From 25efceea2edb7d42b326c1071e3b86c266efd043 Mon Sep 17 00:00:00 2001
From: leestott <leestott@users.noreply.github.com>
Date: Sat, 6 Sep 2025 18:04:07 +0000
Subject: [PATCH] =?UTF-8?q?=F0=9F=8C=90=20Update=20translations=20via=20Co?=
 =?UTF-8?q?-op=20Translator?=
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 .../solution/assignment.ipynb                 |  58 +--
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 .../04-stats-and-probability/notebook.ipynb   | 449 ++++++-----------
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 .../solution/assignment.ipynb                 |  66 +--
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 .../04-stats-and-probability/notebook.ipynb   | 425 +++++-----------
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 .../solution/assignment.ipynb                 |  56 +--
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 .../04-stats-and-probability/notebook.ipynb   | 451 ++++++-----------
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 .../04-stats-and-probability/assignment.ipynb |  26 +-
 .../04-stats-and-probability/notebook.ipynb   | 445 ++++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 .../04-stats-and-probability/assignment.ipynb |  26 +-
 .../04-stats-and-probability/notebook.ipynb   | 441 +++++-----------
 .../solution/assignment.ipynb                 |  62 +--
 .../04-stats-and-probability/assignment.ipynb |  20 +-
 .../04-stats-and-probability/notebook.ipynb   | 443 +++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 .../04-stats-and-probability/assignment.ipynb |  30 +-
 .../04-stats-and-probability/notebook.ipynb   | 456 ++++++-----------
 .../solution/assignment.ipynb                 |  70 +--
 .../04-stats-and-probability/assignment.ipynb |  28 +-
 .../04-stats-and-probability/notebook.ipynb   | 435 +++++-----------
 .../solution/assignment.ipynb                 |  60 +--
 .../04-stats-and-probability/assignment.ipynb |  30 +-
 .../04-stats-and-probability/notebook.ipynb   | 437 +++++-----------
 .../solution/assignment.ipynb                 |  44 +-
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 .../04-stats-and-probability/notebook.ipynb   | 459 ++++++-----------
 .../solution/assignment.ipynb                 |  70 +--
 .../04-stats-and-probability/assignment.ipynb |  36 +-
 .../04-stats-and-probability/notebook.ipynb   | 445 ++++++-----------
 .../solution/assignment.ipynb                 |  52 +-
 .../04-stats-and-probability/assignment.ipynb |  28 +-
 .../04-stats-and-probability/notebook.ipynb   | 463 ++++++-----------
 .../solution/assignment.ipynb                 |  71 +--
 .../04-stats-and-probability/assignment.ipynb |  40 +-
 .../04-stats-and-probability/notebook.ipynb   | 451 ++++++-----------
 .../solution/assignment.ipynb                 |  54 +-
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 .../04-stats-and-probability/notebook.ipynb   | 461 ++++++-----------
 .../solution/assignment.ipynb                 |  60 +--
 .../04-stats-and-probability/assignment.ipynb |  32 +-
 .../04-stats-and-probability/notebook.ipynb   | 441 +++++-----------
 .../solution/assignment.ipynb                 |  60 +--
 .../04-stats-and-probability/assignment.ipynb |  38 +-
 .../04-stats-and-probability/notebook.ipynb   | 471 ++++++------------
 .../solution/assignment.ipynb                 |  70 +--
 .../04-stats-and-probability/assignment.ipynb |  36 +-
 .../04-stats-and-probability/notebook.ipynb   | 453 ++++++-----------
 .../solution/assignment.ipynb                 |  70 +--
 .../04-stats-and-probability/assignment.ipynb |  30 +-
 .../04-stats-and-probability/notebook.ipynb   | 429 +++++-----------
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 .../04-stats-and-probability/notebook.ipynb   | 457 ++++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 .../04-stats-and-probability/assignment.ipynb |  28 +-
 .../04-stats-and-probability/notebook.ipynb   | 427 +++++-----------
 .../solution/assignment.ipynb                 |  56 +--
 .../04-stats-and-probability/assignment.ipynb |  30 +-
 .../04-stats-and-probability/notebook.ipynb   | 431 +++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 .../04-stats-and-probability/assignment.ipynb |  26 +-
 .../04-stats-and-probability/notebook.ipynb   | 439 +++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 .../04-stats-and-probability/assignment.ipynb |  14 +-
 .../04-stats-and-probability/notebook.ipynb   | 435 +++++-----------
 .../solution/assignment.ipynb                 |  46 +-
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 .../04-stats-and-probability/notebook.ipynb   | 433 +++++-----------
 .../solution/assignment.ipynb                 |  56 +--
 .../04-stats-and-probability/assignment.ipynb |  18 +-
 .../04-stats-and-probability/notebook.ipynb   | 435 +++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 .../04-stats-and-probability/assignment.ipynb |  26 +-
 .../04-stats-and-probability/notebook.ipynb   | 435 +++++-----------
 .../solution/assignment.ipynb                 |  54 +-
 .../04-stats-and-probability/assignment.ipynb |  30 +-
 .../04-stats-and-probability/notebook.ipynb   | 443 +++++-----------
 .../solution/assignment.ipynb                 |  64 +--
 .../04-stats-and-probability/assignment.ipynb |  23 +-
 .../04-stats-and-probability/notebook.ipynb   | 433 +++++-----------
 .../solution/assignment.ipynb                 |  56 +--
 .../04-stats-and-probability/assignment.ipynb |  16 +-
 .../04-stats-and-probability/notebook.ipynb   | 451 ++++++-----------
 .../solution/assignment.ipynb                 |  56 +--
 .../04-stats-and-probability/assignment.ipynb |  32 +-
 .../04-stats-and-probability/notebook.ipynb   | 443 +++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 .../04-stats-and-probability/assignment.ipynb |  32 +-
 .../04-stats-and-probability/notebook.ipynb   | 433 +++++-----------
 .../solution/assignment.ipynb                 |  62 +--
 .../04-stats-and-probability/assignment.ipynb |  34 +-
 .../04-stats-and-probability/notebook.ipynb   | 445 ++++++-----------
 .../solution/assignment.ipynb                 |  66 +--
 .../04-stats-and-probability/assignment.ipynb |  16 +-
 .../04-stats-and-probability/notebook.ipynb   | 449 ++++++-----------
 .../solution/assignment.ipynb                 |  50 +-
 .../04-stats-and-probability/assignment.ipynb |  38 +-
 .../04-stats-and-probability/notebook.ipynb   | 463 ++++++-----------
 .../solution/assignment.ipynb                 |  72 +--
 .../04-stats-and-probability/assignment.ipynb |  24 +-
 .../04-stats-and-probability/notebook.ipynb   | 435 +++++-----------
 .../solution/assignment.ipynb                 |  61 ++-
 .../04-stats-and-probability/assignment.ipynb |  20 +-
 .../04-stats-and-probability/notebook.ipynb   | 443 +++++-----------
 .../solution/assignment.ipynb                 |  58 +--
 144 files changed, 8748 insertions(+), 16768 deletions(-)

diff --git a/translations/ar/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ar/1-Introduction/04-stats-and-probability/assignment.ipynb
index 0562577e..67d92773 100644
--- a/translations/ar/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ar/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## مقدمة في الاحتمالات والإحصاء  \n",
     "## الواجب  \n",
     "\n",
-    "في هذا الواجب، سنستخدم مجموعة بيانات مرضى السكري المأخوذة [من هنا](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
+    "في هذا الواجب، سنستخدم مجموعة البيانات الخاصة بمرضى السكري المأخوذة [من هنا](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### المهمة 3: ما هو توزيع العمر، الجنس، مؤشر كتلة الجسم ومتغيرات Y؟\n"
+    "### المهمة 3: ما هو توزيع العمر، الجنس، مؤشر كتلة الجسم والمتغيرات Y؟\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### المهمة 5: اختبار الفرضية بأن درجة تقدم مرض السكري تختلف بين الرجال والنساء\n"
+    "### المهمة 5: اختبار الفرضية بأن درجة تطور مرض السكري تختلف بين الرجال والنساء\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**إخلاء المسؤولية**:  \nتم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الموثوق. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة تنشأ عن استخدام هذه الترجمة.\n"
+    "\n---\n\n**إخلاء المسؤولية**:  \nتمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية هو المصدر الموثوق. للحصول على معلومات حساسة أو هامة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة تنشأ عن استخدام هذه الترجمة.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:16:23+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:05:41+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ar"
   }
diff --git a/translations/ar/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ar/1-Introduction/04-stats-and-probability/notebook.ipynb
index 11341fd6..d45c0c2f 100644
--- a/translations/ar/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ar/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "للتقدير البصري لعدد القيم المختلفة الموجودة في العينة، يمكننا رسم **المخطط البياني**:\n"
+    "للتقدير بصريًا عدد القيم المختلفة الموجودة في العينة، يمكننا رسم **المدرج التكراري**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## تحليل البيانات الحقيقية\n",
     "\n",
-    "المتوسط والتباين لهما أهمية كبيرة عند تحليل البيانات الواقعية. لنقم بتحميل بيانات لاعبي البيسبول من [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "المتوسط والتباين مهمان جدًا عند تحليل البيانات الواقعية. دعونا نقوم بتحميل البيانات المتعلقة بلاعبي البيسبول من [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "نحن نستخدم حزمة تسمى [**Pandas**](https://pandas.pydata.org/) هنا لتحليل البيانات. سنتحدث أكثر عن Pandas والعمل مع البيانات في Python لاحقًا في هذه الدورة.\n",
+    "نحن نستخدم هنا حزمة [**Pandas**](https://pandas.pydata.org/) لتحليل البيانات. سنتحدث أكثر عن Pandas والعمل مع البيانات في Python لاحقًا في هذه الدورة.\n",
     "\n",
     "لنحسب القيم المتوسطة للعمر والطول والوزن:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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o5qGbh24euhXDArlEHR0duUeoHZp56Oahm4duHrp56OahWzEskEt04cKF3CPUDs08dPPQzUM3D908dPPQrRgWyCVasGBB7hFqh2Yeunno5qGbh24eunnoVgwL5BItWrQo9wi1QzMP3Tx089DNQzcP3Tx0K2ZOXsUiIs5KOv6Of+L6WyLpXO4haoZmHrp56Oahm4duHrp56Nbaz6eU3jv5zjlZIKO1iNjT6qVEMDWaeejmoZuHbh66eejmoVsxPMQCAAAAaMICGQAAAGjCArlc/5R7gBqimYduHrp56Oahm4duHroVwGOQAQAAgCbsIAMAAABNWCADAAAATVggz5GIuCUinouIQxFxMCJ+NSLujYjvRsRQROyJiF/OPWeVRMSdjTZXbj+KiE9GxK0R8e8R8YPGx/bcs1bJNN2+0jj+XomIzRFxS+5Zq2Sqbk3//dMRkSJiScYxK2W6ZhHRHRGHI+L7EfF3mUetlGnOUa4JM4iITzWOqQMR0RsRi7gmzGyKblwTCuAxyHMkIp6RtCOl9LWIaJP0HknPSvpqSuk7EfG7kp5IKX0o55xVFRHXSzol6Vck/ZWk11NKX46Iz0hqTymtzzpgRU3qdqekwZTSTyLibyWJbq01d0spHY+IZZK+JmmlpDUpJV5cf5JJx9r7JPVI+r2U0mhE3J5Sei3rgBU1qdvT4powpYj4OUn/IekDKaUfR8Szkv5N0gfENWFK03T7obgmzBo7yHMgIm6S9BuSvi5JKaWxlNL/SUqSbmr8sZs1cbCitYckHU0pHZf0EUnPNO5/RtLv5xqqBq52Syk9n1L6SeP+70rqyDhX1TUfb5L0VUlPaOKcRWvNzf5C0pdTSqOSxOJ4Ws3duCbM7AZJPxURN2hio+mH4powG2/pxjWhGBbIc+N9ks5K+ueI+F5EfC0iFkv6pKSvRMQJSX8v6W8yzlh1fySpt/HPP5NSOi1JjY+3Z5uq+pq7NftTSd8peZY6udotIh6RdCql9HLekSqv+Vh7v6Rfj4hdEbE9Iu7POFfVNXf7pLgmTCmldEoTXV6VdFrScErpeXFNmNY03ZpxTZgBC+S5cYOkD0r6x5TSL0kakfQZTeyyfCqltEzSp9TYYca1Gg9JeUTSt3PPUidTdYuIHkk/kfQvOeaquuZuEfEeTTxU4HN5p6q2FsfaDZLaJT0g6a8lPRsRkWm8ymrRjWvCNBqPLf6IpF+Q9LOSFkfEn+Sdqvpm6sY1YXZYIM+Nk5JOppR2Nf79OU0smD8uqa9x37cl8YSM1n5H0r6U0pnGv5+JiKWS1PjIr29bm9xNEfFxSQ9L+uPEEw6m0tztFzVxUXk5Io5p4leQ+yLijozzVdHkY+2kpL404T8ljUviyY1vNbkb14Tp/bak/0kpnU0pXdZEqy5xTZjJVN24JhTAAnkOpJT+V9KJiLizcddDkv5LE4+d+s3Gfb8l6QcZxquDR3XtwwS2auJCosbHfy19onq4pltEfFjSekmPpJTeyDZV9V3tllLan1K6PaXUmVLq1MTC74ONcxpvmnyObtHE9zRFxPsltUniiY1vNbkb14TpvSrpgYh4T+M3Eg9JOiiuCTNp2Y1rQjG8isUciYh7NfEs+DZJ/y3pMUmrJP2DJn4deUnSX6aU9uaasYoav+I+Iel9KaXhxn23aeIVQJZr4sT/g5TS6/mmrJ4puh2RtFDS+cYf+25KaV2mESupVbdJ//2YpPt4FYs3TXGstUn6hqR7JY1J+nRKaTDbkBU0RbdfE9eEaUXEFyX9oSYeEvA9SX8m6afFNWFaU3T7vrgmzBoLZAAAAKAJD7EAAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmvw/tSpycIADqyoAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **ملاحظة**: يشير هذا الرسم البياني إلى أن متوسط أطوال لاعبي القاعدة الأولى أعلى من متوسط أطوال لاعبي القاعدة الثانية. لاحقًا سنتعلم كيفية اختبار هذه الفرضية بشكل أكثر رسمية، وكيفية إثبات أن بياناتنا ذات دلالة إحصائية لإظهار ذلك.\n",
+    "> **ملاحظة**: يشير هذا الرسم البياني إلى أن متوسط أطوال لاعبي القاعدة الأولى أعلى من متوسط أطوال لاعبي القاعدة الثانية. لاحقًا سنتعلم كيفية اختبار هذه الفرضية بشكل أكثر رسمية، وكيفية إثبات أن بياناتنا ذات دلالة إحصائية لتوضيح ذلك.\n",
     "\n",
     "العمر، الطول، والوزن جميعها متغيرات عشوائية مستمرة. ما رأيك في توزيعها؟ طريقة جيدة لمعرفة ذلك هي رسم المدرج التكراري للقيم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## التوزيع الطبيعي\n",
     "\n",
-    "لنقم بإنشاء عينة اصطناعية من الأوزان التي تتبع توزيعًا طبيعيًا بنفس المتوسط والتباين مثل بياناتنا الحقيقية:\n"
+    "لنقم بإنشاء عينة اصطناعية للأوزان تتبع توزيعًا طبيعيًا بنفس المتوسط والتباين مثل بياناتنا الحقيقية:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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m5Mtf/nIuu+yyXHXVVRk1alTfnBkAwP+xLgAAtXRQHe/W1ta0tLRk9uzZvbavX78+O3bs6LX9+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dWVxx9/fK/P193dna6url43AAAAGAiq7njfcccd+elPf5p169a9Zl97e3tGjRqVcePG9dre2NiY9vb2yjF7hu7d+3fv25vFixfnS1/6UrWlAjAAWeAKABhsqup4b968OZ/97GfzrW99K6NHjy5V02ssWrQonZ2dldvmzZsP23MDAADAoagqeK9fvz5bt27NO97xjowcOTIjR47MmjVrcsMNN2TkyJFpbGzM9u3bs23btl736+joSFNTU5KkqanpNauc7/599zGvVldXl/r6+l43AAAAGAiqCt7vfe9789hjj+XRRx+t3E455ZTMmzev8vMRRxyR1atXV+6zYcOGbNq0Kc3NzUmS5ubmPPbYY9m6dWvlmFWrVqW+vj7Tp0/vo9MCAACA/qGqOd5jx47N2972tl7bxowZkwkTJlS2n3/++Wlra8v48eNTX1+fiy66KM3NzZk1a1aS5Mwzz8z06dNz3nnn5dprr017e3suv/zytLa2pq6uro9OCwAAAPqHg7qc2Ou57rrrMnz48MydOzfd3d2ZM2dObrrppsr+ESNGZMWKFbnwwgvT3NycMWPGZP78+bn66qv7uhQAAACouUMO3j/60Y96/T569OgsXbo0S5cu3ed9pkyZknvuuedQnxoAAAD6vYO6jjcAAABwYPp8qDkAvJ49r9Pdl8cCAPRXOt4AAABQkOANAAAABQneAAAAUJDgDQAAAAVZXA0A4FUs7AdAX9LxBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChoZK0LAGDgmLpwZeXnjUtaalgJAMDAoeMNAAAABQneAAAAUJCh5gAAB8BUCwAOlo43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBFlcDAOgjey7AtieLsQEMbTreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AHLI957WaywoA0JuONwAAABSk4w0AUCWjPACoho43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFAAAMFVMXrqz8vHFJSw0rAeBw0vEGAACAggRvAPrU1IUre3X1AACGOsEbAAAAChK8AQAAoCCLqwFQhOHmDBX+rwOwPzreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AAIWZBw4wtOl4AwAAQEGCNwAAABQkeAMAAEBB5ngDcFDMWQUAODCCNwBADez55dXGJS01rASA0gw1BwAAgIJ0vAF4XYaUAwAcGh1vAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAglxODGAI2vMSYRuXtNSwEgCAwU/HG4CKqQtXum43AEAfE7wBAACgIMEbAAAACjLHGwCgn7IeA8DgoOMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQ4A0AAAAFWdUcgNfYcyVlAAAOTVUd72XLluWkk05KfX196uvr09zcnHvvvbey/+WXX05ra2smTJiQo48+OnPnzk1HR0evx9i0aVNaWlpy1FFHZeLEibn00kvzyiuv9M3ZAAAMQFMXrqzcABh8qgrexx57bJYsWZL169fnJz/5Sc4444x86EMfyuOPP54kueSSS3L33XfnzjvvzJo1a7Jly5acc845lfvv3LkzLS0t2b59ex588MHcdtttufXWW3PFFVf07VkBAABAP1HVUPMPfvCDvX7/m7/5myxbtiwPPfRQjj322Nx88825/fbbc8YZZyRJbrnllpxwwgl56KGHMmvWrPzgBz/IE088kfvvvz+NjY2ZMWNGvvzlL+eyyy7LVVddlVGjRvXdmQEAAEA/cNCLq+3cuTN33HFHXnrppTQ3N2f9+vXZsWNHZs+eXTnm+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dVV6ZrvTXd3d7q6unrdAAAAYCCoOng/9thjOfroo1NXV5fPfOYz+e53v5vp06envb09o0aNyrhx43od39jYmPb29iRJe3t7r9C9e//uffuyePHiNDQ0VG7HHXdctWUDAABATVQdvP/oj/4ojz76aB5++OFceOGFmT9/fp544okStVUsWrQonZ2dldvmzZuLPh8AAAD0laovJzZq1Kj84R/+YZLk5JNPzrp16/L3f//3+djHPpbt27dn27ZtvbreHR0daWpqSpI0NTXlkUce6fV4u1c9333M3tTV1aWurq7aUgEAAKDmDnqO9267du1Kd3d3Tj755BxxxBFZvXp1Zd+GDRuyadOmNDc3J0mam5vz2GOPZevWrZVjVq1alfr6+kyfPv1QSwEAAIB+p6qO96JFi3LWWWdl8uTJeeGFF3L77bfnRz/6Ub7//e+noaEh559/ftra2jJ+/PjU19fnoosuSnNzc2bNmpUkOfPMMzN9+vScd955ufbaa9Pe3p7LL788ra2tOtoAAAAMSlUF761bt+YTn/hEfvWrX6WhoSEnnXRSvv/97+d973tfkuS6667L8OHDM3fu3HR3d2fOnDm56aabKvcfMWJEVqxYkQsvvDDNzc0ZM2ZM5s+fn6uvvrpvzwoAYJCZunBlkmTjkpYaVwJAtaoK3jfffPPr7h89enSWLl2apUuX7vOYKVOm5J577qnmaQEAAGDAOuQ53gAAAMC+Cd4AAABQkOANAAAABQneAAAAUFBVi6sBMPjsXikZAIAydLwBAACgIB1vgEFsz262a//CwGAUCsDgo+MNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdUABgCLpAEADFw63gAAAFCQ4A0AAAAFGWoOADCAmHoCMPDoeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUNLLWBQDQt6YuXFnrEgAA2IPgDTBECOQAALVhqDkAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAU5DreAAPYntfm3rikpYaVAACwL4I3wCCxZwgHhgZfvgEMDIaaAwAAQEGCNwAAABRkqDnAAGNIOQDAwKLjDQAAAAUJ3gAAAFCQ4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJDgDQAAAAUJ3gAAAFDQyFoXAABA35q6cGXl541LWmpYCQCJjjcAAAAUJXgDAABAQYaaA/RTew4VBdgffzMA+i8dbwAAAChI8AYAAICCBG8AAAAoyBxvAIBBzKXFAGpPxxsAAAAK0vEGABhidMEBDi8dbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAgkbWugAAAGpn6sKVlZ83LmmpYSUAg5eONwAAABQkeAMAAEBBhpoD1IjhnQAAQ4OONwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQVUF78WLF+ed73xnxo4dm4kTJ+bss8/Ohg0beh3z8ssvp7W1NRMmTMjRRx+duXPnpqOjo9cxmzZtSktLS4466qhMnDgxl156aV555ZVDPxsAAADoZ6oK3mvWrElra2seeuihrFq1Kjt27MiZZ56Zl156qXLMJZdckrvvvjt33nln1qxZky1btuScc86p7N+5c2daWlqyffv2PPjgg7ntttty66235oorrui7swIAAIB+YlhPT0/Pwd75ueeey8SJE7NmzZq8+93vTmdnZ97whjfk9ttvz5//+Z8nSZ588smccMIJWbt2bWbNmpV77703f/Znf5YtW7aksbExSbJ8+fJcdtllee655zJq1Kj9Pm9XV1caGhrS2dmZ+vr6gy0foKb2dzmxPfcD9IXdf2sO5O+LyxwCvL5qcukhzfHu7OxMkowfPz5Jsn79+uzYsSOzZ8+uHHP88cdn8uTJWbt2bZJk7dq1OfHEEyuhO0nmzJmTrq6uPP7443t9nu7u7nR1dfW6AQAAwEBw0MF7165dufjii3PaaaflbW97W5Kkvb09o0aNyrhx43od29jYmPb29soxe4bu3ft379ubxYsXp6GhoXI77rjjDrZsAAAAOKwOOni3trbmZz/7We64446+rGevFi1alM7Ozspt8+bNxZ8TAAAA+sLIg7nTggULsmLFijzwwAM59thjK9ubmpqyffv2bNu2rVfXu6OjI01NTZVjHnnkkV6Pt3vV893HvFpdXV3q6uoOplQAAACoqao63j09PVmwYEG++93v5oc//GGmTZvWa//JJ5+cI444IqtXr65s27BhQzZt2pTm5uYkSXNzcx577LFs3bq1csyqVatSX1+f6dOnH8q5AADwOqYuXGnhRoAaqKrj3dramttvvz133XVXxo4dW5mT3dDQkCOPPDINDQ05//zz09bWlvHjx6e+vj4XXXRRmpubM2vWrCTJmWeemenTp+e8887Ltddem/b29lx++eVpbW3V1QYAAGDQqSp4L1u2LEly+umn99p+yy235JOf/GSS5Lrrrsvw4cMzd+7cdHd3Z86cObnpppsqx44YMSIrVqzIhRdemObm5owZMybz58/P1VdffWhnAjAI6EQBAAw+VQXvA7nk9+jRo7N06dIsXbp0n8dMmTIl99xzTzVPDQAAAAPSQS2uBsCB27OLvXFJSw0rAQCgFgRvgMPIUHIAgKHnoK/jDQAAAOyf4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJBVzQH6AaudAwAMXoI3AACvsecXghuXtNSwEoCBz1BzAAAAKEjHGwCA16X7DXBodLwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoKCRtS4AYLCYunBl5eeNS1pqWAkAAP2JjjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAB2zqwpW9FpMEYP8EbwAAAChI8AYAAICCBG8AAAAoaGStCwAYjMx/BABgNx1vAAAAKEjwBgAAgIIEbwAAACjIHG+AQ2Q+NzAU7fm3b+OSlhpWAtD/Cd4AABwSIRzg9RlqDgAAAAUJ3gAAAFCQ4A0AAAAFmeMNcIDMYQQA4GDoeAMAAEBBOt4AB8ElxAD2z0ghgN/S8QYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAACjI5cQAAOgzfXG5RZchAwYbwRvgdbheNwAAh8pQcwAAAChI8AYAAICCDDUHAKA487aBoUzwBngV87oBAOhLgjcAAAOWTjowEJjjDQAAAAUJ3gAAAFCQoeYAANSc9TWAwUzHGwAAAArS8QaITgsAAOXoeAMAAEBBgjcAAAAUJHgDAABAQYI3AACH1dSFK62tAQwpgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFANSSVXUBAChNxxsAAAAKErwBAACgIMEbAAAACjLHGwCAmrDOBjBUCN4AAPRbe4bzjUta9rodoL8TvIEhx4c1AAAOJ3O8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgoKqD9wMPPJAPfvCDmTRpUoYNG5bvfe97vfb39PTkiiuuyDHHHJMjjzwys2fPzlNPPdXrmOeffz7z5s1LfX19xo0bl/PPPz8vvvjiIZ0IAAAA9EdVB++XXnopb3/727N06dK97r/22mtzww03ZPny5Xn44YczZsyYzJkzJy+//HLlmHnz5uXxxx/PqlWrsmLFijzwwAP59Kc/ffBnAbAfUxeurNwAAOBwqvo63meddVbOOuusve7r6enJ9ddfn8svvzwf+tCHkiT/9E//lMbGxnzve9/Lueeem5///Oe57777sm7dupxyyilJkhtvvDEf+MAH8rWvfS2TJk16zeN2d3enu7u78ntXV1e1ZQMAAEBN9Okc72eeeSbt7e2ZPXt2ZVtDQ0NmzpyZtWvXJknWrl2bcePGVUJ3ksyePTvDhw/Pww8/vNfHXbx4cRoaGiq34447ri/LBgAAgGL6NHi3t7cnSRobG3ttb2xsrOxrb2/PxIkTe+0fOXJkxo8fXznm1RYtWpTOzs7KbfPmzX1ZNjDAGUYOAEB/VvVQ81qoq6tLXV1drcsAAACAqvVp8G5qakqSdHR05Jhjjqls7+joyIwZMyrHbN26tdf9XnnllTz//POV+wP0BR1wgMHF33VgoOrToebTpk1LU1NTVq9eXdnW1dWVhx9+OM3NzUmS5ubmbNu2LevXr68c88Mf/jC7du3KzJkz+7IcAAAAqLmqO94vvvhinn766crvzzzzTB599NGMHz8+kydPzsUXX5xrrrkmb37zmzNt2rR88YtfzKRJk3L22WcnSU444YS8//3vzwUXXJDly5dnx44dWbBgQc4999y9rmgOAAAAA1nVwfsnP/lJ3vOe91R+b2trS5LMnz8/t956az7/+c/npZdeyqc//els27Yt73rXu3Lfffdl9OjRlft861vfyoIFC/Le9743w4cPz9y5c3PDDTf0wekAg9GeQws3LmmpYSUAAFC9YT09PT21LqJaXV1daWhoSGdnZ+rr62tdDlDY/oK3OX8AJL6cBQ6vanLpgFjVHAAAqmG0FNCf9OniagAAAEBvgjcAAEPG1IUrTVECDjvBGwAAAAoyxxsAgEFNhxuoNR1vAAAAKEjwBgAAgIIMNQf6DZd+AQBgMNLxBgAAgIIEbwAAACjIUHNgQDEcHQCAgUbHGwAAAAoSvAEAAKAgQ82BfmnPIeUAADCQ6XgDAABAQYI3AAAAFGSoOQAAg4JpSkB/peMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdWAw2bPRW82Lmnp08cDgJL6+j0MGFoEbwAAhhxBGjicBG8AAPg/AjlQgjneAAAAUJCONwAA7IW1RIC+IngDADCkCdhAaYaaAwAAQEGCN1ATUxeu1GEAAGBIMNQcKEq4BgBgqBO8gZoSzAEAGOwMNQcAgCqYLgVUS/AGAACAggRvAAAAKEjwBgAAgIIEbwAAACjIquZAn7PgDABDzZ7vfRuXtNSwEqA/0vEGAACAggRvAAAAKMhQc+CgGVYHAAdn93uo908YGnS8AQAAoCAdbwAA6ENGhAGvJngDfcJK5gAAsHeCN1A1IRsAAA6c4A3sM0jvOTxO2AYAgIMjeAP7JGwDAMChE7wBAOAg+IIaOFCCNwAAHAZ7C+pWQIehwXW8AQAAoCDBGwAABqCpC1ca7g4DhKHmAABQiGAMJII3AAD0a+aBw8BnqDkAAAAUJHgDAABAQYaaAwDAAGHOOAxMgjcAAPQzAjYMLoI3DAH7WpTFmzoAAJQneAMAQD/gC3EYvARvAAAYwFxuDPo/wRsGqL19K+7NFgAA+h/BGwYR33gDAED/4zreAAAAUJCONwxSFmgBAID+QfAGAIAhxNQ0OPwEbxhAdLEBgAMlYEP/IXgDAMAgUfJLekEeDp7gDTW0rzdHb2YAADB4WNUcqjR14UpDvgEAgAOm4w19rL8Pw/KlAQCw2+7PBf3xMwsMJjreAAAAUJCONwAADHIHO+KtL0bK9ffRgHA4CN5wAPrizaqaNxrDwQGAw6nazyx7+6wiVMO+Cd5QA4I1ANBfHe6GAwwFgjdDUl+8MXhzAQCojs9PDFWCNxwmutwAAL8jhDOUCN4MefsKxN4AAAD6ByGdgU7whn2opkOtmw0A8Dt9vRo6DHSCNwPagXz76Y82AMDAcCCf23S/GYgEbwYlYRsAYOAYKJ/dhH4OVs2C99KlS/PVr3417e3tefvb354bb7wxp556aq3K4RBU03Uu+QdqoPzBBgCgnIO9JrkgTUk1Cd7f/va309bWluXLl2fmzJm5/vrrM2fOnGzYsCETJ06sRUlF1TJ07vmch1pHX1+Ca1/2VjMAALza/j6fVvP5tdoFd2t5eVqd94GnJsH77/7u73LBBRfkU5/6VJJk+fLlWblyZf7xH/8xCxcufM3x3d3d6e7urvze2dmZJOnq6jo8BR+iXd3/L0nvet925ff3euzPvjTnkJ7j1fZ8zv3Vsb/n3vM59va4r/fY1Zh8yZ0HdT8AAIau/X2GPNjPqQfy2bSaXLKv5979PPv6TL6v++3tuav5jL8vffEYA+E5D8Xuf/uenp79Hjus50CO6kPbt2/PUUcdle985zs5++yzK9vnz5+fbdu25a677nrNfa666qp86UtfOoxVAgAAwP5t3rw5xx577Osec9g73r/+9a+zc+fONDY29tre2NiYJ598cq/3WbRoUdra2iq/79q1K88//3wmTJiQYcOGFa33UHV1deW4447L5s2bU19fX+tyoN/zmoHqed1A9bxuoHpeN7319PTkhRdeyKRJk/Z77IBY1byuri51dXW9to0bN642xRyk+vp6/zmhCl4zUD2vG6ie1w1Uz+vmdxoaGg7ouOGF63iN3//938+IESPS0dHRa3tHR0eampoOdzkAAABQ1GEP3qNGjcrJJ5+c1atXV7bt2rUrq1evTnNz8+EuBwAAAIqqyVDztra2zJ8/P6ecckpOPfXUXH/99XnppZcqq5wPJnV1dbnyyitfM1Qe2DuvGaie1w1Uz+sGqud1c/AO+6rmu33961/PV7/61bS3t2fGjBm54YYbMnPmzFqUAgAAAMXULHgDAADAUHDY53gDAADAUCJ4AwAAQEGCNwAAABQkeAMAAEBBgncNdHd3Z8aMGRk2bFgeffTRWpcD/dbGjRtz/vnnZ9q0aTnyyCPzpje9KVdeeWW2b99e69KgX1m6dGmmTp2a0aNHZ+bMmXnkkUdqXRL0W4sXL8473/nOjB07NhMnTszZZ5+dDRs21LosGDCWLFmSYcOG5eKLL651KQOK4F0Dn//85zNp0qRalwH93pNPPpldu3blG9/4Rh5//PFcd911Wb58eb7whS/UujToN7797W+nra0tV155ZX7605/m7W9/e+bMmZOtW7fWujTol9asWZPW1tY89NBDWbVqVXbs2JEzzzwzL730Uq1Lg35v3bp1+cY3vpGTTjqp1qUMOC4ndpjde++9aWtry7/927/lrW99a/7zP/8zM2bMqHVZMGB89atfzbJly/KLX/yi1qVAvzBz5sy8853vzNe//vUkya5du3LcccfloosuysKFC2tcHfR/zz33XCZOnJg1a9bk3e9+d63LgX7rxRdfzDve8Y7cdNNNueaaazJjxoxcf/31tS5rwNDxPow6OjpywQUX5J//+Z9z1FFH1bocGJA6Ozszfvz4WpcB/cL27duzfv36zJ49u7Jt+PDhmT17dtauXVvDymDg6OzsTBLvLbAfra2taWlp6fWew4EbWesChoqenp588pOfzGc+85mccsop2bhxY61LggHn6aefzo033pivfe1rtS4F+oVf//rX2blzZxobG3ttb2xszJNPPlmjqmDg2LVrVy6++OKcdtppedvb3lbrcqDfuuOOO/LTn/4069atq3UpA5aO9yFauHBhhg0b9rq3J598MjfeeGNeeOGFLFq0qNYlQ80d6OtmT88++2ze//735yMf+UguuOCCGlUOwGDS2tqan/3sZ7njjjtqXQr0W5s3b85nP/vZfOtb38ro0aNrXc6AZY73IXruuefym9/85nWPeeMb35iPfvSjufvuuzNs2LDK9p07d2bEiBGZN29ebrvtttKlQr9xoK+bUaNGJUm2bNmS008/PbNmzcqtt96a4cN9ZwjJb4eaH3XUUfnOd76Ts88+u7J9/vz52bZtW+66667aFQf93IIFC3LXXXflgQceyLRp02pdDvRb3/ve9/LhD384I0aMqGzbuXNnhg0bluHDh6e7u7vXPvZO8D5MNm3alK6ursrvW7ZsyZw5c/Kd73wnM2fOzLHHHlvD6qD/evbZZ/Oe97wnJ598cv7lX/7FH3Z4lZkzZ+bUU0/NjTfemOS3Q2cnT56cBQsWWFwN9qKnpycXXXRRvvvd7+ZHP/pR3vzmN9e6JOjXXnjhhfzP//xPr22f+tSncvzxx+eyyy4zTeMAmeN9mEyePLnX70cffXSS5E1vepPQDfvw7LPP5vTTT8+UKVPyta99Lc8991xlX1NTUw0rg/6jra0t8+fPzymnnJJTTz01119/fV566aV86lOfqnVp0C+1trbm9ttvz1133ZWxY8emvb09SdLQ0JAjjzyyxtVB/zN27NjXhOsxY8ZkwoQJQncVBG+g31q1alWefvrpPP3006/5gspgHfitj33sY3nuuedyxRVXpL29PTNmzMh99933mgXXgN9atmxZkuT000/vtf2WW27JJz/5ycNfEDAkGGoOAAAABVmhCAAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAACvr/ciHiWioJ+MUAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "نظرًا لأن معظم القيم في الحياة الواقعية موزعة بشكل طبيعي، يجب ألا نستخدم مولد أرقام عشوائية موحدًا لتوليد بيانات العينة. إليك ما يحدث إذا حاولنا توليد أوزان بتوزيع موحد (تم توليده بواسطة `np.random.rand`):\n"
+    "نظرًا لأن معظم القيم في الحياة الواقعية تتبع التوزيع الطبيعي، يجب علينا عدم استخدام مولد أرقام عشوائية بتوزيع منتظم لتوليد بيانات العينة. إليك ما يحدث إذا حاولنا توليد أوزان بتوزيع منتظم (تم توليدها بواسطة `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## فترات الثقة\n",
     "\n",
-    "لنحسب الآن فترات الثقة لأوزان وأطوال لاعبي البيسبول. سنستخدم الكود [من هذا النقاش على stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "لنحسب الآن فترات الثقة لأوزان وأطوال لاعبي البيسبول. سنستخدم الكود [من هذا النقاش على StackOverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## اختبار الفرضيات\n",
     "\n",
-    "دعونا نستكشف الأدوار المختلفة في مجموعة بيانات لاعبي البيسبول:\n"
+    "دعونا نستكشف الأدوار المختلفة في مجموعة بيانات لاعبي البيسبول لدينا:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -726,16 +567,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -752,20 +593,20 @@
    "source": [
     "يمكننا أن نرى أن الفترات الزمنية لا تتداخل.\n",
     "\n",
-    "طريقة أكثر دقة من الناحية الإحصائية لإثبات الفرضية هي استخدام **اختبار t للطالب**:\n"
+    "طريقة أكثر دقة من الناحية الإحصائية لإثبات الفرضية هي استخدام **اختبار t لستودنت**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,35 +621,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "القيمتان اللتان تُرجعان بواسطة دالة `ttest_ind` هما:\n",
-    "* p-value يمكن اعتبارها احتمال أن يكون لتوزيعين نفس المتوسط. في حالتنا، هي منخفضة جدًا، مما يعني أن هناك دليلًا قويًا يدعم أن لاعبي القاعدة الأولى أطول.\n",
-    "* t-value هي القيمة الوسيطة للاختلاف المعياري في المتوسط التي تُستخدم في اختبار t، ويتم مقارنتها بقيمة عتبة معينة لمستوى ثقة معين.\n"
+    "القيمتان اللتان تُرجعان بواسطة دالة `ttest_ind` هما:  \n",
+    "* قيمة p يمكن اعتبارها احتمال أن يكون للتوزيعين نفس المتوسط. في حالتنا، هي منخفضة جدًا، مما يعني أن هناك دليلًا قويًا يدعم أن لاعبي القاعدة الأولى أطول.  \n",
+    "* قيمة t هي القيمة الوسيطة لاختلاف المتوسط المُطَبَّع التي تُستخدم في اختبار t، ويتم مقارنتها بقيمة عتبة معينة لمستوى ثقة محدد.  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## محاكاة توزيع طبيعي باستخدام نظرية الحد المركزي\n",
+    "## محاكاة التوزيع الطبيعي باستخدام نظرية الحد المركزي\n",
     "\n",
-    "المولد العشوائي الزائف في بايثون مصمم ليعطينا توزيعًا منتظمًا. إذا أردنا إنشاء مولد لتوزيع طبيعي، يمكننا استخدام نظرية الحد المركزي. للحصول على قيمة موزعة بشكل طبيعي، سنقوم فقط بحساب متوسط عينة تم إنشاؤها بشكل منتظم.\n"
+    "المولد العشوائي الزائف في بايثون مصمم ليعطينا توزيعًا منتظمًا. إذا أردنا إنشاء مولد لتوزيع طبيعي، يمكننا استخدام نظرية الحد المركزي. للحصول على قيمة موزعة طبيعيًا، سنقوم فقط بحساب متوسط عينة تم إنشاؤها بتوزيع منتظم.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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6HavqkUl+Mslbk2SM8Z0xxn/t+8Tra9JjMtu/JeihVbWR5AdztH9P/F7XctW3vdgsvRb2Ofcz6XFln3MvS6/lOu93BPI0O73d9r1+4FTV45O8LMlfXOiTVNVWkmcn+eTqRzw0pq7lNUl+O8l392m+w2LKOj4lyZ1J/mrx34ZvqaqH7eewa27ptRxjfDXJHyb5SpKzSb45xvjovk673nZdy4XnVdVnqupvquoZD/C2R8GUdfwe+5wk09fymtjnnDdlLdd2vyOQp9nL221fk+R3xhj37PgJqh6e7aNPrx9jfGu14x0qS69lVf1sknNjjJv2abbDZMpjciPJc5K8eYzx7CT/k+QoP99zymPy0dk+gvLkJD+c5GFV9Qv7MeQhsZe1/FSSJ40xnpnkT5N88AHc9qiYso7bn8A+57yl19I+536mPC7Xdr+z9BuFkGRvb7d9Isl7qipJjiV5cVXdPcb4YFU9ONs/qN45xrj+IAZeY0uvZZLnJvm5qnpxkockeWRVvWOMcRSDZMo6/nOSO8YY548qvS9r8oNqJlPW8sFJvjzGuDNJqur6JD+e5B37PfSa2nUte6yNMW6oqj+vqmN7ue0RsvQ6jjHuss+5lymPyefHPqeb+v29nvuduZ8EfZhP2f4HxpeyfZTo/BPTn/H/XP/t+f6LeCrJXye5Zu6vYx1OU9byPtuvyBF+wcTUdUzyD0mevjj/piR/MPfXdBjXMtv/aLs12889rmy/COW1c39N67yWSX4o33/zqsuz/fSUeqB/DxfzaeI62uesaC3vc50jvc9ZxVqu637HEeQJxgXebruqfm3x8Qs+7zjb/wJ9VZLPVtXNi21vHGPcsJ8zr6uJa8nCCtbxtUneWVWXZPsH3i/v68BrbMpajjE+WVXvy/Z/K96d5NM5ZG+zukp7XMuXJ/n1xRH4/03yirG9x9zxtrN8ITObso5V9ROxz/meiY9JmhWs5Vrud7zVNAAANF6kBwAAjUAGAIBGIAMAQCOQAQCgEcgAANAIZAAAaAQyAAA0/wceFVFs3MY9ywAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -830,19 +669,19 @@
    "source": [
     "## الارتباط وشركة البيسبول الشريرة\n",
     "\n",
-    "الارتباط يسمح لنا بإيجاد العلاقات بين تسلسلات البيانات. في مثالنا البسيط، دعونا نتخيل وجود شركة بيسبول شريرة تدفع للاعبيها بناءً على طولهم - كلما كان اللاعب أطول، حصل على المزيد من المال. لنفترض أن هناك راتباً أساسياً قدره 1000 دولار، بالإضافة إلى مكافأة تتراوح بين 0 و100 دولار، حسب الطول. سنأخذ اللاعبين الحقيقيين من MLB، ونحسب رواتبهم التخيلية:\n"
+    "يسمح لنا الارتباط بإيجاد العلاقات بين تسلسلات البيانات. في مثالنا البسيط، دعونا نتخيل أن هناك شركة بيسبول شريرة تدفع للاعبيها بناءً على طولهم - كلما كان اللاعب أطول، حصل على أموال أكثر. لنفترض أن هناك راتباً أساسياً قدره 1000 دولار، ومكافأة إضافية تتراوح بين 0 و100 دولار، حسب الطول. سنأخذ لاعبين حقيقيين من MLB، ونحسب رواتبهم التخيلية:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +695,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "دعونا الآن نحسب التغاير والارتباط لتلك التسلسلات. `np.cov` ستعطينا ما يسمى **مصفوفة التغاير**، وهي امتداد للتغاير إلى متغيرات متعددة. العنصر $M_{ij}$ في مصفوفة التغاير $M$ هو ارتباط بين المتغيرات المدخلة $X_i$ و $X_j$، والقيم القطرية $M_{ii}$ هي التباين لـ $X_{i}$. وبالمثل، `np.corrcoef` ستعطينا **مصفوفة الارتباط**.\n"
+    "دعونا الآن نحسب التباين والارتباط لتلك التسلسلات. `np.cov` ستعطينا ما يسمى بـ **مصفوفة التباين**، وهي امتداد للتباين ليشمل متغيرات متعددة. العنصر $M_{ij}$ في مصفوفة التباين $M$ هو ارتباط بين المتغيرات المدخلة $X_i$ و $X_j$، والقيم القطرية $M_{ii}$ هي التباين لـ $X_{i}$. وبالمثل، `np.corrcoef` ستعطينا **مصفوفة الارتباط**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -886,24 +725,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "يعني الارتباط الذي يساوي 1 أن هناك **علاقة خطية قوية** بين متغيرين. يمكننا رؤية العلاقة الخطية بصريًا من خلال رسم قيمة مقابل الأخرى:\n"
+    "يعني الارتباط الذي يساوي 1 أن هناك **علاقة خطية قوية** بين متغيرين. يمكننا رؤية العلاقة الخطية بصريًا من خلال رسم قيمة واحدة مقابل الأخرى:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -923,14 +760,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -943,19 +780,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "في هذه الحالة، الارتباط أصغر قليلاً، لكنه لا يزال مرتفعًا جدًا. الآن، لجعل العلاقة أقل وضوحًا، قد نرغب في إضافة بعض العشوائية الإضافية عن طريق إضافة بعض المتغيرات العشوائية إلى الراتب. دعونا نرى ما سيحدث:\n"
+    "في هذه الحالة، الارتباط أصغر قليلاً، لكنه لا يزال مرتفعاً جداً. الآن، لجعل العلاقة أقل وضوحاً، قد نرغب في إضافة بعض العشوائية الإضافية عن طريق إضافة متغير عشوائي إلى الراتب. لنرَ ما سيحدث:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -995,22 +830,22 @@
    "source": [
     "> هل يمكنك أن تخمن لماذا تصطف النقاط في خطوط عمودية بهذا الشكل؟\n",
     "\n",
-    "لقد لاحظنا العلاقة بين مفهوم مصطنع مثل الراتب والمتغير الملحوظ *الطول*. دعنا نرى أيضًا ما إذا كان هناك ارتباط بين المتغيرين الملحوظين، مثل الطول والوزن:\n"
+    "لقد لاحظنا العلاقة بين مفهوم مصطنع مثل الراتب والمتغير الملاحظ *الطول*. دعونا أيضًا نرى ما إذا كان هناك ارتباط بين المتغيرين الملاحظين، مثل الطول والوزن:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,16 +858,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "للأسف، لم نحصل على أي نتائج - فقط بعض القيم الغريبة مثل `nan`. يعود السبب في ذلك إلى أن بعض القيم في السلسلة لدينا غير معرفة، وممثلة بـ `nan`، مما يؤدي إلى أن تكون نتيجة العملية غير معرفة أيضًا. من خلال النظر إلى المصفوفة، يمكننا ملاحظة أن العمود `Weight` هو العمود المسبب للمشكلة، لأن الارتباط الذاتي بين قيم `Height` قد تم حسابه.\n",
+    "للأسف، لم نحصل على أي نتائج - فقط بعض القيم الغريبة `nan`. يعود السبب إلى أن بعض القيم في السلسلة غير معرفة، ممثلة بـ `nan`، مما يؤدي إلى أن تكون نتيجة العملية غير معرفة أيضًا. من خلال النظر إلى المصفوفة، يمكننا أن نرى أن العمود `Weight` هو العمود المسبب للمشكلة، لأن الارتباط الذاتي بين قيم `Height` قد تم حسابه.\n",
     "\n",
     "> يوضح هذا المثال أهمية **تحضير البيانات** و**تنظيفها**. بدون بيانات مناسبة، لا يمكننا حساب أي شيء.\n",
     "\n",
-    "دعونا نستخدم طريقة `fillna` لملء القيم المفقودة، ثم نحسب الارتباط:\n"
+    "لنستخدم طريقة `fillna` لملء القيم المفقودة، ثم نحسب الارتباط:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,32 +890,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "هناك بالفعل ارتباط، لكنه ليس قويًا كما هو الحال في مثالنا الاصطناعي. في الواقع، إذا نظرنا إلى مخطط التبعثر لقيمة مقابل الأخرى، فستكون العلاقة أقل وضوحًا بكثير:\n"
+    "هناك بالفعل ارتباط، ولكنه ليس قويًا كما هو الحال في مثالنا الاصطناعي. في الواقع، إذا نظرنا إلى مخطط الانتشار لقيمة مقابل الأخرى، فستكون العلاقة أقل وضوحًا بكثير:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1098,7 +931,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**إخلاء المسؤولية**:  \nتم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة.\n"
+    "\n---\n\n**إخلاء المسؤولية**:  \nتم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية هو المصدر الموثوق. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة.\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T22:55:38+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:05:28+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ar"
   }
diff --git a/translations/ar/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ar/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 82addea4..1706df58 100644
--- a/translations/ar/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ar/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## مقدمة في الاحتمالات والإحصاء\n",
-    "## الواجب\n",
+    "## مقدمة في الاحتمالات والإحصاء  \n",
+    "## الواجب  \n",
     "\n",
-    "في هذا الواجب، سنستخدم مجموعة بيانات مرضى السكري المأخوذة [من هنا](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "في هذا الواجب، سنستخدم مجموعة بيانات مرضى السكري المأخوذة [من هنا](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -155,7 +155,7 @@
     "* BMI هو مؤشر كتلة الجسم  \n",
     "* BP هو متوسط ضغط الدم  \n",
     "* S1 إلى S6 هي قياسات دم مختلفة  \n",
-    "* Y هو المقياس النوعي لتطور المرض خلال سنة واحدة  \n",
+    "* Y هو مقياس نوعي لتطور المرض خلال سنة واحدة  \n",
     "\n",
     "لنقم بدراسة هذا الملف باستخدام طرق الاحتمالات والإحصاء.  \n",
     "\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### المهمة 3: ما هو توزيع العمر، الجنس، مؤشر كتلة الجسم ومتغيرات Y؟\n"
+    "### المهمة 3: ما هو توزيع العمر، الجنس، مؤشر كتلة الجسم والمتغيرات Y؟\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -846,8 +846,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "الاستنتاج:  \n",
-    "* أقوى ارتباط لـ Y هو مؤشر كتلة الجسم (BMI) و S5 (مستوى السكر في الدم). يبدو هذا منطقيًا.\n"
+    "الخلاصة:  \n",
+    "* أقوى ارتباط مع Y هو مؤشر كتلة الجسم (BMI) و S5 (مستوى السكر في الدم). هذا يبدو منطقياً.  \n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -879,7 +879,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### المهمة 5: اختبار الفرضية بأن درجة تقدم مرض السكري تختلف بين الرجال والنساء\n"
+    "### المهمة 5: اختبار الفرضية بأن درجة تطور مرض السكري تختلف بين الرجال والنساء\n"
    ],
    "metadata": {}
   },
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -920,7 +920,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**إخلاء المسؤولية**:  \nتم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة تنشأ عن استخدام هذه الترجمة.\n"
+    "\n---\n\n**إخلاء المسؤولية**:  \nتم ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الموثوق. للحصول على معلومات حساسة أو هامة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة.\n"
    ]
   }
  ],
@@ -946,8 +946,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:21:20+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:06:00+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ar"
   }
diff --git a/translations/bg/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/bg/1-Introduction/04-stats-and-probability/assignment.ipynb
index 117bcbd0..9d650daa 100644
--- a/translations/bg/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/bg/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Въведение в вероятностите и статистиката\n",
+    "## Въведение в теорията на вероятностите и статистиката\n",
     "## Задача\n",
     "\n",
     "В тази задача ще използваме набора от данни за пациенти с диабет, взет [оттук](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "В този набор от данни колоните са следните:\n",
-    "* Възраст и пол са ясни сами по себе си\n",
-    "* BMI е индекс на телесната маса\n",
-    "* BP е средното кръвно налягане\n",
-    "* S1 до S6 са различни кръвни измервания\n",
-    "* Y е качествена мярка за прогресията на заболяването за една година\n",
+    "В този набор от данни колоните са следните:  \n",
+    "* Възраст и пол са ясни сами по себе си  \n",
+    "* BMI е индекс на телесната маса  \n",
+    "* BP е средното кръвно налягане  \n",
+    "* S1 до S6 са различни кръвни измервания  \n",
+    "* Y е качествена мярка за прогресията на заболяването за една година  \n",
     "\n",
     "Нека изследваме този набор от данни, използвайки методите на вероятността и статистиката.\n",
     "\n",
-    "### Задача 1: Изчислете средните стойности и вариацията за всички стойности\n"
+    "### Задача 1: Изчислете средните стойности и вариацията за всички стойности  \n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Задача 3: Какво е разпределението на възраст, пол, ИТМ и Y променливи?\n"
+    "### Задача 3: Какво е разпределението на възраст, пол, ИТМ и променливата Y?\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
+    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматичните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия изходен език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален превод от човек. Не носим отговорност за каквито и да било недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:16:51+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:54:53+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "bg"
   }
diff --git a/translations/bg/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/bg/1-Introduction/04-stats-and-probability/notebook.ipynb
index 24027c20..a4a75fb8 100644
--- a/translations/bg/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/bg/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Въведение в вероятностите и статистиката\n",
-    "В този тефтер ще разгледаме някои от концепциите, които обсъдихме по-рано. Много концепции от вероятностите и статистиката са добре представени в основни библиотеки за обработка на данни в Python, като `numpy` и `pandas`.\n"
+    "# Въведение в теорията на вероятностите и статистиката\n",
+    "В този тефтер ще разгледаме някои от концепциите, които обсъдихме по-рано. Много от концепциите в теорията на вероятностите и статистиката са добре представени в основни библиотеки за обработка на данни в Python, като `numpy` и `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Случайни променливи и разпределения\n",
-    "Нека започнем с изтегляне на извадка от 30 стойности от равномерно разпределение между 0 и 9. Ще изчислим също средната стойност и дисперсията.\n"
+    "Нека започнем с изтегляне на извадка от 30 стойности от равномерно разпределение в интервала от 0 до 9. Ще изчислим също средната стойност и дисперсията.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "За да направим визуална оценка на броя на различните стойности в извадката, можем да начертаем **хистограма**:\n"
+    "За да направим визуална оценка на това колко различни стойности има в извадката, можем да начертаем **хистограма**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Тук използваме пакет, наречен [**Pandas**](https://pandas.pydata.org/) за анализ на данни. Ще говорим повече за Pandas и работата с данни в Python по-късно в този курс.\n",
+    "Използваме пакет, наречен [**Pandas**](https://pandas.pydata.org/) тук за анализ на данни. Ще говорим повече за Pandas и работата с данни в Python по-късно в този курс.\n",
     "\n",
     "Нека изчислим средните стойности за възраст, височина и тегло:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Сега нека се съсредоточим върху височината и изчислим стандартното отклонение и вариацията:\n"
+    "Сега нека се фокусираме върху височината и изчислим стандартното отклонение и дисперсията:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "В допълнение към средната стойност, има смисъл да се разгледат медианата и квартилите. Те могат да бъдат визуализирани с помощта на **кутиевидна диаграма**:\n"
+    "В допълнение към средната стойност, има смисъл да се разгледат медианата и квартилите. Те могат да бъдат визуализирани с помощта на **кутия с мустаци**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Можем също да направим кутии диаграми на подмножества от нашия набор от данни, например, групирани по роля на играча.\n"
+    "Можем също да направим кутии на подмножества от нашия набор от данни, например, групирани по роля на играча.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -409,19 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Тъй като повечето стойности в реалния живот са нормално разпределени, не трябва да използваме генератор на случайни числа с равномерно разпределение, за да генерираме примерни данни. Ето какво се случва, ако се опитаме да генерираме тегла с равномерно разпределение (генерирано от `np.random.rand`):\n"
+    "Тъй като повечето стойности в реалния живот са нормално разпределени, не трябва да използваме генератор на случайни числа с равномерно разпределение, за да генерираме примерни данни. Ето какво се случва, ако се опитаме да генерираме тегла с равномерно разпределение (генерирано чрез `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Можем да видим, че интервалите не се припокриват.\n",
     "\n",
-    "Статистически по-коректен начин за доказване на хипотезата е използването на **t-тест на Студент**:\n"
+    "Статистически по-правилен начин за доказване на хипотезата е използването на **t-тест на Стюдент**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,33 +621,31 @@
    "source": [
     "Двете стойности, върнати от функцията `ttest_ind`, са:\n",
     "* p-стойността може да се разглежда като вероятността две разпределения да имат еднаква средна стойност. В нашия случай тя е много ниска, което означава, че има силни доказателства в подкрепа на твърдението, че първите базови играчи са по-високи.\n",
-    "* t-стойността е междинната стойност на нормализираната разлика в средните стойности, която се използва в t-теста и се сравнява с праговата стойност за дадено ниво на доверие.\n"
+    "* t-стойността е междинната стойност на нормализираната разлика в средните стойности, която се използва в t-теста и се сравнява с праговата стойност за дадено ниво на увереност.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Симулиране на нормално разпределение с помощта на теоремата за централната граница\n",
+    "## Симулиране на нормално разпределение с теоремата за централната граница\n",
     "\n",
     "Псевдослучайният генератор в Python е създаден да ни предоставя равномерно разпределение. Ако искаме да създадем генератор за нормално разпределение, можем да използваме теоремата за централната граница. За да получим стойност с нормално разпределение, просто ще изчислим средната стойност на извадка, генерирана с равномерно разпределение.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Корелация и Злата Бейзболна Корпорация\n",
+    "## Корелация и Злата бейзболна корпорация\n",
     "\n",
-    "Корелацията ни позволява да откриваме връзки между последователности от данни. В нашия пример, нека си представим, че съществува зла бейзболна корпорация, която плаща на своите играчи според техния ръст – колкото по-висок е играчът, толкова повече пари получава. Да предположим, че има базова заплата от $1000 и допълнителен бонус от $0 до $100, в зависимост от ръста. Ще вземем реални играчи от MLB и ще изчислим техните въображаеми заплати:\n"
+    "Корелацията ни позволява да откриваме връзки между последователности от данни. В нашия пример, нека си представим, че съществува зла бейзболна корпорация, която плаща на своите играчи според тяхната височина - колкото по-висок е играчът, толкова повече пари получава. Да предположим, че има базова заплата от $1000 и допълнителен бонус от $0 до $100, в зависимост от височината. Ще вземем реални играчи от MLB и ще изчислим техните въображаеми заплати:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Нека сега изчислим ковариацията и корелацията на тези последователности. `np.cov` ще ни даде така наречената **матрица на ковариацията**, която е разширение на ковариацията за множество променливи. Елементът $M_{ij}$ от матрицата на ковариацията $M$ е корелация между входните променливи $X_i$ и $X_j$, а диагоналните стойности $M_{ii}$ са дисперсията на $X_{i}$. По подобен начин, `np.corrcoef` ще ни даде **матрицата на корелацията**.\n"
+    "Нека сега изчислим ковариацията и корелацията на тези последователности. `np.cov` ще ни даде така наречената **ковариационна матрица**, която е разширение на ковариацията за множество променливи. Елементът $M_{ij}$ на ковариационната матрица $M$ е корелация между входните променливи $X_i$ и $X_j$, а диагоналните стойности $M_{ii}$ са дисперсията на $X_{i}$. По подобен начин, `np.corrcoef` ще ни даде **корелационната матрица**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Корелация, равна на 1, означава, че има силна **линейна връзка** между две променливи. Можем визуално да видим линейната връзка, като начертаем една стойност спрямо другата:\n"
+    "Корелация, равна на 1, означава, че има силна **линейна връзка** между две променливи. Можем визуално да видим линейната връзка, като начертаем едната стойност спрямо другата:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Нека видим какво се случва, ако връзката не е линейна. Да предположим, че нашата корпорация реши да скрие очевидната линейна зависимост между височините и заплатите и въведе някаква нелинейност във формулата, като например `sin`:\n"
+    "Нека видим какво се случва, ако зависимостта не е линейна. Да предположим, че нашата корпорация реши да скрие очевидната линейна зависимост между височините и заплатите и въведе някаква нелинейност във формулата, като например `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "В този случай корелацията е малко по-малка, но все още е доста висока. Сега, за да направим връзката още по-малко очевидна, може да искаме да добавим допълнителна случайност, като добавим някаква случайна променлива към заплатата. Нека видим какво ще се случи:\n"
+    "В този случай корелацията е малко по-малка, но все още е доста висока. Сега, за да направим връзката още по-малко очевидна, може да добавим допълнителна случайност, като добавим някаква случайна променлива към заплатата. Нека видим какво ще се случи:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -998,17 +833,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "За съжаление, не получихме никакви резултати - само някои странни стойности `nan`. Това се дължи на факта, че някои от стойностите в нашата серия са неопределени, представени като `nan`, което води до неопределен резултат от операцията. Като разгледаме матрицата, можем да видим, че колоната `Weight` е проблематична, защото само-корелацията между стойностите на `Height` е била изчислена.\n",
+    "За съжаление, не получихме никакви резултати - само някакви странни стойности `nan`. Това се дължи на факта, че някои от стойностите в нашата серия са неопределени, представени като `nan`, което води до неопределен резултат от операцията. Като разгледаме матрицата, можем да видим, че колоната `Weight` е проблемната, защото само-корелацията между стойностите на `Height` е била изчислена.\n",
     "\n",
-    "> Този пример показва значението на **подготовката на данни** и **почистването**. Без подходящи данни не можем да изчислим нищо.\n",
+    "> Този пример показва колко е важно **подготвянето на данни** и **почистването им**. Без подходящи данни не можем да изчислим нищо.\n",
     "\n",
-    "Нека използваме метода `fillna`, за да запълним липсващите стойности и да изчислим корелацията:\n"
+    "Нека използваме метода `fillna`, за да запълним липсващите стойности, и да изчислим корелацията:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,32 +888,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Наистина има корелация, но не толкова силна, колкото в нашия изкуствен пример. Всъщност, ако погледнем диаграмата на разсейване на една стойност спрямо другата, връзката би била много по-малко очевидна:\n"
+    "Наистина има корелация, но не толкова силна, колкото в нашия изкуствен пример. Наистина, ако погледнем разсейващата диаграма на една стойност спрямо другата, връзката би била много по-малко очевидна:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1089,14 +922,14 @@
    "source": [
     "## Заключение\n",
     "\n",
-    "В този тетрадка научихме как да извършваме основни операции с данни за изчисляване на статистически функции. Сега знаем как да използваме надежден апарат от математика и статистика, за да доказваме хипотези и как да изчисляваме доверителни интервали за произволни променливи, базирани на дадена извадка от данни.\n"
+    "В този тефтер научихме как да извършваме основни операции с данни за изчисляване на статистически функции. Сега знаем как да използваме стабилен апарат от математика и статистика, за да доказваме хипотези, както и как да изчисляваме доверителни интервали за произволни променливи, базирани на дадена извадка от данни.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
+    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматичните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия изходен език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален превод от човек. Ние не носим отговорност за каквито и да е недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
    ]
   }
  ],
@@ -1119,11 +952,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T22:57:38+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:54:39+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "bg"
   }
diff --git a/translations/bg/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/bg/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 0f143843..9abe4193 100644
--- a/translations/bg/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/bg/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Въведение в вероятностите и статистиката\n",
+    "## Въведение в теорията на вероятностите и статистиката\n",
     "## Задача\n",
     "\n",
     "В тази задача ще използваме набора от данни за пациенти с диабет, взет [оттук](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "В този набор от данни колоните са следните:  \n",
-    "* Възраст и пол са ясни сами по себе си  \n",
-    "* BMI е индекс на телесната маса  \n",
-    "* BP е средното кръвно налягане  \n",
-    "* S1 до S6 са различни кръвни измервания  \n",
-    "* Y е качествена мярка за прогресията на заболяването за една година  \n",
+    "В този набор от данни колоните са следните:\n",
+    "* Възраст и пол са ясни сами по себе си\n",
+    "* BMI е индекс на телесната маса\n",
+    "* BP е средното кръвно налягане\n",
+    "* S1 до S6 са различни кръвни измервания\n",
+    "* Y е качествена мярка за прогресията на заболяването за една година\n",
     "\n",
     "Нека изследваме този набор от данни, използвайки методите на вероятността и статистиката.\n",
     "\n",
-    "### Задача 1: Изчислете средните стойности и вариацията за всички стойности  \n"
+    "### Задача 1: Изчислете средните стойности и вариацията за всички стойности\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Задача 2: Начертайте boxplots за BMI, BP и Y в зависимост от пола\n"
+    "### Задача 2: Начертайте кутии за BMI, BP и Y в зависимост от пола\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -595,7 +595,7 @@
     "Заключения:\n",
     "* Възраст - нормална\n",
     "* Пол - еднороден\n",
-    "* BMI, Y - трудно за определяне\n"
+    "* ИТМ, Y - трудно е да се определи\n"
    ],
    "metadata": {}
   },
@@ -604,7 +604,7 @@
    "source": [
     "### Задача 4: Тествайте корелацията между различни променливи и прогресията на заболяването (Y)\n",
     "\n",
-    "> **Подсказка** Корелационната матрица ще ви предостави най-полезната информация за това кои стойности са взаимозависими.\n"
+    "> **Подсказка** Корелационната матрица ще ви даде най-полезната информация за това кои стойности са зависими.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -918,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
+    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматичните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия изходен език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален превод от човек. Ние не носим отговорност за каквито и да е недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n"
    ]
   }
  ],
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:21:57+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:55:11+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "bg"
   }
diff --git a/translations/bn/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/bn/1-Introduction/04-stats-and-probability/assignment.ipynb
index 53a5f3b3..9786f57d 100644
--- a/translations/bn/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/bn/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## সম্ভাবনা এবং পরিসংখ্যানের পরিচিতি\n",
-    "## অ্যাসাইনমেন্ট\n",
+    "## সম্ভাবনা এবং পরিসংখ্যানের পরিচিতি  \n",
+    "## অ্যাসাইনমেন্ট  \n",
     "\n",
-    "এই অ্যাসাইনমেন্টে, আমরা ডায়াবেটিস রোগীদের ডেটাসেট ব্যবহার করব যা [এখান থেকে নেওয়া হয়েছে](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।\n"
+    "এই অ্যাসাইনমেন্টে, আমরা ডায়াবেটিস রোগীদের ডেটাসেট ব্যবহার করব যা [এখান থেকে নেওয়া হয়েছে](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।  \n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "এই ডেটাসেটে কলামগুলো নিম্নরূপ:  \n",
-    "* বয়স এবং লিঙ্গ স্বতঃব্যাখ্যামূলক  \n",
-    "* BMI হলো শরীরের ভর সূচক  \n",
-    "* BP হলো গড় রক্তচাপ  \n",
-    "* S1 থেকে S6 হলো বিভিন্ন রক্তের পরিমাপ  \n",
-    "* Y হলো এক বছরের মধ্যে রোগের অগ্রগতির গুণগত পরিমাপ  \n",
+    "এই ডেটাসেটে নিম্নলিখিত কলামগুলো রয়েছে:\n",
+    "* বয়স এবং লিঙ্গ স্বতঃস্পষ্ট\n",
+    "* BMI হলো শরীরের ভর সূচক\n",
+    "* BP হলো গড় রক্তচাপ\n",
+    "* S1 থেকে S6 হলো বিভিন্ন রক্তের পরিমাপ\n",
+    "* Y হলো এক বছরের মধ্যে রোগের অগ্রগতির গুণগত পরিমাপ\n",
     "\n",
-    "চলুন সম্ভাব্যতা এবং পরিসংখ্যানের পদ্ধতি ব্যবহার করে এই ডেটাসেটটি অধ্যয়ন করি।  \n",
+    "চলুন এই ডেটাসেটটি সম্ভাবনা এবং পরিসংখ্যানের পদ্ধতি ব্যবহার করে অধ্যয়ন করি।\n",
     "\n",
-    "### কাজ ১: সমস্ত মানের জন্য গড় মান এবং বৈচিত্র্য গণনা করুন  \n"
+    "### কাজ ১: সমস্ত মানের জন্য গড় মান এবং বৈচিত্র্য গণনা করুন\n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### কাজ ২: লিঙ্গের উপর নির্ভর করে BMI, BP এবং Y এর জন্য বক্সপ্লট আঁকুন\n"
+    "### টাস্ক ২: লিঙ্গের উপর নির্ভর করে BMI, BP এবং Y এর জন্য বক্সপ্লট আঁকুন\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### টাস্ক ৩: বয়স, লিঙ্গ, BMI এবং Y ভেরিয়েবলের বিতরণ কী?\n"
+    "### টাস্ক ৩: বয়স, লিঙ্গ, বিএমআই এবং ওয়াই ভেরিয়েবলের বণ্টন কী?\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### কাজ ৫: পুরুষ এবং নারীদের মধ্যে ডায়াবেটিসের অগ্রগতির মাত্রা ভিন্ন কিনা তা পরীক্ষা করুন\n"
+    "### টাস্ক ৫: পুরুষ এবং নারীদের মধ্যে ডায়াবেটিসের অগ্রগতির মাত্রা ভিন্ন কিনা তা পরীক্ষা করুন\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**অস্বীকৃতি**:  \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসম্ভব সঠিক অনুবাদ প্রদানের চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা দায়বদ্ধ থাকব না।\n"
+    "\n---\n\n**অস্বীকৃতি**:  \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসাধ্য সঠিকতার জন্য চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা দায়বদ্ধ থাকব না।\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:16:37+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:19:49+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "bn"
   }
diff --git a/translations/bn/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/bn/1-Introduction/04-stats-and-probability/notebook.ipynb
index cb49faab..1f196317 100644
--- a/translations/bn/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/bn/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# সম্ভাবনা এবং পরিসংখ্যানের পরিচিতি  \n",
-    "এই নোটবুকে, আমরা পূর্বে আলোচনা করা কিছু ধারণা নিয়ে কাজ করব। সম্ভাবনা এবং পরিসংখ্যানের অনেক ধারণা Python-এর ডেটা প্রক্রিয়াকরণের প্রধান লাইব্রেরিগুলিতে, যেমন `numpy` এবং `pandas`, ভালোভাবে উপস্থাপিত।  \n"
+    "এই নোটবুকে, আমরা পূর্বে আলোচনা করা কিছু ধারণা নিয়ে কাজ করব। সম্ভাবনা এবং পরিসংখ্যানের অনেক ধারণা Python-এর ডেটা প্রক্রিয়াকরণের প্রধান লাইব্রেরিগুলিতে, যেমন `numpy` এবং `pandas`, ভালোভাবে উপস্থাপিত।  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## র্যান্ডম ভেরিয়েবল এবং ডিস্ট্রিবিউশন\n",
-    "চলুন শুরু করি ০ থেকে ৯ পর্যন্ত একটি ইউনিফর্ম ডিস্ট্রিবিউশন থেকে ৩০টি মানের একটি নমুনা আঁকা দিয়ে। আমরা গড় এবং বৈচিত্র্যও গণনা করব।\n"
+    "## র‍্যান্ডম ভেরিয়েবল এবং ডিস্ট্রিবিউশন  \n",
+    "চলুন ০ থেকে ৯ এর মধ্যে একটি ইউনিফর্ম ডিস্ট্রিবিউশন থেকে ৩০টি মানের একটি নমুনা নিই। আমরা গড় এবং ভ্যারিয়েন্সও গণনা করব।  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## বাস্তব ডেটা বিশ্লেষণ\n",
     "\n",
-    "বাস্তব ডেটা বিশ্লেষণের ক্ষেত্রে গড় এবং বৈচিত্র্য খুবই গুরুত্বপূর্ণ। চলুন আমরা বেসবল খেলোয়াড়দের সম্পর্কে ডেটা লোড করি [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) থেকে।\n"
+    "বাস্তব ডেটা বিশ্লেষণের সময় গড় এবং বৈচিত্র্য খুবই গুরুত্বপূর্ণ। চলুন বেসবল খেলোয়াড়দের সম্পর্কে ডেটা লোড করি [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) থেকে।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "আমরা এখানে ডেটা বিশ্লেষণের জন্য [**Pandas**](https://pandas.pydata.org/) নামক একটি প্যাকেজ ব্যবহার করছি। এই কোর্সের পরে আমরা Pandas এবং Python-এ ডেটা নিয়ে কাজ করার বিষয়ে আরও আলোচনা করব।\n",
+    "আমরা এখানে ডেটা বিশ্লেষণের জন্য [**Pandas**](https://pandas.pydata.org/) নামক একটি প্যাকেজ ব্যবহার করছি। এই কোর্সে আমরা পরে Pandas এবং পাইথনে ডেটা নিয়ে কাজ করার বিষয়ে আরও আলোচনা করব।\n",
     "\n",
     "চলুন বয়স, উচ্চতা এবং ওজনের গড় মান গণনা করি:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "গড়ের পাশাপাশি, মধ্যমান এবং চতুর্থাংশগুলোর দিকে নজর দেওয়া যুক্তিসঙ্গত। এগুলোকে **বক্স প্লট** ব্যবহার করে চিত্রিত করা যেতে পারে:\n"
+    "গড়ের পাশাপাশি, মধ্যমান এবং চতুর্থাংশগুলোর দিকে নজর দেওয়া যৌক্তিক। এগুলোকে একটি **বক্স প্লট** ব্যবহার করে চিত্রিত করা যেতে পারে:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "আমরা আমাদের ডেটাসেটের উপসেটগুলির বক্স প্লটও তৈরি করতে পারি, উদাহরণস্বরূপ, খেলোয়াড়ের ভূমিকা অনুযায়ী গোষ্ঠীবদ্ধ।\n"
+    "আমরা আমাদের ডেটাসেটের উপসেটগুলির বক্স প্লটও তৈরি করতে পারি, উদাহরণস্বরূপ, খেলোয়াড়ের ভূমিকা অনুযায়ী গোষ্ঠীবদ্ধ করে।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **নোট**: এই চিত্রটি ইঙ্গিত করে যে, গড়ে প্রথম বেসম্যানদের উচ্চতা দ্বিতীয় বেসম্যানদের উচ্চতার তুলনায় বেশি। পরে আমরা শিখব কীভাবে এই অনুমানটি আরও আনুষ্ঠানিকভাবে পরীক্ষা করা যায় এবং কীভাবে আমাদের ডেটা পরিসংখ্যানগতভাবে গুরুত্বপূর্ণ তা প্রদর্শন করা যায়।  \n",
+    "> **Note**: এই ডায়াগ্রামটি ইঙ্গিত দেয় যে গড়ে, প্রথম বেসম্যানদের উচ্চতা দ্বিতীয় বেসম্যানদের উচ্চতার তুলনায় বেশি। পরে আমরা শিখব কীভাবে এই অনুমানটি আরও আনুষ্ঠানিকভাবে পরীক্ষা করা যায় এবং কীভাবে আমাদের ডেটা পরিসংখ্যানগতভাবে গুরুত্বপূর্ণ তা প্রদর্শন করা যায়।  \n",
     "\n",
-    "বয়স, উচ্চতা এবং ওজন সবই ধারাবাহিক র্যান্ডম ভেরিয়েবল। আপনি কি মনে করেন এদের বণ্টন কেমন? এটি জানার একটি ভালো উপায় হলো মানগুলোর হিস্টোগ্রাম আঁকা:\n"
+    "বয়স, উচ্চতা এবং ওজন সবই ধারাবাহিক র্যান্ডম ভেরিয়েবল। আপনি কি মনে করেন এদের বিতরণ কেমন? এটি জানার একটি ভালো উপায় হলো মানগুলোর হিস্টোগ্রাম আঁকা:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -438,26 +284,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## নরমাল ডিস্ট্রিবিউশন\n",
+    "## স্বাভাবিক বণ্টন\n",
     "\n",
-    "চলুন একটি কৃত্রিম ওজনের নমুনা তৈরি করি যা আমাদের আসল ডেটার মতোই গড় এবং বৈচিত্র্য অনুসরণ করে:\n"
+    "চলুন একটি কৃত্রিম ওজনের নমুনা তৈরি করি যা আমাদের প্রকৃত ডেটার মতোই গড় এবং বৈচিত্র্য অনুসরণ করে স্বাভাবিক বণ্টন মেনে চলে:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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T/OHwsDOTsXOf6/YktyfJNddcs+vCgfkzhQKAg2jqgFxV35rkd5L8VHf/VVVtu+kWY33eQPfdSe5Oko2NjfPuB4B14cMmrJapVrGoqpdmMxy/v7t/dzL81aq6YnL/FUmemYyfSXL18PCrkjw1m3IBAGC+dgzItdkq/rUkj3X3Lw53nUxyZHL9SJL7hvHDVfWyqro2yXVJHppdyQAAMD/TTLF4c5IfS/LHVfWZydjPJjme5ERV3ZbkiSS3Jkl3P1JVJ5I8ms0VMO7o7hdmXTgArJOz0zAeP37zgisBdgzI3f2/svW84iS5cZvHHEtybA91AQDAQjiTHgAADARkAAAYCMgAADDY1YlCAIDZskYyLB8dZAAAGAjIAAAwEJABAGAgIAMAwEBABoAlcujo/Q7cgwUTkAEAYGCZNwDYZzrEsNx0kAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQ4oJyMAAC2JiADwBLyIRYWx4lC4IA7+wf48eM3v+g2ABxUOsgAsAZ0nGF2BGQAABgIyAAAMDAHGUhi7jEAnKWDDAArxFxjmD8BGQAABgIyAAAMzEGGA8ZXs7Bazl2rHJg/HWQAABgIyAAAMDDFAgBWgOlRsH90kAEAYCAgw5qxRioA7I2ADAAAA3OQ4YDQVYb15HcbZk8HGQAABgIyAKwxxyXA7gnIAAAwEJABAGAgIAMAwEBABgCAgWXeYM05OAcAdkdAhjUhCAPAbAjIALCCfCiG+TEHGQAABgIyAAAMTLGAFXP2a9XHj9/8otsAwGzoIAMAwEBABgCAgYAMAAADARmW3KGj95tnDAD7yEF6sKaEagC4OAIyrAiBFwD2hykWAAAwEJABAGCwY0CuqvdV1TNV9flh7NVV9UBVfWly+arhvruq6nRVfbGq3j6vwuGgc/AeAMxHdfeFN6j6gSR/k+TXu/v1k7H/kORr3X28qo4meVV331lV1yf5QJIbkrw2yUeTfGd3v3Ch19jY2OhTp07t/V8Da0gIBmbp7Fk4gaSqHu7ujXPHd+wgd/fHk3ztnOFbktwzuX5PkncN4/d293Pd/ZUkp7MZlgEAYCVc7Bzky7v76SSZXF42Gb8yyZPDdmcmYwAAsBJmfZBebTG25RyOqrq9qk5V1alnn312xmUAAMDFudiA/NWquiJJJpfPTMbPJLl62O6qJE9t9QTdfXd3b3T3xqWXXnqRZQAAwGxdbEA+meTI5PqRJPcN44er6mVVdW2S65I8tLcS4WCxOgUALNaOZ9Krqg8keUuS11TVmSQ/l+R4khNVdVuSJ5LcmiTd/UhVnUjyaJLnk9yx0woWAMD+O/tB3KoWcL4dA3J3/8g2d924zfbHkhzbS1EAALAozqQHAAADARkAAAYCMgAADHacgwzMlwNlgP200yo53pNABxkAAF5EQAYAzmNNdg4yARkAAAYCMgAADBykB3PmgBdgFZhOAd8gIMOS8scKABbDFAsAABjoIMOS0DEGgOWggwz7zNJJwCo59z3LexgHgYAMAAADARkAAAbmIMOC+IoSAJaTgAwA7MiHeg4SARlmzIlBgFVyscHXex3rzBxkAGDPrG7BOhGQAQBgYIoF7BOdFQBYDTrIAAAw0EGGizB2g7c7QEXHGABWk4AMMyIQA0zXQIBlZ4oFAAAMdJBhF3SJAWD96SADAMBAQAYAgIGADADMhbPrsarMQYY98uYPAOtFBxkAAAYCMgCwr0y9YNkJyAAAMBCQAYC50jFm1QjIcAHe1AHg4LGKBWxBKAaAg0tAhnwjED9+/OYL3g/Ai83j/XGn92SYNwEZANgXmg2sCgGZA2HaboQ3b4D9o1PMshKQOdAEYoDF2y4oC9AsioDMWtEpBlh/577XC9LMmoAMACwFzQuWhXWQAQBgoIMMAKwEHWb2S3X3omvIxsZGnzp1atFlsAa8eQIcXOYgs1tV9XB3b5w7booFAAAMBGQAABiYg8xKcCpoAKZl2Tf2SgcZAAAGOsgAwFrY7ttEHWV2S0BmpZz75ufNDgCYNQEZAFhL5zZVtusw78cpq3WxV4s5yAAAMNBBZl+d+wl6uykTPmkDsEr83VovAjIzt9WbxMUuw2b5NgCWkUC83gRkdjTtGsSLeJMQoAHYq93OVWb9zS0gV9VNSX45ySVJ3tvdx+f1WizGXsLpTkvxAMAq02FebXMJyFV1SZJfSfKPk5xJ8qmqOtndj87j9ZjOdr+su/0lnjbECrsArJOt/q5N232e9rmX8dvag2heHeQbkpzu7i8nSVXdm+SWJALyHO0UgLfbfqfnu9jtAYDd2elg9t0+frePu5jHrqPq7tk/adU/T3JTd/+bye0fS/IPu/vdW22/sbHRp06dmnkd09jrJ7aL7b5u9YO/3QoOF/vLcrG/XADAfC3qb/Q0r7vTN8177WYv00m/qurh7t44b3xOAfnWJG8/JyDf0N0/Pmxze5LbJze/K8kXZ17I3r0myZ8tuogVYV/tjv01Pftqd+yv6dlXu2N/Tc++2p1F7q+/292Xnjs4rykWZ5JcPdy+KslT4wbdfXeSu+f0+jNRVae2+lTB+eyr3bG/pmdf7Y79NT37anfsr+nZV7uzjPtrXmfS+1SS66rq2qr65iSHk5yc02sBAMDMzKWD3N3PV9W7k3w4m8u8va+7H5nHawEAwCzNbR3k7v69JL83r+ffJ0s9BWTJ2Fe7Y39Nz77aHftrevbV7thf07Ovdmfp9tdcDtIDAIBVNa85yAAAsJIE5ClV1b+tqq6q1yy6lmVVVf++qj5XVZ+pqo9U1WsXXdMyq6pfqKovTPbZB6vqlYuuaVlV1a1V9UhVfb2qlupI52VRVTdV1Rer6nRVHV10Pcusqt5XVc9U1ecXXcsqqKqrq+oPquqxye/hTy66pmVVVS+vqoeq6rOTffXzi65p2VXVJVX1R1X1oUXXMhKQp1BVV2fztNlPLLqWJfcL3f093f2GJB9K8u8WXM+yeyDJ67v7e5L87yR3LbieZfb5JP8syccXXcgyqqpLkvxKkn+S5PokP1JV1y+2qqX235LctOgiVsjzSX66u787yZuS3OHna1vPJXlrd39vkjckuamq3rTYkpbeTyZ5bNFFnEtAns5/TPIzSUzYvoDu/qvh5itif11Qd3+ku5+f3PzDbK4Xzha6+7HuXsaTCS2LG5Kc7u4vd/ffJrk3yS0LrmlpdffHk3xt0XWsiu5+urs/Pbn+19kMM1cutqrl1Jv+ZnLzpZP//C3cRlVdleTmJO9ddC3nEpB3UFXvTPIn3f3ZRdeyCqrqWFU9meRfRAd5N/51kv++6CJYWVcmeXK4fSYCDHNQVYeSvDHJJxdcytKaTBn4TJJnkjzQ3fbV9n4pmw3Iry+4jvPMbZm3VVJVH03yHVvc9Z4kP5vkB/e3ouV1oX3V3fd193uSvKeq7kry7iQ/t68FLpmd9tdkm/dk8yvM9+9nbctmmn3FtmqLMV0rZqqqvjXJ7yT5qXO+MWTQ3S8kecPkuJIPVtXru9t893NU1TuSPNPdD1fVWxZcznkE5CTd/batxqvqHyS5NslnqyrZ/Ar801V1Q3f/6T6WuDS221db+M0k9+eAB+Sd9ldVHUnyjiQ39gFfc3EXP1uc70ySq4fbVyV5akG1sIaq6qXZDMfv7+7fXXQ9q6C7/7KqPpbN+e4C8vnenOSdVfVDSV6e5Nur6je6+0cXXFcSUywuqLv/uLsv6+5D3X0om3+Evu+ghuOdVNV1w813JvnCompZBVV1U5I7k7yzu//fouthpX0qyXVVdW1VfXOSw0lOLrgm1kRtdoh+Lclj3f2Li65nmVXVpWdXJKqqb0nytvhbuKXuvqu7r5rkq8NJfn9ZwnEiIDNbx6vq81X1uWxOS7EU0IX9pyTfluSBydJ4/2XRBS2rqvqnVXUmyfcnub+qPrzompbJ5GDPdyf5cDYPoDrR3Y8stqrlVVUfSPKJJN9VVWeq6rZF17Tk3pzkx5K8dfJe9ZlJ14/zXZHkDyZ/Bz+VzTnIS7V8GdNxJj0AABjoIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYPD/ASvKmaTtYFHZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "যেহেতু বাস্তব জীবনের বেশিরভাগ মান সাধারণত স্বাভাবিকভাবে বিতরণ করা হয়, আমাদের নমুনা ডেটা তৈরি করতে একটি ইউনিফর্ম র্যান্ডম নম্বর জেনারেটর ব্যবহার করা উচিত নয়। এখানে দেখুন কী ঘটে যদি আমরা ইউনিফর্ম বিতরণ ব্যবহার করে ওজন তৈরি করার চেষ্টা করি (`np.random.rand` দ্বারা তৈরি):\n"
+    "যেহেতু বাস্তব জীবনের বেশিরভাগ মান সাধারণত স্বাভাবিক বণ্টিত, আমাদের নমুনা ডেটা তৈরি করতে একটি অভিন্ন র্যান্ডম সংখ্যা জেনারেটর ব্যবহার করা উচিত নয়। এখানে কী ঘটে যদি আমরা একটি অভিন্ন বণ্টন (যা `np.random.rand` দ্বারা তৈরি) ব্যবহার করে ওজন তৈরি করার চেষ্টা করি:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -554,23 +395,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## আত্মবিশ্বাসের ব্যবধান\n",
+    "## বিশ্বাসযোগ্যতার পরিসীমা\n",
     "\n",
-    "এবার আমরা বেসবল খেলোয়াড়দের ওজন এবং উচ্চতার জন্য আত্মবিশ্বাসের ব্যবধান গণনা করব। আমরা এই কোডটি ব্যবহার করব [এই স্ট্যাকওভারফ্লো আলোচনার থেকে](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "এবার আমরা বেসবল খেলোয়াড়দের ওজন এবং উচ্চতার জন্য বিশ্বাসযোগ্যতার পরিসীমা গণনা করব। আমরা এই কোড ব্যবহার করব [এই স্ট্যাকওভারফ্লো আলোচনার থেকে](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## হাইপোথিসিস টেস্টিং\n",
     "\n",
-    "চলুন আমাদের বেসবল খেলোয়াড়দের ডেটাসেটে বিভিন্ন ভূমিকা পরীক্ষা করি:\n"
+    "চলুন আমাদের বেসবল খেলোয়াড়দের ডেটাসেটে বিভিন্ন ভূমিকা অন্বেষণ করি:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,38 +545,38 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "চলুন এই অনুমান পরীক্ষা করি যে প্রথম বেসম্যানরা দ্বিতীয় বেসম্যানদের তুলনায় লম্বা। এটি পরীক্ষা করার সবচেয়ে সহজ উপায় হল আত্মবিশ্বাসের পরিসীমা পরীক্ষা করা:\n"
+    "চলুন এই অনুমানটি পরীক্ষা করি যে প্রথম বেসম্যানরা দ্বিতীয় বেসম্যানদের চেয়ে লম্বা। এটি পরীক্ষা করার সবচেয়ে সহজ উপায় হল আত্মবিশ্বাসের পরিসীমা পরীক্ষা করা:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,22 +591,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "আমরা দেখতে পাচ্ছি যে সময়সীমাগুলো একে অপরের সাথে ওভারল্যাপ করছে না।\n",
+    "আমরা দেখতে পাচ্ছি যে সময়কালগুলো একে অপরের সাথে ওভারল্যাপ করে না।\n",
     "\n",
-    "পরিসংখ্যানগতভাবে আরও সঠিকভাবে অনুমান প্রমাণ করার একটি উপায় হলো **Student t-test** ব্যবহার করা:\n"
+    "পরিসংখ্যানগতভাবে আরও সঠিক উপায়ে হাইপোথিসিস প্রমাণ করার জন্য একটি **Student t-test** ব্যবহার করা হয়:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,35 +621,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ttest_ind` ফাংশন দ্বারা ফেরত দেওয়া দুটি মান হল:  \n",
-    "* p-value কে দুইটি বিতরণের একই গড় থাকার সম্ভাবনা হিসেবে বিবেচনা করা যেতে পারে। আমাদের ক্ষেত্রে, এটি খুবই কম, যার অর্থ হল প্রথম বেসম্যানদের উচ্চতর হওয়ার পক্ষে শক্তিশালী প্রমাণ রয়েছে।  \n",
-    "* t-value হল স্বাভাবিকীকৃত গড় পার্থক্যের মধ্যবর্তী মান যা t-test-এ ব্যবহৃত হয় এবং এটি একটি নির্দিষ্ট আত্মবিশ্বাস মানের জন্য একটি থ্রেশহোল্ড মানের সাথে তুলনা করা হয়।  \n"
+    "`ttest_ind` ফাংশন দুটি মান প্রদান করে:  \n",
+    "* p-value হলো দুইটি বণ্টনের একই গড় মান থাকার সম্ভাবনা। আমাদের ক্ষেত্রে, এটি খুবই কম, যা নির্দেশ করে যে প্রথম বেসম্যানরা লম্বা হওয়ার পক্ষে শক্তিশালী প্রমাণ রয়েছে।  \n",
+    "* t-value হলো স্বাভাবিকীকৃত গড় পার্থক্যের একটি মধ্যবর্তী মান, যা t-পরীক্ষায় ব্যবহৃত হয় এবং এটি নির্দিষ্ট একটি আত্মবিশ্বাস মানের জন্য একটি সীমা মানের সাথে তুলনা করা হয়।  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## কেন্দ্রীয় সীমা উপপাদ্য ব্যবহার করে একটি স্বাভাবিক বণ্টন সিমুলেট করা\n",
+    "## কেন্দ্রীয় সীমা উপপাদ্য ব্যবহার করে একটি নরমাল বিতরণ সিমুলেট করা\n",
     "\n",
-    "Python-এ থাকা ছদ্ম-র্যান্ডম জেনারেটর আমাদের একটি সমান বণ্টন প্রদান করার জন্য ডিজাইন করা হয়েছে। যদি আমরা একটি স্বাভাবিক বণ্টনের জন্য জেনারেটর তৈরি করতে চাই, তাহলে আমরা কেন্দ্রীয় সীমা উপপাদ্য ব্যবহার করতে পারি। একটি স্বাভাবিক বণ্টিত মান পেতে, আমরা কেবল একটি সমান-বণ্টিত নমুনার গড় হিসাব করব।\n"
+    "পাইথনের ছদ্ম-র্যান্ডম জেনারেটর আমাদের একটি ইউনিফর্ম বিতরণ প্রদান করার জন্য ডিজাইন করা হয়েছে। যদি আমরা একটি নরমাল বিতরণের জন্য জেনারেটর তৈরি করতে চাই, তাহলে আমরা কেন্দ্রীয় সীমা উপপাদ্য ব্যবহার করতে পারি। একটি নরমাল বিতরণযুক্ত মান পেতে, আমরা ইউনিফর্ম-জেনারেট করা নমুনার গড় হিসাব করব।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,21 +667,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## সম্পর্ক এবং দুষ্টু বেসবল কর্পোরেশন\n",
+    "## সম্পর্ক এবং ইভিল বেসবল কর্প\n",
     "\n",
-    "সম্পর্ক আমাদের ডেটা সিকোয়েন্সগুলোর মধ্যে সম্পর্ক খুঁজে বের করতে সাহায্য করে। আমাদের এই খেলনা উদাহরণে, ধরুন একটি দুষ্টু বেসবল কর্পোরেশন আছে যারা তাদের খেলোয়াড়দের উচ্চতার উপর ভিত্তি করে বেতন দেয় - খেলোয়াড় যত লম্বা, তিনি তত বেশি টাকা পান। ধরুন একটি বেসিক বেতন $1000, এবং উচ্চতার উপর নির্ভর করে $0 থেকে $100 পর্যন্ত একটি অতিরিক্ত বোনাস দেওয়া হয়। আমরা MLB-এর আসল খেলোয়াড়দের নেব এবং তাদের কাল্পনিক বেতন হিসাব করব:\n"
+    "সম্পর্ক আমাদের ডেটা সিকোয়েন্সগুলোর মধ্যে সম্পর্ক খুঁজে বের করতে সাহায্য করে। আমাদের খেলনা উদাহরণে, চলুন ধরে নিই একটি ইভিল বেসবল কর্পোরেশন আছে যারা তাদের খেলোয়াড়দের উচ্চতার ভিত্তিতে বেতন দেয় - খেলোয়াড় যত লম্বা, তত বেশি টাকা পায়। ধরে নিই একটি বেসিক বেতন $1000, এবং উচ্চতার উপর নির্ভর করে $0 থেকে $100 পর্যন্ত একটি অতিরিক্ত বোনাস। আমরা MLB-এর আসল খেলোয়াড়দের নেব এবং তাদের কাল্পনিক বেতন হিসাব করব:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +695,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "এখন চলুন সেই সিকোয়েন্সগুলির কোভেরিয়েন্স এবং করেলেশন গণনা করি। `np.cov` আমাদের একটি তথাকথিত **কোভেরিয়েন্স ম্যাট্রিক্স** দেবে, যা কোভেরিয়েন্সের একটি সম্প্রসারণ একাধিক ভেরিয়েবলের জন্য। কোভেরিয়েন্স ম্যাট্রিক্স $M$ এর উপাদান $M_{ij}$ হল ইনপুট ভেরিয়েবল $X_i$ এবং $X_j$ এর মধ্যে করেলেশন, এবং ডায়াগোনাল মান $M_{ii}$ হল $X_{i}$ এর ভ্যারিয়েন্স। অনুরূপভাবে, `np.corrcoef` আমাদের **করেলেশন ম্যাট্রিক্স** দেবে।\n"
+    "এবার চলুন ঐ সিকোয়েন্সগুলোর কোভেরিয়েন্স এবং করেলেশন গণনা করি। `np.cov` আমাদের একটি তথাকথিত **কোভেরিয়েন্স ম্যাট্রিক্স** দেবে, যা কোভেরিয়েন্সকে একাধিক ভেরিয়েবলের জন্য সম্প্রসারিত করে। কোভেরিয়েন্স ম্যাট্রিক্স $M$-এর উপাদান $M_{ij}$ হলো ইনপুট ভেরিয়েবল $X_i$ এবং $X_j$-এর মধ্যে করেলেশন, এবং ডায়াগোনাল মান $M_{ii}$ হলো $X_{i}$-এর ভ্যারিয়েন্স। একইভাবে, `np.corrcoef` আমাদের **করেলেশন ম্যাট্রিক্স** দেবে।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -886,24 +725,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "দুই পরিবর্তনশীলের মধ্যে সম্পর্ক ১ এর সমান হলে এর অর্থ হলো তাদের মধ্যে একটি শক্তিশালী **রৈখিক সম্পর্ক** রয়েছে। আমরা একটির বিপরীতে অন্যটি চিত্রিত করে রৈখিক সম্পর্কটি দৃশ্যমানভাবে দেখতে পারি:\n"
+    "একটি সম্পর্ক ১ এর সমান মানে দুটি ভেরিয়েবলের মধ্যে একটি শক্তিশালী **রৈখিক সম্পর্ক** রয়েছে। আমরা একটি ভেরিয়েবলের বিপরীতে অন্যটি প্লট করে রৈখিক সম্পর্কটি দৃশ্যত দেখতে পারি:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -923,14 +760,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -943,19 +780,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "এই ক্ষেত্রে, সম্পর্কটি সামান্য ছোট, তবে এটি এখনও বেশ উচ্চ। এখন, সম্পর্কটি আরও কম স্পষ্ট করতে, আমরা বেতন এর সাথে কিছু র্যান্ডম ভেরিয়েবল যোগ করে কিছু অতিরিক্ত র্যান্ডমনেস যোগ করতে চাইতে পারি। চলুন দেখি কী ঘটে:\n"
+    "এই ক্ষেত্রে, সম্পর্কটি সামান্য কম, তবে এটি এখনও বেশ উচ্চ। এখন, সম্পর্কটি আরও কম স্পষ্ট করতে, আমরা বেতনের সাথে কিছু র্যান্ডম ভেরিয়েবল যোগ করে কিছু অতিরিক্ত এলোমেলোতা যোগ করতে চাইতে পারি। চলুন দেখি কী ঘটে:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -993,24 +828,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "আপনি কি অনুমান করতে পারেন কেন বিন্দুগুলো এভাবে উল্লম্ব রেখায় সাজানো হয়েছে?\n",
+    "আপনি কি অনুমান করতে পারেন কেন বিন্দুগুলো এমনভাবে উল্লম্ব রেখায় সাজানো হয়েছে?\n",
     "\n",
-    "আমরা একটি কৃত্রিমভাবে তৈরি ধারণা যেমন বেতন এবং পর্যবেক্ষণ করা ভেরিয়েবল *উচ্চতা*-এর মধ্যে সম্পর্ক লক্ষ্য করেছি। চলুন দেখি দুটি পর্যবেক্ষণ করা ভেরিয়েবল, যেমন উচ্চতা এবং ওজন, একে অপরের সাথে সম্পর্কিত কিনা:\n"
+    "আমরা একটি কৃত্রিমভাবে তৈরি ধারণা, যেমন বেতন, এবং পর্যবেক্ষণ করা পরিবর্তনশীল *উচ্চতা*-এর মধ্যে সম্পর্ক লক্ষ্য করেছি। চলুন দেখি দুটি পর্যবেক্ষণ করা পরিবর্তনশীল, যেমন উচ্চতা এবং ওজন, একে অপরের সাথে সম্পর্কিত কিনা:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,16 +858,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "দুঃখজনকভাবে, আমরা কোনো ফলাফল পাইনি - শুধুমাত্র কিছু অদ্ভুত `nan` মান। এর কারণ হলো আমাদের সিরিজের কিছু মান অনির্ধারিত, যা `nan` দ্বারা উপস্থাপিত হয়েছে, এবং এর ফলে অপারেশনের ফলাফলও অনির্ধারিত হয়ে গেছে। ম্যাট্রিক্সটি পর্যবেক্ষণ করলে দেখা যায় যে `Weight` কলামটি সমস্যাজনক, কারণ `Height` মানগুলোর মধ্যে স্ব-সম্পর্ক (self-correlation) গণনা করা হয়েছে।\n",
+    "দুঃখজনকভাবে, আমরা কোনো ফলাফল পাইনি - শুধুমাত্র কিছু অদ্ভুত `nan` মান। এর কারণ হলো আমাদের সিরিজের কিছু মান সংজ্ঞায়িত নয়, যা `nan` দ্বারা উপস্থাপিত হয়েছে, এবং এর ফলে অপারেশনের ফলাফলও সংজ্ঞায়িত হয়নি। ম্যাট্রিক্সটি পর্যবেক্ষণ করলে দেখা যায় যে `Weight` কলামটি সমস্যাজনক, কারণ `Height` মানগুলোর মধ্যে স্ব-সম্পর্ক (self-correlation) গণনা করা হয়েছে।\n",
     "\n",
-    "> এই উদাহরণটি **ডেটা প্রস্তুতি** এবং **পরিষ্কারকরণ** এর গুরুত্বকে তুলে ধরে। সঠিক ডেটা ছাড়া আমরা কিছুই গণনা করতে পারি না।\n",
+    "> এই উদাহরণটি **ডেটা প্রস্তুতি** এবং **পরিষ্কারকরণ** এর গুরুত্বকে তুলে ধরে। সঠিক ডেটা ছাড়া আমরা কিছুই গণনা করতে পারি না।\n",
     "\n",
     "চলুন `fillna` পদ্ধতি ব্যবহার করে অনুপস্থিত মানগুলো পূরণ করি এবং সম্পর্ক (correlation) গণনা করি:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,32 +890,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "আসলেই একটি সম্পর্ক রয়েছে, তবে আমাদের কৃত্রিম উদাহরণের মতো এতটা শক্তিশালী নয়। প্রকৃতপক্ষে, যদি আমরা এক মানের বিপরীতে অন্য মানের স্ক্যাটার প্লট দেখি, সম্পর্কটি অনেক কম স্পষ্ট হবে:\n"
+    "আসলেই একটি সম্পর্ক রয়েছে, তবে আমাদের কৃত্রিম উদাহরণের মতো এতটা শক্তিশালী নয়। প্রকৃতপক্ষে, যদি আমরা একটি মানের বিপরীতে অন্য মানের স্ক্যাটার প্লট দেখি, তবে সম্পর্কটি অনেক কম স্পষ্ট হবে:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1098,7 +931,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**অস্বীকৃতি**:  \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসাধ্য সঠিক অনুবাদের চেষ্টা করি, তবে দয়া করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। নথিটির মূল ভাষায় লেখা সংস্করণটিকেই প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে সৃষ্ট কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যার জন্য আমরা দায়ী নই।\n"
+    "\n---\n\n**অস্বীকৃতি**:  \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসম্ভব সঠিক অনুবাদ প্রদানের চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা তার জন্য দায়ী থাকব না।\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T22:56:40+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:19:36+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "bn"
   }
diff --git a/translations/bn/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/bn/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 7c0babb7..2a562ca4 100644
--- a/translations/bn/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/bn/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -151,15 +151,15 @@
    "cell_type": "markdown",
    "source": [
     "এই ডেটাসেটে কলামগুলো নিম্নরূপ:  \n",
-    "* বয়স এবং লিঙ্গ স্বতঃব্যাখ্যামূলক  \n",
+    "* বয়স এবং লিঙ্গ স্বতঃস্পষ্ট  \n",
     "* BMI হলো শরীরের ভর সূচক  \n",
     "* BP হলো গড় রক্তচাপ  \n",
     "* S1 থেকে S6 হলো বিভিন্ন রক্তের পরিমাপ  \n",
     "* Y হলো এক বছরের মধ্যে রোগের অগ্রগতির গুণগত পরিমাপ  \n",
     "\n",
-    "চলুন সম্ভাব্যতা এবং পরিসংখ্যানের পদ্ধতি ব্যবহার করে এই ডেটাসেটটি অধ্যয়ন করি।  \n",
+    "চলুন এই ডেটাসেটটি সম্ভাবনা এবং পরিসংখ্যানের পদ্ধতি ব্যবহার করে অধ্যয়ন করি।\n",
     "\n",
-    "### কাজ ১: সমস্ত মানের জন্য গড় মান এবং বৈচিত্র্য গণনা করুন  \n"
+    "### কাজ ১: সমস্ত মানের জন্য গড় মান এবং বৈচিত্র্য গণনা করুন\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### কাজ ২: লিঙ্গের উপর নির্ভর করে BMI, BP এবং Y এর জন্য বক্সপ্লট আঁকুন\n"
+    "### টাস্ক ২: লিঙ্গের উপর নির্ভর করে BMI, BP এবং Y এর জন্য বক্সপ্লট আঁকুন\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### টাস্ক ৩: বয়স, লিঙ্গ, BMI এবং Y ভেরিয়েবলগুলোর বণ্টন কী?\n"
+    "### টাস্ক ৩: বয়স, লিঙ্গ, বিএমআই এবং ওয়াই ভেরিয়েবলের বণ্টন কী?\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -592,10 +592,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "উপসংহার:\n",
-    "* বয়স - স্বাভাবিক\n",
-    "* লিঙ্গ - একরূপ\n",
-    "* BMI, Y - বলা কঠিন\n"
+    "উপসংহারসমূহ:  \n",
+    "* বয়স - স্বাভাবিক  \n",
+    "* লিঙ্গ - অভিন্ন  \n",
+    "* BMI, Y - বলা কঠিন  \n"
    ],
    "metadata": {}
   },
@@ -846,8 +846,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "উপসংহার:\n",
-    "* Y এর সাথে সবচেয়ে শক্তিশালী সম্পর্ক হলো BMI এবং S5 (রক্তে শর্করা)। এটি যুক্তিসঙ্গত শোনাচ্ছে।\n"
+    "উপসংহার:  \n",
+    "* Y এর সাথে সবচেয়ে শক্তিশালী সম্পর্ক হলো BMI এবং S5 (রক্তে চিনি)। এটি যুক্তিসঙ্গত শোনাচ্ছে।  \n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -879,7 +879,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### কাজ ৫: পুরুষ এবং নারীদের মধ্যে ডায়াবেটিসের অগ্রগতির মাত্রা ভিন্ন কিনা তা পরীক্ষা করুন\n"
+    "### টাস্ক ৫: পুরুষ এবং নারীদের মধ্যে ডায়াবেটিসের অগ্রগতির মাত্রা ভিন্ন কিনা তা পরীক্ষা করুন\n"
    ],
    "metadata": {}
   },
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -918,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**অস্বীকৃতি**:  \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসাধ্য সঠিকতার জন্য চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা দায়বদ্ধ থাকব না।\n"
+    "\n---\n\n**অস্বীকৃতি**:  \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসম্ভব সঠিক অনুবাদের চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। নথিটির মূল ভাষায় লেখা সংস্করণটিকেই প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ ব্যবহার করার পরামর্শ দেওয়া হচ্ছে। এই অনুবাদ ব্যবহারের ফলে সৃষ্ট কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যার জন্য আমরা দায়ী নই।\n"
    ]
   }
  ],
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:21:38+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:20:08+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "bn"
   }
diff --git a/translations/br/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/br/1-Introduction/04-stats-and-probability/assignment.ipynb
index e4206ad8..4590941b 100644
--- a/translations/br/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/br/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Neste conjunto de dados, as colunas são as seguintes:  \n",
-    "* Idade e sexo são autoexplicativos  \n",
-    "* IMC é o índice de massa corporal  \n",
-    "* PA é a pressão arterial média  \n",
-    "* S1 até S6 são diferentes medições sanguíneas  \n",
-    "* Y é a medida qualitativa da progressão da doença ao longo de um ano  \n",
+    "Neste conjunto de dados, as colunas são as seguintes:\n",
+    "* Idade e sexo são autoexplicativos\n",
+    "* IMC é o índice de massa corporal\n",
+    "* PA é a pressão arterial média\n",
+    "* S1 até S6 são diferentes medições de sangue\n",
+    "* Y é a medida qualitativa da progressão da doença ao longo de um ano\n",
     "\n",
-    "Vamos estudar este conjunto de dados usando métodos de probabilidade e estatística.\n",
+    "Vamos estudar este conjunto de dados utilizando métodos de probabilidade e estatística.\n",
     "\n",
-    "### Tarefa 1: Calcular os valores médios e a variância para todos os valores\n"
+    "### Tarefa 1: Calcular valores médios e variância para todos os valores\n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tarefa 2: Traçar boxplots para IMC, PA e Y dependendo do gênero\n"
+    "### Tarefa 2: Plotar boxplots para IMC, PA e Y dependendo do gênero\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n"
+    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:17:02+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:26:33+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "br"
   }
diff --git a/translations/br/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/br/1-Introduction/04-stats-and-probability/notebook.ipynb
index 58764514..18b38f8c 100644
--- a/translations/br/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/br/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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Xl7d4TDgcVigUarYBAICu6aa2HtjY2KglS5Zo2rRpGjNmTIvrAoGAEhISmu1LSEhQIBBo8Ri/36/ly5e3dTQgqvH/2AFbnfG/QeurOW2+MpKbm6uqqioVFxe35zySpIKCAgWDwaatpqam3c8BAAA6hjZdGVm8eLF27dql/fv3a8CAAa2uTUxMVF1dXbN9dXV1SkxMbPEYt9stt9vdltEAAEAn4+jKSCQS0eLFi7V9+3bt3btXQ4YMueoxPp9PpaWlzfaVlJTI5/M5mxQAAHRJjq6M5ObmqqioSDt37lRsbGzTfR8ej0e9evWSJOXk5Kh///7y+/2SpLy8PKWnp2vlypXKyspScXGxKioqVFhY2M4vBQAAdEaOroysX79ewWBQd911l5KSkpq2rVu3Nq2prq5WbW1t0+O0tDQVFRWpsLBQycnJeu2117Rjx45Wb3oFAADRw9GVkWv5lSRlZWWX7Zs1a5ZmzZrl5FQAACBK8Nk0AADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMCU4xjZv3+/ZsyYoX79+snlcmnHjh2tri8rK5PL5bpsCwQCbZ0ZAAB0IY5jpL6+XsnJyVq3bp2j406dOqXa2tqmLT4+3umpAQBAF3ST0wOmT5+u6dOnOz5RfHy8brvtNsfHAQCAru1bu2ckJSVFSUlJuvfee/XOO++0ujYcDisUCjXbAABA13TDYyQpKUkbNmzQ66+/rtdff11er1d33XWXjh071uIxfr9fHo+nafN6vTd6TAAAYMQViUQibT7Y5dL27duVnZ3t6Lj09HQNHDhQf/rTn6749XA4rHA43PQ4FArJ6/UqGAwqLi6ureNe0eClb7br8wEA0Nl8vCLrhjxvKBSSx+O56vdvx/eMtIfJkyfrwIEDLX7d7XbL7XZ/ixMBAAArJr9npLKyUklJSRanBgAAHYzjKyMXL17U6dOnmx5/9NFHqqysVJ8+fTRw4EAVFBTo008/1R//+EdJ0urVqzVkyBCNHj1aX375pV588UXt3btXf/3rX9vvVQAAgE7LcYxUVFTo7rvvbnqcn58vSZo3b542b96s2tpaVVdXN3390qVL+uUvf6lPP/1UN998s8aNG6e//e1vzZ4DAABEr+u6gfXbcq03wLQFN7ACAKKd9Q2sfDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDlOEb279+vGTNmqF+/fnK5XNqxY8dVjykrK9OECRPkdrs1dOhQbd68uQ2jAgCArshxjNTX1ys5OVnr1q27pvUfffSRsrKydPfdd6uyslJLlizRwoULtWfPHsfDAgCArucmpwdMnz5d06dPv+b1GzZs0JAhQ7Ry5UpJ0siRI3XgwAGtWrVKmZmZTk8PAAC6mBt+z0h5ebkyMjKa7cvMzFR5eXmLx4TDYYVCoWYbAADomm54jAQCASUkJDTbl5CQoFAopP/85z9XPMbv98vj8TRtXq/3Ro8JAACMdMifpikoKFAwGGzaampqrEcCAAA3iON7RpxKTExUXV1ds311dXWKi4tTr169rniM2+2W2+2+0aMBAIAO4IZfGfH5fCotLW22r6SkRD6f70afGgAAdAKOY+TixYuqrKxUZWWlpK9/dLeyslLV1dWSvv4nlpycnKb1DzzwgD788EP96le/0vvvv6/nn39er776qh5++OH2eQUAAKBTcxwjFRUVGj9+vMaPHy9Jys/P1/jx4/X4449Lkmpra5vCRJKGDBmiN998UyUlJUpOTtbKlSv14osv8mO9AABAkuSKRCIR6yGuJhQKyePxKBgMKi4url2fe/DSN9v1+QAA6Gw+XpF1Q573Wr9/d8ifpgEAANGDGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYKpNMbJu3ToNHjxYMTExmjJlig4fPtzi2s2bN8vlcjXbYmJi2jwwAADoWhzHyNatW5Wfn69ly5bp2LFjSk5OVmZmps6ePdviMXFxcaqtrW3azpw5c11DAwCArsNxjDz77LNatGiR5s+fr1GjRmnDhg26+eabtWnTphaPcblcSkxMbNoSEhKua2gAANB1OIqRS5cu6ejRo8rIyPjmCbp1U0ZGhsrLy1s87uLFixo0aJC8Xq9mzpypkydPtnqecDisUCjUbAMAAF2Toxj5/PPP1dDQcNmVjYSEBAUCgSseM3z4cG3atEk7d+7Uli1b1NjYqLS0NH3yySctnsfv98vj8TRtXq/XyZgAAKATueE/TePz+ZSTk6OUlBSlp6frjTfe0B133KGNGze2eExBQYGCwWDTVlNTc6PHBAAARm5ysvj2229X9+7dVVdX12x/XV2dEhMTr+k5evToofHjx+v06dMtrnG73XK73U5GAwAAnZSjKyM9e/ZUamqqSktLm/Y1NjaqtLRUPp/vmp6joaFBJ06cUFJSkrNJAQBAl+Toyogk5efna968eZo4caImT56s1atXq76+XvPnz5ck5eTkqH///vL7/ZKkJ554QlOnTtXQoUP1xRdf6JlnntGZM2e0cOHC9n0lAACgU3IcI7Nnz9a5c+f0+OOPKxAIKCUlRW+//XbTTa3V1dXq1u2bCy7nz5/XokWLFAgE1Lt3b6WmpurgwYMaNWpU+70KAADQabkikUjEeoirCYVC8ng8CgaDiouLa9fnHrz0zXZ9PgAAOpuPV2TdkOe91u/ffDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVphhZt26dBg8erJiYGE2ZMkWHDx9udf22bds0YsQIxcTEaOzYsdq9e3ebhgUAAF2P4xjZunWr8vPztWzZMh07dkzJycnKzMzU2bNnr7j+4MGDmjNnjhYsWKDjx48rOztb2dnZqqqquu7hAQBA5+eKRCIRJwdMmTJFkyZN0tq1ayVJjY2N8nq9euihh7R06dLL1s+ePVv19fXatWtX076pU6cqJSVFGzZsuKZzhkIheTweBYNBxcXFORn3qgYvfbNdnw8AgM7m4xVZN+R5r/X7901OnvTSpUs6evSoCgoKmvZ169ZNGRkZKi8vv+Ix5eXlys/Pb7YvMzNTO3bsaPE84XBY4XC46XEwGJT09Ytqb43hf7f7cwIA0JnciO+v/+/zXu26h6MY+fzzz9XQ0KCEhIRm+xMSEvT+++9f8ZhAIHDF9YFAoMXz+P1+LV++/LL9Xq/XybgAAOAaeFbf2Oe/cOGCPB5Pi193FCPfloKCgmZXUxobG/Wvf/1Lffv2lcvlarfzhEIheb1e1dTUtPs//8A53o+Oh/ekY+H96Fh4P64uEonowoUL6tevX6vrHMXI7bffru7du6uurq7Z/rq6OiUmJl7xmMTEREfrJcntdsvtdjfbd9tttzkZ1ZG4uDj+InUgvB8dD+9Jx8L70bHwfrSutSsi/+Pop2l69uyp1NRUlZaWNu1rbGxUaWmpfD7fFY/x+XzN1ktSSUlJi+sBAEB0cfzPNPn5+Zo3b54mTpyoyZMna/Xq1aqvr9f8+fMlSTk5Oerfv7/8fr8kKS8vT+np6Vq5cqWysrJUXFysiooKFRYWtu8rAQAAnZLjGJk9e7bOnTunxx9/XIFAQCkpKXr77bebblKtrq5Wt27fXHBJS0tTUVGRHnvsMT366KMaNmyYduzYoTFjxrTfq2gjt9utZcuWXfZPQrDB+9Hx8J50LLwfHQvvR/tx/HtGAAAA2hOfTQMAAEwRIwAAwBQxAgAATBEjAADAVFTHyLp16zR48GDFxMRoypQpOnz4sPVIUcnv92vSpEmKjY1VfHy8srOzderUKeux8F8rVqyQy+XSkiVLrEeJWp9++ql++tOfqm/fvurVq5fGjh2riooK67GiVkNDg37zm99oyJAh6tWrl7773e/qySefvOrnr6BlURsjW7duVX5+vpYtW6Zjx44pOTlZmZmZOnv2rPVoUWffvn3Kzc3VoUOHVFJSoq+++kr33Xef6uvrrUeLekeOHNHGjRs1btw461Gi1vnz5zVt2jT16NFDb731lt577z2tXLlSvXv3th4tav3ud7/T+vXrtXbtWv3zn//U7373O/3+97/Xc889Zz1apxW1P9o7ZcoUTZo0SWvXrpX09W+S9Xq9euihh7R06VLj6aLbuXPnFB8fr3379unOO++0HidqXbx4URMmTNDzzz+v3/72t0pJSdHq1autx4o6S5cu1TvvvKO///3v1qPgv374wx8qISFBL730UtO+H/3oR+rVq5e2bNliOFnnFZVXRi5duqSjR48qIyOjaV+3bt2UkZGh8vJyw8kgScFgUJLUp08f40miW25urrKyspr9d4Jv35///GdNnDhRs2bNUnx8vMaPH68XXnjBeqyolpaWptLSUn3wwQeSpH/84x86cOCApk+fbjxZ59UhP7X3Rvv888/V0NDQ9Ftj/ychIUHvv/++0VSQvr5CtWTJEk2bNq1D/JbeaFVcXKxjx47pyJEj1qNEvQ8//FDr169Xfn6+Hn30UR05ckS/+MUv1LNnT82bN896vKi0dOlShUIhjRgxQt27d1dDQ4OeeuopzZ0713q0TisqYwQdV25urqqqqnTgwAHrUaJWTU2N8vLyVFJSopiYGOtxol5jY6MmTpyop59+WpI0fvx4VVVVacOGDcSIkVdffVWvvPKKioqKNHr0aFVWVmrJkiXq168f70kbRWWM3H777erevbvq6uqa7a+rq1NiYqLRVFi8eLF27dql/fv3a8CAAdbjRK2jR4/q7NmzmjBhQtO+hoYG7d+/X2vXrlU4HFb37t0NJ4wuSUlJGjVqVLN9I0eO1Ouvv240ER555BEtXbpUP/nJTyRJY8eO1ZkzZ+T3+4mRNorKe0Z69uyp1NRUlZaWNu1rbGxUaWmpfD6f4WTRKRKJaPHixdq+fbv27t2rIUOGWI8U1e655x6dOHFClZWVTdvEiRM1d+5cVVZWEiLfsmnTpl32o+4ffPCBBg0aZDQR/v3vfzf7QFhJ6t69uxobG40m6vyi8sqIJOXn52vevHmaOHGiJk+erNWrV6u+vl7z58+3Hi3q5ObmqqioSDt37lRsbKwCgYAkyePxqFevXsbTRZ/Y2NjL7te55ZZb1LdvX+7jMfDwww8rLS1NTz/9tH784x/r8OHDKiwsVGFhofVoUWvGjBl66qmnNHDgQI0ePVrHjx/Xs88+q5///OfWo3VekSj23HPPRQYOHBjp2bNnZPLkyZFDhw5ZjxSVJF1xe/nll61Hw3+lp6dH8vLyrMeIWn/5y18iY8aMibjd7siIESMihYWF1iNFtVAoFMnLy4sMHDgwEhMTE/nOd74T+fWvfx0Jh8PWo3VaUft7RgAAQMcQlfeMAACAjoMYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKb+D7cuxelORYM+AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Analisando Dados Reais\n",
     "\n",
-    "Média e variância são muito importantes ao analisar dados do mundo real. Vamos carregar os dados sobre jogadores de beisebol a partir de [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Média e variância são muito importantes ao analisar dados do mundo real. Vamos carregar os dados sobre jogadores de baseball de [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Estamos utilizando um pacote chamado [**Pandas**](https://pandas.pydata.org/) aqui para análise de dados. Falaremos mais sobre o Pandas e como trabalhar com dados em Python mais adiante neste curso.\n",
+    "Estamos utilizando um pacote chamado [**Pandas**](https://pandas.pydata.org/) aqui para análise de dados. Falaremos mais sobre Pandas e como trabalhar com dados em Python mais adiante neste curso.\n",
     "\n",
     "Vamos calcular os valores médios para idade, altura e peso:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Podemos também criar gráficos de caixa de subconjuntos do nosso conjunto de dados, por exemplo, agrupados por função do jogador.\n"
+    "Podemos também criar gráficos de caixa para subconjuntos do nosso conjunto de dados, por exemplo, agrupados por função do jogador.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -404,24 +252,22 @@
    "source": [
     "> **Nota**: Este diagrama sugere que, em média, as alturas dos jogadores de primeira base são maiores do que as alturas dos jogadores de segunda base. Mais adiante, aprenderemos como testar essa hipótese de forma mais formal e como demonstrar que nossos dados são estatisticamente significativos para comprovar isso.\n",
     "\n",
-    "Idade, altura e peso são todas variáveis aleatórias contínuas. Qual você acha que é a distribuição delas? Uma boa maneira de descobrir é traçar o histograma dos valores:\n"
+    "Idade, altura e peso são todas variáveis aleatórias contínuas. O que você acha que é a distribuição delas? Uma boa maneira de descobrir é traçar o histograma dos valores:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Zq7/9GOAQ4GsjqE2SJEmr3DAv83Ym8GngJ5Jcm+Ql/abjuOfyCoCnAl9I8nngfcDLqurmYdUmSZIkzWSYV7F4wQzjJ04zdjZw9rBqkSRJkubLt5qWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKmx97AOnOR04Bjgxqo6vB97PfBS4Fv9bq+uqvP6bScBLwHuAn6rqj4yrNokrV5rN5076hJmteOUo0ddgiStesOcQX4H8Kxpxt9SVUf0H5Ph+FDgOOCw/jF/nWSvIdYmSZIkTWtoAbmqLgRunufuxwJbqurOqroa+Cpw1LBqkyRJkmaSqhrewZO1wIenLLE4EbgN2AZsrKpbkrwVuKiq3tXvdxrwj1X1vmmOuQHYADA2Nnbkli1bhlb/SrBr1y7WrFkz6jJWlFH3dPt1t47s3MM09iDYeceoqxi9dQftO7BjjfprdaWyr4NnT4fDvs5t/fr1F1fV+NTxoa1BnsHbgDcC1X8+FXgxkGn2nTa5V9VmYDPA+Ph4TUxMDKXQlWLr1q3Yo8EadU9PXOJraBdq47rdnLp9sb8lLT07jp8Y2LFG/bW6UtnXwbOnw2FfF25RfxpV1c7J20n+Fvhwf/da4OBm10cB1y9iadIPzPUiro3rdq/YkCpJkhb5Mm9JDmzu/jJwWX/7HOC4JPskeTRwCPDZxaxNkiRJguFe5u1MYAI4IMm1wOuAiSRH0C2f2AH8GkBVXZ7kLOCLwG7gFVV117BqkyRJkmYytIBcVS+YZvi0WfY/GTh5WPVIkiRJ8+E76UmSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSY2hBeQkpye5McllzdifJflSki8k+UCS/frxtUnuSHJp//H2YdUlSZIkzWaYM8jvAJ41Zex84PCq+n+ArwAnNduuqqoj+o+XDbEuSZIkaUZDC8hVdSFw85Sxj1bV7v7uRcCjhnV+SZIkaSFSVcM7eLIW+HBVHT7Ntn8A3ltV7+r3u5xuVvk24A+q6hMzHHMDsAFgbGzsyC1btgyp+pVh165drFmzZtRlLCvbr7t11u1jD4KddyxSMauIfe2sO2jfgR3L///DYV8Hz54Oh32d2/r16y+uqvGp43uPopgkrwF2A+/uh24AfrSqbkpyJPDBJIdV1W1TH1tVm4HNAOPj4zUxMbFIVS9PW7duxR7tmRM3nTvr9o3rdnPq9pH811nR7Gtnx/ETAzuW//+Hw74Onj0dDvu6cIt+FYskJwDHAMdXP31dVXdW1U397YuBq4DHLXZtkiRJ0qIG5CTPAn4f+KWq+l4z/vAke/W3HwMcAnxtMWuTJEmSYIhLLJKcCUwAByS5Fngd3VUr9gHOTwJwUX/FiqcCb0iyG7gLeFlV3TztgSVJkqQhGlpArqoXTDN82gz7ng2cPaxaJEmSpPnynfQkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpMa8AnKSJ89nTJIkSVru5juD/D/nOSZJkiQta3vPtjHJE4EnAQ9P8qpm00OBvYZZmCRJkjQKswZk4AHAmn6/hzTjtwHPHVZRkiRJ0qjMGpCr6gLggiTvqKprFqkmSZIkaWTmmkGetE+SzcDa9jFV9bRhFCVJkiSNynwD8v8C3g78HXDX8MqRJEmSRmu+AXl3Vb1tqJVIkiRJS8B8L/P2D0l+PcmBSR42+THUyiRJkqQRmO8M8gn9599rxgp4zGDLkSRJkkZrXgG5qh497EIkSZKkpWBeATnJi6Ybr6p3DrYcSZIkabTmu8TiCc3tBwJPBy4BDMiSJElaUea7xOI32/tJ9gX+frbHJDkdOAa4saoO78ceBryX7nrKO4DnV9Ut/baTgJfQXUbut6rqI3vyRCRJkqRBmO8M8lTfAw6ZY593AG/lnrPMm4CPVdUpSTb1938/yaHAccBhwCOBf07yuKrymsuSVpW1m84d2LE2rtvNiQM83o5Tjh7YsSRpKZvvGuR/oLtqBcBewOOBs2Z7TFVdmGTtlOFjgYn+9hnAVuD3+/EtVXUncHWSrwJHAZ+eT32SJEnSoKSq5t4p+fnm7m7gmqq6dh6PWwt8uFli8Z2q2q/ZfktV7Z/krcBFVfWufvw04B+r6n3THHMDsAFgbGzsyC1btsxZ/2q2a9cu1qxZM+oylpXt19066/axB8HOOxapmFXEvg7eoHu67qB9B3ewZczvq4NnT4fDvs5t/fr1F1fV+NTx+a5BviDJGHe/WO/KQRYHZLrTzlDLZmAzwPj4eE1MTAy4lJVl69at2KM9M9efpDeu282p2xe6Okkzsa+DN+ie7jh+YmDHWs78vjp49nQ47OvCzeud9JI8H/gs8Dzg+cBnkjx3AefbmeTA/pgHAjf249cCBzf7PQq4fgHHlyRJku6T+b7V9GuAJ1TVCVX1Irr1wX+4gPOdw93vyncC8KFm/Lgk+yR5NN0LAD+7gONLkiRJ98l8//Z2v6q6sbl/E3OE6yRn0r0g74Ak1wKvA04BzkryEuDrdDPSVNXlSc4Cvki3xvkVXsFCkiRJozDfgPxPST4CnNnf/xXgvNkeUFUvmGHT02fY/2Tg5HnWI0mSJA3FrAE5yY8DY1X1e0n+M/AUuhfUfRp49yLUJ0mSJC2qudYg/wVwO0BVvb+qXlVVv0M3e/wXwy1NkiRJWnxzBeS1VfWFqYNVtY3u7aIlSZKkFWWugPzAWbY9aJCFSJIkSUvBXAH5c0leOnWwvwrFxcMpSZIkSRqdua5i8UrgA0mO5+5APA48APjlIdYlSZIkjcSsAbmqdgJPSrIeOLwfPreq/mXolUmSJEkjMK/rIFfVx4GPD7kWSZIkaeTm+1bTkiRJ0qpgQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpsfdinzDJTwDvbYYeA7wW2A94KfCtfvzVVXXe4lYnSZKk1W7RA3JVfRk4AiDJXsB1wAeA/wa8par+fLFrkiRJkiaNeonF04GrquqaEdchSZIkAZCqGt3Jk9OBS6rqrUleD5wI3AZsAzZW1S3TPGYDsAFgbGzsyC1btixewcvQrl27WLNmzajLWFa2X3frrNvHHgQ771ikYlYR+zp4g+7puoP2HdzBljG/rw6ePR0O+zq39evXX1xV41PHRxaQkzwAuB44rKp2JhkDvg0U8EbgwKp68WzHGB8fr23btg2/2GVs69atTExMjLqMZWXtpnNn3b5x3W5O3b7oq5NWPPs6eKutpztOOXpRzuP31cGzp8NhX+eWZNqAPMolFr9IN3u8E6CqdlbVXVX1feBvgaNGWJskSZJWqVFOLbwAOHPyTpIDq+qG/u4vA5eNpCoN3VwztJIkSaM0koCc5IeA/wj8WjP8piRH0C2x2DFlmyRJkrQoRhKQq+p7wA9PGXvhKGqRJEmSWqO+zJskSZK0pBiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqTG3qM4aZIdwO3AXcDuqhpP8jDgvcBaYAfw/Kq6ZRT1SZIkafUa5Qzy+qo6oqrG+/ubgI9V1SHAx/r7kiRJ0qJaSkssjgXO6G+fATxndKVIkiRptUpVLf5Jk6uBW4AC/qaqNif5TlXt1+xzS1XtP81jNwAbAMbGxo7csmXLIlW9PO3atYs1a9aMuox72H7draMu4T4ZexDsvGPUVaw89nXwVltP1x2076KcZyl+X13u7Olw2Ne5rV+//uJmNcMPjGQNMvDkqro+ySOA85N8ab4PrKrNwGaA8fHxmpiYGFKJK8PWrVtZaj06cdO5oy7hPtm4bjenbh/Vf52Vy74O3mrr6Y7jJxblPEvx++pyZ0+Hw74u3EiWWFTV9f3nG4EPAEcBO5McCNB/vnEUtUmSJGl1W/SAnOTBSR4yeRt4BnAZcA5wQr/bCcCHFrs2SZIkaRR/exsDPpBk8vzvqap/SvI54KwkLwG+DjxvBLVJkiRplVv0gFxVXwN+aprxm4CnL3Y9kiRJUmspXeZNkiRJGjkDsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJjb1HXYAkSYOwdtO5i3Kejet2c+ICzrXjlKOHUI2kYXAGWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJauw96gI0eGs3nfuD2xvX7ebE5r4kSZJm5wyyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEmNRQ/ISQ5O8vEkVyS5PMlv9+OvT3Jdkkv7j2cvdm2SJEnSKK5isRvYWFWXJHkIcHGS8/ttb6mqPx9BTZIkSRIwgoBcVTcAN/S3b09yBXDQYtchSZIkTSdVNbqTJ2uBC4HDgVcBJwK3AdvoZplvmeYxG4ANAGNjY0du2bJlscpdNrZfd+sPbo89CHbeMcJiViB7Ohz2dfDs6XAstK/rDtp38MWsELt27WLNmjWjLmPFsa9zW79+/cVVNT51fGQBOcka4ALg5Kp6f5Ix4NtAAW8EDqyqF892jPHx8dq2bdvwi11mpr5RyKnbfT+YQbKnw2FfB8+eDsdC+7rjlKOHUM3KsHXrViYmJkZdxopjX+eWZNqAPJKrWCS5P3A28O6qej9AVe2sqruq6vvA3wJHjaI2SZIkrW6juIpFgNOAK6rqzc34gc1uvwxctti1SZIkSaP429uTgRcC25Nc2o+9GnhBkiPolljsAH5tBLVJkjQU7fK3pcglINLdRnEVi08CmWbTeYtdiyRJkjSV76QnSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSY+9RF7Acrd107qhLkCRJ0pA4gyxJkiQ1nEGWJEkj/evoxnW7OXGO8+845ehFqkZyBlmSJEm6BwOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDa+DLEmSdB8txXfZba8v7XWk98ySm0FO8qwkX07y1SSbRl2PJEmSVpclNYOcZC/gr4D/CFwLfC7JOVX1xdFWJkmSRmkpztAuJ0u9f0tthnupzSAfBXy1qr5WVf8GbAGOHXFNkiRJWkVSVaOu4QeSPBd4VlX9an//hcDPVNVvNPtsADb0d38C+PKiF7q8HAB8e9RFrDD2dDjs6+DZ0+Gwr4NnT4fDvs7tx6rq4VMHl9QSCyDTjN0jwVfVZmDz4pSz/CXZVlXjo65jJbGnw2FfB8+eDod9HTx7Ohz2deGW2hKLa4GDm/uPAq4fUS2SJElahZZaQP4ccEiSRyd5AHAccM6Ia5IkSdIqsqSWWFTV7iS/AXwE2As4vaouH3FZy53LUQbPng6HfR08ezoc9nXw7Olw2NcFWlIv0pMkSZJGbaktsZAkSZJGyoAsSZIkNQzIy1yS05PcmOSyKeO/2b9l9+VJ3tSMn9S/jfeXkzxz8Ste+qbraZIjklyU5NIk25Ic1Wyzp3NIcnCSjye5ov+a/O1+/GFJzk9yZf95/+Yx9nUOs/T1z5J8KckXknwgyX7NY+zrLGbqabP9d5NUkgOaMXs6h9n66s+rhZnl/78/rwahqvxYxh/AU4GfBi5rxtYD/wzs099/RP/5UODzwD7Ao4GrgL1G/RyW2scMPf0o8Iv97WcDW+3pHvX0QOCn+9sPAb7S9+5NwKZ+fBPwp/Z1IH19BrB3P/6n9vW+97S/fzDdi8ivAQ6wp/e9r/68GkpP/Xk1gA9nkJe5qroQuHnK8MuBU6rqzn6fG/vxY4EtVXVnVV0NfJXu7b3VmKGnBTy0v70vd1+f257OQ1XdUFWX9LdvB64ADqLr3xn9bmcAz+lv29d5mKmvVfXRqtrd73YR3TXlwb7OaZavVYC3AP8f93wDK3s6D7P01Z9XCzRLT/15NQAG5JXpccDPJflMkguSPKEfPwj4RrPftdz9jV+zeyXwZ0m+Afw5cFI/bk/3UJK1wH8APgOMVdUN0H2zBx7R72Zf99CUvrZeDPxjf9u+7oG2p0l+Cbiuqj4/ZTd7uoemfK3682oApvT0lfjz6j4zIK9MewP7Az8L/B5wVpIwj7fy1oxeDvxOVR0M/A5wWj9uT/dAkjXA2cArq+q22XadZsy+zmCmviZ5DbAbePfk0DQPt6/TaHtK18PXAK+dbtdpxuzpDKb5WvXn1X00TU/9eTUABuSV6Vrg/dX5LPB94AB8K+/74gTg/f3t/8Xdf5ayp/OU5P5038TfXVWTvdyZ5MB++4HA5J9X7es8zdBXkpwAHAMcX/0CROzrvEzT08fSrdn8fJIddH27JMmPYE/nbYavVX9e3Qcz9NSfVwNgQF6ZPgg8DSDJ44AHAN+me9vu45Lsk+TRwCHAZ0dV5DJzPfDz/e2nAVf2t+3pPPQzQqcBV1TVm5tN59B9M6f//KFm3L7OYaa+JnkW8PvAL1XV95qH2Nc5TNfTqtpeVY+oqrVVtZYuaPx0VX0Tezovs3wP+CD+vFqQWXrqz6sBWFJvNa09l+RMYAI4IMm1wOuA04HT012m7N+AE/oZpMuTnAV8ke5Phq+oqrtGU/nSNUNPXwr8jyR7A/8KbACoKns6P08GXghsT3JpP/Zq4BS6P6m+BPg68Dywr3tgpr7+Jd0r1c/vfoZyUVW9zL7Oy7Q9rarzptvZns7bTF+r/rxauJl66s+rAfCtpiVJkqSGSywkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSlqAkb0nyyub+R5L8XXP/1CSvmuGxb0jyC3Mc//VJfnea8f2S/Pp9KF2Slj0DsiQtTf8beBJAkvvRvbvYYc32JwGfmu6BVfXaqvrnBZ53P8CALGlVMyBL0tL0KfqATBeMLwNuT7J/kn2AxwMkuSDJxf0M8+Tbdr8jyXP7289O8qUkn0zyl0k+3Jzj0CRbk3wtyW/1Y6cAj01yaZI/W4wnKklLje+kJ0lLUFVdn2R3kh+lC8qfBg4CngjcClwBvAU4tqq+leRXgJOBF08eI8kDgb8BnlpVV/fvEtn6SWA98BDgy0neBmwCDq+qI4b6BCVpCTMgS9LSNTmL/CTgzXQB+Ul0Afk64Bnc/XbSewE3THn8TwJfq6qr+/tn0r/tbO/cqroTuDPJjcDYkJ6HJC0rBmRJWrom1yGvo1ti8Q1gI3Ab8C/AQVX1xFkenzmOf2dz+y78mSBJgGuQJWkp+xRwDHBzVd1VVTfTvYjuicB7gYcneSJAkvsnOWzK478EPCbJ2v7+r8zjnLfTLbmQpFXLgCxJS9d2uqtXXDRl7NaquhF4LvCnST4PXMrdL+oDoKruoLsixT8l+SSwk255xoyq6ibgU0ku80V6klarVNWoa5AkDUmSNVW1K91C5b8Crqyqt4y6LklaypxBlqSV7aVJLgUuB/alu6qFJGkWziBLkiRJDWeQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkxv8FiHh2DxCDPowAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Podemos ver que os intervalos não se sobrepõem.\n",
+    "Podemos observar que os intervalos não se sobrepõem.\n",
     "\n",
-    "Uma maneira estatisticamente mais correta de provar a hipótese é usar um **teste t de Student**:\n"
+    "Uma maneira estatisticamente mais correta de comprovar a hipótese é usar um **teste t de Student**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Correlação e a Corporação Maligna de Baseball\n",
     "\n",
-    "A correlação nos permite encontrar relações entre sequências de dados. No nosso exemplo fictício, vamos imaginar que existe uma corporação maligna de baseball que paga seus jogadores de acordo com sua altura - quanto mais alto o jogador, mais dinheiro ele/ela recebe. Suponha que existe um salário base de $1000, e um bônus adicional de $0 a $100, dependendo da altura. Vamos pegar os jogadores reais da MLB e calcular seus salários imaginários:\n"
+    "A correlação nos permite encontrar relações entre sequências de dados. No nosso exemplo fictício, vamos imaginar que existe uma corporação maligna de baseball que paga seus jogadores de acordo com sua altura - quanto mais alto o jogador, mais dinheiro ele/ela recebe. Suponha que exista um salário base de $1000, e um bônus adicional de $0 a $100, dependendo da altura. Vamos pegar jogadores reais da MLB e calcular seus salários imaginários:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Neste caso, a correlação é um pouco menor, mas ainda é bastante alta. Agora, para tornar a relação ainda menos óbvia, podemos querer adicionar um pouco de aleatoriedade extra, adicionando alguma variável aleatória ao salário. Vamos ver o que acontece:\n"
+    "Neste caso, a correlação é ligeiramente menor, mas ainda é bastante alta. Agora, para tornar a relação ainda menos óbvia, podemos querer adicionar um pouco mais de aleatoriedade, adicionando alguma variável aleatória ao salário. Vamos ver o que acontece:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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8UfmA+2xOBN5fj9Ln/kYy2Fb2kV4oLwcAAINGvI2Yun2Wauqbtap2j2rqmx3tG7WTI6n3tu1kJ0dVU8v02I0z5C0ITkT6qtw6ke8tklR+n/1Nqu9vX7IftkS6q+nWKA/pxWNZVlp2G2hra1NBQYFaW1uVn5+f6ssBAAAp1u2zNPuhdRHLre0V0lfvujQoYXNrZTyVo45SMdc42Z832vEvr/AOqjnOXSd8eqZmp3a1tGtCcZ4WVk5U9pD0W1uzH5pJ4Vf20+1BA/o/05yTpBsAAAwKNfXNuuGpjTHjnls0S5WTR0qKPA861i/5kZLcVCS/qRDv9+ZUuO9zbV3ToJrjPNjmVg+2z4v+zTTnZE83AAAYFJw2Yoo1osijnhFFl1d4jVfGL6/wJvAJBoZ4v7d4ZGZ4/A9IpMjJvr19IN1WQgfb55V6SuoHWyUDBj6SbgAAMCg4bcTkZERRrJXxptYO3bJyiwrzsnSo/bj/9XRcoYvne3NDXyb7/cFg+7yBQh+2AP1d+m32AAAACOO8CUWKlXtkeHriJPdXxiUFJdxST/IZrYHbQJSq0U6DbY7zYPu8wEBG0g0AAAaFt3YdVKzm2T6rJ04yXxk/cLhTq2r36OnXGqImQZFY6lmRdKOzdyTJ6CIeSapGOw22Oc6D7fMCAxnl5QAAYFBoaj3mKC7WPGipZ2X8/he2JXxtySi3tg2WueQlw3NcjevvmFsNDBysdAMAgLQVuMK7ZfdBo/e0HO2SFH0etM3NBWPThwJOxDuXPBEpm6Ntei/SZG4Pc6uBgYOkGwAApKXqrY2a/dA63fDURn31V7V6ZuNuo/cVB6yEVk0t02M3zpC3IHi1MBl9qexk3y0me8yTVdYe6XvzFuQmraP2gaOdrsb1dyl7uAHAMcrLAQBA2onURdxE6Yjg8uPQEUUHDne6UlIeqtjlsudUdRG39fVop8FYbm0/3AjdPuBNw674wEBG0g0AAAaMbp8VM4mLtsJr4sRxX6/XAkcUrardE+eRo/Pmp19jsb4c7ZSqveSpxtxqoP8j6QYAAAOCaUOwWCu8sfz+nT367JTSiD83XSm9Z94UlYzIUcmwHN3x23fU1Bb5mpKx93awrfza5dZLVm6RR8Fbt9O93Jq51UD/xp5uAADQ7zlpCJboyu3RzhNRf27awOrLF5VrwfQxuuiMEi2/ukIehd9761FyksHB2GgrFXvJASAWVroBAEDKJFoubqkneVyxpk6XV3iVmeFJeOV2VIwy73hWVFOx9zbwOiNJx5Vfyq0B9Dck3QAAICWqtzZq+eq6oLJrb36ull/trFw8tCGYyXztaM4dXxQzJp4kOhXJYNXUMi2+uFxPbWgIGm+W4ZEWzSlP25Vfyq0B9Cck3QAAoM9Vb23ULWFWYJvaOnTLyi16PKAU2GlDsGgr0SZGFw41iosnie7rZLB6a6OeWN/Q63WfJT2xvkHnji9K28QbAPoLkm4AANCnun2WvvG796LGLP3de47LxQPjIq1EZ3ikaGOpne5x7m8rqoHl+iXDchx9z26dN/Dhg8n2AQBIdyTdAACgT23c0axD7cejxhxsP66NO5p10eklcY+CCrcSffBop/712bcjnncg73EO1909lsDv2c3zlhXk6uppZVr9TmPMbvMAkO7oXg4AAPpUTX2zozi7XFwK3/1bipws2yvRC6aPUeXkkcoYoAl1LJG6u5swvR9OztvY2qEn1jcYdZsHgHRH0g0AAPqY6S7rU3FujIKyu6BHs2JNnbqj1Z8b6vZZqqlv1qraPaqpb/YfM9LriZ4rUnd3M/G9M57zWif/cut7BoCBgPJyAADQpyonleiRV+qN4gIl2v07Vhd0KbgLeiAne5P7utza5HNFE/o998V5I33PAJCOSLoBAECfmjV5pArzsqLu6y7My9KsMAlZIo3LAkeTOYmLlESHS5btcuvQNVy73LrXuU6WW5uu1odj2t09nEjfc7LPK5nfDwAY6CgvBwAASRdYVr25oUX/cc3UqPEPXnu26w3NWo50Oo6LtGc53N7keMutpcTKrU27u4eTyPecyHkl8/sBAAMdK90AACCpqrc2avnquqCVTW9+rr5ycblW1e5VU1tnwOs5Wn71p5PS3bp4WLZRXMvRLq2q3aOS4Tlavvr9sEm0pZ4mbivW1PlHbsVbbm0psXLrWN3dw/FIWnxxeULfczznDWR6P/ojRqEBcIKkGwAAJE311kbdsnJLr9eb2nrKrX/8hRkqGpbdJ8lLab7Zyuyjf46931zqnSwnWm4d7/vt7u5LVm6RR+Zt0Z5c36BzxxfFnXjHe16bt2BoXOdNNSfbDQBAorwcAAAkSbfP0jd+917UmG8+/54uKC/2j/RK6mphkppl28lyouXWibw/Unf3SF+nG2Xt8ZzXVhZmrvpA4GS7AQDYWOkGAABJsXFHc9RmaZJ0sP24Nu5o1kWnx9dB24kDR5Ozh9hOluMtt/aoZ/RZokloaHf3A4c7df8L2yLGJ1rWHum8pSNydfBop2599u2w34NHkeeq92fR9uyH224AADZWugEAQFLU1De7GpeoRFeiQ3kUvGJrl1vbPzM9huReEmp3d18wfYxKRuQYvSfRsvjQ81ZOHqmrzhmtxReX9/oe3NhLniqx9uwHPsQAgEAk3QAAIElM13sTr/sO7I5eU98ctmTaXol2Q6RkOVK5dVlBT+M4b35wIjwqPyfquDCTzxWJ6UMGtx9GSD1l2E+sb+h1Zy1JT6xvSHoZdiLfWySmDyfceIgBIL1QXg4AAJKiclKJHnkldlOyykmJlZabNrbKzPDo6mllYedlO+WN0jgrXLn1BeXFWlvXpFW1oclm5NXtRBt2XVBeHHMeelFelut7q0328n/jd+8lrQw7WY3OUvkQA8DARtINAMAgd6TjhL7+67e1++AxjS8aqv/8p3M1PDfxXxFmTR4ZM+krzMuSJK2q3RNX93K7sVXoOqbd2CpwFbnbZ2n1O85XWO0919/9X9N04Gin0XXa5daxrnNfW+/rdPq5EpGM3nIb62Pv5T/Uflwb65t10Rnu7uVP5vcWa8++W3vzAaQfkm4AAAaxqx/ZoHc/bvP/+YOmw5q6/H90zth8rb5tTkLHzszw6MFrzw47MizQF3+yyf/3TlYknTa2imeOdmAZebwJYqzrVMh1mnyu5avf14jcLB04EvkhwOaGFqPkN9FGaqFqdhwwjnMz6U52o7NoI9Lc3psPIL2wpxsAgEEqNOEO9O7Hbbr6kQ0Jn6Nqapkev3FGr73MhUN7nvuHJoVORi85bWwVz15bb0FuwqvKJsl+4HWafK6mtk598b826au/qtUNT23U7IfW9frOTD/va9sPuLr32XkbOXf0RaOzSHv23fjnBED6YqUbAIBB6EjHiYgJt+3dj9t0pONEwqXmoXucS4bn6I7f1ErHTvSKdbIi6bSxVclws27eN84cr4wMjyYU52lh5URlD0lsjaKpzew67bh4Hg6EK5823Vv8yCvb/X/vxt7nyskjg44ZLc5NfdXoLNKefVa4AUTCSjcAAIPQ1379tqtxsQSOlMrweNTUFnlmtumKpNPGVl2d3UbxKzft1i9qdun+F7bps995JeFO2y1HzOaD23HxNOIKLFO3V6vj6dbupNIgklmTRvr36kdSlJelWZPcTbr7stFZ6Ig0Em4A0ThOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHHwbF/O1vf9OCBQtUUlKi/Px8zZ49W6+88kpQzO7duzVv3jzl5eWptLRU//Zv/6YTJ3o/EQcAAM590HTY1Tgn3FqRtJPKSOlO6Bzt/3rdeddyN5LQ4mHZjuJifa5IQh9W2N3anR5DCk7enbL38kfzwLVnu56oOv3nAQD6iuOk++jRo5o2bZoeffTRsD9/+OGH9cMf/lCPP/64Nm3apGHDhumKK65QR8ep/3D+3d/9nU6cOKF169bprbfe0rRp0/R3f/d3ampqkiR1d3dr3rx56urq0uuvv66f//znevrpp3XvvffG+TEBAEDg7GLThKdgaPQVy3iUDDMr844VZze2knrvDg7X2KqtI3pTsXDcSEK9BUMdxUX7XCbshxXxdmt3a+9zz17+3vPKH0/S3men/zwAQF9xvEnryiuv1JVXXhn2Z5Zl6fvf/77uvvtuLViwQJL0i1/8QqNGjdLzzz+v66+/XgcOHNCHH36on/zkJzrnnHMkSQ8++KB+/OMfa+vWrfJ6vXrppZdUV1enP/3pTxo1apSmT5+u+++/X3fddZeWL1+u7GyzJ8YAAKBHuNnFJr5+6RnuX4yLfbbsxlahny3cHO2powv03p7o+9jDCUxC49mHbDIvuzBkXnakz2XCLp+Op1t7oIG499nJPw8A0FdcbaTW0NCgpqYmXXbZZf7XCgoKNHPmTNXU1Oj666/XyJEjdeaZZ+oXv/iFZsyYoZycHD3xxBMqLS3VeeedJ0mqqanR2WefrVGjRvmPc8UVV2jJkiV6//33de6557p52QAApLVIs4tN5Oa433P1gOEeZ9M40+Ru/Mg8x9caKNEkNJpwaWivBnTDcnTHb9/RvjazOdGJXq+be5/7Eo3OAPQ3rv6X1C4PD0yW7T/bP/N4PPrTn/6ka665RiNGjFBGRoZKS0tVXV2toqIi/3HCHSPwHKE6OzvV2XnqP85tbc6fZAMAkG6izS42kYxE07SLuGmcZJbcJbLqK8WfhJrMyz5oMC87I8Oje/+uQrc+azYnOt7rDU3eB6JUJPsAEEmfjwyzLEu33nqrSktLtWHDBg0dOlT/9V//pfnz5+uNN95QWVl8ZT8PPPCAVqxY4fLVAgAwsCVaYnzgSJej+G6fFXuF0fQJgBsjowOMKzLbWx0q0SQ03sZx4bYElBXkavHF5Vr9TmPM8unzJhQpwyM52YreV3ufjf45AYA04WrS7fV6JUn79u0LSp737dun6dOnS5LWrVunP/zhDzp48KDy8/MlST/+8Y+1du1a/fznP9c3vvENeb1ebd68OejY+/btCzpHqKVLl+r222/3/7mtrU3jxo1z7bMBADAQJbpSfeiYedIdKUkMTQYPHDUsLzeMiyYwubPiSOLdSELjGWUVaUtAU2uHnlzfoEe/cK6KhuVETVrf2nXQUcIt9c3eZ9N/TgAgXbiadJeXl8vr9erll1/2J9ltbW3atGmTlixZIklqb2+XJGVkBDdOz8jIkM/nkyRVVlbq29/+tvbv36/S0lJJ0tq1a5Wfn6+Kioqw587JyVFOjnkZGgAAg0Gi+3I/bmnXqto9MVcjIyWJjSdHbj0W0LHare7lscTbPC6QG0moPcqqqdVsL3a0LQHWyfj7X9imV++6NOqDgKbWY0bX96+fm6wzvSP6ZMU52sOE0H9OACBdOE66jxw5ou3bt/v/3NDQoNraWhUXF2v8+PH62te+pm9961s644wzVF5ernvuuUejR4/WNddcI6knoS4qKtJNN92ke++9V0OHDtVTTz2lhoYGzZs3T5I0d+5cVVRUaOHChXr44YfV1NSku+++W7feeiuJNQAADsRTYhxo1TuNWnVy7FSk1chY+8Yt9YzcurzC25PQudi9PJJEmsfdM2+KSkbkuJaE2qOslqw024sda0uAaTf1lqNmVQojh2VrwfQxRrFOBVYalAzL0fLV0R8mBP1zAgBpwnHS/eabb+qSSy7x/9ku6b7pppv09NNP684779TRo0e1ePFiHTp0SLNnz1Z1dbVyc3uetJeUlKi6ulrf/OY3demll+r48eP69Kc/rVWrVmnatGmSpMzMTP3hD3/QkiVLVFlZqWHDhummm27Sfffd58ZnBgBg0IinxDiSSKuRJvvGA5NEt7uXh0qkeVxRXpa+fFG560mfk1FW8e4BD1Vs2IjONM4pp5UGiY5mA4D+ynHS/bnPfU5WlE1RHo9H9913X9QE+fzzz9f//M//RD3PhAkT9OKLLzq9PAAAEMDN7uORViNNy5jtuHj2ODuRSPM4l3u3BamaWqZLzxqlZ2p2aldLuyYU52lh5URlDwnecufW9+PNNzuOaZwTiVQaJHM0GwCkQp93LwcAAH3HjVnLgcKtRpqWMdtxTvc4O5VI0nbIYHRXvMKt/P7Xqw29Vrrd+n7s40R7AFGWhNFgiY6pc/ufWQBItYzYIQAAoC90+yzV1DdrVe0e1dQ3q9uFunA78XJ7h+xr2w/4r7MoL9voPS1Hu7Sqdo82N7Tonnk9jVFDr6svu4VHkoyVVnvlNzQBtkv2q7c2+l+z94BLiX0/9nE8EY7jMTyOU/FWGniUnIcAAJBqrHQDANAPVG9t1PLVdWpqC9jvm5+r5Vcn1jk7WhOvRDzyyqmmqsXDsoze8+if6/1/b8+bfv7tPdp3+NRKeemIbK1YMDWp3cJjcXul1aQbeWjJvpM94NG4dRwn4nlo0VfzwQEgFTxWtA3aA1hbW5sKCgrU2trqnwcOAEB/VL21Ubes3BLx54+7MEbJjfFZyRD6ICDDIy2aU66lV4UfEWrKXlmWnD1oyPBIf73/yl77rBNRU9+sG57aGDPuuUWzepW1B3b/TqSbulvHMWH6eQMxpxvAQGSac7LSDQBACnX7LH3jd+9FjVn6u/cSHqNUNbVMl1d4/YnX/rZOffvFbXEfLxKnq+mhsT5LemJ9gyQllHhHWuGNxWf1dHx3c093It3IMzM8rlyLW8cxYbInfVR+jv7fP07XgSOdfTIfHABSiaQbAIAU2rijWYfaj0eNOdh+XBt3NOui00sSOldg4rXhb58kdKxIioZlGzdWi+bJ9Q26Y+5ZCa04hz5o+Gtjmx77y46Y79t7yKwbu6mSYWYjuUzj+juTueTLr/50wv88A8BAQSM1AABSqKa+2dW4aAIbtf1uy8dG77lm+mj94Prpuu2SyUbx98yboucWzdIPrp+uWz9n9p5wLElPv9YQ9/tt9oOGBdPH6EjnCaP31H50MOHzBjFdwE3iQm8ymvRFY1caeAuC98d7C3J7zXkHgHTHSjcAACllmvwkliTFu6d7bNFQLZg+RjX1zXrklfqY8d6Cof7V9J9siL2qHM1LdU1a/FmzxL0v9yw7deBIp6txToW7932xhzq00qC/3RcA6Csk3QAApFDlpBKjZLZyknkpbmgCevBop2599u240vaZ5T0JdDwzn4uHJ1oubZacmSaV44uHGR3PNM6UaTf0ZMynthvKhd57e1RZsled+3IvOQD0VyTdAACk0KzJI1WYlxV1X3dhXpZmGSYu4RLQDE/86+QZnp7ENzPDo6unlfmbnIVz9bSyoFVMb35iSeTnp5wWM8ZJUvmp0uFG5zWNM2XSWMybhPnU8YwqAwC4jz3dAACkUGaGRw9ee3bUmAevPdsoKbIT0NDV6ES279odtbt9lla/0xg1dvU7jUF7he1kM14eyxN1D3KspFLqSSrt927e2WJ0XtM4U3ZjsUi3wVJy5lNvbmiJWplgSWps7dDmBnc/LwAgGEk3AAAp9vbu6I27Yv1cip6AJqKprSdpi5XASb0TODvZ9Ci+HmEP/s8H+uqvanXDUxs1+6F1qt4anPQ7TSr3GHYlN43r7xIZVQYAcA9JNwAAKdR1wqenNkTv0v3UhgZ1nfBFjTFJiuPx6ocHJMWfwEXqYu2UXS4emHg7vabRhUON4k3jTNkPRCKxy7zd7iieyr3kAIBTSLoBAEihZ2p2xiz/9lk9cdEka7Xy0LGemduJJHBVU8v06l2X+keJXTjJ+d7lcOXiTq/JdC602/OjU1XmbZf3R6oy8Kh38zsAgPtIugEASKFdLe2uxCVrtdJeYU80gQuclz1tXGFc1xKanF5QXqzCvKyo7ynKy/Jf06xJI5WXnRk1flh2pmZNcrfbdqrKvO3yfql3eb/952TsJQcABCPpBgAghcYV5bkSFysplnq6mDs18mRS62YCVzwssVFiTpLT0CKC7CHRf/WJ9fN4pLLMO1J5v7cgN+njwgAAPRgZBgBACrk1xspOipes3CKPgpNNOw1+5IYZKhqWrf2HO/Toug/1t/1HY573eMBWcjuBCx1J5g0zEzuakcOyjeIisZPTzQ0tUUetSdKh9uPa3NCiyskjjeIPBsS7JVUjw2xVU8t0eYU3aHb7BeXFrHADQB8h6QYAIIWcjLH67FmlUWOcJMWbdzQbJd1neUf0OkeiCVzz0S7j2EChyanTsm27E3sspnGmTB6IJLvM2y7vBwD0PZJuAABSyO0xVqZJ8YSRw4yOFy4u0QTuwFHnSW245NRp2XbLkU6jeNM4J9yqEgAADDwk3QAApFAyxliZJMWW4URv0zgn3v+4zfF7wiWnTsu2iw3L2k3jnKLMGwAGJ5JuAABS6KLTS/TjP9cbxblpzyGz1WbTOCeGxuggbjt/fKEWXjgxYnLqtGzbW2D24MI0Lh6UeQPA4EP3cgAAUmjWpJFGY6/cHmM1odisa7plWVpVu0c19c3++diJ+swEs4Zhcyu8WjB9jConj4y4GuykO/d5E4qMznui2+f6Z7Z1+yzV1Dcn7fgAgP6HlW4AABLU7bPiLhnOzPDoMxOLtLZuf8SY8ycWuV6C/IWZE3T/C9tixj2zcbee2bhbUs8cbjf2H08Zne9qXNXUMl161ig9U7NTu1raNaE4TwsrJ/Ya/7VpR7PR8Rb+dLP/7936zJJUvbWx155uN48PAOifSLoBAAjRdcIXM4GzJZpIdZ3w6U9REm5J+lPdfnWd8Lk6Q/rNBrOu6YGaWju0ZOWWhOc7t7SbdS83jQt3D/7r1YZe9+C/t3zs7ELl3meu3tqoJSu39Np77tbxAQD9F+XlAAAEeODFOp159x91/wvb9IuaXbr/hW068+4/6oEX63rF2olUYLInnUqkqrc2xjzf0681xGxVZp2Mc9P/b8tHjt9jX+eKNXUJlUU77ToejZN70N51wtmFquczW0rsM3f7LK1YUxf2Prv1nQIA+i+SbgAATnrgxTo9sb53EmxJemJ9Q1Di7VYi9dL7TUbXZhpnynQEWShLUmNrhzbHsVJus7uORyqY96inWsDni76f3Ok9+MzE+PfFJ/KZNze09HooEMiN7xQA0H+RdAMAoJ4y7yfXR19NfnJ9g7pO+CS5l0i1dZqtvprGmRpbZNZILZL9h+Pvam53HZfUK/G2u5AfO96tL/5kk776q1rd8NRGzX5oXa/KAaf34MZZE+K+ZklqaovvM5t+V4l8pwCA/oukGwAAST9/3azM++ev9yTmbiVSU7wjjI5jGmfquhljE3p/pNJv0+7ckbqO253cD7UfD3o9XLm403tQ+9Eho/hIWo50xvU+N8vpAQADD43UAACQjEt7Nze0aNHFk11LpK49d6xWvRN77/e15yaWJIeaGecIMo96xnFdUN577JfTpnJVU8t0eYXX3/m9ZHiO7vhNbdjzWifPvWJNnS6v8Cozw+P4HiS6klw8LDuu99nl9E2tHWEf7ET7TgEAAx8r3QAASDrceTx2UECc6b7kWIlUhuEoMNM4U2/EsX/YvoJl8yt6jTCLt6lcZoZHlZNHasH0McrweNTUFnk1ObRc/ILy4pgzzgvzsvz3INGVZG/B0LjeF6ucXgr/nQIA0gNJNwAAkg53dDuKsxOpSCXplswSqU0NZrOjTeNMvV5/wPF7vAW5YUdbJdJULrAc/bXtnxhdh5MV68BvP9aDkmhMHqBEE6mcPtJ3CgBIH5SXAwAgyRNzR3ePo53Htap2j0pH5OqtXdFXi9/efdAgmTJNAd1dBd1zsN0o7qLJxfrHz4xX6YiepDMzw6Nun+UvCS8d0dNl3LShWeXkU2Xt4crRTdgr1psbWnrt/Q51sP24/7z2g5JbVm5xdD6P3FmJDi2nD/xOAQDpi6QbAAD1lA5v3Xs4ZtzO5mP66q9qjY755IYG3TH3LGUPiVxYVjl5pB55ZXvMYwUmq67wmCV6p43I1YLpY/x/DpcoFw6NXuJtC1yhtsvRnUymDt37nIyu4IV5WUGJfLQ96fGwy+kBAIMHSTcAAJIum1KqP23b7+oxLUv6+es7tejiSRFjZowvMjqWaZypMYVm+5MD4yIlyoeOme2Ht1eoo5WjRxJu77PTRmr2eaOdY2hWph69eYYOHO1kJRoA4Ar2dAMAIOn9vW1JOe4bO6OXoK/cuMvoOKZxpkz3J9tx8STKttCmcrHma4cTbu+z02Z2pnO9MzI8WjB9jL8kHQCARLDSDQCAFFcyaSIvOzPqz9/YadYg7Y2dzVFXzE0E7sXebNiY7W/7DuuzZ5bGlShL4VeoTcu9b7vkdJ0xanjEFWd7j/aSlVvkUfA9TOS8iY4WAwAgEEk3AACSxhXFNw4qlutizNfOzYqelDuNiyTepmU7DxyVZJ6IFg7NCio394bZE21aFn7R6SUx9z/bXcFDP1si5010tBgAAIFIugEAaa/rhE/P1OzUrpZ2TSjO08LKib2am1k+98+bl52pC88oiRozPMfsP8WmceHE07TMVtfYKsk8EX30izOU4fFE7c5tl4U3tXaEvabQhmmxmHYFP29CkTI8UpjJZX4Znp44AADcQtINAEhrD7xYp6c2NAQlWt9+cZsWzSnX0qsq/K9t+eig6+deOGt8zD3BpnuG491bnMhebElqPtIpyTxRnjUp9j7oWKO7TGechx4z1qr4W7sORk24pZ6E/K1dB+kwDgBwDY3UAABp64EX6/TE+oZeiZbPkp5Y36AHXjzVyTov2/3n0KvfaVR3jCxvfPEwo2NZlrSqdo9q6ptjHjNQvHuxbT6r51cFO1GWek8MD7d/uj9iTzcAIBVIugEAaanrhE9PbWiIGvPUhgZ1neipK79uRvS91/FobO3Q5obo3cs/VTrc6FgrN+3WV39Vqxue2qjZD61T9dZGo/clmkCeO6HQ//f2/mlvQXCpebjO4tGYjO5asabO0cMFE+zpBgCkAuXlAIABJ7ALd6T9u8/U7DQqJX6mZqdunjNJF55eorzsTLV3dUeMz8vK0FM3fUYHjnTqw32H9cgr9TGvNVbSuznGSLFwmlo7tGTlFqNEN9EE8tppY4L+bLp/OhrT0V2bG1pcLfN2ey85AAAmSLoBAANKuC7cZWE6Ve9qaTc6nh2XmeHRwlnj9cT6yKvjCysn6KLTexqj1dQ3GyXdsZLevYeOGV1nIEunVoMvr/BGTXhjJZqxfPjJEV2iUUGvmeyfjiZVZd5OR4wBAOAGyssBAP1W1wmffrJhh+5dtVU/2bBDa97ZqyUrt/RaJbVXfgNLricU5xmdw47r9lla/U70ku3APdp2MhspPfOo52FArFXT0XGOKgtcDY4m2l5sE2/ucr4SH0sqy7zdKpEHAMAUK90AgH4pXNfxSMKt/C6snKhvv7gt5niohZUTJZk1HAsseXZr1XTWxJF6VLFXzCMxWQ2ONMvaxNAE54OHk+oybzdK5AEAMMVKNwCg34nUdTya0JXf7CEZWjSnPOp7Fs0p98/rjqfk2Y1V04zMxBI909XgqqllevWuS/Xcoln6wfXT9YULxhm979OjC3q91u2zVFPfHFc3denU6nukd8UzMswpu0R+wfQx/ocoAAAkAyvdAICUC2yMVpyXHXVfdSyBSbE9hzt0xTzDo15zuuMteU501fTAyTnYTsWzGhy4F7u729Kzmz+K+Z6S4TlBfzbdUw8AAHqQdAMAUipcEpeI0KR46VUVumPuWXqmZqd2tbRrQnGeFlZO9K9w2+yS52jXEWmPdiKNxeLZt+xG0y/TZD8wrnpro5as3NJrhbrRQTd1yXxkWKwmcQAADAQk3QCAlImUxMUj2spv9pAM3TxnUtT3Z2Z4dPW0sqir7FdPK3M9CTTpLp7hUdBKvdeFleX397Y6irMT5Wgl4aaJcqpGhgEAkAok3QCAlIiVxDnhxsqvaffyO6umuJp4mzRke+SGGSoalu1q06+PD5pVFthxThvNRZOqkWEAAKQCSTcAICVMkjhTbqz8uplUOlU1tUyLLy7XUxsaZAVk3Z6Te8+vOsf9vdI5hr8B2HFNrWbzxE3iUjkyDACAvkbSDQBIiURXMS+aPFL/+JlxCa38BjZw+6DxsNF7Gg+ZJZ9OVG9tDFvW7rOkJ9Y36NzxRa40KQv8vJbH7PsqGd6T+LYc7TKKN4k7b0JRr5L5UBmenjgAAAY6km4AQEokuoo5fVyhFkwfE/f7423g9vZHB3XteWPjPm+obp+l23/zTtSY23/zTsJNxeL9vOOK8yRJxSFdzCMxiXtr18GY4+B8Vk8ce7oBAAMdc7oBAClhNxCLN42cVR5/MmY3cIunvN1nubEL/ZTXPzyg9q7uqDHtXd16/cMDcZ8jkc974eklkiRvvtlDEpM49nQDAAYTkm4AQErYDcQkxZd4x5mtu9nAzQ3/veVjV+NCJfJ5PZI+M7GnG7z9kCSaSCPVQrGnGwAwmJB0AwBSpmpqmR67cYa8MZK5cDY1tBjHdvss1dQ3a1XtHj39WkNCDdxG5GTF/d5w11PXaDa666OD7XGdK5GGdZakN05+z/ZDEo96P++wXzPtHh+rysEj8wQeAID+jj3dAICUqppapssrvP4GX69s26/n39kb832WYZl3vHuZI2lqS+w48V5P5wlfXOdLtET79R0HdNEZPSXm9kOS0Os36R4f2MStdESu7pk3Rbc++3bEMWmJjH8DAKA/IekGAKRcZobH3zCrqbXDKOnOHxp7xdney+xmKXkiW7oTuR5vvlkjs1CJlmjvPRjcrT30IYlJ9/hwDxrKCnK1+OJyrX6n0XECDwDAQELSDQDoV9o6jrsSl6y9258c7tCq2j2OR5Ulej0zJ8XXOM4u5W5q7Yjr3KMLh/Z6LfAhSSyRHjQ0tXboyfUNevQLM1Q0LNs4gQcAYKAh6QYA9CumZeOx4hLZyxzN6zta9PqOnn3OZQ5WZRO9nhtnTYzrffZe7FtWbonr/Yl0iY/2oMFSTyn5/S/U6dW7LiXRBgCkLRqpAQD6lcLcbFfi+mLcVFNrh5as3KLqrY0xYxO9ntqPDiX0/rglkAvHetBgSWps7dBmB03xAAAYaFjpBgC4LrRplpOS4Zb2Llfi+mLclL1au2JNnS6v8Eb9jIlez2vbP4nr+7RXm+NVU9+sOZ86zTg+8N5/uO+I0XuYxw0ASGck3QAAV0VqmmVahv3eHrMRWq9uPxB1b3Wie5lNBa7WRtvnnOj1PPJKvf/v+7Ks/Z2PDxnHxtuZnXncAIB0Rnk5ACCmIx0ntOjnb+iK76/Xop+/oSMdJ8LG2U2zQpMuJ2XYQ7PM/tO0dW+bvvqrWt3w1EbNfmhdr2Pbe5ml8HOlw72eiNdOPgSoqW9Wt693Wh3tepzqy7L2vGyz+xHp3kfDPG4AwGBA0g0AiOrqRzZo6vL/0dpt+/VB02Gt3bZfU5f/j65+ZENQXKymWVJPGXa4hDSQt6B3t+xYIiWh9lxpb0HwSmphXlbQdbnhkVe2R30IEO16nHLyfSa6ivyZCbEbqcXTmZ153ACAwYKkGwAQ0dWPbNC7H7eF/dm7H7cFJd5uNc2aNq7Q8XVGS0Krppbp1bsu1XOLZukH10/XL//3TOUMSe5//qKtRIdez8JZ4+M6h+n3aZe1x+uM0uExY+IpYfcW5OqxG2cwjxsAkPbY0w0ACOtIx4mICbft3Y/bdKTjhIbnDjEuY44Vd/CoWSO1UKZ7q//a2Kamts64zuHkWqI1WAucc/3WroMJnSvW95mZ4dHV08r0xPqGuI6/6t29uqRiVELXYLvtksk6Y9QI5nEDAAYVkm4AQFhf/dVbxnE/+fJM4zLmWHF1jdET/VhCE8B4m3slyvQhwITivITOE+v77PZZWv1O7L3fkXx8sD3ha7BddPppUb8LAADSEeXlAICw3vnIrIu4HWeXMUdauzRtmnW0M3yTNlOBCWA8zb1iueEz4/SD66frtksmG8XHWgVeWDlR8Sz4mn6fiXYvzxmSGTPGrXsPAEA6IukGAITVecLnKM6kW7hJ06zS/Bwnlxl0jsDELp7mXiaOdp7QguljdNHpZrOrY60CZw/J0OenlDq6BiffZ6Ldy88emx8zxq17DwBAOiLpBgCEVTrCLPkNjIvUndtJ06xzxxU5u1CdSuzumTdFmxtatKp2j55+rSEpJeV7Dx2T5N7qbrfP0hs7o+/rDj2Hk+8z0e7lF002e7jgxr0HACAdsacbABDWyBHZqj8Qez/vyBHZQX+umlqmyyu82tzQov2HOxw3zWo9dtzxtXoLcnX1tDLd/8K2pO/dbu86rlW1e1Q6Ilf3zJuiW599Wx4Fjx9zsrq7sb5Zh9qjf2ZL0jevOkul+bmOv8/zJhQpwyPFmCwW/eSGEr33AACkI5JuAEBYRzu7444L7M7tVPGw7NhBkv71c5N0pjdfpSNydfBol259dktcpeT/fuVZGlWQq8df2a5t+47EjK9rOqqv/qpWUs9K9uKLy7X6ncagZN9bkKtl8yuMVndrdhwwus7WY8e16GKzfeSB3tp1MP6EW9Kmnc2ac6bZareU2L0HACAdkXQDAMIzTdRc3jTtLRhqFDfnjFJVTh6pbp+l2Q+ti/syPB5pwfQxerj6r47f29TaoSfXN+jRL8xQ0bDsOFd344/r9llhV5UDX//Q4EFCNJbbm+IBABhkSLoBAGGVFQ7V+42HjeJMRUoSA11QXqzCvKyoJdeFeVn+vdKJdud+Y2eLSvNz4yprt+dx3/9CnV6969K4yqhnlhfrkVfM4gKFG4VWdrLMPnTlPRH5Q7NcOQ4AAIMVSTcAwC8wKR5bbNaA65IzS4ziIiWJpmXYgQJT20S7c79Ut18v1e2P+/2m87gjyfCYJeqBcfYotNBF6MbWDj2xvsHxNURz6GiXq8cDAGCwIekGAEgKnxSbeOWDT/TFWeUxjx0uSWxq7dCSlVuCultvbmiJ2VjsYPtxf5KbaHdut8Sb/B842ukoLlmj0CJpaktuYzoAANIdI8MAAP6kOJ6S5FjviZYk2q+tWFOn7pPdvkyTVzsu1uiuvhJv8m/6Pjsu0XJ6p0YXmW8fAAAAvTlOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHH/Y6zgsvvKCZM2dq6NChKioq0jXXXBP08927d2vevHnKy8tTaWmp/u3f/k0nTpxwerkAMKh1+yzV1DdrVe0e1dQ3+xPb0JhEVk5jdTmPlSQGlmdLzpPQzAyPls2vkNS71Zgn5H+TwXQedyRO530nWk5/2yWT9YPrp+ubV51lFH/hJLPtAwAAIDzH5eVHjx7VtGnT9C//8i+69tpre/384Ycf1g9/+EP9/Oc/V3l5ue655x5dccUVqqurU25uzy9I//3f/61FixbpP/7jP3TppZfqxIkT2rp1q/8Y3d3dmjdvnrxer15//XU1NjbqS1/6krKysvQf//EfCXxcABg8TPdQJ7pyOvm0YVF/7nTl+rwJRfJ4onfN9nh64mxVU8v02I0zen1ee3TXpWeN0jM1O7WrpV1t7cf1/Dt7ja4plljzuE0ax9kPDZas3GI07zvRcvqLTj/N3/X90T/Xx2xYN4vxXwAAJMRx0n3llVfqyiuvDPszy7L0/e9/X3fffbcWLFggSfrFL36hUaNG6fnnn9f111+vEydO6Ktf/aq+853v6Oabb/a/t6Kiwv/3L730kurq6vSnP/1Jo0aN0vTp03X//ffrrrvu0vLly5WdbTbDFQAGKyd7qBNdOT1vfFHUnztduX5jZ0vMMVWW1RN30emnVmGrppbp8gpvxCT35jmTJEk/2bDDtaQ72jxuJ43j7IcGy1e/r6a2U3u8R+XnaPnVnw6Kt1fGm1o7HFUneE5er71inpnh0YPXnq1bVm6J+J4Hrz07ro7sAADgFFf3dDc0NKipqUmXXXaZ/7WCggLNnDlTNTU1kqQtW7Zoz549ysjI0LnnnquysjJdeeWVQSvdNTU1OvvsszVq1Cj/a1dccYXa2tr0/vvvu3nJAJB2nO6hTnTl9ONDx6L+3Gn5dE19s9F5w8VlZnhUOXmkFkwfo8rJI8MmjMXDEntwW1lepB9cP13PLZqlV++6NGLCHW6PvP3Qo3prY4SjRyqQPyVaOX0kkVbkq6aW6fEbZ8ibnxMU783P0eMBD2YAAED8XE26m5qaJCkoWbb/bP9sx44dkqTly5fr7rvv1h/+8AcVFRXpc5/7nFpaWvzHCXeMwHOE6uzsVFtbW9BfADAYOd1DnWgjsl3N7b1eC9xLvrmhRffMmyIpckoZmAx2d/vMzttyNOpe9Ui8BQk2BvN4oib1Th96SKeS9NBO4U1t4ZN0e2XcWxD8wKSsIFdfubhcZSGvewtyg6obQo/12jc+r+cWzfI/THjtG58n4QYAwCV9PjLM5+v5Zeqb3/ymrrvuOknSz372M40dO1a//e1v9ZWvfCWu4z7wwANasWKFa9cJAAOV0z3U0fYUm+g4HtxILVJZ9eKLy7X6ncawe64DE7zWjujjwmxr3mnUmnca/cc3nfdtP2SIdx9785Hoc6udPPSw91ZHa2RnqSdJv7zC22uVOlI5/Z1VU2LuJQ9kVwgAAAD3uZp0e71eSdK+fftUVnbqF599+/Zp+vTpkuR/PXAPd05OjiZNmqTdu3f7j7N58+agY+/bty/oHKGWLl2q22+/3f/ntrY2jRs3LsFPBAD9i0ljLqd7qKXIjchMTApopBZtL/mT6xv06BdmqGhYdtTr/+Sw2dzq0OOH7lWPJPAhQzwd24fnRv9Pp9OHHiaN7AKT9ECRkmWSaAAA+g9Xk+7y8nJ5vV69/PLL/iS7ra1NmzZt0pIlSyRJ5513nnJycvTBBx9o9uzZkqTjx49r586dmjBhgiSpsrJS3/72t7V//36VlpZKktauXav8/PygZD1QTk6OcnJywv4MANJB9dZGLV9dF1SC7M3P1fKrKxw12gptqGULXTl9dN2H+tv+ozGvq6a+Wfeu2qpxRUP1k1d3Riyr9ki6/4U6vXrXpVFXXYflOP9Pk338cCvC4STykOFM7/CoP3f60KOpNfqeeJtpHAAA6F8c/2Zz5MgRbd++3f/nhoYG1dbWqri4WOPHj9fXvvY1fetb39IZZ5zhHxk2evRo/xzu/Px83XLLLVq2bJnGjRunCRMm6Dvf+Y4k6R/+4R8kSXPnzlVFRYUWLlyohx9+WE1NTbr77rt16623klgDGJSqtzaG7TLd1NahW1ZuCWp6ZTKC6p554cuPA1dIn9u8WzJIuj8+1KFf1OyKGRdaVh3JdeeO1fO1zruL28d/+rUGlYzIiVlWHfqQ4a+NbXrsLztinuf88dHncTt96NFyNHq5us00DgAA9C+Ok+4333xTl1xyif/Pdkn3TTfdpKefflp33nmnjh49qsWLF+vQoUOaPXu2qqur/TO6Jek73/mOhgwZooULF+rYsWOaOXOm1q1bp6KinrEzmZmZ+sMf/qAlS5aosrJSw4YN00033aT77rsv0c8LAANOt8/SN373XtSYpb97L2iFN9rc6qunlen+F7bFHGXVFbJX2y2xyq8vPKNEedmZau+K7/z3v7DN//ex9noHPmQoGZZjlHTHasQW+NAjksDGccXDzR4mm8YBAID+xWNZsaahDkxtbW0qKChQa2ur8vPzU305ABC317Yf0Bf/a1PMuF/+75lBc6ul3nvADx7t0q3P9t7LbK8FB+6JvvnpzXr5r5+48AmCPbdoVsz9xpFW9p0K97kiSeR7DueBF+v01IYGBTZWz/BIi+aUa+lVp7ZK1dQ364anNsY8nsn3BgAA+o5pzunqyDAAgPte234g7rjAudUXlBfr/hfMR1mNyM2K84rDC53H3RcijegKZ5/h3u7XPjwQc1RZ9dZGPbk+OOGWJMuSnlzfEDQCzC5Hj6avvzcAAOAekm4A6Of2HOw9BzueOKfzuyu87lUJhZvHHUm3z9Ltv3nHtXOHfq5I3twV/ee2H/+lXl/9Va1ueGqjZj+0rtcMbadzujMzPLp6WvRV+KunlcX83gAAQP9E0g0A/Z5pshU9zukoq6Lh2Ybnjc1bkGtU4i1Jr394IO793NHE+vx/bWxzfEx7VFlg4u304Ua3z9Kv3/w46nl+8+bHMVfqAQBA/+TqyDAAgPtMU61YcU5HWb29+6DhmXvzSPrFv1yglvaumF3EQ/3mrY/iPm80sT7/4c4Tjo8ZblSZ04cbG3c061D78aixB9uPa+OOZqO95AAAoH9hpRsA+jvTfpcx4uy9w5FS39A9139tOmx+jSEWX1yuOZ86TQumj1Hl5JGOSqNrP4o/2Q/HdC/5yDhX9kNXrp0+3KipbzaKN40DAAD9C0k3APRzpvlqrDh7lJXUuxA93J7rIx3RV18jXcNXLg7uzu3UkAz3/tN0ai55hTY3tERtgJZhXMYfnr1y7fThhnu1DAAAoD+ivBwA+rkxxXlxx4WODLu8whtxfnfoPOuRw8y6l08oytFnz/JqQnGeFlZOVPaQxJLmWZOK1dBs1jwullNzyetiziUflpvYfxLtlevAOd0eBafK4R5uVE4q0SOv1Mc8fuUkSssBABiISLoBoB8KTJYLh5olvxeGJGXVWxt7Jdd2svnqXZcGJePh9lyXjBgq6VDM854zrlj3LZhqdI0m5k7x6rk3ojcWi+aeeVNUMiLn5FzyTt367Nu91ojtBmiBzd0umDhSa+v2Oz6fRz3JfWD5etXUMuOHG7Mmj1RhXlbUfd2FeVmaxYxuAAAGJJJuAOhnwiXLseRlZwYlZdVbG7Vk5RajZDMS033Ybo+yeivOPd128vvli8qVmeFRt8/S7IfWRRzdFdoA7aYLJ+o//rjNeAu9fU4p/Ci0qqllurzCG/PhRmaGRw9ee7ZuWbkl4nkevPZsRoYBADBAsacbAPoRO1l2knBLkicgH3M6JzqS4uFmK+ymcabimYwVLvl1Orore0iGzh7jbDa5x9PTNC7SA4zMDI8qJ4+M2VCuamqZHr9xhrz5OUGve/Nz9LjhqDUAANA/sdINAP1EtGQ5lqOd3f6RUk6SzcooJcvrP/jE6NzrP/hE+junVxxZUZ7zLuLhyradju7qOuHT1j3OZnX7LOnJ9Q06d3xRwomx6co4AAAYWEi6AaAPhDY0C5dMxUqWY6mp70m6nSabkbQcNetebhonmX0PxcPMku4ln52ks8ryIx7H6eiuZ2p2xrXKLgWXqSfCXhkHAADpg6QbAJIsWkOzeFZmI+vJGJ0mm4ECk2LTLuQFho3eTL+HQ+1dRscrGZ6jBdPHRPy5PbqrqbUjbPVAaAO0HQeOGp03lGnlAAAAGJxIugEgiZw0NDNNliOxR0qdN6FIGZ7oe6MzPD1xodfqtIGbJC29ckqv10JXtJ10ETctL28+0qVVtXuiNihzMrprf1tiDz0Sf2gCAADSEUk3ACRJrIZmod2zY63MRhPYvfytXQdjlkn7rJ44e2U20sMBE0OzMoP+HC55z/DI+Hs4aLjS/eO/nJptHW7FXHI2uuu0EcFNzJxK9KEJAABITyTdAJAkThuaRVuZjSUnoBTc6Z7uRBq4SdKmnc2ac+ZpkiIn79EeAoR+D6Z7ugNFG4Vm2qAs3u3Y4eZ0AwAA2BgZBgBJEk9DM3tl1lvgbNX0YPtx/+grp3u6E23gZo8eSzR5t7+H0nznK8axRqGZjO4aYbg3Pdy5w83pBgAAkFjpBoCkibehWejK7If7juiRV7bHPI6dtDptIJboXuTmo52SEk/e/d9DnFl7tIZmJl3TMzwkzQAAwH0k3QAQB5MkzmnyGyhwdFRNfbNR0m0nrU4biCW6F/m17c2S4k/eez0EONKZ4PV8EnRf1tY1GXVNL8iJb6U7dE86AABAIJJuAHDIdPSV0+Q3kni6kTtpIJZIAzdJOtTeM6c7nuTd/uT3zJvif4ixZVdLHFdxyiOvnGqwVpiX5b++QOH2gB/qMGvgFoqRYQAAIBqSbgBwwMkIMKkn+V18cbme2tAgK+BNHo+0aE55r6Zf4cTTjdw+t0kDsUQauElSblbP8UyS99CHB96CXF09rUz3v7AtodL0SMIl3FL4rukZnsTanDAyDAAAhEPSDQCGYo0Ak3qXGVdvbdST6xvCdvN+cn2Dzh1fFDPxjqchmy2wTD2aSCvjJs44bZj/XLFW9h+5YYaKhmUHzO/u0q3PxjeqLFGhK9SVk0calfFHwsgwAAAQDkk3ABgyaRQWmMSZdPM22Qscb0M2p0JXxp/d2KBNO1tjvm/k8FPzrZ2UtXf7LM1+aF1KEu5A9sOKWZNGKmdIhjpP+By9n5FhAAAgGpJuAIgisGHaB42Hjd7TeOiYJOdzuiM5b0JRzLJvj4L3dMcrcGX8yb+YrfrubG4P+rNpWXui3c7dYj+s6PZZ6up2nnBLjAwDAACRkXQDQAThGqaZePujg7r2vLEJlYUHeqOhJeZqsHUy7qIzSoJeN+myHkl7l1kCGi7OpKw91XugQ1eon6nZGbTv3kS4FXwAAIBAJN0AEEakhmkm7PeUDMuJGmfb39ahVbV7gpLiwGT5lW37jY7zev2BoKTbtMt6JKMLc9UQsoodKS4eqd4DbSl4hXpXS+zPKklzK0Zp3jlljh9iAACAwYmkGwBCmOzFjmZc0dCevzHMxb794l/9f192spv36ncaHa+w7zlZ1i7F7rL+6BeCG5qFSx7nnHGaXquPPb5rzhmnObpOW6Kjytw2oTjPKG5mebEWTB+T5KsBAADpgqQbAEIkutf4eHdPCnngSKfj9za2duiJ9Q1xnrdbklmX9due2xI8uis/V8uvDl4BT3YDt0RHlQWK9LAi1nzzwEZ2Cysn6tsvbos5D31h5cQErhQAAAw2JN0AECLRvcara/fotkvP6PPy6ff3tEkye2gQmlg2tXXolpVb9HjAnPEDh80eGoSLM91Lbnc7X766Tk1tzr732aeP1D+cPy7o+HdWTfGf98DhTt3/wraoxwhsZJc9JEOL5pRHfeixaE65sockNs8bAAAMLiTdABAi0WT5wJEuST3dxGOttLrp+MnO24k8NFj6u/f8K7/vN8YeFyapV1x8e8mdf0lfuXiy5nwquLQ9sIHb77d8bHScptZTZflLr6qQJD21oSHovmV4ehJu++cAAACmSLoBIESie41zs3pWQt/adbDPEm5Jysvu+Vd6Ig8NDrYf18Ydzbro9JKgPeLRONlL/ljASnq0eBPnT4w+F7vlaJfRcULjll5VoTvmnqVnanZqV0u7JhTnaWHlRFa4AQBAXPgNAgBC2HuNJeNeaEFmjO+Zl93XI7HOPzn6yn5oEG9P7Zr6ZklSlmGSaceZ7CVfsaZO3SefRCTasO7ZTbui/rwoL9voOOHisodk6OY5k3Tfgqm6ec4kEm4AABA3fosAgDDsvcbeAuerxmePLZTU9yOxTj9tuKTEHxrYKfJpw8ySVjsu1l5yS6f2UJvExxJrxNfBdrOVbtM4AACAeFBeDgARVE0t0+UVXn9jrrq9rUadxYtPJqF9ORIrtKu2/dAgdG+1icpJPbO+xxSZjdCy40xX9l/b/on2H+7Qh/sOO7quUGUF0eegFxs+NDCNAwAAiAdJNwBEEdiYa9OOZqP3vL37oP7h/HFRR2LZfy7My9Kh9uP+1yONvsrLzlR7V3fEc4brqh360KBkeI7+9ZdvqfXYiYjHKczL0qyTn7fQsDzbjjNd2X/klXqjuFg+bom+59xbMNToOKZxAAAA8SDpBgCZjbj6oMlsZTYwLtKKs/dkN+/ApDjS6Cv79Yertznuqh340ECSHrruHN2yckvEa3/w2rP9n/ugYSOyrXtatap2j0qG5cibn6t9bclf2Zek3QejJ912pUG0lf6ygp7vFgAAIFlIugEMevGNuDIXuuIcmtQHJsW20GRZks4dX6TThu/RvsOnkuHThmfr3JON20yv5fEbZ2j56vfV1HZqvrY3P0fLr/500Od9d88ho2OuebdRa95tlNSzUm5JvVb2k2FCcfTy98BKg3DX4pG0bH5F2PnhAAAAbiHpBjCoORlxdaZ3hN7afSjmMc/0juj1Wrgk2o3r3He4K+woLlu4FfxYDwFsBw539jpeLK0nS+ULQsrmk+Hys0bFjIlUaeDmQxUAAIBoSLoBDFqxRlx51DPi6vIKrzIzPDp3fJGe3fxRzOM6WXlO9DqlnmsNvE5brBX8WA8BhudkOr5W+3vLHZKhX/7vmTpwpFMf7juiR17Z7vhYsbQcMyt/N33IAAAAkAyMDAMwaDkdcdVy1GzlN1xct89STX2zVtXuUU19s39WtRvXqZDrlE6tjIe+z17Br97aGPO8Z4zKN77GQJakprZOZXg8WjB9jC46vSSu48TSYrjnXDpVabBg+hhVTh5Jwg0AAPoMK90ABi3TEVd23NY9bUbxoXGJ7hn/+GD0edTBcSMdr+BHEq1bugn7e0vW6LQRufwnDAAA9H+sdAMY0BJZQTYdcWXH/c2we3lgnBsrzv9jEBMY53QFP5LGQ9G7g8dy4HCnVtXu0eaGFt0zr6e7upvryy+93+Ti0QAAAJKDZQIAA1aiK8ixVmA96hntZY+UGma4smrHubXiHNhlPBo7zukKfiQ5WfE/l83wSPe/sM3/57KCXC2+uLzX/HF7Tnk83c5NvxcAAIBUIukGMCA56ToeSeBIqdCkz06BA0dKneUdri0G3cvP8g6XZL7i/PRrDSoZkROxwVdhXlbMcwbGOV3Bj2TK6Hy9Vh99NTyS0IKDptYOPbm+QY9+4VwVDcsJami2tq6p18MTE6bfCwAAQCqRdAMYcNxaQZYij5Tyhlkxnza2UM9u/jjm9U0bWyjJfMU5dEU49LyL5kzSq9ubYx5n0ZxJksxX8H0+S6tq90RM9o92ON/TneHpnXBLp+7L/S9s06t3XRp0rtDu4o2HjunB6g9insv+vAAAAP0ZSTeAAcfJnmWT2dimI6XeM2yk9t6eNv2TzFecA4VbqZ99xmnKyvToeHfkAuysTI9mn3GapNgr+JakY8e79cWfbPK/Hi7Z/2Cf2R72M0qH6bZLz9CBw51BDxBCRbsvgXPMu32W/vNPH6rzhC/isXKGZPg/LwAAQH9GIzUAA45be5YDmYyU2mvYWMyOs1ecnTQPsxPkFWvqgprCZWVG/9d16M/tFfxR+cGJv12Sfaj9eNDr4Rq7Hek8YXTNnpOjwUpG5BjFx7ovmRke/eD66VFjfnD9dMZ+AQCAAYGkG8CA49ae5UAmXdB3HDhqdCw7zl5xlpx17Q7tLr6xvjnm+K72rm5trA9Xgh78OUKT7dCowGR/yqjhRtdrxxXnZRvFm8RVTS3T4zfOkDc/OJH35ufqcYP9+gAAAP0F5eUABpzp4wpdjave2qjlq98P6obtzc/R8qs/HZTcDcs2e04ZGBdpz7gJe0X49R0HjOJf33FAF51RIilyo7loHcJDy78XnDtOq96NPZZrwbnjJEl/bTIrv/9rU5vmfCp2abhp2T8AAEB/RtINYMBZuXGncdyiiydHjane2qhbVm7p9XpTW6duWbklaFW1rDBP7zceiXnessK8oD+HJo+x9j7b7JX6vQcNy9pPxkVrNGfCTvY/2GeWRH+wr02XTinVR4bXaRonBe/1BgAAGIgoLwcw4Nhl14nGdfssfeN370WN+cbv3vOXW19RMcrovLHizvLmy5sfea+3Rz2Nzez54KMLhxqd146L1WguFjvZX/t+7FXuwLgJxXkxIuUoDgAAIB2w0g1gwDkWY3+zadzG+uaIe5xth9qPa2N9sy46o0QleWaNwkLjqrc29iovL8zL8o/RijUf/MLJJXr0z/Uxz3vh5J7ScicN5EIV5mX5k/1PDnfGiFZQ3MLKifr2i9vCjgyzZXh64gAAAAYLVroBDAiBjc5GDjdLfs85OS87khrDvdJ23C/f2GUUHxhn760OXXluPZnsF5zsJm7zFuQGjQuTpM+UF8dsxOY5GSfFN6os8Di20/LNjmPHZQ/J0KI55VFjF80pV/YQ/tMDAAAGD1a6AfR74VaKTdhNxSIzbcjVE7fNsFGYHRdtb7W9yp07JEO//N8zdeBIZ8RGYW/tOhhzf7Yl6ZmanSoZkaOS4Tny5udoX1un433dB9uP+xupzZ0ySlt2H4r5nrlTTpXTL72qp1v7Uxsagla8Mzw9Cbf9cwAAgMGCpBtAvxapC3csw3IyNWtS9AZclZNH6pFXtsc8lt3Iq73LZ3RuOy7W3mpLPQ3bMk7OuY5k78F2o/MGNmeLVL5uwi5P//SYAqP40LilV1Xojrln6ZmandrV0q4JxXlaWDmRFW4AADAokXQD6LcS6cKdlRk7wZs1aaQK87Ki7usuysvyJ+/eghwdjLEH3I6TzPdWv7b9QNSRWLUfHzI6TqDA8vVY+9ZD2eXpLe1dRvHh4rKHZOjmOZMcnRcAACAdkXQD6LcS6cJ9KKBMOpLMDI8evPbssCPDbA9ce7Y/CZ4+tlDbDEaGTT+5l9x0b3XgantZQa6Wza8I2tNtXgZ/ir3KPTQrU4/ePEMHjnaqZFiO7vjtO9rX1hH2QYZHPXvK7UZqJcMMG8cZxgEAAAxG1PoB6LcS6cItSU2tsedBV00t0+M3zpA3pGlYWUFu0IxuSRpfaDbqyo67oLxYZQWRR4OFv+YOLVm5RdVbG/2vTRwZ34gtS1Jja4cyMnrK1y86o0TLr+7ZUx16TeG6pncdN+sSbxoHAAAwGJF0A+i3EunCLUkHjpiVR1dNLdNr37hUzy2apR9cP13PLZqlV++6NGS1WdpiWOZtx2VmeLRsfvgkNxJ7BXrFmjr/fPAvzJxg+O7wAh9eVE0t02M3zpC3IPi7Ddc1/anXdhgd3zQOAABgMKK8HEC/Za8UN7WGL4eO5ZDhnmSpJ0GOVoouSUc7TxgdKzDOTnKddF+3V6jt8vjajw4ZvS+S0IcXVVPLdHmFV5sbWqLuJd+x/6jR8U3jAAAABiOSbgD9lr1SvGTllri6cFvxZOpRlIww3OMcEhea5H6477AeeaU+5nHsFWqTMvlwQvdoBzJ5yJBhuDxvGgcAADAYUV4OoF+LVA5torXDfKXbxLhis73V4eLsJHfB9DG66PTTjI5z4HCnVtXu0Zu7WhxdpxR+j7ZThcOyXI0DAAAYjFjpBtDvha4Ur35nj17e9knM97m80K3zxhdLir1C3RMXmUnZfIYneO62U96wXdCdGTUiR3UG3dpHGVYAAAAADEYk3QAGhMBy6Dd3mq38ZnrMV3i7fVbMPc7/8/5eo2P9z/t7demU0sjXZVA274vjicE100frkrNKI16/Ux8fNNuDbhoHAAAwGJF0A3BF1wmfnqnZqV0t7ZpQnKeFlROVPSQ5O1imjS3UM9ptFGeiemujlq+uU1PbqeTRm5+r5VcHrxT/5W8HjI5nEhepwVqGJ76EW5KumzFWcz5lVrpu4pjhKDDTOAAAgMGIpBtAwh54sU5PbWgISha//eI2LZpTrqVXVbh+voOGXclN4qq3NuqWlVt6vd7U1qFbVm4JmtXdcdyse3m4uHAr6aFl8wcOdyZUUp7hYGXfxJBMs4cmpnEAAACDEUk3gIQ88GKdnljf0Ot1nyX/624n3ofaj7sS1+2z9I3fvRc1Zunv3tPlFV5lZnhUkDNEh47FXtUtyAn+V2v11sZeK9plAXuu7bL5VbV7Yh47mpodB3TgaKdr5eWXVZTqvzbsNIoDAABAeCxPAIhb1wmfntrQO+EO9NSGBnWd8Ll6Xo/him6suI07mmMm5gfbj2vjjmZJUvEIsw7qgXHVWxu1ZOWWXjO6m1o7tGTlFlVvbfS/FjpP26lHXqnXV39Vqxue2qjZD60LOnY8LjlzlKtxAAAAgxFJN4C4PVOzM+b+Y5/VExeq22eppr5Zq2r3qKa+Wd0ONjLPDDN3Op64mvpmo+PYcblZmUbxdly3z9KKNXVhG6XZr61YU+f/7HZXczeKxO2k/sV398b9Pc+aNFKFedHHgRXlZWnWpOjzvgEAAAYzyssBxG1nc3tccbHKrWMyzRtjxjk8kGE23HasS6tq9+jA4c5eK9yhR21s7dDmhhZVTh5p1NXclP3e2557O+jBiJPvOTPDowevPTvsnnfbA9eenXAZOwAAQDpjpRtA3CzLLC0MjHNSbh3Jpp1mK9Sx4ionlRgdx44bZrjS/X7jEX31V7XGTdH2Hz71Xdhdzb0FiZWa20IXtp18z/b1PH7jDHnzg6+nrCA3qMkcAAAAwmOlG0Dchuea/SvEjotVbu1RT7m13bgsMtOV1ehxsyb3lE9H29ddmJelWScbnZUMzzE8rzOhe7lDu5pv3HFAz23+2JVzOfuew1+PW43aAAAABgNWugHEzTTnsuM2N7QYl1tHY3f7jiVWnF0+Hc1/XDNVmxtatKp2j97bc8jovE5keKTzJhSFvbbKySO1YPoYXVButiJvyvR7jnQ9dik8AAAAYmOlG0DcivLMVn7tuMAy6mjCxQXOuS7Mid7cyzZjfO9kNlTV1DJ95eJyPbm+IWgF3qOeUVj3v7At6oOCRPks6a1dB6M+IAgt7XaL6f0AAABA/Ei6AcRt5LBsR3GmI7FC48I1XjOxcuNOLbp4ctSY6q2NvRJuqWc1eG3dfkfni1es5Pe8CUUJN1YLJ9ERZQAAAIiN8nIAcXO6ch1rJJZHPQ26LggY9RWp8ZqJN3YejPrzaHvM+1Ks5PeNhhbXrzH0ewYAAEBykHQDiNv6v33iKM4eiSX1bnFm/3nZ/Ar/fuFEk+Jh2dG7jcfaY55s4R4yhFOz44Dr5756Whn7sgEAAPoASTeAuO05ZDanOzAu0kgsb0GuHgsZQZVoUnzNuWOi/jyVe5rDPWSIHe2e1e80qjt0nhgAAABcx55uAHFrPRZ51Fa0ONMRVIkmxUMyoj9X7Is9zWUFubp6WplWv9MY9ADBW5CrZfMrjOZczywv1iOvuHtddvdy007wAAAAiI/jle7169dr/vz5Gj16tDwej55//vmgn1uWpXvvvVdlZWUaOnSoLrvsMn344Ydhj9XZ2anp06fL4/GotrY26Gfvvvuu5syZo9zcXI0bN04PP/yw00sFkGSZMZLaaHEmI6gSTYr3tUVP2mPtMY/XmIIc/eD66Xpu0Sy9etelWnpVhV6961I9t2hW0OsmCbck+brNVqS/ccWZ+sH103XbJdGbx9noXg4AAJB8jpPuo0ePatq0aXr00UfD/vzhhx/WD3/4Qz3++OPatGmThg0bpiuuuEIdHb1/ubvzzjs1evToXq+3tbVp7ty5mjBhgt566y195zvf0fLly/Xkk086vVwASTRqhNnIMNO4UIkmxW9/FL2RWrQ95onwFgzt9TAhkTnXv39nj1HcX/cd1oLpY3TR6acZxdO9HAAAIPkcJ91XXnmlvvWtb+nv//7ve/3Msix9//vf1913360FCxbonHPO0S9+8Qvt3bu314r4H//4R7300kv67ne/2+s4v/zlL9XV1aWf/vSn+vSnP63rr79e//f//l9973vfc3q5AJJo/jm9H5olEhcq0aTYZH040h7zsoJcfeXicpUVOE9MPzVqhOP3RHO084SjuHi6xAMAACA5XN3T3dDQoKamJl122WX+1woKCjRz5kzV1NTo+uuvlyTt27dPixYt0vPPP6+8vLxex6mpqdHFF1+s7OxTM4CvuOIKPfTQQzp48KCKiop6vaezs1OdnZ3+P7e1tbn50YC01e2zYu6tjqS0YKirceHYSXE8c7rHFZmdt2pqmS49a5SeqdmpXS3tmlCcp4WVE5U9JEN3Vk3xfz9/qmvSmnebYh4vP8/ddhmj8s0SfzvOflixZOWWXvO9nTVwAwAAQKJc/c2wqannl9FRo0YFvT5q1Cj/zyzL0pe//GXdcsstOv/887Vz586wxykvL+91DPtn4ZLuBx54QCtWrHDjYwCDRvXWxl7JbJmDBl/7244Zncc0LpLQxmsb6w/ouTc+jvm+nQfatap2T8yHCeG+h/96tcH/PdjNxv781/1G17u/tTN2kAPnji/Syk27jeJskR5WOGngBgAAgMT1effyH/3oRzp8+LCWLl3q6nGXLl2q22+/3f/ntrY2jRs3ztVzAOmkemujlqzc0qsEu6m1Q0tWbuk1viucNe82Gp1rzbuNWnLJGXFeaQ97T7QkvWB43ufe+EjPvfGRpMgPE5x8D6El6JGYxpkaXWi2Yh8aZ9olHgAAAMnj6pxur9crqad8PNC+ffv8P1u3bp1qamqUk5OjIUOG6PTTT5cknX/++brpppv8xwl3jMBzhMrJyVF+fn7QXwBO6fZZqqlv1qraPXpt+wEtX/1+2D3P9msr1tTFnOP88UGzFWzTOFPDsjMdv8dOoqu3nkrYu32WVqypM/4eioZmh4nsrfXYca2q3aOa+mZXZmFPH1cYd1wiDdwAAACQOFdXusvLy+X1evXyyy9r+vTpknpWnDdt2qQlS5ZIkn74wx/qW9/6lv89e/fu1RVXXKFf//rXmjlzpiSpsrJS3/zmN3X8+HFlZWVJktauXaszzzwzbGk5gOjClU9HY8lsjnOW4WM70zhT184Yq9/X7nX0nsAk+vIKrzIzPNrc0BL1Own9Hlo7zeaSP7v5Iz27OfoKuxPPbtplHHfznElxnwcAAADuc/yr8JEjR1RbW+ufq93Q0KDa2lrt3r1bHo9HX/va1/Stb31Lq1ev1nvvvacvfelLGj16tK655hpJ0vjx4zV16lT/X5/61KckSZMnT9bYsWMlSV/4wheUnZ2tm2++We+//75+/etf6wc/+EFQ+TgAM3b5tNMmZFLsOc6nnzbc6DimcaYuPL1EOUPiy+TtJFoyn1OdyDzrcCvsTu1qaXc1DgAAAH3H8Ur3m2++qUsuucT/ZzsRvummm/T000/rzjvv1NGjR7V48WIdOnRIs2fPVnV1tXJzzfc4FhQU6KWXXtKtt96q8847TyUlJbr33nu1ePFip5cLDGrRyqdNxJrjnGGY95rGOZFI0XZTW08SXTLMbH64HVc4NMvxuSz1dAwPXGF3akJx7ykPicQBAACg7zhOuj/3uc/JsiL/uuvxeHTffffpvvvuMzrexIkTwx7vnHPO0YYNG5xeHoAAscqnozGZ49zVbXYs0zhTr28/oK4Tvrjf33LkZHdx0/z3ZFxxntme7lCm5fqRLKycqG+/uE3RtodneHriAAAA0L/0efdyAMnVdcLnnzfddsxsD3I4V08ri7kqO644T2/tPhTzWONcXoH9/70Ve1xYNM1Hu7Sqdo8+3HfEKP7AyST9wNGuhM4bb5l69pAMLZpTrifWN0SMWTSnXNlxltwDAAAgeUi6gTTywIt1empDQ9QVUVOr32nUnVVToibe180Yq+cNGppdN2Ns4hcU4KOWowm9/8d/rncUf+Bwp1bV7tH6v32S0HljletHs/SqCknqdX8zPD0Jt/1zAAAA9C8k3UCaeODFuqgroU6ZlEPPnDRSHkXfX+05GeemzgRKy53K8Ej3v7AtoWN41DO7O1a5fixLr6rQHXPP8lcyTCjO08LKiaxwAwAA9GMk3UAa6Drh01Mb3Eu4bbHKod/adTBmQzPrZFw8e5kjibdzeTwSrRqw6wTumTdFmxtatP9wh0pH9CTg8TRVyx6SwVgwAACAAYSkG0gDz9TsdKWkPFSscmi7C3gspnGmhmZnunq8cDI8iSfcUs8K99XTynT/C9uCmtq5Mb8bAAAA/R9JN5AGTOczz60YpXnnlKlkWI7u+O072tfWEXal2rQc+oBhYzDTOFMlw832Rl84eaT+6TPj9OG+w3rkldj7uG+7ZLLOGDVCBw53JlRS/vmzTtPV08eodESuDh7t0q3Pbun1Pdvzux+7cQaJNwAAQBpjIyCQBkznM88sL9aC6WN00RklWn51T+Ot0AJn+8/L5lfELH9uOWLWzds0zlRZodl87XPG5vd83tNPM4q/6PTTtGD6GJWMMDt+JPvaOrVg+hhdUF6s+18IPyfdfm3Fmjp1J6NMAQAAAP0CSTeQBhZWTpQnxvZgT8gc56qpZXrsxhnyFgSvGnsLco1XX1NVXl481CwptuMuKC9WWUFuxLHcHgXPJU+ky3igWHPSA+d3AwAAID1RXg6kgcwMj4ZmZaq9qztiTF5WZq+V66qpZbq8wht3g6/RRUNdjTPV2mk2f9yOy8zwaNn8Ci1ZuaVXt/VwK/t2kt7UGr78PpaLTu9pGmc6lzve+d0AAADo/1jpBtLA5oaWqAm3JB3t6g67opqZ4VHl5JFaMH2MKiePdNRR+8JJJa7GmTK9wsA4Jyv7dpLu5FyBCvOyJZmvmLu1sg4AAID+h5VuIA2kakV11uSRKszL0qH2yCvPhXlZmuXiuDBJqpxUYtQYrTIk2Y+2st/ts4Jev7zCq8dunKEVa+qiloiH89fGNkmxV8zdmt8NAACA/oukG0gDqVpRzczw6MFrz9YtK7dEjHnw2rPjmkcdzWfKi3uViYfynIwLZa/sB6re2tgrubZHer1616X+ZPwXr+/UW7sPxbw+u+rAaVk7AAAA0g/l5UAacNoozE1VU8v0+I0z5M0Pbm7mzc/R40kah/XWroMx91pbJ+NCdfss1dQ3a1XtHtXUN+vFdxu1ZOWWXqvZ9kivtXVN/vL7KWX5Rtc3Kv/Uww03GtYBAABg4GKlG0gDqV5RrZpapkvPGqVnanZqV0u7JhTnaWHlRGUPSc5zvb0HzeaS98SdWtUOt6Kd4Qm/Ym6p57tbsaZOl1d4lZnh0bnjCrVy0+6Y5z13XGHQnxNtWAcAAICBi6QbSBP2impoUuk9WSadzBXV6q2NWrbqfe073Ol/7cn1O7RiwaeTct7ajw8Zx113/jj/NS5ZuaVXgh1tRHbgSK/KySNVMtxsVFm4uHBl7QAAAEh/JN1AGunrFWepJ5kNt6d73+FO3bJyi2sl5oGNzsznfnv8712xpi6u8V/SqQZ0/1PXZBT/P3VN+uxZpXGeDQAAAOmEpBtII+HKp//r1YakrXR3+yzd/pt3osbc8Zt3/OXZ8Qr3uUxYlqVVtXt04HCn4/cGshvQvftxq1G8aRwAAADSH0k3kCYilU/bDcGS0bTr9e0HjOaDv779gOZ86rS4zhHpc5lYuWm30R7sSEJHelmGV2EaBwAAgPRH0g2kgWjl04ENwUbkZOnA0U7XGnn99s2PjOPiSboTLQtPRLgGdAU5Zv/KNI0DAABA+uM3Q6CfCNyz7DQp3tzQErV82m4I9sWfbPK/VuZCg7Utu3uP5EokLlSsz5VM4RrQDcvNMnqvaRwAAADSH0k30A+E27PsJCm2G3054UbZuelCebwL6vF8rkTcM2+KSkbkRHzocUH5SK3dtj/mcS4op0s5AAAAeiSvpTEAI/ae5dAVXTsprt7aGPMYJcPMRlkFsku2V6ypU3e0uVlRZBl2RTeNC2U3MOsrJSNytGD6GFVOHhm2yuCmCyfKE+MBgsfTEwcAAABIJN1ASsXaiy0ZJsVxriQHzqGOR86QTFfjQl1QXqzCvL4r1Y6V5GcPydDiOeVRYxbPKU/qiDYAAAAMLPxmCKSQ6V7sWElxU4L7nsOVcXf7LNXUN2tV7R7V1DeHTfwzYi37OoxLFY96yvntLuXRLL2qQl+5uLxXyXyGR/rKxeVaelVFci4SAAAAAxJ7uoEUMt2zHCvu7TgbldlCV3hN95jPmlysrXvbYh5/1uTYyWw4mxtadKj9eFzvNRWuS3ksS6+q0B1zz9IzNTu1q6VdE4rztLByIivcAAAA6IWkG0gSk27kpnuWY8Xta4tvpTt0DrXkbN73qBFDjc5jGhcqkUZqedmZQTPEywpydfW0Mq1+pzHoYUK4LuUmsodk6OY5k+K+PgAAAAwOJN1AEpiuFF9QXqyyglw1tXaE3dcdLikOZ1icc6EtBa/wms77vrzCq8wMj0pGmDVwM40LlUgjNY+kX948s9dc8jurpsQ9mg0AAABwilpIwGVOupFnZni0bH7PHuDQtM9J2fPfTx+T6GVLcr7HvHS4WTJtGhfKfigRT0p8tKtbPsvq1Y08M8Ojyskjo3YpBwAAANxC0g24KJ5u5FVTy/TYjTPkLQhe1fUW5BrP0M6IM3G0V67t63G6x/yEz2cUbxonBTdw29zQonvmTfFfq1OP/6U+aiM4AAAAINkoLwdc5GSluHLySP/rVVPLdHmFN+6y540NzXFdb+j1ON1j/vu39xjF//7tPfrsmaUx4yKV5S++uLzXXmwTr9U367X6Zv9x4tm7DQAAACSCpBtwUSLdyO2y53jsOXgsrveFXo/TPeYfG563rrFVq2r3RH2YEK2B25PrG/ToF2aoaFi29h/u0Jp39upP2/Y7+IThG8EBAAAAyUZ5OeAit7qRO2UlWDltX4/TPeZjC80+x9/2HdVXf1WrG57aqNkPrQva1y6ZleXf/0KdLigv1oLpY3RVHElzpPJ+AAAAIJlIugFDgXuNI+0RjtX4y6OeMudY3cidGlMUXxIf7nqc7DH/+xljHZ8zXEM5pw3cygrjG0EWehwAAAAg2SgvBwyYjgCzV4qXrNwS8Vgm3cidqiwv0Y//vMPRe6J1RzfdYz4kw/lzu3Cjx5yW5dsPN5zu8Q49DgAAAJBsrHQDMTgZASb1JKyLLy5XaF6d4ZEWX1yelP3EGZnOk/hY3dFNRms1tcWXvPYaPeawLN9+uBHvowu3y/sBAACASFjpBqKItdc4dMVW6knSn1zf0Os9liU9ub5B544vcj3xPnCk0yjutktO1xmjhjvujh7J27sTK9OOt4GbdOrhxlMbGmS6RTvccQAAAIBkYqUbiMLpXuNYSbql5DTyMl25vej0kqgr107tazNL9iOJt4GbdOrhhpOEO9xxAAAAgGQi6QaicLrXOFaSLiWnkVeqGrjlZcdXLJNoA7doDzdsoXl1rHJ6AAAAIBkoLweicLrX2HSPc7x7oSMJbODmkYKS0WSu8FaMzteqd/Y6eo8bDdxMHm74LOmeeVNUMiLHtXJ6AAAAwCmSbiAKp3uNWwz3VpvGOWGvFId2WfeG6bLultJ85w3JYl2P3cAtGtOHFsXDc7Rg+hjH1wgAAAC4haQbiMLpCnLxsGyj45rGOWW6UuyW0hE5RnHfvGqKSvPdW3H+xDDpNo0DAAAAkoWkG4jByQpy8VDDpNswLh4mK8WuMWxiVlGWr4vOKHHttO/vbXU1DgAAAEgWkm7AgOkK8kvbmoyO99K2Jn12SmkyLrVPNbUeczXO1LHj3a7GAQAAAMlC0g0YMllBfneP2cqqaVw8un1Wn5WX1358yDjuuvPHuXbez0wcqZfq9hvFAQAAAKlE0g24KD83y9U4p6q3NvYqgy9LYiM103njbs8lv+nCifqPP26TFeWwHk9PHAAAAJBKzOkGXPS/Lyp3Nc6J6q2NWrJyS69RWk2tHVqycouqtza6fs5PDpt1YTeNM5U9JEOL50T/DhfPKVf2EP4VBwAAgNTiN1LARUMMkzzTOFPdPksr1tSF7Wtmv7ZiTZ3rK87Fw8xW7E3jnFh6VYW+cnG5QivnMzzSVy4u19KrKlw/JwAAAOAU5eWAizY1tBjHzfnUaa6dd3NDS68V7kCWpMbWDm1uaHG1s/n2/UddjXNq6VUVumPuWXqmZqd2tbRrQnGeFlZOZIUbAAAA/QZJN+Aq05Vkd1ec9x82m0dtGmcqJ8ssuTWNi0f2kAzdPGdS0o4PAAAAJILlIMBFlZPMZlGbxpkqHZHrapypvGyz53amcQAAAEC64TdhIEQiI7dmTR6pwrwsHWo/HjGmMC9Ls1ws8ZakC8qLVVaQq6bWjrBr6B5J3oKez+Km8SOHuhoHAAAApBuSbiBAoiO3MjM8evDas3XLyi0RYx689mzX52ZnZni0bH6FlqzcIo+Ci9ftMy2bX+H6eT9oPOJqHAAAAJBuKC8HTnJr5FbV1DJ95eJyhaa3HvV01U7GvGz7vI/dOEPeguAScm9Brh67cUZSznvseLercQAAAEC6YaUbUOyRWx71jNy6vMIbc7W4emujnljfEPY4T6xv0Lnji5KaeF9e4Y27PN6poVmZrsYBAAAA6YakG5B7I7e6fZa+8bv3op5r6e/eM0re45WZ4XF1LFg0RblmybRpHAAAAJBuKC8H5N7IrY07mqM2UZOkg+3HtXFHs/G19Wc7Dx5zNQ4AAABINyTdgNwbufX69gNGxzGNi0e3z1JNfbNW1e5RTX2zun3uzgQP5HM5DgAAAEg3lJcDcm/k1u7mo0bnM41zKlr39WTs9c7NNHtuZxoHAAAApBuSbkDujdx6b0+r0flM45ywu6+HPjRoau3QLSu39Jof7mQUWiQFQ7NcjQMAAADSDctPwElujNw62mU2Gss0zlSs7uuSeu01dzoKLZxPjnS5GgcAAACkG1a6gQCJjtwalZ9jlGCOys9J9FKDxOq+Ho7TUWjhjCrI0da9ZnEAAADAYMRKNxDCHrm1YPoYVU4e6SgZvePzZ7oaZ8q0+3qowFFo8ZhVXuJqHAAAAJBuSLoBF108pVRDYiTpQzI8unhKqavnNe2+Hkm8SfsNF4x3NQ4AAABINyTdgIsyMzy6efbEqDE3z56YcNfwUBeUF6swL/5mZfEm7b/ctMvVOAAAACDdkHQDLur2WVr9TvTGZKvfaUzK7OyuE86nYXvU08U81ii0SF56v8nVOAAAACDdkHQDLjJpaJbIHupINtY3q91hR3Qno9AiOdxxwtU4AAAAIN2QdAMuamo95mqcqZodBxy/x8kotEjOLBvhahwAAACQbhgZBrio5ajZPGrTOFOmxerXTB+tS84qNRqF1u2zYo5O+8fzxmlNjHJ6Ow4AAAAYjEi6ARcVDzebR20aZyo/16yJ2pSyfC2YPiZmXPXWRq1YUxdUKl9WkKtl8yuCVsYvPL1EedmZUUvbh2Vn6sLTGRkGAACAwYnycsBF3nyzLuCmcabajh13La56a6OWrNzSa296U2uHlqzcouqtp1a2MzM8+t4/Tot6vP/3j9Nc79YOAAAADBQk3Uh73T5LNfXNWlW7RzX1zUnpHG67oLxYZQXRE+pEuoUnW7fP0oo1dWHL1e3XVqypC/oOq6aW6fEbZ2jUiODVe29+jh5PcM84AAAAMNBRXo60Zlom7ZbMDI+Wza/QkpVbJAXvtXajW3gkhXnZrsTF6r5u6VT39crJI/2vV00t0+UV3ph7wAEAAIDBhpVupC0nZdKBEl0Zr5papsdunKFRISXkbnQLj6Q4z2xPd6y4/YejjzuLFpeZ4VHl5JFaMH2MKiePJOEGAAAAxEo30lSsMmmPesqkL6/wBiWHbq6MW5Yv6M8+ny9CZOIOGe7pjhVXOsJsr7lpHAAAADDYsdKNtOSkTNoW78p4qOqtjbpl5RbtOxw8Fmzf4S7d4uA4TiTSNT1wZd/ns+TNz1WkNWqP+veedAAAAKC/YaUbaclpmXS8K+Ohun2WvvG796Ke8xu/ey/mcZyKt2t6uJX9wrws/2fuqz3pAAAAQLpipRtpyWmZdDwr4+FsrG/WofboJdyH2o9rY32z0fWZOm9CkWLlwRmenjhbpJX91pPXXxCy/zuZe9IBAACAdMVKN9KSPbqrqbUj7Oq1Rz1JpF0mnUgDsUCv1X9idJzX6j/RRWeUGMWaeGvXQcXq9+azeuIqJ480WtnPHZKhX/7vmTpwpJNu5AAAAECcHK90r1+/XvPnz9fo0aPl8Xj0/PPPB/3csizde++9Kisr09ChQ3XZZZfpww8/9P98586duvnmm1VeXq6hQ4dq8uTJWrZsmbq6gve/vvvuu5ozZ45yc3M1btw4Pfzww/F9QgxK9uguSb32J4crk3argdjeQ2bJu2mcKacPDUxW9pvaOpXh8dCNHAAAAEiA46T76NGjmjZtmh599NGwP3/44Yf1wx/+UI8//rg2bdqkYcOG6YorrlBHR88v+H/961/l8/n0xBNP6P3339d//ud/6vHHH9e///u/+4/R1tamuXPnasKECXrrrbf0ne98R8uXL9eTTz4Z58fEYGSP7vIWxB7dZa+MJ9pArKzQLHk3jTPl9KGBWyv7AAAAAKJzXF5+5ZVX6sorrwz7M8uy9P3vf1933323FixYIEn6xS9+oVGjRun555/X9ddfr6qqKlVVVfnfM2nSJH3wwQd67LHH9N3vfleS9Mtf/lJdXV366U9/quzsbH36059WbW2tvve972nx4sXxfE70A90+S5sbWrT/cEeflStXTS3TpWeN0jM1O7WrpV0TivO0sHKisocEP2+yV8aXrNySUAOx4jzDLuKGcaacltMzGgwAAADoG67u6W5oaFBTU5Muu+wy/2sFBQWaOXOmampqdP3114d9X2trq4qLT60g1tTU6OKLL1Z2drb/tSuuuEIPPfSQDh48qKKiol7H6OzsVGdnp//PbW1tbnwkuMTN+deJnve/Xm0Ie157ZTw03uvgOouHZceMcRJnyulDA6dJOgAAAID4uNq9vKmpSZI0atSooNdHjRrl/1mo7du360c/+pG+8pWvBB0n3DECzxHqgQceUEFBgf+vcePGxf054C635l/3xXmrppbp1bsu1XOLZukH10/Xc4tm6dW7LjV+MHCovSt2kIM4J5yU0zvd8w4AAAAgPikdGbZnzx5VVVXpH/7hH7Ro0aKEjrV06VK1trb6//roo49cukokIlaXbKln/nV3rNbbLp/XinLezAyPKiePjKuBWPFww/JywzinqqaWad0dn9PCWeM154wSLZw1Xuvu+FzYhwZOknQAAAAA8XG1vNzr9UqS9u3bp7KyU7+w79u3T9OnTw+K3bt3ry655BJdeOGFvRqkeb1e7du3L+g1+8/2OULl5OQoJyc5iQzi52T+deXkkX12XiXpvN58sz3QpnFOPfBinZ7a0OAfH7bhQ+mXm3Zr0ZxyLb2qold81dQyXV7h7fO99gAAAMBg4epKd3l5ubxer15++WX/a21tbdq0aZMqKyv9r+3Zs0ef+9zndN555+lnP/uZMjKCL6OyslLr16/X8ePH/a+tXbtWZ555Ztj93Oi/UtUlu6nN7HimcaYuKC9WYV5W1JiivKyk7JV+4MU6PbG+ode8bp8lPbG+QQ+8WBf2fYms7AMAAACIznHSfeTIEdXW1qq2tlZST/O02tpa7d69Wx6PR1/72tf0rW99S6tXr9Z7772nL33pSxo9erSuueYaSacS7vHjx+u73/2uPvnkEzU1NQXt1f7CF76g7Oxs3XzzzXr//ff161//Wj/4wQ90++23u/Kh0XdS1SW75Uhn7CAHcW5yt5C+R9cJn57a0BA15qkNDeo64UvC2QEAAABE4ri8/M0339Qll1zi/7OdCN900016+umndeedd+ro0aNavHixDh06pNmzZ6u6ulq5uT1J1dq1a7V9+3Zt375dY8eODTq2ZfWkIwUFBXrppZd066236rzzzlNJSYnuvfdexoUNQKnqkp1IF/FERpttbmjRofbjUWMOtR93vaz9mZqdvVa4Q/msnrib50xy7bwAAAAAonOcdH/uc5/zJ8fheDwe3XfffbrvvvvC/vzLX/6yvvzlL8c8zznnnKMNGzY4vTz0M3aX7FtWbgn7c0vJ6ZLtLRgaV1yio82aWo8Zndc0ztTO5nZX4wAAAAC4I6Xdy4FksVfYoykLWWF3Y7TZgSNmo8BM48yZFq0no7gdAAAAQCQk3Ugqe3RXJB45HxnW7bNUU9+sVbV7VFPfHHHs17L5Fb1mUAeeN3CF3a3RZoeOGc7pNowzNX1soatxAAAAANzh6sgwIJTbI8OclH/bc6hN4t26TtMiebf7g48uynM1DgAAAIA7SLqRVG6ODLPLv0PXmu3y78dunBE28TaZQ+3WdVZOKtEjr9THPE7lpBKj85myy+mjPTgILacHAAAAkHyUlyOp3BoZlkj5t8kcareuc9bkkcrLzowaMyw7U7Nc7FwuOS+nBwAAANA3SLqRVBeUF6swLytqTGFeVswVWCfl3/GwV4qjJa2mK8XZQ6L/3yorxs/jZZfThzaQKyvIDVsFAAAAACD5KC9HypmsvbpZph6OvVK8ZOUWeRTc49u+PpOV4lTN6baZltMDAAAA6BusdCOpTJLQgyeT0GjcKv+Oxl4p9oasFHsdrBSnak53IJNyegAAAAB9g5VuJFXjIbPkMlacXf7d1NoRdl+3Rz3Jcbjy726fZbzym+hKcctRs1FgpnEAAAAABjaSbiTV2x8dNI679ryxEX8eb/m3kxFjgeeKt/S7eHiOq3EAAAAABjbKy5FU4Val441zWv5tjxgLbcBmjxir3tpoeHXmvPlm5e2mcQAAAAAGNla6kVTlI4e5Gmda/h1rxJhHPSPGLq/wurrn+bwJRfJ4JCvKUwSPpycOAAAAQPpjpRtJtbByomLltBmenjhTJo3Ckj1iLJI3GlqiJtxST0L+hsvnBQAAANA/kXQjqbKHZOjzU0qjxnx+SmnM2dZOJXvEWCSv7zjgahwAAACAgY2kG0nV7bO0dU9b1Jite9rU7TPd/W2mMCfL1ThTe1raXY0DAAAAMLCRdCOpYpV5S8kp8177132uxpnyuRwHAAAAYGAj6UZSparMe2fzUVfjTHkss6ZspnEAAAAABjaSbiRV6Qiz0VimcaaGZmW6GmfKZzgkzTQOAAAAwMBG0o2kuqC8WGUFuYq0ruuRVFbQM/bLTXM/7XU1zlTzkU5X4wAAAAAMbCTdSKrMDI+Wza+IuK5rSVo2v8LVWdmSNLYoz9U4Ux3Hu12NAwAAADCwkXQjLdkr7NEkY4W9q9usRZppHAAAAICBjaQbSdXts7RiTV3En3skrVhT5/rIMHuFPVpZezJW2L35ZnvTTeMAAAAADGwk3UiqWCPDLCVnZJgkVU0t02M3zui14l1WkKvHbpyhqqllrp9z5qQSV+MAAAAADGxDUn0B6D+6fZY2N7Ro/+EOlY7oKb1OdCU4VSPDbFVTy3R5hdf1zxXJTRdO1H/8cZusKAv3Hk9PHAAAAID0R9I9CIVLrtfWNWnFmrqgVemyglwtm1+R0IpwqkaGBcrM8Khy8sikHT9Q9pAMLZ5TrifWN0SMWTynXNlDKDIBAAAABgOS7kGmemtjr+S6MC9Lh9qP94ptau3QkpVbEirFthuaNbV2hO1g7pHkTUJDs1RaelWFJOmpDQ0K3Kqe4ZEWzSn3/xwAAABA+vNYVrRC2IGrra1NBQUFam1tVX5+fqovp1+o3tqoJSu3RBzfFY6dFL9616Vxl2Tb55UUdG77aMnaX51qXSd8eqZmp3a1tGtCcZ4WVk5khRsAAABIE6Y5Jyvdg4TdRdzpE5bARmfxlmjbDc1CV9i9LpSv92fZQzJ085xJqb4MAAAAAClE0j1IxOoiHkuijc76uqEZAAAAAPQHJN2DRKJJsxuNzvqyoRkAAAAA9Ack3YNEvElzOjY6AwAAAIC+QlenQcLuIu6kmNuOXTa/gjJwAAAAAIgDSfcgkZnh0bL5PaOqQtNn+8+FeVlBr3sLctO2szgAAAAA9AXKyweRWF3EaXQGAAAAAO5iTvcg1O2zSK4BAAAAIAHM6UZEdBEHAAAAgL7Bnm4AAAAAAJKEpBsAAAAAgCShvBx9JlV7ydnDDgAAACBVSLrRJ6q3Nvbqml52smt6MkeSpeq8AAAAACBRXo4+UL21UUtWbglKfCWpqbVDS1ZuUfXWxrQ6LwAAAADYSLqRVN0+SyvW1CncXDr7tRVr6tTtc3dyXarOCwAAAACBSLoHoW6fpZr6Zq2q3aOa+uakJp6bG1p6rTQHsiQ1tnZoc0NLWpwXAAAAAAKxp3uQ6es9zvsPR05844nr7+cFAAAAgECsdA8iqdjjXDoi19W4/n5eAAAAAAhE0p1CfVnmnao9zheUF6usIFeRBnR51LPSfkF5cVqcFwAAAAACUV6eIn1d5u1kj3Pl5JGunTczw6Nl8yu0ZOUWeaSgpN9OiJfNr3B9bnaqzgsAAAAAgVjpToFUlHmnco9z1dQyPXbjDI3Kzwl6fVR+jh67cYZrDxlCKwcur/DqsRtnyFsQXELuLch19bwAAAAAEAkr3X0sVpm3Rz1l3pdXeF1dhe0fe5xDP497ny9a5cCrd12qzQ0t2n+4Q6UjekrKWeEGAAAA0BdY6e5jqRpllco9zvbKflNb8Ofe1+bOyn6syoG1dU2qnDxSC6aPUeXkkSTcAAAAAPoMSXcfS1WZt73HWYq83pyMPc7JbuCWqgZxAAAAAGCCpLuPpbLM295b3Zd7nJO9sp+qygEAAAAAMMGe7j5ml3k3tXaEXZ31qCcJTtYoq6qpZbq8wttne5yTvbKfygZxAAAAABALSXcf6w+jrDIzPK6OBYsm2Sv7/aNBHAAAAACER3l5CqSizDtV7JX9aBJp4JbKBnEAAAAAEAsr3SnS12XeqZKZ4dHV08r0xPqGiDFXTyuL+3P3h8oBAAAAAIiEle4Ussu803mUVbfP0up3oo8EW/1OY0LdxQdT5QAAAACAgYWVbiRVrO7i0qnu4onsMx8slQMAAAAABhaSbiRVX3YX78sGcQAAAABggvJyJBXdxQEAAAAMZiTdSKoLyotVmJcVNaYoL4vu4gAAAADSEkk3kq7rhC/qzztj/BwAAAAABiqSbiTVxvpmtXd1R41p7+rWxvrmProiAAAAAOg7JN1Iqtd3HHA1DgAAAAAGEpJuJNXeg8dcjQMAAACAgYSkG0k1unCoq3EAAAAAMJCQdCOpLpxc4mocAAAAAAwkJN1IqlmTR8YcGVaYl6VZk0f20RUBAAAAQN8h6UZSZWZ49E/nj40a80/nj1VmhqePrggAAAAA+g5JN5Kq22dp9TuNUWNWv9Oobp/VR1cEAAAAAH2HpBtJtbmhRY2tHVFjGls7tLmhpY+uCAAAAAD6Dkk3kmr/4egJt9M4AAAAABhISLqRVCXDclyNAwAAAICBhKQbyWXaH40+agAAAADSEEk3kurAkU5X4wAAAABgICHpRlKVjsh1NQ4AAAAABhKSbiTVBeXFKivIjVg97pFUVpCrC8qL+/KyAAAAAKBPkHQjqTIzPFo2v0JS723b9p+Xza9QZgabugEAAACkH8dJ9/r16zV//nyNHj1aHo9Hzz//fNDPLcvSvffeq7KyMg0dOlSXXXaZPvzww6CYlpYWffGLX1R+fr4KCwt1880368iRI0Ex7777rubMmaPc3FyNGzdODz/8sPNPh36hamqZHrtxhrwFwSXk3oJcPXbjDFVNLUvRlQEAAABAcg1x+oajR49q2rRp+pd/+Rdde+21vX7+8MMP64c//KF+/vOfq7y8XPfcc4+uuOIK1dXVKTe3J+n64he/qMbGRq1du1bHjx/XP//zP2vx4sV69tlnJUltbW2aO3euLrvsMj3++ON677339C//8i8qLCzU4sWLE/zISIWqqWW6vMKrzQ0t2n+4Q6UjekrKWeEGAAAAkM48lmVZcb/Z49Hvf/97XXPNNZJ6VrlHjx6tO+64Q//f//f/SZJaW1s1atQoPf3007r++uu1bds2VVRU6I033tD5558vSaqurtZVV12ljz/+WKNHj9Zjjz2mb37zm2pqalJ2drYk6Rvf+Iaef/55/fWvfzW6tra2NhUUFKi1tVX5+fnxfkQAAAAAAHoxzTld3dPd0NCgpqYmXXbZZf7XCgoKNHPmTNXU1EiSampqVFhY6E+4Jemyyy5TRkaGNm3a5I+5+OKL/Qm3JF1xxRX64IMPdPDgwbDn7uzsVFtbW9BfCK/bZ6mmvlmraveopr5Z3b64n7sAAAAAAKJwXF4eTVNTkyRp1KhRQa+PGjXK/7OmpiaVlpYGX8SQISouLg6KKS8v73UM+2dFRUW9zv3AAw9oxYoV7nyQNFa9tVEr1tSpsbXD/1pZQa6Wza9gbzUAAAAAuCxtupcvXbpUra2t/r8++uijVF9Sv1O9tVFLVm4JSrglqam1Q0tWblH11sYUXRkAAAAApCdXk26v1ytJ2rdvX9Dr+/bt8//M6/Vq//79QT8/ceKEWlpagmLCHSPwHKFycnKUn58f9BdO6fZZWrGmTuEKye3XVqypo9QcAAAAAFzkatJdXl4ur9erl19+2f9aW1ubNm3apMrKSklSZWWlDh06pLfeessfs27dOvl8Ps2cOdMfs379eh0/ftwfs3btWp155plhS8sR2+aGll4r3IEsSY2tHdrc0NJ3FwUAAAAAac5x0n3kyBHV1taqtrZWUk/ztNraWu3evVsej0df+9rX9K1vfUurV6/We++9py996UsaPXq0v8P5lClTVFVVpUWLFmnz5s167bXXdNttt+n666/X6NGjJUlf+MIXlJ2drZtvvlnvv/++fv3rX+sHP/iBbr/9dtc++GCz/3DkhDueOAAAAABAbI4bqb355pu65JJL/H+2E+GbbrpJTz/9tO68804dPXpUixcv1qFDhzR79mxVV1f7Z3RL0i9/+Uvddttt+vznP6+MjAxdd911+uEPf+j/eUFBgV566SXdeuutOu+881RSUqJ7772XGd0JKB2RGzvIQRwAAAAAILaE5nT3Z8zpDtbtszT7oXVqau0Iu6/bI8lbkKtX77pUmRmevr48AAAAABhQUjKnG8705bzszAyPls2vkNSTYAey/7xsfgUJNwAAAAC4yNU53TCXinnZVVPL9NiNM3qd18ucbgAAAABICsrLU8Celx36xdtrzI/dOCOpCXC3z9LmhhbtP9yh0hG5uqC8mBVuAAAAAHDANOdkpbuPxZqX7VHPvOzLK7xJS4QzMzyqnDwyKccGAAAAAJzCnu4+xrxsAAAAABg8SLr7GPOyAQAAAGDwIOnuY8zLBgAAAIDBg6S7j11QXqyygtxeY7tsHvV0Mb+gvLgvLwsAAAAAkAQk3X2MedkAAAAAMHiQdKeAPS/bWxBcQu4tyE36uDAAAAAAQN9hZFiKVE0t0+UVXuZlAwAAAEAaI+lOIeZlAwAAAEB6o7wcAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkGZLqCxjMun2WNje0aP/hDpWOyNUF5cXKzPCk+rIAAAAAAC4h6U6R6q2NWrGmTo2tHf7XygpytWx+haqmlqXwygAAAAAAbqG8PAWqtzZqycotQQm3JDW1dmjJyi2q3tqYoisDAAAAALiJpLuPdfssrVhTJyvMz+zXVqypU7cvXAQAAAAAYCAh6e5jmxtaeq1wB7IkNbZ2aHNDS99dFAAAAAAgKUi6+9j+w5ET7njiAAAAAAD9F0l3HysdketqHAAAAACg/yLp7mMXlBerrCBXkQaDedTTxfyC8uK+vCwAAAAAQBKQdPexzAyPls2vCNtITerZ071sfgXzugEAAAAgDZB0AwAAAACQJCTdfcweGRaJR4wMAwAAAIB0QdLdxxgZBgAAAACDB0l3H2NkGAAAAAAMHiTdfYyRYQAAAAAweJB09zFGhgEAAADA4EHS3cfskWGSeiXe9p8ZGQYAAAAA6YGkOwWqppbpsRtnyFsQXELuLcjVYzfOUNXUshRdGQAAAADATUNSfQGDVdXUMl1e4dXmhhbtP9yh0hE9JeWscAMAAABA+iDpTqHMDI8qJ49M9WUAAAAAAJKE8nIAAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkmRIqi8gWSzLkiS1tbWl+EoAAAAAAOnGzjXt3DOStE26Dx8+LEkaN25ciq8EAAAAAJCuDh8+rIKCgog/91ix0vIByufzae/evRoxYoQ8Hk+qLwcntbW1ady4cfroo4+Un5+f6suBy7i/6Y37m964v+mPe5zeuL/pjfvbP1mWpcOHD2v06NHKyIi8czttV7ozMjI0duzYVF8GIsjPz+dfGGmM+5veuL/pjfub/rjH6Y37m964v/1PtBVuG43UAAAAAABIEpJuAAAAAACShKQbfSonJ0fLli1TTk5Oqi8FScD9TW/c3/TG/U1/3OP0xv1Nb9zfgS1tG6kBAAAAAJBqrHQDAAAAAJAkJN0AAAAAACQJSTcAAAAAAElC0g0AAAAAQJKQdCNh69ev1/z58zV69Gh5PB49//zzvWK2bdumq6++WgUFBRo2bJg+85nPaPfu3f6fd3R06NZbb9XIkSM1fPhwXXfdddq3b18ffgpEE+seHzlyRLfddpvGjh2roUOHqqKiQo8//nhQDPe4f3rggQf0mc98RiNGjFBpaamuueYaffDBB0ExJvdu9+7dmjdvnvLy8lRaWqp/+7d/04kTJ/ryoyCMWPe3paVF/+f//B+deeaZGjp0qMaPH6//+3//r1pbW4OOw/3tn0z+/2uzLEtXXnll2H+Hc3/7L9N7XFNTo0svvVTDhg1Tfn6+Lr74Yh07dsz/85aWFn3xi19Ufn6+CgsLdfPNN+vIkSN9+VEQhsn9bWpq0sKFC+X1ejVs2DDNmDFD//3f/x0Uw/3t/0i6kbCjR49q2rRpevTRR8P+vL6+XrNnz9ZZZ52lP//5z3r33Xd1zz33KDc31x/z9a9/XWvWrNFvf/tb/eUvf9HevXt17bXX9tVHQAyx7vHtt9+u6upqrVy5Utu2bdPXvvY13XbbbVq9erU/hnvcP/3lL3/Rrbfeqo0bN2rt2rU6fvy45s6dq6NHj/pjYt277u5uzZs3T11dXXr99df185//XE8//bTuvffeVHwkBIh1f/fu3au9e/fqu9/9rrZu3aqnn35a1dXVuvnmm/3H4P72Xyb//7V9//vfl8fj6fU697d/M7nHNTU1qqqq0ty5c7V582a98cYbuu2225SRcerX/C9+8Yt6//33tXbtWv3hD3/Q+vXrtXjx4lR8JAQwub9f+tKX9MEHH2j16tV67733dO211+of//Ef9fbbb/tjuL8DgAW4SJL1+9//Pui1f/qnf7JuvPHGiO85dOiQlZWVZf32t7/1v7Zt2zZLklVTU5OsS0Wcwt3jT3/609Z9990X9NqMGTOsb37zm5ZlcY8Hkv3791uSrL/85S+WZZnduxdffNHKyMiwmpqa/DGPPfaYlZ+fb3V2dvbtB0BUofc3nN/85jdWdna2dfz4ccuyuL8DSaT7+/bbb1tjxoyxGhsbe/07nPs7sIS7xzNnzrTuvvvuiO+pq6uzJFlvvPGG/7U//vGPlsfjsfbs2ZPU64Uz4e7vsGHDrF/84hdBccXFxdZTTz1lWRb3d6BgpRtJ5fP59MILL+hTn/qUrrjiCpWWlmrmzJlBpW1vvfWWjh8/rssuu8z/2llnnaXx48erpqYmBVcNpy688EKtXr1ae/bskWVZeuWVV/S3v/1Nc+fOlcQ9HkjssuLi4mJJZveupqZGZ599tkaNGuWPueKKK9TW1qb333+/D68esYTe30gx+fn5GjJkiCTu70AS7v62t7frC1/4gh599FF5vd5e7+H+Diyh93j//v3atGmTSktLdeGFF2rUqFH67Gc/q1dffdX/npqaGhUWFur888/3v3bZZZcpIyNDmzZt6tsPgKjC/X/4wgsv1K9//Wu1tLTI5/PpV7/6lTo6OvS5z31OEvd3oCDpRlLt379fR44c0YMPPqiqqiq99NJL+vu//3tde+21+stf/iKpZ69Kdna2CgsLg947atQoNTU1peCq4dSPfvQjVVRUaOzYscrOzlZVVZUeffRRXXzxxZK4xwOFz+fT1772NV100UWaOnWqJLN719TUFPQLu/1z+2foH8Ld31AHDhzQ/fffH1SWyP0dGCLd369//eu68MILtWDBgrDv4/4OHOHu8Y4dOyRJy5cv16JFi1RdXa0ZM2bo85//vD788ENJPfextLQ06FhDhgxRcXEx97gfifT/4d/85jc6fvy4Ro4cqZycHH3lK1/R73//e51++umSuL8DxZBUXwDSm8/nkyQtWLBAX//61yVJ06dP1+uvv67HH39cn/3sZ1N5eXDJj370I23cuFGrV6/WhAkTtH79et16660aPXp00Aop+rdbb71VW7duDVohQfqIdX/b2to0b948VVRUaPny5X17cUhYuPu7evVqrVu3LmjvJwaucPfY/j3rK1/5iv75n/9ZknTuuefq5Zdf1k9/+lM98MADKblWOBfp39H33HOPDh06pD/96U8qKSnR888/r3/8x3/Uhg0bdPbZZ6foauEUK91IqpKSEg0ZMkQVFRVBr0+ZMsXfvdzr9aqrq0uHDh0Kitm3b1/YUjj0L8eOHdO///u/63vf+57mz5+vc845R7fddpv+6Z/+Sd/97nclcY8Hgttuu01/+MMf9Morr2js2LH+103undfr7dXN3P4z97d/iHR/bYcPH1ZVVZVGjBih3//+98rKyvL/jPvb/0W6v+vWrVN9fb0KCws1ZMgQ/5aB6667zl+ayv0dGCLd47KyMkmK+XvW/v37g35+4sQJtbS0cI/7iUj3t76+Xo888oh++tOf6vOf/7ymTZumZcuW6fzzz/c3t+X+Dgwk3Uiq7OxsfeYzn+k1/uBvf/ubJkyYIEk677zzlJWVpZdfftn/8w8++EC7d+9WZWVln14vnDt+/LiOHz8e1CVVkjIzM/1P4LnH/ZdlWbrtttv0+9//XuvWrVN5eXnQz03uXWVlpd57772g/+ivXbtW+fn5vX4RRN+KdX+lnhXuuXPnKjs7W6tXrw6aLCFxf/uzWPf3G9/4ht59913V1tb6/5Kk//zP/9TPfvYzSdzf/i7WPZ44caJGjx4d9fesyspKHTp0SG+99Zb/5+vWrZPP59PMmTOT/yEQUaz7297eLklRf8fi/g4QqezihvRw+PBh6+2337befvttS5L1ve99z3r77betXbt2WZZlWb/73e+srKws68knn7Q+/PBD60c/+pGVmZlpbdiwwX+MW265xRo/fry1bt06680337QqKyutysrKVH0khIh1jz/72c9an/70p61XXnnF2rFjh/Wzn/3Mys3NtX784x/7j8E97p+WLFliFRQUWH/+85+txsZG/1/t7e3+mFj37sSJE9bUqVOtuXPnWrW1tVZ1dbV12mmnWUuXLk3FR0KAWPe3tbXVmjlzpnX22Wdb27dvD4o5ceKEZVnc3/7M5P+/oRTSvZz727+Z3OP//M//tPLz863f/va31ocffmjdfffdVm5urrV9+3Z/TFVVlXXuuedamzZtsl599VXrjDPOsG644YZUfCQEiHV/u7q6rNNPP92aM2eOtWnTJmv79u3Wd7/7Xcvj8VgvvPCC/zjc3/6PpBsJe+WVVyxJvf666aab/DE/+clPrNNPP93Kzc21pk2bZj3//PNBxzh27Jj1r//6r1ZRUZGVl5dn/f3f/73V2NjYx58EkcS6x42NjdaXv/xla/To0VZubq515plnWv/v//0/y+fz+Y/BPe6fwt1XSdbPfvYzf4zJvdu5c6d15ZVXWkOHDrVKSkqsO+64wz9yCqkT6/5G+v+2JKuhocF/HO5v/2Ty/99w7wkd+8j97b9M7/EDDzxgjR071srLy7MqKyuDFjYsy7Kam5utG264wRo+fLiVn59v/fM//7N1+PDhPvwkCMfk/v7tb3+zrr32Wqu0tNTKy8uzzjnnnF4jxLi//Z/HsizL7dVzAADw/2/fDgkAAAAABP1/7QobvDAIAODpBgAAgI3oBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgEkSp7/Bi3WIFQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,22 +824,22 @@
    "source": [
     "> Você consegue adivinhar por que os pontos se alinham em linhas verticais assim?\n",
     "\n",
-    "Observamos a correlação entre um conceito artificialmente criado, como salário, e a variável observada *altura*. Vamos também verificar se as duas variáveis observadas, como altura e peso, também estão correlacionadas:\n"
+    "Observamos a correlação entre um conceito artificialmente criado, como salário, e a variável observada *altura*. Vamos também verificar se duas variáveis observadas, como altura e peso, também apresentam correlação:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,7 +852,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Infelizmente, não obtivemos nenhum resultado - apenas alguns valores estranhos `nan`. Isso ocorre porque alguns dos valores em nossa série estão indefinidos, representados como `nan`, o que faz com que o resultado da operação também seja indefinido. Ao observar a matriz, podemos ver que a coluna `Weight` é a problemática, porque a autocorrelação entre os valores de `Height` foi calculada.\n",
+    "Infelizmente, não obtivemos nenhum resultado - apenas alguns valores estranhos `nan`. Isso ocorre porque alguns dos valores em nossa série estão indefinidos, representados como `nan`, o que faz com que o resultado da operação também seja indefinido. Ao observar a matriz, podemos ver que a coluna problemática é `Weight`, porque a autocorrelação entre os valores de `Height` foi calculada.\n",
     "\n",
     "> Este exemplo mostra a importância da **preparação** e **limpeza** dos dados. Sem dados adequados, não podemos calcular nada.\n",
     "\n",
@@ -1026,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Conclusão\n",
     "\n",
-    "Neste notebook, aprendemos como realizar operações básicas em dados para calcular funções estatísticas. Agora sabemos como utilizar um conjunto sólido de ferramentas de matemática e estatística para comprovar algumas hipóteses e como calcular intervalos de confiança para variáveis arbitrárias a partir de uma amostra de dados.\n"
+    "Neste notebook, aprendemos como realizar operações básicas em dados para calcular funções estatísticas. Agora sabemos como utilizar um conjunto sólido de ferramentas matemáticas e estatísticas para comprovar algumas hipóteses e como calcular intervalos de confiança para variáveis arbitrárias a partir de uma amostra de dados.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n"
+    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T22:58:30+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:26:22+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "br"
   }
diff --git a/translations/br/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/br/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index fd0c7d2b..66c27333 100644
--- a/translations/br/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/br/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -157,9 +157,9 @@
     "* S1 até S6 são diferentes medições sanguíneas  \n",
     "* Y é a medida qualitativa da progressão da doença ao longo de um ano  \n",
     "\n",
-    "Vamos estudar este conjunto de dados usando métodos de probabilidade e estatística.\n",
+    "Vamos estudar este conjunto de dados utilizando métodos de probabilidade e estatística.\n",
     "\n",
-    "### Tarefa 1: Calcular os valores médios e a variância para todos os valores\n"
+    "### Tarefa 1: Calcular os valores médios e a variância para todos os valores  \n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tarefa 2: Plote boxplots para IMC, PA e Y dependendo do gênero\n"
+    "### Tarefa 2: Plotar boxplots para IMC, PA e Y dependendo do gênero\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -590,10 +590,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Conclusões:\n",
+    "Conclusões:  \n",
     "* Idade - normal  \n",
     "* Sexo - uniforme  \n",
-    "* IMC, Y - difícil dizer  \n"
+    "* IMC, Y - difícil de dizer  \n"
    ],
    "metadata": {}
   },
@@ -845,7 +845,7 @@
    "cell_type": "markdown",
    "source": [
     "Conclusão:  \n",
-    "* As correlações mais fortes de Y são o IMC e S5 (açúcar no sangue). Isso parece razoável.\n"
+    "* A correlação mais forte de Y é com o IMC e S5 (açúcar no sangue). Isso parece razoável.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:22:12+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:26:48+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "br"
   }
diff --git a/translations/cs/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/cs/1-Introduction/04-stats-and-probability/assignment.ipynb
index 5bf3ec2a..934f06f0 100644
--- a/translations/cs/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/cs/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -154,11 +154,11 @@
     "* BMI je index tělesné hmotnosti  \n",
     "* BP je průměrný krevní tlak  \n",
     "* S1 až S6 jsou různé krevní hodnoty  \n",
-    "* Y je kvalitativní měřítko progrese onemocnění během jednoho roku  \n",
+    "* Y je kvalitativní míra progrese onemocnění během jednoho roku  \n",
     "\n",
     "Pojďme tuto datovou sadu prozkoumat pomocí metod pravděpodobnosti a statistiky.\n",
     "\n",
-    "### Úkol 1: Vypočítejte průměrné hodnoty a rozptyl pro všechny hodnoty\n"
+    "### Úkol 1: Vypočítejte průměrné hodnoty a rozptyl pro všechny hodnoty  \n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Úkol 3: Jaké je rozložení věku, pohlaví, BMI a proměnných Y?\n"
+    "### Úkol 3: Jaké je rozložení proměnných Věk, Pohlaví, BMI a Y?\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Prohlášení**:  \nTento dokument byl přeložen pomocí služby pro automatický překlad [Co-op Translator](https://github.com/Azure/co-op-translator). Ačkoli se snažíme o přesnost, mějte na paměti, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za autoritativní zdroj. Pro důležité informace se doporučuje profesionální lidský překlad. Neodpovídáme za žádné nedorozumění nebo nesprávné interpretace vyplývající z použití tohoto překladu.\n"
+    "\n---\n\n**Prohlášení**:  \nTento dokument byl přeložen pomocí služby pro automatický překlad [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o co největší přesnost, mějte prosím na paměti, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Za autoritativní zdroj by měl být považován původní dokument v jeho původním jazyce. Pro důležité informace doporučujeme profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné výklady vyplývající z použití tohoto překladu.\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:17:15+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:50:42+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "cs"
   }
diff --git a/translations/cs/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/cs/1-Introduction/04-stats-and-probability/notebook.ipynb
index 05b0cfae..3578b8ee 100644
--- a/translations/cs/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/cs/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Náhodné proměnné a rozdělení\n",
-    "Začněme tím, že vygenerujeme vzorek 30 hodnot z rovnoměrného rozdělení od 0 do 9. Také vypočítáme průměr a rozptyl.\n"
+    "Začněme tím, že vygenerujeme vzorek 30 hodnot z rovnoměrného rozdělení od 0 do 9. Také spočítáme průměr a rozptyl.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## Analýza reálných dat\n",
     "\n",
-    "Průměr a rozptyl jsou velmi důležité při analýze reálných dat. Načtěme data o hráčích baseballu z [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Průměr a rozptyl jsou velmi důležité při analýze dat z reálného světa. Načtěme data o hráčích baseballu z [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kromě průměru má smysl podívat se na medián a kvartily. Ty lze vizualizovat pomocí **box plotu**:\n"
+    "Kromě průměru má smysl podívat se na medián a kvartily. Ty lze zobrazit pomocí **box plotu**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Poznámka**: Tento diagram naznačuje, že průměrně jsou výšky hráčů na první metě vyšší než výšky hráčů na druhé metě. Později se naučíme, jak tuto hypotézu formálněji otestovat a jak ukázat, že naše data jsou statisticky významná, aby to potvrdila.  \n",
+    "> **Poznámka**: Tento diagram naznačuje, že průměrně jsou výšky hráčů na první metě vyšší než výšky hráčů na druhé metě. Později se naučíme, jak tuto hypotézu formálněji otestovat a jak ukázat, že naše data jsou statisticky významná, aby to potvrdila.\n",
     "\n",
     "Věk, výška a váha jsou všechny spojité náhodné veličiny. Jak si myslíte, že vypadá jejich rozdělení? Dobrým způsobem, jak to zjistit, je vykreslit histogram hodnot:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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qfVW17/Dhw3NOAwAAFmOua5CTvKy7H6qqZyW5paru2eiO3b03yd4k2bVrV885DwAAWIi5ziB390PT7cNJbkxyXpJDVXV6kky3D887SQAA2CqbDuSqelpVPf3o/SQ/m+TOJDcluXTa7NIkn5x3kgAAsFXmucTi2UlurKqj3+f3uvu/VtUXk1xfVW9O8s0kb5h/mgAAsDU2Hcjd/bUk/2jG+LeTnD/PpAAAYLvM+0t6cELYuefm7Z4CAHCC8FHTAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAw8FHTALBgJ8LH2++/4sLtngI8YTmDDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAACDTQdyVZ1ZVX9UVXdX1V1V9SvT+Puq6sGqun36es3ipgsAAMu1Y459jyR5R3d/qaqenuS2qrpleu4D3f3++acHAABba9OB3N0Hkxyc7n+3qu5OcsaiJsaJY+eem7d7CgAAC7OQa5CrameSlyT5/DT0tqq6o6qurqpT19hnd1Xtq6p9hw8fXsQ0AABgbnMHclX9RJIbkry9u7+T5INJnp/k3KyeYb5y1n7dvbe7d3X3rpWVlXmnAQAACzFXIFfVj2U1jj/a3Z9Iku4+1N2PdvcPk3w4yXnzTxMAALbGPO9iUUmuSnJ3d//GMH76sNnrk9y5+ekBAMDWmuddLF6W5E1JvlJVt09j705ySVWdm6ST7E/yljl+BgAAbKl53sXis0lqxlOf2vx0AABge/kkPQAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAY7NjuCQAAW2/nnpu3ewrr2n/Fhds9BU5SziADAMBAIAMAwEAgAwDAQCADAMBAIAMAwEAgAwDAwNu8AQBPSN6Kju3iDDIAAAycQT4BnAj/Bw0A8GThDDIAAAwEMgAADAQyAAAMTvprkF3fCwDAyBlkAAAYCGQAABgsLZCr6oKqureq7q+qPcv6OQAAsEhLuQa5qk5J8p+T/EySA0m+WFU3dfdXl/HzAAC2g99lmt8T8dMIl3UG+bwk93f317r7r5J8LMlFS/pZAACwMMt6F4szknxreHwgyT8ZN6iq3Ul2Tw//sqruXeN7nZbkzxY+Q46yvstlfZfPGi+X9V0u67tc1ne5FrK+9WsLmMnm/b1Zg8sK5Jox1o950L03yd51v1HVvu7etaiJ8VjWd7ms7/JZ4+WyvstlfZfL+i7Xk3l9l3WJxYEkZw6Pn5vkoSX9LAAAWJhlBfIXk5xTVWdX1Y8nuTjJTUv6WQAAsDBLucSiu49U1duS/LckpyS5urvv2uS3W/cyDOZifZfL+i6fNV4u67tc1ne5rO9yPWnXt7p7/a0AAOAk4ZP0AABgIJABAGCwrYFcVc+oqo9X1T1VdXdV/dOqel9VPVhVt09fr1ljXx9lvY411ve6YW33V9Xta+y7v6q+Mm23b4un/oRXVS8c1vH2qvpOVb29qp5ZVbdU1X3T7alr7O/1exzHWd9fn17Pd1TVjVX1jDX29/o9juOsr+PvAhxnfR1/F6Sq/l1V3VVVd1bVtVX1tx1/F2eN9T2pjr/beg1yVV2T5H92929P73bx1CRvT/KX3f3+4+x3SpI/zfBR1kku8VHWjzVrfbv7/wzPX5nkL7r7V2fsuz/Jru72BuvrmF6PD2b1w3AuS/JId18xHXhP7e53ztje63eDjlnfFyb5H9MvAv9akhy7vtM+++P1uyHHrO8vxfF3ocb17e5vDOOOv5tUVWck+WySF3X396vq+iSfSvKiOP7O7Tjr+1BOouPvtp1BrqqfTPKKJFclSXf/1Rhv6/BR1utYb32rqpK8Mcm12zLBJ5fzkzww/eV3UZJrpvFrkrxuxvZev4/PX69vd3+6u49M45/L6nusM5/x9bsRXr+Pz4+sr+PvQuxI8neqakdWT649FMffRfqR9T3Zjr/beYnF85IcTvJfqurLVfXbVfW06bm3Tafwr17jn0hmfZT1GUue74nmeOubJC9Pcqi771tj/07y6aq6rVY/Fpy1XZy/+Yvu2d19MEmm22fN2N7r9/EZ13f0y0n+cI19vH437tj1dfxdrFmvX8ffOXT3g0nen+SbSQ5m9Uz8p+P4uxDHWd/Rk/74u52BvCPJS5N8sLtfkuT/JtmT5INJnp/k3Kz+wVw5Y991P8qaNdf3qEty/LMXL+vulyb5uSSXVdUrljbTE9h06cprk/z+49ltxpjX7wxrrW9VvSfJkSQfXWNXr98NmLG+jr8LdJzjg+PvHKb/cbsoydlJnpPkaVX1rza6+4wxr9/Beut7shx/tzOQDyQ50N2fnx5/PMlLu/tQdz/a3T9M8uGs/nPIrH19lPXxzVzfJJn+yeQXkly31s7d/dB0+3CSGzP7z4HVA8CXuvvQ9PhQVZ2eJNPtwzP28frduGPXN1V1aZKfT/KLvcYvUXj9bthj1tfxd+FmvX4df+f3qiRf7+7D3f3/knwiyT+L4++irLW+J9Xxd9sCubv/d5JvVdULp6Hzk3z16It78vokd87Y3UdZr2Ot9Z3uvyrJPd19YNa+VfW0qnr60ftJfjaz/xz40TNBNyW5dLp/aZJPztjH63fjHrO+VXVBkncmeW13f2/WDl6/j8ux6+v4u1izzhQ7/s7vm0l+uqqeOl3PfX6Su+P4uygz1/ekO/5297Z9ZfWf8fYluSPJHyQ5NcnvJvnKNHZTktOnbZ+T5FPDvq/J6m+iPpDkPdv53/FE/Zq1vtP4R5K89Zht/3p9s3r98p9MX3dZ3zXX96lJvp3k7w5jP5Xk1iT3TbfPPHZ9p8dev5tb3/uzev3g7dPXh45dX6/fudbX8XeJ6zuNO/4uZn3/Y5J7shpfv5vkKY6/S1/fk+r466OmAQBg4JP0AABgIJABAGAgkAEAYCCQAQBgIJABAGAgkAEAYCCQAQBg8P8B40VGjZpezWQAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Intervaly spolehlivosti\n",
     "\n",
-    "Pojďme nyní vypočítat intervaly spolehlivosti pro váhy a výšky baseballových hráčů. Použijeme kód [z této diskuse na stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Nyní vypočítáme intervaly spolehlivosti pro váhy a výšky baseballových hráčů. Použijeme kód [z této diskuse na Stack Overflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## Testování hypotéz\n",
     "\n",
-    "Pojďme prozkoumat různé role v našem datasetu baseballových hráčů:\n"
+    "Pojďme prozkoumat různé role v naší datové sadě baseballových hráčů:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Vidíme, že intervaly se nepřekrývají.\n",
     "\n",
-    "Statisticky správnější způsob, jak dokázat hypotézu, je použít **Studentův t-test**:\n"
+    "Statisticky správnější způsob, jak ověřit hypotézu, je použít **Studentův t-test**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,9 +619,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dvě hodnoty, které vrací funkce `ttest_ind`, jsou:\n",
-    "* p-hodnota může být považována za pravděpodobnost, že dvě rozdělení mají stejný průměr. V našem případě je velmi nízká, což znamená, že existují silné důkazy podporující tvrzení, že první metaři jsou vyšší.\n",
-    "* t-hodnota je mezihodnota normalizovaného rozdílu průměrů, která se používá v t-testu, a porovnává se s prahovou hodnotou pro danou úroveň spolehlivosti.\n"
+    "Dvě hodnoty vrácené funkcí `ttest_ind` jsou:\n",
+    "* p-hodnota může být považována za pravděpodobnost, že dvě distribuce mají stejný průměr. V našem případě je velmi nízká, což znamená, že existují silné důkazy podporující tvrzení, že první metaři jsou vyšší.\n",
+    "* t-hodnota je mezihodnota normalizovaného rozdílu průměrů, která se používá v t-testu a je porovnávána s prahovou hodnotou pro danou úroveň spolehlivosti.\n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Simulace normálního rozdělení pomocí centrální limitní věty\n",
     "\n",
-    "Pseudo-náhodný generátor v Pythonu je navržen tak, aby nám poskytoval rovnoměrné rozdělení. Pokud chceme vytvořit generátor pro normální rozdělení, můžeme využít centrální limitní větu. Pro získání hodnoty s normálním rozdělením jednoduše vypočítáme průměr vzorku generovaného rovnoměrně.\n"
+    "Pseudonáhodný generátor v Pythonu je navržen tak, aby nám poskytoval rovnoměrné rozdělení. Pokud chceme vytvořit generátor pro normální rozdělení, můžeme využít centrální limitní větu. Pro získání hodnoty s normálním rozdělením jednoduše spočítáme průměr vzorku generovaného rovnoměrně.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -833,14 +672,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pojďme nyní vypočítat kovarianci a korelaci těchto sekvencí. `np.cov` nám poskytne takzvanou **kovarianční matici**, což je rozšíření kovariance na více proměnných. Prvek $M_{ij}$ kovarianční matice $M$ je korelace mezi vstupními proměnnými $X_i$ a $X_j$, a diagonální hodnoty $M_{ii}$ jsou rozptyly $X_{i}$. Podobně `np.corrcoef` nám poskytne **korelační matici**.\n"
+    "Pojďme nyní vypočítat kovarianci a korelaci těchto sekvencí. `np.cov` nám poskytne takzvanou **kovarianční matici**, což je rozšíření kovariance na více proměnných. Prvek $M_{ij}$ kovarianční matice $M$ je korelace mezi vstupními proměnnými $X_i$ a $X_j$, a diagonální hodnoty $M_{ii}$ jsou rozptyly $X_{i}$. Podobně nám `np.corrcoef` poskytne **korelační matici**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -889,19 +728,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Podívejme se, co se stane, pokud vztah není lineární. Předpokládejme, že naše společnost se rozhodla skrýt zjevnou lineární závislost mezi výškou a platy a zavedla do vzorce nějakou nelinearitu, například `sin`:\n"
+    "Podívejme se, co se stane, pokud vztah není lineární. Předpokládejme, že naše společnost se rozhodla skrýt zjevnou lineární závislost mezi výškou a platy a zavedla do vzorce určitou nelinearitu, například `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "V tomto případě je korelace o něco menší, ale stále poměrně vysoká. Nyní, abychom udělali vztah ještě méně zřejmým, můžeme přidat trochu náhodnosti přidáním nějaké náhodné proměnné k platu. Podívejme se, co se stane:\n"
+    "V tomto případě je korelace o něco menší, ale stále poměrně vysoká. Nyní, abychom vztah učinili ještě méně zřejmým, bychom mohli přidat trochu náhodnosti přidáním nějaké náhodné proměnné k platu. Podívejme se, co se stane:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Dokážete odhadnout, proč se tečky takto zarovnávají do svislých čar?\n",
+    "> Dokážete odhadnout, proč se tečky seřadí do vertikálních linií takto?\n",
     "\n",
-    "Pozorovali jsme souvislost mezi uměle vytvořeným konceptem, jako je plat, a pozorovanou proměnnou *výška*. Podívejme se také, zda spolu korelují dvě pozorované proměnné, jako jsou výška a váha:\n"
+    "Pozorovali jsme korelaci mezi uměle vytvořeným konceptem, jako je plat, a pozorovanou proměnnou *výška*. Podívejme se také, zda dvě pozorované proměnné, jako výška a váha, spolu korelují:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bohužel jsme nedostali žádné výsledky – pouze nějaké podivné hodnoty `nan`. To je způsobeno tím, že některé hodnoty v naší sérii jsou nedefinované, reprezentované jako `nan`, což způsobuje, že výsledek operace je také nedefinovaný. Při pohledu na matici můžeme vidět, že problémový sloupec je `Weight`, protože byla vypočítána vlastní korelace mezi hodnotami `Height`.\n",
+    "Bohužel jsme nezískali žádné výsledky - pouze nějaké podivné hodnoty `nan`. To je způsobeno tím, že některé hodnoty v naší sérii jsou nedefinované, reprezentované jako `nan`, což způsobuje, že výsledek operace je také nedefinovaný. Při pohledu na matici vidíme, že problémový sloupec je `Weight`, protože korelace mezi hodnotami `Height` byla vypočítána.\n",
     "\n",
-    "> Tento příklad ukazuje důležitost **přípravy dat** a **čištění dat**. Bez správně připravených dat nemůžeme nic spočítat.\n",
+    "> Tento příklad ukazuje důležitost **přípravy dat** a **čištění dat**. Bez správně připravených dat nemůžeme nic vypočítat.\n",
     "\n",
     "Použijme metodu `fillna` k doplnění chybějících hodnot a vypočítejme korelaci:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1087,14 +920,14 @@
    "source": [
     "## Závěr\n",
     "\n",
-    "V tomto zápisníku jsme se naučili, jak provádět základní operace s daty pro výpočet statistických funkcí. Nyní víme, jak používat solidní aparát matematiky a statistiky k ověřování některých hypotéz a jak vypočítat intervaly spolehlivosti pro libovolné proměnné na základě datového vzorku.\n"
+    "V tomto notebooku jsme se naučili, jak provádět základní operace s daty pro výpočet statistických funkcí. Nyní víme, jak používat solidní aparát matematiky a statistiky k ověření některých hypotéz a jak vypočítat intervaly spolehlivosti pro libovolné proměnné na základě vzorku dat.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Prohlášení**:  \nTento dokument byl přeložen pomocí služby pro automatický překlad [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o přesnost, mějte prosím na paměti, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za autoritativní zdroj. Pro důležité informace se doporučuje profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné interpretace vyplývající z použití tohoto překladu.\n"
+    "\n---\n\n**Prohlášení**:  \nTento dokument byl přeložen pomocí služby pro automatický překlad [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o co největší přesnost, mějte prosím na paměti, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Za autoritativní zdroj by měl být považován původní dokument v jeho původním jazyce. Pro důležité informace doporučujeme profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné výklady vyplývající z použití tohoto překladu.\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T22:59:31+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:50:29+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "cs"
   }
diff --git a/translations/cs/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/cs/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 4dfb99c3..25378269 100644
--- a/translations/cs/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/cs/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "V této datové sadě jsou sloupce následující:\n",
-    "* Věk a pohlaví jsou samovysvětlující\n",
-    "* BMI je index tělesné hmotnosti\n",
-    "* BP je průměrný krevní tlak\n",
-    "* S1 až S6 jsou různé krevní měření\n",
-    "* Y je kvalitativní míra progrese nemoci během jednoho roku\n",
+    "V této datové sadě jsou sloupce následující:  \n",
+    "* Věk a pohlaví jsou samovysvětlující  \n",
+    "* BMI je index tělesné hmotnosti  \n",
+    "* BP je průměrný krevní tlak  \n",
+    "* S1 až S6 jsou různé krevní hodnoty  \n",
+    "* Y je kvalitativní míra progrese onemocnění během jednoho roku  \n",
     "\n",
     "Pojďme tuto datovou sadu prozkoumat pomocí metod pravděpodobnosti a statistiky.\n",
     "\n",
-    "### Úkol 1: Vypočítejte průměrné hodnoty a rozptyl pro všechny hodnoty\n"
+    "### Úkol 1: Vypočítejte průměrné hodnoty a rozptyl pro všechny hodnoty  \n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Úkol 3: Jaké je rozložení věku, pohlaví, BMI a proměnných Y?\n"
+    "### Úkol 3: Jaké je rozložení proměnných Věk, Pohlaví, BMI a Y?\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -592,17 +592,17 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Závěry:\n",
-    "* Věk - normální\n",
-    "* Pohlaví - jednotné\n",
-    "* BMI, Y - těžko říct\n"
+    "Závěry:  \n",
+    "* Věk - normální  \n",
+    "* Pohlaví - jednotné  \n",
+    "* BMI, Y - těžko říct  \n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### Úkol 4: Otestujte korelaci mezi různými proměnnými a průběhem nemoci (Y)\n",
+    "### Úkol 4: Otestujte korelaci mezi různými proměnnými a progresí nemoci (Y)\n",
     "\n",
     "> **Tip** Korelační matice vám poskytne nejvíce užitečných informací o tom, které hodnoty jsou závislé.\n"
    ],
@@ -846,8 +846,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Závěr:\n",
-    "* Nejsilnější korelace Y je s BMI a S5 (hladina cukru v krvi). To zní rozumně.\n"
+    "Závěr:  \n",
+    "* Nejsilnější korelace s Y mají BMI a S5 (hladina cukru v krvi). To zní logicky.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Prohlášení**:  \nTento dokument byl přeložen pomocí služby pro automatický překlad [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o přesnost, mějte prosím na paměti, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za autoritativní zdroj. Pro důležité informace se doporučuje profesionální lidský překlad. Neodpovídáme za žádné nedorozumění nebo nesprávné interpretace vyplývající z použití tohoto překladu.\n"
+    "\n---\n\n**Upozornění**:  \nTento dokument byl přeložen pomocí služby pro automatický překlad [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o co největší přesnost, mějte prosím na paměti, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za závazný zdroj. Pro důležité informace doporučujeme profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné výklady vyplývající z použití tohoto překladu.\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:22:28+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:50:58+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "cs"
   }
diff --git a/translations/da/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/da/1-Introduction/04-stats-and-probability/assignment.ipynb
index 7bf38ecb..b4aae446 100644
--- a/translations/da/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/da/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Opgave 2: Plot boksdiagrammer for BMI, BP og Y afhængigt af køn\n"
+    "### Opgave 2: Plot boksplot for BMI, BP og Y afhængigt af køn\n"
    ],
    "metadata": {}
   },
@@ -200,9 +200,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Opgave 4: Test korrelationen mellem forskellige variable og sygdomsprogression (Y)\n",
+    "### Opgave 4: Test korrelationen mellem forskellige variabler og sygdomsprogression (Y)\n",
     "\n",
-    "> **Tip** En korrelationsmatrix vil give dig den mest nyttige information om, hvilke værdier der er afhængige.\n"
+    "> **Tip** Korrelationsmatrixen vil give dig den mest nyttige information om, hvilke værdier der er afhængige.\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi er ikke ansvarlige for eventuelle misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\n"
+    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:17:28+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:36:01+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "da"
   }
diff --git a/translations/da/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/da/1-Introduction/04-stats-and-probability/notebook.ipynb
index 551aa469..9b0ab12e 100644
--- a/translations/da/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/da/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Analyse af virkelige data\n",
     "\n",
-    "Gennemsnit og varians er meget vigtige, når man analyserer data fra den virkelige verden. Lad os indlæse data om baseballspillere fra [SOCR MLB Højde/Vægt Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Gennemsnit og varians er meget vigtige, når man analyserer data fra den virkelige verden. Lad os indlæse data om baseballspillere fra [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vi bruger en pakke kaldet [**Pandas**](https://pandas.pydata.org/) her til dataanalyse. Vi vil tale mere om Pandas og arbejde med data i Python senere i dette kursus.\n",
+    "> Vi bruger en pakke kaldet [**Pandas**](https://pandas.pydata.org/) her til dataanalyse. Vi vil tale mere om Pandas og arbejde med data i Python senere i dette kursus.\n",
     "\n",
     "Lad os beregne gennemsnitsværdier for alder, højde og vægt:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vi kan også lave boksplot af undergrupper af vores datasæt, for eksempel grupperet efter spillerrolle.\n"
+    "Vi kan også lave boksdiagrammer af undergrupper af vores datasæt, for eksempel grupperet efter spillerrolle.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Bemærk**: Dette diagram antyder, at gennemsnitligt er højden på første basemænd højere end højden på anden basemænd. Senere vil vi lære, hvordan vi mere formelt kan teste denne hypotese, og hvordan vi kan demonstrere, at vores data er statistisk signifikante for at vise dette.  \n",
+    "> **Bemærk**: Dette diagram antyder, at gennemsnitligt set er førstebasemandens højde højere end andendebasemandens højde. Senere vil vi lære, hvordan vi mere formelt kan teste denne hypotese, og hvordan vi kan demonstrere, at vores data er statistisk signifikante for at understøtte dette.  \n",
     "\n",
-    "Alder, højde og vægt er alle kontinuerlige stokastiske variable. Hvad tror du, deres fordeling er? En god måde at finde ud af det på er at plotte histogrammet for værdierne:\n"
+    "Alder, højde og vægt er alle kontinuerte stokastiske variable. Hvad tror du, deres fordeling er? En god måde at finde ud af det på er at lave et histogram over værdierne:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## Normalfordeling\n",
     "\n",
-    "Lad os oprette et kunstigt vægtudvalg, der følger en normalfordeling med samme gennemsnit og varians som vores rigtige data:\n"
+    "Lad os skabe et kunstigt vægtudvalg, der følger en normalfordeling med samme gennemsnit og varians som vores rigtige data:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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m5Mtf/nIuu+yyXHXVVRk1alTfnBkAwP+xLgAAtXRQHe/W1ta0tLRk9uzZvbavX78+O3bs6LX9+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dWVxx9/fK/P193dna6url43AAAAGAiq7njfcccd+elPf5p169a9Zl97e3tGjRqVcePG9dre2NiY9vb2yjF7hu7d+3fv25vFixfnS1/6UrWlAjAAWeAKABhsqup4b968OZ/97GfzrW99K6NHjy5V02ssWrQonZ2dldvmzZsP23MDAADAoagqeK9fvz5bt27NO97xjowcOTIjR47MmjVrcsMNN2TkyJFpbGzM9u3bs23btl736+joSFNTU5KkqanpNauc7/599zGvVldXl/r6+l43AAAAGAiqCt7vfe9789hjj+XRRx+t3E455ZTMmzev8vMRRxyR1atXV+6zYcOGbNq0Kc3NzUmS5ubmPPbYY9m6dWvlmFWrVqW+vj7Tp0/vo9MCAACA/qGqOd5jx47N2972tl7bxowZkwkTJlS2n3/++Wlra8v48eNTX1+fiy66KM3NzZk1a1aS5Mwzz8z06dNz3nnn5dprr017e3suv/zytLa2pq6uro9OCwAAAPqHg7qc2Ou57rrrMnz48MydOzfd3d2ZM2dObrrppsr+ESNGZMWKFbnwwgvT3NycMWPGZP78+bn66qv7uhQAAACouUMO3j/60Y96/T569OgsXbo0S5cu3ed9pkyZknvuuedQnxoAAAD6vYO6jjcAAABwYPp8qDkAvJ49r9Pdl8cCAPRXOt4AAABQkOANAAAABQneAAAAUJDgDQAAAAVZXA0A4FUs7AdAX9LxBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChoZK0LAGDgmLpwZeXnjUtaalgJAMDAoeMNAAAABQneAAAAUJCh5gAAB8BUCwAOlo43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBFlcDAOgjey7AtieLsQEMbTreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AHLI957WaywoA0JuONwAAABSk4w0AUCWjPACoho43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFAAAMFVMXrqz8vHFJSw0rAeBw0vEGAACAggRvAPrU1IUre3X1AACGOsEbAAAAChK8AQAAoCCLqwFQhOHmDBX+rwOwPzreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AAIWZBw4wtOl4AwAAQEGCNwAAABQkeAMAAEBB5ngDcFDMWQUAODCCNwBADez55dXGJS01rASA0gw1BwAAgIJ0vAF4XYaUAwAcGh1vAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAglxODGAI2vMSYRuXtNSwEgCAwU/HG4CKqQtXum43AEAfE7wBAACgIMEbAAAACjLHGwCgn7IeA8DgoOMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQ4A0AAAAFWdUcgNfYcyVlAAAOTVUd72XLluWkk05KfX196uvr09zcnHvvvbey/+WXX05ra2smTJiQo48+OnPnzk1HR0evx9i0aVNaWlpy1FFHZeLEibn00kvzyiuv9M3ZAAAMQFMXrqzcABh8qgrexx57bJYsWZL169fnJz/5Sc4444x86EMfyuOPP54kueSSS3L33XfnzjvvzJo1a7Jly5acc845lfvv3LkzLS0t2b59ex588MHcdtttufXWW3PFFVf07VkBAABAP1HVUPMPfvCDvX7/m7/5myxbtiwPPfRQjj322Nx88825/fbbc8YZZyRJbrnllpxwwgl56KGHMmvWrPzgBz/IE088kfvvvz+NjY2ZMWNGvvzlL+eyyy7LVVddlVGjRvXdmQEAAEA/cNCLq+3cuTN33HFHXnrppTQ3N2f9+vXZsWNHZs+eXTnm+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dVV6ZrvTXd3d7q6unrdAAAAYCCoOng/9thjOfroo1NXV5fPfOYz+e53v5vp06envb09o0aNyrhx43od39jYmPb29iRJe3t7r9C9e//uffuyePHiNDQ0VG7HHXdctWUDAABATVQdvP/oj/4ojz76aB5++OFceOGFmT9/fp544okStVUsWrQonZ2dldvmzZuLPh8AAAD0laovJzZq1Kj84R/+YZLk5JNPzrp16/L3f//3+djHPpbt27dn27ZtvbreHR0daWpqSpI0NTXlkUce6fV4u1c9333M3tTV1aWurq7aUgEAAKDmDnqO9267du1Kd3d3Tj755BxxxBFZvXp1Zd+GDRuyadOmNDc3J0mam5vz2GOPZevWrZVjVq1alfr6+kyfPv1QSwEAAIB+p6qO96JFi3LWWWdl8uTJeeGFF3L77bfnRz/6Ub7//e+noaEh559/ftra2jJ+/PjU19fnoosuSnNzc2bNmpUkOfPMMzN9+vScd955ufbaa9Pe3p7LL788ra2tOtoAAAAMSlUF761bt+YTn/hEfvWrX6WhoSEnnXRSvv/97+d973tfkuS6667L8OHDM3fu3HR3d2fOnDm56aabKvcfMWJEVqxYkQsvvDDNzc0ZM2ZM5s+fn6uvvrpvzwoAYJCZunBlkmTjkpYaVwJAtaoK3jfffPPr7h89enSWLl2apUuX7vOYKVOm5J577qnmaQEAAGDAOuQ53gAAAMC+Cd4AAABQkOANAAAABQneAAAAUFBVi6sBMPjsXikZAIAydLwBAACgIB1vgEFsz262a//CwGAUCsDgo+MNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdUABgCLpAEADFw63gAAAFCQ4A0AAAAFGWoOADCAmHoCMPDoeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUNLLWBQDQt6YuXFnrEgAA2IPgDTBECOQAALVhqDkAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAU5DreAAPYntfm3rikpYaVAACwL4I3wCCxZwgHhgZfvgEMDIaaAwAAQEGCNwAAABRkqDnAAGNIOQDAwKLjDQAAAAUJ3gAAAFCQ4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJDgDQAAAAUJ3gAAAFDQyFoXAABA35q6cGXl541LWmpYCQCJjjcAAAAUJXgDAABAQYaaA/RTew4VBdgffzMA+i8dbwAAAChI8AYAAICCBG8AAAAoyBxvAIBBzKXFAGpPxxsAAAAK0vEGABhidMEBDi8dbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAgkbWugAAAGpn6sKVlZ83LmmpYSUAg5eONwAAABQkeAMAAEBBhpoD1IjhnQAAQ4OONwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQVUF78WLF+ed73xnxo4dm4kTJ+bss8/Ohg0beh3z8ssvp7W1NRMmTMjRRx+duXPnpqOjo9cxmzZtSktLS4466qhMnDgxl156aV555ZVDPxsAAADoZ6oK3mvWrElra2seeuihrFq1Kjt27MiZZ56Zl156qXLMJZdckrvvvjt33nln1qxZky1btuScc86p7N+5c2daWlqyffv2PPjgg7ntttty66235oorrui7swIAAIB+YlhPT0/Pwd75ueeey8SJE7NmzZq8+93vTmdnZ97whjfk9ttvz5//+Z8nSZ588smccMIJWbt2bWbNmpV77703f/Znf5YtW7aksbExSbJ8+fJcdtllee655zJq1Kj9Pm9XV1caGhrS2dmZ+vr6gy0foKb2dzmxPfcD9IXdf2sO5O+LyxwCvL5qcukhzfHu7OxMkowfPz5Jsn79+uzYsSOzZ8+uHHP88cdn8uTJWbt2bZJk7dq1OfHEEyuhO0nmzJmTrq6uPP7443t9nu7u7nR1dfW6AQAAwEBw0MF7165dufjii3PaaaflbW97W5Kkvb09o0aNyrhx43od29jYmPb29soxe4bu3ft379ubxYsXp6GhoXI77rjjDrZsAAAAOKwOOni3trbmZz/7We64446+rGevFi1alM7Ozspt8+bNxZ8TAAAA+sLIg7nTggULsmLFijzwwAM59thjK9ubmpqyffv2bNu2rVfXu6OjI01NTZVjHnnkkV6Pt3vV893HvFpdXV3q6uoOplQAAACoqao63j09PVmwYEG++93v5oc//GGmTZvWa//JJ5+cI444IqtXr65s27BhQzZt2pTm5uYkSXNzcx577LFs3bq1csyqVatSX1+f6dOnH8q5AADwOqYuXGnhRoAaqKrj3dramttvvz133XVXxo4dW5mT3dDQkCOPPDINDQ05//zz09bWlvHjx6e+vj4XXXRRmpubM2vWrCTJmWeemenTp+e8887Ltddem/b29lx++eVpbW3V1QYAAGDQqSp4L1u2LEly+umn99p+yy235JOf/GSS5Lrrrsvw4cMzd+7cdHd3Z86cObnpppsqx44YMSIrVqzIhRdemObm5owZMybz58/P1VdffWhnAjAI6EQBAAw+VQXvA7nk9+jRo7N06dIsXbp0n8dMmTIl99xzTzVPDQAAAAPSQS2uBsCB27OLvXFJSw0rAQCgFgRvgMPIUHIAgKHnoK/jDQAAAOyf4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJBVzQH6AaudAwAMXoI3AACvsecXghuXtNSwEoCBz1BzAAAAKEjHGwCA16X7DXBodLwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoKCRtS4AYLCYunBl5eeNS1pqWAkAAP2JjjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAB2zqwpW9FpMEYP8EbwAAAChI8AYAAICCBG8AAAAoaGStCwAYjMx/BABgNx1vAAAAKEjwBgAAgIIEbwAAACjIHG+AQ2Q+NzAU7fm3b+OSlhpWAtD/Cd4AABwSIRzg9RlqDgAAAAUJ3gAAAFCQ4A0AAAAFmeMNcIDMYQQA4GDoeAMAAEBBOt4AB8ElxAD2z0ghgN/S8QYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAACjI5cQAAOgzfXG5RZchAwYbwRvgdbheNwAAh8pQcwAAAChI8AYAAICCDDUHAKA487aBoUzwBngV87oBAOhLgjcAAAOWTjowEJjjDQAAAAUJ3gAAAFCQoeYAANSc9TWAwUzHGwAAAArS8QaITgsAAOXoeAMAAEBBgjcAAAAUJHgDAABAQYI3AACH1dSFK62tAQwpgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFANSSVXUBAChNxxsAAAAKErwBAACgIMEbAAAACjLHGwCAmrDOBjBUCN4AAPRbe4bzjUta9rodoL8TvIEhx4c1AAAOJ3O8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgoKqD9wMPPJAPfvCDmTRpUoYNG5bvfe97vfb39PTkiiuuyDHHHJMjjzwys2fPzlNPPdXrmOeffz7z5s1LfX19xo0bl/PPPz8vvvjiIZ0IAAAA9EdVB++XXnopb3/727N06dK97r/22mtzww03ZPny5Xn44YczZsyYzJkzJy+//HLlmHnz5uXxxx/PqlWrsmLFijzwwAP59Kc/ffBnAbAfUxeurNwAAOBwqvo63meddVbOOuusve7r6enJ9ddfn8svvzwf+tCHkiT/9E//lMbGxnzve9/Lueeem5///Oe57777sm7dupxyyilJkhtvvDEf+MAH8rWvfS2TJk16zeN2d3enu7u78ntXV1e1ZQMAAEBN9Okc72eeeSbt7e2ZPXt2ZVtDQ0NmzpyZtWvXJknWrl2bcePGVUJ3ksyePTvDhw/Pww8/vNfHXbx4cRoaGiq34447ri/LBgAAgGL6NHi3t7cnSRobG3ttb2xsrOxrb2/PxIkTe+0fOXJkxo8fXznm1RYtWpTOzs7KbfPmzX1ZNjDAGUYOAEB/VvVQ81qoq6tLXV1drcsAAACAqvVp8G5qakqSdHR05Jhjjqls7+joyIwZMyrHbN26tdf9XnnllTz//POV+wP0BR1wgMHF33VgoOrToebTpk1LU1NTVq9eXdnW1dWVhx9+OM3NzUmS5ubmbNu2LevXr68c88Mf/jC7du3KzJkz+7IcAAAAqLmqO94vvvhinn766crvzzzzTB599NGMHz8+kydPzsUXX5xrrrkmb37zmzNt2rR88YtfzKRJk3L22WcnSU444YS8//3vzwUXXJDly5dnx44dWbBgQc4999y9rmgOAAAAA1nVwfsnP/lJ3vOe91R+b2trS5LMnz8/t956az7/+c/npZdeyqc//els27Yt73rXu3Lfffdl9OjRlft861vfyoIFC/Le9743w4cPz9y5c3PDDTf0wekAg9GeQws3LmmpYSUAAFC9YT09PT21LqJaXV1daWhoSGdnZ+rr62tdDlDY/oK3OX8AJL6cBQ6vanLpgFjVHAAAqmG0FNCf9OniagAAAEBvgjcAAEPG1IUrTVECDjvBGwAAAAoyxxsAgEFNhxuoNR1vAAAAKEjwBgAAgIIMNQf6DZd+AQBgMNLxBgAAgIIEbwAAACjIUHNgQDEcHQCAgUbHGwAAAAoSvAEAAKAgQ82BfmnPIeUAADCQ6XgDAABAQYI3AAAAFGSoOQAAg4JpSkB/peMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdWAw2bPRW82Lmnp08cDgJL6+j0MGFoEbwAAhhxBGjicBG8AAPg/AjlQgjneAAAAUJCONwAA7IW1RIC+IngDADCkCdhAaYaaAwAAQEGCN1ATUxeu1GEAAGBIMNQcKEq4BgBgqBO8gZoSzAEAGOwMNQcAgCqYLgVUS/AGAACAggRvAAAAKEjwBgAAgIIEbwAAACjIquZAn7PgDABDzZ7vfRuXtNSwEqA/0vEGAACAggRvAAAAKMhQc+CgGVYHAAdn93uo908YGnS8AQAAoCAdbwAA6ENGhAGvJngDfcJK5gAAsHeCN1A1IRsAAA6c4A3sM0jvOTxO2AYAgIMjeAP7JGwDAMChE7wBAOAg+IIaOFCCNwAAHAZ7C+pWQIehwXW8AQAAoCDBGwAABqCpC1ca7g4DhKHmAABQiGAMJII3AAD0a+aBw8BnqDkAAAAUJHgDAABAQYaaAwDAAGHOOAxMgjcAAPQzAjYMLoI3DAH7WpTFmzoAAJQneAMAQD/gC3EYvARvAAAYwFxuDPo/wRsGqL19K+7NFgAA+h/BGwYR33gDAED/4zreAAAAUJCONwxSFmgBAID+QfAGAIAhxNQ0OPwEbxhAdLEBgAMlYEP/IXgDAMAgUfJLekEeDp7gDTW0rzdHb2YAADB4WNUcqjR14UpDvgEAgAOm4w19rL8Pw/KlAQCw2+7PBf3xMwsMJjreAAAAUJCONwAADHIHO+KtL0bK9ffRgHA4CN5wAPrizaqaNxrDwQGAw6nazyx7+6wiVMO+Cd5QA4I1ANBfHe6GAwwFgjdDUl+8MXhzAQCojs9PDFWCNxwmutwAAL8jhDOUCN4MefsKxN4AAAD6ByGdgU7whn2opkOtmw0A8Dt9vRo6DHSCNwPagXz76Y82AMDAcCCf23S/GYgEbwYlYRsAYOAYKJ/dhH4OVs2C99KlS/PVr3417e3tefvb354bb7wxp556aq3K4RBU03Uu+QdqoPzBBgCgnIO9JrkgTUk1Cd7f/va309bWluXLl2fmzJm5/vrrM2fOnGzYsCETJ06sRUlF1TJ07vmch1pHX1+Ca1/2VjMAALza/j6fVvP5tdoFd2t5eVqd94GnJsH77/7u73LBBRfkU5/6VJJk+fLlWblyZf7xH/8xCxcufM3x3d3d6e7urvze2dmZJOnq6jo8BR+iXd3/L0nvet925ff3euzPvjTnkJ7j1fZ8zv3Vsb/n3vM59va4r/fY1Zh8yZ0HdT8AAIau/X2GPNjPqQfy2bSaXLKv5979PPv6TL6v++3tuav5jL8vffEYA+E5D8Xuf/uenp79Hjus50CO6kPbt2/PUUcdle985zs5++yzK9vnz5+fbdu25a677nrNfa666qp86UtfOoxVAgAAwP5t3rw5xx577Osec9g73r/+9a+zc+fONDY29tre2NiYJ598cq/3WbRoUdra2iq/79q1K88//3wmTJiQYcOGFa33UHV1deW4447L5s2bU19fX+tyoN/zmoHqed1A9bxuoHpeN7319PTkhRdeyKRJk/Z77IBY1byuri51dXW9to0bN642xRyk+vp6/zmhCl4zUD2vG6ie1w1Uz+vmdxoaGg7ouOGF63iN3//938+IESPS0dHRa3tHR0eampoOdzkAAABQ1GEP3qNGjcrJJ5+c1atXV7bt2rUrq1evTnNz8+EuBwAAAIqqyVDztra2zJ8/P6ecckpOPfXUXH/99XnppZcqq5wPJnV1dbnyyitfM1Qe2DuvGaie1w1Uz+sGqud1c/AO+6rmu33961/PV7/61bS3t2fGjBm54YYbMnPmzFqUAgAAAMXULHgDAADAUHDY53gDAADAUCJ4AwAAQEGCNwAAABQkeAMAAEBBgncNdHd3Z8aMGRk2bFgeffTRWpcD/dbGjRtz/vnnZ9q0aTnyyCPzpje9KVdeeWW2b99e69KgX1m6dGmmTp2a0aNHZ+bMmXnkkUdqXRL0W4sXL8473/nOjB07NhMnTszZZ5+dDRs21LosGDCWLFmSYcOG5eKLL651KQOK4F0Dn//85zNp0qRalwH93pNPPpldu3blG9/4Rh5//PFcd911Wb58eb7whS/UujToN7797W+nra0tV155ZX7605/m7W9/e+bMmZOtW7fWujTol9asWZPW1tY89NBDWbVqVXbs2JEzzzwzL730Uq1Lg35v3bp1+cY3vpGTTjqp1qUMOC4ndpjde++9aWtry7/927/lrW99a/7zP/8zM2bMqHVZMGB89atfzbJly/KLX/yi1qVAvzBz5sy8853vzNe//vUkya5du3LcccfloosuysKFC2tcHfR/zz33XCZOnJg1a9bk3e9+d63LgX7rxRdfzDve8Y7cdNNNueaaazJjxoxcf/31tS5rwNDxPow6OjpywQUX5J//+Z9z1FFH1bocGJA6Ozszfvz4WpcB/cL27duzfv36zJ49u7Jt+PDhmT17dtauXVvDymDg6OzsTBLvLbAfra2taWlp6fWew4EbWesChoqenp588pOfzGc+85mccsop2bhxY61LggHn6aefzo033pivfe1rtS4F+oVf//rX2blzZxobG3ttb2xszJNPPlmjqmDg2LVrVy6++OKcdtppedvb3lbrcqDfuuOOO/LTn/4069atq3UpA5aO9yFauHBhhg0b9rq3J598MjfeeGNeeOGFLFq0qNYlQ80d6OtmT88++2ze//735yMf+UguuOCCGlUOwGDS2tqan/3sZ7njjjtqXQr0W5s3b85nP/vZfOtb38ro0aNrXc6AZY73IXruuefym9/85nWPeeMb35iPfvSjufvuuzNs2LDK9p07d2bEiBGZN29ebrvtttKlQr9xoK+bUaNGJUm2bNmS008/PbNmzcqtt96a4cN9ZwjJb4eaH3XUUfnOd76Ts88+u7J9/vz52bZtW+66667aFQf93IIFC3LXXXflgQceyLRp02pdDvRb3/ve9/LhD384I0aMqGzbuXNnhg0bluHDh6e7u7vXPvZO8D5MNm3alK6ursrvW7ZsyZw5c/Kd73wnM2fOzLHHHlvD6qD/evbZZ/Oe97wnJ598cv7lX/7FH3Z4lZkzZ+bUU0/NjTfemOS3Q2cnT56cBQsWWFwN9qKnpycXXXRRvvvd7+ZHP/pR3vzmN9e6JOjXXnjhhfzP//xPr22f+tSncvzxx+eyyy4zTeMAmeN9mEyePLnX70cffXSS5E1vepPQDfvw7LPP5vTTT8+UKVPyta99Lc8991xlX1NTUw0rg/6jra0t8+fPzymnnJJTTz01119/fV566aV86lOfqnVp0C+1trbm9ttvz1133ZWxY8emvb09SdLQ0JAjjzyyxtVB/zN27NjXhOsxY8ZkwoQJQncVBG+g31q1alWefvrpPP3006/5gspgHfitj33sY3nuuedyxRVXpL29PTNmzMh99933mgXXgN9atmxZkuT000/vtf2WW27JJz/5ycNfEDAkGGoOAAAABVmhCAAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAACvr/ciHiWioJ+MUAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## Hypotesetestning\n",
     "\n",
-    "Lad os undersøge forskellige roller i vores baseballspiller-datasæt:\n"
+    "Lad os udforske de forskellige roller i vores dataset over baseballspillere:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korrelation og Ond Baseball Corp\n",
     "\n",
-    "Korrelation giver os mulighed for at finde relationer mellem datasæt. I vores simple eksempel, lad os forestille os, at der findes en ond baseballvirksomhed, der betaler sine spillere baseret på deres højde - jo højere spilleren er, desto mere penge får han/hun. Antag, at der er en grundløn på $1000 og en ekstra bonus fra $0 til $100, afhængigt af højden. Vi vil tage de rigtige spillere fra MLB og beregne deres imaginære løn:\n"
+    "Korrelation giver os mulighed for at finde relationer mellem datasæt. I vores simple eksempel, lad os forestille os, at der findes en ond baseballvirksomhed, som betaler sine spillere baseret på deres højde - jo højere spilleren er, desto mere penge får han/hun. Antag, at der er en grundløn på $1000 og en ekstra bonus fra $0 til $100, afhængigt af højden. Vi vil tage de rigtige spillere fra MLB og beregne deres imaginære løn:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lad os nu beregne kovarians og korrelation for disse sekvenser. `np.cov` vil give os en såkaldt **kovariansmatrix**, som er en udvidelse af kovarians til flere variable. Elementet $M_{ij}$ i kovariansmatrixen $M$ er en korrelation mellem inputvariablerne $X_i$ og $X_j$, og diagonalværdierne $M_{ii}$ er variansen af $X_{i}$. Tilsvarende vil `np.corrcoef` give os **korrelationsmatrixen**.\n"
+    "Lad os nu beregne kovarians og korrelation af disse sekvenser. `np.cov` vil give os en såkaldt **kovariansmatrix**, som er en udvidelse af kovarians til flere variable. Elementet $M_{ij}$ i kovariansmatricen $M$ er en korrelation mellem inputvariablerne $X_i$ og $X_j$, og diagonalværdierne $M_{ii}$ er variansen af $X_{i}$. Tilsvarende vil `np.corrcoef` give os **korrelationsmatricen**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "En korrelation lig med 1 betyder, at der er en stærk **lineær relation** mellem to variable. Vi kan visuelt se den lineære relation ved at plotte en værdi mod den anden:\n"
+    "En korrelation lig med 1 betyder, at der er en stærk **lineær relation** mellem to variable. Vi kan visuelt se den lineære relation ved at plotte den ene værdi mod den anden:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -921,14 +758,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I dette tilfælde er korrelationen lidt mindre, men den er stadig ret høj. Nu, for at gøre relationen endnu mindre tydelig, kunne vi overveje at tilføje lidt ekstra tilfældighed ved at tilføje en tilfældig variabel til lønnen. Lad os se, hvad der sker:\n"
+    "I dette tilfælde er korrelationen lidt mindre, men den er stadig ret høj. Nu, for at gøre sammenhængen endnu mindre åbenlys, kunne vi overveje at tilføje lidt ekstra tilfældighed ved at tilføje en tilfældig variabel til lønnen. Lad os se, hvad der sker:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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A0CEgAwBAh4AMAAAdAjIAAHQIyAAA0GEfZNji3vCbD+XTX3zq9P2Xv/CKvP9nf6LHEQFAv6wgwxa2NBwnyae/+FTe8JsP9TQiAOifgAxb2NJwfK46AGwF5wzIVfWeqnqiqj7fqV1RVR+vqkcGt5d3Hvuxqnqoqh6uqiNV9QOD+o2D+49W1Turqi7MPwkAAFZvJSvI703yqiW1O5I82Fq7LsmDg/upqouT3JfkH7bWrk/yiiQnB695V5Lbk1w3+LP0cwIAQO/OGZBba59KsvT3ra9Jcu/g43uTzAw+/s+S/GFr7XOD1/55a22hqq5O8uzW2kOttZbkfZ3XAADA2FhtD/LzWmuPJ8ng9qpB/YeTtKo6UFW/X1W/PKhPJznWef2xQW1ZVXV7VR2qqkNPPvnkKocIAACjW+tt3i5O8teT/LUk307yYFV9NslfLPPcNuyTtNbuSXJPkuzevXvo8wAAYK2tdgX5a4O2iQxunxjUjyX5t621P2utfTvJR5P8+KB+Tef11yQ5vsq/GwAALpjVBuQPJ7lt8PFtST40+PhAkh+rqksHF+z9zSR/NGjD+GZV3TTYveJNndcAAMDYWMk2b/cneSjJzqo6VlVvTnJ3kpur6pEkNw/up7X29STvSPLvk/xBkt9vrX1k8KnekuTdSR5N8sUkH1vbfwoAAJy/c/Ygt9ZuHfLQK4c8/74sbvW2tH4oyYtGGh0AAKwzJ+kBAECHgAywQtddddlIdQA2JgEZYIU+/ouveEYYvu6qy/LxX3xFPwMC4IJY632QATY1YRhg87OCDAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHRf3PQDYKm5+xyfzyBPfOn3/uqsuy8d/8RX9DQgAWJYVZFgHS8NxkjzyxLdy8zs+2c+AAIChBGRYB0vD8bnqAEB/BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkIGxc8lEjVQHgLUkIANj59df++IsjcI1qAPAheYkPWDszOyaTpLsP3A0x0/MZ/vUZPbu2Xm6DgAXkoAMjKWZXdMCMQC90GIBAAAdAjIAAHQIyLCFDdsUwmYRAGxlAjJsYQtttDoAbAUCMgAAdAjIAADQISDDOnAyHABsHAIyrIPvDmnqHVYHAPojIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADOtg25DvtGF1AKA/fjzDOnh6yG5uw+oAQH8EZFgHw7Y7tg0yAIwfARkAADoEZAAA6Li47wHAWrv5HZ/MI0986/T96666LB//xVf0NyAAYEOxgsymsjQcJ8kjT3wrN7/jk/0MCADYcARkNpWl4fhcdQCApQRkAADoOGdArqr3VNUTVfX5Tu2Kqvp4VT0yuL18yWuuraq/rKpf6tRurKojVfVoVb2zqmpt/ykAAHD+VrKC/N4kr1pSuyPJg62165I8OLjf9RtJPrak9q4ktye5bvBn6ecE1tmlQ47yG1YHgK3gnD8FW2ufSvLUkvJrktw7+PjeJDOnHqiqmSRfSvJwp3Z1kme31h5qrbUk7+u+BujHP77lx3LRkt/lXFSLdQDYqla7TPS81trjSTK4vSpJquqyJG9N8qtLnj+d5Fjn/rFBDejRzK7pvONnXpLpqclUkumpybzjZ16SmV2+PQHYutZ6H+RfTfIbrbW/XNJivFy/8dBDdqvq9iy2Y+Taa69d0wECZ5rZNS0QA0DHagPy16rq6tba44P2iScG9ZcleW1V/XqSqSRPV9VfJfntJNd0Xn9NkuPDPnlr7Z4k9yTJ7t27hwZpAABYa6ttsfhwktsGH9+W5ENJ0lr7G621Ha21HUn+5yT/uLX2zwdtGN+sqpsGu1e86dRrAABgnKxkm7f7kzyUZGdVHauqNye5O8nNVfVIkpsH98/lLUneneTRJF/MM3e5gPM2PTU5Uh0AYKlztli01m4d8tArz/G6ty25fyjJi1Y8MliFv/UjV+a+g48tWwcAWAmbnbKpfOQPHx+pDgCwlIDMpvL1b58cqQ4AsJSADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeAzKYyNbltpDoAwFICMpvK008/PVIdAGApAZlN5S++szBSHQBgKQEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOi4uO8BsLHNHp7L/gNHc/zEfLZPTWbvnp2Z2TXd23guv3Rbvv7tk8vWAQBWwgoyqzZ7eC77HjiSuRPzaUnmTsxn3wNHMnt4rrcx/cpPXZ9tE3VGbdtE5Vd+6vqeRgQAbDQCMqu2/8DRzJ888wCO+ZML2X/gaE8jSmZ2TWf/a1+c6anJVJLpqcnsf+2Le13VTpLLLpkYqQ4A9EdAZtWOn5gfqb5eDn3lqfzpN/4qLcmffuOvcugrT/U6niT5tb93QyYuOnNle+Kiyq/9vRt6GhEAMIyAzKptn5ocqb4e7pw9kvsOPpaF1pIkC63lvoOP5c7ZI72NKVlc2f5nrztzZfufva7/lW0A4JlcpMeq7d2zM3s/+LmcXGina9smKnv37OxtTO8/+NjQ+l0z/a7WzuyaFogBYAOwgsz5aee4v86G/fU9DwsA2EAEZFZt/4GjOfn0mdHz5NOt14v0AADOl4DMqo3jRXqXLNni7Vx1AIClBGRWbRwv0ts2sfyX9LA6AMBSUgOrtnfPzmUP5ejzIr1vfXdhpDoAwFICMudnzC7SAwA4XwIyq+YiPQBgMxKQWbVxvEivhlyLN6wOALCUgMyqjeNFem1Ii8ewOgDAUgIyq7Z3z85Mbps4oza5baLXi/QAAM6XgMyqzeyazk/fOJ2JQf/CRFV++kbHKQMAG5uAzKrNHp7Lb392LguD/oWF1vLbn53L7OG5nkcGALB6AjKrtv/A0cyfPHN/4fmTC3axAAA2NAGZVRvHXSwAAM6XgMyqTV26baQ6AMBGICCzarZUAwA2IwGZVfvG/MmR6gAAG4GAzKqN40EhAADnS0Bm1RwUAgBsRhf3PQA2rlMHguw/cDTHT8xn+9Rk9u7Z6aAQAGBDE5A5LzO7nJwHAGwuWizYVGrEOgDAUgIym8qwHebsPAcArJSADAAAHQIym8rU5JDT/YbUAQCWEpDZVN726uuz7aIzO463XVR526uv72lEAMBGYxcLNhVbzwEA5+ucAbmq3pPkJ5M80Vp70aB2RZL/J8mOJF9O8jOtta9X1c1J7k5ySZLvJtnbWvvE4DU3JnlvkskkH03yP7TWXDvFmrP1HABwPlbSYvHeJK9aUrsjyYOtteuSPDi4nyR/luSnWms3JLktyf/Vec27ktye5LrBn6Wfkw1o9vBcXn73J/KCOz6Sl9/9icwenut7SAAA5+WcK8ittU9V1Y4l5dckecXg43uTfDLJW1trhzvPeTjJD1TVs5JckeTZrbWHkqSq3pdkJsnHzmPsW8rs4bmxaxuYPTyXfQ8cyfzJhSTJ3In57HvgSJL0PjYAgNVa7UV6z2utPZ4kg9urlnnOTyc53Fr7TpLpJMc6jx0b1JZVVbdX1aGqOvTkk0+ucoibx6kgOndiPi3fD6J9r9buP3D0dDg+Zf7kQvYfONrTiAAAzt8F2cWiqq5P8k+S/INTpWWeNrT/uLV2T2ttd2tt95VXXnkhhrihjGsQnTsxP1IdAGAjWG1A/lpVXZ0kg9snTj1QVdck+Z0kb2qtfXFQPpbkms7rr0lyfJV/95ZzfEjgHFZfLzXk/OZhdQCAjWC1AfnDWbwIL4PbDyVJVU0l+UiSfa21T5968qAN45tVdVNVVZI3nXoN57Z9anKk+noZtgeJvUkAgI3snAG5qu5P8lCSnVV1rKrenMWt3G6uqkeSnNraLUn+uyT/cZL/sar+YPDnVH/yW5K8O8mjSb4YF+it2N49O7NtYsnhFxOVvXt29jQiAIDNayW7WNw65KFXLvPcu5LcNeTzHEryopFGx/ctXZW1SgsAcEE4anoD2H/gaE4+fWYiPvl06/0iPQCAzUhA3gDG9SI9AIDNSEDeAMb1Ir3LL902Uh0AYCMQkDeAvXt2ZnLbxBm1yW0TvV+k93d/7OqR6gAAG8E5L9Kjf6eObR63o6Z/9wvLn3I4rA4AsBEIyBvEzK7p3gPxUnqjAYDNSIsFq3bpJRMj1QEANgIBmVX71ncXRqoDAGwEAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyKza5Lblv3yG1QEANgJJhlX76RuvGakOALARCMismqOmAYDNyFHTG8Ts4bnsP3A0x0/MZ/vUZPbu2dn70dOOmgYANiMryBvA7OG57HvgSOZOzKclmTsxn30PHMns4blex7V9anKkOgDARiAgbwD7DxzN/Mkzj2+eP7mQ/QeO9jSiRXv37MzktokzapPbJrJ3z86eRgQAcP60WGwA49rKcKrFY9xaPwAAzoeAvAFsn5rM3DJheBxaGWZ2TQvEAMCmosViA9DKAACwfqwgbwBaGQAA1o+AvEFoZQAAWB9aLAAAoENABgCADgEZAAA6BGQAAOhwkd4GMXt4zi4WAADrQEDeAGYPz2XfA0dOHzc9d2I++x44kiRCMgDAGtNisQHsP3D0dDg+Zf7kQvYfONrTiAAANi8BeQM4vswx02erAwCwegLyBrB9anKkOgAAqycgbwB79+zM5LaJM2qT2yayd8/OnkYEALB5uUhvAzh1IZ5dLAAALjwBeYOY2TUtEAMArAMtFgAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0OEkvQ1i9vCco6YBANaBgLzEOAbR2cNz2ffAkcyfXEiSzJ2Yz74HjiRJ72MDANhstFh0nAqicyfm0/L9IDp7eK7Xce0/cPR0OD5l/uRC9h842tOIAAA2LwG5Y1yD6PET8yPVAQBYPQG5Y1yD6PapyZHqAACsnoDcMa5BdMdzlv/7h9UBAFi9cwbkqnpPVT1RVZ/v1K6oqo9X1SOD28s7j+2rqker6mhV7enUb6yqI4PH3llVtfb/nPOzd8/OTG6bOKM2uW0ie/fs7GlEiw5+6esj1QEAWL2VrCC/N8mrltTuSPJga+26JA8O7qeqfjTJ65NcP3jN/15VpxLnu5LcnuS6wZ+ln7N3M7um8/Zbbsj01GQqyfTUZN5+yw297xSx0NpIdQAAVu+c27y11j5VVTuWlF+T5BWDj+9N8skkbx3U/2Vr7TtJ/qSqHk3y0qr6cpJnt9YeSpKqel+SmSQfO+9/wRqb2TXdeyBeaqJq2TA8MX6L8AAAG95qe5Cf11p7PEkGt1cN6tNJvtp53rFBbXrw8dI6K3Dry54/Uh0AgNVb64v0llvSbGepL/9Jqm6vqkNVdejJJ59cs8FtVHfN3JA33nTt6RXjiaq88aZrc9fMDT2PDABg81ntSXpfq6qrW2uPV9XVSZ4Y1I8l6S5rXpPk+KB+zTL1ZbXW7klyT5Ls3r1bo20WQ7JADABw4a12BfnDSW4bfHxbkg916q+vqmdV1QuyeDHe7w3aML5ZVTcNdq94U+c1AAAwNs65glxV92fxgrznVtWxJL+S5O4kH6iqNyd5LMnrkqS19nBVfSDJHyX5XpKfa62dOpruLVncEWMyixfnjd0FegAAUG3MtwrbvXt3O3ToUN/DAABgk6mqz7bWdi+tO0kPAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6Lu57AONm9vBc9h84muMn5rN9ajJ79+zMzK7pvocFAMA6EZA7Zg/PZd8DRzJ/ciFJMndiPvseOJIkQjIAwBahxaJj/4Gjp8PxKfMnF7L/wNGeRgQAwHoTkDuOn5gfqQ4AwOYjIHdsn5ocqQ4AwOYjIHfs3bMzk9smzqhNbpvI3j07exoRAADrzUV6HacuxLOLBQDA1iUgLzGza1ogBgDYwrRYAABAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAR7XW+h7DWVXVk0m+0vc4xshzk/xZ34PYIMzVaMzXaMzXypmr0Ziv0ZivlTNXz/QftdauXFoc+4DMmarqUGttd9/j2AjM1WjM12jM18qZq9GYr9GYr5UzVyunxQIAADoEZAAA6BCQN557+h7ABmKuRmO+RmO+Vs5cjcZ8jcZ8rZy5WiE9yAAA0GEFGQAAOgRkAADoEJDHWFVNVdUHq+oLVfXHVfUTVfWSqjpYVX9QVYeq6qV9j3McVNXOwZyc+vMXVfXzVXVFVX28qh4Z3F7e91jHwVnma//g6+0Pq+p3qmqq77H2bdhcdR7/papqVfXcHoc5Ns42X1X131fV0ap6uKp+veehjoWzfC96r19GVf3C4Ovn81V1f1X9gPf54YbMl/f5FdCDPMaq6t4k/6619u6quiTJpUk+kOQ3Wmsfq6r/Iskvt9Ze0ec4x01VTSSZS/KyJD+X5KnW2t1VdUeSy1trb+11gGNmyXztTPKJ1tr3quqfJIn5+r7uXLXWvlJVz0/y7iQ/kuTG1poN+DuWfG39UJJ/lOTvtta+U1VXtdae6HWAY2bJfP1mvNefoaqmk/x/SX60tTZfVR9I8tEkPxrv889wlvk6Hu/z52QFeUxV1bOT/KdJ/s8kaa19t7V2IklL8uzB0/6DLH6hc6ZXJvlia+0rSV6T5N5B/d4kM30Naoydnq/W2r9urX1vUD+Y5JoexzWOul9bSfIbSX45i9+XPFN3vt6S5O7W2neSRDheVne+vNcv7+Ikk1V1cRYXjY7H+/zZPGO+vM+vjIA8vn4oyZNJ/kVVHa6qd1fVZUl+Psn+qvpqkn+aZF+PYxxXr09y/+Dj57XWHk+Swe1VvY1qfHXnq+u/TvKxdR7LuDs9V1X16iRzrbXP9Tuksdb92vrhJH+jqj5TVf+2qv5aj+MaV935+vl4rz9Da20ui3PxWJLHk3yjtfav431+WWeZry7v80MIyOPr4iQ/nuRdrbVdSb6V5I4srsL8Qmvt+Ul+IYMVZhYNWlFeneS3+h7LRjBsvqrqHyX5XpL39zGucdSdq6q6NIvtAv9Tv6MaX8t8bV2c5PIkNyXZm+QDVVU9DW/sLDNf3uuXGPQWvybJC5JsT3JZVb2x31GNr3PNl/f5sxOQx9exJMdaa58Z3P9gFgPzbUkeGNR+K4kLN870nyf5/dba1wb3v1ZVVyfJ4Navdc+0dL5SVbcl+ckkb2guUujqztULs/hD53NV9eUs/ory96vqP+xxfONm6dfWsSQPtEW/l+TpJC5s/L6l8+W9/pn+TpI/aa092Vo7mcX5+U/ifX6YYfPlfX4FBOQx1Vr70yRfraqdg9Irk/xRFvut/uag9reTPNLD8MbZrTmzXeDDWfxBk8Hth9Z9ROPtjPmqqlcleWuSV7fWvt3bqMbT6blqrR1prV3VWtvRWtuRxfD344PvWxYt/V6czeJ7Vqrqh5NcksRFjd+3dL681z/TY0luqqpLB799eGWSP473+WGWnS/v8ytjF4sxVlUvyeIV8pck+VKS/yrJ9Un+lyz+uvKvkvy3rbXP9jXGcTL4tfdXk/xQa+0bg9pzsrjzx7VZfLN4XWvtqf5GOT6GzNejSZ6V5M8HTzvYWvuHPQ1xbCw3V0se/3KS3XaxWDTka+uSJO9J8pIk303yS621T/Q2yDEyZL7+erzXP0NV/WqSv5/F1oDDSf6bJD8Y7/PLGjJfD8f7/DkJyAAA0KHFAgAAOgRkAADoEJABAKBDQAYAgA4BGQAAOgRkAADoEJABAKDj/wceBaX6Xh706QAAAABJRU5ErkJggg==\n",
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8UfmA+2xOBN5fj9Ln/kYy2Fb2kV4oLwcAAINGvI2Yun2Wauqbtap2j2rqmx3tG7WTI6n3tu1kJ0dVU8v02I0z5C0ITkT6qtw6ke8tklR+n/1Nqu9vX7IftkS6q+nWKA/pxWNZVlp2G2hra1NBQYFaW1uVn5+f6ssBAAAp1u2zNPuhdRHLre0V0lfvujQoYXNrZTyVo45SMdc42Z832vEvr/AOqjnOXSd8eqZmp3a1tGtCcZ4WVk5U9pD0W1uzH5pJ4Vf20+1BA/o/05yTpBsAAAwKNfXNuuGpjTHjnls0S5WTR0qKPA861i/5kZLcVCS/qRDv9+ZUuO9zbV3ToJrjPNjmVg+2z4v+zTTnZE83AAAYFJw2Yoo1osijnhFFl1d4jVfGL6/wJvAJBoZ4v7d4ZGZ4/A9IpMjJvr19IN1WQgfb55V6SuoHWyUDBj6SbgAAMCg4bcTkZERRrJXxptYO3bJyiwrzsnSo/bj/9XRcoYvne3NDXyb7/cFg+7yBQh+2AP1d+m32AAAACOO8CUWKlXtkeHriJPdXxiUFJdxST/IZrYHbQJSq0U6DbY7zYPu8wEBG0g0AAAaFt3YdVKzm2T6rJ04yXxk/cLhTq2r36OnXGqImQZFY6lmRdKOzdyTJ6CIeSapGOw22Oc6D7fMCAxnl5QAAYFBoaj3mKC7WPGipZ2X8/he2JXxtySi3tg2WueQlw3NcjevvmFsNDBysdAMAgLQVuMK7ZfdBo/e0HO2SFH0etM3NBWPThwJOxDuXPBEpm6Ntei/SZG4Pc6uBgYOkGwAApKXqrY2a/dA63fDURn31V7V6ZuNuo/cVB6yEVk0t02M3zpC3IHi1MBl9qexk3y0me8yTVdYe6XvzFuQmraP2gaOdrsb1dyl7uAHAMcrLAQBA2onURdxE6Yjg8uPQEUUHDne6UlIeqtjlsudUdRG39fVop8FYbm0/3AjdPuBNw674wEBG0g0AAAaMbp8VM4mLtsJr4sRxX6/XAkcUrardE+eRo/Pmp19jsb4c7ZSqveSpxtxqoP8j6QYAAAOCaUOwWCu8sfz+nT367JTSiD83XSm9Z94UlYzIUcmwHN3x23fU1Bb5mpKx93awrfza5dZLVm6RR8Fbt9O93Jq51UD/xp5uAADQ7zlpCJboyu3RzhNRf27awOrLF5VrwfQxuuiMEi2/ukIehd9761FyksHB2GgrFXvJASAWVroBAEDKJFoubqkneVyxpk6XV3iVmeFJeOV2VIwy73hWVFOx9zbwOiNJx5Vfyq0B9Dck3QAAICWqtzZq+eq6oLJrb36ull/trFw8tCGYyXztaM4dXxQzJp4kOhXJYNXUMi2+uFxPbWgIGm+W4ZEWzSlP25Vfyq0B9Cck3QAAoM9Vb23ULWFWYJvaOnTLyi16PKAU2GlDsGgr0SZGFw41iosnie7rZLB6a6OeWN/Q63WfJT2xvkHnji9K28QbAPoLkm4AANCnun2WvvG796LGLP3de47LxQPjIq1EZ3ikaGOpne5x7m8rqoHl+iXDchx9z26dN/Dhg8n2AQBIdyTdAACgT23c0axD7cejxhxsP66NO5p10eklcY+CCrcSffBop/712bcjnncg73EO1909lsDv2c3zlhXk6uppZVr9TmPMbvMAkO7oXg4AAPpUTX2zozi7XFwK3/1bipws2yvRC6aPUeXkkcoYoAl1LJG6u5swvR9OztvY2qEn1jcYdZsHgHRH0g0AAPqY6S7rU3FujIKyu6BHs2JNnbqj1Z8b6vZZqqlv1qraPaqpb/YfM9LriZ4rUnd3M/G9M57zWif/cut7BoCBgPJyAADQpyonleiRV+qN4gIl2v07Vhd0KbgLeiAne5P7utza5HNFE/o998V5I33PAJCOSLoBAECfmjV5pArzsqLu6y7My9KsMAlZIo3LAkeTOYmLlESHS5btcuvQNVy73LrXuU6WW5uu1odj2t09nEjfc7LPK5nfDwAY6CgvBwAASRdYVr25oUX/cc3UqPEPXnu26w3NWo50Oo6LtGc53N7keMutpcTKrU27u4eTyPecyHkl8/sBAAMdK90AACCpqrc2avnquqCVTW9+rr5ycblW1e5VU1tnwOs5Wn71p5PS3bp4WLZRXMvRLq2q3aOS4Tlavvr9sEm0pZ4mbivW1PlHbsVbbm0psXLrWN3dw/FIWnxxeULfczznDWR6P/ojRqEBcIKkGwAAJE311kbdsnJLr9eb2nrKrX/8hRkqGpbdJ8lLab7Zyuyjf46931zqnSwnWm4d7/vt7u5LVm6RR+Zt0Z5c36BzxxfFnXjHe16bt2BoXOdNNSfbDQBAorwcAAAkSbfP0jd+917UmG8+/54uKC/2j/RK6mphkppl28lyouXWibw/Unf3SF+nG2Xt8ZzXVhZmrvpA4GS7AQDYWOkGAABJsXFHc9RmaZJ0sP24Nu5o1kWnx9dB24kDR5Ozh9hOluMtt/aoZ/RZokloaHf3A4c7df8L2yLGJ1rWHum8pSNydfBop2599u2w34NHkeeq92fR9uyH224AADZWugEAQFLU1De7GpeoRFeiQ3kUvGJrl1vbPzM9huReEmp3d18wfYxKRuQYvSfRsvjQ81ZOHqmrzhmtxReX9/oe3NhLniqx9uwHPsQAgEAk3QAAIElM13sTr/sO7I5eU98ctmTaXol2Q6RkOVK5dVlBT+M4b35wIjwqPyfquDCTzxWJ6UMGtx9GSD1l2E+sb+h1Zy1JT6xvSHoZdiLfWySmDyfceIgBIL1QXg4AAJKiclKJHnkldlOyykmJlZabNrbKzPDo6mllYedlO+WN0jgrXLn1BeXFWlvXpFW1oclm5NXtRBt2XVBeHHMeelFelut7q0328n/jd+8lrQw7WY3OUvkQA8DARtINAMAgd6TjhL7+67e1++AxjS8aqv/8p3M1PDfxXxFmTR4ZM+krzMuSJK2q3RNX93K7sVXoOqbd2CpwFbnbZ2n1O85XWO0919/9X9N04Gin0XXa5daxrnNfW+/rdPq5EpGM3nIb62Pv5T/Uflwb65t10Rnu7uVP5vcWa8++W3vzAaQfkm4AAAaxqx/ZoHc/bvP/+YOmw5q6/H90zth8rb5tTkLHzszw6MFrzw47MizQF3+yyf/3TlYknTa2imeOdmAZebwJYqzrVMh1mnyu5avf14jcLB04EvkhwOaGFqPkN9FGaqFqdhwwjnMz6U52o7NoI9Lc3psPIL2wpxsAgEEqNOEO9O7Hbbr6kQ0Jn6Nqapkev3FGr73MhUN7nvuHJoVORi85bWwVz15bb0FuwqvKJsl+4HWafK6mtk598b826au/qtUNT23U7IfW9frOTD/va9sPuLr32XkbOXf0RaOzSHv23fjnBED6YqUbAIBB6EjHiYgJt+3dj9t0pONEwqXmoXucS4bn6I7f1ErHTvSKdbIi6bSxVclws27eN84cr4wMjyYU52lh5URlD0lsjaKpzew67bh4Hg6EK5823Vv8yCvb/X/vxt7nyskjg44ZLc5NfdXoLNKefVa4AUTCSjcAAIPQ1379tqtxsQSOlMrweNTUFnlmtumKpNPGVl2d3UbxKzft1i9qdun+F7bps995JeFO2y1HzOaD23HxNOIKLFO3V6vj6dbupNIgklmTRvr36kdSlJelWZPcTbr7stFZ6Ig0Em4A0ThOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHHwbF/O1vf9OCBQtUUlKi/Px8zZ49W6+88kpQzO7duzVv3jzl5eWptLRU//Zv/6YTJ3o/EQcAAM590HTY1Tgn3FqRtJPKSOlO6Bzt/3rdeddyN5LQ4mHZjuJifa5IQh9W2N3anR5DCk7enbL38kfzwLVnu56oOv3nAQD6iuOk++jRo5o2bZoeffTRsD9/+OGH9cMf/lCPP/64Nm3apGHDhumKK65QR8ep/3D+3d/9nU6cOKF169bprbfe0rRp0/R3f/d3ampqkiR1d3dr3rx56urq0uuvv66f//znevrpp3XvvffG+TEBAEDg7GLThKdgaPQVy3iUDDMr844VZze2knrvDg7X2KqtI3pTsXDcSEK9BUMdxUX7XCbshxXxdmt3a+9zz17+3vPKH0/S3men/zwAQF9xvEnryiuv1JVXXhn2Z5Zl6fvf/77uvvtuLViwQJL0i1/8QqNGjdLzzz+v66+/XgcOHNCHH36on/zkJzrnnHMkSQ8++KB+/OMfa+vWrfJ6vXrppZdUV1enP/3pTxo1apSmT5+u+++/X3fddZeWL1+u7GyzJ8YAAKBHuNnFJr5+6RnuX4yLfbbsxlahny3cHO2powv03p7o+9jDCUxC49mHbDIvuzBkXnakz2XCLp+Op1t7oIG499nJPw8A0FdcbaTW0NCgpqYmXXbZZf7XCgoKNHPmTNXU1Oj666/XyJEjdeaZZ+oXv/iFZsyYoZycHD3xxBMqLS3VeeedJ0mqqanR2WefrVGjRvmPc8UVV2jJkiV6//33de6557p52QAApLVIs4tN5Oa433P1gOEeZ9M40+Ru/Mg8x9caKNEkNJpwaWivBnTDcnTHb9/RvjazOdGJXq+be5/7Eo3OAPQ3rv6X1C4PD0yW7T/bP/N4PPrTn/6ka665RiNGjFBGRoZKS0tVXV2toqIi/3HCHSPwHKE6OzvV2XnqP85tbc6fZAMAkG6izS42kYxE07SLuGmcZJbcJbLqK8WfhJrMyz5oMC87I8Oje/+uQrc+azYnOt7rDU3eB6JUJPsAEEmfjwyzLEu33nqrSktLtWHDBg0dOlT/9V//pfnz5+uNN95QWVl8ZT8PPPCAVqxY4fLVAgAwsCVaYnzgSJej+G6fFXuF0fQJgBsjowOMKzLbWx0q0SQ03sZx4bYElBXkavHF5Vr9TmPM8unzJhQpwyM52YreV3ufjf45AYA04WrS7fV6JUn79u0LSp737dun6dOnS5LWrVunP/zhDzp48KDy8/MlST/+8Y+1du1a/fznP9c3vvENeb1ebd68OejY+/btCzpHqKVLl+r222/3/7mtrU3jxo1z7bMBADAQJbpSfeiYedIdKUkMTQYPHDUsLzeMiyYwubPiSOLdSELjGWUVaUtAU2uHnlzfoEe/cK6KhuVETVrf2nXQUcIt9c3eZ9N/TgAgXbiadJeXl8vr9erll1/2J9ltbW3atGmTlixZIklqb2+XJGVkBDdOz8jIkM/nkyRVVlbq29/+tvbv36/S0lJJ0tq1a5Wfn6+Kioqw587JyVFOjnkZGgAAg0Gi+3I/bmnXqto9MVcjIyWJjSdHbj0W0LHare7lscTbPC6QG0moPcqqqdVsL3a0LQHWyfj7X9imV++6NOqDgKbWY0bX96+fm6wzvSP6ZMU52sOE0H9OACBdOE66jxw5ou3bt/v/3NDQoNraWhUXF2v8+PH62te+pm9961s644wzVF5ernvuuUejR4/WNddcI6knoS4qKtJNN92ke++9V0OHDtVTTz2lhoYGzZs3T5I0d+5cVVRUaOHChXr44YfV1NSku+++W7feeiuJNQAADsRTYhxo1TuNWnVy7FSk1chY+8Yt9YzcurzC25PQudi9PJJEmsfdM2+KSkbkuJaE2qOslqw024sda0uAaTf1lqNmVQojh2VrwfQxRrFOBVYalAzL0fLV0R8mBP1zAgBpwnHS/eabb+qSSy7x/9ku6b7pppv09NNP684779TRo0e1ePFiHTp0SLNnz1Z1dbVyc3uetJeUlKi6ulrf/OY3demll+r48eP69Kc/rVWrVmnatGmSpMzMTP3hD3/QkiVLVFlZqWHDhummm27Sfffd58ZnBgBg0IinxDiSSKuRJvvGA5NEt7uXh0qkeVxRXpa+fFG560mfk1FW8e4BD1Vs2IjONM4pp5UGiY5mA4D+ynHS/bnPfU5WlE1RHo9H9913X9QE+fzzz9f//M//RD3PhAkT9OKLLzq9PAAAEMDN7uORViNNy5jtuHj2ODuRSPM4l3u3BamaWqZLzxqlZ2p2aldLuyYU52lh5URlDwnecufW9+PNNzuOaZwTiVQaJHM0GwCkQp93LwcAAH3HjVnLgcKtRpqWMdtxTvc4O5VI0nbIYHRXvMKt/P7Xqw29Vrrd+n7s40R7AFGWhNFgiY6pc/ufWQBItYzYIQAAoC90+yzV1DdrVe0e1dQ3q9uFunA78XJ7h+xr2w/4r7MoL9voPS1Hu7Sqdo82N7Tonnk9jVFDr6svu4VHkoyVVnvlNzQBtkv2q7c2+l+z94BLiX0/9nE8EY7jMTyOU/FWGniUnIcAAJBqrHQDANAPVG9t1PLVdWpqC9jvm5+r5Vcn1jk7WhOvRDzyyqmmqsXDsoze8+if6/1/b8+bfv7tPdp3+NRKeemIbK1YMDWp3cJjcXul1aQbeWjJvpM94NG4dRwn4nlo0VfzwQEgFTxWtA3aA1hbW5sKCgrU2trqnwcOAEB/VL21Ubes3BLx54+7MEbJjfFZyRD6ICDDIy2aU66lV4UfEWrKXlmWnD1oyPBIf73/yl77rBNRU9+sG57aGDPuuUWzepW1B3b/TqSbulvHMWH6eQMxpxvAQGSac7LSDQBACnX7LH3jd+9FjVn6u/cSHqNUNbVMl1d4/YnX/rZOffvFbXEfLxKnq+mhsT5LemJ9gyQllHhHWuGNxWf1dHx3c093It3IMzM8rlyLW8cxYbInfVR+jv7fP07XgSOdfTIfHABSiaQbAIAU2rijWYfaj0eNOdh+XBt3NOui00sSOldg4rXhb58kdKxIioZlGzdWi+bJ9Q26Y+5ZCa04hz5o+Gtjmx77y46Y79t7yKwbu6mSYWYjuUzj+juTueTLr/50wv88A8BAQSM1AABSqKa+2dW4aAIbtf1uy8dG77lm+mj94Prpuu2SyUbx98yboucWzdIPrp+uWz9n9p5wLElPv9YQ9/tt9oOGBdPH6EjnCaP31H50MOHzBjFdwE3iQm8ymvRFY1caeAuC98d7C3J7zXkHgHTHSjcAACllmvwkliTFu6d7bNFQLZg+RjX1zXrklfqY8d6Cof7V9J9siL2qHM1LdU1a/FmzxL0v9yw7deBIp6txToW7932xhzq00qC/3RcA6Csk3QAApFDlpBKjZLZyknkpbmgCevBop2599u240vaZ5T0JdDwzn4uHJ1oubZacmSaV44uHGR3PNM6UaTf0ZMynthvKhd57e1RZsled+3IvOQD0VyTdAACk0KzJI1WYlxV1X3dhXpZmGSYu4RLQDE/86+QZnp7ENzPDo6unlfmbnIVz9bSyoFVMb35iSeTnp5wWM8ZJUvmp0uFG5zWNM2XSWMybhPnU8YwqAwC4jz3dAACkUGaGRw9ee3bUmAevPdsoKbIT0NDV6ES279odtbt9lla/0xg1dvU7jUF7he1kM14eyxN1D3KspFLqSSrt927e2WJ0XtM4U3ZjsUi3wVJy5lNvbmiJWplgSWps7dDmBnc/LwAgGEk3AAAp9vbu6I27Yv1cip6AJqKprSdpi5XASb0TODvZ9Ci+HmEP/s8H+uqvanXDUxs1+6F1qt4anPQ7TSr3GHYlN43r7xIZVQYAcA9JNwAAKdR1wqenNkTv0v3UhgZ1nfBFjTFJiuPx6ocHJMWfwEXqYu2UXS4emHg7vabRhUON4k3jTNkPRCKxy7zd7iieyr3kAIBTSLoBAEihZ2p2xiz/9lk9cdEka7Xy0LGemduJJHBVU8v06l2X+keJXTjJ+d7lcOXiTq/JdC602/OjU1XmbZf3R6oy8Kh38zsAgPtIugEASKFdLe2uxCVrtdJeYU80gQuclz1tXGFc1xKanF5QXqzCvKyo7ynKy/Jf06xJI5WXnRk1flh2pmZNcrfbdqrKvO3yfql3eb/952TsJQcABCPpBgAghcYV5bkSFysplnq6mDs18mRS62YCVzwssVFiTpLT0CKC7CHRf/WJ9fN4pLLMO1J5v7cgN+njwgAAPRgZBgBACrk1xspOipes3CKPgpNNOw1+5IYZKhqWrf2HO/Toug/1t/1HY573eMBWcjuBCx1J5g0zEzuakcOyjeIisZPTzQ0tUUetSdKh9uPa3NCiyskjjeIPBsS7JVUjw2xVU8t0eYU3aHb7BeXFrHADQB8h6QYAIIWcjLH67FmlUWOcJMWbdzQbJd1neUf0OkeiCVzz0S7j2EChyanTsm27E3sspnGmTB6IJLvM2y7vBwD0PZJuAABSyO0xVqZJ8YSRw4yOFy4u0QTuwFHnSW245NRp2XbLkU6jeNM4J9yqEgAADDwk3QAApFAyxliZJMWW4URv0zgn3v+4zfF7wiWnTsu2iw3L2k3jnKLMGwAGJ5JuAABS6KLTS/TjP9cbxblpzyGz1WbTOCeGxuggbjt/fKEWXjgxYnLqtGzbW2D24MI0Lh6UeQPA4EP3cgAAUmjWpJFGY6/cHmM1odisa7plWVpVu0c19c3++diJ+swEs4Zhcyu8WjB9jConj4y4GuykO/d5E4qMznui2+f6Z7Z1+yzV1Dcn7fgAgP6HlW4AABLU7bPiLhnOzPDoMxOLtLZuf8SY8ycWuV6C/IWZE3T/C9tixj2zcbee2bhbUs8cbjf2H08Zne9qXNXUMl161ig9U7NTu1raNaE4TwsrJ/Ya/7VpR7PR8Rb+dLP/7936zJJUvbWx155uN48PAOifSLoBAAjRdcIXM4GzJZpIdZ3w6U9REm5J+lPdfnWd8Lk6Q/rNBrOu6YGaWju0ZOWWhOc7t7SbdS83jQt3D/7r1YZe9+C/t3zs7ELl3meu3tqoJSu39Np77tbxAQD9F+XlAAAEeODFOp159x91/wvb9IuaXbr/hW068+4/6oEX63rF2olUYLInnUqkqrc2xjzf0681xGxVZp2Mc9P/b8tHjt9jX+eKNXUJlUU77ToejZN70N51wtmFquczW0rsM3f7LK1YUxf2Prv1nQIA+i+SbgAATnrgxTo9sb53EmxJemJ9Q1Di7VYi9dL7TUbXZhpnynQEWShLUmNrhzbHsVJus7uORyqY96inWsDni76f3Ok9+MzE+PfFJ/KZNze09HooEMiN7xQA0H+RdAMAoJ4y7yfXR19NfnJ9g7pO+CS5l0i1dZqtvprGmRpbZNZILZL9h+Pvam53HZfUK/G2u5AfO96tL/5kk776q1rd8NRGzX5oXa/KAaf34MZZE+K+ZklqaovvM5t+V4l8pwCA/oukGwAAST9/3azM++ev9yTmbiVSU7wjjI5jGmfquhljE3p/pNJv0+7ckbqO253cD7UfD3o9XLm403tQ+9Eho/hIWo50xvU+N8vpAQADD43UAACQjEt7Nze0aNHFk11LpK49d6xWvRN77/e15yaWJIeaGecIMo96xnFdUN577JfTpnJVU8t0eYXX3/m9ZHiO7vhNbdjzWifPvWJNnS6v8Cozw+P4HiS6klw8LDuu99nl9E2tHWEf7ET7TgEAAx8r3QAASDrceTx2UECc6b7kWIlUhuEoMNM4U2/EsX/YvoJl8yt6jTCLt6lcZoZHlZNHasH0McrweNTUFnk1ObRc/ILy4pgzzgvzsvz3INGVZG/B0LjeF6ucXgr/nQIA0gNJNwAAkg53dDuKsxOpSCXplswSqU0NZrOjTeNMvV5/wPF7vAW5YUdbJdJULrAc/bXtnxhdh5MV68BvP9aDkmhMHqBEE6mcPtJ3CgBIH5SXAwAgyRNzR3ePo53Htap2j0pH5OqtXdFXi9/efdAgmTJNAd1dBd1zsN0o7qLJxfrHz4xX6YiepDMzw6Nun+UvCS8d0dNl3LShWeXkU2Xt4crRTdgr1psbWnrt/Q51sP24/7z2g5JbVm5xdD6P3FmJDi2nD/xOAQDpi6QbAAD1lA5v3Xs4ZtzO5mP66q9qjY755IYG3TH3LGUPiVxYVjl5pB55ZXvMYwUmq67wmCV6p43I1YLpY/x/DpcoFw6NXuJtC1yhtsvRnUymDt37nIyu4IV5WUGJfLQ96fGwy+kBAIMHSTcAAJIum1KqP23b7+oxLUv6+es7tejiSRFjZowvMjqWaZypMYVm+5MD4yIlyoeOme2Ht1eoo5WjRxJu77PTRmr2eaOdY2hWph69eYYOHO1kJRoA4Ar2dAMAIOn9vW1JOe4bO6OXoK/cuMvoOKZxpkz3J9tx8STKttCmcrHma4cTbu+z02Z2pnO9MzI8WjB9jL8kHQCARLDSDQCAFFcyaSIvOzPqz9/YadYg7Y2dzVFXzE0E7sXebNiY7W/7DuuzZ5bGlShL4VeoTcu9b7vkdJ0xanjEFWd7j/aSlVvkUfA9TOS8iY4WAwAgEEk3AACSxhXFNw4qlutizNfOzYqelDuNiyTepmU7DxyVZJ6IFg7NCio394bZE21aFn7R6SUx9z/bXcFDP1si5010tBgAAIFIugEAaa/rhE/P1OzUrpZ2TSjO08LKib2am1k+98+bl52pC88oiRozPMfsP8WmceHE07TMVtfYKsk8EX30izOU4fFE7c5tl4U3tXaEvabQhmmxmHYFP29CkTI8UpjJZX4Znp44AADcQtINAEhrD7xYp6c2NAQlWt9+cZsWzSnX0qsq/K9t+eig6+deOGt8zD3BpnuG491bnMhebElqPtIpyTxRnjUp9j7oWKO7TGechx4z1qr4W7sORk24pZ6E/K1dB+kwDgBwDY3UAABp64EX6/TE+oZeiZbPkp5Y36AHXjzVyTov2/3n0KvfaVR3jCxvfPEwo2NZlrSqdo9q6ptjHjNQvHuxbT6r51cFO1GWek8MD7d/uj9iTzcAIBVIugEAaanrhE9PbWiIGvPUhgZ1neipK79uRvS91/FobO3Q5obo3cs/VTrc6FgrN+3WV39Vqxue2qjZD61T9dZGo/clmkCeO6HQ//f2/mlvQXCpebjO4tGYjO5asabO0cMFE+zpBgCkAuXlAIABJ7ALd6T9u8/U7DQqJX6mZqdunjNJF55eorzsTLV3dUeMz8vK0FM3fUYHjnTqw32H9cgr9TGvNVbSuznGSLFwmlo7tGTlFqNEN9EE8tppY4L+bLp/OhrT0V2bG1pcLfN2ey85AAAmSLoBAANKuC7cZWE6Ve9qaTc6nh2XmeHRwlnj9cT6yKvjCysn6KLTexqj1dQ3GyXdsZLevYeOGV1nIEunVoMvr/BGTXhjJZqxfPjJEV2iUUGvmeyfjiZVZd5OR4wBAOAGyssBAP1W1wmffrJhh+5dtVU/2bBDa97ZqyUrt/RaJbVXfgNLricU5xmdw47r9lla/U70ku3APdp2MhspPfOo52FArFXT0XGOKgtcDY4m2l5sE2/ucr4SH0sqy7zdKpEHAMAUK90AgH4pXNfxSMKt/C6snKhvv7gt5niohZUTJZk1HAsseXZr1XTWxJF6VLFXzCMxWQ2ONMvaxNAE54OHk+oybzdK5AEAMMVKNwCg34nUdTya0JXf7CEZWjSnPOp7Fs0p98/rjqfk2Y1V04zMxBI909XgqqllevWuS/Xcoln6wfXT9YULxhm979OjC3q91u2zVFPfHFc3denU6nukd8UzMswpu0R+wfQx/ocoAAAkAyvdAICUC2yMVpyXHXVfdSyBSbE9hzt0xTzDo15zuuMteU501fTAyTnYTsWzGhy4F7u729Kzmz+K+Z6S4TlBfzbdUw8AAHqQdAMAUipcEpeI0KR46VUVumPuWXqmZqd2tbRrQnGeFlZO9K9w2+yS52jXEWmPdiKNxeLZt+xG0y/TZD8wrnpro5as3NJrhbrRQTd1yXxkWKwmcQAADAQk3QCAlImUxMUj2spv9pAM3TxnUtT3Z2Z4dPW0sqir7FdPK3M9CTTpLp7hUdBKvdeFleX397Y6irMT5Wgl4aaJcqpGhgEAkAok3QCAlIiVxDnhxsqvaffyO6umuJp4mzRke+SGGSoalu1q06+PD5pVFthxThvNRZOqkWEAAKQCSTcAICVMkjhTbqz8uplUOlU1tUyLLy7XUxsaZAVk3Z6Te8+vOsf9vdI5hr8B2HFNrWbzxE3iUjkyDACAvkbSDQBIiURXMS+aPFL/+JlxCa38BjZw+6DxsNF7Gg+ZJZ9OVG9tDFvW7rOkJ9Y36NzxRa40KQv8vJbH7PsqGd6T+LYc7TKKN4k7b0JRr5L5UBmenjgAAAY6km4AQEokuoo5fVyhFkwfE/f7423g9vZHB3XteWPjPm+obp+l23/zTtSY23/zTsJNxeL9vOOK8yRJxSFdzCMxiXtr18GY4+B8Vk8ce7oBAAMdc7oBAClhNxCLN42cVR5/MmY3cIunvN1nubEL/ZTXPzyg9q7uqDHtXd16/cMDcZ8jkc974eklkiRvvtlDEpM49nQDAAYTkm4AQErYDcQkxZd4x5mtu9nAzQ3/veVjV+NCJfJ5PZI+M7GnG7z9kCSaSCPVQrGnGwAwmJB0AwBSpmpqmR67cYa8MZK5cDY1tBjHdvss1dQ3a1XtHj39WkNCDdxG5GTF/d5w11PXaDa666OD7XGdK5GGdZakN05+z/ZDEo96P++wXzPtHh+rysEj8wQeAID+jj3dAICUqppapssrvP4GX69s26/n39kb832WYZl3vHuZI2lqS+w48V5P5wlfXOdLtET79R0HdNEZPSXm9kOS0Os36R4f2MStdESu7pk3Rbc++3bEMWmJjH8DAKA/IekGAKRcZobH3zCrqbXDKOnOHxp7xdney+xmKXkiW7oTuR5vvlkjs1CJlmjvPRjcrT30IYlJ9/hwDxrKCnK1+OJyrX6n0XECDwDAQELSDQDoV9o6jrsSl6y9258c7tCq2j2OR5Ulej0zJ8XXOM4u5W5q7Yjr3KMLh/Z6LfAhSSyRHjQ0tXboyfUNevQLM1Q0LNs4gQcAYKAh6QYA9CumZeOx4hLZyxzN6zta9PqOnn3OZQ5WZRO9nhtnTYzrffZe7FtWbonr/Yl0iY/2oMFSTyn5/S/U6dW7LiXRBgCkLRqpAQD6lcLcbFfi+mLcVFNrh5as3KLqrY0xYxO9ntqPDiX0/rglkAvHetBgSWps7dBmB03xAAAYaFjpBgC4LrRplpOS4Zb2Llfi+mLclL1au2JNnS6v8Eb9jIlez2vbP4nr+7RXm+NVU9+sOZ86zTg+8N5/uO+I0XuYxw0ASGck3QAAV0VqmmVahv3eHrMRWq9uPxB1b3Wie5lNBa7WRtvnnOj1PPJKvf/v+7Ks/Z2PDxnHxtuZnXncAIB0Rnk5ACCmIx0ntOjnb+iK76/Xop+/oSMdJ8LG2U2zQpMuJ2XYQ7PM/tO0dW+bvvqrWt3w1EbNfmhdr2Pbe5ml8HOlw72eiNdOPgSoqW9Wt693Wh3tepzqy7L2vGyz+xHp3kfDPG4AwGBA0g0AiOrqRzZo6vL/0dpt+/VB02Gt3bZfU5f/j65+ZENQXKymWVJPGXa4hDSQt6B3t+xYIiWh9lxpb0HwSmphXlbQdbnhkVe2R30IEO16nHLyfSa6ivyZCbEbqcXTmZ153ACAwYKkGwAQ0dWPbNC7H7eF/dm7H7cFJd5uNc2aNq7Q8XVGS0Krppbp1bsu1XOLZukH10/XL//3TOUMSe5//qKtRIdez8JZ4+M6h+n3aZe1x+uM0uExY+IpYfcW5OqxG2cwjxsAkPbY0w0ACOtIx4mICbft3Y/bdKTjhIbnDjEuY44Vd/CoWSO1UKZ7q//a2Kamts64zuHkWqI1WAucc/3WroMJnSvW95mZ4dHV08r0xPqGuI6/6t29uqRiVELXYLvtksk6Y9QI5nEDAAYVkm4AQFhf/dVbxnE/+fJM4zLmWHF1jdET/VhCE8B4m3slyvQhwITivITOE+v77PZZWv1O7L3fkXx8sD3ha7BddPppUb8LAADSEeXlAICw3vnIrIu4HWeXMUdauzRtmnW0M3yTNlOBCWA8zb1iueEz4/SD66frtksmG8XHWgVeWDlR8Sz4mn6fiXYvzxmSGTPGrXsPAEA6IukGAITVecLnKM6kW7hJ06zS/Bwnlxl0jsDELp7mXiaOdp7QguljdNHpZrOrY60CZw/J0OenlDq6BiffZ6Ldy88emx8zxq17DwBAOiLpBgCEVTrCLPkNjIvUndtJ06xzxxU5u1CdSuzumTdFmxtatKp2j55+rSEpJeV7Dx2T5N7qbrfP0hs7o+/rDj2Hk+8z0e7lF002e7jgxr0HACAdsacbABDWyBHZqj8Qez/vyBHZQX+umlqmyyu82tzQov2HOxw3zWo9dtzxtXoLcnX1tDLd/8K2pO/dbu86rlW1e1Q6Ilf3zJuiW599Wx4Fjx9zsrq7sb5Zh9qjf2ZL0jevOkul+bmOv8/zJhQpwyPFmCwW/eSGEr33AACkI5JuAEBYRzu7444L7M7tVPGw7NhBkv71c5N0pjdfpSNydfBol259dktcpeT/fuVZGlWQq8df2a5t+47EjK9rOqqv/qpWUs9K9uKLy7X6ncagZN9bkKtl8yuMVndrdhwwus7WY8e16GKzfeSB3tp1MP6EW9Kmnc2ac6bZareU2L0HACAdkXQDAMIzTdRc3jTtLRhqFDfnjFJVTh6pbp+l2Q+ti/syPB5pwfQxerj6r47f29TaoSfXN+jRL8xQ0bDsOFd344/r9llhV5UDX//Q4EFCNJbbm+IBABhkSLoBAGGVFQ7V+42HjeJMRUoSA11QXqzCvKyoJdeFeVn+vdKJdud+Y2eLSvNz4yprt+dx3/9CnV6969K4yqhnlhfrkVfM4gKFG4VWdrLMPnTlPRH5Q7NcOQ4AAIMVSTcAwC8wKR5bbNaA65IzS4ziIiWJpmXYgQJT20S7c79Ut18v1e2P+/2m87gjyfCYJeqBcfYotNBF6MbWDj2xvsHxNURz6GiXq8cDAGCwIekGAEgKnxSbeOWDT/TFWeUxjx0uSWxq7dCSlVuCultvbmiJ2VjsYPtxf5KbaHdut8Sb/B842ukoLlmj0CJpaktuYzoAANIdI8MAAP6kOJ6S5FjviZYk2q+tWFOn7pPdvkyTVzsu1uiuvhJv8m/6Pjsu0XJ6p0YXmW8fAAAAvTlOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHH/Y6zgsvvKCZM2dq6NChKioq0jXXXBP08927d2vevHnKy8tTaWmp/u3f/k0nTpxwerkAMKh1+yzV1DdrVe0e1dQ3+xPb0JhEVk5jdTmPlSQGlmdLzpPQzAyPls2vkNS71Zgn5H+TwXQedyRO530nWk5/2yWT9YPrp+ubV51lFH/hJLPtAwAAIDzH5eVHjx7VtGnT9C//8i+69tpre/384Ycf1g9/+EP9/Oc/V3l5ue655x5dccUVqqurU25uzy9I//3f/61FixbpP/7jP3TppZfqxIkT2rp1q/8Y3d3dmjdvnrxer15//XU1NjbqS1/6krKysvQf//EfCXxcABg8TPdQJ7pyOvm0YVF/7nTl+rwJRfJ4onfN9nh64mxVU8v02I0zen1ee3TXpWeN0jM1O7WrpV1t7cf1/Dt7ja4plljzuE0ax9kPDZas3GI07zvRcvqLTj/N3/X90T/Xx2xYN4vxXwAAJMRx0n3llVfqyiuvDPszy7L0/e9/X3fffbcWLFggSfrFL36hUaNG6fnnn9f111+vEydO6Ktf/aq+853v6Oabb/a/t6Kiwv/3L730kurq6vSnP/1Jo0aN0vTp03X//ffrrrvu0vLly5WdbTbDFQAGKyd7qBNdOT1vfFHUnztduX5jZ0vMMVWW1RN30emnVmGrppbp8gpvxCT35jmTJEk/2bDDtaQ72jxuJ43j7IcGy1e/r6a2U3u8R+XnaPnVnw6Kt1fGm1o7HFUneE5er71inpnh0YPXnq1bVm6J+J4Hrz07ro7sAADgFFf3dDc0NKipqUmXXXaZ/7WCggLNnDlTNTU1kqQtW7Zoz549ysjI0LnnnquysjJdeeWVQSvdNTU1OvvsszVq1Cj/a1dccYXa2tr0/vvvu3nJAJB2nO6hTnTl9ONDx6L+3Gn5dE19s9F5w8VlZnhUOXmkFkwfo8rJI8MmjMXDEntwW1lepB9cP13PLZqlV++6NGLCHW6PvP3Qo3prY4SjRyqQPyVaOX0kkVbkq6aW6fEbZ8ibnxMU783P0eMBD2YAAED8XE26m5qaJCkoWbb/bP9sx44dkqTly5fr7rvv1h/+8AcVFRXpc5/7nFpaWvzHCXeMwHOE6uzsVFtbW9BfADAYOd1DnWgjsl3N7b1eC9xLvrmhRffMmyIpckoZmAx2d/vMzttyNOpe9Ui8BQk2BvN4oib1Th96SKeS9NBO4U1t4ZN0e2XcWxD8wKSsIFdfubhcZSGvewtyg6obQo/12jc+r+cWzfI/THjtG58n4QYAwCV9PjLM5+v5Zeqb3/ymrrvuOknSz372M40dO1a//e1v9ZWvfCWu4z7wwANasWKFa9cJAAOV0z3U0fYUm+g4HtxILVJZ9eKLy7X6ncawe64DE7zWjujjwmxr3mnUmnca/cc3nfdtP2SIdx9785Hoc6udPPSw91ZHa2RnqSdJv7zC22uVOlI5/Z1VU2LuJQ9kVwgAAAD3uZp0e71eSdK+fftUVnbqF599+/Zp+vTpkuR/PXAPd05OjiZNmqTdu3f7j7N58+agY+/bty/oHKGWLl2q22+/3f/ntrY2jRs3LsFPBAD9i0ljLqd7qKXIjchMTApopBZtL/mT6xv06BdmqGhYdtTr/+Sw2dzq0OOH7lWPJPAhQzwd24fnRv9Pp9OHHiaN7AKT9ECRkmWSaAAA+g9Xk+7y8nJ5vV69/PLL/iS7ra1NmzZt0pIlSyRJ5513nnJycvTBBx9o9uzZkqTjx49r586dmjBhgiSpsrJS3/72t7V//36VlpZKktauXav8/PygZD1QTk6OcnJywv4MANJB9dZGLV9dF1SC7M3P1fKrKxw12gptqGULXTl9dN2H+tv+ozGvq6a+Wfeu2qpxRUP1k1d3Riyr9ki6/4U6vXrXpVFXXYflOP9Pk338cCvC4STykOFM7/CoP3f60KOpNfqeeJtpHAAA6F8c/2Zz5MgRbd++3f/nhoYG1dbWqri4WOPHj9fXvvY1fetb39IZZ5zhHxk2evRo/xzu/Px83XLLLVq2bJnGjRunCRMm6Dvf+Y4k6R/+4R8kSXPnzlVFRYUWLlyohx9+WE1NTbr77rt16623klgDGJSqtzaG7TLd1NahW1ZuCWp6ZTKC6p554cuPA1dIn9u8WzJIuj8+1KFf1OyKGRdaVh3JdeeO1fO1zruL28d/+rUGlYzIiVlWHfqQ4a+NbXrsLztinuf88dHncTt96NFyNHq5us00DgAA9C+Ok+4333xTl1xyif/Pdkn3TTfdpKefflp33nmnjh49qsWLF+vQoUOaPXu2qqur/TO6Jek73/mOhgwZooULF+rYsWOaOXOm1q1bp6KinrEzmZmZ+sMf/qAlS5aosrJSw4YN00033aT77rsv0c8LAANOt8/SN373XtSYpb97L2iFN9rc6qunlen+F7bFHGXVFbJX2y2xyq8vPKNEedmZau+K7/z3v7DN//ex9noHPmQoGZZjlHTHasQW+NAjksDGccXDzR4mm8YBAID+xWNZsaahDkxtbW0qKChQa2ur8vPzU305ABC317Yf0Bf/a1PMuF/+75lBc6ul3nvADx7t0q3P9t7LbK8FB+6JvvnpzXr5r5+48AmCPbdoVsz9xpFW9p0K97kiSeR7DueBF+v01IYGBTZWz/BIi+aUa+lVp7ZK1dQ364anNsY8nsn3BgAA+o5pzunqyDAAgPte234g7rjAudUXlBfr/hfMR1mNyM2K84rDC53H3RcijegKZ5/h3u7XPjwQc1RZ9dZGPbk+OOGWJMuSnlzfEDQCzC5Hj6avvzcAAOAekm4A6Of2HOw9BzueOKfzuyu87lUJhZvHHUm3z9Ltv3nHtXOHfq5I3twV/ee2H/+lXl/9Va1ueGqjZj+0rtcMbadzujMzPLp6WvRV+KunlcX83gAAQP9E0g0A/Z5pshU9zukoq6Lh2Ybnjc1bkGtU4i1Jr394IO793NHE+vx/bWxzfEx7VFlg4u304Ua3z9Kv3/w46nl+8+bHMVfqAQBA/+TqyDAAgPtMU61YcU5HWb29+6DhmXvzSPrFv1yglvaumF3EQ/3mrY/iPm80sT7/4c4Tjo8ZblSZ04cbG3c061D78aixB9uPa+OOZqO95AAAoH9hpRsA+jvTfpcx4uy9w5FS39A9139tOmx+jSEWX1yuOZ86TQumj1Hl5JGOSqNrP4o/2Q/HdC/5yDhX9kNXrp0+3KipbzaKN40DAAD9C0k3APRzpvlqrDh7lJXUuxA93J7rIx3RV18jXcNXLg7uzu3UkAz3/tN0ai55hTY3tERtgJZhXMYfnr1y7fThhnu1DAAAoD+ivBwA+rkxxXlxx4WODLu8whtxfnfoPOuRw8y6l08oytFnz/JqQnGeFlZOVPaQxJLmWZOK1dBs1jwullNzyetiziUflpvYfxLtlevAOd0eBafK4R5uVE4q0SOv1Mc8fuUkSssBABiISLoBoB8KTJYLh5olvxeGJGXVWxt7Jdd2svnqXZcGJePh9lyXjBgq6VDM854zrlj3LZhqdI0m5k7x6rk3ojcWi+aeeVNUMiLn5FzyTt367Nu91ojtBmiBzd0umDhSa+v2Oz6fRz3JfWD5etXUMuOHG7Mmj1RhXlbUfd2FeVmaxYxuAAAGJJJuAOhnwiXLseRlZwYlZdVbG7Vk5RajZDMS033Ybo+yeivOPd128vvli8qVmeFRt8/S7IfWRRzdFdoA7aYLJ+o//rjNeAu9fU4p/Ci0qqllurzCG/PhRmaGRw9ee7ZuWbkl4nkevPZsRoYBADBAsacbAPoRO1l2knBLkicgH3M6JzqS4uFmK+ymcabimYwVLvl1Orore0iGzh7jbDa5x9PTNC7SA4zMDI8qJ4+M2VCuamqZHr9xhrz5OUGve/Nz9LjhqDUAANA/sdINAP1EtGQ5lqOd3f6RUk6SzcooJcvrP/jE6NzrP/hE+junVxxZUZ7zLuLhyradju7qOuHT1j3OZnX7LOnJ9Q06d3xRwomx6co4AAAYWEi6AaAPhDY0C5dMxUqWY6mp70m6nSabkbQcNetebhonmX0PxcPMku4ln52ks8ryIx7H6eiuZ2p2xrXKLgWXqSfCXhkHAADpg6QbAJIsWkOzeFZmI+vJGJ0mm4ECk2LTLuQFho3eTL+HQ+1dRscrGZ6jBdPHRPy5PbqrqbUjbPVAaAO0HQeOGp03lGnlAAAAGJxIugEgiZw0NDNNliOxR0qdN6FIGZ7oe6MzPD1xodfqtIGbJC29ckqv10JXtJ10ETctL28+0qVVtXuiNihzMrprf1tiDz0Sf2gCAADSEUk3ACRJrIZmod2zY63MRhPYvfytXQdjlkn7rJ44e2U20sMBE0OzMoP+HC55z/DI+Hs4aLjS/eO/nJptHW7FXHI2uuu0EcFNzJxK9KEJAABITyTdAJAkThuaRVuZjSUnoBTc6Z7uRBq4SdKmnc2ac+ZpkiIn79EeAoR+D6Z7ugNFG4Vm2qAs3u3Y4eZ0AwAA2BgZBgBJEk9DM3tl1lvgbNX0YPtx/+grp3u6E23gZo8eSzR5t7+H0nznK8axRqGZjO4aYbg3Pdy5w83pBgAAkFjpBoCkibehWejK7If7juiRV7bHPI6dtDptIJboXuTmo52SEk/e/d9DnFl7tIZmJl3TMzwkzQAAwH0k3QAQB5MkzmnyGyhwdFRNfbNR0m0nrU4biCW6F/m17c2S4k/eez0EONKZ4PV8EnRf1tY1GXVNL8iJb6U7dE86AABAIJJuAHDIdPSV0+Q3kni6kTtpIJZIAzdJOtTeM6c7nuTd/uT3zJvif4ixZVdLHFdxyiOvnGqwVpiX5b++QOH2gB/qMGvgFoqRYQAAIBqSbgBwwMkIMKkn+V18cbme2tAgK+BNHo+0aE55r6Zf4cTTjdw+t0kDsUQauElSblbP8UyS99CHB96CXF09rUz3v7AtodL0SMIl3FL4rukZnsTanDAyDAAAhEPSDQCGYo0Ak3qXGVdvbdST6xvCdvN+cn2Dzh1fFDPxjqchmy2wTD2aSCvjJs44bZj/XLFW9h+5YYaKhmUHzO/u0q3PxjeqLFGhK9SVk0calfFHwsgwAAAQDkk3ABgyaRQWmMSZdPM22Qscb0M2p0JXxp/d2KBNO1tjvm/k8FPzrZ2UtXf7LM1+aF1KEu5A9sOKWZNGKmdIhjpP+By9n5FhAAAgGpJuAIgisGHaB42Hjd7TeOiYJOdzuiM5b0JRzLJvj4L3dMcrcGX8yb+YrfrubG4P+rNpWXui3c7dYj+s6PZZ6up2nnBLjAwDAACRkXQDQAThGqaZePujg7r2vLEJlYUHeqOhJeZqsHUy7qIzSoJeN+myHkl7l1kCGi7OpKw91XugQ1eon6nZGbTv3kS4FXwAAIBAJN0AEEakhmkm7PeUDMuJGmfb39ahVbV7gpLiwGT5lW37jY7zev2BoKTbtMt6JKMLc9UQsoodKS4eqd4DbSl4hXpXS+zPKklzK0Zp3jlljh9iAACAwYmkGwBCmOzFjmZc0dCevzHMxb794l/9f192spv36ncaHa+w7zlZ1i7F7rL+6BeCG5qFSx7nnHGaXquPPb5rzhmnObpOW6Kjytw2oTjPKG5mebEWTB+T5KsBAADpgqQbAEIkutf4eHdPCnngSKfj9za2duiJ9Q1xnrdbklmX9due2xI8uis/V8uvDl4BT3YDt0RHlQWK9LAi1nzzwEZ2Cysn6tsvbos5D31h5cQErhQAAAw2JN0AECLRvcara/fotkvP6PPy6ff3tEkye2gQmlg2tXXolpVb9HjAnPEDh80eGoSLM91Lbnc7X766Tk1tzr732aeP1D+cPy7o+HdWTfGf98DhTt3/wraoxwhsZJc9JEOL5pRHfeixaE65sockNs8bAAAMLiTdABAi0WT5wJEuST3dxGOttLrp+MnO24k8NFj6u/f8K7/vN8YeFyapV1x8e8mdf0lfuXiy5nwquLQ9sIHb77d8bHScptZTZflLr6qQJD21oSHovmV4ehJu++cAAACmSLoBIESie41zs3pWQt/adbDPEm5Jysvu+Vd6Ig8NDrYf18Ydzbro9JKgPeLRONlL/ljASnq0eBPnT4w+F7vlaJfRcULjll5VoTvmnqVnanZqV0u7JhTnaWHlRFa4AQBAXPgNAgBC2HuNJeNeaEFmjO+Zl93XI7HOPzn6yn5oEG9P7Zr6ZklSlmGSaceZ7CVfsaZO3SefRCTasO7ZTbui/rwoL9voOOHisodk6OY5k3Tfgqm6ec4kEm4AABA3fosAgDDsvcbeAuerxmePLZTU9yOxTj9tuKTEHxrYKfJpw8ySVjsu1l5yS6f2UJvExxJrxNfBdrOVbtM4AACAeFBeDgARVE0t0+UVXn9jrrq9rUadxYtPJqF9ORIrtKu2/dAgdG+1icpJPbO+xxSZjdCy40xX9l/b/on2H+7Qh/sOO7quUGUF0eegFxs+NDCNAwAAiAdJNwBEEdiYa9OOZqP3vL37oP7h/HFRR2LZfy7My9Kh9uP+1yONvsrLzlR7V3fEc4brqh360KBkeI7+9ZdvqfXYiYjHKczL0qyTn7fQsDzbjjNd2X/klXqjuFg+bom+59xbMNToOKZxAAAA8SDpBgCZjbj6oMlsZTYwLtKKs/dkN+/ApDjS6Cv79Yertznuqh340ECSHrruHN2yckvEa3/w2rP9n/ugYSOyrXtatap2j0qG5cibn6t9bclf2Zek3QejJ912pUG0lf6ygp7vFgAAIFlIugEMevGNuDIXuuIcmtQHJsW20GRZks4dX6TThu/RvsOnkuHThmfr3JON20yv5fEbZ2j56vfV1HZqvrY3P0fLr/500Od9d88ho2OuebdRa95tlNSzUm5JvVb2k2FCcfTy98BKg3DX4pG0bH5F2PnhAAAAbiHpBjCoORlxdaZ3hN7afSjmMc/0juj1Wrgk2o3r3He4K+woLlu4FfxYDwFsBw539jpeLK0nS+ULQsrmk+Hys0bFjIlUaeDmQxUAAIBoSLoBDFqxRlx51DPi6vIKrzIzPDp3fJGe3fxRzOM6WXlO9DqlnmsNvE5brBX8WA8BhudkOr5W+3vLHZKhX/7vmTpwpFMf7juiR17Z7vhYsbQcMyt/N33IAAAAkAyMDAMwaDkdcdVy1GzlN1xct89STX2zVtXuUU19s39WtRvXqZDrlE6tjIe+z17Br97aGPO8Z4zKN77GQJakprZOZXg8WjB9jC46vSSu48TSYrjnXDpVabBg+hhVTh5Jwg0AAPoMK90ABi3TEVd23NY9bUbxoXGJ7hn/+GD0edTBcSMdr+BHEq1bugn7e0vW6LQRufwnDAAA9H+sdAMY0BJZQTYdcWXH/c2we3lgnBsrzv9jEBMY53QFP5LGQ9G7g8dy4HCnVtXu0eaGFt0zr6e7upvryy+93+Ti0QAAAJKDZQIAA1aiK8ixVmA96hntZY+UGma4smrHubXiHNhlPBo7zukKfiQ5WfE/l83wSPe/sM3/57KCXC2+uLzX/HF7Tnk83c5NvxcAAIBUIukGMCA56ToeSeBIqdCkz06BA0dKneUdri0G3cvP8g6XZL7i/PRrDSoZkROxwVdhXlbMcwbGOV3Bj2TK6Hy9Vh99NTyS0IKDptYOPbm+QY9+4VwVDcsJami2tq6p18MTE6bfCwAAQCqRdAMYcNxaQZYij5Tyhlkxnza2UM9u/jjm9U0bWyjJfMU5dEU49LyL5kzSq9ubYx5n0ZxJksxX8H0+S6tq90RM9o92ON/TneHpnXBLp+7L/S9s06t3XRp0rtDu4o2HjunB6g9insv+vAAAAP0ZSTeAAcfJnmWT2dimI6XeM2yk9t6eNv2TzFecA4VbqZ99xmnKyvToeHfkAuysTI9mn3GapNgr+JakY8e79cWfbPK/Hi7Z/2Cf2R72M0qH6bZLz9CBw51BDxBCRbsvgXPMu32W/vNPH6rzhC/isXKGZPg/LwAAQH9GIzUAA45be5YDmYyU2mvYWMyOs1ecnTQPsxPkFWvqgprCZWVG/9d16M/tFfxR+cGJv12Sfaj9eNDr4Rq7Hek8YXTNnpOjwUpG5BjFx7ovmRke/eD66VFjfnD9dMZ+AQCAAYGkG8CA49ae5UAmXdB3HDhqdCw7zl5xlpx17Q7tLr6xvjnm+K72rm5trA9Xgh78OUKT7dCowGR/yqjhRtdrxxXnZRvFm8RVTS3T4zfOkDc/OJH35ufqcYP9+gAAAP0F5eUABpzp4wpdjave2qjlq98P6obtzc/R8qs/HZTcDcs2e04ZGBdpz7gJe0X49R0HjOJf33FAF51RIilyo7loHcJDy78XnDtOq96NPZZrwbnjJEl/bTIrv/9rU5vmfCp2abhp2T8AAEB/RtINYMBZuXGncdyiiydHjane2qhbVm7p9XpTW6duWbklaFW1rDBP7zceiXnessK8oD+HJo+x9j7b7JX6vQcNy9pPxkVrNGfCTvY/2GeWRH+wr02XTinVR4bXaRonBe/1BgAAGIgoLwcw4Nhl14nGdfssfeN370WN+cbv3vOXW19RMcrovLHizvLmy5sfea+3Rz2Nzez54KMLhxqd146L1WguFjvZX/t+7FXuwLgJxXkxIuUoDgAAIB2w0g1gwDkWY3+zadzG+uaIe5xth9qPa2N9sy46o0QleWaNwkLjqrc29iovL8zL8o/RijUf/MLJJXr0z/Uxz3vh5J7ScicN5EIV5mX5k/1PDnfGiFZQ3MLKifr2i9vCjgyzZXh64gAAAAYLVroBDAiBjc5GDjdLfs85OS87khrDvdJ23C/f2GUUHxhn760OXXluPZnsF5zsJm7zFuQGjQuTpM+UF8dsxOY5GSfFN6os8Di20/LNjmPHZQ/J0KI55VFjF80pV/YQ/tMDAAAGD1a6AfR74VaKTdhNxSIzbcjVE7fNsFGYHRdtb7W9yp07JEO//N8zdeBIZ8RGYW/tOhhzf7Yl6ZmanSoZkaOS4Tny5udoX1un433dB9uP+xupzZ0ySlt2H4r5nrlTTpXTL72qp1v7Uxsagla8Mzw9Cbf9cwAAgMGCpBtAvxapC3csw3IyNWtS9AZclZNH6pFXtsc8lt3Iq73LZ3RuOy7W3mpLPQ3bMk7OuY5k78F2o/MGNmeLVL5uwi5P//SYAqP40LilV1Xojrln6ZmandrV0q4JxXlaWDmRFW4AADAokXQD6LcS6cKdlRk7wZs1aaQK87Ki7usuysvyJ+/eghwdjLEH3I6TzPdWv7b9QNSRWLUfHzI6TqDA8vVY+9ZD2eXpLe1dRvHh4rKHZOjmOZMcnRcAACAdkXQD6LcS6cJ9KKBMOpLMDI8evPbssCPDbA9ce7Y/CZ4+tlDbDEaGTT+5l9x0b3XgantZQa6Wza8I2tNtXgZ/ir3KPTQrU4/ePEMHjnaqZFiO7vjtO9rX1hH2QYZHPXvK7UZqJcMMG8cZxgEAAAxG1PoB6LcS6cItSU2tsedBV00t0+M3zpA3pGlYWUFu0IxuSRpfaDbqyo67oLxYZQWRR4OFv+YOLVm5RdVbG/2vTRwZ34gtS1Jja4cyMnrK1y86o0TLr+7ZUx16TeG6pncdN+sSbxoHAAAwGJF0A+i3EunCLUkHjpiVR1dNLdNr37hUzy2apR9cP13PLZqlV++6NGS1WdpiWOZtx2VmeLRsfvgkNxJ7BXrFmjr/fPAvzJxg+O7wAh9eVE0t02M3zpC3IPi7Ddc1/anXdhgd3zQOAABgMKK8HEC/Za8UN7WGL4eO5ZDhnmSpJ0GOVoouSUc7TxgdKzDOTnKddF+3V6jt8vjajw4ZvS+S0IcXVVPLdHmFV5sbWqLuJd+x/6jR8U3jAAAABiOSbgD9lr1SvGTllri6cFvxZOpRlIww3OMcEhea5H6477AeeaU+5nHsFWqTMvlwQvdoBzJ5yJBhuDxvGgcAADAYUV4OoF+LVA5torXDfKXbxLhis73V4eLsJHfB9DG66PTTjI5z4HCnVtXu0Zu7WhxdpxR+j7ZThcOyXI0DAAAYjFjpBtDvha4Ur35nj17e9knM97m80K3zxhdLir1C3RMXmUnZfIYneO62U96wXdCdGTUiR3UG3dpHGVYAAAAADEYk3QAGhMBy6Dd3mq38ZnrMV3i7fVbMPc7/8/5eo2P9z/t7demU0sjXZVA274vjicE100frkrNKI16/Ux8fNNuDbhoHAAAwGJF0A3BF1wmfnqnZqV0t7ZpQnKeFlROVPSQ5O1imjS3UM9ptFGeiemujlq+uU1PbqeTRm5+r5VcHrxT/5W8HjI5nEhepwVqGJ76EW5KumzFWcz5lVrpu4pjhKDDTOAAAgMGIpBtAwh54sU5PbWgISha//eI2LZpTrqVXVbh+voOGXclN4qq3NuqWlVt6vd7U1qFbVm4JmtXdcdyse3m4uHAr6aFl8wcOdyZUUp7hYGXfxJBMs4cmpnEAAACDEUk3gIQ88GKdnljf0Ot1nyX/624n3ofaj7sS1+2z9I3fvRc1Zunv3tPlFV5lZnhUkDNEh47FXtUtyAn+V2v11sZeK9plAXuu7bL5VbV7Yh47mpodB3TgaKdr5eWXVZTqvzbsNIoDAABAeCxPAIhb1wmfntrQO+EO9NSGBnWd8Ll6Xo/him6suI07mmMm5gfbj2vjjmZJUvEIsw7qgXHVWxu1ZOWWXjO6m1o7tGTlFlVvbfS/FjpP26lHXqnXV39Vqxue2qjZD60LOnY8LjlzlKtxAAAAgxFJN4C4PVOzM+b+Y5/VExeq22eppr5Zq2r3qKa+Wd0ONjLPDDN3Op64mvpmo+PYcblZmUbxdly3z9KKNXVhG6XZr61YU+f/7HZXczeKxO2k/sV398b9Pc+aNFKFedHHgRXlZWnWpOjzvgEAAAYzyssBxG1nc3tccbHKrWMyzRtjxjk8kGE23HasS6tq9+jA4c5eK9yhR21s7dDmhhZVTh5p1NXclP3e2557O+jBiJPvOTPDowevPTvsnnfbA9eenXAZOwAAQDpjpRtA3CzLLC0MjHNSbh3Jpp1mK9Sx4ionlRgdx44bZrjS/X7jEX31V7XGTdH2Hz71Xdhdzb0FiZWa20IXtp18z/b1PH7jDHnzg6+nrCA3qMkcAAAAwmOlG0Dchuea/SvEjotVbu1RT7m13bgsMtOV1ehxsyb3lE9H29ddmJelWScbnZUMzzE8rzOhe7lDu5pv3HFAz23+2JVzOfuew1+PW43aAAAABgNWugHEzTTnsuM2N7QYl1tHY3f7jiVWnF0+Hc1/XDNVmxtatKp2j97bc8jovE5keKTzJhSFvbbKySO1YPoYXVButiJvyvR7jnQ9dik8AAAAYmOlG0DcivLMVn7tuMAy6mjCxQXOuS7Mid7cyzZjfO9kNlTV1DJ95eJyPbm+IWgF3qOeUVj3v7At6oOCRPks6a1dB6M+IAgt7XaL6f0AAABA/Ei6AcRt5LBsR3GmI7FC48I1XjOxcuNOLbp4ctSY6q2NvRJuqWc1eG3dfkfni1es5Pe8CUUJN1YLJ9ERZQAAAIiN8nIAcXO6ch1rJJZHPQ26LggY9RWp8ZqJN3YejPrzaHvM+1Ks5PeNhhbXrzH0ewYAAEBykHQDiNv6v33iKM4eiSX1bnFm/3nZ/Ar/fuFEk+Jh2dG7jcfaY55s4R4yhFOz44Dr5756Whn7sgEAAPoASTeAuO05ZDanOzAu0kgsb0GuHgsZQZVoUnzNuWOi/jyVe5rDPWSIHe2e1e80qjt0nhgAAABcx55uAHFrPRZ51Fa0ONMRVIkmxUMyoj9X7Is9zWUFubp6WplWv9MY9ADBW5CrZfMrjOZczywv1iOvuHtddvdy007wAAAAiI/jle7169dr/vz5Gj16tDwej55//vmgn1uWpXvvvVdlZWUaOnSoLrvsMn344Ydhj9XZ2anp06fL4/GotrY26Gfvvvuu5syZo9zcXI0bN04PP/yw00sFkGSZMZLaaHEmI6gSTYr3tUVP2mPtMY/XmIIc/eD66Xpu0Sy9etelWnpVhV6961I9t2hW0OsmCbck+brNVqS/ccWZ+sH103XbJdGbx9noXg4AAJB8jpPuo0ePatq0aXr00UfD/vzhhx/WD3/4Qz3++OPatGmThg0bpiuuuEIdHb1/ubvzzjs1evToXq+3tbVp7ty5mjBhgt566y195zvf0fLly/Xkk086vVwASTRqhNnIMNO4UIkmxW9/FL2RWrQ95onwFgzt9TAhkTnXv39nj1HcX/cd1oLpY3TR6acZxdO9HAAAIPkcJ91XXnmlvvWtb+nv//7ve/3Msix9//vf1913360FCxbonHPO0S9+8Qvt3bu314r4H//4R7300kv67ne/2+s4v/zlL9XV1aWf/vSn+vSnP63rr79e//f//l9973vfc3q5AJJo/jm9H5olEhcq0aTYZH040h7zsoJcfeXicpUVOE9MPzVqhOP3RHO084SjuHi6xAMAACA5XN3T3dDQoKamJl122WX+1woKCjRz5kzV1NTo+uuvlyTt27dPixYt0vPPP6+8vLxex6mpqdHFF1+s7OxTM4CvuOIKPfTQQzp48KCKiop6vaezs1OdnZ3+P7e1tbn50YC01e2zYu6tjqS0YKirceHYSXE8c7rHFZmdt2pqmS49a5SeqdmpXS3tmlCcp4WVE5U9JEN3Vk3xfz9/qmvSmnebYh4vP8/ddhmj8s0SfzvOflixZOWWXvO9nTVwAwAAQKJc/c2wqannl9FRo0YFvT5q1Cj/zyzL0pe//GXdcsstOv/887Vz586wxykvL+91DPtn4ZLuBx54QCtWrHDjYwCDRvXWxl7JbJmDBl/7244Zncc0LpLQxmsb6w/ouTc+jvm+nQfatap2T8yHCeG+h/96tcH/PdjNxv781/1G17u/tTN2kAPnji/Syk27jeJskR5WOGngBgAAgMT1effyH/3oRzp8+LCWLl3q6nGXLl2q22+/3f/ntrY2jRs3ztVzAOmkemujlqzc0qsEu6m1Q0tWbuk1viucNe82Gp1rzbuNWnLJGXFeaQ97T7QkvWB43ufe+EjPvfGRpMgPE5x8D6El6JGYxpkaXWi2Yh8aZ9olHgAAAMnj6pxur9crqad8PNC+ffv8P1u3bp1qamqUk5OjIUOG6PTTT5cknX/++brpppv8xwl3jMBzhMrJyVF+fn7QXwBO6fZZqqlv1qraPXpt+wEtX/1+2D3P9msr1tTFnOP88UGzFWzTOFPDsjMdv8dOoqu3nkrYu32WVqypM/4eioZmh4nsrfXYca2q3aOa+mZXZmFPH1cYd1wiDdwAAACQOFdXusvLy+X1evXyyy9r+vTpknpWnDdt2qQlS5ZIkn74wx/qW9/6lv89e/fu1RVXXKFf//rXmjlzpiSpsrJS3/zmN3X8+HFlZWVJktauXaszzzwzbGk5gOjClU9HY8lsjnOW4WM70zhT184Yq9/X7nX0nsAk+vIKrzIzPNrc0BL1Own9Hlo7zeaSP7v5Iz27OfoKuxPPbtplHHfznElxnwcAAADuc/yr8JEjR1RbW+ufq93Q0KDa2lrt3r1bHo9HX/va1/Stb31Lq1ev1nvvvacvfelLGj16tK655hpJ0vjx4zV16lT/X5/61KckSZMnT9bYsWMlSV/4wheUnZ2tm2++We+//75+/etf6wc/+EFQ+TgAM3b5tNMmZFLsOc6nnzbc6DimcaYuPL1EOUPiy+TtJFoyn1OdyDzrcCvsTu1qaXc1DgAAAH3H8Ur3m2++qUsuucT/ZzsRvummm/T000/rzjvv1NGjR7V48WIdOnRIs2fPVnV1tXJzzfc4FhQU6KWXXtKtt96q8847TyUlJbr33nu1ePFip5cLDGrRyqdNxJrjnGGY95rGOZFI0XZTW08SXTLMbH64HVc4NMvxuSz1dAwPXGF3akJx7ykPicQBAACg7zhOuj/3uc/JsiL/uuvxeHTffffpvvvuMzrexIkTwx7vnHPO0YYNG5xeHoAAscqnozGZ49zVbXYs0zhTr28/oK4Tvrjf33LkZHdx0/z3ZFxxntme7lCm5fqRLKycqG+/uE3RtodneHriAAAA0L/0efdyAMnVdcLnnzfddsxsD3I4V08ri7kqO644T2/tPhTzWONcXoH9/70Ve1xYNM1Hu7Sqdo8+3HfEKP7AyST9wNGuhM4bb5l69pAMLZpTrifWN0SMWTSnXNlxltwDAAAgeUi6gTTywIt1empDQ9QVUVOr32nUnVVToibe180Yq+cNGppdN2Ns4hcU4KOWowm9/8d/rncUf+Bwp1bV7tH6v32S0HljletHs/SqCknqdX8zPD0Jt/1zAAAA9C8k3UCaeODFuqgroU6ZlEPPnDRSHkXfX+05GeemzgRKy53K8Ej3v7AtoWN41DO7O1a5fixLr6rQHXPP8lcyTCjO08LKiaxwAwAA9GMk3UAa6Drh01Mb3Eu4bbHKod/adTBmQzPrZFw8e5kjibdzeTwSrRqw6wTumTdFmxtatP9wh0pH9CTg8TRVyx6SwVgwAACAAYSkG0gDz9TsdKWkPFSscmi7C3gspnGmhmZnunq8cDI8iSfcUs8K99XTynT/C9uCmtq5Mb8bAAAA/R9JN5AGTOczz60YpXnnlKlkWI7u+O072tfWEXal2rQc+oBhYzDTOFMlw832Rl84eaT+6TPj9OG+w3rkldj7uG+7ZLLOGDVCBw53JlRS/vmzTtPV08eodESuDh7t0q3Pbun1Pdvzux+7cQaJNwAAQBpjIyCQBkznM88sL9aC6WN00RklWn51T+Ot0AJn+8/L5lfELH9uOWLWzds0zlRZodl87XPG5vd83tNPM4q/6PTTtGD6GJWMMDt+JPvaOrVg+hhdUF6s+18IPyfdfm3Fmjp1J6NMAQAAAP0CSTeQBhZWTpQnxvZgT8gc56qpZXrsxhnyFgSvGnsLco1XX1NVXl481CwptuMuKC9WWUFuxLHcHgXPJU+ky3igWHPSA+d3AwAAID1RXg6kgcwMj4ZmZaq9qztiTF5WZq+V66qpZbq8wht3g6/RRUNdjTPV2mk2f9yOy8zwaNn8Ci1ZuaVXt/VwK/t2kt7UGr78PpaLTu9pGmc6lzve+d0AAADo/1jpBtLA5oaWqAm3JB3t6g67opqZ4VHl5JFaMH2MKiePdNRR+8JJJa7GmTK9wsA4Jyv7dpLu5FyBCvOyJZmvmLu1sg4AAID+h5VuIA2kakV11uSRKszL0qH2yCvPhXlZmuXiuDBJqpxUYtQYrTIk2Y+2st/ts4Jev7zCq8dunKEVa+qiloiH89fGNkmxV8zdmt8NAACA/oukG0gDqVpRzczw6MFrz9YtK7dEjHnw2rPjmkcdzWfKi3uViYfynIwLZa/sB6re2tgrubZHer1616X+ZPwXr+/UW7sPxbw+u+rAaVk7AAAA0g/l5UAacNoozE1VU8v0+I0z5M0Pbm7mzc/R40kah/XWroMx91pbJ+NCdfss1dQ3a1XtHtXUN+vFdxu1ZOWWXqvZ9kivtXVN/vL7KWX5Rtc3Kv/Uww03GtYBAABg4GKlG0gDqV5RrZpapkvPGqVnanZqV0u7JhTnaWHlRGUPSc5zvb0HzeaS98SdWtUOt6Kd4Qm/Ym6p57tbsaZOl1d4lZnh0bnjCrVy0+6Y5z13XGHQnxNtWAcAAICBi6QbSBP2impoUuk9WSadzBXV6q2NWrbqfe073Ol/7cn1O7RiwaeTct7ajw8Zx113/jj/NS5ZuaVXgh1tRHbgSK/KySNVMtxsVFm4uHBl7QAAAEh/JN1AGunrFWepJ5kNt6d73+FO3bJyi2sl5oGNzsznfnv8712xpi6u8V/SqQZ0/1PXZBT/P3VN+uxZpXGeDQAAAOmEpBtII+HKp//r1YakrXR3+yzd/pt3osbc8Zt3/OXZ8Qr3uUxYlqVVtXt04HCn4/cGshvQvftxq1G8aRwAAADSH0k3kCYilU/bDcGS0bTr9e0HjOaDv779gOZ86rS4zhHpc5lYuWm30R7sSEJHelmGV2EaBwAAgPRH0g2kgWjl04ENwUbkZOnA0U7XGnn99s2PjOPiSboTLQtPRLgGdAU5Zv/KNI0DAABA+uM3Q6CfCNyz7DQp3tzQErV82m4I9sWfbPK/VuZCg7Utu3uP5EokLlSsz5VM4RrQDcvNMnqvaRwAAADSH0k30A+E27PsJCm2G3054UbZuelCebwL6vF8rkTcM2+KSkbkRHzocUH5SK3dtj/mcS4op0s5AAAAeiSvpTEAI/ae5dAVXTsprt7aGPMYJcPMRlkFsku2V6ypU3e0uVlRZBl2RTeNC2U3MOsrJSNytGD6GFVOHhm2yuCmCyfKE+MBgsfTEwcAAABIJN1ASsXaiy0ZJsVxriQHzqGOR86QTFfjQl1QXqzCvL4r1Y6V5GcPydDiOeVRYxbPKU/qiDYAAAAMLPxmCKSQ6V7sWElxU4L7nsOVcXf7LNXUN2tV7R7V1DeHTfwzYi37OoxLFY96yvntLuXRLL2qQl+5uLxXyXyGR/rKxeVaelVFci4SAAAAAxJ7uoEUMt2zHCvu7TgbldlCV3hN95jPmlysrXvbYh5/1uTYyWw4mxtadKj9eFzvNRWuS3ksS6+q0B1zz9IzNTu1q6VdE4rztLByIivcAAAA6IWkG0gSk27kpnuWY8Xta4tvpTt0DrXkbN73qBFDjc5jGhcqkUZqedmZQTPEywpydfW0Mq1+pzHoYUK4LuUmsodk6OY5k+K+PgAAAAwOJN1AEpiuFF9QXqyyglw1tXaE3dcdLikOZ1icc6EtBa/wms77vrzCq8wMj0pGmDVwM40LlUgjNY+kX948s9dc8jurpsQ9mg0AAABwilpIwGVOupFnZni0bH7PHuDQtM9J2fPfTx+T6GVLcr7HvHS4WTJtGhfKfigRT0p8tKtbPsvq1Y08M8Ojyskjo3YpBwAAANxC0g24KJ5u5FVTy/TYjTPkLQhe1fUW5BrP0M6IM3G0V67t63G6x/yEz2cUbxonBTdw29zQonvmTfFfq1OP/6U+aiM4AAAAINkoLwdc5GSluHLySP/rVVPLdHmFN+6y540NzXFdb+j1ON1j/vu39xjF//7tPfrsmaUx4yKV5S++uLzXXmwTr9U367X6Zv9x4tm7DQAAACSCpBtwUSLdyO2y53jsOXgsrveFXo/TPeYfG563rrFVq2r3RH2YEK2B25PrG/ToF2aoaFi29h/u0Jp39upP2/Y7+IThG8EBAAAAyUZ5OeAit7qRO2UlWDltX4/TPeZjC80+x9/2HdVXf1WrG57aqNkPrQva1y6ZleXf/0KdLigv1oLpY3RVHElzpPJ+AAAAIJlIugFDgXuNI+0RjtX4y6OeMudY3cidGlMUXxIf7nqc7DH/+xljHZ8zXEM5pw3cygrjG0EWehwAAAAg2SgvBwyYjgCzV4qXrNwS8Vgm3cidqiwv0Y//vMPRe6J1RzfdYz4kw/lzu3Cjx5yW5dsPN5zu8Q49DgAAAJBsrHQDMTgZASb1JKyLLy5XaF6d4ZEWX1yelP3EGZnOk/hY3dFNRms1tcWXvPYaPeawLN9+uBHvowu3y/sBAACASFjpBqKItdc4dMVW6knSn1zf0Os9liU9ub5B544vcj3xPnCk0yjutktO1xmjhjvujh7J27sTK9OOt4GbdOrhxlMbGmS6RTvccQAAAIBkYqUbiMLpXuNYSbql5DTyMl25vej0kqgr107tazNL9iOJt4GbdOrhhpOEO9xxAAAAgGQi6QaicLrXOFaSLiWnkVeqGrjlZcdXLJNoA7doDzdsoXl1rHJ6AAAAIBkoLweicLrX2HSPc7x7oSMJbODmkYKS0WSu8FaMzteqd/Y6eo8bDdxMHm74LOmeeVNUMiLHtXJ6AAAAwCmSbiAKp3uNWwz3VpvGOWGvFId2WfeG6bLultJ85w3JYl2P3cAtGtOHFsXDc7Rg+hjH1wgAAAC4haQbiMLpCnLxsGyj45rGOWW6UuyW0hE5RnHfvGqKSvPdW3H+xDDpNo0DAAAAkoWkG4jByQpy8VDDpNswLh4mK8WuMWxiVlGWr4vOKHHttO/vbXU1DgAAAEgWkm7AgOkK8kvbmoyO99K2Jn12SmkyLrVPNbUeczXO1LHj3a7GAQAAAMlC0g0YMllBfneP2cqqaVw8un1Wn5WX1358yDjuuvPHuXbez0wcqZfq9hvFAQAAAKlE0g24KD83y9U4p6q3NvYqgy9LYiM103njbs8lv+nCifqPP26TFeWwHk9PHAAAAJBKzOkGXPS/Lyp3Nc6J6q2NWrJyS69RWk2tHVqycouqtza6fs5PDpt1YTeNM5U9JEOL50T/DhfPKVf2EP4VBwAAgNTiN1LARUMMkzzTOFPdPksr1tSF7Wtmv7ZiTZ3rK87Fw8xW7E3jnFh6VYW+cnG5QivnMzzSVy4u19KrKlw/JwAAAOAU5eWAizY1tBjHzfnUaa6dd3NDS68V7kCWpMbWDm1uaHG1s/n2/UddjXNq6VUVumPuWXqmZqd2tbRrQnGeFlZOZIUbAAAA/QZJN+Aq05Vkd1ec9x82m0dtGmcqJ8ssuTWNi0f2kAzdPGdS0o4PAAAAJILlIMBFlZPMZlGbxpkqHZHrapypvGyz53amcQAAAEC64TdhIEQiI7dmTR6pwrwsHWo/HjGmMC9Ls1ws8ZakC8qLVVaQq6bWjrBr6B5J3oKez+Km8SOHuhoHAAAApBuSbiBAoiO3MjM8evDas3XLyi0RYx689mzX52ZnZni0bH6FlqzcIo+Ci9ftMy2bX+H6eT9oPOJqHAAAAJBuKC8HTnJr5FbV1DJ95eJyhaa3HvV01U7GvGz7vI/dOEPeguAScm9Brh67cUZSznvseLercQAAAEC6YaUbUOyRWx71jNy6vMIbc7W4emujnljfEPY4T6xv0Lnji5KaeF9e4Y27PN6poVmZrsYBAAAA6YakG5B7I7e6fZa+8bv3op5r6e/eM0re45WZ4XF1LFg0RblmybRpHAAAAJBuKC8H5N7IrY07mqM2UZOkg+3HtXFHs/G19Wc7Dx5zNQ4AAABINyTdgNwbufX69gNGxzGNi0e3z1JNfbNW1e5RTX2zun3uzgQP5HM5DgAAAEg3lJcDcm/k1u7mo0bnM41zKlr39WTs9c7NNHtuZxoHAAAApBuSbkDujdx6b0+r0flM45ywu6+HPjRoau3QLSu39Jof7mQUWiQFQ7NcjQMAAADSDctPwElujNw62mU2Gss0zlSs7uuSeu01dzoKLZxPjnS5GgcAAACkG1a6gQCJjtwalZ9jlGCOys9J9FKDxOq+Ho7TUWjhjCrI0da9ZnEAAADAYMRKNxDCHrm1YPoYVU4e6SgZvePzZ7oaZ8q0+3qowFFo8ZhVXuJqHAAAAJBuSLoBF108pVRDYiTpQzI8unhKqavnNe2+Hkm8SfsNF4x3NQ4AAABINyTdgIsyMzy6efbEqDE3z56YcNfwUBeUF6swL/5mZfEm7b/ctMvVOAAAACDdkHQDLur2WVr9TvTGZKvfaUzK7OyuE86nYXvU08U81ii0SF56v8nVOAAAACDdkHQDLjJpaJbIHupINtY3q91hR3Qno9AiOdxxwtU4AAAAIN2QdAMuamo95mqcqZodBxy/x8kotEjOLBvhahwAAACQbhgZBrio5ajZPGrTOFOmxerXTB+tS84qNRqF1u2zYo5O+8fzxmlNjHJ6Ow4AAAAYjEi6ARcVDzebR20aZyo/16yJ2pSyfC2YPiZmXPXWRq1YUxdUKl9WkKtl8yuCVsYvPL1EedmZUUvbh2Vn6sLTGRkGAACAwYnycsBF3nyzLuCmcabajh13La56a6OWrNzSa296U2uHlqzcouqtp1a2MzM8+t4/Tot6vP/3j9Nc79YOAAAADBQk3Uh73T5LNfXNWlW7RzX1zUnpHG67oLxYZQXRE+pEuoUnW7fP0oo1dWHL1e3XVqypC/oOq6aW6fEbZ2jUiODVe29+jh5PcM84AAAAMNBRXo60Zlom7ZbMDI+Wza/QkpVbJAXvtXajW3gkhXnZrsTF6r5u6VT39crJI/2vV00t0+UV3ph7wAEAAIDBhpVupC0nZdKBEl0Zr5papsdunKFRISXkbnQLj6Q4z2xPd6y4/YejjzuLFpeZ4VHl5JFaMH2MKiePJOEGAAAAxEo30lSsMmmPesqkL6/wBiWHbq6MW5Yv6M8+ny9CZOIOGe7pjhVXOsJsr7lpHAAAADDYsdKNtOSkTNoW78p4qOqtjbpl5RbtOxw8Fmzf4S7d4uA4TiTSNT1wZd/ns+TNz1WkNWqP+veedAAAAKC/YaUbaclpmXS8K+Ohun2WvvG796Ke8xu/ey/mcZyKt2t6uJX9wrws/2fuqz3pAAAAQLpipRtpyWmZdDwr4+FsrG/WofboJdyH2o9rY32z0fWZOm9CkWLlwRmenjhbpJX91pPXXxCy/zuZe9IBAACAdMVKN9KSPbqrqbUj7Oq1Rz1JpF0mnUgDsUCv1X9idJzX6j/RRWeUGMWaeGvXQcXq9+azeuIqJ480WtnPHZKhX/7vmTpwpJNu5AAAAECcHK90r1+/XvPnz9fo0aPl8Xj0/PPPB/3csizde++9Kisr09ChQ3XZZZfpww8/9P98586duvnmm1VeXq6hQ4dq8uTJWrZsmbq6gve/vvvuu5ozZ45yc3M1btw4Pfzww/F9QgxK9uguSb32J4crk3argdjeQ2bJu2mcKacPDUxW9pvaOpXh8dCNHAAAAEiA46T76NGjmjZtmh599NGwP3/44Yf1wx/+UI8//rg2bdqkYcOG6YorrlBHR88v+H/961/l8/n0xBNP6P3339d//ud/6vHHH9e///u/+4/R1tamuXPnasKECXrrrbf0ne98R8uXL9eTTz4Z58fEYGSP7vIWxB7dZa+MJ9pArKzQLHk3jTPl9KGBWyv7AAAAAKJzXF5+5ZVX6sorrwz7M8uy9P3vf1933323FixYIEn6xS9+oVGjRun555/X9ddfr6qqKlVVVfnfM2nSJH3wwQd67LHH9N3vfleS9Mtf/lJdXV366U9/quzsbH36059WbW2tvve972nx4sXxfE70A90+S5sbWrT/cEeflStXTS3TpWeN0jM1O7WrpV0TivO0sHKisocEP2+yV8aXrNySUAOx4jzDLuKGcaacltMzGgwAAADoG67u6W5oaFBTU5Muu+wy/2sFBQWaOXOmampqdP3114d9X2trq4qLT60g1tTU6OKLL1Z2drb/tSuuuEIPPfSQDh48qKKiol7H6OzsVGdnp//PbW1tbnwkuMTN+deJnve/Xm0Ie157ZTw03uvgOouHZceMcRJnyulDA6dJOgAAAID4uNq9vKmpSZI0atSooNdHjRrl/1mo7du360c/+pG+8pWvBB0n3DECzxHqgQceUEFBgf+vcePGxf054C635l/3xXmrppbp1bsu1XOLZukH10/Xc4tm6dW7LjV+MHCovSt2kIM4J5yU0zvd8w4AAAAgPikdGbZnzx5VVVXpH/7hH7Ro0aKEjrV06VK1trb6//roo49cukokIlaXbKln/nV3rNbbLp/XinLezAyPKiePjKuBWPFww/JywzinqqaWad0dn9PCWeM154wSLZw1Xuvu+FzYhwZOknQAAAAA8XG1vNzr9UqS9u3bp7KyU7+w79u3T9OnTw+K3bt3ry655BJdeOGFvRqkeb1e7du3L+g1+8/2OULl5OQoJyc5iQzi52T+deXkkX12XiXpvN58sz3QpnFOPfBinZ7a0OAfH7bhQ+mXm3Zr0ZxyLb2qold81dQyXV7h7fO99gAAAMBg4epKd3l5ubxer15++WX/a21tbdq0aZMqKyv9r+3Zs0ef+9zndN555+lnP/uZMjKCL6OyslLr16/X8ePH/a+tXbtWZ555Ztj93Oi/UtUlu6nN7HimcaYuKC9WYV5W1JiivKyk7JV+4MU6PbG+ode8bp8lPbG+QQ+8WBf2fYms7AMAAACIznHSfeTIEdXW1qq2tlZST/O02tpa7d69Wx6PR1/72tf0rW99S6tXr9Z7772nL33pSxo9erSuueYaSacS7vHjx+u73/2uPvnkEzU1NQXt1f7CF76g7Oxs3XzzzXr//ff161//Wj/4wQ90++23u/Kh0XdS1SW75Uhn7CAHcW5yt5C+R9cJn57a0BA15qkNDeo64UvC2QEAAABE4ri8/M0339Qll1zi/7OdCN900016+umndeedd+ro0aNavHixDh06pNmzZ6u6ulq5uT1J1dq1a7V9+3Zt375dY8eODTq2ZfWkIwUFBXrppZd066236rzzzlNJSYnuvfdexoUNQKnqkp1IF/FERpttbmjRofbjUWMOtR93vaz9mZqdvVa4Q/msnrib50xy7bwAAAAAonOcdH/uc5/zJ8fheDwe3XfffbrvvvvC/vzLX/6yvvzlL8c8zznnnKMNGzY4vTz0M3aX7FtWbgn7c0vJ6ZLtLRgaV1yio82aWo8Zndc0ztTO5nZX4wAAAAC4I6Xdy4FksVfYoykLWWF3Y7TZgSNmo8BM48yZFq0no7gdAAAAQCQk3Ugqe3RXJB45HxnW7bNUU9+sVbV7VFPfHHHs17L5Fb1mUAeeN3CF3a3RZoeOGc7pNowzNX1soatxAAAAANzh6sgwIJTbI8OclH/bc6hN4t26TtMiebf7g48uynM1DgAAAIA7SLqRVG6ODLPLv0PXmu3y78dunBE28TaZQ+3WdVZOKtEjr9THPE7lpBKj85myy+mjPTgILacHAAAAkHyUlyOp3BoZlkj5t8kcareuc9bkkcrLzowaMyw7U7Nc7FwuOS+nBwAAANA3SLqRVBeUF6swLytqTGFeVswVWCfl3/GwV4qjJa2mK8XZQ6L/3yorxs/jZZfThzaQKyvIDVsFAAAAACD5KC9HypmsvbpZph6OvVK8ZOUWeRTc49u+PpOV4lTN6baZltMDAAAA6BusdCOpTJLQgyeT0GjcKv+Oxl4p9oasFHsdrBSnak53IJNyegAAAAB9g5VuJFXjIbPkMlacXf7d1NoRdl+3Rz3Jcbjy726fZbzym+hKcctRs1FgpnEAAAAABjaSbiTV2x8dNI679ryxEX8eb/m3kxFjgeeKt/S7eHiOq3EAAAAABjbKy5FU4Val441zWv5tjxgLbcBmjxir3tpoeHXmvPlm5e2mcQAAAAAGNla6kVTlI4e5Gmda/h1rxJhHPSPGLq/wurrn+bwJRfJ4JCvKUwSPpycOAAAAQPpjpRtJtbByomLltBmenjhTJo3Ckj1iLJI3GlqiJtxST0L+hsvnBQAAANA/kXQjqbKHZOjzU0qjxnx+SmnM2dZOJXvEWCSv7zjgahwAAACAgY2kG0nV7bO0dU9b1Jite9rU7TPd/W2mMCfL1ThTe1raXY0DAAAAMLCRdCOpYpV5S8kp8177132uxpnyuRwHAAAAYGAj6UZSparMe2fzUVfjTHkss6ZspnEAAAAABjaSbiRV6Qiz0VimcaaGZmW6GmfKZzgkzTQOAAAAwMBG0o2kuqC8WGUFuYq0ruuRVFbQM/bLTXM/7XU1zlTzkU5X4wAAAAAMbCTdSKrMDI+Wza+IuK5rSVo2v8LVWdmSNLYoz9U4Ux3Hu12NAwAAADCwkXQjLdkr7NEkY4W9q9usRZppHAAAAICBjaQbSdXts7RiTV3En3skrVhT5/rIMHuFPVpZezJW2L35ZnvTTeMAAAAADGwk3UiqWCPDLCVnZJgkVU0t02M3zui14l1WkKvHbpyhqqllrp9z5qQSV+MAAAAADGxDUn0B6D+6fZY2N7Ro/+EOlY7oKb1OdCU4VSPDbFVTy3R5hdf1zxXJTRdO1H/8cZusKAv3Hk9PHAAAAID0R9I9CIVLrtfWNWnFmrqgVemyglwtm1+R0IpwqkaGBcrM8Khy8sikHT9Q9pAMLZ5TrifWN0SMWTynXNlDKDIBAAAABgOS7kGmemtjr+S6MC9Lh9qP94ptau3QkpVbEirFthuaNbV2hO1g7pHkTUJDs1RaelWFJOmpDQ0K3Kqe4ZEWzSn3/xwAAABA+vNYVrRC2IGrra1NBQUFam1tVX5+fqovp1+o3tqoJSu3RBzfFY6dFL9616Vxl2Tb55UUdG77aMnaX51qXSd8eqZmp3a1tGtCcZ4WVk5khRsAAABIE6Y5Jyvdg4TdRdzpE5bARmfxlmjbDc1CV9i9LpSv92fZQzJ085xJqb4MAAAAAClE0j1IxOoiHkuijc76uqEZAAAAAPQHJN2DRKJJsxuNzvqyoRkAAAAA9Ack3YNEvElzOjY6AwAAAIC+QlenQcLuIu6kmNuOXTa/gjJwAAAAAIgDSfcgkZnh0bL5PaOqQtNn+8+FeVlBr3sLctO2szgAAAAA9AXKyweRWF3EaXQGAAAAAO5iTvcg1O2zSK4BAAAAIAHM6UZEdBEHAAAAgL7Bnm4AAAAAAJKEpBsAAAAAgCShvBx9JlV7ydnDDgAAACBVSLrRJ6q3Nvbqml52smt6MkeSpeq8AAAAACBRXo4+UL21UUtWbglKfCWpqbVDS1ZuUfXWxrQ6LwAAAADYSLqRVN0+SyvW1CncXDr7tRVr6tTtc3dyXarOCwAAAACBSLoHoW6fpZr6Zq2q3aOa+uakJp6bG1p6rTQHsiQ1tnZoc0NLWpwXAAAAAAKxp3uQ6es9zvsPR05844nr7+cFAAAAgECsdA8iqdjjXDoi19W4/n5eAAAAAAhE0p1CfVnmnao9zheUF6usIFeRBnR51LPSfkF5cVqcFwAAAAACUV6eIn1d5u1kj3Pl5JGunTczw6Nl8yu0ZOUWeaSgpN9OiJfNr3B9bnaqzgsAAAAAgVjpToFUlHmnco9z1dQyPXbjDI3Kzwl6fVR+jh67cYZrDxlCKwcur/DqsRtnyFsQXELuLch19bwAAAAAEAkr3X0sVpm3Rz1l3pdXeF1dhe0fe5xDP497ny9a5cCrd12qzQ0t2n+4Q6UjekrKWeEGAAAA0BdY6e5jqRpllco9zvbKflNb8Ofe1+bOyn6syoG1dU2qnDxSC6aPUeXkkSTcAAAAAPoMSXcfS1WZt73HWYq83pyMPc7JbuCWqgZxAAAAAGCCpLuPpbLM295b3Zd7nJO9sp+qygEAAAAAMMGe7j5ml3k3tXaEXZ31qCcJTtYoq6qpZbq8wttne5yTvbKfygZxAAAAABALSXcf6w+jrDIzPK6OBYsm2Sv7/aNBHAAAAACER3l5CqSizDtV7JX9aBJp4JbKBnEAAAAAEAsr3SnS12XeqZKZ4dHV08r0xPqGiDFXTyuL+3P3h8oBAAAAAIiEle4Ussu803mUVbfP0up3oo8EW/1OY0LdxQdT5QAAAACAgYWVbiRVrO7i0qnu4onsMx8slQMAAAAABhaSbiRVX3YX78sGcQAAAABggvJyJBXdxQEAAAAMZiTdSKoLyotVmJcVNaYoL4vu4gAAAADSEkk3kq7rhC/qzztj/BwAAAAABiqSbiTVxvpmtXd1R41p7+rWxvrmProiAAAAAOg7JN1Iqtd3HHA1DgAAAAAGEpJuJNXeg8dcjQMAAACAgYSkG0k1unCoq3EAAAAAMJCQdCOpLpxc4mocAAAAAAwkJN1IqlmTR8YcGVaYl6VZk0f20RUBAAAAQN8h6UZSZWZ49E/nj40a80/nj1VmhqePrggAAAAA+g5JN5Kq22dp9TuNUWNWv9Oobp/VR1cEAAAAAH2HpBtJtbmhRY2tHVFjGls7tLmhpY+uCAAAAAD6Dkk3kmr/4egJt9M4AAAAABhISLqRVCXDclyNAwAAAICBhKQbyWXaH40+agAAAADSEEk3kurAkU5X4wAAAABgICHpRlKVjsh1NQ4AAAAABhKSbiTVBeXFKivIjVg97pFUVpCrC8qL+/KyAAAAAKBPkHQjqTIzPFo2v0JS723b9p+Xza9QZgabugEAAACkH8dJ9/r16zV//nyNHj1aHo9Hzz//fNDPLcvSvffeq7KyMg0dOlSXXXaZPvzww6CYlpYWffGLX1R+fr4KCwt1880368iRI0Ex7777rubMmaPc3FyNGzdODz/8sPNPh36hamqZHrtxhrwFwSXk3oJcPXbjDFVNLUvRlQEAAABAcg1x+oajR49q2rRp+pd/+Rdde+21vX7+8MMP64c//KF+/vOfq7y8XPfcc4+uuOIK1dXVKTe3J+n64he/qMbGRq1du1bHjx/XP//zP2vx4sV69tlnJUltbW2aO3euLrvsMj3++ON677339C//8i8qLCzU4sWLE/zISIWqqWW6vMKrzQ0t2n+4Q6UjekrKWeEGAAAAkM48lmVZcb/Z49Hvf/97XXPNNZJ6VrlHjx6tO+64Q//f//f/SZJaW1s1atQoPf3007r++uu1bds2VVRU6I033tD5558vSaqurtZVV12ljz/+WKNHj9Zjjz2mb37zm2pqalJ2drYk6Rvf+Iaef/55/fWvfzW6tra2NhUUFKi1tVX5+fnxfkQAAAAAAHoxzTld3dPd0NCgpqYmXXbZZf7XCgoKNHPmTNXU1EiSampqVFhY6E+4Jemyyy5TRkaGNm3a5I+5+OKL/Qm3JF1xxRX64IMPdPDgwbDn7uzsVFtbW9BfCK/bZ6mmvlmraveopr5Z3b64n7sAAAAAAKJwXF4eTVNTkyRp1KhRQa+PGjXK/7OmpiaVlpYGX8SQISouLg6KKS8v73UM+2dFRUW9zv3AAw9oxYoV7nyQNFa9tVEr1tSpsbXD/1pZQa6Wza9gbzUAAAAAuCxtupcvXbpUra2t/r8++uijVF9Sv1O9tVFLVm4JSrglqam1Q0tWblH11sYUXRkAAAAApCdXk26v1ytJ2rdvX9Dr+/bt8//M6/Vq//79QT8/ceKEWlpagmLCHSPwHKFycnKUn58f9BdO6fZZWrGmTuEKye3XVqypo9QcAAAAAFzkatJdXl4ur9erl19+2f9aW1ubNm3apMrKSklSZWWlDh06pLfeessfs27dOvl8Ps2cOdMfs379eh0/ftwfs3btWp155plhS8sR2+aGll4r3IEsSY2tHdrc0NJ3FwUAAAAAac5x0n3kyBHV1taqtrZWUk/ztNraWu3evVsej0df+9rX9K1vfUurV6/We++9py996UsaPXq0v8P5lClTVFVVpUWLFmnz5s167bXXdNttt+n666/X6NGjJUlf+MIXlJ2drZtvvlnvv/++fv3rX+sHP/iBbr/9dtc++GCz/3DkhDueOAAAAABAbI4bqb355pu65JJL/H+2E+GbbrpJTz/9tO68804dPXpUixcv1qFDhzR79mxVV1f7Z3RL0i9/+Uvddttt+vznP6+MjAxdd911+uEPf+j/eUFBgV566SXdeuutOu+881RSUqJ7772XGd0JKB2RGzvIQRwAAAAAILaE5nT3Z8zpDtbtszT7oXVqau0Iu6/bI8lbkKtX77pUmRmevr48AAAAABhQUjKnG8705bzszAyPls2vkNSTYAey/7xsfgUJNwAAAAC4yNU53TCXinnZVVPL9NiNM3qd18ucbgAAAABICsrLU8Celx36xdtrzI/dOCOpCXC3z9LmhhbtP9yh0hG5uqC8mBVuAAAAAHDANOdkpbuPxZqX7VHPvOzLK7xJS4QzMzyqnDwyKccGAAAAAJzCnu4+xrxsAAAAABg8SLr7GPOyAQAAAGDwIOnuY8zLBgAAAIDBg6S7j11QXqyygtxeY7tsHvV0Mb+gvLgvLwsAAAAAkAQk3X2MedkAAAAAMHiQdKeAPS/bWxBcQu4tyE36uDAAAAAAQN9hZFiKVE0t0+UVXuZlAwAAAEAaI+lOIeZlAwAAAEB6o7wcAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkGZLqCxjMun2WNje0aP/hDpWOyNUF5cXKzPCk+rIAAAAAAC4h6U6R6q2NWrGmTo2tHf7XygpytWx+haqmlqXwygAAAAAAbqG8PAWqtzZqycotQQm3JDW1dmjJyi2q3tqYoisDAAAAALiJpLuPdfssrVhTJyvMz+zXVqypU7cvXAQAAAAAYCAh6e5jmxtaeq1wB7IkNbZ2aHNDS99dFAAAAAAgKUi6+9j+w5ET7njiAAAAAAD9F0l3HysdketqHAAAAACg/yLp7mMXlBerrCBXkQaDedTTxfyC8uK+vCwAAAAAQBKQdPexzAyPls2vCNtITerZ071sfgXzugEAAAAgDZB0AwAAAACQJCTdfcweGRaJR4wMAwAAAIB0QdLdxxgZBgAAAACDB0l3H2NkGAAAAAAMHiTdfYyRYQAAAAAweJB09zFGhgEAAADA4EHS3cfskWGSeiXe9p8ZGQYAAAAA6YGkOwWqppbpsRtnyFsQXELuLcjVYzfOUNXUshRdGQAAAADATUNSfQGDVdXUMl1e4dXmhhbtP9yh0hE9JeWscAMAAABA+iDpTqHMDI8qJ49M9WUAAAAAAJKE8nIAAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkmRIqi8gWSzLkiS1tbWl+EoAAAAAAOnGzjXt3DOStE26Dx8+LEkaN25ciq8EAAAAAJCuDh8+rIKCgog/91ix0vIByufzae/evRoxYoQ8Hk+qLwcntbW1ady4cfroo4+Un5+f6suBy7i/6Y37m964v+mPe5zeuL/pjfvbP1mWpcOHD2v06NHKyIi8czttV7ozMjI0duzYVF8GIsjPz+dfGGmM+5veuL/pjfub/rjH6Y37m964v/1PtBVuG43UAAAAAABIEpJuAAAAAACShKQbfSonJ0fLli1TTk5Oqi8FScD9TW/c3/TG/U1/3OP0xv1Nb9zfgS1tG6kBAAAAAJBqrHQDAAAAAJAkJN0AAAAAACQJSTcAAAAAAElC0g0AAAAAQJKQdCNh69ev1/z58zV69Gh5PB49//zzvWK2bdumq6++WgUFBRo2bJg+85nPaPfu3f6fd3R06NZbb9XIkSM1fPhwXXfdddq3b18ffgpEE+seHzlyRLfddpvGjh2roUOHqqKiQo8//nhQDPe4f3rggQf0mc98RiNGjFBpaamuueYaffDBB0ExJvdu9+7dmjdvnvLy8lRaWqp/+7d/04kTJ/ryoyCMWPe3paVF/+f//B+deeaZGjp0qMaPH6//+3//r1pbW4OOw/3tn0z+/2uzLEtXXnll2H+Hc3/7L9N7XFNTo0svvVTDhg1Tfn6+Lr74Yh07dsz/85aWFn3xi19Ufn6+CgsLdfPNN+vIkSN9+VEQhsn9bWpq0sKFC+X1ejVs2DDNmDFD//3f/x0Uw/3t/0i6kbCjR49q2rRpevTRR8P+vL6+XrNnz9ZZZ52lP//5z3r33Xd1zz33KDc31x/z9a9/XWvWrNFvf/tb/eUvf9HevXt17bXX9tVHQAyx7vHtt9+u6upqrVy5Utu2bdPXvvY13XbbbVq9erU/hnvcP/3lL3/Rrbfeqo0bN2rt2rU6fvy45s6dq6NHj/pjYt277u5uzZs3T11dXXr99df185//XE8//bTuvffeVHwkBIh1f/fu3au9e/fqu9/9rrZu3aqnn35a1dXVuvnmm/3H4P72Xyb//7V9//vfl8fj6fU697d/M7nHNTU1qqqq0ty5c7V582a98cYbuu2225SRcerX/C9+8Yt6//33tXbtWv3hD3/Q+vXrtXjx4lR8JAQwub9f+tKX9MEHH2j16tV67733dO211+of//Ef9fbbb/tjuL8DgAW4SJL1+9//Pui1f/qnf7JuvPHGiO85dOiQlZWVZf32t7/1v7Zt2zZLklVTU5OsS0Wcwt3jT3/609Z9990X9NqMGTOsb37zm5ZlcY8Hkv3791uSrL/85S+WZZnduxdffNHKyMiwmpqa/DGPPfaYlZ+fb3V2dvbtB0BUofc3nN/85jdWdna2dfz4ccuyuL8DSaT7+/bbb1tjxoyxGhsbe/07nPs7sIS7xzNnzrTuvvvuiO+pq6uzJFlvvPGG/7U//vGPlsfjsfbs2ZPU64Uz4e7vsGHDrF/84hdBccXFxdZTTz1lWRb3d6BgpRtJ5fP59MILL+hTn/qUrrjiCpWWlmrmzJlBpW1vvfWWjh8/rssuu8z/2llnnaXx48erpqYmBVcNpy688EKtXr1ae/bskWVZeuWVV/S3v/1Nc+fOlcQ9HkjssuLi4mJJZveupqZGZ599tkaNGuWPueKKK9TW1qb333+/D68esYTe30gx+fn5GjJkiCTu70AS7v62t7frC1/4gh599FF5vd5e7+H+Diyh93j//v3atGmTSktLdeGFF2rUqFH67Gc/q1dffdX/npqaGhUWFur888/3v3bZZZcpIyNDmzZt6tsPgKjC/X/4wgsv1K9//Wu1tLTI5/PpV7/6lTo6OvS5z31OEvd3oCDpRlLt379fR44c0YMPPqiqqiq99NJL+vu//3tde+21+stf/iKpZ69Kdna2CgsLg947atQoNTU1peCq4dSPfvQjVVRUaOzYscrOzlZVVZUeffRRXXzxxZK4xwOFz+fT1772NV100UWaOnWqJLN719TUFPQLu/1z+2foH8Ld31AHDhzQ/fffH1SWyP0dGCLd369//eu68MILtWDBgrDv4/4OHOHu8Y4dOyRJy5cv16JFi1RdXa0ZM2bo85//vD788ENJPfextLQ06FhDhgxRcXEx97gfifT/4d/85jc6fvy4Ro4cqZycHH3lK1/R73//e51++umSuL8DxZBUXwDSm8/nkyQtWLBAX//61yVJ06dP1+uvv67HH39cn/3sZ1N5eXDJj370I23cuFGrV6/WhAkTtH79et16660aPXp00Aop+rdbb71VW7duDVohQfqIdX/b2to0b948VVRUaPny5X17cUhYuPu7evVqrVu3LmjvJwaucPfY/j3rK1/5iv75n/9ZknTuuefq5Zdf1k9/+lM98MADKblWOBfp39H33HOPDh06pD/96U8qKSnR888/r3/8x3/Uhg0bdPbZZ6foauEUK91IqpKSEg0ZMkQVFRVBr0+ZMsXfvdzr9aqrq0uHDh0Kitm3b1/YUjj0L8eOHdO///u/63vf+57mz5+vc845R7fddpv+6Z/+Sd/97nclcY8Hgttuu01/+MMf9Morr2js2LH+103undfr7dXN3P4z97d/iHR/bYcPH1ZVVZVGjBih3//+98rKyvL/jPvb/0W6v+vWrVN9fb0KCws1ZMgQ/5aB6667zl+ayv0dGCLd47KyMkmK+XvW/v37g35+4sQJtbS0cI/7iUj3t76+Xo888oh++tOf6vOf/7ymTZumZcuW6fzzz/c3t+X+Dgwk3Uiq7OxsfeYzn+k1/uBvf/ubJkyYIEk677zzlJWVpZdfftn/8w8++EC7d+9WZWVln14vnDt+/LiOHz8e1CVVkjIzM/1P4LnH/ZdlWbrtttv0+9//XuvWrVN5eXnQz03uXWVlpd57772g/+ivXbtW+fn5vX4RRN+KdX+lnhXuuXPnKjs7W6tXrw6aLCFxf/uzWPf3G9/4ht59913V1tb6/5Kk//zP/9TPfvYzSdzf/i7WPZ44caJGjx4d9fesyspKHTp0SG+99Zb/5+vWrZPP59PMmTOT/yEQUaz7297eLklRf8fi/g4QqezihvRw+PBh6+2337befvttS5L1ve99z3r77betXbt2WZZlWb/73e+srKws68knn7Q+/PBD60c/+pGVmZlpbdiwwX+MW265xRo/fry1bt06680337QqKyutysrKVH0khIh1jz/72c9an/70p61XXnnF2rFjh/Wzn/3Mys3NtX784x/7j8E97p+WLFliFRQUWH/+85+txsZG/1/t7e3+mFj37sSJE9bUqVOtuXPnWrW1tVZ1dbV12mmnWUuXLk3FR0KAWPe3tbXVmjlzpnX22Wdb27dvD4o5ceKEZVnc3/7M5P+/oRTSvZz727+Z3OP//M//tPLz863f/va31ocffmjdfffdVm5urrV9+3Z/TFVVlXXuuedamzZtsl599VXrjDPOsG644YZUfCQEiHV/u7q6rNNPP92aM2eOtWnTJmv79u3Wd7/7Xcvj8VgvvPCC/zjc3/6PpBsJe+WVVyxJvf666aab/DE/+clPrNNPP93Kzc21pk2bZj3//PNBxzh27Jj1r//6r1ZRUZGVl5dn/f3f/73V2NjYx58EkcS6x42NjdaXv/xla/To0VZubq515plnWv/v//0/y+fz+Y/BPe6fwt1XSdbPfvYzf4zJvdu5c6d15ZVXWkOHDrVKSkqsO+64wz9yCqkT6/5G+v+2JKuhocF/HO5v/2Ty/99w7wkd+8j97b9M7/EDDzxgjR071srLy7MqKyuDFjYsy7Kam5utG264wRo+fLiVn59v/fM//7N1+PDhPvwkCMfk/v7tb3+zrr32Wqu0tNTKy8uzzjnnnF4jxLi//Z/HsizL7dVzAADw/2/fDgkAAAAABP1/7QobvDAIAODpBgAAgI3oBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgEkSp7/Bi3WIFQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -993,22 +828,22 @@
    "source": [
     "> Kan du gætte, hvorfor prikkerne danner lodrette linjer på denne måde?\n",
     "\n",
-    "Vi har observeret sammenhængen mellem et kunstigt konstrueret begreb som løn og den observerede variabel *højde*. Lad os også undersøge, om de to observerede variable, såsom højde og vægt, korrelerer med hinanden:\n"
+    "Vi har observeret sammenhængen mellem et kunstigt konstrueret begreb som løn og den observerede variabel *højde*. Lad os også undersøge, om de to observerede variable, som højde og vægt, også korrelerer:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Desværre fik vi ingen resultater - kun nogle mærkelige `nan`-værdier. Dette skyldes, at nogle af værdierne i vores serie er udefinerede, repræsenteret som `nan`, hvilket gør, at resultatet af operationen også bliver udefineret. Ved at kigge på matricen kan vi se, at `Weight` er den problematiske kolonne, fordi selvkorrelationen mellem `Height`-værdier er blevet beregnet.\n",
+    "Desværre fik vi ingen resultater - kun nogle mærkelige `nan`-værdier. Dette skyldes, at nogle af værdierne i vores serie er udefinerede, repræsenteret som `nan`, hvilket gør, at resultatet af operationen også bliver udefineret. Ved at kigge på matricen kan vi se, at `Weight` er den problematiske kolonne, fordi selvkorrelationen mellem `Height`-værdierne er blevet beregnet.\n",
     "\n",
-    "> Dette eksempel viser vigtigheden af **databehandling** og **datarydning**. Uden ordentlige data kan vi ikke beregne noget som helst.\n",
+    "> Dette eksempel viser vigtigheden af **databehandling** og **rensning**. Uden ordentlige data kan vi ikke beregne noget.\n",
     "\n",
     "Lad os bruge metoden `fillna` til at udfylde de manglende værdier og beregne korrelationen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,32 +888,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Der er faktisk en korrelation, men ikke så stærk som i vores kunstige eksempel. Faktisk, hvis vi ser på spredningsdiagrammet af den ene værdi mod den anden, ville forholdet være meget mindre tydeligt:\n"
+    "Der er faktisk en korrelation, men ikke så stærk som i vores kunstige eksempel. Faktisk, hvis vi ser på spredningsdiagrammet af den ene værdi mod den anden, ville sammenhængen være meget mindre tydelig:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1089,14 +922,14 @@
    "source": [
     "## Konklusion\n",
     "\n",
-    "I denne notebook har vi lært, hvordan man udfører grundlæggende operationer på data for at beregne statistiske funktioner. Vi ved nu, hvordan man anvender et solidt apparat af matematik og statistik til at bevise nogle hypoteser, samt hvordan man beregner konfidensintervaller for vilkårlige variable baseret på et datasæt.\n"
+    "I denne notebook har vi lært, hvordan man udfører grundlæggende operationer på data for at beregne statistiske funktioner. Vi ved nu, hvordan man anvender et solidt apparat af matematik og statistik til at bevise nogle hypoteser, og hvordan man beregner konfidensintervaller for vilkårlige variable givet et datasæt.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os ikke ansvar for eventuelle misforståelser eller fejltolkninger, der opstår som følge af brugen af denne oversættelse.\n"
+    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\n"
    ]
   }
  ],
@@ -1119,11 +952,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:00:28+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:35:49+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "da"
   }
diff --git a/translations/da/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/da/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index d843442e..fc13cfdc 100644
--- a/translations/da/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/da/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "I dette datasæt er kolonnerne som følger:  \n",
-    "* Alder og køn er selvforklarende  \n",
-    "* BMI er body mass index  \n",
-    "* BP er gennemsnitligt blodtryk  \n",
-    "* S1 til S6 er forskellige blodmålinger  \n",
-    "* Y er det kvalitative mål for sygdomsprogression over et år  \n",
+    "I dette datasæt er kolonnerne som følger:\n",
+    "* Alder og køn er selvforklarende\n",
+    "* BMI er kropsmasseindeks\n",
+    "* BP er gennemsnitligt blodtryk\n",
+    "* S1 til S6 er forskellige blodmålinger\n",
+    "* Y er det kvalitative mål for sygdomsprogression over et år\n",
     "\n",
     "Lad os undersøge dette datasæt ved hjælp af sandsynligheds- og statistikmetoder.\n",
     "\n",
-    "### Opgave 1: Beregn gennemsnitsværdier og varians for alle værdier  \n"
+    "### Opgave 1: Beregn gennemsnitsværdier og varians for alle værdier\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -592,10 +592,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Konklusioner:\n",
-    "* Alder - normal\n",
-    "* Køn - ensartet\n",
-    "* BMI, Y - svært at vurdere\n"
+    "Konklusioner:  \n",
+    "* Alder - normal  \n",
+    "* Køn - ensartet  \n",
+    "* BMI, Y - svært at sige  \n"
    ],
    "metadata": {}
   },
@@ -604,7 +604,7 @@
    "source": [
     "### Opgave 4: Test korrelationen mellem forskellige variabler og sygdomsprogression (Y)\n",
     "\n",
-    "> **Tip** Korrelationsmatrixen vil give dig den mest nyttige information om, hvilke værdier der er afhængige.\n"
+    "> **Tip** En korrelationsmatrix vil give dig den mest nyttige information om, hvilke værdier der er afhængige.\n"
    ],
    "metadata": {}
   },
@@ -846,7 +846,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Konklusion:\n",
+    "Konklusion:  \n",
     "* Den stærkeste korrelation med Y er BMI og S5 (blodsukker). Dette virker rimeligt.\n"
    ],
    "metadata": {}
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os ikke ansvar for eventuelle misforståelser eller fejltolkninger, der opstår som følge af brugen af denne oversættelse.\n"
+    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:22:44+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:36:18+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "da"
   }
diff --git a/translations/de/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/de/1-Introduction/04-stats-and-probability/assignment.ipynb
index d822cca1..8bf8c4cf 100644
--- a/translations/de/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/de/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -173,7 +173,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Aufgabe 2: Boxplots für BMI, BP und Y abhängig vom Geschlecht erstellen\n"
+    "### Aufgabe 2: Erstelle Boxplots für BMI, BP und Y in Abhängigkeit vom Geschlecht\n"
    ],
    "metadata": {}
   },
@@ -201,9 +201,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Aufgabe 4: Teste die Korrelation zwischen verschiedenen Variablen und dem Krankheitsverlauf (Y)\n",
+    "### Aufgabe 4: Testen Sie die Korrelation zwischen verschiedenen Variablen und dem Krankheitsverlauf (Y)\n",
     "\n",
-    "> **Hinweis** Eine Korrelationsmatrix liefert die nützlichsten Informationen darüber, welche Werte voneinander abhängig sind.\n"
+    "> **Tipp** Eine Korrelationsmatrix liefert Ihnen die nützlichsten Informationen darüber, welche Werte voneinander abhängig sind.\n"
    ],
    "metadata": {}
   },
@@ -226,7 +226,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, beachten Sie bitte, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n"
+    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, weisen wir darauf hin, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n"
    ]
   }
  ],
@@ -252,8 +252,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:17:40+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:03:05+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "de"
   }
diff --git a/translations/de/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/de/1-Introduction/04-stats-and-probability/notebook.ipynb
index 7050e9c3..6ee93c71 100644
--- a/translations/de/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/de/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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Xl7d4TDgcVigUarYBAICu6aa2HtjY2KglS5Zo2rRpGjNmTIvrAoGAEhISmu1LSEhQIBBo8Ri/36/ly5e3dTQgqvH/2AFbnfG/QeurOW2+MpKbm6uqqioVFxe35zySpIKCAgWDwaatpqam3c8BAAA6hjZdGVm8eLF27dql/fv3a8CAAa2uTUxMVF1dXbN9dXV1SkxMbPEYt9stt9vdltEAAEAn4+jKSCQS0eLFi7V9+3bt3btXQ4YMueoxPp9PpaWlzfaVlJTI5/M5mxQAAHRJjq6M5ObmqqioSDt37lRsbGzTfR8ej0e9evWSJOXk5Kh///7y+/2SpLy8PKWnp2vlypXKyspScXGxKioqVFhY2M4vBQAAdEaOroysX79ewWBQd911l5KSkpq2rVu3Nq2prq5WbW1t0+O0tDQVFRWpsLBQycnJeu2117Rjx45Wb3oFAADRw9GVkWv5lSRlZWWX7Zs1a5ZmzZrl5FQAACBK8Nk0AADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMCU4xjZv3+/ZsyYoX79+snlcmnHjh2tri8rK5PL5bpsCwQCbZ0ZAAB0IY5jpL6+XsnJyVq3bp2j406dOqXa2tqmLT4+3umpAQBAF3ST0wOmT5+u6dOnOz5RfHy8brvtNsfHAQCAru1bu2ckJSVFSUlJuvfee/XOO++0ujYcDisUCjXbAABA13TDYyQpKUkbNmzQ66+/rtdff11er1d33XWXjh071uIxfr9fHo+nafN6vTd6TAAAYMQViUQibT7Y5dL27duVnZ3t6Lj09HQNHDhQf/rTn6749XA4rHA43PQ4FArJ6/UqGAwqLi6ureNe0eClb7br8wEA0Nl8vCLrhjxvKBSSx+O56vdvx/eMtIfJkyfrwIEDLX7d7XbL7XZ/ixMBAAArJr9npLKyUklJSRanBgAAHYzjKyMXL17U6dOnmx5/9NFHqqysVJ8+fTRw4EAVFBTo008/1R//+EdJ0urVqzVkyBCNHj1aX375pV588UXt3btXf/3rX9vvVQAAgE7LcYxUVFTo7rvvbnqcn58vSZo3b542b96s2tpaVVdXN3390qVL+uUvf6lPP/1UN998s8aNG6e//e1vzZ4DAABEr+u6gfXbcq03wLQFN7ACAKKd9Q2sfDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDlOEb279+vGTNmqF+/fnK5XNqxY8dVjykrK9OECRPkdrs1dOhQbd68uQ2jAgCArshxjNTX1ys5OVnr1q27pvUfffSRsrKydPfdd6uyslJLlizRwoULtWfPHsfDAgCArucmpwdMnz5d06dPv+b1GzZs0JAhQ7Ry5UpJ0siRI3XgwAGtWrVKmZmZTk8PAAC6mBt+z0h5ebkyMjKa7cvMzFR5eXmLx4TDYYVCoWYbAADomm54jAQCASUkJDTbl5CQoFAopP/85z9XPMbv98vj8TRtXq/3Ro8JAACMdMifpikoKFAwGGzaampqrEcCAAA3iON7RpxKTExUXV1ds311dXWKi4tTr169rniM2+2W2+2+0aMBAIAO4IZfGfH5fCotLW22r6SkRD6f70afGgAAdAKOY+TixYuqrKxUZWWlpK9/dLeyslLV1dWSvv4nlpycnKb1DzzwgD788EP96le/0vvvv6/nn39er776qh5++OH2eQUAAKBTcxwjFRUVGj9+vMaPHy9Jys/P1/jx4/X4449Lkmpra5vCRJKGDBmiN998UyUlJUpOTtbKlSv14osv8mO9AABAkuSKRCIR6yGuJhQKyePxKBgMKi4url2fe/DSN9v1+QAA6Gw+XpF1Q573Wr9/d8ifpgEAANGDGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYKpNMbJu3ToNHjxYMTExmjJlig4fPtzi2s2bN8vlcjXbYmJi2jwwAADoWhzHyNatW5Wfn69ly5bp2LFjSk5OVmZmps6ePdviMXFxcaqtrW3azpw5c11DAwCArsNxjDz77LNatGiR5s+fr1GjRmnDhg26+eabtWnTphaPcblcSkxMbNoSEhKua2gAANB1OIqRS5cu6ejRo8rIyPjmCbp1U0ZGhsrLy1s87uLFixo0aJC8Xq9mzpypkydPtnqecDisUCjUbAMAAF2Toxj5/PPP1dDQcNmVjYSEBAUCgSseM3z4cG3atEk7d+7Uli1b1NjYqLS0NH3yySctnsfv98vj8TRtXq/XyZgAAKATueE/TePz+ZSTk6OUlBSlp6frjTfe0B133KGNGze2eExBQYGCwWDTVlNTc6PHBAAARm5ysvj2229X9+7dVVdX12x/XV2dEhMTr+k5evToofHjx+v06dMtrnG73XK73U5GAwAAnZSjKyM9e/ZUamqqSktLm/Y1NjaqtLRUPp/vmp6joaFBJ06cUFJSkrNJAQBAl+Toyogk5efna968eZo4caImT56s1atXq76+XvPnz5ck5eTkqH///vL7/ZKkJ554QlOnTtXQoUP1xRdf6JlnntGZM2e0cOHC9n0lAACgU3IcI7Nnz9a5c+f0+OOPKxAIKCUlRW+//XbTTa3V1dXq1u2bCy7nz5/XokWLFAgE1Lt3b6WmpurgwYMaNWpU+70KAADQabkikUjEeoirCYVC8ng8CgaDiouLa9fnHrz0zXZ9PgAAOpuPV2TdkOe91u/ffDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVphhZt26dBg8erJiYGE2ZMkWHDx9udf22bds0YsQIxcTEaOzYsdq9e3ebhgUAAF2P4xjZunWr8vPztWzZMh07dkzJycnKzMzU2bNnr7j+4MGDmjNnjhYsWKDjx48rOztb2dnZqqqquu7hAQBA5+eKRCIRJwdMmTJFkyZN0tq1ayVJjY2N8nq9euihh7R06dLL1s+ePVv19fXatWtX076pU6cqJSVFGzZsuKZzhkIheTweBYNBxcXFORn3qgYvfbNdnw8AgM7m4xVZN+R5r/X7901OnvTSpUs6evSoCgoKmvZ169ZNGRkZKi8vv+Ix5eXlys/Pb7YvMzNTO3bsaPE84XBY4XC46XEwGJT09Ytqb43hf7f7cwIA0JnciO+v/+/zXu26h6MY+fzzz9XQ0KCEhIRm+xMSEvT+++9f8ZhAIHDF9YFAoMXz+P1+LV++/LL9Xq/XybgAAOAaeFbf2Oe/cOGCPB5Pi193FCPfloKCgmZXUxobG/Wvf/1Lffv2lcvlarfzhEIheb1e1dTUtPs//8A53o+Oh/ekY+H96Fh4P64uEonowoUL6tevX6vrHMXI7bffru7du6uurq7Z/rq6OiUmJl7xmMTEREfrJcntdsvtdjfbd9tttzkZ1ZG4uDj+InUgvB8dD+9Jx8L70bHwfrSutSsi/+Pop2l69uyp1NRUlZaWNu1rbGxUaWmpfD7fFY/x+XzN1ktSSUlJi+sBAEB0cfzPNPn5+Zo3b54mTpyoyZMna/Xq1aqvr9f8+fMlSTk5Oerfv7/8fr8kKS8vT+np6Vq5cqWysrJUXFysiooKFRYWtu8rAQAAnZLjGJk9e7bOnTunxx9/XIFAQCkpKXr77bebblKtrq5Wt27fXHBJS0tTUVGRHnvsMT366KMaNmyYduzYoTFjxrTfq2gjt9utZcuWXfZPQrDB+9Hx8J50LLwfHQvvR/tx/HtGAAAA2hOfTQMAAEwRIwAAwBQxAgAATBEjAADAVFTHyLp16zR48GDFxMRoypQpOnz4sPVIUcnv92vSpEmKjY1VfHy8srOzderUKeux8F8rVqyQy+XSkiVLrEeJWp9++ql++tOfqm/fvurVq5fGjh2riooK67GiVkNDg37zm99oyJAh6tWrl7773e/qySefvOrnr6BlURsjW7duVX5+vpYtW6Zjx44pOTlZmZmZOnv2rPVoUWffvn3Kzc3VoUOHVFJSoq+++kr33Xef6uvrrUeLekeOHNHGjRs1btw461Gi1vnz5zVt2jT16NFDb731lt577z2tXLlSvXv3th4tav3ud7/T+vXrtXbtWv3zn//U7373O/3+97/Xc889Zz1apxW1P9o7ZcoUTZo0SWvXrpX09W+S9Xq9euihh7R06VLj6aLbuXPnFB8fr3379unOO++0HidqXbx4URMmTNDzzz+v3/72t0pJSdHq1autx4o6S5cu1TvvvKO///3v1qPgv374wx8qISFBL730UtO+H/3oR+rVq5e2bNliOFnnFZVXRi5duqSjR48qIyOjaV+3bt2UkZGh8vJyw8kgScFgUJLUp08f40miW25urrKyspr9d4Jv35///GdNnDhRs2bNUnx8vMaPH68XXnjBeqyolpaWptLSUn3wwQeSpH/84x86cOCApk+fbjxZ59UhP7X3Rvv888/V0NDQ9Ftj/ychIUHvv/++0VSQvr5CtWTJEk2bNq1D/JbeaFVcXKxjx47pyJEj1qNEvQ8//FDr169Xfn6+Hn30UR05ckS/+MUv1LNnT82bN896vKi0dOlShUIhjRgxQt27d1dDQ4OeeuopzZ0713q0TisqYwQdV25urqqqqnTgwAHrUaJWTU2N8vLyVFJSopiYGOtxol5jY6MmTpyop59+WpI0fvx4VVVVacOGDcSIkVdffVWvvPKKioqKNHr0aFVWVmrJkiXq168f70kbRWWM3H777erevbvq6uqa7a+rq1NiYqLRVFi8eLF27dql/fv3a8CAAdbjRK2jR4/q7NmzmjBhQtO+hoYG7d+/X2vXrlU4HFb37t0NJ4wuSUlJGjVqVLN9I0eO1Ouvv240ER555BEtXbpUP/nJTyRJY8eO1ZkzZ+T3+4mRNorKe0Z69uyp1NRUlZaWNu1rbGxUaWmpfD6f4WTRKRKJaPHixdq+fbv27t2rIUOGWI8U1e655x6dOHFClZWVTdvEiRM1d+5cVVZWEiLfsmnTpl32o+4ffPCBBg0aZDQR/v3vfzf7QFhJ6t69uxobG40m6vyi8sqIJOXn52vevHmaOHGiJk+erNWrV6u+vl7z58+3Hi3q5ObmqqioSDt37lRsbKwCgYAkyePxqFevXsbTRZ/Y2NjL7te55ZZb1LdvX+7jMfDwww8rLS1NTz/9tH784x/r8OHDKiwsVGFhofVoUWvGjBl66qmnNHDgQI0ePVrHjx/Xs88+q5///OfWo3VekSj23HPPRQYOHBjp2bNnZPLkyZFDhw5ZjxSVJF1xe/nll61Hw3+lp6dH8vLyrMeIWn/5y18iY8aMibjd7siIESMihYWF1iNFtVAoFMnLy4sMHDgwEhMTE/nOd74T+fWvfx0Jh8PWo3VaUft7RgAAQMcQlfeMAACAjoMYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKb+D7cuxelORYM+AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,168 +89,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lassen Sie uns nun auf die Höhe konzentrieren und die Standardabweichung und Varianz berechnen:\n"
+    "Lassen wir uns nun auf die Größe konzentrieren und die Standardabweichung und Varianz berechnen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -404,24 +252,22 @@
    "source": [
     "> **Hinweis**: Dieses Diagramm deutet darauf hin, dass die Körpergrößen von First Basemen im Durchschnitt höher sind als die von Second Basemen. Später werden wir lernen, wie wir diese Hypothese formeller testen können und wie wir zeigen können, dass unsere Daten statistisch signifikant sind, um dies zu belegen.\n",
     "\n",
-    "Alter, Größe und Gewicht sind alles kontinuierliche Zufallsvariablen. Was denkst du, wie ihre Verteilung aussieht? Eine gute Möglichkeit, dies herauszufinden, ist das Erstellen eines Histogramms der Werte:\n"
+    "Alter, Größe und Gewicht sind allesamt stetige Zufallsvariablen. Was denken Sie, wie ihre Verteilung aussieht? Eine gute Möglichkeit, das herauszufinden, ist, das Histogramm der Werte zu zeichnen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Da die meisten Werte im echten Leben normalverteilt sind, sollten wir keinen gleichmäßigen Zufallszahlengenerator verwenden, um Beispieldaten zu erzeugen. Hier ist, was passiert, wenn wir versuchen, Gewichte mit einer gleichmäßigen Verteilung zu erzeugen (generiert durch `np.random.rand`):\n"
+    "Da die meisten Werte im echten Leben normalverteilt sind, sollten wir keinen gleichmäßigen Zufallszahlengenerator verwenden, um Beispieldaten zu erzeugen. Hier ist, was passiert, wenn wir versuchen, Gewichte mit einer Gleichverteilung zu erzeugen (erzeugt durch `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Konfidenzintervalle\n",
     "\n",
-    "Berechnen wir nun die Konfidenzintervalle für die Gewichte und Größen von Baseballspielern. Wir verwenden den Code [aus dieser Stackoverflow-Diskussion](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Lassen Sie uns nun Konfidenzintervalle für die Gewichte und Größen von Baseballspielern berechnen. Wir verwenden den Code [aus dieser Stackoverflow-Diskussion](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## Hypothesentests\n",
     "\n",
-    "Lass uns die verschiedenen Rollen in unserem Baseballspieler-Datensatz untersuchen:\n"
+    "Lassen Sie uns die verschiedenen Rollen in unserem Baseballspieler-Datensatz untersuchen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -787,26 +628,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simulation einer Normalverteilung mit dem Zentralen Grenzwertsatz\n",
+    "## Simulation einer Normalverteilung mit dem zentralen Grenzwertsatz\n",
     "\n",
-    "Der Pseudozufallsgenerator in Python ist so konzipiert, dass er uns eine gleichmäßige Verteilung liefert. Wenn wir einen Generator für eine Normalverteilung erstellen möchten, können wir den zentralen Grenzwertsatz nutzen. Um einen normalverteilten Wert zu erhalten, berechnen wir einfach den Mittelwert einer gleichmäßig generierten Stichprobe.\n"
+    "Der Pseudo-Zufallsgenerator in Python ist darauf ausgelegt, uns eine gleichmäßige Verteilung zu liefern. Wenn wir einen Generator für die Normalverteilung erstellen möchten, können wir den zentralen Grenzwertsatz verwenden. Um einen normalverteilten Wert zu erhalten, berechnen wir einfach den Mittelwert einer gleichmäßig generierten Stichprobe.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korrelation und die böse Baseballfirma\n",
     "\n",
-    "Korrelation ermöglicht es uns, Beziehungen zwischen Datenreihen zu finden. In unserem Beispiel nehmen wir an, es gibt eine böse Baseballfirma, die ihre Spieler nach deren Größe bezahlt – je größer der Spieler, desto mehr Geld bekommt er/sie. Angenommen, es gibt ein Grundgehalt von 1000 $, und einen zusätzlichen Bonus von 0 $ bis 100 $, abhängig von der Größe. Wir nehmen die echten Spieler aus der MLB und berechnen ihre imaginären Gehälter:\n"
+    "Die Korrelation ermöglicht es uns, Beziehungen zwischen Datenreihen zu finden. In unserem Beispiel nehmen wir an, es gibt eine böse Baseballfirma, die ihre Spieler nach ihrer Körpergröße bezahlt – je größer der Spieler, desto mehr Geld bekommt er/sie. Angenommen, es gibt ein Grundgehalt von 1000 $, und einen zusätzlichen Bonus von 0 bis 100 $, abhängig von der Körpergröße. Wir werden die echten Spieler aus der MLB nehmen und ihre imaginären Gehälter berechnen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lass uns nun die Kovarianz und Korrelation dieser Sequenzen berechnen. `np.cov` gibt uns eine sogenannte **Kovarianzmatrix**, die eine Erweiterung der Kovarianz auf mehrere Variablen darstellt. Das Element $M_{ij}$ der Kovarianzmatrix $M$ ist eine Korrelation zwischen den Eingabevariablen $X_i$ und $X_j$, und die Diagonalwerte $M_{ii}$ sind die Varianz von $X_{i}$. Ebenso gibt uns `np.corrcoef` die **Korrelationsmatrix**.\n"
+    "Lassen Sie uns nun die Kovarianz und Korrelation dieser Sequenzen berechnen. `np.cov` liefert uns eine sogenannte **Kovarianzmatrix**, die eine Erweiterung der Kovarianz auf mehrere Variablen darstellt. Das Element $M_{ij}$ der Kovarianzmatrix $M$ ist eine Korrelation zwischen den Eingabevariablen $X_i$ und $X_j$, und die Diagonalwerte $M_{ii}$ sind die Varianz von $X_{i}$. Ebenso liefert `np.corrcoef` uns die **Korrelationsmatrix**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Eine Korrelation von 1 bedeutet, dass es eine starke **lineare Beziehung** zwischen zwei Variablen gibt. Wir können die lineare Beziehung visuell erkennen, indem wir einen Wert gegen den anderen plotten:\n"
+    "Eine Korrelation von 1 bedeutet, dass eine starke **lineare Beziehung** zwischen zwei Variablen besteht. Wir können die lineare Beziehung visuell erkennen, indem wir einen Wert gegen den anderen plotten:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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vcvwXk1ywrPbNJK9ab3MAAHC8+VPTAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAIPts24AYCU7d+09rHbv7otm0AkAzzauIANzZ6VwfKQ6AGwkARkAAAYCMgAADARkAAAYCMgAADAQkIG5s9pqFVaxAOB4sMwbMJeEYQBmxRVkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAg+2zbgCYrZ279h5Wu3f3RTPoBADmgyvI8Cy2Ujg+Uh0Ang0EZAAAGAjIAAAwEJABAGAgIAMAwEBAhmex1VarsIoFAM9mlnmDZzlhGACebs0ryFV1TVU9UlX7h9oLquoLVfWVaf/8qb6zqh6vqjum7beG97yqqvZV1d1V9RtVVZvzkQAA4NgdzRSLa5NcuKy2K8lN3X12kpum54fc093nTds7h/qHk1yR5OxpW/41AQBg5tYMyN19c5JHl5XfnOS66fF1SS4+0teoqhclOaW7v9TdneTja70HAABm4Vhv0nthdz+UJNP+9OG1l1TV7VX1x1X1mql2RpIDwzEHptqKquqKqlqsqsWDBw8eY4sAALB+G72KxUNJXtzdr0zyS0k+WVWnJFlpvnGv9kW6++ruXujuhR07dmxwiwAAsLpjDcgPT9MmDk2feCRJuvuJ7v6b6fFtSe5J8rIsXTE+c3j/mUkePNamAQBgsxxrQP5sksunx5cn+UySVNWOqto2PX5plm7G++o0DeOxqrpgWr3i7YfeAwAA82TNdZCrak+S1yU5raoOJHl/kt1Jrq+qdyS5L8kl0+GvTfJfq+rbSZ5M8s7uPnSD37uytCLGyUk+P20AADBXamlRifm1sLDQi4uLs24DAIAtpqpu6+6F5XV/ahoAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAYM11kIGNsXPX3sNq9+6+aAadAABH4goyHAcrheMj1QGA2RGQAQBgICADAMBAQAYAgIGADAAAAwEZjoPVVquwigUAzB/LvMFxIgwDwInBFWQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAACD7bNuADbazl17D6vdu/uiGXQCAJyIXEFmS1kpHB+pDgCwnIAMAAADARkAAAYCMgAADARkAAAYCMhsKautVmEVCwDgaFnmjS1HGAYAnglXkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABisGZCr6pqqeqSq9g+1F1TVF6rqK9P++VP9DVV1W1Xtm/Y/Mrzni1V1V1XdMW2nb85HAgCAY3c0V5CvTXLhstquJDd199lJbpqeJ8nXk/xYd5+b5PIkv73sfZd193nT9sixtw0AAJtjzYDc3TcneXRZ+c1JrpseX5fk4unY27v7wal+Z5LnVdVJG9MqAABsvmOdg/zC7n4oSab9StMlfjLJ7d39xFD72DS94n1VVat98aq6oqoWq2rx4MGDx9giAACs36bcpFdVr0jya0l+dihfNk29eM20vW2193f31d290N0LO3bs2IwWAQBgRccakB+uqhclybR/aj5xVZ2Z5NNJ3t7d9xyqd/cD0/6xJJ9M8upjbRoAADbLsQbkz2bpJrxM+88kSVWdmmRvkiu7+08OHVxV26vqtOnxc5K8Kcn+AADAnNm+1gFVtSfJ65KcVlUHkrw/ye4k11fVO5Lcl+SS6fCfT/IDSd5XVe+bav8uyTeT3DiF421J/jDJRzbwczAjO3ftPax27+6LZtAJAMDGqO6edQ9HtLCw0IuLi7NugxWsFI4PEZIBgHlXVbd198Lyur+kBwAAAwEZAAAGAjIAAAwEZAAAGAjIHLPVbsRzgx4AcCJbc5k3OBJhGADYalxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgsH3WDXB0fvC9n8vfP9lPPX/etspfffBHZ9gRAMDW5AryCWB5OE6Sv3+y84Pv/dyMOgIA2LoE5BPA8nC8Vh0AgGMnIAMAwEBABgCAgYB8AnjetlpXHQCAYycgnwD+6oM/elgYtooFAMDmsMzbCUIYBgA4PlxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMFgzIFfVNVX1SFXtH2ovqKovVNVXpv3zh9eurKq7q+quqnrjUH9VVe2bXvuNqqqN/zjP3FU37Mv3X/m57Ny1N99/5edy1Q37Zt0SAADH0dFcQb42yYXLaruS3NTdZye5aXqeqnp5krcmecX0nv9ZVdum93w4yRVJzp625V9z5q66YV8+cct9ebI7SfJkdz5xy31CMgDAs8iaAbm7b07y6LLym5NcNz2+LsnFQ/13u/uJ7v7rJHcneXVVvSjJKd39pe7uJB8f3jM39tx6/7rqAABsPcc6B/mF3f1Qkkz706f6GUnGNHlgqp0xPV5eX1FVXVFVi1W1ePDgwWNscf0OXTk+2joAAFvPRt+kt9K84j5CfUXdfXV3L3T3wo4dOzasubVsW2Va9Gp1AAC2nmMNyA9P0yYy7R+Z6geSnDUcd2aSB6f6mSvU58ql55+1rjoAAFvPsQbkzya5fHp8eZLPDPW3VtVJVfWSLN2M96fTNIzHquqCafWKtw/vmRsfuPjc/PQFL37qivG2qvz0BS/OBy4+d8adAQBwvFSvMb+2qvYkeV2S05I8nOT9SW5Icn2SFye5L8kl3f3odPx7k/xMkm8neXd3f36qL2RpRYyTk3w+yS/0Wt88ycLCQi8uLq7/kwEAwBFU1W3dvXBY/Sgy6kwJyAAAbIbVArK/pAcAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGFR3z7qHI6qqg0m+Nus+5shpSb4+6yZOEMZqfYzX+hivo2es1sd4rY/xOnrG6nD/ort3LC/OfUDm6apqsbsXZt3HicBYrY/xWh/jdfSM1foYr/UxXkfPWB09UywAAGAgIAMAwEBAPvFcPesGTiDGan2M1/oYr6NnrNbHeK2P8Tp6xuoomYMMAAADV5ABAGAgIAMAwEBAnmNVdWpV/X5V/VVVfbmqfriqzquqW6rqjqparKpXz7rPeVBV50xjcmj7f1X17qp6QVV9oaq+Mu2fP+te58ERxutD08/bX1TVp6vq1Fn3OmurjdXw+i9XVVfVaTNsc24cabyq6heq6q6qurOq/tuMW50LR/i36Fy/gqr6xennZ39V7amq5znPr26V8XKePwrmIM+xqrouyf/u7o9W1XOTfHeS65P8end/vqp+NMmvdPfrZtnnvKmqbUkeSHJ+kp9L8mh3766qXUme392/OtMG58yy8TonyR9197er6teSxHj9o3GsuvtrVXVWko8m+cEkr+puC/APlv1svTTJe5Nc1N1PVNXp3f3ITBucM8vG6yNxrn+aqjojyf9J8vLufryqrk/yuSQvj/P8YY4wXg/GeX5NriDPqao6Jclrk/yvJOnub3X3N5J0klOmw/5pln7QebrXJ7mnu7+W5M1Jrpvq1yW5eFZNzbGnxqu7/6C7vz3Vb0ly5gz7mkfjz1aS/HqSX8nSv0sON47Xu5Ls7u4nkkQ4XtE4Xs71K9ue5OSq2p6li0YPxnn+SA4bL+f5oyMgz6+XJjmY5GNVdXtVfbSqvifJu5N8qKruT/Lfk1w5wx7n1VuT7Jkev7C7H0qSaX/6zLqaX+N4jX4myeePcy/z7qmxqqofT/JAd//5bFuaa+PP1suSvKaqbq2qP66qfznDvubVOF7vjnP903T3A1kai/uSPJTkb7v7D+I8v6IjjNfIeX4VAvL82p7kh5J8uLtfmeSbSXZl6SrML3b3WUl+MdMVZpZMU1F+PMnvzbqXE8Fq41VV703y7SS/M4u+5tE4VlX13VmaLvCfZ9vV/FrhZ2t7kucnuSDJe5JcX1U1o/bmzgrj5Vy/zDS3+M1JXpLk+5J8T1X99Gy7ml9rjZfz/JEJyPPrQJID3X3r9Pz3sxSYL0/yqan2e0ncuPF0/z7Jn3X3w9Pzh6vqRUky7f1a9+mWj1eq6vIkb0pyWbtJYTSO1fdn6T86f15V92bpV5R/VlX/fIb9zZvlP1sHknyql/xpku8kcWPjP1o+Xs71h/u3Sf66uw929z9kaXz+VZznV7PaeDnPHwUBeU519/9Ncn9VnTOVXp/kL7M03+rfTLUfSfKVGbQ3zy7N06cLfDZL/6HJtP/Mce9ovj1tvKrqwiS/muTHu/vvZtbVfHpqrLp7X3ef3t07u3tnlsLfD03/blmy/N/iDVk6Z6WqXpbkuUnc1PiPlo+Xc/3h7ktyQVV99/Tbh9cn+XKc51ez4ng5zx8dq1jMsao6L0t3yD83yVeT/Ickr0jyP7L068q/T/Kfuvu2WfU4T6Zfe9+f5KXd/bdT7Z9laeWPF2fpZHFJdz86uy7nxyrjdXeSk5L8zXTYLd39zhm1ODdWGqtlr9+bZMEqFktW+dl6bpJrkpyX5FtJfrm7/2hmTc6RVcbrX8e5/jBV9V+S/FSWpgbcnuQ/JvkncZ5f0SrjdWec59ckIAMAwMAUCwAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAb/H2leqRtP0LMZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lass uns sehen, was passiert, wenn die Beziehung nicht linear ist. Angenommen, unser Unternehmen hat beschlossen, die offensichtliche lineare Abhängigkeit zwischen Größen und Gehältern zu verbergen und eine Nicht-Linearität in die Formel einzuführen, wie zum Beispiel `sin`:\n"
+    "Lass uns sehen, was passiert, wenn die Beziehung nicht linear ist. Angenommen, unser Unternehmen hat beschlossen, die offensichtliche lineare Abhängigkeit zwischen Körpergrößen und Gehältern zu verbergen und eine Nicht-Linearität in die Formel einzuführen, wie zum Beispiel `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -946,14 +783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Kannst du erraten, warum die Punkte sich so in vertikalen Linien anordnen?\n",
+    "> Können Sie erraten, warum sich die Punkte so in vertikale Linien anordnen?\n",
     "\n",
-    "Wir haben die Korrelation zwischen einem künstlich konstruierten Konzept wie Gehalt und der beobachteten Variable *Größe* untersucht. Schauen wir uns nun an, ob auch zwei beobachtete Variablen wie Größe und Gewicht miteinander korrelieren:\n"
+    "Wir haben die Korrelation zwischen einem künstlich konstruierten Konzept wie Gehalt und der beobachteten Variablen *Größe* untersucht. Schauen wir uns nun an, ob auch zwei beobachtete Variablen wie Größe und Gewicht miteinander korrelieren:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Leider haben wir keine Ergebnisse erhalten – nur einige seltsame `nan`-Werte. Das liegt daran, dass einige Werte in unserer Serie undefiniert sind, dargestellt als `nan`, was dazu führt, dass das Ergebnis der Operation ebenfalls undefiniert ist. Wenn wir uns die Matrix ansehen, können wir erkennen, dass die Spalte `Weight` das Problem darstellt, da die Selbstkorrelation zwischen den `Height`-Werten berechnet wurde.\n",
+    "Leider haben wir keine Ergebnisse erhalten - nur einige seltsame `nan`-Werte. Dies liegt daran, dass einige Werte in unserer Serie undefiniert sind, dargestellt als `nan`, was dazu führt, dass das Ergebnis der Operation ebenfalls undefiniert ist. Wenn wir uns die Matrix ansehen, können wir erkennen, dass die Spalte `Weight` das Problem darstellt, da die Selbstkorrelation zwischen den `Height`-Werten berechnet wurde.\n",
     "\n",
-    "> Dieses Beispiel zeigt, wie wichtig **Datenaufbereitung** und **Datenbereinigung** sind. Ohne ordentliche Daten können wir nichts berechnen.\n",
+    "> Dieses Beispiel zeigt die Bedeutung von **Datenvorbereitung** und **Datenbereinigung**. Ohne ordnungsgemäße Daten können wir nichts berechnen.\n",
     "\n",
-    "Lassen Sie uns die Methode `fillna` verwenden, um die fehlenden Werte zu füllen, und die Korrelation berechnen:\n"
+    "Lassen Sie uns die Methode `fillna` verwenden, um die fehlenden Werte zu füllen und die Korrelation zu berechnen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1087,14 +920,14 @@
    "source": [
     "## Fazit\n",
     "\n",
-    "In diesem Notebook haben wir gelernt, wie man grundlegende Operationen mit Daten durchführt, um statistische Funktionen zu berechnen. Wir wissen nun, wie man ein solides Instrumentarium aus Mathematik und Statistik einsetzt, um Hypothesen zu überprüfen, und wie man Konfidenzintervalle für beliebige Variablen anhand einer Datenstichprobe berechnet.\n"
+    "In diesem Notebook haben wir gelernt, wie man grundlegende Operationen mit Daten durchführt, um statistische Funktionen zu berechnen. Wir wissen nun, wie man ein solides Instrumentarium aus Mathematik und Statistik einsetzt, um einige Hypothesen zu überprüfen, und wie man Konfidenzintervalle für beliebige Variablen anhand einer Datenstichprobe berechnet.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, beachten Sie bitte, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n"
+    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mithilfe des KI-Übersetzungsdienstes [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, weisen wir darauf hin, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:01:22+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:02:52+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "de"
   }
diff --git a/translations/de/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/de/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index cfabe3e8..5a2fa4cf 100644
--- a/translations/de/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/de/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -152,11 +152,11 @@
    "source": [
     "In diesem Datensatz sind die Spalten wie folgt:\n",
     "\n",
-    "* Alter und Geschlecht sind selbsterklärend\n",
-    "* BMI ist der Body-Mass-Index\n",
-    "* BP ist der durchschnittliche Blutdruck\n",
-    "* S1 bis S6 sind verschiedene Blutmesswerte\n",
-    "* Y ist das qualitative Maß für den Krankheitsverlauf über ein Jahr\n",
+    "* Alter und Geschlecht sind selbsterklärend  \n",
+    "* BMI ist der Body-Mass-Index  \n",
+    "* BP ist der durchschnittliche Blutdruck  \n",
+    "* S1 bis S6 sind verschiedene Blutmessungen  \n",
+    "* Y ist das qualitative Maß für den Krankheitsverlauf über ein Jahr  \n",
     "\n",
     "Lassen Sie uns diesen Datensatz mit Methoden der Wahrscheinlichkeit und Statistik untersuchen.\n",
     "\n",
@@ -355,7 +355,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -447,7 +447,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -478,7 +478,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Aufgabe 2: Boxplots für BMI, BP und Y abhängig vom Geschlecht erstellen\n"
+    "### Aufgabe 2: Erstelle Boxplots für BMI, BP und Y in Abhängigkeit vom Geschlecht\n"
    ],
    "metadata": {}
   },
@@ -486,8 +486,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -538,8 +538,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -593,19 +593,19 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Schlussfolgerungen:\n",
-    "* Alter - normal\n",
-    "* Geschlecht - einheitlich\n",
-    "* BMI, Y - schwer zu beurteilen\n"
+    "Schlussfolgerungen:  \n",
+    "* Alter - normal  \n",
+    "* Geschlecht - einheitlich  \n",
+    "* BMI, Y - schwer zu sagen  \n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### Aufgabe 4: Teste die Korrelation zwischen verschiedenen Variablen und dem Krankheitsverlauf (Y)\n",
+    "### Aufgabe 4: Testen Sie die Korrelation zwischen verschiedenen Variablen und dem Krankheitsverlauf (Y)\n",
     "\n",
-    "> **Hinweis** Eine Korrelationsmatrix liefert die nützlichsten Informationen darüber, welche Werte voneinander abhängig sind.\n"
+    "> **Tipp** Eine Korrelationsmatrix liefert Ihnen die nützlichsten Informationen darüber, welche Werte voneinander abhängig sind.\n"
    ],
    "metadata": {}
   },
@@ -847,8 +847,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Fazit:\n",
-    "* Die stärkste Korrelation von Y ist BMI und S5 (Blutzucker). Das klingt vernünftig.\n"
+    "Fazit:  \n",
+    "* Die stärkste Korrelation von Y besteht mit BMI und S5 (Blutzucker). Das klingt plausibel.\n"
    ],
    "metadata": {}
   },
@@ -856,10 +856,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -886,9 +886,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -917,7 +917,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, beachten Sie bitte, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n"
+    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mithilfe des KI-Übersetzungsdienstes [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, weisen wir darauf hin, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n"
    ]
   }
  ],
@@ -943,8 +943,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:23:00+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:03:21+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "de"
   }
diff --git a/translations/el/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/el/1-Introduction/04-stats-and-probability/assignment.ipynb
index e1f6caf1..b8fdc7ed 100644
--- a/translations/el/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/el/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Σε αυτό το σύνολο δεδομένων, οι στήλες είναι οι εξής:\n",
-    "* Η ηλικία και το φύλο είναι αυτονόητα\n",
-    "* Το BMI είναι ο δείκτης μάζας σώματος\n",
-    "* Το BP είναι η μέση αρτηριακή πίεση\n",
-    "* Το S1 έως S6 είναι διαφορετικές μετρήσεις αίματος\n",
-    "* Το Y είναι η ποιοτική μέτρηση της εξέλιξης της ασθένειας μέσα σε ένα χρόνο\n",
+    "Σε αυτό το σύνολο δεδομένων, οι στήλες είναι οι εξής:  \n",
+    "* Η ηλικία και το φύλο είναι αυτονόητα  \n",
+    "* Το BMI είναι ο δείκτης μάζας σώματος  \n",
+    "* Το BP είναι η μέση αρτηριακή πίεση  \n",
+    "* Οι S1 έως S6 είναι διαφορετικές μετρήσεις αίματος  \n",
+    "* Το Y είναι το ποιοτικό μέτρο της εξέλιξης της νόσου μέσα σε ένα έτος  \n",
     "\n",
-    "Ας μελετήσουμε αυτό το σύνολο δεδομένων χρησιμοποιώντας μεθόδους πιθανότητας και στατιστικής.\n",
+    "Ας μελετήσουμε αυτό το σύνολο δεδομένων χρησιμοποιώντας μεθόδους πιθανοτήτων και στατιστικής.\n",
     "\n",
-    "### Εργασία 1: Υπολογίστε τις μέσες τιμές και τη διακύμανση για όλες τις τιμές\n"
+    "### Εργασία 1: Υπολογίστε τις μέσες τιμές και τη διακύμανση για όλες τις τιμές  \n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Εργασία 2: Σχεδιάστε boxplots για BMI, BP και Y ανάλογα με το φύλο\n"
+    "### Εργασία 2: Σχεδιάστε διαγράμματα κουτιού για BMI, BP και Y ανάλογα με το φύλο\n"
    ],
    "metadata": {}
   },
@@ -200,7 +200,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Εργασία 4: Δοκιμάστε τη συσχέτιση μεταξύ διαφορετικών μεταβλητών και της εξέλιξης της ασθένειας (Y)\n",
+    "### Εργασία 4: Δοκιμάστε τη συσχέτιση μεταξύ διαφορετικών μεταβλητών και της εξέλιξης της νόσου (Y)\n",
     "\n",
     "> **Υπόδειξη** Ο πίνακας συσχέτισης θα σας δώσει τις πιο χρήσιμες πληροφορίες σχετικά με το ποιες τιμές είναι εξαρτημένες.\n"
    ],
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Αποποίηση ευθύνης**:  \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτοματοποιημένες μεταφράσεις ενδέχεται να περιέχουν λάθη ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n"
+    "\n---\n\n**Αποποίηση ευθύνης**:  \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:17:53+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:31:57+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "el"
   }
diff --git a/translations/el/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/el/1-Introduction/04-stats-and-probability/notebook.ipynb
index 6e7f8380..d167e2c2 100644
--- a/translations/el/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/el/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Εισαγωγή στην Πιθανότητα και τη Στατιστική\n",
-    "Σε αυτό το σημειωματάριο, θα εξερευνήσουμε μερικές από τις έννοιες που έχουμε συζητήσει προηγουμένως. Πολλές έννοιες από την πιθανότητα και τη στατιστική εκπροσωπούνται καλά σε μεγάλες βιβλιοθήκες για την επεξεργασία δεδομένων στην Python, όπως οι `numpy` και `pandas`.\n"
+    "Σε αυτό το σημειωματάριο, θα εξετάσουμε μερικές από τις έννοιες που έχουμε συζητήσει προηγουμένως. Πολλές έννοιες από την πιθανότητα και τη στατιστική εκπροσωπούνται επαρκώς σε μεγάλες βιβλιοθήκες για επεξεργασία δεδομένων στην Python, όπως οι `numpy` και `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Τυχαίες Μεταβλητές και Κατανομές\n",
-    "Ας ξεκινήσουμε με τη δειγματοληψία 30 τιμών από μια ομοιόμορφη κατανομή από το 0 έως το 9. Θα υπολογίσουμε επίσης τη μέση τιμή και τη διασπορά.\n"
+    "Ας ξεκινήσουμε με τη δειγματοληψία 30 τιμών από μια ομοιόμορφη κατανομή από το 0 έως το 9. Θα υπολογίσουμε επίσης τον μέσο όρο και τη διακύμανση.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Ανάλυση Πραγματικών Δεδομένων\n",
     "\n",
-    "Η μέση τιμή και η διασπορά είναι πολύ σημαντικές όταν αναλύουμε δεδομένα από τον πραγματικό κόσμο. Ας φορτώσουμε τα δεδομένα για τους παίκτες του μπέιζμπολ από [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Ο μέσος όρος και η διασπορά είναι πολύ σημαντικά όταν αναλύουμε δεδομένα από τον πραγματικό κόσμο. Ας φορτώσουμε τα δεδομένα για τους παίκτες του μπέιζμπολ από [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Χρησιμοποιούμε ένα πακέτο που ονομάζεται [**Pandas**](https://pandas.pydata.org/) εδώ για ανάλυση δεδομένων. Θα μιλήσουμε περισσότερο για το Pandas και την εργασία με δεδομένα στην Python αργότερα σε αυτό το μάθημα.\n",
+    "> Χρησιμοποιούμε ένα πακέτο που ονομάζεται [**Pandas**](https://pandas.pydata.org/) εδώ για ανάλυση δεδομένων. Θα μιλήσουμε περισσότερο για το Pandas και τη δουλειά με δεδομένα στην Python αργότερα σε αυτό το μάθημα.\n",
     "\n",
     "Ας υπολογίσουμε τις μέσες τιμές για την ηλικία, το ύψος και το βάρος:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Εκτός από τον μέσο όρο, έχει νόημα να εξετάσουμε την τιμή της διάμεσης και τα τεταρτημόρια. Μπορούν να απεικονιστούν χρησιμοποιώντας ένα **διάγραμμα κουτιού**:\n"
+    "Εκτός από τον μέσο όρο, έχει νόημα να εξετάσουμε τη διάμεση τιμή και τα τεταρτημόρια. Μπορούν να απεικονιστούν χρησιμοποιώντας ένα **διάγραμμα κουτιού**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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/6SS5n/zkJx1+TJMnT3aSXGNjY6vH1NDQ4CS5X/3qV20eU/NxTJs2zTmX/u9T878TP/vZz4qyU2vH9H7+7jX/f2reV55+TMuXL3eS3H333Vdwx1RfX+8kufr6eteWnI+at3bFu0+fPjp69KjOP//8gv6Ozdq1azVq1Cg9+eSTuuSSS8x8V9fzPB0+fFg9evRINSj2Ywpip444pmg0qkOHDqlPnz5KJpMFfUx1dXWaNGmSVq9erREjRhTEY0SxXVGI7tmoTk9UK3HPCiW7X97imCKRiG57/jbtPLZTnvvfZ08tCZXo0vMu1ROfeELl5eUFd0yF3Gnjxo0aPny45s+fr8suuyyv51M8Hte+fft00UUXpZ4Nl8e94B5TMT2WW+20a9cuTZo0SWvWrNHw4cPb/bg3ceJEPfvss7rjjjv01FNPyfM87dq1S/369ZMkTZo0SQsWLNDNN98cuCveo0ePTv2bf/oxvfLKKxo5cqReeumlDr/ivXz5clVXV6u2tlaDBg1KO6YNGzZo2LBhevnll9t1xfv04/jvf5/WrVuXdhz/3SkWi+ntt9/WJZdcIs/zCq5TR17xHj16tNauXavrrrsu7ZjWr1+vESNG6MUXXyy4K96nTp1q/49Rt7k1Pwup7Svep8vkuwJ+27x5s5OU+s6KFdFo1M2dO9dFo1G/l4IcK6bWVs/HjhT710bnplW99+dp1u1b1+rV7uaXdfvW+bDi4ubn39liOreRPXoXvvf7eNDY2OgkuVAo5E6ePNmi9cmTJ10oFEpdnQ2SRCLhPvzhD7uxY8e6ZDLZ4n3JZNKNHTvWfeQjH3GJRKKg7zvbz2Xl3Pazd7Yy2dsG96f08b5FIhHdd999qe8EIbhobUtZaVmLP5s55zRn6xyFFGr140IKac7WOTzDeRHh3LaF3sHVpUsXDR48WM45VVRU6Atf+IKuvfZafeELX0g9sdrgwYMD9cRqkhQOhzVr1iw999xzGj9+fItnuR4/fryee+45PfTQQzl5oq2OvO9sP5eVc9vP3vmU8ca7qalJ27Zt07Zt2yRJ//znP7Vt2zbt3bu3o9cGnySTSe3YsSNQv7AeraO1Lcn/jBw3/9ks7sV16PghObW+sXZyOnT8kOKejd8jGgSc27bQO9g2btyY2nz/7ne/01VXXaXf/e53qU33xo0b/V5iTkyYMEHPPPOMXnvtNQ0bNkxVVVUaNmyYtm/frmeeeUYTJkwoivvO5nNZOrf97J0vGT+r+d/+9jeNHj069d/f+MY3JEl333235s+f32ELg3+SyaTWr1+viy++uOi/s4Szo7UtnpdU+L/+bBYJR/SH//MHHTt17Iwfe16n8xQJB/s77kHCuW0LvYNv48aNampq0u23364tW7Zo0KBB+v3vfx+4K92nmzBhgsaNG6e1a9fq4MGD+tCHPqThw4fn5e95R973+/1c1s5tP3vnQ8Yb71GjRjFuGHCRSET33HOP38tAHtDaljONmktSj8491KNzj3wvCTnCuW0LvW3o0qWLampq/F5G3oXDYY0aNaro7/v9fC6L57afvXONn/E+i/79+2vz5s3q37+/30vJq2QyqS1btpgYa7GO1racadQcwcO5bQu9C19HfU1Ja1voHSxsvM+ioqJCgwYNUkVFhd9LyatkMqnXX3+dk9wAWtvieckWfyK4OLdtoXfh66ivKWltC72DJeNRcwRfJBLRpEmT/F4G8oDWtpxt1BzBwrltC73toLUt9A4WNt5Ik0gktGnTJg0ePFilpfwVCbJian3ixAlJ0pYtW3xeSfGK/HunLpe0fccOxQ4xbp5rO3fu9O2+i+ncRvbobQetbaF3sFAQaZxz2rdvn66++mq/l4IcK6bWb7zxhiRp8uTJPq+keF3Zo0Rb7u2iu+66S1vZeOdNZWVl3u+zmM5tZI/edtDaFnoHS8jl+SnKGxoa1LVrV9XX16uqqiqfdw2giB09elRLly5V//79zT3vQkcJJU6pU9NenepyoVxpJ7+XY0JlZaUuvvhiv5cBAAByIJO9LVe8kSaRSGjdunW67rrrGGsJuGJq3a1bN33xi1/0exlF7b3eMV036JqC743sFNO5jezR2w5a20LvYOFZzZHGOaeGhgZ+X7sBtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQoUz2tlzxRppEIqEXXnhBiUTC76Ugx2htC73toLUt9LaD1rbQO1jYeAMAAAAAkEOMmgMAAAAAkCFGzZGVeDyumpoaxeNxv5eCHKO1LfS2g9a20NsOWttC72Bh4400oVBIVVVVCoVCfi8FOUZrW+htB61tobcdtLaF3sHCqDkAAAAAABli1BxZicfjWrRoEWMtBtDaFnrbQWtb6G0HrW2hd7Cw8UaaUCik3r17M9ZiAK1tobcdtLaF3nbQ2hZ6Bwuj5gAAAAAAZIhRc2QlFovp6aefViwW83spyDFa20JvO2htC73toLUt9A4WNt5IEw6HNWDAAIXDYb+XghyjtS30toPWttDbDlrbQu9gYdQcAAAAAIAMMWqOrMRiMc2bN4+xFgNobQu97aC1LfS2g9a20DtY2HgjTTgc1rXXXstYiwG0toXedtDaFnrbQWtb6B0sjJoDAAAAAJAhRs2RlVgspkceeYSxFgNobQu97aC1LfS2g9a20DtY2HgjTWlpqaqrq1VaWur3UpBjtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQIUbNkZVoNKqHH35Y0WjU76Ugx2htC73toLUt9LaD1rbQO1i44o00nudp//796tWrl0pK+N5MkNHaFnrbQWtb6G0HrW2hd+HLZG/LxhsAAAAAgAwxao6sRKNRzZgxg7EWA2htC73toLUt9LaD1rbQO1i44o00nufp6NGj6tatG2MtAUdrW+htB61tobcdtLaF3oWPUXMAAAAAAHKIUXNkJRqN6sEHH2SsxQBa20JvO2htC73toLUt9A4WrngjjXNOjY2NqqysVCgU8ns5yCFa20JvO2htC73toLUt9C58XPFG1srLy/1eAvKE1rbQ2w5a20JvO2htC72Dg4030sRiMc2cOVOxWMzvpSDHaG0Lve2gtS30toPWttA7WBg1RxrnnGKxmCKRCGMtAUdrW+htB61tobcdtLaF3oWPUXNkjSdxsIPWttDbDlrbQm87aG0LvYODjTfSxGIxzZ49m7EWA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMrnufpyJEj8jzP76Ugx2htC73toLUt9LaD1rbQO1jYeCNNPB7XvHnzFI/H/V4KcozWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmy4nme3nnnHcZaDKC1LfS2g9a20NsOWttC72Bh44008XhcixYtYqzFAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5siK53navXs3Yy0G0NoWettBa1vobQetbaF3sLDxRppEIqEXX3xRiUTC76Ugx2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSO3bsUDKZ9HspyDFa20JvO2htC73toLUt9A4WNt5Ik0wmtX79ek5yA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSW7Zs4btrBtDaFnrbQWtb6G0HrW2hd7Cw8UaaZDKp119/nZPcAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5shKIpFQbW0tvzPQAFrbQm87aG0Lve2gtS30DhY23kjjnNO+ffuU52EI+IDWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmykkgktGrVKsZaDKC1LfS2g9a20NsOWttC72Bh4400zjk1NDQw1mIArW2htx20toXedtDaFnoHC6PmAAAAAABkiFFzZCWRSOiFF15grMUAWttCbztobQu97aC1LfQOFjbeAAAAAADkEKPmAAAAAABkKJO9bWme1pTSvM9vaGjI912jneLxuJYvX65PfOITKisr83s5yCFa20JvO2htC73toLUt9C58zXva9lzLzvvGu7GxUZLUp0+ffN81AAAAAAAdqrGxUV27dj3rbfI+au55ng4cOKDKykqFQqF83jXaqaGhQX369NE777zDjwMEHK1tobcdtLaF3nbQ2hZ6Fz7nnBobG9WzZ0+VlJz96dPyfsW7pKREvXv3zvfd4n2oqqriJDeC1rbQ2w5a20JvO2htC70LW1tXupvxrOYAAAAAAOQQG28AAAAAAHKIjTfSlJeXa9q0aSovL/d7KcgxWttCbztobQu97aC1LfQOlrw/uRoAAAAAAJZwxRsAAAAAgBxi4w0AAAAAQA6x8QYAAAAAIIfYeAMAAAAAkENsvI1Ys2aNxo4dq549eyoUCmnp0qVpt9m5c6duvvlmde3aVZ07d9bgwYO1d+/e1PtPnTqlKVOm6Pzzz1eXLl10yy236PDhw3k8CrRHW62bmpo0depU9e7dW+ecc44GDBigRx99tMVtaF08ZsyYocGDB6uyslLdu3fX+PHj9eabb7a4TXt67t27VzfddJMqKirUvXt3ffvb31YikcjnoaANbbU+duyYvvrVr6pfv34655xzdOGFF+prX/ua6uvrW3weWheH9pzbzZxz+tSnPtXqYz69C197W9fW1ur6669X586dVVVVpREjRujkyZOp9x87dkx33HGHqqqqdO655+qee+5RU1NTPg8F7dCe3ocOHdKdd96pHj16qHPnzho0aJD+9Kc/tbgNvYsPG28jjh8/riuuuEJz585t9f1vvfWWrrvuOvXv31+rVq3SP/7xD/3gBz9Qp06dUre5//779ec//1mLFi3S6tWrdeDAAU2YMCFfh4B2aqv1N77xDS1btkxPP/20du7cqa9//euaOnWqampqUrehdfFYvXq1pkyZovXr12v58uWKx+Oqrq7W8ePHU7dpq2cymdRNN92kWCymV199VU888YTmz5+vH/7wh34cEs6grdYHDhzQgQMH9NBDD2n79u2aP3++li1bpnvuuSf1OWhdPNpzbjf7+c9/rlAolPZ2eheH9rSura3VmDFjVF1drY0bN2rTpk2aOnWqSkr+90v5O+64Qzt27NDy5cv13HPPac2aNfrSl77kxyHhLNrT+6677tKbb76pmpoavfbaa5owYYJuvfVWbd26NXUbehchB3MkuSVLlrR428SJE92kSZPO+DHvvvuuKysrc4sWLUq9befOnU6Sq62tzdVSkaXWWl922WXuRz/6UYu3DRo0yH3ve99zztG62B05csRJcqtXr3bOta/nX/7yF1dSUuIOHTqUus2vfvUrV1VV5aLRaH4PAO12euvWLFy40EUiERePx51ztC5mZ+q9detW16tXL3fw4MG0x3x6F6fWWg8ZMsR9//vfP+PHvP76606S27RpU+ptf/3rX10oFHL79+/P6XqRndZ6d+7c2T355JMtbnfeeee5xx57zDlH72LFFW/I8zw9//zzuuSSS/TJT35S3bt315AhQ1qMq23evFnxeFw33nhj6m39+/fXhRdeqNraWh9Wjfdr2LBhqqmp0f79++Wc08qVK7Vr1y5VV1dLonWxax4rPu+88yS1r2dtba0GDhyoCy64IHWbT37yk2poaNCOHTvyuHpk4vTWZ7pNVVWVSktLJdG6mLXW+8SJE7r99ts1d+5c9ejRI+1j6F2cTm995MgRbdiwQd27d9ewYcN0wQUXaOTIkVq3bl3qY2pra3Xuuefq6quvTr3txhtvVElJiTZs2JDfA0BGWju3hw0bpj/+8Y86duyYPM/TH/7wB506dUqjRo2SRO9ixcYbOnLkiJqamjRz5kyNGTNGL774oj796U9rwoQJWr16taT3ftYkEono3HPPbfGxF1xwgQ4dOuTDqvF+zZkzRwMGDFDv3r0ViUQ0ZswYzZ07VyNGjJBE62LmeZ6+/vWv6+Mf/7guv/xySe3reejQoRZfmDe/v/l9KDyttT7d0aNH9eMf/7jF6CGti9OZet9///0aNmyYxo0b1+rH0bv4tNb67bffliRNnz5dkydP1rJlyzRo0CDdcMMNqqurk/Rez+7du7f4XKWlpTrvvPNoXcDOdG4vXLhQ8Xhc559/vsrLy3XvvfdqyZIl6tu3ryR6F6tSvxcA/3meJ0kaN26c7r//fknSxz72Mb366qt69NFHNXLkSD+Xhw42Z84crV+/XjU1Nbrooou0Zs0aTZkyRT179mxxVRTFZ8qUKdq+fXuLqyAIprZaNzQ06KabbtKAAQM0ffr0/C4OHa613jU1NVqxYkWLn/lE8WutdfPXaffee68+//nPS5KuvPJKvfzyy/rtb3+rGTNm+LJWZO9Mj+U/+MEP9O677+qll15St27dtHTpUt16661au3atBg4c6NNqkS2ueEPdunVTaWmpBgwY0OLtl156aepZzXv06KFYLKZ33323xW0OHz7c6ngbCtPJkyf13e9+Vw8//LDGjh2rj370o5o6daomTpyohx56SBKti9XUqVP13HPPaeXKlerdu3fq7e3p2aNHj7RnOW/+b5oXnjO1btbY2KgxY8aosrJSS5YsUVlZWep9tC4+Z+q9YsUKvfXWWzr33HNVWlqa+nGCW265JTWOSu/icqbWH/rQhySpza/Tjhw50uL9iURCx44do3WBOlPvt956S7/85S/129/+VjfccIOuuOIKTZs2TVdffXXqiXPpXZzYeEORSESDBw9O+1UGu3bt0kUXXSRJuuqqq1RWVqaXX3459f4333xTe/fu1dChQ/O6Xrx/8Xhc8Xi8xbOgSlI4HE59R53WxcU5p6lTp2rJkiVasWKFPvKRj7R4f3t6Dh06VK+99lqLf8SXL1+uqqqqtC/04J+2WkvvXemurq5WJBJRTU1Ni99MIdG6mLTV+zvf+Y7+8Y9/aNu2bakXSZo9e7Yef/xxSfQuFm21/vCHP6yePXue9eu0oUOH6t1339XmzZtT71+xYoU8z9OQIUNyfxBot7Z6nzhxQpLO+rUavYuUn8/shvxpbGx0W7dudVu3bnWS3MMPP+y2bt3q/vWvfznnnFu8eLErKytzv/71r11dXZ2bM2eOC4fDbu3atanP8eUvf9ldeOGFbsWKFe5vf/ubGzp0qBs6dKhfh4QzaKv1yJEj3WWXXeZWrlzp3n77bff444+7Tp06uUceeST1OWhdPL7yla+4rl27ulWrVrmDBw+mXk6cOJG6TVs9E4mEu/zyy111dbXbtm2bW7ZsmfvgBz/oHnjgAT8OCWfQVuv6+no3ZMgQN3DgQLd79+4Wt0kkEs45WheT9pzbp9Npz2pO7+LQntazZ892VVVVbtGiRa6urs59//vfd506dXK7d+9O3WbMmDHuyiuvdBs2bHDr1q1zF198sbvtttv8OCScRVu9Y7GY69u3rxs+fLjbsGGD2717t3vooYdcKBRyzz//fOrz0Lv4sPE2YuXKlU5S2svdd9+dus28efNc3759XadOndwVV1zhli5d2uJznDx50t13333uAx/4gKuoqHCf/vSn3cGDB/N8JGhLW60PHjzoPve5z7mePXu6Tp06uX79+rlZs2Y5z/NSn4PWxaO11pLc448/nrpNe3ru2bPHfepTn3LnnHOO69atm/vmN7+Z+hVUKAxttT7TuS/J/fOf/0x9HloXh/ac2619zOm/QpLeha+9rWfMmOF69+7tKioq3NChQ1tcHHHOuX//+9/utttuc126dHFVVVXu85//vGtsbMzjkaA92tN7165dbsKECa579+6uoqLCffSjH0379WL0Lj4h55zr6KvoAAAAAADgPfyMNwAAAAAAOcTGGwAAAACAHGLjDQAAAABADrHxBgAAAAAgh9h4AwAAAACQQ2y8AQAAAADIITbeAAAAAADkEBtvAAAAAAByiI03AAAAAAA5xMYbAAAAAIAcYuMNAAAAAEAOsfEGAAAAACCH/j+8q7kCS2EPGAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Μπορούμε επίσης να δημιουργήσουμε διαγράμματα κουτιού για υποσύνολα του συνόλου δεδομένων μας, για παράδειγμα, ομαδοποιημένα κατά ρόλο παίκτη.\n"
+    "Μπορούμε επίσης να δημιουργήσουμε διαγράμματα κουτιού για υποσύνολα του συνόλου δεδομένων μας, για παράδειγμα, ομαδοποιημένα ανά ρόλο παίκτη.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -409,19 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## Κανονική Κατανομή\n",
     "\n",
-    "Ας δημιουργήσουμε ένα τεχνητό δείγμα βαρών που ακολουθεί μια κανονική κατανομή με τον ίδιο μέσο όρο και διασπορά όπως τα πραγματικά μας δεδομένα:\n"
+    "Ας δημιουργήσουμε ένα τεχνητό δείγμα βαρών που ακολουθεί μια κανονική κατανομή με τον ίδιο μέσο όρο και διακύμανση όπως τα πραγματικά μας δεδομένα:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Δεδομένου ότι οι περισσότερες τιμές στην πραγματική ζωή κατανέμονται κανονικά, δεν πρέπει να χρησιμοποιούμε έναν ομοιόμορφο γεννήτη τυχαίων αριθμών για τη δημιουργία δεδομένων δειγμάτων. Εδώ είναι τι συμβαίνει αν προσπαθήσουμε να δημιουργήσουμε βάρη με μια ομοιόμορφη κατανομή (που δημιουργείται από το `np.random.rand`):\n"
+    "Δεδομένου ότι οι περισσότερες τιμές στην πραγματική ζωή κατανέμονται κανονικά, δεν πρέπει να χρησιμοποιούμε έναν ομοιόμορφο γεννήτορα τυχαίων αριθμών για να δημιουργήσουμε δείγματα δεδομένων. Δείτε τι συμβαίνει αν προσπαθήσουμε να δημιουργήσουμε βάρη με μια ομοιόμορφη κατανομή (δημιουργημένη από το `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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0RzFhwoT4/Oc/H/fdd19p/86dO6OzszPa2tpK2+rr62P69OnR0dFxyHP29fVFb2/voAcAAAAc6coe3b/4xS/innvuiVNPPTX+7d/+Lf7qr/4qvvKVr8QDDzwQERGdnZ0REdHY2DjoeY2NjaV9H7ZixYqor68vPSZNmlTusQEAAKDsyh7dAwMDcdZZZ8U3v/nN+PznPx+LFi2KK6+8MlavXv1bn3PZsmXR09NTeuzatauMEwMAAECOskf3ySefHNOmTRu07bTTTovXX389IiKampoiIqKrq2vQMV1dXaV9H1ZTUxN1dXWDHgAAAHCkK3t0X3DBBbFjx45B21555ZU45ZRTIuL9L1VramqKTZs2lfb39vbGtm3borW1tdzjAAAAQMWU/dvLr7766jj//PPjm9/8ZvzxH/9xPP3003HvvffGvffeGxERVVVVsWTJkrjlllvi1FNPjZaWlrjhhhuiubk55s2bV+5xAAAAoGLKHt3nnnturF+/PpYtWxY333xztLS0xMqVK2PBggWlY6699trYv39/LFq0KLq7u+PCCy+MDRs2xJgxY8o9DgAAAFRMVVEURaWH+Lh6e3ujvr4+enp6PhH3d0+57olKjwAAAPCJ8dqtcyo9wmENtUvLfk83AAAA8D7RDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQJD26b7311qiqqoolS5aUth04cCDa29tj/PjxccIJJ8T8+fOjq6srexQAAAAYVqnR/cwzz8Q//MM/xGc/+9lB26+++up47LHHYt26dbF58+bYvXt3XHLJJZmjAAAAwLBLi+59+/bFggUL4r777osTTzyxtL2npye+973vxZ133hlf/OIX4+yzz441a9bEU089FVu3bs0aBwAAAIZdWnS3t7fHnDlzoq2tbdD27du3R39//6DtU6dOjcmTJ0dHR0fWOAAAADDsRmac9Ac/+EE899xz8cwzz/zavs7Ozhg9enSMGzdu0PbGxsbo7Ow85Pn6+vqir6+v9HNvb29Z5wUAAIAMZb/SvWvXrvjqV78aDz30UIwZM6Ys51yxYkXU19eXHpMmTSrLeQEAACBT2aN7+/btsWfPnjjrrLNi5MiRMXLkyNi8eXPcddddMXLkyGhsbIyDBw9Gd3f3oOd1dXVFU1PTIc+5bNmy6OnpKT127dpV7rEBAACg7Mr+8fIvfelL8cILLwzadsUVV8TUqVPjb/7mb2LSpEkxatSo2LRpU8yfPz8iInbs2BGvv/56tLa2HvKcNTU1UVNTU+5RAQAAIFXZo3vs2LFx+umnD9p2/PHHx/jx40vbFy5cGEuXLo2Ghoaoq6uLq666KlpbW+O8884r9zgAAABQMSlfpHY43/72t6O6ujrmz58ffX19MXPmzPjud79biVEAAAAgTVVRFEWlh/i4ent7o76+Pnp6eqKurq7S4xzWlOueqPQIAAAAnxiv3Tqn0iMc1lC7NO3vdAMAAMCxTnQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJCk7NG9YsWKOPfcc2Ps2LExYcKEmDdvXuzYsWPQMQcOHIj29vYYP358nHDCCTF//vzo6uoq9ygAAABQUWWP7s2bN0d7e3ts3bo1Nm7cGP39/TFjxozYv39/6Zirr746HnvssVi3bl1s3rw5du/eHZdcckm5RwEAAICKGlnuE27YsGHQz/fff39MmDAhtm/fHl/4wheip6cnvve978XatWvji1/8YkRErFmzJk477bTYunVrnHfeeeUeCQAAACoi/Z7unp6eiIhoaGiIiIjt27dHf39/tLW1lY6ZOnVqTJ48OTo6Og55jr6+vujt7R30AAAAgCNdanQPDAzEkiVL4oILLojTTz89IiI6Oztj9OjRMW7cuEHHNjY2Rmdn5yHPs2LFiqivry89Jk2alDk2AAAAlEVqdLe3t8eLL74YP/jBD/5P51m2bFn09PSUHrt27SrThAAAAJCn7Pd0f2Dx4sXx+OOPx5YtW2LixIml7U1NTXHw4MHo7u4edLW7q6srmpqaDnmumpqaqKmpyRoVAAAAUpT9SndRFLF48eJYv359PPnkk9HS0jJo/9lnnx2jRo2KTZs2lbbt2LEjXn/99WhtbS33OAAAAFAxZb/S3d7eHmvXro1HHnkkxo4dW7pPu76+Pmpra6O+vj4WLlwYS5cujYaGhqirq4urrroqWltbfXM5AAAAR5WyR/c999wTEREXXXTRoO1r1qyJyy+/PCIivv3tb0d1dXXMnz8/+vr6YubMmfHd73633KMAAABARZU9uouiOOwxY8aMiVWrVsWqVavK/esBAADgiJH+d7oBAADgWCW6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVCy6V61aFVOmTIkxY8bE9OnT4+mnn67UKAAAAJCiItH9z//8z7F06dK48cYb47nnnoszzzwzZs6cGXv27KnEOAAAAJCiItF95513xpVXXhlXXHFFTJs2LVavXh3HHXdc/OM//mMlxgEAAIAUI4f7Fx48eDC2b98ey5YtK22rrq6Otra26OjoOORz+vr6oq+vr/RzT09PRET09vbmDlsmA33vVHoEAACAT4xPQut9MGNRFL/xuGGP7l/+8pfx3nvvRWNj46DtjY2N8R//8R+HfM6KFSvipptu+rXtkyZNSpkRAACAyqlfWekJhu7tt9+O+vr6j9w/7NH921i2bFksXbq09PPAwEDs3bs3xo8fH1VVVem/v7e3NyZNmhS7du2Kurq69N8Hn0TWCQyNtQKHZ53A0FgrlVUURbz99tvR3Nz8G48b9uj+1Kc+FSNGjIiurq5B27u6uqKpqemQz6mpqYmamppB28aNG5c14keqq6vzHzMchnUCQ2OtwOFZJzA01krl/KYr3B8Y9i9SGz16dJx99tmxadOm0raBgYHYtGlTtLa2Dvc4AAAAkKYiHy9funRpXHbZZXHOOefE7/7u78bKlStj//79ccUVV1RiHAAAAEhRkej+kz/5k/jv//7vWL58eXR2dsbnPve52LBhw699udqRoqamJm688cZf+4g78L+sExgaawUOzzqBobFWPhmqisN9vzkAAADwWxn2e7oBAADgWCG6AQAAIInoBgAAgCSiGwAAAJIcs9G9ZcuWuPjii6O5uTmqqqri4YcfHrR/3759sXjx4pg4cWLU1tbGtGnTYvXq1YOOOXDgQLS3t8f48ePjhBNOiPnz50dXV9cwvgrId7i10tXVFZdffnk0NzfHcccdF7NmzYpXX3110DHWCke7FStWxLnnnhtjx46NCRMmxLx582LHjh2DjhnKOnj99ddjzpw5cdxxx8WECRPimmuuiXfffXc4XwqkGco6uffee+Oiiy6Kurq6qKqqiu7u7l87z969e2PBggVRV1cX48aNi4ULF8a+ffuG6VVAvsOtlb1798ZVV10Vn/70p6O2tjYmT54cX/nKV6Knp2fQebynHDmO2ejev39/nHnmmbFq1apD7l+6dGls2LAhvv/978fLL78cS5YsicWLF8ejjz5aOubqq6+Oxx57LNatWxebN2+O3bt3xyWXXDJcLwGGxW9aK0VRxLx58+IXv/hFPPLII/HTn/40TjnllGhra4v9+/eXjrNWONpt3rw52tvbY+vWrbFx48bo7++PGTNmfKx18N5778WcOXPi4MGD8dRTT8UDDzwQ999/fyxfvrwSLwnKbijr5J133olZs2bF1772tY88z4IFC+Kll16KjRs3xuOPPx5btmyJRYsWDcdLgGFxuLWye/fu2L17d9xxxx3x4osvxv333x8bNmyIhQsXls7hPeUIU1BERLF+/fpB2z7zmc8UN99886BtZ511VvG3f/u3RVEURXd3dzFq1Khi3bp1pf0vv/xyERFFR0dH+sxQCR9eKzt27CgionjxxRdL2957773ipJNOKu67776iKKwVjk179uwpIqLYvHlzURRDWwf/+q//WlRXVxednZ2lY+65556irq6u6OvrG94XAMPgw+vk//fjH/+4iIjirbfeGrT9Zz/7WRERxTPPPFPa9qMf/aioqqoq3njjjeyRoSJ+01r5wA9/+MNi9OjRRX9/f1EU3lOONMfsle7DOf/88+PRRx+NN954I4qiiB//+MfxyiuvxIwZMyIiYvv27dHf3x9tbW2l50ydOjUmT54cHR0dlRobhlVfX19ERIwZM6a0rbq6OmpqauInP/lJRFgrHJs++IhfQ0NDRAxtHXR0dMQZZ5wRjY2NpWNmzpwZvb298dJLLw3j9DA8PrxOhqKjoyPGjRsX55xzTmlbW1tbVFdXx7Zt28o+IxwJhrJWenp6oq6uLkaOHBkR3lOONKL7I9x9990xbdq0mDhxYowePTpmzZoVq1atii984QsREdHZ2RmjR4+OcePGDXpeY2NjdHZ2VmBiGH4fRMOyZcvirbfeioMHD8Ztt90W//Vf/xVvvvlmRFgrHHsGBgZiyZIlccEFF8Tpp58eEUNbB52dnYP+5+iD/R/sg6PJodbJUHR2dsaECRMGbRs5cmQ0NDRYJxyVhrJWfvnLX8bXv/71QbdZeE85soys9ABHqrvvvju2bt0ajz76aJxyyimxZcuWaG9vj+bm5kFXKuBYNmrUqPiXf/mXWLhwYTQ0NMSIESOira0tZs+eHUVRVHo8qIj29vZ48cUXS5/2AH6ddQJDc7i10tvbG3PmzIlp06bF3/3d3w3vcAyZ6D6EX/3qV/G1r30t1q9fH3PmzImIiM9+9rPx/PPPxx133BFtbW3R1NQUBw8ejO7u7kFXLrq6uqKpqalCk8PwO/vss+P555+Pnp6eOHjwYJx00kkxffr00kf/rBWOJYsXLy59sdPEiRNL24eyDpqamuLpp58edL4Pvt3cWuFo8lHrZCiamppiz549g7a9++67sXfvXuuEo87h1srbb78ds2bNirFjx8b69etj1KhRpX3eU44sPl5+CP39/dHf3x/V1YP/9YwYMSIGBgYi4v3QGDVqVGzatKm0f8eOHfH6669Ha2vrsM4LR4L6+vo46aST4tVXX41nn3025s6dGxHWCseGoihi8eLFsX79+njyySejpaVl0P6hrIPW1tZ44YUXBgXFxo0bo66uLqZNmzY8LwQSHW6dDEVra2t0d3fH9u3bS9uefPLJGBgYiOnTp5dzXKiYoayV3t7emDFjRowePToeffTRQd+vE+E95UhzzF7p3rdvX/z85z8v/bxz5854/vnno6GhISZPnhy/93u/F9dcc03U1tbGKaecEps3b44HH3ww7rzzzoh4PzAWLlwYS5cujYaGhqirq4urrroqWltb47zzzqvUy4KyO9xaWbduXZx00kkxefLkeOGFF+KrX/1qzJs3r/Slg9YKx4L29vZYu3ZtPPLIIzF27NjS/XL19fVRW1s7pHUwY8aMmDZtWvz5n/953H777dHZ2RnXX399tLe3R01NTSVfHpTF4dZJxPv3mnZ2dpbed1544YUYO3ZsTJ48ORoaGuK0006LWbNmxZVXXhmrV6+O/v7+WLx4cVx66aXR3NxcsdcG5XS4tfJBcL/zzjvx/e9/P3p7e6O3tzciIk466aQYMWKE95QjTUW/O72CPvhTFB9+XHbZZUVRFMWbb75ZXH755UVzc3MxZsyY4tOf/nTxrW99qxgYGCid41e/+lXx5S9/uTjxxBOL4447rvjDP/zD4s0336zQK4Ich1sr3/nOd4qJEycWo0aNKiZPnlxcf/31v/anKKwVjnaHWiMRUaxZs6Z0zFDWwWuvvVbMnj27qK2tLT71qU8Vf/3Xf1368y/wSTeUdXLjjTce9pj/+Z//Kf70T/+0OOGEE4q6urriiiuuKN5+++3hf0GQ5HBr5aP+3ywiip07d5bO4z3lyFFVFL7tCAAAADK4pxsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAk/w+vxrJ4JXT+ewAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -554,23 +395,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Διάστημα Εμπιστοσύνης\n",
+    "## Διαστήματα Εμπιστοσύνης\n",
     "\n",
-    "Ας υπολογίσουμε τώρα τα διαστήματα εμπιστοσύνης για τα βάρη και τα ύψη των παικτών του μπέιζμπολ. Θα χρησιμοποιήσουμε τον κώδικα [από αυτή τη συζήτηση στο stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Ας υπολογίσουμε τώρα τα διαστήματα εμπιστοσύνης για τα βάρη και τα ύψη των παικτών του μπέιζμπολ. Θα χρησιμοποιήσουμε τον κώδικα [από αυτήν τη συζήτηση στο stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Μπορούμε να δούμε ότι τα διαστήματα δεν επικαλύπτονται.\n",
     "\n",
-    "Ένας στατιστικά πιο σωστός τρόπος για να αποδείξουμε την υπόθεση είναι να χρησιμοποιήσουμε ένα **Student t-test**:\n"
+    "Ένας στατιστικά πιο σωστός τρόπος για να αποδείξουμε την υπόθεση είναι να χρησιμοποιήσουμε ένα **t-test του Student**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "Οι δύο τιμές που επιστρέφονται από τη συνάρτηση `ttest_ind` είναι:\n",
-    "* Η τιμή p μπορεί να θεωρηθεί ως η πιθανότητα δύο κατανομών να έχουν τον ίδιο μέσο όρο. Στην περίπτωσή μας, είναι πολύ χαμηλή, που σημαίνει ότι υπάρχουν ισχυρές ενδείξεις ότι οι πρώτοι παίκτες βάσης είναι ψηλότεροι.\n",
-    "* Η τιμή t είναι η ενδιάμεση τιμή της κανονικοποιημένης διαφοράς μέσου όρου που χρησιμοποιείται στο t-test και συγκρίνεται με μια οριακή τιμή για μια δεδομένη τιμή εμπιστοσύνης.\n"
+    "* Η p-τιμή μπορεί να θεωρηθεί ως η πιθανότητα δύο κατανομών να έχουν τον ίδιο μέσο όρο. Στην περίπτωσή μας, είναι πολύ χαμηλή, που σημαίνει ότι υπάρχουν ισχυρές ενδείξεις που υποστηρίζουν ότι οι πρώτοι βάσεις είναι ψηλότεροι.\n",
+    "* Η t-τιμή είναι η ενδιάμεση τιμή της κανονικοποιημένης διαφοράς μέσων όρων που χρησιμοποιείται στο t-test και συγκρίνεται με μια οριακή τιμή για μια δεδομένη τιμή εμπιστοσύνης.\n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Προσομοίωση Κανονικής Κατανομής με το Θεώρημα Κεντρικού Ορίου\n",
     "\n",
-    "Ο ψευδοτυχαίος γεννήτορας στην Python έχει σχεδιαστεί για να μας δίνει μια ομοιόμορφη κατανομή. Αν θέλουμε να δημιουργήσουμε έναν γεννήτορα για κανονική κατανομή, μπορούμε να χρησιμοποιήσουμε το θεώρημα κεντρικού ορίου. Για να πάρουμε μια τιμή με κανονική κατανομή, απλώς θα υπολογίσουμε τον μέσο όρο ενός δείγματος που έχει παραχθεί με ομοιόμορφη κατανομή.\n"
+    "Ο ψευδοτυχαίος γεννήτορας στην Python έχει σχεδιαστεί για να μας δίνει μια ομοιόμορφη κατανομή. Αν θέλουμε να δημιουργήσουμε έναν γεννήτορα για κανονική κατανομή, μπορούμε να χρησιμοποιήσουμε το θεώρημα κεντρικού ορίου. Για να πάρουμε μια τιμή με κανονική κατανομή, απλώς θα υπολογίσουμε τον μέσο όρο ενός δείγματος που παράγεται ομοιόμορφα.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Συσχέτιση και η Κακόβουλη Εταιρεία Μπέιζμπολ\n",
     "\n",
-    "Η συσχέτιση μας επιτρέπει να βρίσκουμε σχέσεις μεταξύ ακολουθιών δεδομένων. Στο παράδειγμά μας, ας υποθέσουμε ότι υπάρχει μια κακόβουλη εταιρεία μπέιζμπολ που πληρώνει τους παίκτες της ανάλογα με το ύψος τους - όσο πιο ψηλός είναι ο παίκτης, τόσο περισσότερα χρήματα λαμβάνει. Ας πούμε ότι υπάρχει ένας βασικός μισθός $1000, και ένα επιπλέον μπόνους από $0 έως $100, ανάλογα με το ύψος. Θα χρησιμοποιήσουμε τους πραγματικούς παίκτες από το MLB και θα υπολογίσουμε τους φανταστικούς μισθούς τους:\n"
+    "Η συσχέτιση μας επιτρέπει να βρούμε σχέσεις μεταξύ ακολουθιών δεδομένων. Στο παράδειγμά μας, ας υποθέσουμε ότι υπάρχει μια κακόβουλη εταιρεία μπέιζμπολ που πληρώνει τους παίκτες της ανάλογα με το ύψος τους - όσο πιο ψηλός είναι ο παίκτης, τόσο περισσότερα χρήματα λαμβάνει. Ας υποθέσουμε ότι υπάρχει μια βασική αμοιβή των $1000, και ένα επιπλέον μπόνους από $0 έως $100, ανάλογα με το ύψος. Θα πάρουμε τους πραγματικούς παίκτες από το MLB και θα υπολογίσουμε τους φανταστικούς μισθούς τους:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ας υπολογίσουμε τώρα τη συνδιακύμανση και τη συσχέτιση αυτών των ακολουθιών. Το `np.cov` θα μας δώσει έναν λεγόμενο **πίνακα συνδιακύμανσης**, ο οποίος είναι μια επέκταση της συνδιακύμανσης σε πολλαπλές μεταβλητές. Το στοιχείο $M_{ij}$ του πίνακα συνδιακύμανσης $M$ είναι η συσχέτιση μεταξύ των μεταβλητών εισόδου $X_i$ και $X_j$, και οι διαγώνιες τιμές $M_{ii}$ είναι η διασπορά του $X_{i}$. Παρομοίως, το `np.corrcoef` θα μας δώσει τον **πίνακα συσχέτισης**.\n"
+    "Ας υπολογίσουμε τώρα τη συνδιακύμανση και τη συσχέτιση αυτών των ακολουθιών. Η `np.cov` θα μας δώσει έναν λεγόμενο **πίνακα συνδιακύμανσης**, ο οποίος είναι μια επέκταση της συνδιακύμανσης σε πολλαπλές μεταβλητές. Το στοιχείο $M_{ij}$ του πίνακα συνδιακύμανσης $M$ είναι η συσχέτιση μεταξύ των εισόδων $X_i$ και $X_j$, και οι διαγώνιες τιμές $M_{ii}$ είναι η διακύμανση του $X_{i}$. Παρομοίως, η `np.corrcoef` θα μας δώσει τον **πίνακα συσχέτισης**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Μια συσχέτιση ίση με 1 σημαίνει ότι υπάρχει ισχυρή **γραμμική σχέση** μεταξύ δύο μεταβλητών. Μπορούμε να δούμε οπτικά τη γραμμική σχέση σχεδιάζοντας τη μία τιμή σε σχέση με την άλλη:\n"
+    "Μια συσχέτιση ίση με 1 σημαίνει ότι υπάρχει μια ισχυρή **γραμμική σχέση** μεταξύ δύο μεταβλητών. Μπορούμε να δούμε οπτικά τη γραμμική σχέση σχεδιάζοντας τη μία τιμή σε σχέση με την άλλη:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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IoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoTQDQAAAABAgMQEuwAAAAAAXVdX36TbX92l8u+PKb1fvJ66cbj6WPk1HwgV/GkEAAAAwtQ1f9qq3V+5PMf7Kms1ZMn/6oKBNq2fNyaIlQFoxfJyAAAAIAydHLhPtPsrl67509ZurghAWwjdAAAAQJipq29qN3C32v2VS3X1Td1UEYD2ELoBAACAMHP7q7tMbQcgcAjdAAAAQJgp//6Yqe0ABA6hGwAAAAgz6f3iTW0HIHAI3QAAAECYeerG4aa2AxA4hG4AAAAgzPSxxuiCgbbTtrlgoI39uoEQQOgGAAAAwtD6eWPaDd7s0w2EDv7pCwAAAAhT6+eNUV19k25/dZfKvz+m9H7xeurG4cxwAyGEP40AAABAEDS7DRWVVquqtl7JiVaNzExSdJTF5376WGO0esbFAagQgBkI3QAAAEA3y99ToaUbSlRRU+85l2q3anGuU9lDUoNYGQCz8Uw3AAAA0I3y91RozpqdXoFbkipr6jVnzU7l76kIUmUAAoHQDQAAAHSTZrehpRtKZLRxrfXc0g0lana31QJAOPI5dBcUFCg3N1dpaWmyWCxat26d1/XXXntNEyZMUP/+/WWxWFRcXOx1vbq6WrfeeqvOOeccxcfHKz09Xb/97W9VU1Pj1a68vFw5OTlKSEhQcnKy7rrrLjU1Nfn8BQEAAIBQUVRafcoM94kMSRU19Soqre6+ogAElM+h+8iRIxo6dKhWrFjR7vXRo0fr8ccfb/P6wYMHdfDgQT355JPas2eP8vLylJ+fr5kzZ3raNDc3KycnRw0NDfrggw/00ksvKS8vT4sWLfK1XAAAACBkVNW2H7i70g5A6PP5RWqTJk3SpEmT2r0+bdo0SVJZWVmb14cMGaK//OUvnuMzzzxTjzzyiKZOnaqmpibFxMTozTffVElJid566y2lpKRo2LBheuihh3TPPfdoyZIlio2N9bVsAAAAIOiSE62mtgMQ+kLime6amhrZbDbFxLT8G0BhYaHOP/98paSkeNpMnDhRLpdLe/fuDVaZAAAAQLua3YYKP/tO/1P8tQo/+67N57JHZiYp1W5VexuDWdTyFvORmUkBrRVA9wn6lmHffvutHnroIc2ePdtzrrKy0itwS/IcV1ZWttnP8ePHdfz4cc+xy+UKQLUAAADAqTq7BVh0lEWLc52as2anLJLXC9Vag/jiXGeX9usGEJqCOtPtcrmUk5Mjp9OpJUuW+NXXsmXLZLfbPZ9BgwaZUyQAAABwGr5uAZY9JFUrp46Qw+69hNxht2rl1BHs0w1EmKDNdNfW1io7O1uJiYlau3atevXq5bnmcDhUVFTk1f7QoUOea21ZuHCh5s+f7zl2uVwEbwAAAARUR1uAWdSyBdh4p8Nr9jp7SKrGOx0qKq1WVW29khNblpQzww1EnqCEbpfLpYkTJyouLk7r16+X1er9r3xZWVl65JFHVFVVpeTkZEnS5s2bZbPZ5HQ62+wzLi5OcXFxAa8dAAAAPVuz2/CE5W9rj3d6C7CsM/t7XYuOspxyDkDk8Tl019XV6cCBA57j0tJSFRcXKykpSenp6aqurlZ5ebkOHjwoSdq3b5+klhlqh8Mhl8ulCRMm6OjRo1qzZo1cLpfn+eszzjhD0dHRmjBhgpxOp6ZNm6bly5ersrJS999/v+bOnUuwBgAAQNC09ex2Z7AFGNBzWQzDaGs1TLveffddXXHFFaecnzFjhvLy8pSXl6df/OIXp1xfvHixlixZ0u79UkuAz8jIkCR98cUXmjNnjt5991317t1bM2bM0GOPPeZ5w3lHXC6X7Ha7583oAAAAgD9an9326Zfnv/t/sy5hVhuIMJ3NnD6H7nBB6AYAAIBZmt2GRj++xecZbotaXpD2/j1X8rw2EGE6mzlDYp9uAAAAIJQVlVZ3KXBLbAEG9HRB36cbAAAACHVdeSbb0cY+3QB6HkI3AAAA0IHkRGvHjSQ9kHOuBiTGsQUYAA9CNwAAANCBkZlJSrVbVVlT3+aL1Fqf3b7lx5kEbQBeeKYbAAAA6EB0lEWLc52S/vGsdiue3QZwOoRuAAAAoBOyh6Rq5dQRcti9l5o77FatnDqCZ7cBtInl5QAAAEAnZQ9J1XinQ0Wl1aqqrefZbQAdInQDAAAAPoiOsijrzP7BLgNAmGB5OQAAAAAAAULoBgAAAAAgQFheDgAAgIhS/u1RZf/xPR1rdCu+V5Ty/+UypQ9ICHZZAHooQjcAAAAixg/v3agm9z+Ojza6NfbJdxQTJR14NCd4hQHosVheDgAAgIhwcuA+UZO75ToAdDdmugEAABCWmt2GZ+uuqCZ3u4G7VZO7Zek5S80BdCdCNwAAAMJO/p4KLd1Qooqaep/uy/7jeyp5aFKAqgKAUxG6AQAAEFby91RozpqdMrpw77HGDqbDAcBkPNMNAACAsNHsNrR0Q0mXArckxffi118A3Yu/dQAAABA2ikqrfV5SfqL8f7nMxGoAoGOEbgAAAISNqtquB+6YKPESNQDdjtANAACAsJGcaO3SfezTDSBYeJEaAAAAwsbIzCSl2q2qrKlv87lui6S+cRYdd1t0rNGt+F5Ryv+Xy5jhBhA0hG4AAACEjegoixbnOjVnzU5ZJK/gbfn7/y775+HKHpIahOoA4FQsLwcAAEBYyR6SqpVTR8hh915q7rBbtXLqCAI3gJDCTDcAAADCTvaQVI13OlRUWq2q2nolJ1o1MjNJ0VGWjm8GgG5E6AYAAEC3anYbpoTl6CiLss7sH4AKAcA8hG4AAAB0m/w9FVq6ocRrr+1Uu1WLc50sCwcQkXimGwAAAN0if0+F5qzZ6RW4Jamypl5z1uxU/p6KIFUGAIHDTDcAAAACovJwva5+pkCu+ibZrDGyWCxtbvNlqOXN40s3lGi808Fz2QAiCqEbAAAApjv3gTd0rNHtOf72SONp2xuSKmrqVVRazXPaACIKy8sBAABgqpMDty+qaus7bgQAYYTQDQAAANNUHq7vcuCWpOREa8eNACCMsLwcAAAAfjnW0KxHN5Wo7LujKvr8uy71YZHksLdsHwYAkYTQDQAAgC6b9fJ2bS6p8quP1temLc518hI1ABGH0A0AAIAuMSNwSy0z3OzTDSBSEboBAADgs2MNzX4F7md/PlyNhqHkxJYl5cxwA4hUhG4AAAD47NFNJV2+N75XlCYPTTOxGgAIXby9HAAAAD4r++5ol+6L7xWljx+aZHI1ABC6mOkGAACAzzL6J2jr/o7bxUVbZEiyWWP0+q1j5ejLlmAAehZCNwAAAHx272SnXvlbeYftihdPVHxsdDdUBAChieXlAAAA8Fl8bLTGO5NP22a8M5nADaDHI3QDAACgS1ZPv7jd4D3emazV0y/u5ooAIPSwvBwAAABdtnr6xTrW0KxHN5Wo7LujyuifoHsnO5nhBoC/I3QDAAD0UM1uQ0Wl1aqqrfdrv+z42Gg9dN35AagQAMIfoRsAAKAHyt9ToaUbSlRRU+85l2q3anGuU9lDUoNYGQBEFp7pBgAA6GHy91RozpqdXoFbkipr6jVnzU7l76kIUmUAEHmY6QYAAIhw75d8o6kvF3mOYyUZbbQzJFkkLd1QovFOR5eWmgMAvBG6AQAAIljGgo2nnGs4TXtDUkVNvYpKq5V1Zv+A1QUAPQXLywEAACJUW4G7s6pq6ztuBADoEKEbAAAgAr1f8o1f9ycnWk2qBAB6NpaXAwAARIgTtwD7l/8s7lIfFkkOe8v2YQAA/xG6AQAAIkBbW4D5qvW1aYtznbxEDQBMQugGAAAIc61bgLX1RnJfONinGwBM5/Mz3QUFBcrNzVVaWposFovWrVvndf21117ThAkT1L9/f1ksFhUXF5/SR319vebOnav+/furT58+uuGGG3To0CGvNuXl5crJyVFCQoKSk5N11113qampyddyAQAAIk6z21DhZ9/pf4q/1l8PfKsl6/d2OXA/MOlM/XHKMP2/WZfo/XuuJHADgMl8nuk+cuSIhg4dql/+8pe6/vrr27w+evRo/exnP9OsWbPa7OP222/Xxo0b9ec//1l2u13z5s3T9ddfr7/+9a+SpObmZuXk5MjhcOiDDz5QRUWFpk+frl69eunRRx/1tWQAAICIYcYy8hPNvOxHpvQDAGibxTCMLq9EslgsWrt2ra677rpTrpWVlSkzM1O7du3SsGHDPOdramp0xhln6D/+4z/005/+VJL0ySef6Nxzz1VhYaEuueQSvfHGG7r66qt18OBBpaSkSJJWrVqle+65R998841iY2M7rM3lcslut6umpkY2m62rXxEAACBkmLWMvFXZYzkm9QQAPU9nM2e3bxn24YcfqrGxUePGjfOc+9GPfqT09HQVFhZKkgoLC3X++ed7ArckTZw4US6XS3v37m2z3+PHj8vlcnl9AAAAIkWz29DSDSWmBO4100cSuAGgm3T7i9QqKysVGxurvn37ep1PSUlRZWWlp82Jgbv1euu1tixbtkxLly41v2AAAIAgaWhy65XCMn1RfVSGYXRpSXnrFmDv33MlbyQHgCCImLeXL1y4UPPnz/ccu1wuDRo0KIgVAQAAdN2yTSVavbVUbj+mttkCDACCr9tDt8PhUENDgw4fPuw1233o0CE5HA5Pm6KiIq/7Wt9u3trmZHFxcYqLiwtM0QAAAN1o2aYSPVdQ6nc/bAEGAMHX7aH7wgsvVK9evfT222/rhhtukCTt27dP5eXlysrKkiRlZWXpkUceUVVVlZKTkyVJmzdvls1mk9Pp7O6SAQAAuk1Dk1urt/oeuFuXkT/506H69shxJSdaNTIziRluAAgyn0N3XV2dDhw44DkuLS1VcXGxkpKSlJ6erurqapWXl+vgwYOSWgK11DJD7XA4ZLfbNXPmTM2fP19JSUmy2Wy69dZblZWVpUsuuUSSNGHCBDmdTk2bNk3Lly9XZWWl7r//fs2dO5fZbAAAENFeKSzzeUn5icvIf3zWANNrAgB0nc9vL9+xY4eGDx+u4cOHS5Lmz5+v4cOHa9GiRZKk9evXa/jw4crJaXkj5pQpUzR8+HCtWrXK08dTTz2lq6++WjfccIPGjh0rh8Oh1157zXM9Ojpar7/+uqKjo5WVlaWpU6dq+vTpevDBB/36sgAAAKHui+qjPt/jsFu1cuoIlpEDQAjya5/uUMY+3QAAIBz929bP9dDGjztsN+2SdF2UkcQycgAIks5mzoh5ezkAAECo23ewVpOfKVCzIUVbpE23jtU5aYlebaZlZeiRTR+fdol5lEV64OrzFBvj86JFAEA3I3QDAAB0g4wFG72Omw1p4tMFkqSyx3I852NjojRrTOZp314+a0wmgRsAwgR/WwMAAATYyYG7o+sLJzv1q7GZOnnFeJRF+tXYTC2czG4uABAumOkGAAAwWUOTW68UlumL6qNKiOncs9b7DtZ6LTVfONmpOyb8yNPP4KQETcvKYIYbAMIML1IDAAAw0bJNJVq9tdTnbb+iLdJny3I6bggACAm8SA0AAKCbLdtUctpnsU+nOSKnQQAArE8CAAAwQUOTW6u3di1wSy0z3QCAyMNMNwAAQBc1uw0VlVarqrZeO8qqfV5SfqJNt441rzAAQMggdAMAAHRB/p4KLd1QooqaelP6O3m/bgBAZCB0AwAA+Ch/T4XmrNkpsx7DPnGfbgBAZCF0AwAA+KDZbWjphpIuBW7L3z9utTzDvenWscxwA0CEI3QDAAB04MR9tw3D6PKS8tljM7VwstPk6gAAoYzQDQAAcBpd3Xf7RFEWadYYAjcA9ESEbgAAgHb4s+/2tEvSZbFYNDgpQdOyMhQbw06tANATEboBAAD+rq6+Sbe/ukvl3x/TwL5Wvf3JNz73YZHksFu15Johio5i820A6OkI3QAAAJKu+dNW7f7K5TneV1nrcx+tEXtxrpPADQCQROgGAAA4JXB3lcNu1eJcp7KHpJpQFQAgEhC6AQBAj1ZX3+RX4J52SbouykhScqJVIzOTmOEGAHghdAMAgB7nv/9aqjs3lPjdT5RFeuDq83hJGgCgXYRuAADQo2Qs2GhaX7PGZBK4AQCnRegGAAA9hlmBm323AQCdRegGAAARq6HJrVcKy/RF9VF9W+f728hPdPfEs1XpOs6+2wAAnxC6AQBARFq2qUSrt5bKbfjf1wUDbfrNFWf53xEAoMchdAMAgIizbFOJnisoNaWvCwbatH7eGFP6AgD0PIRuAAAQURqa3Fq91b/AfY4jUen94vXUjcPVx8qvSwCAruO/IgAAIOzVHG3UL/OKdLCmXtEW+bWk/Mlcp37640zzigMA9GiEbgAAENYue2KLvvjumGn9EbgBAGbitZsAACBsmR24yx7LMa0vAAAkZroBAEAYaXYbKiqtVlVtvfrERJsWuFlSDgAIFEI3AAAIC/l7KrR0Q4kqaur97utXYzO1cLLThKoAADg9QjcAAAh5+XsqNGfNTvm75XaURZo1hsANAOg+hG4AABDSmt2Glm4o6XLgHtjXqivPTdHgpARNy8pQbAyvtAEAdB9CNwAACDkNTW69UlimL6qPyjAMv5aUb/ztWNkTeplYHQAAnUfoBgAAIWXZphKt3lrq117brQb3jydwAwCCitANAABCxrJNJXquoNSUvgb3j9d7d11pSl8AAHQVoRsAAATNluJK/fI/P/SrD4ukAb1jNKh/H1XU1CvNbtULt4xkhhsAEBII3QAAICgyFmz0uw/L3//3oZ9coOwhqX73BwCA2QjdAACg25kRuCXJYbdqca6TwA0ACFmEbgAA0K22FFf6df+0S9J1UUaSkhOtGpmZpOgoS8c3AQAQJIRuAAAQcDVHG/XLvCIdrKn3a/uvKIv0wNXnsdc2ACBsELoBAEBAXfbEFn3x3TFT+po1JpPADQAIK4RuAAAQMGYF7ihLS+BeONlpQlUAAHQfQjcAADBNXX2Tbn91l8q/P6Y0W5xfgfv6EQPUJ663BiclaFpWBjPcAICwROgGAACmuOZPW7X7K5fneF9lrV/9/f5no/wtCQCAoOOfjAEAgN9ODtz+Knssx7S+AAAIJma6AQCAX+rqm0wL3C9MuVBXDnOY0hcAAKGA0A0AAHx24hZgR443+tXX/y2aIHtCL5MqAwAgtBC6AQCAT8zcAmxw/3gCNwAgovFMNwAA6DSzA/d7d11pSl8AAIQqZroBAECn1Bxt9CtwDxuYqEO1jUqzW/XCLSOZ4QYA9AiEbgAA0K6GJrdeKSzTF9VHteXjQ13u54KBNq2bN8bEygAACA8+Ly8vKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/d7tfn000917bXXasCAAbLZbBo9erTeeecdrzbl5eXKyclRQkKCkpOTddddd6mpqcn3bwgAALpk2aYS/eiBN/TQxo/1cuEX+upwfZf6uWCgTesJ3ACAHsrn0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+/h//ob766qvV1NSkLVu26MMPP9TQoUN19dVXq7KyUpLU3NysnJwcNTQ06IMPPtBLL72kvLw8LVq0qItfEwAA+GLZphI9V1Aqt+H7vTZrtM5xJGr8ucnas2QigRsA0KNZDMPown9O/36zxaK1a9fquuuuk9Qyy52WlqY77rhDd955pySppqZGKSkpysvL05QpU/Ttt9/qjDPOUEFBgcaMafmPcG1trWw2mzZv3qxx48bpjTfe0NVXX62DBw8qJSVFkrRq1Srdc889+uabbxQbG9thbS6XS3a7XTU1NbLZbF39igAA9AhzXsjXG582m9IXW4ABAHqCzmZOU99eXlpaqsrKSo0bN85zzm63a9SoUSosLJQk9e/fX+ecc45efvllHTlyRE1NTXruueeUnJysCy+8UJJUWFio888/3xO4JWnixIlyuVzau3evmSUDANDjZSzYaFrgZgswAAC8mfoitdbl4SeG5dbj1msWi0VvvfWWrrvuOiUmJioqKkrJycnKz89Xv379PP201ceJP+Nkx48f1/Hjxz3HLpfLnC8FAEAEy1iw0bS+2AIMAIBTdfvbyw3D0Ny5c5WcnKytW7cqPj5e//qv/6rc3Fxt375dqampXep32bJlWrp0qcnVAgAQuea8kO/X/QP7WtVsiC3AAAA4DVNDt8PhkCQdOnTIKzwfOnRIw4YNkyRt2bJFr7/+ur7//nvPuvdnn31Wmzdv1ksvvaQFCxbI4XCoqKjIq+9Dhw55/YyTLVy4UPPnz/ccu1wuDRo0yLTvBgBAJKg8XK+rnymQq75JDc1dfq2LoizSljuvUGyMqU+qAQAQcUz9L2VmZqYcDofefvttzzmXy6Vt27YpKytLknT06NGWHxzl/aOjoqLkdrslSVlZWfroo49UVVXlub5582bZbDY5nc42f3ZcXJxsNpvXBwAA/MO5D7yhSx57W98eafQrcEvSrDGZBG4AADrB55nuuro6HThwwHNcWlqq4uJiJSUlKT09XbfddpsefvhhnXXWWcrMzNQDDzygtLQ0zxvOs7Ky1K9fP82YMUOLFi1SfHy8Vq9erdLSUuXk5EiSJkyYIKfTqWnTpmn58uWqrKzU/fffr7lz5youLs6cbw4AQA9y7gNv6Fij2+9+oiwtgXvh5Lb/ERwAAHjzOXTv2LFDV1xxhee4dUn3jBkzlJeXp7vvvltHjhzR7NmzdfjwYY0ePVr5+fmyWq2SpAEDBig/P1/33XefrrzySjU2Nuq8887T//zP/2jo0KGSpOjoaL3++uuaM2eOsrKy1Lt3b82YMUMPPvigGd8ZAICI19Dk1iuFZfqi+qiS4mL8CtxnJ0iXDB2swUkJmpaVwQw3AAA+8Guf7lDGPt0AgJ5q2aYSrd5aKrdJ/4UveyzHnI4AAIggnc2c3f72cgAAEDjLNpXouYJS0/ojcAMA4B/WhwEAECEamtxavdWcwD3p7GgCNwAAJmCmGwCAMNbsNlRUWq2q2nrtKKv2a0n53xZcJUdfq3nFAQAAQjcAAOEqf0+Flm4oUUVNvd99xfeKInADABAAhG4AAMJQ/p4KzVmzU2a8Ky2+V5Q+fmiSCT0BAICTEboBAAgDJ24BNqhfgv5162ddCtwWSUm9e6m2vkk2a4xev3UsM9wAAAQQoRsAgBBn5hZgs8dmauFkp/8dAQCATiF0AwAQwszaAizKIs0aQ+AGAKC7EboBAAhR/m4BNu2SdFksFg1OStC0rAzFxrBTKAAA3Y3QDQBACMl7Z5+W/O8Bv/qwSHLYrVpyzRBFR1nMKQwAAHQJoRsAgBCRsWCj3320RuzFuU4CNwAAIYDQDQBACDAjcEstM9yLc53KHpJqSn8AAMA/hG4AAIKg2W2oqLRaVbX1+mvZV13uJ8oivXTLSFUfa1ByolUjM5OY4QYAIIQQugEA6Gb5eyq0dEOJKmrq/e5r1phMjTnnDBOqAgAAgUDoBgCgG+XvqdCcNTvl75bbbAEGAEB4IHQDANBNmt2Glm4o8StwT88azBZgAACEEUI3AAAB1NDk1iuFZfqi+qgMw/BrSfmSiT/ULVecY2J1AAAg0AjdAAAEyLJNJVq9tVRuf9eS/x2BGwCA8EPoBgAgAJZtKtFzBaWm9Vf2WI5pfQEAgO5D6AYAwGQNTW6t3up74LZIpzzvzZJyAADCG6EbAAATVNc1aMrzH6iqtkExUfJ5SXnrztqrpo5Q9pBU0+sDAADBQegGAMBPFz+8Wd/UNfjVh8Nu1eJcJ4EbAIAIQ+gGAMAP/gTuaZek66KMJCUnWjUyM0nRUZaObwIAAGGF0A0AQCeduIQ8OTFWz950UZcDd5RFeuDq89hrGwCACEfoBgCgE06e0T58rFHj/vBel/ubNSaTwA0AQA9A6AYAoANmPLPdKsrSErgXTnaa0h8AAAhthG4AAE6juq7B78A9oHcvTb4gTYOTEjQtK4MZbgAAehBCNwAAJ5nzQr7e+LTZtP7evP1yJfWJNa0/AAAQPgjdAACcIGPBRlP7O6NPLIEbAIAejPVtAAD8XSAC9/b7x5vaJwAACC/MdAMAeqyao436ZV6RDtbUq6qm3q++3rrtMv3mP3Z4thP7z9mXMsMNAAAI3QCAnumyJ7boi++OmdLXGX1i9UNHH705/3JT+gMAAJGD5eUAgB7H7MDNEnIAANAeZroBAD1KzdFGvwN33/heLCEHAACdQugGAES80qojyv7jezrebPjd16Szo7XylxNMqAoAAPQEhG4AQET7p4Ub5fY/a3us/GW2eZ0BAICIxzPdAICIZXbgLnssx7zOAABAj8BMNwAgYhxraNajm0pU9t1R9U+INi1wtywpZ4YbAAD4jtANAIgIs17ers0lVab0Nbh/vN6760pT+gIAAD0by8sBAGGPwA0AAEIVM90AgLB2rKHZ78CdarcqzW7VC7eMlD2hl0mVAQAAELoBAGHoG9dx/eTZ91V9pFGSfw9uvzP/cmUm9zanMAAAgJMQugEAYeWCJf8rV32TKX1FWUTgBgAAAcUz3QCAsGF24P58GVuAAQCAwGKmGwAQspas3aa8bd+a0ldslNTgluKiLcr/l8uY4QYAAN2C0A0ACEkZCzaa1td4Z7JWT7/YtP4AAAA6i+XlAICQQ+AGAACRgpluAEBIWbJ2m1/3J/SK0oUZScron6B7JzsVHxttUmUAAAC+I3QDAIKurr5Jt7+6S+XfH9O+ylq/+nrvrit1hi3OpMoAAAD8Q+gGAATVNX/aqt1fuUzpy2aNIXADAICQwjPdAICgMTtw714y0ZS+AAAAzMJMNwCg2xxraNajm0pU9t1RpdmtfgfuhF7RSurdS2t/M5oZbgAAEJJ8nukuKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/ef0s/GjRs1atQoxcfHq1+/frruuuu8rpeXlysnJ0cJCQlKTk7WXXfdpaamJl/LBQCEiFkvb9e5i/L1yt/KtXX/t3p1x1d+9Vf2WI5KHsrW+wuuInADAICQ5XPoPnLkiIYOHaoVK1a0eX358uV6+umntWrVKm3btk29e/fWxIkTVV9f72nzl7/8RdOmTdMvfvEL/d///Z/++te/6qabbvJcb25uVk5OjhoaGvTBBx/opZdeUl5enhYtWtSFrwgACLZZL2/X5pIq0/oreyzHtL4AAAACyWIYhtHlmy0WrV271jNLbRiG0tLSdMcdd+jOO++UJNXU1CglJUV5eXmaMmWKmpqalJGRoaVLl2rmzJlt9vvGG2/o6quv1sGDB5WSkiJJWrVqle655x598803io2N7bA2l8slu92umpoa2Wy2rn5FAICfjjU069xF+ab0dcuoAVryk1Gm9AUAAOCPzmZOU5/pLi0tVWVlpcaNG+c5Z7fbNWrUKBUWFmrKlCnauXOnvv76a0VFRWn48OGqrKzUsGHD9MQTT2jIkCGSpMLCQp1//vmewC1JEydO1Jw5c7R3714NHz7czLIBACb7xnVcP3n2fVUfaZTU5X/blSTtWTJRfay8ggQAAIQnU3+LqayslCSvsNx63Hrt888/lyQtWbJEv//975WRkaHf/e53uvzyy/Xpp58qKSlJlZWVbfZx4s842fHjx3X8+HHPsctlzttwAQC+uWDJ/8pVb847OC4YaCNwAwCAsNbtW4a53W5J0n333acbbrhBF154oV588UVZLBb9+c9/7nK/y5Ytk91u93wGDRpkVskAgE4yO3CvnzfGlL4AAACCxdTQ7XA4JEmHDh3yOn/o0CHPtdTUVEmS0+n0XI+Li9M//dM/qby83NNPW32c+DNOtnDhQtXU1Hg+X375pQnfCADQWd+4jvsVuK84Z4DOcSRq/LnJ2rNkIoEbAABEBFPX7GVmZsrhcOjtt9/WsGHDJLUs8962bZvmzJkjSbrwwgsVFxenffv2afTo0ZKkxsZGlZWVafDgwZKkrKwsPfLII6qqqlJycrIkafPmzbLZbF5h/URxcXGKi2PLGADoTifuu/1hWXWX+xnvTNbq6RebWBkAAEBo8Dl019XV6cCBA57j0tJSFRcXKykpSenp6brtttv08MMP66yzzlJmZqYeeOABpaWled5wbrPZ9Otf/1qLFy/WoEGDNHjwYD3xxBOSpH/+53+WJE2YMEFOp1PTpk3T8uXLVVlZqfvvv19z584lWANAiDBrGzACNwAAiGQ+h+4dO3boiiuu8BzPnz9fkjRjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqxWq+eeJ554QjExMZo2bZqOHTumUaNGacuWLerXr58kKTo6Wq+//rrmzJmjrKws9e7dWzNmzNCDDz7o7/cFAJjAn8Cd0CtKF2YkKaN/gu6d7FR8bLTJ1QEAAIQOv/bpDmXs0w0A5hl/70btd5vT1/Z7x+kMG6uWAABAeAvKPt0AgMiTsWCjaX3ZrDEEbgAA0KN0+5ZhAIDwYXbg3r1komn9AQAAhANmugEAbRp/r3+BO6FXlCSLknr30trfjGaGGwAA9EiEbgCAR0OTW68UlumL6qN+P8P94QMTeEkaAADo8QjdAABJ0rJNJVq9tVRuE16vOd6ZTOAGAAAQoRsAoJbA/VxBqSl9se82AADAPxC6AaAHqjnaqF/mFelgTb1SbXHa+WWNX/2NOWsA+24DAAC0gdANAD3MZU9s0RffHfMcV9TU+9Vf2WM5/pYEAAAQsdgyDAB6kJMDt78I3AAAAKdH6AaAHqLmaKNpgfusKAI3AABAZ7C8HAAiWOXhel39TIFc9U1q9uO15FEW6ZOHJik2hn+rBQAA8AWhGwAi1LkPvKFjjX5utv13s8ZkErgBAAC6gNANABHIrMAdZWkJ3AsnO02oCgAAoOchdANABDjW0KxHN5Wo7LujSu4T61fgvnP8Waqqa9DgpARNy8pghhsAAMAPhG4ACHOzXt6uzSVVpvQ1uH+85l11til9AQAAgLeXA0BYMztwv3fXlab0BQAAgBbMdANAmDrW0OxX4I62SMk2q9LsVr1wy0jZE3qZWB0AAAAkQjcAhJXfbyrW0wVfm9LXX++5So6+VlP6AgAAQNsI3QAQJjIWbDStr/heUQRuAACAbsAz3QAQBswO3B8/NMm0/gAAANA+ZroBIASduAVYZcW3fvWVFB+tuga3bNYYvX7rWGa4AQAAuhGhGwBCjJlvJB/vTNbq6Reb0hcAAAB8x/JyAAghBG4AAIDIwkw3AIQIf7cAk6QxZw1QRv8E3TvZqfjYaJMqAwAAQFcRugEgiE58dvtQzTG/+vrt2B9o/uRh5hQGAAAAUxC6ASBIzFxKLonADQAAEIJ4phsAgsDswF32WI5pfQEAAMA8zHQDQDf4uvqYJj39no4cb1bv2Gi5jjeb0i9LygEAAEIboRsAAuzs+zapodnwHPsTuHkjOQAAQHhheTkABNDJgdsfBG4AAIDww0w3AATI19XH/ArcZyf3Voo9ni3AAAAAwhihGwBM1NDk1iuFZfqi+qheLSr3q6//mTeGoA0AABDmCN0AYJJlm0q0emup3CasJh/vTCZwAwAARABCNwCYYNmmEj1XUGpKXzy7DQAAEDkI3QDgp4Ymt1Zv7Xrg/smwFH17pJlntwEAACIQoRsAuuC6RzaquNb/fmKjLXpqykX+dwQAAICQROgGAB9lLNhoSj+x0RZ9+shkU/oCAABAaCJ0A4AP/AnccdEWNbkN9Y6L1hu/vUw/SIo3sTIAAACEIkI3AJzGsYZmPbqpRGXfHdW2/d92uZ8oi/TR0mzFxkSZWB0AAABCHaEbANox6+Xt2lxSZU5fYzIJ3AAAAD0QoRsA2mBW4I6ytATuhZOdJlQFAACAcEPoBoCTHGto9jtwT88arMFJCZqWlcEMNwAAQA9G6AYASV9XH9Okp9/TkePNirL419ewROnBa4eYUxgAAADCGqEbQI939n2b1NBseI5P+H92ybr7cvysCAAAAJGCNY8AerSTA7e/yh4jcAMAAOAfmOkG0KOcuAXYgN4xpgXuYYnMcAMAAOBUhG4APYaZW4CNdyZr9fSLTekLAAAAkYvl5QB6BAI3AAAAgoGZbgARz98twHpFSZecOUAZ/RN072Sn4mOjTawOAAAAkYzQDSAi1dU36fZXd6n8+2M6Ut/oV1/v3nmlfpAUb1JlAAAA6EkI3QAizjV/2qrdX7lM6Ss22kLgBgAAQJfxTDeAiGJ24P70kcmm9AUAAICeyefQXVBQoNzcXKWlpclisWjdunVe1w3D0KJFi5Samqr4+HiNGzdO+/fvb7Ov48ePa9iwYbJYLCouLva6tnv3bo0ZM0ZWq1WDBg3S8uXLfS0VQA/w6Podyliw0fPxJ3AnxkYp2iLZrNH6691XErgBAADgN59D95EjRzR06FCtWLGizevLly/X008/rVWrVmnbtm3q3bu3Jk6cqPr6+lPa3n333UpLSzvlvMvl0oQJEzR48GB9+OGHeuKJJ7RkyRI9//zzvpYLIIJlLNio5z84ZEpf453J+ujBSfpsWY52L8lmSTkAAABM4fMz3ZMmTdKkSZPavGYYhv7whz/o/vvv17XXXitJevnll5WSkqJ169ZpypQpnrZvvPGG3nzzTf3lL3/RG2+84dXPv//7v6uhoUEvvPCCYmNjdd5556m4uFi///3vNXv2bF9LBhCBMhZsNK0vtgADAABAoJj6THdpaakqKys1btw4zzm73a5Ro0apsLDQc+7QoUOaNWuWXnnlFSUkJJzST2FhocaOHavY2FjPuYkTJ2rfvn36/vvv2/zZx48fl8vl8voAiEyPrt/h1/0D+1o15qwBmnZJuj5+MJvADQAAgIAx9e3llZWVkqSUlBSv8ykpKZ5rhmHolltu0a9//WtddNFFKisra7OfzMzMU/povdavX79T7lm2bJmWLl1qxtcAEIJO3AJsX2WtX33l33aZ+ljZvAEAAACB1+2/dT7zzDOqra3VwoULTe134cKFmj9/vufY5XJp0KBBpv4MAMFh5hvJLxhoI3ADAACg25i6vNzhcEhqWT5+okOHDnmubdmyRYWFhYqLi1NMTIx++MMfSpIuuugizZgxw9NPW32c+DNOFhcXJ5vN5vUBEP7MDtzr540xpS8AAACgM0yd7snMzJTD4dDbb7+tYcOGSWqZcd62bZvmzJkjSXr66af18MMPe+45ePCgJk6cqFdffVWjRo2SJGVlZem+++5TY2OjevXqJUnavHmzzjnnnDaXlgOIHM1uQ0Wl1aqqrZctLsavwP0Dm9QnIVHp/eL11I3DmeEGAABAt/P5N9C6ujodOHDAc1xaWqri4mIlJSUpPT1dt912mx5++GGdddZZyszM1AMPPKC0tDRdd911kqT09HSv/vr06SNJOvPMMzVw4EBJ0k033aSlS5dq5syZuueee7Rnzx798Y9/1FNPPdXV7wkgDOTvqdDSDSWqqDl1i8Gu+Ou9Oab0AwAAAHSVz6F7x44duuKKKzzHrc9Rz5gxQ3l5ebr77rt15MgRzZ49W4cPH9bo0aOVn58vq9Xa6Z9ht9v15ptvau7cubrwwgs1YMAALVq0iO3CgAiWv6dCc9bslGFSf2WPEbgBAAAQfBbDMMz6HTekuFwu2e121dTU8Hw3EOKa3YZGP77FlBnu2Zem6N5rLjKhKgAAAKB9nc2cPOAIIChOfHb729rjfgXuPUsm8rw2AAAAQhK/pQLodmY+u80WYAAAAAhl/KYKoFuZ+ew2W4ABAAAg1BG6AQTUicvIB/SJ05L1e30O3BZJyYlxOn+gTV9+X88WYAAAAAgb/MYKIGDMWEZu+fv/Lr32PGUPSTWnMAAAAKCbELoBBIRZy8gddqsW5zoJ3AAAAAhLhG4Apmt2G1q6oaTLgfuBnHM1IDFOyYlWjcxMUnSUpeObAAAAgBBE6AZgiltWbNS7X/rXh0UtM9u3/DiToA0AAICIQOgG4LeMBRv97qM1Yi/OdRK4AQAAEDEI3QD8Ykbglnh2GwAAAJGJ0A2gy25Z0bXA3bqM/MmfDtW3R47z7DYAAAAiFqEbQJd15RnuE5eR//isAabWAwAAAIQaQjeAbsUycgAAAPQkhG4A3eKPU4axjBwAAAA9DqEbQJsamtx6pbBMX1Qf1eCkBE3LylBsTJRXm8sHdW6J+eWDpGuH/SBAlQIAAAChy2IYhhHsIgLB5XLJbrerpqZGNpst2OUAYWXZphKt3loq9wl/O0RZpFljMrVwstOrbWfeXl72WI7ZJQIAAABB1dnMGdXuFQA90rJNJXquwDtwS5LbkJ4rKNWyTSVe5zsK1ARuAAAA9GSEbgAeDU1urd5aeto2q7eWqqHJ7XWu7LEcXT7Iu93lgwjcAAAAAM90Az3csYZmPbqpRGXfHdXR402nzHCfzG1IrxSWaeaYf/I6nzeXgA0AAACcjNAN9GCzXt6uzSVVPt/3RfXRAFQDAAAARB6WlwM9VFcDtyQNTkowuRoAAAAgMjHTDfQQdfVNuv3VXSr//pjS7HF6Z9+3XeonyiJNy8owtzgAAAAgQhG6gR7gmj9t1e6vXJ7jfZW1Xe5r1pjMU/brBgAAANA2QjcQ4U4O3F3V3j7dAAAAANpH6AYiWF19k1+B+8L0vjrvB3YNTkrQtKwMZrgBAAAAHxG6gQjz1s4K/X//tdOUvtb8f5coPjbalL4AAACAnojQDUSQjAUbTetrvDOZwA0AAAD4ibWiQIQwO3Cvnn6xaf0BAAAAPRUz3UAEeGtnhV/333jRQB2sqVdG/wTdO9nJDDcAAABgEkI3EAH8eYb7goE2Pf7ToSZWAwAAAKAVy8uBHuyCgTatnzcm2GUAAAAAEYuZbqCHOceRqPR+8XrqxuHqY+WvAAAAACCQ+I0biAD/+rMRnVpi/q8/G6FxI1K7oSIAAAAAEsvLgYjQ2SBN4AYAAAC6F6EbiBBlj+X4dR0AAACA+VheDoSIZrehotJqVdXWKznRqpGZSYqOsvjUR9ljOXprZ4XXUnOWlAMAAADBQ+gGQkD+ngot3VCiipp6z7lUu1WLc53KHuJbYB43IlVlI5jVBgAAAEIBy8uBIMvfU6E5a3Z6BW5Jqqyp15w1O5W/pyJIlQEAAADwF6EbCKJmt6GlG0pktHGt9dzSDSVqdrfVAgAAAECoI3QDQVRUWn3KDPeJDEkVNfUqKq3uvqIAAAAAmIbQDQRRVW37gbsr7QAAAACEFkI3EETJiVZT2wEAAAAILYRuIIhGZiYp1W5VexuDWdTyFvORmUndWRYAAAAAkxC6gQB5Ycsnyliw0fN5Ycsnp7SJjrJoca5Tkk4J3q3Hi3OdPu/XDQAAACA0WAzDiMjXIrtcLtntdtXU1MhmswW7HPQwGQs2tnut7LFT99A2c59uAAAAAIHX2cxJ6AZMdrrA3aqt4N3sNlRUWq2q2nolJ7YsKWeGGwAAAAhNnc2cMd1YExDx2lpC3l67X175I69z0VEWZZ3ZPxBlAQAAAAgSnukGTPTgm5+Z2g4AAABAeCN0AwAAAAAQIIRuAAAAAAAChNANdFKz21DhZ9/pf4q/VuFn36nZfeo7CBdNOLNTfXW2HQAAAIDw5nPoLigoUG5urtLS0mSxWLRu3Tqv64ZhaNGiRUpNTVV8fLzGjRun/fv3e66XlZVp5syZyszMVHx8vM4880wtXrxYDQ0NXv3s3r1bY8aMkdVq1aBBg7R8+fKufUPABPl7KjT68S36+eq/6V/+s1g/X/03jX58i/L3VHi1O/nlaO3pbDsAAAAA4c3n0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+vmX/4U8++URut1vPPfec9u7dq6eeekqrVq3Svffe6+nD5XJpwoQJGjx4sD788EM98cQTWrJkiZ5//vkufk2g6/L3VGjOmp1ee2hLUmVNveas2XlK8G5rOzBfrgMAAACIHH7t022xWLR27Vpdd911klpmudPS0nTHHXfozjvvlCTV1NQoJSVFeXl5mjJlSpv9PPHEE1q5cqU+//xzSdLKlSt13333qbKyUrGxsZKkBQsWaN26dfrkk85tycQ+3eiqYw3NenRTicq+O6rBSQl6c2+lquoa2mxrkeSwW/X+PVeesqf2C1s+8XpL+aIJZzLDDQAAAESIoOzTXVpaqsrKSo0bN85zzm63a9SoUSosLGw3dNfU1CgpKclzXFhYqLFjx3oCtyRNnDhRjz/+uL7//nv169fPzLIBj1kvb9fmkirP8dYO2huSKmrqVVRafcoe27+88keEbAAAAKCHM/VFapWVlZKklJQUr/MpKSmeayc7cOCAnnnmGf3qV7/y6qetPk78GSc7fvy4XC6X1wfwxcmB2xdVtfUdNwIAAADQ4wT17eVff/21srOz9c///M+aNWuWX30tW7ZMdrvd8xk0aJBJVaInONbQ3OXALUnJiVYTqwEAAAAQKUwN3Q6HQ5J06NAhr/OHDh3yXGt18OBBXXHFFbr00ktPeUGaw+Fos48Tf8bJFi5cqJqaGs/nyy+/9Ou7oGd5dFNJl+6zSEq1WzUyM6nDtgAAAAB6HlNDd2ZmphwOh95++23POZfLpW3btikrK8tz7uuvv9bll1+uCy+8UC+++KKiorzLyMrKUkFBgRobGz3nNm/erHPOOafd57nj4uJks9m8PkBnlX131Od7Wl+btjjXecpL1AAAAABA6kLorqurU3FxsYqLiyW1vDytuLhY5eXlslgsuu222/Twww9r/fr1+uijjzR9+nSlpaV53nDeGrjT09P15JNP6ptvvlFlZaXXs9o33XSTYmNjNXPmTO3du1evvvqq/vjHP2r+/PmmfGngZBn9E3y+x2G3auXUEcoekhqAigAAAABEAp/fXr5jxw5dccUVnuPWIDxjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqzWlmdeN2/erAMHDujAgQMaOHCgV9+tu5fZ7Xa9+eabmjt3ri688EINGDBAixYt0uzZs7v8RdFzfV19TJOefk9Hjjerd1y03vjtZfpBUrxXm3snO/XK38o77OulWy7W4fpGJSe2LClnhhsAAADA6fi1T3coY59uSNLZ921SQ/Op/188NtqiTx+Z7HWuo7eXj3cma/X0i02vEQAAAED46WzmDOrby4FAai9wS1JDs6Gz79vkdW719Is13pncZnsCNwAAAICu8Hl5ORAOvq4+1m7gbtXQbOjr6mNeS81XT79Yxxqa9eimEpV9d1QZ/RN072Sn4mOjA10yAAAAgAjE8nJEjGa3oaLSalXV1mvhX3braKO7w3ts1mjtXpLdDdUBAAAAiCSdzZzMdCMi5O+p0NINJaqoqffpviPHmwNUEQAAAAAQuhEB8vdUaM6anerKko3ecSwbBwAAABA4hG6EnROXkQ/oE6cl6/d2KXBL0hu/vczU2gAAAADgRIRuhJWuLiNvS2y05ZT9ugEAAADATIRuhA1/lpGfrK19ugEAAADAbIRuhIVmt6GlG0q6HLgTekXpeJNbveOi9cZvL2OGGwAAAEC3IHQjLBSVVndpSblFksNu1fv3XKnoKIv5hQEAAADAaUQFuwCgM6pquxa4JWlxrpPADQAAACAomOlGWEhOtPp8j8Nu1eJcp7KHpAagIgAAAADoGKEbYWFkZpJS7VZV1tS3+Vx36zLyJ386VN8eOa7kRKtGZiYxww0AAAAgqAjdCAvRURYtznVqzpqdskhewfvEZeQ/PmtAEKoDAAAAgLbxTDfCRvaQVK2cOkIOu/dSc4fdqpVTR7CMHAAAAEDIYaYbYSV7SKrGOx0qKq1WVW09y8gBAAAAhDRCN7rNR+U1uubZ92WoZUn4+t+M1vnpdp/7iY6yKOvM/qbXBwAAAABmI3SjW2Qs2Oh1bEjKffZ9SVLZYzlBqAgAAAAAAo9nuhFwJwduX68DAAAAQLgidCOgPiqvMbUdAAAAAIQTQjcC6pq/LyE3qx0AAAAAhBNCNwLK6LiJT+0AAAAAIJwQuhFQnd3Iiw2/AAAAAEQiQjcCav1vRpvaDgAAAADCCaEbAdXZfbi7sl83AAAAAIQ6QjcCrqN9uNmnGwAAAECkigl2AegZyh7L0UflNbrm2fdlqOUZ7vW/Gc0MNwAAAICIRuhGtzk/3a5SZrUBAAAA9CAsLwcAAAAAIEAI3QAAAAAABAjLy+HR7DZUVFqtqtp6JSdaNTIzSdFR7KANAAAAAF1F6IYkKX9PhZZuKFFFTb3nXKrdqsW5TmUPSQ1iZQAAAAAQvlheDuXvqdCcNTu9ArckVdbUa86ancrfUxGkygAAAAAgvBG6e7hmt6GlG0pktHGt9dzSDSVqdrfVAgAAAABwOiwv74GONTTr0U0lKvvuqKwxUafMcJ/IkFRRU6+i0mplndm/+4oEAAAAgAhA6O5hZr28XZtLqny+r6q2/WAOAAAAAGgby8t7kK4GbklKTrSaXA0AAAAARD5munuIYw3NXQrcFkkOe8v2YQAAAAAA3zDT3UM8uqnE53tad+henOtkv24AAAAA6AJmunuIsu+O+nyPg326AQAAAMAvhO4eIqN/grbu77jd+HOTdfXQNCUntiwpZ4YbAAAAALqO0N1D3DvZqVf+Vt5hu6d/PkLxsdHdUBEAAAAARD6e6e4h4mOjNd6ZfNo2453JBG4AAAAAMBGhuwdZPf3idoP3eGeyVk+/uJsrAgAAAIDIxvLyHmb19It1rKFZj24qUdl3R5XRP0H3TnYyww0AAAAAAUDo7oHiY6P10HXnB7sMAAAAAIh4LC8HAAAAACBACN0AAAAAAAQIoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoS3lwdRXX2Tbn91l8q/P6b0fvF66sbh6mNlSAAAAAAgUvg8011QUKDc3FylpaXJYrFo3bp1XtcNw9CiRYuUmpqq+Ph4jRs3Tvv37/dqU11drZtvvlk2m019+/bVzJkzVVdX59Vm9+7dGjNmjKxWqwYNGqTly5f7/u1C2DV/2qohS/5Xmz+u0r7KWm3+uEpDlvyvrvnT1mCXBgAAAAAwic+h+8iRIxo6dKhWrFjR5vXly5fr6aef1qpVq7Rt2zb17t1bEydOVH19vafNzTffrL1792rz5s16/fXXVVBQoNmzZ3uuu1wuTZgwQYMHD9aHH36oJ554QkuWLNHzzz/fha8Yeq7501bt/srV5rXdX7kI3gAAAAAQISyGYRhdvtli0dq1a3XddddJapnlTktL0x133KE777xTklRTU6OUlBTl5eVpypQp+vjjj+V0OrV9+3ZddNFFkqT8/HxNnjxZX331ldLS0rRy5Urdd999qqysVGxsrCRpwYIFWrdunT755JNO1eZyuWS321VTUyObzdbVr2i6uvomDVnyvx2227NkIkvNAQAAACBEdTZzmvoitdLSUlVWVmrcuHGec3a7XaNGjVJhYaEkqbCwUH379vUEbkkaN26coqKitG3bNk+bsWPHegK3JE2cOFH79u3T999/3+bPPn78uFwul9cnFN3+6i5T2wEAAAAAQpepobuyslKSlJKS4nU+JSXFc62yslLJycle12NiYpSUlOTVpq0+TvwZJ1u2bJnsdrvnM2jQIP+/UACUf3/M1HYAAAAAgNAVMVuGLVy4UDU1NZ7Pl19+GeyS2pTeL97UdgAAAACA0GVq6HY4HJKkQ4cOeZ0/dOiQ55rD4VBVVZXX9aamJlVXV3u1aauPE3/GyeLi4mSz2bw+oeipG4eb2g4AAAAAELpMDd2ZmZlyOBx6++23PedcLpe2bdumrKwsSVJWVpYOHz6sDz/80NNmy5YtcrvdGjVqlKdNQUGBGhsbPW02b96sc845R/369TOz5G7XxxqjCwae/h8ELhho4yVqAAAAABABfA7ddXV1Ki4uVnFxsaSWl6cVFxervLxcFotFt912mx5++GGtX79eH330kaZPn660tDTPG87PPfdcZWdna9asWSoqKtJf//pXzZs3T1OmTFFaWpok6aabblJsbKxmzpypvXv36tVXX9Uf//hHzZ8/37QvHkzr541pN3hfMNCm9fPGdHNFAAAAAIBA8HnLsHfffVdXXHHFKednzJihvLw8GYahxYsX6/nnn9fhw4c1evRoPfvsszr77LM9baurqzVv3jxt2LBBUVFRuuGGG/T000+rT58+nja7d+/W3LlztX37dg0YMEC33nqr7rnnnk7XGapbhp2orr5Jt7+6S+XfH1N6v3g9deNwZrgBAAAAIAx0NnP6tU93KAuH0A0AAAAACE9B2acbAAAAAAD8A6EbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAESEywCwgUwzAkSS6XK8iVAAAAAAAiTWvWbM2e7YnY0F1bWytJGjRoUJArAQAAAABEqtraWtnt9navW4yOYnmYcrvdOnjwoBITE2WxWIJdDv7O5XJp0KBB+vLLL2Wz2YJdDkzG+EY2xjfyMcaRjfGNbIxvZGN8Q5NhGKqtrVVaWpqiotp/cjtiZ7qjoqI0cODAYJeBdthsNv7CiGCMb2RjfCMfYxzZGN/IxvhGNsY39JxuhrsVL1IDAAAAACBACN0AAAAAAAQIoRvdKi4uTosXL1ZcXFywS0EAML6RjfGNfIxxZGN8IxvjG9kY3/AWsS9SAwAAAAAg2JjpBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG6YoqCgQLm5uUpLS5PFYtG6detOafPxxx/rmmuukd1uV+/evXXxxRervLzcc72+vl5z585V//791adPH91www06dOhQN34LtKej8a2rq9O8efM0cOBAxcfHy+l0atWqVV5tGN/QtGzZMl188cVKTExUcnKyrrvuOu3bt8+rTWfGrry8XDk5OUpISFBycrLuuusuNTU1dedXQRs6Gt/q6mrdeuutOueccxQfH6/09HT99re/VU1NjVc/jG/o6syf4VaGYWjSpElt/j3OGIemzo5vYWGhrrzySvXu3Vs2m01jx47VsWPHPNerq6t18803y2azqW/fvpo5c6bq6uq686ugDZ0Z38rKSk2bNk0Oh0O9e/fWiBEj9Je//MWrDeMb+gjdMMWRI0c0dOhQrVixos3rn332mUaPHq0f/ehHevfdd7V792498MADslqtnja33367NmzYoD//+c967733dPDgQV1//fXd9RVwGh2N7/z585Wfn681a9bo448/1m233aZ58+Zp/fr1njaMb2h67733NHfuXP3tb3/T5s2b1djYqAkTJujIkSOeNh2NXXNzs3JyctTQ0KAPPvhAL730kvLy8rRo0aJgfCWcoKPxPXjwoA4ePKgnn3xSe/bsUV5envLz8zVz5kxPH4xvaOvMn+FWf/jDH2SxWE45zxiHrs6Mb2FhobKzszVhwgQVFRVp+/btmjdvnqKi/vFr/s0336y9e/dq8+bNev3111VQUKDZs2cH4yvhBJ0Z3+nTp2vfvn1av369PvroI11//fX62c9+pl27dnnaML5hwABMJslYu3at17kbb7zRmDp1arv3HD582OjVq5fx5z//2XPu448/NiQZhYWFgSoVXdDW+J533nnGgw8+6HVuxIgRxn333WcYBuMbTqqqqgxJxnvvvWcYRufGbtOmTUZUVJRRWVnpabNy5UrDZrMZx48f794vgNM6eXzb8l//9V9GbGys0djYaBgG4xtu2hvjXbt2GT/4wQ+MioqKU/4eZ4zDR1vjO2rUKOP+++9v956SkhJDkrF9+3bPuTfeeMOwWCzG119/HdB64Zu2xrd3797Gyy+/7NUuKSnJWL16tWEYjG+4YKYbAed2u7Vx40adffbZmjhxopKTkzVq1CivpW0ffvihGhsbNW7cOM+5H/3oR0pPT1dhYWEQqoYvLr30Uq1fv15ff/21DMPQO++8o08//VQTJkyQxPiGk9ZlxUlJSZI6N3aFhYU6//zzlZKS4mkzceJEuVwu7d27txurR0dOHt/22thsNsXExEhifMNNW2N89OhR3XTTTVqxYoUcDscp9zDG4ePk8a2qqtK2bduUnJysSy+9VCkpKbrsssv0/vvve+4pLCxU3759ddFFF3nOjRs3TlFRUdq2bVv3fgGcVlt/fi+99FK9+uqrqq6ultvt1n/+53+qvr5el19+uSTGN1wQuhFwVVVVqqur02OPPabs7Gy9+eab+slPfqLrr79e7733nqSW51ViY2PVt29fr3tTUlJUWVkZhKrhi2eeeUZOp1MDBw5UbGyssrOztWLFCo0dO1YS4xsu3G63brvtNv34xz/WkCFDJHVu7CorK71+WW+93noNoaGt8T3Zt99+q4ceeshrWSLjGz7aG+Pbb79dl156qa699to272OMw0Nb4/v5559LkpYsWaJZs2YpPz9fI0aM0FVXXaX9+/dLahnD5ORkr75iYmKUlJTE+IaQ9v78/td//ZcaGxvVv39/xcXF6Ve/+pXWrl2rH/7wh5IY33ARE+wCEPncbrck6dprr9Xtt98uSRo2bJg++OADrVq1Spdddlkwy4MJnnnmGf3tb3/T+vXrNXjwYBUUFGju3LlKS0vzmiFFaJs7d6727NnjNUOCyNHR+LpcLuXk5MjpdGrJkiXdWxxM0dYYr1+/Xlu2bPF6/hPhqa3xbf0d61e/+pV+8YtfSJKGDx+ut99+Wy+88IKWLVsWlFrhu/b+jn7ggQd0+PBhvfXWWxowYIDWrVunn/3sZ9q6davOP//8IFULXzHTjYAbMGCAYmJi5HQ6vc6fe+65nreXOxwONTQ06PDhw15tDh061OZSOISOY8eO6d5779Xvf/975ebm6oILLtC8efN044036sknn5TE+IaDefPm6fXXX9c777yjgQMHes53ZuwcDscpbzNvPWZ8Q0N749uqtrZW2dnZSkxM1Nq1a9WrVy/PNcY3PLQ3xlu2bNFnn32mvn37KiYmxvPYwA033OBZnsoYh772xjc1NVWSOvwdq6qqyut6U1OTqqurGd8Q0d74fvbZZ/rTn/6kF154QVdddZWGDh2qxYsX66KLLvK83JbxDQ+EbgRcbGysLr744lO2QPj00081ePBgSdKFF16oXr166e233/Zc37dvn8rLy5WVldWt9cI3jY2Namxs9HpLqiRFR0d7/gWe8Q1dhmFo3rx5Wrt2rbZs2aLMzEyv650Zu6ysLH300Ude/9HfvHmzbDbbKb8Iont1NL5Sywz3hAkTFBsbq/Xr13vtKiExvqGuozFesGCBdu/ereLiYs9Hkp566im9+OKLkhjjUNbR+GZkZCgtLe20v2NlZWXp8OHD+vDDDz3Xt2zZIrfbrVGjRgX+S6BdHY3v0aNHJem0v2MxvmEimG9xQ+Sora01du3aZezatcuQZPz+9783du3aZXzxxReGYRjGa6+9ZvTq1ct4/vnnjf379xvPPPOMER0dbWzdutXTx69//WsjPT3d2LJli7Fjxw4jKyvLyMrKCtZXwgk6Gt/LLrvMOO+884x33nnH+Pzzz40XX3zRsFqtxrPPPuvpg/ENTXPmzDHsdrvx7rvvGhUVFZ7P0aNHPW06GrumpiZjyJAhxoQJE4zi4mIjPz/fOOOMM4yFCxcG4yvhBB2Nb01NjTFq1Cjj/PPPNw4cOODVpqmpyTAMxjfUdebP8Ml00tvLGePQ1ZnxfeqppwybzWb8+c9/Nvbv32/cf//9htVqNQ4cOOBpk52dbQwfPtzYtm2b8f777xtnnXWW8fOf/zwYXwkn6Gh8GxoajB/+8IfGmDFjjG3bthkHDhwwnnzyScNisRgbN2709MP4hj5CN0zxzjvvGJJO+cyYMcPT5t/+7d+MH/7wh4bVajWGDh1qrFu3zquPY8eOGb/5zW+Mfv36GQkJCcZPfvITo6Kiopu/CdrS0fhWVFQYt9xyi5GWlmZYrVbjnHPOMX73u98Zbrfb0wfjG5raGldJxosvvuhp05mxKysrMyZNmmTEx8cbAwYMMO644w7PllMIno7Gt70/25KM0tJSTz+Mb+jqzJ/htu45eetHxjg0dXZ8ly1bZgwcONBISEgwsrKyvCY1DMMwvvvuO+PnP/+50adPH8Nmsxm/+MUvjNra2m78JmhLZ8b3008/Na6//nojOTnZSEhIMC644IJTthBjfEOfxTAMw+zZcwAAAAAAwDPdAAAAAAAEDKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAALk/wdw9IA+/qwxiAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ας δούμε τι συμβαίνει αν η σχέση δεν είναι γραμμική. Ας υποθέσουμε ότι η εταιρεία μας αποφάσισε να κρύψει την προφανή γραμμική εξάρτηση μεταξύ ύψους και μισθών, και εισήγαγε κάποια μη γραμμικότητα στον τύπο, όπως το `sin`:\n"
+    "Ας δούμε τι συμβαίνει αν η σχέση δεν είναι γραμμική. Υποθέστε ότι η εταιρεία μας αποφάσισε να κρύψει την προφανή γραμμική εξάρτηση μεταξύ υψών και μισθών, και εισήγαγε κάποια μη γραμμικότητα στον τύπο, όπως το `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Σε αυτή την περίπτωση, η συσχέτιση είναι ελαφρώς μικρότερη, αλλά εξακολουθεί να είναι αρκετά υψηλή. Τώρα, για να κάνουμε τη σχέση ακόμα λιγότερο προφανή, ίσως θέλουμε να προσθέσουμε λίγη επιπλέον τυχαιότητα προσθέτοντας κάποια τυχαία μεταβλητή στον μισθό. Ας δούμε τι συμβαίνει:\n"
+    "Σε αυτή την περίπτωση, η συσχέτιση είναι ελαφρώς μικρότερη, αλλά παραμένει αρκετά υψηλή. Τώρα, για να κάνουμε τη σχέση ακόμα λιγότερο προφανή, ίσως θέλουμε να προσθέσουμε λίγη επιπλέον τυχαιότητα προσθέτοντας κάποια τυχαία μεταβλητή στον μισθό. Ας δούμε τι συμβαίνει:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -998,17 +833,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1030,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,32 +888,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Υπάρχει πράγματι μια συσχέτιση, αλλά όχι τόσο ισχυρή όσο στο τεχνητό μας παράδειγμα. Πράγματι, αν κοιτάξουμε το διάγραμμα διασποράς μιας τιμής σε σχέση με την άλλη, η σχέση θα ήταν πολύ λιγότερο προφανής:\n"
+    "Υπάρχει πράγματι μια συσχέτιση, αλλά όχι τόσο ισχυρή όσο στο τεχνητό μας παράδειγμα. Πράγματι, αν κοιτάξουμε το διάγραμμα διασποράς της μιας τιμής σε σχέση με την άλλη, η σχέση θα ήταν πολύ λιγότερο προφανής:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1096,7 +929,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Αποποίηση ευθύνης**:  \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτοματοποιημένες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n"
+    "\n---\n\n**Αποποίηση Ευθύνης**:  \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n"
    ]
   }
  ],
@@ -1119,11 +952,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:02:24+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:31:44+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "el"
   }
diff --git a/translations/el/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/el/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 7599ef03..835aec7b 100644
--- a/translations/el/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/el/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Εισαγωγή στην Πιθανότητα και Στατιστική\n",
+    "## Εισαγωγή στην Πιθανότητα και τη Στατιστική\n",
     "## Εργασία\n",
     "\n",
     "Σε αυτή την εργασία, θα χρησιμοποιήσουμε το σύνολο δεδομένων ασθενών με διαβήτη που έχει ληφθεί [από εδώ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Σε αυτό το σύνολο δεδομένων, οι στήλες είναι οι εξής:  \n",
-    "* Η ηλικία και το φύλο είναι αυτονόητα  \n",
-    "* Το BMI είναι ο δείκτης μάζας σώματος  \n",
-    "* Το BP είναι η μέση αρτηριακή πίεση  \n",
-    "* Οι S1 έως S6 είναι διαφορετικές μετρήσεις αίματος  \n",
-    "* Το Y είναι το ποιοτικό μέτρο της εξέλιξης της νόσου μέσα σε ένα έτος  \n",
+    "Σε αυτό το σύνολο δεδομένων, οι στήλες είναι οι εξής:\n",
+    "* Η ηλικία και το φύλο είναι αυτονόητα\n",
+    "* Το BMI είναι ο δείκτης μάζας σώματος\n",
+    "* Το BP είναι η μέση αρτηριακή πίεση\n",
+    "* Το S1 έως S6 είναι διαφορετικές μετρήσεις αίματος\n",
+    "* Το Y είναι η ποιοτική μέτρηση της εξέλιξης της ασθένειας μέσα σε ένα χρόνο\n",
     "\n",
-    "Ας μελετήσουμε αυτό το σύνολο δεδομένων χρησιμοποιώντας μεθόδους πιθανοτήτων και στατιστικής.\n",
+    "Ας μελετήσουμε αυτό το σύνολο δεδομένων χρησιμοποιώντας μεθόδους πιθανότητας και στατιστικής.\n",
     "\n",
-    "### Εργασία 1: Υπολογίστε τις μέσες τιμές και τη διακύμανση για όλες τις τιμές  \n"
+    "### Εργασία 1: Υπολογίστε τις μέσες τιμές και τη διακύμανση για όλες τις τιμές\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Εργασία 2: Σχεδιάστε boxplots για BMI, BP και Y ανάλογα με το φύλο\n"
+    "### Εργασία 2: Σχεδιάστε διαγράμματα κουτιού για BMI, BP και Y ανάλογα με το φύλο\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -592,17 +592,17 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Συμπεράσματα:\n",
-    "* Ηλικία - φυσιολογική\n",
-    "* Φύλο - ομοιόμορφο\n",
-    "* ΔΜΣ, Y - δύσκολο να προσδιοριστεί\n"
+    "Συμπεράσματα:  \n",
+    "* Ηλικία - φυσιολογική  \n",
+    "* Φύλο - ομοιόμορφο  \n",
+    "* ΔΜΣ, Υ - δύσκολο να προσδιοριστεί  \n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### Εργασία 4: Δοκιμάστε τη συσχέτιση μεταξύ διαφορετικών μεταβλητών και της εξέλιξης της ασθένειας (Y)\n",
+    "### Εργασία 4: Δοκιμάστε τη συσχέτιση μεταξύ διαφορετικών μεταβλητών και της εξέλιξης της νόσου (Y)\n",
     "\n",
     "> **Υπόδειξη** Ο πίνακας συσχέτισης θα σας δώσει τις πιο χρήσιμες πληροφορίες σχετικά με το ποιες τιμές είναι εξαρτημένες.\n"
    ],
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "Συμπέρασμα:  \n",
-    "* Η ισχυρότερη συσχέτιση του Y είναι με το ΔΜΣ και το S5 (σάκχαρο αίματος). Αυτό ακούγεται λογικό.\n"
+    "* Η ισχυρότερη συσχέτιση του Y είναι με τον ΔΜΣ και το S5 (σάκχαρο αίματος). Αυτό ακούγεται λογικό.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Αποποίηση ευθύνης**:  \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτοματοποιημένες μεταφράσεις ενδέχεται να περιέχουν λάθη ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n"
+    "\n---\n\n**Αποποίηση Ευθύνης**:  \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:23:18+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:32:16+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "el"
   }
diff --git a/translations/en/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/en/1-Introduction/04-stats-and-probability/assignment.ipynb
index 630a03fc..65208144 100644
--- a/translations/en/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/en/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Introduction to Probability and Statistics\n",
     "## Assignment\n",
     "\n",
-    "In this assignment, we will use the dataset of diabetes patients obtained [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "In this assignment, we will use the dataset of diabetes patients available [here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,12 +149,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "In this dataset, the columns are as follows:\n",
-    "* Age and sex are straightforward\n",
-    "* BMI refers to body mass index\n",
-    "* BP represents average blood pressure\n",
-    "* S1 to S6 are various blood measurements\n",
-    "* Y is a qualitative indicator of disease progression over the course of one year\n",
+    "In this dataset, the columns are as follows:  \n",
+    "* Age and sex are self-explanatory  \n",
+    "* BMI is body mass index  \n",
+    "* BP is average blood pressure  \n",
+    "* S1 through S6 are different blood measurements  \n",
+    "* Y is the qualitative measure of disease progression over one year  \n",
     "\n",
     "Let's analyze this dataset using probability and statistical methods.\n",
     "\n",
@@ -202,7 +202,7 @@
    "source": [
     "### Task 4: Test the correlation between different variables and disease progression (Y)\n",
     "\n",
-    "> **Hint** The correlation matrix will provide the most valuable insights into which values are interdependent.\n"
+    "> **Hint** A correlation matrix will provide the most useful insights into which values are interdependent.\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the definitive source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n"
+    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-03T20:43:46+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T16:59:17+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "en"
   }
diff --git a/translations/en/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/en/1-Introduction/04-stats-and-probability/notebook.ipynb
index 1656c46c..c6cbb580 100644
--- a/translations/en/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/en/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Random Variables and Distributions\n",
-    "Let's begin by taking a sample of 30 values from a uniform distribution ranging from 0 to 9. We'll also calculate the mean and variance.\n"
+    "Let's begin by drawing a sample of 30 values from a uniform distribution ranging from 0 to 9. We will also calculate the mean and variance.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,168 +89,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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VeP14VFSU+vTp4/DIMEmaPHmyfZqXX35Zq1evVseOHfXggw+qRYsWOnPmjH744QetXLlSZ86ckSQ9+OCDevPNN3XvvfcqIyNDderU0UcffSR/f/+rWsci+/fv18CBA3Xrrbdqw4YN+vjjj3X33XcXezb3DTfcoKioKPtNy9q0aeNUO5dq0aKF+vXrp3//+9965plndP3112v48OF67733dPbsWcXExGjjxo2aO3euYmNj1bNnz8sua9iwYfrvf/+rhx9+WKtXr1aXLl1UWFionTt36r///a++/vprh/sdAADgwMxbpwMAXF9JjwyrXLmyrXXr1ra3337bZrVaHab/448/bGPGjLHVrVvX5uPjY2vatKntX//6l326jIwMm7e3t8NjwGw2m62goMDWvn17W926dW2///67zWa78MisgIAA2759+2y9e/e2+fv722rXrm177rnnbIWFhQ7z65JHhtlsNtsPP/xg69Onjy0wMNDm7+9v69mzp239+vXF1vH999+3NW7c2Obl5XVVj8P69NNPbZGRkTZfX19bVFSUbcmSJbbBgwfbIiMj7dMUPTLsX//6V4nLWLlypa1Lly42Pz8/W3BwsO22226zbd++vdh0K1assEVFRdkqVapka9asme3jjz++7CPD4uPjbR9//LGtadOmNl9fX9sNN9xQ4rocP37cFh8fb2vQoIHNx8fHFhoaarv55ptt7733nsN0Bw8etA0cONDm7+9vq1mzpu3RRx+1LV++3KlHhm3fvt02ZMgQW1BQkK1atWq2kSNH2vLy8kqcZ+rUqTZJtilTplxx2ReLiYmxtWzZssRxRY9yK/q7sFgstsmTJ9vCw8NtPj4+tgYNGtgmTJhgO3fuXLFlXvzIMJvNZjt//rztlVdesbVs2dLm6+trq1atmq1t27a2yZMn2zIzM6+6XgBAxeNhs/3v+RwAALiY++67T4sWLbrqI8hmat26tWrVqqWUlBRT2vfw8FB8fHyxU/vLk9dee01jxozRgQMH1LBhQ7PLAQDgmuCabgAAnGCxWOw3kCuyZs0abdmyRT169DCnKDdgs9n0wQcfKCYmhsANAHArXNMNAIATDh8+rFtuuUVDhw5V3bp1tXPnTr3zzjsKDQ3Vww8/bHZ55U5OTo6WLFmi1atXa+vWrVq8eLHZJQEAcE0RugEAcEK1atXUtm1b/fvf/9bJkycVEBCg/v376+WXX1aNGjXMLq/cOXnypO6++25VrVpVEydO1MCBA80uCQCAa4prugEAAAAAMAjXdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAFcR9992nsLCwUs8bGBh4bQsCAKACIHQDAOBi5syZIw8PD23atKnE8T169FBUVFQZV3V1cnNzNWnSJK1Zs8bsUgAAcAneZhcAAADKxvvvvy+r1WpoG7m5uZo8ebKkC18OAABQ0RG6AQCoIHx8fMwuAQCACofTywEAcAMff/yx2rZtKz8/P1WvXl133XWXfv31V4dpSrqm+/Tp0xo2bJiCg4NVtWpVDR8+XFu2bJGHh4fmzJlTrJ3Dhw8rNjZWgYGBqlWrlsaNG6fCwkJJ0oEDB1SrVi1J0uTJk+Xh4SEPDw9NmjTJiFUGAKBc4Eg3AAAuKjMzU6dOnSo23GKxOLx+6aWX9Mwzz+iOO+7Q3//+d508eVJvvPGGunfvrh9//FFVq1YtcflWq1W33XabNm7cqEceeUSRkZFavHixhg8fXuL0hYWF6tOnjzp27Khp06Zp5cqVmj59upo0aaJHHnlEtWrV0ttvv61HHnlEt99+u+Li4iRJrVq1+mu/CAAAyjFCNwAALuqWW2657LiWLVtKkg4ePKjnnntOL774oiZOnGgfHxcXpxtuuEFvvfWWw/CLJScna8OGDZo5c6YeffRRSdIjjzyiXr16lTj9uXPndOedd+qZZ56RJD388MNq06aNPvjgAz3yyCMKCAjQkCFD9Mgjj6hVq1YaOnRoqdYbAAB3QugGAMBFzZo1S9ddd12x4QkJCfZTupOSkmS1WnXHHXc4HBUPDQ1V06ZNtXr16suG7uXLl8vHx0cPPvigfZinp6fi4+O1atWqEud5+OGHHV5369ZNH330kdPrBgBARUHoBgDARXXo0EHt2rUrNrxatWr2gL1nzx7ZbDY1bdq0xGVc6eZpBw8eVJ06deTv7+8wPCIiosTpK1eubL9m++Jafv/99yuuBwAAFRmhGwCAcsxqtcrDw0PLli2Tl5dXsfGBgYHXrK2Slg8AAK6M0A0AQDnWpEkT2Ww2hYeHl3gq+pU0atRIq1evVm5ursPR7r1795a6Hg8Pj1LPCwCAO+KRYQAAlGNxcXHy8vLS5MmTZbPZHMbZbDadPn36svP26dNHFotF77//vn2Y1WrVrFmzSl1PUXg/e/ZsqZcBAIA74Ug3AADlWJMmTfTiiy9qwoQJOnDggGJjYxUUFKT9+/fr888/10MPPaRx48aVOG9sbKw6dOighIQE7d27V5GRkVqyZInOnDkjqXRHrf38/NSiRQstWLBA1113napXr66oqChFRUX9pfUEAKC84kg3AADl3Pjx4/XZZ5/J09NTkydP1rhx47RkyRL17t1bAwcOvOx8Xl5e+vLLL3XnnXdq7ty5euqpp1S3bl37ke7KlSuXqp5///vfqlevnsaMGaO//e1vWrRoUamWAwCAO/CwXXouGgAAqNCSk5N1++23a926derSpYvZ5QAAUK4RugEAqMDy8vLk5+dnf11YWKjevXtr06ZNOnbsmMM4AADgPK7pBgCgAhs1apTy8vLUuXNn5efnKykpSevXr9eUKVMI3AAAXAMc6QYAoAKbP3++pk+frr179+rcuXOKiIjQI488opEjR5pdGgAAboHQDQAAAACAQbh7OQAAAAAABiF0AwAAAABgkHJ5IzWr1aojR44oKChIHh4eZpcDAAAAAKhgbDab/vjjD9WtW1eenpc/nl0uQ/eRI0fUoEEDs8sAAAAAAFRwv/76q+rXr3/Z8eUydAcFBUm6sHLBwcEmV/PXWSwWrVixQr1795aPj4/Z5eAS9I9ro39cF33j2ugf10b/uDb6x3XRN67N3fonKytLDRo0sOfTyymXobvolPLg4GC3Cd3+/v4KDg52iz8+d0P/uDb6x3XRN66N/nFt9I9ro39cF33j2ty1f/7skmdupAYAAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAACg3CssLFRqaqrWrl2r1NRUFRYWml0SIInQDQAAAKCcS0pKUkREhHr16qUZM2aoV69eioiIUFJSktmlAYRuAAAAAOVXUlKShgwZoujoaKWlpemTTz5RWlqaoqOjNWTIEII3TEfoBgAAAFAuFRYWKiEhQQMGDFBycrI6duwoPz8/dezYUcnJyRowYIDGjRvHqeYwFaEbAAAAQLmUlpamAwcOaOLEifL0dIw2np6emjBhgvbv36+0tDSTKgQI3QAAAADKqaNHj0qSoqKiShxfNLxoOsAMhG4AAAAA5VKdOnUkSdu2bStxfNHwoukAMxC6AQAAAJRL3bp1U1hYmKZMmSKr1eowzmq1KjExUeHh4erWrZtJFQKEbgAAAADllJeXl6ZPn66lS5cqNjZW6enpysvLU3p6umJjY7V06VJNmzZNXl5eZpeKCszb7AIAAAAAoLTi4uK0aNEiJSQkqHv37vbh4eHhWrRokeLi4kysDiB0AwAAACjn4uLiNGjQIK1evVrLli1T37591bNnT45wwyUQugEAAACUe15eXoqJiVFOTo5iYmII3HAZXNMNAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBnArdiYmJat++vYKCghQSEqLY2Fjt2rXLYZpjx45p2LBhCg0NVUBAgNq0aaPPPvvMYZozZ87onnvuUXBwsKpWraoRI0YoOzv7r68NAAAAAAAuxKnQnZqaqvj4eKWnpyslJUUWi0W9e/dWTk6OfZp7771Xu3bt0pIlS7R161bFxcXpjjvu0I8//mif5p577tHPP/+slJQULV26VGvXrtVDDz107dYKAAAAAAAX4O3MxMuXL3d4PWfOHIWEhCgjI0Pdu3eXJK1fv15vv/22OnToIEl6+umn9eqrryojI0M33HCDduzYoeXLl+v7779Xu3btJElvvPGG+vXrp2nTpqlu3brXYr0AAAAAADCdU6H7UpmZmZKk6tWr24fdeOONWrBggfr376+qVavqv//9r86dO6cePXpIkjZs2KCqVavaA7ck3XLLLfL09NR3332n22+/vVg7+fn5ys/Pt7/OysqSJFksFlkslr+yCi6haB3cYV3cEf3j2ugf10XfuDb6x7XRP66N/nFd9I1rc7f+udr18LDZbLbSNGC1WjVw4ECdPXtW69atsw8/e/as7rzzTq1YsULe3t7y9/fXwoUL1bt3b0nSlClTNHfu3GLXgoeEhGjy5Ml65JFHirU1adIkTZ48udjw+fPny9/fvzTlAwAAAABQarm5ubr77ruVmZmp4ODgy05X6iPd8fHx2rZtm0PglqRnnnlGZ8+e1cqVK1WzZk0lJyfrjjvuUFpamqKjo0vV1oQJEzR27Fj766ysLDVo0EC9e/e+4sqVFxaLRSkpKerVq5d8fHzMLgeXoH9cG/3juugb10b/uDb6x7XRP66LvnFt7tY/RWdg/5lShe6RI0fab4BWv359+/B9+/bpzTff1LZt29SyZUtJ0vXXX6+0tDTNmjVL77zzjkJDQ3XixAmH5RUUFOjMmTMKDQ0tsT1fX1/5+voWG+7j4+MWnVXE3dbH3dA/ro3+cV30jespLCzU+vXrtXbtWgUEBKhnz57y8vIyuyyUgPePa6N/XBd949rcpX+udh2cunu5zWbTyJEj9fnnn2vVqlUKDw93GJ+bm3thoZ6Oi/Xy8pLVapUkde7cWWfPnlVGRoZ9/KpVq2S1WtWxY0dnygEAAE5KSkpSRESEevXqpRkzZqhXr16KiIhQUlKS2aUBAOCWnArd8fHx+vjjjzV//nwFBQXp2LFjOnbsmPLy8iRJkZGRioiI0D/+8Q9t3LhR+/bt0/Tp05WSkqLY2FhJUvPmzXXrrbfqwQcf1MaNG/Xtt99q5MiRuuuuu7hzOQAABkpKStKQIUMUHR2ttLQ0ffLJJ/bLv4YMGULwBgDAAE6F7rfffluZmZnq0aOH6tSpY/+3YMECSRcOr3/11VeqVauWbrvtNrVq1Urz5s3T3Llz1a9fP/ty/vOf/ygyMlI333yz+vXrp65du+q99967tmsGAADsCgsLlZCQoAEDBig5OVkdO3aUn5+fOnbsqOTkZA0YMEDjxo1TYWGh2aUCAOBWnLqm+2pudN60aVN99tlnV5ymevXqmj9/vjNNAwCAvyAtLU0HDhzQJ598Ik9PT4dw7enpqQkTJujGG29UWlqa/TGfAADgr3PqSDcAACifjh49KkmKiooqcXzR8KLpAADAtUHoBgCgAqhTp44kadu2bSWOLxpeNB0AALg2CN0AAFQA3bp1U1hYmKZMmWJ/okgRq9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnTeF43AADXmFM3UgMAAOVXXFycFi1apISEBHXv3t0+PDw8XIsWLVJcXJyJ1QEA4J4I3QAAVCBxcXEaNGiQVq9erWXLlqlv377q2bMnR7gBADAIoRu4gsLCQqWmpmrt2rUKCAhgxxSAW/Dy8lJMTIxycnIUExPDdg0AAANxTTdwGUlJSYqIiFCvXr00Y8YM9erVSxEREUpKSjK7NAAAAADlBKEbKEFSUpKGDBmi6OhopaWl6ZNPPlFaWpqio6M1ZMgQgjcAAACAq0LoBi5RWFiohIQEDRgwQMnJyerYsaP8/PzUsWNHJScna8CAARo3bpwKCwvNLhUAAACAiyN0A5dIS0vTgQMHNHHiRHl6Or5FPD09NWHCBO3fv19paWkmVQgAAACgvCB0A5c4evSoJCkqKqrE8UXDi6YDAAAAgMshdAOXqFOnjiRp27ZtJY4vGl40HQAAAABcDqEbuES3bt0UFhamKVOmyGq1OoyzWq1KTExUeHi4unXrZlKFAAAAAMoLQjdwCS8vL02fPl1Lly5VbGys0tPTlZeXp/T0dMXGxmrp0qWaNm0az7UFAAAA8Ke8zS4AcEVxcXFatGiREhIS1L17d/vw8PBwLVq0SHFxcSZWBwAAAKC8IHQDlxEXF6dBgwZp9erVWrZsmfr27auePXtyhBsAAADAVSN0A1fg5eWlmJgY5eTkKCYmhsANAAAAwClc0w0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AQAVTWFio1NRUrV27VqmpqSosLDS7JAAA3BahGwCACiQpKUkRERHq1auXZsyYoV69eikiIkJJSUlmlwYAgFsidAMAUEEkJSVpyJAhio6OVlpamj755BOlpaUpOjpaQ4YMIXgDAGAAQjcAABVAYWGhEhISNGDAACUnJ6tjx47y8/NTx44dlZycrAEDBmjcuHGcag4AwDVG6AYAoAJIS0vTgQMHNHHiRHl6On78e3p6asKECdq/f7/S0tJMqhAAAPdE6AYAoAI4evSoJCkqKqrE8UXDi6YDAADXBqEbAIAKoE6dOpKkbdu2lTi+aHjRdAAA4NogdAMAUAF069ZNYWFhmjJliqxWq8M4q9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAC4FW+zCwAAAGUjLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAcE+EbgAAKpC4uDgNGjRIq1ev1rJly9S3b1/17NmTI9wAABiE0A0AQAXj5eWlmJgY5eTkKCYmhsANAICBuKYbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDOBW6ExMT1b59ewUFBSkkJESxsbHatWtXsek2bNigm266SQEBAQoODlb37t2Vl5dnH3/mzBndc889Cg4OVtWqVTVixAhlZ2f/9bUBAAAAAMCFOBW6U1NTFR8fr/T0dKWkpMhisah3797KycmxT7Nhwwbdeuut6t27tzZu3Kjvv/9eI0eOlKfn/2/qnnvu0c8//6yUlBQtXbpUa9eu1UMPPXTt1goAAAAAABfg7czEy5cvd3g9Z84chYSEKCMjQ927d5ckjRkzRqNHj9b48ePt0zVr1sz+/x07dmj58uX6/vvv1a5dO0nSG2+8oX79+mnatGmqW7duqVcGAAAAAABX8peu6c7MzJQkVa9eXZJ04sQJfffddwoJCdGNN96o2rVrKyYmRuvWrbPPs2HDBlWtWtUeuCXplltukaenp7777ru/Ug4AAAAAAC7FqSPdF7NarXrsscfUpUsXRUVFSZJ++eUXSdKkSZM0bdo0tW7dWvPmzdPNN9+sbdu2qWnTpjp27JhCQkIci/D2VvXq1XXs2LES28rPz1d+fr79dVZWliTJYrHIYrGUdhVcRtE6uMO6uCP6p+zk5uaWeJ+IK8nOy9f6rfsUVDVdgX6+Ts3brFkz+fv7OzUPrh7vnbLDe8f98P5xbfRP2WDb5n7c7b1ztetR6tAdHx+vbdu2ORzFtlqtkqR//OMfuv/++yVJN9xwg7755ht9+OGHSkxMLFVbiYmJmjx5crHhK1ascKs3RkpKitkl4AroH+Pt27dPCQkJpZp3ainmmT59upo0aVKq9nD1eO8Yj/eO++L949roH2OxbXNf7vLeyc3NvarpShW6R44cab8BWv369e3D69SpI0lq0aKFw/TNmzfXoUOHJEmhoaE6ceKEw/iCggKdOXNGoaGhJbY3YcIEjR071v46KytLDRo0UO/evRUcHFyaVXApFotFKSkp6tWrl3x8fMwuB5egf8pObm6uunbt6tQ8u49m6vHPt+tft7fQdXWqODUv32gbi/dO2eG94354/7g2+qdssG1zP+723ik6A/vPOBW6bTabRo0apc8//1xr1qxReHi4w/iwsDDVrVu32Gkgu3fvVt++fSVJnTt31tmzZ5WRkaG2bdtKklatWiWr1aqOHTuW2K6vr698fYufHuLj4+MWnVXE3dbH3dA/xqtSpYo6dOjg1DyVDp6W74bzimrdRq0b1TCoMvwVvHeMx3vHffH+cW30j7HYtrkvd3nvXO06OBW64+PjNX/+fC1evFhBQUH2a7CrVKkiPz8/eXh46PHHH9dzzz2n66+/Xq1bt9bcuXO1c+dOLVq0SNKFo9633nqrHnzwQb3zzjuyWCwaOXKk7rrrLu5cDgAAAABwK06F7rfffluS1KNHD4fhs2fP1n333SdJeuyxx3Tu3DmNGTNGZ86c0fXXX6+UlBSH6yP+85//aOTIkbr55pvl6empwYMH6/XXX/9rawIAAAAAgItx+vTyqzF+/HiH53Rfqnr16po/f74zTQMAAAAAUO78ped0AwBQksLCQqWmpmrt2rVKTU1VYWGh2SUBAACYgtANALimkpKSFBERoV69emnGjBnq1auXIiIilJSUZHZpAAAAZY7QDQC4ZpKSkjRkyBBFR0crLS1Nn3zyidLS0hQdHa0hQ4YQvAEAQIVD6AYAXBOFhYVKSEjQgAEDlJycrI4dO8rPz08dO3ZUcnKyBgwYoHHjxnGqOQAAqFAI3QCAayItLU0HDhzQxIkT5enp+PHi6empCRMmaP/+/UpLSzOpQgAAgLJH6AYAXBNHjx6VJEVFRZU4vmh40XQAAAAVAaEbAHBN1KlTR5K0bdu2EscXDS+aDgAAoCIgdAMArolu3bopLCxMU6ZMkdVqdRhntVqVmJio8PBwdevWzaQKAQAAyh6hGwBwTXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAAAZcbb7AIAAO4jLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAAMoeoRsAcE3FxcVp0KBBWr16tZYtW6a+ffuqZ8+eHOEGAAAVEqEbAHDNeXl5KSYmRjk5OYqJiSFwAwCACotrugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFAGUpNzdXO3fudGqe7Lx8rd+6T9VqblKgn69T80ZGRsrf39+peQAAAAC4D0I3KpSdO3eqbdu2pZp3ainmycjIUJs2bUrVHgAAAIDyj9CNCiUyMlIZGRlOzbPr6FmNXbhVM/4vWs3qVHW6PQAAAAAVF6EbFYq/v7/TR549D56Wb1qemkddr9aNahhUGQAAAAB3xI3UAAAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDeJtdgLvJzc3Vzp07nZonOy9f67fuU7WamxTo5+vUvJGRkfL393dqHgAAAABA2SB0X2M7d+5U27ZtSzXv1FLMk5GRoTZt2pSqPQAAAACAsQjd11hkZKQyMjKcmmfX0bMau3CrZvxftJrVqep0ewAAAAAA10Tovsb8/f2dPvLsefC0fNPy1DzqerVuVMOgygAAAAAAZY0bqQEAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQp0J3YmKi2rdvr6CgIIWEhCg2Nla7du0qcVqbzaa+ffvKw8NDycnJDuMOHTqk/v37y9/fXyEhIXr88cdVUFBQ6pUAAAAAAMAVORW6U1NTFR8fr/T0dKWkpMhisah3797KyckpNu3MmTPl4eFRbHhhYaH69++v8+fPa/369Zo7d67mzJmjZ599tvRrAQAAAACAC/J2ZuLly5c7vJ4zZ45CQkKUkZGh7t2724dv3rxZ06dP16ZNm1SnTh2HeVasWKHt27dr5cqVql27tlq3bq0XXnhBTz75pCZNmqRKlSr9hdUBAAAAAMB1OBW6L5WZmSlJql69un1Ybm6u7r77bs2aNUuhoaHF5tmwYYOio6NVu3Zt+7A+ffrokUce0c8//6wbbrih2Dz5+fnKz8+3v87KypIkWSwWWSyWv7IKLqHo1PqCggK3WB93Q/+4NvqnbOTm5l72cqLLyc7L1/qt+xRUNV2Bfr5OzdusWTP5+/s7NQ+cw3vHtRX1CX3jmugf18W2reywb3D124BSh26r1arHHntMXbp0UVRUlH34mDFjdOONN2rQoEElznfs2DGHwC3J/vrYsWMlzpOYmKjJkycXG75ixQqX+8WXxq/ZkuSt9PR0Hd5mdjW4FP3j2uifsrFv3z4lJCSUat6ppZhn+vTpatKkSanaw9XhvVM+pKSkmF0CroD+cT1s28oO+wYXvni4GqUO3fHx8dq2bZvWrVtnH7ZkyRKtWrVKP/74Y2kXW6IJEyZo7Nix9tdZWVlq0KCBevfureDg4Gvalhm2HDojbd2kTp066fqG1f98BpQp+se10T9lIzc3V127dnVqnt1HM/X459v1r9tb6Lo6VZya1xW/zXY3vHdcm8ViUUpKinr16iUfHx+zy8El6B/Xxbat7LBv8P/PwP4zpQrdI0eO1NKlS7V27VrVr1/fPnzVqlXat2+fqlat6jD94MGD1a1bN61Zs0ahoaHauHGjw/jjx49LUomno0uSr6+vfH2Ln37g4+PjFhs6b29v+093WB93Q/+4NvqnbFSpUkUdOnRwap5KB0/Ld8N5RbVuo9aNahhUGUqL90754C77Ou6K/nE9bNvKDvsGuuq/MafuXm6z2TRy5Eh9/vnnWrVqlcLDwx3Gjx8/Xj/99JM2b95s/ydJr776qmbPni1J6ty5s7Zu3aoTJ07Y50tJSVFwcLBatGjhTDkAAAAAALg0p450x8fHa/78+Vq8eLGCgoLs12BXqVJFfn5+Cg0NLfFodcOGDe0BvXfv3mrRooWGDRumqVOn6tixY3r66acVHx9f4tFsAAAAAADKK6eOdL/99tvKzMxUjx49VKdOHfu/BQsWXPUyvLy8tHTpUnl5ealz584aOnSo7r33Xj3//PNOFw8AAAAAgCtz6ki3zWZzuoGS5mnUqJG++uorp5cFAAAAAEB54tSRbgAAAAAAcPUI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBvM0uAPgr9p/KUU5+gaFt7DuZY//p7W3sWybA11vhNQMMbQMAAABA2SF0o9zafypHPaetKbP2EhZtLZN2Vo/rQfAGAAAA3AShG+VW0RHumXe2VkRIoHHt5OVr6ZoNGtCjswL8fA1rZ++JbD22YLPhR+4BAAAAlB1CN8q9iJBARdWrYtjyLRaLjtWS2jSqJh8fH8PaAQAAAOB+uJEaAAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQb7MLAOC+9p/KUU5+gaFt7DuZY//p7W3sJi3A11vhNQMMbQMAAADuhdANwBD7T+Wo57Q1ZdZewqKtZdLO6nE9CN4AAAC4aoRulFv5hefkWfmw9mftkmflQMPaKSgo0JGCI9pxZoehR1L3Z2XLs/Jh5Reek1TFsHbKStER7pl3tlZEiHH9k5OXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAgHshdKPcOpJzUAHhb2jixrJp763lbxneRkC4dCSntdqqtuFtlZWIkEBF1TPuSwSLxaJjtaQ2jarJx8fHsHYAAACA0iB0o9yqG9BIOftH6bU7W6uJgUdSCwoK9O26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAAAEDZInSj3PL1qizruXoKD26mFjWMPZK633u/mldvbuiRVOu5TFnPnZSvV2XD2gAAAABQtnhkGAAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGcSp0JyYmqn379goKClJISIhiY2O1a9cu+/gzZ85o1KhRatasmfz8/NSwYUONHj1amZmZDss5dOiQ+vfvL39/f4WEhOjxxx9XQUHBtVkjAAAAAABchFOhOzU1VfHx8UpPT1dKSoosFot69+6tnJwcSdKRI0d05MgRTZs2Tdu2bdOcOXO0fPlyjRgxwr6MwsJC9e/fX+fPn9f69es1d+5czZkzR88+++y1XTMAAAAAAEzm7czEy5cvd3g9Z84chYSEKCMjQ927d1dUVJQ+++wz+/gmTZropZde0tChQ1VQUCBvb2+tWLFC27dv18qVK1W7dm21bt1aL7zwgp588klNmjRJlSpVujZrBgAAAACAyZwK3ZcqOm28evXqV5wmODhY3t4XmtqwYYOio6NVu3Zt+zR9+vTRI488op9//lk33HBDsWXk5+crPz/f/jorK0uSZLFYZLFY/soquISiU+sLCgrcYn3KSln93oqWbXTfuNvfQU5+tjwrH9be37fL6h1gWDsFBQU6UnBEW09stW9njPDL7znyrHxYOfnZslj8DWvHnbjb37S7oX9cW1l99qB06J/SOXA6Rzn5hYa2sftYpsNPIwX4eimshnH7OO7I3T57rnYdSr2HarVa9dhjj6lLly6KiooqcZpTp07phRde0EMPPWQfduzYMYfALcn++tixYyUuJzExUZMnTy42fMWKFfL3L/87v79mS5K30tPTdXib2dWUH0W/t3Xr1ulgoPHtpaSkGLr8sl4fo/3wxxEFhL+lZzLKpr23Vr5leBsB4dJX6wt1LKiu4W25A7Ztro3+KR+M/uzBX0P/XL0TedJLm437cvxST3y+o0zaeap1gUL8yqQpt+Bunz25ublXNV2p//Lj4+O1bds2rVu3rsTxWVlZ6t+/v1q0aKFJkyaVthlJ0oQJEzR27FiHZTdo0EC9e/dWcHDwX1q2K9hy6Iy0dZM6deqk6xte/qwBOPr5SJambU1X165d1bKucX8HFotFKSkp6tWrl3x8fAxrp6zWp6yE/npCH83z0owh0Wpcy9gj3d+lf6eOnToae6T7ZI7GLtqqfvf2V5sGIYa1407YtpVeWRwNyj+WKW3doZCIaDUKrWJoWxwNcl5ZffagdOgf5/18JEvanK5pQ6IVYeB+Qc65fC1P+163dmuvgMq+hrWz92SOxi3aqvad3WO/ray4275B0RnYf6ZUe6gjR47U0qVLtXbtWtWvX7/Y+D/++EO33nqrgoKC9PnnnztsjEJDQ7Vx40aH6Y8fP24fVxJfX1/5+hZ/0/j4+LjFhq4oKHh7e7vF+pSVsv69Gf335m5/BwG+gbKeq6eIai0UVdu4HXqLxaJfvX9VdEi0ob83z4JMWc+dUYBvoFv0T1lwt7/psrL/VI56zfy2zNorq6NBq8f1UHhNgrez3GVfx13RP1ev6DMhsk4VRdUzdr/g1E6pQ+Na7Le5IHf7vV3tOjgVum02m0aNGqXPP/9ca9asUXh4eLFpsrKy1KdPH/n6+mrJkiWqXLmyw/jOnTvrpZde0okTJxQScuFoUUpKioKDg9WiRQtnygEAwO3k5F+43m3mna0VEWLctSY5eflaumaDBvTorAA/A48GncjWYws229cLAICKxqnQHR8fr/nz52vx4sUKCgqyX4NdpUoV+fn5KSsrS71791Zubq4+/vhjZWVl2Q+516pVS15eXurdu7datGihYcOGaerUqTp27JiefvppxcfHl3g0GwCAiigiJNDwo0HHakltGlVzi6MNAAC4KqdC99tvvy1J6tGjh8Pw2bNn67777tMPP/yg7777TpIUERHhMM3+/fsVFhYmLy8vLV26VI888og6d+6sgIAADR8+XM8///xfWA0AAAAAAFyP06eXX0mPHj3+dBpJatSokb766itnmgYAAAAAoNzxNLsAAAAAAADcFaEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFuLr9p3KUk19gaBv7TubYf3p7G9slAb7eCq8ZYGgbAAAAAIALCN1XsP9UjnpOW1Nm7SUs2lom7awe14PgDQAAAABlgNB9BUVHuGfe2VoRIYHGtZOXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAAABcQOi+ChEhgYqqV8Ww5VssFh2rJbVpVE0+Pj6GtQMAAAAAKFvcSA0AAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAABcSGFhoVJTU7V27VqlpqaqsLDQ7JIAAH8BoRsAAMBFJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSAACl5G12AQCAsrf/VI5y8gsMbWPfyRz7T29vYz9uAny9FV4zwNA2AKMlJSVpyJAhGjBggD766CP99ttvql+/vqZOnaohQ4Zo0aJFiouLM7tMAICTCN0AUMHsP5WjntPWlFl7CYu2lkk7q8f1IHij3CosLFRCQoIGDBig5ORkFRYW6vTp0+rYsaOSk5MVGxurcePGadCgQfLy8jK7XACAEwjdAFDBFB3hnnlna0WEBBrXTl6+lq7ZoAE9OivAz9ewdvaeyNZjCzYbfuQeMFJaWpoOHDigTz75RJ6eng7XcXt6emrChAm68cYblZaWph49ephXqBvKzc3Vzp07nZonOy9f67fuU7WamxTo5PYtMjJS/v7+Ts0DlAXOgjMOoRsAKqiIkEBF1ati2PItFouO1ZLaNKomHx8fw9oB3MHRo0clSVFRUSWOLxpeNB2unZ07d6pt27almndqKebJyMhQmzZtStUeYBTOgjMWoRsAAMBkderUkSRt27ZNnTp1KjZ+27ZtDtPh2omMjFRGRoZT8+w6elZjF27VjP+LVrM6VZ1uD3A1nAVnLEI3AACAybp166awsDBNmTJFycnJDuOsVqsSExMVHh6ubt26mVOgG/P393f6yLPnwdPyTctT86jr1bpRDYMqA8oeZ8EZg0eGAQAAmMzLy0vTp0/X0qVLFRsbq/T0dOXl5Sk9PV2xsbFaunSppk2bxk3UAKAc4kg3AACAC4iLi9OiRYuUkJCg7t2724eHh4fzuDAAKMcI3QAAAC4iLi5OgwYN0urVq7Vs2TL17dtXPXv25Ag3AJRjhG4AAAAX4uXlpZiYGOXk5CgmJobADQDlHKEbAADAQDwHGgAqNkI3AACAgXgONABUbIRuAAAAA/EcaACo2AjdAAAABuI50ABQsfGcbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADMJzuq8gv/CcPCsf1v6sXfKsHGhYOwUFBTpScEQ7zuyQt7dxXbI/K1uelQ8rv/CcpCqGtQMAAABcjP1qVGSE7is4knNQAeFvaOLGsmnvreVvGd5GQLh0JKe12qq24W0BAAAAEvvVqNgI3VdQN6CRcvaP0mt3tlaTEGO/kft23bfq0rWLod/I7TuRrUcXbFbdno0MawMAAAC4FPvVqMgI3Vfg61VZ1nP1FB7cTC1qGHfaiMVi0X7v/Wpevbl8fHwMa8d6LlPWcyfl61XZsDYAAACAS7FfjYqMG6kBAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswsASivPUihJ2nY409B2cvLytemkFHrwdwX4+RrWzt4T2YYtGwAAAIA5nArdiYmJSkpK0s6dO+Xn56cbb7xRr7zyipo1a2af5ty5c0pISNCnn36q/Px89enTR2+99ZZq165tn+bQoUN65JFHtHr1agUGBmr48OFKTEyUtzffAeDq7ftfSB2ftLUMWvPWR3u/L4N2pABf3gcAAACAu3Bq7z41NVXx8fFq3769CgoKNHHiRPXu3Vvbt29XQECAJGnMmDH68ssvtXDhQlWpUkUjR45UXFycvv32W0lSYWGh+vfvr9DQUK1fv15Hjx7VvffeKx8fH02ZMuXaryHcVu+WoZKkJiGB8vPxMqydXUczlbBoq6YPiVazOlUMa0e6ELjDawYY2gYAAACAsuNU6F6+fLnD6zlz5igkJEQZGRnq3r27MjMz9cEHH2j+/Pm66aabJEmzZ89W8+bNlZ6erk6dOmnFihXavn27Vq5cqdq1a6t169Z64YUX9OSTT2rSpEmqVKnStVs7uLXqAZV0V4eGhrdTUFAgSWpSK0BR9YwN3QAAAADcy1+6kVpm5oVraatXry5JysjIkMVi0S233GKfJjIyUg0bNtSGDRskSRs2bFB0dLTD6eZ9+vRRVlaWfv75579SDgAAAAAALqXUF49arVY99thj6tKli6KioiRJx44dU6VKlVS1alWHaWvXrq1jx47Zp7k4cBeNLxpXkvz8fOXn59tfZ2VlSZIsFossFktpV+FPFR3hLCgoMLSdomUb2YZUduvjbvi9lc4feRfes1sOnbH/Do2Qc+7Cje5q/nJSAZUNvNHdyRxJ7vF3kJOfLc/Kh7X39+2yeht3OUNBQYGOFBzR1hNbDb1nxy+/58iz8mHl5GfLYvE3rJ2yQv9A4rPH1dE/zmO/2rXx2VM6V9v3pV7T+Ph4bdu2TevWrSvtIq5aYmKiJk+eXGz4ihUr5O9v3C/x12xJ8ta6det0MNCwZuxSUlIMXX5Zr4+7KPq9paen6/A2s6spPzYc95DkpacWby+D1rz10d4fy6Ad6fsN63TQr0yaMswPfxxRQPhbeiajbNp7a+VbhrcREC59tb5Qx4LqGt6W0egfSHz2uDr6x3nsV7s2PntKJzc396qmK1XoHjlypJYuXaq1a9eqfv369uGhoaE6f/68zp4963C0+/jx4woNDbVPs3HjRoflHT9+3D6uJBMmTNDYsWPtr7OystSgQQP17t1bwcHBpVmFq/LzkSxN25qurl27qmVd49qxWCxKSUlRr1695OPjY1g7ZbU+7mbLoTPS1k3q1KmTrm9Y3exyyo1OOecVveOEGtcKMPRGd7uPZeqJz3do6u3NdV2o0Te681JYjfJ/o7vQX0/oo3lemjEkWo1rGftt9nfp36ljp47Gfpt9MkdjF21Vv3v7q02DEMPaKSv0DyQ+e1wd/eM89qtdG589pVN0BvafcWpNbTabRo0apc8//1xr1qxReHi4w/i2bdvKx8dH33zzjQYPHixJ2rVrlw4dOqTOnTtLkjp37qyXXnpJJ06cUEjIhV9ASkqKgoOD1aJFixLb9fX1la9v8dNGfXx8DH0zFf0heHt7G9pOEXdbH3fB7610alf10T2dw/98wmvkutAqat2oRpm1V54F+AbKeq6eIqq1UFRt476osFgs+tX7V0WHRBv63vEsyJT13BkF+Aa6xXuU/oHEZ4+ro3+cx361a+Ozp3SudtlOhe74+HjNnz9fixcvVlBQkP0a7CpVqsjPz09VqlTRiBEjNHbsWFWvXl3BwcEaNWqUOnfurE6dOkmSevfurRYtWmjYsGGaOnWqjh07pqefflrx8fElBmsAAAAAAMorp0L322+/LUnq0aOHw/DZs2frvvvukyS9+uqr8vT01ODBg5Wfn68+ffrorbf+/zn7Xl5eWrp0qR555BF17txZAQEBGj58uJ5//vm/tiYAAAAAALgYp08v/zOVK1fWrFmzNGvWrMtO06hRI3311VfONA0AAAAAQLnzl57TDQAAAAAALo/QDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABjE2+wCgLKUm5urnTt3OjXPrqNnlX9sr3Zs85P1dFWn5o2MjJS/v79T8wAAgNLbfypHOfkFhrax72SO/ae3t7G70wG+3gqvGWBoGwCMRehGhbJz5061bdu2VPPePdf5eTIyMtSmTZtStQcAAJyz/1SOek5bU2btJSzaWibtrB7Xg+ANlGOEblQokZGRysjIcGqe7Lx8fbl6g/r37KxAP1+n2wMAAGWj6Aj3zDtbKyIk0Lh28vK1dM0GDejRWQFO7hs4Y++JbD22YLPhR+4BGIvQjQrF39/f6SPPFotFv586oc4d2snHx8egygAAwLUSERKoqHpVDFu+xWLRsVpSm0bV2DcA8Ke4kRoAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswtwZXmWQknStsOZhraTk5evTSel0IO/K8DP17B29p7INmzZAIBrg88e17f/VI5y8gsMbWPfyRz7T29vY3fXAny9FV4zwNA2ALZtro3+MRah+wr2/a+zxidtLYPWvPXR3u/LoJ0LH64AANfEZ49r238qRz2nrSmz9hIWlcXfgbR6XA+CNwzFts210T/Gco0qXFTvlqGSpCYhgfLz8TKsnV1HM5WwaKumD4lWszpVDGtH4ttsAHB1fPa4tqIj3DPvbK2IkEDj2snL19I1GzSgR2fDjwY9tmCz4UfuAbZtro3+MRah+wqqB1TSXR0aGt5OQcGFD7omtQIUVc/YPz4AgGvjs6d8iAgJNPT3ZrFYdKyW1KZRNfn4+BjWDlBW2La5NvrHWNxIDQAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwiLfZBQAAylaepVCStO1wpqHt5OTla9NJKfTg7wrw8zWsnb0nsg1bNnCp/MJz8qx8WPuzdsmzcqBh7RQUFOhIwRHtOLND3t7G7a7tz8qWZ+XDyi88J6mKYe0AQEVG6AaACmbf/0Lq+KStZdCatz7a+30ZtCMF+PKRBuMdyTmogPA3NHFj2bT31vK3DG8jIFw6ktNabVXb8LYAoCJiDwUAKpjeLUMlSU1CAuXn42VYO7uOZiph0VZNHxKtZnWMPYIW4Out8JoBhrYBSFLdgEbK2T9Kr93ZWk1CjD3S/e26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAqOkI3AFQw1QMq6a4ODQ1vp6CgQJLUpFaAoupx2ircg69XZVnP1VN4cDO1qGHc37XFYtF+7/1qXr25fHx8DGvHei5T1nMn5etV2bA2AKCi40ZqAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGMTp0L127Vrddtttqlu3rjw8PJScnOwwPjs7WyNHjlT9+vXl5+enFi1a6J133nGY5ty5c4qPj1eNGjUUGBiowYMH6/jx439pRQAAAAAAcDVOh+6cnBxdf/31mjVrVonjx44dq+XLl+vjjz/Wjh079Nhjj2nkyJFasmSJfZoxY8boiy++0MKFC5WamqojR44oLi6u9GsBAAAAAIAL8nZ2hr59+6pv376XHb9+/XoNHz5cPXr0kCQ99NBDevfdd7Vx40YNHDhQmZmZ+uCDDzR//nzddNNNkqTZs2erefPmSk9PV6dOnUq3JgAAAAAAuBinQ/efufHGG7VkyRI98MADqlu3rtasWaPdu3fr1VdflSRlZGTIYrHolltusc8TGRmphg0basOGDSWG7vz8fOXn59tfZ2VlSZIsFossFsu1XoUyV1BQYP/pDuvjbor6hL4xXm5urnbt2uXUPLuPZir/2F5t21xJ549XcWreZs2ayd/f36l5cPXYtrk2+qd0yur3VlafPe72d5CTny3Pyoe19/ftsnoHGNZOQUGBjhQc0dYTW+Xtfc13p+1++T1HnpUPKyc/WxYLn1dXw93+pt2Nu/XP1a7DNd9KvPHGG3rooYdUv359eXt7y9PTU++//766d+8uSTp27JgqVaqkqlWrOsxXu3ZtHTt2rMRlJiYmavLkycWGr1ixwi12mH/NliRvpaen6/A2s6vB5aSkpJhdgtvbt2+fEhISSjXvsLnOzzN9+nQ1adKkVO3hz7Ftc230T+kU/d7WrVung4HGt2f0Z09Zr4/RfvjjiALC39IzGWXT3lsr3zK8jYBw6av1hToWVNfwttwB2zbX5m79k5ube1XTGRK609PTtWTJEjVq1Ehr165VfHy86tat63B02xkTJkzQ2LFj7a+zsrLUoEED9e7dW8HBwdeqdNNsOXRG2rpJnTp10vUNq5tdDi5hsViUkpKiXr16ycfHx+xy3Fpubq66du3q1DzZefn6Ou179enWXoF+vk7Ny5FuY7Ftc230T+n8fCRL07amq2vXrmpZ17h9kLL67Cmr9Skrob+e0EfzvDRjSLQa1zL2SPd36d+pY6eOxh7pPpmjsYu2qt+9/dWmQYhh7bgTtm2uzd36p+gM7D9zTbcSeXl5mjhxoj7//HP1799fktSqVStt3rxZ06ZN0y233KLQ0FCdP39eZ8+edTjaffz4cYWGhpa4XF9fX/n6Ft+Z9vHxcYsQVLSx9vb2dov1cVfu8vfmyqpUqaIOHTo4NY/FYtEfZ8+o242d6B8Xw7bNtdE/pVPWvzejP3vc7e8gwDdQ1nP1FFGthaJqO3fJkTMsFot+9f5V0SHRhv7ePAsyZT13RgG+gW7RP2XB3f6m3Y279c/VrsM1fU530TXWnp6Oi/Xy8pLVapUktW3bVj4+Pvrmm2/s43ft2qVDhw6pc+fO17IcAAAAAABM5fSR7uzsbO3du9f+ev/+/dq8ebOqV6+uhg0bKiYmRo8//rj8/PzUqFEjpaamat68eZoxY4akC0eyRowYobFjx6p69eoKDg7WqFGj1LlzZ+5cDgAAAABwK06H7k2bNqlnz57210XXWg8fPlxz5szRp59+qgkTJuiee+7RmTNn1KhRI7300kt6+OGH7fO8+uqr8vT01ODBg5Wfn68+ffrorbeMvxEFAAAAAABlyenQ3aNHD9lstsuODw0N1ezZs6+4jMqVK2vWrFmaNWuWs80DAAAAAFBuXNNrugEAAAAAwP9H6AYAAAAAwCCEbgAAAAAADHJNn9MNAADgzvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAQAXELoBAACu0r7/hdTxSVvLoDVvfbT3+zJoRwrwZZcQAIzCFhYAAOAq9W4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQBARUboBgAAuErVAyrprg4NDW+noKBAktSkVoCi6hkbugEAxuJGagAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQbzNLgAAAAC4FvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAZQdQjcAAADcwr7/hdTxSVvLoDVvfbT3+zJoRwrwZZcdKM94BwMAAMAt9G4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQAwFqEbAAAAbqF6QCXd1aGh4e0UFBRIkprUClBUPWNDN4DyjxupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNrsAd5Obm6udO3c6Nc+uo2eVf2yvdmzzk/V0VafmjYyMlL+/v1PzAAAAAMBfQe65eoTua2znzp1q27Ztqea9e67z82RkZKhNmzalag8AAAAASoPcc/UI3ddYZGSkMjIynJonOy9fX67eoP49OyvQz9fp9gAAAACgLJF7rh6h+xrz9/d3+hsYi8Wi30+dUOcO7eTj42NQZQAAAABwbZB7rh43UgMAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgTofutWvX6rbbblPdunXl4eGh5OTkYtPs2LFDAwcOVJUqVRQQEKD27dvr0KFD9vHnzp1TfHy8atSoocDAQA0ePFjHjx//SysCAAAAAICrcTp05+Tk6Prrr9esWbNKHL9v3z517dpVkZGRWrNmjX766Sc988wzqly5sn2aMWPG6IsvvtDChQuVmpqqI0eOKC4urvRrAQAAAACAC/J2doa+ffuqb9++lx3/1FNPqV+/fpo6dap9WJMmTez/z8zM1AcffKD58+frpptukiTNnj1bzZs3V3p6ujp16uRsSQAAAAAAuCSnQ/eVWK1Wffnll3riiSfUp08f/fjjjwoPD9eECRMUGxsrScrIyJDFYtEtt9xiny8yMlINGzbUhg0bSgzd+fn5ys/Pt7/OysqSJFksFlkslmu5CqYoWgd3WBd3RP+4NvqnbOTm5mrXrl1OzbP7aKbyj+3Vts2VdP54Fafmbdasmfz9/Z2aB84pKCiw/+T9YyzeP+6H94/rom9cm7vtt13telzT0H3ixAllZ2fr5Zdf1osvvqhXXnlFy5cvV1xcnFavXq2YmBgdO3ZMlSpVUtWqVR3mrV27to4dO1bichMTEzV58uRiw1esWOFWHyopKSlml4AroH9cG/1jrH379ikhIaFU8w6b6/w806dPdzhLCtfer9mS5K309HQd3mZ2Ne6N94/74f3juuib8sFd9ttyc3OvarprfqRbkgYNGqQxY8ZIklq3bq3169frnXfeUUxMTKmWO2HCBI0dO9b+OisrSw0aNFDv3r0VHBz81ws3mcViUUpKinr16iUfHx+zy8El6B/XRv+UjdzcXHXt2tWpebLz8vV12vfq0629Av18nZqXI3XG23LojLR1kzp16qTrG1Y3uxy3xvvH/fD+cV30jWtzt/22ojOw/8w1Dd01a9aUt7e3WrRo4TC8efPmWrdunSQpNDRU58+f19mzZx2Odh8/flyhoaElLtfX11e+vsU/cHx8fNyis4q42/q4G/rHtdE/xqpSpYo6dOjg1DwWi0V/nD2jbjd2om9ckLe3t/0n/WMs3j/uh/eP66Jvygd32W+72nW4ps/prlSpktq3b1/suqXdu3erUaNGkqS2bdvKx8dH33zzjX38rl27dOjQIXXu3PlalgMAAAAAgKmcPtKdnZ2tvXv32l/v379fmzdvVvXq1dWwYUM9/vjjuvPOO9W9e3f17NlTy5cv1xdffKE1a9ZIuvBt74gRIzR27FhVr15dwcHBGjVqlDp37sydywEAAAAAbsXp0L1p0yb17NnT/rroWuvhw4drzpw5uv322/XOO+8oMTFRo0ePVrNmzfTZZ585XMv06quvytPTU4MHD1Z+fr769Omjt9566xqsDgAAAAAArsPp0N2jRw/ZbLYrTvPAAw/ogQceuOz4ypUra9asWZo1a5azzQMAAAAAUG5c02u6AQAAAADA/0foBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAACAoQoLC5Wamqq1a9cqNTVVhYWFZpdUZgjdAAAAAADDJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSygShGwAAAABgiKSkJA0ZMkTR0dFKS0vTJ598orS0NEVHR2vIkCEVIngTugEAAAAA11xhYaESEhI0YMAAJScnq2PHjvLz81PHjh2VnJysAQMGaNy4cW5/qjmhGwAAAABwzaWlpenAgQOaOHGiPD0do6enp6cmTJig/fv3Ky0tzaQKywahGwAAAABwzR09elSSFBUVVeL4ouFF07krQjcAAAAA4JqrU6eOJGnbtm0lji8aXjSduyJ0AwAAAACuuW7duiksLExTpkyR1Wp1GGe1WpWYmKjw8HB169bNpArLBqEbAAAAAHDNeXl5afr06Vq6dKliY2OVnp6uvLw8paenKzY2VkuXLtW0adPk5eVldqmG8ja7AAAAAACAe4qLi9OiRYuUkJCg7t2724eHh4dr0aJFiouLM7G6skHoBgAAAAAYJi4uToMGDdLq1au1bNky9e3bVz179nT7I9xFCN0AAAAAAEN5eXkpJiZGOTk5iomJqTCBW+KabgAAAAAADEPoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAg3mYXAAAA/prc3Fzt3LnTqXl2HT2r/GN7tWObn6ynqzo1b2RkpPz9/Z2aB3BVvH9cF30Dd0HoBgCgnNu5c6fatm1bqnnvnuv8PBkZGWrTpk2p2gNcDe8f10XfwF0QugEAKOciIyOVkZHh1DzZefn6cvUG9e/ZWYF+vk63B7gL3j+ui76BuyB0AwBQzvn7+zt9dMZisej3UyfUuUM7+fj4GFQZ4Pp4/7gu+gbughupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNruA0rDZbJKkrKwskyu5NiwWi3Jzc5WVlSUfHx+zy8El6B/XRv+4LvrGtdE/ro3+cW30j+uib1ybu/VPUR4tyqeXUy5D9x9//CFJatCggcmVAAAAAAAqsj/++ENVqlS57HgP25/FchdktVp15MgRBQUFycPDw+xy/rKsrCw1aNBAv/76q4KDg80uB5egf1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1j82m01//PGH6tatK0/Py1+5XS6PdHt6eqp+/fpml3HNBQcHu8Ufn7uif1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1D9XOsJdhBupAQAAAABgEEI3AAAAAAAGIXS7AF9fXz333HPy9fU1uxSUgP5xbfSP66JvXBv949roH9dG/7gu+sa1VdT+KZc3UgMAAAAAoDzgSDcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEK3CQoKCvT888/rt99+M7sUALhm2LYBAMqaxWLRzTffrD179phdCq7g/Pnz+u2333To0CGHfxUFdy83SVBQkLZu3aqwsDCzS8ElLBaLIiMjtXTpUjVv3tzscoByhW2ba2P75vq++eYbffPNNzpx4oSsVqvDuA8//NCkqlBk06ZN2rFjhySpefPmateunckVQZJq1aql9evXq2nTpmaXgkvs2bNHDzzwgNavX+8w3GazycPDQ4WFhSZVVra8zS6gorrpppuUmprKjqkL8vHx0blz58wuAyiX2La5NrZvrm3y5Ml6/vnn1a5dO9WpU0ceHh5ml4T/+e233/S3v/1N3377rapWrSpJOnv2rG688UZ9+umnql+/vrkFVnBDhw7VBx98oJdfftnsUnCJ++67T97e3lq6dGmF3q5xpNsk77zzjiZPnqx77rlHbdu2VUBAgMP4gQMHmlQZJGnKlCnavXu3/v3vf8vbm++mXE1OTo5efvnlyx4N+uWXX0yqDGzbXB/bN9dVp04dTZ06VcOGDTO7FFzi1ltv1dmzZzV37lw1a9ZMkrRr1y7df//9Cg4O1vLly02usGIbNWqU5s2bp6ZNm5b42TNjxgyTKkNAQIAyMjIUGRlpdimmInSbxNPz8pfTV6RTLVzV7bffrm+++UaBgYGKjo4utvFOSkoyqTJI0t/+9jelpqZq2LBhJX5r+uijj5pUGdi2uT62b66rRo0a2rhxo5o0aWJ2KbiEn5+f1q9frxtuuMFheEZGhrp166bc3FyTKoMk9ezZ87LjPDw8tGrVqjKsBhdr3769Xn31VXXt2tXsUkzFV9wmufTIHFxL1apVNXjwYLPLwGUsW7ZMX375pbp06WJ2KbgE2zbXx/bNdf3973/X/Pnz9cwzz5hdCi7RoEEDWSyWYsMLCwtVt25dEyrCxVavXm12CbiMV155RU888YSmTJmi6Oho+fj4OIwPDg42qbKyxZFuF3Du3DlVrlzZ7DKAciM8PFxfffUVN4JycWzbAOc8+uijmjdvnlq1aqVWrVoV2znlFFnzLF68WFOmTNGsWbPsN0/btGmTRo0apSeffFKxsbHmFghJ0t69e7Vv3z51795dfn5+9pt1wTxFZ8Bd2g8V7UZqhG6TFBYWasqUKXrnnXd0/Phx7d69W40bN9YzzzyjsLAwjRgxwuwSK7yCggKtWbNG+/bt0913362goCAdOXJEwcHBCgwMNLu8Cu3jjz/W4sWLNXfuXPn7+5tdDi7Ctq18YPvmmjhF1nVVq1ZNubm5KigosN8Loej/l16icebMGTNKrNBOnz6tO+64Q6tXr5aHh4f27Nmjxo0b64EHHlC1atU0ffp0s0ussFJTU684PiYmpowqMRenl5vkpZde0ty5czV16lQ9+OCD9uFRUVGaOXMmO6YmO3jwoG699VYdOnRI+fn56tWrl4KCgvTKK68oPz9f77zzjtklVmjTp0/Xvn37VLt2bYWFhRU7GvTDDz+YVBnYtrk+tm+ui1NkXdfMmTPNLgFXMGbMGPn4+OjQoUMOZ8HdeeedGjt2LKHbRBUlVP8ZQrdJ5s2bp/fee08333yzHn74Yfvw66+/Xjt37jSxMkgXTvFr166dtmzZoho1atiH33777Q5BAubgND7XxbbN9bF9A5w3fPhws0vAFaxYsUJff/11sUe3NW3aVAcPHjSpKlwsNzdXhw4d0vnz5x2Gt2rVyqSKyhah2ySHDx9WREREseFWq7XEG3WgbKWlpWn9+vWqVKmSw/CwsDAdPnzYpKpQ5LnnnjO7BFwG2zbXx/bNtW3atEn//e9/S9w55c7y5iosLFRycrJ27NghSWrZsqUGDhwoLy8vkytDTk5OiZebnTlzRr6+viZUhCInT57U/fffr2XLlpU4vqJc0335Z7vAUC1atFBaWlqx4YsWLSr2OAqUPavVWuJG4LffflNQUJAJFQHlA9s218f2zXV9+umnuvHGG7Vjxw59/vnnslgs+vnnn7Vq1SpVqVLF7PIqtL1796p58+a69957lZSUpKSkJA0dOlQtW7bUvn37zC6vwuvWrZvmzZtnf+3h4SGr1aqpU6de8V4JMN5jjz2ms2fP6rvvvpOfn5+WL1+uuXPnqmnTplqyZInZ5ZUZjnSb5Nlnn9Xw4cN1+PBhWa1WJSUladeuXZo3b56WLl1qdnkVXu/evTVz5ky99957ki5svLOzs/Xcc8+pX79+JleHwsJCvfrqq5c9GsRNbMzDts31sX1zXVOmTNGrr76q+Ph4BQUF6bXXXlN4eLj+8Y9/qE6dOmaXV6GNHj1aTZo0UXp6uqpXry7pws27hg4dqtGjR+vLL780ucKKberUqbr55pu1adMmnT9/Xk888YR+/vlnnTlzRt9++63Z5VVoq1at0uLFi9WuXTt5enqqUaNG6tWrl4KDg5WYmKj+/fubXWKZ4O7lJkpLS9Pzzz+vLVu2KDs7W23atNGzzz6r3r17m11ahffbb7+pT58+stls2rNnj9q1a6c9e/aoZs2aWrt2rUJCQswusUJ79tln9e9//1sJCQl6+umn9dRTT+nAgQNKTk7Ws88+q9GjR5tdYoXGts21sX1zXQEBAfr5558VFhamGjVqaM2aNYqOjtaOHTt000036ejRo2aXWGEFBAQoPT1d0dHRDsO3bNmiLl26KDs726TKUCQzM1Nvvvmmw2dPfHw8X1iZLDg4WD/99JPCwsLUqFEjzZ8/X126dNH+/fvVsmVL5ebmml1imeBIt4m6deumlJQUs8tACerXr68tW7ZowYIF9o33iBEjdM8998jPz8/s8iq8//znP3r//ffVv39/TZo0SX/729/UpEkTtWrVSunp6YRuk7Ftc21F27dPP/1UP/30E9s3F1KtWjX98ccfkqR69epp27Ztio6O1tmzZyvMjqmr8vX1tffNxbKzs4vdHwHmqFKlip566imzy8AlmjVrpl27diksLEzXX3+93n33XYWFhemdd96pUF+IcKTbZOfPn9eJEydktVodhjds2NCkiiBJa9eu1Y033mh/FmeRgoICrV+/Xt27dzepMkgXjjjs2LFDDRs2VJ06dfTll1+qTZs2+uWXX3TDDTcoMzPT7BIBwGl333232rVrp7Fjx+qFF17QG2+8oUGDBiklJUVt2rThRmomuvfee/XDDz/ogw8+UIcOHSRJ3333nR588EG1bdtWc+bMMbdA6OzZs9q4cWOJ+9X33nuvSVXh448/VkFBge677z5lZGTo1ltv1ZkzZ1SpUiXNmTNHd955p9kllglCt0n27NmjBx54QOvXr3cYbrPZ5OHhUWHu5OeqvLy8dPTo0WKnWZ4+fVohISH0j8maNWumefPmqWPHjuratasGDBig8ePHa8GCBRo1apROnDhhdokVSrVq1eTh4XFV03K9vWvYs2ePVq9eXeLO6bPPPmtSVThz5ozOnTununXr2m8CtX79ejVt2lRPP/20qlWrZnaJFdbZs2c1fPhwffHFF/Lx8ZF04Yv4gQMHavbs2apataq5BVZwX3zxhe655x5lZ2crODjY4TPJw8ODzx4Xkpubq507d6phw4aqWbOm2eWUGUK3Sbp06SJvb2+NHz9ederUKbbDev3115tUGSTJ09NTx48fV61atRyG7969W+3atVNWVpZJlUGSxo8fr+DgYE2cOFELFizQ0KFDFRYWpkOHDmnMmDF6+eWXzS6xQpk7d679/6dPn9aLL76oPn36qHPnzpKkDRs26Ouvv9YzzzyjMWPGmFUm/uf999/XI488opo1ayo0NLTYzukPP/xgYnWAa9u7d6/9kWHNmzcv8RGJKHvXXXed+vXrpylTppT46DDAbIRukwQEBCgjI0ORkZFml4KLxMXFSZIWL16sW2+91eHZjoWFhfrpp5/UrFkzLV++3KwSUYINGzZow4YNatq0qW677Tazy6nQBg8erJ49e2rkyJEOw998802tXLlSycnJ5hQGu0aNGumf//ynnnzySbNLwWWcOHGixLMQWrVqZVJFeP755zVu3LhigS4vL0//+te/OEPEZAEBAdq6dasaN25sdim4hM1m06JFiy57dlVFuWyG0G2S9u3b69VXX1XXrl3NLgUXuf/++yVdOHJ3xx13ONxUqFKlSgoLC9ODDz5YoU6HAZwRGBiozZs3Fzv6s3fvXrVu3Zo7/LqA4OBgbd68mZ1TF5SRkaHhw4drx44dunT3jEvPzMVlZ64tLi5Od911l+644w6zS8ElHn30Ub377rvq2bOnateuXezs3tmzZ5tUWdni7uVl6OJTkl955RU98cQTmjJliqKjo+3XBxUJDg4u6/Kg///GDwsL0+OPP84pSi7syJEjWrduXYnfmnL3cvPUqFFDixcvVkJCgsPwxYsXq0aNGiZVhYv93//9n1asWKGHH37Y7FJwiQceeEDXXXedPvjggxJ3TmGeonvuXGrLli3253ajbC1ZssT+//79++vxxx/X9u3bS9yvHjhwYFmXh//56KOPlJSUpH79+pldiqk40l2GPD09HTbYJW3AuZGaa7jpppuUlJRU7MYoWVlZio2N1apVq8wpDJKkOXPm6B//+IcqVaqkGjVqFLsm9ZdffjGxuoptzpw5+vvf/66+ffuqY8eOki7c4Xf58uV6//33dd9995lbYAX1+uuv2/+fk5OjGTNmqH///iXunPKllXmCgoL0448/cp2wCym6UWRmZmaxG3QVFhYqOztbDz/8sGbNmmVilRWTp6fnVU3HfrW5wsPDtWzZsgp/SS2huwylpqZe9bQxMTEGVoI/c7nTyE6cOKF69erJYrGYVBkkqUGDBnr44Yc1YcKEq/7QRdn57rvv9PrrrzvcbGj06NH2EI6yFx4eflXT8aWVuWJjYzVs2DANHjzY7FLwP3PnzpXNZtMDDzygmTNnqkqVKvZxRZedFd00EkBxc+fO1fLly/Xhhx86XLZZ0RC6gYv89NNPkqTWrVtr1apVDqeMFRYWavny5Xr33Xd14MABkyqEdOEU5o0bN6pJkyZmlwIA18ypU6c0fPhwdejQQVFRUZwi60JSU1PtT54BcPXy8vJ0++2369tvv1VYWFix7VpFeWIGWw6TzJ49W4GBgfq///s/h+ELFy5Ubm6uhg8fblJlFVvr1q3l4eEhDw8P3XTTTcXG+/n56Y033jChMlxsxIgRWrhwocaPH292KZCceoQe96sALm/Dhg369ttvtWzZsmLjOEXWXEFBQdqxY4eio6MlXbhPxezZs9WiRQtNmjRJlSpVMrnCim306NGKiIgodnnMm2++qb1792rmzJnmFAYNHz5cGRkZGjp0aIW+VwVHuk1y3XXX2e/kd7HU1FQ99NBD2rVrl0mVVWwHDx6UzWZT48aNtXHjRofndFeqVEkhISHy8vIysUJIF846GDBggPLy8kq8JnXGjBkmVVYxXXq/ipJwvwrXMXjwYHXo0KHYI8OmTp2q77//XgsXLjSpMoSFhWnAgAF65plnVLt2bbPLwUXat2+v8ePHa/Dgwfrll1/UokULxcXF6fvvv1f//v0JdSarV6+elixZorZt2zoM/+GHHzRw4ED99ttvJlWGgIAAff311xX+iU0c6TbJoUOHSrzGrlGjRjp06JAJFUG68PuXVOxu2HAtiYmJ+vrrr9WsWTNJKnYjNZSt1atXm10CnLB27VpNmjSp2PC+fftq+vTpZV8Q7E6fPq0xY8YQuF3Q7t271bp1a0kXzkqMiYnR/Pnz9e233+quu+4idJvs9OnTDtfbFwkODtapU6dMqAhFGjRowFluInSbJiQkRD/99JPCwsIchm/ZsoXH6phkyZIl6tu3r3x8fBweQ1ESrqsz1/Tp0/Xhhx9yJ2wXwY0fy5fs7OwST4X18fFx6lIBXHtxcXFavXo196twQTabzf6F/MqVKzVgwABJFwIFoc58ERERWr58uUaOHOkwfNmyZWrcuLFJVUG6sM/2xBNP6J133imWeyoSQrdJ/va3v2n06NEKCgpS9+7dJV04tfzRRx/VXXfdZXJ1FVNsbKyOHTumkJAQxcbGXnY6TpE1n6+vr7p06WJ2GbiMtLQ0vfvuu/rll1+0cOFC1atXTx999JHCw8Mr/OllriA6OloLFizQs88+6zD8008/VYsWLUyqCtKFS88mTJigdevW8Tg3F9OuXTu9+OKLuuWWW5Samqq3335bkrR//37OTHABY8eO1ciRI3Xy5En7PXm++eYbTZ8+nbMQTDZ06FDl5uaqSZMm8vf3L7ZdO3PmjEmVlS2u6TbJ+fPnNWzYMC1cuNB+J0yr1ap7771Xb7/9tnx9fU2uEHBdiYmJOnr0qMOzh+EaPvvsMw0bNkz33HOPPvroI23fvl2NGzfWm2++qa+++kpfffWV2SVWeF988YXi4uJ09913O+ycfvLJJ1q4cOEVv3SEsa70aDce52auLVu2aOjQoTp06JDGjh2r5557TpI0atQonT59WvPnzze5Qrz99tt66aWXdOTIEUkX7pEwadIk3XvvvSZXVrHNnTv3iuMrys2jCd0m27NnjzZv3iw/Pz9FR0fbrykGcHm33367Vq1apRo1aqhly5bFvjVNSkoyqTLccMMNGjNmjO69914FBQVpy5Ytaty4sX788Uf17dtXx44dM7tESPryyy81ZcoU++dPq1at9Nxzz3GpAOCkc+fOydvbm0eJuZCTJ0/Kz89PgYGBZpcC2LGFMMnzzz+vcePGqWnTpmratKl9eF5env71r38VO+0PZeNqj5xyip+5qlatqri4OLPLQAl27dplv2TmYlWqVNHZs2fLviCUqH///urfv7/ZZeAyzp8/r/3796tJkyaEORfRuHFjff/998Xuu3Pu3Dm1adOGsxBMdtNNNykpKUlVq1Z1ePJMVlaWYmNjtWrVKhOrw759+zR79mzt27dPr732mkJCQrRs2TI1bNhQLVu2NLu8MsGRbpN4eXnp6NGjCgkJcRh++vRphYSEcM2wSS49te/XX39VnTp1HHZ6OMUPuLzGjRvrvffe0y233OJwpHvevHl6+eWXtX37drNLrPAuFx7Onj1LeDBZbm6uRo0aZT8dc/fu3WrcuLFGjRqlevXqafz48SZXWHF5enra7/tysePHj6tBgwY6f/68SZVBunz/nDhxQvXq1ZPFYjGpMqSmpqpv377q0qWL1q5dqx07dqhx48Z6+eWXtWnTJi1atMjsEssEX5+apOiZtZfasmWLqlevbkJFkC7cEOViQUFBSk1N5c6XLqigoEBr1qzRvn37dPfddysoKEhHjhxRcHAwp5SZ6MEHH9Sjjz6qDz/8UB4eHjpy5Ig2bNigcePG6ZlnnjG7PEg6cOBAiV/s5ufn6/DhwyZUhCITJkzQli1btGbNGt1666324bfccosmTZpE6DbBxU8z+frrrx0eS1VYWKhvvvnmitfiw1g//fST/f/bt293uISpsLBQy5cvV7169cwoDf8zfvx4vfjiixo7dqyCgoLsw2+66Sa9+eabJlZWtgjdZaxatWry8PCQh4eHrrvuOofgXVhYqOzsbD388MMmVgi4voMHD+rWW2/VoUOHlJ+fr169eikoKEivvPKK8vPz9c4775hdYoU1fvx4Wa1W3XzzzcrNzVX37t3l6+urcePGadSoUWaXV6FdTXioyI9zcQXJyclasGCBOnXq5LB/0LJlS+3bt8/Eyiqui28seOkNn3x8fBQWFsbz7U3UunVr+3510Y0hL+bn56c33njDhMpQZOvWrSXeaDAkJKRCPW6P0F3GZs6cKZvNpgceeECTJ0922OmpVKmSwsLC1LlzZxMrBFzfo48+qnbt2hV7rv3tt9+uBx980MTK4OHhoaeeekqPP/649u7dq+zsbLVo0YKzD1xAUXjw8PAgPLiokydPFjs9VpJycnJKPDsOxit6Nnd4eLg2bdpU7LIMmGv//v2y2Wxq3LixNm7c6HA9d6VKlRQSEiIvLy8TK0TVqlV19OjRYmeE/PjjjxXqLARCdxkr2tEJDw/XjTfeWOyuywD+XFpamtavX69KlSo5DA8LC+P0WBdRqVIlnvnsYi4OD99//71q1qxpckW4VLt27fTll1/azwopCtr//ve/+ULeRBaLRY0bN9aZM2cI3S6m6Kk/Rds3uJ677rpLTz75pBYuXCgPDw9ZrVZ9++23GjduXIV6nBuh2yQXP5bl3LlzxW7AERwcXNYlQRfucnkxDw8PZWdnFxtO/5jLarWWeE3qb7/95nC9EMpGXFyc5syZo+Dg4D+9qzyPczPfpfeugOuYMmWK+vbtq+3bt6ugoECvvfaatm/frvXr1ys1NdXs8iosHx8fh2uH4RqWLFmivn37ysfHx+HymZIMHDiwjKrCpaZMmaL4+Hg1aNBAhYWFatGihQoLC3X33Xfr6aefNru8MsPdy02Sm5urJ554Qv/97391+vTpYuO5e7k5PD09HU7hu/SGd0Wv6R9z3XnnnapSpYree+89BQUF6aefflKtWrU0aNAgNWzYULNnzza7xArl/vvv1+uvv66goCDdd999VzwNlr4xx+uvv66HHnpIlStX/tNHI/JIRHPt27dPL7/8srZs2aLs7Gy1adNGTz75pKKjo80urUIbM2aMfH199fLLL5tdCv7n4juWe3p6XnY69ttcw6+//qqtW7cqOztbN9xwg8MjkysCQrdJ4uPjtXr1ar3wwgsaNmyYZs2apcOHD+vdd9/Vyy+/rHvuucfsEiukqz2ScPGZCih7v/32m/r06SObzaY9e/aoXbt22rNnj2rWrKm1a9eWeE0kjHPx0Qa4pouvR73SnZZ5JCJQslGjRmnevHlq2rSp2rZtq4CAAIfxM2bMMKkyoHwpLCzU1q1b1ahRI1WrVs3scsoModskDRs21Lx589SjRw8FBwfrhx9+UEREhD766CN98skn+uqrr8wuEVfh5Zdf1sMPP6yqVauaXUqFU1BQoAULFjgcDbrnnnvk5+dndmkVjpeXl44dO6ZatWrJy8tLR48e5YsPoBR++OEH+fj42I9qL168WLNnz1aLFi00adKkYvexQNnp2bPnZcd5eHho1apVZVgNLnbgwAGlpKTIYrEoJiZGLVu2NLskXOSxxx5TdHS0RowYocLCQsXExGj9+vXy9/fX0qVL1aNHD7NLLBOEbpMEBgZq+/btatiwoerXr6+kpCR16NBB+/fvV3R0tLKzs80uEVchODhYmzdv5jneqNBCQ0P1/vvv67bbbpOnp6eOHz/ucAdZuI709HR98cUXslgsuummmxyeBQ3ztW/fXuPHj9fgwYP1yy+/qEWLFoqLi9P333+v/v37a+bMmWaXCLiU1atXa8CAAcrLy5MkeXt768MPP9TQoUNNrgxF6tevr+TkZLVr107Jycn65z//qTVr1uijjz7SqlWr9O2335pdYpm4/AUQMFTjxo3tN7OJjIzUf//7X0nSF198wVHTcoTvrMwxd+5cffnll/bXTzzxhKpWraobb7xRBw8eNLGyiunhhx/WoEGD5OXlJQ8PD4WGhsrLy6vEfzDPokWL1KVLF7322mt6//331b9/f02bNs3ssnCR3bt3q3Xr1pKkhQsXKiYmRvPnz9ecOXP02WefmVsc7H777Tf99ttvZpcBSc8884x69eqlw4cP6/Tp03rwwQf1xBNPmF0WLnLq1CmFhoZKkr766ivdcccduu666/TAAw9o69atJldXdgjdJrn//vu1ZcsWSdL48eM1a9YsVa5cWY899pgef/xxk6sDXNuUKVPsp5Fv2LBBb775pqZOnaqaNWtqzJgxJldX8UyaNEnbt2/X4sWLZbPZ9OGHHyopKanEfzBPYmKiHnzwQWVmZur333/Xiy++qClTpphdFi5is9nsjz5auXKl+vXrJ0lq0KCBTp06ZWZpFZ7VatXzzz+vKlWqqFGjRmrUqJGqVq2qF154gcdV/b/27jys5rz/H/jztGsvadGEkiUUkoy1QZYy0s1t3GMr21iGYZA9S8LgjsZtbpIlTMNYs0y2iWk0QkTxbSoRNciWUGlR5/dHd+fX0WHM4rxPnefjulxX5/05mWfXXD6d1+f9fr/eAl2/fh3Lly+HjY0NzMzMsHr1ajx8+FBhk2ISw8rKCikpKSgrK8Px48fRq1cvABVNpdXpYTyPDBOkamHg6emJ1NRUXL58GU2aNGGHUqLfkZ2dDUdHRwBAVFQU/vnPf+Kzzz5D586d1WZvkKpp3rw5mjdvjkWLFmHw4MHQ19cXHYlek5aWhu+//172IWfGjBlYuHAhHj58yD34KsLNzQ3BwcHw9PREbGwsNmzYAKDimDcrKyvB6dTb/PnzsWXLFnz11Vfo3LkzACAuLg6LFy9GUVERli1bJjihenr+/DksLCxkr/X19VGnTh08e/aMZ6qriFGjRuGTTz6BjY0NJBIJPD09AQAXLlxA8+bNBadTHhbdSnb69GlMnjwZ58+flzvrufKJaadOnbBx40Z07dpVYEoi1WZoaIgnT56gQYMGOHnyJKZPnw4A0NPTk+3rIjFiY2MxderUakX38+fP4evry2ZDAhUWFsr93tHR0YGenh7y8/NZdKuI0NBQDBs2DFFRUZg/f77s4eK+ffvQqVMnwenU2/bt27F582a5855dXFxga2uLSZMmsegW6MSJEzAxMZG9Li8vR0xMDK5fvy4b4znd4ixevBitWrVCdnY2Bg8eDF1dXQAVTVjnzJkjOJ3ysJGakvn4+KB79+5vXAK7bt06nDlzBgcPHlRyMvozjIyMkJSUxEZqSjZs2DCkpqaibdu22LVrF7KyslC3bl0cPnwY8+bNk/tFS8r1pu7lDx8+hK2tLUpLSwUlIw0NDQQHB8PQ0FA2Nnv2bAQEBMjNFPGcbtVTVFQETU1NHssnkJ6eHpKTk9G0aVO58bS0NLRp04YPfAV52/nclXhON6kCznQrWVJSElauXPnG671792Zjmxqka9euPKJKgG+++QYLFixAdnY29u/fL1tCdvnyZXz66aeC06mn5ORkABV7UlNSUpCTkyO7VrmPy9bWVlQ8QsVRleHh4XJj1tbW2Llzp+y1RCJh0a2C9PT0REdQe61bt8b69euxbt06ufH169ejdevWglIR99PXDAUFBYiNjUVWVhZKSkrkrqnL7xzOdCuZnp4erl+/Llsy9rqMjAw4OzvziakAz58/f+f3Vl2iSUQVsw0SiQSA4q7+derUwX/+8x+MHj1a2dGIaoyysjKsXbsWe/bsUfjhNDc3V1Ayio2NRb9+/dCgQQN07NgRQEUjz+zsbERHR3NbYA3Rr18/bN68GTY2NqKjqI0rV67A29sbhYWFKCgogLm5OR4/fgx9fX1YWlri1q1boiMqBWe6lczW1vatRXdycjJvBIKYmprKiobfw2VKqqGwsFDhB1MXFxdBidRXZmYmpFIpHBwccPHiRblzunV0dGBpaalWXUprA2dnZ0RHR8POzk50FLWxZMkSbN68GTNmzMCCBQswf/583L59G1FRUVi4cKHoeGrNw8MD6enp+Oabb5CamgoAGDhwICZNmoT69esLTkfv6ueff+bElpJ9+eWX6N+/PzZu3AgTExOcP38e2traGD58OKZOnSo6ntJwplvJpkyZgp9++gkJCQnVlou9fPkS7u7u6N69e7XlS/T+xcbGyr6+ffs25syZA39/f7kn2tu3b8eKFSvg5+cnKiYBePToEfz9/XH8+HGF1/lQhOivY88K5WvcuDHWrVuHfv36wcjICFevXpWNnT9/Ht99953oiEQ1Gu9rymdqaooLFy6gWbNmMDU1RXx8PJycnHDhwgX4+fnJHmLVdpzpVrIFCxbgwIEDaNq0KSZPnoxmzZoBAFJTU/HNN9+grKwM8+fPF5xSPXl4eMi+DgoKwpo1a+T2B/v4+MDZ2RmbNm1i0S3YtGnT8OzZM1y4cAEfffQRDh48iAcPHiA4OBghISGi46m1HTt2vPX6yJEjlZSEqObJycmRHRtqaGiIZ8+eAQA+/vhjBAYGioxGAPLy8nDx4kU8fPiw2l5i3tuIFNPW1pY1vLO0tERWVhacnJxgYmKC7OxswemUh0W3kllZWeHcuXOYOHEi5s6dK9v7KJFI0KdPH3zzzTc8i1MFxMfHY+PGjdXG3dzcMHbsWAGJqKrTp0/j0KFDcHNzg4aGBho2bIhevXrB2NgYK1asQL9+/URHVFuvLxUrLS1FYWEhdHR0oK+vzw+mRG/xwQcf4P79+2jQoAEaN26MkydPwtXVFQkJCbJjdkiMI0eOYNiwYcjPz4exsbHcdjSJRMJ7G9EbtG3bFgkJCWjSpAk8PDywcOFCPH78GDt37kSrVq1Ex1Oa3++zT3+7hg0bIjo6Go8fP8aFCxdw/vx5PH78GNHR0bC3txcdjwDY2dlV6/ILAJs3b+b+RhVQUFAgO5LKzMwMjx49AlCxBzUxMVFkNLX39OlTuT/5+flIS0tDly5dsGvXLtHxiFTaP/7xD8TExACo2I4WGBiIJk2aYOTIkWxCKNiMGTMwevRo5OfnIy8vT+4+xwZ3RG+2fPlyWb+qZcuWwczMDBMnTsSjR4+wadMmwemUh3u6iRSIjo7GoEGD4OjoiA4dOgAALl68iBs3bmD//v3w9vYWnFC9tW/fHsHBwejTpw98fHxgamqKFStWYN26ddi3bx9u3rwpOiK95tKlSxg+fLja7N2qDbj3Ubz4+HjEx8ejSZMm6N+/v+g4as3AwADXrl3jv4cajvc1EoXLy4kU8Pb2Rnp6OjZs2CArEvr3748JEyZwplsFTJ06Fffv3wcALFq0CH379kVkZCR0dHQQEREhNhwppKWlhXv37omOQVSjdOzYUdbMk8Tq06cPLl26xGKthps3bx7Mzc1Fx1BrJSUlKCkpgaGhoegoSsWZbiKq8QoLC5GamooGDRrAwsJCdBy1dvjwYbnXUqkU9+/fx/r162FnZ4djx44JSqbezM3NkZ6eDgsLC4wePRpff/01jIyM3vo93333HQYMGAADAwMlpaQnT56gbt26AIDs7GyEh4fj5cuX8PHx4TnQAlS9nz169AhBQUEYNWoUnJ2doa2tLfdeHx8fZcejKl7/3VNJIpFAT08Pjo6O3MIpwLZt25CYmIgPP/wQw4YNw9y5c7FmzRq8evUKPXr0wO7du2X3vNqORTfRG5w9exZhYWG4desW9u7dC1tbW+zcuRP29vbo0qWL6HhEKqmyQ2kliUSCevXqoUePHggJCZHt6yLlMjQ0RHJyMhwcHKCpqYmcnBy5s9RJrGvXrqF///7Izs5GkyZNsHv3bvTt2xcFBQXQ0NBAQUEB9u3bB19fX9FR1crr97M3kUgkPKpSMA0NDUgkErxe1lSOSSQSdOnSBVFRUTAzMxOUUr0sW7YMy5YtQ+fOnZGYmIhPPvkEUVFRmDZtGjQ0NLBu3Tp8/PHH2LBhg+ioSsGim0iB/fv3Y8SIERg2bBh27tyJlJQUODg4YP369YiOjkZ0dLToiGrrxo0bSE5OhqurK+zt7fHDDz9g5cqVePnyJXx9fTFv3jy5rrIkRmVzOxZ2qqFXr1548OAB2rVrh+3bt2PIkCGoU6eOwvdu3bpVyenIy8sLWlpamDNnDnbu3ImjR4+iT58+soaeU6ZMweXLl3H+/HnBSYlUU0xMDObPn49ly5bB3d0dQEUvnsDAQCxYsAAmJiYYP348OnTogC1btghOqx6aNGmCoKAgfPrpp7h06RI6dOiAPXv2YNCgQQCAY8eOYcKECbhz547gpMrB7uVECgQHB2Pjxo0IDw+XW0JW+bSOxDh48CBatGiBoUOHwsnJCTt27MA///lPGBgYwMrKCosXL8aqVatEx1RbeXl5+Pzzz2FhYQFra2tYW1vDwsICkydPRl5enuh4au3bb7+Ft7c38vPzIZFI8OzZs2qd5iv/kPIlJCTIZoT+/e9/4969e5g0aRI0NDSgoaGBKVOmsAmhIPHx8Th69Kjc2I4dO2Bvbw9LS0t89tlnKC4uFpSOKk2dOhVr1qxBz549YWRkBCMjI/Ts2ROrV69GQEAAOnfujNDQUJw6dUp0VLWRlZUlWxnq5uYGLS0tuSPCXFxcZP151AEbqREpkJaWhm7dulUbNzExYfEg0LJlyzBr1iwEBwcjIiICEyZMwIoVKzBt2jQAwKZNm7B27VrMnj1bbFA1lJubi44dO+Lu3bsYNmwYnJycAAApKSmIiIhATEwMzp07x2V9glhZWeGrr74CANjb22Pnzp1qs4+uJsjNzYW1tTWAiq0ABgYGcv9WzMzM8OLFC1Hx1NqSJUvQvXt3fPzxxwAqtgKMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1d/PmTRgbG1cbNzY2xq1btwBUzLw+fvxY2dHUVmlpKXR1dWWvdXR05CaytLS01GpbBme6iRSwtrZGRkZGtfG4uDh2LhUoLS0No0ePhkQigZ+fH0pKSuDp6Sm73rt3b7VZpqRqgoKCoKOjg5s3byIsLAzTpk3DtGnTsGnTJmRkZEBbWxtBQUGiYxKAzMxMWcFdVFQkOA1Ven1bDLfJqIakpCT07NlT9nr37t3o0KEDwsPDMX36dKxbtw579uwRmJAAoF27dggICJBtbQIqtjnNmjUL7du3B1CxPY0n0ChXSkoKkpOTkZycDKlUitTUVNnr//u//xMdT6k4002kwLhx4zB16lRs3boVEokE9+7dQ3x8PGbOnInAwEDR8dRWQUGBrOOyhoYG6tSpA319fdn1OnXqcJmfIFFRUQgLC4OVlVW1a9bW1li1ahUmTJiAtWvXCkhHVZWXl2PZsmXYuHEjHjx4gPT0dDg4OCAwMBCNGjXCmDFjREdUS/7+/rJZoaKiIkyYMEHWOZ73NXGePn0qd1+LjY2Fl5eX7HX79u2RnZ0tIhpVsWXLFgwYMAAffPCBrLDOzs6Gg4MDDh06BADIz8/HggULRMZUOz179pRrble5YqRqgzt1waKbSIE5c+agvLwcPXv2RGFhIbp16wZdXV3MnDkTU6ZMER1PbUkkErkb9OuvSZz79++jZcuWb7zeqlUr5OTkKDERvUlwcDC2b9+OVatWYdy4cbLxVq1aITQ0lEW3AH5+fnKvhw8fXu09I0eOVFYcqsLKygqZmZmws7NDSUkJEhMTsWTJEtn1Fy9eVDs+jJSvWbNmSElJwcmTJ5Geni4b69Wrl6wLPbv/K1dmZqboCCqF3cuJ3qKkpAQZGRnIz89HixYtYGhoKDqSWtPQ0ICJiYms0M7Ly4OxsbHsF6pUKsXz58/Vao+QqrC1tcX333//xuP0zp49iyFDhuDevXtKTkavc3R0RFhYmKzhUFJSEhwcHJCamoqOHTuymVoN8Ntvv6F+/frvfKQV/XkTJ05EUlISVq5ciaioKGzfvh337t2Djo4OACAyMhKhoaFISEgQnJSoZps0aRKCgoJgYWEhOsp7wZluIgVGjx6Nr7/+GkZGRmjRooVsvKCgAFOmTOGROoJs27ZNdAR6gz59+mD+/Pk4deqU7MNopeLiYgQGBqJv376C0lFVd+/ehaOjY7Xx8vJylJaWCkhEf1SLFi1w9epV9hhRgqVLl2LgwIHw8PCAoaEhtm/fLneP27p1K3r37i0wIVWKiYlBTEwMHj58iPLycrlr/Nym+r799lvMnDmz1hbdnOkmUkBTUxP379+HpaWl3Pjjx49hbW2NV69eCUpGf8SuXbvg4+Mj2xdJ789vv/0GNzc36Orq4vPPP0fz5s0hlUrx66+/4r///S+Ki4tx6dIlNrFRAe3atcOXX36J4cOHy810BwUF4dSpUzh79qzoiPQ7qv5/I+V49uwZDA0NoampKTeem5sLQ0PDag8bSbmWLFmCoKAguLm5wcbGptrWs4MHDwpKRu+qtt/XONNNVMXz588hlUohlUrx4sUL6Onpya6VlZUhOjq6WiFOqmv8+PHo0KFDrb2Bq5IPPvgA8fHxmDRpEubOnStrnCKRSNCrVy+sX7+eBbeKWLhwIfz8/HD37l2Ul5fjwIEDSEtLw44dO6qdR0xEFUxMTBSOm5ubKzkJKbJx40ZERERgxIgRoqMQKcSim6gKU1NTWXOupk2bVrsukUjkGqiQauNCHuWyt7fHsWPH8PTpU9y4cQNAxf5hfihVLQMGDMCRI0cQFBQEAwMDLFy4EK6urjhy5Ah69eolOh4R0R9WUlKCTp06iY5B9EYsuomqOHPmDKRSKXr06IH9+/fLFQs6Ojpo2LAh6tevLzAhkeozMzODu7u76Bj0Fl27dsWpU6dExyAi+luMHTsW3333HY91JZXFopuoCg8PDwCQHQ/CzrBERKRqeFQikbyioiJs2rQJP/74I1xcXKod47ZmzRpByYgqsOgmUqBhw4YAgMLCQmRlZaGkpETuuouLi4hYRER/irm5OdLT02FhYQEzM7O3Fm25ublKTEZ/BrfOEMlLTk5GmzZtAADXr1+Xu8aHVDXD8OHDYWxsLDrGe8Oim0iBR48eYdSoUTh27JjC6zwHmohqkrVr18LIyAgAEBoaKjYM/WUpKSnc6kRUxZkzZ0RHoCqSk5Pf+b2VE1kbNmx4X3FUAo8MI1Jg2LBhuHPnDkJDQ/HRRx/h4MGDePDgAYKDgxESEoJ+/fqJjkjvoFWrVjh27Bi7ZhORyho4cOA7v/fAgQPvMQkR0d9DQ0MDEokEUqn0d1caqMtEFme6iRQ4ffo0Dh06BDc3N2hoaKBhw4bo1asXjI2NsWLFChbdgjk4OCAhIQF169aVG8/Ly4Orqytu3boFoPoSMyJ19fz583d+b21e3qeKqh5FJZVKcfDgQZiYmMDNzQ0AcPnyZeTl5f2h4pxIHQwcOBAREREwNjb+3X8ffGClXJmZmbKvr1y5gpkzZyIgIAAdO3YEAMTHxyMkJASrVq0SFVHpWHQTKVBQUCA7j9vMzAyPHj1C06ZN4ezsjMTERMHp6Pbt2wqfjBYXF+Pu3bsCEhGptsrjEN+mckZCXWYdVMW2bdtkX8+ePRuffPIJNm7cCE1NTQAVs0CTJk3iwxCi15iYmMjua8bGxty7rUIqeyMBwODBg7Fu3Tp4e3vLxlxcXGBnZ4fAwED4+voKSKh8LLqJFGjWrBnS0tLQqFEjtG7dGmFhYWjUqBE2btwIGxsb0fHU1uHDh2VfnzhxQm6GqKysDDExMWjUqJGAZESqjfsda4atW7ciLi5OVnADgKamJqZPn45OnTph9erVAtMRqZaqD6wiIiLEBaG3unbtGuzt7auN29vbIyUlRUAiMVh0EykwdepU3L9/HwCwaNEi9O3bF5GRkdDR0eGNXaDKp6ESiQR+fn5y17S1tdGoUSOEhIQISEak2iqPQyTV9urVK6SmpqJZs2Zy46mpqSgvLxeUikj19ejRAwcOHICpqanc+PPnz+Hr64vTp0+LCUZwcnLCihUrsHnzZujo6AAASkpKsGLFCjg5OQlOpzxspEb0DgoLC5GamooGDRrAwsJCdBy1Z29vj4SEBP6/IPqTzp49i7CwMNy6dQt79+6Fra0tdu7cCXt7e3Tp0kV0PLU1ffp07NixA/PmzYO7uzsA4MKFC/jqq68wYsQInjVM9AYaGhrIycmRbQ2s9PDhQ9ja2qK0tFRQMrp48SL69+8PqVQq61SenJwMiUSCI0eOyO51tR1nuonegb6+PlxdXUXHoP+p2qCjUl5eXrUn3ERU3f79+zFixAgMGzYMiYmJKC4uBgA8e/YMy5cvR3R0tOCE6uvf//43rK2tERISIlttZWNjg4CAAMyYMUNwOiLVU/VoqpSUFOTk5Mhel5WV4fjx47C1tRURjf7H3d0dt27dQmRkJFJTUwEAQ4YMwdChQ2FgYCA4nfJwpptIgbKyMkRERCAmJgYPHz6stqyPy5TEWrlyJRo1aoQhQ4YAqGjSsX//ftjY2CA6OhqtW7cWnJBIdbVt2xZffvklRo4cCSMjIyQlJcHBwQFXrlyBl5eX3IdWEqey4zwbqBG9WeXRVEBFM8jX1alTB//5z38wevRoZUcjAKWlpWjevDmOHj2qVkvJFeFMN5ECU6dORUREBPr164dWrVqxI6aK2bhxIyIjIwEAp06dwo8//ojjx49jz549CAgIwMmTJwUnJFJdaWlp6NatW7VxExMT5OXlKT8QKcRim+j3ZWZmQiqVwsHBARcvXkS9evVk13R0dGBpaSnXmJCUS1tbG0VFRaJjqAQW3UQK7N69G3v27JE73oBUR05ODuzs7AAAR48exSeffILevXujUaNG6NChg+B0RKrN2toaGRkZ1Tr9x8XFwcHBQUwoAgA8ePAAM2fOlK2yen3mjse5Eclr2LAhSktL4efnh7p168odVUWq4fPPP8fKlSuxefNmaGmpb+mpvj850Vvo6OjA0dFRdAx6AzMzM2RnZ8POzg7Hjx9HcHAwgIqlZfxQSvR248aNw9SpU7F161ZIJBLcu3cP8fHxmDFjBhYuXCg6nlrz9/dHVlYWAgMDYWNjw1VWRO9AW1sbBw8e5P1LRSUkJCAmJgYnT56Es7NztX3cBw4cEJRMuVh0EykwY8YMfP3111i/fj0/9KiggQMHYujQoWjSpAmePHkCLy8vAMCVK1f4sITod8yZMwfl5eXo2bMnCgsL0a1bN+jq6iIgIABjx44VHU+txcXF4ezZs2jTpo3oKEQ1yoABAxAVFYUvv/xSdBR6jampKQYNGiQ6hnAsuokUiIuLw5kzZ3Ds2DG0bNkS2tractfV5amcqlq7di3s7e2RlZWFVatWwdDQEABw//59TJo0SXA6ItUmkUgwf/58BAQEICMjA/n5+WjRogXCwsJgb2/PRmoC2dnZKWwGRURv16RJEwQFBeGXX35Bu3btqs2mfvHFF4KS0bZt20RHUAnsXk6kwKhRo956nTcQcUpLSzF+/HgEBgbC3t5edByiGqO4uBiLFy/GqVOnZDPbvr6+2LZtGxYsWABNTU18/vnnmD17tuioauvkyZMICQlBWFhYtT33RPRmb/s8IJFIcOvWLSWmIaqORTcR1TgmJia4evUqi26iP2D27NkICwuDp6cnzp07h0ePHmHUqFE4f/485s2bh8GDB7PLr2BmZmYoLCzEq1evoK+vX22VVW5urqBkRETvztXVFTExMTAzM0Pbtm3fulUzMTFRicnE4fJyIqpxfH19uXeL6A/au3cvduzYAR8fH1y/fh0uLi549eoVkpKS2LtCRYSGhoqOQET0lw0YMAC6uroAKj6zEWe6iRR601M5iUQCPT09ODo6wt/fH927dxeQjoKDgxESEoKePXty7xbRO9LR0UFmZiZsbW0BAHXq1MHFixfh7OwsOBkR0V/322+/4fDhw8jKykJJSYnctTVr1ghKRVSBRTeRAnPnzsWGDRvg7OwMd3d3ABVHHiQnJ8Pf3x8pKSmIiYnBgQMHMGDAAMFp1Q/3bhH9cZqamsjJyUG9evUAAEZGRkhOTuY2DRVTVlaGqKgo/PrrrwCAli1bwsfHh0v/id4iJiYGPj4+cHBwQGpqKlq1aoXbt29DKpXC1dUVp0+fFh1R7ZWUlODhw4coLy+XG2/QoIGgRMrFoptIgXHjxqFBgwYIDAyUGw8ODsadO3cQHh6ORYsW4YcffsClS5cEpSQiencaGhrw8vKSLfk7cuQIevToobZnpqqijIwMeHt74+7du2jWrBkAIC0tDXZ2dvjhhx/QuHFjwQmJVJO7uzu8vLywZMkSGBkZISkpCZaWlhg2bBj69u2LiRMnio6ottLT0zFmzBicO3dOblwqlUIikaCsrExQMuVi0U2kgImJCS5fvlztzOeMjAy0a9cOz549Q2pqKtq3b48XL14ISklE9O5+71SGSjydQRxvb29IpVJERkbC3NwcAPDkyRMMHz4cGhoa+OGHHwQnJFJNRkZGuHr1Kho3bgwzMzPExcWhZcuWSEpKwoABA3D79m3REdVW586doaWlhTlz5sDGxqba9s3WrVsLSqZcbKRGpICenh7OnTtXreg+d+4c9PT0AADl5eWyr+n9mz59OpYuXQoDAwNMnz79re/l3i2i6lhMq77Y2FicP39eVnADQN26dfHVV1+hc+fOApMRqTYDAwPZPm4bGxvcvHkTLVu2BAA8fvxYZDS1d/XqVVy+fBnNmzcXHUUoFt1ECkyZMgUTJkzA5cuX0b59ewAVe7o3b96MefPmAQBOnDiBNm3aCEypXq5cuYLU1FS0bdsWV65ceeP72IWZiGoqXV1dhaun8vPzoaOjIyARUc3w4YcfIi4uDk5OTvD29saMGTNw7do1HDhwAB9++KHoeGqtRYsWfPABLi8neqPIyEisX78eaWlpAIBmzZphypQpGDp0KADg5cuXsm7mpByampq4f/8+LC0tAQBDhgzBunXrYGVlJTgZEdFfN3LkSCQmJmLLli2yJp4XLlzAuHHj0K5dO0RERIgNSKSibt26hfz8fLi4uKCgoAAzZszAuXPn0KRJE6xZswYNGzYUHVGtPH/+XPb1pUuXsGDBAixfvhzOzs7Q1taWe6+xsbGy4wnBopuIagwNDQ3k5OTIim5jY2NcvXoVDg4OgpMREf11eXl58PPzw5EjR2QfTF+9egUfHx9ERETAxMREcEIiot+noaEht/KwsmlaVerWSI3Ly4moxuIzQyKqTUxNTXHo0CFkZGTIjgxzcnKq1l+EiOQ5ODggISEBdevWlRvPy8uDq6srjxJVsjNnzoiOoHJYdBP9j7m5OdLT02FhYQEzM7O37g3Ozc1VYjKqJJFIqv1/4R5uIqptHB0dWWgT/QG3b99WOGNaXFyMu3fvCkik3jw8PBAUFISZM2dCX19fdByVwKKb6H/Wrl0LIyMj2dcs5lSPVCqFv7+/7JzhoqIiTJgwgecME1GtMGjQILi7u2P27Nly46tWrUJCQgL27t0rKBmRajp8+LDs6xMnTshtwSgrK0NMTAwaNWokIBktWbIEEyZMYNH9P9zTTUQ1Bs8ZJqLarF69ejh9+jScnZ3lxq9duwZPT088ePBAUDIi1aShoQGgYtXb6yWNtrY2GjVqhJCQEHz88cci4qm11/vwqDvOdBMpkJiYCG1tbdkHn0OHDmHbtm1o0aIFFi9ezKNbBGExTUS12ZuOBtPW1pbrBkxEFcrLywEA9vb2SEhIgIWFheBEVBVXjf5/GqIDEKmi8ePHIz09HUDFMRRDhgyBvr4+9u7di1mzZglOR0REtZGzszO+//77auO7d+9GixYtBCQiUm3x8fE4evQoMjMzZQX3jh07YG9vD0tLS3z22WcoLi4WnFJ9NW3aFObm5m/9oy44002kQHp6Otq0aQMA2Lt3Lzw8PPDdd9/hl19+wb/+9S+EhoYKzUdERLVPYGAgBg4ciJs3b6JHjx4AgJiYGOzatYv7uYkUWLJkCbp37y5bPn7t2jWMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1tWTJEh51+D8suokUkEqlsiVLP/74o+xmbmdnh8ePH4uMRkREtVT//v0RFRWF5cuXY9++fahTpw5cXFzw448/wsPDQ3Q8IpWTlJSE4OBg2evdu3ejQ4cOCA8PB1DxuW3RokUsugX517/+xT3d/8Oim0gBNzc3BAcHw9PTE7GxsdiwYQMAIDMzE1ZWVoLTERFRbdWvXz/069dPdAyiGuHp06dyn8tiY2Ph5eUle92+fXtkZ2eLiKb2uJ9bHvd0EykQGhqKxMRETJ48GfPnz5edl7pv3z506tRJcDoiIqqt8vLysHnzZsybNw+5ubkAKpp78qxhouqsrKyQmZkJACgpKUFiYiI+/PBD2fUXL15AW1tbVDy1xgOy5PHIMKI/oKioCJqamryBExHR3y45ORmenp4wMTHB7du3kZaWBgcHByxYsABZWVnYsWOH6IhEKmXixIlISkrCypUrERUVhe3bt+PevXuyUwAiIyMRGhqKhIQEwUlJ3XGmm+gNKmcb5s6dK5ttSElJwcOHDwUnIyKi2mj69Onw9/fHjRs3oKenJxv39vbGzz//LDAZkWpaunQptLS04OHhgfDwcISHh8sdu7d161b07t1bYEKiCpzpJlIgOTkZPXv2hKmpKWcbiIhIKUxMTJCYmIjGjRvDyMgISUlJcHBwwJ07d9CsWTMUFRWJjkikkp49ewZDQ0NoamrKjefm5sLQ0FCuECcSgTPdRApMnz4do0aN4mwDEREpja6uLp4/f15tPD09HfXq1ROQiKhmMDExqVZwA4C5uTkLblIJLLqJFEhISMD48eOrjdva2iInJ0dAIiIiqu18fHwQFBSE0tJSABXdf7OysjB79mwMGjRIcDoiIvqzWHQTKcDZBiIiUraQkBDk5+fD0tISL1++hIeHBxo3bgxDQ0MsW7ZMdDwiIvqTuKebSIGxY8fiyZMn2LNnD8zNzZGcnAxNTU34+vqiW7duCA0NFR2RiIhqqbi4OCQnJyM/Px/t2rVDz549RUciIqK/gDPdRApUzjbUq1dPNtvg6OgIIyMjzjYQEdHfKj4+HkePHpW97tKlCwwMDPDf//4Xn376KT777DMUFxcLTEhERH8FZ7qJ3uKXX35BUlIS8vPz4erqCk9PT9GRiIiolvHy8sJHH32E2bNnAwCuXbuGdu3awc/PD05OTli9ejXGjx+PxYsXiw1KRER/ipboAESqpry8HBEREThw4ABu374NiUQCe3t7WFtbQyqVQiKRiI5IRES1yNWrV7F06VLZ6927d8Pd3R3h4eEAADs7OyxatIhFNxFRDcXl5URVSKVS+Pj4YOzYsbh79y6cnZ3RsmVL3LlzB/7+/vjHP/4hOiIREdUyT58+hZWVlex1bGwsvLy8ZK/bt2+P7OxsEdGIiOhvwJluoioiIiLw888/IyYmBt27d5e7dvr0afj6+mLHjh0YOXKkoIRERFTbWFlZITMzE3Z2digpKUFiYiKWLFkiu/7ixQtoa2sLTEhERH8FZ7qJqti1axfmzZtXreAGgB49emDOnDmIjIwUkIyIiGorb29vzJkzB2fPnsXcuXOhr6+Prl27yq4nJyejcePGAhMSEdFfwaKbqIrk5GT07dv3jde9vLyQlJSkxERERFTbLV26FFpaWvDw8EB4eDjCw8Oho6Mju75161b07t1bYEIiIvor2L2cqAodHR3cuXMHNjY2Cq/fu3cP9vb2PLqFiIj+ds+ePYOhoSE0NTXlxnNzc2FoaChXiBMRUc3BPd1EVZSVlUFL683/LDQ1NfHq1SslJiIiInVhYmKicNzc3FzJSYiI6O/EopuoCqlUCn9/f+jq6iq8zhluIiIiIiL6I1h0E1Xh5+f3u+9h53IiIiIiInpX3NNNRERERERE9J6wezkRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREX766SdIJBLk5eWJjkJERFSrsOgmIiKqBfz9/SGRSCCRSKCtrQ17e3vMmjULRUVFoqMRERGpNZ7TTUREVEv07dsX27ZtQ2lpKS5fvgw/Pz9IJBKsXLlSdDQiIiK1xZluIiKiWkJXVxfW1taws7ODr68vPD09cerUKQBAcXExvvjiC1haWkJPTw9dunRBQkLCW/++uLg4dO3aFXXq1IGdnR2++OILFBQUKONHISIiqjVYdBMREdVC169fx7lz56CjowMAmDVrFvbv34/t27cjMTERjo6O6NOnD3JzcxV+/82bN9G3b18MGjQIycnJ+P777xEXF4fJkycr88cgIiKq8SRSqVQqOgQRERH9Nf7+/vj222+hp6eHV69eobi4GBoaGtizZw/69u0LMzMzREREYOjQoQCA0tJSNGrUCNOmTUNAQAB++ukndO/eHU+fPoWpqSnGjh0LTU1NhIWFyf4bcXFx8PDwQEFBAfT09ET9qERERDUK93QTERHVEt27d8eGDRtQUFCAtWvXQktLSzZTXVpais6dO8veq62tDXd3d/z6668K/66kpCQkJycjMjJSNiaVSlFeXo7MzEw4OTm995+HiIioNmDRTUREVEsYGBjA0dERALB161a0bt0aW7ZsQfv27f/w35Wfn4/x48fjiy++qHatQYMGfzkrERGRumDRTUREVAtpaGhg3rx5mD59OjIyMqCjo4NffvkFDRs2BFCxvDwhIQHTpk1T+P2urq5ISUmRFfFERET057CRGhERUS01ePBgaGpqYsOGDZg4cSICAgJw/PhxpKSkYNy4cSgsLMSYMWMUfu/s2bNx7tw5TJ48GVevXsWNGzdw6NAhNlIjIiL6gzjTTUREVEtpaWlh8uTJWLVqFTIzM1FeXo4RI0bgxYsXcHNzw4kTJ2BmZqbwe11cXBAbG4v58+eja9eukEqlaNy4MYYMGaLkn4KIiKhmY/dyIiIiIiIioveEy8uJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREREREb0nLLqJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe/L/AJ/aJD9mHqDtAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Note**: This diagram indicates that, on average, first basemen tend to be taller than second basemen. Later, we will explore how to test this hypothesis more rigorously and demonstrate that our data is statistically significant to support this claim.\n",
+    "> **Note**: This diagram suggests that, on average, first basemen are taller than second basemen. Later, we will learn how to formally test this hypothesis and demonstrate that our data is statistically significant to support this claim.\n",
     "\n",
     "Age, height, and weight are all continuous random variables. What do you think their distribution looks like? A good way to find out is by plotting a histogram of the values:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Zq7/9GOAQ4GsjqE2SJEmr3DAv83Ym8GngJ5Jcm+Ql/abjuOfyCoCnAl9I8nngfcDLqurmYdUmSZIkzWSYV7F4wQzjJ04zdjZw9rBqkSRJkubLt5qWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKmx97AOnOR04Bjgxqo6vB97PfBS4Fv9bq+uqvP6bScBLwHuAn6rqj4yrNokrV5rN5076hJmteOUo0ddgiStesOcQX4H8Kxpxt9SVUf0H5Ph+FDgOOCw/jF/nWSvIdYmSZIkTWtoAbmqLgRunufuxwJbqurOqroa+Cpw1LBqkyRJkmaSqhrewZO1wIenLLE4EbgN2AZsrKpbkrwVuKiq3tXvdxrwj1X1vmmOuQHYADA2Nnbkli1bhlb/SrBr1y7WrFkz6jJWlFH3dPt1t47s3MM09iDYeceoqxi9dQftO7BjjfprdaWyr4NnT4fDvs5t/fr1F1fV+NTxoa1BnsHbgDcC1X8+FXgxkGn2nTa5V9VmYDPA+Ph4TUxMDKXQlWLr1q3Yo8EadU9PXOJraBdq47rdnLp9sb8lLT07jp8Y2LFG/bW6UtnXwbOnw2FfF25RfxpV1c7J20n+Fvhwf/da4OBm10cB1y9iadIPzPUiro3rdq/YkCpJkhb5Mm9JDmzu/jJwWX/7HOC4JPskeTRwCPDZxaxNkiRJguFe5u1MYAI4IMm1wOuAiSRH0C2f2AH8GkBVXZ7kLOCLwG7gFVV117BqkyRJkmYytIBcVS+YZvi0WfY/GTh5WPVIkiRJ8+E76UmSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSY2hBeQkpye5McllzdifJflSki8k+UCS/frxtUnuSHJp//H2YdUlSZIkzWaYM8jvAJ41Zex84PCq+n+ArwAnNduuqqoj+o+XDbEuSZIkaUZDC8hVdSFw85Sxj1bV7v7uRcCjhnV+SZIkaSFSVcM7eLIW+HBVHT7Ntn8A3ltV7+r3u5xuVvk24A+q6hMzHHMDsAFgbGzsyC1btgyp+pVh165drFmzZtRlLCvbr7t11u1jD4KddyxSMauIfe2sO2jfgR3L///DYV8Hz54Oh32d2/r16y+uqvGp43uPopgkrwF2A+/uh24AfrSqbkpyJPDBJIdV1W1TH1tVm4HNAOPj4zUxMbFIVS9PW7duxR7tmRM3nTvr9o3rdnPq9pH811nR7Gtnx/ETAzuW//+Hw74Onj0dDvu6cIt+FYskJwDHAMdXP31dVXdW1U397YuBq4DHLXZtkiRJ0qIG5CTPAn4f+KWq+l4z/vAke/W3HwMcAnxtMWuTJEmSYIhLLJKcCUwAByS5Fngd3VUr9gHOTwJwUX/FiqcCb0iyG7gLeFlV3TztgSVJkqQhGlpArqoXTDN82gz7ng2cPaxaJEmSpPnynfQkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpMa8AnKSJ89nTJIkSVru5juD/D/nOSZJkiQta3vPtjHJE4EnAQ9P8qpm00OBvYZZmCRJkjQKswZk4AHAmn6/hzTjtwHPHVZRkiRJ0qjMGpCr6gLggiTvqKprFqkmSZIkaWTmmkGetE+SzcDa9jFV9bRhFCVJkiSNynwD8v8C3g78HXDX8MqRJEmSRmu+AXl3Vb1tqJVIkiRJS8B8L/P2D0l+PcmBSR42+THUyiRJkqQRmO8M8gn9599rxgp4zGDLkSRJkkZrXgG5qh497EIkSZKkpWBeATnJi6Ybr6p3DrYcSZIkabTmu8TiCc3tBwJPBy4BDMiSJElaUea7xOI32/tJ9gX+frbHJDkdOAa4saoO78ceBryX7nrKO4DnV9Ut/baTgJfQXUbut6rqI3vyRCRJkqRBmO8M8lTfAw6ZY593AG/lnrPMm4CPVdUpSTb1938/yaHAccBhwCOBf07yuKrymsuSVpW1m84d2LE2rtvNiQM83o5Tjh7YsSRpKZvvGuR/oLtqBcBewOOBs2Z7TFVdmGTtlOFjgYn+9hnAVuD3+/EtVXUncHWSrwJHAZ+eT32SJEnSoKSq5t4p+fnm7m7gmqq6dh6PWwt8uFli8Z2q2q/ZfktV7Z/krcBFVfWufvw04B+r6n3THHMDsAFgbGzsyC1btsxZ/2q2a9cu1qxZM+oylpXt19066/axB8HOOxapmFXEvg7eoHu67qB9B3ewZczvq4NnT4fDvs5t/fr1F1fV+NTx+a5BviDJGHe/WO/KQRYHZLrTzlDLZmAzwPj4eE1MTAy4lJVl69at2KM9M9efpDeu282p2xe6Okkzsa+DN+ie7jh+YmDHWs78vjp49nQ47OvCzeud9JI8H/gs8Dzg+cBnkjx3AefbmeTA/pgHAjf249cCBzf7PQq4fgHHlyRJku6T+b7V9GuAJ1TVCVX1Irr1wX+4gPOdw93vyncC8KFm/Lgk+yR5NN0LAD+7gONLkiRJ98l8//Z2v6q6sbl/E3OE6yRn0r0g74Ak1wKvA04BzkryEuDrdDPSVNXlSc4Cvki3xvkVXsFCkiRJozDfgPxPST4CnNnf/xXgvNkeUFUvmGHT02fY/2Tg5HnWI0mSJA3FrAE5yY8DY1X1e0n+M/AUuhfUfRp49yLUJ0mSJC2qudYg/wVwO0BVvb+qXlVVv0M3e/wXwy1NkiRJWnxzBeS1VfWFqYNVtY3u7aIlSZKkFWWugPzAWbY9aJCFSJIkSUvBXAH5c0leOnWwvwrFxcMpSZIkSRqdua5i8UrgA0mO5+5APA48APjlIdYlSZIkjcSsAbmqdgJPSrIeOLwfPreq/mXolUmSJEkjMK/rIFfVx4GPD7kWSZIkaeTm+1bTkiRJ0qpgQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpsfdinzDJTwDvbYYeA7wW2A94KfCtfvzVVXXe4lYnSZKk1W7RA3JVfRk4AiDJXsB1wAeA/wa8par+fLFrkiRJkiaNeonF04GrquqaEdchSZIkAZCqGt3Jk9OBS6rqrUleD5wI3AZsAzZW1S3TPGYDsAFgbGzsyC1btixewcvQrl27WLNmzajLWFa2X3frrNvHHgQ771ikYlYR+zp4g+7puoP2HdzBljG/rw6ePR0O+zq39evXX1xV41PHRxaQkzwAuB44rKp2JhkDvg0U8EbgwKp68WzHGB8fr23btg2/2GVs69atTExMjLqMZWXtpnNn3b5x3W5O3b7oq5NWPPs6eKutpztOOXpRzuP31cGzp8NhX+eWZNqAPMolFr9IN3u8E6CqdlbVXVX1feBvgaNGWJskSZJWqVFOLbwAOHPyTpIDq+qG/u4vA5eNpCoN3VwztJIkSaM0koCc5IeA/wj8WjP8piRH0C2x2DFlmyRJkrQoRhKQq+p7wA9PGXvhKGqRJEmSWqO+zJskSZK0pBiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqTG3qM4aZIdwO3AXcDuqhpP8jDgvcBaYAfw/Kq6ZRT1SZIkafUa5Qzy+qo6oqrG+/ubgI9V1SHAx/r7kiRJ0qJaSkssjgXO6G+fATxndKVIkiRptUpVLf5Jk6uBW4AC/qaqNif5TlXt1+xzS1XtP81jNwAbAMbGxo7csmXLIlW9PO3atYs1a9aMuox72H7draMu4T4ZexDsvGPUVaw89nXwVltP1x2076KcZyl+X13u7Olw2Ne5rV+//uJmNcMPjGQNMvDkqro+ySOA85N8ab4PrKrNwGaA8fHxmpiYGFKJK8PWrVtZaj06cdO5oy7hPtm4bjenbh/Vf52Vy74O3mrr6Y7jJxblPEvx++pyZ0+Hw74u3EiWWFTV9f3nG4EPAEcBO5McCNB/vnEUtUmSJGl1W/SAnOTBSR4yeRt4BnAZcA5wQr/bCcCHFrs2SZIkaRR/exsDPpBk8vzvqap/SvI54KwkLwG+DjxvBLVJkiRplVv0gFxVXwN+aprxm4CnL3Y9kiRJUmspXeZNkiRJGjkDsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJjb1HXYAkSYOwdtO5i3Kejet2c+ICzrXjlKOHUI2kYXAGWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJauw96gI0eGs3nfuD2xvX7ebE5r4kSZJm5wyyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEmNRQ/ISQ5O8vEkVyS5PMlv9+OvT3Jdkkv7j2cvdm2SJEnSKK5isRvYWFWXJHkIcHGS8/ttb6mqPx9BTZIkSRIwgoBcVTcAN/S3b09yBXDQYtchSZIkTSdVNbqTJ2uBC4HDgVcBJwK3AdvoZplvmeYxG4ANAGNjY0du2bJlscpdNrZfd+sPbo89CHbeMcJiViB7Ohz2dfDs6XAstK/rDtp38MWsELt27WLNmjWjLmPFsa9zW79+/cVVNT51fGQBOcka4ALg5Kp6f5Ix4NtAAW8EDqyqF892jPHx8dq2bdvwi11mpr5RyKnbfT+YQbKnw2FfB8+eDsdC+7rjlKOHUM3KsHXrViYmJkZdxopjX+eWZNqAPJKrWCS5P3A28O6qej9AVe2sqruq6vvA3wJHjaI2SZIkrW6juIpFgNOAK6rqzc34gc1uvwxctti1SZIkSaP429uTgRcC25Nc2o+9GnhBkiPolljsAH5tBLVJkjQU7fK3pcglINLdRnEVi08CmWbTeYtdiyRJkjSV76QnSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSY+9RF7Acrd107qhLkCRJ0pA4gyxJkiQ1nEGWJEkj/evoxnW7OXGO8+845ehFqkZyBlmSJEm6BwOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDa+DLEmSdB8txXfZba8v7XWk98ySm0FO8qwkX07y1SSbRl2PJEmSVpclNYOcZC/gr4D/CFwLfC7JOVX1xdFWJkmSRmkpztAuJ0u9f0tthnupzSAfBXy1qr5WVf8GbAGOHXFNkiRJWkVSVaOu4QeSPBd4VlX9an//hcDPVNVvNPtsADb0d38C+PKiF7q8HAB8e9RFrDD2dDjs6+DZ0+Gwr4NnT4fDvs7tx6rq4VMHl9QSCyDTjN0jwVfVZmDz4pSz/CXZVlXjo65jJbGnw2FfB8+eDod9HTx7Ohz2deGW2hKLa4GDm/uPAq4fUS2SJElahZZaQP4ccEiSRyd5AHAccM6Ia5IkSdIqsqSWWFTV7iS/AXwE2As4vaouH3FZy53LUQbPng6HfR08ezoc9nXw7Olw2NcFWlIv0pMkSZJGbaktsZAkSZJGyoAsSZIkNQzIy1yS05PcmOSyKeO/2b9l9+VJ3tSMn9S/jfeXkzxz8Ste+qbraZIjklyU5NIk25Ic1Wyzp3NIcnCSjye5ov+a/O1+/GFJzk9yZf95/+Yx9nUOs/T1z5J8KckXknwgyX7NY+zrLGbqabP9d5NUkgOaMXs6h9n66s+rhZnl/78/rwahqvxYxh/AU4GfBi5rxtYD/wzs099/RP/5UODzwD7Ao4GrgL1G/RyW2scMPf0o8Iv97WcDW+3pHvX0QOCn+9sPAb7S9+5NwKZ+fBPwp/Z1IH19BrB3P/6n9vW+97S/fzDdi8ivAQ6wp/e9r/68GkpP/Xk1gA9nkJe5qroQuHnK8MuBU6rqzn6fG/vxY4EtVXVnVV0NfJXu7b3VmKGnBTy0v70vd1+f257OQ1XdUFWX9LdvB64ADqLr3xn9bmcAz+lv29d5mKmvVfXRqtrd73YR3TXlwb7OaZavVYC3AP8f93wDK3s6D7P01Z9XCzRLT/15NQAG5JXpccDPJflMkguSPKEfPwj4RrPftdz9jV+zeyXwZ0m+Afw5cFI/bk/3UJK1wH8APgOMVdUN0H2zBx7R72Zf99CUvrZeDPxjf9u+7oG2p0l+Cbiuqj4/ZTd7uoemfK3682oApvT0lfjz6j4zIK9MewP7Az8L/B5wVpIwj7fy1oxeDvxOVR0M/A5wWj9uT/dAkjXA2cArq+q22XadZsy+zmCmviZ5DbAbePfk0DQPt6/TaHtK18PXAK+dbtdpxuzpDKb5WvXn1X00TU/9eTUABuSV6Vrg/dX5LPB94AB8K+/74gTg/f3t/8Xdf5ayp/OU5P5038TfXVWTvdyZ5MB++4HA5J9X7es8zdBXkpwAHAMcX/0CROzrvEzT08fSrdn8fJIddH27JMmPYE/nbYavVX9e3Qcz9NSfVwNgQF6ZPgg8DSDJ44AHAN+me9vu45Lsk+TRwCHAZ0dV5DJzPfDz/e2nAVf2t+3pPPQzQqcBV1TVm5tN59B9M6f//KFm3L7OYaa+JnkW8PvAL1XV95qH2Nc5TNfTqtpeVY+oqrVVtZYuaPx0VX0Tezovs3wP+CD+vFqQWXrqz6sBWFJvNa09l+RMYAI4IMm1wOuA04HT012m7N+AE/oZpMuTnAV8ke5Phq+oqrtGU/nSNUNPXwr8jyR7A/8KbACoKns6P08GXghsT3JpP/Zq4BS6P6m+BPg68Dywr3tgpr7+Jd0r1c/vfoZyUVW9zL7Oy7Q9rarzptvZns7bTF+r/rxauJl66s+rAfCtpiVJkqSGSywkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSlqAkb0nyyub+R5L8XXP/1CSvmuGxb0jyC3Mc//VJfnea8f2S/Pp9KF2Slj0DsiQtTf8beBJAkvvRvbvYYc32JwGfmu6BVfXaqvrnBZ53P8CALGlVMyBL0tL0KfqATBeMLwNuT7J/kn2AxwMkuSDJxf0M8+Tbdr8jyXP7289O8qUkn0zyl0k+3Jzj0CRbk3wtyW/1Y6cAj01yaZI/W4wnKklLje+kJ0lLUFVdn2R3kh+lC8qfBg4CngjcClwBvAU4tqq+leRXgJOBF08eI8kDgb8BnlpVV/fvEtn6SWA98BDgy0neBmwCDq+qI4b6BCVpCTMgS9LSNTmL/CTgzXQB+Ul0Afk64Bnc/XbSewE3THn8TwJfq6qr+/tn0r/tbO/cqroTuDPJjcDYkJ6HJC0rBmRJWrom1yGvo1ti8Q1gI3Ab8C/AQVX1xFkenzmOf2dz+y78mSBJgGuQJWkp+xRwDHBzVd1VVTfTvYjuicB7gYcneSJAkvsnOWzK478EPCbJ2v7+r8zjnLfTLbmQpFXLgCxJS9d2uqtXXDRl7NaquhF4LvCnST4PXMrdL+oDoKruoLsixT8l+SSwk255xoyq6ibgU0ku80V6klarVNWoa5AkDUmSNVW1K91C5b8Crqyqt4y6LklaypxBlqSV7aVJLgUuB/alu6qFJGkWziBLkiRJDWeQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkxv8FiHh2DxCDPowAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## Normal Distribution\n",
     "\n",
-    "Let's generate an artificial sample of weights that follows a normal distribution with the same mean and variance as our actual data:\n"
+    "Let's generate a synthetic sample of weights that follows a normal distribution with the same mean and variance as our actual data:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Confidence Intervals\n",
     "\n",
-    "Now let's calculate confidence intervals for the weights and heights of baseball players. We'll use the code [from this Stack Overflow discussion](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Let's calculate confidence intervals for the weights and heights of baseball players. We'll use the code [from this Stack Overflow discussion](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -726,16 +567,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -752,20 +593,20 @@
    "source": [
     "We can see that the intervals do not overlap.\n",
     "\n",
-    "A statistically more accurate way to validate the hypothesis is to use a **Student t-test**:\n"
+    "A statistically more accurate way to test the hypothesis is to use a **Student t-test**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -781,8 +622,8 @@
    "metadata": {},
    "source": [
     "The two values returned by the `ttest_ind` function are:\n",
-    "* The p-value represents the probability that the two distributions have the same mean. In our case, it is very low, indicating strong evidence that first basemen are taller.\n",
-    "* The t-value is the intermediate value of the normalized mean difference used in the t-test, which is compared to a threshold value for a given confidence level.\n"
+    "* The p-value can be interpreted as the probability that the two distributions have the same mean. In our case, it is very low, indicating strong evidence that first basemen are taller.\n",
+    "* The t-value represents the standardized mean difference used in the t-test, which is compared to a threshold value for a specified confidence level.\n"
    ]
   },
   {
@@ -791,24 +632,22 @@
    "source": [
     "## Simulating a Normal Distribution with the Central Limit Theorem\n",
     "\n",
-    "The pseudo-random generator in Python is designed to produce a uniform distribution. If we want to create a generator for a normal distribution, we can use the central limit theorem. To obtain a normally distributed value, we simply calculate the mean of a sample generated from a uniform distribution.\n"
+    "The pseudo-random generator in Python is designed to produce a uniform distribution. If we want to create a generator for a normal distribution, we can apply the central limit theorem. To obtain a normally distributed value, we simply calculate the mean of a sample generated from a uniform distribution.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -830,19 +669,19 @@
    "source": [
     "## Correlation and Evil Baseball Corp\n",
     "\n",
-    "Correlation helps us identify relationships between data sequences. In our example, let's imagine an evil baseball corporation that determines players' salaries based on their height—the taller the player, the higher the pay. Assume there is a base salary of $1000, with an additional bonus ranging from $0 to $100, depending on height. We'll use real MLB players and calculate their hypothetical salaries:\n"
+    "Correlation helps us identify relationships between data sequences. In our example, let's imagine there is an evil baseball corporation that determines players' salaries based on their height—the taller the player, the higher the salary. Assume there is a base salary of $1000, with an additional bonus ranging from $0 to $100, depending on height. We will use real MLB players and calculate their hypothetical salaries:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -861,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -891,19 +730,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -923,14 +760,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -948,14 +785,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -995,22 +830,22 @@
    "source": [
     "Can you guess why the dots align into vertical lines like this?\n",
     "\n",
-    "We have examined the relationship between an artificially constructed concept like salary and the observed variable *height*. Now, let's check if there is a correlation between two observed variables, such as height and weight:\n"
+    "We have examined the relationship between an artificially constructed concept like salary and the observed variable *height*. Now, let's check if two observed variables, such as height and weight, are also correlated:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,16 +858,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Unfortunately, we did not get any results - only some strange `nan` values. This happens because some of the values in our series are undefined, represented as `nan`, which makes the result of the operation undefined as well. By examining the matrix, we can see that the `Weight` column is the issue, as the self-correlation between `Height` values has been calculated.\n",
+    "Unfortunately, we did not get any results - only some strange `nan` values. This is because some of the values in our series are undefined, represented as `nan`, which makes the result of the operation undefined as well. By examining the matrix, we can see that the `Weight` column is the problematic one, as the self-correlation between `Height` values has been calculated.\n",
     "\n",
     "> This example highlights the importance of **data preparation** and **cleaning**. Without proper data, we cannot compute anything.\n",
     "\n",
-    "Let's use the `fillna` method to fill in the missing values and calculate the correlation:\n"
+    "Let's use the `fillna` method to fill in the missing values and compute the correlation:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1060,27 +895,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1098,7 +931,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n"
+    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-03T20:43:34+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T16:59:04+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "en"
   }
diff --git a/translations/en/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/en/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index e7ce5f1a..17119d11 100644
--- a/translations/en/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/en/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,12 +150,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "In this dataset, the columns are as follows:\n",
-    "* Age and sex are straightforward.\n",
-    "* BMI refers to body mass index.\n",
-    "* BP represents average blood pressure.\n",
-    "* S1 through S6 are various blood measurements.\n",
-    "* Y is a qualitative indicator of disease progression over the course of one year.\n",
+    "In this dataset, the columns are as follows:  \n",
+    "* Age and sex are self-explanatory  \n",
+    "* BMI is body mass index  \n",
+    "* BP is average blood pressure  \n",
+    "* S1 through S6 are different blood measurements  \n",
+    "* Y is the qualitative measure of disease progression over one year  \n",
     "\n",
     "Let's analyze this dataset using probability and statistical methods.\n",
     "\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -592,19 +592,19 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Conclusions:\n",
-    "* Age - normal\n",
-    "* Sex - uniform\n",
-    "* BMI, Y - hard to tell\n"
+    "Conclusions:  \n",
+    "* Age - normal  \n",
+    "* Sex - uniform  \n",
+    "* BMI, Y - hard to tell  \n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### Task 4: Examine the relationship between various variables and disease progression (Y)\n",
+    "### Task 4: Test the correlation between different variables and disease progression (Y)\n",
     "\n",
-    "> **Hint** A correlation matrix will provide the most valuable insights into which values are interdependent.\n"
+    "> **Hint** A correlation matrix will provide the most useful insights into which values are interdependent.\n"
    ],
    "metadata": {}
   },
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "Conclusion:\n",
-    "* The strongest correlation of Y is BMI and S5 (blood sugar). This seems logical.\n"
+    "* The strongest correlation of Y is BMI and S5 (blood sugar). This sounds reasonable.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -918,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n"
+    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\n"
    ]
   }
  ],
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-03T20:44:02+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T16:59:33+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "en"
   }
diff --git a/translations/es/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/es/1-Introduction/04-stats-and-probability/assignment.ipynb
index ac457fa2..914aaff4 100644
--- a/translations/es/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/es/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "En este conjunto de datos, las columnas son las siguientes:  \n",
-    "* La edad y el sexo son autoexplicativos  \n",
-    "* BMI es el índice de masa corporal  \n",
-    "* BP es la presión arterial promedio  \n",
-    "* S1 a S6 son diferentes mediciones sanguíneas  \n",
-    "* Y es la medida cualitativa de la progresión de la enfermedad a lo largo de un año  \n",
+    "En este conjunto de datos, las columnas son las siguientes:\n",
+    "* Edad y sexo son autoexplicativos\n",
+    "* BMI es el índice de masa corporal\n",
+    "* BP es la presión arterial promedio\n",
+    "* S1 a S6 son diferentes mediciones de sangre\n",
+    "* Y es la medida cualitativa de la progresión de la enfermedad durante un año\n",
     "\n",
     "Estudiemos este conjunto de datos utilizando métodos de probabilidad y estadística.\n",
     "\n",
-    "### Tarea 1: Calcular los valores medios y la varianza para todos los valores  \n"
+    "### Tarea 1: Calcular valores medios y varianza para todos los valores\n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tarea 2: Graficar diagramas de caja para IMC, PA y Y dependiendo del género\n"
+    "### Tarea 2: Graficar diagramas de caja para BMI, BP y Y dependiendo del género\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Descargo de responsabilidad**:  \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por garantizar la precisión, tenga en cuenta que las traducciones automatizadas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n"
+    "\n---\n\n**Descargo de responsabilidad**:  \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Si bien nos esforzamos por lograr precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:18:04+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:01:47+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "es"
   }
diff --git a/translations/es/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/es/1-Introduction/04-stats-and-probability/notebook.ipynb
index a5f39b7d..1f742346 100644
--- a/translations/es/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/es/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Variables Aleatorias y Distribuciones\n",
-    "Comencemos con tomar una muestra de 30 valores de una distribución uniforme de 0 a 9. También calcularemos la media y la varianza.\n"
+    "Comencemos extrayendo una muestra de 30 valores de una distribución uniforme de 0 a 9. También calcularemos la media y la varianza.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Aquí estamos utilizando un paquete llamado [**Pandas**](https://pandas.pydata.org/) para el análisis de datos. Hablaremos más sobre Pandas y cómo trabajar con datos en Python más adelante en este curso.\n",
+    "Estamos utilizando un paquete llamado [**Pandas**](https://pandas.pydata.org/) aquí para el análisis de datos. Hablaremos más sobre Pandas y cómo trabajar con datos en Python más adelante en este curso.\n",
     "\n",
-    "Calculemos los valores promedio de edad, altura y peso:\n"
+    "Calculemos los valores promedio para edad, altura y peso:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Podemos también hacer diagramas de caja de subconjuntos de nuestro conjunto de datos, por ejemplo, agrupados por el rol del jugador.\n"
+    "También podemos hacer diagramas de caja de subconjuntos de nuestro conjunto de datos, por ejemplo, agrupados por el rol del jugador.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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VeP14VFSU+vTp4/DIMEmaPHmyfZqXX35Zq1evVseOHfXggw+qRYsWOnPmjH744QetXLlSZ86ckSQ9+OCDevPNN3XvvfcqIyNDderU0UcffSR/f/+rWsci+/fv18CBA3Xrrbdqw4YN+vjjj3X33XcXezb3DTfcoKioKPtNy9q0aeNUO5dq0aKF+vXrp3//+9965plndP3112v48OF67733dPbsWcXExGjjxo2aO3euYmNj1bNnz8sua9iwYfrvf/+rhx9+WKtXr1aXLl1UWFionTt36r///a++/vprh/sdAADgwMxbpwMAXF9JjwyrXLmyrXXr1ra3337bZrVaHab/448/bGPGjLHVrVvX5uPjY2vatKntX//6l326jIwMm7e3t8NjwGw2m62goMDWvn17W926dW2///67zWa78MisgIAA2759+2y9e/e2+fv722rXrm177rnnbIWFhQ7z65JHhtlsNtsPP/xg69Onjy0wMNDm7+9v69mzp239+vXF1vH999+3NW7c2Obl5XVVj8P69NNPbZGRkTZfX19bVFSUbcmSJbbBgwfbIiMj7dMUPTLsX//6V4nLWLlypa1Lly42Pz8/W3BwsO22226zbd++vdh0K1assEVFRdkqVapka9asme3jjz++7CPD4uPjbR9//LGtadOmNl9fX9sNN9xQ4rocP37cFh8fb2vQoIHNx8fHFhoaarv55ptt7733nsN0Bw8etA0cONDm7+9vq1mzpu3RRx+1LV++3KlHhm3fvt02ZMgQW1BQkK1atWq2kSNH2vLy8kqcZ+rUqTZJtilTplxx2ReLiYmxtWzZssRxRY9yK/q7sFgstsmTJ9vCw8NtPj4+tgYNGtgmTJhgO3fuXLFlXvzIMJvNZjt//rztlVdesbVs2dLm6+trq1atmq1t27a2yZMn2zIzM6+6XgBAxeNhs/3v+RwAALiY++67T4sWLbrqI8hmat26tWrVqqWUlBRT2vfw8FB8fHyxU/vLk9dee01jxozRgQMH1LBhQ7PLAQDgmuCabgAAnGCxWOw3kCuyZs0abdmyRT169DCnKDdgs9n0wQcfKCYmhsANAHArXNMNAIATDh8+rFtuuUVDhw5V3bp1tXPnTr3zzjsKDQ3Vww8/bHZ55U5OTo6WLFmi1atXa+vWrVq8eLHZJQEAcE0RugEAcEK1atXUtm1b/fvf/9bJkycVEBCg/v376+WXX1aNGjXMLq/cOXnypO6++25VrVpVEydO1MCBA80uCQCAa4prugEAAAAAMAjXdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAFcR9992nsLCwUs8bGBh4bQsCAKACIHQDAOBi5syZIw8PD23atKnE8T169FBUVFQZV3V1cnNzNWnSJK1Zs8bsUgAAcAneZhcAAADKxvvvvy+r1WpoG7m5uZo8ebKkC18OAABQ0RG6AQCoIHx8fMwuAQCACofTywEAcAMff/yx2rZtKz8/P1WvXl133XWXfv31V4dpSrqm+/Tp0xo2bJiCg4NVtWpVDR8+XFu2bJGHh4fmzJlTrJ3Dhw8rNjZWgYGBqlWrlsaNG6fCwkJJ0oEDB1SrVi1J0uTJk+Xh4SEPDw9NmjTJiFUGAKBc4Eg3AAAuKjMzU6dOnSo23GKxOLx+6aWX9Mwzz+iOO+7Q3//+d508eVJvvPGGunfvrh9//FFVq1YtcflWq1W33XabNm7cqEceeUSRkZFavHixhg8fXuL0hYWF6tOnjzp27Khp06Zp5cqVmj59upo0aaJHHnlEtWrV0ttvv61HHnlEt99+u+Li4iRJrVq1+mu/CAAAyjFCNwAALuqWW2657LiWLVtKkg4ePKjnnntOL774oiZOnGgfHxcXpxtuuEFvvfWWw/CLJScna8OGDZo5c6YeffRRSdIjjzyiXr16lTj9uXPndOedd+qZZ56RJD388MNq06aNPvjgAz3yyCMKCAjQkCFD9Mgjj6hVq1YaOnRoqdYbAAB3QugGAMBFzZo1S9ddd12x4QkJCfZTupOSkmS1WnXHHXc4HBUPDQ1V06ZNtXr16suG7uXLl8vHx0cPPvigfZinp6fi4+O1atWqEud5+OGHHV5369ZNH330kdPrBgBARUHoBgDARXXo0EHt2rUrNrxatWr2gL1nzx7ZbDY1bdq0xGVc6eZpBw8eVJ06deTv7+8wPCIiosTpK1eubL9m++Jafv/99yuuBwAAFRmhGwCAcsxqtcrDw0PLli2Tl5dXsfGBgYHXrK2Slg8AAK6M0A0AQDnWpEkT2Ww2hYeHl3gq+pU0atRIq1evVm5ursPR7r1795a6Hg8Pj1LPCwCAO+KRYQAAlGNxcXHy8vLS5MmTZbPZHMbZbDadPn36svP26dNHFotF77//vn2Y1WrVrFmzSl1PUXg/e/ZsqZcBAIA74Ug3AADlWJMmTfTiiy9qwoQJOnDggGJjYxUUFKT9+/fr888/10MPPaRx48aVOG9sbKw6dOighIQE7d27V5GRkVqyZInOnDkjqXRHrf38/NSiRQstWLBA1113napXr66oqChFRUX9pfUEAKC84kg3AADl3Pjx4/XZZ5/J09NTkydP1rhx47RkyRL17t1bAwcOvOx8Xl5e+vLLL3XnnXdq7ty5euqpp1S3bl37ke7KlSuXqp5///vfqlevnsaMGaO//e1vWrRoUamWAwCAO/CwXXouGgAAqNCSk5N1++23a926derSpYvZ5QAAUK4RugEAqMDy8vLk5+dnf11YWKjevXtr06ZNOnbsmMM4AADgPK7pBgCgAhs1apTy8vLUuXNn5efnKykpSevXr9eUKVMI3AAAXAMc6QYAoAKbP3++pk+frr179+rcuXOKiIjQI488opEjR5pdGgAAboHQDQAAAACAQbh7OQAAAAAABiF0AwAAAABgkHJ5IzWr1aojR44oKChIHh4eZpcDAAAAAKhgbDab/vjjD9WtW1eenpc/nl0uQ/eRI0fUoEEDs8sAAAAAAFRwv/76q+rXr3/Z8eUydAcFBUm6sHLBwcEmV/PXWSwWrVixQr1795aPj4/Z5eAS9I9ro39cF33j2ugf10b/uDb6x3XRN67N3fonKytLDRo0sOfTyymXobvolPLg4GC3Cd3+/v4KDg52iz8+d0P/uDb6x3XRN66N/nFt9I9ro39cF33j2ty1f/7skmdupAYAAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAACg3CssLFRqaqrWrl2r1NRUFRYWml0SIInQDQAAAKCcS0pKUkREhHr16qUZM2aoV69eioiIUFJSktmlAYRuAAAAAOVXUlKShgwZoujoaKWlpemTTz5RWlqaoqOjNWTIEII3TEfoBgAAAFAuFRYWKiEhQQMGDFBycrI6duwoPz8/dezYUcnJyRowYIDGjRvHqeYwFaEbAAAAQLmUlpamAwcOaOLEifL0dIw2np6emjBhgvbv36+0tDSTKgQI3QAAAADKqaNHj0qSoqKiShxfNLxoOsAMhG4AAAAA5VKdOnUkSdu2bStxfNHwoukAMxC6AQAAAJRL3bp1U1hYmKZMmSKr1eowzmq1KjExUeHh4erWrZtJFQKEbgAAAADllJeXl6ZPn66lS5cqNjZW6enpysvLU3p6umJjY7V06VJNmzZNXl5eZpeKCszb7AIAAAAAoLTi4uK0aNEiJSQkqHv37vbh4eHhWrRokeLi4kysDiB0AwAAACjn4uLiNGjQIK1evVrLli1T37591bNnT45wwyUQugEAAACUe15eXoqJiVFOTo5iYmII3HAZXNMNAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBnArdiYmJat++vYKCghQSEqLY2Fjt2rXLYZpjx45p2LBhCg0NVUBAgNq0aaPPPvvMYZozZ87onnvuUXBwsKpWraoRI0YoOzv7r68NAAAAAAAuxKnQnZqaqvj4eKWnpyslJUUWi0W9e/dWTk6OfZp7771Xu3bt0pIlS7R161bFxcXpjjvu0I8//mif5p577tHPP/+slJQULV26VGvXrtVDDz107dYKAAAAAAAX4O3MxMuXL3d4PWfOHIWEhCgjI0Pdu3eXJK1fv15vv/22OnToIEl6+umn9eqrryojI0M33HCDduzYoeXLl+v7779Xu3btJElvvPGG+vXrp2nTpqlu3brXYr0AAAAAADCdU6H7UpmZmZKk6tWr24fdeOONWrBggfr376+qVavqv//9r86dO6cePXpIkjZs2KCqVavaA7ck3XLLLfL09NR3332n22+/vVg7+fn5ys/Pt7/OysqSJFksFlkslr+yCi6haB3cYV3cEf3j2ugf10XfuDb6x7XRP66N/nFd9I1rc7f+udr18LDZbLbSNGC1WjVw4ECdPXtW69atsw8/e/as7rzzTq1YsULe3t7y9/fXwoUL1bt3b0nSlClTNHfu3GLXgoeEhGjy5Ml65JFHirU1adIkTZ48udjw+fPny9/fvzTlAwAAAABQarm5ubr77ruVmZmp4ODgy05X6iPd8fHx2rZtm0PglqRnnnlGZ8+e1cqVK1WzZk0lJyfrjjvuUFpamqKjo0vV1oQJEzR27Fj766ysLDVo0EC9e/e+4sqVFxaLRSkpKerVq5d8fHzMLgeXoH9cG/3juugb10b/uDb6x7XRP66LvnFt7tY/RWdg/5lShe6RI0fab4BWv359+/B9+/bpzTff1LZt29SyZUtJ0vXXX6+0tDTNmjVL77zzjkJDQ3XixAmH5RUUFOjMmTMKDQ0tsT1fX1/5+voWG+7j4+MWnVXE3dbH3dA/ro3+cV30jespLCzU+vXrtXbtWgUEBKhnz57y8vIyuyyUgPePa6N/XBd949rcpX+udh2cunu5zWbTyJEj9fnnn2vVqlUKDw93GJ+bm3thoZ6Oi/Xy8pLVapUkde7cWWfPnlVGRoZ9/KpVq2S1WtWxY0dnygEAAE5KSkpSRESEevXqpRkzZqhXr16KiIhQUlKS2aUBAOCWnArd8fHx+vjjjzV//nwFBQXp2LFjOnbsmPLy8iRJkZGRioiI0D/+8Q9t3LhR+/bt0/Tp05WSkqLY2FhJUvPmzXXrrbfqwQcf1MaNG/Xtt99q5MiRuuuuu7hzOQAABkpKStKQIUMUHR2ttLQ0ffLJJ/bLv4YMGULwBgDAAE6F7rfffluZmZnq0aOH6tSpY/+3YMECSRcOr3/11VeqVauWbrvtNrVq1Urz5s3T3Llz1a9fP/ty/vOf/ygyMlI333yz+vXrp65du+q99967tmsGAADsCgsLlZCQoAEDBig5OVkdO3aUn5+fOnbsqOTkZA0YMEDjxo1TYWGh2aUCAOBWnLqm+2pudN60aVN99tlnV5ymevXqmj9/vjNNAwCAvyAtLU0HDhzQJ598Ik9PT4dw7enpqQkTJujGG29UWlqa/TGfAADgr3PqSDcAACifjh49KkmKiooqcXzR8KLpAADAtUHoBgCgAqhTp44kadu2bSWOLxpeNB0AALg2CN0AAFQA3bp1U1hYmKZMmWJ/okgRq9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnTeF43AADXmFM3UgMAAOVXXFycFi1apISEBHXv3t0+PDw8XIsWLVJcXJyJ1QEA4J4I3QAAVCBxcXEaNGiQVq9erWXLlqlv377q2bMnR7gBADAIoRu4gsLCQqWmpmrt2rUKCAhgxxSAW/Dy8lJMTIxycnIUExPDdg0AAANxTTdwGUlJSYqIiFCvXr00Y8YM9erVSxEREUpKSjK7NAAAAADlBKEbKEFSUpKGDBmi6OhopaWl6ZNPPlFaWpqio6M1ZMgQgjcAAACAq0LoBi5RWFiohIQEDRgwQMnJyerYsaP8/PzUsWNHJScna8CAARo3bpwKCwvNLhUAAACAiyN0A5dIS0vTgQMHNHHiRHl6Or5FPD09NWHCBO3fv19paWkmVQgAAACgvCB0A5c4evSoJCkqKqrE8UXDi6YDAAAAgMshdAOXqFOnjiRp27ZtJY4vGl40HQAAAABcDqEbuES3bt0UFhamKVOmyGq1OoyzWq1KTExUeHi4unXrZlKFAAAAAMoLQjdwCS8vL02fPl1Lly5VbGys0tPTlZeXp/T0dMXGxmrp0qWaNm0az7UFAAAA8Ke8zS4AcEVxcXFatGiREhIS1L17d/vw8PBwLVq0SHFxcSZWBwAAAKC8IHQDlxEXF6dBgwZp9erVWrZsmfr27auePXtyhBsAAADAVSN0A1fg5eWlmJgY5eTkKCYmhsANAAAAwClc0w0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AQAVTWFio1NRUrV27VqmpqSosLDS7JAAA3BahGwCACiQpKUkRERHq1auXZsyYoV69eikiIkJJSUlmlwYAgFsidAMAUEEkJSVpyJAhio6OVlpamj755BOlpaUpOjpaQ4YMIXgDAGAAQjcAABVAYWGhEhISNGDAACUnJ6tjx47y8/NTx44dlZycrAEDBmjcuHGcag4AwDVG6AYAoAJIS0vTgQMHNHHiRHl6On78e3p6asKECdq/f7/S0tJMqhAAAPdE6AYAoAI4evSoJCkqKqrE8UXDi6YDAADXBqEbAIAKoE6dOpKkbdu2lTi+aHjRdAAA4NogdAMAUAF069ZNYWFhmjJliqxWq8M4q9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAC4FW+zCwAAAGUjLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAcE+EbgAAKpC4uDgNGjRIq1ev1rJly9S3b1/17NmTI9wAABiE0A0AQAXj5eWlmJgY5eTkKCYmhsANAICBuKYbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDOBW6ExMT1b59ewUFBSkkJESxsbHatWtXsek2bNigm266SQEBAQoODlb37t2Vl5dnH3/mzBndc889Cg4OVtWqVTVixAhlZ2f/9bUBAAAAAMCFOBW6U1NTFR8fr/T0dKWkpMhisah3797KycmxT7Nhwwbdeuut6t27tzZu3Kjvv/9eI0eOlKfn/2/qnnvu0c8//6yUlBQtXbpUa9eu1UMPPXTt1goAAAAAABfg7czEy5cvd3g9Z84chYSEKCMjQ927d5ckjRkzRqNHj9b48ePt0zVr1sz+/x07dmj58uX6/vvv1a5dO0nSG2+8oX79+mnatGmqW7duqVcGAAAAAABX8peu6c7MzJQkVa9eXZJ04sQJfffddwoJCdGNN96o2rVrKyYmRuvWrbPPs2HDBlWtWtUeuCXplltukaenp7777ru/Ug4AAAAAAC7FqSPdF7NarXrsscfUpUsXRUVFSZJ++eUXSdKkSZM0bdo0tW7dWvPmzdPNN9+sbdu2qWnTpjp27JhCQkIci/D2VvXq1XXs2LES28rPz1d+fr79dVZWliTJYrHIYrGUdhVcRtE6uMO6uCP6p+zk5uaWeJ+IK8nOy9f6rfsUVDVdgX6+Ts3brFkz+fv7OzUPrh7vnbLDe8f98P5xbfRP2WDb5n7c7b1ztetR6tAdHx+vbdu2ORzFtlqtkqR//OMfuv/++yVJN9xwg7755ht9+OGHSkxMLFVbiYmJmjx5crHhK1ascKs3RkpKitkl4AroH+Pt27dPCQkJpZp3ainmmT59upo0aVKq9nD1eO8Yj/eO++L949roH2OxbXNf7vLeyc3NvarpShW6R44cab8BWv369e3D69SpI0lq0aKFw/TNmzfXoUOHJEmhoaE6ceKEw/iCggKdOXNGoaGhJbY3YcIEjR071v46KytLDRo0UO/evRUcHFyaVXApFotFKSkp6tWrl3x8fMwuB5egf8pObm6uunbt6tQ8u49m6vHPt+tft7fQdXWqODUv32gbi/dO2eG94354/7g2+qdssG1zP+723ik6A/vPOBW6bTabRo0apc8//1xr1qxReHi4w/iwsDDVrVu32Gkgu3fvVt++fSVJnTt31tmzZ5WRkaG2bdtKklatWiWr1aqOHTuW2K6vr698fYufHuLj4+MWnVXE3dbH3dA/xqtSpYo6dOjg1DyVDp6W74bzimrdRq0b1TCoMvwVvHeMx3vHffH+cW30j7HYtrkvd3nvXO06OBW64+PjNX/+fC1evFhBQUH2a7CrVKkiPz8/eXh46PHHH9dzzz2n66+/Xq1bt9bcuXO1c+dOLVq0SNKFo9633nqrHnzwQb3zzjuyWCwaOXKk7rrrLu5cDgAAAABwK06F7rfffluS1KNHD4fhs2fP1n333SdJeuyxx3Tu3DmNGTNGZ86c0fXXX6+UlBSH6yP+85//aOTIkbr55pvl6empwYMH6/XXX/9rawIAAAAAgItx+vTyqzF+/HiH53Rfqnr16po/f74zTQMAAAAAUO78ped0AwBQksLCQqWmpmrt2rVKTU1VYWGh2SUBAACYgtANALimkpKSFBERoV69emnGjBnq1auXIiIilJSUZHZpAAAAZY7QDQC4ZpKSkjRkyBBFR0crLS1Nn3zyidLS0hQdHa0hQ4YQvAEAQIVD6AYAXBOFhYVKSEjQgAEDlJycrI4dO8rPz08dO3ZUcnKyBgwYoHHjxnGqOQAAqFAI3QCAayItLU0HDhzQxIkT5enp+PHi6empCRMmaP/+/UpLSzOpQgAAgLJH6AYAXBNHjx6VJEVFRZU4vmh40XQAAAAVAaEbAHBN1KlTR5K0bdu2EscXDS+aDgAAoCIgdAMArolu3bopLCxMU6ZMkdVqdRhntVqVmJio8PBwdevWzaQKAQAAyh6hGwBwTXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAAAZcbb7AIAAO4jLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAAMoeoRsAcE3FxcVp0KBBWr16tZYtW6a+ffuqZ8+eHOEGAAAVEqEbAHDNeXl5KSYmRjk5OYqJiSFwAwCACotrugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFAGUpNzdXO3fudGqe7Lx8rd+6T9VqblKgn69T80ZGRsrf39+peQAAAAC4D0I3KpSdO3eqbdu2pZp3ainmycjIUJs2bUrVHgAAAIDyj9CNCiUyMlIZGRlOzbPr6FmNXbhVM/4vWs3qVHW6PQAAAAAVF6EbFYq/v7/TR549D56Wb1qemkddr9aNahhUGQAAAAB3xI3UAAAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDeJtdgLvJzc3Vzp07nZonOy9f67fuU7WamxTo5+vUvJGRkfL393dqHgAAAABA2SB0X2M7d+5U27ZtSzXv1FLMk5GRoTZt2pSqPQAAAACAsQjd11hkZKQyMjKcmmfX0bMau3CrZvxftJrVqep0ewAAAAAA10Tovsb8/f2dPvLsefC0fNPy1DzqerVuVMOgygAAAAAAZY0bqQEAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQp0J3YmKi2rdvr6CgIIWEhCg2Nla7du0qcVqbzaa+ffvKw8NDycnJDuMOHTqk/v37y9/fXyEhIXr88cdVUFBQ6pUAAAAAAMAVORW6U1NTFR8fr/T0dKWkpMhisah3797KyckpNu3MmTPl4eFRbHhhYaH69++v8+fPa/369Zo7d67mzJmjZ599tvRrAQAAAACAC/J2ZuLly5c7vJ4zZ45CQkKUkZGh7t2724dv3rxZ06dP16ZNm1SnTh2HeVasWKHt27dr5cqVql27tlq3bq0XXnhBTz75pCZNmqRKlSr9hdUBAAAAAMB1OBW6L5WZmSlJql69un1Ybm6u7r77bs2aNUuhoaHF5tmwYYOio6NVu3Zt+7A+ffrokUce0c8//6wbbrih2Dz5+fnKz8+3v87KypIkWSwWWSyWv7IKLqHo1PqCggK3WB93Q/+4NvqnbOTm5l72cqLLyc7L1/qt+xRUNV2Bfr5OzdusWTP5+/s7NQ+cw3vHtRX1CX3jmugf18W2reywb3D124BSh26r1arHHntMXbp0UVRUlH34mDFjdOONN2rQoEElznfs2DGHwC3J/vrYsWMlzpOYmKjJkycXG75ixQqX+8WXxq/ZkuSt9PR0Hd5mdjW4FP3j2uifsrFv3z4lJCSUat6ppZhn+vTpatKkSanaw9XhvVM+pKSkmF0CroD+cT1s28oO+wYXvni4GqUO3fHx8dq2bZvWrVtnH7ZkyRKtWrVKP/74Y2kXW6IJEyZo7Nix9tdZWVlq0KCBevfureDg4Gvalhm2HDojbd2kTp066fqG1f98BpQp+se10T9lIzc3V127dnVqnt1HM/X459v1r9tb6Lo6VZya1xW/zXY3vHdcm8ViUUpKinr16iUfHx+zy8El6B/Xxbat7LBv8P/PwP4zpQrdI0eO1NKlS7V27VrVr1/fPnzVqlXat2+fqlat6jD94MGD1a1bN61Zs0ahoaHauHGjw/jjx49LUomno0uSr6+vfH2Ln37g4+PjFhs6b29v+093WB93Q/+4NvqnbFSpUkUdOnRwap5KB0/Ld8N5RbVuo9aNahhUGUqL90754C77Ou6K/nE9bNvKDvsGuuq/MafuXm6z2TRy5Eh9/vnnWrVqlcLDwx3Gjx8/Xj/99JM2b95s/ydJr776qmbPni1J6ty5s7Zu3aoTJ07Y50tJSVFwcLBatGjhTDkAAAAAALg0p450x8fHa/78+Vq8eLGCgoLs12BXqVJFfn5+Cg0NLfFodcOGDe0BvXfv3mrRooWGDRumqVOn6tixY3r66acVHx9f4tFsAAAAAADKK6eOdL/99tvKzMxUjx49VKdOHfu/BQsWXPUyvLy8tHTpUnl5ealz584aOnSo7r33Xj3//PNOFw8AAAAAgCtz6ki3zWZzuoGS5mnUqJG++uorp5cFAAAAAEB54tSRbgAAAAAAcPUI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBvM0uAPgr9p/KUU5+gaFt7DuZY//p7W3sWybA11vhNQMMbQMAAABA2SF0o9zafypHPaetKbP2EhZtLZN2Vo/rQfAGAAAA3AShG+VW0RHumXe2VkRIoHHt5OVr6ZoNGtCjswL8fA1rZ++JbD22YLPhR+4BAAAAlB1CN8q9iJBARdWrYtjyLRaLjtWS2jSqJh8fH8PaAQAAAOB+uJEaAAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQb7MLAOC+9p/KUU5+gaFt7DuZY//p7W3sJi3A11vhNQMMbQMAAADuhdANwBD7T+Wo57Q1ZdZewqKtZdLO6nE9CN4AAAC4aoRulFv5hefkWfmw9mftkmflQMPaKSgo0JGCI9pxZoehR1L3Z2XLs/Jh5Reek1TFsHbKStER7pl3tlZEiHH9k5OXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAgHshdKPcOpJzUAHhb2jixrJp763lbxneRkC4dCSntdqqtuFtlZWIkEBF1TPuSwSLxaJjtaQ2jarJx8fHsHYAAACA0iB0o9yqG9BIOftH6bU7W6uJgUdSCwoK9O26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAAAEDZInSj3PL1qizruXoKD26mFjWMPZK633u/mldvbuiRVOu5TFnPnZSvV2XD2gAAAABQtnhkGAAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGcSp0JyYmqn379goKClJISIhiY2O1a9cu+/gzZ85o1KhRatasmfz8/NSwYUONHj1amZmZDss5dOiQ+vfvL39/f4WEhOjxxx9XQUHBtVkjAAAAAABchFOhOzU1VfHx8UpPT1dKSoosFot69+6tnJwcSdKRI0d05MgRTZs2Tdu2bdOcOXO0fPlyjRgxwr6MwsJC9e/fX+fPn9f69es1d+5czZkzR88+++y1XTMAAAAAAEzm7czEy5cvd3g9Z84chYSEKCMjQ927d1dUVJQ+++wz+/gmTZropZde0tChQ1VQUCBvb2+tWLFC27dv18qVK1W7dm21bt1aL7zwgp588klNmjRJlSpVujZrBgAAAACAyZwK3ZcqOm28evXqV5wmODhY3t4XmtqwYYOio6NVu3Zt+zR9+vTRI488op9//lk33HBDsWXk5+crPz/f/jorK0uSZLFYZLFY/soquISiU+sLCgrcYn3KSln93oqWbXTfuNvfQU5+tjwrH9be37fL6h1gWDsFBQU6UnBEW09stW9njPDL7znyrHxYOfnZslj8DWvHnbjb37S7oX9cW1l99qB06J/SOXA6Rzn5hYa2sftYpsNPIwX4eimshnH7OO7I3T57rnYdSr2HarVa9dhjj6lLly6KiooqcZpTp07phRde0EMPPWQfduzYMYfALcn++tixYyUuJzExUZMnTy42fMWKFfL3L/87v79mS5K30tPTdXib2dWUH0W/t3Xr1ulgoPHtpaSkGLr8sl4fo/3wxxEFhL+lZzLKpr23Vr5leBsB4dJX6wt1LKiu4W25A7Ztro3+KR+M/uzBX0P/XL0TedJLm437cvxST3y+o0zaeap1gUL8yqQpt+Bunz25ublXNV2p//Lj4+O1bds2rVu3rsTxWVlZ6t+/v1q0aKFJkyaVthlJ0oQJEzR27FiHZTdo0EC9e/dWcHDwX1q2K9hy6Iy0dZM6deqk6xte/qwBOPr5SJambU1X165d1bKucX8HFotFKSkp6tWrl3x8fAxrp6zWp6yE/npCH83z0owh0Wpcy9gj3d+lf6eOnToae6T7ZI7GLtqqfvf2V5sGIYa1407YtpVeWRwNyj+WKW3doZCIaDUKrWJoWxwNcl5ZffagdOgf5/18JEvanK5pQ6IVYeB+Qc65fC1P+163dmuvgMq+hrWz92SOxi3aqvad3WO/ray4275B0RnYf6ZUe6gjR47U0qVLtXbtWtWvX7/Y+D/++EO33nqrgoKC9PnnnztsjEJDQ7Vx40aH6Y8fP24fVxJfX1/5+hZ/0/j4+LjFhq4oKHh7e7vF+pSVsv69Gf335m5/BwG+gbKeq6eIai0UVdu4HXqLxaJfvX9VdEi0ob83z4JMWc+dUYBvoFv0T1lwt7/psrL/VI56zfy2zNorq6NBq8f1UHhNgrez3GVfx13RP1ev6DMhsk4VRdUzdr/g1E6pQ+Na7Le5IHf7vV3tOjgVum02m0aNGqXPP/9ca9asUXh4eLFpsrKy1KdPH/n6+mrJkiWqXLmyw/jOnTvrpZde0okTJxQScuFoUUpKioKDg9WiRQtnygEAwO3k5F+43m3mna0VEWLctSY5eflaumaDBvTorAA/A48GncjWYws229cLAICKxqnQHR8fr/nz52vx4sUKCgqyX4NdpUoV+fn5KSsrS71791Zubq4+/vhjZWVl2Q+516pVS15eXurdu7datGihYcOGaerUqTp27JiefvppxcfHl3g0GwCAiigiJNDwo0HHakltGlVzi6MNAAC4KqdC99tvvy1J6tGjh8Pw2bNn67777tMPP/yg7777TpIUERHhMM3+/fsVFhYmLy8vLV26VI888og6d+6sgIAADR8+XM8///xfWA0AAAAAAFyP06eXX0mPHj3+dBpJatSokb766itnmgYAAAAAoNzxNLsAAAAAAADcFaEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFuLr9p3KUk19gaBv7TubYf3p7G9slAb7eCq8ZYGgbAAAAAIALCN1XsP9UjnpOW1Nm7SUs2lom7awe14PgDQAAAABlgNB9BUVHuGfe2VoRIYHGtZOXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAAABcQOi+ChEhgYqqV8Ww5VssFh2rJbVpVE0+Pj6GtQMAAAAAKFvcSA0AAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAABcSGFhoVJTU7V27VqlpqaqsLDQ7JIAAH8BoRsAAMBFJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSAACl5G12AQCAsrf/VI5y8gsMbWPfyRz7T29vYz9uAny9FV4zwNA2AKMlJSVpyJAhGjBggD766CP99ttvql+/vqZOnaohQ4Zo0aJFiouLM7tMAICTCN0AUMHsP5WjntPWlFl7CYu2lkk7q8f1IHij3CosLFRCQoIGDBig5ORkFRYW6vTp0+rYsaOSk5MVGxurcePGadCgQfLy8jK7XACAEwjdAFDBFB3hnnlna0WEBBrXTl6+lq7ZoAE9OivAz9ewdvaeyNZjCzYbfuQeMFJaWpoOHDigTz75RJ6eng7XcXt6emrChAm68cYblZaWph49ephXqBvKzc3Vzp07nZonOy9f67fuU7WamxTo5PYtMjJS/v7+Ts0DlAXOgjMOoRsAKqiIkEBF1ati2PItFouO1ZLaNKomHx8fw9oB3MHRo0clSVFRUSWOLxpeNB2unZ07d6pt27almndqKebJyMhQmzZtStUeYBTOgjMWoRsAAMBkderUkSRt27ZNnTp1KjZ+27ZtDtPh2omMjFRGRoZT8+w6elZjF27VjP+LVrM6VZ1uD3A1nAVnLEI3AACAybp166awsDBNmTJFycnJDuOsVqsSExMVHh6ubt26mVOgG/P393f6yLPnwdPyTctT86jr1bpRDYMqA8oeZ8EZg0eGAQAAmMzLy0vTp0/X0qVLFRsbq/T0dOXl5Sk9PV2xsbFaunSppk2bxk3UAKAc4kg3AACAC4iLi9OiRYuUkJCg7t2724eHh4fzuDAAKMcI3QAAAC4iLi5OgwYN0urVq7Vs2TL17dtXPXv25Ag3AJRjhG4AAAAX4uXlpZiYGOXk5CgmJobADQDlHKEbAADAQDwHGgAqNkI3AACAgXgONABUbIRuAAAAA/EcaACo2AjdAAAABuI50ABQsfGcbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADMJzuq8gv/CcPCsf1v6sXfKsHGhYOwUFBTpScEQ7zuyQt7dxXbI/K1uelQ8rv/CcpCqGtQMAAABcjP1qVGSE7is4knNQAeFvaOLGsmnvreVvGd5GQLh0JKe12qq24W0BAAAAEvvVqNgI3VdQN6CRcvaP0mt3tlaTEGO/kft23bfq0rWLod/I7TuRrUcXbFbdno0MawMAAAC4FPvVqMgI3Vfg61VZ1nP1FB7cTC1qGHfaiMVi0X7v/Wpevbl8fHwMa8d6LlPWcyfl61XZsDYAAACAS7FfjYqMG6kBAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswsASivPUihJ2nY409B2cvLytemkFHrwdwX4+RrWzt4T2YYtGwAAAIA5nArdiYmJSkpK0s6dO+Xn56cbb7xRr7zyipo1a2af5ty5c0pISNCnn36q/Px89enTR2+99ZZq165tn+bQoUN65JFHtHr1agUGBmr48OFKTEyUtzffAeDq7ftfSB2ftLUMWvPWR3u/L4N2pABf3gcAAACAu3Bq7z41NVXx8fFq3769CgoKNHHiRPXu3Vvbt29XQECAJGnMmDH68ssvtXDhQlWpUkUjR45UXFycvv32W0lSYWGh+vfvr9DQUK1fv15Hjx7VvffeKx8fH02ZMuXaryHcVu+WoZKkJiGB8vPxMqydXUczlbBoq6YPiVazOlUMa0e6ELjDawYY2gYAAACAsuNU6F6+fLnD6zlz5igkJEQZGRnq3r27MjMz9cEHH2j+/Pm66aabJEmzZ89W8+bNlZ6erk6dOmnFihXavn27Vq5cqdq1a6t169Z64YUX9OSTT2rSpEmqVKnStVs7uLXqAZV0V4eGhrdTUFAgSWpSK0BR9YwN3QAAAADcy1+6kVpm5oVraatXry5JysjIkMVi0S233GKfJjIyUg0bNtSGDRskSRs2bFB0dLTD6eZ9+vRRVlaWfv75579SDgAAAAAALqXUF49arVY99thj6tKli6KioiRJx44dU6VKlVS1alWHaWvXrq1jx47Zp7k4cBeNLxpXkvz8fOXn59tfZ2VlSZIsFossFktpV+FPFR3hLCgoMLSdomUb2YZUduvjbvi9lc4feRfes1sOnbH/Do2Qc+7Cje5q/nJSAZUNvNHdyRxJ7vF3kJOfLc/Kh7X39+2yeht3OUNBQYGOFBzR1hNbDb1nxy+/58iz8mHl5GfLYvE3rJ2yQv9A4rPH1dE/zmO/2rXx2VM6V9v3pV7T+Ph4bdu2TevWrSvtIq5aYmKiJk+eXGz4ihUr5O9v3C/x12xJ8ta6det0MNCwZuxSUlIMXX5Zr4+7KPq9paen6/A2s6spPzYc95DkpacWby+D1rz10d4fy6Ad6fsN63TQr0yaMswPfxxRQPhbeiajbNp7a+VbhrcREC59tb5Qx4LqGt6W0egfSHz2uDr6x3nsV7s2PntKJzc396qmK1XoHjlypJYuXaq1a9eqfv369uGhoaE6f/68zp4963C0+/jx4woNDbVPs3HjRoflHT9+3D6uJBMmTNDYsWPtr7OystSgQQP17t1bwcHBpVmFq/LzkSxN25qurl27qmVd49qxWCxKSUlRr1695OPjY1g7ZbU+7mbLoTPS1k3q1KmTrm9Y3exyyo1OOecVveOEGtcKMPRGd7uPZeqJz3do6u3NdV2o0Te681JYjfJ/o7vQX0/oo3lemjEkWo1rGftt9nfp36ljp47Gfpt9MkdjF21Vv3v7q02DEMPaKSv0DyQ+e1wd/eM89qtdG589pVN0BvafcWpNbTabRo0apc8//1xr1qxReHi4w/i2bdvKx8dH33zzjQYPHixJ2rVrlw4dOqTOnTtLkjp37qyXXnpJJ06cUEjIhV9ASkqKgoOD1aJFixLb9fX1la9v8dNGfXx8DH0zFf0heHt7G9pOEXdbH3fB7610alf10T2dw/98wmvkutAqat2oRpm1V54F+AbKeq6eIqq1UFRt476osFgs+tX7V0WHRBv63vEsyJT13BkF+Aa6xXuU/oHEZ4+ro3+cx361a+Ozp3SudtlOhe74+HjNnz9fixcvVlBQkP0a7CpVqsjPz09VqlTRiBEjNHbsWFWvXl3BwcEaNWqUOnfurE6dOkmSevfurRYtWmjYsGGaOnWqjh07pqefflrx8fElBmsAAAAAAMorp0L322+/LUnq0aOHw/DZs2frvvvukyS9+uqr8vT01ODBg5Wfn68+ffrorbf+/zn7Xl5eWrp0qR555BF17txZAQEBGj58uJ5//vm/tiYAAAAAALgYp08v/zOVK1fWrFmzNGvWrMtO06hRI3311VfONA0AAAAAQLnzl57TDQAAAAAALo/QDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABjE2+wCgLKUm5urnTt3OjXPrqNnlX9sr3Zs85P1dFWn5o2MjJS/v79T8wAAgNLbfypHOfkFhrax72SO/ae3t7G70wG+3gqvGWBoGwCMRehGhbJz5061bdu2VPPePdf5eTIyMtSmTZtStQcAAJyz/1SOek5bU2btJSzaWibtrB7Xg+ANlGOEblQokZGRysjIcGqe7Lx8fbl6g/r37KxAP1+n2wMAAGWj6Aj3zDtbKyIk0Lh28vK1dM0GDejRWQFO7hs4Y++JbD22YLPhR+4BGIvQjQrF39/f6SPPFotFv586oc4d2snHx8egygAAwLUSERKoqHpVDFu+xWLRsVpSm0bV2DcA8Ke4kRoAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswtwZXmWQknStsOZhraTk5evTSel0IO/K8DP17B29p7INmzZAIBrg88e17f/VI5y8gsMbWPfyRz7T29vY3fXAny9FV4zwNA2ALZtro3+MRah+wr2/a+zxidtLYPWvPXR3u/LoJ0LH64AANfEZ49r238qRz2nrSmz9hIWlcXfgbR6XA+CNwzFts210T/Gco0qXFTvlqGSpCYhgfLz8TKsnV1HM5WwaKumD4lWszpVDGtH4ttsAHB1fPa4tqIj3DPvbK2IkEDj2snL19I1GzSgR2fDjwY9tmCz4UfuAbZtro3+MRah+wqqB1TSXR0aGt5OQcGFD7omtQIUVc/YPz4AgGvjs6d8iAgJNPT3ZrFYdKyW1KZRNfn4+BjWDlBW2La5NvrHWNxIDQAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwiLfZBQAAylaepVCStO1wpqHt5OTla9NJKfTg7wrw8zWsnb0nsg1bNnCp/MJz8qx8WPuzdsmzcqBh7RQUFOhIwRHtOLND3t7G7a7tz8qWZ+XDyi88J6mKYe0AQEVG6AaACmbf/0Lq+KStZdCatz7a+30ZtCMF+PKRBuMdyTmogPA3NHFj2bT31vK3DG8jIFw6ktNabVXb8LYAoCJiDwUAKpjeLUMlSU1CAuXn42VYO7uOZiph0VZNHxKtZnWMPYIW4Out8JoBhrYBSFLdgEbK2T9Kr93ZWk1CjD3S/e26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAqOkI3AFQw1QMq6a4ODQ1vp6CgQJLUpFaAoupx2ircg69XZVnP1VN4cDO1qGHc37XFYtF+7/1qXr25fHx8DGvHei5T1nMn5etV2bA2AKCi40ZqAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGMTp0L127Vrddtttqlu3rjw8PJScnOwwPjs7WyNHjlT9+vXl5+enFi1a6J133nGY5ty5c4qPj1eNGjUUGBiowYMH6/jx439pRQAAAAAAcDVOh+6cnBxdf/31mjVrVonjx44dq+XLl+vjjz/Wjh079Nhjj2nkyJFasmSJfZoxY8boiy++0MKFC5WamqojR44oLi6u9GsBAAAAAIAL8nZ2hr59+6pv376XHb9+/XoNHz5cPXr0kCQ99NBDevfdd7Vx40YNHDhQmZmZ+uCDDzR//nzddNNNkqTZs2erefPmSk9PV6dOnUq3JgAAAAAAuBinQ/efufHGG7VkyRI98MADqlu3rtasWaPdu3fr1VdflSRlZGTIYrHolltusc8TGRmphg0basOGDSWG7vz8fOXn59tfZ2VlSZIsFossFsu1XoUyV1BQYP/pDuvjbor6hL4xXm5urnbt2uXUPLuPZir/2F5t21xJ549XcWreZs2ayd/f36l5cPXYtrk2+qd0yur3VlafPe72d5CTny3Pyoe19/ftsnoHGNZOQUGBjhQc0dYTW+Xtfc13p+1++T1HnpUPKyc/WxYLn1dXw93+pt2Nu/XP1a7DNd9KvPHGG3rooYdUv359eXt7y9PTU++//766d+8uSTp27JgqVaqkqlWrOsxXu3ZtHTt2rMRlJiYmavLkycWGr1ixwi12mH/NliRvpaen6/A2s6vB5aSkpJhdgtvbt2+fEhISSjXvsLnOzzN9+nQ1adKkVO3hz7Ftc230T+kU/d7WrVung4HGt2f0Z09Zr4/RfvjjiALC39IzGWXT3lsr3zK8jYBw6av1hToWVNfwttwB2zbX5m79k5ube1XTGRK609PTtWTJEjVq1Ehr165VfHy86tat63B02xkTJkzQ2LFj7a+zsrLUoEED9e7dW8HBwdeqdNNsOXRG2rpJnTp10vUNq5tdDi5hsViUkpKiXr16ycfHx+xy3Fpubq66du3q1DzZefn6Ou179enWXoF+vk7Ny5FuY7Ftc230T+n8fCRL07amq2vXrmpZ17h9kLL67Cmr9Skrob+e0EfzvDRjSLQa1zL2SPd36d+pY6eOxh7pPpmjsYu2qt+9/dWmQYhh7bgTtm2uzd36p+gM7D9zTbcSeXl5mjhxoj7//HP1799fktSqVStt3rxZ06ZN0y233KLQ0FCdP39eZ8+edTjaffz4cYWGhpa4XF9fX/n6Ft+Z9vHxcYsQVLSx9vb2dov1cVfu8vfmyqpUqaIOHTo4NY/FYtEfZ8+o242d6B8Xw7bNtdE/pVPWvzejP3vc7e8gwDdQ1nP1FFGthaJqO3fJkTMsFot+9f5V0SHRhv7ePAsyZT13RgG+gW7RP2XB3f6m3Y279c/VrsM1fU530TXWnp6Oi/Xy8pLVapUktW3bVj4+Pvrmm2/s43ft2qVDhw6pc+fO17IcAAAAAABM5fSR7uzsbO3du9f+ev/+/dq8ebOqV6+uhg0bKiYmRo8//rj8/PzUqFEjpaamat68eZoxY4akC0eyRowYobFjx6p69eoKDg7WqFGj1LlzZ+5cDgAAAABwK06H7k2bNqlnz57210XXWg8fPlxz5szRp59+qgkTJuiee+7RmTNn1KhRI7300kt6+OGH7fO8+uqr8vT01ODBg5Wfn68+ffrorbeMvxEFAAAAAABlyenQ3aNHD9lstsuODw0N1ezZs6+4jMqVK2vWrFmaNWuWs80DAAAAAFBuXNNrugEAAAAAwP9H6AYAAAAAwCCEbgAAAAAADHJNn9MNAADgzvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAQAXELoBAACu0r7/hdTxSVvLoDVvfbT3+zJoRwrwZZcQAIzCFhYAAOAq9W4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQBARUboBgAAuErVAyrprg4NDW+noKBAktSkVoCi6hkbugEAxuJGagAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQbzNLgAAAAC4FvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAZQdQjcAAADcwr7/hdTxSVvLoDVvfbT3+zJoRwrwZZcdKM94BwMAAMAt9G4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQAwFqEbAAAAbqF6QCXd1aGh4e0UFBRIkprUClBUPWNDN4DyjxupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNrsAd5Obm6udO3c6Nc+uo2eVf2yvdmzzk/V0VafmjYyMlL+/v1PzAAAAAMBfQe65eoTua2znzp1q27Ztqea9e67z82RkZKhNmzalag8AAAAASoPcc/UI3ddYZGSkMjIynJonOy9fX67eoP49OyvQz9fp9gAAAACgLJF7rh6h+xrz9/d3+hsYi8Wi30+dUOcO7eTj42NQZQAAAABwbZB7rh43UgMAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgTofutWvX6rbbblPdunXl4eGh5OTkYtPs2LFDAwcOVJUqVRQQEKD27dvr0KFD9vHnzp1TfHy8atSoocDAQA0ePFjHjx//SysCAAAAAICrcTp05+Tk6Prrr9esWbNKHL9v3z517dpVkZGRWrNmjX766Sc988wzqly5sn2aMWPG6IsvvtDChQuVmpqqI0eOKC4urvRrAQAAAACAC/J2doa+ffuqb9++lx3/1FNPqV+/fpo6dap9WJMmTez/z8zM1AcffKD58+frpptukiTNnj1bzZs3V3p6ujp16uRsSQAAAAAAuCSnQ/eVWK1Wffnll3riiSfUp08f/fjjjwoPD9eECRMUGxsrScrIyJDFYtEtt9xiny8yMlINGzbUhg0bSgzd+fn5ys/Pt7/OysqSJFksFlkslmu5CqYoWgd3WBd3RP+4NvqnbOTm5mrXrl1OzbP7aKbyj+3Vts2VdP54Fafmbdasmfz9/Z2aB84pKCiw/+T9YyzeP+6H94/rom9cm7vtt13telzT0H3ixAllZ2fr5Zdf1osvvqhXXnlFy5cvV1xcnFavXq2YmBgdO3ZMlSpVUtWqVR3mrV27to4dO1bichMTEzV58uRiw1esWOFWHyopKSlml4AroH9cG/1jrH379ikhIaFU8w6b6/w806dPdzhLCtfer9mS5K309HQd3mZ2Ne6N94/74f3juuib8sFd9ttyc3OvarprfqRbkgYNGqQxY8ZIklq3bq3169frnXfeUUxMTKmWO2HCBI0dO9b+OisrSw0aNFDv3r0VHBz81ws3mcViUUpKinr16iUfHx+zy8El6B/XRv+UjdzcXHXt2tWpebLz8vV12vfq0629Av18nZqXI3XG23LojLR1kzp16qTrG1Y3uxy3xvvH/fD+cV30jWtzt/22ojOw/8w1Dd01a9aUt7e3WrRo4TC8efPmWrdunSQpNDRU58+f19mzZx2Odh8/flyhoaElLtfX11e+vsU/cHx8fNyis4q42/q4G/rHtdE/xqpSpYo6dOjg1DwWi0V/nD2jbjd2om9ckLe3t/0n/WMs3j/uh/eP66Jvygd32W+72nW4ps/prlSpktq3b1/suqXdu3erUaNGkqS2bdvKx8dH33zzjX38rl27dOjQIXXu3PlalgMAAAAAgKmcPtKdnZ2tvXv32l/v379fmzdvVvXq1dWwYUM9/vjjuvPOO9W9e3f17NlTy5cv1xdffKE1a9ZIuvBt74gRIzR27FhVr15dwcHBGjVqlDp37sydywEAAAAAbsXp0L1p0yb17NnT/rroWuvhw4drzpw5uv322/XOO+8oMTFRo0ePVrNmzfTZZ585XMv06quvytPTU4MHD1Z+fr769Omjt9566xqsDgAAAAAArsPp0N2jRw/ZbLYrTvPAAw/ogQceuOz4ypUra9asWZo1a5azzQMAAAAAUG5c02u6AQAAAADA/0foBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAACAoQoLC5Wamqq1a9cqNTVVhYWFZpdUZgjdAAAAAADDJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSygShGwAAAABgiKSkJA0ZMkTR0dFKS0vTJ598orS0NEVHR2vIkCEVIngTugEAAAAA11xhYaESEhI0YMAAJScnq2PHjvLz81PHjh2VnJysAQMGaNy4cW5/qjmhGwAAAABwzaWlpenAgQOaOHGiPD0do6enp6cmTJig/fv3Ky0tzaQKywahGwAAAABwzR09elSSFBUVVeL4ouFF07krQjcAAAAA4JqrU6eOJGnbtm0lji8aXjSduyJ0AwAAAACuuW7duiksLExTpkyR1Wp1GGe1WpWYmKjw8HB169bNpArLBqEbAAAAAHDNeXl5afr06Vq6dKliY2OVnp6uvLw8paenKzY2VkuXLtW0adPk5eVldqmG8ja7AAAAAACAe4qLi9OiRYuUkJCg7t2724eHh4dr0aJFiouLM7G6skHoBgAAAAAYJi4uToMGDdLq1au1bNky9e3bVz179nT7I9xFCN0AAAAAAEN5eXkpJiZGOTk5iomJqTCBW+KabgAAAAAADEPoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAg3mYXAAAA/prc3Fzt3LnTqXl2HT2r/GN7tWObn6ynqzo1b2RkpPz9/Z2aB3BVvH9cF30Dd0HoBgCgnNu5c6fatm1bqnnvnuv8PBkZGWrTpk2p2gNcDe8f10XfwF0QugEAKOciIyOVkZHh1DzZefn6cvUG9e/ZWYF+vk63B7gL3j+ui76BuyB0AwBQzvn7+zt9dMZisej3UyfUuUM7+fj4GFQZ4Pp4/7gu+gbughupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNruA0rDZbJKkrKwskyu5NiwWi3Jzc5WVlSUfHx+zy8El6B/XRv+4LvrGtdE/ro3+cW30j+uib1ybu/VPUR4tyqeXUy5D9x9//CFJatCggcmVAAAAAAAqsj/++ENVqlS57HgP25/FchdktVp15MgRBQUFycPDw+xy/rKsrCw1aNBAv/76q4KDg80uB5egf1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1j82m01//PGH6tatK0/Py1+5XS6PdHt6eqp+/fpml3HNBQcHu8Ufn7uif1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1D9XOsJdhBupAQAAAABgEEI3AAAAAAAGIXS7AF9fXz333HPy9fU1uxSUgP5xbfSP66JvXBv949roH9dG/7gu+sa1VdT+KZc3UgMAAAAAoDzgSDcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEK3CQoKCvT888/rt99+M7sUALhm2LYBAMqaxWLRzTffrD179phdCq7g/Pnz+u2333To0CGHfxUFdy83SVBQkLZu3aqwsDCzS8ElLBaLIiMjtXTpUjVv3tzscoByhW2ba2P75vq++eYbffPNNzpx4oSsVqvDuA8//NCkqlBk06ZN2rFjhySpefPmateunckVQZJq1aql9evXq2nTpmaXgkvs2bNHDzzwgNavX+8w3GazycPDQ4WFhSZVVra8zS6gorrpppuUmprKjqkL8vHx0blz58wuAyiX2La5NrZvrm3y5Ml6/vnn1a5dO9WpU0ceHh5ml4T/+e233/S3v/1N3377rapWrSpJOnv2rG688UZ9+umnql+/vrkFVnBDhw7VBx98oJdfftnsUnCJ++67T97e3lq6dGmF3q5xpNsk77zzjiZPnqx77rlHbdu2VUBAgMP4gQMHmlQZJGnKlCnavXu3/v3vf8vbm++mXE1OTo5efvnlyx4N+uWXX0yqDGzbXB/bN9dVp04dTZ06VcOGDTO7FFzi1ltv1dmzZzV37lw1a9ZMkrRr1y7df//9Cg4O1vLly02usGIbNWqU5s2bp6ZNm5b42TNjxgyTKkNAQIAyMjIUGRlpdimmInSbxNPz8pfTV6RTLVzV7bffrm+++UaBgYGKjo4utvFOSkoyqTJI0t/+9jelpqZq2LBhJX5r+uijj5pUGdi2uT62b66rRo0a2rhxo5o0aWJ2KbiEn5+f1q9frxtuuMFheEZGhrp166bc3FyTKoMk9ezZ87LjPDw8tGrVqjKsBhdr3769Xn31VXXt2tXsUkzFV9wmufTIHFxL1apVNXjwYLPLwGUsW7ZMX375pbp06WJ2KbgE2zbXx/bNdf3973/X/Pnz9cwzz5hdCi7RoEEDWSyWYsMLCwtVt25dEyrCxVavXm12CbiMV155RU888YSmTJmi6Oho+fj4OIwPDg42qbKyxZFuF3Du3DlVrlzZ7DKAciM8PFxfffUVN4JycWzbAOc8+uijmjdvnlq1aqVWrVoV2znlFFnzLF68WFOmTNGsWbPsN0/btGmTRo0apSeffFKxsbHmFghJ0t69e7Vv3z51795dfn5+9pt1wTxFZ8Bd2g8V7UZqhG6TFBYWasqUKXrnnXd0/Phx7d69W40bN9YzzzyjsLAwjRgxwuwSK7yCggKtWbNG+/bt0913362goCAdOXJEwcHBCgwMNLu8Cu3jjz/W4sWLNXfuXPn7+5tdDi7Ctq18YPvmmjhF1nVVq1ZNubm5KigosN8Loej/l16icebMGTNKrNBOnz6tO+64Q6tXr5aHh4f27Nmjxo0b64EHHlC1atU0ffp0s0ussFJTU684PiYmpowqMRenl5vkpZde0ty5czV16lQ9+OCD9uFRUVGaOXMmO6YmO3jwoG699VYdOnRI+fn56tWrl4KCgvTKK68oPz9f77zzjtklVmjTp0/Xvn37VLt2bYWFhRU7GvTDDz+YVBnYtrk+tm+ui1NkXdfMmTPNLgFXMGbMGPn4+OjQoUMOZ8HdeeedGjt2LKHbRBUlVP8ZQrdJ5s2bp/fee08333yzHn74Yfvw66+/Xjt37jSxMkgXTvFr166dtmzZoho1atiH33777Q5BAubgND7XxbbN9bF9A5w3fPhws0vAFaxYsUJff/11sUe3NW3aVAcPHjSpKlwsNzdXhw4d0vnz5x2Gt2rVyqSKyhah2ySHDx9WREREseFWq7XEG3WgbKWlpWn9+vWqVKmSw/CwsDAdPnzYpKpQ5LnnnjO7BFwG2zbXx/bNtW3atEn//e9/S9w55c7y5iosLFRycrJ27NghSWrZsqUGDhwoLy8vkytDTk5OiZebnTlzRr6+viZUhCInT57U/fffr2XLlpU4vqJc0335Z7vAUC1atFBaWlqx4YsWLSr2OAqUPavVWuJG4LffflNQUJAJFQHlA9s218f2zXV9+umnuvHGG7Vjxw59/vnnslgs+vnnn7Vq1SpVqVLF7PIqtL1796p58+a69957lZSUpKSkJA0dOlQtW7bUvn37zC6vwuvWrZvmzZtnf+3h4SGr1aqpU6de8V4JMN5jjz2ms2fP6rvvvpOfn5+WL1+uuXPnqmnTplqyZInZ5ZUZjnSb5Nlnn9Xw4cN1+PBhWa1WJSUladeuXZo3b56WLl1qdnkVXu/evTVz5ky99957ki5svLOzs/Xcc8+pX79+JleHwsJCvfrqq5c9GsRNbMzDts31sX1zXVOmTNGrr76q+Ph4BQUF6bXXXlN4eLj+8Y9/qE6dOmaXV6GNHj1aTZo0UXp6uqpXry7pws27hg4dqtGjR+vLL780ucKKberUqbr55pu1adMmnT9/Xk888YR+/vlnnTlzRt9++63Z5VVoq1at0uLFi9WuXTt5enqqUaNG6tWrl4KDg5WYmKj+/fubXWKZ4O7lJkpLS9Pzzz+vLVu2KDs7W23atNGzzz6r3r17m11ahffbb7+pT58+stls2rNnj9q1a6c9e/aoZs2aWrt2rUJCQswusUJ79tln9e9//1sJCQl6+umn9dRTT+nAgQNKTk7Ws88+q9GjR5tdYoXGts21sX1zXQEBAfr5558VFhamGjVqaM2aNYqOjtaOHTt000036ejRo2aXWGEFBAQoPT1d0dHRDsO3bNmiLl26KDs726TKUCQzM1Nvvvmmw2dPfHw8X1iZLDg4WD/99JPCwsLUqFEjzZ8/X126dNH+/fvVsmVL5ebmml1imeBIt4m6deumlJQUs8tACerXr68tW7ZowYIF9o33iBEjdM8998jPz8/s8iq8//znP3r//ffVv39/TZo0SX/729/UpEkTtWrVSunp6YRuk7Ftc21F27dPP/1UP/30E9s3F1KtWjX98ccfkqR69epp27Ztio6O1tmzZyvMjqmr8vX1tffNxbKzs4vdHwHmqFKlip566imzy8AlmjVrpl27diksLEzXX3+93n33XYWFhemdd96pUF+IcKTbZOfPn9eJEydktVodhjds2NCkiiBJa9eu1Y033mh/FmeRgoICrV+/Xt27dzepMkgXjjjs2LFDDRs2VJ06dfTll1+qTZs2+uWXX3TDDTcoMzPT7BIBwGl333232rVrp7Fjx+qFF17QG2+8oUGDBiklJUVt2rThRmomuvfee/XDDz/ogw8+UIcOHSRJ3333nR588EG1bdtWc+bMMbdA6OzZs9q4cWOJ+9X33nuvSVXh448/VkFBge677z5lZGTo1ltv1ZkzZ1SpUiXNmTNHd955p9kllglCt0n27NmjBx54QOvXr3cYbrPZ5OHhUWHu5OeqvLy8dPTo0WKnWZ4+fVohISH0j8maNWumefPmqWPHjuratasGDBig8ePHa8GCBRo1apROnDhhdokVSrVq1eTh4XFV03K9vWvYs2ePVq9eXeLO6bPPPmtSVThz5ozOnTununXr2m8CtX79ejVt2lRPP/20qlWrZnaJFdbZs2c1fPhwffHFF/Lx8ZF04Yv4gQMHavbs2apataq5BVZwX3zxhe655x5lZ2crODjY4TPJw8ODzx4Xkpubq507d6phw4aqWbOm2eWUGUK3Sbp06SJvb2+NHz9ederUKbbDev3115tUGSTJ09NTx48fV61atRyG7969W+3atVNWVpZJlUGSxo8fr+DgYE2cOFELFizQ0KFDFRYWpkOHDmnMmDF6+eWXzS6xQpk7d679/6dPn9aLL76oPn36qHPnzpKkDRs26Ouvv9YzzzyjMWPGmFUm/uf999/XI488opo1ayo0NLTYzukPP/xgYnWAa9u7d6/9kWHNmzcv8RGJKHvXXXed+vXrpylTppT46DDAbIRukwQEBCgjI0ORkZFml4KLxMXFSZIWL16sW2+91eHZjoWFhfrpp5/UrFkzLV++3KwSUYINGzZow4YNatq0qW677Tazy6nQBg8erJ49e2rkyJEOw998802tXLlSycnJ5hQGu0aNGumf//ynnnzySbNLwWWcOHGixLMQWrVqZVJFeP755zVu3LhigS4vL0//+te/OEPEZAEBAdq6dasaN25sdim4hM1m06JFiy57dlVFuWyG0G2S9u3b69VXX1XXrl3NLgUXuf/++yVdOHJ3xx13ONxUqFKlSgoLC9ODDz5YoU6HAZwRGBiozZs3Fzv6s3fvXrVu3Zo7/LqA4OBgbd68mZ1TF5SRkaHhw4drx44dunT3jEvPzMVlZ64tLi5Od911l+644w6zS8ElHn30Ub377rvq2bOnateuXezs3tmzZ5tUWdni7uVl6OJTkl955RU98cQTmjJliqKjo+3XBxUJDg4u6/Kg///GDwsL0+OPP84pSi7syJEjWrduXYnfmnL3cvPUqFFDixcvVkJCgsPwxYsXq0aNGiZVhYv93//9n1asWKGHH37Y7FJwiQceeEDXXXedPvjggxJ3TmGeonvuXGrLli3253ajbC1ZssT+//79++vxxx/X9u3bS9yvHjhwYFmXh//56KOPlJSUpH79+pldiqk40l2GPD09HTbYJW3AuZGaa7jpppuUlJRU7MYoWVlZio2N1apVq8wpDJKkOXPm6B//+IcqVaqkGjVqFLsm9ZdffjGxuoptzpw5+vvf/66+ffuqY8eOki7c4Xf58uV6//33dd9995lbYAX1+uuv2/+fk5OjGTNmqH///iXunPKllXmCgoL0448/cp2wCym6UWRmZmaxG3QVFhYqOztbDz/8sGbNmmVilRWTp6fnVU3HfrW5wsPDtWzZsgp/SS2huwylpqZe9bQxMTEGVoI/c7nTyE6cOKF69erJYrGYVBkkqUGDBnr44Yc1YcKEq/7QRdn57rvv9PrrrzvcbGj06NH2EI6yFx4eflXT8aWVuWJjYzVs2DANHjzY7FLwP3PnzpXNZtMDDzygmTNnqkqVKvZxRZedFd00EkBxc+fO1fLly/Xhhx86XLZZ0RC6gYv89NNPkqTWrVtr1apVDqeMFRYWavny5Xr33Xd14MABkyqEdOEU5o0bN6pJkyZmlwIA18ypU6c0fPhwdejQQVFRUZwi60JSU1PtT54BcPXy8vJ0++2369tvv1VYWFix7VpFeWIGWw6TzJ49W4GBgfq///s/h+ELFy5Ubm6uhg8fblJlFVvr1q3l4eEhDw8P3XTTTcXG+/n56Y033jChMlxsxIgRWrhwocaPH292KZCceoQe96sALm/Dhg369ttvtWzZsmLjOEXWXEFBQdqxY4eio6MlXbhPxezZs9WiRQtNmjRJlSpVMrnCim306NGKiIgodnnMm2++qb1792rmzJnmFAYNHz5cGRkZGjp0aIW+VwVHuk1y3XXX2e/kd7HU1FQ99NBD2rVrl0mVVWwHDx6UzWZT48aNtXHjRofndFeqVEkhISHy8vIysUJIF846GDBggPLy8kq8JnXGjBkmVVYxXXq/ipJwvwrXMXjwYHXo0KHYI8OmTp2q77//XgsXLjSpMoSFhWnAgAF65plnVLt2bbPLwUXat2+v8ePHa/Dgwfrll1/UokULxcXF6fvvv1f//v0JdSarV6+elixZorZt2zoM/+GHHzRw4ED99ttvJlWGgIAAff311xX+iU0c6TbJoUOHSrzGrlGjRjp06JAJFUG68PuXVOxu2HAtiYmJ+vrrr9WsWTNJKnYjNZSt1atXm10CnLB27VpNmjSp2PC+fftq+vTpZV8Q7E6fPq0xY8YQuF3Q7t271bp1a0kXzkqMiYnR/Pnz9e233+quu+4idJvs9OnTDtfbFwkODtapU6dMqAhFGjRowFluInSbJiQkRD/99JPCwsIchm/ZsoXH6phkyZIl6tu3r3x8fBweQ1ESrqsz1/Tp0/Xhhx9yJ2wXwY0fy5fs7OwST4X18fFx6lIBXHtxcXFavXo196twQTabzf6F/MqVKzVgwABJFwIFoc58ERERWr58uUaOHOkwfNmyZWrcuLFJVUG6sM/2xBNP6J133imWeyoSQrdJ/va3v2n06NEKCgpS9+7dJV04tfzRRx/VXXfdZXJ1FVNsbKyOHTumkJAQxcbGXnY6TpE1n6+vr7p06WJ2GbiMtLQ0vfvuu/rll1+0cOFC1atXTx999JHCw8Mr/OllriA6OloLFizQs88+6zD8008/VYsWLUyqCtKFS88mTJigdevW8Tg3F9OuXTu9+OKLuuWWW5Samqq3335bkrR//37OTHABY8eO1ciRI3Xy5En7PXm++eYbTZ8+nbMQTDZ06FDl5uaqSZMm8vf3L7ZdO3PmjEmVlS2u6TbJ+fPnNWzYMC1cuNB+J0yr1ap7771Xb7/9tnx9fU2uEHBdiYmJOnr0qMOzh+EaPvvsMw0bNkz33HOPPvroI23fvl2NGzfWm2++qa+++kpfffWV2SVWeF988YXi4uJ09913O+ycfvLJJ1q4cOEVv3SEsa70aDce52auLVu2aOjQoTp06JDGjh2r5557TpI0atQonT59WvPnzze5Qrz99tt66aWXdOTIEUkX7pEwadIk3XvvvSZXVrHNnTv3iuMrys2jCd0m27NnjzZv3iw/Pz9FR0fbrykGcHm33367Vq1apRo1aqhly5bFvjVNSkoyqTLccMMNGjNmjO69914FBQVpy5Ytaty4sX788Uf17dtXx44dM7tESPryyy81ZcoU++dPq1at9Nxzz3GpAOCkc+fOydvbm0eJuZCTJ0/Kz89PgYGBZpcC2LGFMMnzzz+vcePGqWnTpmratKl9eF5env71r38VO+0PZeNqj5xyip+5qlatqri4OLPLQAl27dplv2TmYlWqVNHZs2fLviCUqH///urfv7/ZZeAyzp8/r/3796tJkyaEORfRuHFjff/998Xuu3Pu3Dm1adOGsxBMdtNNNykpKUlVq1Z1ePJMVlaWYmNjtWrVKhOrw759+zR79mzt27dPr732mkJCQrRs2TI1bNhQLVu2NLu8MsGRbpN4eXnp6NGjCgkJcRh++vRphYSEcM2wSS49te/XX39VnTp1HHZ6OMUPuLzGjRvrvffe0y233OJwpHvevHl6+eWXtX37drNLrPAuFx7Onj1LeDBZbm6uRo0aZT8dc/fu3WrcuLFGjRqlevXqafz48SZXWHF5enra7/tysePHj6tBgwY6f/68SZVBunz/nDhxQvXq1ZPFYjGpMqSmpqpv377q0qWL1q5dqx07dqhx48Z6+eWXtWnTJi1atMjsEssEX5+apOiZtZfasmWLqlevbkJFkC7cEOViQUFBSk1N5c6XLqigoEBr1qzRvn37dPfddysoKEhHjhxRcHAwp5SZ6MEHH9Sjjz6qDz/8UB4eHjpy5Ig2bNigcePG6ZlnnjG7PEg6cOBAiV/s5ufn6/DhwyZUhCITJkzQli1btGbNGt1666324bfccosmTZpE6DbBxU8z+frrrx0eS1VYWKhvvvnmitfiw1g//fST/f/bt293uISpsLBQy5cvV7169cwoDf8zfvx4vfjiixo7dqyCgoLsw2+66Sa9+eabJlZWtgjdZaxatWry8PCQh4eHrrvuOofgXVhYqOzsbD388MMmVgi4voMHD+rWW2/VoUOHlJ+fr169eikoKEivvPKK8vPz9c4775hdYoU1fvx4Wa1W3XzzzcrNzVX37t3l6+urcePGadSoUWaXV6FdTXioyI9zcQXJyclasGCBOnXq5LB/0LJlS+3bt8/Eyiqui28seOkNn3x8fBQWFsbz7U3UunVr+3510Y0hL+bn56c33njDhMpQZOvWrSXeaDAkJKRCPW6P0F3GZs6cKZvNpgceeECTJ0922OmpVKmSwsLC1LlzZxMrBFzfo48+qnbt2hV7rv3tt9+uBx980MTK4OHhoaeeekqPP/649u7dq+zsbLVo0YKzD1xAUXjw8PAgPLiokydPFjs9VpJycnJKPDsOxit6Nnd4eLg2bdpU7LIMmGv//v2y2Wxq3LixNm7c6HA9d6VKlRQSEiIvLy8TK0TVqlV19OjRYmeE/PjjjxXqLARCdxkr2tEJDw/XjTfeWOyuywD+XFpamtavX69KlSo5DA8LC+P0WBdRqVIlnvnsYi4OD99//71q1qxpckW4VLt27fTll1/azwopCtr//ve/+ULeRBaLRY0bN9aZM2cI3S6m6Kk/Rds3uJ677rpLTz75pBYuXCgPDw9ZrVZ9++23GjduXIV6nBuh2yQXP5bl3LlzxW7AERwcXNYlQRfucnkxDw8PZWdnFxtO/5jLarWWeE3qb7/95nC9EMpGXFyc5syZo+Dg4D+9qzyPczPfpfeugOuYMmWK+vbtq+3bt6ugoECvvfaatm/frvXr1ys1NdXs8iosHx8fh2uH4RqWLFmivn37ysfHx+HymZIMHDiwjKrCpaZMmaL4+Hg1aNBAhYWFatGihQoLC3X33Xfr6aefNru8MsPdy02Sm5urJ554Qv/97391+vTpYuO5e7k5PD09HU7hu/SGd0Wv6R9z3XnnnapSpYree+89BQUF6aefflKtWrU0aNAgNWzYULNnzza7xArl/vvv1+uvv66goCDdd999VzwNlr4xx+uvv66HHnpIlStX/tNHI/JIRHPt27dPL7/8srZs2aLs7Gy1adNGTz75pKKjo80urUIbM2aMfH199fLLL5tdCv7n4juWe3p6XnY69ttcw6+//qqtW7cqOztbN9xwg8MjkysCQrdJ4uPjtXr1ar3wwgsaNmyYZs2apcOHD+vdd9/Vyy+/rHvuucfsEiukqz2ScPGZCih7v/32m/r06SObzaY9e/aoXbt22rNnj2rWrKm1a9eWeE0kjHPx0Qa4pouvR73SnZZ5JCJQslGjRmnevHlq2rSp2rZtq4CAAIfxM2bMMKkyoHwpLCzU1q1b1ahRI1WrVs3scsoModskDRs21Lx589SjRw8FBwfrhx9+UEREhD766CN98skn+uqrr8wuEVfh5Zdf1sMPP6yqVauaXUqFU1BQoAULFjgcDbrnnnvk5+dndmkVjpeXl44dO6ZatWrJy8tLR48e5YsPoBR++OEH+fj42I9qL168WLNnz1aLFi00adKkYvexQNnp2bPnZcd5eHho1apVZVgNLnbgwAGlpKTIYrEoJiZGLVu2NLskXOSxxx5TdHS0RowYocLCQsXExGj9+vXy9/fX0qVL1aNHD7NLLBOEbpMEBgZq+/btatiwoerXr6+kpCR16NBB+/fvV3R0tLKzs80uEVchODhYmzdv5jneqNBCQ0P1/vvv67bbbpOnp6eOHz/ucAdZuI709HR98cUXslgsuummmxyeBQ3ztW/fXuPHj9fgwYP1yy+/qEWLFoqLi9P333+v/v37a+bMmWaXCLiU1atXa8CAAcrLy5MkeXt768MPP9TQoUNNrgxF6tevr+TkZLVr107Jycn65z//qTVr1uijjz7SqlWr9O2335pdYpm4/AUQMFTjxo3tN7OJjIzUf//7X0nSF198wVHTcoTvrMwxd+5cffnll/bXTzzxhKpWraobb7xRBw8eNLGyiunhhx/WoEGD5OXlJQ8PD4WGhsrLy6vEfzDPokWL1KVLF7322mt6//331b9/f02bNs3ssnCR3bt3q3Xr1pKkhQsXKiYmRvPnz9ecOXP02WefmVsc7H777Tf99ttvZpcBSc8884x69eqlw4cP6/Tp03rwwQf1xBNPmF0WLnLq1CmFhoZKkr766ivdcccduu666/TAAw9o69atJldXdgjdJrn//vu1ZcsWSdL48eM1a9YsVa5cWY899pgef/xxk6sDXNuUKVPsp5Fv2LBBb775pqZOnaqaNWtqzJgxJldX8UyaNEnbt2/X4sWLZbPZ9OGHHyopKanEfzBPYmKiHnzwQWVmZur333/Xiy++qClTpphdFi5is9nsjz5auXKl+vXrJ0lq0KCBTp06ZWZpFZ7VatXzzz+vKlWqqFGjRmrUqJGqVq2qF154gcdV/b/27jys5rz/H/jztGsvadGEkiUUkoy1QZYy0s1t3GMr21iGYZA9S8LgjsZtbpIlTMNYs0y2iWk0QkTxbSoRNciWUGlR5/dHd+fX0WHM4rxPnefjulxX5/05mWfXXD6d1+f9fr/eAl2/fh3Lly+HjY0NzMzMsHr1ajx8+FBhk2ISw8rKCikpKSgrK8Px48fRq1cvABVNpdXpYTyPDBOkamHg6emJ1NRUXL58GU2aNGGHUqLfkZ2dDUdHRwBAVFQU/vnPf+Kzzz5D586d1WZvkKpp3rw5mjdvjkWLFmHw4MHQ19cXHYlek5aWhu+//172IWfGjBlYuHAhHj58yD34KsLNzQ3BwcHw9PREbGwsNmzYAKDimDcrKyvB6dTb/PnzsWXLFnz11Vfo3LkzACAuLg6LFy9GUVERli1bJjihenr+/DksLCxkr/X19VGnTh08e/aMZ6qriFGjRuGTTz6BjY0NJBIJPD09AQAXLlxA8+bNBadTHhbdSnb69GlMnjwZ58+flzvrufKJaadOnbBx40Z07dpVYEoi1WZoaIgnT56gQYMGOHnyJKZPnw4A0NPTk+3rIjFiY2MxderUakX38+fP4evry2ZDAhUWFsr93tHR0YGenh7y8/NZdKuI0NBQDBs2DFFRUZg/f77s4eK+ffvQqVMnwenU2/bt27F582a5855dXFxga2uLSZMmsegW6MSJEzAxMZG9Li8vR0xMDK5fvy4b4znd4ixevBitWrVCdnY2Bg8eDF1dXQAVTVjnzJkjOJ3ysJGakvn4+KB79+5vXAK7bt06nDlzBgcPHlRyMvozjIyMkJSUxEZqSjZs2DCkpqaibdu22LVrF7KyslC3bl0cPnwY8+bNk/tFS8r1pu7lDx8+hK2tLUpLSwUlIw0NDQQHB8PQ0FA2Nnv2bAQEBMjNFPGcbtVTVFQETU1NHssnkJ6eHpKTk9G0aVO58bS0NLRp04YPfAV52/nclXhON6kCznQrWVJSElauXPnG671792Zjmxqka9euPKJKgG+++QYLFixAdnY29u/fL1tCdvnyZXz66aeC06mn5ORkABV7UlNSUpCTkyO7VrmPy9bWVlQ8QsVRleHh4XJj1tbW2Llzp+y1RCJh0a2C9PT0REdQe61bt8b69euxbt06ufH169ejdevWglIR99PXDAUFBYiNjUVWVhZKSkrkrqnL7xzOdCuZnp4erl+/Llsy9rqMjAw4OzvziakAz58/f+f3Vl2iSUQVsw0SiQSA4q7+derUwX/+8x+MHj1a2dGIaoyysjKsXbsWe/bsUfjhNDc3V1Ayio2NRb9+/dCgQQN07NgRQEUjz+zsbERHR3NbYA3Rr18/bN68GTY2NqKjqI0rV67A29sbhYWFKCgogLm5OR4/fgx9fX1YWlri1q1boiMqBWe6lczW1vatRXdycjJvBIKYmprKiobfw2VKqqGwsFDhB1MXFxdBidRXZmYmpFIpHBwccPHiRblzunV0dGBpaalWXUprA2dnZ0RHR8POzk50FLWxZMkSbN68GTNmzMCCBQswf/583L59G1FRUVi4cKHoeGrNw8MD6enp+Oabb5CamgoAGDhwICZNmoT69esLTkfv6ueff+bElpJ9+eWX6N+/PzZu3AgTExOcP38e2traGD58OKZOnSo6ntJwplvJpkyZgp9++gkJCQnVlou9fPkS7u7u6N69e7XlS/T+xcbGyr6+ffs25syZA39/f7kn2tu3b8eKFSvg5+cnKiYBePToEfz9/XH8+HGF1/lQhOivY88K5WvcuDHWrVuHfv36wcjICFevXpWNnT9/Ht99953oiEQ1Gu9rymdqaooLFy6gWbNmMDU1RXx8PJycnHDhwgX4+fnJHmLVdpzpVrIFCxbgwIEDaNq0KSZPnoxmzZoBAFJTU/HNN9+grKwM8+fPF5xSPXl4eMi+DgoKwpo1a+T2B/v4+MDZ2RmbNm1i0S3YtGnT8OzZM1y4cAEfffQRDh48iAcPHiA4OBghISGi46m1HTt2vPX6yJEjlZSEqObJycmRHRtqaGiIZ8+eAQA+/vhjBAYGioxGAPLy8nDx4kU8fPiw2l5i3tuIFNPW1pY1vLO0tERWVhacnJxgYmKC7OxswemUh0W3kllZWeHcuXOYOHEi5s6dK9v7KJFI0KdPH3zzzTc8i1MFxMfHY+PGjdXG3dzcMHbsWAGJqKrTp0/j0KFDcHNzg4aGBho2bIhevXrB2NgYK1asQL9+/URHVFuvLxUrLS1FYWEhdHR0oK+vzw+mRG/xwQcf4P79+2jQoAEaN26MkydPwtXVFQkJCbJjdkiMI0eOYNiwYcjPz4exsbHcdjSJRMJ7G9EbtG3bFgkJCWjSpAk8PDywcOFCPH78GDt37kSrVq1Ex1Oa3++zT3+7hg0bIjo6Go8fP8aFCxdw/vx5PH78GNHR0bC3txcdjwDY2dlV6/ILAJs3b+b+RhVQUFAgO5LKzMwMjx49AlCxBzUxMVFkNLX39OlTuT/5+flIS0tDly5dsGvXLtHxiFTaP/7xD8TExACo2I4WGBiIJk2aYOTIkWxCKNiMGTMwevRo5OfnIy8vT+4+xwZ3RG+2fPlyWb+qZcuWwczMDBMnTsSjR4+wadMmwemUh3u6iRSIjo7GoEGD4OjoiA4dOgAALl68iBs3bmD//v3w9vYWnFC9tW/fHsHBwejTpw98fHxgamqKFStWYN26ddi3bx9u3rwpOiK95tKlSxg+fLja7N2qDbj3Ubz4+HjEx8ejSZMm6N+/v+g4as3AwADXrl3jv4cajvc1EoXLy4kU8Pb2Rnp6OjZs2CArEvr3748JEyZwplsFTJ06Fffv3wcALFq0CH379kVkZCR0dHQQEREhNhwppKWlhXv37omOQVSjdOzYUdbMk8Tq06cPLl26xGKthps3bx7Mzc1Fx1BrJSUlKCkpgaGhoegoSsWZbiKq8QoLC5GamooGDRrAwsJCdBy1dvjwYbnXUqkU9+/fx/r162FnZ4djx44JSqbezM3NkZ6eDgsLC4wePRpff/01jIyM3vo93333HQYMGAADAwMlpaQnT56gbt26AIDs7GyEh4fj5cuX8PHx4TnQAlS9nz169AhBQUEYNWoUnJ2doa2tLfdeHx8fZcejKl7/3VNJIpFAT08Pjo6O3MIpwLZt25CYmIgPP/wQw4YNw9y5c7FmzRq8evUKPXr0wO7du2X3vNqORTfRG5w9exZhYWG4desW9u7dC1tbW+zcuRP29vbo0qWL6HhEKqmyQ2kliUSCevXqoUePHggJCZHt6yLlMjQ0RHJyMhwcHKCpqYmcnBy5s9RJrGvXrqF///7Izs5GkyZNsHv3bvTt2xcFBQXQ0NBAQUEB9u3bB19fX9FR1crr97M3kUgkPKpSMA0NDUgkErxe1lSOSSQSdOnSBVFRUTAzMxOUUr0sW7YMy5YtQ+fOnZGYmIhPPvkEUVFRmDZtGjQ0NLBu3Tp8/PHH2LBhg+ioSsGim0iB/fv3Y8SIERg2bBh27tyJlJQUODg4YP369YiOjkZ0dLToiGrrxo0bSE5OhqurK+zt7fHDDz9g5cqVePnyJXx9fTFv3jy5rrIkRmVzOxZ2qqFXr1548OAB2rVrh+3bt2PIkCGoU6eOwvdu3bpVyenIy8sLWlpamDNnDnbu3ImjR4+iT58+soaeU6ZMweXLl3H+/HnBSYlUU0xMDObPn49ly5bB3d0dQEUvnsDAQCxYsAAmJiYYP348OnTogC1btghOqx6aNGmCoKAgfPrpp7h06RI6dOiAPXv2YNCgQQCAY8eOYcKECbhz547gpMrB7uVECgQHB2Pjxo0IDw+XW0JW+bSOxDh48CBatGiBoUOHwsnJCTt27MA///lPGBgYwMrKCosXL8aqVatEx1RbeXl5+Pzzz2FhYQFra2tYW1vDwsICkydPRl5enuh4au3bb7+Ft7c38vPzIZFI8OzZs2qd5iv/kPIlJCTIZoT+/e9/4969e5g0aRI0NDSgoaGBKVOmsAmhIPHx8Th69Kjc2I4dO2Bvbw9LS0t89tlnKC4uFpSOKk2dOhVr1qxBz549YWRkBCMjI/Ts2ROrV69GQEAAOnfujNDQUJw6dUp0VLWRlZUlWxnq5uYGLS0tuSPCXFxcZP151AEbqREpkJaWhm7dulUbNzExYfEg0LJlyzBr1iwEBwcjIiICEyZMwIoVKzBt2jQAwKZNm7B27VrMnj1bbFA1lJubi44dO+Lu3bsYNmwYnJycAAApKSmIiIhATEwMzp07x2V9glhZWeGrr74CANjb22Pnzp1qs4+uJsjNzYW1tTWAiq0ABgYGcv9WzMzM8OLFC1Hx1NqSJUvQvXt3fPzxxwAqtgKMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1d/PmTRgbG1cbNzY2xq1btwBUzLw+fvxY2dHUVmlpKXR1dWWvdXR05CaytLS01GpbBme6iRSwtrZGRkZGtfG4uDh2LhUoLS0No0ePhkQigZ+fH0pKSuDp6Sm73rt3b7VZpqRqgoKCoKOjg5s3byIsLAzTpk3DtGnTsGnTJmRkZEBbWxtBQUGiYxKAzMxMWcFdVFQkOA1Ven1bDLfJqIakpCT07NlT9nr37t3o0KEDwsPDMX36dKxbtw579uwRmJAAoF27dggICJBtbQIqtjnNmjUL7du3B1CxPY0n0ChXSkoKkpOTkZycDKlUitTUVNnr//u//xMdT6k4002kwLhx4zB16lRs3boVEokE9+7dQ3x8PGbOnInAwEDR8dRWQUGBrOOyhoYG6tSpA319fdn1OnXqcJmfIFFRUQgLC4OVlVW1a9bW1li1ahUmTJiAtWvXCkhHVZWXl2PZsmXYuHEjHjx4gPT0dDg4OCAwMBCNGjXCmDFjREdUS/7+/rJZoaKiIkyYMEHWOZ73NXGePn0qd1+LjY2Fl5eX7HX79u2RnZ0tIhpVsWXLFgwYMAAffPCBrLDOzs6Gg4MDDh06BADIz8/HggULRMZUOz179pRrble5YqRqgzt1waKbSIE5c+agvLwcPXv2RGFhIbp16wZdXV3MnDkTU6ZMER1PbUkkErkb9OuvSZz79++jZcuWb7zeqlUr5OTkKDERvUlwcDC2b9+OVatWYdy4cbLxVq1aITQ0lEW3AH5+fnKvhw8fXu09I0eOVFYcqsLKygqZmZmws7NDSUkJEhMTsWTJEtn1Fy9eVDs+jJSvWbNmSElJwcmTJ5Geni4b69Wrl6wLPbv/K1dmZqboCCqF3cuJ3qKkpAQZGRnIz89HixYtYGhoKDqSWtPQ0ICJiYms0M7Ly4OxsbHsF6pUKsXz58/Vao+QqrC1tcX333//xuP0zp49iyFDhuDevXtKTkavc3R0RFhYmKzhUFJSEhwcHJCamoqOHTuymVoN8Ntvv6F+/frvfKQV/XkTJ05EUlISVq5ciaioKGzfvh337t2Djo4OACAyMhKhoaFISEgQnJSoZps0aRKCgoJgYWEhOsp7wZluIgVGjx6Nr7/+GkZGRmjRooVsvKCgAFOmTOGROoJs27ZNdAR6gz59+mD+/Pk4deqU7MNopeLiYgQGBqJv376C0lFVd+/ehaOjY7Xx8vJylJaWCkhEf1SLFi1w9epV9hhRgqVLl2LgwIHw8PCAoaEhtm/fLneP27p1K3r37i0wIVWKiYlBTEwMHj58iPLycrlr/Nym+r799lvMnDmz1hbdnOkmUkBTUxP379+HpaWl3Pjjx49hbW2NV69eCUpGf8SuXbvg4+Mj2xdJ789vv/0GNzc36Orq4vPPP0fz5s0hlUrx66+/4r///S+Ki4tx6dIlNrFRAe3atcOXX36J4cOHy810BwUF4dSpUzh79qzoiPQ7qv5/I+V49uwZDA0NoampKTeem5sLQ0PDag8bSbmWLFmCoKAguLm5wcbGptrWs4MHDwpKRu+qtt/XONNNVMXz588hlUohlUrx4sUL6Onpya6VlZUhOjq6WiFOqmv8+PHo0KFDrb2Bq5IPPvgA8fHxmDRpEubOnStrnCKRSNCrVy+sX7+eBbeKWLhwIfz8/HD37l2Ul5fjwIEDSEtLw44dO6qdR0xEFUxMTBSOm5ubKzkJKbJx40ZERERgxIgRoqMQKcSim6gKU1NTWXOupk2bVrsukUjkGqiQauNCHuWyt7fHsWPH8PTpU9y4cQNAxf5hfihVLQMGDMCRI0cQFBQEAwMDLFy4EK6urjhy5Ah69eolOh4R0R9WUlKCTp06iY5B9EYsuomqOHPmDKRSKXr06IH9+/fLFQs6Ojpo2LAh6tevLzAhkeozMzODu7u76Bj0Fl27dsWpU6dExyAi+luMHTsW3333HY91JZXFopuoCg8PDwCQHQ/CzrBERKRqeFQikbyioiJs2rQJP/74I1xcXKod47ZmzRpByYgqsOgmUqBhw4YAgMLCQmRlZaGkpETuuouLi4hYRER/irm5OdLT02FhYQEzM7O3Fm25ublKTEZ/BrfOEMlLTk5GmzZtAADXr1+Xu8aHVDXD8OHDYWxsLDrGe8Oim0iBR48eYdSoUTh27JjC6zwHmohqkrVr18LIyAgAEBoaKjYM/WUpKSnc6kRUxZkzZ0RHoCqSk5Pf+b2VE1kbNmx4X3FUAo8MI1Jg2LBhuHPnDkJDQ/HRRx/h4MGDePDgAYKDgxESEoJ+/fqJjkjvoFWrVjh27Bi7ZhORyho4cOA7v/fAgQPvMQkR0d9DQ0MDEokEUqn0d1caqMtEFme6iRQ4ffo0Dh06BDc3N2hoaKBhw4bo1asXjI2NsWLFChbdgjk4OCAhIQF169aVG8/Ly4Orqytu3boFoPoSMyJ19fz583d+b21e3qeKqh5FJZVKcfDgQZiYmMDNzQ0AcPnyZeTl5f2h4pxIHQwcOBAREREwNjb+3X8ffGClXJmZmbKvr1y5gpkzZyIgIAAdO3YEAMTHxyMkJASrVq0SFVHpWHQTKVBQUCA7j9vMzAyPHj1C06ZN4ezsjMTERMHp6Pbt2wqfjBYXF+Pu3bsCEhGptsrjEN+mckZCXWYdVMW2bdtkX8+ePRuffPIJNm7cCE1NTQAVs0CTJk3iwxCi15iYmMjua8bGxty7rUIqeyMBwODBg7Fu3Tp4e3vLxlxcXGBnZ4fAwED4+voKSKh8LLqJFGjWrBnS0tLQqFEjtG7dGmFhYWjUqBE2btwIGxsb0fHU1uHDh2VfnzhxQm6GqKysDDExMWjUqJGAZESqjfsda4atW7ciLi5OVnADgKamJqZPn45OnTph9erVAtMRqZaqD6wiIiLEBaG3unbtGuzt7auN29vbIyUlRUAiMVh0EykwdepU3L9/HwCwaNEi9O3bF5GRkdDR0eGNXaDKp6ESiQR+fn5y17S1tdGoUSOEhIQISEak2iqPQyTV9urVK6SmpqJZs2Zy46mpqSgvLxeUikj19ejRAwcOHICpqanc+PPnz+Hr64vTp0+LCUZwcnLCihUrsHnzZujo6AAASkpKsGLFCjg5OQlOpzxspEb0DgoLC5GamooGDRrAwsJCdBy1Z29vj4SEBP6/IPqTzp49i7CwMNy6dQt79+6Fra0tdu7cCXt7e3Tp0kV0PLU1ffp07NixA/PmzYO7uzsA4MKFC/jqq68wYsQInjVM9AYaGhrIycmRbQ2s9PDhQ9ja2qK0tFRQMrp48SL69+8PqVQq61SenJwMiUSCI0eOyO51tR1nuonegb6+PlxdXUXHoP+p2qCjUl5eXrUn3ERU3f79+zFixAgMGzYMiYmJKC4uBgA8e/YMy5cvR3R0tOCE6uvf//43rK2tERISIlttZWNjg4CAAMyYMUNwOiLVU/VoqpSUFOTk5Mhel5WV4fjx47C1tRURjf7H3d0dt27dQmRkJFJTUwEAQ4YMwdChQ2FgYCA4nfJwpptIgbKyMkRERCAmJgYPHz6stqyPy5TEWrlyJRo1aoQhQ4YAqGjSsX//ftjY2CA6OhqtW7cWnJBIdbVt2xZffvklRo4cCSMjIyQlJcHBwQFXrlyBl5eX3IdWEqey4zwbqBG9WeXRVEBFM8jX1alTB//5z38wevRoZUcjAKWlpWjevDmOHj2qVkvJFeFMN5ECU6dORUREBPr164dWrVqxI6aK2bhxIyIjIwEAp06dwo8//ojjx49jz549CAgIwMmTJwUnJFJdaWlp6NatW7VxExMT5OXlKT8QKcRim+j3ZWZmQiqVwsHBARcvXkS9evVk13R0dGBpaSnXmJCUS1tbG0VFRaJjqAQW3UQK7N69G3v27JE73oBUR05ODuzs7AAAR48exSeffILevXujUaNG6NChg+B0RKrN2toaGRkZ1Tr9x8XFwcHBQUwoAgA8ePAAM2fOlK2yen3mjse5Eclr2LAhSktL4efnh7p168odVUWq4fPPP8fKlSuxefNmaGmpb+mpvj850Vvo6OjA0dFRdAx6AzMzM2RnZ8POzg7Hjx9HcHAwgIqlZfxQSvR248aNw9SpU7F161ZIJBLcu3cP8fHxmDFjBhYuXCg6nlrz9/dHVlYWAgMDYWNjw1VWRO9AW1sbBw8e5P1LRSUkJCAmJgYnT56Es7NztX3cBw4cEJRMuVh0EykwY8YMfP3111i/fj0/9KiggQMHYujQoWjSpAmePHkCLy8vAMCVK1f4sITod8yZMwfl5eXo2bMnCgsL0a1bN+jq6iIgIABjx44VHU+txcXF4ezZs2jTpo3oKEQ1yoABAxAVFYUvv/xSdBR6jampKQYNGiQ6hnAsuokUiIuLw5kzZ3Ds2DG0bNkS2tractfV5amcqlq7di3s7e2RlZWFVatWwdDQEABw//59TJo0SXA6ItUmkUgwf/58BAQEICMjA/n5+WjRogXCwsJgb2/PRmoC2dnZKWwGRURv16RJEwQFBeGXX35Bu3btqs2mfvHFF4KS0bZt20RHUAnsXk6kwKhRo956nTcQcUpLSzF+/HgEBgbC3t5edByiGqO4uBiLFy/GqVOnZDPbvr6+2LZtGxYsWABNTU18/vnnmD17tuioauvkyZMICQlBWFhYtT33RPRmb/s8IJFIcOvWLSWmIaqORTcR1TgmJia4evUqi26iP2D27NkICwuDp6cnzp07h0ePHmHUqFE4f/485s2bh8GDB7PLr2BmZmYoLCzEq1evoK+vX22VVW5urqBkRETvztXVFTExMTAzM0Pbtm3fulUzMTFRicnE4fJyIqpxfH19uXeL6A/au3cvduzYAR8fH1y/fh0uLi549eoVkpKS2LtCRYSGhoqOQET0lw0YMAC6uroAKj6zEWe6iRR601M5iUQCPT09ODo6wt/fH927dxeQjoKDgxESEoKePXty7xbRO9LR0UFmZiZsbW0BAHXq1MHFixfh7OwsOBkR0V/322+/4fDhw8jKykJJSYnctTVr1ghKRVSBRTeRAnPnzsWGDRvg7OwMd3d3ABVHHiQnJ8Pf3x8pKSmIiYnBgQMHMGDAAMFp1Q/3bhH9cZqamsjJyUG9evUAAEZGRkhOTuY2DRVTVlaGqKgo/PrrrwCAli1bwsfHh0v/id4iJiYGPj4+cHBwQGpqKlq1aoXbt29DKpXC1dUVp0+fFh1R7ZWUlODhw4coLy+XG2/QoIGgRMrFoptIgXHjxqFBgwYIDAyUGw8ODsadO3cQHh6ORYsW4YcffsClS5cEpSQiencaGhrw8vKSLfk7cuQIevToobZnpqqijIwMeHt74+7du2jWrBkAIC0tDXZ2dvjhhx/QuHFjwQmJVJO7uzu8vLywZMkSGBkZISkpCZaWlhg2bBj69u2LiRMnio6ottLT0zFmzBicO3dOblwqlUIikaCsrExQMuVi0U2kgImJCS5fvlztzOeMjAy0a9cOz549Q2pqKtq3b48XL14ISklE9O5+71SGSjydQRxvb29IpVJERkbC3NwcAPDkyRMMHz4cGhoa+OGHHwQnJFJNRkZGuHr1Kho3bgwzMzPExcWhZcuWSEpKwoABA3D79m3REdVW586doaWlhTlz5sDGxqba9s3WrVsLSqZcbKRGpICenh7OnTtXreg+d+4c9PT0AADl5eWyr+n9mz59OpYuXQoDAwNMnz79re/l3i2i6lhMq77Y2FicP39eVnADQN26dfHVV1+hc+fOApMRqTYDAwPZPm4bGxvcvHkTLVu2BAA8fvxYZDS1d/XqVVy+fBnNmzcXHUUoFt1ECkyZMgUTJkzA5cuX0b59ewAVe7o3b96MefPmAQBOnDiBNm3aCEypXq5cuYLU1FS0bdsWV65ceeP72IWZiGoqXV1dhaun8vPzoaOjIyARUc3w4YcfIi4uDk5OTvD29saMGTNw7do1HDhwAB9++KHoeGqtRYsWfPABLi8neqPIyEisX78eaWlpAIBmzZphypQpGDp0KADg5cuXsm7mpByampq4f/8+LC0tAQBDhgzBunXrYGVlJTgZEdFfN3LkSCQmJmLLli2yJp4XLlzAuHHj0K5dO0RERIgNSKSibt26hfz8fLi4uKCgoAAzZszAuXPn0KRJE6xZswYNGzYUHVGtPH/+XPb1pUuXsGDBAixfvhzOzs7Q1taWe6+xsbGy4wnBopuIagwNDQ3k5OTIim5jY2NcvXoVDg4OgpMREf11eXl58PPzw5EjR2QfTF+9egUfHx9ERETAxMREcEIiot+noaEht/KwsmlaVerWSI3Ly4moxuIzQyKqTUxNTXHo0CFkZGTIjgxzcnKq1l+EiOQ5ODggISEBdevWlRvPy8uDq6srjxJVsjNnzoiOoHJYdBP9j7m5OdLT02FhYQEzM7O37g3Ozc1VYjKqJJFIqv1/4R5uIqptHB0dWWgT/QG3b99WOGNaXFyMu3fvCkik3jw8PBAUFISZM2dCX19fdByVwKKb6H/Wrl0LIyMj2dcs5lSPVCqFv7+/7JzhoqIiTJgwgecME1GtMGjQILi7u2P27Nly46tWrUJCQgL27t0rKBmRajp8+LDs6xMnTshtwSgrK0NMTAwaNWokIBktWbIEEyZMYNH9P9zTTUQ1Bs8ZJqLarF69ejh9+jScnZ3lxq9duwZPT088ePBAUDIi1aShoQGgYtXb6yWNtrY2GjVqhJCQEHz88cci4qm11/vwqDvOdBMpkJiYCG1tbdkHn0OHDmHbtm1o0aIFFi9ezKNbBGExTUS12ZuOBtPW1pbrBkxEFcrLywEA9vb2SEhIgIWFheBEVBVXjf5/GqIDEKmi8ePHIz09HUDFMRRDhgyBvr4+9u7di1mzZglOR0REtZGzszO+//77auO7d+9GixYtBCQiUm3x8fE4evQoMjMzZQX3jh07YG9vD0tLS3z22WcoLi4WnFJ9NW3aFObm5m/9oy44002kQHp6Otq0aQMA2Lt3Lzw8PPDdd9/hl19+wb/+9S+EhoYKzUdERLVPYGAgBg4ciJs3b6JHjx4AgJiYGOzatYv7uYkUWLJkCbp37y5bPn7t2jWMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1tWTJEh51+D8suokUkEqlsiVLP/74o+xmbmdnh8ePH4uMRkREtVT//v0RFRWF5cuXY9++fahTpw5cXFzw448/wsPDQ3Q8IpWTlJSE4OBg2evdu3ejQ4cOCA8PB1DxuW3RokUsugX517/+xT3d/8Oim0gBNzc3BAcHw9PTE7GxsdiwYQMAIDMzE1ZWVoLTERFRbdWvXz/069dPdAyiGuHp06dyn8tiY2Ph5eUle92+fXtkZ2eLiKb2uJ9bHvd0EykQGhqKxMRETJ48GfPnz5edl7pv3z506tRJcDoiIqqt8vLysHnzZsybNw+5ubkAKpp78qxhouqsrKyQmZkJACgpKUFiYiI+/PBD2fUXL15AW1tbVDy1xgOy5PHIMKI/oKioCJqamryBExHR3y45ORmenp4wMTHB7du3kZaWBgcHByxYsABZWVnYsWOH6IhEKmXixIlISkrCypUrERUVhe3bt+PevXuyUwAiIyMRGhqKhIQEwUlJ3XGmm+gNKmcb5s6dK5ttSElJwcOHDwUnIyKi2mj69Onw9/fHjRs3oKenJxv39vbGzz//LDAZkWpaunQptLS04OHhgfDwcISHh8sdu7d161b07t1bYEKiCpzpJlIgOTkZPXv2hKmpKWcbiIhIKUxMTJCYmIjGjRvDyMgISUlJcHBwwJ07d9CsWTMUFRWJjkikkp49ewZDQ0NoamrKjefm5sLQ0FCuECcSgTPdRApMnz4do0aN4mwDEREpja6uLp4/f15tPD09HfXq1ROQiKhmMDExqVZwA4C5uTkLblIJLLqJFEhISMD48eOrjdva2iInJ0dAIiIiqu18fHwQFBSE0tJSABXdf7OysjB79mwMGjRIcDoiIvqzWHQTKcDZBiIiUraQkBDk5+fD0tISL1++hIeHBxo3bgxDQ0MsW7ZMdDwiIvqTuKebSIGxY8fiyZMn2LNnD8zNzZGcnAxNTU34+vqiW7duCA0NFR2RiIhqqbi4OCQnJyM/Px/t2rVDz549RUciIqK/gDPdRApUzjbUq1dPNtvg6OgIIyMjzjYQEdHfKj4+HkePHpW97tKlCwwMDPDf//4Xn376KT777DMUFxcLTEhERH8FZ7qJ3uKXX35BUlIS8vPz4erqCk9PT9GRiIiolvHy8sJHH32E2bNnAwCuXbuGdu3awc/PD05OTli9ejXGjx+PxYsXiw1KRER/ipboAESqpry8HBEREThw4ABu374NiUQCe3t7WFtbQyqVQiKRiI5IRES1yNWrV7F06VLZ6927d8Pd3R3h4eEAADs7OyxatIhFNxFRDcXl5URVSKVS+Pj4YOzYsbh79y6cnZ3RsmVL3LlzB/7+/vjHP/4hOiIREdUyT58+hZWVlex1bGwsvLy8ZK/bt2+P7OxsEdGIiOhvwJluoioiIiLw888/IyYmBt27d5e7dvr0afj6+mLHjh0YOXKkoIRERFTbWFlZITMzE3Z2digpKUFiYiKWLFkiu/7ixQtoa2sLTEhERH8FZ7qJqti1axfmzZtXreAGgB49emDOnDmIjIwUkIyIiGorb29vzJkzB2fPnsXcuXOhr6+Prl27yq4nJyejcePGAhMSEdFfwaKbqIrk5GT07dv3jde9vLyQlJSkxERERFTbLV26FFpaWvDw8EB4eDjCw8Oho6Mju75161b07t1bYEIiIvor2L2cqAodHR3cuXMHNjY2Cq/fu3cP9vb2PLqFiIj+ds+ePYOhoSE0NTXlxnNzc2FoaChXiBMRUc3BPd1EVZSVlUFL683/LDQ1NfHq1SslJiIiInVhYmKicNzc3FzJSYiI6O/EopuoCqlUCn9/f+jq6iq8zhluIiIiIiL6I1h0E1Xh5+f3u+9h53IiIiIiInpX3NNNRERERERE9J6wezkRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREX766SdIJBLk5eWJjkJERFSrsOgmIiKqBfz9/SGRSCCRSKCtrQ17e3vMmjULRUVFoqMRERGpNZ7TTUREVEv07dsX27ZtQ2lpKS5fvgw/Pz9IJBKsXLlSdDQiIiK1xZluIiKiWkJXVxfW1taws7ODr68vPD09cerUKQBAcXExvvjiC1haWkJPTw9dunRBQkLCW/++uLg4dO3aFXXq1IGdnR2++OILFBQUKONHISIiqjVYdBMREdVC169fx7lz56CjowMAmDVrFvbv34/t27cjMTERjo6O6NOnD3JzcxV+/82bN9G3b18MGjQIycnJ+P777xEXF4fJkycr88cgIiKq8SRSqVQqOgQRERH9Nf7+/vj222+hp6eHV69eobi4GBoaGtizZw/69u0LMzMzREREYOjQoQCA0tJSNGrUCNOmTUNAQAB++ukndO/eHU+fPoWpqSnGjh0LTU1NhIWFyf4bcXFx8PDwQEFBAfT09ET9qERERDUK93QTERHVEt27d8eGDRtQUFCAtWvXQktLSzZTXVpais6dO8veq62tDXd3d/z6668K/66kpCQkJycjMjJSNiaVSlFeXo7MzEw4OTm995+HiIioNmDRTUREVEsYGBjA0dERALB161a0bt0aW7ZsQfv27f/w35Wfn4/x48fjiy++qHatQYMGfzkrERGRumDRTUREVAtpaGhg3rx5mD59OjIyMqCjo4NffvkFDRs2BFCxvDwhIQHTpk1T+P2urq5ISUmRFfFERET057CRGhERUS01ePBgaGpqYsOGDZg4cSICAgJw/PhxpKSkYNy4cSgsLMSYMWMUfu/s2bNx7tw5TJ48GVevXsWNGzdw6NAhNlIjIiL6gzjTTUREVEtpaWlh8uTJWLVqFTIzM1FeXo4RI0bgxYsXcHNzw4kTJ2BmZqbwe11cXBAbG4v58+eja9eukEqlaNy4MYYMGaLkn4KIiKhmY/dyIiIiIiIioveEy8uJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREREREb0nLLqJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe/L/AJ/aJD9mHqDtAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Nota**: Este diagrama sugiere que, en promedio, las alturas de los primera base son mayores que las alturas de los segunda base. Más adelante aprenderemos cómo podemos probar esta hipótesis de manera más formal y cómo demostrar que nuestros datos son estadísticamente significativos para respaldar esto.\n",
+    "> **Nota**: Este diagrama sugiere que, en promedio, las alturas de los primera base son mayores que las alturas de los segunda base. Más adelante aprenderemos cómo podemos probar esta hipótesis de manera más formal y cómo demostrar que nuestros datos son estadísticamente significativos para respaldar esta afirmación.\n",
     "\n",
-    "La edad, la altura y el peso son todas variables aleatorias continuas. ¿Qué piensas sobre su distribución? Una buena manera de averiguarlo es graficar el histograma de los valores:\n"
+    "La edad, la altura y el peso son todas variables aleatorias continuas. ¿Qué crees que podría ser su distribución? Una buena manera de averiguarlo es trazando el histograma de los valores:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,9 +619,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Los dos valores devueltos por la función `ttest_ind` son:  \n",
-    "* El p-valor puede considerarse como la probabilidad de que dos distribuciones tengan la misma media. En nuestro caso, es muy bajo, lo que significa que hay una fuerte evidencia que respalda que los primera base son más altos.  \n",
-    "* El t-valor es el valor intermedio de la diferencia de medias normalizada que se utiliza en la prueba t, y se compara con un valor umbral para un nivel de confianza dado.  \n"
+    "Los dos valores que devuelve la función `ttest_ind` son:\n",
+    "* El p-value puede considerarse como la probabilidad de que dos distribuciones tengan la misma media. En nuestro caso, es muy bajo, lo que significa que hay una fuerte evidencia que respalda que los primera base son más altos.\n",
+    "* El t-value es el valor intermedio de la diferencia de medias normalizada que se utiliza en la prueba t, y se compara con un valor umbral para un nivel de confianza dado.\n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Simulando una Distribución Normal con el Teorema del Límite Central\n",
     "\n",
-    "El generador pseudoaleatorio en Python está diseñado para proporcionarnos una distribución uniforme. Si queremos crear un generador para una distribución normal, podemos usar el teorema del límite central. Para obtener un valor distribuido normalmente, simplemente calcularemos la media de una muestra generada uniformemente.\n"
+    "El generador pseudoaleatorio en Python está diseñado para proporcionarnos una distribución uniforme. Si queremos crear un generador para una distribución normal, podemos utilizar el teorema del límite central. Para obtener un valor con distribución normal, simplemente calcularemos la media de una muestra generada uniformemente.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Correlación y la Malvada Corporación de Béisbol\n",
     "\n",
-    "La correlación nos permite encontrar relaciones entre secuencias de datos. En nuestro ejemplo ficticio, imaginemos que existe una malvada corporación de béisbol que paga a sus jugadores según su altura: cuanto más alto es el jugador, más dinero recibe. Supongamos que hay un salario base de $1000 y un bono adicional de entre $0 y $100, dependiendo de la altura. Tomaremos a los jugadores reales de la MLB y calcularemos sus salarios imaginarios:\n"
+    "La correlación nos permite encontrar relaciones entre secuencias de datos. En nuestro ejemplo ficticio, imaginemos que existe una malvada corporación de béisbol que paga a sus jugadores según su altura: cuanto más alto sea el jugador, más dinero recibe. Supongamos que hay un salario base de $1000 y un bono adicional de entre $0 y $100, dependiendo de la altura. Tomaremos a los jugadores reales de la MLB y calcularemos sus salarios imaginarios:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -942,14 +779,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -994,17 +829,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1026,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Conclusión\n",
     "\n",
-    "En este cuaderno hemos aprendido cómo realizar operaciones básicas con datos para calcular funciones estadísticas. Ahora sabemos cómo utilizar un sólido conjunto de herramientas matemáticas y estadísticas para probar algunas hipótesis y cómo calcular intervalos de confianza para variables arbitrarias a partir de una muestra de datos.\n"
+    "En este cuaderno hemos aprendido cómo realizar operaciones básicas en datos para calcular funciones estadísticas. Ahora sabemos cómo utilizar un sólido conjunto de herramientas matemáticas y estadísticas para probar algunas hipótesis y cómo calcular intervalos de confianza para variables arbitrarias a partir de una muestra de datos.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Descargo de responsabilidad**:  \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por garantizar la precisión, tenga en cuenta que las traducciones automatizadas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n"
+    "\n---\n\n**Descargo de responsabilidad**:  \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Si bien nos esforzamos por lograr precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:03:14+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:01:36+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "es"
   }
diff --git a/translations/es/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/es/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index a499521f..d2511e78 100644
--- a/translations/es/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/es/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "En este conjunto de datos, las columnas son las siguientes:  \n",
-    "* Age y sex son autoexplicativas  \n",
-    "* BMI es el índice de masa corporal  \n",
-    "* BP es la presión arterial promedio  \n",
-    "* S1 a S6 son diferentes mediciones sanguíneas  \n",
-    "* Y es la medida cualitativa de la progresión de la enfermedad a lo largo de un año  \n",
+    "En este conjunto de datos, las columnas son las siguientes:\n",
+    "* Edad y sexo son autoexplicativos\n",
+    "* BMI es el índice de masa corporal\n",
+    "* BP es la presión arterial promedio\n",
+    "* S1 a S6 son diferentes mediciones de sangre\n",
+    "* Y es la medida cualitativa de la progresión de la enfermedad durante un año\n",
     "\n",
-    "Estudiemos este conjunto de datos utilizando métodos de probabilidad y estadística.  \n",
+    "Estudiemos este conjunto de datos utilizando métodos de probabilidad y estadística.\n",
     "\n",
-    "### Tarea 1: Calcular los valores medios y la varianza para todos los valores  \n"
+    "### Tarea 1: Calcular valores medios y varianza para todos los valores\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -591,9 +591,9 @@
    "cell_type": "markdown",
    "source": [
     "Conclusiones:\n",
-    "* Edad - normal\n",
-    "* Sexo - uniforme\n",
-    "* IMC, Y - difícil de determinar\n"
+    "* Edad - normal  \n",
+    "* Sexo - uniforme  \n",
+    "* IMC, Y - difícil de determinar  \n"
    ],
    "metadata": {}
   },
@@ -602,7 +602,7 @@
    "source": [
     "### Tarea 4: Prueba la correlación entre diferentes variables y la progresión de la enfermedad (Y)\n",
     "\n",
-    "> **Sugerencia** La matriz de correlación te proporcionará la información más útil sobre qué valores son dependientes.\n"
+    "> **Sugerencia** Una matriz de correlación te proporcionará la información más útil sobre qué valores son dependientes.\n"
    ],
    "metadata": {}
   },
@@ -845,7 +845,7 @@
    "cell_type": "markdown",
    "source": [
     "Conclusión:  \n",
-    "* La correlación más fuerte de Y es con el IMC y S5 (nivel de azúcar en sangre). Esto parece razonable.\n"
+    "* La correlación más fuerte de Y es con el IMC y S5 (azúcar en sangre). Esto suena razonable.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Descargo de responsabilidad**:  \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por garantizar la precisión, tenga en cuenta que las traducciones automatizadas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n"
+    "\n---\n\n**Descargo de responsabilidad**:  \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Si bien nos esforzamos por lograr precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:23:33+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:02:02+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "es"
   }
diff --git a/translations/fa/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/fa/1-Introduction/04-stats-and-probability/assignment.ipynb
index 53661ee5..e3533560 100644
--- a/translations/fa/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/fa/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "در این مجموعه داده، ستون‌ها به شرح زیر هستند:  \n",
-    "* سن و جنسیت نیازی به توضیح ندارند  \n",
-    "* BMI شاخص توده بدنی است  \n",
-    "* BP میانگین فشار خون است  \n",
-    "* S1 تا S6 اندازه‌گیری‌های مختلف خون هستند  \n",
-    "* Y معیار کیفی پیشرفت بیماری در طول یک سال است  \n",
+    "در این مجموعه داده، ستون‌ها به صورت زیر هستند:\n",
+    "* سن و جنسیت نیازی به توضیح ندارند\n",
+    "* BMI شاخص توده بدنی است\n",
+    "* BP میانگین فشار خون است\n",
+    "* S1 تا S6 اندازه‌گیری‌های مختلف خون هستند\n",
+    "* Y معیار کیفی پیشرفت بیماری در طول یک سال است\n",
     "\n",
     "بیایید این مجموعه داده را با استفاده از روش‌های احتمال و آمار بررسی کنیم.\n",
     "\n",
-    "### وظیفه ۱: محاسبه مقادیر میانگین و واریانس برای تمام مقادیر  \n"
+    "### وظیفه ۱: محاسبه میانگین و واریانس برای تمام مقادیر\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### وظیفه ۳: توزیع متغیرهای سن، جنسیت، شاخص توده بدنی و Y چگونه است؟\n"
+    "### وظیفه ۳: توزیع سن، جنسیت، شاخص توده بدنی و متغیر Y چگونه است؟\n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### وظیفه ۴: آزمایش همبستگی بین متغیرهای مختلف و پیشرفت بیماری (Y)\n",
     "\n",
-    "> **نکته** ماتریس همبستگی اطلاعات مفیدی در مورد اینکه کدام مقادیر وابسته هستند به شما ارائه می‌دهد.\n"
+    "> **نکته** ماتریس همبستگی اطلاعات مفیدی در مورد اینکه کدام مقادیر وابسته هستند به شما می‌دهد.\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### وظیفه ۵: فرضیه را آزمایش کنید که درجه پیشرفت دیابت بین مردان و زنان متفاوت است\n"
+    "### وظیفه ۵: فرضیه را آزمایش کنید که میزان پیشرفت دیابت بین مردان و زنان متفاوت است\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**سلب مسئولیت**:  \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما تلاش می‌کنیم دقت را حفظ کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادرستی‌ها باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، توصیه می‌شود از ترجمه حرفه‌ای انسانی استفاده کنید. ما مسئولیتی در قبال سوء تفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n"
+    "\n---\n\n**سلب مسئولیت**:  \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما برای دقت تلاش می‌کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادرستی‌هایی باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما هیچ مسئولیتی در قبال سوءتفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:18:18+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:07:15+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "fa"
   }
diff --git a/translations/fa/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/fa/1-Introduction/04-stats-and-probability/notebook.ipynb
index a26a9fed..d9787ed2 100644
--- a/translations/fa/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/fa/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# مقدمه‌ای بر احتمال و آمار\n",
-    "در این دفترچه، با برخی از مفاهیمی که قبلاً درباره آن‌ها صحبت کرده‌ایم، کار خواهیم کرد. بسیاری از مفاهیم احتمال و آمار به خوبی در کتابخانه‌های اصلی پردازش داده در پایتون، مانند `numpy` و `pandas`، پیاده‌سازی شده‌اند.\n"
+    "# مقدمه‌ای بر احتمال و آمار  \n",
+    "در این دفترچه، با برخی از مفاهیمی که قبلاً درباره آن‌ها صحبت کرده‌ایم، کار خواهیم کرد. بسیاری از مفاهیم احتمال و آمار به‌خوبی در کتابخانه‌های اصلی پردازش داده در پایتون، مانند `numpy` و `pandas`، پیاده‌سازی شده‌اند.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## متغیرهای تصادفی و توزیع‌ها  \n",
-    "بیایید با نمونه‌گیری 30 مقدار از یک توزیع یکنواخت بین 0 تا 9 شروع کنیم. همچنین میانگین و واریانس را محاسبه خواهیم کرد.  \n"
+    "بیایید با نمونه‌گیری ۳۰ مقدار از یک توزیع یکنواخت از ۰ تا ۹ شروع کنیم. همچنین میانگین و واریانس را محاسبه خواهیم کرد.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ما در اینجا از بسته‌ای به نام [**Pandas**](https://pandas.pydata.org/) برای تحلیل داده‌ها استفاده می‌کنیم. در ادامه این دوره، بیشتر درباره Pandas و کار با داده‌ها در پایتون صحبت خواهیم کرد.\n",
+    "> ما در اینجا از یک بسته به نام [**Pandas**](https://pandas.pydata.org/) برای تحلیل داده‌ها استفاده می‌کنیم. در ادامه این دوره، بیشتر درباره Pandas و کار با داده‌ها در پایتون صحبت خواهیم کرد.\n",
     "\n",
     "بیایید مقادیر میانگین برای سن، قد و وزن را محاسبه کنیم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "حالا بیایید بر ارتفاع تمرکز کنیم و انحراف معیار و واریانس را محاسبه کنیم:\n"
+    "حالا بیایید روی قد تمرکز کنیم و انحراف معیار و واریانس را محاسبه کنیم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "علاوه بر میانگین، منطقی است که به مقدار میانه و چارک‌ها نگاه کنیم. می‌توان آن‌ها را با استفاده از **نمودار جعبه‌ای** نمایش داد:\n"
+    "علاوه بر میانگین، منطقی است که به مقدار میانه و چارک‌ها نیز نگاه کنیم. می‌توان آن‌ها را با استفاده از یک **نمودار جعبه‌ای** نمایش داد:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ما همچنین می‌توانیم نمودارهای جعبه‌ای از زیرمجموعه‌های داده‌های خود ایجاد کنیم، برای مثال، بر اساس نقش بازیکن گروه‌بندی شده.\n"
+    "ما همچنین می‌توانیم نمودارهای جعبه‌ای از زیرمجموعه‌های داده‌های خود ایجاد کنیم، برای مثال، بر اساس نقش بازیکن گروه‌بندی کنیم.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **توجه**: این نمودار نشان می‌دهد که به طور میانگین، قد بازیکنان بیس اول از قد بازیکنان بیس دوم بلندتر است. در ادامه یاد خواهیم گرفت که چگونه می‌توان این فرضیه را به صورت رسمی‌تر آزمایش کرد و نشان داد که داده‌های ما از نظر آماری معنادار هستند تا این موضوع را ثابت کنند.\n",
+    "> **توجه**: این نمودار نشان می‌دهد که به طور میانگین، قد بازیکنان پست اول بیشتر از قد بازیکنان پست دوم است. بعداً یاد خواهیم گرفت که چگونه می‌توان این فرضیه را به صورت رسمی‌تر آزمایش کرد و نشان داد که داده‌های ما از نظر آماری معنادار هستند تا این موضوع را اثبات کنند.\n",
     "\n",
-    "سن، قد و وزن همگی متغیرهای تصادفی پیوسته هستند. به نظر شما توزیع آن‌ها چگونه است؟ یک روش خوب برای فهمیدن این موضوع، رسم هیستوگرام مقادیر است:\n"
+    "سن، قد و وزن همگی متغیرهای تصادفی پیوسته هستند. به نظر شما توزیع آن‌ها چگونه است؟ یک روش خوب برای فهمیدن این موضوع رسم هیستوگرام مقادیر است:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "از آنجا که اکثر مقادیر در زندگی واقعی به طور معمول توزیع شده‌اند، نباید از یک تولیدکننده اعداد تصادفی یکنواخت برای تولید داده‌های نمونه استفاده کنیم. اینجا چیزی است که اتفاق می‌افتد اگر سعی کنیم وزن‌ها را با یک توزیع یکنواخت تولید کنیم (تولید شده توسط `np.random.rand`):\n"
+    "از آنجا که بیشتر مقادیر در زندگی واقعی به طور نرمال توزیع شده‌اند، نباید از یک تولیدکننده اعداد تصادفی یکنواخت برای تولید داده‌های نمونه استفاده کنیم. این چیزی است که اتفاق می‌افتد اگر بخواهیم وزن‌ها را با یک توزیع یکنواخت (تولید شده توسط `np.random.rand`) تولید کنیم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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QAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMexbIVXVDVX2uqh6uqiN79TkAALCb9iSQq+qSJP8+yfcmuS7Jm6rqur34LAAA2E17dQT59Uke7u7f7u6vJHl/kpv36LMAAGDX7Nujn3tVksfG45NJ/tp8QVUdTnJ48fAPq+pzezQLe+/yJF9c9xCcN+wHtrMn2M6e4Fn1E0nWtyf+wpkW9yqQ6wxr/ZwH3UeTHN2jz2eFqupEd2+uew7OD/YD29kTbGdPsN35tif26hSLk0muGY+vTvLEHn0WAADsmr0K5P+Z5GBVXVtVL0lya5J79+izAABg1+zJKRbd/UxV/VCSX0pySZK7uvuBvfgszgtOlWGyH9jOnmA7e4Ltzqs9Ud197lcBAMBFwpX0AABgEMgAADAIZJ63qnpNVX1q/PflqvqRqvrJqvpsVf1mVf1CVb1y3bOyGl9nT/z4Yj98qqo+XFXftO5ZWY2z7Ynx/I9WVVfV5WsckxX5Or8j/mVVPT7Wv2/ds7IaX+93RFX9cFV9rqoeqKp/tdY5nYPMTiwuJ/54ti4A85okH1n8z5k/kSTd/c51zsfqbdsTv9/dX16svz3Jdd391nXOx+rNPdHdX6iqa5K8J8m3JPmr3e1CEReRbb8jfjDJH3b3T613KtZp2554dZJ3Jbmxu5+uqiu6+6l1zeYIMjt1fZL/3d1f6O4Pd/czi/WPZut7r7n4zD3x5bH+imy7UBAXjWf3xOLxv0nyY7EfLlbb9wPMPfFPktzZ3U8nyTrjOBHI7NytSd53hvW3JPnFFc/C+eE5e6Kq3l1VjyV5c5J/sbapWKdn90RV3ZTk8e7+jfWOxBpt/3fjhxanYt1VVZeuayjWau6Jb07yN6vqY1X1q1X1HWucyykWvHCLi788keS13f3kWH9Xks0k/6BtrIvK2fbE4rnbk7ysu+9Yy3CsxdwTSf4gyS8n+bvd/aWqeiTJplMsLh7bf0dU1ZVJvpitvyb8eJL93f2Wdc7Iap1hT3w6yUeSvCPJdyT5uSSvXldPOILMTnxvkk9si+NDSd6Q5M3i+KL0p/bE8F+T/MMVz8P6zT3xF5Ncm+Q3FnF8dZJPVNWfX+N8rNZzfkd095Pd/dXu/lqS/5jk9WudjnXY/u/GySQf7C0fT/K1JGv7n3kFMjvxpjz3T+k3JHlnkpu6+4/WNhXrtH1PHBzP3ZTksyufiHV7dk9092919xXdfaC7D2TrH8Jv7+7fWeeArNT23xH7x3N/P8mnVz4R6/acPZHkvyX520lSVd+c5CXZ+ivDWjjFghekql6e5LFs/dnjS4u1h5O8NMnvLl72Ud9YcPE4y574+Wx9u8nXknwhyVu7+/H1TckqnWlPbHv+kTjF4qJxlt8R/znJX8nWKRaPJPnH3X1qXTOyWmfZEy9Jcle29sVXkvxod39kbTMKZAAA+BNOsQAAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAAhv8PCCPnhqb/Rl0AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## بازه‌های اطمینان\n",
     "\n",
-    "حالا بیایید بازه‌های اطمینان برای وزن‌ها و قدهای بازیکنان بیسبال را محاسبه کنیم. از کدی که [در این بحث در StackOverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) آمده است، استفاده خواهیم کرد:\n"
+    "حالا بیایید بازه‌های اطمینان برای وزن‌ها و قدهای بازیکنان بیسبال محاسبه کنیم. از کدی که [در این بحث در stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) آمده است استفاده خواهیم کرد:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## آزمون فرضیه\n",
     "\n",
-    "بیایید نقش‌های مختلف در مجموعه داده‌های بازیکنان بیسبال را بررسی کنیم:\n"
+    "بیایید نقش‌های مختلف در مجموعه داده بازیکنان بیسبال خود را بررسی کنیم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,38 +545,38 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "بیایید فرضیه‌ای را آزمایش کنیم که می‌گوید بازیکنان بیس اول بلندتر از بازیکنان بیس دوم هستند. ساده‌ترین راه برای انجام این کار، آزمایش بازه‌های اطمینان است:\n"
+    "بیایید فرضیه‌ای را آزمایش کنیم که می‌گوید بازیکنان بیس اول از بازیکنان بیس دوم بلندتر هستند. ساده‌ترین راه برای انجام این کار، آزمایش بازه‌های اطمینان است:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -752,20 +593,20 @@
    "source": [
     "ما می‌توانیم ببینیم که بازه‌ها با یکدیگر همپوشانی ندارند.\n",
     "\n",
-    "یک روش آماری دقیق‌تر برای اثبات این فرضیه استفاده از **آزمون t دانشجویی** است:\n"
+    "یک روش آماری دقیق‌تر برای اثبات این فرضیه استفاده از **آزمون t استیودنت** است:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -791,24 +632,22 @@
    "source": [
     "## شبیه‌سازی توزیع نرمال با استفاده از قضیه حد مرکزی\n",
     "\n",
-    "تولیدکننده شبه‌تصادفی در پایتون به گونه‌ای طراحی شده که یک توزیع یکنواخت به ما بدهد. اگر بخواهیم یک تولیدکننده برای توزیع نرمال ایجاد کنیم، می‌توانیم از قضیه حد مرکزی استفاده کنیم. برای به دست آوردن یک مقدار با توزیع نرمال، کافی است میانگین یک نمونه تولیدشده به صورت یکنواخت را محاسبه کنیم.\n"
+    "مولد شبه‌تصادفی در پایتون به گونه‌ای طراحی شده است که به ما یک توزیع یکنواخت بدهد. اگر بخواهیم یک مولد برای توزیع نرمال ایجاد کنیم، می‌توانیم از قضیه حد مرکزی استفاده کنیم. برای به دست آوردن یک مقدار با توزیع نرمال، کافی است میانگین یک نمونه تولیدشده به صورت یکنواخت را محاسبه کنیم.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -830,19 +669,19 @@
    "source": [
     "## همبستگی و شرکت شیطانی بیسبال\n",
     "\n",
-    "همبستگی به ما این امکان را می‌دهد که روابط بین دنباله‌های داده را پیدا کنیم. در مثال ساده ما، فرض کنید یک شرکت شیطانی بیسبال وجود دارد که به بازیکنانش بر اساس قدشان حقوق می‌دهد - هرچه بازیکن قدبلندتر باشد، پول بیشتری دریافت می‌کند. فرض کنید یک حقوق پایه ۱۰۰۰ دلاری وجود دارد و یک پاداش اضافی از ۰ تا ۱۰۰ دلار، بسته به قد. ما بازیکنان واقعی MLB را در نظر می‌گیریم و حقوق خیالی آن‌ها را محاسبه می‌کنیم:\n"
+    "همبستگی به ما امکان می‌دهد روابط بین دنباله‌های داده را پیدا کنیم. در مثال ساده ما، فرض کنید یک شرکت شیطانی بیسبال وجود دارد که به بازیکنان خود بر اساس قدشان حقوق می‌دهد - هرچه بازیکن قد بلندتر باشد، پول بیشتری دریافت می‌کند. فرض کنید یک حقوق پایه به مبلغ ۱۰۰۰ دلار وجود دارد و یک پاداش اضافی از ۰ تا ۱۰۰ دلار، بسته به قد. ما بازیکنان واقعی MLB را در نظر می‌گیریم و حقوق خیالی آنها را محاسبه می‌کنیم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +695,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "بیایید اکنون کوواریانس و همبستگی این دنباله‌ها را محاسبه کنیم. `np.cov` ماتریسی به نام **ماتریس کوواریانس** به ما می‌دهد که یک گسترش کوواریانس برای چندین متغیر است. عنصر $M_{ij}$ ماتریس کوواریانس $M$ همبستگی بین متغیرهای ورودی $X_i$ و $X_j$ است و مقادیر قطر $M_{ii}$ واریانس $X_{i}$ است. به همین ترتیب، `np.corrcoef` ماتریس **همبستگی** را به ما می‌دهد.\n"
+    "بیایید اکنون کوواریانس و همبستگی این توالی‌ها را محاسبه کنیم. `np.cov` ماتریسی به نام **ماتریس کوواریانس** به ما می‌دهد که تعمیمی از کوواریانس برای چندین متغیر است. عنصر $M_{ij}$ در ماتریس کوواریانس $M$ نشان‌دهنده همبستگی بین متغیرهای ورودی $X_i$ و $X_j$ است و مقادیر قطری $M_{ii}$ واریانس $X_{i}$ را نشان می‌دهند. به همین ترتیب، `np.corrcoef` ماتریس **همبستگی** را به ما می‌دهد.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -886,24 +725,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "یک همبستگی برابر با ۱ به این معناست که یک **رابطه خطی قوی** بین دو متغیر وجود دارد. ما می‌توانیم رابطه خطی را با رسم یک مقدار در مقابل دیگری به صورت بصری مشاهده کنیم:\n"
+    "یک همبستگی برابر با ۱ به این معناست که یک **رابطه خطی** قوی بین دو متغیر وجود دارد. ما می‌توانیم رابطه خطی را با رسم یک مقدار در مقابل دیگری به صورت بصری مشاهده کنیم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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IoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoTQDQAAAABAgMQEuwAAAAAAXVdX36TbX92l8u+PKb1fvJ66cbj6WPk1HwgV/GkEAAAAwtQ1f9qq3V+5PMf7Kms1ZMn/6oKBNq2fNyaIlQFoxfJyAAAAIAydHLhPtPsrl67509ZurghAWwjdAAAAQJipq29qN3C32v2VS3X1Td1UEYD2ELoBAACAMHP7q7tMbQcgcAjdAAAAQJgp//6Yqe0ABA6hGwAAAAgz6f3iTW0HIHAI3QAAAECYeerG4aa2AxA4hG4AAAAgzPSxxuiCgbbTtrlgoI39uoEQQOgGAAAAwtD6eWPaDd7s0w2EDv7pCwAAAAhT6+eNUV19k25/dZfKvz+m9H7xeurG4cxwAyGEP40AAABAEDS7DRWVVquqtl7JiVaNzExSdJTF5376WGO0esbFAagQgBkI3QAAAEA3y99ToaUbSlRRU+85l2q3anGuU9lDUoNYGQCz8Uw3AAAA0I3y91RozpqdXoFbkipr6jVnzU7l76kIUmUAAoHQDQAAAHSTZrehpRtKZLRxrfXc0g0lana31QJAOPI5dBcUFCg3N1dpaWmyWCxat26d1/XXXntNEyZMUP/+/WWxWFRcXOx1vbq6WrfeeqvOOeccxcfHKz09Xb/97W9VU1Pj1a68vFw5OTlKSEhQcnKy7rrrLjU1Nfn8BQEAAIBQUVRafcoM94kMSRU19Soqre6+ogAElM+h+8iRIxo6dKhWrFjR7vXRo0fr8ccfb/P6wYMHdfDgQT355JPas2eP8vLylJ+fr5kzZ3raNDc3KycnRw0NDfrggw/00ksvKS8vT4sWLfK1XAAAACBkVNW2H7i70g5A6PP5RWqTJk3SpEmT2r0+bdo0SVJZWVmb14cMGaK//OUvnuMzzzxTjzzyiKZOnaqmpibFxMTozTffVElJid566y2lpKRo2LBheuihh3TPPfdoyZIlio2N9bVsAAAAIOiSE62mtgMQ+kLime6amhrZbDbFxLT8G0BhYaHOP/98paSkeNpMnDhRLpdLe/fuDVaZAAAAQLua3YYKP/tO/1P8tQo/+67N57JHZiYp1W5VexuDWdTyFvORmUkBrRVA9wn6lmHffvutHnroIc2ePdtzrrKy0itwS/IcV1ZWttnP8ePHdfz4cc+xy+UKQLUAAADAqTq7BVh0lEWLc52as2anLJLXC9Vag/jiXGeX9usGEJqCOtPtcrmUk5Mjp9OpJUuW+NXXsmXLZLfbPZ9BgwaZUyQAAABwGr5uAZY9JFUrp46Qw+69hNxht2rl1BHs0w1EmKDNdNfW1io7O1uJiYlau3atevXq5bnmcDhUVFTk1f7QoUOea21ZuHCh5s+f7zl2uVwEbwAAAARUR1uAWdSyBdh4p8Nr9jp7SKrGOx0qKq1WVW29khNblpQzww1EnqCEbpfLpYkTJyouLk7r16+X1er9r3xZWVl65JFHVFVVpeTkZEnS5s2bZbPZ5HQ62+wzLi5OcXFxAa8dAAAAPVuz2/CE5W9rj3d6C7CsM/t7XYuOspxyDkDk8Tl019XV6cCBA57j0tJSFRcXKykpSenp6aqurlZ5ebkOHjwoSdq3b5+klhlqh8Mhl8ulCRMm6OjRo1qzZo1cLpfn+eszzjhD0dHRmjBhgpxOp6ZNm6bly5ersrJS999/v+bOnUuwBgAAQNC09ex2Z7AFGNBzWQzDaGs1TLveffddXXHFFaecnzFjhvLy8pSXl6df/OIXp1xfvHixlixZ0u79UkuAz8jIkCR98cUXmjNnjt5991317t1bM2bM0GOPPeZ5w3lHXC6X7Ha7583oAAAAgD9an9326Zfnv/t/sy5hVhuIMJ3NnD6H7nBB6AYAAIBZmt2GRj++xecZbotaXpD2/j1X8rw2EGE6mzlDYp9uAAAAIJQVlVZ3KXBLbAEG9HRB36cbAAAACHVdeSbb0cY+3QB6HkI3AAAA0IHkRGvHjSQ9kHOuBiTGsQUYAA9CNwAAANCBkZlJSrVbVVlT3+aL1Fqf3b7lx5kEbQBeeKYbAAAA6EB0lEWLc52S/vGsdiue3QZwOoRuAAAAoBOyh6Rq5dQRcti9l5o77FatnDqCZ7cBtInl5QAAAEAnZQ9J1XinQ0Wl1aqqrefZbQAdInQDAAAAPoiOsijrzP7BLgNAmGB5OQAAAAAAAULoBgAAAAAgQFheDgAAgIhS/u1RZf/xPR1rdCu+V5Ty/+UypQ9ICHZZAHooQjcAAAAixg/v3agm9z+Ojza6NfbJdxQTJR14NCd4hQHosVheDgAAgIhwcuA+UZO75ToAdDdmugEAABCWmt2GZ+uuqCZ3u4G7VZO7Zek5S80BdCdCNwAAAMJO/p4KLd1Qooqaep/uy/7jeyp5aFKAqgKAUxG6AQAAEFby91RozpqdMrpw77HGDqbDAcBkPNMNAACAsNHsNrR0Q0mXArckxffi118A3Yu/dQAAABA2ikqrfV5SfqL8f7nMxGoAoGOEbgAAAISNqtquB+6YKPESNQDdjtANAACAsJGcaO3SfezTDSBYeJEaAAAAwsbIzCSl2q2qrKlv87lui6S+cRYdd1t0rNGt+F5Ryv+Xy5jhBhA0hG4AAACEjegoixbnOjVnzU5ZJK/gbfn7/y775+HKHpIahOoA4FQsLwcAAEBYyR6SqpVTR8hh915q7rBbtXLqCAI3gJDCTDcAAADCTvaQVI13OlRUWq2q2nolJ1o1MjNJ0VGWjm8GgG5E6AYAAEC3anYbpoTl6CiLss7sH4AKAcA8hG4AAAB0m/w9FVq6ocRrr+1Uu1WLc50sCwcQkXimGwAAAN0if0+F5qzZ6RW4Jamypl5z1uxU/p6KIFUGAIHDTDcAAAACovJwva5+pkCu+ibZrDGyWCxtbvNlqOXN40s3lGi808Fz2QAiCqEbAAAApjv3gTd0rNHtOf72SONp2xuSKmrqVVRazXPaACIKy8sBAABgqpMDty+qaus7bgQAYYTQDQAAANNUHq7vcuCWpOREa8eNACCMsLwcAAAAfjnW0KxHN5Wo7LujKvr8uy71YZHksLdsHwYAkYTQDQAAgC6b9fJ2bS6p8quP1temLc518hI1ABGH0A0AAIAuMSNwSy0z3OzTDSBSEboBAADgs2MNzX4F7md/PlyNhqHkxJYl5cxwA4hUhG4AAAD47NFNJV2+N75XlCYPTTOxGgAIXby9HAAAAD4r++5ol+6L7xWljx+aZHI1ABC6mOkGAACAzzL6J2jr/o7bxUVbZEiyWWP0+q1j5ejLlmAAehZCNwAAAHx272SnXvlbeYftihdPVHxsdDdUBAChieXlAAAA8Fl8bLTGO5NP22a8M5nADaDHI3QDAACgS1ZPv7jd4D3emazV0y/u5ooAIPSwvBwAAABdtnr6xTrW0KxHN5Wo7LujyuifoHsnO5nhBoC/I3QDAAD0UM1uQ0Wl1aqqrfdrv+z42Gg9dN35AagQAMIfoRsAAKAHyt9ToaUbSlRRU+85l2q3anGuU9lDUoNYGQBEFp7pBgAA6GHy91RozpqdXoFbkipr6jVnzU7l76kIUmUAEHmY6QYAAIhw75d8o6kvF3mOYyUZbbQzJFkkLd1QovFOR5eWmgMAvBG6AQAAIljGgo2nnGs4TXtDUkVNvYpKq5V1Zv+A1QUAPQXLywEAACJUW4G7s6pq6ztuBADoEKEbAAAgAr1f8o1f9ycnWk2qBAB6NpaXAwAARIgTtwD7l/8s7lIfFkkOe8v2YQAA/xG6AQAAIkBbW4D5qvW1aYtznbxEDQBMQugGAAAIc61bgLX1RnJfONinGwBM5/Mz3QUFBcrNzVVaWposFovWrVvndf21117ThAkT1L9/f1ksFhUXF5/SR319vebOnav+/furT58+uuGGG3To0CGvNuXl5crJyVFCQoKSk5N11113qampyddyAQAAIk6z21DhZ9/pf4q/1l8PfKsl6/d2OXA/MOlM/XHKMP2/WZfo/XuuJHADgMl8nuk+cuSIhg4dql/+8pe6/vrr27w+evRo/exnP9OsWbPa7OP222/Xxo0b9ec//1l2u13z5s3T9ddfr7/+9a+SpObmZuXk5MjhcOiDDz5QRUWFpk+frl69eunRRx/1tWQAAICIYcYy8hPNvOxHpvQDAGibxTCMLq9EslgsWrt2ra677rpTrpWVlSkzM1O7du3SsGHDPOdramp0xhln6D/+4z/005/+VJL0ySef6Nxzz1VhYaEuueQSvfHGG7r66qt18OBBpaSkSJJWrVqle+65R998841iY2M7rM3lcslut6umpkY2m62rXxEAACBkmLWMvFXZYzkm9QQAPU9nM2e3bxn24YcfqrGxUePGjfOc+9GPfqT09HQVFhZKkgoLC3X++ed7ArckTZw4US6XS3v37m2z3+PHj8vlcnl9AAAAIkWz29DSDSWmBO4100cSuAGgm3T7i9QqKysVGxurvn37ep1PSUlRZWWlp82Jgbv1euu1tixbtkxLly41v2AAAIAgaWhy65XCMn1RfVSGYXRpSXnrFmDv33MlbyQHgCCImLeXL1y4UPPnz/ccu1wuDRo0KIgVAQAAdN2yTSVavbVUbj+mttkCDACCr9tDt8PhUENDgw4fPuw1233o0CE5HA5Pm6KiIq/7Wt9u3trmZHFxcYqLiwtM0QAAAN1o2aYSPVdQ6nc/bAEGAMHX7aH7wgsvVK9evfT222/rhhtukCTt27dP5eXlysrKkiRlZWXpkUceUVVVlZKTkyVJmzdvls1mk9Pp7O6SAQAAuk1Dk1urt/oeuFuXkT/506H69shxJSdaNTIziRluAAgyn0N3XV2dDhw44DkuLS1VcXGxkpKSlJ6erurqapWXl+vgwYOSWgK11DJD7XA4ZLfbNXPmTM2fP19JSUmy2Wy69dZblZWVpUsuuUSSNGHCBDmdTk2bNk3Lly9XZWWl7r//fs2dO5fZbAAAENFeKSzzeUn5icvIf3zWANNrAgB0nc9vL9+xY4eGDx+u4cOHS5Lmz5+v4cOHa9GiRZKk9evXa/jw4crJaXkj5pQpUzR8+HCtWrXK08dTTz2lq6++WjfccIPGjh0rh8Oh1157zXM9Ojpar7/+uqKjo5WVlaWpU6dq+vTpevDBB/36sgAAAKHui+qjPt/jsFu1cuoIlpEDQAjya5/uUMY+3QAAIBz929bP9dDGjztsN+2SdF2UkcQycgAIks5mzoh5ezkAAECo23ewVpOfKVCzIUVbpE23jtU5aYlebaZlZeiRTR+fdol5lEV64OrzFBvj86JFAEA3I3QDAAB0g4wFG72Omw1p4tMFkqSyx3I852NjojRrTOZp314+a0wmgRsAwgR/WwMAAATYyYG7o+sLJzv1q7GZOnnFeJRF+tXYTC2czG4uABAumOkGAAAwWUOTW68UlumL6qNKiOncs9b7DtZ6LTVfONmpOyb8yNPP4KQETcvKYIYbAMIML1IDAAAw0bJNJVq9tdTnbb+iLdJny3I6bggACAm8SA0AAKCbLdtUctpnsU+nOSKnQQAArE8CAAAwQUOTW6u3di1wSy0z3QCAyMNMNwAAQBc1uw0VlVarqrZeO8qqfV5SfqJNt441rzAAQMggdAMAAHRB/p4KLd1QooqaelP6O3m/bgBAZCB0AwAA+Ch/T4XmrNkpsx7DPnGfbgBAZCF0AwAA+KDZbWjphpIuBW7L3z9utTzDvenWscxwA0CEI3QDAAB04MR9tw3D6PKS8tljM7VwstPk6gAAoYzQDQAAcBpd3Xf7RFEWadYYAjcA9ESEbgAAgHb4s+/2tEvSZbFYNDgpQdOyMhQbw06tANATEboBAAD+rq6+Sbe/ukvl3x/TwL5Wvf3JNz73YZHksFu15Johio5i820A6OkI3QAAAJKu+dNW7f7K5TneV1nrcx+tEXtxrpPADQCQROgGAAA4JXB3lcNu1eJcp7KHpJpQFQAgEhC6AQBAj1ZX3+RX4J52SbouykhScqJVIzOTmOEGAHghdAMAgB7nv/9aqjs3lPjdT5RFeuDq83hJGgCgXYRuAADQo2Qs2GhaX7PGZBK4AQCnRegGAAA9hlmBm323AQCdRegGAAARq6HJrVcKy/RF9VF9W+f728hPdPfEs1XpOs6+2wAAnxC6AQBARFq2qUSrt5bKbfjf1wUDbfrNFWf53xEAoMchdAMAgIizbFOJnisoNaWvCwbatH7eGFP6AgD0PIRuAAAQURqa3Fq91b/AfY4jUen94vXUjcPVx8qvSwCAruO/IgAAIOzVHG3UL/OKdLCmXtEW+bWk/Mlcp37640zzigMA9GiEbgAAENYue2KLvvjumGn9EbgBAGbitZsAACBsmR24yx7LMa0vAAAkZroBAEAYaXYbKiqtVlVtvfrERJsWuFlSDgAIFEI3AAAIC/l7KrR0Q4kqaur97utXYzO1cLLThKoAADg9QjcAAAh5+XsqNGfNTvm75XaURZo1hsANAOg+hG4AABDSmt2Glm4o6XLgHtjXqivPTdHgpARNy8pQbAyvtAEAdB9CNwAACDkNTW69UlimL6qPyjAMv5aUb/ztWNkTeplYHQAAnUfoBgAAIWXZphKt3lrq117brQb3jydwAwCCitANAABCxrJNJXquoNSUvgb3j9d7d11pSl8AAHQVoRsAAATNluJK/fI/P/SrD4ukAb1jNKh/H1XU1CvNbtULt4xkhhsAEBII3QAAICgyFmz0uw/L3//3oZ9coOwhqX73BwCA2QjdAACg25kRuCXJYbdqca6TwA0ACFmEbgAA0K22FFf6df+0S9J1UUaSkhOtGpmZpOgoS8c3AQAQJIRuAAAQcDVHG/XLvCIdrKn3a/uvKIv0wNXnsdc2ACBsELoBAEBAXfbEFn3x3TFT+po1JpPADQAIK4RuAAAQMGYF7ihLS+BeONlpQlUAAHQfQjcAADBNXX2Tbn91l8q/P6Y0W5xfgfv6EQPUJ663BiclaFpWBjPcAICwROgGAACmuOZPW7X7K5fneF9lrV/9/f5no/wtCQCAoOOfjAEAgN9ODtz+Knssx7S+AAAIJma6AQCAX+rqm0wL3C9MuVBXDnOY0hcAAKGA0A0AAHx24hZgR443+tXX/y2aIHtCL5MqAwAgtBC6AQCAT8zcAmxw/3gCNwAgovFMNwAA6DSzA/d7d11pSl8AAIQqZroBAECn1Bxt9CtwDxuYqEO1jUqzW/XCLSOZ4QYA9AiEbgAA0K6GJrdeKSzTF9VHteXjQ13u54KBNq2bN8bEygAACA8+Ly8vKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/d7tfn000917bXXasCAAbLZbBo9erTeeecdrzbl5eXKyclRQkKCkpOTddddd6mpqcn3bwgAALpk2aYS/eiBN/TQxo/1cuEX+upwfZf6uWCgTesJ3ACAHsrn0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+/h//ob766qvV1NSkLVu26MMPP9TQoUN19dVXq7KyUpLU3NysnJwcNTQ06IMPPtBLL72kvLw8LVq0qItfEwAA+GLZphI9V1Aqt+H7vTZrtM5xJGr8ucnas2QigRsA0KNZDMPown9O/36zxaK1a9fquuuuk9Qyy52WlqY77rhDd955pySppqZGKSkpysvL05QpU/Ttt9/qjDPOUEFBgcaMafmPcG1trWw2mzZv3qxx48bpjTfe0NVXX62DBw8qJSVFkrRq1Srdc889+uabbxQbG9thbS6XS3a7XTU1NbLZbF39igAA9AhzXsjXG582m9IXW4ABAHqCzmZOU99eXlpaqsrKSo0bN85zzm63a9SoUSosLJQk9e/fX+ecc45efvllHTlyRE1NTXruueeUnJysCy+8UJJUWFio888/3xO4JWnixIlyuVzau3evmSUDANDjZSzYaFrgZgswAAC8mfoitdbl4SeG5dbj1msWi0VvvfWWrrvuOiUmJioqKkrJycnKz89Xv379PP201ceJP+Nkx48f1/Hjxz3HLpfLnC8FAEAEy1iw0bS+2AIMAIBTdfvbyw3D0Ny5c5WcnKytW7cqPj5e//qv/6rc3Fxt375dqampXep32bJlWrp0qcnVAgAQuea8kO/X/QP7WtVsiC3AAAA4DVNDt8PhkCQdOnTIKzwfOnRIw4YNkyRt2bJFr7/+ur7//nvPuvdnn31Wmzdv1ksvvaQFCxbI4XCoqKjIq+9Dhw55/YyTLVy4UPPnz/ccu1wuDRo0yLTvBgBAJKg8XK+rnymQq75JDc1dfq2LoizSljuvUGyMqU+qAQAQcUz9L2VmZqYcDofefvttzzmXy6Vt27YpKytLknT06NGWHxzl/aOjoqLkdrslSVlZWfroo49UVVXlub5582bZbDY5nc42f3ZcXJxsNpvXBwAA/MO5D7yhSx57W98eafQrcEvSrDGZBG4AADrB55nuuro6HThwwHNcWlqq4uJiJSUlKT09XbfddpsefvhhnXXWWcrMzNQDDzygtLQ0zxvOs7Ky1K9fP82YMUOLFi1SfHy8Vq9erdLSUuXk5EiSJkyYIKfTqWnTpmn58uWqrKzU/fffr7lz5youLs6cbw4AQA9y7gNv6Fij2+9+oiwtgXvh5Lb/ERwAAHjzOXTv2LFDV1xxhee4dUn3jBkzlJeXp7vvvltHjhzR7NmzdfjwYY0ePVr5+fmyWq2SpAEDBig/P1/33XefrrzySjU2Nuq8887T//zP/2jo0KGSpOjoaL3++uuaM2eOsrKy1Lt3b82YMUMPPvigGd8ZAICI19Dk1iuFZfqi+qiS4mL8CtxnJ0iXDB2swUkJmpaVwQw3AAA+8Guf7lDGPt0AgJ5q2aYSrd5aKrdJ/4UveyzHnI4AAIggnc2c3f72cgAAEDjLNpXouYJS0/ojcAMA4B/WhwEAECEamtxavdWcwD3p7GgCNwAAJmCmGwCAMNbsNlRUWq2q2nrtKKv2a0n53xZcJUdfq3nFAQAAQjcAAOEqf0+Flm4oUUVNvd99xfeKInADABAAhG4AAMJQ/p4KzVmzU2a8Ky2+V5Q+fmiSCT0BAICTEboBAAgDJ24BNqhfgv5162ddCtwWSUm9e6m2vkk2a4xev3UsM9wAAAQQoRsAgBBn5hZgs8dmauFkp/8dAQCATiF0AwAQwszaAizKIs0aQ+AGAKC7EboBAAhR/m4BNu2SdFksFg1OStC0rAzFxrBTKAAA3Y3QDQBACMl7Z5+W/O8Bv/qwSHLYrVpyzRBFR1nMKQwAAHQJoRsAgBCRsWCj3320RuzFuU4CNwAAIYDQDQBACDAjcEstM9yLc53KHpJqSn8AAMA/hG4AAIKg2W2oqLRaVbX1+mvZV13uJ8oivXTLSFUfa1ByolUjM5OY4QYAIIQQugEA6Gb5eyq0dEOJKmrq/e5r1phMjTnnDBOqAgAAgUDoBgCgG+XvqdCcNTvl75bbbAEGAEB4IHQDANBNmt2Glm4o8StwT88azBZgAACEEUI3AAAB1NDk1iuFZfqi+qgMw/BrSfmSiT/ULVecY2J1AAAg0AjdAAAEyLJNJVq9tVRuf9eS/x2BGwCA8EPoBgAgAJZtKtFzBaWm9Vf2WI5pfQEAgO5D6AYAwGQNTW6t3up74LZIpzzvzZJyAADCG6EbAAATVNc1aMrzH6iqtkExUfJ5SXnrztqrpo5Q9pBU0+sDAADBQegGAMBPFz+8Wd/UNfjVh8Nu1eJcJ4EbAIAIQ+gGAMAP/gTuaZek66KMJCUnWjUyM0nRUZaObwIAAGGF0A0AQCeduIQ8OTFWz950UZcDd5RFeuDq89hrGwCACEfoBgCgE06e0T58rFHj/vBel/ubNSaTwA0AQA9A6AYAoANmPLPdKsrSErgXTnaa0h8AAAhthG4AAE6juq7B78A9oHcvTb4gTYOTEjQtK4MZbgAAehBCNwAAJ5nzQr7e+LTZtP7evP1yJfWJNa0/AAAQPgjdAACcIGPBRlP7O6NPLIEbAIAejPVtAAD8XSAC9/b7x5vaJwAACC/MdAMAeqyao436ZV6RDtbUq6qm3q++3rrtMv3mP3Z4thP7z9mXMsMNAAAI3QCAnumyJ7boi++OmdLXGX1i9UNHH705/3JT+gMAAJGD5eUAgB7H7MDNEnIAANAeZroBAD1KzdFGvwN33/heLCEHAACdQugGAES80qojyv7jezrebPjd16Szo7XylxNMqAoAAPQEhG4AQET7p4Ub5fY/a3us/GW2eZ0BAICIxzPdAICIZXbgLnssx7zOAABAj8BMNwAgYhxraNajm0pU9t1R9U+INi1wtywpZ4YbAAD4jtANAIgIs17ers0lVab0Nbh/vN6760pT+gIAAD0by8sBAGGPwA0AAEIVM90AgLB2rKHZ78CdarcqzW7VC7eMlD2hl0mVAQAAELoBAGHoG9dx/eTZ91V9pFGSfw9uvzP/cmUm9zanMAAAgJMQugEAYeWCJf8rV32TKX1FWUTgBgAAAcUz3QCAsGF24P58GVuAAQCAwGKmGwAQspas3aa8bd+a0ldslNTgluKiLcr/l8uY4QYAAN2C0A0ACEkZCzaa1td4Z7JWT7/YtP4AAAA6i+XlAICQQ+AGAACRgpluAEBIWbJ2m1/3J/SK0oUZScron6B7JzsVHxttUmUAAAC+I3QDAIKurr5Jt7+6S+XfH9O+ylq/+nrvrit1hi3OpMoAAAD8Q+gGAATVNX/aqt1fuUzpy2aNIXADAICQwjPdAICgMTtw714y0ZS+AAAAzMJMNwCg2xxraNajm0pU9t1RpdmtfgfuhF7RSurdS2t/M5oZbgAAEJJ8nukuKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/ef0s/GjRs1atQoxcfHq1+/frruuuu8rpeXlysnJ0cJCQlKTk7WXXfdpaamJl/LBQCEiFkvb9e5i/L1yt/KtXX/t3p1x1d+9Vf2WI5KHsrW+wuuInADAICQ5XPoPnLkiIYOHaoVK1a0eX358uV6+umntWrVKm3btk29e/fWxIkTVV9f72nzl7/8RdOmTdMvfvEL/d///Z/++te/6qabbvJcb25uVk5OjhoaGvTBBx/opZdeUl5enhYtWtSFrwgACLZZL2/X5pIq0/oreyzHtL4AAAACyWIYhtHlmy0WrV271jNLbRiG0tLSdMcdd+jOO++UJNXU1CglJUV5eXmaMmWKmpqalJGRoaVLl2rmzJlt9vvGG2/o6quv1sGDB5WSkiJJWrVqle655x598803io2N7bA2l8slu92umpoa2Wy2rn5FAICfjjU069xF+ab0dcuoAVryk1Gm9AUAAOCPzmZOU5/pLi0tVWVlpcaNG+c5Z7fbNWrUKBUWFmrKlCnauXOnvv76a0VFRWn48OGqrKzUsGHD9MQTT2jIkCGSpMLCQp1//vmewC1JEydO1Jw5c7R3714NHz7czLIBACb7xnVcP3n2fVUfaZTU5X/blSTtWTJRfay8ggQAAIQnU3+LqayslCSvsNx63Hrt888/lyQtWbJEv//975WRkaHf/e53uvzyy/Xpp58qKSlJlZWVbfZx4s842fHjx3X8+HHPsctlzttwAQC+uWDJ/8pVb847OC4YaCNwAwCAsNbtW4a53W5J0n333acbbrhBF154oV588UVZLBb9+c9/7nK/y5Ytk91u93wGDRpkVskAgE4yO3CvnzfGlL4AAACCxdTQ7XA4JEmHDh3yOn/o0CHPtdTUVEmS0+n0XI+Li9M//dM/qby83NNPW32c+DNOtnDhQtXU1Hg+X375pQnfCADQWd+4jvsVuK84Z4DOcSRq/LnJ2rNkIoEbAABEBFPX7GVmZsrhcOjtt9/WsGHDJLUs8962bZvmzJkjSbrwwgsVFxenffv2afTo0ZKkxsZGlZWVafDgwZKkrKwsPfLII6qqqlJycrIkafPmzbLZbF5h/URxcXGKi2PLGADoTifuu/1hWXWX+xnvTNbq6RebWBkAAEBo8Dl019XV6cCBA57j0tJSFRcXKykpSenp6brtttv08MMP66yzzlJmZqYeeOABpaWled5wbrPZ9Otf/1qLFy/WoEGDNHjwYD3xxBOSpH/+53+WJE2YMEFOp1PTpk3T8uXLVVlZqfvvv19z584lWANAiDBrGzACNwAAiGQ+h+4dO3boiiuu8BzPnz9fkjRjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqxWq+eeJ554QjExMZo2bZqOHTumUaNGacuWLerXr58kKTo6Wq+//rrmzJmjrKws9e7dWzNmzNCDDz7o7/cFAJjAn8Cd0CtKF2YkKaN/gu6d7FR8bLTJ1QEAAIQOv/bpDmXs0w0A5hl/70btd5vT1/Z7x+kMG6uWAABAeAvKPt0AgMiTsWCjaX3ZrDEEbgAA0KN0+5ZhAIDwYXbg3r1komn9AQAAhANmugEAbRp/r3+BO6FXlCSLknr30trfjGaGGwAA9EiEbgCAR0OTW68UlumL6qN+P8P94QMTeEkaAADo8QjdAABJ0rJNJVq9tVRuE16vOd6ZTOAGAAAQoRsAoJbA/VxBqSl9se82AADAPxC6AaAHqjnaqF/mFelgTb1SbXHa+WWNX/2NOWsA+24DAAC0gdANAD3MZU9s0RffHfMcV9TU+9Vf2WM5/pYEAAAQsdgyDAB6kJMDt78I3AAAAKdH6AaAHqLmaKNpgfusKAI3AABAZ7C8HAAiWOXhel39TIFc9U1q9uO15FEW6ZOHJik2hn+rBQAA8AWhGwAi1LkPvKFjjX5utv13s8ZkErgBAAC6gNANABHIrMAdZWkJ3AsnO02oCgAAoOchdANABDjW0KxHN5Wo7LujSu4T61fgvnP8Waqqa9DgpARNy8pghhsAAMAPhG4ACHOzXt6uzSVVpvQ1uH+85l11til9AQAAgLeXA0BYMztwv3fXlab0BQAAgBbMdANAmDrW0OxX4I62SMk2q9LsVr1wy0jZE3qZWB0AAAAkQjcAhJXfbyrW0wVfm9LXX++5So6+VlP6AgAAQNsI3QAQJjIWbDStr/heUQRuAACAbsAz3QAQBswO3B8/NMm0/gAAANA+ZroBIASduAVYZcW3fvWVFB+tuga3bNYYvX7rWGa4AQAAuhGhGwBCjJlvJB/vTNbq6Reb0hcAAAB8x/JyAAghBG4AAIDIwkw3AIQIf7cAk6QxZw1QRv8E3TvZqfjYaJMqAwAAQFcRugEgiE58dvtQzTG/+vrt2B9o/uRh5hQGAAAAUxC6ASBIzFxKLonADQAAEIJ4phsAgsDswF32WI5pfQEAAMA8zHQDQDf4uvqYJj39no4cb1bv2Gi5jjeb0i9LygEAAEIboRsAAuzs+zapodnwHPsTuHkjOQAAQHhheTkABNDJgdsfBG4AAIDww0w3AATI19XH/ArcZyf3Voo9ni3AAAAAwhihGwBM1NDk1iuFZfqi+qheLSr3q6//mTeGoA0AABDmCN0AYJJlm0q0emup3CasJh/vTCZwAwAARABCNwCYYNmmEj1XUGpKXzy7DQAAEDkI3QDgp4Ymt1Zv7Xrg/smwFH17pJlntwEAACIQoRsAuuC6RzaquNb/fmKjLXpqykX+dwQAAICQROgGAB9lLNhoSj+x0RZ9+shkU/oCAABAaCJ0A4AP/AnccdEWNbkN9Y6L1hu/vUw/SIo3sTIAAACEIkI3AJzGsYZmPbqpRGXfHdW2/d92uZ8oi/TR0mzFxkSZWB0AAABCHaEbANox6+Xt2lxSZU5fYzIJ3AAAAD0QoRsA2mBW4I6ytATuhZOdJlQFAACAcEPoBoCTHGto9jtwT88arMFJCZqWlcEMNwAAQA9G6AYASV9XH9Okp9/TkePNirL419ewROnBa4eYUxgAAADCGqEbQI939n2b1NBseI5P+H92ybr7cvysCAAAAJGCNY8AerSTA7e/yh4jcAMAAOAfmOkG0KOcuAXYgN4xpgXuYYnMcAMAAOBUhG4APYaZW4CNdyZr9fSLTekLAAAAkYvl5QB6BAI3AAAAgoGZbgARz98twHpFSZecOUAZ/RN072Sn4mOjTawOAAAAkYzQDSAi1dU36fZXd6n8+2M6Ut/oV1/v3nmlfpAUb1JlAAAA6EkI3QAizjV/2qrdX7lM6Ss22kLgBgAAQJfxTDeAiGJ24P70kcmm9AUAAICeyefQXVBQoNzcXKWlpclisWjdunVe1w3D0KJFi5Samqr4+HiNGzdO+/fvb7Ov48ePa9iwYbJYLCouLva6tnv3bo0ZM0ZWq1WDBg3S8uXLfS0VQA/w6Podyliw0fPxJ3AnxkYp2iLZrNH6691XErgBAADgN59D95EjRzR06FCtWLGizevLly/X008/rVWrVmnbtm3q3bu3Jk6cqPr6+lPa3n333UpLSzvlvMvl0oQJEzR48GB9+OGHeuKJJ7RkyRI9//zzvpYLIIJlLNio5z84ZEpf453J+ujBSfpsWY52L8lmSTkAAABM4fMz3ZMmTdKkSZPavGYYhv7whz/o/vvv17XXXitJevnll5WSkqJ169ZpypQpnrZvvPGG3nzzTf3lL3/RG2+84dXPv//7v6uhoUEvvPCCYmNjdd5556m4uFi///3vNXv2bF9LBhCBMhZsNK0vtgADAABAoJj6THdpaakqKys1btw4zzm73a5Ro0apsLDQc+7QoUOaNWuWXnnlFSUkJJzST2FhocaOHavY2FjPuYkTJ2rfvn36/vvv2/zZx48fl8vl8voAiEyPrt/h1/0D+1o15qwBmnZJuj5+MJvADQAAgIAx9e3llZWVkqSUlBSv8ykpKZ5rhmHolltu0a9//WtddNFFKisra7OfzMzMU/povdavX79T7lm2bJmWLl1qxtcAEIJO3AJsX2WtX33l33aZ+ljZvAEAAACB1+2/dT7zzDOqra3VwoULTe134cKFmj9/vufY5XJp0KBBpv4MAMFh5hvJLxhoI3ADAACg25i6vNzhcEhqWT5+okOHDnmubdmyRYWFhYqLi1NMTIx++MMfSpIuuugizZgxw9NPW32c+DNOFhcXJ5vN5vUBEP7MDtzr540xpS8AAACgM0yd7snMzJTD4dDbb7+tYcOGSWqZcd62bZvmzJkjSXr66af18MMPe+45ePCgJk6cqFdffVWjRo2SJGVlZem+++5TY2OjevXqJUnavHmzzjnnnDaXlgOIHM1uQ0Wl1aqqrZctLsavwP0Dm9QnIVHp/eL11I3DmeEGAABAt/P5N9C6ujodOHDAc1xaWqri4mIlJSUpPT1dt912mx5++GGdddZZyszM1AMPPKC0tDRdd911kqT09HSv/vr06SNJOvPMMzVw4EBJ0k033aSlS5dq5syZuueee7Rnzx798Y9/1FNPPdXV7wkgDOTvqdDSDSWqqDl1i8Gu+Ou9Oab0AwAAAHSVz6F7x44duuKKKzzHrc9Rz5gxQ3l5ebr77rt15MgRzZ49W4cPH9bo0aOVn58vq9Xa6Z9ht9v15ptvau7cubrwwgs1YMAALVq0iO3CgAiWv6dCc9bslGFSf2WPEbgBAAAQfBbDMMz6HTekuFwu2e121dTU8Hw3EOKa3YZGP77FlBnu2Zem6N5rLjKhKgAAAKB9nc2cPOAIIChOfHb729rjfgXuPUsm8rw2AAAAQhK/pQLodmY+u80WYAAAAAhl/KYKoFuZ+ew2W4ABAAAg1BG6AQTUicvIB/SJ05L1e30O3BZJyYlxOn+gTV9+X88WYAAAAAgb/MYKIGDMWEZu+fv/Lr32PGUPSTWnMAAAAKCbELoBBIRZy8gddqsW5zoJ3AAAAAhLhG4Apmt2G1q6oaTLgfuBnHM1IDFOyYlWjcxMUnSUpeObAAAAgBBE6AZgiltWbNS7X/rXh0UtM9u3/DiToA0AAICIQOgG4LeMBRv97qM1Yi/OdRK4AQAAEDEI3QD8Ykbglnh2GwAAAJGJ0A2gy25Z0bXA3bqM/MmfDtW3R47z7DYAAAAiFqEbQJd15RnuE5eR//isAabWAwAAAIQaQjeAbsUycgAAAPQkhG4A3eKPU4axjBwAAAA9DqEbQJsamtx6pbBMX1Qf1eCkBE3LylBsTJRXm8sHdW6J+eWDpGuH/SBAlQIAAAChy2IYhhHsIgLB5XLJbrerpqZGNpst2OUAYWXZphKt3loq9wl/O0RZpFljMrVwstOrbWfeXl72WI7ZJQIAAABB1dnMGdXuFQA90rJNJXquwDtwS5LbkJ4rKNWyTSVe5zsK1ARuAAAA9GSEbgAeDU1urd5aeto2q7eWqqHJ7XWu7LEcXT7Iu93lgwjcAAAAAM90Az3csYZmPbqpRGXfHdXR402nzHCfzG1IrxSWaeaYf/I6nzeXgA0AAACcjNAN9GCzXt6uzSVVPt/3RfXRAFQDAAAARB6WlwM9VFcDtyQNTkowuRoAAAAgMjHTDfQQdfVNuv3VXSr//pjS7HF6Z9+3XeonyiJNy8owtzgAAAAgQhG6gR7gmj9t1e6vXJ7jfZW1Xe5r1pjMU/brBgAAANA2QjcQ4U4O3F3V3j7dAAAAANpH6AYiWF19k1+B+8L0vjrvB3YNTkrQtKwMZrgBAAAAHxG6gQjz1s4K/X//tdOUvtb8f5coPjbalL4AAACAnojQDUSQjAUbTetrvDOZwA0AAAD4ibWiQIQwO3Cvnn6xaf0BAAAAPRUz3UAEeGtnhV/333jRQB2sqVdG/wTdO9nJDDcAAABgEkI3EAH8eYb7goE2Pf7ToSZWAwAAAKAVy8uBHuyCgTatnzcm2GUAAAAAEYuZbqCHOceRqPR+8XrqxuHqY+WvAAAAACCQ+I0biAD/+rMRnVpi/q8/G6FxI1K7oSIAAAAAEsvLgYjQ2SBN4AYAAAC6F6EbiBBlj+X4dR0AAACA+VheDoSIZrehotJqVdXWKznRqpGZSYqOsvjUR9ljOXprZ4XXUnOWlAMAAADBQ+gGQkD+ngot3VCiipp6z7lUu1WLc53KHuJbYB43IlVlI5jVBgAAAEIBy8uBIMvfU6E5a3Z6BW5Jqqyp15w1O5W/pyJIlQEAAADwF6EbCKJmt6GlG0pktHGt9dzSDSVqdrfVAgAAAECoI3QDQVRUWn3KDPeJDEkVNfUqKq3uvqIAAAAAmIbQDQRRVW37gbsr7QAAAACEFkI3EETJiVZT2wEAAAAILYRuIIhGZiYp1W5VexuDWdTyFvORmUndWRYAAAAAkxC6gQB5Ycsnyliw0fN5Ycsnp7SJjrJoca5Tkk4J3q3Hi3OdPu/XDQAAACA0WAzDiMjXIrtcLtntdtXU1MhmswW7HPQwGQs2tnut7LFT99A2c59uAAAAAIHX2cxJ6AZMdrrA3aqt4N3sNlRUWq2q2nolJ7YsKWeGGwAAAAhNnc2cMd1YExDx2lpC3l67X175I69z0VEWZZ3ZPxBlAQAAAAgSnukGTPTgm5+Z2g4AAABAeCN0AwAAAAAQIIRuAAAAAAAChNANdFKz21DhZ9/pf4q/VuFn36nZfeo7CBdNOLNTfXW2HQAAAIDw5nPoLigoUG5urtLS0mSxWLRu3Tqv64ZhaNGiRUpNTVV8fLzGjRun/fv3e66XlZVp5syZyszMVHx8vM4880wtXrxYDQ0NXv3s3r1bY8aMkdVq1aBBg7R8+fKufUPABPl7KjT68S36+eq/6V/+s1g/X/03jX58i/L3VHi1O/nlaO3pbDsAAAAA4c3n0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+vmX/4U8++URut1vPPfec9u7dq6eeekqrVq3Svffe6+nD5XJpwoQJGjx4sD788EM98cQTWrJkiZ5//vkufk2g6/L3VGjOmp1ee2hLUmVNveas2XlK8G5rOzBfrgMAAACIHH7t022xWLR27Vpdd911klpmudPS0nTHHXfozjvvlCTV1NQoJSVFeXl5mjJlSpv9PPHEE1q5cqU+//xzSdLKlSt13333qbKyUrGxsZKkBQsWaN26dfrkk85tycQ+3eiqYw3NenRTicq+O6rBSQl6c2+lquoa2mxrkeSwW/X+PVeesqf2C1s+8XpL+aIJZzLDDQAAAESIoOzTXVpaqsrKSo0bN85zzm63a9SoUSosLGw3dNfU1CgpKclzXFhYqLFjx3oCtyRNnDhRjz/+uL7//nv169fPzLIBj1kvb9fmkirP8dYO2huSKmrqVVRafcoe27+88keEbAAAAKCHM/VFapWVlZKklJQUr/MpKSmeayc7cOCAnnnmGf3qV7/y6qetPk78GSc7fvy4XC6X1wfwxcmB2xdVtfUdNwIAAADQ4wT17eVff/21srOz9c///M+aNWuWX30tW7ZMdrvd8xk0aJBJVaInONbQ3OXALUnJiVYTqwEAAAAQKUwN3Q6HQ5J06NAhr/OHDh3yXGt18OBBXXHFFbr00ktPeUGaw+Fos48Tf8bJFi5cqJqaGs/nyy+/9Ou7oGd5dFNJl+6zSEq1WzUyM6nDtgAAAAB6HlNDd2ZmphwOh95++23POZfLpW3btikrK8tz7uuvv9bll1+uCy+8UC+++KKiorzLyMrKUkFBgRobGz3nNm/erHPOOafd57nj4uJks9m8PkBnlX131Od7Wl+btjjXecpL1AAAAABA6kLorqurU3FxsYqLiyW1vDytuLhY5eXlslgsuu222/Twww9r/fr1+uijjzR9+nSlpaV53nDeGrjT09P15JNP6ptvvlFlZaXXs9o33XSTYmNjNXPmTO3du1evvvqq/vjHP2r+/PmmfGngZBn9E3y+x2G3auXUEcoekhqAigAAAABEAp/fXr5jxw5dccUVnuPWIDxjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqzWlmdeN2/erAMHDujAgQMaOHCgV9+tu5fZ7Xa9+eabmjt3ri688EINGDBAixYt0uzZs7v8RdFzfV19TJOefk9Hjjerd1y03vjtZfpBUrxXm3snO/XK38o77OulWy7W4fpGJSe2LClnhhsAAADA6fi1T3coY59uSNLZ921SQ/Op/188NtqiTx+Z7HWuo7eXj3cma/X0i02vEQAAAED46WzmDOrby4FAai9wS1JDs6Gz79vkdW719Is13pncZnsCNwAAAICu8Hl5ORAOvq4+1m7gbtXQbOjr6mNeS81XT79Yxxqa9eimEpV9d1QZ/RN072Sn4mOjA10yAAAAgAjE8nJEjGa3oaLSalXV1mvhX3braKO7w3ts1mjtXpLdDdUBAAAAiCSdzZzMdCMi5O+p0NINJaqoqffpviPHmwNUEQAAAAAQuhEB8vdUaM6anerKko3ecSwbBwAAABA4hG6EnROXkQ/oE6cl6/d2KXBL0hu/vczU2gAAAADgRIRuhJWuLiNvS2y05ZT9ugEAAADATIRuhA1/lpGfrK19ugEAAADAbIRuhIVmt6GlG0q6HLgTekXpeJNbveOi9cZvL2OGGwAAAEC3IHQjLBSVVndpSblFksNu1fv3XKnoKIv5hQEAAADAaUQFuwCgM6pquxa4JWlxrpPADQAAACAomOlGWEhOtPp8j8Nu1eJcp7KHpAagIgAAAADoGKEbYWFkZpJS7VZV1tS3+Vx36zLyJ386VN8eOa7kRKtGZiYxww0AAAAgqAjdCAvRURYtznVqzpqdskhewfvEZeQ/PmtAEKoDAAAAgLbxTDfCRvaQVK2cOkIOu/dSc4fdqpVTR7CMHAAAAEDIYaYbYSV7SKrGOx0qKq1WVW09y8gBAAAAhDRCN7rNR+U1uubZ92WoZUn4+t+M1vnpdp/7iY6yKOvM/qbXBwAAAABmI3SjW2Qs2Oh1bEjKffZ9SVLZYzlBqAgAAAAAAo9nuhFwJwduX68DAAAAQLgidCOgPiqvMbUdAAAAAIQTQjcC6pq/LyE3qx0AAAAAhBNCNwLK6LiJT+0AAAAAIJwQuhFQnd3Iiw2/AAAAAEQiQjcCav1vRpvaDgAAAADCCaEbAdXZfbi7sl83AAAAAIQ6QjcCrqN9uNmnGwAAAECkigl2AegZyh7L0UflNbrm2fdlqOUZ7vW/Gc0MNwAAAICIRuhGtzk/3a5SZrUBAAAA9CAsLwcAAAAAIEAI3QAAAAAABAjLy+HR7DZUVFqtqtp6JSdaNTIzSdFR7KANAAAAAF1F6IYkKX9PhZZuKFFFTb3nXKrdqsW5TmUPSQ1iZQAAAAAQvlheDuXvqdCcNTu9ArckVdbUa86ancrfUxGkygAAAAAgvBG6e7hmt6GlG0pktHGt9dzSDSVqdrfVAgAAAABwOiwv74GONTTr0U0lKvvuqKwxUafMcJ/IkFRRU6+i0mplndm/+4oEAAAAgAhA6O5hZr28XZtLqny+r6q2/WAOAAAAAGgby8t7kK4GbklKTrSaXA0AAAAARD5munuIYw3NXQrcFkkOe8v2YQAAAAAA3zDT3UM8uqnE53tad+henOtkv24AAAAA6AJmunuIsu+O+nyPg326AQAAAMAvhO4eIqN/grbu77jd+HOTdfXQNCUntiwpZ4YbAAAAALqO0N1D3DvZqVf+Vt5hu6d/PkLxsdHdUBEAAAAARD6e6e4h4mOjNd6ZfNo2453JBG4AAAAAMBGhuwdZPf3idoP3eGeyVk+/uJsrAgAAAIDIxvLyHmb19It1rKFZj24qUdl3R5XRP0H3TnYyww0AAAAAAUDo7oHiY6P10HXnB7sMAAAAAIh4LC8HAAAAACBACN0AAAAAAAQIoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoS3lwdRXX2Tbn91l8q/P6b0fvF66sbh6mNlSAAAAAAgUvg8011QUKDc3FylpaXJYrFo3bp1XtcNw9CiRYuUmpqq+Ph4jRs3Tvv37/dqU11drZtvvlk2m019+/bVzJkzVVdX59Vm9+7dGjNmjKxWqwYNGqTly5f7/u1C2DV/2qohS/5Xmz+u0r7KWm3+uEpDlvyvrvnT1mCXBgAAAAAwic+h+8iRIxo6dKhWrFjR5vXly5fr6aef1qpVq7Rt2zb17t1bEydOVH19vafNzTffrL1792rz5s16/fXXVVBQoNmzZ3uuu1wuTZgwQYMHD9aHH36oJ554QkuWLNHzzz/fha8Yeq7501bt/srV5rXdX7kI3gAAAAAQISyGYRhdvtli0dq1a3XddddJapnlTktL0x133KE777xTklRTU6OUlBTl5eVpypQp+vjjj+V0OrV9+3ZddNFFkqT8/HxNnjxZX331ldLS0rRy5Urdd999qqysVGxsrCRpwYIFWrdunT755JNO1eZyuWS321VTUyObzdbVr2i6uvomDVnyvx2227NkIkvNAQAAACBEdTZzmvoitdLSUlVWVmrcuHGec3a7XaNGjVJhYaEkqbCwUH379vUEbkkaN26coqKitG3bNk+bsWPHegK3JE2cOFH79u3T999/3+bPPn78uFwul9cnFN3+6i5T2wEAAAAAQpepobuyslKSlJKS4nU+JSXFc62yslLJycle12NiYpSUlOTVpq0+TvwZJ1u2bJnsdrvnM2jQIP+/UACUf3/M1HYAAAAAgNAVMVuGLVy4UDU1NZ7Pl19+GeyS2pTeL97UdgAAAACA0GVq6HY4HJKkQ4cOeZ0/dOiQ55rD4VBVVZXX9aamJlVXV3u1aauPE3/GyeLi4mSz2bw+oeipG4eb2g4AAAAAELpMDd2ZmZlyOBx6++23PedcLpe2bdumrKwsSVJWVpYOHz6sDz/80NNmy5YtcrvdGjVqlKdNQUGBGhsbPW02b96sc845R/369TOz5G7XxxqjCwae/h8ELhho4yVqAAAAABABfA7ddXV1Ki4uVnFxsaSWl6cVFxervLxcFotFt912mx5++GGtX79eH330kaZPn660tDTPG87PPfdcZWdna9asWSoqKtJf//pXzZs3T1OmTFFaWpok6aabblJsbKxmzpypvXv36tVXX9Uf//hHzZ8/37QvHkzr541pN3hfMNCm9fPGdHNFAAAAAIBA8HnLsHfffVdXXHHFKednzJihvLw8GYahxYsX6/nnn9fhw4c1evRoPfvsszr77LM9baurqzVv3jxt2LBBUVFRuuGGG/T000+rT58+nja7d+/W3LlztX37dg0YMEC33nqr7rnnnk7XGapbhp2orr5Jt7+6S+XfH1N6v3g9deNwZrgBAAAAIAx0NnP6tU93KAuH0A0AAAAACE9B2acbAAAAAAD8A6EbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAESEywCwgUwzAkSS6XK8iVAAAAAAAiTWvWbM2e7YnY0F1bWytJGjRoUJArAQAAAABEqtraWtnt9navW4yOYnmYcrvdOnjwoBITE2WxWIJdDv7O5XJp0KBB+vLLL2Wz2YJdDkzG+EY2xjfyMcaRjfGNbIxvZGN8Q5NhGKqtrVVaWpqiotp/cjtiZ7qjoqI0cODAYJeBdthsNv7CiGCMb2RjfCMfYxzZGN/IxvhGNsY39JxuhrsVL1IDAAAAACBACN0AAAAAAAQIoRvdKi4uTosXL1ZcXFywS0EAML6RjfGNfIxxZGN8IxvjG9kY3/AWsS9SAwAAAAAg2JjpBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG6YoqCgQLm5uUpLS5PFYtG6detOafPxxx/rmmuukd1uV+/evXXxxRervLzcc72+vl5z585V//791adPH91www06dOhQN34LtKej8a2rq9O8efM0cOBAxcfHy+l0atWqVV5tGN/QtGzZMl188cVKTExUcnKyrrvuOu3bt8+rTWfGrry8XDk5OUpISFBycrLuuusuNTU1dedXQRs6Gt/q6mrdeuutOueccxQfH6/09HT99re/VU1NjVc/jG/o6syf4VaGYWjSpElt/j3OGIemzo5vYWGhrrzySvXu3Vs2m01jx47VsWPHPNerq6t18803y2azqW/fvpo5c6bq6uq686ugDZ0Z38rKSk2bNk0Oh0O9e/fWiBEj9Je//MWrDeMb+gjdMMWRI0c0dOhQrVixos3rn332mUaPHq0f/ehHevfdd7V792498MADslqtnja33367NmzYoD//+c967733dPDgQV1//fXd9RVwGh2N7/z585Wfn681a9bo448/1m233aZ58+Zp/fr1njaMb2h67733NHfuXP3tb3/T5s2b1djYqAkTJujIkSOeNh2NXXNzs3JyctTQ0KAPPvhAL730kvLy8rRo0aJgfCWcoKPxPXjwoA4ePKgnn3xSe/bsUV5envLz8zVz5kxPH4xvaOvMn+FWf/jDH2SxWE45zxiHrs6Mb2FhobKzszVhwgQVFRVp+/btmjdvnqKi/vFr/s0336y9e/dq8+bNev3111VQUKDZs2cH4yvhBJ0Z3+nTp2vfvn1av369PvroI11//fX62c9+pl27dnnaML5hwABMJslYu3at17kbb7zRmDp1arv3HD582OjVq5fx5z//2XPu448/NiQZhYWFgSoVXdDW+J533nnGgw8+6HVuxIgRxn333WcYBuMbTqqqqgxJxnvvvWcYRufGbtOmTUZUVJRRWVnpabNy5UrDZrMZx48f794vgNM6eXzb8l//9V9GbGys0djYaBgG4xtu2hvjXbt2GT/4wQ+MioqKU/4eZ4zDR1vjO2rUKOP+++9v956SkhJDkrF9+3bPuTfeeMOwWCzG119/HdB64Zu2xrd3797Gyy+/7NUuKSnJWL16tWEYjG+4YKYbAed2u7Vx40adffbZmjhxopKTkzVq1CivpW0ffvihGhsbNW7cOM+5H/3oR0pPT1dhYWEQqoYvLr30Uq1fv15ff/21DMPQO++8o08//VQTJkyQxPiGk9ZlxUlJSZI6N3aFhYU6//zzlZKS4mkzceJEuVwu7d27txurR0dOHt/22thsNsXExEhifMNNW2N89OhR3XTTTVqxYoUcDscp9zDG4ePk8a2qqtK2bduUnJysSy+9VCkpKbrsssv0/vvve+4pLCxU3759ddFFF3nOjRs3TlFRUdq2bVv3fgGcVlt/fi+99FK9+uqrqq6ultvt1n/+53+qvr5el19+uSTGN1wQuhFwVVVVqqur02OPPabs7Gy9+eab+slPfqLrr79e7733nqSW51ViY2PVt29fr3tTUlJUWVkZhKrhi2eeeUZOp1MDBw5UbGyssrOztWLFCo0dO1YS4xsu3G63brvtNv34xz/WkCFDJHVu7CorK71+WW+93noNoaGt8T3Zt99+q4ceeshrWSLjGz7aG+Pbb79dl156qa699to272OMw0Nb4/v5559LkpYsWaJZs2YpPz9fI0aM0FVXXaX9+/dLahnD5ORkr75iYmKUlJTE+IaQ9v78/td//ZcaGxvVv39/xcXF6Ve/+pXWrl2rH/7wh5IY33ARE+wCEPncbrck6dprr9Xtt98uSRo2bJg++OADrVq1Spdddlkwy4MJnnnmGf3tb3/T+vXrNXjwYBUUFGju3LlKS0vzmiFFaJs7d6727NnjNUOCyNHR+LpcLuXk5MjpdGrJkiXdWxxM0dYYr1+/Xlu2bPF6/hPhqa3xbf0d61e/+pV+8YtfSJKGDx+ut99+Wy+88IKWLVsWlFrhu/b+jn7ggQd0+PBhvfXWWxowYIDWrVunn/3sZ9q6davOP//8IFULXzHTjYAbMGCAYmJi5HQ6vc6fe+65nreXOxwONTQ06PDhw15tDh061OZSOISOY8eO6d5779Xvf/975ebm6oILLtC8efN044036sknn5TE+IaDefPm6fXXX9c777yjgQMHes53ZuwcDscpbzNvPWZ8Q0N749uqtrZW2dnZSkxM1Nq1a9WrVy/PNcY3PLQ3xlu2bNFnn32mvn37KiYmxvPYwA033OBZnsoYh772xjc1NVWSOvwdq6qqyut6U1OTqqurGd8Q0d74fvbZZ/rTn/6kF154QVdddZWGDh2qxYsX66KLLvK83JbxDQ+EbgRcbGysLr744lO2QPj00081ePBgSdKFF16oXr166e233/Zc37dvn8rLy5WVldWt9cI3jY2Namxs9HpLqiRFR0d7/gWe8Q1dhmFo3rx5Wrt2rbZs2aLMzEyv650Zu6ysLH300Ude/9HfvHmzbDbbKb8Iont1NL5Sywz3hAkTFBsbq/Xr13vtKiExvqGuozFesGCBdu/ereLiYs9Hkp566im9+OKLkhjjUNbR+GZkZCgtLe20v2NlZWXp8OHD+vDDDz3Xt2zZIrfbrVGjRgX+S6BdHY3v0aNHJem0v2MxvmEimG9xQ+Sora01du3aZezatcuQZPz+9783du3aZXzxxReGYRjGa6+9ZvTq1ct4/vnnjf379xvPPPOMER0dbWzdutXTx69//WsjPT3d2LJli7Fjxw4jKyvLyMrKCtZXwgk6Gt/LLrvMOO+884x33nnH+Pzzz40XX3zRsFqtxrPPPuvpg/ENTXPmzDHsdrvx7rvvGhUVFZ7P0aNHPW06GrumpiZjyJAhxoQJE4zi4mIjPz/fOOOMM4yFCxcG4yvhBB2Nb01NjTFq1Cjj/PPPNw4cOODVpqmpyTAMxjfUdebP8Ml00tvLGePQ1ZnxfeqppwybzWb8+c9/Nvbv32/cf//9htVqNQ4cOOBpk52dbQwfPtzYtm2b8f777xtnnXWW8fOf/zwYXwkn6Gh8GxoajB/+8IfGmDFjjG3bthkHDhwwnnzyScNisRgbN2709MP4hj5CN0zxzjvvGJJO+cyYMcPT5t/+7d+MH/7wh4bVajWGDh1qrFu3zquPY8eOGb/5zW+Mfv36GQkJCcZPfvITo6Kiopu/CdrS0fhWVFQYt9xyi5GWlmZYrVbjnHPOMX73u98Zbrfb0wfjG5raGldJxosvvuhp05mxKysrMyZNmmTEx8cbAwYMMO644w7PllMIno7Gt70/25KM0tJSTz+Mb+jqzJ/htu45eetHxjg0dXZ8ly1bZgwcONBISEgwsrKyvCY1DMMwvvvuO+PnP/+50adPH8Nmsxm/+MUvjNra2m78JmhLZ8b3008/Na6//nojOTnZSEhIMC644IJTthBjfEOfxTAMw+zZcwAAAAAAwDPdAAAAAAAEDKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAALk/wdw9IA+/qwxiAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -923,14 +760,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -943,19 +780,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "در این مورد، همبستگی کمی کمتر است، اما هنوز هم نسبتاً بالا است. حالا، برای اینکه رابطه حتی کمتر آشکار شود، ممکن است بخواهیم با اضافه کردن یک متغیر تصادفی به حقوق، کمی تصادفی بودن بیشتری اضافه کنیم. بیایید ببینیم چه اتفاقی می‌افتد:\n"
+    "در این مورد، همبستگی کمی کمتر است، اما هنوز هم نسبتاً بالا است. حالا، برای کمتر آشکار کردن رابطه، ممکن است بخواهیم با اضافه کردن یک متغیر تصادفی به حقوق، کمی تصادفی بودن اضافه کنیم. بیایید ببینیم چه اتفاقی می‌افتد:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -993,24 +828,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> آیا می‌توانید حدس بزنید چرا نقطه‌ها به این صورت در خطوط عمودی قرار می‌گیرند؟\n",
+    "آیا می‌توانید حدس بزنید چرا نقطه‌ها به صورت خطوط عمودی مرتب می‌شوند؟\n",
     "\n",
-    "ما همبستگی بین یک مفهوم مصنوعی مانند حقوق و متغیر مشاهده‌شده *قد* را بررسی کرده‌ایم. حالا بیایید ببینیم آیا دو متغیر مشاهده‌شده، مانند قد و وزن، نیز با هم همبستگی دارند یا خیر:\n"
+    "ما ارتباط بین یک مفهوم مصنوعی مانند حقوق و متغیر مشاهده‌شده *قد* را بررسی کرده‌ایم. حالا بیایید ببینیم آیا دو متغیر مشاهده‌شده، مانند قد و وزن، نیز با یکدیگر ارتباط دارند:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,16 +858,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "متأسفانه، ما هیچ نتیجه‌ای دریافت نکردیم - فقط چند مقدار عجیب `nan`. این به این دلیل است که برخی از مقادیر در سری ما تعریف نشده‌اند و به صورت `nan` نمایش داده می‌شوند، که باعث می‌شود نتیجه عملیات نیز تعریف نشده باشد. با نگاه به ماتریس می‌توانیم ببینیم که ستون `Weight` مشکل‌ساز است، زیرا همبستگی خودکار بین مقادیر `Height` محاسبه شده است.\n",
+    "متأسفانه، هیچ نتیجه‌ای به دست نیاوردیم - فقط چند مقدار عجیب `nan`. این به این دلیل است که برخی از مقادیر در سری ما تعریف نشده‌اند و به صورت `nan` نمایش داده می‌شوند، که باعث می‌شود نتیجه عملیات نیز تعریف نشده باشد. با نگاه کردن به ماتریس می‌توانیم ببینیم که ستون `Weight` مشکل‌ساز است، زیرا خودهمبستگی بین مقادیر `Height` محاسبه شده است.\n",
     "\n",
-    "> این مثال اهمیت **آماده‌سازی داده‌ها** و **پاکسازی داده‌ها** را نشان می‌دهد. بدون داده‌های مناسب، نمی‌توانیم هیچ چیزی محاسبه کنیم.\n",
+    "> این مثال اهمیت **آماده‌سازی داده‌ها** و **پاکسازی داده‌ها** را نشان می‌دهد. بدون داده‌های مناسب نمی‌توانیم هیچ محاسبه‌ای انجام دهیم.\n",
     "\n",
     "بیایید از متد `fillna` برای پر کردن مقادیر گمشده استفاده کنیم و همبستگی را محاسبه کنیم:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,32 +890,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "در واقع یک همبستگی وجود دارد، اما نه به اندازه‌ای قوی که در مثال مصنوعی ما دیده می‌شود. در حقیقت، اگر به نمودار پراکندگی یک مقدار در مقابل دیگری نگاه کنیم، رابطه بسیار کمتر واضح خواهد بود:\n"
+    "در واقع یک همبستگی وجود دارد، اما نه به اندازه‌ای قوی که در مثال مصنوعی ما دیده می‌شود. در حقیقت، اگر به نمودار پراکندگی یک مقدار در مقابل مقدار دیگر نگاه کنیم، رابطه بسیار کمتر واضح خواهد بود:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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C60H8Mn1s4ibpnjdvnsrKyvTss8+2PhYOhyVJ1113na666iqdeuqpeuCBB3TcccfpiSee6PG/VVRUJL/f3/p15JFH9rr9ANBXqvdG/yPUkzigP+H9AyCC60H8Mn1s4iLpnj9/vlasWKE1a9ZoxIgRrY/n5ras2Q8EAm3iv/a1r2nnzp2SpJycHFVXV7d5vrm5WTU1NcrJyenw31uwYIGCwWDr1yeffOJkdwDAUdnpKY7GAf0J7x8AEVwP4pfpY+Nq0m1ZlubPn6/nn39eq1evVn5+fpvn8/LyNHz48HZlxD744AONGjVKklRQUKA9e/Zo8+bNrc+vXr1a4XBYp59+eof/7sCBA5WRkdHmCwDi1bj8TOX6U1oPEjmUTy0ne47Lz4xlswBP4P0DIILrQfwyfWxcTbrnzZunpUuX6plnnlF6erqqqqpUVVXVWoPb5/Pp1ltv1YMPPqg//OEP+uijj3TXXXfpH//4h6655hpJLbPehYWFmjNnjjZt2qTXX39d8+fP17e//W1bJ5cDQLxLTPBp4cyWFT+H/jGKfL9wZkCJCdH+VAH9F+8fABFcD+KX6WPjaskwn6/jX9qTTz6pK6+8svX7n/zkJ3r44YdVU1Ojk08+WT/96U81YcKE1udramo0f/58LV++XAkJCbrkkkv04IMPavDgwbbaQckwAF5gau1KIBZ4/wCI4HoQv7w2NnbzyLiq0+0Wkm4AXhEKW9pUUaPqvQ3KTm9ZZuXVT32BWGtsDmtJ6XbtqKnTqMw0zS7IU3JSXBxvAyDG+Hsav7w0NiTd3UDSDQCA2bw2ewIAiH9280g+3gUAAEYrKavU3KVb2tWArQo2aO7SLSopq3SpZQCA/oCkGwAAGCsUtrRoebk6WtYXeWzR8nKFwv1+4R8AoI+QdAMAAGNtqqhpN8N9MEtSZbBBmypqYtcoAEC/QtINAACMVb03esLdkzgAALqLpBsAABgrOz3F0TgAALorye0GAAAA9JVx+ZnK9aeoKtjQ4b5un6Qcf0tJGq/xUlkdAOjPSLoBAICxEhN8WjgzoLlLt8gntUm8I+npwpkBzyWrlEADAO9geTkAADBa4ehcLZ41Rjn+tkvIc/wpWjxrjOeSVEqgAYC3MNMNAACMVzg6V1OOP1xLSrdrR02dRmWmaXZBnpKTvDX/0FUJNJ9aSqBNDeR4bvYeAExF0g0AAIzX0XLs326o8Nxy7O6UQCs4Oit2DQMAROWtj3cBAAC6yaTl2JRAAwDvIekGAADG6mo5ttSyHDsU7igi/lACDQC8h6QbAAAYqzvLsb0gUgIt2m5tn1pOMfdiCTQAMBVJNwAAMJZpy7EjJdAktUu8vVwCDQBMRtINAACMZeJybNNKoAGA6Ti9HAAAGCuyHLsq2NDhvm6fWpJVry3HLhydq6mBHG2qqFH13gZlp7f0gRluAIg/JN0AAMBYkeXYc5dukU9qk3h7fTl2YoKPsmAA4AEsLwcAAEZjOTYAwE3MdAMAAOOxHBsA4BaSbgAA0C+wHBsA4AaWlwMAAAAA0EdIugEAAAAA6CMsLwdgtFDYYg9nHGN8EEv1jSHdt7Jc23fVKS8rTXfMCCg1OdHtZvWYaf1BfON6Hd8Yn/jmsyyro7KV/Uptba38fr+CwaAyMjLcbg4Ah5SUVWrR8nJVBhtaH8v1p2jhzACnFccBxgexNOfpt7SqvLrd41MD2Sq+fKwLLeod0/qD+Mb1Or4xPu6xm0eSdIukGzBRSVml5i7dokMvcJHPfCkT5C7GB7EULUGN8Fqialp/EN+4Xsc3xsdddvNI9nQDME4obGnR8vJ2f4AktT62aHm5QuF+/5mjKxgfxFJ9Y6jTBFWSVpVXq74xFKMW9Y5p/TlYKGypdNsuvbj1M5Vu28U1IA5wvY5vjI93kHQDMM6mipo2S6wOZUmqDDZoU0VN7BqFVowPYum+leWOxrnNtP5ElJRVasL9q3Vp8Ubd9OxWXVq8URPuX62Sskq3m9avcb2Ob4yPd5B0AzBO9d7of4B6EgdnMT6Ipe276hyNc5tp/ZH+uTz20OShKtiguUu3kHi7iOt1fGN8vIOkG4BxstNTHI2DsxgfxFJeVpqjcW4zrT8sj41vXK/jG+PjHSTdAIwzLj9Tuf4URSuU4VPLqZ7j8jNj2Sz8H8YHsXTHjICjcW4zrT8sj41vXK/jG+PjHSTdAIyTmODTwpktN5yH/iGKfL9wZoD6lS5hfBBLqcmJmhrI7jRmaiDbM/WtTesPy2PjG9fr+Mb4eAdJNwAjFY7O1eJZY5Tjb7ukKsefQvmMOMD4IJaKLx8bNVH1Ynktk/rD8tj4x/U6vjE+3kCdblGnGzBZKGxpU0WNqvc2KDu9ZYkVn/jGD8YHsVTfGNJ9K8u1fVed8rLSdMeMgGdmhDtiQn9CYUsT7l+tqmBDh/u6fWpJHjbcNoVrg8u4Xsc3xscddvNIkm6RdAMAALglcnq5pDaJdyRdYLYOQLyym0eyvBwAAACuYXksANMlud0AAAAQn1iuiFgpHJ2rqYEcXm8AjETSDQAA2ikpq9Si5eVtyjnl+lO0cGaAmUf0icQEnwqOznK7GQDgOJaXAwCANiJ7bA+tn1wVbNDcpVtUUlbpUssAAPAekm4AANAqFLa0aHl5hydJRx5btLxcoXC/P4cVAABbWF4OAIBDGpvDWlK6XTtq6jQqM02zC/KUnOStz7c3VdS0m+E+mCWpMtigTRU1LAV2mQmvN5NxJgKACJJuAAAcULSyXMXrK3TwBPC9K9/TnIn5WjAj4F7Duql6b/SEuydx6BumvN5MxZkIAA7Gx6EAAPRS0cpyPbaubQIkSWFLemxdhYpWlrvTsB7ITk/pOqgbcXCeSa83E3EmAoBDkXQDgIeEwpZKt+3Si1s/U+m2XeyrjQONzWEVr6/oNKZ4fYUam8MxalHvnHiE39G4eGLC+8e015tpOBMBQEdYXg4AHsFyxfi0pHR7uxnHQ4WtlrhrJh4Vm0b1wv0l79mOu+fCE/u4Nc4x5f1j2uvNNJyJAKAjzHQDgAewXDF+7aipczTObdt32Wun3bh4YNL7x7TXm2k4EwFAR0i6ASDOsVwxvh05NNXROLflZaU5Guc2094/ozLt/d7txsFZnIkAoCMk3QAQ57qzXBGxd3xOhqNxbrvD5snXduPcZtr7Z3ZBnrqqOpXga4lD7I3Lz1SuP0XRhsinlm0N4/IzY9ksAC4j6QaAOMdyxfhWU9foaJzbUpMTNTWQ3WnM1EC2UpMTY9Si3jHt/ZOclKA5E/M7jZkzMZ963S5JTPBp4cyWD6QOTbwj3y+cGaBeN9DPcEUGgDjHcsX4ZuL4FF8+NmriPTWQreLLx8a4RT1n4vgsmBHQdZPy2814J/ik6yZRp9tthaNztXjWGOX4276mcvwpWjxrjKcO7gPgDE4vB4A4F1muWBVs6HBfqk8tN3MsV3SHqeNTfPlY1TeGdN/Kcm3fVae8rDTdMSPgmRnuCFPHZ8GMgL4/7XgtKd2uHTV1GpWZptkFecxwx4nC0bmaGsjRpooaVe9tUHZ6y2uMGW6gf/JZluWNk0P6UG1trfx+v4LBoDIyvLHnDkD/Ejl9WVKbxCFy+8bsibsYn/jG+AAA+oLdPJKPQwHAA1iuGN8Yn/jG+AAA3MRMt5jpBuAdobDFcsU4xvjEN8YHAOAku3kke7oBwEMSE3wqODrL7WYgCsYnvpk2PnyIAADeQNINAADgMSVllVq0vLxNDfJcf4oWzgywXB4A4gx7ugEAADwkcjDcwQm3JFUFGzR36RaVlFW61DIAQEdcTbqLioo0duxYpaenKzs7WxdeeKHef//9DmMty9L06dPl8/n0wgsvtHlu586dOu+885SWlqbs7Gzdeuutam5ujkEPACC2QmFLpdt26cWtn6l02y6Fwt4+lqOxOazH13+sH75YpsfXf6zG5rDbTeoV08anvjGku154V7Mff1N3vfCu6htDbjepV2r2NWraL17TKYte0bRfvKaafY1uN6nbQmFLi5aXd1j+LPLYouXlnnztmXY9oD8AIlxdXr527VrNmzdPY8eOVXNzs+644w5NmzZN5eXlGjRoUJvYX/7yl/L52u9TCoVCOu+885STk6M33nhDlZWVuvzyyzVgwADdd999seoKAPQ505aTFq0sV/H6Ch2cG9y78j3NmZivBTMC7jWsh0wbnzlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM2N/vEpfHpRk76lv0pgfr9Kwwcl6686pLrasezZV1LSb4T6YJaky2KBNFTWe2r9u2vWA/gA4mKsz3SUlJbryyit1wgkn6OSTT9ZTTz2lnTt3avPmzW3itm7dqv/6r//SE0880e5nvPLKKyovL9fSpUt1yimnaPr06brnnnv08MMPq7HRe59gA0BHTFtOWrSyXI+ta3sDJ0lhS3psXYWKVpa707AeMm18Dk24D7aqvFpznn4rxi3qnUMT7oN9ua9RY3+8KsYt6rnqvdET7p7ExQPTrgf0B8Ch4mpPdzAYlCRlZma2PlZXV6fvfOc7evjhh5WTk9PuvyktLdWJJ56oww8/vPWxc889V7W1tfr73//e940GgD5m2nLSxuawitdXdBpTvL7CM0sXTRuf+sZQ1IQ7YlV5tWeWmtfsa4yacEd8ua/RM0vNM1OTHY1zm2nXA/oDoCNxk3SHw2HdfPPNOvPMMzV69OjWx2+55RaNHz9eF1xwQYf/XVVVVZuEW1Lr91VVVR3+NwcOHFBtbW2bLwCIV91ZTuoFS0q3t5sxOVTYaonzAtPG5z6bs1Z249z27d+84Wic2/7xxV5H49xm2vWA/gDoSNyUDJs3b57Kysq0YcOG1seWLVum1atX6+2333b03yoqKtKiRYsc/ZkA0FdMW066o6bO0Ti3mTY+23fZ+73bjXNb9V57M9h249z2yW57v3e7cW4z7XpAfwB0JC5muufPn68VK1ZozZo1GjFiROvjq1ev1rZt2zRkyBAlJSUpKanlM4JLLrlEZ511liQpJydHX3zxRZufF/m+o+XokrRgwQIFg8HWr08++aQPegUAzshOT3E0zm2jMtMcjXObaeOTl2Xv9243zm3D0u0ts7Yb5zbT3j/0J76Z1h/ALa4m3ZZlaf78+Xr++ee1evVq5efnt3n+9ttv1zvvvKOtW7e2fknSAw88oCeffFKSVFBQoHfffVfV1f/cf7Zq1SplZGQoEOj4NMWBAwcqIyOjzRcAxKtx+ZnK9aeoff2GFj61nJI9Lj8zSkR8mV2Qp4Ronfk/Cb6WOC8wbXzusHkSsd04t/3nOcc5Guc2094/9Ce+mdYfwC2uJt3z5s3T0qVL9cwzzyg9PV1VVVWqqqpSfX29pJaZ6tGjR7f5kqSRI0e2JujTpk1TIBDQ7Nmz9be//U1//vOfdeedd2revHkaOHCga30DAKckJvi0cGZLgnPovU/k+4UzA0rs6s4oTiQnJWjOxPxOY+ZMzFdyUlwsxuqSaeOTmpyoqYHsTmOmBrKVmpwYoxb1Tl3Y3gFPduPcZtr7h/7EN9P6A7jF1XfI4sWLFQwGddZZZyk3N7f167nnnrP9MxITE7VixQolJiaqoKBAs2bN0uWXX64f/ehHfdhyAIitwtG5WjxrjHL8bZco5/hTtHjWGM/VgV4wI6DrJuW3m0FJ8EnXTfJe3VfTxueSMSN69Xw8MW35v2Te+4f+xDfT+gO4wWdZljdqmPSh2tpa+f1+BYNBlpoDiGuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2dMTBifUNjShPtXRz2R3aeWDxM23DbFE32L9Kcq2NBhWTev9edgpr1/6E98q28M6b6V5dq+q055WWm6Y0bAMytegL5iN48k6RZJNwAAEaXbdunS4o1dxv1uzhkqODorBi3qvZKySl2/dEvU5x/14GoEIJZKyiq1aHl5mw/jcv0pWjgzwHsH/ZrdPNK7H7cBAADHmVYCDUDvlJRVau7SLe1Wv1QFGzR36RaVlFW61DLAO0i6AQBAK9P2QIfClhYtL4/6vE/SouXlCoX7/cI/oJ3I+6ejd0fkMd4/QNdIugEAQCvTSqBtqqiJuj9dakkcKoMN2lRRE7tGAR7B+wdwBkk3AABoZVoJNJbLAz3H+wdwBkk3AABow6QSaKYtlwdiifcP4IwktxsAAIApTCoRVDg6V5OPzfZ8iaDIcvmuSoZ5Zbn8wUwr4WTS+8cUJr9/gFiiZJgoGQYA6L2ileUqXl+hg88TSvBJcybma8GMgHsN6yGT+mNiybA5T7+lVeXV7R6fGshW8eVjXWhR75j0ejNN5PRySW0S78gGE6+tfgGcRMkwAABipGhluR5b1zZhkKSwJT22rkJFK6Ofnh2PTOvPI6991Kvn4020hFuSVpVXa87Tb8W4Rb1j2uvNNCZtNwHcwvJyAAB6obE5rOL1FZ3GFK+v0PenHe+JpbKm9WdfQ7Pe+bS205h3Pq3VvoZmDU6J/9ui+sZQ1IQ7YlV5teobQ55Yam7a681UhaNzNTWQo00VNare26Ds9JYl5V45UBFwG1cvAAB6YUnp9nYzdIcKWy1xXmBaf2557m1H49x2n81ZX7txbjPt9WayxASfCo7O0gWnHKGCo7NIuIFuIOkGAKAXdtTUORrnNtP6s3N3vaNxbtu+y97v3W6c20x7vQFAR0i6AQDohSOHpjka57ZRmfbaaTfObSOHpjoa57aRmTb7YzPObaa93gCgIyTdAAD0wvE56Y7GuW12QZ66WjWa4GuJ84IHvnWqo3FumxbIcTTObaa93gCgIyTdAAD0Qk1do6NxbktOStCcifmdxsyZmO+ZQ60GpyTppBGdlwM9aUSGJw5Rk6Q99U2OxrnNtNcbAHSEKxgAAL2QnZ7SdVA34uLBghkBXTcpv90MZIJPum6S9+omL5s/MWrifdKIDC2bPzHGLeo5Xm8A4D0+y7K6ODPSfHaLmgMAnBUKW54vQdPYHNbxd73c6QnMCT7pH/dM99xsXX1jSPetLNf2XXXKy0rTHTMCnihDFc2+hmbd8tzb2rm7XiOHpuqBb53qmRnuiFDY0oT7V6sq2KCOXnI+tdRP3nDbFE++l5aUbteOmjqNykzT7II8z71nAPQvdvNIb/2lAQAYo6SsUouWl6sy2ND6WK4/RQtnBlQ4OtfFlnXP5h27bZU82rxjtwqOzopNoxxw6Pis/1D6y3vVnhufgw1OSVLxFWPdbkavJCb4tHBmQHOXbpFPapN4R1LshTMDnku4pZal5tdMPMrtZgCA4/j4EAAQcyVllZq7dEubhFuSqoINmrt0i0rKKl1qWfdV723oOqgbcfHApPExUeHoXC2eNUY5/rZLyHP8KVo8a4xnPxQBAFMx0w0AiKlQ2NKi5eUdLo211DJbt2h5uaYGcjwxW2faHlvTxsdUhaNzNTWQ4/ntGQDQH5B0AwBialNFTbsZ1INZkiqDDdpUUeOJ5djj8jOV60/pco/tuPzMWDetR0wbn4OZtmc4McHnuTEA0DdMOCPFZCTdAICYMm05tml7bE0bn4iileUqXl/RZv/9vSvf05yJnI4NwNtMOSPFZN79eBcA4EmmLceWzNpja+L4FK0s12PrKtodeBe2pMfWVahoZbk7DQOAXuIMDm9gphsAPMSE5WOnjRqqBJ+6LLF12qihsWuUAwpH5+q0kZm66JENqtnfpMxBA/T8dydoWMZAt5vWLaYtl29sDqt4fUWnMcXrK/T9acd7bqm5CSXQDhasa9LVT23S58EGDfen6Ikrx8mfNsDtZvWYCddrk5kwPpzB4R3evTIDQD9jyvIxU0tsjf3xKn25r7H1+7o9IY297y8aNjhZb9051cWWdU9kufz1S7d0+Lwlby2XX1K63dbrbUnpdk+Vqzr/ofV659Pa1u/fr9qr0Xf/WSeNyNCy+RNdbFnPTP7Zau3YVd/6fWWwQSf/6BWNykrV2lunuNiynjHlem0qU8bH5DM4TOOtj3QBoJ8yafmYiXuGD024D/blvkaN/fGqGLeod/7zD+/06vl4sqOmztG4eHBown2wdz6t1fkPrY9xi3rn0IT7YDt21Wvyz1bHuEW9Y9L12kQmjY+Jf09NRdINAHGuq+VjUsvysVBX03lxwrQ9wzX7GqMm3BFf7mtUTRcx8eLL2gOqbWjuNKa2oVlf1h6IUYt6J3OgvUV9duPctq+hOWrCHfHOp7Xa18UYxotgXVPUhDtix656BeuaYtSi3jHtem0a08bHtL+nJiPpBoA4153lY14Q2TMcbXGyTy3L/LyyZ/jbv3nD0Ti3XfTIBkfj3LbkrZ2OxrntlufedjTObVc/tcnROLeZdr02jWnjY9rfU5ORdANAnDNt+Vhkz7CkdjcK3iyxZW8G226c22r225tRtBvntr02Z3ztxrlt5+7OZ4W7G+e2zztJgHoS5zbTrtemMW18TPt7ajKSbgCIcyYuHzOrxFayo3Fuyxxk77Rou3Fuy7B5mrfdOLeNHJrqaJzbhvvtXbfsxrnNxOu1SUwcH5P+nprMZ1mWNzYt9KHa2lr5/X4Fg0FlZGS43RwAaKOxOazj73q5yxJb/7hnuudKHplQsqVmX6PG2DgobcudU5U5OP4T7y9rD2jsfX/pMu6tO87xRDm0z2rqdeZPuz6I6/X/nKIjMuM/Ud3X0KzRd/+5y7iyu8/1RPmwYF2TTv7RK13G/e2H0zxRPiwUtjTh/tVdltzbcNsUz13rTGDy+Jjw99SL7OaR3ro7A4B+qDsltrwmMcGngqOzdMEpR6jg6CxP3iBkDk7WsC6S6WGDkz2RcEvSsIyBXc76ZqQkeSLhlqSdu+2dSm43zm2DU5J00ojOJwhOGpHhiYRbkvxpAzQqq/MPO0ZlpXoi4ZZY7hvvTB4fE/6emoykGwDinGl70Ex08ZgjevV8vPnpv53Uq+fjiYnvn2XzJ0ZNvL1Yp3vB9K/16vl4w3Lf+Mb4wA3e+BgUAPoxE/egmaSxOazi9RWdxhSvr9D3px3vieX/kZI60fjUUlJnaiDHEzMphw22NyNvNy5eLJs/UfsamnXLc29r5+56jRyaqge+dapnZrgjTHu9RRSOztXUQA7LfeMU44NY89aVGQD6oUhJkK72oFESxB1LSrfbWv6/pHS7rpl4VGwa1QvdKalTcHRW7BrWU3ZPrvHgCTeDU5JUfMVYt5vRK8a93g4SWe6L+MT4IJbi/yN3AOjnTN6DZoIdNfb2AtuNc5tpy7G/2n/A0Tg4y7TXGwB0hKQbADyAPWjx68ihaY7Guc207Qym9cc0jA+A/oDl5QDaoORE/GIPWnw6/vB0R+PcZtp2BtP6YxrGB0B/QNINoFVJWaUWLS9vs78u15+ihTMDzKTGCfagxZ+a+kZH49wW2c5w/dItHT5vyVvbGSL9mbt0i3xqu3Wb7RnuY3wA9AcsLwcgqSXhnrt0S7sDbaqCDZq7dItKyipdahkQ31geG//YnhHfGB8ApvNZluXB8zqdVVtbK7/fr2AwqIyMjuteAiYLhS1NuH911BNkI8v7Ntw2xXOzDaYtlzetP6aUPJpw/+oul8d65f1j8vWgsTmsJaXbtaOmTqMy0zS7IM8TZdyiqW8M6b6V5dq+q055WWm6Y0ZAqcmJbjerx0wbHwDms5tHeuvOBkCfMLVki2nL5U3rz/kPrdc7n9a2fv9+1V6NvvvPOmlEhpbNn+hiy7rHtOXY/el68NsNFZ59/8x5+i2tKq9u/X79h9KSjTs1NZCt4su9V0bMtPEBgIP16OPDH/3oR6qra1/6pL6+Xj/60Y963SgAsWViyRbTlsub1p9DE+6DvfNprc5/aH2MW9Q7j7z2Ua+ejydcD+LfoQn3wVaVV2vO02/FuEW9Y9r4AMChepR0L1q0SPv27Wv3eF1dnRYtWtTrRgGILdP2pIbClhYtL+9wqW/ksUXLyxUKe2N3jWn92dfQHDXhjnjn01rta2iOUYt6x7T+JIadjXObae+f+sZQ1IQ7YlV5teobQzFqUe+YNj4A0JEeJd2WZcnna79M7m9/+5syMynpAHhNpGRLtMWvPrUsY/ZKyZbuLI/1AtP6c8tzbzsa5zbT+nPHsncdjXObae+f+1aWOxrnNtPGBwA60q093UOHDpXP55PP59Oxxx7bJvEOhULat2+frr/+escbCaBvmVayxbTlsab1Z+fuekfj3GZaf/YfsDdDajfObaa9f7bvar+9rzdxbjNtfACgI91Kun/5y1/KsixdffXVWrRokfx+f+tzycnJysvLU0FBgeONBND3IiVbDj3IJseDB3WZtlzetP6MHJqq96v22orzAtP6M2hgomobuk6oBw30xinZpr1/8rLStP5De3FeYNr4AEBHupV0X3HFFZKk/Px8jR8/XgMGDOiTRgFwR+HoXE0N5Hi+JNVpo4YqwSd1tgUwwdcS5wWR5f9dlaTyyvL/B751qkbf/WdbcV5gWn/+dP0EnfPLtbbivMC068EdMwJasnGnrTgvMO36BgAd6dGe7smTJysxMVEffPCBNmzYoHXr1rX5AuBdiQk+FRydpQtOOUIFR2d5LuGWpM07dnd6gy213IBv3rE7Ng3qpcjy/2hd8lpJqsEpSTppRPRalpJ00ogMz9TrNq0//72xwtE4t5l2PUhNTtTUQHanMVMD2Z6p123a9Q0AOtKjO4CNGzfqO9/5jnbs2CHLanuZ9Pl8CoW8sc8LgJnYIxj/ls2fGLVsmNfqdEtm9Yc9w/Gv+PKxUcuGebVONwCYrEdJ9/XXX6+vf/3reumll5Sbm9vhSeYA4BbT9ghGSupE41NLSZ2pgRxPzQYtmz9R+xqadctzb2vn7nqNHJqqB751qmdmhA9lSn9M2zOcmZrsaFy8KL58rOobQ7pvZbm276pTXlaa7pgR8MwMd4Sp1zcAOFiP7gQ+/PBD/eEPf9AxxxzjdHsAoNdM2yPYnZI6BUdnxa5hDhickqTiK8yZlUtNTtTVE45qPRPBawmQZN6e4X980fUhd5G4iccN6+PWOCs5KUEzThze+npLTurRrkFXmXx9A4CIHiXdp59+uj766COSbgBxiRJocENJWWW70/9zPXj6f2TPcEdLlyO8tGf4k932lsHbjYsXprzeuL4B6A9sJ93vvPNO6/+/4YYb9P3vf19VVVU68cQT251iftJJJznXQgDoAUqgIZZKyio1d+mWdisrqoINmrt0ixbPGuOp19xRhw3q1fPxZFSmvWXwduPigUmvN65vAPoDn3XoSWhRJCQkyOfztTs4rfUH/d9zXjxIrba2Vn6/X8FgUBkZnZ9AC8BbGpvDWlK6XTtq6jQqM02zC/I8twQzFLY04f7VXS6X33DbFM/M3pskMj7Rlsh6bXwam8M6/q6Xuyyx9Y97pnvivVTfGNLXfljSZdx7Pyr0xOy9aa83rm8AvMxuHml7pruiwhulQQAgoqPll7/dUOG5mW7TlsubxrQ9qUtKt9sqsbWkdLuumXhUbBrVC1s/2WM7zgvjY9rrjesbgP7AdtI9atSovmwHADjKpOWXklnL5U1j2p7UHTX29jbbjXObaeNjWn8krm8AzNejg9SWLVvW4eM+n08pKSk65phjlJ+f36uGAXBHKGxpU0VN62m44/IzPTfDEClB09FknSXvlqApHJ2rcXlZ+vZv3lD13kZlpyfr2WvHK3Owt0odHezL2gO66JENqtnfpMxBA/T8dydoWMZAt5vVLabtSTVtD/Rhg+29nuzGuc2011tE4ehcTThmmOdL7pnMhPsDwC09upJdeOGFHe7vPnhf94QJE/TCCy9o6NChUX9OUVGR/vSnP+kf//iHUlNTNX78eN1///067rjjJEk1NTVauHChXnnlFe3cuVPDhg3ThRdeqHvuuUd+v7/15+zcuVNz587VmjVrNHjwYF1xxRUqKipSUhIXaqA7TDkN17TllxGTf7ZaO3bVt36/p75JY368SqOyUrX21ikutqxnTrr7z6ptaG79vm5PSGPv+4syUpL0zt3nutiy7omUqOvsNZfroRJ1swvydO/K97rc0z27IC9mbeoVWyfXdCPOZaa93iLmPP1WmxPz36/aq9F3/1lTA9kqvtycsoJeZcr9AeCWHp2AsmrVKo0dO1arVq1SMBhUMBjUqlWrdPrpp2vFihVat26ddu3apf/4j//o9OesXbtW8+bN08aNG7Vq1So1NTVp2rRp2r9/vyTp888/1+eff66f//znKisr01NPPaWSkhJdc801rT8jFArpvPPOU2Njo9544w3993//t5566in98Ic/7EnXgH4rshz70Bu5yHLskrJKl1rWfSYuvzw04T7Yjl31mvyz1TFuUe8cmnAfrLahWSfd/ecYt6jnEhN8nSZAUsuHPF6ZEUpOSrC1p9sLh6hJ0lf7Dzga57bEBJ++7OLa9eVe77zepPYJ98FWlVdrztNvxbhFOJhJ9weAW3o0FXzTTTfpN7/5jcaPH9/62De+8Q2lpKTo2muv1d///nf98pe/1NVXX93pzykpaXua6FNPPaXs7Gxt3rxZkyZN0ujRo/XHP/6x9fmjjz5a9957r2bNmqXm5mYlJSXplVdeUXl5uf7yl7/o8MMP1ymnnKJ77rlHt912m+6++24lJ3t32SUQK6Ytxx6SMqDroG7EuS1Y1xQ14Y7Ysatewbom+dPiv09f1h6ImnBH1DY068vaA55Yar7xg1224844Nv5XVpR/Wms7LjAi/it++JrtTWHbjXPbZzX1ag53HtMcbok7IjM1No3qhfrGUKc14aWWxLu+MeSJ0+VNY9r9AeCWHn1MvW3btg6PRM/IyNDHH38sSfqXf/kXffXVV936ucFgUJKUmRl9SVTkOPbI0vHS0lKdeOKJOvzww1tjzj33XNXW1urvf/97hz/jwIEDqq2tbfMF9GfdWY7tBaveq3I0zm1XP7XJ0Ti3XfTIBkfj3PbtJzY6Gue2f31ovaNxbrv9xXcdjXPb9AfXOhrntvtWljsaB2eZdn8AuKVHSfdpp52mW2+9VV9++WXrY19++aX+8z//U2PHtuy7+fDDD3XkkUfa/pnhcFg333yzzjzzTI0ePbrDmK+++kr33HOPrr322tbHqqqq2iTcklq/r6rq+Ia6qKhIfr+/9as77QRMZNpy7B01nc8KdzfObZ93sXS5u3Fuq9nf5GgcnNXFJGq349xW32SvpXbj3Lb/QMjROLdt32XvFHy7cXCWafcHgFt6lHQ//vjjqqio0IgRI3TMMcfomGOO0YgRI7R9+3b99re/lSTt27dPd955p+2fOW/ePJWVlenZZ5/t8Pna2lqdd955CgQCuvvuu3vS7FYLFixo3YseDAb1ySef9OrnAV5n2mm4eVn2TlW2G+e24X57v3e7cW7LHGRvCbzdODjL7o2BN3Z0S6kD7LXUbpzbBg20t8TabpzbTLtem8a0+wPALT36C3PcccepvLxcL774om688UbdeOONWrZsmf7+97/r2GOPldRywvns2bNt/bz58+drxYoVWrNmjUaMGNHu+b1796qwsFDp6el6/vnnNWDAP2/EcnJy9MUXX7SJj3yfk5PT4b83cOBAZWRktPkCeioUtlS6bZde3PqZSrftUqirE4jiUOQ03Gi7sXzy1mm4d8wIOBrntieuHOdonNv+cP2Zjsa57elZ9k5Wthvntj/Z/L3bjXPbSzdMcjTObS/fONnROLfdVvg1R+PgLNPuDwC39Phj3YSEBBUWFrYm3eeee64SErr34yzL0vz58/X8889r9erVHdb2rq2t1bRp05ScnKxly5YpJaXtJ2kFBQV69913VV39z0M4Vq1apYyMDAUC3rihhneVlFVqwv2rdWnxRt307FZdWrxRE+5f7bmTPBMTfFo4s+X9cugf1sj3C2cGPHNISmpyoqYGsjuNmRrI9syhPP60ARqV1fmBSKOyUj1xiJokVeza72ic21Z99EXXQd2Ic9vmT+ztzbQb57Yqm8te7ca57YjMVCUndn4tTk70eeIQNUl697Ogo3Fwlmn3B4BbbJ9e/uCDD+raa69VSkqKHnzwwU5jb7zxRls/c968eXrmmWf04osvKj09vXUPtt/vV2pqamvCXVdXp6VLl7Y59GzYsGFKTEzUtGnTFAgENHv2bP30pz9VVVWV7rzzTs2bN08DB8b/qbfwrkgJjUPntSMlNBbPGuOp2pWFo3O1eNaYdnU4czxah7P48rFRy9B4se7r2lunRC0b5rU63abtETRtT+qOGnvttBvnNtNeb5L0wb0zdOwPVqox1H5lVXKiTx/cO8OFVvWMieNjGtPuDwA32E66H3jgAV122WVKSUnRAw88EDXO5/PZTroXL14sSTrrrLPaPP7kk0/qyiuv1JYtW/Tmm29Kko455pg2MRUVFcrLy1NiYqJWrFihuXPnqqCgQIMGDdIVV1yhH/3oR3a7BnSbqSU0CkfnamogR5sqalS9t0HZ6S1LxrzUh4MVXz5W9Y0h3beyXNt31SkvK013zAh4Zob7UGtvnaJgXZOufmqTPg82aLg/RU9cOc4zM9wRhw2y94Go3Ti35WWlaf2H9uK8YFSmvXbajXObqXtSP7h3hj6rqdf0B9dq/4GQBg1M1Ms3TvbMDHeEqeNjGtPuD4BY81mW5b0NqA6rra2V3+9vLUcGdKV02y5dWtx1+Z/fzTlDBUfHf11eIJZe/+grXfbbN7uM+5//d7rOPOawGLSod+obQ/raD0u6jHvvR4We+MCnsTms4+96WZ0dT5Hgk/5xz3QlJ8X/4WOhsKUJ969WVbChww9KfWqZsdtw2xQSCBcwPgC8zG4e2au/lo2NjXr//ffV3Nzcmx8DeA7L4YCe+2rfAUfj3GbaGQLJSQmaM7H9GSsHmzMx3xMJt8Se1HjH+ADoD3r0F7Ourk7XXHON0tLSdMIJJ2jnzp2SpBtuuEE/+clPHG0gEI9YDgf0nInvn+LLx0ZNvL14hsCpI4f26vl4E9mTmnNIWb0cf4rnzt8wEeMDwHS293QfbMGCBfrb3/6m1157TYWFha2Pn3POObr77rt1++23O9ZAIB5FSmh0tRyOEhpAe6a+f0w5QyByZkU0nFmBvsD4ADBZj5LuF154Qc8995zOOOMM+Xz/vBiecMIJ2rZtm2ONA+JVZDnc3KVb5JPaJA4shwM6Z/L7JzU5UfdceKLbzeiVTRU1bU4oPpQlqTLYoE0VNZ47syIxwee5NvcnjA8AU/VoefmXX36p7Oz2y+j279/fJgkHTMZyOKDneP/EL86sAADAWT2a6f7617+ul156STfccIMktSbav/3tb1VQUOBc64A4x3I4xNqXtQd00SMbVLO/SZmDBuj5707QsAxvlNY6VOHoXB1zWLqmP7hWTWFpQIK05KrTdUzOYLeb1mMmlHTLTE12NC6e7Gto1i3Pva2du+s1cmiqHvjWqRqc0qNbobjQ2BzWktLt2lFTp1GZaZpdkOeZA+76g1DY4v4AgKQelgzbsGGDpk+frlmzZumpp57Sddddp/Lycr3xxhtau3atTjvttL5oa5+hZBgALzjp7j+rtqF9tYiMlCS9c/e5LrSod45a8FKHZakSfNLHRefFvkG9NPlnq7VjV327x0dlpWrtrVNcaFHPFK/7WPeufK/LuB/M+JrmTDoqBi1yxvkPrdc7n9a2e/ykERlaNn+iCy3qnaKV5SpeX9HmPZTgazlZfsGMgHsNgySppKxSi5aXt9mqketP0cKZAVbyAAbp05JhEyZM0NatW9Xc3KwTTzxRr7zyirKzs1VaWuq5hBsAvCBawi1JtQ3NOunuP8e4Rb0TLeGWpLDV8ryXREu4JWnHrnpN/tnqGLeo5z7ZXedoXDyIlnBL0juf1ur8h9bHuEW9U7SyXI+tq2j3Hgpb0mPrKlS0MvpBeOh7JWWVmrt0S7uzEaqCDZq7dItKyipdahkAt3RrTVVt7T//YA0bNkz/9V//1WEMs8UA4Jwvaw9ETbgjahua9WXtAU8sNa+o3h814Y4IWy1x+dmDYtOoXgjWNUVNuCN27KpXsK7JE0vNhwy0d2tgN85t+xqaoybcEe98Wqt9Dc2eWGre2BxW8fqKTmOK11fo+9OOZ6m5CyKn/3d0ibPk3dP/AfROt67GQ4YM0dChQ6N+RZ4HADjnokc2OBrntsJfrXU0zm1XP7XJ0Ti3PVXaeULX3Ti33fLc247GuW1J6XZbH1otKd0ek/agre6c/g+g/+jWR7pr1qxp/f+WZWnGjBn67W9/qyOOOMLxhgEAWtTsb3I0zm0HQvaOErEb57bPO7nB7kmc2/Y3hh2Nc9vO3Z2vQuhunNt21Nhb1m83Ds7i9H8AHelW0j158uQ23ycmJuqMM87QUUd55yAVAPCazEEDVLcnZCvOCwYm+mwl1AMTvbH0crg/pdOZrYPjvGDQwETVNnT9ehs0MDEGrem9kUNT9X7VXltxXjAqM83RODgrO93e+9xuHAAzsNkHAOLc89+d4Gic20pumtx1UDfi3PbEleMcjXPbyzfa+73bjXPbz//9FEfj3Da7IE9dbQVO8LXEIfbG5Wcq15+iaEPkU8sp5uPyM2PZLAAuI+kGgDg3LGOgMro44CkjJckTh6hJUn72IFtJgxcOUZMkf9oAjcrqfJZ0VFaqJw5Rk6QjMlOV3MUqg+REn47I9MbMcHll54eodTfObclJCZozMb/TmDkT8zlEzSWJCT4tnNlSsu3Qd1Hk+4UzAxyiBvQzvb4i+3xcNACgr/30307q1fPx5uOi86Im3l6s07321ilRE2+v1emWpA/unRE18U5O9OmDe2fEuEU9Z+Ie2wUzArpuUn6791CCT7puEnW63VY4OleLZ41RziFbSnL8KVo8awx1uoF+yGdZlu2Tai6++OI23y9fvlxTpkzRoEFtZyP+9Kc/OdO6GLFb1BwA3BAKW5pw/+qo+4Z9armZ23DbFM/NnlRU71fhr9bqQMjSwESfSm6a7JkZ7o4E65p09VOb9HmwQcP9KXriynGemeHuyGc19Zr+4FrtPxDSoIGJevnGyZ6Z4Y54/cOvdNnjb3YZ9z/XnK4z/+WwGLTIOY3NYS0p3a4dNXUalZmm2QV5zHDHkVDY0qaKGlXvbVB2esuScq9dowF0zm4e2a2D1Px+f5vvZ82a1bPWAQBs604JmoKjs2LXMAcckZmq/yw8vjVp8FpCd6jBKUn6j3OPb73J9kLd587kDEnRY7PHtvYnZ4gHD3+ym+N4MBdKTkrQNRM5zDZeJSb4PHdNBtA3unU38OSTT/ZVOwAAUZi4PFaSilaWq3h9RZuaw/eufE9zJnpzeWxJWaUWLS9v8wFJrj9FC2cGPLmc1JT+fLXvgKNxAAB0F2uQACDOmViCpmhluR5b1zbhlqSwJT22rkJFK8vdaVgPlZRVau7SLe1WJFQFGzR36RaVlFW61LKeMak/Jr5/AADeQtINAHHOtBI0jc1hFa+v6DSmeH2FGpvDMWpR74TClhYtL1dHB6REHlu0vFyhQz9hiFOm9eeUI4c4GgcAQHeRdANAnIuUoImW4ljyVgmaJaXb281wHypstcR5QXf23HuBaf155s0djsYBANBdJN0AgJjaUVPnaJzbTNtzb1p/THu9AQC8h6QbAOJcZLlvND55a7nviCH2Tii3G+e2zLRkR+PcZtoe6COH2nsd2Y0DAKC7vF3LBIDjTKsruq+hWbc897Z27q7XyKGpeuBbp3qujJNpJcOsqAvlexbntn9U1dqOm3jssD5uTe9FzhCoCjZ0OAKRuvBeOUPg+JzodVN7EhdPTLi+Hcy0OvcAEOHdKzMAx5lSIiji/IfW651P/5kQvV+1V6Pv/rNOGpGhZfMnutiy7jFtue9ne+y1026c2z7ZXe9onNsiZwhcv3RLh8977QyBmrpGR+PihSnXt4jJP1utHbv++R6pDDbo5B+9olFZqVp76xQXWwYAvcfycgCSzCoRJLW/IT3YO5/W6vyH1se4RT1n2nLfUZlpjsa5zbT+SIqacNt9Pp6Y9v6RzLq+Se0T7oPt2FWvyT9bHeMWAYCzSLoRc6GwpdJtu/Ti1s9Uum2XZ/ahRmNCf0wrEbSvoTnqDWnEO5/Wal9Dc4xa1Dsjh9pL1uzGuW3q13IcjXPbKUcMdTTObW/84ytH49yWnzXI0Ti3mXZ9C9Y1RU24I3bsqlewrilGLQIA55F0I6ZKyio14f7VurR4o256dqsuLd6oCfev9twsaoQp/TGtRNAtz73taJzbLnjY3qyV3Ti3Xbx4g6Nxbvu337zhaJzbvvPUm47Gue3fHn3d0Ti3mXZ9u/qpTY7GAUA8IulGzJi2fNmk/pi2Z3inzb2zduPcVmtzxspunNtM64/d9R/eWCdinpr99mZI7ca5zbTr2+edfODbkzgAiEck3YgJ05Yvm9Yf0/Y8HjnUXjvtxrktw+ZpxHbj3GZaf+weJ+aNY8fMkznI3unXduPcNtJmaTO7cW4b7rd3HbYbF09M2H4GwBkk3YgJ05Yvm9afSImgaEmBTy2nmHulRNBlY0c5Gue2FTdMcjTObab1Z9l3Jzga57ZLxx3haJzbnrf5e7cb57YHvnWqo3Fue+LKcY7GxQtTtp8BcAZJN2LCtOXLpvUnUiJIaj8bF/neSyWCahttLl+2Gee2nCEpSk7s/HefnOhTzhBvzATlDElRVy+lBJ88058TR/odjXPbgER7KwzsxrltWMbALldNZKQkaVjGwBi1qHcGpyRp2ODkTmOGDU72TL1uf9oAW/3xUr1uk7afAXAGSTdiwrTly6b1R5IKR+dq8awxyjlkCV+OP0WLZ43xVJ3uwwbbu3m2G+e2UNhSVhdtzRo80DNLF+sbQ+qqqWGrJc4rtv/kvF49H0+OGGJvWbLduHjwzt3nRk28M1KS9M7d58a4RT3X2BzWrv2d1xTftb9Rjc3hGLWod0JhS0mJnd+OJiUmeOb6Ztr2MwDO8MbHoPC8yPLlqmBDh3+IfGpJ7ryyfNm0/kQUjs7V1ECONlXUqHpvg7LTW/rglRnuVoadbNXVdgbpn9sZCo7OilGreu6+leW24+658MQ+bo1ztv/kPG3+eLcuOeiU8j9eO16nHeWNUmERPpvvC7tx8eKdu8/Vl7UHdNEjG1Szv0mZgwbo+e9O8MwMd8SS0u22PrRaUrpd10w8KjaN6gXTrm/d2X7mhf4AcAZJN2Iisnx57tIt8qltruPF5cum9edgiQk+z98IfLX/gKNxbjNtO8P2XXWOxsWLopXlKl5f0eaxfy9+Q3Mm5mvBjIBLreq+T4P2Tr22GxdPhmUM1Ibbv+F2M3plR42994XdOLeZdn0zrT8AnMHycsSMScuXJfP6YxLTlv+b1p+8rDRH4+JB0cpyPbauot0MZNiSHltXoSKbs/vxYFSmvd+73Tg4y7TxMe36Zlp/ADiDmW7ElDHLl/+Paf0xhWnL/03rzx0zAlqycaetOC9obA63m+E+VPH6Cn1/2vFKTor/z7q/c/oo3fPSe7biEHuzC/J078r3Ol1inuBrifMC065vpvUHgDPi/68/jBNZvnzBKUeo4OgszyeopvXHBKadxh7pT7R7bEve6k9qcqKmBrI7jZkayFZqcmKMWtQ73dlj6wVbP9njaByclZyUoDkT8zuNmTMx3xMf8EjmXq8lM/oDwBneuCIDQDeZtvz/7Z27e/U8+g57bBFrC2YEdN2k/Hal9xJ80nWTvHWGgGTe9dq0/gDoPZaXAzCWKcv/TVu+XN8Y0qry6k5jVpVXq74x5InZ7hE2S2fZjXMbe1K9YcGMgL4/7XgtKd2uHTV1GpWZptkFeZ64BnTElOt1hGn9AdA7JN0A2mhsDhtzEyeZcRq7aSWCTCsZZtmsPWc3zm0m70n9rKZe0x9cq/0HQho0MFEv3zhZR2R648OQ/sCE6/XBGpvDWvnu59q+q055WWk65cghnvggEYDzSLoBtIqUPDo4wbt35XueK3lkGtOWL5tWMuyzPfaWWduNc1tkT+r1S7d0+LzXzhCIOPYHK9UY+ufFrbYhpDN/ulrJiT59cO8MF1vWM1yv49ucp99qs6Jn/YfSko07NTWQreLLx7rYMgBu8O70FQBHmVTyyDSmlQgyrWSYaeMjSfOf6Tjhtvt8vDk04T5YY8jSsT9YGeMW9Q7X6/h2aMJ9sFXl1Zrz9FsxbhEAt5F0A7C9Z7ixORyjFuFgk/+l85O+uxvntqvH21sCbzfObROOHuZonNt2flWnrt7qzeGWOC/4rKY+asId0Riy9FlNfYxa1Dtcr+Nbd86sANB/kHQDMK7kkWnOf3i9o3Fu+/fHXnc0zm2mjU/hr9Y6Gue26Q/aa6fdOLdxvY5v3TmzAkD/QdINwLg9w6apb7I3Y2U3zm21Dc2OxrntQBezqN2Nc5tpr7f9B+zNKNqNcxvX6/hm2pkVAJxB0g3AyD2pJkkdYO9SbTfObekp9s7wtBvntmSbv3a7cW4z7fU2aKC906LtxrmN63V8M+3MCgDO8MZfTAB9anZBnro6iDjB1xKH2Lv//NGOxrlt9tiRjsa57f6LT3Y0zm0lN012NM5tL99or51249zG9Tq+3WHz5Hi7cQDMQNINQMlJCZozMb/TmDkT8z1dr9vLwjZnFO3Gua3mgL1l43bj3JaQZK90lt04t408LE1dvdWTElrivOCIzFQlJ3b+u09O9HmmXjfX6/iWmpyoqYHOD7WcGsimXjfQz3BFBiBJWjAjoOsm5bebQUnwSddNou6rm7LTUxyNc5tpy2NNGx9J+ui+86Im3kkJLc97yYOXntqr5+PNqSOH9up59K3iy8dGTbyp0w30Tz7Lsrxxsksfqq2tld/vVzAYVEZGhtvNAVzV2BzWktLt2lFTp1GZaZpdkOfpGZNQ2NKmihpV721QdnqKxuVnKrGrtZlxprE5rOPvernTE4sTfNI/7pnuibEyrT+hsKUJ969WVbBBHXXJJynHn6INt03x3Gtv51d1KvzVWtU3hZU6IEElN032zAx3RGR8KoMNHT7vtfExrT8mq28M6b6V5dq+q055WWm6Y0aAGW7AMHbzSG+cUgMgZpKTEnTNRG/UR+5KSVmlFi0vb3NzmutP0cKZARWOznWxZd2zecduWyWCNu/YrYKjs2LTqF6ILI99bF30WsNeWh6bmODTwpkBXb90S4fPW5IWzgx4MgEaeViayu+Z7nYzemVTRU3UBFVqGZ/KYIM2VdR44v1jWn9MlpqcqHsuPNHtZgCIA964owGAbiopq9TcpVva3ZxWBRs0d+kWlZRVutSy7qveG/0GuydxcN4ft3zaq+fRd0x7/5jWHwDoD0i6ARgnFLa0aHl5h0t9I48tWl6uUFfTx3HisMEDHY1zW2NzWMXro89yS1Lx+go1NnujDnR9Y0iryqs7jVlVXq36Rm/UgTaNaXvuTesPAPQHJN0AjNOd5ZeeYPezAW98hqAlpdttLZdfUro9Ju3prftWljsaB2eNy89Urj9F0Rb3+9Sy7WRcfmYsm9VjpvUHAPoDkm4AxjFt+eVX+w84Gue2HTV1jsa5bfsue+20GwdnRfbcS2qXqEa+99Kee9P6AwD9AUk3AOOYthzbtP6YVjJslM36znbj4LzC0blaPGuMcvxtl1zn+FO0eNYYTx2sKJnXHwAwnatJd1FRkcaOHav09HRlZ2frwgsv1Pvvv98mpqGhQfPmzVNWVpYGDx6sSy65RF988UWbmJ07d+q8885TWlqasrOzdeutt6q5uTmWXUE/FgpbKt22Sy9u/Uyl23Z5Zp+w0Qxbjm1af2YX5LWrB3+oBF9LnBdM/VqOo3HxZOMHu5R3+0utXxs/2OV2k3qscHSu/nDdeGWkJCrRJ2WkJOoP1433bIJaODpXz80pUNqABPkkpQ1I0HNzCjzbH6nlfIS7XnhXsx9/U3e98K7nz0Hg/gBAhKslw9auXat58+Zp7Nixam5u1h133KFp06apvLxcgwYNkiTdcssteumll/T73/9efr9f8+fP18UXX6zXX39dkhQKhXTeeecpJydHb7zxhiorK3X55ZdrwIABuu+++9zsHvoBU0pSmca05dim9ce0kmF7GpocjYsXebe/1O6xbz+xUZK0/Sfnxbo5vfa1u15WfdM/D+erbQjpzJ+uVuqABL3nwbJox/5gpRpD/0zi6prCmvTzNUpO9OmDe2e42LKemfP0W20OJFz/obRk405NDWSr+PKxLrasZ7g/AHAwV+9oSkpKdOWVV+qEE07QySefrKeeeko7d+7U5s2bJUnBYFCPP/64fvGLX2jKlCk67bTT9OSTT+qNN97Qxo0tf/hfeeUVlZeXa+nSpTrllFM0ffp03XPPPXr44YfV2NjoZvdgOJNKUpnGtNN9TeuPpE4TbjvPxxMTx6ejhLs7z8ebQxPug9U3hfW1u16OcYt659CE+2CNIUvH/mBljFvUO4cm3AdbVV6tOU+/FeMW9Q73BwAOFVfTCMFgUJKUmdly4ubmzZvV1NSkc845pzXm+OOP18iRI1VaWipJKi0t1YknnqjDDz+8Nebcc89VbW2t/v73v8ew9ehPTCtJZZoj/Pb2ztqNc1tmarKjcW5bvbXK0Ti3HdhvbzuT3Ti32V1C7pWl5lV7GqIm3BH1TWFV7fHGwYqf1dRHTbgjGkOWPqupj1GLese0knvcHwDoSNwk3eFwWDfffLPOPPNMjR49WpJUVVWl5ORkDRkypE3s4YcfrqqqqtaYgxPuyPOR5zpy4MAB1dbWtvkCusO4klSG+deH1jka5zbT+nP1s5sdjXPblc/81dE4t0WWkDsV57Z//bXN94/NOLdNf3Cto3FuM63kHvcHADoSN0n3vHnzVFZWpmeffbbP/62ioiL5/f7WryOPPLLP/02YxbSSVKbZf8DejIjdOLd1MUnX7TigP6ltsLfCwG6c20y7vplWco/7AwAdiYuke/78+VqxYoXWrFmjESNGtD6ek5OjxsZG7dmzp038F198oZycnNaYQ08zj3wfiTnUggULFAwGW78++eQTB3uD/sDEPZwmGTQw0dE4tw2weaW2Gwf0Jxkp9s6MtRvnNtOub3lZ9koD2o1zG/cHADri6i2aZVmaP3++nn/+ea1evVr5+fltnj/ttNM0YMAAvfrqq62Pvf/++9q5c6cKCgokSQUFBXr33XdVXf3P/UCrVq1SRkaGAoFAh//uwIEDlZGR0eYL6I5x+ZnK9acoWtUjn1pOKR2XnxnLZjnChBInL9842dE4t5nWnye+fZqjcW77wblHORrntoX/epyjcW5bccMkR+PcZtr14I4ZHd+r9TTObSbfHwDoOVc/1p03b56eeeYZvfjii0pPT2/dg+33+5Wamiq/369rrrlG3/ve95SZmamMjAzdcMMNKigo0BlnnCFJmjZtmgKBgGbPnq2f/vSnqqqq0p133ql58+Zp4MCBbnYPBktM8GnhzIDmLt0in9qWR478oV04M6DErooRxxlTSpwckZmq5ERfp4cNJSf6dESmNw5SOyZncLvX2aF8/xfnBVNOyZFs7CSacoo36lpnD7X3wa3dOLdlDrb3vrAb57acISlKHZDQ6WFqqQMSlDPEGzOPpl3fUpMTNTWQ3elhalMD2UpN9sbMvan3BwB6x9WZ7sWLFysYDOqss85Sbm5u69dzzz3XGvPAAw/oX//1X3XJJZdo0qRJysnJ0Z/+9KfW5xMTE7VixQolJiaqoKBAs2bN0uWXX64f/ehHbnQJ/Ujh6FwtnjVGOf62N2o5/hQtnjXGU0mqZF6JkwcvPbVXz8ebxbPG9Or5ePNoF+3t6vl4YtpyUtP6I0kPfOuUXj0fbz64d4aSEztO2rxYp7v48rGaGsju8Dkv1uk27f4AQO/5LMvy3tpRh9XW1srv9ysYDLLUHN0WClvaVFGj6r0Nyk5vWTLmtU+wQ2FLE+5fHfXEVZ9abhY23DbFE32jP/HN1P5UBRs6XI1Af9xl2uvtYJ/V1Gv6g2u1/0BIgwYm6uUbJ3tmhrsj9Y0h3beyXNt31SkvK013zAh4Zoa7IybcHwDonN080hunhgBxLDHBp4Kjs9xuRq90p8SJF/pKf+Kbaf2JLCe9fumWDp+35K3lpKYtjzXt9XawIzJT9c7dhW43wzGpyYm658IT3W6GY0y4PwDgDM66BWBciRP6E99M64+JTFoey+sNAOA2ZroRcyy3ij+m7eHMTE12NM5t6QMHOBrntsMG2Tvk0m6c20JhS4uWl0d93idp0fJyTQ3keOpaVzg6V1MDOZ6/Xpt2fQMAeA9JN2LKlNOxTRMpcdLVHk6vlDj5xxd7bcdNPG5YH7em9555c7vtuClf6/gworhiN2fzSG5n8vJlE5bHmnZ9AwB4D8vLETOmnY5tksgeTql9nuPFPZyf7K5zNM5tn+yxt+zVbpzbvtp3wNE4t7F8Ob6Zdn0DAHgPSTdiIrL8sqNZhshji5aXKxTu94fpu8akPZyjMtMcjXPbyKH2TiO2G+c205aXHzbYZn9sxsF5Jl3fAADeQ9KNmOjO8ku4p3B0rl66YaKOzR6kIakDdGz2IL10w0TP3ZDOLshTV5NWCb6WOC944Fv2aorbjXOdYcvLO/w0sTdxcWTnV3UK3PWy8m9/SYG7XtbOr7yxOqQjplzfAADew55uxATLL71h8s9Wa8eu+tbv99Q3acyPV2lUVqrW3jrFxZZ1T3JSguZMzNdj6yqixsyZmK/kJG987jg4JUknjcjQO5/WRo05aUSGBqd445Ju2vLyr/bb7I/NuHhxzB0vqTn8z+/rmsKa9PM1SkqQPrrvPPca1kOmXN8AAN7jjTtOeB6nx8a/Q29ID7ZjV70m/2x1jFvUO7/b9Emvno8327uYYezq+Xhi2vXAtP5I7RPugzWHW573EtOubwAAbyHpRkxETo+NtlrUp5ZTzDk91h3BuqaoN6QRO3bVK1jXFKMW9c6XtQdU29DcaUxtQ7O+rPXGzKNp/Tlm2GBH49w2cqi9swHsxrlt51d1URPuiOawPLPU3LTr28FCYUul23bpxa2fqXTbLs5FAYA4RdKNmOD02Ph29VObHI1z20WPbHA0zm2m9eey35Y6Gue2Cx5e72ic2wp/tdbROLeZdn2LKCmr1IT7V+vS4o266dmturR4oybcv5pKIAAQh0i6ETOcHhu/Pu/kkLuexLmtZr+9GSu7cW4zrT/VexsdjXNbV6sQuhvntvqmLqa5uxnnNtOubxIlOAHAa7xx6g6MUTg6V1MDOdpUUaPqvQ3KTm9ZUs4Mt7uG+1M6PV3+4DgvyBw0QHV7QrbivGCozf4M9Uh/hqUna0991x8QDEtPjkFrei8jJUlf2fjAI8MjB92lDkhQnY2EOnWANz63N+361lUJTp9aSnBODeTwtxUA4oQ3/mLCKIkJPhUcnaULTjlCBUdncVMQB564cpyjcW57/rsTHI1z2w8Lv+ZonNv+85zjHI1z24obJjka57aSmyY7Guc2065vlOAEAO8h6QYgf9oAjcpK7TRmVFaq/GkemUnNGNjlrGJGSpKGZQyMUYt6p8FmgWe7cW6rC9tblmw3zm05Q1K6nPVNHZCgnCHemEkdeViauqqml5TQEucFpl3fKMEJAN5D0g1AkrT21ilRb0y9WMf20nFH9ur5eGJaSSrT+iNJ790zPWrinTogQe/dMz3GLeqdj+47L2ri7cU63Qumd74KpKvn44mJ7x8AMB1JN4BWa2+dorfuOEcjhqQobUCiRgxJ0Vt3nOO5hLuxOazi9RWdxhSvr1BjV3WR4oRpJfdM60/Ee/dM18bbv6HDBg1QcqJPhw0aoI23f8NzCXfER/edpzXfO0sDE1tGamCiT2u+d5bnEu7IHuhoInugvVJuy9T3DwCYjKQbQKuileU6vegv+nRPg+qaQvp0T4NOL/qLilZGv2GNR0tKt6ur++ew1RLnBZGSe9G6ZMlbJfdM68/Bcoak6K93TdMH987QX++a5pkl5R0pKavUdx7fqAOhlpE6ELL0ncc3eu5kbNP2QFOCEwC8h6QbgKSWhPuxdRXtktWwJT22rsJTifeOmjpH4+LB2zt39+p5oDtMKkll4h5oSnACgLd4o34JgD5ldzn296cdr+SuTliKA0cM6fzQpO7Guc208bG73JeSR+4wrSTVYYPtHZhoNy5eUIITALwj/u/OAPQ505Zj+2xuzbQb5zbTxse05b6mMW587L7PPXI9OBglOAHAG0i6ARi3HPvTYL2jcW4zbXxMXO5rEtPG56v9BxyNAwCgu0i6AWhUpr16u3bj3EZ/4hslj+KbaeNjWn8AAN5D0g30UihsqXTbLr249TOVbtvlmbIzB5tdkKeuViUm+FrivID+xLfTRg211Z/TRg2NTYMctPnj3cq7/aXWr80fe++AO9PGx+QSW/WNId31wrua/fibuuuFd1XfGHK7Sb1iwt/Tg5k2PgB6joPUgF4oKavUouXlbfY/5vpTtHBmwFOnxyYnJWjOxHw9ti76YV1zJuZ74pAuif7Eu807dtvao755x24VHJ0Vm0Y5IO/2l9o9dslv3pAkbf+Jd2pbmzY+kRJb1y/d0uHzXi1RN+fpt7SqvLr1+/UfSks27tTUQLaKLx/rYst6xpS/pxGmjQ+A3vHGHRoQh0wqqSNJv9v0Sa+ejzedJah2no83j2/ovL1dPR9PTNszLHWccHfn+Xhi4vjc9UJZr56PN4cmdAdbVV6tOU+/FeMW9Y5pf09NGx8AvUfSDfRAVyV1pJaSOl5ZGvdl7QHVNjR3GlPb0Kwva71x0NDW7XscjXPbzq/q1BzuPKY53BLnBdW1+x2Nc5vdJeReWWoesrkE1m6c22r2NerLfY2dxny5r1E1XcTEi/rGUNSELmJVebVnljKb9vfUtPEB4AySbqAHTCupc9EjGxyNc9uFj77uaJzbCn+11tE4t9278kNH49wWWULuVJzbbnvhXUfj3PZtm793u3Fuu29l9Br3PYlzm2l/T00bHwDOIOkGesC05Zc1+5scjYOz6pu6mObuZhzQGbsvI6+83Kr32pvBthvntu277K1osRvnNtP+npo2PgCcQdIN9IBpJWiGptk7U9FuHJyVOsDepdpuHNAZuy8jr7zcstOTHY1zW16WvdKAduPcZtrfU9PGB4AzPPInE4gvppWgueu8ExyNc9sL15/paJzbSm6a7Gic22aNtXcSsd04t/3x2vGOxrnt5RvtvY7sxrntWZu/d7txbrtjRsDROLeZ9vfUtPEB4AySbqAHIiVoJLW7UYh876USNAfC9taJ2o1z2yl5QxyNc9vIw9LUVTWwpISWOC9ISLI3o2g3zm2nHWWvXrXdOLcdkzM4agIU4fu/OC/IHJysYYM7fy0NG5yszC5i4kVqcqKmBrI7jZkayFZqcmKMWtQ7pv09NW18ADiDpBvoocLRuVo8a4xy/G2XvOX4U7R41hhP1RU1bXmf1HVdZC/VTZakaybk9+r5eDIq096HA3bj4sF1kzr//Xf1fLxZPGtMr56PN2/dOTVq4j1scLLeunNqjFvUO8WXj42a2HmxDrRJf08l88YHQO/5LMvyRg2GPlRbWyu/369gMKiMjAy3mwOPCYUtbaqoUfXeBmWntyyB88on8hGhsKUJ969WVbChw7ItPrXc/Gy4bYrn+rZ1+542p5S/cP2ZnpnhjmhsDuv4u15WZxVzEnzSP+6ZruSupsTjQM2+Ro358aou47bcOdUTs4+mjU/kehDtRGkvXw++rD2gix7ZoJr9TcocNEDPf3eChmUMdLtZPVbfGNJ9K8u1fVed8rLSdMeMgKdnUE34e3ow08YHQHt280iSbpF0A5JUUlap65duifr8ox6cbTDF4+s/1j0vvddl3F3nfU3XTDwqBi3qnTn//ZZWvdd5HVtJmvq1bBVfEf8zQqaNT+m2Xbq0eGOXcb+bc4YKjs6KQYucUVJWqUXLy9t8mJDrT9HCmQGubQCAHrGbR8b/R+4A0M/tqLFXWsZunNt27q53NM5tpo2PaSWcpJaEe+7SLe1m76uCDZq7dItKyipdahkAoD8g6QagUNjSouXlUZ/3SVq0vFyhztbPos8cOTTV0Ti3jbTZTrtxbjNtj7ppZzxErm8dXb0ij3F9AwD0JZJuANpUURN1/6bUcmNaGWzQpoqa2DXKIaGwpdJtu/Ti1s9Uum2XJ2+sj8+xt+3FbpzbHvjWqY7GuW12QZ662naa4GuJ8wLTSjiZfH0DAHhDktsNAOA+E5eTSubs4aypa3Q0zm2DU5J00ogMvfNpbdSYk0ZkaHCKN/5EJSclaM7EfD22riJqzJyJ+Z44RE36ZwmnuUu3yCe1mSH2YgknU69vAADv8MYdAIA+ZdpyUsmsPZwmjs+y+RN10oiOZ+ZPGpGhZfMnxrhFvXPqyM5rcHf1fLwxqYSTie8fAIC3eGMaAYhjJpQ4OW3UUCX41GXJo9NGeSNx6GoPZ2SP+tRAjifGyrTxiVg2f6Le/3yvZvx6nUKWlOiTVt4wSccNT3e7ad1i90wEr7zeIgpH52pcXpa+/Zs3VL23UdnpyXr22vGeKON2sMhy+a5KInplufzBTPj7AwD9AUk30AumLF/evGN3pwmd1JLwbd6x2xMlgrqzh9ML/TFtfCKO/cFKNYb+2bGQJZ374DolJ/r0wb0zXGxZ95j2eouY/LPV2rHrnyfI76lv0pgfr9KorFStvXWKiy3rnshy+WglES15a7l8hCl/fwCgP2B5OdBDJi1fNm3PI/2Jf4cm3AdrDFk69gcrY9yinjNxfA5NuA+2Y1e9Jv9sdYxbhIOZ9PcHAPoDkm6gB0wrQTM4KdHROLel2myn3Ti37Wuwd0Ca3Ti3fVZTHzXhjmgMWfqsxht1ugf47M2Q2o1zW7CuKWrCHbFjV72CdU0xalHvmFYS0bS/PwDQH5B0Az1gWgmah9dtczTObT/783uOxrntBy9ETxh6Eue26Q+udTTObT9cVuZonNuufmqTo3FuM+16bVp/AKA/IOkGesC05aSd3cD1JM5tX+6zNwNnNw7O2n8g5Gic22obmh2Nc9vnNt/nduPcZtr12rT+AEB/QNIN9IBpJWiG++21026c24al2ztd2W4cnDVooL1l/Xbj3JZhs5643Ti3mXY9MO16bVp/AKA/IOkGeiBSgibaDk2fWk6R9UoJmieuHOdonNv+85zjHI1z29Xjj3Q0zm33zTzR0Ti3rbhhkqNxbjPtemDa9dq0/gBAf0DSDfRApASNpHY3PpHvvVSCxp82QKOyUjuNGZWVKn/agBi1qHfqwmFH49zWbNm7VNuNc1vI5gS23Ti35QxJUeqAzn/3qQMSlDPEGzOPpl0PTLtem9YfAOgPvHGHBsShwtG5WjxrjHIOWWKZ40/R4lljPFcnde2tU6LeaHutLq9pyy9HZaY5Guc208ZHkt67Z3rUxDt1QILeu2d6jFvUOyZdDyTzrtem9QcATOezLKvf15Sora2V3+9XMBhURkaG282Bx4TCljZV1Kh6b4Oy01uW9Hl5hiFY16Srn9qkz4MNGu5P0RNXjvPMjFZEKGxpwv2rVRVs6LCsjk8tN6cbbpviibFqbA7r+LteVmcVgBJ80j/uma7kpPj/LNW0/hysak+D/vXX61Tb0KyMlCStuGGSZ2a4O2LC9eBgpl2vTesPAHiN3TySpFsk3YCJSsoqNXfpFklqk3hHbke9NhtUtLJcj62riPr8dZPytWBGIIYt6rnSbbt0afHGLuN+N+cMFRydFYMWAQAAdJ/dPNJbUwgAYJNpyy8XzAjoukn5OnQSK8HnrYRbouQRAADoX7xRvwQAeqBwdK6mBnKMWX65YEZA3592vJaUbteOmjqNykzT7II8zy3BPmzwQEfjAAAA4hlJNwCjJSb4jFqinJyUoGsmHuV2M3rH7qamfr/5CQAAmMBb0yMAAM/7av8BR+MAAADiGUk3ACCmTCwZBgAAEA3Lyz3AtJIgpvVnX0Ozbnnube3cXa+RQ1P1wLdO1eAU7761Pqup1/QH12r/gZAGDUzUyzdO1hGZHdfr9YKdX9Wp8FdrVd8UVuqABJXcNFkjD/NGPeuOvPGPr/Sdp95s/f6ZK0/X+OMPc7FF3XfaqKFK8KnLkmGnjRoau0Y55N2dQZ3/yAZZajkpf9l3J+jEkX63m9Vjpl3fAABwg6slw9atW6ef/exn2rx5syorK/X888/rwgsvbH1+3759uv322/XCCy9o165dys/P14033qjrr7++NaahoUHf//739eyzz+rAgQM699xz9cgjj+jwww+33Y54LhlWUlapRcvLVRn85ym+uf4ULZwZ8Nzpy5J5/Tn/ofV659Pado+fNCJDy+ZPdKFFvXPsD1aqMdT+kpCc6NMH985woUW9c8wdL6k53P7xpATpo/vOi32Deinv9peiPrf9J97pj6klw0wZnwjTrm8AADjNEyXD9u/fr5NPPlkPP/xwh89/73vfU0lJiZYuXar33ntPN998s+bPn69ly5a1xtxyyy1avny5fv/732vt2rX6/PPPdfHFF8eqC30qUmf44ARVkqqCDZq7dItKyipdalnPmNafaDekkvTOp7U6/6H1MW5R70RLuCWpMWTp2B+sjHGLeidawi1JzeGW572ks4TOzvPxxMSSYSaNj2Te9Q0AADe5mnRPnz5dP/7xj3XRRRd1+Pwbb7yhK664QmeddZby8vJ07bXX6uSTT9amTZskScFgUI8//rh+8YtfaMqUKTrttNP05JNP6o033tDGjV3PosSzUNjSouXlHR7eG3ls0fJyhTpbnxlHTOvPvobmqDekEe98Wqt9Dc0xalHvfFZTHzXhjmgMWfqspj5GLeqdnV/VRU24I5rDLXFe8MY/vnI0zm176+wl03bj3PbuzqCjcW4z7foGAIDb4vogtfHjx2vZsmX67LPPZFmW1qxZow8++EDTpk2TJG3evFlNTU0655xzWv+b448/XiNHjlRpaWnUn3vgwAHV1ta2+Yo3mypq2s0IH8ySVBls0KaKmtg1qhdM688tz73taJzbpj+41tE4txX+yl477ca57eA93E7Eue3OZf9wNM5t5z+ywdE4t5l2fQMAwG1xnXT/+te/ViAQ0IgRI5ScnKzCwkI9/PDDmjRpkiSpqqpKycnJGjJkSJv/7vDDD1dVVVXUn1tUVCS/39/6deSRR/ZlN3rEtOWXpvVn5257M75249y2/0DI0Ti31Td1Mc3dzTigM6aVHTft+gYAgNviPuneuHGjli1bps2bN+u//uu/NG/ePP3lL3/p1c9dsGCBgsFg69cnn3ziUIudY1pJHdP6M3KovdO87ca5bdDAREfj3JY6wN6lzW4c0Bm7tRe8UqPBtOsbAABui9s7zvr6et1xxx36xS9+oZkzZ+qkk07S/Pnz9a1vfUs///nPJUk5OTlqbGzUnj172vy3X3zxhXJycqL+7IEDByojI6PNV7wZl5+pXH9K1Js0n1pO/R6XnxnLZvWYaf154FunOhrntpdvnOxonNtKbrLXTrtxbnvmytMdjXPbH68d72ic25Z9d4KjcW4z7foGAIDb4jbpbmpqUlNTkxIS2jYxMTFR4XDLktDTTjtNAwYM0Kuvvtr6/Pvvv6+dO3eqoKAgpu11WmKCTwtnBiS1nx2JfL9wZsAz9a1N68/glCSdNKLzD2tOGpHhmXq2R2SmKjmx8999cqLPM/W6Rx6WpqQurm5JCfJMvW67dbi9Uq/7tKPs1d+2G+c2u3W4vVKv27TrGwAAbnM16d63b5+2bt2qrVu3SpIqKiq0detW7dy5UxkZGZo8ebJuvfVWvfbaa6qoqNBTTz2lp59+uvW0c7/fr2uuuUbf+973tGbNGm3evFlXXXWVCgoKdMYZZ7jYM2cUjs7V4lljlONvu+Q6x5+ixbPGeK6utWn9WTZ/YtQbUy/Wsf3g3hlRE28v1ul+6DtjevV8vOmqzrPX6kA/Oqvz339Xz8cb08bHtOsbAABu8lmW5drZLq+99prOPvvsdo9fccUVeuqpp1RVVaUFCxbolVdeUU1NjUaNGqVrr71Wt9xyi3y+luSgoaFB3//+9/W73/1OBw4c0LnnnqtHHnmk0+Xlh7Jb1NwtobClTRU1qt7boOz0liXYXpkR7ohp/dnX0KxbnntbO3fXa+TQVD3wrVM9PQP0WU29pj+4VvsPhDRoYKJevnGyZ2a4I0JhSxPuXx31xHyfWj7s2XDbFM+99jaUf6lZT29q/X7p5eM0ITDMxRZ1n8njs3X7Hl346Out379w/Zk6JW+Iew3qJdOubwAAOMluHulq0h0v4j3pBtA9pdt26dLijV3G/W7OGSo4OisGLXJGSVmlFi0vb5Os5vpTtHBmwFMrRRgfAABgArt5ZNzu6QaAnjKtRJ3UktDNXbql3exwVbBBc5duUUlZpUst6z7GBwAA9Cck3QCMY1qJulDY0qLl5R3WeY48tmh5uUJhbyxcOmzwQEfj3Gba+AAAAGeRdAO91Ngc1uPrP9YPXyzT4+s/VmNz2O0m9UoobKl02y69uPUzlW7b5clEwbQSdZsqaqLuf5ZaErvKYIM2VdTErlG9Yfcl5ZGXnnHjAwAAHMVpKEAvFK0sV/H6Ch2cl9678j3NmZivBTMC7jWsh0zZkxopUTd36Rb51DZ382KJOtOWY3+1/4CjcW4zbXwAAICzmOkGeqhoZbkeW9c24ZaksCU9tq5CRSvL3WlYD5m2J9WkEnWmLZenPwAAoD8h6QZ6oLE5rOL1FZ3GFK+v8MxSc1P3pBaOzlXJTZN02sghyvWn6LSRQ1Ry0yRPJdySdNqooepqUj7B1xLnBaYt/zetPwerbwzprhfe1ezH39RdL7yr+saQ203qFRO2zwAAvIfl5UAPLCnd3m6G+1BhqyXumolHxaZRvdCdPaleKuF0/kPr9c6nta3fVwYbdPKPXtFJIzK0bP5EF1vWPZt37Lb1etu8Y7cnxiey/P/6pVs6fN6St5b/m9afiDlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM6ZsnwEAeA8z3UAP7KipczTObSbuST004T7YO5/W6vyH1se4RT1n4vj8ccunvXoefevQhPtgq8qrNefpt2Lcot4xbfsMAMBbSLqBHjhiiL29mXbj3JaZluxonNv2NTRHTbgj3vm0VvsammPUot5Jara3BNZunNvqG0NRE7qIVeXVnlnKHNmeEY1P3tqeYer4mLZ9BgDgHSTdQA/4ou7e7Fmc2/5RtdfROLfd8tzbjsa5bcGKMkfj3HafzUMG7ca5zbSSYYwPAADOIukGeuDTPfWOxrntk932lsHbjXPbzt32fu9249y2/4C9GUW7cW7bvsve68hunNtMW/7P+AAA4CySbqAHRmWmORrnNtP6M3JoqqNxbhs0MNHROLflZdl7HdmNc5tpJcMYHwAAnEXSDfTA7II8WyWcZhfkxaQ9vWVafx741qmOxrnt5RsnOxrntjtmBByNc5tpJcMYHwAAnEXSDfRAclKC5kzM7zRmzsR8JSd54y1mWn8GpyTppBEZncacNCJDg1O8UTXxiMxUJSd2/qlIcqJPR2R6Y+Y+NTlRUwPZncZMDWQrNdkbM/eRkmGS2iV2ke+9VDKM8QEAwFneuIMG4tCCGQFdNym/3Qxxgk+6blK+FnhkFijCtP4smz8xauLttTrdkvTBvTOiJt7JiT59cO+MGLeod4ovHxs1sfNiHejC0blaPGuMcvxtlyjn+FO0eNYYz9WBZnwAAHCOz7Ksfl8jo7a2Vn6/X8FgUBkZnc+OAYdqbA5rSel27aip06jMNM0uyPPMjHBHTOvPvoZm3fLc29q5u14jh6bqgW+d6pkZ7o58VlOv6Q+u1f4DIQ0amKiXb5zsmRnujtQ3hnTfynJt31WnvKw03TEj4JkZ1I6EwpY2VdSoem+DstNblix7eQaV8QEAIDq7eSRJt0i60TvcxAEAAAD9j9080rvTPUAcKCmr1KLl5W1qwOb6U7RwZoDligAAAADY0w30VElZpeYu3dIm4ZakqmCD5i7dopKySpdaBgAAACBekHQDPRAKW1q0vFwd7c2IPLZoeblC4X6/ewMAAADo10i6gR7YVFHTbob7YJakymCDNlXUxK5RAAAAAOIOSTfQA9V7oyfcPYkDAAAAYCYOUgN6IDs9peugbsTFk2Bdk65+apM+DzZouD9FT1w5Tv60AW43q8dM68/Or+pU+Ku1qm8KK3VAgkpumqyRh6W53aweq9nXqG//5g1V721Udnqynr12vDIHJ7vdrB4zrT+UEIxvVM8AAG+gZJgoGYbuC4UtTbh/dadLzHP9Kdpw2xRP3QBN/tlq7dhV3+7xUVmpWnvrFBda1Dum9eeYO15Sc7j940kJ0kf3nRf7BvXS2B+v0pf7Gts9Pmxwst66c6oLLeod0/pTtLJcxesrdPDRFAk+ac7EfC2YEXCvYT10/kPr9c6nte0eP2lEhpbNn+hCi3qH6hkA4D67eaR3P64GXJSY4NP5J3d+U3P+yblGJNyStGNXvSb/bHWMW9Q7pvUnWsItSc3hlue9JFqCKklf7mvU2B+vinGLese0/hStLNdj69om3JIUtqTH1lWoaGW5Ow3roWgJtyS982mtzn9ofYxb1DtUzwAAbyHpBnogFLa07G+d39Qs+1ulZ04vD9Y1RU1QI3bsqlewrilGLeod0/qz86u6qAl3RHO4Jc4LavY1Rk1QI77c16iaLmLihWn9aWwOq3h9Racxxesr1NjVizJO7GtojppwR7zzaa32NTTHqEW9Q/UMAPAekm6gB7o6vVzy1unlVz+1ydE4t5nWn8JfrXU0zm3f/s0bjsa5zbT+LCnd3m6G+1BhqyXOC2557m1H49xG9QwA8B6SbqAHTDu9/PMuPkDobpzbTOtPfZO9GUW7cW6r3mtvxtdunNtM68+OGnsrJuzGuW3n7s5XvXQ3zm2m/f0BgP6ApBvoAdNOLx/ut9dOu3FuM60/qQPsXartxrktO93ead5249xmWn9GZdo7Dd9unNtGDk11NM5tpv39AYD+wBt3aECcGZefqVx/iqIdk+ZTyymy4/IzY9msHnviynGOxrnNtP6U3DTZ0Ti3PXvteEfj3GZaf2YX5KmrMyATfC1xXvDAt051NM5tpv39AYD+gKQb6IHEBJ8Wzgx0eJCN1LKnbuHMgGdOL/enDdCorM5neUZlpXqmvrVp/Rl5WJq6Ko2clCDP1OvOHJysYV3Urh42ONkz9a1N609yUoLmTMzvNGbOxHzP1OsenJKkk0Z0Xg70pBEZnqnXHfn7I6ld4h353kt/fwCgP/DGX0wAfW7trVOiJqperGttWn8+uu+8qIm3F+t0v3Xn1KiJqhfrWpvWnwUzArpuUn67Ge8En3TdJO/V6V42f2LUxNuLdboLR+dq8awxyjlki0yOP0WLZ42hTjcAxBmfZVn9vqaE3aLmQEQobGnC/aujniDrU8vNz4bbpnhutiFY16Srn9qkz4MNGu5P0RNXjvPMjHBHTOvPzq/qVPirtapvCit1QIJKbprsmRnujtTsa9S3f/OGqvc2Kjs9Wc9eO94zM8IdMa0/jc1hLSndrh01dRqVmabZBXmemeHuyL6GZt3y3NvaubteI4em6oFvneqZGe6OhMKWNlXUqHpvg7LTW5aUe+1vDgB4md08kqRbJN2xZsJNQum2Xbq0eGOXcb+bc4YKjs6KQYsQjQmvt4OZ1h8AAACvsptHevfjXXhSSVmlFi0vbzNDnOtP0cKZAU8th6NkizeY8nqLMK0/AAAA/YF314jBc0rKKjV36ZZ2S7Krgg2au3SLSsoqXWpZ91GyJf6Z9HqTzOsPAABAf0HSjZgIhS0tWl7e4WnfkccWLS9XKOyN3Q6UbIlvpr3eTOsPAABAf0LSjZjYVFET9dAxqSVxqAw2aFNFTewa1QuUbIlvpr3eTOsPAABAf0LSjZgwcQ80JVvil2mvN9P6AwAA0J9wkJoHmHBasal7oAtH52rK8YcbVVKnvjGk+1aWa/uuOuVlpemOGQGlJie63axuMe31Zlp/TGZaiS0AANB7JN1xzpTTiiN7oKuCDR3uS43UtfbaHuiOxue3Gyo8Nz4Rc55+S6vKq1u/X/+htGTjTk0NZKv48rEutqx7Iq+3zpZke2nPvanvH9MUrSxX8foKHby1/t6V72nOxHwtmBFwr2EAAMBVfPwex0w6rdjEPdAmjY/UPuE+2Kryas15+q0Yt6jnEhN8Gn1E9FqJkjT6iAzPvN5MfP+YpmhluR5b1zbhlqSwJT22rkJFK8vdaRgAAHAdSXecMvG0YpP2QJs2PvWNoagJd8Sq8mrVN4Zi1KLeaWwO69X3Ou/Pq+9Vq7E5HKMW9Z5J7x/TNDaHVby+otOY4vUVnnq9AQAA57C8PE5157TigqOzYtewXiocnaupgRzP71E3bXzuszkLd9/Kct1z4Yl93JreW1K6vd2M46HCVkvcNROPik2jHGDK++dgJpxZYerrDQAAOIOkO06ZfFpxYoLPE4loZ0wbn+276hyNc9uOGnvttBsXT0x4/0SYcmaFya83AADQeywvj1OcVhzfTBufvKw0R+PcNirTXjvtxsF5Jp2JwOsNAAB0hqQ7TkVOK462yNInb52+bBrTxucOmycr241z2+yCPHW1QjnB1xKH2DPtTARebwAAoDMk3XGK04rjm2njk5qcqKmB7E5jpgayPVOvOzkpQXMm5ncaM2diPvWTXdKdMxG8gNcbAADoDHcAcYzTiuObaeNzyZgRvXo+3iyYEdB1k/LbzUAm+KTrJlE32U2mnYkg8XoDAADR+SzL8sb6vT5UW1srv9+vYDCojIzOa/u6wYTTfU1mwviEwpYm3L866uyjTy0fJmy4bYrn+tbYHNaS0u3aUVOnUZlpml2Qx4yjy0q37dKlxRu7jPvdnDM8d2gcrzcAAPoPu3kkp5d7gEmnFZvIhPExrQTawZKTEijTFGciZyJUBRs63Ncd+ZDHK2ciHIzXGwAAOBQfvwMwcrkv4pdpZyIAAAB0hqQbgHEl0BD/TDsTAQAAIBqWlwMwerkv4lfh6FxNDeR4/kwEAACAzpB0A2hd7jt36Rb5pDaJN8t90ZdMOBMBAACgMywvByCJ5b4AAABAX2CmG0ArlvsCAAAAznJ1pnvdunWaOXOmhg8fLp/PpxdeeKFdzHvvvafzzz9ffr9fgwYN0tixY7Vz587W5xsaGjRv3jxlZWVp8ODBuuSSS/TFF1/EsBforlDYUum2XXpx62cq3bZLobC3S8U3Nof1+PqP9cMXy/T4+o/V2Bx2u0k4SH1jSHe98K5mP/6m7nrhXdU3htxuUq/Qn/jG9QAAABzKZ1mWaxnPyy+/rNdff12nnXaaLr74Yj3//PO68MILW5/ftm2bxo0bp2uuuUaXXnqpMjIy9Pe//11nnHGGsrOzJUlz587VSy+9pKeeekp+v1/z589XQkKCXn/9ddvtsFvUHL1XUlapRcvL29SEzvWnaOHMgCeXLxetLFfx+god/LlBgk+aMzFfC2YE3GtYD5k2PnOefkuryqvbPT41kK3iy8e60KLeoT/xzbTrAQAA6JzdPNLVpPtgPp+vXdL97W9/WwMGDNCSJUs6/G+CwaCGDRumZ555Rv/2b/8mSfrHP/6hr33tayotLdUZZ5xh698m6Y6NkrJKzV26pd3p2JGFy17bN1y0slyPrauI+vx1k7x1o23a+ERL6CK8ltjRn/hm2vUAAAB0zW4eGbcHqYXDYb300ks69thjde655yo7O1unn356myXomzdvVlNTk84555zWx44//niNHDlSpaWlLrQa0YTClhYtL++wHFXksUXLyz2z1LyxOazi9dFvsCWpeH2FZ5aWmjY+9Y2hThM6SVpVXu2Zpcz0J76Zdj0AAADOituku7q6Wvv27dNPfvITFRYW6pVXXtFFF12kiy++WGvXrpUkVVVVKTk5WUOGDGnz3x5++OGqqqqK+rMPHDig2traNl/oW5sqatosWT6UJaky2KBNFTWxa1QvLCndrq7yz7DVEucFpo3PfSvLHY1zG/2Jb6ZdDwAAgLPi9vTycLhlRuCCCy7QLbfcIkk65ZRT9MYbb+jRRx/V5MmTe/yzi4qKtGjRIkfaCXuq90ZP6HoS57YdNXWOxrnNtPHZvsve791unNvoT3wz7XoAAACcFbcz3YcddpiSkpIUCLTdA/e1r32t9fTynJwcNTY2as+ePW1ivvjiC+Xk5ET92QsWLFAwGGz9+uSTTxxvP9rKTk/pOqgbcW4blZnmaJzbTBufvCx7v3e7cW6jP/HNtOsBAABwVtwm3cnJyRo7dqzef//9No9/8MEHGjVqlCTptNNO04ABA/Tqq6+2Pv/+++9r586dKigoiPqzBw4cqIyMjDZf6Fvj8jOV609RtGrPPrWckj0uPzOWzeqx2QV56qp0dYKvJc4LTBufO2weWGU3zm30J76Zdj0AAADOcjXp3rdvn7Zu3aqtW7dKkioqKrR169bWmexbb71Vzz33nIqLi/XRRx/poYce0vLly/Xd735XkuT3+3XNNdfoe9/7ntasWaPNmzfrqquuUkFBge2TyxEbiQk+LZzZcgN96L1p5PuFMwNK7OrONU4kJyVozsT8TmPmTMxXclLcfq7Vhmnjk5qcqKmB7E5jpgaylZqcGKMW9Q79iW+mXQ8AAICzXC0Z9tprr+nss89u9/gVV1yhp556SpL0xBNPqKioSJ9++qmOO+44LVq0SBdccEFrbENDg77//e/rd7/7nQ4cOKBzzz1XjzzySKfLyw9FybDYMa0OtGl1eU0bH9PqQNOf+Gba9QAAAHTOc3W63UTSHVuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2e0TBuf+saQ7ltZru276pSXlaY7ZgQ8M4PaEfoT30y7HgAAgOhIuruBpBsAAAAA0B1280g+fgcAAAAAoI/EbZ1uAADgLtO2mwAA4AaSbgAA0I5pBysCAOAWlpcDAIA2SsoqNXfpljYJtyRVBRs0d+kWlZRVutQyAAC8h6QbAAC0CoUtLVpero5OWY08tmh5uULhfn8OKwAAtrC8HDHHHsH4RskjoH/bVFHTbob7YJakymCDNlXUqODorNg1DAAAjyLpRkyxRzC+Fa0sV/H6Ch08gXXvyvc0Z2K+FswIuNcwADFTvTd6wt2TOAAA+jumrxAz7BGMb0Ury/XYurYJtySFLemxdRUqWlnuTsMAxFR2eoqjcQAA9Hck3YgJ9gjGt8bmsIrXV3QaU7y+Qo3N4Ri1CIBbxuVnKtefomibfnxqWaE0Lj8zls0CAMCzSLoRE93ZI4jYW1K6vd0M96HCVkscALMlJvi0cGbLdpJDE+/I9wtnBjiLAwAAm0i6ERPsEYxvO2rqHI0D4G2Fo3O1eNYY5fjbLiHP8ado8awxnMEBAEA3cJAaYoI9gvFtVGaao3EAvK9wdK6mBnKoNgEAQC8x042YYI9gfJtdkKeu7qMTfC1xAPqPxASfCo7O0gWnHKGCo7NIuAEA6AGSbsQEewTjW3JSguZMzO80Zs7EfOp1AwAAAN3EHTRihj2C8W3BjICum5TfbsY7wSddN4k63QAAAEBP+CzL6vc1mmpra+X3+xUMBpWRkeF2c4wXClvsEYxjjc1hLSndrh01dRqVmabZBXnMcAMAAACHsJtHcpAaYi6yRxDxKTkpQddMPMrtZgAAAABGYPoKAAAAAIA+QtINAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjnF6OmKNkGGLJtNcbJd0AAAC8haQbMVVSVqlFy8tVGWxofSzXn6KFMwMqHJ3rYstgItNeb0Ury1W8vkJh65+P3bvyPc2ZmK8FMwLuNQwAAABRMT2CmCkpq9TcpVvaJECSVBVs0NylW1RSVulSy2Ai015vRSvL9di6tgm3JIUt6bF1FSpaWe5OwwAAANApkm7ERChsadHyclkdPBd5bNHycoUOzSiAHjDt9dbYHFbx+opOY4rXV6ixORyjFgEAAMAukm7ExKaKmnYzjgezJFUGG7SpoiZ2jYKxTHu9LSnd3m6G+1BhqyUOAAAA8YWkGzFRvTd6AtSTOKAzpr3edtTUORoHAACA2CHpRkxkp6c4Ggd0xrTX26jMNEfjAAAAEDsk3YiJcfmZyvWnKFqhJp9aTpUel58Zy2bBUKa93mYX5KmrKmcJvpY4AAAAxBeSbsREYoJPC2e2lDQ6NHeIfL9wZsDT9ZMRP0x7vSUnJWjOxPxOY+ZMzKdeNwAAQBziDg0xUzg6V4tnjVGOv+2S3hx/ihbPGuPJusmIX6a93hbMCOi6SfntZrwTfNJ1k6jTDQAAEK98lmV5o2ZOH6qtrZXf71cwGFRGRobbzTFeKGxpU0WNqvc2KDu9ZYmvV2Yc4T2mvd4am8NaUrpdO2rqNCozTbML8pjhBgAAcIHdPJKkWyTdAAAAAIDusZtHMj0CAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjJN0AAAAAAPQRkm4AAAAAAPoISTcAAAAAAH2EpBsAAAAAgD5C0g0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIugEAAAAA6CMk3QAAAAAA9BGSbgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPpLkdgPigWVZkqTa2lqXWwIAAAAA8IJI/hjJJ6Mh6Za0d+9eSdKRRx7pcksAAAAAAF6yd+9e+f3+qM/7rK7S8n4gHA7r888/V3p6unw+n9vN6Rdqa2t15JFH6pNPPlFGRobbzcEhGJ/4xvjEN8YnvjE+8Y3xiW+MT3xjfGLPsizt3btXw4cPV0JC9J3bzHRLSkhI0IgRI9xuRr+UkZHBRSGOMT7xjfGJb4xPfGN84hvjE98Yn/jG+MRWZzPcERykBgAAAABAHyHpBgAAAACgj5B0wxUDBw7UwoULNXDgQLebgg4wPvGN8YlvjE98Y3ziG+MT3xif+Mb4xC8OUgMAAAAAoI8w0w0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIutGnPvvsM82aNUtZWVlKTU3ViSeeqL/+9a+tz+/bt0/z58/XiBEjlJqaqkAgoEcffdTFFvcveXl58vl87b7mzZsnSWpoaNC8efOUlZWlwYMH65JLLtEXX3zhcqv7h87GpqamRjfccIOOO+44paamauTIkbrxxhsVDAbdbna/0dV7J8KyLE2fPl0+n08vvPCCO43th+yMT2lpqaZMmaJBgwYpIyNDkyZNUn19vYut7j+6Gp+qqirNnj1bOTk5GjRokMaMGaM//vGPLre6/wiFQrrrrruUn5+v1NRUHX300brnnnt08DFQlmXphz/8oXJzc5WamqpzzjlHH374oYut7j+6Gp+mpibddtttOvHEEzVo0CANHz5cl19+uT7//HOXW96/JbndAJhr9+7dOvPMM3X22Wfr5Zdf1rBhw/Thhx9q6NChrTHf+973tHr1ai1dulR5eXl65ZVX9N3vflfDhw/X+eef72Lr+4e33npLoVCo9fuysjJNnTpV//7v/y5JuuWWW/TSSy/p97//vfx+v+bPn6+LL75Yr7/+ultN7jc6G5vPP/9cn3/+uX7+858rEAhox44duv766/X555/rD3/4g4ut7j+6eu9E/PKXv5TP54t18/q9rsantLRUhYWFWrBggX79618rKSlJf/vb35SQwFxELHQ1Ppdffrn27NmjZcuW6bDDDtMzzzyjb37zm/rrX/+qU0891a1m9xv333+/Fi9erP/+7//WCSecoL/+9a+66qqr5Pf7deONN0qSfvrTn+rBBx/Uf//3fys/P1933XWXzj33XJWXlyslJcXlHpitq/Gpq6vTli1bdNddd+nkk0/W7t27ddNNN+n8889vM/GFGLOAPnLbbbdZEyZM6DTmhBNOsH70ox+1eWzMmDHWD37wg75sGqK46aabrKOPPtoKh8PWnj17rAEDBli///3vW59/7733LElWaWmpi63snw4em4787//+r5WcnGw1NTXFuGWwrI7H5+2337aOOOIIq7Ky0pJkPf/88+41sJ87dHxOP/10684773S5VYg4dHwGDRpkPf30021iMjMzreLiYjea1++cd9551tVXX93msYsvvti67LLLLMuyrHA4bOXk5Fg/+9nPWp/fs2ePNXDgQOt3v/tdTNvaH3U1Ph3ZtGmTJcnasWNHXzcPUfCRLvrMsmXL9PWvf13//u//ruzsbJ166qkqLi5uEzN+/HgtW7ZMn332mSzL0po1a/TBBx9o2rRpLrW6/2psbNTSpUt19dVXy+fzafPmzWpqatI555zTGnP88cdr5MiRKi0tdbGl/c+hY9ORYDCojIwMJSWxgCnWOhqfuro6fec739HDDz+snJwcl1vYvx06PtXV1XrzzTeVnZ2t8ePH6/DDD9fkyZO1YcMGt5vaL3X0/hk/fryee+451dTUKBwO69lnn1VDQ4POOussdxvbT4wfP16vvvqqPvjgA0nS3/72N23YsEHTp0+XJFVUVKiqqqrN/YHf79fpp5/O/UEMdDU+HQkGg/L5fBoyZEiMWolDcXeGPvPxxx9r8eLF+t73vqc77rhDb731lm688UYlJyfriiuukCT9+te/1rXXXqsRI0YoKSlJCQkJKi4u1qRJk1xuff/zwgsvaM+ePbryyislteypS05ObneBPvzww1VVVRX7BvZjh47Nob766ivdc889uvbaa2PbMEjqeHxuueUWjR8/XhdccIF7DYOk9uPz8ccfS5Luvvtu/fznP9cpp5yip59+Wt/4xjdUVlamf/mXf3Gxtf1PR++f//3f/9W3vvUtZWVlKSkpSWlpaXr++ed1zDHHuNfQfuT2229XbW2tjj/+eCUmJioUCunee+/VZZddJkmt9wCHH354m/+O+4PY6Gp8DtXQ0KDbbrtNl156qTIyMmLcWkSQdKPPhMNhff3rX9d9990nSTr11FNVVlamRx99tE3SvXHjRi1btkyjRo3SunXrNG/ePA0fPrzNJ6joe48//rimT5+u4cOHu90UHKKzsamtrdV5552nQCCgu+++O/aNQ7vxWbZsmVavXq23337b5ZZBaj8+4XBYknTdddfpqquuktTy9+nVV1/VE088oaKiItfa2h91dH276667tGfPHv3lL3/RYYcdphdeeEHf/OY3tX79ep144okutrZ/+N///V/9z//8j5555hmdcMIJ2rp1q26++WYNHz689f4N7unO+DQ1Nemb3/ymLMvS4sWLXWoxJLGnG31n5MiR1jXXXNPmsUceecQaPny4ZVmWVVdXZw0YMMBasWJFm5hrrrnGOvfcc2PWTljW9u3brYSEBOuFF15ofezVV1+1JFm7d+9uEzty5EjrF7/4RYxb2H91NDYRtbW1VkFBgfWNb3zDqq+vd6F16Gh8brrpJsvn81mJiYmtX5KshIQEa/Lkye41th/qaHw+/vhjS5K1ZMmSNrHf/OY3re985zuxbmK/1tH4fPTRR5Ykq6ysrE3sN77xDeu6666LdRP7pREjRlgPPfRQm8fuuece67jjjrMsy7K2bdtmSbLefvvtNjGTJk2ybrzxxlg1s9/qanwiGhsbrQsvvNA66aSTrK+++iqWTUQH2NONPnPmmWfq/fffb/PYBx98oFGjRklq+fStqamp3WmxiYmJrTMRiI0nn3xS2dnZOu+881ofO+200zRgwAC9+uqrrY+9//772rlzpwoKCtxoZr/U0dhILTPc06ZNU3JyspYtW8ZpsS7paHxuv/12vfPOO9q6dWvrlyQ98MADevLJJ11qaf/U0fjk5eVp+PDhnf59Qmx0ND51dXWSxL2Bi+rq6jr9/efn5ysnJ6fN/UFtba3efPNN7g9ioKvxkf45w/3hhx/qL3/5i7KysmLdTBzK7awf5tq0aZOVlJRk3XvvvdaHH35o/c///I+VlpZmLV26tDVm8uTJ1gknnGCtWbPG+vjjj60nn3zSSklJsR555BEXW96/hEIha+TIkdZtt93W7rnrr7/eGjlypLV69Wrrr3/9q1VQUGAVFBS40Mr+KdrYBINB6/TTT7dOPPFE66OPPrIqKytbv5qbm11qbf/T2XvnUOL08pjrbHweeOABKyMjw/r9739vffjhh9add95ppaSkWB999JELLe2foo1PY2Ojdcwxx1gTJ0603nzzTeujjz6yfv7zn1s+n8966aWXXGpt/3LFFVdYRxxxhLVixQqroqLC+tOf/mQddthh1n/+53+2xvzkJz+xhgwZYr344ovWO++8Y11wwQVWfn4+q65ioKvxaWxstM4//3xrxIgR1tatW9vcIxw4cMDl1vdfJN3oU8uXL7dGjx5tDRw40Dr++OOt3/zmN22er6ystK688kpr+PDhVkpKinXcccdZ//Vf/xW1LBKc9+c//9mSZL3//vvtnquvr7e++93vWkOHDrXS0tKsiy66yKqsrHShlf1TtLFZs2aNJanDr4qKCnca2w919t45FEl37HU1PkVFRdaIESOstLQ0q6CgwFq/fn2MW9i/dTY+H3zwgXXxxRdb2dnZVlpamnXSSSe1KyGGvlNbW2vddNNN1siRI62UlBTrqKOOsn7wgx+0SdjC4bB11113WYcffrg1cOBA6xvf+IatayF6r6vxqaioiHqPsGbNGncb34/5LMuyYjy5DgAAAABAv8CebgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPkLSDQAAAABAHyHpBgAAAACgj5B0AwAAAADQR0i6AQAAAADoIyTdAAAAAAD0EZJuAADQzmuvvSafz6c9e/bY/m/uvvtunXLKKX3WJgAAvIikGwAAj3v00UeVnp6u5ubm1sf27dunAQMG6KyzzmoTG0mmt23b1unPHD9+vCorK+X3+x1t61lnnaWbb77Z0Z8JAEA8I+kGAMDjzj77bO3bt09//etfWx9bv369cnJy9Oabb6qhoaH18TVr1mjkyJE6+uijO/2ZycnJysnJkc/n67N2AwDQH5B0AwDgcccdd5xyc3P12muvtT722muv6YILLlB+fr42btzY5vGzzz5b4XBYRUVFys/PV2pqqk4++WT94Q9/aBN36PLy4uJiHXnkkUpLS9NFF12kX/ziFxoyZEi79ixZskR5eXny+/369re/rb1790qSrrzySq1du1a/+tWv5PP55PP5tH37dqd/HQAAxBWSbgAADHD22WdrzZo1rd+vWbNGZ511liZPntz6eH19vd58802dffbZKioq0tNPP61HH31Uf//733XLLbdo1qxZWrt2bYc///XXX9f111+vm266SVu3btXUqVN17733tovbtm2bXnjhBa1YsUIrVqzQ2rVr9ZOf/ESS9Ktf/UoFBQWaM2eOKisrVVlZqSOPPLIPfhsAAMSPJLcbAAAAeu/ss8/WzTffrObmZtXX1+vtt9/W5MmT1dTUpEcffVSSVFpaqgMHDuiss85SIBDQX/7yFxUUFEiSjjrqKG3YsEGPPfaYJk+e3O7n//rXv9b06dP1H//xH5KkY489Vm+88YZWrFjRJi4cDuupp55Senq6JGn27Nl69dVXde+998rv9ys5OVlpaWnKycnpy18HAABxg6QbAAADnHXWWdq/f7/eeust7d69W8cee6yGDRumyZMn66qrrlJDQ4Nee+01HXXUUdq3b5/q6uo0derUNj+jsbFRp556aoc///3339dFF13U5rFx48a1S7rz8vJaE25Jys3NVXV1tUO9BADAe0i6AQAwwDHHHKMRI0ZozZo12r17d+ts9fDhw3XkkUfqjTfe0Jo1azRlyhTt27dPkvTSSy/piCOOaPNzBg4c2Kt2DBgwoM33Pp9P4XC4Vz8TAAAvI+kGAMAQZ599tl577TXt3r1bt956a+vjkyZN0ssvv6xNmzZp7ty5CgQCGjhwoHbu3NnhUvKOHHfccXrrrbfaPHbo93YkJycrFAr9//buVyWWMI7j8PdoEfwTBLGqWNbiBaw4TBAsVqtpQdANA1tNBotsXKxehKxJBDF4B0bxDmSDoLB7mnA4J+iBYXV5njgwL79Jw4cZ3vfL9wHATyW6AWBClGWZo6OjvL+//xHTRVHk+Pg4b29vKcsy8/Pz6XQ6qaoqw+EwW1tbeXl5yf39fRYWFnJwcPDX2u12O9vb2+l2u9nb28vNzU36/f6XjxRbWVnJw8NDnp6eMjc3l8XFxUxN2dcVgMnlLQcAE6Isy7y+vmZ9fT3Ly8sf14uiyGAw+DhaLElOT09zcnKSs7OzNBqN7O7u5urqKqurq/9cu9ls5uLiIt1uN5ubm7m+vk5VVZmZmfnSjJ1OJ9PT09nY2MjS0lKen5///4EB4Af4NRqNRuMeAgD4eVqtVh4fH3N3dzfuUQDg2/J7OQDwKefn59nZ2cns7Gz6/X4uLy/T6/XGPRYAfGu+dAMAn7K/v5/b29sMBoOsra2l3W7n8PBw3GMBwLcmugEAAKAmNlIDAACAmohuAAAAqInoBgAAgJqIbgAAAKiJ6AYAAICaiG4AAACoiegGAACAmohuAAAAqInoBgAAgJr8BszmUCbCjQ3yAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1091,14 +924,14 @@
    "source": [
     "## نتیجه‌گیری\n",
     "\n",
-    "در این دفترچه یادداشت یاد گرفتیم که چگونه عملیات پایه‌ای روی داده‌ها انجام دهیم تا توابع آماری را محاسبه کنیم. اکنون می‌دانیم چگونه از ابزارهای مناسب ریاضی و آمار برای اثبات برخی فرضیه‌ها استفاده کنیم و همچنین چگونه بازه‌های اطمینان را برای متغیرهای دلخواه با توجه به نمونه داده محاسبه کنیم.\n"
+    "در این نوت‌بوک یاد گرفتیم که چگونه عملیات پایه‌ای روی داده‌ها انجام دهیم تا توابع آماری را محاسبه کنیم. اکنون می‌دانیم که چگونه از ابزارهای قوی ریاضی و آمار برای اثبات برخی فرضیه‌ها استفاده کنیم و همچنین چطور بازه‌های اطمینان را برای متغیرهای دلخواه با توجه به یک نمونه داده محاسبه کنیم.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**سلب مسئولیت**:  \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما تلاش می‌کنیم دقت را حفظ کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادرستی‌ها باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، توصیه می‌شود از ترجمه حرفه‌ای انسانی استفاده کنید. ما مسئولیتی در قبال سوء تفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n"
+    "\n---\n\n**سلب مسئولیت**:  \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما تلاش می‌کنیم دقت را حفظ کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است حاوی خطاها یا نادقتی‌هایی باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما هیچ مسئولیتی در قبال سوءتفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:04:16+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:06:58+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "fa"
   }
diff --git a/translations/fa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/fa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 14658671..4ce6b9b8 100644
--- a/translations/fa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/fa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### وظیفه ۳: توزیع متغیرهای سن، جنسیت، شاخص توده بدنی و Y چگونه است؟\n"
+    "### وظیفه ۳: توزیع سن، جنسیت، شاخص توده بدنی و متغیر Y چگونه است؟\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -593,9 +593,9 @@
    "cell_type": "markdown",
    "source": [
     "نتیجه‌گیری‌ها:\n",
-    "* سن - عادی\n",
-    "* جنسیت - یکنواخت\n",
-    "* شاخص توده بدنی، Y - سخت است که مشخص شود\n"
+    "* سن - عادی  \n",
+    "* جنسیت - یکنواخت  \n",
+    "* شاخص توده بدنی (BMI)، Y - سخت می‌توان قضاوت کرد  \n"
    ],
    "metadata": {}
   },
@@ -604,7 +604,7 @@
    "source": [
     "### وظیفه ۴: آزمایش همبستگی بین متغیرهای مختلف و پیشرفت بیماری (Y)\n",
     "\n",
-    "> **نکته** ماتریس همبستگی اطلاعات مفیدی در مورد اینکه کدام مقادیر وابسته هستند به شما می‌دهد.\n"
+    "> **نکته** ماتریس همبستگی اطلاعات بسیار مفیدی در مورد اینکه کدام مقادیر وابسته هستند به شما می‌دهد.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -879,7 +879,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### وظیفه ۵: فرضیه را آزمایش کنید که درجه پیشرفت دیابت بین مردان و زنان متفاوت است\n"
+    "### وظیفه ۵: فرضیه را آزمایش کنید که میزان پیشرفت دیابت بین مردان و زنان متفاوت است\n"
    ],
    "metadata": {}
   },
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -907,7 +907,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "نتیجه‌گیری: مقدار p نزدیک به ۰ (معمولاً کمتر از ۰.۰۵) نشان‌دهنده اعتماد بالا به فرضیه ما است. در مورد ما، شواهد قوی وجود ندارد که جنسیت بر پیشرفت دیابت تأثیر بگذارد.\n"
+    "نتیجه‌گیری: مقدار p نزدیک به ۰ (معمولاً کمتر از ۰.۰۵) نشان‌دهنده اعتماد بالا به فرضیه ما است. در مورد ما، شواهد قوی وجود ندارد که نشان دهد جنسیت بر پیشرفت دیابت تأثیر می‌گذارد.\n"
    ],
    "metadata": {}
   },
@@ -920,7 +920,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**سلب مسئولیت**:  \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما تلاش می‌کنیم دقت را حفظ کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادرستی‌ها باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، توصیه می‌شود از ترجمه حرفه‌ای انسانی استفاده کنید. ما هیچ مسئولیتی در قبال سوء تفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n"
+    "\n---\n\n**سلب مسئولیت**:  \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما برای دقت تلاش می‌کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادقتی‌ها باشند. سند اصلی به زبان بومی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما هیچ مسئولیتی در قبال سوءتفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n"
    ]
   }
  ],
@@ -946,8 +946,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:23:53+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:07:35+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "fa"
   }
diff --git a/translations/fi/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/fi/1-Introduction/04-stats-and-probability/assignment.ipynb
index f2003823..984afa21 100644
--- a/translations/fi/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/fi/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Tässä aineistossa sarakkeet ovat seuraavat:  \n",
-    "* Ikä ja sukupuoli ovat itsestään selviä  \n",
-    "* BMI on kehon painoindeksi  \n",
-    "* BP on keskimääräinen verenpaine  \n",
-    "* S1–S6 ovat erilaisia veriarvoja  \n",
-    "* Y on laadullinen mitta taudin etenemisestä yhden vuoden aikana  \n",
+    "Tässä aineistossa sarakkeet ovat seuraavat:\n",
+    "* Ikä ja sukupuoli ovat itsestään selviä\n",
+    "* BMI on kehon massan indeksi\n",
+    "* BP on keskimääräinen verenpaine\n",
+    "* S1–S6 ovat erilaisia verimittauksia\n",
+    "* Y on taudin etenemisen laadullinen mittari yhden vuoden aikana\n",
     "\n",
     "Tutkitaan tätä aineistoa todennäköisyyden ja tilastotieteen menetelmillä.\n",
     "\n",
-    "### Tehtävä 1: Laske kaikkien arvojen keskiarvot ja varianssit  \n"
+    "### Tehtävä 1: Laske kaikkien arvojen keskiarvot ja varianssit\n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### Tehtävä 4: Testaa eri muuttujien ja sairauden etenemisen (Y) välistä korrelaatiota\n",
     "\n",
-    "> **Vinkki** Korrelaatiomatriisi antaa hyödyllisintä tietoa siitä, mitkä arvot ovat riippuvaisia toisistaan.\n"
+    "> **Vinkki** Korrelaatiomatriisi antaa sinulle hyödyllisintä tietoa siitä, mitkä arvot ovat riippuvaisia toisistaan.\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tehtävä 5: Testaa hypoteesi, että diabeteksen etenemisaste eroaa miesten ja naisten välillä\n"
+    "### Tehtävä 5: Testaa hypoteesi, että diabeteksen etenemisaste on erilainen miesten ja naisten välillä\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Vastuuvapauslauseke**:  \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulisi pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskäännöstä. Emme ole vastuussa väärinkäsityksistä tai virhetulkinnoista, jotka johtuvat tämän käännöksen käytöstä.\n"
+    "\n---\n\n**Vastuuvapauslauseke**:  \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulee pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskääntämistä. Emme ole vastuussa tämän käännöksen käytöstä aiheutuvista väärinkäsityksistä tai virhetulkinnoista.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:18:32+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:38:38+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "fi"
   }
diff --git a/translations/fi/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/fi/1-Introduction/04-stats-and-probability/notebook.ipynb
index 7edd7a9a..7b1b9dc4 100644
--- a/translations/fi/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/fi/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Johdanto todennäköisyyteen ja tilastotieteeseen\n",
-    "Tässä muistikirjassa käsittelemme joitakin aiemmin keskusteltuja käsitteitä. Monet todennäköisyyden ja tilastotieteen käsitteet ovat hyvin edustettuina Pythonin tärkeimmissä datankäsittelykirjastoissa, kuten `numpy` ja `pandas`.\n"
+    "# Johdatus todennäköisyyteen ja tilastotieteeseen\n",
+    "Tässä muistikirjassa käsittelemme joitakin aiemmin keskusteltuja käsitteitä. Monet todennäköisyyden ja tilastotieteen käsitteet ovat hyvin edustettuina suurissa Pythonin datankäsittelykirjastoissa, kuten `numpy` ja `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -84,201 +82,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Analysoidaan oikeaa dataa\n",
+    "## Todellisen datan analysointi\n",
     "\n",
-    "Keskiarvo ja varianssi ovat erittäin tärkeitä, kun analysoidaan tosielämän dataa. Ladataan dataa baseball-pelaajista [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Keskiarvo ja varianssi ovat erittäin tärkeitä todellisen maailman datan analysoinnissa. Ladataan tiedot baseball-pelaajista [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) -sivustolta.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Käytämme tässä tietojen analysointiin pakettia nimeltä [**Pandas**](https://pandas.pydata.org/). Puhumme Pandasista ja datan käsittelystä Pythonilla tarkemmin myöhemmin tässä kurssissa.\n",
+    "> Käytämme tässä data-analyysiin pakettia nimeltä [**Pandas**](https://pandas.pydata.org/). Puhumme Pandasista ja datan käsittelystä Pythonissa tarkemmin myöhemmin tässä kurssissa.\n",
     "\n",
     "Lasketaan keskiarvot iälle, pituudelle ja painolle:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Nyt keskitytään pituuteen ja lasketaan keskihajonta ja varianssi:\n"
+    "Keskitytään nyt pituuteen ja lasketaan keskihajonta ja varianssi:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lisäksi keskiarvon ohella on järkevää tarkastella mediaaniarvoa ja kvartiileja. Ne voidaan visualisoida käyttämällä **laatikko-kaaviota**:\n"
+    "Keskiarvon lisäksi on järkevää tarkastella mediaaniarvoa ja kvartiileja. Ne voidaan visualisoida käyttämällä **laatikko-viiksikaaviota**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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CgCxJkiRVGJAlSZKkCgOyJEmSVGFAlqQ2MTQ0xJIlS1i5ciVLlixhaGio7pKkjuNx2Bnm1l2AJGlyQ0ND9Pf3Mzg4yKZNm5gzZw69vb0A9PT01Fyd1Bk8DjuHPciS1AYGBgYYHByku7ubuXPn0t3dzeDgIAMDA3WXJnUMj8POYUCWpDawfv16li9fvsW25cuXs379+poqkjqPx2HnMCBLUhvo6upi3bp1W2xbt24dXV1dNVUkdR6Pw85hQJakNtDf309vby/Dw8Ns3LiR4eFhent76e/vr7s0qWN4HHYOT9KTpDYwegJQX18f69evp6uri4GBAU8MkmaQx2HnMCBLUpvo6emhp6eHkZERVqxYUXc5UkfyOOwMDrGQJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVNBSQI+IdEXFdRFwbEUMRMT8iHh4RF0TEj8p/HzbdxUqSJEnTbdKAHBF7A28DlmXmEmAOcBhwDHBhZj4JuLC8LHW8oaEhlixZwsqVK1myZAlDQ0N1lyRJkqZg7hRut2NE/BHYCfgF8C5gRXn9KcAIsLrJ9UltZWhoiP7+fgYHB9m0aRNz5syht7cXgJ6enpqrkyRJjZi0Bzkzfw58CPgJcCtwR2aeDyzMzFvL29wKPHI6C5XawcDAAIODg3R3dzN37ly6u7sZHBxkYGCg7tIkSVKDIjMnvkExtvjLwGuB3wOnAacD/5mZu1du97vM3GocckQcCRwJsHDhwqWnnnpqs2pvmg0bNrBgwYK6y2gLttXEVq5cyXnnncfcuXMfaKuNGzfyspe9jAsvvLDu8lqar61Cd3d3U/c3PDzc1P21I19bjbOtCh6Hzdeqr63u7u7LM3PZ2O2NDLF4MXBTZt4GEBFnAM8FfhURj87MWyPi0cCvx7tzZp4EnASwbNmyXLFixYN8CtNnZGSEVqyrFdlWE+vq6mLOnDmsWLHigbYaHh6mq6vLdpuEr63CZJ0WAIuOOZub3/+KGahmdvC11TjbquBx2Hzt9tpqZBaLnwDPjoidIiKAlcB64GvA4eVtDge+Oj0lSu2jv7+f3t5ehoeH2bhxI8PDw/T29tLf3193aZIkqUGT9iBn5vci4nTgCmAj8H2KHuEFwJciopciRP/VdBYqtYPRE/H6+vpYv349XV1dDAwMeIKeJEltpKFZLDLzOOC4MZvvo+hNllTR09NDT09P232dJEmSCq6kJ0mSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZKnJhoaGWLJkCStXrmTJkiUMDQ3VXZIkSZqCuXUXIM0mQ0ND9Pf3Mzg4yKZNm5gzZw69vb0A9PT01FydJElqhD3IUhMNDAwwODhId3c3c+fOpbu7m8HBQQYGBuouTZIkNciALDXR+vXrWb58+Rbbli9fzvr162uqSJIkTZUBWWqirq4u1q1bt8W2devW0dXVVVNFkiRpqgzIUhP19/fT29vL8PAwGzduZHh4mN7eXvr7++suTZIkNciT9KQmGj0Rr6+vj/Xr19PV1cXAwIAn6EmS1EYMyFKT9fT00NPTw8jICCtWrKi7HEmSNEUOsZAkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFVMGpAjYr+IuLLyc2dEHB0RB0bEJeW2yyLimTNRsCRJkjSdJg3ImXlDZh6YmQcCS4F7gDOBDwDvLbe/p7wsSVPS19fH/Pnz6e7uZv78+fT19dVdkiSpw82d4u1XAj/OzFsiIoFdy+27Ab9oamWSZr2+vj5OPPFE1qxZw+LFi7n++utZvXo1AGvXrq25OklSp5rqGOTDgKHy96OBD0bET4EPAe9qYl2SOsDJJ5/MmjVrWLVqFfPnz2fVqlWsWbOGk08+ue7SJEkdLDKzsRtG7EDRS7x/Zv4qIj4KXJyZX46IvwaOzMwXj3O/I4EjARYuXLj01FNPbV71TbJhwwYWLFhQdxltwbZqnG01ue7ubs455xzmz5//QHvde++9HHzwwQwPD9ddXst647l385mX71x3GW3DY7FxtlXjPA6nplVfW93d3Zdn5rKx26cyxOJg4IrM/FV5+XDg7eXvpwGfHO9OmXkScBLAsmXLcsWKFVN4yJkxMjJCK9bVimyrxtlWk5s3bx7XX389q1ateqC9TjjhBObNm2fbTeTcs22fKfBYbJxtNQUeh1PSbq+tqQTkHjYPr4CiN/mFwAjwIuBHzStLUic44ogjHhhzvHjxYk444QRWr17NUUcdVXNlkqRO1lBAjoidgJcAf1fZfATwkYiYC9xLOYxCkho1eiLesccey3333ce8efM46qijPEFPklSrhgJyZt4D7DFm2zqKad8k6UFbu3Yta9eubbuv3yRJs5cr6UmSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqWJu3QWodURE0/aVmU3bVytqZlvB7G4v20qSZq/Z+jfeHmQ9IDMn/dln9dcbut1s18y2mu3t1Wgb+NqSpPYzW//GG5AlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpIpJA3JE7BcRV1Z+7oyIo8vr+iLihoi4LiI+MO3VSpIkSdNs7mQ3yMwbgAMBImIO8HPgzIjoBl4JPC0z74uIR05noZIkSdJMmOoQi5XAjzPzFuAtwPsz8z6AzPx1s4uTJEmSZtpUA/JhwFD5+5OB50fE9yLi4og4qLmlSZIkSTNv0iEWoyJiB+BQ4F2V+z4MeDZwEPCliNg3M3PM/Y4EjgRYuHAhIyMjTSi7Md3d3U3d3/DwcFP3165m8v+w3dlWUzOb2+utF97N3X9s3v4WHXN2U/az8/bwsZU7N2VfrWrDhg2z+rXVTJ3QVs08Fj0Op6adXlsNB2TgYOCKzPxVeflnwBllIL40Iv4EPAK4rXqnzDwJOAlg2bJluWLFiodcdKPGZPVtWnTM2dz8/ldMczWzxLlnM5P/h23NtpqaWd5ed5/bvL8zIyMjTWurRcfM7naH5rbXbNcJbdWsY9HjcIra7G/8VIZY9LB5eAXAV4AXAUTEk4EdgNubVpkkSZJUg4YCckTsBLwEOKOy+VPAvhFxLXAqcPjY4RWSJElSu2loiEVm3gPsMWbb/cDrp6MoSZIkqS6upCdJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpIq5dReg6XfAe8/njj/8sWn7W3TM2U3Zz247bs9Vx720Kftqpma212xvK6lOEdHU/WVmU/fXamwvTVUn5wcDcge44w9/5Ob3v6Ip+xoZGWHFihVN2VezDpRma1Z7dUJbSXVqNKAtOubspv0NbGeNtJdtpapOzg8OsZAkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkipcSa8D7NJ1DE895Zjm7fCU5uxmly4AV2ySJEmtxYDcAe5a//6OXSpSkiRpqhxiIUmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaqYNCBHxH4RcWXl586IOLpy/T9GREbEI6a1UkmSJGkGTDoPcmbeABwIEBFzgJ8DZ5aXHwu8BPjJ9JUoSZIkzZypDrFYCfw4M28pL/878E9ANrUqSZIkqSZTDciHAUMAEXEo8PPMvKrpVUmSJEk1iczGOn8jYgfgF8D+wF3AMPDSzLwjIm4GlmXm7ePc70jgSICFCxcuPfXUU5tS+FsvvJu7/9iUXTXVztvDx1buXHcZW3jjuXfzmZc3p6YNGzawYMGCpuyrmXU1U98tfXWXMK61+6ytu4SteBw2rlVfV9Car61matW/Na2oE9qqVY/FVjwOOyE/dHd3X56Zy7a6IjMb+gFeCZxf/v5U4NfAzeXPRopxyI+aaB9Lly7NZtln9debtq/h4eGm7auZdTWLbTU1zarLtpqa2d5etlV9OuE5NksntJV/4xvXCX+3gMtynMw66Ul6FT2Uwysy8xrgkaNXTNSDLEmSJLWThsYgR8ROFLNVnDG95UiSJEn1aqgHOTPvAfaY4PpFzSpIkiRJqpMr6UmSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFZGZM/Zgy5Yty8suu6wp+3rqKU9tyn6mwzWHX1N3CVtYdMzZdZcwrt123J6rjntp3WVspRXbq1XbyuOwca34uoLWfW0d8N7zueMPf6y7jK20YnvZVlPTisdiq7ZVJ/yNj4jLM3PZVldk5oz9LF26NJtln9Vfb9q+hoeHm7avZtbVimb782umTmgrj8N6zPbnl+lraypsq3rM9ueX2RmvLeCyHCezOsRCkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaqYO9kNImI/4IuVTfsC7wH2Bg4B7gd+DLwpM38/DTVKkiRJM2bSHuTMvCEzD8zMA4GlwD3AmcAFwJLMfBrwQ+Bd01moJEmSNBOmOsRiJfDjzLwlM8/PzI3l9kuAxzS3NEmSJGnmTTUgHwYMjbP9zcA5D70cSZIkqV6TjkEeFRE7AIcyZihFRPQDG4H/3sb9jgSOBFi4cCEjIyMPttatNGtfGzZsaMm6WtVsf37N1AltteiYs5u3s3Obs6+dt5/9bT/bn98uXcfw1FOOad4OT2nObnbpgpGRnZuzsyaxreoz249D6OC/8ZnZ0A/wSuD8MdsOB74L7NTIPpYuXZrNss/qrzdtX8PDw03bVzPrakWz/fk1k201NbZX4zqhrfwb3zjbqh6z/fk1W6u2F3BZjpNZG+5BBnqoDK+IiJcDq4EXZuY9zQrskiRJUp0aGoMcETsBLwHOqGz+T2AX4IKIuDIiTpyG+iRJkqQZ1VAPctlDvMeYbU+clookSZKkGrmSniRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKubWXcBDseiYs5u3s3Obs6/ddty+KfuRJKlRvh9KzdW2Afnm97+iaftadMzZTd2fJEkzxfdDqfkcYiFJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVUwakCNiv4i4svJzZ0QcHREPj4gLIuJH5b8Pm4mCJUmSpOk0aUDOzBsy88DMPBBYCtwDnAkcA1yYmU8CLiwvS5IkSW1tqkMsVgI/zsxbgFcCp5TbTwFe1cS6JEmSpFpMNSAfBgyVvy/MzFsByn8f2czCJEmSpDrMbfSGEbEDcCjwrqk8QEQcCRwJsHDhQkZGRqZy9xnTqnXNpO7u7oZuF2smv83w8PBDrKa1NbOtYPa3V6M8DhvXCW216Jizm7ezc5uzr523n/1tP9ufXzPZVlPTTu3VcEAGDgauyMxflZd/FRGPzsxbI+LRwK/Hu1NmngScBLBs2bJcsWLFQ6l3epx7Ni1Z1wzLzElvMzIyYlthW00Lj8PGdUBb3byieftadMzZ3Pz+VzRvh7NZB7y2msa2mpo2a6+pDLHoYfPwCoCvAYeXvx8OfLVZRUmSJEl1aSggR8ROwEuAMyqb3w+8JCJ+VF73/uaXJ0mSJM2shoZYZOY9wB5jtv2GYlYLSZIkadZwJT1JkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFXMrbuA6RQRjd92zeS3ycyHUI3UmTwOp6bR9mqkrWD2t5c0HTwONat7kDOzoZ/h4eGGbidp6jwOp6aZbdUJ7SVNB49DzeqALEmSJE2VAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElSRUMBOSJ2j4jTI+IHEbE+Ip4TEQdGxCURcWVEXBYRz5zuYiVJkqTp1mgP8keAczPzKcABwHrgA8B7M/NA4D3lZUmakr6+PubPn093dzfz58+nr6+v7pJa1tDQEEuWLGHlypUsWbKEoaGhukuSpFlp7mQ3iIhdgRcAbwTIzPuB+yMigV3Lm+0G/GKaapQ0S/X19XHiiSeyZs0aFi9ezPXXX8/q1asBWLt2bc3VtZahoSH6+/sZHBxk06ZNzJkzh97eXgB6enpqrk6SZpdGepD3BW4DPh0R34+IT0bEzsDRwAcj4qfAh4B3TV+Zkmajk08+mTVr1rBq1Srmz5/PqlWrWLNmDSeffHLdpbWcgYEBBgcH6e7uZu7cuXR3dzM4OMjAwEDdpUnSrBOZOfENIpYBlwDPy8zvRcRHgDspeo0vzswvR8RfA0dm5ovHuf+RwJEACxcuXHrqqac2+zk8ZBs2bGDBggV1l9EWbKvG2VaT6+7u5pxzzmH+/PkPtNe9997LwQcfzPDwcN3ltZSVK1dy3nnnMXfu3AfaauPGjbzsZS/jwgsvrLu8lvbGc+/mMy/fue4yatfd3d3U/XX6Merf+EK7v666u7svz8xlW12RmRP+AI8Cbq5cfj5wNnAHmwN2AHdOtq+lS5dmKxoeHq67hLZhWzXOtprcvHnz8sMf/nBmbm6vD3/4wzlv3rwaq2pN+++/f1500UWZubmtLrrootx///1rrKo97LP663WX0Db8u9U422pqWrW9gMtynMw66RjkzPxlRPw0IvbLzBuAlcD1FEMvXgiMAC8CfvSQY7ykjnLEEUc8MOZ48eLFnHDCCaxevZqjjjqq5spaT39/P729vQ+MQR4eHqa3t9chFpI0DSYNyKU+4L8jYgfgRuBNwFeBj0TEXOBeymEUktSo0RPxjj32WO677z7mzZvHUUcd5Ql64xg9Ea+vr4/169fT1dXFwMCAJ+hJ0jRoKCBn5pXA2PEZ64ClzS5IUmdZu3Yta9euZWRkhBUrVtRdTkvr6emhp6fHtpKkaeZKepIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkqcMNDQ2xZMkSVq5cyZIlSxgaGqq7JKlWc+suQJIk1WdoaIj+/n4GBwfZtGkTc+bMobe3F4Cenp6aq5PqYQ+yJEkdbGBggMHBQbq7u5k7dy7d3d0MDg4yMDBQd2lSbexBliS1pYho/LZrJr9NZj6EatrX+vXrWb58+Rbbli9fzvr162uqSKqfPciSpLaUmQ39DA8PN3S7TtXV1cW6deu22LZu3Tq6urpqqkiqnwFZkqQO1t/fT29vL8PDw2zcuJHh4WF6e3vp7++vuzSpNg6xkCSpg42eiNfX18f69evp6upiYGDAE/TU0QzIkiR1uJ6eHnp6ehgZGWHFihV1lyPVziEWkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVRGbO3INF3AbcMmMP2LhHALfXXUSbsK0aZ1tNje3VONtqamyvxtlWjbOtpqZV22ufzNxz7MYZDcitKiIuy8xlddfRDmyrxtlWU2N7Nc62mhrbq3G2VeNsq6lpt/ZyiIUkSZJUYUCWJEmSKgzIhZPqLqCN2FaNs62mxvZqnG01NbZX42yrxtlWU9NW7eUYZEmSJKnCHmRJkiSpwoAsSZIkVcytuwBJnSciAnhMZv607lokSdMjIvYG9qGSNzPzm/VV1LiOG4McEdsBV2fmkrpraRcRMQd4f2a+s+5aNHtExOWZubTuOtqFx+HUtfOb80yLiGcAy4EEvp2ZV9RcUksqj8O3Zea/111Lq4uINcBrgeuBTeXmzMxD66uqcR3Xg5yZf4qIqyLicZn5k7rraQeZuSkilkZEZKd9onoQIuLJwDvZ+o35RbUV1ZouiYiDMvP/1V1IO/A4nJptvTkDBuQxIuI9wF8BZ5SbPh0Rp2Xmv9RYVksqj8NXAgbkyb0K2C8z76u7kAej43qQASLiIuAg4FLg7tHt7fKppg4R8WHgScBpbNlmZ2zzTh0qIq4CTgQuZ/MbM5l5eW1FtaCIuB7YD7iZ4jUVFL0LT6uzrlbmcdi4iLgBeFq7vjnPpIhYDzw9M+8tL+8IXJGZXfVW1poiYgDYDfgiWx6H9rpXRMQ5wF9l5oa6a3kwOq4HufTeugtoQw8HfgNUe0GTzT0O2mxjZn687iLawMF1F9CGPA4bdyOwPWBAntzNwHzg3vLyPODHtVXT+p5b/vvPlW3Jlsel4B7gyoi4kMpxmJlvq6+kxnVkDzJAROwDPCkzvxEROwFzMvOuuutS+4uI44FfA2ey5R+F39ZVU6uKiOUUx+GnI2JPYEFm3lR3XWp/EfFl4ACgLd+cZ1JEfIXiW9ULKILeS4B1FH/HbDM9KBFx+HjbM/OUma7lwejIgBwRRwBHAg/PzCdExJOAEzNzZc2ltaxyXO3HgYWZuSQingYc6hi1rUXEeAEvM3PfGS+mhUXEccAyijFqT46IvYDTMvN5NZfWsjwOG9fub84zaVttNco221JELAT+FdgrMw+OiMXAczJzsObS1ESdGpCvBJ4JfC8zn15uuyYzn1prYS0sIi6mOPHsE5U2u9bZQPRglcfh0ynGOo6+pq52DPK2eRxqukTEDsCTy4s3ZOYf66ynlZVjaz8N9GfmARExF/i+GWJLZefjvwGLKYbwANAunUWdOgb5vsy8v5iKFcoXd+d9UpianTLz0tE2K22sq5hWFxFL2PqPwmfrq6gl3Z+ZGREJEBE7111QG/A4bFC7vznPpIhYAZxCMRY5gMdGxOFOibdNj8jML0XEuwAyc2NEbJrsTh3o08BxFDN+dANvonh9tYVOXUnv4og4FtgxIl5CcUb4WTXX1Opuj4gnUH6QiIjXALfWW1JrKocOrC1/uoEPAM6QsrUvRcQngN3LYU/fAE6uuaZW53HYuE9TDEfZSHEcfhb4XK0Vta4PAy/NzBdm5guAl+E0ZhO5OyL2YPNx+GzgjnpLakk7ZuaFFKMVbsnM42mjExk7dYjFdkAv8FKKTzPnAZ90btFti4h9gZMozt79HXAT8LrMvKXWwlpQRFxDcXLQ98uv3xZSvL4Oqbm0llN+QH3gOMzMC2ouqaVt4zh8fWbeXGddrWh0IZrq8LmI+FZmPr/u2lrNeEObHO60beWiKmuBJcC1wJ7AazLz6loLazER8W3g+cDpwEXAzykWO9qv1sIa1JEBWVMXEY/PzJvKr8G3y8y7RrfVXVuriYhLM/OZEXE5Rc/VXcC1mbl/zaVplqgeh3XX0qra/c15JkXEpyh6Q0d72F8HzM3MN9VXVWsrh2buR/Hh3jHb44iIg4D1wO7A+yjmjv5AZl5SZ12N6siAHBHPA45n80pnowsUODZtGyLiisx8xphtLhU8joj4L+BY4DDgH4ANwJW+2RQi4i4mGPOfmbvOYDltJSLmAX8JLGLLVRr/eVv36VTt/uY8k8rX1VsplpoOitUGP5aZ99daWAuLiOey9XHoeSazSKcG5B8A72Drlc5+U1tRLSoingLsTzGO9p2Vq3YF3mmv6MQiYhGwq1+9bS0i/hn4JUWvVVD0Wu2SmR+otbAWFhHnUox1HPu368O1FaW2FxFvz8yPTLZNhYj4HPAE4Eoqy5g7X/SWImIZ0M/mzkgA2mXoTqcG5O9l5rPqrqMdlGvOv4riJLOvVa66Czg1M79TR12trpyfdhFb/lFwtbOK8Y5Dj82JOaVb49r9zXkmbeMbwu+PTiWoLZVLcy/2vKWJlcu9vxO4BvjT6PZ2OXepo6Z5KwfWAwxHxAcplmetrrDkOupjZOZXga9GxAvGTvlTDlXRGOV4vqcB17H5j4LLAW9tU0S8DjiVon16qPSKalzfiYinZuY1dRfSBv6bcd6ctVlE9AD/B3h8RFQ7QHalWNJc47sWeBTOIDOZ2zLza5PfrDV1VA9yRAxPcHVmZttMPzLTttHDsNU2QURcn5mL666j1ZXDTz4CPI8iIH8bONoZGbZWzoySFJ0aTwJupPhwP3r+hL2iY0TEusxcXncdrSwi9gEeTzFf9DGVq+4Crs5M59iuiIizKI7DXYADgUvZspPN6TwrImIlRcfH2OXe26KzqKN6kDOzu+4a2k1EPIdiSqk9I2JV5apdgTn1VNXyvhsRizPz+roLaWVlEH5l3XW0iT+vu4A2dFxEfJI2fXOeCeVX3bdExIuBP2Tmn8rlzJ9C0fOuLX2o7gLazJsoXkvb04bfpnZUQB4VEf9KcTbz78vLDwP+ITPfXWthrWkHYAHFa2WXyvY7gdfUUlHrO4UiJP8Se/m2EhFrmXgWC090GWN0zF65IMF1o9O7RcQuFCvFtcWYvhnW1m/OM+ybwPPL98ILgcuA11KcOKtSZl4MxbSnwK2ZeW95eUdgYZ21tagD2nn57Y4aYjFqvJMPHC4wsYjYp10G1tctIv4XWEWbnpgw3SLi8Imuz8xTZqqWdhMR3weeMXpyULno0WX+7dpadYEQTWz0/S8i+ihWP/uAJ+ltW0RcBjx3dBq8iNgB+HZmHlRvZa0lIk4G/r1dv03tyB5kYE5EzMvM++CBT3/zaq6pJUXEf2Tm0cB/RsRWn6YcczWun7TziQnTbWwAjoidM/PuuuppM1E9c778SrxT/45P5hKHOjUsyuF0r6NYZRY6Nx80Ym51jujMvL8MydrScuDwiLiJNvw2tVMPgM8DF0bEpym+cnsz4ATf4xtdWcmxV437QUR8ATgLxz5uU/mGPEgxhOdxEXEA8HeZ+ff1VtbSboyItwEfLy//PcUJe9paW785z7C3A+8CzszM68olzSc6qb3T3RYRh452hJTTod5ec02t6OV1F/BQdOQQC4CIeDnwYoo/mudn5nk1l6RZovzgNVZm5ptnvJgWFhHfoxjH/rXRr3Kd53diEfFI4KPAiyg+3F8IvD0zb6u1sBZUztCwFYc6bS0i/iozT5tsmwoR8QSKaQT3Kjf9DHhDZv64vqpaU0QsB56UmZ+OiD2BBZl5U911NaIjA3JErMnM1ZNt0xbTS43L3hg9WKOLglTHOkbEVZl5QN21taqIeF5mfnuybSq085vzTHIaz6mJiMdn5k0RsYAiR901uq3u2lpJRBwHLAP2y8wnR8RewGmZ2RZrKHTqEIuXAGPD8MHjbNPm6aUCOBv4sxpraQvlNEkfBxZm5pJyVb1DM/Nfai6t1fw0Ip4LZDl+723A+ppranVrgbGhZbxtHa/65gx8mmI2i89TzLstICIOpvibvndEfLRy1a6AcyBv25cpTpbdUNl2OrC0pnpa1V8ATweuAMjMX5Qz77SFjgrIEfEWijF7+0bE1ZWrdqFYpEBjVL+OjIj7/HqyISdTrOD1CYDMvLock2xA3tJRFAuF7E3xFeX5wFtrrahFOR/5g9LWb84z5BcUU7odClxe2X4X8I5aKmphEfEUYH9gt4h4deWqXYH59VTV0u7PzBw9wT8idq67oKnoqIAMfAE4h3FWDcrM39ZTkmahnTLz0oiobrM3ZozMvB3nWW2U85FPXVu/Oc+EzLwqIq4FXur0ig3Zj+Jb1d2BQyrb7wKOqKOgFveliPgEsHtEHEExIcLJNdfUsI4KyJl5B3AHxdKHoye8zAcWRMSCzPxJnfW1ooiofnW7Y0Q8nWK4BQCZecXMV9Xybi9P4hh9Y34NcGu9JbWOiPincp7VcRcMcaGQrZULFFwcEZ/xW5yGtfWb80zJzE0RsUdE7FCdukxby8yvAl+NiOdk5nfrrqfVZeaHIuIlFB/k9wPek5kX1FxWwzr1JL1DgBMozkD9NbAPsD4z96+1sBYUERNN9ZOZ+aIZK6ZNlFMknUTxlfjvgJuA15dLK3e8iPjzzPz6thYMsSdra6PzkUfEWYz/ocL5yMdRvjm/lOJD/Xnt9OY8k8oPEs8AvgY8MCd5Zp5QW1EtyA/3U1N+a3Nv+SFsP4qQfE5m/rHm0hrSUT3IFf8CPBv4RmY+PSK6KXuVtaXM7G7kdhHxEt98Cpl5I/Di8o/DdqPLAusBrwW+DuyemR+pu5g24XzkU1Qefxdl5gWjb84RsX27vDnPsF+UP9ux5RAebWn0JOLLaq2ifVSXMP8GbbaEeaf2IF+Wmcsi4irg6eVqVJdm5jPrrq1dOSXQZhHxdoqz5u+i+Er3GcAxmXl+rYW1iIi4nmLWmK8BK6gM2QHwfICtRcR8ipMan0ixhPlgZjqufQIRcTnwfOBhwCUUb873ZGZbvDnXoTyJMcfMzqCKiHgV5XHo+gkTa/clzLeru4Ca/L6cv/CbwH9HxEfwJKqHKia/Scd4c2beSfHV7iOBNwHvr7eklnIicC7wFIoz56s/9syM7xSKKcuuofhw8eF6y2kLkZn3AK8G1mbmXwCLa66pJUXEkoj4PnAtcF1EXB4RDjkcIyL+i2J2jz2A90XE/625pFZXXcL87HJb24xcaJtCmyEinggsBF4J/IHihf46ijHIfTWWNht03lcR2zb6YeHPgE+XZ4r7AaKUmR8FPhoRH8/Mt9RdT5tYnJlPBYiIQeDSmutpB9U3595yW0e9503BScCqzBwGiIgVFN9+PbfGmlrRC4ADyjG1OwHfAt5Xc02trK2XMO+0HuT/oJjS7e7M/FNmbixPCPof4PhaK9NscnlEnE8RkM8rv7b8U801taIFYzdExOfGu6F4YNysQysa1tZvzjNs59FwDJCZI4DT4m3t/szcBFB+O2HHxwQy85uZeWhmrikv39hOJzJ21BjkiLg2M5ds47prRntoNHURcUZmvnryW85+EbEdcCBwY2b+PiL2APbOzKsnvmdnGTtuPSLmAldnpl+DjxERm9g8u0AAOwKjb9CZmbvWVZvaX0ScSbGgyugH1NcDyzLzVbUV1YIi4h7gf0cvAk8oL48eh0+rq7ZWVC7v/k8Ui6s8sJBKu8x+1WlfN0200s2OM1ZFGxmzWtBWMvOM8l/Dcak86fMm4MnlyVWqiIh3AcdSzKt95+hm4H6Kr3o1RmY2tFpeRDwsM3833fW0g3Z/c55hbwbeC5xBcSx+k+LcCW2pq+4C2sx/A1+kWFzlKOBw4LZaK5qCTutBHqKY9ufkMdt7KVYSem09lbWuiPh0+esjKcajXVRe7gZGDMZbi4i/pfh69zHAlRRTCn7XN+YtRcS/Zea76q5jNnE2mc3KYU5fBP6RyptzZq6utTDNehHx3cx8Tt111C0iLs/MpRFx9WjvekRcnJkvrLu2RnRaD/LRwJkR8To2rzu/jGIZ17+oq6hWlplvAoiIr1OcKHRrefnRwMfqrK2FvR04CLgkM7sj4ikUvTPa0jkR8YKxGzPzm3UUM0s4JnKzPTJzMCLeXlmJ8OK6i2pFEfFkig8Si6jkAj/UP2h+c1gYPXfi1oh4BcVc24+psZ4p6aiAnJm/Ap5bLgwyOhb57My8aIK7qbBoNByXfgU8ua5iWty9mXlvRBAR8zLzB+VCBdrSOyu/zweeSfHB1TflB69zvhKcXFu/Oc+w0yimX/wksKnmWmYDj8PCv0TEbsA/AGuBXSlmD2sLHRWQR5Vn63o289SMRMR5wBDFwX8YtuG2/Cwidge+AlwQEb+jeHNWRWYeUr0cEY8FPlBTOZp92vrNeYZtzMyP112EZpfM/Hr56x0UwzLbSkeNQdZDExF/QTEPJMA3M/PMOutpBxHxQmA34NzMvL/uelpZOVf01c4ms7WIeHxm3tTA7dpmlSrVLyIeXv76NuDXwJnAfaPXu6rlg9Ppx2F5cvprgd8BZ1GcLPt84MfA+zLz9hrLa5gBWQ2LiH2AJ2XmN8pJ0udk5l1119WqyjZaDNySmW1z5u5MiYi1bP4qcjvg6cBNmfn6+qpqTZWTXS7MzJUT3O7hnR5qZsub80woZ9tJNo9d3yIQZOa+M17ULBARSzLz2rrrqEtEfIliiNPOFEu9X0txLC4HDszMP6+xvIYZkNWQiDgCOBJ4eGY+ISKeBJw40Zt1p4mIQ4GPAr8F3k1xEuOvKE58WV0uSqNSRLwFmEPxpnwHRTj+dr1VtaZyGeCvAH8L/PvY6zPzhJmuqVXNljfnmRARzwR+Wjn5+nDgL4GbgeM7/cPWtkTEXWw9zvgO4DLgHzLzxpmvqnWMrjlRzm3/s8x8VOW6qzLzgBrLa1hHjkHWg/JWipOovgeQmT+KiEfWW1LLeR/wUoohFcPA0zLzxrKdLgQMyDywIMi/Usy9+hOK3qvHAp+KiEsz848T3b9DHQa8iuJv9i71ltLyFo95cx6dUurciLiqzsJa0InAiwHKGWX+DeijWOjoJOA1tVXW2k6gOK/kCxR/vw4DHgXcAHwKWFFbZa3hfihW/YyIsefftM1JoAZkNeq+zLy/GCb6QMjx64ct/SkzfwjFV5ejvQiZ+euIcHngzT5IEfIePzpEJyJ2BT5U/ry9xtpaUmbeAKwp5xM9p+56WtyseHOeIXMqvcSvBU7KzC8DX46IK+srq+W9PDOfVbl8UkRckpn/HBHH1lZV63hMRHyU4sPD6O+Ul/eur6ypMSCrUReXB/6OEfES4O8pvrbUZttFxMMoxtP+qfx9dGzfdvWV1XL+HHhyVsZ3Zead5ZCLH2BAnsgVETEI7JWZB0fEYuA5mTlYd2EtZFa8Oc+QORExNzM3AisphtGNMh9s258i4q+B08vL1Z52O462nMLzsjHXjb3cshyDrIZExHZAL8UQggDOG7siYaeLiJuBPzH+Yg3pCS+FiPhhZo47h/ZE1wki4hzg00B/Zh5QfpPzfWf+2KwcR7tNnguwWUT0A38G3A48DnhGZmZEPBE4JTOfV2uBLSoi9gU+AjyHIhBfQjGF4M+BpZm5rsby2kZErM3Mvrrr2BYDshpSrkb1kcm2aXIRsX9mXld3HXWJiK8AZ2TmZ8dsfz3w15l5aC2FtYGI+H+ZeVB1GqmIuDIzD6y5tLbT6m/OMyUing08Gjg/M+8utz0ZWJCZV9RanGa1iLgiM59Rdx3b4lcoatThFJ+Yq944zjZN7nNAy/5RmAFvBc6IiDdTrJyXFEtz74hLvk/m7ojYg/Jr3DLc3FFvSW3L3lEgMy8ZZ9sP66ilXUTEnsARbL0095vrqknNZ0DWhCKiB/g/wOMj4muVq3YBflNPVW1vvCEYHSMzfw48KyJeBOxP0R7nZOaF9VbWFlYBXwOeEBHfBvbEmQakmfZV4FvAN/DEz1nLgKzJfAe4FXgE8OHK9ruAq2upqP05rgnIzIuAi+quo51k5hXl6oz7UXywuMFp8aQZt1Nmrq67iFmgpTuLDMiaUGbeAtxCcTKCpBpExIsy86KIePWYq54cEWTmGbUU1t5a+s1ZLe3rEfFnmfk/dRfS5lp6iKYBWQ0pxzquBbqAHShWQLs7M3ettbD2dH/dBajtvJCit/2Qca5LwIA8dS395qyW9nbg2Ii4j2LVxqCYqcj3QyAizmKCb0pHT8TOzM/MVE0PhrNYqCERcRnFakGnAcuAvwGemJn9tRbWgiLiwrFLcI+3TVLzNfrmLGl6lMPAAF5NscLg58vLPcDNmdkWi6nYg6yGZeb/RsSczNwEfDoivlN3Ta0kIuYDOwGPGLNIyK7AXrUVprYXEasmuj4zT5ipWtrAh8p/x31zrqMgzQ4R8ZTM/EFEjDsLkdPiFTLzYoCIeF9mvqBy1VkR8c2aypoyA7IadU9E7ABcGREfoDhxb+eaa2o1fwccTRGGL2dzQL4T+FhNNWl22KXuAtrFbHlzVktaRbHa4IfHuS6BF81sOS1vz4jYNzNvBIiIx1PMvNMWHGKhhkTEPsCvKMYfvwPYDfivzPzfWgtrQRHRl5lr665D6mQRsR54xZg35//JzK56K1O7i4j5mXnvZNs6XUS8DDgZuLHctAg4MjPPr62oKbAHWQ0pZ7MAuBd4b521tIFfRsQumXlXRLybYlGQf/HrNz1U5QpnHwcWZuaSiHgacGhm/kvNpbWidwAjEVF9c/67+srRLPIdtl7sabxtHSsitqPoSHsS8JRy8w8y8776qpoae5DVkIh4HnA8sA9brhy0b101taqIuDoznxYRy4F/oxgTeWxmPqvm0tTmIuJi4J3AJypLTV+bmUvqraw1RcQ82vTNWa0nIh4F7E0xrv3/sOV5Jidm5lO2dd9OFBHfHDPMqa3Yg6xGDVL0yFyOKwdNZrR9XgF8PDO/GhHH11iPZo+dMvPSiC2m8N1YVzFtYCmblwM+oJwz+rP1lqQ29jLgjcBjKMYhV88zaYuZGWbYBRHxj8AXgbtHN2bmb+srqXEGZDXqjsw8p+4i2sTPI+ITwIuBNWUv1nY116TZ4faIeALlNGYR8RqKE2Y1RkR8DngCcCWbP7QmYEDWg5KZp5Svq57M/O+662kDby7/fWtlWwJt8c2zQyzUkIh4P8XiIGcAD3xN6bjarUXETsDLgWsy80cR8Wjgqe1yYoJaV0TsC5wEPBf4HXAT8LrKOQIqlSfpLU7f5NRk7T50QI0xIKshETE8zubMTKe1qShPTLjaMaGaThGxM8W3En8AXmtv1tYi4jTgbZlpD7uaKiL+L8Wx15ZDB6ZbRLwoMy+KiFePd31mtsXKnw6xUEMys7vuGtpBZv4pIq6KiMdl5k/qrkezQ0TsSvE15d7AV4FvlJf/EbgKMCBv7RHA9RFxKVt+6+VKenqo2nrowAx4IXARcMg41yXFN9Etzx5kNWQbK3ndAVyemVfOcDktLSIuAg4CLmXL3gXfmPWgRMRXKYZUfBdYCTyMYk7yt3v8ja+y3O0WRhcSkaSJGJDVkIj4ArAMOKvc9Arg/1FMoXRaZn6grtpajW/MaraIuCYzn1r+Pge4HXhcZt5Vb2WtLSIWUnxYBbg0M39dZz2aPSJiCbAYmD+6zRlStlSeoP6XbJ5JBoDM/Oe6apoKh1ioUXsAz8jMDQARcRxwOvACiqnfDMglg7CmwR9Hf8nMTRFxk+F4YhHx18AHgRGK6bjWRsQ7M/P0WgtT2yvf/1ZQBOT/AQ4G1uEMKWN9lfKbZirDnNqFAVmNehxwf+XyH4F9MvMPEdF2L/zpEBHrMnN5RNxFOQ3X6FUUJzTuWlNpan8HRMSd5e8B7Fhe9rW1bf3AQaO9xhGxJ8XYbQOyHqrXAAcA38/MN5XfVHyy5ppa0WMy8+V1F/FgGZDVqC8Al5RjIaEYfD9Unk1/fX1ltZTXAWTmLnUXotklM+fUXUMb2m7MkIrf4Hzkao4/lCdkbyxPoP01nqA3nu9ExFMz85q6C3kwDMhqSGa+LyL+B1hO0Wt1VGZeVl79uvoqaylnAs8AiIgvZ+Zf1lyP1MnOjYjzgKHy8msBFztSM1wWEbsDJ1MMH9hAcVK2gIi4FvgTRcZ8U0TcSDHEYvQbr6fVWV+jPElPE4qIXTPzzoh4+HjXO+/jZhHx/cx8+tjfJdWjnId19EP9NzPzzJpL0iwTEYuAXTPz6rpraRUR8TvgwG1d3y4LG9mDrMl8Afhzik/JW42rxa+VqnIbv0uaYRHxeOB/RhcliIgdI2JRZt5cb2VqdxFxYWauBBh9PVW3iZvaJQRPxB5kqUkiYhPFvMcB7AjcM3oVnkglzaiIuAx4bmbeX17eAfh2Zh408T2l8UXEfGAnYJhiFosor9oVOCczu2oqraVExM+AE7Z1fWZu87pWYg+yGhIRzwOuzMy7I+L1FGNt/8PV4jbzRCqppcwdDccAmXl/GZKlB+vvgKOBvSi+VR11F/CxOgpqUXOABWz+ANGWDMhq1Mcpppo6APgnYBD4HMWSkpLUam6LiEMz82sAEfFKigVWpAfrO8CXgNdk5tqIOJxiIYybKYYjqnBruywGMhGnvFGjNmYxHueVwEcy8yOA05lJalVHAcdGxE8j4ifAaooeQOnB+gRwXxmOXwD8G3AKxWIYJ9VaWWtp657jUfYgq1F3RcS7gDcAzy+Xu92+5pokaVyZ+WPg2RGxgOJ8G1ce1EM1pzJz02uBkzLzy8CXI+LK+spqObPiZEV7kNWo11LMY/jmzPwlsDfFMq6S1HIiYmFEDAKnZeZdEbE4InrrrkttbU5EjHYsrgQuqlxnh2Nptkz/akBWQ8pQ/GVgXrnpdoqFMSSpFX0GOI/ihCqAH1KcYCU9WEPAxeWKsn8AvgUQEU+kGGahWcSArIZExBHA6RRjsKDoQf5KbQVJ0sQekZlfoljRi8zcCGyqtyS1s8wcAP6B4sPX8tw8T+52QF9ddWl6+JWAGvVW4JnA9wAy80cR8ch6S5Kkbbo7IvagXLQnIp6NvXx6iDLzknG2/bCOWjS9DMhq1H3lPKIAlOOwXGVGUqtaBXwNeEJEfBvYE3hNvSVJahcOsVCjLo6IY4EdI+IlwGnAWTXXJElbiIiDIuJRmXkFxTztx1KcYHw+8LNai5PUNlxqWg2JiO2AXuClFHMcngd8Mn0BSWohEXEF8OLM/G05V+2pFONDDwS6MtNeZEmTMiCrYRGxJ0Bm3lZ3LZI0noi4KjMPKH//GHBbZh5fXr4yMw+ssTxJbcIhFppQFI6PiNuBHwA3RMRtEfGeumuTpHE4V62kh8yArMkcDTwPOCgz98jMhwPPAp4XEe+otTJJ2ppz1Up6yBxioQlFxPeBl2Tm7WO27wmcn5lPr6cySRpfOaXboyn+Rt1dbnsysKA8eU+SJuTXTZrM9mPDMRTjkCNi+zoKkqSJOFetpIfKIRaazP0P8jpJkqS25BALTSgiNgF3j3cVMD8z7UWWJEmzigFZkiRJqnCIhSRJklRhQJYkSZIqDMiS1EIiYlNEXBkR10bEWRGx+yS3/0xEuHyyJDWRAVmSWssfMvPAzFwC/BZ4a90FSVKnMSBLUuv6LrA3QEQcGBGXRMTVEXFmRDxs7I0jYmlEXBwRl0fEeRHx6BmvWJJmAQOyJLWgiJgDrAS+Vm76LLA6M58GXAMcN+b22wNrgddk5lLgU8DAzFUsSbOHK+lJUmvZMSKuBBYBlwMXRMRuwO6ZeXF5m1OA08bcbz9gSXl7gDnArTNRsCTNNgZkSWotf8jMA8tQ/HWKMcinNHC/AK7LzOdMa3WS1AEcYiFJLSgz7wDeBvwjcA/wu4h4fnn1G4CLx9zlBmDPiHgOFEMuImL/mapXkmYTe5AlqUVl5vcj4irgMOBw4MSI2Am4EXjTmNveX0739tGy93ku8B/AdTNbtSS1P5ealiRJkiocYiFJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqeL/Bzi4LlK03SS8AAAAAElFTkSuQmCC\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Huom:** Tämä kaavio viittaa siihen, että keskimäärin ykköspesämiesten pituudet ovat suurempia kuin kakkospesämiesten pituudet. Myöhemmin opimme, kuinka voimme testata tätä hypoteesia muodollisemmin ja osoittaa, että datamme on tilastollisesti merkittävää tämän osoittamiseksi.  \n",
+    "> **Huomio**: Tämä kaavio viittaa siihen, että keskimäärin ensimmäisen pesän pelaajien pituudet ovat suurempia kuin toisen pesän pelaajien pituudet. Myöhemmin opimme, kuinka voimme testata tätä hypoteesia tarkemmin ja osoittaa, että datamme on tilastollisesti merkittävää tämän osoittamiseksi.  \n",
     "\n",
-    "Ikä, pituus ja paino ovat kaikki jatkuvia satunnaismuuttujia. Mitä luulet, millainen niiden jakauma on? Hyvä tapa selvittää tämä on piirtää arvojen histogrammi:\n"
+    "Ikä, pituus ja paino ovat kaikki jatkuvia satunnaismuuttujia. Mitä luulet niiden jakauman olevan? Hyvä tapa selvittää tämä on piirtää arvojen histogrammi:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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qfVW17/Dhw3NOAwAAFmOua5CTvKy7H6qqZyW5paru2eiO3b03yd4k2bVrV885DwAAWIi5ziB390PT7cNJbkxyXpJDVXV6kky3D887SQAA2CqbDuSqelpVPf3o/SQ/m+TOJDcluXTa7NIkn5x3kgAAsFXmucTi2UlurKqj3+f3uvu/VtUXk1xfVW9O8s0kb5h/mgAAsDU2Hcjd/bUk/2jG+LeTnD/PpAAAYLvM+0t6cELYuefm7Z4CAHCC8FHTAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAw8FHTALBgJ8LH2++/4sLtngI8YTmDDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAACDTQdyVZ1ZVX9UVXdX1V1V9SvT+Puq6sGqun36es3ipgsAAMu1Y459jyR5R3d/qaqenuS2qrpleu4D3f3++acHAABba9OB3N0Hkxyc7n+3qu5OcsaiJsaJY+eem7d7CgAAC7OQa5CrameSlyT5/DT0tqq6o6qurqpT19hnd1Xtq6p9hw8fXsQ0AABgbnMHclX9RJIbkry9u7+T5INJnp/k3KyeYb5y1n7dvbe7d3X3rpWVlXmnAQAACzFXIFfVj2U1jj/a3Z9Iku4+1N2PdvcPk3w4yXnzTxMAALbGPO9iUUmuSnJ3d//GMH76sNnrk9y5+ekBAMDWmuddLF6W5E1JvlJVt09j705ySVWdm6ST7E/yljl+BgAAbKl53sXis0lqxlOf2vx0AABge/kkPQAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAY7NjuCQAAW2/nnpu3ewrr2n/Fhds9BU5SziADAMBAIAMAwEAgAwDAQCADAMBAIAMAwEAgAwDAwNu8AQBPSN6Kju3iDDIAAAycQT4BnAj/Bw0A8GThDDIAAAwEMgAADAQyAAAMTvprkF3fCwDAyBlkAAAYCGQAABgsLZCr6oKqureq7q+qPcv6OQAAsEhLuQa5qk5J8p+T/EySA0m+WFU3dfdXl/HzAAC2g99lmt8T8dMIl3UG+bwk93f317r7r5J8LMlFS/pZAACwMMt6F4szknxreHwgyT8ZN6iq3Ul2Tw//sqruXeN7nZbkzxY+Q46yvstlfZfPGi+X9V0u67tc1ne5FrK+9WsLmMnm/b1Zg8sK5Jox1o950L03yd51v1HVvu7etaiJ8VjWd7ms7/JZ4+WyvstlfZfL+i7Xk3l9l3WJxYEkZw6Pn5vkoSX9LAAAWJhlBfIXk5xTVWdX1Y8nuTjJTUv6WQAAsDBLucSiu49U1duS/LckpyS5urvv2uS3W/cyDOZifZfL+i6fNV4u67tc1ne5rO9yPWnXt7p7/a0AAOAk4ZP0AABgIJABAGCwrYFcVc+oqo9X1T1VdXdV/dOqel9VPVhVt09fr1ljXx9lvY411ve6YW33V9Xta+y7v6q+Mm23b4un/oRXVS8c1vH2qvpOVb29qp5ZVbdU1X3T7alr7O/1exzHWd9fn17Pd1TVjVX1jDX29/o9juOsr+PvAhxnfR1/F6Sq/l1V3VVVd1bVtVX1tx1/F2eN9T2pjr/beg1yVV2T5H92929P73bx1CRvT/KX3f3+4+x3SpI/zfBR1kku8VHWjzVrfbv7/wzPX5nkL7r7V2fsuz/Jru72BuvrmF6PD2b1w3AuS/JId18xHXhP7e53ztje63eDjlnfFyb5H9MvAv9akhy7vtM+++P1uyHHrO8vxfF3ocb17e5vDOOOv5tUVWck+WySF3X396vq+iSfSvKiOP7O7Tjr+1BOouPvtp1BrqqfTPKKJFclSXf/1Rhv6/BR1utYb32rqpK8Mcm12zLBJ5fzkzww/eV3UZJrpvFrkrxuxvZev4/PX69vd3+6u49M45/L6nusM5/x9bsRXr+Pz4+sr+PvQuxI8neqakdWT649FMffRfqR9T3Zjr/beYnF85IcTvJfqurLVfXbVfW06bm3Tafwr17jn0hmfZT1GUue74nmeOubJC9Pcqi771tj/07y6aq6rVY/Fpy1XZy/+Yvu2d19MEmm22fN2N7r9/EZ13f0y0n+cI19vH437tj1dfxdrFmvX8ffOXT3g0nen+SbSQ5m9Uz8p+P4uxDHWd/Rk/74u52BvCPJS5N8sLtfkuT/JtmT5INJnp/k3Kz+wVw5Y991P8qaNdf3qEty/LMXL+vulyb5uSSXVdUrljbTE9h06cprk/z+49ltxpjX7wxrrW9VvSfJkSQfXWNXr98NmLG+jr8LdJzjg+PvHKb/cbsoydlJnpPkaVX1rza6+4wxr9/Beut7shx/tzOQDyQ50N2fnx5/PMlLu/tQdz/a3T9M8uGs/nPIrH19lPXxzVzfJJn+yeQXkly31s7d/dB0+3CSGzP7z4HVA8CXuvvQ9PhQVZ2eJNPtwzP28frduGPXN1V1aZKfT/KLvcYvUXj9bthj1tfxd+FmvX4df+f3qiRf7+7D3f3/knwiyT+L4++irLW+J9Xxd9sCubv/d5JvVdULp6Hzk3z16It78vokd87Y3UdZr2Ot9Z3uvyrJPd19YNa+VfW0qnr60ftJfjaz/xz40TNBNyW5dLp/aZJPztjH63fjHrO+VXVBkncmeW13f2/WDl6/j8ux6+v4u1izzhQ7/s7vm0l+uqqeOl3PfX6Su+P4uygz1/ekO/5297Z9ZfWf8fYluSPJHyQ5NcnvJvnKNHZTktOnbZ+T5FPDvq/J6m+iPpDkPdv53/FE/Zq1vtP4R5K89Zht/3p9s3r98p9MX3dZ3zXX96lJvp3k7w5jP5Xk1iT3TbfPPHZ9p8dev5tb3/uzev3g7dPXh45dX6/fudbX8XeJ6zuNO/4uZn3/Y5J7shpfv5vkKY6/S1/fk+r466OmAQBg4JP0AABgIJABAGAgkAEAYCCQAQBgIJABAGAgkAEAYCCQAQBg8P8B40VGjZpezWQAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Koska useimmat arvot tosielämässä ovat normaalijakautuneita, meidän ei pitäisi käyttää tasaisen satunnaislukugeneraattoria näytearvojen luomiseen. Tässä on mitä tapahtuu, jos yritämme luoda painoja tasaisella jakaumalla (luotu `np.random.rand`-funktiolla):\n"
+    "Koska useimmat arvot tosielämässä ovat normaalijakautuneita, meidän ei pitäisi käyttää tasaista satunnaislukugeneraattoria näyteaineiston luomiseen. Tässä on, mitä tapahtuu, jos yritämme luoda painoja tasaisella jakaumalla (luotu `np.random.rand`-funktiolla):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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QAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMexbIVXVDVX2uqh6uqiN79TkAALCb9iSQq+qSJP8+yfcmuS7Jm6rqur34LAAA2E17dQT59Uke7u7f7u6vJHl/kpv36LMAAGDX7Nujn3tVksfG45NJ/tp8QVUdTnJ48fAPq+pzezQLe+/yJF9c9xCcN+wHtrMn2M6e4Fn1E0nWtyf+wpkW9yqQ6wxr/ZwH3UeTHN2jz2eFqupEd2+uew7OD/YD29kTbGdPsN35tif26hSLk0muGY+vTvLEHn0WAADsmr0K5P+Z5GBVXVtVL0lya5J79+izAABg1+zJKRbd/UxV/VCSX0pySZK7uvuBvfgszgtOlWGyH9jOnmA7e4Ltzqs9Ud197lcBAMBFwpX0AABgEMgAADAIZJ63qnpNVX1q/PflqvqRqvrJqvpsVf1mVf1CVb1y3bOyGl9nT/z4Yj98qqo+XFXftO5ZWY2z7Ynx/I9WVVfV5WsckxX5Or8j/mVVPT7Wv2/ds7IaX+93RFX9cFV9rqoeqKp/tdY5nYPMTiwuJ/54ti4A85okH1n8z5k/kSTd/c51zsfqbdsTv9/dX16svz3Jdd391nXOx+rNPdHdX6iqa5K8J8m3JPmr3e1CEReRbb8jfjDJH3b3T613KtZp2554dZJ3Jbmxu5+uqiu6+6l1zeYIMjt1fZL/3d1f6O4Pd/czi/WPZut7r7n4zD3x5bH+imy7UBAXjWf3xOLxv0nyY7EfLlbb9wPMPfFPktzZ3U8nyTrjOBHI7NytSd53hvW3JPnFFc/C+eE5e6Kq3l1VjyV5c5J/sbapWKdn90RV3ZTk8e7+jfWOxBpt/3fjhxanYt1VVZeuayjWau6Jb07yN6vqY1X1q1X1HWucyykWvHCLi788keS13f3kWH9Xks0k/6BtrIvK2fbE4rnbk7ysu+9Yy3CsxdwTSf4gyS8n+bvd/aWqeiTJplMsLh7bf0dU1ZVJvpitvyb8eJL93f2Wdc7Iap1hT3w6yUeSvCPJdyT5uSSvXldPOILMTnxvkk9si+NDSd6Q5M3i+KL0p/bE8F+T/MMVz8P6zT3xF5Ncm+Q3FnF8dZJPVNWfX+N8rNZzfkd095Pd/dXu/lqS/5jk9WudjnXY/u/GySQf7C0fT/K1JGv7n3kFMjvxpjz3T+k3JHlnkpu6+4/WNhXrtH1PHBzP3ZTksyufiHV7dk9092919xXdfaC7D2TrH8Jv7+7fWeeArNT23xH7x3N/P8mnVz4R6/acPZHkvyX520lSVd+c5CXZ+ivDWjjFghekql6e5LFs/dnjS4u1h5O8NMnvLl72Ud9YcPE4y574+Wx9u8nXknwhyVu7+/H1TckqnWlPbHv+kTjF4qJxlt8R/znJX8nWKRaPJPnH3X1qXTOyWmfZEy9Jcle29sVXkvxod39kbTMKZAAA+BNOsQAAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAAhv8PCCPnhqb/Rl0AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -593,14 +434,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Hypoteesien testaus\n",
+    "## Hypoteesin testaus\n",
     "\n",
-    "Tutkitaan eri rooleja baseball-pelaajien aineistossa:\n"
+    "Tutkitaan eri rooleja baseball-pelaajien aineistossamme:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Voimme nähdä, että intervallit eivät mene päällekkäin.\n",
+    "Voimme nähdä, että välejä ei ole päällekkäin.\n",
     "\n",
     "Tilastollisesti tarkempi tapa todistaa hypoteesi on käyttää **Studentin t-testiä**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "`ttest_ind`-funktion palauttamat kaksi arvoa ovat:\n",
-    "* p-arvo voidaan tulkita todennäköisyytenä sille, että kahdella jakaumalla on sama keskiarvo. Meidän tapauksessamme se on hyvin matala, mikä tarkoittaa, että on vahvaa näyttöä siitä, että ykköspesämiehet ovat pidempiä.\n",
-    "* t-arvo on t-testissä käytetty normalisoidun keskiarvoeron väliarvo, jota verrataan tietyn luottamustason kynnysarvoon.\n"
+    "* p-arvo voidaan pitää todennäköisyytenä, että kahdella jakaumalla on sama keskiarvo. Meidän tapauksessamme se on hyvin matala, mikä tarkoittaa, että on vahvaa näyttöä siitä, että ensimmäisen pesän pelaajat ovat pidempiä.\n",
+    "* t-arvo on normalisoidun keskiarvoeron väliarvo, jota käytetään t-testissä, ja sitä verrataan kynnysarvoon tietyn luottamusarvon perusteella.\n"
    ]
   },
   {
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korrelaatio ja Paha Baseball-yhtiö\n",
     "\n",
-    "Korrelaation avulla voimme löytää yhteyksiä datasekvenssien välillä. Kuvitellaanpa leikkimielisessä esimerkissämme, että on olemassa paha baseball-yhtiö, joka maksaa pelaajilleen palkkaa heidän pituutensa perusteella – mitä pidempi pelaaja on, sitä enemmän hän saa rahaa. Oletetaan, että peruspalkka on 1000 dollaria, ja sen lisäksi pituuden mukaan maksetaan 0–100 dollarin bonus. Käytämme oikeita MLB-pelaajia ja laskemme heidän kuvitteelliset palkkansa:\n"
+    "Korrelaation avulla voimme löytää yhteyksiä datasekvenssien välillä. Kuvitellaanpa esimerkkinä, että on olemassa paha baseball-yhtiö, joka maksaa pelaajilleen palkkaa heidän pituutensa mukaan – mitä pidempi pelaaja, sitä enemmän rahaa hän saa. Oletetaan, että peruspalkka on $1000, ja lisäksi korkeintaan $100 bonus, joka riippuu pelaajan pituudesta. Käytämme oikeita MLB-pelaajia ja laskemme heidän kuvitteelliset palkkansa:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lasketaan nyt näiden sekvenssien kovarianssi ja korrelaatio. `np.cov` antaa meille niin sanotun **kovarianssimatriisin**, joka on kovarianssin laajennus useille muuttujille. Kovarianssimatriisin $M$ elementti $M_{ij}$ on syötemuuttujien $X_i$ ja $X_j$ välinen korrelaatio, ja diagonaaliarvot $M_{ii}$ ovat $X_{i}$:n varianssi. Vastaavasti `np.corrcoef` antaa meille **korrelaatiomatriisin**.\n"
+    "Lasketaan nyt näiden sekvenssien kovarianssi ja korrelaatio. `np.cov` antaa meille niin sanotun **kovarianssimatriisin**, joka on kovarianssin laajennus usealle muuttujalle. Kovarianssimatriisin $M$ alkio $M_{ij}$ on syötemuuttujien $X_i$ ja $X_j$ välinen korrelaatio, ja diagonaaliarvot $M_{ii}$ ovat $X_{i}$ varianssi. Vastaavasti `np.corrcoef` antaa meille **korrelaatiomatriisin**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -889,19 +728,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Katsotaan, mitä tapahtuu, jos riippuvuus ei ole lineaarinen. Oletetaan, että yrityksemme päätti piilottaa ilmeisen lineaarisen riippuvuuden pituuksien ja palkkojen välillä ja lisäsi kaavaan jonkin epälineaarisuuden, kuten `sin`:\n"
+    "Katsotaanpa, mitä tapahtuu, jos riippuvuus ei ole lineaarinen. Oletetaan, että yrityksemme päätti piilottaa ilmeisen lineaarisen riippuvuuden pituuksien ja palkkojen välillä ja lisäsi kaavaan jonkin epälineaarisuuden, kuten `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -946,14 +783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Voitko arvata, miksi pisteet asettuvat pystysuoriin linjoihin näin?\n",
+    "> Voitko arvata, miksi pisteet asettuvat pystysuoriin linjoihin tällä tavalla?\n",
     "\n",
     "Olemme havainneet korrelaation keinotekoisesti luodun käsitteen, kuten palkan, ja havaitun muuttujan *pituus* välillä. Katsotaanpa myös, korreloivatko kaksi havaittua muuttujaa, kuten pituus ja paino, keskenään:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Valitettavasti emme saaneet mitään tuloksia - vain outoja `nan`-arvoja. Tämä johtuu siitä, että jotkin sarjamme arvoista ovat määrittelemättömiä, ja ne on merkitty `nan`:illa, mikä aiheuttaa sen, että myös operaation tulos on määrittelemätön. Tarkastelemalla matriisia voimme nähdä, että `Weight` on ongelmallinen sarake, koska `Height`-arvojen itsekorrelaatio on laskettu.\n",
+    "Valitettavasti emme saaneet mitään tuloksia - vain joitakin outoja `nan`-arvoja. Tämä johtuu siitä, että jotkut sarjan arvot ovat määrittelemättömiä, edustettuina `nan`-arvoina, mikä aiheuttaa sen, että operaation tuloskin on määrittelemätön. Tarkastelemalla matriisia voimme nähdä, että `Weight`-sarake on ongelmallinen, koska `Height`-arvojen keskinäinen korrelaatio on laskettu.\n",
     "\n",
-    "> Tämä esimerkki korostaa **datan valmistelun** ja **puhdistamisen** tärkeyttä. Ilman asianmukaista dataa emme voi laskea mitään.\n",
+    "> Tämä esimerkki korostaa **datan valmistelun** ja **puhdistamisen** merkitystä. Ilman asianmukaista dataa emme voi laskea mitään.\n",
     "\n",
     "Käytetään `fillna`-metodia puuttuvien arvojen täyttämiseen ja lasketaan korrelaatio:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,32 +888,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "On todellakin korrelaatio, mutta ei niin vahva kuin keinotekoisessa esimerkissämme. Jos tarkastelemme hajontakaaviota, jossa yksi arvo asetetaan toista vastaan, yhteys olisi paljon vähemmän ilmeinen:\n"
+    "On todellakin korrelaatio, mutta ei niin vahva kuin keinotekoisessa esimerkissämme. Itse asiassa, jos tarkastelemme hajontakaaviota, jossa yksi arvo on toista vastaan, yhteys olisi paljon vähemmän ilmeinen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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C60H8Mn1s4ibpnjdvnsrKyvTss8+2PhYOhyVJ1113na666iqdeuqpeuCBB3TcccfpiSee6PG/VVRUJL/f3/p15JFH9rr9ANBXqvdG/yPUkzigP+H9AyCC60H8Mn1s4iLpnj9/vlasWKE1a9ZoxIgRrY/n5ras2Q8EAm3iv/a1r2nnzp2SpJycHFVXV7d5vrm5WTU1NcrJyenw31uwYIGCwWDr1yeffOJkdwDAUdnpKY7GAf0J7x8AEVwP4pfpY+Nq0m1ZlubPn6/nn39eq1evVn5+fpvn8/LyNHz48HZlxD744AONGjVKklRQUKA9e/Zo8+bNrc+vXr1a4XBYp59+eof/7sCBA5WRkdHmCwDi1bj8TOX6U1oPEjmUTy0ne47Lz4xlswBP4P0DIILrQfwyfWxcTbrnzZunpUuX6plnnlF6erqqqqpUVVXVWoPb5/Pp1ltv1YMPPqg//OEP+uijj3TXXXfpH//4h6655hpJLbPehYWFmjNnjjZt2qTXX39d8+fP17e//W1bJ5cDQLxLTPBp4cyWFT+H/jGKfL9wZkCJCdH+VAH9F+8fABFcD+KX6WPjaskwn6/jX9qTTz6pK6+8svX7n/zkJ3r44YdVU1Ojk08+WT/96U81YcKE1udramo0f/58LV++XAkJCbrkkkv04IMPavDgwbbaQckwAF5gau1KIBZ4/wCI4HoQv7w2NnbzyLiq0+0Wkm4AXhEKW9pUUaPqvQ3KTm9ZZuXVT32BWGtsDmtJ6XbtqKnTqMw0zS7IU3JSXBxvAyDG+Hsav7w0NiTd3UDSDQCA2bw2ewIAiH9280g+3gUAAEYrKavU3KVb2tWArQo2aO7SLSopq3SpZQCA/oCkGwAAGCsUtrRoebk6WtYXeWzR8nKFwv1+4R8AoI+QdAMAAGNtqqhpN8N9MEtSZbBBmypqYtcoAEC/QtINAACMVb03esLdkzgAALqLpBsAABgrOz3F0TgAALorye0GAAAA9JVx+ZnK9aeoKtjQ4b5un6Qcf0tJGq/xUlkdAOjPSLoBAICxEhN8WjgzoLlLt8gntUm8I+npwpkBzyWrlEADAO9geTkAADBa4ehcLZ41Rjn+tkvIc/wpWjxrjOeSVEqgAYC3MNMNAACMVzg6V1OOP1xLSrdrR02dRmWmaXZBnpKTvDX/0FUJNJ9aSqBNDeR4bvYeAExF0g0AAIzX0XLs326o8Nxy7O6UQCs4Oit2DQMAROWtj3cBAAC6yaTl2JRAAwDvIekGAADG6mo5ttSyHDsU7igi/lACDQC8h6QbAAAYqzvLsb0gUgIt2m5tn1pOMfdiCTQAMBVJNwAAMJZpy7EjJdAktUu8vVwCDQBMRtINAACMZeJybNNKoAGA6Ti9HAAAGCuyHLsq2NDhvm6fWpJVry3HLhydq6mBHG2qqFH13gZlp7f0gRluAIg/JN0AAMBYkeXYc5dukU9qk3h7fTl2YoKPsmAA4AEsLwcAAEZjOTYAwE3MdAMAAOOxHBsA4BaSbgAA0C+wHBsA4AaWlwMAAAAA0EdIugEAAAAA6CMsLwdgtFDYYg9nHGN8EEv1jSHdt7Jc23fVKS8rTXfMCCg1OdHtZvWYaf1BfON6Hd8Yn/jmsyyro7KV/Uptba38fr+CwaAyMjLcbg4Ah5SUVWrR8nJVBhtaH8v1p2jhzACnFccBxgexNOfpt7SqvLrd41MD2Sq+fKwLLeod0/qD+Mb1Or4xPu6xm0eSdIukGzBRSVml5i7dokMvcJHPfCkT5C7GB7EULUGN8Fqialp/EN+4Xsc3xsdddvNI9nQDME4obGnR8vJ2f4AktT62aHm5QuF+/5mjKxgfxFJ9Y6jTBFWSVpVXq74xFKMW9Y5p/TlYKGypdNsuvbj1M5Vu28U1IA5wvY5vjI93kHQDMM6mipo2S6wOZUmqDDZoU0VN7BqFVowPYum+leWOxrnNtP5ElJRVasL9q3Vp8Ubd9OxWXVq8URPuX62Sskq3m9avcb2Ob4yPd5B0AzBO9d7of4B6EgdnMT6Ipe276hyNc5tp/ZH+uTz20OShKtiguUu3kHi7iOt1fGN8vIOkG4BxstNTHI2DsxgfxFJeVpqjcW4zrT8sj41vXK/jG+PjHSTdAIwzLj9Tuf4URSuU4VPLqZ7j8jNj2Sz8H8YHsXTHjICjcW4zrT8sj41vXK/jG+PjHSTdAIyTmODTwpktN5yH/iGKfL9wZoD6lS5hfBBLqcmJmhrI7jRmaiDbM/WtTesPy2PjG9fr+Mb4eAdJNwAjFY7O1eJZY5Tjb7ukKsefQvmMOMD4IJaKLx8bNVH1Ynktk/rD8tj4x/U6vjE+3kCdblGnGzBZKGxpU0WNqvc2KDu9ZYkVn/jGD8YHsVTfGNJ9K8u1fVed8rLSdMeMgGdmhDtiQn9CYUsT7l+tqmBDh/u6fWpJHjbcNoVrg8u4Xsc3xscddvNIkm6RdAMAALglcnq5pDaJdyRdYLYOQLyym0eyvBwAAACuYXksANMlud0AAAAQn1iuiFgpHJ2rqYEcXm8AjETSDQAA2ikpq9Si5eVtyjnl+lO0cGaAmUf0icQEnwqOznK7GQDgOJaXAwCANiJ7bA+tn1wVbNDcpVtUUlbpUssAAPAekm4AANAqFLa0aHl5hydJRx5btLxcoXC/P4cVAABbWF4OAIBDGpvDWlK6XTtq6jQqM02zC/KUnOStz7c3VdS0m+E+mCWpMtigTRU1LAV2mQmvN5NxJgKACJJuAAAcULSyXMXrK3TwBPC9K9/TnIn5WjAj4F7Duql6b/SEuydx6BumvN5MxZkIAA7Gx6EAAPRS0cpyPbaubQIkSWFLemxdhYpWlrvTsB7ITk/pOqgbcXCeSa83E3EmAoBDkXQDgIeEwpZKt+3Si1s/U+m2XeyrjQONzWEVr6/oNKZ4fYUam8MxalHvnHiE39G4eGLC+8e015tpOBMBQEdYXg4AHsFyxfi0pHR7uxnHQ4WtlrhrJh4Vm0b1wv0l79mOu+fCE/u4Nc4x5f1j2uvNNJyJAKAjzHQDgAewXDF+7aipczTObdt32Wun3bh4YNL7x7TXm2k4EwFAR0i6ASDOsVwxvh05NNXROLflZaU5Guc2094/ozLt/d7txsFZnIkAoCMk3QAQ57qzXBGxd3xOhqNxbrvD5snXduPcZtr7Z3ZBnrqqOpXga4lD7I3Lz1SuP0XRhsinlm0N4/IzY9ksAC4j6QaAOMdyxfhWU9foaJzbUpMTNTWQ3WnM1EC2UpMTY9Si3jHt/ZOclKA5E/M7jZkzMZ963S5JTPBp4cyWD6QOTbwj3y+cGaBeN9DPcEUGgDjHcsX4ZuL4FF8+NmriPTWQreLLx8a4RT1n4vgsmBHQdZPy2814J/ik6yZRp9tthaNztXjWGOX4276mcvwpWjxrjKcO7gPgDE4vB4A4F1muWBVs6HBfqk8tN3MsV3SHqeNTfPlY1TeGdN/Kcm3fVae8rDTdMSPgmRnuCFPHZ8GMgL4/7XgtKd2uHTV1GpWZptkFecxwx4nC0bmaGsjRpooaVe9tUHZ6y2uMGW6gf/JZluWNk0P6UG1trfx+v4LBoDIyvLHnDkD/Ejl9WVKbxCFy+8bsibsYn/jG+AAA+oLdPJKPQwHAA1iuGN8Yn/jG+AAA3MRMt5jpBuAdobDFcsU4xvjEN8YHAOAku3kke7oBwEMSE3wqODrL7WYgCsYnvpk2PnyIAADeQNINAADgMSVllVq0vLxNDfJcf4oWzgywXB4A4gx7ugEAADwkcjDcwQm3JFUFGzR36RaVlFW61DIAQEdcTbqLioo0duxYpaenKzs7WxdeeKHef//9DmMty9L06dPl8/n0wgsvtHlu586dOu+885SWlqbs7Gzdeuutam5ujkEPACC2QmFLpdt26cWtn6l02y6Fwt4+lqOxOazH13+sH75YpsfXf6zG5rDbTeoV08anvjGku154V7Mff1N3vfCu6htDbjepV2r2NWraL17TKYte0bRfvKaafY1uN6nbQmFLi5aXd1j+LPLYouXlnnztmXY9oD8AIlxdXr527VrNmzdPY8eOVXNzs+644w5NmzZN5eXlGjRoUJvYX/7yl/L52u9TCoVCOu+885STk6M33nhDlZWVuvzyyzVgwADdd999seoKAPQ505aTFq0sV/H6Ch2cG9y78j3NmZivBTMC7jWsh0wbnzlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM2N/vEpfHpRk76lv0pgfr9Kwwcl6686pLrasezZV1LSb4T6YJaky2KBNFTWe2r9u2vWA/gA4mKsz3SUlJbryyit1wgkn6OSTT9ZTTz2lnTt3avPmzW3itm7dqv/6r//SE0880e5nvPLKKyovL9fSpUt1yimnaPr06brnnnv08MMPq7HRe59gA0BHTFtOWrSyXI+ta3sDJ0lhS3psXYWKVpa707AeMm18Dk24D7aqvFpznn4rxi3qnUMT7oN9ua9RY3+8KsYt6rnqvdET7p7ExQPTrgf0B8Ch4mpPdzAYlCRlZma2PlZXV6fvfOc7evjhh5WTk9PuvyktLdWJJ56oww8/vPWxc889V7W1tfr73//e940GgD5m2nLSxuawitdXdBpTvL7CM0sXTRuf+sZQ1IQ7YlV5tWeWmtfsa4yacEd8ua/RM0vNM1OTHY1zm2nXA/oDoCNxk3SHw2HdfPPNOvPMMzV69OjWx2+55RaNHz9eF1xwQYf/XVVVVZuEW1Lr91VVVR3+NwcOHFBtbW2bLwCIV91ZTuoFS0q3t5sxOVTYaonzAtPG5z6bs1Z249z27d+84Wic2/7xxV5H49xm2vWA/gDoSNyUDJs3b57Kysq0YcOG1seWLVum1atX6+2333b03yoqKtKiRYsc/ZkA0FdMW066o6bO0Ti3mTY+23fZ+73bjXNb9V57M9h249z2yW57v3e7cW4z7XpAfwB0JC5muufPn68VK1ZozZo1GjFiROvjq1ev1rZt2zRkyBAlJSUpKanlM4JLLrlEZ511liQpJydHX3zxRZufF/m+o+XokrRgwQIFg8HWr08++aQPegUAzshOT3E0zm2jMtMcjXObaeOTl2Xv9243zm3D0u0ts7Yb5zbT3j/0J76Z1h/ALa4m3ZZlaf78+Xr++ee1evVq5efnt3n+9ttv1zvvvKOtW7e2fknSAw88oCeffFKSVFBQoHfffVfV1f/cf7Zq1SplZGQoEOj4NMWBAwcqIyOjzRcAxKtx+ZnK9aeoff2GFj61nJI9Lj8zSkR8mV2Qp4Ronfk/Cb6WOC8wbXzusHkSsd04t/3nOcc5Guc2094/9Ce+mdYfwC2uJt3z5s3T0qVL9cwzzyg9PV1VVVWqqqpSfX29pJaZ6tGjR7f5kqSRI0e2JujTpk1TIBDQ7Nmz9be//U1//vOfdeedd2revHkaOHCga30DAKckJvi0cGZLgnPovU/k+4UzA0rs6s4oTiQnJWjOxPxOY+ZMzFdyUlwsxuqSaeOTmpyoqYHsTmOmBrKVmpwYoxb1Tl3Y3gFPduPcZtr7h/7EN9P6A7jF1XfI4sWLFQwGddZZZyk3N7f167nnnrP9MxITE7VixQolJiaqoKBAs2bN0uWXX64f/ehHfdhyAIitwtG5WjxrjHL8bZco5/hTtHjWGM/VgV4wI6DrJuW3m0FJ8EnXTfJe3VfTxueSMSN69Xw8MW35v2Te+4f+xDfT+gO4wWdZljdqmPSh2tpa+f1+BYNBlpoDiGuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2dMTBifUNjShPtXRz2R3aeWDxM23DbFE32L9Kcq2NBhWTev9edgpr1/6E98q28M6b6V5dq+q055WWm6Y0bAMytegL5iN48k6RZJNwAAEaXbdunS4o1dxv1uzhkqODorBi3qvZKySl2/dEvU5x/14GoEIJZKyiq1aHl5mw/jcv0pWjgzwHsH/ZrdPNK7H7cBAADHmVYCDUDvlJRVau7SLe1Wv1QFGzR36RaVlFW61DLAO0i6AQBAK9P2QIfClhYtL4/6vE/SouXlCoX7/cI/oJ3I+6ejd0fkMd4/QNdIugEAQCvTSqBtqqiJuj9dakkcKoMN2lRRE7tGAR7B+wdwBkk3AABoZVoJNJbLAz3H+wdwBkk3AABow6QSaKYtlwdiifcP4IwktxsAAIApTCoRVDg6V5OPzfZ8iaDIcvmuSoZ5Zbn8wUwr4WTS+8cUJr9/gFiiZJgoGQYA6L2ileUqXl+hg88TSvBJcybma8GMgHsN6yGT+mNiybA5T7+lVeXV7R6fGshW8eVjXWhR75j0ejNN5PRySW0S78gGE6+tfgGcRMkwAABipGhluR5b1zZhkKSwJT22rkJFK6Ofnh2PTOvPI6991Kvn4020hFuSVpVXa87Tb8W4Rb1j2uvNNCZtNwHcwvJyAAB6obE5rOL1FZ3GFK+v0PenHe+JpbKm9WdfQ7Pe+bS205h3Pq3VvoZmDU6J/9ui+sZQ1IQ7YlV5teobQ55Yam7a681UhaNzNTWQo00VNare26Ds9JYl5V45UBFwG1cvAAB6YUnp9nYzdIcKWy1xXmBaf2557m1H49x2n81ZX7txbjPt9WayxASfCo7O0gWnHKGCo7NIuIFuIOkGAKAXdtTUORrnNtP6s3N3vaNxbtu+y97v3W6c20x7vQFAR0i6AQDohSOHpjka57ZRmfbaaTfObSOHpjoa57aRmTb7YzPObaa93gCgIyTdAAD0wvE56Y7GuW12QZ66WjWa4GuJ84IHvnWqo3FumxbIcTTObaa93gCgIyTdAAD0Qk1do6NxbktOStCcifmdxsyZmO+ZQ60GpyTppBGdlwM9aUSGJw5Rk6Q99U2OxrnNtNcbAHSEKxgAAL2QnZ7SdVA34uLBghkBXTcpv90MZIJPum6S9+omL5s/MWrifdKIDC2bPzHGLeo5Xm8A4D0+y7K6ODPSfHaLmgMAnBUKW54vQdPYHNbxd73c6QnMCT7pH/dM99xsXX1jSPetLNf2XXXKy0rTHTMCnihDFc2+hmbd8tzb2rm7XiOHpuqBb53qmRnuiFDY0oT7V6sq2KCOXnI+tdRP3nDbFE++l5aUbteOmjqNykzT7II8z71nAPQvdvNIb/2lAQAYo6SsUouWl6sy2ND6WK4/RQtnBlQ4OtfFlnXP5h27bZU82rxjtwqOzopNoxxw6Pis/1D6y3vVnhufgw1OSVLxFWPdbkavJCb4tHBmQHOXbpFPapN4R1LshTMDnku4pZal5tdMPMrtZgCA4/j4EAAQcyVllZq7dEubhFuSqoINmrt0i0rKKl1qWfdV723oOqgbcfHApPExUeHoXC2eNUY5/rZLyHP8KVo8a4xnPxQBAFMx0w0AiKlQ2NKi5eUdLo211DJbt2h5uaYGcjwxW2faHlvTxsdUhaNzNTWQ4/ntGQDQH5B0AwBialNFTbsZ1INZkiqDDdpUUeOJ5djj8jOV60/pco/tuPzMWDetR0wbn4OZtmc4McHnuTEA0DdMOCPFZCTdAICYMm05tml7bE0bn4iileUqXl/RZv/9vSvf05yJnI4NwNtMOSPFZN79eBcA4EmmLceWzNpja+L4FK0s12PrKtodeBe2pMfWVahoZbk7DQOAXuIMDm9gphsAPMSE5WOnjRqqBJ+6LLF12qihsWuUAwpH5+q0kZm66JENqtnfpMxBA/T8dydoWMZAt5vWLaYtl29sDqt4fUWnMcXrK/T9acd7bqm5CSXQDhasa9LVT23S58EGDfen6Ikrx8mfNsDtZvWYCddrk5kwPpzB4R3evTIDQD9jyvIxU0tsjf3xKn25r7H1+7o9IY297y8aNjhZb9051cWWdU9kufz1S7d0+Lwlby2XX1K63dbrbUnpdk+Vqzr/ofV659Pa1u/fr9qr0Xf/WSeNyNCy+RNdbFnPTP7Zau3YVd/6fWWwQSf/6BWNykrV2lunuNiynjHlem0qU8bH5DM4TOOtj3QBoJ8yafmYiXuGD024D/blvkaN/fGqGLeod/7zD+/06vl4sqOmztG4eHBown2wdz6t1fkPrY9xi3rn0IT7YDt21Wvyz1bHuEW9Y9L12kQmjY+Jf09NRdINAHGuq+VjUsvysVBX03lxwrQ9wzX7GqMm3BFf7mtUTRcx8eLL2gOqbWjuNKa2oVlf1h6IUYt6J3OgvUV9duPctq+hOWrCHfHOp7Xa18UYxotgXVPUhDtix656BeuaYtSi3jHtem0a08bHtL+nJiPpBoA4153lY14Q2TMcbXGyTy3L/LyyZ/jbv3nD0Ti3XfTIBkfj3LbkrZ2OxrntlufedjTObVc/tcnROLeZdr02jWnjY9rfU5ORdANAnDNt+Vhkz7CkdjcK3iyxZW8G226c22r225tRtBvntr02Z3ztxrlt5+7OZ4W7G+e2zztJgHoS5zbTrtemMW18TPt7ajKSbgCIcyYuHzOrxFayo3Fuyxxk77Rou3Fuy7B5mrfdOLeNHJrqaJzbhvvtXbfsxrnNxOu1SUwcH5P+nprMZ1mWNzYt9KHa2lr5/X4Fg0FlZGS43RwAaKOxOazj73q5yxJb/7hnuudKHplQsqVmX6PG2DgobcudU5U5OP4T7y9rD2jsfX/pMu6tO87xRDm0z2rqdeZPuz6I6/X/nKIjMuM/Ud3X0KzRd/+5y7iyu8/1RPmwYF2TTv7RK13G/e2H0zxRPiwUtjTh/tVdltzbcNsUz13rTGDy+Jjw99SL7OaR3ro7A4B+qDsltrwmMcGngqOzdMEpR6jg6CxP3iBkDk7WsC6S6WGDkz2RcEvSsIyBXc76ZqQkeSLhlqSdu+2dSm43zm2DU5J00ojOJwhOGpHhiYRbkvxpAzQqq/MPO0ZlpXoi4ZZY7hvvTB4fE/6emoykGwDinGl70Ex08ZgjevV8vPnpv53Uq+fjiYnvn2XzJ0ZNvL1Yp3vB9K/16vl4w3Lf+Mb4wA3e+BgUAPoxE/egmaSxOazi9RWdxhSvr9D3px3vieX/kZI60fjUUlJnaiDHEzMphw22NyNvNy5eLJs/UfsamnXLc29r5+56jRyaqge+dapnZrgjTHu9RRSOztXUQA7LfeMU44NY89aVGQD6oUhJkK72oFESxB1LSrfbWv6/pHS7rpl4VGwa1QvdKalTcHRW7BrWU3ZPrvHgCTeDU5JUfMVYt5vRK8a93g4SWe6L+MT4IJbi/yN3AOjnTN6DZoIdNfb2AtuNc5tpy7G/2n/A0Tg4y7TXGwB0hKQbADyAPWjx68ihaY7Guc207Qym9cc0jA+A/oDl5QDaoORE/GIPWnw6/vB0R+PcZtp2BtP6YxrGB0B/QNINoFVJWaUWLS9vs78u15+ihTMDzKTGCfagxZ+a+kZH49wW2c5w/dItHT5vyVvbGSL9mbt0i3xqu3Wb7RnuY3wA9AcsLwcgqSXhnrt0S7sDbaqCDZq7dItKyipdahkQ31geG//YnhHfGB8ApvNZluXB8zqdVVtbK7/fr2AwqIyMjuteAiYLhS1NuH911BNkI8v7Ntw2xXOzDaYtlzetP6aUPJpw/+oul8d65f1j8vWgsTmsJaXbtaOmTqMy0zS7IM8TZdyiqW8M6b6V5dq+q055WWm6Y0ZAqcmJbjerx0wbHwDms5tHeuvOBkCfMLVki2nL5U3rz/kPrdc7n9a2fv9+1V6NvvvPOmlEhpbNn+hiy7rHtOXY/el68NsNFZ59/8x5+i2tKq9u/X79h9KSjTs1NZCt4su9V0bMtPEBgIP16OPDH/3oR6qra1/6pL6+Xj/60Y963SgAsWViyRbTlsub1p9DE+6DvfNprc5/aH2MW9Q7j7z2Ua+ejydcD+LfoQn3wVaVV2vO02/FuEW9Y9r4AMChepR0L1q0SPv27Wv3eF1dnRYtWtTrRgGILdP2pIbClhYtL+9wqW/ksUXLyxUKe2N3jWn92dfQHDXhjnjn01rta2iOUYt6x7T+JIadjXObae+f+sZQ1IQ7YlV5teobQzFqUe+YNj4A0JEeJd2WZcnna79M7m9/+5syMynpAHhNpGRLtMWvPrUsY/ZKyZbuLI/1AtP6c8tzbzsa5zbT+nPHsncdjXObae+f+1aWOxrnNtPGBwA60q093UOHDpXP55PP59Oxxx7bJvEOhULat2+frr/+escbCaBvmVayxbTlsab1Z+fuekfj3GZaf/YfsDdDajfObaa9f7bvar+9rzdxbjNtfACgI91Kun/5y1/KsixdffXVWrRokfx+f+tzycnJysvLU0FBgeONBND3IiVbDj3IJseDB3WZtlzetP6MHJqq96v22orzAtP6M2hgomobuk6oBw30xinZpr1/8rLStP5De3FeYNr4AEBHupV0X3HFFZKk/Px8jR8/XgMGDOiTRgFwR+HoXE0N5Hi+JNVpo4YqwSd1tgUwwdcS5wWR5f9dlaTyyvL/B751qkbf/WdbcV5gWn/+dP0EnfPLtbbivMC068EdMwJasnGnrTgvMO36BgAd6dGe7smTJysxMVEffPCBNmzYoHXr1rX5AuBdiQk+FRydpQtOOUIFR2d5LuGWpM07dnd6gy213IBv3rE7Ng3qpcjy/2hd8lpJqsEpSTppRPRalpJ00ogMz9TrNq0//72xwtE4t5l2PUhNTtTUQHanMVMD2Z6p123a9Q0AOtKjO4CNGzfqO9/5jnbs2CHLanuZ9Pl8CoW8sc8LgJnYIxj/ls2fGLVsmNfqdEtm9Yc9w/Gv+PKxUcuGebVONwCYrEdJ9/XXX6+vf/3reumll5Sbm9vhSeYA4BbT9ghGSupE41NLSZ2pgRxPzQYtmz9R+xqadctzb2vn7nqNHJqqB751qmdmhA9lSn9M2zOcmZrsaFy8KL58rOobQ7pvZbm276pTXlaa7pgR8MwMd4Sp1zcAOFiP7gQ+/PBD/eEPf9AxxxzjdHsAoNdM2yPYnZI6BUdnxa5hDhickqTiK8yZlUtNTtTVE45qPRPBawmQZN6e4X980fUhd5G4iccN6+PWOCs5KUEzThze+npLTurRrkFXmXx9A4CIHiXdp59+uj766COSbgBxiRJocENJWWW70/9zPXj6f2TPcEdLlyO8tGf4k932lsHbjYsXprzeuL4B6A9sJ93vvPNO6/+/4YYb9P3vf19VVVU68cQT251iftJJJznXQgDoAUqgIZZKyio1d+mWdisrqoINmrt0ixbPGuOp19xRhw3q1fPxZFSmvWXwduPigUmvN65vAPoDn3XoSWhRJCQkyOfztTs4rfUH/d9zXjxIrba2Vn6/X8FgUBkZnZ9AC8BbGpvDWlK6XTtq6jQqM02zC/I8twQzFLY04f7VXS6X33DbFM/M3pskMj7Rlsh6bXwam8M6/q6Xuyyx9Y97pnvivVTfGNLXfljSZdx7Pyr0xOy9aa83rm8AvMxuHml7pruiwhulQQAgoqPll7/dUOG5mW7TlsubxrQ9qUtKt9sqsbWkdLuumXhUbBrVC1s/2WM7zgvjY9rrjesbgP7AdtI9atSovmwHADjKpOWXklnL5U1j2p7UHTX29jbbjXObaeNjWn8krm8AzNejg9SWLVvW4eM+n08pKSk65phjlJ+f36uGAXBHKGxpU0VN62m44/IzPTfDEClB09FknSXvlqApHJ2rcXlZ+vZv3lD13kZlpyfr2WvHK3Owt0odHezL2gO66JENqtnfpMxBA/T8dydoWMZAt5vVLabtSTVtD/Rhg+29nuzGuc2011tE4ehcTThmmOdL7pnMhPsDwC09upJdeOGFHe7vPnhf94QJE/TCCy9o6NChUX9OUVGR/vSnP+kf//iHUlNTNX78eN1///067rjjJEk1NTVauHChXnnlFe3cuVPDhg3ThRdeqHvuuUd+v7/15+zcuVNz587VmjVrNHjwYF1xxRUqKipSUhIXaqA7TDkN17TllxGTf7ZaO3bVt36/p75JY368SqOyUrX21ikutqxnTrr7z6ptaG79vm5PSGPv+4syUpL0zt3nutiy7omUqOvsNZfroRJ1swvydO/K97rc0z27IC9mbeoVWyfXdCPOZaa93iLmPP1WmxPz36/aq9F3/1lTA9kqvtycsoJeZcr9AeCWHp2AsmrVKo0dO1arVq1SMBhUMBjUqlWrdPrpp2vFihVat26ddu3apf/4j//o9OesXbtW8+bN08aNG7Vq1So1NTVp2rRp2r9/vyTp888/1+eff66f//znKisr01NPPaWSkhJdc801rT8jFArpvPPOU2Njo9544w3993//t5566in98Ic/7EnXgH4rshz70Bu5yHLskrJKl1rWfSYuvzw04T7Yjl31mvyz1TFuUe8cmnAfrLahWSfd/ecYt6jnEhN8nSZAUsuHPF6ZEUpOSrC1p9sLh6hJ0lf7Dzga57bEBJ++7OLa9eVe77zepPYJ98FWlVdrztNvxbhFOJhJ9weAW3o0FXzTTTfpN7/5jcaPH9/62De+8Q2lpKTo2muv1d///nf98pe/1NVXX93pzykpaXua6FNPPaXs7Gxt3rxZkyZN0ujRo/XHP/6x9fmjjz5a9957r2bNmqXm5mYlJSXplVdeUXl5uf7yl7/o8MMP1ymnnKJ77rlHt912m+6++24lJ3t32SUQK6Ytxx6SMqDroG7EuS1Y1xQ14Y7Ysatewbom+dPiv09f1h6ImnBH1DY068vaA55Yar7xg1224844Nv5XVpR/Wms7LjAi/it++JrtTWHbjXPbZzX1ag53HtMcbok7IjM1No3qhfrGUKc14aWWxLu+MeSJ0+VNY9r9AeCWHn1MvW3btg6PRM/IyNDHH38sSfqXf/kXffXVV936ucFgUJKUmRl9SVTkOPbI0vHS0lKdeOKJOvzww1tjzj33XNXW1urvf/97hz/jwIEDqq2tbfMF9GfdWY7tBaveq3I0zm1XP7XJ0Ti3XfTIBkfj3PbtJzY6Gue2f31ovaNxbrv9xXcdjXPb9AfXOhrntvtWljsaB2eZdn8AuKVHSfdpp52mW2+9VV9++WXrY19++aX+8z//U2PHtuy7+fDDD3XkkUfa/pnhcFg333yzzjzzTI0ePbrDmK+++kr33HOPrr322tbHqqqq2iTcklq/r6rq+Ia6qKhIfr+/9as77QRMZNpy7B01nc8KdzfObZ93sXS5u3Fuq9nf5GgcnNXFJGq349xW32SvpXbj3Lb/QMjROLdt32XvFHy7cXCWafcHgFt6lHQ//vjjqqio0IgRI3TMMcfomGOO0YgRI7R9+3b99re/lSTt27dPd955p+2fOW/ePJWVlenZZ5/t8Pna2lqdd955CgQCuvvuu3vS7FYLFixo3YseDAb1ySef9OrnAV5n2mm4eVn2TlW2G+e24X57v3e7cW7LHGRvCbzdODjL7o2BN3Z0S6kD7LXUbpzbBg20t8TabpzbTLtem8a0+wPALT36C3PcccepvLxcL774om688UbdeOONWrZsmf7+97/r2GOPldRywvns2bNt/bz58+drxYoVWrNmjUaMGNHu+b1796qwsFDp6el6/vnnNWDAP2/EcnJy9MUXX7SJj3yfk5PT4b83cOBAZWRktPkCeioUtlS6bZde3PqZSrftUqirE4jiUOQ03Gi7sXzy1mm4d8wIOBrntieuHOdonNv+cP2Zjsa57elZ9k5Wthvntj/Z/L3bjXPbSzdMcjTObS/fONnROLfdVvg1R+PgLNPuDwC39Phj3YSEBBUWFrYm3eeee64SErr34yzL0vz58/X8889r9erVHdb2rq2t1bRp05ScnKxly5YpJaXtJ2kFBQV69913VV39z0M4Vq1apYyMDAUC3rihhneVlFVqwv2rdWnxRt307FZdWrxRE+5f7bmTPBMTfFo4s+X9cugf1sj3C2cGPHNISmpyoqYGsjuNmRrI9syhPP60ARqV1fmBSKOyUj1xiJokVeza72ic21Z99EXXQd2Ic9vmT+ztzbQb57Yqm8te7ca57YjMVCUndn4tTk70eeIQNUl697Ogo3Fwlmn3B4BbbJ9e/uCDD+raa69VSkqKHnzwwU5jb7zxRls/c968eXrmmWf04osvKj09vXUPtt/vV2pqamvCXVdXp6VLl7Y59GzYsGFKTEzUtGnTFAgENHv2bP30pz9VVVWV7rzzTs2bN08DB8b/qbfwrkgJjUPntSMlNBbPGuOp2pWFo3O1eNaYdnU4czxah7P48rFRy9B4se7r2lunRC0b5rU63abtETRtT+qOGnvttBvnNtNeb5L0wb0zdOwPVqox1H5lVXKiTx/cO8OFVvWMieNjGtPuDwA32E66H3jgAV122WVKSUnRAw88EDXO5/PZTroXL14sSTrrrLPaPP7kk0/qyiuv1JYtW/Tmm29Kko455pg2MRUVFcrLy1NiYqJWrFihuXPnqqCgQIMGDdIVV1yhH/3oR3a7BnSbqSU0CkfnamogR5sqalS9t0HZ6S1LxrzUh4MVXz5W9Y0h3beyXNt31SkvK013zAh4Zob7UGtvnaJgXZOufmqTPg82aLg/RU9cOc4zM9wRhw2y94Go3Ti35WWlaf2H9uK8YFSmvXbajXObqXtSP7h3hj6rqdf0B9dq/4GQBg1M1Ms3TvbMDHeEqeNjGtPuD4BY81mW5b0NqA6rra2V3+9vLUcGdKV02y5dWtx1+Z/fzTlDBUfHf11eIJZe/+grXfbbN7uM+5//d7rOPOawGLSod+obQ/raD0u6jHvvR4We+MCnsTms4+96WZ0dT5Hgk/5xz3QlJ8X/4WOhsKUJ969WVbChww9KfWqZsdtw2xQSCBcwPgC8zG4e2au/lo2NjXr//ffV3Nzcmx8DeA7L4YCe+2rfAUfj3GbaGQLJSQmaM7H9GSsHmzMx3xMJt8Se1HjH+ADoD3r0F7Ourk7XXHON0tLSdMIJJ2jnzp2SpBtuuEE/+clPHG0gEI9YDgf0nInvn+LLx0ZNvL14hsCpI4f26vl4E9mTmnNIWb0cf4rnzt8wEeMDwHS293QfbMGCBfrb3/6m1157TYWFha2Pn3POObr77rt1++23O9ZAIB5FSmh0tRyOEhpAe6a+f0w5QyByZkU0nFmBvsD4ADBZj5LuF154Qc8995zOOOMM+Xz/vBiecMIJ2rZtm2ONA+JVZDnc3KVb5JPaJA4shwM6Z/L7JzU5UfdceKLbzeiVTRU1bU4oPpQlqTLYoE0VNZ47syIxwee5NvcnjA8AU/VoefmXX36p7Oz2y+j279/fJgkHTMZyOKDneP/EL86sAADAWT2a6f7617+ul156STfccIMktSbav/3tb1VQUOBc64A4x3I4xNqXtQd00SMbVLO/SZmDBuj5707QsAxvlNY6VOHoXB1zWLqmP7hWTWFpQIK05KrTdUzOYLeb1mMmlHTLTE12NC6e7Gto1i3Pva2du+s1cmiqHvjWqRqc0qNbobjQ2BzWktLt2lFTp1GZaZpdkOeZA+76g1DY4v4AgKQelgzbsGGDpk+frlmzZumpp57Sddddp/Lycr3xxhtau3atTjvttL5oa5+hZBgALzjp7j+rtqF9tYiMlCS9c/e5LrSod45a8FKHZakSfNLHRefFvkG9NPlnq7VjV327x0dlpWrtrVNcaFHPFK/7WPeufK/LuB/M+JrmTDoqBi1yxvkPrdc7n9a2e/ykERlaNn+iCy3qnaKV5SpeX9HmPZTgazlZfsGMgHsNgySppKxSi5aXt9mqketP0cKZAVbyAAbp05JhEyZM0NatW9Xc3KwTTzxRr7zyirKzs1VaWuq5hBsAvCBawi1JtQ3NOunuP8e4Rb0TLeGWpLDV8ryXREu4JWnHrnpN/tnqGLeo5z7ZXedoXDyIlnBL0juf1ur8h9bHuEW9U7SyXI+tq2j3Hgpb0mPrKlS0MvpBeOh7JWWVmrt0S7uzEaqCDZq7dItKyipdahkAt3RrTVVt7T//YA0bNkz/9V//1WEMs8UA4Jwvaw9ETbgjahua9WXtAU8sNa+o3h814Y4IWy1x+dmDYtOoXgjWNUVNuCN27KpXsK7JE0vNhwy0d2tgN85t+xqaoybcEe98Wqt9Dc2eWGre2BxW8fqKTmOK11fo+9OOZ6m5CyKn/3d0ibPk3dP/AfROt67GQ4YM0dChQ6N+RZ4HADjnokc2OBrntsJfrXU0zm1XP7XJ0Ti3PVXaeULX3Ti33fLc247GuW1J6XZbH1otKd0ek/agre6c/g+g/+jWR7pr1qxp/f+WZWnGjBn67W9/qyOOOMLxhgEAWtTsb3I0zm0HQvaOErEb57bPO7nB7kmc2/Y3hh2Nc9vO3Z2vQuhunNt21Nhb1m83Ds7i9H8AHelW0j158uQ23ycmJuqMM87QUUd55yAVAPCazEEDVLcnZCvOCwYm+mwl1AMTvbH0crg/pdOZrYPjvGDQwETVNnT9ehs0MDEGrem9kUNT9X7VXltxXjAqM83RODgrO93e+9xuHAAzsNkHAOLc89+d4Gic20pumtx1UDfi3PbEleMcjXPbyzfa+73bjXPbz//9FEfj3Da7IE9dbQVO8LXEIfbG5Wcq15+iaEPkU8sp5uPyM2PZLAAuI+kGgDg3LGOgMro44CkjJckTh6hJUn72IFtJgxcOUZMkf9oAjcrqfJZ0VFaqJw5Rk6QjMlOV3MUqg+REn47I9MbMcHll54eodTfObclJCZozMb/TmDkT8zlEzSWJCT4tnNlSsu3Qd1Hk+4UzAxyiBvQzvb4i+3xcNACgr/30307q1fPx5uOi86Im3l6s07321ilRE2+v1emWpA/unRE18U5O9OmDe2fEuEU9Z+Ie2wUzArpuUn6791CCT7puEnW63VY4OleLZ41RziFbSnL8KVo8awx1uoF+yGdZlu2Tai6++OI23y9fvlxTpkzRoEFtZyP+9Kc/OdO6GLFb1BwA3BAKW5pw/+qo+4Z9armZ23DbFM/NnlRU71fhr9bqQMjSwESfSm6a7JkZ7o4E65p09VOb9HmwQcP9KXriynGemeHuyGc19Zr+4FrtPxDSoIGJevnGyZ6Z4Y54/cOvdNnjb3YZ9z/XnK4z/+WwGLTIOY3NYS0p3a4dNXUalZmm2QV5zHDHkVDY0qaKGlXvbVB2esuScq9dowF0zm4e2a2D1Px+f5vvZ82a1bPWAQBs604JmoKjs2LXMAcckZmq/yw8vjVp8FpCd6jBKUn6j3OPb73J9kLd587kDEnRY7PHtvYnZ4gHD3+ym+N4MBdKTkrQNRM5zDZeJSb4PHdNBtA3unU38OSTT/ZVOwAAUZi4PFaSilaWq3h9RZuaw/eufE9zJnpzeWxJWaUWLS9v8wFJrj9FC2cGPLmc1JT+fLXvgKNxAAB0F2uQACDOmViCpmhluR5b1zbhlqSwJT22rkJFK8vdaVgPlZRVau7SLe1WJFQFGzR36RaVlFW61LKeMak/Jr5/AADeQtINAHHOtBI0jc1hFa+v6DSmeH2FGpvDMWpR74TClhYtL1dHB6REHlu0vFyhQz9hiFOm9eeUI4c4GgcAQHeRdANAnIuUoImW4ljyVgmaJaXb281wHypstcR5QXf23HuBaf155s0djsYBANBdJN0AgJjaUVPnaJzbTNtzb1p/THu9AQC8h6QbAOJcZLlvND55a7nviCH2Tii3G+e2zLRkR+PcZtoe6COH2nsd2Y0DAKC7vF3LBIDjTKsruq+hWbc897Z27q7XyKGpeuBbp3qujJNpJcOsqAvlexbntn9U1dqOm3jssD5uTe9FzhCoCjZ0OAKRuvBeOUPg+JzodVN7EhdPTLi+Hcy0OvcAEOHdKzMAx5lSIiji/IfW651P/5kQvV+1V6Pv/rNOGpGhZfMnutiy7jFtue9ne+y1026c2z7ZXe9onNsiZwhcv3RLh8977QyBmrpGR+PihSnXt4jJP1utHbv++R6pDDbo5B+9olFZqVp76xQXWwYAvcfycgCSzCoRJLW/IT3YO5/W6vyH1se4RT1n2nLfUZlpjsa5zbT+SIqacNt9Pp6Y9v6RzLq+Se0T7oPt2FWvyT9bHeMWAYCzSLoRc6GwpdJtu/Ti1s9Uum2XZ/ahRmNCf0wrEbSvoTnqDWnEO5/Wal9Dc4xa1Dsjh9pL1uzGuW3q13IcjXPbKUcMdTTObW/84ytH49yWnzXI0Ti3mXZ9C9Y1RU24I3bsqlewrilGLQIA55F0I6ZKyio14f7VurR4o256dqsuLd6oCfev9twsaoQp/TGtRNAtz73taJzbLnjY3qyV3Ti3Xbx4g6Nxbvu337zhaJzbvvPUm47Gue3fHn3d0Ti3mXZ9u/qpTY7GAUA8IulGzJi2fNmk/pi2Z3inzb2zduPcVmtzxspunNtM64/d9R/eWCdinpr99mZI7ca5zbTr2+edfODbkzgAiEck3YgJ05Yvm9Yf0/Y8HjnUXjvtxrktw+ZpxHbj3GZaf+weJ+aNY8fMkznI3unXduPcNtJmaTO7cW4b7rd3HbYbF09M2H4GwBkk3YgJ05Yvm9afSImgaEmBTy2nmHulRNBlY0c5Gue2FTdMcjTObab1Z9l3Jzga57ZLxx3haJzbnrf5e7cb57YHvnWqo3Fue+LKcY7GxQtTtp8BcAZJN2LCtOXLpvUnUiJIaj8bF/neSyWCahttLl+2Gee2nCEpSk7s/HefnOhTzhBvzATlDElRVy+lBJ88058TR/odjXPbgER7KwzsxrltWMbALldNZKQkaVjGwBi1qHcGpyRp2ODkTmOGDU72TL1uf9oAW/3xUr1uk7afAXAGSTdiwrTly6b1R5IKR+dq8awxyjlkCV+OP0WLZ43xVJ3uwwbbu3m2G+e2UNhSVhdtzRo80DNLF+sbQ+qqqWGrJc4rtv/kvF49H0+OGGJvWbLduHjwzt3nRk28M1KS9M7d58a4RT3X2BzWrv2d1xTftb9Rjc3hGLWod0JhS0mJnd+OJiUmeOb6Ztr2MwDO8MbHoPC8yPLlqmBDh3+IfGpJ7ryyfNm0/kQUjs7V1ECONlXUqHpvg7LTW/rglRnuVoadbNXVdgbpn9sZCo7OilGreu6+leW24+658MQ+bo1ztv/kPG3+eLcuOeiU8j9eO16nHeWNUmERPpvvC7tx8eKdu8/Vl7UHdNEjG1Szv0mZgwbo+e9O8MwMd8SS0u22PrRaUrpd10w8KjaN6gXTrm/d2X7mhf4AcAZJN2Iisnx57tIt8qltruPF5cum9edgiQk+z98IfLX/gKNxbjNtO8P2XXWOxsWLopXlKl5f0eaxfy9+Q3Mm5mvBjIBLreq+T4P2Tr22GxdPhmUM1Ibbv+F2M3plR42994XdOLeZdn0zrT8AnMHycsSMScuXJfP6YxLTlv+b1p+8rDRH4+JB0cpyPbauot0MZNiSHltXoSKbs/vxYFSmvd+73Tg4y7TxMe36Zlp/ADiDmW7ElDHLl/+Paf0xhWnL/03rzx0zAlqycaetOC9obA63m+E+VPH6Cn1/2vFKTor/z7q/c/oo3fPSe7biEHuzC/J078r3Ol1inuBrifMC065vpvUHgDPi/68/jBNZvnzBKUeo4OgszyeopvXHBKadxh7pT7R7bEve6k9qcqKmBrI7jZkayFZqcmKMWtQ73dlj6wVbP9njaByclZyUoDkT8zuNmTMx3xMf8EjmXq8lM/oDwBneuCIDQDeZtvz/7Z27e/U8+g57bBFrC2YEdN2k/Hal9xJ80nWTvHWGgGTe9dq0/gDoPZaXAzCWKcv/TVu+XN8Y0qry6k5jVpVXq74x5InZ7hE2S2fZjXMbe1K9YcGMgL4/7XgtKd2uHTV1GpWZptkFeZ64BnTElOt1hGn9AdA7JN0A2mhsDhtzEyeZcRq7aSWCTCsZZtmsPWc3zm0m70n9rKZe0x9cq/0HQho0MFEv3zhZR2R648OQ/sCE6/XBGpvDWvnu59q+q055WWk65cghnvggEYDzSLoBtIqUPDo4wbt35XueK3lkGtOWL5tWMuyzPfaWWduNc1tkT+r1S7d0+LzXzhCIOPYHK9UY+ufFrbYhpDN/ulrJiT59cO8MF1vWM1yv49ucp99qs6Jn/YfSko07NTWQreLLx7rYMgBu8O70FQBHmVTyyDSmlQgyrWSYaeMjSfOf6Tjhtvt8vDk04T5YY8jSsT9YGeMW9Q7X6/h2aMJ9sFXl1Zrz9FsxbhEAt5F0A7C9Z7ixORyjFuFgk/+l85O+uxvntqvH21sCbzfObROOHuZonNt2flWnrt7qzeGWOC/4rKY+asId0Riy9FlNfYxa1Dtcr+Nbd86sANB/kHQDMK7kkWnOf3i9o3Fu+/fHXnc0zm2mjU/hr9Y6Gue26Q/aa6fdOLdxvY5v3TmzAkD/QdINwLg9w6apb7I3Y2U3zm21Dc2OxrntQBezqN2Nc5tpr7f9B+zNKNqNcxvX6/hm2pkVAJxB0g3AyD2pJkkdYO9SbTfObekp9s7wtBvntmSbv3a7cW4z7fU2aKC906LtxrmN63V8M+3MCgDO8MZfTAB9anZBnro6iDjB1xKH2Lv//NGOxrlt9tiRjsa57f6LT3Y0zm0lN012NM5tL99or51249zG9Tq+3WHz5Hi7cQDMQNINQMlJCZozMb/TmDkT8z1dr9vLwjZnFO3Gua3mgL1l43bj3JaQZK90lt04t408LE1dvdWTElrivOCIzFQlJ3b+u09O9HmmXjfX6/iWmpyoqYHOD7WcGsimXjfQz3BFBiBJWjAjoOsm5bebQUnwSddNou6rm7LTUxyNc5tpy2NNGx9J+ui+86Im3kkJLc97yYOXntqr5+PNqSOH9up59K3iy8dGTbyp0w30Tz7Lsrxxsksfqq2tld/vVzAYVEZGhtvNAVzV2BzWktLt2lFTp1GZaZpdkOfpGZNQ2NKmihpV721QdnqKxuVnKrGrtZlxprE5rOPvernTE4sTfNI/7pnuibEyrT+hsKUJ969WVbBBHXXJJynHn6INt03x3Gtv51d1KvzVWtU3hZU6IEElN032zAx3RGR8KoMNHT7vtfExrT8mq28M6b6V5dq+q055WWm6Y0aAGW7AMHbzSG+cUgMgZpKTEnTNRG/UR+5KSVmlFi0vb3NzmutP0cKZARWOznWxZd2zecduWyWCNu/YrYKjs2LTqF6ILI99bF30WsNeWh6bmODTwpkBXb90S4fPW5IWzgx4MgEaeViayu+Z7nYzemVTRU3UBFVqGZ/KYIM2VdR44v1jWn9MlpqcqHsuPNHtZgCIA964owGAbiopq9TcpVva3ZxWBRs0d+kWlZRVutSy7qveG/0GuydxcN4ft3zaq+fRd0x7/5jWHwDoD0i6ARgnFLa0aHl5h0t9I48tWl6uUFfTx3HisMEDHY1zW2NzWMXro89yS1Lx+go1NnujDnR9Y0iryqs7jVlVXq36Rm/UgTaNaXvuTesPAPQHJN0AjNOd5ZeeYPezAW98hqAlpdttLZdfUro9Ju3prftWljsaB2eNy89Urj9F0Rb3+9Sy7WRcfmYsm9VjpvUHAPoDkm4AxjFt+eVX+w84Gue2HTV1jsa5bfsue+20GwdnRfbcS2qXqEa+99Kee9P6AwD9AUk3AOOYthzbtP6YVjJslM36znbj4LzC0blaPGuMcvxtl1zn+FO0eNYYTx2sKJnXHwAwnatJd1FRkcaOHav09HRlZ2frwgsv1Pvvv98mpqGhQfPmzVNWVpYGDx6sSy65RF988UWbmJ07d+q8885TWlqasrOzdeutt6q5uTmWXUE/FgpbKt22Sy9u/Uyl23Z5Zp+w0Qxbjm1af2YX5LWrB3+oBF9LnBdM/VqOo3HxZOMHu5R3+0utXxs/2OV2k3qscHSu/nDdeGWkJCrRJ2WkJOoP1433bIJaODpXz80pUNqABPkkpQ1I0HNzCjzbH6nlfIS7XnhXsx9/U3e98K7nz0Hg/gBAhKslw9auXat58+Zp7Nixam5u1h133KFp06apvLxcgwYNkiTdcssteumll/T73/9efr9f8+fP18UXX6zXX39dkhQKhXTeeecpJydHb7zxhiorK3X55ZdrwIABuu+++9zsHvoBU0pSmca05dim9ce0kmF7GpocjYsXebe/1O6xbz+xUZK0/Sfnxbo5vfa1u15WfdM/D+erbQjpzJ+uVuqABL3nwbJox/5gpRpD/0zi6prCmvTzNUpO9OmDe2e42LKemfP0W20OJFz/obRk405NDWSr+PKxLrasZ7g/AHAwV+9oSkpKdOWVV+qEE07QySefrKeeeko7d+7U5s2bJUnBYFCPP/64fvGLX2jKlCk67bTT9OSTT+qNN97Qxo0tf/hfeeUVlZeXa+nSpTrllFM0ffp03XPPPXr44YfV2NjoZvdgOJNKUpnGtNN9TeuPpE4TbjvPxxMTx6ejhLs7z8ebQxPug9U3hfW1u16OcYt659CE+2CNIUvH/mBljFvUO4cm3AdbVV6tOU+/FeMW9Q73BwAOFVfTCMFgUJKUmdly4ubmzZvV1NSkc845pzXm+OOP18iRI1VaWipJKi0t1YknnqjDDz+8Nebcc89VbW2t/v73v8ew9ehPTCtJZZoj/Pb2ztqNc1tmarKjcW5bvbXK0Ti3HdhvbzuT3Ti32V1C7pWl5lV7GqIm3BH1TWFV7fHGwYqf1dRHTbgjGkOWPqupj1GLese0knvcHwDoSNwk3eFwWDfffLPOPPNMjR49WpJUVVWl5ORkDRkypE3s4YcfrqqqqtaYgxPuyPOR5zpy4MAB1dbWtvkCusO4klSG+deH1jka5zbT+nP1s5sdjXPblc/81dE4t0WWkDsV57Z//bXN94/NOLdNf3Cto3FuM63kHvcHADoSN0n3vHnzVFZWpmeffbbP/62ioiL5/f7WryOPPLLP/02YxbSSVKbZf8DejIjdOLd1MUnX7TigP6ltsLfCwG6c20y7vplWco/7AwAdiYuke/78+VqxYoXWrFmjESNGtD6ek5OjxsZG7dmzp038F198oZycnNaYQ08zj3wfiTnUggULFAwGW78++eQTB3uD/sDEPZwmGTQw0dE4tw2weaW2Gwf0Jxkp9s6MtRvnNtOub3lZ9koD2o1zG/cHADri6i2aZVmaP3++nn/+ea1evVr5+fltnj/ttNM0YMAAvfrqq62Pvf/++9q5c6cKCgokSQUFBXr33XdVXf3P/UCrVq1SRkaGAoFAh//uwIEDlZGR0eYL6I5x+ZnK9acoWtUjn1pOKR2XnxnLZjnChBInL9842dE4t5nWnye+fZqjcW77wblHORrntoX/epyjcW5bccMkR+PcZtr14I4ZHd+r9TTObSbfHwDoOVc/1p03b56eeeYZvfjii0pPT2/dg+33+5Wamiq/369rrrlG3/ve95SZmamMjAzdcMMNKigo0BlnnCFJmjZtmgKBgGbPnq2f/vSnqqqq0p133ql58+Zp4MCBbnYPBktM8GnhzIDmLt0in9qWR478oV04M6DErooRxxlTSpwckZmq5ERfp4cNJSf6dESmNw5SOyZncLvX2aF8/xfnBVNOyZFs7CSacoo36lpnD7X3wa3dOLdlDrb3vrAb57acISlKHZDQ6WFqqQMSlDPEGzOPpl3fUpMTNTWQ3elhalMD2UpN9sbMvan3BwB6x9WZ7sWLFysYDOqss85Sbm5u69dzzz3XGvPAAw/oX//1X3XJJZdo0qRJysnJ0Z/+9KfW5xMTE7VixQolJiaqoKBAs2bN0uWXX64f/ehHbnQJ/Ujh6FwtnjVGOf62N2o5/hQtnjXGU0mqZF6JkwcvPbVXz8ebxbPG9Or5ePNoF+3t6vl4YtpyUtP6I0kPfOuUXj0fbz64d4aSEztO2rxYp7v48rGaGsju8Dkv1uk27f4AQO/5LMvy3tpRh9XW1srv9ysYDLLUHN0WClvaVFGj6r0Nyk5vWTLmtU+wQ2FLE+5fHfXEVZ9abhY23DbFE32jP/HN1P5UBRs6XI1Af9xl2uvtYJ/V1Gv6g2u1/0BIgwYm6uUbJ3tmhrsj9Y0h3beyXNt31SkvK013zAh4Zoa7IybcHwDonN080hunhgBxLDHBp4Kjs9xuRq90p8SJF/pKf+Kbaf2JLCe9fumWDp+35K3lpKYtjzXt9XawIzJT9c7dhW43wzGpyYm658IT3W6GY0y4PwDgDM66BWBciRP6E99M64+JTFoey+sNAOA2ZroRcyy3ij+m7eHMTE12NM5t6QMHOBrntsMG2Tvk0m6c20JhS4uWl0d93idp0fJyTQ3keOpaVzg6V1MDOZ6/Xpt2fQMAeA9JN2LKlNOxTRMpcdLVHk6vlDj5xxd7bcdNPG5YH7em9555c7vtuClf6/gworhiN2fzSG5n8vJlE5bHmnZ9AwB4D8vLETOmnY5tksgeTql9nuPFPZyf7K5zNM5tn+yxt+zVbpzbvtp3wNE4t7F8Ob6Zdn0DAHgPSTdiIrL8sqNZhshji5aXKxTu94fpu8akPZyjMtMcjXPbyKH2TiO2G+c205aXHzbYZn9sxsF5Jl3fAADeQ9KNmOjO8ku4p3B0rl66YaKOzR6kIakDdGz2IL10w0TP3ZDOLshTV5NWCb6WOC944Fv2aorbjXOdYcvLO/w0sTdxcWTnV3UK3PWy8m9/SYG7XtbOr7yxOqQjplzfAADew55uxATLL71h8s9Wa8eu+tbv99Q3acyPV2lUVqrW3jrFxZZ1T3JSguZMzNdj6yqixsyZmK/kJG987jg4JUknjcjQO5/WRo05aUSGBqd445Ju2vLyr/bb7I/NuHhxzB0vqTn8z+/rmsKa9PM1SkqQPrrvPPca1kOmXN8AAN7jjTtOeB6nx8a/Q29ID7ZjV70m/2x1jFvUO7/b9Emvno8327uYYezq+Xhi2vXAtP5I7RPugzWHW573EtOubwAAbyHpRkxETo+NtlrUp5ZTzDk91h3BuqaoN6QRO3bVK1jXFKMW9c6XtQdU29DcaUxtQ7O+rPXGzKNp/Tlm2GBH49w2cqi9swHsxrlt51d1URPuiOawPLPU3LTr28FCYUul23bpxa2fqXTbLs5FAYA4RdKNmOD02Ph29VObHI1z20WPbHA0zm2m9eey35Y6Gue2Cx5e72ic2wp/tdbROLeZdn2LKCmr1IT7V+vS4o266dmturR4oybcv5pKIAAQh0i6ETOcHhu/Pu/kkLuexLmtZr+9GSu7cW4zrT/VexsdjXNbV6sQuhvntvqmLqa5uxnnNtOubxIlOAHAa7xx6g6MUTg6V1MDOdpUUaPqvQ3KTm9ZUs4Mt7uG+1M6PV3+4DgvyBw0QHV7QrbivGCozf4M9Uh/hqUna0991x8QDEtPjkFrei8jJUlf2fjAI8MjB92lDkhQnY2EOnWANz63N+361lUJTp9aSnBODeTwtxUA4oQ3/mLCKIkJPhUcnaULTjlCBUdncVMQB564cpyjcW57/rsTHI1z2w8Lv+ZonNv+85zjHI1z24obJjka57aSmyY7Guc2065vlOAEAO8h6QYgf9oAjcpK7TRmVFaq/GkemUnNGNjlrGJGSpKGZQyMUYt6p8FmgWe7cW6rC9tblmw3zm05Q1K6nPVNHZCgnCHemEkdeViauqqml5TQEucFpl3fKMEJAN5D0g1AkrT21ilRb0y9WMf20nFH9ur5eGJaSSrT+iNJ790zPWrinTogQe/dMz3GLeqdj+47L2ri7cU63Qumd74KpKvn44mJ7x8AMB1JN4BWa2+dorfuOEcjhqQobUCiRgxJ0Vt3nOO5hLuxOazi9RWdxhSvr1BjV3WR4oRpJfdM60/Ee/dM18bbv6HDBg1QcqJPhw0aoI23f8NzCXfER/edpzXfO0sDE1tGamCiT2u+d5bnEu7IHuhoInugvVJuy9T3DwCYjKQbQKuileU6vegv+nRPg+qaQvp0T4NOL/qLilZGv2GNR0tKt6ur++ew1RLnBZGSe9G6ZMlbJfdM68/Bcoak6K93TdMH987QX++a5pkl5R0pKavUdx7fqAOhlpE6ELL0ncc3eu5kbNP2QFOCEwC8h6QbgKSWhPuxdRXtktWwJT22rsJTifeOmjpH4+LB2zt39+p5oDtMKkll4h5oSnACgLd4o34JgD5ldzn296cdr+SuTliKA0cM6fzQpO7Guc208bG73JeSR+4wrSTVYYPtHZhoNy5eUIITALwj/u/OAPQ505Zj+2xuzbQb5zbTxse05b6mMW587L7PPXI9OBglOAHAG0i6ARi3HPvTYL2jcW4zbXxMXO5rEtPG56v9BxyNAwCgu0i6AWhUpr16u3bj3EZ/4hslj+KbaeNjWn8AAN5D0g30UihsqXTbLr249TOVbtvlmbIzB5tdkKeuViUm+FrivID+xLfTRg211Z/TRg2NTYMctPnj3cq7/aXWr80fe++AO9PGx+QSW/WNId31wrua/fibuuuFd1XfGHK7Sb1iwt/Tg5k2PgB6joPUgF4oKavUouXlbfY/5vpTtHBmwFOnxyYnJWjOxHw9ti76YV1zJuZ74pAuif7Eu807dtvao755x24VHJ0Vm0Y5IO/2l9o9dslv3pAkbf+Jd2pbmzY+kRJb1y/d0uHzXi1RN+fpt7SqvLr1+/UfSks27tTUQLaKLx/rYst6xpS/pxGmjQ+A3vHGHRoQh0wqqSNJv9v0Sa+ejzedJah2no83j2/ovL1dPR9PTNszLHWccHfn+Xhi4vjc9UJZr56PN4cmdAdbVV6tOU+/FeMW9Y5pf09NGx8AvUfSDfRAVyV1pJaSOl5ZGvdl7QHVNjR3GlPb0Kwva71x0NDW7XscjXPbzq/q1BzuPKY53BLnBdW1+x2Nc5vdJeReWWoesrkE1m6c22r2NerLfY2dxny5r1E1XcTEi/rGUNSELmJVebVnljKb9vfUtPEB4AySbqAHTCupc9EjGxyNc9uFj77uaJzbCn+11tE4t9278kNH49wWWULuVJzbbnvhXUfj3PZtm793u3Fuu29l9Br3PYlzm2l/T00bHwDOIOkGesC05Zc1+5scjYOz6pu6mObuZhzQGbsvI6+83Kr32pvBthvntu277K1osRvnNtP+npo2PgCcQdIN9IBpJWiGptk7U9FuHJyVOsDepdpuHNAZuy8jr7zcstOTHY1zW16WvdKAduPcZtrfU9PGB4AzPPInE4gvppWgueu8ExyNc9sL15/paJzbSm6a7Gic22aNtXcSsd04t/3x2vGOxrnt5RvtvY7sxrntWZu/d7txbrtjRsDROLeZ9vfUtPEB4AySbqAHIiVoJLW7UYh876USNAfC9taJ2o1z2yl5QxyNc9vIw9LUVTWwpISWOC9ISLI3o2g3zm2nHWWvXrXdOLcdkzM4agIU4fu/OC/IHJysYYM7fy0NG5yszC5i4kVqcqKmBrI7jZkayFZqcmKMWtQ7pv09NW18ADiDpBvoocLRuVo8a4xy/G2XvOX4U7R41hhP1RU1bXmf1HVdZC/VTZakaybk9+r5eDIq096HA3bj4sF1kzr//Xf1fLxZPGtMr56PN2/dOTVq4j1scLLeunNqjFvUO8WXj42a2HmxDrRJf08l88YHQO/5LMvyRg2GPlRbWyu/369gMKiMjAy3mwOPCYUtbaqoUfXeBmWntyyB88on8hGhsKUJ969WVbChw7ItPrXc/Gy4bYrn+rZ1+542p5S/cP2ZnpnhjmhsDuv4u15WZxVzEnzSP+6ZruSupsTjQM2+Ro358aou47bcOdUTs4+mjU/kehDtRGkvXw++rD2gix7ZoJr9TcocNEDPf3eChmUMdLtZPVbfGNJ9K8u1fVed8rLSdMeMgKdnUE34e3ow08YHQHt280iSbpF0A5JUUlap65duifr8ox6cbTDF4+s/1j0vvddl3F3nfU3XTDwqBi3qnTn//ZZWvdd5HVtJmvq1bBVfEf8zQqaNT+m2Xbq0eGOXcb+bc4YKjs6KQYucUVJWqUXLy9t8mJDrT9HCmQGubQCAHrGbR8b/R+4A0M/tqLFXWsZunNt27q53NM5tpo2PaSWcpJaEe+7SLe1m76uCDZq7dItKyipdahkAoD8g6QagUNjSouXlUZ/3SVq0vFyhztbPos8cOTTV0Ti3jbTZTrtxbjNtj7ppZzxErm8dXb0ij3F9AwD0JZJuANpUURN1/6bUcmNaGWzQpoqa2DXKIaGwpdJtu/Ti1s9Uum2XJ2+sj8+xt+3FbpzbHvjWqY7GuW12QZ662naa4GuJ8wLTSjiZfH0DAHhDktsNAOA+E5eTSubs4aypa3Q0zm2DU5J00ogMvfNpbdSYk0ZkaHCKN/5EJSclaM7EfD22riJqzJyJ+Z44RE36ZwmnuUu3yCe1mSH2YgknU69vAADv8MYdAIA+ZdpyUsmsPZwmjs+y+RN10oiOZ+ZPGpGhZfMnxrhFvXPqyM5rcHf1fLwxqYSTie8fAIC3eGMaAYhjJpQ4OW3UUCX41GXJo9NGeSNx6GoPZ2SP+tRAjifGyrTxiVg2f6Le/3yvZvx6nUKWlOiTVt4wSccNT3e7ad1i90wEr7zeIgpH52pcXpa+/Zs3VL23UdnpyXr22vGeKON2sMhy+a5KInplufzBTPj7AwD9AUk30AumLF/evGN3pwmd1JLwbd6x2xMlgrqzh9ML/TFtfCKO/cFKNYb+2bGQJZ374DolJ/r0wb0zXGxZ95j2eouY/LPV2rHrnyfI76lv0pgfr9KorFStvXWKiy3rnshy+WglES15a7l8hCl/fwCgP2B5OdBDJi1fNm3PI/2Jf4cm3AdrDFk69gcrY9yinjNxfA5NuA+2Y1e9Jv9sdYxbhIOZ9PcHAPoDkm6gB0wrQTM4KdHROLel2myn3Ti37Wuwd0Ca3Ti3fVZTHzXhjmgMWfqsxht1ugf47M2Q2o1zW7CuKWrCHbFjV72CdU0xalHvmFYS0bS/PwDQH5B0Az1gWgmah9dtczTObT/783uOxrntBy9ETxh6Eue26Q+udTTObT9cVuZonNuufmqTo3FuM+16bVp/AKA/IOkGesC05aSd3cD1JM5tX+6zNwNnNw7O2n8g5Gic22obmh2Nc9vnNt/nduPcZtr12rT+AEB/QNIN9IBpJWiG++21026c24al2ztd2W4cnDVooL1l/Xbj3JZhs5643Ti3mXY9MO16bVp/AKA/IOkGeiBSgibaDk2fWk6R9UoJmieuHOdonNv+85zjHI1z29Xjj3Q0zm33zTzR0Ti3rbhhkqNxbjPtemDa9dq0/gBAf0DSDfRApASNpHY3PpHvvVSCxp82QKOyUjuNGZWVKn/agBi1qHfqwmFH49zWbNm7VNuNc1vI5gS23Ti35QxJUeqAzn/3qQMSlDPEGzOPpl0PTLtem9YfAOgPvHGHBsShwtG5WjxrjHIOWWKZ40/R4lljPFcnde2tU6LeaHutLq9pyy9HZaY5Guc208ZHkt67Z3rUxDt1QILeu2d6jFvUOyZdDyTzrtem9QcATOezLKvf15Sora2V3+9XMBhURkaG282Bx4TCljZV1Kh6b4Oy01uW9Hl5hiFY16Srn9qkz4MNGu5P0RNXjvPMjFZEKGxpwv2rVRVs6LCsjk8tN6cbbpviibFqbA7r+LteVmcVgBJ80j/uma7kpPj/LNW0/hysak+D/vXX61Tb0KyMlCStuGGSZ2a4O2LC9eBgpl2vTesPAHiN3TySpFsk3YCJSsoqNXfpFklqk3hHbke9NhtUtLJcj62riPr8dZPytWBGIIYt6rnSbbt0afHGLuN+N+cMFRydFYMWAQAAdJ/dPNJbUwgAYJNpyy8XzAjoukn5OnQSK8HnrYRbouQRAADoX7xRvwQAeqBwdK6mBnKMWX65YEZA3592vJaUbteOmjqNykzT7II8zy3BPmzwQEfjAAAA4hlJNwCjJSb4jFqinJyUoGsmHuV2M3rH7qamfr/5CQAAmMBb0yMAAM/7av8BR+MAAADiGUk3ACCmTCwZBgAAEA3Lyz3AtJIgpvVnX0Ozbnnube3cXa+RQ1P1wLdO1eAU7761Pqup1/QH12r/gZAGDUzUyzdO1hGZHdfr9YKdX9Wp8FdrVd8UVuqABJXcNFkjD/NGPeuOvPGPr/Sdp95s/f6ZK0/X+OMPc7FF3XfaqKFK8KnLkmGnjRoau0Y55N2dQZ3/yAZZajkpf9l3J+jEkX63m9Vjpl3fAABwg6slw9atW6ef/exn2rx5syorK/X888/rwgsvbH1+3759uv322/XCCy9o165dys/P14033qjrr7++NaahoUHf//739eyzz+rAgQM699xz9cgjj+jwww+33Y54LhlWUlapRcvLVRn85ym+uf4ULZwZ8Nzpy5J5/Tn/ofV659Pado+fNCJDy+ZPdKFFvXPsD1aqMdT+kpCc6NMH985woUW9c8wdL6k53P7xpATpo/vOi32Deinv9peiPrf9J97pj6klw0wZnwjTrm8AADjNEyXD9u/fr5NPPlkPP/xwh89/73vfU0lJiZYuXar33ntPN998s+bPn69ly5a1xtxyyy1avny5fv/732vt2rX6/PPPdfHFF8eqC30qUmf44ARVkqqCDZq7dItKyipdalnPmNafaDekkvTOp7U6/6H1MW5R70RLuCWpMWTp2B+sjHGLeidawi1JzeGW572ks4TOzvPxxMSSYSaNj2Te9Q0AADe5mnRPnz5dP/7xj3XRRRd1+Pwbb7yhK664QmeddZby8vJ07bXX6uSTT9amTZskScFgUI8//rh+8YtfaMqUKTrttNP05JNP6o033tDGjV3PosSzUNjSouXlHR7eG3ls0fJyhTpbnxlHTOvPvobmqDekEe98Wqt9Dc0xalHvfFZTHzXhjmgMWfqspj5GLeqdnV/VRU24I5rDLXFe8MY/vnI0zm176+wl03bj3PbuzqCjcW4z7foGAIDb4vogtfHjx2vZsmX67LPPZFmW1qxZow8++EDTpk2TJG3evFlNTU0655xzWv+b448/XiNHjlRpaWnUn3vgwAHV1ta2+Yo3mypq2s0IH8ySVBls0KaKmtg1qhdM688tz73taJzbpj+41tE4txX+yl477ca57eA93E7Eue3OZf9wNM5t5z+ywdE4t5l2fQMAwG1xnXT/+te/ViAQ0IgRI5ScnKzCwkI9/PDDmjRpkiSpqqpKycnJGjJkSJv/7vDDD1dVVVXUn1tUVCS/39/6deSRR/ZlN3rEtOWXpvVn5257M75249y2/0DI0Ti31Td1Mc3dzTigM6aVHTft+gYAgNviPuneuHGjli1bps2bN+u//uu/NG/ePP3lL3/p1c9dsGCBgsFg69cnn3ziUIudY1pJHdP6M3KovdO87ca5bdDAREfj3JY6wN6lzW4c0Bm7tRe8UqPBtOsbAABui9s7zvr6et1xxx36xS9+oZkzZ+qkk07S/Pnz9a1vfUs///nPJUk5OTlqbGzUnj172vy3X3zxhXJycqL+7IEDByojI6PNV7wZl5+pXH9K1Js0n1pO/R6XnxnLZvWYaf154FunOhrntpdvnOxonNtKbrLXTrtxbnvmytMdjXPbH68d72ic25Z9d4KjcW4z7foGAIDb4jbpbmpqUlNTkxIS2jYxMTFR4XDLktDTTjtNAwYM0Kuvvtr6/Pvvv6+dO3eqoKAgpu11WmKCTwtnBiS1nx2JfL9wZsAz9a1N68/glCSdNKLzD2tOGpHhmXq2R2SmKjmx8999cqLPM/W6Rx6WpqQurm5JCfJMvW67dbi9Uq/7tKPs1d+2G+c2u3W4vVKv27TrGwAAbnM16d63b5+2bt2qrVu3SpIqKiq0detW7dy5UxkZGZo8ebJuvfVWvfbaa6qoqNBTTz2lp59+uvW0c7/fr2uuuUbf+973tGbNGm3evFlXXXWVCgoKdMYZZ7jYM2cUjs7V4lljlONvu+Q6x5+ixbPGeK6utWn9WTZ/YtQbUy/Wsf3g3hlRE28v1ul+6DtjevV8vOmqzrPX6kA/Oqvz339Xz8cb08bHtOsbAABu8lmW5drZLq+99prOPvvsdo9fccUVeuqpp1RVVaUFCxbolVdeUU1NjUaNGqVrr71Wt9xyi3y+luSgoaFB3//+9/W73/1OBw4c0LnnnqtHHnmk0+Xlh7Jb1NwtobClTRU1qt7boOz0liXYXpkR7ohp/dnX0KxbnntbO3fXa+TQVD3wrVM9PQP0WU29pj+4VvsPhDRoYKJevnGyZ2a4I0JhSxPuXx31xHyfWj7s2XDbFM+99jaUf6lZT29q/X7p5eM0ITDMxRZ1n8njs3X7Hl346Out379w/Zk6JW+Iew3qJdOubwAAOMluHulq0h0v4j3pBtA9pdt26dLijV3G/W7OGSo4OisGLXJGSVmlFi0vb5Os5vpTtHBmwFMrRRgfAABgArt5ZNzu6QaAnjKtRJ3UktDNXbql3exwVbBBc5duUUlZpUst6z7GBwAA9Cck3QCMY1qJulDY0qLl5R3WeY48tmh5uUJhbyxcOmzwQEfj3Gba+AAAAGeRdAO91Ngc1uPrP9YPXyzT4+s/VmNz2O0m9UoobKl02y69uPUzlW7b5clEwbQSdZsqaqLuf5ZaErvKYIM2VdTErlG9Yfcl5ZGXnnHjAwAAHMVpKEAvFK0sV/H6Ch2cl9678j3NmZivBTMC7jWsh0zZkxopUTd36Rb51DZ382KJOtOWY3+1/4CjcW4zbXwAAICzmOkGeqhoZbkeW9c24ZaksCU9tq5CRSvL3WlYD5m2J9WkEnWmLZenPwAAoD8h6QZ6oLE5rOL1FZ3GFK+v8MxSc1P3pBaOzlXJTZN02sghyvWn6LSRQ1Ry0yRPJdySdNqooepqUj7B1xLnBaYt/zetPwerbwzprhfe1ezH39RdL7yr+saQ203qFRO2zwAAvIfl5UAPLCnd3m6G+1BhqyXumolHxaZRvdCdPaleKuF0/kPr9c6nta3fVwYbdPKPXtFJIzK0bP5EF1vWPZt37Lb1etu8Y7cnxiey/P/6pVs6fN6St5b/m9afiDlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM6ZsnwEAeA8z3UAP7KipczTObSbuST004T7YO5/W6vyH1se4RT1n4vj8ccunvXoefevQhPtgq8qrNefpt2Lcot4xbfsMAMBbSLqBHjhiiL29mXbj3JaZluxonNv2NTRHTbgj3vm0VvsammPUot5Jara3BNZunNvqG0NRE7qIVeXVnlnKHNmeEY1P3tqeYer4mLZ9BgDgHSTdQA/4ou7e7Fmc2/5RtdfROLfd8tzbjsa5bcGKMkfj3HafzUMG7ca5zbSSYYwPAADOIukGeuDTPfWOxrntk932lsHbjXPbzt32fu9249y2/4C9GUW7cW7bvsve68hunNtMW/7P+AAA4CySbqAHRmWmORrnNtP6M3JoqqNxbhs0MNHROLflZdl7HdmNc5tpJcMYHwAAnEXSDfTA7II8WyWcZhfkxaQ9vWVafx741qmOxrnt5RsnOxrntjtmBByNc5tpJcMYHwAAnEXSDfRAclKC5kzM7zRmzsR8JSd54y1mWn8GpyTppBEZncacNCJDg1O8UTXxiMxUJSd2/qlIcqJPR2R6Y+Y+NTlRUwPZncZMDWQrNdkbM/eRkmGS2iV2ke+9VDKM8QEAwFneuIMG4tCCGQFdNym/3Qxxgk+6blK+FnhkFijCtP4smz8xauLttTrdkvTBvTOiJt7JiT59cO+MGLeod4ovHxs1sfNiHejC0blaPGuMcvxtlyjn+FO0eNYYz9WBZnwAAHCOz7Ksfl8jo7a2Vn6/X8FgUBkZnc+OAYdqbA5rSel27aip06jMNM0uyPPMjHBHTOvPvoZm3fLc29q5u14jh6bqgW+d6pkZ7o58VlOv6Q+u1f4DIQ0amKiXb5zsmRnujtQ3hnTfynJt31WnvKw03TEj4JkZ1I6EwpY2VdSoem+DstNblix7eQaV8QEAIDq7eSRJt0i60TvcxAEAAAD9j9080rvTPUAcKCmr1KLl5W1qwOb6U7RwZoDligAAAADY0w30VElZpeYu3dIm4ZakqmCD5i7dopKySpdaBgAAACBekHQDPRAKW1q0vFwd7c2IPLZoeblC4X6/ewMAAADo10i6gR7YVFHTbob7YJakymCDNlXUxK5RAAAAAOIOSTfQA9V7oyfcPYkDAAAAYCYOUgN6IDs9peugbsTFk2Bdk65+apM+DzZouD9FT1w5Tv60AW43q8dM68/Or+pU+Ku1qm8KK3VAgkpumqyRh6W53aweq9nXqG//5g1V721Udnqynr12vDIHJ7vdrB4zrT+UEIxvVM8AAG+gZJgoGYbuC4UtTbh/dadLzHP9Kdpw2xRP3QBN/tlq7dhV3+7xUVmpWnvrFBda1Dum9eeYO15Sc7j940kJ0kf3nRf7BvXS2B+v0pf7Gts9Pmxwst66c6oLLeod0/pTtLJcxesrdPDRFAk+ac7EfC2YEXCvYT10/kPr9c6nte0eP2lEhpbNn+hCi3qH6hkA4D67eaR3P64GXJSY4NP5J3d+U3P+yblGJNyStGNXvSb/bHWMW9Q7pvUnWsItSc3hlue9JFqCKklf7mvU2B+vinGLese0/hStLNdj69om3JIUtqTH1lWoaGW5Ow3roWgJtyS982mtzn9ofYxb1DtUzwAAbyHpBnogFLa07G+d39Qs+1ulZ04vD9Y1RU1QI3bsqlewrilGLeod0/qz86u6qAl3RHO4Jc4LavY1Rk1QI77c16iaLmLihWn9aWwOq3h9Racxxesr1NjVizJO7GtojppwR7zzaa32NTTHqEW9Q/UMAPAekm6gB7o6vVzy1unlVz+1ydE4t5nWn8JfrXU0zm3f/s0bjsa5zbT+LCnd3m6G+1BhqyXOC2557m1H49xG9QwA8B6SbqAHTDu9/PMuPkDobpzbTOtPfZO9GUW7cW6r3mtvxtdunNtM68+OGnsrJuzGuW3n7s5XvXQ3zm2m/f0BgP6ApBvoAdNOLx/ut9dOu3FuM60/qQPsXartxrktO93ead5249xmWn9GZdo7Dd9unNtGDk11NM5tpv39AYD+wBt3aECcGZefqVx/iqIdk+ZTyymy4/IzY9msHnviynGOxrnNtP6U3DTZ0Ti3PXvteEfj3GZaf2YX5KmrMyATfC1xXvDAt051NM5tpv39AYD+gKQb6IHEBJ8Wzgx0eJCN1LKnbuHMgGdOL/enDdCorM5neUZlpXqmvrVp/Rl5WJq6Ko2clCDP1OvOHJysYV3Urh42ONkz9a1N609yUoLmTMzvNGbOxHzP1OsenJKkk0Z0Xg70pBEZnqnXHfn7I6ld4h353kt/fwCgP/DGX0wAfW7trVOiJqperGttWn8+uu+8qIm3F+t0v3Xn1KiJqhfrWpvWnwUzArpuUn67Ge8En3TdJO/V6V42f2LUxNuLdboLR+dq8awxyjlki0yOP0WLZ42hTjcAxBmfZVn9vqaE3aLmQEQobGnC/aujniDrU8vNz4bbpnhutiFY16Srn9qkz4MNGu5P0RNXjvPMjHBHTOvPzq/qVPirtapvCit1QIJKbprsmRnujtTsa9S3f/OGqvc2Kjs9Wc9eO94zM8IdMa0/jc1hLSndrh01dRqVmabZBXmemeHuyL6GZt3y3NvaubteI4em6oFvneqZGe6OhMKWNlXUqHpvg7LTW5aUe+1vDgB4md08kqRbJN2xZsJNQum2Xbq0eGOXcb+bc4YKjs6KQYsQjQmvt4OZ1h8AAACvsptHevfjXXhSSVmlFi0vbzNDnOtP0cKZAU8th6NkizeY8nqLMK0/AAAA/YF314jBc0rKKjV36ZZ2S7Krgg2au3SLSsoqXWpZ91GyJf6Z9HqTzOsPAABAf0HSjZgIhS0tWl7e4WnfkccWLS9XKOyN3Q6UbIlvpr3eTOsPAABAf0LSjZjYVFET9dAxqSVxqAw2aFNFTewa1QuUbIlvpr3eTOsPAABAf0LSjZgwcQ80JVvil2mvN9P6AwAA0J9wkJoHmHBasal7oAtH52rK8YcbVVKnvjGk+1aWa/uuOuVlpemOGQGlJie63axuMe31Zlp/TGZaiS0AANB7JN1xzpTTiiN7oKuCDR3uS43UtfbaHuiOxue3Gyo8Nz4Rc55+S6vKq1u/X/+htGTjTk0NZKv48rEutqx7Iq+3zpZke2nPvanvH9MUrSxX8foKHby1/t6V72nOxHwtmBFwr2EAAMBVfPwex0w6rdjEPdAmjY/UPuE+2Kryas15+q0Yt6jnEhN8Gn1E9FqJkjT6iAzPvN5MfP+YpmhluR5b1zbhlqSwJT22rkJFK8vdaRgAAHAdSXecMvG0YpP2QJs2PvWNoagJd8Sq8mrVN4Zi1KLeaWwO69X3Ou/Pq+9Vq7E5HKMW9Z5J7x/TNDaHVby+otOY4vUVnnq9AQAA57C8PE5157TigqOzYtewXiocnaupgRzP71E3bXzuszkLd9/Kct1z4Yl93JreW1K6vd2M46HCVkvcNROPik2jHGDK++dgJpxZYerrDQAAOIOkO06ZfFpxYoLPE4loZ0wbn+276hyNc9uOGnvttBsXT0x4/0SYcmaFya83AADQeywvj1OcVhzfTBufvKw0R+PcNirTXjvtxsF5Jp2JwOsNAAB0hqQ7TkVOK462yNInb52+bBrTxucOmycr241z2+yCPHW1QjnB1xKH2DPtTARebwAAoDMk3XGK04rjm2njk5qcqKmB7E5jpgayPVOvOzkpQXMm5ncaM2diPvWTXdKdMxG8gNcbAADoDHcAcYzTiuObaeNzyZgRvXo+3iyYEdB1k/LbzUAm+KTrJlE32U2mnYkg8XoDAADR+SzL8sb6vT5UW1srv9+vYDCojIzOa/u6wYTTfU1mwviEwpYm3L866uyjTy0fJmy4bYrn+tbYHNaS0u3aUVOnUZlpml2Qx4yjy0q37dKlxRu7jPvdnDM8d2gcrzcAAPoPu3kkp5d7gEmnFZvIhPExrQTawZKTEijTFGciZyJUBRs63Ncd+ZDHK2ciHIzXGwAAOBQfvwMwcrkv4pdpZyIAAAB0hqQbgHEl0BD/TDsTAQAAIBqWlwMwerkv4lfh6FxNDeR4/kwEAACAzpB0A2hd7jt36Rb5pDaJN8t90ZdMOBMBAACgMywvByCJ5b4AAABAX2CmG0ArlvsCAAAAznJ1pnvdunWaOXOmhg8fLp/PpxdeeKFdzHvvvafzzz9ffr9fgwYN0tixY7Vz587W5xsaGjRv3jxlZWVp8ODBuuSSS/TFF1/EsBforlDYUum2XXpx62cq3bZLobC3S8U3Nof1+PqP9cMXy/T4+o/V2Bx2u0k4SH1jSHe98K5mP/6m7nrhXdU3htxuUq/Qn/jG9QAAABzKZ1mWaxnPyy+/rNdff12nnXaaLr74Yj3//PO68MILW5/ftm2bxo0bp2uuuUaXXnqpMjIy9Pe//11nnHGGsrOzJUlz587VSy+9pKeeekp+v1/z589XQkKCXn/9ddvtsFvUHL1XUlapRcvL29SEzvWnaOHMgCeXLxetLFfx+god/LlBgk+aMzFfC2YE3GtYD5k2PnOefkuryqvbPT41kK3iy8e60KLeoT/xzbTrAQAA6JzdPNLVpPtgPp+vXdL97W9/WwMGDNCSJUs6/G+CwaCGDRumZ555Rv/2b/8mSfrHP/6hr33tayotLdUZZ5xh698m6Y6NkrJKzV26pd3p2JGFy17bN1y0slyPrauI+vx1k7x1o23a+ERL6CK8ltjRn/hm2vUAAAB0zW4eGbcHqYXDYb300ks69thjde655yo7O1unn356myXomzdvVlNTk84555zWx44//niNHDlSpaWlLrQa0YTClhYtL++wHFXksUXLyz2z1LyxOazi9dFvsCWpeH2FZ5aWmjY+9Y2hThM6SVpVXu2Zpcz0J76Zdj0AAADOituku7q6Wvv27dNPfvITFRYW6pVXXtFFF12kiy++WGvXrpUkVVVVKTk5WUOGDGnz3x5++OGqqqqK+rMPHDig2traNl/oW5sqatosWT6UJaky2KBNFTWxa1QvLCndrq7yz7DVEucFpo3PfSvLHY1zG/2Jb6ZdDwAAgLPi9vTycLhlRuCCCy7QLbfcIkk65ZRT9MYbb+jRRx/V5MmTe/yzi4qKtGjRIkfaCXuq90ZP6HoS57YdNXWOxrnNtPHZvsve791unNvoT3wz7XoAAACcFbcz3YcddpiSkpIUCLTdA/e1r32t9fTynJwcNTY2as+ePW1ivvjiC+Xk5ET92QsWLFAwGGz9+uSTTxxvP9rKTk/pOqgbcW4blZnmaJzbTBufvCx7v3e7cW6jP/HNtOsBAABwVtwm3cnJyRo7dqzef//9No9/8MEHGjVqlCTptNNO04ABA/Tqq6+2Pv/+++9r586dKigoiPqzBw4cqIyMjDZf6Fvj8jOV609RtGrPPrWckj0uPzOWzeqx2QV56qp0dYKvJc4LTBufO2weWGU3zm30J76Zdj0AAADOcjXp3rdvn7Zu3aqtW7dKkioqKrR169bWmexbb71Vzz33nIqLi/XRRx/poYce0vLly/Xd735XkuT3+3XNNdfoe9/7ntasWaPNmzfrqquuUkFBge2TyxEbiQk+LZzZcgN96L1p5PuFMwNK7OrONU4kJyVozsT8TmPmTMxXclLcfq7Vhmnjk5qcqKmB7E5jpgaylZqcGKMW9Q79iW+mXQ8AAICzXC0Z9tprr+nss89u9/gVV1yhp556SpL0xBNPqKioSJ9++qmOO+44LVq0SBdccEFrbENDg77//e/rd7/7nQ4cOKBzzz1XjzzySKfLyw9FybDYMa0OtGl1eU0bH9PqQNOf+Gba9QAAAHTOc3W63UTSHVuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2e0TBuf+saQ7ltZru276pSXlaY7ZgQ8M4PaEfoT30y7HgAAgOhIuruBpBsAAAAA0B1280g+fgcAAAAAoI/EbZ1uAADgLtO2mwAA4AaSbgAA0I5pBysCAOAWlpcDAIA2SsoqNXfpljYJtyRVBRs0d+kWlZRVutQyAAC8h6QbAAC0CoUtLVpero5OWY08tmh5uULhfn8OKwAAtrC8HDHHHsH4RskjoH/bVFHTbob7YJakymCDNlXUqODorNg1DAAAjyLpRkyxRzC+Fa0sV/H6Ch08gXXvyvc0Z2K+FswIuNcwADFTvTd6wt2TOAAA+jumrxAz7BGMb0Ury/XYurYJtySFLemxdRUqWlnuTsMAxFR2eoqjcQAA9Hck3YgJ9gjGt8bmsIrXV3QaU7y+Qo3N4Ri1CIBbxuVnKtefomibfnxqWaE0Lj8zls0CAMCzSLoRE93ZI4jYW1K6vd0M96HCVkscALMlJvi0cGbLdpJDE+/I9wtnBjiLAwAAm0i6ERPsEYxvO2rqHI0D4G2Fo3O1eNYY5fjbLiHP8ado8awxnMEBAEA3cJAaYoI9gvFtVGaao3EAvK9wdK6mBnKoNgEAQC8x042YYI9gfJtdkKeu7qMTfC1xAPqPxASfCo7O0gWnHKGCo7NIuAEA6AGSbsQEewTjW3JSguZMzO80Zs7EfOp1AwAAAN3EHTRihj2C8W3BjICum5TfbsY7wSddN4k63QAAAEBP+CzL6vc1mmpra+X3+xUMBpWRkeF2c4wXClvsEYxjjc1hLSndrh01dRqVmabZBXnMcAMAAACHsJtHcpAaYi6yRxDxKTkpQddMPMrtZgAAAABGYPoKAAAAAIA+QtINAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjnF6OmKNkGGLJtNcbJd0AAAC8haQbMVVSVqlFy8tVGWxofSzXn6KFMwMqHJ3rYstgItNeb0Ury1W8vkJh65+P3bvyPc2ZmK8FMwLuNQwAAABRMT2CmCkpq9TcpVvaJECSVBVs0NylW1RSVulSy2Ai015vRSvL9di6tgm3JIUt6bF1FSpaWe5OwwAAANApkm7ERChsadHyclkdPBd5bNHycoUOzSiAHjDt9dbYHFbx+opOY4rXV6ixORyjFgEAAMAukm7ExKaKmnYzjgezJFUGG7SpoiZ2jYKxTHu9LSnd3m6G+1BhqyUOAAAA8YWkGzFRvTd6AtSTOKAzpr3edtTUORoHAACA2CHpRkxkp6c4Ggd0xrTX26jMNEfjAAAAEDsk3YiJcfmZyvWnKFqhJp9aTpUel58Zy2bBUKa93mYX5KmrKmcJvpY4AAAAxBeSbsREYoJPC2e2lDQ6NHeIfL9wZsDT9ZMRP0x7vSUnJWjOxPxOY+ZMzKdeNwAAQBziDg0xUzg6V4tnjVGOv+2S3hx/ihbPGuPJusmIX6a93hbMCOi6SfntZrwTfNJ1k6jTDQAAEK98lmV5o2ZOH6qtrZXf71cwGFRGRobbzTFeKGxpU0WNqvc2KDu9ZYmvV2Yc4T2mvd4am8NaUrpdO2rqNCozTbML8pjhBgAAcIHdPJKkWyTdAAAAAIDusZtHMj0CAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjJN0AAAAAAPQRkm4AAAAAAPoISTcAAAAAAH2EpBsAAAAAgD5C0g0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIugEAAAAA6CMk3QAAAAAA9BGSbgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPpLkdgPigWVZkqTa2lqXWwIAAAAA8IJI/hjJJ6Mh6Za0d+9eSdKRRx7pcksAAAAAAF6yd+9e+f3+qM/7rK7S8n4gHA7r888/V3p6unw+n9vN6Rdqa2t15JFH6pNPPlFGRobbzcEhGJ/4xvjEN8YnvjE+8Y3xiW+MT3xjfGLPsizt3btXw4cPV0JC9J3bzHRLSkhI0IgRI9xuRr+UkZHBRSGOMT7xjfGJb4xPfGN84hvjE98Yn/jG+MRWZzPcERykBgAAAABAHyHpBgAAAACgj5B0wxUDBw7UwoULNXDgQLebgg4wPvGN8YlvjE98Y3ziG+MT3xif+Mb4xC8OUgMAAAAAoI8w0w0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIutGnPvvsM82aNUtZWVlKTU3ViSeeqL/+9a+tz+/bt0/z58/XiBEjlJqaqkAgoEcffdTFFvcveXl58vl87b7mzZsnSWpoaNC8efOUlZWlwYMH65JLLtEXX3zhcqv7h87GpqamRjfccIOOO+44paamauTIkbrxxhsVDAbdbna/0dV7J8KyLE2fPl0+n08vvPCCO43th+yMT2lpqaZMmaJBgwYpIyNDkyZNUn19vYut7j+6Gp+qqirNnj1bOTk5GjRokMaMGaM//vGPLre6/wiFQrrrrruUn5+v1NRUHX300brnnnt08DFQlmXphz/8oXJzc5WamqpzzjlHH374oYut7j+6Gp+mpibddtttOvHEEzVo0CANHz5cl19+uT7//HOXW96/JbndAJhr9+7dOvPMM3X22Wfr5Zdf1rBhw/Thhx9q6NChrTHf+973tHr1ai1dulR5eXl65ZVX9N3vflfDhw/X+eef72Lr+4e33npLoVCo9fuysjJNnTpV//7v/y5JuuWWW/TSSy/p97//vfx+v+bPn6+LL75Yr7/+ultN7jc6G5vPP/9cn3/+uX7+858rEAhox44duv766/X555/rD3/4g4ut7j+6eu9E/PKXv5TP54t18/q9rsantLRUhYWFWrBggX79618rKSlJf/vb35SQwFxELHQ1Ppdffrn27NmjZcuW6bDDDtMzzzyjb37zm/rrX/+qU0891a1m9xv333+/Fi9erP/+7//WCSecoL/+9a+66qqr5Pf7deONN0qSfvrTn+rBBx/Uf//3fys/P1933XWXzj33XJWXlyslJcXlHpitq/Gpq6vTli1bdNddd+nkk0/W7t27ddNNN+n8889vM/GFGLOAPnLbbbdZEyZM6DTmhBNOsH70ox+1eWzMmDHWD37wg75sGqK46aabrKOPPtoKh8PWnj17rAEDBli///3vW59/7733LElWaWmpi63snw4em4787//+r5WcnGw1NTXFuGWwrI7H5+2337aOOOIIq7Ky0pJkPf/88+41sJ87dHxOP/10684773S5VYg4dHwGDRpkPf30021iMjMzreLiYjea1++cd9551tVXX93msYsvvti67LLLLMuyrHA4bOXk5Fg/+9nPWp/fs2ePNXDgQOt3v/tdTNvaH3U1Ph3ZtGmTJcnasWNHXzcPUfCRLvrMsmXL9PWvf13//u//ruzsbJ166qkqLi5uEzN+/HgtW7ZMn332mSzL0po1a/TBBx9o2rRpLrW6/2psbNTSpUt19dVXy+fzafPmzWpqatI555zTGnP88cdr5MiRKi0tdbGl/c+hY9ORYDCojIwMJSWxgCnWOhqfuro6fec739HDDz+snJwcl1vYvx06PtXV1XrzzTeVnZ2t8ePH6/DDD9fkyZO1YcMGt5vaL3X0/hk/fryee+451dTUKBwO69lnn1VDQ4POOussdxvbT4wfP16vvvqqPvjgA0nS3/72N23YsEHTp0+XJFVUVKiqqqrN/YHf79fpp5/O/UEMdDU+HQkGg/L5fBoyZEiMWolDcXeGPvPxxx9r8eLF+t73vqc77rhDb731lm688UYlJyfriiuukCT9+te/1rXXXqsRI0YoKSlJCQkJKi4u1qRJk1xuff/zwgsvaM+ePbryyislteypS05ObneBPvzww1VVVRX7BvZjh47Nob766ivdc889uvbaa2PbMEjqeHxuueUWjR8/XhdccIF7DYOk9uPz8ccfS5Luvvtu/fznP9cpp5yip59+Wt/4xjdUVlamf/mXf3Gxtf1PR++f//3f/9W3vvUtZWVlKSkpSWlpaXr++ed1zDHHuNfQfuT2229XbW2tjj/+eCUmJioUCunee+/VZZddJkmt9wCHH354m/+O+4PY6Gp8DtXQ0KDbbrtNl156qTIyMmLcWkSQdKPPhMNhff3rX9d9990nSTr11FNVVlamRx99tE3SvXHjRi1btkyjRo3SunXrNG/ePA0fPrzNJ6joe48//rimT5+u4cOHu90UHKKzsamtrdV5552nQCCgu+++O/aNQ7vxWbZsmVavXq23337b5ZZBaj8+4XBYknTdddfpqquuktTy9+nVV1/VE088oaKiItfa2h91dH276667tGfPHv3lL3/RYYcdphdeeEHf/OY3tX79ep144okutrZ/+N///V/9z//8j5555hmdcMIJ2rp1q26++WYNHz689f4N7unO+DQ1Nemb3/ymLMvS4sWLXWoxJLGnG31n5MiR1jXXXNPmsUceecQaPny4ZVmWVVdXZw0YMMBasWJFm5hrrrnGOvfcc2PWTljW9u3brYSEBOuFF15ofezVV1+1JFm7d+9uEzty5EjrF7/4RYxb2H91NDYRtbW1VkFBgfWNb3zDqq+vd6F16Gh8brrpJsvn81mJiYmtX5KshIQEa/Lkye41th/qaHw+/vhjS5K1ZMmSNrHf/OY3re985zuxbmK/1tH4fPTRR5Ykq6ysrE3sN77xDeu6666LdRP7pREjRlgPPfRQm8fuuece67jjjrMsy7K2bdtmSbLefvvtNjGTJk2ybrzxxlg1s9/qanwiGhsbrQsvvNA66aSTrK+++iqWTUQH2NONPnPmmWfq/fffb/PYBx98oFGjRklq+fStqamp3WmxiYmJrTMRiI0nn3xS2dnZOu+881ofO+200zRgwAC9+uqrrY+9//772rlzpwoKCtxoZr/U0dhILTPc06ZNU3JyspYtW8ZpsS7paHxuv/12vfPOO9q6dWvrlyQ98MADevLJJ11qaf/U0fjk5eVp+PDhnf59Qmx0ND51dXWSxL2Bi+rq6jr9/efn5ysnJ6fN/UFtba3efPNN7g9ioKvxkf45w/3hhx/qL3/5i7KysmLdTBzK7awf5tq0aZOVlJRk3XvvvdaHH35o/c///I+VlpZmLV26tDVm8uTJ1gknnGCtWbPG+vjjj60nn3zSSklJsR555BEXW96/hEIha+TIkdZtt93W7rnrr7/eGjlypLV69Wrrr3/9q1VQUGAVFBS40Mr+KdrYBINB6/TTT7dOPPFE66OPPrIqKytbv5qbm11qbf/T2XvnUOL08pjrbHweeOABKyMjw/r9739vffjhh9add95ppaSkWB999JELLe2foo1PY2Ojdcwxx1gTJ0603nzzTeujjz6yfv7zn1s+n8966aWXXGpt/3LFFVdYRxxxhLVixQqroqLC+tOf/mQddthh1n/+53+2xvzkJz+xhgwZYr344ovWO++8Y11wwQVWfn4+q65ioKvxaWxstM4//3xrxIgR1tatW9vcIxw4cMDl1vdfJN3oU8uXL7dGjx5tDRw40Dr++OOt3/zmN22er6ystK688kpr+PDhVkpKinXcccdZ//Vf/xW1LBKc9+c//9mSZL3//vvtnquvr7e++93vWkOHDrXS0tKsiy66yKqsrHShlf1TtLFZs2aNJanDr4qKCnca2w919t45FEl37HU1PkVFRdaIESOstLQ0q6CgwFq/fn2MW9i/dTY+H3zwgXXxxRdb2dnZVlpamnXSSSe1KyGGvlNbW2vddNNN1siRI62UlBTrqKOOsn7wgx+0SdjC4bB11113WYcffrg1cOBA6xvf+IatayF6r6vxqaioiHqPsGbNGncb34/5LMuyYjy5DgAAAABAv8CebgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPkLSDQAAAABAHyHpBgAAAACgj5B0AwAAAADQR0i6AQAAAADoIyTdAAAAAAD0EZJuAADQzmuvvSafz6c9e/bY/m/uvvtunXLKKX3WJgAAvIikGwAAj3v00UeVnp6u5ubm1sf27dunAQMG6KyzzmoTG0mmt23b1unPHD9+vCorK+X3+x1t61lnnaWbb77Z0Z8JAEA8I+kGAMDjzj77bO3bt09//etfWx9bv369cnJy9Oabb6qhoaH18TVr1mjkyJE6+uijO/2ZycnJysnJkc/n67N2AwDQH5B0AwDgcccdd5xyc3P12muvtT722muv6YILLlB+fr42btzY5vGzzz5b4XBYRUVFys/PV2pqqk4++WT94Q9/aBN36PLy4uJiHXnkkUpLS9NFF12kX/ziFxoyZEi79ixZskR5eXny+/369re/rb1790qSrrzySq1du1a/+tWv5PP55PP5tH37dqd/HQAAxBWSbgAADHD22WdrzZo1rd+vWbNGZ511liZPntz6eH19vd58802dffbZKioq0tNPP61HH31Uf//733XLLbdo1qxZWrt2bYc///XXX9f111+vm266SVu3btXUqVN17733tovbtm2bXnjhBa1YsUIrVqzQ2rVr9ZOf/ESS9Ktf/UoFBQWaM2eOKisrVVlZqSOPPLIPfhsAAMSPJLcbAAAAeu/ss8/WzTffrObmZtXX1+vtt9/W5MmT1dTUpEcffVSSVFpaqgMHDuiss85SIBDQX/7yFxUUFEiSjjrqKG3YsEGPPfaYJk+e3O7n//rXv9b06dP1H//xH5KkY489Vm+88YZWrFjRJi4cDuupp55Senq6JGn27Nl69dVXde+998rv9ys5OVlpaWnKycnpy18HAABxg6QbAAADnHXWWdq/f7/eeust7d69W8cee6yGDRumyZMn66qrrlJDQ4Nee+01HXXUUdq3b5/q6uo0derUNj+jsbFRp556aoc///3339dFF13U5rFx48a1S7rz8vJaE25Jys3NVXV1tUO9BADAe0i6AQAwwDHHHKMRI0ZozZo12r17d+ts9fDhw3XkkUfqjTfe0Jo1azRlyhTt27dPkvTSSy/piCOOaPNzBg4c2Kt2DBgwoM33Pp9P4XC4Vz8TAAAvI+kGAMAQZ599tl577TXt3r1bt956a+vjkyZN0ssvv6xNmzZp7ty5CgQCGjhwoHbu3NnhUvKOHHfccXrrrbfaPHbo93YkJycrFAr9//buVyWWMI7j8PdoEfwTBLGqWNbiBaw4TBAsVqtpQdANA1tNBotsXKxehKxJBDF4B0bxDmSDoLB7mnA4J+iBYXV5njgwL79Jw4cZ3vfL9wHATyW6AWBClGWZo6OjvL+//xHTRVHk+Pg4b29vKcsy8/Pz6XQ6qaoqw+EwW1tbeXl5yf39fRYWFnJwcPDX2u12O9vb2+l2u9nb28vNzU36/f6XjxRbWVnJw8NDnp6eMjc3l8XFxUxN2dcVgMnlLQcAE6Isy7y+vmZ9fT3Ly8sf14uiyGAw+DhaLElOT09zcnKSs7OzNBqN7O7u5urqKqurq/9cu9ls5uLiIt1uN5ubm7m+vk5VVZmZmfnSjJ1OJ9PT09nY2MjS0lKen5///4EB4Af4NRqNRuMeAgD4eVqtVh4fH3N3dzfuUQDg2/J7OQDwKefn59nZ2cns7Gz6/X4uLy/T6/XGPRYAfGu+dAMAn7K/v5/b29sMBoOsra2l3W7n8PBw3GMBwLcmugEAAKAmNlIDAACAmohuAAAAqInoBgAAgJqIbgAAAKiJ6AYAAICaiG4AAACoiegGAACAmohuAAAAqInoBgAAgJr8BszmUCbCjQ3yAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1087,16 +920,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Johtopäätös\n",
+    "## Yhteenveto\n",
     "\n",
-    "Tässä muistikirjassa olemme oppineet suorittamaan perusoperaatioita datalla tilastollisten funktioiden laskemiseksi. Nyt tiedämme, kuinka käyttää matematiikan ja tilastotieteen vankkaa välineistöä hypoteesien todistamiseen sekä kuinka laskea luottamusvälejä satunnaisille muuttujille annetun otoksen perusteella.\n"
+    "Tässä muistikirjassa olemme oppineet suorittamaan perusoperaatioita datalla tilastollisten funktioiden laskemiseksi. Nyt tiedämme, kuinka käyttää vankkaa matematiikan ja tilastotieteen välineistöä hypoteesien todistamiseen sekä kuinka laskea luottamusvälejä satunnaisille muuttujille annetun otoksen perusteella.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Vastuuvapauslauseke**:  \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulisi pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskäännöstä. Emme ole vastuussa väärinkäsityksistä tai virhetulkinnoista, jotka johtuvat tämän käännöksen käytöstä.\n"
+    "\n---\n\n**Vastuuvapauslauseke**:  \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulee pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskääntämistä. Emme ole vastuussa tämän käännöksen käytöstä aiheutuvista väärinkäsityksistä tai virhetulkinnoista.\n"
    ]
   }
  ],
@@ -1119,11 +952,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:05:18+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:38:24+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "fi"
   }
diff --git a/translations/fi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/fi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 1bef6fc5..5049f8a6 100644
--- a/translations/fi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/fi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -151,13 +151,13 @@
    "cell_type": "markdown",
    "source": [
     "Tässä aineistossa sarakkeet ovat seuraavat:  \n",
-    "* Ikä ja sukupuoli ovat itsestään selviä  \n",
-    "* BMI on kehon painoindeksi  \n",
+    "* Ikä ja sukupuoli ovat itsestäänselviä  \n",
+    "* BMI tarkoittaa painoindeksiä  \n",
     "* BP on keskimääräinen verenpaine  \n",
     "* S1–S6 ovat erilaisia veriarvoja  \n",
-    "* Y on sairauden etenemisen laadullinen mittari yhden vuoden aikana  \n",
+    "* Y on laadullinen mitta taudin etenemisestä yhden vuoden aikana  \n",
     "\n",
-    "Tutkitaan tätä aineistoa todennäköisyyden ja tilastotieteen menetelmillä.\n",
+    "Tutkitaan tätä aineistoa todennäköisyyden ja tilastotieteen menetelmien avulla.  \n",
     "\n",
     "### Tehtävä 1: Laske kaikkien arvojen keskiarvot ja varianssit  \n"
    ],
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -604,7 +604,7 @@
    "source": [
     "### Tehtävä 4: Testaa eri muuttujien ja sairauden etenemisen (Y) välistä korrelaatiota\n",
     "\n",
-    "> **Vinkki** Korrelaatiomatriisi antaa hyödyllisintä tietoa siitä, mitkä arvot ovat riippuvaisia toisistaan.\n"
+    "> **Vinkki** Korrelaatiomatriisi antaa sinulle hyödyllisintä tietoa siitä, mitkä arvot ovat riippuvaisia toisistaan.\n"
    ],
    "metadata": {}
   },
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "Johtopäätös:  \n",
-    "* Vahvin korrelaatio Y:n kanssa on BMI ja S5 (verensokeri). Tämä vaikuttaa järkevältä.\n"
+    "* Vahvin korrelaatio Y:n kanssa on BMI ja S5 (verensokeri). Tämä kuulostaa järkevältä.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -918,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Vastuuvapauslauseke**:  \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulisi pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskäännöstä. Emme ole vastuussa väärinkäsityksistä tai virhetulkinnoista, jotka johtuvat tämän käännöksen käytöstä.\n"
+    "\n---\n\n**Vastuuvapauslauseke**:  \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Pyrimme tarkkuuteen, mutta huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulee pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskääntämistä. Emme ole vastuussa tämän käännöksen käytöstä aiheutuvista väärinkäsityksistä tai virhetulkinnoista.\n"
    ]
   }
  ],
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:24:11+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:38:56+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "fi"
   }
diff --git a/translations/fr/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/fr/1-Introduction/04-stats-and-probability/assignment.ipynb
index 97c52dde..a5ac0fa0 100644
--- a/translations/fr/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/fr/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Introduction à la Probabilité et aux Statistiques\n",
     "## Devoir\n",
     "\n",
-    "Dans ce devoir, nous utiliserons le jeu de données des patients atteints de diabète provenant [d'ici](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Dans ce devoir, nous utiliserons le jeu de données des patients diabétiques disponible [ici](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Dans cet ensemble de données, les colonnes sont les suivantes :  \n",
-    "* L'âge et le sexe sont explicites  \n",
-    "* L'IMC est l'indice de masse corporelle  \n",
-    "* La PA est la pression artérielle moyenne  \n",
-    "* S1 à S6 sont différentes mesures sanguines  \n",
-    "* Y est la mesure qualitative de la progression de la maladie sur une année  \n",
+    "Dans ce jeu de données, les colonnes sont les suivantes :\n",
+    "* L'âge et le sexe sont explicites\n",
+    "* L'IMC est l'indice de masse corporelle\n",
+    "* BP est la pression artérielle moyenne\n",
+    "* S1 à S6 sont différentes mesures sanguines\n",
+    "* Y est la mesure qualitative de la progression de la maladie sur une année\n",
     "\n",
-    "Étudions cet ensemble de données en utilisant des méthodes de probabilité et de statistiques.\n",
+    "Étudions ce jeu de données en utilisant des méthodes de probabilité et de statistiques.\n",
     "\n",
-    "### Tâche 1 : Calculer les valeurs moyennes et la variance pour toutes les valeurs\n"
+    "### Tâche 1 : Calculer les valeurs moyennes et la variance pour tous les éléments\n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tâche 2 : Tracer des boxplots pour l'IMC, la TA et Y en fonction du sexe\n"
+    "### Tâche 2 : Tracer des boîtes à moustaches pour l'IMC, la TA et Y en fonction du sexe\n"
    ],
    "metadata": {}
   },
@@ -200,7 +200,7 @@
    "source": [
     "### Tâche 4 : Tester la corrélation entre différentes variables et la progression de la maladie (Y)\n",
     "\n",
-    "> **Indice** Une matrice de corrélation vous fournira les informations les plus utiles sur les valeurs qui sont dépendantes.\n"
+    "> **Conseil** Une matrice de corrélation vous fournira les informations les plus utiles sur les valeurs qui sont dépendantes.\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Avertissement** :  \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction professionnelle réalisée par un humain. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
+    "\n---\n\n**Avertissement** :  \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de faire appel à une traduction humaine professionnelle. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:18:44+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:00:34+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "fr"
   }
diff --git a/translations/fr/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/fr/1-Introduction/04-stats-and-probability/notebook.ipynb
index 7333ca9b..8084996e 100644
--- a/translations/fr/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/fr/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Variables Aléatoires et Distributions\n",
-    "Commençons par tirer un échantillon de 30 valeurs à partir d'une distribution uniforme allant de 0 à 9. Nous calculerons également la moyenne et la variance.\n"
+    "## Variables aléatoires et distributions\n",
+    "Commençons par tirer un échantillon de 30 valeurs à partir d'une distribution uniforme entre 0 et 9. Nous calculerons également la moyenne et la variance.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Nous utilisons ici un package appelé [**Pandas**](https://pandas.pydata.org/) pour l'analyse de données. Nous parlerons davantage de Pandas et du travail avec les données en Python plus tard dans ce cours.\n",
+    "> Nous utilisons ici un package appelé [**Pandas**](https://pandas.pydata.org/) pour l'analyse de données. Nous parlerons davantage de Pandas et du travail avec les données en Python plus tard dans ce cours.\n",
     "\n",
     "Calculons les valeurs moyennes pour l'âge, la taille et le poids :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Concentrons-nous maintenant sur la taille et calculons l'écart-type et la variance :\n"
+    "Concentrons-nous maintenant sur la taille et calculons l'écart type et la variance :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "En plus de la moyenne, il est logique de regarder la valeur médiane et les quartiles. Ils peuvent être visualisés à l'aide d'un **diagramme en boîte** :\n"
+    "En plus de la moyenne, il est logique de regarder la valeur médiane et les quartiles. Ils peuvent être visualisés à l'aide d'un **boîte à moustaches** :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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/6SS5n/zkJx1+TJMnT3aSXGNjY6vH1NDQ4CS5X/3qV20eU/NxTJs2zTmX/u9T878TP/vZz4qyU2vH9H7+7jX/f2reV55+TMuXL3eS3H333Vdwx1RfX+8kufr6eteWnI+at3bFu0+fPjp69KjOP//8gv6Ozdq1azVq1Cg9+eSTuuSSS8x8V9fzPB0+fFg9evRINSj2Ywpip444pmg0qkOHDqlPnz5KJpMFfUx1dXWaNGmSVq9erREjRhTEY0SxXVGI7tmoTk9UK3HPCiW7X97imCKRiG57/jbtPLZTnvvfZ08tCZXo0vMu1ROfeELl5eUFd0yF3Gnjxo0aPny45s+fr8suuyyv51M8Hte+fft00UUXpZ4Nl8e94B5TMT2WW+20a9cuTZo0SWvWrNHw4cPb/bg3ceJEPfvss7rjjjv01FNPyfM87dq1S/369ZMkTZo0SQsWLNDNN98cuCveo0ePTv2bf/oxvfLKKxo5cqReeumlDr/ivXz5clVXV6u2tlaDBg1KO6YNGzZo2LBhevnll9t1xfv04/jvf5/WrVuXdhz/3SkWi+ntt9/WJZdcIs/zCq5TR17xHj16tNauXavrrrsu7ZjWr1+vESNG6MUXXyy4K96nTp1q/49Rt7k1Pwup7Svep8vkuwJ+27x5s5OU+s6KFdFo1M2dO9dFo1G/l4IcK6bWVs/HjhT710bnplW99+dp1u1b1+rV7uaXdfvW+bDi4ubn39liOreRPXoXvvf7eNDY2OgkuVAo5E6ePNmi9cmTJ10oFEpdnQ2SRCLhPvzhD7uxY8e6ZDLZ4n3JZNKNHTvWfeQjH3GJRKKg7zvbz2Xl3Pazd7Yy2dsG96f08b5FIhHdd999qe8EIbhobUtZaVmLP5s55zRn6xyFFGr140IKac7WOTzDeRHh3LaF3sHVpUsXDR48WM45VVRU6Atf+IKuvfZafeELX0g9sdrgwYMD9cRqkhQOhzVr1iw999xzGj9+fItnuR4/fryee+45PfTQQzl5oq2OvO9sP5eVc9vP3vmU8ca7qalJ27Zt07Zt2yRJ//znP7Vt2zbt3bu3o9cGnySTSe3YsSNQv7AeraO1Lcn/jBw3/9ks7sV16PghObW+sXZyOnT8kOKejd8jGgSc27bQO9g2btyY2nz/7ne/01VXXaXf/e53qU33xo0b/V5iTkyYMEHPPPOMXnvtNQ0bNkxVVVUaNmyYtm/frmeeeUYTJkwoivvO5nNZOrf97J0vGT+r+d/+9jeNHj069d/f+MY3JEl333235s+f32ELg3+SyaTWr1+viy++uOi/s4Szo7UtnpdU+L/+bBYJR/SH//MHHTt17Iwfe16n8xQJB/s77kHCuW0LvYNv48aNampq0u23364tW7Zo0KBB+v3vfx+4K92nmzBhgsaNG6e1a9fq4MGD+tCHPqThw4fn5e95R973+/1c1s5tP3vnQ8Yb71GjRjFuGHCRSET33HOP38tAHtDaljONmktSj8491KNzj3wvCTnCuW0LvW3o0qWLampq/F5G3oXDYY0aNaro7/v9fC6L57afvXONn/E+i/79+2vz5s3q37+/30vJq2QyqS1btpgYa7GO1racadQcwcO5bQu9C19HfU1Ja1voHSxsvM+ioqJCgwYNUkVFhd9LyatkMqnXX3+dk9wAWtvieckWfyK4OLdtoXfh66ivKWltC72DJeNRcwRfJBLRpEmT/F4G8oDWtpxt1BzBwrltC73toLUt9A4WNt5Ik0gktGnTJg0ePFilpfwVCbJian3ixAlJ0pYtW3xeSfGK/HunLpe0fccOxQ4xbp5rO3fu9O2+i+ncRvbobQetbaF3sFAQaZxz2rdvn66++mq/l4IcK6bWb7zxhiRp8uTJPq+keF3Zo0Rb7u2iu+66S1vZeOdNZWVl3u+zmM5tZI/edtDaFnoHS8jl+SnKGxoa1LVrV9XX16uqqiqfdw2giB09elRLly5V//79zT3vQkcJJU6pU9NenepyoVxpJ7+XY0JlZaUuvvhiv5cBAAByIJO9LVe8kSaRSGjdunW67rrrGGsJuGJq3a1bN33xi1/0exlF7b3eMV036JqC743sFNO5jezR2w5a20LvYOFZzZHGOaeGhgZ+X7sBtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQoUz2tlzxRppEIqEXXnhBiUTC76Ugx2htC73toLUt9LaD1rbQO1jYeAMAAAAAkEOMmgMAAAAAkCFGzZGVeDyumpoaxeNxv5eCHKO1LfS2g9a20NsOWttC72Bh4400oVBIVVVVCoVCfi8FOUZrW+htB61tobcdtLaF3sHCqDkAAAAAABli1BxZicfjWrRoEWMtBtDaFnrbQWtb6G0HrW2hd7Cw8UaaUCik3r17M9ZiAK1tobcdtLaF3nbQ2hZ6Bwuj5gAAAAAAZIhRc2QlFovp6aefViwW83spyDFa20JvO2htC73toLUt9A4WNt5IEw6HNWDAAIXDYb+XghyjtS30toPWttDbDlrbQu9gYdQcAAAAAIAMMWqOrMRiMc2bN4+xFgNobQu97aC1LfS2g9a20DtY2HgjTTgc1rXXXstYiwG0toXedtDaFnrbQWtb6B0sjJoDAAAAAJAhRs2RlVgspkceeYSxFgNobQu97aC1LfS2g9a20DtY2HgjTWlpqaqrq1VaWur3UpBjtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQIUbNkZVoNKqHH35Y0WjU76Ugx2htC73toLUt9LaD1rbQO1i44o00nudp//796tWrl0pK+N5MkNHaFnrbQWtb6G0HrW2hd+HLZG/LxhsAAAAAgAwxao6sRKNRzZgxg7EWA2htC73toLUt9LaD1rbQO1i44o00nufp6NGj6tatG2MtAUdrW+htB61tobcdtLaF3oWPUXMAAAAAAHKIUXNkJRqN6sEHH2SsxQBa20JvO2htC73toLUt9A4WrngjjXNOjY2NqqysVCgU8ns5yCFa20JvO2htC73toLUt9C58XPFG1srLy/1eAvKE1rbQ2w5a20JvO2htC72Dg4030sRiMc2cOVOxWMzvpSDHaG0Lve2gtS30toPWttA7WBg1RxrnnGKxmCKRCGMtAUdrW+htB61tobcdtLaF3oWPUXNkjSdxsIPWttDbDlrbQm87aG0LvYODjTfSxGIxzZ49m7EWA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMrnufpyJEj8jzP76Ugx2htC73toLUt9LaD1rbQO1jYeCNNPB7XvHnzFI/H/V4KcozWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmy4nme3nnnHcZaDKC1LfS2g9a20NsOWttC72Bh44008XhcixYtYqzFAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5siK53navXs3Yy0G0NoWettBa1vobQetbaF3sLDxRppEIqEXX3xRiUTC76Ugx2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSO3bsUDKZ9HspyDFa20JvO2htC73toLUt9A4WNt5Ik0wmtX79ek5yA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSW7Zs4btrBtDaFnrbQWtb6G0HrW2hd7Cw8UaaZDKp119/nZPcAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5shKIpFQbW0tvzPQAFrbQm87aG0Lve2gtS30DhY23kjjnNO+ffuU52EI+IDWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmykkgktGrVKsZaDKC1LfS2g9a20NsOWttC72Bh4400zjk1NDQw1mIArW2htx20toXedtDaFnoHC6PmAAAAAABkiFFzZCWRSOiFF15grMUAWttCbztobQu97aC1LfQOFjbeAAAAAADkEKPmAAAAAABkKJO9bWme1pTSvM9vaGjI912jneLxuJYvX65PfOITKisr83s5yCFa20JvO2htC73toLUt9C58zXva9lzLzvvGu7GxUZLUp0+ffN81AAAAAAAdqrGxUV27dj3rbfI+au55ng4cOKDKykqFQqF83jXaqaGhQX369NE777zDjwMEHK1tobcdtLaF3nbQ2hZ6Fz7nnBobG9WzZ0+VlJz96dPyfsW7pKREvXv3zvfd4n2oqqriJDeC1rbQ2w5a20JvO2htC70LW1tXupvxrOYAAAAAAOQQG28AAAAAAHKIjTfSlJeXa9q0aSovL/d7KcgxWttCbztobQu97aC1LfQOlrw/uRoAAAAAAJZwxRsAAAAAgBxi4w0AAAAAQA6x8QYAAAAAIIfYeAMAAAAAkENsvI1Ys2aNxo4dq549eyoUCmnp0qVpt9m5c6duvvlmde3aVZ07d9bgwYO1d+/e1PtPnTqlKVOm6Pzzz1eXLl10yy236PDhw3k8CrRHW62bmpo0depU9e7dW+ecc44GDBigRx99tMVtaF08ZsyYocGDB6uyslLdu3fX+PHj9eabb7a4TXt67t27VzfddJMqKirUvXt3ffvb31YikcjnoaANbbU+duyYvvrVr6pfv34655xzdOGFF+prX/ua6uvrW3weWheH9pzbzZxz+tSnPtXqYz69C197W9fW1ur6669X586dVVVVpREjRujkyZOp9x87dkx33HGHqqqqdO655+qee+5RU1NTPg8F7dCe3ocOHdKdd96pHj16qHPnzho0aJD+9Kc/tbgNvYsPG28jjh8/riuuuEJz585t9f1vvfWWrrvuOvXv31+rVq3SP/7xD/3gBz9Qp06dUre5//779ec//1mLFi3S6tWrdeDAAU2YMCFfh4B2aqv1N77xDS1btkxPP/20du7cqa9//euaOnWqampqUrehdfFYvXq1pkyZovXr12v58uWKx+Oqrq7W8ePHU7dpq2cymdRNN92kWCymV199VU888YTmz5+vH/7wh34cEs6grdYHDhzQgQMH9NBDD2n79u2aP3++li1bpnvuuSf1OWhdPNpzbjf7+c9/rlAolPZ2eheH9rSura3VmDFjVF1drY0bN2rTpk2aOnWqSkr+90v5O+64Qzt27NDy5cv13HPPac2aNfrSl77kxyHhLNrT+6677tKbb76pmpoavfbaa5owYYJuvfVWbd26NXUbehchB3MkuSVLlrR428SJE92kSZPO+DHvvvuuKysrc4sWLUq9befOnU6Sq62tzdVSkaXWWl922WXuRz/6UYu3DRo0yH3ve99zztG62B05csRJcqtXr3bOta/nX/7yF1dSUuIOHTqUus2vfvUrV1VV5aLRaH4PAO12euvWLFy40EUiERePx51ztC5mZ+q9detW16tXL3fw4MG0x3x6F6fWWg8ZMsR9//vfP+PHvP76606S27RpU+ptf/3rX10oFHL79+/P6XqRndZ6d+7c2T355JMtbnfeeee5xx57zDlH72LFFW/I8zw9//zzuuSSS/TJT35S3bt315AhQ1qMq23evFnxeFw33nhj6m39+/fXhRdeqNraWh9Wjfdr2LBhqqmp0f79++Wc08qVK7Vr1y5VV1dLonWxax4rPu+88yS1r2dtba0GDhyoCy64IHWbT37yk2poaNCOHTvyuHpk4vTWZ7pNVVWVSktLJdG6mLXW+8SJE7r99ts1d+5c9ejRI+1j6F2cTm995MgRbdiwQd27d9ewYcN0wQUXaOTIkVq3bl3qY2pra3Xuuefq6quvTr3txhtvVElJiTZs2JDfA0BGWju3hw0bpj/+8Y86duyYPM/TH/7wB506dUqjRo2SRO9ixcYbOnLkiJqamjRz5kyNGTNGL774oj796U9rwoQJWr16taT3ftYkEono3HPPbfGxF1xwgQ4dOuTDqvF+zZkzRwMGDFDv3r0ViUQ0ZswYzZ07VyNGjJBE62LmeZ6+/vWv6+Mf/7guv/xySe3reejQoRZfmDe/v/l9KDyttT7d0aNH9eMf/7jF6CGti9OZet9///0aNmyYxo0b1+rH0bv4tNb67bffliRNnz5dkydP1rJlyzRo0CDdcMMNqqurk/Rez+7du7f4XKWlpTrvvPNoXcDOdG4vXLhQ8Xhc559/vsrLy3XvvfdqyZIl6tu3ryR6F6tSvxcA/3meJ0kaN26c7r//fknSxz72Mb366qt69NFHNXLkSD+Xhw42Z84crV+/XjU1Nbrooou0Zs0aTZkyRT179mxxVRTFZ8qUKdq+fXuLqyAIprZaNzQ06KabbtKAAQM0ffr0/C4OHa613jU1NVqxYkWLn/lE8WutdfPXaffee68+//nPS5KuvPJKvfzyy/rtb3+rGTNm+LJWZO9Mj+U/+MEP9O677+qll15St27dtHTpUt16661au3atBg4c6NNqkS2ueEPdunVTaWmpBgwY0OLtl156aepZzXv06KFYLKZ33323xW0OHz7c6ngbCtPJkyf13e9+Vw8//LDGjh2rj370o5o6daomTpyohx56SBKti9XUqVP13HPPaeXKlerdu3fq7e3p2aNHj7RnOW/+b5oXnjO1btbY2KgxY8aosrJSS5YsUVlZWep9tC4+Z+q9YsUKvfXWWzr33HNVWlqa+nGCW265JTWOSu/icqbWH/rQhySpza/Tjhw50uL9iURCx44do3WBOlPvt956S7/85S/129/+VjfccIOuuOIKTZs2TVdffXXqiXPpXZzYeEORSESDBw9O+1UGu3bt0kUXXSRJuuqqq1RWVqaXX3459f4333xTe/fu1dChQ/O6Xrx/8Xhc8Xi8xbOgSlI4HE59R53WxcU5p6lTp2rJkiVasWKFPvKRj7R4f3t6Dh06VK+99lqLf8SXL1+uqqqqtC/04J+2WkvvXemurq5WJBJRTU1Ni99MIdG6mLTV+zvf+Y7+8Y9/aNu2bakXSZo9e7Yef/xxSfQuFm21/vCHP6yePXue9eu0oUOH6t1339XmzZtT71+xYoU8z9OQIUNyfxBot7Z6nzhxQpLO+rUavYuUn8/shvxpbGx0W7dudVu3bnWS3MMPP+y2bt3q/vWvfznnnFu8eLErKytzv/71r11dXZ2bM2eOC4fDbu3atanP8eUvf9ldeOGFbsWKFe5vf/ubGzp0qBs6dKhfh4QzaKv1yJEj3WWXXeZWrlzp3n77bff444+7Tp06uUceeST1OWhdPL7yla+4rl27ulWrVrmDBw+mXk6cOJG6TVs9E4mEu/zyy111dbXbtm2bW7ZsmfvgBz/oHnjgAT8OCWfQVuv6+no3ZMgQN3DgQLd79+4Wt0kkEs45WheT9pzbp9Npz2pO7+LQntazZ892VVVVbtGiRa6urs59//vfd506dXK7d+9O3WbMmDHuyiuvdBs2bHDr1q1zF198sbvtttv8OCScRVu9Y7GY69u3rxs+fLjbsGGD2717t3vooYdcKBRyzz//fOrz0Lv4sPE2YuXKlU5S2svdd9+dus28efNc3759XadOndwVV1zhli5d2uJznDx50t13333uAx/4gKuoqHCf/vSn3cGDB/N8JGhLW60PHjzoPve5z7mePXu6Tp06uX79+rlZs2Y5z/NSn4PWxaO11pLc448/nrpNe3ru2bPHfepTn3LnnHOO69atm/vmN7+Z+hVUKAxttT7TuS/J/fOf/0x9HloXh/ac2619zOm/QpLeha+9rWfMmOF69+7tKioq3NChQ1tcHHHOuX//+9/utttuc126dHFVVVXu85//vGtsbMzjkaA92tN7165dbsKECa579+6uoqLCffSjH0379WL0Lj4h55zr6KvoAAAAAADgPfyMNwAAAAAAOcTGGwAAAACAHGLjDQAAAABADrHxBgAAAAAgh9h4AwAAAACQQ2y8AQAAAADIITbeAAAAAADkEBtvAAAAAAByiI03AAAAAAA5xMYbAAAAAIAcYuMNAAAAAEAOsfEGAAAAACCH/j+8q7kCS2EPGAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Nous pouvons également créer des diagrammes en boîte pour des sous-ensembles de notre ensemble de données, par exemple, regroupés par rôle de joueur.\n"
+    "Nous pouvons également créer des boîtes à moustaches pour des sous-ensembles de notre ensemble de données, par exemple, regroupés par rôle de joueur.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Note**: Ce diagramme suggère qu'en moyenne, les tailles des joueurs de première base sont supérieures à celles des joueurs de deuxième base. Plus tard, nous apprendrons comment tester cette hypothèse de manière plus formelle et comment démontrer que nos données sont statistiquement significatives pour le prouver.\n",
+    "> **Note** : Ce diagramme suggère qu'en moyenne, les tailles des joueurs de première base sont supérieures à celles des joueurs de deuxième base. Plus tard, nous apprendrons comment tester cette hypothèse de manière plus formelle et comment démontrer que nos données sont statistiquement significatives pour le prouver.\n",
     "\n",
     "L'âge, la taille et le poids sont tous des variables aléatoires continues. À votre avis, quelle est leur distribution ? Une bonne façon de le découvrir est de tracer l'histogramme des valeurs :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Étant donné que la plupart des valeurs dans la vie réelle suivent une distribution normale, nous ne devrions pas utiliser un générateur de nombres aléatoires uniformes pour créer des données d'échantillon. Voici ce qui se passe si nous essayons de générer des poids avec une distribution uniforme (générée par `np.random.rand`) :\n"
+    "Étant donné que la plupart des valeurs dans la vie réelle suivent une distribution normale, nous ne devrions pas utiliser un générateur de nombres aléatoires uniformes pour générer des données d'échantillon. Voici ce qui se passe si nous essayons de générer des poids avec une distribution uniforme (générée par `np.random.rand`) :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -554,23 +395,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Intervalles de confiance\n",
+    "## Intervalles de Confiance\n",
     "\n",
-    "Calculons maintenant les intervalles de confiance pour les poids et les tailles des joueurs de baseball. Nous utiliserons le code [de cette discussion sur stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) :\n"
+    "Calculons maintenant les intervalles de confiance pour les poids et les tailles des joueurs de baseball. Nous utiliserons le code [de cette discussion sur Stack Overflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -593,14 +434,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Test d'hypothèse\n",
+    "## Test d'hypothèses\n",
     "\n",
     "Explorons les différents rôles dans notre ensemble de données sur les joueurs de baseball :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Nous pouvons constater que les intervalles ne se chevauchent pas.\n",
     "\n",
-    "Une méthode statistiquement plus correcte pour prouver l'hypothèse consiste à utiliser un **test t de Student** :\n"
+    "Une méthode statistiquement plus correcte pour prouver l'hypothèse est d'utiliser un **test t de Student** :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -833,14 +672,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Une corrélation égale à 1 signifie qu'il existe une forte **relation linéaire** entre deux variables. Nous pouvons visualiser la relation linéaire en traçant une valeur par rapport à l'autre :\n"
+    "Une corrélation égale à 1 signifie qu'il existe une **relation linéaire** forte entre deux variables. Nous pouvons visualiser la relation linéaire en traçant une valeur par rapport à l'autre :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -919,14 +756,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -939,19 +776,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dans ce cas, la corrélation est légèrement plus faible, mais elle reste assez élevée. Maintenant, pour rendre la relation encore moins évidente, nous pourrions ajouter un peu de hasard en ajoutant une variable aléatoire au salaire. Voyons ce qui se passe :\n"
+    "Dans ce cas, la corrélation est légèrement plus faible, mais elle reste assez élevée. Maintenant, pour rendre la relation encore moins évidente, nous pourrions vouloir ajouter un peu plus d'aléatoire en ajoutant une variable aléatoire au salaire. Voyons ce qui se passe :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -962,19 +799,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,24 +824,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Pouvez-vous deviner pourquoi les points s'alignent en lignes verticales comme ça ?\n",
+    "> Pouvez-vous deviner pourquoi les points s'alignent en lignes verticales comme ceci ?\n",
     "\n",
-    "Nous avons observé la corrélation entre un concept artificiellement conçu comme le salaire et la variable observée *taille*. Voyons également si les deux variables observées, comme la taille et le poids, sont corrélées :\n"
+    "Nous avons observé la corrélation entre un concept artificiellement conçu comme le salaire et la variable observée *taille*. Voyons également si deux variables observées, comme la taille et le poids, sont corrélées :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,16 +854,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Malheureusement, nous n'avons obtenu aucun résultat - seulement des valeurs étranges `nan`. Cela est dû au fait que certaines des valeurs dans notre série sont indéfinies, représentées par `nan`, ce qui rend le résultat de l'opération également indéfini. En examinant la matrice, nous pouvons voir que la colonne `Weight` est problématique, car l'auto-corrélation entre les valeurs de `Height` a été calculée.\n",
+    "Malheureusement, nous n'avons obtenu aucun résultat - seulement des valeurs étranges `nan`. Cela est dû au fait que certaines des valeurs dans notre série sont indéfinies, représentées par `nan`, ce qui rend également le résultat de l'opération indéfini. En examinant la matrice, nous pouvons voir que la colonne problématique est `Weight`, car l'auto-corrélation entre les valeurs de `Height` a été calculée.\n",
     "\n",
-    "> Cet exemple montre l'importance de la **préparation des données** et du **nettoyage**. Sans données adéquates, nous ne pouvons rien calculer.\n",
+    "> Cet exemple montre l'importance de la **préparation** et du **nettoyage** des données. Sans données adéquates, nous ne pouvons rien calculer.\n",
     "\n",
-    "Utilisons la méthode `fillna` pour remplir les valeurs manquantes et calculer la corrélation :\n"
+    "Utilisons la méthode `fillna` pour remplir les valeurs manquantes, puis calculons la corrélation :\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +873,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1054,27 +889,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1085,14 +918,14 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "Dans ce notebook, nous avons appris à effectuer des opérations de base sur des données pour calculer des fonctions statistiques. Nous savons désormais utiliser un solide ensemble d'outils mathématiques et statistiques pour prouver certaines hypothèses, ainsi que calculer des intervalles de confiance pour des variables arbitraires à partir d'un échantillon de données.\n"
+    "Dans ce notebook, nous avons appris à effectuer des opérations de base sur les données pour calculer des fonctions statistiques. Nous savons désormais comment utiliser un ensemble solide d'outils mathématiques et statistiques pour prouver certaines hypothèses, ainsi que comment calculer des intervalles de confiance pour des variables arbitraires à partir d'un échantillon de données.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Avertissement** :  \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction professionnelle réalisée par un humain. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
+    "\n---\n\n**Avertissement** :  \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de faire appel à une traduction humaine professionnelle. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
    ]
   }
  ],
@@ -1115,11 +948,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:06:10+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:00:22+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "fr"
   }
diff --git a/translations/fr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/fr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 88fedf18..805738bc 100644
--- a/translations/fr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/fr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Introduction à la Probabilité et aux Statistiques\n",
     "## Devoir\n",
     "\n",
-    "Dans ce devoir, nous utiliserons le jeu de données des patients atteints de diabète provenant [d'ici](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Dans ce devoir, nous utiliserons le jeu de données des patients diabétiques disponible [ici](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Dans ce jeu de données, les colonnes sont les suivantes :\n",
-    "* L'âge et le sexe sont explicites\n",
-    "* L'IMC est l'indice de masse corporelle\n",
-    "* BP est la pression artérielle moyenne\n",
-    "* S1 à S6 sont différentes mesures sanguines\n",
-    "* Y est la mesure qualitative de la progression de la maladie sur une année\n",
+    "Dans cet ensemble de données, les colonnes sont les suivantes :  \n",
+    "* L'âge et le sexe sont explicites  \n",
+    "* L'IMC correspond à l'indice de masse corporelle  \n",
+    "* La PA est la pression artérielle moyenne  \n",
+    "* S1 à S6 sont différentes mesures sanguines  \n",
+    "* Y est la mesure qualitative de la progression de la maladie sur une année  \n",
     "\n",
-    "Étudions ce jeu de données en utilisant des méthodes de probabilité et de statistiques.\n",
+    "Étudions cet ensemble de données à l'aide des méthodes de probabilité et de statistiques.\n",
     "\n",
-    "### Tâche 1 : Calculer les valeurs moyennes et la variance pour tous les éléments\n"
+    "### Tâche 1 : Calculer les valeurs moyennes et la variance pour toutes les valeurs  \n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tâche 2 : Tracer des boxplots pour l'IMC, la TA et Y en fonction du sexe\n"
+    "### Tâche 2 : Tracer des boîtes à moustaches pour l'IMC, la TA et Y en fonction du genre\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -602,7 +602,7 @@
    "source": [
     "### Tâche 4 : Tester la corrélation entre différentes variables et la progression de la maladie (Y)\n",
     "\n",
-    "> **Indice** Une matrice de corrélation vous fournira les informations les plus utiles sur les valeurs qui sont dépendantes.\n"
+    "> **Conseil** Une matrice de corrélation vous fournira les informations les plus utiles pour identifier quelles valeurs sont dépendantes.\n"
    ],
    "metadata": {}
   },
@@ -845,7 +845,7 @@
    "cell_type": "markdown",
    "source": [
     "Conclusion :  \n",
-    "* La corrélation la plus forte avec Y est l'IMC et S5 (sucre dans le sang). Cela semble raisonnable.\n"
+    "* La corrélation la plus forte avec Y est l'IMC et S5 (sucre dans le sang). Cela semble logique.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:24:26+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:00:48+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "fr"
   }
diff --git a/translations/he/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/he/1-Introduction/04-stats-and-probability/assignment.ipynb
index 6afe33ba..d6b74855 100644
--- a/translations/he/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/he/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## מבוא להסתברות וסטטיסטיקה  \n",
-    "## משימה  \n",
+    "## מבוא להסתברות וסטטיסטיקה\n",
+    "## משימה\n",
     "\n",
-    "במשימה זו, נשתמש במאגר הנתונים של חולי סוכרת שנלקח [מכאן](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
+    "במשימה זו, נשתמש במאגר הנתונים של חולי סוכרת שנלקח [מכאן](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "במערך הנתונים הזה, העמודות הן כדלקמן:  \n",
-    "* גיל ומין מובנים מאליהם  \n",
-    "* BMI הוא מדד מסת הגוף  \n",
-    "* BP הוא לחץ דם ממוצע  \n",
-    "* S1 עד S6 הם מדידות דם שונות  \n",
-    "* Y הוא מדד איכותי להתקדמות המחלה לאורך שנה אחת  \n",
+    "בעזרת מערך הנתונים הזה, העמודות הן כדלקמן:\n",
+    "* גיל ומין מובנים מאליהם\n",
+    "* BMI הוא מדד מסת הגוף\n",
+    "* BP הוא לחץ דם ממוצע\n",
+    "* S1 עד S6 הם מדידות דם שונות\n",
+    "* Y הוא מדד איכותי להתקדמות המחלה לאורך שנה אחת\n",
     "\n",
-    "בואו נלמד את מערך הנתונים הזה באמצעות שיטות של הסתברות וסטטיסטיקה.\n",
+    "בואו נלמד את מערך הנתונים הזה באמצעות שיטות הסתברות וסטטיסטיקה.\n",
     "\n",
-    "### משימה 1: חישוב ערכי ממוצע ושונות לכל הערכים  \n"
+    "### משימה 1: חישוב ערכי ממוצע ושונות לכל הערכים\n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### משימה 2: שרטט תרשימי קופסה עבור BMI, BP ו-Y בהתאם למגדר\n"
+    "### משימה 2: שרטטו תרשימי קופסה עבור BMI, BP ו-Y בהתאם למגדר\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### משימה 3: מהי ההתפלגות של משתני גיל, מין, BMI ו-Y?\n"
+    "### משימה 3: מהי התפלגות הגיל, המין, ה-BMI והמשתנה Y?\n"
    ],
    "metadata": {}
   },
@@ -200,9 +200,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### משימה 4: בדיקת הקשר בין משתנים שונים להתקדמות המחלה (Y)\n",
+    "### משימה 4: בדיקת הקורלציה בין משתנים שונים להתקדמות המחלה (Y)\n",
     "\n",
-    "> **רמז** מטריצת מתאם תספק לך את המידע השימושי ביותר על אילו ערכים תלויים זה בזה.\n"
+    "> **רמז** מטריצת קורלציה תספק לך את המידע השימושי ביותר על אילו ערכים תלויים זה בזה.\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### משימה 5: בדיקת ההשערה שהדרגת התקדמות הסוכרת שונה בין גברים לנשים\n"
+    "### משימה 5: בדיקת ההשערה שהדרגה של התקדמות הסוכרת שונה בין גברים לנשים\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). בעוד שאנו שואפים לדיוק, יש להיות מודעים לכך שתרגומים אוטומטיים עשויים להכיל שגיאות או אי דיוקים. המסמך המקורי בשפתו המקורית צריך להיחשב כמקור סמכותי. עבור מידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי אדם. איננו נושאים באחריות לאי הבנות או לפרשנויות שגויות הנובעות משימוש בתרגום זה.\n"
+    "\n---\n\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). למרות שאנו שואפים לדיוק, יש לקחת בחשבון שתרגומים אוטומטיים עשויים להכיל שגיאות או אי דיוקים. המסמך המקורי בשפתו המקורית צריך להיחשב כמקור סמכותי. עבור מידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי אדם. איננו נושאים באחריות לאי הבנות או לפרשנויות שגויות הנובעות משימוש בתרגום זה.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:18:58+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:41:18+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "he"
   }
diff --git a/translations/he/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/he/1-Introduction/04-stats-and-probability/notebook.ipynb
index 9ad2d09d..e455b218 100644
--- a/translations/he/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/he/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# מבוא להסתברות וסטטיסטיקה  \n",
-    "במחברת זו נשחק עם כמה מהקונספטים שדיברנו עליהם בעבר. רבים מהקונספטים בהסתברות וסטטיסטיקה מיוצגים היטב בספריות מרכזיות לעיבוד נתונים בפייתון, כמו `numpy` ו-`pandas`.  \n"
+    "במחברת זו, נתנסה בכמה מהעקרונות שדיברנו עליהם בעבר. רבים מהעקרונות בהסתברות וסטטיסטיקה מיוצגים היטב בספריות מרכזיות לעיבוד נתונים בפייתון, כמו `numpy` ו-`pandas`.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## משתנים מקריים והתפלגויות  \n",
+    "## משתנים אקראיים והתפלגויות  \n",
     "נתחיל בדגימה של 30 ערכים מתוך התפלגות אחידה בין 0 ל-9. בנוסף, נחשב את הממוצע והשונות.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "כדי להעריך באופן חזותי כמה ערכים שונים יש במדגם, נוכל לשרטט את **ההיסטוגרמה**:\n"
+    "כדי להעריך באופן חזותי כמה ערכים שונים יש במדגם, ניתן לשרטט את **ההיסטוגרמה**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## ניתוח נתונים אמיתיים\n",
     "\n",
-    "הממוצע והשונות הם מאוד חשובים כשמנתחים נתונים מהעולם האמיתי. בואו נטען את הנתונים על שחקני בייסבול מתוך [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "ממוצע ושונות הם מאוד חשובים כאשר מנתחים נתונים מהעולם האמיתי. בואו נטען את הנתונים על שחקני בייסבול מתוך [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -261,24 +113,24 @@
    "source": [
     "אנו משתמשים כאן בחבילה שנקראת [**Pandas**](https://pandas.pydata.org/) לניתוח נתונים. נדבר יותר על Pandas ועל עבודה עם נתונים ב-Python בהמשך הקורס.\n",
     "\n",
-    "בואו נחשב ערכים ממוצעים לגיל, גובה ומשקל:\n"
+    "בואו נחשב ערכים ממוצעים עבור גיל, גובה ומשקל:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "בנוסף לממוצע, יש הגיון להסתכל על הערך החציוני והרבעונים. ניתן להמחיש אותם באמצעות **תרשים קופסה**:\n"
+    "בנוסף לממוצע, יש היגיון להסתכל על הערך החציוני והרבעונים. ניתן להמחיש אותם באמצעות **תרשים קופסה**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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o5qGbh24euhXDArlEHR0duUeoHZp56Oahm4duHrp56OahWzEskEt04cKF3CPUDs08dPPQzUM3D908dPPQrRgWyCVasGBB7hFqh2Yeunno5qGbh24eunnoVgwL5BItWrQo9wi1QzMP3Tx089DNQzcP3Tx0K2ZOXsUiIs5KOv6Of+L6WyLpXO4haoZmHrp56Oahm4duHrp56Nbaz6eU3jv5zjlZIKO1iNjT6qVEMDWaeejmoZuHbh66eejmoVsxPMQCAAAAaMICGQAAAGjCArlc/5R7gBqimYduHrp56Oahm4duHroVwGOQAQAAgCbsIAMAAABNWCADAAAATVggz5GIuCUinouIQxFxMCJ+NSLujYjvRsRQROyJiF/OPWeVRMSdjTZXbj+KiE9GxK0R8e8R8YPGx/bcs1bJNN2+0jj+XomIzRFxS+5Zq2Sqbk3//dMRkSJiScYxK2W6ZhHRHRGHI+L7EfF3mUetlGnOUa4JM4iITzWOqQMR0RsRi7gmzGyKblwTCuAxyHMkIp6RtCOl9LWIaJP0HknPSvpqSuk7EfG7kp5IKX0o55xVFRHXSzol6Vck/ZWk11NKX46Iz0hqTymtzzpgRU3qdqekwZTSTyLibyWJbq01d0spHY+IZZK+JmmlpDUpJV5cf5JJx9r7JPVI+r2U0mhE3J5Sei3rgBU1qdvT4powpYj4OUn/IekDKaUfR8Szkv5N0gfENWFK03T7obgmzBo7yHMgIm6S9BuSvi5JKaWxlNL/SUqSbmr8sZs1cbCitYckHU0pHZf0EUnPNO5/RtLv5xqqBq52Syk9n1L6SeP+70rqyDhX1TUfb5L0VUlPaOKcRWvNzf5C0pdTSqOSxOJ4Ws3duCbM7AZJPxURN2hio+mH4powG2/pxjWhGBbIc+N9ks5K+ueI+F5EfC0iFkv6pKSvRMQJSX8v6W8yzlh1fySpt/HPP5NSOi1JjY+3Z5uq+pq7NftTSd8peZY6udotIh6RdCql9HLekSqv+Vh7v6Rfj4hdEbE9Iu7POFfVNXf7pLgmTCmldEoTXV6VdFrScErpeXFNmNY03ZpxTZgBC+S5cYOkD0r6x5TSL0kakfQZTeyyfCqltEzSp9TYYca1Gg9JeUTSt3PPUidTdYuIHkk/kfQvOeaquuZuEfEeTTxU4HN5p6q2FsfaDZLaJT0g6a8lPRsRkWm8ymrRjWvCNBqPLf6IpF+Q9LOSFkfEn+Sdqvpm6sY1YXZYIM+Nk5JOppR2Nf79OU0smD8uqa9x37cl8YSM1n5H0r6U0pnGv5+JiKWS1PjIr29bm9xNEfFxSQ9L+uPEEw6m0tztFzVxUXk5Io5p4leQ+yLijozzVdHkY+2kpL404T8ljUviyY1vNbkb14Tp/bak/0kpnU0pXdZEqy5xTZjJVN24JhTAAnkOpJT+V9KJiLizcddDkv5LE4+d+s3Gfb8l6QcZxquDR3XtwwS2auJCosbHfy19onq4pltEfFjSekmPpJTeyDZV9V3tllLan1K6PaXUmVLq1MTC74ONcxpvmnyObtHE9zRFxPsltUniiY1vNbkb14TpvSrpgYh4T+M3Eg9JOiiuCTNp2Y1rQjG8isUciYh7NfEs+DZJ/y3pMUmrJP2DJn4deUnSX6aU9uaasYoav+I+Iel9KaXhxn23aeIVQJZr4sT/g5TS6/mmrJ4puh2RtFDS+cYf+25KaV2mESupVbdJ//2YpPt4FYs3TXGstUn6hqR7JY1J+nRKaTDbkBU0RbdfE9eEaUXEFyX9oSYeEvA9SX8m6afFNWFaU3T7vrgmzBoLZAAAAKAJD7EAAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmvw/tSpycIADqyoAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -409,19 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## התפלגות נורמלית\n",
     "\n",
-    "בואו ניצור מדגם מלאכותי של משקלים שמקיים התפלגות נורמלית עם אותו ממוצע ושונות כמו הנתונים האמיתיים שלנו:\n"
+    "בואו ניצור מדגם מלאכותי של משקלים שמתקיים בו התפלגות נורמלית עם אותו ממוצע ושונות כמו בנתונים האמיתיים שלנו:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "מכיוון שרוב הערכים בחיים האמיתיים מתפלגים נורמלית, אין להשתמש במחולל מספרים אקראיים אחידים כדי ליצור נתוני דגימה. הנה מה שקורה אם ננסה ליצור משקלים עם התפלגות אחידה (נוצר על ידי `np.random.rand`):\n"
+    "מכיוון שרוב הערכים בחיים האמיתיים מתפלגים נורמלית, אין להשתמש במחולל מספרים אקראיים אחיד ליצירת נתוני דגימה. הנה מה שקורה אם ננסה ליצור משקלים עם התפלגות אחידה (נוצר על ידי `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## רווחי סמך\n",
     "\n",
-    "בואו נחשב עכשיו רווחי סמך למשקלים ולגובה של שחקני בייסבול. נשתמש בקוד [מהדיון הזה ב-Stack Overflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "בואו נחשב עכשיו רווחי סמך למשקל ולגובה של שחקני בייסבול. נשתמש בקוד [מהדיון הזה ב-StackOverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,38 +545,38 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "בואו נבדוק את ההשערה שראשוני הבסיס גבוהים יותר משני הבסיס. הדרך הפשוטה ביותר לעשות זאת היא לבדוק את רווחי הביטחון:\n"
+    "בואו נבחן את ההשערה ששחקני בסיס ראשון גבוהים יותר משחקני בסיס שני. הדרך הפשוטה ביותר לעשות זאת היא לבדוק את רווחי הביטחון:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,22 +591,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "אנו יכולים לראות שהמרווחים אינם חופפים.\n",
+    "אנו יכולים לראות שהטווחים אינם חופפים.\n",
     "\n",
-    "דרך סטטיסטית מדויקת יותר להוכיח את ההשערה היא להשתמש ב-**Student t-test**:\n"
+    "דרך סטטיסטית מדויקת יותר להוכחת ההשערה היא להשתמש ב-**Student t-test**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -781,8 +622,8 @@
    "metadata": {},
    "source": [
     "שני הערכים שמחזירה הפונקציה `ttest_ind` הם:\n",
-    "* p-value ניתן להתייחס אליו כהסתברות ששתי ההתפלגויות בעלות ממוצע זהה. במקרה שלנו, הוא נמוך מאוד, מה שמעיד על כך שיש ראיות חזקות לכך ששחקני בסיס ראשון גבוהים יותר.\n",
-    "* t-value הוא הערך הביניים של ההפרש המנורמל של הממוצעים, שמשמש במבחן t, והוא מושווה לערך סף עבור רמת ביטחון נתונה.\n"
+    "* ערך ה-p ניתן להיחשב כהסתברות ששתי ההתפלגויות בעלות ממוצע זהה. במקרה שלנו, הוא נמוך מאוד, מה שמעיד על כך שיש ראיות חזקות לכך ששחקני בסיס ראשון גבוהים יותר.\n",
+    "* ערך ה-t הוא הערך הביניים של ההבדל המנורמל בממוצעים, שמשמש במבחן ה-t, והוא מושווה לערך סף עבור רמת ביטחון נתונה.\n"
    ]
   },
   {
@@ -791,24 +632,22 @@
    "source": [
     "## סימולציה של התפלגות נורמלית באמצעות משפט הגבול המרכזי\n",
     "\n",
-    "מחולל המספרים הפסאודו-אקראיים בפייתון נועד לספק לנו התפלגות אחידה. אם נרצה ליצור מחולל להתפלגות נורמלית, נוכל להשתמש במשפט הגבול המרכזי. כדי לקבל ערך שמתפלג נורמלית, פשוט נחשב ממוצע של מדגם שנוצר בהתפלגות אחידה.\n"
+    "מחולל המספרים הפסאודו-אקראי בפייתון מתוכנן לספק לנו התפלגות אחידה. אם נרצה ליצור מחולל להתפלגות נורמלית, נוכל להשתמש במשפט הגבול המרכזי. כדי לקבל ערך שמתפלג נורמלית, פשוט נחשב ממוצע של מדגם שנוצר בהתפלגות אחידה.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -830,19 +669,19 @@
    "source": [
     "## מתאם ותאגיד הבייסבול המרושע\n",
     "\n",
-    "מתאם מאפשר לנו למצוא קשרים בין רצפי נתונים. בדוגמה שלנו, נניח שיש תאגיד בייסבול מרושע שמשלם לשחקניו לפי גובהם - ככל שהשחקן גבוה יותר, כך הוא מקבל יותר כסף. נניח שיש משכורת בסיס של $1000, ובונוס נוסף שנע בין $0 ל-$100, בהתאם לגובה. ניקח את השחקנים האמיתיים מ-MLB, ונחשב את המשכורות הדמיוניות שלהם:\n"
+    "מתאם מאפשר לנו למצוא קשרים בין רצפי נתונים. בדוגמה שלנו, נניח שקיים תאגיד בייסבול מרושע שמשלם לשחקניו לפי הגובה שלהם - ככל שהשחקן גבוה יותר, כך הוא/היא מקבל/ת יותר כסף. נניח שיש שכר בסיס של $1000, ובונוס נוסף שנע בין $0 ל-$100, בהתאם לגובה. ניקח את השחקנים האמיתיים מ-MLB, ונחשב את המשכורות הדמיוניות שלהם:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +695,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "בואו עכשיו נחשב את השונות והמתאם של הרצפים הללו. `np.cov` ייתן לנו את מה שנקרא **מטריצת השונות**, שהיא הרחבה של שונות למספר משתנים. האלמנט $M_{ij}$ של מטריצת השונות $M$ הוא מתאם בין משתני הקלט $X_i$ ו-$X_j$, והערכים האלכסוניים $M_{ii}$ הם השונות של $X_{i}$. באופן דומה, `np.corrcoef` ייתן לנו את **מטריצת המתאם**.\n"
+    "בואו כעת נחשב את השונות המשותפת והמתאם של הרצפים הללו. `np.cov` ייתן לנו את מה שנקרא **מטריצת השונות המשותפת**, שהיא הרחבה של שונות משותפת למספר משתנים. האלמנט $M_{ij}$ של מטריצת השונות המשותפת $M$ הוא המתאם בין משתני הקלט $X_i$ ו-$X_j$, והערכים האלכסוניים $M_{ii}$ הם השונות של $X_{i}$. באופן דומה, `np.corrcoef` ייתן לנו את **מטריצת המתאם**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -886,24 +725,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "מתאם השווה ל-1 אומר שיש **קשר ליניארי** חזק בין שני משתנים. ניתן לראות את הקשר הליניארי באופן חזותי על ידי גרף של ערך אחד מול השני:\n"
+    "מתאם השווה ל-1 אומר שיש **קשר ליניארי חזק** בין שני משתנים. ניתן לראות את הקשר הליניארי באופן חזותי על ידי שרטוט ערך אחד מול השני:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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vcvwXk1ywrPbNJK9ab3MAAHC8+VPTAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAIPts24AYCU7d+09rHbv7otm0AkAzzauIANzZ6VwfKQ6AGwkARkAAAYCMgAADARkAAAYCMgAADAQkIG5s9pqFVaxAOB4sMwbMJeEYQBmxRVkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAg+2zbgCYrZ279h5Wu3f3RTPoBADmgyvI8Cy2Ujg+Uh0Ang0EZAAAGAjIAAAwEJABAGAgIAMAwEBAhmex1VarsIoFAM9mlnmDZzlhGACebs0ryFV1TVU9UlX7h9oLquoLVfWVaf/8qb6zqh6vqjum7beG97yqqvZV1d1V9RtVVZvzkQAA4NgdzRSLa5NcuKy2K8lN3X12kpum54fc093nTds7h/qHk1yR5OxpW/41AQBg5tYMyN19c5JHl5XfnOS66fF1SS4+0teoqhclOaW7v9TdneTja70HAABm4Vhv0nthdz+UJNP+9OG1l1TV7VX1x1X1mql2RpIDwzEHptqKquqKqlqsqsWDBw8eY4sAALB+G72KxUNJXtzdr0zyS0k+WVWnJFlpvnGv9kW6++ruXujuhR07dmxwiwAAsLpjDcgPT9MmDk2feCRJuvuJ7v6b6fFtSe5J8rIsXTE+c3j/mUkePNamAQBgsxxrQP5sksunx5cn+UySVNWOqto2PX5plm7G++o0DeOxqrpgWr3i7YfeAwAA82TNdZCrak+S1yU5raoOJHl/kt1Jrq+qdyS5L8kl0+GvTfJfq+rbSZ5M8s7uPnSD37uytCLGyUk+P20AADBXamlRifm1sLDQi4uLs24DAIAtpqpu6+6F5XV/ahoAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAYM11kIGNsXPX3sNq9+6+aAadAABH4goyHAcrheMj1QGA2RGQAQBgICADAMBAQAYAgIGADAAAAwEZjoPVVquwigUAzB/LvMFxIgwDwInBFWQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAACD7bNuADbazl17D6vdu/uiGXQCAJyIXEFmS1kpHB+pDgCwnIAMAAADARkAAAYCMgAADARkAAAYCMhsKautVmEVCwDgaFnmjS1HGAYAnglXkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABisGZCr6pqqeqSq9g+1F1TVF6rqK9P++VP9DVV1W1Xtm/Y/Mrzni1V1V1XdMW2nb85HAgCAY3c0V5CvTXLhstquJDd199lJbpqeJ8nXk/xYd5+b5PIkv73sfZd193nT9sixtw0AAJtjzYDc3TcneXRZ+c1JrpseX5fk4unY27v7wal+Z5LnVdVJG9MqAABsvmOdg/zC7n4oSab9StMlfjLJ7d39xFD72DS94n1VVat98aq6oqoWq2rx4MGDx9giAACs36bcpFdVr0jya0l+dihfNk29eM20vW2193f31d290N0LO3bs2IwWAQBgRccakB+uqhclybR/aj5xVZ2Z5NNJ3t7d9xyqd/cD0/6xJJ9M8upjbRoAADbLsQbkz2bpJrxM+88kSVWdmmRvkiu7+08OHVxV26vqtOnxc5K8Kcn+AADAnNm+1gFVtSfJ65KcVlUHkrw/ye4k11fVO5Lcl+SS6fCfT/IDSd5XVe+bav8uyTeT3DiF421J/jDJRzbwczAjO3ftPax27+6LZtAJAMDGqO6edQ9HtLCw0IuLi7NugxWsFI4PEZIBgHlXVbd198Lyur+kBwAAAwEZAAAGAjIAAAwEZAAAGAjIHLPVbsRzgx4AcCJbc5k3OBJhGADYalxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgsH3WDXB0fvC9n8vfP9lPPX/etspfffBHZ9gRAMDW5AryCWB5OE6Sv3+y84Pv/dyMOgIA2LoE5BPA8nC8Vh0AgGMnIAMAwEBABgCAgYB8AnjetlpXHQCAYycgnwD+6oM/elgYtooFAMDmsMzbCUIYBgA4PlxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMFgzIFfVNVX1SFXtH2ovqKovVNVXpv3zh9eurKq7q+quqnrjUH9VVe2bXvuNqqqN/zjP3FU37Mv3X/m57Ny1N99/5edy1Q37Zt0SAADH0dFcQb42yYXLaruS3NTdZye5aXqeqnp5krcmecX0nv9ZVdum93w4yRVJzp625V9z5q66YV8+cct9ebI7SfJkdz5xy31CMgDAs8iaAbm7b07y6LLym5NcNz2+LsnFQ/13u/uJ7v7rJHcneXVVvSjJKd39pe7uJB8f3jM39tx6/7rqAABsPcc6B/mF3f1Qkkz706f6GUnGNHlgqp0xPV5eX1FVXVFVi1W1ePDgwWNscf0OXTk+2joAAFvPRt+kt9K84j5CfUXdfXV3L3T3wo4dOzasubVsW2Va9Gp1AAC2nmMNyA9P0yYy7R+Z6geSnDUcd2aSB6f6mSvU58ql55+1rjoAAFvPsQbkzya5fHp8eZLPDPW3VtVJVfWSLN2M96fTNIzHquqCafWKtw/vmRsfuPjc/PQFL37qivG2qvz0BS/OBy4+d8adAQBwvFSvMb+2qvYkeV2S05I8nOT9SW5Icn2SFye5L8kl3f3odPx7k/xMkm8neXd3f36qL2RpRYyTk3w+yS/0Wt88ycLCQi8uLq7/kwEAwBFU1W3dvXBY/Sgy6kwJyAAAbIbVArK/pAcAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGFR3z7qHI6qqg0m+Nus+5shpSb4+6yZOEMZqfYzX+hivo2es1sd4rY/xOnrG6nD/ort3LC/OfUDm6apqsbsXZt3HicBYrY/xWh/jdfSM1foYr/UxXkfPWB09UywAAGAgIAMAwEBAPvFcPesGTiDGan2M1/oYr6NnrNbHeK2P8Tp6xuoomYMMAAADV5ABAGAgIAMAwEBAnmNVdWpV/X5V/VVVfbmqfriqzquqW6rqjqparKpXz7rPeVBV50xjcmj7f1X17qp6QVV9oaq+Mu2fP+te58ERxutD08/bX1TVp6vq1Fn3OmurjdXw+i9XVVfVaTNsc24cabyq6heq6q6qurOq/tuMW50LR/i36Fy/gqr6xennZ39V7amq5znPr26V8XKePwrmIM+xqrouyf/u7o9W1XOTfHeS65P8end/vqp+NMmvdPfrZtnnvKmqbUkeSHJ+kp9L8mh3766qXUme392/OtMG58yy8TonyR9197er6teSxHj9o3GsuvtrVXVWko8m+cEkr+puC/APlv1svTTJe5Nc1N1PVNXp3f3ITBucM8vG6yNxrn+aqjojyf9J8vLufryqrk/yuSQvj/P8YY4wXg/GeX5NriDPqao6Jclrk/yvJOnub3X3N5J0klOmw/5pln7QebrXJ7mnu7+W5M1Jrpvq1yW5eFZNzbGnxqu7/6C7vz3Vb0ly5gz7mkfjz1aS/HqSX8nSv0sON47Xu5Ls7u4nkkQ4XtE4Xs71K9ue5OSq2p6li0YPxnn+SA4bL+f5oyMgz6+XJjmY5GNVdXtVfbSqvifJu5N8qKruT/Lfk1w5wx7n1VuT7Jkev7C7H0qSaX/6zLqaX+N4jX4myeePcy/z7qmxqqofT/JAd//5bFuaa+PP1suSvKaqbq2qP66qfznDvubVOF7vjnP903T3A1kai/uSPJTkb7v7D+I8v6IjjNfIeX4VAvL82p7kh5J8uLtfmeSbSXZl6SrML3b3WUl+MdMVZpZMU1F+PMnvzbqXE8Fq41VV703y7SS/M4u+5tE4VlX13VmaLvCfZ9vV/FrhZ2t7kucnuSDJe5JcX1U1o/bmzgrj5Vy/zDS3+M1JXpLk+5J8T1X99Gy7ml9rjZfz/JEJyPPrQJID3X3r9Pz3sxSYL0/yqan2e0ncuPF0/z7Jn3X3w9Pzh6vqRUky7f1a9+mWj1eq6vIkb0pyWbtJYTSO1fdn6T86f15V92bpV5R/VlX/fIb9zZvlP1sHknyql/xpku8kcWPjP1o+Xs71h/u3Sf66uw929z9kaXz+VZznV7PaeDnPHwUBeU519/9Ncn9VnTOVXp/kL7M03+rfTLUfSfKVGbQ3zy7N06cLfDZL/6HJtP/Mce9ovj1tvKrqwiS/muTHu/vvZtbVfHpqrLp7X3ef3t07u3tnlsLfD03/blmy/N/iDVk6Z6WqXpbkuUnc1PiPlo+Xc/3h7ktyQVV99/Tbh9cn+XKc51ez4ng5zx8dq1jMsao6L0t3yD83yVeT/Ickr0jyP7L068q/T/Kfuvu2WfU4T6Zfe9+f5KXd/bdT7Z9laeWPF2fpZHFJdz86uy7nxyrjdXeSk5L8zXTYLd39zhm1ODdWGqtlr9+bZMEqFktW+dl6bpJrkpyX5FtJfrm7/2hmTc6RVcbrX8e5/jBV9V+S/FSWpgbcnuQ/JvkncZ5f0SrjdWec59ckIAMAwMAUCwAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAb/H2leqRtP0LMZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -918,19 +755,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "בואו נראה מה קורה אם הקשר אינו ליניארי. נניח שהחברה שלנו החליטה להסתיר את התלות הליניארית הברורה בין גבהים למשכורות, והכניסה אי-ליניאריות כלשהי לנוסחה, כמו `sin`:\n"
+    "בואו נראה מה קורה אם הקשר אינו ליניארי. נניח שהתאגיד שלנו החליט להסתיר את התלות הליניארית הברורה בין גבהים למשכורות, והכניס אי-ליניאריות כלשהי לנוסחה, כמו `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -943,19 +780,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "במקרה זה, הקורלציה מעט קטנה יותר, אך היא עדיין די גבוהה. עכשיו, כדי להפוך את הקשר לפחות ברור, ייתכן שנרצה להוסיף קצת אקראיות נוספת על ידי הוספת משתנה אקראי למשכורת. בואו נראה מה קורה:\n"
+    "במקרה זה, הקורלציה קטנה מעט, אך היא עדיין די גבוהה. כעת, כדי להפוך את הקשר לפחות ברור, ייתכן שנרצה להוסיף מעט אקראיות נוספת על ידי הוספת משתנה אקראי למשכורת. בואו נראה מה קורה:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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A0CEgAwBAh4AMAAAdAjIAAHQIyAAA0GEfZNji3vCbD+XTX3zq9P2Xv/CKvP9nf6LHEQFAv6wgwxa2NBwnyae/+FTe8JsP9TQiAOifgAxb2NJwfK46AGwF5wzIVfWeqnqiqj7fqV1RVR+vqkcGt5d3Hvuxqnqoqh6uqiNV9QOD+o2D+49W1Turqi7MPwkAAFZvJSvI703yqiW1O5I82Fq7LsmDg/upqouT3JfkH7bWrk/yiiQnB695V5Lbk1w3+LP0cwIAQO/OGZBba59KsvT3ra9Jcu/g43uTzAw+/s+S/GFr7XOD1/55a22hqq5O8uzW2kOttZbkfZ3XAADA2FhtD/LzWmuPJ8ng9qpB/YeTtKo6UFW/X1W/PKhPJznWef2xQW1ZVXV7VR2qqkNPPvnkKocIAACjW+tt3i5O8teT/LUk307yYFV9NslfLPPcNuyTtNbuSXJPkuzevXvo8wAAYK2tdgX5a4O2iQxunxjUjyX5t621P2utfTvJR5P8+KB+Tef11yQ5vsq/GwAALpjVBuQPJ7lt8PFtST40+PhAkh+rqksHF+z9zSR/NGjD+GZV3TTYveJNndcAAMDYWMk2b/cneSjJzqo6VlVvTnJ3kpur6pEkNw/up7X29STvSPLvk/xBkt9vrX1k8KnekuTdSR5N8sUkH1vbfwoAAJy/c/Ygt9ZuHfLQK4c8/74sbvW2tH4oyYtGGh0AAKwzJ+kBAECHgAywQtddddlIdQA2JgEZYIU+/ouveEYYvu6qy/LxX3xFPwMC4IJY632QATY1YRhg87OCDAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHRf3PQDYKm5+xyfzyBPfOn3/uqsuy8d/8RX9DQgAWJYVZFgHS8NxkjzyxLdy8zs+2c+AAIChBGRYB0vD8bnqAEB/BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkIGxc8lEjVQHgLUkIANj59df++IsjcI1qAPAheYkPWDszOyaTpLsP3A0x0/MZ/vUZPbu2Xm6DgAXkoAMjKWZXdMCMQC90GIBAAAdAjIAAHQIyLCFDdsUwmYRAGxlAjJsYQtttDoAbAUCMgAAdAjIAADQISDDOnAyHABsHAIyrIPvDmnqHVYHAPojIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADOtg25DvtGF1AKA/fjzDOnh6yG5uw+oAQH8EZFgHw7Y7tg0yAIwfARkAADoEZAAA6Li47wHAWrv5HZ/MI0986/T96666LB//xVf0NyAAYEOxgsymsjQcJ8kjT3wrN7/jk/0MCADYcARkNpWl4fhcdQCApQRkAADoOGdArqr3VNUTVfX5Tu2Kqvp4VT0yuL18yWuuraq/rKpf6tRurKojVfVoVb2zqmpt/ykAAHD+VrKC/N4kr1pSuyPJg62165I8OLjf9RtJPrak9q4ktye5bvBn6ecE1tmlQ47yG1YHgK3gnD8FW2ufSvLUkvJrktw7+PjeJDOnHqiqmSRfSvJwp3Z1kme31h5qrbUk7+u+BujHP77lx3LRkt/lXFSLdQDYqla7TPS81trjSTK4vSpJquqyJG9N8qtLnj+d5Fjn/rFBDejRzK7pvONnXpLpqclUkumpybzjZ16SmV2+PQHYutZ6H+RfTfIbrbW/XNJivFy/8dBDdqvq9iy2Y+Taa69d0wECZ5rZNS0QA0DHagPy16rq6tba44P2iScG9ZcleW1V/XqSqSRPV9VfJfntJNd0Xn9NkuPDPnlr7Z4k9yTJ7t27hwZpAABYa6ttsfhwktsGH9+W5ENJ0lr7G621Ha21HUn+5yT/uLX2zwdtGN+sqpsGu1e86dRrAABgnKxkm7f7kzyUZGdVHauqNye5O8nNVfVIkpsH98/lLUneneTRJF/MM3e5gPM2PTU5Uh0AYKlztli01m4d8tArz/G6ty25fyjJi1Y8MliFv/UjV+a+g48tWwcAWAmbnbKpfOQPHx+pDgCwlIDMpvL1b58cqQ4AsJSADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeAzKYyNbltpDoAwFICMpvK008/PVIdAGApAZlN5S++szBSHQBgKQEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOi4uO8BsLHNHp7L/gNHc/zEfLZPTWbvnp2Z2TXd23guv3Rbvv7tk8vWAQBWwgoyqzZ7eC77HjiSuRPzaUnmTsxn3wNHMnt4rrcx/cpPXZ9tE3VGbdtE5Vd+6vqeRgQAbDQCMqu2/8DRzJ888wCO+ZML2X/gaE8jSmZ2TWf/a1+c6anJVJLpqcnsf+2Le13VTpLLLpkYqQ4A9EdAZtWOn5gfqb5eDn3lqfzpN/4qLcmffuOvcugrT/U6niT5tb93QyYuOnNle+Kiyq/9vRt6GhEAMIyAzKptn5ocqb4e7pw9kvsOPpaF1pIkC63lvoOP5c7ZI72NKVlc2f5nrztzZfufva7/lW0A4JlcpMeq7d2zM3s/+LmcXGina9smKnv37OxtTO8/+NjQ+l0z/a7WzuyaFogBYAOwgsz5aee4v86G/fU9DwsA2EAEZFZt/4GjOfn0mdHz5NOt14v0AADOl4DMqo3jRXqXLNni7Vx1AIClBGRWbRwv0ts2sfyX9LA6AMBSUgOrtnfPzmUP5ejzIr1vfXdhpDoAwFICMudnzC7SAwA4XwIyq+YiPQBgMxKQWbVxvEivhlyLN6wOALCUgMyqjeNFem1Ii8ewOgDAUgIyq7Z3z85Mbps4oza5baLXi/QAAM6XgMyqzeyazk/fOJ2JQf/CRFV++kbHKQMAG5uAzKrNHp7Lb392LguD/oWF1vLbn53L7OG5nkcGALB6AjKrtv/A0cyfPHN/4fmTC3axAAA2NAGZVRvHXSwAAM6XgMyqTV26baQ6AMBGICCzarZUAwA2IwGZVfvG/MmR6gAAG4GAzKqN40EhAADnS0Bm1RwUAgBsRhf3PQA2rlMHguw/cDTHT8xn+9Rk9u7Z6aAQAGBDE5A5LzO7nJwHAGwuWizYVGrEOgDAUgIym8qwHebsPAcArJSADAAAHQIym8rU5JDT/YbUAQCWEpDZVN726uuz7aIzO463XVR526uv72lEAMBGYxcLNhVbzwEA5+ucAbmq3pPkJ5M80Vp70aB2RZL/J8mOJF9O8jOtta9X1c1J7k5ySZLvJtnbWvvE4DU3JnlvkskkH03yP7TWXDvFmrP1HABwPlbSYvHeJK9aUrsjyYOtteuSPDi4nyR/luSnWms3JLktyf/Vec27ktye5LrBn6Wfkw1o9vBcXn73J/KCOz6Sl9/9icwenut7SAAA5+WcK8ittU9V1Y4l5dckecXg43uTfDLJW1trhzvPeTjJD1TVs5JckeTZrbWHkqSq3pdkJsnHzmPsW8rs4bmxaxuYPTyXfQ8cyfzJhSTJ3In57HvgSJL0PjYAgNVa7UV6z2utPZ4kg9urlnnOTyc53Fr7TpLpJMc6jx0b1JZVVbdX1aGqOvTkk0+ucoibx6kgOndiPi3fD6J9r9buP3D0dDg+Zf7kQvYfONrTiAAAzt8F2cWiqq5P8k+S/INTpWWeNrT/uLV2T2ttd2tt95VXXnkhhrihjGsQnTsxP1IdAGAjWG1A/lpVXZ0kg9snTj1QVdck+Z0kb2qtfXFQPpbkms7rr0lyfJV/95ZzfEjgHFZfLzXk/OZhdQCAjWC1AfnDWbwIL4PbDyVJVU0l+UiSfa21T5968qAN45tVdVNVVZI3nXoN57Z9anKk+noZtgeJvUkAgI3snAG5qu5P8lCSnVV1rKrenMWt3G6uqkeSnNraLUn+uyT/cZL/sar+YPDnVH/yW5K8O8mjSb4YF+it2N49O7NtYsnhFxOVvXt29jQiAIDNayW7WNw65KFXLvPcu5LcNeTzHEryopFGx/ctXZW1SgsAcEE4anoD2H/gaE4+fWYiPvl06/0iPQCAzUhA3gDG9SI9AIDNSEDeAMb1Ir3LL902Uh0AYCMQkDeAvXt2ZnLbxBm1yW0TvV+k93d/7OqR6gAAG8E5L9Kjf6eObR63o6Z/9wvLn3I4rA4AsBEIyBvEzK7p3gPxUnqjAYDNSIsFq3bpJRMj1QEANgIBmVX71ncXRqoDAGwEAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyKza5Lblv3yG1QEANgJJhlX76RuvGakOALARCMismqOmAYDNyFHTG8Ts4bnsP3A0x0/MZ/vUZPbu2dn70dOOmgYANiMryBvA7OG57HvgSOZOzKclmTsxn30PHMns4blex7V9anKkOgDARiAgbwD7DxzN/Mkzj2+eP7mQ/QeO9jSiRXv37MzktokzapPbJrJ3z86eRgQAcP60WGwA49rKcKrFY9xaPwAAzoeAvAFsn5rM3DJheBxaGWZ2TQvEAMCmosViA9DKAACwfqwgbwBaGQAA1o+AvEFoZQAAWB9aLAAAoENABgCADgEZAAA6BGQAAOhwkd4GMXt4zi4WAADrQEDeAGYPz2XfA0dOHzc9d2I++x44kiRCMgDAGtNisQHsP3D0dDg+Zf7kQvYfONrTiAAANi8BeQM4vswx02erAwCwegLyBrB9anKkOgAAqycgbwB79+zM5LaJM2qT2yayd8/OnkYEALB5uUhvAzh1IZ5dLAAALjwBeYOY2TUtEAMArAMtFgAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0OEkvQ1i9vCco6YBANaBgLzEOAbR2cNz2ffAkcyfXEiSzJ2Yz74HjiRJ72MDANhstFh0nAqicyfm0/L9IDp7eK7Xce0/cPR0OD5l/uRC9h842tOIAAA2LwG5Y1yD6PET8yPVAQBYPQG5Y1yD6PapyZHqAACsnoDcMa5BdMdzlv/7h9UBAFi9cwbkqnpPVT1RVZ/v1K6oqo9X1SOD28s7j+2rqker6mhV7enUb6yqI4PH3llVtfb/nPOzd8/OTG6bOKM2uW0ie/fs7GlEiw5+6esj1QEAWL2VrCC/N8mrltTuSPJga+26JA8O7qeqfjTJ65NcP3jN/15VpxLnu5LcnuS6wZ+ln7N3M7um8/Zbbsj01GQqyfTUZN5+yw297xSx0NpIdQAAVu+c27y11j5VVTuWlF+T5BWDj+9N8skkbx3U/2Vr7TtJ/qSqHk3y0qr6cpJnt9YeSpKqel+SmSQfO+9/wRqb2TXdeyBeaqJq2TA8MX6L8AAAG95qe5Cf11p7PEkGt1cN6tNJvtp53rFBbXrw8dI6K3Dry54/Uh0AgNVb64v0llvSbGepL/9Jqm6vqkNVdejJJ59cs8FtVHfN3JA33nTt6RXjiaq88aZrc9fMDT2PDABg81ntSXpfq6qrW2uPV9XVSZ4Y1I8l6S5rXpPk+KB+zTL1ZbXW7klyT5Ls3r1bo20WQ7JADABw4a12BfnDSW4bfHxbkg916q+vqmdV1QuyeDHe7w3aML5ZVTcNdq94U+c1AAAwNs65glxV92fxgrznVtWxJL+S5O4kH6iqNyd5LMnrkqS19nBVfSDJHyX5XpKfa62dOpruLVncEWMyixfnjd0FegAAUG3MtwrbvXt3O3ToUN/DAABgk6mqz7bWdi+tO0kPAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6Lu57AONm9vBc9h84muMn5rN9ajJ79+zMzK7pvocFAMA6EZA7Zg/PZd8DRzJ/ciFJMndiPvseOJIkQjIAwBahxaJj/4Gjp8PxKfMnF7L/wNGeRgQAwHoTkDuOn5gfqQ4AwOYjIHdsn5ocqQ4AwOYjIHfs3bMzk9smzqhNbpvI3j07exoRAADrzUV6HacuxLOLBQDA1iUgLzGza1ogBgDYwrRYAABAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAR7XW+h7DWVXVk0m+0vc4xshzk/xZ34PYIMzVaMzXaMzXypmr0Ziv0ZivlTNXz/QftdauXFoc+4DMmarqUGttd9/j2AjM1WjM12jM18qZq9GYr9GYr5UzVyunxQIAADoEZAAA6BCQN557+h7ABmKuRmO+RmO+Vs5cjcZ8jcZ8rZy5WiE9yAAA0GEFGQAAOgRkAADoEJDHWFVNVdUHq+oLVfXHVfUTVfWSqjpYVX9QVYeq6qV9j3McVNXOwZyc+vMXVfXzVXVFVX28qh4Z3F7e91jHwVnma//g6+0Pq+p3qmqq77H2bdhcdR7/papqVfXcHoc5Ns42X1X131fV0ap6uKp+veehjoWzfC96r19GVf3C4Ovn81V1f1X9gPf54YbMl/f5FdCDPMaq6t4k/6619u6quiTJpUk+kOQ3Wmsfq6r/Iskvt9Ze0ec4x01VTSSZS/KyJD+X5KnW2t1VdUeSy1trb+11gGNmyXztTPKJ1tr3quqfJIn5+r7uXLXWvlJVz0/y7iQ/kuTG1poN+DuWfG39UJJ/lOTvtta+U1VXtdae6HWAY2bJfP1mvNefoaqmk/x/SX60tTZfVR9I8tEkPxrv889wlvk6Hu/z52QFeUxV1bOT/KdJ/s8kaa19t7V2IklL8uzB0/6DLH6hc6ZXJvlia+0rSV6T5N5B/d4kM30Naoydnq/W2r9urX1vUD+Y5JoexzWOul9bSfIbSX45i9+XPFN3vt6S5O7W2neSRDheVne+vNcv7+Ikk1V1cRYXjY7H+/zZPGO+vM+vjIA8vn4oyZNJ/kVVHa6qd1fVZUl+Psn+qvpqkn+aZF+PYxxXr09y/+Dj57XWHk+Swe1VvY1qfHXnq+u/TvKxdR7LuDs9V1X16iRzrbXP9Tuksdb92vrhJH+jqj5TVf+2qv5aj+MaV935+vl4rz9Da20ui3PxWJLHk3yjtfav431+WWeZry7v80MIyOPr4iQ/nuRdrbVdSb6V5I4srsL8Qmvt+Ul+IYMVZhYNWlFeneS3+h7LRjBsvqrqHyX5XpL39zGucdSdq6q6NIvtAv9Tv6MaX8t8bV2c5PIkNyXZm+QDVVU9DW/sLDNf3uuXGPQWvybJC5JsT3JZVb2x31GNr3PNl/f5sxOQx9exJMdaa58Z3P9gFgPzbUkeGNR+K4kLN870nyf5/dba1wb3v1ZVVyfJ4Navdc+0dL5SVbcl+ckkb2guUujqztULs/hD53NV9eUs/ory96vqP+xxfONm6dfWsSQPtEW/l+TpJC5s/L6l8+W9/pn+TpI/aa092Vo7mcX5+U/ifX6YYfPlfX4FBOQx1Vr70yRfraqdg9Irk/xRFvut/uag9reTPNLD8MbZrTmzXeDDWfxBk8Hth9Z9ROPtjPmqqlcleWuSV7fWvt3bqMbT6blqrR1prV3VWtvRWtuRxfD344PvWxYt/V6czeJ7Vqrqh5NcksRFjd+3dL681z/TY0luqqpLB799eGWSP473+WGWnS/v8ytjF4sxVlUvyeIV8pck+VKS/yrJ9Un+lyz+uvKvkvy3rbXP9jXGcTL4tfdXk/xQa+0bg9pzsrjzx7VZfLN4XWvtqf5GOT6GzNejSZ6V5M8HTzvYWvuHPQ1xbCw3V0se/3KS3XaxWDTka+uSJO9J8pIk303yS621T/Q2yDEyZL7+erzXP0NV/WqSv5/F1oDDSf6bJD8Y7/PLGjJfD8f7/DkJyAAA0KHFAgAAOgRkAADoEJABAKBDQAYAgA4BGQAAOgRkAADoEJABAKDj/wceBaX6Xh706QAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1000,17 +835,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,16 +858,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "למרבה הצער, לא קיבלנו שום תוצאות - רק ערכים מוזרים מסוג `nan`. הסיבה לכך היא שחלק מהערכים בסדרה שלנו אינם מוגדרים, ומיוצגים כ-`nan`, מה שגורם לתוצאה של הפעולה להיות לא מוגדרת גם כן. אם נסתכל על המטריצה, נוכל לראות שעמודת `Weight` היא הבעייתית, מכיוון שחישוב הקורלציה העצמית בין ערכי `Height` בוצע.\n",
+    "למרבה הצער, לא קיבלנו תוצאות - רק ערכים מוזרים מסוג `nan`. הסיבה לכך היא שחלק מהערכים בסדרה שלנו אינם מוגדרים, ומיוצגים כ-`nan`, מה שגורם לכך שגם תוצאת החישוב אינה מוגדרת. אם נסתכל על המטריצה, נוכל לראות שעמודת `Weight` היא הבעייתית, מכיוון שהחישוב של המתאם העצמי בין ערכי `Height` בוצע.\n",
     "\n",
-    "> הדוגמה הזו מדגישה את החשיבות של **הכנת נתונים** ו**ניקוי נתונים**. ללא נתונים תקינים, לא נוכל לחשב דבר.\n",
+    "> דוגמה זו מדגישה את החשיבות של **הכנת נתונים** ו**ניקוי נתונים**. ללא נתונים תקינים, לא נוכל לחשב דבר.\n",
     "\n",
-    "בואו נשתמש בשיטת `fillna` כדי למלא את הערכים החסרים, ונחשב את הקורלציה:\n"
+    "בואו נשתמש בשיטת `fillna` כדי למלא את הערכים החסרים, ונחשב את המתאם:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,32 +890,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "אכן יש מתאם, אך לא כזה חזק כמו בדוגמה המלאכותית שלנו. למעשה, אם נסתכל על תרשים הפיזור של ערך אחד מול השני, הקשר יהיה הרבה פחות ברור:\n"
+    "אכן ישנה קורלציה, אך לא חזקה כל כך כמו בדוגמה המלאכותית שלנו. למעשה, אם נסתכל על תרשים הפיזור של ערך אחד מול השני, הקשר יהיה הרבה פחות ברור:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1091,14 +924,14 @@
    "source": [
     "## סיכום\n",
     "\n",
-    "במחברת זו למדנו כיצד לבצע פעולות בסיסיות על נתונים כדי לחשב פונקציות סטטיסטיות. כעת אנו יודעים כיצד להשתמש במערך כלים מתמטי וסטטיסטי כדי להוכיח השערות מסוימות, וכיצד לחשב רווחי סמך עבור משתנים שרירותיים בהתבסס על מדגם נתונים.\n"
+    "במחברת זו למדנו כיצד לבצע פעולות בסיסיות על נתונים כדי לחשב פונקציות סטטיסטיות. כעת אנו יודעים כיצד להשתמש בכלים מתמטיים וסטטיסטיים כדי להוכיח השערות מסוימות, וכיצד לחשב רווחי סמך עבור משתנים שונים בהתבסס על מדגם נתונים.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). בעוד שאנו שואפים לדיוק, יש להיות מודעים לכך שתרגומים אוטומטיים עשויים להכיל שגיאות או אי דיוקים. המסמך המקורי בשפתו המקורית צריך להיחשב כמקור הסמכותי. עבור מידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי אדם. איננו נושאים באחריות לאי הבנות או לפרשנויות שגויות הנובעות משימוש בתרגום זה.\n"
+    "\n---\n\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). למרות שאנו שואפים לדיוק, יש לקחת בחשבון שתרגומים אוטומטיים עשויים להכיל שגיאות או אי-דיוקים. המסמך המקורי בשפתו המקורית נחשב למקור הסמכותי. למידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי בני אדם. איננו נושאים באחריות לכל אי-הבנה או פרשנות שגויה הנובעת משימוש בתרגום זה.\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:07:08+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:41:04+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "he"
   }
diff --git a/translations/he/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/he/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 66b1e47e..6e1a422c 100644
--- a/translations/he/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/he/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## מבוא להסתברות וסטטיסטיקה  \n",
-    "## משימה  \n",
+    "## מבוא להסתברות וסטטיסטיקה\n",
+    "## משימה\n",
     "\n",
-    "במשימה זו, נשתמש במאגר הנתונים של חולי סוכרת שנלקח [מכאן](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
+    "במשימה זו, נשתמש במאגר הנתונים של חולי סוכרת שנלקח [מכאן](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,8 +150,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "במערך נתונים זה, העמודות הן כדלקמן:  \n",
-    "* גיל ומין מובנים מאליהם  \n",
+    "בעבור מערך הנתונים הזה, העמודות הן כדלקמן:  \n",
+    "* גיל ומין הם מובנים מאליהם  \n",
     "* BMI הוא מדד מסת הגוף  \n",
     "* BP הוא לחץ דם ממוצע  \n",
     "* S1 עד S6 הם מדידות דם שונות  \n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### משימה 3: מהי ההתפלגות של משתני גיל, מין, BMI ו-Y?\n"
+    "### משימה 3: מהי התפלגות הגיל, המין, ה-BMI והמשתנה Y?\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -602,9 +602,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### משימה 4: בדיקת הקשר בין משתנים שונים להתקדמות המחלה (Y)\n",
+    "### משימה 4: בדיקת הקורלציה בין משתנים שונים להתקדמות המחלה (Y)\n",
     "\n",
-    "> **רמז** מטריצת מתאם תספק את המידע השימושי ביותר על אילו ערכים תלויים.\n"
+    "> **רמז** מטריצת קורלציה תספק לך את המידע השימושי ביותר על אילו ערכים תלויים זה בזה.\n"
    ],
    "metadata": {}
   },
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "סיכום:  \n",
-    "* הקשר החזק ביותר של Y הוא עם BMI ו-S5 (רמת סוכר בדם). זה נשמע הגיוני.\n"
+    "* הקשר החזק ביותר של Y הוא עם BMI ו-S5 (רמת סוכר בדם). זה נשמע הגיוני.  \n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -879,7 +879,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### משימה 5: בדוק את ההשערה שהדרגת התקדמות הסוכרת שונה בין גברים לנשים\n"
+    "### משימה 5: בדיקת ההשערה שהדרגה של התקדמות הסוכרת שונה בין גברים לנשים\n"
    ],
    "metadata": {}
   },
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -920,7 +920,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). בעוד שאנו שואפים לדיוק, יש להיות מודעים לכך שתרגומים אוטומטיים עשויים להכיל שגיאות או אי דיוקים. המסמך המקורי בשפתו המקורית צריך להיחשב כמקור הסמכותי. עבור מידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי אדם. איננו נושאים באחריות לאי הבנות או לפרשנויות שגויות הנובעות משימוש בתרגום זה.\n"
+    "\n---\n\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). למרות שאנו שואפים לדיוק, יש לקחת בחשבון שתרגומים אוטומטיים עשויים להכיל שגיאות או אי-דיוקים. המסמך המקורי בשפתו המקורית נחשב למקור הסמכותי. למידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי בני אדם. איננו נושאים באחריות לכל אי-הבנה או פרשנות שגויה הנובעת משימוש בתרגום זה.  \n"
    ]
   }
  ],
@@ -946,8 +946,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:24:44+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:41:36+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "he"
   }
diff --git a/translations/hi/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/hi/1-Introduction/04-stats-and-probability/assignment.ipynb
index 334bb090..bc192d60 100644
--- a/translations/hi/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/hi/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## संभावना और सांख्यिकी का परिचय\n",
-    "## असाइनमेंट\n",
+    "## संभावना और सांख्यिकी का परिचय  \n",
+    "## असाइनमेंट  \n",
     "\n",
-    "इस असाइनमेंट में, हम मधुमेह रोगियों के डेटा सेट का उपयोग करेंगे, जो [यहां से लिया गया है](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।\n"
+    "इस असाइनमेंट में, हम मधुमेह रोगियों के डेटा सेट का उपयोग करेंगे, जो [यहां से लिया गया है](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।  \n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### कार्य 2: लिंग के आधार पर BMI, BP और Y के लिए बॉक्सप्लॉट बनाएं\n"
+    "### कार्य 2: लिंग के अनुसार BMI, BP और Y के लिए बॉक्सप्लॉट बनाएं\n"
    ],
    "metadata": {}
   },
@@ -198,9 +198,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### कार्य 4: विभिन्न चर और बीमारी की प्रगति (Y) के बीच सहसंबंध का परीक्षण करें\n",
+    "### कार्य 4: विभिन्न चर और रोग की प्रगति (Y) के बीच सहसंबंध का परीक्षण करें\n",
     "\n",
-    "> **संकेत** सहसंबंध मैट्रिक्स आपको यह समझने में सबसे अधिक मदद करेगा कि कौन से मान एक-दूसरे पर निर्भर हैं।\n"
+    "> **संकेत** सहसंबंध मैट्रिक्स आपको यह समझने में सबसे अधिक सहायक होगा कि कौन से मान एक-दूसरे पर निर्भर हैं।\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता सुनिश्चित करने का प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को प्रामाणिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं।\n"
+    "\n---\n\n**अस्वीकरण**:  \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता के लिए प्रयासरत हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को आधिकारिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं।\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:19:09+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:18:25+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "hi"
   }
diff --git a/translations/hi/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/hi/1-Introduction/04-stats-and-probability/notebook.ipynb
index 645eb0e3..502604c3 100644
--- a/translations/hi/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/hi/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# संभावना और सांख्यिकी का परिचय\n",
-    "इस नोटबुक में, हम उन कुछ अवधारणाओं के साथ प्रयोग करेंगे जिन पर हमने पहले चर्चा की है। संभावना और सांख्यिकी की कई अवधारणाएं डेटा प्रोसेसिंग के लिए Python की प्रमुख लाइब्रेरीज़, जैसे `numpy` और `pandas`, में अच्छी तरह से प्रस्तुत की गई हैं।\n"
+    "इस नोटबुक में, हम उन कुछ अवधारणाओं के साथ प्रयोग करेंगे जिन पर हमने पहले चर्चा की है। संभावना और सांख्यिकी से जुड़ी कई अवधारणाएं Python में डेटा प्रोसेसिंग के लिए प्रमुख लाइब्रेरीज़, जैसे `numpy` और `pandas`, में अच्छी तरह से प्रस्तुत की गई हैं।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## रैंडम वेरिएबल्स और वितरण\n",
-    "चलो 0 से 9 तक के यूनिफॉर्म वितरण से 30 मानों का एक सैंपल लेते हैं। हम इसका औसत और विचरण भी गणना करेंगे।\n"
+    "## रैंडम वेरिएबल्स और डिस्ट्रीब्यूशन्स  \n",
+    "आइए 0 से 9 के बीच की एक समान डिस्ट्रीब्यूशन से 30 मानों का एक सैंपल निकालते हैं। साथ ही, हम इसका औसत (mean) और वैरिएंस (variance) भी निकालेंगे।  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "नमूने में कितने विभिन्न मान हैं इसका अनुमान लगाने के लिए, हम **हिस्टोग्राम** बना सकते हैं:\n"
+    "नमूने में कितने विभिन्न मान हैं, इसका दृष्टिगत अनुमान लगाने के लिए, हम **हिस्टोग्राम** बना सकते हैं:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -84,201 +82,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## वास्तविक डेटा का विश्लेषण\n",
+    "## वास्तविक डेटा का विश्लेषण करना\n",
     "\n",
     "औसत और विचरण वास्तविक दुनिया के डेटा का विश्लेषण करते समय बहुत महत्वपूर्ण होते हैं। आइए बेसबॉल खिलाड़ियों के बारे में डेटा [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) से लोड करें।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "हम यहाँ डेटा विश्लेषण के लिए [**Pandas**](https://pandas.pydata.org/) नामक पैकेज का उपयोग कर रहे हैं। इस कोर्स में आगे चलकर हम Pandas और Python में डेटा के साथ काम करने के बारे में अधिक चर्चा करेंगे।\n",
+    "हम यहाँ डेटा विश्लेषण के लिए [**Pandas**](https://pandas.pydata.org/) नामक पैकेज का उपयोग कर रहे हैं। इस कोर्स में आगे हम Pandas और Python में डेटा के साथ काम करने के बारे में और अधिक चर्चा करेंगे।\n",
     "\n",
     "आइए उम्र, ऊंचाई और वजन के औसत मानों की गणना करें:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "औसत के अलावा, माध्य मान और चतुर्थांशों को देखना भी समझदारी है। इन्हें **बॉक्स प्लॉट** का उपयोग करके दर्शाया जा सकता है:\n"
+    "औसत के अलावा, माध्यिका मान और चतुर्थक देखना भी समझदारी है। इन्हें एक **बॉक्स प्लॉट** का उपयोग करके दर्शाया जा सकता है:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **नोट**: यह आरेख सुझाव देता है कि औसतन, पहले बेसमैन की ऊंचाई दूसरे बेसमैन की ऊंचाई से अधिक होती है। बाद में हम सीखेंगे कि इस परिकल्पना को अधिक औपचारिक रूप से कैसे परीक्षण किया जा सकता है, और यह दिखाने के लिए कि हमारे डेटा सांख्यिकीय रूप से महत्वपूर्ण है, इसे कैसे प्रदर्शित किया जा सकता है।  \n",
+    "> **नोट**: यह आरेख सुझाव देता है कि औसतन, पहले बेसमैन की ऊंचाई दूसरे बेसमैन की ऊंचाई से अधिक होती है। बाद में हम सीखेंगे कि इस परिकल्पना को अधिक औपचारिक रूप से कैसे परीक्षण किया जा सकता है, और यह दिखाने के लिए कि हमारे डेटा सांख्यिकीय रूप से महत्वपूर्ण है।  \n",
     "\n",
     "आयु, ऊंचाई और वजन सभी सतत यादृच्छिक चर हैं। आपको क्या लगता है कि उनका वितरण कैसा है? इसका पता लगाने का एक अच्छा तरीका है कि मानों का हिस्टोग्राम बनाएं:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## सामान्य वितरण\n",
     "\n",
-    "आइए एक कृत्रिम नमूना बनाते हैं जो हमारे वास्तविक डेटा के समान औसत और विचरण के साथ सामान्य वितरण का अनुसरण करता है:\n"
+    "आइए एक कृत्रिम नमूना बनाते हैं जो भारों का अनुसरण करता है और हमारे वास्तविक डेटा के समान औसत और विचरण रखता है:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "चूंकि वास्तविक जीवन में अधिकांश मान सामान्य रूप से वितरित होते हैं, हमें नमूना डेटा उत्पन्न करने के लिए एक समान रैंडम नंबर जनरेटर का उपयोग नहीं करना चाहिए। यहां यह होता है यदि हम एक समान वितरण (जो `np.random.rand` द्वारा उत्पन्न होता है) के साथ वजन उत्पन्न करने का प्रयास करते हैं:\n"
+    "वास्तविक जीवन में अधिकांश मान सामान्य रूप से वितरित होते हैं, इसलिए नमूना डेटा उत्पन्न करने के लिए हमें एक समान रैंडम नंबर जनरेटर का उपयोग नहीं करना चाहिए। यदि हम एक समान वितरण (जो `np.random.rand` द्वारा उत्पन्न होता है) के साथ भार उत्पन्न करने का प्रयास करते हैं, तो यह होता है:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## परिकल्पना परीक्षण\n",
     "\n",
-    "आइए हमारे बेसबॉल खिलाड़ियों के डेटा सेट में विभिन्न भूमिकाओं का पता लगाएं:\n"
+    "आइए हमारे बेसबॉल खिलाड़ियों के डेटा सेट में विभिन्न भूमिकाओं का अन्वेषण करें:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "हम देख सकते हैं कि अंतराल एक-दूसरे से ओवरलैप नहीं करते हैं।\n",
     "\n",
-    "परिकल्पना को साबित करने का सांख्यिकीय रूप से अधिक सही तरीका **Student t-test** का उपयोग करना है:\n"
+    "परिकल्पना को सिद्ध करने का सांख्यिकीय रूप से अधिक सटीक तरीका **Student t-test** का उपयोग करना है:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,7 +620,7 @@
    "metadata": {},
    "source": [
     "`ttest_ind` फ़ंक्शन द्वारा लौटाए गए दो मान हैं:\n",
-    "* p-value को इस बात की संभावना के रूप में माना जा सकता है कि दो वितरणों का औसत समान है। हमारे मामले में, यह बहुत कम है, जिसका मतलब है कि इस बात के ठोस प्रमाण हैं कि पहले बेसमैन अधिक लंबे होते हैं।\n",
+    "* p-value को इस बात की संभावना के रूप में देखा जा सकता है कि दो वितरणों का औसत समान है। हमारे मामले में, यह बहुत कम है, जिसका मतलब है कि इस बात के लिए मजबूत प्रमाण हैं कि पहले बेसमैन अधिक लंबे होते हैं।\n",
     "* t-value सामान्यीकृत औसत अंतर का मध्यवर्ती मान है, जिसका उपयोग t-परीक्षण में किया जाता है, और इसे दिए गए आत्मविश्वास मान के लिए एक सीमा मान के साथ तुलना की जाती है।\n"
    ]
   },
@@ -789,24 +630,22 @@
    "source": [
     "## केंद्रीय सीमा प्रमेय के साथ सामान्य वितरण का अनुकरण\n",
     "\n",
-    "Python में छद्म-यादृच्छिक जनरेटर हमें एक समान वितरण प्रदान करने के लिए डिज़ाइन किया गया है। यदि हम सामान्य वितरण के लिए एक जनरेटर बनाना चाहते हैं, तो हम केंद्रीय सीमा प्रमेय का उपयोग कर सकते हैं। सामान्य रूप से वितरित मान प्राप्त करने के लिए, हम बस समान-जनित नमूने का औसत निकालेंगे।\n"
+    "Python में छद्म-यादृच्छिक जनरेटर हमें एक समान वितरण प्रदान करने के लिए डिज़ाइन किया गया है। यदि हम सामान्य वितरण के लिए एक जनरेटर बनाना चाहते हैं, तो हम केंद्रीय सीमा प्रमेय का उपयोग कर सकते हैं। सामान्य रूप से वितरित मान प्राप्त करने के लिए, हम बस एक समान-जनित नमूने का औसत निकालेंगे।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## सहसंबंध और ईविल बेसबॉल कॉर्प\n",
     "\n",
-    "सहसंबंध हमें डेटा अनुक्रमों के बीच संबंध खोजने की अनुमति देता है। हमारे खिलौना उदाहरण में, मान लीजिए कि एक दुष्ट बेसबॉल निगम है जो अपने खिलाड़ियों को उनकी ऊंचाई के अनुसार भुगतान करता है - खिलाड़ी जितना लंबा होगा, उसे उतना अधिक पैसा मिलेगा। मान लीजिए कि $1000 का एक आधार वेतन है, और ऊंचाई के आधार पर $0 से $100 तक का अतिरिक्त बोनस। हम MLB के असली खिलाड़ियों को लेंगे और उनकी काल्पनिक सैलरी की गणना करेंगे:\n"
+    "सहसंबंध हमें डेटा अनुक्रमों के बीच संबंध खोजने की अनुमति देता है। हमारे खिलौना उदाहरण में, मान लें कि एक दुष्ट बेसबॉल निगम है जो अपने खिलाड़ियों को उनकी ऊंचाई के अनुसार भुगतान करता है - खिलाड़ी जितना लंबा होगा, उसे उतना ही अधिक पैसा मिलेगा। मान लें कि $1000 का एक आधार वेतन है, और ऊंचाई के आधार पर $0 से $100 तक का अतिरिक्त बोनस। हम MLB के असली खिलाड़ियों को लेंगे और उनकी काल्पनिक सैलरी की गणना करेंगे:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "अब आइए उन अनुक्रमों का सहसंबंध और सहसंबद्धता की गणना करें। `np.cov` हमें तथाकथित **सहसंबद्धता मैट्रिक्स** देगा, जो कई चर के लिए सहसंबद्धता का विस्तार है। सहसंबद्धता मैट्रिक्स $M$ का तत्व $M_{ij}$ इनपुट चर $X_i$ और $X_j$ के बीच सहसंबंध है, और विकर्ण मान $M_{ii}$ $X_{i}$ का विचलन है। इसी प्रकार, `np.corrcoef` हमें **सहसंबंध मैट्रिक्स** देगा।\n"
+    "आइए अब उन अनुक्रमों का सह-संवेदनशीलता (covariance) और सहसंबंध (correlation) की गणना करें। `np.cov` हमें तथाकथित **सह-संवेदनशीलता मैट्रिक्स** देगा, जो कई चर के लिए सह-संवेदनशीलता का विस्तार है। सह-संवेदनशीलता मैट्रिक्स $M$ का तत्व $M_{ij}$ इनपुट चर $X_i$ और $X_j$ के बीच सहसंबंध है, और विकर्ण मान $M_{ii}$ चर $X_{i}$ का विचलन (variance) है। इसी प्रकार, `np.corrcoef` हमें **सहसंबंध मैट्रिक्स** देगा।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "इस मामले में, सहसंबंध थोड़ा छोटा है, लेकिन यह अभी भी काफी उच्च है। अब, संबंध को और भी कम स्पष्ट बनाने के लिए, हम वेतन में कुछ यादृच्छिक चर जोड़कर कुछ अतिरिक्त यादृच्छता जोड़ना चाह सकते हैं। आइए देखें क्या होता है:\n"
+    "इस मामले में, सहसंबंध थोड़ा छोटा है, लेकिन यह अभी भी काफी उच्च है। अब, संबंध को और भी कम स्पष्ट बनाने के लिए, हम वेतन में कुछ यादृच्छिक चर जोड़कर कुछ अतिरिक्त यादृच्छिकता जोड़ना चाह सकते हैं। आइए देखें क्या होता है:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "क्या आप अनुमान लगा सकते हैं कि बिंदु इस तरह से लंबवत रेखाओं में क्यों संरेखित होते हैं?\n",
+    "क्या आप अंदाज़ा लगा सकते हैं कि बिंदु इस तरह से सीधी रेखाओं में क्यों आते हैं?\n",
     "\n",
-    "हमने एक कृत्रिम रूप से निर्मित अवधारणा जैसे वेतन और देखे गए चर *ऊंचाई* के बीच संबंध देखा है। चलिए यह भी देखते हैं कि क्या दो देखे गए चर, जैसे ऊंचाई और वजन, भी आपस में संबंधित हैं:\n"
+    "हमने एक कृत्रिम रूप से निर्मित अवधारणा, जैसे वेतन, और देखे गए चर *ऊंचाई* के बीच संबंध देखा है। आइए अब देखें कि क्या दो देखे गए चर, जैसे ऊंचाई और वजन, भी आपस में संबंधित हैं:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,7 +852,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "दुर्भाग्यवश, हमें कोई परिणाम नहीं मिला - केवल कुछ अजीब `nan` मान प्राप्त हुए। इसका कारण यह है कि हमारी श्रृंखला में कुछ मान अपरिभाषित हैं, जिन्हें `nan` के रूप में दर्शाया गया है, जिससे ऑपरेशन का परिणाम भी अपरिभाषित हो जाता है। मैट्रिक्स को देखकर हम देख सकते हैं कि `Weight` समस्या वाली कॉलम है, क्योंकि `Height` मानों के बीच आत्म-संबंध की गणना की गई है।\n",
+    "दुर्भाग्यवश, हमें कोई परिणाम नहीं मिला - केवल कुछ अजीब `nan` मान प्राप्त हुए। इसका कारण यह है कि हमारी श्रृंखला में कुछ मान अपरिभाषित हैं, जिन्हें `nan` के रूप में दर्शाया गया है, और यह ऑपरेशन का परिणाम भी अपरिभाषित कर देता है। मैट्रिक्स को देखकर हम देख सकते हैं कि `Weight` समस्या वाली कॉलम है, क्योंकि `Height` मानों के बीच आत्म-संबंध की गणना की गई है।\n",
     "\n",
     "> यह उदाहरण **डेटा तैयारी** और **सफाई** के महत्व को दर्शाता है। बिना सही डेटा के हम कुछ भी गणना नहीं कर सकते।\n",
     "\n",
@@ -1026,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## निष्कर्ष\n",
     "\n",
-    "इस नोटबुक में हमने डेटा पर बुनियादी संचालन करके सांख्यिकीय कार्यों की गणना करना सीखा। अब हम जानते हैं कि गणित और सांख्यिकी के एक मजबूत उपकरण का उपयोग करके कुछ परिकल्पनाओं को साबित कैसे करना है, और दिए गए डेटा नमूने के आधार पर किसी भी चर के लिए विश्वास अंतराल की गणना कैसे करनी है।\n"
+    "इस नोटबुक में हमने डेटा पर बुनियादी संचालन करके सांख्यिकीय कार्यों की गणना करना सीखा। अब हम जानते हैं कि गणित और सांख्यिकी के एक ठोस उपकरण का उपयोग करके कुछ परिकल्पनाओं को सिद्ध कैसे किया जाए, और किसी डेटा सैंपल के आधार पर मनमाने वेरिएबल्स के लिए विश्वास अंतराल (confidence intervals) की गणना कैसे की जाए।\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता सुनिश्चित करने का प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को प्रामाणिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम जिम्मेदार नहीं हैं।\n"
+    "\n---\n\n**अस्वीकरण**:  \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता के लिए प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को आधिकारिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं।  \n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:08:01+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:18:14+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "hi"
   }
diff --git a/translations/hi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/hi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 01201cbd..40f92774 100644
--- a/translations/hi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/hi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## संभावना और सांख्यिकी का परिचय\n",
-    "## असाइनमेंट\n",
+    "## संभावना और सांख्यिकी का परिचय  \n",
+    "## असाइनमेंट  \n",
     "\n",
-    "इस असाइनमेंट में, हम मधुमेह रोगियों के डेटा सेट का उपयोग करेंगे जो [यहां से लिया गया है](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।\n"
+    "इस असाइनमेंट में, हम मधुमेह रोगियों के डेटा सेट का उपयोग करेंगे, जिसे [यहां से लिया गया है](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।  \n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### कार्य 2: लिंग के आधार पर BMI, BP और Y के लिए बॉक्सप्लॉट बनाएं\n"
+    "### कार्य 2: लिंग के अनुसार BMI, BP और Y के लिए बॉक्सप्लॉट बनाएं\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -590,7 +590,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "निष्कर्ष:\n",
+    "निष्कर्ष:  \n",
     "* आयु - सामान्य  \n",
     "* लिंग - समान  \n",
     "* बीएमआई, वाई - कहना मुश्किल  \n"
@@ -600,9 +600,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### कार्य 4: विभिन्न चर और रोग की प्रगति (Y) के बीच संबंध का परीक्षण करें\n",
+    "### कार्य 4: विभिन्न चर और रोग की प्रगति (Y) के बीच सहसंबंध का परीक्षण करें\n",
     "\n",
-    "> **संकेत** संबंध मैट्रिक्स आपको यह समझने में सबसे अधिक सहायक होगा कि कौन से मान एक-दूसरे पर निर्भर हैं।\n"
+    "> **संकेत** सहसंबंध मैट्रिक्स आपको यह समझने में सबसे अधिक सहायक होगा कि कौन-कौन से मान एक-दूसरे पर निर्भर हैं।\n"
    ],
    "metadata": {}
   },
@@ -845,7 +845,7 @@
    "cell_type": "markdown",
    "source": [
     "निष्कर्ष:\n",
-    "* Y का सबसे मजबूत संबंध BMI और S5 (ब्लड शुगर) से है। यह उचित लगता है।\n"
+    "* Y का सबसे मजबूत सहसंबंध BMI और S5 (ब्लड शुगर) के साथ है। यह तर्कसंगत लगता है।\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता सुनिश्चित करने का प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को प्रामाणिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं।\n"
+    "\n---\n\n**अस्वीकरण**:  \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता के लिए प्रयासरत हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को आधिकारिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं।  \n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:24:59+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:18:40+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "hi"
   }
diff --git a/translations/hk/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/hk/1-Introduction/04-stats-and-probability/assignment.ipynb
index 3972ea7b..f34deadc 100644
--- a/translations/hk/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/hk/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,14 +149,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "在此數據集中，列的含義如下：\n",
-    "* 年齡和性別不言自明\n",
+    "在此數據集中，列包含以下內容：\n",
+    "* 年齡和性別不需額外解釋\n",
     "* BMI 是身體質量指數\n",
     "* BP 是平均血壓\n",
-    "* S1 到 S6 是不同的血液測量值\n",
+    "* S1 至 S6 是不同的血液測量值\n",
     "* Y 是疾病在一年內進展的定性指標\n",
     "\n",
-    "讓我們使用概率和統計的方法來研究這個數據集。\n",
+    "讓我們使用概率和統計方法來研究這個數據集。\n",
     "\n",
     "### 任務 1：計算所有值的平均值和方差\n"
    ],
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 任務 3: 年齡、性別、BMI 和 Y 變量的分佈是什麼？\n"
+    "### 任務 3：年齡、性別、BMI 和 Y 變數的分佈是什麼？\n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### 任務 4：測試不同變數與疾病進展（Y）之間的相關性\n",
     "\n",
-    "> **提示** 相關性矩陣可以為你提供最有用的資訊，幫助判斷哪些數值是相關的。\n"
+    "> **提示** 相關性矩陣可以為你提供最有用的資訊，幫助判斷哪些值是相關的。\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
+    "\n---\n\n**免責聲明**：  \n此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議使用專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:49:25+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:12:43+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "hk"
   }
diff --git a/translations/hk/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/hk/1-Introduction/04-stats-and-probability/notebook.ipynb
index 44bd21bc..f385532e 100644
--- a/translations/hk/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/hk/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# 概率與統計入門  \n",
-    "在這份筆記中，我們將探索一些之前討論過的概念。許多概率與統計的概念在 Python 的主要數據處理庫中都有良好的呈現，例如 `numpy` 和 `pandas`。\n"
+    "在這份筆記中，我們將嘗試一些之前討論過的概念。概率與統計中的許多概念在 Python 的主要數據處理庫中都有良好的實現，例如 `numpy` 和 `pandas`。  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## 分析真實數據\n",
     "\n",
-    "在分析真實世界的數據時，平均值和方差是非常重要的。我們來載入有關棒球運動員的數據，數據來源於 [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "平均值和方差在分析現實世界的數據時非常重要。讓我們從 [SOCR MLB 身高/體重數據](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) 加載有關棒球球員的數據。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -261,24 +113,24 @@
    "source": [
     "我們在這裡使用一個名為 [**Pandas**](https://pandas.pydata.org/) 的套件進行數據分析。在這門課程的後續部分，我們會更詳細地討論 Pandas 以及如何在 Python 中處理數據。\n",
     "\n",
-    "現在讓我們計算年齡、身高和體重的平均值：\n"
+    "現在，讓我們計算年齡、身高和體重的平均值：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "除了平均值，查看中位數和四分位數也是有意義的。它們可以使用一個**箱型圖**來可視化：\n"
+    "除了平均值外，查看中位數值和四分位數也是有意義的。它們可以使用一個**箱型圖**來可視化：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "我們也可以製作數據集子集的箱型圖，例如按玩家角色分組。\n"
+    "我們亦可以為數據集的子集繪製箱型圖，例如按玩家角色分組。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -404,24 +252,22 @@
    "source": [
     "> **注意**：這個圖表顯示，平均而言，一壘手的身高比二壘手的身高更高。我們稍後會學習如何更正式地檢驗這個假設，以及如何證明我們的數據在統計上具有顯著性來支持這一點。\n",
     "\n",
-    "年齡、身高和體重都是連續隨機變量。你認為它們的分佈是怎樣的？一個好的方法是繪製它們的直方圖：\n"
+    "年齡、身高和體重都是連續隨機變量。你認為它們的分佈是怎樣的？一個好的方法是繪製數值的直方圖：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## 常態分佈\n",
     "\n",
-    "讓我們建立一個符合常態分佈的人工樣本，其平均值和變異數與我們的真實數據相同：\n"
+    "讓我們建立一個模擬的重量樣本，該樣本遵循與我們的真實數據相同的平均值和方差的常態分佈：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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T/OHwsDOTsXOf6/YktyfJNddcs+vCgfkzhQKAg2jqgFxV35rkd5L8VHf/VVVtu+kWY33eQPfdSe5Oko2NjfPuB4B14cMmrJapVrGoqpdmMxy/v7t/dzL81aq6YnL/FUmemYyfSXL18PCrkjw1m3IBAGC+dgzItdkq/rUkj3X3Lw53nUxyZHL9SJL7hvHDVfWyqro2yXVJHppdyQAAMD/TTLF4c5IfS/LHVfWZydjPJjme5ERV3ZbkiSS3Jkl3P1JVJ5I8ms0VMO7o7hdmXTgArJOz0zAeP37zgisBdgzI3f2/svW84iS5cZvHHEtybA91AQDAQjiTHgAADARkAAAYCMgAADDY1YlCAIDZskYyLB8dZAAAGAjIAAAwEJABAGAgIAMAwEBABoAlcujo/Q7cgwUTkAEAYGCZNwDYZzrEsNx0kAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQ4oJyMAAC2JiADwBLyIRYWx4lC4IA7+wf48eM3v+g2ABxUOsgAsAZ0nGF2BGQAABgIyAAAMDAHGUhi7jEAnKWDDAArxFxjmD8BGQAABgIyAAAMzEGGA8ZXs7Bazl2rHJg/HWQAABgIyAAAMDDFAgBWgOlRsH90kAEAYCAgw5qxRioA7I2ADAAAA3OQ4YDQVYb15HcbZk8HGQAABgIyAKwxxyXA7gnIAAAwEJABAGAgIAMAwEBABgCAgWXeYM05OAcAdkdAhjUhCAPAbAjIALCCfCiG+TEHGQAABgIyAAAMTLGAFXP2a9XHj9/8otsAwGzoIAMAwEBABgCAgYAMAAADARmW3KGj95tnDAD7yEF6sKaEagC4OAIyrAiBFwD2hykWAAAwEJABAGCwY0CuqvdV1TNV9flh7NVV9UBVfWly+arhvruq6nRVfbGq3j6vwuGgc/AeAMxHdfeFN6j6gSR/k+TXu/v1k7H/kORr3X28qo4meVV331lV1yf5QJIbkrw2yUeTfGd3v3Ch19jY2OhTp07t/V8Da0gIBmbp7Fk4gaSqHu7ujXPHd+wgd/fHk3ztnOFbktwzuX5PkncN4/d293Pd/ZUkp7MZlgEAYCVc7Bzky7v76SSZXF42Gb8yyZPDdmcmYwAAsBJmfZBebTG25RyOqrq9qk5V1alnn312xmUAAMDFudiA/NWquiJJJpfPTMbPJLl62O6qJE9t9QTdfXd3b3T3xqWXXnqRZQAAwGxdbEA+meTI5PqRJPcN44er6mVVdW2S65I8tLcS4WCxOgUALNaOZ9Krqg8keUuS11TVmSQ/l+R4khNVdVuSJ5LcmiTd/UhVnUjyaJLnk9yx0woWAMD+O/tB3KoWcL4dA3J3/8g2d924zfbHkhzbS1EAALAozqQHAAADARkAAAYCMgAADHacgwzMlwNlgP200yo53pNABxkAAF5EQAYAzmNNdg4yARkAAAYCMgAADBykB3PmgBdgFZhOAd8gIMOS8scKABbDFAsAABjoIMOS0DEGgOWggwz7zNJJwCo59z3LexgHgYAMAAADARkAAAbmIMOC+IoSAJaTgAwA7MiHeg4SARlmzIlBgFVyscHXex3rzBxkAGDPrG7BOhGQAQBgYIoF7BOdFQBYDTrIAAAw0EGGizB2g7c7QEXHGABWk4AMMyIQA0zXQIBlZ4oFAAAMdJBhF3SJAWD96SADAMBAQAYAgIGADADMhbPrsarMQYY98uYPAOtFBxkAAAYCMgCwr0y9YNkJyAAAMBCQAYC50jFm1QjIcAHe1AHg4LGKBWxBKAaAg0tAhnwjED9+/OYL3g/Ai83j/XGn92SYNwEZANgXmg2sCgGZA2HaboQ3b4D9o1PMshKQOdAEYoDF2y4oC9AsioDMWtEpBlh/577XC9LMmoAMACwFzQuWhXWQAQBgoIMMAKwEHWb2S3X3omvIxsZGnzp1atFlsAa8eQIcXOYgs1tV9XB3b5w7booFAAAMBGQAABiYg8xKcCpoAKZl2Tf2SgcZAAAGOsgAwFrY7ttEHWV2S0BmpZz75ufNDgCYNQEZAFhL5zZVtusw78cpq3WxV4s5yAAAMNBBZl+d+wl6uykTPmkDsEr83VovAjIzt9WbxMUuw2b5NgCWkUC83gRkdjTtGsSLeJMQoAHYq93OVWb9zS0gV9VNSX45ySVJ3tvdx+f1WizGXsLpTkvxAMAq02FebXMJyFV1SZJfSfKPk5xJ8qmqOtndj87j9ZjOdr+su/0lnjbECrsArJOt/q5N232e9rmX8dvag2heHeQbkpzu7i8nSVXdm+SWJALyHO0UgLfbfqfnu9jtAYDd2elg9t0+frePu5jHrqPq7tk/adU/T3JTd/+bye0fS/IPu/vdW22/sbHRp06dmnkd09jrJ7aL7b5u9YO/3QoOF/vLcrG/XADAfC3qb/Q0r7vTN8177WYv00m/qurh7t44b3xOAfnWJG8/JyDf0N0/Pmxze5LbJze/K8kXZ17I3r0myZ8tuogVYV/tjv01Pftqd+yv6dlXu2N/Tc++2p1F7q+/292Xnjs4rykWZ5JcPdy+KslT4wbdfXeSu+f0+jNRVae2+lTB+eyr3bG/pmdf7Y79NT37anfsr+nZV7uzjPtrXmfS+1SS66rq2qr65iSHk5yc02sBAMDMzKWD3N3PV9W7k3w4m8u8va+7H5nHawEAwCzNbR3k7v69JL83r+ffJ0s9BWTJ2Fe7Y39Nz77aHftrevbV7thf07Ovdmfp9tdcDtIDAIBVNa85yAAAsJIE5ClV1b+tqq6q1yy6lmVVVf++qj5XVZ+pqo9U1WsXXdMyq6pfqKovTPbZB6vqlYuuaVlV1a1V9UhVfb2qlupI52VRVTdV1Rer6nRVHV10Pcusqt5XVc9U1ecXXcsqqKqrq+oPquqxye/hTy66pmVVVS+vqoeq6rOTffXzi65p2VXVJVX1R1X1oUXXMhKQp1BVV2fztNlPLLqWJfcL3f093f2GJB9K8u8WXM+yeyDJ67v7e5L87yR3LbieZfb5JP8syccXXcgyqqpLkvxKkn+S5PokP1JV1y+2qqX235LctOgiVsjzSX66u787yZuS3OHna1vPJXlrd39vkjckuamq3rTYkpbeTyZ5bNFFnEtAns5/TPIzSUzYvoDu/qvh5itif11Qd3+ku5+f3PzDbK4Xzha6+7HuXsaTCS2LG5Kc7u4vd/ffJrk3yS0LrmlpdffHk3xt0XWsiu5+urs/Pbn+19kMM1cutqrl1Jv+ZnLzpZP//C3cRlVdleTmJO9ddC3nEpB3UFXvTPIn3f3ZRdeyCqrqWFU9meRfRAd5N/51kv++6CJYWVcmeXK4fSYCDHNQVYeSvDHJJxdcytKaTBn4TJJnkjzQ3fbV9n4pmw3Iry+4jvPMbZm3VVJVH03yHVvc9Z4kP5vkB/e3ouV1oX3V3fd193uSvKeq7kry7iQ/t68FLpmd9tdkm/dk8yvM9+9nbctmmn3FtmqLMV0rZqqqvjXJ7yT5qXO+MWTQ3S8kecPkuJIPVtXru9t893NU1TuSPNPdD1fVWxZcznkE5CTd/batxqvqHyS5NslnqyrZ/Ar801V1Q3f/6T6WuDS221db+M0k9+eAB+Sd9ldVHUnyjiQ39gFfc3EXP1uc70ySq4fbVyV5akG1sIaq6qXZDMfv7+7fXXQ9q6C7/7KqPpbN+e4C8vnenOSdVfVDSV6e5Nur6je6+0cXXFcSUywuqLv/uLsv6+5D3X0om3+Evu+ghuOdVNV1w813JvnCompZBVV1U5I7k7yzu//fouthpX0qyXVVdW1VfXOSw0lOLrgm1kRtdoh+Lclj3f2Li65nmVXVpWdXJKqqb0nytvhbuKXuvqu7r5rkq8NJfn9ZwnEiIDNbx6vq81X1uWxOS7EU0IX9pyTfluSBydJ4/2XRBS2rqvqnVXUmyfcnub+qPrzompbJ5GDPdyf5cDYPoDrR3Y8stqrlVVUfSPKJJN9VVWeq6rZF17Tk3pzkx5K8dfJe9ZlJ14/zXZHkDyZ/Bz+VzTnIS7V8GdNxJj0AABjoIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYPD/ASvKmaTtYFHZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "我們可以看到這些區間並沒有重疊。\n",
     "\n",
-    "一個在統計學上更正確的方法來驗證這個假設是使用 **Student t-test**：\n"
+    "一個在統計學上更正確的方法來證明這個假設是使用 **Student t-test**：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "`ttest_ind` 函數返回的兩個值分別是：  \n",
-    "* p-value 可視為兩個分佈具有相同平均值的概率。在我們的情況下，p-value 非常低，這意味著有強烈的證據支持一壘手的身高較高。  \n",
-    "* t-value 是 t 檢驗中使用的標準化平均差異的中間值，並且會與給定置信值的閾值進行比較。  \n"
+    "* p-value 可視為兩個分佈具有相同平均值的概率。在我們的情況下，p-value 非常低，這意味著有強烈的證據支持一壘手的身高更高。  \n",
+    "* t-value 是 t 檢驗中用於比較的標準化平均差異的中間值，並且會根據給定的置信值與閾值進行比較。  \n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## 使用中央極限定理模擬正態分佈\n",
     "\n",
-    "Python 的偽隨機生成器旨在提供均勻分佈。如果我們想創建一個正態分佈的生成器，可以利用中央極限定理。要獲得一個正態分佈的值，我們只需計算均勻生成樣本的平均值。\n"
+    "Python 的偽隨機生成器旨在提供均勻分佈。如果我們想創建一個正態分佈的生成器，可以利用中央極限定理。要獲得正態分佈的值，我們只需計算均勻生成樣本的平均值。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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6HavqkUl+Mslbk2SM8Z0xxn/t+8Tra9JjMtu/JeihVbWR5AdztH9P/F7XctW3vdgsvRb2Ofcz6XFln3MvS6/lOu93BPI0O73d9r1+4FTV45O8LMlfXOiTVNVWkmcn+eTqRzw0pq7lNUl+O8l392m+w2LKOj4lyZ1J/mrx34ZvqaqH7eewa27ptRxjfDXJHyb5SpKzSb45xvjovk673nZdy4XnVdVnqupvquoZD/C2R8GUdfwe+5wk09fymtjnnDdlLdd2vyOQp9nL221fk+R3xhj37PgJqh6e7aNPrx9jfGu14x0qS69lVf1sknNjjJv2abbDZMpjciPJc5K8eYzx7CT/k+QoP99zymPy0dk+gvLkJD+c5GFV9Qv7MeQhsZe1/FSSJ40xnpnkT5N88AHc9qiYso7bn8A+57yl19I+536mPC7Xdr+z9BuFkGRvb7d9Isl7qipJjiV5cVXdPcb4YFU9ONs/qN45xrj+IAZeY0uvZZLnJvm5qnpxkockeWRVvWOMcRSDZMo6/nOSO8YY548qvS9r8oNqJlPW8sFJvjzGuDNJqur6JD+e5B37PfSa2nUte6yNMW6oqj+vqmN7ue0RsvQ6jjHuss+5lymPyefHPqeb+v29nvuduZ8EfZhP2f4HxpeyfZTo/BPTn/H/XP/t+f6LeCrJXye5Zu6vYx1OU9byPtuvyBF+wcTUdUzyD0mevjj/piR/MPfXdBjXMtv/aLs12889rmy/COW1c39N67yWSX4o33/zqsuz/fSUeqB/DxfzaeI62uesaC3vc50jvc9ZxVqu637HEeQJxgXebruqfm3x8Qs+7zjb/wJ9VZLPVtXNi21vHGPcsJ8zr6uJa8nCCtbxtUneWVWXZPsH3i/v68BrbMpajjE+WVXvy/Z/K96d5NM5ZG+zukp7XMuXJ/n1xRH4/03yirG9x9zxtrN8ITObso5V9ROxz/meiY9JmhWs5Vrud7zVNAAANF6kBwAAjUAGAIBGIAMAQCOQAQCgEcgAANAIZAAAaAQyAAA0/wceFVFs3MY9ywAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## 相關性與邪惡棒球公司\n",
     "\n",
-    "相關性讓我們能夠找出數據序列之間的關係。在我們的簡單例子中，假設有一家邪惡的棒球公司，根據球員的身高來支付薪水——球員越高，薪水就越多。假設基本薪水是 $1000，並根據身高額外提供 $0 至 $100 的獎金。我們將使用 MLB 的真實球員數據，計算他們的虛構薪水：\n"
+    "相關性讓我們能夠找出數據序列之間的關係。在我們的玩具例子中，假設有一家邪惡的棒球公司，根據球員的身高來支付薪水——球員越高，薪水就越多。假設基本薪水是 $1000，並根據身高額外提供 $0 至 $100 的獎金。我們將使用 MLB 的真實球員數據，計算他們的虛構薪水：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "現在讓我們計算這些序列的共變異數和相關性。`np.cov` 會給我們一個所謂的 **共變異數矩陣**，這是共變異數在多個變量上的延伸。共變異數矩陣 $M$ 的元素 $M_{ij}$ 是輸入變量 $X_i$ 和 $X_j$ 之間的相關性，而對角線上的值 $M_{ii}$ 是 $X_{i}$ 的變異數。同樣地，`np.corrcoef` 會給我們 **相關矩陣**。\n"
+    "讓我們現在計算這些序列的共變異數和相關性。`np.cov` 會給我們一個所謂的 **共變異數矩陣**，這是共變異數在多個變量上的延伸。共變異數矩陣 $M$ 的元素 $M_{ij}$ 是輸入變量 $X_i$ 和 $X_j$ 之間的相關性，而對角線上的值 $M_{ii}$ 是 $X_{i}$ 的變異數。同樣地，`np.corrcoef` 會給我們 **相關性矩陣**。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -889,19 +728,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "讓我們看看如果關係不是線性的會發生什麼。假設我們的公司決定隱藏高度和薪水之間明顯的線性依賴性，並在公式中引入一些非線性，例如 `sin`:\n"
+    "讓我們看看如果關係不是線性的會發生什麼。假設我們的公司決定隱藏高度和薪水之間明顯的線性依賴性，並在公式中引入一些非線性，例如 `sin`：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "在這種情況下，相關性略低一些，但仍然相當高。現在，為了使關係更不明顯，我們可能想通過向薪水添加一些隨機變量來增加一些額外的隨機性。讓我們看看會發生什麼：\n"
+    "在這種情況下，相關性略小，但仍然相當高。現在，為了使關係更不明顯，我們可能需要通過向薪水添加一些隨機變量來增加一些額外的隨機性。讓我們看看會發生什麼：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "你能猜到為什麼這些點會排列成垂直線嗎？\n",
+    "> 你能猜到為什麼這些點會排列成垂直線嗎？\n",
     "\n",
-    "我們已經觀察到像薪水這樣的人為設計概念與觀察變數*身高*之間的關聯。現在讓我們看看兩個觀察變數，例如身高和體重，是否也有相關性：\n"
+    "我們已經觀察到像薪水這樣的人為設計概念與觀察變數*身高*之間的相關性。現在讓我們看看兩個觀察變數，例如身高和體重，是否也有相關性：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,7 +856,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "很可惜，我們沒有得到任何結果——只有一些奇怪的 `nan` 值。這是因為我們的數列中有一些值是未定義的，用 `nan` 表示，這導致運算的結果也變成未定義。通過查看矩陣，我們可以看到問題出在 `Weight` 這一列，因為 `Height` 值之間的自相關已經被計算出來。\n",
+    "不幸地，我們沒有得到任何結果——只有一些奇怪的 `nan` 值。這是因為我們的數據序列中有一些值是未定義的，用 `nan` 表示，這導致操作的結果也變成未定義。通過查看矩陣，我們可以看到問題出在 `Weight` 這一列，因為 `Height` 值之間的自相關已經被計算出來。\n",
     "\n",
     "> 這個例子顯示了**數據準備**和**清理**的重要性。沒有適當的數據，我們無法計算出任何結果。\n",
     "\n",
@@ -1030,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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C60H8Mn1s4ibpnjdvnsrKyvTss8+2PhYOhyVJ1113na666iqdeuqpeuCBB3TcccfpiSee6PG/VVRUJL/f3/p15JFH9rr9ANBXqvdG/yPUkzigP+H9AyCC60H8Mn1s4iLpnj9/vlasWKE1a9ZoxIgRrY/n5ras2Q8EAm3iv/a1r2nnzp2SpJycHFVXV7d5vrm5WTU1NcrJyenw31uwYIGCwWDr1yeffOJkdwDAUdnpKY7GAf0J7x8AEVwP4pfpY+Nq0m1ZlubPn6/nn39eq1evVn5+fpvn8/LyNHz48HZlxD744AONGjVKklRQUKA9e/Zo8+bNrc+vXr1a4XBYp59+eof/7sCBA5WRkdHmCwDi1bj8TOX6U1oPEjmUTy0ne47Lz4xlswBP4P0DIILrQfwyfWxcTbrnzZunpUuX6plnnlF6erqqqqpUVVXVWoPb5/Pp1ltv1YMPPqg//OEP+uijj3TXXXfpH//4h6655hpJLbPehYWFmjNnjjZt2qTXX39d8+fP17e//W1bJ5cDQLxLTPBp4cyWFT+H/jGKfL9wZkCJCdH+VAH9F+8fABFcD+KX6WPjaskwn6/jX9qTTz6pK6+8svX7n/zkJ3r44YdVU1Ojk08+WT/96U81YcKE1udramo0f/58LV++XAkJCbrkkkv04IMPavDgwbbaQckwAF5gau1KIBZ4/wCI4HoQv7w2NnbzyLiq0+0Wkm4AXhEKW9pUUaPqvQ3KTm9ZZuXVT32BWGtsDmtJ6XbtqKnTqMw0zS7IU3JSXBxvAyDG+Hsav7w0NiTd3UDSDQCA2bw2ewIAiH9280g+3gUAAEYrKavU3KVb2tWArQo2aO7SLSopq3SpZQCA/oCkGwAAGCsUtrRoebk6WtYXeWzR8nKFwv1+4R8AoI+QdAMAAGNtqqhpN8N9MEtSZbBBmypqYtcoAEC/QtINAACMVb03esLdkzgAALqLpBsAABgrOz3F0TgAALorye0GAAAA9JVx+ZnK9aeoKtjQ4b5un6Qcf0tJGq/xUlkdAOjPSLoBAICxEhN8WjgzoLlLt8gntUm8I+npwpkBzyWrlEADAO9geTkAADBa4ehcLZ41Rjn+tkvIc/wpWjxrjOeSVEqgAYC3MNMNAACMVzg6V1OOP1xLSrdrR02dRmWmaXZBnpKTvDX/0FUJNJ9aSqBNDeR4bvYeAExF0g0AAIzX0XLs326o8Nxy7O6UQCs4Oit2DQMAROWtj3cBAAC6yaTl2JRAAwDvIekGAADG6mo5ttSyHDsU7igi/lACDQC8h6QbAAAYqzvLsb0gUgIt2m5tn1pOMfdiCTQAMBVJNwAAMJZpy7EjJdAktUu8vVwCDQBMRtINAACMZeJybNNKoAGA6Ti9HAAAGCuyHLsq2NDhvm6fWpJVry3HLhydq6mBHG2qqFH13gZlp7f0gRluAIg/JN0AAMBYkeXYc5dukU9qk3h7fTl2YoKPsmAA4AEsLwcAAEZjOTYAwE3MdAMAAOOxHBsA4BaSbgAA0C+wHBsA4AaWlwMAAAAA0EdIugEAAAAA6CMsLwdgtFDYYg9nHGN8EEv1jSHdt7Jc23fVKS8rTXfMCCg1OdHtZvWYaf1BfON6Hd8Yn/jmsyyro7KV/Uptba38fr+CwaAyMjLcbg4Ah5SUVWrR8nJVBhtaH8v1p2jhzACnFccBxgexNOfpt7SqvLrd41MD2Sq+fKwLLeod0/qD+Mb1Or4xPu6xm0eSdIukGzBRSVml5i7dokMvcJHPfCkT5C7GB7EULUGN8Fqialp/EN+4Xsc3xsdddvNI9nQDME4obGnR8vJ2f4AktT62aHm5QuF+/5mjKxgfxFJ9Y6jTBFWSVpVXq74xFKMW9Y5p/TlYKGypdNsuvbj1M5Vu28U1IA5wvY5vjI93kHQDMM6mipo2S6wOZUmqDDZoU0VN7BqFVowPYum+leWOxrnNtP5ElJRVasL9q3Vp8Ubd9OxWXVq8URPuX62Sskq3m9avcb2Ob4yPd5B0AzBO9d7of4B6EgdnMT6Ipe276hyNc5tp/ZH+uTz20OShKtiguUu3kHi7iOt1fGN8vIOkG4BxstNTHI2DsxgfxFJeVpqjcW4zrT8sj41vXK/jG+PjHSTdAIwzLj9Tuf4URSuU4VPLqZ7j8jNj2Sz8H8YHsXTHjICjcW4zrT8sj41vXK/jG+PjHSTdAIyTmODTwpktN5yH/iGKfL9wZoD6lS5hfBBLqcmJmhrI7jRmaiDbM/WtTesPy2PjG9fr+Mb4eAdJNwAjFY7O1eJZY5Tjb7ukKsefQvmMOMD4IJaKLx8bNVH1Ynktk/rD8tj4x/U6vjE+3kCdblGnGzBZKGxpU0WNqvc2KDu9ZYkVn/jGD8YHsVTfGNJ9K8u1fVed8rLSdMeMgGdmhDtiQn9CYUsT7l+tqmBDh/u6fWpJHjbcNoVrg8u4Xsc3xscddvNIkm6RdAMAALglcnq5pDaJdyRdYLYOQLyym0eyvBwAAACuYXksANMlud0AAAAQn1iuiFgpHJ2rqYEcXm8AjETSDQAA2ikpq9Si5eVtyjnl+lO0cGaAmUf0icQEnwqOznK7GQDgOJaXAwCANiJ7bA+tn1wVbNDcpVtUUlbpUssAAPAekm4AANAqFLa0aHl5hydJRx5btLxcoXC/P4cVAABbWF4OAIBDGpvDWlK6XTtq6jQqM02zC/KUnOStz7c3VdS0m+E+mCWpMtigTRU1LAV2mQmvN5NxJgKACJJuAAAcULSyXMXrK3TwBPC9K9/TnIn5WjAj4F7Duql6b/SEuydx6BumvN5MxZkIAA7Gx6EAAPRS0cpyPbaubQIkSWFLemxdhYpWlrvTsB7ITk/pOqgbcXCeSa83E3EmAoBDkXQDgIeEwpZKt+3Si1s/U+m2XeyrjQONzWEVr6/oNKZ4fYUam8MxalHvnHiE39G4eGLC+8e015tpOBMBQEdYXg4AHsFyxfi0pHR7uxnHQ4WtlrhrJh4Vm0b1wv0l79mOu+fCE/u4Nc4x5f1j2uvNNJyJAKAjzHQDgAewXDF+7aipczTObdt32Wun3bh4YNL7x7TXm2k4EwFAR0i6ASDOsVwxvh05NNXROLflZaU5Guc2094/ozLt/d7txsFZnIkAoCMk3QAQ57qzXBGxd3xOhqNxbrvD5snXduPcZtr7Z3ZBnrqqOpXga4lD7I3Lz1SuP0XRhsinlm0N4/IzY9ksAC4j6QaAOMdyxfhWU9foaJzbUpMTNTWQ3WnM1EC2UpMTY9Si3jHt/ZOclKA5E/M7jZkzMZ963S5JTPBp4cyWD6QOTbwj3y+cGaBeN9DPcEUGgDjHcsX4ZuL4FF8+NmriPTWQreLLx8a4RT1n4vgsmBHQdZPy2814J/ik6yZRp9tthaNztXjWGOX4276mcvwpWjxrjKcO7gPgDE4vB4A4F1muWBVs6HBfqk8tN3MsV3SHqeNTfPlY1TeGdN/Kcm3fVae8rDTdMSPgmRnuCFPHZ8GMgL4/7XgtKd2uHTV1GpWZptkFecxwx4nC0bmaGsjRpooaVe9tUHZ6y2uMGW6gf/JZluWNk0P6UG1trfx+v4LBoDIyvLHnDkD/Ejl9WVKbxCFy+8bsibsYn/jG+AAA+oLdPJKPQwHAA1iuGN8Yn/jG+AAA3MRMt5jpBuAdobDFcsU4xvjEN8YHAOAku3kke7oBwEMSE3wqODrL7WYgCsYnvpk2PnyIAADeQNINAADgMSVllVq0vLxNDfJcf4oWzgywXB4A4gx7ugEAADwkcjDcwQm3JFUFGzR36RaVlFW61DIAQEdcTbqLioo0duxYpaenKzs7WxdeeKHef//9DmMty9L06dPl8/n0wgsvtHlu586dOu+885SWlqbs7Gzdeuutam5ujkEPACC2QmFLpdt26cWtn6l02y6Fwt4+lqOxOazH13+sH75YpsfXf6zG5rDbTeoV08anvjGku154V7Mff1N3vfCu6htDbjepV2r2NWraL17TKYte0bRfvKaafY1uN6nbQmFLi5aXd1j+LPLYouXlnnztmXY9oD8AIlxdXr527VrNmzdPY8eOVXNzs+644w5NmzZN5eXlGjRoUJvYX/7yl/L52u9TCoVCOu+885STk6M33nhDlZWVuvzyyzVgwADdd999seoKAPQ505aTFq0sV/H6Ch2cG9y78j3NmZivBTMC7jWsh0wbnzlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM2N/vEpfHpRk76lv0pgfr9Kwwcl6686pLrasezZV1LSb4T6YJaky2KBNFTWe2r9u2vWA/gA4mKsz3SUlJbryyit1wgkn6OSTT9ZTTz2lnTt3avPmzW3itm7dqv/6r//SE0880e5nvPLKKyovL9fSpUt1yimnaPr06brnnnv08MMPq7HRe59gA0BHTFtOWrSyXI+ta3sDJ0lhS3psXYWKVpa707AeMm18Dk24D7aqvFpznn4rxi3qnUMT7oN9ua9RY3+8KsYt6rnqvdET7p7ExQPTrgf0B8Ch4mpPdzAYlCRlZma2PlZXV6fvfOc7evjhh5WTk9PuvyktLdWJJ56oww8/vPWxc889V7W1tfr73//e940GgD5m2nLSxuawitdXdBpTvL7CM0sXTRuf+sZQ1IQ7YlV5tWeWmtfsa4yacEd8ua/RM0vNM1OTHY1zm2nXA/oDoCNxk3SHw2HdfPPNOvPMMzV69OjWx2+55RaNHz9eF1xwQYf/XVVVVZuEW1Lr91VVVR3+NwcOHFBtbW2bLwCIV91ZTuoFS0q3t5sxOVTYaonzAtPG5z6bs1Z249z27d+84Wic2/7xxV5H49xm2vWA/gDoSNyUDJs3b57Kysq0YcOG1seWLVum1atX6+2333b03yoqKtKiRYsc/ZkA0FdMW066o6bO0Ti3mTY+23fZ+73bjXNb9V57M9h249z2yW57v3e7cW4z7XpAfwB0JC5muufPn68VK1ZozZo1GjFiROvjq1ev1rZt2zRkyBAlJSUpKanlM4JLLrlEZ511liQpJydHX3zxRZufF/m+o+XokrRgwQIFg8HWr08++aQPegUAzshOT3E0zm2jMtMcjXObaeOTl2Xv9243zm3D0u0ts7Yb5zbT3j/0J76Z1h/ALa4m3ZZlaf78+Xr++ee1evVq5efnt3n+9ttv1zvvvKOtW7e2fknSAw88oCeffFKSVFBQoHfffVfV1f/cf7Zq1SplZGQoEOj4NMWBAwcqIyOjzRcAxKtx+ZnK9aeoff2GFj61nJI9Lj8zSkR8mV2Qp4Ronfk/Cb6WOC8wbXzusHkSsd04t/3nOcc5Guc2094/9Ce+mdYfwC2uJt3z5s3T0qVL9cwzzyg9PV1VVVWqqqpSfX29pJaZ6tGjR7f5kqSRI0e2JujTpk1TIBDQ7Nmz9be//U1//vOfdeedd2revHkaOHCga30DAKckJvi0cGZLgnPovU/k+4UzA0rs6s4oTiQnJWjOxPxOY+ZMzFdyUlwsxuqSaeOTmpyoqYHsTmOmBrKVmpwYoxb1Tl3Y3gFPduPcZtr7h/7EN9P6A7jF1XfI4sWLFQwGddZZZyk3N7f167nnnrP9MxITE7VixQolJiaqoKBAs2bN0uWXX64f/ehHfdhyAIitwtG5WjxrjHL8bZco5/hTtHjWGM/VgV4wI6DrJuW3m0FJ8EnXTfJe3VfTxueSMSN69Xw8MW35v2Te+4f+xDfT+gO4wWdZljdqmPSh2tpa+f1+BYNBlpoDiGuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2dMTBifUNjShPtXRz2R3aeWDxM23DbFE32L9Kcq2NBhWTev9edgpr1/6E98q28M6b6V5dq+q055WWm6Y0bAMytegL5iN48k6RZJNwAAEaXbdunS4o1dxv1uzhkqODorBi3qvZKySl2/dEvU5x/14GoEIJZKyiq1aHl5mw/jcv0pWjgzwHsH/ZrdPNK7H7cBAADHmVYCDUDvlJRVau7SLe1Wv1QFGzR36RaVlFW61DLAO0i6AQBAK9P2QIfClhYtL4/6vE/SouXlCoX7/cI/oJ3I+6ejd0fkMd4/QNdIugEAQCvTSqBtqqiJuj9dakkcKoMN2lRRE7tGAR7B+wdwBkk3AABoZVoJNJbLAz3H+wdwBkk3AABow6QSaKYtlwdiifcP4IwktxsAAIApTCoRVDg6V5OPzfZ8iaDIcvmuSoZ5Zbn8wUwr4WTS+8cUJr9/gFiiZJgoGQYA6L2ileUqXl+hg88TSvBJcybma8GMgHsN6yGT+mNiybA5T7+lVeXV7R6fGshW8eVjXWhR75j0ejNN5PRySW0S78gGE6+tfgGcRMkwAABipGhluR5b1zZhkKSwJT22rkJFK6Ofnh2PTOvPI6991Kvn4020hFuSVpVXa87Tb8W4Rb1j2uvNNCZtNwHcwvJyAAB6obE5rOL1FZ3GFK+v0PenHe+JpbKm9WdfQ7Pe+bS205h3Pq3VvoZmDU6J/9ui+sZQ1IQ7YlV5teobQ55Yam7a681UhaNzNTWQo00VNare26Ds9JYl5V45UBFwG1cvAAB6YUnp9nYzdIcKWy1xXmBaf2557m1H49x2n81ZX7txbjPt9WayxASfCo7O0gWnHKGCo7NIuIFuIOkGAKAXdtTUORrnNtP6s3N3vaNxbtu+y97v3W6c20x7vQFAR0i6AQDohSOHpjka57ZRmfbaaTfObSOHpjoa57aRmTb7YzPObaa93gCgIyTdAAD0wvE56Y7GuW12QZ66WjWa4GuJ84IHvnWqo3FumxbIcTTObaa93gCgIyTdAAD0Qk1do6NxbktOStCcifmdxsyZmO+ZQ60GpyTppBGdlwM9aUSGJw5Rk6Q99U2OxrnNtNcbAHSEKxgAAL2QnZ7SdVA34uLBghkBXTcpv90MZIJPum6S9+omL5s/MWrifdKIDC2bPzHGLeo5Xm8A4D0+y7K6ODPSfHaLmgMAnBUKW54vQdPYHNbxd73c6QnMCT7pH/dM99xsXX1jSPetLNf2XXXKy0rTHTMCnihDFc2+hmbd8tzb2rm7XiOHpuqBb53qmRnuiFDY0oT7V6sq2KCOXnI+tdRP3nDbFE++l5aUbteOmjqNykzT7II8z71nAPQvdvNIb/2lAQAYo6SsUouWl6sy2ND6WK4/RQtnBlQ4OtfFlnXP5h27bZU82rxjtwqOzopNoxxw6Pis/1D6y3vVnhufgw1OSVLxFWPdbkavJCb4tHBmQHOXbpFPapN4R1LshTMDnku4pZal5tdMPMrtZgCA4/j4EAAQcyVllZq7dEubhFuSqoINmrt0i0rKKl1qWfdV723oOqgbcfHApPExUeHoXC2eNUY5/rZLyHP8KVo8a4xnPxQBAFMx0w0AiKlQ2NKi5eUdLo211DJbt2h5uaYGcjwxW2faHlvTxsdUhaNzNTWQ4/ntGQDQH5B0AwBialNFTbsZ1INZkiqDDdpUUeOJ5djj8jOV60/pco/tuPzMWDetR0wbn4OZtmc4McHnuTEA0DdMOCPFZCTdAICYMm05tml7bE0bn4iileUqXl/RZv/9vSvf05yJnI4NwNtMOSPFZN79eBcA4EmmLceWzNpja+L4FK0s12PrKtodeBe2pMfWVahoZbk7DQOAXuIMDm9gphsAPMSE5WOnjRqqBJ+6LLF12qihsWuUAwpH5+q0kZm66JENqtnfpMxBA/T8dydoWMZAt5vWLaYtl29sDqt4fUWnMcXrK/T9acd7bqm5CSXQDhasa9LVT23S58EGDfen6Ikrx8mfNsDtZvWYCddrk5kwPpzB4R3evTIDQD9jyvIxU0tsjf3xKn25r7H1+7o9IY297y8aNjhZb9051cWWdU9kufz1S7d0+Lwlby2XX1K63dbrbUnpdk+Vqzr/ofV659Pa1u/fr9qr0Xf/WSeNyNCy+RNdbFnPTP7Zau3YVd/6fWWwQSf/6BWNykrV2lunuNiynjHlem0qU8bH5DM4TOOtj3QBoJ8yafmYiXuGD024D/blvkaN/fGqGLeod/7zD+/06vl4sqOmztG4eHBown2wdz6t1fkPrY9xi3rn0IT7YDt21Wvyz1bHuEW9Y9L12kQmjY+Jf09NRdINAHGuq+VjUsvysVBX03lxwrQ9wzX7GqMm3BFf7mtUTRcx8eLL2gOqbWjuNKa2oVlf1h6IUYt6J3OgvUV9duPctq+hOWrCHfHOp7Xa18UYxotgXVPUhDtix656BeuaYtSi3jHtem0a08bHtL+nJiPpBoA4153lY14Q2TMcbXGyTy3L/LyyZ/jbv3nD0Ti3XfTIBkfj3LbkrZ2OxrntlufedjTObVc/tcnROLeZdr02jWnjY9rfU5ORdANAnDNt+Vhkz7CkdjcK3iyxZW8G226c22r225tRtBvntr02Z3ztxrlt5+7OZ4W7G+e2zztJgHoS5zbTrtemMW18TPt7ajKSbgCIcyYuHzOrxFayo3Fuyxxk77Rou3Fuy7B5mrfdOLeNHJrqaJzbhvvtXbfsxrnNxOu1SUwcH5P+nprMZ1mWNzYt9KHa2lr5/X4Fg0FlZGS43RwAaKOxOazj73q5yxJb/7hnuudKHplQsqVmX6PG2DgobcudU5U5OP4T7y9rD2jsfX/pMu6tO87xRDm0z2rqdeZPuz6I6/X/nKIjMuM/Ud3X0KzRd/+5y7iyu8/1RPmwYF2TTv7RK13G/e2H0zxRPiwUtjTh/tVdltzbcNsUz13rTGDy+Jjw99SL7OaR3ro7A4B+qDsltrwmMcGngqOzdMEpR6jg6CxP3iBkDk7WsC6S6WGDkz2RcEvSsIyBXc76ZqQkeSLhlqSdu+2dSm43zm2DU5J00ojOJwhOGpHhiYRbkvxpAzQqq/MPO0ZlpXoi4ZZY7hvvTB4fE/6emoykGwDinGl70Ex08ZgjevV8vPnpv53Uq+fjiYnvn2XzJ0ZNvL1Yp3vB9K/16vl4w3Lf+Mb4wA3e+BgUAPoxE/egmaSxOazi9RWdxhSvr9D3px3vieX/kZI60fjUUlJnaiDHEzMphw22NyNvNy5eLJs/UfsamnXLc29r5+56jRyaqge+dapnZrgjTHu9RRSOztXUQA7LfeMU44NY89aVGQD6oUhJkK72oFESxB1LSrfbWv6/pHS7rpl4VGwa1QvdKalTcHRW7BrWU3ZPrvHgCTeDU5JUfMVYt5vRK8a93g4SWe6L+MT4IJbi/yN3AOjnTN6DZoIdNfb2AtuNc5tpy7G/2n/A0Tg4y7TXGwB0hKQbADyAPWjx68ihaY7Guc207Qym9cc0jA+A/oDl5QDaoORE/GIPWnw6/vB0R+PcZtp2BtP6YxrGB0B/QNINoFVJWaUWLS9vs78u15+ihTMDzKTGCfagxZ+a+kZH49wW2c5w/dItHT5vyVvbGSL9mbt0i3xqu3Wb7RnuY3wA9AcsLwcgqSXhnrt0S7sDbaqCDZq7dItKyipdahkQ31geG//YnhHfGB8ApvNZluXB8zqdVVtbK7/fr2AwqIyMjuteAiYLhS1NuH911BNkI8v7Ntw2xXOzDaYtlzetP6aUPJpw/+oul8d65f1j8vWgsTmsJaXbtaOmTqMy0zS7IM8TZdyiqW8M6b6V5dq+q055WWm6Y0ZAqcmJbjerx0wbHwDms5tHeuvOBkCfMLVki2nL5U3rz/kPrdc7n9a2fv9+1V6NvvvPOmlEhpbNn+hiy7rHtOXY/el68NsNFZ59/8x5+i2tKq9u/X79h9KSjTs1NZCt4su9V0bMtPEBgIP16OPDH/3oR6qra1/6pL6+Xj/60Y963SgAsWViyRbTlsub1p9DE+6DvfNprc5/aH2MW9Q7j7z2Ua+ejydcD+LfoQn3wVaVV2vO02/FuEW9Y9r4AMChepR0L1q0SPv27Wv3eF1dnRYtWtTrRgGILdP2pIbClhYtL+9wqW/ksUXLyxUKe2N3jWn92dfQHDXhjnjn01rta2iOUYt6x7T+JIadjXObae+f+sZQ1IQ7YlV5teobQzFqUe+YNj4A0JEeJd2WZcnna79M7m9/+5syMynpAHhNpGRLtMWvPrUsY/ZKyZbuLI/1AtP6c8tzbzsa5zbT+nPHsncdjXObae+f+1aWOxrnNtPGBwA60q093UOHDpXP55PP59Oxxx7bJvEOhULat2+frr/+escbCaBvmVayxbTlsab1Z+fuekfj3GZaf/YfsDdDajfObaa9f7bvar+9rzdxbjNtfACgI91Kun/5y1/KsixdffXVWrRokfx+f+tzycnJysvLU0FBgeONBND3IiVbDj3IJseDB3WZtlzetP6MHJqq96v22orzAtP6M2hgomobuk6oBw30xinZpr1/8rLStP5De3FeYNr4AEBHupV0X3HFFZKk/Px8jR8/XgMGDOiTRgFwR+HoXE0N5Hi+JNVpo4YqwSd1tgUwwdcS5wWR5f9dlaTyyvL/B751qkbf/WdbcV5gWn/+dP0EnfPLtbbivMC068EdMwJasnGnrTgvMO36BgAd6dGe7smTJysxMVEffPCBNmzYoHXr1rX5AuBdiQk+FRydpQtOOUIFR2d5LuGWpM07dnd6gy213IBv3rE7Ng3qpcjy/2hd8lpJqsEpSTppRPRalpJ00ogMz9TrNq0//72xwtE4t5l2PUhNTtTUQHanMVMD2Z6p123a9Q0AOtKjO4CNGzfqO9/5jnbs2CHLanuZ9Pl8CoW8sc8LgJnYIxj/ls2fGLVsmNfqdEtm9Yc9w/Gv+PKxUcuGebVONwCYrEdJ9/XXX6+vf/3reumll5Sbm9vhSeYA4BbT9ghGSupE41NLSZ2pgRxPzQYtmz9R+xqadctzb2vn7nqNHJqqB751qmdmhA9lSn9M2zOcmZrsaFy8KL58rOobQ7pvZbm276pTXlaa7pgR8MwMd4Sp1zcAOFiP7gQ+/PBD/eEPf9AxxxzjdHsAoNdM2yPYnZI6BUdnxa5hDhickqTiK8yZlUtNTtTVE45qPRPBawmQZN6e4X980fUhd5G4iccN6+PWOCs5KUEzThze+npLTurRrkFXmXx9A4CIHiXdp59+uj766COSbgBxiRJocENJWWW70/9zPXj6f2TPcEdLlyO8tGf4k932lsHbjYsXprzeuL4B6A9sJ93vvPNO6/+/4YYb9P3vf19VVVU68cQT251iftJJJznXQgDoAUqgIZZKyio1d+mWdisrqoINmrt0ixbPGuOp19xRhw3q1fPxZFSmvWXwduPigUmvN65vAPoDn3XoSWhRJCQkyOfztTs4rfUH/d9zXjxIrba2Vn6/X8FgUBkZnZ9AC8BbGpvDWlK6XTtq6jQqM02zC/I8twQzFLY04f7VXS6X33DbFM/M3pskMj7Rlsh6bXwam8M6/q6Xuyyx9Y97pnvivVTfGNLXfljSZdx7Pyr0xOy9aa83rm8AvMxuHml7pruiwhulQQAgoqPll7/dUOG5mW7TlsubxrQ9qUtKt9sqsbWkdLuumXhUbBrVC1s/2WM7zgvjY9rrjesbgP7AdtI9atSovmwHADjKpOWXklnL5U1j2p7UHTX29jbbjXObaeNjWn8krm8AzNejg9SWLVvW4eM+n08pKSk65phjlJ+f36uGAXBHKGxpU0VN62m44/IzPTfDEClB09FknSXvlqApHJ2rcXlZ+vZv3lD13kZlpyfr2WvHK3Owt0odHezL2gO66JENqtnfpMxBA/T8dydoWMZAt5vVLabtSTVtD/Rhg+29nuzGuc2011tE4ehcTThmmOdL7pnMhPsDwC09upJdeOGFHe7vPnhf94QJE/TCCy9o6NChUX9OUVGR/vSnP+kf//iHUlNTNX78eN1///067rjjJEk1NTVauHChXnnlFe3cuVPDhg3ThRdeqHvuuUd+v7/15+zcuVNz587VmjVrNHjwYF1xxRUqKipSUhIXaqA7TDkN17TllxGTf7ZaO3bVt36/p75JY368SqOyUrX21ikutqxnTrr7z6ptaG79vm5PSGPv+4syUpL0zt3nutiy7omUqOvsNZfroRJ1swvydO/K97rc0z27IC9mbeoVWyfXdCPOZaa93iLmPP1WmxPz36/aq9F3/1lTA9kqvtycsoJeZcr9AeCWHp2AsmrVKo0dO1arVq1SMBhUMBjUqlWrdPrpp2vFihVat26ddu3apf/4j//o9OesXbtW8+bN08aNG7Vq1So1NTVp2rRp2r9/vyTp888/1+eff66f//znKisr01NPPaWSkhJdc801rT8jFArpvPPOU2Njo9544w3993//t5566in98Ic/7EnXgH4rshz70Bu5yHLskrJKl1rWfSYuvzw04T7Yjl31mvyz1TFuUe8cmnAfrLahWSfd/ecYt6jnEhN8nSZAUsuHPF6ZEUpOSrC1p9sLh6hJ0lf7Dzga57bEBJ++7OLa9eVe77zepPYJ98FWlVdrztNvxbhFOJhJ9weAW3o0FXzTTTfpN7/5jcaPH9/62De+8Q2lpKTo2muv1d///nf98pe/1NVXX93pzykpaXua6FNPPaXs7Gxt3rxZkyZN0ujRo/XHP/6x9fmjjz5a9957r2bNmqXm5mYlJSXplVdeUXl5uf7yl7/o8MMP1ymnnKJ77rlHt912m+6++24lJ3t32SUQK6Ytxx6SMqDroG7EuS1Y1xQ14Y7Ysatewbom+dPiv09f1h6ImnBH1DY068vaA55Yar7xg1224844Nv5XVpR/Wms7LjAi/it++JrtTWHbjXPbZzX1ag53HtMcbok7IjM1No3qhfrGUKc14aWWxLu+MeSJ0+VNY9r9AeCWHn1MvW3btg6PRM/IyNDHH38sSfqXf/kXffXVV936ucFgUJKUmRl9SVTkOPbI0vHS0lKdeOKJOvzww1tjzj33XNXW1urvf/97hz/jwIEDqq2tbfMF9GfdWY7tBaveq3I0zm1XP7XJ0Ti3XfTIBkfj3PbtJzY6Gue2f31ovaNxbrv9xXcdjXPb9AfXOhrntvtWljsaB2eZdn8AuKVHSfdpp52mW2+9VV9++WXrY19++aX+8z//U2PHtuy7+fDDD3XkkUfa/pnhcFg333yzzjzzTI0ePbrDmK+++kr33HOPrr322tbHqqqq2iTcklq/r6rq+Ia6qKhIfr+/9as77QRMZNpy7B01nc8KdzfObZ93sXS5u3Fuq9nf5GgcnNXFJGq349xW32SvpXbj3Lb/QMjROLdt32XvFHy7cXCWafcHgFt6lHQ//vjjqqio0IgRI3TMMcfomGOO0YgRI7R9+3b99re/lSTt27dPd955p+2fOW/ePJWVlenZZ5/t8Pna2lqdd955CgQCuvvuu3vS7FYLFixo3YseDAb1ySef9OrnAV5n2mm4eVn2TlW2G+e24X57v3e7cW7LHGRvCbzdODjL7o2BN3Z0S6kD7LXUbpzbBg20t8TabpzbTLtem8a0+wPALT36C3PcccepvLxcL774om688UbdeOONWrZsmf7+97/r2GOPldRywvns2bNt/bz58+drxYoVWrNmjUaMGNHu+b1796qwsFDp6el6/vnnNWDAP2/EcnJy9MUXX7SJj3yfk5PT4b83cOBAZWRktPkCeioUtlS6bZde3PqZSrftUqirE4jiUOQ03Gi7sXzy1mm4d8wIOBrntieuHOdonNv+cP2Zjsa57elZ9k5Wthvntj/Z/L3bjXPbSzdMcjTObS/fONnROLfdVvg1R+PgLNPuDwC39Phj3YSEBBUWFrYm3eeee64SErr34yzL0vz58/X8889r9erVHdb2rq2t1bRp05ScnKxly5YpJaXtJ2kFBQV69913VV39z0M4Vq1apYyMDAUC3rihhneVlFVqwv2rdWnxRt307FZdWrxRE+5f7bmTPBMTfFo4s+X9cugf1sj3C2cGPHNISmpyoqYGsjuNmRrI9syhPP60ARqV1fmBSKOyUj1xiJokVeza72ic21Z99EXXQd2Ic9vmT+ztzbQb57Yqm8te7ca57YjMVCUndn4tTk70eeIQNUl697Ogo3Fwlmn3B4BbbJ9e/uCDD+raa69VSkqKHnzwwU5jb7zxRls/c968eXrmmWf04osvKj09vXUPtt/vV2pqamvCXVdXp6VLl7Y59GzYsGFKTEzUtGnTFAgENHv2bP30pz9VVVWV7rzzTs2bN08DB8b/qbfwrkgJjUPntSMlNBbPGuOp2pWFo3O1eNaYdnU4czxah7P48rFRy9B4se7r2lunRC0b5rU63abtETRtT+qOGnvttBvnNtNeb5L0wb0zdOwPVqox1H5lVXKiTx/cO8OFVvWMieNjGtPuDwA32E66H3jgAV122WVKSUnRAw88EDXO5/PZTroXL14sSTrrrLPaPP7kk0/qyiuv1JYtW/Tmm29Kko455pg2MRUVFcrLy1NiYqJWrFihuXPnqqCgQIMGDdIVV1yhH/3oR3a7BnSbqSU0CkfnamogR5sqalS9t0HZ6S1LxrzUh4MVXz5W9Y0h3beyXNt31SkvK013zAh4Zob7UGtvnaJgXZOufmqTPg82aLg/RU9cOc4zM9wRhw2y94Go3Ti35WWlaf2H9uK8YFSmvXbajXObqXtSP7h3hj6rqdf0B9dq/4GQBg1M1Ms3TvbMDHeEqeNjGtPuD4BY81mW5b0NqA6rra2V3+9vLUcGdKV02y5dWtx1+Z/fzTlDBUfHf11eIJZe/+grXfbbN7uM+5//d7rOPOawGLSod+obQ/raD0u6jHvvR4We+MCnsTms4+96WZ0dT5Hgk/5xz3QlJ8X/4WOhsKUJ969WVbChww9KfWqZsdtw2xQSCBcwPgC8zG4e2au/lo2NjXr//ffV3Nzcmx8DeA7L4YCe+2rfAUfj3GbaGQLJSQmaM7H9GSsHmzMx3xMJt8Se1HjH+ADoD3r0F7Ourk7XXHON0tLSdMIJJ2jnzp2SpBtuuEE/+clPHG0gEI9YDgf0nInvn+LLx0ZNvL14hsCpI4f26vl4E9mTmnNIWb0cf4rnzt8wEeMDwHS293QfbMGCBfrb3/6m1157TYWFha2Pn3POObr77rt1++23O9ZAIB5FSmh0tRyOEhpAe6a+f0w5QyByZkU0nFmBvsD4ADBZj5LuF154Qc8995zOOOMM+Xz/vBiecMIJ2rZtm2ONA+JVZDnc3KVb5JPaJA4shwM6Z/L7JzU5UfdceKLbzeiVTRU1bU4oPpQlqTLYoE0VNZ47syIxwee5NvcnjA8AU/VoefmXX36p7Oz2y+j279/fJgkHTMZyOKDneP/EL86sAADAWT2a6f7617+ul156STfccIMktSbav/3tb1VQUOBc64A4x3I4xNqXtQd00SMbVLO/SZmDBuj5707QsAxvlNY6VOHoXB1zWLqmP7hWTWFpQIK05KrTdUzOYLeb1mMmlHTLTE12NC6e7Gto1i3Pva2du+s1cmiqHvjWqRqc0qNbobjQ2BzWktLt2lFTp1GZaZpdkOeZA+76g1DY4v4AgKQelgzbsGGDpk+frlmzZumpp57Sddddp/Lycr3xxhtau3atTjvttL5oa5+hZBgALzjp7j+rtqF9tYiMlCS9c/e5LrSod45a8FKHZakSfNLHRefFvkG9NPlnq7VjV327x0dlpWrtrVNcaFHPFK/7WPeufK/LuB/M+JrmTDoqBi1yxvkPrdc7n9a2e/ykERlaNn+iCy3qnaKV5SpeX9HmPZTgazlZfsGMgHsNgySppKxSi5aXt9mqketP0cKZAVbyAAbp05JhEyZM0NatW9Xc3KwTTzxRr7zyirKzs1VaWuq5hBsAvCBawi1JtQ3NOunuP8e4Rb0TLeGWpLDV8ryXREu4JWnHrnpN/tnqGLeo5z7ZXedoXDyIlnBL0juf1ur8h9bHuEW9U7SyXI+tq2j3Hgpb0mPrKlS0MvpBeOh7JWWVmrt0S7uzEaqCDZq7dItKyipdahkAt3RrTVVt7T//YA0bNkz/9V//1WEMs8UA4Jwvaw9ETbgjahua9WXtAU8sNa+o3h814Y4IWy1x+dmDYtOoXgjWNUVNuCN27KpXsK7JE0vNhwy0d2tgN85t+xqaoybcEe98Wqt9Dc2eWGre2BxW8fqKTmOK11fo+9OOZ6m5CyKn/3d0ibPk3dP/AfROt67GQ4YM0dChQ6N+RZ4HADjnokc2OBrntsJfrXU0zm1XP7XJ0Ti3PVXaeULX3Ti33fLc247GuW1J6XZbH1otKd0ek/agre6c/g+g/+jWR7pr1qxp/f+WZWnGjBn67W9/qyOOOMLxhgEAWtTsb3I0zm0HQvaOErEb57bPO7nB7kmc2/Y3hh2Nc9vO3Z2vQuhunNt21Nhb1m83Ds7i9H8AHelW0j158uQ23ycmJuqMM87QUUd55yAVAPCazEEDVLcnZCvOCwYm+mwl1AMTvbH0crg/pdOZrYPjvGDQwETVNnT9ehs0MDEGrem9kUNT9X7VXltxXjAqM83RODgrO93e+9xuHAAzsNkHAOLc89+d4Gic20pumtx1UDfi3PbEleMcjXPbyzfa+73bjXPbz//9FEfj3Da7IE9dbQVO8LXEIfbG5Wcq15+iaEPkU8sp5uPyM2PZLAAuI+kGgDg3LGOgMro44CkjJckTh6hJUn72IFtJgxcOUZMkf9oAjcrqfJZ0VFaqJw5Rk6QjMlOV3MUqg+REn47I9MbMcHll54eodTfObclJCZozMb/TmDkT8zlEzSWJCT4tnNlSsu3Qd1Hk+4UzAxyiBvQzvb4i+3xcNACgr/30307q1fPx5uOi86Im3l6s07321ilRE2+v1emWpA/unRE18U5O9OmDe2fEuEU9Z+Ie2wUzArpuUn6791CCT7puEnW63VY4OleLZ41RziFbSnL8KVo8awx1uoF+yGdZlu2Tai6++OI23y9fvlxTpkzRoEFtZyP+9Kc/OdO6GLFb1BwA3BAKW5pw/+qo+4Z9armZ23DbFM/NnlRU71fhr9bqQMjSwESfSm6a7JkZ7o4E65p09VOb9HmwQcP9KXriynGemeHuyGc19Zr+4FrtPxDSoIGJevnGyZ6Z4Y54/cOvdNnjb3YZ9z/XnK4z/+WwGLTIOY3NYS0p3a4dNXUalZmm2QV5zHDHkVDY0qaKGlXvbVB2esuScq9dowF0zm4e2a2D1Px+f5vvZ82a1bPWAQBs604JmoKjs2LXMAcckZmq/yw8vjVp8FpCd6jBKUn6j3OPb73J9kLd587kDEnRY7PHtvYnZ4gHD3+ym+N4MBdKTkrQNRM5zDZeJSb4PHdNBtA3unU38OSTT/ZVOwAAUZi4PFaSilaWq3h9RZuaw/eufE9zJnpzeWxJWaUWLS9v8wFJrj9FC2cGPLmc1JT+fLXvgKNxAAB0F2uQACDOmViCpmhluR5b1zbhlqSwJT22rkJFK8vdaVgPlZRVau7SLe1WJFQFGzR36RaVlFW61LKeMak/Jr5/AADeQtINAHHOtBI0jc1hFa+v6DSmeH2FGpvDMWpR74TClhYtL1dHB6REHlu0vFyhQz9hiFOm9eeUI4c4GgcAQHeRdANAnIuUoImW4ljyVgmaJaXb281wHypstcR5QXf23HuBaf155s0djsYBANBdJN0AgJjaUVPnaJzbTNtzb1p/THu9AQC8h6QbAOJcZLlvND55a7nviCH2Tii3G+e2zLRkR+PcZtoe6COH2nsd2Y0DAKC7vF3LBIDjTKsruq+hWbc897Z27q7XyKGpeuBbp3qujJNpJcOsqAvlexbntn9U1dqOm3jssD5uTe9FzhCoCjZ0OAKRuvBeOUPg+JzodVN7EhdPTLi+Hcy0OvcAEOHdKzMAx5lSIiji/IfW651P/5kQvV+1V6Pv/rNOGpGhZfMnutiy7jFtue9ne+y1026c2z7ZXe9onNsiZwhcv3RLh8977QyBmrpGR+PihSnXt4jJP1utHbv++R6pDDbo5B+9olFZqVp76xQXWwYAvcfycgCSzCoRJLW/IT3YO5/W6vyH1se4RT1n2nLfUZlpjsa5zbT+SIqacNt9Pp6Y9v6RzLq+Se0T7oPt2FWvyT9bHeMWAYCzSLoRc6GwpdJtu/Ti1s9Uum2XZ/ahRmNCf0wrEbSvoTnqDWnEO5/Wal9Dc4xa1Dsjh9pL1uzGuW3q13IcjXPbKUcMdTTObW/84ytH49yWnzXI0Ti3mXZ9C9Y1RU24I3bsqlewrilGLQIA55F0I6ZKyio14f7VurR4o256dqsuLd6oCfev9twsaoQp/TGtRNAtz73taJzbLnjY3qyV3Ti3Xbx4g6Nxbvu337zhaJzbvvPUm47Gue3fHn3d0Ti3mXZ9u/qpTY7GAUA8IulGzJi2fNmk/pi2Z3inzb2zduPcVmtzxspunNtM64/d9R/eWCdinpr99mZI7ca5zbTr2+edfODbkzgAiEck3YgJ05Yvm9Yf0/Y8HjnUXjvtxrktw+ZpxHbj3GZaf+weJ+aNY8fMkznI3unXduPcNtJmaTO7cW4b7rd3HbYbF09M2H4GwBkk3YgJ05Yvm9afSImgaEmBTy2nmHulRNBlY0c5Gue2FTdMcjTObab1Z9l3Jzga57ZLxx3haJzbnrf5e7cb57YHvnWqo3Fue+LKcY7GxQtTtp8BcAZJN2LCtOXLpvUnUiJIaj8bF/neSyWCahttLl+2Gee2nCEpSk7s/HefnOhTzhBvzATlDElRVy+lBJ88058TR/odjXPbgER7KwzsxrltWMbALldNZKQkaVjGwBi1qHcGpyRp2ODkTmOGDU72TL1uf9oAW/3xUr1uk7afAXAGSTdiwrTly6b1R5IKR+dq8awxyjlkCV+OP0WLZ43xVJ3uwwbbu3m2G+e2UNhSVhdtzRo80DNLF+sbQ+qqqWGrJc4rtv/kvF49H0+OGGJvWbLduHjwzt3nRk28M1KS9M7d58a4RT3X2BzWrv2d1xTftb9Rjc3hGLWod0JhS0mJnd+OJiUmeOb6Ztr2MwDO8MbHoPC8yPLlqmBDh3+IfGpJ7ryyfNm0/kQUjs7V1ECONlXUqHpvg7LTW/rglRnuVoadbNXVdgbpn9sZCo7OilGreu6+leW24+658MQ+bo1ztv/kPG3+eLcuOeiU8j9eO16nHeWNUmERPpvvC7tx8eKdu8/Vl7UHdNEjG1Szv0mZgwbo+e9O8MwMd8SS0u22PrRaUrpd10w8KjaN6gXTrm/d2X7mhf4AcAZJN2Iisnx57tIt8qltruPF5cum9edgiQk+z98IfLX/gKNxbjNtO8P2XXWOxsWLopXlKl5f0eaxfy9+Q3Mm5mvBjIBLreq+T4P2Tr22GxdPhmUM1Ibbv+F2M3plR42994XdOLeZdn0zrT8AnMHycsSMScuXJfP6YxLTlv+b1p+8rDRH4+JB0cpyPbauot0MZNiSHltXoSKbs/vxYFSmvd+73Tg4y7TxMe36Zlp/ADiDmW7ElDHLl/+Paf0xhWnL/03rzx0zAlqycaetOC9obA63m+E+VPH6Cn1/2vFKTor/z7q/c/oo3fPSe7biEHuzC/J078r3Ol1inuBrifMC065vpvUHgDPi/68/jBNZvnzBKUeo4OgszyeopvXHBKadxh7pT7R7bEve6k9qcqKmBrI7jZkayFZqcmKMWtQ73dlj6wVbP9njaByclZyUoDkT8zuNmTMx3xMf8EjmXq8lM/oDwBneuCIDQDeZtvz/7Z27e/U8+g57bBFrC2YEdN2k/Hal9xJ80nWTvHWGgGTe9dq0/gDoPZaXAzCWKcv/TVu+XN8Y0qry6k5jVpVXq74x5InZ7hE2S2fZjXMbe1K9YcGMgL4/7XgtKd2uHTV1GpWZptkFeZ64BnTElOt1hGn9AdA7JN0A2mhsDhtzEyeZcRq7aSWCTCsZZtmsPWc3zm0m70n9rKZe0x9cq/0HQho0MFEv3zhZR2R648OQ/sCE6/XBGpvDWvnu59q+q055WWk65cghnvggEYDzSLoBtIqUPDo4wbt35XueK3lkGtOWL5tWMuyzPfaWWduNc1tkT+r1S7d0+LzXzhCIOPYHK9UY+ufFrbYhpDN/ulrJiT59cO8MF1vWM1yv49ucp99qs6Jn/YfSko07NTWQreLLx7rYMgBu8O70FQBHmVTyyDSmlQgyrWSYaeMjSfOf6Tjhtvt8vDk04T5YY8jSsT9YGeMW9Q7X6/h2aMJ9sFXl1Zrz9FsxbhEAt5F0A7C9Z7ixORyjFuFgk/+l85O+uxvntqvH21sCbzfObROOHuZonNt2flWnrt7qzeGWOC/4rKY+asId0Riy9FlNfYxa1Dtcr+Nbd86sANB/kHQDMK7kkWnOf3i9o3Fu+/fHXnc0zm2mjU/hr9Y6Gue26Q/aa6fdOLdxvY5v3TmzAkD/QdINwLg9w6apb7I3Y2U3zm21Dc2OxrntQBezqN2Nc5tpr7f9B+zNKNqNcxvX6/hm2pkVAJxB0g3AyD2pJkkdYO9SbTfObekp9s7wtBvntmSbv3a7cW4z7fU2aKC906LtxrmN63V8M+3MCgDO8MZfTAB9anZBnro6iDjB1xKH2Lv//NGOxrlt9tiRjsa57f6LT3Y0zm0lN012NM5tL99or51249zG9Tq+3WHz5Hi7cQDMQNINQMlJCZozMb/TmDkT8z1dr9vLwjZnFO3Gua3mgL1l43bj3JaQZK90lt04t408LE1dvdWTElrivOCIzFQlJ3b+u09O9HmmXjfX6/iWmpyoqYHOD7WcGsimXjfQz3BFBiBJWjAjoOsm5bebQUnwSddNou6rm7LTUxyNc5tpy2NNGx9J+ui+86Im3kkJLc97yYOXntqr5+PNqSOH9up59K3iy8dGTbyp0w30Tz7Lsrxxsksfqq2tld/vVzAYVEZGhtvNAVzV2BzWktLt2lFTp1GZaZpdkOfpGZNQ2NKmihpV721QdnqKxuVnKrGrtZlxprE5rOPvernTE4sTfNI/7pnuibEyrT+hsKUJ969WVbBBHXXJJynHn6INt03x3Gtv51d1KvzVWtU3hZU6IEElN032zAx3RGR8KoMNHT7vtfExrT8mq28M6b6V5dq+q055WWm6Y0aAGW7AMHbzSG+cUgMgZpKTEnTNRG/UR+5KSVmlFi0vb3NzmutP0cKZARWOznWxZd2zecduWyWCNu/YrYKjs2LTqF6ILI99bF30WsNeWh6bmODTwpkBXb90S4fPW5IWzgx4MgEaeViayu+Z7nYzemVTRU3UBFVqGZ/KYIM2VdR44v1jWn9MlpqcqHsuPNHtZgCIA964owGAbiopq9TcpVva3ZxWBRs0d+kWlZRVutSy7qveG/0GuydxcN4ft3zaq+fRd0x7/5jWHwDoD0i6ARgnFLa0aHl5h0t9I48tWl6uUFfTx3HisMEDHY1zW2NzWMXro89yS1Lx+go1NnujDnR9Y0iryqs7jVlVXq36Rm/UgTaNaXvuTesPAPQHJN0AjNOd5ZeeYPezAW98hqAlpdttLZdfUro9Ju3prftWljsaB2eNy89Urj9F0Rb3+9Sy7WRcfmYsm9VjpvUHAPoDkm4AxjFt+eVX+w84Gue2HTV1jsa5bfsue+20GwdnRfbcS2qXqEa+99Kee9P6AwD9AUk3AOOYthzbtP6YVjJslM36znbj4LzC0blaPGuMcvxtl1zn+FO0eNYYTx2sKJnXHwAwnatJd1FRkcaOHav09HRlZ2frwgsv1Pvvv98mpqGhQfPmzVNWVpYGDx6sSy65RF988UWbmJ07d+q8885TWlqasrOzdeutt6q5uTmWXUE/FgpbKt22Sy9u/Uyl23Z5Zp+w0Qxbjm1af2YX5LWrB3+oBF9LnBdM/VqOo3HxZOMHu5R3+0utXxs/2OV2k3qscHSu/nDdeGWkJCrRJ2WkJOoP1433bIJaODpXz80pUNqABPkkpQ1I0HNzCjzbH6nlfIS7XnhXsx9/U3e98K7nz0Hg/gBAhKslw9auXat58+Zp7Nixam5u1h133KFp06apvLxcgwYNkiTdcssteumll/T73/9efr9f8+fP18UXX6zXX39dkhQKhXTeeecpJydHb7zxhiorK3X55ZdrwIABuu+++9zsHvoBU0pSmca05dim9ce0kmF7GpocjYsXebe/1O6xbz+xUZK0/Sfnxbo5vfa1u15WfdM/D+erbQjpzJ+uVuqABL3nwbJox/5gpRpD/0zi6prCmvTzNUpO9OmDe2e42LKemfP0W20OJFz/obRk405NDWSr+PKxLrasZ7g/AHAwV+9oSkpKdOWVV+qEE07QySefrKeeeko7d+7U5s2bJUnBYFCPP/64fvGLX2jKlCk67bTT9OSTT+qNN97Qxo0tf/hfeeUVlZeXa+nSpTrllFM0ffp03XPPPXr44YfV2NjoZvdgOJNKUpnGtNN9TeuPpE4TbjvPxxMTx6ejhLs7z8ebQxPug9U3hfW1u16OcYt659CE+2CNIUvH/mBljFvUO4cm3AdbVV6tOU+/FeMW9Q73BwAOFVfTCMFgUJKUmdly4ubmzZvV1NSkc845pzXm+OOP18iRI1VaWipJKi0t1YknnqjDDz+8Nebcc89VbW2t/v73v8ew9ehPTCtJZZoj/Pb2ztqNc1tmarKjcW5bvbXK0Ti3HdhvbzuT3Ti32V1C7pWl5lV7GqIm3BH1TWFV7fHGwYqf1dRHTbgjGkOWPqupj1GLese0knvcHwDoSNwk3eFwWDfffLPOPPNMjR49WpJUVVWl5ORkDRkypE3s4YcfrqqqqtaYgxPuyPOR5zpy4MAB1dbWtvkCusO4klSG+deH1jka5zbT+nP1s5sdjXPblc/81dE4t0WWkDsV57Z//bXN94/NOLdNf3Cto3FuM63kHvcHADoSN0n3vHnzVFZWpmeffbbP/62ioiL5/f7WryOPPLLP/02YxbSSVKbZf8DejIjdOLd1MUnX7TigP6ltsLfCwG6c20y7vplWco/7AwAdiYuke/78+VqxYoXWrFmjESNGtD6ek5OjxsZG7dmzp038F198oZycnNaYQ08zj3wfiTnUggULFAwGW78++eQTB3uD/sDEPZwmGTQw0dE4tw2weaW2Gwf0Jxkp9s6MtRvnNtOub3lZ9koD2o1zG/cHADri6i2aZVmaP3++nn/+ea1evVr5+fltnj/ttNM0YMAAvfrqq62Pvf/++9q5c6cKCgokSQUFBXr33XdVXf3P/UCrVq1SRkaGAoFAh//uwIEDlZGR0eYL6I5x+ZnK9acoWtUjn1pOKR2XnxnLZjnChBInL9842dE4t5nWnye+fZqjcW77wblHORrntoX/epyjcW5bccMkR+PcZtr14I4ZHd+r9TTObSbfHwDoOVc/1p03b56eeeYZvfjii0pPT2/dg+33+5Wamiq/369rrrlG3/ve95SZmamMjAzdcMMNKigo0BlnnCFJmjZtmgKBgGbPnq2f/vSnqqqq0p133ql58+Zp4MCBbnYPBktM8GnhzIDmLt0in9qWR478oV04M6DErooRxxlTSpwckZmq5ERfp4cNJSf6dESmNw5SOyZncLvX2aF8/xfnBVNOyZFs7CSacoo36lpnD7X3wa3dOLdlDrb3vrAb57acISlKHZDQ6WFqqQMSlDPEGzOPpl3fUpMTNTWQ3elhalMD2UpN9sbMvan3BwB6x9WZ7sWLFysYDOqss85Sbm5u69dzzz3XGvPAAw/oX//1X3XJJZdo0qRJysnJ0Z/+9KfW5xMTE7VixQolJiaqoKBAs2bN0uWXX64f/ehHbnQJ/Ujh6FwtnjVGOf62N2o5/hQtnjXGU0mqZF6JkwcvPbVXz8ebxbPG9Or5ePNoF+3t6vl4YtpyUtP6I0kPfOuUXj0fbz64d4aSEztO2rxYp7v48rGaGsju8Dkv1uk27f4AQO/5LMvy3tpRh9XW1srv9ysYDLLUHN0WClvaVFGj6r0Nyk5vWTLmtU+wQ2FLE+5fHfXEVZ9abhY23DbFE32jP/HN1P5UBRs6XI1Af9xl2uvtYJ/V1Gv6g2u1/0BIgwYm6uUbJ3tmhrsj9Y0h3beyXNt31SkvK013zAh4Zoa7IybcHwDonN080hunhgBxLDHBp4Kjs9xuRq90p8SJF/pKf+Kbaf2JLCe9fumWDp+35K3lpKYtjzXt9XawIzJT9c7dhW43wzGpyYm658IT3W6GY0y4PwDgDM66BWBciRP6E99M64+JTFoey+sNAOA2ZroRcyy3ij+m7eHMTE12NM5t6QMHOBrntsMG2Tvk0m6c20JhS4uWl0d93idp0fJyTQ3keOpaVzg6V1MDOZ6/Xpt2fQMAeA9JN2LKlNOxTRMpcdLVHk6vlDj5xxd7bcdNPG5YH7em9555c7vtuClf6/gworhiN2fzSG5n8vJlE5bHmnZ9AwB4D8vLETOmnY5tksgeTql9nuPFPZyf7K5zNM5tn+yxt+zVbpzbvtp3wNE4t7F8Ob6Zdn0DAHgPSTdiIrL8sqNZhshji5aXKxTu94fpu8akPZyjMtMcjXPbyKH2TiO2G+c205aXHzbYZn9sxsF5Jl3fAADeQ9KNmOjO8ku4p3B0rl66YaKOzR6kIakDdGz2IL10w0TP3ZDOLshTV5NWCb6WOC944Fv2aorbjXOdYcvLO/w0sTdxcWTnV3UK3PWy8m9/SYG7XtbOr7yxOqQjplzfAADew55uxATLL71h8s9Wa8eu+tbv99Q3acyPV2lUVqrW3jrFxZZ1T3JSguZMzNdj6yqixsyZmK/kJG987jg4JUknjcjQO5/WRo05aUSGBqd445Ju2vLyr/bb7I/NuHhxzB0vqTn8z+/rmsKa9PM1SkqQPrrvPPca1kOmXN8AAN7jjTtOeB6nx8a/Q29ID7ZjV70m/2x1jFvUO7/b9Emvno8327uYYezq+Xhi2vXAtP5I7RPugzWHW573EtOubwAAbyHpRkxETo+NtlrUp5ZTzDk91h3BuqaoN6QRO3bVK1jXFKMW9c6XtQdU29DcaUxtQ7O+rPXGzKNp/Tlm2GBH49w2cqi9swHsxrlt51d1URPuiOawPLPU3LTr28FCYUul23bpxa2fqXTbLs5FAYA4RdKNmOD02Ph29VObHI1z20WPbHA0zm2m9eey35Y6Gue2Cx5e72ic2wp/tdbROLeZdn2LKCmr1IT7V+vS4o266dmturR4oybcv5pKIAAQh0i6ETOcHhu/Pu/kkLuexLmtZr+9GSu7cW4zrT/VexsdjXNbV6sQuhvntvqmLqa5uxnnNtOubxIlOAHAa7xx6g6MUTg6V1MDOdpUUaPqvQ3KTm9ZUs4Mt7uG+1M6PV3+4DgvyBw0QHV7QrbivGCozf4M9Uh/hqUna0991x8QDEtPjkFrei8jJUlf2fjAI8MjB92lDkhQnY2EOnWANz63N+361lUJTp9aSnBODeTwtxUA4oQ3/mLCKIkJPhUcnaULTjlCBUdncVMQB564cpyjcW57/rsTHI1z2w8Lv+ZonNv+85zjHI1z24obJjka57aSmyY7Guc2065vlOAEAO8h6QYgf9oAjcpK7TRmVFaq/GkemUnNGNjlrGJGSpKGZQyMUYt6p8FmgWe7cW6rC9tblmw3zm05Q1K6nPVNHZCgnCHemEkdeViauqqml5TQEucFpl3fKMEJAN5D0g1AkrT21ilRb0y9WMf20nFH9ur5eGJaSSrT+iNJ790zPWrinTogQe/dMz3GLeqdj+47L2ri7cU63Qumd74KpKvn44mJ7x8AMB1JN4BWa2+dorfuOEcjhqQobUCiRgxJ0Vt3nOO5hLuxOazi9RWdxhSvr1BjV3WR4oRpJfdM60/Ee/dM18bbv6HDBg1QcqJPhw0aoI23f8NzCXfER/edpzXfO0sDE1tGamCiT2u+d5bnEu7IHuhoInugvVJuy9T3DwCYjKQbQKuileU6vegv+nRPg+qaQvp0T4NOL/qLilZGv2GNR0tKt6ur++ew1RLnBZGSe9G6ZMlbJfdM68/Bcoak6K93TdMH987QX++a5pkl5R0pKavUdx7fqAOhlpE6ELL0ncc3eu5kbNP2QFOCEwC8h6QbgKSWhPuxdRXtktWwJT22rsJTifeOmjpH4+LB2zt39+p5oDtMKkll4h5oSnACgLd4o34JgD5ldzn296cdr+SuTliKA0cM6fzQpO7Guc208bG73JeSR+4wrSTVYYPtHZhoNy5eUIITALwj/u/OAPQ505Zj+2xuzbQb5zbTxse05b6mMW587L7PPXI9OBglOAHAG0i6ARi3HPvTYL2jcW4zbXxMXO5rEtPG56v9BxyNAwCgu0i6AWhUpr16u3bj3EZ/4hslj+KbaeNjWn8AAN5D0g30UihsqXTbLr249TOVbtvlmbIzB5tdkKeuViUm+FrivID+xLfTRg211Z/TRg2NTYMctPnj3cq7/aXWr80fe++AO9PGx+QSW/WNId31wrua/fibuuuFd1XfGHK7Sb1iwt/Tg5k2PgB6joPUgF4oKavUouXlbfY/5vpTtHBmwFOnxyYnJWjOxHw9ti76YV1zJuZ74pAuif7Eu807dtvao755x24VHJ0Vm0Y5IO/2l9o9dslv3pAkbf+Jd2pbmzY+kRJb1y/d0uHzXi1RN+fpt7SqvLr1+/UfSks27tTUQLaKLx/rYst6xpS/pxGmjQ+A3vHGHRoQh0wqqSNJv9v0Sa+ejzedJah2no83j2/ovL1dPR9PTNszLHWccHfn+Xhi4vjc9UJZr56PN4cmdAdbVV6tOU+/FeMW9Y5pf09NGx8AvUfSDfRAVyV1pJaSOl5ZGvdl7QHVNjR3GlPb0Kwva71x0NDW7XscjXPbzq/q1BzuPKY53BLnBdW1+x2Nc5vdJeReWWoesrkE1m6c22r2NerLfY2dxny5r1E1XcTEi/rGUNSELmJVebVnljKb9vfUtPEB4AySbqAHTCupc9EjGxyNc9uFj77uaJzbCn+11tE4t9278kNH49wWWULuVJzbbnvhXUfj3PZtm793u3Fuu29l9Br3PYlzm2l/T00bHwDOIOkGesC05Zc1+5scjYOz6pu6mObuZhzQGbsvI6+83Kr32pvBthvntu277K1osRvnNtP+npo2PgCcQdIN9IBpJWiGptk7U9FuHJyVOsDepdpuHNAZuy8jr7zcstOTHY1zW16WvdKAduPcZtrfU9PGB4AzPPInE4gvppWgueu8ExyNc9sL15/paJzbSm6a7Gic22aNtXcSsd04t/3x2vGOxrnt5RvtvY7sxrntWZu/d7txbrtjRsDROLeZ9vfUtPEB4AySbqAHIiVoJLW7UYh876USNAfC9taJ2o1z2yl5QxyNc9vIw9LUVTWwpISWOC9ISLI3o2g3zm2nHWWvXrXdOLcdkzM4agIU4fu/OC/IHJysYYM7fy0NG5yszC5i4kVqcqKmBrI7jZkayFZqcmKMWtQ7pv09NW18ADiDpBvoocLRuVo8a4xy/G2XvOX4U7R41hhP1RU1bXmf1HVdZC/VTZakaybk9+r5eDIq096HA3bj4sF1kzr//Xf1fLxZPGtMr56PN2/dOTVq4j1scLLeunNqjFvUO8WXj42a2HmxDrRJf08l88YHQO/5LMvyRg2GPlRbWyu/369gMKiMjAy3mwOPCYUtbaqoUfXeBmWntyyB88on8hGhsKUJ969WVbChw7ItPrXc/Gy4bYrn+rZ1+542p5S/cP2ZnpnhjmhsDuv4u15WZxVzEnzSP+6ZruSupsTjQM2+Ro358aou47bcOdUTs4+mjU/kehDtRGkvXw++rD2gix7ZoJr9TcocNEDPf3eChmUMdLtZPVbfGNJ9K8u1fVed8rLSdMeMgKdnUE34e3ow08YHQHt280iSbpF0A5JUUlap65duifr8ox6cbTDF4+s/1j0vvddl3F3nfU3XTDwqBi3qnTn//ZZWvdd5HVtJmvq1bBVfEf8zQqaNT+m2Xbq0eGOXcb+bc4YKjs6KQYucUVJWqUXLy9t8mJDrT9HCmQGubQCAHrGbR8b/R+4A0M/tqLFXWsZunNt27q53NM5tpo2PaSWcpJaEe+7SLe1m76uCDZq7dItKyipdahkAoD8g6QagUNjSouXlUZ/3SVq0vFyhztbPos8cOTTV0Ti3jbTZTrtxbjNtj7ppZzxErm8dXb0ij3F9AwD0JZJuANpUURN1/6bUcmNaGWzQpoqa2DXKIaGwpdJtu/Ti1s9Uum2XJ2+sj8+xt+3FbpzbHvjWqY7GuW12QZ662naa4GuJ8wLTSjiZfH0DAHhDktsNAOA+E5eTSubs4aypa3Q0zm2DU5J00ogMvfNpbdSYk0ZkaHCKN/5EJSclaM7EfD22riJqzJyJ+Z44RE36ZwmnuUu3yCe1mSH2YgknU69vAADv8MYdAIA+ZdpyUsmsPZwmjs+y+RN10oiOZ+ZPGpGhZfMnxrhFvXPqyM5rcHf1fLwxqYSTie8fAIC3eGMaAYhjJpQ4OW3UUCX41GXJo9NGeSNx6GoPZ2SP+tRAjifGyrTxiVg2f6Le/3yvZvx6nUKWlOiTVt4wSccNT3e7ad1i90wEr7zeIgpH52pcXpa+/Zs3VL23UdnpyXr22vGeKON2sMhy+a5KInplufzBTPj7AwD9AUk30AumLF/evGN3pwmd1JLwbd6x2xMlgrqzh9ML/TFtfCKO/cFKNYb+2bGQJZ374DolJ/r0wb0zXGxZ95j2eouY/LPV2rHrnyfI76lv0pgfr9KorFStvXWKiy3rnshy+WglES15a7l8hCl/fwCgP2B5OdBDJi1fNm3PI/2Jf4cm3AdrDFk69gcrY9yinjNxfA5NuA+2Y1e9Jv9sdYxbhIOZ9PcHAPoDkm6gB0wrQTM4KdHROLel2myn3Ti37Wuwd0Ca3Ti3fVZTHzXhjmgMWfqsxht1ugf47M2Q2o1zW7CuKWrCHbFjV72CdU0xalHvmFYS0bS/PwDQH5B0Az1gWgmah9dtczTObT/783uOxrntBy9ETxh6Eue26Q+udTTObT9cVuZonNuufmqTo3FuM+16bVp/AKA/IOkGesC05aSd3cD1JM5tX+6zNwNnNw7O2n8g5Gic22obmh2Nc9vnNt/nduPcZtr12rT+AEB/QNIN9IBpJWiG++21026c24al2ztd2W4cnDVooL1l/Xbj3JZhs5643Ti3mXY9MO16bVp/AKA/IOkGeiBSgibaDk2fWk6R9UoJmieuHOdonNv+85zjHI1z29Xjj3Q0zm33zTzR0Ti3rbhhkqNxbjPtemDa9dq0/gBAf0DSDfRApASNpHY3PpHvvVSCxp82QKOyUjuNGZWVKn/agBi1qHfqwmFH49zWbNm7VNuNc1vI5gS23Ti35QxJUeqAzn/3qQMSlDPEGzOPpl0PTLtem9YfAOgPvHGHBsShwtG5WjxrjHIOWWKZ40/R4lljPFcnde2tU6LeaHutLq9pyy9HZaY5Guc208ZHkt67Z3rUxDt1QILeu2d6jFvUOyZdDyTzrtem9QcATOezLKvf15Sora2V3+9XMBhURkaG282Bx4TCljZV1Kh6b4Oy01uW9Hl5hiFY16Srn9qkz4MNGu5P0RNXjvPMjFZEKGxpwv2rVRVs6LCsjk8tN6cbbpviibFqbA7r+LteVmcVgBJ80j/uma7kpPj/LNW0/hysak+D/vXX61Tb0KyMlCStuGGSZ2a4O2LC9eBgpl2vTesPAHiN3TySpFsk3YCJSsoqNXfpFklqk3hHbke9NhtUtLJcj62riPr8dZPytWBGIIYt6rnSbbt0afHGLuN+N+cMFRydFYMWAQAAdJ/dPNJbUwgAYJNpyy8XzAjoukn5OnQSK8HnrYRbouQRAADoX7xRvwQAeqBwdK6mBnKMWX65YEZA3592vJaUbteOmjqNykzT7II8zy3BPmzwQEfjAAAA4hlJNwCjJSb4jFqinJyUoGsmHuV2M3rH7qamfr/5CQAAmMBb0yMAAM/7av8BR+MAAADiGUk3ACCmTCwZBgAAEA3Lyz3AtJIgpvVnX0Ozbnnube3cXa+RQ1P1wLdO1eAU7761Pqup1/QH12r/gZAGDUzUyzdO1hGZHdfr9YKdX9Wp8FdrVd8UVuqABJXcNFkjD/NGPeuOvPGPr/Sdp95s/f6ZK0/X+OMPc7FF3XfaqKFK8KnLkmGnjRoau0Y55N2dQZ3/yAZZajkpf9l3J+jEkX63m9Vjpl3fAABwg6slw9atW6ef/exn2rx5syorK/X888/rwgsvbH1+3759uv322/XCCy9o165dys/P14033qjrr7++NaahoUHf//739eyzz+rAgQM699xz9cgjj+jwww+33Y54LhlWUlapRcvLVRn85ym+uf4ULZwZ8Nzpy5J5/Tn/ofV659Pado+fNCJDy+ZPdKFFvXPsD1aqMdT+kpCc6NMH985woUW9c8wdL6k53P7xpATpo/vOi32Deinv9peiPrf9J97pj6klw0wZnwjTrm8AADjNEyXD9u/fr5NPPlkPP/xwh89/73vfU0lJiZYuXar33ntPN998s+bPn69ly5a1xtxyyy1avny5fv/732vt2rX6/PPPdfHFF8eqC30qUmf44ARVkqqCDZq7dItKyipdalnPmNafaDekkvTOp7U6/6H1MW5R70RLuCWpMWTp2B+sjHGLeidawi1JzeGW572ks4TOzvPxxMSSYSaNj2Te9Q0AADe5mnRPnz5dP/7xj3XRRRd1+Pwbb7yhK664QmeddZby8vJ07bXX6uSTT9amTZskScFgUI8//rh+8YtfaMqUKTrttNP05JNP6o033tDGjV3PosSzUNjSouXlHR7eG3ls0fJyhTpbnxlHTOvPvobmqDekEe98Wqt9Dc0xalHvfFZTHzXhjmgMWfqspj5GLeqdnV/VRU24I5rDLXFe8MY/vnI0zm176+wl03bj3PbuzqCjcW4z7foGAIDb4vogtfHjx2vZsmX67LPPZFmW1qxZow8++EDTpk2TJG3evFlNTU0655xzWv+b448/XiNHjlRpaWnUn3vgwAHV1ta2+Yo3mypq2s0IH8ySVBls0KaKmtg1qhdM688tz73taJzbpj+41tE4txX+yl477ca57eA93E7Eue3OZf9wNM5t5z+ywdE4t5l2fQMAwG1xnXT/+te/ViAQ0IgRI5ScnKzCwkI9/PDDmjRpkiSpqqpKycnJGjJkSJv/7vDDD1dVVVXUn1tUVCS/39/6deSRR/ZlN3rEtOWXpvVn5257M75249y2/0DI0Ti31Td1Mc3dzTigM6aVHTft+gYAgNviPuneuHGjli1bps2bN+u//uu/NG/ePP3lL3/p1c9dsGCBgsFg69cnn3ziUIudY1pJHdP6M3KovdO87ca5bdDAREfj3JY6wN6lzW4c0Bm7tRe8UqPBtOsbAABui9s7zvr6et1xxx36xS9+oZkzZ+qkk07S/Pnz9a1vfUs///nPJUk5OTlqbGzUnj172vy3X3zxhXJycqL+7IEDByojI6PNV7wZl5+pXH9K1Js0n1pO/R6XnxnLZvWYaf154FunOhrntpdvnOxonNtKbrLXTrtxbnvmytMdjXPbH68d72ic25Z9d4KjcW4z7foGAIDb4jbpbmpqUlNTkxIS2jYxMTFR4XDLktDTTjtNAwYM0Kuvvtr6/Pvvv6+dO3eqoKAgpu11WmKCTwtnBiS1nx2JfL9wZsAz9a1N68/glCSdNKLzD2tOGpHhmXq2R2SmKjmx8999cqLPM/W6Rx6WpqQurm5JCfJMvW67dbi9Uq/7tKPs1d+2G+c2u3W4vVKv27TrGwAAbnM16d63b5+2bt2qrVu3SpIqKiq0detW7dy5UxkZGZo8ebJuvfVWvfbaa6qoqNBTTz2lp59+uvW0c7/fr2uuuUbf+973tGbNGm3evFlXXXWVCgoKdMYZZ7jYM2cUjs7V4lljlONvu+Q6x5+ixbPGeK6utWn9WTZ/YtQbUy/Wsf3g3hlRE28v1ul+6DtjevV8vOmqzrPX6kA/Oqvz339Xz8cb08bHtOsbAABu8lmW5drZLq+99prOPvvsdo9fccUVeuqpp1RVVaUFCxbolVdeUU1NjUaNGqVrr71Wt9xyi3y+luSgoaFB3//+9/W73/1OBw4c0LnnnqtHHnmk0+Xlh7Jb1NwtobClTRU1qt7boOz0liXYXpkR7ohp/dnX0KxbnntbO3fXa+TQVD3wrVM9PQP0WU29pj+4VvsPhDRoYKJevnGyZ2a4I0JhSxPuXx31xHyfWj7s2XDbFM+99jaUf6lZT29q/X7p5eM0ITDMxRZ1n8njs3X7Hl346Out379w/Zk6JW+Iew3qJdOubwAAOMluHulq0h0v4j3pBtA9pdt26dLijV3G/W7OGSo4OisGLXJGSVmlFi0vb5Os5vpTtHBmwFMrRRgfAABgArt5ZNzu6QaAnjKtRJ3UktDNXbql3exwVbBBc5duUUlZpUst6z7GBwAA9Cck3QCMY1qJulDY0qLl5R3WeY48tmh5uUJhbyxcOmzwQEfj3Gba+AAAAGeRdAO91Ngc1uPrP9YPXyzT4+s/VmNz2O0m9UoobKl02y69uPUzlW7b5clEwbQSdZsqaqLuf5ZaErvKYIM2VdTErlG9Yfcl5ZGXnnHjAwAAHMVpKEAvFK0sV/H6Ch2cl9678j3NmZivBTMC7jWsh0zZkxopUTd36Rb51DZ382KJOtOWY3+1/4CjcW4zbXwAAICzmOkGeqhoZbkeW9c24ZaksCU9tq5CRSvL3WlYD5m2J9WkEnWmLZenPwAAoD8h6QZ6oLE5rOL1FZ3GFK+v8MxSc1P3pBaOzlXJTZN02sghyvWn6LSRQ1Ry0yRPJdySdNqooepqUj7B1xLnBaYt/zetPwerbwzprhfe1ezH39RdL7yr+saQ203qFRO2zwAAvIfl5UAPLCnd3m6G+1BhqyXumolHxaZRvdCdPaleKuF0/kPr9c6nta3fVwYbdPKPXtFJIzK0bP5EF1vWPZt37Lb1etu8Y7cnxiey/P/6pVs6fN6St5b/m9afiDlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM6ZsnwEAeA8z3UAP7KipczTObSbuST004T7YO5/W6vyH1se4RT1n4vj8ccunvXoefevQhPtgq8qrNefpt2Lcot4xbfsMAMBbSLqBHjhiiL29mXbj3JaZluxonNv2NTRHTbgj3vm0VvsammPUot5Jara3BNZunNvqG0NRE7qIVeXVnlnKHNmeEY1P3tqeYer4mLZ9BgDgHSTdQA/4ou7e7Fmc2/5RtdfROLfd8tzbjsa5bcGKMkfj3HafzUMG7ca5zbSSYYwPAADOIukGeuDTPfWOxrntk932lsHbjXPbzt32fu9249y2/4C9GUW7cW7bvsve68hunNtMW/7P+AAA4CySbqAHRmWmORrnNtP6M3JoqqNxbhs0MNHROLflZdl7HdmNc5tpJcMYHwAAnEXSDfTA7II8WyWcZhfkxaQ9vWVafx741qmOxrnt5RsnOxrntjtmBByNc5tpJcMYHwAAnEXSDfRAclKC5kzM7zRmzsR8JSd54y1mWn8GpyTppBEZncacNCJDg1O8UTXxiMxUJSd2/qlIcqJPR2R6Y+Y+NTlRUwPZncZMDWQrNdkbM/eRkmGS2iV2ke+9VDKM8QEAwFneuIMG4tCCGQFdNym/3Qxxgk+6blK+FnhkFijCtP4smz8xauLttTrdkvTBvTOiJt7JiT59cO+MGLeod4ovHxs1sfNiHejC0blaPGuMcvxtlyjn+FO0eNYYz9WBZnwAAHCOz7Ksfl8jo7a2Vn6/X8FgUBkZnc+OAYdqbA5rSel27aip06jMNM0uyPPMjHBHTOvPvoZm3fLc29q5u14jh6bqgW+d6pkZ7o58VlOv6Q+u1f4DIQ0amKiXb5zsmRnujtQ3hnTfynJt31WnvKw03TEj4JkZ1I6EwpY2VdSoem+DstNblix7eQaV8QEAIDq7eSRJt0i60TvcxAEAAAD9j9080rvTPUAcKCmr1KLl5W1qwOb6U7RwZoDligAAAADY0w30VElZpeYu3dIm4ZakqmCD5i7dopKySpdaBgAAACBekHQDPRAKW1q0vFwd7c2IPLZoeblC4X6/ewMAAADo10i6gR7YVFHTbob7YJakymCDNlXUxK5RAAAAAOIOSTfQA9V7oyfcPYkDAAAAYCYOUgN6IDs9peugbsTFk2Bdk65+apM+DzZouD9FT1w5Tv60AW43q8dM68/Or+pU+Ku1qm8KK3VAgkpumqyRh6W53aweq9nXqG//5g1V721Udnqynr12vDIHJ7vdrB4zrT+UEIxvVM8AAG+gZJgoGYbuC4UtTbh/dadLzHP9Kdpw2xRP3QBN/tlq7dhV3+7xUVmpWnvrFBda1Dum9eeYO15Sc7j940kJ0kf3nRf7BvXS2B+v0pf7Gts9Pmxwst66c6oLLeod0/pTtLJcxesrdPDRFAk+ac7EfC2YEXCvYT10/kPr9c6nte0eP2lEhpbNn+hCi3qH6hkA4D67eaR3P64GXJSY4NP5J3d+U3P+yblGJNyStGNXvSb/bHWMW9Q7pvUnWsItSc3hlue9JFqCKklf7mvU2B+vinGLese0/hStLNdj69om3JIUtqTH1lWoaGW5Ow3roWgJtyS982mtzn9ofYxb1DtUzwAAbyHpBnogFLa07G+d39Qs+1ulZ04vD9Y1RU1QI3bsqlewrilGLeod0/qz86u6qAl3RHO4Jc4LavY1Rk1QI77c16iaLmLihWn9aWwOq3h9Racxxesr1NjVizJO7GtojppwR7zzaa32NTTHqEW9Q/UMAPAekm6gB7o6vVzy1unlVz+1ydE4t5nWn8JfrXU0zm3f/s0bjsa5zbT+LCnd3m6G+1BhqyXOC2557m1H49xG9QwA8B6SbqAHTDu9/PMuPkDobpzbTOtPfZO9GUW7cW6r3mtvxtdunNtM68+OGnsrJuzGuW3n7s5XvXQ3zm2m/f0BgP6ApBvoAdNOLx/ut9dOu3FuM60/qQPsXartxrktO93ead5249xmWn9GZdo7Dd9unNtGDk11NM5tpv39AYD+wBt3aECcGZefqVx/iqIdk+ZTyymy4/IzY9msHnviynGOxrnNtP6U3DTZ0Ti3PXvteEfj3GZaf2YX5KmrMyATfC1xXvDAt051NM5tpv39AYD+gKQb6IHEBJ8Wzgx0eJCN1LKnbuHMgGdOL/enDdCorM5neUZlpXqmvrVp/Rl5WJq6Ko2clCDP1OvOHJysYV3Urh42ONkz9a1N609yUoLmTMzvNGbOxHzP1OsenJKkk0Z0Xg70pBEZnqnXHfn7I6ld4h353kt/fwCgP/DGX0wAfW7trVOiJqperGttWn8+uu+8qIm3F+t0v3Xn1KiJqhfrWpvWnwUzArpuUn67Ge8En3TdJO/V6V42f2LUxNuLdboLR+dq8awxyjlki0yOP0WLZ42hTjcAxBmfZVn9vqaE3aLmQEQobGnC/aujniDrU8vNz4bbpnhutiFY16Srn9qkz4MNGu5P0RNXjvPMjHBHTOvPzq/qVPirtapvCit1QIJKbprsmRnujtTsa9S3f/OGqvc2Kjs9Wc9eO94zM8IdMa0/jc1hLSndrh01dRqVmabZBXmemeHuyL6GZt3y3NvaubteI4em6oFvneqZGe6OhMKWNlXUqHpvg7LTW5aUe+1vDgB4md08kqRbJN2xZsJNQum2Xbq0eGOXcb+bc4YKjs6KQYsQjQmvt4OZ1h8AAACvsptHevfjXXhSSVmlFi0vbzNDnOtP0cKZAU8th6NkizeY8nqLMK0/AAAA/YF314jBc0rKKjV36ZZ2S7Krgg2au3SLSsoqXWpZ91GyJf6Z9HqTzOsPAABAf0HSjZgIhS0tWl7e4WnfkccWLS9XKOyN3Q6UbIlvpr3eTOsPAABAf0LSjZjYVFET9dAxqSVxqAw2aFNFTewa1QuUbIlvpr3eTOsPAABAf0LSjZgwcQ80JVvil2mvN9P6AwAA0J9wkJoHmHBasal7oAtH52rK8YcbVVKnvjGk+1aWa/uuOuVlpemOGQGlJie63axuMe31Zlp/TGZaiS0AANB7JN1xzpTTiiN7oKuCDR3uS43UtfbaHuiOxue3Gyo8Nz4Rc55+S6vKq1u/X/+htGTjTk0NZKv48rEutqx7Iq+3zpZke2nPvanvH9MUrSxX8foKHby1/t6V72nOxHwtmBFwr2EAAMBVfPwex0w6rdjEPdAmjY/UPuE+2Kryas15+q0Yt6jnEhN8Gn1E9FqJkjT6iAzPvN5MfP+YpmhluR5b1zbhlqSwJT22rkJFK8vdaRgAAHAdSXecMvG0YpP2QJs2PvWNoagJd8Sq8mrVN4Zi1KLeaWwO69X3Ou/Pq+9Vq7E5HKMW9Z5J7x/TNDaHVby+otOY4vUVnnq9AQAA57C8PE5157TigqOzYtewXiocnaupgRzP71E3bXzuszkLd9/Kct1z4Yl93JreW1K6vd2M46HCVkvcNROPik2jHGDK++dgJpxZYerrDQAAOIOkO06ZfFpxYoLPE4loZ0wbn+276hyNc9uOGnvttBsXT0x4/0SYcmaFya83AADQeywvj1OcVhzfTBufvKw0R+PcNirTXjvtxsF5Jp2JwOsNAAB0hqQ7TkVOK462yNInb52+bBrTxucOmycr241z2+yCPHW1QjnB1xKH2DPtTARebwAAoDMk3XGK04rjm2njk5qcqKmB7E5jpgayPVOvOzkpQXMm5ncaM2diPvWTXdKdMxG8gNcbAADoDHcAcYzTiuObaeNzyZgRvXo+3iyYEdB1k/LbzUAm+KTrJlE32U2mnYkg8XoDAADR+SzL8sb6vT5UW1srv9+vYDCojIzOa/u6wYTTfU1mwviEwpYm3L866uyjTy0fJmy4bYrn+tbYHNaS0u3aUVOnUZlpml2Qx4yjy0q37dKlxRu7jPvdnDM8d2gcrzcAAPoPu3kkp5d7gEmnFZvIhPExrQTawZKTEijTFGciZyJUBRs63Ncd+ZDHK2ciHIzXGwAAOBQfvwMwcrkv4pdpZyIAAAB0hqQbgHEl0BD/TDsTAQAAIBqWlwMwerkv4lfh6FxNDeR4/kwEAACAzpB0A2hd7jt36Rb5pDaJN8t90ZdMOBMBAACgMywvByCJ5b4AAABAX2CmG0ArlvsCAAAAznJ1pnvdunWaOXOmhg8fLp/PpxdeeKFdzHvvvafzzz9ffr9fgwYN0tixY7Vz587W5xsaGjRv3jxlZWVp8ODBuuSSS/TFF1/EsBforlDYUum2XXpx62cq3bZLobC3S8U3Nof1+PqP9cMXy/T4+o/V2Bx2u0k4SH1jSHe98K5mP/6m7nrhXdU3htxuUq/Qn/jG9QAAABzKZ1mWaxnPyy+/rNdff12nnXaaLr74Yj3//PO68MILW5/ftm2bxo0bp2uuuUaXXnqpMjIy9Pe//11nnHGGsrOzJUlz587VSy+9pKeeekp+v1/z589XQkKCXn/9ddvtsFvUHL1XUlapRcvL29SEzvWnaOHMgCeXLxetLFfx+god/LlBgk+aMzFfC2YE3GtYD5k2PnOefkuryqvbPT41kK3iy8e60KLeoT/xzbTrAQAA6JzdPNLVpPtgPp+vXdL97W9/WwMGDNCSJUs6/G+CwaCGDRumZ555Rv/2b/8mSfrHP/6hr33tayotLdUZZ5xh698m6Y6NkrJKzV26pd3p2JGFy17bN1y0slyPrauI+vx1k7x1o23a+ERL6CK8ltjRn/hm2vUAAAB0zW4eGbcHqYXDYb300ks69thjde655yo7O1unn356myXomzdvVlNTk84555zWx44//niNHDlSpaWlLrQa0YTClhYtL++wHFXksUXLyz2z1LyxOazi9dFvsCWpeH2FZ5aWmjY+9Y2hThM6SVpVXu2Zpcz0J76Zdj0AAADOituku7q6Wvv27dNPfvITFRYW6pVXXtFFF12kiy++WGvXrpUkVVVVKTk5WUOGDGnz3x5++OGqqqqK+rMPHDig2traNl/oW5sqatosWT6UJaky2KBNFTWxa1QvLCndrq7yz7DVEucFpo3PfSvLHY1zG/2Jb6ZdDwAAgLPi9vTycLhlRuCCCy7QLbfcIkk65ZRT9MYbb+jRRx/V5MmTe/yzi4qKtGjRIkfaCXuq90ZP6HoS57YdNXWOxrnNtPHZvsve791unNvoT3wz7XoAAACcFbcz3YcddpiSkpIUCLTdA/e1r32t9fTynJwcNTY2as+ePW1ivvjiC+Xk5ET92QsWLFAwGGz9+uSTTxxvP9rKTk/pOqgbcW4blZnmaJzbTBufvCx7v3e7cW6jP/HNtOsBAABwVtwm3cnJyRo7dqzef//9No9/8MEHGjVqlCTptNNO04ABA/Tqq6+2Pv/+++9r586dKigoiPqzBw4cqIyMjDZf6Fvj8jOV609RtGrPPrWckj0uPzOWzeqx2QV56qp0dYKvJc4LTBufO2weWGU3zm30J76Zdj0AAADOcjXp3rdvn7Zu3aqtW7dKkioqKrR169bWmexbb71Vzz33nIqLi/XRRx/poYce0vLly/Xd735XkuT3+3XNNdfoe9/7ntasWaPNmzfrqquuUkFBge2TyxEbiQk+LZzZcgN96L1p5PuFMwNK7OrONU4kJyVozsT8TmPmTMxXclLcfq7Vhmnjk5qcqKmB7E5jpgaylZqcGKMW9Q79iW+mXQ8AAICzXC0Z9tprr+nss89u9/gVV1yhp556SpL0xBNPqKioSJ9++qmOO+44LVq0SBdccEFrbENDg77//e/rd7/7nQ4cOKBzzz1XjzzySKfLyw9FybDYMa0OtGl1eU0bH9PqQNOf+Gba9QAAAHTOc3W63UTSHVuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2e0TBuf+saQ7ltZru276pSXlaY7ZgQ8M4PaEfoT30y7HgAAgOhIuruBpBsAAAAA0B1280g+fgcAAAAAoI/EbZ1uAADgLtO2mwAA4AaSbgAA0I5pBysCAOAWlpcDAIA2SsoqNXfpljYJtyRVBRs0d+kWlZRVutQyAAC8h6QbAAC0CoUtLVpero5OWY08tmh5uULhfn8OKwAAtrC8HDHHHsH4RskjoH/bVFHTbob7YJakymCDNlXUqODorNg1DAAAjyLpRkyxRzC+Fa0sV/H6Ch08gXXvyvc0Z2K+FswIuNcwADFTvTd6wt2TOAAA+jumrxAz7BGMb0Ury/XYurYJtySFLemxdRUqWlnuTsMAxFR2eoqjcQAA9Hck3YgJ9gjGt8bmsIrXV3QaU7y+Qo3N4Ri1CIBbxuVnKtefomibfnxqWaE0Lj8zls0CAMCzSLoRE93ZI4jYW1K6vd0M96HCVkscALMlJvi0cGbLdpJDE+/I9wtnBjiLAwAAm0i6ERPsEYxvO2rqHI0D4G2Fo3O1eNYY5fjbLiHP8ado8awxnMEBAEA3cJAaYoI9gvFtVGaao3EAvK9wdK6mBnKoNgEAQC8x042YYI9gfJtdkKeu7qMTfC1xAPqPxASfCo7O0gWnHKGCo7NIuAEA6AGSbsQEewTjW3JSguZMzO80Zs7EfOp1AwAAAN3EHTRihj2C8W3BjICum5TfbsY7wSddN4k63QAAAEBP+CzL6vc1mmpra+X3+xUMBpWRkeF2c4wXClvsEYxjjc1hLSndrh01dRqVmabZBXnMcAMAAACHsJtHcpAaYi6yRxDxKTkpQddMPMrtZgAAAABGYPoKAAAAAIA+QtINAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjnF6OmKNkGGLJtNcbJd0AAAC8haQbMVVSVqlFy8tVGWxofSzXn6KFMwMqHJ3rYstgItNeb0Ury1W8vkJh65+P3bvyPc2ZmK8FMwLuNQwAAABRMT2CmCkpq9TcpVvaJECSVBVs0NylW1RSVulSy2Ai015vRSvL9di6tgm3JIUt6bF1FSpaWe5OwwAAANApkm7ERChsadHyclkdPBd5bNHycoUOzSiAHjDt9dbYHFbx+opOY4rXV6ixORyjFgEAAMAukm7ExKaKmnYzjgezJFUGG7SpoiZ2jYKxTHu9LSnd3m6G+1BhqyUOAAAA8YWkGzFRvTd6AtSTOKAzpr3edtTUORoHAACA2CHpRkxkp6c4Ggd0xrTX26jMNEfjAAAAEDsk3YiJcfmZyvWnKFqhJp9aTpUel58Zy2bBUKa93mYX5KmrKmcJvpY4AAAAxBeSbsREYoJPC2e2lDQ6NHeIfL9wZsDT9ZMRP0x7vSUnJWjOxPxOY+ZMzKdeNwAAQBziDg0xUzg6V4tnjVGOv+2S3hx/ihbPGuPJusmIX6a93hbMCOi6SfntZrwTfNJ1k6jTDQAAEK98lmV5o2ZOH6qtrZXf71cwGFRGRobbzTFeKGxpU0WNqvc2KDu9ZYmvV2Yc4T2mvd4am8NaUrpdO2rqNCozTbML8pjhBgAAcIHdPJKkWyTdAAAAAIDusZtHMj0CAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjJN0AAAAAAPQRkm4AAAAAAPoISTcAAAAAAH2EpBsAAAAAgD5C0g0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIugEAAAAA6CMk3QAAAAAA9BGSbgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPpLkdgPigWVZkqTa2lqXWwIAAAAA8IJI/hjJJ6Mh6Za0d+9eSdKRRx7pcksAAAAAAF6yd+9e+f3+qM/7rK7S8n4gHA7r888/V3p6unw+n9vN6Rdqa2t15JFH6pNPPlFGRobbzcEhGJ/4xvjEN8YnvjE+8Y3xiW+MT3xjfGLPsizt3btXw4cPV0JC9J3bzHRLSkhI0IgRI9xuRr+UkZHBRSGOMT7xjfGJb4xPfGN84hvjE98Yn/jG+MRWZzPcERykBgAAAABAHyHpBgAAAACgj5B0wxUDBw7UwoULNXDgQLebgg4wPvGN8YlvjE98Y3ziG+MT3xif+Mb4xC8OUgMAAAAAoI8w0w0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIutGnPvvsM82aNUtZWVlKTU3ViSeeqL/+9a+tz+/bt0/z58/XiBEjlJqaqkAgoEcffdTFFvcveXl58vl87b7mzZsnSWpoaNC8efOUlZWlwYMH65JLLtEXX3zhcqv7h87GpqamRjfccIOOO+44paamauTIkbrxxhsVDAbdbna/0dV7J8KyLE2fPl0+n08vvPCCO43th+yMT2lpqaZMmaJBgwYpIyNDkyZNUn19vYut7j+6Gp+qqirNnj1bOTk5GjRokMaMGaM//vGPLre6/wiFQrrrrruUn5+v1NRUHX300brnnnt08DFQlmXphz/8oXJzc5WamqpzzjlHH374oYut7j+6Gp+mpibddtttOvHEEzVo0CANHz5cl19+uT7//HOXW96/JbndAJhr9+7dOvPMM3X22Wfr5Zdf1rBhw/Thhx9q6NChrTHf+973tHr1ai1dulR5eXl65ZVX9N3vflfDhw/X+eef72Lr+4e33npLoVCo9fuysjJNnTpV//7v/y5JuuWWW/TSSy/p97//vfx+v+bPn6+LL75Yr7/+ultN7jc6G5vPP/9cn3/+uX7+858rEAhox44duv766/X555/rD3/4g4ut7j+6eu9E/PKXv5TP54t18/q9rsantLRUhYWFWrBggX79618rKSlJf/vb35SQwFxELHQ1Ppdffrn27NmjZcuW6bDDDtMzzzyjb37zm/rrX/+qU0891a1m9xv333+/Fi9erP/+7//WCSecoL/+9a+66qqr5Pf7deONN0qSfvrTn+rBBx/Uf//3fys/P1933XWXzj33XJWXlyslJcXlHpitq/Gpq6vTli1bdNddd+nkk0/W7t27ddNNN+n8889vM/GFGLOAPnLbbbdZEyZM6DTmhBNOsH70ox+1eWzMmDHWD37wg75sGqK46aabrKOPPtoKh8PWnj17rAEDBli///3vW59/7733LElWaWmpi63snw4em4787//+r5WcnGw1NTXFuGWwrI7H5+2337aOOOIIq7Ky0pJkPf/88+41sJ87dHxOP/10684773S5VYg4dHwGDRpkPf30021iMjMzreLiYjea1++cd9551tVXX93msYsvvti67LLLLMuyrHA4bOXk5Fg/+9nPWp/fs2ePNXDgQOt3v/tdTNvaH3U1Ph3ZtGmTJcnasWNHXzcPUfCRLvrMsmXL9PWvf13//u//ruzsbJ166qkqLi5uEzN+/HgtW7ZMn332mSzL0po1a/TBBx9o2rRpLrW6/2psbNTSpUt19dVXy+fzafPmzWpqatI555zTGnP88cdr5MiRKi0tdbGl/c+hY9ORYDCojIwMJSWxgCnWOhqfuro6fec739HDDz+snJwcl1vYvx06PtXV1XrzzTeVnZ2t8ePH6/DDD9fkyZO1YcMGt5vaL3X0/hk/fryee+451dTUKBwO69lnn1VDQ4POOussdxvbT4wfP16vvvqqPvjgA0nS3/72N23YsEHTp0+XJFVUVKiqqqrN/YHf79fpp5/O/UEMdDU+HQkGg/L5fBoyZEiMWolDcXeGPvPxxx9r8eLF+t73vqc77rhDb731lm688UYlJyfriiuukCT9+te/1rXXXqsRI0YoKSlJCQkJKi4u1qRJk1xuff/zwgsvaM+ePbryyislteypS05ObneBPvzww1VVVRX7BvZjh47Nob766ivdc889uvbaa2PbMEjqeHxuueUWjR8/XhdccIF7DYOk9uPz8ccfS5Luvvtu/fznP9cpp5yip59+Wt/4xjdUVlamf/mXf3Gxtf1PR++f//3f/9W3vvUtZWVlKSkpSWlpaXr++ed1zDHHuNfQfuT2229XbW2tjj/+eCUmJioUCunee+/VZZddJkmt9wCHH354m/+O+4PY6Gp8DtXQ0KDbbrtNl156qTIyMmLcWkSQdKPPhMNhff3rX9d9990nSTr11FNVVlamRx99tE3SvXHjRi1btkyjRo3SunXrNG/ePA0fPrzNJ6joe48//rimT5+u4cOHu90UHKKzsamtrdV5552nQCCgu+++O/aNQ7vxWbZsmVavXq23337b5ZZBaj8+4XBYknTdddfpqquuktTy9+nVV1/VE088oaKiItfa2h91dH276667tGfPHv3lL3/RYYcdphdeeEHf/OY3tX79ep144okutrZ/+N///V/9z//8j5555hmdcMIJ2rp1q26++WYNHz689f4N7unO+DQ1Nemb3/ymLMvS4sWLXWoxJLGnG31n5MiR1jXXXNPmsUceecQaPny4ZVmWVVdXZw0YMMBasWJFm5hrrrnGOvfcc2PWTljW9u3brYSEBOuFF15ofezVV1+1JFm7d+9uEzty5EjrF7/4RYxb2H91NDYRtbW1VkFBgfWNb3zDqq+vd6F16Gh8brrpJsvn81mJiYmtX5KshIQEa/Lkye41th/qaHw+/vhjS5K1ZMmSNrHf/OY3re985zuxbmK/1tH4fPTRR5Ykq6ysrE3sN77xDeu6666LdRP7pREjRlgPPfRQm8fuuece67jjjrMsy7K2bdtmSbLefvvtNjGTJk2ybrzxxlg1s9/qanwiGhsbrQsvvNA66aSTrK+++iqWTUQH2NONPnPmmWfq/fffb/PYBx98oFGjRklq+fStqamp3WmxiYmJrTMRiI0nn3xS2dnZOu+881ofO+200zRgwAC9+uqrrY+9//772rlzpwoKCtxoZr/U0dhILTPc06ZNU3JyspYtW8ZpsS7paHxuv/12vfPOO9q6dWvrlyQ98MADevLJJ11qaf/U0fjk5eVp+PDhnf59Qmx0ND51dXWSxL2Bi+rq6jr9/efn5ysnJ6fN/UFtba3efPNN7g9ioKvxkf45w/3hhx/qL3/5i7KysmLdTBzK7awf5tq0aZOVlJRk3XvvvdaHH35o/c///I+VlpZmLV26tDVm8uTJ1gknnGCtWbPG+vjjj60nn3zSSklJsR555BEXW96/hEIha+TIkdZtt93W7rnrr7/eGjlypLV69Wrrr3/9q1VQUGAVFBS40Mr+KdrYBINB6/TTT7dOPPFE66OPPrIqKytbv5qbm11qbf/T2XvnUOL08pjrbHweeOABKyMjw/r9739vffjhh9add95ppaSkWB999JELLe2foo1PY2Ojdcwxx1gTJ0603nzzTeujjz6yfv7zn1s+n8966aWXXGpt/3LFFVdYRxxxhLVixQqroqLC+tOf/mQddthh1n/+53+2xvzkJz+xhgwZYr344ovWO++8Y11wwQVWfn4+q65ioKvxaWxstM4//3xrxIgR1tatW9vcIxw4cMDl1vdfJN3oU8uXL7dGjx5tDRw40Dr++OOt3/zmN22er6ystK688kpr+PDhVkpKinXcccdZ//Vf/xW1LBKc9+c//9mSZL3//vvtnquvr7e++93vWkOHDrXS0tKsiy66yKqsrHShlf1TtLFZs2aNJanDr4qKCnca2w919t45FEl37HU1PkVFRdaIESOstLQ0q6CgwFq/fn2MW9i/dTY+H3zwgXXxxRdb2dnZVlpamnXSSSe1KyGGvlNbW2vddNNN1siRI62UlBTrqKOOsn7wgx+0SdjC4bB11113WYcffrg1cOBA6xvf+IatayF6r6vxqaioiHqPsGbNGncb34/5LMuyYjy5DgAAAABAv8CebgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPkLSDQAAAABAHyHpBgAAAACgj5B0AwAAAADQR0i6AQAAAADoIyTdAAAAAAD0EZJuAADQzmuvvSafz6c9e/bY/m/uvvtunXLKKX3WJgAAvIikGwAAj3v00UeVnp6u5ubm1sf27dunAQMG6KyzzmoTG0mmt23b1unPHD9+vCorK+X3+x1t61lnnaWbb77Z0Z8JAEA8I+kGAMDjzj77bO3bt09//etfWx9bv369cnJy9Oabb6qhoaH18TVr1mjkyJE6+uijO/2ZycnJysnJkc/n67N2AwDQH5B0AwDgcccdd5xyc3P12muvtT722muv6YILLlB+fr42btzY5vGzzz5b4XBYRUVFys/PV2pqqk4++WT94Q9/aBN36PLy4uJiHXnkkUpLS9NFF12kX/ziFxoyZEi79ixZskR5eXny+/369re/rb1790qSrrzySq1du1a/+tWv5PP55PP5tH37dqd/HQAAxBWSbgAADHD22WdrzZo1rd+vWbNGZ511liZPntz6eH19vd58802dffbZKioq0tNPP61HH31Uf//733XLLbdo1qxZWrt2bYc///XXX9f111+vm266SVu3btXUqVN17733tovbtm2bXnjhBa1YsUIrVqzQ2rVr9ZOf/ESS9Ktf/UoFBQWaM2eOKisrVVlZqSOPPLIPfhsAAMSPJLcbAAAAeu/ss8/WzTffrObmZtXX1+vtt9/W5MmT1dTUpEcffVSSVFpaqgMHDuiss85SIBDQX/7yFxUUFEiSjjrqKG3YsEGPPfaYJk+e3O7n//rXv9b06dP1H//xH5KkY489Vm+88YZWrFjRJi4cDuupp55Senq6JGn27Nl69dVXde+998rv9ys5OVlpaWnKycnpy18HAABxg6QbAAADnHXWWdq/f7/eeust7d69W8cee6yGDRumyZMn66qrrlJDQ4Nee+01HXXUUdq3b5/q6uo0derUNj+jsbFRp556aoc///3339dFF13U5rFx48a1S7rz8vJaE25Jys3NVXV1tUO9BADAe0i6AQAwwDHHHKMRI0ZozZo12r17d+ts9fDhw3XkkUfqjTfe0Jo1azRlyhTt27dPkvTSSy/piCOOaPNzBg4c2Kt2DBgwoM33Pp9P4XC4Vz8TAAAvI+kGAMAQZ599tl577TXt3r1bt956a+vjkyZN0ssvv6xNmzZp7ty5CgQCGjhwoHbu3NnhUvKOHHfccXrrrbfaPHbo93YkJycrFAr9//buVyWWMI7j8PdoEfwTBLGqWNbiBaw4TBAsVqtpQdANA1tNBotsXKxehKxJBDF4B0bxDmSDoLB7mnA4J+iBYXV5njgwL79Jw4cZ3vfL9wHATyW6AWBClGWZo6OjvL+//xHTRVHk+Pg4b29vKcsy8/Pz6XQ6qaoqw+EwW1tbeXl5yf39fRYWFnJwcPDX2u12O9vb2+l2u9nb28vNzU36/f6XjxRbWVnJw8NDnp6eMjc3l8XFxUxN2dcVgMnlLQcAE6Isy7y+vmZ9fT3Ly8sf14uiyGAw+DhaLElOT09zcnKSs7OzNBqN7O7u5urqKqurq/9cu9ls5uLiIt1uN5ubm7m+vk5VVZmZmfnSjJ1OJ9PT09nY2MjS0lKen5///4EB4Af4NRqNRuMeAgD4eVqtVh4fH3N3dzfuUQDg2/J7OQDwKefn59nZ2cns7Gz6/X4uLy/T6/XGPRYAfGu+dAMAn7K/v5/b29sMBoOsra2l3W7n8PBw3GMBwLcmugEAAKAmNlIDAACAmohuAAAAqInoBgAAgJqIbgAAAKiJ6AYAAICaiG4AAACoiegGAACAmohuAAAAqInoBgAAgJr8BszmUCbCjQ3yAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1087,14 +920,14 @@
    "source": [
     "## 結論\n",
     "\n",
-    "在這份筆記中，我們學習了如何對數據進行基本操作以計算統計函數。我們現在知道如何使用完善的數學和統計工具來驗證一些假設，以及如何根據數據樣本計算任意變量的置信區間。\n"
+    "在這份筆記中，我們學習了如何對數據執行基本操作以計算統計函數。我們現在知道如何運用數學和統計的可靠工具來驗證一些假設，以及如何根據數據樣本計算任意變量的置信區間。\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n此文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤詮釋概不負責。  \n"
+    "\n---\n\n**免責聲明**：  \n此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業的人類翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:42:57+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:12:30+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "hk"
   }
diff --git a/translations/hk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/hk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index ba549525..8b73d308 100644
--- a/translations/hk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/hk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "在此數據集中，欄位如下：  \n",
-    "* Age 和 sex 不需多作解釋  \n",
-    "* BMI 是身體質量指數  \n",
-    "* BP 是平均血壓  \n",
-    "* S1 到 S6 是不同的血液測量值  \n",
-    "* Y 是一年內疾病進展的定性指標  \n",
+    "在此數據集中，列包含以下內容：\n",
+    "* 年齡和性別不需額外解釋\n",
+    "* BMI 是身體質量指數\n",
+    "* BP 是平均血壓\n",
+    "* S1 至 S6 是不同的血液測量值\n",
+    "* Y 是疾病在一年內進展的定性指標\n",
     "\n",
-    "讓我們使用概率和統計的方法來研究這個數據集。\n",
+    "讓我們使用概率和統計方法來研究這個數據集。\n",
     "\n",
-    "### 任務 1：計算所有值的平均值和方差  \n"
+    "### 任務 1：計算所有值的平均值和方差\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -604,7 +604,7 @@
    "source": [
     "### 任務 4：測試不同變數與疾病進展（Y）之間的相關性\n",
     "\n",
-    "> **提示** 相關性矩陣可以為你提供最有用的資訊，幫助判斷哪些值是相互依賴的。\n"
+    "> **提示** 相關矩陣可以為你提供最有用的資訊，幫助判斷哪些值是相關的。\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -879,7 +879,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 任務 5: 測試糖尿病進展程度在男性和女性之間是否存在差異的假設\n"
+    "### 任務 5：檢驗糖尿病進展程度在男性和女性之間是否存在差異的假設\n"
    ],
    "metadata": {}
   },
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -918,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
+    "\n---\n\n**免責聲明**：  \n此文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
    ]
   }
  ],
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:58:14+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:13:00+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "hk"
   }
diff --git a/translations/hr/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/hr/1-Introduction/04-stats-and-probability/assignment.ipynb
index ca850d26..f18737e3 100644
--- a/translations/hr/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/hr/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Uvod u vjerojatnost i statistiku\n",
     "## Zadatak\n",
     "\n",
-    "U ovom zadatku koristit ćemo skup podataka o pacijentima s dijabetesom preuzet [odavde](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "U ovom zadatku koristit ćemo skup podataka o pacijentima s dijabetesom preuzet [s ove stranice](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -158,7 +158,7 @@
     "\n",
     "Proučimo ovaj skup podataka koristeći metode vjerojatnosti i statistike.\n",
     "\n",
-    "### Zadatak 1: Izračunajte srednje vrijednosti i varijancu za sve vrijednosti\n"
+    "### Zadatak 1: Izračunajte srednje vrijednosti i varijancu za sve vrijednosti  \n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### Zadatak 4: Testirajte korelaciju između različitih varijabli i napredovanja bolesti (Y)\n",
     "\n",
-    "> **Savjet** Korelacijska matrica pružit će vam najkorisnije informacije o tome koje vrijednosti su međusobno ovisne.\n"
+    "> **Savjet** Korelacijska matrica pružit će vam najkorisnije informacije o tome koje su vrijednosti međusobno ovisne.\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Odricanje od odgovornosti**:  \nOvaj dokument je preveden pomoću AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane ljudskog prevoditelja. Ne preuzimamo odgovornost za bilo kakve nesporazume ili pogrešne interpretacije koje proizlaze iz korištenja ovog prijevoda.\n"
+    "\n---\n\n**Odricanje od odgovornosti**:  \nOvaj dokument je preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati mjerodavnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane stručnjaka. Ne preuzimamo odgovornost za bilo kakva nesporazuma ili pogrešna tumačenja koja mogu proizaći iz korištenja ovog prijevoda.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:19:23+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:57:44+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "hr"
   }
diff --git a/translations/hr/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/hr/1-Introduction/04-stats-and-probability/notebook.ipynb
index 6b2ae056..6f744121 100644
--- a/translations/hr/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/hr/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Za vizualnu procjenu koliko različitih vrijednosti ima u uzorku, možemo nacrtati **histogram**:\n"
+    "Da bismo vizualno procijenili koliko različitih vrijednosti ima u uzorku, možemo nacrtati **histogram**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Koristimo paket pod nazivom [**Pandas**](https://pandas.pydata.org/) ovdje za analizu podataka. Više ćemo govoriti o Pandasu i radu s podacima u Pythonu kasnije u ovom tečaju.\n",
+    "Koristimo paket pod nazivom [**Pandas**](https://pandas.pydata.org/) za analizu podataka. O Pandasu i radu s podacima u Pythonu razgovarat ćemo kasnije u ovom tečaju.\n",
     "\n",
     "Izračunajmo prosječne vrijednosti za dob, visinu i težinu:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Također možemo napraviti box plotove podskupova našeg skupa podataka, na primjer, grupiranih prema ulozi igrača.\n"
+    "Također možemo napraviti box plotove podskupova našeg skupa podataka, na primjer, grupirane prema ulozi igrača.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Napomena**: Ovaj dijagram sugerira da su, u prosjeku, visine prvih baznih igrača veće od visina drugih baznih igrača. Kasnije ćemo naučiti kako možemo formalnije testirati ovu hipotezu i kako pokazati da su naši podaci statistički značajni kako bismo to dokazali.  \n",
+    "> **Napomena**: Ovaj dijagram sugerira da su, u prosjeku, visine prvih bazena veće od visina drugih bazena. Kasnije ćemo naučiti kako možemo formalnije testirati ovu hipotezu i kako pokazati da su naši podaci statistički značajni kako bismo to dokazali.\n",
     "\n",
     "Dob, visina i težina su sve kontinuirane slučajne varijable. Što mislite, kakva je njihova distribucija? Dobar način da to saznate je da nacrtate histogram vrijednosti:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Zq7/9GOAQ4GsjqE2SJEmr3DAv83Ym8GngJ5Jcm+Ql/abjuOfyCoCnAl9I8nngfcDLqurmYdUmSZIkzWSYV7F4wQzjJ04zdjZw9rBqkSRJkubLt5qWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKmx97AOnOR04Bjgxqo6vB97PfBS4Fv9bq+uqvP6bScBLwHuAn6rqj4yrNokrV5rN5076hJmteOUo0ddgiStesOcQX4H8Kxpxt9SVUf0H5Ph+FDgOOCw/jF/nWSvIdYmSZIkTWtoAbmqLgRunufuxwJbqurOqroa+Cpw1LBqkyRJkmaSqhrewZO1wIenLLE4EbgN2AZsrKpbkrwVuKiq3tXvdxrwj1X1vmmOuQHYADA2Nnbkli1bhlb/SrBr1y7WrFkz6jJWlFH3dPt1t47s3MM09iDYeceoqxi9dQftO7BjjfprdaWyr4NnT4fDvs5t/fr1F1fV+NTxoa1BnsHbgDcC1X8+FXgxkGn2nTa5V9VmYDPA+Ph4TUxMDKXQlWLr1q3Yo8EadU9PXOJraBdq47rdnLp9sb8lLT07jp8Y2LFG/bW6UtnXwbOnw2FfF25RfxpV1c7J20n+Fvhwf/da4OBm10cB1y9iadIPzPUiro3rdq/YkCpJkhb5Mm9JDmzu/jJwWX/7HOC4JPskeTRwCPDZxaxNkiRJguFe5u1MYAI4IMm1wOuAiSRH0C2f2AH8GkBVXZ7kLOCLwG7gFVV117BqkyRJkmYytIBcVS+YZvi0WfY/GTh5WPVIkiRJ8+E76UmSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSY2hBeQkpye5McllzdifJflSki8k+UCS/frxtUnuSHJp//H2YdUlSZIkzWaYM8jvAJ41Zex84PCq+n+ArwAnNduuqqoj+o+XDbEuSZIkaUZDC8hVdSFw85Sxj1bV7v7uRcCjhnV+SZIkaSFSVcM7eLIW+HBVHT7Ntn8A3ltV7+r3u5xuVvk24A+q6hMzHHMDsAFgbGzsyC1btgyp+pVh165drFmzZtRlLCvbr7t11u1jD4KddyxSMauIfe2sO2jfgR3L///DYV8Hz54Oh32d2/r16y+uqvGp43uPopgkrwF2A+/uh24AfrSqbkpyJPDBJIdV1W1TH1tVm4HNAOPj4zUxMbFIVS9PW7duxR7tmRM3nTvr9o3rdnPq9pH811nR7Gtnx/ETAzuW//+Hw74Onj0dDvu6cIt+FYskJwDHAMdXP31dVXdW1U397YuBq4DHLXZtkiRJ0qIG5CTPAn4f+KWq+l4z/vAke/W3HwMcAnxtMWuTJEmSYIhLLJKcCUwAByS5Fngd3VUr9gHOTwJwUX/FiqcCb0iyG7gLeFlV3TztgSVJkqQhGlpArqoXTDN82gz7ng2cPaxaJEmSpPnynfQkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpMa8AnKSJ89nTJIkSVru5juD/D/nOSZJkiQta3vPtjHJE4EnAQ9P8qpm00OBvYZZmCRJkjQKswZk4AHAmn6/hzTjtwHPHVZRkiRJ0qjMGpCr6gLggiTvqKprFqkmSZIkaWTmmkGetE+SzcDa9jFV9bRhFCVJkiSNynwD8v8C3g78HXDX8MqRJEmSRmu+AXl3Vb1tqJVIkiRJS8B8L/P2D0l+PcmBSR42+THUyiRJkqQRmO8M8gn9599rxgp4zGDLkSRJkkZrXgG5qh497EIkSZKkpWBeATnJi6Ybr6p3DrYcSZIkabTmu8TiCc3tBwJPBy4BDMiSJElaUea7xOI32/tJ9gX+frbHJDkdOAa4saoO78ceBryX7nrKO4DnV9Ut/baTgJfQXUbut6rqI3vyRCRJkqRBmO8M8lTfAw6ZY593AG/lnrPMm4CPVdUpSTb1938/yaHAccBhwCOBf07yuKrymsuSVpW1m84d2LE2rtvNiQM83o5Tjh7YsSRpKZvvGuR/oLtqBcBewOOBs2Z7TFVdmGTtlOFjgYn+9hnAVuD3+/EtVXUncHWSrwJHAZ+eT32SJEnSoKSq5t4p+fnm7m7gmqq6dh6PWwt8uFli8Z2q2q/ZfktV7Z/krcBFVfWufvw04B+r6n3THHMDsAFgbGzsyC1btsxZ/2q2a9cu1qxZM+oylpXt19066/axB8HOOxapmFXEvg7eoHu67qB9B3ewZczvq4NnT4fDvs5t/fr1F1fV+NTx+a5BviDJGHe/WO/KQRYHZLrTzlDLZmAzwPj4eE1MTAy4lJVl69at2KM9M9efpDeu282p2xe6Okkzsa+DN+ie7jh+YmDHWs78vjp49nQ47OvCzeud9JI8H/gs8Dzg+cBnkjx3AefbmeTA/pgHAjf249cCBzf7PQq4fgHHlyRJku6T+b7V9GuAJ1TVCVX1Irr1wX+4gPOdw93vyncC8KFm/Lgk+yR5NN0LAD+7gONLkiRJ98l8//Z2v6q6sbl/E3OE6yRn0r0g74Ak1wKvA04BzkryEuDrdDPSVNXlSc4Cvki3xvkVXsFCkiRJozDfgPxPST4CnNnf/xXgvNkeUFUvmGHT02fY/2Tg5HnWI0mSJA3FrAE5yY8DY1X1e0n+M/AUuhfUfRp49yLUJ0mSJC2qudYg/wVwO0BVvb+qXlVVv0M3e/wXwy1NkiRJWnxzBeS1VfWFqYNVtY3u7aIlSZKkFWWugPzAWbY9aJCFSJIkSUvBXAH5c0leOnWwvwrFxcMpSZIkSRqdua5i8UrgA0mO5+5APA48APjlIdYlSZIkjcSsAbmqdgJPSrIeOLwfPreq/mXolUmSJEkjMK/rIFfVx4GPD7kWSZIkaeTm+1bTkiRJ0qpgQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpsfdinzDJTwDvbYYeA7wW2A94KfCtfvzVVXXe4lYnSZKk1W7RA3JVfRk4AiDJXsB1wAeA/wa8par+fLFrkiRJkiaNeonF04GrquqaEdchSZIkAZCqGt3Jk9OBS6rqrUleD5wI3AZsAzZW1S3TPGYDsAFgbGzsyC1btixewcvQrl27WLNmzajLWFa2X3frrNvHHgQ771ikYlYR+zp4g+7puoP2HdzBljG/rw6ePR0O+zq39evXX1xV41PHRxaQkzwAuB44rKp2JhkDvg0U8EbgwKp68WzHGB8fr23btg2/2GVs69atTExMjLqMZWXtpnNn3b5x3W5O3b7oq5NWPPs6eKutpztOOXpRzuP31cGzp8NhX+eWZNqAPMolFr9IN3u8E6CqdlbVXVX1feBvgaNGWJskSZJWqVFOLbwAOHPyTpIDq+qG/u4vA5eNpCoN3VwztJIkSaM0koCc5IeA/wj8WjP8piRH0C2x2DFlmyRJkrQoRhKQq+p7wA9PGXvhKGqRJEmSWqO+zJskSZK0pBiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqTG3qM4aZIdwO3AXcDuqhpP8jDgvcBaYAfw/Kq6ZRT1SZIkafUa5Qzy+qo6oqrG+/ubgI9V1SHAx/r7kiRJ0qJaSkssjgXO6G+fATxndKVIkiRptUpVLf5Jk6uBW4AC/qaqNif5TlXt1+xzS1XtP81jNwAbAMbGxo7csmXLIlW9PO3atYs1a9aMuox72H7draMu4T4ZexDsvGPUVaw89nXwVltP1x2076KcZyl+X13u7Olw2Ne5rV+//uJmNcMPjGQNMvDkqro+ySOA85N8ab4PrKrNwGaA8fHxmpiYGFKJK8PWrVtZaj06cdO5oy7hPtm4bjenbh/Vf52Vy74O3mrr6Y7jJxblPEvx++pyZ0+Hw74u3EiWWFTV9f3nG4EPAEcBO5McCNB/vnEUtUmSJGl1W/SAnOTBSR4yeRt4BnAZcA5wQr/bCcCHFrs2SZIkaRR/exsDPpBk8vzvqap/SvI54KwkLwG+DjxvBLVJkiRplVv0gFxVXwN+aprxm4CnL3Y9kiRJUmspXeZNkiRJGjkDsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJjb1HXYAkSYOwdtO5i3Kejet2c+ICzrXjlKOHUI2kYXAGWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJauw96gI0eGs3nfuD2xvX7ebE5r4kSZJm5wyyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEmNRQ/ISQ5O8vEkVyS5PMlv9+OvT3Jdkkv7j2cvdm2SJEnSKK5isRvYWFWXJHkIcHGS8/ttb6mqPx9BTZIkSRIwgoBcVTcAN/S3b09yBXDQYtchSZIkTSdVNbqTJ2uBC4HDgVcBJwK3AdvoZplvmeYxG4ANAGNjY0du2bJlscpdNrZfd+sPbo89CHbeMcJiViB7Ohz2dfDs6XAstK/rDtp38MWsELt27WLNmjWjLmPFsa9zW79+/cVVNT51fGQBOcka4ALg5Kp6f5Ix4NtAAW8EDqyqF892jPHx8dq2bdvwi11mpr5RyKnbfT+YQbKnw2FfB8+eDsdC+7rjlKOHUM3KsHXrViYmJkZdxopjX+eWZNqAPJKrWCS5P3A28O6qej9AVe2sqruq6vvA3wJHjaI2SZIkrW6juIpFgNOAK6rqzc34gc1uvwxctti1SZIkSaP429uTgRcC25Nc2o+9GnhBkiPolljsAH5tBLVJkjQU7fK3pcglINLdRnEVi08CmWbTeYtdiyRJkjSV76QnSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSY+9RF7Acrd107qhLkCRJ0pA4gyxJkiQ1nEGWJEkj/evoxnW7OXGO8+845ehFqkZyBlmSJEm6BwOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDa+DLEmSdB8txXfZba8v7XWk98ySm0FO8qwkX07y1SSbRl2PJEmSVpclNYOcZC/gr4D/CFwLfC7JOVX1xdFWJkmSRmkpztAuJ0u9f0tthnupzSAfBXy1qr5WVf8GbAGOHXFNkiRJWkVSVaOu4QeSPBd4VlX9an//hcDPVNVvNPtsADb0d38C+PKiF7q8HAB8e9RFrDD2dDjs6+DZ0+Gwr4NnT4fDvs7tx6rq4VMHl9QSCyDTjN0jwVfVZmDz4pSz/CXZVlXjo65jJbGnw2FfB8+eDod9HTx7Ohz2deGW2hKLa4GDm/uPAq4fUS2SJElahZZaQP4ccEiSRyd5AHAccM6Ia5IkSdIqsqSWWFTV7iS/AXwE2As4vaouH3FZy53LUQbPng6HfR08ezoc9nXw7Olw2NcFWlIv0pMkSZJGbaktsZAkSZJGyoAsSZIkNQzIy1yS05PcmOSyKeO/2b9l9+VJ3tSMn9S/jfeXkzxz8Ste+qbraZIjklyU5NIk25Ic1Wyzp3NIcnCSjye5ov+a/O1+/GFJzk9yZf95/+Yx9nUOs/T1z5J8KckXknwgyX7NY+zrLGbqabP9d5NUkgOaMXs6h9n66s+rhZnl/78/rwahqvxYxh/AU4GfBi5rxtYD/wzs099/RP/5UODzwD7Ao4GrgL1G/RyW2scMPf0o8Iv97WcDW+3pHvX0QOCn+9sPAb7S9+5NwKZ+fBPwp/Z1IH19BrB3P/6n9vW+97S/fzDdi8ivAQ6wp/e9r/68GkpP/Xk1gA9nkJe5qroQuHnK8MuBU6rqzn6fG/vxY4EtVXVnVV0NfJXu7b3VmKGnBTy0v70vd1+f257OQ1XdUFWX9LdvB64ADqLr3xn9bmcAz+lv29d5mKmvVfXRqtrd73YR3TXlwb7OaZavVYC3AP8f93wDK3s6D7P01Z9XCzRLT/15NQAG5JXpccDPJflMkguSPKEfPwj4RrPftdz9jV+zeyXwZ0m+Afw5cFI/bk/3UJK1wH8APgOMVdUN0H2zBx7R72Zf99CUvrZeDPxjf9u+7oG2p0l+Cbiuqj4/ZTd7uoemfK3682oApvT0lfjz6j4zIK9MewP7Az8L/B5wVpIwj7fy1oxeDvxOVR0M/A5wWj9uT/dAkjXA2cArq+q22XadZsy+zmCmviZ5DbAbePfk0DQPt6/TaHtK18PXAK+dbtdpxuzpDKb5WvXn1X00TU/9eTUABuSV6Vrg/dX5LPB94AB8K+/74gTg/f3t/8Xdf5ayp/OU5P5038TfXVWTvdyZ5MB++4HA5J9X7es8zdBXkpwAHAMcX/0CROzrvEzT08fSrdn8fJIddH27JMmPYE/nbYavVX9e3Qcz9NSfVwNgQF6ZPgg8DSDJ44AHAN+me9vu45Lsk+TRwCHAZ0dV5DJzPfDz/e2nAVf2t+3pPPQzQqcBV1TVm5tN59B9M6f//KFm3L7OYaa+JnkW8PvAL1XV95qH2Nc5TNfTqtpeVY+oqrVVtZYuaPx0VX0Tezovs3wP+CD+vFqQWXrqz6sBWFJvNa09l+RMYAI4IMm1wOuA04HT012m7N+AE/oZpMuTnAV8ke5Phq+oqrtGU/nSNUNPXwr8jyR7A/8KbACoKns6P08GXghsT3JpP/Zq4BS6P6m+BPg68Dywr3tgpr7+Jd0r1c/vfoZyUVW9zL7Oy7Q9rarzptvZns7bTF+r/rxauJl66s+rAfCtpiVJkqSGSywkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSlqAkb0nyyub+R5L8XXP/1CSvmuGxb0jyC3Mc//VJfnea8f2S/Pp9KF2Slj0DsiQtTf8beBJAkvvRvbvYYc32JwGfmu6BVfXaqvrnBZ53P8CALGlVMyBL0tL0KfqATBeMLwNuT7J/kn2AxwMkuSDJxf0M8+Tbdr8jyXP7289O8qUkn0zyl0k+3Jzj0CRbk3wtyW/1Y6cAj01yaZI/W4wnKklLje+kJ0lLUFVdn2R3kh+lC8qfBg4CngjcClwBvAU4tqq+leRXgJOBF08eI8kDgb8BnlpVV/fvEtn6SWA98BDgy0neBmwCDq+qI4b6BCVpCTMgS9LSNTmL/CTgzXQB+Ul0Afk64Bnc/XbSewE3THn8TwJfq6qr+/tn0r/tbO/cqroTuDPJjcDYkJ6HJC0rBmRJWrom1yGvo1ti8Q1gI3Ab8C/AQVX1xFkenzmOf2dz+y78mSBJgGuQJWkp+xRwDHBzVd1VVTfTvYjuicB7gYcneSJAkvsnOWzK478EPCbJ2v7+r8zjnLfTLbmQpFXLgCxJS9d2uqtXXDRl7NaquhF4LvCnST4PXMrdL+oDoKruoLsixT8l+SSwk255xoyq6ibgU0ku80V6klarVNWoa5AkDUmSNVW1K91C5b8Crqyqt4y6LklaypxBlqSV7aVJLgUuB/alu6qFJGkWziBLkiRJDWeQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkxv8FiHh2DxCDPowAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -438,26 +284,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Normalna distribucija\n",
+    "## Normalna raspodjela\n",
     "\n",
-    "Napravimo umjetni uzorak težina koji slijedi normalnu distribuciju s istim srednjim vrijednostima i varijansom kao naši stvarni podaci:\n"
+    "Stvorimo umjetni uzorak težina koji slijedi normalnu raspodjelu s istim srednjim vrijednostima i varijancom kao naši stvarni podaci:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Budući da su većina vrijednosti u stvarnom životu normalno raspodijeljene, ne bismo trebali koristiti generator slučajnih brojeva s uniformnom raspodjelom za generiranje uzoraka podataka. Evo što se događa ako pokušamo generirati težine s uniformnom raspodjelom (generirano pomoću `np.random.rand`):\n"
+    "Budući da su većina vrijednosti u stvarnom životu normalno raspodijeljene, ne bismo trebali koristiti generator uniformnih slučajnih brojeva za generiranje uzoraka podataka. Evo što se događa ako pokušamo generirati težine s uniformnom raspodjelom (generirano pomoću `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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QAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMexbIVXVDVX2uqh6uqiN79TkAALCb9iSQq+qSJP8+yfcmuS7Jm6rqur34LAAA2E17dQT59Uke7u7f7u6vJHl/kpv36LMAAGDX7Nujn3tVksfG45NJ/tp8QVUdTnJ48fAPq+pzezQLe+/yJF9c9xCcN+wHtrMn2M6e4Fn1E0nWtyf+wpkW9yqQ6wxr/ZwH3UeTHN2jz2eFqupEd2+uew7OD/YD29kTbGdPsN35tif26hSLk0muGY+vTvLEHn0WAADsmr0K5P+Z5GBVXVtVL0lya5J79+izAABg1+zJKRbd/UxV/VCSX0pySZK7uvuBvfgszgtOlWGyH9jOnmA7e4Ltzqs9Ud197lcBAMBFwpX0AABgEMgAADAIZJ63qnpNVX1q/PflqvqRqvrJqvpsVf1mVf1CVb1y3bOyGl9nT/z4Yj98qqo+XFXftO5ZWY2z7Ynx/I9WVVfV5WsckxX5Or8j/mVVPT7Wv2/ds7IaX+93RFX9cFV9rqoeqKp/tdY5nYPMTiwuJ/54ti4A85okH1n8z5k/kSTd/c51zsfqbdsTv9/dX16svz3Jdd391nXOx+rNPdHdX6iqa5K8J8m3JPmr3e1CEReRbb8jfjDJH3b3T613KtZp2554dZJ3Jbmxu5+uqiu6+6l1zeYIMjt1fZL/3d1f6O4Pd/czi/WPZut7r7n4zD3x5bH+imy7UBAXjWf3xOLxv0nyY7EfLlbb9wPMPfFPktzZ3U8nyTrjOBHI7NytSd53hvW3JPnFFc/C+eE5e6Kq3l1VjyV5c5J/sbapWKdn90RV3ZTk8e7+jfWOxBpt/3fjhxanYt1VVZeuayjWau6Jb07yN6vqY1X1q1X1HWucyykWvHCLi788keS13f3kWH9Xks0k/6BtrIvK2fbE4rnbk7ysu+9Yy3CsxdwTSf4gyS8n+bvd/aWqeiTJplMsLh7bf0dU1ZVJvpitvyb8eJL93f2Wdc7Iap1hT3w6yUeSvCPJdyT5uSSvXldPOILMTnxvkk9si+NDSd6Q5M3i+KL0p/bE8F+T/MMVz8P6zT3xF5Ncm+Q3FnF8dZJPVNWfX+N8rNZzfkd095Pd/dXu/lqS/5jk9WudjnXY/u/GySQf7C0fT/K1JGv7n3kFMjvxpjz3T+k3JHlnkpu6+4/WNhXrtH1PHBzP3ZTksyufiHV7dk9092919xXdfaC7D2TrH8Jv7+7fWeeArNT23xH7x3N/P8mnVz4R6/acPZHkvyX520lSVd+c5CXZ+ivDWjjFghekql6e5LFs/dnjS4u1h5O8NMnvLl72Ud9YcPE4y574+Wx9u8nXknwhyVu7+/H1TckqnWlPbHv+kTjF4qJxlt8R/znJX8nWKRaPJPnH3X1qXTOyWmfZEy9Jcle29sVXkvxod39kbTMKZAAA+BNOsQAAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAAhv8PCCPnhqb/Rl0AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Intervali pouzdanosti\n",
     "\n",
-    "Sada ćemo izračunati intervale pouzdanosti za težine i visine bejzbol igrača. Koristit ćemo kod [iz ove rasprave na Stack Overflowu](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Sada ćemo izračunati intervale pouzdanosti za težine i visine baseball igrača. Koristit ćemo kod [iz ove rasprave na stackoverflowu](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,35 +619,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dvije vrijednosti koje vraća funkcija `ttest_ind` su:  \n",
-    "* p-vrijednost može se smatrati vjerojatnošću da dvije distribucije imaju isti prosjek. U našem slučaju, ona je vrlo niska, što znači da postoje snažni dokazi koji podržavaju tvrdnju da su prvi bazni igrači viši.  \n",
-    "* t-vrijednost je međuvrijednost normalizirane razlike srednjih vrijednosti koja se koristi u t-testu, i uspoređuje se s graničnom vrijednošću za zadanu razinu pouzdanosti.  \n"
+    "Dvije vrijednosti koje vraća funkcija `ttest_ind` su:\n",
+    "* p-vrijednost može se smatrati vjerojatnošću da dvije distribucije imaju isti prosjek. U našem slučaju, ona je vrlo niska, što znači da postoje snažni dokazi koji podržavaju tvrdnju da su prvi igrači baze viši.\n",
+    "* t-vrijednost je srednja vrijednost normalizirane razlike prosjeka koja se koristi u t-testu, i uspoređuje se s graničnom vrijednošću za zadanu razinu pouzdanosti.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simulacija normalne distribucije s teoremom centralne granične vrijednosti\n",
+    "## Simulacija normalne distribucije pomoću teorema centralne granice\n",
     "\n",
-    "Pseudo-slučajni generator u Pythonu dizajniran je da nam daje uniformnu distribuciju. Ako želimo stvoriti generator za normalnu distribuciju, možemo koristiti teorem centralne granične vrijednosti. Da bismo dobili vrijednost s normalnom distribucijom, jednostavno ćemo izračunati srednju vrijednost uzorka generiranog uniformno.\n"
+    "Pseudo-slučajni generator u Pythonu dizajniran je da nam daje uniformnu distribuciju. Ako želimo stvoriti generator za normalnu distribuciju, možemo koristiti teorem centralne granice. Da bismo dobili vrijednost koja je normalno distribuirana, jednostavno ćemo izračunati srednju vrijednost uzorka generiranog uniformno.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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6HavqkUl+Mslbk2SM8Z0xxn/t+8Tra9JjMtu/JeihVbWR5AdztH9P/F7XctW3vdgsvRb2Ofcz6XFln3MvS6/lOu93BPI0O73d9r1+4FTV45O8LMlfXOiTVNVWkmcn+eTqRzw0pq7lNUl+O8l392m+w2LKOj4lyZ1J/mrx34ZvqaqH7eewa27ptRxjfDXJHyb5SpKzSb45xvjovk673nZdy4XnVdVnqupvquoZD/C2R8GUdfwe+5wk09fymtjnnDdlLdd2vyOQp9nL221fk+R3xhj37PgJqh6e7aNPrx9jfGu14x0qS69lVf1sknNjjJv2abbDZMpjciPJc5K8eYzx7CT/k+QoP99zymPy0dk+gvLkJD+c5GFV9Qv7MeQhsZe1/FSSJ40xnpnkT5N88AHc9qiYso7bn8A+57yl19I+536mPC7Xdr+z9BuFkGRvb7d9Isl7qipJjiV5cVXdPcb4YFU9ONs/qN45xrj+IAZeY0uvZZLnJvm5qnpxkockeWRVvWOMcRSDZMo6/nOSO8YY548qvS9r8oNqJlPW8sFJvjzGuDNJqur6JD+e5B37PfSa2nUte6yNMW6oqj+vqmN7ue0RsvQ6jjHuss+5lymPyefHPqeb+v29nvuduZ8EfZhP2f4HxpeyfZTo/BPTn/H/XP/t+f6LeCrJXye5Zu6vYx1OU9byPtuvyBF+wcTUdUzyD0mevjj/piR/MPfXdBjXMtv/aLs12889rmy/COW1c39N67yWSX4o33/zqsuz/fSUeqB/DxfzaeI62uesaC3vc50jvc9ZxVqu637HEeQJxgXebruqfm3x8Qs+7zjb/wJ9VZLPVtXNi21vHGPcsJ8zr6uJa8nCCtbxtUneWVWXZPsH3i/v68BrbMpajjE+WVXvy/Z/K96d5NM5ZG+zukp7XMuXJ/n1xRH4/03yirG9x9zxtrN8ITObso5V9ROxz/meiY9JmhWs5Vrud7zVNAAANF6kBwAAjUAGAIBGIAMAQCOQAQCgEcgAANAIZAAAaAQyAAA0/wceFVFs3MY9ywAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Korelacija i Zla Baseball Korporacija\n",
+    "## Korelacija i Zla Bejzbol Korporacija\n",
     "\n",
-    "Korelacija nam omogućuje pronalaženje odnosa između nizova podataka. U našem jednostavnom primjeru, zamislimo da postoji zla baseball korporacija koja plaća svoje igrače prema njihovoj visini - što je igrač viši, to više novca dobiva. Pretpostavimo da postoji osnovna plaća od 1000 dolara, uz dodatni bonus od 0 do 100 dolara, ovisno o visini. Uzet ćemo stvarne igrače iz MLB-a i izračunati njihove zamišljene plaće:\n"
+    "Korelacija nam omogućuje pronalaženje odnosa između nizova podataka. U našem jednostavnom primjeru, zamislimo da postoji zla bejzbol korporacija koja plaća svoje igrače prema njihovoj visini - što je igrač viši, to više novca dobiva. Pretpostavimo da postoji osnovna plaća od 1000 dolara, uz dodatni bonus od 0 do 100 dolara, ovisno o visini. Uzet ćemo stvarne igrače iz MLB-a i izračunati njihove zamišljene plaće:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Izračunajmo sada kovarijancu i korelaciju tih sekvenci. `np.cov` će nam dati takozvanu **matricu kovarijance**, koja je proširenje kovarijance na više varijabli. Element $M_{ij}$ matrice kovarijance $M$ je korelacija između ulaznih varijabli $X_i$ i $X_j$, a dijagonalne vrijednosti $M_{ii}$ su varijance $X_{i}$. Slično, `np.corrcoef` će nam dati **matricu korelacije**.\n"
+    "Izračunajmo sada kovarijancu i korelaciju tih sekvenci. `np.cov` će nam dati takozvanu **matricu kovarijance**, koja je proširenje kovarijance na više varijabli. Element $M_{ij}$ matrice kovarijance $M$ je korelacija između ulaznih varijabli $X_i$ i $X_j$, a dijagonalne vrijednosti $M_{ii}$ su varijance $X_{i}$. Slično tome, `np.corrcoef` će nam dati **matricu korelacije**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Korelacija jednaka 1 znači da postoji jaka **linearna veza** između dvije varijable. Linearnu vezu možemo vizualno vidjeti prikazivanjem jedne vrijednosti u odnosu na drugu:\n"
+    "Korelacija jednaka 1 znači da postoji jaka **linearna veza** između dvije varijable. Linearna veza može se vizualno uočiti iscrtavanjem jedne vrijednosti u odnosu na drugu:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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vcvwXk1ywrPbNJK9ab3MAAHC8+VPTAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAIPts24AYCU7d+09rHbv7otm0AkAzzauIANzZ6VwfKQ6AGwkARkAAAYCMgAADARkAAAYCMgAADAQkIG5s9pqFVaxAOB4sMwbMJeEYQBmxRVkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAg+2zbgCYrZ279h5Wu3f3RTPoBADmgyvI8Cy2Ujg+Uh0Ang0EZAAAGAjIAAAwEJABAGAgIAMAwEBAhmex1VarsIoFAM9mlnmDZzlhGACebs0ryFV1TVU9UlX7h9oLquoLVfWVaf/8qb6zqh6vqjum7beG97yqqvZV1d1V9RtVVZvzkQAA4NgdzRSLa5NcuKy2K8lN3X12kpum54fc093nTds7h/qHk1yR5OxpW/41AQBg5tYMyN19c5JHl5XfnOS66fF1SS4+0teoqhclOaW7v9TdneTja70HAABm4Vhv0nthdz+UJNP+9OG1l1TV7VX1x1X1mql2RpIDwzEHptqKquqKqlqsqsWDBw8eY4sAALB+G72KxUNJXtzdr0zyS0k+WVWnJFlpvnGv9kW6++ruXujuhR07dmxwiwAAsLpjDcgPT9MmDk2feCRJuvuJ7v6b6fFtSe5J8rIsXTE+c3j/mUkePNamAQBgsxxrQP5sksunx5cn+UySVNWOqto2PX5plm7G++o0DeOxqrpgWr3i7YfeAwAA82TNdZCrak+S1yU5raoOJHl/kt1Jrq+qdyS5L8kl0+GvTfJfq+rbSZ5M8s7uPnSD37uytCLGyUk+P20AADBXamlRifm1sLDQi4uLs24DAIAtpqpu6+6F5XV/ahoAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAYM11kIGNsXPX3sNq9+6+aAadAABH4goyHAcrheMj1QGA2RGQAQBgICADAMBAQAYAgIGADAAAAwEZjoPVVquwigUAzB/LvMFxIgwDwInBFWQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAACD7bNuADbazl17D6vdu/uiGXQCAJyIXEFmS1kpHB+pDgCwnIAMAAADARkAAAYCMgAADARkAAAYCMhsKautVmEVCwDgaFnmjS1HGAYAnglXkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABisGZCr6pqqeqSq9g+1F1TVF6rqK9P++VP9DVV1W1Xtm/Y/Mrzni1V1V1XdMW2nb85HAgCAY3c0V5CvTXLhstquJDd199lJbpqeJ8nXk/xYd5+b5PIkv73sfZd193nT9sixtw0AAJtjzYDc3TcneXRZ+c1JrpseX5fk4unY27v7wal+Z5LnVdVJG9MqAABsvmOdg/zC7n4oSab9StMlfjLJ7d39xFD72DS94n1VVat98aq6oqoWq2rx4MGDx9giAACs36bcpFdVr0jya0l+dihfNk29eM20vW2193f31d290N0LO3bs2IwWAQBgRccakB+uqhclybR/aj5xVZ2Z5NNJ3t7d9xyqd/cD0/6xJJ9M8upjbRoAADbLsQbkz2bpJrxM+88kSVWdmmRvkiu7+08OHVxV26vqtOnxc5K8Kcn+AADAnNm+1gFVtSfJ65KcVlUHkrw/ye4k11fVO5Lcl+SS6fCfT/IDSd5XVe+bav8uyTeT3DiF421J/jDJRzbwczAjO3ftPax27+6LZtAJAMDGqO6edQ9HtLCw0IuLi7NugxWsFI4PEZIBgHlXVbd198Lyur+kBwAAAwEZAAAGAjIAAAwEZAAAGAjIHLPVbsRzgx4AcCJbc5k3OBJhGADYalxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgsH3WDXB0fvC9n8vfP9lPPX/etspfffBHZ9gRAMDW5AryCWB5OE6Sv3+y84Pv/dyMOgIA2LoE5BPA8nC8Vh0AgGMnIAMAwEBABgCAgYB8AnjetlpXHQCAYycgnwD+6oM/elgYtooFAMDmsMzbCUIYBgA4PlxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMFgzIFfVNVX1SFXtH2ovqKovVNVXpv3zh9eurKq7q+quqnrjUH9VVe2bXvuNqqqN/zjP3FU37Mv3X/m57Ny1N99/5edy1Q37Zt0SAADH0dFcQb42yYXLaruS3NTdZye5aXqeqnp5krcmecX0nv9ZVdum93w4yRVJzp625V9z5q66YV8+cct9ebI7SfJkdz5xy31CMgDAs8iaAbm7b07y6LLym5NcNz2+LsnFQ/13u/uJ7v7rJHcneXVVvSjJKd39pe7uJB8f3jM39tx6/7rqAABsPcc6B/mF3f1Qkkz706f6GUnGNHlgqp0xPV5eX1FVXVFVi1W1ePDgwWNscf0OXTk+2joAAFvPRt+kt9K84j5CfUXdfXV3L3T3wo4dOzasubVsW2Va9Gp1AAC2nmMNyA9P0yYy7R+Z6geSnDUcd2aSB6f6mSvU58ql55+1rjoAAFvPsQbkzya5fHp8eZLPDPW3VtVJVfWSLN2M96fTNIzHquqCafWKtw/vmRsfuPjc/PQFL37qivG2qvz0BS/OBy4+d8adAQBwvFSvMb+2qvYkeV2S05I8nOT9SW5Icn2SFye5L8kl3f3odPx7k/xMkm8neXd3f36qL2RpRYyTk3w+yS/0Wt88ycLCQi8uLq7/kwEAwBFU1W3dvXBY/Sgy6kwJyAAAbIbVArK/pAcAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGFR3z7qHI6qqg0m+Nus+5shpSb4+6yZOEMZqfYzX+hivo2es1sd4rY/xOnrG6nD/ort3LC/OfUDm6apqsbsXZt3HicBYrY/xWh/jdfSM1foYr/UxXkfPWB09UywAAGAgIAMAwEBAPvFcPesGTiDGan2M1/oYr6NnrNbHeK2P8Tp6xuoomYMMAAADV5ABAGAgIAMAwEBAnmNVdWpV/X5V/VVVfbmqfriqzquqW6rqjqparKpXz7rPeVBV50xjcmj7f1X17qp6QVV9oaq+Mu2fP+te58ERxutD08/bX1TVp6vq1Fn3OmurjdXw+i9XVVfVaTNsc24cabyq6heq6q6qurOq/tuMW50LR/i36Fy/gqr6xennZ39V7amq5znPr26V8XKePwrmIM+xqrouyf/u7o9W1XOTfHeS65P8end/vqp+NMmvdPfrZtnnvKmqbUkeSHJ+kp9L8mh3766qXUme392/OtMG58yy8TonyR9197er6teSxHj9o3GsuvtrVXVWko8m+cEkr+puC/APlv1svTTJe5Nc1N1PVNXp3f3ITBucM8vG6yNxrn+aqjojyf9J8vLufryqrk/yuSQvj/P8YY4wXg/GeX5NriDPqao6Jclrk/yvJOnub3X3N5J0klOmw/5pln7QebrXJ7mnu7+W5M1Jrpvq1yW5eFZNzbGnxqu7/6C7vz3Vb0ly5gz7mkfjz1aS/HqSX8nSv0sON47Xu5Ls7u4nkkQ4XtE4Xs71K9ue5OSq2p6li0YPxnn+SA4bL+f5oyMgz6+XJjmY5GNVdXtVfbSqvifJu5N8qKruT/Lfk1w5wx7n1VuT7Jkev7C7H0qSaX/6zLqaX+N4jX4myeePcy/z7qmxqqofT/JAd//5bFuaa+PP1suSvKaqbq2qP66qfznDvubVOF7vjnP903T3A1kai/uSPJTkb7v7D+I8v6IjjNfIeX4VAvL82p7kh5J8uLtfmeSbSXZl6SrML3b3WUl+MdMVZpZMU1F+PMnvzbqXE8Fq41VV703y7SS/M4u+5tE4VlX13VmaLvCfZ9vV/FrhZ2t7kucnuSDJe5JcX1U1o/bmzgrj5Vy/zDS3+M1JXpLk+5J8T1X99Gy7ml9rjZfz/JEJyPPrQJID3X3r9Pz3sxSYL0/yqan2e0ncuPF0/z7Jn3X3w9Pzh6vqRUky7f1a9+mWj1eq6vIkb0pyWbtJYTSO1fdn6T86f15V92bpV5R/VlX/fIb9zZvlP1sHknyql/xpku8kcWPjP1o+Xs71h/u3Sf66uw929z9kaXz+VZznV7PaeDnPHwUBeU519/9Ncn9VnTOVXp/kL7M03+rfTLUfSfKVGbQ3zy7N06cLfDZL/6HJtP/Mce9ovj1tvKrqwiS/muTHu/vvZtbVfHpqrLp7X3ef3t07u3tnlsLfD03/blmy/N/iDVk6Z6WqXpbkuUnc1PiPlo+Xc/3h7ktyQVV99/Tbh9cn+XKc51ez4ng5zx8dq1jMsao6L0t3yD83yVeT/Ickr0jyP7L068q/T/Kfuvu2WfU4T6Zfe9+f5KXd/bdT7Z9laeWPF2fpZHFJdz86uy7nxyrjdXeSk5L8zXTYLd39zhm1ODdWGqtlr9+bZMEqFktW+dl6bpJrkpyX5FtJfrm7/2hmTc6RVcbrX8e5/jBV9V+S/FSWpgbcnuQ/JvkncZ5f0SrjdWec59ckIAMAwMAUCwAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAb/H2leqRtP0LMZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pogledajmo što se događa ako odnos nije linearan. Pretpostavimo da je naša korporacija odlučila sakriti očitu linearnu ovisnost između visina i plaća, te uvela neku nelinearnost u formulu, poput `sin`:\n"
+    "Pogledajmo što se događa ako odnos nije linearan. Pretpostavimo da je naša korporacija odlučila sakriti očitu linearnu ovisnost između visina i plaća te uvela neku nelinearnost u formulu, poput `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "U ovom slučaju, korelacija je nešto manja, ali je i dalje prilično visoka. Sada, kako bismo učinili vezu još manje očitom, mogli bismo dodati malo dodatne nasumičnosti dodavanjem neke nasumične varijable plaći. Pogledajmo što se događa:\n"
+    "U ovom slučaju, korelacija je nešto manja, ali je i dalje prilično visoka. Sada, kako bismo vezu učinili još manje očitom, mogli bismo dodati malo dodatne nasumičnosti dodavanjem neke slučajne varijable plaći. Pogledajmo što će se dogoditi:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Možete li pogoditi zašto se točke poravnavaju u vertikalne linije na ovaj način?\n",
+    "> Možete li pogoditi zašto se točkice slažu u vertikalne linije na ovaj način?\n",
     "\n",
     "Primijetili smo povezanost između umjetno stvorenog koncepta poput plaće i promatrane varijable *visina*. Pogledajmo također jesu li dvije promatrane varijable, poput visine i težine, međusobno povezane:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1030,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1094,7 +927,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Odricanje od odgovornosti**:  \nOvaj dokument je preveden pomoću AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane ljudskog prevoditelja. Ne preuzimamo odgovornost za bilo kakve nesporazume ili pogrešne interpretacije koje proizlaze iz korištenja ovog prijevoda.\n"
+    "\n---\n\n**Odricanje od odgovornosti**:  \nOvaj dokument je preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati mjerodavnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane stručnjaka. Ne preuzimamo odgovornost za bilo kakve nesporazume ili pogrešne interpretacije proizašle iz korištenja ovog prijevoda.\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:08:57+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:57:31+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "hr"
   }
diff --git a/translations/hr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/hr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 9e283ec5..b1807d35 100644
--- a/translations/hr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/hr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Uvod u vjerojatnost i statistiku\n",
     "## Zadatak\n",
     "\n",
-    "U ovom zadatku koristit ćemo skup podataka o pacijentima s dijabetesom preuzet [odavde](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "U ovom zadatku koristit ćemo skup podataka o pacijentima s dijabetesom preuzet [s ove stranice](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,12 +150,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "U ovom skupu podataka, stupci su sljedeći:  \n",
-    "* Dob i spol su sami po sebi razumljivi  \n",
-    "* BMI je indeks tjelesne mase  \n",
-    "* BP je prosječni krvni tlak  \n",
-    "* S1 do S6 su različita mjerenja krvi  \n",
-    "* Y je kvalitativna mjera napredovanja bolesti tijekom jedne godine  \n",
+    "U ovom skupu podataka, stupci su sljedeći:\n",
+    "* Dob i spol su sami po sebi razumljivi\n",
+    "* BMI je indeks tjelesne mase\n",
+    "* BP je prosječni krvni tlak\n",
+    "* S1 do S6 su različita mjerenja krvi\n",
+    "* Y je kvalitativna mjera napredovanja bolesti tijekom jedne godine\n",
     "\n",
     "Proučimo ovaj skup podataka koristeći metode vjerojatnosti i statistike.\n",
     "\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -604,7 +604,7 @@
    "source": [
     "### Zadatak 4: Testirajte korelaciju između različitih varijabli i napredovanja bolesti (Y)\n",
     "\n",
-    "> **Savjet** Korelacijska matrica pružit će vam najkorisnije informacije o tome koje vrijednosti su međusobno ovisne.\n"
+    "> **Savjet** Korelacijska matrica pružit će vam najkorisnije informacije o tome koje su vrijednosti međusobno ovisne.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -918,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Odricanje od odgovornosti**:  \nOvaj dokument je preveden pomoću AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane čovjeka. Ne preuzimamo odgovornost za bilo kakva nesporazuma ili pogrešna tumačenja koja proizlaze iz korištenja ovog prijevoda.\n"
+    "\n---\n\n**Odricanje od odgovornosti**:  \nOvaj dokument je preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati mjerodavnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane stručnjaka. Ne preuzimamo odgovornost za bilo kakva nesporazuma ili pogrešna tumačenja koja mogu proizaći iz korištenja ovog prijevoda.\n"
    ]
   }
  ],
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:25:18+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:58:02+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "hr"
   }
diff --git a/translations/hu/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/hu/1-Introduction/04-stats-and-probability/assignment.ipynb
index 64e3162f..0b108281 100644
--- a/translations/hu/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/hu/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Bevezetés a valószínűségszámításba és statisztikába\n",
     "## Feladat\n",
     "\n",
-    "Ebben a feladatban a cukorbeteg páciensek adatállományát fogjuk használni, amely [innen származik](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Ebben a feladatban a cukorbeteg páciensek adathalmazát fogjuk használni, amely [innen származik](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,14 +149,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Ebben az adatállományban az oszlopok a következők:\n",
+    "Ebben az adathalmazban az oszlopok a következők:\n",
     "* Az életkor és nem magától értetődőek\n",
-    "* A BMI a testtömeg-indexet jelenti\n",
-    "* A BP az átlagos vérnyomást jelöli\n",
+    "* A BMI a testtömeg-index\n",
+    "* A BP az átlagos vérnyomás\n",
     "* Az S1-től S6-ig különböző vérvizsgálati eredmények\n",
     "* Az Y a betegség egyéves előrehaladásának kvalitatív mértéke\n",
     "\n",
-    "Vizsgáljuk meg ezt az adatállományt a valószínűség és statisztika módszereivel.\n",
+    "Vizsgáljuk meg ezt az adathalmazt a valószínűség és statisztika módszereivel.\n",
     "\n",
     "### Feladat 1: Számítsuk ki az átlagértékeket és a szórást minden értékre\n"
    ],
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Feladat 2: Készítsen boxplotokat a BMI, BP és Y értékekről nemek szerint\n"
+    "### 2. feladat: Készítsen boxplotokat a BMI, BP és Y értékekről nemek szerint\n"
    ],
    "metadata": {}
   },
@@ -198,9 +198,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 4. feladat: Vizsgálja meg a különböző változók és a betegség előrehaladása (Y) közötti korrelációt\n",
+    "### Feladat 4: Vizsgáld meg a különböző változók és a betegség előrehaladása (Y) közötti korrelációt\n",
     "\n",
-    "> **Tipp** A korrelációs mátrix nyújtja a leghasznosabb információt arról, hogy mely értékek függenek egymástól.\n"
+    "> **Tipp** A korrelációs mátrix nyújtja a leghasznosabb információt arról, hogy mely értékek függnek egymástól.\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Felelősség kizárása**:  \nEz a dokumentum az AI fordítási szolgáltatás, a [Co-op Translator](https://github.com/Azure/co-op-translator) segítségével lett lefordítva. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt professzionális emberi fordítást igénybe venni. Nem vállalunk felelősséget semmilyen félreértésért vagy téves értelmezésért, amely a fordítás használatából eredhet.\n"
+    "\n---\n\n**Felelősségkizárás**:  \nEz a dokumentum az [Co-op Translator](https://github.com/Azure/co-op-translator) AI fordítási szolgáltatás segítségével készült. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt professzionális, emberi fordítást igénybe venni. Nem vállalunk felelősséget a fordítás használatából eredő félreértésekért vagy téves értelmezésekért.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:19:36+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:49:19+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "hu"
   }
diff --git a/translations/hu/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/hu/1-Introduction/04-stats-and-probability/notebook.ipynb
index f2de04f0..a0092d6c 100644
--- a/translations/hu/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/hu/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Bevezetés a valószínűségszámításba és statisztikába\n",
-    "Ebben a jegyzetfüzetben néhány korábban tárgyalt fogalommal fogunk játszani. A valószínűségszámítás és statisztika számos fogalma jól képviselteti magát a Python adatfeldolgozó főbb könyvtáraiban, mint például a `numpy` és a `pandas`.\n"
+    "Ebben a jegyzetfüzetben néhány korábban tárgyalt fogalommal fogunk játszani. A valószínűségszámítás és statisztika számos fogalma jól reprezentált a Python adatfeldolgozó főbb könyvtáraiban, mint például a `numpy` és a `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Véletlen változók és eloszlások\n",
-    "Kezdjük azzal, hogy 30 értéket mintázunk egy 0-tól 9-ig terjedő egyenletes eloszlásból. Emellett kiszámítjuk az átlagot és a szórást.\n"
+    "Kezdjük azzal, hogy veszünk egy 30 értékből álló mintát egy 0 és 9 közötti egyenletes eloszlásból. Számítsuk ki az átlagot és a szórást is.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Valós adatok elemzése\n",
     "\n",
-    "Az átlag és a szórás nagyon fontosak a valós adatok elemzésekor. Töltsük be az adatokat a baseball játékosokról a [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) oldalról.\n"
+    "Az átlag és a szórás nagyon fontos szerepet játszik a valós adatok elemzése során. Töltsük be a baseball játékosokról szóló adatokat innen: [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Egy csomagot használunk itt, amelyet [**Pandas**](https://pandas.pydata.org/) néven ismerünk, adat elemzéshez. Később ebben a kurzusban többet fogunk beszélni a Pandas-ról és az adatokkal való munkáról Pythonban.\n",
+    "Egy [**Pandas**](https://pandas.pydata.org/) nevű csomagot használunk itt adatelemzéshez. Később a kurzus során többet fogunk beszélni a Pandas-ról és az adatokkal való munkáról Pythonban.\n",
     "\n",
-    "Számoljuk ki az átlagos értékeket az életkorra, magasságra és testsúlyra:\n"
+    "Számítsuk ki az életkor, magasság és testsúly átlagértékeit:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Most nézzük a magasságot, és számítsuk ki a szórást és a varianciát:\n"
+    "Most koncentráljunk a magasságra, és számítsuk ki a szórást és a varianciát:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Készíthetünk boxplotokat az adataink részhalmazairól is, például játékos szerepkörök szerint csoportosítva.\n"
+    "Készíthetünk dobozdiagramokat az adathalmazunk részhalmazairól is, például játékos szerepkörök szerint csoportosítva.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Megjegyzés**: Ez a diagram azt sugallja, hogy átlagosan az első bázisemberek magassága nagyobb, mint a második bázisembereké. Később megtanuljuk, hogyan tesztelhetjük ezt a hipotézist formálisabban, és hogyan mutathatjuk ki, hogy adataink statisztikailag szignifikánsak ennek alátámasztására.  \n",
+    "> **Megjegyzés**: Ez a diagram azt sugallja, hogy átlagosan az első bázisemberek magassága nagyobb, mint a második bázisembereké. Később megtanuljuk, hogyan tesztelhetjük ezt a hipotézist formálisabban, és hogyan bizonyíthatjuk, hogy adataink statisztikailag szignifikánsak ennek alátámasztására.  \n",
     "\n",
-    "Az életkor, a magasság és a testsúly mind folytonos valószínűségi változók. Mit gondolsz, milyen az eloszlásuk? Egy jó módszer ennek kiderítésére, ha ábrázoljuk az értékek hisztogramját:\n"
+    "Az életkor, a magasság és a testsúly mind folytonos valószínűségi változók. Mit gondolsz, milyen az eloszlásuk? Egy jó módszer ennek kiderítésére az értékek hisztogramjának megrajzolása:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -438,26 +284,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Normális eloszlás\n",
+    "## Normál eloszlás\n",
     "\n",
-    "Hozzunk létre egy mesterséges súlymintát, amely normális eloszlást követ, ugyanazzal az átlaggal és szórással, mint a valós adataink:\n"
+    "Hozzunk létre egy mesterséges mintát súlyokból, amely normál eloszlást követ, ugyanazzal az átlaggal és szórással, mint a valós adataink:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Mivel a való életben a legtöbb érték normál eloszlású, nem szabad egyenletes véletlenszám-generátort használni mintavételi adatok előállítására. Íme, mi történik, ha megpróbálunk súlyokat generálni egy egyenletes eloszlással (amit a `np.random.rand` generál):\n"
+    "Mivel a való életben a legtöbb érték normál eloszlású, nem szabad egyenletes eloszlású véletlenszám-generátort használni mintaadatok előállítására. Íme, mi történik, ha egyenletes eloszlással próbálunk súlyokat generálni (amit a `np.random.rand` generál):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## Hipotézisvizsgálat\n",
     "\n",
-    "Nézzük meg a baseball játékosok adatállományában szereplő különböző szerepeket:\n"
+    "Vizsgáljuk meg a baseball játékosok adatbázisában szereplő különböző szerepeket:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Láthatjuk, hogy az intervallumok nem fedik egymást.\n",
+    "Láthatjuk, hogy az intervallumok nem fedik át egymást.\n",
     "\n",
-    "Egy statisztikailag helyesebb módja a hipotézis bizonyításának a **Student t-teszt** használata:\n"
+    "Egy statisztikailag helyesebb módja a hipotézis bizonyításának a **Student t-teszt** alkalmazása:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,10 +619,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A `ttest_ind` függvény által visszaadott két érték a következő:\n",
-    "\n",
-    "* A p-érték annak valószínűségét jelzi, hogy a két eloszlásnak ugyanaz az átlaga. Esetünkben ez nagyon alacsony, ami erős bizonyítékot szolgáltat arra, hogy az első bázisjátékosok magasabbak.\n",
-    "* A t-érték a normált átlagkülönbség köztes értéke, amelyet a t-teszt során használnak, és egy adott megbízhatósági érték küszöbértékével hasonlítanak össze.\n"
+    "A `ttest_ind` függvény által visszaadott két érték a következők:  \n",
+    "* A p-érték annak a valószínűségét jelzi, hogy a két eloszlásnak azonos az átlaga. Esetünkben ez nagyon alacsony, ami erős bizonyítékot szolgáltat arra, hogy az első bázisemberek magasabbak.  \n",
+    "* A t-érték a normalizált átlagkülönbség köztes értéke, amelyet a t-teszt során használnak, és egy adott megbízhatósági szinthez tartozó küszöbértékkel vetik össze.  \n"
    ]
   },
   {
@@ -795,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -829,19 +667,19 @@
    "source": [
     "## Korreláció és a Gonosz Baseball Vállalat\n",
     "\n",
-    "A korreláció lehetővé teszi, hogy kapcsolatokat találjunk adat-sorozatok között. Példánkban tegyük fel, hogy létezik egy gonosz baseball vállalat, amely a játékosait a magasságuk alapján fizeti - minél magasabb a játékos, annál több pénzt kap. Tegyük fel, hogy van egy alapfizetés, ami $1000, és egy további bónusz $0 és $100 között, a magasságtól függően. Vegyük az MLB valódi játékosait, és számítsuk ki képzeletbeli fizetésüket:\n"
+    "A korreláció lehetővé teszi, hogy kapcsolatokat találjunk adat-sorozatok között. Példánkban tegyük fel, hogy létezik egy gonosz baseball vállalat, amely a játékosait a magasságuk alapján fizeti - minél magasabb a játékos, annál több pénzt kap. Tegyük fel, hogy van egy alapfizetés, ami $1000, és egy további bónusz $0 és $100 között, a magasságtól függően. Vegyük az MLB valódi játékosait, és számítsuk ki az elképzelt fizetésüket:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -855,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Számítsuk ki most ezeknek a sorozatoknak a kovarianciáját és korrelációját. A `np.cov` egy úgynevezett **kovariancia mátrixot** ad nekünk, amely a kovariancia kiterjesztése több változóra. A kovariancia mátrix $M$ eleme $M_{ij}$ az input változók $X_i$ és $X_j$ közötti korrelációt jelenti, míg a diagonális értékek $M_{ii}$ az $X_{i}$ varianciája. Hasonlóképpen, a `np.corrcoef` a **korrelációs mátrixot** adja nekünk.\n"
+    "Számítsuk ki most ezeknek a sorozatoknak a kovarianciáját és korrelációját. Az `np.cov` egy úgynevezett **kovariancia mátrixot** ad nekünk, amely a kovariancia kiterjesztése több változóra. A kovariancia mátrix $M$ eleme $M_{ij}$ az input változók $X_i$ és $X_j$ közötti korrelációt jelenti, míg a diagonális értékek $M_{ii}$ az $X_{i}$ varianciáját adják. Hasonlóképpen, az `np.corrcoef` a **korrelációs mátrixot** adja meg.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -868,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -890,19 +728,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lássuk, mi történik, ha a kapcsolat nem lineáris. Tegyük fel, hogy vállalatunk úgy döntött, hogy elrejti a magasságok és fizetések közötti nyilvánvaló lineáris függőséget, és valamilyen nem-linearitást vezetett be a képletbe, például `sin`:\n"
+    "Nézzük meg, mi történik, ha a kapcsolat nem lineáris. Tegyük fel, hogy vállalatunk úgy döntött, hogy elrejti a magasságok és fizetések közötti nyilvánvaló lineáris összefüggést, és valamilyen nemlinearitást vezetett be a képletbe, például a `sin` függvényt:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -947,14 +783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -965,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -992,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Szerinted miért rendeződnek a pontok ilyen függőleges vonalakba?\n",
+    "> Tudod, miért rendeződnek a pontok ilyen függőleges vonalakba?\n",
     "\n",
-    "Megfigyeltük az összefüggést egy mesterségesen létrehozott fogalom, például a fizetés, és a megfigyelt változó, a *magasság* között. Nézzük meg azt is, hogy a két megfigyelt változó, például a magasság és a súly, korrelálnak-e egymással:\n"
+    "Megfigyeltük az összefüggést egy mesterségesen létrehozott fogalom, mint például a fizetés, és a megfigyelt változó, *magasság* között. Nézzük meg, hogy a két megfigyelt változó, mint például a magasság és a súly, is mutat-e összefüggést:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1022,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sajnos nem kaptunk semmilyen eredményt – csak néhány furcsa `nan` értéket. Ennek oka, hogy a sorozatunkban néhány érték nincs meghatározva, és ezek `nan`-ként jelennek meg, ami miatt a művelet eredménye is meghatározatlan lesz. Ha megnézzük a mátrixot, láthatjuk, hogy a `Weight` az a problémás oszlop, mert a `Height` értékek közötti önkorreláció lett kiszámítva.\n",
+    "Sajnos nem kaptunk semmilyen eredményt - csak néhány furcsa `nan` értéket. Ennek az az oka, hogy a sorozatunkban néhány érték nincs meghatározva, és `nan`-ként van jelölve, ami miatt a művelet eredménye is meghatározatlan lesz. Ha megnézzük a mátrixot, láthatjuk, hogy a `Weight` az a problémás oszlop, mivel a `Height` értékek közötti önkorrelációt számítottuk ki.\n",
     "\n",
-    "> Ez a példa jól mutatja a **adatelőkészítés** és **adat-tisztítás** fontosságát. Megfelelő adatok nélkül nem tudunk semmit kiszámítani.\n",
+    "> Ez a példa jól mutatja a **adatelőkészítés** és **adat-tisztítás** fontosságát. Megfelelő adatok nélkül semmit sem tudunk kiszámítani.\n",
     "\n",
     "Használjuk a `fillna` metódust a hiányzó értékek kitöltésére, majd számítsuk ki a korrelációt:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1041,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1057,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1088,14 +920,14 @@
    "source": [
     "## Következtetés\n",
     "\n",
-    "Ebben a jegyzetfüzetben megtanultuk, hogyan végezzünk alapvető műveleteket az adatokon statisztikai függvények kiszámításához. Most már tudjuk, hogyan használjunk egy jól megalapozott matematikai és statisztikai eszköztárat hipotézisek bizonyítására, valamint hogyan számítsunk konfidencia-intervallumokat tetszőleges változókra egy adott adatmintából.\n"
+    "Ebben a jegyzetfüzetben megtanultuk, hogyan végezzünk alapvető műveleteket az adatokon statisztikai függvények kiszámításához. Most már tudjuk, hogyan használjunk egy megbízható matematikai és statisztikai eszköztárat bizonyos hipotézisek igazolására, valamint hogyan számítsunk konfidencia-intervallumokat tetszőleges változókra egy adott adatminta alapján.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Felelősség kizárása**:  \nEz a dokumentum az AI fordítási szolgáltatás, a [Co-op Translator](https://github.com/Azure/co-op-translator) segítségével lett lefordítva. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt professzionális emberi fordítást igénybe venni. Nem vállalunk felelősséget semmilyen félreértésért vagy téves értelmezésért, amely a fordítás használatából eredhet.\n"
+    "\n---\n\n**Felelősségkizárás**:  \nEz a dokumentum az [Co-op Translator](https://github.com/Azure/co-op-translator) AI fordítási szolgáltatás segítségével készült. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt a professzionális, emberi fordítás igénybevétele. Nem vállalunk felelősséget a fordítás használatából eredő félreértésekért vagy téves értelmezésekért.\n"
    ]
   }
  ],
@@ -1118,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:09:55+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:49:07+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "hu"
   }
diff --git a/translations/hu/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/hu/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 91449b7a..a115baf9 100644
--- a/translations/hu/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/hu/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Bevezetés a valószínűségszámításba és statisztikába\n",
     "## Feladat\n",
     "\n",
-    "Ebben a feladatban a cukorbeteg páciensek adatállományát fogjuk használni, amely [innen származik](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Ebben a feladatban a cukorbeteg páciensek adathalmazát fogjuk használni, amely [innen származik](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Ebben az adathalmazban az oszlopok a következők:\n",
-    "* Az életkor és nem magától értetődőek\n",
-    "* A BMI a testtömeg-indexet jelenti\n",
-    "* A BP az átlagos vérnyomást jelöli\n",
-    "* Az S1-től S6-ig különböző vérvizsgálati eredmények\n",
-    "* Az Y a betegség egyéves előrehaladásának kvalitatív mértéke\n",
+    "Ebben az adathalmazban az oszlopok a következők:  \n",
+    "* Az életkor és a nem magától értetődő  \n",
+    "* A BMI a testtömegindex  \n",
+    "* A BP az átlagos vérnyomás  \n",
+    "* Az S1-től S6-ig különböző vérvizsgálati eredmények  \n",
+    "* Az Y a betegség egyéves előrehaladásának kvalitatív mértéke  \n",
     "\n",
-    "Vizsgáljuk meg ezt az adathalmazt a valószínűség és statisztika módszereivel.\n",
+    "Vizsgáljuk meg ezt az adathalmazt a valószínűség és a statisztika módszereivel.\n",
     "\n",
-    "### Feladat 1: Számítsuk ki az átlagértékeket és a szórást minden értékre\n"
+    "### 1. feladat: Számítsuk ki az összes érték átlagát és szórását\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Feladat 2: Készítsen boxplotokat a BMI, BP és Y értékekről nemek szerint\n"
+    "### 2. feladat: Készítsen boxplotokat a BMI, BP és Y értékekről nemek szerint\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -591,7 +591,7 @@
    "cell_type": "markdown",
    "source": [
     "Következtetések:\n",
-    "* Kor - normális\n",
+    "* Életkor - normális\n",
     "* Nem - egységes\n",
     "* BMI, Y - nehéz megállapítani\n"
    ],
@@ -602,7 +602,7 @@
    "source": [
     "### Feladat 4: Vizsgáld meg a különböző változók és a betegség előrehaladása (Y) közötti korrelációt\n",
     "\n",
-    "> **Tipp** A korrelációs mátrix nyújtja a leghasznosabb információt arról, hogy mely értékek függnek egymástól.\n"
+    "> **Tipp** A korrelációs mátrix nyújtja a leghasznosabb információt arról, hogy mely értékek függenek egymástól.\n"
    ],
    "metadata": {}
   },
@@ -844,8 +844,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Következtetés:\n",
-    "* Y legerősebb korrelációja a BMI és az S5 (vércukor). Ez logikusnak tűnik.\n"
+    "Következtetés:  \n",
+    "* Az Y legerősebb korrelációja a BMI-vel és az S5-tel (vércukor). Ez ésszerűnek tűnik.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Felelősség kizárása**:  \nEz a dokumentum az AI fordítási szolgáltatás, a [Co-op Translator](https://github.com/Azure/co-op-translator) segítségével lett lefordítva. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt professzionális emberi fordítást igénybe venni. Nem vállalunk felelősséget semmilyen félreértésért vagy téves értelmezésért, amely a fordítás használatából eredhet.\n"
+    "\n---\n\n**Felelősségkizárás**:  \nEz a dokumentum az [Co-op Translator](https://github.com/Azure/co-op-translator) AI fordítási szolgáltatás segítségével készült. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt professzionális, emberi fordítást igénybe venni. Nem vállalunk felelősséget a fordítás használatából eredő félreértésekért vagy téves értelmezésekért.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:25:34+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:49:36+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "hu"
   }
diff --git a/translations/id/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/id/1-Introduction/04-stats-and-probability/assignment.ipynb
index e78bc575..b204dd42 100644
--- a/translations/id/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/id/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,12 +149,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Dalam dataset ini, kolom-kolomnya adalah sebagai berikut:  \n",
-    "* Usia dan jenis kelamin sudah jelas.  \n",
-    "* BMI adalah indeks massa tubuh.  \n",
-    "* BP adalah tekanan darah rata-rata.  \n",
-    "* S1 hingga S6 adalah berbagai pengukuran darah.  \n",
-    "* Y adalah ukuran kualitatif dari perkembangan penyakit selama satu tahun.  \n",
+    "Dalam dataset ini, kolom-kolomnya adalah sebagai berikut:\n",
+    "* Usia dan jenis kelamin sudah jelas\n",
+    "* BMI adalah indeks massa tubuh\n",
+    "* BP adalah tekanan darah rata-rata\n",
+    "* S1 hingga S6 adalah berbagai pengukuran darah\n",
+    "* Y adalah ukuran kualitatif dari perkembangan penyakit selama satu tahun\n",
     "\n",
     "Mari kita pelajari dataset ini menggunakan metode probabilitas dan statistik.\n",
     "\n",
@@ -200,7 +200,7 @@
    "source": [
     "### Tugas 4: Uji korelasi antara berbagai variabel dan perkembangan penyakit (Y)\n",
     "\n",
-    "> **Petunjuk** Matriks korelasi akan memberikan informasi paling berguna tentang nilai-nilai yang saling bergantung.\n"
+    "> **Petunjuk** Matriks korelasi akan memberikan informasi paling berguna tentang nilai-nilai mana yang saling bergantung.\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berusaha untuk memberikan hasil yang akurat, harap diingat bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang otoritatif. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan profesional oleh manusia. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\n"
+    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berupaya untuk memberikan hasil yang akurat, harap diperhatikan bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang berwenang. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan manusia profesional. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:19:47+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:43:52+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "id"
   }
diff --git a/translations/id/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/id/1-Introduction/04-stats-and-probability/notebook.ipynb
index 8724ef2d..4b91454c 100644
--- a/translations/id/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/id/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Pengantar Probabilitas dan Statistik\n",
-    "Dalam notebook ini, kita akan mencoba beberapa konsep yang telah kita bahas sebelumnya. Banyak konsep dari probabilitas dan statistik yang terwakili dengan baik dalam pustaka utama untuk pemrosesan data di Python, seperti `numpy` dan `pandas`.\n"
+    "Dalam notebook ini, kita akan mengeksplorasi beberapa konsep yang telah kita bahas sebelumnya. Banyak konsep dari probabilitas dan statistik yang terwakili dengan baik dalam pustaka utama untuk pemrosesan data di Python, seperti `numpy` dan `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Variabel Acak dan Distribusi\n",
-    "Mari kita mulai dengan mengambil sampel sebanyak 30 nilai dari distribusi uniform antara 0 hingga 9. Kita juga akan menghitung rata-rata dan varians.\n"
+    "Mari kita mulai dengan mengambil sampel sebanyak 30 nilai dari distribusi uniform dari 0 hingga 9. Kita juga akan menghitung rata-rata dan varians.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## Menganalisis Data Nyata\n",
     "\n",
-    "Rata-rata dan varians sangat penting saat menganalisis data dunia nyata. Mari kita muat data tentang pemain baseball dari [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Rata-rata dan variansi sangat penting saat menganalisis data dunia nyata. Mari kita muat data tentang pemain baseball dari [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sekarang mari fokus pada tinggi badan, dan hitung standar deviasi dan varians:\n"
+    "Sekarang mari kita fokus pada tinggi, dan hitung standar deviasi dan varians:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kita juga dapat membuat plot kotak dari subset dataset kita, misalnya, dikelompokkan berdasarkan peran pemain.\n"
+    "Kita juga dapat membuat diagram kotak dari subset dataset kita, misalnya, dikelompokkan berdasarkan peran pemain.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Catatan**: Diagram ini menunjukkan bahwa, rata-rata, tinggi pemain base pertama lebih tinggi dibandingkan dengan pemain base kedua. Nantinya kita akan belajar bagaimana menguji hipotesis ini secara lebih formal, dan bagaimana menunjukkan bahwa data kita secara statistik signifikan untuk membuktikannya.\n",
+    "> **Catatan**: Diagram ini menunjukkan bahwa, rata-rata, tinggi badan pemain base pertama lebih tinggi dibandingkan dengan pemain base kedua. Nantinya kita akan belajar bagaimana menguji hipotesis ini secara lebih formal, dan bagaimana menunjukkan bahwa data kita secara statistik signifikan untuk membuktikannya.\n",
     "\n",
-    "Usia, tinggi, dan berat badan semuanya adalah variabel acak kontinu. Menurutmu, bagaimana distribusinya? Cara yang baik untuk mengetahuinya adalah dengan membuat histogram nilai:\n"
+    "Usia, tinggi badan, dan berat badan semuanya adalah variabel acak kontinu. Menurutmu, bagaimana distribusinya? Cara yang baik untuk mengetahuinya adalah dengan membuat histogram dari nilai-nilainya:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Karena sebagian besar nilai dalam kehidupan nyata terdistribusi secara normal, kita tidak seharusnya menggunakan generator angka acak uniform untuk menghasilkan data sampel. Berikut adalah apa yang terjadi jika kita mencoba menghasilkan berat dengan distribusi uniform (dihasilkan oleh `np.random.rand`):\n"
+    "Karena sebagian besar nilai dalam kehidupan nyata terdistribusi normal, kita tidak seharusnya menggunakan generator angka acak uniform untuk menghasilkan data sampel. Berikut adalah apa yang terjadi jika kita mencoba menghasilkan berat dengan distribusi uniform (dihasilkan oleh `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Interval Kepercayaan\n",
     "\n",
-    "Sekarang mari kita hitung interval kepercayaan untuk berat badan dan tinggi badan pemain baseball. Kita akan menggunakan kode [dari diskusi stackoverflow ini](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Sekarang, mari kita hitung interval kepercayaan untuk berat dan tinggi badan pemain baseball. Kita akan menggunakan kode [dari diskusi stackoverflow ini](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Kita dapat melihat bahwa interval tersebut tidak saling tumpang tindih.\n",
     "\n",
-    "Cara yang lebih tepat secara statistik untuk membuktikan hipotesis adalah dengan menggunakan **Student t-test**:\n"
+    "Cara yang secara statistik lebih tepat untuk membuktikan hipotesis adalah dengan menggunakan **Student t-test**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "Dua nilai yang dikembalikan oleh fungsi `ttest_ind` adalah:\n",
-    "* p-value dapat dianggap sebagai probabilitas bahwa dua distribusi memiliki rata-rata yang sama. Dalam kasus kita, nilainya sangat rendah, yang berarti ada bukti kuat yang mendukung bahwa pemain base pertama lebih tinggi.\n",
-    "* t-value adalah nilai tengah dari perbedaan rata-rata yang dinormalisasi yang digunakan dalam uji t, dan nilai ini dibandingkan dengan nilai ambang untuk tingkat kepercayaan tertentu.\n"
+    "* p-value dapat dianggap sebagai probabilitas bahwa dua distribusi memiliki rata-rata yang sama. Dalam kasus kita, nilainya sangat rendah, yang berarti ada bukti kuat bahwa pemain base pertama lebih tinggi.\n",
+    "* t-value adalah nilai perbedaan rata-rata yang dinormalisasi yang digunakan dalam uji-t, dan nilai ini dibandingkan dengan nilai ambang untuk tingkat kepercayaan tertentu.\n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Mensimulasikan Distribusi Normal dengan Teorema Limit Tengah\n",
     "\n",
-    "Generator pseudo-acak di Python dirancang untuk memberikan kita distribusi uniform. Jika kita ingin membuat generator untuk distribusi normal, kita dapat menggunakan teorema limit tengah. Untuk mendapatkan nilai yang terdistribusi normal, kita hanya perlu menghitung rata-rata dari sampel yang dihasilkan secara uniform.\n"
+    "Generator pseudo-acak di Python dirancang untuk memberikan distribusi uniform. Jika kita ingin membuat generator untuk distribusi normal, kita dapat menggunakan teorema limit tengah. Untuk mendapatkan nilai yang terdistribusi normal, kita hanya perlu menghitung rata-rata dari sampel yang dihasilkan secara uniform.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korelasi dan Perusahaan Baseball Jahat\n",
     "\n",
-    "Korelasi memungkinkan kita menemukan hubungan antara rangkaian data. Dalam contoh sederhana kita, mari kita bayangkan ada sebuah perusahaan baseball jahat yang membayar pemainnya berdasarkan tinggi badan mereka - semakin tinggi pemainnya, semakin banyak uang yang dia dapatkan. Misalkan ada gaji dasar sebesar $1000, dan bonus tambahan dari $0 hingga $100, tergantung pada tinggi badan. Kita akan mengambil data pemain nyata dari MLB, dan menghitung gaji imajiner mereka:\n"
+    "Korelasi memungkinkan kita menemukan hubungan antara urutan data. Dalam contoh sederhana kita, mari kita berpura-pura ada sebuah perusahaan baseball jahat yang membayar pemainnya berdasarkan tinggi badan mereka - semakin tinggi pemain, semakin banyak uang yang dia dapatkan. Misalkan ada gaji dasar sebesar $1000, dan bonus tambahan dari $0 hingga $100, tergantung pada tinggi badan. Kita akan mengambil data pemain nyata dari MLB, dan menghitung gaji imajiner mereka:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dalam kasus ini, korelasi sedikit lebih kecil, tetapi masih cukup tinggi. Sekarang, untuk membuat hubungan menjadi kurang jelas, kita mungkin ingin menambahkan beberapa keacakan tambahan dengan menambahkan beberapa variabel acak ke gaji. Mari kita lihat apa yang terjadi:\n"
+    "Dalam kasus ini, korelasinya sedikit lebih kecil, tetapi masih cukup tinggi. Sekarang, untuk membuat hubungan tersebut menjadi kurang jelas, kita mungkin ingin menambahkan beberapa keacakan tambahan dengan menambahkan beberapa variabel acak ke gaji. Mari kita lihat apa yang terjadi:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -994,17 +829,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,7 +852,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sayangnya, kami tidak mendapatkan hasil apa pun - hanya beberapa nilai `nan` yang aneh. Hal ini terjadi karena beberapa nilai dalam seri kami tidak terdefinisi, yang direpresentasikan sebagai `nan`, sehingga hasil operasi juga menjadi tidak terdefinisi. Dengan melihat matriks, kita dapat melihat bahwa kolom `Weight` adalah kolom yang bermasalah, karena korelasi diri antara nilai `Height` telah dihitung.\n",
+    "Sayangnya, kami tidak mendapatkan hasil apa pun - hanya beberapa nilai `nan` yang aneh. Hal ini terjadi karena beberapa nilai dalam seri kami tidak terdefinisi, yang diwakili sebagai `nan`, sehingga hasil operasi juga menjadi tidak terdefinisi. Dengan melihat matriks, kita dapat melihat bahwa kolom `Weight` adalah kolom yang bermasalah, karena korelasi diri antara nilai `Height` telah dihitung.\n",
     "\n",
     "> Contoh ini menunjukkan pentingnya **persiapan data** dan **pembersihan data**. Tanpa data yang tepat, kita tidak dapat menghitung apa pun.\n",
     "\n",
@@ -1026,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1090,7 +923,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berusaha untuk memberikan hasil yang akurat, harap diketahui bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang otoritatif. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan profesional oleh manusia. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\n"
+    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berupaya untuk memberikan hasil yang akurat, harap diperhatikan bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang berwenang. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa terjemahan manusia profesional. Kami tidak bertanggung jawab atas kesalahpahaman atau interpretasi yang keliru yang timbul dari penggunaan terjemahan ini.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:10:45+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:43:41+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "id"
   }
diff --git a/translations/id/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/id/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 4e510604..b408a737 100644
--- a/translations/id/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/id/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,12 +150,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Dalam dataset ini, kolom-kolomnya adalah sebagai berikut:  \n",
-    "* Usia dan jenis kelamin sudah jelas.  \n",
-    "* BMI adalah indeks massa tubuh.  \n",
-    "* BP adalah tekanan darah rata-rata.  \n",
-    "* S1 hingga S6 adalah berbagai pengukuran darah.  \n",
-    "* Y adalah ukuran kualitatif dari perkembangan penyakit selama satu tahun.  \n",
+    "Dalam dataset ini, kolom-kolomnya adalah sebagai berikut:\n",
+    "* Usia dan jenis kelamin sudah jelas\n",
+    "* BMI adalah indeks massa tubuh\n",
+    "* BP adalah tekanan darah rata-rata\n",
+    "* S1 hingga S6 adalah berbagai pengukuran darah\n",
+    "* Y adalah ukuran kualitatif dari perkembangan penyakit selama satu tahun\n",
     "\n",
     "Mari kita pelajari dataset ini menggunakan metode probabilitas dan statistik.\n",
     "\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tugas 2: Plot boxplot untuk BMI, BP, dan Y berdasarkan jenis kelamin\n"
+    "### Tugas 2: Buat boxplot untuk BMI, BP, dan Y berdasarkan jenis kelamin\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -593,7 +593,7 @@
     "Kesimpulan:\n",
     "* Usia - normal  \n",
     "* Jenis Kelamin - seragam  \n",
-    "* BMI, Y - sulit untuk menentukan  \n"
+    "* BMI, Y - sulit untuk ditentukan  \n"
    ],
    "metadata": {}
   },
@@ -602,7 +602,7 @@
    "source": [
     "### Tugas 4: Uji korelasi antara berbagai variabel dan perkembangan penyakit (Y)\n",
     "\n",
-    "> **Hint** Matriks korelasi akan memberikan informasi paling berguna tentang nilai-nilai yang saling bergantung.\n"
+    "> **Petunjuk** Matriks korelasi akan memberikan informasi paling berguna tentang nilai-nilai mana yang saling bergantung.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berupaya untuk memberikan hasil yang akurat, harap diperhatikan bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang berwenang. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan manusia profesional. Kami tidak bertanggung jawab atas kesalahpahaman atau interpretasi yang keliru yang timbul dari penggunaan terjemahan ini.\n"
+    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berupaya untuk memberikan hasil yang akurat, harap diperhatikan bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang berwenang. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan manusia profesional. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:25:50+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:44:07+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "id"
   }
diff --git a/translations/it/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/it/1-Introduction/04-stats-and-probability/assignment.ipynb
index f0b83d36..0642bc8c 100644
--- a/translations/it/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/it/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "In questo dataset, le colonne sono le seguenti:  \n",
-    "* Età e sesso sono autoesplicativi  \n",
-    "* BMI è l'indice di massa corporea  \n",
-    "* BP è la pressione sanguigna media  \n",
-    "* S1 fino a S6 sono diverse misurazioni del sangue  \n",
-    "* Y è la misura qualitativa della progressione della malattia nell'arco di un anno  \n",
+    "In questo dataset, le colonne sono le seguenti:\n",
+    "* Età e sesso sono autoesplicativi\n",
+    "* BMI è l'indice di massa corporea\n",
+    "* BP è la pressione sanguigna media\n",
+    "* S1 fino a S6 sono diverse misurazioni del sangue\n",
+    "* Y è la misura qualitativa della progressione della malattia nell'arco di un anno\n",
     "\n",
     "Studiamo questo dataset utilizzando metodi di probabilità e statistica.\n",
     "\n",
-    "### Attività 1: Calcolare i valori medi e la varianza per tutti i valori  \n"
+    "### Compito 1: Calcolare i valori medi e la varianza per tutti i valori\n"
    ],
    "metadata": {}
   },
@@ -196,7 +196,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Compito 4: Testare la correlazione tra diverse variabili e la progressione della malattia (Y)\n",
+    "### Attività 4: Testare la correlazione tra diverse variabili e la progressione della malattia (Y)\n",
     "\n",
     "> **Suggerimento** La matrice di correlazione ti fornirà le informazioni più utili su quali valori sono dipendenti.\n"
    ],
@@ -221,7 +221,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Disclaimer**:  \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali fraintendimenti o interpretazioni errate derivanti dall'uso di questa traduzione.\n"
+    "\n---\n\n**Disclaimer**:  \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si consiglia una traduzione professionale eseguita da un traduttore umano. Non siamo responsabili per eventuali fraintendimenti o interpretazioni errate derivanti dall'uso di questa traduzione.\n"
    ]
   }
  ],
@@ -247,8 +247,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:19:57+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:27:46+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "it"
   }
diff --git a/translations/it/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/it/1-Introduction/04-stats-and-probability/notebook.ipynb
index b314b0e1..530bc686 100644
--- a/translations/it/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/it/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Variabili casuali e distribuzioni\n",
-    "Iniziamo con il prelevare un campione di 30 valori da una distribuzione uniforme tra 0 e 9. Calcoleremo anche la media e la varianza.\n"
+    "Iniziamo estraendo un campione di 30 valori da una distribuzione uniforme da 0 a 9. Calcoleremo anche la media e la varianza.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Stiamo utilizzando un pacchetto chiamato [**Pandas**](https://pandas.pydata.org/) qui per l'analisi dei dati. Parleremo più approfonditamente di Pandas e del lavoro con i dati in Python più avanti in questo corso.\n",
+    "Stiamo utilizzando un pacchetto chiamato [**Pandas**](https://pandas.pydata.org/) qui per l'analisi dei dati. Parleremo più approfonditamente di Pandas e del lavoro con i dati in Python più avanti in questo corso.\n",
     "\n",
     "Calcoliamo i valori medi per età, altezza e peso:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Possiamo anche creare diagrammi a scatola di sottoinsiemi del nostro dataset, ad esempio raggruppati per ruolo del giocatore.\n"
+    "Possiamo anche creare box plot di sottoinsiemi del nostro dataset, ad esempio raggruppati per ruolo del giocatore.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Nota**: Questo diagramma suggerisce che, in media, le altezze dei primi basemen sono maggiori rispetto alle altezze dei secondi basemen. Più avanti impareremo come testare questa ipotesi in modo più formale e come dimostrare che i nostri dati sono statisticamente significativi per supportare questa affermazione.\n",
+    "> **Nota**: Questo diagramma suggerisce che, in media, l'altezza dei primi base è maggiore rispetto a quella dei secondi base. Più avanti impareremo come testare formalmente questa ipotesi e come dimostrare che i nostri dati sono statisticamente significativi per supportarla.\n",
     "\n",
-    "Età, altezza e peso sono tutte variabili casuali continue. Quale pensi sia la loro distribuzione? Un buon modo per scoprirlo è tracciare l'istogramma dei valori:\n"
+    "Età, altezza e peso sono tutte variabili casuali continue. Quale pensi che sia la loro distribuzione? Un buon modo per scoprirlo è tracciare l'istogramma dei valori:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Intervalli di Confidenza\n",
     "\n",
-    "Calcoliamo ora gli intervalli di confidenza per i pesi e le altezze dei giocatori di baseball. Utilizzeremo il codice [da questa discussione su Stack Overflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Calcoliamo ora gli intervalli di confidenza per i pesi e le altezze dei giocatori di baseball. Utilizzeremo il codice [da questa discussione su stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,9 +619,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I due valori restituiti dalla funzione `ttest_ind` sono:\n",
-    "* Il p-value può essere considerato come la probabilità che due distribuzioni abbiano la stessa media. Nel nostro caso, è molto basso, il che significa che ci sono forti evidenze a supporto del fatto che i prima base siano più alti.\n",
-    "* Il t-value è il valore intermedio della differenza media normalizzata utilizzato nel t-test, ed è confrontato con un valore soglia per un determinato livello di confidenza.\n"
+    "I due valori restituiti dalla funzione `ttest_ind` sono:  \n",
+    "* Il p-value può essere considerato come la probabilità che due distribuzioni abbiano la stessa media. Nel nostro caso, è molto basso, il che significa che ci sono forti evidenze a supporto del fatto che i primi basi siano più alti.  \n",
+    "* Il t-value è il valore intermedio della differenza media normalizzata utilizzato nel t-test, ed è confrontato con un valore soglia per un determinato livello di confidenza.  \n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Simulare una Distribuzione Normale con il Teorema del Limite Centrale\n",
     "\n",
-    "Il generatore pseudo-casuale in Python è progettato per fornirci una distribuzione uniforme. Se vogliamo creare un generatore per una distribuzione normale, possiamo utilizzare il teorema del limite centrale. Per ottenere un valore distribuito normalmente, calcoleremo semplicemente la media di un campione generato uniformemente.\n"
+    "Il generatore pseudo-casuale in Python è progettato per fornirci una distribuzione uniforme. Se vogliamo creare un generatore per la distribuzione normale, possiamo utilizzare il teorema del limite centrale. Per ottenere un valore distribuito normalmente, calcoleremo semplicemente la media di un campione generato uniformemente.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Correlazione e la Malvagia Corporazione di Baseball\n",
+    "## Correlazione e Malvagia Corporazione di Baseball\n",
     "\n",
-    "La correlazione ci permette di trovare relazioni tra sequenze di dati. Nel nostro esempio fittizio, immaginiamo che esista una malvagia corporazione di baseball che paga i suoi giocatori in base alla loro altezza: più il giocatore è alto, più soldi riceve. Supponiamo che ci sia uno stipendio base di $1000, e un bonus aggiuntivo che varia da $0 a $100, a seconda dell'altezza. Prenderemo i veri giocatori della MLB e calcoleremo i loro stipendi immaginari:\n"
+    "La correlazione ci permette di trovare relazioni tra sequenze di dati. Nel nostro esempio, immaginiamo che esista una malvagia corporazione di baseball che paga i suoi giocatori in base alla loro altezza - più alto è il giocatore, più soldi riceve. Supponiamo che ci sia uno stipendio base di $1000, e un bonus aggiuntivo da $0 a $100, a seconda dell'altezza. Prenderemo i veri giocatori della MLB e calcoleremo i loro stipendi immaginari:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In questo caso, la correlazione è leggermente più bassa, ma è comunque abbastanza alta. Ora, per rendere la relazione ancora meno evidente, potremmo voler aggiungere un po' di casualità extra aggiungendo una variabile casuale allo stipendio. Vediamo cosa succede:\n"
+    "In questo caso, la correlazione è leggermente più bassa, ma è comunque piuttosto alta. Ora, per rendere la relazione ancora meno evidente, potremmo voler aggiungere un po' di casualità in più aggiungendo una variabile casuale allo stipendio. Vediamo cosa succede:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,22 +824,22 @@
    "source": [
     "> Riesci a indovinare perché i punti si allineano in linee verticali in questo modo?\n",
     "\n",
-    "Abbiamo osservato la correlazione tra un concetto artificiale come lo stipendio e la variabile osservata *altezza*. Vediamo anche se le due variabili osservate, come altezza e peso, sono correlate:\n"
+    "Abbiamo osservato la correlazione tra un concetto artificialmente costruito come lo stipendio e la variabile osservata *altezza*. Vediamo anche se le due variabili osservate, come altezza e peso, sono correlate tra loro:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,14 +854,14 @@
    "source": [
     "Purtroppo, non abbiamo ottenuto alcun risultato - solo alcuni strani valori `nan`. Questo è dovuto al fatto che alcuni dei valori nella nostra serie sono indefiniti, rappresentati come `nan`, il che rende il risultato dell'operazione anch'esso indefinito. Osservando la matrice, possiamo vedere che la colonna problematica è `Weight`, perché l'autocorrelazione tra i valori di `Height` è stata calcolata.\n",
     "\n",
-    "> Questo esempio mostra l'importanza della **preparazione dei dati** e della **pulizia**. Senza dati adeguati non possiamo calcolare nulla.\n",
+    "> Questo esempio mostra l'importanza della **preparazione** e della **pulizia** dei dati. Senza dati adeguati non possiamo calcolare nulla.\n",
     "\n",
-    "Utilizziamo il metodo `fillna` per riempire i valori mancanti e calcolare la correlazione:\n"
+    "Utilizziamo il metodo `fillna` per riempire i valori mancanti e calcoliamo la correlazione:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Conclusione\n",
     "\n",
-    "In questo notebook abbiamo imparato come eseguire operazioni di base sui dati per calcolare funzioni statistiche. Ora sappiamo come utilizzare un solido apparato di matematica e statistica per dimostrare alcune ipotesi e come calcolare intervalli di confidenza per variabili arbitrarie a partire da un campione di dati.\n"
+    "In questo notebook abbiamo imparato come eseguire operazioni di base sui dati per calcolare funzioni statistiche. Ora sappiamo come utilizzare un solido apparato di matematica e statistica per verificare alcune ipotesi e come calcolare intervalli di confidenza per variabili arbitrarie a partire da un campione di dati.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Disclaimer**:  \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali incomprensioni o interpretazioni errate derivanti dall'uso di questa traduzione.\n"
+    "\n---\n\n**Disclaimer**:  \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche potrebbero contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale eseguita da un traduttore umano. Non siamo responsabili per eventuali fraintendimenti o interpretazioni errate derivanti dall'uso di questa traduzione.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:11:36+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:27:36+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "it"
   }
diff --git a/translations/it/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/it/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index eaeb3396..2469ae8e 100644
--- a/translations/it/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/it/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -483,8 +483,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -533,8 +533,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -591,7 +591,7 @@
     "Conclusioni:\n",
     "* Età - normale\n",
     "* Sesso - uniforme\n",
-    "* BMI, Y - difficile da dire\n"
+    "* BMI, Y - difficile da determinare\n"
    ],
    "metadata": {}
   },
@@ -851,10 +851,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -881,9 +881,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -912,7 +912,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Disclaimer**:  \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali incomprensioni o interpretazioni errate derivanti dall'uso di questa traduzione.\n"
+    "\n---\n\n**Disclaimer**:  \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si consiglia una traduzione professionale eseguita da un traduttore umano. Non siamo responsabili per eventuali fraintendimenti o interpretazioni errate derivanti dall'uso di questa traduzione.\n"
    ]
   }
  ],
@@ -938,8 +938,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:26:04+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:28:00+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "it"
   }
diff --git a/translations/ja/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ja/1-Introduction/04-stats-and-probability/assignment.ipynb
index 48386b84..e768475a 100644
--- a/translations/ja/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ja/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 確率と統計の入門  \n",
-    "## 課題  \n",
+    "## 確率と統計の入門\n",
+    "## 課題\n",
     "\n",
-    "この課題では、[こちら](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)から取得した糖尿病患者のデータセットを使用します。  \n"
+    "この課題では、[こちら](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)から取得した糖尿病患者のデータセットを使用します。\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### タスク3: 年齢、性別、BMI、Y変数の分布は何ですか？\n"
+    "### タスク3: 年齢、性別、BMI、およびY変数の分布はどうなっていますか？\n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### タスク 4: 異なる変数と病気の進行 (Y) の相関をテストする\n",
     "\n",
-    "> **ヒント** 相関行列は、どの値が依存しているかについて最も有益な情報を提供します。\n"
+    "> **ヒント** 相関行列は、どの値が依存しているかについて最も有用な情報を提供します。\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責事項**:  \nこの文書はAI翻訳サービス[Co-op Translator](https://github.com/Azure/co-op-translator)を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があります。元の言語で記載された文書を正式な情報源としてご参照ください。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤解釈について、当社は責任を負いません。\n"
+    "\n---\n\n**免責事項**:  \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を期すよう努めておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があります。元の言語で記載された原文を公式な情報源としてご参照ください。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の利用に起因する誤解や誤認について、当方は一切の責任を負いません。\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:20:10+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:15:31+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ja"
   }
diff --git a/translations/ja/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ja/1-Introduction/04-stats-and-probability/notebook.ipynb
index 777788d5..7b115eb9 100644
--- a/translations/ja/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ja/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# 確率と統計の入門  \n",
-    "このノートブックでは、これまでに議論したいくつかの概念を試してみます。確率と統計の多くの概念は、Pythonのデータ処理用主要ライブラリである`numpy`や`pandas`でよく表現されています。  \n"
+    "このノートブックでは、これまでに議論したいくつかの概念を試してみます。確率と統計の多くの概念は、Pythonのデータ処理用主要ライブラリである`numpy`や`pandas`でよく表現されています。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ランダム変数と分布\n",
-    "まず、0から9までの一様分布から30個の値をサンプルとして抽出してみましょう。また、平均と分散も計算します。\n"
+    "## 確率変数と分布\n",
+    "まず、0から9までの一様分布から30個の値をサンプリングしてみましょう。また、平均と分散も計算します。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "サンプル内にどれだけ異なる値があるかを視覚的に推定するために、**ヒストグラム**をプロットすることができます。\n"
+    "サンプルにどれくらい異なる値があるかを視覚的に推定するために、**ヒストグラム**をプロットすることができます。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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Xl7d4TDgcVigUarYBAICu6aa2HtjY2KglS5Zo2rRpGjNmTIvrAoGAEhISmu1LSEhQIBBo8Ri/36/ly5e3dTQgqvH/2AFbnfG/QeurOW2+MpKbm6uqqioVFxe35zySpIKCAgWDwaatpqam3c8BAAA6hjZdGVm8eLF27dql/fv3a8CAAa2uTUxMVF1dXbN9dXV1SkxMbPEYt9stt9vdltEAAEAn4+jKSCQS0eLFi7V9+3bt3btXQ4YMueoxPp9PpaWlzfaVlJTI5/M5mxQAAHRJjq6M5ObmqqioSDt37lRsbGzTfR8ej0e9evWSJOXk5Kh///7y+/2SpLy8PKWnp2vlypXKyspScXGxKioqVFhY2M4vBQAAdEaOroysX79ewWBQd911l5KSkpq2rVu3Nq2prq5WbW1t0+O0tDQVFRWpsLBQycnJeu2117Rjx45Wb3oFAADRw9GVkWv5lSRlZWWX7Zs1a5ZmzZrl5FQAACBK8Nk0AADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMCU4xjZv3+/ZsyYoX79+snlcmnHjh2tri8rK5PL5bpsCwQCbZ0ZAAB0IY5jpL6+XsnJyVq3bp2j406dOqXa2tqmLT4+3umpAQBAF3ST0wOmT5+u6dOnOz5RfHy8brvtNsfHAQCAru1bu2ckJSVFSUlJuvfee/XOO++0ujYcDisUCjXbAABA13TDYyQpKUkbNmzQ66+/rtdff11er1d33XWXjh071uIxfr9fHo+nafN6vTd6TAAAYMQViUQibT7Y5dL27duVnZ3t6Lj09HQNHDhQf/rTn6749XA4rHA43PQ4FArJ6/UqGAwqLi6ureNe0eClb7br8wEA0Nl8vCLrhjxvKBSSx+O56vdvx/eMtIfJkyfrwIEDLX7d7XbL7XZ/ixMBAAArJr9npLKyUklJSRanBgAAHYzjKyMXL17U6dOnmx5/9NFHqqysVJ8+fTRw4EAVFBTo008/1R//+EdJ0urVqzVkyBCNHj1aX375pV588UXt3btXf/3rX9vvVQAAgE7LcYxUVFTo7rvvbnqcn58vSZo3b542b96s2tpaVVdXN3390qVL+uUvf6lPP/1UN998s8aNG6e//e1vzZ4DAABEr+u6gfXbcq03wLQFN7ACAKKd9Q2sfDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDlOEb279+vGTNmqF+/fnK5XNqxY8dVjykrK9OECRPkdrs1dOhQbd68uQ2jAgCArshxjNTX1ys5OVnr1q27pvUfffSRsrKydPfdd6uyslJLlizRwoULtWfPHsfDAgCArucmpwdMnz5d06dPv+b1GzZs0JAhQ7Ry5UpJ0siRI3XgwAGtWrVKmZmZTk8PAAC6mBt+z0h5ebkyMjKa7cvMzFR5eXmLx4TDYYVCoWYbAADomm54jAQCASUkJDTbl5CQoFAopP/85z9XPMbv98vj8TRtXq/3Ro8JAACMdMifpikoKFAwGGzaampqrEcCAAA3iON7RpxKTExUXV1ds311dXWKi4tTr169rniM2+2W2+2+0aMBAIAO4IZfGfH5fCotLW22r6SkRD6f70afGgAAdAKOY+TixYuqrKxUZWWlpK9/dLeyslLV1dWSvv4nlpycnKb1DzzwgD788EP96le/0vvvv6/nn39er776qh5++OH2eQUAAKBTcxwjFRUVGj9+vMaPHy9Jys/P1/jx4/X4449Lkmpra5vCRJKGDBmiN998UyUlJUpOTtbKlSv14osv8mO9AABAkuSKRCIR6yGuJhQKyePxKBgMKi4url2fe/DSN9v1+QAA6Gw+XpF1Q573Wr9/d8ifpgEAANGDGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYKpNMbJu3ToNHjxYMTExmjJlig4fPtzi2s2bN8vlcjXbYmJi2jwwAADoWhzHyNatW5Wfn69ly5bp2LFjSk5OVmZmps6ePdviMXFxcaqtrW3azpw5c11DAwCArsNxjDz77LNatGiR5s+fr1GjRmnDhg26+eabtWnTphaPcblcSkxMbNoSEhKua2gAANB1OIqRS5cu6ejRo8rIyPjmCbp1U0ZGhsrLy1s87uLFixo0aJC8Xq9mzpypkydPtnqecDisUCjUbAMAAF2Toxj5/PPP1dDQcNmVjYSEBAUCgSseM3z4cG3atEk7d+7Uli1b1NjYqLS0NH3yySctnsfv98vj8TRtXq/XyZgAAKATueE/TePz+ZSTk6OUlBSlp6frjTfe0B133KGNGze2eExBQYGCwWDTVlNTc6PHBAAARm5ysvj2229X9+7dVVdX12x/XV2dEhMTr+k5evToofHjx+v06dMtrnG73XK73U5GAwAAnZSjKyM9e/ZUamqqSktLm/Y1NjaqtLRUPp/vmp6joaFBJ06cUFJSkrNJAQBAl+Toyogk5efna968eZo4caImT56s1atXq76+XvPnz5ck5eTkqH///vL7/ZKkJ554QlOnTtXQoUP1xRdf6JlnntGZM2e0cOHC9n0lAACgU3IcI7Nnz9a5c+f0+OOPKxAIKCUlRW+//XbTTa3V1dXq1u2bCy7nz5/XokWLFAgE1Lt3b6WmpurgwYMaNWpU+70KAADQabkikUjEeoirCYVC8ng8CgaDiouLa9fnHrz0zXZ9PgAAOpuPV2TdkOe91u/ffDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVphhZt26dBg8erJiYGE2ZMkWHDx9udf22bds0YsQIxcTEaOzYsdq9e3ebhgUAAF2P4xjZunWr8vPztWzZMh07dkzJycnKzMzU2bNnr7j+4MGDmjNnjhYsWKDjx48rOztb2dnZqqqquu7hAQBA5+eKRCIRJwdMmTJFkyZN0tq1ayVJjY2N8nq9euihh7R06dLL1s+ePVv19fXatWtX076pU6cqJSVFGzZsuKZzhkIheTweBYNBxcXFORn3qgYvfbNdnw8AgM7m4xVZN+R5r/X7901OnvTSpUs6evSoCgoKmvZ169ZNGRkZKi8vv+Ix5eXlys/Pb7YvMzNTO3bsaPE84XBY4XC46XEwGJT09Ytqb43hf7f7cwIA0JnciO+v/+/zXu26h6MY+fzzz9XQ0KCEhIRm+xMSEvT+++9f8ZhAIHDF9YFAoMXz+P1+LV++/LL9Xq/XybgAAOAaeFbf2Oe/cOGCPB5Pi193FCPfloKCgmZXUxobG/Wvf/1Lffv2lcvlarfzhEIheb1e1dTUtPs//8A53o+Oh/ekY+H96Fh4P64uEonowoUL6tevX6vrHMXI7bffru7du6uurq7Z/rq6OiUmJl7xmMTEREfrJcntdsvtdjfbd9tttzkZ1ZG4uDj+InUgvB8dD+9Jx8L70bHwfrSutSsi/+Pop2l69uyp1NRUlZaWNu1rbGxUaWmpfD7fFY/x+XzN1ktSSUlJi+sBAEB0cfzPNPn5+Zo3b54mTpyoyZMna/Xq1aqvr9f8+fMlSTk5Oerfv7/8fr8kKS8vT+np6Vq5cqWysrJUXFysiooKFRYWtu8rAQAAnZLjGJk9e7bOnTunxx9/XIFAQCkpKXr77bebblKtrq5Wt27fXHBJS0tTUVGRHnvsMT366KMaNmyYduzYoTFjxrTfq2gjt9utZcuWXfZPQrDB+9Hx8J50LLwfHQvvR/tx/HtGAAAA2hOfTQMAAEwRIwAAwBQxAgAATBEjAADAVFTHyLp16zR48GDFxMRoypQpOnz4sPVIUcnv92vSpEmKjY1VfHy8srOzderUKeux8F8rVqyQy+XSkiVLrEeJWp9++ql++tOfqm/fvurVq5fGjh2riooK67GiVkNDg37zm99oyJAh6tWrl7773e/qySefvOrnr6BlURsjW7duVX5+vpYtW6Zjx44pOTlZmZmZOnv2rPVoUWffvn3Kzc3VoUOHVFJSoq+++kr33Xef6uvrrUeLekeOHNHGjRs1btw461Gi1vnz5zVt2jT16NFDb731lt577z2tXLlSvXv3th4tav3ud7/T+vXrtXbtWv3zn//U7373O/3+97/Xc889Zz1apxW1P9o7ZcoUTZo0SWvXrpX09W+S9Xq9euihh7R06VLj6aLbuXPnFB8fr3379unOO++0HidqXbx4URMmTNDzzz+v3/72t0pJSdHq1autx4o6S5cu1TvvvKO///3v1qPgv374wx8qISFBL730UtO+H/3oR+rVq5e2bNliOFnnFZVXRi5duqSjR48qIyOjaV+3bt2UkZGh8vJyw8kgScFgUJLUp08f40miW25urrKyspr9d4Jv35///GdNnDhRs2bNUnx8vMaPH68XXnjBeqyolpaWptLSUn3wwQeSpH/84x86cOCApk+fbjxZ59UhP7X3Rvv888/V0NDQ9Ftj/ychIUHvv/++0VSQvr5CtWTJEk2bNq1D/JbeaFVcXKxjx47pyJEj1qNEvQ8//FDr169Xfn6+Hn30UR05ckS/+MUv1LNnT82bN896vKi0dOlShUIhjRgxQt27d1dDQ4OeeuopzZ0713q0TisqYwQdV25urqqqqnTgwAHrUaJWTU2N8vLyVFJSopiYGOtxol5jY6MmTpyop59+WpI0fvx4VVVVacOGDcSIkVdffVWvvPKKioqKNHr0aFVWVmrJkiXq168f70kbRWWM3H777erevbvq6uqa7a+rq1NiYqLRVFi8eLF27dql/fv3a8CAAdbjRK2jR4/q7NmzmjBhQtO+hoYG7d+/X2vXrlU4HFb37t0NJ4wuSUlJGjVqVLN9I0eO1Ouvv240ER555BEtXbpUP/nJTyRJY8eO1ZkzZ+T3+4mRNorKe0Z69uyp1NRUlZaWNu1rbGxUaWmpfD6f4WTRKRKJaPHixdq+fbv27t2rIUOGWI8U1e655x6dOHFClZWVTdvEiRM1d+5cVVZWEiLfsmnTpl32o+4ffPCBBg0aZDQR/v3vfzf7QFhJ6t69uxobG40m6vyi8sqIJOXn52vevHmaOHGiJk+erNWrV6u+vl7z58+3Hi3q5ObmqqioSDt37lRsbKwCgYAkyePxqFevXsbTRZ/Y2NjL7te55ZZb1LdvX+7jMfDwww8rLS1NTz/9tH784x/r8OHDKiwsVGFhofVoUWvGjBl66qmnNHDgQI0ePVrHjx/Xs88+q5///OfWo3VekSj23HPPRQYOHBjp2bNnZPLkyZFDhw5ZjxSVJF1xe/nll61Hw3+lp6dH8vLyrMeIWn/5y18iY8aMibjd7siIESMihYWF1iNFtVAoFMnLy4sMHDgwEhMTE/nOd74T+fWvfx0Jh8PWo3VaUft7RgAAQMcQlfeMAACAjoMYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKb+D7cuxelORYM+AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## 実データの分析\n",
     "\n",
-    "平均と分散は、実世界のデータを分析する際に非常に重要です。[SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)から野球選手に関するデータを読み込みましょう。\n"
+    "平均と分散は、実世界のデータを分析する際に非常に重要です。野球選手に関するデータを [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) から読み込みましょう。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "次に身長に注目し、標準偏差と分散を計算しましょう。\n"
+    "では、高さに注目し、標準偏差と分散を計算しましょう：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "平均値に加えて、中央値や四分位数を確認することは理にかなっています。それらは**箱ひげ図**を使用して視覚化できます。\n"
+    "平均値に加えて、中央値や四分位数を確認するのも理にかなっています。それらは**箱ひげ図**を使用して視覚化できます。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "私たちは、例えばプレイヤーの役割ごとにグループ化されたデータセットのサブセットの箱ひげ図を作成することもできます。\n"
+    "私たちは、例えばプレイヤーの役割ごとにグループ化するなどして、データセットのサブセットの箱ひげ図を作成することもできます。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **注**: この図は、平均的に一塁手の身長が二塁手の身長よりも高いことを示唆しています。後ほど、この仮説をより正式に検証する方法や、データが統計的に有意であることを示す方法について学びます。\n",
+    "> **Note**: この図は、平均的に一塁手の身長が二塁手の身長よりも高いことを示唆しています。後ほど、この仮説をより正式に検証する方法や、データが統計的に有意であることを示す方法について学びます。\n",
     "\n",
-    "年齢、身長、体重はすべて連続型の確率変数です。それらの分布はどのような形だと思いますか？それを調べる良い方法は、値のヒストグラムをプロットすることです。\n"
+    "年齢、身長、体重はすべて連続型の確率変数です。それらの分布はどのような形だと思いますか？調べる良い方法は、値のヒストグラムをプロットすることです。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "実生活のほとんどの値は正規分布しているため、サンプルデータを生成する際に一様乱数生成器を使用すべきではありません。一様分布（`np.random.rand`によって生成）で体重を生成しようとすると、次のようなことが起こります:\n"
+    "実生活のほとんどの値は正規分布しているため、サンプルデータを生成する際に一様乱数生成器を使用すべきではありません。一様分布（`np.random.rand`によって生成される）を使用して体重を生成しようとすると、次のようなことが起こります：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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0RzFhwoT4/Oc/H/fdd19p/86dO6OzszPa2tpK2+rr62P69OnR0dFxyHP29fVFb2/voAcAAAAc6coe3b/4xS/innvuiVNPPTX+7d/+Lf7qr/4qvvKVr8QDDzwQERGdnZ0REdHY2DjoeY2NjaV9H7ZixYqor68vPSZNmlTusQEAAKDsyh7dAwMDcdZZZ8U3v/nN+PznPx+LFi2KK6+8MlavXv1bn3PZsmXR09NTeuzatauMEwMAAECOskf3ySefHNOmTRu07bTTTovXX389IiKampoiIqKrq2vQMV1dXaV9H1ZTUxN1dXWDHgAAAHCkK3t0X3DBBbFjx45B21555ZU45ZRTIuL9L1VramqKTZs2lfb39vbGtm3borW1tdzjAAAAQMWU/dvLr7766jj//PPjm9/8ZvzxH/9xPP3003HvvffGvffeGxERVVVVsWTJkrjlllvi1FNPjZaWlrjhhhuiubk55s2bV+5xAAAAoGLKHt3nnnturF+/PpYtWxY333xztLS0xMqVK2PBggWlY6699trYv39/LFq0KLq7u+PCCy+MDRs2xJgxY8o9DgAAAFRMVVEURaWH+Lh6e3ujvr4+enp6PhH3d0+57olKjwAAAPCJ8dqtcyo9wmENtUvLfk83AAAA8D7RDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQJD26b7311qiqqoolS5aUth04cCDa29tj/PjxccIJJ8T8+fOjq6srexQAAAAYVqnR/cwzz8Q//MM/xGc/+9lB26+++up47LHHYt26dbF58+bYvXt3XHLJJZmjAAAAwLBLi+59+/bFggUL4r777osTTzyxtL2npye+973vxZ133hlf/OIX4+yzz441a9bEU089FVu3bs0aBwAAAIZdWnS3t7fHnDlzoq2tbdD27du3R39//6DtU6dOjcmTJ0dHR0fWOAAAADDsRmac9Ac/+EE899xz8cwzz/zavs7Ozhg9enSMGzdu0PbGxsbo7Ow85Pn6+vqir6+v9HNvb29Z5wUAAIAMZb/SvWvXrvjqV78aDz30UIwZM6Ys51yxYkXU19eXHpMmTSrLeQEAACBT2aN7+/btsWfPnjjrrLNi5MiRMXLkyNi8eXPcddddMXLkyGhsbIyDBw9Gd3f3oOd1dXVFU1PTIc+5bNmy6OnpKT127dpV7rEBAACg7Mr+8fIvfelL8cILLwzadsUVV8TUqVPjb/7mb2LSpEkxatSo2LRpU8yfPz8iInbs2BGvv/56tLa2HvKcNTU1UVNTU+5RAQAAIFXZo3vs2LFx+umnD9p2/PHHx/jx40vbFy5cGEuXLo2Ghoaoq6uLq666KlpbW+O8884r9zgAAABQMSlfpHY43/72t6O6ujrmz58ffX19MXPmzPjud79biVEAAAAgTVVRFEWlh/i4ent7o76+Pnp6eqKurq7S4xzWlOueqPQIAAAAnxiv3Tqn0iMc1lC7NO3vdAMAAMCxTnQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJCk7NG9YsWKOPfcc2Ps2LExYcKEmDdvXuzYsWPQMQcOHIj29vYYP358nHDCCTF//vzo6uoq9ygAAABQUWWP7s2bN0d7e3ts3bo1Nm7cGP39/TFjxozYv39/6Zirr746HnvssVi3bl1s3rw5du/eHZdcckm5RwEAAICKGlnuE27YsGHQz/fff39MmDAhtm/fHl/4wheip6cnvve978XatWvji1/8YkRErFmzJk477bTYunVrnHfeeeUeCQAAACoi/Z7unp6eiIhoaGiIiIjt27dHf39/tLW1lY6ZOnVqTJ48OTo6Og55jr6+vujt7R30AAAAgCNdanQPDAzEkiVL4oILLojTTz89IiI6Oztj9OjRMW7cuEHHNjY2Rmdn5yHPs2LFiqivry89Jk2alDk2AAAAlEVqdLe3t8eLL74YP/jBD/5P51m2bFn09PSUHrt27SrThAAAAJCn7Pd0f2Dx4sXx+OOPx5YtW2LixIml7U1NTXHw4MHo7u4edLW7q6srmpqaDnmumpqaqKmpyRoVAAAAUpT9SndRFLF48eJYv359PPnkk9HS0jJo/9lnnx2jRo2KTZs2lbbt2LEjXn/99WhtbS33OAAAAFAxZb/S3d7eHmvXro1HHnkkxo4dW7pPu76+Pmpra6O+vj4WLlwYS5cujYaGhqirq4urrroqWltbfXM5AAAAR5WyR/c999wTEREXXXTRoO1r1qyJyy+/PCIivv3tb0d1dXXMnz8/+vr6YubMmfHd73633KMAAABARZU9uouiOOwxY8aMiVWrVsWqVavK/esBAADgiJH+d7oBAADgWCW6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVCy6V61aFVOmTIkxY8bE9OnT4+mnn67UKAAAAJCiItH9z//8z7F06dK48cYb47nnnoszzzwzZs6cGXv27KnEOAAAAJCiItF95513xpVXXhlXXHFFTJs2LVavXh3HHXdc/OM//mMlxgEAAIAUI4f7Fx48eDC2b98ey5YtK22rrq6Otra26OjoOORz+vr6oq+vr/RzT09PRET09vbmDlsmA33vVHoEAACAT4xPQut9MGNRFL/xuGGP7l/+8pfx3nvvRWNj46DtjY2N8R//8R+HfM6KFSvipptu+rXtkyZNSpkRAACAyqlfWekJhu7tt9+O+vr6j9w/7NH921i2bFksXbq09PPAwEDs3bs3xo8fH1VVVem/v7e3NyZNmhS7du2Kurq69N8Hn0TWCQyNtQKHZ53A0FgrlVUURbz99tvR3Nz8G48b9uj+1Kc+FSNGjIiurq5B27u6uqKpqemQz6mpqYmamppB28aNG5c14keqq6vzHzMchnUCQ2OtwOFZJzA01krl/KYr3B8Y9i9SGz16dJx99tmxadOm0raBgYHYtGlTtLa2Dvc4AAAAkKYiHy9funRpXHbZZXHOOefE7/7u78bKlStj//79ccUVV1RiHAAAAEhRkej+kz/5k/jv//7vWL58eXR2dsbnPve52LBhw699udqRoqamJm688cZf+4g78L+sExgaawUOzzqBobFWPhmqisN9vzkAAADwWxn2e7oBAADgWCG6AQAAIInoBgAAgCSiGwAAAJIcs9G9ZcuWuPjii6O5uTmqqqri4YcfHrR/3759sXjx4pg4cWLU1tbGtGnTYvXq1YOOOXDgQLS3t8f48ePjhBNOiPnz50dXV9cwvgrId7i10tXVFZdffnk0NzfHcccdF7NmzYpXX3110DHWCke7FStWxLnnnhtjx46NCRMmxLx582LHjh2DjhnKOnj99ddjzpw5cdxxx8WECRPimmuuiXfffXc4XwqkGco6uffee+Oiiy6Kurq6qKqqiu7u7l87z969e2PBggVRV1cX48aNi4ULF8a+ffuG6VVAvsOtlb1798ZVV10Vn/70p6O2tjYmT54cX/nKV6Knp2fQebynHDmO2ejev39/nHnmmbFq1apD7l+6dGls2LAhvv/978fLL78cS5YsicWLF8ejjz5aOubqq6+Oxx57LNatWxebN2+O3bt3xyWXXDJcLwGGxW9aK0VRxLx58+IXv/hFPPLII/HTn/40TjnllGhra4v9+/eXjrNWONpt3rw52tvbY+vWrbFx48bo7++PGTNmfKx18N5778WcOXPi4MGD8dRTT8UDDzwQ999/fyxfvrwSLwnKbijr5J133olZs2bF1772tY88z4IFC+Kll16KjRs3xuOPPx5btmyJRYsWDcdLgGFxuLWye/fu2L17d9xxxx3x4osvxv333x8bNmyIhQsXls7hPeUIU1BERLF+/fpB2z7zmc8UN99886BtZ511VvG3f/u3RVEURXd3dzFq1Khi3bp1pf0vv/xyERFFR0dH+sxQCR9eKzt27CgionjxxRdL2957773ipJNOKu67776iKKwVjk179uwpIqLYvHlzURRDWwf/+q//WlRXVxednZ2lY+65556irq6u6OvrG94XAMPgw+vk//fjH/+4iIjirbfeGrT9Zz/7WRERxTPPPFPa9qMf/aioqqoq3njjjeyRoSJ+01r5wA9/+MNi9OjRRX9/f1EU3lOONMfsle7DOf/88+PRRx+NN954I4qiiB//+MfxyiuvxIwZMyIiYvv27dHf3x9tbW2l50ydOjUmT54cHR0dlRobhlVfX19ERIwZM6a0rbq6OmpqauInP/lJRFgrHJs++IhfQ0NDRAxtHXR0dMQZZ5wRjY2NpWNmzpwZvb298dJLLw3j9DA8PrxOhqKjoyPGjRsX55xzTmlbW1tbVFdXx7Zt28o+IxwJhrJWenp6oq6uLkaOHBkR3lOONKL7I9x9990xbdq0mDhxYowePTpmzZoVq1atii984QsREdHZ2RmjR4+OcePGDXpeY2NjdHZ2VmBiGH4fRMOyZcvirbfeioMHD8Ztt90W//Vf/xVvvvlmRFgrHHsGBgZiyZIlccEFF8Tpp58eEUNbB52dnYP+5+iD/R/sg6PJodbJUHR2dsaECRMGbRs5cmQ0NDRYJxyVhrJWfvnLX8bXv/71QbdZeE85soys9ABHqrvvvju2bt0ajz76aJxyyimxZcuWaG9vj+bm5kFXKuBYNmrUqPiXf/mXWLhwYTQ0NMSIESOira0tZs+eHUVRVHo8qIj29vZ48cUXS5/2AH6ddQJDc7i10tvbG3PmzIlp06bF3/3d3w3vcAyZ6D6EX/3qV/G1r30t1q9fH3PmzImIiM9+9rPx/PPPxx133BFtbW3R1NQUBw8ejO7u7kFXLrq6uqKpqalCk8PwO/vss+P555+Pnp6eOHjwYJx00kkxffr00kf/rBWOJYsXLy59sdPEiRNL24eyDpqamuLpp58edL4Pvt3cWuFo8lHrZCiamppiz549g7a9++67sXfvXuuEo87h1srbb78ds2bNirFjx8b69etj1KhRpX3eU44sPl5+CP39/dHf3x/V1YP/9YwYMSIGBgYi4v3QGDVqVGzatKm0f8eOHfH6669Ha2vrsM4LR4L6+vo46aST4tVXX41nn3025s6dGxHWCseGoihi8eLFsX79+njyySejpaVl0P6hrIPW1tZ44YUXBgXFxo0bo66uLqZNmzY8LwQSHW6dDEVra2t0d3fH9u3bS9uefPLJGBgYiOnTp5dzXKiYoayV3t7emDFjRowePToeffTRQd+vE+E95UhzzF7p3rdvX/z85z8v/bxz5854/vnno6GhISZPnhy/93u/F9dcc03U1tbGKaecEps3b44HH3ww7rzzzoh4PzAWLlwYS5cujYaGhqirq4urrroqWltb47zzzqvUy4KyO9xaWbduXZx00kkxefLkeOGFF+KrX/1qzJs3r/Slg9YKx4L29vZYu3ZtPPLIIzF27NjS/XL19fVRW1s7pHUwY8aMmDZtWvz5n/953H777dHZ2RnXX399tLe3R01NTSVfHpTF4dZJxPv3mnZ2dpbed1544YUYO3ZsTJ48ORoaGuK0006LWbNmxZVXXhmrV6+O/v7+WLx4cVx66aXR3NxcsdcG5XS4tfJBcL/zzjvx/e9/P3p7e6O3tzciIk466aQYMWKE95QjTUW/O72CPvhTFB9+XHbZZUVRFMWbb75ZXH755UVzc3MxZsyY4tOf/nTxrW99qxgYGCid41e/+lXx5S9/uTjxxBOL4447rvjDP/zD4s0336zQK4Ich1sr3/nOd4qJEycWo0aNKiZPnlxcf/31v/anKKwVjnaHWiMRUaxZs6Z0zFDWwWuvvVbMnj27qK2tLT71qU8Vf/3Xf1368y/wSTeUdXLjjTce9pj/+Z//Kf70T/+0OOGEE4q6urriiiuuKN5+++3hf0GQ5HBr5aP+3ywiip07d5bO4z3lyFFVFL7tCAAAADK4pxsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAk/w+vxrJ4JXT+ewAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## 信頼区間\n",
     "\n",
-    "次に、野球選手の体重と身長について信頼区間を計算してみましょう。このコードは、[このStack Overflowの議論](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)から引用しています。\n"
+    "次に、野球選手の体重と身長の信頼区間を計算してみましょう。このコードは[このStack Overflowの議論](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)から引用しています。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## 仮説検定\n",
     "\n",
-    "野球選手のデータセットで異なる役割を探ってみましょう:\n"
+    "野球選手のデータセットで異なる役割を見てみましょう:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "間隔が重ならないことが確認できます。\n",
+    "私たちは、区間が重ならないことを確認できます。\n",
     "\n",
-    "仮説をより統計的に正確に証明する方法は、**スチューデントのt検定**を使用することです：\n"
+    "仮説を証明するための統計的により正確な方法は、**スチューデントのt検定**を使用することです：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,34 +620,32 @@
    "metadata": {},
    "source": [
     "`ttest_ind` 関数によって返される2つの値は以下の通りです：\n",
-    "* p値は、2つの分布が同じ平均を持つ確率と考えることができます。この場合、p値は非常に低く、これにより一塁手が背が高いという強い証拠が示されています。\n",
-    "* t値は、t検定で使用される正規化された平均差の中間値であり、特定の信頼値に対する閾値と比較されます。\n"
+    "* p値は、2つの分布が同じ平均を持つ確率と考えることができます。今回の場合、p値は非常に低く、これにより一塁手が背が高いという強い証拠が示されています。\n",
+    "* t値は、t検定で使用される正規化された平均差の中間値であり、指定された信頼値に対する閾値と比較されます。\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 中央極限定理を使った正規分布のシミュレーション\n",
+    "## 中心極限定理を使った正規分布のシミュレーション\n",
     "\n",
-    "Pythonの疑似乱数生成器は、一様分布を提供するように設計されています。正規分布の生成器を作りたい場合は、中央極限定理を利用することができます。正規分布の値を得るには、一様分布で生成されたサンプルの平均を計算するだけです。\n"
+    "Pythonの疑似乱数生成器は、一様分布を生成するように設計されています。正規分布の生成器を作成したい場合、中心極限定理を利用することができます。正規分布に従う値を得るには、一様分布で生成されたサンプルの平均を計算するだけです。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## 相関と悪の野球企業\n",
     "\n",
-    "相関を使うと、データの系列間の関係を見つけることができます。このおもちゃの例では、悪の野球企業が選手の身長に応じて給料を支払うと仮定しましょう。つまり、選手が背が高いほど、より多くのお金をもらえるという仕組みです。基本給は$1000で、身長に応じて$0から$100のボーナスが追加されるとします。ここでは、MLBの実際の選手を使って、彼らの仮想的な給料を計算してみましょう。\n"
+    "相関を利用すると、データの並びの間にある関係を見つけることができます。このおもちゃの例では、悪の野球企業が選手の身長に応じて給料を支払うと仮定してみましょう。選手が背が高いほど、より多くのお金をもらえる仕組みです。基本給は$1000で、身長に応じて$0から$100のボーナスが追加されるとします。ここでは、MLBの実際の選手を使い、彼らの仮想的な給料を計算してみましょう。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "これらのシーケンスの共分散と相関を計算してみましょう。`np.cov`は、いわゆる**共分散行列**を提供します。これは、共分散を複数の変数に拡張したものです。共分散行列$M$の要素$M_{ij}$は、入力変数$X_i$と$X_j$の間の相関を表し、対角要素$M_{ii}$は$X_{i}$の分散を表します。同様に、`np.corrcoef`は**相関行列**を提供します。\n"
+    "これらのシーケンスの共分散と相関を計算してみましょう。`np.cov`は、いわゆる**共分散行列**を提供します。これは、共分散を複数の変数に拡張したものです。共分散行列$M$の要素$M_{ij}$は、入力変数$X_i$と$X_j$の間の相関を表し、対角成分$M_{ii}$は$X_{i}$の分散を表します。同様に、`np.corrcoef`は**相関行列**を提供します。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "相関が1であるということは、2つの変数間に強い**線形関係**があることを意味します。一方の値をもう一方の値に対してプロットすることで、線形関係を視覚的に確認することができます。\n"
+    "相関が1に等しいということは、2つの変数の間に強い**線形関係**があることを意味します。一方の値をもう一方の値に対してプロットすることで、線形関係を視覚的に確認できます。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "高さと給与の間の明らかな線形依存性を隠すことを決定し、`sin`のような非線形性を式に導入したと仮定します。\n"
+    "高さと給与の間の明らかな線形依存関係を隠すことを決定し、`sin`のような非線形性を式に導入したと仮定しましょう。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "この場合、相関はやや小さいですが、それでもかなり高いです。さて、関係をさらに目立たなくするために、給与にいくつかのランダムな変数を追加して、追加のランダム性を加えたいと思います。どうなるか見てみましょう。\n"
+    "この場合、相関はやや小さくなりますが、それでもかなり高いです。さて、関係をさらに目立たなくするために、給与にいくつかのランダムな変数を追加して、追加のランダム性を加えたいと思います。何が起こるか見てみましょう:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> なぜ点がこのように縦の線状に並ぶのか、わかりますか？\n",
+    "> なぜドットがこのように縦の線に並ぶのか、わかりますか？\n",
     "\n",
-    "私たちは、給与のような人工的に設計された概念と観測された変数 *身長* の間の相関を観察しました。それでは、身長と体重のような2つの観測変数が相関するかどうかも見てみましょう:\n"
+    "私たちは、給与のような人工的に作られた概念と観測された変数 *身長* の間の相関を確認しました。それでは、身長と体重のような2つの観測された変数が相関するかどうかも見てみましょう:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "残念ながら、結果は得られず、奇妙な `nan` 値だけが出てきました。これは、シリーズ内のいくつかの値が未定義であり、それが `nan` として表されているためです。その結果、演算の結果も未定義になってしまいます。マトリックスを見てみると、`Weight` が問題のある列であることがわかります。なぜなら、`Height` 値間の自己相関が計算されているからです。\n",
+    "残念ながら、結果は得られず、奇妙な `nan` 値だけが表示されました。これは、シリーズ内のいくつかの値が未定義であり、それが `nan` として表されているためです。その結果、操作の結果も未定義となります。マトリックスを確認すると、`Weight` が問題のある列であることが分かります。なぜなら、`Height` 値間の自己相関は計算されているからです。\n",
     "\n",
     "> この例は、**データの準備**と**クリーニング**の重要性を示しています。適切なデータがなければ、何も計算することはできません。\n",
     "\n",
-    "`fillna` メソッドを使って欠損値を埋め、相関を計算してみましょう。\n"
+    "`fillna` メソッドを使用して欠損値を埋め、相関を計算してみましょう。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1094,7 +927,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責事項**:  \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があることをご承知ください。元の言語で記載された文書が正式な情報源とみなされるべきです。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤った解釈について、当方は責任を負いません。\n"
+    "\n---\n\n**免責事項**:  \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を期すよう努めておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があります。元の言語で記載された原文を公式な情報源としてご参照ください。重要な情報については、専門の人間による翻訳を推奨します。本翻訳の利用に起因する誤解や誤認について、当社は一切の責任を負いません。\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:12:31+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:15:18+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ja"
   }
diff --git a/translations/ja/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ja/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 7459810f..eaa8acfe 100644
--- a/translations/ja/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ja/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 確率と統計の入門  \n",
-    "## 課題  \n",
+    "## 確率と統計の入門\n",
+    "## 課題\n",
     "\n",
-    "この課題では、[こちら](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)から取得した糖尿病患者のデータセットを使用します。  \n"
+    "この課題では、[こちら](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)から取得した糖尿病患者のデータセットを使用します。\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,14 +150,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "このデータセットには、以下の列が含まれています：\n",
-    "* 年齢と性別はそのままの意味です\n",
+    "このデータセットには以下の列があります：\n",
+    "* 年齢と性別はそのまま説明不要です\n",
     "* BMIは体格指数を表します\n",
-    "* BPは平均血圧を表します\n",
-    "* S1からS6は異なる血液測定値を表します\n",
-    "* Yは1年間の病気の進行度を定性的に測定したものです\n",
+    "* BPは平均血圧を示します\n",
+    "* S1からS6は異なる血液測定値です\n",
+    "* Yは1年間の疾患進行の定性的な指標です\n",
     "\n",
-    "このデータセットを確率と統計の手法を用いて分析してみましょう。\n",
+    "このデータセットを確率と統計の手法を使って分析してみましょう。\n",
     "\n",
     "### タスク 1: 全ての値の平均値と分散を計算する\n"
    ],
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### タスク3: 年齢、性別、BMI、Y変数の分布は何ですか？\n"
+    "### タスク3: 年齢、性別、BMI、およびY変数の分布はどうなっていますか？\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -602,9 +602,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### タスク 4: 異なる変数と病気の進行 (Y) の相関をテストする\n",
+    "### タスク 4: 異なる変数と病気の進行 (Y) との相関をテストする\n",
     "\n",
-    "> **ヒント** 相関行列は、どの値が依存しているかについて最も有益な情報を提供します。\n"
+    "> **ヒント** 相関行列は、どの値が依存しているかについて最も有用な情報を提供します。\n"
    ],
    "metadata": {}
   },
@@ -846,8 +846,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "結論:\n",
-    "* Yとの最も強い相関はBMIとS5（血糖値）です。これは妥当な結果のように思われます。\n"
+    "結論:  \n",
+    "* Yと最も強い相関があるのはBMIとS5（血糖値）です。これは妥当な結果のように思われます。\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責事項**:  \nこの文書はAI翻訳サービス[Co-op Translator](https://github.com/Azure/co-op-translator)を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があります。元の言語で記載された文書を正式な情報源としてお考えください。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤った解釈について、当方は一切の責任を負いません。\n"
+    "\n---\n\n**免責事項**:  \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を期すよう努めておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があります。元の言語で記載された原文を公式な情報源としてご参照ください。重要な情報については、専門の人間による翻訳を推奨します。本翻訳の利用に起因する誤解や誤認について、当社は一切の責任を負いません。\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:26:26+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:15:47+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ja"
   }
diff --git a/translations/ko/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ko/1-Introduction/04-stats-and-probability/assignment.ipynb
index 22452f0b..e76493be 100644
--- a/translations/ko/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ko/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## 확률과 통계 소개\n",
     "## 과제\n",
     "\n",
-    "이 과제에서는 [여기에서](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) 가져온 당뇨병 환자 데이터셋을 사용할 것입니다.\n"
+    "이 과제에서는 [여기에서 가져온](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) 당뇨병 환자 데이터셋을 사용할 것입니다.\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "이 데이터셋의 열은 다음과 같습니다:  \n",
-    "* Age와 sex는 별도의 설명이 필요 없습니다.  \n",
-    "* BMI는 체질량지수입니다.  \n",
-    "* BP는 평균 혈압입니다.  \n",
-    "* S1부터 S6까지는 서로 다른 혈액 측정값입니다.  \n",
-    "* Y는 1년 동안의 질병 진행 정도를 나타내는 정성적 지표입니다.  \n",
+    "이 데이터셋의 열은 다음과 같습니다:\n",
+    "* Age와 sex는 별도의 설명이 필요 없습니다\n",
+    "* BMI는 체질량지수입니다\n",
+    "* BP는 평균 혈압입니다\n",
+    "* S1부터 S6까지는 서로 다른 혈액 측정값입니다\n",
+    "* Y는 1년 동안 질병 진행의 정성적 측정값입니다\n",
     "\n",
     "이 데이터셋을 확률과 통계 방법을 사용하여 분석해 봅시다.\n",
     "\n",
-    "### 작업 1: 모든 값에 대한 평균값과 분산 계산\n"
+    "### 작업 1: 모든 값의 평균값과 분산을 계산하세요\n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Task 2: 성별에 따라 BMI, BP 및 Y의 박스플롯 그리기\n"
+    "### 작업 2: 성별에 따라 BMI, BP 및 Y에 대한 박스플롯 그리기\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 작업 3: 연령, 성별, BMI 및 Y 변수의 분포는 무엇입니까?\n"
+    "### 작업 3: 나이, 성별, BMI 및 Y 변수의 분포는 무엇인가요?\n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### 작업 4: 다양한 변수와 질병 진행(Y) 간의 상관관계 테스트\n",
     "\n",
-    "> **힌트** 상관관계 행렬은 어떤 값들이 서로 의존적인지에 대한 가장 유용한 정보를 제공합니다.\n"
+    "> **힌트** 상관 행렬은 어떤 값들이 서로 의존적인지에 대한 가장 유용한 정보를 제공합니다.\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**면책 조항**:  \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있지만, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서의 원어 버전을 권위 있는 출처로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생하는 오해나 잘못된 해석에 대해 책임을 지지 않습니다.\n"
+    "\n---\n\n**면책 조항**:  \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서(원어로 작성된 문서)를 권위 있는 자료로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생하는 오해나 잘못된 해석에 대해 당사는 책임을 지지 않습니다.  \n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:20:23+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:16:58+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ko"
   }
diff --git a/translations/ko/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ko/1-Introduction/04-stats-and-probability/notebook.ipynb
index a0ed9d7e..3d8fffe8 100644
--- a/translations/ko/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ko/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 랜덤 변수와 분포\n",
+    "## 확률 변수와 분포\n",
     "0에서 9까지의 균등 분포에서 30개의 값을 샘플링해 봅시다. 또한 평균과 분산도 계산해 보겠습니다.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## 실제 데이터 분석\n",
     "\n",
-    "평균과 분산은 실제 데이터를 분석할 때 매우 중요합니다. [SOCR MLB 키/몸무게 데이터](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)에서 야구 선수에 대한 데이터를 불러와 봅시다.\n"
+    "평균과 분산은 실제 데이터를 분석할 때 매우 중요합니다. [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)에서 야구 선수에 대한 데이터를 불러옵시다.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "우리는 여기서 데이터 분석을 위해 [**Pandas**](https://pandas.pydata.org/)라는 패키지를 사용하고 있습니다. 이 과정에서 Pandas와 Python을 사용한 데이터 작업에 대해 더 자세히 이야기할 예정입니다.\n",
+    "우리는 여기서 데이터 분석을 위해 [**Pandas**](https://pandas.pydata.org/)라는 패키지를 사용하고 있습니다. Pandas와 Python에서 데이터를 다루는 방법에 대해서는 이 강의 후반부에서 더 자세히 다룰 예정입니다.\n",
     "\n",
     "이제 나이, 키, 몸무게의 평균값을 계산해 봅시다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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o5qGbh24euhXDArlEHR0duUeoHZp56Oahm4duHrp56OahWzEskEt04cKF3CPUDs08dPPQzUM3D908dPPQrRgWyCVasGBB7hFqh2Yeunno5qGbh24eunnoVgwL5BItWrQo9wi1QzMP3Tx089DNQzcP3Tx0K2ZOXsUiIs5KOv6Of+L6WyLpXO4haoZmHrp56Oahm4duHrp56Nbaz6eU3jv5zjlZIKO1iNjT6qVEMDWaeejmoZuHbh66eejmoVsxPMQCAAAAaMICGQAAAGjCArlc/5R7gBqimYduHrp56Oahm4duHroVwGOQAQAAgCbsIAMAAABNWCADAAAATVggz5GIuCUinouIQxFxMCJ+NSLujYjvRsRQROyJiF/OPWeVRMSdjTZXbj+KiE9GxK0R8e8R8YPGx/bcs1bJNN2+0jj+XomIzRFxS+5Zq2Sqbk3//dMRkSJiScYxK2W6ZhHRHRGHI+L7EfF3mUetlGnOUa4JM4iITzWOqQMR0RsRi7gmzGyKblwTCuAxyHMkIp6RtCOl9LWIaJP0HknPSvpqSuk7EfG7kp5IKX0o55xVFRHXSzol6Vck/ZWk11NKX46Iz0hqTymtzzpgRU3qdqekwZTSTyLibyWJbq01d0spHY+IZZK+JmmlpDUpJV5cf5JJx9r7JPVI+r2U0mhE3J5Sei3rgBU1qdvT4powpYj4OUn/IekDKaUfR8Szkv5N0gfENWFK03T7obgmzBo7yHMgIm6S9BuSvi5JKaWxlNL/SUqSbmr8sZs1cbCitYckHU0pHZf0EUnPNO5/RtLv5xqqBq52Syk9n1L6SeP+70rqyDhX1TUfb5L0VUlPaOKcRWvNzf5C0pdTSqOSxOJ4Ws3duCbM7AZJPxURN2hio+mH4powG2/pxjWhGBbIc+N9ks5K+ueI+F5EfC0iFkv6pKSvRMQJSX8v6W8yzlh1fySpt/HPP5NSOi1JjY+3Z5uq+pq7NftTSd8peZY6udotIh6RdCql9HLekSqv+Vh7v6Rfj4hdEbE9Iu7POFfVNXf7pLgmTCmldEoTXV6VdFrScErpeXFNmNY03ZpxTZgBC+S5cYOkD0r6x5TSL0kakfQZTeyyfCqltEzSp9TYYca1Gg9JeUTSt3PPUidTdYuIHkk/kfQvOeaquuZuEfEeTTxU4HN5p6q2FsfaDZLaJT0g6a8lPRsRkWm8ymrRjWvCNBqPLf6IpF+Q9LOSFkfEn+Sdqvpm6sY1YXZYIM+Nk5JOppR2Nf79OU0smD8uqa9x37cl8YSM1n5H0r6U0pnGv5+JiKWS1PjIr29bm9xNEfFxSQ9L+uPEEw6m0tztFzVxUXk5Io5p4leQ+yLijozzVdHkY+2kpL404T8ljUviyY1vNbkb14Tp/bak/0kpnU0pXdZEqy5xTZjJVN24JhTAAnkOpJT+V9KJiLizcddDkv5LE4+d+s3Gfb8l6QcZxquDR3XtwwS2auJCosbHfy19onq4pltEfFjSekmPpJTeyDZV9V3tllLan1K6PaXUmVLq1MTC74ONcxpvmnyObtHE9zRFxPsltUniiY1vNbkb14TpvSrpgYh4T+M3Eg9JOiiuCTNp2Y1rQjG8isUciYh7NfEs+DZJ/y3pMUmrJP2DJn4deUnSX6aU9uaasYoav+I+Iel9KaXhxn23aeIVQJZr4sT/g5TS6/mmrJ4puh2RtFDS+cYf+25KaV2mESupVbdJ//2YpPt4FYs3TXGstUn6hqR7JY1J+nRKaTDbkBU0RbdfE9eEaUXEFyX9oSYeEvA9SX8m6afFNWFaU3T7vrgmzBoLZAAAAKAJD7EAAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmvw/tSpycIADqyoAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "우리는 또한 데이터셋의 하위 집합에 대해 박스 플롯을 만들 수 있습니다. 예를 들어, 플레이어 역할별로 그룹화하여 만들 수 있습니다.\n"
+    "우리는 또한 데이터셋의 부분 집합에 대해 박스 플롯을 만들 수 있습니다. 예를 들어, 플레이어 역할별로 그룹화하여 만들 수 있습니다.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Note**: 이 다이어그램은 평균적으로 1루수의 키가 2루수의 키보다 더 크다는 것을 시사합니다. 나중에 우리는 이 가설을 더 공식적으로 테스트하는 방법과 우리의 데이터가 통계적으로 유의미하다는 것을 보여주는 방법을 배우게 될 것입니다.\n",
+    "> **참고**: 이 다이어그램은 평균적으로 1루수의 키가 2루수의 키보다 더 크다는 것을 시사합니다. 나중에 우리는 이 가설을 더 공식적으로 검증하는 방법과, 우리의 데이터가 이를 보여주기에 통계적으로 유의미하다는 것을 입증하는 방법을 배우게 될 것입니다.\n",
     "\n",
-    "나이, 키, 몸무게는 모두 연속적인 확률 변수입니다. 이들의 분포가 어떻게 생겼을 것 같나요? 이를 알아보는 좋은 방법은 값의 히스토그램을 그려보는 것입니다:\n"
+    "나이, 키, 몸무게는 모두 연속 확률 변수입니다. 이들의 분포가 어떻게 생겼을지 생각해보세요. 이를 알아보는 좋은 방법은 값들의 히스토그램을 그려보는 것입니다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Zq7/9GOAQ4GsjqE2SJEmr3DAv83Ym8GngJ5Jcm+Ql/abjuOfyCoCnAl9I8nngfcDLqurmYdUmSZIkzWSYV7F4wQzjJ04zdjZw9rBqkSRJkubLt5qWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKmx97AOnOR04Bjgxqo6vB97PfBS4Fv9bq+uqvP6bScBLwHuAn6rqj4yrNokrV5rN5076hJmteOUo0ddgiStesOcQX4H8Kxpxt9SVUf0H5Ph+FDgOOCw/jF/nWSvIdYmSZIkTWtoAbmqLgRunufuxwJbqurOqroa+Cpw1LBqkyRJkmaSqhrewZO1wIenLLE4EbgN2AZsrKpbkrwVuKiq3tXvdxrwj1X1vmmOuQHYADA2Nnbkli1bhlb/SrBr1y7WrFkz6jJWlFH3dPt1t47s3MM09iDYeceoqxi9dQftO7BjjfprdaWyr4NnT4fDvs5t/fr1F1fV+NTxoa1BnsHbgDcC1X8+FXgxkGn2nTa5V9VmYDPA+Ph4TUxMDKXQlWLr1q3Yo8EadU9PXOJraBdq47rdnLp9sb8lLT07jp8Y2LFG/bW6UtnXwbOnw2FfF25RfxpV1c7J20n+Fvhwf/da4OBm10cB1y9iadIPzPUiro3rdq/YkCpJkhb5Mm9JDmzu/jJwWX/7HOC4JPskeTRwCPDZxaxNkiRJguFe5u1MYAI4IMm1wOuAiSRH0C2f2AH8GkBVXZ7kLOCLwG7gFVV117BqkyRJkmYytIBcVS+YZvi0WfY/GTh5WPVIkiRJ8+E76UmSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSY2hBeQkpye5McllzdifJflSki8k+UCS/frxtUnuSHJp//H2YdUlSZIkzWaYM8jvAJ41Zex84PCq+n+ArwAnNduuqqoj+o+XDbEuSZIkaUZDC8hVdSFw85Sxj1bV7v7uRcCjhnV+SZIkaSFSVcM7eLIW+HBVHT7Ntn8A3ltV7+r3u5xuVvk24A+q6hMzHHMDsAFgbGzsyC1btgyp+pVh165drFmzZtRlLCvbr7t11u1jD4KddyxSMauIfe2sO2jfgR3L///DYV8Hz54Oh32d2/r16y+uqvGp43uPopgkrwF2A+/uh24AfrSqbkpyJPDBJIdV1W1TH1tVm4HNAOPj4zUxMbFIVS9PW7duxR7tmRM3nTvr9o3rdnPq9pH811nR7Gtnx/ETAzuW//+Hw74Onj0dDvu6cIt+FYskJwDHAMdXP31dVXdW1U397YuBq4DHLXZtkiRJ0qIG5CTPAn4f+KWq+l4z/vAke/W3HwMcAnxtMWuTJEmSYIhLLJKcCUwAByS5Fngd3VUr9gHOTwJwUX/FiqcCb0iyG7gLeFlV3TztgSVJkqQhGlpArqoXTDN82gz7ng2cPaxaJEmSpPnynfQkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpMa8AnKSJ89nTJIkSVru5juD/D/nOSZJkiQta3vPtjHJE4EnAQ9P8qpm00OBvYZZmCRJkjQKswZk4AHAmn6/hzTjtwHPHVZRkiRJ0qjMGpCr6gLggiTvqKprFqkmSZIkaWTmmkGetE+SzcDa9jFV9bRhFCVJkiSNynwD8v8C3g78HXDX8MqRJEmSRmu+AXl3Vb1tqJVIkiRJS8B8L/P2D0l+PcmBSR42+THUyiRJkqQRmO8M8gn9599rxgp4zGDLkSRJkkZrXgG5qh497EIkSZKkpWBeATnJi6Ybr6p3DrYcSZIkabTmu8TiCc3tBwJPBy4BDMiSJElaUea7xOI32/tJ9gX+frbHJDkdOAa4saoO78ceBryX7nrKO4DnV9Ut/baTgJfQXUbut6rqI3vyRCRJkqRBmO8M8lTfAw6ZY593AG/lnrPMm4CPVdUpSTb1938/yaHAccBhwCOBf07yuKrymsuSVpW1m84d2LE2rtvNiQM83o5Tjh7YsSRpKZvvGuR/oLtqBcBewOOBs2Z7TFVdmGTtlOFjgYn+9hnAVuD3+/EtVXUncHWSrwJHAZ+eT32SJEnSoKSq5t4p+fnm7m7gmqq6dh6PWwt8uFli8Z2q2q/ZfktV7Z/krcBFVfWufvw04B+r6n3THHMDsAFgbGzsyC1btsxZ/2q2a9cu1qxZM+oylpXt19066/axB8HOOxapmFXEvg7eoHu67qB9B3ewZczvq4NnT4fDvs5t/fr1F1fV+NTx+a5BviDJGHe/WO/KQRYHZLrTzlDLZmAzwPj4eE1MTAy4lJVl69at2KM9M9efpDeu282p2xe6Okkzsa+DN+ie7jh+YmDHWs78vjp49nQ47OvCzeud9JI8H/gs8Dzg+cBnkjx3AefbmeTA/pgHAjf249cCBzf7PQq4fgHHlyRJku6T+b7V9GuAJ1TVCVX1Irr1wX+4gPOdw93vyncC8KFm/Lgk+yR5NN0LAD+7gONLkiRJ98l8//Z2v6q6sbl/E3OE6yRn0r0g74Ak1wKvA04BzkryEuDrdDPSVNXlSc4Cvki3xvkVXsFCkiRJozDfgPxPST4CnNnf/xXgvNkeUFUvmGHT02fY/2Tg5HnWI0mSJA3FrAE5yY8DY1X1e0n+M/AUuhfUfRp49yLUJ0mSJC2qudYg/wVwO0BVvb+qXlVVv0M3e/wXwy1NkiRJWnxzBeS1VfWFqYNVtY3u7aIlSZKkFWWugPzAWbY9aJCFSJIkSUvBXAH5c0leOnWwvwrFxcMpSZIkSRqdua5i8UrgA0mO5+5APA48APjlIdYlSZIkjcSsAbmqdgJPSrIeOLwfPreq/mXolUmSJEkjMK/rIFfVx4GPD7kWSZIkaeTm+1bTkiRJ0qpgQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpsfdinzDJTwDvbYYeA7wW2A94KfCtfvzVVXXe4lYnSZKk1W7RA3JVfRk4AiDJXsB1wAeA/wa8par+fLFrkiRJkiaNeonF04GrquqaEdchSZIkAZCqGt3Jk9OBS6rqrUleD5wI3AZsAzZW1S3TPGYDsAFgbGzsyC1btixewcvQrl27WLNmzajLWFa2X3frrNvHHgQ771ikYlYR+zp4g+7puoP2HdzBljG/rw6ePR0O+zq39evXX1xV41PHRxaQkzwAuB44rKp2JhkDvg0U8EbgwKp68WzHGB8fr23btg2/2GVs69atTExMjLqMZWXtpnNn3b5x3W5O3b7oq5NWPPs6eKutpztOOXpRzuP31cGzp8NhX+eWZNqAPMolFr9IN3u8E6CqdlbVXVX1feBvgaNGWJskSZJWqVFOLbwAOHPyTpIDq+qG/u4vA5eNpCoN3VwztJIkSaM0koCc5IeA/wj8WjP8piRH0C2x2DFlmyRJkrQoRhKQq+p7wA9PGXvhKGqRJEmSWqO+zJskSZK0pBiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqTG3qM4aZIdwO3AXcDuqhpP8jDgvcBaYAfw/Kq6ZRT1SZIkafUa5Qzy+qo6oqrG+/ubgI9V1SHAx/r7kiRJ0qJaSkssjgXO6G+fATxndKVIkiRptUpVLf5Jk6uBW4AC/qaqNif5TlXt1+xzS1XtP81jNwAbAMbGxo7csmXLIlW9PO3atYs1a9aMuox72H7draMu4T4ZexDsvGPUVaw89nXwVltP1x2076KcZyl+X13u7Olw2Ne5rV+//uJmNcMPjGQNMvDkqro+ySOA85N8ab4PrKrNwGaA8fHxmpiYGFKJK8PWrVtZaj06cdO5oy7hPtm4bjenbh/Vf52Vy74O3mrr6Y7jJxblPEvx++pyZ0+Hw74u3EiWWFTV9f3nG4EPAEcBO5McCNB/vnEUtUmSJGl1W/SAnOTBSR4yeRt4BnAZcA5wQr/bCcCHFrs2SZIkaRR/exsDPpBk8vzvqap/SvI54KwkLwG+DjxvBLVJkiRplVv0gFxVXwN+aprxm4CnL3Y9kiRJUmspXeZNkiRJGjkDsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJjb1HXYAkSYOwdtO5i3Kejet2c+ICzrXjlKOHUI2kYXAGWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJauw96gI0eGs3nfuD2xvX7ebE5r4kSZJm5wyyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEmNRQ/ISQ5O8vEkVyS5PMlv9+OvT3Jdkkv7j2cvdm2SJEnSKK5isRvYWFWXJHkIcHGS8/ttb6mqPx9BTZIkSRIwgoBcVTcAN/S3b09yBXDQYtchSZIkTSdVNbqTJ2uBC4HDgVcBJwK3AdvoZplvmeYxG4ANAGNjY0du2bJlscpdNrZfd+sPbo89CHbeMcJiViB7Ohz2dfDs6XAstK/rDtp38MWsELt27WLNmjWjLmPFsa9zW79+/cVVNT51fGQBOcka4ALg5Kp6f5Ix4NtAAW8EDqyqF892jPHx8dq2bdvwi11mpr5RyKnbfT+YQbKnw2FfB8+eDsdC+7rjlKOHUM3KsHXrViYmJkZdxopjX+eWZNqAPJKrWCS5P3A28O6qej9AVe2sqruq6vvA3wJHjaI2SZIkrW6juIpFgNOAK6rqzc34gc1uvwxctti1SZIkSaP429uTgRcC25Nc2o+9GnhBkiPolljsAH5tBLVJkjQU7fK3pcglINLdRnEVi08CmWbTeYtdiyRJkjSV76QnSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSY+9RF7Acrd107qhLkCRJ0pA4gyxJkiQ1nEGWJEkj/evoxnW7OXGO8+845ehFqkZyBlmSJEm6BwOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDa+DLEmSdB8txXfZba8v7XWk98ySm0FO8qwkX07y1SSbRl2PJEmSVpclNYOcZC/gr4D/CFwLfC7JOVX1xdFWJkmSRmkpztAuJ0u9f0tthnupzSAfBXy1qr5WVf8GbAGOHXFNkiRJWkVSVaOu4QeSPBd4VlX9an//hcDPVNVvNPtsADb0d38C+PKiF7q8HAB8e9RFrDD2dDjs6+DZ0+Gwr4NnT4fDvs7tx6rq4VMHl9QSCyDTjN0jwVfVZmDz4pSz/CXZVlXjo65jJbGnw2FfB8+eDod9HTx7Ohz2deGW2hKLa4GDm/uPAq4fUS2SJElahZZaQP4ccEiSRyd5AHAccM6Ia5IkSdIqsqSWWFTV7iS/AXwE2As4vaouH3FZy53LUQbPng6HfR08ezoc9nXw7Olw2NcFWlIv0pMkSZJGbaktsZAkSZJGyoAsSZIkNQzIy1yS05PcmOSyKeO/2b9l9+VJ3tSMn9S/jfeXkzxz8Ste+qbraZIjklyU5NIk25Ic1Wyzp3NIcnCSjye5ov+a/O1+/GFJzk9yZf95/+Yx9nUOs/T1z5J8KckXknwgyX7NY+zrLGbqabP9d5NUkgOaMXs6h9n66s+rhZnl/78/rwahqvxYxh/AU4GfBi5rxtYD/wzs099/RP/5UODzwD7Ao4GrgL1G/RyW2scMPf0o8Iv97WcDW+3pHvX0QOCn+9sPAb7S9+5NwKZ+fBPwp/Z1IH19BrB3P/6n9vW+97S/fzDdi8ivAQ6wp/e9r/68GkpP/Xk1gA9nkJe5qroQuHnK8MuBU6rqzn6fG/vxY4EtVXVnVV0NfJXu7b3VmKGnBTy0v70vd1+f257OQ1XdUFWX9LdvB64ADqLr3xn9bmcAz+lv29d5mKmvVfXRqtrd73YR3TXlwb7OaZavVYC3AP8f93wDK3s6D7P01Z9XCzRLT/15NQAG5JXpccDPJflMkguSPKEfPwj4RrPftdz9jV+zeyXwZ0m+Afw5cFI/bk/3UJK1wH8APgOMVdUN0H2zBx7R72Zf99CUvrZeDPxjf9u+7oG2p0l+Cbiuqj4/ZTd7uoemfK3682oApvT0lfjz6j4zIK9MewP7Az8L/B5wVpIwj7fy1oxeDvxOVR0M/A5wWj9uT/dAkjXA2cArq+q22XadZsy+zmCmviZ5DbAbePfk0DQPt6/TaHtK18PXAK+dbtdpxuzpDKb5WvXn1X00TU/9eTUABuSV6Vrg/dX5LPB94AB8K+/74gTg/f3t/8Xdf5ayp/OU5P5038TfXVWTvdyZ5MB++4HA5J9X7es8zdBXkpwAHAMcX/0CROzrvEzT08fSrdn8fJIddH27JMmPYE/nbYavVX9e3Qcz9NSfVwNgQF6ZPgg8DSDJ44AHAN+me9vu45Lsk+TRwCHAZ0dV5DJzPfDz/e2nAVf2t+3pPPQzQqcBV1TVm5tN59B9M6f//KFm3L7OYaa+JnkW8PvAL1XV95qH2Nc5TNfTqtpeVY+oqrVVtZYuaPx0VX0Tezovs3wP+CD+vFqQWXrqz6sBWFJvNa09l+RMYAI4IMm1wOuA04HT012m7N+AE/oZpMuTnAV8ke5Phq+oqrtGU/nSNUNPXwr8jyR7A/8KbACoKns6P08GXghsT3JpP/Zq4BS6P6m+BPg68Dywr3tgpr7+Jd0r1c/vfoZyUVW9zL7Oy7Q9rarzptvZns7bTF+r/rxauJl66s+rAfCtpiVJkqSGSywkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSlqAkb0nyyub+R5L8XXP/1CSvmuGxb0jyC3Mc//VJfnea8f2S/Pp9KF2Slj0DsiQtTf8beBJAkvvRvbvYYc32JwGfmu6BVfXaqvrnBZ53P8CALGlVMyBL0tL0KfqATBeMLwNuT7J/kn2AxwMkuSDJxf0M8+Tbdr8jyXP7289O8qUkn0zyl0k+3Jzj0CRbk3wtyW/1Y6cAj01yaZI/W4wnKklLje+kJ0lLUFVdn2R3kh+lC8qfBg4CngjcClwBvAU4tqq+leRXgJOBF08eI8kDgb8BnlpVV/fvEtn6SWA98BDgy0neBmwCDq+qI4b6BCVpCTMgS9LSNTmL/CTgzXQB+Ul0Afk64Bnc/XbSewE3THn8TwJfq6qr+/tn0r/tbO/cqroTuDPJjcDYkJ6HJC0rBmRJWrom1yGvo1ti8Q1gI3Ab8C/AQVX1xFkenzmOf2dz+y78mSBJgGuQJWkp+xRwDHBzVd1VVTfTvYjuicB7gYcneSJAkvsnOWzK478EPCbJ2v7+r8zjnLfTLbmQpFXLgCxJS9d2uqtXXDRl7NaquhF4LvCnST4PXMrdL+oDoKruoLsixT8l+SSwk255xoyq6ibgU0ku80V6klarVNWoa5AkDUmSNVW1K91C5b8Crqyqt4y6LklaypxBlqSV7aVJLgUuB/alu6qFJGkWziBLkiRJDWeQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkxv8FiHh2DxCDPowAAAAASUVORK5CYII=\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## 정규 분포\n",
     "\n",
-    "우리의 실제 데이터와 동일한 평균과 분산을 가진 정규 분포를 따르는 인공적인 체중 샘플을 만들어봅시다:\n"
+    "우리의 실제 데이터와 동일한 평균과 분산을 가지는 정규 분포를 따르는 인공적인 체중 샘플을 만들어 봅시다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "실제 생활에서 대부분의 값은 정규 분포를 따르므로 샘플 데이터를 생성하기 위해 균일한 난수 생성기를 사용해서는 안 됩니다. 균일 분포로 무게를 생성하려고 하면 (`np.random.rand`로 생성됨) 다음과 같은 일이 발생합니다:\n"
+    "실제 생활에서 대부분의 값은 정규 분포를 따르기 때문에, 샘플 데이터를 생성할 때 균일한 난수 생성기를 사용해서는 안 됩니다. 균일 분포(`np.random.rand`로 생성됨)를 사용하여 무게를 생성하려고 하면 다음과 같은 일이 발생합니다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## 신뢰 구간\n",
     "\n",
-    "이제 야구 선수들의 몸무게와 키에 대한 신뢰 구간을 계산해 보겠습니다. 우리는 [이 StackOverflow 토론](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)의 코드를 사용할 것입니다:\n"
+    "이제 야구 선수들의 몸무게와 키에 대한 신뢰 구간을 계산해 봅시다. 우리는 [이 stackoverflow 토론](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)에서 제공된 코드를 사용할 것입니다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "우리는 간격이 겹치지 않는다는 것을 확인할 수 있습니다.\n",
+    "우리는 구간들이 겹치지 않는다는 것을 알 수 있습니다.\n",
     "\n",
     "가설을 증명하는 더 통계적으로 올바른 방법은 **Student t-test**를 사용하는 것입니다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "`ttest_ind` 함수가 반환하는 두 가지 값은 다음과 같습니다:\n",
-    "* p-value는 두 분포가 동일한 평균을 가질 확률로 간주될 수 있습니다. 우리의 경우, p-value가 매우 낮아 첫 번째 베이스맨이 더 키가 크다는 강력한 증거를 제공합니다.\n",
-    "* t-value는 t-검정에서 사용되는 정규화된 평균 차이의 중간 값이며, 주어진 신뢰 값에 대한 임계값과 비교됩니다.\n"
+    "* p-value는 두 분포가 동일한 평균을 가질 확률로 간주될 수 있습니다. 우리의 경우, p-value가 매우 낮아, 1루수가 더 키가 크다는 강력한 증거를 지지합니다.\n",
+    "* t-value는 t-검정에서 사용되는 정규화된 평균 차이의 중간 값으로, 주어진 신뢰 수준에 대한 임계값과 비교됩니다.\n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## 중심극한정리를 이용한 정규분포 시뮬레이션\n",
     "\n",
-    "Python의 의사 난수 생성기는 균등분포를 제공하도록 설계되어 있습니다. 정규분포를 생성하려면 중심극한정리를 활용할 수 있습니다. 정규분포 값을 얻기 위해서는 균등분포로 생성된 샘플의 평균을 계산하면 됩니다.\n"
+    "Python의 의사난수 생성기는 균등분포를 제공하도록 설계되어 있습니다. 정규분포를 생성하려면 중심극한정리를 활용할 수 있습니다. 정규분포 값을 얻기 위해서는 균등분포로 생성된 샘플의 평균을 계산하면 됩니다.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## 상관관계와 사악한 야구 회사\n",
     "\n",
-    "상관관계는 데이터 시퀀스 간의 관계를 찾을 수 있게 해줍니다. 우리의 간단한 예시에서, 사악한 야구 회사가 선수들의 키에 따라 급여를 지급한다고 가정해봅시다. 키가 클수록 더 많은 돈을 받는 구조입니다. 기본 급여는 $1000이고, 키에 따라 $0에서 $100까지 추가 보너스를 받는다고 가정합니다. 실제 MLB 선수 데이터를 사용하여 그들의 가상의 급여를 계산해보겠습니다:\n"
+    "상관관계는 데이터 시퀀스 간의 관계를 찾을 수 있게 해줍니다. 우리의 간단한 예제에서, 어떤 사악한 야구 회사가 선수들의 키에 따라 급여를 지급한다고 가정해 봅시다. 즉, 선수가 키가 클수록 더 많은 돈을 받는다는 설정입니다. 기본 급여는 $1000이고, 키에 따라 $0에서 $100까지 추가 보너스가 지급된다고 가정합니다. 우리는 MLB의 실제 선수 데이터를 사용하여 그들의 가상의 급여를 계산해 보겠습니다.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "이제 이러한 시퀀스의 공분산과 상관관계를 계산해 봅시다. `np.cov`는 소위 **공분산 행렬**을 제공하며, 이는 공분산을 다변수로 확장한 것입니다. 공분산 행렬 $M$의 요소 $M_{ij}$는 입력 변수 $X_i$와 $X_j$ 간의 상관관계이고, 대각선 값 $M_{ii}$는 $X_{i}$의 분산입니다. 마찬가지로, `np.corrcoef`는 **상관관계 행렬**을 제공합니다.\n"
+    "이제 이러한 시퀀스의 공분산과 상관관계를 계산해 봅시다. `np.cov`는 **공분산 행렬**이라고 불리는 것을 제공하며, 이는 공분산을 다변수로 확장한 것입니다. 공분산 행렬 $M$의 요소 $M_{ij}$는 입력 변수 $X_i$와 $X_j$ 간의 상관관계를 나타내며, 대각선 값 $M_{ii}$는 $X_{i}$의 분산을 나타냅니다. 마찬가지로, `np.corrcoef`는 **상관관계 행렬**을 제공합니다.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "상관계수가 1이라는 것은 두 변수 간에 강한 **선형 관계**가 있다는 것을 의미합니다. 한 값을 다른 값에 대해 플롯하여 선형 관계를 시각적으로 확인할 수 있습니다:\n"
+    "두 변수 간에 강한 **선형 관계**가 있음을 상관계수가 1이라는 것은 의미합니다. 한 값을 다른 값에 대해 플로팅하여 선형 관계를 시각적으로 확인할 수 있습니다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -919,14 +756,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -939,19 +776,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "이 경우 상관관계는 약간 작아졌지만 여전히 상당히 높습니다. 이제 관계를 더욱 덜 명확하게 만들기 위해 급여에 임의의 변수를 추가하여 약간의 추가 랜덤성을 추가하고자 합니다. 어떻게 되는지 봅시다:\n"
+    "이 경우 상관관계는 약간 작아졌지만 여전히 꽤 높습니다. 이제 관계를 더욱 덜 명확하게 만들기 위해 급여에 임의의 변수를 추가하여 약간의 추가 무작위성을 더하고자 합니다. 어떻게 되는지 봅시다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -962,19 +799,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,22 +826,22 @@
    "source": [
     "왜 점들이 이렇게 수직선으로 정렬되는지 추측할 수 있나요?\n",
     "\n",
-    "우리는 급여와 같은 인위적으로 설계된 개념과 관찰된 변수 *키* 사이의 상관관계를 관찰했습니다. 이제 키와 몸무게 같은 두 관찰 변수도 상관관계가 있는지 살펴봅시다:\n"
+    "우리는 급여와 같은 인위적으로 설계된 개념과 관찰된 변수 *키* 사이의 상관관계를 관찰했습니다. 이제 키와 몸무게 같은 두 개의 관찰된 변수도 상관관계가 있는지 살펴봅시다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,16 +854,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "안타깝게도 결과를 얻지 못했고, 대신 이상한 `nan` 값들만 나왔습니다. 이는 시리즈 내 일부 값이 정의되지 않은 상태로 `nan`으로 표시되었기 때문이며, 이로 인해 연산 결과도 정의되지 않게 됩니다. 행렬을 살펴보면 `Weight`가 문제를 일으키는 열임을 알 수 있습니다. 이는 `Height` 값 간의 자기 상관 관계가 계산되었기 때문입니다.\n",
+    "안타깝게도 결과를 얻지 못했고, 대신 이상한 `nan` 값들만 나왔습니다. 이는 시리즈 내 일부 값이 정의되지 않은 상태로 `nan`으로 표시되었기 때문이며, 이로 인해 연산 결과도 정의되지 않은 상태가 됩니다. 행렬을 살펴보면 `Weight`가 문제의 열임을 알 수 있습니다. 이는 `Height` 값 간의 자기 상관이 계산되었기 때문입니다.\n",
     "\n",
     "> 이 예시는 **데이터 준비**와 **정리**의 중요성을 보여줍니다. 적절한 데이터 없이는 아무것도 계산할 수 없습니다.\n",
     "\n",
-    "이제 `fillna` 메서드를 사용하여 누락된 값을 채우고 상관 관계를 계산해 봅시다.\n"
+    "이제 `fillna` 메서드를 사용하여 누락된 값을 채우고 상관관계를 계산해 봅시다:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +873,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1054,27 +889,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1085,14 +918,14 @@
    "source": [
     "## 결론\n",
     "\n",
-    "이 노트북에서는 데이터를 활용하여 기본적인 작업을 수행하고 통계적 기능을 계산하는 방법을 배웠습니다. 이제 수학과 통계의 탄탄한 도구를 사용하여 몇 가지 가설을 증명하는 방법과 데이터 샘플을 기반으로 임의 변수에 대한 신뢰 구간을 계산하는 방법을 알게 되었습니다.\n"
+    "이 노트북에서는 데이터를 활용하여 기본적인 연산을 수행하고 통계 함수를 계산하는 방법을 배웠습니다. 이제 우리는 수학과 통계의 탄탄한 도구를 사용하여 가설을 증명하는 방법과, 주어진 데이터 샘플에서 임의의 변수에 대한 신뢰 구간을 계산하는 방법을 알게 되었습니다.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**면책 조항**:  \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서의 원어 버전을 권위 있는 출처로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생하는 오해나 잘못된 해석에 대해 책임을 지지 않습니다.\n"
+    "\n---\n\n**면책 조항**:  \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서를 해당 언어로 작성된 상태에서 권위 있는 자료로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생할 수 있는 오해나 잘못된 해석에 대해 당사는 책임을 지지 않습니다.  \n"
    ]
   }
  ],
@@ -1115,11 +948,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:13:20+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:16:45+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ko"
   }
diff --git a/translations/ko/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ko/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index fd53173f..2a5ff1b7 100644
--- a/translations/ko/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ko/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## 확률과 통계 소개\n",
     "## 과제\n",
     "\n",
-    "이 과제에서는 [여기에서](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) 가져온 당뇨병 환자 데이터셋을 사용할 것입니다.\n"
+    "이 과제에서는 [여기에서 가져온](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) 당뇨병 환자 데이터셋을 사용할 것입니다.\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 작업 3: Age, Sex, BMI 및 Y 변수의 분포는 무엇입니까?\n"
+    "### 작업 3: 나이, 성별, BMI 및 Y 변수의 분포는 무엇입니까?\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -602,9 +602,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 작업 4: 다양한 변수와 질병 진행(Y) 간의 상관관계를 테스트하세요\n",
+    "### 작업 4: 다양한 변수와 질병 진행(Y) 간의 상관관계 테스트\n",
     "\n",
-    "> **힌트** 상관관계 행렬은 어떤 값들이 서로 의존적인지에 대한 가장 유용한 정보를 제공합니다.\n"
+    "> **힌트** 상관 행렬은 어떤 값들이 서로 의존적인지에 대한 가장 유용한 정보를 제공합니다.\n"
    ],
    "metadata": {}
   },
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "결론:  \n",
-    "* Y와 가장 강한 상관관계를 가진 변수는 BMI와 S5(혈당)입니다. 이는 합리적으로 들립니다.\n"
+    "* Y와 가장 강한 상관관계를 보이는 것은 BMI와 S5(혈당)입니다. 이는 합리적으로 들립니다.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**면책 조항**:  \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있지만, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서를 해당 언어로 작성된 상태에서 권위 있는 자료로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생하는 오해나 잘못된 해석에 대해 책임을 지지 않습니다.\n"
+    "\n---\n\n**면책 조항**:  \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서를 해당 언어로 작성된 상태에서 권위 있는 자료로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생할 수 있는 오해나 잘못된 해석에 대해 당사는 책임을 지지 않습니다.  \n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:26:42+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:17:21+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ko"
   }
diff --git a/translations/lt/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/lt/1-Introduction/04-stats-and-probability/assignment.ipynb
index ac6ef5d6..f3c0020a 100644
--- a/translations/lt/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/lt/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Įvadas į tikimybes ir statistiką\n",
     "## Užduotis\n",
     "\n",
-    "Šioje užduotyje naudosime diabetu sergančių pacientų duomenų rinkinį, paimtą [iš čia](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Šioje užduotyje naudosime diabeto pacientų duomenų rinkinį, paimtą [iš čia](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -152,8 +152,8 @@
     "Šiame duomenų rinkinyje stulpeliai yra tokie:  \n",
     "* Amžius ir lytis yra savaime suprantami  \n",
     "* KMI yra kūno masės indeksas  \n",
-    "* AKS yra vidutinis kraujo spaudimas  \n",
-    "* S1 iki S6 yra skirtingi kraujo matavimai  \n",
+    "* AKS yra vidutinis kraujospūdis  \n",
+    "* S1 iki S6 yra skirtingi kraujo tyrimų rodikliai  \n",
     "* Y yra kokybinis ligos progresavimo matas per vienerius metus  \n",
     "\n",
     "Išnagrinėkime šį duomenų rinkinį naudodami tikimybių ir statistikos metodus.\n",
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Užduotis 2: Nubraižykite dėžės diagramas BMI, BP ir Y priklausomai nuo lyties\n"
+    "### Užduotis 2: Nubraižykite BMI, BP ir Y dėžutinius grafikus pagal lytį\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Užduotis 3: Koks yra amžiaus, lyties, KMI ir Y kintamųjų pasiskirstymas?\n"
+    "### Užduotis 3: Kokia yra Amžiaus, Lyties, KMI ir Y kintamųjų pasiskirstymas?\n"
    ],
    "metadata": {}
   },
@@ -200,9 +200,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Užduotis 4: Patikrinti koreliaciją tarp skirtingų kintamųjų ir ligos progresavimo (Y)\n",
+    "### Užduotis 4: Ištirkite skirtingų kintamųjų ir ligos progresavimo (Y) koreliaciją\n",
     "\n",
-    "> **Patarimas** Koreliacijos matrica suteiks naudingiausią informaciją apie tai, kurie rodikliai yra priklausomi.\n"
+    "> **Patarimas** Koreliacijos matrica suteiks jums naudingiausią informaciją apie tai, kurie dydžiai yra tarpusavyje priklausomi.\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Atsakomybės apribojimas**:  \nŠis dokumentas buvo išverstas naudojant AI vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama profesionali žmogaus vertimo paslauga. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus interpretavimus, atsiradusius dėl šio vertimo naudojimo.\n"
+    "\n---\n\n**Atsakomybės apribojimas**:  \nŠis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, atkreipiame dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Dėl svarbios informacijos rekomenduojame kreiptis į profesionalius vertėjus. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus aiškinimus, kylančius dėl šio vertimo naudojimo.\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:20:36+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T18:03:38+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "lt"
   }
diff --git a/translations/lt/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/lt/1-Introduction/04-stats-and-probability/notebook.ipynb
index 13a579ee..f9daefd6 100644
--- a/translations/lt/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/lt/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Atsitiktiniai kintamieji ir skirstiniai\n",
-    "Pradėkime nuo 30 reikšmių imties iš vienodo skirstinio nuo 0 iki 9. Taip pat apskaičiuosime vidurkį ir dispersiją.\n"
+    "## Atsitiktiniai kintamieji ir pasiskirstymai\n",
+    "Pradėkime nuo 30 reikšmių imties paėmimo iš tolygaus pasiskirstymo nuo 0 iki 9. Taip pat apskaičiuosime vidurkį ir dispersiją.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Norint vizualiai įvertinti, kiek skirtingų reikšmių yra imtyje, galime nubraižyti **histogramą**:\n"
+    "Norėdami vizualiai įvertinti, kiek skirtingų reikšmių yra imtyje, galime nubraižyti **histogramą**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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Xl7d4TDgcVigUarYBAICu6aa2HtjY2KglS5Zo2rRpGjNmTIvrAoGAEhISmu1LSEhQIBBo8Ri/36/ly5e3dTQgqvH/2AFbnfG/QeurOW2+MpKbm6uqqioVFxe35zySpIKCAgWDwaatpqam3c8BAAA6hjZdGVm8eLF27dql/fv3a8CAAa2uTUxMVF1dXbN9dXV1SkxMbPEYt9stt9vdltEAAEAn4+jKSCQS0eLFi7V9+3bt3btXQ4YMueoxPp9PpaWlzfaVlJTI5/M5mxQAAHRJjq6M5ObmqqioSDt37lRsbGzTfR8ej0e9evWSJOXk5Kh///7y+/2SpLy8PKWnp2vlypXKyspScXGxKioqVFhY2M4vBQAAdEaOroysX79ewWBQd911l5KSkpq2rVu3Nq2prq5WbW1t0+O0tDQVFRWpsLBQycnJeu2117Rjx45Wb3oFAADRw9GVkWv5lSRlZWWX7Zs1a5ZmzZrl5FQAACBK8Nk0AADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMCU4xjZv3+/ZsyYoX79+snlcmnHjh2tri8rK5PL5bpsCwQCbZ0ZAAB0IY5jpL6+XsnJyVq3bp2j406dOqXa2tqmLT4+3umpAQBAF3ST0wOmT5+u6dOnOz5RfHy8brvtNsfHAQCAru1bu2ckJSVFSUlJuvfee/XOO++0ujYcDisUCjXbAABA13TDYyQpKUkbNmzQ66+/rtdff11er1d33XWXjh071uIxfr9fHo+nafN6vTd6TAAAYMQViUQibT7Y5dL27duVnZ3t6Lj09HQNHDhQf/rTn6749XA4rHA43PQ4FArJ6/UqGAwqLi6ureNe0eClb7br8wEA0Nl8vCLrhjxvKBSSx+O56vdvx/eMtIfJkyfrwIEDLX7d7XbL7XZ/ixMBAAArJr9npLKyUklJSRanBgAAHYzjKyMXL17U6dOnmx5/9NFHqqysVJ8+fTRw4EAVFBTo008/1R//+EdJ0urVqzVkyBCNHj1aX375pV588UXt3btXf/3rX9vvVQAAgE7LcYxUVFTo7rvvbnqcn58vSZo3b542b96s2tpaVVdXN3390qVL+uUvf6lPP/1UN998s8aNG6e//e1vzZ4DAABEr+u6gfXbcq03wLQFN7ACAKKd9Q2sfDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDlOEb279+vGTNmqF+/fnK5XNqxY8dVjykrK9OECRPkdrs1dOhQbd68uQ2jAgCArshxjNTX1ys5OVnr1q27pvUfffSRsrKydPfdd6uyslJLlizRwoULtWfPHsfDAgCArucmpwdMnz5d06dPv+b1GzZs0JAhQ7Ry5UpJ0siRI3XgwAGtWrVKmZmZTk8PAAC6mBt+z0h5ebkyMjKa7cvMzFR5eXmLx4TDYYVCoWYbAADomm54jAQCASUkJDTbl5CQoFAopP/85z9XPMbv98vj8TRtXq/3Ro8JAACMdMifpikoKFAwGGzaampqrEcCAAA3iON7RpxKTExUXV1ds311dXWKi4tTr169rniM2+2W2+2+0aMBAIAO4IZfGfH5fCotLW22r6SkRD6f70afGgAAdAKOY+TixYuqrKxUZWWlpK9/dLeyslLV1dWSvv4nlpycnKb1DzzwgD788EP96le/0vvvv6/nn39er776qh5++OH2eQUAAKBTcxwjFRUVGj9+vMaPHy9Jys/P1/jx4/X4449Lkmpra5vCRJKGDBmiN998UyUlJUpOTtbKlSv14osv8mO9AABAkuSKRCIR6yGuJhQKyePxKBgMKi4url2fe/DSN9v1+QAA6Gw+XpF1Q573Wr9/d8ifpgEAANGDGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYKpNMbJu3ToNHjxYMTExmjJlig4fPtzi2s2bN8vlcjXbYmJi2jwwAADoWhzHyNatW5Wfn69ly5bp2LFjSk5OVmZmps6ePdviMXFxcaqtrW3azpw5c11DAwCArsNxjDz77LNatGiR5s+fr1GjRmnDhg26+eabtWnTphaPcblcSkxMbNoSEhKua2gAANB1OIqRS5cu6ejRo8rIyPjmCbp1U0ZGhsrLy1s87uLFixo0aJC8Xq9mzpypkydPtnqecDisUCjUbAMAAF2Toxj5/PPP1dDQcNmVjYSEBAUCgSseM3z4cG3atEk7d+7Uli1b1NjYqLS0NH3yySctnsfv98vj8TRtXq/XyZgAAKATueE/TePz+ZSTk6OUlBSlp6frjTfe0B133KGNGze2eExBQYGCwWDTVlNTc6PHBAAARm5ysvj2229X9+7dVVdX12x/XV2dEhMTr+k5evToofHjx+v06dMtrnG73XK73U5GAwAAnZSjKyM9e/ZUamqqSktLm/Y1NjaqtLRUPp/vmp6joaFBJ06cUFJSkrNJAQBAl+Toyogk5efna968eZo4caImT56s1atXq76+XvPnz5ck5eTkqH///vL7/ZKkJ554QlOnTtXQoUP1xRdf6JlnntGZM2e0cOHC9n0lAACgU3IcI7Nnz9a5c+f0+OOPKxAIKCUlRW+//XbTTa3V1dXq1u2bCy7nz5/XokWLFAgE1Lt3b6WmpurgwYMaNWpU+70KAADQabkikUjEeoirCYVC8ng8CgaDiouLa9fnHrz0zXZ9PgAAOpuPV2TdkOe91u/ffDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVphhZt26dBg8erJiYGE2ZMkWHDx9udf22bds0YsQIxcTEaOzYsdq9e3ebhgUAAF2P4xjZunWr8vPztWzZMh07dkzJycnKzMzU2bNnr7j+4MGDmjNnjhYsWKDjx48rOztb2dnZqqqquu7hAQBA5+eKRCIRJwdMmTJFkyZN0tq1ayVJjY2N8nq9euihh7R06dLL1s+ePVv19fXatWtX076pU6cqJSVFGzZsuKZzhkIheTweBYNBxcXFORn3qgYvfbNdnw8AgM7m4xVZN+R5r/X7901OnvTSpUs6evSoCgoKmvZ169ZNGRkZKi8vv+Ix5eXlys/Pb7YvMzNTO3bsaPE84XBY4XC46XEwGJT09Ytqb43hf7f7cwIA0JnciO+v/+/zXu26h6MY+fzzz9XQ0KCEhIRm+xMSEvT+++9f8ZhAIHDF9YFAoMXz+P1+LV++/LL9Xq/XybgAAOAaeFbf2Oe/cOGCPB5Pi193FCPfloKCgmZXUxobG/Wvf/1Lffv2lcvlarfzhEIheb1e1dTUtPs//8A53o+Oh/ekY+H96Fh4P64uEonowoUL6tevX6vrHMXI7bffru7du6uurq7Z/rq6OiUmJl7xmMTEREfrJcntdsvtdjfbd9tttzkZ1ZG4uDj+InUgvB8dD+9Jx8L70bHwfrSutSsi/+Pop2l69uyp1NRUlZaWNu1rbGxUaWmpfD7fFY/x+XzN1ktSSUlJi+sBAEB0cfzPNPn5+Zo3b54mTpyoyZMna/Xq1aqvr9f8+fMlSTk5Oerfv7/8fr8kKS8vT+np6Vq5cqWysrJUXFysiooKFRYWtu8rAQAAnZLjGJk9e7bOnTunxx9/XIFAQCkpKXr77bebblKtrq5Wt27fXHBJS0tTUVGRHnvsMT366KMaNmyYduzYoTFjxrTfq2gjt9utZcuWXfZPQrDB+9Hx8J50LLwfHQvvR/tx/HtGAAAA2hOfTQMAAEwRIwAAwBQxAgAATBEjAADAVFTHyLp16zR48GDFxMRoypQpOnz4sPVIUcnv92vSpEmKjY1VfHy8srOzderUKeux8F8rVqyQy+XSkiVLrEeJWp9++ql++tOfqm/fvurVq5fGjh2riooK67GiVkNDg37zm99oyJAh6tWrl7773e/qySefvOrnr6BlURsjW7duVX5+vpYtW6Zjx44pOTlZmZmZOnv2rPVoUWffvn3Kzc3VoUOHVFJSoq+++kr33Xef6uvrrUeLekeOHNHGjRs1btw461Gi1vnz5zVt2jT16NFDb731lt577z2tXLlSvXv3th4tav3ud7/T+vXrtXbtWv3zn//U7373O/3+97/Xc889Zz1apxW1P9o7ZcoUTZo0SWvXrpX09W+S9Xq9euihh7R06VLj6aLbuXPnFB8fr3379unOO++0HidqXbx4URMmTNDzzz+v3/72t0pJSdHq1autx4o6S5cu1TvvvKO///3v1qPgv374wx8qISFBL730UtO+H/3oR+rVq5e2bNliOFnnFZVXRi5duqSjR48qIyOjaV+3bt2UkZGh8vJyw8kgScFgUJLUp08f40miW25urrKyspr9d4Jv35///GdNnDhRs2bNUnx8vMaPH68XXnjBeqyolpaWptLSUn3wwQeSpH/84x86cOCApk+fbjxZ59UhP7X3Rvv888/V0NDQ9Ftj/ychIUHvv/++0VSQvr5CtWTJEk2bNq1D/JbeaFVcXKxjx47pyJEj1qNEvQ8//FDr169Xfn6+Hn30UR05ckS/+MUv1LNnT82bN896vKi0dOlShUIhjRgxQt27d1dDQ4OeeuopzZ0713q0TisqYwQdV25urqqqqnTgwAHrUaJWTU2N8vLyVFJSopiYGOtxol5jY6MmTpyop59+WpI0fvx4VVVVacOGDcSIkVdffVWvvPKKioqKNHr0aFVWVmrJkiXq168f70kbRWWM3H777erevbvq6uqa7a+rq1NiYqLRVFi8eLF27dql/fv3a8CAAdbjRK2jR4/q7NmzmjBhQtO+hoYG7d+/X2vXrlU4HFb37t0NJ4wuSUlJGjVqVLN9I0eO1Ouvv240ER555BEtXbpUP/nJTyRJY8eO1ZkzZ+T3+4mRNorKe0Z69uyp1NRUlZaWNu1rbGxUaWmpfD6f4WTRKRKJaPHixdq+fbv27t2rIUOGWI8U1e655x6dOHFClZWVTdvEiRM1d+5cVVZWEiLfsmnTpl32o+4ffPCBBg0aZDQR/v3vfzf7QFhJ6t69uxobG40m6vyi8sqIJOXn52vevHmaOHGiJk+erNWrV6u+vl7z58+3Hi3q5ObmqqioSDt37lRsbKwCgYAkyePxqFevXsbTRZ/Y2NjL7te55ZZb1LdvX+7jMfDwww8rLS1NTz/9tH784x/r8OHDKiwsVGFhofVoUWvGjBl66qmnNHDgQI0ePVrHjx/Xs88+q5///OfWo3VekSj23HPPRQYOHBjp2bNnZPLkyZFDhw5ZjxSVJF1xe/nll61Hw3+lp6dH8vLyrMeIWn/5y18iY8aMibjd7siIESMihYWF1iNFtVAoFMnLy4sMHDgwEhMTE/nOd74T+fWvfx0Jh8PWo3VaUft7RgAAQMcQlfeMAACAjoMYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKb+D7cuxelORYM+AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -84,201 +82,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Analizuojant tikrus duomenis\n",
+    "## Tikrų duomenų analizė\n",
     "\n",
     "Vidurkis ir dispersija yra labai svarbūs analizuojant realius duomenis. Pakraukime duomenis apie beisbolo žaidėjus iš [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Šiame kurse naudojame paketą [**Pandas**](https://pandas.pydata.org/) duomenų analizei. Vėliau aptarsime daugiau apie Pandas ir darbą su duomenimis Python kalboje.\n",
+    "> Šiame kurse naudojame paketą [**Pandas**](https://pandas.pydata.org/) duomenų analizei. Vėliau kurse daugiau kalbėsime apie Pandas ir darbą su duomenimis Python kalba.\n",
     "\n",
     "Apskaičiuokime vidutines amžiaus, ūgio ir svorio reikšmes:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Be papildomai vidurkio, prasminga pažvelgti į medianos vertę ir kvartilius. Juos galima vizualizuoti naudojant **dėžės diagramą**:\n"
+    "Be vidurkio, prasminga atsižvelgti į medianos reikšmę ir kvartilius. Juos galima vizualizuoti naudojant **dėžės diagramą**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Mes taip pat galime sudaryti dėžės diagramas iš mūsų duomenų rinkinio poskyrių, pavyzdžiui, suskirstytų pagal žaidėjo vaidmenį.\n"
+    "Mes taip pat galime sudaryti dėžutės diagramas iš mūsų duomenų rinkinio poskyrių, pavyzdžiui, suskirstytų pagal žaidėjo vaidmenį.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Pastaba**: Ši diagrama rodo, kad vidutiniškai pirmosios bazės žaidėjų ūgis yra didesnis nei antrosios bazės žaidėjų ūgis. Vėliau sužinosime, kaip formaliau patikrinti šią hipotezę ir kaip parodyti, kad mūsų duomenys yra statistiškai reikšmingi, kad tai įrodytume.  \n",
+    "> **Pastaba**: Ši diagrama rodo, kad vidutiniškai pirmosios bazės žaidėjų ūgis yra didesnis nei antrosios bazės žaidėjų ūgis. Vėliau sužinosime, kaip formaliau patikrinti šią hipotezę ir kaip įrodyti, kad mūsų duomenys yra statistiškai reikšmingi, kad tai parodytų.\n",
     "\n",
-    "Amžius, ūgis ir svoris yra visi tęstiniai atsitiktiniai kintamieji. Kaip manote, kokia yra jų pasiskirstymo forma? Geras būdas tai sužinoti – nubraižyti reikšmių histogramą:\n"
+    "Amžius, ūgis ir svoris yra visi nenutrūkstami atsitiktiniai kintamieji. Kaip manote, kokia yra jų pasiskirstymo forma? Geras būdas tai sužinoti – nubraižyti reikšmių histogramą:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -438,26 +284,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Normali skirstinio funkcija\n",
+    "## Normali skirstinys\n",
     "\n",
-    "Sukurkime dirbtinį svorių pavyzdį, kuris atitinka normalią skirstinio funkciją su tuo pačiu vidurkiu ir dispersija kaip mūsų tikrieji duomenys:\n"
+    "Sukurkime dirbtinį svorių pavyzdį, kuris atitinka normalųjį skirstinį su tokia pačia vidurkiu ir dispersija kaip mūsų tikrieji duomenys:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kadangi dauguma realaus gyvenimo reikšmių yra normaliai pasiskirsčiusios, neturėtume naudoti vienodo atsitiktinių skaičių generatoriaus, kad sugeneruotume pavyzdinius duomenis. Štai kas nutinka, jei bandome generuoti svorius su vienodu pasiskirstymu (generuojamu naudojant `np.random.rand`):\n"
+    "Kadangi dauguma realių gyvenimo reikšmių yra normaliai pasiskirsčiusios, neturėtume naudoti vienodo atsitiktinių skaičių generatoriaus mėginių duomenims generuoti. Štai kas nutinka, jei bandome generuoti svorius su vienodu pasiskirstymu (generuojamu naudojant `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Pasitikėjimo intervalai\n",
     "\n",
-    "Dabar apskaičiuokime beisbolo žaidėjų svorio ir ūgio pasitikėjimo intervalus. Naudosime kodą [iš šios „stackoverflow“ diskusijos](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Dabar apskaičiuokime pasitikėjimo intervalus beisbolo žaidėjų svoriams ir ūgiams. Naudosime kodą [iš šios stackoverflow diskusijos](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Matome, kad intervalai nesikerta.\n",
+    "Galime matyti, kad intervalai nesikerta.\n",
     "\n",
-    "Statistiškai teisingesnis būdas hipotezei patvirtinti yra naudoti **Studento t-testą**:\n"
+    "Statistiškai tikslesnis būdas hipotezei patikrinti yra naudoti **Studento t-testą**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,35 +619,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dvi reikšmės, kurias grąžina `ttest_ind` funkcija, yra šios:  \n",
-    "* p-reikšmė gali būti laikoma tikimybe, kad dvi skirstiniai turi tą patį vidurkį. Mūsų atveju ji yra labai maža, o tai reiškia, kad yra stiprių įrodymų, jog pirmosios bazės žaidėjai yra aukštesni.  \n",
-    "* t-reikšmė yra normalizuoto vidurkių skirtumo tarpinė reikšmė, kuri naudojama t-teste ir lyginama su slenkstine reikšme, atitinkančia tam tikrą pasitikėjimo lygį.  \n"
+    "Funkcija `ttest_ind` grąžina dvi reikšmes:  \n",
+    "* p-reikšmė gali būti laikoma tikimybe, kad dvi skirstiniai turi tą pačią vidutinę reikšmę. Mūsų atveju ji yra labai maža, o tai reiškia, kad yra stiprių įrodymų, jog pirmos bazės žaidėjai yra aukštesni.  \n",
+    "* t-reikšmė yra normalizuoto vidurkio skirtumo tarpinė reikšmė, kuri naudojama t-teste ir lyginama su slenkstine reikšme, atitinkančia tam tikrą pasitikėjimo lygį.  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Normaliojo skirstinio modeliavimas naudojant centrinę ribinę teoremą\n",
+    "## Normalaus pasiskirstymo simuliavimas naudojant Centrinės ribos teoremą\n",
     "\n",
-    "Pseudoatsitiktinių skaičių generatorius Python'e yra sukurtas taip, kad pateiktų vienodą skirstinį. Jei norime sukurti generatorių normaliajam skirstiniui, galime pasinaudoti centrine ribine teorema. Norėdami gauti normaliai paskirstytą reikšmę, tiesiog apskaičiuosime vienodai paskirstytos imties vidurkį.\n"
+    "Pseudo-atsitiktinių skaičių generatorius Python'e yra sukurtas taip, kad suteiktų mums vienodą pasiskirstymą. Jei norime sukurti generatorių normaliam pasiskirstymui, galime pasinaudoti Centrinės ribos teorema. Norėdami gauti normaliai pasiskirsčiusį reikšmę, tiesiog apskaičiuosime vienodai generuoto mėginio vidurkį.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Koreliacija ir Piktoji Beisbolo Korporacija\n",
     "\n",
-    "Koreliacija leidžia mums nustatyti ryšius tarp duomenų sekų. Mūsų žaisliniame pavyzdyje įsivaizduokime, kad yra piktoji beisbolo korporacija, kuri moka savo žaidėjams pagal jų ūgį – kuo aukštesnis žaidėjas, tuo daugiau pinigų jis/ji gauna. Tarkime, yra bazinis atlyginimas – 1000 $, ir papildomas priedas nuo 0 iki 100 $, priklausomai nuo ūgio. Mes paimsime tikrus MLB žaidėjus ir apskaičiuosime jų įsivaizduojamus atlyginimus:\n"
+    "Koreliacija leidžia mums nustatyti ryšius tarp duomenų sekų. Mūsų žaisliniame pavyzdyje įsivaizduokime, kad egzistuoja piktoji beisbolo korporacija, kuri moka savo žaidėjams pagal jų ūgį – kuo aukštesnis žaidėjas, tuo daugiau pinigų jis/ji gauna. Tarkime, kad yra bazinis atlyginimas – 1000 $, ir papildomas priedas nuo 0 $ iki 100 $, priklausomai nuo ūgio. Paimsime tikrus MLB žaidėjus ir apskaičiuosime jų įsivaizduojamus atlyginimus:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dabar apskaičiuokime tų sekų kovariaciją ir koreliaciją. `np.cov` pateiks vadinamąją **kovariacijos matricą**, kuri yra kovariacijos išplėtimas keliems kintamiesiems. Kovariacijos matricos $M$ elementas $M_{ij}$ yra koreliacija tarp įvesties kintamųjų $X_i$ ir $X_j$, o diagonalės reikšmės $M_{ii}$ yra $X_{i}$ dispersija. Panašiai, `np.corrcoef` pateiks **koreliacijos matricą**.\n"
+    "Dabar apskaičiuokime tų sekų kovariaciją ir koreliaciją. `np.cov` pateiks vadinamąją **kovariacijos matricą**, kuri yra kovariacijos išplėtimas keliems kintamiesiems. Kovariacijos matricos $M$ elementas $M_{ij}$ yra įvesties kintamųjų $X_i$ ir $X_j$ koreliacija, o diagonalės reikšmės $M_{ii}$ yra $X_{i}$ dispersija. Panašiai, `np.corrcoef` pateiks **koreliacijos matricą**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Koreliacija lygi 1 reiškia, kad tarp dviejų kintamųjų yra stiprus **linijinis ryšys**. Linijinį ryšį galime vizualiai pamatyti, nubraižydami vieną reikšmę prieš kitą:\n"
+    "Koreliacija, lygi 1, reiškia, kad tarp dviejų kintamųjų yra stiprus **linijinis ryšys**. Linijinį ryšį galime vizualiai pamatyti, nubraižydami vieną reikšmę prieš kitą:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -919,14 +756,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -939,19 +776,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Šiuo atveju koreliacija yra šiek tiek mažesnė, tačiau vis dar gana aukšta. Dabar, kad ryšys būtų dar mažiau akivaizdus, galime pridėti šiek tiek papildomo atsitiktinumo, pridėdami atsitiktinį kintamąjį prie atlyginimo. Pažiūrėkime, kas nutiks:\n"
+    "Šiuo atveju koreliacija yra šiek tiek mažesnė, tačiau vis dar gana didelė. Dabar, norėdami padaryti ryšį dar mažiau akivaizdų, galime pridėti šiek tiek papildomo atsitiktinumo, pridėdami atsitiktinį kintamąjį prie atlyginimo. Pažiūrėkime, kas nutiks:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -962,19 +799,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,24 +824,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ar pastebėjote, kodėl taškai išsidėsto vertikaliomis linijomis taip?\n",
+    "> Ar galite atspėti, kodėl taškai išsirikiuoja į vertikalias linijas taip?\n",
     "\n",
-    "Mes pastebėjome ryšį tarp dirbtinai sukurto koncepto, kaip atlyginimas, ir stebimo kintamojo *ūgis*. Pažiūrėkime, ar du stebimi kintamieji, tokie kaip ūgis ir svoris, taip pat koreliuoja:\n"
+    "Mes pastebėjome ryšį tarp dirbtinai sukurto koncepto, kaip atlyginimas, ir stebimojo kintamojo *ūgis*. Pažiūrėkime, ar du stebimieji kintamieji, tokie kaip ūgis ir svoris, taip pat koreliuoja:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,16 +854,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Deja, mes negavome jokių rezultatų – tik keletą keistų `nan` reikšmių. Taip nutiko dėl to, kad kai kurios mūsų serijos reikšmės yra neapibrėžtos, pažymėtos kaip `nan`, todėl ir operacijos rezultatas tampa neapibrėžtas. Pažvelgę į matricą matome, kad problematiška yra `Weight` stulpelis, nes buvo apskaičiuota `Height` reikšmių tarpusavio koreliacija.\n",
+    "Deja, negavome jokių rezultatų – tik keletą keistų `nan` reikšmių. Taip nutiko dėl to, kad kai kurios mūsų serijos reikšmės yra neapibrėžtos, pažymėtos kaip `nan`, todėl operacijos rezultatas taip pat tampa neapibrėžtas. Pažvelgę į matricą matome, kad `Weight` yra probleminė stulpelis, nes buvo apskaičiuota savikoreliacija tarp `Height` reikšmių.\n",
     "\n",
-    "> Šis pavyzdys parodo, kokia svarbi yra **duomenų paruošimo** ir **valymo** procedūra. Be tinkamų duomenų negalime nieko apskaičiuoti.\n",
+    "> Šis pavyzdys parodo, kokia svarbi yra **duomenų paruošimas** ir **valymas**. Be tinkamų duomenų negalime nieko apskaičiuoti.\n",
     "\n",
-    "Panaudokime `fillna` metodą, kad užpildytume trūkstamas reikšmes, ir apskaičiuokime koreliaciją:\n"
+    "Naudokime `fillna` metodą, kad užpildytume trūkstamas reikšmes, ir apskaičiuokime koreliaciją:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +873,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1054,27 +889,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1085,14 +918,14 @@
    "source": [
     "## Išvada\n",
     "\n",
-    "Šiame užrašų knygelėje išmokome atlikti pagrindines operacijas su duomenimis, kad galėtume apskaičiuoti statistines funkcijas. Dabar žinome, kaip naudoti patikimą matematikos ir statistikos aparatą, kad patvirtintume tam tikras hipotezes, ir kaip apskaičiuoti pasitikėjimo intervalus atsitiktiniams kintamiesiems, remiantis duomenų pavyzdžiu.\n"
+    "Šiame užrašų knygelėje išmokome atlikti pagrindines operacijas su duomenimis, kad apskaičiuotume statistines funkcijas. Dabar žinome, kaip naudoti patikimą matematikos ir statistikos aparatą, siekiant patvirtinti tam tikras hipotezes, ir kaip apskaičiuoti pasikliautinuosius intervalus atsitiktiniams kintamiesiems, remiantis duomenų imtimi.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Atsakomybės apribojimas**:  \nŠis dokumentas buvo išverstas naudojant AI vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudoti profesionalų žmogaus vertimą. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus interpretavimus, atsiradusius dėl šio vertimo naudojimo.\n"
+    "\n---\n\n**Atsakomybės apribojimas**:  \nŠis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, atkreipiame dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudotis profesionalių vertėjų paslaugomis. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus aiškinimus, kylančius dėl šio vertimo naudojimo.\n"
    ]
   }
  ],
@@ -1115,11 +948,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:14:18+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T18:03:25+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "lt"
   }
diff --git a/translations/lt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/lt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 73327b7e..362327b9 100644
--- a/translations/lt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/lt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Įvadas į tikimybes ir statistiką\n",
     "## Užduotis\n",
     "\n",
-    "Šioje užduotyje naudosime diabetu sergančių pacientų duomenų rinkinį, paimtą [iš čia](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Šioje užduotyje naudosime diabeto pacientų duomenų rinkinį, paimtą [iš čia](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,12 +150,13 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Šiame duomenų rinkinyje stulpeliai yra tokie:  \n",
-    "* Amžius ir lytis yra savaime suprantami  \n",
-    "* KMI yra kūno masės indeksas  \n",
-    "* AKS yra vidutinis kraujo spaudimas  \n",
-    "* S1 iki S6 yra skirtingi kraujo matavimai  \n",
-    "* Y yra kokybinis ligos progresavimo matas per vienerius metus  \n",
+    "Šiame duomenų rinkinyje stulpeliai yra tokie:\n",
+    "\n",
+    "* Amžius ir lytis yra savaime suprantami\n",
+    "* KMI yra kūno masės indeksas\n",
+    "* AKS yra vidutinis kraujo spaudimas\n",
+    "* S1 iki S6 yra skirtingi kraujo matavimai\n",
+    "* Y yra kokybinis ligos progresavimo matas per vienerius metus\n",
     "\n",
     "Išnagrinėkime šį duomenų rinkinį naudodami tikimybių ir statistikos metodus.\n",
     "\n",
@@ -354,7 +355,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +447,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +478,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Užduotis 2: Nubraižykite dėžės diagramas BMI, BP ir Y priklausomai nuo lyties\n"
+    "### Užduotis 2: Nubraižykite BMI, BP ir Y dėžutės diagramas pagal lytį\n"
    ],
    "metadata": {}
   },
@@ -485,8 +486,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +530,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Užduotis 3: Koks yra amžiaus, lyties, KMI ir Y kintamųjų pasiskirstymas?\n"
+    "### Užduotis 3: Kokia yra Amžiaus, Lyties, KMI ir Y kintamųjų pasiskirstymo analizė?\n"
    ],
    "metadata": {}
   },
@@ -537,8 +538,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -593,16 +594,16 @@
    "cell_type": "markdown",
    "source": [
     "Išvados:  \n",
-    "* Amžius - normalus  \n",
-    "* Lytis - vienoda  \n",
-    "* KMI, Y - sunku pasakyti  \n"
+    "* Amžius – normalus  \n",
+    "* Lytis – vienoda  \n",
+    "* KMI, Y – sunku pasakyti  \n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### Užduotis 4: Patikrinti koreliaciją tarp skirtingų kintamųjų ir ligos progresavimo (Y)\n",
+    "### Užduotis 4: Ištirkite koreliaciją tarp skirtingų kintamųjų ir ligos progresavimo (Y)\n",
     "\n",
     "> **Patarimas** Koreliacijos matrica suteiks naudingiausią informaciją apie tai, kurie kintamieji yra priklausomi.\n"
    ],
@@ -847,7 +848,7 @@
    "cell_type": "markdown",
    "source": [
     "Išvada:  \n",
-    "* Stipriausia Y koreliacija yra su KMI ir S5 (cukraus kiekis kraujyje). Tai skamba logiškai.\n"
+    "* Stipriausia Y koreliacija yra su KMI ir S5 (cukraus kiekis kraujyje). Tai atrodo logiška.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +856,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +886,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +917,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Atsakomybės apribojimas**:  \nŠis dokumentas buvo išverstas naudojant AI vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama profesionali žmogaus vertimo paslauga. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus interpretavimus, atsiradusius naudojant šį vertimą.\n"
+    "\n---\n\n**Atsakomybės apribojimas**:  \nŠis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, atkreipiame dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Dėl svarbios informacijos rekomenduojame kreiptis į profesionalius vertėjus. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus aiškinimus, kylančius dėl šio vertimo naudojimo.\n"
    ]
   }
  ],
@@ -942,8 +943,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:26:59+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T18:03:59+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "lt"
   }
diff --git a/translations/mo/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/mo/1-Introduction/04-stats-and-probability/assignment.ipynb
index ef3a4532..4c161a3b 100644
--- a/translations/mo/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/mo/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 概率與統計簡介\n",
-    "## 作業\n",
+    "## 概論：機率與統計  \n",
+    "## 作業  \n",
     "\n",
-    "在這次作業中，我們將使用糖尿病患者的數據集，該數據集取自[此處](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)。\n"
+    "在這次作業中，我們將使用糖尿病患者的數據集，該數據集取自[這裡](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)。  \n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "在這個數據集中，欄位如下：  \n",
-    "* 年齡和性別不需多作解釋  \n",
-    "* BMI 是身體質量指數  \n",
-    "* BP 是平均血壓  \n",
-    "* S1 到 S6 是不同的血液測量值  \n",
-    "* Y 是一年內疾病進展的定性指標  \n",
+    "在此數據集中，欄位如下：\n",
+    "* 年齡和性別不需額外解釋\n",
+    "* BMI 是身體質量指數\n",
+    "* BP 是平均血壓\n",
+    "* S1 到 S6 是不同的血液測量值\n",
+    "* Y 是疾病在一年內進展的定性指標\n",
     "\n",
-    "讓我們使用機率與統計的方法來研究這個數據集。\n",
+    "讓我們使用概率和統計方法來研究這個數據集。\n",
     "\n",
-    "### 任務 1：計算所有值的平均值和變異數\n"
+    "### 任務 1：計算所有值的平均值和方差\n"
    ],
    "metadata": {}
   },
@@ -198,9 +198,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 任務 4：測試不同變數與疾病進展（Y）之間的相關性\n",
+    "### 任務 4：測試不同變數與疾病進展 (Y) 之間的相關性\n",
     "\n",
-    "> **提示** 相關矩陣可以提供最有用的資訊，幫助判斷哪些數值是相互依賴的。\n"
+    "> **提示** 相關矩陣可以提供最有用的資訊，幫助判斷哪些值是相互依賴的。\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。\n"
+    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對於因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:43:11+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:11:24+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "mo"
   }
diff --git a/translations/mo/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/mo/1-Introduction/04-stats-and-probability/notebook.ipynb
index acfedf9d..45d17506 100644
--- a/translations/mo/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/mo/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# 概率與統計入門  \n",
-    "在這份筆記中，我們將嘗試一些之前討論過的概念。許多概率與統計的概念在 Python 的主要數據處理庫中都有良好的呈現，例如 `numpy` 和 `pandas`。\n"
+    "在這份筆記中，我們將實際操作一些之前討論過的概念。許多來自概率與統計的概念在 Python 的主要數據處理庫中都有良好的實現，例如 `numpy` 和 `pandas`。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## 隨機變數與分佈\n",
-    "我們先從 0 到 9 的均勻分佈中抽取一組包含 30 個值的樣本。我們還會計算平均值和變異數。\n"
+    "我們先從 0 到 9 的均勻分佈中抽取 30 個值作為樣本。接著，我們將計算平均值和變異數。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "要視覺化估算樣本中有多少不同的值，我們可以繪製**直方圖**：\n"
+    "要直觀地估計樣本中有多少不同的值，我們可以繪製**直方圖**：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## 分析真實數據\n",
     "\n",
-    "在分析真實世界的數據時，平均值和變異數是非常重要的。我們來載入有關棒球選手的數據，數據來源於 [SOCR MLB 身高/體重數據](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "在分析真實世界的數據時，平均值和變異數是非常重要的。我們來載入有關棒球運動員的數據，數據來源於 [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "現在讓我們專注於身高，並計算標準差和變異數：\n"
+    "現在讓我們專注於高度，並計算標準差和方差：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "除了平均值之外，查看中位數值和四分位數也是有意義的。它們可以使用一個**箱型圖**來可視化：\n"
+    "除了平均值，查看中位數值和四分位數也是有意義的。它們可以使用一個**箱型圖**來可視化：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **注意**：此圖表顯示，平均而言，一壘手的身高比二壘手的身高更高。稍後我們將學習如何更正式地檢驗這一假設，以及如何證明我們的數據在統計上具有顯著性來支持這一點。\n",
+    "> **注意**：此圖表顯示，平均而言，一壘手的身高比二壘手的身高更高。我們稍後將學習如何更正式地檢驗這一假設，以及如何證明我們的數據在統計上具有顯著性來支持這一點。\n",
     "\n",
-    "年齡、身高和體重都是連續隨機變數。你認為它們的分佈是什麼樣的？一個好的方法是繪製值的直方圖：\n"
+    "年齡、身高和體重都是連續隨機變數。你認為它們的分佈是什麼樣的？一個好的方法是繪製數值的直方圖：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## 常態分佈\n",
     "\n",
-    "讓我們建立一個人工樣本的重量數據，該數據遵循與我們真實數據相同的平均值和方差的常態分佈：\n"
+    "讓我們建立一個符合常態分佈的人工樣本，其平均值和變異數與我們的真實數據相同：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "由於現實生活中的大多數數值通常是正態分佈的，我們不應該使用均勻隨機數生成器來生成樣本數據。以下是如果我們嘗試使用均勻分佈（由 `np.random.rand` 生成）來生成重量時會發生的情況：\n"
+    "由於現實生活中的大多數數值呈正態分佈，我們不應該使用均勻隨機數生成器來生成樣本數據。以下是如果我們嘗試使用均勻分佈（由 `np.random.rand` 生成）來生成重量時會發生的情況：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## 信賴區間\n",
     "\n",
-    "現在讓我們計算棒球選手體重和身高的信賴區間。我們將使用[這篇 StackOverflow 討論中的程式碼](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)：\n"
+    "現在讓我們計算棒球選手體重和身高的信賴區間。我們將使用[這篇 stackoverflow 討論](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)中的程式碼：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## 假設檢定\n",
     "\n",
-    "讓我們來探索棒球球員數據集中的不同角色：\n"
+    "讓我們來探討棒球球員數據集中的不同角色：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "我們可以看到這些區間並沒有重疊。\n",
     "\n",
-    "一種在統計上更正確的方法來驗證這個假設是使用 **Student t檢定**：\n"
+    "一種在統計上更正確的方法來證明這個假設是使用 **Student t檢定**：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "`ttest_ind` 函數返回的兩個值分別是：  \n",
-    "* p-value 可視為兩個分佈具有相同平均值的概率。在我們的情況下，p-value 非常低，這意味著有強烈的證據支持一壘手更高。  \n",
-    "* t-value 是 t 檢驗中用於比較的標準化平均差的中間值，並且會與給定置信值的閾值進行比較。  \n"
+    "* p-value 可被視為兩個分佈具有相同平均值的概率。在我們的情況下，p-value 非常低，這意味著有強有力的證據支持一壘手更高。  \n",
+    "* t-value 是 t 檢驗中使用的標準化平均差異的中間值，並且會與給定置信值的閾值進行比較。  \n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## 使用中央極限定理模擬常態分佈\n",
     "\n",
-    "Python 的偽隨機生成器是設計用來產生均勻分佈的。如果我們想要創建一個生成常態分佈的生成器，可以利用中央極限定理。為了獲得一個常態分佈的值，我們只需要計算一組均勻分佈樣本的平均值。\n"
+    "Python 的偽隨機生成器旨在提供均勻分佈。如果我們想要創建一個常態分佈的生成器，可以利用中央極限定理。要獲得一個常態分佈的值，我們只需計算均勻生成樣本的平均值。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 關聯性與邪惡棒球公司\n",
+    "## 相關性與邪惡棒球公司\n",
     "\n",
-    "關聯性讓我們能夠找出數據序列之間的關係。在我們的這個範例中，假設有一家邪惡的棒球公司，他們根據球員的身高來支付薪水——球員越高，薪水就越多。假設基本薪水是 $1000，另外根據身高提供 $0 到 $100 的額外獎金。我們將使用 MLB 的真實球員數據，來計算他們的假想薪水：\n"
+    "相關性讓我們能夠找出數據序列之間的關係。在我們的範例中，假設有一家邪惡的棒球公司，根據球員的身高來支付薪水——球員越高，薪水就越多。假設基本薪水是 $1000，並根據身高額外提供 $0 到 $100 的獎金。我們將使用 MLB 的真實球員數據，計算他們的假想薪水：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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vcvwXk1ywrPbNJK9ab3MAAHC8+VPTAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAIPts24AYCU7d+09rHbv7otm0AkAzzauIANzZ6VwfKQ6AGwkARkAAAYCMgAADARkAAAYCMgAADAQkIG5s9pqFVaxAOB4sMwbMJeEYQBmxRVkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAg+2zbgCYrZ279h5Wu3f3RTPoBADmgyvI8Cy2Ujg+Uh0Ang0EZAAAGAjIAAAwEJABAGAgIAMAwEBAhmex1VarsIoFAM9mlnmDZzlhGACebs0ryFV1TVU9UlX7h9oLquoLVfWVaf/8qb6zqh6vqjum7beG97yqqvZV1d1V9RtVVZvzkQAA4NgdzRSLa5NcuKy2K8lN3X12kpum54fc093nTds7h/qHk1yR5OxpW/41AQBg5tYMyN19c5JHl5XfnOS66fF1SS4+0teoqhclOaW7v9TdneTja70HAABm4Vhv0nthdz+UJNP+9OG1l1TV7VX1x1X1mql2RpIDwzEHptqKquqKqlqsqsWDBw8eY4sAALB+G72KxUNJXtzdr0zyS0k+WVWnJFlpvnGv9kW6++ruXujuhR07dmxwiwAAsLpjDcgPT9MmDk2feCRJuvuJ7v6b6fFtSe5J8rIsXTE+c3j/mUkePNamAQBgsxxrQP5sksunx5cn+UySVNWOqto2PX5plm7G++o0DeOxqrpgWr3i7YfeAwAA82TNdZCrak+S1yU5raoOJHl/kt1Jrq+qdyS5L8kl0+GvTfJfq+rbSZ5M8s7uPnSD37uytCLGyUk+P20AADBXamlRifm1sLDQi4uLs24DAIAtpqpu6+6F5XV/ahoAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAYM11kIGNsXPX3sNq9+6+aAadAABH4goyHAcrheMj1QGA2RGQAQBgICADAMBAQAYAgIGADAAAAwEZjoPVVquwigUAzB/LvMFxIgwDwInBFWQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAACD7bNuADbazl17D6vdu/uiGXQCAJyIXEFmS1kpHB+pDgCwnIAMAAADARkAAAYCMgAADARkAAAYCMhsKautVmEVCwDgaFnmjS1HGAYAnglXkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABisGZCr6pqqeqSq9g+1F1TVF6rqK9P++VP9DVV1W1Xtm/Y/Mrzni1V1V1XdMW2nb85HAgCAY3c0V5CvTXLhstquJDd199lJbpqeJ8nXk/xYd5+b5PIkv73sfZd193nT9sixtw0AAJtjzYDc3TcneXRZ+c1JrpseX5fk4unY27v7wal+Z5LnVdVJG9MqAABsvmOdg/zC7n4oSab9StMlfjLJ7d39xFD72DS94n1VVat98aq6oqoWq2rx4MGDx9giAACs36bcpFdVr0jya0l+dihfNk29eM20vW2193f31d290N0LO3bs2IwWAQBgRccakB+uqhclybR/aj5xVZ2Z5NNJ3t7d9xyqd/cD0/6xJJ9M8upjbRoAADbLsQbkz2bpJrxM+88kSVWdmmRvkiu7+08OHVxV26vqtOnxc5K8Kcn+AADAnNm+1gFVtSfJ65KcVlUHkrw/ye4k11fVO5Lcl+SS6fCfT/IDSd5XVe+bav8uyTeT3DiF421J/jDJRzbwczAjO3ftPax27+6LZtAJAMDGqO6edQ9HtLCw0IuLi7NugxWsFI4PEZIBgHlXVbd198Lyur+kBwAAAwEZAAAGAjIAAAwEZAAAGAjIHLPVbsRzgx4AcCJbc5k3OBJhGADYalxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgsH3WDXB0fvC9n8vfP9lPPX/etspfffBHZ9gRAMDW5AryCWB5OE6Sv3+y84Pv/dyMOgIA2LoE5BPA8nC8Vh0AgGMnIAMAwEBABgCAgYB8AnjetlpXHQCAYycgnwD+6oM/elgYtooFAMDmsMzbCUIYBgA4PlxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMFgzIFfVNVX1SFXtH2ovqKovVNVXpv3zh9eurKq7q+quqnrjUH9VVe2bXvuNqqqN/zjP3FU37Mv3X/m57Ny1N99/5edy1Q37Zt0SAADH0dFcQb42yYXLaruS3NTdZye5aXqeqnp5krcmecX0nv9ZVdum93w4yRVJzp625V9z5q66YV8+cct9ebI7SfJkdz5xy31CMgDAs8iaAbm7b07y6LLym5NcNz2+LsnFQ/13u/uJ7v7rJHcneXVVvSjJKd39pe7uJB8f3jM39tx6/7rqAABsPcc6B/mF3f1Qkkz706f6GUnGNHlgqp0xPV5eX1FVXVFVi1W1ePDgwWNscf0OXTk+2joAAFvPRt+kt9K84j5CfUXdfXV3L3T3wo4dOzasubVsW2Va9Gp1AAC2nmMNyA9P0yYy7R+Z6geSnDUcd2aSB6f6mSvU58ql55+1rjoAAFvPsQbkzya5fHp8eZLPDPW3VtVJVfWSLN2M96fTNIzHquqCafWKtw/vmRsfuPjc/PQFL37qivG2qvz0BS/OBy4+d8adAQBwvFSvMb+2qvYkeV2S05I8nOT9SW5Icn2SFye5L8kl3f3odPx7k/xMkm8neXd3f36qL2RpRYyTk3w+yS/0Wt88ycLCQi8uLq7/kwEAwBFU1W3dvXBY/Sgy6kwJyAAAbIbVArK/pAcAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGFR3z7qHI6qqg0m+Nus+5shpSb4+6yZOEMZqfYzX+hivo2es1sd4rY/xOnrG6nD/ort3LC/OfUDm6apqsbsXZt3HicBYrY/xWh/jdfSM1foYr/UxXkfPWB09UywAAGAgIAMAwEBAPvFcPesGTiDGan2M1/oYr6NnrNbHeK2P8Tp6xuoomYMMAAADV5ABAGAgIAMAwEBAnmNVdWpV/X5V/VVVfbmqfriqzquqW6rqjqparKpXz7rPeVBV50xjcmj7f1X17qp6QVV9oaq+Mu2fP+te58ERxutD08/bX1TVp6vq1Fn3OmurjdXw+i9XVVfVaTNsc24cabyq6heq6q6qurOq/tuMW50LR/i36Fy/gqr6xennZ39V7amq5znPr26V8XKePwrmIM+xqrouyf/u7o9W1XOTfHeS65P8end/vqp+NMmvdPfrZtnnvKmqbUkeSHJ+kp9L8mh3766qXUme392/OtMG58yy8TonyR9197er6teSxHj9o3GsuvtrVXVWko8m+cEkr+puC/APlv1svTTJe5Nc1N1PVNXp3f3ITBucM8vG6yNxrn+aqjojyf9J8vLufryqrk/yuSQvj/P8YY4wXg/GeX5NriDPqao6Jclrk/yvJOnub3X3N5J0klOmw/5pln7QebrXJ7mnu7+W5M1Jrpvq1yW5eFZNzbGnxqu7/6C7vz3Vb0ly5gz7mkfjz1aS/HqSX8nSv0sON47Xu5Ls7u4nkkQ4XtE4Xs71K9ue5OSq2p6li0YPxnn+SA4bL+f5oyMgz6+XJjmY5GNVdXtVfbSqvifJu5N8qKruT/Lfk1w5wx7n1VuT7Jkev7C7H0qSaX/6zLqaX+N4jX4myeePcy/z7qmxqqofT/JAd//5bFuaa+PP1suSvKaqbq2qP66qfznDvubVOF7vjnP903T3A1kai/uSPJTkb7v7D+I8v6IjjNfIeX4VAvL82p7kh5J8uLtfmeSbSXZl6SrML3b3WUl+MdMVZpZMU1F+PMnvzbqXE8Fq41VV703y7SS/M4u+5tE4VlX13VmaLvCfZ9vV/FrhZ2t7kucnuSDJe5JcX1U1o/bmzgrj5Vy/zDS3+M1JXpLk+5J8T1X99Gy7ml9rjZfz/JEJyPPrQJID3X3r9Pz3sxSYL0/yqan2e0ncuPF0/z7Jn3X3w9Pzh6vqRUky7f1a9+mWj1eq6vIkb0pyWbtJYTSO1fdn6T86f15V92bpV5R/VlX/fIb9zZvlP1sHknyql/xpku8kcWPjP1o+Xs71h/u3Sf66uw929z9kaXz+VZznV7PaeDnPHwUBeU519/9Ncn9VnTOVXp/kL7M03+rfTLUfSfKVGbQ3zy7N06cLfDZL/6HJtP/Mce9ovj1tvKrqwiS/muTHu/vvZtbVfHpqrLp7X3ef3t07u3tnlsLfD03/blmy/N/iDVk6Z6WqXpbkuUnc1PiPlo+Xc/3h7ktyQVV99/Tbh9cn+XKc51ez4ng5zx8dq1jMsao6L0t3yD83yVeT/Ickr0jyP7L068q/T/Kfuvu2WfU4T6Zfe9+f5KXd/bdT7Z9laeWPF2fpZHFJdz86uy7nxyrjdXeSk5L8zXTYLd39zhm1ODdWGqtlr9+bZMEqFktW+dl6bpJrkpyX5FtJfrm7/2hmTc6RVcbrX8e5/jBV9V+S/FSWpgbcnuQ/JvkncZ5f0SrjdWec59ckIAMAwMAUCwAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAb/H2leqRtP0LMZAAAAAElFTkSuQmCC\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "在這種情況下，相關性略小一些，但仍然相當高。現在，為了使關係更不明顯，我們可能想通過向薪水添加一些隨機變量來增加一些額外的隨機性。讓我們看看會發生什麼：\n"
+    "在這種情況下，相關性略小一些，但仍然相當高。現在，為了使關係更加不明顯，我們可能想通過向薪水添加一些隨機變量來增加一些額外的隨機性。讓我們看看會發生什麼：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> 你能猜出為什麼這些點會排列成垂直線嗎？\n",
+    "> 你能猜出為什麼這些點會排列成這樣的垂直線嗎？\n",
     "\n",
-    "我們已經觀察到像薪水這樣的人工設計概念與觀察變數*身高*之間的關聯。現在讓我們看看兩個觀察變數，例如身高和體重，是否也存在關聯：\n"
+    "我們已經觀察到一個像薪水這樣的人為設計概念與觀察變數*身高*之間的相關性。現在讓我們看看兩個觀察變數，例如身高和體重，是否也存在相關性：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "很不幸地，我們並未獲得任何結果——只有一些奇怪的 `nan` 值。這是因為我們的系列中有些值是未定義的，以 `nan` 表示，這導致運算結果也變成未定義。透過觀察矩陣，我們可以看到 `Weight` 是問題所在的欄位，因為 `Height` 值之間的自相關已被計算出來。\n",
+    "很遺憾，我們沒有得到任何結果——只有一些奇怪的 `nan` 值。這是因為我們的數據序列中有一些值是未定義的，用 `nan` 表示，這導致運算結果也變成未定義。通過觀察矩陣，我們可以看到問題出在 `Weight` 這一列，因為 `Height` 值之間的自相關已經被計算出來。\n",
     "\n",
-    "> 這個例子顯示了**資料準備**和**清理**的重要性。沒有適當的資料，我們無法計算任何東西。\n",
+    "> 這個例子顯示了**數據準備**和**清理**的重要性。沒有適當的數據，我們無法計算出任何結果。\n",
     "\n",
-    "讓我們使用 `fillna` 方法填補缺失值，並計算相關性：\n"
+    "讓我們使用 `fillna` 方法來填補缺失值，然後計算相關性：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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C60H8Mn1s4ibpnjdvnsrKyvTss8+2PhYOhyVJ1113na666iqdeuqpeuCBB3TcccfpiSee6PG/VVRUJL/f3/p15JFH9rr9ANBXqvdG/yPUkzigP+H9AyCC60H8Mn1s4iLpnj9/vlasWKE1a9ZoxIgRrY/n5ras2Q8EAm3iv/a1r2nnzp2SpJycHFVXV7d5vrm5WTU1NcrJyenw31uwYIGCwWDr1yeffOJkdwDAUdnpKY7GAf0J7x8AEVwP4pfpY+Nq0m1ZlubPn6/nn39eq1evVn5+fpvn8/LyNHz48HZlxD744AONGjVKklRQUKA9e/Zo8+bNrc+vXr1a4XBYp59+eof/7sCBA5WRkdHmCwDi1bj8TOX6U1oPEjmUTy0ne47Lz4xlswBP4P0DIILrQfwyfWxcTbrnzZunpUuX6plnnlF6erqqqqpUVVXVWoPb5/Pp1ltv1YMPPqg//OEP+uijj3TXXXfpH//4h6655hpJLbPehYWFmjNnjjZt2qTXX39d8+fP17e//W1bJ5cDQLxLTPBp4cyWFT+H/jGKfL9wZkCJCdH+VAH9F+8fABFcD+KX6WPjaskwn6/jX9qTTz6pK6+8svX7n/zkJ3r44YdVU1Ojk08+WT/96U81YcKE1udramo0f/58LV++XAkJCbrkkkv04IMPavDgwbbaQckwAF5gau1KIBZ4/wCI4HoQv7w2NnbzyLiq0+0Wkm4AXhEKW9pUUaPqvQ3KTm9ZZuXVT32BWGtsDmtJ6XbtqKnTqMw0zS7IU3JSXBxvAyDG+Hsav7w0NiTd3UDSDQCA2bw2ewIAiH9280g+3gUAAEYrKavU3KVb2tWArQo2aO7SLSopq3SpZQCA/oCkGwAAGCsUtrRoebk6WtYXeWzR8nKFwv1+4R8AoI+QdAMAAGNtqqhpN8N9MEtSZbBBmypqYtcoAEC/QtINAACMVb03esLdkzgAALqLpBsAABgrOz3F0TgAALorye0GAAAA9JVx+ZnK9aeoKtjQ4b5un6Qcf0tJGq/xUlkdAOjPSLoBAICxEhN8WjgzoLlLt8gntUm8I+npwpkBzyWrlEADAO9geTkAADBa4ehcLZ41Rjn+tkvIc/wpWjxrjOeSVEqgAYC3MNMNAACMVzg6V1OOP1xLSrdrR02dRmWmaXZBnpKTvDX/0FUJNJ9aSqBNDeR4bvYeAExF0g0AAIzX0XLs326o8Nxy7O6UQCs4Oit2DQMAROWtj3cBAAC6yaTl2JRAAwDvIekGAADG6mo5ttSyHDsU7igi/lACDQC8h6QbAAAYqzvLsb0gUgIt2m5tn1pOMfdiCTQAMBVJNwAAMJZpy7EjJdAktUu8vVwCDQBMRtINAACMZeJybNNKoAGA6Ti9HAAAGCuyHLsq2NDhvm6fWpJVry3HLhydq6mBHG2qqFH13gZlp7f0gRluAIg/JN0AAMBYkeXYc5dukU9qk3h7fTl2YoKPsmAA4AEsLwcAAEZjOTYAwE3MdAMAAOOxHBsA4BaSbgAA0C+wHBsA4AaWlwMAAAAA0EdIugEAAAAA6CMsLwdgtFDYYg9nHGN8EEv1jSHdt7Jc23fVKS8rTXfMCCg1OdHtZvWYaf1BfON6Hd8Yn/jmsyyro7KV/Uptba38fr+CwaAyMjLcbg4Ah5SUVWrR8nJVBhtaH8v1p2jhzACnFccBxgexNOfpt7SqvLrd41MD2Sq+fKwLLeod0/qD+Mb1Or4xPu6xm0eSdIukGzBRSVml5i7dokMvcJHPfCkT5C7GB7EULUGN8Fqialp/EN+4Xsc3xsdddvNI9nQDME4obGnR8vJ2f4AktT62aHm5QuF+/5mjKxgfxFJ9Y6jTBFWSVpVXq74xFKMW9Y5p/TlYKGypdNsuvbj1M5Vu28U1IA5wvY5vjI93kHQDMM6mipo2S6wOZUmqDDZoU0VN7BqFVowPYum+leWOxrnNtP5ElJRVasL9q3Vp8Ubd9OxWXVq8URPuX62Sskq3m9avcb2Ob4yPd5B0AzBO9d7of4B6EgdnMT6Ipe276hyNc5tp/ZH+uTz20OShKtiguUu3kHi7iOt1fGN8vIOkG4BxstNTHI2DsxgfxFJeVpqjcW4zrT8sj41vXK/jG+PjHSTdAIwzLj9Tuf4URSuU4VPLqZ7j8jNj2Sz8H8YHsXTHjICjcW4zrT8sj41vXK/jG+PjHSTdAIyTmODTwpktN5yH/iGKfL9wZoD6lS5hfBBLqcmJmhrI7jRmaiDbM/WtTesPy2PjG9fr+Mb4eAdJNwAjFY7O1eJZY5Tjb7ukKsefQvmMOMD4IJaKLx8bNVH1Ynktk/rD8tj4x/U6vjE+3kCdblGnGzBZKGxpU0WNqvc2KDu9ZYkVn/jGD8YHsVTfGNJ9K8u1fVed8rLSdMeMgGdmhDtiQn9CYUsT7l+tqmBDh/u6fWpJHjbcNoVrg8u4Xsc3xscddvNIkm6RdAMAALglcnq5pDaJdyRdYLYOQLyym0eyvBwAAACuYXksANMlud0AAAAQn1iuiFgpHJ2rqYEcXm8AjETSDQAA2ikpq9Si5eVtyjnl+lO0cGaAmUf0icQEnwqOznK7GQDgOJaXAwCANiJ7bA+tn1wVbNDcpVtUUlbpUssAAPAekm4AANAqFLa0aHl5hydJRx5btLxcoXC/P4cVAABbWF4OAIBDGpvDWlK6XTtq6jQqM02zC/KUnOStz7c3VdS0m+E+mCWpMtigTRU1LAV2mQmvN5NxJgKACJJuAAAcULSyXMXrK3TwBPC9K9/TnIn5WjAj4F7Duql6b/SEuydx6BumvN5MxZkIAA7Gx6EAAPRS0cpyPbaubQIkSWFLemxdhYpWlrvTsB7ITk/pOqgbcXCeSa83E3EmAoBDkXQDgIeEwpZKt+3Si1s/U+m2XeyrjQONzWEVr6/oNKZ4fYUam8MxalHvnHiE39G4eGLC+8e015tpOBMBQEdYXg4AHsFyxfi0pHR7uxnHQ4WtlrhrJh4Vm0b1wv0l79mOu+fCE/u4Nc4x5f1j2uvNNJyJAKAjzHQDgAewXDF+7aipczTObdt32Wun3bh4YNL7x7TXm2k4EwFAR0i6ASDOsVwxvh05NNXROLflZaU5Guc2094/ozLt/d7txsFZnIkAoCMk3QAQ57qzXBGxd3xOhqNxbrvD5snXduPcZtr7Z3ZBnrqqOpXga4lD7I3Lz1SuP0XRhsinlm0N4/IzY9ksAC4j6QaAOMdyxfhWU9foaJzbUpMTNTWQ3WnM1EC2UpMTY9Si3jHt/ZOclKA5E/M7jZkzMZ963S5JTPBp4cyWD6QOTbwj3y+cGaBeN9DPcEUGgDjHcsX4ZuL4FF8+NmriPTWQreLLx8a4RT1n4vgsmBHQdZPy2814J/ik6yZRp9tthaNztXjWGOX4276mcvwpWjxrjKcO7gPgDE4vB4A4F1muWBVs6HBfqk8tN3MsV3SHqeNTfPlY1TeGdN/Kcm3fVae8rDTdMSPgmRnuCFPHZ8GMgL4/7XgtKd2uHTV1GpWZptkFecxwx4nC0bmaGsjRpooaVe9tUHZ6y2uMGW6gf/JZluWNk0P6UG1trfx+v4LBoDIyvLHnDkD/Ejl9WVKbxCFy+8bsibsYn/jG+AAA+oLdPJKPQwHAA1iuGN8Yn/jG+AAA3MRMt5jpBuAdobDFcsU4xvjEN8YHAOAku3kke7oBwEMSE3wqODrL7WYgCsYnvpk2PnyIAADeQNINAADgMSVllVq0vLxNDfJcf4oWzgywXB4A4gx7ugEAADwkcjDcwQm3JFUFGzR36RaVlFW61DIAQEdcTbqLioo0duxYpaenKzs7WxdeeKHef//9DmMty9L06dPl8/n0wgsvtHlu586dOu+885SWlqbs7Gzdeuutam5ujkEPACC2QmFLpdt26cWtn6l02y6Fwt4+lqOxOazH13+sH75YpsfXf6zG5rDbTeoV08anvjGku154V7Mff1N3vfCu6htDbjepV2r2NWraL17TKYte0bRfvKaafY1uN6nbQmFLi5aXd1j+LPLYouXlnnztmXY9oD8AIlxdXr527VrNmzdPY8eOVXNzs+644w5NmzZN5eXlGjRoUJvYX/7yl/L52u9TCoVCOu+885STk6M33nhDlZWVuvzyyzVgwADdd999seoKAPQ505aTFq0sV/H6Ch2cG9y78j3NmZivBTMC7jWsh0wbnzlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM2N/vEpfHpRk76lv0pgfr9Kwwcl6686pLrasezZV1LSb4T6YJaky2KBNFTWe2r9u2vWA/gA4mKsz3SUlJbryyit1wgkn6OSTT9ZTTz2lnTt3avPmzW3itm7dqv/6r//SE0880e5nvPLKKyovL9fSpUt1yimnaPr06brnnnv08MMPq7HRe59gA0BHTFtOWrSyXI+ta3sDJ0lhS3psXYWKVpa707AeMm18Dk24D7aqvFpznn4rxi3qnUMT7oN9ua9RY3+8KsYt6rnqvdET7p7ExQPTrgf0B8Ch4mpPdzAYlCRlZma2PlZXV6fvfOc7evjhh5WTk9PuvyktLdWJJ56oww8/vPWxc889V7W1tfr73//e940GgD5m2nLSxuawitdXdBpTvL7CM0sXTRuf+sZQ1IQ7YlV5tWeWmtfsa4yacEd8ua/RM0vNM1OTHY1zm2nXA/oDoCNxk3SHw2HdfPPNOvPMMzV69OjWx2+55RaNHz9eF1xwQYf/XVVVVZuEW1Lr91VVVR3+NwcOHFBtbW2bLwCIV91ZTuoFS0q3t5sxOVTYaonzAtPG5z6bs1Z249z27d+84Wic2/7xxV5H49xm2vWA/gDoSNyUDJs3b57Kysq0YcOG1seWLVum1atX6+2333b03yoqKtKiRYsc/ZkA0FdMW066o6bO0Ti3mTY+23fZ+73bjXNb9V57M9h249z2yW57v3e7cW4z7XpAfwB0JC5muufPn68VK1ZozZo1GjFiROvjq1ev1rZt2zRkyBAlJSUpKanlM4JLLrlEZ511liQpJydHX3zxRZufF/m+o+XokrRgwQIFg8HWr08++aQPegUAzshOT3E0zm2jMtMcjXObaeOTl2Xv9243zm3D0u0ts7Yb5zbT3j/0J76Z1h/ALa4m3ZZlaf78+Xr++ee1evVq5efnt3n+9ttv1zvvvKOtW7e2fknSAw88oCeffFKSVFBQoHfffVfV1f/cf7Zq1SplZGQoEOj4NMWBAwcqIyOjzRcAxKtx+ZnK9aeoff2GFj61nJI9Lj8zSkR8mV2Qp4Ronfk/Cb6WOC8wbXzusHkSsd04t/3nOcc5Guc2094/9Ce+mdYfwC2uJt3z5s3T0qVL9cwzzyg9PV1VVVWqqqpSfX29pJaZ6tGjR7f5kqSRI0e2JujTpk1TIBDQ7Nmz9be//U1//vOfdeedd2revHkaOHCga30DAKckJvi0cGZLgnPovU/k+4UzA0rs6s4oTiQnJWjOxPxOY+ZMzFdyUlwsxuqSaeOTmpyoqYHsTmOmBrKVmpwYoxb1Tl3Y3gFPduPcZtr7h/7EN9P6A7jF1XfI4sWLFQwGddZZZyk3N7f167nnnrP9MxITE7VixQolJiaqoKBAs2bN0uWXX64f/ehHfdhyAIitwtG5WjxrjHL8bZco5/hTtHjWGM/VgV4wI6DrJuW3m0FJ8EnXTfJe3VfTxueSMSN69Xw8MW35v2Te+4f+xDfT+gO4wWdZljdqmPSh2tpa+f1+BYNBlpoDiGuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2dMTBifUNjShPtXRz2R3aeWDxM23DbFE32L9Kcq2NBhWTev9edgpr1/6E98q28M6b6V5dq+q055WWm6Y0bAMytegL5iN48k6RZJNwAAEaXbdunS4o1dxv1uzhkqODorBi3qvZKySl2/dEvU5x/14GoEIJZKyiq1aHl5mw/jcv0pWjgzwHsH/ZrdPNK7H7cBAADHmVYCDUDvlJRVau7SLe1Wv1QFGzR36RaVlFW61DLAO0i6AQBAK9P2QIfClhYtL4/6vE/SouXlCoX7/cI/oJ3I+6ejd0fkMd4/QNdIugEAQCvTSqBtqqiJuj9dakkcKoMN2lRRE7tGAR7B+wdwBkk3AABoZVoJNJbLAz3H+wdwBkk3AABow6QSaKYtlwdiifcP4IwktxsAAIApTCoRVDg6V5OPzfZ8iaDIcvmuSoZ5Zbn8wUwr4WTS+8cUJr9/gFiiZJgoGQYA6L2ileUqXl+hg88TSvBJcybma8GMgHsN6yGT+mNiybA5T7+lVeXV7R6fGshW8eVjXWhR75j0ejNN5PRySW0S78gGE6+tfgGcRMkwAABipGhluR5b1zZhkKSwJT22rkJFK6Ofnh2PTOvPI6991Kvn4020hFuSVpVXa87Tb8W4Rb1j2uvNNCZtNwHcwvJyAAB6obE5rOL1FZ3GFK+v0PenHe+JpbKm9WdfQ7Pe+bS205h3Pq3VvoZmDU6J/9ui+sZQ1IQ7YlV5teobQ55Yam7a681UhaNzNTWQo00VNare26Ds9JYl5V45UBFwG1cvAAB6YUnp9nYzdIcKWy1xXmBaf2557m1H49x2n81ZX7txbjPt9WayxASfCo7O0gWnHKGCo7NIuIFuIOkGAKAXdtTUORrnNtP6s3N3vaNxbtu+y97v3W6c20x7vQFAR0i6AQDohSOHpjka57ZRmfbaaTfObSOHpjoa57aRmTb7YzPObaa93gCgIyTdAAD0wvE56Y7GuW12QZ66WjWa4GuJ84IHvnWqo3FumxbIcTTObaa93gCgIyTdAAD0Qk1do6NxbktOStCcifmdxsyZmO+ZQ60GpyTppBGdlwM9aUSGJw5Rk6Q99U2OxrnNtNcbAHSEKxgAAL2QnZ7SdVA34uLBghkBXTcpv90MZIJPum6S9+omL5s/MWrifdKIDC2bPzHGLeo5Xm8A4D0+y7K6ODPSfHaLmgMAnBUKW54vQdPYHNbxd73c6QnMCT7pH/dM99xsXX1jSPetLNf2XXXKy0rTHTMCnihDFc2+hmbd8tzb2rm7XiOHpuqBb53qmRnuiFDY0oT7V6sq2KCOXnI+tdRP3nDbFE++l5aUbteOmjqNykzT7II8z71nAPQvdvNIb/2lAQAYo6SsUouWl6sy2ND6WK4/RQtnBlQ4OtfFlnXP5h27bZU82rxjtwqOzopNoxxw6Pis/1D6y3vVnhufgw1OSVLxFWPdbkavJCb4tHBmQHOXbpFPapN4R1LshTMDnku4pZal5tdMPMrtZgCA4/j4EAAQcyVllZq7dEubhFuSqoINmrt0i0rKKl1qWfdV723oOqgbcfHApPExUeHoXC2eNUY5/rZLyHP8KVo8a4xnPxQBAFMx0w0AiKlQ2NKi5eUdLo211DJbt2h5uaYGcjwxW2faHlvTxsdUhaNzNTWQ4/ntGQDQH5B0AwBialNFTbsZ1INZkiqDDdpUUeOJ5djj8jOV60/pco/tuPzMWDetR0wbn4OZtmc4McHnuTEA0DdMOCPFZCTdAICYMm05tml7bE0bn4iileUqXl/RZv/9vSvf05yJnI4NwNtMOSPFZN79eBcA4EmmLceWzNpja+L4FK0s12PrKtodeBe2pMfWVahoZbk7DQOAXuIMDm9gphsAPMSE5WOnjRqqBJ+6LLF12qihsWuUAwpH5+q0kZm66JENqtnfpMxBA/T8dydoWMZAt5vWLaYtl29sDqt4fUWnMcXrK/T9acd7bqm5CSXQDhasa9LVT23S58EGDfen6Ikrx8mfNsDtZvWYCddrk5kwPpzB4R3evTIDQD9jyvIxU0tsjf3xKn25r7H1+7o9IY297y8aNjhZb9051cWWdU9kufz1S7d0+Lwlby2XX1K63dbrbUnpdk+Vqzr/ofV659Pa1u/fr9qr0Xf/WSeNyNCy+RNdbFnPTP7Zau3YVd/6fWWwQSf/6BWNykrV2lunuNiynjHlem0qU8bH5DM4TOOtj3QBoJ8yafmYiXuGD024D/blvkaN/fGqGLeod/7zD+/06vl4sqOmztG4eHBown2wdz6t1fkPrY9xi3rn0IT7YDt21Wvyz1bHuEW9Y9L12kQmjY+Jf09NRdINAHGuq+VjUsvysVBX03lxwrQ9wzX7GqMm3BFf7mtUTRcx8eLL2gOqbWjuNKa2oVlf1h6IUYt6J3OgvUV9duPctq+hOWrCHfHOp7Xa18UYxotgXVPUhDtix656BeuaYtSi3jHtem0a08bHtL+nJiPpBoA4153lY14Q2TMcbXGyTy3L/LyyZ/jbv3nD0Ti3XfTIBkfj3LbkrZ2OxrntlufedjTObVc/tcnROLeZdr02jWnjY9rfU5ORdANAnDNt+Vhkz7CkdjcK3iyxZW8G226c22r225tRtBvntr02Z3ztxrlt5+7OZ4W7G+e2zztJgHoS5zbTrtemMW18TPt7ajKSbgCIcyYuHzOrxFayo3Fuyxxk77Rou3Fuy7B5mrfdOLeNHJrqaJzbhvvtXbfsxrnNxOu1SUwcH5P+nprMZ1mWNzYt9KHa2lr5/X4Fg0FlZGS43RwAaKOxOazj73q5yxJb/7hnuudKHplQsqVmX6PG2DgobcudU5U5OP4T7y9rD2jsfX/pMu6tO87xRDm0z2rqdeZPuz6I6/X/nKIjMuM/Ud3X0KzRd/+5y7iyu8/1RPmwYF2TTv7RK13G/e2H0zxRPiwUtjTh/tVdltzbcNsUz13rTGDy+Jjw99SL7OaR3ro7A4B+qDsltrwmMcGngqOzdMEpR6jg6CxP3iBkDk7WsC6S6WGDkz2RcEvSsIyBXc76ZqQkeSLhlqSdu+2dSm43zm2DU5J00ojOJwhOGpHhiYRbkvxpAzQqq/MPO0ZlpXoi4ZZY7hvvTB4fE/6emoykGwDinGl70Ex08ZgjevV8vPnpv53Uq+fjiYnvn2XzJ0ZNvL1Yp3vB9K/16vl4w3Lf+Mb4wA3e+BgUAPoxE/egmaSxOazi9RWdxhSvr9D3px3vieX/kZI60fjUUlJnaiDHEzMphw22NyNvNy5eLJs/UfsamnXLc29r5+56jRyaqge+dapnZrgjTHu9RRSOztXUQA7LfeMU44NY89aVGQD6oUhJkK72oFESxB1LSrfbWv6/pHS7rpl4VGwa1QvdKalTcHRW7BrWU3ZPrvHgCTeDU5JUfMVYt5vRK8a93g4SWe6L+MT4IJbi/yN3AOjnTN6DZoIdNfb2AtuNc5tpy7G/2n/A0Tg4y7TXGwB0hKQbADyAPWjx68ihaY7Guc207Qym9cc0jA+A/oDl5QDaoORE/GIPWnw6/vB0R+PcZtp2BtP6YxrGB0B/QNINoFVJWaUWLS9vs78u15+ihTMDzKTGCfagxZ+a+kZH49wW2c5w/dItHT5vyVvbGSL9mbt0i3xqu3Wb7RnuY3wA9AcsLwcgqSXhnrt0S7sDbaqCDZq7dItKyipdahkQ31geG//YnhHfGB8ApvNZluXB8zqdVVtbK7/fr2AwqIyMjuteAiYLhS1NuH911BNkI8v7Ntw2xXOzDaYtlzetP6aUPJpw/+oul8d65f1j8vWgsTmsJaXbtaOmTqMy0zS7IM8TZdyiqW8M6b6V5dq+q055WWm6Y0ZAqcmJbjerx0wbHwDms5tHeuvOBkCfMLVki2nL5U3rz/kPrdc7n9a2fv9+1V6NvvvPOmlEhpbNn+hiy7rHtOXY/el68NsNFZ59/8x5+i2tKq9u/X79h9KSjTs1NZCt4su9V0bMtPEBgIP16OPDH/3oR6qra1/6pL6+Xj/60Y963SgAsWViyRbTlsub1p9DE+6DvfNprc5/aH2MW9Q7j7z2Ua+ejydcD+LfoQn3wVaVV2vO02/FuEW9Y9r4AMChepR0L1q0SPv27Wv3eF1dnRYtWtTrRgGILdP2pIbClhYtL+9wqW/ksUXLyxUKe2N3jWn92dfQHDXhjnjn01rta2iOUYt6x7T+JIadjXObae+f+sZQ1IQ7YlV5teobQzFqUe+YNj4A0JEeJd2WZcnna79M7m9/+5syMynpAHhNpGRLtMWvPrUsY/ZKyZbuLI/1AtP6c8tzbzsa5zbT+nPHsncdjXObae+f+1aWOxrnNtPGBwA60q093UOHDpXP55PP59Oxxx7bJvEOhULat2+frr/+escbCaBvmVayxbTlsab1Z+fuekfj3GZaf/YfsDdDajfObaa9f7bvar+9rzdxbjNtfACgI91Kun/5y1/KsixdffXVWrRokfx+f+tzycnJysvLU0FBgeONBND3IiVbDj3IJseDB3WZtlzetP6MHJqq96v22orzAtP6M2hgomobuk6oBw30xinZpr1/8rLStP5De3FeYNr4AEBHupV0X3HFFZKk/Px8jR8/XgMGDOiTRgFwR+HoXE0N5Hi+JNVpo4YqwSd1tgUwwdcS5wWR5f9dlaTyyvL/B751qkbf/WdbcV5gWn/+dP0EnfPLtbbivMC068EdMwJasnGnrTgvMO36BgAd6dGe7smTJysxMVEffPCBNmzYoHXr1rX5AuBdiQk+FRydpQtOOUIFR2d5LuGWpM07dnd6gy213IBv3rE7Ng3qpcjy/2hd8lpJqsEpSTppRPRalpJ00ogMz9TrNq0//72xwtE4t5l2PUhNTtTUQHanMVMD2Z6p123a9Q0AOtKjO4CNGzfqO9/5jnbs2CHLanuZ9Pl8CoW8sc8LgJnYIxj/ls2fGLVsmNfqdEtm9Yc9w/Gv+PKxUcuGebVONwCYrEdJ9/XXX6+vf/3reumll5Sbm9vhSeYA4BbT9ghGSupE41NLSZ2pgRxPzQYtmz9R+xqadctzb2vn7nqNHJqqB751qmdmhA9lSn9M2zOcmZrsaFy8KL58rOobQ7pvZbm276pTXlaa7pgR8MwMd4Sp1zcAOFiP7gQ+/PBD/eEPf9AxxxzjdHsAoNdM2yPYnZI6BUdnxa5hDhickqTiK8yZlUtNTtTVE45qPRPBawmQZN6e4X980fUhd5G4iccN6+PWOCs5KUEzThze+npLTurRrkFXmXx9A4CIHiXdp59+uj766COSbgBxiRJocENJWWW70/9zPXj6f2TPcEdLlyO8tGf4k932lsHbjYsXprzeuL4B6A9sJ93vvPNO6/+/4YYb9P3vf19VVVU68cQT251iftJJJznXQgDoAUqgIZZKyio1d+mWdisrqoINmrt0ixbPGuOp19xRhw3q1fPxZFSmvWXwduPigUmvN65vAPoDn3XoSWhRJCQkyOfztTs4rfUH/d9zXjxIrba2Vn6/X8FgUBkZnZ9AC8BbGpvDWlK6XTtq6jQqM02zC/I8twQzFLY04f7VXS6X33DbFM/M3pskMj7Rlsh6bXwam8M6/q6Xuyyx9Y97pnvivVTfGNLXfljSZdx7Pyr0xOy9aa83rm8AvMxuHml7pruiwhulQQAgoqPll7/dUOG5mW7TlsubxrQ9qUtKt9sqsbWkdLuumXhUbBrVC1s/2WM7zgvjY9rrjesbgP7AdtI9atSovmwHADjKpOWXklnL5U1j2p7UHTX29jbbjXObaeNjWn8krm8AzNejg9SWLVvW4eM+n08pKSk65phjlJ+f36uGAXBHKGxpU0VN62m44/IzPTfDEClB09FknSXvlqApHJ2rcXlZ+vZv3lD13kZlpyfr2WvHK3Owt0odHezL2gO66JENqtnfpMxBA/T8dydoWMZAt5vVLabtSTVtD/Rhg+29nuzGuc2011tE4ehcTThmmOdL7pnMhPsDwC09upJdeOGFHe7vPnhf94QJE/TCCy9o6NChUX9OUVGR/vSnP+kf//iHUlNTNX78eN1///067rjjJEk1NTVauHChXnnlFe3cuVPDhg3ThRdeqHvuuUd+v7/15+zcuVNz587VmjVrNHjwYF1xxRUqKipSUhIXaqA7TDkN17TllxGTf7ZaO3bVt36/p75JY368SqOyUrX21ikutqxnTrr7z6ptaG79vm5PSGPv+4syUpL0zt3nutiy7omUqOvsNZfroRJ1swvydO/K97rc0z27IC9mbeoVWyfXdCPOZaa93iLmPP1WmxPz36/aq9F3/1lTA9kqvtycsoJeZcr9AeCWHp2AsmrVKo0dO1arVq1SMBhUMBjUqlWrdPrpp2vFihVat26ddu3apf/4j//o9OesXbtW8+bN08aNG7Vq1So1NTVp2rRp2r9/vyTp888/1+eff66f//znKisr01NPPaWSkhJdc801rT8jFArpvPPOU2Njo9544w3993//t5566in98Ic/7EnXgH4rshz70Bu5yHLskrJKl1rWfSYuvzw04T7Yjl31mvyz1TFuUe8cmnAfrLahWSfd/ecYt6jnEhN8nSZAUsuHPF6ZEUpOSrC1p9sLh6hJ0lf7Dzga57bEBJ++7OLa9eVe77zepPYJ98FWlVdrztNvxbhFOJhJ9weAW3o0FXzTTTfpN7/5jcaPH9/62De+8Q2lpKTo2muv1d///nf98pe/1NVXX93pzykpaXua6FNPPaXs7Gxt3rxZkyZN0ujRo/XHP/6x9fmjjz5a9957r2bNmqXm5mYlJSXplVdeUXl5uf7yl7/o8MMP1ymnnKJ77rlHt912m+6++24lJ3t32SUQK6Ytxx6SMqDroG7EuS1Y1xQ14Y7Ysatewbom+dPiv09f1h6ImnBH1DY068vaA55Yar7xg1224844Nv5XVpR/Wms7LjAi/it++JrtTWHbjXPbZzX1ag53HtMcbok7IjM1No3qhfrGUKc14aWWxLu+MeSJ0+VNY9r9AeCWHn1MvW3btg6PRM/IyNDHH38sSfqXf/kXffXVV936ucFgUJKUmRl9SVTkOPbI0vHS0lKdeOKJOvzww1tjzj33XNXW1urvf/97hz/jwIEDqq2tbfMF9GfdWY7tBaveq3I0zm1XP7XJ0Ti3XfTIBkfj3PbtJzY6Gue2f31ovaNxbrv9xXcdjXPb9AfXOhrntvtWljsaB2eZdn8AuKVHSfdpp52mW2+9VV9++WXrY19++aX+8z//U2PHtuy7+fDDD3XkkUfa/pnhcFg333yzzjzzTI0ePbrDmK+++kr33HOPrr322tbHqqqq2iTcklq/r6rq+Ia6qKhIfr+/9as77QRMZNpy7B01nc8KdzfObZ93sXS5u3Fuq9nf5GgcnNXFJGq349xW32SvpXbj3Lb/QMjROLdt32XvFHy7cXCWafcHgFt6lHQ//vjjqqio0IgRI3TMMcfomGOO0YgRI7R9+3b99re/lSTt27dPd955p+2fOW/ePJWVlenZZ5/t8Pna2lqdd955CgQCuvvuu3vS7FYLFixo3YseDAb1ySef9OrnAV5n2mm4eVn2TlW2G+e24X57v3e7cW7LHGRvCbzdODjL7o2BN3Z0S6kD7LXUbpzbBg20t8TabpzbTLtem8a0+wPALT36C3PcccepvLxcL774om688UbdeOONWrZsmf7+97/r2GOPldRywvns2bNt/bz58+drxYoVWrNmjUaMGNHu+b1796qwsFDp6el6/vnnNWDAP2/EcnJy9MUXX7SJj3yfk5PT4b83cOBAZWRktPkCeioUtlS6bZde3PqZSrftUqirE4jiUOQ03Gi7sXzy1mm4d8wIOBrntieuHOdonNv+cP2Zjsa57elZ9k5Wthvntj/Z/L3bjXPbSzdMcjTObS/fONnROLfdVvg1R+PgLNPuDwC39Phj3YSEBBUWFrYm3eeee64SErr34yzL0vz58/X8889r9erVHdb2rq2t1bRp05ScnKxly5YpJaXtJ2kFBQV69913VV39z0M4Vq1apYyMDAUC3rihhneVlFVqwv2rdWnxRt307FZdWrxRE+5f7bmTPBMTfFo4s+X9cugf1sj3C2cGPHNISmpyoqYGsjuNmRrI9syhPP60ARqV1fmBSKOyUj1xiJokVeza72ic21Z99EXXQd2Ic9vmT+ztzbQb57Yqm8te7ca57YjMVCUndn4tTk70eeIQNUl697Ogo3Fwlmn3B4BbbJ9e/uCDD+raa69VSkqKHnzwwU5jb7zxRls/c968eXrmmWf04osvKj09vXUPtt/vV2pqamvCXVdXp6VLl7Y59GzYsGFKTEzUtGnTFAgENHv2bP30pz9VVVWV7rzzTs2bN08DB8b/qbfwrkgJjUPntSMlNBbPGuOp2pWFo3O1eNaYdnU4czxah7P48rFRy9B4se7r2lunRC0b5rU63abtETRtT+qOGnvttBvnNtNeb5L0wb0zdOwPVqox1H5lVXKiTx/cO8OFVvWMieNjGtPuDwA32E66H3jgAV122WVKSUnRAw88EDXO5/PZTroXL14sSTrrrLPaPP7kk0/qyiuv1JYtW/Tmm29Kko455pg2MRUVFcrLy1NiYqJWrFihuXPnqqCgQIMGDdIVV1yhH/3oR3a7BnSbqSU0CkfnamogR5sqalS9t0HZ6S1LxrzUh4MVXz5W9Y0h3beyXNt31SkvK013zAh4Zob7UGtvnaJgXZOufmqTPg82aLg/RU9cOc4zM9wRhw2y94Go3Ti35WWlaf2H9uK8YFSmvXbajXObqXtSP7h3hj6rqdf0B9dq/4GQBg1M1Ms3TvbMDHeEqeNjGtPuD4BY81mW5b0NqA6rra2V3+9vLUcGdKV02y5dWtx1+Z/fzTlDBUfHf11eIJZe/+grXfbbN7uM+5//d7rOPOawGLSod+obQ/raD0u6jHvvR4We+MCnsTms4+96WZ0dT5Hgk/5xz3QlJ8X/4WOhsKUJ969WVbChww9KfWqZsdtw2xQSCBcwPgC8zG4e2au/lo2NjXr//ffV3Nzcmx8DeA7L4YCe+2rfAUfj3GbaGQLJSQmaM7H9GSsHmzMx3xMJt8Se1HjH+ADoD3r0F7Ourk7XXHON0tLSdMIJJ2jnzp2SpBtuuEE/+clPHG0gEI9YDgf0nInvn+LLx0ZNvL14hsCpI4f26vl4E9mTmnNIWb0cf4rnzt8wEeMDwHS293QfbMGCBfrb3/6m1157TYWFha2Pn3POObr77rt1++23O9ZAIB5FSmh0tRyOEhpAe6a+f0w5QyByZkU0nFmBvsD4ADBZj5LuF154Qc8995zOOOMM+Xz/vBiecMIJ2rZtm2ONA+JVZDnc3KVb5JPaJA4shwM6Z/L7JzU5UfdceKLbzeiVTRU1bU4oPpQlqTLYoE0VNZ47syIxwee5NvcnjA8AU/VoefmXX36p7Oz2y+j279/fJgkHTMZyOKDneP/EL86sAADAWT2a6f7617+ul156STfccIMktSbav/3tb1VQUOBc64A4x3I4xNqXtQd00SMbVLO/SZmDBuj5707QsAxvlNY6VOHoXB1zWLqmP7hWTWFpQIK05KrTdUzOYLeb1mMmlHTLTE12NC6e7Gto1i3Pva2du+s1cmiqHvjWqRqc0qNbobjQ2BzWktLt2lFTp1GZaZpdkOeZA+76g1DY4v4AgKQelgzbsGGDpk+frlmzZumpp57Sddddp/Lycr3xxhtau3atTjvttL5oa5+hZBgALzjp7j+rtqF9tYiMlCS9c/e5LrSod45a8FKHZakSfNLHRefFvkG9NPlnq7VjV327x0dlpWrtrVNcaFHPFK/7WPeufK/LuB/M+JrmTDoqBi1yxvkPrdc7n9a2e/ykERlaNn+iCy3qnaKV5SpeX9HmPZTgazlZfsGMgHsNgySppKxSi5aXt9mqketP0cKZAVbyAAbp05JhEyZM0NatW9Xc3KwTTzxRr7zyirKzs1VaWuq5hBsAvCBawi1JtQ3NOunuP8e4Rb0TLeGWpLDV8ryXREu4JWnHrnpN/tnqGLeo5z7ZXedoXDyIlnBL0juf1ur8h9bHuEW9U7SyXI+tq2j3Hgpb0mPrKlS0MvpBeOh7JWWVmrt0S7uzEaqCDZq7dItKyipdahkAt3RrTVVt7T//YA0bNkz/9V//1WEMs8UA4Jwvaw9ETbgjahua9WXtAU8sNa+o3h814Y4IWy1x+dmDYtOoXgjWNUVNuCN27KpXsK7JE0vNhwy0d2tgN85t+xqaoybcEe98Wqt9Dc2eWGre2BxW8fqKTmOK11fo+9OOZ6m5CyKn/3d0ibPk3dP/AfROt67GQ4YM0dChQ6N+RZ4HADjnokc2OBrntsJfrXU0zm1XP7XJ0Ti3PVXaeULX3Ti33fLc247GuW1J6XZbH1otKd0ek/agre6c/g+g/+jWR7pr1qxp/f+WZWnGjBn67W9/qyOOOMLxhgEAWtTsb3I0zm0HQvaOErEb57bPO7nB7kmc2/Y3hh2Nc9vO3Z2vQuhunNt21Nhb1m83Ds7i9H8AHelW0j158uQ23ycmJuqMM87QUUd55yAVAPCazEEDVLcnZCvOCwYm+mwl1AMTvbH0crg/pdOZrYPjvGDQwETVNnT9ehs0MDEGrem9kUNT9X7VXltxXjAqM83RODgrO93e+9xuHAAzsNkHAOLc89+d4Gic20pumtx1UDfi3PbEleMcjXPbyzfa+73bjXPbz//9FEfj3Da7IE9dbQVO8LXEIfbG5Wcq15+iaEPkU8sp5uPyM2PZLAAuI+kGgDg3LGOgMro44CkjJckTh6hJUn72IFtJgxcOUZMkf9oAjcrqfJZ0VFaqJw5Rk6QjMlOV3MUqg+REn47I9MbMcHll54eodTfObclJCZozMb/TmDkT8zlEzSWJCT4tnNlSsu3Qd1Hk+4UzAxyiBvQzvb4i+3xcNACgr/30307q1fPx5uOi86Im3l6s07321ilRE2+v1emWpA/unRE18U5O9OmDe2fEuEU9Z+Ie2wUzArpuUn6791CCT7puEnW63VY4OleLZ41RziFbSnL8KVo8awx1uoF+yGdZlu2Tai6++OI23y9fvlxTpkzRoEFtZyP+9Kc/OdO6GLFb1BwA3BAKW5pw/+qo+4Z9armZ23DbFM/NnlRU71fhr9bqQMjSwESfSm6a7JkZ7o4E65p09VOb9HmwQcP9KXriynGemeHuyGc19Zr+4FrtPxDSoIGJevnGyZ6Z4Y54/cOvdNnjb3YZ9z/XnK4z/+WwGLTIOY3NYS0p3a4dNXUalZmm2QV5zHDHkVDY0qaKGlXvbVB2esuScq9dowF0zm4e2a2D1Px+f5vvZ82a1bPWAQBs604JmoKjs2LXMAcckZmq/yw8vjVp8FpCd6jBKUn6j3OPb73J9kLd587kDEnRY7PHtvYnZ4gHD3+ym+N4MBdKTkrQNRM5zDZeJSb4PHdNBtA3unU38OSTT/ZVOwAAUZi4PFaSilaWq3h9RZuaw/eufE9zJnpzeWxJWaUWLS9v8wFJrj9FC2cGPLmc1JT+fLXvgKNxAAB0F2uQACDOmViCpmhluR5b1zbhlqSwJT22rkJFK8vdaVgPlZRVau7SLe1WJFQFGzR36RaVlFW61LKeMak/Jr5/AADeQtINAHHOtBI0jc1hFa+v6DSmeH2FGpvDMWpR74TClhYtL1dHB6REHlu0vFyhQz9hiFOm9eeUI4c4GgcAQHeRdANAnIuUoImW4ljyVgmaJaXb281wHypstcR5QXf23HuBaf155s0djsYBANBdJN0AgJjaUVPnaJzbTNtzb1p/THu9AQC8h6QbAOJcZLlvND55a7nviCH2Tii3G+e2zLRkR+PcZtoe6COH2nsd2Y0DAKC7vF3LBIDjTKsruq+hWbc897Z27q7XyKGpeuBbp3qujJNpJcOsqAvlexbntn9U1dqOm3jssD5uTe9FzhCoCjZ0OAKRuvBeOUPg+JzodVN7EhdPTLi+Hcy0OvcAEOHdKzMAx5lSIiji/IfW651P/5kQvV+1V6Pv/rNOGpGhZfMnutiy7jFtue9ne+y1026c2z7ZXe9onNsiZwhcv3RLh8977QyBmrpGR+PihSnXt4jJP1utHbv++R6pDDbo5B+9olFZqVp76xQXWwYAvcfycgCSzCoRJLW/IT3YO5/W6vyH1se4RT1n2nLfUZlpjsa5zbT+SIqacNt9Pp6Y9v6RzLq+Se0T7oPt2FWvyT9bHeMWAYCzSLoRc6GwpdJtu/Ti1s9Uum2XZ/ahRmNCf0wrEbSvoTnqDWnEO5/Wal9Dc4xa1Dsjh9pL1uzGuW3q13IcjXPbKUcMdTTObW/84ytH49yWnzXI0Ti3mXZ9C9Y1RU24I3bsqlewrilGLQIA55F0I6ZKyio14f7VurR4o256dqsuLd6oCfev9twsaoQp/TGtRNAtz73taJzbLnjY3qyV3Ti3Xbx4g6Nxbvu337zhaJzbvvPUm47Gue3fHn3d0Ti3mXZ9u/qpTY7GAUA8IulGzJi2fNmk/pi2Z3inzb2zduPcVmtzxspunNtM64/d9R/eWCdinpr99mZI7ca5zbTr2+edfODbkzgAiEck3YgJ05Yvm9Yf0/Y8HjnUXjvtxrktw+ZpxHbj3GZaf+weJ+aNY8fMkznI3unXduPcNtJmaTO7cW4b7rd3HbYbF09M2H4GwBkk3YgJ05Yvm9afSImgaEmBTy2nmHulRNBlY0c5Gue2FTdMcjTObab1Z9l3Jzga57ZLxx3haJzbnrf5e7cb57YHvnWqo3Fue+LKcY7GxQtTtp8BcAZJN2LCtOXLpvUnUiJIaj8bF/neSyWCahttLl+2Gee2nCEpSk7s/HefnOhTzhBvzATlDElRVy+lBJ88058TR/odjXPbgER7KwzsxrltWMbALldNZKQkaVjGwBi1qHcGpyRp2ODkTmOGDU72TL1uf9oAW/3xUr1uk7afAXAGSTdiwrTly6b1R5IKR+dq8awxyjlkCV+OP0WLZ43xVJ3uwwbbu3m2G+e2UNhSVhdtzRo80DNLF+sbQ+qqqWGrJc4rtv/kvF49H0+OGGJvWbLduHjwzt3nRk28M1KS9M7d58a4RT3X2BzWrv2d1xTftb9Rjc3hGLWod0JhS0mJnd+OJiUmeOb6Ztr2MwDO8MbHoPC8yPLlqmBDh3+IfGpJ7ryyfNm0/kQUjs7V1ECONlXUqHpvg7LTW/rglRnuVoadbNXVdgbpn9sZCo7OilGreu6+leW24+658MQ+bo1ztv/kPG3+eLcuOeiU8j9eO16nHeWNUmERPpvvC7tx8eKdu8/Vl7UHdNEjG1Szv0mZgwbo+e9O8MwMd8SS0u22PrRaUrpd10w8KjaN6gXTrm/d2X7mhf4AcAZJN2Iisnx57tIt8qltruPF5cum9edgiQk+z98IfLX/gKNxbjNtO8P2XXWOxsWLopXlKl5f0eaxfy9+Q3Mm5mvBjIBLreq+T4P2Tr22GxdPhmUM1Ibbv+F2M3plR42994XdOLeZdn0zrT8AnMHycsSMScuXJfP6YxLTlv+b1p+8rDRH4+JB0cpyPbauot0MZNiSHltXoSKbs/vxYFSmvd+73Tg4y7TxMe36Zlp/ADiDmW7ElDHLl/+Paf0xhWnL/03rzx0zAlqycaetOC9obA63m+E+VPH6Cn1/2vFKTor/z7q/c/oo3fPSe7biEHuzC/J078r3Ol1inuBrifMC065vpvUHgDPi/68/jBNZvnzBKUeo4OgszyeopvXHBKadxh7pT7R7bEve6k9qcqKmBrI7jZkayFZqcmKMWtQ73dlj6wVbP9njaByclZyUoDkT8zuNmTMx3xMf8EjmXq8lM/oDwBneuCIDQDeZtvz/7Z27e/U8+g57bBFrC2YEdN2k/Hal9xJ80nWTvHWGgGTe9dq0/gDoPZaXAzCWKcv/TVu+XN8Y0qry6k5jVpVXq74x5InZ7hE2S2fZjXMbe1K9YcGMgL4/7XgtKd2uHTV1GpWZptkFeZ64BnTElOt1hGn9AdA7JN0A2mhsDhtzEyeZcRq7aSWCTCsZZtmsPWc3zm0m70n9rKZe0x9cq/0HQho0MFEv3zhZR2R648OQ/sCE6/XBGpvDWvnu59q+q055WWk65cghnvggEYDzSLoBtIqUPDo4wbt35XueK3lkGtOWL5tWMuyzPfaWWduNc1tkT+r1S7d0+LzXzhCIOPYHK9UY+ufFrbYhpDN/ulrJiT59cO8MF1vWM1yv49ucp99qs6Jn/YfSko07NTWQreLLx7rYMgBu8O70FQBHmVTyyDSmlQgyrWSYaeMjSfOf6Tjhtvt8vDk04T5YY8jSsT9YGeMW9Q7X6/h2aMJ9sFXl1Zrz9FsxbhEAt5F0A7C9Z7ixORyjFuFgk/+l85O+uxvntqvH21sCbzfObROOHuZonNt2flWnrt7qzeGWOC/4rKY+asId0Riy9FlNfYxa1Dtcr+Nbd86sANB/kHQDMK7kkWnOf3i9o3Fu+/fHXnc0zm2mjU/hr9Y6Gue26Q/aa6fdOLdxvY5v3TmzAkD/QdINwLg9w6apb7I3Y2U3zm21Dc2OxrntQBezqN2Nc5tpr7f9B+zNKNqNcxvX6/hm2pkVAJxB0g3AyD2pJkkdYO9SbTfObekp9s7wtBvntmSbv3a7cW4z7fU2aKC906LtxrmN63V8M+3MCgDO8MZfTAB9anZBnro6iDjB1xKH2Lv//NGOxrlt9tiRjsa57f6LT3Y0zm0lN012NM5tL99or51249zG9Tq+3WHz5Hi7cQDMQNINQMlJCZozMb/TmDkT8z1dr9vLwjZnFO3Gua3mgL1l43bj3JaQZK90lt04t408LE1dvdWTElrivOCIzFQlJ3b+u09O9HmmXjfX6/iWmpyoqYHOD7WcGsimXjfQz3BFBiBJWjAjoOsm5bebQUnwSddNou6rm7LTUxyNc5tpy2NNGx9J+ui+86Im3kkJLc97yYOXntqr5+PNqSOH9up59K3iy8dGTbyp0w30Tz7Lsrxxsksfqq2tld/vVzAYVEZGhtvNAVzV2BzWktLt2lFTp1GZaZpdkOfpGZNQ2NKmihpV721QdnqKxuVnKrGrtZlxprE5rOPvernTE4sTfNI/7pnuibEyrT+hsKUJ969WVbBBHXXJJynHn6INt03x3Gtv51d1KvzVWtU3hZU6IEElN032zAx3RGR8KoMNHT7vtfExrT8mq28M6b6V5dq+q055WWm6Y0aAGW7AMHbzSG+cUgMgZpKTEnTNRG/UR+5KSVmlFi0vb3NzmutP0cKZARWOznWxZd2zecduWyWCNu/YrYKjs2LTqF6ILI99bF30WsNeWh6bmODTwpkBXb90S4fPW5IWzgx4MgEaeViayu+Z7nYzemVTRU3UBFVqGZ/KYIM2VdR44v1jWn9MlpqcqHsuPNHtZgCIA964owGAbiopq9TcpVva3ZxWBRs0d+kWlZRVutSy7qveG/0GuydxcN4ft3zaq+fRd0x7/5jWHwDoD0i6ARgnFLa0aHl5h0t9I48tWl6uUFfTx3HisMEDHY1zW2NzWMXro89yS1Lx+go1NnujDnR9Y0iryqs7jVlVXq36Rm/UgTaNaXvuTesPAPQHJN0AjNOd5ZeeYPezAW98hqAlpdttLZdfUro9Ju3prftWljsaB2eNy89Urj9F0Rb3+9Sy7WRcfmYsm9VjpvUHAPoDkm4AxjFt+eVX+w84Gue2HTV1jsa5bfsue+20GwdnRfbcS2qXqEa+99Kee9P6AwD9AUk3AOOYthzbtP6YVjJslM36znbj4LzC0blaPGuMcvxtl1zn+FO0eNYYTx2sKJnXHwAwnatJd1FRkcaOHav09HRlZ2frwgsv1Pvvv98mpqGhQfPmzVNWVpYGDx6sSy65RF988UWbmJ07d+q8885TWlqasrOzdeutt6q5uTmWXUE/FgpbKt22Sy9u/Uyl23Z5Zp+w0Qxbjm1af2YX5LWrB3+oBF9LnBdM/VqOo3HxZOMHu5R3+0utXxs/2OV2k3qscHSu/nDdeGWkJCrRJ2WkJOoP1433bIJaODpXz80pUNqABPkkpQ1I0HNzCjzbH6nlfIS7XnhXsx9/U3e98K7nz0Hg/gBAhKslw9auXat58+Zp7Nixam5u1h133KFp06apvLxcgwYNkiTdcssteumll/T73/9efr9f8+fP18UXX6zXX39dkhQKhXTeeecpJydHb7zxhiorK3X55ZdrwIABuu+++9zsHvoBU0pSmca05dim9ce0kmF7GpocjYsXebe/1O6xbz+xUZK0/Sfnxbo5vfa1u15WfdM/D+erbQjpzJ+uVuqABL3nwbJox/5gpRpD/0zi6prCmvTzNUpO9OmDe2e42LKemfP0W20OJFz/obRk405NDWSr+PKxLrasZ7g/AHAwV+9oSkpKdOWVV+qEE07QySefrKeeeko7d+7U5s2bJUnBYFCPP/64fvGLX2jKlCk67bTT9OSTT+qNN97Qxo0tf/hfeeUVlZeXa+nSpTrllFM0ffp03XPPPXr44YfV2NjoZvdgOJNKUpnGtNN9TeuPpE4TbjvPxxMTx6ejhLs7z8ebQxPug9U3hfW1u16OcYt659CE+2CNIUvH/mBljFvUO4cm3AdbVV6tOU+/FeMW9Q73BwAOFVfTCMFgUJKUmdly4ubmzZvV1NSkc845pzXm+OOP18iRI1VaWipJKi0t1YknnqjDDz+8Nebcc89VbW2t/v73v8ew9ehPTCtJZZoj/Pb2ztqNc1tmarKjcW5bvbXK0Ti3HdhvbzuT3Ti32V1C7pWl5lV7GqIm3BH1TWFV7fHGwYqf1dRHTbgjGkOWPqupj1GLese0knvcHwDoSNwk3eFwWDfffLPOPPNMjR49WpJUVVWl5ORkDRkypE3s4YcfrqqqqtaYgxPuyPOR5zpy4MAB1dbWtvkCusO4klSG+deH1jka5zbT+nP1s5sdjXPblc/81dE4t0WWkDsV57Z//bXN94/NOLdNf3Cto3FuM63kHvcHADoSN0n3vHnzVFZWpmeffbbP/62ioiL5/f7WryOPPLLP/02YxbSSVKbZf8DejIjdOLd1MUnX7TigP6ltsLfCwG6c20y7vplWco/7AwAdiYuke/78+VqxYoXWrFmjESNGtD6ek5OjxsZG7dmzp038F198oZycnNaYQ08zj3wfiTnUggULFAwGW78++eQTB3uD/sDEPZwmGTQw0dE4tw2weaW2Gwf0Jxkp9s6MtRvnNtOub3lZ9koD2o1zG/cHADri6i2aZVmaP3++nn/+ea1evVr5+fltnj/ttNM0YMAAvfrqq62Pvf/++9q5c6cKCgokSQUFBXr33XdVXf3P/UCrVq1SRkaGAoFAh//uwIEDlZGR0eYL6I5x+ZnK9acoWtUjn1pOKR2XnxnLZjnChBInL9842dE4t5nWnye+fZqjcW77wblHORrntoX/epyjcW5bccMkR+PcZtr14I4ZHd+r9TTObSbfHwDoOVc/1p03b56eeeYZvfjii0pPT2/dg+33+5Wamiq/369rrrlG3/ve95SZmamMjAzdcMMNKigo0BlnnCFJmjZtmgKBgGbPnq2f/vSnqqqq0p133ql58+Zp4MCBbnYPBktM8GnhzIDmLt0in9qWR478oV04M6DErooRxxlTSpwckZmq5ERfp4cNJSf6dESmNw5SOyZncLvX2aF8/xfnBVNOyZFs7CSacoo36lpnD7X3wa3dOLdlDrb3vrAb57acISlKHZDQ6WFqqQMSlDPEGzOPpl3fUpMTNTWQ3elhalMD2UpN9sbMvan3BwB6x9WZ7sWLFysYDOqss85Sbm5u69dzzz3XGvPAAw/oX//1X3XJJZdo0qRJysnJ0Z/+9KfW5xMTE7VixQolJiaqoKBAs2bN0uWXX64f/ehHbnQJ/Ujh6FwtnjVGOf62N2o5/hQtnjXGU0mqZF6JkwcvPbVXz8ebxbPG9Or5ePNoF+3t6vl4YtpyUtP6I0kPfOuUXj0fbz64d4aSEztO2rxYp7v48rGaGsju8Dkv1uk27f4AQO/5LMvy3tpRh9XW1srv9ysYDLLUHN0WClvaVFGj6r0Nyk5vWTLmtU+wQ2FLE+5fHfXEVZ9abhY23DbFE32jP/HN1P5UBRs6XI1Af9xl2uvtYJ/V1Gv6g2u1/0BIgwYm6uUbJ3tmhrsj9Y0h3beyXNt31SkvK013zAh4Zoa7IybcHwDonN080hunhgBxLDHBp4Kjs9xuRq90p8SJF/pKf+Kbaf2JLCe9fumWDp+35K3lpKYtjzXt9XawIzJT9c7dhW43wzGpyYm658IT3W6GY0y4PwDgDM66BWBciRP6E99M64+JTFoey+sNAOA2ZroRcyy3ij+m7eHMTE12NM5t6QMHOBrntsMG2Tvk0m6c20JhS4uWl0d93idp0fJyTQ3keOpaVzg6V1MDOZ6/Xpt2fQMAeA9JN2LKlNOxTRMpcdLVHk6vlDj5xxd7bcdNPG5YH7em9555c7vtuClf6/gworhiN2fzSG5n8vJlE5bHmnZ9AwB4D8vLETOmnY5tksgeTql9nuPFPZyf7K5zNM5tn+yxt+zVbpzbvtp3wNE4t7F8Ob6Zdn0DAHgPSTdiIrL8sqNZhshji5aXKxTu94fpu8akPZyjMtMcjXPbyKH2TiO2G+c205aXHzbYZn9sxsF5Jl3fAADeQ9KNmOjO8ku4p3B0rl66YaKOzR6kIakDdGz2IL10w0TP3ZDOLshTV5NWCb6WOC944Fv2aorbjXOdYcvLO/w0sTdxcWTnV3UK3PWy8m9/SYG7XtbOr7yxOqQjplzfAADew55uxATLL71h8s9Wa8eu+tbv99Q3acyPV2lUVqrW3jrFxZZ1T3JSguZMzNdj6yqixsyZmK/kJG987jg4JUknjcjQO5/WRo05aUSGBqd445Ju2vLyr/bb7I/NuHhxzB0vqTn8z+/rmsKa9PM1SkqQPrrvPPca1kOmXN8AAN7jjTtOeB6nx8a/Q29ID7ZjV70m/2x1jFvUO7/b9Emvno8327uYYezq+Xhi2vXAtP5I7RPugzWHW573EtOubwAAbyHpRkxETo+NtlrUp5ZTzDk91h3BuqaoN6QRO3bVK1jXFKMW9c6XtQdU29DcaUxtQ7O+rPXGzKNp/Tlm2GBH49w2cqi9swHsxrlt51d1URPuiOawPLPU3LTr28FCYUul23bpxa2fqXTbLs5FAYA4RdKNmOD02Ph29VObHI1z20WPbHA0zm2m9eey35Y6Gue2Cx5e72ic2wp/tdbROLeZdn2LKCmr1IT7V+vS4o266dmturR4oybcv5pKIAAQh0i6ETOcHhu/Pu/kkLuexLmtZr+9GSu7cW4zrT/VexsdjXNbV6sQuhvntvqmLqa5uxnnNtOubxIlOAHAa7xx6g6MUTg6V1MDOdpUUaPqvQ3KTm9ZUs4Mt7uG+1M6PV3+4DgvyBw0QHV7QrbivGCozf4M9Uh/hqUna0991x8QDEtPjkFrei8jJUlf2fjAI8MjB92lDkhQnY2EOnWANz63N+361lUJTp9aSnBODeTwtxUA4oQ3/mLCKIkJPhUcnaULTjlCBUdncVMQB564cpyjcW57/rsTHI1z2w8Lv+ZonNv+85zjHI1z24obJjka57aSmyY7Guc2065vlOAEAO8h6QYgf9oAjcpK7TRmVFaq/GkemUnNGNjlrGJGSpKGZQyMUYt6p8FmgWe7cW6rC9tblmw3zm05Q1K6nPVNHZCgnCHemEkdeViauqqml5TQEucFpl3fKMEJAN5D0g1AkrT21ilRb0y9WMf20nFH9ur5eGJaSSrT+iNJ790zPWrinTogQe/dMz3GLeqdj+47L2ri7cU63Qumd74KpKvn44mJ7x8AMB1JN4BWa2+dorfuOEcjhqQobUCiRgxJ0Vt3nOO5hLuxOazi9RWdxhSvr1BjV3WR4oRpJfdM60/Ee/dM18bbv6HDBg1QcqJPhw0aoI23f8NzCXfER/edpzXfO0sDE1tGamCiT2u+d5bnEu7IHuhoInugvVJuy9T3DwCYjKQbQKuileU6vegv+nRPg+qaQvp0T4NOL/qLilZGv2GNR0tKt6ur++ew1RLnBZGSe9G6ZMlbJfdM68/Bcoak6K93TdMH987QX++a5pkl5R0pKavUdx7fqAOhlpE6ELL0ncc3eu5kbNP2QFOCEwC8h6QbgKSWhPuxdRXtktWwJT22rsJTifeOmjpH4+LB2zt39+p5oDtMKkll4h5oSnACgLd4o34JgD5ldzn296cdr+SuTliKA0cM6fzQpO7Guc208bG73JeSR+4wrSTVYYPtHZhoNy5eUIITALwj/u/OAPQ505Zj+2xuzbQb5zbTxse05b6mMW587L7PPXI9OBglOAHAG0i6ARi3HPvTYL2jcW4zbXxMXO5rEtPG56v9BxyNAwCgu0i6AWhUpr16u3bj3EZ/4hslj+KbaeNjWn8AAN5D0g30UihsqXTbLr249TOVbtvlmbIzB5tdkKeuViUm+FrivID+xLfTRg211Z/TRg2NTYMctPnj3cq7/aXWr80fe++AO9PGx+QSW/WNId31wrua/fibuuuFd1XfGHK7Sb1iwt/Tg5k2PgB6joPUgF4oKavUouXlbfY/5vpTtHBmwFOnxyYnJWjOxHw9ti76YV1zJuZ74pAuif7Eu807dtvao755x24VHJ0Vm0Y5IO/2l9o9dslv3pAkbf+Jd2pbmzY+kRJb1y/d0uHzXi1RN+fpt7SqvLr1+/UfSks27tTUQLaKLx/rYst6xpS/pxGmjQ+A3vHGHRoQh0wqqSNJv9v0Sa+ejzedJah2no83j2/ovL1dPR9PTNszLHWccHfn+Xhi4vjc9UJZr56PN4cmdAdbVV6tOU+/FeMW9Y5pf09NGx8AvUfSDfRAVyV1pJaSOl5ZGvdl7QHVNjR3GlPb0Kwva71x0NDW7XscjXPbzq/q1BzuPKY53BLnBdW1+x2Nc5vdJeReWWoesrkE1m6c22r2NerLfY2dxny5r1E1XcTEi/rGUNSELmJVebVnljKb9vfUtPEB4AySbqAHTCupc9EjGxyNc9uFj77uaJzbCn+11tE4t9278kNH49wWWULuVJzbbnvhXUfj3PZtm793u3Fuu29l9Br3PYlzm2l/T00bHwDOIOkGesC05Zc1+5scjYOz6pu6mObuZhzQGbsvI6+83Kr32pvBthvntu277K1osRvnNtP+npo2PgCcQdIN9IBpJWiGptk7U9FuHJyVOsDepdpuHNAZuy8jr7zcstOTHY1zW16WvdKAduPcZtrfU9PGB4AzPPInE4gvppWgueu8ExyNc9sL15/paJzbSm6a7Gic22aNtXcSsd04t/3x2vGOxrnt5RvtvY7sxrntWZu/d7txbrtjRsDROLeZ9vfUtPEB4AySbqAHIiVoJLW7UYh876USNAfC9taJ2o1z2yl5QxyNc9vIw9LUVTWwpISWOC9ISLI3o2g3zm2nHWWvXrXdOLcdkzM4agIU4fu/OC/IHJysYYM7fy0NG5yszC5i4kVqcqKmBrI7jZkayFZqcmKMWtQ7pv09NW18ADiDpBvoocLRuVo8a4xy/G2XvOX4U7R41hhP1RU1bXmf1HVdZC/VTZakaybk9+r5eDIq096HA3bj4sF1kzr//Xf1fLxZPGtMr56PN2/dOTVq4j1scLLeunNqjFvUO8WXj42a2HmxDrRJf08l88YHQO/5LMvyRg2GPlRbWyu/369gMKiMjAy3mwOPCYUtbaqoUfXeBmWntyyB88on8hGhsKUJ969WVbChw7ItPrXc/Gy4bYrn+rZ1+542p5S/cP2ZnpnhjmhsDuv4u15WZxVzEnzSP+6ZruSupsTjQM2+Ro358aou47bcOdUTs4+mjU/kehDtRGkvXw++rD2gix7ZoJr9TcocNEDPf3eChmUMdLtZPVbfGNJ9K8u1fVed8rLSdMeMgKdnUE34e3ow08YHQHt280iSbpF0A5JUUlap65duifr8ox6cbTDF4+s/1j0vvddl3F3nfU3XTDwqBi3qnTn//ZZWvdd5HVtJmvq1bBVfEf8zQqaNT+m2Xbq0eGOXcb+bc4YKjs6KQYucUVJWqUXLy9t8mJDrT9HCmQGubQCAHrGbR8b/R+4A0M/tqLFXWsZunNt27q53NM5tpo2PaSWcpJaEe+7SLe1m76uCDZq7dItKyipdahkAoD8g6QagUNjSouXlUZ/3SVq0vFyhztbPos8cOTTV0Ti3jbTZTrtxbjNtj7ppZzxErm8dXb0ij3F9AwD0JZJuANpUURN1/6bUcmNaGWzQpoqa2DXKIaGwpdJtu/Ti1s9Uum2XJ2+sj8+xt+3FbpzbHvjWqY7GuW12QZ662naa4GuJ8wLTSjiZfH0DAHhDktsNAOA+E5eTSubs4aypa3Q0zm2DU5J00ogMvfNpbdSYk0ZkaHCKN/5EJSclaM7EfD22riJqzJyJ+Z44RE36ZwmnuUu3yCe1mSH2YgknU69vAADv8MYdAIA+ZdpyUsmsPZwmjs+y+RN10oiOZ+ZPGpGhZfMnxrhFvXPqyM5rcHf1fLwxqYSTie8fAIC3eGMaAYhjJpQ4OW3UUCX41GXJo9NGeSNx6GoPZ2SP+tRAjifGyrTxiVg2f6Le/3yvZvx6nUKWlOiTVt4wSccNT3e7ad1i90wEr7zeIgpH52pcXpa+/Zs3VL23UdnpyXr22vGeKON2sMhy+a5KInplufzBTPj7AwD9AUk30AumLF/evGN3pwmd1JLwbd6x2xMlgrqzh9ML/TFtfCKO/cFKNYb+2bGQJZ374DolJ/r0wb0zXGxZ95j2eouY/LPV2rHrnyfI76lv0pgfr9KorFStvXWKiy3rnshy+WglES15a7l8hCl/fwCgP2B5OdBDJi1fNm3PI/2Jf4cm3AdrDFk69gcrY9yinjNxfA5NuA+2Y1e9Jv9sdYxbhIOZ9PcHAPoDkm6gB0wrQTM4KdHROLel2myn3Ti37Wuwd0Ca3Ti3fVZTHzXhjmgMWfqsxht1ugf47M2Q2o1zW7CuKWrCHbFjV72CdU0xalHvmFYS0bS/PwDQH5B0Az1gWgmah9dtczTObT/783uOxrntBy9ETxh6Eue26Q+udTTObT9cVuZonNuufmqTo3FuM+16bVp/AKA/IOkGesC05aSd3cD1JM5tX+6zNwNnNw7O2n8g5Gic22obmh2Nc9vnNt/nduPcZtr12rT+AEB/QNIN9IBpJWiG++21026c24al2ztd2W4cnDVooL1l/Xbj3JZhs5643Ti3mXY9MO16bVp/AKA/IOkGeiBSgibaDk2fWk6R9UoJmieuHOdonNv+85zjHI1z29Xjj3Q0zm33zTzR0Ti3rbhhkqNxbjPtemDa9dq0/gBAf0DSDfRApASNpHY3PpHvvVSCxp82QKOyUjuNGZWVKn/agBi1qHfqwmFH49zWbNm7VNuNc1vI5gS23Ti35QxJUeqAzn/3qQMSlDPEGzOPpl0PTLtem9YfAOgPvHGHBsShwtG5WjxrjHIOWWKZ40/R4lljPFcnde2tU6LeaHutLq9pyy9HZaY5Guc208ZHkt67Z3rUxDt1QILeu2d6jFvUOyZdDyTzrtem9QcATOezLKvf15Sora2V3+9XMBhURkaG282Bx4TCljZV1Kh6b4Oy01uW9Hl5hiFY16Srn9qkz4MNGu5P0RNXjvPMjFZEKGxpwv2rVRVs6LCsjk8tN6cbbpviibFqbA7r+LteVmcVgBJ80j/uma7kpPj/LNW0/hysak+D/vXX61Tb0KyMlCStuGGSZ2a4O2LC9eBgpl2vTesPAHiN3TySpFsk3YCJSsoqNXfpFklqk3hHbke9NhtUtLJcj62riPr8dZPytWBGIIYt6rnSbbt0afHGLuN+N+cMFRydFYMWAQAAdJ/dPNJbUwgAYJNpyy8XzAjoukn5OnQSK8HnrYRbouQRAADoX7xRvwQAeqBwdK6mBnKMWX65YEZA3592vJaUbteOmjqNykzT7II8zy3BPmzwQEfjAAAA4hlJNwCjJSb4jFqinJyUoGsmHuV2M3rH7qamfr/5CQAAmMBb0yMAAM/7av8BR+MAAADiGUk3ACCmTCwZBgAAEA3Lyz3AtJIgpvVnX0Ozbnnube3cXa+RQ1P1wLdO1eAU7761Pqup1/QH12r/gZAGDUzUyzdO1hGZHdfr9YKdX9Wp8FdrVd8UVuqABJXcNFkjD/NGPeuOvPGPr/Sdp95s/f6ZK0/X+OMPc7FF3XfaqKFK8KnLkmGnjRoau0Y55N2dQZ3/yAZZajkpf9l3J+jEkX63m9Vjpl3fAABwg6slw9atW6ef/exn2rx5syorK/X888/rwgsvbH1+3759uv322/XCCy9o165dys/P14033qjrr7++NaahoUHf//739eyzz+rAgQM699xz9cgjj+jwww+33Y54LhlWUlapRcvLVRn85ym+uf4ULZwZ8Nzpy5J5/Tn/ofV659Pado+fNCJDy+ZPdKFFvXPsD1aqMdT+kpCc6NMH985woUW9c8wdL6k53P7xpATpo/vOi32Deinv9peiPrf9J97pj6klw0wZnwjTrm8AADjNEyXD9u/fr5NPPlkPP/xwh89/73vfU0lJiZYuXar33ntPN998s+bPn69ly5a1xtxyyy1avny5fv/732vt2rX6/PPPdfHFF8eqC30qUmf44ARVkqqCDZq7dItKyipdalnPmNafaDekkvTOp7U6/6H1MW5R70RLuCWpMWTp2B+sjHGLeidawi1JzeGW572ks4TOzvPxxMSSYSaNj2Te9Q0AADe5mnRPnz5dP/7xj3XRRRd1+Pwbb7yhK664QmeddZby8vJ07bXX6uSTT9amTZskScFgUI8//rh+8YtfaMqUKTrttNP05JNP6o033tDGjV3PosSzUNjSouXlHR7eG3ls0fJyhTpbnxlHTOvPvobmqDekEe98Wqt9Dc0xalHvfFZTHzXhjmgMWfqspj5GLeqdnV/VRU24I5rDLXFe8MY/vnI0zm176+wl03bj3PbuzqCjcW4z7foGAIDb4vogtfHjx2vZsmX67LPPZFmW1qxZow8++EDTpk2TJG3evFlNTU0655xzWv+b448/XiNHjlRpaWnUn3vgwAHV1ta2+Yo3mypq2s0IH8ySVBls0KaKmtg1qhdM688tz73taJzbpj+41tE4txX+yl477ca57eA93E7Eue3OZf9wNM5t5z+ywdE4t5l2fQMAwG1xnXT/+te/ViAQ0IgRI5ScnKzCwkI9/PDDmjRpkiSpqqpKycnJGjJkSJv/7vDDD1dVVVXUn1tUVCS/39/6deSRR/ZlN3rEtOWXpvVn5257M75249y2/0DI0Ti31Td1Mc3dzTigM6aVHTft+gYAgNviPuneuHGjli1bps2bN+u//uu/NG/ePP3lL3/p1c9dsGCBgsFg69cnn3ziUIudY1pJHdP6M3KovdO87ca5bdDAREfj3JY6wN6lzW4c0Bm7tRe8UqPBtOsbAABui9s7zvr6et1xxx36xS9+oZkzZ+qkk07S/Pnz9a1vfUs///nPJUk5OTlqbGzUnj172vy3X3zxhXJycqL+7IEDByojI6PNV7wZl5+pXH9K1Js0n1pO/R6XnxnLZvWYaf154FunOhrntpdvnOxonNtKbrLXTrtxbnvmytMdjXPbH68d72ic25Z9d4KjcW4z7foGAIDb4jbpbmpqUlNTkxIS2jYxMTFR4XDLktDTTjtNAwYM0Kuvvtr6/Pvvv6+dO3eqoKAgpu11WmKCTwtnBiS1nx2JfL9wZsAz9a1N68/glCSdNKLzD2tOGpHhmXq2R2SmKjmx8999cqLPM/W6Rx6WpqQurm5JCfJMvW67dbi9Uq/7tKPs1d+2G+c2u3W4vVKv27TrGwAAbnM16d63b5+2bt2qrVu3SpIqKiq0detW7dy5UxkZGZo8ebJuvfVWvfbaa6qoqNBTTz2lp59+uvW0c7/fr2uuuUbf+973tGbNGm3evFlXXXWVCgoKdMYZZ7jYM2cUjs7V4lljlONvu+Q6x5+ixbPGeK6utWn9WTZ/YtQbUy/Wsf3g3hlRE28v1ul+6DtjevV8vOmqzrPX6kA/Oqvz339Xz8cb08bHtOsbAABu8lmW5drZLq+99prOPvvsdo9fccUVeuqpp1RVVaUFCxbolVdeUU1NjUaNGqVrr71Wt9xyi3y+luSgoaFB3//+9/W73/1OBw4c0LnnnqtHHnmk0+Xlh7Jb1NwtobClTRU1qt7boOz0liXYXpkR7ohp/dnX0KxbnntbO3fXa+TQVD3wrVM9PQP0WU29pj+4VvsPhDRoYKJevnGyZ2a4I0JhSxPuXx31xHyfWj7s2XDbFM+99jaUf6lZT29q/X7p5eM0ITDMxRZ1n8njs3X7Hl346Out379w/Zk6JW+Iew3qJdOubwAAOMluHulq0h0v4j3pBtA9pdt26dLijV3G/W7OGSo4OisGLXJGSVmlFi0vb5Os5vpTtHBmwFMrRRgfAABgArt5ZNzu6QaAnjKtRJ3UktDNXbql3exwVbBBc5duUUlZpUst6z7GBwAA9Cck3QCMY1qJulDY0qLl5R3WeY48tmh5uUJhbyxcOmzwQEfj3Gba+AAAAGeRdAO91Ngc1uPrP9YPXyzT4+s/VmNz2O0m9UoobKl02y69uPUzlW7b5clEwbQSdZsqaqLuf5ZaErvKYIM2VdTErlG9Yfcl5ZGXnnHjAwAAHMVpKEAvFK0sV/H6Ch2cl9678j3NmZivBTMC7jWsh0zZkxopUTd36Rb51DZ382KJOtOWY3+1/4CjcW4zbXwAAICzmOkGeqhoZbkeW9c24ZaksCU9tq5CRSvL3WlYD5m2J9WkEnWmLZenPwAAoD8h6QZ6oLE5rOL1FZ3GFK+v8MxSc1P3pBaOzlXJTZN02sghyvWn6LSRQ1Ry0yRPJdySdNqooepqUj7B1xLnBaYt/zetPwerbwzprhfe1ezH39RdL7yr+saQ203qFRO2zwAAvIfl5UAPLCnd3m6G+1BhqyXumolHxaZRvdCdPaleKuF0/kPr9c6nta3fVwYbdPKPXtFJIzK0bP5EF1vWPZt37Lb1etu8Y7cnxiey/P/6pVs6fN6St5b/m9afiDlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM6ZsnwEAeA8z3UAP7KipczTObSbuST004T7YO5/W6vyH1se4RT1n4vj8ccunvXoefevQhPtgq8qrNefpt2Lcot4xbfsMAMBbSLqBHjhiiL29mXbj3JaZluxonNv2NTRHTbgj3vm0VvsammPUot5Jara3BNZunNvqG0NRE7qIVeXVnlnKHNmeEY1P3tqeYer4mLZ9BgDgHSTdQA/4ou7e7Fmc2/5RtdfROLfd8tzbjsa5bcGKMkfj3HafzUMG7ca5zbSSYYwPAADOIukGeuDTPfWOxrntk932lsHbjXPbzt32fu9249y2/4C9GUW7cW7bvsve68hunNtMW/7P+AAA4CySbqAHRmWmORrnNtP6M3JoqqNxbhs0MNHROLflZdl7HdmNc5tpJcMYHwAAnEXSDfTA7II8WyWcZhfkxaQ9vWVafx741qmOxrnt5RsnOxrntjtmBByNc5tpJcMYHwAAnEXSDfRAclKC5kzM7zRmzsR8JSd54y1mWn8GpyTppBEZncacNCJDg1O8UTXxiMxUJSd2/qlIcqJPR2R6Y+Y+NTlRUwPZncZMDWQrNdkbM/eRkmGS2iV2ke+9VDKM8QEAwFneuIMG4tCCGQFdNym/3Qxxgk+6blK+FnhkFijCtP4smz8xauLttTrdkvTBvTOiJt7JiT59cO+MGLeod4ovHxs1sfNiHejC0blaPGuMcvxtlyjn+FO0eNYYz9WBZnwAAHCOz7Ksfl8jo7a2Vn6/X8FgUBkZnc+OAYdqbA5rSel27aip06jMNM0uyPPMjHBHTOvPvoZm3fLc29q5u14jh6bqgW+d6pkZ7o58VlOv6Q+u1f4DIQ0amKiXb5zsmRnujtQ3hnTfynJt31WnvKw03TEj4JkZ1I6EwpY2VdSoem+DstNblix7eQaV8QEAIDq7eSRJt0i60TvcxAEAAAD9j9080rvTPUAcKCmr1KLl5W1qwOb6U7RwZoDligAAAADY0w30VElZpeYu3dIm4ZakqmCD5i7dopKySpdaBgAAACBekHQDPRAKW1q0vFwd7c2IPLZoeblC4X6/ewMAAADo10i6gR7YVFHTbob7YJakymCDNlXUxK5RAAAAAOIOSTfQA9V7oyfcPYkDAAAAYCYOUgN6IDs9peugbsTFk2Bdk65+apM+DzZouD9FT1w5Tv60AW43q8dM68/Or+pU+Ku1qm8KK3VAgkpumqyRh6W53aweq9nXqG//5g1V721Udnqynr12vDIHJ7vdrB4zrT+UEIxvVM8AAG+gZJgoGYbuC4UtTbh/dadLzHP9Kdpw2xRP3QBN/tlq7dhV3+7xUVmpWnvrFBda1Dum9eeYO15Sc7j940kJ0kf3nRf7BvXS2B+v0pf7Gts9Pmxwst66c6oLLeod0/pTtLJcxesrdPDRFAk+ac7EfC2YEXCvYT10/kPr9c6nte0eP2lEhpbNn+hCi3qH6hkA4D67eaR3P64GXJSY4NP5J3d+U3P+yblGJNyStGNXvSb/bHWMW9Q7pvUnWsItSc3hlue9JFqCKklf7mvU2B+vinGLese0/hStLNdj69om3JIUtqTH1lWoaGW5Ow3roWgJtyS982mtzn9ofYxb1DtUzwAAbyHpBnogFLa07G+d39Qs+1ulZ04vD9Y1RU1QI3bsqlewrilGLeod0/qz86u6qAl3RHO4Jc4LavY1Rk1QI77c16iaLmLihWn9aWwOq3h9Racxxesr1NjVizJO7GtojppwR7zzaa32NTTHqEW9Q/UMAPAekm6gB7o6vVzy1unlVz+1ydE4t5nWn8JfrXU0zm3f/s0bjsa5zbT+LCnd3m6G+1BhqyXOC2557m1H49xG9QwA8B6SbqAHTDu9/PMuPkDobpzbTOtPfZO9GUW7cW6r3mtvxtdunNtM68+OGnsrJuzGuW3n7s5XvXQ3zm2m/f0BgP6ApBvoAdNOLx/ut9dOu3FuM60/qQPsXartxrktO93ead5249xmWn9GZdo7Dd9unNtGDk11NM5tpv39AYD+wBt3aECcGZefqVx/iqIdk+ZTyymy4/IzY9msHnviynGOxrnNtP6U3DTZ0Ti3PXvteEfj3GZaf2YX5KmrMyATfC1xXvDAt051NM5tpv39AYD+gKQb6IHEBJ8Wzgx0eJCN1LKnbuHMgGdOL/enDdCorM5neUZlpXqmvrVp/Rl5WJq6Ko2clCDP1OvOHJysYV3Urh42ONkz9a1N609yUoLmTMzvNGbOxHzP1OsenJKkk0Z0Xg70pBEZnqnXHfn7I6ld4h353kt/fwCgP/DGX0wAfW7trVOiJqperGttWn8+uu+8qIm3F+t0v3Xn1KiJqhfrWpvWnwUzArpuUn67Ge8En3TdJO/V6V42f2LUxNuLdboLR+dq8awxyjlki0yOP0WLZ42hTjcAxBmfZVn9vqaE3aLmQEQobGnC/aujniDrU8vNz4bbpnhutiFY16Srn9qkz4MNGu5P0RNXjvPMjHBHTOvPzq/qVPirtapvCit1QIJKbprsmRnujtTsa9S3f/OGqvc2Kjs9Wc9eO94zM8IdMa0/jc1hLSndrh01dRqVmabZBXmemeHuyL6GZt3y3NvaubteI4em6oFvneqZGe6OhMKWNlXUqHpvg7LTW5aUe+1vDgB4md08kqRbJN2xZsJNQum2Xbq0eGOXcb+bc4YKjs6KQYsQjQmvt4OZ1h8AAACvsptHevfjXXhSSVmlFi0vbzNDnOtP0cKZAU8th6NkizeY8nqLMK0/AAAA/YF314jBc0rKKjV36ZZ2S7Krgg2au3SLSsoqXWpZ91GyJf6Z9HqTzOsPAABAf0HSjZgIhS0tWl7e4WnfkccWLS9XKOyN3Q6UbIlvpr3eTOsPAABAf0LSjZjYVFET9dAxqSVxqAw2aFNFTewa1QuUbIlvpr3eTOsPAABAf0LSjZgwcQ80JVvil2mvN9P6AwAA0J9wkJoHmHBasal7oAtH52rK8YcbVVKnvjGk+1aWa/uuOuVlpemOGQGlJie63axuMe31Zlp/TGZaiS0AANB7JN1xzpTTiiN7oKuCDR3uS43UtfbaHuiOxue3Gyo8Nz4Rc55+S6vKq1u/X/+htGTjTk0NZKv48rEutqx7Iq+3zpZke2nPvanvH9MUrSxX8foKHby1/t6V72nOxHwtmBFwr2EAAMBVfPwex0w6rdjEPdAmjY/UPuE+2Kryas15+q0Yt6jnEhN8Gn1E9FqJkjT6iAzPvN5MfP+YpmhluR5b1zbhlqSwJT22rkJFK8vdaRgAAHAdSXecMvG0YpP2QJs2PvWNoagJd8Sq8mrVN4Zi1KLeaWwO69X3Ou/Pq+9Vq7E5HKMW9Z5J7x/TNDaHVby+otOY4vUVnnq9AQAA57C8PE5157TigqOzYtewXiocnaupgRzP71E3bXzuszkLd9/Kct1z4Yl93JreW1K6vd2M46HCVkvcNROPik2jHGDK++dgJpxZYerrDQAAOIOkO06ZfFpxYoLPE4loZ0wbn+276hyNc9uOGnvttBsXT0x4/0SYcmaFya83AADQeywvj1OcVhzfTBufvKw0R+PcNirTXjvtxsF5Jp2JwOsNAAB0hqQ7TkVOK462yNInb52+bBrTxucOmycr241z2+yCPHW1QjnB1xKH2DPtTARebwAAoDMk3XGK04rjm2njk5qcqKmB7E5jpgayPVOvOzkpQXMm5ncaM2diPvWTXdKdMxG8gNcbAADoDHcAcYzTiuObaeNzyZgRvXo+3iyYEdB1k/LbzUAm+KTrJlE32U2mnYkg8XoDAADR+SzL8sb6vT5UW1srv9+vYDCojIzOa/u6wYTTfU1mwviEwpYm3L866uyjTy0fJmy4bYrn+tbYHNaS0u3aUVOnUZlpml2Qx4yjy0q37dKlxRu7jPvdnDM8d2gcrzcAAPoPu3kkp5d7gEmnFZvIhPExrQTawZKTEijTFGciZyJUBRs63Ncd+ZDHK2ciHIzXGwAAOBQfvwMwcrkv4pdpZyIAAAB0hqQbgHEl0BD/TDsTAQAAIBqWlwMwerkv4lfh6FxNDeR4/kwEAACAzpB0A2hd7jt36Rb5pDaJN8t90ZdMOBMBAACgMywvByCJ5b4AAABAX2CmG0ArlvsCAAAAznJ1pnvdunWaOXOmhg8fLp/PpxdeeKFdzHvvvafzzz9ffr9fgwYN0tixY7Vz587W5xsaGjRv3jxlZWVp8ODBuuSSS/TFF1/EsBforlDYUum2XXpx62cq3bZLobC3S8U3Nof1+PqP9cMXy/T4+o/V2Bx2u0k4SH1jSHe98K5mP/6m7nrhXdU3htxuUq/Qn/jG9QAAABzKZ1mWaxnPyy+/rNdff12nnXaaLr74Yj3//PO68MILW5/ftm2bxo0bp2uuuUaXXnqpMjIy9Pe//11nnHGGsrOzJUlz587VSy+9pKeeekp+v1/z589XQkKCXn/9ddvtsFvUHL1XUlapRcvL29SEzvWnaOHMgCeXLxetLFfx+god/LlBgk+aMzFfC2YE3GtYD5k2PnOefkuryqvbPT41kK3iy8e60KLeoT/xzbTrAQAA6JzdPNLVpPtgPp+vXdL97W9/WwMGDNCSJUs6/G+CwaCGDRumZ555Rv/2b/8mSfrHP/6hr33tayotLdUZZ5xh698m6Y6NkrJKzV26pd3p2JGFy17bN1y0slyPrauI+vx1k7x1o23a+ERL6CK8ltjRn/hm2vUAAAB0zW4eGbcHqYXDYb300ks69thjde655yo7O1unn356myXomzdvVlNTk84555zWx44//niNHDlSpaWlLrQa0YTClhYtL++wHFXksUXLyz2z1LyxOazi9dFvsCWpeH2FZ5aWmjY+9Y2hThM6SVpVXu2Zpcz0J76Zdj0AAADOituku7q6Wvv27dNPfvITFRYW6pVXXtFFF12kiy++WGvXrpUkVVVVKTk5WUOGDGnz3x5++OGqqqqK+rMPHDig2traNl/oW5sqatosWT6UJaky2KBNFTWxa1QvLCndrq7yz7DVEucFpo3PfSvLHY1zG/2Jb6ZdDwAAgLPi9vTycLhlRuCCCy7QLbfcIkk65ZRT9MYbb+jRRx/V5MmTe/yzi4qKtGjRIkfaCXuq90ZP6HoS57YdNXWOxrnNtPHZvsve791unNvoT3wz7XoAAACcFbcz3YcddpiSkpIUCLTdA/e1r32t9fTynJwcNTY2as+ePW1ivvjiC+Xk5ET92QsWLFAwGGz9+uSTTxxvP9rKTk/pOqgbcW4blZnmaJzbTBufvCx7v3e7cW6jP/HNtOsBAABwVtwm3cnJyRo7dqzef//9No9/8MEHGjVqlCTptNNO04ABA/Tqq6+2Pv/+++9r586dKigoiPqzBw4cqIyMjDZf6Fvj8jOV609RtGrPPrWckj0uPzOWzeqx2QV56qp0dYKvJc4LTBufO2weWGU3zm30J76Zdj0AAADOcjXp3rdvn7Zu3aqtW7dKkioqKrR169bWmexbb71Vzz33nIqLi/XRRx/poYce0vLly/Xd735XkuT3+3XNNdfoe9/7ntasWaPNmzfrqquuUkFBge2TyxEbiQk+LZzZcgN96L1p5PuFMwNK7OrONU4kJyVozsT8TmPmTMxXclLcfq7Vhmnjk5qcqKmB7E5jpgaylZqcGKMW9Q79iW+mXQ8AAICzXC0Z9tprr+nss89u9/gVV1yhp556SpL0xBNPqKioSJ9++qmOO+44LVq0SBdccEFrbENDg77//e/rd7/7nQ4cOKBzzz1XjzzySKfLyw9FybDYMa0OtGl1eU0bH9PqQNOf+Gba9QAAAHTOc3W63UTSHVuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2e0TBuf+saQ7ltZru276pSXlaY7ZgQ8M4PaEfoT30y7HgAAgOhIuruBpBsAAAAA0B1280g+fgcAAAAAoI/EbZ1uAADgLtO2mwAA4AaSbgAA0I5pBysCAOAWlpcDAIA2SsoqNXfpljYJtyRVBRs0d+kWlZRVutQyAAC8h6QbAAC0CoUtLVpero5OWY08tmh5uULhfn8OKwAAtrC8HDHHHsH4RskjoH/bVFHTbob7YJakymCDNlXUqODorNg1DAAAjyLpRkyxRzC+Fa0sV/H6Ch08gXXvyvc0Z2K+FswIuNcwADFTvTd6wt2TOAAA+jumrxAz7BGMb0Ury/XYurYJtySFLemxdRUqWlnuTsMAxFR2eoqjcQAA9Hck3YgJ9gjGt8bmsIrXV3QaU7y+Qo3N4Ri1CIBbxuVnKtefomibfnxqWaE0Lj8zls0CAMCzSLoRE93ZI4jYW1K6vd0M96HCVkscALMlJvi0cGbLdpJDE+/I9wtnBjiLAwAAm0i6ERPsEYxvO2rqHI0D4G2Fo3O1eNYY5fjbLiHP8ado8awxnMEBAEA3cJAaYoI9gvFtVGaao3EAvK9wdK6mBnKoNgEAQC8x042YYI9gfJtdkKeu7qMTfC1xAPqPxASfCo7O0gWnHKGCo7NIuAEA6AGSbsQEewTjW3JSguZMzO80Zs7EfOp1AwAAAN3EHTRihj2C8W3BjICum5TfbsY7wSddN4k63QAAAEBP+CzL6vc1mmpra+X3+xUMBpWRkeF2c4wXClvsEYxjjc1hLSndrh01dRqVmabZBXnMcAMAAACHsJtHcpAaYi6yRxDxKTkpQddMPMrtZgAAAABGYPoKAAAAAIA+QtINAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjnF6OmKNkGGLJtNcbJd0AAAC8haQbMVVSVqlFy8tVGWxofSzXn6KFMwMqHJ3rYstgItNeb0Ury1W8vkJh65+P3bvyPc2ZmK8FMwLuNQwAAABRMT2CmCkpq9TcpVvaJECSVBVs0NylW1RSVulSy2Ai015vRSvL9di6tgm3JIUt6bF1FSpaWe5OwwAAANApkm7ERChsadHyclkdPBd5bNHycoUOzSiAHjDt9dbYHFbx+opOY4rXV6ixORyjFgEAAMAukm7ExKaKmnYzjgezJFUGG7SpoiZ2jYKxTHu9LSnd3m6G+1BhqyUOAAAA8YWkGzFRvTd6AtSTOKAzpr3edtTUORoHAACA2CHpRkxkp6c4Ggd0xrTX26jMNEfjAAAAEDsk3YiJcfmZyvWnKFqhJp9aTpUel58Zy2bBUKa93mYX5KmrKmcJvpY4AAAAxBeSbsREYoJPC2e2lDQ6NHeIfL9wZsDT9ZMRP0x7vSUnJWjOxPxOY+ZMzKdeNwAAQBziDg0xUzg6V4tnjVGOv+2S3hx/ihbPGuPJusmIX6a93hbMCOi6SfntZrwTfNJ1k6jTDQAAEK98lmV5o2ZOH6qtrZXf71cwGFRGRobbzTFeKGxpU0WNqvc2KDu9ZYmvV2Yc4T2mvd4am8NaUrpdO2rqNCozTbML8pjhBgAAcIHdPJKkWyTdAAAAAIDusZtHMj0CAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjJN0AAAAAAPQRkm4AAAAAAPoISTcAAAAAAH2EpBsAAAAAgD5C0g0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIugEAAAAA6CMk3QAAAAAA9BGSbgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPpLkdgPigWVZkqTa2lqXWwIAAAAA8IJI/hjJJ6Mh6Za0d+9eSdKRRx7pcksAAAAAAF6yd+9e+f3+qM/7rK7S8n4gHA7r888/V3p6unw+n9vN6Rdqa2t15JFH6pNPPlFGRobbzcEhGJ/4xvjEN8YnvjE+8Y3xiW+MT3xjfGLPsizt3btXw4cPV0JC9J3bzHRLSkhI0IgRI9xuRr+UkZHBRSGOMT7xjfGJb4xPfGN84hvjE98Yn/jG+MRWZzPcERykBgAAAABAHyHpBgAAAACgj5B0wxUDBw7UwoULNXDgQLebgg4wPvGN8YlvjE98Y3ziG+MT3xif+Mb4xC8OUgMAAAAAoI8w0w0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIutGnPvvsM82aNUtZWVlKTU3ViSeeqL/+9a+tz+/bt0/z58/XiBEjlJqaqkAgoEcffdTFFvcveXl58vl87b7mzZsnSWpoaNC8efOUlZWlwYMH65JLLtEXX3zhcqv7h87GpqamRjfccIOOO+44paamauTIkbrxxhsVDAbdbna/0dV7J8KyLE2fPl0+n08vvPCCO43th+yMT2lpqaZMmaJBgwYpIyNDkyZNUn19vYut7j+6Gp+qqirNnj1bOTk5GjRokMaMGaM//vGPLre6/wiFQrrrrruUn5+v1NRUHX300brnnnt08DFQlmXphz/8oXJzc5WamqpzzjlHH374oYut7j+6Gp+mpibddtttOvHEEzVo0CANHz5cl19+uT7//HOXW96/JbndAJhr9+7dOvPMM3X22Wfr5Zdf1rBhw/Thhx9q6NChrTHf+973tHr1ai1dulR5eXl65ZVX9N3vflfDhw/X+eef72Lr+4e33npLoVCo9fuysjJNnTpV//7v/y5JuuWWW/TSSy/p97//vfx+v+bPn6+LL75Yr7/+ultN7jc6G5vPP/9cn3/+uX7+858rEAhox44duv766/X555/rD3/4g4ut7j+6eu9E/PKXv5TP54t18/q9rsantLRUhYWFWrBggX79618rKSlJf/vb35SQwFxELHQ1Ppdffrn27NmjZcuW6bDDDtMzzzyjb37zm/rrX/+qU0891a1m9xv333+/Fi9erP/+7//WCSecoL/+9a+66qqr5Pf7deONN0qSfvrTn+rBBx/Uf//3fys/P1933XWXzj33XJWXlyslJcXlHpitq/Gpq6vTli1bdNddd+nkk0/W7t27ddNNN+n8889vM/GFGLOAPnLbbbdZEyZM6DTmhBNOsH70ox+1eWzMmDHWD37wg75sGqK46aabrKOPPtoKh8PWnj17rAEDBli///3vW59/7733LElWaWmpi63snw4em4787//+r5WcnGw1NTXFuGWwrI7H5+2337aOOOIIq7Ky0pJkPf/88+41sJ87dHxOP/10684773S5VYg4dHwGDRpkPf30021iMjMzreLiYjea1++cd9551tVXX93msYsvvti67LLLLMuyrHA4bOXk5Fg/+9nPWp/fs2ePNXDgQOt3v/tdTNvaH3U1Ph3ZtGmTJcnasWNHXzcPUfCRLvrMsmXL9PWvf13//u//ruzsbJ166qkqLi5uEzN+/HgtW7ZMn332mSzL0po1a/TBBx9o2rRpLrW6/2psbNTSpUt19dVXy+fzafPmzWpqatI555zTGnP88cdr5MiRKi0tdbGl/c+hY9ORYDCojIwMJSWxgCnWOhqfuro6fec739HDDz+snJwcl1vYvx06PtXV1XrzzTeVnZ2t8ePH6/DDD9fkyZO1YcMGt5vaL3X0/hk/fryee+451dTUKBwO69lnn1VDQ4POOussdxvbT4wfP16vvvqqPvjgA0nS3/72N23YsEHTp0+XJFVUVKiqqqrN/YHf79fpp5/O/UEMdDU+HQkGg/L5fBoyZEiMWolDcXeGPvPxxx9r8eLF+t73vqc77rhDb731lm688UYlJyfriiuukCT9+te/1rXXXqsRI0YoKSlJCQkJKi4u1qRJk1xuff/zwgsvaM+ePbryyislteypS05ObneBPvzww1VVVRX7BvZjh47Nob766ivdc889uvbaa2PbMEjqeHxuueUWjR8/XhdccIF7DYOk9uPz8ccfS5Luvvtu/fznP9cpp5yip59+Wt/4xjdUVlamf/mXf3Gxtf1PR++f//3f/9W3vvUtZWVlKSkpSWlpaXr++ed1zDHHuNfQfuT2229XbW2tjj/+eCUmJioUCunee+/VZZddJkmt9wCHH354m/+O+4PY6Gp8DtXQ0KDbbrtNl156qTIyMmLcWkSQdKPPhMNhff3rX9d9990nSTr11FNVVlamRx99tE3SvXHjRi1btkyjRo3SunXrNG/ePA0fPrzNJ6joe48//rimT5+u4cOHu90UHKKzsamtrdV5552nQCCgu+++O/aNQ7vxWbZsmVavXq23337b5ZZBaj8+4XBYknTdddfpqquuktTy9+nVV1/VE088oaKiItfa2h91dH276667tGfPHv3lL3/RYYcdphdeeEHf/OY3tX79ep144okutrZ/+N///V/9z//8j5555hmdcMIJ2rp1q26++WYNHz689f4N7unO+DQ1Nemb3/ymLMvS4sWLXWoxJLGnG31n5MiR1jXXXNPmsUceecQaPny4ZVmWVVdXZw0YMMBasWJFm5hrrrnGOvfcc2PWTljW9u3brYSEBOuFF15ofezVV1+1JFm7d+9uEzty5EjrF7/4RYxb2H91NDYRtbW1VkFBgfWNb3zDqq+vd6F16Gh8brrpJsvn81mJiYmtX5KshIQEa/Lkye41th/qaHw+/vhjS5K1ZMmSNrHf/OY3re985zuxbmK/1tH4fPTRR5Ykq6ysrE3sN77xDeu6666LdRP7pREjRlgPPfRQm8fuuece67jjjrMsy7K2bdtmSbLefvvtNjGTJk2ybrzxxlg1s9/qanwiGhsbrQsvvNA66aSTrK+++iqWTUQH2NONPnPmmWfq/fffb/PYBx98oFGjRklq+fStqamp3WmxiYmJrTMRiI0nn3xS2dnZOu+881ofO+200zRgwAC9+uqrrY+9//772rlzpwoKCtxoZr/U0dhILTPc06ZNU3JyspYtW8ZpsS7paHxuv/12vfPOO9q6dWvrlyQ98MADevLJJ11qaf/U0fjk5eVp+PDhnf59Qmx0ND51dXWSxL2Bi+rq6jr9/efn5ysnJ6fN/UFtba3efPNN7g9ioKvxkf45w/3hhx/qL3/5i7KysmLdTBzK7awf5tq0aZOVlJRk3XvvvdaHH35o/c///I+VlpZmLV26tDVm8uTJ1gknnGCtWbPG+vjjj60nn3zSSklJsR555BEXW96/hEIha+TIkdZtt93W7rnrr7/eGjlypLV69Wrrr3/9q1VQUGAVFBS40Mr+KdrYBINB6/TTT7dOPPFE66OPPrIqKytbv5qbm11qbf/T2XvnUOL08pjrbHweeOABKyMjw/r9739vffjhh9add95ppaSkWB999JELLe2foo1PY2Ojdcwxx1gTJ0603nzzTeujjz6yfv7zn1s+n8966aWXXGpt/3LFFVdYRxxxhLVixQqroqLC+tOf/mQddthh1n/+53+2xvzkJz+xhgwZYr344ovWO++8Y11wwQVWfn4+q65ioKvxaWxstM4//3xrxIgR1tatW9vcIxw4cMDl1vdfJN3oU8uXL7dGjx5tDRw40Dr++OOt3/zmN22er6ystK688kpr+PDhVkpKinXcccdZ//Vf/xW1LBKc9+c//9mSZL3//vvtnquvr7e++93vWkOHDrXS0tKsiy66yKqsrHShlf1TtLFZs2aNJanDr4qKCnca2w919t45FEl37HU1PkVFRdaIESOstLQ0q6CgwFq/fn2MW9i/dTY+H3zwgXXxxRdb2dnZVlpamnXSSSe1KyGGvlNbW2vddNNN1siRI62UlBTrqKOOsn7wgx+0SdjC4bB11113WYcffrg1cOBA6xvf+IatayF6r6vxqaioiHqPsGbNGncb34/5LMuyYjy5DgAAAABAv8CebgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPkLSDQAAAABAHyHpBgAAAACgj5B0AwAAAADQR0i6AQAAAADoIyTdAAAAAAD0EZJuAADQzmuvvSafz6c9e/bY/m/uvvtunXLKKX3WJgAAvIikGwAAj3v00UeVnp6u5ubm1sf27dunAQMG6KyzzmoTG0mmt23b1unPHD9+vCorK+X3+x1t61lnnaWbb77Z0Z8JAEA8I+kGAMDjzj77bO3bt09//etfWx9bv369cnJy9Oabb6qhoaH18TVr1mjkyJE6+uijO/2ZycnJysnJkc/n67N2AwDQH5B0AwDgcccdd5xyc3P12muvtT722muv6YILLlB+fr42btzY5vGzzz5b4XBYRUVFys/PV2pqqk4++WT94Q9/aBN36PLy4uJiHXnkkUpLS9NFF12kX/ziFxoyZEi79ixZskR5eXny+/369re/rb1790qSrrzySq1du1a/+tWv5PP55PP5tH37dqd/HQAAxBWSbgAADHD22WdrzZo1rd+vWbNGZ511liZPntz6eH19vd58802dffbZKioq0tNPP61HH31Uf//733XLLbdo1qxZWrt2bYc///XXX9f111+vm266SVu3btXUqVN17733tovbtm2bXnjhBa1YsUIrVqzQ2rVr9ZOf/ESS9Ktf/UoFBQWaM2eOKisrVVlZqSOPPLIPfhsAAMSPJLcbAAAAeu/ss8/WzTffrObmZtXX1+vtt9/W5MmT1dTUpEcffVSSVFpaqgMHDuiss85SIBDQX/7yFxUUFEiSjjrqKG3YsEGPPfaYJk+e3O7n//rXv9b06dP1H//xH5KkY489Vm+88YZWrFjRJi4cDuupp55Senq6JGn27Nl69dVXde+998rv9ys5OVlpaWnKycnpy18HAABxg6QbAAADnHXWWdq/f7/eeust7d69W8cee6yGDRumyZMn66qrrlJDQ4Nee+01HXXUUdq3b5/q6uo0derUNj+jsbFRp556aoc///3339dFF13U5rFx48a1S7rz8vJaE25Jys3NVXV1tUO9BADAe0i6AQAwwDHHHKMRI0ZozZo12r17d+ts9fDhw3XkkUfqjTfe0Jo1azRlyhTt27dPkvTSSy/piCOOaPNzBg4c2Kt2DBgwoM33Pp9P4XC4Vz8TAAAvI+kGAMAQZ599tl577TXt3r1bt956a+vjkyZN0ssvv6xNmzZp7ty5CgQCGjhwoHbu3NnhUvKOHHfccXrrrbfaPHbo93YkJycrFAr9//buVyWWMI7j8PdoEfwTBLGqWNbiBaw4TBAsVqtpQdANA1tNBotsXKxehKxJBDF4B0bxDmSDoLB7mnA4J+iBYXV5njgwL79Jw4cZ3vfL9wHATyW6AWBClGWZo6OjvL+//xHTRVHk+Pg4b29vKcsy8/Pz6XQ6qaoqw+EwW1tbeXl5yf39fRYWFnJwcPDX2u12O9vb2+l2u9nb28vNzU36/f6XjxRbWVnJw8NDnp6eMjc3l8XFxUxN2dcVgMnlLQcAE6Isy7y+vmZ9fT3Ly8sf14uiyGAw+DhaLElOT09zcnKSs7OzNBqN7O7u5urqKqurq/9cu9ls5uLiIt1uN5ubm7m+vk5VVZmZmfnSjJ1OJ9PT09nY2MjS0lKen5///4EB4Af4NRqNRuMeAgD4eVqtVh4fH3N3dzfuUQDg2/J7OQDwKefn59nZ2cns7Gz6/X4uLy/T6/XGPRYAfGu+dAMAn7K/v5/b29sMBoOsra2l3W7n8PBw3GMBwLcmugEAAKAmNlIDAACAmohuAAAAqInoBgAAgJqIbgAAAKiJ6AYAAICaiG4AAACoiegGAACAmohuAAAAqInoBgAAgJr8BszmUCbCjQ3yAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1090,7 +923,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對於因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。  \n"
+    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對於因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:16:44+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:11:13+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "mo"
   }
diff --git a/translations/mo/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/mo/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index a6838d5a..5a6c7831 100644
--- a/translations/mo/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/mo/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 概率與統計簡介\n",
+    "## 概率與統計學簡介\n",
     "## 作業\n",
     "\n",
-    "在這次作業中，我們將使用糖尿病患者的數據集，該數據集取自[此處](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)。\n"
+    "在這次作業中，我們將使用從[這裡](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)取得的糖尿病患者數據集。\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,8 +150,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "在此數據集中，欄位如下：\n",
-    "* 年齡和性別不需額外解釋\n",
+    "在此數據集中，列包含以下內容：\n",
+    "* 年齡和性別不需多作解釋\n",
     "* BMI 是身體質量指數\n",
     "* BP 是平均血壓\n",
     "* S1 到 S6 是不同的血液測量值\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -600,9 +600,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 任務 4：測試不同變數與疾病進展（Y）之間的相關性\n",
+    "### 任務 4：測試不同變數與疾病進展 (Y) 之間的相關性\n",
     "\n",
-    "> **提示** 相關性矩陣可以提供最有用的資訊，幫助判斷哪些數值是相互依賴的。\n"
+    "> **提示** 相關矩陣可以提供最有用的資訊，幫助判斷哪些值是相互依賴的。\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。  \n"
+    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:49:43+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:11:39+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "mo"
   }
diff --git a/translations/mr/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/mr/1-Introduction/04-stats-and-probability/assignment.ipynb
index 82ef5b87..d6bc963a 100644
--- a/translations/mr/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/mr/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -196,7 +196,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### कार्य ४: विविध चल आणि आजार प्रगती (Y) यांच्यातील परस्परसंबंध तपासा\n",
+    "### कार्य ४: विविध चल आणि रोग प्रगती (Y) यांच्यातील परस्परसंबंध तपासा\n",
     "\n",
     "> **सूचना** परस्परसंबंध मॅट्रिक्स तुम्हाला कोणते मूल्ये परस्पर अवलंबून आहेत याबद्दल सर्वात उपयुक्त माहिती देईल.\n"
    ],
@@ -221,7 +221,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nहा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) चा वापर करून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी कृपया लक्षात ठेवा की स्वयंचलित भाषांतरे त्रुटी किंवा अचूकतेच्या अभावाने युक्त असू शकतात. मूळ भाषेतील दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर करून उद्भवलेल्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\n"
+    "\n---\n\n**अस्वीकरण**:  \nहा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) चा वापर करून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात घ्या की स्वयंचलित भाषांतरांमध्ये त्रुटी किंवा अचूकतेचा अभाव असू शकतो. मूळ भाषेतील मूळ दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर केल्यामुळे उद्भवणाऱ्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\n"
    ]
   }
  ],
@@ -247,8 +247,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:43:44+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:21:12+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "mr"
   }
diff --git a/translations/mr/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/mr/1-Introduction/04-stats-and-probability/notebook.ipynb
index a20ccfff..7ea6e3e0 100644
--- a/translations/mr/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/mr/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# संभाव्यता आणि सांख्यिकीची ओळख  \n",
-    "या नोटबुकमध्ये, आपण यापूर्वी चर्चा केलेल्या काही संकल्पनांशी खेळ करू. संभाव्यता आणि सांख्यिकीमधील अनेक संकल्पना Python मधील डेटा प्रक्रिया करण्यासाठी प्रमुख लायब्ररींमध्ये चांगल्या प्रकारे सादर केल्या जातात, जसे की `numpy` आणि `pandas`.\n"
+    "या नोटबुकमध्ये, आपण यापूर्वी चर्चा केलेल्या काही संकल्पनांशी थोडेसे प्रयोग करू. संभाव्यता आणि सांख्यिकीमधील अनेक संकल्पना डेटाच्या प्रक्रियेसाठी Python मधील प्रमुख लायब्ररींमध्ये चांगल्या प्रकारे सादर केल्या जातात, जसे की `numpy` आणि `pandas`.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## रँडम व्हेरिएबल्स आणि वितरणे  \n",
-    "चला 0 ते 9 पर्यंतच्या युनिफॉर्म वितरणातून 30 मूल्यांचे नमुना काढण्यापासून सुरुवात करूया. आपण याचा सरासरी (mean) आणि विचलन (variance) देखील मोजू.  \n"
+    "चला 0 ते 9 पर्यंतच्या युनिफॉर्म वितरणातून 30 मूल्यांचे नमुने काढण्यापासून सुरुवात करूया. आपण याचा सरासरी (mean) आणि विचलन (variance) देखील मोजू.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "नमुन्यात किती वेगवेगळ्या मूल्ये आहेत याचा दृश्यमान अंदाज लावण्यासाठी, आपण **हिस्टोग्राम** प्लॉट करू शकतो:\n"
+    "नमुन्यात किती वेगवेगळ्या मूल्ये आहेत याचा दृश्यमान अंदाज घेण्यासाठी, आपण **हिस्टोग्राम** प्लॉट करू शकतो:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -84,175 +82,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## वास्तविक डेटा विश्लेषण करणे\n",
+    "## वास्तविक डेटा विश्लेषण\n",
     "\n",
-    "वास्तविक जगातील डेटा विश्लेषण करताना सरासरी आणि विचलन खूप महत्त्वाचे असते. चला बेसबॉल खेळाडूंच्या डेटाबद्दल माहिती [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) मधून लोड करूया.\n"
+    "वास्तविक जगातील डेटा विश्लेषण करताना सरासरी (mean) आणि विचलन (variance) खूप महत्त्वाचे असतात. चला बेसबॉल खेळाडूंबद्दलचा डेटा [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) येथून लोड करूया.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -261,24 +113,24 @@
    "source": [
     "आम्ही येथे डेटा विश्लेषणासाठी [**Pandas**](https://pandas.pydata.org/) नावाचे पॅकेज वापरत आहोत. या कोर्समध्ये पुढे Pandas आणि Python मध्ये डेटा हाताळण्याबद्दल अधिक चर्चा करू.\n",
     "\n",
-    "चला वय, उंची आणि वजन यांचे सरासरी मूल्ये गणना करूया:\n"
+    "चला वय, उंची आणि वजनासाठी सरासरी मूल्ये गणना करू:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "आता उंचीवर लक्ष केंद्रित करूया आणि मानक विचलन आणि विचलन गणना करूया:\n"
+    "आता उंचीवर लक्ष केंद्रित करूया आणि प्रमाणित विचलन आणि वैविध्य मोजूया:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "म्हणाच्या व्यतिरिक्त, मध्यम मूल्य आणि चतुर्थांश पाहणे योग्य ठरेल. त्यांना **बॉक्स प्लॉट** च्या मदतीने दृश्यमान केले जाऊ शकते:\n"
+    "म्हणाच्या व्यतिरिक्त, मध्यम मूल्य आणि चतुर्थांश पाहणे योग्य ठरेल. त्यांना **बॉक्स प्लॉट** च्या मदतीने दृश्यरूप दिले जाऊ शकते:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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o5qGbh24euhXDArlEHR0duUeoHZp56Oahm4duHrp56OahWzEskEt04cKF3CPUDs08dPPQzUM3D908dPPQrRgWyCVasGBB7hFqh2Yeunno5qGbh24eunnoVgwL5BItWrQo9wi1QzMP3Tx089DNQzcP3Tx0K2ZOXsUiIs5KOv6Of+L6WyLpXO4haoZmHrp56Oahm4duHrp56Nbaz6eU3jv5zjlZIKO1iNjT6qVEMDWaeejmoZuHbh66eejmoVsxPMQCAAAAaMICGQAAAGjCArlc/5R7gBqimYduHrp56Oahm4duHroVwGOQAQAAgCbsIAMAAABNWCADAAAATVggz5GIuCUinouIQxFxMCJ+NSLujYjvRsRQROyJiF/OPWeVRMSdjTZXbj+KiE9GxK0R8e8R8YPGx/bcs1bJNN2+0jj+XomIzRFxS+5Zq2Sqbk3//dMRkSJiScYxK2W6ZhHRHRGHI+L7EfF3mUetlGnOUa4JM4iITzWOqQMR0RsRi7gmzGyKblwTCuAxyHMkIp6RtCOl9LWIaJP0HknPSvpqSuk7EfG7kp5IKX0o55xVFRHXSzol6Vck/ZWk11NKX46Iz0hqTymtzzpgRU3qdqekwZTSTyLibyWJbq01d0spHY+IZZK+JmmlpDUpJV5cf5JJx9r7JPVI+r2U0mhE3J5Sei3rgBU1qdvT4powpYj4OUn/IekDKaUfR8Szkv5N0gfENWFK03T7obgmzBo7yHMgIm6S9BuSvi5JKaWxlNL/SUqSbmr8sZs1cbCitYckHU0pHZf0EUnPNO5/RtLv5xqqBq52Syk9n1L6SeP+70rqyDhX1TUfb5L0VUlPaOKcRWvNzf5C0pdTSqOSxOJ4Ws3duCbM7AZJPxURN2hio+mH4powG2/pxjWhGBbIc+N9ks5K+ueI+F5EfC0iFkv6pKSvRMQJSX8v6W8yzlh1fySpt/HPP5NSOi1JjY+3Z5uq+pq7NftTSd8peZY6udotIh6RdCql9HLekSqv+Vh7v6Rfj4hdEbE9Iu7POFfVNXf7pLgmTCmldEoTXV6VdFrScErpeXFNmNY03ZpxTZgBC+S5cYOkD0r6x5TSL0kakfQZTeyyfCqltEzSp9TYYca1Gg9JeUTSt3PPUidTdYuIHkk/kfQvOeaquuZuEfEeTTxU4HN5p6q2FsfaDZLaJT0g6a8lPRsRkWm8ymrRjWvCNBqPLf6IpF+Q9LOSFkfEn+Sdqvpm6sY1YXZYIM+Nk5JOppR2Nf79OU0smD8uqa9x37cl8YSM1n5H0r6U0pnGv5+JiKWS1PjIr29bm9xNEfFxSQ9L+uPEEw6m0tztFzVxUXk5Io5p4leQ+yLijozzVdHkY+2kpL404T8ljUviyY1vNbkb14Tp/bak/0kpnU0pXdZEqy5xTZjJVN24JhTAAnkOpJT+V9KJiLizcddDkv5LE4+d+s3Gfb8l6QcZxquDR3XtwwS2auJCosbHfy19onq4pltEfFjSekmPpJTeyDZV9V3tllLan1K6PaXUmVLq1MTC74ONcxpvmnyObtHE9zRFxPsltUniiY1vNbkb14TpvSrpgYh4T+M3Eg9JOiiuCTNp2Y1rQjG8isUciYh7NfEs+DZJ/y3pMUmrJP2DJn4deUnSX6aU9uaasYoav+I+Iel9KaXhxn23aeIVQJZr4sT/g5TS6/mmrJ4puh2RtFDS+cYf+25KaV2mESupVbdJ//2YpPt4FYs3TXGstUn6hqR7JY1J+nRKaTDbkBU0RbdfE9eEaUXEFyX9oSYeEvA9SX8m6afFNWFaU3T7vrgmzBoLZAAAAKAJD7EAAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmvw/tSpycIADqyoAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **टीप**: या आकृतीवरून असे सुचवले जाते की, सरासरीने पहिले बेसमनचे उंची दुसऱ्या बेसमनच्या उंचीपेक्षा जास्त असते. नंतर आपण शिकू की ही गृहितक अधिक औपचारिकपणे कसे तपासायचे आणि आपले डेटा सांख्यिकदृष्ट्या महत्त्वपूर्ण असल्याचे कसे दाखवायचे.  \n",
+    "> **Note**: या आकृतीनुसार, सरासरीने पहिले बेसमनचे उंची दुसऱ्या बेसमनच्या उंचीपेक्षा जास्त असते. नंतर आपण शिकू की ही गृहितक अधिक औपचारिकपणे कशी तपासता येईल आणि आपले डेटा सांख्यिकदृष्ट्या महत्त्वपूर्ण असल्याचे कसे दाखवता येईल.  \n",
     "\n",
-    "वय, उंची आणि वजन हे सर्व सतत यादृच्छिक चल आहेत. तुम्हाला त्यांचे वितरण काय वाटते? हे शोधण्याचा एक चांगला मार्ग म्हणजे मूल्यांचे हिस्टोग्राम तयार करणे:\n"
+    "वय, उंची आणि वजन हे सर्व सातत्यपूर्ण यादृच्छिक चल आहेत. तुम्हाला त्यांचे वितरण काय वाटते? हे शोधण्याचा एक चांगला मार्ग म्हणजे मूल्यांचे हिस्टोग्राम तयार करणे:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## सामान्य वितरण\n",
     "\n",
-    "चला वजनांचे एक कृत्रिम नमुना तयार करूया, जो आपल्या वास्तविक डेटासारख्याच सरासरी आणि वैविध्याने सामान्य वितरणाचे अनुसरण करतो:\n"
+    "चला वजनांचे एक कृत्रिम नमुना तयार करूया, जो आपल्या वास्तविक डेटासारख्याच सरासरी आणि विचलनासह सामान्य वितरणाचे अनुसरण करतो:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -554,23 +395,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## विश्वास अंतर\n",
+    "## विश्वास अंतराल\n",
     "\n",
-    "आता आपण बेसबॉल खेळाडूंच्या वजन आणि उंचींसाठी विश्वास अंतर गणना करूया. आपण [या स्टॅकओव्हरफ्लो चर्चेतील कोड](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) वापरणार आहोत:\n"
+    "आता आपण बेसबॉल खेळाडूंच्या वजन आणि उंचींसाठी विश्वास अंतरालाची गणना करूया. यासाठी आपण [या स्टॅकओव्हरफ्लो चर्चेतील कोड](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) वापरणार आहोत:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -593,14 +434,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## गृहीतक चाचणी\n",
+    "## गृहितक चाचणी\n",
     "\n",
-    "आपल्या बेसबॉल खेळाडूंच्या डेटासेटमधील वेगवेगळ्या भूमिका पाहूया:\n"
+    "आपल्या बेसबॉल खेळाडूंच्या डेटासेटमधील विविध भूमिका पाहूया:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "आपण पाहू शकतो की अंतराल एकमेकांवर आच्छादित होत नाहीत.\n",
     "\n",
-    "परिकल्पना सिद्ध करण्यासाठी सांख्यिकदृष्ट्या अधिक योग्य पद्धत म्हणजे **Student t-test** वापरणे:\n"
+    "परिकल्पना सिद्ध करण्याचा सांख्यिकदृष्ट्या अधिक योग्य मार्ग म्हणजे **Student t-test** वापरणे:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,35 +619,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ttest_ind` फंक्शनद्वारे परत केलेल्या दोन मूल्यांचा अर्थ असा आहे:  \n",
-    "* p-value म्हणजे दोन वितरणांमध्ये समान सरासरी असण्याची शक्यता. आपल्या बाबतीत, हे मूल्य खूपच कमी आहे, याचा अर्थ असा की पहिल्या बेसमेन उंच असल्याचे समर्थन करणारे ठोस पुरावे आहेत.  \n",
-    "* t-value म्हणजे t-test मध्ये वापरले जाणारे सामान्यीकृत सरासरी फरकाचे मध्यम मूल्य, आणि ते दिलेल्या आत्मविश्वासाच्या मूल्यासाठी एका मर्यादात्मक मूल्याशी तुलना केले जाते.  \n"
+    "`ttest_ind` फंक्शनद्वारे परत केलेल्या दोन मूल्ये आहेत:\n",
+    "* p-value म्हणजे दोन वितरणांमध्ये समान सरासरी असण्याची शक्यता मानली जाऊ शकते. आपल्या प्रकरणात, ती खूप कमी आहे, ज्याचा अर्थ असा आहे की पहिल्या बेसमेन उंच असल्याचे समर्थन करणारे मजबूत पुरावे आहेत.\n",
+    "* t-value म्हणजे सामान्यीकृत सरासरी फरकाचे मध्यम मूल्य जे t-test मध्ये वापरले जाते, आणि ते दिलेल्या विश्वासार्हतेसाठी एका थ्रेशोल्ड मूल्याशी तुलना केले जाते.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## सेंट्रल लिमिट थिअरमसह नॉर्मल वितरणाचे अनुकरण करणे\n",
+    "## केंद्रीय मर्यादा प्रमेयासह सामान्य वितरणाचे अनुकरण\n",
     "\n",
-    "Python मधील प्स्यूडो-रँडम जनरेटर आपल्याला एकसमान वितरण प्रदान करण्यासाठी डिझाइन केलेला आहे. जर आपल्याला नॉर्मल वितरणासाठी जनरेटर तयार करायचा असेल, तर आपण सेंट्रल लिमिट थिअरमचा वापर करू शकतो. नॉर्मल वितरणासाठी मूल्य मिळवण्यासाठी, आपण एकसमान-निर्मित नमुन्याचा सरासरी (mean) काढू.\n"
+    "Python मधील छद्म-यादृच्छिक जनक आपल्याला एकसमान वितरण देण्यासाठी तयार केलेला आहे. जर आपल्याला सामान्य वितरणासाठी जनक तयार करायचा असेल, तर आपण केंद्रीय मर्यादा प्रमेयाचा उपयोग करू शकतो. सामान्य वितरणासाठी मूल्य मिळवण्यासाठी, आपण एकसमान-निर्मित नमुन्याचा सरासरी गणना करू.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## संबंध आणि दुष्ट बेसबॉल कॉर्प\n",
+    "## परस्परसंबंध आणि दुष्ट बेसबॉल कॉर्प\n",
     "\n",
-    "संबंध आपल्याला डेटा अनुक्रमांमधील संबंध शोधण्यास मदत करतो. आपल्या खेळण्याच्या उदाहरणात, असे समजूया की एक दुष्ट बेसबॉल कंपनी आहे जी आपल्या खेळाडूंना त्यांच्या उंचीच्या आधारावर पैसे देते - खेळाडू जितका उंच असेल, तितके अधिक पैसे त्याला/तिला मिळतात. समजा, $1000 चा मूलभूत पगार आहे आणि उंचीच्या आधारावर $0 ते $100 पर्यंतचा अतिरिक्त बोनस आहे. आपण MLB मधील वास्तविक खेळाडू घेऊ आणि त्यांच्या काल्पनिक पगाराची गणना करू:\n"
+    "परस्परसंबंध आपल्याला डेटा अनुक्रमांमधील संबंध शोधण्यास मदत करतो. आपल्या खेळण्याच्या उदाहरणात, असे समजूया की एक दुष्ट बेसबॉल कंपनी आहे जी आपल्या खेळाडूंना त्यांच्या उंचीच्या आधारावर पैसे देते - खेळाडू जितका उंच असेल तितके अधिक पैसे त्याला/तिला मिळतात. समजा, $1000 ची मूळ पगार रक्कम आहे आणि उंचीच्या आधारावर $0 ते $100 पर्यंत अतिरिक्त बोनस दिला जातो. आपण MLB मधील वास्तविक खेळाडू घेऊ आणि त्यांच्या काल्पनिक पगारांची गणना करू:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "चला आता त्या अनुक्रमांची सहसंबंध आणि परस्परसंबंध मोजूया. `np.cov` आपल्याला तथाकथित **सहसंबंध मॅट्रिक्स** देईल, जो अनेक चलांमध्ये सहसंबंधाचा विस्तार आहे. सहसंबंध मॅट्रिक्स $M$ मधील घटक $M_{ij}$ हा इनपुट चल $X_i$ आणि $X_j$ यांच्यातील परस्परसंबंध आहे, आणि तिरप्या मूल्ये $M_{ii}$ ही $X_{i}$ ची विचलन आहे. त्याचप्रमाणे, `np.corrcoef` आपल्याला **परस्परसंबंध मॅट्रिक्स** देईल.\n"
+    "चला आता त्या अनुक्रमांची सहसंबंध आणि परस्परसंबंध मोजूया. `np.cov` आपल्याला तथाकथित **सहसंबंध मॅट्रिक्स** देईल, जो अनेक चलांमध्ये सहसंबंधाचा विस्तार आहे. सहसंबंध मॅट्रिक्स $M$ चा घटक $M_{ij}$ हा इनपुट चल $X_i$ आणि $X_j$ यांच्यातील परस्परसंबंध आहे, आणि तिरप्या मूल्ये $M_{ii}$ ही $X_{i}$ ची विचलन आहे. त्याचप्रमाणे, `np.corrcoef` आपल्याला **परस्परसंबंध मॅट्रिक्स** देईल.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -942,14 +779,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,22 +824,22 @@
    "source": [
     "> तुम्ही अंदाज लावू शकता का की ठिपके अशा प्रकारे उभ्या रेषांमध्ये का जुळतात?\n",
     "\n",
-    "आम्ही पगारासारख्या कृत्रिमपणे तयार केलेल्या संकल्पनेचा आणि निरीक्षित बदलणारा घटक *उंची* यांच्यातील परस्परसंबंध पाहिला आहे. चला आता दोन निरीक्षित घटक, जसे की उंची आणि वजन, यांच्यातही परस्परसंबंध आहे का ते पाहूया:\n"
+    "आम्ही पगारासारख्या कृत्रिमरित्या तयार केलेल्या संकल्पनेचा आणि निरीक्षित बदलणाऱ्या *उंची* या घटकाचा परस्परसंबंध पाहिला आहे. चला आता दोन निरीक्षित घटक, जसे की उंची आणि वजन, यांच्यातही परस्परसंबंध आहे का ते पाहूया:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "दुर्दैवाने, आपल्याला कोणतेही परिणाम मिळाले नाहीत - फक्त काही विचित्र `nan` मूल्ये मिळाली. याचे कारण असे की, आपल्या मालिकेतील काही मूल्ये अनिर्दिष्ट आहेत, ज्यांचे प्रतिनिधित्व `nan` ने केले जाते, आणि त्यामुळे ऑपरेशनचा परिणामही अनिर्दिष्ट होतो. मॅट्रिक्सकडे पाहिल्यावर आपल्याला दिसते की `Weight` हा समस्याग्रस्त स्तंभ आहे, कारण `Height` मूल्यांमधील स्व-परस्परसंबंधाची गणना केली गेली आहे.\n",
+    "दुर्दैवाने, आम्हाला कोणतेही परिणाम मिळाले नाहीत - फक्त काही विचित्र `nan` मूल्ये मिळाली. याचे कारण असे आहे की आमच्या मालिकेतील काही मूल्ये अनिर्दिष्ट आहेत, ज्यांचे प्रतिनिधित्व `nan` ने केले जाते, ज्यामुळे ऑपरेशनचा परिणाम देखील अनिर्दिष्ट होतो. मॅट्रिक्सकडे पाहिल्यास आपण पाहू शकतो की `Weight` ही समस्या निर्माण करणारी स्तंभ आहे, कारण `Height` मूल्यांमधील स्व-संबंध गणना केला गेला आहे.\n",
     "\n",
-    "> हा उदाहरण **डेटा तयारी** आणि **स्वच्छता** यांचे महत्त्व दाखवते. योग्य डेटा नसल्यास आपण काहीही गणना करू शकत नाही.\n",
+    "> हा उदाहरण **डेटा तयारी** आणि **स्वच्छता** याचे महत्त्व दाखवतो. योग्य डेटा नसल्यास आपण काहीही गणना करू शकत नाही.\n",
     "\n",
-    "चला `fillna` पद्धत वापरून हरवलेली मूल्ये भरूया आणि परस्परसंबंधाची गणना करूया:\n"
+    "चला `fillna` पद्धत वापरून गहाळ मूल्ये भरूया आणि संबंधाची गणना करूया:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## निष्कर्ष\n",
     "\n",
-    "या नोटबुकमध्ये आपण डेटावर मूलभूत ऑपरेशन्स कसे करायचे आणि सांख्यिकीय फंक्शन्स कसे मोजायचे हे शिकले. आता आपल्याला गणित आणि सांख्यिकीच्या ठोस साधनांचा वापर करून काही गृहीते सिद्ध कशी करायची आणि दिलेल्या डेटा नमुन्यावरून कोणत्याही बदलत्या घटकांसाठी विश्वास अंतर कसे मोजायचे हे माहित आहे.\n"
+    "या नोटबुकमध्ये आपण डेटावर मूलभूत ऑपरेशन्स कसे करायचे आणि सांख्यिकीय फंक्शन्स कसे मोजायचे हे शिकले. आता आपल्याला गणित आणि सांख्यिकीच्या ठोस साधनांचा वापर करून काही गृहीतके सिद्ध कसे करायचे आणि दिलेल्या डेटा नमुन्यावरून कोणत्याही बदलत्या घटकांसाठी विश्वास अंतर कसे मोजायचे हे माहित आहे.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nहा दस्तऐवज [Co-op Translator](https://github.com/Azure/co-op-translator) या एआय भाषांतर सेवेचा वापर करून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी कृपया लक्षात घ्या की स्वयंचलित भाषांतरांमध्ये त्रुटी किंवा अचूकतेचा अभाव असू शकतो. मूळ भाषेतील दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर केल्यामुळे उद्भवलेल्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\n"
+    "\n---\n\n**अस्वीकरण**:  \nहा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) चा वापर करून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात घ्या की स्वयंचलित भाषांतरांमध्ये त्रुटी किंवा अचूकतेचा अभाव असू शकतो. मूळ भाषेतील मूळ दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी, व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर केल्यामुळे उद्भवलेल्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:18:52+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:21:01+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "mr"
   }
diff --git a/translations/mr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/mr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 2757bb1d..180875f1 100644
--- a/translations/mr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/mr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## संभाव्यता आणि सांख्यिकीची ओळख\n",
-    "## असाइनमेंट\n",
+    "## संभाव्यता आणि सांख्यिकीची ओळख  \n",
+    "## असाइनमेंट  \n",
     "\n",
-    "या असाइनमेंटमध्ये, आपण मधुमेह रुग्णांचा डेटासेट वापरणार आहोत जो [येथून घेतलेला आहे](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "या असाइनमेंटमध्ये, आपण मधुमेह रुग्णांचा डेटासेट वापरणार आहोत जो [येथून घेतलेला आहे](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "या डेटासेटमध्ये, स्तंभ खालीलप्रमाणे आहेत:\n",
-    "* वय आणि लिंग हे स्वतः स्पष्ट आहेत\n",
+    "या डेटासेटमध्ये खालील प्रकारचे स्तंभ आहेत:\n",
+    "* वय आणि लिंग स्वतः स्पष्ट आहेत\n",
     "* BMI म्हणजे शरीराचा वस्तुमान निर्देशांक\n",
     "* BP म्हणजे सरासरी रक्तदाब\n",
-    "* S1 ते S6 हे वेगवेगळ्या रक्ताच्या मोजमापांचे प्रतिनिधित्व करतात\n",
-    "* Y म्हणजे एका वर्षातील आजाराच्या प्रगतीचे गुणात्मक मोजमाप\n",
+    "* S1 ते S6 हे वेगवेगळ्या रक्ताचे मोजमाप आहेत\n",
+    "* Y म्हणजे एका वर्षाच्या कालावधीत रोगाच्या प्रगतीचे गुणात्मक मोजमाप\n",
     "\n",
-    "चला या डेटासेटचा अभ्यास संभाव्यता आणि सांख्यिकीच्या पद्धतींचा वापर करून करूया.\n",
+    "चला संभाव्यता आणि सांख्यिकीच्या पद्धती वापरून या डेटासेटचा अभ्यास करूया.\n",
     "\n",
-    "### कार्य 1: सर्व मूल्यांसाठी सरासरी आणि वैविध्य (variance) मोजा\n"
+    "### कार्य 1: सर्व मूल्यांसाठी सरासरी आणि विचलन गणना करा\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -483,8 +483,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -533,8 +533,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -851,10 +851,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -881,9 +881,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -912,7 +912,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nहा दस्तऐवज [Co-op Translator](https://github.com/Azure/co-op-translator) या एआय भाषांतर सेवेचा वापर करून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात घ्या की स्वयंचलित भाषांतरांमध्ये त्रुटी किंवा अचूकतेचा अभाव असू शकतो. मूळ भाषेतील दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर केल्यामुळे उद्भवणाऱ्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\n"
+    "\n---\n\n**अस्वीकरण**:  \nहा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) वापरून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी कृपया लक्षात ठेवा की स्वयंचलित भाषांतरांमध्ये त्रुटी किंवा अचूकतेचा अभाव असू शकतो. मूळ भाषेतील दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर करून निर्माण होणाऱ्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\n"
    ]
   }
  ],
@@ -938,8 +938,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:50:28+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:21:27+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "mr"
   }
diff --git a/translations/ms/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ms/1-Introduction/04-stats-and-probability/assignment.ipynb
index fdc47e24..c8999d00 100644
--- a/translations/ms/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ms/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Dalam set data ini, lajur-lajur adalah seperti berikut:  \n",
-    "* Umur dan jantina adalah jelas dengan sendirinya  \n",
-    "* BMI ialah indeks jisim badan  \n",
-    "* BP ialah tekanan darah purata  \n",
-    "* S1 hingga S6 adalah pelbagai ukuran darah  \n",
-    "* Y ialah ukuran kualitatif perkembangan penyakit sepanjang satu tahun  \n",
+    "Dalam dataset ini, lajur-lajur adalah seperti berikut:\n",
+    "* Umur dan jantina adalah jelas dengan sendirinya\n",
+    "* BMI adalah indeks jisim badan\n",
+    "* BP adalah tekanan darah purata\n",
+    "* S1 hingga S6 adalah pelbagai ukuran darah\n",
+    "* Y adalah ukuran kualitatif bagi perkembangan penyakit sepanjang satu tahun\n",
     "\n",
-    "Mari kita kaji set data ini menggunakan kaedah kebarangkalian dan statistik.  \n",
+    "Mari kita kaji dataset ini menggunakan kaedah kebarangkalian dan statistik.\n",
     "\n",
-    "### Tugasan 1: Kira nilai purata dan varians untuk semua nilai  \n"
+    "### Tugasan 1: Kira nilai purata dan varians untuk semua nilai\n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tugasan 2: Plot kotak plot untuk BMI, BP dan Y bergantung kepada jantina\n"
+    "### Tugasan 2: Plot kotak plot untuk BMI, BP dan Y bergantung pada jantina\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk memastikan ketepatan, sila ambil maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang berwibawa. Untuk maklumat yang kritikal, terjemahan manusia profesional adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.\n"
+    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk memastikan ketepatan, sila ambil maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang berwibawa. Untuk maklumat penting, terjemahan manusia profesional adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:43:27+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:45:12+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ms"
   }
diff --git a/translations/ms/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ms/1-Introduction/04-stats-and-probability/notebook.ipynb
index bdfaa73c..39f59945 100644
--- a/translations/ms/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ms/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Pembolehubah Rawak dan Taburan  \n",
-    "Mari kita mulakan dengan mengambil sampel sebanyak 30 nilai daripada taburan seragam dari 0 hingga 9. Kita juga akan mengira min dan varians.  \n"
+    "## Pembolehubah Rawak dan Taburan\n",
+    "Mari kita mulakan dengan mengambil sampel sebanyak 30 nilai daripada taburan seragam dari 0 hingga 9. Kita juga akan mengira min dan varians.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Untuk menganggarkan secara visual berapa banyak nilai berbeza yang terdapat dalam sampel, kita boleh plot **histogram**:\n"
+    "Untuk menganggar secara visual berapa banyak nilai berbeza yang terdapat dalam sampel, kita boleh plot **histogram**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kita menggunakan pakej yang dipanggil [**Pandas**](https://pandas.pydata.org/) di sini untuk analisis data. Kita akan membincangkan lebih lanjut tentang Pandas dan bekerja dengan data dalam Python kemudian dalam kursus ini.\n",
+    "Kita menggunakan pakej yang dipanggil [**Pandas**](https://pandas.pydata.org/) di sini untuk analisis data. Kita akan berbincang lebih lanjut tentang Pandas dan bekerja dengan data dalam Python kemudian dalam kursus ini.\n",
     "\n",
     "Mari kita kira nilai purata untuk umur, ketinggian, dan berat:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sebagai tambahan kepada min, adalah wajar untuk melihat nilai median dan kuartil. Nilai-nilai ini boleh divisualisasikan menggunakan **graf kotak**:\n"
+    "Sebagai tambahan kepada min, adalah masuk akal untuk melihat nilai median dan kuartil. Ia boleh digambarkan menggunakan **graf kotak**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -404,24 +252,22 @@
    "source": [
     "> **Nota**: Rajah ini mencadangkan bahawa, secara purata, ketinggian pemain base pertama lebih tinggi daripada ketinggian pemain base kedua. Kemudian kita akan belajar bagaimana untuk menguji hipotesis ini dengan lebih formal, dan bagaimana untuk menunjukkan bahawa data kita adalah signifikan secara statistik untuk membuktikannya.\n",
     "\n",
-    "Umur, ketinggian dan berat badan semuanya adalah pemboleh ubah rawak berterusan. Apa pendapat anda tentang taburan mereka? Cara yang baik untuk mengetahuinya adalah dengan melukis histogram nilai:\n"
+    "Umur, ketinggian, dan berat badan semuanya adalah pemboleh ubah rawak berterusan. Apa pendapat anda tentang taburan mereka? Cara yang baik untuk mengetahuinya adalah dengan melukis histogram nilai:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,9 +619,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dua nilai yang dikembalikan oleh fungsi `ttest_ind` adalah:\n",
-    "* p-value boleh dianggap sebagai kebarangkalian dua taburan mempunyai purata yang sama. Dalam kes kita, ia sangat rendah, bermaksud terdapat bukti kukuh yang menyokong bahawa pemain base pertama lebih tinggi.\n",
-    "* t-value adalah nilai perantaraan bagi perbezaan purata yang dinormalisasi yang digunakan dalam ujian-t, dan ia dibandingkan dengan nilai ambang untuk tahap keyakinan tertentu.\n"
+    "Dua nilai yang dikembalikan oleh fungsi `ttest_ind` adalah:  \n",
+    "* p-value boleh dianggap sebagai kebarangkalian dua taburan mempunyai min yang sama. Dalam kes kita, ia sangat rendah, yang bermaksud terdapat bukti kukuh menyokong bahawa pemain base pertama lebih tinggi.  \n",
+    "* t-value ialah nilai perantaraan bagi perbezaan min yang dinormalisasi yang digunakan dalam ujian-t, dan ia dibandingkan dengan nilai ambang untuk tahap keyakinan tertentu.  \n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Mensimulasikan Taburan Normal dengan Teorem Had Pusat\n",
     "\n",
-    "Penjana pseudo-rawak dalam Python direka untuk memberikan kita taburan seragam. Jika kita ingin mencipta penjana untuk taburan normal, kita boleh menggunakan teorem had pusat. Untuk mendapatkan nilai yang ditaburkan secara normal, kita hanya akan mengira purata sampel yang dijana secara seragam.\n"
+    "Penjana pseudo-rawak dalam Python direka untuk memberikan kita taburan seragam. Jika kita ingin mencipta penjana untuk taburan normal, kita boleh menggunakan teorem had pusat. Untuk mendapatkan nilai yang bertaburan secara normal, kita hanya perlu mengira purata daripada sampel yang dijana secara seragam.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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eyrcCAACALq/T316+YcOGeOWVV9rur1q1KpYuXRp9+/aNffbZJy699NL4yU9+El/60pdi6NChcc0110RdXV2ccsoppZwbAAAAurxOR/dzzz0Xxx9/fNv9iRMnRkTE+PHjY9asWXHllVfGxo0b48ILL4x169bFyJEjY/78+dG7d+/STQ0AAADdQEVRFEW5h/hfzc3NUVNTE01NTT7fDXR5QybNK/cIAPCprJ56YrlHgJ3Kp23Xsn97OQAAAOysRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAkspyDwAAAOQbMmleuUfY6ayeemK5R6AbcKUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkpQ8un/84x9HRUVFu9sBBxxQ6rcBAACALq8y40UPPvjgePjhh///TSpT3gYAAAC6tJQarqysjNra2oyXBgAAgG4j5TPdK1asiLq6uth3333j7LPPjtdee22r+7a0tERzc3O7GwAAAOwMSh7dw4YNi1mzZsX8+fNjxowZsWrVqjj66KNj/fr1He4/ZcqUqKmpabvV19eXeiQAAAAoi4qiKIrMN1i3bl0MHjw4brrppjj//PO3eLylpSVaWlra7jc3N0d9fX00NTVFdXV15mgA223IpHnlHgEAKJPVU08s9wiUUXNzc9TU1Hxiu6Z/w1mfPn1iv/32i1deeaXDx6uqqqKqqip7DAAAANjh0v9O94YNG2LlypUxcODA7LcCAACALqXk0X355ZfH448/HqtXr46nn346Tj311OjZs2ecddZZpX4rAAAA6NJK/uvlr7/+epx11lnx7rvvxt577x0jR46MRYsWxd57713qtwIAAIAureTRPXv27FK/JAAAAHRL6Z/pBgAAgF2V6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIUlnuAQAAALqjIZPmlXuEndLqqSeWe4SScqUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AOjIkEnzyj3CTmn11BPLPQIAAOxSXOkGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AGDHGTJpXrlHAACAXYor3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJLKcg/Q3Q2ZNK/cIwAAANBFudINAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAEnSonv69OkxZMiQ6N27dwwbNiyeffbZrLcCAACALikluu+7776YOHFiXHfddbFkyZI47LDDYvTo0bF27dqMtwMAAIAuKSW6b7rpprjgggvivPPOi4MOOihmzpwZn/nMZ+LOO+/MeDsAAADokipL/YIffPBBLF68OCZPnty2rUePHjFq1KhYuHDhFvu3tLRES0tL2/2mpqaIiGhubi71aClaW/5V7hEAAAB2Gt2lBT+csyiKj92v5NH9zjvvxObNm2PAgAHttg8YMCD+/ve/b7H/lClT4vrrr99ie319falHAwAAoIurmVbuCTpn/fr1UVNTs9XHSx7dnTV58uSYOHFi2/3W1tZ47733Yq+99oqKiooyTkaG5ubmqK+vjzVr1kR1dXW5x6GLsC7oiHXBR1kTdMS6oCPWBR0p9booiiLWr18fdXV1H7tfyaO7X79+0bNnz2hsbGy3vbGxMWpra7fYv6qqKqqqqtpt69OnT6nHoouprq72A5AtWBd0xLrgo6wJOmJd0BHrgo6Ucl183BXuD5X8i9R69eoVRxxxRCxYsKBtW2trayxYsCCGDx9e6rcDAACALivl18snTpwY48ePj6997Wtx1FFHxbRp02Ljxo1x3nnnZbwdAAAAdEkp0X3GGWfE22+/Hddee200NDTE4YcfHvPnz9/iy9XY9VRVVcV11123xUcK2LVZF3TEuuCjrAk6Yl3QEeuCjpRrXVQUn/T95gAAAMA2KflnugEAAID/Et0AAACQRHQDAABAEtENAAAASUQ322X69OkxZMiQ6N27dwwbNiyeffbZT/W82bNnR0VFRZxyyilb3eeiiy6KioqKmDZtWmmGZYfJWBcvvfRSnHzyyVFTUxN77LFHHHnkkfHaa6+VeHIylXpdbNiwIS6++OIYNGhQ7L777nHQQQfFzJkzEyYnU2fWxaxZs6KioqLdrXfv3u32KYoirr322hg4cGDsvvvuMWrUqFixYkX2YVBipVwXmzZtiquuuioOOeSQ2GOPPaKuri6++93vxptvvrkjDoUSKvXPi//lvLN7ylgTGeecopttdt9998XEiRPjuuuuiyVLlsRhhx0Wo0ePjrVr137s81avXh2XX355HH300Vvd54EHHohFixZFXV1dqccmWca6WLlyZYwcOTIOOOCAeOyxx2LZsmVxzTXXfOz/POlaMtbFxIkTY/78+XH33XfHSy+9FJdeemlcfPHFMXfu3KzDoMS2ZV1UV1fHW2+91XZ79dVX2z3+85//PG655ZaYOXNmPPPMM7HHHnvE6NGj4/33388+HEqk1OviX//6VyxZsiSuueaaWLJkSdx///2xfPnyOPnkk3fE4VAiGT8vPuS8s3vKWBNp55wFbKOjjjqqmDBhQtv9zZs3F3V1dcWUKVO2+pz//Oc/xYgRI4rf/va3xfjx44tx48Ztsc/rr79efP7zny9eeOGFYvDgwcXNN9+cMD1ZMtbFGWecUXznO9/JGpkdIGNdHHzwwcUNN9zQbttXv/rV4oc//GFJZydPZ9fFXXfdVdTU1Gz19VpbW4va2triF7/4Rdu2devWFVVVVcW9995bsrnJVep10ZFnn322iIji1Vdf3Z5R2YGy1oXzzu4rY01knXO60s02+eCDD2Lx4sUxatSotm09evSIUaNGxcKFC7f6vBtuuCH69+8f559/foePt7a2xjnnnBNXXHFFHHzwwSWfm1wZ66K1tTXmzZsX++23X4wePTr69+8fw4YNizlz5mQcAgmyfl6MGDEi5s6dG2+88UYURRGPPvpovPzyy3HCCSeU/BgovW1dFxs2bIjBgwdHfX19jBs3Ll588cW2x1atWhUNDQ3tXrOmpiaGDRv2sa9J15GxLjrS1NQUFRUV0adPn1KNTqKsdeG8s/vKWBOZ55yim23yzjvvxObNm2PAgAHttg8YMCAaGho6fM5TTz0Vd9xxR9x+++1bfd2f/exnUVlZGZdccklJ52XHyFgXa9eujQ0bNsTUqVNjzJgx8Ze//CVOPfXUOO200+Lxxx8v+TFQelk/L2699dY46KCDYtCgQdGrV68YM2ZMTJ8+PY455piSzk+ObVkX+++/f9x5553x4IMPxt133x2tra0xYsSIeP311yMi2p7Xmdeka8lYFx/1/vvvx1VXXRVnnXVWVFdXl/wYKL2sdeG8s/vKWBOZ55yV2/Vs+JTWr18f55xzTtx+++3Rr1+/DvdZvHhx/OpXv4olS5ZERUXFDp6Qcvg066K1tTUiIsaNGxeXXXZZREQcfvjh8fTTT8fMmTPj2GOP3WHzsmN8mnUR8d/oXrRoUcydOzcGDx4cTzzxREyYMCHq6ura/cs3O4/hw4fH8OHD2+6PGDEiDjzwwPj1r38dN954Yxkno5w6sy42bdoU3/72t6MoipgxY8aOHpUd6JPWhfPOXc8nrYnMc07RzTbp169f9OzZMxobG9ttb2xsjNra2i32X7lyZaxevTpOOumktm0fLuzKyspYvnx5PPnkk7F27drYZ5992vbZvHlz/OAHP4hp06bF6tWrcw6GkslYF/X19VFZWRkHHXRQu+ceeOCB8dRTTyUcBaWWsS7q6uri6quvjgceeCBOPPHEiIg49NBDY+nSpfHLX/5SdHcDnV0XHdltt93iK1/5SrzyyisREW3Pa2xsjIEDB7Z7zcMPP7w0g5MqY1186MPgfvXVV+ORRx5xlbsbyVgXzju7t4w10a9fv7RzTr9ezjbp1atXHHHEEbFgwYK2ba2trbFgwYJ2/4L0oQMOOCCef/75WLp0advt5JNPjuOPPz6WLl0a9fX1cc4558SyZcva7VNXVxdXXHFFPPTQQzvy8NhGGeuiV69eceSRR8by5cvbPffll1+OwYMHpx8T2y9jXWzatCk2bdoUPXq0/99Yz5492wKdrq2z66Ijmzdvjueff74tsIcOHRq1tbXtXrO5uTmeeeaZT/2alFfGuoj4/+BesWJFPPzww7HXXnuVfHbyZKwL553dW8aaSD3nLPlXs7HLmD17dlFVVVXMmjWr+Nvf/lZceOGFRZ8+fYqGhoaiKIrinHPOKSZNmrTV52/t28v/l2+R7H4y1sX9999f7LbbbsVvfvObYsWKFcWtt95a9OzZs3jyySczD4USylgXxx57bHHwwQcXjz76aPGPf/yjuOuuu4revXsXt912W+ahUEKdXRfXX3998dBDDxUrV64sFi9eXJx55plF7969ixdffLFtn6lTpxZ9+vQpHnzwwWLZsmXFuHHjiqFDhxb//ve/d/jxsW1KvS4++OCD4uSTTy4GDRpULF26tHjrrbfabi0tLWU5Rjov4+fFRznv7F4y1kTWOadfL2ebnXHGGfH222/HtddeGw0NDXH44YfH/Pnz277Q4LXXXtviKhQ7v4x1ceqpp8bMmTNjypQpcckll8T+++8ff/zjH2PkyJEZh0CCjHUxe/bsmDx5cpx99tnx3nvvxeDBg+OnP/1pXHTRRRmHQILOrot//vOfccEFF0RDQ0N87nOfiyOOOCKefvrpdr8KeOWVV8bGjRvjwgsvjHXr1sXIkSNj/vz52/83VtlhSr0u3njjjZg7d25ExBYfM3j00UfjuOOO2yHHxfbJ+HlB95axJrLOOSuKoii26xUAAACADrkMCQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJL/A9iNnCdIIuhfAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Korelasi dan Syarikat Besbol Jahat\n",
+    "## Korelasi dan Korporasi Besbol Jahat\n",
     "\n",
-    "Korelasi membolehkan kita mencari hubungan antara urutan data. Dalam contoh mainan kita, mari kita bayangkan terdapat sebuah syarikat besbol jahat yang membayar pemainnya berdasarkan ketinggian mereka - semakin tinggi pemain, semakin banyak wang yang diterima. Katakan terdapat gaji asas sebanyak $1000, dan bonus tambahan dari $0 hingga $100, bergantung pada ketinggian. Kita akan mengambil pemain sebenar dari MLB, dan mengira gaji khayalan mereka:\n"
+    "Korelasi membolehkan kita mencari hubungan antara urutan data. Dalam contoh mainan kita, mari kita bayangkan terdapat sebuah korporasi besbol jahat yang membayar pemainnya berdasarkan ketinggian mereka - semakin tinggi pemain itu, semakin banyak wang yang dia dapat. Katakan terdapat gaji asas sebanyak $1000, dan bonus tambahan dari $0 hingga $100, bergantung pada ketinggian. Kita akan mengambil pemain sebenar dari MLB, dan mengira gaji khayalan mereka:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Mari kita hitung kovarians dan korelasi bagi jujukan tersebut. `np.cov` akan memberikan kita **matriks kovarians**, yang merupakan lanjutan kovarians kepada pelbagai pemboleh ubah. Elemen $M_{ij}$ dalam matriks kovarians $M$ adalah korelasi antara pemboleh ubah input $X_i$ dan $X_j$, dan nilai pepenjuru $M_{ii}$ adalah varians bagi $X_{i}$. Begitu juga, `np.corrcoef` akan memberikan kita **matriks korelasi**.\n"
+    "Mari kita kira kovarians dan korelasi bagi jujukan tersebut. `np.cov` akan memberikan kita **matriks kovarians**, yang merupakan lanjutan kovarians kepada pelbagai pemboleh ubah. Elemen $M_{ij}$ dalam matriks kovarians $M$ adalah korelasi antara pemboleh ubah input $X_i$ dan $X_j$, dan nilai diagonal $M_{ii}$ adalah varians bagi $X_{i}$. Begitu juga, `np.corrcoef` akan memberikan kita **matriks korelasi**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Korelasi sama dengan 1 bermaksud terdapat **hubungan linear** yang kuat antara dua pemboleh ubah. Kita boleh melihat hubungan linear secara visual dengan memplotkan satu nilai terhadap yang lain:\n"
+    "Hubungan sama dengan 1 bermaksud terdapat **hubungan linear** yang kuat antara dua pemboleh ubah. Kita boleh melihat hubungan linear secara visual dengan melukis satu nilai terhadap yang lain:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Mari kita lihat apa yang berlaku jika hubungan tersebut tidak linear. Katakanlah syarikat kita memutuskan untuk menyembunyikan kebergantungan linear yang jelas antara ketinggian dan gaji, dan memperkenalkan beberapa ketidaklinearan ke dalam formula, seperti `sin`:\n"
+    "Mari kita lihat apa yang berlaku jika hubungan tersebut tidak linear. Katakan bahawa syarikat kita memutuskan untuk menyembunyikan kebergantungan linear yang jelas antara ketinggian dan gaji, dan memperkenalkan beberapa ketidaklinearan ke dalam formula, seperti `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dalam kes ini, korelasi adalah sedikit lebih kecil, tetapi ia masih agak tinggi. Sekarang, untuk menjadikan hubungan itu kurang jelas, kita mungkin ingin menambah sedikit kebarangkalian dengan menambah beberapa pembolehubah rawak kepada gaji. Mari kita lihat apa yang berlaku:\n"
+    "Dalam kes ini, korelasi adalah sedikit lebih kecil, tetapi ia masih agak tinggi. Sekarang, untuk menjadikan hubungan itu kurang jelas, kita mungkin mahu menambah sedikit keacakan dengan menambah beberapa pemboleh ubah rawak kepada gaji. Mari kita lihat apa yang berlaku:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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8UfmA+2xOBN5fj9Ln/kYy2Fb2kV4oLwcAAINGvI2Yun2Wauqbtap2j2rqmx3tG7WTI6n3tu1kJ0dVU8v02I0z5C0ITkT6qtw6ke8tklR+n/1Nqu9vX7IftkS6q+nWKA/pxWNZVlp2G2hra1NBQYFaW1uVn5+f6ssBAAAp1u2zNPuhdRHLre0V0lfvujQoYXNrZTyVo45SMdc42Z832vEvr/AOqjnOXSd8eqZmp3a1tGtCcZ4WVk5U9pD0W1uzH5pJ4Vf20+1BA/o/05yTpBsAAAwKNfXNuuGpjTHjnls0S5WTR0qKPA861i/5kZLcVCS/qRDv9+ZUuO9zbV3ToJrjPNjmVg+2z4v+zTTnZE83AAAYFJw2Yoo1osijnhFFl1d4jVfGL6/wJvAJBoZ4v7d4ZGZ4/A9IpMjJvr19IN1WQgfb55V6SuoHWyUDBj6SbgAAMCg4bcTkZERRrJXxptYO3bJyiwrzsnSo/bj/9XRcoYvne3NDXyb7/cFg+7yBQh+2AP1d+m32AAAACOO8CUWKlXtkeHriJPdXxiUFJdxST/IZrYHbQJSq0U6DbY7zYPu8wEBG0g0AAAaFt3YdVKzm2T6rJ04yXxk/cLhTq2r36OnXGqImQZFY6lmRdKOzdyTJ6CIeSapGOw22Oc6D7fMCAxnl5QAAYFBoaj3mKC7WPGipZ2X8/he2JXxtySi3tg2WueQlw3NcjevvmFsNDBysdAMAgLQVuMK7ZfdBo/e0HO2SFH0etM3NBWPThwJOxDuXPBEpm6Ntei/SZG4Pc6uBgYOkGwAApKXqrY2a/dA63fDURn31V7V6ZuNuo/cVB6yEVk0t02M3zpC3IHi1MBl9qexk3y0me8yTVdYe6XvzFuQmraP2gaOdrsb1dyl7uAHAMcrLAQBA2onURdxE6Yjg8uPQEUUHDne6UlIeqtjlsudUdRG39fVop8FYbm0/3AjdPuBNw674wEBG0g0AAAaMbp8VM4mLtsJr4sRxX6/XAkcUrardE+eRo/Pmp19jsb4c7ZSqveSpxtxqoP8j6QYAAAOCaUOwWCu8sfz+nT367JTSiD83XSm9Z94UlYzIUcmwHN3x23fU1Bb5mpKx93awrfza5dZLVm6RR8Fbt9O93Jq51UD/xp5uAADQ7zlpCJboyu3RzhNRf27awOrLF5VrwfQxuuiMEi2/ukIehd9761FyksHB2GgrFXvJASAWVroBAEDKJFoubqkneVyxpk6XV3iVmeFJeOV2VIwy73hWVFOx9zbwOiNJx5Vfyq0B9Dck3QAAICWqtzZq+eq6oLJrb36ull/trFw8tCGYyXztaM4dXxQzJp4kOhXJYNXUMi2+uFxPbWgIGm+W4ZEWzSlP25Vfyq0B9Cck3QAAoM9Vb23ULWFWYJvaOnTLyi16PKAU2GlDsGgr0SZGFw41iosnie7rZLB6a6OeWN/Q63WfJT2xvkHnji9K28QbAPoLkm4AANCnun2WvvG796LGLP3de47LxQPjIq1EZ3ikaGOpne5x7m8rqoHl+iXDchx9z26dN/Dhg8n2AQBIdyTdAACgT23c0axD7cejxhxsP66NO5p10eklcY+CCrcSffBop/712bcjnncg73EO1909lsDv2c3zlhXk6uppZVr9TmPMbvMAkO7oXg4AAPpUTX2zozi7XFwK3/1bipws2yvRC6aPUeXkkcoYoAl1LJG6u5swvR9OztvY2qEn1jcYdZsHgHRH0g0AAPqY6S7rU3FujIKyu6BHs2JNnbqj1Z8b6vZZqqlv1qraPaqpb/YfM9LriZ4rUnd3M/G9M57zWif/cut7BoCBgPJyAADQpyonleiRV+qN4gIl2v07Vhd0KbgLeiAne5P7utza5HNFE/o998V5I33PAJCOSLoBAECfmjV5pArzsqLu6y7My9KsMAlZIo3LAkeTOYmLlESHS5btcuvQNVy73LrXuU6WW5uu1odj2t09nEjfc7LPK5nfDwAY6CgvBwAASRdYVr25oUX/cc3UqPEPXnu26w3NWo50Oo6LtGc53N7keMutpcTKrU27u4eTyPecyHkl8/sBAAMdK90AACCpqrc2avnquqCVTW9+rr5ycblW1e5VU1tnwOs5Wn71p5PS3bp4WLZRXMvRLq2q3aOS4Tlavvr9sEm0pZ4mbivW1PlHbsVbbm0psXLrWN3dw/FIWnxxeULfczznDWR6P/ojRqEBcIKkGwAAJE311kbdsnJLr9eb2nrKrX/8hRkqGpbdJ8lLab7Zyuyjf46931zqnSwnWm4d7/vt7u5LVm6RR+Zt0Z5c36BzxxfFnXjHe16bt2BoXOdNNSfbDQBAorwcAAAkSbfP0jd+917UmG8+/54uKC/2j/RK6mphkppl28lyouXWibw/Unf3SF+nG2Xt8ZzXVhZmrvpA4GS7AQDYWOkGAABJsXFHc9RmaZJ0sP24Nu5o1kWnx9dB24kDR5Ozh9hOluMtt/aoZ/RZokloaHf3A4c7df8L2yLGJ1rWHum8pSNydfBop2599u2w34NHkeeq92fR9uyH224AADZWugEAQFLU1De7GpeoRFeiQ3kUvGJrl1vbPzM9huReEmp3d18wfYxKRuQYvSfRsvjQ81ZOHqmrzhmtxReX9/oe3NhLniqx9uwHPsQAgEAk3QAAIElM13sTr/sO7I5eU98ctmTaXol2Q6RkOVK5dVlBT+M4b35wIjwqPyfquDCTzxWJ6UMGtx9GSD1l2E+sb+h1Zy1JT6xvSHoZdiLfWySmDyfceIgBIL1QXg4AAJKiclKJHnkldlOyykmJlZabNrbKzPDo6mllYedlO+WN0jgrXLn1BeXFWlvXpFW1oclm5NXtRBt2XVBeHHMeelFelut7q0328n/jd+8lrQw7WY3OUvkQA8DARtINAMAgd6TjhL7+67e1++AxjS8aqv/8p3M1PDfxXxFmTR4ZM+krzMuSJK2q3RNX93K7sVXoOqbd2CpwFbnbZ2n1O85XWO0919/9X9N04Gin0XXa5daxrnNfW+/rdPq5EpGM3nIb62Pv5T/Uflwb65t10Rnu7uVP5vcWa8++W3vzAaQfkm4AAAaxqx/ZoHc/bvP/+YOmw5q6/H90zth8rb5tTkLHzszw6MFrzw47MizQF3+yyf/3TlYknTa2imeOdmAZebwJYqzrVMh1mnyu5avf14jcLB04EvkhwOaGFqPkN9FGaqFqdhwwjnMz6U52o7NoI9Lc3psPIL2wpxsAgEEqNOEO9O7Hbbr6kQ0Jn6Nqapkev3FGr73MhUN7nvuHJoVORi85bWwVz15bb0FuwqvKJsl+4HWafK6mtk598b826au/qtUNT23U7IfW9frOTD/va9sPuLr32XkbOXf0RaOzSHv23fjnBED6YqUbAIBB6EjHiYgJt+3dj9t0pONEwqXmoXucS4bn6I7f1ErHTvSKdbIi6bSxVclws27eN84cr4wMjyYU52lh5URlD0lsjaKpzew67bh4Hg6EK5823Vv8yCvb/X/vxt7nyskjg44ZLc5NfdXoLNKefVa4AUTCSjcAAIPQ1379tqtxsQSOlMrweNTUFnlmtumKpNPGVl2d3UbxKzft1i9qdun+F7bps995JeFO2y1HzOaD23HxNOIKLFO3V6vj6dbupNIgklmTRvr36kdSlJelWZPcTbr7stFZ6Ig0Em4A0ThOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHHwbF/O1vf9OCBQtUUlKi/Px8zZ49W6+88kpQzO7duzVv3jzl5eWptLRU//Zv/6YTJ3o/EQcAAM590HTY1Tgn3FqRtJPKSOlO6Bzt/3rdeddyN5LQ4mHZjuJifa5IQh9W2N3anR5DCk7enbL38kfzwLVnu56oOv3nAQD6iuOk++jRo5o2bZoeffTRsD9/+OGH9cMf/lCPP/64Nm3apGHDhumKK65QR8ep/3D+3d/9nU6cOKF169bprbfe0rRp0/R3f/d3ampqkiR1d3dr3rx56urq0uuvv66f//znevrpp3XvvffG+TEBAEDg7GLThKdgaPQVy3iUDDMr844VZze2knrvDg7X2KqtI3pTsXDcSEK9BUMdxUX7XCbshxXxdmt3a+9zz17+3vPKH0/S3men/zwAQF9xvEnryiuv1JVXXhn2Z5Zl6fvf/77uvvtuLViwQJL0i1/8QqNGjdLzzz+v66+/XgcOHNCHH36on/zkJzrnnHMkSQ8++KB+/OMfa+vWrfJ6vXrppZdUV1enP/3pTxo1apSmT5+u+++/X3fddZeWL1+u7GyzJ8YAAKBHuNnFJr5+6RnuX4yLfbbsxlahny3cHO2powv03p7o+9jDCUxC49mHbDIvuzBkXnakz2XCLp+Op1t7oIG499nJPw8A0FdcbaTW0NCgpqYmXXbZZf7XCgoKNHPmTNXU1Oj666/XyJEjdeaZZ+oXv/iFZsyYoZycHD3xxBMqLS3VeeedJ0mqqanR2WefrVGjRvmPc8UVV2jJkiV6//33de6557p52QAApLVIs4tN5Oa433P1gOEeZ9M40+Ru/Mg8x9caKNEkNJpwaWivBnTDcnTHb9/RvjazOdGJXq+be5/7Eo3OAPQ3rv6X1C4PD0yW7T/bP/N4PPrTn/6ka665RiNGjFBGRoZKS0tVXV2toqIi/3HCHSPwHKE6OzvV2XnqP85tbc6fZAMAkG6izS42kYxE07SLuGmcZJbcJbLqK8WfhJrMyz5oMC87I8Oje/+uQrc+azYnOt7rDU3eB6JUJPsAEEmfjwyzLEu33nqrSktLtWHDBg0dOlT/9V//pfnz5+uNN95QWVl8ZT8PPPCAVqxY4fLVAgAwsCVaYnzgSJej+G6fFXuF0fQJgBsjowOMKzLbWx0q0SQ03sZx4bYElBXkavHF5Vr9TmPM8unzJhQpwyM52YreV3ufjf45AYA04WrS7fV6JUn79u0LSp737dun6dOnS5LWrVunP/zhDzp48KDy8/MlST/+8Y+1du1a/fznP9c3vvENeb1ebd68OejY+/btCzpHqKVLl+r222/3/7mtrU3jxo1z7bMBADAQJbpSfeiYedIdKUkMTQYPHDUsLzeMiyYwubPiSOLdSELjGWUVaUtAU2uHnlzfoEe/cK6KhuVETVrf2nXQUcIt9c3eZ9N/TgAgXbiadJeXl8vr9erll1/2J9ltbW3atGmTlixZIklqb2+XJGVkBDdOz8jIkM/nkyRVVlbq29/+tvbv36/S0lJJ0tq1a5Wfn6+Kioqw587JyVFOjnkZGgAAg0Gi+3I/bmnXqto9MVcjIyWJjSdHbj0W0LHare7lscTbPC6QG0moPcqqqdVsL3a0LQHWyfj7X9imV++6NOqDgKbWY0bX96+fm6wzvSP6ZMU52sOE0H9OACBdOE66jxw5ou3bt/v/3NDQoNraWhUXF2v8+PH62te+pm9961s644wzVF5ernvuuUejR4/WNddcI6knoS4qKtJNN92ke++9V0OHDtVTTz2lhoYGzZs3T5I0d+5cVVRUaOHChXr44YfV1NSku+++W7feeiuJNQAADsRTYhxo1TuNWnVy7FSk1chY+8Yt9YzcurzC25PQudi9PJJEmsfdM2+KSkbkuJaE2qOslqw024sda0uAaTf1lqNmVQojh2VrwfQxRrFOBVYalAzL0fLV0R8mBP1zAgBpwnHS/eabb+qSSy7x/9ku6b7pppv09NNP684779TRo0e1ePFiHTp0SLNnz1Z1dbVyc3uetJeUlKi6ulrf/OY3demll+r48eP69Kc/rVWrVmnatGmSpMzMTP3hD3/QkiVLVFlZqWHDhummm27Sfffd58ZnBgBg0IinxDiSSKuRJvvGA5NEt7uXh0qkeVxRXpa+fFG560mfk1FW8e4BD1Vs2IjONM4pp5UGiY5mA4D+ynHS/bnPfU5WlE1RHo9H9913X9QE+fzzz9f//M//RD3PhAkT9OKLLzq9PAAAEMDN7uORViNNy5jtuHj2ODuRSPM4l3u3BamaWqZLzxqlZ2p2aldLuyYU52lh5URlDwnecufW9+PNNzuOaZwTiVQaJHM0GwCkQp93LwcAAH3HjVnLgcKtRpqWMdtxTvc4O5VI0nbIYHRXvMKt/P7Xqw29Vrrd+n7s40R7AFGWhNFgiY6pc/ufWQBItYzYIQAAoC90+yzV1DdrVe0e1dQ3q9uFunA78XJ7h+xr2w/4r7MoL9voPS1Hu7Sqdo82N7Tonnk9jVFDr6svu4VHkoyVVnvlNzQBtkv2q7c2+l+z94BLiX0/9nE8EY7jMTyOU/FWGniUnIcAAJBqrHQDANAPVG9t1PLVdWpqC9jvm5+r5Vcn1jk7WhOvRDzyyqmmqsXDsoze8+if6/1/b8+bfv7tPdp3+NRKeemIbK1YMDWp3cJjcXul1aQbeWjJvpM94NG4dRwn4nlo0VfzwQEgFTxWtA3aA1hbW5sKCgrU2trqnwcOAEB/VL21Ubes3BLx54+7MEbJjfFZyRD6ICDDIy2aU66lV4UfEWrKXlmWnD1oyPBIf73/yl77rBNRU9+sG57aGDPuuUWzepW1B3b/TqSbulvHMWH6eQMxpxvAQGSac7LSDQBACnX7LH3jd+9FjVn6u/cSHqNUNbVMl1d4/YnX/rZOffvFbXEfLxKnq+mhsT5LemJ9gyQllHhHWuGNxWf1dHx3c093It3IMzM8rlyLW8cxYbInfVR+jv7fP07XgSOdfTIfHABSiaQbAIAU2rijWYfaj0eNOdh+XBt3NOui00sSOldg4rXhb58kdKxIioZlGzdWi+bJ9Q26Y+5ZCa04hz5o+Gtjmx77y46Y79t7yKwbu6mSYWYjuUzj+juTueTLr/50wv88A8BAQSM1AABSqKa+2dW4aAIbtf1uy8dG77lm+mj94Prpuu2SyUbx98yboucWzdIPrp+uWz9n9p5wLElPv9YQ9/tt9oOGBdPH6EjnCaP31H50MOHzBjFdwE3iQm8ymvRFY1caeAuC98d7C3J7zXkHgHTHSjcAACllmvwkliTFu6d7bNFQLZg+RjX1zXrklfqY8d6Cof7V9J9siL2qHM1LdU1a/FmzxL0v9yw7deBIp6txToW7932xhzq00qC/3RcA6Csk3QAApFDlpBKjZLZyknkpbmgCevBop2599u240vaZ5T0JdDwzn4uHJ1oubZacmSaV44uHGR3PNM6UaTf0ZMynthvKhd57e1RZsled+3IvOQD0VyTdAACk0KzJI1WYlxV1X3dhXpZmGSYu4RLQDE/86+QZnp7ENzPDo6unlfmbnIVz9bSyoFVMb35iSeTnp5wWM8ZJUvmp0uFG5zWNM2XSWMybhPnU8YwqAwC4jz3dAACkUGaGRw9ee3bUmAevPdsoKbIT0NDV6ES279odtbt9lla/0xg1dvU7jUF7he1kM14eyxN1D3KspFLqSSrt927e2WJ0XtM4U3ZjsUi3wVJy5lNvbmiJWplgSWps7dDmBnc/LwAgGEk3AAAp9vbu6I27Yv1cip6AJqKprSdpi5XASb0TODvZ9Ci+HmEP/s8H+uqvanXDUxs1+6F1qt4anPQ7TSr3GHYlN43r7xIZVQYAcA9JNwAAKdR1wqenNkTv0v3UhgZ1nfBFjTFJiuPx6ocHJMWfwEXqYu2UXS4emHg7vabRhUON4k3jTNkPRCKxy7zd7iieyr3kAIBTSLoBAEihZ2p2xiz/9lk9cdEka7Xy0LGemduJJHBVU8v06l2X+keJXTjJ+d7lcOXiTq/JdC602/OjU1XmbZf3R6oy8Kh38zsAgPtIugEASKFdLe2uxCVrtdJeYU80gQuclz1tXGFc1xKanF5QXqzCvKyo7ynKy/Jf06xJI5WXnRk1flh2pmZNcrfbdqrKvO3yfql3eb/952TsJQcABCPpBgAghcYV5bkSFysplnq6mDs18mRS62YCVzwssVFiTpLT0CKC7CHRf/WJ9fN4pLLMO1J5v7cgN+njwgAAPRgZBgBACrk1xspOipes3CKPgpNNOw1+5IYZKhqWrf2HO/Toug/1t/1HY573eMBWcjuBCx1J5g0zEzuakcOyjeIisZPTzQ0tUUetSdKh9uPa3NCiyskjjeIPBsS7JVUjw2xVU8t0eYU3aHb7BeXFrHADQB8h6QYAIIWcjLH67FmlUWOcJMWbdzQbJd1neUf0OkeiCVzz0S7j2EChyanTsm27E3sspnGmTB6IJLvM2y7vBwD0PZJuAABSyO0xVqZJ8YSRw4yOFy4u0QTuwFHnSW245NRp2XbLkU6jeNM4J9yqEgAADDwk3QAApFAyxliZJMWW4URv0zgn3v+4zfF7wiWnTsu2iw3L2k3jnKLMGwAGJ5JuAABS6KLTS/TjP9cbxblpzyGz1WbTOCeGxuggbjt/fKEWXjgxYnLqtGzbW2D24MI0Lh6UeQPA4EP3cgAAUmjWpJFGY6/cHmM1odisa7plWVpVu0c19c3++diJ+swEs4Zhcyu8WjB9jConj4y4GuykO/d5E4qMznui2+f6Z7Z1+yzV1Dcn7fgAgP6HlW4AABLU7bPiLhnOzPDoMxOLtLZuf8SY8ycWuV6C/IWZE3T/C9tixj2zcbee2bhbUs8cbjf2H08Zne9qXNXUMl161ig9U7NTu1raNaE4TwsrJ/Ya/7VpR7PR8Rb+dLP/7936zJJUvbWx155uN48PAOifSLoBAAjRdcIXM4GzJZpIdZ3w6U9REm5J+lPdfnWd8Lk6Q/rNBrOu6YGaWju0ZOWWhOc7t7SbdS83jQt3D/7r1YZe9+C/t3zs7ELl3meu3tqoJSu39Np77tbxAQD9F+XlAAAEeODFOp159x91/wvb9IuaXbr/hW068+4/6oEX63rF2olUYLInnUqkqrc2xjzf0681xGxVZp2Mc9P/b8tHjt9jX+eKNXUJlUU77ToejZN70N51wtmFquczW0rsM3f7LK1YUxf2Prv1nQIA+i+SbgAATnrgxTo9sb53EmxJemJ9Q1Di7VYi9dL7TUbXZhpnynQEWShLUmNrhzbHsVJus7uORyqY96inWsDni76f3Ok9+MzE+PfFJ/KZNze09HooEMiN7xQA0H+RdAMAoJ4y7yfXR19NfnJ9g7pO+CS5l0i1dZqtvprGmRpbZNZILZL9h+Pvam53HZfUK/G2u5AfO96tL/5kk776q1rd8NRGzX5oXa/KAaf34MZZE+K+ZklqaovvM5t+V4l8pwCA/oukGwAAST9/3azM++ev9yTmbiVSU7wjjI5jGmfquhljE3p/pNJv0+7ckbqO253cD7UfD3o9XLm403tQ+9Eho/hIWo50xvU+N8vpAQADD43UAACQjEt7Nze0aNHFk11LpK49d6xWvRN77/e15yaWJIeaGecIMo96xnFdUN577JfTpnJVU8t0eYXX3/m9ZHiO7vhNbdjzWifPvWJNnS6v8Cozw+P4HiS6klw8LDuu99nl9E2tHWEf7ET7TgEAAx8r3QAASDrceTx2UECc6b7kWIlUhuEoMNM4U2/EsX/YvoJl8yt6jTCLt6lcZoZHlZNHasH0McrweNTUFnk1ObRc/ILy4pgzzgvzsvz3INGVZG/B0LjeF6ucXgr/nQIA0gNJNwAAkg53dDuKsxOpSCXplswSqU0NZrOjTeNMvV5/wPF7vAW5YUdbJdJULrAc/bXtnxhdh5MV68BvP9aDkmhMHqBEE6mcPtJ3CgBIH5SXAwAgyRNzR3ePo53Htap2j0pH5OqtXdFXi9/efdAgmTJNAd1dBd1zsN0o7qLJxfrHz4xX6YiepDMzw6Nun+UvCS8d0dNl3LShWeXkU2Xt4crRTdgr1psbWnrt/Q51sP24/7z2g5JbVm5xdD6P3FmJDi2nD/xOAQDpi6QbAAD1lA5v3Xs4ZtzO5mP66q9qjY755IYG3TH3LGUPiVxYVjl5pB55ZXvMYwUmq67wmCV6p43I1YLpY/x/DpcoFw6NXuJtC1yhtsvRnUymDt37nIyu4IV5WUGJfLQ96fGwy+kBAIMHSTcAAJIum1KqP23b7+oxLUv6+es7tejiSRFjZowvMjqWaZypMYVm+5MD4yIlyoeOme2Ht1eoo5WjRxJu77PTRmr2eaOdY2hWph69eYYOHO1kJRoA4Ar2dAMAIOn9vW1JOe4bO6OXoK/cuMvoOKZxpkz3J9tx8STKttCmcrHma4cTbu+z02Z2pnO9MzI8WjB9jL8kHQCARLDSDQCAFFcyaSIvOzPqz9/YadYg7Y2dzVFXzE0E7sXebNiY7W/7DuuzZ5bGlShL4VeoTcu9b7vkdJ0xanjEFWd7j/aSlVvkUfA9TOS8iY4WAwAgEEk3AACSxhXFNw4qlutizNfOzYqelDuNiyTepmU7DxyVZJ6IFg7NCio394bZE21aFn7R6SUx9z/bXcFDP1si5010tBgAAIFIugEAaa/rhE/P1OzUrpZ2TSjO08LKib2am1k+98+bl52pC88oiRozPMfsP8WmceHE07TMVtfYKsk8EX30izOU4fFE7c5tl4U3tXaEvabQhmmxmHYFP29CkTI8UpjJZX4Znp44AADcQtINAEhrD7xYp6c2NAQlWt9+cZsWzSnX0qsq/K9t+eig6+deOGt8zD3BpnuG491bnMhebElqPtIpyTxRnjUp9j7oWKO7TGechx4z1qr4W7sORk24pZ6E/K1dB+kwDgBwDY3UAABp64EX6/TE+oZeiZbPkp5Y36AHXjzVyTov2/3n0KvfaVR3jCxvfPEwo2NZlrSqdo9q6ptjHjNQvHuxbT6r51cFO1GWek8MD7d/uj9iTzcAIBVIugEAaanrhE9PbWiIGvPUhgZ1neipK79uRvS91/FobO3Q5obo3cs/VTrc6FgrN+3WV39Vqxue2qjZD61T9dZGo/clmkCeO6HQ//f2/mlvQXCpebjO4tGYjO5asabO0cMFE+zpBgCkAuXlAIABJ7ALd6T9u8/U7DQqJX6mZqdunjNJF55eorzsTLV3dUeMz8vK0FM3fUYHjnTqw32H9cgr9TGvNVbSuznGSLFwmlo7tGTlFqNEN9EE8tppY4L+bLp/OhrT0V2bG1pcLfN2ey85AAAmSLoBAANKuC7cZWE6Ve9qaTc6nh2XmeHRwlnj9cT6yKvjCysn6KLTexqj1dQ3GyXdsZLevYeOGV1nIEunVoMvr/BGTXhjJZqxfPjJEV2iUUGvmeyfjiZVZd5OR4wBAOAGyssBAP1W1wmffrJhh+5dtVU/2bBDa97ZqyUrt/RaJbVXfgNLricU5xmdw47r9lla/U70ku3APdp2MhspPfOo52FArFXT0XGOKgtcDY4m2l5sE2/ucr4SH0sqy7zdKpEHAMAUK90AgH4pXNfxSMKt/C6snKhvv7gt5niohZUTJZk1HAsseXZr1XTWxJF6VLFXzCMxWQ2ONMvaxNAE54OHk+oybzdK5AEAMMVKNwCg34nUdTya0JXf7CEZWjSnPOp7Fs0p98/rjqfk2Y1V04zMxBI909XgqqllevWuS/Xcoln6wfXT9YULxhm979OjC3q91u2zVFPfHFc3denU6nukd8UzMswpu0R+wfQx/ocoAAAkAyvdAICUC2yMVpyXHXVfdSyBSbE9hzt0xTzDo15zuuMteU501fTAyTnYTsWzGhy4F7u729Kzmz+K+Z6S4TlBfzbdUw8AAHqQdAMAUipcEpeI0KR46VUVumPuWXqmZqd2tbRrQnGeFlZO9K9w2+yS52jXEWmPdiKNxeLZt+xG0y/TZD8wrnpro5as3NJrhbrRQTd1yXxkWKwmcQAADAQk3QCAlImUxMUj2spv9pAM3TxnUtT3Z2Z4dPW0sqir7FdPK3M9CTTpLp7hUdBKvdeFleX397Y6irMT5Wgl4aaJcqpGhgEAkAok3QCAlIiVxDnhxsqvaffyO6umuJp4mzRke+SGGSoalu1q06+PD5pVFthxThvNRZOqkWEAAKQCSTcAICVMkjhTbqz8uplUOlU1tUyLLy7XUxsaZAVk3Z6Te8+vOsf9vdI5hr8B2HFNrWbzxE3iUjkyDACAvkbSDQBIiURXMS+aPFL/+JlxCa38BjZw+6DxsNF7Gg+ZJZ9OVG9tDFvW7rOkJ9Y36NzxRa40KQv8vJbH7PsqGd6T+LYc7TKKN4k7b0JRr5L5UBmenjgAAAY6km4AQEokuoo5fVyhFkwfE/f7423g9vZHB3XteWPjPm+obp+l23/zTtSY23/zTsJNxeL9vOOK8yRJxSFdzCMxiXtr18GY4+B8Vk8ce7oBAAMdc7oBAClhNxCLN42cVR5/MmY3cIunvN1nubEL/ZTXPzyg9q7uqDHtXd16/cMDcZ8jkc974eklkiRvvtlDEpM49nQDAAYTkm4AQErYDcQkxZd4x5mtu9nAzQ3/veVjV+NCJfJ5PZI+M7GnG7z9kCSaSCPVQrGnGwAwmJB0AwBSpmpqmR67cYa8MZK5cDY1tBjHdvss1dQ3a1XtHj39WkNCDdxG5GTF/d5w11PXaDa666OD7XGdK5GGdZakN05+z/ZDEo96P++wXzPtHh+rysEj8wQeAID+jj3dAICUqppapssrvP4GX69s26/n39kb832WYZl3vHuZI2lqS+w48V5P5wlfXOdLtET79R0HdNEZPSXm9kOS0Os36R4f2MStdESu7pk3Rbc++3bEMWmJjH8DAKA/IekGAKRcZobH3zCrqbXDKOnOHxp7xdney+xmKXkiW7oTuR5vvlkjs1CJlmjvPRjcrT30IYlJ9/hwDxrKCnK1+OJyrX6n0XECDwDAQELSDQDoV9o6jrsSl6y9258c7tCq2j2OR5Ulej0zJ8XXOM4u5W5q7Yjr3KMLh/Z6LfAhSSyRHjQ0tXboyfUNevQLM1Q0LNs4gQcAYKAh6QYA9CumZeOx4hLZyxzN6zta9PqOnn3OZQ5WZRO9nhtnTYzrffZe7FtWbonr/Yl0iY/2oMFSTyn5/S/U6dW7LiXRBgCkLRqpAQD6lcLcbFfi+mLcVFNrh5as3KLqrY0xYxO9ntqPDiX0/rglkAvHetBgSWps7dBmB03xAAAYaFjpBgC4LrRplpOS4Zb2Llfi+mLclL1au2JNnS6v8Eb9jIlez2vbP4nr+7RXm+NVU9+sOZ86zTg+8N5/uO+I0XuYxw0ASGck3QAAV0VqmmVahv3eHrMRWq9uPxB1b3Wie5lNBa7WRtvnnOj1PPJKvf/v+7Ks/Z2PDxnHxtuZnXncAIB0Rnk5ACCmIx0ntOjnb+iK76/Xop+/oSMdJ8LG2U2zQpMuJ2XYQ7PM/tO0dW+bvvqrWt3w1EbNfmhdr2Pbe5ml8HOlw72eiNdOPgSoqW9Wt693Wh3tepzqy7L2vGyz+xHp3kfDPG4AwGBA0g0AiOrqRzZo6vL/0dpt+/VB02Gt3bZfU5f/j65+ZENQXKymWVJPGXa4hDSQt6B3t+xYIiWh9lxpb0HwSmphXlbQdbnhkVe2R30IEO16nHLyfSa6ivyZCbEbqcXTmZ153ACAwYKkGwAQ0dWPbNC7H7eF/dm7H7cFJd5uNc2aNq7Q8XVGS0Krppbp1bsu1XOLZukH10/XL//3TOUMSe5//qKtRIdez8JZ4+M6h+n3aZe1x+uM0uExY+IpYfcW5OqxG2cwjxsAkPbY0w0ACOtIx4mICbft3Y/bdKTjhIbnDjEuY44Vd/CoWSO1UKZ7q//a2Kamts64zuHkWqI1WAucc/3WroMJnSvW95mZ4dHV08r0xPqGuI6/6t29uqRiVELXYLvtksk6Y9QI5nEDAAYVkm4AQFhf/dVbxnE/+fJM4zLmWHF1jdET/VhCE8B4m3slyvQhwITivITOE+v77PZZWv1O7L3fkXx8sD3ha7BddPppUb8LAADSEeXlAICw3vnIrIu4HWeXMUdauzRtmnW0M3yTNlOBCWA8zb1iueEz4/SD66frtksmG8XHWgVeWDlR8Sz4mn6fiXYvzxmSGTPGrXsPAEA6IukGAITVecLnKM6kW7hJ06zS/Bwnlxl0jsDELp7mXiaOdp7QguljdNHpZrOrY60CZw/J0OenlDq6BiffZ6Ldy88emx8zxq17DwBAOiLpBgCEVTrCLPkNjIvUndtJ06xzxxU5u1CdSuzumTdFmxtatKp2j55+rSEpJeV7Dx2T5N7qbrfP0hs7o+/rDj2Hk+8z0e7lF002e7jgxr0HACAdsacbABDWyBHZqj8Qez/vyBHZQX+umlqmyyu82tzQov2HOxw3zWo9dtzxtXoLcnX1tDLd/8K2pO/dbu86rlW1e1Q6Ilf3zJuiW599Wx4Fjx9zsrq7sb5Zh9qjf2ZL0jevOkul+bmOv8/zJhQpwyPFmCwW/eSGEr33AACkI5JuAEBYRzu7444L7M7tVPGw7NhBkv71c5N0pjdfpSNydfBol259dktcpeT/fuVZGlWQq8df2a5t+47EjK9rOqqv/qpWUs9K9uKLy7X6ncagZN9bkKtl8yuMVndrdhwwus7WY8e16GKzfeSB3tp1MP6EW9Kmnc2ac6bZareU2L0HACAdkXQDAMIzTdRc3jTtLRhqFDfnjFJVTh6pbp+l2Q+ti/syPB5pwfQxerj6r47f29TaoSfXN+jRL8xQ0bDsOFd344/r9llhV5UDX//Q4EFCNJbbm+IBABhkSLoBAGGVFQ7V+42HjeJMRUoSA11QXqzCvKyoJdeFeVn+vdKJdud+Y2eLSvNz4yprt+dx3/9CnV6969K4yqhnlhfrkVfM4gKFG4VWdrLMPnTlPRH5Q7NcOQ4AAIMVSTcAwC8wKR5bbNaA65IzS4ziIiWJpmXYgQJT20S7c79Ut18v1e2P+/2m87gjyfCYJeqBcfYotNBF6MbWDj2xvsHxNURz6GiXq8cDAGCwIekGAEgKnxSbeOWDT/TFWeUxjx0uSWxq7dCSlVuCultvbmiJ2VjsYPtxf5KbaHdut8Sb/B842ukoLlmj0CJpaktuYzoAANIdI8MAAP6kOJ6S5FjviZYk2q+tWFOn7pPdvkyTVzsu1uiuvhJv8m/6Pjsu0XJ6p0YXmW8fAAAAvTlOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHH/Y6zgsvvKCZM2dq6NChKioq0jXXXBP08927d2vevHnKy8tTaWmp/u3f/k0nTpxwerkAMKh1+yzV1DdrVe0e1dQ3+xPb0JhEVk5jdTmPlSQGlmdLzpPQzAyPls2vkNS71Zgn5H+TwXQedyRO530nWk5/2yWT9YPrp+ubV51lFH/hJLPtAwAAIDzH5eVHjx7VtGnT9C//8i+69tpre/384Ycf1g9/+EP9/Oc/V3l5ue655x5dccUVqqurU25uzy9I//3f/61FixbpP/7jP3TppZfqxIkT2rp1q/8Y3d3dmjdvnrxer15//XU1NjbqS1/6krKysvQf//EfCXxcABg8TPdQJ7pyOvm0YVF/7nTl+rwJRfJ4onfN9nh64mxVU8v02I0zen1ee3TXpWeN0jM1O7WrpV1t7cf1/Dt7ja4plljzuE0ax9kPDZas3GI07zvRcvqLTj/N3/X90T/Xx2xYN4vxXwAAJMRx0n3llVfqyiuvDPszy7L0/e9/X3fffbcWLFggSfrFL36hUaNG6fnnn9f111+vEydO6Ktf/aq+853v6Oabb/a/t6Kiwv/3L730kurq6vSnP/1Jo0aN0vTp03X//ffrrrvu0vLly5WdbTbDFQAGKyd7qBNdOT1vfFHUnztduX5jZ0vMMVWW1RN30emnVmGrppbp8gpvxCT35jmTJEk/2bDDtaQ72jxuJ43j7IcGy1e/r6a2U3u8R+XnaPnVnw6Kt1fGm1o7HFUneE5er71inpnh0YPXnq1bVm6J+J4Hrz07ro7sAADgFFf3dDc0NKipqUmXXXaZ/7WCggLNnDlTNTU1kqQtW7Zoz549ysjI0LnnnquysjJdeeWVQSvdNTU1OvvsszVq1Cj/a1dccYXa2tr0/vvvu3nJAJB2nO6hTnTl9ONDx6L+3Gn5dE19s9F5w8VlZnhUOXmkFkwfo8rJI8MmjMXDEntwW1lepB9cP13PLZqlV++6NGLCHW6PvP3Qo3prY4SjRyqQPyVaOX0kkVbkq6aW6fEbZ8ibnxMU783P0eMBD2YAAED8XE26m5qaJCkoWbb/bP9sx44dkqTly5fr7rvv1h/+8AcVFRXpc5/7nFpaWvzHCXeMwHOE6uzsVFtbW9BfADAYOd1DnWgjsl3N7b1eC9xLvrmhRffMmyIpckoZmAx2d/vMzttyNOpe9Ui8BQk2BvN4oib1Th96SKeS9NBO4U1t4ZN0e2XcWxD8wKSsIFdfubhcZSGvewtyg6obQo/12jc+r+cWzfI/THjtG58n4QYAwCV9PjLM5+v5Zeqb3/ymrrvuOknSz372M40dO1a//e1v9ZWvfCWu4z7wwANasWKFa9cJAAOV0z3U0fYUm+g4HtxILVJZ9eKLy7X6ncawe64DE7zWjujjwmxr3mnUmnca/cc3nfdtP2SIdx9785Hoc6udPPSw91ZHa2RnqSdJv7zC22uVOlI5/Z1VU2LuJQ9kVwgAAAD3uZp0e71eSdK+fftUVnbqF599+/Zp+vTpkuR/PXAPd05OjiZNmqTdu3f7j7N58+agY+/bty/oHKGWLl2q22+/3f/ntrY2jRs3LsFPBAD9i0ljLqd7qKXIjchMTApopBZtL/mT6xv06BdmqGhYdtTr/+Sw2dzq0OOH7lWPJPAhQzwd24fnRv9Pp9OHHiaN7AKT9ECRkmWSaAAA+g9Xk+7y8nJ5vV69/PLL/iS7ra1NmzZt0pIlSyRJ5513nnJycvTBBx9o9uzZkqTjx49r586dmjBhgiSpsrJS3/72t7V//36VlpZKktauXav8/PygZD1QTk6OcnJywv4MANJB9dZGLV9dF1SC7M3P1fKrKxw12gptqGULXTl9dN2H+tv+ozGvq6a+Wfeu2qpxRUP1k1d3Riyr9ki6/4U6vXrXpVFXXYflOP9Pk338cCvC4STykOFM7/CoP3f60KOpNfqeeJtpHAAA6F8c/2Zz5MgRbd++3f/nhoYG1dbWqri4WOPHj9fXvvY1fetb39IZZ5zhHxk2evRo/xzu/Px83XLLLVq2bJnGjRunCRMm6Dvf+Y4k6R/+4R8kSXPnzlVFRYUWLlyohx9+WE1NTbr77rt16623klgDGJSqtzaG7TLd1NahW1ZuCWp6ZTKC6p554cuPA1dIn9u8WzJIuj8+1KFf1OyKGRdaVh3JdeeO1fO1zruL28d/+rUGlYzIiVlWHfqQ4a+NbXrsLztinuf88dHncTt96NFyNHq5us00DgAA9C+Ok+4333xTl1xyif/Pdkn3TTfdpKefflp33nmnjh49qsWLF+vQoUOaPXu2qqur/TO6Jek73/mOhgwZooULF+rYsWOaOXOm1q1bp6KinrEzmZmZ+sMf/qAlS5aosrJSw4YN00033aT77rsv0c8LAANOt8/SN373XtSYpb97L2iFN9rc6qunlen+F7bFHGXVFbJX2y2xyq8vPKNEedmZau+K7/z3v7DN//ex9noHPmQoGZZjlHTHasQW+NAjksDGccXDzR4mm8YBAID+xWNZsaahDkxtbW0qKChQa2ur8vPzU305ABC317Yf0Bf/a1PMuF/+75lBc6ul3nvADx7t0q3P9t7LbK8FB+6JvvnpzXr5r5+48AmCPbdoVsz9xpFW9p0K97kiSeR7DueBF+v01IYGBTZWz/BIi+aUa+lVp7ZK1dQ364anNsY8nsn3BgAA+o5pzunqyDAAgPte234g7rjAudUXlBfr/hfMR1mNyM2K84rDC53H3RcijegKZ5/h3u7XPjwQc1RZ9dZGPbk+OOGWJMuSnlzfEDQCzC5Hj6avvzcAAOAekm4A6Of2HOw9BzueOKfzuyu87lUJhZvHHUm3z9Ltv3nHtXOHfq5I3twV/ee2H/+lXl/9Va1ueGqjZj+0rtcMbadzujMzPLp6WvRV+KunlcX83gAAQP9E0g0A/Z5pshU9zukoq6Lh2Ybnjc1bkGtU4i1Jr394IO793NHE+vx/bWxzfEx7VFlg4u304Ua3z9Kv3/w46nl+8+bHMVfqAQBA/+TqyDAAgPtMU61YcU5HWb29+6DhmXvzSPrFv1yglvaumF3EQ/3mrY/iPm80sT7/4c4Tjo8ZblSZ04cbG3c061D78aixB9uPa+OOZqO95AAAoH9hpRsA+jvTfpcx4uy9w5FS39A9139tOmx+jSEWX1yuOZ86TQumj1Hl5JGOSqNrP4o/2Q/HdC/5yDhX9kNXrp0+3KipbzaKN40DAAD9C0k3APRzpvlqrDh7lJXUuxA93J7rIx3RV18jXcNXLg7uzu3UkAz3/tN0ai55hTY3tERtgJZhXMYfnr1y7fThhnu1DAAAoD+ivBwA+rkxxXlxx4WODLu8whtxfnfoPOuRw8y6l08oytFnz/JqQnGeFlZOVPaQxJLmWZOK1dBs1jwullNzyetiziUflpvYfxLtlevAOd0eBafK4R5uVE4q0SOv1Mc8fuUkSssBABiISLoBoB8KTJYLh5olvxeGJGXVWxt7Jdd2svnqXZcGJePh9lyXjBgq6VDM854zrlj3LZhqdI0m5k7x6rk3ojcWi+aeeVNUMiLn5FzyTt367Nu91ojtBmiBzd0umDhSa+v2Oz6fRz3JfWD5etXUMuOHG7Mmj1RhXlbUfd2FeVmaxYxuAAAGJJJuAOhnwiXLseRlZwYlZdVbG7Vk5RajZDMS033Ybo+yeivOPd128vvli8qVmeFRt8/S7IfWRRzdFdoA7aYLJ+o//rjNeAu9fU4p/Ci0qqllurzCG/PhRmaGRw9ee7ZuWbkl4nkevPZsRoYBADBAsacbAPoRO1l2knBLkicgH3M6JzqS4uFmK+ymcabimYwVLvl1Orore0iGzh7jbDa5x9PTNC7SA4zMDI8qJ4+M2VCuamqZHr9xhrz5OUGve/Nz9LjhqDUAANA/sdINAP1EtGQ5lqOd3f6RUk6SzcooJcvrP/jE6NzrP/hE+junVxxZUZ7zLuLhyradju7qOuHT1j3OZnX7LOnJ9Q06d3xRwomx6co4AAAYWEi6AaAPhDY0C5dMxUqWY6mp70m6nSabkbQcNetebhonmX0PxcPMku4ln52ks8ryIx7H6eiuZ2p2xrXKLgWXqSfCXhkHAADpg6QbAJIsWkOzeFZmI+vJGJ0mm4ECk2LTLuQFho3eTL+HQ+1dRscrGZ6jBdPHRPy5PbqrqbUjbPVAaAO0HQeOGp03lGnlAAAAGJxIugEgiZw0NDNNliOxR0qdN6FIGZ7oe6MzPD1xodfqtIGbJC29ckqv10JXtJ10ETctL28+0qVVtXuiNihzMrprf1tiDz0Sf2gCAADSEUk3ACRJrIZmod2zY63MRhPYvfytXQdjlkn7rJ44e2U20sMBE0OzMoP+HC55z/DI+Hs4aLjS/eO/nJptHW7FXHI2uuu0EcFNzJxK9KEJAABITyTdAJAkThuaRVuZjSUnoBTc6Z7uRBq4SdKmnc2ac+ZpkiIn79EeAoR+D6Z7ugNFG4Vm2qAs3u3Y4eZ0AwAA2BgZBgBJEk9DM3tl1lvgbNX0YPtx/+grp3u6E23gZo8eSzR5t7+H0nznK8axRqGZjO4aYbg3Pdy5w83pBgAAkFjpBoCkibehWejK7If7juiRV7bHPI6dtDptIJboXuTmo52SEk/e/d9DnFl7tIZmJl3TMzwkzQAAwH0k3QAQB5MkzmnyGyhwdFRNfbNR0m0nrU4biCW6F/m17c2S4k/eez0EONKZ4PV8EnRf1tY1GXVNL8iJb6U7dE86AABAIJJuAHDIdPSV0+Q3kni6kTtpIJZIAzdJOtTeM6c7nuTd/uT3zJvif4ixZVdLHFdxyiOvnGqwVpiX5b++QOH2gB/qMGvgFoqRYQAAIBqSbgBwwMkIMKkn+V18cbme2tAgK+BNHo+0aE55r6Zf4cTTjdw+t0kDsUQauElSblbP8UyS99CHB96CXF09rUz3v7AtodL0SMIl3FL4rukZnsTanDAyDAAAhEPSDQCGYo0Ak3qXGVdvbdST6xvCdvN+cn2Dzh1fFDPxjqchmy2wTD2aSCvjJs44bZj/XLFW9h+5YYaKhmUHzO/u0q3PxjeqLFGhK9SVk0calfFHwsgwAAAQDkk3ABgyaRQWmMSZdPM22Qscb0M2p0JXxp/d2KBNO1tjvm/k8FPzrZ2UtXf7LM1+aF1KEu5A9sOKWZNGKmdIhjpP+By9n5FhAAAgGpJuAIgisGHaB42Hjd7TeOiYJOdzuiM5b0JRzLJvj4L3dMcrcGX8yb+YrfrubG4P+rNpWXui3c7dYj+s6PZZ6up2nnBLjAwDAACRkXQDQAThGqaZePujg7r2vLEJlYUHeqOhJeZqsHUy7qIzSoJeN+myHkl7l1kCGi7OpKw91XugQ1eon6nZGbTv3kS4FXwAAIBAJN0AEEakhmkm7PeUDMuJGmfb39ahVbV7gpLiwGT5lW37jY7zev2BoKTbtMt6JKMLc9UQsoodKS4eqd4DbSl4hXpXS+zPKklzK0Zp3jlljh9iAACAwYmkGwBCmOzFjmZc0dCevzHMxb794l/9f192spv36ncaHa+w7zlZ1i7F7rL+6BeCG5qFSx7nnHGaXquPPb5rzhmnObpOW6Kjytw2oTjPKG5mebEWTB+T5KsBAADpgqQbAEIkutf4eHdPCnngSKfj9za2duiJ9Q1xnrdbklmX9due2xI8uis/V8uvDl4BT3YDt0RHlQWK9LAi1nzzwEZ2Cysn6tsvbos5D31h5cQErhQAAAw2JN0AECLRvcara/fotkvP6PPy6ff3tEkye2gQmlg2tXXolpVb9HjAnPEDh80eGoSLM91Lbnc7X766Tk1tzr732aeP1D+cPy7o+HdWTfGf98DhTt3/wraoxwhsZJc9JEOL5pRHfeixaE65sockNs8bAAAMLiTdABAi0WT5wJEuST3dxGOttLrp+MnO24k8NFj6u/f8K7/vN8YeFyapV1x8e8mdf0lfuXiy5nwquLQ9sIHb77d8bHScptZTZflLr6qQJD21oSHovmV4ehJu++cAAACmSLoBIESie41zs3pWQt/adbDPEm5Jysvu+Vd6Ig8NDrYf18Ydzbro9JKgPeLRONlL/ljASnq0eBPnT4w+F7vlaJfRcULjll5VoTvmnqVnanZqV0u7JhTnaWHlRFa4AQBAXPgNAgBC2HuNJeNeaEFmjO+Zl93XI7HOPzn6yn5oEG9P7Zr6ZklSlmGSaceZ7CVfsaZO3SefRCTasO7ZTbui/rwoL9voOOHisodk6OY5k3Tfgqm6ec4kEm4AABA3fosAgDDsvcbeAuerxmePLZTU9yOxTj9tuKTEHxrYKfJpw8ySVjsu1l5yS6f2UJvExxJrxNfBdrOVbtM4AACAeFBeDgARVE0t0+UVXn9jrrq9rUadxYtPJqF9ORIrtKu2/dAgdG+1icpJPbO+xxSZjdCy40xX9l/b/on2H+7Qh/sOO7quUGUF0eegFxs+NDCNAwAAiAdJNwBEEdiYa9OOZqP3vL37oP7h/HFRR2LZfy7My9Kh9uP+1yONvsrLzlR7V3fEc4brqh360KBkeI7+9ZdvqfXYiYjHKczL0qyTn7fQsDzbjjNd2X/klXqjuFg+bom+59xbMNToOKZxAAAA8SDpBgCZjbj6oMlsZTYwLtKKs/dkN+/ApDjS6Cv79Yertznuqh340ECSHrruHN2yckvEa3/w2rP9n/ugYSOyrXtatap2j0qG5cibn6t9bclf2Zek3QejJ912pUG0lf6ygp7vFgAAIFlIugEMevGNuDIXuuIcmtQHJsW20GRZks4dX6TThu/RvsOnkuHThmfr3JON20yv5fEbZ2j56vfV1HZqvrY3P0fLr/500Od9d88ho2OuebdRa95tlNSzUm5JvVb2k2FCcfTy98BKg3DX4pG0bH5F2PnhAAAAbiHpBjCoORlxdaZ3hN7afSjmMc/0juj1Wrgk2o3r3He4K+woLlu4FfxYDwFsBw539jpeLK0nS+ULQsrmk+Hys0bFjIlUaeDmQxUAAIBoSLoBDFqxRlx51DPi6vIKrzIzPDp3fJGe3fxRzOM6WXlO9DqlnmsNvE5brBX8WA8BhudkOr5W+3vLHZKhX/7vmTpwpFMf7juiR17Z7vhYsbQcMyt/N33IAAAAkAyMDAMwaDkdcdVy1GzlN1xct89STX2zVtXuUU19s39WtRvXqZDrlE6tjIe+z17Br97aGPO8Z4zKN77GQJakprZOZXg8WjB9jC46vSSu48TSYrjnXDpVabBg+hhVTh5Jwg0AAPoMK90ABi3TEVd23NY9bUbxoXGJ7hn/+GD0edTBcSMdr+BHEq1bugn7e0vW6LQRufwnDAAA9H+sdAMY0BJZQTYdcWXH/c2we3lgnBsrzv9jEBMY53QFP5LGQ9G7g8dy4HCnVtXu0eaGFt0zr6e7upvryy+93+Ti0QAAAJKDZQIAA1aiK8ixVmA96hntZY+UGma4smrHubXiHNhlPBo7zukKfiQ5WfE/l83wSPe/sM3/57KCXC2+uLzX/HF7Tnk83c5NvxcAAIBUIukGMCA56ToeSeBIqdCkz06BA0dKneUdri0G3cvP8g6XZL7i/PRrDSoZkROxwVdhXlbMcwbGOV3Bj2TK6Hy9Vh99NTyS0IKDptYOPbm+QY9+4VwVDcsJami2tq6p18MTE6bfCwAAQCqRdAMYcNxaQZYij5Tyhlkxnza2UM9u/jjm9U0bWyjJfMU5dEU49LyL5kzSq9ubYx5n0ZxJksxX8H0+S6tq90RM9o92ON/TneHpnXBLp+7L/S9s06t3XRp0rtDu4o2HjunB6g9insv+vAAAAP0ZSTeAAcfJnmWT2dimI6XeM2yk9t6eNv2TzFecA4VbqZ99xmnKyvToeHfkAuysTI9mn3GapNgr+JakY8e79cWfbPK/Hi7Z/2Cf2R72M0qH6bZLz9CBw51BDxBCRbsvgXPMu32W/vNPH6rzhC/isXKGZPg/LwAAQH9GIzUAA45be5YDmYyU2mvYWMyOs1ecnTQPsxPkFWvqgprCZWVG/9d16M/tFfxR+cGJv12Sfaj9eNDr4Rq7Hek8YXTNnpOjwUpG5BjFx7ovmRke/eD66VFjfnD9dMZ+AQCAAYGkG8CA49ae5UAmXdB3HDhqdCw7zl5xlpx17Q7tLr6xvjnm+K72rm5trA9Xgh78OUKT7dCowGR/yqjhRtdrxxXnZRvFm8RVTS3T4zfOkDc/OJH35ufqcYP9+gAAAP0F5eUABpzp4wpdjave2qjlq98P6obtzc/R8qs/HZTcDcs2e04ZGBdpz7gJe0X49R0HjOJf33FAF51RIilyo7loHcJDy78XnDtOq96NPZZrwbnjJEl/bTIrv/9rU5vmfCp2abhp2T8AAEB/RtINYMBZuXGncdyiiydHjane2qhbVm7p9XpTW6duWbklaFW1rDBP7zceiXnessK8oD+HJo+x9j7b7JX6vQcNy9pPxkVrNGfCTvY/2GeWRH+wr02XTinVR4bXaRonBe/1BgAAGIgoLwcw4Nhl14nGdfssfeN370WN+cbv3vOXW19RMcrovLHizvLmy5sfea+3Rz2Nzez54KMLhxqd146L1WguFjvZX/t+7FXuwLgJxXkxIuUoDgAAIB2w0g1gwDkWY3+zadzG+uaIe5xth9qPa2N9sy46o0QleWaNwkLjqrc29iovL8zL8o/RijUf/MLJJXr0z/Uxz3vh5J7ScicN5EIV5mX5k/1PDnfGiFZQ3MLKifr2i9vCjgyzZXh64gAAAAYLVroBDAiBjc5GDjdLfs85OS87khrDvdJ23C/f2GUUHxhn760OXXluPZnsF5zsJm7zFuQGjQuTpM+UF8dsxOY5GSfFN6os8Di20/LNjmPHZQ/J0KI55VFjF80pV/YQ/tMDAAAGD1a6AfR74VaKTdhNxSIzbcjVE7fNsFGYHRdtb7W9yp07JEO//N8zdeBIZ8RGYW/tOhhzf7Yl6ZmanSoZkaOS4Tny5udoX1un433dB9uP+xupzZ0ySlt2H4r5nrlTTpXTL72qp1v7Uxsagla8Mzw9Cbf9cwAAgMGCpBtAvxapC3csw3IyNWtS9AZclZNH6pFXtsc8lt3Iq73LZ3RuOy7W3mpLPQ3bMk7OuY5k78F2o/MGNmeLVL5uwi5P//SYAqP40LilV1Xojrln6ZmandrV0q4JxXlaWDmRFW4AADAokXQD6LcS6cKdlRk7wZs1aaQK87Ki7usuysvyJ+/eghwdjLEH3I6TzPdWv7b9QNSRWLUfHzI6TqDA8vVY+9ZD2eXpLe1dRvHh4rKHZOjmOZMcnRcAACAdkXQD6LcS6cJ9KKBMOpLMDI8evPbssCPDbA9ce7Y/CZ4+tlDbDEaGTT+5l9x0b3XgantZQa6Wza8I2tNtXgZ/ir3KPTQrU4/ePEMHjnaqZFiO7vjtO9rX1hH2QYZHPXvK7UZqJcMMG8cZxgEAAAxG1PoB6LcS6cItSU2tsedBV00t0+M3zpA3pGlYWUFu0IxuSRpfaDbqyo67oLxYZQWRR4OFv+YOLVm5RdVbG/2vTRwZ34gtS1Jja4cyMnrK1y86o0TLr+7ZUx16TeG6pncdN+sSbxoHAAAwGJF0A+i3EunCLUkHjpiVR1dNLdNr37hUzy2apR9cP13PLZqlV++6NGS1WdpiWOZtx2VmeLRsfvgkNxJ7BXrFmjr/fPAvzJxg+O7wAh9eVE0t02M3zpC3IPi7Ddc1/anXdhgd3zQOAABgMKK8HEC/Za8UN7WGL4eO5ZDhnmSpJ0GOVoouSUc7TxgdKzDOTnKddF+3V6jt8vjajw4ZvS+S0IcXVVPLdHmFV5sbWqLuJd+x/6jR8U3jAAAABiOSbgD9lr1SvGTllri6cFvxZOpRlIww3OMcEhea5H6477AeeaU+5nHsFWqTMvlwQvdoBzJ5yJBhuDxvGgcAADAYUV4OoF+LVA5torXDfKXbxLhis73V4eLsJHfB9DG66PTTjI5z4HCnVtXu0Zu7WhxdpxR+j7ZThcOyXI0DAAAYjFjpBtDvha4Ur35nj17e9knM97m80K3zxhdLir1C3RMXmUnZfIYneO62U96wXdCdGTUiR3UG3dpHGVYAAAAADEYk3QAGhMBy6Dd3mq38ZnrMV3i7fVbMPc7/8/5eo2P9z/t7demU0sjXZVA274vjicE100frkrNKI16/Ux8fNNuDbhoHAAAwGJF0A3BF1wmfnqnZqV0t7ZpQnKeFlROVPSQ5O1imjS3UM9ptFGeiemujlq+uU1PbqeTRm5+r5VcHrxT/5W8HjI5nEhepwVqGJ76EW5KumzFWcz5lVrpu4pjhKDDTOAAAgMGIpBtAwh54sU5PbWgISha//eI2LZpTrqVXVbh+voOGXclN4qq3NuqWlVt6vd7U1qFbVm4JmtXdcdyse3m4uHAr6aFl8wcOdyZUUp7hYGXfxJBMs4cmpnEAAACDEUk3gIQ88GKdnljf0Ot1nyX/624n3ofaj7sS1+2z9I3fvRc1Zunv3tPlFV5lZnhUkDNEh47FXtUtyAn+V2v11sZeK9plAXuu7bL5VbV7Yh47mpodB3TgaKdr5eWXVZTqvzbsNIoDAABAeCxPAIhb1wmfntrQO+EO9NSGBnWd8Ll6Xo/him6suI07mmMm5gfbj2vjjmZJUvEIsw7qgXHVWxu1ZOWWXjO6m1o7tGTlFlVvbfS/FjpP26lHXqnXV39Vqxue2qjZD60LOnY8LjlzlKtxAAAAgxFJN4C4PVOzM+b+Y5/VExeq22eppr5Zq2r3qKa+Wd0ONjLPDDN3Op64mvpmo+PYcblZmUbxdly3z9KKNXVhG6XZr61YU+f/7HZXczeKxO2k/sV398b9Pc+aNFKFedHHgRXlZWnWpOjzvgEAAAYzyssBxG1nc3tccbHKrWMyzRtjxjk8kGE23HasS6tq9+jA4c5eK9yhR21s7dDmhhZVTh5p1NXclP3e2557O+jBiJPvOTPDowevPTvsnnfbA9eenXAZOwAAQDpjpRtA3CzLLC0MjHNSbh3Jpp1mK9Sx4ionlRgdx44bZrjS/X7jEX31V7XGTdH2Hz71Xdhdzb0FiZWa20IXtp18z/b1PH7jDHnzg6+nrCA3qMkcAAAAwmOlG0Dchuea/SvEjotVbu1RT7m13bgsMtOV1ehxsyb3lE9H29ddmJelWScbnZUMzzE8rzOhe7lDu5pv3HFAz23+2JVzOfuew1+PW43aAAAABgNWugHEzTTnsuM2N7QYl1tHY3f7jiVWnF0+Hc1/XDNVmxtatKp2j97bc8jovE5keKTzJhSFvbbKySO1YPoYXVButiJvyvR7jnQ9dik8AAAAYmOlG0DcivLMVn7tuMAy6mjCxQXOuS7Mid7cyzZjfO9kNlTV1DJ95eJyPbm+IWgF3qOeUVj3v7At6oOCRPks6a1dB6M+IAgt7XaL6f0AAABA/Ei6AcRt5LBsR3GmI7FC48I1XjOxcuNOLbp4ctSY6q2NvRJuqWc1eG3dfkfni1es5Pe8CUUJN1YLJ9ERZQAAAIiN8nIAcXO6ch1rJJZHPQ26LggY9RWp8ZqJN3YejPrzaHvM+1Ks5PeNhhbXrzH0ewYAAEBykHQDiNv6v33iKM4eiSX1bnFm/3nZ/Ar/fuFEk+Jh2dG7jcfaY55s4R4yhFOz44Dr5756Whn7sgEAAPoASTeAuO05ZDanOzAu0kgsb0GuHgsZQZVoUnzNuWOi/jyVe5rDPWSIHe2e1e80qjt0nhgAAABcx55uAHFrPRZ51Fa0ONMRVIkmxUMyoj9X7Is9zWUFubp6WplWv9MY9ADBW5CrZfMrjOZczywv1iOvuHtddvdy007wAAAAiI/jle7169dr/vz5Gj16tDwej55//vmgn1uWpXvvvVdlZWUaOnSoLrvsMn344Ydhj9XZ2anp06fL4/GotrY26Gfvvvuu5syZo9zcXI0bN04PP/yw00sFkGSZMZLaaHEmI6gSTYr3tUVP2mPtMY/XmIIc/eD66Xpu0Sy9etelWnpVhV6961I9t2hW0OsmCbck+brNVqS/ccWZ+sH103XbJdGbx9noXg4AAJB8jpPuo0ePatq0aXr00UfD/vzhhx/WD3/4Qz3++OPatGmThg0bpiuuuEIdHb1/ubvzzjs1evToXq+3tbVp7ty5mjBhgt566y195zvf0fLly/Xkk086vVwASTRqhNnIMNO4UIkmxW9/FL2RWrQ95onwFgzt9TAhkTnXv39nj1HcX/cd1oLpY3TR6acZxdO9HAAAIPkcJ91XXnmlvvWtb+nv//7ve/3Msix9//vf1913360FCxbonHPO0S9+8Qvt3bu314r4H//4R7300kv67ne/2+s4v/zlL9XV1aWf/vSn+vSnP63rr79e//f//l9973vfc3q5AJJo/jm9H5olEhcq0aTYZH040h7zsoJcfeXicpUVOE9MPzVqhOP3RHO084SjuHi6xAMAACA5XN3T3dDQoKamJl122WX+1woKCjRz5kzV1NTo+uuvlyTt27dPixYt0vPPP6+8vLxex6mpqdHFF1+s7OxTM4CvuOIKPfTQQzp48KCKiop6vaezs1OdnZ3+P7e1tbn50YC01e2zYu6tjqS0YKirceHYSXE8c7rHFZmdt2pqmS49a5SeqdmpXS3tmlCcp4WVE5U9JEN3Vk3xfz9/qmvSmnebYh4vP8/ddhmj8s0SfzvOflixZOWWXvO9nTVwAwAAQKJc/c2wqannl9FRo0YFvT5q1Cj/zyzL0pe//GXdcsstOv/887Vz586wxykvL+91DPtn4ZLuBx54QCtWrHDjYwCDRvXWxl7JbJmDBl/7244Zncc0LpLQxmsb6w/ouTc+jvm+nQfatap2T8yHCeG+h/96tcH/PdjNxv781/1G17u/tTN2kAPnji/Syk27jeJskR5WOGngBgAAgMT1effyH/3oRzp8+LCWLl3q6nGXLl2q22+/3f/ntrY2jRs3ztVzAOmkemujlqzc0qsEu6m1Q0tWbuk1viucNe82Gp1rzbuNWnLJGXFeaQ97T7QkvWB43ufe+EjPvfGRpMgPE5x8D6El6JGYxpkaXWi2Yh8aZ9olHgAAAMnj6pxur9crqad8PNC+ffv8P1u3bp1qamqUk5OjIUOG6PTTT5cknX/++brpppv8xwl3jMBzhMrJyVF+fn7QXwBO6fZZqqlv1qraPXpt+wEtX/1+2D3P9msr1tTFnOP88UGzFWzTOFPDsjMdv8dOoqu3nkrYu32WVqypM/4eioZmh4nsrfXYca2q3aOa+mZXZmFPH1cYd1wiDdwAAACQOFdXusvLy+X1evXyyy9r+vTpknpWnDdt2qQlS5ZIkn74wx/qW9/6lv89e/fu1RVXXKFf//rXmjlzpiSpsrJS3/zmN3X8+HFlZWVJktauXaszzzwzbGk5gOjClU9HY8lsjnOW4WM70zhT184Yq9/X7nX0nsAk+vIKrzIzPNrc0BL1Own9Hlo7zeaSP7v5Iz27OfoKuxPPbtplHHfznElxnwcAAADuc/yr8JEjR1RbW+ufq93Q0KDa2lrt3r1bHo9HX/va1/Stb31Lq1ev1nvvvacvfelLGj16tK655hpJ0vjx4zV16lT/X5/61KckSZMnT9bYsWMlSV/4wheUnZ2tm2++We+//75+/etf6wc/+EFQ+TgAM3b5tNMmZFLsOc6nnzbc6DimcaYuPL1EOUPiy+TtJFoyn1OdyDzrcCvsTu1qaXc1DgAAAH3H8Ur3m2++qUsuucT/ZzsRvummm/T000/rzjvv1NGjR7V48WIdOnRIs2fPVnV1tXJzzfc4FhQU6KWXXtKtt96q8847TyUlJbr33nu1ePFip5cLDGrRyqdNxJrjnGGY95rGOZFI0XZTW08SXTLMbH64HVc4NMvxuSz1dAwPXGF3akJx7ykPicQBAACg7zhOuj/3uc/JsiL/uuvxeHTffffpvvvuMzrexIkTwx7vnHPO0YYNG5xeHoAAscqnozGZ49zVbXYs0zhTr28/oK4Tvrjf33LkZHdx0/z3ZFxxntme7lCm5fqRLKycqG+/uE3RtodneHriAAAA0L/0efdyAMnVdcLnnzfddsxsD3I4V08ri7kqO644T2/tPhTzWONcXoH9/70Ve1xYNM1Hu7Sqdo8+3HfEKP7AyST9wNGuhM4bb5l69pAMLZpTrifWN0SMWTSnXNlxltwDAAAgeUi6gTTywIt1empDQ9QVUVOr32nUnVVToibe180Yq+cNGppdN2Ns4hcU4KOWowm9/8d/rncUf+Bwp1bV7tH6v32S0HljletHs/SqCknqdX8zPD0Jt/1zAAAA9C8k3UCaeODFuqgroU6ZlEPPnDRSHkXfX+05GeemzgRKy53K8Ej3v7AtoWN41DO7O1a5fixLr6rQHXPP8lcyTCjO08LKiaxwAwAA9GMk3UAa6Drh01Mb3Eu4bbHKod/adTBmQzPrZFw8e5kjibdzeTwSrRqw6wTumTdFmxtatP9wh0pH9CTg8TRVyx6SwVgwAACAAYSkG0gDz9TsdKWkPFSscmi7C3gspnGmhmZnunq8cDI8iSfcUs8K99XTynT/C9uCmtq5Mb8bAAAA/R9JN5AGTOczz60YpXnnlKlkWI7u+O072tfWEXal2rQc+oBhYzDTOFMlw832Rl84eaT+6TPj9OG+w3rkldj7uG+7ZLLOGDVCBw53JlRS/vmzTtPV08eodESuDh7t0q3Pbun1Pdvzux+7cQaJNwAAQBpjIyCQBkznM88sL9aC6WN00RklWn51T+Ot0AJn+8/L5lfELH9uOWLWzds0zlRZodl87XPG5vd83tNPM4q/6PTTtGD6GJWMMDt+JPvaOrVg+hhdUF6s+18IPyfdfm3Fmjp1J6NMAQAAAP0CSTeQBhZWTpQnxvZgT8gc56qpZXrsxhnyFgSvGnsLco1XX1NVXl481CwptuMuKC9WWUFuxLHcHgXPJU+ky3igWHPSA+d3AwAAID1RXg6kgcwMj4ZmZaq9qztiTF5WZq+V66qpZbq8wht3g6/RRUNdjTPV2mk2f9yOy8zwaNn8Ci1ZuaVXt/VwK/t2kt7UGr78PpaLTu9pGmc6lzve+d0AAADo/1jpBtLA5oaWqAm3JB3t6g67opqZ4VHl5JFaMH2MKiePdNRR+8JJJa7GmTK9wsA4Jyv7dpLu5FyBCvOyJZmvmLu1sg4AAID+h5VuIA2kakV11uSRKszL0qH2yCvPhXlZmuXiuDBJqpxUYtQYrTIk2Y+2st/ts4Jev7zCq8dunKEVa+qiloiH89fGNkmxV8zdmt8NAACA/oukG0gDqVpRzczw6MFrz9YtK7dEjHnw2rPjmkcdzWfKi3uViYfynIwLZa/sB6re2tgrubZHer1616X+ZPwXr+/UW7sPxbw+u+rAaVk7AAAA0g/l5UAacNoozE1VU8v0+I0z5M0Pbm7mzc/R40kah/XWroMx91pbJ+NCdfss1dQ3a1XtHtXUN+vFdxu1ZOWWXqvZ9kivtXVN/vL7KWX5Rtc3Kv/Uww03GtYBAABg4GKlG0gDqV5RrZpapkvPGqVnanZqV0u7JhTnaWHlRGUPSc5zvb0HzeaS98SdWtUOt6Kd4Qm/Ym6p57tbsaZOl1d4lZnh0bnjCrVy0+6Y5z13XGHQnxNtWAcAAICBi6QbSBP2impoUuk9WSadzBXV6q2NWrbqfe073Ol/7cn1O7RiwaeTct7ajw8Zx113/jj/NS5ZuaVXgh1tRHbgSK/KySNVMtxsVFm4uHBl7QAAAEh/JN1AGunrFWepJ5kNt6d73+FO3bJyi2sl5oGNzsznfnv8712xpi6u8V/SqQZ0/1PXZBT/P3VN+uxZpXGeDQAAAOmEpBtII+HKp//r1YakrXR3+yzd/pt3osbc8Zt3/OXZ8Qr3uUxYlqVVtXt04HCn4/cGshvQvftxq1G8aRwAAADSH0k3kCYilU/bDcGS0bTr9e0HjOaDv779gOZ86rS4zhHpc5lYuWm30R7sSEJHelmGV2EaBwAAgPRH0g2kgWjl04ENwUbkZOnA0U7XGnn99s2PjOPiSboTLQtPRLgGdAU5Zv/KNI0DAABA+uM3Q6CfCNyz7DQp3tzQErV82m4I9sWfbPK/VuZCg7Utu3uP5EokLlSsz5VM4RrQDcvNMnqvaRwAAADSH0k30A+E27PsJCm2G3054UbZuelCebwL6vF8rkTcM2+KSkbkRHzocUH5SK3dtj/mcS4op0s5AAAAeiSvpTEAI/ae5dAVXTsprt7aGPMYJcPMRlkFsku2V6ypU3e0uVlRZBl2RTeNC2U3MOsrJSNytGD6GFVOHhm2yuCmCyfKE+MBgsfTEwcAAABIJN1ASsXaiy0ZJsVxriQHzqGOR86QTFfjQl1QXqzCvL4r1Y6V5GcPydDiOeVRYxbPKU/qiDYAAAAMLPxmCKSQ6V7sWElxU4L7nsOVcXf7LNXUN2tV7R7V1DeHTfwzYi37OoxLFY96yvntLuXRLL2qQl+5uLxXyXyGR/rKxeVaelVFci4SAAAAAxJ7uoEUMt2zHCvu7TgbldlCV3hN95jPmlysrXvbYh5/1uTYyWw4mxtadKj9eFzvNRWuS3ksS6+q0B1zz9IzNTu1q6VdE4rztLByIivcAAAA6IWkG0gSk27kpnuWY8Xta4tvpTt0DrXkbN73qBFDjc5jGhcqkUZqedmZQTPEywpydfW0Mq1+pzHoYUK4LuUmsodk6OY5k+K+PgAAAAwOJN1AEpiuFF9QXqyyglw1tXaE3dcdLikOZ1icc6EtBa/wms77vrzCq8wMj0pGmDVwM40LlUgjNY+kX948s9dc8jurpsQ9mg0AAABwilpIwGVOupFnZni0bH7PHuDQtM9J2fPfTx+T6GVLcr7HvHS4WTJtGhfKfigRT0p8tKtbPsvq1Y08M8Ojyskjo3YpBwAAANxC0g24KJ5u5FVTy/TYjTPkLQhe1fUW5BrP0M6IM3G0V67t63G6x/yEz2cUbxonBTdw29zQonvmTfFfq1OP/6U+aiM4AAAAINkoLwdc5GSluHLySP/rVVPLdHmFN+6y540NzXFdb+j1ON1j/vu39xjF//7tPfrsmaUx4yKV5S++uLzXXmwTr9U367X6Zv9x4tm7DQAAACSCpBtwUSLdyO2y53jsOXgsrveFXo/TPeYfG563rrFVq2r3RH2YEK2B25PrG/ToF2aoaFi29h/u0Jp39upP2/Y7+IThG8EBAAAAyUZ5OeAit7qRO2UlWDltX4/TPeZjC80+x9/2HdVXf1WrG57aqNkPrQva1y6ZleXf/0KdLigv1oLpY3RVHElzpPJ+AAAAIJlIugFDgXuNI+0RjtX4y6OeMudY3cidGlMUXxIf7nqc7DH/+xljHZ8zXEM5pw3cygrjG0EWehwAAAAg2SgvBwyYjgCzV4qXrNwS8Vgm3cidqiwv0Y//vMPRe6J1RzfdYz4kw/lzu3Cjx5yW5dsPN5zu8Q49DgAAAJBsrHQDMTgZASb1JKyLLy5XaF6d4ZEWX1yelP3EGZnOk/hY3dFNRms1tcWXvPYaPeawLN9+uBHvowu3y/sBAACASFjpBqKItdc4dMVW6knSn1zf0Os9liU9ub5B544vcj3xPnCk0yjutktO1xmjhjvujh7J27sTK9OOt4GbdOrhxlMbGmS6RTvccQAAAIBkYqUbiMLpXuNYSbql5DTyMl25vej0kqgr107tazNL9iOJt4GbdOrhhpOEO9xxAAAAgGQi6QaicLrXOFaSLiWnkVeqGrjlZcdXLJNoA7doDzdsoXl1rHJ6AAAAIBkoLweicLrX2HSPc7x7oSMJbODmkYKS0WSu8FaMzteqd/Y6eo8bDdxMHm74LOmeeVNUMiLHtXJ6AAAAwCmSbiAKp3uNWwz3VpvGOWGvFId2WfeG6bLultJ85w3JYl2P3cAtGtOHFsXDc7Rg+hjH1wgAAAC4haQbiMLpCnLxsGyj45rGOWW6UuyW0hE5RnHfvGqKSvPdW3H+xDDpNo0DAAAAkoWkG4jByQpy8VDDpNswLh4mK8WuMWxiVlGWr4vOKHHttO/vbXU1DgAAAEgWkm7AgOkK8kvbmoyO99K2Jn12SmkyLrVPNbUeczXO1LHj3a7GAQAAAMlC0g0YMllBfneP2cqqaVw8un1Wn5WX1358yDjuuvPHuXbez0wcqZfq9hvFAQAAAKlE0g24KD83y9U4p6q3NvYqgy9LYiM103njbs8lv+nCifqPP26TFeWwHk9PHAAAAJBKzOkGXPS/Lyp3Nc6J6q2NWrJyS69RWk2tHVqycouqtza6fs5PDpt1YTeNM5U9JEOL50T/DhfPKVf2EP4VBwAAgNTiN1LARUMMkzzTOFPdPksr1tSF7Wtmv7ZiTZ3rK87Fw8xW7E3jnFh6VYW+cnG5QivnMzzSVy4u19KrKlw/JwAAAOAU5eWAizY1tBjHzfnUaa6dd3NDS68V7kCWpMbWDm1uaHG1s/n2/UddjXNq6VUVumPuWXqmZqd2tbRrQnGeFlZOZIUbAAAA/QZJN+Aq05Vkd1ec9x82m0dtGmcqJ8ssuTWNi0f2kAzdPGdS0o4PAAAAJILlIMBFlZPMZlGbxpkqHZHrapypvGyz53amcQAAAEC64TdhIEQiI7dmTR6pwrwsHWo/HjGmMC9Ls1ws8ZakC8qLVVaQq6bWjrBr6B5J3oKez+Km8SOHuhoHAAAApBuSbiBAoiO3MjM8evDas3XLyi0RYx689mzX52ZnZni0bH6FlqzcIo+Ci9ftMy2bX+H6eT9oPOJqHAAAAJBuKC8HTnJr5FbV1DJ95eJyhaa3HvV01U7GvGz7vI/dOEPeguAScm9Brh67cUZSznvseLercQAAAEC6YaUbUOyRWx71jNy6vMIbc7W4emujnljfEPY4T6xv0Lnji5KaeF9e4Y27PN6poVmZrsYBAAAA6YakG5B7I7e6fZa+8bv3op5r6e/eM0re45WZ4XF1LFg0RblmybRpHAAAAJBuKC8H5N7IrY07mqM2UZOkg+3HtXFHs/G19Wc7Dx5zNQ4AAABINyTdgNwbufX69gNGxzGNi0e3z1JNfbNW1e5RTX2zun3uzgQP5HM5DgAAAEg3lJcDcm/k1u7mo0bnM41zKlr39WTs9c7NNHtuZxoHAAAApBuSbkDujdx6b0+r0flM45ywu6+HPjRoau3QLSu39Jof7mQUWiQFQ7NcjQMAAADSDctPwElujNw62mU2Gss0zlSs7uuSeu01dzoKLZxPjnS5GgcAAACkG1a6gQCJjtwalZ9jlGCOys9J9FKDxOq+Ho7TUWjhjCrI0da9ZnEAAADAYMRKNxDCHrm1YPoYVU4e6SgZvePzZ7oaZ8q0+3qowFFo8ZhVXuJqHAAAAJBuSLoBF108pVRDYiTpQzI8unhKqavnNe2+Hkm8SfsNF4x3NQ4AAABINyTdgIsyMzy6efbEqDE3z56YcNfwUBeUF6swL/5mZfEm7b/ctMvVOAAAACDdkHQDLur2WVr9TvTGZKvfaUzK7OyuE86nYXvU08U81ii0SF56v8nVOAAAACDdkHQDLjJpaJbIHupINtY3q91hR3Qno9AiOdxxwtU4AAAAIN2QdAMuamo95mqcqZodBxy/x8kotEjOLBvhahwAAACQbhgZBrio5ajZPGrTOFOmxerXTB+tS84qNRqF1u2zYo5O+8fzxmlNjHJ6Ow4AAAAYjEi6ARcVDzebR20aZyo/16yJ2pSyfC2YPiZmXPXWRq1YUxdUKl9WkKtl8yuCVsYvPL1EedmZUUvbh2Vn6sLTGRkGAACAwYnycsBF3nyzLuCmcabajh13La56a6OWrNzSa296U2uHlqzcouqtp1a2MzM8+t4/Tot6vP/3j9Nc79YOAAAADBQk3Uh73T5LNfXNWlW7RzX1zUnpHG67oLxYZQXRE+pEuoUnW7fP0oo1dWHL1e3XVqypC/oOq6aW6fEbZ2jUiODVe29+jh5PcM84AAAAMNBRXo60Zlom7ZbMDI+Wza/QkpVbJAXvtXajW3gkhXnZrsTF6r5u6VT39crJI/2vV00t0+UV3ph7wAEAAIDBhpVupC0nZdKBEl0Zr5papsdunKFRISXkbnQLj6Q4z2xPd6y4/YejjzuLFpeZ4VHl5JFaMH2MKiePJOEGAAAAxEo30lSsMmmPesqkL6/wBiWHbq6MW5Yv6M8+ny9CZOIOGe7pjhVXOsJsr7lpHAAAADDYsdKNtOSkTNoW78p4qOqtjbpl5RbtOxw8Fmzf4S7d4uA4TiTSNT1wZd/ns+TNz1WkNWqP+veedAAAAKC/YaUbaclpmXS8K+Ohun2WvvG796Ke8xu/ey/mcZyKt2t6uJX9wrws/2fuqz3pAAAAQLpipRtpyWmZdDwr4+FsrG/WofboJdyH2o9rY32z0fWZOm9CkWLlwRmenjhbpJX91pPXXxCy/zuZe9IBAACAdMVKN9KSPbqrqbUj7Oq1Rz1JpF0mnUgDsUCv1X9idJzX6j/RRWeUGMWaeGvXQcXq9+azeuIqJ480WtnPHZKhX/7vmTpwpJNu5AAAAECcHK90r1+/XvPnz9fo0aPl8Xj0/PPPB/3csizde++9Kisr09ChQ3XZZZfpww8/9P98586duvnmm1VeXq6hQ4dq8uTJWrZsmbq6gve/vvvuu5ozZ45yc3M1btw4Pfzww/F9QgxK9uguSb32J4crk3argdjeQ2bJu2mcKacPDUxW9pvaOpXh8dCNHAAAAEiA46T76NGjmjZtmh599NGwP3/44Yf1wx/+UI8//rg2bdqkYcOG6YorrlBHR88v+H/961/l8/n0xBNP6P3339d//ud/6vHHH9e///u/+4/R1tamuXPnasKECXrrrbf0ne98R8uXL9eTTz4Z58fEYGSP7vIWxB7dZa+MJ9pArKzQLHk3jTPl9KGBWyv7AAAAAKJzXF5+5ZVX6sorrwz7M8uy9P3vf1933323FixYIEn6xS9+oVGjRun555/X9ddfr6qqKlVVVfnfM2nSJH3wwQd67LHH9N3vfleS9Mtf/lJdXV366U9/quzsbH36059WbW2tvve972nx4sXxfE70A90+S5sbWrT/cEeflStXTS3TpWeN0jM1O7WrpV0TivO0sHKisocEP2+yV8aXrNySUAOx4jzDLuKGcaacltMzGgwAAADoG67u6W5oaFBTU5Muu+wy/2sFBQWaOXOmampqdP3114d9X2trq4qLT60g1tTU6OKLL1Z2drb/tSuuuEIPPfSQDh48qKKiol7H6OzsVGdnp//PbW1tbnwkuMTN+deJnve/Xm0Ie157ZTw03uvgOouHZceMcRJnyulDA6dJOgAAAID4uNq9vKmpSZI0atSooNdHjRrl/1mo7du360c/+pG+8pWvBB0n3DECzxHqgQceUEFBgf+vcePGxf054C635l/3xXmrppbp1bsu1XOLZukH10/Xc4tm6dW7LjV+MHCovSt2kIM4J5yU0zvd8w4AAAAgPikdGbZnzx5VVVXpH/7hH7Ro0aKEjrV06VK1trb6//roo49cukokIlaXbKln/nV3rNbbLp/XinLezAyPKiePjKuBWPFww/JywzinqqaWad0dn9PCWeM154wSLZw1Xuvu+FzYhwZOknQAAAAA8XG1vNzr9UqS9u3bp7KyU7+w79u3T9OnTw+K3bt3ry655BJdeOGFvRqkeb1e7du3L+g1+8/2OULl5OQoJyc5iQzi52T+deXkkX12XiXpvN58sz3QpnFOPfBinZ7a0OAfH7bhQ+mXm3Zr0ZxyLb2qold81dQyXV7h7fO99gAAAMBg4epKd3l5ubxer15++WX/a21tbdq0aZMqKyv9r+3Zs0ef+9zndN555+lnP/uZMjKCL6OyslLr16/X8ePH/a+tXbtWZ555Ztj93Oi/UtUlu6nN7HimcaYuKC9WYV5W1JiivKyk7JV+4MU6PbG+ode8bp8lPbG+QQ+8WBf2fYms7AMAAACIznHSfeTIEdXW1qq2tlZST/O02tpa7d69Wx6PR1/72tf0rW99S6tXr9Z7772nL33pSxo9erSuueYaSacS7vHjx+u73/2uPvnkEzU1NQXt1f7CF76g7Oxs3XzzzXr//ff161//Wj/4wQ90++23u/Kh0XdS1SW75Uhn7CAHcW5yt5C+R9cJn57a0BA15qkNDeo64UvC2QEAAABE4ri8/M0339Qll1zi/7OdCN900016+umndeedd+ro0aNavHixDh06pNmzZ6u6ulq5uT1J1dq1a7V9+3Zt375dY8eODTq2ZfWkIwUFBXrppZd066236rzzzlNJSYnuvfdexoUNQKnqkp1IF/FERpttbmjRofbjUWMOtR93vaz9mZqdvVa4Q/msnrib50xy7bwAAAAAonOcdH/uc5/zJ8fheDwe3XfffbrvvvvC/vzLX/6yvvzlL8c8zznnnKMNGzY4vTz0M3aX7FtWbgn7c0vJ6ZLtLRgaV1yio82aWo8Zndc0ztTO5nZX4wAAAAC4I6Xdy4FksVfYoykLWWF3Y7TZgSNmo8BM48yZFq0no7gdAAAAQCQk3Ugqe3RXJB45HxnW7bNUU9+sVbV7VFPfHHHs17L5Fb1mUAeeN3CF3a3RZoeOGc7pNowzNX1soatxAAAAANzh6sgwIJTbI8OclH/bc6hN4t26TtMiebf7g48uynM1DgAAAIA7SLqRVG6ODLPLv0PXmu3y78dunBE28TaZQ+3WdVZOKtEjr9THPE7lpBKj85myy+mjPTgILacHAAAAkHyUlyOp3BoZlkj5t8kcareuc9bkkcrLzowaMyw7U7Nc7FwuOS+nBwAAANA3SLqRVBeUF6swLytqTGFeVswVWCfl3/GwV4qjJa2mK8XZQ6L/3yorxs/jZZfThzaQKyvIDVsFAAAAACD5KC9HypmsvbpZph6OvVK8ZOUWeRTc49u+PpOV4lTN6baZltMDAAAA6BusdCOpTJLQgyeT0GjcKv+Oxl4p9oasFHsdrBSnak53IJNyegAAAAB9g5VuJFXjIbPkMlacXf7d1NoRdl+3Rz3Jcbjy726fZbzym+hKcctRs1FgpnEAAAAABjaSbiTV2x8dNI679ryxEX8eb/m3kxFjgeeKt/S7eHiOq3EAAAAABjbKy5FU4Val441zWv5tjxgLbcBmjxir3tpoeHXmvPlm5e2mcQAAAAAGNla6kVTlI4e5Gmda/h1rxJhHPSPGLq/wurrn+bwJRfJ4JCvKUwSPpycOAAAAQPpjpRtJtbByomLltBmenjhTJo3Ckj1iLJI3GlqiJtxST0L+hsvnBQAAANA/kXQjqbKHZOjzU0qjxnx+SmnM2dZOJXvEWCSv7zjgahwAAACAgY2kG0nV7bO0dU9b1Jite9rU7TPd/W2mMCfL1ThTe1raXY0DAAAAMLCRdCOpYpV5S8kp8177132uxpnyuRwHAAAAYGAj6UZSparMe2fzUVfjTHkss6ZspnEAAAAABjaSbiRV6Qiz0VimcaaGZmW6GmfKZzgkzTQOAAAAwMBG0o2kuqC8WGUFuYq0ruuRVFbQM/bLTXM/7XU1zlTzkU5X4wAAAAAMbCTdSKrMDI+Wza+IuK5rSVo2v8LVWdmSNLYoz9U4Ux3Hu12NAwAAADCwkXQjLdkr7NEkY4W9q9usRZppHAAAAICBjaQbSdXts7RiTV3En3skrVhT5/rIMHuFPVpZezJW2L35ZnvTTeMAAAAADGwk3UiqWCPDLCVnZJgkVU0t02M3zui14l1WkKvHbpyhqqllrp9z5qQSV+MAAAAADGxDUn0B6D+6fZY2N7Ro/+EOlY7oKb1OdCU4VSPDbFVTy3R5hdf1zxXJTRdO1H/8cZusKAv3Hk9PHAAAAID0R9I9CIVLrtfWNWnFmrqgVemyglwtm1+R0IpwqkaGBcrM8Khy8sikHT9Q9pAMLZ5TrifWN0SMWTynXNlDKDIBAAAABgOS7kGmemtjr+S6MC9Lh9qP94ptau3QkpVbEirFthuaNbV2hO1g7pHkTUJDs1RaelWFJOmpDQ0K3Kqe4ZEWzSn3/xwAAABA+vNYVrRC2IGrra1NBQUFam1tVX5+fqovp1+o3tqoJSu3RBzfFY6dFL9616Vxl2Tb55UUdG77aMnaX51qXSd8eqZmp3a1tGtCcZ4WVk5khRsAAABIE6Y5Jyvdg4TdRdzpE5bARmfxlmjbDc1CV9i9LpSv92fZQzJ085xJqb4MAAAAAClE0j1IxOoiHkuijc76uqEZAAAAAPQHJN2DRKJJsxuNzvqyoRkAAAAA9Ack3YNEvElzOjY6AwAAAIC+QlenQcLuIu6kmNuOXTa/gjJwAAAAAIgDSfcgkZnh0bL5PaOqQtNn+8+FeVlBr3sLctO2szgAAAAA9AXKyweRWF3EaXQGAAAAAO5iTvcg1O2zSK4BAAAAIAHM6UZEdBEHAAAAgL7Bnm4AAAAAAJKEpBsAAAAAgCShvBx9JlV7ydnDDgAAACBVSLrRJ6q3Nvbqml52smt6MkeSpeq8AAAAACBRXo4+UL21UUtWbglKfCWpqbVDS1ZuUfXWxrQ6LwAAAADYSLqRVN0+SyvW1CncXDr7tRVr6tTtc3dyXarOCwAAAACBSLoHoW6fpZr6Zq2q3aOa+uakJp6bG1p6rTQHsiQ1tnZoc0NLWpwXAAAAAAKxp3uQ6es9zvsPR05844nr7+cFAAAAgECsdA8iqdjjXDoi19W4/n5eAAAAAAhE0p1CfVnmnao9zheUF6usIFeRBnR51LPSfkF5cVqcFwAAAAACUV6eIn1d5u1kj3Pl5JGunTczw6Nl8yu0ZOUWeaSgpN9OiJfNr3B9bnaqzgsAAAAAgVjpToFUlHmnco9z1dQyPXbjDI3Kzwl6fVR+jh67cYZrDxlCKwcur/DqsRtnyFsQXELuLch19bwAAAAAEAkr3X0sVpm3Rz1l3pdXeF1dhe0fe5xDP497ny9a5cCrd12qzQ0t2n+4Q6UjekrKWeEGAAAA0BdY6e5jqRpllco9zvbKflNb8Ofe1+bOyn6syoG1dU2qnDxSC6aPUeXkkSTcAAAAAPoMSXcfS1WZt73HWYq83pyMPc7JbuCWqgZxAAAAAGCCpLuPpbLM295b3Zd7nJO9sp+qygEAAAAAMMGe7j5ml3k3tXaEXZ31qCcJTtYoq6qpZbq8wttne5yTvbKfygZxAAAAABALSXcf6w+jrDIzPK6OBYsm2Sv7/aNBHAAAAACER3l5CqSizDtV7JX9aBJp4JbKBnEAAAAAEAsr3SnS12XeqZKZ4dHV08r0xPqGiDFXTyuL+3P3h8oBAAAAAIiEle4Ussu803mUVbfP0up3oo8EW/1OY0LdxQdT5QAAAACAgYWVbiRVrO7i0qnu4onsMx8slQMAAAAABhaSbiRVX3YX78sGcQAAAABggvJyJBXdxQEAAAAMZiTdSKoLyotVmJcVNaYoL4vu4gAAAADSEkk3kq7rhC/qzztj/BwAAAAABiqSbiTVxvpmtXd1R41p7+rWxvrmProiAAAAAOg7JN1Iqtd3HHA1DgAAAAAGEpJuJNXeg8dcjQMAAACAgYSkG0k1unCoq3EAAAAAMJCQdCOpLpxc4mocAAAAAAwkJN1IqlmTR8YcGVaYl6VZk0f20RUBAAAAQN8h6UZSZWZ49E/nj40a80/nj1VmhqePrggAAAAA+g5JN5Kq22dp9TuNUWNWv9Oobp/VR1cEAAAAAH2HpBtJtbmhRY2tHVFjGls7tLmhpY+uCAAAAAD6Dkk3kmr/4egJt9M4AAAAABhISLqRVCXDclyNAwAAAICBhKQbyWXaH40+agAAAADSEEk3kurAkU5X4wAAAABgICHpRlKVjsh1NQ4AAAAABhKSbiTVBeXFKivIjVg97pFUVpCrC8qL+/KyAAAAAKBPkHQjqTIzPFo2v0JS723b9p+Xza9QZgabugEAAACkH8dJ9/r16zV//nyNHj1aHo9Hzz//fNDPLcvSvffeq7KyMg0dOlSXXXaZPvzww6CYlpYWffGLX1R+fr4KCwt1880368iRI0Ex7777rubMmaPc3FyNGzdODz/8sPNPh36hamqZHrtxhrwFwSXk3oJcPXbjDFVNLUvRlQEAAABAcg1x+oajR49q2rRp+pd/+Rdde+21vX7+8MMP64c//KF+/vOfq7y8XPfcc4+uuOIK1dXVKTe3J+n64he/qMbGRq1du1bHjx/XP//zP2vx4sV69tlnJUltbW2aO3euLrvsMj3++ON677339C//8i8qLCzU4sWLE/zISIWqqWW6vMKrzQ0t2n+4Q6UjekrKWeEGAAAAkM48lmVZcb/Z49Hvf/97XXPNNZJ6VrlHjx6tO+64Q//f//f/SZJaW1s1atQoPf3007r++uu1bds2VVRU6I033tD5558vSaqurtZVV12ljz/+WKNHj9Zjjz2mb37zm2pqalJ2drYk6Rvf+Iaef/55/fWvfzW6tra2NhUUFKi1tVX5+fnxfkQAAAAAAHoxzTld3dPd0NCgpqYmXXbZZf7XCgoKNHPmTNXU1EiSampqVFhY6E+4Jemyyy5TRkaGNm3a5I+5+OKL/Qm3JF1xxRX64IMPdPDgwbDn7uzsVFtbW9BfCK/bZ6mmvlmraveopr5Z3b64n7sAAAAAAKJwXF4eTVNTkyRp1KhRQa+PGjXK/7OmpiaVlpYGX8SQISouLg6KKS8v73UM+2dFRUW9zv3AAw9oxYoV7nyQNFa9tVEr1tSpsbXD/1pZQa6Wza9gbzUAAAAAuCxtupcvXbpUra2t/r8++uijVF9Sv1O9tVFLVm4JSrglqam1Q0tWblH11sYUXRkAAAAApCdXk26v1ytJ2rdvX9Dr+/bt8//M6/Vq//79QT8/ceKEWlpagmLCHSPwHKFycnKUn58f9BdO6fZZWrGmTuEKye3XVqypo9QcAAAAAFzkatJdXl4ur9erl19+2f9aW1ubNm3apMrKSklSZWWlDh06pLfeessfs27dOvl8Ps2cOdMfs379eh0/ftwfs3btWp155plhS8sR2+aGll4r3IEsSY2tHdrc0NJ3FwUAAAAAac5x0n3kyBHV1taqtrZWUk/ztNraWu3evVsej0df+9rX9K1vfUurV6/We++9py996UsaPXq0v8P5lClTVFVVpUWLFmnz5s167bXXdNttt+n666/X6NGjJUlf+MIXlJ2drZtvvlnvv/++fv3rX+sHP/iBbr/9dtc++GCz/3DkhDueOAAAAABAbI4bqb355pu65JJL/H+2E+GbbrpJTz/9tO68804dPXpUixcv1qFDhzR79mxVV1f7Z3RL0i9/+Uvddttt+vznP6+MjAxdd911+uEPf+j/eUFBgV566SXdeuutOu+881RSUqJ7772XGd0JKB2RGzvIQRwAAAAAILaE5nT3Z8zpDtbtszT7oXVqau0Iu6/bI8lbkKtX77pUmRmevr48AAAAABhQUjKnG8705bzszAyPls2vkNSTYAey/7xsfgUJNwAAAAC4yNU53TCXinnZVVPL9NiNM3qd18ucbgAAAABICsrLU8Celx36xdtrzI/dOCOpCXC3z9LmhhbtP9yh0hG5uqC8mBVuAAAAAHDANOdkpbuPxZqX7VHPvOzLK7xJS4QzMzyqnDwyKccGAAAAAJzCnu4+xrxsAAAAABg8SLr7GPOyAQAAAGDwIOnuY8zLBgAAAIDBg6S7j11QXqyygtxeY7tsHvV0Mb+gvLgvLwsAAAAAkAQk3X2MedkAAAAAMHiQdKeAPS/bWxBcQu4tyE36uDAAAAAAQN9hZFiKVE0t0+UVXuZlAwAAAEAaI+lOIeZlAwAAAEB6o7wcAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkGZLqCxjMun2WNje0aP/hDpWOyNUF5cXKzPCk+rIAAAAAAC4h6U6R6q2NWrGmTo2tHf7XygpytWx+haqmlqXwygAAAAAAbqG8PAWqtzZqycotQQm3JDW1dmjJyi2q3tqYoisDAAAAALiJpLuPdfssrVhTJyvMz+zXVqypU7cvXAQAAAAAYCAh6e5jmxtaeq1wB7IkNbZ2aHNDS99dFAAAAAAgKUi6+9j+w5ET7njiAAAAAAD9F0l3HysdketqHAAAAACg/yLp7mMXlBerrCBXkQaDedTTxfyC8uK+vCwAAAAAQBKQdPexzAyPls2vCNtITerZ071sfgXzugEAAAAgDZB0AwAAAACQJCTdfcweGRaJR4wMAwAAAIB0QdLdxxgZBgAAAACDB0l3H2NkGAAAAAAMHiTdfYyRYQAAAAAweJB09zFGhgEAAADA4EHS3cfskWGSeiXe9p8ZGQYAAAAA6YGkOwWqppbpsRtnyFsQXELuLcjVYzfOUNXUshRdGQAAAADATUNSfQGDVdXUMl1e4dXmhhbtP9yh0hE9JeWscAMAAABA+iDpTqHMDI8qJ49M9WUAAAAAAJKE8nIAAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkmRIqi8gWSzLkiS1tbWl+EoAAAAAAOnGzjXt3DOStE26Dx8+LEkaN25ciq8EAAAAAJCuDh8+rIKCgog/91ix0vIByufzae/evRoxYoQ8Hk+qLwcntbW1ady4cfroo4+Un5+f6suBy7i/6Y37m964v+mPe5zeuL/pjfvbP1mWpcOHD2v06NHKyIi8czttV7ozMjI0duzYVF8GIsjPz+dfGGmM+5veuL/pjfub/rjH6Y37m964v/1PtBVuG43UAAAAAABIEpJuAAAAAACShKQbfSonJ0fLli1TTk5Oqi8FScD9TW/c3/TG/U1/3OP0xv1Nb9zfgS1tG6kBAAAAAJBqrHQDAAAAAJAkJN0AAAAAACQJSTcAAAAAAElC0g0AAAAAQJKQdCNh69ev1/z58zV69Gh5PB49//zzvWK2bdumq6++WgUFBRo2bJg+85nPaPfu3f6fd3R06NZbb9XIkSM1fPhwXXfdddq3b18ffgpEE+seHzlyRLfddpvGjh2roUOHqqKiQo8//nhQDPe4f3rggQf0mc98RiNGjFBpaamuueYaffDBB0ExJvdu9+7dmjdvnvLy8lRaWqp/+7d/04kTJ/ryoyCMWPe3paVF/+f//B+deeaZGjp0qMaPH6//+3//r1pbW4OOw/3tn0z+/2uzLEtXXnll2H+Hc3/7L9N7XFNTo0svvVTDhg1Tfn6+Lr74Yh07dsz/85aWFn3xi19Ufn6+CgsLdfPNN+vIkSN9+VEQhsn9bWpq0sKFC+X1ejVs2DDNmDFD//3f/x0Uw/3t/0i6kbCjR49q2rRpevTRR8P+vL6+XrNnz9ZZZ52lP//5z3r33Xd1zz33KDc31x/z9a9/XWvWrNFvf/tb/eUvf9HevXt17bXX9tVHQAyx7vHtt9+u6upqrVy5Utu2bdPXvvY13XbbbVq9erU/hnvcP/3lL3/Rrbfeqo0bN2rt2rU6fvy45s6dq6NHj/pjYt277u5uzZs3T11dXXr99df185//XE8//bTuvffeVHwkBIh1f/fu3au9e/fqu9/9rrZu3aqnn35a1dXVuvnmm/3H4P72Xyb//7V9//vfl8fj6fU697d/M7nHNTU1qqqq0ty5c7V582a98cYbuu2225SRcerX/C9+8Yt6//33tXbtWv3hD3/Q+vXrtXjx4lR8JAQwub9f+tKX9MEHH2j16tV67733dO211+of//Ef9fbbb/tjuL8DgAW4SJL1+9//Pui1f/qnf7JuvPHGiO85dOiQlZWVZf32t7/1v7Zt2zZLklVTU5OsS0Wcwt3jT3/609Z9990X9NqMGTOsb37zm5ZlcY8Hkv3791uSrL/85S+WZZnduxdffNHKyMiwmpqa/DGPPfaYlZ+fb3V2dvbtB0BUofc3nN/85jdWdna2dfz4ccuyuL8DSaT7+/bbb1tjxoyxGhsbe/07nPs7sIS7xzNnzrTuvvvuiO+pq6uzJFlvvPGG/7U//vGPlsfjsfbs2ZPU64Uz4e7vsGHDrF/84hdBccXFxdZTTz1lWRb3d6BgpRtJ5fP59MILL+hTn/qUrrjiCpWWlmrmzJlBpW1vvfWWjh8/rssuu8z/2llnnaXx48erpqYmBVcNpy688EKtXr1ae/bskWVZeuWVV/S3v/1Nc+fOlcQ9HkjssuLi4mJJZveupqZGZ599tkaNGuWPueKKK9TW1qb333+/D68esYTe30gx+fn5GjJkiCTu70AS7v62t7frC1/4gh599FF5vd5e7+H+Diyh93j//v3atGmTSktLdeGFF2rUqFH67Gc/q1dffdX/npqaGhUWFur888/3v3bZZZcpIyNDmzZt6tsPgKjC/X/4wgsv1K9//Wu1tLTI5/PpV7/6lTo6OvS5z31OEvd3oCDpRlLt379fR44c0YMPPqiqqiq99NJL+vu//3tde+21+stf/iKpZ69Kdna2CgsLg947atQoNTU1peCq4dSPfvQjVVRUaOzYscrOzlZVVZUeffRRXXzxxZK4xwOFz+fT1772NV100UWaOnWqJLN719TUFPQLu/1z+2foH8Ld31AHDhzQ/fffH1SWyP0dGCLd369//eu68MILtWDBgrDv4/4OHOHu8Y4dOyRJy5cv16JFi1RdXa0ZM2bo85//vD788ENJPfextLQ06FhDhgxRcXEx97gfifT/4d/85jc6fvy4Ro4cqZycHH3lK1/R73//e51++umSuL8DxZBUXwDSm8/nkyQtWLBAX//61yVJ06dP1+uvv67HH39cn/3sZ1N5eXDJj370I23cuFGrV6/WhAkTtH79et16660aPXp00Aop+rdbb71VW7duDVohQfqIdX/b2to0b948VVRUaPny5X17cUhYuPu7evVqrVu3LmjvJwaucPfY/j3rK1/5iv75n/9ZknTuuefq5Zdf1k9/+lM98MADKblWOBfp39H33HOPDh06pD/96U8qKSnR888/r3/8x3/Uhg0bdPbZZ6foauEUK91IqpKSEg0ZMkQVFRVBr0+ZMsXfvdzr9aqrq0uHDh0Kitm3b1/YUjj0L8eOHdO///u/63vf+57mz5+vc845R7fddpv+6Z/+Sd/97nclcY8Hgttuu01/+MMf9Morr2js2LH+103undfr7dXN3P4z97d/iHR/bYcPH1ZVVZVGjBih3//+98rKyvL/jPvb/0W6v+vWrVN9fb0KCws1ZMgQ/5aB6667zl+ayv0dGCLd47KyMkmK+XvW/v37g35+4sQJtbS0cI/7iUj3t76+Xo888oh++tOf6vOf/7ymTZumZcuW6fzzz/c3t+X+Dgwk3Uiq7OxsfeYzn+k1/uBvf/ubJkyYIEk677zzlJWVpZdfftn/8w8++EC7d+9WZWVln14vnDt+/LiOHz8e1CVVkjIzM/1P4LnH/ZdlWbrtttv0+9//XuvWrVN5eXnQz03uXWVlpd57772g/+ivXbtW+fn5vX4RRN+KdX+lnhXuuXPnKjs7W6tXrw6aLCFxf/uzWPf3G9/4ht59913V1tb6/5Kk//zP/9TPfvYzSdzf/i7WPZ44caJGjx4d9fesyspKHTp0SG+99Zb/5+vWrZPP59PMmTOT/yEQUaz7297eLklRf8fi/g4QqezihvRw+PBh6+2337befvttS5L1ve99z3r77betXbt2WZZlWb/73e+srKws68knn7Q+/PBD60c/+pGVmZlpbdiwwX+MW265xRo/fry1bt06680337QqKyutysrKVH0khIh1jz/72c9an/70p61XXnnF2rFjh/Wzn/3Mys3NtX784x/7j8E97p+WLFliFRQUWH/+85+txsZG/1/t7e3+mFj37sSJE9bUqVOtuXPnWrW1tVZ1dbV12mmnWUuXLk3FR0KAWPe3tbXVmjlzpnX22Wdb27dvD4o5ceKEZVnc3/7M5P+/oRTSvZz727+Z3OP//M//tPLz863f/va31ocffmjdfffdVm5urrV9+3Z/TFVVlXXuuedamzZtsl599VXrjDPOsG644YZUfCQEiHV/u7q6rNNPP92aM2eOtWnTJmv79u3Wd7/7Xcvj8VgvvPCC/zjc3/6PpBsJe+WVVyxJvf666aab/DE/+clPrNNPP93Kzc21pk2bZj3//PNBxzh27Jj1r//6r1ZRUZGVl5dn/f3f/73V2NjYx58EkcS6x42NjdaXv/xla/To0VZubq515plnWv/v//0/y+fz+Y/BPe6fwt1XSdbPfvYzf4zJvdu5c6d15ZVXWkOHDrVKSkqsO+64wz9yCqkT6/5G+v+2JKuhocF/HO5v/2Ty/99w7wkd+8j97b9M7/EDDzxgjR071srLy7MqKyuDFjYsy7Kam5utG264wRo+fLiVn59v/fM//7N1+PDhPvwkCMfk/v7tb3+zrr32Wqu0tNTKy8uzzjnnnF4jxLi//Z/HsizL7dVzAADw/2/fDgkAAAAABP1/7QobvDAIAODpBgAAgI3oBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgEkSp7/Bi3WIFQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bolehkah anda meneka mengapa titik-titik ini tersusun dalam garis menegak seperti ini?\n",
+    "> Bolehkah anda meneka mengapa titik-titik ini berbaris menjadi garisan menegak seperti ini?\n",
     "\n",
-    "Kami telah memerhatikan hubungan antara konsep yang direka secara buatan seperti gaji dan pemboleh ubah yang diperhatikan *ketinggian*. Mari kita lihat juga sama ada dua pemboleh ubah yang diperhatikan, seperti ketinggian dan berat, mempunyai hubungan:\n"
+    "Kami telah memerhatikan hubungan antara konsep yang direka secara buatan seperti gaji dan pemboleh ubah yang diperhatikan *ketinggian*. Mari kita lihat juga jika dua pemboleh ubah yang diperhatikan, seperti ketinggian dan berat, turut berkorelasi:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,7 +856,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Malangnya, kami tidak mendapat sebarang hasil - hanya beberapa nilai `nan` yang pelik. Ini disebabkan oleh fakta bahawa beberapa nilai dalam siri kami tidak ditentukan, diwakili sebagai `nan`, yang menyebabkan hasil operasi juga tidak ditentukan. Dengan melihat matriks, kita dapat melihat bahawa lajur `Weight` adalah lajur yang bermasalah, kerana korelasi diri antara nilai `Height` telah dikira.\n",
+    "Malangnya, kami tidak mendapat sebarang hasil - hanya beberapa nilai `nan` yang pelik. Ini disebabkan oleh fakta bahawa beberapa nilai dalam siri kami tidak ditakrifkan, diwakili sebagai `nan`, yang menyebabkan hasil operasi juga tidak ditakrifkan. Dengan melihat matriks, kita dapat melihat bahawa `Weight` adalah kolum yang bermasalah, kerana korelasi diri antara nilai `Height` telah dikira.\n",
     "\n",
     "> Contoh ini menunjukkan kepentingan **penyediaan data** dan **pembersihan data**. Tanpa data yang betul, kita tidak dapat mengira apa-apa.\n",
     "\n",
@@ -1030,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1087,7 +920,7 @@
    "source": [
     "## Kesimpulan\n",
     "\n",
-    "Dalam buku nota ini, kita telah mempelajari cara melakukan operasi asas pada data untuk mengira fungsi statistik. Kita kini tahu cara menggunakan alat matematik dan statistik yang kukuh untuk membuktikan beberapa hipotesis, serta cara mengira selang keyakinan untuk pemboleh ubah sewenang-wenangnya berdasarkan sampel data.\n"
+    "Dalam notebook ini, kita telah mempelajari cara melaksanakan operasi asas pada data untuk mengira fungsi statistik. Kita kini tahu cara menggunakan alat matematik dan statistik yang kukuh untuk membuktikan beberapa hipotesis, serta cara mengira selang keyakinan untuk pemboleh ubah sewenang-wenangnya berdasarkan sampel data.\n"
    ]
   },
   {
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:17:46+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:44:59+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ms"
   }
diff --git a/translations/ms/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ms/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 611b7682..dfabb85f 100644
--- a/translations/ms/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ms/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Pengenalan kepada Kebarangkalian dan Statistik\n",
     "## Tugasan\n",
     "\n",
-    "Dalam tugasan ini, kita akan menggunakan dataset pesakit diabetes yang diambil [dari sini](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Dalam tugasan ini, kita akan menggunakan set data pesakit diabetes yang diambil [dari sini](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Dalam set data ini, lajur-lajur adalah seperti berikut:  \n",
-    "* Umur dan jantina adalah jelas dengan sendirinya  \n",
-    "* BMI adalah indeks jisim badan  \n",
-    "* BP adalah tekanan darah purata  \n",
-    "* S1 hingga S6 adalah pelbagai ukuran darah  \n",
-    "* Y adalah ukuran kualitatif perkembangan penyakit sepanjang satu tahun  \n",
+    "Dalam dataset ini, lajur-lajur adalah seperti berikut:\n",
+    "* Umur dan jantina adalah jelas dengan sendirinya\n",
+    "* BMI ialah indeks jisim badan\n",
+    "* BP ialah tekanan darah purata\n",
+    "* S1 hingga S6 adalah pelbagai ukuran darah\n",
+    "* Y ialah ukuran kualitatif bagi perkembangan penyakit sepanjang satu tahun\n",
     "\n",
-    "Mari kita kaji set data ini menggunakan kaedah kebarangkalian dan statistik.  \n",
+    "Mari kita kaji dataset ini menggunakan kaedah kebarangkalian dan statistik.\n",
     "\n",
-    "### Tugasan 1: Kira nilai purata dan varians untuk semua nilai  \n"
+    "### Tugasan 1: Kira nilai purata dan varians untuk semua nilai\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tugasan 2: Plot kotak plot untuk BMI, BP dan Y bergantung kepada jantina\n"
+    "### Tugasan 2: Plot kotak plot untuk BMI, BP dan Y bergantung pada jantina\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:50:06+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:45:29+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ms"
   }
diff --git a/translations/my/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/my/1-Introduction/04-stats-and-probability/assignment.ipynb
index 3b7684a3..19a35fb2 100644
--- a/translations/my/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/my/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## စွမ်းဆောင်ရည်နှင့် သင်္ချာဆိုင်ရာ သင်္ချာအခြေခံ\n",
+    "## စွမ်းဆောင်ရည်နှင့် စာရင်းအင်းဆိုင်ရာ သဘောတရားအကြောင်း\n",
     "## လုပ်ငန်းတာဝန်\n",
     "\n",
-    "ဒီလုပ်ငန်းတာဝန်မှာတော့ [ဒီနေရာမှ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) ယူထားတဲ့ ဆီးချိုရောဂါရှိသူများ၏ ဒေတာစနစ်ကို အသုံးပြုသွားမှာ ဖြစ်ပါတယ်။\n"
+    "ဒီလုပ်ငန်းတာဝန်မှာ ကျွန်တော်တို့ [ဒီနေရာ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) မှ ယူထားတဲ့ ဆီးချိုရောဂါရှိသူများ၏ ဒေတာဆက်တင်ကို အသုံးပြုသွားမှာ ဖြစ်ပါတယ်။\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "ဒီဒေတာဆက်တင်တွင် ကော်လံများမှာ အောက်ပါအတိုင်းဖြစ်သည် -  \n",
-    "* အသက်နှင့် လိင်သည် အလွယ်တကူနားလည်နိုင်သည်  \n",
-    "* BMI သည် ကိုယ်အလေးချိန်နှင့် အရပ်အမတ်အချိုး  \n",
-    "* BP သည် ပျမ်းမျှ သွေးပေါင်ချိန်  \n",
-    "* S1 မှ S6 အထိသည် သွေးစစ်ဆေးမှုအမျိုးမျိုး  \n",
-    "* Y သည် တစ်နှစ်အတွင်း ရောဂါတိုးတက်မှုအရည်အသွေးတိုင်းတာချက်  \n",
+    "ဒီဒေတာဆက်တင်ထဲမှာ ကော်လံတွေက အောက်ပါအတိုင်းဖြစ်ပါတယ်-  \n",
+    "* အသက်နဲ့ လိင်က အလွယ်တကူနားလည်နိုင်ပါတယ်  \n",
+    "* BMI က ကိုယ်အလေးချိန်နှင့် အရွယ်အစားညှိထားသော အညွှန်းကိန်းဖြစ်ပါတယ်  \n",
+    "* BP က ပျမ်းမျှ သွေးဖိအား  \n",
+    "* S1 ကနေ S6 အထိက သွေးစစ်ဆေးမှုအမျိုးမျိုး  \n",
+    "* Y က တစ်နှစ်အတွင်း ရောဂါတိုးတက်မှုအရည်အချင်းအတိုင်းအတာ  \n",
     "\n",
-    "Probability နှင့် Statistics နည်းလမ်းများကို အသုံးပြု၍ ဒီဒေတာဆက်တင်ကို လေ့လာကြမည်။\n",
+    "ဒီဒေတာဆက်တင်ကို သက်မှတ်နှုန်းနှင့် သင်္ချာဆိုင်ရာ နည်းလမ်းများကို အသုံးပြုပြီး လေ့လာကြမယ်။\n",
     "\n",
-    "### Task 1: တန်ဖိုးအားလုံးအတွက် ပျမ်းမျှနှင့် အပြောင်းအလဲကိုတွက်ချက်ပါ  \n"
+    "### တာဝန် ၁: တန်ဖိုးအားလုံးအတွက် ပျမ်းမျှနှုန်းနဲ့ မျိုးကွဲမှုကို တွက်ချက်ပါ  \n"
    ],
    "metadata": {}
   },
@@ -196,9 +196,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### အလုပ် ၄: အမျိုးမျိုးသော အပြောင်းအလဲများနှင့် ရောဂါတိုးတက်မှု (Y) အကြား ဆက်စပ်မှုကို စမ်းသပ်ပါ\n",
+    "### တာဝန် ၄: အမျိုးမျိုးသော အပြောင်းအလဲများနှင့် ရောဂါတိုးတက်မှု (Y) အကြား ဆက်စပ်မှုကို စမ်းသပ်ပါ\n",
     "\n",
-    "> **အကြံပြုချက်** ဆက်စပ်မှုအမီတာဇယားက ဘယ်တန်ဖိုးတွေက အချင်းချင်းမူတည်နေတယ်ဆိုတာ အထောက်အကူဖြစ်စေမယ့် အချက်အလက်တွေကို ပိုမိုပေးနိုင်ပါတယ်။\n"
+    "> **အကြံပြုချက်** ဆက်စပ်မှုအချိုးဇယားသည် ဘယ်တန်ဖိုးများသည် အချင်းချင်းမူတည်နေသည်ကို အထောက်အကူဖြစ်စေမည့် အရေးကြီးသော အချက်အလက်များကို ပေးနိုင်ပါသည်။\n"
    ],
    "metadata": {}
   },
@@ -221,7 +221,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ဝက်ဘ်ဆိုက်မှတ်ချက်**:  \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုအတွက် ကြိုးစားနေပါသော်လည်း၊ အလိုအလျောက်ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် မမှန်ကန်မှုများ ပါဝင်နိုင်သည်ကို ကျေးဇူးပြု၍ သတိပြုပါ။ မူရင်းစာရွက်စာတမ်းကို ၎င်း၏ မူလဘာသာစကားဖြင့် အာဏာတည်သောရင်းမြစ်အဖြစ် သတ်မှတ်ရန် လိုအပ်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူ့ဘာသာပြန်ပညာရှင်များမှ အတည်ပြုထားသော ဘာသာပြန်မှုကို အသုံးပြုရန် အကြံပြုပါသည်။ ဤဘာသာပြန်မှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော နားလည်မှုမှားမှုများ သို့မဟုတ် အဓိပ္ပာယ်မှားမှုများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n"
+    "\n---\n\n**ဝက်ဘ်ဆိုက်မှတ်ချက်**:  \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုအတွက် ကြိုးစားနေပါသော်လည်း၊ အလိုအလျောက်ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် မမှန်ကန်မှုများ ပါဝင်နိုင်သည်ကို ကျေးဇူးပြု၍ သတိပြုပါ။ မူရင်းစာရွက်စာတမ်းကို ၎င်း၏ မူလဘာသာစကားဖြင့် အာဏာတည်သောရင်းမြစ်အဖြစ် သတ်မှတ်သင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူကူးဘာသာပြန်မှုကို အကြံပြုပါသည်။ ဤဘာသာပြန်မှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော နားလည်မှုမှားများ သို့မဟုတ် အဓိပ္ပါယ်မှားများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n"
    ]
   }
  ],
@@ -247,8 +247,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:44:02+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T18:00:47+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "my"
   }
diff --git a/translations/my/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/my/1-Introduction/04-stats-and-probability/notebook.ipynb
index 3f290e26..76d247b0 100644
--- a/translations/my/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/my/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# မူလအခြေခံအချက်များနှင့် သင်္ချာဆိုင်ရာ သင်္ချာသိပ္ပံ  \n",
-    "ဒီ notebook မှာ ကျွန်တော်တို့ အရင်က ဆွေးနွေးခဲ့တဲ့ အချို့သော အကြောင်းအရာတွေကို ပြန်လည်လေ့လာသုံးသပ်သွားမှာ ဖြစ်ပါတယ်။ သင်္ချာနှင့် သင်္ချာသိပ္ပံဆိုင်ရာ အကြောင်းအရာများစွာကို Python ရဲ့ `numpy` နဲ့ `pandas` လို မျိုးကွဲ library တွေမှာ အလွန်ကောင်းမွန်စွာ ကိုယ်စားပြုထားပါတယ်။\n"
+    "# အခြေခံ သင်ခန်းစာ - အလားအလာနှင့် စာရင်းအင်း\n",
+    "ဒီ notebook မှာ ကျွန်တော်တို့ အရင်က ဆွေးနွေးခဲ့တဲ့ အချို့သော အယူအဆတွေကို လေ့ကျင့်ကြမယ်။ အလားအလာနှင့် စာရင်းအင်းဆိုင်ရာ အယူအဆများစွာကို Python ရဲ့ `numpy` နဲ့ `pandas` ကဲ့သို့သော အချက်အလက်များကို 처리ဖို့ အဓိက library တွေမှာ ကောင်းစွာ ဖော်ပြထားပါတယ်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## အကြောင်းအရာ မတူညီမှုများနှင့် ဖြန့်ဖြူးမှုများ\n",
-    "အရင်ဆုံး 0 မှ 9 အထိ တန်းတူဖြန့်ဖြူးမှုမှ တန်ဖိုး 30 ခုကို နမူနာယူကြမယ်။ ထို့အပြင် အလယ်ပျမ်းမျှနှင့် အပြောင်းအလဲမှုကိုလည်း တွက်ချက်ပါမယ်။\n"
+    "## အလွတ်ရွေးကောက် အပြောင်းအလဲများနှင့် ဖြန့်ဖြူးမှုများ  \n",
+    "အရင်ဆုံး 0 မှ 9 အတွင်းရှိ တူညီညီမျှဖြန့်ဖြူးမှုမှ တန်ဖိုး 30 ခုကို ရွေးချယ်ကြမယ်။ ထို့အပြင် အလယ်ပျမ်းမျှနှင့် အပြောင်းအလဲကိုလည်းတွက်ချက်ပါမယ်။  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "နမူနာထဲမှာ ဘယ်လောက်ကွဲပြားတဲ့တန်ဖိုးတွေရှိတယ်ဆိုတာကို မြင်သာအောင် ခန့်မှန်းဖို့၊ **histogram** ကို ပုံဆွဲနိုင်ပါတယ်။\n"
+    "နမူနာတွင် မတူညီသောတန်ဖိုးများ ဘယ်နှစ်ခုရှိသည်ကို မြင်သာစွာ ခန့်မှန်းရန်၊ **histogram** ကို ရှု့ဆင်နိုင်ပါသည်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## အချက်အလက်များကို စစ်ဆေးခြင်း\n",
     "\n",
-    "အလယ်တန်းနှင့် မျိုးစုံမှုသည် အမှန်တကယ်ကမ္ဘာကြီးမှ အချက်အလက်များကို စစ်ဆေးရာတွင် အလွန်အရေးကြီးပါသည်။ [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) မှ ဘေ့စ်ဘောကစားသမားများအကြောင်း အချက်အလက်များကို ဖိုင်ထဲသို့ တင်ပါ။\n"
+    "အလတ်နှင့် အပြောင်းအလဲသည် အမှန်တကယ်သော ဒေတာများကို စစ်ဆေးရာတွင် အလွန်အရေးကြီးပါသည်။ [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) မှ ဘေ့စ်ဘောကစားသမားများနှင့် ပတ်သက်သော ဒေတာကို လုပ်ဆောင်ကြည့်ရအောင်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ကျွန်တော်တို့ ဒီမှာ [**Pandas**](https://pandas.pydata.org/) ဆိုတဲ့ package ကို ဒေတာခွဲခြမ်းစိတ်ဖြာလုပ်ဆောင်ရန် အသုံးပြုနေပါတယ်။ ဒီ course ရဲ့ နောက်ပိုင်းမှာ Pandas နဲ့ Python ကို အသုံးပြုပြီး ဒေတာနဲ့အလုပ်လုပ်ခြင်းအကြောင်းကို ပိုမိုဆွေးနွေးသွားပါမယ်။\n",
+    "ဒီမှာ [**Pandas**](https://pandas.pydata.org/) ဆိုတဲ့ package ကို data analysis အတွက် အသုံးပြုနေပါတယ်။ ဒီ course ရဲ့ နောက်ပိုင်းမှာ Pandas နဲ့ Python ကို အသုံးပြုပြီး data တွေကို အလုပ်လုပ်ပုံအကြောင်းကို ပိုမိုဆွေးနွေးသွားမှာ ဖြစ်ပါတယ်။\n",
     "\n",
-    "အရွယ်အစား၊ အရပ်အမြင့်နဲ့ ကိုယ်အလေးချိန်အတွက် ပျမ်းမျှတန်ဖိုးတွေကို တွက်ကြည့်ရအောင်:\n"
+    "အခုတော့ အသက်၊ အရပ်အမြင့်နဲ့ ကိုယ်အလေးချိန်အတွက် ပျမ်းမျှတန်ဖိုးတွေကို တွက်ကြည့်ရအောင်:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "အလယ်ပမာဏအပြင်၊ အလယ်တန်းတန်ဖိုးနှင့် လေးပုံတစ်ပုံတန်ဖိုးများကိုလည်း ကြည့်ရှုရန် အဓိကရှိပါသည်။ ၎င်းတို့ကို **box plot** အသုံးပြု၍ ရှင်းလင်းစွာ ဖော်ပြနိုင်ပါသည်။\n"
+    "အလယ်ပမာဏအပြင်၊ အလယ်တန်းတန်ဖိုးနှင့် လေးပုံတစ်ပုံတန်ဖိုးများကိုလည်း ကြည့်ရှုခြင်းမှာ အဓိကရှိပါသည်။ ၎င်းတို့ကို **box plot** အသုံးပြု၍ မြင်နိုင်ပါသည်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ကျွန်ုပ်တို့၏ဒေတာအစုအပြုံကို အပိုင်းအခြားများအလိုက်၊ ဥပမာအားဖြင့် ကစားသမား၏အခန်းကဏ္ဍအလိုက် အုပ်စုဖွဲ့ပြီး box plot များကိုလည်း ပြုလုပ်နိုင်ပါသည်။\n"
+    "ကျွန်ုပ်တို့၏ဒေတာအစုအဖွဲ့၏အပိုင်းခွဲများကိုလည်း ဥပမာအားဖြင့် ကစားသမား၏အခန်းကဏ္ဍအလိုက် အုပ်စုဖွဲ့ပြီး box plot များပြုလုပ်နိုင်ပါသည်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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VeP14VFSU+vTp4/DIMEmaPHmyfZqXX35Zq1evVseOHfXggw+qRYsWOnPmjH744QetXLlSZ86ckSQ9+OCDevPNN3XvvfcqIyNDderU0UcffSR/f/+rWsci+/fv18CBA3Xrrbdqw4YN+vjjj3X33XcXezb3DTfcoKioKPtNy9q0aeNUO5dq0aKF+vXrp3//+9965plndP3112v48OF67733dPbsWcXExGjjxo2aO3euYmNj1bNnz8sua9iwYfrvf/+rhx9+WKtXr1aXLl1UWFionTt36r///a++/vprh/sdAADgwMxbpwMAXF9JjwyrXLmyrXXr1ra3337bZrVaHab/448/bGPGjLHVrVvX5uPjY2vatKntX//6l326jIwMm7e3t8NjwGw2m62goMDWvn17W926dW2///67zWa78MisgIAA2759+2y9e/e2+fv722rXrm177rnnbIWFhQ7z65JHhtlsNtsPP/xg69Onjy0wMNDm7+9v69mzp239+vXF1vH999+3NW7c2Obl5XVVj8P69NNPbZGRkTZfX19bVFSUbcmSJbbBgwfbIiMj7dMUPTLsX//6V4nLWLlypa1Lly42Pz8/W3BwsO22226zbd++vdh0K1assEVFRdkqVapka9asme3jjz++7CPD4uPjbR9//LGtadOmNl9fX9sNN9xQ4rocP37cFh8fb2vQoIHNx8fHFhoaarv55ptt7733nsN0Bw8etA0cONDm7+9vq1mzpu3RRx+1LV++3KlHhm3fvt02ZMgQW1BQkK1atWq2kSNH2vLy8kqcZ+rUqTZJtilTplxx2ReLiYmxtWzZssRxRY9yK/q7sFgstsmTJ9vCw8NtPj4+tgYNGtgmTJhgO3fuXLFlXvzIMJvNZjt//rztlVdesbVs2dLm6+trq1atmq1t27a2yZMn2zIzM6+6XgBAxeNhs/3v+RwAALiY++67T4sWLbrqI8hmat26tWrVqqWUlBRT2vfw8FB8fHyxU/vLk9dee01jxozRgQMH1LBhQ7PLAQDgmuCabgAAnGCxWOw3kCuyZs0abdmyRT169DCnKDdgs9n0wQcfKCYmhsANAHArXNMNAIATDh8+rFtuuUVDhw5V3bp1tXPnTr3zzjsKDQ3Vww8/bHZ55U5OTo6WLFmi1atXa+vWrVq8eLHZJQEAcE0RugEAcEK1atXUtm1b/fvf/9bJkycVEBCg/v376+WXX1aNGjXMLq/cOXnypO6++25VrVpVEydO1MCBA80uCQCAa4prugEAAAAAMAjXdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAFcR9992nsLCwUs8bGBh4bQsCAKACIHQDAOBi5syZIw8PD23atKnE8T169FBUVFQZV3V1cnNzNWnSJK1Zs8bsUgAAcAneZhcAAADKxvvvvy+r1WpoG7m5uZo8ebKkC18OAABQ0RG6AQCoIHx8fMwuAQCACofTywEAcAMff/yx2rZtKz8/P1WvXl133XWXfv31V4dpSrqm+/Tp0xo2bJiCg4NVtWpVDR8+XFu2bJGHh4fmzJlTrJ3Dhw8rNjZWgYGBqlWrlsaNG6fCwkJJ0oEDB1SrVi1J0uTJk+Xh4SEPDw9NmjTJiFUGAKBc4Eg3AAAuKjMzU6dOnSo23GKxOLx+6aWX9Mwzz+iOO+7Q3//+d508eVJvvPGGunfvrh9//FFVq1YtcflWq1W33XabNm7cqEceeUSRkZFavHixhg8fXuL0hYWF6tOnjzp27Khp06Zp5cqVmj59upo0aaJHHnlEtWrV0ttvv61HHnlEt99+u+Li4iRJrVq1+mu/CAAAyjFCNwAALuqWW2657LiWLVtKkg4ePKjnnntOL774oiZOnGgfHxcXpxtuuEFvvfWWw/CLJScna8OGDZo5c6YeffRRSdIjjzyiXr16lTj9uXPndOedd+qZZ56RJD388MNq06aNPvjgAz3yyCMKCAjQkCFD9Mgjj6hVq1YaOnRoqdYbAAB3QugGAMBFzZo1S9ddd12x4QkJCfZTupOSkmS1WnXHHXc4HBUPDQ1V06ZNtXr16suG7uXLl8vHx0cPPvigfZinp6fi4+O1atWqEud5+OGHHV5369ZNH330kdPrBgBARUHoBgDARXXo0EHt2rUrNrxatWr2gL1nzx7ZbDY1bdq0xGVc6eZpBw8eVJ06deTv7+8wPCIiosTpK1eubL9m++Jafv/99yuuBwAAFRmhGwCAcsxqtcrDw0PLli2Tl5dXsfGBgYHXrK2Slg8AAK6M0A0AQDnWpEkT2Ww2hYeHl3gq+pU0atRIq1evVm5ursPR7r1795a6Hg8Pj1LPCwCAO+KRYQAAlGNxcXHy8vLS5MmTZbPZHMbZbDadPn36svP26dNHFotF77//vn2Y1WrVrFmzSl1PUXg/e/ZsqZcBAIA74Ug3AADlWJMmTfTiiy9qwoQJOnDggGJjYxUUFKT9+/fr888/10MPPaRx48aVOG9sbKw6dOighIQE7d27V5GRkVqyZInOnDkjqXRHrf38/NSiRQstWLBA1113napXr66oqChFRUX9pfUEAKC84kg3AADl3Pjx4/XZZ5/J09NTkydP1rhx47RkyRL17t1bAwcOvOx8Xl5e+vLLL3XnnXdq7ty5euqpp1S3bl37ke7KlSuXqp5///vfqlevnsaMGaO//e1vWrRoUamWAwCAO/CwXXouGgAAqNCSk5N1++23a926derSpYvZ5QAAUK4RugEAqMDy8vLk5+dnf11YWKjevXtr06ZNOnbsmMM4AADgPK7pBgCgAhs1apTy8vLUuXNn5efnKykpSevXr9eUKVMI3AAAXAMc6QYAoAKbP3++pk+frr179+rcuXOKiIjQI488opEjR5pdGgAAboHQDQAAAACAQbh7OQAAAAAABiF0AwAAAABgkHJ5IzWr1aojR44oKChIHh4eZpcDAAAAAKhgbDab/vjjD9WtW1eenpc/nl0uQ/eRI0fUoEEDs8sAAAAAAFRwv/76q+rXr3/Z8eUydAcFBUm6sHLBwcEmV/PXWSwWrVixQr1795aPj4/Z5eAS9I9ro39cF33j2ugf10b/uDb6x3XRN67N3fonKytLDRo0sOfTyymXobvolPLg4GC3Cd3+/v4KDg52iz8+d0P/uDb6x3XRN66N/nFt9I9ro39cF33j2ty1f/7skmdupAYAAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAACg3CssLFRqaqrWrl2r1NRUFRYWml0SIInQDQAAAKCcS0pKUkREhHr16qUZM2aoV69eioiIUFJSktmlAYRuAAAAAOVXUlKShgwZoujoaKWlpemTTz5RWlqaoqOjNWTIEII3TEfoBgAAAFAuFRYWKiEhQQMGDFBycrI6duwoPz8/dezYUcnJyRowYIDGjRvHqeYwFaEbAAAAQLmUlpamAwcOaOLEifL0dIw2np6emjBhgvbv36+0tDSTKgQI3QAAAADKqaNHj0qSoqKiShxfNLxoOsAMhG4AAAAA5VKdOnUkSdu2bStxfNHwoukAMxC6AQAAAJRL3bp1U1hYmKZMmSKr1eowzmq1KjExUeHh4erWrZtJFQKEbgAAAADllJeXl6ZPn66lS5cqNjZW6enpysvLU3p6umJjY7V06VJNmzZNXl5eZpeKCszb7AIAAAAAoLTi4uK0aNEiJSQkqHv37vbh4eHhWrRokeLi4kysDiB0AwAAACjn4uLiNGjQIK1evVrLli1T37591bNnT45wwyUQugEAAACUe15eXoqJiVFOTo5iYmII3HAZXNMNAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBnArdiYmJat++vYKCghQSEqLY2Fjt2rXLYZpjx45p2LBhCg0NVUBAgNq0aaPPPvvMYZozZ87onnvuUXBwsKpWraoRI0YoOzv7r68NAAAAAAAuxKnQnZqaqvj4eKWnpyslJUUWi0W9e/dWTk6OfZp7771Xu3bt0pIlS7R161bFxcXpjjvu0I8//mif5p577tHPP/+slJQULV26VGvXrtVDDz107dYKAAAAAAAX4O3MxMuXL3d4PWfOHIWEhCgjI0Pdu3eXJK1fv15vv/22OnToIEl6+umn9eqrryojI0M33HCDduzYoeXLl+v7779Xu3btJElvvPGG+vXrp2nTpqlu3brXYr0AAAAAADCdU6H7UpmZmZKk6tWr24fdeOONWrBggfr376+qVavqv//9r86dO6cePXpIkjZs2KCqVavaA7ck3XLLLfL09NR3332n22+/vVg7+fn5ys/Pt7/OysqSJFksFlkslr+yCi6haB3cYV3cEf3j2ugf10XfuDb6x7XRP66N/nFd9I1rc7f+udr18LDZbLbSNGC1WjVw4ECdPXtW69atsw8/e/as7rzzTq1YsULe3t7y9/fXwoUL1bt3b0nSlClTNHfu3GLXgoeEhGjy5Ml65JFHirU1adIkTZ48udjw+fPny9/fvzTlAwAAAABQarm5ubr77ruVmZmp4ODgy05X6iPd8fHx2rZtm0PglqRnnnlGZ8+e1cqVK1WzZk0lJyfrjjvuUFpamqKjo0vV1oQJEzR27Fj766ysLDVo0EC9e/e+4sqVFxaLRSkpKerVq5d8fHzMLgeXoH9cG/3juugb10b/uDb6x7XRP66LvnFt7tY/RWdg/5lShe6RI0fab4BWv359+/B9+/bpzTff1LZt29SyZUtJ0vXXX6+0tDTNmjVL77zzjkJDQ3XixAmH5RUUFOjMmTMKDQ0tsT1fX1/5+voWG+7j4+MWnVXE3dbH3dA/ro3+cV30jespLCzU+vXrtXbtWgUEBKhnz57y8vIyuyyUgPePa6N/XBd949rcpX+udh2cunu5zWbTyJEj9fnnn2vVqlUKDw93GJ+bm3thoZ6Oi/Xy8pLVapUkde7cWWfPnlVGRoZ9/KpVq2S1WtWxY0dnygEAAE5KSkpSRESEevXqpRkzZqhXr16KiIhQUlKS2aUBAOCWnArd8fHx+vjjjzV//nwFBQXp2LFjOnbsmPLy8iRJkZGRioiI0D/+8Q9t3LhR+/bt0/Tp05WSkqLY2FhJUvPmzXXrrbfqwQcf1MaNG/Xtt99q5MiRuuuuu7hzOQAABkpKStKQIUMUHR2ttLQ0ffLJJ/bLv4YMGULwBgDAAE6F7rfffluZmZnq0aOH6tSpY/+3YMECSRcOr3/11VeqVauWbrvtNrVq1Urz5s3T3Llz1a9fP/ty/vOf/ygyMlI333yz+vXrp65du+q99967tmsGAADsCgsLlZCQoAEDBig5OVkdO3aUn5+fOnbsqOTkZA0YMEDjxo1TYWGh2aUCAOBWnLqm+2pudN60aVN99tlnV5ymevXqmj9/vjNNAwCAvyAtLU0HDhzQJ598Ik9PT4dw7enpqQkTJujGG29UWlqa/TGfAADgr3PqSDcAACifjh49KkmKiooqcXzR8KLpAADAtUHoBgCgAqhTp44kadu2bSWOLxpeNB0AALg2CN0AAFQA3bp1U1hYmKZMmWJ/okgRq9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnTeF43AADXmFM3UgMAAOVXXFycFi1apISEBHXv3t0+PDw8XIsWLVJcXJyJ1QEA4J4I3QAAVCBxcXEaNGiQVq9erWXLlqlv377q2bMnR7gBADAIoRu4gsLCQqWmpmrt2rUKCAhgxxSAW/Dy8lJMTIxycnIUExPDdg0AAANxTTdwGUlJSYqIiFCvXr00Y8YM9erVSxEREUpKSjK7NAAAAADlBKEbKEFSUpKGDBmi6OhopaWl6ZNPPlFaWpqio6M1ZMgQgjcAAACAq0LoBi5RWFiohIQEDRgwQMnJyerYsaP8/PzUsWNHJScna8CAARo3bpwKCwvNLhUAAACAiyN0A5dIS0vTgQMHNHHiRHl6Or5FPD09NWHCBO3fv19paWkmVQgAAACgvCB0A5c4evSoJCkqKqrE8UXDi6YDAAAAgMshdAOXqFOnjiRp27ZtJY4vGl40HQAAAABcDqEbuES3bt0UFhamKVOmyGq1OoyzWq1KTExUeHi4unXrZlKFAAAAAMoLQjdwCS8vL02fPl1Lly5VbGys0tPTlZeXp/T0dMXGxmrp0qWaNm0az7UFAAAA8Ke8zS4AcEVxcXFatGiREhIS1L17d/vw8PBwLVq0SHFxcSZWBwAAAKC8IHQDlxEXF6dBgwZp9erVWrZsmfr27auePXtyhBsAAADAVSN0A1fg5eWlmJgY5eTkKCYmhsANAAAAwClc0w0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AQAVTWFio1NRUrV27VqmpqSosLDS7JAAA3BahGwCACiQpKUkRERHq1auXZsyYoV69eikiIkJJSUlmlwYAgFsidAMAUEEkJSVpyJAhio6OVlpamj755BOlpaUpOjpaQ4YMIXgDAGAAQjcAABVAYWGhEhISNGDAACUnJ6tjx47y8/NTx44dlZycrAEDBmjcuHGcag4AwDVG6AYAoAJIS0vTgQMHNHHiRHl6On78e3p6asKECdq/f7/S0tJMqhAAAPdE6AYAoAI4evSoJCkqKqrE8UXDi6YDAADXBqEbAIAKoE6dOpKkbdu2lTi+aHjRdAAA4NogdAMAUAF069ZNYWFhmjJliqxWq8M4q9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAC4FW+zCwAAAGUjLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAcE+EbgAAKpC4uDgNGjRIq1ev1rJly9S3b1/17NmTI9wAABiE0A0AQAXj5eWlmJgY5eTkKCYmhsANAICBuKYbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDOBW6ExMT1b59ewUFBSkkJESxsbHatWtXsek2bNigm266SQEBAQoODlb37t2Vl5dnH3/mzBndc889Cg4OVtWqVTVixAhlZ2f/9bUBAAAAAMCFOBW6U1NTFR8fr/T0dKWkpMhisah3797KycmxT7Nhwwbdeuut6t27tzZu3Kjvv/9eI0eOlKfn/2/qnnvu0c8//6yUlBQtXbpUa9eu1UMPPXTt1goAAAAAABfg7czEy5cvd3g9Z84chYSEKCMjQ927d5ckjRkzRqNHj9b48ePt0zVr1sz+/x07dmj58uX6/vvv1a5dO0nSG2+8oX79+mnatGmqW7duqVcGAAAAAABX8peu6c7MzJQkVa9eXZJ04sQJfffddwoJCdGNN96o2rVrKyYmRuvWrbPPs2HDBlWtWtUeuCXplltukaenp7777ru/Ug4AAAAAAC7FqSPdF7NarXrsscfUpUsXRUVFSZJ++eUXSdKkSZM0bdo0tW7dWvPmzdPNN9+sbdu2qWnTpjp27JhCQkIci/D2VvXq1XXs2LES28rPz1d+fr79dVZWliTJYrHIYrGUdhVcRtE6uMO6uCP6p+zk5uaWeJ+IK8nOy9f6rfsUVDVdgX6+Ts3brFkz+fv7OzUPrh7vnbLDe8f98P5xbfRP2WDb5n7c7b1ztetR6tAdHx+vbdu2ORzFtlqtkqR//OMfuv/++yVJN9xwg7755ht9+OGHSkxMLFVbiYmJmjx5crHhK1ascKs3RkpKitkl4AroH+Pt27dPCQkJpZp3ainmmT59upo0aVKq9nD1eO8Yj/eO++L949roH2OxbXNf7vLeyc3NvarpShW6R44cab8BWv369e3D69SpI0lq0aKFw/TNmzfXoUOHJEmhoaE6ceKEw/iCggKdOXNGoaGhJbY3YcIEjR071v46KytLDRo0UO/evRUcHFyaVXApFotFKSkp6tWrl3x8fMwuB5egf8pObm6uunbt6tQ8u49m6vHPt+tft7fQdXWqODUv32gbi/dO2eG94354/7g2+qdssG1zP+723ik6A/vPOBW6bTabRo0apc8//1xr1qxReHi4w/iwsDDVrVu32Gkgu3fvVt++fSVJnTt31tmzZ5WRkaG2bdtKklatWiWr1aqOHTuW2K6vr698fYufHuLj4+MWnVXE3dbH3dA/xqtSpYo6dOjg1DyVDp6W74bzimrdRq0b1TCoMvwVvHeMx3vHffH+cW30j7HYtrkvd3nvXO06OBW64+PjNX/+fC1evFhBQUH2a7CrVKkiPz8/eXh46PHHH9dzzz2n66+/Xq1bt9bcuXO1c+dOLVq0SNKFo9633nqrHnzwQb3zzjuyWCwaOXKk7rrrLu5cDgAAAABwK06F7rfffluS1KNHD4fhs2fP1n333SdJeuyxx3Tu3DmNGTNGZ86c0fXXX6+UlBSH6yP+85//aOTIkbr55pvl6empwYMH6/XXX/9rawIAAAAAgItx+vTyqzF+/HiH53Rfqnr16po/f74zTQMAAAAAUO78ped0AwBQksLCQqWmpmrt2rVKTU1VYWGh2SUBAACYgtANALimkpKSFBERoV69emnGjBnq1auXIiIilJSUZHZpAAAAZY7QDQC4ZpKSkjRkyBBFR0crLS1Nn3zyidLS0hQdHa0hQ4YQvAEAQIVD6AYAXBOFhYVKSEjQgAEDlJycrI4dO8rPz08dO3ZUcnKyBgwYoHHjxnGqOQAAqFAI3QCAayItLU0HDhzQxIkT5enp+PHi6empCRMmaP/+/UpLSzOpQgAAgLJH6AYAXBNHjx6VJEVFRZU4vmh40XQAAAAVAaEbAHBN1KlTR5K0bdu2EscXDS+aDgAAoCIgdAMArolu3bopLCxMU6ZMkdVqdRhntVqVmJio8PBwdevWzaQKAQAAyh6hGwBwTXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAAAZcbb7AIAAO4jLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAAMoeoRsAcE3FxcVp0KBBWr16tZYtW6a+ffuqZ8+eHOEGAAAVEqEbAHDNeXl5KSYmRjk5OYqJiSFwAwCACotrugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFAGUpNzdXO3fudGqe7Lx8rd+6T9VqblKgn69T80ZGRsrf39+peQAAAAC4D0I3KpSdO3eqbdu2pZp3ainmycjIUJs2bUrVHgAAAIDyj9CNCiUyMlIZGRlOzbPr6FmNXbhVM/4vWs3qVHW6PQAAAAAVF6EbFYq/v7/TR549D56Wb1qemkddr9aNahhUGQAAAAB3xI3UAAAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDeJtdgLvJzc3Vzp07nZonOy9f67fuU7WamxTo5+vUvJGRkfL393dqHgAAAABA2SB0X2M7d+5U27ZtSzXv1FLMk5GRoTZt2pSqPQAAAACAsQjd11hkZKQyMjKcmmfX0bMau3CrZvxftJrVqep0ewAAAAAA10Tovsb8/f2dPvLsefC0fNPy1DzqerVuVMOgygAAAAAAZY0bqQEAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQp0J3YmKi2rdvr6CgIIWEhCg2Nla7du0qcVqbzaa+ffvKw8NDycnJDuMOHTqk/v37y9/fXyEhIXr88cdVUFBQ6pUAAAAAAMAVORW6U1NTFR8fr/T0dKWkpMhisah3797KyckpNu3MmTPl4eFRbHhhYaH69++v8+fPa/369Zo7d67mzJmjZ599tvRrAQAAAACAC/J2ZuLly5c7vJ4zZ45CQkKUkZGh7t2724dv3rxZ06dP16ZNm1SnTh2HeVasWKHt27dr5cqVql27tlq3bq0XXnhBTz75pCZNmqRKlSr9hdUBAAAAAMB1OBW6L5WZmSlJql69un1Ybm6u7r77bs2aNUuhoaHF5tmwYYOio6NVu3Zt+7A+ffrokUce0c8//6wbbrih2Dz5+fnKz8+3v87KypIkWSwWWSyWv7IKLqHo1PqCggK3WB93Q/+4NvqnbOTm5l72cqLLyc7L1/qt+xRUNV2Bfr5OzdusWTP5+/s7NQ+cw3vHtRX1CX3jmugf18W2reywb3D124BSh26r1arHHntMXbp0UVRUlH34mDFjdOONN2rQoEElznfs2DGHwC3J/vrYsWMlzpOYmKjJkycXG75ixQqX+8WXxq/ZkuSt9PR0Hd5mdjW4FP3j2uifsrFv3z4lJCSUat6ppZhn+vTpatKkSanaw9XhvVM+pKSkmF0CroD+cT1s28oO+wYXvni4GqUO3fHx8dq2bZvWrVtnH7ZkyRKtWrVKP/74Y2kXW6IJEyZo7Nix9tdZWVlq0KCBevfureDg4Gvalhm2HDojbd2kTp066fqG1f98BpQp+se10T9lIzc3V127dnVqnt1HM/X459v1r9tb6Lo6VZya1xW/zXY3vHdcm8ViUUpKinr16iUfHx+zy8El6B/Xxbat7LBv8P/PwP4zpQrdI0eO1NKlS7V27VrVr1/fPnzVqlXat2+fqlat6jD94MGD1a1bN61Zs0ahoaHauHGjw/jjx49LUomno0uSr6+vfH2Ln37g4+PjFhs6b29v+093WB93Q/+4NvqnbFSpUkUdOnRwap5KB0/Ld8N5RbVuo9aNahhUGUqL90754C77Ou6K/nE9bNvKDvsGuuq/MafuXm6z2TRy5Eh9/vnnWrVqlcLDwx3Gjx8/Xj/99JM2b95s/ydJr776qmbPni1J6ty5s7Zu3aoTJ07Y50tJSVFwcLBatGjhTDkAAAAAALg0p450x8fHa/78+Vq8eLGCgoLs12BXqVJFfn5+Cg0NLfFodcOGDe0BvXfv3mrRooWGDRumqVOn6tixY3r66acVHx9f4tFsAAAAAADKK6eOdL/99tvKzMxUjx49VKdOHfu/BQsWXPUyvLy8tHTpUnl5ealz584aOnSo7r33Xj3//PNOFw8AAAAAgCtz6ki3zWZzuoGS5mnUqJG++uorp5cFAAAAAEB54tSRbgAAAAAAcPUI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBvM0uAPgr9p/KUU5+gaFt7DuZY//p7W3sWybA11vhNQMMbQMAAABA2SF0o9zafypHPaetKbP2EhZtLZN2Vo/rQfAGAAAA3AShG+VW0RHumXe2VkRIoHHt5OVr6ZoNGtCjswL8fA1rZ++JbD22YLPhR+4BAAAAlB1CN8q9iJBARdWrYtjyLRaLjtWS2jSqJh8fH8PaAQAAAOB+uJEaAAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQb7MLAOC+9p/KUU5+gaFt7DuZY//p7W3sJi3A11vhNQMMbQMAAADuhdANwBD7T+Wo57Q1ZdZewqKtZdLO6nE9CN4AAAC4aoRulFv5hefkWfmw9mftkmflQMPaKSgo0JGCI9pxZoehR1L3Z2XLs/Jh5Reek1TFsHbKStER7pl3tlZEiHH9k5OXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAgHshdKPcOpJzUAHhb2jixrJp763lbxneRkC4dCSntdqqtuFtlZWIkEBF1TPuSwSLxaJjtaQ2jarJx8fHsHYAAACA0iB0o9yqG9BIOftH6bU7W6uJgUdSCwoK9O26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAAAEDZInSj3PL1qizruXoKD26mFjWMPZK633u/mldvbuiRVOu5TFnPnZSvV2XD2gAAAABQtnhkGAAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGcSp0JyYmqn379goKClJISIhiY2O1a9cu+/gzZ85o1KhRatasmfz8/NSwYUONHj1amZmZDss5dOiQ+vfvL39/f4WEhOjxxx9XQUHBtVkjAAAAAABchFOhOzU1VfHx8UpPT1dKSoosFot69+6tnJwcSdKRI0d05MgRTZs2Tdu2bdOcOXO0fPlyjRgxwr6MwsJC9e/fX+fPn9f69es1d+5czZkzR88+++y1XTMAAAAAAEzm7czEy5cvd3g9Z84chYSEKCMjQ927d1dUVJQ+++wz+/gmTZropZde0tChQ1VQUCBvb2+tWLFC27dv18qVK1W7dm21bt1aL7zwgp588klNmjRJlSpVujZrBgAAAACAyZwK3ZcqOm28evXqV5wmODhY3t4XmtqwYYOio6NVu3Zt+zR9+vTRI488op9//lk33HBDsWXk5+crPz/f/jorK0uSZLFYZLFY/soquISiU+sLCgrcYn3KSln93oqWbXTfuNvfQU5+tjwrH9be37fL6h1gWDsFBQU6UnBEW09stW9njPDL7znyrHxYOfnZslj8DWvHnbjb37S7oX9cW1l99qB06J/SOXA6Rzn5hYa2sftYpsNPIwX4eimshnH7OO7I3T57rnYdSr2HarVa9dhjj6lLly6KiooqcZpTp07phRde0EMPPWQfduzYMYfALcn++tixYyUuJzExUZMnTy42fMWKFfL3L/87v79mS5K30tPTdXib2dWUH0W/t3Xr1ulgoPHtpaSkGLr8sl4fo/3wxxEFhL+lZzLKpr23Vr5leBsB4dJX6wt1LKiu4W25A7Ztro3+KR+M/uzBX0P/XL0TedJLm437cvxST3y+o0zaeap1gUL8yqQpt+Bunz25ublXNV2p//Lj4+O1bds2rVu3rsTxWVlZ6t+/v1q0aKFJkyaVthlJ0oQJEzR27FiHZTdo0EC9e/dWcHDwX1q2K9hy6Iy0dZM6deqk6xte/qwBOPr5SJambU1X165d1bKucX8HFotFKSkp6tWrl3x8fAxrp6zWp6yE/npCH83z0owh0Wpcy9gj3d+lf6eOnToae6T7ZI7GLtqqfvf2V5sGIYa1407YtpVeWRwNyj+WKW3doZCIaDUKrWJoWxwNcl5ZffagdOgf5/18JEvanK5pQ6IVYeB+Qc65fC1P+163dmuvgMq+hrWz92SOxi3aqvad3WO/ray4275B0RnYf6ZUe6gjR47U0qVLtXbtWtWvX7/Y+D/++EO33nqrgoKC9PnnnztsjEJDQ7Vx40aH6Y8fP24fVxJfX1/5+hZ/0/j4+LjFhq4oKHh7e7vF+pSVsv69Gf335m5/BwG+gbKeq6eIai0UVdu4HXqLxaJfvX9VdEi0ob83z4JMWc+dUYBvoFv0T1lwt7/psrL/VI56zfy2zNorq6NBq8f1UHhNgrez3GVfx13RP1ev6DMhsk4VRdUzdr/g1E6pQ+Na7Le5IHf7vV3tOjgVum02m0aNGqXPP/9ca9asUXh4eLFpsrKy1KdPH/n6+mrJkiWqXLmyw/jOnTvrpZde0okTJxQScuFoUUpKioKDg9WiRQtnygEAwO3k5F+43m3mna0VEWLctSY5eflaumaDBvTorAA/A48GncjWYws229cLAICKxqnQHR8fr/nz52vx4sUKCgqyX4NdpUoV+fn5KSsrS71791Zubq4+/vhjZWVl2Q+516pVS15eXurdu7datGihYcOGaerUqTp27JiefvppxcfHl3g0GwCAiigiJNDwo0HHakltGlVzi6MNAAC4KqdC99tvvy1J6tGjh8Pw2bNn67777tMPP/yg7777TpIUERHhMM3+/fsVFhYmLy8vLV26VI888og6d+6sgIAADR8+XM8///xfWA0AAAAAAFyP06eXX0mPHj3+dBpJatSokb766itnmgYAAAAAoNzxNLsAAAAAAADcFaEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFuLr9p3KUk19gaBv7TubYf3p7G9slAb7eCq8ZYGgbAAAAAIALCN1XsP9UjnpOW1Nm7SUs2lom7awe14PgDQAAAABlgNB9BUVHuGfe2VoRIYHGtZOXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAAABcQOi+ChEhgYqqV8Ww5VssFh2rJbVpVE0+Pj6GtQMAAAAAKFvcSA0AAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAABcSGFhoVJTU7V27VqlpqaqsLDQ7JIAAH8BoRsAAMBFJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSAACl5G12AQCAsrf/VI5y8gsMbWPfyRz7T29vYz9uAny9FV4zwNA2AKMlJSVpyJAhGjBggD766CP99ttvql+/vqZOnaohQ4Zo0aJFiouLM7tMAICTCN0AUMHsP5WjntPWlFl7CYu2lkk7q8f1IHij3CosLFRCQoIGDBig5ORkFRYW6vTp0+rYsaOSk5MVGxurcePGadCgQfLy8jK7XACAEwjdAFDBFB3hnnlna0WEBBrXTl6+lq7ZoAE9OivAz9ewdvaeyNZjCzYbfuQeMFJaWpoOHDigTz75RJ6eng7XcXt6emrChAm68cYblZaWph49ephXqBvKzc3Vzp07nZonOy9f67fuU7WamxTo5PYtMjJS/v7+Ts0DlAXOgjMOoRsAKqiIkEBF1ati2PItFouO1ZLaNKomHx8fw9oB3MHRo0clSVFRUSWOLxpeNB2unZ07d6pt27almndqKebJyMhQmzZtStUeYBTOgjMWoRsAAMBkderUkSRt27ZNnTp1KjZ+27ZtDtPh2omMjFRGRoZT8+w6elZjF27VjP+LVrM6VZ1uD3A1nAVnLEI3AACAybp166awsDBNmTJFycnJDuOsVqsSExMVHh6ubt26mVOgG/P393f6yLPnwdPyTctT86jr1bpRDYMqA8oeZ8EZg0eGAQAAmMzLy0vTp0/X0qVLFRsbq/T0dOXl5Sk9PV2xsbFaunSppk2bxk3UAKAc4kg3AACAC4iLi9OiRYuUkJCg7t2724eHh4fzuDAAKMcI3QAAAC4iLi5OgwYN0urVq7Vs2TL17dtXPXv25Ag3AJRjhG4AAAAX4uXlpZiYGOXk5CgmJobADQDlHKEbAADAQDwHGgAqNkI3AACAgXgONABUbIRuAAAAA/EcaACo2AjdAAAABuI50ABQsfGcbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADMJzuq8gv/CcPCsf1v6sXfKsHGhYOwUFBTpScEQ7zuyQt7dxXbI/K1uelQ8rv/CcpCqGtQMAAABcjP1qVGSE7is4knNQAeFvaOLGsmnvreVvGd5GQLh0JKe12qq24W0BAAAAEvvVqNgI3VdQN6CRcvaP0mt3tlaTEGO/kft23bfq0rWLod/I7TuRrUcXbFbdno0MawMAAAC4FPvVqMgI3Vfg61VZ1nP1FB7cTC1qGHfaiMVi0X7v/Wpevbl8fHwMa8d6LlPWcyfl61XZsDYAAACAS7FfjYqMG6kBAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswsASivPUihJ2nY409B2cvLytemkFHrwdwX4+RrWzt4T2YYtGwAAAIA5nArdiYmJSkpK0s6dO+Xn56cbb7xRr7zyipo1a2af5ty5c0pISNCnn36q/Px89enTR2+99ZZq165tn+bQoUN65JFHtHr1agUGBmr48OFKTEyUtzffAeDq7ftfSB2ftLUMWvPWR3u/L4N2pABf3gcAAACAu3Bq7z41NVXx8fFq3769CgoKNHHiRPXu3Vvbt29XQECAJGnMmDH68ssvtXDhQlWpUkUjR45UXFycvv32W0lSYWGh+vfvr9DQUK1fv15Hjx7VvffeKx8fH02ZMuXaryHcVu+WoZKkJiGB8vPxMqydXUczlbBoq6YPiVazOlUMa0e6ELjDawYY2gYAAACAsuNU6F6+fLnD6zlz5igkJEQZGRnq3r27MjMz9cEHH2j+/Pm66aabJEmzZ89W8+bNlZ6erk6dOmnFihXavn27Vq5cqdq1a6t169Z64YUX9OSTT2rSpEmqVKnStVs7uLXqAZV0V4eGhrdTUFAgSWpSK0BR9YwN3QAAAADcy1+6kVpm5oVraatXry5JysjIkMVi0S233GKfJjIyUg0bNtSGDRskSRs2bFB0dLTD6eZ9+vRRVlaWfv75579SDgAAAAAALqXUF49arVY99thj6tKli6KioiRJx44dU6VKlVS1alWHaWvXrq1jx47Zp7k4cBeNLxpXkvz8fOXn59tfZ2VlSZIsFossFktpV+FPFR3hLCgoMLSdomUb2YZUduvjbvi9lc4feRfes1sOnbH/Do2Qc+7Cje5q/nJSAZUNvNHdyRxJ7vF3kJOfLc/Kh7X39+2yeht3OUNBQYGOFBzR1hNbDb1nxy+/58iz8mHl5GfLYvE3rJ2yQv9A4rPH1dE/zmO/2rXx2VM6V9v3pV7T+Ph4bdu2TevWrSvtIq5aYmKiJk+eXGz4ihUr5O9v3C/x12xJ8ta6det0MNCwZuxSUlIMXX5Zr4+7KPq9paen6/A2s6spPzYc95DkpacWby+D1rz10d4fy6Ad6fsN63TQr0yaMswPfxxRQPhbeiajbNp7a+VbhrcREC59tb5Qx4LqGt6W0egfSHz2uDr6x3nsV7s2PntKJzc396qmK1XoHjlypJYuXaq1a9eqfv369uGhoaE6f/68zp4963C0+/jx4woNDbVPs3HjRoflHT9+3D6uJBMmTNDYsWPtr7OystSgQQP17t1bwcHBpVmFq/LzkSxN25qurl27qmVd49qxWCxKSUlRr1695OPjY1g7ZbU+7mbLoTPS1k3q1KmTrm9Y3exyyo1OOecVveOEGtcKMPRGd7uPZeqJz3do6u3NdV2o0Te681JYjfJ/o7vQX0/oo3lemjEkWo1rGftt9nfp36ljp47Gfpt9MkdjF21Vv3v7q02DEMPaKSv0DyQ+e1wd/eM89qtdG589pVN0BvafcWpNbTabRo0apc8//1xr1qxReHi4w/i2bdvKx8dH33zzjQYPHixJ2rVrlw4dOqTOnTtLkjp37qyXXnpJJ06cUEjIhV9ASkqKgoOD1aJFixLb9fX1la9v8dNGfXx8DH0zFf0heHt7G9pOEXdbH3fB7610alf10T2dw/98wmvkutAqat2oRpm1V54F+AbKeq6eIqq1UFRt476osFgs+tX7V0WHRBv63vEsyJT13BkF+Aa6xXuU/oHEZ4+ro3+cx361a+Ozp3SudtlOhe74+HjNnz9fixcvVlBQkP0a7CpVqsjPz09VqlTRiBEjNHbsWFWvXl3BwcEaNWqUOnfurE6dOkmSevfurRYtWmjYsGGaOnWqjh07pqefflrx8fElBmsAAAAAAMorp0L322+/LUnq0aOHw/DZs2frvvvukyS9+uqr8vT01ODBg5Wfn68+ffrorbf+/zn7Xl5eWrp0qR555BF17txZAQEBGj58uJ5//vm/tiYAAAAAALgYp08v/zOVK1fWrFmzNGvWrMtO06hRI3311VfONA0AAAAAQLnzl57TDQAAAAAALo/QDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABjE2+wCgLKUm5urnTt3OjXPrqNnlX9sr3Zs85P1dFWn5o2MjJS/v79T8wAAgNLbfypHOfkFhrax72SO/ae3t7G70wG+3gqvGWBoGwCMRehGhbJz5061bdu2VPPePdf5eTIyMtSmTZtStQcAAJyz/1SOek5bU2btJSzaWibtrB7Xg+ANlGOEblQokZGRysjIcGqe7Lx8fbl6g/r37KxAP1+n2wMAAGWj6Aj3zDtbKyIk0Lh28vK1dM0GDejRWQFO7hs4Y++JbD22YLPhR+4BGIvQjQrF39/f6SPPFotFv586oc4d2snHx8egygAAwLUSERKoqHpVDFu+xWLRsVpSm0bV2DcA8Ke4kRoAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswtwZXmWQknStsOZhraTk5evTSel0IO/K8DP17B29p7INmzZAIBrg88e17f/VI5y8gsMbWPfyRz7T29vY3fXAny9FV4zwNA2ALZtro3+MRah+wr2/a+zxidtLYPWvPXR3u/LoJ0LH64AANfEZ49r238qRz2nrSmz9hIWlcXfgbR6XA+CNwzFts210T/Gco0qXFTvlqGSpCYhgfLz8TKsnV1HM5WwaKumD4lWszpVDGtH4ttsAHB1fPa4tqIj3DPvbK2IkEDj2snL19I1GzSgR2fDjwY9tmCz4UfuAbZtro3+MRah+wqqB1TSXR0aGt5OQcGFD7omtQIUVc/YPz4AgGvjs6d8iAgJNPT3ZrFYdKyW1KZRNfn4+BjWDlBW2La5NvrHWNxIDQAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwiLfZBQAAylaepVCStO1wpqHt5OTla9NJKfTg7wrw8zWsnb0nsg1bNnCp/MJz8qx8WPuzdsmzcqBh7RQUFOhIwRHtOLND3t7G7a7tz8qWZ+XDyi88J6mKYe0AQEVG6AaACmbf/0Lq+KStZdCatz7a+30ZtCMF+PKRBuMdyTmogPA3NHFj2bT31vK3DG8jIFw6ktNabVXb8LYAoCJiDwUAKpjeLUMlSU1CAuXn42VYO7uOZiph0VZNHxKtZnWMPYIW4Out8JoBhrYBSFLdgEbK2T9Kr93ZWk1CjD3S/e26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAqOkI3AFQw1QMq6a4ODQ1vp6CgQJLUpFaAoupx2ircg69XZVnP1VN4cDO1qGHc37XFYtF+7/1qXr25fHx8DGvHei5T1nMn5etV2bA2AKCi40ZqAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGMTp0L127Vrddtttqlu3rjw8PJScnOwwPjs7WyNHjlT9+vXl5+enFi1a6J133nGY5ty5c4qPj1eNGjUUGBiowYMH6/jx439pRQAAAAAAcDVOh+6cnBxdf/31mjVrVonjx44dq+XLl+vjjz/Wjh079Nhjj2nkyJFasmSJfZoxY8boiy++0MKFC5WamqojR44oLi6u9GsBAAAAAIAL8nZ2hr59+6pv376XHb9+/XoNHz5cPXr0kCQ99NBDevfdd7Vx40YNHDhQmZmZ+uCDDzR//nzddNNNkqTZs2erefPmSk9PV6dOnUq3JgAAAAAAuBinQ/efufHGG7VkyRI98MADqlu3rtasWaPdu3fr1VdflSRlZGTIYrHolltusc8TGRmphg0basOGDSWG7vz8fOXn59tfZ2VlSZIsFossFsu1XoUyV1BQYP/pDuvjbor6hL4xXm5urnbt2uXUPLuPZir/2F5t21xJ549XcWreZs2ayd/f36l5cPXYtrk2+qd0yur3VlafPe72d5CTny3Pyoe19/ftsnoHGNZOQUGBjhQc0dYTW+Xtfc13p+1++T1HnpUPKyc/WxYLn1dXw93+pt2Nu/XP1a7DNd9KvPHGG3rooYdUv359eXt7y9PTU++//766d+8uSTp27JgqVaqkqlWrOsxXu3ZtHTt2rMRlJiYmavLkycWGr1ixwi12mH/NliRvpaen6/A2s6vB5aSkpJhdgtvbt2+fEhISSjXvsLnOzzN9+nQ1adKkVO3hz7Ftc230T+kU/d7WrVung4HGt2f0Z09Zr4/RfvjjiALC39IzGWXT3lsr3zK8jYBw6av1hToWVNfwttwB2zbX5m79k5ube1XTGRK609PTtWTJEjVq1Ehr165VfHy86tat63B02xkTJkzQ2LFj7a+zsrLUoEED9e7dW8HBwdeqdNNsOXRG2rpJnTp10vUNq5tdDi5hsViUkpKiXr16ycfHx+xy3Fpubq66du3q1DzZefn6Ou179enWXoF+vk7Ny5FuY7Ftc230T+n8fCRL07amq2vXrmpZ17h9kLL67Cmr9Skrob+e0EfzvDRjSLQa1zL2SPd36d+pY6eOxh7pPpmjsYu2qt+9/dWmQYhh7bgTtm2uzd36p+gM7D9zTbcSeXl5mjhxoj7//HP1799fktSqVStt3rxZ06ZN0y233KLQ0FCdP39eZ8+edTjaffz4cYWGhpa4XF9fX/n6Ft+Z9vHxcYsQVLSx9vb2dov1cVfu8vfmyqpUqaIOHTo4NY/FYtEfZ8+o242d6B8Xw7bNtdE/pVPWvzejP3vc7e8gwDdQ1nP1FFGthaJqO3fJkTMsFot+9f5V0SHRhv7ePAsyZT13RgG+gW7RP2XB3f6m3Y279c/VrsM1fU530TXWnp6Oi/Xy8pLVapUktW3bVj4+Pvrmm2/s43ft2qVDhw6pc+fO17IcAAAAAABM5fSR7uzsbO3du9f+ev/+/dq8ebOqV6+uhg0bKiYmRo8//rj8/PzUqFEjpaamat68eZoxY4akC0eyRowYobFjx6p69eoKDg7WqFGj1LlzZ+5cDgAAAABwK06H7k2bNqlnz57210XXWg8fPlxz5szRp59+qgkTJuiee+7RmTNn1KhRI7300kt6+OGH7fO8+uqr8vT01ODBg5Wfn68+ffrorbeMvxEFAAAAAABlyenQ3aNHD9lstsuODw0N1ezZs6+4jMqVK2vWrFmaNWuWs80DAAAAAFBuXNNrugEAAAAAwP9H6AYAAAAAwCCEbgAAAAAADHJNn9MNAADgzvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAQAXELoBAACu0r7/hdTxSVvLoDVvfbT3+zJoRwrwZZcQAIzCFhYAAOAq9W4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQBARUboBgAAuErVAyrprg4NDW+noKBAktSkVoCi6hkbugEAxuJGagAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQbzNLgAAAAC4FvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAZQdQjcAAADcwr7/hdTxSVvLoDVvfbT3+zJoRwrwZZcdKM94BwMAAMAt9G4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQAwFqEbAAAAbqF6QCXd1aGh4e0UFBRIkprUClBUPWNDN4DyjxupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNrsAd5Obm6udO3c6Nc+uo2eVf2yvdmzzk/V0VafmjYyMlL+/v1PzAAAAAMBfQe65eoTua2znzp1q27Ztqea9e67z82RkZKhNmzalag8AAAAASoPcc/UI3ddYZGSkMjIynJonOy9fX67eoP49OyvQz9fp9gAAAACgLJF7rh6h+xrz9/d3+hsYi8Wi30+dUOcO7eTj42NQZQAAAABwbZB7rh43UgMAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgTofutWvX6rbbblPdunXl4eGh5OTkYtPs2LFDAwcOVJUqVRQQEKD27dvr0KFD9vHnzp1TfHy8atSoocDAQA0ePFjHjx//SysCAAAAAICrcTp05+Tk6Prrr9esWbNKHL9v3z517dpVkZGRWrNmjX766Sc988wzqly5sn2aMWPG6IsvvtDChQuVmpqqI0eOKC4urvRrAQAAAACAC/J2doa+ffuqb9++lx3/1FNPqV+/fpo6dap9WJMmTez/z8zM1AcffKD58+frpptukiTNnj1bzZs3V3p6ujp16uRsSQAAAAAAuCSnQ/eVWK1Wffnll3riiSfUp08f/fjjjwoPD9eECRMUGxsrScrIyJDFYtEtt9xiny8yMlINGzbUhg0bSgzd+fn5ys/Pt7/OysqSJFksFlkslmu5CqYoWgd3WBd3RP+4NvqnbOTm5mrXrl1OzbP7aKbyj+3Vts2VdP54Fafmbdasmfz9/Z2aB84pKCiw/+T9YyzeP+6H94/rom9cm7vtt13telzT0H3ixAllZ2fr5Zdf1osvvqhXXnlFy5cvV1xcnFavXq2YmBgdO3ZMlSpVUtWqVR3mrV27to4dO1bichMTEzV58uRiw1esWOFWHyopKSlml4AroH9cG/1jrH379ikhIaFU8w6b6/w806dPdzhLCtfer9mS5K309HQd3mZ2Ne6N94/74f3juuib8sFd9ttyc3OvarprfqRbkgYNGqQxY8ZIklq3bq3169frnXfeUUxMTKmWO2HCBI0dO9b+OisrSw0aNFDv3r0VHBz81ws3mcViUUpKinr16iUfHx+zy8El6B/XRv+UjdzcXHXt2tWpebLz8vV12vfq0629Av18nZqXI3XG23LojLR1kzp16qTrG1Y3uxy3xvvH/fD+cV30jWtzt/22ojOw/8w1Dd01a9aUt7e3WrRo4TC8efPmWrdunSQpNDRU58+f19mzZx2Odh8/flyhoaElLtfX11e+vsU/cHx8fNyis4q42/q4G/rHtdE/xqpSpYo6dOjg1DwWi0V/nD2jbjd2om9ckLe3t/0n/WMs3j/uh/eP66Jvygd32W+72nW4ps/prlSpktq3b1/suqXdu3erUaNGkqS2bdvKx8dH33zzjX38rl27dOjQIXXu3PlalgMAAAAAgKmcPtKdnZ2tvXv32l/v379fmzdvVvXq1dWwYUM9/vjjuvPOO9W9e3f17NlTy5cv1xdffKE1a9ZIuvBt74gRIzR27FhVr15dwcHBGjVqlDp37sydywEAAAAAbsXp0L1p0yb17NnT/rroWuvhw4drzpw5uv322/XOO+8oMTFRo0ePVrNmzfTZZ585XMv06quvytPTU4MHD1Z+fr769Omjt9566xqsDgAAAAAArsPp0N2jRw/ZbLYrTvPAAw/ogQceuOz4ypUra9asWZo1a5azzQMAAAAAUG5c02u6AQAAAADA/0foBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAACAoQoLC5Wamqq1a9cqNTVVhYWFZpdUZgjdAAAAAADDJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSygShGwAAAABgiKSkJA0ZMkTR0dFKS0vTJ598orS0NEVHR2vIkCEVIngTugEAAAAA11xhYaESEhI0YMAAJScnq2PHjvLz81PHjh2VnJysAQMGaNy4cW5/qjmhGwAAAABwzaWlpenAgQOaOHGiPD0do6enp6cmTJig/fv3Ky0tzaQKywahGwAAAABwzR09elSSFBUVVeL4ouFF07krQjcAAAAA4JqrU6eOJGnbtm0lji8aXjSduyJ0AwAAAACuuW7duiksLExTpkyR1Wp1GGe1WpWYmKjw8HB169bNpArLBqEbAAAAAHDNeXl5afr06Vq6dKliY2OVnp6uvLw8paenKzY2VkuXLtW0adPk5eVldqmG8ja7AAAAAACAe4qLi9OiRYuUkJCg7t2724eHh4dr0aJFiouLM7G6skHoBgAAAAAYJi4uToMGDdLq1au1bNky9e3bVz179nT7I9xFCN0AAAAAAEN5eXkpJiZGOTk5iomJqTCBW+KabgAAAAAADEPoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAg3mYXAAAA/prc3Fzt3LnTqXl2HT2r/GN7tWObn6ynqzo1b2RkpPz9/Z2aB3BVvH9cF30Dd0HoBgCgnNu5c6fatm1bqnnvnuv8PBkZGWrTpk2p2gNcDe8f10XfwF0QugEAKOciIyOVkZHh1DzZefn6cvUG9e/ZWYF+vk63B7gL3j+ui76BuyB0AwBQzvn7+zt9dMZisej3UyfUuUM7+fj4GFQZ4Pp4/7gu+gbughupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNruA0rDZbJKkrKwskyu5NiwWi3Jzc5WVlSUfHx+zy8El6B/XRv+4LvrGtdE/ro3+cW30j+uib1ybu/VPUR4tyqeXUy5D9x9//CFJatCggcmVAAAAAAAqsj/++ENVqlS57HgP25/FchdktVp15MgRBQUFycPDw+xy/rKsrCw1aNBAv/76q4KDg80uB5egf1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1j82m01//PGH6tatK0/Py1+5XS6PdHt6eqp+/fpml3HNBQcHu8Ufn7uif1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1D9XOsJdhBupAQAAAABgEEI3AAAAAAAGIXS7AF9fXz333HPy9fU1uxSUgP5xbfSP66JvXBv949roH9dG/7gu+sa1VdT+KZc3UgMAAAAAoDzgSDcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEK3CQoKCvT888/rt99+M7sUALhm2LYBAMqaxWLRzTffrD179phdCq7g/Pnz+u2333To0CGHfxUFdy83SVBQkLZu3aqwsDCzS8ElLBaLIiMjtXTpUjVv3tzscoByhW2ba2P75vq++eYbffPNNzpx4oSsVqvDuA8//NCkqlBk06ZN2rFjhySpefPmateunckVQZJq1aql9evXq2nTpmaXgkvs2bNHDzzwgNavX+8w3GazycPDQ4WFhSZVVra8zS6gorrpppuUmprKjqkL8vHx0blz58wuAyiX2La5NrZvrm3y5Ml6/vnn1a5dO9WpU0ceHh5ml4T/+e233/S3v/1N3377rapWrSpJOnv2rG688UZ9+umnql+/vrkFVnBDhw7VBx98oJdfftnsUnCJ++67T97e3lq6dGmF3q5xpNsk77zzjiZPnqx77rlHbdu2VUBAgMP4gQMHmlQZJGnKlCnavXu3/v3vf8vbm++mXE1OTo5efvnlyx4N+uWXX0yqDGzbXB/bN9dVp04dTZ06VcOGDTO7FFzi1ltv1dmzZzV37lw1a9ZMkrRr1y7df//9Cg4O1vLly02usGIbNWqU5s2bp6ZNm5b42TNjxgyTKkNAQIAyMjIUGRlpdimmInSbxNPz8pfTV6RTLVzV7bffrm+++UaBgYGKjo4utvFOSkoyqTJI0t/+9jelpqZq2LBhJX5r+uijj5pUGdi2uT62b66rRo0a2rhxo5o0aWJ2KbiEn5+f1q9frxtuuMFheEZGhrp166bc3FyTKoMk9ezZ87LjPDw8tGrVqjKsBhdr3769Xn31VXXt2tXsUkzFV9wmufTIHFxL1apVNXjwYLPLwGUsW7ZMX375pbp06WJ2KbgE2zbXx/bNdf3973/X/Pnz9cwzz5hdCi7RoEEDWSyWYsMLCwtVt25dEyrCxVavXm12CbiMV155RU888YSmTJmi6Oho+fj4OIwPDg42qbKyxZFuF3Du3DlVrlzZ7DKAciM8PFxfffUVN4JycWzbAOc8+uijmjdvnlq1aqVWrVoV2znlFFnzLF68WFOmTNGsWbPsN0/btGmTRo0apSeffFKxsbHmFghJ0t69e7Vv3z51795dfn5+9pt1wTxFZ8Bd2g8V7UZqhG6TFBYWasqUKXrnnXd0/Phx7d69W40bN9YzzzyjsLAwjRgxwuwSK7yCggKtWbNG+/bt0913362goCAdOXJEwcHBCgwMNLu8Cu3jjz/W4sWLNXfuXPn7+5tdDi7Ctq18YPvmmjhF1nVVq1ZNubm5KigosN8Loej/l16icebMGTNKrNBOnz6tO+64Q6tXr5aHh4f27Nmjxo0b64EHHlC1atU0ffp0s0ussFJTU684PiYmpowqMRenl5vkpZde0ty5czV16lQ9+OCD9uFRUVGaOXMmO6YmO3jwoG699VYdOnRI+fn56tWrl4KCgvTKK68oPz9f77zzjtklVmjTp0/Xvn37VLt2bYWFhRU7GvTDDz+YVBnYtrk+tm+ui1NkXdfMmTPNLgFXMGbMGPn4+OjQoUMOZ8HdeeedGjt2LKHbRBUlVP8ZQrdJ5s2bp/fee08333yzHn74Yfvw66+/Xjt37jSxMkgXTvFr166dtmzZoho1atiH33777Q5BAubgND7XxbbN9bF9A5w3fPhws0vAFaxYsUJff/11sUe3NW3aVAcPHjSpKlwsNzdXhw4d0vnz5x2Gt2rVyqSKyhah2ySHDx9WREREseFWq7XEG3WgbKWlpWn9+vWqVKmSw/CwsDAdPnzYpKpQ5LnnnjO7BFwG2zbXx/bNtW3atEn//e9/S9w55c7y5iosLFRycrJ27NghSWrZsqUGDhwoLy8vkytDTk5OiZebnTlzRr6+viZUhCInT57U/fffr2XLlpU4vqJc0335Z7vAUC1atFBaWlqx4YsWLSr2OAqUPavVWuJG4LffflNQUJAJFQHlA9s218f2zXV9+umnuvHGG7Vjxw59/vnnslgs+vnnn7Vq1SpVqVLF7PIqtL1796p58+a69957lZSUpKSkJA0dOlQtW7bUvn37zC6vwuvWrZvmzZtnf+3h4SGr1aqpU6de8V4JMN5jjz2ms2fP6rvvvpOfn5+WL1+uuXPnqmnTplqyZInZ5ZUZjnSb5Nlnn9Xw4cN1+PBhWa1WJSUladeuXZo3b56WLl1qdnkVXu/evTVz5ky99957ki5svLOzs/Xcc8+pX79+JleHwsJCvfrqq5c9GsRNbMzDts31sX1zXVOmTNGrr76q+Ph4BQUF6bXXXlN4eLj+8Y9/qE6dOmaXV6GNHj1aTZo0UXp6uqpXry7pws27hg4dqtGjR+vLL780ucKKberUqbr55pu1adMmnT9/Xk888YR+/vlnnTlzRt9++63Z5VVoq1at0uLFi9WuXTt5enqqUaNG6tWrl4KDg5WYmKj+/fubXWKZ4O7lJkpLS9Pzzz+vLVu2KDs7W23atNGzzz6r3r17m11ahffbb7+pT58+stls2rNnj9q1a6c9e/aoZs2aWrt2rUJCQswusUJ79tln9e9//1sJCQl6+umn9dRTT+nAgQNKTk7Ws88+q9GjR5tdYoXGts21sX1zXQEBAfr5558VFhamGjVqaM2aNYqOjtaOHTt000036ejRo2aXWGEFBAQoPT1d0dHRDsO3bNmiLl26KDs726TKUCQzM1Nvvvmmw2dPfHw8X1iZLDg4WD/99JPCwsLUqFEjzZ8/X126dNH+/fvVsmVL5ebmml1imeBIt4m6deumlJQUs8tACerXr68tW7ZowYIF9o33iBEjdM8998jPz8/s8iq8//znP3r//ffVv39/TZo0SX/729/UpEkTtWrVSunp6YRuk7Ftc21F27dPP/1UP/30E9s3F1KtWjX98ccfkqR69epp27Ztio6O1tmzZyvMjqmr8vX1tffNxbKzs4vdHwHmqFKlip566imzy8AlmjVrpl27diksLEzXX3+93n33XYWFhemdd96pUF+IcKTbZOfPn9eJEydktVodhjds2NCkiiBJa9eu1Y033mh/FmeRgoICrV+/Xt27dzepMkgXjjjs2LFDDRs2VJ06dfTll1+qTZs2+uWXX3TDDTcoMzPT7BIBwGl333232rVrp7Fjx+qFF17QG2+8oUGDBiklJUVt2rThRmomuvfee/XDDz/ogw8+UIcOHSRJ3333nR588EG1bdtWc+bMMbdA6OzZs9q4cWOJ+9X33nuvSVXh448/VkFBge677z5lZGTo1ltv1ZkzZ1SpUiXNmTNHd955p9kllglCt0n27NmjBx54QOvXr3cYbrPZ5OHhUWHu5OeqvLy8dPTo0WKnWZ4+fVohISH0j8maNWumefPmqWPHjuratasGDBig8ePHa8GCBRo1apROnDhhdokVSrVq1eTh4XFV03K9vWvYs2ePVq9eXeLO6bPPPmtSVThz5ozOnTununXr2m8CtX79ejVt2lRPP/20qlWrZnaJFdbZs2c1fPhwffHFF/Lx8ZF04Yv4gQMHavbs2apataq5BVZwX3zxhe655x5lZ2crODjY4TPJw8ODzx4Xkpubq507d6phw4aqWbOm2eWUGUK3Sbp06SJvb2+NHz9ederUKbbDev3115tUGSTJ09NTx48fV61atRyG7969W+3atVNWVpZJlUGSxo8fr+DgYE2cOFELFizQ0KFDFRYWpkOHDmnMmDF6+eWXzS6xQpk7d679/6dPn9aLL76oPn36qHPnzpKkDRs26Ouvv9YzzzyjMWPGmFUm/uf999/XI488opo1ayo0NLTYzukPP/xgYnWAa9u7d6/9kWHNmzcv8RGJKHvXXXed+vXrpylTppT46DDAbIRukwQEBCgjI0ORkZFml4KLxMXFSZIWL16sW2+91eHZjoWFhfrpp5/UrFkzLV++3KwSUYINGzZow4YNatq0qW677Tazy6nQBg8erJ49e2rkyJEOw998802tXLlSycnJ5hQGu0aNGumf//ynnnzySbNLwWWcOHGixLMQWrVqZVJFeP755zVu3LhigS4vL0//+te/OEPEZAEBAdq6dasaN25sdim4hM1m06JFiy57dlVFuWyG0G2S9u3b69VXX1XXrl3NLgUXuf/++yVdOHJ3xx13ONxUqFKlSgoLC9ODDz5YoU6HAZwRGBiozZs3Fzv6s3fvXrVu3Zo7/LqA4OBgbd68mZ1TF5SRkaHhw4drx44dunT3jEvPzMVlZ64tLi5Od911l+644w6zS8ElHn30Ub377rvq2bOnateuXezs3tmzZ5tUWdni7uVl6OJTkl955RU98cQTmjJliqKjo+3XBxUJDg4u6/Kg///GDwsL0+OPP84pSi7syJEjWrduXYnfmnL3cvPUqFFDixcvVkJCgsPwxYsXq0aNGiZVhYv93//9n1asWKGHH37Y7FJwiQceeEDXXXedPvjggxJ3TmGeonvuXGrLli3253ajbC1ZssT+//79++vxxx/X9u3bS9yvHjhwYFmXh//56KOPlJSUpH79+pldiqk40l2GPD09HTbYJW3AuZGaa7jpppuUlJRU7MYoWVlZio2N1apVq8wpDJKkOXPm6B//+IcqVaqkGjVqFLsm9ZdffjGxuoptzpw5+vvf/66+ffuqY8eOki7c4Xf58uV6//33dd9995lbYAX1+uuv2/+fk5OjGTNmqH///iXunPKllXmCgoL0448/cp2wCym6UWRmZmaxG3QVFhYqOztbDz/8sGbNmmVilRWTp6fnVU3HfrW5wsPDtWzZsgp/SS2huwylpqZe9bQxMTEGVoI/c7nTyE6cOKF69erJYrGYVBkkqUGDBnr44Yc1YcKEq/7QRdn57rvv9PrrrzvcbGj06NH2EI6yFx4eflXT8aWVuWJjYzVs2DANHjzY7FLwP3PnzpXNZtMDDzygmTNnqkqVKvZxRZedFd00EkBxc+fO1fLly/Xhhx86XLZZ0RC6gYv89NNPkqTWrVtr1apVDqeMFRYWavny5Xr33Xd14MABkyqEdOEU5o0bN6pJkyZmlwIA18ypU6c0fPhwdejQQVFRUZwi60JSU1PtT54BcPXy8vJ0++2369tvv1VYWFix7VpFeWIGWw6TzJ49W4GBgfq///s/h+ELFy5Ubm6uhg8fblJlFVvr1q3l4eEhDw8P3XTTTcXG+/n56Y033jChMlxsxIgRWrhwocaPH292KZCceoQe96sALm/Dhg369ttvtWzZsmLjOEXWXEFBQdqxY4eio6MlXbhPxezZs9WiRQtNmjRJlSpVMrnCim306NGKiIgodnnMm2++qb1792rmzJnmFAYNHz5cGRkZGjp0aIW+VwVHuk1y3XXX2e/kd7HU1FQ99NBD2rVrl0mVVWwHDx6UzWZT48aNtXHjRofndFeqVEkhISHy8vIysUJIF846GDBggPLy8kq8JnXGjBkmVVYxXXq/ipJwvwrXMXjwYHXo0KHYI8OmTp2q77//XgsXLjSpMoSFhWnAgAF65plnVLt2bbPLwUXat2+v8ePHa/Dgwfrll1/UokULxcXF6fvvv1f//v0JdSarV6+elixZorZt2zoM/+GHHzRw4ED99ttvJlWGgIAAff311xX+iU0c6TbJoUOHSrzGrlGjRjp06JAJFUG68PuXVOxu2HAtiYmJ+vrrr9WsWTNJKnYjNZSt1atXm10CnLB27VpNmjSp2PC+fftq+vTpZV8Q7E6fPq0xY8YQuF3Q7t271bp1a0kXzkqMiYnR/Pnz9e233+quu+4idJvs9OnTDtfbFwkODtapU6dMqAhFGjRowFluInSbJiQkRD/99JPCwsIchm/ZsoXH6phkyZIl6tu3r3x8fBweQ1ESrqsz1/Tp0/Xhhx9yJ2wXwY0fy5fs7OwST4X18fFx6lIBXHtxcXFavXo196twQTabzf6F/MqVKzVgwABJFwIFoc58ERERWr58uUaOHOkwfNmyZWrcuLFJVUG6sM/2xBNP6J133imWeyoSQrdJ/va3v2n06NEKCgpS9+7dJV04tfzRRx/VXXfdZXJ1FVNsbKyOHTumkJAQxcbGXnY6TpE1n6+vr7p06WJ2GbiMtLQ0vfvuu/rll1+0cOFC1atXTx999JHCw8Mr/OllriA6OloLFizQs88+6zD8008/VYsWLUyqCtKFS88mTJigdevW8Tg3F9OuXTu9+OKLuuWWW5Samqq3335bkrR//37OTHABY8eO1ciRI3Xy5En7PXm++eYbTZ8+nbMQTDZ06FDl5uaqSZMm8vf3L7ZdO3PmjEmVlS2u6TbJ+fPnNWzYMC1cuNB+J0yr1ap7771Xb7/9tnx9fU2uEHBdiYmJOnr0qMOzh+EaPvvsMw0bNkz33HOPPvroI23fvl2NGzfWm2++qa+++kpfffWV2SVWeF988YXi4uJ09913O+ycfvLJJ1q4cOEVv3SEsa70aDce52auLVu2aOjQoTp06JDGjh2r5557TpI0atQonT59WvPnzze5Qrz99tt66aWXdOTIEUkX7pEwadIk3XvvvSZXVrHNnTv3iuMrys2jCd0m27NnjzZv3iw/Pz9FR0fbrykGcHm33367Vq1apRo1aqhly5bFvjVNSkoyqTLccMMNGjNmjO69914FBQVpy5Ytaty4sX788Uf17dtXx44dM7tESPryyy81ZcoU++dPq1at9Nxzz3GpAOCkc+fOydvbm0eJuZCTJ0/Kz89PgYGBZpcC2LGFMMnzzz+vcePGqWnTpmratKl9eF5env71r38VO+0PZeNqj5xyip+5qlatqri4OLPLQAl27dplv2TmYlWqVNHZs2fLviCUqH///urfv7/ZZeAyzp8/r/3796tJkyaEORfRuHFjff/998Xuu3Pu3Dm1adOGsxBMdtNNNykpKUlVq1Z1ePJMVlaWYmNjtWrVKhOrw759+zR79mzt27dPr732mkJCQrRs2TI1bNhQLVu2NLu8MsGRbpN4eXnp6NGjCgkJcRh++vRphYSEcM2wSS49te/XX39VnTp1HHZ6OMUPuLzGjRvrvffe0y233OJwpHvevHl6+eWXtX37drNLrPAuFx7Onj1LeDBZbm6uRo0aZT8dc/fu3WrcuLFGjRqlevXqafz48SZXWHF5enra7/tysePHj6tBgwY6f/68SZVBunz/nDhxQvXq1ZPFYjGpMqSmpqpv377q0qWL1q5dqx07dqhx48Z6+eWXtWnTJi1atMjsEssEX5+apOiZtZfasmWLqlevbkJFkC7cEOViQUFBSk1N5c6XLqigoEBr1qzRvn37dPfddysoKEhHjhxRcHAwp5SZ6MEHH9Sjjz6qDz/8UB4eHjpy5Ig2bNigcePG6ZlnnjG7PEg6cOBAiV/s5ufn6/DhwyZUhCITJkzQli1btGbNGt1666324bfccosmTZpE6DbBxU8z+frrrx0eS1VYWKhvvvnmitfiw1g//fST/f/bt293uISpsLBQy5cvV7169cwoDf8zfvx4vfjiixo7dqyCgoLsw2+66Sa9+eabJlZWtgjdZaxatWry8PCQh4eHrrvuOofgXVhYqOzsbD388MMmVgi4voMHD+rWW2/VoUOHlJ+fr169eikoKEivvPKK8vPz9c4775hdYoU1fvx4Wa1W3XzzzcrNzVX37t3l6+urcePGadSoUWaXV6FdTXioyI9zcQXJyclasGCBOnXq5LB/0LJlS+3bt8/Eyiqui28seOkNn3x8fBQWFsbz7U3UunVr+3510Y0hL+bn56c33njDhMpQZOvWrSXeaDAkJKRCPW6P0F3GZs6cKZvNpgceeECTJ0922OmpVKmSwsLC1LlzZxMrBFzfo48+qnbt2hV7rv3tt9+uBx980MTK4OHhoaeeekqPP/649u7dq+zsbLVo0YKzD1xAUXjw8PAgPLiokydPFjs9VpJycnJKPDsOxit6Nnd4eLg2bdpU7LIMmGv//v2y2Wxq3LixNm7c6HA9d6VKlRQSEiIvLy8TK0TVqlV19OjRYmeE/PjjjxXqLARCdxkr2tEJDw/XjTfeWOyuywD+XFpamtavX69KlSo5DA8LC+P0WBdRqVIlnvnsYi4OD99//71q1qxpckW4VLt27fTll1/azwopCtr//ve/+ULeRBaLRY0bN9aZM2cI3S6m6Kk/Rds3uJ677rpLTz75pBYuXCgPDw9ZrVZ9++23GjduXIV6nBuh2yQXP5bl3LlzxW7AERwcXNYlQRfucnkxDw8PZWdnFxtO/5jLarWWeE3qb7/95nC9EMpGXFyc5syZo+Dg4D+9qzyPczPfpfeugOuYMmWK+vbtq+3bt6ugoECvvfaatm/frvXr1ys1NdXs8iosHx8fh2uH4RqWLFmivn37ysfHx+HymZIMHDiwjKrCpaZMmaL4+Hg1aNBAhYWFatGihQoLC3X33Xfr6aefNru8MsPdy02Sm5urJ554Qv/97391+vTpYuO5e7k5PD09HU7hu/SGd0Wv6R9z3XnnnapSpYree+89BQUF6aefflKtWrU0aNAgNWzYULNnzza7xArl/vvv1+uvv66goCDdd999VzwNlr4xx+uvv66HHnpIlStX/tNHI/JIRHPt27dPL7/8srZs2aLs7Gy1adNGTz75pKKjo80urUIbM2aMfH199fLLL5tdCv7n4juWe3p6XnY69ttcw6+//qqtW7cqOztbN9xwg8MjkysCQrdJ4uPjtXr1ar3wwgsaNmyYZs2apcOHD+vdd9/Vyy+/rHvuucfsEiukqz2ScPGZCih7v/32m/r06SObzaY9e/aoXbt22rNnj2rWrKm1a9eWeE0kjHPx0Qa4pouvR73SnZZ5JCJQslGjRmnevHlq2rSp2rZtq4CAAIfxM2bMMKkyoHwpLCzU1q1b1ahRI1WrVs3scsoModskDRs21Lx589SjRw8FBwfrhx9+UEREhD766CN98skn+uqrr8wuEVfh5Zdf1sMPP6yqVauaXUqFU1BQoAULFjgcDbrnnnvk5+dndmkVjpeXl44dO6ZatWrJy8tLR48e5YsPoBR++OEH+fj42I9qL168WLNnz1aLFi00adKkYvexQNnp2bPnZcd5eHho1apVZVgNLnbgwAGlpKTIYrEoJiZGLVu2NLskXOSxxx5TdHS0RowYocLCQsXExGj9+vXy9/fX0qVL1aNHD7NLLBOEbpMEBgZq+/btatiwoerXr6+kpCR16NBB+/fvV3R0tLKzs80uEVchODhYmzdv5jneqNBCQ0P1/vvv67bbbpOnp6eOHz/ucAdZuI709HR98cUXslgsuummmxyeBQ3ztW/fXuPHj9fgwYP1yy+/qEWLFoqLi9P333+v/v37a+bMmWaXCLiU1atXa8CAAcrLy5MkeXt768MPP9TQoUNNrgxF6tevr+TkZLVr107Jycn65z//qTVr1uijjz7SqlWr9O2335pdYpm4/AUQMFTjxo3tN7OJjIzUf//7X0nSF198wVHTcoTvrMwxd+5cffnll/bXTzzxhKpWraobb7xRBw8eNLGyiunhhx/WoEGD5OXlJQ8PD4WGhsrLy6vEfzDPokWL1KVLF7322mt6//331b9/f02bNs3ssnCR3bt3q3Xr1pKkhQsXKiYmRvPnz9ecOXP02WefmVsc7H777Tf99ttvZpcBSc8884x69eqlw4cP6/Tp03rwwQf1xBNPmF0WLnLq1CmFhoZKkr766ivdcccduu666/TAAw9o69atJldXdgjdJrn//vu1ZcsWSdL48eM1a9YsVa5cWY899pgef/xxk6sDXNuUKVPsp5Fv2LBBb775pqZOnaqaNWtqzJgxJldX8UyaNEnbt2/X4sWLZbPZ9OGHHyopKanEfzBPYmKiHnzwQWVmZur333/Xiy++qClTpphdFi5is9nsjz5auXKl+vXrJ0lq0KCBTp06ZWZpFZ7VatXzzz+vKlWqqFGjRmrUqJGqVq2qF154gcdV/b/27jys5rz/H/jztGsvadGEkiUUkoy1QZYy0s1t3GMr21iGYZA9S8LgjsZtbpIlTMNYs0y2iWk0QkTxbSoRNciWUGlR5/dHd+fX0WHM4rxPnefjulxX5/05mWfXXD6d1+f9fr/eAl2/fh3Lly+HjY0NzMzMsHr1ajx8+FBhk2ISw8rKCikpKSgrK8Px48fRq1cvABVNpdXpYTyPDBOkamHg6emJ1NRUXL58GU2aNGGHUqLfkZ2dDUdHRwBAVFQU/vnPf+Kzzz5D586d1WZvkKpp3rw5mjdvjkWLFmHw4MHQ19cXHYlek5aWhu+//172IWfGjBlYuHAhHj58yD34KsLNzQ3BwcHw9PREbGwsNmzYAKDimDcrKyvB6dTb/PnzsWXLFnz11Vfo3LkzACAuLg6LFy9GUVERli1bJjihenr+/DksLCxkr/X19VGnTh08e/aMZ6qriFGjRuGTTz6BjY0NJBIJPD09AQAXLlxA8+bNBadTHhbdSnb69GlMnjwZ58+flzvrufKJaadOnbBx40Z07dpVYEoi1WZoaIgnT56gQYMGOHnyJKZPnw4A0NPTk+3rIjFiY2MxderUakX38+fP4evry2ZDAhUWFsr93tHR0YGenh7y8/NZdKuI0NBQDBs2DFFRUZg/f77s4eK+ffvQqVMnwenU2/bt27F582a5855dXFxga2uLSZMmsegW6MSJEzAxMZG9Li8vR0xMDK5fvy4b4znd4ixevBitWrVCdnY2Bg8eDF1dXQAVTVjnzJkjOJ3ysJGakvn4+KB79+5vXAK7bt06nDlzBgcPHlRyMvozjIyMkJSUxEZqSjZs2DCkpqaibdu22LVrF7KyslC3bl0cPnwY8+bNk/tFS8r1pu7lDx8+hK2tLUpLSwUlIw0NDQQHB8PQ0FA2Nnv2bAQEBMjNFPGcbtVTVFQETU1NHssnkJ6eHpKTk9G0aVO58bS0NLRp04YPfAV52/nclXhON6kCznQrWVJSElauXPnG671792Zjmxqka9euPKJKgG+++QYLFixAdnY29u/fL1tCdvnyZXz66aeC06mn5ORkABV7UlNSUpCTkyO7VrmPy9bWVlQ8QsVRleHh4XJj1tbW2Llzp+y1RCJh0a2C9PT0REdQe61bt8b69euxbt06ufH169ejdevWglIR99PXDAUFBYiNjUVWVhZKSkrkrqnL7xzOdCuZnp4erl+/Llsy9rqMjAw4OzvziakAz58/f+f3Vl2iSUQVsw0SiQSA4q7+derUwX/+8x+MHj1a2dGIaoyysjKsXbsWe/bsUfjhNDc3V1Ayio2NRb9+/dCgQQN07NgRQEUjz+zsbERHR3NbYA3Rr18/bN68GTY2NqKjqI0rV67A29sbhYWFKCgogLm5OR4/fgx9fX1YWlri1q1boiMqBWe6lczW1vatRXdycjJvBIKYmprKiobfw2VKqqGwsFDhB1MXFxdBidRXZmYmpFIpHBwccPHiRblzunV0dGBpaalWXUprA2dnZ0RHR8POzk50FLWxZMkSbN68GTNmzMCCBQswf/583L59G1FRUVi4cKHoeGrNw8MD6enp+Oabb5CamgoAGDhwICZNmoT69esLTkfv6ueff+bElpJ9+eWX6N+/PzZu3AgTExOcP38e2traGD58OKZOnSo6ntJwplvJpkyZgp9++gkJCQnVlou9fPkS7u7u6N69e7XlS/T+xcbGyr6+ffs25syZA39/f7kn2tu3b8eKFSvg5+cnKiYBePToEfz9/XH8+HGF1/lQhOivY88K5WvcuDHWrVuHfv36wcjICFevXpWNnT9/Ht99953oiEQ1Gu9rymdqaooLFy6gWbNmMDU1RXx8PJycnHDhwgX4+fnJHmLVdpzpVrIFCxbgwIEDaNq0KSZPnoxmzZoBAFJTU/HNN9+grKwM8+fPF5xSPXl4eMi+DgoKwpo1a+T2B/v4+MDZ2RmbNm1i0S3YtGnT8OzZM1y4cAEfffQRDh48iAcPHiA4OBghISGi46m1HTt2vPX6yJEjlZSEqObJycmRHRtqaGiIZ8+eAQA+/vhjBAYGioxGAPLy8nDx4kU8fPiw2l5i3tuIFNPW1pY1vLO0tERWVhacnJxgYmKC7OxswemUh0W3kllZWeHcuXOYOHEi5s6dK9v7KJFI0KdPH3zzzTc8i1MFxMfHY+PGjdXG3dzcMHbsWAGJqKrTp0/j0KFDcHNzg4aGBho2bIhevXrB2NgYK1asQL9+/URHVFuvLxUrLS1FYWEhdHR0oK+vzw+mRG/xwQcf4P79+2jQoAEaN26MkydPwtXVFQkJCbJjdkiMI0eOYNiwYcjPz4exsbHcdjSJRMJ7G9EbtG3bFgkJCWjSpAk8PDywcOFCPH78GDt37kSrVq1Ex1Oa3++zT3+7hg0bIjo6Go8fP8aFCxdw/vx5PH78GNHR0bC3txcdjwDY2dlV6/ILAJs3b+b+RhVQUFAgO5LKzMwMjx49AlCxBzUxMVFkNLX39OlTuT/5+flIS0tDly5dsGvXLtHxiFTaP/7xD8TExACo2I4WGBiIJk2aYOTIkWxCKNiMGTMwevRo5OfnIy8vT+4+xwZ3RG+2fPlyWb+qZcuWwczMDBMnTsSjR4+wadMmwemUh3u6iRSIjo7GoEGD4OjoiA4dOgAALl68iBs3bmD//v3w9vYWnFC9tW/fHsHBwejTpw98fHxgamqKFStWYN26ddi3bx9u3rwpOiK95tKlSxg+fLja7N2qDbj3Ubz4+HjEx8ejSZMm6N+/v+g4as3AwADXrl3jv4cajvc1EoXLy4kU8Pb2Rnp6OjZs2CArEvr3748JEyZwplsFTJ06Fffv3wcALFq0CH379kVkZCR0dHQQEREhNhwppKWlhXv37omOQVSjdOzYUdbMk8Tq06cPLl26xGKthps3bx7Mzc1Fx1BrJSUlKCkpgaGhoegoSsWZbiKq8QoLC5GamooGDRrAwsJCdBy1dvjwYbnXUqkU9+/fx/r162FnZ4djx44JSqbezM3NkZ6eDgsLC4wePRpff/01jIyM3vo93333HQYMGAADAwMlpaQnT56gbt26AIDs7GyEh4fj5cuX8PHx4TnQAlS9nz169AhBQUEYNWoUnJ2doa2tLfdeHx8fZcejKl7/3VNJIpFAT08Pjo6O3MIpwLZt25CYmIgPP/wQw4YNw9y5c7FmzRq8evUKPXr0wO7du2X3vNqORTfRG5w9exZhYWG4desW9u7dC1tbW+zcuRP29vbo0qWL6HhEKqmyQ2kliUSCevXqoUePHggJCZHt6yLlMjQ0RHJyMhwcHKCpqYmcnBy5s9RJrGvXrqF///7Izs5GkyZNsHv3bvTt2xcFBQXQ0NBAQUEB9u3bB19fX9FR1crr97M3kUgkPKpSMA0NDUgkErxe1lSOSSQSdOnSBVFRUTAzMxOUUr0sW7YMy5YtQ+fOnZGYmIhPPvkEUVFRmDZtGjQ0NLBu3Tp8/PHH2LBhg+ioSsGim0iB/fv3Y8SIERg2bBh27tyJlJQUODg4YP369YiOjkZ0dLToiGrrxo0bSE5OhqurK+zt7fHDDz9g5cqVePnyJXx9fTFv3jy5rrIkRmVzOxZ2qqFXr1548OAB2rVrh+3bt2PIkCGoU6eOwvdu3bpVyenIy8sLWlpamDNnDnbu3ImjR4+iT58+soaeU6ZMweXLl3H+/HnBSYlUU0xMDObPn49ly5bB3d0dQEUvnsDAQCxYsAAmJiYYP348OnTogC1btghOqx6aNGmCoKAgfPrpp7h06RI6dOiAPXv2YNCgQQCAY8eOYcKECbhz547gpMrB7uVECgQHB2Pjxo0IDw+XW0JW+bSOxDh48CBatGiBoUOHwsnJCTt27MA///lPGBgYwMrKCosXL8aqVatEx1RbeXl5+Pzzz2FhYQFra2tYW1vDwsICkydPRl5enuh4au3bb7+Ft7c38vPzIZFI8OzZs2qd5iv/kPIlJCTIZoT+/e9/4969e5g0aRI0NDSgoaGBKVOmsAmhIPHx8Th69Kjc2I4dO2Bvbw9LS0t89tlnKC4uFpSOKk2dOhVr1qxBz549YWRkBCMjI/Ts2ROrV69GQEAAOnfujNDQUJw6dUp0VLWRlZUlWxnq5uYGLS0tuSPCXFxcZP151AEbqREpkJaWhm7dulUbNzExYfEg0LJlyzBr1iwEBwcjIiICEyZMwIoVKzBt2jQAwKZNm7B27VrMnj1bbFA1lJubi44dO+Lu3bsYNmwYnJycAAApKSmIiIhATEwMzp07x2V9glhZWeGrr74CANjb22Pnzp1qs4+uJsjNzYW1tTWAiq0ABgYGcv9WzMzM8OLFC1Hx1NqSJUvQvXt3fPzxxwAqtgKMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1d/PmTRgbG1cbNzY2xq1btwBUzLw+fvxY2dHUVmlpKXR1dWWvdXR05CaytLS01GpbBme6iRSwtrZGRkZGtfG4uDh2LhUoLS0No0ePhkQigZ+fH0pKSuDp6Sm73rt3b7VZpqRqgoKCoKOjg5s3byIsLAzTpk3DtGnTsGnTJmRkZEBbWxtBQUGiYxKAzMxMWcFdVFQkOA1Ven1bDLfJqIakpCT07NlT9nr37t3o0KEDwsPDMX36dKxbtw579uwRmJAAoF27dggICJBtbQIqtjnNmjUL7du3B1CxPY0n0ChXSkoKkpOTkZycDKlUitTUVNnr//u//xMdT6k4002kwLhx4zB16lRs3boVEokE9+7dQ3x8PGbOnInAwEDR8dRWQUGBrOOyhoYG6tSpA319fdn1OnXqcJmfIFFRUQgLC4OVlVW1a9bW1li1ahUmTJiAtWvXCkhHVZWXl2PZsmXYuHEjHjx4gPT0dDg4OCAwMBCNGjXCmDFjREdUS/7+/rJZoaKiIkyYMEHWOZ73NXGePn0qd1+LjY2Fl5eX7HX79u2RnZ0tIhpVsWXLFgwYMAAffPCBrLDOzs6Gg4MDDh06BADIz8/HggULRMZUOz179pRrble5YqRqgzt1waKbSIE5c+agvLwcPXv2RGFhIbp16wZdXV3MnDkTU6ZMER1PbUkkErkb9OuvSZz79++jZcuWb7zeqlUr5OTkKDERvUlwcDC2b9+OVatWYdy4cbLxVq1aITQ0lEW3AH5+fnKvhw8fXu09I0eOVFYcqsLKygqZmZmws7NDSUkJEhMTsWTJEtn1Fy9eVDs+jJSvWbNmSElJwcmTJ5Geni4b69Wrl6wLPbv/K1dmZqboCCqF3cuJ3qKkpAQZGRnIz89HixYtYGhoKDqSWtPQ0ICJiYms0M7Ly4OxsbHsF6pUKsXz58/Vao+QqrC1tcX333//xuP0zp49iyFDhuDevXtKTkavc3R0RFhYmKzhUFJSEhwcHJCamoqOHTuymVoN8Ntvv6F+/frvfKQV/XkTJ05EUlISVq5ciaioKGzfvh337t2Djo4OACAyMhKhoaFISEgQnJSoZps0aRKCgoJgYWEhOsp7wZluIgVGjx6Nr7/+GkZGRmjRooVsvKCgAFOmTOGROoJs27ZNdAR6gz59+mD+/Pk4deqU7MNopeLiYgQGBqJv376C0lFVd+/ehaOjY7Xx8vJylJaWCkhEf1SLFi1w9epV9hhRgqVLl2LgwIHw8PCAoaEhtm/fLneP27p1K3r37i0wIVWKiYlBTEwMHj58iPLycrlr/Nym+r799lvMnDmz1hbdnOkmUkBTUxP379+HpaWl3Pjjx49hbW2NV69eCUpGf8SuXbvg4+Mj2xdJ789vv/0GNzc36Orq4vPPP0fz5s0hlUrx66+/4r///S+Ki4tx6dIlNrFRAe3atcOXX36J4cOHy810BwUF4dSpUzh79qzoiPQ7qv5/I+V49uwZDA0NoampKTeem5sLQ0PDag8bSbmWLFmCoKAguLm5wcbGptrWs4MHDwpKRu+qtt/XONNNVMXz588hlUohlUrx4sUL6Onpya6VlZUhOjq6WiFOqmv8+PHo0KFDrb2Bq5IPPvgA8fHxmDRpEubOnStrnCKRSNCrVy+sX7+eBbeKWLhwIfz8/HD37l2Ul5fjwIEDSEtLw44dO6qdR0xEFUxMTBSOm5ubKzkJKbJx40ZERERgxIgRoqMQKcSim6gKU1NTWXOupk2bVrsukUjkGqiQauNCHuWyt7fHsWPH8PTpU9y4cQNAxf5hfihVLQMGDMCRI0cQFBQEAwMDLFy4EK6urjhy5Ah69eolOh4R0R9WUlKCTp06iY5B9EYsuomqOHPmDKRSKXr06IH9+/fLFQs6Ojpo2LAh6tevLzAhkeozMzODu7u76Bj0Fl27dsWpU6dExyAi+luMHTsW3333HY91JZXFopuoCg8PDwCQHQ/CzrBERKRqeFQikbyioiJs2rQJP/74I1xcXKod47ZmzRpByYgqsOgmUqBhw4YAgMLCQmRlZaGkpETuuouLi4hYRER/irm5OdLT02FhYQEzM7O3Fm25ublKTEZ/BrfOEMlLTk5GmzZtAADXr1+Xu8aHVDXD8OHDYWxsLDrGe8Oim0iBR48eYdSoUTh27JjC6zwHmohqkrVr18LIyAgAEBoaKjYM/WUpKSnc6kRUxZkzZ0RHoCqSk5Pf+b2VE1kbNmx4X3FUAo8MI1Jg2LBhuHPnDkJDQ/HRRx/h4MGDePDgAYKDgxESEoJ+/fqJjkjvoFWrVjh27Bi7ZhORyho4cOA7v/fAgQPvMQkR0d9DQ0MDEokEUqn0d1caqMtEFme6iRQ4ffo0Dh06BDc3N2hoaKBhw4bo1asXjI2NsWLFChbdgjk4OCAhIQF169aVG8/Ly4Orqytu3boFoPoSMyJ19fz583d+b21e3qeKqh5FJZVKcfDgQZiYmMDNzQ0AcPnyZeTl5f2h4pxIHQwcOBAREREwNjb+3X8ffGClXJmZmbKvr1y5gpkzZyIgIAAdO3YEAMTHxyMkJASrVq0SFVHpWHQTKVBQUCA7j9vMzAyPHj1C06ZN4ezsjMTERMHp6Pbt2wqfjBYXF+Pu3bsCEhGptsrjEN+mckZCXWYdVMW2bdtkX8+ePRuffPIJNm7cCE1NTQAVs0CTJk3iwxCi15iYmMjua8bGxty7rUIqeyMBwODBg7Fu3Tp4e3vLxlxcXGBnZ4fAwED4+voKSKh8LLqJFGjWrBnS0tLQqFEjtG7dGmFhYWjUqBE2btwIGxsb0fHU1uHDh2VfnzhxQm6GqKysDDExMWjUqJGAZESqjfsda4atW7ciLi5OVnADgKamJqZPn45OnTph9erVAtMRqZaqD6wiIiLEBaG3unbtGuzt7auN29vbIyUlRUAiMVh0EykwdepU3L9/HwCwaNEi9O3bF5GRkdDR0eGNXaDKp6ESiQR+fn5y17S1tdGoUSOEhIQISEak2iqPQyTV9urVK6SmpqJZs2Zy46mpqSgvLxeUikj19ejRAwcOHICpqanc+PPnz+Hr64vTp0+LCUZwcnLCihUrsHnzZujo6AAASkpKsGLFCjg5OQlOpzxspEb0DgoLC5GamooGDRrAwsJCdBy1Z29vj4SEBP6/IPqTzp49i7CwMNy6dQt79+6Fra0tdu7cCXt7e3Tp0kV0PLU1ffp07NixA/PmzYO7uzsA4MKFC/jqq68wYsQInjVM9AYaGhrIycmRbQ2s9PDhQ9ja2qK0tFRQMrp48SL69+8PqVQq61SenJwMiUSCI0eOyO51tR1nuonegb6+PlxdXUXHoP+p2qCjUl5eXrUn3ERU3f79+zFixAgMGzYMiYmJKC4uBgA8e/YMy5cvR3R0tOCE6uvf//43rK2tERISIlttZWNjg4CAAMyYMUNwOiLVU/VoqpSUFOTk5Mhel5WV4fjx47C1tRURjf7H3d0dt27dQmRkJFJTUwEAQ4YMwdChQ2FgYCA4nfJwpptIgbKyMkRERCAmJgYPHz6stqyPy5TEWrlyJRo1aoQhQ4YAqGjSsX//ftjY2CA6OhqtW7cWnJBIdbVt2xZffvklRo4cCSMjIyQlJcHBwQFXrlyBl5eX3IdWEqey4zwbqBG9WeXRVEBFM8jX1alTB//5z38wevRoZUcjAKWlpWjevDmOHj2qVkvJFeFMN5ECU6dORUREBPr164dWrVqxI6aK2bhxIyIjIwEAp06dwo8//ojjx49jz549CAgIwMmTJwUnJFJdaWlp6NatW7VxExMT5OXlKT8QKcRim+j3ZWZmQiqVwsHBARcvXkS9evVk13R0dGBpaSnXmJCUS1tbG0VFRaJjqAQW3UQK7N69G3v27JE73oBUR05ODuzs7AAAR48exSeffILevXujUaNG6NChg+B0RKrN2toaGRkZ1Tr9x8XFwcHBQUwoAgA8ePAAM2fOlK2yen3mjse5Eclr2LAhSktL4efnh7p168odVUWq4fPPP8fKlSuxefNmaGmpb+mpvj850Vvo6OjA0dFRdAx6AzMzM2RnZ8POzg7Hjx9HcHAwgIqlZfxQSvR248aNw9SpU7F161ZIJBLcu3cP8fHxmDFjBhYuXCg6nlrz9/dHVlYWAgMDYWNjw1VWRO9AW1sbBw8e5P1LRSUkJCAmJgYnT56Es7NztX3cBw4cEJRMuVh0EykwY8YMfP3111i/fj0/9KiggQMHYujQoWjSpAmePHkCLy8vAMCVK1f4sITod8yZMwfl5eXo2bMnCgsL0a1bN+jq6iIgIABjx44VHU+txcXF4ezZs2jTpo3oKEQ1yoABAxAVFYUvv/xSdBR6jampKQYNGiQ6hnAsuokUiIuLw5kzZ3Ds2DG0bNkS2tractfV5amcqlq7di3s7e2RlZWFVatWwdDQEABw//59TJo0SXA6ItUmkUgwf/58BAQEICMjA/n5+WjRogXCwsJgb2/PRmoC2dnZKWwGRURv16RJEwQFBeGXX35Bu3btqs2mfvHFF4KS0bZt20RHUAnsXk6kwKhRo956nTcQcUpLSzF+/HgEBgbC3t5edByiGqO4uBiLFy/GqVOnZDPbvr6+2LZtGxYsWABNTU18/vnnmD17tuioauvkyZMICQlBWFhYtT33RPRmb/s8IJFIcOvWLSWmIaqORTcR1TgmJia4evUqi26iP2D27NkICwuDp6cnzp07h0ePHmHUqFE4f/485s2bh8GDB7PLr2BmZmYoLCzEq1evoK+vX22VVW5urqBkRETvztXVFTExMTAzM0Pbtm3fulUzMTFRicnE4fJyIqpxfH19uXeL6A/au3cvduzYAR8fH1y/fh0uLi549eoVkpKS2LtCRYSGhoqOQET0lw0YMAC6uroAKj6zEWe6iRR601M5iUQCPT09ODo6wt/fH927dxeQjoKDgxESEoKePXty7xbRO9LR0UFmZiZsbW0BAHXq1MHFixfh7OwsOBkR0V/322+/4fDhw8jKykJJSYnctTVr1ghKRVSBRTeRAnPnzsWGDRvg7OwMd3d3ABVHHiQnJ8Pf3x8pKSmIiYnBgQMHMGDAAMFp1Q/3bhH9cZqamsjJyUG9evUAAEZGRkhOTuY2DRVTVlaGqKgo/PrrrwCAli1bwsfHh0v/id4iJiYGPj4+cHBwQGpqKlq1aoXbt29DKpXC1dUVp0+fFh1R7ZWUlODhw4coLy+XG2/QoIGgRMrFoptIgXHjxqFBgwYIDAyUGw8ODsadO3cQHh6ORYsW4YcffsClS5cEpSQiencaGhrw8vKSLfk7cuQIevToobZnpqqijIwMeHt74+7du2jWrBkAIC0tDXZ2dvjhhx/QuHFjwQmJVJO7uzu8vLywZMkSGBkZISkpCZaWlhg2bBj69u2LiRMnio6ottLT0zFmzBicO3dOblwqlUIikaCsrExQMuVi0U2kgImJCS5fvlztzOeMjAy0a9cOz549Q2pqKtq3b48XL14ISklE9O5+71SGSjydQRxvb29IpVJERkbC3NwcAPDkyRMMHz4cGhoa+OGHHwQnJFJNRkZGuHr1Kho3bgwzMzPExcWhZcuWSEpKwoABA3D79m3REdVW586doaWlhTlz5sDGxqba9s3WrVsLSqZcbKRGpICenh7OnTtXreg+d+4c9PT0AADl5eWyr+n9mz59OpYuXQoDAwNMnz79re/l3i2i6lhMq77Y2FicP39eVnADQN26dfHVV1+hc+fOApMRqTYDAwPZPm4bGxvcvHkTLVu2BAA8fvxYZDS1d/XqVVy+fBnNmzcXHUUoFt1ECkyZMgUTJkzA5cuX0b59ewAVe7o3b96MefPmAQBOnDiBNm3aCEypXq5cuYLU1FS0bdsWV65ceeP72IWZiGoqXV1dhaun8vPzoaOjIyARUc3w4YcfIi4uDk5OTvD29saMGTNw7do1HDhwAB9++KHoeGqtRYsWfPABLi8neqPIyEisX78eaWlpAIBmzZphypQpGDp0KADg5cuXsm7mpByampq4f/8+LC0tAQBDhgzBunXrYGVlJTgZEdFfN3LkSCQmJmLLli2yJp4XLlzAuHHj0K5dO0RERIgNSKSibt26hfz8fLi4uKCgoAAzZszAuXPn0KRJE6xZswYNGzYUHVGtPH/+XPb1pUuXsGDBAixfvhzOzs7Q1taWe6+xsbGy4wnBopuIagwNDQ3k5OTIim5jY2NcvXoVDg4OgpMREf11eXl58PPzw5EjR2QfTF+9egUfHx9ERETAxMREcEIiot+noaEht/KwsmlaVerWSI3Ly4moxuIzQyKqTUxNTXHo0CFkZGTIjgxzcnKq1l+EiOQ5ODggISEBdevWlRvPy8uDq6srjxJVsjNnzoiOoHJYdBP9j7m5OdLT02FhYQEzM7O37g3Ozc1VYjKqJJFIqv1/4R5uIqptHB0dWWgT/QG3b99WOGNaXFyMu3fvCkik3jw8PBAUFISZM2dCX19fdByVwKKb6H/Wrl0LIyMj2dcs5lSPVCqFv7+/7JzhoqIiTJgwgecME1GtMGjQILi7u2P27Nly46tWrUJCQgL27t0rKBmRajp8+LDs6xMnTshtwSgrK0NMTAwaNWokIBktWbIEEyZMYNH9P9zTTUQ1Bs8ZJqLarF69ejh9+jScnZ3lxq9duwZPT088ePBAUDIi1aShoQGgYtXb6yWNtrY2GjVqhJCQEHz88cci4qm11/vwqDvOdBMpkJiYCG1tbdkHn0OHDmHbtm1o0aIFFi9ezKNbBGExTUS12ZuOBtPW1pbrBkxEFcrLywEA9vb2SEhIgIWFheBEVBVXjf5/GqIDEKmi8ePHIz09HUDFMRRDhgyBvr4+9u7di1mzZglOR0REtZGzszO+//77auO7d+9GixYtBCQiUm3x8fE4evQoMjMzZQX3jh07YG9vD0tLS3z22WcoLi4WnFJ9NW3aFObm5m/9oy44002kQHp6Otq0aQMA2Lt3Lzw8PPDdd9/hl19+wb/+9S+EhoYKzUdERLVPYGAgBg4ciJs3b6JHjx4AgJiYGOzatYv7uYkUWLJkCbp37y5bPn7t2jWMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1tWTJEh51+D8suokUkEqlsiVLP/74o+xmbmdnh8ePH4uMRkREtVT//v0RFRWF5cuXY9++fahTpw5cXFzw448/wsPDQ3Q8IpWTlJSE4OBg2evdu3ejQ4cOCA8PB1DxuW3RokUsugX517/+xT3d/8Oim0gBNzc3BAcHw9PTE7GxsdiwYQMAIDMzE1ZWVoLTERFRbdWvXz/069dPdAyiGuHp06dyn8tiY2Ph5eUle92+fXtkZ2eLiKb2uJ9bHvd0EykQGhqKxMRETJ48GfPnz5edl7pv3z506tRJcDoiIqqt8vLysHnzZsybNw+5ubkAKpp78qxhouqsrKyQmZkJACgpKUFiYiI+/PBD2fUXL15AW1tbVDy1xgOy5PHIMKI/oKioCJqamryBExHR3y45ORmenp4wMTHB7du3kZaWBgcHByxYsABZWVnYsWOH6IhEKmXixIlISkrCypUrERUVhe3bt+PevXuyUwAiIyMRGhqKhIQEwUlJ3XGmm+gNKmcb5s6dK5ttSElJwcOHDwUnIyKi2mj69Onw9/fHjRs3oKenJxv39vbGzz//LDAZkWpaunQptLS04OHhgfDwcISHh8sdu7d161b07t1bYEKiCpzpJlIgOTkZPXv2hKmpKWcbiIhIKUxMTJCYmIjGjRvDyMgISUlJcHBwwJ07d9CsWTMUFRWJjkikkp49ewZDQ0NoamrKjefm5sLQ0FCuECcSgTPdRApMnz4do0aN4mwDEREpja6uLp4/f15tPD09HfXq1ROQiKhmMDExqVZwA4C5uTkLblIJLLqJFEhISMD48eOrjdva2iInJ0dAIiIiqu18fHwQFBSE0tJSABXdf7OysjB79mwMGjRIcDoiIvqzWHQTKcDZBiIiUraQkBDk5+fD0tISL1++hIeHBxo3bgxDQ0MsW7ZMdDwiIvqTuKebSIGxY8fiyZMn2LNnD8zNzZGcnAxNTU34+vqiW7duCA0NFR2RiIhqqbi4OCQnJyM/Px/t2rVDz549RUciIqK/gDPdRApUzjbUq1dPNtvg6OgIIyMjzjYQEdHfKj4+HkePHpW97tKlCwwMDPDf//4Xn376KT777DMUFxcLTEhERH8FZ7qJ3uKXX35BUlIS8vPz4erqCk9PT9GRiIiolvHy8sJHH32E2bNnAwCuXbuGdu3awc/PD05OTli9ejXGjx+PxYsXiw1KRER/ipboAESqpry8HBEREThw4ABu374NiUQCe3t7WFtbQyqVQiKRiI5IRES1yNWrV7F06VLZ6927d8Pd3R3h4eEAADs7OyxatIhFNxFRDcXl5URVSKVS+Pj4YOzYsbh79y6cnZ3RsmVL3LlzB/7+/vjHP/4hOiIREdUyT58+hZWVlex1bGwsvLy8ZK/bt2+P7OxsEdGIiOhvwJluoioiIiLw888/IyYmBt27d5e7dvr0afj6+mLHjh0YOXKkoIRERFTbWFlZITMzE3Z2digpKUFiYiKWLFkiu/7ixQtoa2sLTEhERH8FZ7qJqti1axfmzZtXreAGgB49emDOnDmIjIwUkIyIiGorb29vzJkzB2fPnsXcuXOhr6+Prl27yq4nJyejcePGAhMSEdFfwaKbqIrk5GT07dv3jde9vLyQlJSkxERERFTbLV26FFpaWvDw8EB4eDjCw8Oho6Mju75161b07t1bYEIiIvor2L2cqAodHR3cuXMHNjY2Cq/fu3cP9vb2PLqFiIj+ds+ePYOhoSE0NTXlxnNzc2FoaChXiBMRUc3BPd1EVZSVlUFL683/LDQ1NfHq1SslJiIiInVhYmKicNzc3FzJSYiI6O/EopuoCqlUCn9/f+jq6iq8zhluIiIiIiL6I1h0E1Xh5+f3u+9h53IiIiIiInpX3NNNRERERERE9J6wezkRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREX766SdIJBLk5eWJjkJERFSrsOgmIiKqBfz9/SGRSCCRSKCtrQ17e3vMmjULRUVFoqMRERGpNZ7TTUREVEv07dsX27ZtQ2lpKS5fvgw/Pz9IJBKsXLlSdDQiIiK1xZluIiKiWkJXVxfW1taws7ODr68vPD09cerUKQBAcXExvvjiC1haWkJPTw9dunRBQkLCW/++uLg4dO3aFXXq1IGdnR2++OILFBQUKONHISIiqjVYdBMREdVC169fx7lz56CjowMAmDVrFvbv34/t27cjMTERjo6O6NOnD3JzcxV+/82bN9G3b18MGjQIycnJ+P777xEXF4fJkycr88cgIiKq8SRSqVQqOgQRERH9Nf7+/vj222+hp6eHV69eobi4GBoaGtizZw/69u0LMzMzREREYOjQoQCA0tJSNGrUCNOmTUNAQAB++ukndO/eHU+fPoWpqSnGjh0LTU1NhIWFyf4bcXFx8PDwQEFBAfT09ET9qERERDUK93QTERHVEt27d8eGDRtQUFCAtWvXQktLSzZTXVpais6dO8veq62tDXd3d/z6668K/66kpCQkJycjMjJSNiaVSlFeXo7MzEw4OTm995+HiIioNmDRTUREVEsYGBjA0dERALB161a0bt0aW7ZsQfv27f/w35Wfn4/x48fjiy++qHatQYMGfzkrERGRumDRTUREVAtpaGhg3rx5mD59OjIyMqCjo4NffvkFDRs2BFCxvDwhIQHTpk1T+P2urq5ISUmRFfFERET057CRGhERUS01ePBgaGpqYsOGDZg4cSICAgJw/PhxpKSkYNy4cSgsLMSYMWMUfu/s2bNx7tw5TJ48GVevXsWNGzdw6NAhNlIjIiL6gzjTTUREVEtpaWlh8uTJWLVqFTIzM1FeXo4RI0bgxYsXcHNzw4kTJ2BmZqbwe11cXBAbG4v58+eja9eukEqlaNy4MYYMGaLkn4KIiKhmY/dyIiIiIiIioveEy8uJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREREREb0nLLqJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe/L/AJ/aJD9mHqDtAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **မှတ်ချက်**: ဒီဇယားက ပထမအခြေစိုက်ကစားသမားတွေရဲ့ အမြင့်တွေဟာ ဒုတိယအခြေစိုက်ကစားသမားတွေရဲ့ အမြင့်တွေထက် ပျမ်းမျှအားဖြင့် ပိုမြင့်တယ်ဆိုတာကို ဖော်ပြပါတယ်။ နောက်ပိုင်းမှာ ဒီအယူအဆကို ပိုမိုတိကျစွာ စမ်းသပ်နိုင်တဲ့နည်းလမ်းတွေနဲ့၊ ဒါကို သက်သေပြဖို့ အချက်အလက်တွေဟာ သင်္ချာပိုင်းအရ အရေးပါကြောင်း ပြသနိုင်မယ့် နည်းလမ်းတွေကို လေ့လာသွားပါမယ်။\n",
+    "> **Note**: ဒီဇယားက ပထမအခြေခံကစားသမားတွေရဲ့ အမြင့်တွေဟာ ဒုတိယအခြေခံကစားသမားတွေရဲ့ အမြင့်ထက် ပျမ်းမျှအားဖြင့် ပိုမြင့်တယ်ဆိုတာကို ဖော်ပြထားပါတယ်။ နောက်ပိုင်းမှာ ဒီအယူအဆကို ပိုမိုတိကျစွာ စမ်းသပ်နိုင်တဲ့နည်းလမ်းတွေကို သင်ယူမယ်၊ အဲဒီအချက်အလက်တွေက သက်သေပြနိုင်ဖို့ စာရင်းအင်းအရ အရေးပါကြောင်းကို ပြသနိုင်မယ်။\n",
     "\n",
-    "အသက်၊ အမြင့်နဲ့ ကိုယ်အလေးချိန်ဟာ ဆက်တိုက်ဖြစ်တဲ့ အခွင့်အလမ်းဆိုင်ရာ အပြောင်းအလဲတွေဖြစ်ပါတယ်။ ဒါတွေက ဘယ်လိုဖြန့်ဖြူးမှုရှိမလဲလို့ သင်ထင်ပါသလဲ။ သိနိုင်ဖို့ အကောင်းဆုံးနည်းလမ်းတစ်ခုကတော့ တန်ဖိုးတွေကို ဟစ်စတိုဂရမ်အဖြစ် ရှုထောင့်ဖော်ပြတာပါ:\n"
+    "အသက်၊ အမြင့်နဲ့ အလေးချိန်ဟာ ဆက်လက်ဖြစ်နေတဲ့ အလှည့်အပြောင်းရှိတဲ့ အချက်အလက်တွေဖြစ်ပါတယ်။ အဲဒီအချက်အလက်တွေက ဘယ်လိုဖြန့်ဖြူးမှုရှိမလဲလို့ သင်ထင်ပါသလဲ? သိနိုင်ဖို့ နည်းလမ်းကတော့ အတန်းပုံစံကို ရှုထောင့်ပုံစံ (histogram) အဖြစ် ရေးဆွဲကြည့်ဖို့ ဖြစ်ပါတယ်:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -438,26 +284,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## သာမန်ဖြန့်ဖြူးမှု\n",
+    "## သာမန်ဖြန့်ဖြူးမှု\n",
     "\n",
-    "အမှန်တကယ်ရှိတဲ့ဒေတာရဲ့ ပျမ်းမျှနှုန်းနဲ့ မျိုးကွဲမှုတူညီတဲ့ သာမန်ဖြန့်ဖြူးမှုကိုလိုက်နာတဲ့ အလေးချိန်အတုနမူနာတစ်ခုကို ဖန်တီးကြရအောင်:\n"
+    "အမှန်တကယ် ဒေတာတွေရဲ့ ပျမ်းမျှနှုန်းနှင့် မျိုးကွဲမှုအတူတူရှိတဲ့ သာမန်ဖြန့်ဖြူးမှုကို လိုက်နာတဲ့ အလေးချိန်အတုနမူနာတစ်ခု ဖန်တီးကြရအောင်:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "အမှန်တကယ်ဘဝရှိတန်ဖိုးများအများစုသည် သာမန်ဖြန့်ဖြူးမှုရှိကြသဖြင့်၊ နမူနာဒေတာများကို ထုတ်လုပ်ရန် အညီမျှကျပန်းနံပါတ်ထုတ်လုပ်ရေးကို မသုံးသင့်ပါ။ သာမန်ဖြန့်ဖြူးမှုမရှိဘဲ အညီမျှဖြန့်ဖြူးမှုဖြင့် အလေးချိန်များကို ထုတ်လုပ်ရန် ကြိုးစားပါက (ဥပမာ `np.random.rand` ဖြင့် ထုတ်လုပ်ခြင်း) ဖြစ်နိုင်သောအရာမှာ အောက်ပါအတိုင်းဖြစ်ပါသည်။\n"
+    "အမှန်တကယ်ဘဝရှိတန်ဖိုးများအများစုသည် သာမန်ဖြန့်ဖြူးမှုရှိကြသဖြင့်၊ နမူနာဒေတာများကို ထုတ်လုပ်ရန် အညီမျှကျပန်းနံပါတ်ထုတ်လုပ်စက်ကို မသုံးသင့်ပါ။ သို့သော် အညီမျှဖြန့်ဖြူးမှု (ဥပမာ `np.random.rand` ဖြင့် ထုတ်လုပ်ထားသည်) ဖြင့် အလေးချိန်များကို ထုတ်လုပ်ရန် ကြိုးစားပါက ဖြစ်နိုင်သောအရာမှာ အောက်ပါအတိုင်းဖြစ်သည်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## ယုံကြည်မှုအကွာအဝေးများ\n",
     "\n",
-    "အခုတော့ ဘေ့စ်ဘောကစားသမားများ၏ အလေးချိန်နှင့် အရပ်အမြင့်များအတွက် ယုံကြည်မှုအကွာအဝေးများကိုတွက်ချက်ကြမယ်။ [ဒီ stackoverflow ဆွေးနွေးမှု](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) မှ ကုဒ်ကို အသုံးပြုပါမယ်။\n"
+    "အခုတော့ ဘေ့စ်ဘောကစားသမားများ၏ အလေးချိန်နှင့် အမြင့်အတွက် ယုံကြည်မှုအကွာအဝေးများကိုတွက်ချက်ကြမည်။ ဤအတွက် [ဒီ StackOverflow ဆွေးနွေးမှုမှ ကုဒ်](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) ကို အသုံးပြုမည်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -593,14 +434,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## အကြောင်းအရာ စမ်းသပ်ခြင်း\n",
+    "## သီအိုရီစမ်းသပ်ခြင်း\n",
     "\n",
-    "ကျွန်တော်တို့ရဲ့ ဘေ့စ်ဘော ကစားသမားများ ဒေတာစနစ်မှာ အမျိုးမျိုးသော အခန်းကဏ္ဍများကို လေ့လာကြမယ်:\n"
+    "အောက်ပါအချက်အလက်များကို အသုံးပြု၍ ကျွန်တော်တို့ရဲ့ ဘေ့စ်ဘောကစားသမားများ ဒေတာစနစ်အတွင်း အမျိုးမျိုးသော အခန်းကဏ္ဍများကို စူးစမ်းကြည့်ပါ။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,37 +619,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ttest_ind` လုပ်ဆောင်ချက်မှ ပြန်လာသော တန်ဖိုးနှစ်ခုမှာ -\n",
-    "\n",
-    "* p-value ကို နှစ်ခုသော distribution များတွင် အလယ်တန်းတန်ဖိုးတူညီမှုရှိနိုင်ခြေဟု သတ်မှတ်နိုင်သည်။ ကျွန်ုပ်တို့၏ အနေအထားတွင်၊ p-value သည် အလွန်နိမ့်ပြီး၊ ပထမတန်းကစားသမားများသည် ပိုမြင့်ကြောင်း အထောက်အထားအားကောင်းစွာထောက်ခံသည်။\n",
-    "\n",
-    "* t-value သည် t-test တွင် အသုံးပြုသည့် အလယ်တန်းတန်ဖိုးကွာခြားမှုကို သာမန်ပြုထားသော အလယ်တန်းတန်ဖိုးဖြစ်ပြီး၊ ယင်းကို သတ်မှတ်ထားသော ယုံကြည်မှုတန်ဖိုးအတွက် စမ်းသပ်မှုအကန့်အသတ်တန်ဖိုးနှင့် နှိုင်းယှဉ်သည်။\n"
+    "`ttest_ind` function မှ ပြန်လာသော တန်ဖိုးနှစ်ခုမှာ -  \n",
+    "* p-value သည် အနှစ်ချုပ်အားဖြင့် အနှစ်ချုပ်နှစ်ခု၏ ပျမ်းမျှတန်ဖိုးတူညီမှု ဖြစ်နိုင်ခြေကို ဖော်ပြသည်။ ကျွန်ုပ်တို့၏ အမှုအတွက်တော့ p-value သည် အလွန်နိမ့်ပြီး၊ ပထမအခြေခံကစားသမားများ ပိုအရပ်ရှည်ကြောင်း အထောက်အထားပြင်းပြင်းရှိသည်ကို ဖော်ပြသည်။  \n",
+    "* t-value သည် t-test တွင် အသုံးပြုသည့် ပျမ်းမျှတန်ဖိုးကွာခြားမှုကို သာမာန်ပြုထားသော အလယ်တန်းတန်ဖိုးဖြစ်ပြီး၊ ယင်းကို သတ်မှတ်ထားသော ယုံကြည်မှုတန်ဖိုးအတွက် စံချိန်တန်ဖိုးနှင့် နှိုင်းယှဉ်သည်။  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## အလယ်ပိုင်းကန့်သတ်သီအိုရီဖြင့် သာမန်ဖြန့်ဖြူးမှုကို အတုယူခြင်း\n",
+    "## အလယ်ဗဟိုကန့်သတ်သီအိုရီဖြင့် သာမန်ဖြန့်ဖြူးမှုကို အတုယူခြင်း\n",
     "\n",
-    "Python ရှိ pseudo-random generator သည် uniform distribution ကို ပေးရန် ဒီဇိုင်းထုတ်ထားသည်။ သာမန်ဖြန့်ဖြူးမှုအတွက် generator တစ်ခု ဖန်တီးလိုပါက အလယ်ပိုင်းကန့်သတ်သီအိုရီကို အသုံးပြုနိုင်သည်။ သာမန်ဖြန့်ဖြူးမှုရှိသော တန်ဖိုးတစ်ခု ရရှိရန် uniform-generated sample ၏ ပျမ်းမျှတန်ဖိုးကိုသာ တွက်ချက်ရမည်ဖြစ်သည်။\n"
+    "Python ရှိ pseudo-random generator သည် ကျွန်ုပ်တို့အား တန်းတူဖြန့်ဖြူးမှုကို ပေးရန် ဒီဇိုင်းထုတ်ထားသည်။ သာမန်ဖြန့်ဖြူးမှုအတွက် generator တစ်ခုကို ဖန်တီးလိုပါက အလယ်ဗဟိုကန့်သတ်သီအိုရီကို အသုံးပြုနိုင်သည်။ သာမန်ဖြန့်ဖြူးမှုရှိသော တန်ဖိုးတစ်ခုကို ရယူရန် ကျွန်ုပ်တို့သည် တန်းတူဖြန့်ဖြူးမှုဖြင့် ဖန်တီးထားသော နမူနာ၏ အလယ်ပမာဏကို တွက်ချက်လိမ့်မည်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## အချင်းချင်းဆက်စပ်မှုနှင့် မကောင်းသော ဘေ့စ်ဘော ကော်ပိုရေးရှင်း\n",
+    "## အချိုးအဆက်နှင့် အဆိုးဝါးသော ဘေ့စ်ဘော ကော်ပိုရေးရှင်း\n",
     "\n",
-    "အချင်းချင်းဆက်စပ်မှုက ဒေတာအစုများအကြား ဆက်စပ်မှုများကို ရှာဖွေခွင့်ပေးပါတယ်။ ဥပမာအနေနဲ့, မကောင်းသော ဘေ့စ်ဘော ကော်ပိုရေးရှင်းတစ်ခုက သူ့ရဲ့ ကစားသမားတွေကို အရပ်အမြင့်အပေါ်မူတည်ပြီး လစာပေးတယ်လို့ သတ်မှတ်ကြည့်ကြရအောင်။ ကစားသမားတစ်ဦးရဲ့ အရပ်အမြင့်က မြင့်လျှင် မြင့်သလို, သူ/သူမရဲ့ လစာလည်း မြင့်တယ်။ အခြေခံလစာ $1000 ရှိတယ်လို့ သတ်မှတ်ပြီး, အရပ်အမြင့်ပေါ်မူတည်ပြီး $0 ကနေ $100 အထိ အပိုဆုပေးတယ်လို့ ယူဆကြည့်ပါမယ်။ အခုတော့ MLB က အမှန်တကယ်ကစားသမားတွေကို ယူပြီး, သူတို့ရဲ့ စိတ်ကူးယဉ်လစာတွေကို တွက်ကြည့်ကြမယ်။\n"
+    "အချိုးအဆက်သည် ဒေတာအတိုင်းအတာများအကြား ဆက်နွယ်မှုများကို ရှာဖွေနိုင်စေသည်။ ကျွန်ုပ်တို့၏ ကစားစရာ ဥပမာတွင် အဆိုးဝါးသော ဘေ့စ်ဘော ကော်ပိုရေးရှင်းတစ်ခုရှိပြီး၊ ၎င်းသည် ကစားသမားများကို ၎င်းတို့၏အရပ်အမြင့်အတိုင်း လစာပေးသည်ဟု သက်မှတ်ကြပါစို့။ ကစားသမား၏အရပ်အမြင့်မြင့်လျှင် ပိုမိုများသောငွေကို ရရှိမည်ဖြစ်သည်။ အခြေခံလစာ $1000 ရှိပြီး၊ အရပ်အမြင့်ပေါ်မူတည်၍ $0 မှ $100 အထိ အပိုဆုပေးမည်ဖြစ်သည်။ ကျွန်ုပ်တို့သည် MLB မှ အစစ်အမှန် ကစားသမားများကို ယူပြီး၊ ၎င်းတို့၏ စိတ်ကူးယဉ်လစာများကို တွက်ချက်မည်ဖြစ်သည်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ယခုအခါ အဆိုပါ အစဉ်များ၏ covariance နှင့် correlation ကိုတွက်ချက်ကြည့်ပါစို့။ `np.cov` သည် **covariance matrix** ဟုခေါ်သော အရာကို ပေးမည်ဖြစ်ပြီး၊ ၎င်းသည် အပြောင်းအလဲများစွာအတွက် covariance ကို တိုးချဲ့ထားခြင်းဖြစ်သည်။ Covariance matrix $M$ ၏ အစိတ်အပိုင်း $M_{ij}$ သည် အဝင်အပြောင်းအလဲများ $X_i$ နှင့် $X_j$ အကြား correlation ဖြစ်ပြီး၊ အနံဖြင့် diagonal အတန်းများ $M_{ii}$ သည် $X_{i}$ ၏ variance ဖြစ်သည်။ ထို့အတူတူပင် `np.corrcoef` သည် **correlation matrix** ကို ပေးမည်ဖြစ်သည်။\n"
+    "အခုတော့ အဲဒီအဆင့်များ၏ covariance နှင့် correlation ကိုတွက်ချက်ကြည့်ရအောင်။ `np.cov` က **covariance matrix** လို့ခေါ်တဲ့အရာကို ပေးပါလိမ့်မယ်၊ ဒါဟာ covariance ကို မျိုးစုံအပြောင်းအလဲများအထိ တိုးချဲ့ထားတဲ့အရာဖြစ်ပါတယ်။ Covariance matrix $M$ ရဲ့ အစိတ်အပိုင်း $M_{ij}$ က input variables $X_i$ နဲ့ $X_j$ အကြား correlation ဖြစ်ပြီး၊ အနံဖြတ်တန်း $M_{ii}$ ကတော့ $X_{i}$ ရဲ့ variance ဖြစ်ပါတယ်။ အတူတူပဲ၊ `np.corrcoef` က **correlation matrix** ကို ပေးပါလိမ့်မယ်။\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -889,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -919,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -939,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ဤအခါတွင်၊ အဆက်စပ်မှုသည် အနည်းငယ်ပိုမိုသေးငယ်သော်လည်း၊ ထိုသို့မဟုတ် အတော်လေးမြင့်မားနေဆဲဖြစ်သည်။ ယခု၊ အဆက်စပ်မှုကို ပိုမိုမရှင်းလင်းစေရန်အတွက်၊ အချို့သောကျပန်းအလားအလာများကို လုပ်ဆောင်ရန်အတွက် လုပ်ခနှင့်အတူ ကျပန်းအလားအလာတစ်ခုကို ထည့်သွင်းလိုက်နိုင်သည်။ ဘာဖြစ်မလဲဆိုတာ ကြည့်ကြရအောင်:\n"
+    "ဤအခါတွင်၊ အဆက်နွယ်မှုသည် အနည်းငယ်ပိုမိုသေးငယ်သော်လည်း၊ သို့သော်၎င်းသည် အတော်လေးမြင့်မားနေဆဲဖြစ်သည်။ ယခု၊ အဆက်နွယ်မှုကို ပိုမိုမရှင်းလင်းစေရန်၊ အချို့သောကျပန်းအချက်အလက်များကို လုပ်ခလစာတွင် ထည့်သွင်းခြင်းဖြင့် ကျပန်းဖြစ်မှုအနည်းငယ်ထပ်မံထည့်သွင်းလိုကြောင်း ဖြစ်နိုင်ပါသည်။ ဘာဖြစ်မလဲဆိုတာ ကြည့်ကြရအောင်:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -962,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> အဲဒီအကြောင်းကြောင့် အမှတ်များဟာ ဒီလို တန်းစီနေတယ်လို့ ခန့်မှန်းနိုင်မလား?\n",
+    "> အဲဒီအတိုင်း အစက်တွေ ဘာကြောင့် ဒေါင်လိုက်လိုက်လျောစေတယ်ဆိုတာ ခန့်မှန်းနိုင်မလား?\n",
     "\n",
-    "ကျွန်တော်တို့ salary လို့ခေါ်တဲ့ အတုအယောင် ဖန်တီးထားတဲ့ အယူအဆနဲ့ *height* လို့ခေါ်တဲ့ ကြည့်ရှုတွေ့ရှိထားတဲ့ အပြောင်းအလဲကြားမှာ ဆက်စပ်မှုရှိတယ်ဆိုတာ တွေ့ရှိခဲ့ပါတယ်။ height နဲ့ weight လို့ခေါ်တဲ့ ကြည့်ရှုတွေ့ရှိထားတဲ့ အပြောင်းအလဲနှစ်ခုကြားမှာလည်း ဆက်စပ်မှုရှိမရှိ ကြည့်ကြရအောင်:\n"
+    "ကျွန်တော်တို့ salary လိုမျိုး အတုလုပ်ထားတဲ့ အယူအဆနဲ့ *အမြင့်* ဆိုတဲ့ ကြည့်ရှုတွေ့ရှိထားတဲ့ အပြောင်းအလဲတစ်ခုအကြား ဆက်စပ်မှုကို သတိထားမိခဲ့ပါတယ်။ အခုတော့ *အမြင့်* နဲ့ *အလေးချိန်*လိုမျိုး ကြည့်ရှုတွေ့ရှိထားတဲ့ အပြောင်းအလဲနှစ်ခုအကြားလည်း ဆက်စပ်မှုရှိမရှိ ကြည့်လိုက်ရအောင်:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ကံမကောင်းစွာဖြင့်၊ ကျွန်တော်တို့ရဲ့ရလဒ်မှာ မည်သည့်အရာမှမရရှိခဲ့ပါဘူး - `nan` အဖြစ်သိပ်ထူးဆန်းတဲ့တန်ဖိုးတွေကိုသာရရှိခဲ့ပါတယ်။ ဒါဟာ ကျွန်တော်တို့ရဲ့ series ထဲမှာ အချို့သောတန်ဖိုးတွေဟာ မသတ်မှတ်ထားတဲ့အခြေအနေဖြစ်နေတဲ့အတွက်ဖြစ်ပြီး၊ အဲဒီအရာတွေကို `nan` အဖြစ်ဖော်ပြထားတာကြောင့် ဖြစ်ပါတယ်။ ဒီအခြေအနေကြောင့် လုပ်ဆောင်မှုရဲ့ရလဒ်လည်း မသတ်မှတ်နိုင်တဲ့အခြေအနေဖြစ်သွားပါတယ်။ Matrix ကိုကြည့်လိုက်ရင် `Weight` က column ပြဿနာဖြစ်နေတယ်ဆိုတာမြင်နိုင်ပါတယ်၊ အကြောင်းကတော့ `Height` တန်ဖိုးတွေကြားမှာ self-correlation ကိုတွက်ချက်ထားတာကြောင့်ဖြစ်ပါတယ်။\n",
+    "ကံမကောင်းစွာဖြင့်၊ ကျွန်တော်တို့ရလဒ်မရခဲ့ပါဘူး - `nan` ဆိုတဲ့ ထူးဆန်းတဲ့တန်ဖိုးတွေကိုသာရရှိခဲ့ပါတယ်။ ဒါဟာ ကျွန်တော်တို့ရဲ့ series ထဲမှာ တချို့တန်ဖိုးတွေ မသတ်မှတ်ထားတဲ့အခြေအနေကြောင့်ဖြစ်ပြီး၊ အဲဒီတန်ဖိုးတွေကို `nan` အဖြစ်ဖော်ပြထားတာကြောင့် လုပ်ဆောင်မှုရဲ့ရလဒ်ကလည်း မသတ်မှတ်နိုင်တဲ့အခြေအနေဖြစ်သွားပါတယ်။ Matrix ကိုကြည့်မယ်ဆိုရင် `Weight` က column အနေနဲ့ ပြဿနာဖြစ်နေတဲ့အချက်ကိုတွေ့ရပါတယ်၊ အဲဒါက `Height` တန်ဖိုးတွေကြားမှာ self-correlation ကိုတွက်ချက်ထားတာကြောင့်ဖြစ်ပါတယ်။\n",
     "\n",
-    "> ဒီဥပမာက **ဒေတာပြင်ဆင်မှု** နဲ့ **ရှင်းလင်းမှု** ရဲ့အရေးပါမှုကို ပြသနေပါတယ်။ မှန်ကန်တဲ့ဒေတာမရှိရင် ဘာမှတွက်ချက်လို့မရနိုင်ပါဘူး။\n",
+    "> ဒီဥပမာက **ဒေတာပြင်ဆင်မှု** နဲ့ **ရှင်းလင်းမှု** ရဲ့အရေးကြီးမှုကို ပြသနေပါတယ်။ သင့်တော်တဲ့ဒေတာမရှိရင် ဘာမှတွက်ချက်လို့မရနိုင်ပါဘူး။\n",
     "\n",
-    "အခုတော့ `fillna` method ကိုသုံးပြီး ပျက်ကွက်နေတဲ့တန်ဖိုးတွေကို ဖြည့်စွက်ပြီး correlation ကိုတွက်ချက်ကြည့်ရအောင်:\n"
+    "အခုတော့ `fillna` method ကိုသုံးပြီး ပျောက်နေတဲ့တန်ဖိုးတွေကို ဖြည့်ပြီး correlation ကိုတွက်ချက်ကြည့်ရအောင်:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1054,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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C60H8Mn1s4ibpnjdvnsrKyvTss8+2PhYOhyVJ1113na666iqdeuqpeuCBB3TcccfpiSee6PG/VVRUJL/f3/p15JFH9rr9ANBXqvdG/yPUkzigP+H9AyCC60H8Mn1s4iLpnj9/vlasWKE1a9ZoxIgRrY/n5ras2Q8EAm3iv/a1r2nnzp2SpJycHFVXV7d5vrm5WTU1NcrJyenw31uwYIGCwWDr1yeffOJkdwDAUdnpKY7GAf0J7x8AEVwP4pfpY+Nq0m1ZlubPn6/nn39eq1evVn5+fpvn8/LyNHz48HZlxD744AONGjVKklRQUKA9e/Zo8+bNrc+vXr1a4XBYp59+eof/7sCBA5WRkdHmCwDi1bj8TOX6U1oPEjmUTy0ne47Lz4xlswBP4P0DIILrQfwyfWxcTbrnzZunpUuX6plnnlF6erqqqqpUVVXVWoPb5/Pp1ltv1YMPPqg//OEP+uijj3TXXXfpH//4h6655hpJLbPehYWFmjNnjjZt2qTXX39d8+fP17e//W1bJ5cDQLxLTPBp4cyWFT+H/jGKfL9wZkCJCdH+VAH9F+8fABFcD+KX6WPjaskwn6/jX9qTTz6pK6+8svX7n/zkJ3r44YdVU1Ojk08+WT/96U81YcKE1udramo0f/58LV++XAkJCbrkkkv04IMPavDgwbbaQckwAF5gau1KIBZ4/wCI4HoQv7w2NnbzyLiq0+0Wkm4AXhEKW9pUUaPqvQ3KTm9ZZuXVT32BWGtsDmtJ6XbtqKnTqMw0zS7IU3JSXBxvAyDG+Hsav7w0NiTd3UDSDQCA2bw2ewIAiH9280g+3gUAAEYrKavU3KVb2tWArQo2aO7SLSopq3SpZQCA/oCkGwAAGCsUtrRoebk6WtYXeWzR8nKFwv1+4R8AoI+QdAMAAGNtqqhpN8N9MEtSZbBBmypqYtcoAEC/QtINAACMVb03esLdkzgAALqLpBsAABgrOz3F0TgAALorye0GAAAA9JVx+ZnK9aeoKtjQ4b5un6Qcf0tJGq/xUlkdAOjPSLoBAICxEhN8WjgzoLlLt8gntUm8I+npwpkBzyWrlEADAO9geTkAADBa4ehcLZ41Rjn+tkvIc/wpWjxrjOeSVEqgAYC3MNMNAACMVzg6V1OOP1xLSrdrR02dRmWmaXZBnpKTvDX/0FUJNJ9aSqBNDeR4bvYeAExF0g0AAIzX0XLs326o8Nxy7O6UQCs4Oit2DQMAROWtj3cBAAC6yaTl2JRAAwDvIekGAADG6mo5ttSyHDsU7igi/lACDQC8h6QbAAAYqzvLsb0gUgIt2m5tn1pOMfdiCTQAMBVJNwAAMJZpy7EjJdAktUu8vVwCDQBMRtINAACMZeJybNNKoAGA6Ti9HAAAGCuyHLsq2NDhvm6fWpJVry3HLhydq6mBHG2qqFH13gZlp7f0gRluAIg/JN0AAMBYkeXYc5dukU9qk3h7fTl2YoKPsmAA4AEsLwcAAEZjOTYAwE3MdAMAAOOxHBsA4BaSbgAA0C+wHBsA4AaWlwMAAAAA0EdIugEAAAAA6CMsLwdgtFDYYg9nHGN8EEv1jSHdt7Jc23fVKS8rTXfMCCg1OdHtZvWYaf1BfON6Hd8Yn/jmsyyro7KV/Uptba38fr+CwaAyMjLcbg4Ah5SUVWrR8nJVBhtaH8v1p2jhzACnFccBxgexNOfpt7SqvLrd41MD2Sq+fKwLLeod0/qD+Mb1Or4xPu6xm0eSdIukGzBRSVml5i7dokMvcJHPfCkT5C7GB7EULUGN8Fqialp/EN+4Xsc3xsdddvNI9nQDME4obGnR8vJ2f4AktT62aHm5QuF+/5mjKxgfxFJ9Y6jTBFWSVpVXq74xFKMW9Y5p/TlYKGypdNsuvbj1M5Vu28U1IA5wvY5vjI93kHQDMM6mipo2S6wOZUmqDDZoU0VN7BqFVowPYum+leWOxrnNtP5ElJRVasL9q3Vp8Ubd9OxWXVq8URPuX62Sskq3m9avcb2Ob4yPd5B0AzBO9d7of4B6EgdnMT6Ipe276hyNc5tp/ZH+uTz20OShKtiguUu3kHi7iOt1fGN8vIOkG4BxstNTHI2DsxgfxFJeVpqjcW4zrT8sj41vXK/jG+PjHSTdAIwzLj9Tuf4URSuU4VPLqZ7j8jNj2Sz8H8YHsXTHjICjcW4zrT8sj41vXK/jG+PjHSTdAIyTmODTwpktN5yH/iGKfL9wZoD6lS5hfBBLqcmJmhrI7jRmaiDbM/WtTesPy2PjG9fr+Mb4eAdJNwAjFY7O1eJZY5Tjb7ukKsefQvmMOMD4IJaKLx8bNVH1Ynktk/rD8tj4x/U6vjE+3kCdblGnGzBZKGxpU0WNqvc2KDu9ZYkVn/jGD8YHsVTfGNJ9K8u1fVed8rLSdMeMgGdmhDtiQn9CYUsT7l+tqmBDh/u6fWpJHjbcNoVrg8u4Xsc3xscddvNIkm6RdAMAALglcnq5pDaJdyRdYLYOQLyym0eyvBwAAACuYXksANMlud0AAAAQn1iuiFgpHJ2rqYEcXm8AjETSDQAA2ikpq9Si5eVtyjnl+lO0cGaAmUf0icQEnwqOznK7GQDgOJaXAwCANiJ7bA+tn1wVbNDcpVtUUlbpUssAAPAekm4AANAqFLa0aHl5hydJRx5btLxcoXC/P4cVAABbWF4OAIBDGpvDWlK6XTtq6jQqM02zC/KUnOStz7c3VdS0m+E+mCWpMtigTRU1LAV2mQmvN5NxJgKACJJuAAAcULSyXMXrK3TwBPC9K9/TnIn5WjAj4F7Duql6b/SEuydx6BumvN5MxZkIAA7Gx6EAAPRS0cpyPbaubQIkSWFLemxdhYpWlrvTsB7ITk/pOqgbcXCeSa83E3EmAoBDkXQDgIeEwpZKt+3Si1s/U+m2XeyrjQONzWEVr6/oNKZ4fYUam8MxalHvnHiE39G4eGLC+8e015tpOBMBQEdYXg4AHsFyxfi0pHR7uxnHQ4WtlrhrJh4Vm0b1wv0l79mOu+fCE/u4Nc4x5f1j2uvNNJyJAKAjzHQDgAewXDF+7aipczTObdt32Wun3bh4YNL7x7TXm2k4EwFAR0i6ASDOsVwxvh05NNXROLflZaU5Guc2094/ozLt/d7txsFZnIkAoCMk3QAQ57qzXBGxd3xOhqNxbrvD5snXduPcZtr7Z3ZBnrqqOpXga4lD7I3Lz1SuP0XRhsinlm0N4/IzY9ksAC4j6QaAOMdyxfhWU9foaJzbUpMTNTWQ3WnM1EC2UpMTY9Si3jHt/ZOclKA5E/M7jZkzMZ963S5JTPBp4cyWD6QOTbwj3y+cGaBeN9DPcEUGgDjHcsX4ZuL4FF8+NmriPTWQreLLx8a4RT1n4vgsmBHQdZPy2814J/ik6yZRp9tthaNztXjWGOX4276mcvwpWjxrjKcO7gPgDE4vB4A4F1muWBVs6HBfqk8tN3MsV3SHqeNTfPlY1TeGdN/Kcm3fVae8rDTdMSPgmRnuCFPHZ8GMgL4/7XgtKd2uHTV1GpWZptkFecxwx4nC0bmaGsjRpooaVe9tUHZ6y2uMGW6gf/JZluWNk0P6UG1trfx+v4LBoDIyvLHnDkD/Ejl9WVKbxCFy+8bsibsYn/jG+AAA+oLdPJKPQwHAA1iuGN8Yn/jG+AAA3MRMt5jpBuAdobDFcsU4xvjEN8YHAOAku3kke7oBwEMSE3wqODrL7WYgCsYnvpk2PnyIAADeQNINAADgMSVllVq0vLxNDfJcf4oWzgywXB4A4gx7ugEAADwkcjDcwQm3JFUFGzR36RaVlFW61DIAQEdcTbqLioo0duxYpaenKzs7WxdeeKHef//9DmMty9L06dPl8/n0wgsvtHlu586dOu+885SWlqbs7Gzdeuutam5ujkEPACC2QmFLpdt26cWtn6l02y6Fwt4+lqOxOazH13+sH75YpsfXf6zG5rDbTeoV08anvjGku154V7Mff1N3vfCu6htDbjepV2r2NWraL17TKYte0bRfvKaafY1uN6nbQmFLi5aXd1j+LPLYouXlnnztmXY9oD8AIlxdXr527VrNmzdPY8eOVXNzs+644w5NmzZN5eXlGjRoUJvYX/7yl/L52u9TCoVCOu+885STk6M33nhDlZWVuvzyyzVgwADdd999seoKAPQ505aTFq0sV/H6Ch2cG9y78j3NmZivBTMC7jWsh0wbnzlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM2N/vEpfHpRk76lv0pgfr9Kwwcl6686pLrasezZV1LSb4T6YJaky2KBNFTWe2r9u2vWA/gA4mKsz3SUlJbryyit1wgkn6OSTT9ZTTz2lnTt3avPmzW3itm7dqv/6r//SE0880e5nvPLKKyovL9fSpUt1yimnaPr06brnnnv08MMPq7HRe59gA0BHTFtOWrSyXI+ta3sDJ0lhS3psXYWKVpa707AeMm18Dk24D7aqvFpznn4rxi3qnUMT7oN9ua9RY3+8KsYt6rnqvdET7p7ExQPTrgf0B8Ch4mpPdzAYlCRlZma2PlZXV6fvfOc7evjhh5WTk9PuvyktLdWJJ56oww8/vPWxc889V7W1tfr73//e940GgD5m2nLSxuawitdXdBpTvL7CM0sXTRuf+sZQ1IQ7YlV5tWeWmtfsa4yacEd8ua/RM0vNM1OTHY1zm2nXA/oDoCNxk3SHw2HdfPPNOvPMMzV69OjWx2+55RaNHz9eF1xwQYf/XVVVVZuEW1Lr91VVVR3+NwcOHFBtbW2bLwCIV91ZTuoFS0q3t5sxOVTYaonzAtPG5z6bs1Z249z27d+84Wic2/7xxV5H49xm2vWA/gDoSNyUDJs3b57Kysq0YcOG1seWLVum1atX6+2333b03yoqKtKiRYsc/ZkA0FdMW066o6bO0Ti3mTY+23fZ+73bjXNb9V57M9h249z2yW57v3e7cW4z7XpAfwB0JC5muufPn68VK1ZozZo1GjFiROvjq1ev1rZt2zRkyBAlJSUpKanlM4JLLrlEZ511liQpJydHX3zxRZufF/m+o+XokrRgwQIFg8HWr08++aQPegUAzshOT3E0zm2jMtMcjXObaeOTl2Xv9243zm3D0u0ts7Yb5zbT3j/0J76Z1h/ALa4m3ZZlaf78+Xr++ee1evVq5efnt3n+9ttv1zvvvKOtW7e2fknSAw88oCeffFKSVFBQoHfffVfV1f/cf7Zq1SplZGQoEOj4NMWBAwcqIyOjzRcAxKtx+ZnK9aeoff2GFj61nJI9Lj8zSkR8mV2Qp4Ronfk/Cb6WOC8wbXzusHkSsd04t/3nOcc5Guc2094/9Ce+mdYfwC2uJt3z5s3T0qVL9cwzzyg9PV1VVVWqqqpSfX29pJaZ6tGjR7f5kqSRI0e2JujTpk1TIBDQ7Nmz9be//U1//vOfdeedd2revHkaOHCga30DAKckJvi0cGZLgnPovU/k+4UzA0rs6s4oTiQnJWjOxPxOY+ZMzFdyUlwsxuqSaeOTmpyoqYHsTmOmBrKVmpwYoxb1Tl3Y3gFPduPcZtr7h/7EN9P6A7jF1XfI4sWLFQwGddZZZyk3N7f167nnnrP9MxITE7VixQolJiaqoKBAs2bN0uWXX64f/ehHfdhyAIitwtG5WjxrjHL8bZco5/hTtHjWGM/VgV4wI6DrJuW3m0FJ8EnXTfJe3VfTxueSMSN69Xw8MW35v2Te+4f+xDfT+gO4wWdZljdqmPSh2tpa+f1+BYNBlpoDiGuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2dMTBifUNjShPtXRz2R3aeWDxM23DbFE32L9Kcq2NBhWTev9edgpr1/6E98q28M6b6V5dq+q055WWm6Y0bAMytegL5iN48k6RZJNwAAEaXbdunS4o1dxv1uzhkqODorBi3qvZKySl2/dEvU5x/14GoEIJZKyiq1aHl5mw/jcv0pWjgzwHsH/ZrdPNK7H7cBAADHmVYCDUDvlJRVau7SLe1Wv1QFGzR36RaVlFW61DLAO0i6AQBAK9P2QIfClhYtL4/6vE/SouXlCoX7/cI/oJ3I+6ejd0fkMd4/QNdIugEAQCvTSqBtqqiJuj9dakkcKoMN2lRRE7tGAR7B+wdwBkk3AABoZVoJNJbLAz3H+wdwBkk3AABow6QSaKYtlwdiifcP4IwktxsAAIApTCoRVDg6V5OPzfZ8iaDIcvmuSoZ5Zbn8wUwr4WTS+8cUJr9/gFiiZJgoGQYA6L2ileUqXl+hg88TSvBJcybma8GMgHsN6yGT+mNiybA5T7+lVeXV7R6fGshW8eVjXWhR75j0ejNN5PRySW0S78gGE6+tfgGcRMkwAABipGhluR5b1zZhkKSwJT22rkJFK6Ofnh2PTOvPI6991Kvn4020hFuSVpVXa87Tb8W4Rb1j2uvNNCZtNwHcwvJyAAB6obE5rOL1FZ3GFK+v0PenHe+JpbKm9WdfQ7Pe+bS205h3Pq3VvoZmDU6J/9ui+sZQ1IQ7YlV5teobQ55Yam7a681UhaNzNTWQo00VNare26Ds9JYl5V45UBFwG1cvAAB6YUnp9nYzdIcKWy1xXmBaf2557m1H49x2n81ZX7txbjPt9WayxASfCo7O0gWnHKGCo7NIuIFuIOkGAKAXdtTUORrnNtP6s3N3vaNxbtu+y97v3W6c20x7vQFAR0i6AQDohSOHpjka57ZRmfbaaTfObSOHpjoa57aRmTb7YzPObaa93gCgIyTdAAD0wvE56Y7GuW12QZ66WjWa4GuJ84IHvnWqo3FumxbIcTTObaa93gCgIyTdAAD0Qk1do6NxbktOStCcifmdxsyZmO+ZQ60GpyTppBGdlwM9aUSGJw5Rk6Q99U2OxrnNtNcbAHSEKxgAAL2QnZ7SdVA34uLBghkBXTcpv90MZIJPum6S9+omL5s/MWrifdKIDC2bPzHGLeo5Xm8A4D0+y7K6ODPSfHaLmgMAnBUKW54vQdPYHNbxd73c6QnMCT7pH/dM99xsXX1jSPetLNf2XXXKy0rTHTMCnihDFc2+hmbd8tzb2rm7XiOHpuqBb53qmRnuiFDY0oT7V6sq2KCOXnI+tdRP3nDbFE++l5aUbteOmjqNykzT7II8z71nAPQvdvNIb/2lAQAYo6SsUouWl6sy2ND6WK4/RQtnBlQ4OtfFlnXP5h27bZU82rxjtwqOzopNoxxw6Pis/1D6y3vVnhufgw1OSVLxFWPdbkavJCb4tHBmQHOXbpFPapN4R1LshTMDnku4pZal5tdMPMrtZgCA4/j4EAAQcyVllZq7dEubhFuSqoINmrt0i0rKKl1qWfdV723oOqgbcfHApPExUeHoXC2eNUY5/rZLyHP8KVo8a4xnPxQBAFMx0w0AiKlQ2NKi5eUdLo211DJbt2h5uaYGcjwxW2faHlvTxsdUhaNzNTWQ4/ntGQDQH5B0AwBialNFTbsZ1INZkiqDDdpUUeOJ5djj8jOV60/pco/tuPzMWDetR0wbn4OZtmc4McHnuTEA0DdMOCPFZCTdAICYMm05tml7bE0bn4iileUqXl/RZv/9vSvf05yJnI4NwNtMOSPFZN79eBcA4EmmLceWzNpja+L4FK0s12PrKtodeBe2pMfWVahoZbk7DQOAXuIMDm9gphsAPMSE5WOnjRqqBJ+6LLF12qihsWuUAwpH5+q0kZm66JENqtnfpMxBA/T8dydoWMZAt5vWLaYtl29sDqt4fUWnMcXrK/T9acd7bqm5CSXQDhasa9LVT23S58EGDfen6Ikrx8mfNsDtZvWYCddrk5kwPpzB4R3evTIDQD9jyvIxU0tsjf3xKn25r7H1+7o9IY297y8aNjhZb9051cWWdU9kufz1S7d0+Lwlby2XX1K63dbrbUnpdk+Vqzr/ofV659Pa1u/fr9qr0Xf/WSeNyNCy+RNdbFnPTP7Zau3YVd/6fWWwQSf/6BWNykrV2lunuNiynjHlem0qU8bH5DM4TOOtj3QBoJ8yafmYiXuGD024D/blvkaN/fGqGLeod/7zD+/06vl4sqOmztG4eHBown2wdz6t1fkPrY9xi3rn0IT7YDt21Wvyz1bHuEW9Y9L12kQmjY+Jf09NRdINAHGuq+VjUsvysVBX03lxwrQ9wzX7GqMm3BFf7mtUTRcx8eLL2gOqbWjuNKa2oVlf1h6IUYt6J3OgvUV9duPctq+hOWrCHfHOp7Xa18UYxotgXVPUhDtix656BeuaYtSi3jHtem0a08bHtL+nJiPpBoA4153lY14Q2TMcbXGyTy3L/LyyZ/jbv3nD0Ti3XfTIBkfj3LbkrZ2OxrntlufedjTObVc/tcnROLeZdr02jWnjY9rfU5ORdANAnDNt+Vhkz7CkdjcK3iyxZW8G226c22r225tRtBvntr02Z3ztxrlt5+7OZ4W7G+e2zztJgHoS5zbTrtemMW18TPt7ajKSbgCIcyYuHzOrxFayo3Fuyxxk77Rou3Fuy7B5mrfdOLeNHJrqaJzbhvvtXbfsxrnNxOu1SUwcH5P+nprMZ1mWNzYt9KHa2lr5/X4Fg0FlZGS43RwAaKOxOazj73q5yxJb/7hnuudKHplQsqVmX6PG2DgobcudU5U5OP4T7y9rD2jsfX/pMu6tO87xRDm0z2rqdeZPuz6I6/X/nKIjMuM/Ud3X0KzRd/+5y7iyu8/1RPmwYF2TTv7RK13G/e2H0zxRPiwUtjTh/tVdltzbcNsUz13rTGDy+Jjw99SL7OaR3ro7A4B+qDsltrwmMcGngqOzdMEpR6jg6CxP3iBkDk7WsC6S6WGDkz2RcEvSsIyBXc76ZqQkeSLhlqSdu+2dSm43zm2DU5J00ojOJwhOGpHhiYRbkvxpAzQqq/MPO0ZlpXoi4ZZY7hvvTB4fE/6emoykGwDinGl70Ex08ZgjevV8vPnpv53Uq+fjiYnvn2XzJ0ZNvL1Yp3vB9K/16vl4w3Lf+Mb4wA3e+BgUAPoxE/egmaSxOazi9RWdxhSvr9D3px3vieX/kZI60fjUUlJnaiDHEzMphw22NyNvNy5eLJs/UfsamnXLc29r5+56jRyaqge+dapnZrgjTHu9RRSOztXUQA7LfeMU44NY89aVGQD6oUhJkK72oFESxB1LSrfbWv6/pHS7rpl4VGwa1QvdKalTcHRW7BrWU3ZPrvHgCTeDU5JUfMVYt5vRK8a93g4SWe6L+MT4IJbi/yN3AOjnTN6DZoIdNfb2AtuNc5tpy7G/2n/A0Tg4y7TXGwB0hKQbADyAPWjx68ihaY7Guc207Qym9cc0jA+A/oDl5QDaoORE/GIPWnw6/vB0R+PcZtp2BtP6YxrGB0B/QNINoFVJWaUWLS9vs78u15+ihTMDzKTGCfagxZ+a+kZH49wW2c5w/dItHT5vyVvbGSL9mbt0i3xqu3Wb7RnuY3wA9AcsLwcgqSXhnrt0S7sDbaqCDZq7dItKyipdahkQ31geG//YnhHfGB8ApvNZluXB8zqdVVtbK7/fr2AwqIyMjuteAiYLhS1NuH911BNkI8v7Ntw2xXOzDaYtlzetP6aUPJpw/+oul8d65f1j8vWgsTmsJaXbtaOmTqMy0zS7IM8TZdyiqW8M6b6V5dq+q055WWm6Y0ZAqcmJbjerx0wbHwDms5tHeuvOBkCfMLVki2nL5U3rz/kPrdc7n9a2fv9+1V6NvvvPOmlEhpbNn+hiy7rHtOXY/el68NsNFZ59/8x5+i2tKq9u/X79h9KSjTs1NZCt4su9V0bMtPEBgIP16OPDH/3oR6qra1/6pL6+Xj/60Y963SgAsWViyRbTlsub1p9DE+6DvfNprc5/aH2MW9Q7j7z2Ua+ejydcD+LfoQn3wVaVV2vO02/FuEW9Y9r4AMChepR0L1q0SPv27Wv3eF1dnRYtWtTrRgGILdP2pIbClhYtL+9wqW/ksUXLyxUKe2N3jWn92dfQHDXhjnjn01rta2iOUYt6x7T+JIadjXObae+f+sZQ1IQ7YlV5teobQzFqUe+YNj4A0JEeJd2WZcnna79M7m9/+5syMynpAHhNpGRLtMWvPrUsY/ZKyZbuLI/1AtP6c8tzbzsa5zbT+nPHsncdjXObae+f+1aWOxrnNtPGBwA60q093UOHDpXP55PP59Oxxx7bJvEOhULat2+frr/+escbCaBvmVayxbTlsab1Z+fuekfj3GZaf/YfsDdDajfObaa9f7bvar+9rzdxbjNtfACgI91Kun/5y1/KsixdffXVWrRokfx+f+tzycnJysvLU0FBgeONBND3IiVbDj3IJseDB3WZtlzetP6MHJqq96v22orzAtP6M2hgomobuk6oBw30xinZpr1/8rLStP5De3FeYNr4AEBHupV0X3HFFZKk/Px8jR8/XgMGDOiTRgFwR+HoXE0N5Hi+JNVpo4YqwSd1tgUwwdcS5wWR5f9dlaTyyvL/B751qkbf/WdbcV5gWn/+dP0EnfPLtbbivMC068EdMwJasnGnrTgvMO36BgAd6dGe7smTJysxMVEffPCBNmzYoHXr1rX5AuBdiQk+FRydpQtOOUIFR2d5LuGWpM07dnd6gy213IBv3rE7Ng3qpcjy/2hd8lpJqsEpSTppRPRalpJ00ogMz9TrNq0//72xwtE4t5l2PUhNTtTUQHanMVMD2Z6p123a9Q0AOtKjO4CNGzfqO9/5jnbs2CHLanuZ9Pl8CoW8sc8LgJnYIxj/ls2fGLVsmNfqdEtm9Yc9w/Gv+PKxUcuGebVONwCYrEdJ9/XXX6+vf/3reumll5Sbm9vhSeYA4BbT9ghGSupE41NLSZ2pgRxPzQYtmz9R+xqadctzb2vn7nqNHJqqB751qmdmhA9lSn9M2zOcmZrsaFy8KL58rOobQ7pvZbm276pTXlaa7pgR8MwMd4Sp1zcAOFiP7gQ+/PBD/eEPf9AxxxzjdHsAoNdM2yPYnZI6BUdnxa5hDhickqTiK8yZlUtNTtTVE45qPRPBawmQZN6e4X980fUhd5G4iccN6+PWOCs5KUEzThze+npLTurRrkFXmXx9A4CIHiXdp59+uj766COSbgBxiRJocENJWWW70/9zPXj6f2TPcEdLlyO8tGf4k932lsHbjYsXprzeuL4B6A9sJ93vvPNO6/+/4YYb9P3vf19VVVU68cQT251iftJJJznXQgDoAUqgIZZKyio1d+mWdisrqoINmrt0ixbPGuOp19xRhw3q1fPxZFSmvWXwduPigUmvN65vAPoDn3XoSWhRJCQkyOfztTs4rfUH/d9zXjxIrba2Vn6/X8FgUBkZnZ9AC8BbGpvDWlK6XTtq6jQqM02zC/I8twQzFLY04f7VXS6X33DbFM/M3pskMj7Rlsh6bXwam8M6/q6Xuyyx9Y97pnvivVTfGNLXfljSZdx7Pyr0xOy9aa83rm8AvMxuHml7pruiwhulQQAgoqPll7/dUOG5mW7TlsubxrQ9qUtKt9sqsbWkdLuumXhUbBrVC1s/2WM7zgvjY9rrjesbgP7AdtI9atSovmwHADjKpOWXklnL5U1j2p7UHTX29jbbjXObaeNjWn8krm8AzNejg9SWLVvW4eM+n08pKSk65phjlJ+f36uGAXBHKGxpU0VN62m44/IzPTfDEClB09FknSXvlqApHJ2rcXlZ+vZv3lD13kZlpyfr2WvHK3Owt0odHezL2gO66JENqtnfpMxBA/T8dydoWMZAt5vVLabtSTVtD/Rhg+29nuzGuc2011tE4ehcTThmmOdL7pnMhPsDwC09upJdeOGFHe7vPnhf94QJE/TCCy9o6NChUX9OUVGR/vSnP+kf//iHUlNTNX78eN1///067rjjJEk1NTVauHChXnnlFe3cuVPDhg3ThRdeqHvuuUd+v7/15+zcuVNz587VmjVrNHjwYF1xxRUqKipSUhIXaqA7TDkN17TllxGTf7ZaO3bVt36/p75JY368SqOyUrX21ikutqxnTrr7z6ptaG79vm5PSGPv+4syUpL0zt3nutiy7omUqOvsNZfroRJ1swvydO/K97rc0z27IC9mbeoVWyfXdCPOZaa93iLmPP1WmxPz36/aq9F3/1lTA9kqvtycsoJeZcr9AeCWHp2AsmrVKo0dO1arVq1SMBhUMBjUqlWrdPrpp2vFihVat26ddu3apf/4j//o9OesXbtW8+bN08aNG7Vq1So1NTVp2rRp2r9/vyTp888/1+eff66f//znKisr01NPPaWSkhJdc801rT8jFArpvPPOU2Njo9544w3993//t5566in98Ic/7EnXgH4rshz70Bu5yHLskrJKl1rWfSYuvzw04T7Yjl31mvyz1TFuUe8cmnAfrLahWSfd/ecYt6jnEhN8nSZAUsuHPF6ZEUpOSrC1p9sLh6hJ0lf7Dzga57bEBJ++7OLa9eVe77zepPYJ98FWlVdrztNvxbhFOJhJ9weAW3o0FXzTTTfpN7/5jcaPH9/62De+8Q2lpKTo2muv1d///nf98pe/1NVXX93pzykpaXua6FNPPaXs7Gxt3rxZkyZN0ujRo/XHP/6x9fmjjz5a9957r2bNmqXm5mYlJSXplVdeUXl5uf7yl7/o8MMP1ymnnKJ77rlHt912m+6++24lJ3t32SUQK6Ytxx6SMqDroG7EuS1Y1xQ14Y7Ysatewbom+dPiv09f1h6ImnBH1DY068vaA55Yar7xg1224844Nv5XVpR/Wms7LjAi/it++JrtTWHbjXPbZzX1ag53HtMcbok7IjM1No3qhfrGUKc14aWWxLu+MeSJ0+VNY9r9AeCWHn1MvW3btg6PRM/IyNDHH38sSfqXf/kXffXVV936ucFgUJKUmRl9SVTkOPbI0vHS0lKdeOKJOvzww1tjzj33XNXW1urvf/97hz/jwIEDqq2tbfMF9GfdWY7tBaveq3I0zm1XP7XJ0Ti3XfTIBkfj3PbtJzY6Gue2f31ovaNxbrv9xXcdjXPb9AfXOhrntvtWljsaB2eZdn8AuKVHSfdpp52mW2+9VV9++WXrY19++aX+8z//U2PHtuy7+fDDD3XkkUfa/pnhcFg333yzzjzzTI0ePbrDmK+++kr33HOPrr322tbHqqqq2iTcklq/r6rq+Ia6qKhIfr+/9as77QRMZNpy7B01nc8KdzfObZ93sXS5u3Fuq9nf5GgcnNXFJGq349xW32SvpXbj3Lb/QMjROLdt32XvFHy7cXCWafcHgFt6lHQ//vjjqqio0IgRI3TMMcfomGOO0YgRI7R9+3b99re/lSTt27dPd955p+2fOW/ePJWVlenZZ5/t8Pna2lqdd955CgQCuvvuu3vS7FYLFixo3YseDAb1ySef9OrnAV5n2mm4eVn2TlW2G+e24X57v3e7cW7LHGRvCbzdODjL7o2BN3Z0S6kD7LXUbpzbBg20t8TabpzbTLtem8a0+wPALT36C3PcccepvLxcL774om688UbdeOONWrZsmf7+97/r2GOPldRywvns2bNt/bz58+drxYoVWrNmjUaMGNHu+b1796qwsFDp6el6/vnnNWDAP2/EcnJy9MUXX7SJj3yfk5PT4b83cOBAZWRktPkCeioUtlS6bZde3PqZSrftUqirE4jiUOQ03Gi7sXzy1mm4d8wIOBrntieuHOdonNv+cP2Zjsa57elZ9k5Wthvntj/Z/L3bjXPbSzdMcjTObS/fONnROLfdVvg1R+PgLNPuDwC39Phj3YSEBBUWFrYm3eeee64SErr34yzL0vz58/X8889r9erVHdb2rq2t1bRp05ScnKxly5YpJaXtJ2kFBQV69913VV39z0M4Vq1apYyMDAUC3rihhneVlFVqwv2rdWnxRt307FZdWrxRE+5f7bmTPBMTfFo4s+X9cugf1sj3C2cGPHNISmpyoqYGsjuNmRrI9syhPP60ARqV1fmBSKOyUj1xiJokVeza72ic21Z99EXXQd2Ic9vmT+ztzbQb57Yqm8te7ca57YjMVCUndn4tTk70eeIQNUl697Ogo3Fwlmn3B4BbbJ9e/uCDD+raa69VSkqKHnzwwU5jb7zxRls/c968eXrmmWf04osvKj09vXUPtt/vV2pqamvCXVdXp6VLl7Y59GzYsGFKTEzUtGnTFAgENHv2bP30pz9VVVWV7rzzTs2bN08DB8b/qbfwrkgJjUPntSMlNBbPGuOp2pWFo3O1eNaYdnU4czxah7P48rFRy9B4se7r2lunRC0b5rU63abtETRtT+qOGnvttBvnNtNeb5L0wb0zdOwPVqox1H5lVXKiTx/cO8OFVvWMieNjGtPuDwA32E66H3jgAV122WVKSUnRAw88EDXO5/PZTroXL14sSTrrrLPaPP7kk0/qyiuv1JYtW/Tmm29Kko455pg2MRUVFcrLy1NiYqJWrFihuXPnqqCgQIMGDdIVV1yhH/3oR3a7BnSbqSU0CkfnamogR5sqalS9t0HZ6S1LxrzUh4MVXz5W9Y0h3beyXNt31SkvK013zAh4Zob7UGtvnaJgXZOufmqTPg82aLg/RU9cOc4zM9wRhw2y94Go3Ti35WWlaf2H9uK8YFSmvXbajXObqXtSP7h3hj6rqdf0B9dq/4GQBg1M1Ms3TvbMDHeEqeNjGtPuD4BY81mW5b0NqA6rra2V3+9vLUcGdKV02y5dWtx1+Z/fzTlDBUfHf11eIJZe/+grXfbbN7uM+5//d7rOPOawGLSod+obQ/raD0u6jHvvR4We+MCnsTms4+96WZ0dT5Hgk/5xz3QlJ8X/4WOhsKUJ969WVbChww9KfWqZsdtw2xQSCBcwPgC8zG4e2au/lo2NjXr//ffV3Nzcmx8DeA7L4YCe+2rfAUfj3GbaGQLJSQmaM7H9GSsHmzMx3xMJt8Se1HjH+ADoD3r0F7Ourk7XXHON0tLSdMIJJ2jnzp2SpBtuuEE/+clPHG0gEI9YDgf0nInvn+LLx0ZNvL14hsCpI4f26vl4E9mTmnNIWb0cf4rnzt8wEeMDwHS293QfbMGCBfrb3/6m1157TYWFha2Pn3POObr77rt1++23O9ZAIB5FSmh0tRyOEhpAe6a+f0w5QyByZkU0nFmBvsD4ADBZj5LuF154Qc8995zOOOMM+Xz/vBiecMIJ2rZtm2ONA+JVZDnc3KVb5JPaJA4shwM6Z/L7JzU5UfdceKLbzeiVTRU1bU4oPpQlqTLYoE0VNZ47syIxwee5NvcnjA8AU/VoefmXX36p7Oz2y+j279/fJgkHTMZyOKDneP/EL86sAADAWT2a6f7617+ul156STfccIMktSbav/3tb1VQUOBc64A4x3I4xNqXtQd00SMbVLO/SZmDBuj5707QsAxvlNY6VOHoXB1zWLqmP7hWTWFpQIK05KrTdUzOYLeb1mMmlHTLTE12NC6e7Gto1i3Pva2du+s1cmiqHvjWqRqc0qNbobjQ2BzWktLt2lFTp1GZaZpdkOeZA+76g1DY4v4AgKQelgzbsGGDpk+frlmzZumpp57Sddddp/Lycr3xxhtau3atTjvttL5oa5+hZBgALzjp7j+rtqF9tYiMlCS9c/e5LrSod45a8FKHZakSfNLHRefFvkG9NPlnq7VjV327x0dlpWrtrVNcaFHPFK/7WPeufK/LuB/M+JrmTDoqBi1yxvkPrdc7n9a2e/ykERlaNn+iCy3qnaKV5SpeX9HmPZTgazlZfsGMgHsNgySppKxSi5aXt9mqketP0cKZAVbyAAbp05JhEyZM0NatW9Xc3KwTTzxRr7zyirKzs1VaWuq5hBsAvCBawi1JtQ3NOunuP8e4Rb0TLeGWpLDV8ryXREu4JWnHrnpN/tnqGLeo5z7ZXedoXDyIlnBL0juf1ur8h9bHuEW9U7SyXI+tq2j3Hgpb0mPrKlS0MvpBeOh7JWWVmrt0S7uzEaqCDZq7dItKyipdahkAt3RrTVVt7T//YA0bNkz/9V//1WEMs8UA4Jwvaw9ETbgjahua9WXtAU8sNa+o3h814Y4IWy1x+dmDYtOoXgjWNUVNuCN27KpXsK7JE0vNhwy0d2tgN85t+xqaoybcEe98Wqt9Dc2eWGre2BxW8fqKTmOK11fo+9OOZ6m5CyKn/3d0ibPk3dP/AfROt67GQ4YM0dChQ6N+RZ4HADjnokc2OBrntsJfrXU0zm1XP7XJ0Ti3PVXaeULX3Ti33fLc247GuW1J6XZbH1otKd0ek/agre6c/g+g/+jWR7pr1qxp/f+WZWnGjBn67W9/qyOOOMLxhgEAWtTsb3I0zm0HQvaOErEb57bPO7nB7kmc2/Y3hh2Nc9vO3Z2vQuhunNt21Nhb1m83Ds7i9H8AHelW0j158uQ23ycmJuqMM87QUUd55yAVAPCazEEDVLcnZCvOCwYm+mwl1AMTvbH0crg/pdOZrYPjvGDQwETVNnT9ehs0MDEGrem9kUNT9X7VXltxXjAqM83RODgrO93e+9xuHAAzsNkHAOLc89+d4Gic20pumtx1UDfi3PbEleMcjXPbyzfa+73bjXPbz//9FEfj3Da7IE9dbQVO8LXEIfbG5Wcq15+iaEPkU8sp5uPyM2PZLAAuI+kGgDg3LGOgMro44CkjJckTh6hJUn72IFtJgxcOUZMkf9oAjcrqfJZ0VFaqJw5Rk6QjMlOV3MUqg+REn47I9MbMcHll54eodTfObclJCZozMb/TmDkT8zlEzSWJCT4tnNlSsu3Qd1Hk+4UzAxyiBvQzvb4i+3xcNACgr/30307q1fPx5uOi86Im3l6s07321ilRE2+v1emWpA/unRE18U5O9OmDe2fEuEU9Z+Ie2wUzArpuUn6791CCT7puEnW63VY4OleLZ41RziFbSnL8KVo8awx1uoF+yGdZlu2Tai6++OI23y9fvlxTpkzRoEFtZyP+9Kc/OdO6GLFb1BwA3BAKW5pw/+qo+4Z9armZ23DbFM/NnlRU71fhr9bqQMjSwESfSm6a7JkZ7o4E65p09VOb9HmwQcP9KXriynGemeHuyGc19Zr+4FrtPxDSoIGJevnGyZ6Z4Y54/cOvdNnjb3YZ9z/XnK4z/+WwGLTIOY3NYS0p3a4dNXUalZmm2QV5zHDHkVDY0qaKGlXvbVB2esuScq9dowF0zm4e2a2D1Px+f5vvZ82a1bPWAQBs604JmoKjs2LXMAcckZmq/yw8vjVp8FpCd6jBKUn6j3OPb73J9kLd587kDEnRY7PHtvYnZ4gHD3+ym+N4MBdKTkrQNRM5zDZeJSb4PHdNBtA3unU38OSTT/ZVOwAAUZi4PFaSilaWq3h9RZuaw/eufE9zJnpzeWxJWaUWLS9v8wFJrj9FC2cGPLmc1JT+fLXvgKNxAAB0F2uQACDOmViCpmhluR5b1zbhlqSwJT22rkJFK8vdaVgPlZRVau7SLe1WJFQFGzR36RaVlFW61LKeMak/Jr5/AADeQtINAHHOtBI0jc1hFa+v6DSmeH2FGpvDMWpR74TClhYtL1dHB6REHlu0vFyhQz9hiFOm9eeUI4c4GgcAQHeRdANAnIuUoImW4ljyVgmaJaXb281wHypstcR5QXf23HuBaf155s0djsYBANBdJN0AgJjaUVPnaJzbTNtzb1p/THu9AQC8h6QbAOJcZLlvND55a7nviCH2Tii3G+e2zLRkR+PcZtoe6COH2nsd2Y0DAKC7vF3LBIDjTKsruq+hWbc897Z27q7XyKGpeuBbp3qujJNpJcOsqAvlexbntn9U1dqOm3jssD5uTe9FzhCoCjZ0OAKRuvBeOUPg+JzodVN7EhdPTLi+Hcy0OvcAEOHdKzMAx5lSIiji/IfW651P/5kQvV+1V6Pv/rNOGpGhZfMnutiy7jFtue9ne+y1026c2z7ZXe9onNsiZwhcv3RLh8977QyBmrpGR+PihSnXt4jJP1utHbv++R6pDDbo5B+9olFZqVp76xQXWwYAvcfycgCSzCoRJLW/IT3YO5/W6vyH1se4RT1n2nLfUZlpjsa5zbT+SIqacNt9Pp6Y9v6RzLq+Se0T7oPt2FWvyT9bHeMWAYCzSLoRc6GwpdJtu/Ti1s9Uum2XZ/ahRmNCf0wrEbSvoTnqDWnEO5/Wal9Dc4xa1Dsjh9pL1uzGuW3q13IcjXPbKUcMdTTObW/84ytH49yWnzXI0Ti3mXZ9C9Y1RU24I3bsqlewrilGLQIA55F0I6ZKyio14f7VurR4o256dqsuLd6oCfev9twsaoQp/TGtRNAtz73taJzbLnjY3qyV3Ti3Xbx4g6Nxbvu337zhaJzbvvPUm47Gue3fHn3d0Ti3mXZ9u/qpTY7GAUA8IulGzJi2fNmk/pi2Z3inzb2zduPcVmtzxspunNtM64/d9R/eWCdinpr99mZI7ca5zbTr2+edfODbkzgAiEck3YgJ05Yvm9Yf0/Y8HjnUXjvtxrktw+ZpxHbj3GZaf+weJ+aNY8fMkznI3unXduPcNtJmaTO7cW4b7rd3HbYbF09M2H4GwBkk3YgJ05Yvm9afSImgaEmBTy2nmHulRNBlY0c5Gue2FTdMcjTObab1Z9l3Jzga57ZLxx3haJzbnrf5e7cb57YHvnWqo3Fue+LKcY7GxQtTtp8BcAZJN2LCtOXLpvUnUiJIaj8bF/neSyWCahttLl+2Gee2nCEpSk7s/HefnOhTzhBvzATlDElRVy+lBJ88058TR/odjXPbgER7KwzsxrltWMbALldNZKQkaVjGwBi1qHcGpyRp2ODkTmOGDU72TL1uf9oAW/3xUr1uk7afAXAGSTdiwrTly6b1R5IKR+dq8awxyjlkCV+OP0WLZ43xVJ3uwwbbu3m2G+e2UNhSVhdtzRo80DNLF+sbQ+qqqWGrJc4rtv/kvF49H0+OGGJvWbLduHjwzt3nRk28M1KS9M7d58a4RT3X2BzWrv2d1xTftb9Rjc3hGLWod0JhS0mJnd+OJiUmeOb6Ztr2MwDO8MbHoPC8yPLlqmBDh3+IfGpJ7ryyfNm0/kQUjs7V1ECONlXUqHpvg7LTW/rglRnuVoadbNXVdgbpn9sZCo7OilGreu6+leW24+658MQ+bo1ztv/kPG3+eLcuOeiU8j9eO16nHeWNUmERPpvvC7tx8eKdu8/Vl7UHdNEjG1Szv0mZgwbo+e9O8MwMd8SS0u22PrRaUrpd10w8KjaN6gXTrm/d2X7mhf4AcAZJN2Iisnx57tIt8qltruPF5cum9edgiQk+z98IfLX/gKNxbjNtO8P2XXWOxsWLopXlKl5f0eaxfy9+Q3Mm5mvBjIBLreq+T4P2Tr22GxdPhmUM1Ibbv+F2M3plR42994XdOLeZdn0zrT8AnMHycsSMScuXJfP6YxLTlv+b1p+8rDRH4+JB0cpyPbauot0MZNiSHltXoSKbs/vxYFSmvd+73Tg4y7TxMe36Zlp/ADiDmW7ElDHLl/+Paf0xhWnL/03rzx0zAlqycaetOC9obA63m+E+VPH6Cn1/2vFKTor/z7q/c/oo3fPSe7biEHuzC/J078r3Ol1inuBrifMC065vpvUHgDPi/68/jBNZvnzBKUeo4OgszyeopvXHBKadxh7pT7R7bEve6k9qcqKmBrI7jZkayFZqcmKMWtQ73dlj6wVbP9njaByclZyUoDkT8zuNmTMx3xMf8EjmXq8lM/oDwBneuCIDQDeZtvz/7Z27e/U8+g57bBFrC2YEdN2k/Hal9xJ80nWTvHWGgGTe9dq0/gDoPZaXAzCWKcv/TVu+XN8Y0qry6k5jVpVXq74x5InZ7hE2S2fZjXMbe1K9YcGMgL4/7XgtKd2uHTV1GpWZptkFeZ64BnTElOt1hGn9AdA7JN0A2mhsDhtzEyeZcRq7aSWCTCsZZtmsPWc3zm0m70n9rKZe0x9cq/0HQho0MFEv3zhZR2R648OQ/sCE6/XBGpvDWvnu59q+q055WWk65cghnvggEYDzSLoBtIqUPDo4wbt35XueK3lkGtOWL5tWMuyzPfaWWduNc1tkT+r1S7d0+LzXzhCIOPYHK9UY+ufFrbYhpDN/ulrJiT59cO8MF1vWM1yv49ucp99qs6Jn/YfSko07NTWQreLLx7rYMgBu8O70FQBHmVTyyDSmlQgyrWSYaeMjSfOf6Tjhtvt8vDk04T5YY8jSsT9YGeMW9Q7X6/h2aMJ9sFXl1Zrz9FsxbhEAt5F0A7C9Z7ixORyjFuFgk/+l85O+uxvntqvH21sCbzfObROOHuZonNt2flWnrt7qzeGWOC/4rKY+asId0Riy9FlNfYxa1Dtcr+Nbd86sANB/kHQDMK7kkWnOf3i9o3Fu+/fHXnc0zm2mjU/hr9Y6Gue26Q/aa6fdOLdxvY5v3TmzAkD/QdINwLg9w6apb7I3Y2U3zm21Dc2OxrntQBezqN2Nc5tpr7f9B+zNKNqNcxvX6/hm2pkVAJxB0g3AyD2pJkkdYO9SbTfObekp9s7wtBvntmSbv3a7cW4z7fU2aKC906LtxrmN63V8M+3MCgDO8MZfTAB9anZBnro6iDjB1xKH2Lv//NGOxrlt9tiRjsa57f6LT3Y0zm0lN012NM5tL99or51249zG9Tq+3WHz5Hi7cQDMQNINQMlJCZozMb/TmDkT8z1dr9vLwjZnFO3Gua3mgL1l43bj3JaQZK90lt04t408LE1dvdWTElrivOCIzFQlJ3b+u09O9HmmXjfX6/iWmpyoqYHOD7WcGsimXjfQz3BFBiBJWjAjoOsm5bebQUnwSddNou6rm7LTUxyNc5tpy2NNGx9J+ui+86Im3kkJLc97yYOXntqr5+PNqSOH9up59K3iy8dGTbyp0w30Tz7Lsrxxsksfqq2tld/vVzAYVEZGhtvNAVzV2BzWktLt2lFTp1GZaZpdkOfpGZNQ2NKmihpV721QdnqKxuVnKrGrtZlxprE5rOPvernTE4sTfNI/7pnuibEyrT+hsKUJ969WVbBBHXXJJynHn6INt03x3Gtv51d1KvzVWtU3hZU6IEElN032zAx3RGR8KoMNHT7vtfExrT8mq28M6b6V5dq+q055WWm6Y0aAGW7AMHbzSG+cUgMgZpKTEnTNRG/UR+5KSVmlFi0vb3NzmutP0cKZARWOznWxZd2zecduWyWCNu/YrYKjs2LTqF6ILI99bF30WsNeWh6bmODTwpkBXb90S4fPW5IWzgx4MgEaeViayu+Z7nYzemVTRU3UBFVqGZ/KYIM2VdR44v1jWn9MlpqcqHsuPNHtZgCIA964owGAbiopq9TcpVva3ZxWBRs0d+kWlZRVutSy7qveG/0GuydxcN4ft3zaq+fRd0x7/5jWHwDoD0i6ARgnFLa0aHl5h0t9I48tWl6uUFfTx3HisMEDHY1zW2NzWMXro89yS1Lx+go1NnujDnR9Y0iryqs7jVlVXq36Rm/UgTaNaXvuTesPAPQHJN0AjNOd5ZeeYPezAW98hqAlpdttLZdfUro9Ju3prftWljsaB2eNy89Urj9F0Rb3+9Sy7WRcfmYsm9VjpvUHAPoDkm4AxjFt+eVX+w84Gue2HTV1jsa5bfsue+20GwdnRfbcS2qXqEa+99Kee9P6AwD9AUk3AOOYthzbtP6YVjJslM36znbj4LzC0blaPGuMcvxtl1zn+FO0eNYYTx2sKJnXHwAwnatJd1FRkcaOHav09HRlZ2frwgsv1Pvvv98mpqGhQfPmzVNWVpYGDx6sSy65RF988UWbmJ07d+q8885TWlqasrOzdeutt6q5uTmWXUE/FgpbKt22Sy9u/Uyl23Z5Zp+w0Qxbjm1af2YX5LWrB3+oBF9LnBdM/VqOo3HxZOMHu5R3+0utXxs/2OV2k3qscHSu/nDdeGWkJCrRJ2WkJOoP1433bIJaODpXz80pUNqABPkkpQ1I0HNzCjzbH6nlfIS7XnhXsx9/U3e98K7nz0Hg/gBAhKslw9auXat58+Zp7Nixam5u1h133KFp06apvLxcgwYNkiTdcssteumll/T73/9efr9f8+fP18UXX6zXX39dkhQKhXTeeecpJydHb7zxhiorK3X55ZdrwIABuu+++9zsHvoBU0pSmca05dim9ce0kmF7GpocjYsXebe/1O6xbz+xUZK0/Sfnxbo5vfa1u15WfdM/D+erbQjpzJ+uVuqABL3nwbJox/5gpRpD/0zi6prCmvTzNUpO9OmDe2e42LKemfP0W20OJFz/obRk405NDWSr+PKxLrasZ7g/AHAwV+9oSkpKdOWVV+qEE07QySefrKeeeko7d+7U5s2bJUnBYFCPP/64fvGLX2jKlCk67bTT9OSTT+qNN97Qxo0tf/hfeeUVlZeXa+nSpTrllFM0ffp03XPPPXr44YfV2NjoZvdgOJNKUpnGtNN9TeuPpE4TbjvPxxMTx6ejhLs7z8ebQxPug9U3hfW1u16OcYt659CE+2CNIUvH/mBljFvUO4cm3AdbVV6tOU+/FeMW9Q73BwAOFVfTCMFgUJKUmdly4ubmzZvV1NSkc845pzXm+OOP18iRI1VaWipJKi0t1YknnqjDDz+8Nebcc89VbW2t/v73v8ew9ehPTCtJZZoj/Pb2ztqNc1tmarKjcW5bvbXK0Ti3HdhvbzuT3Ti32V1C7pWl5lV7GqIm3BH1TWFV7fHGwYqf1dRHTbgjGkOWPqupj1GLese0knvcHwDoSNwk3eFwWDfffLPOPPNMjR49WpJUVVWl5ORkDRkypE3s4YcfrqqqqtaYgxPuyPOR5zpy4MAB1dbWtvkCusO4klSG+deH1jka5zbT+nP1s5sdjXPblc/81dE4t0WWkDsV57Z//bXN94/NOLdNf3Cto3FuM63kHvcHADoSN0n3vHnzVFZWpmeffbbP/62ioiL5/f7WryOPPLLP/02YxbSSVKbZf8DejIjdOLd1MUnX7TigP6ltsLfCwG6c20y7vplWco/7AwAdiYuke/78+VqxYoXWrFmjESNGtD6ek5OjxsZG7dmzp038F198oZycnNaYQ08zj3wfiTnUggULFAwGW78++eQTB3uD/sDEPZwmGTQw0dE4tw2weaW2Gwf0Jxkp9s6MtRvnNtOub3lZ9koD2o1zG/cHADri6i2aZVmaP3++nn/+ea1evVr5+fltnj/ttNM0YMAAvfrqq62Pvf/++9q5c6cKCgokSQUFBXr33XdVXf3P/UCrVq1SRkaGAoFAh//uwIEDlZGR0eYL6I5x+ZnK9acoWtUjn1pOKR2XnxnLZjnChBInL9842dE4t5nWnye+fZqjcW77wblHORrntoX/epyjcW5bccMkR+PcZtr14I4ZHd+r9TTObSbfHwDoOVc/1p03b56eeeYZvfjii0pPT2/dg+33+5Wamiq/369rrrlG3/ve95SZmamMjAzdcMMNKigo0BlnnCFJmjZtmgKBgGbPnq2f/vSnqqqq0p133ql58+Zp4MCBbnYPBktM8GnhzIDmLt0in9qWR478oV04M6DErooRxxlTSpwckZmq5ERfp4cNJSf6dESmNw5SOyZncLvX2aF8/xfnBVNOyZFs7CSacoo36lpnD7X3wa3dOLdlDrb3vrAb57acISlKHZDQ6WFqqQMSlDPEGzOPpl3fUpMTNTWQ3elhalMD2UpN9sbMvan3BwB6x9WZ7sWLFysYDOqss85Sbm5u69dzzz3XGvPAAw/oX//1X3XJJZdo0qRJysnJ0Z/+9KfW5xMTE7VixQolJiaqoKBAs2bN0uWXX64f/ehHbnQJ/Ujh6FwtnjVGOf62N2o5/hQtnjXGU0mqZF6JkwcvPbVXz8ebxbPG9Or5ePNoF+3t6vl4YtpyUtP6I0kPfOuUXj0fbz64d4aSEztO2rxYp7v48rGaGsju8Dkv1uk27f4AQO/5LMvy3tpRh9XW1srv9ysYDLLUHN0WClvaVFGj6r0Nyk5vWTLmtU+wQ2FLE+5fHfXEVZ9abhY23DbFE32jP/HN1P5UBRs6XI1Af9xl2uvtYJ/V1Gv6g2u1/0BIgwYm6uUbJ3tmhrsj9Y0h3beyXNt31SkvK013zAh4Zoa7IybcHwDonN080hunhgBxLDHBp4Kjs9xuRq90p8SJF/pKf+Kbaf2JLCe9fumWDp+35K3lpKYtjzXt9XawIzJT9c7dhW43wzGpyYm658IT3W6GY0y4PwDgDM66BWBciRP6E99M64+JTFoey+sNAOA2ZroRcyy3ij+m7eHMTE12NM5t6QMHOBrntsMG2Tvk0m6c20JhS4uWl0d93idp0fJyTQ3keOpaVzg6V1MDOZ6/Xpt2fQMAeA9JN2LKlNOxTRMpcdLVHk6vlDj5xxd7bcdNPG5YH7em9555c7vtuClf6/gworhiN2fzSG5n8vJlE5bHmnZ9AwB4D8vLETOmnY5tksgeTql9nuPFPZyf7K5zNM5tn+yxt+zVbpzbvtp3wNE4t7F8Ob6Zdn0DAHgPSTdiIrL8sqNZhshji5aXKxTu94fpu8akPZyjMtMcjXPbyKH2TiO2G+c205aXHzbYZn9sxsF5Jl3fAADeQ9KNmOjO8ku4p3B0rl66YaKOzR6kIakDdGz2IL10w0TP3ZDOLshTV5NWCb6WOC944Fv2aorbjXOdYcvLO/w0sTdxcWTnV3UK3PWy8m9/SYG7XtbOr7yxOqQjplzfAADew55uxATLL71h8s9Wa8eu+tbv99Q3acyPV2lUVqrW3jrFxZZ1T3JSguZMzNdj6yqixsyZmK/kJG987jg4JUknjcjQO5/WRo05aUSGBqd445Ju2vLyr/bb7I/NuHhxzB0vqTn8z+/rmsKa9PM1SkqQPrrvPPca1kOmXN8AAN7jjTtOeB6nx8a/Q29ID7ZjV70m/2x1jFvUO7/b9Emvno8327uYYezq+Xhi2vXAtP5I7RPugzWHW573EtOubwAAbyHpRkxETo+NtlrUp5ZTzDk91h3BuqaoN6QRO3bVK1jXFKMW9c6XtQdU29DcaUxtQ7O+rPXGzKNp/Tlm2GBH49w2cqi9swHsxrlt51d1URPuiOawPLPU3LTr28FCYUul23bpxa2fqXTbLs5FAYA4RdKNmOD02Ph29VObHI1z20WPbHA0zm2m9eey35Y6Gue2Cx5e72ic2wp/tdbROLeZdn2LKCmr1IT7V+vS4o266dmturR4oybcv5pKIAAQh0i6ETOcHhu/Pu/kkLuexLmtZr+9GSu7cW4zrT/VexsdjXNbV6sQuhvntvqmLqa5uxnnNtOubxIlOAHAa7xx6g6MUTg6V1MDOdpUUaPqvQ3KTm9ZUs4Mt7uG+1M6PV3+4DgvyBw0QHV7QrbivGCozf4M9Uh/hqUna0991x8QDEtPjkFrei8jJUlf2fjAI8MjB92lDkhQnY2EOnWANz63N+361lUJTp9aSnBODeTwtxUA4oQ3/mLCKIkJPhUcnaULTjlCBUdncVMQB564cpyjcW57/rsTHI1z2w8Lv+ZonNv+85zjHI1z24obJjka57aSmyY7Guc2065vlOAEAO8h6QYgf9oAjcpK7TRmVFaq/GkemUnNGNjlrGJGSpKGZQyMUYt6p8FmgWe7cW6rC9tblmw3zm05Q1K6nPVNHZCgnCHemEkdeViauqqml5TQEucFpl3fKMEJAN5D0g1AkrT21ilRb0y9WMf20nFH9ur5eGJaSSrT+iNJ790zPWrinTogQe/dMz3GLeqdj+47L2ri7cU63Qumd74KpKvn44mJ7x8AMB1JN4BWa2+dorfuOEcjhqQobUCiRgxJ0Vt3nOO5hLuxOazi9RWdxhSvr1BjV3WR4oRpJfdM60/Ee/dM18bbv6HDBg1QcqJPhw0aoI23f8NzCXfER/edpzXfO0sDE1tGamCiT2u+d5bnEu7IHuhoInugvVJuy9T3DwCYjKQbQKuileU6vegv+nRPg+qaQvp0T4NOL/qLilZGv2GNR0tKt6ur++ew1RLnBZGSe9G6ZMlbJfdM68/Bcoak6K93TdMH987QX++a5pkl5R0pKavUdx7fqAOhlpE6ELL0ncc3eu5kbNP2QFOCEwC8h6QbgKSWhPuxdRXtktWwJT22rsJTifeOmjpH4+LB2zt39+p5oDtMKkll4h5oSnACgLd4o34JgD5ldzn296cdr+SuTliKA0cM6fzQpO7Guc208bG73JeSR+4wrSTVYYPtHZhoNy5eUIITALwj/u/OAPQ505Zj+2xuzbQb5zbTxse05b6mMW587L7PPXI9OBglOAHAG0i6ARi3HPvTYL2jcW4zbXxMXO5rEtPG56v9BxyNAwCgu0i6AWhUpr16u3bj3EZ/4hslj+KbaeNjWn8AAN5D0g30UihsqXTbLr249TOVbtvlmbIzB5tdkKeuViUm+FrivID+xLfTRg211Z/TRg2NTYMctPnj3cq7/aXWr80fe++AO9PGx+QSW/WNId31wrua/fibuuuFd1XfGHK7Sb1iwt/Tg5k2PgB6joPUgF4oKavUouXlbfY/5vpTtHBmwFOnxyYnJWjOxHw9ti76YV1zJuZ74pAuif7Eu807dtvao755x24VHJ0Vm0Y5IO/2l9o9dslv3pAkbf+Jd2pbmzY+kRJb1y/d0uHzXi1RN+fpt7SqvLr1+/UfSks27tTUQLaKLx/rYst6xpS/pxGmjQ+A3vHGHRoQh0wqqSNJv9v0Sa+ejzedJah2no83j2/ovL1dPR9PTNszLHWccHfn+Xhi4vjc9UJZr56PN4cmdAdbVV6tOU+/FeMW9Y5pf09NGx8AvUfSDfRAVyV1pJaSOl5ZGvdl7QHVNjR3GlPb0Kwva71x0NDW7XscjXPbzq/q1BzuPKY53BLnBdW1+x2Nc5vdJeReWWoesrkE1m6c22r2NerLfY2dxny5r1E1XcTEi/rGUNSELmJVebVnljKb9vfUtPEB4AySbqAHTCupc9EjGxyNc9uFj77uaJzbCn+11tE4t9278kNH49wWWULuVJzbbnvhXUfj3PZtm793u3Fuu29l9Br3PYlzm2l/T00bHwDOIOkGesC05Zc1+5scjYOz6pu6mObuZhzQGbsvI6+83Kr32pvBthvntu277K1osRvnNtP+npo2PgCcQdIN9IBpJWiGptk7U9FuHJyVOsDepdpuHNAZuy8jr7zcstOTHY1zW16WvdKAduPcZtrfU9PGB4AzPPInE4gvppWgueu8ExyNc9sL15/paJzbSm6a7Gic22aNtXcSsd04t/3x2vGOxrnt5RvtvY7sxrntWZu/d7txbrtjRsDROLeZ9vfUtPEB4AySbqAHIiVoJLW7UYh876USNAfC9taJ2o1z2yl5QxyNc9vIw9LUVTWwpISWOC9ISLI3o2g3zm2nHWWvXrXdOLcdkzM4agIU4fu/OC/IHJysYYM7fy0NG5yszC5i4kVqcqKmBrI7jZkayFZqcmKMWtQ7pv09NW18ADiDpBvoocLRuVo8a4xy/G2XvOX4U7R41hhP1RU1bXmf1HVdZC/VTZakaybk9+r5eDIq096HA3bj4sF1kzr//Xf1fLxZPGtMr56PN2/dOTVq4j1scLLeunNqjFvUO8WXj42a2HmxDrRJf08l88YHQO/5LMvyRg2GPlRbWyu/369gMKiMjAy3mwOPCYUtbaqoUfXeBmWntyyB88on8hGhsKUJ969WVbChw7ItPrXc/Gy4bYrn+rZ1+542p5S/cP2ZnpnhjmhsDuv4u15WZxVzEnzSP+6ZruSupsTjQM2+Ro358aou47bcOdUTs4+mjU/kehDtRGkvXw++rD2gix7ZoJr9TcocNEDPf3eChmUMdLtZPVbfGNJ9K8u1fVed8rLSdMeMgKdnUE34e3ow08YHQHt280iSbpF0A5JUUlap65duifr8ox6cbTDF4+s/1j0vvddl3F3nfU3XTDwqBi3qnTn//ZZWvdd5HVtJmvq1bBVfEf8zQqaNT+m2Xbq0eGOXcb+bc4YKjs6KQYucUVJWqUXLy9t8mJDrT9HCmQGubQCAHrGbR8b/R+4A0M/tqLFXWsZunNt27q53NM5tpo2PaSWcpJaEe+7SLe1m76uCDZq7dItKyipdahkAoD8g6QagUNjSouXlUZ/3SVq0vFyhztbPos8cOTTV0Ti3jbTZTrtxbjNtj7ppZzxErm8dXb0ij3F9AwD0JZJuANpUURN1/6bUcmNaGWzQpoqa2DXKIaGwpdJtu/Ti1s9Uum2XJ2+sj8+xt+3FbpzbHvjWqY7GuW12QZ662naa4GuJ8wLTSjiZfH0DAHhDktsNAOA+E5eTSubs4aypa3Q0zm2DU5J00ogMvfNpbdSYk0ZkaHCKN/5EJSclaM7EfD22riJqzJyJ+Z44RE36ZwmnuUu3yCe1mSH2YgknU69vAADv8MYdAIA+ZdpyUsmsPZwmjs+y+RN10oiOZ+ZPGpGhZfMnxrhFvXPqyM5rcHf1fLwxqYSTie8fAIC3eGMaAYhjJpQ4OW3UUCX41GXJo9NGeSNx6GoPZ2SP+tRAjifGyrTxiVg2f6Le/3yvZvx6nUKWlOiTVt4wSccNT3e7ad1i90wEr7zeIgpH52pcXpa+/Zs3VL23UdnpyXr22vGeKON2sMhy+a5KInplufzBTPj7AwD9AUk30AumLF/evGN3pwmd1JLwbd6x2xMlgrqzh9ML/TFtfCKO/cFKNYb+2bGQJZ374DolJ/r0wb0zXGxZ95j2eouY/LPV2rHrnyfI76lv0pgfr9KorFStvXWKiy3rnshy+WglES15a7l8hCl/fwCgP2B5OdBDJi1fNm3PI/2Jf4cm3AdrDFk69gcrY9yinjNxfA5NuA+2Y1e9Jv9sdYxbhIOZ9PcHAPoDkm6gB0wrQTM4KdHROLel2myn3Ti37Wuwd0Ca3Ti3fVZTHzXhjmgMWfqsxht1ugf47M2Q2o1zW7CuKWrCHbFjV72CdU0xalHvmFYS0bS/PwDQH5B0Az1gWgmah9dtczTObT/783uOxrntBy9ETxh6Eue26Q+udTTObT9cVuZonNuufmqTo3FuM+16bVp/AKA/IOkGesC05aSd3cD1JM5tX+6zNwNnNw7O2n8g5Gic22obmh2Nc9vnNt/nduPcZtr12rT+AEB/QNIN9IBpJWiG++21026c24al2ztd2W4cnDVooL1l/Xbj3JZhs5643Ti3mXY9MO16bVp/AKA/IOkGeiBSgibaDk2fWk6R9UoJmieuHOdonNv+85zjHI1z29Xjj3Q0zm33zTzR0Ti3rbhhkqNxbjPtemDa9dq0/gBAf0DSDfRApASNpHY3PpHvvVSCxp82QKOyUjuNGZWVKn/agBi1qHfqwmFH49zWbNm7VNuNc1vI5gS23Ti35QxJUeqAzn/3qQMSlDPEGzOPpl0PTLtem9YfAOgPvHGHBsShwtG5WjxrjHIOWWKZ40/R4lljPFcnde2tU6LeaHutLq9pyy9HZaY5Guc208ZHkt67Z3rUxDt1QILeu2d6jFvUOyZdDyTzrtem9QcATOezLKvf15Sora2V3+9XMBhURkaG282Bx4TCljZV1Kh6b4Oy01uW9Hl5hiFY16Srn9qkz4MNGu5P0RNXjvPMjFZEKGxpwv2rVRVs6LCsjk8tN6cbbpviibFqbA7r+LteVmcVgBJ80j/uma7kpPj/LNW0/hysak+D/vXX61Tb0KyMlCStuGGSZ2a4O2LC9eBgpl2vTesPAHiN3TySpFsk3YCJSsoqNXfpFklqk3hHbke9NhtUtLJcj62riPr8dZPytWBGIIYt6rnSbbt0afHGLuN+N+cMFRydFYMWAQAAdJ/dPNJbUwgAYJNpyy8XzAjoukn5OnQSK8HnrYRbouQRAADoX7xRvwQAeqBwdK6mBnKMWX65YEZA3592vJaUbteOmjqNykzT7II8zy3BPmzwQEfjAAAA4hlJNwCjJSb4jFqinJyUoGsmHuV2M3rH7qamfr/5CQAAmMBb0yMAAM/7av8BR+MAAADiGUk3ACCmTCwZBgAAEA3Lyz3AtJIgpvVnX0Ozbnnube3cXa+RQ1P1wLdO1eAU7761Pqup1/QH12r/gZAGDUzUyzdO1hGZHdfr9YKdX9Wp8FdrVd8UVuqABJXcNFkjD/NGPeuOvPGPr/Sdp95s/f6ZK0/X+OMPc7FF3XfaqKFK8KnLkmGnjRoau0Y55N2dQZ3/yAZZajkpf9l3J+jEkX63m9Vjpl3fAABwg6slw9atW6ef/exn2rx5syorK/X888/rwgsvbH1+3759uv322/XCCy9o165dys/P14033qjrr7++NaahoUHf//739eyzz+rAgQM699xz9cgjj+jwww+33Y54LhlWUlapRcvLVRn85ym+uf4ULZwZ8Nzpy5J5/Tn/ofV659Pado+fNCJDy+ZPdKFFvXPsD1aqMdT+kpCc6NMH985woUW9c8wdL6k53P7xpATpo/vOi32Deinv9peiPrf9J97pj6klw0wZnwjTrm8AADjNEyXD9u/fr5NPPlkPP/xwh89/73vfU0lJiZYuXar33ntPN998s+bPn69ly5a1xtxyyy1avny5fv/732vt2rX6/PPPdfHFF8eqC30qUmf44ARVkqqCDZq7dItKyipdalnPmNafaDekkvTOp7U6/6H1MW5R70RLuCWpMWTp2B+sjHGLeidawi1JzeGW572ks4TOzvPxxMSSYSaNj2Te9Q0AADe5mnRPnz5dP/7xj3XRRRd1+Pwbb7yhK664QmeddZby8vJ07bXX6uSTT9amTZskScFgUI8//rh+8YtfaMqUKTrttNP05JNP6o033tDGjV3PosSzUNjSouXlHR7eG3ls0fJyhTpbnxlHTOvPvobmqDekEe98Wqt9Dc0xalHvfFZTHzXhjmgMWfqspj5GLeqdnV/VRU24I5rDLXFe8MY/vnI0zm176+wl03bj3PbuzqCjcW4z7foGAIDb4vogtfHjx2vZsmX67LPPZFmW1qxZow8++EDTpk2TJG3evFlNTU0655xzWv+b448/XiNHjlRpaWnUn3vgwAHV1ta2+Yo3mypq2s0IH8ySVBls0KaKmtg1qhdM688tz73taJzbpj+41tE4txX+yl477ca57eA93E7Eue3OZf9wNM5t5z+ywdE4t5l2fQMAwG1xnXT/+te/ViAQ0IgRI5ScnKzCwkI9/PDDmjRpkiSpqqpKycnJGjJkSJv/7vDDD1dVVVXUn1tUVCS/39/6deSRR/ZlN3rEtOWXpvVn5257M75249y2/0DI0Ti31Td1Mc3dzTigM6aVHTft+gYAgNviPuneuHGjli1bps2bN+u//uu/NG/ePP3lL3/p1c9dsGCBgsFg69cnn3ziUIudY1pJHdP6M3KovdO87ca5bdDAREfj3JY6wN6lzW4c0Bm7tRe8UqPBtOsbAABui9s7zvr6et1xxx36xS9+oZkzZ+qkk07S/Pnz9a1vfUs///nPJUk5OTlqbGzUnj172vy3X3zxhXJycqL+7IEDByojI6PNV7wZl5+pXH9K1Js0n1pO/R6XnxnLZvWYaf154FunOhrntpdvnOxonNtKbrLXTrtxbnvmytMdjXPbH68d72ic25Z9d4KjcW4z7foGAIDb4jbpbmpqUlNTkxIS2jYxMTFR4XDLktDTTjtNAwYM0Kuvvtr6/Pvvv6+dO3eqoKAgpu11WmKCTwtnBiS1nx2JfL9wZsAz9a1N68/glCSdNKLzD2tOGpHhmXq2R2SmKjmx8999cqLPM/W6Rx6WpqQurm5JCfJMvW67dbi9Uq/7tKPs1d+2G+c2u3W4vVKv27TrGwAAbnM16d63b5+2bt2qrVu3SpIqKiq0detW7dy5UxkZGZo8ebJuvfVWvfbaa6qoqNBTTz2lp59+uvW0c7/fr2uuuUbf+973tGbNGm3evFlXXXWVCgoKdMYZZ7jYM2cUjs7V4lljlONvu+Q6x5+ixbPGeK6utWn9WTZ/YtQbUy/Wsf3g3hlRE28v1ul+6DtjevV8vOmqzrPX6kA/Oqvz339Xz8cb08bHtOsbAABu8lmW5drZLq+99prOPvvsdo9fccUVeuqpp1RVVaUFCxbolVdeUU1NjUaNGqVrr71Wt9xyi3y+luSgoaFB3//+9/W73/1OBw4c0LnnnqtHHnmk0+Xlh7Jb1NwtobClTRU1qt7boOz0liXYXpkR7ohp/dnX0KxbnntbO3fXa+TQVD3wrVM9PQP0WU29pj+4VvsPhDRoYKJevnGyZ2a4I0JhSxPuXx31xHyfWj7s2XDbFM+99jaUf6lZT29q/X7p5eM0ITDMxRZ1n8njs3X7Hl346Out379w/Zk6JW+Iew3qJdOubwAAOMluHulq0h0v4j3pBtA9pdt26dLijV3G/W7OGSo4OisGLXJGSVmlFi0vb5Os5vpTtHBmwFMrRRgfAABgArt5ZNzu6QaAnjKtRJ3UktDNXbql3exwVbBBc5duUUlZpUst6z7GBwAA9Cck3QCMY1qJulDY0qLl5R3WeY48tmh5uUJhbyxcOmzwQEfj3Gba+AAAAGeRdAO91Ngc1uPrP9YPXyzT4+s/VmNz2O0m9UoobKl02y69uPUzlW7b5clEwbQSdZsqaqLuf5ZaErvKYIM2VdTErlG9Yfcl5ZGXnnHjAwAAHMVpKEAvFK0sV/H6Ch2cl9678j3NmZivBTMC7jWsh0zZkxopUTd36Rb51DZ382KJOtOWY3+1/4CjcW4zbXwAAICzmOkGeqhoZbkeW9c24ZaksCU9tq5CRSvL3WlYD5m2J9WkEnWmLZenPwAAoD8h6QZ6oLE5rOL1FZ3GFK+v8MxSc1P3pBaOzlXJTZN02sghyvWn6LSRQ1Ry0yRPJdySdNqooepqUj7B1xLnBaYt/zetPwerbwzprhfe1ezH39RdL7yr+saQ203qFRO2zwAAvIfl5UAPLCnd3m6G+1BhqyXumolHxaZRvdCdPaleKuF0/kPr9c6nta3fVwYbdPKPXtFJIzK0bP5EF1vWPZt37Lb1etu8Y7cnxiey/P/6pVs6fN6St5b/m9afiDlPv6VV5dWt36//UFqycaemBrJVfPlYF1vWM6ZsnwEAeA8z3UAP7KipczTObSbuST004T7YO5/W6vyH1se4RT1n4vj8ccunvXoefevQhPtgq8qrNefpt2Lcot4xbfsMAMBbSLqBHjhiiL29mXbj3JaZluxonNv2NTRHTbgj3vm0VvsammPUot5Jara3BNZunNvqG0NRE7qIVeXVnlnKHNmeEY1P3tqeYer4mLZ9BgDgHSTdQA/4ou7e7Fmc2/5RtdfROLfd8tzbjsa5bcGKMkfj3HafzUMG7ca5zbSSYYwPAADOIukGeuDTPfWOxrntk932lsHbjXPbzt32fu9249y2/4C9GUW7cW7bvsve68hunNtMW/7P+AAA4CySbqAHRmWmORrnNtP6M3JoqqNxbhs0MNHROLflZdl7HdmNc5tpJcMYHwAAnEXSDfTA7II8WyWcZhfkxaQ9vWVafx741qmOxrnt5RsnOxrntjtmBByNc5tpJcMYHwAAnEXSDfRAclKC5kzM7zRmzsR8JSd54y1mWn8GpyTppBEZncacNCJDg1O8UTXxiMxUJSd2/qlIcqJPR2R6Y+Y+NTlRUwPZncZMDWQrNdkbM/eRkmGS2iV2ke+9VDKM8QEAwFneuIMG4tCCGQFdNym/3Qxxgk+6blK+FnhkFijCtP4smz8xauLttTrdkvTBvTOiJt7JiT59cO+MGLeod4ovHxs1sfNiHejC0blaPGuMcvxtlyjn+FO0eNYYz9WBZnwAAHCOz7Ksfl8jo7a2Vn6/X8FgUBkZnc+OAYdqbA5rSel27aip06jMNM0uyPPMjHBHTOvPvoZm3fLc29q5u14jh6bqgW+d6pkZ7o58VlOv6Q+u1f4DIQ0amKiXb5zsmRnujtQ3hnTfynJt31WnvKw03TEj4JkZ1I6EwpY2VdSoem+DstNblix7eQaV8QEAIDq7eSRJt0i60TvcxAEAAAD9j9080rvTPUAcKCmr1KLl5W1qwOb6U7RwZoDligAAAADY0w30VElZpeYu3dIm4ZakqmCD5i7dopKySpdaBgAAACBekHQDPRAKW1q0vFwd7c2IPLZoeblC4X6/ewMAAADo10i6gR7YVFHTbob7YJakymCDNlXUxK5RAAAAAOIOSTfQA9V7oyfcPYkDAAAAYCYOUgN6IDs9peugbsTFk2Bdk65+apM+DzZouD9FT1w5Tv60AW43q8dM68/Or+pU+Ku1qm8KK3VAgkpumqyRh6W53aweq9nXqG//5g1V721Udnqynr12vDIHJ7vdrB4zrT+UEIxvVM8AAG+gZJgoGYbuC4UtTbh/dadLzHP9Kdpw2xRP3QBN/tlq7dhV3+7xUVmpWnvrFBda1Dum9eeYO15Sc7j940kJ0kf3nRf7BvXS2B+v0pf7Gts9Pmxwst66c6oLLeod0/pTtLJcxesrdPDRFAk+ac7EfC2YEXCvYT10/kPr9c6nte0eP2lEhpbNn+hCi3qH6hkA4D67eaR3P64GXJSY4NP5J3d+U3P+yblGJNyStGNXvSb/bHWMW9Q7pvUnWsItSc3hlue9JFqCKklf7mvU2B+vinGLese0/hStLNdj69om3JIUtqTH1lWoaGW5Ow3roWgJtyS982mtzn9ofYxb1DtUzwAAbyHpBnogFLa07G+d39Qs+1ulZ04vD9Y1RU1QI3bsqlewrilGLeod0/qz86u6qAl3RHO4Jc4LavY1Rk1QI77c16iaLmLihWn9aWwOq3h9Racxxesr1NjVizJO7GtojppwR7zzaa32NTTHqEW9Q/UMAPAekm6gB7o6vVzy1unlVz+1ydE4t5nWn8JfrXU0zm3f/s0bjsa5zbT+LCnd3m6G+1BhqyXOC2557m1H49xG9QwA8B6SbqAHTDu9/PMuPkDobpzbTOtPfZO9GUW7cW6r3mtvxtdunNtM68+OGnsrJuzGuW3n7s5XvXQ3zm2m/f0BgP6ApBvoAdNOLx/ut9dOu3FuM60/qQPsXartxrktO93ead5249xmWn9GZdo7Dd9unNtGDk11NM5tpv39AYD+wBt3aECcGZefqVx/iqIdk+ZTyymy4/IzY9msHnviynGOxrnNtP6U3DTZ0Ti3PXvteEfj3GZaf2YX5KmrMyATfC1xXvDAt051NM5tpv39AYD+gKQb6IHEBJ8Wzgx0eJCN1LKnbuHMgGdOL/enDdCorM5neUZlpXqmvrVp/Rl5WJq6Ko2clCDP1OvOHJysYV3Urh42ONkz9a1N609yUoLmTMzvNGbOxHzP1OsenJKkk0Z0Xg70pBEZnqnXHfn7I6ld4h353kt/fwCgP/DGX0wAfW7trVOiJqperGttWn8+uu+8qIm3F+t0v3Xn1KiJqhfrWpvWnwUzArpuUn67Ge8En3TdJO/V6V42f2LUxNuLdboLR+dq8awxyjlki0yOP0WLZ42hTjcAxBmfZVn9vqaE3aLmQEQobGnC/aujniDrU8vNz4bbpnhutiFY16Srn9qkz4MNGu5P0RNXjvPMjHBHTOvPzq/qVPirtapvCit1QIJKbprsmRnujtTsa9S3f/OGqvc2Kjs9Wc9eO94zM8IdMa0/jc1hLSndrh01dRqVmabZBXmemeHuyL6GZt3y3NvaubteI4em6oFvneqZGe6OhMKWNlXUqHpvg7LTW5aUe+1vDgB4md08kqRbJN2xZsJNQum2Xbq0eGOXcb+bc4YKjs6KQYsQjQmvt4OZ1h8AAACvsptHevfjXXhSSVmlFi0vbzNDnOtP0cKZAU8th6NkizeY8nqLMK0/AAAA/YF314jBc0rKKjV36ZZ2S7Krgg2au3SLSsoqXWpZ91GyJf6Z9HqTzOsPAABAf0HSjZgIhS0tWl7e4WnfkccWLS9XKOyN3Q6UbIlvpr3eTOsPAABAf0LSjZjYVFET9dAxqSVxqAw2aFNFTewa1QuUbIlvpr3eTOsPAABAf0LSjZgwcQ80JVvil2mvN9P6AwAA0J9wkJoHmHBasal7oAtH52rK8YcbVVKnvjGk+1aWa/uuOuVlpemOGQGlJie63axuMe31Zlp/TGZaiS0AANB7JN1xzpTTiiN7oKuCDR3uS43UtfbaHuiOxue3Gyo8Nz4Rc55+S6vKq1u/X/+htGTjTk0NZKv48rEutqx7Iq+3zpZke2nPvanvH9MUrSxX8foKHby1/t6V72nOxHwtmBFwr2EAAMBVfPwex0w6rdjEPdAmjY/UPuE+2Kryas15+q0Yt6jnEhN8Gn1E9FqJkjT6iAzPvN5MfP+YpmhluR5b1zbhlqSwJT22rkJFK8vdaRgAAHAdSXecMvG0YpP2QJs2PvWNoagJd8Sq8mrVN4Zi1KLeaWwO69X3Ou/Pq+9Vq7E5HKMW9Z5J7x/TNDaHVby+otOY4vUVnnq9AQAA57C8PE5157TigqOzYtewXiocnaupgRzP71E3bXzuszkLd9/Kct1z4Yl93JreW1K6vd2M46HCVkvcNROPik2jHGDK++dgJpxZYerrDQAAOIOkO06ZfFpxYoLPE4loZ0wbn+276hyNc9uOGnvttBsXT0x4/0SYcmaFya83AADQeywvj1OcVhzfTBufvKw0R+PcNirTXjvtxsF5Jp2JwOsNAAB0hqQ7TkVOK462yNInb52+bBrTxucOmycr241z2+yCPHW1QjnB1xKH2DPtTARebwAAoDMk3XGK04rjm2njk5qcqKmB7E5jpgayPVOvOzkpQXMm5ncaM2diPvWTXdKdMxG8gNcbAADoDHcAcYzTiuObaeNzyZgRvXo+3iyYEdB1k/LbzUAm+KTrJlE32U2mnYkg8XoDAADR+SzL8sb6vT5UW1srv9+vYDCojIzOa/u6wYTTfU1mwviEwpYm3L866uyjTy0fJmy4bYrn+tbYHNaS0u3aUVOnUZlpml2Qx4yjy0q37dKlxRu7jPvdnDM8d2gcrzcAAPoPu3kkp5d7gEmnFZvIhPExrQTawZKTEijTFGciZyJUBRs63Ncd+ZDHK2ciHIzXGwAAOBQfvwMwcrkv4pdpZyIAAAB0hqQbgHEl0BD/TDsTAQAAIBqWlwMwerkv4lfh6FxNDeR4/kwEAACAzpB0A2hd7jt36Rb5pDaJN8t90ZdMOBMBAACgMywvByCJ5b4AAABAX2CmG0ArlvsCAAAAznJ1pnvdunWaOXOmhg8fLp/PpxdeeKFdzHvvvafzzz9ffr9fgwYN0tixY7Vz587W5xsaGjRv3jxlZWVp8ODBuuSSS/TFF1/EsBforlDYUum2XXpx62cq3bZLobC3S8U3Nof1+PqP9cMXy/T4+o/V2Bx2u0k4SH1jSHe98K5mP/6m7nrhXdU3htxuUq/Qn/jG9QAAABzKZ1mWaxnPyy+/rNdff12nnXaaLr74Yj3//PO68MILW5/ftm2bxo0bp2uuuUaXXnqpMjIy9Pe//11nnHGGsrOzJUlz587VSy+9pKeeekp+v1/z589XQkKCXn/9ddvtsFvUHL1XUlapRcvL29SEzvWnaOHMgCeXLxetLFfx+god/LlBgk+aMzFfC2YE3GtYD5k2PnOefkuryqvbPT41kK3iy8e60KLeoT/xzbTrAQAA6JzdPNLVpPtgPp+vXdL97W9/WwMGDNCSJUs6/G+CwaCGDRumZ555Rv/2b/8mSfrHP/6hr33tayotLdUZZ5xh698m6Y6NkrJKzV26pd3p2JGFy17bN1y0slyPrauI+vx1k7x1o23a+ERL6CK8ltjRn/hm2vUAAAB0zW4eGbcHqYXDYb300ks69thjde655yo7O1unn356myXomzdvVlNTk84555zWx44//niNHDlSpaWlLrQa0YTClhYtL++wHFXksUXLyz2z1LyxOazi9dFvsCWpeH2FZ5aWmjY+9Y2hThM6SVpVXu2Zpcz0J76Zdj0AAADOituku7q6Wvv27dNPfvITFRYW6pVXXtFFF12kiy++WGvXrpUkVVVVKTk5WUOGDGnz3x5++OGqqqqK+rMPHDig2traNl/oW5sqatosWT6UJaky2KBNFTWxa1QvLCndrq7yz7DVEucFpo3PfSvLHY1zG/2Jb6ZdDwAAgLPi9vTycLhlRuCCCy7QLbfcIkk65ZRT9MYbb+jRRx/V5MmTe/yzi4qKtGjRIkfaCXuq90ZP6HoS57YdNXWOxrnNtPHZvsve791unNvoT3wz7XoAAACcFbcz3YcddpiSkpIUCLTdA/e1r32t9fTynJwcNTY2as+ePW1ivvjiC+Xk5ET92QsWLFAwGGz9+uSTTxxvP9rKTk/pOqgbcW4blZnmaJzbTBufvCx7v3e7cW6jP/HNtOsBAABwVtwm3cnJyRo7dqzef//9No9/8MEHGjVqlCTptNNO04ABA/Tqq6+2Pv/+++9r586dKigoiPqzBw4cqIyMjDZf6Fvj8jOV609RtGrPPrWckj0uPzOWzeqx2QV56qp0dYKvJc4LTBufO2weWGU3zm30J76Zdj0AAADOcjXp3rdvn7Zu3aqtW7dKkioqKrR169bWmexbb71Vzz33nIqLi/XRRx/poYce0vLly/Xd735XkuT3+3XNNdfoe9/7ntasWaPNmzfrqquuUkFBge2TyxEbiQk+LZzZcgN96L1p5PuFMwNK7OrONU4kJyVozsT8TmPmTMxXclLcfq7Vhmnjk5qcqKmB7E5jpgaylZqcGKMW9Q79iW+mXQ8AAICzXC0Z9tprr+nss89u9/gVV1yhp556SpL0xBNPqKioSJ9++qmOO+44LVq0SBdccEFrbENDg77//e/rd7/7nQ4cOKBzzz1XjzzySKfLyw9FybDYMa0OtGl1eU0bH9PqQNOf+Gba9QAAAHTOc3W63UTSHVuhsKVNFTWq3tug7PSWJctemUHtSGNzWEtKt2tHTZ1GZaZpdkGep2e0TBuf+saQ7ltZru276pSXlaY7ZgQ8M4PaEfoT30y7HgAAgOhIuruBpBsAAAAA0B1280g+fgcAAAAAoI/EbZ1uAADgLtO2mwAA4AaSbgAA0I5pBysCAOAWlpcDAIA2SsoqNXfpljYJtyRVBRs0d+kWlZRVutQyAAC8h6QbAAC0CoUtLVpero5OWY08tmh5uULhfn8OKwAAtrC8HDHHHsH4RskjoH/bVFHTbob7YJakymCDNlXUqODorNg1DAAAjyLpRkyxRzC+Fa0sV/H6Ch08gXXvyvc0Z2K+FswIuNcwADFTvTd6wt2TOAAA+jumrxAz7BGMb0Ury/XYurYJtySFLemxdRUqWlnuTsMAxFR2eoqjcQAA9Hck3YgJ9gjGt8bmsIrXV3QaU7y+Qo3N4Ri1CIBbxuVnKtefomibfnxqWaE0Lj8zls0CAMCzSLoRE93ZI4jYW1K6vd0M96HCVkscALMlJvi0cGbLdpJDE+/I9wtnBjiLAwAAm0i6ERPsEYxvO2rqHI0D4G2Fo3O1eNYY5fjbLiHP8ado8awxnMEBAEA3cJAaYoI9gvFtVGaao3EAvK9wdK6mBnKoNgEAQC8x042YYI9gfJtdkKeu7qMTfC1xAPqPxASfCo7O0gWnHKGCo7NIuAEA6AGSbsQEewTjW3JSguZMzO80Zs7EfOp1AwAAAN3EHTRihj2C8W3BjICum5TfbsY7wSddN4k63QAAAEBP+CzL6vc1mmpra+X3+xUMBpWRkeF2c4wXClvsEYxjjc1hLSndrh01dRqVmabZBXnMcAMAAACHsJtHcpAaYi6yRxDxKTkpQddMPMrtZgAAAABGYPoKAAAAAIA+QtINAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjnF6OmKNkGGLJtNcbJd0AAAC8haQbMVVSVqlFy8tVGWxofSzXn6KFMwMqHJ3rYstgItNeb0Ury1W8vkJh65+P3bvyPc2ZmK8FMwLuNQwAAABRMT2CmCkpq9TcpVvaJECSVBVs0NylW1RSVulSy2Ai015vRSvL9di6tgm3JIUt6bF1FSpaWe5OwwAAANApkm7ERChsadHyclkdPBd5bNHycoUOzSiAHjDt9dbYHFbx+opOY4rXV6ixORyjFgEAAMAukm7ExKaKmnYzjgezJFUGG7SpoiZ2jYKxTHu9LSnd3m6G+1BhqyUOAAAA8YWkGzFRvTd6AtSTOKAzpr3edtTUORoHAACA2CHpRkxkp6c4Ggd0xrTX26jMNEfjAAAAEDsk3YiJcfmZyvWnKFqhJp9aTpUel58Zy2bBUKa93mYX5KmrKmcJvpY4AAAAxBeSbsREYoJPC2e2lDQ6NHeIfL9wZsDT9ZMRP0x7vSUnJWjOxPxOY+ZMzKdeNwAAQBziDg0xUzg6V4tnjVGOv+2S3hx/ihbPGuPJusmIX6a93hbMCOi6SfntZrwTfNJ1k6jTDQAAEK98lmV5o2ZOH6qtrZXf71cwGFRGRobbzTFeKGxpU0WNqvc2KDu9ZYmvV2Yc4T2mvd4am8NaUrpdO2rqNCozTbML8pjhBgAAcIHdPJKkWyTdAAAAAIDusZtHMj0CAAAAAEAfIekGAAAAAKCPkHQDAAAAANBHSLoBAAAAAOgjJN0AAAAAAPQRkm4AAAAAAPoISTcAAAAAAH2EpBsAAAAAgD5C0g0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIugEAAAAA6CMk3QAAAAAA9BGSbgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPpLkdgPigWVZkqTa2lqXWwIAAAAA8IJI/hjJJ6Mh6Za0d+9eSdKRRx7pcksAAAAAAF6yd+9e+f3+qM/7rK7S8n4gHA7r888/V3p6unw+n9vN6Rdqa2t15JFH6pNPPlFGRobbzcEhGJ/4xvjEN8YnvjE+8Y3xiW+MT3xjfGLPsizt3btXw4cPV0JC9J3bzHRLSkhI0IgRI9xuRr+UkZHBRSGOMT7xjfGJb4xPfGN84hvjE98Yn/jG+MRWZzPcERykBgAAAABAHyHpBgAAAACgj5B0wxUDBw7UwoULNXDgQLebgg4wPvGN8YlvjE98Y3ziG+MT3xif+Mb4xC8OUgMAAAAAoI8w0w0AAAAAQB8h6QYAAAAAoI+QdAMAAAAA0EdIutGnPvvsM82aNUtZWVlKTU3ViSeeqL/+9a+tz+/bt0/z58/XiBEjlJqaqkAgoEcffdTFFvcveXl58vl87b7mzZsnSWpoaNC8efOUlZWlwYMH65JLLtEXX3zhcqv7h87GpqamRjfccIOOO+44paamauTIkbrxxhsVDAbdbna/0dV7J8KyLE2fPl0+n08vvPCCO43th+yMT2lpqaZMmaJBgwYpIyNDkyZNUn19vYut7j+6Gp+qqirNnj1bOTk5GjRokMaMGaM//vGPLre6/wiFQrrrrruUn5+v1NRUHX300brnnnt08DFQlmXphz/8oXJzc5WamqpzzjlHH374oYut7j+6Gp+mpibddtttOvHEEzVo0CANHz5cl19+uT7//HOXW96/JbndAJhr9+7dOvPMM3X22Wfr5Zdf1rBhw/Thhx9q6NChrTHf+973tHr1ai1dulR5eXl65ZVX9N3vflfDhw/X+eef72Lr+4e33npLoVCo9fuysjJNnTpV//7v/y5JuuWWW/TSSy/p97//vfx+v+bPn6+LL75Yr7/+ultN7jc6G5vPP/9cn3/+uX7+858rEAhox44duv766/X555/rD3/4g4ut7j+6eu9E/PKXv5TP54t18/q9rsantLRUhYWFWrBggX79618rKSlJf/vb35SQwFxELHQ1Ppdffrn27NmjZcuW6bDDDtMzzzyjb37zm/rrX/+qU0891a1m9xv333+/Fi9erP/+7//WCSecoL/+9a+66qqr5Pf7deONN0qSfvrTn+rBBx/Uf//3fys/P1933XWXzj33XJWXlyslJcXlHpitq/Gpq6vTli1bdNddd+nkk0/W7t27ddNNN+n8889vM/GFGLOAPnLbbbdZEyZM6DTmhBNOsH70ox+1eWzMmDHWD37wg75sGqK46aabrKOPPtoKh8PWnj17rAEDBli///3vW59/7733LElWaWmpi63snw4em4787//+r5WcnGw1NTXFuGWwrI7H5+2337aOOOIIq7Ky0pJkPf/88+41sJ87dHxOP/10684773S5VYg4dHwGDRpkPf30021iMjMzreLiYjea1++cd9551tVXX93msYsvvti67LLLLMuyrHA4bOXk5Fg/+9nPWp/fs2ePNXDgQOt3v/tdTNvaH3U1Ph3ZtGmTJcnasWNHXzcPUfCRLvrMsmXL9PWvf13//u//ruzsbJ166qkqLi5uEzN+/HgtW7ZMn332mSzL0po1a/TBBx9o2rRpLrW6/2psbNTSpUt19dVXy+fzafPmzWpqatI555zTGnP88cdr5MiRKi0tdbGl/c+hY9ORYDCojIwMJSWxgCnWOhqfuro6fec739HDDz+snJwcl1vYvx06PtXV1XrzzTeVnZ2t8ePH6/DDD9fkyZO1YcMGt5vaL3X0/hk/fryee+451dTUKBwO69lnn1VDQ4POOussdxvbT4wfP16vvvqqPvjgA0nS3/72N23YsEHTp0+XJFVUVKiqqqrN/YHf79fpp5/O/UEMdDU+HQkGg/L5fBoyZEiMWolDcXeGPvPxxx9r8eLF+t73vqc77rhDb731lm688UYlJyfriiuukCT9+te/1rXXXqsRI0YoKSlJCQkJKi4u1qRJk1xuff/zwgsvaM+ePbryyislteypS05ObneBPvzww1VVVRX7BvZjh47Nob766ivdc889uvbaa2PbMEjqeHxuueUWjR8/XhdccIF7DYOk9uPz8ccfS5Luvvtu/fznP9cpp5yip59+Wt/4xjdUVlamf/mXf3Gxtf1PR++f//3f/9W3vvUtZWVlKSkpSWlpaXr++ed1zDHHuNfQfuT2229XbW2tjj/+eCUmJioUCunee+/VZZddJkmt9wCHH354m/+O+4PY6Gp8DtXQ0KDbbrtNl156qTIyMmLcWkSQdKPPhMNhff3rX9d9990nSTr11FNVVlamRx99tE3SvXHjRi1btkyjRo3SunXrNG/ePA0fPrzNJ6joe48//rimT5+u4cOHu90UHKKzsamtrdV5552nQCCgu+++O/aNQ7vxWbZsmVavXq23337b5ZZBaj8+4XBYknTdddfpqquuktTy9+nVV1/VE088oaKiItfa2h91dH276667tGfPHv3lL3/RYYcdphdeeEHf/OY3tX79ep144okutrZ/+N///V/9z//8j5555hmdcMIJ2rp1q26++WYNHz689f4N7unO+DQ1Nemb3/ymLMvS4sWLXWoxJLGnG31n5MiR1jXXXNPmsUceecQaPny4ZVmWVVdXZw0YMMBasWJFm5hrrrnGOvfcc2PWTljW9u3brYSEBOuFF15ofezVV1+1JFm7d+9uEzty5EjrF7/4RYxb2H91NDYRtbW1VkFBgfWNb3zDqq+vd6F16Gh8brrpJsvn81mJiYmtX5KshIQEa/Lkye41th/qaHw+/vhjS5K1ZMmSNrHf/OY3re985zuxbmK/1tH4fPTRR5Ykq6ysrE3sN77xDeu6666LdRP7pREjRlgPPfRQm8fuuece67jjjrMsy7K2bdtmSbLefvvtNjGTJk2ybrzxxlg1s9/qanwiGhsbrQsvvNA66aSTrK+++iqWTUQH2NONPnPmmWfq/fffb/PYBx98oFGjRklq+fStqamp3WmxiYmJrTMRiI0nn3xS2dnZOu+881ofO+200zRgwAC9+uqrrY+9//772rlzpwoKCtxoZr/U0dhILTPc06ZNU3JyspYtW8ZpsS7paHxuv/12vfPOO9q6dWvrlyQ98MADevLJJ11qaf/U0fjk5eVp+PDhnf59Qmx0ND51dXWSxL2Bi+rq6jr9/efn5ysnJ6fN/UFtba3efPNN7g9ioKvxkf45w/3hhx/qL3/5i7KysmLdTBzK7awf5tq0aZOVlJRk3XvvvdaHH35o/c///I+VlpZmLV26tDVm8uTJ1gknnGCtWbPG+vjjj60nn3zSSklJsR555BEXW96/hEIha+TIkdZtt93W7rnrr7/eGjlypLV69Wrrr3/9q1VQUGAVFBS40Mr+KdrYBINB6/TTT7dOPPFE66OPPrIqKytbv5qbm11qbf/T2XvnUOL08pjrbHweeOABKyMjw/r9739vffjhh9add95ppaSkWB999JELLe2foo1PY2Ojdcwxx1gTJ0603nzzTeujjz6yfv7zn1s+n8966aWXXGpt/3LFFVdYRxxxhLVixQqroqLC+tOf/mQddthh1n/+53+2xvzkJz+xhgwZYr344ovWO++8Y11wwQVWfn4+q65ioKvxaWxstM4//3xrxIgR1tatW9vcIxw4cMDl1vdfJN3oU8uXL7dGjx5tDRw40Dr++OOt3/zmN22er6ystK688kpr+PDhVkpKinXcccdZ//Vf/xW1LBKc9+c//9mSZL3//vvtnquvr7e++93vWkOHDrXS0tKsiy66yKqsrHShlf1TtLFZs2aNJanDr4qKCnca2w919t45FEl37HU1PkVFRdaIESOstLQ0q6CgwFq/fn2MW9i/dTY+H3zwgXXxxRdb2dnZVlpamnXSSSe1KyGGvlNbW2vddNNN1siRI62UlBTrqKOOsn7wgx+0SdjC4bB11113WYcffrg1cOBA6xvf+IatayF6r6vxqaioiHqPsGbNGncb34/5LMuyYjy5DgAAAABAv8CebgAAAAAA+ghJNwAAAAAAfYSkGwAAAACAPkLSDQAAAABAHyHpBgAAAACgj5B0AwAAAADQR0i6AQAAAADoIyTdAAAAAAD0EZJuAADQzmuvvSafz6c9e/bY/m/uvvtunXLKKX3WJgAAvIikGwAAj3v00UeVnp6u5ubm1sf27dunAQMG6KyzzmoTG0mmt23b1unPHD9+vCorK+X3+x1t61lnnaWbb77Z0Z8JAEA8I+kGAMDjzj77bO3bt09//etfWx9bv369cnJy9Oabb6qhoaH18TVr1mjkyJE6+uijO/2ZycnJysnJkc/n67N2AwDQH5B0AwDgcccdd5xyc3P12muvtT722muv6YILLlB+fr42btzY5vGzzz5b4XBYRUVFys/PV2pqqk4++WT94Q9/aBN36PLy4uJiHXnkkUpLS9NFF12kX/ziFxoyZEi79ixZskR5eXny+/369re/rb1790qSrrzySq1du1a/+tWv5PP55PP5tH37dqd/HQAAxBWSbgAADHD22WdrzZo1rd+vWbNGZ511liZPntz6eH19vd58802dffbZKioq0tNPP61HH31Uf//733XLLbdo1qxZWrt2bYc///XXX9f111+vm266SVu3btXUqVN17733tovbtm2bXnjhBa1YsUIrVqzQ2rVr9ZOf/ESS9Ktf/UoFBQWaM2eOKisrVVlZqSOPPLIPfhsAAMSPJLcbAAAAeu/ss8/WzTffrObmZtXX1+vtt9/W5MmT1dTUpEcffVSSVFpaqgMHDuiss85SIBDQX/7yFxUUFEiSjjrqKG3YsEGPPfaYJk+e3O7n//rXv9b06dP1H//xH5KkY489Vm+88YZWrFjRJi4cDuupp55Senq6JGn27Nl69dVXde+998rv9ys5OVlpaWnKycnpy18HAABxg6QbAAADnHXWWdq/f7/eeust7d69W8cee6yGDRumyZMn66qrrlJDQ4Nee+01HXXUUdq3b5/q6uo0derUNj+jsbFRp556aoc///3339dFF13U5rFx48a1S7rz8vJaE25Jys3NVXV1tUO9BADAe0i6AQAwwDHHHKMRI0ZozZo12r17d+ts9fDhw3XkkUfqjTfe0Jo1azRlyhTt27dPkvTSSy/piCOOaPNzBg4c2Kt2DBgwoM33Pp9P4XC4Vz8TAAAvI+kGAMAQZ599tl577TXt3r1bt956a+vjkyZN0ssvv6xNmzZp7ty5CgQCGjhwoHbu3NnhUvKOHHfccXrrrbfaPHbo93YkJycrFAr9//buVyWWMI7j8PdoEfwTBLGqWNbiBaw4TBAsVqtpQdANA1tNBotsXKxehKxJBDF4B0bxDmSDoLB7mnA4J+iBYXV5njgwL79Jw4cZ3vfL9wHATyW6AWBClGWZo6OjvL+//xHTRVHk+Pg4b29vKcsy8/Pz6XQ6qaoqw+EwW1tbeXl5yf39fRYWFnJwcPDX2u12O9vb2+l2u9nb28vNzU36/f6XjxRbWVnJw8NDnp6eMjc3l8XFxUxN2dcVgMnlLQcAE6Isy7y+vmZ9fT3Ly8sf14uiyGAw+DhaLElOT09zcnKSs7OzNBqN7O7u5urqKqurq/9cu9ls5uLiIt1uN5ubm7m+vk5VVZmZmfnSjJ1OJ9PT09nY2MjS0lKen5///4EB4Af4NRqNRuMeAgD4eVqtVh4fH3N3dzfuUQDg2/J7OQDwKefn59nZ2cns7Gz6/X4uLy/T6/XGPRYAfGu+dAMAn7K/v5/b29sMBoOsra2l3W7n8PBw3GMBwLcmugEAAKAmNlIDAACAmohuAAAAqInoBgAAgJqIbgAAAKiJ6AYAAICaiG4AAACoiegGAACAmohuAAAAqInoBgAAgJr8BszmUCbCjQ3yAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1085,14 +916,14 @@
    "source": [
     "## နိဂုံးချုပ်\n",
     "\n",
-    "ဒီနိုတ်ဘွတ်မှာ ကျွန်တော်တို့ ဒေတာပေါ်မှာ အခြေခံအလုပ်ဆောင်မှုတွေ ပြုလုပ်ပြီး သင်္ချာဆိုင်ရာ အဆင့်အတန်းတွေကို တွက်ချက်နိုင်တဲ့ နည်းလမ်းတွေကို လေ့လာခဲ့ပါတယ်။ ဒါ့အပြင် သက်သေခံချက်တစ်ချို့ကို အတည်ပြုဖို့ သင်္ချာနဲ့ သက်ဆိုင်ရာ အဆင့်မြင့်နည်းလမ်းတွေကို ဘယ်လိုအသုံးပြုရမလဲ၊ ဒါ့အပြင် ဒေတာနမူနာတစ်ခုရဲ့ အခြေခံပေါ်မှာ မည်သည့် အပြောင်းလဲမှုမဆို အတည်ပြုကွင်းအတိုင်းအတာတွေကို တွက်ချက်နိုင်မလဲဆိုတာကိုလည်း သိရှိလာခဲ့ပါတယ်။\n"
+    "ဒီနိုတ်ဘွတ်မှာ ကျွန်တော်တို့ ဒေတာပေါ်မှာ အခြေခံအလုပ်ဆောင်မှုတွေ ပြုလုပ်ပြီး သင်္ချာဆိုင်ရာ အဆင့်မြင့်လုပ်ဆောင်ချက်တွေကို တွက်ချက်နိုင်တဲ့ နည်းလမ်းတွေကို လေ့လာခဲ့ပါတယ်။ ဒါ့အပြင် သင်္ချာနဲ့ သက်ဆိုင်ရာ စက်ကိရိယာတွေကို သုံးပြီး အချို့သော သီအိုရီတွေကို သက်သေပြနိုင်တဲ့ နည်းလမ်းတွေ၊ ဒါ့အပြင် ဒေတာနမူနာတစ်ခုရဲ့ အခြေခံပေါ်မှာ မည်သည့် အပြောင်းအလဲမဆို အတည်ပြုကွင်းအတိုင်းအတာတွေကို တွက်ချက်နိုင်တဲ့ နည်းလမ်းတွေကိုလည်း သိရှိခဲ့ပါပြီ။\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ဝက်ဘ်ဆိုက်မှတ်ချက်**:  \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုအတွက် ကြိုးစားနေသော်လည်း၊ အလိုအလျောက်ဘာသာပြန်ဆိုမှုများတွင် အမှားများ သို့မဟုတ် မတိကျမှုများ ပါဝင်နိုင်သည်ကို ကျေးဇူးပြု၍ သတိပြုပါ။ မူရင်းဘာသာစကားဖြင့် ရေးသားထားသော စာရွက်စာတမ်းကို အာဏာတည်သော ရင်းမြစ်အဖြစ် သတ်မှတ်သင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူ့ဘာသာပြန်ပညာရှင်များမှ ဘာသာပြန်ဆိုမှုကို အကြံပြုပါသည်။ ဤဘာသာပြန်ကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော နားလည်မှုမှားများ သို့မဟုတ် အဓိပ္ပာယ်မှားများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n"
+    "\n---\n\n**ဝက်ဘ်ဆိုက်မှတ်ချက်**:  \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုအတွက် ကြိုးစားနေသော်လည်း၊ အလိုအလျောက်ဘာသာပြန်ဆိုမှုများတွင် အမှားများ သို့မဟုတ် မတိကျမှုများ ပါဝင်နိုင်သည်ကို ကျေးဇူးပြု၍ သတိပြုပါ။ မူရင်းဘာသာစကားဖြင့် ရေးသားထားသော စာရွက်စာတမ်းကို အာဏာတည်သော ရင်းမြစ်အဖြစ် သတ်မှတ်သင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူပညာရှင်များမှ ဘာသာပြန်ဆိုမှုကို အကြံပြုပါသည်။ ဤဘာသာပြန်ဆိုမှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော နားလည်မှုမှားမှုများ သို့မဟုတ် အဓိပ္ပာယ်မှားမှုများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n"
    ]
   }
  ],
@@ -1115,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:20:23+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T18:00:33+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "my"
   }
diff --git a/translations/my/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/my/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 3d5ce149..c8d07744 100644
--- a/translations/my/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/my/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## မူလိကျ Probability နှင့် Statistics  \n",
-    "## လုပ်ငန်းတာဝန်  \n",
+    "## စွမ်းဆောင်ရည်နှင့် စာရင်းအင်းဆိုင်ရာ သဘောတရားအကြောင်း\n",
+    "## လုပ်ငန်းတာဝန်\n",
     "\n",
-    "ဒီလုပ်ငန်းတာဝန်မှာ ကျွန်တော်တို့ [ဒီနေရာ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) မှာရရှိတဲ့ ဆီးချိုရောဂါရှိသူများရဲ့ ဒေတာစနစ်ကို အသုံးပြုသွားမှာ ဖြစ်ပါတယ်။  \n"
+    "ဒီလုပ်ငန်းတာဝန်မှာ ကျွန်တော်တို့ [ဒီနေရာ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) မှ ယူထားတဲ့ ဆီးချိုရောဂါရှိသူများ၏ ဒေတာဆက်တင်ကို အသုံးပြုသွားမှာ ဖြစ်ပါတယ်။\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "ဒီဒေတာစနစ်တွင် ကော်လံများမှာ အောက်ပါအတိုင်းဖြစ်သည်။\n",
-    "* အသက်နှင့် လိင်သည် အလွယ်တကူနားလည်နိုင်သည်။\n",
-    "* BMI သည် ကိုယ်အလေးချိန်နှင့် အရပ်အမောင်းအချိုးကို ဖော်ပြသည်။\n",
-    "* BP သည် ပျမ်းမျှ သွေးပေါင်ချိန်ကို ဆိုလိုသည်။\n",
-    "* S1 မှ S6 သည် သွေးစစ်ဆေးမှုအမျိုးမျိုးကို ဖော်ပြသည်။\n",
-    "* Y သည် တစ်နှစ်အတွင်း ရောဂါတိုးတက်မှုအရည်အသွေးကို ဖော်ပြသည်။\n",
+    "ဒီဒေတာဆက်တင်ထဲမှာ ကော်လံတွေက အောက်ပါအတိုင်းဖြစ်ပါတယ်-  \n",
+    "* အသက်နဲ့ လိင်က အလွယ်တကူနားလည်နိုင်ပါတယ်  \n",
+    "* BMI က ကိုယ်အလေးချိန်ညွှန်းကိန်းဖြစ်ပါတယ်  \n",
+    "* BP က ပျမ်းမျှ သွေးဖိအားဖြစ်ပါတယ်  \n",
+    "* S1 ကနေ S6 အထိက သွေးစစ်ဆေးမှုအမျိုးမျိုးဖြစ်ပါတယ်  \n",
+    "* Y က တစ်နှစ်အတွင်း ရောဂါတိုးတက်မှုအရည်အချင်းကို တိုင်းတာထားတဲ့ တန်ဖိုးဖြစ်ပါတယ်  \n",
     "\n",
-    "Probability နှင့် Statistics နည်းလမ်းများကို အသုံးပြု၍ ဒီဒေတာစနစ်ကို လေ့လာကြမည်။\n",
+    "ဒီဒေတာဆက်တင်ကို သက်မှတ်နှုန်းနဲ့ သင်္ချာဆိုင်ရာ နည်းလမ်းတွေကို အသုံးပြုပြီး လေ့လာကြရအောင်။\n",
     "\n",
-    "### Task 1: တန်ဖိုးအားလုံးအတွက် ပျမ်းမျှနှင့် အပြောင်းအလဲကို တွက်ချက်ပါ\n"
+    "### တာဝန် ၁: တန်ဖိုးအားလုံးအတွက် ပျမ်းမျှတန်ဖိုးနဲ့ မျိုးကွဲမှုကို တွက်ချက်ပါ  \n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -483,8 +483,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -533,8 +533,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -598,9 +598,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### အလုပ် ၄: အမျိုးမျိုးသော အပြောင်းအလဲများနှင့် ရောဂါတိုးတက်မှု (Y) အကြား ဆက်စပ်မှုကို စမ်းသပ်ပါ\n",
+    "### အလုပ် ၄: အမျိုးမျိုးသောအပြောင်းအလဲများနှင့် ရောဂါတိုးတက်မှု (Y) အကြား ဆက်စပ်မှုကို စမ်းသပ်ပါ\n",
     "\n",
-    "> **အကြံပြုချက်** ဆက်စပ်မှုအမီတာစ် (correlation matrix) သည် ဘယ်တန်ဖိုးများသည် အချင်းချင်းမူတည်နေသည်ကို အထောက်အကူဖြစ်စေသော အချက်အလက်များကို ပေးနိုင်ပါသည်။\n"
+    "> **အကြံပြုချက်** ဆက်စပ်မှု အကျဉ်းချုပ်ဇယားသည် ဘယ်တန်ဖိုးများသည် အချင်းချင်းပေါ်မူတည်နေသည်ကို သိရှိရန် အထောက်အကူဖြစ်စေပါမည်။\n"
    ],
    "metadata": {}
   },
@@ -843,7 +843,7 @@
    "cell_type": "markdown",
    "source": [
     "အနှစ်ချုပ်:  \n",
-    "* Y နှင့် အပြင်းထန်ဆုံး ဆက်စပ်မှုမှာ BMI နဲ့ S5 (သွေးချို) ဖြစ်ပါတယ်။ ဒါဟာ အကျိုးရှိတယ်လို့ ထင်ရပါတယ်။  \n"
+    "* Y နှင့် အပြင်းထန်ဆုံး ဆက်စပ်မှုမှာ BMI နှင့် S5 (သွေးချို) ဖြစ်ပါတယ်။ ဒါဟာ အကျိုးရှိမယ်လို့ ထင်ရပါတယ်။  \n"
    ],
    "metadata": {}
   },
@@ -851,10 +851,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -881,9 +881,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -912,7 +912,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ဝက်ဘ်ဆိုက်မှတ်ချက်**:  \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုအတွက် ကြိုးစားနေပါသော်လည်း၊ အလိုအလျောက်ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် မတိကျမှုများ ပါဝင်နိုင်သည်ကို ကျေးဇူးပြု၍ သတိပြုပါ။ မူရင်းဘာသာစကားဖြင့် ရေးသားထားသော စာရွက်စာတမ်းကို အာဏာတည်သော ရင်းမြစ်အဖြစ် သတ်မှတ်သင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူသားဘာသာပြန်ပညာရှင်များမှ ပြန်ဆိုမှုကို အကြံပြုပါသည်။ ဤဘာသာပြန်မှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော နားလည်မှုမှားများ သို့မဟုတ် အဓိပ္ပာယ်မှားများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n"
+    "\n---\n\n**ဝက်ဘ်ဆိုက်မှတ်ချက်**:  \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုအတွက် ကြိုးစားနေပါသော်လည်း၊ အလိုအလျောက်ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် မမှန်ကန်မှုများ ပါဝင်နိုင်သည်ကို ကျေးဇူးပြု၍ သတိပြုပါ။ မူရင်းစာရွက်စာတမ်းကို ၎င်း၏ မူလဘာသာစကားဖြင့် အာဏာတည်သောရင်းမြစ်အဖြစ် သတ်မှတ်ရန် လိုအပ်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူ့ဘာသာပြန်ပညာရှင်များမှ ပြန်ဆိုမှုကို အကြံပြုပါသည်။ ဤဘာသာပြန်မှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော နားလည်မှုမှားများ သို့မဟုတ် အဓိပ္ပါယ်မှားများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n"
    ]
   }
  ],
@@ -938,8 +938,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:50:58+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T18:01:06+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "my"
   }
diff --git a/translations/ne/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ne/1-Introduction/04-stats-and-probability/assignment.ipynb
index 57169379..470f4986 100644
--- a/translations/ne/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ne/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## सम्भाव्यता र तथ्यांकको परिचय  \n",
     "## असाइनमेन्ट  \n",
     "\n",
-    "यस असाइनमेन्टमा, हामी मधुमेहका बिरामीहरूको डाटासेट प्रयोग गर्नेछौं जुन [यहाँबाट लिइएको हो](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।  \n"
+    "यस असाइनमेन्टमा, हामी मधुमेहका बिरामीहरूको डेटासेट प्रयोग गर्नेछौं जुन [यहाँबाट लिइएको हो](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।  \n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "यस डेटासेटमा, स्तम्भहरू निम्नानुसार छन्:  \n",
-    "* उमेर र लिङ्ग स्वाभाविक रूपमा स्पष्ट छन्  \n",
-    "* BMI भनेको शरीरको मास सूचकांक हो  \n",
-    "* BP भनेको औसत रक्तचाप हो  \n",
-    "* S1 देखि S6 सम्म विभिन्न रक्त परीक्षणका मापनहरू हुन्  \n",
-    "* Y भनेको एक वर्षको अवधिमा रोगको प्रगतिको गुणात्मक मापन हो  \n",
+    "यस डेटासेटमा स्तम्भहरू निम्न प्रकारका छन्:\n",
+    "* उमेर र लिङ्ग स्पष्ट छन्\n",
+    "* BMI भनेको शरीरको मास सूचकांक हो\n",
+    "* BP भनेको औसत रक्तचाप हो\n",
+    "* S1 देखि S6 विभिन्न रक्त मापनहरू हुन्\n",
+    "* Y भनेको एक वर्षको अवधिमा रोगको प्रगतिको गुणात्मक मापन हो\n",
     "\n",
-    "आउनुहोस्, सम्भाव्यता र तथ्याङ्कका विधिहरू प्रयोग गरेर यस डेटासेटको अध्ययन गरौँ।  \n",
+    "आउनुहोस्, सम्भाव्यता र तथ्याङ्कका विधिहरू प्रयोग गरेर यस डेटासेटको अध्ययन गरौं।\n",
     "\n",
-    "### कार्य १: सबै मानहरूको औसत र विचलन गणना गर्नुहोस्  \n"
+    "### कार्य १: सबै मानहरूको औसत मान र विचलन गणना गर्नुहोस्\n"
    ],
    "metadata": {}
   },
@@ -198,9 +198,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### कार्य ४: विभिन्न चरहरू र रोगको प्रगति (Y) बीचको सम्बन्ध परीक्षण गर्नुहोस्\n",
+    "### कार्य ४: विभिन्न भेरिएबलहरू र रोगको प्रगति (Y) बीचको सम्बन्ध परीक्षण गर्नुहोस्\n",
     "\n",
-    "> **सूचना** सम्बन्ध म्याट्रिक्सले कुन मानहरू परस्पर निर्भर छन् भन्ने सबैभन्दा उपयोगी जानकारी प्रदान गर्नेछ।\n"
+    "> **संकेत** सम्बन्ध म्याट्रिक्सले कुन मानहरू परनिर्भर छन् भन्ने बारेमा सबैभन्दा उपयोगी जानकारी दिन्छ।\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव सटीकता सुनिश्चित गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटि वा अशुद्धता हुन सक्छ। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याको लागि हामी जिम्मेवार हुने छैनौं।  \n"
+    "\n---\n\n**अस्वीकरण**:  \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव सटीकता सुनिश्चित गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटिहरू वा अशुद्धताहरू हुन सक्छन्। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं।\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:44:25+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:22:31+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ne"
   }
diff --git a/translations/ne/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ne/1-Introduction/04-stats-and-probability/notebook.ipynb
index 87917161..3ca3f83d 100644
--- a/translations/ne/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ne/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# सम्भाव्यता र तथ्यांकको परिचय  \n",
-    "यस नोटबुकमा, हामी पहिले चर्चा गरिएका केही अवधारणाहरूको अभ्यास गर्नेछौं। सम्भाव्यता र तथ्यांकका धेरै अवधारणाहरू `numpy` र `pandas` जस्ता पाइथनका प्रमुख डाटा प्रशोधन पुस्तकालयहरूमा राम्रोसँग प्रतिनिधित्व गरिएका छन्।  \n"
+    "यस नोटबुकमा, हामी पहिले चर्चा गरिएका केही अवधारणाहरूसँग खेल्नेछौं। सम्भाव्यता र तथ्यांकका धेरै अवधारणाहरू `numpy` र `pandas` जस्ता पाइथनका प्रमुख डाटा प्रशोधन पुस्तकालयहरूमा राम्रोसँग प्रतिनिधित्व गरिएका छन्।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## र्‍यान्डम भेरिएबल्स र वितरणहरू  \n",
-    "हामी ० देखि ९ सम्मको युनिफर्म वितरणबाट ३० मानहरूको नमूना निकालेर सुरु गरौं। साथै, हामी यसका औसत (mean) र भिन्नता (variance) पनि गणना गर्नेछौं।  \n"
+    "## र्‍यान्डम भेरिएबलहरू र वितरणहरू  \n",
+    "हामी ० देखि ९ सम्मको युनिफर्म वितरणबाट ३० मानहरूको नमूना निकालेर सुरु गरौं। साथै, हामी औसत र भिन्नता पनि गणना गर्नेछौं।  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -84,201 +82,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## वास्तविक डाटा विश्लेषण गर्दै\n",
+    "## वास्तविक डाटाको विश्लेषण\n",
     "\n",
-    "औसत र भिन्नता वास्तविक संसारको डाटा विश्लेषण गर्दा धेरै महत्त्वपूर्ण हुन्छ। आउनुहोस्, बेसबल खेलाडीहरूको डाटा [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) बाट लोड गरौं।\n"
+    "औसत (Mean) र भिन्नता (Variance) वास्तविक संसारको डाटाको विश्लेषण गर्दा धेरै महत्त्वपूर्ण हुन्छन्। अब, बेसबल खेलाडीहरूको डाटा [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) बाट लोड गरौं।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "हामी यहाँ डेटा विश्लेषणको लागि [**Pandas**](https://pandas.pydata.org/) नामक प्याकेज प्रयोग गर्दैछौं। यस कोर्सको पछि हामी Pandas र Python मा डेटा संग काम गर्ने बारेमा थप कुरा गर्नेछौं।\n",
+    "हामी यहाँ डेटा विश्लेषणका लागि [**Pandas**](https://pandas.pydata.org/) नामक प्याकेज प्रयोग गर्दैछौं। यस कोर्समा पछि हामी Pandas र Python मा डेटा संग काम गर्ने बारेमा थप कुरा गर्नेछौं।\n",
     "\n",
-    "आउनुहोस् उमेर, उचाइ र तौलको औसत मानहरू गणना गरौं:\n"
+    "आउनुहोस्, उमेर, उचाइ र तौलको औसत मानहरू गणना गरौं:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "अब उचाइमा ध्यान केन्द्रित गरौं, र मानक विचलन र भिन्नता गणना गरौं:\n"
+    "अब उचाइमा ध्यान केन्द्रित गरौं, र मानक विचलन र विचरण गणना गरौं:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "औसतको अतिरिक्त, माध्य मान र चतुर्थांशहरू हेर्नु पनि तर्कसंगत हुन्छ। तिनीहरूलाई **बक्स प्लट** प्रयोग गरेर दृश्यात्मक बनाउन सकिन्छ:\n"
+    "औसतको अतिरिक्त, मध्य मान र चतुर्थांशहरू हेर्नु उपयुक्त हुन्छ। तिनीहरूलाई **बक्स प्लट** प्रयोग गरेर दृश्यात्मक बनाउन सकिन्छ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **नोट**: यो चित्रले सुझाव दिन्छ कि औसतमा, पहिलो बेसम्यानहरूको उचाइ दोस्रो बेसम्यानहरूको उचाइभन्दा बढी छ। पछि हामी सिक्नेछौं कि कसरी यो परिकल्पनालाई औपचारिक रूपमा परीक्षण गर्न सकिन्छ, र कसरी हाम्रो डाटाले यो देखाउन सांख्यिकीय रूपमा महत्त्वपूर्ण छ भनेर प्रमाणित गर्न सकिन्छ।  \n",
+    "> **नोट**: यो चित्रले सुझाव दिन्छ कि औसतमा, पहिलो बेसम्यानहरूको उचाइ दोस्रो बेसम्यानहरूको उचाइभन्दा बढी छ। पछि हामी सिक्नेछौं कि कसरी यो परिकल्पनालाई औपचारिक रूपमा परीक्षण गर्न सकिन्छ, र कसरी हाम्रो डाटालाई सांख्यिकीय रूपमा महत्त्वपूर्ण देखाउन सकिन्छ।  \n",
     "\n",
     "उमेर, उचाइ र तौल सबै निरन्तर र्‍यान्डम भेरिएबलहरू हुन्। तपाईंलाई के लाग्छ, तिनीहरूको वितरण कस्तो छ? पत्ता लगाउने राम्रो तरिका भनेको मानहरूको हिस्टोग्राम बनाउनु हो:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "यद्यपि वास्तविक जीवनका अधिकांश मानहरू सामान्य रूपमा वितरण गरिएका हुन्छन्, नमूना डाटा उत्पन्न गर्नको लागि हामीले समान रूपमा वितरण गरिएको र्यान्डम नम्बर जेनेरेटर प्रयोग गर्नु हुँदैन। यदि हामीले समान वितरण (जसलाई `np.random.rand` द्वारा उत्पन्न गरिन्छ) प्रयोग गरेर तौलहरू उत्पन्न गर्न प्रयास गर्यौं भने यस्तो हुन्छ:\n"
+    "वास्तविक जीवनका अधिकांश मानहरू सामान्य रूपमा वितरण गरिएका हुन्छन्, त्यसैले नमूना डाटा उत्पन्न गर्नको लागि हामीले समान रूपमा वितरण गरिएको र्यान्डम नम्बर जेनेरेटर प्रयोग गर्नु हुँदैन। यदि हामीले समान वितरण (जसलाई `np.random.rand` द्वारा उत्पन्न गरिन्छ) प्रयोग गरेर तौलहरू उत्पन्न गर्न प्रयास गर्यौं भने यस्तो हुन्छ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## विश्वास अन्तरालहरू\n",
     "\n",
-    "अब हामी बेसबल खेलाडीहरूको तौल र उचाइका लागि विश्वास अन्तरालहरू गणना गर्नेछौं। हामी यो कोड प्रयोग गर्नेछौं [यो stackoverflow छलफलबाट](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "अब हामी बेसबल खेलाडीहरूको तौल र उचाइका लागि विश्वास अन्तरालहरू गणना गर्नेछौं। हामी यो कोड प्रयोग गर्नेछौं [यो StackOverflow छलफलबाट](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,35 +619,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ttest_ind` फंक्शनले फर्काउने दुई मानहरू हुन्:\n",
-    "* p-value लाई दुई वितरणहरूको औसत समान हुने सम्भावना भनेर मान्न सकिन्छ। हाम्रो अवस्थामा, यो धेरै कम छ, जसको अर्थ यो हो कि पहिलो बेसम्यानहरू अग्लो हुने बलियो प्रमाण छ।\n",
-    "* t-value भनेको t-test मा प्रयोग गरिने सामान्यीकृत औसत भिन्नताको मध्यवर्ती मान हो, र यो दिइएको विश्वासस्तरको लागि थ्रेसहोल्ड मानसँग तुलना गरिन्छ।\n"
+    "`ttest_ind` फङ्क्सनले फर्काउने दुई मानहरू हुन्:  \n",
+    "* p-value लाई दुई वितरणहरूको औसत समान हुने सम्भावना भनेर मान्न सकिन्छ। हाम्रो अवस्थामा, यो धेरै कम छ, जसको अर्थ पहिलो बेसम्यानहरू अग्लो हुने बलियो प्रमाण छ।  \n",
+    "* t-value भनेको t-test मा प्रयोग गरिने सामान्यीकृत औसत भिन्नताको मध्यवर्ती मान हो, र यो दिइएको विश्वासस्तरको लागि थ्रेसहोल्ड मानसँग तुलना गरिन्छ।  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## केन्द्रीय सीमा प्रमेयको प्रयोग गरेर सामान्य वितरणको अनुकरण\n",
+    "## केन्द्रीय सिमाको प्रमेयको प्रयोग गरेर सामान्य वितरणको अनुकरण\n",
     "\n",
-    "Python मा रहेको pseudo-random generator ले हामीलाई समान वितरण प्रदान गर्न डिजाइन गरिएको छ। यदि हामी सामान्य वितरणको लागि एक जनरेटर बनाउन चाहन्छौं भने, हामी केन्द्रीय सीमा प्रमेय प्रयोग गर्न सक्छौं। सामान्य रूपमा वितरण गरिएको मान प्राप्त गर्नका लागि, हामी समान रूपमा उत्पन्न गरिएको नमूनाको औसत गणना गर्नेछौं।\n"
+    "Python मा रहेको pseudo-random जनरेटरले हामीलाई समान वितरण प्रदान गर्न डिजाइन गरिएको छ। यदि हामी सामान्य वितरणको लागि जनरेटर बनाउन चाहन्छौं भने, हामी केन्द्रीय सिमाको प्रमेय प्रयोग गर्न सक्छौं। सामान्य वितरण भएको मान प्राप्त गर्नका लागि, हामी समान-जनित नमूनाको औसत गणना गर्नेछौं।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -833,14 +672,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "अब ती क्रमहरूको सहसंबन्ध र सम्बन्ध गणना गरौं। `np.cov` ले हामीलाई तथाकथित **सहसंबन्ध म्याट्रिक्स** दिनेछ, जुन बहुविध चरहरूमा सहसंबन्धको विस्तार हो। सहसंबन्ध म्याट्रिक्स $M$ को तत्त्व $M_{ij}$ इनपुट चरहरू $X_i$ र $X_j$ बीचको सम्बन्ध हो, र विकर्ण मानहरू $M_{ii}$ $X_{i}$ को विचलन हो। त्यस्तै, `np.corrcoef` ले हामीलाई **सम्बन्ध म्याट्रिक्स** दिनेछ।\n"
+    "अब ती क्रमहरूको सहसंबन्ध र सम्बन्ध गणना गरौं। `np.cov` ले हामीलाई तथाकथित **सहसंबन्ध म्याट्रिक्स** दिनेछ, जुन बहुविध चरहरूमा सहसंबन्धको विस्तार हो। सहसंबन्ध म्याट्रिक्स $M$ को तत्त्व $M_{ij}$ इनपुट चरहरू $X_i$ र $X_j$ बीचको सम्बन्ध हो, र विकर्ण मानहरू $M_{ii}$ $X_{i}$ को विचलन हो। त्यसैगरी, `np.corrcoef` ले हामीलाई **सम्बन्ध म्याट्रिक्स** दिनेछ।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "यस अवस्थामा, सम्बन्ध अलिकति सानो छ, तर यो अझै पनि धेरै उच्च छ। अब, सम्बन्धलाई अझ कम स्पष्ट बनाउनका लागि, हामीले तलबमा केही अनियमित चर थपेर केही अतिरिक्त अनियमितता थप्न चाहन सक्छौं। हेरौं के हुन्छ:\n"
+    "यस अवस्थामा, सम्बन्ध अलि सानो छ, तर यो अझै पनि धेरै उच्च छ। अब, सम्बन्धलाई अझ कम स्पष्ट बनाउनको लागि, हामी तलबमा केही अनियमित चर जोडेर केही अतिरिक्त अनियमितता थप्न चाहन सक्छौं। हेरौं के हुन्छ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "के dots यसरी ठाडो रेखामा किन मिल्छन् भनेर अनुमान गर्न सक्नुहुन्छ?\n",
+    "के तपाईं अनुमान गर्न सक्नुहुन्छ किन बिन्दुहरू यसरी ठाडो रेखाहरूमा मिल्छन्?\n",
     "\n",
-    "हामीले कृत्रिम रूपमा निर्माण गरिएको अवधारणा जस्तै तलब र देखिएको चर *उचाइ* बीचको सम्बन्धलाई अवलोकन गरेका छौं। अब, दुई देखिएका चरहरू, जस्तै उचाइ र तौल, पनि सम्बन्धित छन् कि छैनन् भनेर हेरौं:\n"
+    "हामीले तलब जस्तो कृत्रिम रूपमा सिर्जना गरिएको अवधारणा र अवलोकन गरिएको चर *उचाइ* बीचको सम्बन्धलाई अवलोकन गरेका छौं। अब हेरौं कि दुई अवलोकन गरिएका चरहरू, जस्तै उचाइ र तौल, पनि आपसमा सम्बन्धित छन् कि छैनन्:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "दुर्भाग्यवश, हामीले कुनै नतिजा प्राप्त गर्न सकेनौं - केवल केही अजीब `nan` मानहरू मात्र देख्यौं। यसको कारण के हो भने हाम्रो श्रृंखलाका केही मानहरू अपरिभाषित छन्, जसलाई `nan` द्वारा प्रतिनिधित्व गरिएको छ, जसले गर्दा अपरेशनको नतिजा पनि अपरिभाषित हुन्छ। म्याट्रिक्स हेर्दा हामी देख्न सक्छौं कि `Weight` समस्या भएको स्तम्भ हो, किनभने `Height` मानहरूको आत्म-सम्बन्ध गणना गरिएको छ।\n",
+    "दुर्भाग्यवश, हामीले कुनै परिणाम प्राप्त गर्न सकेनौं - केवल केही अजीब `nan` मानहरू। यसको कारण हाम्रो श्रृंखलाका केही मानहरू अपरिभाषित छन्, जसलाई `nan` द्वारा प्रतिनिधित्व गरिएको छ, जसले गर्दा अपरेशनको परिणाम पनि अपरिभाषित हुन्छ। म्याट्रिक्स हेर्दा हामी देख्न सक्छौं कि `Weight` समस्या भएको स्तम्भ हो, किनकि `Height` मानहरूको आत्म-सम्बन्ध गणना गरिएको छ।\n",
     "\n",
-    "> यो उदाहरणले **डाटा तयारी** र **सफाई** को महत्त्व देखाउँछ। उचित डाटा बिना हामीले केही पनि गणना गर्न सक्दैनौं।\n",
+    "> यो उदाहरणले **डाटा तयारी** र **सफाई** को महत्त्व देखाउँछ। उचित डाटा बिना हामी केही पनि गणना गर्न सक्दैनौं।\n",
     "\n",
-    "आउनुहोस्, `fillna` विधि प्रयोग गरेर हराएका मानहरू भर्नुहोस्, र सम्बन्ध गणना गरौं:\n"
+    "आउनुहोस्, `fillna` विधि प्रयोग गरेर हराएका मानहरू भर्ने र सम्बन्ध गणना गर्ने प्रयास गरौं:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## निष्कर्ष\n",
     "\n",
-    "यस नोटबुकमा, हामीले तथ्यांकमा आधारित आधारभूत कार्यहरू कसरी गर्ने भनेर सिक्यौं। हामीले केही परिकल्पनाहरू प्रमाणित गर्न गणित र तथ्यांकको सुदृढ उपकरण कसरी प्रयोग गर्ने भन्ने कुरा पनि बुझेका छौं। साथै, कुनै पनि भेरिएबलहरूको लागि, दिइएको डेटा नमूनाबाट विश्वास अन्तरालहरू कसरी गणना गर्ने भन्ने कुरा पनि सिक्यौं।\n"
+    "यस नोटबुकमा, हामीले डाटामा आधारभूत कार्यहरू गरेर सांख्यिकीय कार्यहरू कसरी गणना गर्ने भन्ने कुरा सिक्यौं। अब हामीलाई थाहा छ कि केही परिकल्पनाहरू प्रमाणित गर्न गणित र सांख्यिकीको एक सुदृढ उपकरण कसरी प्रयोग गर्ने, र डाटा नमुनाको आधारमा कुनै पनि चरहरूको लागि विश्वास अन्तराल कसरी गणना गर्ने।\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरेर अनुवाद गरिएको छ। हामी शुद्धताको लागि प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटि वा अशुद्धता हुन सक्छ। यसको मूल भाषा मा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्वपूर्ण जानकारीको लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याको लागि हामी जिम्मेवार हुने छैनौं।\n"
+    "\n---\n\n**अस्वीकरण**:  \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव शुद्धता सुनिश्चित गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादमा त्रुटिहरू वा अशुद्धताहरू हुन सक्छन्। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं।\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:21:32+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:22:20+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ne"
   }
diff --git a/translations/ne/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ne/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 5be49f51..479aa839 100644
--- a/translations/ne/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ne/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## सम्भाव्यता र तथ्यांकको परिचय  \n",
     "## असाइनमेन्ट  \n",
     "\n",
-    "यस असाइनमेन्टमा, हामी मधुमेहका बिरामीहरूको डेटासेट प्रयोग गर्नेछौं जुन [यहाँबाट लिइएको छ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।  \n"
+    "यस असाइनमेन्टमा, हामी मधुमेहका बिरामीहरूको डेटासेट प्रयोग गर्नेछौं जुन [यहाँबाट लिइएको हो](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।  \n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "यस डेटासेटमा स्तम्भहरू निम्न प्रकारका छन्:  \n",
-    "* उमेर र लिङ्ग स्वाभाविक रूपमा स्पष्ट छन्  \n",
-    "* BMI भनेको शरीरको मास सूचकांक हो  \n",
-    "* BP भनेको औसत रक्तचाप हो  \n",
-    "* S1 देखि S6 सम्म विभिन्न रक्त परीक्षणका मापनहरू हुन्  \n",
-    "* Y भनेको एक वर्षको अवधिमा रोगको प्रगतिको गुणात्मक मापन हो  \n",
+    "यस डेटासेटमा स्तम्भहरू निम्न प्रकारका छन्:\n",
+    "* उमेर र लिङ्ग स्पष्ट छन्\n",
+    "* BMI भनेको शरीरको मास सूचकांक हो\n",
+    "* BP भनेको औसत रक्तचाप हो\n",
+    "* S1 देखि S6 विभिन्न रक्त मापनहरू हुन्\n",
+    "* Y भनेको एक वर्षको अवधिमा रोगको प्रगतिको गुणात्मक मापन हो\n",
     "\n",
-    "आउनुहोस्, सम्भाव्यता र तथ्याङ्कका विधिहरू प्रयोग गरेर यस डेटासेटको अध्ययन गरौँ।  \n",
+    "आउनुहोस्, सम्भाव्यता र तथ्यांकका विधिहरू प्रयोग गरेर यस डेटासेटको अध्ययन गरौं।\n",
     "\n",
-    "### कार्य १: सबै मानहरूको औसत र विचलन गणना गर्नुहोस्  \n"
+    "### कार्य १: सबै मानहरूको औसत मान र विचलन गणना गर्नुहोस्\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -590,10 +590,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "निष्कर्षहरू:  \n",
-    "* उमेर - सामान्य  \n",
-    "* लिङ्ग - समान  \n",
-    "* BMI, Y - भन्न गाह्रो  \n"
+    "निष्कर्षहरू:\n",
+    "* उमेर - सामान्य\n",
+    "* लिङ्ग - समान\n",
+    "* BMI, Y - भन्न गाह्रो\n"
    ],
    "metadata": {}
   },
@@ -602,7 +602,7 @@
    "source": [
     "### कार्य ४: विभिन्न भेरिएबलहरू र रोगको प्रगति (Y) बीचको सम्बन्ध परीक्षण गर्नुहोस्\n",
     "\n",
-    "> **संकेत** सम्बन्ध म्याट्रिक्सले कुन मानहरू परस्पर निर्भर छन् भन्ने सबैभन्दा उपयोगी जानकारी दिन्छ।\n"
+    "> **संकेत** सम्बन्ध म्याट्रिक्सले कुन मानहरू परनिर्भर छन् भन्ने बारेमा सबैभन्दा उपयोगी जानकारी दिन्छ।\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**अस्वीकरण**:  \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव शुद्धताको प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटि वा अशुद्धता हुन सक्छ। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं।  \n"
+    "\n---\n\n**अस्वीकरण**:  \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव सटीकता सुनिश्चित गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटि वा अशुद्धता हुन सक्छ। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याको लागि हामी जिम्मेवार हुने छैनौं।\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:51:17+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:22:47+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ne"
   }
diff --git a/translations/nl/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/nl/1-Introduction/04-stats-and-probability/assignment.ipynb
index aef83536..aa184698 100644
--- a/translations/nl/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/nl/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "In deze dataset zijn de kolommen als volgt:  \n",
-    "* Leeftijd en geslacht spreken voor zich  \n",
-    "* BMI is de body mass index  \n",
-    "* BP is de gemiddelde bloeddruk  \n",
-    "* S1 tot en met S6 zijn verschillende bloedmetingen  \n",
-    "* Y is de kwalitatieve maat voor ziekteprogressie over één jaar  \n",
+    "In deze dataset zijn de kolommen als volgt:\n",
+    "* Leeftijd en geslacht spreken voor zich\n",
+    "* BMI is de body mass index\n",
+    "* BP is de gemiddelde bloeddruk\n",
+    "* S1 tot en met S6 zijn verschillende bloedmetingen\n",
+    "* Y is de kwalitatieve maat voor ziekteprogressie over één jaar\n",
     "\n",
-    "Laten we deze dataset bestuderen met behulp van methoden uit de waarschijnlijkheidsleer en statistiek.  \n",
+    "Laten we deze dataset bestuderen met behulp van methoden uit de kansrekening en statistiek.\n",
     "\n",
-    "### Taak 1: Bereken gemiddelde waarden en variantie voor alle waarden  \n"
+    "### Taak 1: Bereken gemiddelde waarden en variantie voor alle waarden\n"
    ],
    "metadata": {}
   },
@@ -200,7 +200,7 @@
    "source": [
     "### Taak 4: Test de correlatie tussen verschillende variabelen en ziekteprogressie (Y)\n",
     "\n",
-    "> **Tip** Een correlatiematrix geeft je de meest bruikbare informatie over welke waarden afhankelijk zijn.\n"
+    "> **Hint** Een correlatiematrix geeft je de meest bruikbare informatie over welke waarden afhankelijk zijn.\n"
    ],
    "metadata": {}
   },
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:44:44+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:39:55+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "nl"
   }
diff --git a/translations/nl/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/nl/1-Introduction/04-stats-and-probability/notebook.ipynb
index 3cff46e8..047f8e93 100644
--- a/translations/nl/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/nl/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Inleiding tot Kansrekening en Statistiek\n",
-    "In dit notitieboek gaan we aan de slag met enkele concepten die we eerder hebben besproken. Veel concepten uit kansrekening en statistiek worden goed ondersteund in belangrijke bibliotheken voor gegevensverwerking in Python, zoals `numpy` en `pandas`.\n"
+    "# Introductie tot Kansrekening en Statistiek\n",
+    "In dit notebook gaan we aan de slag met enkele concepten die we eerder hebben besproken. Veel concepten uit kansrekening en statistiek zijn goed vertegenwoordigd in belangrijke bibliotheken voor gegevensverwerking in Python, zoals `numpy` en `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,168 +89,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Naast het gemiddelde is het zinvol om naar de mediaanwaarde en kwartielen te kijken. Deze kunnen worden gevisualiseerd met behulp van een **boxplot**:\n"
+    "Naast het gemiddelde is het logisch om naar de mediaanwaarde en kwartielen te kijken. Deze kunnen worden weergegeven met een **boxplot**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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/6SS5n/zkJx1+TJMnT3aSXGNjY6vH1NDQ4CS5X/3qV20eU/NxTJs2zTmX/u9T878TP/vZz4qyU2vH9H7+7jX/f2reV55+TMuXL3eS3H333Vdwx1RfX+8kufr6eteWnI+at3bFu0+fPjp69KjOP//8gv6Ozdq1azVq1Cg9+eSTuuSSS8x8V9fzPB0+fFg9evRINSj2Ywpip444pmg0qkOHDqlPnz5KJpMFfUx1dXWaNGmSVq9erREjRhTEY0SxXVGI7tmoTk9UK3HPCiW7X97imCKRiG57/jbtPLZTnvvfZ08tCZXo0vMu1ROfeELl5eUFd0yF3Gnjxo0aPny45s+fr8suuyyv51M8Hte+fft00UUXpZ4Nl8e94B5TMT2WW+20a9cuTZo0SWvWrNHw4cPb/bg3ceJEPfvss7rjjjv01FNPyfM87dq1S/369ZMkTZo0SQsWLNDNN98cuCveo0ePTv2bf/oxvfLKKxo5cqReeumlDr/ivXz5clVXV6u2tlaDBg1KO6YNGzZo2LBhevnll9t1xfv04/jvf5/WrVuXdhz/3SkWi+ntt9/WJZdcIs/zCq5TR17xHj16tNauXavrrrsu7ZjWr1+vESNG6MUXXyy4K96nTp1q/49Rt7k1Pwup7Svep8vkuwJ+27x5s5OU+s6KFdFo1M2dO9dFo1G/l4IcK6bWVs/HjhT710bnplW99+dp1u1b1+rV7uaXdfvW+bDi4ubn39liOreRPXoXvvf7eNDY2OgkuVAo5E6ePNmi9cmTJ10oFEpdnQ2SRCLhPvzhD7uxY8e6ZDLZ4n3JZNKNHTvWfeQjH3GJRKKg7zvbz2Xl3Pazd7Yy2dsG96f08b5FIhHdd999qe8EIbhobUtZaVmLP5s55zRn6xyFFGr140IKac7WOTzDeRHh3LaF3sHVpUsXDR48WM45VVRU6Atf+IKuvfZafeELX0g9sdrgwYMD9cRqkhQOhzVr1iw999xzGj9+fItnuR4/fryee+45PfTQQzl5oq2OvO9sP5eVc9vP3vmU8ca7qalJ27Zt07Zt2yRJ//znP7Vt2zbt3bu3o9cGnySTSe3YsSNQv7AeraO1Lcn/jBw3/9ks7sV16PghObW+sXZyOnT8kOKejd8jGgSc27bQO9g2btyY2nz/7ne/01VXXaXf/e53qU33xo0b/V5iTkyYMEHPPPOMXnvtNQ0bNkxVVVUaNmyYtm/frmeeeUYTJkwoivvO5nNZOrf97J0vGT+r+d/+9jeNHj069d/f+MY3JEl333235s+f32ELg3+SyaTWr1+viy++uOi/s4Szo7UtnpdU+L/+bBYJR/SH//MHHTt17Iwfe16n8xQJB/s77kHCuW0LvYNv48aNampq0u23364tW7Zo0KBB+v3vfx+4K92nmzBhgsaNG6e1a9fq4MGD+tCHPqThw4fn5e95R973+/1c1s5tP3vnQ8Yb71GjRjFuGHCRSET33HOP38tAHtDaljONmktSj8491KNzj3wvCTnCuW0LvW3o0qWLampq/F5G3oXDYY0aNaro7/v9fC6L57afvXONn/E+i/79+2vz5s3q37+/30vJq2QyqS1btpgYa7GO1racadQcwcO5bQu9C19HfU1Ja1voHSxsvM+ioqJCgwYNUkVFhd9LyatkMqnXX3+dk9wAWtvieckWfyK4OLdtoXfh66ivKWltC72DJeNRcwRfJBLRpEmT/F4G8oDWtpxt1BzBwrltC73toLUt9A4WNt5Ik0gktGnTJg0ePFilpfwVCbJian3ixAlJ0pYtW3xeSfGK/HunLpe0fccOxQ4xbp5rO3fu9O2+i+ncRvbobQetbaF3sFAQaZxz2rdvn66++mq/l4IcK6bWb7zxhiRp8uTJPq+keF3Zo0Rb7u2iu+66S1vZeOdNZWVl3u+zmM5tZI/edtDaFnoHS8jl+SnKGxoa1LVrV9XX16uqqiqfdw2giB09elRLly5V//79zT3vQkcJJU6pU9NenepyoVxpJ7+XY0JlZaUuvvhiv5cBAAByIJO9LVe8kSaRSGjdunW67rrrGGsJuGJq3a1bN33xi1/0exlF7b3eMV036JqC743sFNO5jezR2w5a20LvYOFZzZHGOaeGhgZ+X7sBtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQoUz2tlzxRppEIqEXXnhBiUTC76Ugx2htC73toLUt9LaD1rbQO1jYeAMAAAAAkEOMmgMAAAAAkCFGzZGVeDyumpoaxeNxv5eCHKO1LfS2g9a20NsOWttC72Bh4400oVBIVVVVCoVCfi8FOUZrW+htB61tobcdtLaF3sHCqDkAAAAAABli1BxZicfjWrRoEWMtBtDaFnrbQWtb6G0HrW2hd7Cw8UaaUCik3r17M9ZiAK1tobcdtLaF3nbQ2hZ6Bwuj5gAAAAAAZIhRc2QlFovp6aefViwW83spyDFa20JvO2htC73toLUt9A4WNt5IEw6HNWDAAIXDYb+XghyjtS30toPWttDbDlrbQu9gYdQcAAAAAIAMMWqOrMRiMc2bN4+xFgNobQu97aC1LfS2g9a20DtY2HgjTTgc1rXXXstYiwG0toXedtDaFnrbQWtb6B0sjJoDAAAAAJAhRs2RlVgspkceeYSxFgNobQu97aC1LfS2g9a20DtY2HgjTWlpqaqrq1VaWur3UpBjtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQIUbNkZVoNKqHH35Y0WjU76Ugx2htC73toLUt9LaD1rbQO1i44o00nudp//796tWrl0pK+N5MkNHaFnrbQWtb6G0HrW2hd+HLZG/LxhsAAAAAgAwxao6sRKNRzZgxg7EWA2htC73toLUt9LaD1rbQO1i44o00nufp6NGj6tatG2MtAUdrW+htB61tobcdtLaF3oWPUXMAAAAAAHKIUXNkJRqN6sEHH2SsxQBa20JvO2htC73toLUt9A4WrngjjXNOjY2NqqysVCgU8ns5yCFa20JvO2htC73toLUt9C58XPFG1srLy/1eAvKE1rbQ2w5a20JvO2htC72Dg4030sRiMc2cOVOxWMzvpSDHaG0Lve2gtS30toPWttA7WBg1RxrnnGKxmCKRCGMtAUdrW+htB61tobcdtLaF3oWPUXNkjSdxsIPWttDbDlrbQm87aG0LvYODjTfSxGIxzZ49m7EWA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMrnufpyJEj8jzP76Ugx2htC73toLUt9LaD1rbQO1jYeCNNPB7XvHnzFI/H/V4KcozWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmy4nme3nnnHcZaDKC1LfS2g9a20NsOWttC72Bh44008XhcixYtYqzFAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5siK53navXs3Yy0G0NoWettBa1vobQetbaF3sLDxRppEIqEXX3xRiUTC76Ugx2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSO3bsUDKZ9HspyDFa20JvO2htC73toLUt9A4WNt5Ik0wmtX79ek5yA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSW7Zs4btrBtDaFnrbQWtb6G0HrW2hd7Cw8UaaZDKp119/nZPcAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5shKIpFQbW0tvzPQAFrbQm87aG0Lve2gtS30DhY23kjjnNO+ffuU52EI+IDWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmykkgktGrVKsZaDKC1LfS2g9a20NsOWttC72Bh4400zjk1NDQw1mIArW2htx20toXedtDaFnoHC6PmAAAAAABkiFFzZCWRSOiFF15grMUAWttCbztobQu97aC1LfQOFjbeAAAAAADkEKPmAAAAAABkKJO9bWme1pTSvM9vaGjI912jneLxuJYvX65PfOITKisr83s5yCFa20JvO2htC73toLUt9C58zXva9lzLzvvGu7GxUZLUp0+ffN81AAAAAAAdqrGxUV27dj3rbfI+au55ng4cOKDKykqFQqF83jXaqaGhQX369NE777zDjwMEHK1tobcdtLaF3nbQ2hZ6Fz7nnBobG9WzZ0+VlJz96dPyfsW7pKREvXv3zvfd4n2oqqriJDeC1rbQ2w5a20JvO2htC70LW1tXupvxrOYAAAAAAOQQG28AAAAAAHKIjTfSlJeXa9q0aSovL/d7KcgxWttCbztobQu97aC1LfQOlrw/uRoAAAAAAJZwxRsAAAAAgBxi4w0AAAAAQA6x8QYAAAAAIIfYeAMAAAAAkENsvI1Ys2aNxo4dq549eyoUCmnp0qVpt9m5c6duvvlmde3aVZ07d9bgwYO1d+/e1PtPnTqlKVOm6Pzzz1eXLl10yy236PDhw3k8CrRHW62bmpo0depU9e7dW+ecc44GDBigRx99tMVtaF08ZsyYocGDB6uyslLdu3fX+PHj9eabb7a4TXt67t27VzfddJMqKirUvXt3ffvb31YikcjnoaANbbU+duyYvvrVr6pfv34655xzdOGFF+prX/ua6uvrW3weWheH9pzbzZxz+tSnPtXqYz69C197W9fW1ur6669X586dVVVVpREjRujkyZOp9x87dkx33HGHqqqqdO655+qee+5RU1NTPg8F7dCe3ocOHdKdd96pHj16qHPnzho0aJD+9Kc/tbgNvYsPG28jjh8/riuuuEJz585t9f1vvfWWrrvuOvXv31+rVq3SP/7xD/3gBz9Qp06dUre5//779ec//1mLFi3S6tWrdeDAAU2YMCFfh4B2aqv1N77xDS1btkxPP/20du7cqa9//euaOnWqampqUrehdfFYvXq1pkyZovXr12v58uWKx+Oqrq7W8ePHU7dpq2cymdRNN92kWCymV199VU888YTmz5+vH/7wh34cEs6grdYHDhzQgQMH9NBDD2n79u2aP3++li1bpnvuuSf1OWhdPNpzbjf7+c9/rlAolPZ2eheH9rSura3VmDFjVF1drY0bN2rTpk2aOnWqSkr+90v5O+64Qzt27NDy5cv13HPPac2aNfrSl77kxyHhLNrT+6677tKbb76pmpoavfbaa5owYYJuvfVWbd26NXUbehchB3MkuSVLlrR428SJE92kSZPO+DHvvvuuKysrc4sWLUq9befOnU6Sq62tzdVSkaXWWl922WXuRz/6UYu3DRo0yH3ve99zztG62B05csRJcqtXr3bOta/nX/7yF1dSUuIOHTqUus2vfvUrV1VV5aLRaH4PAO12euvWLFy40EUiERePx51ztC5mZ+q9detW16tXL3fw4MG0x3x6F6fWWg8ZMsR9//vfP+PHvP76606S27RpU+ptf/3rX10oFHL79+/P6XqRndZ6d+7c2T355JMtbnfeeee5xx57zDlH72LFFW/I8zw9//zzuuSSS/TJT35S3bt315AhQ1qMq23evFnxeFw33nhj6m39+/fXhRdeqNraWh9Wjfdr2LBhqqmp0f79++Wc08qVK7Vr1y5VV1dLonWxax4rPu+88yS1r2dtba0GDhyoCy64IHWbT37yk2poaNCOHTvyuHpk4vTWZ7pNVVWVSktLJdG6mLXW+8SJE7r99ts1d+5c9ejRI+1j6F2cTm995MgRbdiwQd27d9ewYcN0wQUXaOTIkVq3bl3qY2pra3Xuuefq6quvTr3txhtvVElJiTZs2JDfA0BGWju3hw0bpj/+8Y86duyYPM/TH/7wB506dUqjRo2SRO9ixcYbOnLkiJqamjRz5kyNGTNGL774oj796U9rwoQJWr16taT3ftYkEono3HPPbfGxF1xwgQ4dOuTDqvF+zZkzRwMGDFDv3r0ViUQ0ZswYzZ07VyNGjJBE62LmeZ6+/vWv6+Mf/7guv/xySe3reejQoRZfmDe/v/l9KDyttT7d0aNH9eMf/7jF6CGti9OZet9///0aNmyYxo0b1+rH0bv4tNb67bffliRNnz5dkydP1rJlyzRo0CDdcMMNqqurk/Rez+7du7f4XKWlpTrvvPNoXcDOdG4vXLhQ8Xhc559/vsrLy3XvvfdqyZIl6tu3ryR6F6tSvxcA/3meJ0kaN26c7r//fknSxz72Mb366qt69NFHNXLkSD+Xhw42Z84crV+/XjU1Nbrooou0Zs0aTZkyRT179mxxVRTFZ8qUKdq+fXuLqyAIprZaNzQ06KabbtKAAQM0ffr0/C4OHa613jU1NVqxYkWLn/lE8WutdfPXaffee68+//nPS5KuvPJKvfzyy/rtb3+rGTNm+LJWZO9Mj+U/+MEP9O677+qll15St27dtHTpUt16661au3atBg4c6NNqkS2ueEPdunVTaWmpBgwY0OLtl156aepZzXv06KFYLKZ33323xW0OHz7c6ngbCtPJkyf13e9+Vw8//LDGjh2rj370o5o6daomTpyohx56SBKti9XUqVP13HPPaeXKlerdu3fq7e3p2aNHj7RnOW/+b5oXnjO1btbY2KgxY8aosrJSS5YsUVlZWep9tC4+Z+q9YsUKvfXWWzr33HNVWlqa+nGCW265JTWOSu/icqbWH/rQhySpza/Tjhw50uL9iURCx44do3WBOlPvt956S7/85S/129/+VjfccIOuuOIKTZs2TVdffXXqiXPpXZzYeEORSESDBw9O+1UGu3bt0kUXXSRJuuqqq1RWVqaXX3459f4333xTe/fu1dChQ/O6Xrx/8Xhc8Xi8xbOgSlI4HE59R53WxcU5p6lTp2rJkiVasWKFPvKRj7R4f3t6Dh06VK+99lqLf8SXL1+uqqqqtC/04J+2WkvvXemurq5WJBJRTU1Ni99MIdG6mLTV+zvf+Y7+8Y9/aNu2bakXSZo9e7Yef/xxSfQuFm21/vCHP6yePXue9eu0oUOH6t1339XmzZtT71+xYoU8z9OQIUNyfxBot7Z6nzhxQpLO+rUavYuUn8/shvxpbGx0W7dudVu3bnWS3MMPP+y2bt3q/vWvfznnnFu8eLErKytzv/71r11dXZ2bM2eOC4fDbu3atanP8eUvf9ldeOGFbsWKFe5vf/ubGzp0qBs6dKhfh4QzaKv1yJEj3WWXXeZWrlzp3n77bff444+7Tp06uUceeST1OWhdPL7yla+4rl27ulWrVrmDBw+mXk6cOJG6TVs9E4mEu/zyy111dbXbtm2bW7ZsmfvgBz/oHnjgAT8OCWfQVuv6+no3ZMgQN3DgQLd79+4Wt0kkEs45WheT9pzbp9Npz2pO7+LQntazZ892VVVVbtGiRa6urs59//vfd506dXK7d+9O3WbMmDHuyiuvdBs2bHDr1q1zF198sbvtttv8OCScRVu9Y7GY69u3rxs+fLjbsGGD2717t3vooYdcKBRyzz//fOrz0Lv4sPE2YuXKlU5S2svdd9+dus28efNc3759XadOndwVV1zhli5d2uJznDx50t13333uAx/4gKuoqHCf/vSn3cGDB/N8JGhLW60PHjzoPve5z7mePXu6Tp06uX79+rlZs2Y5z/NSn4PWxaO11pLc448/nrpNe3ru2bPHfepTn3LnnHOO69atm/vmN7+Z+hVUKAxttT7TuS/J/fOf/0x9HloXh/ac2619zOm/QpLeha+9rWfMmOF69+7tKioq3NChQ1tcHHHOuX//+9/utttuc126dHFVVVXu85//vGtsbMzjkaA92tN7165dbsKECa579+6uoqLCffSjH0379WL0Lj4h55zr6KvoAAAAAADgPfyMNwAAAAAAOcTGGwAAAACAHGLjDQAAAABADrHxBgAAAAAgh9h4AwAAAACQQ2y8AQAAAADIITbeAAAAAADkEBtvAAAAAAByiI03AAAAAAA5xMYbAAAAAIAcYuMNAAAAAEAOsfEGAAAAACCH/j+8q7kCS2EPGAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Opmerking**: Dit diagram suggereert dat de gemiddelde lengtes van eerste honkspelers hoger zijn dan de lengtes van tweede honkspelers. Later zullen we leren hoe we deze hypothese formeler kunnen testen en hoe we kunnen aantonen dat onze gegevens statistisch significant zijn om dit te bewijzen.  \n",
+    "> **Opmerking**: Dit diagram suggereert dat de gemiddelde lengte van eerste honkspelers hoger is dan die van tweede honkspelers. Later zullen we leren hoe we deze hypothese formeler kunnen testen en hoe we kunnen aantonen dat onze gegevens statistisch significant zijn om dit te bewijzen.  \n",
     "\n",
     "Leeftijd, lengte en gewicht zijn allemaal continue willekeurige variabelen. Wat denk je dat hun verdeling is? Een goede manier om dit te ontdekken is door het histogram van de waarden te plotten:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## Normale verdeling\n",
     "\n",
-    "Laten we een kunstmatige steekproef van gewichten maken die een normale verdeling volgt met dezelfde gemiddelde en variantie als onze echte gegevens:\n"
+    "Laten we een kunstmatige steekproef van gewichten maken die een normale verdeling volgt met hetzelfde gemiddelde en dezelfde variantie als onze echte gegevens:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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qfVW17/Dhw3NOAwAAFmOua5CTvKy7H6qqZyW5paru2eiO3b03yd4k2bVrV885DwAAWIi5ziB390PT7cNJbkxyXpJDVXV6kky3D887SQAA2CqbDuSqelpVPf3o/SQ/m+TOJDcluXTa7NIkn5x3kgAAsFXmucTi2UlurKqj3+f3uvu/VtUXk1xfVW9O8s0kb5h/mgAAsDU2Hcjd/bUk/2jG+LeTnD/PpAAAYLvM+0t6cELYuefm7Z4CAHCC8FHTAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAw8FHTALBgJ8LH2++/4sLtngI8YTmDDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAACDTQdyVZ1ZVX9UVXdX1V1V9SvT+Puq6sGqun36es3ipgsAAMu1Y459jyR5R3d/qaqenuS2qrpleu4D3f3++acHAABba9OB3N0Hkxyc7n+3qu5OcsaiJsaJY+eem7d7CgAAC7OQa5CrameSlyT5/DT0tqq6o6qurqpT19hnd1Xtq6p9hw8fXsQ0AABgbnMHclX9RJIbkry9u7+T5INJnp/k3KyeYb5y1n7dvbe7d3X3rpWVlXmnAQAACzFXIFfVj2U1jj/a3Z9Iku4+1N2PdvcPk3w4yXnzTxMAALbGPO9iUUmuSnJ3d//GMH76sNnrk9y5+ekBAMDWmuddLF6W5E1JvlJVt09j705ySVWdm6ST7E/yljl+BgAAbKl53sXis0lqxlOf2vx0AABge/kkPQAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAY7NjuCQAAW2/nnpu3ewrr2n/Fhds9BU5SziADAMBAIAMAwEAgAwDAQCADAMBAIAMAwEAgAwDAwNu8AQBPSN6Kju3iDDIAAAycQT4BnAj/Bw0A8GThDDIAAAwEMgAADAQyAAAMTvprkF3fCwDAyBlkAAAYCGQAABgsLZCr6oKqureq7q+qPcv6OQAAsEhLuQa5qk5J8p+T/EySA0m+WFU3dfdXl/HzAAC2g99lmt8T8dMIl3UG+bwk93f317r7r5J8LMlFS/pZAACwMMt6F4szknxreHwgyT8ZN6iq3Ul2Tw//sqruXeN7nZbkzxY+Q46yvstlfZfPGi+X9V0u67tc1ne5FrK+9WsLmMnm/b1Zg8sK5Jox1o950L03yd51v1HVvu7etaiJ8VjWd7ms7/JZ4+WyvstlfZfL+i7Xk3l9l3WJxYEkZw6Pn5vkoSX9LAAAWJhlBfIXk5xTVWdX1Y8nuTjJTUv6WQAAsDBLucSiu49U1duS/LckpyS5urvv2uS3W/cyDOZifZfL+i6fNV4u67tc1ne5rO9yPWnXt7p7/a0AAOAk4ZP0AABgIJABAGCwrYFcVc+oqo9X1T1VdXdV/dOqel9VPVhVt09fr1ljXx9lvY411ve6YW33V9Xta+y7v6q+Mm23b4un/oRXVS8c1vH2qvpOVb29qp5ZVbdU1X3T7alr7O/1exzHWd9fn17Pd1TVjVX1jDX29/o9juOsr+PvAhxnfR1/F6Sq/l1V3VVVd1bVtVX1tx1/F2eN9T2pjr/beg1yVV2T5H92929P73bx1CRvT/KX3f3+4+x3SpI/zfBR1kku8VHWjzVrfbv7/wzPX5nkL7r7V2fsuz/Jru72BuvrmF6PD2b1w3AuS/JId18xHXhP7e53ztje63eDjlnfFyb5H9MvAv9akhy7vtM+++P1uyHHrO8vxfF3ocb17e5vDOOOv5tUVWck+WySF3X396vq+iSfSvKiOP7O7Tjr+1BOouPvtp1BrqqfTPKKJFclSXf/1Rhv6/BR1utYb32rqpK8Mcm12zLBJ5fzkzww/eV3UZJrpvFrkrxuxvZev4/PX69vd3+6u49M45/L6nusM5/x9bsRXr+Pz4+sr+PvQuxI8neqakdWT649FMffRfqR9T3Zjr/beYnF85IcTvJfqurLVfXbVfW06bm3Tafwr17jn0hmfZT1GUue74nmeOubJC9Pcqi771tj/07y6aq6rVY/Fpy1XZy/+Yvu2d19MEmm22fN2N7r9/EZ13f0y0n+cI19vH437tj1dfxdrFmvX8ffOXT3g0nen+SbSQ5m9Uz8p+P4uxDHWd/Rk/74u52BvCPJS5N8sLtfkuT/JtmT5INJnp/k3Kz+wVw5Y991P8qaNdf3qEty/LMXL+vulyb5uSSXVdUrljbTE9h06cprk/z+49ltxpjX7wxrrW9VvSfJkSQfXWNXr98NmLG+jr8LdJzjg+PvHKb/cbsoydlJnpPkaVX1rza6+4wxr9/Beut7shx/tzOQDyQ50N2fnx5/PMlLu/tQdz/a3T9M8uGs/nPIrH19lPXxzVzfJJn+yeQXkly31s7d/dB0+3CSGzP7z4HVA8CXuvvQ9PhQVZ2eJNPtwzP28frduGPXN1V1aZKfT/KLvcYvUXj9bthj1tfxd+FmvX4df+f3qiRf7+7D3f3/knwiyT+L4++irLW+J9Xxd9sCubv/d5JvVdULp6Hzk3z16It78vokd87Y3UdZr2Ot9Z3uvyrJPd19YNa+VfW0qnr60ftJfjaz/xz40TNBNyW5dLp/aZJPztjH63fjHrO+VXVBkncmeW13f2/WDl6/j8ux6+v4u1izzhQ7/s7vm0l+uqqeOl3PfX6Su+P4uygz1/ekO/5297Z9ZfWf8fYluSPJHyQ5NcnvJvnKNHZTktOnbZ+T5FPDvq/J6m+iPpDkPdv53/FE/Zq1vtP4R5K89Zht/3p9s3r98p9MX3dZ3zXX96lJvp3k7w5jP5Xk1iT3TbfPPHZ9p8dev5tb3/uzev3g7dPXh45dX6/fudbX8XeJ6zuNO/4uZn3/Y5J7shpfv5vkKY6/S1/fk+r466OmAQBg4JP0AABgIJABAGAgkAEAYCCQAQBgIJABAGAgkAEAYCCQAQBg8P8B40VGjZpezWQAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Aangezien de meeste waarden in het echte leven normaal verdeeld zijn, zouden we geen uniforme willekeurige getallengenerator moeten gebruiken om steekproefgegevens te genereren. Hier is wat er gebeurt als we proberen gewichten te genereren met een uniforme verdeling (gegenereerd door `np.random.rand`):\n"
+    "Aangezien de meeste waarden in het echte leven normaal verdeeld zijn, moeten we geen uniforme willekeurige getallengenerator gebruiken om steekproefgegevens te genereren. Hier is wat er gebeurt als we proberen gewichten te genereren met een uniforme verdeling (gegenereerd door `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Betrouwbaarheidsintervallen\n",
     "\n",
-    "Laten we nu betrouwbaarheidsintervallen berekenen voor het gewicht en de lengte van honkbalspelers. We zullen de code gebruiken [uit deze stackoverflow-discussie](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Laten we nu betrouwbaarheidsintervallen berekenen voor de gewichten en lengtes van honkbalspelers. We zullen de code gebruiken [uit deze stackoverflow-discussie](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -593,14 +434,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Hypothesetoetsing\n",
+    "## Hypothese Toetsen\n",
     "\n",
-    "Laten we verschillende rollen in onze dataset van honkbalspelers verkennen:\n"
+    "Laten we de verschillende rollen in onze dataset van honkbalspelers verkennen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "We kunnen zien dat de intervallen elkaar niet overlappen.\n",
     "\n",
-    "Een statistisch meer correcte manier om de hypothese te bewijzen is door gebruik te maken van een **Student t-test**:\n"
+    "Een statistisch correctere manier om de hypothese te bewijzen is door gebruik te maken van een **Student t-test**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,7 +621,7 @@
    "source": [
     "De twee waarden die door de functie `ttest_ind` worden geretourneerd zijn:  \n",
     "* De p-waarde kan worden beschouwd als de waarschijnlijkheid dat twee verdelingen hetzelfde gemiddelde hebben. In ons geval is deze erg laag, wat betekent dat er sterk bewijs is dat eerste honkspelers langer zijn.  \n",
-    "* De t-waarde is de tussenliggende waarde van het genormaliseerde gemiddelde verschil die wordt gebruikt in de t-test, en deze wordt vergeleken met een drempelwaarde voor een gegeven betrouwbaarheidsniveau.  \n"
+    "* De t-waarde is de genormaliseerde gemiddelde verschilwaarde die wordt gebruikt in de t-test, en deze wordt vergeleken met een drempelwaarde voor een gegeven betrouwbaarheidsniveau.  \n"
    ]
   },
   {
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -833,14 +672,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Laten we nu de covariantie en correlatie van die reeksen berekenen. `np.cov` geeft ons een zogenaamde **covariantiematrix**, wat een uitbreiding is van covariantie naar meerdere variabelen. Het element $M_{ij}$ van de covariantiematrix $M$ is een correlatie tussen invoervariabelen $X_i$ en $X_j$, en diagonale waarden $M_{ii}$ zijn de variantie van $X_{i}$. Op dezelfde manier geeft `np.corrcoef` ons de **correlatiematrix**.\n"
+    "Laten we nu de covariantie en correlatie van die reeksen berekenen. `np.cov` geeft ons een zogenaamde **covariantiematrix**, wat een uitbreiding is van covariantie naar meerdere variabelen. Het element $M_{ij}$ van de covariantiematrix $M$ is een correlatie tussen invoervariabelen $X_i$ en $X_j$, en de diagonale waarden $M_{ii}$ zijn de variantie van $X_{i}$. Op dezelfde manier geeft `np.corrcoef` ons de **correlatiematrix**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -889,19 +728,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -919,14 +756,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -939,19 +776,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In dit geval is de correlatie iets kleiner, maar deze is nog steeds behoorlijk hoog. Nu, om de relatie nog minder duidelijk te maken, zouden we wat extra willekeurigheid kunnen toevoegen door een willekeurige variabele aan het salaris toe te voegen. Laten we kijken wat er gebeurt:\n"
+    "In dit geval is de correlatie iets kleiner, maar deze is nog steeds behoorlijk hoog. Nu, om de relatie nog minder duidelijk te maken, willen we misschien wat extra willekeurigheid toevoegen door een willekeurige variabele aan het salaris toe te voegen. Laten we kijken wat er gebeurt:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -962,19 +799,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -996,17 +831,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,7 +854,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Helaas hebben we geen resultaten gekregen - alleen enkele vreemde `nan` waarden. Dit komt doordat sommige waarden in onze reeks niet gedefinieerd zijn, weergegeven als `nan`, wat ervoor zorgt dat het resultaat van de bewerking ook niet gedefinieerd is. Als we naar de matrix kijken, zien we dat de kolom `Weight` het probleem vormt, omdat de zelfcorrelatie tussen `Height` waarden is berekend.\n",
+    "Helaas hebben we geen resultaten gekregen - alleen enkele vreemde `nan` waarden. Dit komt doordat sommige waarden in onze reeks niet gedefinieerd zijn, weergegeven als `nan`, wat ervoor zorgt dat het resultaat van de bewerking ook niet gedefinieerd is. Als we naar de matrix kijken, zien we dat de kolom `Weight` het probleem vormt, omdat de zelf-correlatie tussen `Height` waarden is berekend.\n",
     "\n",
     "> Dit voorbeeld toont het belang van **datapreparatie** en **opschoning**. Zonder correcte data kunnen we niets berekenen.\n",
     "\n",
@@ -1028,7 +863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +873,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1054,27 +889,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1115,11 +948,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:22:32+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:39:45+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "nl"
   }
diff --git a/translations/nl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/nl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 0a56f8c4..167dfe61 100644
--- a/translations/nl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/nl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -157,7 +157,7 @@
     "* S1 tot en met S6 zijn verschillende bloedmetingen\n",
     "* Y is de kwalitatieve maat voor ziekteprogressie over één jaar\n",
     "\n",
-    "Laten we deze dataset bestuderen met behulp van methoden uit de waarschijnlijkheidsleer en statistiek.\n",
+    "Laten we deze dataset bestuderen met behulp van methoden uit de kansrekening en statistiek.\n",
     "\n",
     "### Taak 1: Bereken gemiddelde waarden en variantie voor alle waarden\n"
    ],
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -590,7 +590,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Conclusies:  \n",
+    "Conclusies:\n",
     "* Leeftijd - normaal  \n",
     "* Geslacht - uniform  \n",
     "* BMI, Y - moeilijk te zeggen  \n"
@@ -602,7 +602,7 @@
    "source": [
     "### Taak 4: Test de correlatie tussen verschillende variabelen en ziekteprogressie (Y)\n",
     "\n",
-    "> **Hint** Een correlatiematrix geeft je de meest bruikbare informatie over welke waarden afhankelijk zijn.\n"
+    "> **Tip** Een correlatiematrix geeft je de meest bruikbare informatie over welke waarden afhankelijk zijn.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Disclaimer**:  \nDit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, dient u zich ervan bewust te zijn dat geautomatiseerde vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal moet worden beschouwd als de gezaghebbende bron. Voor kritieke informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling.\n"
+    "\n---\n\n**Disclaimer**:  \nDit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, willen we u erop wijzen dat geautomatiseerde vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal moet worden beschouwd als de gezaghebbende bron. Voor kritieke informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:51:40+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:40:10+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "nl"
   }
diff --git a/translations/no/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/no/1-Introduction/04-stats-and-probability/assignment.ipynb
index 61507ada..461b4b7e 100644
--- a/translations/no/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/no/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,7 +149,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "I dette datasettet har kolonnene følgende betydning:\n",
+    "I dette datasettet er kolonnene som følger:\n",
     "* Alder og kjønn er selvforklarende\n",
     "* BMI er kroppsmasseindeks\n",
     "* BP er gjennomsnittlig blodtrykk\n",
@@ -247,8 +247,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:44:58+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:37:17+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "no"
   }
diff --git a/translations/no/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/no/1-Introduction/04-stats-and-probability/notebook.ipynb
index e9b0bcff..00273052 100644
--- a/translations/no/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/no/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Analysere ekte data\n",
     "\n",
-    "Gjennomsnitt og varians er svært viktige når man analyserer data fra virkeligheten. La oss laste inn dataene om baseballspillere fra [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Gjennomsnitt og varians er svært viktige når man analyserer data fra virkeligheten. La oss laste inn data om baseballspillere fra [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vi bruker en pakke kalt [**Pandas**](https://pandas.pydata.org/) her for dataanalyse. Vi kommer til å snakke mer om Pandas og arbeid med data i Python senere i dette kurset.\n",
+    "Vi bruker en pakke som heter [**Pandas**](https://pandas.pydata.org/) her for dataanalyse. Vi skal snakke mer om Pandas og arbeid med data i Python senere i dette kurset.\n",
     "\n",
     "La oss beregne gjennomsnittsverdier for alder, høyde og vekt:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vi kan også lage boksplott av delmengder av datasettet vårt, for eksempel gruppert etter spillerrolle.\n"
+    "Vi kan også lage boksplott av undergrupper av datasettet vårt, for eksempel gruppert etter spillerrolle.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Merk**: Dette diagrammet antyder at førstebasemen i gjennomsnitt er høyere enn andrebasemen. Senere skal vi lære hvordan vi kan teste denne hypotesen mer formelt, og hvordan vi kan vise at dataene våre er statistisk signifikante for å underbygge dette.  \n",
+    "> **Merk**: Dette diagrammet antyder at gjennomsnittlig høyde på førstemenn er høyere enn høyden på andremenn. Senere skal vi lære hvordan vi kan teste denne hypotesen mer formelt, og hvordan vi kan demonstrere at dataene våre er statistisk signifikante for å vise dette.\n",
     "\n",
-    "Alder, høyde og vekt er alle kontinuerlige stokastiske variabler. Hva tror du fordelingen deres er? En god måte å finne ut av det på er å plotte histogrammet av verdiene:\n"
+    "Alder, høyde og vekt er alle kontinuerlige tilfeldige variabler. Hva tror du deres fordeling er? En god måte å finne ut av det på er å lage et histogram av verdiene:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## Hypotesetesting\n",
     "\n",
-    "La oss utforske ulike roller i datasettet vårt med baseballspillere:\n"
+    "La oss utforske de ulike rollene i datasettet vårt for baseballspillere:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -789,24 +630,22 @@
    "source": [
     "## Simulere en normalfordeling med sentralgrenseteoremet\n",
     "\n",
-    "Den pseudo-tilfeldige generatoren i Python er designet for å gi oss en uniform fordeling. Hvis vi ønsker å lage en generator for normalfordeling, kan vi bruke sentralgrenseteoremet. For å få en normalfordelt verdi, beregner vi bare gjennomsnittet av et uniform-generert utvalg.\n"
+    "Den pseudotilfeldige generatoren i Python er designet for å gi oss en uniform fordeling. Hvis vi ønsker å lage en generator for normalfordeling, kan vi bruke sentralgrenseteoremet. For å få en normalfordelt verdi, beregner vi bare gjennomsnittet av et uniform-generert utvalg.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Korrelasjon og den onde baseballbedriften\n",
+    "## Korrelasjon og Ond Baseball Corp\n",
     "\n",
-    "Korrelasjon lar oss finne sammenhenger mellom datasett. I vårt enkle eksempel kan vi late som om det finnes en ond baseballbedrift som betaler spillerne sine basert på høyden deres – jo høyere spilleren er, desto mer penger får han/henne. La oss si at det er en grunnlønn på $1000, og en ekstra bonus fra $0 til $100, avhengig av høyden. Vi skal bruke ekte spillere fra MLB og beregne deres tenkte lønninger:\n"
+    "Korrelasjon lar oss finne sammenhenger mellom datasett. I vårt enkle eksempel, la oss late som det finnes et ondt baseballselskap som betaler spillerne sine basert på høyde – jo høyere spilleren er, desto mer penger får han/hun. Anta at det finnes en grunnlønn på $1000, og en ekstra bonus fra $0 til $100, avhengig av høyde. Vi skal ta de ekte spillerne fra MLB og beregne deres imaginære lønninger:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -942,14 +779,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Kan du gjette hvorfor prikkene stiller seg opp i vertikale linjer på denne måten?\n",
+    "> Kan du gjette hvorfor prikkene danner vertikale linjer på denne måten?\n",
     "\n",
-    "Vi har sett på sammenhengen mellom et kunstig konstruert konsept som lønn og den observerte variabelen *høyde*. La oss også undersøke om de to observerte variablene, som høyde og vekt, har en sammenheng:\n"
+    "Vi har observert sammenhengen mellom et kunstig konstruert konsept som lønn og den observerte variabelen *høyde*. La oss også se om de to observerte variablene, som høyde og vekt, korrelerer:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1026,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:23:34+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:37:06+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "no"
   }
diff --git a/translations/no/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/no/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index a8c69d04..f8a2138a 100644
--- a/translations/no/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/no/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -483,8 +483,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -533,8 +533,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -598,9 +598,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Oppgave 4: Test korrelasjonen mellom ulike variabler og sykdomsutvikling (Y)\n",
+    "### Oppgave 4: Test korrelasjonen mellom ulike variabler og sykdomsprogresjon (Y)\n",
     "\n",
-    "> **Tips** En korrelasjonsmatrise vil gi deg den mest nyttige informasjonen om hvilke verdier som er avhengige.\n"
+    "> **Hint** Korrelasjonsmatrisen vil gi deg den mest nyttige informasjonen om hvilke verdier som er avhengige.\n"
    ],
    "metadata": {}
   },
@@ -851,10 +851,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -881,9 +881,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -912,7 +912,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi tilstreber nøyaktighet, vennligst vær oppmerksom på at automatiske oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på sitt opprinnelige språk bør anses som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for eventuelle misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen.\n"
+    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi streber etter nøyaktighet, vær oppmerksom på at automatiserte oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på sitt opprinnelige språk bør anses som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen.\n"
    ]
   }
  ],
@@ -938,8 +938,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:52:02+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:37:30+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "no"
   }
diff --git a/translations/pa/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/pa/1-Introduction/04-stats-and-probability/assignment.ipynb
index f23be099..41b8be34 100644
--- a/translations/pa/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/pa/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## ਪਰਿਚਯ: ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜੇ\n",
     "## ਅਸਾਈਨਮੈਂਟ\n",
     "\n",
-    "ਇਸ ਅਸਾਈਨਮੈਂਟ ਵਿੱਚ, ਅਸੀਂ ਸ਼ੂਗਰ ਦੇ ਮਰੀਜ਼ਾਂ ਦੇ ਡਾਟਾਸੈੱਟ ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ ਜੋ [ਇਥੋਂ ਲਿਆ ਗਿਆ ਹੈ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।\n"
+    "ਇਸ ਅਸਾਈਨਮੈਂਟ ਵਿੱਚ, ਅਸੀਂ ਸ਼ੂਗਰ ਮਰੀਜ਼ਾਂ ਦੇ ਡਾਟਾਸੈੱਟ ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ ਜੋ [ਇਥੋਂ ਲਿਆ ਗਿਆ ਹੈ](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)।\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,14 +149,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "ਇਸ ਡਾਟਾਸੈੱਟ ਵਿੱਚ ਕਾਲਮ ਹੇਠ ਲਿਖੇ ਹਨ:  \n",
+    "ਇਸ ਡੇਟਾਸੈੱਟ ਵਿੱਚ ਕਾਲਮ ਹੇਠ ਲਿਖੇ ਹਨ:  \n",
     "* ਉਮਰ ਅਤੇ ਲਿੰਗ ਸਵੈ-ਸਪਸ਼ਟ ਹਨ  \n",
     "* BMI ਮਤਲਬ ਬਾਡੀ ਮਾਸ ਇੰਡੈਕਸ ਹੈ  \n",
     "* BP ਮਤਲਬ ਔਸਤ ਰਕਤ ਦਬਾਅ ਹੈ  \n",
     "* S1 ਤੋਂ S6 ਵੱਖ-ਵੱਖ ਰਕਤ ਦੇ ਮਾਪ ਹਨ  \n",
-    "* Y ਇੱਕ ਸਾਲ ਵਿੱਚ ਬਿਮਾਰੀ ਦੇ ਵਿਕਾਸ ਦਾ ਗੁਣਾਤਮਕ ਮਾਪ ਹੈ  \n",
+    "* Y ਇੱਕ ਗੁਣਾਤਮਕ ਮਾਪ ਹੈ ਜੋ ਇੱਕ ਸਾਲ ਵਿੱਚ ਬਿਮਾਰੀ ਦੇ ਵਿਕਾਸ ਨੂੰ ਦਰਸਾਉਂਦਾ ਹੈ  \n",
     "\n",
-    "ਆਓ ਇਸ ਡਾਟਾਸੈੱਟ ਦਾ ਅਧਿਐਨ ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜਾ ਵਿਗਿਆਨ ਦੇ ਤਰੀਕਿਆਂ ਨਾਲ ਕਰੀਏ।  \n",
+    "ਆਓ ਇਸ ਡੇਟਾਸੈੱਟ ਦਾ ਅਧਿਐਨ ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜਾ ਵਿਗਿਆਨ ਦੇ ਤਰੀਕਿਆਂ ਨਾਲ ਕਰੀਏ।  \n",
     "\n",
     "### ਕੰਮ 1: ਸਾਰੇ ਮੁੱਲਾਂ ਲਈ ਔਸਤ ਅਤੇ ਵੈਰੀਅੰਸ ਦੀ ਗਣਨਾ ਕਰੋ  \n"
    ],
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### ਟਾਸਕ 2: ਲਿੰਗ ਦੇ ਅਧਾਰ 'ਤੇ BMI, BP ਅਤੇ Y ਲਈ ਬਾਕਸਪਲਾਟ ਬਣਾਓ\n"
+    "### ਟਾਸਕ 2: ਲਿੰਗ ਦੇ ਆਧਾਰ 'ਤੇ BMI, BP ਅਤੇ Y ਲਈ ਬਾਕਸਪਲਾਟ ਬਣਾਓ\n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### ਟਾਸਕ 4: ਵੱਖ-ਵੱਖ ਵੈਰੀਏਬਲਾਂ ਅਤੇ ਬਿਮਾਰੀ ਦੇ ਵਿਕਾਸ (Y) ਦੇ ਵਿਚਕਾਰ ਸਬੰਧ ਦੀ ਜਾਂਚ ਕਰੋ\n",
     "\n",
-    "> **ਸੁਝਾਅ** ਸਬੰਧ ਮੈਟ੍ਰਿਕਸ ਤੁਹਾਨੂੰ ਇਹ ਜਾਣਨ ਲਈ ਸਭ ਤੋਂ ਜ਼ਿਆਦਾ ਲਾਭਦਾਇਕ ਜਾਣਕਾਰੀ ਦੇਵੇਗਾ ਕਿ ਕਿਹੜੀਆਂ ਮੁੱਲਾਂ ਦਾ ਇੱਕ ਦੂਜੇ ਨਾਲ ਸਬੰਧ ਹੈ।\n"
+    "> **ਸੁਝਾਅ** ਸਬੰਧ ਮੈਟ੍ਰਿਕਸ ਤੁਹਾਨੂੰ ਇਹ ਸਮਝਣ ਲਈ ਸਭ ਤੋਂ ਜ਼ਿਆਦਾ ਮਦਦਗਾਰ ਜਾਣਕਾਰੀ ਦੇਵੇਗਾ ਕਿ ਕਿਹੜੀਆਂ ਮੁੱਲਾਂ ਦਾ ਇੱਕ ਦੂਜੇ ਨਾਲ ਸਬੰਧ ਹੈ।\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ਅਸਵੀਕਰਤੀ**:  \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀ ਹੋਣ ਦਾ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁੱਤੀਆਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਮੌਜੂਦ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੇ ਪ੍ਰਯੋਗ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੀਆਂ ਕਿਸੇ ਵੀ ਗਲਤਫਹਮੀਆਂ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆਵਾਂ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।  \n"
+    "\n---\n\n**ਅਸਵੀਕਤੀ**:  \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀਅਤ ਲਈ ਯਤਨਸ਼ੀਲ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁਚਤਤਾਵਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਇਸਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਅਧਿਕਾਰਤ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੀ ਵਰਤੋਂ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤ ਫਹਿਮੀ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:45:21+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:24:05+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "pa"
   }
diff --git a/translations/pa/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/pa/1-Introduction/04-stats-and-probability/notebook.ipynb
index c504a2b2..5bc23174 100644
--- a/translations/pa/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/pa/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ਪਰਿਚਯ ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜੇ\n",
-    "ਇਸ ਨੋਟਬੁੱਕ ਵਿੱਚ, ਅਸੀਂ ਕੁਝ ਧਾਰਨਾਵਾਂ ਨਾਲ ਖੇਡਾਂਗੇ ਜਿਨ੍ਹਾਂ ਬਾਰੇ ਅਸੀਂ ਪਹਿਲਾਂ ਚਰਚਾ ਕੀਤੀ ਹੈ। ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜਿਆਂ ਦੇ ਕਈ ਧਾਰਨਾਵਾਂ ਪਾਇਥਨ ਵਿੱਚ ਡਾਟਾ ਪ੍ਰੋਸੈਸਿੰਗ ਲਈ ਮੁੱਖ ਲਾਇਬ੍ਰੇਰੀਆਂ ਜਿਵੇਂ ਕਿ `numpy` ਅਤੇ `pandas` ਵਿੱਚ ਚੰਗੀ ਤਰ੍ਹਾਂ ਦਰਸਾਈ ਗਈਆਂ ਹਨ।\n"
+    "# ਪਰਿਚਯ: ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜੇ\n",
+    "ਇਸ ਨੋਟਬੁੱਕ ਵਿੱਚ, ਅਸੀਂ ਪਹਿਲਾਂ ਚਰਚਾ ਕੀਤੇ ਕੁਝ ਧਾਰਨਾਵਾਂ ਨਾਲ ਖੇਡਾਂਗੇ। ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜਿਆਂ ਦੇ ਬਹੁਤ ਸਾਰੇ ਧਾਰਨਾਵਾਂ ਪਾਇਥਨ ਵਿੱਚ ਡਾਟਾ ਪ੍ਰੋਸੈਸਿੰਗ ਲਈ ਮੁੱਖ ਲਾਇਬ੍ਰੇਰੀਆਂ, ਜਿਵੇਂ ਕਿ `numpy` ਅਤੇ `pandas`, ਵਿੱਚ ਚੰਗੀ ਤਰ੍ਹਾਂ ਦਰਸਾਏ ਗਏ ਹਨ।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ਰੈਂਡਮ ਵੈਰੀਏਬਲ ਅਤੇ ਵੰਡਾਂ  \n",
-    "ਆਓ 0 ਤੋਂ 9 ਤੱਕ ਦੀ ਯੂਨੀਫਾਰਮ ਵੰਡ ਤੋਂ 30 ਮੁੱਲਾਂ ਦਾ ਨਮੂਨਾ ਖਿੱਚਣ ਨਾਲ ਸ਼ੁਰੂ ਕਰੀਏ। ਅਸੀਂ ਮੀਨ ਅਤੇ ਵੈਰੀਅੰਸ ਦੀ ਗਿਣਤੀ ਵੀ ਕਰਾਂਗੇ।  \n"
+    "## ਰੈਂਡਮ ਵੈਰੀਏਬਲ ਅਤੇ ਵੰਡਾਂ\n",
+    "ਆਓ 0 ਤੋਂ 9 ਤੱਕ ਦੀ ਇੱਕ ਯੂਨੀਫਾਰਮ ਵੰਡ ਤੋਂ 30 ਮੁੱਲਾਂ ਦਾ ਨਮੂਨਾ ਖਿੱਚਣ ਨਾਲ ਸ਼ੁਰੂ ਕਰੀਏ। ਅਸੀਂ ਮੀਨ ਅਤੇ ਵੈਰੀਅੰਸ ਦੀ ਗਣਨਾ ਵੀ ਕਰਾਂਗੇ।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -84,201 +82,55 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ਅਸਲ ਡਾਟਾ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ\n",
+    "## ਅਸਲ ਡਾਟਾ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ\n",
     "\n",
-    "ਅਸਲ-ਜਗਤ ਦੇ ਡਾਟਾ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ ਕਰਦੇ ਸਮੇਂ ਔਸਤ ਅਤੇ ਵੈਰੀਅੰਸ ਬਹੁਤ ਮਹੱਤਵਪੂਰਨ ਹੁੰਦੇ ਹਨ। ਆਓ ਬੇਸਬਾਲ ਖਿਡਾਰੀਆਂ ਬਾਰੇ ਡਾਟਾ ਨੂੰ ਲੋਡ ਕਰੀਏ [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) ਤੋਂ।\n"
+    "ਅਸਲ-ਜੀਵਨ ਡਾਟਾ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ ਕਰਦੇ ਸਮੇਂ ਔਸਤ ਅਤੇ ਵੈਰੀਅੰਸ ਬਹੁਤ ਮਹੱਤਵਪੂਰਨ ਹੁੰਦੇ ਹਨ। ਆਓ ਬੇਸਬਾਲ ਖਿਡਾਰੀਆਂ ਬਾਰੇ ਡਾਟਾ ਨੂੰ ਲੋਡ ਕਰੀਏ [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) ਤੋਂ।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ਅਸੀਂ ਇੱਥੇ ਡਾਟਾ ਵਿਸ਼ਲੇਸ਼ਣ ਲਈ [**Pandas**](https://pandas.pydata.org/) ਨਾਮਕ ਪੈਕੇਜ ਦੀ ਵਰਤੋਂ ਕਰ ਰਹੇ ਹਾਂ। ਇਸ ਕੋਰਸ ਵਿੱਚ ਅੱਗੇ ਚੱਲ ਕੇ ਅਸੀਂ Pandas ਅਤੇ Python ਵਿੱਚ ਡਾਟਾ ਨਾਲ ਕੰਮ ਕਰਨ ਬਾਰੇ ਹੋਰ ਗੱਲਬਾਤ ਕਰਾਂਗੇ।\n",
+    "ਅਸੀਂ ਇੱਥੇ ਡਾਟਾ ਵਿਸ਼ਲੇਸ਼ਣ ਲਈ [**Pandas**](https://pandas.pydata.org/) ਨਾਮਕ ਪੈਕੇਜ ਦੀ ਵਰਤੋਂ ਕਰ ਰਹੇ ਹਾਂ। ਇਸ ਕੋਰਸ ਵਿੱਚ ਅੱਗੇ ਚੱਲ ਕੇ ਅਸੀਂ Pandas ਅਤੇ Python ਵਿੱਚ ਡਾਟਾ ਨਾਲ ਕੰਮ ਕਰਨ ਬਾਰੇ ਹੋਰ ਗੱਲ ਕਰਾਂਗੇ।\n",
     "\n",
-    "ਚਲੋ, ਉਮਰ, ਲੰਬਾਈ ਅਤੇ ਵਜ਼ਨ ਲਈ ਔਸਤ ਮੁੱਲ ਗਣਨਾ ਕਰੀਏ:\n"
+    "ਚਲੋ, ਉਮਰ, ਕੱਦ ਅਤੇ ਵਜ਼ਨ ਲਈ ਔਸਤ ਮੁੱਲ ਗਣਨਾ ਕਰੀਏ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ਇਸਦੇ ਨਾਲ ਨਾਲ, ਮੀਨ ਦੇ ਨਾਲ ਮੀਡੀਅਨ ਮੁੱਲ ਅਤੇ ਕਵਾਰਟਾਈਲਜ਼ ਨੂੰ ਦੇਖਣਾ ਸਮਝਦਾਰੀ ਹੈ। ਇਨ੍ਹਾਂ ਨੂੰ ਇੱਕ **ਬਾਕਸ ਪਲਾਟ** ਦੀ ਵਰਤੋਂ ਨਾਲ ਦਿਖਾਇਆ ਜਾ ਸਕਦਾ ਹੈ:\n"
+    "ਇਸਦੇ ਨਾਲ ਨਾਲ, ਮੀਨ ਦੇ ਨਾਲ, ਮੀਡੀਅਨ ਮੁੱਲ ਅਤੇ ਕਵਾਰਟਾਈਲਜ਼ ਨੂੰ ਦੇਖਣਾ ਸਮਝਦਾਰੀ ਹੈ। ਇਨ੍ਹਾਂ ਨੂੰ ਇੱਕ **ਬਾਕਸ ਪਲਾਟ** ਦੀ ਵਰਤੋਂ ਨਾਲ ਦਿਖਾਇਆ ਜਾ ਸਕਦਾ ਹੈ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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/6SS5n/zkJx1+TJMnT3aSXGNjY6vH1NDQ4CS5X/3qV20eU/NxTJs2zTmX/u9T878TP/vZz4qyU2vH9H7+7jX/f2reV55+TMuXL3eS3H333Vdwx1RfX+8kufr6eteWnI+at3bFu0+fPjp69KjOP//8gv6Ozdq1azVq1Cg9+eSTuuSSS8x8V9fzPB0+fFg9evRINSj2Ywpip444pmg0qkOHDqlPnz5KJpMFfUx1dXWaNGmSVq9erREjRhTEY0SxXVGI7tmoTk9UK3HPCiW7X97imCKRiG57/jbtPLZTnvvfZ08tCZXo0vMu1ROfeELl5eUFd0yF3Gnjxo0aPny45s+fr8suuyyv51M8Hte+fft00UUXpZ4Nl8e94B5TMT2WW+20a9cuTZo0SWvWrNHw4cPb/bg3ceJEPfvss7rjjjv01FNPyfM87dq1S/369ZMkTZo0SQsWLNDNN98cuCveo0ePTv2bf/oxvfLKKxo5cqReeumlDr/ivXz5clVXV6u2tlaDBg1KO6YNGzZo2LBhevnll9t1xfv04/jvf5/WrVuXdhz/3SkWi+ntt9/WJZdcIs/zCq5TR17xHj16tNauXavrrrsu7ZjWr1+vESNG6MUXXyy4K96nTp1q/49Rt7k1Pwup7Svep8vkuwJ+27x5s5OU+s6KFdFo1M2dO9dFo1G/l4IcK6bWVs/HjhT710bnplW99+dp1u1b1+rV7uaXdfvW+bDi4ubn39liOreRPXoXvvf7eNDY2OgkuVAo5E6ePNmi9cmTJ10oFEpdnQ2SRCLhPvzhD7uxY8e6ZDLZ4n3JZNKNHTvWfeQjH3GJRKKg7zvbz2Xl3Pazd7Yy2dsG96f08b5FIhHdd999qe8EIbhobUtZaVmLP5s55zRn6xyFFGr140IKac7WOTzDeRHh3LaF3sHVpUsXDR48WM45VVRU6Atf+IKuvfZafeELX0g9sdrgwYMD9cRqkhQOhzVr1iw999xzGj9+fItnuR4/fryee+45PfTQQzl5oq2OvO9sP5eVc9vP3vmU8ca7qalJ27Zt07Zt2yRJ//znP7Vt2zbt3bu3o9cGnySTSe3YsSNQv7AeraO1Lcn/jBw3/9ks7sV16PghObW+sXZyOnT8kOKejd8jGgSc27bQO9g2btyY2nz/7ne/01VXXaXf/e53qU33xo0b/V5iTkyYMEHPPPOMXnvtNQ0bNkxVVVUaNmyYtm/frmeeeUYTJkwoivvO5nNZOrf97J0vGT+r+d/+9jeNHj069d/f+MY3JEl333235s+f32ELg3+SyaTWr1+viy++uOi/s4Szo7UtnpdU+L/+bBYJR/SH//MHHTt17Iwfe16n8xQJB/s77kHCuW0LvYNv48aNampq0u23364tW7Zo0KBB+v3vfx+4K92nmzBhgsaNG6e1a9fq4MGD+tCHPqThw4fn5e95R973+/1c1s5tP3vnQ8Yb71GjRjFuGHCRSET33HOP38tAHtDaljONmktSj8491KNzj3wvCTnCuW0LvW3o0qWLampq/F5G3oXDYY0aNaro7/v9fC6L57afvXONn/E+i/79+2vz5s3q37+/30vJq2QyqS1btpgYa7GO1racadQcwcO5bQu9C19HfU1Ja1voHSxsvM+ioqJCgwYNUkVFhd9LyatkMqnXX3+dk9wAWtvieckWfyK4OLdtoXfh66ivKWltC72DJeNRcwRfJBLRpEmT/F4G8oDWtpxt1BzBwrltC73toLUt9A4WNt5Ik0gktGnTJg0ePFilpfwVCbJian3ixAlJ0pYtW3xeSfGK/HunLpe0fccOxQ4xbp5rO3fu9O2+i+ncRvbobQetbaF3sFAQaZxz2rdvn66++mq/l4IcK6bWb7zxhiRp8uTJPq+keF3Zo0Rb7u2iu+66S1vZeOdNZWVl3u+zmM5tZI/edtDaFnoHS8jl+SnKGxoa1LVrV9XX16uqqiqfdw2giB09elRLly5V//79zT3vQkcJJU6pU9NenepyoVxpJ7+XY0JlZaUuvvhiv5cBAAByIJO9LVe8kSaRSGjdunW67rrrGGsJuGJq3a1bN33xi1/0exlF7b3eMV036JqC743sFNO5jezR2w5a20LvYOFZzZHGOaeGhgZ+X7sBtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQoUz2tlzxRppEIqEXXnhBiUTC76Ugx2htC73toLUt9LaD1rbQO1jYeAMAAAAAkEOMmgMAAAAAkCFGzZGVeDyumpoaxeNxv5eCHKO1LfS2g9a20NsOWttC72Bh4400oVBIVVVVCoVCfi8FOUZrW+htB61tobcdtLaF3sHCqDkAAAAAABli1BxZicfjWrRoEWMtBtDaFnrbQWtb6G0HrW2hd7Cw8UaaUCik3r17M9ZiAK1tobcdtLaF3nbQ2hZ6Bwuj5gAAAAAAZIhRc2QlFovp6aefViwW83spyDFa20JvO2htC73toLUt9A4WNt5IEw6HNWDAAIXDYb+XghyjtS30toPWttDbDlrbQu9gYdQcAAAAAIAMMWqOrMRiMc2bN4+xFgNobQu97aC1LfS2g9a20DtY2HgjTTgc1rXXXstYiwG0toXedtDaFnrbQWtb6B0sjJoDAAAAAJAhRs2RlVgspkceeYSxFgNobQu97aC1LfS2g9a20DtY2HgjTWlpqaqrq1VaWur3UpBjtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQIUbNkZVoNKqHH35Y0WjU76Ugx2htC73toLUt9LaD1rbQO1i44o00nudp//796tWrl0pK+N5MkNHaFnrbQWtb6G0HrW2hd+HLZG/LxhsAAAAAgAwxao6sRKNRzZgxg7EWA2htC73toLUt9LaD1rbQO1i44o00nufp6NGj6tatG2MtAUdrW+htB61tobcdtLaF3oWPUXMAAAAAAHKIUXNkJRqN6sEHH2SsxQBa20JvO2htC73toLUt9A4WrngjjXNOjY2NqqysVCgU8ns5yCFa20JvO2htC73toLUt9C58XPFG1srLy/1eAvKE1rbQ2w5a20JvO2htC72Dg4030sRiMc2cOVOxWMzvpSDHaG0Lve2gtS30toPWttA7WBg1RxrnnGKxmCKRCGMtAUdrW+htB61tobcdtLaF3oWPUXNkjSdxsIPWttDbDlrbQm87aG0LvYODjTfSxGIxzZ49m7EWA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMrnufpyJEj8jzP76Ugx2htC73toLUt9LaD1rbQO1jYeCNNPB7XvHnzFI/H/V4KcozWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmy4nme3nnnHcZaDKC1LfS2g9a20NsOWttC72Bh44008XhcixYtYqzFAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5siK53navXs3Yy0G0NoWettBa1vobQetbaF3sLDxRppEIqEXX3xRiUTC76Ugx2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSO3bsUDKZ9HspyDFa20JvO2htC73toLUt9A4WNt5Ik0wmtX79ek5yA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSW7Zs4btrBtDaFnrbQWtb6G0HrW2hd7Cw8UaaZDKp119/nZPcAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5shKIpFQbW0tvzPQAFrbQm87aG0Lve2gtS30DhY23kjjnNO+ffuU52EI+IDWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmykkgktGrVKsZaDKC1LfS2g9a20NsOWttC72Bh4400zjk1NDQw1mIArW2htx20toXedtDaFnoHC6PmAAAAAABkiFFzZCWRSOiFF15grMUAWttCbztobQu97aC1LfQOFjbeAAAAAADkEKPmAAAAAABkKJO9bWme1pTSvM9vaGjI912jneLxuJYvX65PfOITKisr83s5yCFa20JvO2htC73toLUt9C58zXva9lzLzvvGu7GxUZLUp0+ffN81AAAAAAAdqrGxUV27dj3rbfI+au55ng4cOKDKykqFQqF83jXaqaGhQX369NE777zDjwMEHK1tobcdtLaF3nbQ2hZ6Fz7nnBobG9WzZ0+VlJz96dPyfsW7pKREvXv3zvfd4n2oqqriJDeC1rbQ2w5a20JvO2htC70LW1tXupvxrOYAAAAAAOQQG28AAAAAAHKIjTfSlJeXa9q0aSovL/d7KcgxWttCbztobQu97aC1LfQOlrw/uRoAAAAAAJZwxRsAAAAAgBxi4w0AAAAAQA6x8QYAAAAAIIfYeAMAAAAAkENsvI1Ys2aNxo4dq549eyoUCmnp0qVpt9m5c6duvvlmde3aVZ07d9bgwYO1d+/e1PtPnTqlKVOm6Pzzz1eXLl10yy236PDhw3k8CrRHW62bmpo0depU9e7dW+ecc44GDBigRx99tMVtaF08ZsyYocGDB6uyslLdu3fX+PHj9eabb7a4TXt67t27VzfddJMqKirUvXt3ffvb31YikcjnoaANbbU+duyYvvrVr6pfv34655xzdOGFF+prX/ua6uvrW3weWheH9pzbzZxz+tSnPtXqYz69C197W9fW1ur6669X586dVVVVpREjRujkyZOp9x87dkx33HGHqqqqdO655+qee+5RU1NTPg8F7dCe3ocOHdKdd96pHj16qHPnzho0aJD+9Kc/tbgNvYsPG28jjh8/riuuuEJz585t9f1vvfWWrrvuOvXv31+rVq3SP/7xD/3gBz9Qp06dUre5//779ec//1mLFi3S6tWrdeDAAU2YMCFfh4B2aqv1N77xDS1btkxPP/20du7cqa9//euaOnWqampqUrehdfFYvXq1pkyZovXr12v58uWKx+Oqrq7W8ePHU7dpq2cymdRNN92kWCymV199VU888YTmz5+vH/7wh34cEs6grdYHDhzQgQMH9NBDD2n79u2aP3++li1bpnvuuSf1OWhdPNpzbjf7+c9/rlAolPZ2eheH9rSura3VmDFjVF1drY0bN2rTpk2aOnWqSkr+90v5O+64Qzt27NDy5cv13HPPac2aNfrSl77kxyHhLNrT+6677tKbb76pmpoavfbaa5owYYJuvfVWbd26NXUbehchB3MkuSVLlrR428SJE92kSZPO+DHvvvuuKysrc4sWLUq9befOnU6Sq62tzdVSkaXWWl922WXuRz/6UYu3DRo0yH3ve99zztG62B05csRJcqtXr3bOta/nX/7yF1dSUuIOHTqUus2vfvUrV1VV5aLRaH4PAO12euvWLFy40EUiERePx51ztC5mZ+q9detW16tXL3fw4MG0x3x6F6fWWg8ZMsR9//vfP+PHvP76606S27RpU+ptf/3rX10oFHL79+/P6XqRndZ6d+7c2T355JMtbnfeeee5xx57zDlH72LFFW/I8zw9//zzuuSSS/TJT35S3bt315AhQ1qMq23evFnxeFw33nhj6m39+/fXhRdeqNraWh9Wjfdr2LBhqqmp0f79++Wc08qVK7Vr1y5VV1dLonWxax4rPu+88yS1r2dtba0GDhyoCy64IHWbT37yk2poaNCOHTvyuHpk4vTWZ7pNVVWVSktLJdG6mLXW+8SJE7r99ts1d+5c9ejRI+1j6F2cTm995MgRbdiwQd27d9ewYcN0wQUXaOTIkVq3bl3qY2pra3Xuuefq6quvTr3txhtvVElJiTZs2JDfA0BGWju3hw0bpj/+8Y86duyYPM/TH/7wB506dUqjRo2SRO9ixcYbOnLkiJqamjRz5kyNGTNGL774oj796U9rwoQJWr16taT3ftYkEono3HPPbfGxF1xwgQ4dOuTDqvF+zZkzRwMGDFDv3r0ViUQ0ZswYzZ07VyNGjJBE62LmeZ6+/vWv6+Mf/7guv/xySe3reejQoRZfmDe/v/l9KDyttT7d0aNH9eMf/7jF6CGti9OZet9///0aNmyYxo0b1+rH0bv4tNb67bffliRNnz5dkydP1rJlyzRo0CDdcMMNqqurk/Rez+7du7f4XKWlpTrvvPNoXcDOdG4vXLhQ8Xhc559/vsrLy3XvvfdqyZIl6tu3ryR6F6tSvxcA/3meJ0kaN26c7r//fknSxz72Mb366qt69NFHNXLkSD+Xhw42Z84crV+/XjU1Nbrooou0Zs0aTZkyRT179mxxVRTFZ8qUKdq+fXuLqyAIprZaNzQ06KabbtKAAQM0ffr0/C4OHa613jU1NVqxYkWLn/lE8WutdfPXaffee68+//nPS5KuvPJKvfzyy/rtb3+rGTNm+LJWZO9Mj+U/+MEP9O677+qll15St27dtHTpUt16661au3atBg4c6NNqkS2ueEPdunVTaWmpBgwY0OLtl156aepZzXv06KFYLKZ33323xW0OHz7c6ngbCtPJkyf13e9+Vw8//LDGjh2rj370o5o6daomTpyohx56SBKti9XUqVP13HPPaeXKlerdu3fq7e3p2aNHj7RnOW/+b5oXnjO1btbY2KgxY8aosrJSS5YsUVlZWep9tC4+Z+q9YsUKvfXWWzr33HNVWlqa+nGCW265JTWOSu/icqbWH/rQhySpza/Tjhw50uL9iURCx44do3WBOlPvt956S7/85S/129/+VjfccIOuuOIKTZs2TVdffXXqiXPpXZzYeEORSESDBw9O+1UGu3bt0kUXXSRJuuqqq1RWVqaXX3459f4333xTe/fu1dChQ/O6Xrx/8Xhc8Xi8xbOgSlI4HE59R53WxcU5p6lTp2rJkiVasWKFPvKRj7R4f3t6Dh06VK+99lqLf8SXL1+uqqqqtC/04J+2WkvvXemurq5WJBJRTU1Ni99MIdG6mLTV+zvf+Y7+8Y9/aNu2bakXSZo9e7Yef/xxSfQuFm21/vCHP6yePXue9eu0oUOH6t1339XmzZtT71+xYoU8z9OQIUNyfxBot7Z6nzhxQpLO+rUavYuUn8/shvxpbGx0W7dudVu3bnWS3MMPP+y2bt3q/vWvfznnnFu8eLErKytzv/71r11dXZ2bM2eOC4fDbu3atanP8eUvf9ldeOGFbsWKFe5vf/ubGzp0qBs6dKhfh4QzaKv1yJEj3WWXXeZWrlzp3n77bff444+7Tp06uUceeST1OWhdPL7yla+4rl27ulWrVrmDBw+mXk6cOJG6TVs9E4mEu/zyy111dbXbtm2bW7ZsmfvgBz/oHnjgAT8OCWfQVuv6+no3ZMgQN3DgQLd79+4Wt0kkEs45WheT9pzbp9Npz2pO7+LQntazZ892VVVVbtGiRa6urs59//vfd506dXK7d+9O3WbMmDHuyiuvdBs2bHDr1q1zF198sbvtttv8OCScRVu9Y7GY69u3rxs+fLjbsGGD2717t3vooYdcKBRyzz//fOrz0Lv4sPE2YuXKlU5S2svdd9+dus28efNc3759XadOndwVV1zhli5d2uJznDx50t13333uAx/4gKuoqHCf/vSn3cGDB/N8JGhLW60PHjzoPve5z7mePXu6Tp06uX79+rlZs2Y5z/NSn4PWxaO11pLc448/nrpNe3ru2bPHfepTn3LnnHOO69atm/vmN7+Z+hVUKAxttT7TuS/J/fOf/0x9HloXh/ac2619zOm/QpLeha+9rWfMmOF69+7tKioq3NChQ1tcHHHOuX//+9/utttuc126dHFVVVXu85//vGtsbMzjkaA92tN7165dbsKECa579+6uoqLCffSjH0379WL0Lj4h55zr6KvoAAAAAADgPfyMNwAAAAAAOcTGGwAAAACAHGLjDQAAAABADrHxBgAAAAAgh9h4AwAAAACQQ2y8AQAAAADIITbeAAAAAADkEBtvAAAAAAByiI03AAAAAAA5xMYbAAAAAIAcYuMNAAAAAEAOsfEGAAAAACCH/j+8q7kCS2EPGAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ਅਸੀਂ ਆਪਣੇ ਡੇਟਾਸੈੱਟ ਦੇ ਉਪਸੈੱਟਾਂ ਦੇ ਬਾਕਸ ਪਲਾਟ ਵੀ ਬਣਾ ਸਕਦੇ ਹਾਂ, ਉਦਾਹਰਣ ਵਜੋਂ, ਖਿਡਾਰੀ ਦੀ ਭੂਮਿਕਾ ਅਨੁਸਾਰ ਗਰੁੱਪ ਕੀਤਾ ਹੋਇਆ।\n"
+    "ਅਸੀਂ ਆਪਣੇ ਡੇਟਾਸੈੱਟ ਦੇ ਉਪਸੈੱਟਾਂ ਦੇ ਬਾਕਸ ਪਲਾਟ ਵੀ ਬਣਾ ਸਕਦੇ ਹਾਂ, ਉਦਾਹਰਣ ਵਜੋਂ, ਖਿਡਾਰੀ ਦੀ ਭੂਮਿਕਾ ਅਨੁਸਾਰ ਗਰੁੱਪਬੱਧ।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **ਨੋਟ**: ਇਸ ਡਾਇਗ੍ਰਾਮ ਤੋਂ ਪਤਾ ਲਗਦਾ ਹੈ ਕਿ ਔਸਤ ਵਿੱਚ ਪਹਿਲੇ ਬੇਸਮੈਨ ਦੀ ਉਚਾਈ ਦੂਜੇ ਬੇਸਮੈਨ ਦੀ ਉਚਾਈ ਤੋਂ ਵੱਧ ਹੁੰਦੀ ਹੈ। ਅੱਗੇ ਚੱਲ ਕੇ ਅਸੀਂ ਸਿੱਖਾਂਗੇ ਕਿ ਇਸ ਧਾਰਨਾ ਨੂੰ ਹੋਰ ਵਿਗਿਆਨਕ ਢੰਗ ਨਾਲ ਕਿਵੇਂ ਪਰਖਿਆ ਜਾ ਸਕਦਾ ਹੈ ਅਤੇ ਕਿਵੇਂ ਸਾਬਤ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ ਕਿ ਸਾਡੇ ਡਾਟਾ ਵਿੱਚ ਅੰਕੜੇਗਣਿਤ ਰੂਪ ਵਿੱਚ ਮਹੱਤਵਪੂਰਨ ਸਬੂਤ ਹਨ।  \n",
+    "> **Note**: ਇਹ ਚਿੱਤਰ ਦਰਸਾਉਂਦਾ ਹੈ ਕਿ ਆਮ ਤੌਰ 'ਤੇ ਪਹਿਲੇ ਬੇਸਮੈਨ ਦੀਆਂ ਉਚਾਈਆਂ ਦੂਜੇ ਬੇਸਮੈਨ ਦੀਆਂ ਉਚਾਈਆਂ ਨਾਲੋਂ ਵੱਧ ਹੁੰਦੀਆਂ ਹਨ। ਬਾਅਦ ਵਿੱਚ ਅਸੀਂ ਸਿੱਖਾਂਗੇ ਕਿ ਅਸੀਂ ਇਸ ਧਾਰਨਾ ਨੂੰ ਹੋਰ ਵਿਧੀਵਤ ਤਰੀਕੇ ਨਾਲ ਕਿਵੇਂ ਪਰਖ ਸਕਦੇ ਹਾਂ, ਅਤੇ ਕਿਵੇਂ ਸਾਬਤ ਕਰ ਸਕਦੇ ਹਾਂ ਕਿ ਸਾਡੇ ਡਾਟਾ ਨੂੰ ਸਾਂਖਿਆਤਮਿਕ ਤੌਰ 'ਤੇ ਮਹੱਤਵਪੂਰਨ ਦਿਖਾਉਣ ਲਈ ਵਰਤਿਆ ਜਾ ਸਕਦਾ ਹੈ।  \n",
     "\n",
-    "ਉਮਰ, ਉਚਾਈ ਅਤੇ ਵਜ਼ਨ ਸਾਰੇ ਲਗਾਤਾਰ ਰੈਂਡਮ ਵੈਰੀਏਬਲ ਹਨ। ਤੁਹਾਨੂੰ ਕੀ ਲੱਗਦਾ ਹੈ ਕਿ ਇਹਨਾਂ ਦੀ ਵੰਡ ਕਿਹੋ ਜਿਹੀ ਹੈ? ਇਸਨੂੰ ਪਤਾ ਕਰਨ ਦਾ ਇੱਕ ਵਧੀਆ ਤਰੀਕਾ ਇਹ ਹੈ ਕਿ ਮੁੱਲਾਂ ਦਾ ਹਿਸਟੋਗ੍ਰਾਮ ਬਣਾਇਆ ਜਾਵੇ:\n"
+    "ਉਮਰ, ਉਚਾਈ ਅਤੇ ਵਜ਼ਨ ਸਾਰੇ ਲਗਾਤਾਰ ਰੈਂਡਮ ਵੈਰੀਏਬਲ ਹਨ। ਤੁਹਾਨੂੰ ਕੀ ਲੱਗਦਾ ਹੈ ਕਿ ਇਹਨਾਂ ਦਾ ਵੰਡਨ ਕਿਹੜਾ ਹੈ? ਪਤਾ ਕਰਨ ਦਾ ਇੱਕ ਵਧੀਆ ਤਰੀਕਾ ਇਹ ਹੈ ਕਿ ਮੁੱਲਾਂ ਦਾ ਹਿਸਟੋਗ੍ਰਾਮ ਪਲਾਟ ਕੀਤਾ ਜਾਵੇ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ਜਿਵੇਂ ਕਿ ਜ਼ਿਆਦਾਤਰ ਮੁੱਲ ਵਾਸਤਵਿਕ ਜੀਵਨ ਵਿੱਚ ਆਮ ਤੌਰ 'ਤੇ ਵੰਡੇ ਜਾਂਦੇ ਹਨ, ਸਾਨੂੰ ਨਮੂਨਾ ਡਾਟਾ ਤਿਆਰ ਕਰਨ ਲਈ ਇੱਕ ਸਮਾਨ ਰੈਂਡਮ ਨੰਬਰ ਜਨਰੇਟਰ ਦੀ ਵਰਤੋਂ ਨਹੀਂ ਕਰਨੀ ਚਾਹੀਦੀ। ਇੱਥੇ ਇਹ ਹੈ ਕਿ ਜੇ ਅਸੀਂ ਸਮਾਨ ਵੰਡ ਨਾਲ ਭਾਰ ਤਿਆਰ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਦੇ ਹਾਂ (ਜੋ `np.random.rand` ਦੁਆਰਾ ਤਿਆਰ ਕੀਤਾ ਗਿਆ ਹੈ):\n"
+    "ਜਿਵੇਂ ਕਿ ਜ਼ਿਆਦਾਤਰ ਮੁੱਲ ਵਾਸਤਵਿਕ ਜੀਵਨ ਵਿੱਚ ਆਮ ਤੌਰ 'ਤੇ ਵੰਡੇ ਜਾਂਦੇ ਹਨ, ਸਾਨੂੰ ਨਮੂਨਾ ਡਾਟਾ ਤਿਆਰ ਕਰਨ ਲਈ ਇੱਕ ਸਮਾਨ ਰੈਂਡਮ ਨੰਬਰ ਜਨਰੇਟਰ ਦੀ ਵਰਤੋਂ ਨਹੀਂ ਕਰਨੀ ਚਾਹੀਦੀ। ਇੱਥੇ ਇਹ ਹੈ ਕਿ ਕੀ ਹੁੰਦਾ ਹੈ ਜੇ ਅਸੀਂ ਸਮਾਨ ਵੰਡ ਨਾਲ ਭਾਰ ਤਿਆਰ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਦੇ ਹਾਂ (ਜੋ `np.random.rand` ਦੁਆਰਾ ਤਿਆਰ ਕੀਤਾ ਗਿਆ ਹੈ):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -554,23 +395,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ਭਰੋਸੇਯੋਗ ਅੰਤਰਾਲ\n",
+    "## ਭਰੋਸੇਮੰਦ ਅੰਤਰਾਲ\n",
     "\n",
-    "ਆਓ ਹੁਣ ਬੇਸਬਾਲ ਖਿਡਾਰੀਆਂ ਦੇ ਵਜ਼ਨਾਂ ਅਤੇ ਕਦਾਂ ਲਈ ਭਰੋਸੇਯੋਗ ਅੰਤਰਾਲ ਦੀ ਗਣਨਾ ਕਰੀਏ। ਅਸੀਂ ਇਸ ਕੋਡ ਦਾ ਇਸਤੇਮਾਲ ਕਰਾਂਗੇ [ਇਸ ਸਟੈਕਓਵਰਫਲ ਡਿਸਕਸ਼ਨ ਤੋਂ](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "ਆਓ ਹੁਣ ਬੇਸਬਾਲ ਖਿਡਾਰੀਆਂ ਦੇ ਵਜ਼ਨ ਅਤੇ ਕਦ ਲਈ ਭਰੋਸੇਮੰਦ ਅੰਤਰਾਲ ਦੀ ਗਣਨਾ ਕਰੀਏ। ਅਸੀਂ ਇਸ ਕੋਡ ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ [ਇਸ ਸਟੈਕਓਵਰਫਲੋ ਚਰਚਾ ਤੋਂ](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "ਅਸੀਂ ਵੇਖ ਸਕਦੇ ਹਾਂ ਕਿ ਇੰਟਰਵਲ ਇੱਕ ਦੂਜੇ ਨਾਲ ਓਵਰਲੈਪ ਨਹੀਂ ਕਰਦੇ।\n",
     "\n",
-    "ਇੱਕ ਅੰਕੜਿਆਂ ਅਧਾਰਤ ਹੋਰ ਸਹੀ ਤਰੀਕਾ ਪਰਿਕਲਪਨਾ ਨੂੰ ਸਾਬਤ ਕਰਨ ਦਾ **ਸਟੂਡੈਂਟ ਟੀ-ਟੈਸਟ** ਵਰਤਣਾ ਹੈ:\n"
+    "ਇੱਕ ਅੰਕੜਿਆਂ ਅਧਾਰਤ ਹੋਰ ਸਹੀ ਤਰੀਕਾ ਪਰਮਾਣਿਤ ਕਰਨ ਲਈ ਹੈ ਕਿ **ਸਟੂਡੈਂਟ ਟੀ-ਟੈਸਟ** ਦੀ ਵਰਤੋਂ ਕੀਤੀ ਜਾਵੇ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,34 +620,32 @@
    "metadata": {},
    "source": [
     "`ttest_ind` ਫੰਕਸ਼ਨ ਦੁਆਰਾ ਵਾਪਸ ਕੀਤੇ ਗਏ ਦੋ ਮੁੱਲ ਹਨ:  \n",
-    "* p-value ਨੂੰ ਦੋ ਵੰਡਾਂ ਦੇ ਇੱਕੋ ਜਿਹੇ ਮੀਨ ਹੋਣ ਦੀ ਸੰਭਾਵਨਾ ਵਜੋਂ ਮੰਨਿਆ ਜਾ ਸਕਦਾ ਹੈ। ਸਾਡੇ ਕੇਸ ਵਿੱਚ, ਇਹ ਬਹੁਤ ਘੱਟ ਹੈ, ਜਿਸਦਾ ਮਤਲਬ ਹੈ ਕਿ ਪਹਿਲੇ ਬੇਸਮੈਨ ਦੇ ਲੰਬੇ ਹੋਣ ਦੇ ਹੱਕ ਵਿੱਚ ਮਜ਼ਬੂਤ ਸਬੂਤ ਹਨ।  \n",
-    "* t-value ਨਾਰਮਲਾਈਜ਼ਡ ਮੀਨ ਅੰਤਰ ਦਾ ਮੱਧਵਰਤੀ ਮੁੱਲ ਹੈ ਜੋ t-ਟੈਸਟ ਵਿੱਚ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਇਸਨੂੰ ਦਿੱਤੇ ਗਏ ਭਰੋਸੇ ਦੇ ਮੁੱਲ ਲਈ ਇੱਕ ਥ੍ਰੈਸ਼ਹੋਲਡ ਮੁੱਲ ਦੇ ਖਿਲਾਫ ਤੁਲਨਾ ਕੀਤੀ ਜਾਂਦੀ ਹੈ।  \n"
+    "* p-value ਨੂੰ ਦੋ ਵੰਡਾਂ ਦੇ ਇੱਕੋ ਜਿਹੇ ਔਸਤ ਹੋਣ ਦੀ ਸੰਭਾਵਨਾ ਵਜੋਂ ਸਮਝਿਆ ਜਾ ਸਕਦਾ ਹੈ। ਸਾਡੇ ਕੇਸ ਵਿੱਚ, ਇਹ ਬਹੁਤ ਘੱਟ ਹੈ, ਜਿਸਦਾ ਮਤਲਬ ਹੈ ਕਿ ਪਹਿਲੇ ਬੇਸਮੈਨ ਦੇ ਲੰਬੇ ਹੋਣ ਦੇ ਹੱਕ ਵਿੱਚ ਮਜ਼ਬੂਤ ਸਬੂਤ ਹਨ।  \n",
+    "* t-value ਸਧਾਰਿਤ ਔਸਤ ਅੰਤਰ ਦਾ ਵਿਚਾਰਧਾਰਾ ਮੁੱਲ ਹੈ ਜੋ t-ਟੈਸਟ ਵਿੱਚ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਇਸਨੂੰ ਦਿੱਤੇ ਗਏ ਭਰੋਸੇ ਦੇ ਮੁੱਲ ਲਈ ਇੱਕ ਥ੍ਰੈਸ਼ਹੋਲਡ ਮੁੱਲ ਦੇ ਖਿਲਾਫ ਤੁਲਨਾ ਕੀਤੀ ਜਾਂਦੀ ਹੈ।  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ਸੈਂਟਰਲ ਲਿਮਟ ਥਿਓਰਮ ਨਾਲ ਨਾਰਮਲ ਡਿਸਟ੍ਰੀਬਿਊਸ਼ਨ ਦੀ ਨਕਲ ਕਰਨਾ\n",
+    "## ਸੈਂਟਰਲ ਲਿਮਿਟ ਥਿਊਰਮ ਨਾਲ ਨਾਰਮਲ ਡਿਸਟ੍ਰੀਬਿਊਸ਼ਨ ਦੀ ਨਕਲ ਕਰਨਾ\n",
     "\n",
-    "ਪਾਇਥਨ ਵਿੱਚ ਪਸੂਡੋ-ਰੈਂਡਮ ਜਨਰੇਟਰ ਸਾਨੂੰ ਇੱਕ ਯੂਨੀਫਾਰਮ ਡਿਸਟ੍ਰੀਬਿਊਸ਼ਨ ਦਿੰਦਾ ਹੈ। ਜੇਕਰ ਅਸੀਂ ਨਾਰਮਲ ਡਿਸਟ੍ਰੀਬਿਊਸ਼ਨ ਲਈ ਜਨਰੇਟਰ ਬਣਾਉਣਾ ਚਾਹੁੰਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ ਸੈਂਟਰਲ ਲਿਮਟ ਥਿਓਰਮ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹਾਂ। ਨਾਰਮਲ ਤੌਰ 'ਤੇ ਵੰਡਿਆ ਹੋਇਆ ਮੁੱਲ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ, ਅਸੀਂ ਯੂਨੀਫਾਰਮ-ਜਨਰੇਟ ਕੀਤੇ ਨਮੂਨੇ ਦਾ ਔਸਤ ਗਿਣਾਂਗੇ।\n"
+    "ਪਾਇਥਨ ਵਿੱਚ ਪਸੂਡੋ-ਰੈਂਡਮ ਜਨਰੇਟਰ ਸਾਨੂੰ ਇੱਕ ਯੂਨੀਫਾਰਮ ਡਿਸਟ੍ਰੀਬਿਊਸ਼ਨ ਦੇਣ ਲਈ ਤਿਆਰ ਕੀਤਾ ਗਿਆ ਹੈ। ਜੇਕਰ ਅਸੀਂ ਨਾਰਮਲ ਡਿਸਟ੍ਰੀਬਿਊਸ਼ਨ ਲਈ ਇੱਕ ਜਨਰੇਟਰ ਬਣਾਉਣਾ ਚਾਹੁੰਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ ਸੈਂਟਰਲ ਲਿਮਿਟ ਥਿਊਰਮ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹਾਂ। ਨਾਰਮਲ ਡਿਸਟ੍ਰੀਬਿਊਟ ਕੀਤੀ ਗਈ ਵੈਲਿਊ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ, ਅਸੀਂ ਸਿਰਫ਼ ਯੂਨੀਫਾਰਮ-ਜਨਰੇਟ ਕੀਤੇ ਸੈਂਪਲ ਦਾ ਔਸਤ ਗਣਨਾ ਕਰਾਂਗੇ।\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## ਸਬੰਧ ਅਤੇ ਦੁਰਾਚਾਰੀ ਬੇਸਬਾਲ ਕਾਰਪੋਰੇਸ਼ਨ\n",
     "\n",
-    "ਸਬੰਧ ਸਾਨੂੰ ਡਾਟਾ ਕ੍ਰਮਾਂ ਦੇ ਵਿਚਕਾਰ ਸੰਬੰਧ ਲੱਭਣ ਵਿੱਚ ਸਹਾਇਤਾ ਕਰਦਾ ਹੈ। ਸਾਡੇ ਖੇਡ ਦੇ ਉਦਾਹਰਨ ਵਿੱਚ, ਆਓ ਕਲਪਨਾ ਕਰੀਏ ਕਿ ਇੱਕ ਦੁਰਾਚਾਰੀ ਬੇਸਬਾਲ ਕਾਰਪੋਰੇਸ਼ਨ ਹੈ ਜੋ ਆਪਣੇ ਖਿਡਾਰੀਆਂ ਨੂੰ ਉਨ੍ਹਾਂ ਦੀ ਉਚਾਈ ਦੇ ਅਧਾਰ 'ਤੇ ਤਨਖਾਹ ਦਿੰਦਾ ਹੈ - ਜਿੰਨਾ ਲੰਬਾ ਖਿਡਾਰੀ ਹੋਵੇਗਾ, ਉਸਨੂੰ ਉਤਨਾ ਹੀ ਵੱਧ ਪੈਸਾ ਮਿਲੇਗਾ। ਮੰਨ ਲਓ ਕਿ ਮੁੱਢਲੀ ਤਨਖਾਹ $1000 ਹੈ, ਅਤੇ ਉਚਾਈ ਦੇ ਅਧਾਰ 'ਤੇ $0 ਤੋਂ $100 ਤੱਕ ਦਾ ਵਾਧੂ ਬੋਨਸ ਮਿਲਦਾ ਹੈ। ਅਸੀਂ MLB ਦੇ ਅਸਲੀ ਖਿਡਾਰੀਆਂ ਨੂੰ ਲਵਾਂਗੇ ਅਤੇ ਉਨ੍ਹਾਂ ਦੀ ਕਲਪਨਾਤਮਕ ਤਨਖਾਹਾਂ ਦੀ ਗਣਨਾ ਕਰਾਂਗੇ:\n"
+    "ਸਬੰਧ ਸਾਨੂੰ ਡਾਟਾ ਕ੍ਰਮਾਂ ਦੇ ਵਿਚਕਾਰ ਸੰਬੰਧ ਲੱਭਣ ਵਿੱਚ ਸਹਾਇਤਾ ਕਰਦਾ ਹੈ। ਸਾਡੇ ਖੇਡ ਦੇ ਉਦਾਹਰਨ ਵਿੱਚ, ਆਓ ਕਲਪਨਾ ਕਰੀਏ ਕਿ ਇੱਕ ਦੁਰਾਚਾਰੀ ਬੇਸਬਾਲ ਕਾਰਪੋਰੇਸ਼ਨ ਹੈ ਜੋ ਆਪਣੇ ਖਿਡਾਰੀਆਂ ਨੂੰ ਉਨ੍ਹਾਂ ਦੀ ਉਚਾਈ ਦੇ ਅਧਾਰ 'ਤੇ ਤਨਖਾਹ ਦਿੰਦਾ ਹੈ - ਜਿੰਨਾ ਲੰਮਾ ਖਿਡਾਰੀ ਹੋਵੇਗਾ, ਉਸਨੂੰ ਉਤਨਾ ਜ਼ਿਆਦਾ ਪੈਸਾ ਮਿਲੇਗਾ। ਮੰਨ ਲਓ ਕਿ ਇੱਕ ਬੇਸ ਤਨਖਾਹ $1000 ਹੈ, ਅਤੇ ਉਚਾਈ ਦੇ ਅਧਾਰ 'ਤੇ $0 ਤੋਂ $100 ਤੱਕ ਦਾ ਵਾਧੂ ਬੋਨਸ ਮਿਲਦਾ ਹੈ। ਅਸੀਂ MLB ਦੇ ਅਸਲ ਖਿਡਾਰੀਆਂ ਨੂੰ ਲਵਾਂਗੇ ਅਤੇ ਉਨ੍ਹਾਂ ਦੀ ਕਲਪਨਾਤਮਕ ਤਨਖਾਹਾਂ ਦੀ ਗਣਨਾ ਕਰਾਂਗੇ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ਇਸ ਮਾਮਲੇ ਵਿੱਚ, ਸਬੰਧ ਥੋੜ੍ਹਾ ਘੱਟ ਹੈ, ਪਰ ਇਹ ਫਿਰ ਵੀ ਕਾਫ਼ੀ ਉੱਚਾ ਹੈ। ਹੁਣ, ਸਬੰਧ ਨੂੰ ਹੋਰ ਘੱਟ ਸਪਸ਼ਟ ਬਣਾਉਣ ਲਈ, ਅਸੀਂ ਤਨਖਾਹ ਵਿੱਚ ਕੁਝ ਰੈਂਡਮ ਵੈਰੀਏਬਲ ਸ਼ਾਮਲ ਕਰਕੇ ਕੁਝ ਵਾਧੂ ਰੈਂਡਮਨੈੱਸ ਸ਼ਾਮਲ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹਾਂ। ਆਓ ਵੇਖੀਏ ਕਿ ਕੀ ਹੁੰਦਾ ਹੈ:\n"
+    "ਇਸ ਮਾਮਲੇ ਵਿੱਚ, ਸਬੰਧ ਥੋੜ੍ਹਾ ਘੱਟ ਹੈ, ਪਰ ਇਹ ਫਿਰ ਵੀ ਕਾਫ਼ੀ ਉੱਚਾ ਹੈ। ਹੁਣ, ਸਬੰਧ ਨੂੰ ਹੋਰ ਵੀ ਘੱਟ ਸਪਸ਼ਟ ਬਣਾਉਣ ਲਈ, ਅਸੀਂ ਤਨਖਾਹ ਵਿੱਚ ਕੁਝ ਰੈਂਡਮ ਵੈਰੀਏਬਲ ਸ਼ਾਮਲ ਕਰਕੇ ਕੁਝ ਵਾਧੂ ਰੈਂਡਮਨੈੱਸ ਸ਼ਾਮਲ ਕਰਨਾ ਚਾਹਾਂਗੇ। ਆਓ ਵੇਖੀਏ ਕਿ ਕੀ ਹੁੰਦਾ ਹੈ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ਕੀ ਤੁਸੀਂ ਅਨੁਮਾਨ ਲਗਾ ਸਕਦੇ ਹੋ ਕਿ ਡਾਟਾਂ ਇਸ ਤਰ੍ਹਾਂ ਖੜੇ ਲਾਈਨਾਂ ਵਿੱਚ ਕਿਉਂ ਸਜ ਜਾਂਦੇ ਹਨ?\n",
+    "ਕੀ ਤੁਸੀਂ ਅਨੁਮਾਨ ਲਗਾ ਸਕਦੇ ਹੋ ਕਿ ਡਾਟਸ ਇਸ ਤਰ੍ਹਾਂ ਖੜੇ ਲਾਈਨਾਂ ਵਿੱਚ ਕਿਉਂ ਸੱਜੇ ਹੋ ਜਾਂਦੇ ਹਨ?\n",
     "\n",
-    "ਅਸੀਂ ਇੱਕ ਕ੍ਰਿਤ੍ਰਿਮ ਤਰੀਕੇ ਨਾਲ ਬਣਾਈ ਗਈ ਧਾਰਨਾ ਜਿਵੇਂ ਕਿ ਤਨਖਾਹ ਅਤੇ ਦੇਖੀ ਗਈ ਚਰ **ਉਚਾਈ** ਦੇ ਵਿਚਕਾਰ ਸਬੰਧ ਦਾ ਅਧਿਐਨ ਕੀਤਾ ਹੈ। ਆਓ ਵੇਖੀਏ ਕਿ ਕੀ ਦੋ ਦੇਖੇ ਗਏ ਚਰ, ਜਿਵੇਂ ਕਿ ਉਚਾਈ ਅਤੇ ਵਜ਼ਨ, ਵੀ ਆਪਸ ਵਿੱਚ ਸਬੰਧਿਤ ਹਨ:\n"
+    "ਅਸੀਂ ਇੱਕ ਕ੍ਰਿਤ੍ਰਿਮ ਤਰੀਕੇ ਨਾਲ ਬਣਾਈ ਗਈ ਧਾਰਨਾ ਜਿਵੇਂ ਕਿ ਤਨਖਾਹ ਅਤੇ ਦੇਖੀ ਗਈ ਚਰ *ਉਚਾਈ* ਦੇ ਵਿਚਕਾਰ ਸਬੰਧ ਨੂੰ ਦੇਖਿਆ ਹੈ। ਆਓ ਇਹ ਵੀ ਵੇਖੀਏ ਕਿ ਕੀ ਦੋ ਦੇਖੇ ਗਏ ਚਰ, ਜਿਵੇਂ ਕਿ ਉਚਾਈ ਅਤੇ ਵਜ਼ਨ, ਵੀ ਆਪਸ ਵਿੱਚ ਸਬੰਧਿਤ ਹਨ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ਅਫਸੋਸ, ਸਾਨੂੰ ਕੋਈ ਨਤੀਜੇ ਨਹੀਂ ਮਿਲੇ - ਸਿਰਫ ਕੁਝ ਅਜੀਬ `nan` ਮੁੱਲ ਪ੍ਰਾਪਤ ਹੋਏ। ਇਹ ਇਸ ਕਰਕੇ ਹੈ ਕਿਉਂਕਿ ਸਾਡੀ ਸਿਰੀਜ਼ ਵਿੱਚ ਕੁਝ ਮੁੱਲ ਅਣਪਛਾਤੇ ਹਨ, ਜੋ `nan` ਵਜੋਂ ਦਰਸਾਏ ਗਏ ਹਨ, ਜਿਸ ਕਾਰਨ ਕਾਰਵਾਈ ਦਾ ਨਤੀਜਾ ਵੀ ਅਣਪਛਾਤਾ ਰਹਿੰਦਾ ਹੈ। ਮੈਟ੍ਰਿਕਸ ਨੂੰ ਦੇਖ ਕੇ ਸਾਨੂੰ ਪਤਾ ਲੱਗਦਾ ਹੈ ਕਿ `Weight` ਸਮੱਸਿਆ ਵਾਲਾ ਕਾਲਮ ਹੈ, ਕਿਉਂਕਿ `Height` ਮੁੱਲਾਂ ਦੇ ਆਪਸੀ ਸਬੰਧ ਦੀ ਗਣਨਾ ਕੀਤੀ ਗਈ ਹੈ।\n",
+    "ਅਫਸੋਸ, ਸਾਨੂੰ ਕੋਈ ਨਤੀਜੇ ਨਹੀਂ ਮਿਲੇ - ਸਿਰਫ ਕੁਝ ਅਜੀਬ `nan` ਮੁੱਲ ਪ੍ਰਾਪਤ ਹੋਏ। ਇਹ ਇਸ ਕਰਕੇ ਹੈ ਕਿਉਂਕਿ ਸਾਡੀ ਸਿਰੀਜ਼ ਵਿੱਚ ਕੁਝ ਮੁੱਲ ਅਣਪਛਾਤੇ ਹਨ, ਜੋ `nan` ਵਜੋਂ ਦਰਸਾਏ ਗਏ ਹਨ, ਜਿਸ ਕਾਰਨ ਕਾਰਵਾਈ ਦਾ ਨਤੀਜਾ ਵੀ ਅਣਪਛਾਤਾ ਰਹਿੰਦਾ ਹੈ। ਮੈਟ੍ਰਿਕਸ ਨੂੰ ਦੇਖਣ 'ਤੇ ਸਾਨੂੰ ਪਤਾ ਲੱਗਦਾ ਹੈ ਕਿ `Weight` ਸਮੱਸਿਆ ਵਾਲਾ ਕਾਲਮ ਹੈ, ਕਿਉਂਕਿ `Height` ਮੁੱਲਾਂ ਦੇ ਆਪਸੀ-ਸਬੰਧ ਦੀ ਗਣਨਾ ਕੀਤੀ ਗਈ ਹੈ।\n",
     "\n",
     "> ਇਹ ਉਦਾਹਰਨ **ਡਾਟਾ ਤਿਆਰੀ** ਅਤੇ **ਸਾਫ਼-ਸਫਾਈ** ਦੀ ਮਹੱਤਤਾ ਦਿਖਾਉਂਦਾ ਹੈ। ਬਿਨਾਂ ਠੀਕ ਡਾਟਾ ਦੇ ਅਸੀਂ ਕੁਝ ਵੀ ਗਣਨਾ ਨਹੀਂ ਕਰ ਸਕਦੇ।\n",
     "\n",
-    "ਆਓ `fillna` ਵਿਧੀ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਗੁੰਮ ਹੋਏ ਮੁੱਲਾਂ ਨੂੰ ਭਰਦੇ ਹਾਂ ਅਤੇ ਸਬੰਧ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ:\n"
+    "ਆਓ `fillna` ਵਿਧੀ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਗੁੰਮ ਹੋਏ ਮੁੱਲ ਭਰਦੇ ਹਾਂ ਅਤੇ ਸਬੰਧ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## ਨਤੀਜਾ\n",
     "\n",
-    "ਇਸ ਨੋਟਬੁੱਕ ਵਿੱਚ ਅਸੀਂ ਸਿੱਖਿਆ ਹੈ ਕਿ ਡਾਟਾ 'ਤੇ ਮੁਢਲੀ ਕਾਰਵਾਈਆਂ ਕਰਕੇ ਅੰਕੜੇਵਿਦੀ ਫੰਕਸ਼ਨਾਂ ਦੀ ਗਣਨਾ ਕਿਵੇਂ ਕਰਨੀ ਹੈ। ਹੁਣ ਅਸੀਂ ਜਾਣਦੇ ਹਾਂ ਕਿ ਗਣਿਤ ਅਤੇ ਅੰਕੜੇਵਿਦੀ ਦੇ ਮਜ਼ਬੂਤ ਸਾਧਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਕੁਝ ਧਾਰਨਾਵਾਂ ਨੂੰ ਸਾਬਤ ਕਿਵੇਂ ਕਰਨਾ ਹੈ, ਅਤੇ ਦਿੱਤੇ ਗਏ ਡਾਟਾ ਨਮੂਨੇ ਲਈ ਮਨਮਾਨੇ ਚਰਾਂ ਦੇ ਭਰੋਸੇਯੋਗ ਅੰਤਰਾਲ ਦੀ ਗਣਨਾ ਕਿਵੇਂ ਕਰਨੀ ਹੈ।\n"
+    "ਇਸ ਨੋਟਬੁੱਕ ਵਿੱਚ ਅਸੀਂ ਸਿੱਖਿਆ ਹੈ ਕਿ ਡਾਟਾ 'ਤੇ ਮੁਢਲੀ ਕਾਰਵਾਈਆਂ ਕਰਕੇ ਅੰਕੜੇਵਿਦੀ ਫੰਕਸ਼ਨਾਂ ਦੀ ਗਣਨਾ ਕਿਵੇਂ ਕਰਨੀ ਹੈ। ਹੁਣ ਅਸੀਂ ਜਾਣਦੇ ਹਾਂ ਕਿ ਗਣਿਤ ਅਤੇ ਅੰਕੜੇਵਿਦੀ ਦੇ ਮਜ਼ਬੂਤ ਸਾਧਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਕੁਝ ਪਰਿਕਲਪਨਾਵਾਂ ਨੂੰ ਸਾਬਤ ਕਰਨਾ ਅਤੇ ਦਿੱਤੇ ਗਏ ਡਾਟਾ ਨਮੂਨੇ ਲਈ ਮਨਮਾਨੇ ਚਰਾਂ ਦੇ ਭਰੋਸੇਯੋਗ ਅੰਤਰਾਲ ਦੀ ਗਣਨਾ ਕਿਵੇਂ ਕਰਨੀ ਹੈ।\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ਅਸਵੀਕਰਤੀ**:  \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀ ਹੋਣ ਦਾ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁੱਤੀਆਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਮੌਜੂਦ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੇ ਪ੍ਰਯੋਗ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤਫਹਮੀਆਂ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆਵਾਂ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।  \n"
+    "\n---\n\n**ਅਸਵੀਕਰਤੀ**:  \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀ ਹੋਣ ਦਾ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁਣਜੀਆਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਮੌਜੂਦ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੀ ਵਰਤੋਂ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।  \n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:24:57+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:23:50+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "pa"
   }
diff --git a/translations/pa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/pa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index ef8577cf..5db92443 100644
--- a/translations/pa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/pa/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "ਇਸ ਡਾਟਾਸੈੱਟ ਵਿੱਚ ਕਾਲਮ ਹੇਠਾਂ ਦਿੱਤੇ ਗਏ ਹਨ:  \n",
+    "ਇਸ ਡਾਟਾਸੈਟ ਵਿੱਚ, ਕਾਲਮ ਹੇਠਾਂ ਦਿੱਤੇ ਗਏ ਹਨ:  \n",
     "* ਉਮਰ ਅਤੇ ਲਿੰਗ ਸਵੈ-ਸਪਸ਼ਟ ਹਨ  \n",
     "* BMI ਸ਼ਰੀਰ ਦਾ ਭਾਰ ਸੂਚਕਾਂਕ ਹੈ  \n",
     "* BP ਔਸਤ ਰਕਤ ਦਬਾਅ ਹੈ  \n",
     "* S1 ਤੋਂ S6 ਵੱਖ-ਵੱਖ ਰਕਤ ਮਾਪ ਹਨ  \n",
-    "* Y ਇੱਕ ਸਾਲ ਵਿੱਚ ਬਿਮਾਰੀ ਦੇ ਵਿਕਾਸ ਦਾ ਗੁਣਾਤਮਕ ਮਾਪ ਹੈ  \n",
+    "* Y ਇੱਕ ਸਾਲ ਦੇ ਦੌਰਾਨ ਬਿਮਾਰੀ ਦੀ ਪ੍ਰਗਤੀ ਦਾ ਗੁਣਾਤਮਕ ਮਾਪ ਹੈ  \n",
     "\n",
-    "ਆਓ ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜਾ ਵਿਗਿਆਨ ਦੇ ਤਰੀਕਿਆਂ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਇਸ ਡਾਟਾਸੈੱਟ ਦਾ ਅਧਿਐਨ ਕਰੀਏ।  \n",
+    "ਆਓ ਸੰਭਾਵਨਾ ਅਤੇ ਅੰਕੜਾ ਵਿਗਿਆਨ ਦੇ ਤਰੀਕਿਆਂ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਇਸ ਡਾਟਾਸੈਟ ਦਾ ਅਧਿਐਨ ਕਰੀਏ।  \n",
     "\n",
-    "### ਕੰਮ 1: ਸਾਰੇ ਮੁੱਲਾਂ ਲਈ ਔਸਤ ਅਤੇ ਵਿਆਪਨ ਦੀ ਗਣਨਾ ਕਰੋ  \n"
+    "### ਕੰਮ 1: ਸਾਰੇ ਮੁੱਲਾਂ ਲਈ ਔਸਤ ਅਤੇ ਵੈਰੀਅੰਸ ਦੀ ਗਣਨਾ ਕਰੋ  \n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -593,9 +593,9 @@
    "cell_type": "markdown",
    "source": [
     "ਨਤੀਜੇ:  \n",
-    "* ਉਮਰ - ਸਧਾਰਨ  \n",
+    "* ਉਮਰ - ਸਧਾਰਣ  \n",
     "* ਲਿੰਗ - ਇਕਸਾਰ  \n",
-    "* ਬੀਐਮਆਈ, ਵਾਈ - ਦੱਸਣਾ ਔਖਾ\n"
+    "* BMI, Y - ਕਹਿਣਾ ਔਖਾ  \n"
    ],
    "metadata": {}
   },
@@ -604,7 +604,7 @@
    "source": [
     "### ਟਾਸਕ 4: ਵੱਖ-ਵੱਖ ਵੈਰੀਏਬਲਾਂ ਅਤੇ ਬਿਮਾਰੀ ਦੇ ਵਿਕਾਸ (Y) ਦੇ ਵਿਚਕਾਰ ਸਬੰਧ ਦੀ ਜਾਂਚ ਕਰੋ\n",
     "\n",
-    "> **ਸੁਝਾਅ** ਸਬੰਧ ਮੈਟ੍ਰਿਕਸ ਤੁਹਾਨੂੰ ਇਹ ਜਾਣਨ ਲਈ ਸਭ ਤੋਂ ਜ਼ਿਆਦਾ ਲਾਭਦਾਇਕ ਜਾਣਕਾਰੀ ਦੇਵੇਗਾ ਕਿ ਕਿਹੜੀਆਂ ਮੁੱਲਾਂ ਦਾ ਇੱਕ ਦੂਜੇ ਨਾਲ ਸਬੰਧ ਹੈ।\n"
+    "> **ਸੁਝਾਅ** ਸਬੰਧ ਮੈਟ੍ਰਿਕਸ ਤੁਹਾਨੂੰ ਇਹ ਪਤਾ ਲਗਾਉਣ ਲਈ ਸਭ ਤੋਂ ਜ਼ਿਆਦਾ ਮਦਦਗਾਰ ਜਾਣਕਾਰੀ ਦੇਵੇਗਾ ਕਿ ਕਿਹੜੀਆਂ ਮੁੱਲਾਂ ਇੱਕ ਦੂਜੇ 'ਤੇ ਨਿਰਭਰ ਹਨ।\n"
    ],
    "metadata": {}
   },
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "ਨਤੀਜਾ:  \n",
-    "* Y ਦੇ ਨਾਲ ਸਭ ਤੋਂ ਮਜ਼ਬੂਤ ਸੰਬੰਧ BMI ਅਤੇ S5 (ਖੂਨ ਵਿੱਚ ਸ਼ੂਗਰ) ਹੈ। ਇਹ ਵਾਜਬ ਲੱਗਦਾ ਹੈ।\n"
+    "* Y ਦਾ ਸਭ ਤੋਂ ਮਜ਼ਬੂਤ ਸੰਬੰਧ BMI ਅਤੇ S5 (ਖੂਨ ਵਿੱਚ ਸ਼ੱਕਰ) ਨਾਲ ਹੈ। ਇਹ ਵਾਜਬ ਲੱਗਦਾ ਹੈ।\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ਅਸਵੀਕਰਤੀ**:  \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀ ਹੋਣ ਦਾ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁੱਤੀਆਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਮੌਜੂਦ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੇ ਪ੍ਰਯੋਗ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।  \n"
+    "\n---\n\n**ਅਸਵੀਕਰਤੀ**:  \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀ ਹੋਣ ਦਾ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁੱਚੀਤਤਾਵਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਮੌਜੂਦ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੀ ਵਰਤੋਂ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।  \n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:52:28+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:24:25+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "pa"
   }
diff --git a/translations/pl/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/pl/1-Introduction/04-stats-and-probability/assignment.ipynb
index 77f93833..a03c1237 100644
--- a/translations/pl/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/pl/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,14 +149,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "W tym zestawie danych kolumny są następujące:\n",
-    "* Wiek i płeć są oczywiste\n",
-    "* BMI to wskaźnik masy ciała\n",
-    "* BP to średnie ciśnienie krwi\n",
-    "* S1 do S6 to różne pomiary krwi\n",
-    "* Y to jakościowy wskaźnik postępu choroby w ciągu jednego roku\n",
+    "W tym zbiorze danych kolumny przedstawiają następujące informacje:  \n",
+    "* Wiek i płeć są oczywiste  \n",
+    "* BMI to wskaźnik masy ciała  \n",
+    "* BP to średnie ciśnienie krwi  \n",
+    "* S1 do S6 to różne pomiary krwi  \n",
+    "* Y to jakościowa miara postępu choroby w ciągu jednego roku  \n",
     "\n",
-    "Przeanalizujmy ten zestaw danych za pomocą metod prawdopodobieństwa i statystyki.\n",
+    "Przeanalizujmy ten zbiór danych za pomocą metod prawdopodobieństwa i statystyki.\n",
     "\n",
     "### Zadanie 1: Oblicz średnie wartości i wariancję dla wszystkich danych\n"
    ],
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Zastrzeżenie**:  \nTen dokument został przetłumaczony za pomocą usługi tłumaczeniowej AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy wszelkich starań, aby zapewnić dokładność, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji krytycznych zaleca się skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia.\n"
+    "\n---\n\n**Zastrzeżenie**:  \nTen dokument został przetłumaczony za pomocą usługi tłumaczeniowej AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy wszelkich starań, aby tłumaczenie było precyzyjne, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji krytycznych zaleca się skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:45:38+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:29:04+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "pl"
   }
diff --git a/translations/pl/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/pl/1-Introduction/04-stats-and-probability/notebook.ipynb
index 77a53103..45fb3fa7 100644
--- a/translations/pl/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/pl/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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Xl7d4TDgcVigUarYBAICu6aa2HtjY2KglS5Zo2rRpGjNmTIvrAoGAEhISmu1LSEhQIBBo8Ri/36/ly5e3dTQgqvH/2AFbnfG/QeurOW2+MpKbm6uqqioVFxe35zySpIKCAgWDwaatpqam3c8BAAA6hjZdGVm8eLF27dql/fv3a8CAAa2uTUxMVF1dXbN9dXV1SkxMbPEYt9stt9vdltEAAEAn4+jKSCQS0eLFi7V9+3bt3btXQ4YMueoxPp9PpaWlzfaVlJTI5/M5mxQAAHRJjq6M5ObmqqioSDt37lRsbGzTfR8ej0e9evWSJOXk5Kh///7y+/2SpLy8PKWnp2vlypXKyspScXGxKioqVFhY2M4vBQAAdEaOroysX79ewWBQd911l5KSkpq2rVu3Nq2prq5WbW1t0+O0tDQVFRWpsLBQycnJeu2117Rjx45Wb3oFAADRw9GVkWv5lSRlZWWX7Zs1a5ZmzZrl5FQAACBK8Nk0AADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMCU4xjZv3+/ZsyYoX79+snlcmnHjh2tri8rK5PL5bpsCwQCbZ0ZAAB0IY5jpL6+XsnJyVq3bp2j406dOqXa2tqmLT4+3umpAQBAF3ST0wOmT5+u6dOnOz5RfHy8brvtNsfHAQCAru1bu2ckJSVFSUlJuvfee/XOO++0ujYcDisUCjXbAABA13TDYyQpKUkbNmzQ66+/rtdff11er1d33XWXjh071uIxfr9fHo+nafN6vTd6TAAAYMQViUQibT7Y5dL27duVnZ3t6Lj09HQNHDhQf/rTn6749XA4rHA43PQ4FArJ6/UqGAwqLi6ureNe0eClb7br8wEA0Nl8vCLrhjxvKBSSx+O56vdvx/eMtIfJkyfrwIEDLX7d7XbL7XZ/ixMBAAArJr9npLKyUklJSRanBgAAHYzjKyMXL17U6dOnmx5/9NFHqqysVJ8+fTRw4EAVFBTo008/1R//+EdJ0urVqzVkyBCNHj1aX375pV588UXt3btXf/3rX9vvVQAAgE7LcYxUVFTo7rvvbnqcn58vSZo3b542b96s2tpaVVdXN3390qVL+uUvf6lPP/1UN998s8aNG6e//e1vzZ4DAABEr+u6gfXbcq03wLQFN7ACAKKd9Q2sfDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDlOEb279+vGTNmqF+/fnK5XNqxY8dVjykrK9OECRPkdrs1dOhQbd68uQ2jAgCArshxjNTX1ys5OVnr1q27pvUfffSRsrKydPfdd6uyslJLlizRwoULtWfPHsfDAgCArucmpwdMnz5d06dPv+b1GzZs0JAhQ7Ry5UpJ0siRI3XgwAGtWrVKmZmZTk8PAAC6mBt+z0h5ebkyMjKa7cvMzFR5eXmLx4TDYYVCoWYbAADomm54jAQCASUkJDTbl5CQoFAopP/85z9XPMbv98vj8TRtXq/3Ro8JAACMdMifpikoKFAwGGzaampqrEcCAAA3iON7RpxKTExUXV1ds311dXWKi4tTr169rniM2+2W2+2+0aMBAIAO4IZfGfH5fCotLW22r6SkRD6f70afGgAAdAKOY+TixYuqrKxUZWWlpK9/dLeyslLV1dWSvv4nlpycnKb1DzzwgD788EP96le/0vvvv6/nn39er776qh5++OH2eQUAAKBTcxwjFRUVGj9+vMaPHy9Jys/P1/jx4/X4449Lkmpra5vCRJKGDBmiN998UyUlJUpOTtbKlSv14osv8mO9AABAkuSKRCIR6yGuJhQKyePxKBgMKi4url2fe/DSN9v1+QAA6Gw+XpF1Q573Wr9/d8ifpgEAANGDGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYKpNMbJu3ToNHjxYMTExmjJlig4fPtzi2s2bN8vlcjXbYmJi2jwwAADoWhzHyNatW5Wfn69ly5bp2LFjSk5OVmZmps6ePdviMXFxcaqtrW3azpw5c11DAwCArsNxjDz77LNatGiR5s+fr1GjRmnDhg26+eabtWnTphaPcblcSkxMbNoSEhKua2gAANB1OIqRS5cu6ejRo8rIyPjmCbp1U0ZGhsrLy1s87uLFixo0aJC8Xq9mzpypkydPtnqecDisUCjUbAMAAF2Toxj5/PPP1dDQcNmVjYSEBAUCgSseM3z4cG3atEk7d+7Uli1b1NjYqLS0NH3yySctnsfv98vj8TRtXq/XyZgAAKATueE/TePz+ZSTk6OUlBSlp6frjTfe0B133KGNGze2eExBQYGCwWDTVlNTc6PHBAAARm5ysvj2229X9+7dVVdX12x/XV2dEhMTr+k5evToofHjx+v06dMtrnG73XK73U5GAwAAnZSjKyM9e/ZUamqqSktLm/Y1NjaqtLRUPp/vmp6joaFBJ06cUFJSkrNJAQBAl+Toyogk5efna968eZo4caImT56s1atXq76+XvPnz5ck5eTkqH///vL7/ZKkJ554QlOnTtXQoUP1xRdf6JlnntGZM2e0cOHC9n0lAACgU3IcI7Nnz9a5c+f0+OOPKxAIKCUlRW+//XbTTa3V1dXq1u2bCy7nz5/XokWLFAgE1Lt3b6WmpurgwYMaNWpU+70KAADQabkikUjEeoirCYVC8ng8CgaDiouLa9fnHrz0zXZ9PgAAOpuPV2TdkOe91u/ffDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVphhZt26dBg8erJiYGE2ZMkWHDx9udf22bds0YsQIxcTEaOzYsdq9e3ebhgUAAF2P4xjZunWr8vPztWzZMh07dkzJycnKzMzU2bNnr7j+4MGDmjNnjhYsWKDjx48rOztb2dnZqqqquu7hAQBA5+eKRCIRJwdMmTJFkyZN0tq1ayVJjY2N8nq9euihh7R06dLL1s+ePVv19fXatWtX076pU6cqJSVFGzZsuKZzhkIheTweBYNBxcXFORn3qgYvfbNdnw8AgM7m4xVZN+R5r/X7901OnvTSpUs6evSoCgoKmvZ169ZNGRkZKi8vv+Ix5eXlys/Pb7YvMzNTO3bsaPE84XBY4XC46XEwGJT09Ytqb43hf7f7cwIA0JnciO+v/+/zXu26h6MY+fzzz9XQ0KCEhIRm+xMSEvT+++9f8ZhAIHDF9YFAoMXz+P1+LV++/LL9Xq/XybgAAOAaeFbf2Oe/cOGCPB5Pi193FCPfloKCgmZXUxobG/Wvf/1Lffv2lcvlarfzhEIheb1e1dTUtPs//8A53o+Oh/ekY+H96Fh4P64uEonowoUL6tevX6vrHMXI7bffru7du6uurq7Z/rq6OiUmJl7xmMTEREfrJcntdsvtdjfbd9tttzkZ1ZG4uDj+InUgvB8dD+9Jx8L70bHwfrSutSsi/+Pop2l69uyp1NRUlZaWNu1rbGxUaWmpfD7fFY/x+XzN1ktSSUlJi+sBAEB0cfzPNPn5+Zo3b54mTpyoyZMna/Xq1aqvr9f8+fMlSTk5Oerfv7/8fr8kKS8vT+np6Vq5cqWysrJUXFysiooKFRYWtu8rAQAAnZLjGJk9e7bOnTunxx9/XIFAQCkpKXr77bebblKtrq5Wt27fXHBJS0tTUVGRHnvsMT366KMaNmyYduzYoTFjxrTfq2gjt9utZcuWXfZPQrDB+9Hx8J50LLwfHQvvR/tx/HtGAAAA2hOfTQMAAEwRIwAAwBQxAgAATBEjAADAVFTHyLp16zR48GDFxMRoypQpOnz4sPVIUcnv92vSpEmKjY1VfHy8srOzderUKeux8F8rVqyQy+XSkiVLrEeJWp9++ql++tOfqm/fvurVq5fGjh2riooK67GiVkNDg37zm99oyJAh6tWrl7773e/qySefvOrnr6BlURsjW7duVX5+vpYtW6Zjx44pOTlZmZmZOnv2rPVoUWffvn3Kzc3VoUOHVFJSoq+++kr33Xef6uvrrUeLekeOHNHGjRs1btw461Gi1vnz5zVt2jT16NFDb731lt577z2tXLlSvXv3th4tav3ud7/T+vXrtXbtWv3zn//U7373O/3+97/Xc889Zz1apxW1P9o7ZcoUTZo0SWvXrpX09W+S9Xq9euihh7R06VLj6aLbuXPnFB8fr3379unOO++0HidqXbx4URMmTNDzzz+v3/72t0pJSdHq1autx4o6S5cu1TvvvKO///3v1qPgv374wx8qISFBL730UtO+H/3oR+rVq5e2bNliOFnnFZVXRi5duqSjR48qIyOjaV+3bt2UkZGh8vJyw8kgScFgUJLUp08f40miW25urrKyspr9d4Jv35///GdNnDhRs2bNUnx8vMaPH68XXnjBeqyolpaWptLSUn3wwQeSpH/84x86cOCApk+fbjxZ59UhP7X3Rvv888/V0NDQ9Ftj/ychIUHvv/++0VSQvr5CtWTJEk2bNq1D/JbeaFVcXKxjx47pyJEj1qNEvQ8//FDr169Xfn6+Hn30UR05ckS/+MUv1LNnT82bN896vKi0dOlShUIhjRgxQt27d1dDQ4OeeuopzZ0713q0TisqYwQdV25urqqqqnTgwAHrUaJWTU2N8vLyVFJSopiYGOtxol5jY6MmTpyop59+WpI0fvx4VVVVacOGDcSIkVdffVWvvPKKioqKNHr0aFVWVmrJkiXq168f70kbRWWM3H777erevbvq6uqa7a+rq1NiYqLRVFi8eLF27dql/fv3a8CAAdbjRK2jR4/q7NmzmjBhQtO+hoYG7d+/X2vXrlU4HFb37t0NJ4wuSUlJGjVqVLN9I0eO1Ouvv240ER555BEtXbpUP/nJTyRJY8eO1ZkzZ+T3+4mRNorKe0Z69uyp1NRUlZaWNu1rbGxUaWmpfD6f4WTRKRKJaPHixdq+fbv27t2rIUOGWI8U1e655x6dOHFClZWVTdvEiRM1d+5cVVZWEiLfsmnTpl32o+4ffPCBBg0aZDQR/v3vfzf7QFhJ6t69uxobG40m6vyi8sqIJOXn52vevHmaOHGiJk+erNWrV6u+vl7z58+3Hi3q5ObmqqioSDt37lRsbKwCgYAkyePxqFevXsbTRZ/Y2NjL7te55ZZb1LdvX+7jMfDwww8rLS1NTz/9tH784x/r8OHDKiwsVGFhofVoUWvGjBl66qmnNHDgQI0ePVrHjx/Xs88+q5///OfWo3VekSj23HPPRQYOHBjp2bNnZPLkyZFDhw5ZjxSVJF1xe/nll61Hw3+lp6dH8vLyrMeIWn/5y18iY8aMibjd7siIESMihYWF1iNFtVAoFMnLy4sMHDgwEhMTE/nOd74T+fWvfx0Jh8PWo3VaUft7RgAAQMcQlfeMAACAjoMYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKb+D7cuxelORYM+AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## Analiza rzeczywistych danych\n",
     "\n",
-    "Średnia i wariancja są bardzo ważne podczas analizy danych z rzeczywistego świata. Załadujmy dane o graczach baseballu z [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Średnia i wariancja są bardzo ważne podczas analizy danych z rzeczywistego świata. Załadujmy dane dotyczące graczy baseballowych z [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Możemy również tworzyć wykresy pudełkowe dla podzbiorów naszego zestawu danych, na przykład pogrupowanych według roli gracza.\n"
+    "Możemy również tworzyć wykresy pudełkowe dla podzbiorów naszego zbioru danych, na przykład pogrupowanych według roli gracza.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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VeP14VFSU+vTp4/DIMEmaPHmyfZqXX35Zq1evVseOHfXggw+qRYsWOnPmjH744QetXLlSZ86ckSQ9+OCDevPNN3XvvfcqIyNDderU0UcffSR/f/+rWsci+/fv18CBA3Xrrbdqw4YN+vjjj3X33XcXezb3DTfcoKioKPtNy9q0aeNUO5dq0aKF+vXrp3//+9965plndP3112v48OF67733dPbsWcXExGjjxo2aO3euYmNj1bNnz8sua9iwYfrvf/+rhx9+WKtXr1aXLl1UWFionTt36r///a++/vprh/sdAADgwMxbpwMAXF9JjwyrXLmyrXXr1ra3337bZrVaHab/448/bGPGjLHVrVvX5uPjY2vatKntX//6l326jIwMm7e3t8NjwGw2m62goMDWvn17W926dW2///67zWa78MisgIAA2759+2y9e/e2+fv722rXrm177rnnbIWFhQ7z65JHhtlsNtsPP/xg69Onjy0wMNDm7+9v69mzp239+vXF1vH999+3NW7c2Obl5XVVj8P69NNPbZGRkTZfX19bVFSUbcmSJbbBgwfbIiMj7dMUPTLsX//6V4nLWLlypa1Lly42Pz8/W3BwsO22226zbd++vdh0K1assEVFRdkqVapka9asme3jjz++7CPD4uPjbR9//LGtadOmNl9fX9sNN9xQ4rocP37cFh8fb2vQoIHNx8fHFhoaarv55ptt7733nsN0Bw8etA0cONDm7+9vq1mzpu3RRx+1LV++3KlHhm3fvt02ZMgQW1BQkK1atWq2kSNH2vLy8kqcZ+rUqTZJtilTplxx2ReLiYmxtWzZssRxRY9yK/q7sFgstsmTJ9vCw8NtPj4+tgYNGtgmTJhgO3fuXLFlXvzIMJvNZjt//rztlVdesbVs2dLm6+trq1atmq1t27a2yZMn2zIzM6+6XgBAxeNhs/3v+RwAALiY++67T4sWLbrqI8hmat26tWrVqqWUlBRT2vfw8FB8fHyxU/vLk9dee01jxozRgQMH1LBhQ7PLAQDgmuCabgAAnGCxWOw3kCuyZs0abdmyRT169DCnKDdgs9n0wQcfKCYmhsANAHArXNMNAIATDh8+rFtuuUVDhw5V3bp1tXPnTr3zzjsKDQ3Vww8/bHZ55U5OTo6WLFmi1atXa+vWrVq8eLHZJQEAcE0RugEAcEK1atXUtm1b/fvf/9bJkycVEBCg/v376+WXX1aNGjXMLq/cOXnypO6++25VrVpVEydO1MCBA80uCQCAa4prugEAAAAAMAjXdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAFcR9992nsLCwUs8bGBh4bQsCAKACIHQDAOBi5syZIw8PD23atKnE8T169FBUVFQZV3V1cnNzNWnSJK1Zs8bsUgAAcAneZhcAAADKxvvvvy+r1WpoG7m5uZo8ebKkC18OAABQ0RG6AQCoIHx8fMwuAQCACofTywEAcAMff/yx2rZtKz8/P1WvXl133XWXfv31V4dpSrqm+/Tp0xo2bJiCg4NVtWpVDR8+XFu2bJGHh4fmzJlTrJ3Dhw8rNjZWgYGBqlWrlsaNG6fCwkJJ0oEDB1SrVi1J0uTJk+Xh4SEPDw9NmjTJiFUGAKBc4Eg3AAAuKjMzU6dOnSo23GKxOLx+6aWX9Mwzz+iOO+7Q3//+d508eVJvvPGGunfvrh9//FFVq1YtcflWq1W33XabNm7cqEceeUSRkZFavHixhg8fXuL0hYWF6tOnjzp27Khp06Zp5cqVmj59upo0aaJHHnlEtWrV0ttvv61HHnlEt99+u+Li4iRJrVq1+mu/CAAAyjFCNwAALuqWW2657LiWLVtKkg4ePKjnnntOL774oiZOnGgfHxcXpxtuuEFvvfWWw/CLJScna8OGDZo5c6YeffRRSdIjjzyiXr16lTj9uXPndOedd+qZZ56RJD388MNq06aNPvjgAz3yyCMKCAjQkCFD9Mgjj6hVq1YaOnRoqdYbAAB3QugGAMBFzZo1S9ddd12x4QkJCfZTupOSkmS1WnXHHXc4HBUPDQ1V06ZNtXr16suG7uXLl8vHx0cPPvigfZinp6fi4+O1atWqEud5+OGHHV5369ZNH330kdPrBgBARUHoBgDARXXo0EHt2rUrNrxatWr2gL1nzx7ZbDY1bdq0xGVc6eZpBw8eVJ06deTv7+8wPCIiosTpK1eubL9m++Jafv/99yuuBwAAFRmhGwCAcsxqtcrDw0PLli2Tl5dXsfGBgYHXrK2Slg8AAK6M0A0AQDnWpEkT2Ww2hYeHl3gq+pU0atRIq1evVm5ursPR7r1795a6Hg8Pj1LPCwCAO+KRYQAAlGNxcXHy8vLS5MmTZbPZHMbZbDadPn36svP26dNHFotF77//vn2Y1WrVrFmzSl1PUXg/e/ZsqZcBAIA74Ug3AADlWJMmTfTiiy9qwoQJOnDggGJjYxUUFKT9+/fr888/10MPPaRx48aVOG9sbKw6dOighIQE7d27V5GRkVqyZInOnDkjqXRHrf38/NSiRQstWLBA1113napXr66oqChFRUX9pfUEAKC84kg3AADl3Pjx4/XZZ5/J09NTkydP1rhx47RkyRL17t1bAwcOvOx8Xl5e+vLLL3XnnXdq7ty5euqpp1S3bl37ke7KlSuXqp5///vfqlevnsaMGaO//e1vWrRoUamWAwCAO/CwXXouGgAAqNCSk5N1++23a926derSpYvZ5QAAUK4RugEAqMDy8vLk5+dnf11YWKjevXtr06ZNOnbsmMM4AADgPK7pBgCgAhs1apTy8vLUuXNn5efnKykpSevXr9eUKVMI3AAAXAMc6QYAoAKbP3++pk+frr179+rcuXOKiIjQI488opEjR5pdGgAAboHQDQAAAACAQbh7OQAAAAAABiF0AwAAAABgkHJ5IzWr1aojR44oKChIHh4eZpcDAAAAAKhgbDab/vjjD9WtW1eenpc/nl0uQ/eRI0fUoEEDs8sAAAAAAFRwv/76q+rXr3/Z8eUydAcFBUm6sHLBwcEmV/PXWSwWrVixQr1795aPj4/Z5eAS9I9ro39cF33j2ugf10b/uDb6x3XRN67N3fonKytLDRo0sOfTyymXobvolPLg4GC3Cd3+/v4KDg52iz8+d0P/uDb6x3XRN66N/nFt9I9ro39cF33j2ty1f/7skmdupAYAAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAACg3CssLFRqaqrWrl2r1NRUFRYWml0SIInQDQAAAKCcS0pKUkREhHr16qUZM2aoV69eioiIUFJSktmlAYRuAAAAAOVXUlKShgwZoujoaKWlpemTTz5RWlqaoqOjNWTIEII3TEfoBgAAAFAuFRYWKiEhQQMGDFBycrI6duwoPz8/dezYUcnJyRowYIDGjRvHqeYwFaEbAAAAQLmUlpamAwcOaOLEifL0dIw2np6emjBhgvbv36+0tDSTKgQI3QAAAADKqaNHj0qSoqKiShxfNLxoOsAMhG4AAAAA5VKdOnUkSdu2bStxfNHwoukAMxC6AQAAAJRL3bp1U1hYmKZMmSKr1eowzmq1KjExUeHh4erWrZtJFQKEbgAAAADllJeXl6ZPn66lS5cqNjZW6enpysvLU3p6umJjY7V06VJNmzZNXl5eZpeKCszb7AIAAAAAoLTi4uK0aNEiJSQkqHv37vbh4eHhWrRokeLi4kysDiB0AwAAACjn4uLiNGjQIK1evVrLli1T37591bNnT45wwyUQugEAAACUe15eXoqJiVFOTo5iYmII3HAZXNMNAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBnArdiYmJat++vYKCghQSEqLY2Fjt2rXLYZpjx45p2LBhCg0NVUBAgNq0aaPPPvvMYZozZ87onnvuUXBwsKpWraoRI0YoOzv7r68NAAAAAAAuxKnQnZqaqvj4eKWnpyslJUUWi0W9e/dWTk6OfZp7771Xu3bt0pIlS7R161bFxcXpjjvu0I8//mif5p577tHPP/+slJQULV26VGvXrtVDDz107dYKAAAAAAAX4O3MxMuXL3d4PWfOHIWEhCgjI0Pdu3eXJK1fv15vv/22OnToIEl6+umn9eqrryojI0M33HCDduzYoeXLl+v7779Xu3btJElvvPGG+vXrp2nTpqlu3brXYr0AAAAAADCdU6H7UpmZmZKk6tWr24fdeOONWrBggfr376+qVavqv//9r86dO6cePXpIkjZs2KCqVavaA7ck3XLLLfL09NR3332n22+/vVg7+fn5ys/Pt7/OysqSJFksFlkslr+yCi6haB3cYV3cEf3j2ugf10XfuDb6x7XRP66N/nFd9I1rc7f+udr18LDZbLbSNGC1WjVw4ECdPXtW69atsw8/e/as7rzzTq1YsULe3t7y9/fXwoUL1bt3b0nSlClTNHfu3GLXgoeEhGjy5Ml65JFHirU1adIkTZ48udjw+fPny9/fvzTlAwAAAABQarm5ubr77ruVmZmp4ODgy05X6iPd8fHx2rZtm0PglqRnnnlGZ8+e1cqVK1WzZk0lJyfrjjvuUFpamqKjo0vV1oQJEzR27Fj766ysLDVo0EC9e/e+4sqVFxaLRSkpKerVq5d8fHzMLgeXoH9cG/3juugb10b/uDb6x7XRP66LvnFt7tY/RWdg/5lShe6RI0fab4BWv359+/B9+/bpzTff1LZt29SyZUtJ0vXXX6+0tDTNmjVL77zzjkJDQ3XixAmH5RUUFOjMmTMKDQ0tsT1fX1/5+voWG+7j4+MWnVXE3dbH3dA/ro3+cV30jespLCzU+vXrtXbtWgUEBKhnz57y8vIyuyyUgPePa6N/XBd949rcpX+udh2cunu5zWbTyJEj9fnnn2vVqlUKDw93GJ+bm3thoZ6Oi/Xy8pLVapUkde7cWWfPnlVGRoZ9/KpVq2S1WtWxY0dnygEAAE5KSkpSRESEevXqpRkzZqhXr16KiIhQUlKS2aUBAOCWnArd8fHx+vjjjzV//nwFBQXp2LFjOnbsmPLy8iRJkZGRioiI0D/+8Q9t3LhR+/bt0/Tp05WSkqLY2FhJUvPmzXXrrbfqwQcf1MaNG/Xtt99q5MiRuuuuu7hzOQAABkpKStKQIUMUHR2ttLQ0ffLJJ/bLv4YMGULwBgDAAE6F7rfffluZmZnq0aOH6tSpY/+3YMECSRcOr3/11VeqVauWbrvtNrVq1Urz5s3T3Llz1a9fP/ty/vOf/ygyMlI333yz+vXrp65du+q99967tmsGAADsCgsLlZCQoAEDBig5OVkdO3aUn5+fOnbsqOTkZA0YMEDjxo1TYWGh2aUCAOBWnLqm+2pudN60aVN99tlnV5ymevXqmj9/vjNNAwCAvyAtLU0HDhzQJ598Ik9PT4dw7enpqQkTJujGG29UWlqa/TGfAADgr3PqSDcAACifjh49KkmKiooqcXzR8KLpAADAtUHoBgCgAqhTp44kadu2bSWOLxpeNB0AALg2CN0AAFQA3bp1U1hYmKZMmWJ/okgRq9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnTeF43AADXmFM3UgMAAOVXXFycFi1apISEBHXv3t0+PDw8XIsWLVJcXJyJ1QEA4J4I3QAAVCBxcXEaNGiQVq9erWXLlqlv377q2bMnR7gBADAIoRu4gsLCQqWmpmrt2rUKCAhgxxSAW/Dy8lJMTIxycnIUExPDdg0AAANxTTdwGUlJSYqIiFCvXr00Y8YM9erVSxEREUpKSjK7NAAAAADlBKEbKEFSUpKGDBmi6OhopaWl6ZNPPlFaWpqio6M1ZMgQgjcAAACAq0LoBi5RWFiohIQEDRgwQMnJyerYsaP8/PzUsWNHJScna8CAARo3bpwKCwvNLhUAAACAiyN0A5dIS0vTgQMHNHHiRHl6Or5FPD09NWHCBO3fv19paWkmVQgAAACgvCB0A5c4evSoJCkqKqrE8UXDi6YDAAAAgMshdAOXqFOnjiRp27ZtJY4vGl40HQAAAABcDqEbuES3bt0UFhamKVOmyGq1OoyzWq1KTExUeHi4unXrZlKFAAAAAMoLQjdwCS8vL02fPl1Lly5VbGys0tPTlZeXp/T0dMXGxmrp0qWaNm0az7UFAAAA8Ke8zS4AcEVxcXFatGiREhIS1L17d/vw8PBwLVq0SHFxcSZWBwAAAKC8IHQDlxEXF6dBgwZp9erVWrZsmfr27auePXtyhBsAAADAVSN0A1fg5eWlmJgY5eTkKCYmhsANAAAAwClc0w0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AQAVTWFio1NRUrV27VqmpqSosLDS7JAAA3BahGwCACiQpKUkRERHq1auXZsyYoV69eikiIkJJSUlmlwYAgFsidAMAUEEkJSVpyJAhio6OVlpamj755BOlpaUpOjpaQ4YMIXgDAGAAQjcAABVAYWGhEhISNGDAACUnJ6tjx47y8/NTx44dlZycrAEDBmjcuHGcag4AwDVG6AYAoAJIS0vTgQMHNHHiRHl6On78e3p6asKECdq/f7/S0tJMqhAAAPdE6AYAoAI4evSoJCkqKqrE8UXDi6YDAADXBqEbAIAKoE6dOpKkbdu2lTi+aHjRdAAA4NogdAMAUAF069ZNYWFhmjJliqxWq8M4q9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAC4FW+zCwAAAGUjLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAcE+EbgAAKpC4uDgNGjRIq1ev1rJly9S3b1/17NmTI9wAABiE0A0AQAXj5eWlmJgY5eTkKCYmhsANAICBuKYbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDOBW6ExMT1b59ewUFBSkkJESxsbHatWtXsek2bNigm266SQEBAQoODlb37t2Vl5dnH3/mzBndc889Cg4OVtWqVTVixAhlZ2f/9bUBAAAAAMCFOBW6U1NTFR8fr/T0dKWkpMhisah3797KycmxT7Nhwwbdeuut6t27tzZu3Kjvv/9eI0eOlKfn/2/qnnvu0c8//6yUlBQtXbpUa9eu1UMPPXTt1goAAAAAABfg7czEy5cvd3g9Z84chYSEKCMjQ927d5ckjRkzRqNHj9b48ePt0zVr1sz+/x07dmj58uX6/vvv1a5dO0nSG2+8oX79+mnatGmqW7duqVcGAAAAAABX8peu6c7MzJQkVa9eXZJ04sQJfffddwoJCdGNN96o2rVrKyYmRuvWrbPPs2HDBlWtWtUeuCXplltukaenp7777ru/Ug4AAAAAAC7FqSPdF7NarXrsscfUpUsXRUVFSZJ++eUXSdKkSZM0bdo0tW7dWvPmzdPNN9+sbdu2qWnTpjp27JhCQkIci/D2VvXq1XXs2LES28rPz1d+fr79dVZWliTJYrHIYrGUdhVcRtE6uMO6uCP6p+zk5uaWeJ+IK8nOy9f6rfsUVDVdgX6+Ts3brFkz+fv7OzUPrh7vnbLDe8f98P5xbfRP2WDb5n7c7b1ztetR6tAdHx+vbdu2ORzFtlqtkqR//OMfuv/++yVJN9xwg7755ht9+OGHSkxMLFVbiYmJmjx5crHhK1ascKs3RkpKitkl4AroH+Pt27dPCQkJpZp3ainmmT59upo0aVKq9nD1eO8Yj/eO++L949roH2OxbXNf7vLeyc3NvarpShW6R44cab8BWv369e3D69SpI0lq0aKFw/TNmzfXoUOHJEmhoaE6ceKEw/iCggKdOXNGoaGhJbY3YcIEjR071v46KytLDRo0UO/evRUcHFyaVXApFotFKSkp6tWrl3x8fMwuB5egf8pObm6uunbt6tQ8u49m6vHPt+tft7fQdXWqODUv32gbi/dO2eG94354/7g2+qdssG1zP+723ik6A/vPOBW6bTabRo0apc8//1xr1qxReHi4w/iwsDDVrVu32Gkgu3fvVt++fSVJnTt31tmzZ5WRkaG2bdtKklatWiWr1aqOHTuW2K6vr698fYufHuLj4+MWnVXE3dbH3dA/xqtSpYo6dOjg1DyVDp6W74bzimrdRq0b1TCoMvwVvHeMx3vHffH+cW30j7HYtrkvd3nvXO06OBW64+PjNX/+fC1evFhBQUH2a7CrVKkiPz8/eXh46PHHH9dzzz2n66+/Xq1bt9bcuXO1c+dOLVq0SNKFo9633nqrHnzwQb3zzjuyWCwaOXKk7rrrLu5cDgAAAABwK06F7rfffluS1KNHD4fhs2fP1n333SdJeuyxx3Tu3DmNGTNGZ86c0fXXX6+UlBSH6yP+85//aOTIkbr55pvl6empwYMH6/XXX/9rawIAAAAAgItx+vTyqzF+/HiH53Rfqnr16po/f74zTQMAAAAAUO78ped0AwBQksLCQqWmpmrt2rVKTU1VYWGh2SUBAACYgtANALimkpKSFBERoV69emnGjBnq1auXIiIilJSUZHZpAAAAZY7QDQC4ZpKSkjRkyBBFR0crLS1Nn3zyidLS0hQdHa0hQ4YQvAEAQIVD6AYAXBOFhYVKSEjQgAEDlJycrI4dO8rPz08dO3ZUcnKyBgwYoHHjxnGqOQAAqFAI3QCAayItLU0HDhzQxIkT5enp+PHi6empCRMmaP/+/UpLSzOpQgAAgLJH6AYAXBNHjx6VJEVFRZU4vmh40XQAAAAVAaEbAHBN1KlTR5K0bdu2EscXDS+aDgAAoCIgdAMArolu3bopLCxMU6ZMkdVqdRhntVqVmJio8PBwdevWzaQKAQAAyh6hGwBwTXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAAAZcbb7AIAAO4jLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAAMoeoRsAcE3FxcVp0KBBWr16tZYtW6a+ffuqZ8+eHOEGAAAVEqEbAHDNeXl5KSYmRjk5OYqJiSFwAwCACotrugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFAGUpNzdXO3fudGqe7Lx8rd+6T9VqblKgn69T80ZGRsrf39+peQAAAAC4D0I3KpSdO3eqbdu2pZp3ainmycjIUJs2bUrVHgAAAIDyj9CNCiUyMlIZGRlOzbPr6FmNXbhVM/4vWs3qVHW6PQAAAAAVF6EbFYq/v7/TR549D56Wb1qemkddr9aNahhUGQAAAAB3xI3UAAAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDeJtdgLvJzc3Vzp07nZonOy9f67fuU7WamxTo5+vUvJGRkfL393dqHgAAAABA2SB0X2M7d+5U27ZtSzXv1FLMk5GRoTZt2pSqPQAAAACAsQjd11hkZKQyMjKcmmfX0bMau3CrZvxftJrVqep0ewAAAAAA10Tovsb8/f2dPvLsefC0fNPy1DzqerVuVMOgygAAAAAAZY0bqQEAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQp0J3YmKi2rdvr6CgIIWEhCg2Nla7du0qcVqbzaa+ffvKw8NDycnJDuMOHTqk/v37y9/fXyEhIXr88cdVUFBQ6pUAAAAAAMAVORW6U1NTFR8fr/T0dKWkpMhisah3797KyckpNu3MmTPl4eFRbHhhYaH69++v8+fPa/369Zo7d67mzJmjZ599tvRrAQAAAACAC/J2ZuLly5c7vJ4zZ45CQkKUkZGh7t2724dv3rxZ06dP16ZNm1SnTh2HeVasWKHt27dr5cqVql27tlq3bq0XXnhBTz75pCZNmqRKlSr9hdUBAAAAAMB1OBW6L5WZmSlJql69un1Ybm6u7r77bs2aNUuhoaHF5tmwYYOio6NVu3Zt+7A+ffrokUce0c8//6wbbrih2Dz5+fnKz8+3v87KypIkWSwWWSyWv7IKLqHo1PqCggK3WB93Q/+4NvqnbOTm5l72cqLLyc7L1/qt+xRUNV2Bfr5OzdusWTP5+/s7NQ+cw3vHtRX1CX3jmugf18W2reywb3D124BSh26r1arHHntMXbp0UVRUlH34mDFjdOONN2rQoEElznfs2DGHwC3J/vrYsWMlzpOYmKjJkycXG75ixQqX+8WXxq/ZkuSt9PR0Hd5mdjW4FP3j2uifsrFv3z4lJCSUat6ppZhn+vTpatKkSanaw9XhvVM+pKSkmF0CroD+cT1s28oO+wYXvni4GqUO3fHx8dq2bZvWrVtnH7ZkyRKtWrVKP/74Y2kXW6IJEyZo7Nix9tdZWVlq0KCBevfureDg4Gvalhm2HDojbd2kTp066fqG1f98BpQp+se10T9lIzc3V127dnVqnt1HM/X459v1r9tb6Lo6VZya1xW/zXY3vHdcm8ViUUpKinr16iUfHx+zy8El6B/Xxbat7LBv8P/PwP4zpQrdI0eO1NKlS7V27VrVr1/fPnzVqlXat2+fqlat6jD94MGD1a1bN61Zs0ahoaHauHGjw/jjx49LUomno0uSr6+vfH2Ln37g4+PjFhs6b29v+093WB93Q/+4NvqnbFSpUkUdOnRwap5KB0/Ld8N5RbVuo9aNahhUGUqL90754C77Ou6K/nE9bNvKDvsGuuq/MafuXm6z2TRy5Eh9/vnnWrVqlcLDwx3Gjx8/Xj/99JM2b95s/ydJr776qmbPni1J6ty5s7Zu3aoTJ07Y50tJSVFwcLBatGjhTDkAAAAAALg0p450x8fHa/78+Vq8eLGCgoLs12BXqVJFfn5+Cg0NLfFodcOGDe0BvXfv3mrRooWGDRumqVOn6tixY3r66acVHx9f4tFsAAAAAADKK6eOdL/99tvKzMxUjx49VKdOHfu/BQsWXPUyvLy8tHTpUnl5ealz584aOnSo7r33Xj3//PNOFw8AAAAAgCtz6ki3zWZzuoGS5mnUqJG++uorp5cFAAAAAEB54tSRbgAAAAAAcPUI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBvM0uAPgr9p/KUU5+gaFt7DuZY//p7W3sWybA11vhNQMMbQMAAABA2SF0o9zafypHPaetKbP2EhZtLZN2Vo/rQfAGAAAA3AShG+VW0RHumXe2VkRIoHHt5OVr6ZoNGtCjswL8fA1rZ++JbD22YLPhR+4BAAAAlB1CN8q9iJBARdWrYtjyLRaLjtWS2jSqJh8fH8PaAQAAAOB+uJEaAAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQb7MLAOC+9p/KUU5+gaFt7DuZY//p7W3sJi3A11vhNQMMbQMAAADuhdANwBD7T+Wo57Q1ZdZewqKtZdLO6nE9CN4AAAC4aoRulFv5hefkWfmw9mftkmflQMPaKSgo0JGCI9pxZoehR1L3Z2XLs/Jh5Reek1TFsHbKStER7pl3tlZEiHH9k5OXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAgHshdKPcOpJzUAHhb2jixrJp763lbxneRkC4dCSntdqqtuFtlZWIkEBF1TPuSwSLxaJjtaQ2jarJx8fHsHYAAACA0iB0o9yqG9BIOftH6bU7W6uJgUdSCwoK9O26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAAAEDZInSj3PL1qizruXoKD26mFjWMPZK633u/mldvbuiRVOu5TFnPnZSvV2XD2gAAAABQtnhkGAAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGcSp0JyYmqn379goKClJISIhiY2O1a9cu+/gzZ85o1KhRatasmfz8/NSwYUONHj1amZmZDss5dOiQ+vfvL39/f4WEhOjxxx9XQUHBtVkjAAAAAABchFOhOzU1VfHx8UpPT1dKSoosFot69+6tnJwcSdKRI0d05MgRTZs2Tdu2bdOcOXO0fPlyjRgxwr6MwsJC9e/fX+fPn9f69es1d+5czZkzR88+++y1XTMAAAAAAEzm7czEy5cvd3g9Z84chYSEKCMjQ927d1dUVJQ+++wz+/gmTZropZde0tChQ1VQUCBvb2+tWLFC27dv18qVK1W7dm21bt1aL7zwgp588klNmjRJlSpVujZrBgAAAACAyZwK3ZcqOm28evXqV5wmODhY3t4XmtqwYYOio6NVu3Zt+zR9+vTRI488op9//lk33HBDsWXk5+crPz/f/jorK0uSZLFYZLFY/soquISiU+sLCgrcYn3KSln93oqWbXTfuNvfQU5+tjwrH9be37fL6h1gWDsFBQU6UnBEW09stW9njPDL7znyrHxYOfnZslj8DWvHnbjb37S7oX9cW1l99qB06J/SOXA6Rzn5hYa2sftYpsNPIwX4eimshnH7OO7I3T57rnYdSr2HarVa9dhjj6lLly6KiooqcZpTp07phRde0EMPPWQfduzYMYfALcn++tixYyUuJzExUZMnTy42fMWKFfL3L/87v79mS5K30tPTdXib2dWUH0W/t3Xr1ulgoPHtpaSkGLr8sl4fo/3wxxEFhL+lZzLKpr23Vr5leBsB4dJX6wt1LKiu4W25A7Ztro3+KR+M/uzBX0P/XL0TedJLm437cvxST3y+o0zaeap1gUL8yqQpt+Bunz25ublXNV2p//Lj4+O1bds2rVu3rsTxWVlZ6t+/v1q0aKFJkyaVthlJ0oQJEzR27FiHZTdo0EC9e/dWcHDwX1q2K9hy6Iy0dZM6deqk6xte/qwBOPr5SJambU1X165d1bKucX8HFotFKSkp6tWrl3x8fAxrp6zWp6yE/npCH83z0owh0Wpcy9gj3d+lf6eOnToae6T7ZI7GLtqqfvf2V5sGIYa1407YtpVeWRwNyj+WKW3doZCIaDUKrWJoWxwNcl5ZffagdOgf5/18JEvanK5pQ6IVYeB+Qc65fC1P+163dmuvgMq+hrWz92SOxi3aqvad3WO/ray4275B0RnYf6ZUe6gjR47U0qVLtXbtWtWvX7/Y+D/++EO33nqrgoKC9PnnnztsjEJDQ7Vx40aH6Y8fP24fVxJfX1/5+hZ/0/j4+LjFhq4oKHh7e7vF+pSVsv69Gf335m5/BwG+gbKeq6eIai0UVdu4HXqLxaJfvX9VdEi0ob83z4JMWc+dUYBvoFv0T1lwt7/psrL/VI56zfy2zNorq6NBq8f1UHhNgrez3GVfx13RP1ev6DMhsk4VRdUzdr/g1E6pQ+Na7Le5IHf7vV3tOjgVum02m0aNGqXPP/9ca9asUXh4eLFpsrKy1KdPH/n6+mrJkiWqXLmyw/jOnTvrpZde0okTJxQScuFoUUpKioKDg9WiRQtnygEAwO3k5F+43m3mna0VEWLctSY5eflaumaDBvTorAA/A48GncjWYws229cLAICKxqnQHR8fr/nz52vx4sUKCgqyX4NdpUoV+fn5KSsrS71791Zubq4+/vhjZWVl2Q+516pVS15eXurdu7datGihYcOGaerUqTp27JiefvppxcfHl3g0GwCAiigiJNDwo0HHakltGlVzi6MNAAC4KqdC99tvvy1J6tGjh8Pw2bNn67777tMPP/yg7777TpIUERHhMM3+/fsVFhYmLy8vLV26VI888og6d+6sgIAADR8+XM8///xfWA0AAAAAAFyP06eXX0mPHj3+dBpJatSokb766itnmgYAAAAAoNzxNLsAAAAAAADcFaEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFuLr9p3KUk19gaBv7TubYf3p7G9slAb7eCq8ZYGgbAAAAAIALCN1XsP9UjnpOW1Nm7SUs2lom7awe14PgDQAAAABlgNB9BUVHuGfe2VoRIYHGtZOXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAAABcQOi+ChEhgYqqV8Ww5VssFh2rJbVpVE0+Pj6GtQMAAAAAKFvcSA0AAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAABcSGFhoVJTU7V27VqlpqaqsLDQ7JIAAH8BoRsAAMBFJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSAACl5G12AQCAsrf/VI5y8gsMbWPfyRz7T29vYz9uAny9FV4zwNA2AKMlJSVpyJAhGjBggD766CP99ttvql+/vqZOnaohQ4Zo0aJFiouLM7tMAICTCN0AUMHsP5WjntPWlFl7CYu2lkk7q8f1IHij3CosLFRCQoIGDBig5ORkFRYW6vTp0+rYsaOSk5MVGxurcePGadCgQfLy8jK7XACAEwjdAFDBFB3hnnlna0WEBBrXTl6+lq7ZoAE9OivAz9ewdvaeyNZjCzYbfuQeMFJaWpoOHDigTz75RJ6eng7XcXt6emrChAm68cYblZaWph49ephXqBvKzc3Vzp07nZonOy9f67fuU7WamxTo5PYtMjJS/v7+Ts0DlAXOgjMOoRsAKqiIkEBF1ati2PItFouO1ZLaNKomHx8fw9oB3MHRo0clSVFRUSWOLxpeNB2unZ07d6pt27almndqKebJyMhQmzZtStUeYBTOgjMWoRsAAMBkderUkSRt27ZNnTp1KjZ+27ZtDtPh2omMjFRGRoZT8+w6elZjF27VjP+LVrM6VZ1uD3A1nAVnLEI3AACAybp166awsDBNmTJFycnJDuOsVqsSExMVHh6ubt26mVOgG/P393f6yLPnwdPyTctT86jr1bpRDYMqA8oeZ8EZg0eGAQAAmMzLy0vTp0/X0qVLFRsbq/T0dOXl5Sk9PV2xsbFaunSppk2bxk3UAKAc4kg3AACAC4iLi9OiRYuUkJCg7t2724eHh4fzuDAAKMcI3QAAAC4iLi5OgwYN0urVq7Vs2TL17dtXPXv25Ag3AJRjhG4AAAAX4uXlpZiYGOXk5CgmJobADQDlHKEbAADAQDwHGgAqNkI3AACAgXgONABUbIRuAAAAA/EcaACo2AjdAAAABuI50ABQsfGcbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADMJzuq8gv/CcPCsf1v6sXfKsHGhYOwUFBTpScEQ7zuyQt7dxXbI/K1uelQ8rv/CcpCqGtQMAAABcjP1qVGSE7is4knNQAeFvaOLGsmnvreVvGd5GQLh0JKe12qq24W0BAAAAEvvVqNgI3VdQN6CRcvaP0mt3tlaTEGO/kft23bfq0rWLod/I7TuRrUcXbFbdno0MawMAAAC4FPvVqMgI3Vfg61VZ1nP1FB7cTC1qGHfaiMVi0X7v/Wpevbl8fHwMa8d6LlPWcyfl61XZsDYAAACAS7FfjYqMG6kBAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswsASivPUihJ2nY409B2cvLytemkFHrwdwX4+RrWzt4T2YYtGwAAAIA5nArdiYmJSkpK0s6dO+Xn56cbb7xRr7zyipo1a2af5ty5c0pISNCnn36q/Px89enTR2+99ZZq165tn+bQoUN65JFHtHr1agUGBmr48OFKTEyUtzffAeDq7ftfSB2ftLUMWvPWR3u/L4N2pABf3gcAAACAu3Bq7z41NVXx8fFq3769CgoKNHHiRPXu3Vvbt29XQECAJGnMmDH68ssvtXDhQlWpUkUjR45UXFycvv32W0lSYWGh+vfvr9DQUK1fv15Hjx7VvffeKx8fH02ZMuXaryHcVu+WoZKkJiGB8vPxMqydXUczlbBoq6YPiVazOlUMa0e6ELjDawYY2gYAAACAsuNU6F6+fLnD6zlz5igkJEQZGRnq3r27MjMz9cEHH2j+/Pm66aabJEmzZ89W8+bNlZ6erk6dOmnFihXavn27Vq5cqdq1a6t169Z64YUX9OSTT2rSpEmqVKnStVs7uLXqAZV0V4eGhrdTUFAgSWpSK0BR9YwN3QAAAADcy1+6kVpm5oVraatXry5JysjIkMVi0S233GKfJjIyUg0bNtSGDRskSRs2bFB0dLTD6eZ9+vRRVlaWfv75579SDgAAAAAALqXUF49arVY99thj6tKli6KioiRJx44dU6VKlVS1alWHaWvXrq1jx47Zp7k4cBeNLxpXkvz8fOXn59tfZ2VlSZIsFossFktpV+FPFR3hLCgoMLSdomUb2YZUduvjbvi9lc4feRfes1sOnbH/Do2Qc+7Cje5q/nJSAZUNvNHdyRxJ7vF3kJOfLc/Kh7X39+2yeht3OUNBQYGOFBzR1hNbDb1nxy+/58iz8mHl5GfLYvE3rJ2yQv9A4rPH1dE/zmO/2rXx2VM6V9v3pV7T+Ph4bdu2TevWrSvtIq5aYmKiJk+eXGz4ihUr5O9v3C/x12xJ8ta6det0MNCwZuxSUlIMXX5Zr4+7KPq9paen6/A2s6spPzYc95DkpacWby+D1rz10d4fy6Ad6fsN63TQr0yaMswPfxxRQPhbeiajbNp7a+VbhrcREC59tb5Qx4LqGt6W0egfSHz2uDr6x3nsV7s2PntKJzc396qmK1XoHjlypJYuXaq1a9eqfv369uGhoaE6f/68zp4963C0+/jx4woNDbVPs3HjRoflHT9+3D6uJBMmTNDYsWPtr7OystSgQQP17t1bwcHBpVmFq/LzkSxN25qurl27qmVd49qxWCxKSUlRr1695OPjY1g7ZbU+7mbLoTPS1k3q1KmTrm9Y3exyyo1OOecVveOEGtcKMPRGd7uPZeqJz3do6u3NdV2o0Te681JYjfJ/o7vQX0/oo3lemjEkWo1rGftt9nfp36ljp47Gfpt9MkdjF21Vv3v7q02DEMPaKSv0DyQ+e1wd/eM89qtdG589pVN0BvafcWpNbTabRo0apc8//1xr1qxReHi4w/i2bdvKx8dH33zzjQYPHixJ2rVrlw4dOqTOnTtLkjp37qyXXnpJJ06cUEjIhV9ASkqKgoOD1aJFixLb9fX1la9v8dNGfXx8DH0zFf0heHt7G9pOEXdbH3fB7610alf10T2dw/98wmvkutAqat2oRpm1V54F+AbKeq6eIqq1UFRt476osFgs+tX7V0WHRBv63vEsyJT13BkF+Aa6xXuU/oHEZ4+ro3+cx361a+Ozp3SudtlOhe74+HjNnz9fixcvVlBQkP0a7CpVqsjPz09VqlTRiBEjNHbsWFWvXl3BwcEaNWqUOnfurE6dOkmSevfurRYtWmjYsGGaOnWqjh07pqefflrx8fElBmsAAAAAAMorp0L322+/LUnq0aOHw/DZs2frvvvukyS9+uqr8vT01ODBg5Wfn68+ffrorbf+/zn7Xl5eWrp0qR555BF17txZAQEBGj58uJ5//vm/tiYAAAAAALgYp08v/zOVK1fWrFmzNGvWrMtO06hRI3311VfONA0AAAAAQLnzl57TDQAAAAAALo/QDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABjE2+wCgLKUm5urnTt3OjXPrqNnlX9sr3Zs85P1dFWn5o2MjJS/v79T8wAAgNLbfypHOfkFhrax72SO/ae3t7G70wG+3gqvGWBoGwCMRehGhbJz5061bdu2VPPePdf5eTIyMtSmTZtStQcAAJyz/1SOek5bU2btJSzaWibtrB7Xg+ANlGOEblQokZGRysjIcGqe7Lx8fbl6g/r37KxAP1+n2wMAAGWj6Aj3zDtbKyIk0Lh28vK1dM0GDejRWQFO7hs4Y++JbD22YLPhR+4BGIvQjQrF39/f6SPPFotFv586oc4d2snHx8egygAAwLUSERKoqHpVDFu+xWLRsVpSm0bV2DcA8Ke4kRoAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswtwZXmWQknStsOZhraTk5evTSel0IO/K8DP17B29p7INmzZAIBrg88e17f/VI5y8gsMbWPfyRz7T29vY3fXAny9FV4zwNA2ALZtro3+MRah+wr2/a+zxidtLYPWvPXR3u/LoJ0LH64AANfEZ49r238qRz2nrSmz9hIWlcXfgbR6XA+CNwzFts210T/Gco0qXFTvlqGSpCYhgfLz8TKsnV1HM5WwaKumD4lWszpVDGtH4ttsAHB1fPa4tqIj3DPvbK2IkEDj2snL19I1GzSgR2fDjwY9tmCz4UfuAbZtro3+MRah+wqqB1TSXR0aGt5OQcGFD7omtQIUVc/YPz4AgGvjs6d8iAgJNPT3ZrFYdKyW1KZRNfn4+BjWDlBW2La5NvrHWNxIDQAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwiLfZBQAAylaepVCStO1wpqHt5OTla9NJKfTg7wrw8zWsnb0nsg1bNnCp/MJz8qx8WPuzdsmzcqBh7RQUFOhIwRHtOLND3t7G7a7tz8qWZ+XDyi88J6mKYe0AQEVG6AaACmbf/0Lq+KStZdCatz7a+30ZtCMF+PKRBuMdyTmogPA3NHFj2bT31vK3DG8jIFw6ktNabVXb8LYAoCJiDwUAKpjeLUMlSU1CAuXn42VYO7uOZiph0VZNHxKtZnWMPYIW4Out8JoBhrYBSFLdgEbK2T9Kr93ZWk1CjD3S/e26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAqOkI3AFQw1QMq6a4ODQ1vp6CgQJLUpFaAoupx2ircg69XZVnP1VN4cDO1qGHc37XFYtF+7/1qXr25fHx8DGvHei5T1nMn5etV2bA2AKCi40ZqAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGMTp0L127Vrddtttqlu3rjw8PJScnOwwPjs7WyNHjlT9+vXl5+enFi1a6J133nGY5ty5c4qPj1eNGjUUGBiowYMH6/jx439pRQAAAAAAcDVOh+6cnBxdf/31mjVrVonjx44dq+XLl+vjjz/Wjh079Nhjj2nkyJFasmSJfZoxY8boiy++0MKFC5WamqojR44oLi6u9GsBAAAAAIAL8nZ2hr59+6pv376XHb9+/XoNHz5cPXr0kCQ99NBDevfdd7Vx40YNHDhQmZmZ+uCDDzR//nzddNNNkqTZs2erefPmSk9PV6dOnUq3JgAAAAAAuBinQ/efufHGG7VkyRI98MADqlu3rtasWaPdu3fr1VdflSRlZGTIYrHolltusc8TGRmphg0basOGDSWG7vz8fOXn59tfZ2VlSZIsFossFsu1XoUyV1BQYP/pDuvjbor6hL4xXm5urnbt2uXUPLuPZir/2F5t21xJ549XcWreZs2ayd/f36l5cPXYtrk2+qd0yur3VlafPe72d5CTny3Pyoe19/ftsnoHGNZOQUGBjhQc0dYTW+Xtfc13p+1++T1HnpUPKyc/WxYLn1dXw93+pt2Nu/XP1a7DNd9KvPHGG3rooYdUv359eXt7y9PTU++//766d+8uSTp27JgqVaqkqlWrOsxXu3ZtHTt2rMRlJiYmavLkycWGr1ixwi12mH/NliRvpaen6/A2s6vB5aSkpJhdgtvbt2+fEhISSjXvsLnOzzN9+nQ1adKkVO3hz7Ftc230T+kU/d7WrVung4HGt2f0Z09Zr4/RfvjjiALC39IzGWXT3lsr3zK8jYBw6av1hToWVNfwttwB2zbX5m79k5ube1XTGRK609PTtWTJEjVq1Ehr165VfHy86tat63B02xkTJkzQ2LFj7a+zsrLUoEED9e7dW8HBwdeqdNNsOXRG2rpJnTp10vUNq5tdDi5hsViUkpKiXr16ycfHx+xy3Fpubq66du3q1DzZefn6Ou179enWXoF+vk7Ny5FuY7Ftc230T+n8fCRL07amq2vXrmpZ17h9kLL67Cmr9Skrob+e0EfzvDRjSLQa1zL2SPd36d+pY6eOxh7pPpmjsYu2qt+9/dWmQYhh7bgTtm2uzd36p+gM7D9zTbcSeXl5mjhxoj7//HP1799fktSqVStt3rxZ06ZN0y233KLQ0FCdP39eZ8+edTjaffz4cYWGhpa4XF9fX/n6Ft+Z9vHxcYsQVLSx9vb2dov1cVfu8vfmyqpUqaIOHTo4NY/FYtEfZ8+o242d6B8Xw7bNtdE/pVPWvzejP3vc7e8gwDdQ1nP1FFGthaJqO3fJkTMsFot+9f5V0SHRhv7ePAsyZT13RgG+gW7RP2XB3f6m3Y279c/VrsM1fU530TXWnp6Oi/Xy8pLVapUktW3bVj4+Pvrmm2/s43ft2qVDhw6pc+fO17IcAAAAAABM5fSR7uzsbO3du9f+ev/+/dq8ebOqV6+uhg0bKiYmRo8//rj8/PzUqFEjpaamat68eZoxY4akC0eyRowYobFjx6p69eoKDg7WqFGj1LlzZ+5cDgAAAABwK06H7k2bNqlnz57210XXWg8fPlxz5szRp59+qgkTJuiee+7RmTNn1KhRI7300kt6+OGH7fO8+uqr8vT01ODBg5Wfn68+ffrorbeMvxEFAAAAAABlyenQ3aNHD9lstsuODw0N1ezZs6+4jMqVK2vWrFmaNWuWs80DAAAAAFBuXNNrugEAAAAAwP9H6AYAAAAAwCCEbgAAAAAADHJNn9MNAADgzvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAQAXELoBAACu0r7/hdTxSVvLoDVvfbT3+zJoRwrwZZcQAIzCFhYAAOAq9W4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQBARUboBgAAuErVAyrprg4NDW+noKBAktSkVoCi6hkbugEAxuJGagAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQbzNLgAAAAC4FvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAZQdQjcAAADcwr7/hdTxSVvLoDVvfbT3+zJoRwrwZZcdKM94BwMAAMAt9G4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQAwFqEbAAAAbqF6QCXd1aGh4e0UFBRIkprUClBUPWNDN4DyjxupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNrsAd5Obm6udO3c6Nc+uo2eVf2yvdmzzk/V0VafmjYyMlL+/v1PzAAAAAMBfQe65eoTua2znzp1q27Ztqea9e67z82RkZKhNmzalag8AAAAASoPcc/UI3ddYZGSkMjIynJonOy9fX67eoP49OyvQz9fp9gAAAACgLJF7rh6h+xrz9/d3+hsYi8Wi30+dUOcO7eTj42NQZQAAAABwbZB7rh43UgMAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgTofutWvX6rbbblPdunXl4eGh5OTkYtPs2LFDAwcOVJUqVRQQEKD27dvr0KFD9vHnzp1TfHy8atSoocDAQA0ePFjHjx//SysCAAAAAICrcTp05+Tk6Prrr9esWbNKHL9v3z517dpVkZGRWrNmjX766Sc988wzqly5sn2aMWPG6IsvvtDChQuVmpqqI0eOKC4urvRrAQAAAACAC/J2doa+ffuqb9++lx3/1FNPqV+/fpo6dap9WJMmTez/z8zM1AcffKD58+frpptukiTNnj1bzZs3V3p6ujp16uRsSQAAAAAAuCSnQ/eVWK1Wffnll3riiSfUp08f/fjjjwoPD9eECRMUGxsrScrIyJDFYtEtt9xiny8yMlINGzbUhg0bSgzd+fn5ys/Pt7/OysqSJFksFlkslmu5CqYoWgd3WBd3RP+4NvqnbOTm5mrXrl1OzbP7aKbyj+3Vts2VdP54Fafmbdasmfz9/Z2aB84pKCiw/+T9YyzeP+6H94/rom9cm7vtt13telzT0H3ixAllZ2fr5Zdf1osvvqhXXnlFy5cvV1xcnFavXq2YmBgdO3ZMlSpVUtWqVR3mrV27to4dO1bichMTEzV58uRiw1esWOFWHyopKSlml4AroH9cG/1jrH379ikhIaFU8w6b6/w806dPdzhLCtfer9mS5K309HQd3mZ2Ne6N94/74f3juuib8sFd9ttyc3OvarprfqRbkgYNGqQxY8ZIklq3bq3169frnXfeUUxMTKmWO2HCBI0dO9b+OisrSw0aNFDv3r0VHBz81ws3mcViUUpKinr16iUfHx+zy8El6B/XRv+UjdzcXHXt2tWpebLz8vV12vfq0629Av18nZqXI3XG23LojLR1kzp16qTrG1Y3uxy3xvvH/fD+cV30jWtzt/22ojOw/8w1Dd01a9aUt7e3WrRo4TC8efPmWrdunSQpNDRU58+f19mzZx2Odh8/flyhoaElLtfX11e+vsU/cHx8fNyis4q42/q4G/rHtdE/xqpSpYo6dOjg1DwWi0V/nD2jbjd2om9ckLe3t/0n/WMs3j/uh/eP66Jvygd32W+72nW4ps/prlSpktq3b1/suqXdu3erUaNGkqS2bdvKx8dH33zzjX38rl27dOjQIXXu3PlalgMAAAAAgKmcPtKdnZ2tvXv32l/v379fmzdvVvXq1dWwYUM9/vjjuvPOO9W9e3f17NlTy5cv1xdffKE1a9ZIuvBt74gRIzR27FhVr15dwcHBGjVqlDp37sydywEAAAAAbsXp0L1p0yb17NnT/rroWuvhw4drzpw5uv322/XOO+8oMTFRo0ePVrNmzfTZZ585XMv06quvytPTU4MHD1Z+fr769Omjt9566xqsDgAAAAAArsPp0N2jRw/ZbLYrTvPAAw/ogQceuOz4ypUra9asWZo1a5azzQMAAAAAUG5c02u6AQAAAADA/0foBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAACAoQoLC5Wamqq1a9cqNTVVhYWFZpdUZgjdAAAAAADDJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSygShGwAAAABgiKSkJA0ZMkTR0dFKS0vTJ598orS0NEVHR2vIkCEVIngTugEAAAAA11xhYaESEhI0YMAAJScnq2PHjvLz81PHjh2VnJysAQMGaNy4cW5/qjmhGwAAAABwzaWlpenAgQOaOHGiPD0do6enp6cmTJig/fv3Ky0tzaQKywahGwAAAABwzR09elSSFBUVVeL4ouFF07krQjcAAAAA4JqrU6eOJGnbtm0lji8aXjSduyJ0AwAAAACuuW7duiksLExTpkyR1Wp1GGe1WpWYmKjw8HB169bNpArLBqEbAAAAAHDNeXl5afr06Vq6dKliY2OVnp6uvLw8paenKzY2VkuXLtW0adPk5eVldqmG8ja7AAAAAACAe4qLi9OiRYuUkJCg7t2724eHh4dr0aJFiouLM7G6skHoBgAAAAAYJi4uToMGDdLq1au1bNky9e3bVz179nT7I9xFCN0AAAAAAEN5eXkpJiZGOTk5iomJqTCBW+KabgAAAAAADEPoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAg3mYXAAAA/prc3Fzt3LnTqXl2HT2r/GN7tWObn6ynqzo1b2RkpPz9/Z2aB3BVvH9cF30Dd0HoBgCgnNu5c6fatm1bqnnvnuv8PBkZGWrTpk2p2gNcDe8f10XfwF0QugEAKOciIyOVkZHh1DzZefn6cvUG9e/ZWYF+vk63B7gL3j+ui76BuyB0AwBQzvn7+zt9dMZisej3UyfUuUM7+fj4GFQZ4Pp4/7gu+gbughupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNruA0rDZbJKkrKwskyu5NiwWi3Jzc5WVlSUfHx+zy8El6B/XRv+4LvrGtdE/ro3+cW30j+uib1ybu/VPUR4tyqeXUy5D9x9//CFJatCggcmVAAAAAAAqsj/++ENVqlS57HgP25/FchdktVp15MgRBQUFycPDw+xy/rKsrCw1aNBAv/76q4KDg80uB5egf1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1j82m01//PGH6tatK0/Py1+5XS6PdHt6eqp+/fpml3HNBQcHu8Ufn7uif1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1D9XOsJdhBupAQAAAABgEEI3AAAAAAAGIXS7AF9fXz333HPy9fU1uxSUgP5xbfSP66JvXBv949roH9dG/7gu+sa1VdT+KZc3UgMAAAAAoDzgSDcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEK3CQoKCvT888/rt99+M7sUALhm2LYBAMqaxWLRzTffrD179phdCq7g/Pnz+u2333To0CGHfxUFdy83SVBQkLZu3aqwsDCzS8ElLBaLIiMjtXTpUjVv3tzscoByhW2ba2P75vq++eYbffPNNzpx4oSsVqvDuA8//NCkqlBk06ZN2rFjhySpefPmateunckVQZJq1aql9evXq2nTpmaXgkvs2bNHDzzwgNavX+8w3GazycPDQ4WFhSZVVra8zS6gorrpppuUmprKjqkL8vHx0blz58wuAyiX2La5NrZvrm3y5Ml6/vnn1a5dO9WpU0ceHh5ml4T/+e233/S3v/1N3377rapWrSpJOnv2rG688UZ9+umnql+/vrkFVnBDhw7VBx98oJdfftnsUnCJ++67T97e3lq6dGmF3q5xpNsk77zzjiZPnqx77rlHbdu2VUBAgMP4gQMHmlQZJGnKlCnavXu3/v3vf8vbm++mXE1OTo5efvnlyx4N+uWXX0yqDGzbXB/bN9dVp04dTZ06VcOGDTO7FFzi1ltv1dmzZzV37lw1a9ZMkrRr1y7df//9Cg4O1vLly02usGIbNWqU5s2bp6ZNm5b42TNjxgyTKkNAQIAyMjIUGRlpdimmInSbxNPz8pfTV6RTLVzV7bffrm+++UaBgYGKjo4utvFOSkoyqTJI0t/+9jelpqZq2LBhJX5r+uijj5pUGdi2uT62b66rRo0a2rhxo5o0aWJ2KbiEn5+f1q9frxtuuMFheEZGhrp166bc3FyTKoMk9ezZ87LjPDw8tGrVqjKsBhdr3769Xn31VXXt2tXsUkzFV9wmufTIHFxL1apVNXjwYLPLwGUsW7ZMX375pbp06WJ2KbgE2zbXx/bNdf3973/X/Pnz9cwzz5hdCi7RoEEDWSyWYsMLCwtVt25dEyrCxVavXm12CbiMV155RU888YSmTJmi6Oho+fj4OIwPDg42qbKyxZFuF3Du3DlVrlzZ7DKAciM8PFxfffUVN4JycWzbAOc8+uijmjdvnlq1aqVWrVoV2znlFFnzLF68WFOmTNGsWbPsN0/btGmTRo0apSeffFKxsbHmFghJ0t69e7Vv3z51795dfn5+9pt1wTxFZ8Bd2g8V7UZqhG6TFBYWasqUKXrnnXd0/Phx7d69W40bN9YzzzyjsLAwjRgxwuwSK7yCggKtWbNG+/bt0913362goCAdOXJEwcHBCgwMNLu8Cu3jjz/W4sWLNXfuXPn7+5tdDi7Ctq18YPvmmjhF1nVVq1ZNubm5KigosN8Loej/l16icebMGTNKrNBOnz6tO+64Q6tXr5aHh4f27Nmjxo0b64EHHlC1atU0ffp0s0ussFJTU684PiYmpowqMRenl5vkpZde0ty5czV16lQ9+OCD9uFRUVGaOXMmO6YmO3jwoG699VYdOnRI+fn56tWrl4KCgvTKK68oPz9f77zzjtklVmjTp0/Xvn37VLt2bYWFhRU7GvTDDz+YVBnYtrk+tm+ui1NkXdfMmTPNLgFXMGbMGPn4+OjQoUMOZ8HdeeedGjt2LKHbRBUlVP8ZQrdJ5s2bp/fee08333yzHn74Yfvw66+/Xjt37jSxMkgXTvFr166dtmzZoho1atiH33777Q5BAubgND7XxbbN9bF9A5w3fPhws0vAFaxYsUJff/11sUe3NW3aVAcPHjSpKlwsNzdXhw4d0vnz5x2Gt2rVyqSKyhah2ySHDx9WREREseFWq7XEG3WgbKWlpWn9+vWqVKmSw/CwsDAdPnzYpKpQ5LnnnjO7BFwG2zbXx/bNtW3atEn//e9/S9w55c7y5iosLFRycrJ27NghSWrZsqUGDhwoLy8vkytDTk5OiZebnTlzRr6+viZUhCInT57U/fffr2XLlpU4vqJc0335Z7vAUC1atFBaWlqx4YsWLSr2OAqUPavVWuJG4LffflNQUJAJFQHlA9s218f2zXV9+umnuvHGG7Vjxw59/vnnslgs+vnnn7Vq1SpVqVLF7PIqtL1796p58+a69957lZSUpKSkJA0dOlQtW7bUvn37zC6vwuvWrZvmzZtnf+3h4SGr1aqpU6de8V4JMN5jjz2ms2fP6rvvvpOfn5+WL1+uuXPnqmnTplqyZInZ5ZUZjnSb5Nlnn9Xw4cN1+PBhWa1WJSUladeuXZo3b56WLl1qdnkVXu/evTVz5ky99957ki5svLOzs/Xcc8+pX79+JleHwsJCvfrqq5c9GsRNbMzDts31sX1zXVOmTNGrr76q+Ph4BQUF6bXXXlN4eLj+8Y9/qE6dOmaXV6GNHj1aTZo0UXp6uqpXry7pws27hg4dqtGjR+vLL780ucKKberUqbr55pu1adMmnT9/Xk888YR+/vlnnTlzRt9++63Z5VVoq1at0uLFi9WuXTt5enqqUaNG6tWrl4KDg5WYmKj+/fubXWKZ4O7lJkpLS9Pzzz+vLVu2KDs7W23atNGzzz6r3r17m11ahffbb7+pT58+stls2rNnj9q1a6c9e/aoZs2aWrt2rUJCQswusUJ79tln9e9//1sJCQl6+umn9dRTT+nAgQNKTk7Ws88+q9GjR5tdYoXGts21sX1zXQEBAfr5558VFhamGjVqaM2aNYqOjtaOHTt000036ejRo2aXWGEFBAQoPT1d0dHRDsO3bNmiLl26KDs726TKUCQzM1Nvvvmmw2dPfHw8X1iZLDg4WD/99JPCwsLUqFEjzZ8/X126dNH+/fvVsmVL5ebmml1imeBIt4m6deumlJQUs8tACerXr68tW7ZowYIF9o33iBEjdM8998jPz8/s8iq8//znP3r//ffVv39/TZo0SX/729/UpEkTtWrVSunp6YRuk7Ftc21F27dPP/1UP/30E9s3F1KtWjX98ccfkqR69epp27Ztio6O1tmzZyvMjqmr8vX1tffNxbKzs4vdHwHmqFKlip566imzy8AlmjVrpl27diksLEzXX3+93n33XYWFhemdd96pUF+IcKTbZOfPn9eJEydktVodhjds2NCkiiBJa9eu1Y033mh/FmeRgoICrV+/Xt27dzepMkgXjjjs2LFDDRs2VJ06dfTll1+qTZs2+uWXX3TDDTcoMzPT7BIBwGl333232rVrp7Fjx+qFF17QG2+8oUGDBiklJUVt2rThRmomuvfee/XDDz/ogw8+UIcOHSRJ3333nR588EG1bdtWc+bMMbdA6OzZs9q4cWOJ+9X33nuvSVXh448/VkFBge677z5lZGTo1ltv1ZkzZ1SpUiXNmTNHd955p9kllglCt0n27NmjBx54QOvXr3cYbrPZ5OHhUWHu5OeqvLy8dPTo0WKnWZ4+fVohISH0j8maNWumefPmqWPHjuratasGDBig8ePHa8GCBRo1apROnDhhdokVSrVq1eTh4XFV03K9vWvYs2ePVq9eXeLO6bPPPmtSVThz5ozOnTununXr2m8CtX79ejVt2lRPP/20qlWrZnaJFdbZs2c1fPhwffHFF/Lx8ZF04Yv4gQMHavbs2apataq5BVZwX3zxhe655x5lZ2crODjY4TPJw8ODzx4Xkpubq507d6phw4aqWbOm2eWUGUK3Sbp06SJvb2+NHz9ederUKbbDev3115tUGSTJ09NTx48fV61atRyG7969W+3atVNWVpZJlUGSxo8fr+DgYE2cOFELFizQ0KFDFRYWpkOHDmnMmDF6+eWXzS6xQpk7d679/6dPn9aLL76oPn36qHPnzpKkDRs26Ouvv9YzzzyjMWPGmFUm/uf999/XI488opo1ayo0NLTYzukPP/xgYnWAa9u7d6/9kWHNmzcv8RGJKHvXXXed+vXrpylTppT46DDAbIRukwQEBCgjI0ORkZFml4KLxMXFSZIWL16sW2+91eHZjoWFhfrpp5/UrFkzLV++3KwSUYINGzZow4YNatq0qW677Tazy6nQBg8erJ49e2rkyJEOw998802tXLlSycnJ5hQGu0aNGumf//ynnnzySbNLwWWcOHGixLMQWrVqZVJFeP755zVu3LhigS4vL0//+te/OEPEZAEBAdq6dasaN25sdim4hM1m06JFiy57dlVFuWyG0G2S9u3b69VXX1XXrl3NLgUXuf/++yVdOHJ3xx13ONxUqFKlSgoLC9ODDz5YoU6HAZwRGBiozZs3Fzv6s3fvXrVu3Zo7/LqA4OBgbd68mZ1TF5SRkaHhw4drx44dunT3jEvPzMVlZ64tLi5Od911l+644w6zS8ElHn30Ub377rvq2bOnateuXezs3tmzZ5tUWdni7uVl6OJTkl955RU98cQTmjJliqKjo+3XBxUJDg4u6/Kg///GDwsL0+OPP84pSi7syJEjWrduXYnfmnL3cvPUqFFDixcvVkJCgsPwxYsXq0aNGiZVhYv93//9n1asWKGHH37Y7FJwiQceeEDXXXedPvjggxJ3TmGeonvuXGrLli3253ajbC1ZssT+//79++vxxx/X9u3bS9yvHjhwYFmXh//56KOPlJSUpH79+pldiqk40l2GPD09HTbYJW3AuZGaa7jpppuUlJRU7MYoWVlZio2N1apVq8wpDJKkOXPm6B//+IcqVaqkGjVqFLsm9ZdffjGxuoptzpw5+vvf/66+ffuqY8eOki7c4Xf58uV6//33dd9995lbYAX1+uuv2/+fk5OjGTNmqH///iXunPKllXmCgoL0448/cp2wCym6UWRmZmaxG3QVFhYqOztbDz/8sGbNmmVilRWTp6fnVU3HfrW5wsPDtWzZsgp/SS2huwylpqZe9bQxMTEGVoI/c7nTyE6cOKF69erJYrGYVBkkqUGDBnr44Yc1YcKEq/7QRdn57rvv9PrrrzvcbGj06NH2EI6yFx4eflXT8aWVuWJjYzVs2DANHjzY7FLwP3PnzpXNZtMDDzygmTNnqkqVKvZxRZedFd00EkBxc+fO1fLly/Xhhx86XLZZ0RC6gYv89NNPkqTWrVtr1apVDqeMFRYWavny5Xr33Xd14MABkyqEdOEU5o0bN6pJkyZmlwIA18ypU6c0fPhwdejQQVFRUZwi60JSU1PtT54BcPXy8vJ0++2369tvv1VYWFix7VpFeWIGWw6TzJ49W4GBgfq///s/h+ELFy5Ubm6uhg8fblJlFVvr1q3l4eEhDw8P3XTTTcXG+/n56Y033jChMlxsxIgRWrhwocaPH292KZCceoQe96sALm/Dhg369ttvtWzZsmLjOEXWXEFBQdqxY4eio6MlXbhPxezZs9WiRQtNmjRJlSpVMrnCim306NGKiIgodnnMm2++qb1792rmzJnmFAYNHz5cGRkZGjp0aIW+VwVHuk1y3XXX2e/kd7HU1FQ99NBD2rVrl0mVVWwHDx6UzWZT48aNtXHjRofndFeqVEkhISHy8vIysUJIF846GDBggPLy8kq8JnXGjBkmVVYxXXq/ipJwvwrXMXjwYHXo0KHYI8OmTp2q77//XgsXLjSpMoSFhWnAgAF65plnVLt2bbPLwUXat2+v8ePHa/Dgwfrll1/UokULxcXF6fvvv1f//v0JdSarV6+elixZorZt2zoM/+GHHzRw4ED99ttvJlWGgIAAff311xX+iU0c6TbJoUOHSrzGrlGjRjp06JAJFUG68PuXVOxu2HAtiYmJ+vrrr9WsWTNJKnYjNZSt1atXm10CnLB27VpNmjSp2PC+fftq+vTpZV8Q7E6fPq0xY8YQuF3Q7t271bp1a0kXzkqMiYnR/Pnz9e233+quu+4idJvs9OnTDtfbFwkODtapU6dMqAhFGjRowFluInSbJiQkRD/99JPCwsIchm/ZsoXH6phkyZIl6tu3r3x8fBweQ1ESrqsz1/Tp0/Xhhx9yJ2wXwY0fy5fs7OwST4X18fFx6lIBXHtxcXFavXo196twQTabzf6F/MqVKzVgwABJFwIFoc58ERERWr58uUaOHOkwfNmyZWrcuLFJVUG6sM/2xBNP6J133imWeyoSQrdJ/va3v2n06NEKCgpS9+7dJV04tfzRRx/VXXfdZXJ1FVNsbKyOHTumkJAQxcbGXnY6TpE1n6+vr7p06WJ2GbiMtLQ0vfvuu/rll1+0cOFC1atXTx999JHCw8Mr/OllriA6OloLFizQs88+6zD8008/VYsWLUyqCtKFS88mTJigdevW8Tg3F9OuXTu9+OKLuuWWW5Samqq3335bkrR//37OTHABY8eO1ciRI3Xy5En7PXm++eYbTZ8+nbMQTDZ06FDl5uaqSZMm8vf3L7ZdO3PmjEmVlS2u6TbJ+fPnNWzYMC1cuNB+J0yr1ap7771Xb7/9tnx9fU2uEHBdiYmJOnr0qMOzh+EaPvvsMw0bNkz33HOPPvroI23fvl2NGzfWm2++qa+++kpfffWV2SVWeF988YXi4uJ09913O+ycfvLJJ1q4cOEVv3SEsa70aDce52auLVu2aOjQoTp06JDGjh2r5557TpI0atQonT59WvPnzze5Qrz99tt66aWXdOTIEUkX7pEwadIk3XvvvSZXVrHNnTv3iuMrys2jCd0m27NnjzZv3iw/Pz9FR0fbrykGcHm33367Vq1apRo1aqhly5bFvjVNSkoyqTLccMMNGjNmjO69914FBQVpy5Ytaty4sX788Uf17dtXx44dM7tESPryyy81ZcoU++dPq1at9Nxzz3GpAOCkc+fOydvbm0eJuZCTJ0/Kz89PgYGBZpcC2LGFMMnzzz+vcePGqWnTpmratKl9eF5env71r38VO+0PZeNqj5xyip+5qlatqri4OLPLQAl27dplv2TmYlWqVNHZs2fLviCUqH///urfv7/ZZeAyzp8/r/3796tJkyaEORfRuHFjff/998Xuu3Pu3Dm1adOGsxBMdtNNNykpKUlVq1Z1ePJMVlaWYmNjtWrVKhOrw759+zR79mzt27dPr732mkJCQrRs2TI1bNhQLVu2NLu8MsGRbpN4eXnp6NGjCgkJcRh++vRphYSEcM2wSS49te/XX39VnTp1HHZ6OMUPuLzGjRvrvffe0y233OJwpHvevHl6+eWXtX37drNLrPAuFx7Onj1LeDBZbm6uRo0aZT8dc/fu3WrcuLFGjRqlevXqafz48SZXWHF5enra7/tysePHj6tBgwY6f/68SZVBunz/nDhxQvXq1ZPFYjGpMqSmpqpv377q0qWL1q5dqx07dqhx48Z6+eWXtWnTJi1atMjsEssEX5+apOiZtZfasmWLqlevbkJFkC7cEOViQUFBSk1N5c6XLqigoEBr1qzRvn37dPfddysoKEhHjhxRcHAwp5SZ6MEHH9Sjjz6qDz/8UB4eHjpy5Ig2bNigcePG6ZlnnjG7PEg6cOBAiV/s5ufn6/DhwyZUhCITJkzQli1btGbNGt1666324bfccosmTZpE6DbBxU8z+frrrx0eS1VYWKhvvvnmitfiw1g//fST/f/bt293uISpsLBQy5cvV7169cwoDf8zfvx4vfjiixo7dqyCgoLsw2+66Sa9+eabJlZWtgjdZaxatWry8PCQh4eHrrvuOofgXVhYqOzsbD388MMmVgi4voMHD+rWW2/VoUOHlJ+fr169eikoKEivvPKK8vPz9c4775hdYoU1fvx4Wa1W3XzzzcrNzVX37t3l6+urcePGadSoUWaXV6FdTXioyI9zcQXJyclasGCBOnXq5LB/0LJlS+3bt8/Eyiqui28seOkNn3x8fBQWFsbz7U3UunVr+3510Y0hL+bn56c33njDhMpQZOvWrSXeaDAkJKRCPW6P0F3GZs6cKZvNpgceeECTJ0922OmpVKmSwsLC1LlzZxMrBFzfo48+qnbt2hV7rv3tt9+uBx980MTK4OHhoaeeekqPP/649u7dq+zsbLVo0YKzD1xAUXjw8PAgPLiokydPFjs9VpJycnJKPDsOxit6Nnd4eLg2bdpU7LIMmGv//v2y2Wxq3LixNm7c6HA9d6VKlRQSEiIvLy8TK0TVqlV19OjRYmeE/PjjjxXqLARCdxkr2tEJDw/XjTfeWOyuywD+XFpamtavX69KlSo5DA8LC+P0WBdRqVIlnvnsYi4OD99//71q1qxpckW4VLt27fTll1/azwopCtr//ve/+ULeRBaLRY0bN9aZM2cI3S6m6Kk/Rds3uJ677rpLTz75pBYuXCgPDw9ZrVZ9++23GjduXIV6nBuh2yQXP5bl3LlzxW7AERwcXNYlQRfucnkxDw8PZWdnFxtO/5jLarWWeE3qb7/95nC9EMpGXFyc5syZo+Dg4D+9qzyPczPfpfeugOuYMmWK+vbtq+3bt6ugoECvvfaatm/frvXr1ys1NdXs8iosHx8fh2uH4RqWLFmivn37ysfHx+HymZIMHDiwjKrCpaZMmaL4+Hg1aNBAhYWFatGihQoLC3X33Xfr6aefNru8MsPdy02Sm5urJ554Qv/97391+vTpYuO5e7k5PD09HU7hu/SGd0Wv6R9z3XnnnapSpYree+89BQUF6aefflKtWrU0aNAgNWzYULNnzza7xArl/vvv1+uvv66goCDdd999VzwNlr4xx+uvv66HHnpIlStX/tNHI/JIRHPt27dPL7/8srZs2aLs7Gy1adNGTz75pKKjo80urUIbM2aMfH199fLLL5tdCv7n4juWe3p6XnY69ttcw6+//qqtW7cqOztbN9xwg8MjkysCQrdJ4uPjtXr1ar3wwgsaNmyYZs2apcOHD+vdd9/Vyy+/rHvuucfsEiukqz2ScPGZCih7v/32m/r06SObzaY9e/aoXbt22rNnj2rWrKm1a9eWeE0kjHPx0Qa4pouvR73SnZZ5JCJQslGjRmnevHlq2rSp2rZtq4CAAIfxM2bMMKkyoHwpLCzU1q1b1ahRI1WrVs3scsoModskDRs21Lx589SjRw8FBwfrhx9+UEREhD766CN98skn+uqrr8wuEVfh5Zdf1sMPP6yqVauaXUqFU1BQoAULFjgcDbrnnnvk5+dndmkVjpeXl44dO6ZatWrJy8tLR48e5YsPoBR++OEH+fj42I9qL168WLNnz1aLFi00adKkYvexQNnp2bPnZcd5eHho1apVZVgNLnbgwAGlpKTIYrEoJiZGLVu2NLskXOSxxx5TdHS0RowYocLCQsXExGj9+vXy9/fX0qVL1aNHD7NLLBOEbpMEBgZq+/btatiwoerXr6+kpCR16NBB+/fvV3R0tLKzs80uEVchODhYmzdv5jneqNBCQ0P1/vvv67bbbpOnp6eOHz/ucAdZuI709HR98cUXslgsuummmxyeBQ3ztW/fXuPHj9fgwYP1yy+/qEWLFoqLi9P333+v/v37a+bMmWaXCLiU1atXa8CAAcrLy5MkeXt768MPP9TQoUNNrgxF6tevr+TkZLVr107Jycn65z//qTVr1uijjz7SqlWr9O2335pdYpm4/AUQMFTjxo3tN7OJjIzUf//7X0nSF198wVHTcoTvrMwxd+5cffnll/bXTzzxhKpWraobb7xRBw8eNLGyiunhhx/WoEGD5OXlJQ8PD4WGhsrLy6vEfzDPokWL1KVLF7322mt6//331b9/f02bNs3ssnCR3bt3q3Xr1pKkhQsXKiYmRvPnz9ecOXP02WefmVsc7H777Tf99ttvZpcBSc8884x69eqlw4cP6/Tp03rwwQf1xBNPmF0WLnLq1CmFhoZKkr766ivdcccduu666/TAAw9o69atJldXdgjdJrn//vu1ZcsWSdL48eM1a9YsVa5cWY899pgef/xxk6sDXNuUKVPsp5Fv2LBBb775pqZOnaqaNWtqzJgxJldX8UyaNEnbt2/X4sWLZbPZ9OGHHyopKanEfzBPYmKiHnzwQWVmZur333/Xiy++qClTpphdFi5is9nsjz5auXKl+vXrJ0lq0KCBTp06ZWZpFZ7VatXzzz+vKlWqqFGjRmrUqJGqVq2qF154gcdV/b/27jys5rz/H/jztGsvadGEkiUUkoy1QZYy0s1t3GMr21iGYZA9S8LgjsZtbpIlTMNYs0y2iWk0QkTxbSoRNciWUGlR5/dHd+fX0WHM4rxPnefjulxX5/05mWfXXD6d1+f9fr/eAl2/fh3Lly+HjY0NzMzMsHr1ajx8+FBhk2ISw8rKCikpKSgrK8Px48fRq1cvABVNpdXpYTyPDBOkamHg6emJ1NRUXL58GU2aNGGHUqLfkZ2dDUdHRwBAVFQU/vnPf+Kzzz5D586d1WZvkKpp3rw5mjdvjkWLFmHw4MHQ19cXHYlek5aWhu+//172IWfGjBlYuHAhHj58yD34KsLNzQ3BwcHw9PREbGwsNmzYAKDimDcrKyvB6dTb/PnzsWXLFnz11Vfo3LkzACAuLg6LFy9GUVERli1bJjihenr+/DksLCxkr/X19VGnTh08e/aMZ6qriFGjRuGTTz6BjY0NJBIJPD09AQAXLlxA8+bNBadTHhbdSnb69GlMnjwZ58+flzvrufKJaadOnbBx40Z07dpVYEoi1WZoaIgnT56gQYMGOHnyJKZPnw4A0NPTk+3rIjFiY2MxderUakX38+fP4evry2ZDAhUWFsr93tHR0YGenh7y8/NZdKuI0NBQDBs2DFFRUZg/f77s4eK+ffvQqVMnwenU2/bt27F582a5855dXFxga2uLSZMmsegW6MSJEzAxMZG9Li8vR0xMDK5fvy4b4znd4ixevBitWrVCdnY2Bg8eDF1dXQAVTVjnzJkjOJ3ysJGakvn4+KB79+5vXAK7bt06nDlzBgcPHlRyMvozjIyMkJSUxEZqSjZs2DCkpqaibdu22LVrF7KyslC3bl0cPnwY8+bNk/tFS8r1pu7lDx8+hK2tLUpLSwUlIw0NDQQHB8PQ0FA2Nnv2bAQEBMjNFPGcbtVTVFQETU1NHssnkJ6eHpKTk9G0aVO58bS0NLRp04YPfAV52/nclXhON6kCznQrWVJSElauXPnG671792Zjmxqka9euPKJKgG+++QYLFixAdnY29u/fL1tCdvnyZXz66aeC06mn5ORkABV7UlNSUpCTkyO7VrmPy9bWVlQ8QsVRleHh4XJj1tbW2Llzp+y1RCJh0a2C9PT0REdQe61bt8b69euxbt06ufH169ejdevWglIR99PXDAUFBYiNjUVWVhZKSkrkrqnL7xzOdCuZnp4erl+/Llsy9rqMjAw4OzvziakAz58/f+f3Vl2iSUQVsw0SiQSA4q7+derUwX/+8x+MHj1a2dGIaoyysjKsXbsWe/bsUfjhNDc3V1Ayio2NRb9+/dCgQQN07NgRQEUjz+zsbERHR3NbYA3Rr18/bN68GTY2NqKjqI0rV67A29sbhYWFKCgogLm5OR4/fgx9fX1YWlri1q1boiMqBWe6lczW1vatRXdycjJvBIKYmprKiobfw2VKqqGwsFDhB1MXFxdBidRXZmYmpFIpHBwccPHiRblzunV0dGBpaalWXUprA2dnZ0RHR8POzk50FLWxZMkSbN68GTNmzMCCBQswf/583L59G1FRUVi4cKHoeGrNw8MD6enp+Oabb5CamgoAGDhwICZNmoT69esLTkfv6ueff+bElpJ9+eWX6N+/PzZu3AgTExOcP38e2traGD58OKZOnSo6ntJwplvJpkyZgp9++gkJCQnVlou9fPkS7u7u6N69e7XlS/T+xcbGyr6+ffs25syZA39/f7kn2tu3b8eKFSvg5+cnKiYBePToEfz9/XH8+HGF1/lQhOivY88K5WvcuDHWrVuHfv36wcjICFevXpWNnT9/Ht99953oiEQ1Gu9rymdqaooLFy6gWbNmMDU1RXx8PJycnHDhwgX4+fnJHmLVdpzpVrIFCxbgwIEDaNq0KSZPnoxmzZoBAFJTU/HNN9+grKwM8+fPF5xSPXl4eMi+DgoKwpo1a+T2B/v4+MDZ2RmbNm1i0S3YtGnT8OzZM1y4cAEfffQRDh48iAcPHiA4OBghISGi46m1HTt2vPX6yJEjlZSEqObJycmRHRtqaGiIZ8+eAQA+/vhjBAYGioxGAPLy8nDx4kU8fPiw2l5i3tuIFNPW1pY1vLO0tERWVhacnJxgYmKC7OxswemUh0W3kllZWeHcuXOYOHEi5s6dK9v7KJFI0KdPH3zzzTc8i1MFxMfHY+PGjdXG3dzcMHbsWAGJqKrTp0/j0KFDcHNzg4aGBho2bIhevXrB2NgYK1asQL9+/URHVFuvLxUrLS1FYWEhdHR0oK+vzw+mRG/xwQcf4P79+2jQoAEaN26MkydPwtXVFQkJCbJjdkiMI0eOYNiwYcjPz4exsbHcdjSJRMJ7G9EbtG3bFgkJCWjSpAk8PDywcOFCPH78GDt37kSrVq1Ex1Oa3++zT3+7hg0bIjo6Go8fP8aFCxdw/vx5PH78GNHR0bC3txcdjwDY2dlV6/ILAJs3b+b+RhVQUFAgO5LKzMwMjx49AlCxBzUxMVFkNLX39OlTuT/5+flIS0tDly5dsGvXLtHxiFTaP/7xD8TExACo2I4WGBiIJk2aYOTIkWxCKNiMGTMwevRo5OfnIy8vT+4+xwZ3RG+2fPlyWb+qZcuWwczMDBMnTsSjR4+wadMmwemUh3u6iRSIjo7GoEGD4OjoiA4dOgAALl68iBs3bmD//v3w9vYWnFC9tW/fHsHBwejTpw98fHxgamqKFStWYN26ddi3bx9u3rwpOiK95tKlSxg+fLja7N2qDbj3Ubz4+HjEx8ejSZMm6N+/v+g4as3AwADXrl3jv4cajvc1EoXLy4kU8Pb2Rnp6OjZs2CArEvr3748JEyZwplsFTJ06Fffv3wcALFq0CH379kVkZCR0dHQQEREhNhwppKWlhXv37omOQVSjdOzYUdbMk8Tq06cPLl26xGKthps3bx7Mzc1Fx1BrJSUlKCkpgaGhoegoSsWZbiKq8QoLC5GamooGDRrAwsJCdBy1dvjwYbnXUqkU9+/fx/r162FnZ4djx44JSqbezM3NkZ6eDgsLC4wePRpff/01jIyM3vo93333HQYMGAADAwMlpaQnT56gbt26AIDs7GyEh4fj5cuX8PHx4TnQAlS9nz169AhBQUEYNWoUnJ2doa2tLfdeHx8fZcejKl7/3VNJIpFAT08Pjo6O3MIpwLZt25CYmIgPP/wQw4YNw9y5c7FmzRq8evUKPXr0wO7du2X3vNqORTfRG5w9exZhYWG4desW9u7dC1tbW+zcuRP29vbo0qWL6HhEKqmyQ2kliUSCevXqoUePHggJCZHt6yLlMjQ0RHJyMhwcHKCpqYmcnBy5s9RJrGvXrqF///7Izs5GkyZNsHv3bvTt2xcFBQXQ0NBAQUEB9u3bB19fX9FR1crr97M3kUgkPKpSMA0NDUgkErxe1lSOSSQSdOnSBVFRUTAzMxOUUr0sW7YMy5YtQ+fOnZGYmIhPPvkEUVFRmDZtGjQ0NLBu3Tp8/PHH2LBhg+ioSsGim0iB/fv3Y8SIERg2bBh27tyJlJQUODg4YP369YiOjkZ0dLToiGrrxo0bSE5OhqurK+zt7fHDDz9g5cqVePnyJXx9fTFv3jy5rrIkRmVzOxZ2qqFXr1548OAB2rVrh+3bt2PIkCGoU6eOwvdu3bpVyenIy8sLWlpamDNnDnbu3ImjR4+iT58+soaeU6ZMweXLl3H+/HnBSYlUU0xMDObPn49ly5bB3d0dQEUvnsDAQCxYsAAmJiYYP348OnTogC1btghOqx6aNGmCoKAgfPrpp7h06RI6dOiAPXv2YNCgQQCAY8eOYcKECbhz547gpMrB7uVECgQHB2Pjxo0IDw+XW0JW+bSOxDh48CBatGiBoUOHwsnJCTt27MA///lPGBgYwMrKCosXL8aqVatEx1RbeXl5+Pzzz2FhYQFra2tYW1vDwsICkydPRl5enuh4au3bb7+Ft7c38vPzIZFI8OzZs2qd5iv/kPIlJCTIZoT+/e9/4969e5g0aRI0NDSgoaGBKVOmsAmhIPHx8Th69Kjc2I4dO2Bvbw9LS0t89tlnKC4uFpSOKk2dOhVr1qxBz549YWRkBCMjI/Ts2ROrV69GQEAAOnfujNDQUJw6dUp0VLWRlZUlWxnq5uYGLS0tuSPCXFxcZP151AEbqREpkJaWhm7dulUbNzExYfEg0LJlyzBr1iwEBwcjIiICEyZMwIoVKzBt2jQAwKZNm7B27VrMnj1bbFA1lJubi44dO+Lu3bsYNmwYnJycAAApKSmIiIhATEwMzp07x2V9glhZWeGrr74CANjb22Pnzp1qs4+uJsjNzYW1tTWAiq0ABgYGcv9WzMzM8OLFC1Hx1NqSJUvQvXt3fPzxxwAqtgKMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1d/PmTRgbG1cbNzY2xq1btwBUzLw+fvxY2dHUVmlpKXR1dWWvdXR05CaytLS01GpbBme6iRSwtrZGRkZGtfG4uDh2LhUoLS0No0ePhkQigZ+fH0pKSuDp6Sm73rt3b7VZpqRqgoKCoKOjg5s3byIsLAzTpk3DtGnTsGnTJmRkZEBbWxtBQUGiYxKAzMxMWcFdVFQkOA1Ven1bDLfJqIakpCT07NlT9nr37t3o0KEDwsPDMX36dKxbtw579uwRmJAAoF27dggICJBtbQIqtjnNmjUL7du3B1CxPY0n0ChXSkoKkpOTkZycDKlUitTUVNnr//u//xMdT6k4002kwLhx4zB16lRs3boVEokE9+7dQ3x8PGbOnInAwEDR8dRWQUGBrOOyhoYG6tSpA319fdn1OnXqcJmfIFFRUQgLC4OVlVW1a9bW1li1ahUmTJiAtWvXCkhHVZWXl2PZsmXYuHEjHjx4gPT0dDg4OCAwMBCNGjXCmDFjREdUS/7+/rJZoaKiIkyYMEHWOZ73NXGePn0qd1+LjY2Fl5eX7HX79u2RnZ0tIhpVsWXLFgwYMAAffPCBrLDOzs6Gg4MDDh06BADIz8/HggULRMZUOz179pRrble5YqRqgzt1waKbSIE5c+agvLwcPXv2RGFhIbp16wZdXV3MnDkTU6ZMER1PbUkkErkb9OuvSZz79++jZcuWb7zeqlUr5OTkKDERvUlwcDC2b9+OVatWYdy4cbLxVq1aITQ0lEW3AH5+fnKvhw8fXu09I0eOVFYcqsLKygqZmZmws7NDSUkJEhMTsWTJEtn1Fy9eVDs+jJSvWbNmSElJwcmTJ5Geni4b69Wrl6wLPbv/K1dmZqboCCqF3cuJ3qKkpAQZGRnIz89HixYtYGhoKDqSWtPQ0ICJiYms0M7Ly4OxsbHsF6pUKsXz58/Vao+QqrC1tcX333//xuP0zp49iyFDhuDevXtKTkavc3R0RFhYmKzhUFJSEhwcHJCamoqOHTuymVoN8Ntvv6F+/frvfKQV/XkTJ05EUlISVq5ciaioKGzfvh337t2Djo4OACAyMhKhoaFISEgQnJSoZps0aRKCgoJgYWEhOsp7wZluIgVGjx6Nr7/+GkZGRmjRooVsvKCgAFOmTOGROoJs27ZNdAR6gz59+mD+/Pk4deqU7MNopeLiYgQGBqJv376C0lFVd+/ehaOjY7Xx8vJylJaWCkhEf1SLFi1w9epV9hhRgqVLl2LgwIHw8PCAoaEhtm/fLneP27p1K3r37i0wIVWKiYlBTEwMHj58iPLycrlr/Nym+r799lvMnDmz1hbdnOkmUkBTUxP379+HpaWl3Pjjx49hbW2NV69eCUpGf8SuXbvg4+Mj2xdJ789vv/0GNzc36Orq4vPPP0fz5s0hlUrx66+/4r///S+Ki4tx6dIlNrFRAe3atcOXX36J4cOHy810BwUF4dSpUzh79qzoiPQ7qv5/I+V49uwZDA0NoampKTeem5sLQ0PDag8bSbmWLFmCoKAguLm5wcbGptrWs4MHDwpKRu+qtt/XONNNVMXz588hlUohlUrx4sUL6Onpya6VlZUhOjq6WiFOqmv8+PHo0KFDrb2Bq5IPPvgA8fHxmDRpEubOnStrnCKRSNCrVy+sX7+eBbeKWLhwIfz8/HD37l2Ul5fjwIEDSEtLw44dO6qdR0xEFUxMTBSOm5ubKzkJKbJx40ZERERgxIgRoqMQKcSim6gKU1NTWXOupk2bVrsukUjkGqiQauNCHuWyt7fHsWPH8PTpU9y4cQNAxf5hfihVLQMGDMCRI0cQFBQEAwMDLFy4EK6urjhy5Ah69eolOh4R0R9WUlKCTp06iY5B9EYsuomqOHPmDKRSKXr06IH9+/fLFQs6Ojpo2LAh6tevLzAhkeozMzODu7u76Bj0Fl27dsWpU6dExyAi+luMHTsW3333HY91JZXFopuoCg8PDwCQHQ/CzrBERKRqeFQikbyioiJs2rQJP/74I1xcXKod47ZmzRpByYgqsOgmUqBhw4YAgMLCQmRlZaGkpETuuouLi4hYRER/irm5OdLT02FhYQEzM7O3Fm25ublKTEZ/BrfOEMlLTk5GmzZtAADXr1+Xu8aHVDXD8OHDYWxsLDrGe8Oim0iBR48eYdSoUTh27JjC6zwHmohqkrVr18LIyAgAEBoaKjYM/WUpKSnc6kRUxZkzZ0RHoCqSk5Pf+b2VE1kbNmx4X3FUAo8MI1Jg2LBhuHPnDkJDQ/HRRx/h4MGDePDgAYKDgxESEoJ+/fqJjkjvoFWrVjh27Bi7ZhORyho4cOA7v/fAgQPvMQkR0d9DQ0MDEokEUqn0d1caqMtEFme6iRQ4ffo0Dh06BDc3N2hoaKBhw4bo1asXjI2NsWLFChbdgjk4OCAhIQF169aVG8/Ly4Orqytu3boFoPoSMyJ19fz583d+b21e3qeKqh5FJZVKcfDgQZiYmMDNzQ0AcPnyZeTl5f2h4pxIHQwcOBAREREwNjb+3X8ffGClXJmZmbKvr1y5gpkzZyIgIAAdO3YEAMTHxyMkJASrVq0SFVHpWHQTKVBQUCA7j9vMzAyPHj1C06ZN4ezsjMTERMHp6Pbt2wqfjBYXF+Pu3bsCEhGptsrjEN+mckZCXWYdVMW2bdtkX8+ePRuffPIJNm7cCE1NTQAVs0CTJk3iwxCi15iYmMjua8bGxty7rUIqeyMBwODBg7Fu3Tp4e3vLxlxcXGBnZ4fAwED4+voKSKh8LLqJFGjWrBnS0tLQqFEjtG7dGmFhYWjUqBE2btwIGxsb0fHU1uHDh2VfnzhxQm6GqKysDDExMWjUqJGAZESqjfsda4atW7ciLi5OVnADgKamJqZPn45OnTph9erVAtMRqZaqD6wiIiLEBaG3unbtGuzt7auN29vbIyUlRUAiMVh0EykwdepU3L9/HwCwaNEi9O3bF5GRkdDR0eGNXaDKp6ESiQR+fn5y17S1tdGoUSOEhIQISEak2iqPQyTV9urVK6SmpqJZs2Zy46mpqSgvLxeUikj19ejRAwcOHICpqanc+PPnz+Hr64vTp0+LCUZwcnLCihUrsHnzZujo6AAASkpKsGLFCjg5OQlOpzxspEb0DgoLC5GamooGDRrAwsJCdBy1Z29vj4SEBP6/IPqTzp49i7CwMNy6dQt79+6Fra0tdu7cCXt7e3Tp0kV0PLU1ffp07NixA/PmzYO7uzsA4MKFC/jqq68wYsQInjVM9AYaGhrIycmRbQ2s9PDhQ9ja2qK0tFRQMrp48SL69+8PqVQq61SenJwMiUSCI0eOyO51tR1nuonegb6+PlxdXUXHoP+p2qCjUl5eXrUn3ERU3f79+zFixAgMGzYMiYmJKC4uBgA8e/YMy5cvR3R0tOCE6uvf//43rK2tERISIlttZWNjg4CAAMyYMUNwOiLVU/VoqpSUFOTk5Mhel5WV4fjx47C1tRURjf7H3d0dt27dQmRkJFJTUwEAQ4YMwdChQ2FgYCA4nfJwpptIgbKyMkRERCAmJgYPHz6stqyPy5TEWrlyJRo1aoQhQ4YAqGjSsX//ftjY2CA6OhqtW7cWnJBIdbVt2xZffvklRo4cCSMjIyQlJcHBwQFXrlyBl5eX3IdWEqey4zwbqBG9WeXRVEBFM8jX1alTB//5z38wevRoZUcjAKWlpWjevDmOHj2qVkvJFeFMN5ECU6dORUREBPr164dWrVqxI6aK2bhxIyIjIwEAp06dwo8//ojjx49jz549CAgIwMmTJwUnJFJdaWlp6NatW7VxExMT5OXlKT8QKcRim+j3ZWZmQiqVwsHBARcvXkS9evVk13R0dGBpaSnXmJCUS1tbG0VFRaJjqAQW3UQK7N69G3v27JE73oBUR05ODuzs7AAAR48exSeffILevXujUaNG6NChg+B0RKrN2toaGRkZ1Tr9x8XFwcHBQUwoAgA8ePAAM2fOlK2yen3mjse5Eclr2LAhSktL4efnh7p168odVUWq4fPPP8fKlSuxefNmaGmpb+mpvj850Vvo6OjA0dFRdAx6AzMzM2RnZ8POzg7Hjx9HcHAwgIqlZfxQSvR248aNw9SpU7F161ZIJBLcu3cP8fHxmDFjBhYuXCg6nlrz9/dHVlYWAgMDYWNjw1VWRO9AW1sbBw8e5P1LRSUkJCAmJgYnT56Es7NztX3cBw4cEJRMuVh0EykwY8YMfP3111i/fj0/9KiggQMHYujQoWjSpAmePHkCLy8vAMCVK1f4sITod8yZMwfl5eXo2bMnCgsL0a1bN+jq6iIgIABjx44VHU+txcXF4ezZs2jTpo3oKEQ1yoABAxAVFYUvv/xSdBR6jampKQYNGiQ6hnAsuokUiIuLw5kzZ3Ds2DG0bNkS2tractfV5amcqlq7di3s7e2RlZWFVatWwdDQEABw//59TJo0SXA6ItUmkUgwf/58BAQEICMjA/n5+WjRogXCwsJgb2/PRmoC2dnZKWwGRURv16RJEwQFBeGXX35Bu3btqs2mfvHFF4KS0bZt20RHUAnsXk6kwKhRo956nTcQcUpLSzF+/HgEBgbC3t5edByiGqO4uBiLFy/GqVOnZDPbvr6+2LZtGxYsWABNTU18/vnnmD17tuioauvkyZMICQlBWFhYtT33RPRmb/s8IJFIcOvWLSWmIaqORTcR1TgmJia4evUqi26iP2D27NkICwuDp6cnzp07h0ePHmHUqFE4f/485s2bh8GDB7PLr2BmZmYoLCzEq1evoK+vX22VVW5urqBkRETvztXVFTExMTAzM0Pbtm3fulUzMTFRicnE4fJyIqpxfH19uXeL6A/au3cvduzYAR8fH1y/fh0uLi549eoVkpKS2LtCRYSGhoqOQET0lw0YMAC6uroAKj6zEWe6iRR601M5iUQCPT09ODo6wt/fH927dxeQjoKDgxESEoKePXty7xbRO9LR0UFmZiZsbW0BAHXq1MHFixfh7OwsOBkR0V/322+/4fDhw8jKykJJSYnctTVr1ghKRVSBRTeRAnPnzsWGDRvg7OwMd3d3ABVHHiQnJ8Pf3x8pKSmIiYnBgQMHMGDAAMFp1Q/3bhH9cZqamsjJyUG9evUAAEZGRkhOTuY2DRVTVlaGqKgo/PrrrwCAli1bwsfHh0v/id4iJiYGPj4+cHBwQGpqKlq1aoXbt29DKpXC1dUVp0+fFh1R7ZWUlODhw4coLy+XG2/QoIGgRMrFoptIgXHjxqFBgwYIDAyUGw8ODsadO3cQHh6ORYsW4YcffsClS5cEpSQiencaGhrw8vKSLfk7cuQIevToobZnpqqijIwMeHt74+7du2jWrBkAIC0tDXZ2dvjhhx/QuHFjwQmJVJO7uzu8vLywZMkSGBkZISkpCZaWlhg2bBj69u2LiRMnio6ottLT0zFmzBicO3dOblwqlUIikaCsrExQMuVi0U2kgImJCS5fvlztzOeMjAy0a9cOz549Q2pqKtq3b48XL14ISklE9O5+71SGSjydQRxvb29IpVJERkbC3NwcAPDkyRMMHz4cGhoa+OGHHwQnJFJNRkZGuHr1Kho3bgwzMzPExcWhZcuWSEpKwoABA3D79m3REdVW586doaWlhTlz5sDGxqba9s3WrVsLSqZcbKRGpICenh7OnTtXreg+d+4c9PT0AADl5eWyr+n9mz59OpYuXQoDAwNMnz79re/l3i2i6lhMq77Y2FicP39eVnADQN26dfHVV1+hc+fOApMRqTYDAwPZPm4bGxvcvHkTLVu2BAA8fvxYZDS1d/XqVVy+fBnNmzcXHUUoFt1ECkyZMgUTJkzA5cuX0b59ewAVe7o3b96MefPmAQBOnDiBNm3aCEypXq5cuYLU1FS0bdsWV65ceeP72IWZiGoqXV1dhaun8vPzoaOjIyARUc3w4YcfIi4uDk5OTvD29saMGTNw7do1HDhwAB9++KHoeGqtRYsWfPABLi8neqPIyEisX78eaWlpAIBmzZphypQpGDp0KADg5cuXsm7mpByampq4f/8+LC0tAQBDhgzBunXrYGVlJTgZEdFfN3LkSCQmJmLLli2yJp4XLlzAuHHj0K5dO0RERIgNSKSibt26hfz8fLi4uKCgoAAzZszAuXPn0KRJE6xZswYNGzYUHVGtPH/+XPb1pUuXsGDBAixfvhzOzs7Q1taWe6+xsbGy4wnBopuIagwNDQ3k5OTIim5jY2NcvXoVDg4OgpMREf11eXl58PPzw5EjR2QfTF+9egUfHx9ERETAxMREcEIiot+noaEht/KwsmlaVerWSI3Ly4moxuIzQyKqTUxNTXHo0CFkZGTIjgxzcnKq1l+EiOQ5ODggISEBdevWlRvPy8uDq6srjxJVsjNnzoiOoHJYdBP9j7m5OdLT02FhYQEzM7O37g3Ozc1VYjKqJJFIqv1/4R5uIqptHB0dWWgT/QG3b99WOGNaXFyMu3fvCkik3jw8PBAUFISZM2dCX19fdByVwKKb6H/Wrl0LIyMj2dcs5lSPVCqFv7+/7JzhoqIiTJgwgecME1GtMGjQILi7u2P27Nly46tWrUJCQgL27t0rKBmRajp8+LDs6xMnTshtwSgrK0NMTAwaNWokIBktWbIEEyZMYNH9P9zTTUQ1Bs8ZJqLarF69ejh9+jScnZ3lxq9duwZPT088ePBAUDIi1aShoQGgYtXb6yWNtrY2GjVqhJCQEHz88cci4qm11/vwqDvOdBMpkJiYCG1tbdkHn0OHDmHbtm1o0aIFFi9ezKNbBGExTUS12ZuOBtPW1pbrBkxEFcrLywEA9vb2SEhIgIWFheBEVBVXjf5/GqIDEKmi8ePHIz09HUDFMRRDhgyBvr4+9u7di1mzZglOR0REtZGzszO+//77auO7d+9GixYtBCQiUm3x8fE4evQoMjMzZQX3jh07YG9vD0tLS3z22WcoLi4WnFJ9NW3aFObm5m/9oy44002kQHp6Otq0aQMA2Lt3Lzw8PPDdd9/hl19+wb/+9S+EhoYKzUdERLVPYGAgBg4ciJs3b6JHjx4AgJiYGOzatYv7uYkUWLJkCbp37y5bPn7t2jWMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1tWTJEh51+D8suokUkEqlsiVLP/74o+xmbmdnh8ePH4uMRkREtVT//v0RFRWF5cuXY9++fahTpw5cXFzw448/wsPDQ3Q8IpWTlJSE4OBg2evdu3ejQ4cOCA8PB1DxuW3RokUsugX517/+xT3d/8Oim0gBNzc3BAcHw9PTE7GxsdiwYQMAIDMzE1ZWVoLTERFRbdWvXz/069dPdAyiGuHp06dyn8tiY2Ph5eUle92+fXtkZ2eLiKb2uJ9bHvd0EykQGhqKxMRETJ48GfPnz5edl7pv3z506tRJcDoiIqqt8vLysHnzZsybNw+5ubkAKpp78qxhouqsrKyQmZkJACgpKUFiYiI+/PBD2fUXL15AW1tbVDy1xgOy5PHIMKI/oKioCJqamryBExHR3y45ORmenp4wMTHB7du3kZaWBgcHByxYsABZWVnYsWOH6IhEKmXixIlISkrCypUrERUVhe3bt+PevXuyUwAiIyMRGhqKhIQEwUlJ3XGmm+gNKmcb5s6dK5ttSElJwcOHDwUnIyKi2mj69Onw9/fHjRs3oKenJxv39vbGzz//LDAZkWpaunQptLS04OHhgfDwcISHh8sdu7d161b07t1bYEKiCpzpJlIgOTkZPXv2hKmpKWcbiIhIKUxMTJCYmIjGjRvDyMgISUlJcHBwwJ07d9CsWTMUFRWJjkikkp49ewZDQ0NoamrKjefm5sLQ0FCuECcSgTPdRApMnz4do0aN4mwDEREpja6uLp4/f15tPD09HfXq1ROQiKhmMDExqVZwA4C5uTkLblIJLLqJFEhISMD48eOrjdva2iInJ0dAIiIiqu18fHwQFBSE0tJSABXdf7OysjB79mwMGjRIcDoiIvqzWHQTKcDZBiIiUraQkBDk5+fD0tISL1++hIeHBxo3bgxDQ0MsW7ZMdDwiIvqTuKebSIGxY8fiyZMn2LNnD8zNzZGcnAxNTU34+vqiW7duCA0NFR2RiIhqqbi4OCQnJyM/Px/t2rVDz549RUciIqK/gDPdRApUzjbUq1dPNtvg6OgIIyMjzjYQEdHfKj4+HkePHpW97tKlCwwMDPDf//4Xn376KT777DMUFxcLTEhERH8FZ7qJ3uKXX35BUlIS8vPz4erqCk9PT9GRiIiolvHy8sJHH32E2bNnAwCuXbuGdu3awc/PD05OTli9ejXGjx+PxYsXiw1KRER/ipboAESqpry8HBEREThw4ABu374NiUQCe3t7WFtbQyqVQiKRiI5IRES1yNWrV7F06VLZ6927d8Pd3R3h4eEAADs7OyxatIhFNxFRDcXl5URVSKVS+Pj4YOzYsbh79y6cnZ3RsmVL3LlzB/7+/vjHP/4hOiIREdUyT58+hZWVlex1bGwsvLy8ZK/bt2+P7OxsEdGIiOhvwJluoioiIiLw888/IyYmBt27d5e7dvr0afj6+mLHjh0YOXKkoIRERFTbWFlZITMzE3Z2digpKUFiYiKWLFkiu/7ixQtoa2sLTEhERH8FZ7qJqti1axfmzZtXreAGgB49emDOnDmIjIwUkIyIiGorb29vzJkzB2fPnsXcuXOhr6+Prl27yq4nJyejcePGAhMSEdFfwaKbqIrk5GT07dv3jde9vLyQlJSkxERERFTbLV26FFpaWvDw8EB4eDjCw8Oho6Mju75161b07t1bYEIiIvor2L2cqAodHR3cuXMHNjY2Cq/fu3cP9vb2PLqFiIj+ds+ePYOhoSE0NTXlxnNzc2FoaChXiBMRUc3BPd1EVZSVlUFL683/LDQ1NfHq1SslJiIiInVhYmKicNzc3FzJSYiI6O/EopuoCqlUCn9/f+jq6iq8zhluIiIiIiL6I1h0E1Xh5+f3u+9h53IiIiIiInpX3NNNRERERERE9J6wezkRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREX766SdIJBLk5eWJjkJERFSrsOgmIiKqBfz9/SGRSCCRSKCtrQ17e3vMmjULRUVFoqMRERGpNZ7TTUREVEv07dsX27ZtQ2lpKS5fvgw/Pz9IJBKsXLlSdDQiIiK1xZluIiKiWkJXVxfW1taws7ODr68vPD09cerUKQBAcXExvvjiC1haWkJPTw9dunRBQkLCW/++uLg4dO3aFXXq1IGdnR2++OILFBQUKONHISIiqjVYdBMREdVC169fx7lz56CjowMAmDVrFvbv34/t27cjMTERjo6O6NOnD3JzcxV+/82bN9G3b18MGjQIycnJ+P777xEXF4fJkycr88cgIiKq8SRSqVQqOgQRERH9Nf7+/vj222+hp6eHV69eobi4GBoaGtizZw/69u0LMzMzREREYOjQoQCA0tJSNGrUCNOmTUNAQAB++ukndO/eHU+fPoWpqSnGjh0LTU1NhIWFyf4bcXFx8PDwQEFBAfT09ET9qERERDUK93QTERHVEt27d8eGDRtQUFCAtWvXQktLSzZTXVpais6dO8veq62tDXd3d/z6668K/66kpCQkJycjMjJSNiaVSlFeXo7MzEw4OTm995+HiIioNmDRTUREVEsYGBjA0dERALB161a0bt0aW7ZsQfv27f/w35Wfn4/x48fjiy++qHatQYMGfzkrERGRumDRTUREVAtpaGhg3rx5mD59OjIyMqCjo4NffvkFDRs2BFCxvDwhIQHTpk1T+P2urq5ISUmRFfFERET057CRGhERUS01ePBgaGpqYsOGDZg4cSICAgJw/PhxpKSkYNy4cSgsLMSYMWMUfu/s2bNx7tw5TJ48GVevXsWNGzdw6NAhNlIjIiL6gzjTTUREVEtpaWlh8uTJWLVqFTIzM1FeXo4RI0bgxYsXcHNzw4kTJ2BmZqbwe11cXBAbG4v58+eja9eukEqlaNy4MYYMGaLkn4KIiKhmY/dyIiIiIiIioveEy8uJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREREREb0nLLqJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe/L/AJ/aJD9mHqDtAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Uwaga**: Ten diagram sugeruje, że średnio wzrost pierwszobazowych jest wyższy niż wzrost drugobazowych. Później dowiemy się, jak można formalniej przetestować tę hipotezę i jak wykazać, że nasze dane są statystycznie istotne, aby to udowodnić.  \n",
+    "> **Uwaga**: Ten diagram sugeruje, że średnio wzrost pierwszobazowych jest wyższy niż wzrost drugobazowych. Później dowiemy się, jak można bardziej formalnie przetestować tę hipotezę i jak wykazać, że nasze dane są statystycznie istotne, aby to udowodnić.\n",
     "\n",
-    "Wiek, wzrost i waga to wszystkie ciągłe zmienne losowe. Jak myślisz, jaki jest ich rozkład? Dobrym sposobem, aby się tego dowiedzieć, jest narysowanie histogramu wartości:\n"
+    "Wiek, wzrost i waga to zmienne losowe ciągłe. Jak myślisz, jaki jest ich rozkład? Dobrym sposobem, aby się tego dowiedzieć, jest narysowanie histogramu wartości:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,7 +620,7 @@
    "metadata": {},
    "source": [
     "Dwie wartości zwracane przez funkcję `ttest_ind` to:\n",
-    "* p-wartość można uznać za prawdopodobieństwo, że dwie rozkłady mają tę samą średnią. W naszym przypadku jest ona bardzo niska, co oznacza, że istnieją silne dowody na to, że pierwszobazowi są wyżsi.\n",
+    "* p-wartość można traktować jako prawdopodobieństwo, że dwie rozkłady mają tę samą średnią. W naszym przypadku jest ona bardzo niska, co oznacza, że istnieją silne dowody na to, że pierwszobazowi są wyżsi.\n",
     "* t-wartość to pośrednia wartość znormalizowanej różnicy średnich, która jest używana w teście t i porównywana z wartością progową dla danego poziomu ufności.\n"
    ]
   },
@@ -787,26 +628,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Symulacja rozkładu normalnego za pomocą twierdzenia granicznego\n",
+    "## Symulacja rozkładu normalnego za pomocą centralnego twierdzenia granicznego\n",
     "\n",
-    "Generator pseudolosowy w Pythonie jest zaprojektowany tak, aby zapewniać rozkład jednostajny. Jeśli chcemy stworzyć generator dla rozkładu normalnego, możemy skorzystać z twierdzenia granicznego. Aby uzyskać wartość o rozkładzie normalnym, wystarczy obliczyć średnią z próby wygenerowanej w sposób jednostajny.\n"
+    "Generator pseudolosowy w Pythonie został zaprojektowany tak, aby dostarczać rozkład jednostajny. Jeśli chcemy stworzyć generator dla rozkładu normalnego, możemy skorzystać z centralnego twierdzenia granicznego. Aby uzyskać wartość o rozkładzie normalnym, wystarczy obliczyć średnią próbki wygenerowanej w sposób jednostajny.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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6HavqkUl+Mslbk2SM8Z0xxn/t+8Tra9JjMtu/JeihVbWR5AdztH9P/F7XctW3vdgsvRb2Ofcz6XFln3MvS6/lOu93BPI0O73d9r1+4FTV45O8LMlfXOiTVNVWkmcn+eTqRzw0pq7lNUl+O8l392m+w2LKOj4lyZ1J/mrx34ZvqaqH7eewa27ptRxjfDXJHyb5SpKzSb45xvjovk673nZdy4XnVdVnqupvquoZD/C2R8GUdfwe+5wk09fymtjnnDdlLdd2vyOQp9nL221fk+R3xhj37PgJqh6e7aNPrx9jfGu14x0qS69lVf1sknNjjJv2abbDZMpjciPJc5K8eYzx7CT/k+QoP99zymPy0dk+gvLkJD+c5GFV9Qv7MeQhsZe1/FSSJ40xnpnkT5N88AHc9qiYso7bn8A+57yl19I+536mPC7Xdr+z9BuFkGRvb7d9Isl7qipJjiV5cVXdPcb4YFU9ONs/qN45xrj+IAZeY0uvZZLnJvm5qnpxkockeWRVvWOMcRSDZMo6/nOSO8YY548qvS9r8oNqJlPW8sFJvjzGuDNJqur6JD+e5B37PfSa2nUte6yNMW6oqj+vqmN7ue0RsvQ6jjHuss+5lymPyefHPqeb+v29nvuduZ8EfZhP2f4HxpeyfZTo/BPTn/H/XP/t+f6LeCrJXye5Zu6vYx1OU9byPtuvyBF+wcTUdUzyD0mevjj/piR/MPfXdBjXMtv/aLs12889rmy/COW1c39N67yWSX4o33/zqsuz/fSUeqB/DxfzaeI62uesaC3vc50jvc9ZxVqu637HEeQJxgXebruqfm3x8Qs+7zjb/wJ9VZLPVtXNi21vHGPcsJ8zr6uJa8nCCtbxtUneWVWXZPsH3i/v68BrbMpajjE+WVXvy/Z/K96d5NM5ZG+zukp7XMuXJ/n1xRH4/03yirG9x9zxtrN8ITObso5V9ROxz/meiY9JmhWs5Vrud7zVNAAANF6kBwAAjUAGAIBGIAMAQCOQAQCgEcgAANAIZAAAaAQyAAA0/wceFVFs3MY9ywAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korelacja i Zła Korporacja Baseballowa\n",
     "\n",
-    "Korelacja pozwala nam znaleźć zależności między sekwencjami danych. W naszym prostym przykładzie wyobraźmy sobie, że istnieje zła korporacja baseballowa, która płaci swoim graczom w zależności od ich wzrostu - im wyższy gracz, tym więcej zarabia. Załóżmy, że istnieje podstawowa pensja w wysokości 1000 dolarów oraz dodatkowy bonus od 0 do 100 dolarów, w zależności od wzrostu. Weźmiemy prawdziwych graczy z MLB i obliczymy ich wyimaginowane pensje:\n"
+    "Korelacja pozwala nam znaleźć zależności między sekwencjami danych. W naszym przykładzie zabawowym załóżmy, że istnieje zła korporacja baseballowa, która płaci swoim graczom w zależności od ich wzrostu – im wyższy gracz, tym więcej zarabia. Załóżmy, że istnieje podstawowa pensja w wysokości 1000 dolarów oraz dodatkowy bonus od 0 do 100 dolarów, w zależności od wzrostu. Weźmiemy prawdziwych graczy z MLB i obliczymy ich wyimaginowane pensje:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Policzmy teraz kowariancję i korelację tych sekwencji. `np.cov` zwróci nam tak zwaną **macierz kowariancji**, która jest rozszerzeniem kowariancji na wiele zmiennych. Element $M_{ij}$ macierzy kowariancji $M$ to korelacja między zmiennymi wejściowymi $X_i$ i $X_j$, a wartości diagonalne $M_{ii}$ to wariancja $X_{i}$. Podobnie, `np.corrcoef` zwróci nam **macierz korelacji**.\n"
+    "Obliczmy teraz kowariancję i korelację tych sekwencji. `np.cov` zwróci tzw. **macierz kowariancji**, która jest rozszerzeniem kowariancji na wiele zmiennych. Element $M_{ij}$ macierzy kowariancji $M$ to korelacja między zmiennymi wejściowymi $X_i$ i $X_j$, a wartości diagonalne $M_{ii}$ to wariancja $X_{i}$. Podobnie, `np.corrcoef` zwróci **macierz korelacji**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Korelacja równa 1 oznacza, że istnieje silna **relacja liniowa** między dwiema zmiennymi. Możemy wizualnie zobaczyć relację liniową, rysując jeden parametr względem drugiego:\n"
+    "Korelacja równa 1 oznacza, że istnieje silna **liniowa zależność** między dwiema zmiennymi. Możemy wizualnie zobaczyć liniową zależność, rysując jedną wartość względem drugiej:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -919,14 +756,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -944,14 +781,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -962,19 +799,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,24 +824,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Czy potrafisz zgadnąć, dlaczego kropki układają się w pionowe linie w ten sposób?\n",
+    "> Czy możesz zgadnąć, dlaczego kropki układają się w takie pionowe linie?\n",
     "\n",
-    "Zaobserwowaliśmy korelację między sztucznie stworzonym pojęciem, takim jak wynagrodzenie, a obserwowaną zmienną *wzrost*. Zobaczmy teraz, czy dwie obserwowane zmienne, takie jak wzrost i waga, również się ze sobą korelują:\n"
+    "Zaobserwowaliśmy korelację pomiędzy sztucznie skonstruowaną zmienną, taką jak pensja, a obserwowaną zmienną *wzrost*. Sprawdźmy teraz, czy dwie obserwowane zmienne, takie jak wzrost i waga, również są ze sobą skorelowane:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,14 +856,14 @@
    "source": [
     "Niestety, nie uzyskaliśmy żadnych wyników - tylko dziwne wartości `nan`. Wynika to z faktu, że niektóre wartości w naszej serii są niezdefiniowane, reprezentowane jako `nan`, co powoduje, że wynik operacji również jest niezdefiniowany. Patrząc na macierz, możemy zauważyć, że problematyczną kolumną jest `Weight`, ponieważ autokorelacja między wartościami `Height` została obliczona.\n",
     "\n",
-    "> Ten przykład pokazuje, jak ważne są **przygotowanie danych** i **ich czyszczenie**. Bez odpowiednich danych nie możemy niczego obliczyć.\n",
+    "> Ten przykład pokazuje, jak ważne są **przygotowanie danych** i **ich oczyszczenie**. Bez odpowiednich danych nie możemy niczego obliczyć.\n",
     "\n",
     "Użyjmy metody `fillna`, aby uzupełnić brakujące wartości i obliczyć korelację:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +873,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1054,27 +889,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1115,11 +948,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:26:07+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:28:52+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "pl"
   }
diff --git a/translations/pl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/pl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index ecdf0c34..ac742bc4 100644
--- a/translations/pl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/pl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,14 +150,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "W tym zestawie danych kolumny są następujące:\n",
-    "* Wiek i płeć są oczywiste\n",
-    "* BMI to wskaźnik masy ciała\n",
-    "* BP to średnie ciśnienie krwi\n",
-    "* S1 do S6 to różne pomiary krwi\n",
-    "* Y to jakościowy wskaźnik postępu choroby w ciągu jednego roku\n",
+    "W tym zbiorze danych kolumny przedstawiają następujące informacje:  \n",
+    "* Wiek i płeć są oczywiste  \n",
+    "* BMI to wskaźnik masy ciała  \n",
+    "* BP to średnie ciśnienie krwi  \n",
+    "* S1 do S6 to różne pomiary krwi  \n",
+    "* Y to jakościowa miara postępu choroby w ciągu jednego roku  \n",
     "\n",
-    "Przeanalizujmy ten zestaw danych za pomocą metod prawdopodobieństwa i statystyki.\n",
+    "Przeanalizujmy ten zbiór danych za pomocą metod prawdopodobieństwa i statystyki.\n",
     "\n",
     "### Zadanie 1: Oblicz średnie wartości i wariancję dla wszystkich danych\n"
    ],
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Zadanie 2: Wykreśl wykresy pudełkowe dla BMI, BP i Y w zależności od płci\n"
+    "### Zadanie 2: Narysuj wykresy pudełkowe dla BMI, BP i Y w zależności od płci\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:52:50+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:29:20+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "pl"
   }
diff --git a/translations/pt/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/pt/1-Introduction/04-stats-and-probability/assignment.ipynb
index 707f9ca8..cbcf9581 100644
--- a/translations/pt/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/pt/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Neste conjunto de dados, as colunas são as seguintes:\n",
-    "* Idade e sexo são autoexplicativos\n",
-    "* IMC é o índice de massa corporal\n",
-    "* PA é a pressão arterial média\n",
-    "* S1 até S6 são diferentes medições sanguíneas\n",
-    "* Y é a medida qualitativa da progressão da doença ao longo de um ano\n",
+    "Neste conjunto de dados, as colunas são as seguintes:  \n",
+    "* Idade e sexo são autoexplicativos  \n",
+    "* IMC é o índice de massa corporal  \n",
+    "* PA é a pressão arterial média  \n",
+    "* S1 até S6 são diferentes medições sanguíneas  \n",
+    "* Y é a medida qualitativa da progressão da doença ao longo de um ano  \n",
     "\n",
     "Vamos estudar este conjunto de dados utilizando métodos de probabilidade e estatística.\n",
     "\n",
-    "### Tarefa 1: Calcular os valores médios e a variância para todos os valores\n"
+    "### Tarefa 1: Calcular os valores médios e a variância para todos os valores  \n"
    ],
    "metadata": {}
   },
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Tarefa 2: Traçar boxplots para IMC, TA e Y dependendo do género\n"
+    "### Tarefa 2: Traçar boxplots para IMC, PA e Y dependendo do género\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução automática [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas resultantes do uso desta tradução.\n"
+    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, é importante notar que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes da utilização desta tradução.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:45:51+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:25:21+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "pt"
   }
diff --git a/translations/pt/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/pt/1-Introduction/04-stats-and-probability/notebook.ipynb
index a371c448..8ba4c23e 100644
--- a/translations/pt/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/pt/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Estamos a utilizar um pacote chamado [**Pandas**](https://pandas.pydata.org/) aqui para análise de dados. Falaremos mais sobre o Pandas e como trabalhar com dados em Python mais adiante neste curso.\n",
+    "Estamos a utilizar um pacote chamado [**Pandas**](https://pandas.pydata.org/) aqui para análise de dados. Falaremos mais sobre o Pandas e como trabalhar com dados em Python mais tarde neste curso.\n",
     "\n",
     "Vamos calcular os valores médios para idade, altura e peso:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Podemos também criar diagramas de caixa de subconjuntos do nosso conjunto de dados, por exemplo, agrupados por função do jogador.\n"
+    "Podemos também criar diagramas de caixa de subconjuntos do nosso conjunto de dados, por exemplo, agrupados pelo papel do jogador.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Nota**: Este diagrama sugere que, em média, as alturas dos primeiros bases são maiores do que as alturas dos segundos bases. Mais tarde, iremos aprender como testar esta hipótese de forma mais rigorosa e como demonstrar que os nossos dados são estatisticamente significativos para comprovar isso.\n",
+    "> **Nota**: Este diagrama sugere que, em média, as alturas dos primeiros bases são maiores do que as alturas dos segundos bases. Mais tarde, iremos aprender como testar esta hipótese de forma mais formal e como demonstrar que os nossos dados são estatisticamente significativos para comprovar isso.\n",
     "\n",
-    "A idade, altura e peso são todas variáveis aleatórias contínuas. Qual achas que é a sua distribuição? Uma boa forma de descobrir é traçar o histograma dos valores:\n"
+    "Idade, altura e peso são todas variáveis aleatórias contínuas. Qual achas que é a sua distribuição? Uma boa forma de descobrir é criar o histograma dos valores:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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T/OHwsDOTsXOf6/YktyfJNddcs+vCgfkzhQKAg2jqgFxV35rkd5L8VHf/VVVtu+kWY33eQPfdSe5Oko2NjfPuB4B14cMmrJapVrGoqpdmMxy/v7t/dzL81aq6YnL/FUmemYyfSXL18PCrkjw1m3IBAGC+dgzItdkq/rUkj3X3Lw53nUxyZHL9SJL7hvHDVfWyqro2yXVJHppdyQAAMD/TTLF4c5IfS/LHVfWZydjPJjme5ERV3ZbkiSS3Jkl3P1JVJ5I8ms0VMO7o7hdmXTgArJOz0zAeP37zgisBdgzI3f2/svW84iS5cZvHHEtybA91AQDAQjiTHgAADARkAAAYCMgAADDY1YlCAIDZskYyLB8dZAAAGAjIAAAwEJABAGAgIAMAwEBABoAlcujo/Q7cgwUTkAEAYGCZNwDYZzrEsNx0kAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQ4oJyMAAC2JiADwBLyIRYWx4lC4IA7+wf48eM3v+g2ABxUOsgAsAZ0nGF2BGQAABgIyAAAMDAHGUhi7jEAnKWDDAArxFxjmD8BGQAABgIyAAAMzEGGA8ZXs7Bazl2rHJg/HWQAABgIyAAAMDDFAgBWgOlRsH90kAEAYCAgw5qxRioA7I2ADAAAA3OQ4YDQVYb15HcbZk8HGQAABgIyAKwxxyXA7gnIAAAwEJABAGAgIAMAwEBABgCAgWXeYM05OAcAdkdAhjUhCAPAbAjIALCCfCiG+TEHGQAABgIyAAAMTLGAFXP2a9XHj9/8otsAwGzoIAMAwEBABgCAgYAMAAADARmW3KGj95tnDAD7yEF6sKaEagC4OAIyrAiBFwD2hykWAAAwEJABAGCwY0CuqvdV1TNV9flh7NVV9UBVfWly+arhvruq6nRVfbGq3j6vwuGgc/AeAMxHdfeFN6j6gSR/k+TXu/v1k7H/kORr3X28qo4meVV331lV1yf5QJIbkrw2yUeTfGd3v3Ch19jY2OhTp07t/V8Da0gIBmbp7Fk4gaSqHu7ujXPHd+wgd/fHk3ztnOFbktwzuX5PkncN4/d293Pd/ZUkp7MZlgEAYCVc7Bzky7v76SSZXF42Gb8yyZPDdmcmYwAAsBJmfZBebTG25RyOqrq9qk5V1alnn312xmUAAMDFudiA/NWquiJJJpfPTMbPJLl62O6qJE9t9QTdfXd3b3T3xqWXXnqRZQAAwGxdbEA+meTI5PqRJPcN44er6mVVdW2S65I8tLcS4WCxOgUALNaOZ9Krqg8keUuS11TVmSQ/l+R4khNVdVuSJ5LcmiTd/UhVnUjyaJLnk9yx0woWAMD+O/tB3KoWcL4dA3J3/8g2d924zfbHkhzbS1EAALAozqQHAAADARkAAAYCMgAADHacgwzMlwNlgP200yo53pNABxkAAF5EQAYAzmNNdg4yARkAAAYCMgAADBykB3PmgBdgFZhOAd8gIMOS8scKABbDFAsAABjoIMOS0DEGgOWggwz7zNJJwCo59z3LexgHgYAMAAADARkAAAbmIMOC+IoSAJaTgAwA7MiHeg4SARlmzIlBgFVyscHXex3rzBxkAGDPrG7BOhGQAQBgYIoF7BOdFQBYDTrIAAAw0EGGizB2g7c7QEXHGABWk4AMMyIQA0zXQIBlZ4oFAAAMdJBhF3SJAWD96SADAMBAQAYAgIGADADMhbPrsarMQYY98uYPAOtFBxkAAAYCMgCwr0y9YNkJyAAAMBCQAYC50jFm1QjIcAHe1AHg4LGKBWxBKAaAg0tAhnwjED9+/OYL3g/Ai83j/XGn92SYNwEZANgXmg2sCgGZA2HaboQ3b4D9o1PMshKQOdAEYoDF2y4oC9AsioDMWtEpBlh/577XC9LMmoAMACwFzQuWhXWQAQBgoIMMAKwEHWb2S3X3omvIxsZGnzp1atFlsAa8eQIcXOYgs1tV9XB3b5w7booFAAAMBGQAABiYg8xKcCpoAKZl2Tf2SgcZAAAGOsgAwFrY7ttEHWV2S0BmpZz75ufNDgCYNQEZAFhL5zZVtusw78cpq3WxV4s5yAAAMNBBZl+d+wl6uykTPmkDsEr83VovAjIzt9WbxMUuw2b5NgCWkUC83gRkdjTtGsSLeJMQoAHYq93OVWb9zS0gV9VNSX45ySVJ3tvdx+f1WizGXsLpTkvxAMAq02FebXMJyFV1SZJfSfKPk5xJ8qmqOtndj87j9ZjOdr+su/0lnjbECrsArJOt/q5N232e9rmX8dvag2heHeQbkpzu7i8nSVXdm+SWJALyHO0UgLfbfqfnu9jtAYDd2elg9t0+frePu5jHrqPq7tk/adU/T3JTd/+bye0fS/IPu/vdW22/sbHRp06dmnkd09jrJ7aL7b5u9YO/3QoOF/vLcrG/XADAfC3qb/Q0r7vTN8177WYv00m/qurh7t44b3xOAfnWJG8/JyDf0N0/Pmxze5LbJze/K8kXZ17I3r0myZ8tuogVYV/tjv01Pftqd+yv6dlXu2N/Tc++2p1F7q+/292Xnjs4rykWZ5JcPdy+KslT4wbdfXeSu+f0+jNRVae2+lTB+eyr3bG/pmdf7Y79NT37anfsr+nZV7uzjPtrXmfS+1SS66rq2qr65iSHk5yc02sBAMDMzKWD3N3PV9W7k3w4m8u8va+7H5nHawEAwCzNbR3k7v69JL83r+ffJ0s9BWTJ2Fe7Y39Nz77aHftrevbV7thf07Ovdmfp9tdcDtIDAIBVNa85yAAAsJIE5ClV1b+tqq6q1yy6lmVVVf++qj5XVZ+pqo9U1WsXXdMyq6pfqKovTPbZB6vqlYuuaVlV1a1V9UhVfb2qlupI52VRVTdV1Rer6nRVHV10Pcusqt5XVc9U1ecXXcsqqKqrq+oPquqxye/hTy66pmVVVS+vqoeq6rOTffXzi65p2VXVJVX1R1X1oUXXMhKQp1BVV2fztNlPLLqWJfcL3f093f2GJB9K8u8WXM+yeyDJ67v7e5L87yR3LbieZfb5JP8syccXXcgyqqpLkvxKkn+S5PokP1JV1y+2qqX235LctOgiVsjzSX66u787yZuS3OHna1vPJXlrd39vkjckuamq3rTYkpbeTyZ5bNFFnEtAns5/TPIzSUzYvoDu/qvh5itif11Qd3+ku5+f3PzDbK4Xzha6+7HuXsaTCS2LG5Kc7u4vd/ffJrk3yS0LrmlpdffHk3xt0XWsiu5+urs/Pbn+19kMM1cutqrl1Jv+ZnLzpZP//C3cRlVdleTmJO9ddC3nEpB3UFXvTPIn3f3ZRdeyCqrqWFU9meRfRAd5N/51kv++6CJYWVcmeXK4fSYCDHNQVYeSvDHJJxdcytKaTBn4TJJnkjzQ3fbV9n4pmw3Iry+4jvPMbZm3VVJVH03yHVvc9Z4kP5vkB/e3ouV1oX3V3fd193uSvKeq7kry7iQ/t68FLpmd9tdkm/dk8yvM9+9nbctmmn3FtmqLMV0rZqqqvjXJ7yT5qXO+MWTQ3S8kecPkuJIPVtXru9t893NU1TuSPNPdD1fVWxZcznkE5CTd/batxqvqHyS5NslnqyrZ/Ar801V1Q3f/6T6WuDS221db+M0k9+eAB+Sd9ldVHUnyjiQ39gFfc3EXP1uc70ySq4fbVyV5akG1sIaq6qXZDMfv7+7fXXQ9q6C7/7KqPpbN+e4C8vnenOSdVfVDSV6e5Nur6je6+0cXXFcSUywuqLv/uLsv6+5D3X0om3+Evu+ghuOdVNV1w813JvnCompZBVV1U5I7k7yzu//fouthpX0qyXVVdW1VfXOSw0lOLrgm1kRtdoh+Lclj3f2Li65nmVXVpWdXJKqqb0nytvhbuKXuvqu7r5rkq8NJfn9ZwnEiIDNbx6vq81X1uWxOS7EU0IX9pyTfluSBydJ4/2XRBS2rqvqnVXUmyfcnub+qPrzompbJ5GDPdyf5cDYPoDrR3Y8stqrlVVUfSPKJJN9VVWeq6rZF17Tk3pzkx5K8dfJe9ZlJ14/zXZHkDyZ/Bz+VzTnIS7V8GdNxJj0AABjoIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYPD/ASvKmaTtYFHZAAAAAElFTkSuQmCC\n",
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m5Mtf/nIuu+yyXHXVVRk1alTfnBkAwP+xLgAAtXRQHe/W1ta0tLRk9uzZvbavX78+O3bs6LX9+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dWVxx9/fK/P193dna6url43AAAAGAiq7njfcccd+elPf5p169a9Zl97e3tGjRqVcePG9dre2NiY9vb2yjF7hu7d+3fv25vFixfnS1/6UrWlAjAAWeAKABhsqup4b968OZ/97GfzrW99K6NHjy5V02ssWrQonZ2dldvmzZsP23MDAADAoagqeK9fvz5bt27NO97xjowcOTIjR47MmjVrcsMNN2TkyJFpbGzM9u3bs23btl736+joSFNTU5KkqanpNauc7/599zGvVldXl/r6+l43AAAAGAiqCt7vfe9789hjj+XRRx+t3E455ZTMmzev8vMRRxyR1atXV+6zYcOGbNq0Kc3NzUmS5ubmPPbYY9m6dWvlmFWrVqW+vj7Tp0/vo9MCAACA/qGqOd5jx47N2972tl7bxowZkwkTJlS2n3/++Wlra8v48eNTX1+fiy66KM3NzZk1a1aS5Mwzz8z06dNz3nnn5dprr017e3suv/zytLa2pq6uro9OCwAAAPqHg7qc2Ou57rrrMnz48MydOzfd3d2ZM2dObrrppsr+ESNGZMWKFbnwwgvT3NycMWPGZP78+bn66qv7uhQAAACouUMO3j/60Y96/T569OgsXbo0S5cu3ed9pkyZknvuuedQnxoAAAD6vYO6jjcAAABwYPp8qDkAvJ49r9Pdl8cCAPRXOt4AAABQkOANAAAABQneAAAAUJDgDQAAAAVZXA0A4FUs7AdAX9LxBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChoZK0LAGDgmLpwZeXnjUtaalgJAMDAoeMNAAAABQneAAAAUJCh5gAAB8BUCwAOlo43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBFlcDAOgjey7AtieLsQEMbTreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AHLI957WaywoA0JuONwAAABSk4w0AUCWjPACoho43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFAAAMFVMXrqz8vHFJSw0rAeBw0vEGAACAggRvAPrU1IUre3X1AACGOsEbAAAAChK8AQAAoCCLqwFQhOHmDBX+rwOwPzreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AAIWZBw4wtOl4AwAAQEGCNwAAABQkeAMAAEBB5ngDcFDMWQUAODCCNwBADez55dXGJS01rASA0gw1BwAAgIJ0vAF4XYaUAwAcGh1vAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAglxODGAI2vMSYRuXtNSwEgCAwU/HG4CKqQtXum43AEAfE7wBAACgIMEbAAAACjLHGwCgn7IeA8DgoOMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQ4A0AAAAFWdUcgNfYcyVlAAAOTVUd72XLluWkk05KfX196uvr09zcnHvvvbey/+WXX05ra2smTJiQo48+OnPnzk1HR0evx9i0aVNaWlpy1FFHZeLEibn00kvzyiuv9M3ZAAAMQFMXrqzcABh8qgrexx57bJYsWZL169fnJz/5Sc4444x86EMfyuOPP54kueSSS3L33XfnzjvvzJo1a7Jly5acc845lfvv3LkzLS0t2b59ex588MHcdtttufXWW3PFFVf07VkBAABAP1HVUPMPfvCDvX7/m7/5myxbtiwPPfRQjj322Nx88825/fbbc8YZZyRJbrnllpxwwgl56KGHMmvWrPzgBz/IE088kfvvvz+NjY2ZMWNGvvzlL+eyyy7LVVddlVGjRvXdmQEAAEA/cNCLq+3cuTN33HFHXnrppTQ3N2f9+vXZsWNHZs+eXTnm+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dVV6ZrvTXd3d7q6unrdAAAAYCCoOng/9thjOfroo1NXV5fPfOYz+e53v5vp06envb09o0aNyrhx43od39jYmPb29iRJe3t7r9C9e//uffuyePHiNDQ0VG7HHXdctWUDAABATVQdvP/oj/4ojz76aB5++OFceOGFmT9/fp544okStVUsWrQonZ2dldvmzZuLPh8AAAD0laovJzZq1Kj84R/+YZLk5JNPzrp16/L3f//3+djHPpbt27dn27ZtvbreHR0daWpqSpI0NTXlkUce6fV4u1c9333M3tTV1aWurq7aUgEAAKDmDnqO9267du1Kd3d3Tj755BxxxBFZvXp1Zd+GDRuyadOmNDc3J0mam5vz2GOPZevWrZVjVq1alfr6+kyfPv1QSwEAAIB+p6qO96JFi3LWWWdl8uTJeeGFF3L77bfnRz/6Ub7//e+noaEh559/ftra2jJ+/PjU19fnoosuSnNzc2bNmpUkOfPMMzN9+vScd955ufbaa9Pe3p7LL788ra2tOtoAAAAMSlUF761bt+YTn/hEfvWrX6WhoSEnnXRSvv/97+d973tfkuS6667L8OHDM3fu3HR3d2fOnDm56aabKvcfMWJEVqxYkQsvvDDNzc0ZM2ZM5s+fn6uvvrpvzwoAYJCZunBlkmTjkpYaVwJAtaoK3jfffPPr7h89enSWLl2apUuX7vOYKVOm5J577qnmaQEAAGDAOuQ53gAAAMC+Cd4AAABQkOANAAAABQneAAAAUFBVi6sBMPjsXikZAIAydLwBAACgIB1vgEFsz262a//CwGAUCsDgo+MNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdUABgCLpAEADFw63gAAAFCQ4A0AAAAFGWoOADCAmHoCMPDoeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUNLLWBQDQt6YuXFnrEgAA2IPgDTBECOQAALVhqDkAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAU5DreAAPYntfm3rikpYaVAACwL4I3wCCxZwgHhgZfvgEMDIaaAwAAQEGCNwAAABRkqDnAAGNIOQDAwKLjDQAAAAUJ3gAAAFCQ4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJDgDQAAAAUJ3gAAAFDQyFoXAABA35q6cGXl541LWmpYCQCJjjcAAAAUJXgDAABAQYaaA/RTew4VBdgffzMA+i8dbwAAAChI8AYAAICCBG8AAAAoyBxvAIBBzKXFAGpPxxsAAAAK0vEGABhidMEBDi8dbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAgkbWugAAAGpn6sKVlZ83LmmpYSUAg5eONwAAABQkeAMAAEBBhpoD1IjhnQAAQ4OONwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQVUF78WLF+ed73xnxo4dm4kTJ+bss8/Ohg0beh3z8ssvp7W1NRMmTMjRRx+duXPnpqOjo9cxmzZtSktLS4466qhMnDgxl156aV555ZVDPxsAAADoZ6oK3mvWrElra2seeuihrFq1Kjt27MiZZ56Zl156qXLMJZdckrvvvjt33nln1qxZky1btuScc86p7N+5c2daWlqyffv2PPjgg7ntttty66235oorrui7swIAAIB+YlhPT0/Pwd75ueeey8SJE7NmzZq8+93vTmdnZ97whjfk9ttvz5//+Z8nSZ588smccMIJWbt2bWbNmpV77703f/Znf5YtW7aksbExSbJ8+fJcdtllee655zJq1Kj9Pm9XV1caGhrS2dmZ+vr6gy0foKb2dzmxPfcD9IXdf2sO5O+LyxwCvL5qcukhzfHu7OxMkowfPz5Jsn79+uzYsSOzZ8+uHHP88cdn8uTJWbt2bZJk7dq1OfHEEyuhO0nmzJmTrq6uPP7443t9nu7u7nR1dfW6AQAAwEBw0MF7165dufjii3PaaaflbW97W5Kkvb09o0aNyrhx43od29jYmPb29soxe4bu3ft379ubxYsXp6GhoXI77rjjDrZsAAAAOKwOOni3trbmZz/7We64446+rGevFi1alM7Ozspt8+bNxZ8TAAAA+sLIg7nTggULsmLFijzwwAM59thjK9ubmpqyffv2bNu2rVfXu6OjI01NTZVjHnnkkV6Pt3vV893HvFpdXV3q6uoOplQAAACoqao63j09PVmwYEG++93v5oc//GGmTZvWa//JJ5+cI444IqtXr65s27BhQzZt2pTm5uYkSXNzcx577LFs3bq1csyqVatSX1+f6dOnH8q5AADwOqYuXGnhRoAaqKrj3dramttvvz133XVXxo4dW5mT3dDQkCOPPDINDQ05//zz09bWlvHjx6e+vj4XXXRRmpubM2vWrCTJmWeemenTp+e8887Ltddem/b29lx++eVpbW3V1QYAAGDQqSp4L1u2LEly+umn99p+yy235JOf/GSS5Lrrrsvw4cMzd+7cdHd3Z86cObnpppsqx44YMSIrVqzIhRdemObm5owZMybz58/P1VdffWhnAjAI6EQBAAw+VQXvA7nk9+jRo7N06dIsXbp0n8dMmTIl99xzTzVPDQAAAAPSQS2uBsCB27OLvXFJSw0rAQCgFgRvgMPIUHIAgKHnoK/jDQAAAOyf4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJBVzQH6AaudAwAMXoI3AACvsecXghuXtNSwEoCBz1BzAAAAKEjHGwCA16X7DXBodLwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoKCRtS4AYLCYunBl5eeNS1pqWAkAAP2JjjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAB2zqwpW9FpMEYP8EbwAAAChI8AYAAICCBG8AAAAoaGStCwAYjMx/BABgNx1vAAAAKEjwBgAAgIIEbwAAACjIHG+AQ2Q+NzAU7fm3b+OSlhpWAtD/Cd4AABwSIRzg9RlqDgAAAAUJ3gAAAFCQ4A0AAAAFmeMNcIDMYQQA4GDoeAMAAEBBOt4AB8ElxAD2z0ghgN/S8QYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAACjI5cQAAOgzfXG5RZchAwYbwRvgdbheNwAAh8pQcwAAAChI8AYAAICCDDUHAKA487aBoUzwBngV87oBAOhLgjcAAAOWTjowEJjjDQAAAAUJ3gAAAFCQoeYAANSc9TWAwUzHGwAAAArS8QaITgsAAOXoeAMAAEBBgjcAAAAUJHgDAABAQYI3AACH1dSFK62tAQwpgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFANSSVXUBAChNxxsAAAAKErwBAACgIMEbAAAACjLHGwCAmrDOBjBUCN4AAPRbe4bzjUta9rodoL8TvIEhx4c1AAAOJ3O8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgoKqD9wMPPJAPfvCDmTRpUoYNG5bvfe97vfb39PTkiiuuyDHHHJMjjzwys2fPzlNPPdXrmOeffz7z5s1LfX19xo0bl/PPPz8vvvjiIZ0IAAAA9EdVB++XXnopb3/727N06dK97r/22mtzww03ZPny5Xn44YczZsyYzJkzJy+//HLlmHnz5uXxxx/PqlWrsmLFijzwwAP59Kc/ffBnAbAfUxeurNwAAOBwqvo63meddVbOOuusve7r6enJ9ddfn8svvzwf+tCHkiT/9E//lMbGxnzve9/Lueeem5///Oe57777sm7dupxyyilJkhtvvDEf+MAH8rWvfS2TJk16zeN2d3enu7u78ntXV1e1ZQMAAEBN9Okc72eeeSbt7e2ZPXt2ZVtDQ0NmzpyZtWvXJknWrl2bcePGVUJ3ksyePTvDhw/Pww8/vNfHXbx4cRoaGiq34447ri/LBgAAgGL6NHi3t7cnSRobG3ttb2xsrOxrb2/PxIkTe+0fOXJkxo8fXznm1RYtWpTOzs7KbfPmzX1ZNjDAGUYOAEB/VvVQ81qoq6tLXV1drcsAAACAqvVp8G5qakqSdHR05Jhjjqls7+joyIwZMyrHbN26tdf9XnnllTz//POV+wP0BR1wgMHF33VgoOrToebTpk1LU1NTVq9eXdnW1dWVhx9+OM3NzUmS5ubmbNu2LevXr68c88Mf/jC7du3KzJkz+7IcAAAAqLmqO94vvvhinn766crvzzzzTB599NGMHz8+kydPzsUXX5xrrrkmb37zmzNt2rR88YtfzKRJk3L22WcnSU444YS8//3vzwUXXJDly5dnx44dWbBgQc4999y9rmgOAAAAA1nVwfsnP/lJ3vOe91R+b2trS5LMnz8/t956az7/+c/npZdeyqc//els27Yt73rXu3Lfffdl9OjRlft861vfyoIFC/Le9743w4cPz9y5c3PDDTf0wekAg9GeQws3LmmpYSUAAFC9YT09PT21LqJaXV1daWhoSGdnZ+rr62tdDlDY/oK3OX8AJL6cBQ6vanLpgFjVHAAAqmG0FNCf9OniagAAAEBvgjcAAEPG1IUrTVECDjvBGwAAAAoyxxsAgEFNhxuoNR1vAAAAKEjwBgAAgIIMNQf6DZd+AQBgMNLxBgAAgIIEbwAAACjIUHNgQDEcHQCAgUbHGwAAAAoSvAEAAKAgQ82BfmnPIeUAADCQ6XgDAABAQYI3AAAAFGSoOQAAg4JpSkB/peMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdWAw2bPRW82Lmnp08cDgJL6+j0MGFoEbwAAhhxBGjicBG8AAPg/AjlQgjneAAAAUJCONwAA7IW1RIC+IngDADCkCdhAaYaaAwAAQEGCN1ATUxeu1GEAAGBIMNQcKEq4BgBgqBO8gZoSzAEAGOwMNQcAgCqYLgVUS/AGAACAggRvAAAAKEjwBgAAgIIEbwAAACjIquZAn7PgDABDzZ7vfRuXtNSwEqA/0vEGAACAggRvAAAAKMhQc+CgGVYHAAdn93uo908YGnS8AQAAoCAdbwAA6ENGhAGvJngDfcJK5gAAsHeCN1A1IRsAAA6c4A3sM0jvOTxO2AYAgIMjeAP7JGwDAMChE7wBAOAg+IIaOFCCNwAAHAZ7C+pWQIehwXW8AQAAoCDBGwAABqCpC1ca7g4DhKHmAABQiGAMJII3AAD0a+aBw8BnqDkAAAAUJHgDAABAQYaaAwDAAGHOOAxMgjcAAPQzAjYMLoI3DAH7WpTFmzoAAJQneAMAQD/gC3EYvARvAAAYwFxuDPo/wRsGqL19K+7NFgAA+h/BGwYR33gDAED/4zreAAAAUJCONwxSFmgBAID+QfAGAIAhxNQ0OPwEbxhAdLEBgAMlYEP/IXgDAMAgUfJLekEeDp7gDTW0rzdHb2YAADB4WNUcqjR14UpDvgEAgAOm4w19rL8Pw/KlAQCw2+7PBf3xMwsMJjreAAAAUJCONwAADHIHO+KtL0bK9ffRgHA4CN5wAPrizaqaNxrDwQGAw6nazyx7+6wiVMO+Cd5QA4I1ANBfHe6GAwwFgjdDUl+8MXhzAQCojs9PDFWCNxwmutwAAL8jhDOUCN4MefsKxN4AAAD6ByGdgU7whn2opkOtmw0A8Dt9vRo6DHSCNwPagXz76Y82AMDAcCCf23S/GYgEbwYlYRsAYOAYKJ/dhH4OVs2C99KlS/PVr3417e3tefvb354bb7wxp556aq3K4RBU03Uu+QdqoPzBBgCgnIO9JrkgTUk1Cd7f/va309bWluXLl2fmzJm5/vrrM2fOnGzYsCETJ06sRUlF1TJ07vmch1pHX1+Ca1/2VjMAALza/j6fVvP5tdoFd2t5eVqd94GnJsH77/7u73LBBRfkU5/6VJJk+fLlWblyZf7xH/8xCxcufM3x3d3d6e7urvze2dmZJOnq6jo8BR+iXd3/L0nvet925ff3euzPvjTnkJ7j1fZ8zv3Vsb/n3vM59va4r/fY1Zh8yZ0HdT8AAIau/X2GPNjPqQfy2bSaXLKv5979PPv6TL6v++3tuav5jL8vffEYA+E5D8Xuf/uenp79Hjus50CO6kPbt2/PUUcdle985zs5++yzK9vnz5+fbdu25a677nrNfa666qp86UtfOoxVAgAAwP5t3rw5xx577Osec9g73r/+9a+zc+fONDY29tre2NiYJ598cq/3WbRoUdra2iq/79q1K88//3wmTJiQYcOGFa33UHV1deW4447L5s2bU19fX+tyoN/zmoHqed1A9bxuoHpeN7319PTkhRdeyKRJk/Z77IBY1byuri51dXW9to0bN642xRyk+vp6/zmhCl4zUD2vG6ie1w1Uz+vmdxoaGg7ouOGF63iN3//938+IESPS0dHRa3tHR0eampoOdzkAAABQ1GEP3qNGjcrJJ5+c1atXV7bt2rUrq1evTnNz8+EuBwAAAIqqyVDztra2zJ8/P6ecckpOPfXUXH/99XnppZcqq5wPJnV1dbnyyitfM1Qe2DuvGaie1w1Uz+sGqud1c/AO+6rmu33961/PV7/61bS3t2fGjBm54YYbMnPmzFqUAgAAAMXULHgDAADAUHDY53gDAADAUCJ4AwAAQEGCNwAAABQkeAMAAEBBgncNdHd3Z8aMGRk2bFgeffTRWpcD/dbGjRtz/vnnZ9q0aTnyyCPzpje9KVdeeWW2b99e69KgX1m6dGmmTp2a0aNHZ+bMmXnkkUdqXRL0W4sXL8473/nOjB07NhMnTszZZ5+dDRs21LosGDCWLFmSYcOG5eKLL651KQOK4F0Dn//85zNp0qRalwH93pNPPpldu3blG9/4Rh5//PFcd911Wb58eb7whS/UujToN7797W+nra0tV155ZX7605/m7W9/e+bMmZOtW7fWujTol9asWZPW1tY89NBDWbVqVXbs2JEzzzwzL730Uq1Lg35v3bp1+cY3vpGTTjqp1qUMOC4ndpjde++9aWtry7/927/lrW99a/7zP/8zM2bMqHVZMGB89atfzbJly/KLX/yi1qVAvzBz5sy8853vzNe//vUkya5du3LcccfloosuysKFC2tcHfR/zz33XCZOnJg1a9bk3e9+d63LgX7rxRdfzDve8Y7cdNNNueaaazJjxoxcf/31tS5rwNDxPow6OjpywQUX5J//+Z9z1FFH1bocGJA6Ozszfvz4WpcB/cL27duzfv36zJ49u7Jt+PDhmT17dtauXVvDymDg6OzsTBLvLbAfra2taWlp6fWew4EbWesChoqenp588pOfzGc+85mccsop2bhxY61LggHn6aefzo033pivfe1rtS4F+oVf//rX2blzZxobG3ttb2xszJNPPlmjqmDg2LVrVy6++OKcdtppedvb3lbrcqDfuuOOO/LTn/4069atq3UpA5aO9yFauHBhhg0b9rq3J598MjfeeGNeeOGFLFq0qNYlQ80d6OtmT88++2ze//735yMf+UguuOCCGlUOwGDS2tqan/3sZ7njjjtqXQr0W5s3b85nP/vZfOtb38ro0aNrXc6AZY73IXruuefym9/85nWPeeMb35iPfvSjufvuuzNs2LDK9p07d2bEiBGZN29ebrvtttKlQr9xoK+bUaNGJUm2bNmS008/PbNmzcqtt96a4cN9ZwjJb4eaH3XUUfnOd76Ts88+u7J9/vz52bZtW+66667aFQf93IIFC3LXXXflgQceyLRp02pdDvRb3/ve9/LhD384I0aMqGzbuXNnhg0bluHDh6e7u7vXPvZO8D5MNm3alK6ursrvW7ZsyZw5c/Kd73wnM2fOzLHHHlvD6qD/evbZZ/Oe97wnJ598cv7lX/7FH3Z4lZkzZ+bUU0/NjTfemOS3Q2cnT56cBQsWWFwN9qKnpycXXXRRvvvd7+ZHP/pR3vzmN9e6JOjXXnjhhfzP//xPr22f+tSncvzxx+eyyy4zTeMAmeN9mEyePLnX70cffXSS5E1vepPQDfvw7LPP5vTTT8+UKVPyta99Lc8991xlX1NTUw0rg/6jra0t8+fPzymnnJJTTz01119/fV566aV86lOfqnVp0C+1trbm9ttvz1133ZWxY8emvb09SdLQ0JAjjzyyxtVB/zN27NjXhOsxY8ZkwoQJQncVBG+g31q1alWefvrpPP3006/5gspgHfitj33sY3nuuedyxRVXpL29PTNmzMh99933mgXXgN9atmxZkuT000/vtf2WW27JJz/5ycNfEDAkGGoOAAAABVmhCAAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAACvr/ciHiWioJ+MUAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Intervalos de Confiança\n",
     "\n",
-    "Vamos agora calcular intervalos de confiança para os pesos e alturas dos jogadores de baseball. Vamos utilizar o código [desta discussão no Stack Overflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Vamos agora calcular intervalos de confiança para os pesos e alturas dos jogadores de baseball. Utilizaremos o código [desta discussão no stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,7 +621,7 @@
    "source": [
     "Os dois valores devolvidos pela função `ttest_ind` são:\n",
     "* p-value pode ser considerado como a probabilidade de duas distribuições terem a mesma média. No nosso caso, é muito baixo, o que significa que há fortes evidências de que os primeiros bases são mais altos.\n",
-    "* t-value é o valor intermediário da diferença normalizada das médias que é utilizado no teste t, e é comparado com um valor limite para um determinado nível de confiança.\n"
+    "* t-value é o valor intermédio da diferença normalizada das médias que é utilizado no teste t, e é comparado com um valor limite para um determinado nível de confiança.\n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Simular uma Distribuição Normal com o Teorema do Limite Central\n",
     "\n",
-    "O gerador pseudo-aleatório em Python é projetado para nos fornecer uma distribuição uniforme. Se quisermos criar um gerador para distribuição normal, podemos usar o teorema do limite central. Para obter um valor com distribuição normal, basta calcular a média de uma amostra gerada uniformemente.\n"
+    "O gerador pseudo-aleatório em Python foi concebido para nos fornecer uma distribuição uniforme. Se quisermos criar um gerador para distribuição normal, podemos usar o teorema do limite central. Para obter um valor com distribuição normal, basta calcular a média de uma amostra gerada uniformemente.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Correlação e a Malvada Corporação de Baseball\n",
+    "## Correlação e a Corporação Maligna de Baseball\n",
     "\n",
-    "A correlação permite-nos encontrar relações entre sequências de dados. No nosso exemplo fictício, vamos imaginar que existe uma malvada corporação de baseball que paga aos seus jogadores com base na altura - quanto mais alto for o jogador, mais dinheiro ele/ela recebe. Suponha que existe um salário base de $1000, e um bónus adicional entre $0 e $100, dependendo da altura. Vamos usar jogadores reais da MLB e calcular os seus salários imaginários:\n"
+    "A correlação permite-nos encontrar relações entre sequências de dados. No nosso exemplo fictício, vamos imaginar que existe uma corporação maligna de baseball que paga aos seus jogadores com base na altura - quanto mais alto for o jogador, mais dinheiro ele/ela recebe. Suponha que existe um salário base de $1000, e um bónus adicional de $0 a $100, dependendo da altura. Vamos usar os jogadores reais da MLB e calcular os seus salários imaginários:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -942,14 +779,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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8UfmA+2xOBN5fj9Ln/kYy2Fb2kV4oLwcAAINGvI2Yun2Wauqbtap2j2rqmx3tG7WTI6n3tu1kJ0dVU8v02I0z5C0ITkT6qtw6ke8tklR+n/1Nqu9vX7IftkS6q+nWKA/pxWNZVlp2G2hra1NBQYFaW1uVn5+f6ssBAAAp1u2zNPuhdRHLre0V0lfvujQoYXNrZTyVo45SMdc42Z832vEvr/AOqjnOXSd8eqZmp3a1tGtCcZ4WVk5U9pD0W1uzH5pJ4Vf20+1BA/o/05yTpBsAAAwKNfXNuuGpjTHjnls0S5WTR0qKPA861i/5kZLcVCS/qRDv9+ZUuO9zbV3ToJrjPNjmVg+2z4v+zTTnZE83AAAYFJw2Yoo1osijnhFFl1d4jVfGL6/wJvAJBoZ4v7d4ZGZ4/A9IpMjJvr19IN1WQgfb55V6SuoHWyUDBj6SbgAAMCg4bcTkZERRrJXxptYO3bJyiwrzsnSo/bj/9XRcoYvne3NDXyb7/cFg+7yBQh+2AP1d+m32AAAACOO8CUWKlXtkeHriJPdXxiUFJdxST/IZrYHbQJSq0U6DbY7zYPu8wEBG0g0AAAaFt3YdVKzm2T6rJ04yXxk/cLhTq2r36OnXGqImQZFY6lmRdKOzdyTJ6CIeSapGOw22Oc6D7fMCAxnl5QAAYFBoaj3mKC7WPGipZ2X8/he2JXxtySi3tg2WueQlw3NcjevvmFsNDBysdAMAgLQVuMK7ZfdBo/e0HO2SFH0etM3NBWPThwJOxDuXPBEpm6Ntei/SZG4Pc6uBgYOkGwAApKXqrY2a/dA63fDURn31V7V6ZuNuo/cVB6yEVk0t02M3zpC3IHi1MBl9qexk3y0me8yTVdYe6XvzFuQmraP2gaOdrsb1dyl7uAHAMcrLAQBA2onURdxE6Yjg8uPQEUUHDne6UlIeqtjlsudUdRG39fVop8FYbm0/3AjdPuBNw674wEBG0g0AAAaMbp8VM4mLtsJr4sRxX6/XAkcUrardE+eRo/Pmp19jsb4c7ZSqveSpxtxqoP8j6QYAAAOCaUOwWCu8sfz+nT367JTSiD83XSm9Z94UlYzIUcmwHN3x23fU1Bb5mpKx93awrfza5dZLVm6RR8Fbt9O93Jq51UD/xp5uAADQ7zlpCJboyu3RzhNRf27awOrLF5VrwfQxuuiMEi2/ukIehd9761FyksHB2GgrFXvJASAWVroBAEDKJFoubqkneVyxpk6XV3iVmeFJeOV2VIwy73hWVFOx9zbwOiNJx5Vfyq0B9Dck3QAAICWqtzZq+eq6oLJrb36ull/trFw8tCGYyXztaM4dXxQzJp4kOhXJYNXUMi2+uFxPbWgIGm+W4ZEWzSlP25Vfyq0B9Cck3QAAoM9Vb23ULWFWYJvaOnTLyi16PKAU2GlDsGgr0SZGFw41iosnie7rZLB6a6OeWN/Q63WfJT2xvkHnji9K28QbAPoLkm4AANCnun2WvvG796LGLP3de47LxQPjIq1EZ3ikaGOpne5x7m8rqoHl+iXDchx9z26dN/Dhg8n2AQBIdyTdAACgT23c0axD7cejxhxsP66NO5p10eklcY+CCrcSffBop/712bcjnncg73EO1909lsDv2c3zlhXk6uppZVr9TmPMbvMAkO7oXg4AAPpUTX2zozi7XFwK3/1bipws2yvRC6aPUeXkkcoYoAl1LJG6u5swvR9OztvY2qEn1jcYdZsHgHRH0g0AAPqY6S7rU3FujIKyu6BHs2JNnbqj1Z8b6vZZqqlv1qraPaqpb/YfM9LriZ4rUnd3M/G9M57zWif/cut7BoCBgPJyAADQpyonleiRV+qN4gIl2v07Vhd0KbgLeiAne5P7utza5HNFE/o998V5I33PAJCOSLoBAECfmjV5pArzsqLu6y7My9KsMAlZIo3LAkeTOYmLlESHS5btcuvQNVy73LrXuU6WW5uu1odj2t09nEjfc7LPK5nfDwAY6CgvBwAASRdYVr25oUX/cc3UqPEPXnu26w3NWo50Oo6LtGc53N7keMutpcTKrU27u4eTyPecyHkl8/sBAAMdK90AACCpqrc2avnquqCVTW9+rr5ycblW1e5VU1tnwOs5Wn71p5PS3bp4WLZRXMvRLq2q3aOS4Tlavvr9sEm0pZ4mbivW1PlHbsVbbm0psXLrWN3dw/FIWnxxeULfczznDWR6P/ojRqEBcIKkGwAAJE311kbdsnJLr9eb2nrKrX/8hRkqGpbdJ8lLab7Zyuyjf46931zqnSwnWm4d7/vt7u5LVm6RR+Zt0Z5c36BzxxfFnXjHe16bt2BoXOdNNSfbDQBAorwcAAAkSbfP0jd+917UmG8+/54uKC/2j/RK6mphkppl28lyouXWibw/Unf3SF+nG2Xt8ZzXVhZmrvpA4GS7AQDYWOkGAABJsXFHc9RmaZJ0sP24Nu5o1kWnx9dB24kDR5Ozh9hOluMtt/aoZ/RZokloaHf3A4c7df8L2yLGJ1rWHum8pSNydfBop2599u2w34NHkeeq92fR9uyH224AADZWugEAQFLU1De7GpeoRFeiQ3kUvGJrl1vbPzM9huReEmp3d18wfYxKRuQYvSfRsvjQ81ZOHqmrzhmtxReX9/oe3NhLniqx9uwHPsQAgEAk3QAAIElM13sTr/sO7I5eU98ctmTaXol2Q6RkOVK5dVlBT+M4b35wIjwqPyfquDCTzxWJ6UMGtx9GSD1l2E+sb+h1Zy1JT6xvSHoZdiLfWySmDyfceIgBIL1QXg4AAJKiclKJHnkldlOyykmJlZabNrbKzPDo6mllYedlO+WN0jgrXLn1BeXFWlvXpFW1oclm5NXtRBt2XVBeHHMeelFelut7q0328n/jd+8lrQw7WY3OUvkQA8DARtINAMAgd6TjhL7+67e1++AxjS8aqv/8p3M1PDfxXxFmTR4ZM+krzMuSJK2q3RNX93K7sVXoOqbd2CpwFbnbZ2n1O85XWO0919/9X9N04Gin0XXa5daxrnNfW+/rdPq5EpGM3nIb62Pv5T/Uflwb65t10Rnu7uVP5vcWa8++W3vzAaQfkm4AAAaxqx/ZoHc/bvP/+YOmw5q6/H90zth8rb5tTkLHzszw6MFrzw47MizQF3+yyf/3TlYknTa2imeOdmAZebwJYqzrVMh1mnyu5avf14jcLB04EvkhwOaGFqPkN9FGaqFqdhwwjnMz6U52o7NoI9Lc3psPIL2wpxsAgEEqNOEO9O7Hbbr6kQ0Jn6Nqapkev3FGr73MhUN7nvuHJoVORi85bWwVz15bb0FuwqvKJsl+4HWafK6mtk598b826au/qtUNT23U7IfW9frOTD/va9sPuLr32XkbOXf0RaOzSHv23fjnBED6YqUbAIBB6EjHiYgJt+3dj9t0pONEwqXmoXucS4bn6I7f1ErHTvSKdbIi6bSxVclws27eN84cr4wMjyYU52lh5URlD0lsjaKpzew67bh4Hg6EK5823Vv8yCvb/X/vxt7nyskjg44ZLc5NfdXoLNKefVa4AUTCSjcAAIPQ1379tqtxsQSOlMrweNTUFnlmtumKpNPGVl2d3UbxKzft1i9qdun+F7bps995JeFO2y1HzOaD23HxNOIKLFO3V6vj6dbupNIgklmTRvr36kdSlJelWZPcTbr7stFZ6Ig0Em4A0ThOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHHwbF/O1vf9OCBQtUUlKi/Px8zZ49W6+88kpQzO7duzVv3jzl5eWptLRU//Zv/6YTJ3o/EQcAAM590HTY1Tgn3FqRtJPKSOlO6Bzt/3rdeddyN5LQ4mHZjuJifa5IQh9W2N3anR5DCk7enbL38kfzwLVnu56oOv3nAQD6iuOk++jRo5o2bZoeffTRsD9/+OGH9cMf/lCPP/64Nm3apGHDhumKK65QR8ep/3D+3d/9nU6cOKF169bprbfe0rRp0/R3f/d3ampqkiR1d3dr3rx56urq0uuvv66f//znevrpp3XvvffG+TEBAEDg7GLThKdgaPQVy3iUDDMr844VZze2knrvDg7X2KqtI3pTsXDcSEK9BUMdxUX7XCbshxXxdmt3a+9zz17+3vPKH0/S3men/zwAQF9xvEnryiuv1JVXXhn2Z5Zl6fvf/77uvvtuLViwQJL0i1/8QqNGjdLzzz+v66+/XgcOHNCHH36on/zkJzrnnHMkSQ8++KB+/OMfa+vWrfJ6vXrppZdUV1enP/3pTxo1apSmT5+u+++/X3fddZeWL1+u7GyzJ8YAAKBHuNnFJr5+6RnuX4yLfbbsxlahny3cHO2powv03p7o+9jDCUxC49mHbDIvuzBkXnakz2XCLp+Op1t7oIG499nJPw8A0FdcbaTW0NCgpqYmXXbZZf7XCgoKNHPmTNXU1Oj666/XyJEjdeaZZ+oXv/iFZsyYoZycHD3xxBMqLS3VeeedJ0mqqanR2WefrVGjRvmPc8UVV2jJkiV6//33de6557p52QAApLVIs4tN5Oa433P1gOEeZ9M40+Ru/Mg8x9caKNEkNJpwaWivBnTDcnTHb9/RvjazOdGJXq+be5/7Eo3OAPQ3rv6X1C4PD0yW7T/bP/N4PPrTn/6ka665RiNGjFBGRoZKS0tVXV2toqIi/3HCHSPwHKE6OzvV2XnqP85tbc6fZAMAkG6izS42kYxE07SLuGmcZJbcJbLqK8WfhJrMyz5oMC87I8Oje/+uQrc+azYnOt7rDU3eB6JUJPsAEEmfjwyzLEu33nqrSktLtWHDBg0dOlT/9V//pfnz5+uNN95QWVl8ZT8PPPCAVqxY4fLVAgAwsCVaYnzgSJej+G6fFXuF0fQJgBsjowOMKzLbWx0q0SQ03sZx4bYElBXkavHF5Vr9TmPM8unzJhQpwyM52YreV3ufjf45AYA04WrS7fV6JUn79u0LSp737dun6dOnS5LWrVunP/zhDzp48KDy8/MlST/+8Y+1du1a/fznP9c3vvENeb1ebd68OejY+/btCzpHqKVLl+r222/3/7mtrU3jxo1z7bMBADAQJbpSfeiYedIdKUkMTQYPHDUsLzeMiyYwubPiSOLdSELjGWUVaUtAU2uHnlzfoEe/cK6KhuVETVrf2nXQUcIt9c3eZ9N/TgAgXbiadJeXl8vr9erll1/2J9ltbW3atGmTlixZIklqb2+XJGVkBDdOz8jIkM/nkyRVVlbq29/+tvbv36/S0lJJ0tq1a5Wfn6+Kioqw587JyVFOjnkZGgAAg0Gi+3I/bmnXqto9MVcjIyWJjSdHbj0W0LHare7lscTbPC6QG0moPcqqqdVsL3a0LQHWyfj7X9imV++6NOqDgKbWY0bX96+fm6wzvSP6ZMU52sOE0H9OACBdOE66jxw5ou3bt/v/3NDQoNraWhUXF2v8+PH62te+pm9961s644wzVF5ernvuuUejR4/WNddcI6knoS4qKtJNN92ke++9V0OHDtVTTz2lhoYGzZs3T5I0d+5cVVRUaOHChXr44YfV1NSku+++W7feeiuJNQAADsRTYhxo1TuNWnVy7FSk1chY+8Yt9YzcurzC25PQudi9PJJEmsfdM2+KSkbkuJaE2qOslqw024sda0uAaTf1lqNmVQojh2VrwfQxRrFOBVYalAzL0fLV0R8mBP1zAgBpwnHS/eabb+qSSy7x/9ku6b7pppv09NNP684779TRo0e1ePFiHTp0SLNnz1Z1dbVyc3uetJeUlKi6ulrf/OY3demll+r48eP69Kc/rVWrVmnatGmSpMzMTP3hD3/QkiVLVFlZqWHDhummm27Sfffd58ZnBgBg0IinxDiSSKuRJvvGA5NEt7uXh0qkeVxRXpa+fFG560mfk1FW8e4BD1Vs2IjONM4pp5UGiY5mA4D+ynHS/bnPfU5WlE1RHo9H9913X9QE+fzzz9f//M//RD3PhAkT9OKLLzq9PAAAEMDN7uORViNNy5jtuHj2ODuRSPM4l3u3BamaWqZLzxqlZ2p2aldLuyYU52lh5URlDwnecufW9+PNNzuOaZwTiVQaJHM0GwCkQp93LwcAAH3HjVnLgcKtRpqWMdtxTvc4O5VI0nbIYHRXvMKt/P7Xqw29Vrrd+n7s40R7AFGWhNFgiY6pc/ufWQBItYzYIQAAoC90+yzV1DdrVe0e1dQ3q9uFunA78XJ7h+xr2w/4r7MoL9voPS1Hu7Sqdo82N7Tonnk9jVFDr6svu4VHkoyVVnvlNzQBtkv2q7c2+l+z94BLiX0/9nE8EY7jMTyOU/FWGniUnIcAAJBqrHQDANAPVG9t1PLVdWpqC9jvm5+r5Vcn1jk7WhOvRDzyyqmmqsXDsoze8+if6/1/b8+bfv7tPdp3+NRKeemIbK1YMDWp3cJjcXul1aQbeWjJvpM94NG4dRwn4nlo0VfzwQEgFTxWtA3aA1hbW5sKCgrU2trqnwcOAEB/VL21Ubes3BLx54+7MEbJjfFZyRD6ICDDIy2aU66lV4UfEWrKXlmWnD1oyPBIf73/yl77rBNRU9+sG57aGDPuuUWzepW1B3b/TqSbulvHMWH6eQMxpxvAQGSac7LSDQBACnX7LH3jd+9FjVn6u/cSHqNUNbVMl1d4/YnX/rZOffvFbXEfLxKnq+mhsT5LemJ9gyQllHhHWuGNxWf1dHx3c093It3IMzM8rlyLW8cxYbInfVR+jv7fP07XgSOdfTIfHABSiaQbAIAU2rijWYfaj0eNOdh+XBt3NOui00sSOldg4rXhb58kdKxIioZlGzdWi+bJ9Q26Y+5ZCa04hz5o+Gtjmx77y46Y79t7yKwbu6mSYWYjuUzj+juTueTLr/50wv88A8BAQSM1AABSqKa+2dW4aAIbtf1uy8dG77lm+mj94Prpuu2SyUbx98yboucWzdIPrp+uWz9n9p5wLElPv9YQ9/tt9oOGBdPH6EjnCaP31H50MOHzBjFdwE3iQm8ymvRFY1caeAuC98d7C3J7zXkHgHTHSjcAACllmvwkliTFu6d7bNFQLZg+RjX1zXrklfqY8d6Cof7V9J9siL2qHM1LdU1a/FmzxL0v9yw7deBIp6txToW7932xhzq00qC/3RcA6Csk3QAApFDlpBKjZLZyknkpbmgCevBop2599u240vaZ5T0JdDwzn4uHJ1oubZacmSaV44uHGR3PNM6UaTf0ZMynthvKhd57e1RZsled+3IvOQD0VyTdAACk0KzJI1WYlxV1X3dhXpZmGSYu4RLQDE/86+QZnp7ENzPDo6unlfmbnIVz9bSyoFVMb35iSeTnp5wWM8ZJUvmp0uFG5zWNM2XSWMybhPnU8YwqAwC4jz3dAACkUGaGRw9ee3bUmAevPdsoKbIT0NDV6ES279odtbt9lla/0xg1dvU7jUF7he1kM14eyxN1D3KspFLqSSrt927e2WJ0XtM4U3ZjsUi3wVJy5lNvbmiJWplgSWps7dDmBnc/LwAgGEk3AAAp9vbu6I27Yv1cip6AJqKprSdpi5XASb0TODvZ9Ci+HmEP/s8H+uqvanXDUxs1+6F1qt4anPQ7TSr3GHYlN43r7xIZVQYAcA9JNwAAKdR1wqenNkTv0v3UhgZ1nfBFjTFJiuPx6ocHJMWfwEXqYu2UXS4emHg7vabRhUON4k3jTNkPRCKxy7zd7iieyr3kAIBTSLoBAEihZ2p2xiz/9lk9cdEka7Xy0LGemduJJHBVU8v06l2X+keJXTjJ+d7lcOXiTq/JdC602/OjU1XmbZf3R6oy8Kh38zsAgPtIugEASKFdLe2uxCVrtdJeYU80gQuclz1tXGFc1xKanF5QXqzCvKyo7ynKy/Jf06xJI5WXnRk1flh2pmZNcrfbdqrKvO3yfql3eb/952TsJQcABCPpBgAghcYV5bkSFysplnq6mDs18mRS62YCVzwssVFiTpLT0CKC7CHRf/WJ9fN4pLLMO1J5v7cgN+njwgAAPRgZBgBACrk1xspOipes3CKPgpNNOw1+5IYZKhqWrf2HO/Toug/1t/1HY573eMBWcjuBCx1J5g0zEzuakcOyjeIisZPTzQ0tUUetSdKh9uPa3NCiyskjjeIPBsS7JVUjw2xVU8t0eYU3aHb7BeXFrHADQB8h6QYAIIWcjLH67FmlUWOcJMWbdzQbJd1neUf0OkeiCVzz0S7j2EChyanTsm27E3sspnGmTB6IJLvM2y7vBwD0PZJuAABSyO0xVqZJ8YSRw4yOFy4u0QTuwFHnSW245NRp2XbLkU6jeNM4J9yqEgAADDwk3QAApFAyxliZJMWW4URv0zgn3v+4zfF7wiWnTsu2iw3L2k3jnKLMGwAGJ5JuAABS6KLTS/TjP9cbxblpzyGz1WbTOCeGxuggbjt/fKEWXjgxYnLqtGzbW2D24MI0Lh6UeQPA4EP3cgAAUmjWpJFGY6/cHmM1odisa7plWVpVu0c19c3++diJ+swEs4Zhcyu8WjB9jConj4y4GuykO/d5E4qMznui2+f6Z7Z1+yzV1Dcn7fgAgP6HlW4AABLU7bPiLhnOzPDoMxOLtLZuf8SY8ycWuV6C/IWZE3T/C9tixj2zcbee2bhbUs8cbjf2H08Zne9qXNXUMl161ig9U7NTu1raNaE4TwsrJ/Ya/7VpR7PR8Rb+dLP/7936zJJUvbWx155uN48PAOifSLoBAAjRdcIXM4GzJZpIdZ3w6U9REm5J+lPdfnWd8Lk6Q/rNBrOu6YGaWju0ZOWWhOc7t7SbdS83jQt3D/7r1YZe9+C/t3zs7ELl3meu3tqoJSu39Np77tbxAQD9F+XlAAAEeODFOp159x91/wvb9IuaXbr/hW068+4/6oEX63rF2olUYLInnUqkqrc2xjzf0681xGxVZp2Mc9P/b8tHjt9jX+eKNXUJlUU77ToejZN70N51wtmFquczW0rsM3f7LK1YUxf2Prv1nQIA+i+SbgAATnrgxTo9sb53EmxJemJ9Q1Di7VYi9dL7TUbXZhpnynQEWShLUmNrhzbHsVJus7uORyqY96inWsDni76f3Ok9+MzE+PfFJ/KZNze09HooEMiN7xQA0H+RdAMAoJ4y7yfXR19NfnJ9g7pO+CS5l0i1dZqtvprGmRpbZNZILZL9h+Pvam53HZfUK/G2u5AfO96tL/5kk776q1rd8NRGzX5oXa/KAaf34MZZE+K+ZklqaovvM5t+V4l8pwCA/oukGwAAST9/3azM++ev9yTmbiVSU7wjjI5jGmfquhljE3p/pNJv0+7ckbqO253cD7UfD3o9XLm403tQ+9Eho/hIWo50xvU+N8vpAQADD43UAACQjEt7Nze0aNHFk11LpK49d6xWvRN77/e15yaWJIeaGecIMo96xnFdUN577JfTpnJVU8t0eYXX3/m9ZHiO7vhNbdjzWifPvWJNnS6v8Cozw+P4HiS6klw8LDuu99nl9E2tHWEf7ET7TgEAAx8r3QAASDrceTx2UECc6b7kWIlUhuEoMNM4U2/EsX/YvoJl8yt6jTCLt6lcZoZHlZNHasH0McrweNTUFnk1ObRc/ILy4pgzzgvzsvz3INGVZG/B0LjeF6ucXgr/nQIA0gNJNwAAkg53dDuKsxOpSCXplswSqU0NZrOjTeNMvV5/wPF7vAW5YUdbJdJULrAc/bXtnxhdh5MV68BvP9aDkmhMHqBEE6mcPtJ3CgBIH5SXAwAgyRNzR3ePo53Htap2j0pH5OqtXdFXi9/efdAgmTJNAd1dBd1zsN0o7qLJxfrHz4xX6YiepDMzw6Nun+UvCS8d0dNl3LShWeXkU2Xt4crRTdgr1psbWnrt/Q51sP24/7z2g5JbVm5xdD6P3FmJDi2nD/xOAQDpi6QbAAD1lA5v3Xs4ZtzO5mP66q9qjY755IYG3TH3LGUPiVxYVjl5pB55ZXvMYwUmq67wmCV6p43I1YLpY/x/DpcoFw6NXuJtC1yhtsvRnUymDt37nIyu4IV5WUGJfLQ96fGwy+kBAIMHSTcAAJIum1KqP23b7+oxLUv6+es7tejiSRFjZowvMjqWaZypMYVm+5MD4yIlyoeOme2Ht1eoo5WjRxJu77PTRmr2eaOdY2hWph69eYYOHO1kJRoA4Ar2dAMAIOn9vW1JOe4bO6OXoK/cuMvoOKZxpkz3J9tx8STKttCmcrHma4cTbu+z02Z2pnO9MzI8WjB9jL8kHQCARLDSDQCAFFcyaSIvOzPqz9/YadYg7Y2dzVFXzE0E7sXebNiY7W/7DuuzZ5bGlShL4VeoTcu9b7vkdJ0xanjEFWd7j/aSlVvkUfA9TOS8iY4WAwAgEEk3AACSxhXFNw4qlutizNfOzYqelDuNiyTepmU7DxyVZJ6IFg7NCio394bZE21aFn7R6SUx9z/bXcFDP1si5010tBgAAIFIugEAaa/rhE/P1OzUrpZ2TSjO08LKib2am1k+98+bl52pC88oiRozPMfsP8WmceHE07TMVtfYKsk8EX30izOU4fFE7c5tl4U3tXaEvabQhmmxmHYFP29CkTI8UpjJZX4Znp44AADcQtINAEhrD7xYp6c2NAQlWt9+cZsWzSnX0qsq/K9t+eig6+deOGt8zD3BpnuG491bnMhebElqPtIpyTxRnjUp9j7oWKO7TGechx4z1qr4W7sORk24pZ6E/K1dB+kwDgBwDY3UAABp64EX6/TE+oZeiZbPkp5Y36AHXjzVyTov2/3n0KvfaVR3jCxvfPEwo2NZlrSqdo9q6ptjHjNQvHuxbT6r51cFO1GWek8MD7d/uj9iTzcAIBVIugEAaanrhE9PbWiIGvPUhgZ1neipK79uRvS91/FobO3Q5obo3cs/VTrc6FgrN+3WV39Vqxue2qjZD61T9dZGo/clmkCeO6HQ//f2/mlvQXCpebjO4tGYjO5asabO0cMFE+zpBgCkAuXlAIABJ7ALd6T9u8/U7DQqJX6mZqdunjNJF55eorzsTLV3dUeMz8vK0FM3fUYHjnTqw32H9cgr9TGvNVbSuznGSLFwmlo7tGTlFqNEN9EE8tppY4L+bLp/OhrT0V2bG1pcLfN2ey85AAAmSLoBAANKuC7cZWE6Ve9qaTc6nh2XmeHRwlnj9cT6yKvjCysn6KLTexqj1dQ3GyXdsZLevYeOGV1nIEunVoMvr/BGTXhjJZqxfPjJEV2iUUGvmeyfjiZVZd5OR4wBAOAGyssBAP1W1wmffrJhh+5dtVU/2bBDa97ZqyUrt/RaJbVXfgNLricU5xmdw47r9lla/U70ku3APdp2MhspPfOo52FArFXT0XGOKgtcDY4m2l5sE2/ucr4SH0sqy7zdKpEHAMAUK90AgH4pXNfxSMKt/C6snKhvv7gt5niohZUTJZk1HAsseXZr1XTWxJF6VLFXzCMxWQ2ONMvaxNAE54OHk+oybzdK5AEAMMVKNwCg34nUdTya0JXf7CEZWjSnPOp7Fs0p98/rjqfk2Y1V04zMxBI909XgqqllevWuS/Xcoln6wfXT9YULxhm979OjC3q91u2zVFPfHFc3denU6nukd8UzMswpu0R+wfQx/ocoAAAkAyvdAICUC2yMVpyXHXVfdSyBSbE9hzt0xTzDo15zuuMteU501fTAyTnYTsWzGhy4F7u729Kzmz+K+Z6S4TlBfzbdUw8AAHqQdAMAUipcEpeI0KR46VUVumPuWXqmZqd2tbRrQnGeFlZO9K9w2+yS52jXEWmPdiKNxeLZt+xG0y/TZD8wrnpro5as3NJrhbrRQTd1yXxkWKwmcQAADAQk3QCAlImUxMUj2spv9pAM3TxnUtT3Z2Z4dPW0sqir7FdPK3M9CTTpLp7hUdBKvdeFleX397Y6irMT5Wgl4aaJcqpGhgEAkAok3QCAlIiVxDnhxsqvaffyO6umuJp4mzRke+SGGSoalu1q06+PD5pVFthxThvNRZOqkWEAAKQCSTcAICVMkjhTbqz8uplUOlU1tUyLLy7XUxsaZAVk3Z6Te8+vOsf9vdI5hr8B2HFNrWbzxE3iUjkyDACAvkbSDQBIiURXMS+aPFL/+JlxCa38BjZw+6DxsNF7Gg+ZJZ9OVG9tDFvW7rOkJ9Y36NzxRa40KQv8vJbH7PsqGd6T+LYc7TKKN4k7b0JRr5L5UBmenjgAAAY6km4AQEokuoo5fVyhFkwfE/f7423g9vZHB3XteWPjPm+obp+l23/zTtSY23/zTsJNxeL9vOOK8yRJxSFdzCMxiXtr18GY4+B8Vk8ce7oBAAMdc7oBAClhNxCLN42cVR5/MmY3cIunvN1nubEL/ZTXPzyg9q7uqDHtXd16/cMDcZ8jkc974eklkiRvvtlDEpM49nQDAAYTkm4AQErYDcQkxZd4x5mtu9nAzQ3/veVjV+NCJfJ5PZI+M7GnG7z9kCSaSCPVQrGnGwAwmJB0AwBSpmpqmR67cYa8MZK5cDY1tBjHdvss1dQ3a1XtHj39WkNCDdxG5GTF/d5w11PXaDa666OD7XGdK5GGdZakN05+z/ZDEo96P++wXzPtHh+rysEj8wQeAID+jj3dAICUqppapssrvP4GX69s26/n39kb832WYZl3vHuZI2lqS+w48V5P5wlfXOdLtET79R0HdNEZPSXm9kOS0Os36R4f2MStdESu7pk3Rbc++3bEMWmJjH8DAKA/IekGAKRcZobH3zCrqbXDKOnOHxp7xdney+xmKXkiW7oTuR5vvlkjs1CJlmjvPRjcrT30IYlJ9/hwDxrKCnK1+OJyrX6n0XECDwDAQELSDQDoV9o6jrsSl6y9258c7tCq2j2OR5Ulej0zJ8XXOM4u5W5q7Yjr3KMLh/Z6LfAhSSyRHjQ0tXboyfUNevQLM1Q0LNs4gQcAYKAh6QYA9CumZeOx4hLZyxzN6zta9PqOnn3OZQ5WZRO9nhtnTYzrffZe7FtWbonr/Yl0iY/2oMFSTyn5/S/U6dW7LiXRBgCkLRqpAQD6lcLcbFfi+mLcVFNrh5as3KLqrY0xYxO9ntqPDiX0/rglkAvHetBgSWps7dBmB03xAAAYaFjpBgC4LrRplpOS4Zb2Llfi+mLclL1au2JNnS6v8Eb9jIlez2vbP4nr+7RXm+NVU9+sOZ86zTg+8N5/uO+I0XuYxw0ASGck3QAAV0VqmmVahv3eHrMRWq9uPxB1b3Wie5lNBa7WRtvnnOj1PPJKvf/v+7Ks/Z2PDxnHxtuZnXncAIB0Rnk5ACCmIx0ntOjnb+iK76/Xop+/oSMdJ8LG2U2zQpMuJ2XYQ7PM/tO0dW+bvvqrWt3w1EbNfmhdr2Pbe5ml8HOlw72eiNdOPgSoqW9Wt693Wh3tepzqy7L2vGyz+xHp3kfDPG4AwGBA0g0AiOrqRzZo6vL/0dpt+/VB02Gt3bZfU5f/j65+ZENQXKymWVJPGXa4hDSQt6B3t+xYIiWh9lxpb0HwSmphXlbQdbnhkVe2R30IEO16nHLyfSa6ivyZCbEbqcXTmZ153ACAwYKkGwAQ0dWPbNC7H7eF/dm7H7cFJd5uNc2aNq7Q8XVGS0Krppbp1bsu1XOLZukH10/XL//3TOUMSe5//qKtRIdez8JZ4+M6h+n3aZe1x+uM0uExY+IpYfcW5OqxG2cwjxsAkPbY0w0ACOtIx4mICbft3Y/bdKTjhIbnDjEuY44Vd/CoWSO1UKZ7q//a2Kamts64zuHkWqI1WAucc/3WroMJnSvW95mZ4dHV08r0xPqGuI6/6t29uqRiVELXYLvtksk6Y9QI5nEDAAYVkm4AQFhf/dVbxnE/+fJM4zLmWHF1jdET/VhCE8B4m3slyvQhwITivITOE+v77PZZWv1O7L3fkXx8sD3ha7BddPppUb8LAADSEeXlAICw3vnIrIu4HWeXMUdauzRtmnW0M3yTNlOBCWA8zb1iueEz4/SD66frtksmG8XHWgVeWDlR8Sz4mn6fiXYvzxmSGTPGrXsPAEA6IukGAITVecLnKM6kW7hJ06zS/Bwnlxl0jsDELp7mXiaOdp7QguljdNHpZrOrY60CZw/J0OenlDq6BiffZ6Ldy88emx8zxq17DwBAOiLpBgCEVTrCLPkNjIvUndtJ06xzxxU5u1CdSuzumTdFmxtatKp2j55+rSEpJeV7Dx2T5N7qbrfP0hs7o+/rDj2Hk+8z0e7lF002e7jgxr0HACAdsacbABDWyBHZqj8Qez/vyBHZQX+umlqmyyu82tzQov2HOxw3zWo9dtzxtXoLcnX1tDLd/8K2pO/dbu86rlW1e1Q6Ilf3zJuiW599Wx4Fjx9zsrq7sb5Zh9qjf2ZL0jevOkul+bmOv8/zJhQpwyPFmCwW/eSGEr33AACkI5JuAEBYRzu7444L7M7tVPGw7NhBkv71c5N0pjdfpSNydfBol259dktcpeT/fuVZGlWQq8df2a5t+47EjK9rOqqv/qpWUs9K9uKLy7X6ncagZN9bkKtl8yuMVndrdhwwus7WY8e16GKzfeSB3tp1MP6EW9Kmnc2ac6bZareU2L0HACAdkXQDAMIzTdRc3jTtLRhqFDfnjFJVTh6pbp+l2Q+ti/syPB5pwfQxerj6r47f29TaoSfXN+jRL8xQ0bDsOFd344/r9llhV5UDX//Q4EFCNJbbm+IBABhkSLoBAGGVFQ7V+42HjeJMRUoSA11QXqzCvKyoJdeFeVn+vdKJdud+Y2eLSvNz4yprt+dx3/9CnV6969K4yqhnlhfrkVfM4gKFG4VWdrLMPnTlPRH5Q7NcOQ4AAIMVSTcAwC8wKR5bbNaA65IzS4ziIiWJpmXYgQJT20S7c79Ut18v1e2P+/2m87gjyfCYJeqBcfYotNBF6MbWDj2xvsHxNURz6GiXq8cDAGCwIekGAEgKnxSbeOWDT/TFWeUxjx0uSWxq7dCSlVuCultvbmiJ2VjsYPtxf5KbaHdut8Sb/B842ukoLlmj0CJpaktuYzoAANIdI8MAAP6kOJ6S5FjviZYk2q+tWFOn7pPdvkyTVzsu1uiuvhJv8m/6Pjsu0XJ6p0YXmW8fAAAAvTlOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHH/Y6zgsvvKCZM2dq6NChKioq0jXXXBP08927d2vevHnKy8tTaWmp/u3f/k0nTpxwerkAMKh1+yzV1DdrVe0e1dQ3+xPb0JhEVk5jdTmPlSQGlmdLzpPQzAyPls2vkNS71Zgn5H+TwXQedyRO530nWk5/2yWT9YPrp+ubV51lFH/hJLPtAwAAIDzH5eVHjx7VtGnT9C//8i+69tpre/384Ycf1g9/+EP9/Oc/V3l5ue655x5dccUVqqurU25uzy9I//3f/61FixbpP/7jP3TppZfqxIkT2rp1q/8Y3d3dmjdvnrxer15//XU1NjbqS1/6krKysvQf//EfCXxcABg8TPdQJ7pyOvm0YVF/7nTl+rwJRfJ4onfN9nh64mxVU8v02I0zen1ee3TXpWeN0jM1O7WrpV1t7cf1/Dt7ja4plljzuE0ax9kPDZas3GI07zvRcvqLTj/N3/X90T/Xx2xYN4vxXwAAJMRx0n3llVfqyiuvDPszy7L0/e9/X3fffbcWLFggSfrFL36hUaNG6fnnn9f111+vEydO6Ktf/aq+853v6Oabb/a/t6Kiwv/3L730kurq6vSnP/1Jo0aN0vTp03X//ffrrrvu0vLly5WdbTbDFQAGKyd7qBNdOT1vfFHUnztduX5jZ0vMMVWW1RN30emnVmGrppbp8gpvxCT35jmTJEk/2bDDtaQ72jxuJ43j7IcGy1e/r6a2U3u8R+XnaPnVnw6Kt1fGm1o7HFUneE5er71inpnh0YPXnq1bVm6J+J4Hrz07ro7sAADgFFf3dDc0NKipqUmXXXaZ/7WCggLNnDlTNTU1kqQtW7Zoz549ysjI0LnnnquysjJdeeWVQSvdNTU1OvvsszVq1Cj/a1dccYXa2tr0/vvvu3nJAJB2nO6hTnTl9ONDx6L+3Gn5dE19s9F5w8VlZnhUOXmkFkwfo8rJI8MmjMXDEntwW1lepB9cP13PLZqlV++6NGLCHW6PvP3Qo3prY4SjRyqQPyVaOX0kkVbkq6aW6fEbZ8ibnxMU783P0eMBD2YAAED8XE26m5qaJCkoWbb/bP9sx44dkqTly5fr7rvv1h/+8AcVFRXpc5/7nFpaWvzHCXeMwHOE6uzsVFtbW9BfADAYOd1DnWgjsl3N7b1eC9xLvrmhRffMmyIpckoZmAx2d/vMzttyNOpe9Ui8BQk2BvN4oib1Th96SKeS9NBO4U1t4ZN0e2XcWxD8wKSsIFdfubhcZSGvewtyg6obQo/12jc+r+cWzfI/THjtG58n4QYAwCV9PjLM5+v5Zeqb3/ymrrvuOknSz372M40dO1a//e1v9ZWvfCWu4z7wwANasWKFa9cJAAOV0z3U0fYUm+g4HtxILVJZ9eKLy7X6ncawe64DE7zWjujjwmxr3mnUmnca/cc3nfdtP2SIdx9785Hoc6udPPSw91ZHa2RnqSdJv7zC22uVOlI5/Z1VU2LuJQ9kVwgAAAD3uZp0e71eSdK+fftUVnbqF599+/Zp+vTpkuR/PXAPd05OjiZNmqTdu3f7j7N58+agY+/bty/oHKGWLl2q22+/3f/ntrY2jRs3LsFPBAD9i0ljLqd7qKXIjchMTApopBZtL/mT6xv06BdmqGhYdtTr/+Sw2dzq0OOH7lWPJPAhQzwd24fnRv9Pp9OHHiaN7AKT9ECRkmWSaAAA+g9Xk+7y8nJ5vV69/PLL/iS7ra1NmzZt0pIlSyRJ5513nnJycvTBBx9o9uzZkqTjx49r586dmjBhgiSpsrJS3/72t7V//36VlpZKktauXav8/PygZD1QTk6OcnJywv4MANJB9dZGLV9dF1SC7M3P1fKrKxw12gptqGULXTl9dN2H+tv+ozGvq6a+Wfeu2qpxRUP1k1d3Riyr9ki6/4U6vXrXpVFXXYflOP9Pk338cCvC4STykOFM7/CoP3f60KOpNfqeeJtpHAAA6F8c/2Zz5MgRbd++3f/nhoYG1dbWqri4WOPHj9fXvvY1fetb39IZZ5zhHxk2evRo/xzu/Px83XLLLVq2bJnGjRunCRMm6Dvf+Y4k6R/+4R8kSXPnzlVFRYUWLlyohx9+WE1NTbr77rt16623klgDGJSqtzaG7TLd1NahW1ZuCWp6ZTKC6p554cuPA1dIn9u8WzJIuj8+1KFf1OyKGRdaVh3JdeeO1fO1zruL28d/+rUGlYzIiVlWHfqQ4a+NbXrsLztinuf88dHncTt96NFyNHq5us00DgAA9C+Ok+4333xTl1xyif/Pdkn3TTfdpKefflp33nmnjh49qsWLF+vQoUOaPXu2qqur/TO6Jek73/mOhgwZooULF+rYsWOaOXOm1q1bp6KinrEzmZmZ+sMf/qAlS5aosrJSw4YN00033aT77rsv0c8LAANOt8/SN373XtSYpb97L2iFN9rc6qunlen+F7bFHGXVFbJX2y2xyq8vPKNEedmZau+K7/z3v7DN//ex9noHPmQoGZZjlHTHasQW+NAjksDGccXDzR4mm8YBAID+xWNZsaahDkxtbW0qKChQa2ur8vPzU305ABC317Yf0Bf/a1PMuF/+75lBc6ul3nvADx7t0q3P9t7LbK8FB+6JvvnpzXr5r5+48AmCPbdoVsz9xpFW9p0K97kiSeR7DueBF+v01IYGBTZWz/BIi+aUa+lVp7ZK1dQ364anNsY8nsn3BgAA+o5pzunqyDAAgPte234g7rjAudUXlBfr/hfMR1mNyM2K84rDC53H3RcijegKZ5/h3u7XPjwQc1RZ9dZGPbk+OOGWJMuSnlzfEDQCzC5Hj6avvzcAAOAekm4A6Of2HOw9BzueOKfzuyu87lUJhZvHHUm3z9Ltv3nHtXOHfq5I3twV/ee2H/+lXl/9Va1ueGqjZj+0rtcMbadzujMzPLp6WvRV+KunlcX83gAAQP9E0g0A/Z5pshU9zukoq6Lh2Ybnjc1bkGtU4i1Jr394IO793NHE+vx/bWxzfEx7VFlg4u304Ua3z9Kv3/w46nl+8+bHMVfqAQBA/+TqyDAAgPtMU61YcU5HWb29+6DhmXvzSPrFv1yglvaumF3EQ/3mrY/iPm80sT7/4c4Tjo8ZblSZ04cbG3c061D78aixB9uPa+OOZqO95AAAoH9hpRsA+jvTfpcx4uy9w5FS39A9139tOmx+jSEWX1yuOZ86TQumj1Hl5JGOSqNrP4o/2Q/HdC/5yDhX9kNXrp0+3KipbzaKN40DAAD9C0k3APRzpvlqrDh7lJXUuxA93J7rIx3RV18jXcNXLg7uzu3UkAz3/tN0ai55hTY3tERtgJZhXMYfnr1y7fThhnu1DAAAoD+ivBwA+rkxxXlxx4WODLu8whtxfnfoPOuRw8y6l08oytFnz/JqQnGeFlZOVPaQxJLmWZOK1dBs1jwullNzyetiziUflpvYfxLtlevAOd0eBafK4R5uVE4q0SOv1Mc8fuUkSssBABiISLoBoB8KTJYLh5olvxeGJGXVWxt7Jdd2svnqXZcGJePh9lyXjBgq6VDM854zrlj3LZhqdI0m5k7x6rk3ojcWi+aeeVNUMiLn5FzyTt367Nu91ojtBmiBzd0umDhSa+v2Oz6fRz3JfWD5etXUMuOHG7Mmj1RhXlbUfd2FeVmaxYxuAAAGJJJuAOhnwiXLseRlZwYlZdVbG7Vk5RajZDMS033Ybo+yeivOPd128vvli8qVmeFRt8/S7IfWRRzdFdoA7aYLJ+o//rjNeAu9fU4p/Ci0qqllurzCG/PhRmaGRw9ee7ZuWbkl4nkevPZsRoYBADBAsacbAPoRO1l2knBLkicgH3M6JzqS4uFmK+ymcabimYwVLvl1Orore0iGzh7jbDa5x9PTNC7SA4zMDI8qJ4+M2VCuamqZHr9xhrz5OUGve/Nz9LjhqDUAANA/sdINAP1EtGQ5lqOd3f6RUk6SzcooJcvrP/jE6NzrP/hE+junVxxZUZ7zLuLhyradju7qOuHT1j3OZnX7LOnJ9Q06d3xRwomx6co4AAAYWEi6AaAPhDY0C5dMxUqWY6mp70m6nSabkbQcNetebhonmX0PxcPMku4ln52ks8ryIx7H6eiuZ2p2xrXKLgWXqSfCXhkHAADpg6QbAJIsWkOzeFZmI+vJGJ0mm4ECk2LTLuQFho3eTL+HQ+1dRscrGZ6jBdPHRPy5PbqrqbUjbPVAaAO0HQeOGp03lGnlAAAAGJxIugEgiZw0NDNNliOxR0qdN6FIGZ7oe6MzPD1xodfqtIGbJC29ckqv10JXtJ10ETctL28+0qVVtXuiNihzMrprf1tiDz0Sf2gCAADSEUk3ACRJrIZmod2zY63MRhPYvfytXQdjlkn7rJ44e2U20sMBE0OzMoP+HC55z/DI+Hs4aLjS/eO/nJptHW7FXHI2uuu0EcFNzJxK9KEJAABITyTdAJAkThuaRVuZjSUnoBTc6Z7uRBq4SdKmnc2ac+ZpkiIn79EeAoR+D6Z7ugNFG4Vm2qAs3u3Y4eZ0AwAA2BgZBgBJEk9DM3tl1lvgbNX0YPtx/+grp3u6E23gZo8eSzR5t7+H0nznK8axRqGZjO4aYbg3Pdy5w83pBgAAkFjpBoCkibehWejK7If7juiRV7bHPI6dtDptIJboXuTmo52SEk/e/d9DnFl7tIZmJl3TMzwkzQAAwH0k3QAQB5MkzmnyGyhwdFRNfbNR0m0nrU4biCW6F/m17c2S4k/eez0EONKZ4PV8EnRf1tY1GXVNL8iJb6U7dE86AABAIJJuAHDIdPSV0+Q3kni6kTtpIJZIAzdJOtTeM6c7nuTd/uT3zJvif4ixZVdLHFdxyiOvnGqwVpiX5b++QOH2gB/qMGvgFoqRYQAAIBqSbgBwwMkIMKkn+V18cbme2tAgK+BNHo+0aE55r6Zf4cTTjdw+t0kDsUQauElSblbP8UyS99CHB96CXF09rUz3v7AtodL0SMIl3FL4rukZnsTanDAyDAAAhEPSDQCGYo0Ak3qXGVdvbdST6xvCdvN+cn2Dzh1fFDPxjqchmy2wTD2aSCvjJs44bZj/XLFW9h+5YYaKhmUHzO/u0q3PxjeqLFGhK9SVk0calfFHwsgwAAAQDkk3ABgyaRQWmMSZdPM22Qscb0M2p0JXxp/d2KBNO1tjvm/k8FPzrZ2UtXf7LM1+aF1KEu5A9sOKWZNGKmdIhjpP+By9n5FhAAAgGpJuAIgisGHaB42Hjd7TeOiYJOdzuiM5b0JRzLJvj4L3dMcrcGX8yb+YrfrubG4P+rNpWXui3c7dYj+s6PZZ6up2nnBLjAwDAACRkXQDQAThGqaZePujg7r2vLEJlYUHeqOhJeZqsHUy7qIzSoJeN+myHkl7l1kCGi7OpKw91XugQ1eon6nZGbTv3kS4FXwAAIBAJN0AEEakhmkm7PeUDMuJGmfb39ahVbV7gpLiwGT5lW37jY7zev2BoKTbtMt6JKMLc9UQsoodKS4eqd4DbSl4hXpXS+zPKklzK0Zp3jlljh9iAACAwYmkGwBCmOzFjmZc0dCevzHMxb794l/9f192spv36ncaHa+w7zlZ1i7F7rL+6BeCG5qFSx7nnHGaXquPPb5rzhmnObpOW6Kjytw2oTjPKG5mebEWTB+T5KsBAADpgqQbAEIkutf4eHdPCnngSKfj9za2duiJ9Q1xnrdbklmX9due2xI8uis/V8uvDl4BT3YDt0RHlQWK9LAi1nzzwEZ2Cysn6tsvbos5D31h5cQErhQAAAw2JN0AECLRvcara/fotkvP6PPy6ff3tEkye2gQmlg2tXXolpVb9HjAnPEDh80eGoSLM91Lbnc7X766Tk1tzr732aeP1D+cPy7o+HdWTfGf98DhTt3/wraoxwhsZJc9JEOL5pRHfeixaE65sockNs8bAAAMLiTdABAi0WT5wJEuST3dxGOttLrp+MnO24k8NFj6u/f8K7/vN8YeFyapV1x8e8mdf0lfuXiy5nwquLQ9sIHb77d8bHScptZTZflLr6qQJD21oSHovmV4ehJu++cAAACmSLoBIESie41zs3pWQt/adbDPEm5Jysvu+Vd6Ig8NDrYf18Ydzbro9JKgPeLRONlL/ljASnq0eBPnT4w+F7vlaJfRcULjll5VoTvmnqVnanZqV0u7JhTnaWHlRFa4AQBAXPgNAgBC2HuNJeNeaEFmjO+Zl93XI7HOPzn6yn5oEG9P7Zr6ZklSlmGSaceZ7CVfsaZO3SefRCTasO7ZTbui/rwoL9voOOHisodk6OY5k3Tfgqm6ec4kEm4AABA3fosAgDDsvcbeAuerxmePLZTU9yOxTj9tuKTEHxrYKfJpw8ySVjsu1l5yS6f2UJvExxJrxNfBdrOVbtM4AACAeFBeDgARVE0t0+UVXn9jrrq9rUadxYtPJqF9ORIrtKu2/dAgdG+1icpJPbO+xxSZjdCy40xX9l/b/on2H+7Qh/sOO7quUGUF0eegFxs+NDCNAwAAiAdJNwBEEdiYa9OOZqP3vL37oP7h/HFRR2LZfy7My9Kh9uP+1yONvsrLzlR7V3fEc4brqh360KBkeI7+9ZdvqfXYiYjHKczL0qyTn7fQsDzbjjNd2X/klXqjuFg+bom+59xbMNToOKZxAAAA8SDpBgCZjbj6oMlsZTYwLtKKs/dkN+/ApDjS6Cv79Yertznuqh340ECSHrruHN2yckvEa3/w2rP9n/ugYSOyrXtatap2j0qG5cibn6t9bclf2Zek3QejJ912pUG0lf6ygp7vFgAAIFlIugEMevGNuDIXuuIcmtQHJsW20GRZks4dX6TThu/RvsOnkuHThmfr3JON20yv5fEbZ2j56vfV1HZqvrY3P0fLr/500Od9d88ho2OuebdRa95tlNSzUm5JvVb2k2FCcfTy98BKg3DX4pG0bH5F2PnhAAAAbiHpBjCoORlxdaZ3hN7afSjmMc/0juj1Wrgk2o3r3He4K+woLlu4FfxYDwFsBw539jpeLK0nS+ULQsrmk+Hys0bFjIlUaeDmQxUAAIBoSLoBDFqxRlx51DPi6vIKrzIzPDp3fJGe3fxRzOM6WXlO9DqlnmsNvE5brBX8WA8BhudkOr5W+3vLHZKhX/7vmTpwpFMf7juiR17Z7vhYsbQcMyt/N33IAAAAkAyMDAMwaDkdcdVy1GzlN1xct89STX2zVtXuUU19s39WtRvXqZDrlE6tjIe+z17Br97aGPO8Z4zKN77GQJakprZOZXg8WjB9jC46vSSu48TSYrjnXDpVabBg+hhVTh5Jwg0AAPoMK90ABi3TEVd23NY9bUbxoXGJ7hn/+GD0edTBcSMdr+BHEq1bugn7e0vW6LQRufwnDAAA9H+sdAMY0BJZQTYdcWXH/c2we3lgnBsrzv9jEBMY53QFP5LGQ9G7g8dy4HCnVtXu0eaGFt0zr6e7upvryy+93+Ti0QAAAJKDZQIAA1aiK8ixVmA96hntZY+UGma4smrHubXiHNhlPBo7zukKfiQ5WfE/l83wSPe/sM3/57KCXC2+uLzX/HF7Tnk83c5NvxcAAIBUIukGMCA56ToeSeBIqdCkz06BA0dKneUdri0G3cvP8g6XZL7i/PRrDSoZkROxwVdhXlbMcwbGOV3Bj2TK6Hy9Vh99NTyS0IKDptYOPbm+QY9+4VwVDcsJami2tq6p18MTE6bfCwAAQCqRdAMYcNxaQZYij5Tyhlkxnza2UM9u/jjm9U0bWyjJfMU5dEU49LyL5kzSq9ubYx5n0ZxJksxX8H0+S6tq90RM9o92ON/TneHpnXBLp+7L/S9s06t3XRp0rtDu4o2HjunB6g9insv+vAAAAP0ZSTeAAcfJnmWT2dimI6XeM2yk9t6eNv2TzFecA4VbqZ99xmnKyvToeHfkAuysTI9mn3GapNgr+JakY8e79cWfbPK/Hi7Z/2Cf2R72M0qH6bZLz9CBw51BDxBCRbsvgXPMu32W/vNPH6rzhC/isXKGZPg/LwAAQH9GIzUAA45be5YDmYyU2mvYWMyOs1ecnTQPsxPkFWvqgprCZWVG/9d16M/tFfxR+cGJv12Sfaj9eNDr4Rq7Hek8YXTNnpOjwUpG5BjFx7ovmRke/eD66VFjfnD9dMZ+AQCAAYGkG8CA49ae5UAmXdB3HDhqdCw7zl5xlpx17Q7tLr6xvjnm+K72rm5trA9Xgh78OUKT7dCowGR/yqjhRtdrxxXnZRvFm8RVTS3T4zfOkDc/OJH35ufqcYP9+gAAAP0F5eUABpzp4wpdjave2qjlq98P6obtzc/R8qs/HZTcDcs2e04ZGBdpz7gJe0X49R0HjOJf33FAF51RIilyo7loHcJDy78XnDtOq96NPZZrwbnjJEl/bTIrv/9rU5vmfCp2abhp2T8AAEB/RtINYMBZuXGncdyiiydHjane2qhbVm7p9XpTW6duWbklaFW1rDBP7zceiXnessK8oD+HJo+x9j7b7JX6vQcNy9pPxkVrNGfCTvY/2GeWRH+wr02XTinVR4bXaRonBe/1BgAAGIgoLwcw4Nhl14nGdfssfeN370WN+cbv3vOXW19RMcrovLHizvLmy5sfea+3Rz2Nzez54KMLhxqd146L1WguFjvZX/t+7FXuwLgJxXkxIuUoDgAAIB2w0g1gwDkWY3+zadzG+uaIe5xth9qPa2N9sy46o0QleWaNwkLjqrc29iovL8zL8o/RijUf/MLJJXr0z/Uxz3vh5J7ScicN5EIV5mX5k/1PDnfGiFZQ3MLKifr2i9vCjgyzZXh64gAAAAYLVroBDAiBjc5GDjdLfs85OS87khrDvdJ23C/f2GUUHxhn760OXXluPZnsF5zsJm7zFuQGjQuTpM+UF8dsxOY5GSfFN6os8Di20/LNjmPHZQ/J0KI55VFjF80pV/YQ/tMDAAAGD1a6AfR74VaKTdhNxSIzbcjVE7fNsFGYHRdtb7W9yp07JEO//N8zdeBIZ8RGYW/tOhhzf7Yl6ZmanSoZkaOS4Tny5udoX1un433dB9uP+xupzZ0ySlt2H4r5nrlTTpXTL72qp1v7Uxsagla8Mzw9Cbf9cwAAgMGCpBtAvxapC3csw3IyNWtS9AZclZNH6pFXtsc8lt3Iq73LZ3RuOy7W3mpLPQ3bMk7OuY5k78F2o/MGNmeLVL5uwi5P//SYAqP40LilV1Xojrln6ZmandrV0q4JxXlaWDmRFW4AADAokXQD6LcS6cKdlRk7wZs1aaQK87Ki7usuysvyJ+/eghwdjLEH3I6TzPdWv7b9QNSRWLUfHzI6TqDA8vVY+9ZD2eXpLe1dRvHh4rKHZOjmOZMcnRcAACAdkXQD6LcS6cJ9KKBMOpLMDI8evPbssCPDbA9ce7Y/CZ4+tlDbDEaGTT+5l9x0b3XgantZQa6Wza8I2tNtXgZ/ir3KPTQrU4/ePEMHjnaqZFiO7vjtO9rX1hH2QYZHPXvK7UZqJcMMG8cZxgEAAAxG1PoB6LcS6cItSU2tsedBV00t0+M3zpA3pGlYWUFu0IxuSRpfaDbqyo67oLxYZQWRR4OFv+YOLVm5RdVbG/2vTRwZ34gtS1Jja4cyMnrK1y86o0TLr+7ZUx16TeG6pncdN+sSbxoHAAAwGJF0A+i3EunCLUkHjpiVR1dNLdNr37hUzy2apR9cP13PLZqlV++6NGS1WdpiWOZtx2VmeLRsfvgkNxJ7BXrFmjr/fPAvzJxg+O7wAh9eVE0t02M3zpC3IPi7Ddc1/anXdhgd3zQOAABgMKK8HEC/Za8UN7WGL4eO5ZDhnmSpJ0GOVoouSUc7TxgdKzDOTnKddF+3V6jt8vjajw4ZvS+S0IcXVVPLdHmFV5sbWqLuJd+x/6jR8U3jAAAABiOSbgD9lr1SvGTllri6cFvxZOpRlIww3OMcEhea5H6477AeeaU+5nHsFWqTMvlwQvdoBzJ5yJBhuDxvGgcAADAYUV4OoF+LVA5torXDfKXbxLhis73V4eLsJHfB9DG66PTTjI5z4HCnVtXu0Zu7WhxdpxR+j7ZThcOyXI0DAAAYjFjpBtDvha4Ur35nj17e9knM97m80K3zxhdLir1C3RMXmUnZfIYneO62U96wXdCdGTUiR3UG3dpHGVYAAAAADEYk3QAGhMBy6Dd3mq38ZnrMV3i7fVbMPc7/8/5eo2P9z/t7demU0sjXZVA274vjicE100frkrNKI16/Ux8fNNuDbhoHAAAwGJF0A3BF1wmfnqnZqV0t7ZpQnKeFlROVPSQ5O1imjS3UM9ptFGeiemujlq+uU1PbqeTRm5+r5VcHrxT/5W8HjI5nEhepwVqGJ76EW5KumzFWcz5lVrpu4pjhKDDTOAAAgMGIpBtAwh54sU5PbWgISha//eI2LZpTrqVXVbh+voOGXclN4qq3NuqWlVt6vd7U1qFbVm4JmtXdcdyse3m4uHAr6aFl8wcOdyZUUp7hYGXfxJBMs4cmpnEAAACDEUk3gIQ88GKdnljf0Ot1nyX/624n3ofaj7sS1+2z9I3fvRc1Zunv3tPlFV5lZnhUkDNEh47FXtUtyAn+V2v11sZeK9plAXuu7bL5VbV7Yh47mpodB3TgaKdr5eWXVZTqvzbsNIoDAABAeCxPAIhb1wmfntrQO+EO9NSGBnWd8Ll6Xo/him6suI07mmMm5gfbj2vjjmZJUvEIsw7qgXHVWxu1ZOWWXjO6m1o7tGTlFlVvbfS/FjpP26lHXqnXV39Vqxue2qjZD60LOnY8LjlzlKtxAAAAgxFJN4C4PVOzM+b+Y5/VExeq22eppr5Zq2r3qKa+Wd0ONjLPDDN3Op64mvpmo+PYcblZmUbxdly3z9KKNXVhG6XZr61YU+f/7HZXczeKxO2k/sV398b9Pc+aNFKFedHHgRXlZWnWpOjzvgEAAAYzyssBxG1nc3tccbHKrWMyzRtjxjk8kGE23HasS6tq9+jA4c5eK9yhR21s7dDmhhZVTh5p1NXclP3e2557O+jBiJPvOTPDowevPTvsnnfbA9eenXAZOwAAQDpjpRtA3CzLLC0MjHNSbh3Jpp1mK9Sx4ionlRgdx44bZrjS/X7jEX31V7XGTdH2Hz71Xdhdzb0FiZWa20IXtp18z/b1PH7jDHnzg6+nrCA3qMkcAAAAwmOlG0Dchuea/SvEjotVbu1RT7m13bgsMtOV1ehxsyb3lE9H29ddmJelWScbnZUMzzE8rzOhe7lDu5pv3HFAz23+2JVzOfuew1+PW43aAAAABgNWugHEzTTnsuM2N7QYl1tHY3f7jiVWnF0+Hc1/XDNVmxtatKp2j97bc8jovE5keKTzJhSFvbbKySO1YPoYXVButiJvyvR7jnQ9dik8AAAAYmOlG0DcivLMVn7tuMAy6mjCxQXOuS7Mid7cyzZjfO9kNlTV1DJ95eJyPbm+IWgF3qOeUVj3v7At6oOCRPks6a1dB6M+IAgt7XaL6f0AAABA/Ei6AcRt5LBsR3GmI7FC48I1XjOxcuNOLbp4ctSY6q2NvRJuqWc1eG3dfkfni1es5Pe8CUUJN1YLJ9ERZQAAAIiN8nIAcXO6ch1rJJZHPQ26LggY9RWp8ZqJN3YejPrzaHvM+1Ks5PeNhhbXrzH0ewYAAEBykHQDiNv6v33iKM4eiSX1bnFm/3nZ/Ar/fuFEk+Jh2dG7jcfaY55s4R4yhFOz44Dr5756Whn7sgEAAPoASTeAuO05ZDanOzAu0kgsb0GuHgsZQZVoUnzNuWOi/jyVe5rDPWSIHe2e1e80qjt0nhgAAABcx55uAHFrPRZ51Fa0ONMRVIkmxUMyoj9X7Is9zWUFubp6WplWv9MY9ADBW5CrZfMrjOZczywv1iOvuHtddvdy007wAAAAiI/jle7169dr/vz5Gj16tDwej55//vmgn1uWpXvvvVdlZWUaOnSoLrvsMn344Ydhj9XZ2anp06fL4/GotrY26Gfvvvuu5syZo9zcXI0bN04PP/yw00sFkGSZMZLaaHEmI6gSTYr3tUVP2mPtMY/XmIIc/eD66Xpu0Sy9etelWnpVhV6961I9t2hW0OsmCbck+brNVqS/ccWZ+sH103XbJdGbx9noXg4AAJB8jpPuo0ePatq0aXr00UfD/vzhhx/WD3/4Qz3++OPatGmThg0bpiuuuEIdHb1/ubvzzjs1evToXq+3tbVp7ty5mjBhgt566y195zvf0fLly/Xkk086vVwASTRqhNnIMNO4UIkmxW9/FL2RWrQ95onwFgzt9TAhkTnXv39nj1HcX/cd1oLpY3TR6acZxdO9HAAAIPkcJ91XXnmlvvWtb+nv//7ve/3Msix9//vf1913360FCxbonHPO0S9+8Qvt3bu314r4H//4R7300kv67ne/2+s4v/zlL9XV1aWf/vSn+vSnP63rr79e//f//l9973vfc3q5AJJo/jm9H5olEhcq0aTYZH040h7zsoJcfeXicpUVOE9MPzVqhOP3RHO084SjuHi6xAMAACA5XN3T3dDQoKamJl122WX+1woKCjRz5kzV1NTo+uuvlyTt27dPixYt0vPPP6+8vLxex6mpqdHFF1+s7OxTM4CvuOIKPfTQQzp48KCKiop6vaezs1OdnZ3+P7e1tbn50YC01e2zYu6tjqS0YKirceHYSXE8c7rHFZmdt2pqmS49a5SeqdmpXS3tmlCcp4WVE5U9JEN3Vk3xfz9/qmvSmnebYh4vP8/ddhmj8s0SfzvOflixZOWWXvO9nTVwAwAAQKJc/c2wqannl9FRo0YFvT5q1Cj/zyzL0pe//GXdcsstOv/887Vz586wxykvL+91DPtn4ZLuBx54QCtWrHDjYwCDRvXWxl7JbJmDBl/7244Zncc0LpLQxmsb6w/ouTc+jvm+nQfatap2T8yHCeG+h/96tcH/PdjNxv781/1G17u/tTN2kAPnji/Syk27jeJskR5WOGngBgAAgMT1effyH/3oRzp8+LCWLl3q6nGXLl2q22+/3f/ntrY2jRs3ztVzAOmkemujlqzc0qsEu6m1Q0tWbuk1viucNe82Gp1rzbuNWnLJGXFeaQ97T7QkvWB43ufe+EjPvfGRpMgPE5x8D6El6JGYxpkaXWi2Yh8aZ9olHgAAAMnj6pxur9crqad8PNC+ffv8P1u3bp1qamqUk5OjIUOG6PTTT5cknX/++brpppv8xwl3jMBzhMrJyVF+fn7QXwBO6fZZqqlv1qraPXpt+wEtX/1+2D3P9msr1tTFnOP88UGzFWzTOFPDsjMdv8dOoqu3nkrYu32WVqypM/4eioZmh4nsrfXYca2q3aOa+mZXZmFPH1cYd1wiDdwAAACQOFdXusvLy+X1evXyyy9r+vTpknpWnDdt2qQlS5ZIkn74wx/qW9/6lv89e/fu1RVXXKFf//rXmjlzpiSpsrJS3/zmN3X8+HFlZWVJktauXaszzzwzbGk5gOjClU9HY8lsjnOW4WM70zhT184Yq9/X7nX0nsAk+vIKrzIzPNrc0BL1Own9Hlo7zeaSP7v5Iz27OfoKuxPPbtplHHfznElxnwcAAADuc/yr8JEjR1RbW+ufq93Q0KDa2lrt3r1bHo9HX/va1/Stb31Lq1ev1nvvvacvfelLGj16tK655hpJ0vjx4zV16lT/X5/61KckSZMnT9bYsWMlSV/4wheUnZ2tm2++We+//75+/etf6wc/+EFQ+TgAM3b5tNMmZFLsOc6nnzbc6DimcaYuPL1EOUPiy+TtJFoyn1OdyDzrcCvsTu1qaXc1DgAAAH3H8Ur3m2++qUsuucT/ZzsRvummm/T000/rzjvv1NGjR7V48WIdOnRIs2fPVnV1tXJzzfc4FhQU6KWXXtKtt96q8847TyUlJbr33nu1ePFip5cLDGrRyqdNxJrjnGGY95rGOZFI0XZTW08SXTLMbH64HVc4NMvxuSz1dAwPXGF3akJx7ykPicQBAACg7zhOuj/3uc/JsiL/uuvxeHTffffpvvvuMzrexIkTwx7vnHPO0YYNG5xeHoAAscqnozGZ49zVbXYs0zhTr28/oK4Tvrjf33LkZHdx0/z3ZFxxntme7lCm5fqRLKycqG+/uE3RtodneHriAAAA0L/0efdyAMnVdcLnnzfddsxsD3I4V08ri7kqO644T2/tPhTzWONcXoH9/70Ve1xYNM1Hu7Sqdo8+3HfEKP7AyST9wNGuhM4bb5l69pAMLZpTrifWN0SMWTSnXNlxltwDAAAgeUi6gTTywIt1empDQ9QVUVOr32nUnVVToibe180Yq+cNGppdN2Ns4hcU4KOWowm9/8d/rncUf+Bwp1bV7tH6v32S0HljletHs/SqCknqdX8zPD0Jt/1zAAAA9C8k3UCaeODFuqgroU6ZlEPPnDRSHkXfX+05GeemzgRKy53K8Ej3v7AtoWN41DO7O1a5fixLr6rQHXPP8lcyTCjO08LKiaxwAwAA9GMk3UAa6Drh01Mb3Eu4bbHKod/adTBmQzPrZFw8e5kjibdzeTwSrRqw6wTumTdFmxtatP9wh0pH9CTg8TRVyx6SwVgwAACAAYSkG0gDz9TsdKWkPFSscmi7C3gspnGmhmZnunq8cDI8iSfcUs8K99XTynT/C9uCmtq5Mb8bAAAA/R9JN5AGTOczz60YpXnnlKlkWI7u+O072tfWEXal2rQc+oBhYzDTOFMlw832Rl84eaT+6TPj9OG+w3rkldj7uG+7ZLLOGDVCBw53JlRS/vmzTtPV08eodESuDh7t0q3Pbun1Pdvzux+7cQaJNwAAQBpjIyCQBkznM88sL9aC6WN00RklWn51T+Ot0AJn+8/L5lfELH9uOWLWzds0zlRZodl87XPG5vd83tNPM4q/6PTTtGD6GJWMMDt+JPvaOrVg+hhdUF6s+18IPyfdfm3Fmjp1J6NMAQAAAP0CSTeQBhZWTpQnxvZgT8gc56qpZXrsxhnyFgSvGnsLco1XX1NVXl481CwptuMuKC9WWUFuxLHcHgXPJU+ky3igWHPSA+d3AwAAID1RXg6kgcwMj4ZmZaq9qztiTF5WZq+V66qpZbq8wht3g6/RRUNdjTPV2mk2f9yOy8zwaNn8Ci1ZuaVXt/VwK/t2kt7UGr78PpaLTu9pGmc6lzve+d0AAADo/1jpBtLA5oaWqAm3JB3t6g67opqZ4VHl5JFaMH2MKiePdNRR+8JJJa7GmTK9wsA4Jyv7dpLu5FyBCvOyJZmvmLu1sg4AAID+h5VuIA2kakV11uSRKszL0qH2yCvPhXlZmuXiuDBJqpxUYtQYrTIk2Y+2st/ts4Jev7zCq8dunKEVa+qiloiH89fGNkmxV8zdmt8NAACA/oukG0gDqVpRzczw6MFrz9YtK7dEjHnw2rPjmkcdzWfKi3uViYfynIwLZa/sB6re2tgrubZHer1616X+ZPwXr+/UW7sPxbw+u+rAaVk7AAAA0g/l5UAacNoozE1VU8v0+I0z5M0Pbm7mzc/R40kah/XWroMx91pbJ+NCdfss1dQ3a1XtHtXUN+vFdxu1ZOWWXqvZ9kivtXVN/vL7KWX5Rtc3Kv/Uww03GtYBAABg4GKlG0gDqV5RrZpapkvPGqVnanZqV0u7JhTnaWHlRGUPSc5zvb0HzeaS98SdWtUOt6Kd4Qm/Ym6p57tbsaZOl1d4lZnh0bnjCrVy0+6Y5z13XGHQnxNtWAcAAICBi6QbSBP2impoUuk9WSadzBXV6q2NWrbqfe073Ol/7cn1O7RiwaeTct7ajw8Zx113/jj/NS5ZuaVXgh1tRHbgSK/KySNVMtxsVFm4uHBl7QAAAEh/JN1AGunrFWepJ5kNt6d73+FO3bJyi2sl5oGNzsznfnv8712xpi6u8V/SqQZ0/1PXZBT/P3VN+uxZpXGeDQAAAOmEpBtII+HKp//r1YakrXR3+yzd/pt3osbc8Zt3/OXZ8Qr3uUxYlqVVtXt04HCn4/cGshvQvftxq1G8aRwAAADSH0k3kCYilU/bDcGS0bTr9e0HjOaDv779gOZ86rS4zhHpc5lYuWm30R7sSEJHelmGV2EaBwAAgPRH0g2kgWjl04ENwUbkZOnA0U7XGnn99s2PjOPiSboTLQtPRLgGdAU5Zv/KNI0DAABA+uM3Q6CfCNyz7DQp3tzQErV82m4I9sWfbPK/VuZCg7Utu3uP5EokLlSsz5VM4RrQDcvNMnqvaRwAAADSH0k30A+E27PsJCm2G3054UbZuelCebwL6vF8rkTcM2+KSkbkRHzocUH5SK3dtj/mcS4op0s5AAAAeiSvpTEAI/ae5dAVXTsprt7aGPMYJcPMRlkFsku2V6ypU3e0uVlRZBl2RTeNC2U3MOsrJSNytGD6GFVOHhm2yuCmCyfKE+MBgsfTEwcAAABIJN1ASsXaiy0ZJsVxriQHzqGOR86QTFfjQl1QXqzCvL4r1Y6V5GcPydDiOeVRYxbPKU/qiDYAAAAMLPxmCKSQ6V7sWElxU4L7nsOVcXf7LNXUN2tV7R7V1DeHTfwzYi37OoxLFY96yvntLuXRLL2qQl+5uLxXyXyGR/rKxeVaelVFci4SAAAAAxJ7uoEUMt2zHCvu7TgbldlCV3hN95jPmlysrXvbYh5/1uTYyWw4mxtadKj9eFzvNRWuS3ksS6+q0B1zz9IzNTu1q6VdE4rztLByIivcAAAA6IWkG0gSk27kpnuWY8Xta4tvpTt0DrXkbN73qBFDjc5jGhcqkUZqedmZQTPEywpydfW0Mq1+pzHoYUK4LuUmsodk6OY5k+K+PgAAAAwOJN1AEpiuFF9QXqyyglw1tXaE3dcdLikOZ1icc6EtBa/wms77vrzCq8wMj0pGmDVwM40LlUgjNY+kX948s9dc8jurpsQ9mg0AAABwilpIwGVOupFnZni0bH7PHuDQtM9J2fPfTx+T6GVLcr7HvHS4WTJtGhfKfigRT0p8tKtbPsvq1Y08M8Ojyskjo3YpBwAAANxC0g24KJ5u5FVTy/TYjTPkLQhe1fUW5BrP0M6IM3G0V67t63G6x/yEz2cUbxonBTdw29zQonvmTfFfq1OP/6U+aiM4AAAAINkoLwdc5GSluHLySP/rVVPLdHmFN+6y540NzXFdb+j1ON1j/vu39xjF//7tPfrsmaUx4yKV5S++uLzXXmwTr9U367X6Zv9x4tm7DQAAACSCpBtwUSLdyO2y53jsOXgsrveFXo/TPeYfG563rrFVq2r3RH2YEK2B25PrG/ToF2aoaFi29h/u0Jp39upP2/Y7+IThG8EBAAAAyUZ5OeAit7qRO2UlWDltX4/TPeZjC80+x9/2HdVXf1WrG57aqNkPrQva1y6ZleXf/0KdLigv1oLpY3RVHElzpPJ+AAAAIJlIugFDgXuNI+0RjtX4y6OeMudY3cidGlMUXxIf7nqc7DH/+xljHZ8zXEM5pw3cygrjG0EWehwAAAAg2SgvBwyYjgCzV4qXrNwS8Vgm3cidqiwv0Y//vMPRe6J1RzfdYz4kw/lzu3Cjx5yW5dsPN5zu8Q49DgAAAJBsrHQDMTgZASb1JKyLLy5XaF6d4ZEWX1yelP3EGZnOk/hY3dFNRms1tcWXvPYaPeawLN9+uBHvowu3y/sBAACASFjpBqKItdc4dMVW6knSn1zf0Os9liU9ub5B544vcj3xPnCk0yjutktO1xmjhjvujh7J27sTK9OOt4GbdOrhxlMbGmS6RTvccQAAAIBkYqUbiMLpXuNYSbql5DTyMl25vej0kqgr107tazNL9iOJt4GbdOrhhpOEO9xxAAAAgGQi6QaicLrXOFaSLiWnkVeqGrjlZcdXLJNoA7doDzdsoXl1rHJ6AAAAIBkoLweicLrX2HSPc7x7oSMJbODmkYKS0WSu8FaMzteqd/Y6eo8bDdxMHm74LOmeeVNUMiLHtXJ6AAAAwCmSbiAKp3uNWwz3VpvGOWGvFId2WfeG6bLultJ85w3JYl2P3cAtGtOHFsXDc7Rg+hjH1wgAAAC4haQbiMLpCnLxsGyj45rGOWW6UuyW0hE5RnHfvGqKSvPdW3H+xDDpNo0DAAAAkoWkG4jByQpy8VDDpNswLh4mK8WuMWxiVlGWr4vOKHHttO/vbXU1DgAAAEgWkm7AgOkK8kvbmoyO99K2Jn12SmkyLrVPNbUeczXO1LHj3a7GAQAAAMlC0g0YMllBfneP2cqqaVw8un1Wn5WX1358yDjuuvPHuXbez0wcqZfq9hvFAQAAAKlE0g24KD83y9U4p6q3NvYqgy9LYiM103njbs8lv+nCifqPP26TFeWwHk9PHAAAAJBKzOkGXPS/Lyp3Nc6J6q2NWrJyS69RWk2tHVqycouqtza6fs5PDpt1YTeNM5U9JEOL50T/DhfPKVf2EP4VBwAAgNTiN1LARUMMkzzTOFPdPksr1tSF7Wtmv7ZiTZ3rK87Fw8xW7E3jnFh6VYW+cnG5QivnMzzSVy4u19KrKlw/JwAAAOAU5eWAizY1tBjHzfnUaa6dd3NDS68V7kCWpMbWDm1uaHG1s/n2/UddjXNq6VUVumPuWXqmZqd2tbRrQnGeFlZOZIUbAAAA/QZJN+Aq05Vkd1ec9x82m0dtGmcqJ8ssuTWNi0f2kAzdPGdS0o4PAAAAJILlIMBFlZPMZlGbxpkqHZHrapypvGyz53amcQAAAEC64TdhIEQiI7dmTR6pwrwsHWo/HjGmMC9Ls1ws8ZakC8qLVVaQq6bWjrBr6B5J3oKez+Km8SOHuhoHAAAApBuSbiBAoiO3MjM8evDas3XLyi0RYx689mzX52ZnZni0bH6FlqzcIo+Ci9ftMy2bX+H6eT9oPOJqHAAAAJBuKC8HTnJr5FbV1DJ95eJyhaa3HvV01U7GvGz7vI/dOEPeguAScm9Brh67cUZSznvseLercQAAAEC6YaUbUOyRWx71jNy6vMIbc7W4emujnljfEPY4T6xv0Lnji5KaeF9e4Y27PN6poVmZrsYBAAAA6YakG5B7I7e6fZa+8bv3op5r6e/eM0re45WZ4XF1LFg0RblmybRpHAAAAJBuKC8H5N7IrY07mqM2UZOkg+3HtXFHs/G19Wc7Dx5zNQ4AAABINyTdgNwbufX69gNGxzGNi0e3z1JNfbNW1e5RTX2zun3uzgQP5HM5DgAAAEg3lJcDcm/k1u7mo0bnM41zKlr39WTs9c7NNHtuZxoHAAAApBuSbkDujdx6b0+r0flM45ywu6+HPjRoau3QLSu39Jof7mQUWiQFQ7NcjQMAAADSDctPwElujNw62mU2Gss0zlSs7uuSeu01dzoKLZxPjnS5GgcAAACkG1a6gQCJjtwalZ9jlGCOys9J9FKDxOq+Ho7TUWjhjCrI0da9ZnEAAADAYMRKNxDCHrm1YPoYVU4e6SgZvePzZ7oaZ8q0+3qowFFo8ZhVXuJqHAAAAJBuSLoBF108pVRDYiTpQzI8unhKqavnNe2+Hkm8SfsNF4x3NQ4AAABINyTdgIsyMzy6efbEqDE3z56YcNfwUBeUF6swL/5mZfEm7b/ctMvVOAAAACDdkHQDLur2WVr9TvTGZKvfaUzK7OyuE86nYXvU08U81ii0SF56v8nVOAAAACDdkHQDLjJpaJbIHupINtY3q91hR3Qno9AiOdxxwtU4AAAAIN2QdAMuamo95mqcqZodBxy/x8kotEjOLBvhahwAAACQbhgZBrio5ajZPGrTOFOmxerXTB+tS84qNRqF1u2zYo5O+8fzxmlNjHJ6Ow4AAAAYjEi6ARcVDzebR20aZyo/16yJ2pSyfC2YPiZmXPXWRq1YUxdUKl9WkKtl8yuCVsYvPL1EedmZUUvbh2Vn6sLTGRkGAACAwYnycsBF3nyzLuCmcabajh13La56a6OWrNzSa296U2uHlqzcouqtp1a2MzM8+t4/Tot6vP/3j9Nc79YOAAAADBQk3Uh73T5LNfXNWlW7RzX1zUnpHG67oLxYZQXRE+pEuoUnW7fP0oo1dWHL1e3XVqypC/oOq6aW6fEbZ2jUiODVe29+jh5PcM84AAAAMNBRXo60Zlom7ZbMDI+Wza/QkpVbJAXvtXajW3gkhXnZrsTF6r5u6VT39crJI/2vV00t0+UV3ph7wAEAAIDBhpVupC0nZdKBEl0Zr5papsdunKFRISXkbnQLj6Q4z2xPd6y4/YejjzuLFpeZ4VHl5JFaMH2MKiePJOEGAAAAxEo30lSsMmmPesqkL6/wBiWHbq6MW5Yv6M8+ny9CZOIOGe7pjhVXOsJsr7lpHAAAADDYsdKNtOSkTNoW78p4qOqtjbpl5RbtOxw8Fmzf4S7d4uA4TiTSNT1wZd/ns+TNz1WkNWqP+veedAAAAKC/YaUbaclpmXS8K+Ohun2WvvG796Ke8xu/ey/mcZyKt2t6uJX9wrws/2fuqz3pAAAAQLpipRtpyWmZdDwr4+FsrG/WofboJdyH2o9rY32z0fWZOm9CkWLlwRmenjhbpJX91pPXXxCy/zuZe9IBAACAdMVKN9KSPbqrqbUj7Oq1Rz1JpF0mnUgDsUCv1X9idJzX6j/RRWeUGMWaeGvXQcXq9+azeuIqJ480WtnPHZKhX/7vmTpwpJNu5AAAAECcHK90r1+/XvPnz9fo0aPl8Xj0/PPPB/3csizde++9Kisr09ChQ3XZZZfpww8/9P98586duvnmm1VeXq6hQ4dq8uTJWrZsmbq6gve/vvvuu5ozZ45yc3M1btw4Pfzww/F9QgxK9uguSb32J4crk3argdjeQ2bJu2mcKacPDUxW9pvaOpXh8dCNHAAAAEiA46T76NGjmjZtmh599NGwP3/44Yf1wx/+UI8//rg2bdqkYcOG6YorrlBHR88v+H/961/l8/n0xBNP6P3339d//ud/6vHHH9e///u/+4/R1tamuXPnasKECXrrrbf0ne98R8uXL9eTTz4Z58fEYGSP7vIWxB7dZa+MJ9pArKzQLHk3jTPl9KGBWyv7AAAAAKJzXF5+5ZVX6sorrwz7M8uy9P3vf1933323FixYIEn6xS9+oVGjRun555/X9ddfr6qqKlVVVfnfM2nSJH3wwQd67LHH9N3vfleS9Mtf/lJdXV366U9/quzsbH36059WbW2tvve972nx4sXxfE70A90+S5sbWrT/cEeflStXTS3TpWeN0jM1O7WrpV0TivO0sHKisocEP2+yV8aXrNySUAOx4jzDLuKGcaacltMzGgwAAADoG67u6W5oaFBTU5Muu+wy/2sFBQWaOXOmampqdP3114d9X2trq4qLT60g1tTU6OKLL1Z2drb/tSuuuEIPPfSQDh48qKKiol7H6OzsVGdnp//PbW1tbnwkuMTN+deJnve/Xm0Ie157ZTw03uvgOouHZceMcRJnyulDA6dJOgAAAID4uNq9vKmpSZI0atSooNdHjRrl/1mo7du360c/+pG+8pWvBB0n3DECzxHqgQceUEFBgf+vcePGxf054C635l/3xXmrppbp1bsu1XOLZukH10/Xc4tm6dW7LjV+MHCovSt2kIM4J5yU0zvd8w4AAAAgPikdGbZnzx5VVVXpH/7hH7Ro0aKEjrV06VK1trb6//roo49cukokIlaXbKln/nV3rNbbLp/XinLezAyPKiePjKuBWPFww/JywzinqqaWad0dn9PCWeM154wSLZw1Xuvu+FzYhwZOknQAAAAA8XG1vNzr9UqS9u3bp7KyU7+w79u3T9OnTw+K3bt3ry655BJdeOGFvRqkeb1e7du3L+g1+8/2OULl5OQoJyc5iQzi52T+deXkkX12XiXpvN58sz3QpnFOPfBinZ7a0OAfH7bhQ+mXm3Zr0ZxyLb2qold81dQyXV7h7fO99gAAAMBg4epKd3l5ubxer15++WX/a21tbdq0aZMqKyv9r+3Zs0ef+9zndN555+lnP/uZMjKCL6OyslLr16/X8ePH/a+tXbtWZ555Ztj93Oi/UtUlu6nN7HimcaYuKC9WYV5W1JiivKyk7JV+4MU6PbG+ode8bp8lPbG+QQ+8WBf2fYms7AMAAACIznHSfeTIEdXW1qq2tlZST/O02tpa7d69Wx6PR1/72tf0rW99S6tXr9Z7772nL33pSxo9erSuueYaSacS7vHjx+u73/2uPvnkEzU1NQXt1f7CF76g7Oxs3XzzzXr//ff161//Wj/4wQ90++23u/Kh0XdS1SW75Uhn7CAHcW5yt5C+R9cJn57a0BA15qkNDeo64UvC2QEAAABE4ri8/M0339Qll1zi/7OdCN900016+umndeedd+ro0aNavHixDh06pNmzZ6u6ulq5uT1J1dq1a7V9+3Zt375dY8eODTq2ZfWkIwUFBXrppZd066236rzzzlNJSYnuvfdexoUNQKnqkp1IF/FERpttbmjRofbjUWMOtR93vaz9mZqdvVa4Q/msnrib50xy7bwAAAAAonOcdH/uc5/zJ8fheDwe3XfffbrvvvvC/vzLX/6yvvzlL8c8zznnnKMNGzY4vTz0M3aX7FtWbgn7c0vJ6ZLtLRgaV1yio82aWo8Zndc0ztTO5nZX4wAAAAC4I6Xdy4FksVfYoykLWWF3Y7TZgSNmo8BM48yZFq0no7gdAAAAQCQk3Ugqe3RXJB45HxnW7bNUU9+sVbV7VFPfHHHs17L5Fb1mUAeeN3CF3a3RZoeOGc7pNowzNX1soatxAAAAANzh6sgwIJTbI8OclH/bc6hN4t26TtMiebf7g48uynM1DgAAAIA7SLqRVG6ODLPLv0PXmu3y78dunBE28TaZQ+3WdVZOKtEjr9THPE7lpBKj85myy+mjPTgILacHAAAAkHyUlyOp3BoZlkj5t8kcareuc9bkkcrLzowaMyw7U7Nc7FwuOS+nBwAAANA3SLqRVBeUF6swLytqTGFeVswVWCfl3/GwV4qjJa2mK8XZQ6L/3yorxs/jZZfThzaQKyvIDVsFAAAAACD5KC9HypmsvbpZph6OvVK8ZOUWeRTc49u+PpOV4lTN6baZltMDAAAA6BusdCOpTJLQgyeT0GjcKv+Oxl4p9oasFHsdrBSnak53IJNyegAAAAB9g5VuJFXjIbPkMlacXf7d1NoRdl+3Rz3Jcbjy726fZbzym+hKcctRs1FgpnEAAAAABjaSbiTV2x8dNI679ryxEX8eb/m3kxFjgeeKt/S7eHiOq3EAAAAABjbKy5FU4Val441zWv5tjxgLbcBmjxir3tpoeHXmvPlm5e2mcQAAAAAGNla6kVTlI4e5Gmda/h1rxJhHPSPGLq/wurrn+bwJRfJ4JCvKUwSPpycOAAAAQPpjpRtJtbByomLltBmenjhTJo3Ckj1iLJI3GlqiJtxST0L+hsvnBQAAANA/kXQjqbKHZOjzU0qjxnx+SmnM2dZOJXvEWCSv7zjgahwAAACAgY2kG0nV7bO0dU9b1Jite9rU7TPd/W2mMCfL1ThTe1raXY0DAAAAMLCRdCOpYpV5S8kp8177132uxpnyuRwHAAAAYGAj6UZSparMe2fzUVfjTHkss6ZspnEAAAAABjaSbiRV6Qiz0VimcaaGZmW6GmfKZzgkzTQOAAAAwMBG0o2kuqC8WGUFuYq0ruuRVFbQM/bLTXM/7XU1zlTzkU5X4wAAAAAMbCTdSKrMDI+Wza+IuK5rSVo2v8LVWdmSNLYoz9U4Ux3Hu12NAwAAADCwkXQjLdkr7NEkY4W9q9usRZppHAAAAICBjaQbSdXts7RiTV3En3skrVhT5/rIMHuFPVpZezJW2L35ZnvTTeMAAAAADGwk3UiqWCPDLCVnZJgkVU0t02M3zui14l1WkKvHbpyhqqllrp9z5qQSV+MAAAAADGxDUn0B6D+6fZY2N7Ro/+EOlY7oKb1OdCU4VSPDbFVTy3R5hdf1zxXJTRdO1H/8cZusKAv3Hk9PHAAAAID0R9I9CIVLrtfWNWnFmrqgVemyglwtm1+R0IpwqkaGBcrM8Khy8sikHT9Q9pAMLZ5TrifWN0SMWTynXNlDKDIBAAAABgOS7kGmemtjr+S6MC9Lh9qP94ptau3QkpVbEirFthuaNbV2hO1g7pHkTUJDs1RaelWFJOmpDQ0K3Kqe4ZEWzSn3/xwAAABA+vNYVrRC2IGrra1NBQUFam1tVX5+fqovp1+o3tqoJSu3RBzfFY6dFL9616Vxl2Tb55UUdG77aMnaX51qXSd8eqZmp3a1tGtCcZ4WVk5khRsAAABIE6Y5Jyvdg4TdRdzpE5bARmfxlmjbDc1CV9i9LpSv92fZQzJ085xJqb4MAAAAAClE0j1IxOoiHkuijc76uqEZAAAAAPQHJN2DRKJJsxuNzvqyoRkAAAAA9Ack3YNEvElzOjY6AwAAAIC+QlenQcLuIu6kmNuOXTa/gjJwAAAAAIgDSfcgkZnh0bL5PaOqQtNn+8+FeVlBr3sLctO2szgAAAAA9AXKyweRWF3EaXQGAAAAAO5iTvcg1O2zSK4BAAAAIAHM6UZEdBEHAAAAgL7Bnm4AAAAAAJKEpBsAAAAAgCShvBx9JlV7ydnDDgAAACBVSLrRJ6q3Nvbqml52smt6MkeSpeq8AAAAACBRXo4+UL21UUtWbglKfCWpqbVDS1ZuUfXWxrQ6LwAAAADYSLqRVN0+SyvW1CncXDr7tRVr6tTtc3dyXarOCwAAAACBSLoHoW6fpZr6Zq2q3aOa+uakJp6bG1p6rTQHsiQ1tnZoc0NLWpwXAAAAAAKxp3uQ6es9zvsPR05844nr7+cFAAAAgECsdA8iqdjjXDoi19W4/n5eAAAAAAhE0p1CfVnmnao9zheUF6usIFeRBnR51LPSfkF5cVqcFwAAAAACUV6eIn1d5u1kj3Pl5JGunTczw6Nl8yu0ZOUWeaSgpN9OiJfNr3B9bnaqzgsAAAAAgVjpToFUlHmnco9z1dQyPXbjDI3Kzwl6fVR+jh67cYZrDxlCKwcur/DqsRtnyFsQXELuLch19bwAAAAAEAkr3X0sVpm3Rz1l3pdXeF1dhe0fe5xDP497ny9a5cCrd12qzQ0t2n+4Q6UjekrKWeEGAAAA0BdY6e5jqRpllco9zvbKflNb8Ofe1+bOyn6syoG1dU2qnDxSC6aPUeXkkSTcAAAAAPoMSXcfS1WZt73HWYq83pyMPc7JbuCWqgZxAAAAAGCCpLuPpbLM295b3Zd7nJO9sp+qygEAAAAAMMGe7j5ml3k3tXaEXZ31qCcJTtYoq6qpZbq8wttne5yTvbKfygZxAAAAABALSXcf6w+jrDIzPK6OBYsm2Sv7/aNBHAAAAACER3l5CqSizDtV7JX9aBJp4JbKBnEAAAAAEAsr3SnS12XeqZKZ4dHV08r0xPqGiDFXTyuL+3P3h8oBAAAAAIiEle4Ussu803mUVbfP0up3oo8EW/1OY0LdxQdT5QAAAACAgYWVbiRVrO7i0qnu4onsMx8slQMAAAAABhaSbiRVX3YX78sGcQAAAABggvJyJBXdxQEAAAAMZiTdSKoLyotVmJcVNaYoL4vu4gAAAADSEkk3kq7rhC/qzztj/BwAAAAABiqSbiTVxvpmtXd1R41p7+rWxvrmProiAAAAAOg7JN1Iqtd3HHA1DgAAAAAGEpJuJNXeg8dcjQMAAACAgYSkG0k1unCoq3EAAAAAMJCQdCOpLpxc4mocAAAAAAwkJN1IqlmTR8YcGVaYl6VZk0f20RUBAAAAQN8h6UZSZWZ49E/nj40a80/nj1VmhqePrggAAAAA+g5JN5Kq22dp9TuNUWNWv9Oobp/VR1cEAAAAAH2HpBtJtbmhRY2tHVFjGls7tLmhpY+uCAAAAAD6Dkk3kmr/4egJt9M4AAAAABhISLqRVCXDclyNAwAAAICBhKQbyWXaH40+agAAAADSEEk3kurAkU5X4wAAAABgICHpRlKVjsh1NQ4AAAAABhKSbiTVBeXFKivIjVg97pFUVpCrC8qL+/KyAAAAAKBPkHQjqTIzPFo2v0JS723b9p+Xza9QZgabugEAAACkH8dJ9/r16zV//nyNHj1aHo9Hzz//fNDPLcvSvffeq7KyMg0dOlSXXXaZPvzww6CYlpYWffGLX1R+fr4KCwt1880368iRI0Ex7777rubMmaPc3FyNGzdODz/8sPNPh36hamqZHrtxhrwFwSXk3oJcPXbjDFVNLUvRlQEAAABAcg1x+oajR49q2rRp+pd/+Rdde+21vX7+8MMP64c//KF+/vOfq7y8XPfcc4+uuOIK1dXVKTe3J+n64he/qMbGRq1du1bHjx/XP//zP2vx4sV69tlnJUltbW2aO3euLrvsMj3++ON677339C//8i8qLCzU4sWLE/zISIWqqWW6vMKrzQ0t2n+4Q6UjekrKWeEGAAAAkM48lmVZcb/Z49Hvf/97XXPNNZJ6VrlHjx6tO+64Q//f//f/SZJaW1s1atQoPf3007r++uu1bds2VVRU6I033tD5558vSaqurtZVV12ljz/+WKNHj9Zjjz2mb37zm2pqalJ2drYk6Rvf+Iaef/55/fWvfzW6tra2NhUUFKi1tVX5+fnxfkQAAAAAAHoxzTld3dPd0NCgpqYmXXbZZf7XCgoKNHPmTNXU1EiSampqVFhY6E+4Jemyyy5TRkaGNm3a5I+5+OKL/Qm3JF1xxRX64IMPdPDgwbDn7uzsVFtbW9BfCK/bZ6mmvlmraveopr5Z3b64n7sAAAAAAKJwXF4eTVNTkyRp1KhRQa+PGjXK/7OmpiaVlpYGX8SQISouLg6KKS8v73UM+2dFRUW9zv3AAw9oxYoV7nyQNFa9tVEr1tSpsbXD/1pZQa6Wza9gbzUAAAAAuCxtupcvXbpUra2t/r8++uijVF9Sv1O9tVFLVm4JSrglqam1Q0tWblH11sYUXRkAAAAApCdXk26v1ytJ2rdvX9Dr+/bt8//M6/Vq//79QT8/ceKEWlpagmLCHSPwHKFycnKUn58f9BdO6fZZWrGmTuEKye3XVqypo9QcAAAAAFzkatJdXl4ur9erl19+2f9aW1ubNm3apMrKSklSZWWlDh06pLfeessfs27dOvl8Ps2cOdMfs379eh0/ftwfs3btWp155plhS8sR2+aGll4r3IEsSY2tHdrc0NJ3FwUAAAAAac5x0n3kyBHV1taqtrZWUk/ztNraWu3evVsej0df+9rX9K1vfUurV6/We++9py996UsaPXq0v8P5lClTVFVVpUWLFmnz5s167bXXdNttt+n666/X6NGjJUlf+MIXlJ2drZtvvlnvv/++fv3rX+sHP/iBbr/9dtc++GCz/3DkhDueOAAAAABAbI4bqb355pu65JJL/H+2E+GbbrpJTz/9tO68804dPXpUixcv1qFDhzR79mxVV1f7Z3RL0i9/+Uvddttt+vznP6+MjAxdd911+uEPf+j/eUFBgV566SXdeuutOu+881RSUqJ7772XGd0JKB2RGzvIQRwAAAAAILaE5nT3Z8zpDtbtszT7oXVqau0Iu6/bI8lbkKtX77pUmRmevr48AAAAABhQUjKnG8705bzszAyPls2vkNSTYAey/7xsfgUJNwAAAAC4yNU53TCXinnZVVPL9NiNM3qd18ucbgAAAABICsrLU8Celx36xdtrzI/dOCOpCXC3z9LmhhbtP9yh0hG5uqC8mBVuAAAAAHDANOdkpbuPxZqX7VHPvOzLK7xJS4QzMzyqnDwyKccGAAAAAJzCnu4+xrxsAAAAABg8SLr7GPOyAQAAAGDwIOnuY8zLBgAAAIDBg6S7j11QXqyygtxeY7tsHvV0Mb+gvLgvLwsAAAAAkAQk3X2MedkAAAAAMHiQdKeAPS/bWxBcQu4tyE36uDAAAAAAQN9hZFiKVE0t0+UVXuZlAwAAAEAaI+lOIeZlAwAAAEB6o7wcAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkGZLqCxjMun2WNje0aP/hDpWOyNUF5cXKzPCk+rIAAAAAAC4h6U6R6q2NWrGmTo2tHf7XygpytWx+haqmlqXwygAAAAAAbqG8PAWqtzZqycotQQm3JDW1dmjJyi2q3tqYoisDAAAAALiJpLuPdfssrVhTJyvMz+zXVqypU7cvXAQAAAAAYCAh6e5jmxtaeq1wB7IkNbZ2aHNDS99dFAAAAAAgKUi6+9j+w5ET7njiAAAAAAD9F0l3HysdketqHAAAAACg/yLp7mMXlBerrCBXkQaDedTTxfyC8uK+vCwAAAAAQBKQdPexzAyPls2vCNtITerZ071sfgXzugEAAAAgDZB0AwAAAACQJCTdfcweGRaJR4wMAwAAAIB0QdLdxxgZBgAAAACDB0l3H2NkGAAAAAAMHiTdfYyRYQAAAAAweJB09zFGhgEAAADA4EHS3cfskWGSeiXe9p8ZGQYAAAAA6YGkOwWqppbpsRtnyFsQXELuLcjVYzfOUNXUshRdGQAAAADATUNSfQGDVdXUMl1e4dXmhhbtP9yh0hE9JeWscAMAAABA+iDpTqHMDI8qJ49M9WUAAAAAAJKE8nIAAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkmRIqi8gWSzLkiS1tbWl+EoAAAAAAOnGzjXt3DOStE26Dx8+LEkaN25ciq8EAAAAAJCuDh8+rIKCgog/91ix0vIByufzae/evRoxYoQ8Hk+qLwcntbW1ady4cfroo4+Un5+f6suBy7i/6Y37m964v+mPe5zeuL/pjfvbP1mWpcOHD2v06NHKyIi8czttV7ozMjI0duzYVF8GIsjPz+dfGGmM+5veuL/pjfub/rjH6Y37m964v/1PtBVuG43UAAAAAABIEpJuAAAAAACShKQbfSonJ0fLli1TTk5Oqi8FScD9TW/c3/TG/U1/3OP0xv1Nb9zfgS1tG6kBAAAAAJBqrHQDAAAAAJAkJN0AAAAAACQJSTcAAAAAAElC0g0AAAAAQJKQdCNh69ev1/z58zV69Gh5PB49//zzvWK2bdumq6++WgUFBRo2bJg+85nPaPfu3f6fd3R06NZbb9XIkSM1fPhwXXfdddq3b18ffgpEE+seHzlyRLfddpvGjh2roUOHqqKiQo8//nhQDPe4f3rggQf0mc98RiNGjFBpaamuueYaffDBB0ExJvdu9+7dmjdvnvLy8lRaWqp/+7d/04kTJ/ryoyCMWPe3paVF/+f//B+deeaZGjp0qMaPH6//+3//r1pbW4OOw/3tn0z+/2uzLEtXXnll2H+Hc3/7L9N7XFNTo0svvVTDhg1Tfn6+Lr74Yh07dsz/85aWFn3xi19Ufn6+CgsLdfPNN+vIkSN9+VEQhsn9bWpq0sKFC+X1ejVs2DDNmDFD//3f/x0Uw/3t/0i6kbCjR49q2rRpevTRR8P+vL6+XrNnz9ZZZ52lP//5z3r33Xd1zz33KDc31x/z9a9/XWvWrNFvf/tb/eUvf9HevXt17bXX9tVHQAyx7vHtt9+u6upqrVy5Utu2bdPXvvY13XbbbVq9erU/hnvcP/3lL3/Rrbfeqo0bN2rt2rU6fvy45s6dq6NHj/pjYt277u5uzZs3T11dXXr99df185//XE8//bTuvffeVHwkBIh1f/fu3au9e/fqu9/9rrZu3aqnn35a1dXVuvnmm/3H4P72Xyb//7V9//vfl8fj6fU697d/M7nHNTU1qqqq0ty5c7V582a98cYbuu2225SRcerX/C9+8Yt6//33tXbtWv3hD3/Q+vXrtXjx4lR8JAQwub9f+tKX9MEHH2j16tV67733dO211+of//Ef9fbbb/tjuL8DgAW4SJL1+9//Pui1f/qnf7JuvPHGiO85dOiQlZWVZf32t7/1v7Zt2zZLklVTU5OsS0Wcwt3jT3/609Z9990X9NqMGTOsb37zm5ZlcY8Hkv3791uSrL/85S+WZZnduxdffNHKyMiwmpqa/DGPPfaYlZ+fb3V2dvbtB0BUofc3nN/85jdWdna2dfz4ccuyuL8DSaT7+/bbb1tjxoyxGhsbe/07nPs7sIS7xzNnzrTuvvvuiO+pq6uzJFlvvPGG/7U//vGPlsfjsfbs2ZPU64Uz4e7vsGHDrF/84hdBccXFxdZTTz1lWRb3d6BgpRtJ5fP59MILL+hTn/qUrrjiCpWWlmrmzJlBpW1vvfWWjh8/rssuu8z/2llnnaXx48erpqYmBVcNpy688EKtXr1ae/bskWVZeuWVV/S3v/1Nc+fOlcQ9HkjssuLi4mJJZveupqZGZ599tkaNGuWPueKKK9TW1qb333+/D68esYTe30gx+fn5GjJkiCTu70AS7v62t7frC1/4gh599FF5vd5e7+H+Diyh93j//v3atGmTSktLdeGFF2rUqFH67Gc/q1dffdX/npqaGhUWFur888/3v3bZZZcpIyNDmzZt6tsPgKjC/X/4wgsv1K9//Wu1tLTI5/PpV7/6lTo6OvS5z31OEvd3oCDpRlLt379fR44c0YMPPqiqqiq99NJL+vu//3tde+21+stf/iKpZ69Kdna2CgsLg947atQoNTU1peCq4dSPfvQjVVRUaOzYscrOzlZVVZUeffRRXXzxxZK4xwOFz+fT1772NV100UWaOnWqJLN719TUFPQLu/1z+2foH8Ld31AHDhzQ/fffH1SWyP0dGCLd369//eu68MILtWDBgrDv4/4OHOHu8Y4dOyRJy5cv16JFi1RdXa0ZM2bo85//vD788ENJPfextLQ06FhDhgxRcXEx97gfifT/4d/85jc6fvy4Ro4cqZycHH3lK1/R73//e51++umSuL8DxZBUXwDSm8/nkyQtWLBAX//61yVJ06dP1+uvv67HH39cn/3sZ1N5eXDJj370I23cuFGrV6/WhAkTtH79et16660aPXp00Aop+rdbb71VW7duDVohQfqIdX/b2to0b948VVRUaPny5X17cUhYuPu7evVqrVu3LmjvJwaucPfY/j3rK1/5iv75n/9ZknTuuefq5Zdf1k9/+lM98MADKblWOBfp39H33HOPDh06pD/96U8qKSnR888/r3/8x3/Uhg0bdPbZZ6foauEUK91IqpKSEg0ZMkQVFRVBr0+ZMsXfvdzr9aqrq0uHDh0Kitm3b1/YUjj0L8eOHdO///u/63vf+57mz5+vc845R7fddpv+6Z/+Sd/97nclcY8Hgttuu01/+MMf9Morr2js2LH+103undfr7dXN3P4z97d/iHR/bYcPH1ZVVZVGjBih3//+98rKyvL/jPvb/0W6v+vWrVN9fb0KCws1ZMgQ/5aB6667zl+ayv0dGCLd47KyMkmK+XvW/v37g35+4sQJtbS0cI/7iUj3t76+Xo888oh++tOf6vOf/7ymTZumZcuW6fzzz/c3t+X+Dgwk3Uiq7OxsfeYzn+k1/uBvf/ubJkyYIEk677zzlJWVpZdfftn/8w8++EC7d+9WZWVln14vnDt+/LiOHz8e1CVVkjIzM/1P4LnH/ZdlWbrtttv0+9//XuvWrVN5eXnQz03uXWVlpd57772g/+ivXbtW+fn5vX4RRN+KdX+lnhXuuXPnKjs7W6tXrw6aLCFxf/uzWPf3G9/4ht59913V1tb6/5Kk//zP/9TPfvYzSdzf/i7WPZ44caJGjx4d9fesyspKHTp0SG+99Zb/5+vWrZPP59PMmTOT/yEQUaz7297eLklRf8fi/g4QqezihvRw+PBh6+2337befvttS5L1ve99z3r77betXbt2WZZlWb/73e+srKws68knn7Q+/PBD60c/+pGVmZlpbdiwwX+MW265xRo/fry1bt06680337QqKyutysrKVH0khIh1jz/72c9an/70p61XXnnF2rFjh/Wzn/3Mys3NtX784x/7j8E97p+WLFliFRQUWH/+85+txsZG/1/t7e3+mFj37sSJE9bUqVOtuXPnWrW1tVZ1dbV12mmnWUuXLk3FR0KAWPe3tbXVmjlzpnX22Wdb27dvD4o5ceKEZVnc3/7M5P+/oRTSvZz727+Z3OP//M//tPLz863f/va31ocffmjdfffdVm5urrV9+3Z/TFVVlXXuuedamzZtsl599VXrjDPOsG644YZUfCQEiHV/u7q6rNNPP92aM2eOtWnTJmv79u3Wd7/7Xcvj8VgvvPCC/zjc3/6PpBsJe+WVVyxJvf666aab/DE/+clPrNNPP93Kzc21pk2bZj3//PNBxzh27Jj1r//6r1ZRUZGVl5dn/f3f/73V2NjYx58EkcS6x42NjdaXv/xla/To0VZubq515plnWv/v//0/y+fz+Y/BPe6fwt1XSdbPfvYzf4zJvdu5c6d15ZVXWkOHDrVKSkqsO+64wz9yCqkT6/5G+v+2JKuhocF/HO5v/2Ty/99w7wkd+8j97b9M7/EDDzxgjR071srLy7MqKyuDFjYsy7Kam5utG264wRo+fLiVn59v/fM//7N1+PDhPvwkCMfk/v7tb3+zrr32Wqu0tNTKy8uzzjnnnF4jxLi//Z/HsizL7dVzAADw/2/fDgkAAAAABP1/7QobvDAIAODpBgAAgI3oBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgEkSp7/Bi3WIFQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Consegue adivinhar porque é que os pontos se alinham em linhas verticais desta forma?\n",
+    "> Consegue adivinhar por que os pontos se alinham em linhas verticais assim?\n",
     "\n",
-    "Observámos a correlação entre um conceito artificialmente criado, como o salário, e a variável observada *altura*. Vamos também verificar se as duas variáveis observadas, como altura e peso, também estão correlacionadas:\n"
+    "Observámos a correlação entre um conceito artificialmente criado, como o salário, e a variável observada *altura*. Vamos também verificar se as duas variáveis observadas, como altura e peso, também têm correlação:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Infelizmente, não obtivemos nenhum resultado - apenas alguns valores estranhos `nan`. Isto deve-se ao facto de alguns dos valores na nossa série estarem indefinidos, representados como `nan`, o que faz com que o resultado da operação também seja indefinido. Ao observar a matriz, podemos ver que a coluna `Weight` é a problemática, porque a autocorrelação entre os valores de `Height` foi calculada.\n",
+    "Infelizmente, não obtivemos quaisquer resultados - apenas alguns valores estranhos `nan`. Isto deve-se ao facto de alguns dos valores na nossa série estarem indefinidos, representados como `nan`, o que faz com que o resultado da operação também seja indefinido. Ao observarmos a matriz, podemos ver que a coluna problemática é `Weight`, porque a autocorrelação entre os valores de `Height` foi calculada.\n",
     "\n",
     "> Este exemplo demonstra a importância da **preparação** e **limpeza** dos dados. Sem dados adequados, não conseguimos calcular nada.\n",
     "\n",
-    "Vamos usar o método `fillna` para preencher os valores em falta e calcular a correlação:\n"
+    "Vamos utilizar o método `fillna` para preencher os valores em falta e calcular a correlação:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Conclusão\n",
     "\n",
-    "Neste caderno, aprendemos a realizar operações básicas em dados para calcular funções estatísticas. Agora sabemos como utilizar um conjunto sólido de ferramentas de matemática e estatística para comprovar algumas hipóteses e como calcular intervalos de confiança para variáveis arbitrárias com base numa amostra de dados.\n"
+    "Neste notebook, aprendemos a realizar operações básicas em dados para calcular funções estatísticas. Agora sabemos como utilizar um conjunto sólido de ferramentas de matemática e estatística para comprovar algumas hipóteses e como calcular intervalos de confiança para variáveis arbitrárias com base numa amostra de dados.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, é importante ter em conta que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes da utilização desta tradução.\n"
+    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original no seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se uma tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas resultantes do uso desta tradução.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:27:08+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:25:11+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "pt"
   }
diff --git a/translations/pt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/pt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 8b73a21f..706e72c9 100644
--- a/translations/pt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/pt/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Neste conjunto de dados, as colunas são as seguintes:\n",
-    "* Idade e sexo são autoexplicativos\n",
-    "* IMC é o índice de massa corporal\n",
-    "* PA é a pressão arterial média\n",
-    "* S1 até S6 são diferentes medições de sangue\n",
-    "* Y é a medida qualitativa da progressão da doença ao longo de um ano\n",
+    "Neste conjunto de dados, as colunas são as seguintes:  \n",
+    "* Idade e sexo são autoexplicativos  \n",
+    "* IMC é o índice de massa corporal  \n",
+    "* PA é a pressão arterial média  \n",
+    "* S1 até S6 são diferentes medições sanguíneas  \n",
+    "* Y é a medida qualitativa da progressão da doença ao longo de um ano  \n",
     "\n",
     "Vamos estudar este conjunto de dados utilizando métodos de probabilidade e estatística.\n",
     "\n",
-    "### Tarefa 1: Calcular os valores médios e a variância para todos os valores\n"
+    "### Tarefa 1: Calcular os valores médios e a variância para todos os valores  \n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -590,10 +590,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Conclusões:\n",
+    "Conclusões:  \n",
     "* Idade - normal  \n",
     "* Sexo - uniforme  \n",
-    "* IMC, Y - difícil de dizer  \n"
+    "* IMC, Y - difícil de determinar  \n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:53:10+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:25:36+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "pt"
   }
diff --git a/translations/ro/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ro/1-Introduction/04-stats-and-probability/assignment.ipynb
index e5792c85..ed0d4be3 100644
--- a/translations/ro/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ro/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "În acest set de date, coloanele sunt următoarele:  \n",
-    "* Vârsta și sexul sunt auto-explicative  \n",
-    "* BMI este indicele de masă corporală  \n",
-    "* BP este tensiunea arterială medie  \n",
-    "* S1 până la S6 sunt diferite măsurători ale sângelui  \n",
-    "* Y este măsura calitativă a progresiei bolii pe parcursul unui an  \n",
+    "În acest set de date, coloanele sunt următoarele:\n",
+    "* Vârsta și sexul sunt auto-explicative\n",
+    "* BMI este indicele de masă corporală\n",
+    "* BP este tensiunea arterială medie\n",
+    "* S1 până la S6 sunt diferite măsurători ale sângelui\n",
+    "* Y este măsura calitativă a progresiei bolii pe parcursul unui an\n",
     "\n",
     "Să studiem acest set de date folosind metode de probabilitate și statistică.\n",
     "\n",
-    "### Sarcina 1: Calculați valorile medii și varianța pentru toate valorile  \n"
+    "### Sarcina 1: Calculați valorile medii și varianța pentru toate valorile\n"
    ],
    "metadata": {}
   },
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:46:06+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:53:28+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ro"
   }
diff --git a/translations/ro/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ro/1-Introduction/04-stats-and-probability/notebook.ipynb
index 15cd2e59..543acf70 100644
--- a/translations/ro/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ro/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Variabile aleatoare și distribuții\n",
-    "Să începem prin a extrage un eșantion de 30 de valori dintr-o distribuție uniformă de la 0 la 9. Vom calcula, de asemenea, media și varianța.\n"
+    "## Variabile aleatoare și distribuții  \n",
+    "Să începem prin a extrage un eșantion de 30 de valori dintr-o distribuție uniformă de la 0 la 9. Vom calcula, de asemenea, media și varianța.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## Analizând Date Reale\n",
     "\n",
-    "Media și variația sunt foarte importante atunci când analizăm date din lumea reală. Haideți să încărcăm datele despre jucătorii de baseball de la [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Media și variația sunt foarte importante atunci când analizăm date din lumea reală. Să încărcăm datele despre jucătorii de baseball de la [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Putem, de asemenea, să realizăm diagrame boxplot pentru subseturi ale setului nostru de date, de exemplu, grupate după rolul jucătorului.\n"
+    "Putem, de asemenea, să realizăm diagrame boxplot pentru subansambluri ale setului nostru de date, de exemplu, grupate după rolul jucătorului.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Notă**: Acest diagram sugerează că, în medie, înălțimea jucătorilor de primă bază este mai mare decât înălțimea jucătorilor de a doua bază. Mai târziu vom învăța cum putem testa această ipoteză într-un mod mai formal și cum să demonstrăm că datele noastre sunt semnificative din punct de vedere statistic pentru a susține acest lucru.\n",
+    "> **Notă**: Acest diagram sugerează că, în medie, înălțimea primilor basiști este mai mare decât înălțimea celor de pe a doua bază. Mai târziu vom învăța cum putem testa această ipoteză într-un mod mai formal și cum să demonstrăm că datele noastre sunt semnificative din punct de vedere statistic pentru a susține acest lucru.\n",
     "\n",
-    "Vârsta, înălțimea și greutatea sunt toate variabile aleatoare continue. Ce crezi că reprezintă distribuția lor? O modalitate bună de a afla este să construiești histograma valorilor:\n"
+    "Vârsta, înălțimea și greutatea sunt toate variabile aleatoare continue. Ce crezi că este distribuția lor? O modalitate bună de a afla este să trasezi histograma valorilor:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Deoarece majoritatea valorilor din viața reală sunt distribuite normal, nu ar trebui să folosim un generator de numere aleatoare uniform pentru a genera date de eșantion. Iată ce se întâmplă dacă încercăm să generăm greutăți cu o distribuție uniformă (generată de `np.random.rand`):\n"
+    "Deoarece majoritatea valorilor din viața reală sunt distribuite normal, nu ar trebui să folosim un generator uniform de numere aleatoare pentru a genera date de eșantion. Iată ce se întâmplă dacă încercăm să generăm greutăți cu o distribuție uniformă (generată de `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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0RzFhwoT4/Oc/H/fdd19p/86dO6OzszPa2tpK2+rr62P69OnR0dFxyHP29fVFb2/voAcAAAAc6coe3b/4xS/innvuiVNPPTX+7d/+Lf7qr/4qvvKVr8QDDzwQERGdnZ0REdHY2DjoeY2NjaV9H7ZixYqor68vPSZNmlTusQEAAKDsyh7dAwMDcdZZZ8U3v/nN+PznPx+LFi2KK6+8MlavXv1bn3PZsmXR09NTeuzatauMEwMAAECOskf3ySefHNOmTRu07bTTTovXX389IiKampoiIqKrq2vQMV1dXaV9H1ZTUxN1dXWDHgAAAHCkK3t0X3DBBbFjx45B21555ZU45ZRTIuL9L1VramqKTZs2lfb39vbGtm3borW1tdzjAAAAQMWU/dvLr7766jj//PPjm9/8ZvzxH/9xPP3003HvvffGvffeGxERVVVVsWTJkrjlllvi1FNPjZaWlrjhhhuiubk55s2bV+5xAAAAoGLKHt3nnnturF+/PpYtWxY333xztLS0xMqVK2PBggWlY6699trYv39/LFq0KLq7u+PCCy+MDRs2xJgxY8o9DgAAAFRMVVEURaWH+Lh6e3ujvr4+enp6PhH3d0+57olKjwAAAPCJ8dqtcyo9wmENtUvLfk83AAAA8D7RDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQJD26b7311qiqqoolS5aUth04cCDa29tj/PjxccIJJ8T8+fOjq6srexQAAAAYVqnR/cwzz8Q//MM/xGc/+9lB26+++up47LHHYt26dbF58+bYvXt3XHLJJZmjAAAAwLBLi+59+/bFggUL4r777osTTzyxtL2npye+973vxZ133hlf/OIX4+yzz441a9bEU089FVu3bs0aBwAAAIZdWnS3t7fHnDlzoq2tbdD27du3R39//6DtU6dOjcmTJ0dHR0fWOAAAADDsRmac9Ac/+EE899xz8cwzz/zavs7Ozhg9enSMGzdu0PbGxsbo7Ow85Pn6+vqir6+v9HNvb29Z5wUAAIAMZb/SvWvXrvjqV78aDz30UIwZM6Ys51yxYkXU19eXHpMmTSrLeQEAACBT2aN7+/btsWfPnjjrrLNi5MiRMXLkyNi8eXPcddddMXLkyGhsbIyDBw9Gd3f3oOd1dXVFU1PTIc+5bNmy6OnpKT127dpV7rEBAACg7Mr+8fIvfelL8cILLwzadsUVV8TUqVPjb/7mb2LSpEkxatSo2LRpU8yfPz8iInbs2BGvv/56tLa2HvKcNTU1UVNTU+5RAQAAIFXZo3vs2LFx+umnD9p2/PHHx/jx40vbFy5cGEuXLo2Ghoaoq6uLq666KlpbW+O8884r9zgAAABQMSlfpHY43/72t6O6ujrmz58ffX19MXPmzPjud79biVEAAAAgTVVRFEWlh/i4ent7o76+Pnp6eqKurq7S4xzWlOueqPQIAAAAnxiv3Tqn0iMc1lC7NO3vdAMAAMCxTnQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJCk7NG9YsWKOPfcc2Ps2LExYcKEmDdvXuzYsWPQMQcOHIj29vYYP358nHDCCTF//vzo6uoq9ygAAABQUWWP7s2bN0d7e3ts3bo1Nm7cGP39/TFjxozYv39/6Zirr746HnvssVi3bl1s3rw5du/eHZdcckm5RwEAAICKGlnuE27YsGHQz/fff39MmDAhtm/fHl/4wheip6cnvve978XatWvji1/8YkRErFmzJk477bTYunVrnHfeeeUeCQAAACoi/Z7unp6eiIhoaGiIiIjt27dHf39/tLW1lY6ZOnVqTJ48OTo6Og55jr6+vujt7R30AAAAgCNdanQPDAzEkiVL4oILLojTTz89IiI6Oztj9OjRMW7cuEHHNjY2Rmdn5yHPs2LFiqivry89Jk2alDk2AAAAlEVqdLe3t8eLL74YP/jBD/5P51m2bFn09PSUHrt27SrThAAAAJCn7Pd0f2Dx4sXx+OOPx5YtW2LixIml7U1NTXHw4MHo7u4edLW7q6srmpqaDnmumpqaqKmpyRoVAAAAUpT9SndRFLF48eJYv359PPnkk9HS0jJo/9lnnx2jRo2KTZs2lbbt2LEjXn/99WhtbS33OAAAAFAxZb/S3d7eHmvXro1HHnkkxo4dW7pPu76+Pmpra6O+vj4WLlwYS5cujYaGhqirq4urrroqWltbfXM5AAAAR5WyR/c999wTEREXXXTRoO1r1qyJyy+/PCIivv3tb0d1dXXMnz8/+vr6YubMmfHd73633KMAAABARZU9uouiOOwxY8aMiVWrVsWqVavK/esBAADgiJH+d7oBAADgWCW6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVCy6V61aFVOmTIkxY8bE9OnT4+mnn67UKAAAAJCiItH9z//8z7F06dK48cYb47nnnoszzzwzZs6cGXv27KnEOAAAAJCiItF95513xpVXXhlXXHFFTJs2LVavXh3HHXdc/OM//mMlxgEAAIAUI4f7Fx48eDC2b98ey5YtK22rrq6Otra26OjoOORz+vr6oq+vr/RzT09PRET09vbmDlsmA33vVHoEAACAT4xPQut9MGNRFL/xuGGP7l/+8pfx3nvvRWNj46DtjY2N8R//8R+HfM6KFSvipptu+rXtkyZNSpkRAACAyqlfWekJhu7tt9+O+vr6j9w/7NH921i2bFksXbq09PPAwEDs3bs3xo8fH1VVVem/v7e3NyZNmhS7du2Kurq69N8Hn0TWCQyNtQKHZ53A0FgrlVUURbz99tvR3Nz8G48b9uj+1Kc+FSNGjIiurq5B27u6uqKpqemQz6mpqYmamppB28aNG5c14keqq6vzHzMchnUCQ2OtwOFZJzA01krl/KYr3B8Y9i9SGz16dJx99tmxadOm0raBgYHYtGlTtLa2Dvc4AAAAkKYiHy9funRpXHbZZXHOOefE7/7u78bKlStj//79ccUVV1RiHAAAAEhRkej+kz/5k/jv//7vWL58eXR2dsbnPve52LBhw699udqRoqamJm688cZf+4g78L+sExgaawUOzzqBobFWPhmqisN9vzkAAADwWxn2e7oBAADgWCG6AQAAIInoBgAAgCSiGwAAAJIcs9G9ZcuWuPjii6O5uTmqqqri4YcfHrR/3759sXjx4pg4cWLU1tbGtGnTYvXq1YOOOXDgQLS3t8f48ePjhBNOiPnz50dXV9cwvgrId7i10tXVFZdffnk0NzfHcccdF7NmzYpXX3110DHWCke7FStWxLnnnhtjx46NCRMmxLx582LHjh2DjhnKOnj99ddjzpw5cdxxx8WECRPimmuuiXfffXc4XwqkGco6uffee+Oiiy6Kurq6qKqqiu7u7l87z969e2PBggVRV1cX48aNi4ULF8a+ffuG6VVAvsOtlb1798ZVV10Vn/70p6O2tjYmT54cX/nKV6Knp2fQebynHDmO2ejev39/nHnmmbFq1apD7l+6dGls2LAhvv/978fLL78cS5YsicWLF8ejjz5aOubqq6+Oxx57LNatWxebN2+O3bt3xyWXXDJcLwGGxW9aK0VRxLx58+IXv/hFPPLII/HTn/40TjnllGhra4v9+/eXjrNWONpt3rw52tvbY+vWrbFx48bo7++PGTNmfKx18N5778WcOXPi4MGD8dRTT8UDDzwQ999/fyxfvrwSLwnKbijr5J133olZs2bF1772tY88z4IFC+Kll16KjRs3xuOPPx5btmyJRYsWDcdLgGFxuLWye/fu2L17d9xxxx3x4osvxv333x8bNmyIhQsXls7hPeUIU1BERLF+/fpB2z7zmc8UN99886BtZ511VvG3f/u3RVEURXd3dzFq1Khi3bp1pf0vv/xyERFFR0dH+sxQCR9eKzt27CgionjxxRdL2957773ipJNOKu67776iKKwVjk179uwpIqLYvHlzURRDWwf/+q//WlRXVxednZ2lY+65556irq6u6OvrG94XAMPgw+vk//fjH/+4iIjirbfeGrT9Zz/7WRERxTPPPFPa9qMf/aioqqoq3njjjeyRoSJ+01r5wA9/+MNi9OjRRX9/f1EU3lOONMfsle7DOf/88+PRRx+NN954I4qiiB//+MfxyiuvxIwZMyIiYvv27dHf3x9tbW2l50ydOjUmT54cHR0dlRobhlVfX19ERIwZM6a0rbq6OmpqauInP/lJRFgrHJs++IhfQ0NDRAxtHXR0dMQZZ5wRjY2NpWNmzpwZvb298dJLLw3j9DA8PrxOhqKjoyPGjRsX55xzTmlbW1tbVFdXx7Zt28o+IxwJhrJWenp6oq6uLkaOHBkR3lOONKL7I9x9990xbdq0mDhxYowePTpmzZoVq1atii984QsREdHZ2RmjR4+OcePGDXpeY2NjdHZ2VmBiGH4fRMOyZcvirbfeioMHD8Ztt90W//Vf/xVvvvlmRFgrHHsGBgZiyZIlccEFF8Tpp58eEUNbB52dnYP+5+iD/R/sg6PJodbJUHR2dsaECRMGbRs5cmQ0NDRYJxyVhrJWfvnLX8bXv/71QbdZeE85soys9ABHqrvvvju2bt0ajz76aJxyyimxZcuWaG9vj+bm5kFXKuBYNmrUqPiXf/mXWLhwYTQ0NMSIESOira0tZs+eHUVRVHo8qIj29vZ48cUXS5/2AH6ddQJDc7i10tvbG3PmzIlp06bF3/3d3w3vcAyZ6D6EX/3qV/G1r30t1q9fH3PmzImIiM9+9rPx/PPPxx133BFtbW3R1NQUBw8ejO7u7kFXLrq6uqKpqalCk8PwO/vss+P555+Pnp6eOHjwYJx00kkxffr00kf/rBWOJYsXLy59sdPEiRNL24eyDpqamuLpp58edL4Pvt3cWuFo8lHrZCiamppiz549g7a9++67sXfvXuuEo87h1srbb78ds2bNirFjx8b69etj1KhRpX3eU44sPl5+CP39/dHf3x/V1YP/9YwYMSIGBgYi4v3QGDVqVGzatKm0f8eOHfH6669Ha2vrsM4LR4L6+vo46aST4tVXX41nn3025s6dGxHWCseGoihi8eLFsX79+njyySejpaVl0P6hrIPW1tZ44YUXBgXFxo0bo66uLqZNmzY8LwQSHW6dDEVra2t0d3fH9u3bS9uefPLJGBgYiOnTp5dzXKiYoayV3t7emDFjRowePToeffTRQd+vE+E95UhzzF7p3rdvX/z85z8v/bxz5854/vnno6GhISZPnhy/93u/F9dcc03U1tbGKaecEps3b44HH3ww7rzzzoh4PzAWLlwYS5cujYaGhqirq4urrroqWltb47zzzqvUy4KyO9xaWbduXZx00kkxefLkeOGFF+KrX/1qzJs3r/Slg9YKx4L29vZYu3ZtPPLIIzF27NjS/XL19fVRW1s7pHUwY8aMmDZtWvz5n/953H777dHZ2RnXX399tLe3R01NTSVfHpTF4dZJxPv3mnZ2dpbed1544YUYO3ZsTJ48ORoaGuK0006LWbNmxZVXXhmrV6+O/v7+WLx4cVx66aXR3NxcsdcG5XS4tfJBcL/zzjvx/e9/P3p7e6O3tzciIk466aQYMWKE95QjTUW/O72CPvhTFB9+XHbZZUVRFMWbb75ZXH755UVzc3MxZsyY4tOf/nTxrW99qxgYGCid41e/+lXx5S9/uTjxxBOL4447rvjDP/zD4s0336zQK4Ich1sr3/nOd4qJEycWo0aNKiZPnlxcf/31v/anKKwVjnaHWiMRUaxZs6Z0zFDWwWuvvVbMnj27qK2tLT71qU8Vf/3Xf1368y/wSTeUdXLjjTce9pj/+Z//Kf70T/+0OOGEE4q6urriiiuuKN5+++3hf0GQ5HBr5aP+3ywiip07d5bO4z3lyFFVFL7tCAAAADK4pxsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAk/w+vxrJ4JXT+ewAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -554,23 +395,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Intervaluri de Încredere\n",
+    "## Intervale de încredere\n",
     "\n",
-    "Să calculăm acum intervalele de încredere pentru greutățile și înălțimile jucătorilor de baseball. Vom folosi codul [din această discuție de pe stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Acum să calculăm intervalele de încredere pentru greutățile și înălțimile jucătorilor de baseball. Vom folosi codul [din această discuție de pe stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Putem observa că intervalele nu se suprapun.\n",
     "\n",
-    "O modalitate statistic mai corectă de a demonstra ipoteza este utilizarea unui **test t de Student**:\n"
+    "O modalitate statistică mai corectă de a demonstra ipoteza este utilizarea unui **test t de Student**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "Cele două valori returnate de funcția `ttest_ind` sunt:\n",
-    "* p-value poate fi considerată probabilitatea ca două distribuții să aibă aceeași medie. În cazul nostru, este foarte mică, ceea ce înseamnă că există dovezi puternice care susțin că primii jucători de bază sunt mai înalți.\n",
-    "* t-value este valoarea intermediară a diferenței medii normalizate care este utilizată în testul t și este comparată cu o valoare prag pentru un anumit nivel de încredere.\n"
+    "* p-value poate fi considerat probabilitatea ca două distribuții să aibă aceeași medie. În cazul nostru, este foarte mică, ceea ce înseamnă că există dovezi puternice care susțin că primii jucători de bază sunt mai înalți.\n",
+    "* t-value este valoarea intermediară a diferenței medii normalizate utilizată în testul t și este comparată cu o valoare prag pentru un nivel de încredere dat.\n"
    ]
   },
   {
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Corelație și Corporația Malefică de Baseball\n",
     "\n",
-    "Corelația ne permite să găsim relații între secvențe de date. În exemplul nostru imaginar, să presupunem că există o corporație malefică de baseball care își plătește jucătorii în funcție de înălțimea lor - cu cât jucătorul este mai înalt, cu atât primește mai mulți bani. Să presupunem că există un salariu de bază de 1000 de dolari și un bonus suplimentar între 0 și 100 de dolari, în funcție de înălțime. Vom lua jucătorii reali din MLB și le vom calcula salariile imaginare:\n"
+    "Corelația ne permite să găsim relații între secvențe de date. În exemplul nostru fictiv, să presupunem că există o corporație malefică de baseball care își plătește jucătorii în funcție de înălțimea lor - cu cât jucătorul este mai înalt, cu atât primește mai mulți bani. Să presupunem că există un salariu de bază de 1000 de dolari și un bonus suplimentar între 0 și 100 de dolari, în funcție de înălțime. Vom lua jucătorii reali din MLB și vom calcula salariile lor imaginare:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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IoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoTQDQAAAABAgMQEuwAAAAAAXVdX36TbX92l8u+PKb1fvJ66cbj6WPk1HwgV/GkEAAAAwtQ1f9qq3V+5PMf7Kms1ZMn/6oKBNq2fNyaIlQFoxfJyAAAAIAydHLhPtPsrl67509ZurghAWwjdAAAAQJipq29qN3C32v2VS3X1Td1UEYD2ELoBAACAMHP7q7tMbQcgcAjdAAAAQJgp//6Yqe0ABA6hGwAAAAgz6f3iTW0HIHAI3QAAAECYeerG4aa2AxA4hG4AAAAgzPSxxuiCgbbTtrlgoI39uoEQQOgGAAAAwtD6eWPaDd7s0w2EDv7pCwAAAAhT6+eNUV19k25/dZfKvz+m9H7xeurG4cxwAyGEP40AAABAEDS7DRWVVquqtl7JiVaNzExSdJTF5376WGO0esbFAagQgBkI3QAAAEA3y99ToaUbSlRRU+85l2q3anGuU9lDUoNYGQCz8Uw3AAAA0I3y91RozpqdXoFbkipr6jVnzU7l76kIUmUAAoHQDQAAAHSTZrehpRtKZLRxrfXc0g0lana31QJAOPI5dBcUFCg3N1dpaWmyWCxat26d1/XXXntNEyZMUP/+/WWxWFRcXOx1vbq6WrfeeqvOOeccxcfHKz09Xb/97W9VU1Pj1a68vFw5OTlKSEhQcnKy7rrrLjU1Nfn8BQEAAIBQUVRafcoM94kMSRU19Soqre6+ogAElM+h+8iRIxo6dKhWrFjR7vXRo0fr8ccfb/P6wYMHdfDgQT355JPas2eP8vLylJ+fr5kzZ3raNDc3KycnRw0NDfrggw/00ksvKS8vT4sWLfK1XAAAACBkVNW2H7i70g5A6PP5RWqTJk3SpEmT2r0+bdo0SVJZWVmb14cMGaK//OUvnuMzzzxTjzzyiKZOnaqmpibFxMTozTffVElJid566y2lpKRo2LBheuihh3TPPfdoyZIlio2N9bVsAAAAIOiSE62mtgMQ+kLime6amhrZbDbFxLT8G0BhYaHOP/98paSkeNpMnDhRLpdLe/fuDVaZAAAAQLua3YYKP/tO/1P8tQo/+67N57JHZiYp1W5VexuDWdTyFvORmUkBrRVA9wn6lmHffvutHnroIc2ePdtzrrKy0itwS/IcV1ZWttnP8ePHdfz4cc+xy+UKQLUAAADAqTq7BVh0lEWLc52as2anLJLXC9Vag/jiXGeX9usGEJqCOtPtcrmUk5Mjp9OpJUuW+NXXsmXLZLfbPZ9BgwaZUyQAAABwGr5uAZY9JFUrp46Qw+69hNxht2rl1BHs0w1EmKDNdNfW1io7O1uJiYlau3atevXq5bnmcDhUVFTk1f7QoUOea21ZuHCh5s+f7zl2uVwEbwAAAARUR1uAWdSyBdh4p8Nr9jp7SKrGOx0qKq1WVW29khNblpQzww1EnqCEbpfLpYkTJyouLk7r16+X1er9r3xZWVl65JFHVFVVpeTkZEnS5s2bZbPZ5HQ62+wzLi5OcXFxAa8dAAAAPVuz2/CE5W9rj3d6C7CsM/t7XYuOspxyDkDk8Tl019XV6cCBA57j0tJSFRcXKykpSenp6aqurlZ5ebkOHjwoSdq3b5+klhlqh8Mhl8ulCRMm6OjRo1qzZo1cLpfn+eszzjhD0dHRmjBhgpxOp6ZNm6bly5ersrJS999/v+bOnUuwBgAAQNC09ex2Z7AFGNBzWQzDaGs1TLveffddXXHFFaecnzFjhvLy8pSXl6df/OIXp1xfvHixlixZ0u79UkuAz8jIkCR98cUXmjNnjt5991317t1bM2bM0GOPPeZ5w3lHXC6X7Ha7583oAAAAgD9an9326Zfnv/t/sy5hVhuIMJ3NnD6H7nBB6AYAAIBZmt2GRj++xecZbotaXpD2/j1X8rw2EGE6mzlDYp9uAAAAIJQVlVZ3KXBLbAEG9HRB36cbAAAACHVdeSbb0cY+3QB6HkI3AAAA0IHkRGvHjSQ9kHOuBiTGsQUYAA9CNwAAANCBkZlJSrVbVVlT3+aL1Fqf3b7lx5kEbQBeeKYbAAAA6EB0lEWLc52S/vGsdiue3QZwOoRuAAAAoBOyh6Rq5dQRcti9l5o77FatnDqCZ7cBtInl5QAAAEAnZQ9J1XinQ0Wl1aqqrefZbQAdInQDAAAAPoiOsijrzP7BLgNAmGB5OQAAAAAAAULoBgAAAAAgQFheDgAAgIhS/u1RZf/xPR1rdCu+V5Ty/+UypQ9ICHZZAHooQjcAAAAixg/v3agm9z+Ojza6NfbJdxQTJR14NCd4hQHosVheDgAAgIhwcuA+UZO75ToAdDdmugEAABCWmt2GZ+uuqCZ3u4G7VZO7Zek5S80BdCdCNwAAAMJO/p4KLd1Qooqaep/uy/7jeyp5aFKAqgKAUxG6AQAAEFby91RozpqdMrpw77HGDqbDAcBkPNMNAACAsNHsNrR0Q0mXArckxffi118A3Yu/dQAAABA2ikqrfV5SfqL8f7nMxGoAoGOEbgAAAISNqtquB+6YKPESNQDdjtANAACAsJGcaO3SfezTDSBYeJEaAAAAwsbIzCSl2q2qrKlv87lui6S+cRYdd1t0rNGt+F5Ryv+Xy5jhBhA0hG4AAACEjegoixbnOjVnzU5ZJK/gbfn7/y775+HKHpIahOoA4FQsLwcAAEBYyR6SqpVTR8hh915q7rBbtXLqCAI3gJDCTDcAAADCTvaQVI13OlRUWq2q2nolJ1o1MjNJ0VGWjm8GgG5E6AYAAEC3anYbpoTl6CiLss7sH4AKAcA8hG4AAAB0m/w9FVq6ocRrr+1Uu1WLc50sCwcQkXimGwAAAN0if0+F5qzZ6RW4Jamypl5z1uxU/p6KIFUGAIHDTDcAAAACovJwva5+pkCu+ibZrDGyWCxtbvNlqOXN40s3lGi808Fz2QAiCqEbAAAApjv3gTd0rNHtOf72SONp2xuSKmrqVVRazXPaACIKy8sBAABgqpMDty+qaus7bgQAYYTQDQAAANNUHq7vcuCWpOREa8eNACCMsLwcAAAAfjnW0KxHN5Wo7LujKvr8uy71YZHksLdsHwYAkYTQDQAAgC6b9fJ2bS6p8quP1temLc518hI1ABGH0A0AAIAuMSNwSy0z3OzTDSBSEboBAADgs2MNzX4F7md/PlyNhqHkxJYl5cxwA4hUhG4AAAD47NFNJV2+N75XlCYPTTOxGgAIXby9HAAAAD4r++5ol+6L7xWljx+aZHI1ABC6mOkGAACAzzL6J2jr/o7bxUVbZEiyWWP0+q1j5ejLlmAAehZCNwAAAHx272SnXvlbeYftihdPVHxsdDdUBAChieXlAAAA8Fl8bLTGO5NP22a8M5nADaDHI3QDAACgS1ZPv7jd4D3emazV0y/u5ooAIPSwvBwAAABdtnr6xTrW0KxHN5Wo7LujyuifoHsnO5nhBoC/I3QDAAD0UM1uQ0Wl1aqqrfdrv+z42Gg9dN35AagQAMIfoRsAAKAHyt9ToaUbSlRRU+85l2q3anGuU9lDUoNYGQBEFp7pBgAA6GHy91RozpqdXoFbkipr6jVnzU7l76kIUmUAEHmY6QYAAIhw75d8o6kvF3mOYyUZbbQzJFkkLd1QovFOR5eWmgMAvBG6AQAAIljGgo2nnGs4TXtDUkVNvYpKq5V1Zv+A1QUAPQXLywEAACJUW4G7s6pq6ztuBADoEKEbAAAgAr1f8o1f9ycnWk2qBAB6NpaXAwAARIgTtwD7l/8s7lIfFkkOe8v2YQAA/xG6AQAAIkBbW4D5qvW1aYtznbxEDQBMQugGAAAIc61bgLX1RnJfONinGwBM5/Mz3QUFBcrNzVVaWposFovWrVvndf21117ThAkT1L9/f1ksFhUXF5/SR319vebOnav+/furT58+uuGGG3To0CGvNuXl5crJyVFCQoKSk5N11113qampyddyAQAAIk6z21DhZ9/pf4q/1l8PfKsl6/d2OXA/MOlM/XHKMP2/WZfo/XuuJHADgMl8nuk+cuSIhg4dql/+8pe6/vrr27w+evRo/exnP9OsWbPa7OP222/Xxo0b9ec//1l2u13z5s3T9ddfr7/+9a+SpObmZuXk5MjhcOiDDz5QRUWFpk+frl69eunRRx/1tWQAAICIYcYy8hPNvOxHpvQDAGibxTCMLq9EslgsWrt2ra677rpTrpWVlSkzM1O7du3SsGHDPOdramp0xhln6D/+4z/005/+VJL0ySef6Nxzz1VhYaEuueQSvfHGG7r66qt18OBBpaSkSJJWrVqle+65R998841iY2M7rM3lcslut6umpkY2m62rXxEAACBkmLWMvFXZYzkm9QQAPU9nM2e3bxn24YcfqrGxUePGjfOc+9GPfqT09HQVFhZKkgoLC3X++ed7ArckTZw4US6XS3v37m2z3+PHj8vlcnl9AAAAIkWz29DSDSWmBO4100cSuAGgm3T7i9QqKysVGxurvn37ep1PSUlRZWWlp82Jgbv1euu1tixbtkxLly41v2AAAIAgaWhy65XCMn1RfVSGYXRpSXnrFmDv33MlbyQHgCCImLeXL1y4UPPnz/ccu1wuDRo0KIgVAQAAdN2yTSVavbVUbj+mttkCDACCr9tDt8PhUENDgw4fPuw1233o0CE5HA5Pm6KiIq/7Wt9u3trmZHFxcYqLiwtM0QAAAN1o2aYSPVdQ6nc/bAEGAMHX7aH7wgsvVK9evfT222/rhhtukCTt27dP5eXlysrKkiRlZWXpkUceUVVVlZKTkyVJmzdvls1mk9Pp7O6SAQAAuk1Dk1urt/oeuFuXkT/506H69shxJSdaNTIziRluAAgyn0N3XV2dDhw44DkuLS1VcXGxkpKSlJ6erurqapWXl+vgwYOSWgK11DJD7XA4ZLfbNXPmTM2fP19JSUmy2Wy69dZblZWVpUsuuUSSNGHCBDmdTk2bNk3Lly9XZWWl7r//fs2dO5fZbAAAENFeKSzzeUn5icvIf3zWANNrAgB0nc9vL9+xY4eGDx+u4cOHS5Lmz5+v4cOHa9GiRZKk9evXa/jw4crJaXkj5pQpUzR8+HCtWrXK08dTTz2lq6++WjfccIPGjh0rh8Oh1157zXM9Ojpar7/+uqKjo5WVlaWpU6dq+vTpevDBB/36sgAAAKHui+qjPt/jsFu1cuoIlpEDQAjya5/uUMY+3QAAIBz929bP9dDGjztsN+2SdF2UkcQycgAIks5mzoh5ezkAAECo23ewVpOfKVCzIUVbpE23jtU5aYlebaZlZeiRTR+fdol5lEV64OrzFBvj86JFAEA3I3QDAAB0g4wFG72Omw1p4tMFkqSyx3I852NjojRrTOZp314+a0wmgRsAwgR/WwMAAATYyYG7o+sLJzv1q7GZOnnFeJRF+tXYTC2czG4uABAumOkGAAAwWUOTW68UlumL6qNKiOncs9b7DtZ6LTVfONmpOyb8yNPP4KQETcvKYIYbAMIML1IDAAAw0bJNJVq9tdTnbb+iLdJny3I6bggACAm8SA0AAKCbLdtUctpnsU+nOSKnQQAArE8CAAAwQUOTW6u3di1wSy0z3QCAyMNMNwAAQBc1uw0VlVarqrZeO8qqfV5SfqJNt441rzAAQMggdAMAAHRB/p4KLd1QooqaelP6O3m/bgBAZCB0AwAA+Ch/T4XmrNkpsx7DPnGfbgBAZCF0AwAA+KDZbWjphpIuBW7L3z9utTzDvenWscxwA0CEI3QDAAB04MR9tw3D6PKS8tljM7VwstPk6gAAoYzQDQAAcBpd3Xf7RFEWadYYAjcA9ESEbgAAgHb4s+/2tEvSZbFYNDgpQdOyMhQbw06tANATEboBAAD+rq6+Sbe/ukvl3x/TwL5Wvf3JNz73YZHksFu15Johio5i820A6OkI3QAAAJKu+dNW7f7K5TneV1nrcx+tEXtxrpPADQCQROgGAAA4JXB3lcNu1eJcp7KHpJpQFQAgEhC6AQBAj1ZX3+RX4J52SbouykhScqJVIzOTmOEGAHghdAMAgB7nv/9aqjs3lPjdT5RFeuDq83hJGgCgXYRuAADQo2Qs2GhaX7PGZBK4AQCnRegGAAA9hlmBm323AQCdRegGAAARq6HJrVcKy/RF9VF9W+f728hPdPfEs1XpOs6+2wAAnxC6AQBARFq2qUSrt5bKbfjf1wUDbfrNFWf53xEAoMchdAMAgIizbFOJnisoNaWvCwbatH7eGFP6AgD0PIRuAAAQURqa3Fq91b/AfY4jUen94vXUjcPVx8qvSwCAruO/IgAAIOzVHG3UL/OKdLCmXtEW+bWk/Mlcp37640zzigMA9GiEbgAAENYue2KLvvjumGn9EbgBAGbitZsAACBsmR24yx7LMa0vAAAkZroBAEAYaXYbKiqtVlVtvfrERJsWuFlSDgAIFEI3AAAIC/l7KrR0Q4kqaur97utXYzO1cLLThKoAADg9QjcAAAh5+XsqNGfNTvm75XaURZo1hsANAOg+hG4AABDSmt2Glm4o6XLgHtjXqivPTdHgpARNy8pQbAyvtAEAdB9CNwAACDkNTW69UlimL6qPyjAMv5aUb/ztWNkTeplYHQAAnUfoBgAAIWXZphKt3lrq117brQb3jydwAwCCitANAABCxrJNJXquoNSUvgb3j9d7d11pSl8AAHQVoRsAAATNluJK/fI/P/SrD4ukAb1jNKh/H1XU1CvNbtULt4xkhhsAEBII3QAAICgyFmz0uw/L3//3oZ9coOwhqX73BwCA2QjdAACg25kRuCXJYbdqca6TwA0ACFmEbgAA0K22FFf6df+0S9J1UUaSkhOtGpmZpOgoS8c3AQAQJIRuAAAQcDVHG/XLvCIdrKn3a/uvKIv0wNXnsdc2ACBsELoBAEBAXfbEFn3x3TFT+po1JpPADQAIK4RuAAAQMGYF7ihLS+BeONlpQlUAAHQfQjcAADBNXX2Tbn91l8q/P6Y0W5xfgfv6EQPUJ663BiclaFpWBjPcAICwROgGAACmuOZPW7X7K5fneF9lrV/9/f5no/wtCQCAoOOfjAEAgN9ODtz+Knssx7S+AAAIJma6AQCAX+rqm0wL3C9MuVBXDnOY0hcAAKGA0A0AAHx24hZgR443+tXX/y2aIHtCL5MqAwAgtBC6AQCAT8zcAmxw/3gCNwAgovFMNwAA6DSzA/d7d11pSl8AAIQqZroBAECn1Bxt9CtwDxuYqEO1jUqzW/XCLSOZ4QYA9AiEbgAA0K6GJrdeKSzTF9VHteXjQ13u54KBNq2bN8bEygAACA8+Ly8vKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/d7tfn000917bXXasCAAbLZbBo9erTeeecdrzbl5eXKyclRQkKCkpOTddddd6mpqcn3bwgAALpk2aYS/eiBN/TQxo/1cuEX+upwfZf6uWCgTesJ3ACAHsrn0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+/h//ob766qvV1NSkLVu26MMPP9TQoUN19dVXq7KyUpLU3NysnJwcNTQ06IMPPtBLL72kvLw8LVq0qItfEwAA+GLZphI9V1Aqt+H7vTZrtM5xJGr8ucnas2QigRsA0KNZDMPown9O/36zxaK1a9fquuuuk9Qyy52WlqY77rhDd955pySppqZGKSkpysvL05QpU/Ttt9/qjDPOUEFBgcaMafmPcG1trWw2mzZv3qxx48bpjTfe0NVXX62DBw8qJSVFkrRq1Srdc889+uabbxQbG9thbS6XS3a7XTU1NbLZbF39igAA9AhzXsjXG582m9IXW4ABAHqCzmZOU99eXlpaqsrKSo0bN85zzm63a9SoUSosLJQk9e/fX+ecc45efvllHTlyRE1NTXruueeUnJysCy+8UJJUWFio888/3xO4JWnixIlyuVzau3evmSUDANDjZSzYaFrgZgswAAC8mfoitdbl4SeG5dbj1msWi0VvvfWWrrvuOiUmJioqKkrJycnKz89Xv379PP201ceJP+Nkx48f1/Hjxz3HLpfLnC8FAEAEy1iw0bS+2AIMAIBTdfvbyw3D0Ny5c5WcnKytW7cqPj5e//qv/6rc3Fxt375dqampXep32bJlWrp0qcnVAgAQuea8kO/X/QP7WtVsiC3AAAA4DVNDt8PhkCQdOnTIKzwfOnRIw4YNkyRt2bJFr7/+ur7//nvPuvdnn31Wmzdv1ksvvaQFCxbI4XCoqKjIq+9Dhw55/YyTLVy4UPPnz/ccu1wuDRo0yLTvBgBAJKg8XK+rnymQq75JDc1dfq2LoizSljuvUGyMqU+qAQAQcUz9L2VmZqYcDofefvttzzmXy6Vt27YpKytLknT06NGWHxzl/aOjoqLkdrslSVlZWfroo49UVVXlub5582bZbDY5nc42f3ZcXJxsNpvXBwAA/MO5D7yhSx57W98eafQrcEvSrDGZBG4AADrB55nuuro6HThwwHNcWlqq4uJiJSUlKT09XbfddpsefvhhnXXWWcrMzNQDDzygtLQ0zxvOs7Ky1K9fP82YMUOLFi1SfHy8Vq9erdLSUuXk5EiSJkyYIKfTqWnTpmn58uWqrKzU/fffr7lz5youLs6cbw4AQA9y7gNv6Fij2+9+oiwtgXvh5Lb/ERwAAHjzOXTv2LFDV1xxhee4dUn3jBkzlJeXp7vvvltHjhzR7NmzdfjwYY0ePVr5+fmyWq2SpAEDBig/P1/33XefrrzySjU2Nuq8887T//zP/2jo0KGSpOjoaL3++uuaM2eOsrKy1Lt3b82YMUMPPvigGd8ZAICI19Dk1iuFZfqi+qiS4mL8CtxnJ0iXDB2swUkJmpaVwQw3AAA+8Guf7lDGPt0AgJ5q2aYSrd5aKrdJ/4UveyzHnI4AAIggnc2c3f72cgAAEDjLNpXouYJS0/ojcAMA4B/WhwEAECEamtxavdWcwD3p7GgCNwAAJmCmGwCAMNbsNlRUWq2q2nrtKKv2a0n53xZcJUdfq3nFAQAAQjcAAOEqf0+Flm4oUUVNvd99xfeKInADABAAhG4AAMJQ/p4KzVmzU2a8Ky2+V5Q+fmiSCT0BAICTEboBAAgDJ24BNqhfgv5162ddCtwWSUm9e6m2vkk2a4xev3UsM9wAAAQQoRsAgBBn5hZgs8dmauFkp/8dAQCATiF0AwAQwszaAizKIs0aQ+AGAKC7EboBAAhR/m4BNu2SdFksFg1OStC0rAzFxrBTKAAA3Y3QDQBACMl7Z5+W/O8Bv/qwSHLYrVpyzRBFR1nMKQwAAHQJoRsAgBCRsWCj3320RuzFuU4CNwAAIYDQDQBACDAjcEstM9yLc53KHpJqSn8AAMA/hG4AAIKg2W2oqLRaVbX1+mvZV13uJ8oivXTLSFUfa1ByolUjM5OY4QYAIIQQugEA6Gb5eyq0dEOJKmrq/e5r1phMjTnnDBOqAgAAgUDoBgCgG+XvqdCcNTvl75bbbAEGAEB4IHQDANBNmt2Glm4o8StwT88azBZgAACEEUI3AAAB1NDk1iuFZfqi+qgMw/BrSfmSiT/ULVecY2J1AAAg0AjdAAAEyLJNJVq9tVRuf9eS/x2BGwCA8EPoBgAgAJZtKtFzBaWm9Vf2WI5pfQEAgO5D6AYAwGQNTW6t3up74LZIpzzvzZJyAADCG6EbAAATVNc1aMrzH6iqtkExUfJ5SXnrztqrpo5Q9pBU0+sDAADBQegGAMBPFz+8Wd/UNfjVh8Nu1eJcJ4EbAIAIQ+gGAMAP/gTuaZek66KMJCUnWjUyM0nRUZaObwIAAGGF0A0AQCeduIQ8OTFWz950UZcDd5RFeuDq89hrGwCACEfoBgCgE06e0T58rFHj/vBel/ubNSaTwA0AQA9A6AYAoANmPLPdKsrSErgXTnaa0h8AAAhthG4AAE6juq7B78A9oHcvTb4gTYOTEjQtK4MZbgAAehBCNwAAJ5nzQr7e+LTZtP7evP1yJfWJNa0/AAAQPgjdAACcIGPBRlP7O6NPLIEbAIAejPVtAAD8XSAC9/b7x5vaJwAACC/MdAMAeqyao436ZV6RDtbUq6qm3q++3rrtMv3mP3Z4thP7z9mXMsMNAAAI3QCAnumyJ7boi++OmdLXGX1i9UNHH705/3JT+gMAAJGD5eUAgB7H7MDNEnIAANAeZroBAD1KzdFGvwN33/heLCEHAACdQugGAES80qojyv7jezrebPjd16Szo7XylxNMqAoAAPQEhG4AQET7p4Ub5fY/a3us/GW2eZ0BAICIxzPdAICIZXbgLnssx7zOAABAj8BMNwAgYhxraNajm0pU9t1R9U+INi1wtywpZ4YbAAD4jtANAIgIs17ers0lVab0Nbh/vN6760pT+gIAAD0by8sBAGGPwA0AAEIVM90AgLB2rKHZ78CdarcqzW7VC7eMlD2hl0mVAQAAELoBAGHoG9dx/eTZ91V9pFGSfw9uvzP/cmUm9zanMAAAgJMQugEAYeWCJf8rV32TKX1FWUTgBgAAAcUz3QCAsGF24P58GVuAAQCAwGKmGwAQspas3aa8bd+a0ldslNTgluKiLcr/l8uY4QYAAN2C0A0ACEkZCzaa1td4Z7JWT7/YtP4AAAA6i+XlAICQQ+AGAACRgpluAEBIWbJ2m1/3J/SK0oUZScron6B7JzsVHxttUmUAAAC+I3QDAIKurr5Jt7+6S+XfH9O+ylq/+nrvrit1hi3OpMoAAAD8Q+gGAATVNX/aqt1fuUzpy2aNIXADAICQwjPdAICgMTtw714y0ZS+AAAAzMJMNwCg2xxraNajm0pU9t1RpdmtfgfuhF7RSurdS2t/M5oZbgAAEJJ8nukuKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/ef0s/GjRs1atQoxcfHq1+/frruuuu8rpeXlysnJ0cJCQlKTk7WXXfdpaamJl/LBQCEiFkvb9e5i/L1yt/KtXX/t3p1x1d+9Vf2WI5KHsrW+wuuInADAICQ5XPoPnLkiIYOHaoVK1a0eX358uV6+umntWrVKm3btk29e/fWxIkTVV9f72nzl7/8RdOmTdMvfvEL/d///Z/++te/6qabbvJcb25uVk5OjhoaGvTBBx/opZdeUl5enhYtWtSFrwgACLZZL2/X5pIq0/oreyzHtL4AAAACyWIYhtHlmy0WrV271jNLbRiG0tLSdMcdd+jOO++UJNXU1CglJUV5eXmaMmWKmpqalJGRoaVLl2rmzJlt9vvGG2/o6quv1sGDB5WSkiJJWrVqle655x598803io2N7bA2l8slu92umpoa2Wy2rn5FAICfjjU069xF+ab0dcuoAVryk1Gm9AUAAOCPzmZOU5/pLi0tVWVlpcaNG+c5Z7fbNWrUKBUWFmrKlCnauXOnvv76a0VFRWn48OGqrKzUsGHD9MQTT2jIkCGSpMLCQp1//vmewC1JEydO1Jw5c7R3714NHz7czLIBACb7xnVcP3n2fVUfaZTU5X/blSTtWTJRfay8ggQAAIQnU3+LqayslCSvsNx63Hrt888/lyQtWbJEv//975WRkaHf/e53uvzyy/Xpp58qKSlJlZWVbfZx4s842fHjx3X8+HHPsctlzttwAQC+uWDJ/8pVb847OC4YaCNwAwCAsNbtW4a53W5J0n333acbbrhBF154oV588UVZLBb9+c9/7nK/y5Ytk91u93wGDRpkVskAgE4yO3CvnzfGlL4AAACCxdTQ7XA4JEmHDh3yOn/o0CHPtdTUVEmS0+n0XI+Li9M//dM/qby83NNPW32c+DNOtnDhQtXU1Hg+X375pQnfCADQWd+4jvsVuK84Z4DOcSRq/LnJ2rNkIoEbAABEBFPX7GVmZsrhcOjtt9/WsGHDJLUs8962bZvmzJkjSbrwwgsVFxenffv2afTo0ZKkxsZGlZWVafDgwZKkrKwsPfLII6qqqlJycrIkafPmzbLZbF5h/URxcXGKi2PLGADoTifuu/1hWXWX+xnvTNbq6RebWBkAAEBo8Dl019XV6cCBA57j0tJSFRcXKykpSenp6brtttv08MMP66yzzlJmZqYeeOABpaWled5wbrPZ9Otf/1qLFy/WoEGDNHjwYD3xxBOSpH/+53+WJE2YMEFOp1PTpk3T8uXLVVlZqfvvv19z584lWANAiDBrGzACNwAAiGQ+h+4dO3boiiuu8BzPnz9fkjRjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqxWq+eeJ554QjExMZo2bZqOHTumUaNGacuWLerXr58kKTo6Wq+//rrmzJmjrKws9e7dWzNmzNCDDz7o7/cFAJjAn8Cd0CtKF2YkKaN/gu6d7FR8bLTJ1QEAAIQOv/bpDmXs0w0A5hl/70btd5vT1/Z7x+kMG6uWAABAeAvKPt0AgMiTsWCjaX3ZrDEEbgAA0KN0+5ZhAIDwYXbg3r1komn9AQAAhANmugEAbRp/r3+BO6FXlCSLknr30trfjGaGGwAA9EiEbgCAR0OTW68UlumL6qN+P8P94QMTeEkaAADo8QjdAABJ0rJNJVq9tVRuE16vOd6ZTOAGAAAQoRsAoJbA/VxBqSl9se82AADAPxC6AaAHqjnaqF/mFelgTb1SbXHa+WWNX/2NOWsA+24DAAC0gdANAD3MZU9s0RffHfMcV9TU+9Vf2WM5/pYEAAAQsdgyDAB6kJMDt78I3AAAAKdH6AaAHqLmaKNpgfusKAI3AABAZ7C8HAAiWOXhel39TIFc9U1q9uO15FEW6ZOHJik2hn+rBQAA8AWhGwAi1LkPvKFjjX5utv13s8ZkErgBAAC6gNANABHIrMAdZWkJ3AsnO02oCgAAoOchdANABDjW0KxHN5Wo7LujSu4T61fgvnP8Waqqa9DgpARNy8pghhsAAMAPhG4ACHOzXt6uzSVVpvQ1uH+85l11til9AQAAgLeXA0BYMztwv3fXlab0BQAAgBbMdANAmDrW0OxX4I62SMk2q9LsVr1wy0jZE3qZWB0AAAAkQjcAhJXfbyrW0wVfm9LXX++5So6+VlP6AgAAQNsI3QAQJjIWbDStr/heUQRuAACAbsAz3QAQBswO3B8/NMm0/gAAANA+ZroBIASduAVYZcW3fvWVFB+tuga3bNYYvX7rWGa4AQAAuhGhGwBCjJlvJB/vTNbq6Reb0hcAAAB8x/JyAAghBG4AAIDIwkw3AIQIf7cAk6QxZw1QRv8E3TvZqfjYaJMqAwAAQFcRugEgiE58dvtQzTG/+vrt2B9o/uRh5hQGAAAAUxC6ASBIzFxKLonADQAAEIJ4phsAgsDswF32WI5pfQEAAMA8zHQDQDf4uvqYJj39no4cb1bv2Gi5jjeb0i9LygEAAEIboRsAAuzs+zapodnwHPsTuHkjOQAAQHhheTkABNDJgdsfBG4AAIDww0w3AATI19XH/ArcZyf3Voo9ni3AAAAAwhihGwBM1NDk1iuFZfqi+qheLSr3q6//mTeGoA0AABDmCN0AYJJlm0q0emup3CasJh/vTCZwAwAARABCNwCYYNmmEj1XUGpKXzy7DQAAEDkI3QDgp4Ymt1Zv7Xrg/smwFH17pJlntwEAACIQoRsAuuC6RzaquNb/fmKjLXpqykX+dwQAAICQROgGAB9lLNhoSj+x0RZ9+shkU/oCAABAaCJ0A4AP/AnccdEWNbkN9Y6L1hu/vUw/SIo3sTIAAACEIkI3AJzGsYZmPbqpRGXfHdW2/d92uZ8oi/TR0mzFxkSZWB0AAABCHaEbANox6+Xt2lxSZU5fYzIJ3AAAAD0QoRsA2mBW4I6ytATuhZOdJlQFAACAcEPoBoCTHGto9jtwT88arMFJCZqWlcEMNwAAQA9G6AYASV9XH9Okp9/TkePNirL419ewROnBa4eYUxgAAADCGqEbQI939n2b1NBseI5P+H92ybr7cvysCAAAAJGCNY8AerSTA7e/yh4jcAMAAOAfmOkG0KOcuAXYgN4xpgXuYYnMcAMAAOBUhG4APYaZW4CNdyZr9fSLTekLAAAAkYvl5QB6BAI3AAAAgoGZbgARz98twHpFSZecOUAZ/RN072Sn4mOjTawOAAAAkYzQDSAi1dU36fZXd6n8+2M6Ut/oV1/v3nmlfpAUb1JlAAAA6EkI3QAizjV/2qrdX7lM6Ss22kLgBgAAQJfxTDeAiGJ24P70kcmm9AUAAICeyefQXVBQoNzcXKWlpclisWjdunVe1w3D0KJFi5Samqr4+HiNGzdO+/fvb7Ov48ePa9iwYbJYLCouLva6tnv3bo0ZM0ZWq1WDBg3S8uXLfS0VQA/w6Podyliw0fPxJ3AnxkYp2iLZrNH6691XErgBAADgN59D95EjRzR06FCtWLGizevLly/X008/rVWrVmnbtm3q3bu3Jk6cqPr6+lPa3n333UpLSzvlvMvl0oQJEzR48GB9+OGHeuKJJ7RkyRI9//zzvpYLIIJlLNio5z84ZEpf453J+ujBSfpsWY52L8lmSTkAAABM4fMz3ZMmTdKkSZPavGYYhv7whz/o/vvv17XXXitJevnll5WSkqJ169ZpypQpnrZvvPGG3nzzTf3lL3/RG2+84dXPv//7v6uhoUEvvPCCYmNjdd5556m4uFi///3vNXv2bF9LBhCBMhZsNK0vtgADAABAoJj6THdpaakqKys1btw4zzm73a5Ro0apsLDQc+7QoUOaNWuWXnnlFSUkJJzST2FhocaOHavY2FjPuYkTJ2rfvn36/vvv2/zZx48fl8vl8voAiEyPrt/h1/0D+1o15qwBmnZJuj5+MJvADQAAgIAx9e3llZWVkqSUlBSv8ykpKZ5rhmHolltu0a9//WtddNFFKisra7OfzMzMU/povdavX79T7lm2bJmWLl1qxtcAEIJO3AJsX2WtX33l33aZ+ljZvAEAAACB1+2/dT7zzDOqra3VwoULTe134cKFmj9/vufY5XJp0KBBpv4MAMFh5hvJLxhoI3ADAACg25i6vNzhcEhqWT5+okOHDnmubdmyRYWFhYqLi1NMTIx++MMfSpIuuugizZgxw9NPW32c+DNOFhcXJ5vN5vUBEP7MDtzr540xpS8AAACgM0yd7snMzJTD4dDbb7+tYcOGSWqZcd62bZvmzJkjSXr66af18MMPe+45ePCgJk6cqFdffVWjRo2SJGVlZem+++5TY2OjevXqJUnavHmzzjnnnDaXlgOIHM1uQ0Wl1aqqrZctLsavwP0Dm9QnIVHp/eL11I3DmeEGAABAt/P5N9C6ujodOHDAc1xaWqri4mIlJSUpPT1dt912mx5++GGdddZZyszM1AMPPKC0tDRdd911kqT09HSv/vr06SNJOvPMMzVw4EBJ0k033aSlS5dq5syZuueee7Rnzx798Y9/1FNPPdXV7wkgDOTvqdDSDSWqqDl1i8Gu+Ou9Oab0AwAAAHSVz6F7x44duuKKKzzHrc9Rz5gxQ3l5ebr77rt15MgRzZ49W4cPH9bo0aOVn58vq9Xa6Z9ht9v15ptvau7cubrwwgs1YMAALVq0iO3CgAiWv6dCc9bslGFSf2WPEbgBAAAQfBbDMMz6HTekuFwu2e121dTU8Hw3EOKa3YZGP77FlBnu2Zem6N5rLjKhKgAAAKB9nc2cPOAIIChOfHb729rjfgXuPUsm8rw2AAAAQhK/pQLodmY+u80WYAAAAAhl/KYKoFuZ+ew2W4ABAAAg1BG6AQTUicvIB/SJ05L1e30O3BZJyYlxOn+gTV9+X88WYAAAAAgb/MYKIGDMWEZu+fv/Lr32PGUPSTWnMAAAAKCbELoBBIRZy8gddqsW5zoJ3AAAAAhLhG4Apmt2G1q6oaTLgfuBnHM1IDFOyYlWjcxMUnSUpeObAAAAgBBE6AZgiltWbNS7X/rXh0UtM9u3/DiToA0AAICIQOgG4LeMBRv97qM1Yi/OdRK4AQAAEDEI3QD8Ykbglnh2GwAAAJGJ0A2gy25Z0bXA3bqM/MmfDtW3R47z7DYAAAAiFqEbQJd15RnuE5eR//isAabWAwAAAIQaQjeAbsUycgAAAPQkhG4A3eKPU4axjBwAAAA9DqEbQJsamtx6pbBMX1Qf1eCkBE3LylBsTJRXm8sHdW6J+eWDpGuH/SBAlQIAAAChy2IYhhHsIgLB5XLJbrerpqZGNpst2OUAYWXZphKt3loq9wl/O0RZpFljMrVwstOrbWfeXl72WI7ZJQIAAABB1dnMGdXuFQA90rJNJXquwDtwS5LbkJ4rKNWyTSVe5zsK1ARuAAAA9GSEbgAeDU1urd5aeto2q7eWqqHJ7XWu7LEcXT7Iu93lgwjcAAAAAM90Az3csYZmPbqpRGXfHdXR402nzHCfzG1IrxSWaeaYf/I6nzeXgA0AAACcjNAN9GCzXt6uzSVVPt/3RfXRAFQDAAAARB6WlwM9VFcDtyQNTkowuRoAAAAgMjHTDfQQdfVNuv3VXSr//pjS7HF6Z9+3XeonyiJNy8owtzgAAAAgQhG6gR7gmj9t1e6vXJ7jfZW1Xe5r1pjMU/brBgAAANA2QjcQ4U4O3F3V3j7dAAAAANpH6AYiWF19k1+B+8L0vjrvB3YNTkrQtKwMZrgBAAAAHxG6gQjz1s4K/X//tdOUvtb8f5coPjbalL4AAACAnojQDUSQjAUbTetrvDOZwA0AAAD4ibWiQIQwO3Cvnn6xaf0BAAAAPRUz3UAEeGtnhV/333jRQB2sqVdG/wTdO9nJDDcAAABgEkI3EAH8eYb7goE2Pf7ToSZWAwAAAKAVy8uBHuyCgTatnzcm2GUAAAAAEYuZbqCHOceRqPR+8XrqxuHqY+WvAAAAACCQ+I0biAD/+rMRnVpi/q8/G6FxI1K7oSIAAAAAEsvLgYjQ2SBN4AYAAAC6F6EbiBBlj+X4dR0AAACA+VheDoSIZrehotJqVdXWKznRqpGZSYqOsvjUR9ljOXprZ4XXUnOWlAMAAADBQ+gGQkD+ngot3VCiipp6z7lUu1WLc53KHuJbYB43IlVlI5jVBgAAAEIBy8uBIMvfU6E5a3Z6BW5Jqqyp15w1O5W/pyJIlQEAAADwF6EbCKJmt6GlG0pktHGt9dzSDSVqdrfVAgAAAECoI3QDQVRUWn3KDPeJDEkVNfUqKq3uvqIAAAAAmIbQDQRRVW37gbsr7QAAAACEFkI3EETJiVZT2wEAAAAILYRuIIhGZiYp1W5VexuDWdTyFvORmUndWRYAAAAAkxC6gQB5Ycsnyliw0fN5Ycsnp7SJjrJoca5Tkk4J3q3Hi3OdPu/XDQAAACA0WAzDiMjXIrtcLtntdtXU1MhmswW7HPQwGQs2tnut7LFT99A2c59uAAAAAIHX2cxJ6AZMdrrA3aqt4N3sNlRUWq2q2nolJ7YsKWeGGwAAAAhNnc2cMd1YExDx2lpC3l67X175I69z0VEWZZ3ZPxBlAQAAAAgSnukGTPTgm5+Z2g4AAABAeCN0AwAAAAAQIIRuAAAAAAAChNANdFKz21DhZ9/pf4q/VuFn36nZfeo7CBdNOLNTfXW2HQAAAIDw5nPoLigoUG5urtLS0mSxWLRu3Tqv64ZhaNGiRUpNTVV8fLzGjRun/fv3e66XlZVp5syZyszMVHx8vM4880wtXrxYDQ0NXv3s3r1bY8aMkdVq1aBBg7R8+fKufUPABPl7KjT68S36+eq/6V/+s1g/X/03jX58i/L3VHi1O/nlaO3pbDsAAAAA4c3n0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+vmX/4U8++URut1vPPfec9u7dq6eeekqrVq3Svffe6+nD5XJpwoQJGjx4sD788EM98cQTWrJkiZ5//vkufk2g6/L3VGjOmp1ee2hLUmVNveas2XlK8G5rOzBfrgMAAACIHH7t022xWLR27Vpdd911klpmudPS0nTHHXfozjvvlCTV1NQoJSVFeXl5mjJlSpv9PPHEE1q5cqU+//xzSdLKlSt13333qbKyUrGxsZKkBQsWaN26dfrkk85tycQ+3eiqYw3NenRTicq+O6rBSQl6c2+lquoa2mxrkeSwW/X+PVeesqf2C1s+8XpL+aIJZzLDDQAAAESIoOzTXVpaqsrKSo0bN85zzm63a9SoUSosLGw3dNfU1CgpKclzXFhYqLFjx3oCtyRNnDhRjz/+uL7//nv169fPzLIBj1kvb9fmkirP8dYO2huSKmrqVVRafcoe27+88keEbAAAAKCHM/VFapWVlZKklJQUr/MpKSmeayc7cOCAnnnmGf3qV7/y6qetPk78GSc7fvy4XC6X1wfwxcmB2xdVtfUdNwIAAADQ4wT17eVff/21srOz9c///M+aNWuWX30tW7ZMdrvd8xk0aJBJVaInONbQ3OXALUnJiVYTqwEAAAAQKUwN3Q6HQ5J06NAhr/OHDh3yXGt18OBBXXHFFbr00ktPeUGaw+Fos48Tf8bJFi5cqJqaGs/nyy+/9Ou7oGd5dFNJl+6zSEq1WzUyM6nDtgAAAAB6HlNDd2ZmphwOh95++23POZfLpW3btikrK8tz7uuvv9bll1+uCy+8UC+++KKiorzLyMrKUkFBgRobGz3nNm/erHPOOafd57nj4uJks9m8PkBnlX131Od7Wl+btjjXecpL1AAAAABA6kLorqurU3FxsYqLiyW1vDytuLhY5eXlslgsuu222/Twww9r/fr1+uijjzR9+nSlpaV53nDeGrjT09P15JNP6ptvvlFlZaXXs9o33XSTYmNjNXPmTO3du1evvvqq/vjHP2r+/PmmfGngZBn9E3y+x2G3auXUEcoekhqAigAAAABEAp/fXr5jxw5dccUVnuPWIDxjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqzWlmdeN2/erAMHDujAgQMaOHCgV9+tu5fZ7Xa9+eabmjt3ri688EINGDBAixYt0uzZs7v8RdFzfV19TJOefk9Hjjerd1y03vjtZfpBUrxXm3snO/XK38o77OulWy7W4fpGJSe2LClnhhsAAADA6fi1T3coY59uSNLZ921SQ/Op/188NtqiTx+Z7HWuo7eXj3cma/X0i02vEQAAAED46WzmDOrby4FAai9wS1JDs6Gz79vkdW719Is13pncZnsCNwAAAICu8Hl5ORAOvq4+1m7gbtXQbOjr6mNeS81XT79Yxxqa9eimEpV9d1QZ/RN072Sn4mOjA10yAAAAgAjE8nJEjGa3oaLSalXV1mvhX3braKO7w3ts1mjtXpLdDdUBAAAAiCSdzZzMdCMi5O+p0NINJaqoqffpviPHmwNUEQAAAAAQuhEB8vdUaM6anerKko3ecSwbBwAAABA4hG6EnROXkQ/oE6cl6/d2KXBL0hu/vczU2gAAAADgRIRuhJWuLiNvS2y05ZT9ugEAAADATIRuhA1/lpGfrK19ugEAAADAbIRuhIVmt6GlG0q6HLgTekXpeJNbveOi9cZvL2OGGwAAAEC3IHQjLBSVVndpSblFksNu1fv3XKnoKIv5hQEAAADAaUQFuwCgM6pquxa4JWlxrpPADQAAACAomOlGWEhOtPp8j8Nu1eJcp7KHpAagIgAAAADoGKEbYWFkZpJS7VZV1tS3+Vx36zLyJ386VN8eOa7kRKtGZiYxww0AAAAgqAjdCAvRURYtznVqzpqdskhewfvEZeQ/PmtAEKoDAAAAgLbxTDfCRvaQVK2cOkIOu/dSc4fdqpVTR7CMHAAAAEDIYaYbYSV7SKrGOx0qKq1WVW09y8gBAAAAhDRCN7rNR+U1uubZ92WoZUn4+t+M1vnpdp/7iY6yKOvM/qbXBwAAAABmI3SjW2Qs2Oh1bEjKffZ9SVLZYzlBqAgAAAAAAo9nuhFwJwduX68DAAAAQLgidCOgPiqvMbUdAAAAAIQTQjcC6pq/LyE3qx0AAAAAhBNCNwLK6LiJT+0AAAAAIJwQuhFQnd3Iiw2/AAAAAEQiQjcCav1vRpvaDgAAAADCCaEbAdXZfbi7sl83AAAAAIQ6QjcCrqN9uNmnGwAAAECkigl2AegZyh7L0UflNbrm2fdlqOUZ7vW/Gc0MNwAAAICIRuhGtzk/3a5SZrUBAAAA9CAsLwcAAAAAIEAI3QAAAAAABAjLy+HR7DZUVFqtqtp6JSdaNTIzSdFR7KANAAAAAF1F6IYkKX9PhZZuKFFFTb3nXKrdqsW5TmUPSQ1iZQAAAAAQvlheDuXvqdCcNTu9ArckVdbUa86ancrfUxGkygAAAAAgvBG6e7hmt6GlG0pktHGt9dzSDSVqdrfVAgAAAABwOiwv74GONTTr0U0lKvvuqKwxUafMcJ/IkFRRU6+i0mplndm/+4oEAAAAgAhA6O5hZr28XZtLqny+r6q2/WAOAAAAAGgby8t7kK4GbklKTrSaXA0AAAAARD5munuIYw3NXQrcFkkOe8v2YQAAAAAA3zDT3UM8uqnE53tad+henOtkv24AAAAA6AJmunuIsu+O+nyPg326AQAAAMAvhO4eIqN/grbu77jd+HOTdfXQNCUntiwpZ4YbAAAAALqO0N1D3DvZqVf+Vt5hu6d/PkLxsdHdUBEAAAAARD6e6e4h4mOjNd6ZfNo2453JBG4AAAAAMBGhuwdZPf3idoP3eGeyVk+/uJsrAgAAAIDIxvLyHmb19It1rKFZj24qUdl3R5XRP0H3TnYyww0AAAAAAUDo7oHiY6P10HXnB7sMAAAAAIh4LC8HAAAAACBACN0AAAAAAAQIoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoS3lwdRXX2Tbn91l8q/P6b0fvF66sbh6mNlSAAAAAAgUvg8011QUKDc3FylpaXJYrFo3bp1XtcNw9CiRYuUmpqq+Ph4jRs3Tvv37/dqU11drZtvvlk2m019+/bVzJkzVVdX59Vm9+7dGjNmjKxWqwYNGqTly5f7/u1C2DV/2qohS/5Xmz+u0r7KWm3+uEpDlvyvrvnT1mCXBgAAAAAwic+h+8iRIxo6dKhWrFjR5vXly5fr6aef1qpVq7Rt2zb17t1bEydOVH19vafNzTffrL1792rz5s16/fXXVVBQoNmzZ3uuu1wuTZgwQYMHD9aHH36oJ554QkuWLNHzzz/fha8Yeq7501bt/srV5rXdX7kI3gAAAAAQISyGYRhdvtli0dq1a3XddddJapnlTktL0x133KE777xTklRTU6OUlBTl5eVpypQp+vjjj+V0OrV9+3ZddNFFkqT8/HxNnjxZX331ldLS0rRy5Urdd999qqysVGxsrCRpwYIFWrdunT755JNO1eZyuWS321VTUyObzdbVr2i6uvomDVnyvx2227NkIkvNAQAAACBEdTZzmvoitdLSUlVWVmrcuHGec3a7XaNGjVJhYaEkqbCwUH379vUEbkkaN26coqKitG3bNk+bsWPHegK3JE2cOFH79u3T999/3+bPPn78uFwul9cnFN3+6i5T2wEAAAAAQpepobuyslKSlJKS4nU+JSXFc62yslLJycle12NiYpSUlOTVpq0+TvwZJ1u2bJnsdrvnM2jQIP+/UACUf3/M1HYAAAAAgNAVMVuGLVy4UDU1NZ7Pl19+GeyS2pTeL97UdgAAAACA0GVq6HY4HJKkQ4cOeZ0/dOiQ55rD4VBVVZXX9aamJlVXV3u1aauPE3/GyeLi4mSz2bw+oeipG4eb2g4AAAAAELpMDd2ZmZlyOBx6++23PedcLpe2bdumrKwsSVJWVpYOHz6sDz/80NNmy5YtcrvdGjVqlKdNQUGBGhsbPW02b96sc845R/369TOz5G7XxxqjCwae/h8ELhho4yVqAAAAABABfA7ddXV1Ki4uVnFxsaSWl6cVFxervLxcFotFt912mx5++GGtX79eH330kaZPn660tDTPG87PPfdcZWdna9asWSoqKtJf//pXzZs3T1OmTFFaWpok6aabblJsbKxmzpypvXv36tVXX9Uf//hHzZ8/37QvHkzr541pN3hfMNCm9fPGdHNFAAAAAIBA8HnLsHfffVdXXHHFKednzJihvLw8GYahxYsX6/nnn9fhw4c1evRoPfvsszr77LM9baurqzVv3jxt2LBBUVFRuuGGG/T000+rT58+nja7d+/W3LlztX37dg0YMEC33nqr7rnnnk7XGapbhp2orr5Jt7+6S+XfH1N6v3g9deNwZrgBAAAAIAx0NnP6tU93KAuH0A0AAAAACE9B2acbAAAAAAD8A6EbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAESEywCwgUwzAkSS6XK8iVAAAAAAAiTWvWbM2e7YnY0F1bWytJGjRoUJArAQAAAABEqtraWtnt9navW4yOYnmYcrvdOnjwoBITE2WxWIJdDv7O5XJp0KBB+vLLL2Wz2YJdDkzG+EY2xjfyMcaRjfGNbIxvZGN8Q5NhGKqtrVVaWpqiotp/cjtiZ7qjoqI0cODAYJeBdthsNv7CiGCMb2RjfCMfYxzZGN/IxvhGNsY39JxuhrsVL1IDAAAAACBACN0AAAAAAAQIoRvdKi4uTosXL1ZcXFywS0EAML6RjfGNfIxxZGN8IxvjG9kY3/AWsS9SAwAAAAAg2JjpBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG6YoqCgQLm5uUpLS5PFYtG6detOafPxxx/rmmuukd1uV+/evXXxxRervLzcc72+vl5z585V//791adPH91www06dOhQN34LtKej8a2rq9O8efM0cOBAxcfHy+l0atWqVV5tGN/QtGzZMl188cVKTExUcnKyrrvuOu3bt8+rTWfGrry8XDk5OUpISFBycrLuuusuNTU1dedXQRs6Gt/q6mrdeuutOueccxQfH6/09HT99re/VU1NjVc/jG/o6syf4VaGYWjSpElt/j3OGIemzo5vYWGhrrzySvXu3Vs2m01jx47VsWPHPNerq6t18803y2azqW/fvpo5c6bq6uq686ugDZ0Z38rKSk2bNk0Oh0O9e/fWiBEj9Je//MWrDeMb+gjdMMWRI0c0dOhQrVixos3rn332mUaPHq0f/ehHevfdd7V792498MADslqtnja33367NmzYoD//+c967733dPDgQV1//fXd9RVwGh2N7/z585Wfn681a9bo448/1m233aZ58+Zp/fr1njaMb2h67733NHfuXP3tb3/T5s2b1djYqAkTJujIkSOeNh2NXXNzs3JyctTQ0KAPPvhAL730kvLy8rRo0aJgfCWcoKPxPXjwoA4ePKgnn3xSe/bsUV5envLz8zVz5kxPH4xvaOvMn+FWf/jDH2SxWE45zxiHrs6Mb2FhobKzszVhwgQVFRVp+/btmjdvnqKi/vFr/s0336y9e/dq8+bNev3111VQUKDZs2cH4yvhBJ0Z3+nTp2vfvn1av369PvroI11//fX62c9+pl27dnnaML5hwABMJslYu3at17kbb7zRmDp1arv3HD582OjVq5fx5z//2XPu448/NiQZhYWFgSoVXdDW+J533nnGgw8+6HVuxIgRxn333WcYBuMbTqqqqgxJxnvvvWcYRufGbtOmTUZUVJRRWVnpabNy5UrDZrMZx48f794vgNM6eXzb8l//9V9GbGys0djYaBgG4xtu2hvjXbt2GT/4wQ+MioqKU/4eZ4zDR1vjO2rUKOP+++9v956SkhJDkrF9+3bPuTfeeMOwWCzG119/HdB64Zu2xrd3797Gyy+/7NUuKSnJWL16tWEYjG+4YKYbAed2u7Vx40adffbZmjhxopKTkzVq1CivpW0ffvihGhsbNW7cOM+5H/3oR0pPT1dhYWEQqoYvLr30Uq1fv15ff/21DMPQO++8o08//VQTJkyQxPiGk9ZlxUlJSZI6N3aFhYU6//zzlZKS4mkzceJEuVwu7d27txurR0dOHt/22thsNsXExEhifMNNW2N89OhR3XTTTVqxYoUcDscp9zDG4ePk8a2qqtK2bduUnJysSy+9VCkpKbrsssv0/vvve+4pLCxU3759ddFFF3nOjRs3TlFRUdq2bVv3fgGcVlt/fi+99FK9+uqrqq6ultvt1n/+53+qvr5el19+uSTGN1wQuhFwVVVVqqur02OPPabs7Gy9+eab+slPfqLrr79e7733nqSW51ViY2PVt29fr3tTUlJUWVkZhKrhi2eeeUZOp1MDBw5UbGyssrOztWLFCo0dO1YS4xsu3G63brvtNv34xz/WkCFDJHVu7CorK71+WW+93noNoaGt8T3Zt99+q4ceeshrWSLjGz7aG+Pbb79dl156qa699to272OMw0Nb4/v5559LkpYsWaJZs2YpPz9fI0aM0FVXXaX9+/dLahnD5ORkr75iYmKUlJTE+IaQ9v78/td//ZcaGxvVv39/xcXF6Ve/+pXWrl2rH/7wh5IY33ARE+wCEPncbrck6dprr9Xtt98uSRo2bJg++OADrVq1Spdddlkwy4MJnnnmGf3tb3/T+vXrNXjwYBUUFGju3LlKS0vzmiFFaJs7d6727NnjNUOCyNHR+LpcLuXk5MjpdGrJkiXdWxxM0dYYr1+/Xlu2bPF6/hPhqa3xbf0d61e/+pV+8YtfSJKGDx+ut99+Wy+88IKWLVsWlFrhu/b+jn7ggQd0+PBhvfXWWxowYIDWrVunn/3sZ9q6davOP//8IFULXzHTjYAbMGCAYmJi5HQ6vc6fe+65nreXOxwONTQ06PDhw15tDh061OZSOISOY8eO6d5779Xvf/975ebm6oILLtC8efN044036sknn5TE+IaDefPm6fXXX9c777yjgQMHes53ZuwcDscpbzNvPWZ8Q0N749uqtrZW2dnZSkxM1Nq1a9WrVy/PNcY3PLQ3xlu2bNFnn32mvn37KiYmxvPYwA033OBZnsoYh772xjc1NVWSOvwdq6qqyut6U1OTqqurGd8Q0d74fvbZZ/rTn/6kF154QVdddZWGDh2qxYsX66KLLvK83JbxDQ+EbgRcbGysLr744lO2QPj00081ePBgSdKFF16oXr166e233/Zc37dvn8rLy5WVldWt9cI3jY2Namxs9HpLqiRFR0d7/gWe8Q1dhmFo3rx5Wrt2rbZs2aLMzEyv650Zu6ysLH300Ude/9HfvHmzbDbbKb8Iont1NL5Sywz3hAkTFBsbq/Xr13vtKiExvqGuozFesGCBdu/ereLiYs9Hkp566im9+OKLkhjjUNbR+GZkZCgtLe20v2NlZWXp8OHD+vDDDz3Xt2zZIrfbrVGjRgX+S6BdHY3v0aNHJem0v2MxvmEimG9xQ+Sora01du3aZezatcuQZPz+9783du3aZXzxxReGYRjGa6+9ZvTq1ct4/vnnjf379xvPPPOMER0dbWzdutXTx69//WsjPT3d2LJli7Fjxw4jKyvLyMrKCtZXwgk6Gt/LLrvMOO+884x33nnH+Pzzz40XX3zRsFqtxrPPPuvpg/ENTXPmzDHsdrvx7rvvGhUVFZ7P0aNHPW06GrumpiZjyJAhxoQJE4zi4mIjPz/fOOOMM4yFCxcG4yvhBB2Nb01NjTFq1Cjj/PPPNw4cOODVpqmpyTAMxjfUdebP8Ml00tvLGePQ1ZnxfeqppwybzWb8+c9/Nvbv32/cf//9htVqNQ4cOOBpk52dbQwfPtzYtm2b8f777xtnnXWW8fOf/zwYXwkn6Gh8GxoajB/+8IfGmDFjjG3bthkHDhwwnnzyScNisRgbN2709MP4hj5CN0zxzjvvGJJO+cyYMcPT5t/+7d+MH/7wh4bVajWGDh1qrFu3zquPY8eOGb/5zW+Mfv36GQkJCcZPfvITo6Kiopu/CdrS0fhWVFQYt9xyi5GWlmZYrVbjnHPOMX73u98Zbrfb0wfjG5raGldJxosvvuhp05mxKysrMyZNmmTEx8cbAwYMMO644w7PllMIno7Gt70/25KM0tJSTz+Mb+jqzJ/htu45eetHxjg0dXZ8ly1bZgwcONBISEgwsrKyvCY1DMMwvvvuO+PnP/+50adPH8Nmsxm/+MUvjNra2m78JmhLZ8b3008/Na6//nojOTnZSEhIMC644IJTthBjfEOfxTAMw+zZcwAAAAAAwDPdAAAAAAAEDKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAALk/wdw9IA+/qwxiAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "În acest caz, corelația este puțin mai mică, dar este încă destul de mare. Acum, pentru a face relația și mai puțin evidentă, am putea dori să adăugăm un plus de aleatoriu prin adăugarea unei variabile aleatoare la salariu. Să vedem ce se întâmplă:\n"
+    "În acest caz, corelația este ușor mai mică, dar este totuși destul de mare. Acum, pentru a face relația și mai puțin evidentă, am putea dori să adăugăm un plus de aleatoriu prin adăugarea unei variabile aleatoare la salariu. Să vedem ce se întâmplă:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Poți ghici de ce punctele se aliniază în linii verticale așa?\n",
+    "> Poți ghici de ce punctele se aliniază în linii verticale astfel?\n",
     "\n",
-    "Am observat corelația dintre un concept artificial creat, cum ar fi salariul, și variabila observată *înălțime*. Să vedem, de asemenea, dacă cele două variabile observate, cum ar fi înălțimea și greutatea, sunt corelate:\n"
+    "Am observat corelația dintre un concept artificial, cum ar fi salariul, și variabila observată *înălțime*. Haide să vedem dacă cele două variabile observate, cum ar fi înălțimea și greutatea, se corelează și ele:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,14 +854,14 @@
    "source": [
     "Din păcate, nu am obținut niciun rezultat - doar niște valori ciudate `nan`. Acest lucru se datorează faptului că unele dintre valorile din seria noastră sunt nedefinite, reprezentate ca `nan`, ceea ce face ca rezultatul operației să fie și el nedefinit. Privind matricea, putem observa că coloana `Weight` este cea problematică, deoarece corelația dintre valorile `Height` a fost calculată.\n",
     "\n",
-    "> Acest exemplu evidențiază importanța **pregătirii datelor** și **curățării** acestora. Fără date adecvate, nu putem calcula nimic.\n",
+    "> Acest exemplu evidențiază importanța **pregătirii** și **curățării** datelor. Fără date adecvate, nu putem calcula nimic.\n",
     "\n",
     "Să folosim metoda `fillna` pentru a completa valorile lipsă și să calculăm corelația:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Concluzie\n",
     "\n",
-    "În acest notebook am învățat cum să efectuăm operațiuni de bază pe date pentru a calcula funcții statistice. Acum știm cum să utilizăm un aparat solid de matematică și statistică pentru a demonstra anumite ipoteze și cum să calculăm intervale de încredere pentru variabile arbitrare, având un eșantion de date.\n"
+    "În acest notebook am învățat cum să efectuăm operațiuni de bază pe date pentru a calcula funcții statistice. Acum știm cum să utilizăm un aparat solid de matematică și statistici pentru a demonstra anumite ipoteze și cum să calculăm intervale de încredere pentru variabile arbitrare, având un eșantion de date.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
+    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus utilizând serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să aveți în vedere că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:28:15+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:53:17+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ro"
   }
diff --git a/translations/ro/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ro/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 7b6249e7..cd49ea3a 100644
--- a/translations/ro/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ro/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "În acest set de date, coloanele sunt următoarele:  \n",
-    "* Vârsta și sexul sunt auto-explicative  \n",
-    "* BMI este indicele de masă corporală  \n",
-    "* BP este tensiunea arterială medie  \n",
-    "* S1 până la S6 sunt diferite măsurători ale sângelui  \n",
-    "* Y este măsura calitativă a progresiei bolii pe parcursul unui an  \n",
+    "În acest set de date, coloanele sunt următoarele:\n",
+    "* Vârsta și sexul sunt evidente\n",
+    "* BMI este indicele de masă corporală\n",
+    "* BP este tensiunea arterială medie\n",
+    "* S1 până la S6 sunt diferite măsurători ale sângelui\n",
+    "* Y este măsura calitativă a progresiei bolii pe parcursul unui an\n",
     "\n",
     "Să studiem acest set de date folosind metode de probabilitate și statistică.\n",
     "\n",
-    "### Sarcina 1: Calculați valorile medii și varianța pentru toate valorile  \n"
+    "### Sarcina 1: Calculați valorile medii și variația pentru toate valorile\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Sarcina 2: Realizați boxplot-uri pentru BMI, BP și Y în funcție de gen\n"
+    "### Sarcina 2: Realizați boxplot-uri pentru IMC, TA și Y în funcție de gen\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus utilizând serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să aveți în vedere că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
+    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să aveți în vedere că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:53:36+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:53:43+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ro"
   }
diff --git a/translations/ru/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ru/1-Introduction/04-stats-and-probability/assignment.ipynb
index afd8e51a..ff383d9d 100644
--- a/translations/ru/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ru/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -200,7 +200,7 @@
    "source": [
     "### Задание 4: Проверьте корреляцию между различными переменными и прогрессированием заболевания (Y)\n",
     "\n",
-    "> **Подсказка** Матрица корреляции предоставит наиболее полезную информацию о том, какие значения зависят друг от друга.\n"
+    "> **Подсказка** Матрица корреляции предоставит наиболее полезную информацию о том, какие значения взаимосвязаны.\n"
    ],
    "metadata": {}
   },
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:46:19+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:04:19+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ru"
   }
diff --git a/translations/ru/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ru/1-Introduction/04-stats-and-probability/notebook.ipynb
index f91d38fc..14118f63 100644
--- a/translations/ru/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ru/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Введение в теорию вероятностей и статистику\n",
-    "В этом блокноте мы будем рассматривать некоторые из концепций, которые обсуждали ранее. Многие понятия из теории вероятностей и статистики хорошо представлены в основных библиотеках для обработки данных в Python, таких как `numpy` и `pandas`.\n"
+    "В этом блокноте мы будем изучать некоторые из концепций, которые обсуждали ранее. Многие понятия из теории вероятностей и статистики хорошо представлены в основных библиотеках для обработки данных в Python, таких как `numpy` и `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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Xl7d4TDgcVigUarYBAICu6aa2HtjY2KglS5Zo2rRpGjNmTIvrAoGAEhISmu1LSEhQIBBo8Ri/36/ly5e3dTQgqvH/2AFbnfG/QeurOW2+MpKbm6uqqioVFxe35zySpIKCAgWDwaatpqam3c8BAAA6hjZdGVm8eLF27dql/fv3a8CAAa2uTUxMVF1dXbN9dXV1SkxMbPEYt9stt9vdltEAAEAn4+jKSCQS0eLFi7V9+3bt3btXQ4YMueoxPp9PpaWlzfaVlJTI5/M5mxQAAHRJjq6M5ObmqqioSDt37lRsbGzTfR8ej0e9evWSJOXk5Kh///7y+/2SpLy8PKWnp2vlypXKyspScXGxKioqVFhY2M4vBQAAdEaOroysX79ewWBQd911l5KSkpq2rVu3Nq2prq5WbW1t0+O0tDQVFRWpsLBQycnJeu2117Rjx45Wb3oFAADRw9GVkWv5lSRlZWWX7Zs1a5ZmzZrl5FQAACBK8Nk0AADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMCU4xjZv3+/ZsyYoX79+snlcmnHjh2tri8rK5PL5bpsCwQCbZ0ZAAB0IY5jpL6+XsnJyVq3bp2j406dOqXa2tqmLT4+3umpAQBAF3ST0wOmT5+u6dOnOz5RfHy8brvtNsfHAQCAru1bu2ckJSVFSUlJuvfee/XOO++0ujYcDisUCjXbAABA13TDYyQpKUkbNmzQ66+/rtdff11er1d33XWXjh071uIxfr9fHo+nafN6vTd6TAAAYMQViUQibT7Y5dL27duVnZ3t6Lj09HQNHDhQf/rTn6749XA4rHA43PQ4FArJ6/UqGAwqLi6ureNe0eClb7br8wEA0Nl8vCLrhjxvKBSSx+O56vdvx/eMtIfJkyfrwIEDLX7d7XbL7XZ/ixMBAAArJr9npLKyUklJSRanBgAAHYzjKyMXL17U6dOnmx5/9NFHqqysVJ8+fTRw4EAVFBTo008/1R//+EdJ0urVqzVkyBCNHj1aX375pV588UXt3btXf/3rX9vvVQAAgE7LcYxUVFTo7rvvbnqcn58vSZo3b542b96s2tpaVVdXN3390qVL+uUvf6lPP/1UN998s8aNG6e//e1vzZ4DAABEr+u6gfXbcq03wLQFN7ACAKKd9Q2sfDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDlOEb279+vGTNmqF+/fnK5XNqxY8dVjykrK9OECRPkdrs1dOhQbd68uQ2jAgCArshxjNTX1ys5OVnr1q27pvUfffSRsrKydPfdd6uyslJLlizRwoULtWfPHsfDAgCArucmpwdMnz5d06dPv+b1GzZs0JAhQ7Ry5UpJ0siRI3XgwAGtWrVKmZmZTk8PAAC6mBt+z0h5ebkyMjKa7cvMzFR5eXmLx4TDYYVCoWYbAADomm54jAQCASUkJDTbl5CQoFAopP/85z9XPMbv98vj8TRtXq/3Ro8JAACMdMifpikoKFAwGGzaampqrEcCAAA3iON7RpxKTExUXV1ds311dXWKi4tTr169rniM2+2W2+2+0aMBAIAO4IZfGfH5fCotLW22r6SkRD6f70afGgAAdAKOY+TixYuqrKxUZWWlpK9/dLeyslLV1dWSvv4nlpycnKb1DzzwgD788EP96le/0vvvv6/nn39er776qh5++OH2eQUAAKBTcxwjFRUVGj9+vMaPHy9Jys/P1/jx4/X4449Lkmpra5vCRJKGDBmiN998UyUlJUpOTtbKlSv14osv8mO9AABAkuSKRCIR6yGuJhQKyePxKBgMKi4url2fe/DSN9v1+QAA6Gw+XpF1Q573Wr9/d8ifpgEAANGDGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYKpNMbJu3ToNHjxYMTExmjJlig4fPtzi2s2bN8vlcjXbYmJi2jwwAADoWhzHyNatW5Wfn69ly5bp2LFjSk5OVmZmps6ePdviMXFxcaqtrW3azpw5c11DAwCArsNxjDz77LNatGiR5s+fr1GjRmnDhg26+eabtWnTphaPcblcSkxMbNoSEhKua2gAANB1OIqRS5cu6ejRo8rIyPjmCbp1U0ZGhsrLy1s87uLFixo0aJC8Xq9mzpypkydPtnqecDisUCjUbAMAAF2Toxj5/PPP1dDQcNmVjYSEBAUCgSseM3z4cG3atEk7d+7Uli1b1NjYqLS0NH3yySctnsfv98vj8TRtXq/XyZgAAKATueE/TePz+ZSTk6OUlBSlp6frjTfe0B133KGNGze2eExBQYGCwWDTVlNTc6PHBAAARm5ysvj2229X9+7dVVdX12x/XV2dEhMTr+k5evToofHjx+v06dMtrnG73XK73U5GAwAAnZSjKyM9e/ZUamqqSktLm/Y1NjaqtLRUPp/vmp6joaFBJ06cUFJSkrNJAQBAl+Toyogk5efna968eZo4caImT56s1atXq76+XvPnz5ck5eTkqH///vL7/ZKkJ554QlOnTtXQoUP1xRdf6JlnntGZM2e0cOHC9n0lAACgU3IcI7Nnz9a5c+f0+OOPKxAIKCUlRW+//XbTTa3V1dXq1u2bCy7nz5/XokWLFAgE1Lt3b6WmpurgwYMaNWpU+70KAADQabkikUjEeoirCYVC8ng8CgaDiouLa9fnHrz0zXZ9PgAAOpuPV2TdkOe91u/ffDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVphhZt26dBg8erJiYGE2ZMkWHDx9udf22bds0YsQIxcTEaOzYsdq9e3ebhgUAAF2P4xjZunWr8vPztWzZMh07dkzJycnKzMzU2bNnr7j+4MGDmjNnjhYsWKDjx48rOztb2dnZqqqquu7hAQBA5+eKRCIRJwdMmTJFkyZN0tq1ayVJjY2N8nq9euihh7R06dLL1s+ePVv19fXatWtX076pU6cqJSVFGzZsuKZzhkIheTweBYNBxcXFORn3qgYvfbNdnw8AgM7m4xVZN+R5r/X7901OnvTSpUs6evSoCgoKmvZ169ZNGRkZKi8vv+Ix5eXlys/Pb7YvMzNTO3bsaPE84XBY4XC46XEwGJT09Ytqb43hf7f7cwIA0JnciO+v/+/zXu26h6MY+fzzz9XQ0KCEhIRm+xMSEvT+++9f8ZhAIHDF9YFAoMXz+P1+LV++/LL9Xq/XybgAAOAaeFbf2Oe/cOGCPB5Pi193FCPfloKCgmZXUxobG/Wvf/1Lffv2lcvlarfzhEIheb1e1dTUtPs//8A53o+Oh/ekY+H96Fh4P64uEonowoUL6tevX6vrHMXI7bffru7du6uurq7Z/rq6OiUmJl7xmMTEREfrJcntdsvtdjfbd9tttzkZ1ZG4uDj+InUgvB8dD+9Jx8L70bHwfrSutSsi/+Pop2l69uyp1NRUlZaWNu1rbGxUaWmpfD7fFY/x+XzN1ktSSUlJi+sBAEB0cfzPNPn5+Zo3b54mTpyoyZMna/Xq1aqvr9f8+fMlSTk5Oerfv7/8fr8kKS8vT+np6Vq5cqWysrJUXFysiooKFRYWtu8rAQAAnZLjGJk9e7bOnTunxx9/XIFAQCkpKXr77bebblKtrq5Wt27fXHBJS0tTUVGRHnvsMT366KMaNmyYduzYoTFjxrTfq2gjt9utZcuWXfZPQrDB+9Hx8J50LLwfHQvvR/tx/HtGAAAA2hOfTQMAAEwRIwAAwBQxAgAATBEjAADAVFTHyLp16zR48GDFxMRoypQpOnz4sPVIUcnv92vSpEmKjY1VfHy8srOzderUKeux8F8rVqyQy+XSkiVLrEeJWp9++ql++tOfqm/fvurVq5fGjh2riooK67GiVkNDg37zm99oyJAh6tWrl7773e/qySefvOrnr6BlURsjW7duVX5+vpYtW6Zjx44pOTlZmZmZOnv2rPVoUWffvn3Kzc3VoUOHVFJSoq+++kr33Xef6uvrrUeLekeOHNHGjRs1btw461Gi1vnz5zVt2jT16NFDb731lt577z2tXLlSvXv3th4tav3ud7/T+vXrtXbtWv3zn//U7373O/3+97/Xc889Zz1apxW1P9o7ZcoUTZo0SWvXrpX09W+S9Xq9euihh7R06VLj6aLbuXPnFB8fr3379unOO++0HidqXbx4URMmTNDzzz+v3/72t0pJSdHq1autx4o6S5cu1TvvvKO///3v1qPgv374wx8qISFBL730UtO+H/3oR+rVq5e2bNliOFnnFZVXRi5duqSjR48qIyOjaV+3bt2UkZGh8vJyw8kgScFgUJLUp08f40miW25urrKyspr9d4Jv35///GdNnDhRs2bNUnx8vMaPH68XXnjBeqyolpaWptLSUn3wwQeSpH/84x86cOCApk+fbjxZ59UhP7X3Rvv888/V0NDQ9Ftj/ychIUHvv/++0VSQvr5CtWTJEk2bNq1D/JbeaFVcXKxjx47pyJEj1qNEvQ8//FDr169Xfn6+Hn30UR05ckS/+MUv1LNnT82bN896vKi0dOlShUIhjRgxQt27d1dDQ4OeeuopzZ0713q0TisqYwQdV25urqqqqnTgwAHrUaJWTU2N8vLyVFJSopiYGOtxol5jY6MmTpyop59+WpI0fvx4VVVVacOGDcSIkVdffVWvvPKKioqKNHr0aFVWVmrJkiXq168f70kbRWWM3H777erevbvq6uqa7a+rq1NiYqLRVFi8eLF27dql/fv3a8CAAdbjRK2jR4/q7NmzmjBhQtO+hoYG7d+/X2vXrlU4HFb37t0NJ4wuSUlJGjVqVLN9I0eO1Ouvv240ER555BEtXbpUP/nJTyRJY8eO1ZkzZ+T3+4mRNorKe0Z69uyp1NRUlZaWNu1rbGxUaWmpfD6f4WTRKRKJaPHixdq+fbv27t2rIUOGWI8U1e655x6dOHFClZWVTdvEiRM1d+5cVVZWEiLfsmnTpl32o+4ffPCBBg0aZDQR/v3vfzf7QFhJ6t69uxobG40m6vyi8sqIJOXn52vevHmaOHGiJk+erNWrV6u+vl7z58+3Hi3q5ObmqqioSDt37lRsbKwCgYAkyePxqFevXsbTRZ/Y2NjL7te55ZZb1LdvX+7jMfDwww8rLS1NTz/9tH784x/r8OHDKiwsVGFhofVoUWvGjBl66qmnNHDgQI0ePVrHjx/Xs88+q5///OfWo3VekSj23HPPRQYOHBjp2bNnZPLkyZFDhw5ZjxSVJF1xe/nll61Hw3+lp6dH8vLyrMeIWn/5y18iY8aMibjd7siIESMihYWF1iNFtVAoFMnLy4sMHDgwEhMTE/nOd74T+fWvfx0Jh8PWo3VaUft7RgAAQMcQlfeMAACAjoMYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKb+D7cuxelORYM+AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Анализ реальных данных\n",
     "\n",
-    "Среднее значение и дисперсия играют очень важную роль при анализе данных из реального мира. Давайте загрузим данные о бейсболистах с [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Среднее значение и дисперсия играют важную роль при анализе данных из реального мира. Давайте загрузим данные о бейсболистах с [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Здесь мы используем пакет [**Pandas**](https://pandas.pydata.org/) для анализа данных. Позже в этом курсе мы подробнее поговорим о Pandas и работе с данными в Python.\n",
+    "> Мы используем пакет [**Pandas**](https://pandas.pydata.org/) для анализа данных. Позже в этом курсе мы подробнее поговорим о Pandas и работе с данными в Python.\n",
     "\n",
     "Давайте вычислим средние значения для возраста, роста и веса:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Примечание**: Эта диаграмма предполагает, что в среднем рост игроков на первой базе выше, чем рост игроков на второй базе. Позже мы узнаем, как можно более формально проверить эту гипотезу и как продемонстрировать, что наши данные статистически значимы, чтобы это показать.  \n",
+    "> **Примечание**: Эта диаграмма предполагает, что в среднем рост первых базовых игроков выше, чем рост вторых базовых игроков. Позже мы узнаем, как можно более формально проверить эту гипотезу и как продемонстрировать, что наши данные статистически значимы, чтобы это показать.  \n",
     "\n",
     "Возраст, рост и вес — это все непрерывные случайные величины. Как вы думаете, какова их распределение? Хороший способ узнать это — построить гистограмму значений:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## Нормальное распределение\n",
     "\n",
-    "Давайте создадим искусственную выборку весов, которая следует нормальному распределению с теми же средним и дисперсией, что и наши реальные данные:\n"
+    "Давайте создадим искусственную выборку весов, которая будет следовать нормальному распределению с тем же средним и дисперсией, что и наши реальные данные:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Поскольку большинство значений в реальной жизни имеют нормальное распределение, мы не должны использовать генератор случайных чисел с равномерным распределением для создания выборочных данных. Вот что произойдет, если мы попытаемся сгенерировать веса с равномерным распределением (созданным с помощью `np.random.rand`):\n"
+    "Поскольку большинство значений в реальной жизни имеют нормальное распределение, мы не должны использовать генератор случайных чисел с равномерным распределением для создания выборочных данных. Вот что происходит, если мы пытаемся сгенерировать веса с равномерным распределением (созданным с помощью `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,7 +620,7 @@
    "metadata": {},
    "source": [
     "Два значения, возвращаемые функцией `ttest_ind`, это:\n",
-    "* p-value можно рассматривать как вероятность того, что два распределения имеют одинаковое среднее значение. В нашем случае оно очень низкое, что означает наличие убедительных доказательств того, что первые игроки базы выше.\n",
+    "* p-value можно рассматривать как вероятность того, что два распределения имеют одинаковое среднее значение. В нашем случае оно очень низкое, что означает наличие убедительных доказательств того, что первые базы выше.\n",
     "* t-value — это промежуточное значение нормализованной разницы средних, которое используется в t-тесте и сравнивается с пороговым значением для заданного уровня доверия.\n"
    ]
   },
@@ -789,24 +630,22 @@
    "source": [
     "## Симуляция нормального распределения с использованием центральной предельной теоремы\n",
     "\n",
-    "Псевдослучайный генератор в Python предназначен для создания равномерного распределения. Если мы хотим создать генератор для нормального распределения, мы можем использовать центральную предельную теорему. Чтобы получить значение с нормальным распределением, мы просто вычислим среднее значение выборки, сгенерированной равномерно.\n"
+    "Псевдослучайный генератор в Python предназначен для создания равномерного распределения. Если мы хотим создать генератор для нормального распределения, мы можем воспользоваться центральной предельной теоремой. Чтобы получить значение с нормальным распределением, мы просто вычислим среднее значение выборки, сгенерированной равномерно.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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eyrcCAACALq/T316+YcOGeOWVV9rur1q1KpYuXRp9+/aNffbZJy699NL4yU9+El/60pdi6NChcc0110RdXV2ccsoppZwbAAAAurxOR/dzzz0Xxx9/fNv9iRMnRkTE+PHjY9asWXHllVfGxo0b48ILL4x169bFyJEjY/78+dG7d+/STQ0AAADdQEVRFEW5h/hfzc3NUVNTE01NTT7fDXR5QybNK/cIAPCprJ56YrlHgJ3Kp23Xsn97OQAAAOysRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAkspyDwAAAOQbMmleuUfY6ayeemK5R6AbcKUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkpQ8un/84x9HRUVFu9sBBxxQ6rcBAACALq8y40UPPvjgePjhh///TSpT3gYAAAC6tJQarqysjNra2oyXBgAAgG4j5TPdK1asiLq6uth3333j7LPPjtdee22r+7a0tERzc3O7GwAAAOwMSh7dw4YNi1mzZsX8+fNjxowZsWrVqjj66KNj/fr1He4/ZcqUqKmpabvV19eXeiQAAAAoi4qiKIrMN1i3bl0MHjw4brrppjj//PO3eLylpSVaWlra7jc3N0d9fX00NTVFdXV15mgA223IpHnlHgEAKJPVU08s9wiUUXNzc9TU1Hxiu6Z/w1mfPn1iv/32i1deeaXDx6uqqqKqqip7DAAAANjh0v9O94YNG2LlypUxcODA7LcCAACALqXk0X355ZfH448/HqtXr46nn346Tj311OjZs2ecddZZpX4rAAAA6NJK/uvlr7/+epx11lnx7rvvxt577x0jR46MRYsWxd57713qtwIAAIAureTRPXv27FK/JAAAAHRL6Z/pBgAAgF2V6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIUlnuAQAAALqjIZPmlXuEndLqqSeWe4SScqUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AOjIkEnzyj3CTmn11BPLPQIAAOxSXOkGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AGDHGTJpXrlHAACAXYor3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJLKcg/Q3Q2ZNK/cIwAAANBFudINAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAEnSonv69OkxZMiQ6N27dwwbNiyeffbZrLcCAACALikluu+7776YOHFiXHfddbFkyZI47LDDYvTo0bF27dqMtwMAAIAuKSW6b7rpprjgggvivPPOi4MOOihmzpwZn/nMZ+LOO+/MeDsAAADokipL/YIffPBBLF68OCZPnty2rUePHjFq1KhYuHDhFvu3tLRES0tL2/2mpqaIiGhubi71aClaW/5V7hEAAAB2Gt2lBT+csyiKj92v5NH9zjvvxObNm2PAgAHttg8YMCD+/ve/b7H/lClT4vrrr99ie319falHAwAAoIurmVbuCTpn/fr1UVNTs9XHSx7dnTV58uSYOHFi2/3W1tZ47733Yq+99oqKiooyTkaG5ubmqK+vjzVr1kR1dXW5x6GLsC7oiHXBR1kTdMS6oCPWBR0p9booiiLWr18fdXV1H7tfyaO7X79+0bNnz2hsbGy3vbGxMWpra7fYv6qqKqqqqtpt69OnT6nHoouprq72A5AtWBd0xLrgo6wJOmJd0BHrgo6Ucl183BXuD5X8i9R69eoVRxxxRCxYsKBtW2trayxYsCCGDx9e6rcDAACALivl18snTpwY48ePj6997Wtx1FFHxbRp02Ljxo1x3nnnZbwdAAAAdEkp0X3GGWfE22+/Hddee200NDTE4YcfHvPnz9/iy9XY9VRVVcV11123xUcK2LVZF3TEuuCjrAk6Yl3QEeuCjpRrXVQUn/T95gAAAMA2KflnugEAAID/Et0AAACQRHQDAABAEtENAAAASUQ322X69OkxZMiQ6N27dwwbNiyeffbZT/W82bNnR0VFRZxyyilb3eeiiy6KioqKmDZtWmmGZYfJWBcvvfRSnHzyyVFTUxN77LFHHHnkkfHaa6+VeHIylXpdbNiwIS6++OIYNGhQ7L777nHQQQfFzJkzEyYnU2fWxaxZs6KioqLdrXfv3u32KYoirr322hg4cGDsvvvuMWrUqFixYkX2YVBipVwXmzZtiquuuioOOeSQ2GOPPaKuri6++93vxptvvrkjDoUSKvXPi//lvLN7ylgTGeecopttdt9998XEiRPjuuuuiyVLlsRhhx0Wo0ePjrVr137s81avXh2XX355HH300Vvd54EHHohFixZFXV1dqccmWca6WLlyZYwcOTIOOOCAeOyxx2LZsmVxzTXXfOz/POlaMtbFxIkTY/78+XH33XfHSy+9FJdeemlcfPHFMXfu3KzDoMS2ZV1UV1fHW2+91XZ79dVX2z3+85//PG655ZaYOXNmPPPMM7HHHnvE6NGj4/33388+HEqk1OviX//6VyxZsiSuueaaWLJkSdx///2xfPnyOPnkk3fE4VAiGT8vPuS8s3vKWBNp55wFbKOjjjqqmDBhQtv9zZs3F3V1dcWUKVO2+pz//Oc/xYgRI4rf/va3xfjx44tx48Ztsc/rr79efP7zny9eeOGFYvDgwcXNN9+cMD1ZMtbFGWecUXznO9/JGpkdIGNdHHzwwcUNN9zQbttXv/rV4oc//GFJZydPZ9fFXXfdVdTU1Gz19VpbW4va2triF7/4Rdu2devWFVVVVcW9995bsrnJVep10ZFnn322iIji1Vdf3Z5R2YGy1oXzzu4rY01knXO60s02+eCDD2Lx4sUxatSotm09evSIUaNGxcKFC7f6vBtuuCH69+8f559/foePt7a2xjnnnBNXXHFFHHzwwSWfm1wZ66K1tTXmzZsX++23X4wePTr69+8fw4YNizlz5mQcAgmyfl6MGDEi5s6dG2+88UYURRGPPvpovPzyy3HCCSeU/BgovW1dFxs2bIjBgwdHfX19jBs3Ll588cW2x1atWhUNDQ3tXrOmpiaGDRv2sa9J15GxLjrS1NQUFRUV0adPn1KNTqKsdeG8s/vKWBOZ55yim23yzjvvxObNm2PAgAHttg8YMCAaGho6fM5TTz0Vd9xxR9x+++1bfd2f/exnUVlZGZdccklJ52XHyFgXa9eujQ0bNsTUqVNjzJgx8Ze//CVOPfXUOO200+Lxxx8v+TFQelk/L2699dY46KCDYtCgQdGrV68YM2ZMTJ8+PY455piSzk+ObVkX+++/f9x5553x4IMPxt133x2tra0xYsSIeP311yMi2p7Xmdeka8lYFx/1/vvvx1VXXRVnnXVWVFdXl/wYKL2sdeG8s/vKWBOZ55yV2/Vs+JTWr18f55xzTtx+++3Rr1+/DvdZvHhx/OpXv4olS5ZERUXFDp6Qcvg066K1tTUiIsaNGxeXXXZZREQcfvjh8fTTT8fMmTPj2GOP3WHzsmN8mnUR8d/oXrRoUcydOzcGDx4cTzzxREyYMCHq6ura/cs3O4/hw4fH8OHD2+6PGDEiDjzwwPj1r38dN954Yxkno5w6sy42bdoU3/72t6MoipgxY8aOHpUd6JPWhfPOXc8nrYnMc07RzTbp169f9OzZMxobG9ttb2xsjNra2i32X7lyZaxevTpOOumktm0fLuzKyspYvnx5PPnkk7F27drYZ5992vbZvHlz/OAHP4hp06bF6tWrcw6GkslYF/X19VFZWRkHHXRQu+ceeOCB8dRTTyUcBaWWsS7q6uri6quvjgceeCBOPPHEiIg49NBDY+nSpfHLX/5SdHcDnV0XHdltt93iK1/5SrzyyisREW3Pa2xsjIEDB7Z7zcMPP7w0g5MqY1186MPgfvXVV+ORRx5xlbsbyVgXzju7t4w10a9fv7RzTr9ezjbp1atXHHHEEbFgwYK2ba2trbFgwYJ2/4L0oQMOOCCef/75WLp0advt5JNPjuOPPz6WLl0a9fX1cc4558SyZcva7VNXVxdXXHFFPPTQQzvy8NhGGeuiV69eceSRR8by5cvbPffll1+OwYMHpx8T2y9jXWzatCk2bdoUPXq0/99Yz5492wKdrq2z66Ijmzdvjueff74tsIcOHRq1tbXtXrO5uTmeeeaZT/2alFfGuoj4/+BesWJFPPzww7HXXnuVfHbyZKwL553dW8aaSD3nLPlXs7HLmD17dlFVVVXMmjWr+Nvf/lZceOGFRZ8+fYqGhoaiKIrinHPOKSZNmrTV52/t28v/l2+R7H4y1sX9999f7LbbbsVvfvObYsWKFcWtt95a9OzZs3jyySczD4USylgXxx57bHHwwQcXjz76aPGPf/yjuOuuu4revXsXt912W+ahUEKdXRfXX3998dBDDxUrV64sFi9eXJx55plF7969ixdffLFtn6lTpxZ9+vQpHnzwwWLZsmXFuHHjiqFDhxb//ve/d/jxsW1KvS4++OCD4uSTTy4GDRpULF26tHjrrbfabi0tLWU5Rjov4+fFRznv7F4y1kTWOadfL2ebnXHGGfH222/HtddeGw0NDXH44YfH/Pnz277Q4LXXXtviKhQ7v4x1ceqpp8bMmTNjypQpcckll8T+++8ff/zjH2PkyJEZh0CCjHUxe/bsmDx5cpx99tnx3nvvxeDBg+OnP/1pXHTRRRmHQILOrot//vOfccEFF0RDQ0N87nOfiyOOOCKefvrpdr8KeOWVV8bGjRvjwgsvjHXr1sXIkSNj/vz52/83VtlhSr0u3njjjZg7d25ExBYfM3j00UfjuOOO2yHHxfbJ+HlB95axJrLOOSuKoii26xUAAACADrkMCQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJL/A9iNnCdIIuhfAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Корреляция и Злая Бейсбольная Корпорация\n",
     "\n",
-    "Корреляция позволяет нам находить связи между последовательностями данных. В нашем примере представим, что существует злая бейсбольная корпорация, которая платит своим игрокам в зависимости от их роста — чем выше игрок, тем больше он/она получает. Предположим, что существует базовая зарплата в размере $1000 и дополнительный бонус от $0 до $100, зависящий от роста. Мы возьмем реальных игроков из MLB и вычислим их воображаемые зарплаты:\n"
+    "Корреляция позволяет нам находить связи между последовательностями данных. В нашем учебном примере представим, что существует злая бейсбольная корпорация, которая платит своим игрокам в зависимости от их роста — чем выше игрок, тем больше он/она получает денег. Предположим, что существует базовая зарплата в размере $1000, а также дополнительный бонус от $0 до $100, в зависимости от роста. Мы возьмем реальных игроков из MLB и вычислим их воображаемые зарплаты:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Давайте теперь вычислим ковариацию и корреляцию этих последовательностей. `np.cov` даст нам так называемую **ковариационную матрицу**, которая является расширением ковариации на несколько переменных. Элемент $M_{ij}$ ковариационной матрицы $M$ — это корреляция между входными переменными $X_i$ и $X_j$, а диагональные значения $M_{ii}$ — это дисперсия $X_{i}$. Аналогично, `np.corrcoef` даст нам **матрицу корреляции**.\n"
+    "Давайте теперь вычислим ковариацию и корреляцию этих последовательностей. `np.cov` даст нам так называемую **ковариационную матрицу**, которая является расширением ковариации на несколько переменных. Элемент $M_{ij}$ ковариационной матрицы $M$ представляет собой корреляцию между входными переменными $X_i$ и $X_j$, а диагональные значения $M_{ii}$ — это дисперсия $X_{i}$. Аналогично, `np.corrcoef` даст нам **матрицу корреляции**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "В данном случае корреляция немного меньше, но всё же довольно высокая. Теперь, чтобы сделать связь ещё менее очевидной, мы могли бы добавить немного дополнительной случайности, добавив некоторую случайную переменную к зарплате. Давайте посмотрим, что произойдёт:\n"
+    "В этом случае корреляция немного меньше, но все же довольно высокая. Теперь, чтобы сделать связь еще менее очевидной, мы могли бы добавить немного дополнительной случайности, добавив некоторую случайную переменную к зарплате. Давайте посмотрим, что произойдет:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,22 +824,22 @@
    "source": [
     "> Можете догадаться, почему точки выстраиваются в вертикальные линии таким образом?\n",
     "\n",
-    "Мы заметили связь между искусственно созданной концепцией, такой как зарплата, и наблюдаемой переменной *рост*. Давайте также посмотрим, коррелируют ли две наблюдаемые переменные, такие как рост и вес:\n"
+    "Мы наблюдали корреляцию между искусственно созданной концепцией, такой как зарплата, и наблюдаемой переменной *рост*. Давайте также посмотрим, коррелируют ли две наблюдаемые переменные, такие как рост и вес:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "К сожалению, мы не получили никаких результатов — только странные значения `nan`. Это связано с тем, что некоторые значения в нашей серии неопределены, представлены как `nan`, что приводит к тому, что результат операции также становится неопределенным. Если посмотреть на матрицу, можно заметить, что проблемной колонкой является `Weight`, так как автокорреляция между значениями `Height` была вычислена.\n",
+    "К сожалению, мы не получили никаких результатов — только странные значения `nan`. Это связано с тем, что некоторые значения в нашей серии неопределены и представлены как `nan`, что приводит к тому, что результат операции также становится неопределенным. Если посмотреть на матрицу, можно заметить, что проблемной колонкой является `Weight`, так как была вычислена автокорреляция значений `Height`.\n",
     "\n",
-    "> Этот пример демонстрирует важность **подготовки данных** и **очистки**. Без качественных данных мы не можем ничего вычислить.\n",
+    "> Этот пример подчеркивает важность **подготовки данных** и **очистки данных**. Без корректных данных мы не можем ничего вычислить.\n",
     "\n",
-    "Давайте используем метод `fillna`, чтобы заполнить пропущенные значения, и вычислим корреляцию:\n"
+    "Давайте воспользуемся методом `fillna`, чтобы заполнить пропущенные значения, и вычислим корреляцию:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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kpZK+ZWbPS7pa0jfN7O0qzBgvKvv2qyUda2V+ANrHulWLmornYWSoXz3dlVslerq7NDLUnygj1RwzPVu8k0W8pmb691PmRU7oNC0rkM3MJH1W0lPufr8kufu4u1/p7kvcfYkKRfFPu/s/SPqqpNvM7EIzWyppuaTHWpUfgPZy7/AK3bF68fTsUJeZ7li9OOmO9eGVfdq8doX6entkkvp6e5Ifnfz+gcWaVzWBNs8K8ZT6MmbVs+J5iHhNRc2LnNBpWnaSnpn9C0n/XdK4Cm3eJOn33P3hsq95XtJAWZu3eyT9mgpLMda7+yMz/RucpAcAlQa37NVEnTXQfb092rfhxgQZFYwemtDIl7+lU1Nnfud0d5m2/vJPJW+Lh/YVsc0i2kvuJ+m5+zdUf11x+dcsqfr4Pkn3tSonAJjrQm8crJ6PYS8VzgEnIaKVOEkPAOaQqBsHt+4+rFNV7SFOnXZOrcNZi9hmEXMHBTIAzCERNw5KwWe20Za4ptBKFMgAMIdE3DgoxZ3ZRvvimkIrtWwNMgB0gk2j49px8Kim3NVlpnWrFiXfRT/2veP6h1d+JJf0D6/8SGPfO568QB4Z6tf6Lz5RN55S9dHcg8uu0PY7r0+YUUHE6yqakaH+ijXIUoxXSzA3MIMMAGdp0+i4th04Mn1y15S7th04ok2j4+RU5Y+//nRT8TxUF8dS4Uju2x/cnyijgqiPYTRRXy3B3ECBDABnacfBo03F8xAxJ0l6+qXXm4rnobo4ni2el6iPYUTDK/u0b8ONem7LLdq34UaKY5w3FMgAcJamMvrIZ8XzEDEnNIfHEEiPAhkAzpJldHrPiuehK+Mfz4ojHh5DID0KZAA4Sz0X1L+FZsXzsG7VoqbieVl+5SVNxfNwYcbjlBXPS9THEOgkFMgAcJZ+eOp0U/E83Du8QnesXjw929hlpjtWL07eAWHP3TfUFMPLr7xEe+6+IU1Ckt54s/7jlBXPS9THEOgktHkDgLPUZVZ3XWjql8LvHV4RsphKWQzXs7C3RxN1DpWI0Ec36mMIdAoKZABtY/TQhLbuPqxjJya1sLdHI0P9SXetR91MFW2coqKPLoAsFMgA2sLooYmKYmbixKQ27iz0hU1V/PVlzED2JZyBjDhOUZXGgz8mAFSjQAbQFrbuPlwx0ydJk6emtHX34WQFTcQZyIjjVBLxdLjhlX3JxwXIA68sNYcCGUBbqDdTO1M8DxFnII9ljEdWPC+l0+FKSqfDSUpeJANzHa8sNY8CGUCNiDMNUTfE/f7ouF49eeaXzu+Pjicdq6gbz2Y6HS5lgbzm/kcrTvNL3VmjZNV9e/Tia29Mf3zVpfN18J41CTOKeV+IKtpYRX5lKSravAGoUJppmDgxKdeZmYbRQxNJ84q4Ie7dn/jadHFc8urJKb37E19LlJH04iv1Z4qz4nmJ+PhVF8dS4ejrNfc/miahouriWJJefO0NrbpvT6KM4t4XIoo4VlFfWYqMAhlAhZlmGlLK2viWckNcdXE8WzwPb2bUm1nxTlZdHM8Wz0t1cTxbPA9R7wsRRRyrrFeQUr+yFBkFMoAKUWcaRob61dPdVRFLvSEO6BRR7wsRRRwr7p/No0AGUKH34u6m4nkZXtmnzWtXqK+3R6bCzPHmtStYPwfkgBnIxkUcK+6fzWOTHoAKWUtCE599ISleS67LLuyqu5zisgu76nx1Pi7qMv1oqvbBuqgr7WbGiJZfeUnd5RTVR2Ln7apL59ddTnHVpfMTZFMQsaVhVFHHKtr9MzpmkAFUeGXyVFPxTva+jF82WfE8bPnln2oq3sn23H1DTTEcoYvFxpuvaSqeB2YgG8dYzQ3MIAOoELVNWEQRW5dlbQRK3c4papu+1MVwPVEfQ2YgG8dYtT9mkAFUYDNH4yK2Lou4QUiKOVZRRX0MgU5CgQygAi8PNm5exuRnVjwPETcISTHb9EUV9TEEOglLLNAxop1sFBkvDzbmwgvmafLU6brxVEaG+jXy5W/pVNlGve4uS/4KQNSNSxExVkB6FMjoCJxDj1b4UZ3ieKZ4bqpXLQRYxVB6nvFH6uwYKyA9CmR0BM6hRytE3NC4dfdhnTpdWRGfOu0hrnVemWgcYwWk1VCBbGb/zt1/d7YYEFXkTS8Rl35EzEmS1tz/aEXf2tQtuUaG+rX+i0/UjafCtd6c2x/cr33PHp/+eHDZFdp+5/UJMyqIOFZAJ2l0odyaOrF/dT4TAVop6qaX0tKPiROTcp1Z+jF6aIKcqlQXx5L09Euva839j6ZJSNIff/3ppuJ5yFpNkXqVRcTrqro4lqR9zx7X7Q/uT5RRQcSxAjrNjAWymf2GmY1L6jezb5e9PSfp2/mkCJy7qK3LZlr6kUrEnCTVPfFspngeIuYUVcTrqro4ni2el4hjBXSa2ZZY/IWkRyRtlrShLP6au6e9gwBNiLrppd761ZnieYj8Ej3aV8RrPSqeg0B6MxbI7v6KpFckrTOzLklXFb/nLWb2Fnc/kkOOwHkRcdNLxNPFIm48Q/uLeK1HxXMQSK+hNchm9puSXpS0R9Ku4tt/aWFeQEeIeLrYyFC/uqtOuuiel76P7vIrL2kqnoeIOUUV8VofXHZFU/G8RH0OAp2k0U166yX1u/u17r6i+PbuFuYFdISwp4tVT+oFmOTbc/cNNYVn6i4WEXOKqqe7/q+brHgett95fU0xHKWLRcTnINBJGr0zHVVhqQWA8yji5sGtuw9XnMImSaemPMQGoT1336Dnt9wy/RahEL3rF5ZXHMt91y8sT5pP1FnRk2/WPzwlK56X9w8srnj83j+wOGk+UuznINApZlyDbGZ3F9/9e0mPmtkuSSdLn3f3+1uYGzDnRdw8yAahxkU8oXHpgrfU7cKwdMFbEmRzxumMlRRZ8TxEfPwknoNABLN1sbi0+N8jxbf5xTcA50m0zYNsEGpcxBMadxw8mhm/d3hFztnEFvHxk3gOAhHM1sXik3klAnSqaCdmjQz1V8yqSemXfUQVcaYv4ma4qCI+fhLPQSCCRo+a/s+qPYjpFUljkv5fd//R+U4M6AQRX+KNuOwjqogzfVHbqV1+cbd+8MNTdeOpRHz8JJ6DQASNbtL7e0n/U9KDxbdXVWj79s7ixzXMbJGZfd3MnjKz75jZbxfjW83su8UT+f6TmfWWfc9GM3vGzA6b2dA5/H8BNUYPTWhwy14t3bBLg1v2hji2NeqJWZsffrLimNvNDz+ZNJ+Sd93zsJZs2DX99q57Hk6aT9aMXsqZvnWrFjUVz0vWBHbKie2Ij1/Jl8aOVDwHvzTGsQP1RLyvY25otEBe6e7/u7v/5+LbHZLe6+53SfrpjO95U9LH3P0nJa2WdJeZXaNCL+V/XmwT9z8kbZSk4uduk3StpJsk/UnxcBLgnJVmast/4WzcOZ78ZhrxdLFV9+3Ri6+9URF78bU3tOq+PYkyKnjXPQ/rR1U7+3805UmL5PVffKKpeB62HahfSGXF83Jisnb2eKZ4Hj6a8ThlxfNy+4P7azZa7nv2uG5/cH+ijGKKel/H3NBogbzAzKZ73xTff1vxwzfqfYO7f9/dv1l8/zVJT0nqc/e/cvc3i192QNLVxfdvlfQFdz/p7s9JekbSe5v6vwEyRJ2pjai6OJ4tnpfq4ni2ODCbrCsn9RVVrwvJTPFOxX0drdTQGmRJH5P0DTN7VoV25Usl/Vszu0TS52f7ZjNbImmlpINVn/o1SV8svt+nQsFc8kIxVv2zPizpw5K0eHH6fpVoD1E34wAAzg73dbRSQwWyuz9sZsslvUuFAvm7ZRvz/nCm7zWzt0j6iqT17v5qWfweFZZhbC+F6v3TdXJ5QNIDkjQwMJD6D320iaibcQAAZ4f7OlppxiUWZnZj8b9rJd0iaZmkd0i6uRibkZl1q1Acb3f3nWXxD0r6JUm3u09v0XhBUvkukqslHWv8fwWRRNs4EfHEOinmqWdXXVq/1XlWHGhXWX09Up/qnHX6dsJTuUOKel/H3DDb0+3ni//9X+u8/dJM32hmJumzkp4qP3HPzG6S9LuS3ufuPyz7lq9Kus3MLjSzpZKWS3qsif8XBBFx48Twyj5tXrui4kjZzWtXJG+btP3O62uK4cFlV2j7ndcnykg6eM+ammL4qkvn6+A9axJlhLkgYjH63JZbav59K8ZTyjp9O/Gp3OFEva9jbpjtoJBPFP/7b87iZw9K+lVJ42b2RDH2e5L+vaQLJe0p1NA64O6/7u7fMbOHJD2pwtKLu9x9qvbHIrqop1NFO7GuJGUxnCViMRy1v280Uccp6svhqYvheqKOVURR7+tofw29YGNmV5nZZ83skeLH15jZh2b6Hnf/hrubu7/b3a8rvj3s7v/M3ReVxX697Hvuc/dl7t7v7o+c2/8aUmHjBFoh4glxEZfIrH7H5U3F88LL4Y1jrID0Gl3R9GeSdktaWPz4f0ha34J8MAdkzXIw+4Fz0Zdx/WTF8xBxiczz/1T/D9GseF54ObxxjBWQXqNt3t7m7g+Z2UZJcvc3zYzlD6hrZKi/4vhkidkPnLuo11W0JTKRX8Hh5fDGMVZAWo0WyK+b2Y+p2HbNzFZLeqVlWaGtlW7qW3cf1rETk1rY26ORoX5u9jgnXFeNuah7niZP1e7muogWCADQsBkLZDNbL2mfpN+R9JeS3mFm+yQtkPT+lmeHtsXsR3urPtb5oi7Td++7OWFGBeVHOE+cmNT6Lz6R/DqrPpo7dcePkxmtDrLieVpz/6N6+qXXpz9efuUl2nP3DekSkrRpdFw7Dh7VlLu6zLRu1SLdO7wiaU5RjR6a4A9UdIzZphSulvQZSV8rfu0eSX8h6Wfd/Vstzg1AAtXFsVQ4zvld9zycKKOCJRt2NRXPQ3VxLBWO5F51355EGUmnM/YsZsXzUl0cS9LTL72uNfc/miYhFYrjbQeOTG/0nHLXtgNHtGl0PFlOUUVs3wm00owFsrt/3N1/VtLbJX1chaOib5T0bTN7Mof8AOSsujieLd7Jqovj2eKdrLo4ni2ehx0HjzYV72Qzte8E5qJG1yD3SLpM0luLb8ck8Sc2AKBtRWwdGFXkzZ9AK8y2BvkBSddKek2F2eP/T9L97v6DHHIDAKBloh6qEhGHl6DTzLYGebEKp979g6QJSS9IOtHinAAkdFFX/eIgK97Jqo/kni2eh4hHOkvZv2xS9tZYt2pRU/FOxuEl6DSzrUG+SdJ7JH2qGPqYpL81s78ys0+2OjkA+dvyyz/VVDwvz2ccCZwVz8PBe9bUFMOpu1g8t+WWmmLYlP5I5aweGil7a9w7vEJ3rF48PWPcZaY7Vi+mi0UdHF6CTmPe4ForM7ta0qCkn5X0S5J+zN17W5fa7AYGBnxsbCxlCsCcM7hlb92XUvt6e7Rvw40JMsJcsGzjw5nLGZ7dnL6FIIDOZGaPu/tAdXy2NcgfUaEgHpR0SoWeyPslfU5s0gPmpMibcSL2rI3YGzZiTmyIA9BOZutisUTSlyV91N2/3/p0AKQWdTNOqWdtSalnraRkRXKpN2yp/VWpN6ykZAVpxJwk6ZL5XXr9jam6cQCIZrY1yHe7+5cpjoHOMTLUr+55latYu+dZ8s04EXvWRuwNGzEnSfphneJ4pjgApJRyAzGAqOrt8kos4kv09WbaZ4rnIeoSmaxHiQUWACKiQAZQYevuwzpVdWreqSlPPgOJxmQthUm9RAYA2kmjJ+kBbS/ixiVJWnP/oxXH7S6/8hLtufuGZPlEnYFEY0aG+rX+i0/UjaNWtOdfyar79lQcWZ66fSDQaZhBRkcobVyaODEp15mNS6OHJpLmVf3LWZKeful1rbn/0TQJiZfC21294nimeCeL+PyTaotjSXrxtTe06r49iTICOg8zyHNAxJnRaDnNtHEpZV7Vv5xniwM4f6I+/6qL49niAM4/CuQ2F7GlU8ScWDYAAAAaxRKLNhexpVPEnNi4BAAAGkWB3OYizoxGzGlkqF893ZUHEvR0d7FxqY4LMlq6ZcXzEjUvNOairvoPVFY8D8uvvKSpeF6uunR+U/G8jB6a0OCWvVq6YZcGt+xNvocDaCUK5DYXcWY0Yk7DK/u0ee0K9fX2yCT19fZo89oVyddqX35xd1PxPHzqV65rKp6XVe+4oql4Hp7fcktT8U723fturimGL+oyffe+mxNlJK16x481Fc/LxpuvaSqeh6gbnYFWYQ1ymxsZ6q9Y7yulnxmNmJNUKJJTF8TVss64SHj2ReZSmNQbGvc9e7ypeF7uWL1YOw4e1ZS7usy0btWipPmY6ncciTDRnrIYrmem0xlTHV8uxXwORt3oDLQKBXKbK92YInWMiJhTVK9MnmoqnoeIS2Si2jQ6rm0Hjkx/POU+/XGqAos2fY2LeDqjFPM5GDEnoJUokOeAiDOjEXOKaGFvT91jiVMvkYmWU1RRZyDRmC6zusVwl6Wdb4/4HIyYE9BKrEEGEoq4eXBkqF9d8yoLhK55lnyJzOCy+muNs+J5iDoDicZkLYdJvUwm6n0hWk5AK1EgAwlF3Dw49r3jmjpdWeBNnXaNfS/tWt+IsuYZU84/9mXM6GXFO9m9wyt0x+rF0zPGXWa6Y/Xi5LP/Ee8LEXMCWoklFugYm0bHazZTpf5FKMVbjhJ12UDETXoR1/uODPXXPVY6wkzf0g27KsbGJD1Hx4+6ot0XJOlLY0eml1lMnJjUl8aOhMsROF+YQUZHKG2mKr30XdpMtWl0PHFm8bBsoL3VK45niuelujiWCn9ILN2wK0U6krgvNOP2B/fX/DG679njuv3B/YkyAlqLAhkdYaZZUQCtF3G2nftC4yK+ggO0EgUyOgKzogCqcV8AkIUCGR0hq21T6nZOANLhvgAgCwUyOkLUdk5Ap4jY8YP7QuMitlkEWokCGR0hajsntLeIRd/zGV0hsuJ5+fQHrmsqnoeBn7ii5pfgvGIclbbfeX1NMTy47Aptv/P6RBkBrUWbN3SMe4dXhCyIRw9NhDqWO+rpYhHzinq6WOpiuJ6tuw9nxlNd71t3H9bpqthppc0pMophdBJmkIGERg9NaOPOcU2cmJSr0Ft0485xjR6aSJZT1I1L71hwcVPxPFw8v/4tNCveyY7V+UNipngeIuYEIAbu4kBCW3cf1uSpqYrY5KmpzNm2PETduPT3L/+wqXgenn7p9abinSxrVj3lbHvEnADEQIEMJBRxBivqDHLUvNCYkaF+9XR3VcR6uruSnvAXMScAMbAGGUio9+Ju/eCHp+rGU7k8I6fLE+YkxVyDjMaV1vRGWm8fMScAMbSsQDazRZL+XNLbVdj38IC7f8bMrpD0RUlLJD0v6Vfc/QfF79ko6UOSpiR9xN13tyo/tFa0jWdRZU1+ppwUjZiTVGi9te3AkbrxVOZ3md6Yqh2Y+V1pi/YldY5vjrBx7+MPPaE3i8M1cWJSH3/oieT3hY9+8Ynp0/wmTkzqo19Mn5NUe7RzhI4R3NfRSVq5xOJNSR9z95+UtFrSXWZ2jaQNkv6ruy+X9F+LH6v4udskXSvpJkl/YmZddX8yQou48SyqE5O1M7UzxfMQMSdJdYvjmeJ5qFcczxTPQ73ieKZ4Xv7Zxl3TxXHJm16Ip7J0w66ao669GE+pujiWCkc63/7g/kQZcV9H52lZgezu33f3bxbff03SU5L6JN0q6fPFL/u8pOHi+7dK+oK7n3T35yQ9I+m9rcoPrRNx4xmAtKqL49niecj6p1Ovaq8ujmeL54H7OjpNLpv0zGyJpJWSDkq6yt2/LxWKaElXFr+sT9LRsm97oRir/lkfNrMxMxt7+eWXW5o3zk7EjWcAgLPHfR2dpuUFspm9RdJXJK1391dn+tI6sZo/5N39AXcfcPeBBQsWnK80cR7ROgkA5hbu6+g0LS2QzaxbheJ4u7vvLIZfNLMfL37+xyW9VIy/IKl8t83Vko61Mj+0RtTWSaOHJjS4Za+WbtilwS17Q6ydi3hUMdAKF2Rc1FnxPER9/lUf6TxbPA9R7+tAq7SsQDYzk/RZSU+5+/1ln/qqpA8W3/+gpL8si99mZhea2VJJyyU91qr80DrDK/u0ee0K9fX2yCT19fZo89oVSXc7R91g8ukPXNdUPA9/mPFvZ8XzEjGviAVWxJwk6VO/cl1T8Tw8t+WWmnGxYjyl7XdeX1MMp+5iEfG+DrRSK/sgD0r6VUnjZvZEMfZ7krZIesjMPiTpiKT3S5K7f8fMHpL0pAodMO5y96man4q2MLyyL9SNc6YNJinzzNrgkjKviDmV/v2seKq8Fvb2aKLOGszUp8NFy0mK+fhJ6YvhLKlbutUT7b4OtFIru1h8w93N3d/t7tcV3x52939y91909+XF/x4v+5773H2Zu/e7+yOtyg2dJ+oGk4h5Rcxppn8/ZV4jQ/3qnlc5B9k9zzgdro6Ijx8AZOGoaXSEqBtMLuqu/xTMiueha179F+Oz4nl5a0/9k/yy4rmp9xp9QlFfCo/6HASAejhqeg7gdKPZjQz1a+PO8YplFhFm1U6+ebqpeB7ePF2/C2xWPC9ZJ0qnPGl66+7DOlV1KMipKU++bCDiS+FRn4MAUA8FcpsrbT4r/dIpbT6TFO4XZEqlsYj2h0RWzZm4Fg3pBz+sf5JfVjwP9db6zhTvZFGfgwBQDwVym4u6+SyiiLNqXWaa8tpquCvltGhQEccqYk6RRXwOAkA9FMhtjo0vjVt13x69+Nob0x9fdel8HbxnTcKMpHWrFmnbgSN146ksv/ISPf3S63XjKdUrRGeK5yFiTpK0ZMOumtjzAbo1RHwOAkA9bNJrc2x8aUz1L2ZJevG1N7Tqvj2JMir48t8ebSqeh6P/9MOm4oilXnE8UzwvUZ+DAFAPBXKbi9rSKZrqX8yzxfPyo6n6M41Z8TxEzAntL+pzMOIJmwDSY4lFm2PjCwCcHTY5A8hCgTwHsPEFAJrHJmcAWVhigY5w1aXzm4rn5aKu+t0OsuKI5YKMhykr3skiPgfZ5AwgCwUyOsLBe9bU/CKOsIP+l99Tv1tFVjwPfRkbPLPieYmY1zObb6kphi+wQjyVrG4VqbtYbLz5mqbieWCTM4AsFMjoGGuufft0f9ouM6259u2JM5J2HKzfrSIrnoeRof6aG8O8YjylkaF+dVcdd909z5Ln9czmW/T8ljNvKYvjkj/8wHUVR03/4QeuS52Stu4+3FQ8D2xyBpCFAhkdYdPouLYdODLdn3bKXdsOHNGm0fGkeUXsozv2veOqPuj6dDGeXPXSBZYy1ChtPJs4MSnXmY1nqbszRDx1cHhlnzavXVHxx8TmtStYfwyAAhmdIeJMbVRRx2rr7sM6VdVq7tSUJ52BjGimjWcpZZ0umPrUweGVfdq34UY9t+UW7dtwI8UxAEkUyOgQEWdqo4o6VmyoakzUcYp6XQFAPRTI6AhRZ68i5hUxJ0l6a093U/FO1Xtx/fHIiucl4iZLAMhCH2S0xOihiVCHl6xbtUjbDhypG08pYl4Rc5KkrPo8cd1e9wjnlB0jsiZkU0/Ujgz1a/0Xn6gbT+n2B/dr37Nn1tcPLrtC2++8PmFGBdHuoVLcsQJagRlknHcRNwl9+W/rr5/NiuelXiE6UzwPEXOSpB/88FRT8TzUK45niufhxGT98ciK5+X3MzbEZsXzUF3wSdK+Z4/r9gf3J8qoIOI9NOpYAa1CgYzzLuImoR9N1Z8+y4oDOL9ePTnVVDwP1QXfbPG8RLyHRh0roFUokHHeRd0kBADtgHsokB4FMs47TqcCgLPHPRRIjwIZ5x2nUwGodtmFXU3F8zC47Iqm4nmJeA+NOlZAq1Ag47yLeDpV1BZTWd0OUnZBuGP14qbinSziWGUdK536uOlvf/KmmmL4sgu79O1P3pQoI2n7ndfXFHgROjNEvIdGHSugVcxT9/45BwMDAz42NpY6DbSB0UMTGvnytypOYuvuMm395Z9K3jopmmUbH657eEOXmZ7dfHOCjApm6gyR6g+KiGM1uGVv3eOb+3p7tG/DjQkyOiNi6zIAnc3MHnf3geo4fZDROarrmPb927ClOPGscRHHKuoGr1LrslJ3hlLrMkkUyQDCYYkFOsLW3Yd16nRl0XLqtCdtm4T2F/HUwagbvCK2LgOALBTI6AhRZ9Wkwsza4Ja9Wrphlwa37E16GEBky6+8pKl4HrJOF0x56mDEDV5S7OcgAFSjQEZH6L24u6l4XiKemNXbkzFWGfG87Ln7hppiePmVl2jP3TekSUjSwE9coa55lbPFXfNMAz+Rbmd/xA1eUtyZbQCohwIZLRFtVjRrSWjqZbURX3bOWh2QcNXAtGdeen3Gj/O2dfdhTVUt3ZkKsHRn88NPVvzRtfnhJ5PmIxVmtrur/pjonmfJZ7aj3asAxECBjPMu4qzoiclTTcXzUq/bwEzxPPzgh/XHJCuel6UbdtXdZ7l0hu4WrRbx8Vt13x69+NobFbEXX3tDq+7bkyijMtV/ZCX+oyvivQpADBTIOO8izoqi/WVN9tNbo1J1cTxbPC9bdx+uaLMoSaem0s62c68CkIUCGecdm3EAVIt4X4iYE4AYKJBx3rEZB0C1iPeFiDkBiIECGeddxDZTV106v6k40IgLMtbQZsXzEPVaj3hfiJgTgBgokHHeRWwzdfCeNTUFwlWXztfBe9Ykyqggq44K0DACDXhm8y01xfAFVoinEvVaj3hfiJgTgBg4ahotMbyyL9wvmdQFQj0Le3vqdjxI+RJvl1ndo5JTng5X+vcj5pWyGM4S8VqXYt4XIuYEID1mkIGEIr7EG/F0OEl1i+OZ4gAAnC1mkIGESjNXW3cf1rETk1rY26ORof6kM1r3Dq+QJO04eFRT7uoy07pVi6bjqVx+cXfdXsyXJz4NEQAw91AgA4lFfIn33uEVyQvialFPQwQAzD0ssQDQFqKehggAmHtaViCb2efM7CUz+7uy2HVmdsDMnjCzMTN7b9nnNprZM2Z22MyGWpUXgPaUtRkv9SY9AMDc08olFn8m6Y8k/XlZ7A8kfdLdHzGzm4sf32Bm10i6TdK1khZK+msze6e7TymQ0UMTodaKRs5rzf2P6umXXp/+ePmVl2jP3TekS0jS0g27Ko4lNknPbUnfgWDJhl01secT5xUxp6ib9CKOVcTnHwC0k5bNILv7f5N0vDos6bLi+2+VdKz4/q2SvuDuJ939OUnPSHqvAhk9NKGNO8c1cWJSLmnixKQ27hzX6KEJ8qpS/ctZkp5+6XWtuf/RNAmptjiWChfj0jrFTZ7qFVczxfMQMaeoIo5VxOcfALSbvNcgr5e01cyOSvqUpI3FeJ+ko2Vf90IxFsbW3Yc1eapyQnvy1JS27j6cKKOCiHlV/3KeLZ6HrDlG9ndhron4/AOAdpN3gfwbkj7q7oskfVTSZ4vxeosI69YuZvbh4vrlsZdffrlFadY6Vucwh5nieYmaFwAAQLvKu0D+oKSdxfe/pDPLKF6QVH4KwdU6s/yigrs/4O4D7j6wYMGCliVaLetks5Qnns3076fOCwAAoF3lXSAfk/TzxfdvlPR08f2vSrrNzC40s6WSlkt6LOfcZhTxxDMpZl7Lr7ykqXgesvoc0P8Ac03E5x8AtJtWtnnbIWm/pH4ze8HMPiTpTkn/t5l9S9L/JenDkuTu35H0kKQnJX1N0l3ROlgMr+zT5rUr1NfbI5PU19ujzWtXJO8WETGvPXffUPPLOPUu+ue23FJTDEfoYvGHH7iuqXgesjowpO7MwFg1JuLzDwDajXkbH0M1MDDgY2NjqdMAztrglr2aqLNevK+3R/s23Jggo4KIrQMZKwDA+WZmj7v7QHWco6aBhCJusiy1Dix1Rym1DpSUtPCrVxzPFM9D1LECAJwbjpoGEoq4yTJi60Ap5kl6UccKAHBumEFGS2waHdeOg0c15a4uM61btUj3Dq9ImlPEl8JHhvorZiCl9JssI85qSzFP0os6VgCAc8MMMs67TaPj2nbgyHThMuWubQeOaNPoeLKcIp44KMXcZNl7cXdT8bz0ZcyqZ8XzEPEVAADAuaNAxnm34+DRpuJ5iPxS+B9//emKwv2Pv/70rN/TSlkTsqn382bNqqecbR8Z6ld3V+USj+4uS97+8fYH92vJhl3Tb7c/uD9pPiWjhyY0uGWvlm7YpcEte5P/gQoAWSiQcd5FfCk84gYvSVpz/6M1RwA//dLrWnP/o2kSknRi8lRT8bx8aexIU/HcVF/Wif+QuP3B/dr37PGK2L5njycvkqO+igMA9VAgAwlVF8ezxTtZddE3WzwPW3cf1qnTlRXxqdOe9JWJiOMkxX4VBwCqUSADwFlik17jGCsA7YQCGeddxHZcEXNC+2OTXuMYKwDthAIZ5926VYuaiuchYk6Sao4Eni2eh6w/GVL/KTG47Iqm4nkYGepXT3dXRSx1m76I4yTFHCsAyEKBjPPu3uEVumP14unZ2S4z3bF6cdI+yBFzkqQ9d99QUwwvv/IS7bn7hjQJSXpuyy01xbAV4yltv/P6miJvcNkV2n7n9YkyitmmL+I4STHHCgCymKfu3XQOBgYGfGxsLHUaqCPioRwAAADlzOxxdx+ojnOSHs67Ujun0o71UjsnSRTJAAAgPJZY4LyjnRMAAGhnFMg472jnBAAA2hkFMs472jkBAIB2xhpknHcjQ/0a+dK3Kk4Y655nyds5rbpvj1587Y3pj6+6dL4O3rMmYUYFEfNasmFXTez5xF0sJOndn/iaXj15ZvnOZRd26dufvClhRjFz2jQ6rh0Hj2rKXV1mWrdqUfKOLVLt0eqpO7ZIbCgGUB8zyGiNen3CEqouQiXpxdfe0Kr79iTKqCBiXvWK45nieakuRCXp1ZNTevcnvpYoo5g5bRod17YDRzRV7FA05a5tB45o0+h4spyk2uJYKhypvub+R9MkpDMbiidOTMp1ZkPx6KGJZDkBiIECGefd1t2HdWqqsn3gqSlPukmvugidLZ6XqHlFVF2IzhbPQ8Scdhw82lQ8L9XF8WzxPLChGEAWCmScd2zSA9KZyuhtnxXvZNyrAGShQMZ5xyY9IJ3SaZGNxjsZ9yoAWSiQcd6NDPWre17lL+PUm/SuunR+U/G8RM0rossu7GoqnoeIOa1btaipeF6qj1SfLZ6HkaF+9XRXPlY93V3JNxQDSI8CGa0RbJPewXvW1BSdEbpFRMwrq1tF6i4W3/7kTTWFZ+qOERFzund4he5YvXh6xrjLTHesXpy8i8Weu2+oKYZTd7EYXtmnzWtXqK+3Ryapr7dHm9euoIsFAJm38bq0gYEBHxsbS50Gqgxu2auJOmv4+np7tG/DjQkyAgAAqGVmj7v7QHWcPshzQLQ+nmx8aU60xy9qTgAA5IUCuc2V+niWWhWV+nhKSlbQLOztqTuDzMaXWhEfv4g5AQCQJ9Ygt7mIfTzZ+NK4iI9fxJwAAMgTM8htLuJyhtIsIy/Rzy7i4xcxJwAA8kSB3OaiLmcYXtlHQdyAiI9fxJwAAMgTBXITIm5cGhnqr1gvKsVYzhBxrG5/cL/2PXt8+uPBZVdo+53XJ8yo8Pjd/dATOl3WTGaeKenjF/WaimrT6Lh2HDyqKXd1mWndqkXJW6oBAM4Na5AbVNq4NHFiUq4zG5dGD00kzStiH8+IY1VdHEvSvmeP6/YH9yfKqGDse8crimNJOu2FeCoRr6moNo2Oa9uBI9PHOE+5a9uBI9o0Op44MwDAuaAPcoPo7du4iGO1ZMOuzM+lPABj2caHp4urcl1menbzzQkyii3aKxM8fgDQ3uiDfI7YuNQ4xqpx9YqrmeKdLGL7OR4/AJibWGLRoKwNSmxcqsVYNa50HHCj8U4Wsf0cjx8AzE0UyA2it2/jIo7V4LIrmornZd2qRU3FO1nEVyZ4/ABgbqJAbhAblxoXcazeP7BY86om9eZZIZ7SvcMrdMfqxdMzjl1mumP1Yrog1BHxlQkePwCYm9ikh44QceMgmlO9BlkqvDKR+o8vAED7YpMeOlrEl+fRHE5oBADkhQIZHYHT4eYGTmgEAOSBNcjoCBE3DgIAgJiYQUZH4OV5AADQqJYVyGb2OUm/JOkld//nZfHfkvSbkt6UtMvdf6cY3yjpQ5KmJH3E3Xe3Kre5JtrpYlLhCN4dB49qyl1dZlq3ahE7+zNUH4M9uOwKbb/z+oQZxcxJipkX1zoAzD2tXGLxZ5JuKg+Y2S9IulXSu939WkmfKsavkXSbpGuL3/MnZlb5ejjqKu3snzgxKdeZ08VGD00ky2nT6Li2HTgyfZrYlLu2HTiiTaPjyXKKOE5SbcEnSfuePa7bH9yfKKOYOUkx84p4rQMAzl3LCmR3/2+SjleFf0PSFnc/Wfyal4rxWyV9wd1Puvtzkp6R9N5W5TaXRDxdbMfBo03F8xBxnCTVFHyzxfMQMaeZ/v2UeUW81gEA5y7vTXrvlPRzZnbQzP7GzN5TjPdJKv+N8kIxVsPMPmxmY2Y29vLLL7c43fgiti+byuitnRXPQ8RxQvuLeK0DAM5d3gXyBZIul7Ra0oikh8zMJFmdr637G8bdH3D3AXcfWLBgQesybRMRTxcrnSrWaDwPEccJ7S/itQ4AOHd5F8gvSNrpBY9JOi3pbcX4orKvu1rSsZxza0sR25etW7WoqXgeIo6TVNhk1kw8DxFzmunfT5lXxGsdAHDu8i6QRyXdKElm9k5J8yX9o6SvSrrNzC40s6WSlkt6LOfc2tLwyj5tXrtCfb09MhWOTk599O69wyt0x+rF07NoXWa6Y/XipDv7I46TJG2/8/qaAi91Z4aIOUkx84p4rQMAzp15i9bKmdkOSTeoMEP8oqRPSPqPkj4n6TpJb0j6uLvvLX79PZJ+TYX2b+vd/ZHZ/o2BgQEfGxtrRfoAAACY48zscXcfqIm3qkDOAwUyAAAAzlZWgcxR0wAAAEAZjpoGgDkm4umaANBOKJABYA4pnRpZOhindGqkJIpkAGgQBfIcwGxRYzaNjmvHwaOacleXmdatWkS3Acw5M50ayX0BABpDgdzmmC1qzKbRcW07cGT64yn36Y8pkjGXcGokAJw7Num1uZlmi3DGjoNHm4oD7YpTIwHg3FEgtzlmixozldHOMCsOtKuop0YCQDuhQG5zzBY1pnTSWaNxoF1FPTUSANoJa5Db3MhQf8UaZInZonrWrVpUsQa5PA7MNcMr+yiIAeAcUCC3udIvQbpYzKy0EY8uFgAAYDYcNQ0AAICOxFHTAAAAQAMokAEAAIAyFMgAAABAGQpkAAAAoAwFMgAAAFCGNm9zwOihCdq8tbGIj9+m0XFa4gEAOhYFcpsbPTRRcVDIxIlJbdw5LknJiyzMLuLjt2l0vOJQlSn36Y8pkgEAnYAlFm1u6+7DFafoSdLkqSlt3X04UUZoRsTHb8fBo03FAQCYayiQ29yxE5NNxRFLxMdvKuPwoKw4AABzDQVym1vY29NUHLFEfPy6zJqKAwAw11Agt7mRoX71dHdVxHq6uzQy1J8oIzQj4uO3btWipuIAAMw1bNJrc6WNXNG6IKAxER+/0kY8ulgAADqVeRuvKxwYGPCxsbHUaQAAAKANmdnj7j5QHWeJBQAAAFCGAhkAAAAoQ4EMAAAAlKFABgAAAMpQIAMAAABlKJABAACAMhTIAAAAQBkKZAAAAKAMBTIAAABQhgIZAAAAKEOBDAAAAJShQAYAAADKmLunzuGsmdnLkr6XOo9A3ibpH1Mn0QYYp8YxVo1jrBrHWDWOsWoM49Q4xqrST7j7gupgWxfIqGRmY+4+kDqP6BinxjFWjWOsGsdYNY6xagzj1DjGqjEssQAAAADKUCADAAAAZSiQ55YHUifQJhinxjFWjWOsGsdYNY6xagzj1DjGqgGsQQYAAADKMIMMAAAAlKFABgAAAMpQILcpM+s1sy+b2XfN7Ckzu97MrjOzA2b2hJmNmdl7U+eZmpn1F8ej9Paqma03syvMbI+ZPV387+Wpc01thrHaWrzOvm1m/8nMelPnmlLWOJV9/uNm5mb2toRphjDTWJnZb5nZYTP7jpn9QeJUk5vh+cd9vQ4z+2jx2vk7M9thZhdxX68vY6y4r8+CNchtysw+L+m/u/ufmtl8SRdLekjSp939ETO7WdLvuPsNKfOMxMy6JE1IWiXpLknH3X2LmW2QdLm7/27SBAOpGqt+SXvd/U0z+3eSxFgVlI+Tu3/PzBZJ+lNJ75L0M+5OM/6iqmvqHZLukXSLu580syvd/aWkCQZSNVYPivt6BTPrk/QNSde4+6SZPSTpYUnXiPt6hRnG6pi4r8+IGeQ2ZGaXSfpfJH1Wktz9DXc/IcklXVb8sreq8ATAGb8o6Vl3/56kWyV9vhj/vKThVEkFNT1W7v5X7v5mMX5A0tUJ84qm/JqSpE9L+h0VnouoVD5WvyFpi7uflCSK4xrlY8V9vb4LJPWY2QUqTBAdE/f1LDVjxX19dhTI7ekdkl6W9B/M7JCZ/amZXSJpvaStZnZU0qckbUyYY0S3SdpRfP8qd/++JBX/e2WyrGIqH6tyvybpkZxziWx6nMzsfZIm3P1baVMKq/yaeqeknzOzg2b2N2b2noR5RVQ+VuvFfb2Cu0+oMBZHJH1f0ivu/lfivl5jhrEqx329Dgrk9nSBpJ+W9P+4+0pJr0vaoMKszEfdfZGkj6o4wwypuAzlfZK+lDqX6LLGyszukfSmpO0p8oqmfJzM7GIVlgz8ftqsYqpzTV0g6XJJqyWNSHrIzCxReqHUGSvu61WKa4tvlbRU0kJJl5jZHWmzimm2seK+no0CuT29IOkFdz9Y/PjLKhTMH5S0sxj7kiQ2c5zxryR9091fLH78opn9uCQV/8tLvGdUj5XM7IOSfknS7c7GhZLycVqmwi+gb5nZ8yq8XPlNM3t7wvwiqb6mXpC00wsek3RaUsdvaiyqHivu67X+paTn3P1ldz+lwvj8rLiv15M1VtzXZ0GB3Ibc/R8kHTWz/mLoFyU9qcIarJ8vxm6U9HSC9KJap8olA19V4RePiv/9y9wziqtirMzsJkm/K+l97v7DZFnFMz1O7j7u7le6+xJ3X6JCAfjTxecqap9/oyrco2Rm75Q0XxIbGguqx4r7eq0jklab2cXFVx5+UdJT4r5eT92x4r4+O7pYtCkzu06F3fLzJf29pH8j6VpJn1Hh5csfSfq37v54qhyjKL78fVTSO9z9lWLsx1To+rFYhRvI+939eLosY8gYq2ckXSjpn4pfdsDdfz1RiiHUG6eqzz8vaYAuFpnX1HxJn5N0naQ3JH3c3fcmSzKIjLH6F+K+XsPMPinpAyosDzgk6f+Q9BZxX6+RMVbfEff1GVEgAwAAAGVYYgEAAACUoUAGAAAAylAgAwAAAGUokAEAAIAyFMgAAABAGQpkAAjMzP5n1cf/2sz+aJbveZ+ZbZjla24ws/+S8bn1xZZjANCRKJABYI5x96+6+5Zz+BHrJVEgA+hYFMgA0KbMbIGZfcXM/rb4NliMT88ym9kyMztQ/Pz/WTUj/RYz+7KZfdfMtlvBRyQtlPR1M/t6gv8tAEjugtQJAABm1GNmT5R9fIUKR+pKhRPWPu3u3zCzxZJ2S/rJqu//jKTPuPsOM6s+KWulCidwHpO0T9Kgu/97M7tb0i9wEiCATkWBDACxTbr7daUPzOxfSxoofvgvJV1jZqVPX2Zml1Z9//WShovv/4WkT5V97jF3f6H4c5+QtETSN85b5gDQpiiQAaB9zZN0vbtPlgfLCubZnCx7f0r8TgAASaxBBoB29leSfrP0gZldV+drDkj634rv39bgz31NUvVMNAB0DApkAGhfH5E0YGbfNrMnJVWvMZYKHSnuNrPHJP24pFca+LkPSHqETXoAOpW5e+ocAAAtUuxnPOnubma3SVrn7remzgsAImO9GQDMbT8j6Y+ssDD5hKRfS5sOAMTHDDIAAABQhjXIAAAAQBkKZAAAAKAMBTIAAABQhgIZAAAAKEOBDAAAAJT5/wEF2g87zs/PPwAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Заключение\n",
     "\n",
-    "В этом блокноте мы научились выполнять базовые операции с данными для вычисления статистических функций. Теперь мы знаем, как использовать надежный аппарат математики и статистики для подтверждения гипотез, а также как вычислять доверительные интервалы для произвольных переменных на основе выборки данных.\n"
+    "В этом блокноте мы изучили, как выполнять базовые операции с данными для вычисления статистических функций. Теперь мы знаем, как использовать надежный аппарат математики и статистики для проверки гипотез, а также как вычислять доверительные интервалы для произвольных переменных на основе выборки данных.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от ответственности**:  \nЭтот документ был переведен с использованием сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Хотя мы стремимся к точности, пожалуйста, имейте в виду, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные интерпретации, возникшие в результате использования данного перевода.\n"
+    "\n---\n\n**Отказ от ответственности**:  \nЭтот документ был переведен с помощью сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Хотя мы стремимся к точности, пожалуйста, имейте в виду, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникшие в результате использования данного перевода.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:29:19+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:04:08+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ru"
   }
diff --git a/translations/ru/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ru/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 94d83dc1..2af85b17 100644
--- a/translations/ru/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ru/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -592,7 +592,7 @@
    "source": [
     "Выводы:\n",
     "* Возраст - в пределах нормы\n",
-    "* Пол - однородный\n",
+    "* Пол - равномерно распределен\n",
     "* ИМТ, Y - сложно сказать\n"
    ],
    "metadata": {}
@@ -602,7 +602,7 @@
    "source": [
     "### Задание 4: Проверьте корреляцию между различными переменными и прогрессированием заболевания (Y)\n",
     "\n",
-    "> **Подсказка** Матрица корреляции предоставит наиболее полезную информацию о том, какие значения зависят друг от друга.\n"
+    "> **Подсказка** Матрица корреляции предоставит наиболее полезную информацию о том, какие значения взаимосвязаны.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Отказ от ответственности**:  \nЭтот документ был переведен с использованием сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия обеспечить точность, автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные интерпретации, возникшие в результате использования данного перевода.\n"
+    "\n---\n\n**Отказ от ответственности**:  \nЭтот документ был переведен с помощью сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Хотя мы стремимся к точности, пожалуйста, имейте в виду, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникшие в результате использования данного перевода.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:53:57+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:04:34+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ru"
   }
diff --git a/translations/sk/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/sk/1-Introduction/04-stats-and-probability/assignment.ipynb
index 6cce61ec..e7d39591 100644
--- a/translations/sk/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/sk/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,14 +149,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "V tejto množine údajov sú stĺpce nasledovné:\n",
+    "V tomto datasete sú stĺpce nasledovné:\n",
     "* Vek a pohlavie sú samozrejmé\n",
     "* BMI je index telesnej hmotnosti\n",
     "* BP je priemerný krvný tlak\n",
     "* S1 až S6 sú rôzne merania krvi\n",
-    "* Y je kvalitatívne meranie progresie ochorenia počas jedného roka\n",
+    "* Y je kvalitatívne hodnotenie progresie ochorenia počas jedného roka\n",
     "\n",
-    "Poďme preskúmať túto množinu údajov pomocou metód pravdepodobnosti a štatistiky.\n",
+    "Poďme preskúmať tento dataset pomocou metód pravdepodobnosti a štatistiky.\n",
     "\n",
     "### Úloha 1: Vypočítajte priemerné hodnoty a rozptyl pre všetky hodnoty\n"
    ],
@@ -200,7 +200,7 @@
    "source": [
     "### Úloha 4: Otestujte koreláciu medzi rôznymi premennými a progresiou ochorenia (Y)\n",
     "\n",
-    "> **Tip** Korelačná matica vám poskytne najužitočnejšie informácie o tom, ktoré hodnoty sú závislé.\n"
+    "> **Tip** Korelačná matica vám poskytne najviac užitočných informácií o tom, ktoré hodnoty sú závislé.\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:46:34+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:52:10+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "sk"
   }
diff --git a/translations/sk/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/sk/1-Introduction/04-stats-and-probability/notebook.ipynb
index f95b9cf7..f62ebe0c 100644
--- a/translations/sk/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/sk/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Náhodné premenné a rozdelenia\n",
-    "Začnime s výberom vzorky 30 hodnôt z rovnomerného rozdelenia od 0 do 9. Tiež vypočítame priemer a rozptyl.\n"
+    "Začnime odobratím vzorky 30 hodnôt z rovnomerného rozdelenia od 0 do 9. Tiež vypočítame priemer a rozptyl.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Analýza reálnych údajov\n",
     "\n",
-    "Pri analýze údajov z reálneho sveta sú priemerná hodnota a rozptyl veľmi dôležité. Poďme načítať údaje o hráčoch baseballu zo [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Pri analýze reálnych údajov sú priemer a rozptyl veľmi dôležité. Poďme načítať údaje o hráčoch baseballu zo [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Používame balík nazvaný [**Pandas**](https://pandas.pydata.org/) na analýzu údajov. O Pandas a práci s údajmi v Pythone budeme hovoriť neskôr v tomto kurze.\n",
+    "Používame balík nazvaný [**Pandas**](https://pandas.pydata.org/) na analýzu dát. O Pandas a práci s dátami v Pythone budeme hovoriť neskôr v tomto kurze.\n",
     "\n",
     "Poďme vypočítať priemerné hodnoty pre vek, výšku a váhu:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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o5qGbh24euhXDArlEHR0duUeoHZp56Oahm4duHrp56OahWzEskEt04cKF3CPUDs08dPPQzUM3D908dPPQrRgWyCVasGBB7hFqh2Yeunno5qGbh24eunnoVgwL5BItWrQo9wi1QzMP3Tx089DNQzcP3Tx0K2ZOXsUiIs5KOv6Of+L6WyLpXO4haoZmHrp56Oahm4duHrp56Nbaz6eU3jv5zjlZIKO1iNjT6qVEMDWaeejmoZuHbh66eejmoVsxPMQCAAAAaMICGQAAAGjCArlc/5R7gBqimYduHrp56Oahm4duHroVwGOQAQAAgCbsIAMAAABNWCADAAAATVggz5GIuCUinouIQxFxMCJ+NSLujYjvRsRQROyJiF/OPWeVRMSdjTZXbj+KiE9GxK0R8e8R8YPGx/bcs1bJNN2+0jj+XomIzRFxS+5Zq2Sqbk3//dMRkSJiScYxK2W6ZhHRHRGHI+L7EfF3mUetlGnOUa4JM4iITzWOqQMR0RsRi7gmzGyKblwTCuAxyHMkIp6RtCOl9LWIaJP0HknPSvpqSuk7EfG7kp5IKX0o55xVFRHXSzol6Vck/ZWk11NKX46Iz0hqTymtzzpgRU3qdqekwZTSTyLibyWJbq01d0spHY+IZZK+JmmlpDUpJV5cf5JJx9r7JPVI+r2U0mhE3J5Sei3rgBU1qdvT4powpYj4OUn/IekDKaUfR8Szkv5N0gfENWFK03T7obgmzBo7yHMgIm6S9BuSvi5JKaWxlNL/SUqSbmr8sZs1cbCitYckHU0pHZf0EUnPNO5/RtLv5xqqBq52Syk9n1L6SeP+70rqyDhX1TUfb5L0VUlPaOKcRWvNzf5C0pdTSqOSxOJ4Ws3duCbM7AZJPxURN2hio+mH4powG2/pxjWhGBbIc+N9ks5K+ueI+F5EfC0iFkv6pKSvRMQJSX8v6W8yzlh1fySpt/HPP5NSOi1JjY+3Z5uq+pq7NftTSd8peZY6udotIh6RdCql9HLekSqv+Vh7v6Rfj4hdEbE9Iu7POFfVNXf7pLgmTCmldEoTXV6VdFrScErpeXFNmNY03ZpxTZgBC+S5cYOkD0r6x5TSL0kakfQZTeyyfCqltEzSp9TYYca1Gg9JeUTSt3PPUidTdYuIHkk/kfQvOeaquuZuEfEeTTxU4HN5p6q2FsfaDZLaJT0g6a8lPRsRkWm8ymrRjWvCNBqPLf6IpF+Q9LOSFkfEn+Sdqvpm6sY1YXZYIM+Nk5JOppR2Nf79OU0smD8uqa9x37cl8YSM1n5H0r6U0pnGv5+JiKWS1PjIr29bm9xNEfFxSQ9L+uPEEw6m0tztFzVxUXk5Io5p4leQ+yLijozzVdHkY+2kpL404T8ljUviyY1vNbkb14Tp/bak/0kpnU0pXdZEqy5xTZjJVN24JhTAAnkOpJT+V9KJiLizcddDkv5LE4+d+s3Gfb8l6QcZxquDR3XtwwS2auJCosbHfy19onq4pltEfFjSekmPpJTeyDZV9V3tllLan1K6PaXUmVLq1MTC74ONcxpvmnyObtHE9zRFxPsltUniiY1vNbkb14TpvSrpgYh4T+M3Eg9JOiiuCTNp2Y1rQjG8isUciYh7NfEs+DZJ/y3pMUmrJP2DJn4deUnSX6aU9uaasYoav+I+Iel9KaXhxn23aeIVQJZr4sT/g5TS6/mmrJ4puh2RtFDS+cYf+25KaV2mESupVbdJ//2YpPt4FYs3TXGstUn6hqR7JY1J+nRKaTDbkBU0RbdfE9eEaUXEFyX9oSYeEvA9SX8m6afFNWFaU3T7vrgmzBoLZAAAAKAJD7EAAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmvw/tSpycIADqyoAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Môžeme tiež vytvoriť boxploty podmnožín nášho datasetu, napríklad zoskupené podľa role hráča.\n"
+    "Môžeme tiež vytvoriť boxploty podmnožín našej množiny údajov, napríklad zoskupené podľa role hráča.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Poznámka**: Tento diagram naznačuje, že priemerne sú výšky hráčov na prvej méte vyššie ako výšky hráčov na druhej méte. Neskôr sa naučíme, ako môžeme túto hypotézu formálnejšie otestovať a ako ukázať, že naše údaje sú štatisticky významné na jej potvrdenie.\n",
+    "> **Poznámka**: Tento diagram naznačuje, že priemerne sú výšky hráčov na prvej méte vyššie ako výšky hráčov na druhej méte. Neskôr sa naučíme, ako môžeme túto hypotézu formálnejšie otestovať a ako ukázať, že naše údaje sú štatisticky významné na jej potvrdenie.  \n",
     "\n",
-    "Vek, výška a váha sú všetko spojité náhodné premenné. Aký si myslíte, že je ich rozdelenie? Dobrý spôsob, ako to zistiť, je vykresliť histogram hodnôt:\n"
+    "Vek, výška a váha sú všetko spojité náhodné premenné. Aký si myslíte, že majú rozdelenie? Dobrý spôsob, ako to zistiť, je vykresliť histogram hodnôt:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,9 +619,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dve hodnoty, ktoré vracia funkcia `ttest_ind`, sú:\n",
-    "* p-hodnota môže byť považovaná za pravdepodobnosť, že dve rozdelenia majú rovnaký priemer. V našom prípade je veľmi nízka, čo znamená, že existuje silný dôkaz podporujúci tvrdenie, že hráči na prvej méte sú vyšší.\n",
-    "* t-hodnota je medzihodnota normalizovaného rozdielu priemerov, ktorá sa používa v t-teste a porovnáva sa s prahovou hodnotou pre danú úroveň spoľahlivosti.\n"
+    "Dve hodnoty, ktoré vracia funkcia `ttest_ind`, sú:  \n",
+    "* p-hodnota môže byť považovaná za pravdepodobnosť, že dve rozdelenia majú rovnaký priemer. V našom prípade je veľmi nízka, čo znamená, že existuje silný dôkaz podporujúci tvrdenie, že prví meta sú vyšší.  \n",
+    "* t-hodnota je medzihodnota normalizovaného rozdielu priemerov, ktorá sa používa v t-teste a porovnáva sa s prahovou hodnotou pre danú úroveň spoľahlivosti.  \n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## Simulácia normálneho rozdelenia pomocou centrálnej limitnej vety\n",
     "\n",
-    "Pseudo-náhodný generátor v Pythone je navrhnutý tak, aby nám poskytol rovnomerné rozdelenie. Ak chceme vytvoriť generátor pre normálne rozdelenie, môžeme použiť centrálnu limitnú vetu. Na získanie hodnoty s normálnym rozdelením jednoducho vypočítame priemer vzorky generovanej rovnomerne.\n"
+    "Pseudo-náhodný generátor v Pythone je navrhnutý tak, aby nám poskytoval rovnomerné rozdelenie. Ak chceme vytvoriť generátor pre normálne rozdelenie, môžeme použiť centrálnu limitnú vetu. Na získanie hodnoty s normálnym rozdelením jednoducho vypočítame priemer vzorky generovanej rovnomerne.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korelácia a Zlá Baseballová Korporácia\n",
     "\n",
-    "Korelácia nám umožňuje nájsť vzťahy medzi sekvenciami údajov. V našom jednoduchom príklade si predstavme, že existuje zlá baseballová korporácia, ktorá platí svojim hráčom podľa ich výšky – čím je hráč vyšší, tým viac peňazí dostane. Predpokladajme, že existuje základný plat vo výške 1000 dolárov a dodatočný bonus od 0 do 100 dolárov, v závislosti od výšky. Vezmeme skutočných hráčov z MLB a vypočítame ich imaginárne platy:\n"
+    "Korelácia nám umožňuje nájsť vzťahy medzi dátovými sekvenciami. V našom jednoduchom príklade si predstavme, že existuje zlá baseballová korporácia, ktorá platí svojim hráčom podľa ich výšky - čím je hráč vyšší, tým viac peňazí dostane. Predpokladajme, že existuje základný plat vo výške $1000 a dodatočný bonus od $0 do $100, v závislosti od výšky. Vezmeme skutočných hráčov z MLB a vypočítame ich imaginárne platy:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Poďme teraz vypočítať kovarianciu a koreláciu týchto sekvencií. `np.cov` nám poskytne takzvanú **kovariančnú maticu**, ktorá je rozšírením kovariancie na viacero premenných. Prvok $M_{ij}$ kovariančnej matice $M$ je korelácia medzi vstupnými premennými $X_i$ a $X_j$, a diagonálne hodnoty $M_{ii}$ sú rozptyl $X_{i}$. Podobne, `np.corrcoef` nám poskytne **korelačnú maticu**.\n"
+    "Poďme teraz vypočítať kovarianciu a koreláciu týchto sekvencií. `np.cov` nám poskytne takzvanú **kovariančnú maticu**, čo je rozšírenie kovariancie na viacero premenných. Prvok $M_{ij}$ kovariančnej matice $M$ je korelácia medzi vstupnými premennými $X_i$ a $X_j$, a diagonálne hodnoty $M_{ii}$ sú rozptyl $X_{i}$. Podobne, `np.corrcoef` nám poskytne **korelačnú maticu**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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IoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoTQDQAAAABAgMQEuwAAAAAAXVdX36TbX92l8u+PKb1fvJ66cbj6WPk1HwgV/GkEAAAAwtQ1f9qq3V+5PMf7Kms1ZMn/6oKBNq2fNyaIlQFoxfJyAAAAIAydHLhPtPsrl67509ZurghAWwjdAAAAQJipq29qN3C32v2VS3X1Td1UEYD2ELoBAACAMHP7q7tMbQcgcAjdAAAAQJgp//6Yqe0ABA6hGwAAAAgz6f3iTW0HIHAI3QAAAECYeerG4aa2AxA4hG4AAAAgzPSxxuiCgbbTtrlgoI39uoEQQOgGAAAAwtD6eWPaDd7s0w2EDv7pCwAAAAhT6+eNUV19k25/dZfKvz+m9H7xeurG4cxwAyGEP40AAABAEDS7DRWVVquqtl7JiVaNzExSdJTF5376WGO0esbFAagQgBkI3QAAAEA3y99ToaUbSlRRU+85l2q3anGuU9lDUoNYGQCz8Uw3AAAA0I3y91RozpqdXoFbkipr6jVnzU7l76kIUmUAAoHQDQAAAHSTZrehpRtKZLRxrfXc0g0lana31QJAOPI5dBcUFCg3N1dpaWmyWCxat26d1/XXXntNEyZMUP/+/WWxWFRcXOx1vbq6WrfeeqvOOeccxcfHKz09Xb/97W9VU1Pj1a68vFw5OTlKSEhQcnKy7rrrLjU1Nfn8BQEAAIBQUVRafcoM94kMSRU19Soqre6+ogAElM+h+8iRIxo6dKhWrFjR7vXRo0fr8ccfb/P6wYMHdfDgQT355JPas2eP8vLylJ+fr5kzZ3raNDc3KycnRw0NDfrggw/00ksvKS8vT4sWLfK1XAAAACBkVNW2H7i70g5A6PP5RWqTJk3SpEmT2r0+bdo0SVJZWVmb14cMGaK//OUvnuMzzzxTjzzyiKZOnaqmpibFxMTozTffVElJid566y2lpKRo2LBheuihh3TPPfdoyZIlio2N9bVsAAAAIOiSE62mtgMQ+kLime6amhrZbDbFxLT8G0BhYaHOP/98paSkeNpMnDhRLpdLe/fuDVaZAAAAQLua3YYKP/tO/1P8tQo/+67N57JHZiYp1W5VexuDWdTyFvORmUkBrRVA9wn6lmHffvutHnroIc2ePdtzrrKy0itwS/IcV1ZWttnP8ePHdfz4cc+xy+UKQLUAAADAqTq7BVh0lEWLc52as2anLJLXC9Vag/jiXGeX9usGEJqCOtPtcrmUk5Mjp9OpJUuW+NXXsmXLZLfbPZ9BgwaZUyQAAABwGr5uAZY9JFUrp46Qw+69hNxht2rl1BHs0w1EmKDNdNfW1io7O1uJiYlau3atevXq5bnmcDhUVFTk1f7QoUOea21ZuHCh5s+f7zl2uVwEbwAAAARUR1uAWdSyBdh4p8Nr9jp7SKrGOx0qKq1WVW29khNblpQzww1EnqCEbpfLpYkTJyouLk7r16+X1er9r3xZWVl65JFHVFVVpeTkZEnS5s2bZbPZ5HQ62+wzLi5OcXFxAa8dAAAAPVuz2/CE5W9rj3d6C7CsM/t7XYuOspxyDkDk8Tl019XV6cCBA57j0tJSFRcXKykpSenp6aqurlZ5ebkOHjwoSdq3b5+klhlqh8Mhl8ulCRMm6OjRo1qzZo1cLpfn+eszzjhD0dHRmjBhgpxOp6ZNm6bly5ersrJS999/v+bOnUuwBgAAQNC09ex2Z7AFGNBzWQzDaGs1TLveffddXXHFFaecnzFjhvLy8pSXl6df/OIXp1xfvHixlixZ0u79UkuAz8jIkCR98cUXmjNnjt5991317t1bM2bM0GOPPeZ5w3lHXC6X7Ha7583oAAAAgD9an9326Zfnv/t/sy5hVhuIMJ3NnD6H7nBB6AYAAIBZmt2GRj++xecZbotaXpD2/j1X8rw2EGE6mzlDYp9uAAAAIJQVlVZ3KXBLbAEG9HRB36cbAAAACHVdeSbb0cY+3QB6HkI3AAAA0IHkRGvHjSQ9kHOuBiTGsQUYAA9CNwAAANCBkZlJSrVbVVlT3+aL1Fqf3b7lx5kEbQBeeKYbAAAA6EB0lEWLc52S/vGsdiue3QZwOoRuAAAAoBOyh6Rq5dQRcti9l5o77FatnDqCZ7cBtInl5QAAAEAnZQ9J1XinQ0Wl1aqqrefZbQAdInQDAAAAPoiOsijrzP7BLgNAmGB5OQAAAAAAAULoBgAAAAAgQFheDgAAgIhS/u1RZf/xPR1rdCu+V5Ty/+UypQ9ICHZZAHooQjcAAAAixg/v3agm9z+Ojza6NfbJdxQTJR14NCd4hQHosVheDgAAgIhwcuA+UZO75ToAdDdmugEAABCWmt2GZ+uuqCZ3u4G7VZO7Zek5S80BdCdCNwAAAMJO/p4KLd1Qooqaep/uy/7jeyp5aFKAqgKAUxG6AQAAEFby91RozpqdMrpw77HGDqbDAcBkPNMNAACAsNHsNrR0Q0mXArckxffi118A3Yu/dQAAABA2ikqrfV5SfqL8f7nMxGoAoGOEbgAAAISNqtquB+6YKPESNQDdjtANAACAsJGcaO3SfezTDSBYeJEaAAAAwsbIzCSl2q2qrKlv87lui6S+cRYdd1t0rNGt+F5Ryv+Xy5jhBhA0hG4AAACEjegoixbnOjVnzU5ZJK/gbfn7/y775+HKHpIahOoA4FQsLwcAAEBYyR6SqpVTR8hh915q7rBbtXLqCAI3gJDCTDcAAADCTvaQVI13OlRUWq2q2nolJ1o1MjNJ0VGWjm8GgG5E6AYAAEC3anYbpoTl6CiLss7sH4AKAcA8hG4AAAB0m/w9FVq6ocRrr+1Uu1WLc50sCwcQkXimGwAAAN0if0+F5qzZ6RW4Jamypl5z1uxU/p6KIFUGAIHDTDcAAAACovJwva5+pkCu+ibZrDGyWCxtbvNlqOXN40s3lGi808Fz2QAiCqEbAAAApjv3gTd0rNHtOf72SONp2xuSKmrqVVRazXPaACIKy8sBAABgqpMDty+qaus7bgQAYYTQDQAAANNUHq7vcuCWpOREa8eNACCMsLwcAAAAfjnW0KxHN5Wo7LujKvr8uy71YZHksLdsHwYAkYTQDQAAgC6b9fJ2bS6p8quP1temLc518hI1ABGH0A0AAIAuMSNwSy0z3OzTDSBSEboBAADgs2MNzX4F7md/PlyNhqHkxJYl5cxwA4hUhG4AAAD47NFNJV2+N75XlCYPTTOxGgAIXby9HAAAAD4r++5ol+6L7xWljx+aZHI1ABC6mOkGAACAzzL6J2jr/o7bxUVbZEiyWWP0+q1j5ejLlmAAehZCNwAAAHx272SnXvlbeYftihdPVHxsdDdUBAChieXlAAAA8Fl8bLTGO5NP22a8M5nADaDHI3QDAACgS1ZPv7jd4D3emazV0y/u5ooAIPSwvBwAAABdtnr6xTrW0KxHN5Wo7LujyuifoHsnO5nhBoC/I3QDAAD0UM1uQ0Wl1aqqrfdrv+z42Gg9dN35AagQAMIfoRsAAKAHyt9ToaUbSlRRU+85l2q3anGuU9lDUoNYGQBEFp7pBgAA6GHy91RozpqdXoFbkipr6jVnzU7l76kIUmUAEHmY6QYAAIhw75d8o6kvF3mOYyUZbbQzJFkkLd1QovFOR5eWmgMAvBG6AQAAIljGgo2nnGs4TXtDUkVNvYpKq5V1Zv+A1QUAPQXLywEAACJUW4G7s6pq6ztuBADoEKEbAAAgAr1f8o1f9ycnWk2qBAB6NpaXAwAARIgTtwD7l/8s7lIfFkkOe8v2YQAA/xG6AQAAIkBbW4D5qvW1aYtznbxEDQBMQugGAAAIc61bgLX1RnJfONinGwBM5/Mz3QUFBcrNzVVaWposFovWrVvndf21117ThAkT1L9/f1ksFhUXF5/SR319vebOnav+/furT58+uuGGG3To0CGvNuXl5crJyVFCQoKSk5N11113qampyddyAQAAIk6z21DhZ9/pf4q/1l8PfKsl6/d2OXA/MOlM/XHKMP2/WZfo/XuuJHADgMl8nuk+cuSIhg4dql/+8pe6/vrr27w+evRo/exnP9OsWbPa7OP222/Xxo0b9ec//1l2u13z5s3T9ddfr7/+9a+SpObmZuXk5MjhcOiDDz5QRUWFpk+frl69eunRRx/1tWQAAICIYcYy8hPNvOxHpvQDAGibxTCMLq9EslgsWrt2ra677rpTrpWVlSkzM1O7du3SsGHDPOdramp0xhln6D/+4z/005/+VJL0ySef6Nxzz1VhYaEuueQSvfHGG7r66qt18OBBpaSkSJJWrVqle+65R998841iY2M7rM3lcslut6umpkY2m62rXxEAACBkmLWMvFXZYzkm9QQAPU9nM2e3bxn24YcfqrGxUePGjfOc+9GPfqT09HQVFhZKkgoLC3X++ed7ArckTZw4US6XS3v37m2z3+PHj8vlcnl9AAAAIkWz29DSDSWmBO4100cSuAGgm3T7i9QqKysVGxurvn37ep1PSUlRZWWlp82Jgbv1euu1tixbtkxLly41v2AAAIAgaWhy65XCMn1RfVSGYXRpSXnrFmDv33MlbyQHgCCImLeXL1y4UPPnz/ccu1wuDRo0KIgVAQAAdN2yTSVavbVUbj+mttkCDACCr9tDt8PhUENDgw4fPuw1233o0CE5HA5Pm6KiIq/7Wt9u3trmZHFxcYqLiwtM0QAAAN1o2aYSPVdQ6nc/bAEGAMHX7aH7wgsvVK9evfT222/rhhtukCTt27dP5eXlysrKkiRlZWXpkUceUVVVlZKTkyVJmzdvls1mk9Pp7O6SAQAAuk1Dk1urt/oeuFuXkT/506H69shxJSdaNTIziRluAAgyn0N3XV2dDhw44DkuLS1VcXGxkpKSlJ6erurqapWXl+vgwYOSWgK11DJD7XA4ZLfbNXPmTM2fP19JSUmy2Wy69dZblZWVpUsuuUSSNGHCBDmdTk2bNk3Lly9XZWWl7r//fs2dO5fZbAAAENFeKSzzeUn5icvIf3zWANNrAgB0nc9vL9+xY4eGDx+u4cOHS5Lmz5+v4cOHa9GiRZKk9evXa/jw4crJaXkj5pQpUzR8+HCtWrXK08dTTz2lq6++WjfccIPGjh0rh8Oh1157zXM9Ojpar7/+uqKjo5WVlaWpU6dq+vTpevDBB/36sgAAAKHui+qjPt/jsFu1cuoIlpEDQAjya5/uUMY+3QAAIBz929bP9dDGjztsN+2SdF2UkcQycgAIks5mzoh5ezkAAECo23ewVpOfKVCzIUVbpE23jtU5aYlebaZlZeiRTR+fdol5lEV64OrzFBvj86JFAEA3I3QDAAB0g4wFG72Omw1p4tMFkqSyx3I852NjojRrTOZp314+a0wmgRsAwgR/WwMAAATYyYG7o+sLJzv1q7GZOnnFeJRF+tXYTC2czG4uABAumOkGAAAwWUOTW68UlumL6qNKiOncs9b7DtZ6LTVfONmpOyb8yNPP4KQETcvKYIYbAMIML1IDAAAw0bJNJVq9tdTnbb+iLdJny3I6bggACAm8SA0AAKCbLdtUctpnsU+nOSKnQQAArE8CAAAwQUOTW6u3di1wSy0z3QCAyMNMNwAAQBc1uw0VlVarqrZeO8qqfV5SfqJNt441rzAAQMggdAMAAHRB/p4KLd1QooqaelP6O3m/bgBAZCB0AwAA+Ch/T4XmrNkpsx7DPnGfbgBAZCF0AwAA+KDZbWjphpIuBW7L3z9utTzDvenWscxwA0CEI3QDAAB04MR9tw3D6PKS8tljM7VwstPk6gAAoYzQDQAAcBpd3Xf7RFEWadYYAjcA9ESEbgAAgHb4s+/2tEvSZbFYNDgpQdOyMhQbw06tANATEboBAAD+rq6+Sbe/ukvl3x/TwL5Wvf3JNz73YZHksFu15Johio5i820A6OkI3QAAAJKu+dNW7f7K5TneV1nrcx+tEXtxrpPADQCQROgGAAA4JXB3lcNu1eJcp7KHpJpQFQAgEhC6AQBAj1ZX3+RX4J52SbouykhScqJVIzOTmOEGAHghdAMAgB7nv/9aqjs3lPjdT5RFeuDq83hJGgCgXYRuAADQo2Qs2GhaX7PGZBK4AQCnRegGAAA9hlmBm323AQCdRegGAAARq6HJrVcKy/RF9VF9W+f728hPdPfEs1XpOs6+2wAAnxC6AQBARFq2qUSrt5bKbfjf1wUDbfrNFWf53xEAoMchdAMAgIizbFOJnisoNaWvCwbatH7eGFP6AgD0PIRuAAAQURqa3Fq91b/AfY4jUen94vXUjcPVx8qvSwCAruO/IgAAIOzVHG3UL/OKdLCmXtEW+bWk/Mlcp37640zzigMA9GiEbgAAENYue2KLvvjumGn9EbgBAGbitZsAACBsmR24yx7LMa0vAAAkZroBAEAYaXYbKiqtVlVtvfrERJsWuFlSDgAIFEI3AAAIC/l7KrR0Q4kqaur97utXYzO1cLLThKoAADg9QjcAAAh5+XsqNGfNTvm75XaURZo1hsANAOg+hG4AABDSmt2Glm4o6XLgHtjXqivPTdHgpARNy8pQbAyvtAEAdB9CNwAACDkNTW69UlimL6qPyjAMv5aUb/ztWNkTeplYHQAAnUfoBgAAIWXZphKt3lrq117brQb3jydwAwCCitANAABCxrJNJXquoNSUvgb3j9d7d11pSl8AAHQVoRsAAATNluJK/fI/P/SrD4ukAb1jNKh/H1XU1CvNbtULt4xkhhsAEBII3QAAICgyFmz0uw/L3//3oZ9coOwhqX73BwCA2QjdAACg25kRuCXJYbdqca6TwA0ACFmEbgAA0K22FFf6df+0S9J1UUaSkhOtGpmZpOgoS8c3AQAQJIRuAAAQcDVHG/XLvCIdrKn3a/uvKIv0wNXnsdc2ACBsELoBAEBAXfbEFn3x3TFT+po1JpPADQAIK4RuAAAQMGYF7ihLS+BeONlpQlUAAHQfQjcAADBNXX2Tbn91l8q/P6Y0W5xfgfv6EQPUJ663BiclaFpWBjPcAICwROgGAACmuOZPW7X7K5fneF9lrV/9/f5no/wtCQCAoOOfjAEAgN9ODtz+Knssx7S+AAAIJma6AQCAX+rqm0wL3C9MuVBXDnOY0hcAAKGA0A0AAHx24hZgR443+tXX/y2aIHtCL5MqAwAgtBC6AQCAT8zcAmxw/3gCNwAgovFMNwAA6DSzA/d7d11pSl8AAIQqZroBAECn1Bxt9CtwDxuYqEO1jUqzW/XCLSOZ4QYA9AiEbgAA0K6GJrdeKSzTF9VHteXjQ13u54KBNq2bN8bEygAACA8+Ly8vKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/d7tfn000917bXXasCAAbLZbBo9erTeeecdrzbl5eXKyclRQkKCkpOTddddd6mpqcn3bwgAALpk2aYS/eiBN/TQxo/1cuEX+upwfZf6uWCgTesJ3ACAHsrn0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+/h//ob766qvV1NSkLVu26MMPP9TQoUN19dVXq7KyUpLU3NysnJwcNTQ06IMPPtBLL72kvLw8LVq0qItfEwAA+GLZphI9V1Aqt+H7vTZrtM5xJGr8ucnas2QigRsA0KNZDMPown9O/36zxaK1a9fquuuuk9Qyy52WlqY77rhDd955pySppqZGKSkpysvL05QpU/Ttt9/qjDPOUEFBgcaMafmPcG1trWw2mzZv3qxx48bpjTfe0NVXX62DBw8qJSVFkrRq1Srdc889+uabbxQbG9thbS6XS3a7XTU1NbLZbF39igAA9AhzXsjXG582m9IXW4ABAHqCzmZOU99eXlpaqsrKSo0bN85zzm63a9SoUSosLJQk9e/fX+ecc45efvllHTlyRE1NTXruueeUnJysCy+8UJJUWFio888/3xO4JWnixIlyuVzau3evmSUDANDjZSzYaFrgZgswAAC8mfoitdbl4SeG5dbj1msWi0VvvfWWrrvuOiUmJioqKkrJycnKz89Xv379PP201ceJP+Nkx48f1/Hjxz3HLpfLnC8FAEAEy1iw0bS+2AIMAIBTdfvbyw3D0Ny5c5WcnKytW7cqPj5e//qv/6rc3Fxt375dqampXep32bJlWrp0qcnVAgAQuea8kO/X/QP7WtVsiC3AAAA4DVNDt8PhkCQdOnTIKzwfOnRIw4YNkyRt2bJFr7/+ur7//nvPuvdnn31Wmzdv1ksvvaQFCxbI4XCoqKjIq+9Dhw55/YyTLVy4UPPnz/ccu1wuDRo0yLTvBgBAJKg8XK+rnymQq75JDc1dfq2LoizSljuvUGyMqU+qAQAQcUz9L2VmZqYcDofefvttzzmXy6Vt27YpKytLknT06NGWHxzl/aOjoqLkdrslSVlZWfroo49UVVXlub5582bZbDY5nc42f3ZcXJxsNpvXBwAA/MO5D7yhSx57W98eafQrcEvSrDGZBG4AADrB55nuuro6HThwwHNcWlqq4uJiJSUlKT09XbfddpsefvhhnXXWWcrMzNQDDzygtLQ0zxvOs7Ky1K9fP82YMUOLFi1SfHy8Vq9erdLSUuXk5EiSJkyYIKfTqWnTpmn58uWqrKzU/fffr7lz5youLs6cbw4AQA9y7gNv6Fij2+9+oiwtgXvh5Lb/ERwAAHjzOXTv2LFDV1xxhee4dUn3jBkzlJeXp7vvvltHjhzR7NmzdfjwYY0ePVr5+fmyWq2SpAEDBig/P1/33XefrrzySjU2Nuq8887T//zP/2jo0KGSpOjoaL3++uuaM2eOsrKy1Lt3b82YMUMPPvigGd8ZAICI19Dk1iuFZfqi+qiS4mL8CtxnJ0iXDB2swUkJmpaVwQw3AAA+8Guf7lDGPt0AgJ5q2aYSrd5aKrdJ/4UveyzHnI4AAIggnc2c3f72cgAAEDjLNpXouYJS0/ojcAMA4B/WhwEAECEamtxavdWcwD3p7GgCNwAAJmCmGwCAMNbsNlRUWq2q2nrtKKv2a0n53xZcJUdfq3nFAQAAQjcAAOEqf0+Flm4oUUVNvd99xfeKInADABAAhG4AAMJQ/p4KzVmzU2a8Ky2+V5Q+fmiSCT0BAICTEboBAAgDJ24BNqhfgv5162ddCtwWSUm9e6m2vkk2a4xev3UsM9wAAAQQoRsAgBBn5hZgs8dmauFkp/8dAQCATiF0AwAQwszaAizKIs0aQ+AGAKC7EboBAAhR/m4BNu2SdFksFg1OStC0rAzFxrBTKAAA3Y3QDQBACMl7Z5+W/O8Bv/qwSHLYrVpyzRBFR1nMKQwAAHQJoRsAgBCRsWCj3320RuzFuU4CNwAAIYDQDQBACDAjcEstM9yLc53KHpJqSn8AAMA/hG4AAIKg2W2oqLRaVbX1+mvZV13uJ8oivXTLSFUfa1ByolUjM5OY4QYAIIQQugEA6Gb5eyq0dEOJKmrq/e5r1phMjTnnDBOqAgAAgUDoBgCgG+XvqdCcNTvl75bbbAEGAEB4IHQDANBNmt2Glm4o8StwT88azBZgAACEEUI3AAAB1NDk1iuFZfqi+qgMw/BrSfmSiT/ULVecY2J1AAAg0AjdAAAEyLJNJVq9tVRuf9eS/x2BGwCA8EPoBgAgAJZtKtFzBaWm9Vf2WI5pfQEAgO5D6AYAwGQNTW6t3up74LZIpzzvzZJyAADCG6EbAAATVNc1aMrzH6iqtkExUfJ5SXnrztqrpo5Q9pBU0+sDAADBQegGAMBPFz+8Wd/UNfjVh8Nu1eJcJ4EbAIAIQ+gGAMAP/gTuaZek66KMJCUnWjUyM0nRUZaObwIAAGGF0A0AQCeduIQ8OTFWz950UZcDd5RFeuDq89hrGwCACEfoBgCgE06e0T58rFHj/vBel/ubNSaTwA0AQA9A6AYAoANmPLPdKsrSErgXTnaa0h8AAAhthG4AAE6juq7B78A9oHcvTb4gTYOTEjQtK4MZbgAAehBCNwAAJ5nzQr7e+LTZtP7evP1yJfWJNa0/AAAQPgjdAACcIGPBRlP7O6NPLIEbAIAejPVtAAD8XSAC9/b7x5vaJwAACC/MdAMAeqyao436ZV6RDtbUq6qm3q++3rrtMv3mP3Z4thP7z9mXMsMNAAAI3QCAnumyJ7boi++OmdLXGX1i9UNHH705/3JT+gMAAJGD5eUAgB7H7MDNEnIAANAeZroBAD1KzdFGvwN33/heLCEHAACdQugGAES80qojyv7jezrebPjd16Szo7XylxNMqAoAAPQEhG4AQET7p4Ub5fY/a3us/GW2eZ0BAICIxzPdAICIZXbgLnssx7zOAABAj8BMNwAgYhxraNajm0pU9t1R9U+INi1wtywpZ4YbAAD4jtANAIgIs17ers0lVab0Nbh/vN6760pT+gIAAD0by8sBAGGPwA0AAEIVM90AgLB2rKHZ78CdarcqzW7VC7eMlD2hl0mVAQAAELoBAGHoG9dx/eTZ91V9pFGSfw9uvzP/cmUm9zanMAAAgJMQugEAYeWCJf8rV32TKX1FWUTgBgAAAcUz3QCAsGF24P58GVuAAQCAwGKmGwAQspas3aa8bd+a0ldslNTgluKiLcr/l8uY4QYAAN2C0A0ACEkZCzaa1td4Z7JWT7/YtP4AAAA6i+XlAICQQ+AGAACRgpluAEBIWbJ2m1/3J/SK0oUZScron6B7JzsVHxttUmUAAAC+I3QDAIKurr5Jt7+6S+XfH9O+ylq/+nrvrit1hi3OpMoAAAD8Q+gGAATVNX/aqt1fuUzpy2aNIXADAICQwjPdAICgMTtw714y0ZS+AAAAzMJMNwCg2xxraNajm0pU9t1RpdmtfgfuhF7RSurdS2t/M5oZbgAAEJJ8nukuKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/ef0s/GjRs1atQoxcfHq1+/frruuuu8rpeXlysnJ0cJCQlKTk7WXXfdpaamJl/LBQCEiFkvb9e5i/L1yt/KtXX/t3p1x1d+9Vf2WI5KHsrW+wuuInADAICQ5XPoPnLkiIYOHaoVK1a0eX358uV6+umntWrVKm3btk29e/fWxIkTVV9f72nzl7/8RdOmTdMvfvEL/d///Z/++te/6qabbvJcb25uVk5OjhoaGvTBBx/opZdeUl5enhYtWtSFrwgACLZZL2/X5pIq0/oreyzHtL4AAAACyWIYhtHlmy0WrV271jNLbRiG0tLSdMcdd+jOO++UJNXU1CglJUV5eXmaMmWKmpqalJGRoaVLl2rmzJlt9vvGG2/o6quv1sGDB5WSkiJJWrVqle655x598803io2N7bA2l8slu92umpoa2Wy2rn5FAICfjjU069xF+ab0dcuoAVryk1Gm9AUAAOCPzmZOU5/pLi0tVWVlpcaNG+c5Z7fbNWrUKBUWFmrKlCnauXOnvv76a0VFRWn48OGqrKzUsGHD9MQTT2jIkCGSpMLCQp1//vmewC1JEydO1Jw5c7R3714NHz7czLIBACb7xnVcP3n2fVUfaZTU5X/blSTtWTJRfay8ggQAAIQnU3+LqayslCSvsNx63Hrt888/lyQtWbJEv//975WRkaHf/e53uvzyy/Xpp58qKSlJlZWVbfZx4s842fHjx3X8+HHPsctlzttwAQC+uWDJ/8pVb847OC4YaCNwAwCAsNbtW4a53W5J0n333acbbrhBF154oV588UVZLBb9+c9/7nK/y5Ytk91u93wGDRpkVskAgE4yO3CvnzfGlL4AAACCxdTQ7XA4JEmHDh3yOn/o0CHPtdTUVEmS0+n0XI+Li9M//dM/qby83NNPW32c+DNOtnDhQtXU1Hg+X375pQnfCADQWd+4jvsVuK84Z4DOcSRq/LnJ2rNkIoEbAABEBFPX7GVmZsrhcOjtt9/WsGHDJLUs8962bZvmzJkjSbrwwgsVFxenffv2afTo0ZKkxsZGlZWVafDgwZKkrKwsPfLII6qqqlJycrIkafPmzbLZbF5h/URxcXGKi2PLGADoTifuu/1hWXWX+xnvTNbq6RebWBkAAEBo8Dl019XV6cCBA57j0tJSFRcXKykpSenp6brtttv08MMP66yzzlJmZqYeeOABpaWled5wbrPZ9Otf/1qLFy/WoEGDNHjwYD3xxBOSpH/+53+WJE2YMEFOp1PTpk3T8uXLVVlZqfvvv19z584lWANAiDBrGzACNwAAiGQ+h+4dO3boiiuu8BzPnz9fkjRjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqxWq+eeJ554QjExMZo2bZqOHTumUaNGacuWLerXr58kKTo6Wq+//rrmzJmjrKws9e7dWzNmzNCDDz7o7/cFAJjAn8Cd0CtKF2YkKaN/gu6d7FR8bLTJ1QEAAIQOv/bpDmXs0w0A5hl/70btd5vT1/Z7x+kMG6uWAABAeAvKPt0AgMiTsWCjaX3ZrDEEbgAA0KN0+5ZhAIDwYXbg3r1komn9AQAAhANmugEAbRp/r3+BO6FXlCSLknr30trfjGaGGwAA9EiEbgCAR0OTW68UlumL6qN+P8P94QMTeEkaAADo8QjdAABJ0rJNJVq9tVRuE16vOd6ZTOAGAAAQoRsAoJbA/VxBqSl9se82AADAPxC6AaAHqjnaqF/mFelgTb1SbXHa+WWNX/2NOWsA+24DAAC0gdANAD3MZU9s0RffHfMcV9TU+9Vf2WM5/pYEAAAQsdgyDAB6kJMDt78I3AAAAKdH6AaAHqLmaKNpgfusKAI3AABAZ7C8HAAiWOXhel39TIFc9U1q9uO15FEW6ZOHJik2hn+rBQAA8AWhGwAi1LkPvKFjjX5utv13s8ZkErgBAAC6gNANABHIrMAdZWkJ3AsnO02oCgAAoOchdANABDjW0KxHN5Wo7LujSu4T61fgvnP8Waqqa9DgpARNy8pghhsAAMAPhG4ACHOzXt6uzSVVpvQ1uH+85l11til9AQAAgLeXA0BYMztwv3fXlab0BQAAgBbMdANAmDrW0OxX4I62SMk2q9LsVr1wy0jZE3qZWB0AAAAkQjcAhJXfbyrW0wVfm9LXX++5So6+VlP6AgAAQNsI3QAQJjIWbDStr/heUQRuAACAbsAz3QAQBswO3B8/NMm0/gAAANA+ZroBIASduAVYZcW3fvWVFB+tuga3bNYYvX7rWGa4AQAAuhGhGwBCjJlvJB/vTNbq6Reb0hcAAAB8x/JyAAghBG4AAIDIwkw3AIQIf7cAk6QxZw1QRv8E3TvZqfjYaJMqAwAAQFcRugEgiE58dvtQzTG/+vrt2B9o/uRh5hQGAAAAUxC6ASBIzFxKLonADQAAEIJ4phsAgsDswF32WI5pfQEAAMA8zHQDQDf4uvqYJj39no4cb1bv2Gi5jjeb0i9LygEAAEIboRsAAuzs+zapodnwHPsTuHkjOQAAQHhheTkABNDJgdsfBG4AAIDww0w3AATI19XH/ArcZyf3Voo9ni3AAAAAwhihGwBM1NDk1iuFZfqi+qheLSr3q6//mTeGoA0AABDmCN0AYJJlm0q0emup3CasJh/vTCZwAwAARABCNwCYYNmmEj1XUGpKXzy7DQAAEDkI3QDgp4Ymt1Zv7Xrg/smwFH17pJlntwEAACIQoRsAuuC6RzaquNb/fmKjLXpqykX+dwQAAICQROgGAB9lLNhoSj+x0RZ9+shkU/oCAABAaCJ0A4AP/AnccdEWNbkN9Y6L1hu/vUw/SIo3sTIAAACEIkI3AJzGsYZmPbqpRGXfHdW2/d92uZ8oi/TR0mzFxkSZWB0AAABCHaEbANox6+Xt2lxSZU5fYzIJ3AAAAD0QoRsA2mBW4I6ytATuhZOdJlQFAACAcEPoBoCTHGto9jtwT88arMFJCZqWlcEMNwAAQA9G6AYASV9XH9Okp9/TkePNirL419ewROnBa4eYUxgAAADCGqEbQI939n2b1NBseI5P+H92ybr7cvysCAAAAJGCNY8AerSTA7e/yh4jcAMAAOAfmOkG0KOcuAXYgN4xpgXuYYnMcAMAAOBUhG4APYaZW4CNdyZr9fSLTekLAAAAkYvl5QB6BAI3AAAAgoGZbgARz98twHpFSZecOUAZ/RN072Sn4mOjTawOAAAAkYzQDSAi1dU36fZXd6n8+2M6Ut/oV1/v3nmlfpAUb1JlAAAA6EkI3QAizjV/2qrdX7lM6Ss22kLgBgAAQJfxTDeAiGJ24P70kcmm9AUAAICeyefQXVBQoNzcXKWlpclisWjdunVe1w3D0KJFi5Samqr4+HiNGzdO+/fvb7Ov48ePa9iwYbJYLCouLva6tnv3bo0ZM0ZWq1WDBg3S8uXLfS0VQA/w6Podyliw0fPxJ3AnxkYp2iLZrNH6691XErgBAADgN59D95EjRzR06FCtWLGizevLly/X008/rVWrVmnbtm3q3bu3Jk6cqPr6+lPa3n333UpLSzvlvMvl0oQJEzR48GB9+OGHeuKJJ7RkyRI9//zzvpYLIIJlLNio5z84ZEpf453J+ujBSfpsWY52L8lmSTkAAABM4fMz3ZMmTdKkSZPavGYYhv7whz/o/vvv17XXXitJevnll5WSkqJ169ZpypQpnrZvvPGG3nzzTf3lL3/RG2+84dXPv//7v6uhoUEvvPCCYmNjdd5556m4uFi///3vNXv2bF9LBhCBMhZsNK0vtgADAABAoJj6THdpaakqKys1btw4zzm73a5Ro0apsLDQc+7QoUOaNWuWXnnlFSUkJJzST2FhocaOHavY2FjPuYkTJ2rfvn36/vvv2/zZx48fl8vl8voAiEyPrt/h1/0D+1o15qwBmnZJuj5+MJvADQAAgIAx9e3llZWVkqSUlBSv8ykpKZ5rhmHolltu0a9//WtddNFFKisra7OfzMzMU/povdavX79T7lm2bJmWLl1qxtcAEIJO3AJsX2WtX33l33aZ+ljZvAEAAACB1+2/dT7zzDOqra3VwoULTe134cKFmj9/vufY5XJp0KBBpv4MAMFh5hvJLxhoI3ADAACg25i6vNzhcEhqWT5+okOHDnmubdmyRYWFhYqLi1NMTIx++MMfSpIuuugizZgxw9NPW32c+DNOFhcXJ5vN5vUBEP7MDtzr540xpS8AAACgM0yd7snMzJTD4dDbb7+tYcOGSWqZcd62bZvmzJkjSXr66af18MMPe+45ePCgJk6cqFdffVWjRo2SJGVlZem+++5TY2OjevXqJUnavHmzzjnnnDaXlgOIHM1uQ0Wl1aqqrZctLsavwP0Dm9QnIVHp/eL11I3DmeEGAABAt/P5N9C6ujodOHDAc1xaWqri4mIlJSUpPT1dt912mx5++GGdddZZyszM1AMPPKC0tDRdd911kqT09HSv/vr06SNJOvPMMzVw4EBJ0k033aSlS5dq5syZuueee7Rnzx798Y9/1FNPPdXV7wkgDOTvqdDSDSWqqDl1i8Gu+Ou9Oab0AwAAAHSVz6F7x44duuKKKzzHrc9Rz5gxQ3l5ebr77rt15MgRzZ49W4cPH9bo0aOVn58vq9Xa6Z9ht9v15ptvau7cubrwwgs1YMAALVq0iO3CgAiWv6dCc9bslGFSf2WPEbgBAAAQfBbDMMz6HTekuFwu2e121dTU8Hw3EOKa3YZGP77FlBnu2Zem6N5rLjKhKgAAAKB9nc2cPOAIIChOfHb729rjfgXuPUsm8rw2AAAAQhK/pQLodmY+u80WYAAAAAhl/KYKoFuZ+ew2W4ABAAAg1BG6AQTUicvIB/SJ05L1e30O3BZJyYlxOn+gTV9+X88WYAAAAAgb/MYKIGDMWEZu+fv/Lr32PGUPSTWnMAAAAKCbELoBBIRZy8gddqsW5zoJ3AAAAAhLhG4Apmt2G1q6oaTLgfuBnHM1IDFOyYlWjcxMUnSUpeObAAAAgBBE6AZgiltWbNS7X/rXh0UtM9u3/DiToA0AAICIQOgG4LeMBRv97qM1Yi/OdRK4AQAAEDEI3QD8Ykbglnh2GwAAAJGJ0A2gy25Z0bXA3bqM/MmfDtW3R47z7DYAAAAiFqEbQJd15RnuE5eR//isAabWAwAAAIQaQjeAbsUycgAAAPQkhG4A3eKPU4axjBwAAAA9DqEbQJsamtx6pbBMX1Qf1eCkBE3LylBsTJRXm8sHdW6J+eWDpGuH/SBAlQIAAAChy2IYhhHsIgLB5XLJbrerpqZGNpst2OUAYWXZphKt3loq9wl/O0RZpFljMrVwstOrbWfeXl72WI7ZJQIAAABB1dnMGdXuFQA90rJNJXquwDtwS5LbkJ4rKNWyTSVe5zsK1ARuAAAA9GSEbgAeDU1urd5aeto2q7eWqqHJ7XWu7LEcXT7Iu93lgwjcAAAAAM90Az3csYZmPbqpRGXfHdXR402nzHCfzG1IrxSWaeaYf/I6nzeXgA0AAACcjNAN9GCzXt6uzSVVPt/3RfXRAFQDAAAARB6WlwM9VFcDtyQNTkowuRoAAAAgMjHTDfQQdfVNuv3VXSr//pjS7HF6Z9+3XeonyiJNy8owtzgAAAAgQhG6gR7gmj9t1e6vXJ7jfZW1Xe5r1pjMU/brBgAAANA2QjcQ4U4O3F3V3j7dAAAAANpH6AYiWF19k1+B+8L0vjrvB3YNTkrQtKwMZrgBAAAAHxG6gQjz1s4K/X//tdOUvtb8f5coPjbalL4AAACAnojQDUSQjAUbTetrvDOZwA0AAAD4ibWiQIQwO3Cvnn6xaf0BAAAAPRUz3UAEeGtnhV/333jRQB2sqVdG/wTdO9nJDDcAAABgEkI3EAH8eYb7goE2Pf7ToSZWAwAAAKAVy8uBHuyCgTatnzcm2GUAAAAAEYuZbqCHOceRqPR+8XrqxuHqY+WvAAAAACCQ+I0biAD/+rMRnVpi/q8/G6FxI1K7oSIAAAAAEsvLgYjQ2SBN4AYAAAC6F6EbiBBlj+X4dR0AAACA+VheDoSIZrehotJqVdXWKznRqpGZSYqOsvjUR9ljOXprZ4XXUnOWlAMAAADBQ+gGQkD+ngot3VCiipp6z7lUu1WLc53KHuJbYB43IlVlI5jVBgAAAEIBy8uBIMvfU6E5a3Z6BW5Jqqyp15w1O5W/pyJIlQEAAADwF6EbCKJmt6GlG0pktHGt9dzSDSVqdrfVAgAAAECoI3QDQVRUWn3KDPeJDEkVNfUqKq3uvqIAAAAAmIbQDQRRVW37gbsr7QAAAACEFkI3EETJiVZT2wEAAAAILYRuIIhGZiYp1W5VexuDWdTyFvORmUndWRYAAAAAkxC6gQB5Ycsnyliw0fN5Ycsnp7SJjrJoca5Tkk4J3q3Hi3OdPu/XDQAAACA0WAzDiMjXIrtcLtntdtXU1MhmswW7HPQwGQs2tnut7LFT99A2c59uAAAAAIHX2cxJ6AZMdrrA3aqt4N3sNlRUWq2q2nolJ7YsKWeGGwAAAAhNnc2cMd1YExDx2lpC3l67X175I69z0VEWZZ3ZPxBlAQAAAAgSnukGTPTgm5+Z2g4AAABAeCN0AwAAAAAQIIRuAAAAAAAChNANdFKz21DhZ9/pf4q/VuFn36nZfeo7CBdNOLNTfXW2HQAAAIDw5nPoLigoUG5urtLS0mSxWLRu3Tqv64ZhaNGiRUpNTVV8fLzGjRun/fv3e66XlZVp5syZyszMVHx8vM4880wtXrxYDQ0NXv3s3r1bY8aMkdVq1aBBg7R8+fKufUPABPl7KjT68S36+eq/6V/+s1g/X/03jX58i/L3VHi1O/nlaO3pbDsAAAAA4c3n0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+vmX/4U8++URut1vPPfec9u7dq6eeekqrVq3Svffe6+nD5XJpwoQJGjx4sD788EM98cQTWrJkiZ5//vkufk2g6/L3VGjOmp1ee2hLUmVNveas2XlK8G5rOzBfrgMAAACIHH7t022xWLR27Vpdd911klpmudPS0nTHHXfozjvvlCTV1NQoJSVFeXl5mjJlSpv9PPHEE1q5cqU+//xzSdLKlSt13333qbKyUrGxsZKkBQsWaN26dfrkk85tycQ+3eiqYw3NenRTicq+O6rBSQl6c2+lquoa2mxrkeSwW/X+PVeesqf2C1s+8XpL+aIJZzLDDQAAAESIoOzTXVpaqsrKSo0bN85zzm63a9SoUSosLGw3dNfU1CgpKclzXFhYqLFjx3oCtyRNnDhRjz/+uL7//nv169fPzLIBj1kvb9fmkirP8dYO2huSKmrqVVRafcoe27+88keEbAAAAKCHM/VFapWVlZKklJQUr/MpKSmeayc7cOCAnnnmGf3qV7/y6qetPk78GSc7fvy4XC6X1wfwxcmB2xdVtfUdNwIAAADQ4wT17eVff/21srOz9c///M+aNWuWX30tW7ZMdrvd8xk0aJBJVaInONbQ3OXALUnJiVYTqwEAAAAQKUwN3Q6HQ5J06NAhr/OHDh3yXGt18OBBXXHFFbr00ktPeUGaw+Fos48Tf8bJFi5cqJqaGs/nyy+/9Ou7oGd5dFNJl+6zSEq1WzUyM6nDtgAAAAB6HlNDd2ZmphwOh95++23POZfLpW3btikrK8tz7uuvv9bll1+uCy+8UC+++KKiorzLyMrKUkFBgRobGz3nNm/erHPOOafd57nj4uJks9m8PkBnlX131Od7Wl+btjjXecpL1AAAAABA6kLorqurU3FxsYqLiyW1vDytuLhY5eXlslgsuu222/Twww9r/fr1+uijjzR9+nSlpaV53nDeGrjT09P15JNP6ptvvlFlZaXXs9o33XSTYmNjNXPmTO3du1evvvqq/vjHP2r+/PmmfGngZBn9E3y+x2G3auXUEcoekhqAigAAAABEAp/fXr5jxw5dccUVnuPWIDxjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqzWlmdeN2/erAMHDujAgQMaOHCgV9+tu5fZ7Xa9+eabmjt3ri688EINGDBAixYt0uzZs7v8RdFzfV19TJOefk9Hjjerd1y03vjtZfpBUrxXm3snO/XK38o77OulWy7W4fpGJSe2LClnhhsAAADA6fi1T3coY59uSNLZ921SQ/Op/188NtqiTx+Z7HWuo7eXj3cma/X0i02vEQAAAED46WzmDOrby4FAai9wS1JDs6Gz79vkdW719Is13pncZnsCNwAAAICu8Hl5ORAOvq4+1m7gbtXQbOjr6mNeS81XT79Yxxqa9eimEpV9d1QZ/RN072Sn4mOjA10yAAAAgAjE8nJEjGa3oaLSalXV1mvhX3braKO7w3ts1mjtXpLdDdUBAAAAiCSdzZzMdCMi5O+p0NINJaqoqffpviPHmwNUEQAAAAAQuhEB8vdUaM6anerKko3ecSwbBwAAABA4hG6EnROXkQ/oE6cl6/d2KXBL0hu/vczU2gAAAADgRIRuhJWuLiNvS2y05ZT9ugEAAADATIRuhA1/lpGfrK19ugEAAADAbIRuhIVmt6GlG0q6HLgTekXpeJNbveOi9cZvL2OGGwAAAEC3IHQjLBSVVndpSblFksNu1fv3XKnoKIv5hQEAAADAaUQFuwCgM6pquxa4JWlxrpPADQAAACAomOlGWEhOtPp8j8Nu1eJcp7KHpAagIgAAAADoGKEbYWFkZpJS7VZV1tS3+Vx36zLyJ386VN8eOa7kRKtGZiYxww0AAAAgqAjdCAvRURYtznVqzpqdskhewfvEZeQ/PmtAEKoDAAAAgLbxTDfCRvaQVK2cOkIOu/dSc4fdqpVTR7CMHAAAAEDIYaYbYSV7SKrGOx0qKq1WVW09y8gBAAAAhDRCN7rNR+U1uubZ92WoZUn4+t+M1vnpdp/7iY6yKOvM/qbXBwAAAABmI3SjW2Qs2Oh1bEjKffZ9SVLZYzlBqAgAAAAAAo9nuhFwJwduX68DAAAAQLgidCOgPiqvMbUdAAAAAIQTQjcC6pq/LyE3qx0AAAAAhBNCNwLK6LiJT+0AAAAAIJwQuhFQnd3Iiw2/AAAAAEQiQjcCav1vRpvaDgAAAADCCaEbAdXZfbi7sl83AAAAAIQ6QjcCrqN9uNmnGwAAAECkigl2AegZyh7L0UflNbrm2fdlqOUZ7vW/Gc0MNwAAAICIRuhGtzk/3a5SZrUBAAAA9CAsLwcAAAAAIEAI3QAAAAAABAjLy+HR7DZUVFqtqtp6JSdaNTIzSdFR7KANAAAAAF1F6IYkKX9PhZZuKFFFTb3nXKrdqsW5TmUPSQ1iZQAAAAAQvlheDuXvqdCcNTu9ArckVdbUa86ancrfUxGkygAAAAAgvBG6e7hmt6GlG0pktHGt9dzSDSVqdrfVAgAAAABwOiwv74GONTTr0U0lKvvuqKwxUafMcJ/IkFRRU6+i0mplndm/+4oEAAAAgAhA6O5hZr28XZtLqny+r6q2/WAOAAAAAGgby8t7kK4GbklKTrSaXA0AAAAARD5munuIYw3NXQrcFkkOe8v2YQAAAAAA3zDT3UM8uqnE53tad+henOtkv24AAAAA6AJmunuIsu+O+nyPg326AQAAAMAvhO4eIqN/grbu77jd+HOTdfXQNCUntiwpZ4YbAAAAALqO0N1D3DvZqVf+Vt5hu6d/PkLxsdHdUBEAAAAARD6e6e4h4mOjNd6ZfNo2453JBG4AAAAAMBGhuwdZPf3idoP3eGeyVk+/uJsrAgAAAIDIxvLyHmb19It1rKFZj24qUdl3R5XRP0H3TnYyww0AAAAAAUDo7oHiY6P10HXnB7sMAAAAAIh4LC8HAAAAACBACN0AAAAAAAQIoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoS3lwdRXX2Tbn91l8q/P6b0fvF66sbh6mNlSAAAAAAgUvg8011QUKDc3FylpaXJYrFo3bp1XtcNw9CiRYuUmpqq+Ph4jRs3Tvv37/dqU11drZtvvlk2m019+/bVzJkzVVdX59Vm9+7dGjNmjKxWqwYNGqTly5f7/u1C2DV/2qohS/5Xmz+u0r7KWm3+uEpDlvyvrvnT1mCXBgAAAAAwic+h+8iRIxo6dKhWrFjR5vXly5fr6aef1qpVq7Rt2zb17t1bEydOVH19vafNzTffrL1792rz5s16/fXXVVBQoNmzZ3uuu1wuTZgwQYMHD9aHH36oJ554QkuWLNHzzz/fha8Yeq7501bt/srV5rXdX7kI3gAAAAAQISyGYRhdvtli0dq1a3XddddJapnlTktL0x133KE777xTklRTU6OUlBTl5eVpypQp+vjjj+V0OrV9+3ZddNFFkqT8/HxNnjxZX331ldLS0rRy5Urdd999qqysVGxsrCRpwYIFWrdunT755JNO1eZyuWS321VTUyObzdbVr2i6uvomDVnyvx2227NkIkvNAQAAACBEdTZzmvoitdLSUlVWVmrcuHGec3a7XaNGjVJhYaEkqbCwUH379vUEbkkaN26coqKitG3bNk+bsWPHegK3JE2cOFH79u3T999/3+bPPn78uFwul9cnFN3+6i5T2wEAAAAAQpepobuyslKSlJKS4nU+JSXFc62yslLJycle12NiYpSUlOTVpq0+TvwZJ1u2bJnsdrvnM2jQIP+/UACUf3/M1HYAAAAAgNAVMVuGLVy4UDU1NZ7Pl19+GeyS2pTeL97UdgAAAACA0GVq6HY4HJKkQ4cOeZ0/dOiQ55rD4VBVVZXX9aamJlVXV3u1aauPE3/GyeLi4mSz2bw+oeipG4eb2g4AAAAAELpMDd2ZmZlyOBx6++23PedcLpe2bdumrKwsSVJWVpYOHz6sDz/80NNmy5YtcrvdGjVqlKdNQUGBGhsbPW02b96sc845R/369TOz5G7XxxqjCwae/h8ELhho4yVqAAAAABABfA7ddXV1Ki4uVnFxsaSWl6cVFxervLxcFotFt912mx5++GGtX79eH330kaZPn660tDTPG87PPfdcZWdna9asWSoqKtJf//pXzZs3T1OmTFFaWpok6aabblJsbKxmzpypvXv36tVXX9Uf//hHzZ8/37QvHkzr541pN3hfMNCm9fPGdHNFAAAAAIBA8HnLsHfffVdXXHHFKednzJihvLw8GYahxYsX6/nnn9fhw4c1evRoPfvsszr77LM9baurqzVv3jxt2LBBUVFRuuGGG/T000+rT58+nja7d+/W3LlztX37dg0YMEC33nqr7rnnnk7XGapbhp2orr5Jt7+6S+XfH1N6v3g9deNwZrgBAAAAIAx0NnP6tU93KAuH0A0AAAAACE9B2acbAAAAAAD8A6EbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAESEywCwgUwzAkSS6XK8iVAAAAAAAiTWvWbM2e7YnY0F1bWytJGjRoUJArAQAAAABEqtraWtnt9navW4yOYnmYcrvdOnjwoBITE2WxWIJdDv7O5XJp0KBB+vLLL2Wz2YJdDkzG+EY2xjfyMcaRjfGNbIxvZGN8Q5NhGKqtrVVaWpqiotp/cjtiZ7qjoqI0cODAYJeBdthsNv7CiGCMb2RjfCMfYxzZGN/IxvhGNsY39JxuhrsVL1IDAAAAACBACN0AAAAAAAQIoRvdKi4uTosXL1ZcXFywS0EAML6RjfGNfIxxZGN8IxvjG9kY3/AWsS9SAwAAAAAg2JjpBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG6YoqCgQLm5uUpLS5PFYtG6detOafPxxx/rmmuukd1uV+/evXXxxRervLzcc72+vl5z585V//791adPH91www06dOhQN34LtKej8a2rq9O8efM0cOBAxcfHy+l0atWqVV5tGN/QtGzZMl188cVKTExUcnKyrrvuOu3bt8+rTWfGrry8XDk5OUpISFBycrLuuusuNTU1dedXQRs6Gt/q6mrdeuutOueccxQfH6/09HT99re/VU1NjVc/jG/o6syf4VaGYWjSpElt/j3OGIemzo5vYWGhrrzySvXu3Vs2m01jx47VsWPHPNerq6t18803y2azqW/fvpo5c6bq6uq686ugDZ0Z38rKSk2bNk0Oh0O9e/fWiBEj9Je//MWrDeMb+gjdMMWRI0c0dOhQrVixos3rn332mUaPHq0f/ehHevfdd7V792498MADslqtnja33367NmzYoD//+c967733dPDgQV1//fXd9RVwGh2N7/z585Wfn681a9bo448/1m233aZ58+Zp/fr1njaMb2h67733NHfuXP3tb3/T5s2b1djYqAkTJujIkSOeNh2NXXNzs3JyctTQ0KAPPvhAL730kvLy8rRo0aJgfCWcoKPxPXjwoA4ePKgnn3xSe/bsUV5envLz8zVz5kxPH4xvaOvMn+FWf/jDH2SxWE45zxiHrs6Mb2FhobKzszVhwgQVFRVp+/btmjdvnqKi/vFr/s0336y9e/dq8+bNev3111VQUKDZs2cH4yvhBJ0Z3+nTp2vfvn1av369PvroI11//fX62c9+pl27dnnaML5hwABMJslYu3at17kbb7zRmDp1arv3HD582OjVq5fx5z//2XPu448/NiQZhYWFgSoVXdDW+J533nnGgw8+6HVuxIgRxn333WcYBuMbTqqqqgxJxnvvvWcYRufGbtOmTUZUVJRRWVnpabNy5UrDZrMZx48f794vgNM6eXzb8l//9V9GbGys0djYaBgG4xtu2hvjXbt2GT/4wQ+MioqKU/4eZ4zDR1vjO2rUKOP+++9v956SkhJDkrF9+3bPuTfeeMOwWCzG119/HdB64Zu2xrd3797Gyy+/7NUuKSnJWL16tWEYjG+4YKYbAed2u7Vx40adffbZmjhxopKTkzVq1CivpW0ffvihGhsbNW7cOM+5H/3oR0pPT1dhYWEQqoYvLr30Uq1fv15ff/21DMPQO++8o08//VQTJkyQxPiGk9ZlxUlJSZI6N3aFhYU6//zzlZKS4mkzceJEuVwu7d27txurR0dOHt/22thsNsXExEhifMNNW2N89OhR3XTTTVqxYoUcDscp9zDG4ePk8a2qqtK2bduUnJysSy+9VCkpKbrsssv0/vvve+4pLCxU3759ddFFF3nOjRs3TlFRUdq2bVv3fgGcVlt/fi+99FK9+uqrqq6ultvt1n/+53+qvr5el19+uSTGN1wQuhFwVVVVqqur02OPPabs7Gy9+eab+slPfqLrr79e7733nqSW51ViY2PVt29fr3tTUlJUWVkZhKrhi2eeeUZOp1MDBw5UbGyssrOztWLFCo0dO1YS4xsu3G63brvtNv34xz/WkCFDJHVu7CorK71+WW+93noNoaGt8T3Zt99+q4ceeshrWSLjGz7aG+Pbb79dl156qa699to272OMw0Nb4/v5559LkpYsWaJZs2YpPz9fI0aM0FVXXaX9+/dLahnD5ORkr75iYmKUlJTE+IaQ9v78/td//ZcaGxvVv39/xcXF6Ve/+pXWrl2rH/7wh5IY33ARE+wCEPncbrck6dprr9Xtt98uSRo2bJg++OADrVq1Spdddlkwy4MJnnnmGf3tb3/T+vXrNXjwYBUUFGju3LlKS0vzmiFFaJs7d6727NnjNUOCyNHR+LpcLuXk5MjpdGrJkiXdWxxM0dYYr1+/Xlu2bPF6/hPhqa3xbf0d61e/+pV+8YtfSJKGDx+ut99+Wy+88IKWLVsWlFrhu/b+jn7ggQd0+PBhvfXWWxowYIDWrVunn/3sZ9q6davOP//8IFULXzHTjYAbMGCAYmJi5HQ6vc6fe+65nreXOxwONTQ06PDhw15tDh061OZSOISOY8eO6d5779Xvf/975ebm6oILLtC8efN044036sknn5TE+IaDefPm6fXXX9c777yjgQMHes53ZuwcDscpbzNvPWZ8Q0N749uqtrZW2dnZSkxM1Nq1a9WrVy/PNcY3PLQ3xlu2bNFnn32mvn37KiYmxvPYwA033OBZnsoYh772xjc1NVWSOvwdq6qqyut6U1OTqqurGd8Q0d74fvbZZ/rTn/6kF154QVdddZWGDh2qxYsX66KLLvK83JbxDQ+EbgRcbGysLr744lO2QPj00081ePBgSdKFF16oXr166e233/Zc37dvn8rLy5WVldWt9cI3jY2Namxs9HpLqiRFR0d7/gWe8Q1dhmFo3rx5Wrt2rbZs2aLMzEyv650Zu6ysLH300Ude/9HfvHmzbDbbKb8Iont1NL5Sywz3hAkTFBsbq/Xr13vtKiExvqGuozFesGCBdu/ereLiYs9Hkp566im9+OKLkhjjUNbR+GZkZCgtLe20v2NlZWXp8OHD+vDDDz3Xt2zZIrfbrVGjRgX+S6BdHY3v0aNHJem0v2MxvmEimG9xQ+Sora01du3aZezatcuQZPz+9783du3aZXzxxReGYRjGa6+9ZvTq1ct4/vnnjf379xvPPPOMER0dbWzdutXTx69//WsjPT3d2LJli7Fjxw4jKyvLyMrKCtZXwgk6Gt/LLrvMOO+884x33nnH+Pzzz40XX3zRsFqtxrPPPuvpg/ENTXPmzDHsdrvx7rvvGhUVFZ7P0aNHPW06GrumpiZjyJAhxoQJE4zi4mIjPz/fOOOMM4yFCxcG4yvhBB2Nb01NjTFq1Cjj/PPPNw4cOODVpqmpyTAMxjfUdebP8Ml00tvLGePQ1ZnxfeqppwybzWb8+c9/Nvbv32/cf//9htVqNQ4cOOBpk52dbQwfPtzYtm2b8f777xtnnXWW8fOf/zwYXwkn6Gh8GxoajB/+8IfGmDFjjG3bthkHDhwwnnzyScNisRgbN2709MP4hj5CN0zxzjvvGJJO+cyYMcPT5t/+7d+MH/7wh4bVajWGDh1qrFu3zquPY8eOGb/5zW+Mfv36GQkJCcZPfvITo6Kiopu/CdrS0fhWVFQYt9xyi5GWlmZYrVbjnHPOMX73u98Zbrfb0wfjG5raGldJxosvvuhp05mxKysrMyZNmmTEx8cbAwYMMO644w7PllMIno7Gt70/25KM0tJSTz+Mb+jqzJ/htu45eetHxjg0dXZ8ly1bZgwcONBISEgwsrKyvCY1DMMwvvvuO+PnP/+50adPH8Nmsxm/+MUvjNra2m78JmhLZ8b3008/Na6//nojOTnZSEhIMC644IJTthBjfEOfxTAMw+zZcwAAAAAAwDPdAAAAAAAEDKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAALk/wdw9IA+/qwxiAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -942,14 +779,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Dokážete uhádnuť, prečo sa bodky zoradia do vertikálnych línií takto?\n",
+    "> Dokážete uhádnuť, prečo sa bodky zoradia do takýchto vertikálnych línií?\n",
     "\n",
-    "Pozorovali sme koreláciu medzi umelo vytvoreným konceptom, ako je plat, a pozorovanou premennou *výška*. Pozrime sa tiež, či dve pozorované premenné, ako výška a váha, korelujú:\n"
+    "Pozorovali sme koreláciu medzi umelo vytvoreným konceptom, ako je plat, a pozorovanou premennou *výška*. Pozrime sa tiež, či medzi dvoma pozorovanými premennými, ako sú výška a váha, existuje korelácia:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bohužiaľ, nedostali sme žiadne výsledky – iba nejaké zvláštne hodnoty `nan`. Je to spôsobené tým, že niektoré hodnoty v našej sérii sú nedefinované, reprezentované ako `nan`, čo spôsobuje, že výsledok operácie je tiež nedefinovaný. Pri pohľade na maticu môžeme vidieť, že problematickým stĺpcom je `Weight`, pretože samokorelácia medzi hodnotami `Height` bola vypočítaná.\n",
+    "Bohužiaľ, nedostali sme žiadne výsledky – iba nejaké zvláštne hodnoty `nan`. Je to spôsobené tým, že niektoré hodnoty v našej sérii sú nedefinované, reprezentované ako `nan`, čo spôsobuje, že výsledok operácie je tiež nedefinovaný. Pri pohľade na maticu môžeme vidieť, že problematickým stĺpcom je `Weight`, pretože bola vypočítaná samokorelácia medzi hodnotami `Height`.\n",
     "\n",
     "> Tento príklad ukazuje dôležitosť **prípravy dát** a **čistenia dát**. Bez správnych dát nemôžeme nič vypočítať.\n",
     "\n",
-    "Použime metódu `fillna` na vyplnenie chýbajúcich hodnôt a vypočítajme koreláciu:\n"
+    "Použime metódu `fillna` na doplnenie chýbajúcich hodnôt a vypočítajme koreláciu:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:30:29+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:51:58+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "sk"
   }
diff --git a/translations/sk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/sk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 959c3187..bf0d837e 100644
--- a/translations/sk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/sk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,14 +150,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "V tomto datasete sú stĺpce nasledovné:\n",
+    "V tejto množine údajov sú stĺpce nasledovné:\n",
     "* Vek a pohlavie sú samozrejmé\n",
     "* BMI je index telesnej hmotnosti\n",
     "* BP je priemerný krvný tlak\n",
     "* S1 až S6 sú rôzne merania krvi\n",
     "* Y je kvalitatívne hodnotenie progresie ochorenia počas jedného roka\n",
     "\n",
-    "Poďme preskúmať tento dataset pomocou metód pravdepodobnosti a štatistiky.\n",
+    "Poďme preskúmať túto množinu údajov pomocou metód pravdepodobnosti a štatistiky.\n",
     "\n",
     "### Úloha 1: Vypočítajte priemerné hodnoty a rozptyl pre všetky hodnoty\n"
    ],
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -590,10 +590,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Závery:  \n",
-    "* Vek - normálny  \n",
-    "* Pohlavie - jednotné  \n",
-    "* BMI, Y - ťažko povedať  \n"
+    "Závery:\n",
+    "* Vek - normálny\n",
+    "* Pohlavie - jednotné\n",
+    "* BMI, Y - ťažko povedať\n"
    ],
    "metadata": {}
   },
@@ -845,7 +845,7 @@
    "cell_type": "markdown",
    "source": [
     "Záver:  \n",
-    "* Najsilnejšia korelácia s Y je BMI a S5 (hladina cukru v krvi). To znie logicky.\n"
+    "* Najsilnejšia korelácia Y je s BMI a S5 (hladina cukru v krvi). To znie logicky.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:54:16+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:52:26+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "sk"
   }
diff --git a/translations/sl/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/sl/1-Introduction/04-stats-and-probability/assignment.ipynb
index 97f318d7..7fdcae43 100644
--- a/translations/sl/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/sl/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Naloga 2: Prikaži škatlaste diagrame za ITM, KT in Y glede na spol\n"
+    "### Naloga 2: Prikažite boxplote za ITM, KT in Y glede na spol\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Naloga 5: Preizkusite hipotezo, da je stopnja napredovanja sladkorne bolezni različna med moškimi in ženskami\n"
+    "### Naloga 5: Preverite hipotezo, da je stopnja napredovanja sladkorne bolezni različna med moškimi in ženskami\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:46:52+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:59:10+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "sl"
   }
diff --git a/translations/sl/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/sl/1-Introduction/04-stats-and-probability/notebook.ipynb
index 98adcd8c..7091280a 100644
--- a/translations/sl/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/sl/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Uvod v verjetnost in statistiko\n",
-    "V tem zvezku se bomo poigrali z nekaterimi koncepti, o katerih smo že prej razpravljali. Veliko konceptov iz verjetnosti in statistike je dobro zastopanih v glavnih knjižnicah za obdelavo podatkov v Pythonu, kot sta `numpy` in `pandas`.\n"
+    "V tem zvezku se bomo poigrali z nekaterimi koncepti, o katerih smo že prej razpravljali. Številni koncepti iz verjetnosti in statistike so dobro zastopani v glavnih knjižnicah za obdelavo podatkov v Pythonu, kot sta `numpy` in `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Tukaj uporabljamo paket [**Pandas**](https://pandas.pydata.org/) za analizo podatkov. Več o Pandas in delu s podatki v Pythonu bomo govorili kasneje v tem tečaju.\n",
+    "Uporabljamo paket, imenovan [**Pandas**](https://pandas.pydata.org/) za analizo podatkov. O Pandas in delu s podatki v Pythonu bomo govorili več kasneje v tem tečaju.\n",
     "\n",
     "Izračunajmo povprečne vrednosti za starost, višino in težo:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Poleg povprečja je smiselno pogledati tudi mediano in kvartile. Te je mogoče prikazati z uporabo **škatlastega diagrama**:\n"
+    "Poleg povprečja je smiselno pogledati tudi mediano in kvartile. Te lahko prikažemo z uporabo **škatlastega diagrama**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Prav tako lahko naredimo škatlaste diagrame za podmnožice našega nabora podatkov, na primer, razvrščene po vlogi igralca.\n"
+    "Lahko naredimo tudi škatlaste diagrame podmnožic našega nabora podatkov, na primer, razvrščenih po vlogi igralca.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Opomba**: Ta diagram nakazuje, da so povprečne višine igralcev na prvi bazi višje od višin igralcev na drugi bazi. Kasneje bomo spoznali, kako lahko to hipotezo bolj formalno preverimo in kako lahko pokažemo, da so naši podatki statistično pomembni za dokazovanje tega.\n",
+    "> **Opomba**: Ta diagram nakazuje, da so povprečne višine igralcev na prvi bazi višje od višin igralcev na drugi bazi. Kasneje bomo spoznali, kako lahko to hipotezo bolj formalno preverimo in kako pokažemo, da so naši podatki statistično pomembni za dokazovanje tega.\n",
     "\n",
-    "Starost, višina in teža so vse zvezne naključne spremenljivke. Kaj mislite, kakšna je njihova porazdelitev? Dober način, da to ugotovimo, je, da narišemo histogram vrednosti:\n"
+    "Starost, višina in teža so vse zvezne naključne spremenljivke. Kaj menite, kakšna je njihova porazdelitev? Dober način, da to ugotovimo, je, da narišemo histogram vrednosti:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ker je večina vrednosti v resničnem življenju normalno porazdeljenih, ne bi smeli uporabljati enakomernega generatorja naključnih števil za ustvarjanje vzorčnih podatkov. Tukaj je, kaj se zgodi, če poskusimo generirati teže z enakomerno porazdelitvijo (ustvarjeno z `np.random.rand`):\n"
+    "Ker je večina vrednosti v resničnem življenju normalno porazdeljenih, ne bi smeli uporabljati enakomernega generatorja naključnih števil za ustvarjanje vzorčnih podatkov. Tukaj je, kaj se zgodi, če poskušamo generirati teže z enakomerno porazdelitvijo (ustvarjeno z `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Intervali zaupanja\n",
     "\n",
-    "Zdaj bomo izračunali intervale zaupanja za težo in višino igralcev baseballa. Uporabili bomo kodo [iz te razprave na stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Zdaj bomo izračunali intervale zaupanja za teže in višine igralcev baseballa. Uporabili bomo kodo [iz te razprave na stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -726,16 +567,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -757,15 +598,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -791,24 +632,22 @@
    "source": [
     "## Simulacija normalne porazdelitve s pomočjo centralnega limitnega izreka\n",
     "\n",
-    "Psevdonaključni generator v Pythonu je zasnovan tako, da nam daje enakomerno porazdelitev. Če želimo ustvariti generator za normalno porazdelitev, lahko uporabimo centralni limitni izrek. Za pridobitev vrednosti z normalno porazdelitvijo bomo preprosto izračunali povprečje vzorca, ustvarjenega z enakomerno porazdelitvijo.\n"
+    "Psevdonaključni generator v Pythonu je zasnovan tako, da nam daje enakomerno porazdelitev. Če želimo ustvariti generator za normalno porazdelitev, lahko uporabimo centralni limitni izrek. Za pridobitev vrednosti z normalno porazdelitvijo bomo preprosto izračunali povprečje vzorca, generiranega z enakomerno porazdelitvijo.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -835,14 +674,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +695,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Zdaj izračunajmo kovarianco in korelacijo teh zaporedij. `np.cov` nam bo dal tako imenovano **kovariančno matriko**, ki je razširitev kovariance na več spremenljivk. Element $M_{ij}$ kovariančne matrike $M$ je korelacija med vhodnima spremenljivkama $X_i$ in $X_j$, diagonalne vrednosti $M_{ii}$ pa so varianca $X_{i}$. Podobno nam bo `np.corrcoef` dal **korelacijsko matriko**.\n"
+    "Izračunajmo zdaj kovarianco in korelacijo teh zaporedij. `np.cov` nam bo dal tako imenovano **kovariančno matriko**, ki je razširitev kovariance na več spremenljivk. Element $M_{ij}$ kovariančne matrike $M$ je korelacija med vhodnima spremenljivkama $X_i$ in $X_j$, diagonalne vrednosti $M_{ii}$ pa predstavljajo varianco $X_{i}$. Podobno nam bo `np.corrcoef` dal **korelacijsko matriko**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -891,19 +730,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -918,19 +755,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Poglejmo, kaj se zgodi, če razmerje ni linearno. Recimo, da se je naše podjetje odločilo skriti očitno linearno odvisnost med višinami in plačami ter je v formulo uvedlo nekaj nelinearnosti, na primer `sin`:\n"
+    "Poglejmo, kaj se zgodi, če razmerje ni linearno. Recimo, da se je naše podjetje odločilo skriti očitno linearno odvisnost med višino in plačami ter je v formulo uvedlo nekaj nelinearnosti, kot je `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -943,19 +780,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "V tem primeru je korelacija nekoliko manjša, vendar je še vedno precej visoka. Zdaj, da bi naredili povezavo še manj očitno, bi morda želeli dodati nekaj dodatne naključnosti z dodajanjem naključne spremenljivke k plači. Poglejmo, kaj se zgodi:\n"
+    "V tem primeru je korelacija nekoliko manjša, vendar je še vedno precej visoka. Zdaj, da bi bila povezava še manj očitna, bi morda želeli dodati nekaj dodatne naključnosti z dodajanjem neke naključne spremenljivke k plači. Poglejmo, kaj se zgodi:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -993,24 +828,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Ali lahko uganeš, zakaj se pike poravnajo v navpične črte na ta način?\n",
+    "Ali lahko uganeš, zakaj se pike poravnajo v navpične črte na ta način?\n",
     "\n",
-    "Opazili smo povezavo med umetno ustvarjenim konceptom, kot je plača, in opazovano spremenljivko *višina*. Poglejmo še, ali sta dve opazovani spremenljivki, kot sta višina in teža, prav tako povezani:\n"
+    "Opazili smo povezavo med umetno ustvarjenim konceptom, kot je plača, in opazovano spremenljivko *višina*. Poglejmo še, ali se dve opazovani spremenljivki, kot sta višina in teža, med seboj povezujeta:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1032,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,32 +890,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dejansko obstaja korelacija, vendar ne tako močna kot v našem umetnem primeru. Če pogledamo razpršeni diagram ene vrednosti glede na drugo, bi bila povezava veliko manj očitna:\n"
+    "Dejansko obstaja korelacija, vendar ne tako močna kot v našem umetnem primeru. Če pogledamo razpršeni diagram ene vrednosti v primerjavi z drugo, bi bila povezava veliko manj očitna:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1091,14 +924,14 @@
    "source": [
     "## Zaključek\n",
     "\n",
-    "V tem zvezku smo se naučili, kako izvajati osnovne operacije na podatkih za izračun statističnih funkcij. Zdaj vemo, kako uporabiti zanesljiv nabor matematičnih in statističnih orodij za dokazovanje nekaterih hipotez ter kako izračunati intervale zaupanja za poljubne spremenljivke na podlagi vzorca podatkov.\n"
+    "V tem zvezku smo se naučili, kako izvajati osnovne operacije na podatkih za izračun statističnih funkcij. Zdaj vemo, kako uporabiti zanesljiv nabor matematičnih in statističnih orodij za preverjanje nekaterih hipotez ter kako izračunati intervale zaupanja za poljubne spremenljivke na podlagi vzorca podatkov.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:31:43+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:58:57+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "sl"
   }
diff --git a/translations/sl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/sl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index fe9ada58..b1a62236 100644
--- a/translations/sl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/sl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Uvod v verjetnost in statistiko\n",
     "## Naloga\n",
     "\n",
-    "V tej nalogi bomo uporabili podatkovni niz bolnikov s sladkorno boleznijo, ki je na voljo [tukaj](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "V tej nalogi bomo uporabili podatkovni niz bolnikov s sladkorno boleznijo, ki je vzet [od tukaj](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -152,8 +152,8 @@
    "source": [
     "V tem naboru podatkov so stolpci naslednji:  \n",
     "* Starost in spol sta samoumevna  \n",
-    "* BMI je indeks telesne mase  \n",
-    "* BP je povprečni krvni tlak  \n",
+    "* ITM je indeks telesne mase  \n",
+    "* KT je povprečen krvni tlak  \n",
     "* S1 do S6 so različne meritve krvi  \n",
     "* Y je kvalitativna mera napredovanja bolezni v enem letu  \n",
     "\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Naloga 2: Prikažite škatlaste diagrame za ITM, KT in Y glede na spol\n"
+    "### Naloga 2: Prikaži škatlaste diagrame za ITM, KT in Y glede na spol\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -593,9 +593,9 @@
    "cell_type": "markdown",
    "source": [
     "Zaključki:\n",
-    "* Starost - normalno\n",
-    "* Spol - enakomerno\n",
-    "* ITM, Y - težko reči\n"
+    "* Starost - normalno  \n",
+    "* Spol - enakomerno  \n",
+    "* ITM, Y - težko reči  \n"
    ],
    "metadata": {}
   },
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "Zaključek:\n",
-    "* Najmočnejša korelacija Y je z ITM (indeks telesne mase) in S5 (krvni sladkor). To se zdi smiselno.\n"
+    "* Najmočnejša korelacija z Y sta ITM (indeks telesne mase) in S5 (krvni sladkor). To se zdi smiselno.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -879,7 +879,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Naloga 5: Preizkusite hipotezo, da je stopnja napredovanja sladkorne bolezni različna med moškimi in ženskami\n"
+    "### Naloga 5: Preverite hipotezo, da je stopnja napredovanja sladkorne bolezni različna med moškimi in ženskami\n"
    ],
    "metadata": {}
   },
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:54:36+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:59:27+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "sl"
   }
diff --git a/translations/sr/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/sr/1-Introduction/04-stats-and-probability/assignment.ipynb
index 22472634..53d88e68 100644
--- a/translations/sr/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/sr/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Увод у вероватноћу и статистику\n",
-    "## Задатак\n",
+    "## Увод у вероватноћу и статистику  \n",
+    "## Задатак  \n",
     "\n",
-    "У овом задатку ћемо користити скуп података о пацијентима са дијабетесом који је преузет [са овог линка](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "У овом задатку ћемо користити скуп података о пацијентима са дијабетесом који је преузет [одатле](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,7 +150,7 @@
    "cell_type": "markdown",
    "source": [
     "У овом скупу података, колоне су следеће:\n",
-    "* Узраст и пол су сами по себи јасни\n",
+    "* Узраст и пол су самообјашњиви\n",
     "* BMI је индекс телесне масе\n",
     "* BP је просечан крвни притисак\n",
     "* S1 до S6 су различита мерења крви\n",
@@ -172,7 +172,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Задатак 2: Прикажите боксплотове за БМИ, БП и Y у зависности од пола\n"
+    "### Задатак 2: Прикажи боксплоте за БМИ, БП и Y у зависности од пола\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Задатак 3: Каква је расподела променљивих Старост, Пол, БМИ и Y?\n"
+    "### Задатак 3: Каква је расподела старости, пола, БМИ и Y променљивих?\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:47:09+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:56:22+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "sr"
   }
diff --git a/translations/sr/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/sr/1-Introduction/04-stats-and-probability/notebook.ipynb
index 5fb20a6e..03d36d80 100644
--- a/translations/sr/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/sr/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Увод у вероватноћу и статистику\n",
-    "У овом бележнику ћемо се позабавити неким од концепата које смо раније разматрали. Многи концепти из вероватноће и статистике добро су заступљени у главним библиотекама за обраду података у Python-у, као што су `numpy` и `pandas`.\n"
+    "# Увод у вероватноћу и статистику  \n",
+    "У овом бележнику ћемо се позабавити неким од концепата које смо раније разматрали. Многи концепти из вероватноће и статистике добро су заступљени у главним библиотекама за обраду података у Python-у, као што су `numpy` и `pandas`.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Случајне променљиве и расподеле\n",
-    "Хајде да започнемо са узорком од 30 вредности из униформне расподеле од 0 до 9. Такође ћемо израчунати средњу вредност и варијансу.\n"
+    "## Случајне променљиве и расподеле  \n",
+    "Хајде да почнемо са узорком од 30 вредности из униформне расподеле од 0 до 9. Такође ћемо израчунати средњу вредност и варијансу.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Анализа стварних података\n",
     "\n",
-    "Средња вредност и варијанса су веома важни при анализи података из стварног света. Хајде да учитамо податке о бејзбол играчима са [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Средња вредност и варијанса су веома важни при анализи података из стварног света. Хајде да учитамо податке о бејзбол играчима са [SOCR MLB подаци о висини/тежини](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Користимо пакет под називом [**Pandas**](https://pandas.pydata.org/) овде за анализу података. О Пандасу и раду са подацима у Пајтону ћемо више говорити касније у овом курсу.\n",
+    "> Користимо пакет под називом [**Pandas**](https://pandas.pydata.org/) овде за анализу података. О Pandas-у и раду са подацима у Python-у ћемо више говорити касније у овом курсу.\n",
     "\n",
     "Хајде да израчунамо просечне вредности за године, висину и тежину:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Сада хајде да се фокусирамо на висину и израчунамо стандардну девијацију и варијансу:\n"
+    "Сада се фокусирамо на висину и израчунавамо стандардну девијацију и варијансу:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Напомена**: Овај дијаграм указује да су, у просеку, висине играча на првој бази веће од висина играча на другој бази. Касније ћемо научити како можемо формалније тестирати ову хипотезу и како да покажемо да су наши подаци статистички значајни да то потврде.\n",
+    "> **Напомена**: Овај дијаграм указује на то да су, у просеку, висине првих базмена веће од висина других базмена. Касније ћемо научити како можемо формалније тестирати ову хипотезу и како да покажемо да су наши подаци статистички значајни да би то доказали.  \n",
     "\n",
-    "Старост, висина и тежина су све континуиране случајне променљиве. Шта мислите, како изгледа њихова расподела? Добар начин да то сазнате је да нацртате хистограм вредности:\n"
+    "Године, висина и тежина су све континуиране случајне променљиве. Шта мислите, како изгледа њихова расподела? Добар начин да то сазнате је да нацртате хистограм вредности:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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qfVW17/Dhw3NOAwAAFmOua5CTvKy7H6qqZyW5paru2eiO3b03yd4k2bVrV885DwAAWIi5ziB390PT7cNJbkxyXpJDVXV6kky3D887SQAA2CqbDuSqelpVPf3o/SQ/m+TOJDcluXTa7NIkn5x3kgAAsFXmucTi2UlurKqj3+f3uvu/VtUXk1xfVW9O8s0kb5h/mgAAsDU2Hcjd/bUk/2jG+LeTnD/PpAAAYLvM+0t6cELYuefm7Z4CAHCC8FHTAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAw8FHTALBgJ8LH2++/4sLtngI8YTmDDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAACDTQdyVZ1ZVX9UVXdX1V1V9SvT+Puq6sGqun36es3ipgsAAMu1Y459jyR5R3d/qaqenuS2qrpleu4D3f3++acHAABba9OB3N0Hkxyc7n+3qu5OcsaiJsaJY+eem7d7CgAAC7OQa5CrameSlyT5/DT0tqq6o6qurqpT19hnd1Xtq6p9hw8fXsQ0AABgbnMHclX9RJIbkry9u7+T5INJnp/k3KyeYb5y1n7dvbe7d3X3rpWVlXmnAQAACzFXIFfVj2U1jj/a3Z9Iku4+1N2PdvcPk3w4yXnzTxMAALbGPO9iUUmuSnJ3d//GMH76sNnrk9y5+ekBAMDWmuddLF6W5E1JvlJVt09j705ySVWdm6ST7E/yljl+BgAAbKl53sXis0lqxlOf2vx0AABge/kkPQAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAY7NjuCQAAW2/nnpu3ewrr2n/Fhds9BU5SziADAMBAIAMAwEAgAwDAQCADAMBAIAMAwEAgAwDAwNu8AQBPSN6Kju3iDDIAAAycQT4BnAj/Bw0A8GThDDIAAAwEMgAADAQyAAAMTvprkF3fCwDAyBlkAAAYCGQAABgsLZCr6oKqureq7q+qPcv6OQAAsEhLuQa5qk5J8p+T/EySA0m+WFU3dfdXl/HzAAC2g99lmt8T8dMIl3UG+bwk93f317r7r5J8LMlFS/pZAACwMMt6F4szknxreHwgyT8ZN6iq3Ul2Tw//sqruXeN7nZbkzxY+Q46yvstlfZfPGi+X9V0u67tc1ne5FrK+9WsLmMnm/b1Zg8sK5Jox1o950L03yd51v1HVvu7etaiJ8VjWd7ms7/JZ4+WyvstlfZfL+i7Xk3l9l3WJxYEkZw6Pn5vkoSX9LAAAWJhlBfIXk5xTVWdX1Y8nuTjJTUv6WQAAsDBLucSiu49U1duS/LckpyS5urvv2uS3W/cyDOZifZfL+i6fNV4u67tc1ne5rO9yPWnXt7p7/a0AAOAk4ZP0AABgIJABAGCwrYFcVc+oqo9X1T1VdXdV/dOqel9VPVhVt09fr1ljXx9lvY411ve6YW33V9Xta+y7v6q+Mm23b4un/oRXVS8c1vH2qvpOVb29qp5ZVbdU1X3T7alr7O/1exzHWd9fn17Pd1TVjVX1jDX29/o9juOsr+PvAhxnfR1/F6Sq/l1V3VVVd1bVtVX1tx1/F2eN9T2pjr/beg1yVV2T5H92929P73bx1CRvT/KX3f3+4+x3SpI/zfBR1kku8VHWjzVrfbv7/wzPX5nkL7r7V2fsuz/Jru72BuvrmF6PD2b1w3AuS/JId18xHXhP7e53ztje63eDjlnfFyb5H9MvAv9akhy7vtM+++P1uyHHrO8vxfF3ocb17e5vDOOOv5tUVWck+WySF3X396vq+iSfSvKiOP7O7Tjr+1BOouPvtp1BrqqfTPKKJFclSXf/1Rhv6/BR1utYb32rqpK8Mcm12zLBJ5fzkzww/eV3UZJrpvFrkrxuxvZev4/PX69vd3+6u49M45/L6nusM5/x9bsRXr+Pz4+sr+PvQuxI8neqakdWT649FMffRfqR9T3Zjr/beYnF85IcTvJfqurLVfXbVfW06bm3Tafwr17jn0hmfZT1GUue74nmeOubJC9Pcqi771tj/07y6aq6rVY/Fpy1XZy/+Yvu2d19MEmm22fN2N7r9/EZ13f0y0n+cI19vH437tj1dfxdrFmvX8ffOXT3g0nen+SbSQ5m9Uz8p+P4uxDHWd/Rk/74u52BvCPJS5N8sLtfkuT/JtmT5INJnp/k3Kz+wVw5Y991P8qaNdf3qEty/LMXL+vulyb5uSSXVdUrljbTE9h06cprk/z+49ltxpjX7wxrrW9VvSfJkSQfXWNXr98NmLG+jr8LdJzjg+PvHKb/cbsoydlJnpPkaVX1rza6+4wxr9/Beut7shx/tzOQDyQ50N2fnx5/PMlLu/tQdz/a3T9M8uGs/nPIrH19lPXxzVzfJJn+yeQXkly31s7d/dB0+3CSGzP7z4HVA8CXuvvQ9PhQVZ2eJNPtwzP28frduGPXN1V1aZKfT/KLvcYvUXj9bthj1tfxd+FmvX4df+f3qiRf7+7D3f3/knwiyT+L4++irLW+J9Xxd9sCubv/d5JvVdULp6Hzk3z16It78vokd87Y3UdZr2Ot9Z3uvyrJPd19YNa+VfW0qnr60ftJfjaz/xz40TNBNyW5dLp/aZJPztjH63fjHrO+VXVBkncmeW13f2/WDl6/j8ux6+v4u1izzhQ7/s7vm0l+uqqeOl3PfX6Su+P4uygz1/ekO/5297Z9ZfWf8fYluSPJHyQ5NcnvJvnKNHZTktOnbZ+T5FPDvq/J6m+iPpDkPdv53/FE/Zq1vtP4R5K89Zht/3p9s3r98p9MX3dZ3zXX96lJvp3k7w5jP5Xk1iT3TbfPPHZ9p8dev5tb3/uzev3g7dPXh45dX6/fudbX8XeJ6zuNO/4uZn3/Y5J7shpfv5vkKY6/S1/fk+r466OmAQBg4JP0AABgIJABAGAgkAEAYCCQAQBgIJABAGAgkAEAYCCQAQBg8P8B40VGjZpezWQAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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T/OHwsDOTsXOf6/YktyfJNddcs+vCgfkzhQKAg2jqgFxV35rkd5L8VHf/VVVtu+kWY33eQPfdSe5Oko2NjfPuB4B14cMmrJapVrGoqpdmMxy/v7t/dzL81aq6YnL/FUmemYyfSXL18PCrkjw1m3IBAGC+dgzItdkq/rUkj3X3Lw53nUxyZHL9SJL7hvHDVfWyqro2yXVJHppdyQAAMD/TTLF4c5IfS/LHVfWZydjPJjme5ERV3ZbkiSS3Jkl3P1JVJ5I8ms0VMO7o7hdmXTgArJOz0zAeP37zgisBdgzI3f2/svW84iS5cZvHHEtybA91AQDAQjiTHgAADARkAAAYCMgAADDY1YlCAIDZskYyLB8dZAAAGAjIAAAwEJABAGAgIAMAwEBABoAlcujo/Q7cgwUTkAEAYGCZNwDYZzrEsNx0kAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQ4oJyMAAC2JiADwBLyIRYWx4lC4IA7+wf48eM3v+g2ABxUOsgAsAZ0nGF2BGQAABgIyAAAMDAHGUhi7jEAnKWDDAArxFxjmD8BGQAABgIyAAAMzEGGA8ZXs7Bazl2rHJg/HWQAABgIyAAAMDDFAgBWgOlRsH90kAEAYCAgw5qxRioA7I2ADAAAA3OQ4YDQVYb15HcbZk8HGQAABgIyAKwxxyXA7gnIAAAwEJABAGAgIAMAwEBABgCAgWXeYM05OAcAdkdAhjUhCAPAbAjIALCCfCiG+TEHGQAABgIyAAAMTLGAFXP2a9XHj9/8otsAwGzoIAMAwEBABgCAgYAMAAADARmW3KGj95tnDAD7yEF6sKaEagC4OAIyrAiBFwD2hykWAAAwEJABAGCwY0CuqvdV1TNV9flh7NVV9UBVfWly+arhvruq6nRVfbGq3j6vwuGgc/AeAMxHdfeFN6j6gSR/k+TXu/v1k7H/kORr3X28qo4meVV331lV1yf5QJIbkrw2yUeTfGd3v3Ch19jY2OhTp07t/V8Da0gIBmbp7Fk4gaSqHu7ujXPHd+wgd/fHk3ztnOFbktwzuX5PkncN4/d293Pd/ZUkp7MZlgEAYCVc7Bzky7v76SSZXF42Gb8yyZPDdmcmYwAAsBJmfZBebTG25RyOqrq9qk5V1alnn312xmUAAMDFudiA/NWquiJJJpfPTMbPJLl62O6qJE9t9QTdfXd3b3T3xqWXXnqRZQAAwGxdbEA+meTI5PqRJPcN44er6mVVdW2S65I8tLcS4WCxOgUALNaOZ9Krqg8keUuS11TVmSQ/l+R4khNVdVuSJ5LcmiTd/UhVnUjyaJLnk9yx0woWAMD+O/tB3KoWcL4dA3J3/8g2d924zfbHkhzbS1EAALAozqQHAAADARkAAAYCMgAADHacgwzMlwNlgP200yo53pNABxkAAF5EQAYAzmNNdg4yARkAAAYCMgAADBykB3PmgBdgFZhOAd8gIMOS8scKABbDFAsAABjoIMOS0DEGgOWggwz7zNJJwCo59z3LexgHgYAMAAADARkAAAbmIMOC+IoSAJaTgAwA7MiHeg4SARlmzIlBgFVyscHXex3rzBxkAGDPrG7BOhGQAQBgYIoF7BOdFQBYDTrIAAAw0EGGizB2g7c7QEXHGABWk4AMMyIQA0zXQIBlZ4oFAAAMdJBhF3SJAWD96SADAMBAQAYAgIGADADMhbPrsarMQYY98uYPAOtFBxkAAAYCMgCwr0y9YNkJyAAAMBCQAYC50jFm1QjIcAHe1AHg4LGKBWxBKAaAg0tAhnwjED9+/OYL3g/Ai83j/XGn92SYNwEZANgXmg2sCgGZA2HaboQ3b4D9o1PMshKQOdAEYoDF2y4oC9AsioDMWtEpBlh/577XC9LMmoAMACwFzQuWhXWQAQBgoIMMAKwEHWb2S3X3omvIxsZGnzp1atFlsAa8eQIcXOYgs1tV9XB3b5w7booFAAAMBGQAABiYg8xKcCpoAKZl2Tf2SgcZAAAGOsgAwFrY7ttEHWV2S0BmpZz75ufNDgCYNQEZAFhL5zZVtusw78cpq3WxV4s5yAAAMNBBZl+d+wl6uykTPmkDsEr83VovAjIzt9WbxMUuw2b5NgCWkUC83gRkdjTtGsSLeJMQoAHYq93OVWb9zS0gV9VNSX45ySVJ3tvdx+f1WizGXsLpTkvxAMAq02FebXMJyFV1SZJfSfKPk5xJ8qmqOtndj87j9ZjOdr+su/0lnjbECrsArJOt/q5N232e9rmX8dvag2heHeQbkpzu7i8nSVXdm+SWJALyHO0UgLfbfqfnu9jtAYDd2elg9t0+frePu5jHrqPq7tk/adU/T3JTd/+bye0fS/IPu/vdW22/sbHRp06dmnkd09jrJ7aL7b5u9YO/3QoOF/vLcrG/XADAfC3qb/Q0r7vTN8177WYv00m/qurh7t44b3xOAfnWJG8/JyDf0N0/Pmxze5LbJze/K8kXZ17I3r0myZ8tuogVYV/tjv01Pftqd+yv6dlXu2N/Tc++2p1F7q+/292Xnjs4rykWZ5JcPdy+KslT4wbdfXeSu+f0+jNRVae2+lTB+eyr3bG/pmdf7Y79NT37anfsr+nZV7uzjPtrXmfS+1SS66rq2qr65iSHk5yc02sBAMDMzKWD3N3PV9W7k3w4m8u8va+7H5nHawEAwCzNbR3k7v69JL83r+ffJ0s9BWTJ2Fe7Y39Nz77aHftrevbV7thf07Ovdmfp9tdcDtIDAIBVNa85yAAAsJIE5ClV1b+tqq6q1yy6lmVVVf++qj5XVZ+pqo9U1WsXXdMyq6pfqKovTPbZB6vqlYuuaVlV1a1V9UhVfb2qlupI52VRVTdV1Rer6nRVHV10Pcusqt5XVc9U1ecXXcsqqKqrq+oPquqxye/hTy66pmVVVS+vqoeq6rOTffXzi65p2VXVJVX1R1X1oUXXMhKQp1BVV2fztNlPLLqWJfcL3f093f2GJB9K8u8WXM+yeyDJ67v7e5L87yR3LbieZfb5JP8syccXXcgyqqpLkvxKkn+S5PokP1JV1y+2qqX235LctOgiVsjzSX66u787yZuS3OHna1vPJXlrd39vkjckuamq3rTYkpbeTyZ5bNFFnEtAns5/TPIzSUzYvoDu/qvh5itif11Qd3+ku5+f3PzDbK4Xzha6+7HuXsaTCS2LG5Kc7u4vd/ffJrk3yS0LrmlpdffHk3xt0XWsiu5+urs/Pbn+19kMM1cutqrl1Jv+ZnLzpZP//C3cRlVdleTmJO9ddC3nEpB3UFXvTPIn3f3ZRdeyCqrqWFU9meRfRAd5N/51kv++6CJYWVcmeXK4fSYCDHNQVYeSvDHJJxdcytKaTBn4TJJnkjzQ3fbV9n4pmw3Iry+4jvPMbZm3VVJVH03yHVvc9Z4kP5vkB/e3ouV1oX3V3fd193uSvKeq7kry7iQ/t68FLpmd9tdkm/dk8yvM9+9nbctmmn3FtmqLMV0rZqqqvjXJ7yT5qXO+MWTQ3S8kecPkuJIPVtXru9t893NU1TuSPNPdD1fVWxZcznkE5CTd/batxqvqHyS5NslnqyrZ/Ar801V1Q3f/6T6WuDS221db+M0k9+eAB+Sd9ldVHUnyjiQ39gFfc3EXP1uc70ySq4fbVyV5akG1sIaq6qXZDMfv7+7fXXQ9q6C7/7KqPpbN+e4C8vnenOSdVfVDSV6e5Nur6je6+0cXXFcSUywuqLv/uLsv6+5D3X0om3+Evu+ghuOdVNV1w813JvnCompZBVV1U5I7k7yzu//fouthpX0qyXVVdW1VfXOSw0lOLrgm1kRtdoh+Lclj3f2Li65nmVXVpWdXJKqqb0nytvhbuKXuvqu7r5rkq8NJfn9ZwnEiIDNbx6vq81X1uWxOS7EU0IX9pyTfluSBydJ4/2XRBS2rqvqnVXUmyfcnub+qPrzompbJ5GDPdyf5cDYPoDrR3Y8stqrlVVUfSPKJJN9VVWeq6rZF17Tk3pzkx5K8dfJe9ZlJ14/zXZHkDyZ/Bz+VzTnIS7V8GdNxJj0AABjoIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYPD/ASvKmaTtYFHZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Пошто је већина вредности у стварном животу нормално расподељена, не бисмо требали користити генератор случајних бројева са униформном расподелом за генерисање узорака података. Ево шта се дешава ако покушамо да генеришемо тежине са униформном расподелом (генерисано помоћу `np.random.rand`):\n"
+    "Пошто је већина вредности у стварном животу нормално расподељена, не бисмо требали користити генератор случајних бројева са равномерном расподелом за генерисање узорака података. Ево шта се дешава ако покушамо да генеришемо тежине са равномерном расподелом (генерисано помоћу `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,33 +621,31 @@
    "source": [
     "Две вредности које враћа функција `ttest_ind` су:\n",
     "* p-вредност може се сматрати вероватноћом да две дистрибуције имају исти просек. У нашем случају, она је веома ниска, што значи да постоје јаки докази који подржавају да су први базмени виши.\n",
-    "* t-вредност је интермедијарна вредност нормализоване разлике у просеку која се користи у t-тесту и упоређује се са граничном вредношћу за дати ниво поузданости.\n"
+    "* t-вредност је интермедијарна вредност нормализоване разлике просека која се користи у t-тесту и пореди се са граничном вредношћу за дати ниво поузданости.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Симулирање нормалне расподеле уз помоћ теореме о централној граничној вредности\n",
+    "## Симулирање нормалне расподеле уз помоћ теореме централног лимита\n",
     "\n",
-    "Псеудо-случајни генератор у Python-у је дизајниран да нам пружи униформну расподелу. Ако желимо да направимо генератор за нормалну расподелу, можемо користити теорему о централној граничној вредности. Да бисмо добили вредност са нормалном расподелом, једноставно ћемо израчунати средњу вредност узорка генерисаног униформно.\n"
+    "Псеудо-случајни генератор у Пајтону је дизајниран да нам даје униформну расподелу. Ако желимо да направимо генератор за нормалну расподелу, можемо користити теорему централног лимита. Да бисмо добили вредност са нормалном расподелом, једноставно ћемо израчунати просек узорка генерисаног униформно.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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eyrcCAACALq/T316+YcOGeOWVV9rur1q1KpYuXRp9+/aNffbZJy699NL4yU9+El/60pdi6NChcc0110RdXV2ccsoppZwbAAAAurxOR/dzzz0Xxx9/fNv9iRMnRkTE+PHjY9asWXHllVfGxo0b48ILL4x169bFyJEjY/78+dG7d+/STQ0AAADdQEVRFEW5h/hfzc3NUVNTE01NTT7fDXR5QybNK/cIAPCprJ56YrlHgJ3Kp23Xsn97OQAAAOysRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAkspyDwAAAOQbMmleuUfY6ayeemK5R6AbcKUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkpQ8un/84x9HRUVFu9sBBxxQ6rcBAACALq8y40UPPvjgePjhh///TSpT3gYAAAC6tJQarqysjNra2oyXBgAAgG4j5TPdK1asiLq6uth3333j7LPPjtdee22r+7a0tERzc3O7GwAAAOwMSh7dw4YNi1mzZsX8+fNjxowZsWrVqjj66KNj/fr1He4/ZcqUqKmpabvV19eXeiQAAAAoi4qiKIrMN1i3bl0MHjw4brrppjj//PO3eLylpSVaWlra7jc3N0d9fX00NTVFdXV15mgA223IpHnlHgEAKJPVU08s9wiUUXNzc9TU1Hxiu6Z/w1mfPn1iv/32i1deeaXDx6uqqqKqqip7DAAAANjh0v9O94YNG2LlypUxcODA7LcCAACALqXk0X355ZfH448/HqtXr46nn346Tj311OjZs2ecddZZpX4rAAAA6NJK/uvlr7/+epx11lnx7rvvxt577x0jR46MRYsWxd57713qtwIAAIAureTRPXv27FK/JAAAAHRL6Z/pBgAAgF2V6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIUlnuAQAAALqjIZPmlXuEndLqqSeWe4SScqUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AOjIkEnzyj3CTmn11BPLPQIAAOxSXOkGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AGDHGTJpXrlHAACAXYor3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJLKcg/Q3Q2ZNK/cIwAAANBFudINAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAEnSonv69OkxZMiQ6N27dwwbNiyeffbZrLcCAACALikluu+7776YOHFiXHfddbFkyZI47LDDYvTo0bF27dqMtwMAAIAuKSW6b7rpprjgggvivPPOi4MOOihmzpwZn/nMZ+LOO+/MeDsAAADokipL/YIffPBBLF68OCZPnty2rUePHjFq1KhYuHDhFvu3tLRES0tL2/2mpqaIiGhubi71aClaW/5V7hEAAAB2Gt2lBT+csyiKj92v5NH9zjvvxObNm2PAgAHttg8YMCD+/ve/b7H/lClT4vrrr99ie319falHAwAAoIurmVbuCTpn/fr1UVNTs9XHSx7dnTV58uSYOHFi2/3W1tZ47733Yq+99oqKiooyTkaG5ubmqK+vjzVr1kR1dXW5x6GLsC7oiHXBR1kTdMS6oCPWBR0p9booiiLWr18fdXV1H7tfyaO7X79+0bNnz2hsbGy3vbGxMWpra7fYv6qqKqqqqtpt69OnT6nHoouprq72A5AtWBd0xLrgo6wJOmJd0BHrgo6Ucl183BXuD5X8i9R69eoVRxxxRCxYsKBtW2trayxYsCCGDx9e6rcDAACALivl18snTpwY48ePj6997Wtx1FFHxbRp02Ljxo1x3nnnZbwdAAAAdEkp0X3GGWfE22+/Hddee200NDTE4YcfHvPnz9/iy9XY9VRVVcV11123xUcK2LVZF3TEuuCjrAk6Yl3QEeuCjpRrXVQUn/T95gAAAMA2KflnugEAAID/Et0AAACQRHQDAABAEtENAAAASUQ322X69OkxZMiQ6N27dwwbNiyeffbZT/W82bNnR0VFRZxyyilb3eeiiy6KioqKmDZtWmmGZYfJWBcvvfRSnHzyyVFTUxN77LFHHHnkkfHaa6+VeHIylXpdbNiwIS6++OIYNGhQ7L777nHQQQfFzJkzEyYnU2fWxaxZs6KioqLdrXfv3u32KYoirr322hg4cGDsvvvuMWrUqFixYkX2YVBipVwXmzZtiquuuioOOeSQ2GOPPaKuri6++93vxptvvrkjDoUSKvXPi//lvLN7ylgTGeecopttdt9998XEiRPjuuuuiyVLlsRhhx0Wo0ePjrVr137s81avXh2XX355HH300Vvd54EHHohFixZFXV1dqccmWca6WLlyZYwcOTIOOOCAeOyxx2LZsmVxzTXXfOz/POlaMtbFxIkTY/78+XH33XfHSy+9FJdeemlcfPHFMXfu3KzDoMS2ZV1UV1fHW2+91XZ79dVX2z3+85//PG655ZaYOXNmPPPMM7HHHnvE6NGj4/33388+HEqk1OviX//6VyxZsiSuueaaWLJkSdx///2xfPnyOPnkk3fE4VAiGT8vPuS8s3vKWBNp55wFbKOjjjqqmDBhQtv9zZs3F3V1dcWUKVO2+pz//Oc/xYgRI4rf/va3xfjx44tx48Ztsc/rr79efP7zny9eeOGFYvDgwcXNN9+cMD1ZMtbFGWecUXznO9/JGpkdIGNdHHzwwcUNN9zQbttXv/rV4oc//GFJZydPZ9fFXXfdVdTU1Gz19VpbW4va2triF7/4Rdu2devWFVVVVcW9995bsrnJVep10ZFnn322iIji1Vdf3Z5R2YGy1oXzzu4rY01knXO60s02+eCDD2Lx4sUxatSotm09evSIUaNGxcKFC7f6vBtuuCH69+8f559/foePt7a2xjnnnBNXXHFFHHzwwSWfm1wZ66K1tTXmzZsX++23X4wePTr69+8fw4YNizlz5mQcAgmyfl6MGDEi5s6dG2+88UYURRGPPvpovPzyy3HCCSeU/BgovW1dFxs2bIjBgwdHfX19jBs3Ll588cW2x1atWhUNDQ3tXrOmpiaGDRv2sa9J15GxLjrS1NQUFRUV0adPn1KNTqKsdeG8s/vKWBOZ55yim23yzjvvxObNm2PAgAHttg8YMCAaGho6fM5TTz0Vd9xxR9x+++1bfd2f/exnUVlZGZdccklJ52XHyFgXa9eujQ0bNsTUqVNjzJgx8Ze//CVOPfXUOO200+Lxxx8v+TFQelk/L2699dY46KCDYtCgQdGrV68YM2ZMTJ8+PY455piSzk+ObVkX+++/f9x5553x4IMPxt133x2tra0xYsSIeP311yMi2p7Xmdeka8lYFx/1/vvvx1VXXRVnnXVWVFdXl/wYKL2sdeG8s/vKWBOZ55yV2/Vs+JTWr18f55xzTtx+++3Rr1+/DvdZvHhx/OpXv4olS5ZERUXFDp6Qcvg066K1tTUiIsaNGxeXXXZZREQcfvjh8fTTT8fMmTPj2GOP3WHzsmN8mnUR8d/oXrRoUcydOzcGDx4cTzzxREyYMCHq6ura/cs3O4/hw4fH8OHD2+6PGDEiDjzwwPj1r38dN954Yxkno5w6sy42bdoU3/72t6MoipgxY8aOHpUd6JPWhfPOXc8nrYnMc07RzTbp169f9OzZMxobG9ttb2xsjNra2i32X7lyZaxevTpOOumktm0fLuzKyspYvnx5PPnkk7F27drYZ5992vbZvHlz/OAHP4hp06bF6tWrcw6GkslYF/X19VFZWRkHHXRQu+ceeOCB8dRTTyUcBaWWsS7q6uri6quvjgceeCBOPPHEiIg49NBDY+nSpfHLX/5SdHcDnV0XHdltt93iK1/5SrzyyisREW3Pa2xsjIEDB7Z7zcMPP7w0g5MqY1186MPgfvXVV+ORRx5xlbsbyVgXzju7t4w10a9fv7RzTr9ezjbp1atXHHHEEbFgwYK2ba2trbFgwYJ2/4L0oQMOOCCef/75WLp0advt5JNPjuOPPz6WLl0a9fX1cc4558SyZcva7VNXVxdXXHFFPPTQQzvy8NhGGeuiV69eceSRR8by5cvbPffll1+OwYMHpx8T2y9jXWzatCk2bdoUPXq0/99Yz5492wKdrq2z66Ijmzdvjueff74tsIcOHRq1tbXtXrO5uTmeeeaZT/2alFfGuoj4/+BesWJFPPzww7HXXnuVfHbyZKwL553dW8aaSD3nLPlXs7HLmD17dlFVVVXMmjWr+Nvf/lZceOGFRZ8+fYqGhoaiKIrinHPOKSZNmrTV52/t28v/l2+R7H4y1sX9999f7LbbbsVvfvObYsWKFcWtt95a9OzZs3jyySczD4USylgXxx57bHHwwQcXjz76aPGPf/yjuOuuu4revXsXt912W+ahUEKdXRfXX3998dBDDxUrV64sFi9eXJx55plF7969ixdffLFtn6lTpxZ9+vQpHnzwwWLZsmXFuHHjiqFDhxb//ve/d/jxsW1KvS4++OCD4uSTTy4GDRpULF26tHjrrbfabi0tLWU5Rjov4+fFRznv7F4y1kTWOadfL2ebnXHGGfH222/HtddeGw0NDXH44YfH/Pnz277Q4LXXXtviKhQ7v4x1ceqpp8bMmTNjypQpcckll8T+++8ff/zjH2PkyJEZh0CCjHUxe/bsmDx5cpx99tnx3nvvxeDBg+OnP/1pXHTRRRmHQILOrot//vOfccEFF0RDQ0N87nOfiyOOOCKefvrpdr8KeOWVV8bGjRvjwgsvjHXr1sXIkSNj/vz52/83VtlhSr0u3njjjZg7d25ExBYfM3j00UfjuOOO2yHHxfbJ+HlB95axJrLOOSuKoii26xUAAACADrkMCQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJL/A9iNnCdIIuhfAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -833,14 +672,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Корелација једнака 1 значи да постоји јака **линеарна веза** између две променљиве. Линеарну везу можемо визуелно уочити тако што ћемо једну вредност нацртати у односу на другу:\n"
+    "Корелација једнака 1 значи да постоји јака **линеарна веза** између две променљиве. Линеарну везу можемо визуелно видети тако што ћемо једну вредност приказати у односу на другу:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -921,14 +758,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -946,14 +783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -993,22 +828,22 @@
    "source": [
     "> Можете ли да погодите зашто се тачке поређају у вертикалне линије овако?\n",
     "\n",
-    "Приметили смо корелацију између вештачки створеног концепта као што је плата и посматране променљиве *висина*. Хајде да видимо да ли се и две посматране променљиве, као што су висина и тежина, такође корелирају:\n"
+    "Приметили смо корелацију између вештачки конструисаног концепта као што је плата и посматране променљиве *висина*. Хајде да видимо да ли се и две посматране променљиве, као што су висина и тежина, такође корелирају:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,7 +856,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Нажалост, нисмо добили никакве резултате - само неке чудне `nan` вредности. Ово је због тога што су неке од вредности у нашој серији недефинисане, представљене као `nan`, што доводи до тога да је резултат операције такође недефинисан. Посматрајући матрицу, можемо видети да је `Weight` проблематична колона, јер је само-корелација између вредности `Height` израчуната.\n",
+    "Нажалост, нисмо добили никакве резултате - само неке чудне `nan` вредности. Ово је због тога што су неке вредности у нашој серији недефинисане, представљене као `nan`, што узрокује да резултат операције такође буде недефинисан. Гледајући матрицу, можемо видети да је `Weight` проблематична колона, јер је само-корелација између вредности `Height` израчуната.\n",
     "\n",
     "> Овај пример показује важност **припреме података** и **чишћења**. Без одговарајућих података не можемо израчунати ништа.\n",
     "\n",
@@ -1030,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1094,7 +927,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:32:54+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:56:09+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "sr"
   }
diff --git a/translations/sr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/sr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 9724f553..bf5a3c53 100644
--- a/translations/sr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/sr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Увод у вероватноћу и статистику  \n",
     "## Задатак  \n",
     "\n",
-    "У овом задатку ћемо користити скуп података о пацијентима са дијабетесом који је преузет [са овог линка](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
+    "У овом задатку ћемо користити скуп података о пацијентима са дијабетесом, преузет [са овог линка](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -151,7 +151,7 @@
    "cell_type": "markdown",
    "source": [
     "У овом скупу података, колоне су следеће:\n",
-    "* Године и пол су сами по себи јасни\n",
+    "* Узраст и пол су самообјашњиви\n",
     "* BMI је индекс телесне масе\n",
     "* BP је просечан крвни притисак\n",
     "* S1 до S6 су различита мерења крви\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Задатак 3: Каква је расподела старости, пола, БМИ и Y променљивих?\n"
+    "### Задатак 3: Каква је расподела променљивих старости, пола, БМИ и Y?\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -595,7 +595,7 @@
     "Закључци:  \n",
     "* Године - нормално  \n",
     "* Пол - уједначено  \n",
-    "* БМИ, Y - тешко рећи  \n"
+    "* BMI, Y - тешко рећи  \n"
    ],
    "metadata": {}
   },
@@ -846,8 +846,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Закључак:  \n",
-    "* Најјача корелација са Y је BMI и S5 (шећер у крви). Ово звучи разумно.\n"
+    "Закључак:\n",
+    "* Најјача корелација Y је са BMI и S5 (шећер у крви). Ово звучи разумно.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:55:04+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:56:40+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "sr"
   }
diff --git a/translations/sv/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/sv/1-Introduction/04-stats-and-probability/assignment.ipynb
index 3ad9d17e..00a60690 100644
--- a/translations/sv/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/sv/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "I den här datasetet är kolumnerna följande:  \n",
-    "* Ålder och kön är självförklarande  \n",
-    "* BMI är kroppsmassindex  \n",
-    "* BP är genomsnittligt blodtryck  \n",
-    "* S1 till S6 är olika blodmätningar  \n",
-    "* Y är det kvalitativa måttet på sjukdomsprogression över ett år  \n",
+    "I det här datasetet är kolumnerna följande:\n",
+    "* Ålder och kön är självförklarande\n",
+    "* BMI är kroppsmassaindex\n",
+    "* BP är genomsnittligt blodtryck\n",
+    "* S1 till S6 är olika blodmätningar\n",
+    "* Y är det kvalitativa måttet på sjukdomsprogression under ett år\n",
     "\n",
-    "Låt oss studera detta dataset med hjälp av sannolikhets- och statistikmetoder.\n",
+    "Låt oss studera detta dataset med hjälp av sannolikhets- och statistiska metoder.\n",
     "\n",
-    "### Uppgift 1: Beräkna medelvärden och varians för alla värden  \n"
+    "### Uppgift 1: Beräkna medelvärden och varians för alla värden\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Uppgift 3: Hur ser fördelningen av Ålder, Kön, BMI och Y-variabler ut?\n"
+    "### Uppgift 3: Vad är fördelningen av Ålder, Kön, BMI och Y-variabler?\n"
    ],
    "metadata": {}
   },
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:47:24+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:34:38+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "sv"
   }
diff --git a/translations/sv/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/sv/1-Introduction/04-stats-and-probability/notebook.ipynb
index 4a5c7640..4b5c24d7 100644
--- a/translations/sv/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/sv/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "För att visuellt uppskatta hur många olika värden som finns i urvalet kan vi rita upp **histogrammet**:\n"
+    "För att visuellt uppskatta hur många olika värden som finns i urvalet kan vi plotta **histogrammet**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## Analysera verkliga data\n",
     "\n",
-    "Medelvärde och varians är mycket viktiga när man analyserar verkliga data. Låt oss ladda data om basebollspelare från [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Medelvärde och varians är mycket viktiga när man analyserar data från verkligheten. Låt oss ladda data om basebollspelare från [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -261,24 +113,24 @@
    "source": [
     "> Vi använder ett paket som heter [**Pandas**](https://pandas.pydata.org/) här för dataanalys. Vi kommer att prata mer om Pandas och att arbeta med data i Python senare i den här kursen.\n",
     "\n",
-    "Låt oss beräkna genomsnittliga värden för ålder, längd och vikt:\n"
+    "Låt oss beräkna medelvärden för ålder, längd och vikt:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Låt oss nu fokusera på längd och beräkna standardavvikelse och varians:\n"
+    "Nu låt oss fokusera på höjd och beräkna standardavvikelse och varians:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Observera**: Detta diagram antyder att genomsnittligt sett är förstabasens spelare längre än andrabasens spelare. Senare kommer vi att lära oss hur vi kan testa denna hypotes mer formellt och hur vi kan visa att våra data är statistiskt signifikanta för att styrka detta.  \n",
+    "> **Observera**: Detta diagram antyder att, i genomsnitt, är förstabasens längder högre än andrabasens längder. Senare kommer vi att lära oss hur vi kan testa denna hypotes mer formellt, och hur vi kan visa att våra data är statistiskt signifikanta för att styrka detta.\n",
     "\n",
-    "Ålder, längd och vikt är alla kontinuerliga slumpvariabler. Vad tror du att deras fördelning är? Ett bra sätt att ta reda på det är att rita ett histogram över värdena:\n"
+    "Ålder, längd och vikt är alla kontinuerliga slumpmässiga variabler. Vad tror du att deras fördelning är? Ett bra sätt att ta reda på det är att rita ett histogram över värdena:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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qfVW17/Dhw3NOAwAAFmOua5CTvKy7H6qqZyW5paru2eiO3b03yd4k2bVrV885DwAAWIi5ziB390PT7cNJbkxyXpJDVXV6kky3D887SQAA2CqbDuSqelpVPf3o/SQ/m+TOJDcluXTa7NIkn5x3kgAAsFXmucTi2UlurKqj3+f3uvu/VtUXk1xfVW9O8s0kb5h/mgAAsDU2Hcjd/bUk/2jG+LeTnD/PpAAAYLvM+0t6cELYuefm7Z4CAHCC8FHTAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAwEMgAADAQyAAAMBDIAAAw8FHTALBgJ8LH2++/4sLtngI8YTmDDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAAADgQwAAAOBDAAAA4EMAACDTQdyVZ1ZVX9UVXdX1V1V9SvT+Puq6sGqun36es3ipgsAAMu1Y459jyR5R3d/qaqenuS2qrpleu4D3f3++acHAABba9OB3N0Hkxyc7n+3qu5OcsaiJsaJY+eem7d7CgAAC7OQa5CrameSlyT5/DT0tqq6o6qurqpT19hnd1Xtq6p9hw8fXsQ0AABgbnMHclX9RJIbkry9u7+T5INJnp/k3KyeYb5y1n7dvbe7d3X3rpWVlXmnAQAACzFXIFfVj2U1jj/a3Z9Iku4+1N2PdvcPk3w4yXnzTxMAALbGPO9iUUmuSnJ3d//GMH76sNnrk9y5+ekBAMDWmuddLF6W5E1JvlJVt09j705ySVWdm6ST7E/yljl+BgAAbKl53sXis0lqxlOf2vx0AABge/kkPQAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAYCGQAABgIZAAAGAhkAAAY7NjuCQAAW2/nnpu3ewrr2n/Fhds9BU5SziADAMBAIAMAwEAgAwDAQCADAMBAIAMAwEAgAwDAwNu8AQBPSN6Kju3iDDIAAAycQT4BnAj/Bw0A8GThDDIAAAwEMgAADAQyAAAMTvprkF3fCwDAyBlkAAAYCGQAABgsLZCr6oKqureq7q+qPcv6OQAAsEhLuQa5qk5J8p+T/EySA0m+WFU3dfdXl/HzAAC2g99lmt8T8dMIl3UG+bwk93f317r7r5J8LMlFS/pZAACwMMt6F4szknxreHwgyT8ZN6iq3Ul2Tw//sqruXeN7nZbkzxY+Q46yvstlfZfPGi+X9V0u67tc1ne5FrK+9WsLmMnm/b1Zg8sK5Jox1o950L03yd51v1HVvu7etaiJ8VjWd7ms7/JZ4+WyvstlfZfL+i7Xk3l9l3WJxYEkZw6Pn5vkoSX9LAAAWJhlBfIXk5xTVWdX1Y8nuTjJTUv6WQAAsDBLucSiu49U1duS/LckpyS5urvv2uS3W/cyDOZifZfL+i6fNV4u67tc1ne5rO9yPWnXt7p7/a0AAOAk4ZP0AABgIJABAGCwrYFcVc+oqo9X1T1VdXdV/dOqel9VPVhVt09fr1ljXx9lvY411ve6YW33V9Xta+y7v6q+Mm23b4un/oRXVS8c1vH2qvpOVb29qp5ZVbdU1X3T7alr7O/1exzHWd9fn17Pd1TVjVX1jDX29/o9juOsr+PvAhxnfR1/F6Sq/l1V3VVVd1bVtVX1tx1/F2eN9T2pjr/beg1yVV2T5H92929P73bx1CRvT/KX3f3+4+x3SpI/zfBR1kku8VHWjzVrfbv7/wzPX5nkL7r7V2fsuz/Jru72BuvrmF6PD2b1w3AuS/JId18xHXhP7e53ztje63eDjlnfFyb5H9MvAv9akhy7vtM+++P1uyHHrO8vxfF3ocb17e5vDOOOv5tUVWck+WySF3X396vq+iSfSvKiOP7O7Tjr+1BOouPvtp1BrqqfTPKKJFclSXf/1Rhv6/BR1utYb32rqpK8Mcm12zLBJ5fzkzww/eV3UZJrpvFrkrxuxvZev4/PX69vd3+6u49M45/L6nusM5/x9bsRXr+Pz4+sr+PvQuxI8neqakdWT649FMffRfqR9T3Zjr/beYnF85IcTvJfqurLVfXbVfW06bm3Tafwr17jn0hmfZT1GUue74nmeOubJC9Pcqi771tj/07y6aq6rVY/Fpy1XZy/+Yvu2d19MEmm22fN2N7r9/EZ13f0y0n+cI19vH437tj1dfxdrFmvX8ffOXT3g0nen+SbSQ5m9Uz8p+P4uxDHWd/Rk/74u52BvCPJS5N8sLtfkuT/JtmT5INJnp/k3Kz+wVw5Y991P8qaNdf3qEty/LMXL+vulyb5uSSXVdUrljbTE9h06cprk/z+49ltxpjX7wxrrW9VvSfJkSQfXWNXr98NmLG+jr8LdJzjg+PvHKb/cbsoydlJnpPkaVX1rza6+4wxr9/Beut7shx/tzOQDyQ50N2fnx5/PMlLu/tQdz/a3T9M8uGs/nPIrH19lPXxzVzfJJn+yeQXkly31s7d/dB0+3CSGzP7z4HVA8CXuvvQ9PhQVZ2eJNPtwzP28frduGPXN1V1aZKfT/KLvcYvUXj9bthj1tfxd+FmvX4df+f3qiRf7+7D3f3/knwiyT+L4++irLW+J9Xxd9sCubv/d5JvVdULp6Hzk3z16It78vokd87Y3UdZr2Ot9Z3uvyrJPd19YNa+VfW0qnr60ftJfjaz/xz40TNBNyW5dLp/aZJPztjH63fjHrO+VXVBkncmeW13f2/WDl6/j8ux6+v4u1izzhQ7/s7vm0l+uqqeOl3PfX6Su+P4uygz1/ekO/5297Z9ZfWf8fYluSPJHyQ5NcnvJvnKNHZTktOnbZ+T5FPDvq/J6m+iPpDkPdv53/FE/Zq1vtP4R5K89Zht/3p9s3r98p9MX3dZ3zXX96lJvp3k7w5jP5Xk1iT3TbfPPHZ9p8dev5tb3/uzev3g7dPXh45dX6/fudbX8XeJ6zuNO/4uZn3/Y5J7shpfv5vkKY6/S1/fk+r466OmAQBg4JP0AABgIJABAGAgkAEAYCCQAQBgIJABAGAgkAEAYCCQAQBg8P8B40VGjZpezWQAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Konfidensintervall\n",
     "\n",
-    "Låt oss nu beräkna konfidensintervall för vikter och längder hos basebollspelare. Vi kommer att använda koden [från denna diskussion på Stack Overflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Låt oss nu beräkna konfidensintervall för vikter och längder hos basebollspelare. Vi kommer att använda koden [från denna stackoverflow-diskussion](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,35 +619,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "De två värdena som returneras av funktionen `ttest_ind` är:\n",
-    "* p-värdet kan betraktas som sannolikheten för att två fördelningar har samma medelvärde. I vårt fall är det väldigt lågt, vilket innebär att det finns starka bevis för att förstabasmän är längre.\n",
-    "* t-värdet är det intermediära värdet av normaliserad medelvärdesskillnad som används i t-testet, och det jämförs med ett tröskelvärde för en given konfidensnivå.\n"
+    "De två värdena som returneras av funktionen `ttest_ind` är:  \n",
+    "* p-värdet kan betraktas som sannolikheten för att två fördelningar har samma medelvärde. I vårt fall är det mycket lågt, vilket innebär att det finns starka bevis som stöder att första basmän är längre.  \n",
+    "* t-värdet är det mellanliggande värdet av den normaliserade medelvärdesskillnaden som används i t-testet, och det jämförs med ett tröskelvärde för en given konfidensnivå.  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simulera en normalfördelning med den centrala gränsvärdessatsen\n",
+    "## Simulera en normalfördelning med centrala gränsvärdessatsen\n",
     "\n",
-    "Den pseudo-slumpgeneratorn i Python är utformad för att ge oss en jämn fördelning. Om vi vill skapa en generator för normalfördelning kan vi använda den centrala gränsvärdessatsen. För att få ett normalt fördelat värde beräknar vi helt enkelt medelvärdet av ett jämnt genererat urval.\n"
+    "Den pseudorandom-generator som finns i Python är utformad för att ge oss en jämn fördelning. Om vi vill skapa en generator för normalfördelning kan vi använda den centrala gränsvärdessatsen. För att få ett normalfördelat värde beräknar vi helt enkelt medelvärdet av ett jämnt fördelat urval.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korrelation och Ondskefulla Baseballbolaget\n",
     "\n",
-    "Korrelation gör det möjligt för oss att hitta samband mellan dataserier. I vårt enkla exempel kan vi låtsas att det finns ett ondskefullt baseballbolag som betalar sina spelare baserat på deras längd – ju längre spelaren är, desto mer pengar får han/hon. Anta att det finns en grundlön på $1000 och en extra bonus från $0 till $100, beroende på längd. Vi kommer att ta riktiga spelare från MLB och beräkna deras påhittade löner:\n"
+    "Korrelation hjälper oss att hitta samband mellan dataserier. I vårt enkla exempel kan vi låtsas att det finns ett ondskefullt baseballbolag som betalar sina spelare baserat på deras längd – ju längre spelaren är, desto mer pengar får han/hon. Anta att det finns en grundlön på $1000 och en extra bonus från $0 till $100, beroende på längd. Vi kommer att använda riktiga spelare från MLB och beräkna deras påhittade löner:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "En korrelation som är lika med 1 betyder att det finns en stark **linjär relation** mellan två variabler. Vi kan visuellt se den linjära relationen genom att plotta ett värde mot det andra:\n"
+    "En korrelation som är lika med 1 innebär att det finns en stark **linjär relation** mellan två variabler. Vi kan visuellt se den linjära relationen genom att plotta ett värde mot det andra:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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IoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoTQDQAAAABAgMQEuwAAAAAAXVdX36TbX92l8u+PKb1fvJ66cbj6WPk1HwgV/GkEAAAAwtQ1f9qq3V+5PMf7Kms1ZMn/6oKBNq2fNyaIlQFoxfJyAAAAIAydHLhPtPsrl67509ZurghAWwjdAAAAQJipq29qN3C32v2VS3X1Td1UEYD2ELoBAACAMHP7q7tMbQcgcAjdAAAAQJgp//6Yqe0ABA6hGwAAAAgz6f3iTW0HIHAI3QAAAECYeerG4aa2AxA4hG4AAAAgzPSxxuiCgbbTtrlgoI39uoEQQOgGAAAAwtD6eWPaDd7s0w2EDv7pCwAAAAhT6+eNUV19k25/dZfKvz+m9H7xeurG4cxwAyGEP40AAABAEDS7DRWVVquqtl7JiVaNzExSdJTF5376WGO0esbFAagQgBkI3QAAAEA3y99ToaUbSlRRU+85l2q3anGuU9lDUoNYGQCz8Uw3AAAA0I3y91RozpqdXoFbkipr6jVnzU7l76kIUmUAAoHQDQAAAHSTZrehpRtKZLRxrfXc0g0lana31QJAOPI5dBcUFCg3N1dpaWmyWCxat26d1/XXXntNEyZMUP/+/WWxWFRcXOx1vbq6WrfeeqvOOeccxcfHKz09Xb/97W9VU1Pj1a68vFw5OTlKSEhQcnKy7rrrLjU1Nfn8BQEAAIBQUVRafcoM94kMSRU19Soqre6+ogAElM+h+8iRIxo6dKhWrFjR7vXRo0fr8ccfb/P6wYMHdfDgQT355JPas2eP8vLylJ+fr5kzZ3raNDc3KycnRw0NDfrggw/00ksvKS8vT4sWLfK1XAAAACBkVNW2H7i70g5A6PP5RWqTJk3SpEmT2r0+bdo0SVJZWVmb14cMGaK//OUvnuMzzzxTjzzyiKZOnaqmpibFxMTozTffVElJid566y2lpKRo2LBheuihh3TPPfdoyZIlio2N9bVsAAAAIOiSE62mtgMQ+kLime6amhrZbDbFxLT8G0BhYaHOP/98paSkeNpMnDhRLpdLe/fuDVaZAAAAQLua3YYKP/tO/1P8tQo/+67N57JHZiYp1W5VexuDWdTyFvORmUkBrRVA9wn6lmHffvutHnroIc2ePdtzrrKy0itwS/IcV1ZWttnP8ePHdfz4cc+xy+UKQLUAAADAqTq7BVh0lEWLc52as2anLJLXC9Vag/jiXGeX9usGEJqCOtPtcrmUk5Mjp9OpJUuW+NXXsmXLZLfbPZ9BgwaZUyQAAABwGr5uAZY9JFUrp46Qw+69hNxht2rl1BHs0w1EmKDNdNfW1io7O1uJiYlau3atevXq5bnmcDhUVFTk1f7QoUOea21ZuHCh5s+f7zl2uVwEbwAAAARUR1uAWdSyBdh4p8Nr9jp7SKrGOx0qKq1WVW29khNblpQzww1EnqCEbpfLpYkTJyouLk7r16+X1er9r3xZWVl65JFHVFVVpeTkZEnS5s2bZbPZ5HQ62+wzLi5OcXFxAa8dAAAAPVuz2/CE5W9rj3d6C7CsM/t7XYuOspxyDkDk8Tl019XV6cCBA57j0tJSFRcXKykpSenp6aqurlZ5ebkOHjwoSdq3b5+klhlqh8Mhl8ulCRMm6OjRo1qzZo1cLpfn+eszzjhD0dHRmjBhgpxOp6ZNm6bly5ersrJS999/v+bOnUuwBgAAQNC09ex2Z7AFGNBzWQzDaGs1TLveffddXXHFFaecnzFjhvLy8pSXl6df/OIXp1xfvHixlixZ0u79UkuAz8jIkCR98cUXmjNnjt5991317t1bM2bM0GOPPeZ5w3lHXC6X7Ha7583oAAAAgD9an9326Zfnv/t/sy5hVhuIMJ3NnD6H7nBB6AYAAIBZmt2GRj++xecZbotaXpD2/j1X8rw2EGE6mzlDYp9uAAAAIJQVlVZ3KXBLbAEG9HRB36cbAAAACHVdeSbb0cY+3QB6HkI3AAAA0IHkRGvHjSQ9kHOuBiTGsQUYAA9CNwAAANCBkZlJSrVbVVlT3+aL1Fqf3b7lx5kEbQBeeKYbAAAA6EB0lEWLc52S/vGsdiue3QZwOoRuAAAAoBOyh6Rq5dQRcti9l5o77FatnDqCZ7cBtInl5QAAAEAnZQ9J1XinQ0Wl1aqqrefZbQAdInQDAAAAPoiOsijrzP7BLgNAmGB5OQAAAAAAAULoBgAAAAAgQFheDgAAgIhS/u1RZf/xPR1rdCu+V5Ty/+UypQ9ICHZZAHooQjcAAAAixg/v3agm9z+Ojza6NfbJdxQTJR14NCd4hQHosVheDgAAgIhwcuA+UZO75ToAdDdmugEAABCWmt2GZ+uuqCZ3u4G7VZO7Zek5S80BdCdCNwAAAMJO/p4KLd1Qooqaep/uy/7jeyp5aFKAqgKAUxG6AQAAEFby91RozpqdMrpw77HGDqbDAcBkPNMNAACAsNHsNrR0Q0mXArckxffi118A3Yu/dQAAABA2ikqrfV5SfqL8f7nMxGoAoGOEbgAAAISNqtquB+6YKPESNQDdjtANAACAsJGcaO3SfezTDSBYeJEaAAAAwsbIzCSl2q2qrKlv87lui6S+cRYdd1t0rNGt+F5Ryv+Xy5jhBhA0hG4AAACEjegoixbnOjVnzU5ZJK/gbfn7/y775+HKHpIahOoA4FQsLwcAAEBYyR6SqpVTR8hh915q7rBbtXLqCAI3gJDCTDcAAADCTvaQVI13OlRUWq2q2nolJ1o1MjNJ0VGWjm8GgG5E6AYAAEC3anYbpoTl6CiLss7sH4AKAcA8hG4AAAB0m/w9FVq6ocRrr+1Uu1WLc50sCwcQkXimGwAAAN0if0+F5qzZ6RW4Jamypl5z1uxU/p6KIFUGAIHDTDcAAAACovJwva5+pkCu+ibZrDGyWCxtbvNlqOXN40s3lGi808Fz2QAiCqEbAAAApjv3gTd0rNHtOf72SONp2xuSKmrqVVRazXPaACIKy8sBAABgqpMDty+qaus7bgQAYYTQDQAAANNUHq7vcuCWpOREa8eNACCMsLwcAAAAfjnW0KxHN5Wo7LujKvr8uy71YZHksLdsHwYAkYTQDQAAgC6b9fJ2bS6p8quP1temLc518hI1ABGH0A0AAIAuMSNwSy0z3OzTDSBSEboBAADgs2MNzX4F7md/PlyNhqHkxJYl5cxwA4hUhG4AAAD47NFNJV2+N75XlCYPTTOxGgAIXby9HAAAAD4r++5ol+6L7xWljx+aZHI1ABC6mOkGAACAzzL6J2jr/o7bxUVbZEiyWWP0+q1j5ejLlmAAehZCNwAAAHx272SnXvlbeYftihdPVHxsdDdUBAChieXlAAAA8Fl8bLTGO5NP22a8M5nADaDHI3QDAACgS1ZPv7jd4D3emazV0y/u5ooAIPSwvBwAAABdtnr6xTrW0KxHN5Wo7LujyuifoHsnO5nhBoC/I3QDAAD0UM1uQ0Wl1aqqrfdrv+z42Gg9dN35AagQAMIfoRsAAKAHyt9ToaUbSlRRU+85l2q3anGuU9lDUoNYGQBEFp7pBgAA6GHy91RozpqdXoFbkipr6jVnzU7l76kIUmUAEHmY6QYAAIhw75d8o6kvF3mOYyUZbbQzJFkkLd1QovFOR5eWmgMAvBG6AQAAIljGgo2nnGs4TXtDUkVNvYpKq5V1Zv+A1QUAPQXLywEAACJUW4G7s6pq6ztuBADoEKEbAAAgAr1f8o1f9ycnWk2qBAB6NpaXAwAARIgTtwD7l/8s7lIfFkkOe8v2YQAA/xG6AQAAIkBbW4D5qvW1aYtznbxEDQBMQugGAAAIc61bgLX1RnJfONinGwBM5/Mz3QUFBcrNzVVaWposFovWrVvndf21117ThAkT1L9/f1ksFhUXF5/SR319vebOnav+/furT58+uuGGG3To0CGvNuXl5crJyVFCQoKSk5N11113qampyddyAQAAIk6z21DhZ9/pf4q/1l8PfKsl6/d2OXA/MOlM/XHKMP2/WZfo/XuuJHADgMl8nuk+cuSIhg4dql/+8pe6/vrr27w+evRo/exnP9OsWbPa7OP222/Xxo0b9ec//1l2u13z5s3T9ddfr7/+9a+SpObmZuXk5MjhcOiDDz5QRUWFpk+frl69eunRRx/1tWQAAICIYcYy8hPNvOxHpvQDAGibxTCMLq9EslgsWrt2ra677rpTrpWVlSkzM1O7du3SsGHDPOdramp0xhln6D/+4z/005/+VJL0ySef6Nxzz1VhYaEuueQSvfHGG7r66qt18OBBpaSkSJJWrVqle+65R998841iY2M7rM3lcslut6umpkY2m62rXxEAACBkmLWMvFXZYzkm9QQAPU9nM2e3bxn24YcfqrGxUePGjfOc+9GPfqT09HQVFhZKkgoLC3X++ed7ArckTZw4US6XS3v37m2z3+PHj8vlcnl9AAAAIkWz29DSDSWmBO4100cSuAGgm3T7i9QqKysVGxurvn37ep1PSUlRZWWlp82Jgbv1euu1tixbtkxLly41v2AAAIAgaWhy65XCMn1RfVSGYXRpSXnrFmDv33MlbyQHgCCImLeXL1y4UPPnz/ccu1wuDRo0KIgVAQAAdN2yTSVavbVUbj+mttkCDACCr9tDt8PhUENDgw4fPuw1233o0CE5HA5Pm6KiIq/7Wt9u3trmZHFxcYqLiwtM0QAAAN1o2aYSPVdQ6nc/bAEGAMHX7aH7wgsvVK9evfT222/rhhtukCTt27dP5eXlysrKkiRlZWXpkUceUVVVlZKTkyVJmzdvls1mk9Pp7O6SAQAAuk1Dk1urt/oeuFuXkT/506H69shxJSdaNTIziRluAAgyn0N3XV2dDhw44DkuLS1VcXGxkpKSlJ6erurqapWXl+vgwYOSWgK11DJD7XA4ZLfbNXPmTM2fP19JSUmy2Wy69dZblZWVpUsuuUSSNGHCBDmdTk2bNk3Lly9XZWWl7r//fs2dO5fZbAAAENFeKSzzeUn5icvIf3zWANNrAgB0nc9vL9+xY4eGDx+u4cOHS5Lmz5+v4cOHa9GiRZKk9evXa/jw4crJaXkj5pQpUzR8+HCtWrXK08dTTz2lq6++WjfccIPGjh0rh8Oh1157zXM9Ojpar7/+uqKjo5WVlaWpU6dq+vTpevDBB/36sgAAAKHui+qjPt/jsFu1cuoIlpEDQAjya5/uUMY+3QAAIBz929bP9dDGjztsN+2SdF2UkcQycgAIks5mzoh5ezkAAECo23ewVpOfKVCzIUVbpE23jtU5aYlebaZlZeiRTR+fdol5lEV64OrzFBvj86JFAEA3I3QDAAB0g4wFG72Omw1p4tMFkqSyx3I852NjojRrTOZp314+a0wmgRsAwgR/WwMAAATYyYG7o+sLJzv1q7GZOnnFeJRF+tXYTC2czG4uABAumOkGAAAwWUOTW68UlumL6qNKiOncs9b7DtZ6LTVfONmpOyb8yNPP4KQETcvKYIYbAMIML1IDAAAw0bJNJVq9tdTnbb+iLdJny3I6bggACAm8SA0AAKCbLdtUctpnsU+nOSKnQQAArE8CAAAwQUOTW6u3di1wSy0z3QCAyMNMNwAAQBc1uw0VlVarqrZeO8qqfV5SfqJNt441rzAAQMggdAMAAHRB/p4KLd1QooqaelP6O3m/bgBAZCB0AwAA+Ch/T4XmrNkpsx7DPnGfbgBAZCF0AwAA+KDZbWjphpIuBW7L3z9utTzDvenWscxwA0CEI3QDAAB04MR9tw3D6PKS8tljM7VwstPk6gAAoYzQDQAAcBpd3Xf7RFEWadYYAjcA9ESEbgAAgHb4s+/2tEvSZbFYNDgpQdOyMhQbw06tANATEboBAAD+rq6+Sbe/ukvl3x/TwL5Wvf3JNz73YZHksFu15Johio5i820A6OkI3QAAAJKu+dNW7f7K5TneV1nrcx+tEXtxrpPADQCQROgGAAA4JXB3lcNu1eJcp7KHpJpQFQAgEhC6AQBAj1ZX3+RX4J52SbouykhScqJVIzOTmOEGAHghdAMAgB7nv/9aqjs3lPjdT5RFeuDq83hJGgCgXYRuAADQo2Qs2GhaX7PGZBK4AQCnRegGAAA9hlmBm323AQCdRegGAAARq6HJrVcKy/RF9VF9W+f728hPdPfEs1XpOs6+2wAAnxC6AQBARFq2qUSrt5bKbfjf1wUDbfrNFWf53xEAoMchdAMAgIizbFOJnisoNaWvCwbatH7eGFP6AgD0PIRuAAAQURqa3Fq91b/AfY4jUen94vXUjcPVx8qvSwCAruO/IgAAIOzVHG3UL/OKdLCmXtEW+bWk/Mlcp37640zzigMA9GiEbgAAENYue2KLvvjumGn9EbgBAGbitZsAACBsmR24yx7LMa0vAAAkZroBAEAYaXYbKiqtVlVtvfrERJsWuFlSDgAIFEI3AAAIC/l7KrR0Q4kqaur97utXYzO1cLLThKoAADg9QjcAAAh5+XsqNGfNTvm75XaURZo1hsANAOg+hG4AABDSmt2Glm4o6XLgHtjXqivPTdHgpARNy8pQbAyvtAEAdB9CNwAACDkNTW69UlimL6qPyjAMv5aUb/ztWNkTeplYHQAAnUfoBgAAIWXZphKt3lrq117brQb3jydwAwCCitANAABCxrJNJXquoNSUvgb3j9d7d11pSl8AAHQVoRsAAATNluJK/fI/P/SrD4ukAb1jNKh/H1XU1CvNbtULt4xkhhsAEBII3QAAICgyFmz0uw/L3//3oZ9coOwhqX73BwCA2QjdAACg25kRuCXJYbdqca6TwA0ACFmEbgAA0K22FFf6df+0S9J1UUaSkhOtGpmZpOgoS8c3AQAQJIRuAAAQcDVHG/XLvCIdrKn3a/uvKIv0wNXnsdc2ACBsELoBAEBAXfbEFn3x3TFT+po1JpPADQAIK4RuAAAQMGYF7ihLS+BeONlpQlUAAHQfQjcAADBNXX2Tbn91l8q/P6Y0W5xfgfv6EQPUJ663BiclaFpWBjPcAICwROgGAACmuOZPW7X7K5fneF9lrV/9/f5no/wtCQCAoOOfjAEAgN9ODtz+Knssx7S+AAAIJma6AQCAX+rqm0wL3C9MuVBXDnOY0hcAAKGA0A0AAHx24hZgR443+tXX/y2aIHtCL5MqAwAgtBC6AQCAT8zcAmxw/3gCNwAgovFMNwAA6DSzA/d7d11pSl8AAIQqZroBAECn1Bxt9CtwDxuYqEO1jUqzW/XCLSOZ4QYA9AiEbgAA0K6GJrdeKSzTF9VHteXjQ13u54KBNq2bN8bEygAACA8+Ly8vKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/d7tfn000917bXXasCAAbLZbBo9erTeeecdrzbl5eXKyclRQkKCkpOTddddd6mpqcn3bwgAALpk2aYS/eiBN/TQxo/1cuEX+upwfZf6uWCgTesJ3ACAHsrn0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+/h//ob766qvV1NSkLVu26MMPP9TQoUN19dVXq7KyUpLU3NysnJwcNTQ06IMPPtBLL72kvLw8LVq0qItfEwAA+GLZphI9V1Aqt+H7vTZrtM5xJGr8ucnas2QigRsA0KNZDMPown9O/36zxaK1a9fquuuuk9Qyy52WlqY77rhDd955pySppqZGKSkpysvL05QpU/Ttt9/qjDPOUEFBgcaMafmPcG1trWw2mzZv3qxx48bpjTfe0NVXX62DBw8qJSVFkrRq1Srdc889+uabbxQbG9thbS6XS3a7XTU1NbLZbF39igAA9AhzXsjXG582m9IXW4ABAHqCzmZOU99eXlpaqsrKSo0bN85zzm63a9SoUSosLJQk9e/fX+ecc45efvllHTlyRE1NTXruueeUnJysCy+8UJJUWFio888/3xO4JWnixIlyuVzau3evmSUDANDjZSzYaFrgZgswAAC8mfoitdbl4SeG5dbj1msWi0VvvfWWrrvuOiUmJioqKkrJycnKz89Xv379PP201ceJP+Nkx48f1/Hjxz3HLpfLnC8FAEAEy1iw0bS+2AIMAIBTdfvbyw3D0Ny5c5WcnKytW7cqPj5e//qv/6rc3Fxt375dqampXep32bJlWrp0qcnVAgAQuea8kO/X/QP7WtVsiC3AAAA4DVNDt8PhkCQdOnTIKzwfOnRIw4YNkyRt2bJFr7/+ur7//nvPuvdnn31Wmzdv1ksvvaQFCxbI4XCoqKjIq+9Dhw55/YyTLVy4UPPnz/ccu1wuDRo0yLTvBgBAJKg8XK+rnymQq75JDc1dfq2LoizSljuvUGyMqU+qAQAQcUz9L2VmZqYcDofefvttzzmXy6Vt27YpKytLknT06NGWHxzl/aOjoqLkdrslSVlZWfroo49UVVXlub5582bZbDY5nc42f3ZcXJxsNpvXBwAA/MO5D7yhSx57W98eafQrcEvSrDGZBG4AADrB55nuuro6HThwwHNcWlqq4uJiJSUlKT09XbfddpsefvhhnXXWWcrMzNQDDzygtLQ0zxvOs7Ky1K9fP82YMUOLFi1SfHy8Vq9erdLSUuXk5EiSJkyYIKfTqWnTpmn58uWqrKzU/fffr7lz5youLs6cbw4AQA9y7gNv6Fij2+9+oiwtgXvh5Lb/ERwAAHjzOXTv2LFDV1xxhee4dUn3jBkzlJeXp7vvvltHjhzR7NmzdfjwYY0ePVr5+fmyWq2SpAEDBig/P1/33XefrrzySjU2Nuq8887T//zP/2jo0KGSpOjoaL3++uuaM2eOsrKy1Lt3b82YMUMPPvigGd8ZAICI19Dk1iuFZfqi+qiS4mL8CtxnJ0iXDB2swUkJmpaVwQw3AAA+8Guf7lDGPt0AgJ5q2aYSrd5aKrdJ/4UveyzHnI4AAIggnc2c3f72cgAAEDjLNpXouYJS0/ojcAMA4B/WhwEAECEamtxavdWcwD3p7GgCNwAAJmCmGwCAMNbsNlRUWq2q2nrtKKv2a0n53xZcJUdfq3nFAQAAQjcAAOEqf0+Flm4oUUVNvd99xfeKInADABAAhG4AAMJQ/p4KzVmzU2a8Ky2+V5Q+fmiSCT0BAICTEboBAAgDJ24BNqhfgv5162ddCtwWSUm9e6m2vkk2a4xev3UsM9wAAAQQoRsAgBBn5hZgs8dmauFkp/8dAQCATiF0AwAQwszaAizKIs0aQ+AGAKC7EboBAAhR/m4BNu2SdFksFg1OStC0rAzFxrBTKAAA3Y3QDQBACMl7Z5+W/O8Bv/qwSHLYrVpyzRBFR1nMKQwAAHQJoRsAgBCRsWCj3320RuzFuU4CNwAAIYDQDQBACDAjcEstM9yLc53KHpJqSn8AAMA/hG4AAIKg2W2oqLRaVbX1+mvZV13uJ8oivXTLSFUfa1ByolUjM5OY4QYAIIQQugEA6Gb5eyq0dEOJKmrq/e5r1phMjTnnDBOqAgAAgUDoBgCgG+XvqdCcNTvl75bbbAEGAEB4IHQDANBNmt2Glm4o8StwT88azBZgAACEEUI3AAAB1NDk1iuFZfqi+qgMw/BrSfmSiT/ULVecY2J1AAAg0AjdAAAEyLJNJVq9tVRuf9eS/x2BGwCA8EPoBgAgAJZtKtFzBaWm9Vf2WI5pfQEAgO5D6AYAwGQNTW6t3up74LZIpzzvzZJyAADCG6EbAAATVNc1aMrzH6iqtkExUfJ5SXnrztqrpo5Q9pBU0+sDAADBQegGAMBPFz+8Wd/UNfjVh8Nu1eJcJ4EbAIAIQ+gGAMAP/gTuaZek66KMJCUnWjUyM0nRUZaObwIAAGGF0A0AQCeduIQ8OTFWz950UZcDd5RFeuDq89hrGwCACEfoBgCgE06e0T58rFHj/vBel/ubNSaTwA0AQA9A6AYAoANmPLPdKsrSErgXTnaa0h8AAAhthG4AAE6juq7B78A9oHcvTb4gTYOTEjQtK4MZbgAAehBCNwAAJ5nzQr7e+LTZtP7evP1yJfWJNa0/AAAQPgjdAACcIGPBRlP7O6NPLIEbAIAejPVtAAD8XSAC9/b7x5vaJwAACC/MdAMAeqyao436ZV6RDtbUq6qm3q++3rrtMv3mP3Z4thP7z9mXMsMNAAAI3QCAnumyJ7boi++OmdLXGX1i9UNHH705/3JT+gMAAJGD5eUAgB7H7MDNEnIAANAeZroBAD1KzdFGvwN33/heLCEHAACdQugGAES80qojyv7jezrebPjd16Szo7XylxNMqAoAAPQEhG4AQET7p4Ub5fY/a3us/GW2eZ0BAICIxzPdAICIZXbgLnssx7zOAABAj8BMNwAgYhxraNajm0pU9t1R9U+INi1wtywpZ4YbAAD4jtANAIgIs17ers0lVab0Nbh/vN6760pT+gIAAD0by8sBAGGPwA0AAEIVM90AgLB2rKHZ78CdarcqzW7VC7eMlD2hl0mVAQAAELoBAGHoG9dx/eTZ91V9pFGSfw9uvzP/cmUm9zanMAAAgJMQugEAYeWCJf8rV32TKX1FWUTgBgAAAcUz3QCAsGF24P58GVuAAQCAwGKmGwAQspas3aa8bd+a0ldslNTgluKiLcr/l8uY4QYAAN2C0A0ACEkZCzaa1td4Z7JWT7/YtP4AAAA6i+XlAICQQ+AGAACRgpluAEBIWbJ2m1/3J/SK0oUZScron6B7JzsVHxttUmUAAAC+I3QDAIKurr5Jt7+6S+XfH9O+ylq/+nrvrit1hi3OpMoAAAD8Q+gGAATVNX/aqt1fuUzpy2aNIXADAICQwjPdAICgMTtw714y0ZS+AAAAzMJMNwCg2xxraNajm0pU9t1RpdmtfgfuhF7RSurdS2t/M5oZbgAAEJJ8nukuKChQbm6u0tLSZLFYtG7dOq/rhmFo0aJFSk1NVXx8vMaNG6f9+/ef0s/GjRs1atQoxcfHq1+/frruuuu8rpeXlysnJ0cJCQlKTk7WXXfdpaamJl/LBQCEiFkvb9e5i/L1yt/KtXX/t3p1x1d+9Vf2WI5KHsrW+wuuInADAICQ5XPoPnLkiIYOHaoVK1a0eX358uV6+umntWrVKm3btk29e/fWxIkTVV9f72nzl7/8RdOmTdMvfvEL/d///Z/++te/6qabbvJcb25uVk5OjhoaGvTBBx/opZdeUl5enhYtWtSFrwgACLZZL2/X5pIq0/oreyzHtL4AAAACyWIYhtHlmy0WrV271jNLbRiG0tLSdMcdd+jOO++UJNXU1CglJUV5eXmaMmWKmpqalJGRoaVLl2rmzJlt9vvGG2/o6quv1sGDB5WSkiJJWrVqle655x598803io2N7bA2l8slu92umpoa2Wy2rn5FAICfjjU069xF+ab0dcuoAVryk1Gm9AUAAOCPzmZOU5/pLi0tVWVlpcaNG+c5Z7fbNWrUKBUWFmrKlCnauXOnvv76a0VFRWn48OGqrKzUsGHD9MQTT2jIkCGSpMLCQp1//vmewC1JEydO1Jw5c7R3714NHz7czLIBACb7xnVcP3n2fVUfaZTU5X/blSTtWTJRfay8ggQAAIQnU3+LqayslCSvsNx63Hrt888/lyQtWbJEv//975WRkaHf/e53uvzyy/Xpp58qKSlJlZWVbfZx4s842fHjx3X8+HHPsctlzttwAQC+uWDJ/8pVb847OC4YaCNwAwCAsNbtW4a53W5J0n333acbbrhBF154oV588UVZLBb9+c9/7nK/y5Ytk91u93wGDRpkVskAgE4yO3CvnzfGlL4AAACCxdTQ7XA4JEmHDh3yOn/o0CHPtdTUVEmS0+n0XI+Li9M//dM/qby83NNPW32c+DNOtnDhQtXU1Hg+X375pQnfCADQWd+4jvsVuK84Z4DOcSRq/LnJ2rNkIoEbAABEBFPX7GVmZsrhcOjtt9/WsGHDJLUs8962bZvmzJkjSbrwwgsVFxenffv2afTo0ZKkxsZGlZWVafDgwZKkrKwsPfLII6qqqlJycrIkafPmzbLZbF5h/URxcXGKi2PLGADoTifuu/1hWXWX+xnvTNbq6RebWBkAAEBo8Dl019XV6cCBA57j0tJSFRcXKykpSenp6brtttv08MMP66yzzlJmZqYeeOABpaWled5wbrPZ9Otf/1qLFy/WoEGDNHjwYD3xxBOSpH/+53+WJE2YMEFOp1PTpk3T8uXLVVlZqfvvv19z584lWANAiDBrGzACNwAAiGQ+h+4dO3boiiuu8BzPnz9fkjRjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqxWq+eeJ554QjExMZo2bZqOHTumUaNGacuWLerXr58kKTo6Wq+//rrmzJmjrKws9e7dWzNmzNCDDz7o7/cFAJjAn8Cd0CtKF2YkKaN/gu6d7FR8bLTJ1QEAAIQOv/bpDmXs0w0A5hl/70btd5vT1/Z7x+kMG6uWAABAeAvKPt0AgMiTsWCjaX3ZrDEEbgAA0KN0+5ZhAIDwYXbg3r1komn9AQAAhANmugEAbRp/r3+BO6FXlCSLknr30trfjGaGGwAA9EiEbgCAR0OTW68UlumL6qN+P8P94QMTeEkaAADo8QjdAABJ0rJNJVq9tVRuE16vOd6ZTOAGAAAQoRsAoJbA/VxBqSl9se82AADAPxC6AaAHqjnaqF/mFelgTb1SbXHa+WWNX/2NOWsA+24DAAC0gdANAD3MZU9s0RffHfMcV9TU+9Vf2WM5/pYEAAAQsdgyDAB6kJMDt78I3AAAAKdH6AaAHqLmaKNpgfusKAI3AABAZ7C8HAAiWOXhel39TIFc9U1q9uO15FEW6ZOHJik2hn+rBQAA8AWhGwAi1LkPvKFjjX5utv13s8ZkErgBAAC6gNANABHIrMAdZWkJ3AsnO02oCgAAoOchdANABDjW0KxHN5Wo7LujSu4T61fgvnP8Waqqa9DgpARNy8pghhsAAMAPhG4ACHOzXt6uzSVVpvQ1uH+85l11til9AQAAgLeXA0BYMztwv3fXlab0BQAAgBbMdANAmDrW0OxX4I62SMk2q9LsVr1wy0jZE3qZWB0AAAAkQjcAhJXfbyrW0wVfm9LXX++5So6+VlP6AgAAQNsI3QAQJjIWbDStr/heUQRuAACAbsAz3QAQBswO3B8/NMm0/gAAANA+ZroBIASduAVYZcW3fvWVFB+tuga3bNYYvX7rWGa4AQAAuhGhGwBCjJlvJB/vTNbq6Reb0hcAAAB8x/JyAAghBG4AAIDIwkw3AIQIf7cAk6QxZw1QRv8E3TvZqfjYaJMqAwAAQFcRugEgiE58dvtQzTG/+vrt2B9o/uRh5hQGAAAAUxC6ASBIzFxKLonADQAAEIJ4phsAgsDswF32WI5pfQEAAMA8zHQDQDf4uvqYJj39no4cb1bv2Gi5jjeb0i9LygEAAEIboRsAAuzs+zapodnwHPsTuHkjOQAAQHhheTkABNDJgdsfBG4AAIDww0w3AATI19XH/ArcZyf3Voo9ni3AAAAAwhihGwBM1NDk1iuFZfqi+qheLSr3q6//mTeGoA0AABDmCN0AYJJlm0q0emup3CasJh/vTCZwAwAARABCNwCYYNmmEj1XUGpKXzy7DQAAEDkI3QDgp4Ymt1Zv7Xrg/smwFH17pJlntwEAACIQoRsAuuC6RzaquNb/fmKjLXpqykX+dwQAAICQROgGAB9lLNhoSj+x0RZ9+shkU/oCAABAaCJ0A4AP/AnccdEWNbkN9Y6L1hu/vUw/SIo3sTIAAACEIkI3AJzGsYZmPbqpRGXfHdW2/d92uZ8oi/TR0mzFxkSZWB0AAABCHaEbANox6+Xt2lxSZU5fYzIJ3AAAAD0QoRsA2mBW4I6ytATuhZOdJlQFAACAcEPoBoCTHGto9jtwT88arMFJCZqWlcEMNwAAQA9G6AYASV9XH9Okp9/TkePNirL419ewROnBa4eYUxgAAADCGqEbQI939n2b1NBseI5P+H92ybr7cvysCAAAAJGCNY8AerSTA7e/yh4jcAMAAOAfmOkG0KOcuAXYgN4xpgXuYYnMcAMAAOBUhG4APYaZW4CNdyZr9fSLTekLAAAAkYvl5QB6BAI3AAAAgoGZbgARz98twHpFSZecOUAZ/RN072Sn4mOjTawOAAAAkYzQDSAi1dU36fZXd6n8+2M6Ut/oV1/v3nmlfpAUb1JlAAAA6EkI3QAizjV/2qrdX7lM6Ss22kLgBgAAQJfxTDeAiGJ24P70kcmm9AUAAICeyefQXVBQoNzcXKWlpclisWjdunVe1w3D0KJFi5Samqr4+HiNGzdO+/fvb7Ov48ePa9iwYbJYLCouLva6tnv3bo0ZM0ZWq1WDBg3S8uXLfS0VQA/w6Podyliw0fPxJ3AnxkYp2iLZrNH6691XErgBAADgN59D95EjRzR06FCtWLGizevLly/X008/rVWrVmnbtm3q3bu3Jk6cqPr6+lPa3n333UpLSzvlvMvl0oQJEzR48GB9+OGHeuKJJ7RkyRI9//zzvpYLIIJlLNio5z84ZEpf453J+ujBSfpsWY52L8lmSTkAAABM4fMz3ZMmTdKkSZPavGYYhv7whz/o/vvv17XXXitJevnll5WSkqJ169ZpypQpnrZvvPGG3nzzTf3lL3/RG2+84dXPv//7v6uhoUEvvPCCYmNjdd5556m4uFi///3vNXv2bF9LBhCBMhZsNK0vtgADAABAoJj6THdpaakqKys1btw4zzm73a5Ro0apsLDQc+7QoUOaNWuWXnnlFSUkJJzST2FhocaOHavY2FjPuYkTJ2rfvn36/vvv2/zZx48fl8vl8voAiEyPrt/h1/0D+1o15qwBmnZJuj5+MJvADQAAgIAx9e3llZWVkqSUlBSv8ykpKZ5rhmHolltu0a9//WtddNFFKisra7OfzMzMU/povdavX79T7lm2bJmWLl1qxtcAEIJO3AJsX2WtX33l33aZ+ljZvAEAAACB1+2/dT7zzDOqra3VwoULTe134cKFmj9/vufY5XJp0KBBpv4MAMFh5hvJLxhoI3ADAACg25i6vNzhcEhqWT5+okOHDnmubdmyRYWFhYqLi1NMTIx++MMfSpIuuugizZgxw9NPW32c+DNOFhcXJ5vN5vUBEP7MDtzr540xpS8AAACgM0yd7snMzJTD4dDbb7+tYcOGSWqZcd62bZvmzJkjSXr66af18MMPe+45ePCgJk6cqFdffVWjRo2SJGVlZem+++5TY2OjevXqJUnavHmzzjnnnDaXlgOIHM1uQ0Wl1aqqrZctLsavwP0Dm9QnIVHp/eL11I3DmeEGAABAt/P5N9C6ujodOHDAc1xaWqri4mIlJSUpPT1dt912mx5++GGdddZZyszM1AMPPKC0tDRdd911kqT09HSv/vr06SNJOvPMMzVw4EBJ0k033aSlS5dq5syZuueee7Rnzx798Y9/1FNPPdXV7wkgDOTvqdDSDSWqqDl1i8Gu+Ou9Oab0AwAAAHSVz6F7x44duuKKKzzHrc9Rz5gxQ3l5ebr77rt15MgRzZ49W4cPH9bo0aOVn58vq9Xa6Z9ht9v15ptvau7cubrwwgs1YMAALVq0iO3CgAiWv6dCc9bslGFSf2WPEbgBAAAQfBbDMMz6HTekuFwu2e121dTU8Hw3EOKa3YZGP77FlBnu2Zem6N5rLjKhKgAAAKB9nc2cPOAIIChOfHb729rjfgXuPUsm8rw2AAAAQhK/pQLodmY+u80WYAAAAAhl/KYKoFuZ+ew2W4ABAAAg1BG6AQTUicvIB/SJ05L1e30O3BZJyYlxOn+gTV9+X88WYAAAAAgb/MYKIGDMWEZu+fv/Lr32PGUPSTWnMAAAAKCbELoBBIRZy8gddqsW5zoJ3AAAAAhLhG4Apmt2G1q6oaTLgfuBnHM1IDFOyYlWjcxMUnSUpeObAAAAgBBE6AZgiltWbNS7X/rXh0UtM9u3/DiToA0AAICIQOgG4LeMBRv97qM1Yi/OdRK4AQAAEDEI3QD8Ykbglnh2GwAAAJGJ0A2gy25Z0bXA3bqM/MmfDtW3R47z7DYAAAAiFqEbQJd15RnuE5eR//isAabWAwAAAIQaQjeAbsUycgAAAPQkhG4A3eKPU4axjBwAAAA9DqEbQJsamtx6pbBMX1Qf1eCkBE3LylBsTJRXm8sHdW6J+eWDpGuH/SBAlQIAAAChy2IYhhHsIgLB5XLJbrerpqZGNpst2OUAYWXZphKt3loq9wl/O0RZpFljMrVwstOrbWfeXl72WI7ZJQIAAABB1dnMGdXuFQA90rJNJXquwDtwS5LbkJ4rKNWyTSVe5zsK1ARuAAAA9GSEbgAeDU1urd5aeto2q7eWqqHJ7XWu7LEcXT7Iu93lgwjcAAAAAM90Az3csYZmPbqpRGXfHdXR402nzHCfzG1IrxSWaeaYf/I6nzeXgA0AAACcjNAN9GCzXt6uzSVVPt/3RfXRAFQDAAAARB6WlwM9VFcDtyQNTkowuRoAAAAgMjHTDfQQdfVNuv3VXSr//pjS7HF6Z9+3XeonyiJNy8owtzgAAAAgQhG6gR7gmj9t1e6vXJ7jfZW1Xe5r1pjMU/brBgAAANA2QjcQ4U4O3F3V3j7dAAAAANpH6AYiWF19k1+B+8L0vjrvB3YNTkrQtKwMZrgBAAAAHxG6gQjz1s4K/X//tdOUvtb8f5coPjbalL4AAACAnojQDUSQjAUbTetrvDOZwA0AAAD4ibWiQIQwO3Cvnn6xaf0BAAAAPRUz3UAEeGtnhV/333jRQB2sqVdG/wTdO9nJDDcAAABgEkI3EAH8eYb7goE2Pf7ToSZWAwAAAKAVy8uBHuyCgTatnzcm2GUAAAAAEYuZbqCHOceRqPR+8XrqxuHqY+WvAAAAACCQ+I0biAD/+rMRnVpi/q8/G6FxI1K7oSIAAAAAEsvLgYjQ2SBN4AYAAAC6F6EbiBBlj+X4dR0AAACA+VheDoSIZrehotJqVdXWKznRqpGZSYqOsvjUR9ljOXprZ4XXUnOWlAMAAADBQ+gGQkD+ngot3VCiipp6z7lUu1WLc53KHuJbYB43IlVlI5jVBgAAAEIBy8uBIMvfU6E5a3Z6BW5Jqqyp15w1O5W/pyJIlQEAAADwF6EbCKJmt6GlG0pktHGt9dzSDSVqdrfVAgAAAECoI3QDQVRUWn3KDPeJDEkVNfUqKq3uvqIAAAAAmIbQDQRRVW37gbsr7QAAAACEFkI3EETJiVZT2wEAAAAILYRuIIhGZiYp1W5VexuDWdTyFvORmUndWRYAAAAAkxC6gQB5Ycsnyliw0fN5Ycsnp7SJjrJoca5Tkk4J3q3Hi3OdPu/XDQAAACA0WAzDiMjXIrtcLtntdtXU1MhmswW7HPQwGQs2tnut7LFT99A2c59uAAAAAIHX2cxJ6AZMdrrA3aqt4N3sNlRUWq2q2nolJ7YsKWeGGwAAAAhNnc2cMd1YExDx2lpC3l67X175I69z0VEWZZ3ZPxBlAQAAAAgSnukGTPTgm5+Z2g4AAABAeCN0AwAAAAAQIIRuAAAAAAAChNANdFKz21DhZ9/pf4q/VuFn36nZfeo7CBdNOLNTfXW2HQAAAIDw5nPoLigoUG5urtLS0mSxWLRu3Tqv64ZhaNGiRUpNTVV8fLzGjRun/fv3e66XlZVp5syZyszMVHx8vM4880wtXrxYDQ0NXv3s3r1bY8aMkdVq1aBBg7R8+fKufUPABPl7KjT68S36+eq/6V/+s1g/X/03jX58i/L3VHi1O/nlaO3pbDsAAAAA4c3n0H3kyBENHTpUK1asaPP68uXL9fTTT2vVqlXatm2bevfurYkTJ6q+vmX/4U8++URut1vPPfec9u7dq6eeekqrVq3Svffe6+nD5XJpwoQJGjx4sD788EM98cQTWrJkiZ5//vkufk2g6/L3VGjOmp1ee2hLUmVNveas2XlK8G5rOzBfrgMAAACIHH7t022xWLR27Vpdd911klpmudPS0nTHHXfozjvvlCTV1NQoJSVFeXl5mjJlSpv9PPHEE1q5cqU+//xzSdLKlSt13333qbKyUrGxsZKkBQsWaN26dfrkk85tycQ+3eiqYw3NenRTicq+O6rBSQl6c2+lquoa2mxrkeSwW/X+PVeesqf2C1s+8XpL+aIJZzLDDQAAAESIoOzTXVpaqsrKSo0bN85zzm63a9SoUSosLGw3dNfU1CgpKclzXFhYqLFjx3oCtyRNnDhRjz/+uL7//nv169fPzLIBj1kvb9fmkirP8dYO2huSKmrqVVRafcoe27+88keEbAAAAKCHM/VFapWVlZKklJQUr/MpKSmeayc7cOCAnnnmGf3qV7/y6qetPk78GSc7fvy4XC6X1wfwxcmB2xdVtfUdNwIAAADQ4wT17eVff/21srOz9c///M+aNWuWX30tW7ZMdrvd8xk0aJBJVaInONbQ3OXALUnJiVYTqwEAAAAQKUwN3Q6HQ5J06NAhr/OHDh3yXGt18OBBXXHFFbr00ktPeUGaw+Fos48Tf8bJFi5cqJqaGs/nyy+/9Ou7oGd5dFNJl+6zSEq1WzUyM6nDtgAAAAB6HlNDd2ZmphwOh95++23POZfLpW3btikrK8tz7uuvv9bll1+uCy+8UC+++KKiorzLyMrKUkFBgRobGz3nNm/erHPOOafd57nj4uJks9m8PkBnlX131Od7Wl+btjjXecpL1AAAAABA6kLorqurU3FxsYqLiyW1vDytuLhY5eXlslgsuu222/Twww9r/fr1+uijjzR9+nSlpaV53nDeGrjT09P15JNP6ptvvlFlZaXXs9o33XSTYmNjNXPmTO3du1evvvqq/vjHP2r+/PmmfGngZBn9E3y+x2G3auXUEcoekhqAigAAAABEAp/fXr5jxw5dccUVnuPWIDxjxgzl5eXp7rvv1pEjRzR79mwdPnxYo0ePVn5+vqzWlmdeN2/erAMHDujAgQMaOHCgV9+tu5fZ7Xa9+eabmjt3ri688EINGDBAixYt0uzZs7v8RdFzfV19TJOefk9Hjjerd1y03vjtZfpBUrxXm3snO/XK38o77OulWy7W4fpGJSe2LClnhhsAAADA6fi1T3coY59uSNLZ921SQ/Op/188NtqiTx+Z7HWuo7eXj3cma/X0i02vEQAAAED46WzmDOrby4FAai9wS1JDs6Gz79vkdW719Is13pncZnsCNwAAAICu8Hl5ORAOvq4+1m7gbtXQbOjr6mNeS81XT79Yxxqa9eimEpV9d1QZ/RN072Sn4mOjA10yAAAAgAjE8nJEjGa3oaLSalXV1mvhX3braKO7w3ts1mjtXpLdDdUBAAAAiCSdzZzMdCMi5O+p0NINJaqoqffpviPHmwNUEQAAAAAQuhEB8vdUaM6anerKko3ecSwbBwAAABA4hG6EnROXkQ/oE6cl6/d2KXBL0hu/vczU2gAAAADgRIRuhJWuLiNvS2y05ZT9ugEAAADATIRuhA1/lpGfrK19ugEAAADAbIRuhIVmt6GlG0q6HLgTekXpeJNbveOi9cZvL2OGGwAAAEC3IHQjLBSVVndpSblFksNu1fv3XKnoKIv5hQEAAADAaUQFuwCgM6pquxa4JWlxrpPADQAAACAomOlGWEhOtPp8j8Nu1eJcp7KHpAagIgAAAADoGKEbYWFkZpJS7VZV1tS3+Vx36zLyJ386VN8eOa7kRKtGZiYxww0AAAAgqAjdCAvRURYtznVqzpqdskhewfvEZeQ/PmtAEKoDAAAAgLbxTDfCRvaQVK2cOkIOu/dSc4fdqpVTR7CMHAAAAEDIYaYbYSV7SKrGOx0qKq1WVW09y8gBAAAAhDRCN7rNR+U1uubZ92WoZUn4+t+M1vnpdp/7iY6yKOvM/qbXBwAAAABmI3SjW2Qs2Oh1bEjKffZ9SVLZYzlBqAgAAAAAAo9nuhFwJwduX68DAAAAQLgidCOgPiqvMbUdAAAAAIQTQjcC6pq/LyE3qx0AAAAAhBNCNwLK6LiJT+0AAAAAIJwQuhFQnd3Iiw2/AAAAAEQiQjcCav1vRpvaDgAAAADCCaEbAdXZfbi7sl83AAAAAIQ6QjcCrqN9uNmnGwAAAECkigl2AegZyh7L0UflNbrm2fdlqOUZ7vW/Gc0MNwAAAICIRuhGtzk/3a5SZrUBAAAA9CAsLwcAAAAAIEAI3QAAAAAABAjLy+HR7DZUVFqtqtp6JSdaNTIzSdFR7KANAAAAAF1F6IYkKX9PhZZuKFFFTb3nXKrdqsW5TmUPSQ1iZQAAAAAQvlheDuXvqdCcNTu9ArckVdbUa86ancrfUxGkygAAAAAgvBG6e7hmt6GlG0pktHGt9dzSDSVqdrfVAgAAAABwOiwv74GONTTr0U0lKvvuqKwxUafMcJ/IkFRRU6+i0mplndm/+4oEAAAAgAhA6O5hZr28XZtLqny+r6q2/WAOAAAAAGgby8t7kK4GbklKTrSaXA0AAAAARD5munuIYw3NXQrcFkkOe8v2YQAAAAAA3zDT3UM8uqnE53tad+henOtkv24AAAAA6AJmunuIsu+O+nyPg326AQAAAMAvhO4eIqN/grbu77jd+HOTdfXQNCUntiwpZ4YbAAAAALqO0N1D3DvZqVf+Vt5hu6d/PkLxsdHdUBEAAAAARD6e6e4h4mOjNd6ZfNo2453JBG4AAAAAMBGhuwdZPf3idoP3eGeyVk+/uJsrAgAAAIDIxvLyHmb19It1rKFZj24qUdl3R5XRP0H3TnYyww0AAAAAAUDo7oHiY6P10HXnB7sMAAAAAIh4LC8HAAAAACBACN0AAAAAAAQIoRsAAAAAgAAhdAMAAAAAECCEbgAAAAAAAoS3lwdRXX2Tbn91l8q/P6b0fvF66sbh6mNlSAAAAAAgUvg8011QUKDc3FylpaXJYrFo3bp1XtcNw9CiRYuUmpqq+Ph4jRs3Tvv37/dqU11drZtvvlk2m019+/bVzJkzVVdX59Vm9+7dGjNmjKxWqwYNGqTly5f7/u1C2DV/2qohS/5Xmz+u0r7KWm3+uEpDlvyvrvnT1mCXBgAAAAAwic+h+8iRIxo6dKhWrFjR5vXly5fr6aef1qpVq7Rt2zb17t1bEydOVH19vafNzTffrL1792rz5s16/fXXVVBQoNmzZ3uuu1wuTZgwQYMHD9aHH36oJ554QkuWLNHzzz/fha8Yeq7501bt/srV5rXdX7kI3gAAAAAQISyGYRhdvtli0dq1a3XddddJapnlTktL0x133KE777xTklRTU6OUlBTl5eVpypQp+vjjj+V0OrV9+3ZddNFFkqT8/HxNnjxZX331ldLS0rRy5Urdd999qqysVGxsrCRpwYIFWrdunT755JNO1eZyuWS321VTUyObzdbVr2i6uvomDVnyvx2227NkIkvNAQAAACBEdTZzmvoitdLSUlVWVmrcuHGec3a7XaNGjVJhYaEkqbCwUH379vUEbkkaN26coqKitG3bNk+bsWPHegK3JE2cOFH79u3T999/3+bPPn78uFwul9cnFN3+6i5T2wEAAAAAQpepobuyslKSlJKS4nU+JSXFc62yslLJycle12NiYpSUlOTVpq0+TvwZJ1u2bJnsdrvnM2jQIP+/UACUf3/M1HYAAAAAgNAVMVuGLVy4UDU1NZ7Pl19+GeyS2pTeL97UdgAAAACA0GVq6HY4HJKkQ4cOeZ0/dOiQ55rD4VBVVZXX9aamJlVXV3u1aauPE3/GyeLi4mSz2bw+oeipG4eb2g4AAAAAELpMDd2ZmZlyOBx6++23PedcLpe2bdumrKwsSVJWVpYOHz6sDz/80NNmy5YtcrvdGjVqlKdNQUGBGhsbPW02b96sc845R/369TOz5G7XxxqjCwae/h8ELhho4yVqAAAAABABfA7ddXV1Ki4uVnFxsaSWl6cVFxervLxcFotFt912mx5++GGtX79eH330kaZPn660tDTPG87PPfdcZWdna9asWSoqKtJf//pXzZs3T1OmTFFaWpok6aabblJsbKxmzpypvXv36tVXX9Uf//hHzZ8/37QvHkzr541pN3hfMNCm9fPGdHNFAAAAAIBA8HnLsHfffVdXXHHFKednzJihvLw8GYahxYsX6/nnn9fhw4c1evRoPfvsszr77LM9baurqzVv3jxt2LBBUVFRuuGGG/T000+rT58+nja7d+/W3LlztX37dg0YMEC33nqr7rnnnk7XGapbhp2orr5Jt7+6S+XfH1N6v3g9deNwZrgBAAAAIAx0NnP6tU93KAuH0A0AAAAACE9B2acbAAAAAAD8A6EbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAESEywCwgUwzAkSS6XK8iVAAAAAAAiTWvWbM2e7YnY0F1bWytJGjRoUJArAQAAAABEqtraWtnt9navW4yOYnmYcrvdOnjwoBITE2WxWIJdDv7O5XJp0KBB+vLLL2Wz2YJdDkzG+EY2xjfyMcaRjfGNbIxvZGN8Q5NhGKqtrVVaWpqiotp/cjtiZ7qjoqI0cODAYJeBdthsNv7CiGCMb2RjfCMfYxzZGN/IxvhGNsY39JxuhrsVL1IDAAAAACBACN0AAAAAAAQIoRvdKi4uTosXL1ZcXFywS0EAML6RjfGNfIxxZGN8IxvjG9kY3/AWsS9SAwAAAAAg2JjpBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG6YoqCgQLm5uUpLS5PFYtG6detOafPxxx/rmmuukd1uV+/evXXxxRervLzcc72+vl5z585V//791adPH91www06dOhQN34LtKej8a2rq9O8efM0cOBAxcfHy+l0atWqVV5tGN/QtGzZMl188cVKTExUcnKyrrvuOu3bt8+rTWfGrry8XDk5OUpISFBycrLuuusuNTU1dedXQRs6Gt/q6mrdeuutOueccxQfH6/09HT99re/VU1NjVc/jG/o6syf4VaGYWjSpElt/j3OGIemzo5vYWGhrrzySvXu3Vs2m01jx47VsWPHPNerq6t18803y2azqW/fvpo5c6bq6uq686ugDZ0Z38rKSk2bNk0Oh0O9e/fWiBEj9Je//MWrDeMb+gjdMMWRI0c0dOhQrVixos3rn332mUaPHq0f/ehHevfdd7V792498MADslqtnja33367NmzYoD//+c967733dPDgQV1//fXd9RVwGh2N7/z585Wfn681a9bo448/1m233aZ58+Zp/fr1njaMb2h67733NHfuXP3tb3/T5s2b1djYqAkTJujIkSOeNh2NXXNzs3JyctTQ0KAPPvhAL730kvLy8rRo0aJgfCWcoKPxPXjwoA4ePKgnn3xSe/bsUV5envLz8zVz5kxPH4xvaOvMn+FWf/jDH2SxWE45zxiHrs6Mb2FhobKzszVhwgQVFRVp+/btmjdvnqKi/vFr/s0336y9e/dq8+bNev3111VQUKDZs2cH4yvhBJ0Z3+nTp2vfvn1av369PvroI11//fX62c9+pl27dnnaML5hwABMJslYu3at17kbb7zRmDp1arv3HD582OjVq5fx5z//2XPu448/NiQZhYWFgSoVXdDW+J533nnGgw8+6HVuxIgRxn333WcYBuMbTqqqqgxJxnvvvWcYRufGbtOmTUZUVJRRWVnpabNy5UrDZrMZx48f794vgNM6eXzb8l//9V9GbGys0djYaBgG4xtu2hvjXbt2GT/4wQ+MioqKU/4eZ4zDR1vjO2rUKOP+++9v956SkhJDkrF9+3bPuTfeeMOwWCzG119/HdB64Zu2xrd3797Gyy+/7NUuKSnJWL16tWEYjG+4YKYbAed2u7Vx40adffbZmjhxopKTkzVq1CivpW0ffvihGhsbNW7cOM+5H/3oR0pPT1dhYWEQqoYvLr30Uq1fv15ff/21DMPQO++8o08//VQTJkyQxPiGk9ZlxUlJSZI6N3aFhYU6//zzlZKS4mkzceJEuVwu7d27txurR0dOHt/22thsNsXExEhifMNNW2N89OhR3XTTTVqxYoUcDscp9zDG4ePk8a2qqtK2bduUnJysSy+9VCkpKbrsssv0/vvve+4pLCxU3759ddFFF3nOjRs3TlFRUdq2bVv3fgGcVlt/fi+99FK9+uqrqq6ultvt1n/+53+qvr5el19+uSTGN1wQuhFwVVVVqqur02OPPabs7Gy9+eab+slPfqLrr79e7733nqSW51ViY2PVt29fr3tTUlJUWVkZhKrhi2eeeUZOp1MDBw5UbGyssrOztWLFCo0dO1YS4xsu3G63brvtNv34xz/WkCFDJHVu7CorK71+WW+93noNoaGt8T3Zt99+q4ceeshrWSLjGz7aG+Pbb79dl156qa699to272OMw0Nb4/v5559LkpYsWaJZs2YpPz9fI0aM0FVXXaX9+/dLahnD5ORkr75iYmKUlJTE+IaQ9v78/td//ZcaGxvVv39/xcXF6Ve/+pXWrl2rH/7wh5IY33ARE+wCEPncbrck6dprr9Xtt98uSRo2bJg++OADrVq1Spdddlkwy4MJnnnmGf3tb3/T+vXrNXjwYBUUFGju3LlKS0vzmiFFaJs7d6727NnjNUOCyNHR+LpcLuXk5MjpdGrJkiXdWxxM0dYYr1+/Xlu2bPF6/hPhqa3xbf0d61e/+pV+8YtfSJKGDx+ut99+Wy+88IKWLVsWlFrhu/b+jn7ggQd0+PBhvfXWWxowYIDWrVunn/3sZ9q6davOP//8IFULXzHTjYAbMGCAYmJi5HQ6vc6fe+65nreXOxwONTQ06PDhw15tDh061OZSOISOY8eO6d5779Xvf/975ebm6oILLtC8efN044036sknn5TE+IaDefPm6fXXX9c777yjgQMHes53ZuwcDscpbzNvPWZ8Q0N749uqtrZW2dnZSkxM1Nq1a9WrVy/PNcY3PLQ3xlu2bNFnn32mvn37KiYmxvPYwA033OBZnsoYh772xjc1NVWSOvwdq6qqyut6U1OTqqurGd8Q0d74fvbZZ/rTn/6kF154QVdddZWGDh2qxYsX66KLLvK83JbxDQ+EbgRcbGysLr744lO2QPj00081ePBgSdKFF16oXr166e233/Zc37dvn8rLy5WVldWt9cI3jY2Namxs9HpLqiRFR0d7/gWe8Q1dhmFo3rx5Wrt2rbZs2aLMzEyv650Zu6ysLH300Ude/9HfvHmzbDbbKb8Iont1NL5Sywz3hAkTFBsbq/Xr13vtKiExvqGuozFesGCBdu/ereLiYs9Hkp566im9+OKLkhjjUNbR+GZkZCgtLe20v2NlZWXp8OHD+vDDDz3Xt2zZIrfbrVGjRgX+S6BdHY3v0aNHJem0v2MxvmEimG9xQ+Sora01du3aZezatcuQZPz+9783du3aZXzxxReGYRjGa6+9ZvTq1ct4/vnnjf379xvPPPOMER0dbWzdutXTx69//WsjPT3d2LJli7Fjxw4jKyvLyMrKCtZXwgk6Gt/LLrvMOO+884x33nnH+Pzzz40XX3zRsFqtxrPPPuvpg/ENTXPmzDHsdrvx7rvvGhUVFZ7P0aNHPW06GrumpiZjyJAhxoQJE4zi4mIjPz/fOOOMM4yFCxcG4yvhBB2Nb01NjTFq1Cjj/PPPNw4cOODVpqmpyTAMxjfUdebP8Ml00tvLGePQ1ZnxfeqppwybzWb8+c9/Nvbv32/cf//9htVqNQ4cOOBpk52dbQwfPtzYtm2b8f777xtnnXWW8fOf/zwYXwkn6Gh8GxoajB/+8IfGmDFjjG3bthkHDhwwnnzyScNisRgbN2709MP4hj5CN0zxzjvvGJJO+cyYMcPT5t/+7d+MH/7wh4bVajWGDh1qrFu3zquPY8eOGb/5zW+Mfv36GQkJCcZPfvITo6Kiopu/CdrS0fhWVFQYt9xyi5GWlmZYrVbjnHPOMX73u98Zbrfb0wfjG5raGldJxosvvuhp05mxKysrMyZNmmTEx8cbAwYMMO644w7PllMIno7Gt70/25KM0tJSTz+Mb+jqzJ/htu45eetHxjg0dXZ8ly1bZgwcONBISEgwsrKyvCY1DMMwvvvuO+PnP/+50adPH8Nmsxm/+MUvjNra2m78JmhLZ8b3008/Na6//nojOTnZSEhIMC644IJTthBjfEOfxTAMw+zZcwAAAAAAwDPdAAAAAAAEDKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAAKE0A0AAAAAQIAQugEAAAAACBBCNwAAAAAAAULoBgAAAAAgQAjdAAAAAAAECKEbAAAAAIAAIXQDAAAAABAghG4AAAAAAALk/wdw9IA+/qwxiAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Låt oss se vad som händer om relationen inte är linjär. Anta att vårt företag bestämde sig för att dölja det uppenbara linjära beroendet mellan höjder och löner, och införde någon icke-linjärhet i formeln, såsom `sin`:\n"
+    "Låt oss se vad som händer om relationen inte är linjär. Anta att vårt företag bestämde sig för att dölja det uppenbara linjära sambandet mellan höjder och löner, och införde en viss icke-linjärhet i formeln, såsom `sin`:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -946,14 +783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -991,24 +826,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Kan du gissa varför prickarna bildar vertikala linjer på det här sättet?\n",
+    "> Kan du gissa varför prickarna radar upp sig i vertikala linjer så här?\n",
     "\n",
-    "Vi har observerat sambandet mellan ett konstgjort koncept som lön och den observerade variabeln *längd*. Låt oss också se om de två observerade variablerna, såsom längd och vikt, korrelerar med varandra:\n"
+    "Vi har observerat sambandet mellan ett konstgjort koncept som lön och den observerade variabeln *längd*. Låt oss också se om de två observerade variablerna, som längd och vikt, också korrelerar:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,14 +858,14 @@
    "source": [
     "Tyvärr fick vi inga resultat - bara några konstiga `nan`-värden. Detta beror på att vissa av värdena i vår serie är odefinierade, representerade som `nan`, vilket gör att resultatet av operationen också blir odefinierat. Genom att titta på matrisen kan vi se att `Weight` är den problematiska kolumnen, eftersom självkorrelationen mellan `Height`-värden har beräknats.\n",
     "\n",
-    "> Det här exemplet visar vikten av **databearbetning** och **rengöring**. Utan korrekt data kan vi inte beräkna något.\n",
+    "> Detta exempel visar vikten av **databearbetning** och **rengöring**. Utan korrekt data kan vi inte beräkna något.\n",
     "\n",
     "Låt oss använda metoden `fillna` för att fylla i de saknade värdena och beräkna korrelationen:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,32 +888,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Det finns verkligen en korrelation, men inte en så stark som i vårt artificiella exempel. Faktum är att om vi tittar på spridningsdiagrammet för ett värde mot det andra, skulle sambandet vara mycket mindre uppenbart:\n"
+    "Det finns verkligen en korrelation, men inte en så stark som i vårt konstgjorda exempel. Faktum är att om vi tittar på spridningsdiagrammet av ett värde mot det andra, skulle sambandet vara mycket mindre uppenbart:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1089,14 +922,14 @@
    "source": [
     "## Slutsats\n",
     "\n",
-    "I den här anteckningsboken har vi lärt oss att utföra grundläggande operationer på data för att beräkna statistiska funktioner. Vi vet nu hur man använder ett gediget verktyg av matematik och statistik för att bevisa vissa hypoteser, samt hur man beräknar konfidensintervall för godtyckliga variabler baserat på ett dataprovsample.\n"
+    "I den här anteckningsboken har vi lärt oss hur man utför grundläggande operationer på data för att beräkna statistiska funktioner. Vi vet nu hur man använder ett gediget verktyg inom matematik och statistik för att bevisa vissa hypoteser, samt hur man beräknar konfidensintervall för godtyckliga variabler baserat på ett dataprovsample.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör det noteras att automatiserade översättningar kan innehålla fel eller brister. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som kan uppstå vid användning av denna översättning.\n"
    ]
   }
  ],
@@ -1119,11 +952,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:34:02+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:34:26+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "sv"
   }
diff --git a/translations/sv/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/sv/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 777c3740..d2da38ca 100644
--- a/translations/sv/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/sv/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Introduktion till sannolikhet och statistik\n",
     "## Uppgift\n",
     "\n",
-    "I den här uppgiften kommer vi att använda datasetet med diabetespatienter som hämtats [härifrån](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "I denna uppgift kommer vi att använda datasetet med diabetespatienter hämtat [härifrån](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "I den här datasetet är kolumnerna följande:  \n",
-    "* Ålder och kön är självförklarande  \n",
-    "* BMI är kroppsmassindex  \n",
-    "* BP är genomsnittligt blodtryck  \n",
-    "* S1 till S6 är olika blodmätningar  \n",
-    "* Y är det kvalitativa måttet på sjukdomsprogression över ett år  \n",
+    "I denna dataset är kolumnerna följande:\n",
+    "* Ålder och kön är självförklarande\n",
+    "* BMI är kroppsmassaindex\n",
+    "* BP är genomsnittligt blodtryck\n",
+    "* S1 till S6 är olika blodmätningar\n",
+    "* Y är det kvalitativa måttet på sjukdomsprogression över ett år\n",
     "\n",
-    "Låt oss studera detta dataset med hjälp av sannolikhets- och statistikmetoder.\n",
+    "Låt oss studera denna dataset med hjälp av sannolikhets- och statistiska metoder.\n",
     "\n",
-    "### Uppgift 1: Beräkna medelvärden och varians för alla värden  \n"
+    "### Uppgift 1: Beräkna medelvärden och varians för alla värden\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Uppgift 2: Rita lådagram för BMI, BP och Y beroende på kön\n"
+    "### Uppgift 2: Rita boxplottar för BMI, BP och Y beroende på kön\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -592,7 +592,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Slutsatser:  \n",
+    "Slutsatser:\n",
     "* Ålder - normal  \n",
     "* Kön - enhetlig  \n",
     "* BMI, Y - svårt att avgöra  \n"
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:55:24+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:34:54+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "sv"
   }
diff --git a/translations/sw/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/sw/1-Introduction/04-stats-and-probability/assignment.ipynb
index 2a0e79a9..bbe4d196 100644
--- a/translations/sw/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/sw/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,15 +149,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Katika seti hii ya data, safu zifuatazo zinaelezea:\n",
-    "\n",
-    "* Umri na jinsia vinaeleweka kirahisi\n",
+    "Katika seti hii ya data, safu zifuatazo zinaelezwa kama ifuatavyo:\n",
+    "* Umri na jinsia vinaeleweka wazi\n",
     "* BMI ni kipimo cha uzito wa mwili kulingana na urefu\n",
-    "* BP ni wastani wa shinikizo la damu\n",
+    "* BP ni shinikizo la damu la wastani\n",
     "* S1 hadi S6 ni vipimo tofauti vya damu\n",
     "* Y ni kipimo cha ubora wa maendeleo ya ugonjwa kwa kipindi cha mwaka mmoja\n",
     "\n",
-    "Tuchambue seti hii ya data kwa kutumia mbinu za uwezekano na takwimu.\n",
+    "Tuchunguze seti hii ya data kwa kutumia mbinu za uwezekano na takwimu.\n",
     "\n",
     "### Kazi ya 1: Hesabu wastani wa thamani na tofauti kwa thamani zote\n"
    ],
@@ -224,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
    ]
   }
  ],
@@ -250,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:47:39+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:47:50+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "sw"
   }
diff --git a/translations/sw/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/sw/1-Introduction/04-stats-and-probability/notebook.ipynb
index d5bfbd72..be1e7679 100644
--- a/translations/sw/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/sw/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Utangulizi wa Uwezekano na Takwimu  \n",
-    "Katika daftari hili, tutacheza na baadhi ya dhana ambazo tumejadili hapo awali. Dhana nyingi za uwezekano na takwimu zimewakilishwa vyema katika maktaba kuu za usindikaji wa data katika Python, kama `numpy` na `pandas`.  \n"
+    "Katika daftari hili, tutachunguza baadhi ya dhana ambazo tumejadili hapo awali. Dhana nyingi za uwezekano na takwimu zimewakilishwa vyema katika maktaba kuu za usindikaji wa data katika Python, kama `numpy` na `pandas`.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## Kuchambua Data Halisi\n",
     "\n",
-    "Wastani na tofauti ni muhimu sana wakati wa kuchambua data ya ulimwengu halisi. Hebu tuweke data kuhusu wachezaji wa baseball kutoka [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Wastani na tofauti ni muhimu sana wakati wa kuchambua data halisi. Hebu tupakie data kuhusu wachezaji wa baseball kutoka [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kwa kuongezea wastani, inafaa kuangalia thamani ya kati na robo. Zinaweza kuonyeshwa kwa kutumia **box plot**:\n"
+    "Mbali na wastani, inafaa kuangalia thamani ya kati na robo. Zinaweza kuonyeshwa kwa kutumia **mchoro wa kisanduku**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Tunaweza pia kutengeneza chati za kisanduku za sehemu ndogo za seti yetu ya data, kwa mfano, zilizopangwa kulingana na jukumu la mchezaji.\n"
+    "Tunaweza pia kutengeneza grafu za sanduku za sehemu ndogo za seti yetu ya data, kwa mfano, zilizopangwa kulingana na jukumu la mchezaji.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Note**: Mchoro huu unapendekeza kwamba, kwa wastani, urefu wa wachezaji wa kwanza wa msingi ni mrefu zaidi kuliko urefu wa wachezaji wa pili wa msingi. Baadaye tutajifunza jinsi tunavyoweza kujaribu dhana hii kwa njia rasmi zaidi, na jinsi ya kuonyesha kwamba data yetu ina umuhimu wa takwimu kuonyesha hilo.  \n",
+    "> **Kumbuka**: Mchoro huu unapendekeza kwamba, kwa wastani, urefu wa wachezaji wa kwanza wa msingi ni mrefu zaidi kuliko urefu wa wachezaji wa pili wa msingi. Baadaye tutajifunza jinsi tunavyoweza kujaribu dhana hii kwa njia rasmi zaidi, na jinsi ya kuonyesha kwamba data yetu ina umuhimu wa takwimu kuthibitisha hilo.  \n",
     "\n",
-    "Umri, urefu na uzito vyote ni vigezo vya nasibu vinavyoendelea. Unadhani usambazaji wao ukoje? Njia nzuri ya kugundua ni kuchora histogramu ya thamani:\n"
+    "Umri, urefu, na uzito vyote ni vigezo endelevu vya nasibu. Unadhani usambazaji wao ukoje? Njia nzuri ya kugundua ni kuchora histogramu ya thamani:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Zq7/9GOAQ4GsjqE2SJEmr3DAv83Ym8GngJ5Jcm+Ql/abjuOfyCoCnAl9I8nngfcDLqurmYdUmSZIkzWSYV7F4wQzjJ04zdjZw9rBqkSRJkubLt5qWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKmx97AOnOR04Bjgxqo6vB97PfBS4Fv9bq+uqvP6bScBLwHuAn6rqj4yrNokrV5rN5076hJmteOUo0ddgiStesOcQX4H8Kxpxt9SVUf0H5Ph+FDgOOCw/jF/nWSvIdYmSZIkTWtoAbmqLgRunufuxwJbqurOqroa+Cpw1LBqkyRJkmaSqhrewZO1wIenLLE4EbgN2AZsrKpbkrwVuKiq3tXvdxrwj1X1vmmOuQHYADA2Nnbkli1bhlb/SrBr1y7WrFkz6jJWlFH3dPt1t47s3MM09iDYeceoqxi9dQftO7BjjfprdaWyr4NnT4fDvs5t/fr1F1fV+NTxoa1BnsHbgDcC1X8+FXgxkGn2nTa5V9VmYDPA+Ph4TUxMDKXQlWLr1q3Yo8EadU9PXOJraBdq47rdnLp9sb8lLT07jp8Y2LFG/bW6UtnXwbOnw2FfF25RfxpV1c7J20n+Fvhwf/da4OBm10cB1y9iadIPzPUiro3rdq/YkCpJkhb5Mm9JDmzu/jJwWX/7HOC4JPskeTRwCPDZxaxNkiRJguFe5u1MYAI4IMm1wOuAiSRH0C2f2AH8GkBVXZ7kLOCLwG7gFVV117BqkyRJkmYytIBcVS+YZvi0WfY/GTh5WPVIkiRJ8+E76UmSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSY2hBeQkpye5McllzdifJflSki8k+UCS/frxtUnuSHJp//H2YdUlSZIkzWaYM8jvAJ41Zex84PCq+n+ArwAnNduuqqoj+o+XDbEuSZIkaUZDC8hVdSFw85Sxj1bV7v7uRcCjhnV+SZIkaSFSVcM7eLIW+HBVHT7Ntn8A3ltV7+r3u5xuVvk24A+q6hMzHHMDsAFgbGzsyC1btgyp+pVh165drFmzZtRlLCvbr7t11u1jD4KddyxSMauIfe2sO2jfgR3L///DYV8Hz54Oh32d2/r16y+uqvGp43uPopgkrwF2A+/uh24AfrSqbkpyJPDBJIdV1W1TH1tVm4HNAOPj4zUxMbFIVS9PW7duxR7tmRM3nTvr9o3rdnPq9pH811nR7Gtnx/ETAzuW//+Hw74Onj0dDvu6cIt+FYskJwDHAMdXP31dVXdW1U397YuBq4DHLXZtkiRJ0qIG5CTPAn4f+KWq+l4z/vAke/W3HwMcAnxtMWuTJEmSYIhLLJKcCUwAByS5Fngd3VUr9gHOTwJwUX/FiqcCb0iyG7gLeFlV3TztgSVJkqQhGlpArqoXTDN82gz7ng2cPaxaJEmSpPnynfQkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpMa8AnKSJ89nTJIkSVru5juD/D/nOSZJkiQta3vPtjHJE4EnAQ9P8qpm00OBvYZZmCRJkjQKswZk4AHAmn6/hzTjtwHPHVZRkiRJ0qjMGpCr6gLggiTvqKprFqkmSZIkaWTmmkGetE+SzcDa9jFV9bRhFCVJkiSNynwD8v8C3g78HXDX8MqRJEmSRmu+AXl3Vb1tqJVIkiRJS8B8L/P2D0l+PcmBSR42+THUyiRJkqQRmO8M8gn9599rxgp4zGDLkSRJkkZrXgG5qh497EIkSZKkpWBeATnJi6Ybr6p3DrYcSZIkabTmu8TiCc3tBwJPBy4BDMiSJElaUea7xOI32/tJ9gX+frbHJDkdOAa4saoO78ceBryX7nrKO4DnV9Ut/baTgJfQXUbut6rqI3vyRCRJkqRBmO8M8lTfAw6ZY593AG/lnrPMm4CPVdUpSTb1938/yaHAccBhwCOBf07yuKrymsuSVpW1m84d2LE2rtvNiQM83o5Tjh7YsSRpKZvvGuR/oLtqBcBewOOBs2Z7TFVdmGTtlOFjgYn+9hnAVuD3+/EtVXUncHWSrwJHAZ+eT32SJEnSoKSq5t4p+fnm7m7gmqq6dh6PWwt8uFli8Z2q2q/ZfktV7Z/krcBFVfWufvw04B+r6n3THHMDsAFgbGzsyC1btsxZ/2q2a9cu1qxZM+oylpXt19066/axB8HOOxapmFXEvg7eoHu67qB9B3ewZczvq4NnT4fDvs5t/fr1F1fV+NTx+a5BviDJGHe/WO/KQRYHZLrTzlDLZmAzwPj4eE1MTAy4lJVl69at2KM9M9efpDeu282p2xe6Okkzsa+DN+ie7jh+YmDHWs78vjp49nQ47OvCzeud9JI8H/gs8Dzg+cBnkjx3AefbmeTA/pgHAjf249cCBzf7PQq4fgHHlyRJku6T+b7V9GuAJ1TVCVX1Irr1wX+4gPOdw93vyncC8KFm/Lgk+yR5NN0LAD+7gONLkiRJ98l8//Z2v6q6sbl/E3OE6yRn0r0g74Ak1wKvA04BzkryEuDrdDPSVNXlSc4Cvki3xvkVXsFCkiRJozDfgPxPST4CnNnf/xXgvNkeUFUvmGHT02fY/2Tg5HnWI0mSJA3FrAE5yY8DY1X1e0n+M/AUuhfUfRp49yLUJ0mSJC2qudYg/wVwO0BVvb+qXlVVv0M3e/wXwy1NkiRJWnxzBeS1VfWFqYNVtY3u7aIlSZKkFWWugPzAWbY9aJCFSJIkSUvBXAH5c0leOnWwvwrFxcMpSZIkSRqdua5i8UrgA0mO5+5APA48APjlIdYlSZIkjcSsAbmqdgJPSrIeOLwfPreq/mXolUmSJEkjMK/rIFfVx4GPD7kWSZIkaeTm+1bTkiRJ0qpgQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpsfdinzDJTwDvbYYeA7wW2A94KfCtfvzVVXXe4lYnSZKk1W7RA3JVfRk4AiDJXsB1wAeA/wa8par+fLFrkiRJkiaNeonF04GrquqaEdchSZIkAZCqGt3Jk9OBS6rqrUleD5wI3AZsAzZW1S3TPGYDsAFgbGzsyC1btixewcvQrl27WLNmzajLWFa2X3frrNvHHgQ771ikYlYR+zp4g+7puoP2HdzBljG/rw6ePR0O+zq39evXX1xV41PHRxaQkzwAuB44rKp2JhkDvg0U8EbgwKp68WzHGB8fr23btg2/2GVs69atTExMjLqMZWXtpnNn3b5x3W5O3b7oq5NWPPs6eKutpztOOXpRzuP31cGzp8NhX+eWZNqAPMolFr9IN3u8E6CqdlbVXVX1feBvgaNGWJskSZJWqVFOLbwAOHPyTpIDq+qG/u4vA5eNpCoN3VwztJIkSaM0koCc5IeA/wj8WjP8piRH0C2x2DFlmyRJkrQoRhKQq+p7wA9PGXvhKGqRJEmSWqO+zJskSZK0pBiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqTG3qM4aZIdwO3AXcDuqhpP8jDgvcBaYAfw/Kq6ZRT1SZIkafUa5Qzy+qo6oqrG+/ubgI9V1SHAx/r7kiRJ0qJaSkssjgXO6G+fATxndKVIkiRptUpVLf5Jk6uBW4AC/qaqNif5TlXt1+xzS1XtP81jNwAbAMbGxo7csmXLIlW9PO3atYs1a9aMuox72H7draMu4T4ZexDsvGPUVaw89nXwVltP1x2076KcZyl+X13u7Olw2Ne5rV+//uJmNcMPjGQNMvDkqro+ySOA85N8ab4PrKrNwGaA8fHxmpiYGFKJK8PWrVtZaj06cdO5oy7hPtm4bjenbh/Vf52Vy74O3mrr6Y7jJxblPEvx++pyZ0+Hw74u3EiWWFTV9f3nG4EPAEcBO5McCNB/vnEUtUmSJGl1W/SAnOTBSR4yeRt4BnAZcA5wQr/bCcCHFrs2SZIkaRR/exsDPpBk8vzvqap/SvI54KwkLwG+DjxvBLVJkiRplVv0gFxVXwN+aprxm4CnL3Y9kiRJUmspXeZNkiRJGjkDsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJjb1HXYAkSYOwdtO5i3Kejet2c+ICzrXjlKOHUI2kYXAGWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJauw96gI0eGs3nfuD2xvX7ebE5r4kSZJm5wyyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEmNRQ/ISQ5O8vEkVyS5PMlv9+OvT3Jdkkv7j2cvdm2SJEnSKK5isRvYWFWXJHkIcHGS8/ttb6mqPx9BTZIkSRIwgoBcVTcAN/S3b09yBXDQYtchSZIkTSdVNbqTJ2uBC4HDgVcBJwK3AdvoZplvmeYxG4ANAGNjY0du2bJlscpdNrZfd+sPbo89CHbeMcJiViB7Ohz2dfDs6XAstK/rDtp38MWsELt27WLNmjWjLmPFsa9zW79+/cVVNT51fGQBOcka4ALg5Kp6f5Ix4NtAAW8EDqyqF892jPHx8dq2bdvwi11mpr5RyKnbfT+YQbKnw2FfB8+eDsdC+7rjlKOHUM3KsHXrViYmJkZdxopjX+eWZNqAPJKrWCS5P3A28O6qej9AVe2sqruq6vvA3wJHjaI2SZIkrW6juIpFgNOAK6rqzc34gc1uvwxctti1SZIkSaP429uTgRcC25Nc2o+9GnhBkiPolljsAH5tBLVJkjQU7fK3pcglINLdRnEVi08CmWbTeYtdiyRJkjSV76QnSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSY+9RF7Acrd107qhLkCRJ0pA4gyxJkiQ1nEGWJEkj/evoxnW7OXGO8+845ehFqkZyBlmSJEm6BwOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDa+DLEmSdB8txXfZba8v7XWk98ySm0FO8qwkX07y1SSbRl2PJEmSVpclNYOcZC/gr4D/CFwLfC7JOVX1xdFWJkmSRmkpztAuJ0u9f0tthnupzSAfBXy1qr5WVf8GbAGOHXFNkiRJWkVSVaOu4QeSPBd4VlX9an//hcDPVNVvNPtsADb0d38C+PKiF7q8HAB8e9RFrDD2dDjs6+DZ0+Gwr4NnT4fDvs7tx6rq4VMHl9QSCyDTjN0jwVfVZmDz4pSz/CXZVlXjo65jJbGnw2FfB8+eDod9HTx7Ohz2deGW2hKLa4GDm/uPAq4fUS2SJElahZZaQP4ccEiSRyd5AHAccM6Ia5IkSdIqsqSWWFTV7iS/AXwE2As4vaouH3FZy53LUQbPng6HfR08ezoc9nXw7Olw2NcFWlIv0pMkSZJGbaktsZAkSZJGyoAsSZIkNQzIy1yS05PcmOSyKeO/2b9l9+VJ3tSMn9S/jfeXkzxz8Ste+qbraZIjklyU5NIk25Ic1Wyzp3NIcnCSjye5ov+a/O1+/GFJzk9yZf95/+Yx9nUOs/T1z5J8KckXknwgyX7NY+zrLGbqabP9d5NUkgOaMXs6h9n66s+rhZnl/78/rwahqvxYxh/AU4GfBi5rxtYD/wzs099/RP/5UODzwD7Ao4GrgL1G/RyW2scMPf0o8Iv97WcDW+3pHvX0QOCn+9sPAb7S9+5NwKZ+fBPwp/Z1IH19BrB3P/6n9vW+97S/fzDdi8ivAQ6wp/e9r/68GkpP/Xk1gA9nkJe5qroQuHnK8MuBU6rqzn6fG/vxY4EtVXVnVV0NfJXu7b3VmKGnBTy0v70vd1+f257OQ1XdUFWX9LdvB64ADqLr3xn9bmcAz+lv29d5mKmvVfXRqtrd73YR3TXlwb7OaZavVYC3AP8f93wDK3s6D7P01Z9XCzRLT/15NQAG5JXpccDPJflMkguSPKEfPwj4RrPftdz9jV+zeyXwZ0m+Afw5cFI/bk/3UJK1wH8APgOMVdUN0H2zBx7R72Zf99CUvrZeDPxjf9u+7oG2p0l+Cbiuqj4/ZTd7uoemfK3682oApvT0lfjz6j4zIK9MewP7Az8L/B5wVpIwj7fy1oxeDvxOVR0M/A5wWj9uT/dAkjXA2cArq+q22XadZsy+zmCmviZ5DbAbePfk0DQPt6/TaHtK18PXAK+dbtdpxuzpDKb5WvXn1X00TU/9eTUABuSV6Vrg/dX5LPB94AB8K+/74gTg/f3t/8Xdf5ayp/OU5P5038TfXVWTvdyZ5MB++4HA5J9X7es8zdBXkpwAHAMcX/0CROzrvEzT08fSrdn8fJIddH27JMmPYE/nbYavVX9e3Qcz9NSfVwNgQF6ZPgg8DSDJ44AHAN+me9vu45Lsk+TRwCHAZ0dV5DJzPfDz/e2nAVf2t+3pPPQzQqcBV1TVm5tN59B9M6f//KFm3L7OYaa+JnkW8PvAL1XV95qH2Nc5TNfTqtpeVY+oqrVVtZYuaPx0VX0Tezovs3wP+CD+vFqQWXrqz6sBWFJvNa09l+RMYAI4IMm1wOuA04HT012m7N+AE/oZpMuTnAV8ke5Phq+oqrtGU/nSNUNPXwr8jyR7A/8KbACoKns6P08GXghsT3JpP/Zq4BS6P6m+BPg68Dywr3tgpr7+Jd0r1c/vfoZyUVW9zL7Oy7Q9rarzptvZns7bTF+r/rxauJl66s+rAfCtpiVJkqSGSywkSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSlqAkb0nyyub+R5L8XXP/1CSvmuGxb0jyC3Mc//VJfnea8f2S/Pp9KF2Slj0DsiQtTf8beBJAkvvRvbvYYc32JwGfmu6BVfXaqvrnBZ53P8CALGlVMyBL0tL0KfqATBeMLwNuT7J/kn2AxwMkuSDJxf0M8+Tbdr8jyXP7289O8qUkn0zyl0k+3Jzj0CRbk3wtyW/1Y6cAj01yaZI/W4wnKklLje+kJ0lLUFVdn2R3kh+lC8qfBg4CngjcClwBvAU4tqq+leRXgJOBF08eI8kDgb8BnlpVV/fvEtn6SWA98BDgy0neBmwCDq+qI4b6BCVpCTMgS9LSNTmL/CTgzXQB+Ul0Afk64Bnc/XbSewE3THn8TwJfq6qr+/tn0r/tbO/cqroTuDPJjcDYkJ6HJC0rBmRJWrom1yGvo1ti8Q1gI3Ab8C/AQVX1xFkenzmOf2dz+y78mSBJgGuQJWkp+xRwDHBzVd1VVTfTvYjuicB7gYcneSJAkvsnOWzK478EPCbJ2v7+r8zjnLfTLbmQpFXLgCxJS9d2uqtXXDRl7NaquhF4LvCnST4PXMrdL+oDoKruoLsixT8l+SSwk255xoyq6ibgU0ku80V6klarVNWoa5AkDUmSNVW1K91C5b8Crqyqt4y6LklaypxBlqSV7aVJLgUuB/alu6qFJGkWziBLkiRJDWeQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkxv8FiHh2DxCDPowAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -561,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Tunaweza kuona kwamba vipindi havigongani.\n",
     "\n",
-    "Njia sahihi zaidi ya kuthibitisha dhana hii ni kutumia **Student t-test**:\n"
+    "Njia sahihi zaidi ya kuthibitisha dhana hii kwa njia ya takwimu ni kutumia **Student t-test**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "Thamani mbili zinazorejeshwa na kazi ya `ttest_ind` ni:  \n",
-    "* p-value inaweza kuchukuliwa kama uwezekano wa usambazaji mbili kuwa na wastani sawa. Katika hali yetu, ni ya chini sana, ikimaanisha kuwa kuna ushahidi mkubwa unaoonyesha kuwa mabaseman wa kwanza ni warefu zaidi.  \n",
-    "* t-value ni thamani ya kati ya tofauti ya wastani iliyonormalishwa inayotumika katika t-test, na inalinganishwa na thamani ya kizingiti kwa thamani fulani ya kujiamini.  \n"
+    "* p-value inaweza kuchukuliwa kama uwezekano wa usambazaji mbili kuwa na wastani sawa. Katika hali yetu, ni ndogo sana, ikimaanisha kuna ushahidi mkubwa unaounga mkono kwamba wachezaji wa kwanza wa msingi ni warefu zaidi.  \n",
+    "* t-value ni thamani ya kati ya tofauti ya wastani iliyonormalishwa ambayo hutumika katika t-test, na inalinganishwa na thamani ya kizingiti kwa thamani fulani ya kujiamini.  \n"
    ]
   },
   {
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -833,14 +672,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Hebu sasa tuhifadhi kovariansi na uhusiano wa mfuatano huo. `np.cov` itatupa kinachoitwa **matriki ya kovariansi**, ambayo ni upanuzi wa kovariansi kwa vigezo vingi. Kipengele $M_{ij}$ cha matriki ya kovariansi $M$ ni uhusiano kati ya vigezo vya ingizo $X_i$ na $X_j$, na thamani za diagonal $M_{ii}$ ni tofauti ya $X_{i}$. Vivyo hivyo, `np.corrcoef` itatupa **matriki ya uhusiano**.\n"
+    "Hebu sasa tukokotoe kovarians na uhusiano wa mfuatano huo. `np.cov` itatupa kinachoitwa **matriki ya kovarians**, ambayo ni upanuzi wa kovarians kwa vigezo vingi. Kipengele $M_{ij}$ cha matriki ya kovarians $M$ ni uhusiano kati ya vigezo vya ingizo $X_i$ na $X_j$, na thamani za diagonal $M_{ii}$ ni tofauti ya $X_{i}$. Vivyo hivyo, `np.corrcoef` itatupa **matriki ya uhusiano**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Katika kesi hii, uhusiano ni mdogo kidogo, lakini bado ni wa juu sana. Sasa, ili kufanya uhusiano huo usiwe dhahiri zaidi, tunaweza kutaka kuongeza baadhi ya mambo ya nasibu kwa kuongeza kipengele cha nasibu kwenye mshahara. Hebu tuone kitakachotokea:\n"
+    "Katika kesi hii, uhusiano ni mdogo kidogo, lakini bado uko juu sana. Sasa, ili kufanya uhusiano usiwe dhahiri zaidi, tunaweza kutaka kuongeza nasibu fulani kwa kuongeza kigezo fulani cha nasibu kwenye mshahara. Hebu tuone kinachotokea:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -989,22 +824,22 @@
    "source": [
     "> Je, unaweza kukisia kwa nini nukta zinajipanga katika mistari ya wima kama hii?\n",
     "\n",
-    "Tumegundua uhusiano kati ya dhana iliyotengenezwa kama mshahara na kigezo kilichotazamwa *urefu*. Hebu tuone pia kama vigezo viwili vilivyotazamwa, kama vile urefu na uzito, vina uhusiano pia:\n"
+    "Tumetazama uhusiano kati ya dhana iliyotengenezwa kama mshahara na kipimo kilichotazamwa *urefu*. Hebu tuone pia kama vipimo viwili vilivyotazamwa, kama vile urefu na uzito, vina uhusiano pia:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kwa bahati mbaya, hatukupata matokeo yoyote - ni baadhi tu ya thamani za ajabu `nan`. Hii inatokana na ukweli kwamba baadhi ya thamani katika mfululizo wetu hazijafafanuliwa, zikiwa zimewakilishwa kama `nan`, jambo ambalo husababisha matokeo ya operesheni pia kuwa hayajafafanuliwa. Kwa kuangalia matriki tunaweza kuona kwamba `Weight` ni safu yenye tatizo, kwa sababu uhusiano wa kibinafsi kati ya thamani za `Height` tayari umehesabiwa.\n",
+    "Kwa bahati mbaya, hatukupata matokeo yoyote - ni baadhi tu ya thamani za ajabu `nan`. Hii ni kwa sababu baadhi ya thamani katika mfululizo wetu hazijafafanuliwa, zikiwa zimewakilishwa kama `nan`, jambo ambalo husababisha matokeo ya operesheni pia kuwa hayajafafanuliwa. Kwa kuangalia matriki tunaweza kuona kwamba `Weight` ni safu yenye tatizo, kwa sababu uhusiano wa ndani kati ya thamani za `Height` tayari umehesabiwa.\n",
     "\n",
     "> Mfano huu unaonyesha umuhimu wa **kuandaa data** na **kusafisha data**. Bila data sahihi hatuwezi kuhesabu chochote.\n",
     "\n",
-    "Tumia mbinu ya `fillna` kujaza thamani zilizokosekana, na kisha hesabu uhusiano:\n"
+    "Hebu tutumie mbinu ya `fillna` kujaza thamani zilizokosekana, na kuhesabu uhusiano:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Hitimisho\n",
     "\n",
-    "Katika daftari hili tumejifunza jinsi ya kufanya shughuli za msingi kwenye data ili kuhesabu kazi za takwimu. Sasa tunajua jinsi ya kutumia mbinu thabiti za hisabati na takwimu kuthibitisha baadhi ya dhana, na jinsi ya kuhesabu viwango vya kujiamini kwa vigezo vilivyopewa sampuli ya data.\n"
+    "Katika daftari hili tumejifunza jinsi ya kufanya shughuli za msingi kwenye data ili kuhesabu kazi za takwimu. Sasa tunajua jinsi ya kutumia mbinu thabiti za hisabati na takwimu kuthibitisha baadhi ya dhana, na jinsi ya kuhesabu viwango vya kujiamini kwa mabadiliko yoyote tukipewa sampuli ya data.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kibinadamu ya kitaalamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:35:09+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:47:38+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "sw"
   }
diff --git a/translations/sw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/sw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 4e32554b..fa78720b 100644
--- a/translations/sw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/sw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Katika seti hii ya data, safu ni kama ifuatavyo:  \n",
-    "* Umri na jinsia vinaeleweka vyenyewe  \n",
-    "* BMI ni kipimo cha uzito wa mwili kulingana na urefu  \n",
-    "* BP ni shinikizo la damu la wastani  \n",
-    "* S1 hadi S6 ni vipimo tofauti vya damu  \n",
-    "* Y ni kipimo cha ubora wa maendeleo ya ugonjwa kwa kipindi cha mwaka mmoja  \n",
+    "Katika seti hii ya data, safu zifuatazo zinaelezwa kama ifuatavyo:\n",
+    "* Umri na jinsia vinaeleweka wazi\n",
+    "* BMI ni kipimo cha uzito wa mwili kulingana na urefu\n",
+    "* BP ni shinikizo la damu la wastani\n",
+    "* S1 hadi S6 ni vipimo tofauti vya damu\n",
+    "* Y ni kipimo cha ubora wa maendeleo ya ugonjwa kwa kipindi cha mwaka mmoja\n",
     "\n",
-    "Tuchunguze seti hii ya data kwa kutumia mbinu za uwezekano na takwimu.  \n",
+    "Tuchunguze seti hii ya data kwa kutumia mbinu za uwezekano na takwimu.\n",
     "\n",
-    "### Kazi ya 1: Hesabu wastani wa thamani na tofauti kwa thamani zote  \n"
+    "### Kazi ya 1: Hesabu wastani wa thamani na tofauti kwa thamani zote\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:55:44+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:48:11+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "sw"
   }
diff --git a/translations/th/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/th/1-Introduction/04-stats-and-probability/assignment.ipynb
index 75096354..ec0a5e1d 100644
--- a/translations/th/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/th/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -196,7 +196,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### งานที่ 4: ทดสอบความสัมพันธ์ระหว่างตัวแปรต่าง ๆ กับการพัฒนาของโรค (Y)\n",
+    "### งานที่ 4: ทดสอบความสัมพันธ์ระหว่างตัวแปรต่างๆ กับการพัฒนาของโรค (Y)\n",
     "\n",
     "> **คำแนะนำ** เมทริกซ์ความสัมพันธ์จะให้ข้อมูลที่มีประโยชน์ที่สุดเกี่ยวกับค่าที่มีความสัมพันธ์กัน\n"
    ],
@@ -221,7 +221,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
    ]
   }
  ],
@@ -247,8 +247,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:47:52+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:33:19+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "th"
   }
diff --git a/translations/th/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/th/1-Introduction/04-stats-and-probability/notebook.ipynb
index 082b9aa4..98f3ab6f 100644
--- a/translations/th/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/th/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# บทนำสู่ความน่าจะเป็นและสถิติ  \n",
-    "ในสมุดบันทึกนี้ เราจะทดลองกับแนวคิดบางอย่างที่เราได้พูดถึงก่อนหน้านี้ แนวคิดหลายอย่างเกี่ยวกับความน่าจะเป็นและสถิติได้รับการนำเสนออย่างดีในไลบรารีหลักสำหรับการประมวลผลข้อมูลใน Python เช่น `numpy` และ `pandas`  \n"
+    "ในสมุดบันทึกนี้ เราจะมาลองเล่นกับแนวคิดบางอย่างที่เราได้พูดถึงไปก่อนหน้านี้ แนวคิดหลายอย่างจากความน่าจะเป็นและสถิตินั้นมีการนำเสนอไว้อย่างดีในไลบรารีหลักสำหรับการประมวลผลข้อมูลใน Python เช่น `numpy` และ `pandas`\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## ตัวแปรสุ่มและการแจกแจง\n",
-    "มาเริ่มต้นด้วยการสุ่มตัวอย่างจำนวน 30 ค่า จากการแจกแจงแบบสม่ำเสมอในช่วง 0 ถึง 9 นอกจากนี้เรายังจะคำนวณค่าเฉลี่ยและความแปรปรวนด้วย\n"
+    "มาเริ่มต้นด้วยการสุ่มตัวอย่างจำนวน 30 ค่า จากการแจกแจงแบบสม่ำเสมอระหว่าง 0 ถึง 9 เราจะคำนวณค่าเฉลี่ยและความแปรปรวนด้วย\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "เพื่อประเมินด้วยสายตาว่ามีค่าที่แตกต่างกันกี่ค่าในตัวอย่าง เราสามารถสร้าง **ฮิสโตแกรม**:\n"
+    "ในการประมาณจำนวนค่าที่แตกต่างกันในตัวอย่างโดยการมองเห็น เราสามารถสร้าง **ฮิสโตแกรม**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## การวิเคราะห์ข้อมูลจริง\n",
     "\n",
-    "ค่าเฉลี่ยและความแปรปรวนมีความสำคัญมากเมื่อวิเคราะห์ข้อมูลในโลกจริง ลองโหลดข้อมูลเกี่ยวกับนักเบสบอลจาก [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "ค่าเฉลี่ยและความแปรปรวนเป็นสิ่งสำคัญมากเมื่อวิเคราะห์ข้อมูลในโลกความเป็นจริง มาลองโหลดข้อมูลเกี่ยวกับนักเบสบอลจาก [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> เรากำลังใช้แพ็กเกจที่เรียกว่า [**Pandas**](https://pandas.pydata.org/) สำหรับการวิเคราะห์ข้อมูล ที่เราจะพูดถึง Pandas และการทำงานกับข้อมูลใน Python เพิ่มเติมในภายหลังในคอร์สนี้\n",
+    "เราใช้แพ็กเกจที่เรียกว่า [**Pandas**](https://pandas.pydata.org/) สำหรับการวิเคราะห์ข้อมูล ในบทเรียนนี้เราจะพูดถึง Pandas และการทำงานกับข้อมูลใน Python เพิ่มเติมในภายหลัง\n",
     "\n",
-    "มาคำนวณค่าเฉลี่ยของอายุ ความสูง และน้ำหนักกัน:\n"
+    "มาคำนวณค่าเฉลี่ยสำหรับอายุ ความสูง และน้ำหนักกัน:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "นอกเหนือจากค่าเฉลี่ยแล้ว การพิจารณาค่ามัธยฐานและควอไทล์ก็มีความสำคัญ สามารถแสดงผลได้โดยใช้ **แผนภาพกล่อง**:\n"
+    "นอกเหนือจากค่าเฉลี่ยแล้ว การพิจารณาค่ามัธยฐานและควอไทล์ก็มีความสำคัญ สามารถแสดงผลได้โดยใช้ **box plot**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **หมายเหตุ**: แผนภาพนี้แสดงให้เห็นว่า โดยเฉลี่ยแล้ว ความสูงของผู้เล่นเบสคนแรก (first basemen) มักจะสูงกว่าความสูงของผู้เล่นเบสคนที่สอง (second basemen) ในภายหลังเราจะได้เรียนรู้วิธีการทดสอบสมมติฐานนี้อย่างเป็นทางการ และวิธีแสดงให้เห็นว่าข้อมูลของเรามีนัยสำคัญทางสถิติที่สามารถยืนยันข้อสรุปนี้ได้  \n",
+    "> **หมายเหตุ**: แผนภาพนี้แสดงให้เห็นว่า โดยเฉลี่ยแล้ว ความสูงของผู้เล่นเบสคนแรกมักจะสูงกว่าความสูงของผู้เล่นเบสคนที่สอง ต่อไปเราจะเรียนรู้วิธีทดสอบสมมติฐานนี้อย่างเป็นทางการ และวิธีแสดงให้เห็นว่าข้อมูลของเรามีนัยสำคัญทางสถิติที่สามารถยืนยันข้อสรุปนี้ได้  \n",
     "\n",
-    "อายุ ความสูง และน้ำหนัก ล้วนเป็นตัวแปรสุ่มแบบต่อเนื่อง คุณคิดว่าการแจกแจงของพวกมันเป็นอย่างไร? วิธีที่ดีในการค้นหาคือการพล็อตฮิสโตแกรมของค่าเหล่านั้น:\n"
+    "อายุ ความสูง และน้ำหนัก ล้วนเป็นตัวแปรสุ่มแบบต่อเนื่อง คุณคิดว่าการแจกแจงของพวกมันเป็นอย่างไร? วิธีที่ดีในการค้นหาคือการพล็อตฮิสโตแกรมของค่า:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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m5Mtf/nIuu+yyXHXVVRk1alTfnBkAwP+xLgAAtXRQHe/W1ta0tLRk9uzZvbavX78+O3bs6LX9+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dWVxx9/fK/P193dna6url43AAAAGAiq7njfcccd+elPf5p169a9Zl97e3tGjRqVcePG9dre2NiY9vb2yjF7hu7d+3fv25vFixfnS1/6UrWlAjAAWeAKABhsqup4b968OZ/97GfzrW99K6NHjy5V02ssWrQonZ2dldvmzZsP23MDAADAoagqeK9fvz5bt27NO97xjowcOTIjR47MmjVrcsMNN2TkyJFpbGzM9u3bs23btl736+joSFNTU5KkqanpNauc7/599zGvVldXl/r6+l43AAAAGAiqCt7vfe9789hjj+XRRx+t3E455ZTMmzev8vMRRxyR1atXV+6zYcOGbNq0Kc3NzUmS5ubmPPbYY9m6dWvlmFWrVqW+vj7Tp0/vo9MCAACA/qGqOd5jx47N2972tl7bxowZkwkTJlS2n3/++Wlra8v48eNTX1+fiy66KM3NzZk1a1aS5Mwzz8z06dNz3nnn5dprr017e3suv/zytLa2pq6uro9OCwAAAPqHg7qc2Ou57rrrMnz48MydOzfd3d2ZM2dObrrppsr+ESNGZMWKFbnwwgvT3NycMWPGZP78+bn66qv7uhQAAACouUMO3j/60Y96/T569OgsXbo0S5cu3ed9pkyZknvuuedQnxoAAAD6vYO6jjcAAABwYPp8qDkAvJ49r9Pdl8cCAPRXOt4AAABQkOANAAAABQneAAAAUJDgDQAAAAVZXA0A4FUs7AdAX9LxBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChoZK0LAGDgmLpwZeXnjUtaalgJAMDAoeMNAAAABQneAAAAUJCh5gAAB8BUCwAOlo43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBFlcDAOgjey7AtieLsQEMbTreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AHLI957WaywoA0JuONwAAABSk4w0AUCWjPACoho43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFAAAMFVMXrqz8vHFJSw0rAeBw0vEGAACAggRvAPrU1IUre3X1AACGOsEbAAAAChK8AQAAoCCLqwFQhOHmDBX+rwOwPzreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AAIWZBw4wtOl4AwAAQEGCNwAAABQkeAMAAEBB5ngDcFDMWQUAODCCNwBADez55dXGJS01rASA0gw1BwAAgIJ0vAF4XYaUAwAcGh1vAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAglxODGAI2vMSYRuXtNSwEgCAwU/HG4CKqQtXum43AEAfE7wBAACgIMEbAAAACjLHGwCgn7IeA8DgoOMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQ4A0AAAAFWdUcgNfYcyVlAAAOTVUd72XLluWkk05KfX196uvr09zcnHvvvbey/+WXX05ra2smTJiQo48+OnPnzk1HR0evx9i0aVNaWlpy1FFHZeLEibn00kvzyiuv9M3ZAAAMQFMXrqzcABh8qgrexx57bJYsWZL169fnJz/5Sc4444x86EMfyuOPP54kueSSS3L33XfnzjvvzJo1a7Jly5acc845lfvv3LkzLS0t2b59ex588MHcdtttufXWW3PFFVf07VkBAABAP1HVUPMPfvCDvX7/m7/5myxbtiwPPfRQjj322Nx88825/fbbc8YZZyRJbrnllpxwwgl56KGHMmvWrPzgBz/IE088kfvvvz+NjY2ZMWNGvvzlL+eyyy7LVVddlVGjRvXdmQEAAEA/cNCLq+3cuTN33HFHXnrppTQ3N2f9+vXZsWNHZs+eXTnm+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dVV6ZrvTXd3d7q6unrdAAAAYCCoOng/9thjOfroo1NXV5fPfOYz+e53v5vp06envb09o0aNyrhx43od39jYmPb29iRJe3t7r9C9e//uffuyePHiNDQ0VG7HHXdctWUDAABATVQdvP/oj/4ojz76aB5++OFceOGFmT9/fp544okStVUsWrQonZ2dldvmzZuLPh8AAAD0laovJzZq1Kj84R/+YZLk5JNPzrp16/L3f//3+djHPpbt27dn27ZtvbreHR0daWpqSpI0NTXlkUce6fV4u1c9333M3tTV1aWurq7aUgEAAKDmDnqO9267du1Kd3d3Tj755BxxxBFZvXp1Zd+GDRuyadOmNDc3J0mam5vz2GOPZevWrZVjVq1alfr6+kyfPv1QSwEAAIB+p6qO96JFi3LWWWdl8uTJeeGFF3L77bfnRz/6Ub7//e+noaEh559/ftra2jJ+/PjU19fnoosuSnNzc2bNmpUkOfPMMzN9+vScd955ufbaa9Pe3p7LL788ra2tOtoAAAAMSlUF761bt+YTn/hEfvWrX6WhoSEnnXRSvv/97+d973tfkuS6667L8OHDM3fu3HR3d2fOnDm56aabKvcfMWJEVqxYkQsvvDDNzc0ZM2ZM5s+fn6uvvrpvzwoAYJCZunBlkmTjkpYaVwJAtaoK3jfffPPr7h89enSWLl2apUuX7vOYKVOm5J577qnmaQEAAGDAOuQ53gAAAMC+Cd4AAABQkOANAAAABQneAAAAUFBVi6sBMPjsXikZAIAydLwBAACgIB1vgEFsz262a//CwGAUCsDgo+MNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdUABgCLpAEADFw63gAAAFCQ4A0AAAAFGWoOADCAmHoCMPDoeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUNLLWBQDQt6YuXFnrEgAA2IPgDTBECOQAALVhqDkAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAU5DreAAPYntfm3rikpYaVAACwL4I3wCCxZwgHhgZfvgEMDIaaAwAAQEGCNwAAABRkqDnAAGNIOQDAwKLjDQAAAAUJ3gAAAFCQ4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJDgDQAAAAUJ3gAAAFDQyFoXAABA35q6cGXl541LWmpYCQCJjjcAAAAUJXgDAABAQYaaA/RTew4VBdgffzMA+i8dbwAAAChI8AYAAICCBG8AAAAoyBxvAIBBzKXFAGpPxxsAAAAK0vEGABhidMEBDi8dbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAgkbWugAAAGpn6sKVlZ83LmmpYSUAg5eONwAAABQkeAMAAEBBhpoD1IjhnQAAQ4OONwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQVUF78WLF+ed73xnxo4dm4kTJ+bss8/Ohg0beh3z8ssvp7W1NRMmTMjRRx+duXPnpqOjo9cxmzZtSktLS4466qhMnDgxl156aV555ZVDPxsAAADoZ6oK3mvWrElra2seeuihrFq1Kjt27MiZZ56Zl156qXLMJZdckrvvvjt33nln1qxZky1btuScc86p7N+5c2daWlqyffv2PPjgg7ntttty66235oorrui7swIAAIB+YlhPT0/Pwd75ueeey8SJE7NmzZq8+93vTmdnZ97whjfk9ttvz5//+Z8nSZ588smccMIJWbt2bWbNmpV77703f/Znf5YtW7aksbExSbJ8+fJcdtllee655zJq1Kj9Pm9XV1caGhrS2dmZ+vr6gy0foKb2dzmxPfcD9IXdf2sO5O+LyxwCvL5qcukhzfHu7OxMkowfPz5Jsn79+uzYsSOzZ8+uHHP88cdn8uTJWbt2bZJk7dq1OfHEEyuhO0nmzJmTrq6uPP7443t9nu7u7nR1dfW6AQAAwEBw0MF7165dufjii3PaaaflbW97W5Kkvb09o0aNyrhx43od29jYmPb29soxe4bu3ft379ubxYsXp6GhoXI77rjjDrZsAAAAOKwOOni3trbmZz/7We64446+rGevFi1alM7Ozspt8+bNxZ8TAAAA+sLIg7nTggULsmLFijzwwAM59thjK9ubmpqyffv2bNu2rVfXu6OjI01NTZVjHnnkkV6Pt3vV893HvFpdXV3q6uoOplQAAACoqao63j09PVmwYEG++93v5oc//GGmTZvWa//JJ5+cI444IqtXr65s27BhQzZt2pTm5uYkSXNzcx577LFs3bq1csyqVatSX1+f6dOnH8q5AADwOqYuXGnhRoAaqKrj3dramttvvz133XVXxo4dW5mT3dDQkCOPPDINDQ05//zz09bWlvHjx6e+vj4XXXRRmpubM2vWrCTJmWeemenTp+e8887Ltddem/b29lx++eVpbW3V1QYAAGDQqSp4L1u2LEly+umn99p+yy235JOf/GSS5Lrrrsvw4cMzd+7cdHd3Z86cObnpppsqx44YMSIrVqzIhRdemObm5owZMybz58/P1VdffWhnAjAI6EQBAAw+VQXvA7nk9+jRo7N06dIsXbp0n8dMmTIl99xzTzVPDQAAAAPSQS2uBsCB27OLvXFJSw0rAQCgFgRvgMPIUHIAgKHnoK/jDQAAAOyf4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJBVzQH6AaudAwAMXoI3AACvsecXghuXtNSwEoCBz1BzAAAAKEjHGwCA16X7DXBodLwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoKCRtS4AYLCYunBl5eeNS1pqWAkAAP2JjjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAB2zqwpW9FpMEYP8EbwAAAChI8AYAAICCBG8AAAAoaGStCwAYjMx/BABgNx1vAAAAKEjwBgAAgIIEbwAAACjIHG+AQ2Q+NzAU7fm3b+OSlhpWAtD/Cd4AABwSIRzg9RlqDgAAAAUJ3gAAAFCQ4A0AAAAFmeMNcIDMYQQA4GDoeAMAAEBBOt4AB8ElxAD2z0ghgN/S8QYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAACjI5cQAAOgzfXG5RZchAwYbwRvgdbheNwAAh8pQcwAAAChI8AYAAICCDDUHAKA487aBoUzwBngV87oBAOhLgjcAAAOWTjowEJjjDQAAAAUJ3gAAAFCQoeYAANSc9TWAwUzHGwAAAArS8QaITgsAAOXoeAMAAEBBgjcAAAAUJHgDAABAQYI3AACH1dSFK62tAQwpgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFANSSVXUBAChNxxsAAAAKErwBAACgIMEbAAAACjLHGwCAmrDOBjBUCN4AAPRbe4bzjUta9rodoL8TvIEhx4c1AAAOJ3O8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgoKqD9wMPPJAPfvCDmTRpUoYNG5bvfe97vfb39PTkiiuuyDHHHJMjjzwys2fPzlNPPdXrmOeffz7z5s1LfX19xo0bl/PPPz8vvvjiIZ0IAAAA9EdVB++XXnopb3/727N06dK97r/22mtzww03ZPny5Xn44YczZsyYzJkzJy+//HLlmHnz5uXxxx/PqlWrsmLFijzwwAP59Kc/ffBnAbAfUxeurNwAAOBwqvo63meddVbOOuusve7r6enJ9ddfn8svvzwf+tCHkiT/9E//lMbGxnzve9/Lueeem5///Oe57777sm7dupxyyilJkhtvvDEf+MAH8rWvfS2TJk16zeN2d3enu7u78ntXV1e1ZQMAAEBN9Okc72eeeSbt7e2ZPXt2ZVtDQ0NmzpyZtWvXJknWrl2bcePGVUJ3ksyePTvDhw/Pww8/vNfHXbx4cRoaGiq34447ri/LBgAAgGL6NHi3t7cnSRobG3ttb2xsrOxrb2/PxIkTe+0fOXJkxo8fXznm1RYtWpTOzs7KbfPmzX1ZNjDAGUYOAEB/VvVQ81qoq6tLXV1drcsAAACAqvVp8G5qakqSdHR05Jhjjqls7+joyIwZMyrHbN26tdf9XnnllTz//POV+wP0BR1wgMHF33VgoOrToebTpk1LU1NTVq9eXdnW1dWVhx9+OM3NzUmS5ubmbNu2LevXr68c88Mf/jC7du3KzJkz+7IcAAAAqLmqO94vvvhinn766crvzzzzTB599NGMHz8+kydPzsUXX5xrrrkmb37zmzNt2rR88YtfzKRJk3L22WcnSU444YS8//3vzwUXXJDly5dnx44dWbBgQc4999y9rmgOAAAAA1nVwfsnP/lJ3vOe91R+b2trS5LMnz8/t956az7/+c/npZdeyqc//els27Yt73rXu3Lfffdl9OjRlft861vfyoIFC/Le9743w4cPz9y5c3PDDTf0wekAg9GeQws3LmmpYSUAAFC9YT09PT21LqJaXV1daWhoSGdnZ+rr62tdDlDY/oK3OX8AJL6cBQ6vanLpgFjVHAAAqmG0FNCf9OniagAAAEBvgjcAAEPG1IUrTVECDjvBGwAAAAoyxxsAgEFNhxuoNR1vAAAAKEjwBgAAgIIMNQf6DZd+AQBgMNLxBgAAgIIEbwAAACjIUHNgQDEcHQCAgUbHGwAAAAoSvAEAAKAgQ82BfmnPIeUAADCQ6XgDAABAQYI3AAAAFGSoOQAAg4JpSkB/peMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdWAw2bPRW82Lmnp08cDgJL6+j0MGFoEbwAAhhxBGjicBG8AAPg/AjlQgjneAAAAUJCONwAA7IW1RIC+IngDADCkCdhAaYaaAwAAQEGCN1ATUxeu1GEAAGBIMNQcKEq4BgBgqBO8gZoSzAEAGOwMNQcAgCqYLgVUS/AGAACAggRvAAAAKEjwBgAAgIIEbwAAACjIquZAn7PgDABDzZ7vfRuXtNSwEqA/0vEGAACAggRvAAAAKMhQc+CgGVYHAAdn93uo908YGnS8AQAAoCAdbwAA6ENGhAGvJngDfcJK5gAAsHeCN1A1IRsAAA6c4A3sM0jvOTxO2AYAgIMjeAP7JGwDAMChE7wBAOAg+IIaOFCCNwAAHAZ7C+pWQIehwXW8AQAAoCDBGwAABqCpC1ca7g4DhKHmAABQiGAMJII3AAD0a+aBw8BnqDkAAAAUJHgDAABAQYaaAwDAAGHOOAxMgjcAAPQzAjYMLoI3DAH7WpTFmzoAAJQneAMAQD/gC3EYvARvAAAYwFxuDPo/wRsGqL19K+7NFgAA+h/BGwYR33gDAED/4zreAAAAUJCONwxSFmgBAID+QfAGAIAhxNQ0OPwEbxhAdLEBgAMlYEP/IXgDAMAgUfJLekEeDp7gDTW0rzdHb2YAADB4WNUcqjR14UpDvgEAgAOm4w19rL8Pw/KlAQCw2+7PBf3xMwsMJjreAAAAUJCONwAADHIHO+KtL0bK9ffRgHA4CN5wAPrizaqaNxrDwQGAw6nazyx7+6wiVMO+Cd5QA4I1ANBfHe6GAwwFgjdDUl+8MXhzAQCojs9PDFWCNxwmutwAAL8jhDOUCN4MefsKxN4AAAD6ByGdgU7whn2opkOtmw0A8Dt9vRo6DHSCNwPagXz76Y82AMDAcCCf23S/GYgEbwYlYRsAYOAYKJ/dhH4OVs2C99KlS/PVr3417e3tefvb354bb7wxp556aq3K4RBU03Uu+QdqoPzBBgCgnIO9JrkgTUk1Cd7f/va309bWluXLl2fmzJm5/vrrM2fOnGzYsCETJ06sRUlF1TJ07vmch1pHX1+Ca1/2VjMAALza/j6fVvP5tdoFd2t5eVqd94GnJsH77/7u73LBBRfkU5/6VJJk+fLlWblyZf7xH/8xCxcufM3x3d3d6e7urvze2dmZJOnq6jo8BR+iXd3/L0nvet925ff3euzPvjTnkJ7j1fZ8zv3Vsb/n3vM59va4r/fY1Zh8yZ0HdT8AAIau/X2GPNjPqQfy2bSaXLKv5979PPv6TL6v++3tuav5jL8vffEYA+E5D8Xuf/uenp79Hjus50CO6kPbt2/PUUcdle985zs5++yzK9vnz5+fbdu25a677nrNfa666qp86UtfOoxVAgAAwP5t3rw5xx577Osec9g73r/+9a+zc+fONDY29tre2NiYJ598cq/3WbRoUdra2iq/79q1K88//3wmTJiQYcOGFa33UHV1deW4447L5s2bU19fX+tyoN/zmoHqed1A9bxuoHpeN7319PTkhRdeyKRJk/Z77IBY1byuri51dXW9to0bN642xRyk+vp6/zmhCl4zUD2vG6ie1w1Uz+vmdxoaGg7ouOGF63iN3//938+IESPS0dHRa3tHR0eampoOdzkAAABQ1GEP3qNGjcrJJ5+c1atXV7bt2rUrq1evTnNz8+EuBwAAAIqqyVDztra2zJ8/P6ecckpOPfXUXH/99XnppZcqq5wPJnV1dbnyyitfM1Qe2DuvGaie1w1Uz+sGqud1c/AO+6rmu33961/PV7/61bS3t2fGjBm54YYbMnPmzFqUAgAAAMXULHgDAADAUHDY53gDAADAUCJ4AwAAQEGCNwAAABQkeAMAAEBBgncNdHd3Z8aMGRk2bFgeffTRWpcD/dbGjRtz/vnnZ9q0aTnyyCPzpje9KVdeeWW2b99e69KgX1m6dGmmTp2a0aNHZ+bMmXnkkUdqXRL0W4sXL8473/nOjB07NhMnTszZZ5+dDRs21LosGDCWLFmSYcOG5eKLL651KQOK4F0Dn//85zNp0qRalwH93pNPPpldu3blG9/4Rh5//PFcd911Wb58eb7whS/UujToN7797W+nra0tV155ZX7605/m7W9/e+bMmZOtW7fWujTol9asWZPW1tY89NBDWbVqVXbs2JEzzzwzL730Uq1Lg35v3bp1+cY3vpGTTjqp1qUMOC4ndpjde++9aWtry7/927/lrW99a/7zP/8zM2bMqHVZMGB89atfzbJly/KLX/yi1qVAvzBz5sy8853vzNe//vUkya5du3LcccfloosuysKFC2tcHfR/zz33XCZOnJg1a9bk3e9+d63LgX7rxRdfzDve8Y7cdNNNueaaazJjxoxcf/31tS5rwNDxPow6OjpywQUX5J//+Z9z1FFH1bocGJA6Ozszfvz4WpcB/cL27duzfv36zJ49u7Jt+PDhmT17dtauXVvDymDg6OzsTBLvLbAfra2taWlp6fWew4EbWesChoqenp588pOfzGc+85mccsop2bhxY61LggHn6aefzo033pivfe1rtS4F+oVf//rX2blzZxobG3ttb2xszJNPPlmjqmDg2LVrVy6++OKcdtppedvb3lbrcqDfuuOOO/LTn/4069atq3UpA5aO9yFauHBhhg0b9rq3J598MjfeeGNeeOGFLFq0qNYlQ80d6OtmT88++2ze//735yMf+UguuOCCGlUOwGDS2tqan/3sZ7njjjtqXQr0W5s3b85nP/vZfOtb38ro0aNrXc6AZY73IXruuefym9/85nWPeeMb35iPfvSjufvuuzNs2LDK9p07d2bEiBGZN29ebrvtttKlQr9xoK+bUaNGJUm2bNmS008/PbNmzcqtt96a4cN9ZwjJb4eaH3XUUfnOd76Ts88+u7J9/vz52bZtW+66667aFQf93IIFC3LXXXflgQceyLRp02pdDvRb3/ve9/LhD384I0aMqGzbuXNnhg0bluHDh6e7u7vXPvZO8D5MNm3alK6ursrvW7ZsyZw5c/Kd73wnM2fOzLHHHlvD6qD/evbZZ/Oe97wnJ598cv7lX/7FH3Z4lZkzZ+bUU0/NjTfemOS3Q2cnT56cBQsWWFwN9qKnpycXXXRRvvvd7+ZHP/pR3vzmN9e6JOjXXnjhhfzP//xPr22f+tSncvzxx+eyyy4zTeMAmeN9mEyePLnX70cffXSS5E1vepPQDfvw7LPP5vTTT8+UKVPyta99Lc8991xlX1NTUw0rg/6jra0t8+fPzymnnJJTTz01119/fV566aV86lOfqnVp0C+1trbm9ttvz1133ZWxY8emvb09SdLQ0JAjjzyyxtVB/zN27NjXhOsxY8ZkwoQJQncVBG+g31q1alWefvrpPP3006/5gspgHfitj33sY3nuuedyxRVXpL29PTNmzMh99933mgXXgN9atmxZkuT000/vtf2WW27JJz/5ycNfEDAkGGoOAAAABVmhCAAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAACvr/ciHiWioJ+MUAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## ช่วงความเชื่อมั่น\n",
     "\n",
-    "ตอนนี้เรามาคำนวณช่วงความเชื่อมั่นสำหรับน้ำหนักและส่วนสูงของนักเบสบอลกัน เราจะใช้โค้ด [จากการอภิปรายใน Stack Overflow นี้](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "ตอนนี้เรามาคำนวณช่วงความเชื่อมั่นสำหรับน้ำหนักและส่วนสูงของนักเบสบอลกัน เราจะใช้โค้ด [จากการอภิปรายใน stackoverflow นี้](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "เราสามารถเห็นได้ว่า ช่วงเวลาเหล่านี้ไม่ทับซ้อนกัน\n",
+    "เราสามารถเห็นได้ว่าช่วงเวลาเหล่านี้ไม่มีการทับซ้อนกัน\n",
     "\n",
-    "วิธีที่ถูกต้องทางสถิติมากกว่าในการพิสูจน์สมมติฐานคือการใช้ **Student t-test**:\n"
+    "วิธีที่ถูกต้องทางสถิติมากขึ้นในการพิสูจน์สมมติฐานคือการใช้ **Student t-test**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,8 +620,8 @@
    "metadata": {},
    "source": [
     "ค่าทั้งสองที่ได้จากฟังก์ชัน `ttest_ind` คือ:\n",
-    "* p-value สามารถพิจารณาได้ว่าเป็นความน่าจะเป็นที่การแจกแจงสองชุดมีค่าเฉลี่ยเท่ากัน ในกรณีนี้ p-value ต่ำมาก ซึ่งหมายความว่ามีหลักฐานที่ชัดเจนสนับสนุนว่าผู้เล่นตำแหน่งเบสแรกมีความสูงมากกว่า\n",
-    "* t-value เป็นค่ากลางของความแตกต่างค่าเฉลี่ยที่ถูกปรับให้เป็นมาตรฐาน ซึ่งใช้ใน t-test และจะถูกเปรียบเทียบกับค่าขีดจำกัดสำหรับค่าความเชื่อมั่นที่กำหนด\n"
+    "* p-value สามารถพิจารณาได้ว่าเป็นความน่าจะเป็นที่การแจกแจงสองชุดมีค่าเฉลี่ยเท่ากัน ในกรณีของเรา ค่า p-value ต่ำมาก ซึ่งหมายความว่ามีหลักฐานที่ชัดเจนสนับสนุนว่าผู้เล่นเบสคนแรกมีความสูงมากกว่า\n",
+    "* t-value คือค่ากลางของความแตกต่างของค่าเฉลี่ยที่ถูกปรับให้เป็นมาตรฐาน ซึ่งใช้ใน t-test และจะถูกเปรียบเทียบกับค่าขีดจำกัดสำหรับค่าความเชื่อมั่นที่กำหนด\n"
    ]
   },
   {
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ความสัมพันธ์และบริษัทเบสบอลชั่วร้าย\n",
+    "## ความสัมพันธ์และบริษัทเบสบอลจอมวายร้าย\n",
     "\n",
-    "ความสัมพันธ์ช่วยให้เราค้นหาความเชื่อมโยงระหว่างลำดับข้อมูล ในตัวอย่างสมมติของเรา ลองจินตนาการว่ามีบริษัทเบสบอลชั่วร้ายที่จ่ายเงินให้ผู้เล่นตามความสูงของพวกเขา - ยิ่งผู้เล่นสูงเท่าไหร่ ก็ยิ่งได้รับเงินมากขึ้นเท่านั้น สมมติว่ามีเงินเดือนพื้นฐานอยู่ที่ $1000 และโบนัสเพิ่มเติมตั้งแต่ $0 ถึง $100 ขึ้นอยู่กับความสูง เราจะใช้ข้อมูลผู้เล่นจริงจาก MLB และคำนวณเงินเดือนในจินตนาการของพวกเขา:\n"
+    "ความสัมพันธ์ช่วยให้เราค้นหาความเชื่อมโยงระหว่างลำดับข้อมูลต่าง ๆ ได้ ในตัวอย่างสมมติของเรา ลองจินตนาการว่ามีบริษัทเบสบอลจอมวายร้ายที่จ่ายเงินให้ผู้เล่นตามความสูงของพวกเขา - ยิ่งผู้เล่นสูงมากเท่าไร ก็ยิ่งได้เงินมากขึ้นเท่านั้น สมมติว่ามีเงินเดือนพื้นฐานอยู่ที่ $1000 และมีโบนัสเพิ่มเติมตั้งแต่ $0 ถึง $100 ขึ้นอยู่กับความสูง เราจะใช้ข้อมูลผู้เล่นจริงจาก MLB และคำนวณเงินเดือนในจินตนาการของพวกเขา:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "เรามาคำนวณความแปรปรวนร่วมและความสัมพันธ์ของลำดับเหล่านั้นกัน `np.cov` จะให้สิ่งที่เรียกว่า **เมทริกซ์ความแปรปรวนร่วม** ซึ่งเป็นการขยายความแปรปรวนร่วมไปยังตัวแปรหลายตัว องค์ประกอบ $M_{ij}$ ของเมทริกซ์ความแปรปรวนร่วม $M$ คือความสัมพันธ์ระหว่างตัวแปรอินพุต $X_i$ และ $X_j$ และค่าบนเส้นทแยงมุม $M_{ii}$ คือความแปรปรวนของ $X_{i}$ ในทำนองเดียวกัน `np.corrcoef` จะให้ **เมทริกซ์ความสัมพันธ์** แก่เรา\n"
+    "ตอนนี้เรามาคำนวณความสัมพันธ์ร่วมและความสัมพันธ์ของลำดับเหล่านั้นกัน `np.cov` จะให้สิ่งที่เรียกว่า **เมทริกซ์ความสัมพันธ์ร่วม** ซึ่งเป็นการขยายความสัมพันธ์ร่วมไปยังตัวแปรหลายตัว องค์ประกอบ $M_{ij}$ ของเมทริกซ์ความสัมพันธ์ร่วม $M$ คือความสัมพันธ์ระหว่างตัวแปรอินพุต $X_i$ และ $X_j$ และค่าบนเส้นทแยงมุม $M_{ii}$ คือความแปรปรวนของ $X_{i}$ ในทำนองเดียวกัน `np.corrcoef` จะให้ **เมทริกซ์ความสัมพันธ์** แก่เรา\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ในกรณีนี้ ความสัมพันธ์มีขนาดเล็กลงเล็กน้อย แต่ยังคงสูงอยู่มาก ตอนนี้ เพื่อทำให้ความสัมพันธ์ดูไม่ชัดเจนยิ่งขึ้น เราอาจต้องการเพิ่มความสุ่มเพิ่มเติมโดยการเพิ่มตัวแปรสุ่มบางตัวเข้าไปในเงินเดือน ลองมาดูกันว่าจะเกิดอะไรขึ้น:\n"
+    "ในกรณีนี้ ความสัมพันธ์มีขนาดเล็กลงเล็กน้อย แต่ก็ยังค่อนข้างสูงอยู่ ตอนนี้ เพื่อทำให้ความสัมพันธ์ดูไม่ชัดเจนยิ่งขึ้น เราอาจต้องการเพิ่มความสุ่มเพิ่มเติมโดยการเพิ่มตัวแปรสุ่มบางตัวเข้าไปในเงินเดือน มาดูกันว่าจะเกิดอะไรขึ้น:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "คุณสามารถเดาได้ไหมว่าทำไมจุดถึงเรียงตัวเป็นเส้นแนวตั้งแบบนี้?\n",
+    "คุณพอจะเดาได้ไหมว่าทำไมจุดถึงเรียงตัวเป็นเส้นแนวตั้งแบบนี้?\n",
     "\n",
-    "เราได้สังเกตความสัมพันธ์ระหว่างแนวคิดที่ถูกสร้างขึ้นอย่างเงินเดือนกับตัวแปรที่สังเกตได้อย่าง *ความสูง* ลองมาดูกันว่าตัวแปรที่สังเกตได้สองตัว เช่น ความสูงและน้ำหนัก มีความสัมพันธ์กันหรือไม่:\n"
+    "เราได้สังเกตความสัมพันธ์ระหว่างแนวคิดที่ถูกสร้างขึ้นอย่างเงินเดือน กับตัวแปรที่สังเกตได้อย่าง *ส่วนสูง* แล้ว ลองมาดูกันว่าตัวแปรที่สังเกตได้สองตัว เช่น ส่วนสูงและน้ำหนัก มีความสัมพันธ์กันด้วยหรือไม่:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "น่าเสียดายที่เราไม่ได้ผลลัพธ์ใด ๆ เลย - มีเพียงค่าประหลาด ๆ อย่าง `nan` เท่านั้น นี่เป็นเพราะว่าค่าบางค่าในซีรีส์ของเรายังไม่ได้กำหนดค่าไว้ ซึ่งแสดงเป็น `nan` และทำให้ผลลัพธ์ของการดำเนินการกลายเป็นค่าที่ไม่ได้กำหนดไปด้วย เมื่อดูที่เมทริกซ์ เราจะเห็นว่า `Weight` เป็นคอลัมน์ที่มีปัญหา เพราะการคำนวณ self-correlation ระหว่างค่าของ `Height` ได้ถูกดำเนินการไปแล้ว\n",
+    "น่าเสียดายที่เราไม่ได้ผลลัพธ์ใดๆ นอกจากค่าที่แปลกๆ อย่าง `nan` เท่านั้น สาเหตุเกิดจากบางค่าที่อยู่ในซีรีส์ของเรานั้นไม่มีการกำหนดค่า ซึ่งแสดงเป็น `nan` ทำให้ผลลัพธ์ของการดำเนินการกลายเป็นค่าที่ไม่มีการกำหนดเช่นกัน เมื่อเราดูที่เมทริกซ์ เราจะเห็นว่า `Weight` เป็นคอลัมน์ที่มีปัญหา เนื่องจากการคำนวณการสัมพันธ์ตัวเองระหว่างค่าของ `Height` ได้ถูกดำเนินการไปแล้ว\n",
     "\n",
     "> ตัวอย่างนี้แสดงให้เห็นถึงความสำคัญของ **การเตรียมข้อมูล** และ **การทำความสะอาดข้อมูล** หากไม่มีข้อมูลที่เหมาะสม เราจะไม่สามารถคำนวณอะไรได้เลย\n",
     "\n",
-    "ลองใช้เมธอด `fillna` เพื่อเติมค่าที่ขาดหายไป และคำนวณค่า correlation กัน:\n"
+    "ลองใช้เมธอด `fillna` เพื่อเติมค่าที่หายไป และคำนวณการสัมพันธ์:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1081,16 +914,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## บทสรุป\n",
+    "## สรุป\n",
     "\n",
-    "ในสมุดบันทึกนี้ เราได้เรียนรู้วิธีการดำเนินการพื้นฐานกับข้อมูลเพื่อคำนวณฟังก์ชันทางสถิติ เราได้เข้าใจวิธีการใช้เครื่องมือทางคณิตศาสตร์และสถิติอย่างถูกต้องเพื่อพิสูจน์สมมติฐานบางอย่าง และวิธีการคำนวณช่วงความเชื่อมั่นสำหรับตัวแปรใดๆ โดยอ้างอิงจากตัวอย่างข้อมูล\n"
+    "ในสมุดบันทึกนี้ เราได้เรียนรู้วิธีการดำเนินการพื้นฐานกับข้อมูลเพื่อคำนวณฟังก์ชันทางสถิติ เราเข้าใจแล้วว่าควรใช้อุปกรณ์ทางคณิตศาสตร์และสถิติอย่างไรเพื่อพิสูจน์สมมติฐานบางอย่าง และวิธีการคำนวณช่วงความเชื่อมั่นสำหรับตัวแปรใดๆ โดยอ้างอิงจากตัวอย่างข้อมูล\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:36:15+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:33:08+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "th"
   }
diff --git a/translations/th/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/th/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 591b8147..dfcac717 100644
--- a/translations/th/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/th/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -151,11 +151,11 @@
    "cell_type": "markdown",
    "source": [
     "ในชุดข้อมูลนี้ คอลัมน์มีดังนี้:\n",
-    "* อายุและเพศมีความหมายชัดเจนในตัวเอง\n",
+    "* Age และ sex อธิบายตัวเองได้ชัดเจน\n",
     "* BMI คือดัชนีมวลกาย\n",
     "* BP คือความดันโลหิตเฉลี่ย\n",
-    "* S1 ถึง S6 คือค่าการวัดเลือดที่แตกต่างกัน\n",
-    "* Y คือค่าคุณภาพที่แสดงถึงการพัฒนาของโรคในช่วงหนึ่งปี\n",
+    "* S1 ถึง S6 เป็นการวัดค่าต่าง ๆ ของเลือด\n",
+    "* Y คือการวัดเชิงคุณภาพของการพัฒนาของโรคในช่วงหนึ่งปี\n",
     "\n",
     "มาศึกษาชุดข้อมูลนี้โดยใช้วิธีการของความน่าจะเป็นและสถิติ\n",
     "\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -483,8 +483,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -533,8 +533,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -588,9 +588,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "บทสรุป:  \n",
-    "* อายุ - ปกติ  \n",
-    "* เพศ - สม่ำเสมอ  \n",
+    "บทสรุป:\n",
+    "* อายุ - ปกติ\n",
+    "* เพศ - สม่ำเสมอ\n",
     "* BMI, Y - ยากที่จะบอก\n"
    ],
    "metadata": {}
@@ -598,7 +598,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### งานที่ 4: ทดสอบความสัมพันธ์ระหว่างตัวแปรต่างๆ กับการพัฒนาของโรค (Y)\n",
+    "### งานที่ 4: ทดสอบความสัมพันธ์ระหว่างตัวแปรต่าง ๆ กับการพัฒนาของโรค (Y)\n",
     "\n",
     "> **คำแนะนำ** เมทริกซ์ความสัมพันธ์จะให้ข้อมูลที่มีประโยชน์ที่สุดเกี่ยวกับค่าที่มีความสัมพันธ์กัน\n"
    ],
@@ -851,10 +851,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -881,9 +881,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -912,7 +912,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์มืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
    ]
   }
  ],
@@ -938,8 +938,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:56:04+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:33:33+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "th"
   }
diff --git a/translations/tl/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/tl/1-Introduction/04-stats-and-probability/assignment.ipynb
index 2a4e7148..012b7e92 100644
--- a/translations/tl/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/tl/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Sa dataset na ito, ang mga column ay ang mga sumusunod:  \n",
-    "* Ang Edad at Kasarian ay madaling maunawaan  \n",
-    "* Ang BMI ay ang body mass index  \n",
-    "* Ang BP ay ang karaniwang blood pressure  \n",
-    "* Ang S1 hanggang S6 ay iba't ibang sukat ng dugo  \n",
-    "* Ang Y ay ang kwalitatibong sukat ng pag-usad ng sakit sa loob ng isang taon  \n",
+    "Sa dataset na ito, ang mga column ay ang mga sumusunod:\n",
+    "* Ang Edad at Kasarian ay madaling maintindihan\n",
+    "* Ang BMI ay ang body mass index\n",
+    "* Ang BP ay ang average na presyon ng dugo\n",
+    "* Ang S1 hanggang S6 ay iba't ibang sukat ng dugo\n",
+    "* Ang Y ay ang kwalitatibong sukat ng pag-usad ng sakit sa loob ng isang taon\n",
     "\n",
     "Pag-aralan natin ang dataset na ito gamit ang mga pamamaraan ng probabilidad at estadistika.\n",
     "\n",
-    "### Gawain 1: Kalkulahin ang mga mean value at variance para sa lahat ng halaga\n"
+    "### Gawain 1: Kalkulahin ang mga mean na halaga at variance para sa lahat ng mga halaga\n"
    ],
    "metadata": {}
   },
@@ -198,9 +198,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### Gawain 4: Subukan ang ugnayan sa pagitan ng iba't ibang mga variable at pag-usad ng sakit (Y)\n",
+    "### Gawain 4: Subukin ang ugnayan sa pagitan ng iba't ibang variable at pag-usad ng sakit (Y)\n",
     "\n",
-    "> **Pahiwatig** Ang correlation matrix ang magbibigay sa iyo ng pinakakapaki-pakinabang na impormasyon kung aling mga halaga ang magkakaugnay.\n"
+    "> **Pahiwatig** Ang correlation matrix ang magbibigay sa iyo ng pinakakapaki-pakinabang na impormasyon kung aling mga halaga ang may kaugnayan.\n"
    ],
    "metadata": {}
   },
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Paunawa**:  \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa kanyang katutubong wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na dulot ng paggamit ng pagsasaling ito.\n"
+    "\n---\n\n**Paunawa**:  \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa kanyang orihinal na wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:48:09+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:46:32+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "tl"
   }
diff --git a/translations/tl/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/tl/1-Introduction/04-stats-and-probability/notebook.ipynb
index 2f4ad1df..068608c8 100644
--- a/translations/tl/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/tl/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Panimula sa Probabilidad at Estadistika\n",
-    "Sa notebook na ito, pag-aaralan natin ang ilang mga konsepto na tinalakay natin dati. Maraming mga konsepto mula sa probabilidad at estadistika ang mahusay na naipapakita sa mga pangunahing library para sa pagproseso ng datos sa Python, tulad ng `numpy` at `pandas`.\n"
+    "Sa notebook na ito, mag-eeksperimento tayo sa ilang mga konsepto na napag-usapan na natin dati. Maraming konsepto mula sa probabilidad at estadistika ang mahusay na naipapakita sa mga pangunahing library para sa pagproseso ng datos sa Python, tulad ng `numpy` at `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Mga Random na Baryable at Pamamahagi  \n",
-    "Magsimula tayo sa pagkuha ng sample na may 30 halaga mula sa isang uniform distribution mula 0 hanggang 9. Kukuwentahin din natin ang mean at variance.  \n"
+    "## Mga Random na Variable at Distribusyon  \n",
+    "Magsimula tayo sa pagkuha ng sample na may 30 halaga mula sa isang uniform na distribusyon mula 0 hanggang 9. Kalkulahin din natin ang mean at variance.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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Xl7d4TDgcVigUarYBAICu6aa2HtjY2KglS5Zo2rRpGjNmTIvrAoGAEhISmu1LSEhQIBBo8Ri/36/ly5e3dTQgqvH/2AFbnfG/QeurOW2+MpKbm6uqqioVFxe35zySpIKCAgWDwaatpqam3c8BAAA6hjZdGVm8eLF27dql/fv3a8CAAa2uTUxMVF1dXbN9dXV1SkxMbPEYt9stt9vdltEAAEAn4+jKSCQS0eLFi7V9+3bt3btXQ4YMueoxPp9PpaWlzfaVlJTI5/M5mxQAAHRJjq6M5ObmqqioSDt37lRsbGzTfR8ej0e9evWSJOXk5Kh///7y+/2SpLy8PKWnp2vlypXKyspScXGxKioqVFhY2M4vBQAAdEaOroysX79ewWBQd911l5KSkpq2rVu3Nq2prq5WbW1t0+O0tDQVFRWpsLBQycnJeu2117Rjx45Wb3oFAADRw9GVkWv5lSRlZWWX7Zs1a5ZmzZrl5FQAACBK8Nk0AADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMAUMQIAAEwRIwAAwBQxAgAATBEjAADAFDECAABMESMAAMCU4xjZv3+/ZsyYoX79+snlcmnHjh2tri8rK5PL5bpsCwQCbZ0ZAAB0IY5jpL6+XsnJyVq3bp2j406dOqXa2tqmLT4+3umpAQBAF3ST0wOmT5+u6dOnOz5RfHy8brvtNsfHAQCAru1bu2ckJSVFSUlJuvfee/XOO++0ujYcDisUCjXbAABA13TDYyQpKUkbNmzQ66+/rtdff11er1d33XWXjh071uIxfr9fHo+nafN6vTd6TAAAYMQViUQibT7Y5dL27duVnZ3t6Lj09HQNHDhQf/rTn6749XA4rHA43PQ4FArJ6/UqGAwqLi6ureNe0eClb7br8wEA0Nl8vCLrhjxvKBSSx+O56vdvx/eMtIfJkyfrwIEDLX7d7XbL7XZ/ixMBAAArJr9npLKyUklJSRanBgAAHYzjKyMXL17U6dOnmx5/9NFHqqysVJ8+fTRw4EAVFBTo008/1R//+EdJ0urVqzVkyBCNHj1aX375pV588UXt3btXf/3rX9vvVQAAgE7LcYxUVFTo7rvvbnqcn58vSZo3b542b96s2tpaVVdXN3390qVL+uUvf6lPP/1UN998s8aNG6e//e1vzZ4DAABEr+u6gfXbcq03wLQFN7ACAKKd9Q2sfDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDlOEb279+vGTNmqF+/fnK5XNqxY8dVjykrK9OECRPkdrs1dOhQbd68uQ2jAgCArshxjNTX1ys5OVnr1q27pvUfffSRsrKydPfdd6uyslJLlizRwoULtWfPHsfDAgCArucmpwdMnz5d06dPv+b1GzZs0JAhQ7Ry5UpJ0siRI3XgwAGtWrVKmZmZTk8PAAC6mBt+z0h5ebkyMjKa7cvMzFR5eXmLx4TDYYVCoWYbAADomm54jAQCASUkJDTbl5CQoFAopP/85z9XPMbv98vj8TRtXq/3Ro8JAACMdMifpikoKFAwGGzaampqrEcCAAA3iON7RpxKTExUXV1ds311dXWKi4tTr169rniM2+2W2+2+0aMBAIAO4IZfGfH5fCotLW22r6SkRD6f70afGgAAdAKOY+TixYuqrKxUZWWlpK9/dLeyslLV1dWSvv4nlpycnKb1DzzwgD788EP96le/0vvvv6/nn39er776qh5++OH2eQUAAKBTcxwjFRUVGj9+vMaPHy9Jys/P1/jx4/X4449Lkmpra5vCRJKGDBmiN998UyUlJUpOTtbKlSv14osv8mO9AABAkuSKRCIR6yGuJhQKyePxKBgMKi4url2fe/DSN9v1+QAA6Gw+XpF1Q573Wr9/d8ifpgEAANGDGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYKpNMbJu3ToNHjxYMTExmjJlig4fPtzi2s2bN8vlcjXbYmJi2jwwAADoWhzHyNatW5Wfn69ly5bp2LFjSk5OVmZmps6ePdviMXFxcaqtrW3azpw5c11DAwCArsNxjDz77LNatGiR5s+fr1GjRmnDhg26+eabtWnTphaPcblcSkxMbNoSEhKua2gAANB1OIqRS5cu6ejRo8rIyPjmCbp1U0ZGhsrLy1s87uLFixo0aJC8Xq9mzpypkydPtnqecDisUCjUbAMAAF2Toxj5/PPP1dDQcNmVjYSEBAUCgSseM3z4cG3atEk7d+7Uli1b1NjYqLS0NH3yySctnsfv98vj8TRtXq/XyZgAAKATueE/TePz+ZSTk6OUlBSlp6frjTfe0B133KGNGze2eExBQYGCwWDTVlNTc6PHBAAARm5ysvj2229X9+7dVVdX12x/XV2dEhMTr+k5evToofHjx+v06dMtrnG73XK73U5GAwAAnZSjKyM9e/ZUamqqSktLm/Y1NjaqtLRUPp/vmp6joaFBJ06cUFJSkrNJAQBAl+Toyogk5efna968eZo4caImT56s1atXq76+XvPnz5ck5eTkqH///vL7/ZKkJ554QlOnTtXQoUP1xRdf6JlnntGZM2e0cOHC9n0lAACgU3IcI7Nnz9a5c+f0+OOPKxAIKCUlRW+//XbTTa3V1dXq1u2bCy7nz5/XokWLFAgE1Lt3b6WmpurgwYMaNWpU+70KAADQabkikUjEeoirCYVC8ng8CgaDiouLa9fnHrz0zXZ9PgAAOpuPV2TdkOe91u/ffDYNAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADBFjAAAAFPECAAAMEWMAAAAU8QIAAAwRYwAAABTxAgAADDVphhZt26dBg8erJiYGE2ZMkWHDx9udf22bds0YsQIxcTEaOzYsdq9e3ebhgUAAF2P4xjZunWr8vPztWzZMh07dkzJycnKzMzU2bNnr7j+4MGDmjNnjhYsWKDjx48rOztb2dnZqqqquu7hAQBA5+eKRCIRJwdMmTJFkyZN0tq1ayVJjY2N8nq9euihh7R06dLL1s+ePVv19fXatWtX076pU6cqJSVFGzZsuKZzhkIheTweBYNBxcXFORn3qgYvfbNdnw8AgM7m4xVZN+R5r/X7901OnvTSpUs6evSoCgoKmvZ169ZNGRkZKi8vv+Ix5eXlys/Pb7YvMzNTO3bsaPE84XBY4XC46XEwGJT09Ytqb43hf7f7cwIA0JnciO+v/+/zXu26h6MY+fzzz9XQ0KCEhIRm+xMSEvT+++9f8ZhAIHDF9YFAoMXz+P1+LV++/LL9Xq/XybgAAOAaeFbf2Oe/cOGCPB5Pi193FCPfloKCgmZXUxobG/Wvf/1Lffv2lcvlarfzhEIheb1e1dTUtPs//8A53o+Oh/ekY+H96Fh4P64uEonowoUL6tevX6vrHMXI7bffru7du6uurq7Z/rq6OiUmJl7xmMTEREfrJcntdsvtdjfbd9tttzkZ1ZG4uDj+InUgvB8dD+9Jx8L70bHwfrSutSsi/+Pop2l69uyp1NRUlZaWNu1rbGxUaWmpfD7fFY/x+XzN1ktSSUlJi+sBAEB0cfzPNPn5+Zo3b54mTpyoyZMna/Xq1aqvr9f8+fMlSTk5Oerfv7/8fr8kKS8vT+np6Vq5cqWysrJUXFysiooKFRYWtu8rAQAAnZLjGJk9e7bOnTunxx9/XIFAQCkpKXr77bebblKtrq5Wt27fXHBJS0tTUVGRHnvsMT366KMaNmyYduzYoTFjxrTfq2gjt9utZcuWXfZPQrDB+9Hx8J50LLwfHQvvR/tx/HtGAAAA2hOfTQMAAEwRIwAAwBQxAgAATBEjAADAVFTHyLp16zR48GDFxMRoypQpOnz4sPVIUcnv92vSpEmKjY1VfHy8srOzderUKeux8F8rVqyQy+XSkiVLrEeJWp9++ql++tOfqm/fvurVq5fGjh2riooK67GiVkNDg37zm99oyJAh6tWrl7773e/qySefvOrnr6BlURsjW7duVX5+vpYtW6Zjx44pOTlZmZmZOnv2rPVoUWffvn3Kzc3VoUOHVFJSoq+++kr33Xef6uvrrUeLekeOHNHGjRs1btw461Gi1vnz5zVt2jT16NFDb731lt577z2tXLlSvXv3th4tav3ud7/T+vXrtXbtWv3zn//U7373O/3+97/Xc889Zz1apxW1P9o7ZcoUTZo0SWvXrpX09W+S9Xq9euihh7R06VLj6aLbuXPnFB8fr3379unOO++0HidqXbx4URMmTNDzzz+v3/72t0pJSdHq1autx4o6S5cu1TvvvKO///3v1qPgv374wx8qISFBL730UtO+H/3oR+rVq5e2bNliOFnnFZVXRi5duqSjR48qIyOjaV+3bt2UkZGh8vJyw8kgScFgUJLUp08f40miW25urrKyspr9d4Jv35///GdNnDhRs2bNUnx8vMaPH68XXnjBeqyolpaWptLSUn3wwQeSpH/84x86cOCApk+fbjxZ59UhP7X3Rvv888/V0NDQ9Ftj/ychIUHvv/++0VSQvr5CtWTJEk2bNq1D/JbeaFVcXKxjx47pyJEj1qNEvQ8//FDr169Xfn6+Hn30UR05ckS/+MUv1LNnT82bN896vKi0dOlShUIhjRgxQt27d1dDQ4OeeuopzZ0713q0TisqYwQdV25urqqqqnTgwAHrUaJWTU2N8vLyVFJSopiYGOtxol5jY6MmTpyop59+WpI0fvx4VVVVacOGDcSIkVdffVWvvPKKioqKNHr0aFVWVmrJkiXq168f70kbRWWM3H777erevbvq6uqa7a+rq1NiYqLRVFi8eLF27dql/fv3a8CAAdbjRK2jR4/q7NmzmjBhQtO+hoYG7d+/X2vXrlU4HFb37t0NJ4wuSUlJGjVqVLN9I0eO1Ouvv240ER555BEtXbpUP/nJTyRJY8eO1ZkzZ+T3+4mRNorKe0Z69uyp1NRUlZaWNu1rbGxUaWmpfD6f4WTRKRKJaPHixdq+fbv27t2rIUOGWI8U1e655x6dOHFClZWVTdvEiRM1d+5cVVZWEiLfsmnTpl32o+4ffPCBBg0aZDQR/v3vfzf7QFhJ6t69uxobG40m6vyi8sqIJOXn52vevHmaOHGiJk+erNWrV6u+vl7z58+3Hi3q5ObmqqioSDt37lRsbKwCgYAkyePxqFevXsbTRZ/Y2NjL7te55ZZb1LdvX+7jMfDwww8rLS1NTz/9tH784x/r8OHDKiwsVGFhofVoUWvGjBl66qmnNHDgQI0ePVrHjx/Xs88+q5///OfWo3VekSj23HPPRQYOHBjp2bNnZPLkyZFDhw5ZjxSVJF1xe/nll61Hw3+lp6dH8vLyrMeIWn/5y18iY8aMibjd7siIESMihYWF1iNFtVAoFMnLy4sMHDgwEhMTE/nOd74T+fWvfx0Jh8PWo3VaUft7RgAAQMcQlfeMAACAjoMYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKaIEQAAYIoYAQAApogRAABgihgBAACmiBEAAGCKGAEAAKb+D7cuxelORYM+AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Gumagamit tayo ng isang package na tinatawag na [**Pandas**](https://pandas.pydata.org/) dito para sa pagsusuri ng datos. Tatalakayin pa natin ang tungkol sa Pandas at ang paggamit ng datos sa Python sa mga susunod na bahagi ng kursong ito.\n",
+    "Gumagamit tayo ng package na tinatawag na [**Pandas**](https://pandas.pydata.org/) dito para sa pagsusuri ng datos. Pag-uusapan pa natin ang tungkol sa Pandas at ang paggamit ng datos sa Python sa mga susunod na bahagi ng kursong ito.\n",
     "\n",
-    "Kunin natin ang mga karaniwang halaga para sa edad, taas, at timbang:\n"
+    "Ngayon, kalkulahin natin ang mga karaniwang halaga para sa edad, taas, at timbang:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bilang karagdagan sa mean, may saysay na tingnan ang median na halaga at mga quartile. Maaari itong maipakita gamit ang isang **box plot**:\n"
+    "Bilang karagdagan sa mean, makatuwiran ding tingnan ang median na halaga at mga quartile. Maaari itong maipakita gamit ang isang **box plot**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Maaari rin tayong gumawa ng mga box plot ng mga subset ng ating dataset, halimbawa, ayon sa pangkat ng papel ng manlalaro.\n"
+    "Maaari rin tayong gumawa ng mga box plot ng mga subset ng ating dataset, halimbawa, naka-grupo ayon sa papel ng manlalaro.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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VeP14VFSU+vTp4/DIMEmaPHmyfZqXX35Zq1evVseOHfXggw+qRYsWOnPmjH744QetXLlSZ86ckSQ9+OCDevPNN3XvvfcqIyNDderU0UcffSR/f/+rWsci+/fv18CBA3Xrrbdqw4YN+vjjj3X33XcXezb3DTfcoKioKPtNy9q0aeNUO5dq0aKF+vXrp3//+9965plndP3112v48OF67733dPbsWcXExGjjxo2aO3euYmNj1bNnz8sua9iwYfrvf/+rhx9+WKtXr1aXLl1UWFionTt36r///a++/vprh/sdAADgwMxbpwMAXF9JjwyrXLmyrXXr1ra3337bZrVaHab/448/bGPGjLHVrVvX5uPjY2vatKntX//6l326jIwMm7e3t8NjwGw2m62goMDWvn17W926dW2///67zWa78MisgIAA2759+2y9e/e2+fv722rXrm177rnnbIWFhQ7z65JHhtlsNtsPP/xg69Onjy0wMNDm7+9v69mzp239+vXF1vH999+3NW7c2Obl5XVVj8P69NNPbZGRkTZfX19bVFSUbcmSJbbBgwfbIiMj7dMUPTLsX//6V4nLWLlypa1Lly42Pz8/W3BwsO22226zbd++vdh0K1assEVFRdkqVapka9asme3jjz++7CPD4uPjbR9//LGtadOmNl9fX9sNN9xQ4rocP37cFh8fb2vQoIHNx8fHFhoaarv55ptt7733nsN0Bw8etA0cONDm7+9vq1mzpu3RRx+1LV++3KlHhm3fvt02ZMgQW1BQkK1atWq2kSNH2vLy8kqcZ+rUqTZJtilTplxx2ReLiYmxtWzZssRxRY9yK/q7sFgstsmTJ9vCw8NtPj4+tgYNGtgmTJhgO3fuXLFlXvzIMJvNZjt//rztlVdesbVs2dLm6+trq1atmq1t27a2yZMn2zIzM6+6XgBAxeNhs/3v+RwAALiY++67T4sWLbrqI8hmat26tWrVqqWUlBRT2vfw8FB8fHyxU/vLk9dee01jxozRgQMH1LBhQ7PLAQDgmuCabgAAnGCxWOw3kCuyZs0abdmyRT169DCnKDdgs9n0wQcfKCYmhsANAHArXNMNAIATDh8+rFtuuUVDhw5V3bp1tXPnTr3zzjsKDQ3Vww8/bHZ55U5OTo6WLFmi1atXa+vWrVq8eLHZJQEAcE0RugEAcEK1atXUtm1b/fvf/9bJkycVEBCg/v376+WXX1aNGjXMLq/cOXnypO6++25VrVpVEydO1MCBA80uCQCAa4prugEAAAAAMAjXdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAFcR9992nsLCwUs8bGBh4bQsCAKACIHQDAOBi5syZIw8PD23atKnE8T169FBUVFQZV3V1cnNzNWnSJK1Zs8bsUgAAcAneZhcAAADKxvvvvy+r1WpoG7m5uZo8ebKkC18OAABQ0RG6AQCoIHx8fMwuAQCACofTywEAcAMff/yx2rZtKz8/P1WvXl133XWXfv31V4dpSrqm+/Tp0xo2bJiCg4NVtWpVDR8+XFu2bJGHh4fmzJlTrJ3Dhw8rNjZWgYGBqlWrlsaNG6fCwkJJ0oEDB1SrVi1J0uTJk+Xh4SEPDw9NmjTJiFUGAKBc4Eg3AAAuKjMzU6dOnSo23GKxOLx+6aWX9Mwzz+iOO+7Q3//+d508eVJvvPGGunfvrh9//FFVq1YtcflWq1W33XabNm7cqEceeUSRkZFavHixhg8fXuL0hYWF6tOnjzp27Khp06Zp5cqVmj59upo0aaJHHnlEtWrV0ttvv61HHnlEt99+u+Li4iRJrVq1+mu/CAAAyjFCNwAALuqWW2657LiWLVtKkg4ePKjnnntOL774oiZOnGgfHxcXpxtuuEFvvfWWw/CLJScna8OGDZo5c6YeffRRSdIjjzyiXr16lTj9uXPndOedd+qZZ56RJD388MNq06aNPvjgAz3yyCMKCAjQkCFD9Mgjj6hVq1YaOnRoqdYbAAB3QugGAMBFzZo1S9ddd12x4QkJCfZTupOSkmS1WnXHHXc4HBUPDQ1V06ZNtXr16suG7uXLl8vHx0cPPvigfZinp6fi4+O1atWqEud5+OGHHV5369ZNH330kdPrBgBARUHoBgDARXXo0EHt2rUrNrxatWr2gL1nzx7ZbDY1bdq0xGVc6eZpBw8eVJ06deTv7+8wPCIiosTpK1eubL9m++Jafv/99yuuBwAAFRmhGwCAcsxqtcrDw0PLli2Tl5dXsfGBgYHXrK2Slg8AAK6M0A0AQDnWpEkT2Ww2hYeHl3gq+pU0atRIq1evVm5ursPR7r1795a6Hg8Pj1LPCwCAO+KRYQAAlGNxcXHy8vLS5MmTZbPZHMbZbDadPn36svP26dNHFotF77//vn2Y1WrVrFmzSl1PUXg/e/ZsqZcBAIA74Ug3AADlWJMmTfTiiy9qwoQJOnDggGJjYxUUFKT9+/fr888/10MPPaRx48aVOG9sbKw6dOighIQE7d27V5GRkVqyZInOnDkjqXRHrf38/NSiRQstWLBA1113napXr66oqChFRUX9pfUEAKC84kg3AADl3Pjx4/XZZ5/J09NTkydP1rhx47RkyRL17t1bAwcOvOx8Xl5e+vLLL3XnnXdq7ty5euqpp1S3bl37ke7KlSuXqp5///vfqlevnsaMGaO//e1vWrRoUamWAwCAO/CwXXouGgAAqNCSk5N1++23a926derSpYvZ5QAAUK4RugEAqMDy8vLk5+dnf11YWKjevXtr06ZNOnbsmMM4AADgPK7pBgCgAhs1apTy8vLUuXNn5efnKykpSevXr9eUKVMI3AAAXAMc6QYAoAKbP3++pk+frr179+rcuXOKiIjQI488opEjR5pdGgAAboHQDQAAAACAQbh7OQAAAAAABiF0AwAAAABgkHJ5IzWr1aojR44oKChIHh4eZpcDAAAAAKhgbDab/vjjD9WtW1eenpc/nl0uQ/eRI0fUoEEDs8sAAAAAAFRwv/76q+rXr3/Z8eUydAcFBUm6sHLBwcEmV/PXWSwWrVixQr1795aPj4/Z5eAS9I9ro39cF33j2ugf10b/uDb6x3XRN67N3fonKytLDRo0sOfTyymXobvolPLg4GC3Cd3+/v4KDg52iz8+d0P/uDb6x3XRN66N/nFt9I9ro39cF33j2ty1f/7skmdupAYAAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAACg3CssLFRqaqrWrl2r1NRUFRYWml0SIInQDQAAAKCcS0pKUkREhHr16qUZM2aoV69eioiIUFJSktmlAYRuAAAAAOVXUlKShgwZoujoaKWlpemTTz5RWlqaoqOjNWTIEII3TEfoBgAAAFAuFRYWKiEhQQMGDFBycrI6duwoPz8/dezYUcnJyRowYIDGjRvHqeYwFaEbAAAAQLmUlpamAwcOaOLEifL0dIw2np6emjBhgvbv36+0tDSTKgQI3QAAAADKqaNHj0qSoqKiShxfNLxoOsAMhG4AAAAA5VKdOnUkSdu2bStxfNHwoukAMxC6AQAAAJRL3bp1U1hYmKZMmSKr1eowzmq1KjExUeHh4erWrZtJFQKEbgAAAADllJeXl6ZPn66lS5cqNjZW6enpysvLU3p6umJjY7V06VJNmzZNXl5eZpeKCszb7AIAAAAAoLTi4uK0aNEiJSQkqHv37vbh4eHhWrRokeLi4kysDiB0AwAAACjn4uLiNGjQIK1evVrLli1T37591bNnT45wwyUQugEAAACUe15eXoqJiVFOTo5iYmII3HAZXNMNAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBnArdiYmJat++vYKCghQSEqLY2Fjt2rXLYZpjx45p2LBhCg0NVUBAgNq0aaPPPvvMYZozZ87onnvuUXBwsKpWraoRI0YoOzv7r68NAAAAAAAuxKnQnZqaqvj4eKWnpyslJUUWi0W9e/dWTk6OfZp7771Xu3bt0pIlS7R161bFxcXpjjvu0I8//mif5p577tHPP/+slJQULV26VGvXrtVDDz107dYKAAAAAAAX4O3MxMuXL3d4PWfOHIWEhCgjI0Pdu3eXJK1fv15vv/22OnToIEl6+umn9eqrryojI0M33HCDduzYoeXLl+v7779Xu3btJElvvPGG+vXrp2nTpqlu3brXYr0AAAAAADCdU6H7UpmZmZKk6tWr24fdeOONWrBggfr376+qVavqv//9r86dO6cePXpIkjZs2KCqVavaA7ck3XLLLfL09NR3332n22+/vVg7+fn5ys/Pt7/OysqSJFksFlkslr+yCi6haB3cYV3cEf3j2ugf10XfuDb6x7XRP66N/nFd9I1rc7f+udr18LDZbLbSNGC1WjVw4ECdPXtW69atsw8/e/as7rzzTq1YsULe3t7y9/fXwoUL1bt3b0nSlClTNHfu3GLXgoeEhGjy5Ml65JFHirU1adIkTZ48udjw+fPny9/fvzTlAwAAAABQarm5ubr77ruVmZmp4ODgy05X6iPd8fHx2rZtm0PglqRnnnlGZ8+e1cqVK1WzZk0lJyfrjjvuUFpamqKjo0vV1oQJEzR27Fj766ysLDVo0EC9e/e+4sqVFxaLRSkpKerVq5d8fHzMLgeXoH9cG/3juugb10b/uDb6x7XRP66LvnFt7tY/RWdg/5lShe6RI0fab4BWv359+/B9+/bpzTff1LZt29SyZUtJ0vXXX6+0tDTNmjVL77zzjkJDQ3XixAmH5RUUFOjMmTMKDQ0tsT1fX1/5+voWG+7j4+MWnVXE3dbH3dA/ro3+cV30jespLCzU+vXrtXbtWgUEBKhnz57y8vIyuyyUgPePa6N/XBd949rcpX+udh2cunu5zWbTyJEj9fnnn2vVqlUKDw93GJ+bm3thoZ6Oi/Xy8pLVapUkde7cWWfPnlVGRoZ9/KpVq2S1WtWxY0dnygEAAE5KSkpSRESEevXqpRkzZqhXr16KiIhQUlKS2aUBAOCWnArd8fHx+vjjjzV//nwFBQXp2LFjOnbsmPLy8iRJkZGRioiI0D/+8Q9t3LhR+/bt0/Tp05WSkqLY2FhJUvPmzXXrrbfqwQcf1MaNG/Xtt99q5MiRuuuuu7hzOQAABkpKStKQIUMUHR2ttLQ0ffLJJ/bLv4YMGULwBgDAAE6F7rfffluZmZnq0aOH6tSpY/+3YMECSRcOr3/11VeqVauWbrvtNrVq1Urz5s3T3Llz1a9fP/ty/vOf/ygyMlI333yz+vXrp65du+q99967tmsGAADsCgsLlZCQoAEDBig5OVkdO3aUn5+fOnbsqOTkZA0YMEDjxo1TYWGh2aUCAOBWnLqm+2pudN60aVN99tlnV5ymevXqmj9/vjNNAwCAvyAtLU0HDhzQJ598Ik9PT4dw7enpqQkTJujGG29UWlqa/TGfAADgr3PqSDcAACifjh49KkmKiooqcXzR8KLpAADAtUHoBgCgAqhTp44kadu2bSWOLxpeNB0AALg2CN0AAFQA3bp1U1hYmKZMmWJ/okgRq9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnTeF43AADXmFM3UgMAAOVXXFycFi1apISEBHXv3t0+PDw8XIsWLVJcXJyJ1QEA4J4I3QAAVCBxcXEaNGiQVq9erWXLlqlv377q2bMnR7gBADAIoRu4gsLCQqWmpmrt2rUKCAhgxxSAW/Dy8lJMTIxycnIUExPDdg0AAANxTTdwGUlJSYqIiFCvXr00Y8YM9erVSxEREUpKSjK7NAAAAADlBKEbKEFSUpKGDBmi6OhopaWl6ZNPPlFaWpqio6M1ZMgQgjcAAACAq0LoBi5RWFiohIQEDRgwQMnJyerYsaP8/PzUsWNHJScna8CAARo3bpwKCwvNLhUAAACAiyN0A5dIS0vTgQMHNHHiRHl6Or5FPD09NWHCBO3fv19paWkmVQgAAACgvCB0A5c4evSoJCkqKqrE8UXDi6YDAAAAgMshdAOXqFOnjiRp27ZtJY4vGl40HQAAAABcDqEbuES3bt0UFhamKVOmyGq1OoyzWq1KTExUeHi4unXrZlKFAAAAAMoLQjdwCS8vL02fPl1Lly5VbGys0tPTlZeXp/T0dMXGxmrp0qWaNm0az7UFAAAA8Ke8zS4AcEVxcXFatGiREhIS1L17d/vw8PBwLVq0SHFxcSZWBwAAAKC8IHQDlxEXF6dBgwZp9erVWrZsmfr27auePXtyhBsAAADAVSN0A1fg5eWlmJgY5eTkKCYmhsANAAAAwClc0w0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AQAVTWFio1NRUrV27VqmpqSosLDS7JAAA3BahGwCACiQpKUkRERHq1auXZsyYoV69eikiIkJJSUlmlwYAgFsidAMAUEEkJSVpyJAhio6OVlpamj755BOlpaUpOjpaQ4YMIXgDAGAAQjcAABVAYWGhEhISNGDAACUnJ6tjx47y8/NTx44dlZycrAEDBmjcuHGcag4AwDVG6AYAoAJIS0vTgQMHNHHiRHl6On78e3p6asKECdq/f7/S0tJMqhAAAPdE6AYAoAI4evSoJCkqKqrE8UXDi6YDAADXBqEbAIAKoE6dOpKkbdu2lTi+aHjRdAAA4NogdAMAUAF069ZNYWFhmjJliqxWq8M4q9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAC4FW+zCwAAAGUjLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAcE+EbgAAKpC4uDgNGjRIq1ev1rJly9S3b1/17NmTI9wAABiE0A0AQAXj5eWlmJgY5eTkKCYmhsANAICBuKYbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDOBW6ExMT1b59ewUFBSkkJESxsbHatWtXsek2bNigm266SQEBAQoODlb37t2Vl5dnH3/mzBndc889Cg4OVtWqVTVixAhlZ2f/9bUBAAAAAMCFOBW6U1NTFR8fr/T0dKWkpMhisah3797KycmxT7Nhwwbdeuut6t27tzZu3Kjvv/9eI0eOlKfn/2/qnnvu0c8//6yUlBQtXbpUa9eu1UMPPXTt1goAAAAAABfg7czEy5cvd3g9Z84chYSEKCMjQ927d5ckjRkzRqNHj9b48ePt0zVr1sz+/x07dmj58uX6/vvv1a5dO0nSG2+8oX79+mnatGmqW7duqVcGAAAAAABX8peu6c7MzJQkVa9eXZJ04sQJfffddwoJCdGNN96o2rVrKyYmRuvWrbPPs2HDBlWtWtUeuCXplltukaenp7777ru/Ug4AAAAAAC7FqSPdF7NarXrsscfUpUsXRUVFSZJ++eUXSdKkSZM0bdo0tW7dWvPmzdPNN9+sbdu2qWnTpjp27JhCQkIci/D2VvXq1XXs2LES28rPz1d+fr79dVZWliTJYrHIYrGUdhVcRtE6uMO6uCP6p+zk5uaWeJ+IK8nOy9f6rfsUVDVdgX6+Ts3brFkz+fv7OzUPrh7vnbLDe8f98P5xbfRP2WDb5n7c7b1ztetR6tAdHx+vbdu2ORzFtlqtkqR//OMfuv/++yVJN9xwg7755ht9+OGHSkxMLFVbiYmJmjx5crHhK1ascKs3RkpKitkl4AroH+Pt27dPCQkJpZp3ainmmT59upo0aVKq9nD1eO8Yj/eO++L949roH2OxbXNf7vLeyc3NvarpShW6R44cab8BWv369e3D69SpI0lq0aKFw/TNmzfXoUOHJEmhoaE6ceKEw/iCggKdOXNGoaGhJbY3YcIEjR071v46KytLDRo0UO/evRUcHFyaVXApFotFKSkp6tWrl3x8fMwuB5egf8pObm6uunbt6tQ8u49m6vHPt+tft7fQdXWqODUv32gbi/dO2eG94354/7g2+qdssG1zP+723ik6A/vPOBW6bTabRo0apc8//1xr1qxReHi4w/iwsDDVrVu32Gkgu3fvVt++fSVJnTt31tmzZ5WRkaG2bdtKklatWiWr1aqOHTuW2K6vr698fYufHuLj4+MWnVXE3dbH3dA/xqtSpYo6dOjg1DyVDp6W74bzimrdRq0b1TCoMvwVvHeMx3vHffH+cW30j7HYtrkvd3nvXO06OBW64+PjNX/+fC1evFhBQUH2a7CrVKkiPz8/eXh46PHHH9dzzz2n66+/Xq1bt9bcuXO1c+dOLVq0SNKFo9633nqrHnzwQb3zzjuyWCwaOXKk7rrrLu5cDgAAAABwK06F7rfffluS1KNHD4fhs2fP1n333SdJeuyxx3Tu3DmNGTNGZ86c0fXXX6+UlBSH6yP+85//aOTIkbr55pvl6empwYMH6/XXX/9rawIAAAAAgItx+vTyqzF+/HiH53Rfqnr16po/f74zTQMAAAAAUO78ped0AwBQksLCQqWmpmrt2rVKTU1VYWGh2SUBAACYgtANALimkpKSFBERoV69emnGjBnq1auXIiIilJSUZHZpAAAAZY7QDQC4ZpKSkjRkyBBFR0crLS1Nn3zyidLS0hQdHa0hQ4YQvAEAQIVD6AYAXBOFhYVKSEjQgAEDlJycrI4dO8rPz08dO3ZUcnKyBgwYoHHjxnGqOQAAqFAI3QCAayItLU0HDhzQxIkT5enp+PHi6empCRMmaP/+/UpLSzOpQgAAgLJH6AYAXBNHjx6VJEVFRZU4vmh40XQAAAAVAaEbAHBN1KlTR5K0bdu2EscXDS+aDgAAoCIgdAMArolu3bopLCxMU6ZMkdVqdRhntVqVmJio8PBwdevWzaQKAQAAyh6hGwBwTXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAAAZcbb7AIAAO4jLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAAMoeoRsAcE3FxcVp0KBBWr16tZYtW6a+ffuqZ8+eHOEGAAAVEqEbAHDNeXl5KSYmRjk5OYqJiSFwAwCACotrugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFAGUpNzdXO3fudGqe7Lx8rd+6T9VqblKgn69T80ZGRsrf39+peQAAAAC4D0I3KpSdO3eqbdu2pZp3ainmycjIUJs2bUrVHgAAAIDyj9CNCiUyMlIZGRlOzbPr6FmNXbhVM/4vWs3qVHW6PQAAAAAVF6EbFYq/v7/TR549D56Wb1qemkddr9aNahhUGQAAAAB3xI3UAAAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDeJtdgLvJzc3Vzp07nZonOy9f67fuU7WamxTo5+vUvJGRkfL393dqHgAAAABA2SB0X2M7d+5U27ZtSzXv1FLMk5GRoTZt2pSqPQAAAACAsQjd11hkZKQyMjKcmmfX0bMau3CrZvxftJrVqep0ewAAAAAA10Tovsb8/f2dPvLsefC0fNPy1DzqerVuVMOgygAAAAAAZY0bqQEAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQp0J3YmKi2rdvr6CgIIWEhCg2Nla7du0qcVqbzaa+ffvKw8NDycnJDuMOHTqk/v37y9/fXyEhIXr88cdVUFBQ6pUAAAAAAMAVORW6U1NTFR8fr/T0dKWkpMhisah3797KyckpNu3MmTPl4eFRbHhhYaH69++v8+fPa/369Zo7d67mzJmjZ599tvRrAQAAAACAC/J2ZuLly5c7vJ4zZ45CQkKUkZGh7t2724dv3rxZ06dP16ZNm1SnTh2HeVasWKHt27dr5cqVql27tlq3bq0XXnhBTz75pCZNmqRKlSr9hdUBAAAAAMB1OBW6L5WZmSlJql69un1Ybm6u7r77bs2aNUuhoaHF5tmwYYOio6NVu3Zt+7A+ffrokUce0c8//6wbbrih2Dz5+fnKz8+3v87KypIkWSwWWSyWv7IKLqHo1PqCggK3WB93Q/+4NvqnbOTm5l72cqLLyc7L1/qt+xRUNV2Bfr5OzdusWTP5+/s7NQ+cw3vHtRX1CX3jmugf18W2reywb3D124BSh26r1arHHntMXbp0UVRUlH34mDFjdOONN2rQoEElznfs2DGHwC3J/vrYsWMlzpOYmKjJkycXG75ixQqX+8WXxq/ZkuSt9PR0Hd5mdjW4FP3j2uifsrFv3z4lJCSUat6ppZhn+vTpatKkSanaw9XhvVM+pKSkmF0CroD+cT1s28oO+wYXvni4GqUO3fHx8dq2bZvWrVtnH7ZkyRKtWrVKP/74Y2kXW6IJEyZo7Nix9tdZWVlq0KCBevfureDg4Gvalhm2HDojbd2kTp066fqG1f98BpQp+se10T9lIzc3V127dnVqnt1HM/X459v1r9tb6Lo6VZya1xW/zXY3vHdcm8ViUUpKinr16iUfHx+zy8El6B/Xxbat7LBv8P/PwP4zpQrdI0eO1NKlS7V27VrVr1/fPnzVqlXat2+fqlat6jD94MGD1a1bN61Zs0ahoaHauHGjw/jjx49LUomno0uSr6+vfH2Ln37g4+PjFhs6b29v+093WB93Q/+4NvqnbFSpUkUdOnRwap5KB0/Ld8N5RbVuo9aNahhUGUqL90754C77Ou6K/nE9bNvKDvsGuuq/MafuXm6z2TRy5Eh9/vnnWrVqlcLDwx3Gjx8/Xj/99JM2b95s/ydJr776qmbPni1J6ty5s7Zu3aoTJ07Y50tJSVFwcLBatGjhTDkAAAAAALg0p450x8fHa/78+Vq8eLGCgoLs12BXqVJFfn5+Cg0NLfFodcOGDe0BvXfv3mrRooWGDRumqVOn6tixY3r66acVHx9f4tFsAAAAAADKK6eOdL/99tvKzMxUjx49VKdOHfu/BQsWXPUyvLy8tHTpUnl5ealz584aOnSo7r33Xj3//PNOFw8AAAAAgCtz6ki3zWZzuoGS5mnUqJG++uorp5cFAAAAAEB54tSRbgAAAAAAcPUI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBvM0uAPgr9p/KUU5+gaFt7DuZY//p7W3sWybA11vhNQMMbQMAAABA2SF0o9zafypHPaetKbP2EhZtLZN2Vo/rQfAGAAAA3AShG+VW0RHumXe2VkRIoHHt5OVr6ZoNGtCjswL8fA1rZ++JbD22YLPhR+4BAAAAlB1CN8q9iJBARdWrYtjyLRaLjtWS2jSqJh8fH8PaAQAAAOB+uJEaAAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQb7MLAOC+9p/KUU5+gaFt7DuZY//p7W3sJi3A11vhNQMMbQMAAADuhdANwBD7T+Wo57Q1ZdZewqKtZdLO6nE9CN4AAAC4aoRulFv5hefkWfmw9mftkmflQMPaKSgo0JGCI9pxZoehR1L3Z2XLs/Jh5Reek1TFsHbKStER7pl3tlZEiHH9k5OXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAgHshdKPcOpJzUAHhb2jixrJp763lbxneRkC4dCSntdqqtuFtlZWIkEBF1TPuSwSLxaJjtaQ2jarJx8fHsHYAAACA0iB0o9yqG9BIOftH6bU7W6uJgUdSCwoK9O26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAAAEDZInSj3PL1qizruXoKD26mFjWMPZK633u/mldvbuiRVOu5TFnPnZSvV2XD2gAAAABQtnhkGAAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGcSp0JyYmqn379goKClJISIhiY2O1a9cu+/gzZ85o1KhRatasmfz8/NSwYUONHj1amZmZDss5dOiQ+vfvL39/f4WEhOjxxx9XQUHBtVkjAAAAAABchFOhOzU1VfHx8UpPT1dKSoosFot69+6tnJwcSdKRI0d05MgRTZs2Tdu2bdOcOXO0fPlyjRgxwr6MwsJC9e/fX+fPn9f69es1d+5czZkzR88+++y1XTMAAAAAAEzm7czEy5cvd3g9Z84chYSEKCMjQ927d1dUVJQ+++wz+/gmTZropZde0tChQ1VQUCBvb2+tWLFC27dv18qVK1W7dm21bt1aL7zwgp588klNmjRJlSpVujZrBgAAAACAyZwK3ZcqOm28evXqV5wmODhY3t4XmtqwYYOio6NVu3Zt+zR9+vTRI488op9//lk33HBDsWXk5+crPz/f/jorK0uSZLFYZLFY/soquISiU+sLCgrcYn3KSln93oqWbXTfuNvfQU5+tjwrH9be37fL6h1gWDsFBQU6UnBEW09stW9njPDL7znyrHxYOfnZslj8DWvHnbjb37S7oX9cW1l99qB06J/SOXA6Rzn5hYa2sftYpsNPIwX4eimshnH7OO7I3T57rnYdSr2HarVa9dhjj6lLly6KiooqcZpTp07phRde0EMPPWQfduzYMYfALcn++tixYyUuJzExUZMnTy42fMWKFfL3L/87v79mS5K30tPTdXib2dWUH0W/t3Xr1ulgoPHtpaSkGLr8sl4fo/3wxxEFhL+lZzLKpr23Vr5leBsB4dJX6wt1LKiu4W25A7Ztro3+KR+M/uzBX0P/XL0TedJLm437cvxST3y+o0zaeap1gUL8yqQpt+Bunz25ublXNV2p//Lj4+O1bds2rVu3rsTxWVlZ6t+/v1q0aKFJkyaVthlJ0oQJEzR27FiHZTdo0EC9e/dWcHDwX1q2K9hy6Iy0dZM6deqk6xte/qwBOPr5SJambU1X165d1bKucX8HFotFKSkp6tWrl3x8fAxrp6zWp6yE/npCH83z0owh0Wpcy9gj3d+lf6eOnToae6T7ZI7GLtqqfvf2V5sGIYa1407YtpVeWRwNyj+WKW3doZCIaDUKrWJoWxwNcl5ZffagdOgf5/18JEvanK5pQ6IVYeB+Qc65fC1P+163dmuvgMq+hrWz92SOxi3aqvad3WO/ray4275B0RnYf6ZUe6gjR47U0qVLtXbtWtWvX7/Y+D/++EO33nqrgoKC9PnnnztsjEJDQ7Vx40aH6Y8fP24fVxJfX1/5+hZ/0/j4+LjFhq4oKHh7e7vF+pSVsv69Gf335m5/BwG+gbKeq6eIai0UVdu4HXqLxaJfvX9VdEi0ob83z4JMWc+dUYBvoFv0T1lwt7/psrL/VI56zfy2zNorq6NBq8f1UHhNgrez3GVfx13RP1ev6DMhsk4VRdUzdr/g1E6pQ+Na7Le5IHf7vV3tOjgVum02m0aNGqXPP/9ca9asUXh4eLFpsrKy1KdPH/n6+mrJkiWqXLmyw/jOnTvrpZde0okTJxQScuFoUUpKioKDg9WiRQtnygEAwO3k5F+43m3mna0VEWLctSY5eflaumaDBvTorAA/A48GncjWYws229cLAICKxqnQHR8fr/nz52vx4sUKCgqyX4NdpUoV+fn5KSsrS71791Zubq4+/vhjZWVl2Q+516pVS15eXurdu7datGihYcOGaerUqTp27JiefvppxcfHl3g0GwCAiigiJNDwo0HHakltGlVzi6MNAAC4KqdC99tvvy1J6tGjh8Pw2bNn67777tMPP/yg7777TpIUERHhMM3+/fsVFhYmLy8vLV26VI888og6d+6sgIAADR8+XM8///xfWA0AAAAAAFyP06eXX0mPHj3+dBpJatSokb766itnmgYAAAAAoNzxNLsAAAAAAADcFaEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFuLr9p3KUk19gaBv7TubYf3p7G9slAb7eCq8ZYGgbAAAAAIALCN1XsP9UjnpOW1Nm7SUs2lom7awe14PgDQAAAABlgNB9BUVHuGfe2VoRIYHGtZOXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAAABcQOi+ChEhgYqqV8Ww5VssFh2rJbVpVE0+Pj6GtQMAAAAAKFvcSA0AAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAABcSGFhoVJTU7V27VqlpqaqsLDQ7JIAAH8BoRsAAMBFJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSAACl5G12AQCAsrf/VI5y8gsMbWPfyRz7T29vYz9uAny9FV4zwNA2AKMlJSVpyJAhGjBggD766CP99ttvql+/vqZOnaohQ4Zo0aJFiouLM7tMAICTCN0AUMHsP5WjntPWlFl7CYu2lkk7q8f1IHij3CosLFRCQoIGDBig5ORkFRYW6vTp0+rYsaOSk5MVGxurcePGadCgQfLy8jK7XACAEwjdAFDBFB3hnnlna0WEBBrXTl6+lq7ZoAE9OivAz9ewdvaeyNZjCzYbfuQeMFJaWpoOHDigTz75RJ6eng7XcXt6emrChAm68cYblZaWph49ephXqBvKzc3Vzp07nZonOy9f67fuU7WamxTo5PYtMjJS/v7+Ts0DlAXOgjMOoRsAKqiIkEBF1ati2PItFouO1ZLaNKomHx8fw9oB3MHRo0clSVFRUSWOLxpeNB2unZ07d6pt27almndqKebJyMhQmzZtStUeYBTOgjMWoRsAAMBkderUkSRt27ZNnTp1KjZ+27ZtDtPh2omMjFRGRoZT8+w6elZjF27VjP+LVrM6VZ1uD3A1nAVnLEI3AACAybp166awsDBNmTJFycnJDuOsVqsSExMVHh6ubt26mVOgG/P393f6yLPnwdPyTctT86jr1bpRDYMqA8oeZ8EZg0eGAQAAmMzLy0vTp0/X0qVLFRsbq/T0dOXl5Sk9PV2xsbFaunSppk2bxk3UAKAc4kg3AACAC4iLi9OiRYuUkJCg7t2724eHh4fzuDAAKMcI3QAAAC4iLi5OgwYN0urVq7Vs2TL17dtXPXv25Ag3AJRjhG4AAAAX4uXlpZiYGOXk5CgmJobADQDlHKEbAADAQDwHGgAqNkI3AACAgXgONABUbIRuAAAAA/EcaACo2AjdAAAABuI50ABQsfGcbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADMJzuq8gv/CcPCsf1v6sXfKsHGhYOwUFBTpScEQ7zuyQt7dxXbI/K1uelQ8rv/CcpCqGtQMAAABcjP1qVGSE7is4knNQAeFvaOLGsmnvreVvGd5GQLh0JKe12qq24W0BAAAAEvvVqNgI3VdQN6CRcvaP0mt3tlaTEGO/kft23bfq0rWLod/I7TuRrUcXbFbdno0MawMAAAC4FPvVqMgI3Vfg61VZ1nP1FB7cTC1qGHfaiMVi0X7v/Wpevbl8fHwMa8d6LlPWcyfl61XZsDYAAACAS7FfjYqMG6kBAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswsASivPUihJ2nY409B2cvLytemkFHrwdwX4+RrWzt4T2YYtGwAAAIA5nArdiYmJSkpK0s6dO+Xn56cbb7xRr7zyipo1a2af5ty5c0pISNCnn36q/Px89enTR2+99ZZq165tn+bQoUN65JFHtHr1agUGBmr48OFKTEyUtzffAeDq7ftfSB2ftLUMWvPWR3u/L4N2pABf3gcAAACAu3Bq7z41NVXx8fFq3769CgoKNHHiRPXu3Vvbt29XQECAJGnMmDH68ssvtXDhQlWpUkUjR45UXFycvv32W0lSYWGh+vfvr9DQUK1fv15Hjx7VvffeKx8fH02ZMuXaryHcVu+WoZKkJiGB8vPxMqydXUczlbBoq6YPiVazOlUMa0e6ELjDawYY2gYAAACAsuNU6F6+fLnD6zlz5igkJEQZGRnq3r27MjMz9cEHH2j+/Pm66aabJEmzZ89W8+bNlZ6erk6dOmnFihXavn27Vq5cqdq1a6t169Z64YUX9OSTT2rSpEmqVKnStVs7uLXqAZV0V4eGhrdTUFAgSWpSK0BR9YwN3QAAAADcy1+6kVpm5oVraatXry5JysjIkMVi0S233GKfJjIyUg0bNtSGDRskSRs2bFB0dLTD6eZ9+vRRVlaWfv75579SDgAAAAAALqXUF49arVY99thj6tKli6KioiRJx44dU6VKlVS1alWHaWvXrq1jx47Zp7k4cBeNLxpXkvz8fOXn59tfZ2VlSZIsFossFktpV+FPFR3hLCgoMLSdomUb2YZUduvjbvi9lc4feRfes1sOnbH/Do2Qc+7Cje5q/nJSAZUNvNHdyRxJ7vF3kJOfLc/Kh7X39+2yeht3OUNBQYGOFBzR1hNbDb1nxy+/58iz8mHl5GfLYvE3rJ2yQv9A4rPH1dE/zmO/2rXx2VM6V9v3pV7T+Ph4bdu2TevWrSvtIq5aYmKiJk+eXGz4ihUr5O9v3C/x12xJ8ta6det0MNCwZuxSUlIMXX5Zr4+7KPq9paen6/A2s6spPzYc95DkpacWby+D1rz10d4fy6Ad6fsN63TQr0yaMswPfxxRQPhbeiajbNp7a+VbhrcREC59tb5Qx4LqGt6W0egfSHz2uDr6x3nsV7s2PntKJzc396qmK1XoHjlypJYuXaq1a9eqfv369uGhoaE6f/68zp4963C0+/jx4woNDbVPs3HjRoflHT9+3D6uJBMmTNDYsWPtr7OystSgQQP17t1bwcHBpVmFq/LzkSxN25qurl27qmVd49qxWCxKSUlRr1695OPjY1g7ZbU+7mbLoTPS1k3q1KmTrm9Y3exyyo1OOecVveOEGtcKMPRGd7uPZeqJz3do6u3NdV2o0Te681JYjfJ/o7vQX0/oo3lemjEkWo1rGftt9nfp36ljp47Gfpt9MkdjF21Vv3v7q02DEMPaKSv0DyQ+e1wd/eM89qtdG589pVN0BvafcWpNbTabRo0apc8//1xr1qxReHi4w/i2bdvKx8dH33zzjQYPHixJ2rVrlw4dOqTOnTtLkjp37qyXXnpJJ06cUEjIhV9ASkqKgoOD1aJFixLb9fX1la9v8dNGfXx8DH0zFf0heHt7G9pOEXdbH3fB7610alf10T2dw/98wmvkutAqat2oRpm1V54F+AbKeq6eIqq1UFRt476osFgs+tX7V0WHRBv63vEsyJT13BkF+Aa6xXuU/oHEZ4+ro3+cx361a+Ozp3SudtlOhe74+HjNnz9fixcvVlBQkP0a7CpVqsjPz09VqlTRiBEjNHbsWFWvXl3BwcEaNWqUOnfurE6dOkmSevfurRYtWmjYsGGaOnWqjh07pqefflrx8fElBmsAAAAAAMorp0L322+/LUnq0aOHw/DZs2frvvvukyS9+uqr8vT01ODBg5Wfn68+ffrorbf+/zn7Xl5eWrp0qR555BF17txZAQEBGj58uJ5//vm/tiYAAAAAALgYp08v/zOVK1fWrFmzNGvWrMtO06hRI3311VfONA0AAAAAQLnzl57TDQAAAAAALo/QDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABjE2+wCgLKUm5urnTt3OjXPrqNnlX9sr3Zs85P1dFWn5o2MjJS/v79T8wAAgNLbfypHOfkFhrax72SO/ae3t7G70wG+3gqvGWBoGwCMRehGhbJz5061bdu2VPPePdf5eTIyMtSmTZtStQcAAJyz/1SOek5bU2btJSzaWibtrB7Xg+ANlGOEblQokZGRysjIcGqe7Lx8fbl6g/r37KxAP1+n2wMAAGWj6Aj3zDtbKyIk0Lh28vK1dM0GDejRWQFO7hs4Y++JbD22YLPhR+4BGIvQjQrF39/f6SPPFotFv586oc4d2snHx8egygAAwLUSERKoqHpVDFu+xWLRsVpSm0bV2DcA8Ke4kRoAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswtwZXmWQknStsOZhraTk5evTSel0IO/K8DP17B29p7INmzZAIBrg88e17f/VI5y8gsMbWPfyRz7T29vY3fXAny9FV4zwNA2ALZtro3+MRah+wr2/a+zxidtLYPWvPXR3u/LoJ0LH64AANfEZ49r238qRz2nrSmz9hIWlcXfgbR6XA+CNwzFts210T/Gco0qXFTvlqGSpCYhgfLz8TKsnV1HM5WwaKumD4lWszpVDGtH4ttsAHB1fPa4tqIj3DPvbK2IkEDj2snL19I1GzSgR2fDjwY9tmCz4UfuAbZtro3+MRah+wqqB1TSXR0aGt5OQcGFD7omtQIUVc/YPz4AgGvjs6d8iAgJNPT3ZrFYdKyW1KZRNfn4+BjWDlBW2La5NvrHWNxIDQAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwiLfZBQAAylaepVCStO1wpqHt5OTla9NJKfTg7wrw8zWsnb0nsg1bNnCp/MJz8qx8WPuzdsmzcqBh7RQUFOhIwRHtOLND3t7G7a7tz8qWZ+XDyi88J6mKYe0AQEVG6AaACmbf/0Lq+KStZdCatz7a+30ZtCMF+PKRBuMdyTmogPA3NHFj2bT31vK3DG8jIFw6ktNabVXb8LYAoCJiDwUAKpjeLUMlSU1CAuXn42VYO7uOZiph0VZNHxKtZnWMPYIW4Out8JoBhrYBSFLdgEbK2T9Kr93ZWk1CjD3S/e26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAqOkI3AFQw1QMq6a4ODQ1vp6CgQJLUpFaAoupx2ircg69XZVnP1VN4cDO1qGHc37XFYtF+7/1qXr25fHx8DGvHei5T1nMn5etV2bA2AKCi40ZqAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGMTp0L127Vrddtttqlu3rjw8PJScnOwwPjs7WyNHjlT9+vXl5+enFi1a6J133nGY5ty5c4qPj1eNGjUUGBiowYMH6/jx439pRQAAAAAAcDVOh+6cnBxdf/31mjVrVonjx44dq+XLl+vjjz/Wjh079Nhjj2nkyJFasmSJfZoxY8boiy++0MKFC5WamqojR44oLi6u9GsBAAAAAIAL8nZ2hr59+6pv376XHb9+/XoNHz5cPXr0kCQ99NBDevfdd7Vx40YNHDhQmZmZ+uCDDzR//nzddNNNkqTZs2erefPmSk9PV6dOnUq3JgAAAAAAuBinQ/efufHGG7VkyRI98MADqlu3rtasWaPdu3fr1VdflSRlZGTIYrHolltusc8TGRmphg0basOGDSWG7vz8fOXn59tfZ2VlSZIsFossFsu1XoUyV1BQYP/pDuvjbor6hL4xXm5urnbt2uXUPLuPZir/2F5t21xJ549XcWreZs2ayd/f36l5cPXYtrk2+qd0yur3VlafPe72d5CTny3Pyoe19/ftsnoHGNZOQUGBjhQc0dYTW+Xtfc13p+1++T1HnpUPKyc/WxYLn1dXw93+pt2Nu/XP1a7DNd9KvPHGG3rooYdUv359eXt7y9PTU++//766d+8uSTp27JgqVaqkqlWrOsxXu3ZtHTt2rMRlJiYmavLkycWGr1ixwi12mH/NliRvpaen6/A2s6vB5aSkpJhdgtvbt2+fEhISSjXvsLnOzzN9+nQ1adKkVO3hz7Ftc230T+kU/d7WrVung4HGt2f0Z09Zr4/RfvjjiALC39IzGWXT3lsr3zK8jYBw6av1hToWVNfwttwB2zbX5m79k5ube1XTGRK609PTtWTJEjVq1Ehr165VfHy86tat63B02xkTJkzQ2LFj7a+zsrLUoEED9e7dW8HBwdeqdNNsOXRG2rpJnTp10vUNq5tdDi5hsViUkpKiXr16ycfHx+xy3Fpubq66du3q1DzZefn6Ou179enWXoF+vk7Ny5FuY7Ftc230T+n8fCRL07amq2vXrmpZ17h9kLL67Cmr9Skrob+e0EfzvDRjSLQa1zL2SPd36d+pY6eOxh7pPpmjsYu2qt+9/dWmQYhh7bgTtm2uzd36p+gM7D9zTbcSeXl5mjhxoj7//HP1799fktSqVStt3rxZ06ZN0y233KLQ0FCdP39eZ8+edTjaffz4cYWGhpa4XF9fX/n6Ft+Z9vHxcYsQVLSx9vb2dov1cVfu8vfmyqpUqaIOHTo4NY/FYtEfZ8+o242d6B8Xw7bNtdE/pVPWvzejP3vc7e8gwDdQ1nP1FFGthaJqO3fJkTMsFot+9f5V0SHRhv7ePAsyZT13RgG+gW7RP2XB3f6m3Y279c/VrsM1fU530TXWnp6Oi/Xy8pLVapUktW3bVj4+Pvrmm2/s43ft2qVDhw6pc+fO17IcAAAAAABM5fSR7uzsbO3du9f+ev/+/dq8ebOqV6+uhg0bKiYmRo8//rj8/PzUqFEjpaamat68eZoxY4akC0eyRowYobFjx6p69eoKDg7WqFGj1LlzZ+5cDgAAAABwK06H7k2bNqlnz57210XXWg8fPlxz5szRp59+qgkTJuiee+7RmTNn1KhRI7300kt6+OGH7fO8+uqr8vT01ODBg5Wfn68+ffrorbeMvxEFAAAAAABlyenQ3aNHD9lstsuODw0N1ezZs6+4jMqVK2vWrFmaNWuWs80DAAAAAFBuXNNrugEAAAAAwP9H6AYAAAAAwCCEbgAAAAAADHJNn9MNAADgzvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAQAXELoBAACu0r7/hdTxSVvLoDVvfbT3+zJoRwrwZZcQAIzCFhYAAOAq9W4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQBARUboBgAAuErVAyrprg4NDW+noKBAktSkVoCi6hkbugEAxuJGagAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQbzNLgAAAAC4FvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAZQdQjcAAADcwr7/hdTxSVvLoDVvfbT3+zJoRwrwZZcdKM94BwMAAMAt9G4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQAwFqEbAAAAbqF6QCXd1aGh4e0UFBRIkprUClBUPWNDN4DyjxupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNrsAd5Obm6udO3c6Nc+uo2eVf2yvdmzzk/V0VafmjYyMlL+/v1PzAAAAAMBfQe65eoTua2znzp1q27Ztqea9e67z82RkZKhNmzalag8AAAAASoPcc/UI3ddYZGSkMjIynJonOy9fX67eoP49OyvQz9fp9gAAAACgLJF7rh6h+xrz9/d3+hsYi8Wi30+dUOcO7eTj42NQZQAAAABwbZB7rh43UgMAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgTofutWvX6rbbblPdunXl4eGh5OTkYtPs2LFDAwcOVJUqVRQQEKD27dvr0KFD9vHnzp1TfHy8atSoocDAQA0ePFjHjx//SysCAAAAAICrcTp05+Tk6Prrr9esWbNKHL9v3z517dpVkZGRWrNmjX766Sc988wzqly5sn2aMWPG6IsvvtDChQuVmpqqI0eOKC4urvRrAQAAAACAC/J2doa+ffuqb9++lx3/1FNPqV+/fpo6dap9WJMmTez/z8zM1AcffKD58+frpptukiTNnj1bzZs3V3p6ujp16uRsSQAAAAAAuCSnQ/eVWK1Wffnll3riiSfUp08f/fjjjwoPD9eECRMUGxsrScrIyJDFYtEtt9xiny8yMlINGzbUhg0bSgzd+fn5ys/Pt7/OysqSJFksFlkslmu5CqYoWgd3WBd3RP+4NvqnbOTm5mrXrl1OzbP7aKbyj+3Vts2VdP54Fafmbdasmfz9/Z2aB84pKCiw/+T9YyzeP+6H94/rom9cm7vtt13telzT0H3ixAllZ2fr5Zdf1osvvqhXXnlFy5cvV1xcnFavXq2YmBgdO3ZMlSpVUtWqVR3mrV27to4dO1bichMTEzV58uRiw1esWOFWHyopKSlml4AroH9cG/1jrH379ikhIaFU8w6b6/w806dPdzhLCtfer9mS5K309HQd3mZ2Ne6N94/74f3juuib8sFd9ttyc3OvarprfqRbkgYNGqQxY8ZIklq3bq3169frnXfeUUxMTKmWO2HCBI0dO9b+OisrSw0aNFDv3r0VHBz81ws3mcViUUpKinr16iUfHx+zy8El6B/XRv+UjdzcXHXt2tWpebLz8vV12vfq0629Av18nZqXI3XG23LojLR1kzp16qTrG1Y3uxy3xvvH/fD+cV30jWtzt/22ojOw/8w1Dd01a9aUt7e3WrRo4TC8efPmWrdunSQpNDRU58+f19mzZx2Odh8/flyhoaElLtfX11e+vsU/cHx8fNyis4q42/q4G/rHtdE/xqpSpYo6dOjg1DwWi0V/nD2jbjd2om9ckLe3t/0n/WMs3j/uh/eP66Jvygd32W+72nW4ps/prlSpktq3b1/suqXdu3erUaNGkqS2bdvKx8dH33zzjX38rl27dOjQIXXu3PlalgMAAAAAgKmcPtKdnZ2tvXv32l/v379fmzdvVvXq1dWwYUM9/vjjuvPOO9W9e3f17NlTy5cv1xdffKE1a9ZIuvBt74gRIzR27FhVr15dwcHBGjVqlDp37sydywEAAAAAbsXp0L1p0yb17NnT/rroWuvhw4drzpw5uv322/XOO+8oMTFRo0ePVrNmzfTZZ585XMv06quvytPTU4MHD1Z+fr769Omjt9566xqsDgAAAAAArsPp0N2jRw/ZbLYrTvPAAw/ogQceuOz4ypUra9asWZo1a5azzQMAAAAAUG5c02u6AQAAAADA/0foBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAACAoQoLC5Wamqq1a9cqNTVVhYWFZpdUZgjdAAAAAADDJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSygShGwAAAABgiKSkJA0ZMkTR0dFKS0vTJ598orS0NEVHR2vIkCEVIngTugEAAAAA11xhYaESEhI0YMAAJScnq2PHjvLz81PHjh2VnJysAQMGaNy4cW5/qjmhGwAAAABwzaWlpenAgQOaOHGiPD0do6enp6cmTJig/fv3Ky0tzaQKywahGwAAAABwzR09elSSFBUVVeL4ouFF07krQjcAAAAA4JqrU6eOJGnbtm0lji8aXjSduyJ0AwAAAACuuW7duiksLExTpkyR1Wp1GGe1WpWYmKjw8HB169bNpArLBqEbAAAAAHDNeXl5afr06Vq6dKliY2OVnp6uvLw8paenKzY2VkuXLtW0adPk5eVldqmG8ja7AAAAAACAe4qLi9OiRYuUkJCg7t2724eHh4dr0aJFiouLM7G6skHoBgAAAAAYJi4uToMGDdLq1au1bNky9e3bVz179nT7I9xFCN0AAAAAAEN5eXkpJiZGOTk5iomJqTCBW+KabgAAAAAADEPoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAg3mYXAAAA/prc3Fzt3LnTqXl2HT2r/GN7tWObn6ynqzo1b2RkpPz9/Z2aB3BVvH9cF30Dd0HoBgCgnNu5c6fatm1bqnnvnuv8PBkZGWrTpk2p2gNcDe8f10XfwF0QugEAKOciIyOVkZHh1DzZefn6cvUG9e/ZWYF+vk63B7gL3j+ui76BuyB0AwBQzvn7+zt9dMZisej3UyfUuUM7+fj4GFQZ4Pp4/7gu+gbughupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNruA0rDZbJKkrKwskyu5NiwWi3Jzc5WVlSUfHx+zy8El6B/XRv+4LvrGtdE/ro3+cW30j+uib1ybu/VPUR4tyqeXUy5D9x9//CFJatCggcmVAAAAAAAqsj/++ENVqlS57HgP25/FchdktVp15MgRBQUFycPDw+xy/rKsrCw1aNBAv/76q4KDg80uB5egf1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1j82m01//PGH6tatK0/Py1+5XS6PdHt6eqp+/fpml3HNBQcHu8Ufn7uif1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1D9XOsJdhBupAQAAAABgEEI3AAAAAAAGIXS7AF9fXz333HPy9fU1uxSUgP5xbfSP66JvXBv949roH9dG/7gu+sa1VdT+KZc3UgMAAAAAoDzgSDcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEK3CQoKCvT888/rt99+M7sUALhm2LYBAMqaxWLRzTffrD179phdCq7g/Pnz+u2333To0CGHfxUFdy83SVBQkLZu3aqwsDCzS8ElLBaLIiMjtXTpUjVv3tzscoByhW2ba2P75vq++eYbffPNNzpx4oSsVqvDuA8//NCkqlBk06ZN2rFjhySpefPmateunckVQZJq1aql9evXq2nTpmaXgkvs2bNHDzzwgNavX+8w3GazycPDQ4WFhSZVVra8zS6gorrpppuUmprKjqkL8vHx0blz58wuAyiX2La5NrZvrm3y5Ml6/vnn1a5dO9WpU0ceHh5ml4T/+e233/S3v/1N3377rapWrSpJOnv2rG688UZ9+umnql+/vrkFVnBDhw7VBx98oJdfftnsUnCJ++67T97e3lq6dGmF3q5xpNsk77zzjiZPnqx77rlHbdu2VUBAgMP4gQMHmlQZJGnKlCnavXu3/v3vf8vbm++mXE1OTo5efvnlyx4N+uWXX0yqDGzbXB/bN9dVp04dTZ06VcOGDTO7FFzi1ltv1dmzZzV37lw1a9ZMkrRr1y7df//9Cg4O1vLly02usGIbNWqU5s2bp6ZNm5b42TNjxgyTKkNAQIAyMjIUGRlpdimmInSbxNPz8pfTV6RTLVzV7bffrm+++UaBgYGKjo4utvFOSkoyqTJI0t/+9jelpqZq2LBhJX5r+uijj5pUGdi2uT62b66rRo0a2rhxo5o0aWJ2KbiEn5+f1q9frxtuuMFheEZGhrp166bc3FyTKoMk9ezZ87LjPDw8tGrVqjKsBhdr3769Xn31VXXt2tXsUkzFV9wmufTIHFxL1apVNXjwYLPLwGUsW7ZMX375pbp06WJ2KbgE2zbXx/bNdf3973/X/Pnz9cwzz5hdCi7RoEEDWSyWYsMLCwtVt25dEyrCxVavXm12CbiMV155RU888YSmTJmi6Oho+fj4OIwPDg42qbKyxZFuF3Du3DlVrlzZ7DKAciM8PFxfffUVN4JycWzbAOc8+uijmjdvnlq1aqVWrVoV2znlFFnzLF68WFOmTNGsWbPsN0/btGmTRo0apSeffFKxsbHmFghJ0t69e7Vv3z51795dfn5+9pt1wTxFZ8Bd2g8V7UZqhG6TFBYWasqUKXrnnXd0/Phx7d69W40bN9YzzzyjsLAwjRgxwuwSK7yCggKtWbNG+/bt0913362goCAdOXJEwcHBCgwMNLu8Cu3jjz/W4sWLNXfuXPn7+5tdDi7Ctq18YPvmmjhF1nVVq1ZNubm5KigosN8Loej/l16icebMGTNKrNBOnz6tO+64Q6tXr5aHh4f27Nmjxo0b64EHHlC1atU0ffp0s0ussFJTU684PiYmpowqMRenl5vkpZde0ty5czV16lQ9+OCD9uFRUVGaOXMmO6YmO3jwoG699VYdOnRI+fn56tWrl4KCgvTKK68oPz9f77zzjtklVmjTp0/Xvn37VLt2bYWFhRU7GvTDDz+YVBnYtrk+tm+ui1NkXdfMmTPNLgFXMGbMGPn4+OjQoUMOZ8HdeeedGjt2LKHbRBUlVP8ZQrdJ5s2bp/fee08333yzHn74Yfvw66+/Xjt37jSxMkgXTvFr166dtmzZoho1atiH33777Q5BAubgND7XxbbN9bF9A5w3fPhws0vAFaxYsUJff/11sUe3NW3aVAcPHjSpKlwsNzdXhw4d0vnz5x2Gt2rVyqSKyhah2ySHDx9WREREseFWq7XEG3WgbKWlpWn9+vWqVKmSw/CwsDAdPnzYpKpQ5LnnnjO7BFwG2zbXx/bNtW3atEn//e9/S9w55c7y5iosLFRycrJ27NghSWrZsqUGDhwoLy8vkytDTk5OiZebnTlzRr6+viZUhCInT57U/fffr2XLlpU4vqJc0335Z7vAUC1atFBaWlqx4YsWLSr2OAqUPavVWuJG4LffflNQUJAJFQHlA9s218f2zXV9+umnuvHGG7Vjxw59/vnnslgs+vnnn7Vq1SpVqVLF7PIqtL1796p58+a69957lZSUpKSkJA0dOlQtW7bUvn37zC6vwuvWrZvmzZtnf+3h4SGr1aqpU6de8V4JMN5jjz2ms2fP6rvvvpOfn5+WL1+uuXPnqmnTplqyZInZ5ZUZjnSb5Nlnn9Xw4cN1+PBhWa1WJSUladeuXZo3b56WLl1qdnkVXu/evTVz5ky99957ki5svLOzs/Xcc8+pX79+JleHwsJCvfrqq5c9GsRNbMzDts31sX1zXVOmTNGrr76q+Ph4BQUF6bXXXlN4eLj+8Y9/qE6dOmaXV6GNHj1aTZo0UXp6uqpXry7pws27hg4dqtGjR+vLL780ucKKberUqbr55pu1adMmnT9/Xk888YR+/vlnnTlzRt9++63Z5VVoq1at0uLFi9WuXTt5enqqUaNG6tWrl4KDg5WYmKj+/fubXWKZ4O7lJkpLS9Pzzz+vLVu2KDs7W23atNGzzz6r3r17m11ahffbb7+pT58+stls2rNnj9q1a6c9e/aoZs2aWrt2rUJCQswusUJ79tln9e9//1sJCQl6+umn9dRTT+nAgQNKTk7Ws88+q9GjR5tdYoXGts21sX1zXQEBAfr5558VFhamGjVqaM2aNYqOjtaOHTt000036ejRo2aXWGEFBAQoPT1d0dHRDsO3bNmiLl26KDs726TKUCQzM1Nvvvmmw2dPfHw8X1iZLDg4WD/99JPCwsLUqFEjzZ8/X126dNH+/fvVsmVL5ebmml1imeBIt4m6deumlJQUs8tACerXr68tW7ZowYIF9o33iBEjdM8998jPz8/s8iq8//znP3r//ffVv39/TZo0SX/729/UpEkTtWrVSunp6YRuk7Ftc21F27dPP/1UP/30E9s3F1KtWjX98ccfkqR69epp27Ztio6O1tmzZyvMjqmr8vX1tffNxbKzs4vdHwHmqFKlip566imzy8AlmjVrpl27diksLEzXX3+93n33XYWFhemdd96pUF+IcKTbZOfPn9eJEydktVodhjds2NCkiiBJa9eu1Y033mh/FmeRgoICrV+/Xt27dzepMkgXjjjs2LFDDRs2VJ06dfTll1+qTZs2+uWXX3TDDTcoMzPT7BIBwGl333232rVrp7Fjx+qFF17QG2+8oUGDBiklJUVt2rThRmomuvfee/XDDz/ogw8+UIcOHSRJ3333nR588EG1bdtWc+bMMbdA6OzZs9q4cWOJ+9X33nuvSVXh448/VkFBge677z5lZGTo1ltv1ZkzZ1SpUiXNmTNHd955p9kllglCt0n27NmjBx54QOvXr3cYbrPZ5OHhUWHu5OeqvLy8dPTo0WKnWZ4+fVohISH0j8maNWumefPmqWPHjuratasGDBig8ePHa8GCBRo1apROnDhhdokVSrVq1eTh4XFV03K9vWvYs2ePVq9eXeLO6bPPPmtSVThz5ozOnTununXr2m8CtX79ejVt2lRPP/20qlWrZnaJFdbZs2c1fPhwffHFF/Lx8ZF04Yv4gQMHavbs2apataq5BVZwX3zxhe655x5lZ2crODjY4TPJw8ODzx4Xkpubq507d6phw4aqWbOm2eWUGUK3Sbp06SJvb2+NHz9ederUKbbDev3115tUGSTJ09NTx48fV61atRyG7969W+3atVNWVpZJlUGSxo8fr+DgYE2cOFELFizQ0KFDFRYWpkOHDmnMmDF6+eWXzS6xQpk7d679/6dPn9aLL76oPn36qHPnzpKkDRs26Ouvv9YzzzyjMWPGmFUm/uf999/XI488opo1ayo0NLTYzukPP/xgYnWAa9u7d6/9kWHNmzcv8RGJKHvXXXed+vXrpylTppT46DDAbIRukwQEBCgjI0ORkZFml4KLxMXFSZIWL16sW2+91eHZjoWFhfrpp5/UrFkzLV++3KwSUYINGzZow4YNatq0qW677Tazy6nQBg8erJ49e2rkyJEOw998802tXLlSycnJ5hQGu0aNGumf//ynnnzySbNLwWWcOHGixLMQWrVqZVJFeP755zVu3LhigS4vL0//+te/OEPEZAEBAdq6dasaN25sdim4hM1m06JFiy57dlVFuWyG0G2S9u3b69VXX1XXrl3NLgUXuf/++yVdOHJ3xx13ONxUqFKlSgoLC9ODDz5YoU6HAZwRGBiozZs3Fzv6s3fvXrVu3Zo7/LqA4OBgbd68mZ1TF5SRkaHhw4drx44dunT3jEvPzMVlZ64tLi5Od911l+644w6zS8ElHn30Ub377rvq2bOnateuXezs3tmzZ5tUWdni7uVl6OJTkl955RU98cQTmjJliqKjo+3XBxUJDg4u6/Kg///GDwsL0+OPP84pSi7syJEjWrduXYnfmnL3cvPUqFFDixcvVkJCgsPwxYsXq0aNGiZVhYv93//9n1asWKGHH37Y7FJwiQceeEDXXXedPvjggxJ3TmGeonvuXGrLli3253ajbC1ZssT+//79++vxxx/X9u3bS9yvHjhwYFmXh//56KOPlJSUpH79+pldiqk40l2GPD09HTbYJW3AuZGaa7jpppuUlJRU7MYoWVlZio2N1apVq8wpDJKkOXPm6B//+IcqVaqkGjVqFLsm9ZdffjGxuoptzpw5+vvf/66+ffuqY8eOki7c4Xf58uV6//33dd9995lbYAX1+uuv2/+fk5OjGTNmqH///iXunPKllXmCgoL0448/cp2wCym6UWRmZmaxG3QVFhYqOztbDz/8sGbNmmVilRWTp6fnVU3HfrW5wsPDtWzZsgp/SS2huwylpqZe9bQxMTEGVoI/c7nTyE6cOKF69erJYrGYVBkkqUGDBnr44Yc1YcKEq/7QRdn57rvv9PrrrzvcbGj06NH2EI6yFx4eflXT8aWVuWJjYzVs2DANHjzY7FLwP3PnzpXNZtMDDzygmTNnqkqVKvZxRZedFd00EkBxc+fO1fLly/Xhhx86XLZZ0RC6gYv89NNPkqTWrVtr1apVDqeMFRYWavny5Xr33Xd14MABkyqEdOEU5o0bN6pJkyZmlwIA18ypU6c0fPhwdejQQVFRUZwi60JSU1PtT54BcPXy8vJ0++2369tvv1VYWFix7VpFeWIGWw6TzJ49W4GBgfq///s/h+ELFy5Ubm6uhg8fblJlFVvr1q3l4eEhDw8P3XTTTcXG+/n56Y033jChMlxsxIgRWrhwocaPH292KZCceoQe96sALm/Dhg369ttvtWzZsmLjOEXWXEFBQdqxY4eio6MlXbhPxezZs9WiRQtNmjRJlSpVMrnCim306NGKiIgodnnMm2++qb1792rmzJnmFAYNHz5cGRkZGjp0aIW+VwVHuk1y3XXX2e/kd7HU1FQ99NBD2rVrl0mVVWwHDx6UzWZT48aNtXHjRofndFeqVEkhISHy8vIysUJIF846GDBggPLy8kq8JnXGjBkmVVYxXXq/ipJwvwrXMXjwYHXo0KHYI8OmTp2q77//XgsXLjSpMoSFhWnAgAF65plnVLt2bbPLwUXat2+v8ePHa/Dgwfrll1/UokULxcXF6fvvv1f//v0JdSarV6+elixZorZt2zoM/+GHHzRw4ED99ttvJlWGgIAAff311xX+iU0c6TbJoUOHSrzGrlGjRjp06JAJFUG68PuXVOxu2HAtiYmJ+vrrr9WsWTNJKnYjNZSt1atXm10CnLB27VpNmjSp2PC+fftq+vTpZV8Q7E6fPq0xY8YQuF3Q7t271bp1a0kXzkqMiYnR/Pnz9e233+quu+4idJvs9OnTDtfbFwkODtapU6dMqAhFGjRowFluInSbJiQkRD/99JPCwsIchm/ZsoXH6phkyZIl6tu3r3x8fBweQ1ESrqsz1/Tp0/Xhhx9yJ2wXwY0fy5fs7OwST4X18fFx6lIBXHtxcXFavXo196twQTabzf6F/MqVKzVgwABJFwIFoc58ERERWr58uUaOHOkwfNmyZWrcuLFJVUG6sM/2xBNP6J133imWeyoSQrdJ/va3v2n06NEKCgpS9+7dJV04tfzRRx/VXXfdZXJ1FVNsbKyOHTumkJAQxcbGXnY6TpE1n6+vr7p06WJ2GbiMtLQ0vfvuu/rll1+0cOFC1atXTx999JHCw8Mr/OllriA6OloLFizQs88+6zD8008/VYsWLUyqCtKFS88mTJigdevW8Tg3F9OuXTu9+OKLuuWWW5Samqq3335bkrR//37OTHABY8eO1ciRI3Xy5En7PXm++eYbTZ8+nbMQTDZ06FDl5uaqSZMm8vf3L7ZdO3PmjEmVlS2u6TbJ+fPnNWzYMC1cuNB+J0yr1ap7771Xb7/9tnx9fU2uEHBdiYmJOnr0qMOzh+EaPvvsMw0bNkz33HOPPvroI23fvl2NGzfWm2++qa+++kpfffWV2SVWeF988YXi4uJ09913O+ycfvLJJ1q4cOEVv3SEsa70aDce52auLVu2aOjQoTp06JDGjh2r5557TpI0atQonT59WvPnzze5Qrz99tt66aWXdOTIEUkX7pEwadIk3XvvvSZXVrHNnTv3iuMrys2jCd0m27NnjzZv3iw/Pz9FR0fbrykGcHm33367Vq1apRo1aqhly5bFvjVNSkoyqTLccMMNGjNmjO69914FBQVpy5Ytaty4sX788Uf17dtXx44dM7tESPryyy81ZcoU++dPq1at9Nxzz3GpAOCkc+fOydvbm0eJuZCTJ0/Kz89PgYGBZpcC2LGFMMnzzz+vcePGqWnTpmratKl9eF5env71r38VO+0PZeNqj5xyip+5qlatqri4OLPLQAl27dplv2TmYlWqVNHZs2fLviCUqH///urfv7/ZZeAyzp8/r/3796tJkyaEORfRuHFjff/998Xuu3Pu3Dm1adOGsxBMdtNNNykpKUlVq1Z1ePJMVlaWYmNjtWrVKhOrw759+zR79mzt27dPr732mkJCQrRs2TI1bNhQLVu2NLu8MsGRbpN4eXnp6NGjCgkJcRh++vRphYSEcM2wSS49te/XX39VnTp1HHZ6OMUPuLzGjRvrvffe0y233OJwpHvevHl6+eWXtX37drNLrPAuFx7Onj1LeDBZbm6uRo0aZT8dc/fu3WrcuLFGjRqlevXqafz48SZXWHF5enra7/tysePHj6tBgwY6f/68SZVBunz/nDhxQvXq1ZPFYjGpMqSmpqpv377q0qWL1q5dqx07dqhx48Z6+eWXtWnTJi1atMjsEssEX5+apOiZtZfasmWLqlevbkJFkC7cEOViQUFBSk1N5c6XLqigoEBr1qzRvn37dPfddysoKEhHjhxRcHAwp5SZ6MEHH9Sjjz6qDz/8UB4eHjpy5Ig2bNigcePG6ZlnnjG7PEg6cOBAiV/s5ufn6/DhwyZUhCITJkzQli1btGbNGt1666324bfccosmTZpE6DbBxU8z+frrrx0eS1VYWKhvvvnmitfiw1g//fST/f/bt293uISpsLBQy5cvV7169cwoDf8zfvx4vfjiixo7dqyCgoLsw2+66Sa9+eabJlZWtgjdZaxatWry8PCQh4eHrrvuOofgXVhYqOzsbD388MMmVgi4voMHD+rWW2/VoUOHlJ+fr169eikoKEivvPKK8vPz9c4775hdYoU1fvx4Wa1W3XzzzcrNzVX37t3l6+urcePGadSoUWaXV6FdTXioyI9zcQXJyclasGCBOnXq5LB/0LJlS+3bt8/Eyiqui28seOkNn3x8fBQWFsbz7U3UunVr+3510Y0hL+bn56c33njDhMpQZOvWrSXeaDAkJKRCPW6P0F3GZs6cKZvNpgceeECTJ0922OmpVKmSwsLC1LlzZxMrBFzfo48+qnbt2hV7rv3tt9+uBx980MTK4OHhoaeeekqPP/649u7dq+zsbLVo0YKzD1xAUXjw8PAgPLiokydPFjs9VpJycnJKPDsOxit6Nnd4eLg2bdpU7LIMmGv//v2y2Wxq3LixNm7c6HA9d6VKlRQSEiIvLy8TK0TVqlV19OjRYmeE/PjjjxXqLARCdxkr2tEJDw/XjTfeWOyuywD+XFpamtavX69KlSo5DA8LC+P0WBdRqVIlnvnsYi4OD99//71q1qxpckW4VLt27fTll1/azwopCtr//ve/+ULeRBaLRY0bN9aZM2cI3S6m6Kk/Rds3uJ677rpLTz75pBYuXCgPDw9ZrVZ9++23GjduXIV6nBuh2yQXP5bl3LlzxW7AERwcXNYlQRfucnkxDw8PZWdnFxtO/5jLarWWeE3qb7/95nC9EMpGXFyc5syZo+Dg4D+9qzyPczPfpfeugOuYMmWK+vbtq+3bt6ugoECvvfaatm/frvXr1ys1NdXs8iosHx8fh2uH4RqWLFmivn37ysfHx+HymZIMHDiwjKrCpaZMmaL4+Hg1aNBAhYWFatGihQoLC3X33Xfr6aefNru8MsPdy02Sm5urJ554Qv/97391+vTpYuO5e7k5PD09HU7hu/SGd0Wv6R9z3XnnnapSpYree+89BQUF6aefflKtWrU0aNAgNWzYULNnzza7xArl/vvv1+uvv66goCDdd999VzwNlr4xx+uvv66HHnpIlStX/tNHI/JIRHPt27dPL7/8srZs2aLs7Gy1adNGTz75pKKjo80urUIbM2aMfH199fLLL5tdCv7n4juWe3p6XnY69ttcw6+//qqtW7cqOztbN9xwg8MjkysCQrdJ4uPjtXr1ar3wwgsaNmyYZs2apcOHD+vdd9/Vyy+/rHvuucfsEiukqz2ScPGZCih7v/32m/r06SObzaY9e/aoXbt22rNnj2rWrKm1a9eWeE0kjHPx0Qa4pouvR73SnZZ5JCJQslGjRmnevHlq2rSp2rZtq4CAAIfxM2bMMKkyoHwpLCzU1q1b1ahRI1WrVs3scsoModskDRs21Lx589SjRw8FBwfrhx9+UEREhD766CN98skn+uqrr8wuEVfh5Zdf1sMPP6yqVauaXUqFU1BQoAULFjgcDbrnnnvk5+dndmkVjpeXl44dO6ZatWrJy8tLR48e5YsPoBR++OEH+fj42I9qL168WLNnz1aLFi00adKkYvexQNnp2bPnZcd5eHho1apVZVgNLnbgwAGlpKTIYrEoJiZGLVu2NLskXOSxxx5TdHS0RowYocLCQsXExGj9+vXy9/fX0qVL1aNHD7NLLBOEbpMEBgZq+/btatiwoerXr6+kpCR16NBB+/fvV3R0tLKzs80uEVchODhYmzdv5jneqNBCQ0P1/vvv67bbbpOnp6eOHz/ucAdZuI709HR98cUXslgsuummmxyeBQ3ztW/fXuPHj9fgwYP1yy+/qEWLFoqLi9P333+v/v37a+bMmWaXCLiU1atXa8CAAcrLy5MkeXt768MPP9TQoUNNrgxF6tevr+TkZLVr107Jycn65z//qTVr1uijjz7SqlWr9O2335pdYpm4/AUQMFTjxo3tN7OJjIzUf//7X0nSF198wVHTcoTvrMwxd+5cffnll/bXTzzxhKpWraobb7xRBw8eNLGyiunhhx/WoEGD5OXlJQ8PD4WGhsrLy6vEfzDPokWL1KVLF7322mt6//331b9/f02bNs3ssnCR3bt3q3Xr1pKkhQsXKiYmRvPnz9ecOXP02WefmVsc7H777Tf99ttvZpcBSc8884x69eqlw4cP6/Tp03rwwQf1xBNPmF0WLnLq1CmFhoZKkr766ivdcccduu666/TAAw9o69atJldXdgjdJrn//vu1ZcsWSdL48eM1a9YsVa5cWY899pgef/xxk6sDXNuUKVPsp5Fv2LBBb775pqZOnaqaNWtqzJgxJldX8UyaNEnbt2/X4sWLZbPZ9OGHHyopKanEfzBPYmKiHnzwQWVmZur333/Xiy++qClTpphdFi5is9nsjz5auXKl+vXrJ0lq0KCBTp06ZWZpFZ7VatXzzz+vKlWqqFGjRmrUqJGqVq2qF154gcdV/b/27jys5rz/H/jztGsvadGEkiUUkoy1QZYy0s1t3GMr21iGYZA9S8LgjsZtbpIlTMNYs0y2iWk0QkTxbSoRNciWUGlR5/dHd+fX0WHM4rxPnefjulxX5/05mWfXXD6d1+f9fr/eAl2/fh3Lly+HjY0NzMzMsHr1ajx8+FBhk2ISw8rKCikpKSgrK8Px48fRq1cvABVNpdXpYTyPDBOkamHg6emJ1NRUXL58GU2aNGGHUqLfkZ2dDUdHRwBAVFQU/vnPf+Kzzz5D586d1WZvkKpp3rw5mjdvjkWLFmHw4MHQ19cXHYlek5aWhu+//172IWfGjBlYuHAhHj58yD34KsLNzQ3BwcHw9PREbGwsNmzYAKDimDcrKyvB6dTb/PnzsWXLFnz11Vfo3LkzACAuLg6LFy9GUVERli1bJjihenr+/DksLCxkr/X19VGnTh08e/aMZ6qriFGjRuGTTz6BjY0NJBIJPD09AQAXLlxA8+bNBadTHhbdSnb69GlMnjwZ58+flzvrufKJaadOnbBx40Z07dpVYEoi1WZoaIgnT56gQYMGOHnyJKZPnw4A0NPTk+3rIjFiY2MxderUakX38+fP4evry2ZDAhUWFsr93tHR0YGenh7y8/NZdKuI0NBQDBs2DFFRUZg/f77s4eK+ffvQqVMnwenU2/bt27F582a5855dXFxga2uLSZMmsegW6MSJEzAxMZG9Li8vR0xMDK5fvy4b4znd4ixevBitWrVCdnY2Bg8eDF1dXQAVTVjnzJkjOJ3ysJGakvn4+KB79+5vXAK7bt06nDlzBgcPHlRyMvozjIyMkJSUxEZqSjZs2DCkpqaibdu22LVrF7KyslC3bl0cPnwY8+bNk/tFS8r1pu7lDx8+hK2tLUpLSwUlIw0NDQQHB8PQ0FA2Nnv2bAQEBMjNFPGcbtVTVFQETU1NHssnkJ6eHpKTk9G0aVO58bS0NLRp04YPfAV52/nclXhON6kCznQrWVJSElauXPnG671792Zjmxqka9euPKJKgG+++QYLFixAdnY29u/fL1tCdvnyZXz66aeC06mn5ORkABV7UlNSUpCTkyO7VrmPy9bWVlQ8QsVRleHh4XJj1tbW2Llzp+y1RCJh0a2C9PT0REdQe61bt8b69euxbt06ufH169ejdevWglIR99PXDAUFBYiNjUVWVhZKSkrkrqnL7xzOdCuZnp4erl+/Llsy9rqMjAw4OzvziakAz58/f+f3Vl2iSUQVsw0SiQSA4q7+derUwX/+8x+MHj1a2dGIaoyysjKsXbsWe/bsUfjhNDc3V1Ayio2NRb9+/dCgQQN07NgRQEUjz+zsbERHR3NbYA3Rr18/bN68GTY2NqKjqI0rV67A29sbhYWFKCgogLm5OR4/fgx9fX1YWlri1q1boiMqBWe6lczW1vatRXdycjJvBIKYmprKiobfw2VKqqGwsFDhB1MXFxdBidRXZmYmpFIpHBwccPHiRblzunV0dGBpaalWXUprA2dnZ0RHR8POzk50FLWxZMkSbN68GTNmzMCCBQswf/583L59G1FRUVi4cKHoeGrNw8MD6enp+Oabb5CamgoAGDhwICZNmoT69esLTkfv6ueff+bElpJ9+eWX6N+/PzZu3AgTExOcP38e2traGD58OKZOnSo6ntJwplvJpkyZgp9++gkJCQnVlou9fPkS7u7u6N69e7XlS/T+xcbGyr6+ffs25syZA39/f7kn2tu3b8eKFSvg5+cnKiYBePToEfz9/XH8+HGF1/lQhOivY88K5WvcuDHWrVuHfv36wcjICFevXpWNnT9/Ht99953oiEQ1Gu9rymdqaooLFy6gWbNmMDU1RXx8PJycnHDhwgX4+fnJHmLVdpzpVrIFCxbgwIEDaNq0KSZPnoxmzZoBAFJTU/HNN9+grKwM8+fPF5xSPXl4eMi+DgoKwpo1a+T2B/v4+MDZ2RmbNm1i0S3YtGnT8OzZM1y4cAEfffQRDh48iAcPHiA4OBghISGi46m1HTt2vPX6yJEjlZSEqObJycmRHRtqaGiIZ8+eAQA+/vhjBAYGioxGAPLy8nDx4kU8fPiw2l5i3tuIFNPW1pY1vLO0tERWVhacnJxgYmKC7OxswemUh0W3kllZWeHcuXOYOHEi5s6dK9v7KJFI0KdPH3zzzTc8i1MFxMfHY+PGjdXG3dzcMHbsWAGJqKrTp0/j0KFDcHNzg4aGBho2bIhevXrB2NgYK1asQL9+/URHVFuvLxUrLS1FYWEhdHR0oK+vzw+mRG/xwQcf4P79+2jQoAEaN26MkydPwtXVFQkJCbJjdkiMI0eOYNiwYcjPz4exsbHcdjSJRMJ7G9EbtG3bFgkJCWjSpAk8PDywcOFCPH78GDt37kSrVq1Ex1Oa3++zT3+7hg0bIjo6Go8fP8aFCxdw/vx5PH78GNHR0bC3txcdjwDY2dlV6/ILAJs3b+b+RhVQUFAgO5LKzMwMjx49AlCxBzUxMVFkNLX39OlTuT/5+flIS0tDly5dsGvXLtHxiFTaP/7xD8TExACo2I4WGBiIJk2aYOTIkWxCKNiMGTMwevRo5OfnIy8vT+4+xwZ3RG+2fPlyWb+qZcuWwczMDBMnTsSjR4+wadMmwemUh3u6iRSIjo7GoEGD4OjoiA4dOgAALl68iBs3bmD//v3w9vYWnFC9tW/fHsHBwejTpw98fHxgamqKFStWYN26ddi3bx9u3rwpOiK95tKlSxg+fLja7N2qDbj3Ubz4+HjEx8ejSZMm6N+/v+g4as3AwADXrl3jv4cajvc1EoXLy4kU8Pb2Rnp6OjZs2CArEvr3748JEyZwplsFTJ06Fffv3wcALFq0CH379kVkZCR0dHQQEREhNhwppKWlhXv37omOQVSjdOzYUdbMk8Tq06cPLl26xGKthps3bx7Mzc1Fx1BrJSUlKCkpgaGhoegoSsWZbiKq8QoLC5GamooGDRrAwsJCdBy1dvjwYbnXUqkU9+/fx/r162FnZ4djx44JSqbezM3NkZ6eDgsLC4wePRpff/01jIyM3vo93333HQYMGAADAwMlpaQnT56gbt26AIDs7GyEh4fj5cuX8PHx4TnQAlS9nz169AhBQUEYNWoUnJ2doa2tLfdeHx8fZcejKl7/3VNJIpFAT08Pjo6O3MIpwLZt25CYmIgPP/wQw4YNw9y5c7FmzRq8evUKPXr0wO7du2X3vNqORTfRG5w9exZhYWG4desW9u7dC1tbW+zcuRP29vbo0qWL6HhEKqmyQ2kliUSCevXqoUePHggJCZHt6yLlMjQ0RHJyMhwcHKCpqYmcnBy5s9RJrGvXrqF///7Izs5GkyZNsHv3bvTt2xcFBQXQ0NBAQUEB9u3bB19fX9FR1crr97M3kUgkPKpSMA0NDUgkErxe1lSOSSQSdOnSBVFRUTAzMxOUUr0sW7YMy5YtQ+fOnZGYmIhPPvkEUVFRmDZtGjQ0NLBu3Tp8/PHH2LBhg+ioSsGim0iB/fv3Y8SIERg2bBh27tyJlJQUODg4YP369YiOjkZ0dLToiGrrxo0bSE5OhqurK+zt7fHDDz9g5cqVePnyJXx9fTFv3jy5rrIkRmVzOxZ2qqFXr1548OAB2rVrh+3bt2PIkCGoU6eOwvdu3bpVyenIy8sLWlpamDNnDnbu3ImjR4+iT58+soaeU6ZMweXLl3H+/HnBSYlUU0xMDObPn49ly5bB3d0dQEUvnsDAQCxYsAAmJiYYP348OnTogC1btghOqx6aNGmCoKAgfPrpp7h06RI6dOiAPXv2YNCgQQCAY8eOYcKECbhz547gpMrB7uVECgQHB2Pjxo0IDw+XW0JW+bSOxDh48CBatGiBoUOHwsnJCTt27MA///lPGBgYwMrKCosXL8aqVatEx1RbeXl5+Pzzz2FhYQFra2tYW1vDwsICkydPRl5enuh4au3bb7+Ft7c38vPzIZFI8OzZs2qd5iv/kPIlJCTIZoT+/e9/4969e5g0aRI0NDSgoaGBKVOmsAmhIPHx8Th69Kjc2I4dO2Bvbw9LS0t89tlnKC4uFpSOKk2dOhVr1qxBz549YWRkBCMjI/Ts2ROrV69GQEAAOnfujNDQUJw6dUp0VLWRlZUlWxnq5uYGLS0tuSPCXFxcZP151AEbqREpkJaWhm7dulUbNzExYfEg0LJlyzBr1iwEBwcjIiICEyZMwIoVKzBt2jQAwKZNm7B27VrMnj1bbFA1lJubi44dO+Lu3bsYNmwYnJycAAApKSmIiIhATEwMzp07x2V9glhZWeGrr74CANjb22Pnzp1qs4+uJsjNzYW1tTWAiq0ABgYGcv9WzMzM8OLFC1Hx1NqSJUvQvXt3fPzxxwAqtgKMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1d/PmTRgbG1cbNzY2xq1btwBUzLw+fvxY2dHUVmlpKXR1dWWvdXR05CaytLS01GpbBme6iRSwtrZGRkZGtfG4uDh2LhUoLS0No0ePhkQigZ+fH0pKSuDp6Sm73rt3b7VZpqRqgoKCoKOjg5s3byIsLAzTpk3DtGnTsGnTJmRkZEBbWxtBQUGiYxKAzMxMWcFdVFQkOA1Ven1bDLfJqIakpCT07NlT9nr37t3o0KEDwsPDMX36dKxbtw579uwRmJAAoF27dggICJBtbQIqtjnNmjUL7du3B1CxPY0n0ChXSkoKkpOTkZycDKlUitTUVNnr//u//xMdT6k4002kwLhx4zB16lRs3boVEokE9+7dQ3x8PGbOnInAwEDR8dRWQUGBrOOyhoYG6tSpA319fdn1OnXqcJmfIFFRUQgLC4OVlVW1a9bW1li1ahUmTJiAtWvXCkhHVZWXl2PZsmXYuHEjHjx4gPT0dDg4OCAwMBCNGjXCmDFjREdUS/7+/rJZoaKiIkyYMEHWOZ73NXGePn0qd1+LjY2Fl5eX7HX79u2RnZ0tIhpVsWXLFgwYMAAffPCBrLDOzs6Gg4MDDh06BADIz8/HggULRMZUOz179pRrble5YqRqgzt1waKbSIE5c+agvLwcPXv2RGFhIbp16wZdXV3MnDkTU6ZMER1PbUkkErkb9OuvSZz79++jZcuWb7zeqlUr5OTkKDERvUlwcDC2b9+OVatWYdy4cbLxVq1aITQ0lEW3AH5+fnKvhw8fXu09I0eOVFYcqsLKygqZmZmws7NDSUkJEhMTsWTJEtn1Fy9eVDs+jJSvWbNmSElJwcmTJ5Geni4b69Wrl6wLPbv/K1dmZqboCCqF3cuJ3qKkpAQZGRnIz89HixYtYGhoKDqSWtPQ0ICJiYms0M7Ly4OxsbHsF6pUKsXz58/Vao+QqrC1tcX333//xuP0zp49iyFDhuDevXtKTkavc3R0RFhYmKzhUFJSEhwcHJCamoqOHTuymVoN8Ntvv6F+/frvfKQV/XkTJ05EUlISVq5ciaioKGzfvh337t2Djo4OACAyMhKhoaFISEgQnJSoZps0aRKCgoJgYWEhOsp7wZluIgVGjx6Nr7/+GkZGRmjRooVsvKCgAFOmTOGROoJs27ZNdAR6gz59+mD+/Pk4deqU7MNopeLiYgQGBqJv376C0lFVd+/ehaOjY7Xx8vJylJaWCkhEf1SLFi1w9epV9hhRgqVLl2LgwIHw8PCAoaEhtm/fLneP27p1K3r37i0wIVWKiYlBTEwMHj58iPLycrlr/Nym+r799lvMnDmz1hbdnOkmUkBTUxP379+HpaWl3Pjjx49hbW2NV69eCUpGf8SuXbvg4+Mj2xdJ789vv/0GNzc36Orq4vPPP0fz5s0hlUrx66+/4r///S+Ki4tx6dIlNrFRAe3atcOXX36J4cOHy810BwUF4dSpUzh79qzoiPQ7qv5/I+V49uwZDA0NoampKTeem5sLQ0PDag8bSbmWLFmCoKAguLm5wcbGptrWs4MHDwpKRu+qtt/XONNNVMXz588hlUohlUrx4sUL6Onpya6VlZUhOjq6WiFOqmv8+PHo0KFDrb2Bq5IPPvgA8fHxmDRpEubOnStrnCKRSNCrVy+sX7+eBbeKWLhwIfz8/HD37l2Ul5fjwIEDSEtLw44dO6qdR0xEFUxMTBSOm5ubKzkJKbJx40ZERERgxIgRoqMQKcSim6gKU1NTWXOupk2bVrsukUjkGqiQauNCHuWyt7fHsWPH8PTpU9y4cQNAxf5hfihVLQMGDMCRI0cQFBQEAwMDLFy4EK6urjhy5Ah69eolOh4R0R9WUlKCTp06iY5B9EYsuomqOHPmDKRSKXr06IH9+/fLFQs6Ojpo2LAh6tevLzAhkeozMzODu7u76Bj0Fl27dsWpU6dExyAi+luMHTsW3333HY91JZXFopuoCg8PDwCQHQ/CzrBERKRqeFQikbyioiJs2rQJP/74I1xcXKod47ZmzRpByYgqsOgmUqBhw4YAgMLCQmRlZaGkpETuuouLi4hYRER/irm5OdLT02FhYQEzM7O3Fm25ublKTEZ/BrfOEMlLTk5GmzZtAADXr1+Xu8aHVDXD8OHDYWxsLDrGe8Oim0iBR48eYdSoUTh27JjC6zwHmohqkrVr18LIyAgAEBoaKjYM/WUpKSnc6kRUxZkzZ0RHoCqSk5Pf+b2VE1kbNmx4X3FUAo8MI1Jg2LBhuHPnDkJDQ/HRRx/h4MGDePDgAYKDgxESEoJ+/fqJjkjvoFWrVjh27Bi7ZhORyho4cOA7v/fAgQPvMQkR0d9DQ0MDEokEUqn0d1caqMtEFme6iRQ4ffo0Dh06BDc3N2hoaKBhw4bo1asXjI2NsWLFChbdgjk4OCAhIQF169aVG8/Ly4Orqytu3boFoPoSMyJ19fz583d+b21e3qeKqh5FJZVKcfDgQZiYmMDNzQ0AcPnyZeTl5f2h4pxIHQwcOBAREREwNjb+3X8ffGClXJmZmbKvr1y5gpkzZyIgIAAdO3YEAMTHxyMkJASrVq0SFVHpWHQTKVBQUCA7j9vMzAyPHj1C06ZN4ezsjMTERMHp6Pbt2wqfjBYXF+Pu3bsCEhGptsrjEN+mckZCXWYdVMW2bdtkX8+ePRuffPIJNm7cCE1NTQAVs0CTJk3iwxCi15iYmMjua8bGxty7rUIqeyMBwODBg7Fu3Tp4e3vLxlxcXGBnZ4fAwED4+voKSKh8LLqJFGjWrBnS0tLQqFEjtG7dGmFhYWjUqBE2btwIGxsb0fHU1uHDh2VfnzhxQm6GqKysDDExMWjUqJGAZESqjfsda4atW7ciLi5OVnADgKamJqZPn45OnTph9erVAtMRqZaqD6wiIiLEBaG3unbtGuzt7auN29vbIyUlRUAiMVh0EykwdepU3L9/HwCwaNEi9O3bF5GRkdDR0eGNXaDKp6ESiQR+fn5y17S1tdGoUSOEhIQISEak2iqPQyTV9urVK6SmpqJZs2Zy46mpqSgvLxeUikj19ejRAwcOHICpqanc+PPnz+Hr64vTp0+LCUZwcnLCihUrsHnzZujo6AAASkpKsGLFCjg5OQlOpzxspEb0DgoLC5GamooGDRrAwsJCdBy1Z29vj4SEBP6/IPqTzp49i7CwMNy6dQt79+6Fra0tdu7cCXt7e3Tp0kV0PLU1ffp07NixA/PmzYO7uzsA4MKFC/jqq68wYsQInjVM9AYaGhrIycmRbQ2s9PDhQ9ja2qK0tFRQMrp48SL69+8PqVQq61SenJwMiUSCI0eOyO51tR1nuonegb6+PlxdXUXHoP+p2qCjUl5eXrUn3ERU3f79+zFixAgMGzYMiYmJKC4uBgA8e/YMy5cvR3R0tOCE6uvf//43rK2tERISIlttZWNjg4CAAMyYMUNwOiLVU/VoqpSUFOTk5Mhel5WV4fjx47C1tRURjf7H3d0dt27dQmRkJFJTUwEAQ4YMwdChQ2FgYCA4nfJwpptIgbKyMkRERCAmJgYPHz6stqyPy5TEWrlyJRo1aoQhQ4YAqGjSsX//ftjY2CA6OhqtW7cWnJBIdbVt2xZffvklRo4cCSMjIyQlJcHBwQFXrlyBl5eX3IdWEqey4zwbqBG9WeXRVEBFM8jX1alTB//5z38wevRoZUcjAKWlpWjevDmOHj2qVkvJFeFMN5ECU6dORUREBPr164dWrVqxI6aK2bhxIyIjIwEAp06dwo8//ojjx49jz549CAgIwMmTJwUnJFJdaWlp6NatW7VxExMT5OXlKT8QKcRim+j3ZWZmQiqVwsHBARcvXkS9evVk13R0dGBpaSnXmJCUS1tbG0VFRaJjqAQW3UQK7N69G3v27JE73oBUR05ODuzs7AAAR48exSeffILevXujUaNG6NChg+B0RKrN2toaGRkZ1Tr9x8XFwcHBQUwoAgA8ePAAM2fOlK2yen3mjse5Eclr2LAhSktL4efnh7p168odVUWq4fPPP8fKlSuxefNmaGmpb+mpvj850Vvo6OjA0dFRdAx6AzMzM2RnZ8POzg7Hjx9HcHAwgIqlZfxQSvR248aNw9SpU7F161ZIJBLcu3cP8fHxmDFjBhYuXCg6nlrz9/dHVlYWAgMDYWNjw1VWRO9AW1sbBw8e5P1LRSUkJCAmJgYnT56Es7NztX3cBw4cEJRMuVh0EykwY8YMfP3111i/fj0/9KiggQMHYujQoWjSpAmePHkCLy8vAMCVK1f4sITod8yZMwfl5eXo2bMnCgsL0a1bN+jq6iIgIABjx44VHU+txcXF4ezZs2jTpo3oKEQ1yoABAxAVFYUvv/xSdBR6jampKQYNGiQ6hnAsuokUiIuLw5kzZ3Ds2DG0bNkS2tractfV5amcqlq7di3s7e2RlZWFVatWwdDQEABw//59TJo0SXA6ItUmkUgwf/58BAQEICMjA/n5+WjRogXCwsJgb2/PRmoC2dnZKWwGRURv16RJEwQFBeGXX35Bu3btqs2mfvHFF4KS0bZt20RHUAnsXk6kwKhRo956nTcQcUpLSzF+/HgEBgbC3t5edByiGqO4uBiLFy/GqVOnZDPbvr6+2LZtGxYsWABNTU18/vnnmD17tuioauvkyZMICQlBWFhYtT33RPRmb/s8IJFIcOvWLSWmIaqORTcR1TgmJia4evUqi26iP2D27NkICwuDp6cnzp07h0ePHmHUqFE4f/485s2bh8GDB7PLr2BmZmYoLCzEq1evoK+vX22VVW5urqBkRETvztXVFTExMTAzM0Pbtm3fulUzMTFRicnE4fJyIqpxfH19uXeL6A/au3cvduzYAR8fH1y/fh0uLi549eoVkpKS2LtCRYSGhoqOQET0lw0YMAC6uroAKj6zEWe6iRR601M5iUQCPT09ODo6wt/fH927dxeQjoKDgxESEoKePXty7xbRO9LR0UFmZiZsbW0BAHXq1MHFixfh7OwsOBkR0V/322+/4fDhw8jKykJJSYnctTVr1ghKRVSBRTeRAnPnzsWGDRvg7OwMd3d3ABVHHiQnJ8Pf3x8pKSmIiYnBgQMHMGDAAMFp1Q/3bhH9cZqamsjJyUG9evUAAEZGRkhOTuY2DRVTVlaGqKgo/PrrrwCAli1bwsfHh0v/id4iJiYGPj4+cHBwQGpqKlq1aoXbt29DKpXC1dUVp0+fFh1R7ZWUlODhw4coLy+XG2/QoIGgRMrFoptIgXHjxqFBgwYIDAyUGw8ODsadO3cQHh6ORYsW4YcffsClS5cEpSQiencaGhrw8vKSLfk7cuQIevToobZnpqqijIwMeHt74+7du2jWrBkAIC0tDXZ2dvjhhx/QuHFjwQmJVJO7uzu8vLywZMkSGBkZISkpCZaWlhg2bBj69u2LiRMnio6ottLT0zFmzBicO3dOblwqlUIikaCsrExQMuVi0U2kgImJCS5fvlztzOeMjAy0a9cOz549Q2pqKtq3b48XL14ISklE9O5+71SGSjydQRxvb29IpVJERkbC3NwcAPDkyRMMHz4cGhoa+OGHHwQnJFJNRkZGuHr1Kho3bgwzMzPExcWhZcuWSEpKwoABA3D79m3REdVW586doaWlhTlz5sDGxqba9s3WrVsLSqZcbKRGpICenh7OnTtXreg+d+4c9PT0AADl5eWyr+n9mz59OpYuXQoDAwNMnz79re/l3i2i6lhMq77Y2FicP39eVnADQN26dfHVV1+hc+fOApMRqTYDAwPZPm4bGxvcvHkTLVu2BAA8fvxYZDS1d/XqVVy+fBnNmzcXHUUoFt1ECkyZMgUTJkzA5cuX0b59ewAVe7o3b96MefPmAQBOnDiBNm3aCEypXq5cuYLU1FS0bdsWV65ceeP72IWZiGoqXV1dhaun8vPzoaOjIyARUc3w4YcfIi4uDk5OTvD29saMGTNw7do1HDhwAB9++KHoeGqtRYsWfPABLi8neqPIyEisX78eaWlpAIBmzZphypQpGDp0KADg5cuXsm7mpByampq4f/8+LC0tAQBDhgzBunXrYGVlJTgZEdFfN3LkSCQmJmLLli2yJp4XLlzAuHHj0K5dO0RERIgNSKSibt26hfz8fLi4uKCgoAAzZszAuXPn0KRJE6xZswYNGzYUHVGtPH/+XPb1pUuXsGDBAixfvhzOzs7Q1taWe6+xsbGy4wnBopuIagwNDQ3k5OTIim5jY2NcvXoVDg4OgpMREf11eXl58PPzw5EjR2QfTF+9egUfHx9ERETAxMREcEIiot+noaEht/KwsmlaVerWSI3Ly4moxuIzQyKqTUxNTXHo0CFkZGTIjgxzcnKq1l+EiOQ5ODggISEBdevWlRvPy8uDq6srjxJVsjNnzoiOoHJYdBP9j7m5OdLT02FhYQEzM7O37g3Ozc1VYjKqJJFIqv1/4R5uIqptHB0dWWgT/QG3b99WOGNaXFyMu3fvCkik3jw8PBAUFISZM2dCX19fdByVwKKb6H/Wrl0LIyMj2dcs5lSPVCqFv7+/7JzhoqIiTJgwgecME1GtMGjQILi7u2P27Nly46tWrUJCQgL27t0rKBmRajp8+LDs6xMnTshtwSgrK0NMTAwaNWokIBktWbIEEyZMYNH9P9zTTUQ1Bs8ZJqLarF69ejh9+jScnZ3lxq9duwZPT088ePBAUDIi1aShoQGgYtXb6yWNtrY2GjVqhJCQEHz88cci4qm11/vwqDvOdBMpkJiYCG1tbdkHn0OHDmHbtm1o0aIFFi9ezKNbBGExTUS12ZuOBtPW1pbrBkxEFcrLywEA9vb2SEhIgIWFheBEVBVXjf5/GqIDEKmi8ePHIz09HUDFMRRDhgyBvr4+9u7di1mzZglOR0REtZGzszO+//77auO7d+9GixYtBCQiUm3x8fE4evQoMjMzZQX3jh07YG9vD0tLS3z22WcoLi4WnFJ9NW3aFObm5m/9oy44002kQHp6Otq0aQMA2Lt3Lzw8PPDdd9/hl19+wb/+9S+EhoYKzUdERLVPYGAgBg4ciJs3b6JHjx4AgJiYGOzatYv7uYkUWLJkCbp37y5bPn7t2jWMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1tWTJEh51+D8suokUkEqlsiVLP/74o+xmbmdnh8ePH4uMRkREtVT//v0RFRWF5cuXY9++fahTpw5cXFzw448/wsPDQ3Q8IpWTlJSE4OBg2evdu3ejQ4cOCA8PB1DxuW3RokUsugX517/+xT3d/8Oim0gBNzc3BAcHw9PTE7GxsdiwYQMAIDMzE1ZWVoLTERFRbdWvXz/069dPdAyiGuHp06dyn8tiY2Ph5eUle92+fXtkZ2eLiKb2uJ9bHvd0EykQGhqKxMRETJ48GfPnz5edl7pv3z506tRJcDoiIqqt8vLysHnzZsybNw+5ubkAKpp78qxhouqsrKyQmZkJACgpKUFiYiI+/PBD2fUXL15AW1tbVDy1xgOy5PHIMKI/oKioCJqamryBExHR3y45ORmenp4wMTHB7du3kZaWBgcHByxYsABZWVnYsWOH6IhEKmXixIlISkrCypUrERUVhe3bt+PevXuyUwAiIyMRGhqKhIQEwUlJ3XGmm+gNKmcb5s6dK5ttSElJwcOHDwUnIyKi2mj69Onw9/fHjRs3oKenJxv39vbGzz//LDAZkWpaunQptLS04OHhgfDwcISHh8sdu7d161b07t1bYEKiCpzpJlIgOTkZPXv2hKmpKWcbiIhIKUxMTJCYmIjGjRvDyMgISUlJcHBwwJ07d9CsWTMUFRWJjkikkp49ewZDQ0NoamrKjefm5sLQ0FCuECcSgTPdRApMnz4do0aN4mwDEREpja6uLp4/f15tPD09HfXq1ROQiKhmMDExqVZwA4C5uTkLblIJLLqJFEhISMD48eOrjdva2iInJ0dAIiIiqu18fHwQFBSE0tJSABXdf7OysjB79mwMGjRIcDoiIvqzWHQTKcDZBiIiUraQkBDk5+fD0tISL1++hIeHBxo3bgxDQ0MsW7ZMdDwiIvqTuKebSIGxY8fiyZMn2LNnD8zNzZGcnAxNTU34+vqiW7duCA0NFR2RiIhqqbi4OCQnJyM/Px/t2rVDz549RUciIqK/gDPdRApUzjbUq1dPNtvg6OgIIyMjzjYQEdHfKj4+HkePHpW97tKlCwwMDPDf//4Xn376KT777DMUFxcLTEhERH8FZ7qJ3uKXX35BUlIS8vPz4erqCk9PT9GRiIiolvHy8sJHH32E2bNnAwCuXbuGdu3awc/PD05OTli9ejXGjx+PxYsXiw1KRER/ipboAESqpry8HBEREThw4ABu374NiUQCe3t7WFtbQyqVQiKRiI5IRES1yNWrV7F06VLZ6927d8Pd3R3h4eEAADs7OyxatIhFNxFRDcXl5URVSKVS+Pj4YOzYsbh79y6cnZ3RsmVL3LlzB/7+/vjHP/4hOiIREdUyT58+hZWVlex1bGwsvLy8ZK/bt2+P7OxsEdGIiOhvwJluoioiIiLw888/IyYmBt27d5e7dvr0afj6+mLHjh0YOXKkoIRERFTbWFlZITMzE3Z2digpKUFiYiKWLFkiu/7ixQtoa2sLTEhERH8FZ7qJqti1axfmzZtXreAGgB49emDOnDmIjIwUkIyIiGorb29vzJkzB2fPnsXcuXOhr6+Prl27yq4nJyejcePGAhMSEdFfwaKbqIrk5GT07dv3jde9vLyQlJSkxERERFTbLV26FFpaWvDw8EB4eDjCw8Oho6Mju75161b07t1bYEIiIvor2L2cqAodHR3cuXMHNjY2Cq/fu3cP9vb2PLqFiIj+ds+ePYOhoSE0NTXlxnNzc2FoaChXiBMRUc3BPd1EVZSVlUFL683/LDQ1NfHq1SslJiIiInVhYmKicNzc3FzJSYiI6O/EopuoCqlUCn9/f+jq6iq8zhluIiIiIiL6I1h0E1Xh5+f3u+9h53IiIiIiInpX3NNNRERERERE9J6wezkRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREX766SdIJBLk5eWJjkJERFSrsOgmIiKqBfz9/SGRSCCRSKCtrQ17e3vMmjULRUVFoqMRERGpNZ7TTUREVEv07dsX27ZtQ2lpKS5fvgw/Pz9IJBKsXLlSdDQiIiK1xZluIiKiWkJXVxfW1taws7ODr68vPD09cerUKQBAcXExvvjiC1haWkJPTw9dunRBQkLCW/++uLg4dO3aFXXq1IGdnR2++OILFBQUKONHISIiqjVYdBMREdVC169fx7lz56CjowMAmDVrFvbv34/t27cjMTERjo6O6NOnD3JzcxV+/82bN9G3b18MGjQIycnJ+P777xEXF4fJkycr88cgIiKq8SRSqVQqOgQRERH9Nf7+/vj222+hp6eHV69eobi4GBoaGtizZw/69u0LMzMzREREYOjQoQCA0tJSNGrUCNOmTUNAQAB++ukndO/eHU+fPoWpqSnGjh0LTU1NhIWFyf4bcXFx8PDwQEFBAfT09ET9qERERDUK93QTERHVEt27d8eGDRtQUFCAtWvXQktLSzZTXVpais6dO8veq62tDXd3d/z6668K/66kpCQkJycjMjJSNiaVSlFeXo7MzEw4OTm995+HiIioNmDRTUREVEsYGBjA0dERALB161a0bt0aW7ZsQfv27f/w35Wfn4/x48fjiy++qHatQYMGfzkrERGRumDRTUREVAtpaGhg3rx5mD59OjIyMqCjo4NffvkFDRs2BFCxvDwhIQHTpk1T+P2urq5ISUmRFfFERET057CRGhERUS01ePBgaGpqYsOGDZg4cSICAgJw/PhxpKSkYNy4cSgsLMSYMWMUfu/s2bNx7tw5TJ48GVevXsWNGzdw6NAhNlIjIiL6gzjTTUREVEtpaWlh8uTJWLVqFTIzM1FeXo4RI0bgxYsXcHNzw4kTJ2BmZqbwe11cXBAbG4v58+eja9eukEqlaNy4MYYMGaLkn4KIiKhmY/dyIiIiIiIioveEy8uJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREREREb0nLLqJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe/L/AJ/aJD9mHqDtAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Tandaan**: Ipinapakita ng diagram na ito na, sa karaniwan, ang taas ng mga first basemen ay mas mataas kaysa sa taas ng mga second basemen. Sa mga susunod na bahagi, matututunan natin kung paano mas pormal na masusubok ang hypothesis na ito, at kung paano maipapakita na ang ating datos ay may estadistikal na kabuluhan upang patunayan ito.\n",
+    "> **Tandaan**: Ipinapakita ng diagram na ito na, sa karaniwan, ang taas ng mga first basemen ay mas mataas kaysa sa taas ng mga second basemen. Sa mga susunod na bahagi, matututunan natin kung paano mas pormal na masusubukan ang hypothesis na ito, at kung paano maipapakita na ang ating datos ay may estadistikal na kabuluhan upang patunayan ito.\n",
     "\n",
     "Ang edad, taas, at timbang ay pawang mga tuloy-tuloy na random na variable. Ano sa tingin mo ang distribusyon ng mga ito? Isang magandang paraan upang malaman ay ang pag-plot ng histogram ng mga halaga:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Dahil karamihan sa mga halaga sa totoong buhay ay karaniwang ipinamamahagi, hindi natin dapat gamitin ang isang uniform random number generator upang lumikha ng sample na datos. Narito ang mangyayari kung susubukan nating lumikha ng mga timbang gamit ang isang uniform distribution (na nilikha ng `np.random.rand`):\n"
+    "Dahil karamihan sa mga halaga sa totoong buhay ay karaniwang may normal na distribusyon, hindi natin dapat gamitin ang isang uniform random number generator upang makabuo ng sample na datos. Narito ang mangyayari kung susubukan nating bumuo ng mga timbang gamit ang isang uniform na distribusyon (na binuo gamit ang `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Mga Interval ng Kumpiyansa\n",
     "\n",
-    "Ngayon, kalkulahin natin ang mga interval ng kumpiyansa para sa mga timbang at taas ng mga manlalaro ng baseball. Gagamitin natin ang code [mula sa diskusyon na ito sa stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Ngayon, kalkulahin natin ang mga interval ng kumpiyansa para sa mga timbang at taas ng mga manlalaro ng baseball. Gagamitin natin ang code [mula sa talakayan sa stackoverflow na ito](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,8 +619,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ang dalawang halaga na ibinabalik ng `ttest_ind` function ay:  \n",
-    "* Ang p-value ay maituturing na posibilidad na ang dalawang distribusyon ay may parehong mean. Sa ating kaso, ito ay napakababa, nangangahulugan na may matibay na ebidensiya na sumusuporta sa ideya na mas matangkad ang mga first basemen.  \n",
+    "Ang dalawang halagang ibinabalik ng `ttest_ind` function ay:  \n",
+    "* Ang p-value ay maituturing na posibilidad na ang dalawang distribusyon ay may parehong mean. Sa ating kaso, ito ay napakababa, na nangangahulugang may malakas na ebidensiya na sumusuporta sa ideya na mas matangkad ang mga first basemen.  \n",
     "* Ang t-value ay ang pansamantalang halaga ng normalisadong pagkakaiba ng mean na ginagamit sa t-test, at ito ay ikinukumpara sa isang threshold value para sa isang tiyak na antas ng kumpiyansa.  \n"
    ]
   },
@@ -789,24 +630,22 @@
    "source": [
     "## Pagsasagawa ng Normal na Pamamahagi gamit ang Central Limit Theorem\n",
     "\n",
-    "Ang pseudo-random generator sa Python ay idinisenyo upang magbigay sa atin ng pantay na pamamahagi. Kung nais nating gumawa ng generator para sa normal na pamamahagi, maaari nating gamitin ang central limit theorem. Upang makakuha ng normal na pamamahaging halaga, kukunin lang natin ang mean ng isang sample na nabuo mula sa pantay na pamamahagi.\n"
+    "Ang pseudo-random generator sa Python ay idinisenyo upang magbigay sa atin ng pantay na pamamahagi. Kung nais nating lumikha ng generator para sa normal na pamamahagi, maaari nating gamitin ang central limit theorem. Upang makakuha ng normal na ipinamamahaging halaga, kukunin lamang natin ang mean ng isang sample na nabuo mula sa pantay na pamamahagi.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korelasyon at Masamang Baseball Corp\n",
     "\n",
-    "Ang korelasyon ay nagbibigay-daan sa atin upang mahanap ang ugnayan sa pagitan ng mga sunod-sunod na datos. Sa ating simpleng halimbawa, magpanggap tayo na may isang masamang korporasyon ng baseball na nagbabayad sa kanilang mga manlalaro batay sa kanilang tangkad - mas matangkad ang manlalaro, mas malaki ang kanyang kita. Ipagpalagay na mayroong base na suweldo na $1000, at isang karagdagang bonus mula $0 hanggang $100, depende sa tangkad. Kukunin natin ang mga totoong manlalaro mula sa MLB, at kakalkulahin ang kanilang mga imahinaryong suweldo:\n"
+    "Ang korelasyon ay nagbibigay-daan sa atin upang matukoy ang ugnayan sa pagitan ng mga hanay ng datos. Sa ating simpleng halimbawa, magkunwari tayo na may isang masamang korporasyon ng baseball na nagbabayad sa kanilang mga manlalaro batay sa kanilang tangkad - mas matangkad ang manlalaro, mas malaki ang bayad na natatanggap niya. Ipagpalagay na may base na sahod na $1000, at isang karagdagang bonus mula $0 hanggang $100, depende sa tangkad. Kukunin natin ang totoong mga manlalaro mula sa MLB, at kakalkulahin ang kanilang imahinaryong mga sahod:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ngayon, kalkulahin natin ang covariance at correlation ng mga sequence na iyon. Ang `np.cov` ay magbibigay sa atin ng tinatawag na **covariance matrix**, na isang extension ng covariance sa maraming variable. Ang elemento $M_{ij}$ ng covariance matrix $M$ ay isang correlation sa pagitan ng mga input variable $X_i$ at $X_j$, at ang mga diagonal na halaga $M_{ii}$ ay ang variance ng $X_{i}$. Katulad nito, ang `np.corrcoef` ay magbibigay sa atin ng **correlation matrix**.\n"
+    "Ngayon, kalkulahin natin ang covariance at correlation ng mga sequence na iyon. Ang `np.cov` ay magbibigay sa atin ng tinatawag na **covariance matrix**, na isang pagpapalawig ng covariance sa maraming variable. Ang elemento $M_{ij}$ ng covariance matrix $M$ ay isang correlation sa pagitan ng mga input variable na $X_i$ at $X_j$, at ang mga diagonal na halaga $M_{ii}$ ay ang variance ng $X_{i}$. Sa parehong paraan, ang `np.corrcoef` ay magbibigay sa atin ng **correlation matrix**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -942,14 +779,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Mapapansin mo ba kung bakit ang mga tuldok ay pumila nang patayo nang ganito?\n",
+    "> Alam mo ba kung bakit ang mga tuldok ay nagkakahanay sa patayong linya nang ganito?\n",
     "\n",
-    "Napansin natin ang ugnayan sa pagitan ng isang artipisyal na konsepto tulad ng sahod at ng naobserbahang variable na *taas*. Tingnan din natin kung ang dalawang naobserbahang variable, tulad ng taas at timbang, ay may ugnayan din:\n"
+    "Napansin namin ang ugnayan sa pagitan ng isang artipisyal na konsepto tulad ng sahod at ng naobserbahang variable na *taas*. Tingnan din natin kung ang dalawang naobserbahang variable, tulad ng taas at timbang, ay may ugnayan:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sa kasamaang-palad, wala tayong nakuha na resulta - tanging ilang kakaibang `nan` na halaga lamang. Ito ay dahil ang ilang mga halaga sa ating serye ay hindi natukoy, na kinakatawan bilang `nan`, na nagdudulot ng hindi natukoy na resulta sa operasyon. Sa pagtingin sa matrix, makikita natin na ang `Weight` ang may problema, dahil ang self-correlation sa pagitan ng mga halaga ng `Height` ay na-compute.\n",
+    "Sa kasamaang-palad, wala tayong nakuha kahit anong resulta - tanging ilang kakaibang `nan` na halaga lamang. Ito ay dahil ang ilan sa mga halaga sa ating serye ay hindi natukoy, na kinakatawan bilang `nan`, na nagiging sanhi upang ang resulta ng operasyon ay maging hindi rin natukoy. Sa pagtingin sa matrix, makikita natin na ang `Weight` ang may problema, dahil ang self-correlation sa pagitan ng mga halaga ng `Height` ay na-compute.\n",
     "\n",
-    "> Ipinapakita ng halimbawang ito ang kahalagahan ng **paghahanda ng datos** at **paglilinis**. Kung walang maayos na datos, hindi tayo makakagawa ng anumang kalkulasyon.\n",
+    "> Ipinapakita ng halimbawang ito ang kahalagahan ng **paghahanda ng datos** at **paglilinis**. Kung walang maayos na datos, wala tayong maikukuwenta.\n",
     "\n",
     "Gamitin natin ang `fillna` na pamamaraan upang punan ang mga nawawalang halaga, at i-compute ang correlation:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Konklusyon\n",
     "\n",
-    "Sa notebook na ito, natutunan natin kung paano magsagawa ng mga pangunahing operasyon sa datos upang makalkula ang mga estadistikal na function. Alam na natin kung paano gamitin ang maayos na kasangkapan ng matematika at estadistika upang patunayan ang ilang mga hypothesis, at kung paano kalkulahin ang mga confidence interval para sa mga arbitraryong variable batay sa isang sample ng datos.\n"
+    "Sa notebook na ito, natutunan natin kung paano magsagawa ng mga pangunahing operasyon sa datos upang makalkula ang mga estadistikal na function. Alam na natin kung paano gamitin ang maayos na kasangkapan ng matematika at estadistika upang patunayan ang ilang mga hypothesis, at kung paano kalkulahin ang confidence intervals para sa mga arbitraryong variable gamit ang isang sample ng datos.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Paunawa**:  \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.\n"
+    "\n---\n\n**Paunawa**:  \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa kanyang orihinal na wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:37:22+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:46:21+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "tl"
   }
diff --git a/translations/tl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/tl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 95589bb3..07af32f7 100644
--- a/translations/tl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/tl/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Panimula sa Probabilidad at Estadistika\n",
+    "## Panimula sa Probability at Statistics\n",
     "## Takdang-Aralin\n",
     "\n",
     "Sa takdang-araling ito, gagamitin natin ang dataset ng mga pasyenteng may diabetes na kinuha [mula rito](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,12 +150,12 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Sa dataset na ito, ang mga kolum ay ang mga sumusunod:  \n",
-    "* Ang Edad at Kasarian ay madaling maunawaan  \n",
-    "* Ang BMI ay ang body mass index  \n",
-    "* Ang BP ay ang karaniwang blood pressure  \n",
-    "* Ang S1 hanggang S6 ay iba't ibang sukat ng dugo  \n",
-    "* Ang Y ay ang kwalitatibong sukat ng pag-usad ng sakit sa loob ng isang taon  \n",
+    "Sa dataset na ito, ang mga column ay ang mga sumusunod:\n",
+    "* Ang Edad at Kasarian ay madaling maintindihan\n",
+    "* Ang BMI ay ang body mass index\n",
+    "* Ang BP ay ang average na presyon ng dugo\n",
+    "* Ang S1 hanggang S6 ay iba't ibang sukat ng dugo\n",
+    "* Ang Y ay ang kwalitatibong sukat ng pag-usad ng sakit sa loob ng isang taon\n",
     "\n",
     "Pag-aralan natin ang dataset na ito gamit ang mga pamamaraan ng probabilidad at estadistika.\n",
     "\n",
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -590,7 +590,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Mga Konklusyon:\n",
+    "Mga Konklusyon:  \n",
     "* Edad - normal  \n",
     "* Kasarian - pare-pareho  \n",
     "* BMI, Y - mahirap sabihin  \n"
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Paunawa**:  \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa kanyang orihinal na wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.\n"
+    "\n---\n\n**Paunawa**:  \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:56:25+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:46:48+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "tl"
   }
diff --git a/translations/tr/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/tr/1-Introduction/04-stats-and-probability/assignment.ipynb
index 17d538c5..eef23aa1 100644
--- a/translations/tr/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/tr/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Bu veri setinde sütunlar şu şekildedir:\n",
-    "* Yaş ve cinsiyet kendini açıklayıcıdır\n",
-    "* BMI vücut kitle indeksidir\n",
-    "* BP ortalama kan basıncıdır\n",
-    "* S1'den S6'ya kadar olanlar farklı kan ölçümleridir\n",
-    "* Y, hastalığın bir yıl içindeki ilerlemesinin niteliksel ölçüsüdür\n",
+    "Bu veri kümesinde, sütunlar şu şekildedir:  \n",
+    "* Yaş ve cinsiyet kendiliğinden anlaşılır.  \n",
+    "* BMI, vücut kitle indeksidir.  \n",
+    "* BP, ortalama kan basıncıdır.  \n",
+    "* S1'den S6'ya kadar olanlar, farklı kan ölçümleridir.  \n",
+    "* Y, bir yıl boyunca hastalık ilerlemesinin nitel ölçüsüdür.  \n",
     "\n",
-    "Hadi bu veri setini olasılık ve istatistik yöntemleriyle inceleyelim.\n",
+    "Hadi bu veri kümesini olasılık ve istatistik yöntemleriyle inceleyelim.\n",
     "\n",
-    "### Görev 1: Tüm değerler için ortalama ve varyans hesaplayın\n"
+    "### Görev 1: Tüm değerler için ortalama ve varyans hesaplayın  \n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### Görev 4: Farklı değişkenler ile hastalık ilerlemesi (Y) arasındaki korelasyonu test edin\n",
     "\n",
-    "> **İpucu** Korelasyon matrisi, hangi değerlerin birbirine bağımlı olduğunu anlamak için en faydalı bilgiyi sağlar.\n"
+    "> **İpucu** Korelasyon matrisi, hangi değerlerin birbirine bağımlı olduğunu anlamak için size en faydalı bilgiyi sağlayacaktır.\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel bir insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:48:24+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:30:24+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "tr"
   }
diff --git a/translations/tr/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/tr/1-Introduction/04-stats-and-probability/notebook.ipynb
index 7102baf8..eaa48116 100644
--- a/translations/tr/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/tr/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Olasılık ve İstatistiğe Giriş\n",
-    "Bu not defterinde, daha önce tartıştığımız bazı kavramlarla ilgili uygulamalar yapacağız. Olasılık ve istatistikle ilgili birçok kavram, Python'daki veri işleme için kullanılan büyük kütüphanelerde, örneğin `numpy` ve `pandas` içinde iyi bir şekilde temsil edilmektedir.\n"
+    "Bu not defterinde, daha önce tartıştığımız bazı kavramlarla ilgili pratik yapacağız. Olasılık ve istatistikle ilgili birçok kavram, Python'daki `numpy` ve `pandas` gibi veri işleme için kullanılan büyük kütüphanelerde iyi bir şekilde temsil edilmektedir.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Rastgele Değişkenler ve Dağılımlar\n",
-    "0 ile 9 arasında bir uniform dağılımdan 30 değerlik bir örneklem çekerek başlayalım. Ayrıca, ortalama ve varyansı da hesaplayacağız.\n"
+    "0 ile 9 arasında bir uniform dağılımdan 30 değerlik bir örnek alarak başlayalım. Ayrıca ortalama ve varyansı hesaplayacağız.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -54,24 +54,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Örneklemde kaç farklı değer olduğunu görsel olarak tahmin etmek için **histogram** çizebiliriz:\n"
+    "Örneklemde kaç farklı değer olduğunu görsel olarak tahmin etmek için, **histogramı** çizebiliriz:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -84,175 +82,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Gerçek Verileri Analiz Etme\n",
+    "## Gerçek Verileri Analiz Etmek\n",
     "\n",
-    "Gerçek dünya verilerini analiz ederken, ortalama ve varyans oldukça önemlidir. Hadi, beyzbol oyuncularıyla ilgili verileri [SOCR MLB Boy/Kilo Verisi](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) adresinden yükleyelim.\n"
+    "Gerçek dünya verilerini analiz ederken ortalama ve varyans çok önemlidir. Hadi beyzbol oyuncuları hakkındaki verileri [SOCR MLB Boy/Kilo Verisi](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) adresinden yükleyelim.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -266,19 +118,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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/6SS5n/zkJx1+TJMnT3aSXGNjY6vH1NDQ4CS5X/3qV20eU/NxTJs2zTmX/u9T878TP/vZz4qyU2vH9H7+7jX/f2reV55+TMuXL3eS3H333Vdwx1RfX+8kufr6eteWnI+at3bFu0+fPjp69KjOP//8gv6Ozdq1azVq1Cg9+eSTuuSSS8x8V9fzPB0+fFg9evRINSj2Ywpip444pmg0qkOHDqlPnz5KJpMFfUx1dXWaNGmSVq9erREjRhTEY0SxXVGI7tmoTk9UK3HPCiW7X97imCKRiG57/jbtPLZTnvvfZ08tCZXo0vMu1ROfeELl5eUFd0yF3Gnjxo0aPny45s+fr8suuyyv51M8Hte+fft00UUXpZ4Nl8e94B5TMT2WW+20a9cuTZo0SWvWrNHw4cPb/bg3ceJEPfvss7rjjjv01FNPyfM87dq1S/369ZMkTZo0SQsWLNDNN98cuCveo0ePTv2bf/oxvfLKKxo5cqReeumlDr/ivXz5clVXV6u2tlaDBg1KO6YNGzZo2LBhevnll9t1xfv04/jvf5/WrVuXdhz/3SkWi+ntt9/WJZdcIs/zCq5TR17xHj16tNauXavrrrsu7ZjWr1+vESNG6MUXXyy4K96nTp1q/49Rt7k1Pwup7Svep8vkuwJ+27x5s5OU+s6KFdFo1M2dO9dFo1G/l4IcK6bWVs/HjhT710bnplW99+dp1u1b1+rV7uaXdfvW+bDi4ubn39liOreRPXoXvvf7eNDY2OgkuVAo5E6ePNmi9cmTJ10oFEpdnQ2SRCLhPvzhD7uxY8e6ZDLZ4n3JZNKNHTvWfeQjH3GJRKKg7zvbz2Xl3Pazd7Yy2dsG96f08b5FIhHdd999qe8EIbhobUtZaVmLP5s55zRn6xyFFGr140IKac7WOTzDeRHh3LaF3sHVpUsXDR48WM45VVRU6Atf+IKuvfZafeELX0g9sdrgwYMD9cRqkhQOhzVr1iw999xzGj9+fItnuR4/fryee+45PfTQQzl5oq2OvO9sP5eVc9vP3vmU8ca7qalJ27Zt07Zt2yRJ//znP7Vt2zbt3bu3o9cGnySTSe3YsSNQv7AeraO1Lcn/jBw3/9ks7sV16PghObW+sXZyOnT8kOKejd8jGgSc27bQO9g2btyY2nz/7ne/01VXXaXf/e53qU33xo0b/V5iTkyYMEHPPPOMXnvtNQ0bNkxVVVUaNmyYtm/frmeeeUYTJkwoivvO5nNZOrf97J0vGT+r+d/+9jeNHj069d/f+MY3JEl333235s+f32ELg3+SyaTWr1+viy++uOi/s4Szo7UtnpdU+L/+bBYJR/SH//MHHTt17Iwfe16n8xQJB/s77kHCuW0LvYNv48aNampq0u23364tW7Zo0KBB+v3vfx+4K92nmzBhgsaNG6e1a9fq4MGD+tCHPqThw4fn5e95R973+/1c1s5tP3vnQ8Yb71GjRjFuGHCRSET33HOP38tAHtDaljONmktSj8491KNzj3wvCTnCuW0LvW3o0qWLampq/F5G3oXDYY0aNaro7/v9fC6L57afvXONn/E+i/79+2vz5s3q37+/30vJq2QyqS1btpgYa7GO1racadQcwcO5bQu9C19HfU1Ja1voHSxsvM+ioqJCgwYNUkVFhd9LyatkMqnXX3+dk9wAWtvieckWfyK4OLdtoXfh66ivKWltC72DJeNRcwRfJBLRpEmT/F4G8oDWtpxt1BzBwrltC73toLUt9A4WNt5Ik0gktGnTJg0ePFilpfwVCbJian3ixAlJ0pYtW3xeSfGK/HunLpe0fccOxQ4xbp5rO3fu9O2+i+ncRvbobQetbaF3sFAQaZxz2rdvn66++mq/l4IcK6bWb7zxhiRp8uTJPq+keF3Zo0Rb7u2iu+66S1vZeOdNZWVl3u+zmM5tZI/edtDaFnoHS8jl+SnKGxoa1LVrV9XX16uqqiqfdw2giB09elRLly5V//79zT3vQkcJJU6pU9NenepyoVxpJ7+XY0JlZaUuvvhiv5cBAAByIJO9LVe8kSaRSGjdunW67rrrGGsJuGJq3a1bN33xi1/0exlF7b3eMV036JqC743sFNO5jezR2w5a20LvYOFZzZHGOaeGhgZ+X7sBtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQoUz2tlzxRppEIqEXXnhBiUTC76Ugx2htC73toLUt9LaD1rbQO1jYeAMAAAAAkEOMmgMAAAAAkCFGzZGVeDyumpoaxeNxv5eCHKO1LfS2g9a20NsOWttC72Bh4400oVBIVVVVCoVCfi8FOUZrW+htB61tobcdtLaF3sHCqDkAAAAAABli1BxZicfjWrRoEWMtBtDaFnrbQWtb6G0HrW2hd7Cw8UaaUCik3r17M9ZiAK1tobcdtLaF3nbQ2hZ6Bwuj5gAAAAAAZIhRc2QlFovp6aefViwW83spyDFa20JvO2htC73toLUt9A4WNt5IEw6HNWDAAIXDYb+XghyjtS30toPWttDbDlrbQu9gYdQcAAAAAIAMMWqOrMRiMc2bN4+xFgNobQu97aC1LfS2g9a20DtY2HgjTTgc1rXXXstYiwG0toXedtDaFnrbQWtb6B0sjJoDAAAAAJAhRs2RlVgspkceeYSxFgNobQu97aC1LfS2g9a20DtY2HgjTWlpqaqrq1VaWur3UpBjtLaF3nbQ2hZ620FrW+gdLIyaAwAAAACQIUbNkZVoNKqHH35Y0WjU76Ugx2htC73toLUt9LaD1rbQO1i44o00nudp//796tWrl0pK+N5MkNHaFnrbQWtb6G0HrW2hd+HLZG/LxhsAAAAAgAwxao6sRKNRzZgxg7EWA2htC73toLUt9LaD1rbQO1i44o00nufp6NGj6tatG2MtAUdrW+htB61tobcdtLaF3oWPUXMAAAAAAHKIUXNkJRqN6sEHH2SsxQBa20JvO2htC73toLUt9A4WrngjjXNOjY2NqqysVCgU8ns5yCFa20JvO2htC73toLUt9C58XPFG1srLy/1eAvKE1rbQ2w5a20JvO2htC72Dg4030sRiMc2cOVOxWMzvpSDHaG0Lve2gtS30toPWttA7WBg1RxrnnGKxmCKRCGMtAUdrW+htB61tobcdtLaF3oWPUXNkjSdxsIPWttDbDlrbQm87aG0LvYODjTfSxGIxzZ49m7EWA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMrnufpyJEj8jzP76Ugx2htC73toLUt9LaD1rbQO1jYeCNNPB7XvHnzFI/H/V4KcozWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmy4nme3nnnHcZaDKC1LfS2g9a20NsOWttC72Bh44008XhcixYtYqzFAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5siK53navXs3Yy0G0NoWettBa1vobQetbaF3sLDxRppEIqEXX3xRiUTC76Ugx2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSO3bsUDKZ9HspyDFa20JvO2htC73toLUt9A4WNt5Ik0wmtX79ek5yA2htC73toLUt9LaD1rbQO1gYNQcAAAAAIEOMmiMryWRSW7Zs4btrBtDaFnrbQWtb6G0HrW2hd7Cw8UaaZDKp119/nZPcAFrbQm87aG0Lve2gtS30DhZGzQEAAAAAyBCj5shKIpFQbW0tvzPQAFrbQm87aG0Lve2gtS30DhY23kjjnNO+ffuU52EI+IDWttDbDlrbQm87aG0LvYOFUXMAAAAAADLEqDmykkgktGrVKsZaDKC1LfS2g9a20NsOWttC72Bh4400zjk1NDQw1mIArW2htx20toXedtDaFnoHC6PmAAAAAABkiFFzZCWRSOiFF15grMUAWttCbztobQu97aC1LfQOFjbeAAAAAADkEKPmAAAAAABkKJO9bWme1pTSvM9vaGjI912jneLxuJYvX65PfOITKisr83s5yCFa20JvO2htC73toLUt9C58zXva9lzLzvvGu7GxUZLUp0+ffN81AAAAAAAdqrGxUV27dj3rbfI+au55ng4cOKDKykqFQqF83jXaqaGhQX369NE777zDjwMEHK1tobcdtLaF3nbQ2hZ6Fz7nnBobG9WzZ0+VlJz96dPyfsW7pKREvXv3zvfd4n2oqqriJDeC1rbQ2w5a20JvO2htC70LW1tXupvxrOYAAAAAAOQQG28AAAAAAHKIjTfSlJeXa9q0aSovL/d7KcgxWttCbztobQu97aC1LfQOlrw/uRoAAAAAAJZwxRsAAAAAgBxi4w0AAAAAQA6x8QYAAAAAIIfYeAMAAAAAkENsvI1Ys2aNxo4dq549eyoUCmnp0qVpt9m5c6duvvlmde3aVZ07d9bgwYO1d+/e1PtPnTqlKVOm6Pzzz1eXLl10yy236PDhw3k8CrRHW62bmpo0depU9e7dW+ecc44GDBigRx99tMVtaF08ZsyYocGDB6uyslLdu3fX+PHj9eabb7a4TXt67t27VzfddJMqKirUvXt3ffvb31YikcjnoaANbbU+duyYvvrVr6pfv34655xzdOGFF+prX/ua6uvrW3weWheH9pzbzZxz+tSnPtXqYz69C197W9fW1ur6669X586dVVVVpREjRujkyZOp9x87dkx33HGHqqqqdO655+qee+5RU1NTPg8F7dCe3ocOHdKdd96pHj16qHPnzho0aJD+9Kc/tbgNvYsPG28jjh8/riuuuEJz585t9f1vvfWWrrvuOvXv31+rVq3SP/7xD/3gBz9Qp06dUre5//779ec//1mLFi3S6tWrdeDAAU2YMCFfh4B2aqv1N77xDS1btkxPP/20du7cqa9//euaOnWqampqUrehdfFYvXq1pkyZovXr12v58uWKx+Oqrq7W8ePHU7dpq2cymdRNN92kWCymV199VU888YTmz5+vH/7wh34cEs6grdYHDhzQgQMH9NBDD2n79u2aP3++li1bpnvuuSf1OWhdPNpzbjf7+c9/rlAolPZ2eheH9rSura3VmDFjVF1drY0bN2rTpk2aOnWqSkr+90v5O+64Qzt27NDy5cv13HPPac2aNfrSl77kxyHhLNrT+6677tKbb76pmpoavfbaa5owYYJuvfVWbd26NXUbehchB3MkuSVLlrR428SJE92kSZPO+DHvvvuuKysrc4sWLUq9befOnU6Sq62tzdVSkaXWWl922WXuRz/6UYu3DRo0yH3ve99zztG62B05csRJcqtXr3bOta/nX/7yF1dSUuIOHTqUus2vfvUrV1VV5aLRaH4PAO12euvWLFy40EUiERePx51ztC5mZ+q9detW16tXL3fw4MG0x3x6F6fWWg8ZMsR9//vfP+PHvP76606S27RpU+ptf/3rX10oFHL79+/P6XqRndZ6d+7c2T355JMtbnfeeee5xx57zDlH72LFFW/I8zw9//zzuuSSS/TJT35S3bt315AhQ1qMq23evFnxeFw33nhj6m39+/fXhRdeqNraWh9Wjfdr2LBhqqmp0f79++Wc08qVK7Vr1y5VV1dLonWxax4rPu+88yS1r2dtba0GDhyoCy64IHWbT37yk2poaNCOHTvyuHpk4vTWZ7pNVVWVSktLJdG6mLXW+8SJE7r99ts1d+5c9ejRI+1j6F2cTm995MgRbdiwQd27d9ewYcN0wQUXaOTIkVq3bl3qY2pra3Xuuefq6quvTr3txhtvVElJiTZs2JDfA0BGWju3hw0bpj/+8Y86duyYPM/TH/7wB506dUqjRo2SRO9ixcYbOnLkiJqamjRz5kyNGTNGL774oj796U9rwoQJWr16taT3ftYkEono3HPPbfGxF1xwgQ4dOuTDqvF+zZkzRwMGDFDv3r0ViUQ0ZswYzZ07VyNGjJBE62LmeZ6+/vWv6+Mf/7guv/xySe3reejQoRZfmDe/v/l9KDyttT7d0aNH9eMf/7jF6CGti9OZet9///0aNmyYxo0b1+rH0bv4tNb67bffliRNnz5dkydP1rJlyzRo0CDdcMMNqqurk/Rez+7du7f4XKWlpTrvvPNoXcDOdG4vXLhQ8Xhc559/vsrLy3XvvfdqyZIl6tu3ryR6F6tSvxcA/3meJ0kaN26c7r//fknSxz72Mb366qt69NFHNXLkSD+Xhw42Z84crV+/XjU1Nbrooou0Zs0aTZkyRT179mxxVRTFZ8qUKdq+fXuLqyAIprZaNzQ06KabbtKAAQM0ffr0/C4OHa613jU1NVqxYkWLn/lE8WutdfPXaffee68+//nPS5KuvPJKvfzyy/rtb3+rGTNm+LJWZO9Mj+U/+MEP9O677+qll15St27dtHTpUt16661au3atBg4c6NNqkS2ueEPdunVTaWmpBgwY0OLtl156aepZzXv06KFYLKZ33323xW0OHz7c6ngbCtPJkyf13e9+Vw8//LDGjh2rj370o5o6daomTpyohx56SBKti9XUqVP13HPPaeXKlerdu3fq7e3p2aNHj7RnOW/+b5oXnjO1btbY2KgxY8aosrJSS5YsUVlZWep9tC4+Z+q9YsUKvfXWWzr33HNVWlqa+nGCW265JTWOSu/icqbWH/rQhySpza/Tjhw50uL9iURCx44do3WBOlPvt956S7/85S/129/+VjfccIOuuOIKTZs2TVdffXXqiXPpXZzYeEORSESDBw9O+1UGu3bt0kUXXSRJuuqqq1RWVqaXX3459f4333xTe/fu1dChQ/O6Xrx/8Xhc8Xi8xbOgSlI4HE59R53WxcU5p6lTp2rJkiVasWKFPvKRj7R4f3t6Dh06VK+99lqLf8SXL1+uqqqqtC/04J+2WkvvXemurq5WJBJRTU1Ni99MIdG6mLTV+zvf+Y7+8Y9/aNu2bakXSZo9e7Yef/xxSfQuFm21/vCHP6yePXue9eu0oUOH6t1339XmzZtT71+xYoU8z9OQIUNyfxBot7Z6nzhxQpLO+rUavYuUn8/shvxpbGx0W7dudVu3bnWS3MMPP+y2bt3q/vWvfznnnFu8eLErKytzv/71r11dXZ2bM2eOC4fDbu3atanP8eUvf9ldeOGFbsWKFe5vf/ubGzp0qBs6dKhfh4QzaKv1yJEj3WWXXeZWrlzp3n77bff444+7Tp06uUceeST1OWhdPL7yla+4rl27ulWrVrmDBw+mXk6cOJG6TVs9E4mEu/zyy111dbXbtm2bW7ZsmfvgBz/oHnjgAT8OCWfQVuv6+no3ZMgQN3DgQLd79+4Wt0kkEs45WheT9pzbp9Npz2pO7+LQntazZ892VVVVbtGiRa6urs59//vfd506dXK7d+9O3WbMmDHuyiuvdBs2bHDr1q1zF198sbvtttv8OCScRVu9Y7GY69u3rxs+fLjbsGGD2717t3vooYdcKBRyzz//fOrz0Lv4sPE2YuXKlU5S2svdd9+dus28efNc3759XadOndwVV1zhli5d2uJznDx50t13333uAx/4gKuoqHCf/vSn3cGDB/N8JGhLW60PHjzoPve5z7mePXu6Tp06uX79+rlZs2Y5z/NSn4PWxaO11pLc448/nrpNe3ru2bPHfepTn3LnnHOO69atm/vmN7+Z+hVUKAxttT7TuS/J/fOf/0x9HloXh/ac2619zOm/QpLeha+9rWfMmOF69+7tKioq3NChQ1tcHHHOuX//+9/utttuc126dHFVVVXu85//vGtsbMzjkaA92tN7165dbsKECa579+6uoqLCffSjH0379WL0Lj4h55zr6KvoAAAAAADgPfyMNwAAAAAAOcTGGwAAAACAHGLjDQAAAABADrHxBgAAAAAgh9h4AwAAAACQQ2y8AQAAAADIITbeAAAAAADkEBtvAAAAAAByiI03AAAAAAA5xMYbAAAAAIAcYuMNAAAAAEAOsfEGAAAAACCH/j+8q7kCS2EPGAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -404,24 +252,22 @@
    "source": [
     "> **Not**: Bu diyagram, ortalama olarak birinci kalecilerin boylarının ikinci kalecilerin boylarından daha uzun olduğunu öne sürüyor. Daha sonra bu hipotezi daha resmi bir şekilde nasıl test edebileceğimizi ve verilerimizin bunu göstermek için istatistiksel olarak anlamlı olduğunu nasıl kanıtlayabileceğimizi öğreneceğiz.\n",
     "\n",
-    "Yaş, boy ve kilo sürekli rastgele değişkenlerdir. Sizce bunların dağılımı nedir? Bunu öğrenmenin iyi bir yolu, değerlerin histogramını çizmek:\n"
+    "Yaş, boy ve kilo, hepsi sürekli rastgele değişkenlerdir. Sizce bunların dağılımı nedir? Bunu öğrenmenin iyi bir yolu, değerlerin histogramını çizmektir:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## Normal Dağılım\n",
     "\n",
-    "Gerçek verilerimizle aynı ortalama ve varyansa sahip normal bir dağılımı takip eden yapay bir ağırlık örneği oluşturalım:\n"
+    "Gerçek verilerimizle aynı ortalama ve varyansa sahip, normal dağılıma uyan yapay bir ağırlık örneği oluşturalım:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -526,19 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Güven Aralıkları\n",
     "\n",
-    "Şimdi beyzbol oyuncularının ağırlıkları ve boyları için güven aralıklarını hesaplayalım. [Bu stackoverflow tartışmasından](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) alınan kodu kullanacağız:\n"
+    "Şimdi beyzbol oyuncularının ağırlıkları ve boyları için güven aralıklarını hesaplayalım. [Bu stackoverflow tartışmasındaki](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data) kodu kullanacağız:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -748,22 +589,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Aralıkların örtüşmediğini görebiliyoruz.\n",
+    "Aralıkların çakışmadığını görebiliyoruz.\n",
     "\n",
     "Hipotezi kanıtlamak için istatistiksel olarak daha doğru bir yöntem, **Student t-testi** kullanmaktır:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -794,19 +635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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eyrcCAACALq/T316+YcOGeOWVV9rur1q1KpYuXRp9+/aNffbZJy699NL4yU9+El/60pdi6NChcc0110RdXV2ccsoppZwbAAAAurxOR/dzzz0Xxx9/fNv9iRMnRkTE+PHjY9asWXHllVfGxo0b48ILL4x169bFyJEjY/78+dG7d+/STQ0AAADdQEVRFEW5h/hfzc3NUVNTE01NTT7fDXR5QybNK/cIAPCprJ56YrlHgJ3Kp23Xsn97OQAAAOysRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAkspyDwAAAOQbMmleuUfY6ayeemK5R6AbcKUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkpQ8un/84x9HRUVFu9sBBxxQ6rcBAACALq8y40UPPvjgePjhh///TSpT3gYAAAC6tJQarqysjNra2oyXBgAAgG4j5TPdK1asiLq6uth3333j7LPPjtdee22r+7a0tERzc3O7GwAAAOwMSh7dw4YNi1mzZsX8+fNjxowZsWrVqjj66KNj/fr1He4/ZcqUqKmpabvV19eXeiQAAAAoi4qiKIrMN1i3bl0MHjw4brrppjj//PO3eLylpSVaWlra7jc3N0d9fX00NTVFdXV15mgA223IpHnlHgEAKJPVU08s9wiUUXNzc9TU1Hxiu6Z/w1mfPn1iv/32i1deeaXDx6uqqqKqqip7DAAAANjh0v9O94YNG2LlypUxcODA7LcCAACALqXk0X355ZfH448/HqtXr46nn346Tj311OjZs2ecddZZpX4rAAAA6NJK/uvlr7/+epx11lnx7rvvxt577x0jR46MRYsWxd57713qtwIAAIAureTRPXv27FK/JAAAAHRL6Z/pBgAAgF2V6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIUlnuAQAAALqjIZPmlXuEndLqqSeWe4SScqUbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AOjIkEnzyj3CTmn11BPLPQIAAOxSXOkGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSiG4AAABIIroBAAAgiegGAACAJKIbAAAAkohuAAAASCK6AQAAIInoBgAAgCSiGwAAAJKIbgAAAEgiugEAACCJ6AYAAIAkohsAAACSVJZ7AGDHGTJpXrlHAACAXYor3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJLKcg/Q3Q2ZNK/cIwAAANBFudINAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQBLRDQAAAEnSonv69OkxZMiQ6N27dwwbNiyeffbZrLcCAACALikluu+7776YOHFiXHfddbFkyZI47LDDYvTo0bF27dqMtwMAAIAuKSW6b7rpprjgggvivPPOi4MOOihmzpwZn/nMZ+LOO+/MeDsAAADokipL/YIffPBBLF68OCZPnty2rUePHjFq1KhYuHDhFvu3tLRES0tL2/2mpqaIiGhubi71aClaW/5V7hEAAAB2Gt2lBT+csyiKj92v5NH9zjvvxObNm2PAgAHttg8YMCD+/ve/b7H/lClT4vrrr99ie319falHAwAAoIurmVbuCTpn/fr1UVNTs9XHSx7dnTV58uSYOHFi2/3W1tZ47733Yq+99oqKiooyTkaG5ubmqK+vjzVr1kR1dXW5x6GLsC7oiHXBR1kTdMS6oCPWBR0p9booiiLWr18fdXV1H7tfyaO7X79+0bNnz2hsbGy3vbGxMWpra7fYv6qqKqqqqtpt69OnT6nHoouprq72A5AtWBd0xLrgo6wJOmJd0BHrgo6Ucl183BXuD5X8i9R69eoVRxxxRCxYsKBtW2trayxYsCCGDx9e6rcDAACALivl18snTpwY48ePj6997Wtx1FFHxbRp02Ljxo1x3nnnZbwdAAAAdEkp0X3GGWfE22+/Hddee200NDTE4YcfHvPnz9/iy9XY9VRVVcV11123xUcK2LVZF3TEuuCjrAk6Yl3QEeuCjpRrXVQUn/T95gAAAMA2KflnugEAAID/Et0AAACQRHQDAABAEtENAAAASUQ322X69OkxZMiQ6N27dwwbNiyeffbZT/W82bNnR0VFRZxyyilb3eeiiy6KioqKmDZtWmmGZYfJWBcvvfRSnHzyyVFTUxN77LFHHHnkkfHaa6+VeHIylXpdbNiwIS6++OIYNGhQ7L777nHQQQfFzJkzEyYnU2fWxaxZs6KioqLdrXfv3u32KYoirr322hg4cGDsvvvuMWrUqFixYkX2YVBipVwXmzZtiquuuioOOeSQ2GOPPaKuri6++93vxptvvrkjDoUSKvXPi//lvLN7ylgTGeecopttdt9998XEiRPjuuuuiyVLlsRhhx0Wo0ePjrVr137s81avXh2XX355HH300Vvd54EHHohFixZFXV1dqccmWca6WLlyZYwcOTIOOOCAeOyxx2LZsmVxzTXXfOz/POlaMtbFxIkTY/78+XH33XfHSy+9FJdeemlcfPHFMXfu3KzDoMS2ZV1UV1fHW2+91XZ79dVX2z3+85//PG655ZaYOXNmPPPMM7HHHnvE6NGj4/33388+HEqk1OviX//6VyxZsiSuueaaWLJkSdx///2xfPnyOPnkk3fE4VAiGT8vPuS8s3vKWBNp55wFbKOjjjqqmDBhQtv9zZs3F3V1dcWUKVO2+pz//Oc/xYgRI4rf/va3xfjx44tx48Ztsc/rr79efP7zny9eeOGFYvDgwcXNN9+cMD1ZMtbFGWecUXznO9/JGpkdIGNdHHzwwcUNN9zQbttXv/rV4oc//GFJZydPZ9fFXXfdVdTU1Gz19VpbW4va2triF7/4Rdu2devWFVVVVcW9995bsrnJVep10ZFnn322iIji1Vdf3Z5R2YGy1oXzzu4rY01knXO60s02+eCDD2Lx4sUxatSotm09evSIUaNGxcKFC7f6vBtuuCH69+8f559/foePt7a2xjnnnBNXXHFFHHzwwSWfm1wZ66K1tTXmzZsX++23X4wePTr69+8fw4YNizlz5mQcAgmyfl6MGDEi5s6dG2+88UYURRGPPvpovPzyy3HCCSeU/BgovW1dFxs2bIjBgwdHfX19jBs3Ll588cW2x1atWhUNDQ3tXrOmpiaGDRv2sa9J15GxLjrS1NQUFRUV0adPn1KNTqKsdeG8s/vKWBOZ55yim23yzjvvxObNm2PAgAHttg8YMCAaGho6fM5TTz0Vd9xxR9x+++1bfd2f/exnUVlZGZdccklJ52XHyFgXa9eujQ0bNsTUqVNjzJgx8Ze//CVOPfXUOO200+Lxxx8v+TFQelk/L2699dY46KCDYtCgQdGrV68YM2ZMTJ8+PY455piSzk+ObVkX+++/f9x5553x4IMPxt133x2tra0xYsSIeP311yMi2p7Xmdeka8lYFx/1/vvvx1VXXRVnnXVWVFdXl/wYKL2sdeG8s/vKWBOZ55yV2/Vs+JTWr18f55xzTtx+++3Rr1+/DvdZvHhx/OpXv4olS5ZERUXFDp6Qcvg066K1tTUiIsaNGxeXXXZZREQcfvjh8fTTT8fMmTPj2GOP3WHzsmN8mnUR8d/oXrRoUcydOzcGDx4cTzzxREyYMCHq6ura/cs3O4/hw4fH8OHD2+6PGDEiDjzwwPj1r38dN954Yxkno5w6sy42bdoU3/72t6MoipgxY8aOHpUd6JPWhfPOXc8nrYnMc07RzTbp169f9OzZMxobG9ttb2xsjNra2i32X7lyZaxevTpOOumktm0fLuzKyspYvnx5PPnkk7F27drYZ5992vbZvHlz/OAHP4hp06bF6tWrcw6GkslYF/X19VFZWRkHHXRQu+ceeOCB8dRTTyUcBaWWsS7q6uri6quvjgceeCBOPPHEiIg49NBDY+nSpfHLX/5SdHcDnV0XHdltt93iK1/5SrzyyisREW3Pa2xsjIEDB7Z7zcMPP7w0g5MqY1186MPgfvXVV+ORRx5xlbsbyVgXzju7t4w10a9fv7RzTr9ezjbp1atXHHHEEbFgwYK2ba2trbFgwYJ2/4L0oQMOOCCef/75WLp0advt5JNPjuOPPz6WLl0a9fX1cc4558SyZcva7VNXVxdXXHFFPPTQQzvy8NhGGeuiV69eceSRR8by5cvbPffll1+OwYMHpx8T2y9jXWzatCk2bdoUPXq0/99Yz5492wKdrq2z66Ijmzdvjueff74tsIcOHRq1tbXtXrO5uTmeeeaZT/2alFfGuoj4/+BesWJFPPzww7HXXnuVfHbyZKwL553dW8aaSD3nLPlXs7HLmD17dlFVVVXMmjWr+Nvf/lZceOGFRZ8+fYqGhoaiKIrinHPOKSZNmrTV52/t28v/l2+R7H4y1sX9999f7LbbbsVvfvObYsWKFcWtt95a9OzZs3jyySczD4USylgXxx57bHHwwQcXjz76aPGPf/yjuOuuu4revXsXt912W+ahUEKdXRfXX3998dBDDxUrV64sFi9eXJx55plF7969ixdffLFtn6lTpxZ9+vQpHnzwwWLZsmXFuHHjiqFDhxb//ve/d/jxsW1KvS4++OCD4uSTTy4GDRpULF26tHjrrbfabi0tLWU5Rjov4+fFRznv7F4y1kTWOadfL2ebnXHGGfH222/HtddeGw0NDXH44YfH/Pnz277Q4LXXXtviKhQ7v4x1ceqpp8bMmTNjypQpcckll8T+++8ff/zjH2PkyJEZh0CCjHUxe/bsmDx5cpx99tnx3nvvxeDBg+OnP/1pXHTRRRmHQILOrot//vOfccEFF0RDQ0N87nOfiyOOOCKefvrpdr8KeOWVV8bGjRvjwgsvjHXr1sXIkSNj/vz52/83VtlhSr0u3njjjZg7d25ExBYfM3j00UfjuOOO2yHHxfbJ+HlB95axJrLOOSuKoii26xUAAACADrkMCQAAAElENwAAACQR3QAAAJBEdAMAAEAS0Q0AAABJRDcAAAAkEd0AAACQRHQDAABAEtENAAAASUQ3AAAAJBHdAAAAkER0AwAAQJL/A9iNnCdIIuhfAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Korelasyon ve Kötü Beyzbol Şirketi\n",
     "\n",
-    "Korelasyon, veri dizileri arasındaki ilişkileri bulmamıza olanak tanır. Oyuncak örneğimizde, oyuncularına boylarına göre ödeme yapan kötü bir beyzbol şirketi olduğunu varsayalım - oyuncu ne kadar uzun boyluysa, o kadar fazla para alır. Diyelim ki temel maaş $1000 ve boya bağlı olarak $0 ile $100 arasında değişen bir ek bonus var. Gerçek MLB oyuncularını alıp, onların hayali maaşlarını hesaplayacağız:\n"
+    "Korelasyon, veri dizileri arasındaki ilişkileri bulmamıza olanak tanır. Oyuncak örneğimizde, oyuncularına boylarına göre ödeme yapan kötü bir beyzbol şirketi olduğunu varsayalım - oyuncu ne kadar uzun boyluysa, o kadar fazla para alır. Diyelim ki temel maaş $1000 ve boya bağlı olarak $0 ile $100 arasında ek bir bonus var. Gerçek MLB oyuncularını alacağız ve hayali maaşlarını hesaplayacağız:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Haydi şimdi bu dizilerin kovaryansını ve korelasyonunu hesaplayalım. `np.cov` bize **kovaryans matrisi** olarak adlandırılan bir matris verecektir, bu da kovaryansın birden fazla değişkene genişletilmiş halidir. Kovaryans matrisi $M$'nin elemanı $M_{ij}$, giriş değişkenleri $X_i$ ve $X_j$ arasındaki korelasyonu ifade eder ve köşegen değerler $M_{ii}$, $X_{i}$'nin varyansıdır. Benzer şekilde, `np.corrcoef` bize **korelasyon matrisi** verecektir.\n"
+    "Haydi şimdi bu dizilerin kovaryansını ve korelasyonunu hesaplayalım. `np.cov` bize **kovaryans matrisi** olarak adlandırılan bir matris verecektir, bu da kovaryansın birden fazla değişkene genişletilmiş halidir. Kovaryans matrisi $M$'nin $M_{ij}$ elemanı, giriş değişkenleri $X_i$ ve $X_j$ arasındaki korelasyonu ifade eder ve köşegen değerler $M_{ii}$, $X_{i}$'nin varyansıdır. Benzer şekilde, `np.corrcoef` bize **korelasyon matrisi** verecektir.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -884,24 +723,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bir korelasyonun 1'e eşit olması, iki değişken arasında güçlü bir **doğrusal ilişki** olduğu anlamına gelir. Bir değeri diğerine karşı çizerek doğrusal ilişkiyi görsel olarak görebiliriz:\n"
+    "1'e eşit bir korelasyon, iki değişken arasında güçlü bir **doğrusal ilişki** olduğu anlamına gelir. Bir değeri diğerine karşı çizerek doğrusal ilişkiyi görsel olarak görebiliriz:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -921,14 +758,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -941,19 +778,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bu durumda, korelasyon biraz daha küçük, ancak yine de oldukça yüksek. Şimdi, ilişkiyi daha az belirgin hale getirmek için, maaşa bazı rastgele değişkenler ekleyerek biraz daha fazla rastgelelik eklemek isteyebiliriz. Bakalım ne oluyor:\n"
+    "Bu durumda, korelasyon biraz daha küçük, ancak yine de oldukça yüksek. Şimdi, ilişkiyi daha az belirgin hale getirmek için maaşa bazı rastgele değişkenler ekleyerek biraz daha rastgelelik eklemek isteyebiliriz. Bakalım ne oluyor:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -998,17 +833,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,14 +858,14 @@
    "source": [
     "Ne yazık ki herhangi bir sonuç elde edemedik - sadece garip `nan` değerleri aldık. Bunun nedeni, serimizdeki bazı değerlerin tanımsız olması ve `nan` olarak temsil edilmesidir. Bu da işlemin sonucunun tanımsız olmasına yol açar. Matrisi incelediğimizde, `Weight` sütununun sorunlu olduğunu görebiliriz, çünkü `Height` değerleri arasındaki öz-korelasyon hesaplanmış durumda.\n",
     "\n",
-    "> Bu örnek, **veri hazırlama** ve **temizleme**nin önemini göstermektedir. Doğru veriler olmadan hiçbir şey hesaplayamayız.\n",
+    "> Bu örnek, **veri hazırlama** ve **temizleme**nin önemini göstermektedir. Doğru veri olmadan hiçbir şey hesaplayamayız.\n",
     "\n",
     "Hadi eksik değerleri doldurmak için `fillna` metodunu kullanalım ve korelasyonu hesaplayalım:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1087,14 +920,14 @@
    "source": [
     "## Sonuç\n",
     "\n",
-    "Bu not defterinde, veriler üzerinde temel işlemleri gerçekleştirerek istatistiksel fonksiyonları nasıl hesaplayacağımızı öğrendik. Artık bazı hipotezleri kanıtlamak için sağlam bir matematik ve istatistik donanımını nasıl kullanacağımızı ve bir veri örneği verildiğinde rastgele değişkenler için güven aralıklarını nasıl hesaplayacağımızı biliyoruz.\n"
+    "Bu not defterinde, veriler üzerinde temel işlemleri gerçekleştirerek istatistiksel fonksiyonları hesaplamayı öğrendik. Artık bazı hipotezleri kanıtlamak için sağlam bir matematik ve istatistik araç setini nasıl kullanacağımızı ve bir veri örneği verildiğinde rastgele değişkenler için güven aralıklarını nasıl hesaplayacağımızı biliyoruz.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Orijinal belgenin kendi dilindeki hali, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalardan sorumlu değiliz.\n"
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:38:29+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:30:11+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "tr"
   }
diff --git a/translations/tr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/tr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 42535968..74090739 100644
--- a/translations/tr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/tr/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Bu veri setinde sütunlar şu şekildedir:\n",
-    "* Yaş ve cinsiyet kendini açıklayıcıdır\n",
-    "* BMI vücut kitle indeksidir\n",
-    "* BP ortalama kan basıncıdır\n",
-    "* S1'den S6'ya kadar olanlar farklı kan ölçümleridir\n",
-    "* Y, hastalığın bir yıl içindeki ilerlemesinin niteliksel ölçüsüdür\n",
+    "Bu veri kümesinde sütunlar şu şekildedir:  \n",
+    "* Yaş ve cinsiyet kendiliğinden anlaşılır.  \n",
+    "* BMI, vücut kitle indeksidir.  \n",
+    "* BP, ortalama kan basıncıdır.  \n",
+    "* S1'den S6'ya kadar olanlar farklı kan ölçümleridir.  \n",
+    "* Y, bir yıl boyunca hastalık ilerlemesinin nitel ölçüsüdür.  \n",
     "\n",
-    "Hadi bu veri setini olasılık ve istatistik yöntemleri kullanarak inceleyelim.\n",
+    "Hadi bu veri kümesini olasılık ve istatistik yöntemleriyle inceleyelim.\n",
     "\n",
-    "### Görev 1: Tüm değerler için ortalama ve varyans hesaplayın\n"
+    "### Görev 1: Tüm değerler için ortalama ve varyansı hesaplayın  \n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -594,7 +594,7 @@
    "source": [
     "Sonuçlar:\n",
     "* Yaş - normal  \n",
-    "* Cinsiyet - tek tip  \n",
+    "* Cinsiyet - homojen  \n",
     "* BMI, Y - söylemesi zor  \n"
    ],
    "metadata": {}
@@ -604,7 +604,7 @@
    "source": [
     "### Görev 4: Farklı değişkenler ile hastalık ilerlemesi (Y) arasındaki korelasyonu test edin\n",
     "\n",
-    "> **İpucu** Korelasyon matrisi, hangi değerlerin bağımlı olduğunu anlamak için size en faydalı bilgiyi sağlayacaktır.\n"
+    "> **İpucu** Korelasyon matrisi, hangi değerlerin birbirine bağımlı olduğunu anlamak için size en faydalı bilgiyi sağlayacaktır.\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:56:55+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:30:41+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "tr"
   }
diff --git a/translations/tw/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/tw/1-Introduction/04-stats-and-probability/assignment.ipynb
index ddaa0d8f..2179e7fa 100644
--- a/translations/tw/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/tw/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -6,7 +6,7 @@
     "## 概率與統計簡介\n",
     "## 作業\n",
     "\n",
-    "在這次作業中，我們將使用[這裡](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)提供的糖尿病患者數據集。\n"
+    "在這次作業中，我們將使用糖尿病患者的數據集，該數據集取自[此處](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)。\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "在此數據集中，欄位如下：  \n",
-    "* Age 和 sex 不需多作解釋  \n",
-    "* BMI 是身體質量指數  \n",
-    "* BP 是平均血壓  \n",
-    "* S1 到 S6 是不同的血液測量值  \n",
-    "* Y 是一年內疾病進展的定性指標  \n",
+    "在此數據集中，欄位如下：\n",
+    "* 年齡和性別不需額外解釋\n",
+    "* BMI 是身體質量指數\n",
+    "* BP 是平均血壓\n",
+    "* S1 到 S6 是不同的血液測量值\n",
+    "* Y 是一年內疾病進展的定性指標\n",
     "\n",
-    "讓我們使用概率與統計的方法來研究這個數據集。\n",
+    "讓我們使用概率和統計方法來研究這個數據集。\n",
     "\n",
-    "### 任務 1：計算所有值的平均值和方差  \n"
+    "### 任務 1：計算所有值的平均值和方差\n"
    ],
    "metadata": {}
   },
@@ -202,7 +202,7 @@
    "source": [
     "### 任務 4：測試不同變數與疾病進展（Y）之間的相關性\n",
     "\n",
-    "> **提示** 相關矩陣可以為您提供最有用的信息，幫助判斷哪些值是相互依賴的。\n"
+    "> **提示** 相關矩陣可以提供最有用的資訊，幫助您了解哪些值是相互依賴的。\n"
    ],
    "metadata": {}
   },
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
+    "\n---\n\n**免責聲明**：  \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
    ]
   }
  ],
@@ -251,8 +251,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:48:39+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:14:03+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "tw"
   }
diff --git a/translations/tw/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/tw/1-Introduction/04-stats-and-probability/notebook.ipynb
index 38b2ee60..4ad2e146 100644
--- a/translations/tw/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/tw/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# 概率與統計入門\n",
-    "在這份筆記中，我們將探索一些之前討論過的概念。許多概率與統計的概念在 Python 的主要數據處理庫中都有良好的呈現，例如 `numpy` 和 `pandas`。\n"
+    "在這份筆記中，我們將嘗試一些之前討論過的概念。許多概率與統計的概念在 Python 的主要數據處理庫中都有良好的呈現，例如 `numpy` 和 `pandas`。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,173 +84,27 @@
    "source": [
     "## 分析真實數據\n",
     "\n",
-    "在分析真實世界的數據時，平均值和變異數是非常重要的。我們來載入有關棒球運動員的數據，數據來源於 [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)。\n"
+    "平均值和方差在分析真實世界數據時非常重要。讓我們從 [SOCR MLB 身高/體重數據](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) 加載有關棒球球員的數據。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
@@ -261,24 +113,24 @@
    "source": [
     "> 我們在這裡使用一個名為 [**Pandas**](https://pandas.pydata.org/) 的套件進行資料分析。在這門課程的後續部分，我們會更深入討論 Pandas 以及如何在 Python 中處理資料。\n",
     "\n",
-    "現在讓我們計算年齡、身高和體重的平均值：\n"
+    "現在讓我們來計算年齡、身高和體重的平均值：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "除了平均值之外，查看中位數值和四分位數也是有意義的。它們可以使用一個**箱型圖**來可視化：\n"
+    "除了平均值之外，查看中位數和四分位數也是有意義的。它們可以使用**盒形圖**來可視化：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "我們也可以根據玩家角色分組，為我們的數據集的子集製作箱型圖。\n"
+    "我們也可以製作數據集子集的箱型圖，例如，按玩家角色分組。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -409,19 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## 常態分佈\n",
     "\n",
-    "讓我們建立一個符合常態分佈的人工體重樣本，其平均值和變異數與我們的真實數據相同：\n"
+    "讓我們建立一個符合常態分佈的人工樣本，其平均值和變異數與我們的真實數據相同：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "由於現實生活中的大多數數值通常是正態分佈的，我們不應該使用均勻隨機數生成器來生成樣本數據。以下是如果我們嘗試使用均勻分佈（由 `np.random.rand` 生成）來生成重量時會發生的情況：\n"
+    "由於現實生活中的大多數數值呈正態分佈，我們不應該使用均勻隨機數生成器來生成樣本數據。以下是如果我們嘗試使用均勻分佈（由 `np.random.rand` 生成）來生成重量時會發生的情況：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## 信賴區間\n",
     "\n",
-    "現在讓我們計算棒球選手體重和身高的信賴區間。我們將使用[這篇 stackoverflow 討論中的程式碼](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)：\n"
+    "現在讓我們計算棒球選手體重和身高的信賴區間。我們將使用[這篇 stackoverflow 討論](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)中的程式碼：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "我們可以看到這些區間並未重疊。\n",
     "\n",
-    "一種在統計上更正確的方法來驗證假設是使用 **Student t檢定**：\n"
+    "一種在統計上更正確的方式來驗證假設是使用 **Student t檢定**：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,9 +619,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ttest_ind` 函數返回的兩個值是：  \n",
-    "* p 值可以被視為兩個分佈具有相同平均值的概率。在我們的例子中，p 值非常低，這表示有強有力的證據支持一壘手的身高較高。  \n",
-    "* t 值是 t 檢驗中用於比較的標準化平均差異的中間值，並且會與給定置信值的閾值進行比較。  \n"
+    "`ttest_ind` 函數返回的兩個值是：\n",
+    "* p-value 可以被視為兩個分佈具有相同平均值的概率。在我們的例子中，p-value 非常低，這表示有強有力的證據支持一壘手的身高較高。\n",
+    "* t-value 是在 t 檢驗中使用的標準化平均差異的中間值，並且會與給定置信值的閾值進行比較。\n"
    ]
   },
   {
@@ -789,24 +630,22 @@
    "source": [
     "## 使用中央極限定理模擬常態分佈\n",
     "\n",
-    "Python 的偽隨機生成器旨在為我們提供均勻分佈。如果我們想要創建一個常態分佈的生成器，可以利用中央極限定理。為了獲得一個常態分佈的值，我們只需計算一個均勻生成樣本的平均值。\n"
+    "Python 中的偽隨機生成器旨在提供均勻分佈。如果我們想要創建一個常態分佈的生成器，可以利用中央極限定理。要獲得一個常態分佈的值，我們只需計算均勻生成樣本的平均值。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -826,21 +665,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 關聯性與邪惡棒球公司\n",
+    "## 相關性與邪惡棒球公司\n",
     "\n",
-    "關聯性讓我們能夠找出數據序列之間的關係。在我們的範例中，假設有一家邪惡的棒球公司，根據球員的身高來支付薪水——球員越高，薪水就越多。假設基本薪資是 $1000，並根據身高額外提供 $0 到 $100 的獎金。我們將使用真實的 MLB 球員數據，來計算他們的假想薪水：\n"
+    "相關性讓我們能夠找出數據序列之間的關係。在我們的範例中，假設有一家邪惡的棒球公司，根據球員的身高來支付薪水——球員越高，薪水就越多。假設基本薪水是 $1000，並根據身高額外提供 $0 到 $100 的獎金。我們將使用 MLB 的真實球員數據，計算他們的虛構薪水：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -859,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -889,19 +728,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -916,19 +753,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "讓我們看看如果關係不是線性的會發生什麼。假設我們的公司決定隱藏高度和薪水之間明顯的線性依賴性，並在公式中引入一些非線性，例如 `sin`:\n"
+    "讓我們看看如果關係不是線性的會發生什麼。假設我們的公司決定隱藏高度和薪水之間明顯的線性依賴性，並在公式中引入一些非線性，例如 `sin`：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -946,14 +783,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -964,19 +801,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -993,22 +828,22 @@
    "source": [
     "> 你能猜出為什麼這些點會排列成這樣的垂直線嗎？\n",
     "\n",
-    "我們已經觀察到像薪水這樣的人為設計概念與觀察變數*身高*之間的相關性。現在，我們也來看看兩個觀察變數，例如身高和體重，是否也存在相關性：\n"
+    "我們已經觀察到像薪資這樣的人為設計概念與觀察變數*身高*之間的相關性。現在，我們也來看看兩個觀察變數，例如身高和體重，是否也存在相關性：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,16 +856,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "很遺憾，我們沒有得到任何結果——只有一些奇怪的 `nan` 值。這是因為我們的系列中有些值是未定義的，用 `nan` 表示，這導致操作的結果也未定義。透過查看矩陣，我們可以看到 `Weight` 是問題所在的欄位，因為已計算出 `Height` 值之間的自相關。\n",
+    "很遺憾，我們沒有得到任何結果——只有一些奇怪的 `nan` 值。這是因為我們的數據序列中有一些值是未定義的，用 `nan` 表示，這導致操作的結果也變成未定義。通過觀察矩陣，我們可以看到問題出在 `Weight` 這一列，因為 `Height` 值之間的自相關已經被計算出來。\n",
     "\n",
-    "> 這個例子顯示了**資料準備**和**清理**的重要性。沒有適當的資料，我們無法計算任何東西。\n",
+    "> 這個例子顯示了**數據準備**和**清理**的重要性。沒有適當的數據，我們無法計算出任何結果。\n",
     "\n",
-    "讓我們使用 `fillna` 方法填補缺失值，並計算相關性：\n"
+    "讓我們使用 `fillna` 方法來填補缺失值，然後計算相關性：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1040,7 +875,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1056,27 +891,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1087,14 +920,14 @@
    "source": [
     "## 結論\n",
     "\n",
-    "在這份筆記中，我們學習了如何對數據進行基本操作以計算統計函數。我們現在知道如何使用完善的數學和統計工具來驗證一些假設，以及如何根據數據樣本計算任意變數的信賴區間。\n"
+    "在這份筆記中，我們學習了如何對數據進行基本操作以計算統計函數。我們現在知道如何使用完善的數學和統計工具來驗證一些假設，以及如何根據數據樣本計算任意變量的信賴區間。\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
+    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或誤釋不承擔責任。\n"
    ]
   }
  ],
@@ -1117,11 +950,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:39:39+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:13:51+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "tw"
   }
diff --git a/translations/tw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/tw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index cffd9149..0e50d52c 100644
--- a/translations/tw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/tw/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 概率與統計學簡介\n",
+    "## 概率與統計簡介\n",
     "## 作業\n",
     "\n",
-    "在這次作業中，我們將使用[這裡](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)提供的糖尿病患者數據集。\n"
+    "在這次作業中，我們將使用從[這裡](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)取得的糖尿病患者數據集。\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "在此數據集中，欄位如下：  \n",
-    "* Age 和 sex 不需多作解釋  \n",
-    "* BMI 是身體質量指數  \n",
-    "* BP 是平均血壓  \n",
-    "* S1 到 S6 是不同的血液測量值  \n",
-    "* Y 是一年內疾病進展的定性測量值  \n",
+    "在此數據集中，欄位如下：\n",
+    "* 年齡和性別不需額外解釋\n",
+    "* BMI 是身體質量指數\n",
+    "* BP 是平均血壓\n",
+    "* S1 到 S6 是不同的血液測量值\n",
+    "* Y 是疾病在一年內進展的定性指標\n",
     "\n",
-    "讓我們使用概率與統計的方法來研究這個數據集。\n",
+    "讓我們使用概率和統計方法來研究這個數據集。\n",
     "\n",
-    "### 任務 1：計算所有值的平均值和方差  \n"
+    "### 任務 1：計算所有值的平均值和方差\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -602,9 +602,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 任務 4：測試不同變數與疾病進展 (Y) 之間的相關性\n",
+    "### 任務 4：測試不同變數與疾病進展（Y）之間的相關性\n",
     "\n",
-    "> **提示** 相關矩陣可以為您提供最有用的信息，幫助判斷哪些值是相互依賴的。\n"
+    "> **提示** 相關性矩陣將為您提供最有用的信息，幫助判斷哪些值是相互依賴的。\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -885,9 +885,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -916,7 +916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用本翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
+    "\n---\n\n**免責聲明**：  \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
    ]
   }
  ],
@@ -942,8 +942,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:57:16+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:14:18+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "tw"
   }
diff --git a/translations/uk/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/uk/1-Introduction/04-stats-and-probability/assignment.ipynb
index ee55e036..e611e2d4 100644
--- a/translations/uk/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/uk/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -154,7 +154,7 @@
     "* BMI — індекс маси тіла  \n",
     "* BP — середній артеріальний тиск  \n",
     "* S1 до S6 — різні показники крові  \n",
-    "* Y — якісна міра прогресування хвороби протягом одного року  \n",
+    "* Y — якісна оцінка прогресування хвороби протягом одного року  \n",
     "\n",
     "Давайте вивчимо цей набір даних за допомогою методів ймовірності та статистики.\n",
     "\n",
@@ -223,7 +223,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Відмова від відповідальності**:  \nЦей документ був перекладений за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, будь ласка, майте на увазі, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ на його рідній мові слід вважати авторитетним джерелом. Для критичної інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникають внаслідок використання цього перекладу.\n"
+    "\n---\n\n**Відмова від відповідальності**:  \nЦей документ було перекладено за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, зверніть увагу, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ мовою оригіналу слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникли внаслідок використання цього перекладу.\n"
    ]
   }
  ],
@@ -249,8 +249,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:20:49+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T18:02:13+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "uk"
   }
diff --git a/translations/uk/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/uk/1-Introduction/04-stats-and-probability/notebook.ipynb
index b921e6a5..15145cf8 100644
--- a/translations/uk/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/uk/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Вступ до ймовірності та статистики\n",
-    "У цьому зошиті ми розглянемо деякі концепції, які обговорювали раніше. Багато понять із ймовірності та статистики добре представлені в основних бібліотеках для обробки даних у Python, таких як `numpy` та `pandas`.\n"
+    "У цьому зошиті ми розглянемо деякі з концепцій, які ми обговорювали раніше. Багато понять з ймовірності та статистики добре представлені в основних бібліотеках для обробки даних у Python, таких як `numpy` та `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## Випадкові змінні та розподіли\n",
-    "Почнемо з вибірки з 30 значень із рівномірного розподілу від 0 до 9. Також обчислимо середнє значення та дисперсію.\n"
+    "Почнемо з вибірки з 30 значень із рівномірного розподілу від 0 до 9. Ми також обчислимо середнє значення та дисперсію.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -86,199 +84,53 @@
    "source": [
     "## Аналіз реальних даних\n",
     "\n",
-    "Середнє значення та дисперсія є дуже важливими при аналізі реальних даних. Давайте завантажимо дані про бейсболістів із [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
+    "Середнє значення та дисперсія є дуже важливими при аналізі даних з реального світу. Давайте завантажимо дані про бейсболістів із [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ми використовуємо пакет під назвою [**Pandas**](https://pandas.pydata.org/) для аналізу даних. Більше про Pandas і роботу з даними в Python ми поговоримо пізніше в цьому курсі.\n",
+    "Ми використовуємо пакет під назвою [**Pandas**](https://pandas.pydata.org/) для аналізу даних. Ми детальніше поговоримо про Pandas і роботу з даними в Python пізніше в цьому курсі.\n",
     "\n",
     "Давайте обчислимо середні значення для віку, зросту та ваги:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Тепер давайте зосередимося на зрості та обчислимо стандартне відхилення і дисперсію:\n"
+    "Тепер зосередимося на зрості та обчислимо стандартне відхилення і дисперсію:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "На додаток до середнього, має сенс розглянути медіанне значення та квартилі. Їх можна візуалізувати за допомогою **коробкової діаграми**:\n"
+    "На додаток до середнього, має сенс розглянути медіанне значення та квартилі. Їх можна візуалізувати за допомогою **коробчатої діаграми**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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o5qGbh24euhXDArlEHR0duUeoHZp56Oahm4duHrp56OahWzEskEt04cKF3CPUDs08dPPQzUM3D908dPPQrRgWyCVasGBB7hFqh2Yeunno5qGbh24eunnoVgwL5BItWrQo9wi1QzMP3Tx089DNQzcP3Tx0K2ZOXsUiIs5KOv6Of+L6WyLpXO4haoZmHrp56Oahm4duHrp56Nbaz6eU3jv5zjlZIKO1iNjT6qVEMDWaeejmoZuHbh66eejmoVsxPMQCAAAAaMICGQAAAGjCArlc/5R7gBqimYduHrp56Oahm4duHroVwGOQAQAAgCbsIAMAAABNWCADAAAATVggz5GIuCUinouIQxFxMCJ+NSLujYjvRsRQROyJiF/OPWeVRMSdjTZXbj+KiE9GxK0R8e8R8YPGx/bcs1bJNN2+0jj+XomIzRFxS+5Zq2Sqbk3//dMRkSJiScYxK2W6ZhHRHRGHI+L7EfF3mUetlGnOUa4JM4iITzWOqQMR0RsRi7gmzGyKblwTCuAxyHMkIp6RtCOl9LWIaJP0HknPSvpqSuk7EfG7kp5IKX0o55xVFRHXSzol6Vck/ZWk11NKX46Iz0hqTymtzzpgRU3qdqekwZTSTyLibyWJbq01d0spHY+IZZK+JmmlpDUpJV5cf5JJx9r7JPVI+r2U0mhE3J5Sei3rgBU1qdvT4powpYj4OUn/IekDKaUfR8Szkv5N0gfENWFK03T7obgmzBo7yHMgIm6S9BuSvi5JKaWxlNL/SUqSbmr8sZs1cbCitYckHU0pHZf0EUnPNO5/RtLv5xqqBq52Syk9n1L6SeP+70rqyDhX1TUfb5L0VUlPaOKcRWvNzf5C0pdTSqOSxOJ4Ws3duCbM7AZJPxURN2hio+mH4powG2/pxjWhGBbIc+N9ks5K+ueI+F5EfC0iFkv6pKSvRMQJSX8v6W8yzlh1fySpt/HPP5NSOi1JjY+3Z5uq+pq7NftTSd8peZY6udotIh6RdCql9HLekSqv+Vh7v6Rfj4hdEbE9Iu7POFfVNXf7pLgmTCmldEoTXV6VdFrScErpeXFNmNY03ZpxTZgBC+S5cYOkD0r6x5TSL0kakfQZTeyyfCqltEzSp9TYYca1Gg9JeUTSt3PPUidTdYuIHkk/kfQvOeaquuZuEfEeTTxU4HN5p6q2FsfaDZLaJT0g6a8lPRsRkWm8ymrRjWvCNBqPLf6IpF+Q9LOSFkfEn+Sdqvpm6sY1YXZYIM+Nk5JOppR2Nf79OU0smD8uqa9x37cl8YSM1n5H0r6U0pnGv5+JiKWS1PjIr29bm9xNEfFxSQ9L+uPEEw6m0tztFzVxUXk5Io5p4leQ+yLijozzVdHkY+2kpL404T8ljUviyY1vNbkb14Tp/bak/0kpnU0pXdZEqy5xTZjJVN24JhTAAnkOpJT+V9KJiLizcddDkv5LE4+d+s3Gfb8l6QcZxquDR3XtwwS2auJCosbHfy19onq4pltEfFjSekmPpJTeyDZV9V3tllLan1K6PaXUmVLq1MTC74ONcxpvmnyObtHE9zRFxPsltUniiY1vNbkb14TpvSrpgYh4T+M3Eg9JOiiuCTNp2Y1rQjG8isUciYh7NfEs+DZJ/y3pMUmrJP2DJn4deUnSX6aU9uaasYoav+I+Iel9KaXhxn23aeIVQJZr4sT/g5TS6/mmrJ4puh2RtFDS+cYf+25KaV2mESupVbdJ//2YpPt4FYs3TXGstUn6hqR7JY1J+nRKaTDbkBU0RbdfE9eEaUXEFyX9oSYeEvA9SX8m6afFNWFaU3T7vrgmzBoLZAAAAKAJD7EAAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmrBABgAAAJqwQAYAAACasEAGAAAAmvw/tSpycIADqyoAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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CgCxJkiRVGJAlSZKkCgOyJEmSVGFAlqQ2MTQ0xJIlS1i5ciVLlixhaGio7pKkjuNx2Bnm1l2AJGlyQ0ND9Pf3Mzg4yKZNm5gzZw69vb0A9PT01Fyd1Bk8DjuHPciS1AYGBgYYHByku7ubuXPn0t3dzeDgIAMDA3WXJnUMj8POYUCWpDawfv16li9fvsW25cuXs379+poqkjqPx2HnMCBLUhvo6upi3bp1W2xbt24dXV1dNVUkdR6Pw85hQJakNtDf309vby/Dw8Ns3LiR4eFhent76e/vr7s0qWN4HHYOT9KTpDYwegJQX18f69evp6uri4GBAU8MkmaQx2HnMCBLUpvo6emhp6eHkZERVqxYUXc5UkfyOOwMDrGQJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVNBSQI+IdEXFdRFwbEUMRMT8iHh4RF0TEj8p/HzbdxUqSJEnTbdKAHBF7A28DlmXmEmAOcBhwDHBhZj4JuLC8LHW8oaEhlixZwsqVK1myZAlDQ0N1lyRJkqZg7hRut2NE/BHYCfgF8C5gRXn9KcAIsLrJ9UltZWhoiP7+fgYHB9m0aRNz5syht7cXgJ6enpqrkyRJjZi0Bzkzfw58CPgJcCtwR2aeDyzMzFvL29wKPHI6C5XawcDAAIODg3R3dzN37ly6u7sZHBxkYGCg7tIkSVKDIjMnvkExtvjLwGuB3wOnAacD/5mZu1du97vM3GocckQcCRwJsHDhwqWnnnpqs2pvmg0bNrBgwYK6y2gLttXEVq5cyXnnncfcuXMfaKuNGzfyspe9jAsvvLDu8lqar61Cd3d3U/c3PDzc1P21I19bjbOtCh6Hzdeqr63u7u7LM3PZ2O2NDLF4MXBTZt4GEBFnAM8FfhURj87MWyPi0cCvx7tzZp4EnASwbNmyXLFixYN8CtNnZGSEVqyrFdlWE+vq6mLOnDmsWLHigbYaHh6mq6vLdpuEr63CZJ0WAIuOOZub3/+KGahmdvC11TjbquBx2Hzt9tpqZBaLnwDPjoidIiKAlcB64GvA4eVtDge+Oj0lSu2jv7+f3t5ehoeH2bhxI8PDw/T29tLf3193aZIkqUGT9iBn5vci4nTgCmAj8H2KHuEFwJciopciRP/VdBYqtYPRE/H6+vpYv349XV1dDAwMeIKeJEltpKFZLDLzOOC4MZvvo+hNllTR09NDT09P232dJEmSCq6kJ0mSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZKnJhoaGWLJkCStXrmTJkiUMDQ3VXZIkSZqCuXUXIM0mQ0ND9Pf3Mzg4yKZNm5gzZw69vb0A9PT01FydJElqhD3IUhMNDAwwODhId3c3c+fOpbu7m8HBQQYGBuouTZIkNciALDXR+vXrWb58+Rbbli9fzvr162uqSJIkTZUBWWqirq4u1q1bt8W2devW0dXVVVNFkiRpqgzIUhP19/fT29vL8PAwGzduZHh4mN7eXvr7++suTZIkNciT9KQmGj0Rr6+vj/Xr19PV1cXAwIAn6EmS1EYMyFKT9fT00NPTw8jICCtWrKi7HEmSNEUOsZAkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFVMGpAjYr+IuLLyc2dEHB0RB0bEJeW2yyLimTNRsCRJkjSdJg3ImXlDZh6YmQcCS4F7gDOBDwDvLbe/p7wsSVPS19fH/Pnz6e7uZv78+fT19dVdkiSpw82d4u1XAj/OzFsiIoFdy+27Ab9oamWSZr2+vj5OPPFE1qxZw+LFi7n++utZvXo1AGvXrq25OklSp5rqGOTDgKHy96OBD0bET4EPAe9qYl2SOsDJJ5/MmjVrWLVqFfPnz2fVqlWsWbOGk08+ue7SJEkdLDKzsRtG7EDRS7x/Zv4qIj4KXJyZX46IvwaOzMwXj3O/I4EjARYuXLj01FNPbV71TbJhwwYWLFhQdxltwbZqnG01ue7ubs455xzmz5//QHvde++9HHzwwQwPD9ddXst647l385mX71x3GW3DY7FxtlXjPA6nplVfW93d3Zdn5rKx26cyxOJg4IrM/FV5+XDg7eXvpwGfHO9OmXkScBLAsmXLcsWKFVN4yJkxMjJCK9bVimyrxtlWk5s3bx7XX389q1ateqC9TjjhBObNm2fbTeTcs22fKfBYbJxtNQUeh1PSbq+tqQTkHjYPr4CiN/mFwAjwIuBHzStLUic44ogjHhhzvHjxYk444QRWr17NUUcdVXNlkqRO1lBAjoidgJcAf1fZfATwkYiYC9xLOYxCkho1eiLesccey3333ce8efM46qijPEFPklSrhgJyZt4D7DFm2zqKad8k6UFbu3Yta9eubbuv3yRJs5cr6UmSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqWJu3QWodURE0/aVmU3bVytqZlvB7G4v20qSZq/Z+jfeHmQ9IDMn/dln9dcbut1s18y2mu3t1Wgb+NqSpPYzW//GG5AlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpIpJA3JE7BcRV1Z+7oyIo8vr+iLihoi4LiI+MO3VSpIkSdNs7mQ3yMwbgAMBImIO8HPgzIjoBl4JPC0z74uIR05noZIkSdJMmOoQi5XAjzPzFuAtwPsz8z6AzPx1s4uTJEmSZtpUA/JhwFD5+5OB50fE9yLi4og4qLmlSZIkSTNv0iEWoyJiB+BQ4F2V+z4MeDZwEPCliNg3M3PM/Y4EjgRYuHAhIyMjTSi7Md3d3U3d3/DwcFP3165m8v+w3dlWUzOb2+utF97N3X9s3v4WHXN2U/az8/bwsZU7N2VfrWrDhg2z+rXVTJ3QVs08Fj0Op6adXlsNB2TgYOCKzPxVeflnwBllIL40Iv4EPAK4rXqnzDwJOAlg2bJluWLFiodcdKPGZPVtWnTM2dz8/ldMczWzxLlnM5P/h23NtpqaWd5ed5/bvL8zIyMjTWurRcfM7naH5rbXbNcJbdWsY9HjcIra7G/8VIZY9LB5eAXAV4AXAUTEk4EdgNubVpkkSZJUg4YCckTsBLwEOKOy+VPAvhFxLXAqcPjY4RWSJElSu2loiEVm3gPsMWbb/cDrp6MoSZIkqS6upCdJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpIq5dReg6XfAe8/njj/8sWn7W3TM2U3Zz247bs9Vx720Kftqpma212xvK6lOEdHU/WVmU/fXamwvTVUn5wcDcge44w9/5Ob3v6Ip+xoZGWHFihVN2VezDpRma1Z7dUJbSXVqNKAtOubspv0NbGeNtJdtpapOzg8OsZAkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkipcSa8D7NJ1DE895Zjm7fCU5uxmly4AV2ySJEmtxYDcAe5a//6OXSpSkiRpqhxiIUmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaqYNCBHxH4RcWXl586IOLpy/T9GREbEI6a1UkmSJGkGTDoPcmbeABwIEBFzgJ8DZ5aXHwu8BPjJ9JUoSZIkzZypDrFYCfw4M28pL/878E9ANrUqSZIkqSZTDciHAUMAEXEo8PPMvKrpVUmSJEk1iczGOn8jYgfgF8D+wF3AMPDSzLwjIm4GlmXm7ePc70jgSICFCxcuPfXUU5tS+FsvvJu7/9iUXTXVztvDx1buXHcZW3jjuXfzmZc3p6YNGzawYMGCpuyrmXU1U98tfXWXMK61+6ytu4SteBw2rlVfV9Car61matW/Na2oE9qqVY/FVjwOOyE/dHd3X56Zy7a6IjMb+gFeCZxf/v5U4NfAzeXPRopxyI+aaB9Lly7NZtln9debtq/h4eGm7auZdTWLbTU1zarLtpqa2d5etlV9OuE5NksntJV/4xvXCX+3gMtynMw66Ul6FT2Uwysy8xrgkaNXTNSDLEmSJLWThsYgR8ROFLNVnDG95UiSJEn1aqgHOTPvAfaY4PpFzSpIkiRJqpMr6UmSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFZGZM/Zgy5Yty8suu6wp+3rqKU9tyn6mwzWHX1N3CVtYdMzZdZcwrt123J6rjntp3WVspRXbq1XbyuOwca34uoLWfW0d8N7zueMPf6y7jK20YnvZVlPTisdiq7ZVJ/yNj4jLM3PZVldk5oz9LF26NJtln9Vfb9q+hoeHm7avZtbVimb782umTmgrj8N6zPbnl+lraypsq3rM9ueX2RmvLeCyHCezOsRCkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaqYO9kNImI/4IuVTfsC7wH2Bg4B7gd+DLwpM38/DTVKkiRJM2bSHuTMvCEzD8zMA4GlwD3AmcAFwJLMfBrwQ+Bd01moJEmSNBOmOsRiJfDjzLwlM8/PzI3l9kuAxzS3NEmSJGnmTTUgHwYMjbP9zcA5D70cSZIkqV6TjkEeFRE7AIcyZihFRPQDG4H/3sb9jgSOBFi4cCEjIyMPttatNGtfGzZsaMm6WtVsf37N1AltteiYs5u3s3Obs6+dt5/9bT/bn98uXcfw1FOOad4OT2nObnbpgpGRnZuzsyaxreoz249D6OC/8ZnZ0A/wSuD8MdsOB74L7NTIPpYuXZrNss/qrzdtX8PDw03bVzPrakWz/fk1k201NbZX4zqhrfwb3zjbqh6z/fk1W6u2F3BZjpNZG+5BBnqoDK+IiJcDq4EXZuY9zQrskiRJUp0aGoMcETsBLwHOqGz+T2AX4IKIuDIiTpyG+iRJkqQZ1VAPctlDvMeYbU+clookSZKkGrmSniRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKubWXcBDseiYs5u3s3Obs6/ddty+KfuRJKlRvh9KzdW2Afnm97+iaftadMzZTd2fJEkzxfdDqfkcYiFJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVUwakCNiv4i4svJzZ0QcHREPj4gLIuJH5b8Pm4mCJUmSpOk0aUDOzBsy88DMPBBYCtwDnAkcA1yYmU8CLiwvS5IkSW1tqkMsVgI/zsxbgFcCp5TbTwFe1cS6JEmSpFpMNSAfBgyVvy/MzFsByn8f2czCJEmSpDrMbfSGEbEDcCjwrqk8QEQcCRwJsHDhQkZGRqZy9xnTqnXNpO7u7oZuF2smv83w8PBDrKa1NbOtYPa3V6M8DhvXCW216Jizm7ezc5uzr523n/1tP9ufXzPZVlPTTu3VcEAGDgauyMxflZd/FRGPzsxbI+LRwK/Hu1NmngScBLBs2bJcsWLFQ6l3epx7Ni1Z1wzLzElvMzIyYlthW00Lj8PGdUBb3byieftadMzZ3Pz+VzRvh7NZB7y2msa2mpo2a6+pDLHoYfPwCoCvAYeXvx8OfLVZRUmSJEl1aSggR8ROwEuAMyqb3w+8JCJ+VF73/uaXJ0mSJM2shoZYZOY9wB5jtv2GYlYLSZIkadZwJT1JkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFXMrbuA6RQRjd92zeS3ycyHUI3UmTwOp6bR9mqkrWD2t5c0HTwONat7kDOzoZ/h4eGGbidp6jwOp6aZbdUJ7SVNB49DzeqALEmSJE2VAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElSRUMBOSJ2j4jTI+IHEbE+Ip4TEQdGxCURcWVEXBYRz5zuYiVJkqTp1mgP8keAczPzKcABwHrgA8B7M/NA4D3lZUmakr6+PubPn093dzfz58+nr6+v7pJa1tDQEEuWLGHlypUsWbKEoaGhukuSpFlp7mQ3iIhdgRcAbwTIzPuB+yMigV3Lm+0G/GKaapQ0S/X19XHiiSeyZs0aFi9ezPXXX8/q1asBWLt2bc3VtZahoSH6+/sZHBxk06ZNzJkzh97eXgB6enpqrk6SZpdGepD3BW4DPh0R34+IT0bEzsDRwAcj4qfAh4B3TV+Zkmajk08+mTVr1rBq1Srmz5/PqlWrWLNmDSeffHLdpbWcgYEBBgcH6e7uZu7cuXR3dzM4OMjAwEDdpUnSrBOZOfENIpYBlwDPy8zvRcRHgDspeo0vzswvR8RfA0dm5ovHuf+RwJEACxcuXHrqqac2+zk8ZBs2bGDBggV1l9EWbKvG2VaT6+7u5pxzzmH+/PkPtNe9997LwQcfzPDwcN3ltZSVK1dy3nnnMXfu3AfaauPGjbzsZS/jwgsvrLu8lvbGc+/mMy/fue4yatfd3d3U/XX6Merf+EK7v666u7svz8xlW12RmRP+AI8Cbq5cfj5wNnAHmwN2AHdOtq+lS5dmKxoeHq67hLZhWzXOtprcvHnz8sMf/nBmbm6vD3/4wzlv3rwaq2pN+++/f1500UWZubmtLrrootx///1rrKo97LP663WX0Db8u9U422pqWrW9gMtynMw66RjkzPxlRPw0IvbLzBuAlcD1FEMvXgiMAC8CfvSQY7ykjnLEEUc8MOZ48eLFnHDCCaxevZqjjjqq5spaT39/P729vQ+MQR4eHqa3t9chFpI0DSYNyKU+4L8jYgfgRuBNwFeBj0TEXOBeymEUktSo0RPxjj32WO677z7mzZvHUUcd5Ql64xg9Ea+vr4/169fT1dXFwMCAJ+hJ0jRoKCBn5pXA2PEZ64ClzS5IUmdZu3Yta9euZWRkhBUrVtRdTkvr6emhp6fHtpKkaeZKepIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqcKALEmSJFUYkCVJkqQKA7IkSZJUYUCWJEmSKgzIkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkqcMNDQ2xZMkSVq5cyZIlSxgaGqq7JKlWc+suQJIk1WdoaIj+/n4GBwfZtGkTc+bMobe3F4Cenp6aq5PqYQ+yJEkdbGBggMHBQbq7u5k7dy7d3d0MDg4yMDBQd2lSbexBliS1pYho/LZrJr9NZj6EatrX+vXrWb58+Rbbli9fzvr162uqSKqfPciSpLaUmQ39DA8PN3S7TtXV1cW6deu22LZu3Tq6urpqqkiqnwFZkqQO1t/fT29vL8PDw2zcuJHh4WF6e3vp7++vuzSpNg6xkCSpg42eiNfX18f69evp6upiYGDAE/TU0QzIkiR1uJ6eHnp6ehgZGWHFihV1lyPVziEWkiRJUoUBWZIkSaowIEuSJEkVBmRJkiSpwoAsSZIkVRiQJUmSpAoDsiRJklRhQJYkSZIqDMiSJElShQFZkiRJqjAgS5IkSRUGZEmSJKnCgCxJkiRVRGbO3INF3AbcMmMP2LhHALfXXUSbsK0aZ1tNje3VONtqamyvxtlWjbOtpqZV22ufzNxz7MYZDcitKiIuy8xlddfRDmyrxtlWU2N7Nc62mhrbq3G2VeNsq6lpt/ZyiIUkSZJUYUCWJEmSKgzIhZPqLqCN2FaNs62mxvZqnG01NbZX42yrxtlWU9NW7eUYZEmSJKnCHmRJkiSpwoAsSZIkVcytuwBJnSciAnhMZv607lokSdMjIvYG9qGSNzPzm/VV1LiOG4McEdsBV2fmkrpraRcRMQd4f2a+s+5aNHtExOWZubTuOtqFx+HUtfOb80yLiGcAy4EEvp2ZV9RcUksqj8O3Zea/111Lq4uINcBrgeuBTeXmzMxD66uqcR3Xg5yZf4qIqyLicZn5k7rraQeZuSkilkZEZKd9onoQIuLJwDvZ+o35RbUV1ZouiYiDMvP/1V1IO/A4nJptvTkDBuQxIuI9wF8BZ5SbPh0Rp2Xmv9RYVksqj8NXAgbkyb0K2C8z76u7kAej43qQASLiIuAg4FLg7tHt7fKppg4R8WHgScBpbNlmZ2zzTh0qIq4CTgQuZ/MbM5l5eW1FtaCIuB7YD7iZ4jUVFL0LT6uzrlbmcdi4iLgBeFq7vjnPpIhYDzw9M+8tL+8IXJGZXfVW1poiYgDYDfgiWx6H9rpXRMQ5wF9l5oa6a3kwOq4HufTeugtoQw8HfgNUe0GTzT0O2mxjZn687iLawMF1F9CGPA4bdyOwPWBAntzNwHzg3vLyPODHtVXT+p5b/vvPlW3Jlsel4B7gyoi4kMpxmJlvq6+kxnVkDzJAROwDPCkzvxEROwFzMvOuuutS+4uI44FfA2ey5R+F39ZVU6uKiOUUx+GnI2JPYEFm3lR3XWp/EfFl4ACgLd+cZ1JEfIXiW9ULKILeS4B1FH/HbDM9KBFx+HjbM/OUma7lwejIgBwRRwBHAg/PzCdExJOAEzNzZc2ltaxyXO3HgYWZuSQingYc6hi1rUXEeAEvM3PfGS+mhUXEccAyijFqT46IvYDTMvN5NZfWsjwOG9fub84zaVttNco221JELAT+FdgrMw+OiMXAczJzsObS1ESdGpCvBJ4JfC8zn15uuyYzn1prYS0sIi6mOPHsE5U2u9bZQPRglcfh0ynGOo6+pq52DPK2eRxqukTEDsCTy4s3ZOYf66ynlZVjaz8N9GfmARExF/i+GWJLZefjvwGLKYbwANAunUWdOgb5vsy8v5iKFcoXd+d9UpianTLz0tE2K22sq5hWFxFL2PqPwmfrq6gl3Z+ZGREJEBE7111QG/A4bFC7vznPpIhYAZxCMRY5gMdGxOFOibdNj8jML0XEuwAyc2NEbJrsTh3o08BxFDN+dANvonh9tYVOXUnv4og4FtgxIl5CcUb4WTXX1Opuj4gnUH6QiIjXALfWW1JrKocOrC1/uoEPAM6QsrUvRcQngN3LYU/fAE6uuaZW53HYuE9TDEfZSHEcfhb4XK0Vta4PAy/NzBdm5guAl+E0ZhO5OyL2YPNx+GzgjnpLakk7ZuaFFKMVbsnM42mjExk7dYjFdkAv8FKKTzPnAZ90btFti4h9gZMozt79HXAT8LrMvKXWwlpQRFxDcXLQ98uv3xZSvL4Oqbm0llN+QH3gOMzMC2ouqaVt4zh8fWbeXGddrWh0IZrq8LmI+FZmPr/u2lrNeEObHO60beWiKmuBJcC1wJ7AazLz6loLazER8W3g+cDpwEXAzykWO9qv1sIa1JEBWVMXEY/PzJvKr8G3y8y7RrfVXVuriYhLM/OZEXE5Rc/VXcC1mbl/zaVplqgeh3XX0qra/c15JkXEpyh6Q0d72F8HzM3MN9VXVWsrh2buR/Hh3jHb44iIg4D1wO7A+yjmjv5AZl5SZ12N6siAHBHPA45n80pnowsUODZtGyLiisx8xphtLhU8joj4L+BY4DDgH4ANwJW+2RQi4i4mGPOfmbvOYDltJSLmAX8JLGLLVRr/eVv36VTt/uY8k8rX1VsplpoOitUGP5aZ99daWAuLiOey9XHoeSazSKcG5B8A72Drlc5+U1tRLSoingLsTzGO9p2Vq3YF3mmv6MQiYhGwq1+9bS0i/hn4JUWvVVD0Wu2SmR+otbAWFhHnUox1HPu368O1FaW2FxFvz8yPTLZNhYj4HPAE4Eoqy5g7X/SWImIZ0M/mzkgA2mXoTqcG5O9l5rPqrqMdlGvOv4riJLOvVa66Czg1M79TR12trpyfdhFb/lFwtbOK8Y5Dj82JOaVb49r9zXkmbeMbwu+PTiWoLZVLcy/2vKWJlcu9vxO4BvjT6PZ2OXepo6Z5KwfWAwxHxAcplmetrrDkOupjZOZXga9GxAvGTvlTDlXRGOV4vqcB17H5j4LLAW9tU0S8DjiVon16qPSKalzfiYinZuY1dRfSBv6bcd6ctVlE9AD/B3h8RFQ7QHalWNJc47sWeBTOIDOZ2zLza5PfrDV1VA9yRAxPcHVmZttMPzLTttHDsNU2QURcn5mL666j1ZXDTz4CPI8iIH8bONoZGbZWzoySFJ0aTwJupPhwP3r+hL2iY0TEusxcXncdrSwi9gEeTzFf9DGVq+4Crs5M59iuiIizKI7DXYADgUvZspPN6TwrImIlRcfH2OXe26KzqKN6kDOzu+4a2k1EPIdiSqk9I2JV5apdgTn1VNXyvhsRizPz+roLaWVlEH5l3XW0iT+vu4A2dFxEfJI2fXOeCeVX3bdExIuBP2Tmn8rlzJ9C0fOuLX2o7gLazJsoXkvb04bfpnZUQB4VEf9KcTbz78vLDwP+ITPfXWthrWkHYAHFa2WXyvY7gdfUUlHrO4UiJP8Se/m2EhFrmXgWC090GWN0zF65IMF1o9O7RcQuFCvFtcWYvhnW1m/OM+ybwPPL98ILgcuA11KcOKtSZl4MxbSnwK2ZeW95eUdgYZ21tagD2nn57Y4aYjFqvJMPHC4wsYjYp10G1tctIv4XWEWbnpgw3SLi8Imuz8xTZqqWdhMR3weeMXpyULno0WX+7dpadYEQTWz0/S8i+ihWP/uAJ+ltW0RcBjx3dBq8iNgB+HZmHlRvZa0lIk4G/r1dv03tyB5kYE5EzMvM++CBT3/zaq6pJUXEf2Tm0cB/RsRWn6YcczWun7TziQnTbWwAjoidM/PuuuppM1E9c778SrxT/45P5hKHOjUsyuF0r6NYZRY6Nx80Ym51jujMvL8MydrScuDwiLiJNvw2tVMPgM8DF0bEpym+cnsz4ATf4xtdWcmxV437QUR8ATgLxz5uU/mGPEgxhOdxEXEA8HeZ+ff1VtbSboyItwEfLy//PcUJe9paW785z7C3A+8CzszM68olzSc6qb3T3RYRh452hJTTod5ec02t6OV1F/BQdOQQC4CIeDnwYoo/mudn5nk1l6RZovzgNVZm5ptnvJgWFhHfoxjH/rXRr3Kd53diEfFI4KPAiyg+3F8IvD0zb6u1sBZUztCwFYc6bS0i/iozT5tsmwoR8QSKaQT3Kjf9DHhDZv64vqpaU0QsB56UmZ+OiD2BBZl5U911NaIjA3JErMnM1ZNt0xbTS43L3hg9WKOLglTHOkbEVZl5QN21taqIeF5mfnuybSq085vzTHIaz6mJiMdn5k0RsYAiR901uq3u2lpJRBwHLAP2y8wnR8RewGmZ2RZrKHTqEIuXAGPD8MHjbNPm6aUCOBv4sxpraQvlNEkfBxZm5pJyVb1DM/Nfai6t1fw0Ip4LZDl+723A+ppranVrgbGhZbxtHa/65gx8mmI2i89TzLstICIOpvibvndEfLRy1a6AcyBv25cpTpbdUNl2OrC0pnpa1V8ATweuAMjMX5Qz77SFjgrIEfEWijF7+0bE1ZWrdqFYpEBjVL+OjIj7/HqyISdTrOD1CYDMvLock2xA3tJRFAuF7E3xFeX5wFtrrahFOR/5g9LWb84z5BcUU7odClxe2X4X8I5aKmphEfEUYH9gt4h4deWqXYH59VTV0u7PzBw9wT8idq67oKnoqIAMfAE4h3FWDcrM39ZTkmahnTLz0oiobrM3ZozMvB3nWW2U85FPXVu/Oc+EzLwqIq4FXur0ig3Zj+Jb1d2BQyrb7wKOqKOgFveliPgEsHtEHEExIcLJNdfUsI4KyJl5B3AHxdKHoye8zAcWRMSCzPxJnfW1ooiofnW7Y0Q8nWK4BQCZecXMV9Xybi9P4hh9Y34NcGu9JbWOiPincp7VcRcMcaGQrZULFFwcEZ/xW5yGtfWb80zJzE0RsUdE7FCdukxby8yvAl+NiOdk5nfrrqfVZeaHIuIlFB/k9wPek5kX1FxWwzr1JL1DgBMozkD9NbAPsD4z96+1sBYUERNN9ZOZ+aIZK6ZNlFMknUTxlfjvgJuA15dLK3e8iPjzzPz6thYMsSdra6PzkUfEWYz/ocL5yMdRvjm/lOJD/Xnt9OY8k8oPEs8AvgY8MCd5Zp5QW1EtyA/3U1N+a3Nv+SFsP4qQfE5m/rHm0hrSUT3IFf8CPBv4RmY+PSK6KXuVtaXM7G7kdhHxEt98Cpl5I/Di8o/DdqPLAusBrwW+DuyemR+pu5g24XzkU1Qefxdl5gWjb84RsX27vDnPsF+UP9ux5RAebWn0JOLLaq2ifVSXMP8GbbaEeaf2IF+Wmcsi4irg6eVqVJdm5jPrrq1dOSXQZhHxdoqz5u+i+Er3GcAxmXl+rYW1iIi4nmLWmK8BK6gM2QHwfICtRcR8ipMan0ixhPlgZjqufQIRcTnwfOBhwCUUb873ZGZbvDnXoTyJMcfMzqCKiHgV5XHo+gkTa/clzLeru4Ca/L6cv/CbwH9HxEfwJKqHKia/Scd4c2beSfHV7iOBNwHvr7eklnIicC7wFIoz56s/9syM7xSKKcuuofhw8eF6y2kLkZn3AK8G1mbmXwCLa66pJUXEkoj4PnAtcF1EXB4RDjkcIyL+i2J2jz2A90XE/625pFZXXcL87HJb24xcaJtCmyEinggsBF4J/IHihf46ijHIfTWWNht03lcR2zb6YeHPgE+XZ4r7AaKUmR8FPhoRH8/Mt9RdT5tYnJlPBYiIQeDSmutpB9U3595yW0e9503BScCqzBwGiIgVFN9+PbfGmlrRC4ADyjG1OwHfAt5Xc02trK2XMO+0HuT/oJjS7e7M/FNmbixPCPof4PhaK9NscnlEnE8RkM8rv7b8U801taIFYzdExOfGu6F4YNysQysa1tZvzjNs59FwDJCZI4DT4m3t/szcBFB+O2HHxwQy85uZeWhmrikv39hOJzJ21BjkiLg2M5ds47prRntoNHURcUZmvnryW85+EbEdcCBwY2b+PiL2APbOzKsnvmdnGTtuPSLmAldnpl+DjxERm9g8u0AAOwKjb9CZmbvWVZvaX0ScSbGgyugH1NcDyzLzVbUV1YIi4h7gf0cvAk8oL48eh0+rq7ZWVC7v/k8Ui6s8sJBKu8x+1WlfN0200s2OM1ZFGxmzWtBWMvOM8l/Dcak86fMm4MnlyVWqiIh3AcdSzKt95+hm4H6Kr3o1RmY2tFpeRDwsM3833fW0g3Z/c55hbwbeC5xBcSx+k+LcCW2pq+4C2sx/A1+kWFzlKOBw4LZaK5qCTutBHqKY9ufkMdt7KVYSem09lbWuiPh0+esjKcajXVRe7gZGDMZbi4i/pfh69zHAlRRTCn7XN+YtRcS/Zea76q5jNnE2mc3KYU5fBP6RyptzZq6utTDNehHx3cx8Tt111C0iLs/MpRFx9WjvekRcnJkvrLu2RnRaD/LRwJkR8To2rzu/jGIZ17+oq6hWlplvAoiIr1OcKHRrefnRwMfqrK2FvR04CLgkM7sj4ikUvTPa0jkR8YKxGzPzm3UUM0s4JnKzPTJzMCLeXlmJ8OK6i2pFEfFkig8Si6jkAj/UP2h+c1gYPXfi1oh4BcVc24+psZ4p6aiAnJm/Ap5bLgwyOhb57My8aIK7qbBoNByXfgU8ua5iWty9mXlvRBAR8zLzB+VCBdrSOyu/zweeSfHB1TflB69zvhKcXFu/Oc+w0yimX/wksKnmWmYDj8PCv0TEbsA/AGuBXSlmD2sLHRWQR5Vn63o289SMRMR5wBDFwX8YtuG2/Cwidge+AlwQEb+jeHNWRWYeUr0cEY8FPlBTOZp92vrNeYZtzMyP112EZpfM/Hr56x0UwzLbSkeNQdZDExF/QTEPJMA3M/PMOutpBxHxQmA34NzMvL/uelpZOVf01c4ms7WIeHxm3tTA7dpmlSrVLyIeXv76NuDXwJnAfaPXu6rlg9Ppx2F5cvprgd8BZ1GcLPt84MfA+zLz9hrLa5gBWQ2LiH2AJ2XmN8pJ0udk5l1119WqyjZaDNySmW1z5u5MiYi1bP4qcjvg6cBNmfn6+qpqTZWTXS7MzJUT3O7hnR5qZsub80woZ9tJNo9d3yIQZOa+M17ULBARSzLz2rrrqEtEfIliiNPOFEu9X0txLC4HDszMP6+xvIYZkNWQiDgCOBJ4eGY+ISKeBJw40Zt1p4mIQ4GPAr8F3k1xEuOvKE58WV0uSqNSRLwFmEPxpnwHRTj+dr1VtaZyGeCvAH8L/PvY6zPzhJmuqVXNljfnmRARzwR+Wjn5+nDgL4GbgeM7/cPWtkTEXWw9zvgO4DLgHzLzxpmvqnWMrjlRzm3/s8x8VOW6qzLzgBrLa1hHjkHWg/JWipOovgeQmT+KiEfWW1LLeR/wUoohFcPA0zLzxrKdLgQMyDywIMi/Usy9+hOK3qvHAp+KiEsz848T3b9DHQa8iuJv9i71ltLyFo95cx6dUurciLiqzsJa0InAiwHKGWX+DeijWOjoJOA1tVXW2k6gOK/kCxR/vw4DHgXcAHwKWFFbZa3hfihW/YyIsefftM1JoAZkNeq+zLy/GCb6QMjx64ct/SkzfwjFV5ejvQiZ+euIcHngzT5IEfIePzpEJyJ2BT5U/ry9xtpaUmbeAKwp5xM9p+56WtyseHOeIXMqvcSvBU7KzC8DX46IK+srq+W9PDOfVbl8UkRckpn/HBHH1lZV63hMRHyU4sPD6O+Ul/eur6ypMSCrUReXB/6OEfES4O8pvrbUZttFxMMoxtP+qfx9dGzfdvWV1XL+HHhyVsZ3Zead5ZCLH2BAnsgVETEI7JWZB0fEYuA5mTlYd2EtZFa8Oc+QORExNzM3AisphtGNMh9s258i4q+B08vL1Z52O462nMLzsjHXjb3cshyDrIZExHZAL8UQggDOG7siYaeLiJuBPzH+Yg3pCS+FiPhhZo47h/ZE1wki4hzg00B/Zh5QfpPzfWf+2KwcR7tNnguwWUT0A38G3A48DnhGZmZEPBE4JTOfV2uBLSoi9gU+AjyHIhBfQjGF4M+BpZm5rsby2kZErM3Mvrrr2BYDshpSrkb1kcm2aXIRsX9mXld3HXWJiK8AZ2TmZ8dsfz3w15l5aC2FtYGI+H+ZeVB1GqmIuDIzD6y5tLbT6m/OMyUing08Gjg/M+8utz0ZWJCZV9RanGa1iLgiM59Rdx3b4lcoatThFJ+Yq944zjZN7nNAy/5RmAFvBc6IiDdTrJyXFEtz74hLvk/m7ojYg/Jr3DLc3FFvSW3L3lEgMy8ZZ9sP66ilXUTEnsARbL0095vrqknNZ0DWhCKiB/g/wOMj4muVq3YBflNPVW1vvCEYHSMzfw48KyJeBOxP0R7nZOaF9VbWFlYBXwOeEBHfBvbEmQakmfZV4FvAN/DEz1nLgKzJfAe4FXgE8OHK9ruAq2upqP05rgnIzIuAi+quo51k5hXl6oz7UXywuMFp8aQZt1Nmrq67iFmgpTuLDMiaUGbeAtxCcTKCpBpExIsy86KIePWYq54cEWTmGbUU1t5a+s1ZLe3rEfFnmfk/dRfS5lp6iKYBWQ0pxzquBbqAHShWQLs7M3ettbD2dH/dBajtvJCit/2Qca5LwIA8dS395qyW9nbg2Ii4j2LVxqCYqcj3QyAizmKCb0pHT8TOzM/MVE0PhrNYqCERcRnFakGnAcuAvwGemJn9tRbWgiLiwrFLcI+3TVLzNfrmLGl6lMPAAF5NscLg58vLPcDNmdkWi6nYg6yGZeb/RsSczNwEfDoivlN3Ta0kIuYDOwGPGLNIyK7AXrUVprYXEasmuj4zT5ipWtrAh8p/x31zrqMgzQ4R8ZTM/EFEjDsLkdPiFTLzYoCIeF9mvqBy1VkR8c2aypoyA7IadU9E7ABcGREfoDhxb+eaa2o1fwccTRGGL2dzQL4T+FhNNWl22KXuAtrFbHlzVktaRbHa4IfHuS6BF81sOS1vz4jYNzNvBIiIx1PMvNMWHGKhhkTEPsCvKMYfvwPYDfivzPzfWgtrQRHRl5lr665D6mQRsR54xZg35//JzK56K1O7i4j5mXnvZNs6XUS8DDgZuLHctAg4MjPPr62oKbAHWQ0pZ7MAuBd4b521tIFfRsQumXlXRLybYlGQf/HrNz1U5QpnHwcWZuaSiHgacGhm/kvNpbWidwAjEVF9c/67+srRLPIdtl7sabxtHSsitqPoSHsS8JRy8w8y8776qpoae5DVkIh4HnA8sA9brhy0b101taqIuDoznxYRy4F/oxgTeWxmPqvm0tTmIuJi4J3AJypLTV+bmUvqraw1RcQ82vTNWa0nIh4F7E0xrv3/sOV5Jidm5lO2dd9OFBHfHDPMqa3Yg6xGDVL0yFyOKwdNZrR9XgF8PDO/GhHH11iPZo+dMvPSiC2m8N1YVzFtYCmblwM+oJwz+rP1lqQ29jLgjcBjKMYhV88zaYuZGWbYBRHxj8AXgbtHN2bmb+srqXEGZDXqjsw8p+4i2sTPI+ITwIuBNWUv1nY116TZ4faIeALlNGYR8RqKE2Y1RkR8DngCcCWbP7QmYEDWg5KZp5Svq57M/O+662kDby7/fWtlWwJt8c2zQyzUkIh4P8XiIGcAD3xN6bjarUXETsDLgWsy80cR8Wjgqe1yYoJaV0TsC5wEPBf4HXAT8LrKOQIqlSfpLU7f5NRk7T50QI0xIKshETE8zubMTKe1qShPTLjaMaGaThGxM8W3En8AXmtv1tYi4jTgbZlpD7uaKiL+L8Wx15ZDB6ZbRLwoMy+KiFePd31mtsXKnw6xUEMys7vuGtpBZv4pIq6KiMdl5k/qrkezQ0TsSvE15d7AV4FvlJf/EbgKMCBv7RHA9RFxKVt+6+VKenqo2nrowAx4IXARcMg41yXFN9Etzx5kNWQbK3ndAVyemVfOcDktLSIuAg4CLmXL3gXfmPWgRMRXKYZUfBdYCTyMYk7yt3v8ja+y3O0WRhcSkaSJGJDVkIj4ArAMOKvc9Arg/1FMoXRaZn6grtpajW/MaraIuCYzn1r+Pge4HXhcZt5Vb2WtLSIWUnxYBbg0M39dZz2aPSJiCbAYmD+6zRlStlSeoP6XbJ5JBoDM/Oe6apoKh1ioUXsAz8jMDQARcRxwOvACiqnfDMglg7CmwR9Hf8nMTRFxk+F4YhHx18AHgRGK6bjWRsQ7M/P0WgtT2yvf/1ZQBOT/AQ4G1uEMKWN9lfKbZirDnNqFAVmNehxwf+XyH4F9MvMPEdF2L/zpEBHrMnN5RNxFOQ3X6FUUJzTuWlNpan8HRMSd5e8B7Fhe9rW1bf3AQaO9xhGxJ8XYbQOyHqrXAAcA38/MN5XfVHyy5ppa0WMy8+V1F/FgGZDVqC8Al5RjIaEYfD9Unk1/fX1ltZTXAWTmLnUXotklM+fUXUMb2m7MkIrf4Hzkao4/lCdkbyxPoP01nqA3nu9ExFMz85q6C3kwDMhqSGa+LyL+B1hO0Wt1VGZeVl79uvoqaylnAs8AiIgvZ+Zf1lyP1MnOjYjzgKHy8msBFztSM1wWEbsDJ1MMH9hAcVK2gIi4FvgTRcZ8U0TcSDHEYvQbr6fVWV+jPElPE4qIXTPzzoh4+HjXO+/jZhHx/cx8+tjfJdWjnId19EP9NzPzzJpL0iwTEYuAXTPz6rpraRUR8TvgwG1d3y4LG9mDrMl8Afhzik/JW42rxa+VqnIbv0uaYRHxeOB/RhcliIgdI2JRZt5cb2VqdxFxYWauBBh9PVW3iZvaJQRPxB5kqUkiYhPFvMcB7AjcM3oVnkglzaiIuAx4bmbeX17eAfh2Zh408T2l8UXEfGAnYJhiFosor9oVOCczu2oqraVExM+AE7Z1fWZu87pWYg+yGhIRzwOuzMy7I+L1FGNt/8PV4jbzRCqppcwdDccAmXl/GZKlB+vvgKOBvSi+VR11F/CxOgpqUXOABWz+ANGWDMhq1Mcpppo6APgnYBD4HMWSkpLUam6LiEMz82sAEfFKigVWpAfrO8CXgNdk5tqIOJxiIYybKYYjqnBruywGMhGnvFGjNmYxHueVwEcy8yOA05lJalVHAcdGxE8j4ifAaooeQOnB+gRwXxmOXwD8G3AKxWIYJ9VaWWtp657jUfYgq1F3RcS7gDcAzy+Xu92+5pokaVyZ+WPg2RGxgOJ8G1ce1EM1pzJz02uBkzLzy8CXI+LK+spqObPiZEV7kNWo11LMY/jmzPwlsDfFMq6S1HIiYmFEDAKnZeZdEbE4InrrrkttbU5EjHYsrgQuqlxnh2Nptkz/akBWQ8pQ/GVgXrnpdoqFMSSpFX0GOI/ihCqAH1KcYCU9WEPAxeWKsn8AvgUQEU+kGGahWcSArIZExBHA6RRjsKDoQf5KbQVJ0sQekZlfoljRi8zcCGyqtyS1s8wcAP6B4sPX8tw8T+52QF9ddWl6+JWAGvVW4JnA9wAy80cR8ch6S5Kkbbo7IvagXLQnIp6NvXx6iDLzknG2/bCOWjS9DMhq1H3lPKIAlOOwXGVGUqtaBXwNeEJEfBvYE3hNvSVJahcOsVCjLo6IY4EdI+IlwGnAWTXXJElbiIiDIuJRmXkFxTztx1KcYHw+8LNai5PUNlxqWg2JiO2AXuClFHMcngd8Mn0BSWohEXEF8OLM/G05V+2pFONDDwS6MtNeZEmTMiCrYRGxJ0Bm3lZ3LZI0noi4KjMPKH//GHBbZh5fXr4yMw+ssTxJbcIhFppQFI6PiNuBHwA3RMRtEfGeumuTpHE4V62kh8yArMkcDTwPOCgz98jMhwPPAp4XEe+otTJJ2ppz1Up6yBxioQlFxPeBl2Tm7WO27wmcn5lPr6cySRpfOaXboyn+Rt1dbnsysKA8eU+SJuTXTZrM9mPDMRTjkCNi+zoKkqSJOFetpIfKIRaazP0P8jpJkqS25BALTSgiNgF3j3cVMD8z7UWWJEmzigFZkiRJqnCIhSRJklRhQJYkSZIqDMiS1EIiYlNEXBkR10bEWRGx+yS3/0xEuHyyJDWRAVmSWssfMvPAzFwC/BZ4a90FSVKnMSBLUuv6LrA3QEQcGBGXRMTVEXFmRDxs7I0jYmlEXBwRl0fEeRHx6BmvWJJmAQOyJLWgiJgDrAS+Vm76LLA6M58GXAMcN+b22wNrgddk5lLgU8DAzFUsSbOHK+lJUmvZMSKuBBYBlwMXRMRuwO6ZeXF5m1OA08bcbz9gSXl7gDnArTNRsCTNNgZkSWotf8jMA8tQ/HWKMcinNHC/AK7LzOdMa3WS1AEcYiFJLSgz7wDeBvwjcA/wu4h4fnn1G4CLx9zlBmDPiHgOFEMuImL/mapXkmYTe5AlqUVl5vcj4irgMOBw4MSI2Am4EXjTmNveX0739tGy93ku8B/AdTNbtSS1P5ealiRJkiocYiFJkiRVGJAlSZKkCgOyJEmSVGFAliRJkioMyJIkSVKFAVmSJEmqMCBLkiRJFQZkSZIkqeL/Bzi4LlK03SS8AAAAAElFTkSuQmCC\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -409,19 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ZM3TjjTdq5syZxXp+AICSRfAGAIf98Xreq1ev1t133+1b1qpVK7ndbq1YsULr169Xr169fMvq1asnY4ySkpJ8e8WKqk6dOlq+fLmysrL89noX9ezX+Tlb8Cyohm3btuUZ37p1a5HuX7FiRf31r3/VX//6V506dUr9+/fX448/rnHjxik8PLzY9RRm27ZtfnsZt2/fLq/X6zspW+6e5SNHjvjd78w90lLxtlWdOnXy3Sbff/+9b/n5ql69uiIiIs76OEFBQXmC37moXbu2Lr/8cq1YsUJ33HGH1eulnz59WpJ07NixQoP3uXjnnXdUt25dzZs3z6+fjzzySJ65MTEx6t27t2bPnq0hQ4Zo9erVevbZZ/3m1KtXT19//bW6du16Xq/dsLAw9enTR3369JHX69Wdd96pl156SePHjz/rJ1oAAPbxUXMAcFjr1q0VHh6u2bNn69dff/Xb4+12u3XZZZdp2rRpOn78uN9xvv3791dwcLAmTJiQZ2+mMUa//fbbWR+zR48e8ng8euWVV3xjXq9X06ZNO+fnkRvgzwyeZ9OrVy+tW7dOn332mW/swIEDfsfCns2Zzy0sLEyNGzeWMUYej0eSfGGrqPUU5sxtM3XqVElSSkqKJCkqKkrVqlXTypUr/ea98MILedZVnNp69eqlzz77TGvXrvWNHT9+XC+//LISExOLdZz62QQHB6t79+56//33/T46v2/fPqWnp6tDhw6Kioo678eRpMcee0yPPPJIkT8Gfi4OHTqkNWvWKC4uTrGxsVYeI/eTBX/83lu/fr1fn/7ohhtu0Lfffqt7771XwcHBGjRokN/ygQMH6tdff/X7nsx14sQJHT9+vNCazvy+CAoK8l1Z4MxLkgEAShd7vAHAYWFhYfrTn/6kTz/9VG63W61atfJb3r59ez399NOS5Be869Wrp8cee0zjxo3Trl271K9fP0VGRmrnzp167733NGLECN1zzz35Pma/fv3Upk0b/d///Z+2b9+uhg0b6oMPPvBdlulc9rhVqFBBjRs31r/+9S/Vr19fMTExatq0qZo2bZrv/Pvuu09vvvmmevbsqTFjxvguJ1anTh1t2rSpwMfq3r274uLidPnll6tGjRr67rvv9M9//lO9e/dWZGSkJPm244MPPqhBgwYpNDRUffr0Oee9nzt37lTfvn3Vs2dPrV27VrNmzdLgwYPVvHlz35xbb71VkydP1q233qrWrVtr5cqV+uGHH/Ksqzi1PfDAA3rrrbeUkpKiu+66SzExMZo5c6Z27typd999V0FBJfM39Mcee0yLFy9Whw4ddOeddyokJEQvvfSSsrOz9cQTT5TIY0i/nxSsU6dORZp74MABPfbYY3nGk5KS/E7C984776hSpUoyxmj37t169dVXdfjwYU2fPr3EP/mQ6y9/+YvmzZunq6++Wr1799bOnTs1ffp0NW7cWMeOHcszv3fv3qpatarmzp2rlJSUPH8QuOGGGzRnzhzdfvvtWr58uS6//HLl5OTo+++/15w5c/TRRx+pdevWBdZ066236tChQ+rSpYtq1aqln376SVOnTlWLFi185wQAADjEuROqAwByjRs3zkgy7du3z7Ns3rx5RpKJjIzM9zJB7777runQoYOpWLGiqVixomnYsKEZOXKk2bp1q2/OmZcTM+b3y38NHjzYREZGmujoaDNs2DCzevVqI8m8/fbbfvc981JPxvzvUk5/tGbNGtOqVSsTFhZWpEuLbdq0yXTq1MmEh4ebiy66yEycONG8+uqrhV5O7KWXXjIdO3Y0VatWNW6329SrV8/ce++9JiMjw2/9EydONBdddJEJCgryW6ckM3LkyHxrOrPu3Of57bffmmuuucZERkaaKlWqmFGjRpkTJ0743TcrK8vccsstJjo62kRGRpqBAwea/fv357stzlbbmZcTM8aYHTt2mGuuucZUrlzZhIeHmzZt2pgFCxb4zcm9nNjcuXP9xgu6zNmZvvzyS9OjRw9TqVIlExERYZKTk82aNWvyXV9xLydWkLNdTkz5XCpMkunatasxJv/LiVWsWNG0a9fOzJkzp9D6jPl9e/fu3bvQeWe+Br1er/n73/9u6tSpY9xut2nZsqVZsGBBvt9ruXIvQZeenp7v8lOnTpl//OMfpkmTJsbtdpsqVaqYVq1amQkTJvi9ts/2+n3nnXdM9+7dTWxsrAkLCzO1a9c2t912m9mzZ0+hzw8AYJfLmAA42woAICDMnz9fV199tVatWqXLL7/c6XKAC0pqaqpeffVV7d27t0QuowYAKDs4xhsAyqkTJ0743c7JydHUqVMVFRWlyy67zKGqgAvTyZMnNWvWLA0YMIDQDQDlEMd4A0A5NXr0aJ04cULt2rVTdna25s2bpzVr1ujvf//7eV9qC8Dv9u/fryVLluidd97Rb7/9pjFjxjhdEgDAAQRvACinunTpoqeffloLFizQyZMndfHFF2vq1KkaNWqU06UBF4xvv/1WQ4YMUWxsrJ5//nm1aNHC6ZIAAA7gGG8AAAAAACziGG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALAoxOkCAoHX69Xu3bsVGRkpl8vldDkAAAAAgABnjNHRo0cVHx+voKCC92kTvCXt3r1bCQkJTpcBAAAAAChjfvnlF9WqVavAOQRvSZGRkZJ+32BRUVEOV1M+eDweffzxx+revbtCQ0OdLgdnoD+Bjf4ENvoT2OhPYKM/gY3+BC5644zMzEwlJCT48mRBCN6S7+PlUVFRBO9S4vF4FBERoaioKH44BCD6E9joT2CjP4GN/gQ2+hPY6E/gojfOKsrhypxcDQAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLHA3eK1euVJ8+fRQfHy+Xy6X58+f7LXe5XPl+Pfnkk745iYmJeZZPnjy5lJ8JAAAAAAD5czR4Hz9+XM2bN9e0adPyXb5nzx6/r9dee00ul0sDBgzwm/foo4/6zRs9enRplA8AAAAAQKFCnHzwlJQUpaSknHV5XFyc3+33339fycnJqlu3rt94ZGRknrkAAAAAAAQCR4N3cezbt08LFy7UzJkz8yybPHmyJk6cqNq1a2vw4MFKTU1VSMjZn1p2drays7N9tzMzMyVJHo9HHo+n5ItHHrnbme0dmOhPYKM/gY3+BDb6E9joT2CjP4GL3jijONvbZYwxFmspMpfLpffee0/9+vXLd/kTTzyhyZMna/fu3QoPD/eNT5kyRZdddpliYmK0Zs0ajRs3TjfddJOmTJly1sdKS0vThAkT8oynp6crIiLivJ8LAAAAAODClpWVpcGDBysjI0NRUVEFzi0zwbthw4bq1q2bpk6dWuB6XnvtNd122206duyY3G53vnPy2+OdkJCggwcPFrrBUDI8Ho8WL16sbt26KTQ01OlycAb6E9joT9E0TfvIkcd1BxlNbO3V+A1Byva6rDzG5rQeVtZbHvD9E9joT2CjP4GL3jgjMzNT1apVK1LwLhMfNf/000+1detW/etf/yp0btu2bXX69Gnt2rVLDRo0yHeO2+3ON5SHhobyQi1lbPPARn8CG/0pWHaOndBb5Mf3uqzVQN/PH98/gY3+BDb6E7joTekqzrYuE9fxfvXVV9WqVSs1b9680LkbN25UUFCQYmNjS6EyAAAAAAAK5uge72PHjmn79u2+2zt37tTGjRsVExOj2rVrS/p99/3cuXP19NNP57n/2rVrtX79eiUnJysyMlJr165Vamqqrr/+elWpUqXUngcAAAAAAGfjaPDesGGDkpOTfbfHjh0rSRo6dKhef/11SdLbb78tY4yuu+66PPd3u916++23lZaWpuzsbCUlJSk1NdW3HgAAAAAAnOZo8O7cubMKO7fbiBEjNGLEiHyXXXbZZVq3bp2N0gAAAAAAKBFl4hhvAAAAAADKKoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wUAAJyR+MBCp0sAAAAoF9jjDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwKcboAAABQPIkPLHS6BKt2Te7tdAkAAJQo9ngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAIBAlvjAQqdLyMMdbPREG6lp2kfKznE5XQ4AAAAKwR5vAAAAAAAscjR4r1y5Un369FF8fLxcLpfmz5/vt3zYsGFyuVx+Xz179vSbc+jQIQ0ZMkRRUVGqXLmybrnlFh07dqwUnwUAAAAAAGfnaPA+fvy4mjdvrmnTpp11Ts+ePbVnzx7f11tvveW3fMiQIdqyZYsWL16sBQsWaOXKlRoxYoTt0gEAAAAAKBJHj/FOSUlRSkpKgXPcbrfi4uLyXfbdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+PL/GaAQAAAAAojoA/udqKFSsUGxurKlWqqEuXLnrsscdUtWpVSdLatWtVuXJlX+iWpCuvvFJBQUFav369rr766nzXmZ2drezsbN/tzMxMSZLH45HH47H4bJArdzuzvQMT/fkfd7BxuoQ83EHG718EFvpz/mz+7OHnW2CjP4GN/gQueuOM4mxvlzEmIN4ZuFwuvffee+rXr59v7O2331ZERISSkpK0Y8cO/e1vf1OlSpW0du1aBQcH6+9//7tmzpyprVu3+q0rNjZWEyZM0B133JHvY6WlpWnChAl5xtPT0xUREVGizwsAAAAAcOHJysrS4MGDlZGRoaioqALnBvQe70GDBvn+f+mll6pZs2aqV6+eVqxYoa5du57zeseNG6exY8f6bmdmZiohIUHdu3cvdIOhZHg8Hi1evFjdunVTaGio0+XgDPTnf5qmfeR0CXm4g4wmtvZq/IYgZXu5nFigoT/nb3NaD2vr5udbYKM/gY3+BC5644zcT04XRUAH7zPVrVtX1apV0/bt29W1a1fFxcVp//79fnNOnz6tQ4cOnfW4cOn348bdbnee8dDQUF6opYxtHtjojwL6OtnZXldA11fe0Z9zVxo/d/j5FtjoT2CjP4GL3pSu4mzrMnUd7//+97/67bffVLNmTUlSu3btdOTIEX3xxRe+OcuWLZPX61Xbtm2dKhMAAAAAAB9H93gfO3ZM27dv993euXOnNm7cqJiYGMXExGjChAkaMGCA4uLitGPHDt133326+OKL1aPH7x9Ba9SokXr27Knhw4dr+vTp8ng8GjVqlAYNGsQZzQEAAAAAAcHRPd4bNmxQy5Yt1bJlS0nS2LFj1bJlSz388MMKDg7Wpk2b1LdvX9WvX1+33HKLWrVqpU8//dTvY+KzZ89Ww4YN1bVrV/Xq1UsdOnTQyy+/7NRTAgAAAADAj6N7vDt37qyCTqr+0UeFn9QoJiZG6enpJVkWAAAAAAAlpkwd4w0AAAAAQFlD8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAixwN3itXrlSfPn0UHx8vl8ul+fPn+5Z5PB7df//9uvTSS1WxYkXFx8frxhtv1O7du/3WkZiYKJfL5fc1efLkUn4mAAAAAADkz9Hgffz4cTVv3lzTpk3LsywrK0tffvmlxo8fry+//FLz5s3T1q1b1bdv3zxzH330Ue3Zs8f3NXr06NIoHwAAAACAQoU4+eApKSlKSUnJd1l0dLQWL17sN/bPf/5Tbdq00c8//6zatWv7xiMjIxUXF2e1VgAAAAAAzoWjwbu4MjIy5HK5VLlyZb/xyZMna+LEiapdu7YGDx6s1NRUhYSc/allZ2crOzvbdzszM1PS7x9v93g8VmqHv9ztzPYOTPTnf9zBxukS8nAHGb9/EVjoz/mz+bOHn2+Bjf4ENvoTuOiNM4qzvV3GmIB4Z+ByufTee++pX79++S4/efKkLr/8cjVs2FCzZ8/2jU+ZMkWXXXaZYmJitGbNGo0bN0433XSTpkyZctbHSktL04QJE/KMp6enKyIi4ryfCwAAAADgwpaVlaXBgwcrIyNDUVFRBc4tE8Hb4/FowIAB+u9//6sVK1YU+KRee+013XbbbTp27Jjcbne+c/Lb452QkKCDBw8WusFQMjwejxYvXqxu3bopNDTU6XJwBvrzP03TPnK6hDzcQUYTW3s1fkOQsr0up8vBGejP+duc1sPauvn5FtjoT2CjP4GL3jgjMzNT1apVK1LwDviPmns8Hg0cOFA//fSTli1bVugTatu2rU6fPq1du3apQYMG+c5xu935hvLQ0FBeqKWMbR7Y6I+UnRO4wSnb6wro+so7+nPuSuPnDj/fAhv9CWz0J3DRm9JVnG0d0ME7N3Rv27ZNy5cvV9WqVQu9z8aNGxUUFKTY2NhSqBAAAAAAgII5GryPHTum7du3+27v3LlTGzduVExMjGrWrKlrrrlGX375pRYsWKCcnBzt3btXkhQTE6OwsDCtXbtW69evV3JysiIjI7V27Vqlpqbq+uuvV5UqVZx6WgAAAAAA+DgavDds2KDk5GTf7bFjx0qShg4dqrS0NH3wwQeSpBYtWvjdb/ny5ercubPcbrfefvttpaWlKTs7W0lJSUpNTfWtBwAAAAAApzkavDt37qyCzu1W2HnfLrvsMq1bt66kywIAAAAAoMQEOV0AAAAAAAAXMoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUhThcAAADwR4kPLLS2bnew0RNtpKZpHyk7x2Xtcc5m1+Tepf6YAADnsccbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFjkavFeuXKk+ffooPj5eLpdL8+fP91tujNHDDz+smjVrqkKFCrryyiu1bds2vzmHDh3SkCFDFBUVpcqVK+uWW27RsWPHSvFZAAAAAABwdo4G7+PHj6t58+aaNm1avsufeOIJPf/885o+fbrWr1+vihUrqkePHjp58qRvzpAhQ7RlyxYtXrxYCxYs0MqVKzVixIjSegoAAAAAABTI0cuJpaSkKCUlJd9lxhg9++yzeuihh3TVVVdJkt544w3VqFFD8+fP16BBg/Tdd99p0aJF+vzzz9W6dWtJ0tSpU9WrVy899dRTio+Pz3fd2dnZys7O9t3OzMyUJHk8Hnk8npJ8ijiL3O3M9g5M9Od/3MHG6RLycAcZv38RWOhPYHO6P/xcLRi/fwIb/Qlc9MYZxdneLmNMQLwzcLlceu+999SvXz9J0o8//qh69erpq6++UosWLXzzOnXqpBYtWui5557Ta6+9pv/7v//T4cOHfctPnz6t8PBwzZ07V1dffXW+j5WWlqYJEybkGU9PT1dERESJPi8AAAAAwIUnKytLgwcPVkZGhqKiogqc6+ge74Ls3btXklSjRg2/8Ro1aviW7d27V7GxsX7LQ0JCFBMT45uTn3Hjxmns2LG+25mZmUpISFD37t0L3WAoGR6PR4sXL1a3bt0UGhrqdDk4A/35n6ZpHzldQh7uIKOJrb0avyFI2V6X0+XgDPQnsDndn81pPUr9McsSfv8ENvoTuOiNM3I/OV0UARu8bXK73XK73XnGQ0NDeaGWMrZ5YKM/UnZO4AanbK8roOsr7+hPYHOqP+X9Z2pR8fsnsNGfwEVvSldxtnXAXk4sLi5OkrRv3z6/8X379vmWxcXFaf/+/X7LT58+rUOHDvnmAAAAAADgpHMK3nXr1tVvv/2WZ/zIkSOqW7fueRclSUlJSYqLi9PSpUt9Y5mZmVq/fr3atWsnSWrXrp2OHDmiL774wjdn2bJl8nq9atu2bYnUAQAAAADA+Tinj5rv2rVLOTk5ecazs7P166+/Fnk9x44d0/bt2323d+7cqY0bNyomJka1a9fW3Xffrccee0yXXHKJkpKSNH78eMXHx/tOwNaoUSP17NlTw4cP1/Tp0+XxeDRq1CgNGjTorGc0BwAAAACgNBUreH/wwQe+/3/00UeKjo723c7JydHSpUuVmJhY5PVt2LBBycnJvtu5JzwbOnSoXn/9dd133306fvy4RowYoSNHjqhDhw5atGiRwsPDffeZPXu2Ro0apa5duyooKEgDBgzQ888/X5ynBQAAAACANcUK3rl7ml0ul4YOHeq3LDQ0VImJiXr66aeLvL7OnTuroKuZuVwuPfroo3r00UfPOicmJkbp6elFfkwAAAAAAEpTsYK31+uV9Pvx159//rmqVatmpSgAAAAAAC4U53SM986dO0u6DgAAAAAALkjnfB3vpUuXaunSpdq/f79vT3iu11577bwLAwAAAADgQnBOwXvChAl69NFH1bp1a9WsWVMul6uk6wIAAAAA4IJwTsF7+vTpev3113XDDTeUdD0AAAAAAFxQgs7lTqdOnVL79u1LuhYAAAAAAC445xS8b731Vi7hBQAAAABAEZzTR81Pnjypl19+WUuWLFGzZs0UGhrqt3zKlCklUhwAAAAAAGXdOQXvTZs2qUWLFpKkzZs3+y3jRGsAAAAAAPzPOQXv5cuXl3QdAAAAAABckM7pGG8AAAAAAFA057THOzk5ucCPlC9btuycCwIAAAAA4EJyTsE79/juXB6PRxs3btTmzZs1dOjQkqgLAAAAAIALwjkF72eeeSbf8bS0NB07duy8CgIAAAAA4EJSosd4X3/99XrttddKcpUAAAAAAJRpJRq8165dq/Dw8JJcJQAAAAAAZdo5fdS8f//+freNMdqzZ482bNig8ePHl0hhAAAAAABcCM4peEdHR/vdDgoKUoMGDfToo4+qe/fuJVIYAAAAAAAXgnMK3jNmzCjpOgAAAAAAuCCdU/DO9cUXX+i7776TJDVp0kQtW7YskaIAAAAAALhQnFPw3r9/vwYNGqQVK1aocuXKkqQjR44oOTlZb7/9tqpXr16SNQIAAAAAUGad01nNR48eraNHj2rLli06dOiQDh06pM2bNyszM1N33XVXSdcIAAAAAECZdU57vBctWqQlS5aoUaNGvrHGjRtr2rRpnFwNKGcSH1jodAkAAABAQDunPd5er1ehoaF5xkNDQ+X1es+7KAAAAAAALhTnFLy7dOmiMWPGaPfu3b6xX3/9VampqeratWuJFQcAAAAAQFl3TsH7n//8pzIzM5WYmKh69eqpXr16SkpKUmZmpqZOnVrSNQIAAAAAUGad0zHeCQkJ+vLLL7VkyRJ9//33kqRGjRrpyiuvLNHiAAAAAAAo64q1x3vZsmVq3LixMjMz5XK51K1bN40ePVqjR4/Wn/70JzVp0kSffvqprVoBAAAAAChzihW8n332WQ0fPlxRUVF5lkVHR+u2227TlClTSqw4AAAAAADKumIF76+//lo9e/Y86/Lu3bvriy++OO+iAAAAAAC4UBQreO/bty/fy4jlCgkJ0YEDB867KAAAAAAALhTFCt4XXXSRNm/efNblmzZtUs2aNc+7KAAAAAAALhTFCt69evXS+PHjdfLkyTzLTpw4oUceeUR/+ctfSqw4AAAAAADKumJdTuyhhx7SvHnzVL9+fY0aNUoNGjSQJH3//feaNm2acnJy9OCDD1opFAAAAACAsqhYwbtGjRpas2aN7rjjDo0bN07GGEmSy+VSjx49NG3aNNWoUcNKoQAAAAAAlEXFCt6SVKdOHf3nP//R4cOHtX37dhljdMkll6hKlSo26gMAAAAAoEwrdvDOVaVKFf3pT38qyVoAAAAAALjgFOvkagAAAAAAoHgI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCigA/eiYmJcrlceb5GjhwpSercuXOeZbfffrvDVQMAAAAA8LsQpwsozOeff66cnBzf7c2bN6tbt2669tprfWPDhw/Xo48+6rsdERFRqjUCAAAAAHA2AR+8q1ev7nd78uTJqlevnjp16uQbi4iIUFxcXJHXmZ2drezsbN/tzMxMSZLH45HH4znPilEUuduZ7R2YitMfd7CxXQ7O4A4yfv8isNCfwOZ0f/i9VzDeHwQ2+hO46I0zirO9XcaYMvPO4NSpU4qPj9fYsWP1t7/9TdLvHzXfsmWLjDGKi4tTnz59NH78+AL3eqelpWnChAl5xtPT09lbDgAAAAAoVFZWlgYPHqyMjAxFRUUVOLdMBe85c+Zo8ODB+vnnnxUfHy9Jevnll1WnTh3Fx8dr06ZNuv/++9WmTRvNmzfvrOvJb493QkKCDh48WOgGQ8nweDxavHixunXrptDQUKfLwRmK05+maR+VUlXI5Q4ymtjaq/EbgpTtdTldDs5AfwKb0/3ZnNaj1B+zLOH9QWCjP4GL3jgjMzNT1apVK1LwDviPmv/Rq6++qpSUFF/olqQRI0b4/n/ppZeqZs2a6tq1q3bs2KF69erlux632y23251nPDQ0lBdqKWObB7ai9Cc7h2DhlGyvi+0fwOhPYHOqP/zOKxreHwQ2+hO46E3pKs62Dvizmuf66aeftGTJEt16660Fzmvbtq0kafv27aVRFgAAAAAABSozwXvGjBmKjY1V7969C5y3ceNGSVLNmjVLoSoAAAAAAApWJj5q7vV6NWPGDA0dOlQhIf8receOHUpPT1evXr1UtWpVbdq0SampqerYsaOaNWvmYMUAAAAAAPyuTATvJUuW6Oeff9bNN9/sNx4WFqYlS5bo2Wef1fHjx5WQkKABAwbooYcecqhSAAAAAAD8lYng3b17d+V38vWEhAR98sknDlQEAAAAAEDRlJljvAEAAAAAKIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAi0KcLgAAAKC8SHxgodMlWLNrcm+nSwCAgMUebwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYFOJ0AUB5kPjAQqdLKBZ3sNETbaSmaR8pO8fldDkAAABAmcYebwAAAAAALAro4J2WliaXy+X31bBhQ9/ykydPauTIkapataoqVaqkAQMGaN++fQ5WDAAAAACAv4AO3pLUpEkT7dmzx/e1atUq37LU1FT9+9//1ty5c/XJJ59o9+7d6t+/v4PVAgAAAADgL+CP8Q4JCVFcXFye8YyMDL366qtKT09Xly5dJEkzZsxQo0aNtG7dOv35z38+6zqzs7OVnZ3tu52ZmSlJ8ng88ng8JfwMkJ/c7Vxetrc72DhdQrG4g4zfvwgs9Cew0Z/ARn/sKYnf6eXt/UFZQ38CF71xRnG2t8sYE7C/edLS0vTkk08qOjpa4eHhateunSZNmqTatWtr2bJl6tq1qw4fPqzKlSv77lOnTh3dfffdSk1NLXC9EyZMyDOenp6uiIgIG08FAAAAAHABycrK0uDBg5WRkaGoqKgC5wb0Hu+2bdvq9ddfV4MGDbRnzx5NmDBBV1xxhTZv3qy9e/cqLCzML3RLUo0aNbR3794C1ztu3DiNHTvWdzszM1MJCQnq3r17oRsMJcPj8Wjx4sXq1q2bQkNDnS7HuqZpHzldQrG4g4wmtvZq/IYgZXs5q3mgoT+Bjf4ENvpjz+a0Hue9jvL2/qCsoT+Bi944I/eT00UR0ME7JSXF9/9mzZqpbdu2qlOnjubMmaMKFSqc83rdbrfcbnee8dDQUF6opay8bPOyekmubK+rzNZeHtCfwEZ/Ahv9KXkl+fu8vLw/KKvoT+CiN6WrONs64E+u9keVK1dW/fr1tX37dsXFxenUqVM6cuSI35x9+/ble0w4AAAAAABOKFPB+9ixY9qxY4dq1qypVq1aKTQ0VEuXLvUt37p1q37++We1a9fOwSoBAAAAAPifgP6o+T333KM+ffqoTp062r17tx555BEFBwfruuuuU3R0tG655RaNHTtWMTExioqK0ujRo9WuXbsCz2gOAAAAAEBpCujg/d///lfXXXedfvvtN1WvXl0dOnTQunXrVL16dUnSM888o6CgIA0YMEDZ2dnq0aOHXnjhBYerBgAAAADgfwI6eL/99tsFLg8PD9e0adM0bdq0UqoIAAAAAIDiKVPHeAMAAAAAUNYQvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAItCnC4AAAAAZV/iAwvPex3uYKMn2khN0z5Sdo6rBKoqObsm93a6BABlGHu8AQAAAACwKKCD96RJk/SnP/1JkZGRio2NVb9+/bR161a/OZ07d5bL5fL7uv322x2qGAAAAAAAfwEdvD/55BONHDlS69at0+LFi+XxeNS9e3cdP37cb97w4cO1Z88e39cTTzzhUMUAAAAAAPgL6GO8Fy1a5Hf79ddfV2xsrL744gt17NjRNx4REaG4uLjSLg8AAAAAgEIFdPA+U0ZGhiQpJibGb3z27NmaNWuW4uLi1KdPH40fP14RERFnXU92drays7N9tzMzMyVJHo9HHo/HQuU4U+52Li/b2x1snC6hWNxBxu9fBBb6E9joT2CjP4EtkPtTXt6zFKS8vX8rS+iNM4qzvV3GmMD7yZYPr9ervn376siRI1q1apVv/OWXX1adOnUUHx+vTZs26f7771ebNm00b968s64rLS1NEyZMyDOenp5eYGAHAAAAAECSsrKyNHjwYGVkZCgqKqrAuWUmeN9xxx368MMPtWrVKtWqVeus85YtW6auXbtq+/btqlevXr5z8tvjnZCQoIMHDxa6wVAyPB6PFi9erG7duik0NNTpcqxrmvaR0yUUizvIaGJrr8ZvCFK2N7Au5wL6E+joT2CjP4EtkPuzOa2H0yU4rry9fytL6I0zMjMzVa1atSIF7zLxUfNRo0ZpwYIFWrlyZYGhW5Latm0rSQUGb7fbLbfbnWc8NDSUF2opKy/bPNCuRVpU2V5Xma29PKA/gY3+BDb6E9gCsT/l4f1KUZWX929lEb0pXcXZ1gEdvI0xGj16tN577z2tWLFCSUlJhd5n48aNkqSaNWtarg4AAAAAgMIFdPAeOXKk0tPT9f777ysyMlJ79+6VJEVHR6tChQrasWOH0tPT1atXL1WtWlWbNm1SamqqOnbsqGbNmjlcPQAAAAAAAR68X3zxRUlS586d/cZnzJihYcOGKSwsTEuWLNGzzz6r48ePKyEhQQMGDNBDDz3kQLUAAAAAAOQV0MG7sPO+JSQk6JNPPimlagAAAAAAKL4gpwsAAAAAAOBCRvAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWBTidAFArsQHFjpdAgAAAACUOPZ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMCiEKcLAAAAAAJd4gMLnS7Bml2TeztdAnDBY483AAAAAAAWEbwBAAAAALCI4A0AAAAAgEUEbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAAAAACwieAMAAAAAYFGI0wWg6BIfWOh0CSXGHWz0RBupadpHys5xOV0OAAAAAFjDHm8AAAAAACwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAigjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvAAAAAAAsCnG6AAAAAADOSXxgYZHmuYONnmgjNU37SNk5LstVlZxdk3s7XQLAHm8AAAAAAGwieAMAAAAAYBHBGwAAAAAAiwjeAAAAAABYRPAGAAAAAMAizmoOAAAAAGVQ7hnpy+oZ5wtyoZ2Nnj3eAAAAAABYxB5vAAAAABesol6nHLCJPd4AAAAAAFh0wQTvadOmKTExUeHh4Wrbtq0+++wzp0sCAAAAAODCCN7/+te/NHbsWD3yyCP68ssv1bx5c/Xo0UP79+93ujQAAAAAQDl3QQTvKVOmaPjw4brpppvUuHFjTZ8+XREREXrttdecLg0AAAAAUM6V+ZOrnTp1Sl988YXGjRvnGwsKCtKVV16ptWvX5nuf7OxsZWdn+25nZGRIkg4dOiSPx2O34PMQcvq40yWUmBCvUVaWVyGeIOV4L4xLHlxI6E9goz+Bjf4ENvoT2OhPYKM/getC7M1vv/3mdAmFOnr0qCTJGFPo3DIfvA8ePKicnBzVqFHDb7xGjRr6/vvv873PpEmTNGHChDzjSUlJVmpE/gY7XQAKRH8CG/0JbPQnsNGfwEZ/Ahv9CVwXWm+qPe10BUV39OhRRUdHFzinzAfvczFu3DiNHTvWd9vr9erQoUOqWrWqXK4L4y9EgS4zM1MJCQn65ZdfFBUV5XQ5OAP9CWz0J7DRn8BGfwIb/Qls9Cdw0RtnGGN09OhRxcfHFzq3zAfvatWqKTg4WPv27fMb37dvn+Li4vK9j9vtltvt9hurXLmyrRJRgKioKH44BDD6E9joT2CjP4GN/gQ2+hPY6E/gojelr7A93bnK/MnVwsLC1KpVKy1dutQ35vV6tXTpUrVr187BygAAAAAAuAD2eEvS2LFjNXToULVu3Vpt2rTRs88+q+PHj+umm25yujQAAAAAQDl3QQTvv/71rzpw4IAefvhh7d27Vy1atNCiRYvynHANgcPtduuRRx7J85F/BAb6E9joT2CjP4GN/gQ2+hPY6E/gojeBz2WKcu5zAAAAAABwTsr8Md4AAAAAAAQygjcAAAAAABYRvAEAAAAAsIjgDQAAAACARQRvWPXrr7/q+uuvV9WqVVWhQgVdeuml2rBhg2/5sWPHNGrUKNWqVUsVKlRQ48aNNX36dAcrLj8SExPlcrnyfI0cOVKSdPLkSY0cOVJVq1ZVpUqVNGDAAO3bt8/hqsuPgvpz6NAhjR49Wg0aNFCFChVUu3Zt3XXXXcrIyHC67HKjsO+fXMYYpaSkyOVyaf78+c4UWw4VpT9r165Vly5dVLFiRUVFRaljx446ceKEg1WXH4X1Z+/evbrhhhsUFxenihUr6rLLLtO7777rcNXlR05OjsaPH6+kpCRVqFBB9erV08SJE/XH8zEbY/Twww+rZs2aqlChgq688kpt27bNwarLj8L64/F4dP/99+vSSy9VxYoVFR8frxtvvFG7d+92uHJcEJcTQ2A6fPiwLr/8ciUnJ+vDDz9U9erVtW3bNlWpUsU3Z+zYsVq2bJlmzZqlxMREffzxx7rzzjsVHx+vvn37Olj9he/zzz9XTk6O7/bmzZvVrVs3XXvttZKk1NRULVy4UHPnzlV0dLRGjRql/v37a/Xq1U6VXK4U1J/du3dr9+7deuqpp9S4cWP99NNPuv3227V792698847DlZdfhT2/ZPr2WeflcvlKu3yyr3C+rN27Vr17NlT48aN09SpUxUSEqKvv/5aQUHsjygNhfXnxhtv1JEjR/TBBx+oWrVqSk9P18CBA7Vhwwa1bNnSqbLLjX/84x968cUXNXPmTDVp0kQbNmzQTTfdpOjoaN11112SpCeeeELPP/+8Zs6cqaSkJI0fP149evTQt99+q/DwcIefwYWtsP5kZWXpyy+/1Pjx49W8eXMdPnxYY8aMUd++ff12fsEBBrDk/vvvNx06dChwTpMmTcyjjz7qN3bZZZeZBx980GZpyMeYMWNMvXr1jNfrNUeOHDGhoaFm7ty5vuXfffedkWTWrl3rYJXl1x/7k585c+aYsLAw4/F4SrkyGJN/f7766itz0UUXmT179hhJ5r333nOuwHLuzP60bdvWPPTQQw5XhVxn9qdixYrmjTfe8JsTExNjXnnlFSfKK3d69+5tbr75Zr+x/v37myFDhhhjjPF6vSYuLs48+eSTvuVHjhwxbrfbvPXWW6Vaa3lUWH/y89lnnxlJ5qeffrJdHgrAn3ZhzQcffKDWrVvr2muvVWxsrFq2bKlXXnnFb0779u31wQcf6Ndff5UxRsuXL9cPP/yg7t27O1R1+XTq1CnNmjVLN998s1wul7744gt5PB5deeWVvjkNGzZU7dq1tXbtWgcrLZ/O7E9+MjIyFBUVpZAQPshU2vLrT1ZWlgYPHqxp06YpLi7O4QrLtzP7s3//fq1fv16xsbFq3769atSooU6dOmnVqlVOl1ou5ff90759e/3rX//SoUOH5PV69fbbb+vkyZPq3Lmzs8WWE+3bt9fSpUv1ww8/SJK+/vprrVq1SikpKZKknTt3au/evX7vEaKjo9W2bVveI5SCwvqTn4yMDLlcLlWuXLmUqkR+eIcGa3788Ue9+OKLGjt2rP72t7/p888/11133aWwsDANHTpUkjR16lSNGDFCtWrVUkhIiIKCgvTKK6+oY8eODldfvsyfP19HjhzRsGHDJP1+fF1YWFieH9A1atTQ3r17S7/Acu7M/pzp4MGDmjhxokaMGFG6hUFS/v1JTU1V+/btddVVVzlXGCTl7c+PP/4oSUpLS9NTTz2lFi1a6I033lDXrl21efNmXXLJJQ5WW/7k9/0zZ84c/fWvf1XVqlUVEhKiiIgIvffee7r44oudK7QceeCBB5SZmamGDRsqODhYOTk5evzxxzVkyBBJ8r0PqFGjht/9eI9QOgrrz5lOnjyp+++/X9ddd52ioqJKuVr8EcEb1ni9XrVu3Vp///vfJUktW7bU5s2bNX36dL/gvW7dOn3wwQeqU6eOVq5cqZEjRyo+Pt7vL6mw69VXX1VKSori4+OdLgX5KKg/mZmZ6t27txo3bqy0tLTSLw55+vPBBx9o2bJl+uqrrxyuDFLe/ni9XknSbbfdpptuuknS77+fli5dqtdee02TJk1yrNbyKL+fb+PHj9eRI0e0ZMkSVatWTfPnz9fAgQP16aef6tJLL3Ww2vJhzpw5mj17ttLT09WkSRNt3LhRd999t+Lj433v3+Cc4vTH4/Fo4MCBMsboxRdfdKhi+Dj9WXdcuGrXrm1uueUWv7EXXnjBxMfHG2OMycrKMqGhoWbBggV+c2655RbTo0ePUquzvNu1a5cJCgoy8+fP940tXbrUSDKHDx/2m1u7dm0zZcqUUq6wfMuvP7kyMzNNu3btTNeuXc2JEyccqA759WfMmDHG5XKZ4OBg35ckExQUZDp16uRcseVQfv358ccfjSTz5ptv+s0dOHCgGTx4cGmXWK7l15/t27cbSWbz5s1+c7t27Wpuu+220i6xXKpVq5b55z//6Tc2ceJE06BBA2OMMTt27DCSzFdffeU3p2PHjuauu+4qrTLLrcL6k+vUqVOmX79+plmzZubgwYOlWSLOgmO8Yc3ll1+urVu3+o398MMPqlOnjqTf/wrn8XjynEU2ODjYt0cC9s2YMUOxsbHq3bu3b6xVq1YKDQ3V0qVLfWNbt27Vzz//rHbt2jlRZrmVX3+k3/d0d+/eXWFhYfrggw84i6xD8uvPAw88oE2bNmnjxo2+L0l65plnNGPGDIcqLZ/y609iYqLi4+ML/P2E0pFff7KysiSJ9wYOysrKKnD7JyUlKS4uzu89QmZmptavX897hFJQWH+k/+3p3rZtm5YsWaKqVauWdpnIj9PJHxeuzz77zISEhJjHH3/cbNu2zcyePdtERESYWbNm+eZ06tTJNGnSxCxfvtz8+OOPZsaMGSY8PNy88MILDlZefuTk5JjatWub+++/P8+y22+/3dSuXdssW7bMbNiwwbRr1860a9fOgSrLr7P1JyMjw7Rt29ZceumlZvv27WbPnj2+r9OnTztUbflT0PfPmcRZzUtdQf155plnTFRUlJk7d67Ztm2beeihh0x4eLjZvn27A5WWT2frz6lTp8zFF19srrjiCrN+/Xqzfft289RTTxmXy2UWLlzoULXly9ChQ81FF11kFixYYHbu3GnmzZtnqlWrZu677z7fnMmTJ5vKlSub999/32zatMlcddVVJikpiU9flYLC+nPq1CnTt29fU6tWLbNx40a/9wjZ2dkOV1++Ebxh1b///W/TtGlT43a7TcOGDc3LL7/st3zPnj1m2LBhJj4+3oSHh5sGDRqYp59++qyXTELJ+uijj4wks3Xr1jzLTpw4Ye68805TpUoVExERYa6++mqzZ88eB6osv87Wn+XLlxtJ+X7t3LnTmWLLoYK+f85E8C59hfVn0qRJplatWiYiIsK0a9fOfPrpp6VcYflWUH9++OEH079/fxMbG2siIiJMs2bN8lxeDPZkZmaaMWPGmNq1a5vw8HBTt25d8+CDD/qFNq/Xa8aPH29q1Khh3G636dq1a5F+FuL8FdafnTt3nvU9wvLly50tvpxzGWNMKe9kBwAAAACg3OAYbwAAAAAALCJ4AwAAAABgEcEbAAAAAACLCN4AAAAAAFhE8AYAAAAAwCKCNwAAAAAAFhG8AQAAAACwiOANAAAAAIBFBG8AAJDHihUr5HK5dOTIkSLfJy0tTS1atLBWEwAAZRXBGwCAMm769OmKjIzU6dOnfWPHjh1TaGioOnfu7Dc3N1Dv2LGjwHW2b99ee/bsUXR0dInW2rlzZ919990luk4AAAIdwRsAgDIuOTlZx44d04YNG3xjn376qeLi4rR+/XqdPHnSN758+XLVrl1b9erVK3CdYWFhiouLk8vlslY3AADlBcEbAIAyrkGDBqpZs6ZWrFjhG1uxYoWuuuoqJSUlad26dX7jycnJ8nq9mjRpkpKSklShQgU1b95c77zzjt+8Mz9q/sorryghIUERERG6+uqrNWXKFFWuXDlPPW+++aYSExMVHR2tQYMG6ejRo5KkYcOG6ZNPPtFzzz0nl8sll8ulXbt2lfTmAAAg4BC8AQC4ACQnJ2v58uW+28uXL1fnzp3VqVMn3/iJEye0fv16JScna9KkSXrjjTc0ffp0bdmyRampqbr++uv1ySef5Lv+1atX6/bbb9eYMWO0ceNGdevWTY8//nieeTt27ND8+fO1YMECLViwQJ988okmT54sSXruuefUrl07DR8+XHv27NGePXuUkJBgYWsAABBYQpwuAAAAnL/k5GTdfffdOn36tE6cOKGvvvpKnTp1ksfj0fTp0yVJa9euVXZ2tjp37qzGjRtryZIlateunSSpbt26WrVqlV566SV16tQpz/qnTp2qlJQU3XPPPZKk+vXra82aNVqwYIHfPK/Xq9dff12RkZGSpBtuuEFLly7V448/rujoaIWFhSkiIkJxcXE2NwcAAAGF4A0AwAWgc+fOOn78uD7//HMdPnxY9evXV/Xq1dWpUyfddNNNOnnypFasWKG6devq2LFjysrKUrdu3fzWcerUKbVs2TLf9W/dulVXX32131ibNm3yBO/ExERf6JakmjVrav/+/SX0LAEAKJsI3gAAXAAuvvhi1apVS8uXL9fhw4d9e63j4+OVkJCgNWvWaPny5erSpYuOHTsmSVq4cKEuuugiv/W43e7zqiM0NNTvtsvlktfrPa91AgBQ1hG8AQC4QCQnJ2vFihU6fPiw7r33Xt94x44d9eGHH+qzzz7THXfcocaNG8vtduvnn3/O92Pl+WnQoIE+//xzv7EzbxdFWFiYcnJyin0/AADKMoI3AAAXiOTkZI0cOVIej8cvUHfq1EmjRo3SqVOnlJycrMjISN1zzz1KTU2V1+tVhw4dlJGRodWrVysqKkpDhw7Ns+7Ro0erY8eOmjJlivr06aNly5bpww8/LPblxhITE7V+/Xrt2rVLlSpVUkxMjIKCONcrAODCxm86AAAuEMnJyTpx4oQuvvhi1ahRwzfeqVMnHT161HfZMUmaOHGixo8fr0mTJqlRo0bq2bOnFi5cqKSkpHzXffnll2v69OmaMmWKmjdvrkWLFik1NVXh4eHFqvGee+5RcHCwGjdurOrVq+vnn38+9ycMAEAZ4TLGGKeLAAAAZc/w4cP1/fff69NPP3W6FAAAAhofNQcAAEXy1FNPqVu3bqpYsaI+/PBDzZw5Uy+88ILTZQEAEPDY4w0AAIpk4MCBWrFihY4ePaq6detq9OjRuv32250uCwCAgEfwBgAAAADAIk6uBgAAAACARQRvAAAAAAAsIngDAAAAAGARwRsAAAAAAIsI3gAAAAAAWETwBgAAAADAIoI3AAAAAAAWEbwBAAAAALDo/wNsvhmawwrF2gAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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AABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgoeJCDwAAcKLoP2tFoUdI7sW5Yws9AkCn44o3AAAAJCS8AQAAICFvNQc4Qbwf3uIKANAZueINAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIqLvQAAAB0Hv1nrSj0CEm9OHdsoUcATkCueAMAAEBCRx3e69ati3HjxkV1dXUUFRXF0qVL2+3PsixuvfXWOO2006JHjx5RW1sb27Zta3fMnj17YtKkSVFaWhrl5eUxefLk2Ldv33t6IQAAAHA8Ourw3r9/fwwZMiQWLFjQ4f477rgj7rnnnli4cGFs2LAhevbsGaNGjYoDBw7kjpk0aVI899xzsWrVqli+fHmsW7cupkyZ8u5fBQAAABynjvoz3mPGjIkxY8Z0uC/Lspg3b17ccsstMX78+IiI+MEPfhCVlZWxdOnSmDhxYmzZsiVWrlwZGzdujGHDhkVExPz58+OKK66IO++8M6qrq9/DywEAAIDjS14/4719+/ZoaGiI2tra3LaysrIYMWJE1NfXR0REfX19lJeX56I7IqK2tja6dOkSGzZs6PC8LS0t0dzc3O4BAAAAnUFew7uhoSEiIiorK9ttr6yszO1raGiIPn36tNtfXFwcFRUVuWPeqq6uLsrKynKPvn375nNsAAAASKZT3NV89uzZ0dTUlHvs3Lmz0CMBAADAEclreFdVVUVERGNjY7vtjY2NuX1VVVWxe/fudvsPHjwYe/bsyR3zViUlJVFaWtruAQAAAJ1BXsN7wIABUVVVFatXr85ta25ujg0bNkRNTU1ERNTU1MTevXtj06ZNuWPWrFkTbW1tMWLEiHyOAwAAAAV31Hc137dvX7zwwgu5r7dv3x7PPPNMVFRURL9+/WL69Olx2223xZlnnhkDBgyIOXPmRHV1dVx55ZURETFo0KAYPXp03HDDDbFw4cJobW2NadOmxcSJE93RHAAAgBPOUYf3U089FZdccknu65kzZ0ZExHXXXRcPPvhgfPnLX479+/fHlClTYu/evTFy5MhYuXJldO/ePfechx9+OKZNmxaXXXZZdOnSJSZMmBD33HNPHl4OAAAAHF+KsizLCj3E0Wpubo6ysrJoamryeW+A/6//rBWFHgGg03tx7thCjwB0EkfTpZ3iruYAAADQWQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACeU9vN94442YM2dODBgwIHr06BEf+tCH4h/+4R8iy7LcMVmWxa233hqnnXZa9OjRI2pra2Pbtm35HgUAAAAKLu/hffvtt8d9990X//iP/xhbtmyJ22+/Pe64446YP39+7pg77rgj7rnnnli4cGFs2LAhevbsGaNGjYoDBw7kexwAAAAoqOJ8n/AXv/hFjB8/PsaOHRsREf37949/+Zd/iSeffDIi/ni1e968eXHLLbfE+PHjIyLiBz/4QVRWVsbSpUtj4sSJ+R4JAAAACibvV7wvuOCCWL16dTz//PMREfFf//Vf8cQTT8SYMWMiImL79u3R0NAQtbW1ueeUlZXFiBEjor6+Pt/jAAAAQEHl/Yr3rFmzorm5OQYOHBgnnXRSvPHGG/Gtb30rJk2aFBERDQ0NERFRWVnZ7nmVlZW5fW/V0tISLS0tua+bm5vzPTYAAAAkkfcr3j/60Y/i4YcfjsWLF8fmzZvjoYceijvvvDMeeuihd33Ourq6KCsryz369u2bx4kBAAAgnbyH98033xyzZs2KiRMnxrnnnhvXXnttzJgxI+rq6iIioqqqKiIiGhsb2z2vsbExt++tZs+eHU1NTbnHzp078z02AAAAJJH38H7ttdeiS5f2pz3ppJOira0tIiIGDBgQVVVVsXr16tz+5ubm2LBhQ9TU1HR4zpKSkigtLW33AAAAgM4g75/xHjduXHzrW9+Kfv36xdlnnx1PP/103HXXXfG3f/u3ERFRVFQU06dPj9tuuy3OPPPMGDBgQMyZMyeqq6vjyiuvzPc4AAAAUFB5D+/58+fHnDlz4otf/GLs3r07qqur43Of+1zceuutuWO+/OUvx/79+2PKlCmxd+/eGDlyZKxcuTK6d++e73EAAACgoIqyLMsKPcTRam5ujrKysmhqavK2c4D/r/+sFYUeAaDTe3Hu2EKPAHQSR9Olef+MNwAAAPAnwhsAAAASEt4AAACQkPAGAACAhIQ3AAAAJCS8AQAAICHhDQAAAAkJbwAAAEhIeAMAAEBCwhsAAAASEt4AAACQkPAGAACAhIQ3AAAAJCS8AQAAICHhDQAAAAkJbwAAAEhIeAMAAEBCwhsAAAASEt4AAACQkPAGAACAhIQ3AAAAJCS8AQAAICHhDQAAAAkJbwAAAEhIeAMAAEBCwhsAAAASEt4AAACQkPAGAACAhIQ3AAAAJCS8AQAAICHhDQAAAAkJbwAAAEhIeAMAAEBCwhsAAAASEt4AAACQkPAGAACAhIQ3AAAAJCS8AQAAICHhDQAAAAkJbwAAAEhIeAMAAEBCwhsAAAASEt4AAACQkPAGAACAhIQ3AAAAJCS8AQAAIKHiQg8AcCz0n7Wi0CMAAPA+5Yo3AAAAJCS8AQAAICHhDQAAAAklCe+XX345PvOZz0Tv3r2jR48ece6558ZTTz2V259lWdx6661x2mmnRY8ePaK2tja2bduWYhQAAAAoqLyH9//93//FhRdeGF27do2f/OQn8etf/zq+853vxAc+8IHcMXfccUfcc889sXDhwtiwYUP07NkzRo0aFQcOHMj3OAAAAFBQeb+r+e233x59+/aNRYsW5bYNGDAg989ZlsW8efPilltuifHjx0dExA9+8IOorKyMpUuXxsSJE/M9EgAAABRM3q94L1u2LIYNGxZ/9Vd/FX369ImhQ4fG9773vdz+7du3R0NDQ9TW1ua2lZWVxYgRI6K+vj7f4wAAAEBB5T28f/vb38Z9990XZ555Zvz0pz+NL3zhC3HTTTfFQw89FBERDQ0NERFRWVnZ7nmVlZW5fW/V0tISzc3N7R4AAADQGeT9reZtbW0xbNiw+Pa3vx0REUOHDo1nn302Fi5cGNddd927OmddXV184xvfyOeYAAAAcEzk/Yr3aaedFoMHD263bdCgQbFjx46IiKiqqoqIiMbGxnbHNDY25va91ezZs6OpqSn32LlzZ77HBgAAgCTyHt4XXnhhbN26td22559/Ps4444yI+OON1qqqqmL16tW5/c3NzbFhw4aoqanp8JwlJSVRWlra7gEAAACdQd7faj5jxoy44IIL4tvf/nZ86lOfiieffDLuv//+uP/++yMioqioKKZPnx633XZbnHnmmTFgwICYM2dOVFdXx5VXXpnvcQAAAKCg8h7ew4cPjyVLlsTs2bPjm9/8ZgwYMCDmzZsXkyZNyh3z5S9/Ofbv3x9TpkyJvXv3xsiRI2PlypXRvXv3fI8DAAAABVWUZVlW6CGOVnNzc5SVlUVTU5O3nQNHpP+sFYUeAYBO4MW5Yws9AtBJHE2X5v0z3gAAAMCfCG8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJFRd6AAAAOF70n7Wi0CMk9+LcsYUeAd53XPEGAACAhIQ3AAAAJCS8AQAAICHhDQAAAAkJbwAAAEhIeAMAAEBCwhsAAAASEt4AAACQkPAGAACAhIQ3AAAAJCS8AQAAICHhDQAAAAkJbwAAAEhIeAMAAEBCwhsAAAASEt4AAACQUPLwnjt3bhQVFcX06dNz2w4cOBBTp06N3r17xymnnBITJkyIxsbG1KMAAADAMZc0vDdu3Bj/9E//FB/5yEfabZ8xY0Y8+uij8cgjj8TatWtj165dcfXVV6ccBQAAAAqiONWJ9+3bF5MmTYrvfe97cdttt+W2NzU1xQMPPBCLFy+OSy+9NCIiFi1aFIMGDYr169fHxz/+8VQjAW+j/6wVhR4BAABOWMmueE+dOjXGjh0btbW17bZv2rQpWltb220fOHBg9OvXL+rr61ONAwAAAAWR5Ir3D3/4w9i8eXNs3LjxkH0NDQ3RrVu3KC8vb7e9srIyGhoaOjxfS0tLtLS05L5ubm7O67wAAACQSt6veO/cuTP+7u/+Lh5++OHo3r17Xs5ZV1cXZWVluUffvn3zcl4AAABILe/hvWnTpti9e3d89KMfjeLi4iguLo61a9fGPffcE8XFxVFZWRmvv/567N27t93zGhsbo6qqqsNzzp49O5qamnKPnTt35ntsAAAASCLvbzW/7LLL4le/+lW7bddff30MHDgwvvKVr0Tfvn2ja9eusXr16pgwYUJERGzdujV27NgRNTU1HZ6zpKQkSkpK8j0qAAAAJJf38O7Vq1ecc8457bb17Nkzevfunds+efLkmDlzZlRUVERpaWnceOONUVNT447mAAAAnHCS/Tqxd3L33XdHly5dYsKECdHS0hKjRo2Ke++9txCjAAAAQFJFWZZlhR7iaDU3N0dZWVk0NTVFaWlpoceBTs/v8QaA948X544t9AhwQjiaLk32e7wBAAAA4Q0AAABJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACRUXOgBAACAY6f/rBWFHiGpF+eOLfQIcAhXvAEAACChvId3XV1dDB8+PHr16hV9+vSJK6+8MrZu3drumAMHDsTUqVOjd+/eccopp8SECROisbEx36MAAABAweU9vNeuXRtTp06N9evXx6pVq6K1tTUuv/zy2L9/f+6YGTNmxKOPPhqPPPJIrF27Nnbt2hVXX311vkcBAACAgsv7Z7xXrlzZ7usHH3ww+vTpE5s2bYqLLroompqa4oEHHojFixfHpZdeGhERixYtikGDBsX69evj4x//eL5HAgAAgIJJ/hnvpqamiIioqKiIiIhNmzZFa2tr1NbW5o4ZOHBg9OvXL+rr6zs8R0tLSzQ3N7d7AAAAQGeQ9K7mbW1tMX369LjwwgvjnHPOiYiIhoaG6NatW5SXl7c7trKyMhoaGjo8T11dXXzjG99IOSq8oxP97p8AAEA6Sa94T506NZ599tn44Q9/+J7OM3v27Ghqaso9du7cmacJAQAAIK1kV7ynTZsWy5cvj3Xr1sXpp5+e215VVRWvv/567N27t91V78bGxqiqqurwXCUlJVFSUpJqVAAAAEgm71e8syyLadOmxZIlS2LNmjUxYMCAdvvPP//86Nq1a6xevTq3bevWrbFjx46oqanJ9zgAAABQUHm/4j116tRYvHhx/Pu//3v06tUr97ntsrKy6NGjR5SVlcXkyZNj5syZUVFREaWlpXHjjTdGTU2NO5oDAABwwsl7eN93330REXHxxRe3275o0aL47Gc/GxERd999d3Tp0iUmTJgQLS0tMWrUqLj33nvzPQoAAAAUXN7DO8uywx7TvXv3WLBgQSxYsCDffzwAAAAcV5L/Hm8AAAB4PxPeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsWFHgAAACBf+s9aUegRkntx7thCj8BRcsUbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJDwBgAAgISENwAAACQkvAEAACAh4Q0AAAAJCW8AAABISHgDAABAQsIbAAAAEiou9AB0fv1nrSj0CAAA8L7xfvj5+8W5Yws9Ql654g0AAAAJCW8AAABISHgDAABAQsIbAAAAEhLeAAAAkJC7mh8D74e7DgIAANAxV7wBAAAgoYKG94IFC6J///7RvXv3GDFiRDz55JOFHAcAAADyrmDh/a//+q8xc+bM+NrXvhabN2+OIUOGxKhRo2L37t2FGgkAAADyrmDhfdddd8UNN9wQ119/fQwePDgWLlwYJ598cnz/+98v1EgAAACQdwW5udrrr78emzZtitmzZ+e2denSJWpra6O+vv6Q41taWqKlpSX3dVNTU0RENDc3px82D9paXiv0CAAAAJ1GZ2i9N2fMsuywxxYkvH//+9/HG2+8EZWVle22V1ZWxn//938fcnxdXV184xvfOGR73759k80IAABAYZTNK/QER+7VV1+NsrKydzymU/w6sdmzZ8fMmTNzX7e1tcWePXuid+/eUVRUVMDJji/Nzc3Rt2/f2LlzZ5SWlhZ6HArIWiDCOuBPrAUirAP+xFogwjrIhyzL4tVXX43q6urDHluQ8D711FPjpJNOisbGxnbbGxsbo6qq6pDjS0pKoqSkpN228vLylCN2aqWlpf7lISKsBf7IOuBN1gIR1gF/Yi0QYR28V4e70v2mgtxcrVu3bnH++efH6tWrc9va2tpi9erVUVNTU4iRAAAAIImCvdV85syZcd1118WwYcPiYx/7WMybNy/2798f119/faFGAgAAgLwrWHhfc8018T//8z9x6623RkNDQ5x33nmxcuXKQ264xpErKSmJr33ta4e8LZ/3H2uBCOuAP7EWiLAO+BNrgQjr4Fgryo7k3ucAAADAu1KQz3gDAADA+4XwBgAAgISENwAAACQkvAEAACAh4X2cW7duXYwbNy6qq6ujqKgoli5d+rbHfv7zn4+ioqKYN29eu+179uyJSZMmRWlpaZSXl8fkyZNj3759aQcn745kLWzZsiU++clPRllZWfTs2TOGDx8eO3bsyO0/cOBATJ06NXr37h2nnHJKTJgwIRobG4/hq+C9Otw62LdvX0ybNi1OP/306NGjRwwePDgWLlzY7hjr4MRQV1cXw4cPj169ekWfPn3iyiuvjK1bt7Y75ki+1zt27IixY8fGySefHH369Imbb745Dh48eCxfCu/B4dbBnj174sYbb4yzzjorevToEf369Yubbropmpqa2p3HOuj8juTvhDdlWRZjxozp8L8j1kLndqTroL6+Pi699NLo2bNnlJaWxkUXXRR/+MMfcvv1Q/4J7+Pc/v37Y8iQIbFgwYJ3PG7JkiWxfv36qK6uPmTfpEmT4rnnnotVq1bF8uXLY926dTFlypRUI5PI4dbCb37zmxg5cmQMHDgwHn/88fjlL38Zc+bMie7du+eOmTFjRjz66KPxyCOPxNq1a2PXrl1x9dVXH6uXQB4cbh3MnDkzVq5cGf/8z/8cW7ZsienTp8e0adNi2bJluWOsgxPD2rVrY+rUqbF+/fpYtWpVtLa2xuWXXx779+/PHXO47/Ubb7wRY8eOjddffz1+8YtfxEMPPRQPPvhg3HrrrYV4SbwLh1sHu3btil27dsWdd94Zzz77bDz44IOxcuXKmDx5cu4c1sGJ4Uj+TnjTvHnzoqio6JDt1kLndyTroL6+PkaPHh2XX355PPnkk7Fx48aYNm1adOnypzTUDwlkdBoRkS1ZsuSQ7b/73e+yD37wg9mzzz6bnXHGGdndd9+d2/frX/86i4hs48aNuW0/+clPsqKiouzll18+BlOTQkdr4Zprrsk+85nPvO1z9u7dm3Xt2jV75JFHctu2bNmSRURWX1+falQS6mgdnH322dk3v/nNdts++tGPZl/96lezLLMOTmS7d+/OIiJbu3ZtlmVH9r3+j//4j6xLly5ZQ0ND7pj77rsvKy0tzVpaWo7tCyAv3roOOvKjH/0o69atW9ba2pplmXVwonq7tfD0009nH/zgB7NXXnnlkP+OWAsnno7WwYgRI7JbbrnlbZ+jH9JwxbuTa2tri2uvvTZuvvnmOPvssw/ZX19fH+Xl5TFs2LDcttra2ujSpUts2LDhWI5KQm1tbbFixYr48Ic/HKNGjYo+ffrEiBEj2r19bNOmTdHa2hq1tbW5bQMHDox+/fpFfX19AaYmhQsuuCCWLVsWL7/8cmRZFo899lg8//zzcfnll0eEdXAie/OtwxUVFRFxZN/r+vr6OPfcc6OysjJ3zKhRo6K5uTmee+65Yzg9+fLWdfB2x5SWlkZxcXFEWAcnqo7WwmuvvRZ//dd/HQsWLIiqqqpDnmMtnHjeug52794dGzZsiD59+sQFF1wQlZWV8YlPfCKeeOKJ3HP0QxrCu5O7/fbbo7i4OG666aYO9zc0NESfPn3abSsuLo6KiopoaGg4FiNyDOzevTv27dsXc+fOjdGjR8fPfvazuOqqq+Lqq6+OtWvXRsQf10K3bt2ivLy83XMrKyuthRPI/PnzY/DgwXH66adHt27dYvTo0bFgwYK46KKLIsI6OFG1tbXF9OnT48ILL4xzzjknIo7se93Q0NDuB+w397+5j86lo3XwVr///e/jH/7hH9q9ZdQ6OPG83VqYMWNGXHDBBTF+/PgOn2ctnFg6Wge//e1vIyLi61//etxwww2xcuXK+OhHPxqXXXZZbNu2LSL0QyrFhR6Ad2/Tpk3x3e9+NzZv3tzh53R4/2hra4uIiPHjx8eMGTMiIuK8886LX/ziF7Fw4cL4xCc+UcjxOIbmz58f69evj2XLlsUZZ5wR69ati6lTp0Z1dXW7K5+cWKZOnRrPPvtsuysWvP8cbh00NzfH2LFjY/DgwfH1r3/92A7HMdXRWli2bFmsWbMmnn766QJOxrHU0Tp482fGz33uc3H99ddHRMTQoUNj9erV8f3vfz/q6uoKMuv7gSvendjPf/7z2L17d/Tr1y+Ki4ujuLg4XnrppfjSl74U/fv3j4iIqqqq2L17d7vnHTx4MPbs2dPhW4zonE499dQoLi6OwYMHt9s+aNCg3F3Nq6qq4vXXX4+9e/e2O6axsdFaOEH84Q9/iL//+7+Pu+66K8aNGxcf+chHYtq0aXHNNdfEnXfeGRHWwYlo2rRpsXz58njsscfi9NNPz20/ku91VVXVIXc5f/Nr66Fzebt18KZXX301Ro8eHb169YolS5ZE165dc/usgxPL262FNWvWxG9+85soLy/P/dwYETFhwoS4+OKLI8JaOJG83To47bTTIiIO+zOjfsg/4d2JXXvttfHLX/4ynnnmmdyjuro6br755vjpT38aERE1NTWxd+/e2LRpU+55a9asiba2thgxYkShRifPunXrFsOHDz/k10U8//zzccYZZ0RExPnnnx9du3aN1atX5/Zv3bo1duzYETU1Ncd0XtJobW2N1tbWdncljYg46aSTcv+H2zo4cWRZFtOmTYslS5bEmjVrYsCAAe32H8n3uqamJn71q1+1+wFr1apVUVpaesgPZRyfDrcOIv54pfvyyy+Pbt26xbJly9r9tosI6+BEcbi1MGvWrEN+boyIuPvuu2PRokURYS2cCA63Dvr37x/V1dXv+DOjfkikoLd247BeffXV7Omnn86efvrpLCKyu+66K3v66aezl156qcPj33pX8yzLstGjR2dDhw7NNmzYkD3xxBPZmWeemX36058+BtOTT4dbCz/+8Y+zrl27Zvfff3+2bdu2bP78+dlJJ52U/fznP8+d4/Of/3zWr1+/bM2aNdlTTz2V1dTUZDU1NYV6SbwLh1sHn/jEJ7Kzzz47e+yxx7Lf/va32aJFi7Lu3btn9957b+4c1sGJ4Qtf+EJWVlaWPf7449krr7ySe7z22mu5Yw73vT548GB2zjnnZJdffnn2zDPPZCtXrsz+7M/+LJs9e3YhXhLvwuHWQVNTUzZixIjs3HPPzV544YV2xxw8eDDLMuvgRHEkfye8VbzlrubWQud3JOvg7rvvzkpLS7NHHnkk27ZtW3bLLbdk3bt3z1544YXcMfoh/4T3ce6xxx7LIuKQx3XXXdfh8R2F9//+7/9mn/70p7NTTjklKy0tza6//vrs1VdfTT88eXUka+GBBx7I/vzP/zzr3r17NmTIkGzp0qXtzvGHP/wh++IXv5h94AMfyE4++eTsqquuyl555ZVj/Ep4Lw63Dl555ZXss5/9bFZdXZ117949O+uss7LvfOc7WVtbW+4c1sGJoaN1EBHZokWLcsccyff6xRdfzMaMGZP16NEjO/XUU7MvfelLuV8zxfHvcOvg7f7OiIhs+/btufNYB53fkfyd0NFz3vprKa2Fzu1I10FdXV12+umnZyeffHJWU1PT7kJNlumHFIqyLMvyfRUdAAAA+COf8QYAAICEhDcAAAAkJLwBAAAgIeENAAAACQlvAAAASEh4AwAAQELCGwAAABIS3gAAAJCQ8AYAAICEhDcAAAAkJLwBAAAgIeENAAAACf0/dtWYQ6W8SI4AAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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T/OHwsDOTsXOf6/YktyfJNddcs+vCgfkzhQKAg2jqgFxV35rkd5L8VHf/VVVtu+kWY33eQPfdSe5Oko2NjfPuB4B14cMmrJapVrGoqpdmMxy/v7t/dzL81aq6YnL/FUmemYyfSXL18PCrkjw1m3IBAGC+dgzItdkq/rUkj3X3Lw53nUxyZHL9SJL7hvHDVfWyqro2yXVJHppdyQAAMD/TTLF4c5IfS/LHVfWZydjPJjme5ERV3ZbkiSS3Jkl3P1JVJ5I8ms0VMO7o7hdmXTgArJOz0zAeP37zgisBdgzI3f2/svW84iS5cZvHHEtybA91AQDAQjiTHgAADARkAAAYCMgAADDY1YlCAIDZskYyLB8dZAAAGAjIAAAwEJABAGAgIAMAwEBABoAlcujo/Q7cgwUTkAEAYGCZNwDYZzrEsNx0kAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQ4oJyMAAC2JiADwBLyIRYWx4lC4IA7+wf48eM3v+g2ABxUOsgAsAZ0nGF2BGQAABgIyAAAMDAHGUhi7jEAnKWDDAArxFxjmD8BGQAABgIyAAAMzEGGA8ZXs7Bazl2rHJg/HWQAABgIyAAAMDDFAgBWgOlRsH90kAEAYCAgw5qxRioA7I2ADAAAA3OQ4YDQVYb15HcbZk8HGQAABgIyAKwxxyXA7gnIAAAwEJABAGAgIAMAwEBABgCAgWXeYM05OAcAdkdAhjUhCAPAbAjIALCCfCiG+TEHGQAABgIyAAAMTLGAFXP2a9XHj9/8otsAwGzoIAMAwEBABgCAgYAMAAADARmW3KGj95tnDAD7yEF6sKaEagC4OAIyrAiBFwD2hykWAAAwEJABAGCwY0CuqvdV1TNV9flh7NVV9UBVfWly+arhvruq6nRVfbGq3j6vwuGgc/AeAMxHdfeFN6j6gSR/k+TXu/v1k7H/kORr3X28qo4meVV331lV1yf5QJIbkrw2yUeTfGd3v3Ch19jY2OhTp07t/V8Da0gIBmbp7Fk4gaSqHu7ujXPHd+wgd/fHk3ztnOFbktwzuX5PkncN4/d293Pd/ZUkp7MZlgEAYCVc7Bzky7v76SSZXF42Gb8yyZPDdmcmYwAAsBJmfZBebTG25RyOqrq9qk5V1alnn312xmUAAMDFudiA/NWquiJJJpfPTMbPJLl62O6qJE9t9QTdfXd3b3T3xqWXXnqRZQAAwGxdbEA+meTI5PqRJPcN44er6mVVdW2S65I8tLcS4WCxOgUALNaOZ9Krqg8keUuS11TVmSQ/l+R4khNVdVuSJ5LcmiTd/UhVnUjyaJLnk9yx0woWAMD+O/tB3KoWcL4dA3J3/8g2d924zfbHkhzbS1EAALAozqQHAAADARkAAAYCMgAADHacgwzMlwNlgP200yo53pNABxkAAF5EQAYAzmNNdg4yARkAAAYCMgAADBykB3PmgBdgFZhOAd8gIMOS8scKABbDFAsAABjoIMOS0DEGgOWggwz7zNJJwCo59z3LexgHgYAMAAADARkAAAbmIMOC+IoSAJaTgAwA7MiHeg4SARlmzIlBgFVyscHXex3rzBxkAGDPrG7BOhGQAQBgYIoF7BOdFQBYDTrIAAAw0EGGizB2g7c7QEXHGABWk4AMMyIQA0zXQIBlZ4oFAAAMdJBhF3SJAWD96SADAMBAQAYAgIGADADMhbPrsarMQYY98uYPAOtFBxkAAAYCMgCwr0y9YNkJyAAAMBCQAYC50jFm1QjIcAHe1AHg4LGKBWxBKAaAg0tAhnwjED9+/OYL3g/Ai83j/XGn92SYNwEZANgXmg2sCgGZA2HaboQ3b4D9o1PMshKQOdAEYoDF2y4oC9AsioDMWtEpBlh/577XC9LMmoAMACwFzQuWhXWQAQBgoIMMAKwEHWb2S3X3omvIxsZGnzp1atFlsAa8eQIcXOYgs1tV9XB3b5w7booFAAAMBGQAABiYg8xKcCpoAKZl2Tf2SgcZAAAGOsgAwFrY7ttEHWV2S0BmpZz75ufNDgCYNQEZAFhL5zZVtusw78cpq3WxV4s5yAAAMNBBZl+d+wl6uykTPmkDsEr83VovAjIzt9WbxMUuw2b5NgCWkUC83gRkdjTtGsSLeJMQoAHYq93OVWb9zS0gV9VNSX45ySVJ3tvdx+f1WizGXsLpTkvxAMAq02FebXMJyFV1SZJfSfKPk5xJ8qmqOtndj87j9ZjOdr+su/0lnjbECrsArJOt/q5N232e9rmX8dvag2heHeQbkpzu7i8nSVXdm+SWJALyHO0UgLfbfqfnu9jtAYDd2elg9t0+frePu5jHrqPq7tk/adU/T3JTd/+bye0fS/IPu/vdW22/sbHRp06dmnkd09jrJ7aL7b5u9YO/3QoOF/vLcrG/XADAfC3qb/Q0r7vTN8177WYv00m/qurh7t44b3xOAfnWJG8/JyDf0N0/Pmxze5LbJze/K8kXZ17I3r0myZ8tuogVYV/tjv01Pftqd+yv6dlXu2N/Tc++2p1F7q+/292Xnjs4rykWZ5JcPdy+KslT4wbdfXeSu+f0+jNRVae2+lTB+eyr3bG/pmdf7Y79NT37anfsr+nZV7uzjPtrXmfS+1SS66rq2qr65iSHk5yc02sBAMDMzKWD3N3PV9W7k3w4m8u8va+7H5nHawEAwCzNbR3k7v69JL83r+ffJ0s9BWTJ2Fe7Y39Nz77aHftrevbV7thf07Ovdmfp9tdcDtIDAIBVNa85yAAAsJIE5ClV1b+tqq6q1yy6lmVVVf++qj5XVZ+pqo9U1WsXXdMyq6pfqKovTPbZB6vqlYuuaVlV1a1V9UhVfb2qlupI52VRVTdV1Rer6nRVHV10Pcusqt5XVc9U1ecXXcsqqKqrq+oPquqxye/hTy66pmVVVS+vqoeq6rOTffXzi65p2VXVJVX1R1X1oUXXMhKQp1BVV2fztNlPLLqWJfcL3f093f2GJB9K8u8WXM+yeyDJ67v7e5L87yR3LbieZfb5JP8syccXXcgyqqpLkvxKkn+S5PokP1JV1y+2qqX235LctOgiVsjzSX66u787yZuS3OHna1vPJXlrd39vkjckuamq3rTYkpbeTyZ5bNFFnEtAns5/TPIzSUzYvoDu/qvh5itif11Qd3+ku5+f3PzDbK4Xzha6+7HuXsaTCS2LG5Kc7u4vd/ffJrk3yS0LrmlpdffHk3xt0XWsiu5+urs/Pbn+19kMM1cutqrl1Jv+ZnLzpZP//C3cRlVdleTmJO9ddC3nEpB3UFXvTPIn3f3ZRdeyCqrqWFU9meRfRAd5N/51kv++6CJYWVcmeXK4fSYCDHNQVYeSvDHJJxdcytKaTBn4TJJnkjzQ3fbV9n4pmw3Iry+4jvPMbZm3VVJVH03yHVvc9Z4kP5vkB/e3ouV1oX3V3fd193uSvKeq7kry7iQ/t68FLpmd9tdkm/dk8yvM9+9nbctmmn3FtmqLMV0rZqqqvjXJ7yT5qXO+MWTQ3S8kecPkuJIPVtXru9t893NU1TuSPNPdD1fVWxZcznkE5CTd/batxqvqHyS5NslnqyrZ/Ar801V1Q3f/6T6WuDS221db+M0k9+eAB+Sd9ldVHUnyjiQ39gFfc3EXP1uc70ySq4fbVyV5akG1sIaq6qXZDMfv7+7fXXQ9q6C7/7KqPpbN+e4C8vnenOSdVfVDSV6e5Nur6je6+0cXXFcSUywuqLv/uLsv6+5D3X0om3+Evu+ghuOdVNV1w813JvnCompZBVV1U5I7k7yzu//fouthpX0qyXVVdW1VfXOSw0lOLrgm1kRtdoh+Lclj3f2Li65nmVXVpWdXJKqqb0nytvhbuKXuvqu7r5rkq8NJfn9ZwnEiIDNbx6vq81X1uWxOS7EU0IX9pyTfluSBydJ4/2XRBS2rqvqnVXUmyfcnub+qPrzompbJ5GDPdyf5cDYPoDrR3Y8stqrlVVUfSPKJJN9VVWeq6rZF17Tk3pzkx5K8dfJe9ZlJ14/zXZHkDyZ/Bz+VzTnIS7V8GdNxJj0AABjoIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYPD/ASvKmaTtYFHZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Оскільки більшість значень у реальному житті мають нормальний розподіл, ми не повинні використовувати генератор випадкових чисел з рівномірним розподілом для створення вибіркових даних. Ось що відбувається, якщо ми спробуємо створити ваги з рівномірним розподілом (згенерованим за допомогою `np.random.rand`):\n"
+    "Оскільки більшість значень у реальному житті мають нормальний розподіл, ми не повинні використовувати генератор випадкових чисел з рівномірним розподілом для створення вибіркових даних. Ось що відбувається, якщо ми спробуємо згенерувати ваги з рівномірним розподілом (згенерованим за допомогою `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -554,23 +395,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Інтервали довіри\n",
+    "## Довірчі інтервали\n",
     "\n",
-    "Давайте тепер обчислимо інтервали довіри для ваги та зросту бейсболістів. Ми використаємо код [з цього обговорення на stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Давайте тепер обчислимо довірчі інтервали для ваги та зросту бейсболістів. Ми використаємо код [з цієї дискусії на stackoverflow](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## Перевірка гіпотез\n",
     "\n",
-    "Давайте дослідимо різні ролі в нашому наборі даних про бейсболістів:\n"
+    "Давайте розглянемо різні ролі в нашому наборі даних про бейсболістів:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,20 +591,20 @@
    "source": [
     "Ми бачимо, що інтервали не перетинаються.\n",
     "\n",
-    "Статистично більш коректний спосіб довести гіпотезу — використати **t-тест Стьюдента**:\n"
+    "Статистично більш правильний спосіб довести гіпотезу — використати **t-тест Стьюдента**:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -778,35 +619,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Два значення, які повертає функція `ttest_ind`, це:\n",
-    "* p-value можна розглядати як ймовірність того, що два розподіли мають однакове середнє значення. У нашому випадку вона дуже низька, що свідчить про сильні докази того, що перші базові гравці вищі.\n",
-    "* t-value — це проміжне значення нормалізованої різниці середніх, яке використовується в t-тесті і порівнюється з пороговим значенням для заданого рівня довіри.\n"
+    "Два значення, які повертає функція `ttest_ind`, це:  \n",
+    "* p-значення можна розглядати як ймовірність того, що два розподіли мають однакове середнє значення. У нашому випадку воно дуже низьке, що свідчить про вагомі докази того, що перші базові гравці вищі.  \n",
+    "* t-значення є проміжним значенням нормалізованої різниці середніх, яке використовується в t-тесті, і його порівнюють із пороговим значенням для заданого рівня довіри.  \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Симуляція нормального розподілу за допомогою теореми центральної граничної\n",
+    "## Імітація нормального розподілу за допомогою теореми центральної граничної\n",
     "\n",
-    "Псевдовипадковий генератор у Python створений для того, щоб забезпечувати рівномірний розподіл. Якщо ми хочемо створити генератор для нормального розподілу, можемо скористатися теоремою центральної граничної. Щоб отримати значення з нормальним розподілом, ми просто обчислимо середнє значення вибірки, згенерованої рівномірно.\n"
+    "Псевдовипадковий генератор у Python створений для отримання рівномірного розподілу. Якщо ми хочемо створити генератор для нормального розподілу, можемо скористатися теоремою центральної граничної. Щоб отримати значення з нормального розподілу, ми просто обчислимо середнє значення вибірки, згенерованої рівномірно.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Кореляція та Зла Бейсбольна Корпорація\n",
     "\n",
-    "Кореляція дозволяє нам знаходити зв’язки між послідовностями даних. У нашому умовному прикладі уявімо, що існує зла бейсбольна корпорація, яка платить своїм гравцям залежно від їхнього зросту — чим вищий гравець, тим більше він/вона отримує грошей. Припустимо, що є базова зарплата у розмірі $1000, а також додаткова премія від $0 до $100, залежно від зросту. Ми візьмемо реальних гравців з MLB і розрахуємо їхні уявні зарплати:\n"
+    "Кореляція дозволяє нам знаходити зв’язки між послідовностями даних. У нашому умовному прикладі уявімо, що існує зла бейсбольна корпорація, яка платить своїм гравцям залежно від їхнього зросту — чим вищий гравець, тим більше грошей він/вона отримує. Припустимо, що є базова зарплата у розмірі $1000, а також додаткова премія від $0 до $100, залежно від зросту. Ми візьмемо реальних гравців з MLB і розрахуємо їхні уявні зарплати:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Давайте тепер обчислимо коваріацію та кореляцію цих послідовностей. `np.cov` надасть нам так звану **матрицю коваріації**, яка є розширенням коваріації для кількох змінних. Елемент $M_{ij}$ матриці коваріації $M$ є кореляцією між вхідними змінними $X_i$ та $X_j$, а діагональні значення $M_{ii}$ — це дисперсія $X_{i}$. Аналогічно, `np.corrcoef` надасть нам **матрицю кореляції**.\n"
+    "Давайте тепер обчислимо коваріацію та кореляцію цих послідовностей. `np.cov` надасть нам так звану **коваріаційну матрицю**, яка є розширенням коваріації на кілька змінних. Елемент $M_{ij}$ коваріаційної матриці $M$ є кореляцією між вхідними змінними $X_i$ та $X_j$, а діагональні значення $M_{ii}$ є дисперсією $X_{i}$. Аналогічно, `np.corrcoef` надасть нам **кореляційну матрицю**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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vcvwXk1ywrPbNJK9ab3MAAHC8+VPTAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAIPts24AYCU7d+09rHbv7otm0AkAzzauIANzZ6VwfKQ6AGwkARkAAAYCMgAADARkAAAYCMgAADAQkIG5s9pqFVaxAOB4sMwbMJeEYQBmxRVkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAg+2zbgCYrZ279h5Wu3f3RTPoBADmgyvI8Cy2Ujg+Uh0Ang0EZAAAGAjIAAAwEJABAGAgIAMAwEBAhmex1VarsIoFAM9mlnmDZzlhGACebs0ryFV1TVU9UlX7h9oLquoLVfWVaf/8qb6zqh6vqjum7beG97yqqvZV1d1V9RtVVZvzkQAA4NgdzRSLa5NcuKy2K8lN3X12kpum54fc093nTds7h/qHk1yR5OxpW/41AQBg5tYMyN19c5JHl5XfnOS66fF1SS4+0teoqhclOaW7v9TdneTja70HAABm4Vhv0nthdz+UJNP+9OG1l1TV7VX1x1X1mql2RpIDwzEHptqKquqKqlqsqsWDBw8eY4sAALB+G72KxUNJXtzdr0zyS0k+WVWnJFlpvnGv9kW6++ruXujuhR07dmxwiwAAsLpjDcgPT9MmDk2feCRJuvuJ7v6b6fFtSe5J8rIsXTE+c3j/mUkePNamAQBgsxxrQP5sksunx5cn+UySVNWOqto2PX5plm7G++o0DeOxqrpgWr3i7YfeAwAA82TNdZCrak+S1yU5raoOJHl/kt1Jrq+qdyS5L8kl0+GvTfJfq+rbSZ5M8s7uPnSD37uytCLGyUk+P20AADBXamlRifm1sLDQi4uLs24DAIAtpqpu6+6F5XV/ahoAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAYM11kIGNsXPX3sNq9+6+aAadAABH4goyHAcrheMj1QGA2RGQAQBgICADAMBAQAYAgIGADAAAAwEZjoPVVquwigUAzB/LvMFxIgwDwInBFWQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAACD7bNuADbazl17D6vdu/uiGXQCAJyIXEFmS1kpHB+pDgCwnIAMAAADARkAAAYCMgAADARkAAAYCMhsKautVmEVCwDgaFnmjS1HGAYAnglXkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABisGZCr6pqqeqSq9g+1F1TVF6rqK9P++VP9DVV1W1Xtm/Y/Mrzni1V1V1XdMW2nb85HAgCAY3c0V5CvTXLhstquJDd199lJbpqeJ8nXk/xYd5+b5PIkv73sfZd193nT9sixtw0AAJtjzYDc3TcneXRZ+c1JrpseX5fk4unY27v7wal+Z5LnVdVJG9MqAABsvmOdg/zC7n4oSab9StMlfjLJ7d39xFD72DS94n1VVat98aq6oqoWq2rx4MGDx9giAACs36bcpFdVr0jya0l+dihfNk29eM20vW2193f31d290N0LO3bs2IwWAQBgRccakB+uqhclybR/aj5xVZ2Z5NNJ3t7d9xyqd/cD0/6xJJ9M8upjbRoAADbLsQbkz2bpJrxM+88kSVWdmmRvkiu7+08OHVxV26vqtOnxc5K8Kcn+AADAnNm+1gFVtSfJ65KcVlUHkrw/ye4k11fVO5Lcl+SS6fCfT/IDSd5XVe+bav8uyTeT3DiF421J/jDJRzbwczAjO3ftPax27+6LZtAJAMDGqO6edQ9HtLCw0IuLi7NugxWsFI4PEZIBgHlXVbd198Lyur+kBwAAAwEZAAAGAjIAAAwEZAAAGAjIHLPVbsRzgx4AcCJbc5k3OBJhGADYalxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgsH3WDXB0fvC9n8vfP9lPPX/etspfffBHZ9gRAMDW5AryCWB5OE6Sv3+y84Pv/dyMOgIA2LoE5BPA8nC8Vh0AgGMnIAMAwEBABgCAgYB8AnjetlpXHQCAYycgnwD+6oM/elgYtooFAMDmsMzbCUIYBgA4PlxBBgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQAABgIyAAAMFgzIFfVNVX1SFXtH2ovqKovVNVXpv3zh9eurKq7q+quqnrjUH9VVe2bXvuNqqqN/zjP3FU37Mv3X/m57Ny1N99/5edy1Q37Zt0SAADH0dFcQb42yYXLaruS3NTdZye5aXqeqnp5krcmecX0nv9ZVdum93w4yRVJzp625V9z5q66YV8+cct9ebI7SfJkdz5xy31CMgDAs8iaAbm7b07y6LLym5NcNz2+LsnFQ/13u/uJ7v7rJHcneXVVvSjJKd39pe7uJB8f3jM39tx6/7rqAABsPcc6B/mF3f1Qkkz706f6GUnGNHlgqp0xPV5eX1FVXVFVi1W1ePDgwWNscf0OXTk+2joAAFvPRt+kt9K84j5CfUXdfXV3L3T3wo4dOzasubVsW2Va9Gp1AAC2nmMNyA9P0yYy7R+Z6geSnDUcd2aSB6f6mSvU58ql55+1rjoAAFvPsQbkzya5fHp8eZLPDPW3VtVJVfWSLN2M96fTNIzHquqCafWKtw/vmRsfuPjc/PQFL37qivG2qvz0BS/OBy4+d8adAQBwvFSvMb+2qvYkeV2S05I8nOT9SW5Icn2SFye5L8kl3f3odPx7k/xMkm8neXd3f36qL2RpRYyTk3w+yS/0Wt88ycLCQi8uLq7/kwEAwBFU1W3dvXBY/Sgy6kwJyAAAbIbVArK/pAcAAAMBGQAABgIyAAAMBGQAABgIyAAAMBCQAQBgICADAMBAQAYAgIGADAAAAwEZAAAGAjIAAAwEZAAAGFR3z7qHI6qqg0m+Nus+5shpSb4+6yZOEMZqfYzX+hivo2es1sd4rY/xOnrG6nD/ort3LC/OfUDm6apqsbsXZt3HicBYrY/xWh/jdfSM1foYr/UxXkfPWB09UywAAGAgIAMAwEBAPvFcPesGTiDGan2M1/oYr6NnrNbHeK2P8Tp6xuoomYMMAAADV5ABAGAgIAMAwEBAnmNVdWpV/X5V/VVVfbmqfriqzquqW6rqjqparKpXz7rPeVBV50xjcmj7f1X17qp6QVV9oaq+Mu2fP+te58ERxutD08/bX1TVp6vq1Fn3OmurjdXw+i9XVVfVaTNsc24cabyq6heq6q6qurOq/tuMW50LR/i36Fy/gqr6xennZ39V7amq5znPr26V8XKePwrmIM+xqrouyf/u7o9W1XOTfHeS65P8end/vqp+NMmvdPfrZtnnvKmqbUkeSHJ+kp9L8mh3766qXUme392/OtMG58yy8TonyR9197er6teSxHj9o3GsuvtrVXVWko8m+cEkr+puC/APlv1svTTJe5Nc1N1PVNXp3f3ITBucM8vG6yNxrn+aqjojyf9J8vLufryqrk/yuSQvj/P8YY4wXg/GeX5NriDPqao6Jclrk/yvJOnub3X3N5J0klOmw/5pln7QebrXJ7mnu7+W5M1Jrpvq1yW5eFZNzbGnxqu7/6C7vz3Vb0ly5gz7mkfjz1aS/HqSX8nSv0sON47Xu5Ls7u4nkkQ4XtE4Xs71K9ue5OSq2p6li0YPxnn+SA4bL+f5oyMgz6+XJjmY5GNVdXtVfbSqvifJu5N8qKruT/Lfk1w5wx7n1VuT7Jkev7C7H0qSaX/6zLqaX+N4jX4myeePcy/z7qmxqqofT/JAd//5bFuaa+PP1suSvKaqbq2qP66qfznDvubVOF7vjnP903T3A1kai/uSPJTkb7v7D+I8v6IjjNfIeX4VAvL82p7kh5J8uLtfmeSbSXZl6SrML3b3WUl+MdMVZpZMU1F+PMnvzbqXE8Fq41VV703y7SS/M4u+5tE4VlX13VmaLvCfZ9vV/FrhZ2t7kucnuSDJe5JcX1U1o/bmzgrj5Vy/zDS3+M1JXpLk+5J8T1X99Gy7ml9rjZfz/JEJyPPrQJID3X3r9Pz3sxSYL0/yqan2e0ncuPF0/z7Jn3X3w9Pzh6vqRUky7f1a9+mWj1eq6vIkb0pyWbtJYTSO1fdn6T86f15V92bpV5R/VlX/fIb9zZvlP1sHknyql/xpku8kcWPjP1o+Xs71h/u3Sf66uw929z9kaXz+VZznV7PaeDnPHwUBeU519/9Ncn9VnTOVXp/kL7M03+rfTLUfSfKVGbQ3zy7N06cLfDZL/6HJtP/Mce9ovj1tvKrqwiS/muTHu/vvZtbVfHpqrLp7X3ef3t07u3tnlsLfD03/blmy/N/iDVk6Z6WqXpbkuUnc1PiPlo+Xc/3h7ktyQVV99/Tbh9cn+XKc51ez4ng5zx8dq1jMsao6L0t3yD83yVeT/Ickr0jyP7L068q/T/Kfuvu2WfU4T6Zfe9+f5KXd/bdT7Z9laeWPF2fpZHFJdz86uy7nxyrjdXeSk5L8zXTYLd39zhm1ODdWGqtlr9+bZMEqFktW+dl6bpJrkpyX5FtJfrm7/2hmTc6RVcbrX8e5/jBV9V+S/FSWpgbcnuQ/JvkncZ5f0SrjdWec59ckIAMAwMAUCwAAGAjIAAAwEJABAGAgIAMAwEBABgCAgYAMAAADARkAAAb/H2leqRtP0LMZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "У цьому випадку кореляція трохи менша, але все ще досить висока. Тепер, щоб зробити зв'язок ще менш очевидним, ми можемо додати трохи додаткової випадковості, додавши деяку випадкову змінну до зарплати. Давайте подивимось, що станеться:\n"
+    "У цьому випадку кореляція трохи менша, але все ще досить висока. Тепер, щоб зробити зв'язок ще менш очевидним, ми можемо додати трохи додаткової випадковості, додавши деяку випадкову змінну до зарплати. Давайте подивимося, що станеться:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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8UfmA+2xOBN5fj9Ln/kYy2Fb2kV4oLwcAAINGvI2Yun2Wauqbtap2j2rqmx3tG7WTI6n3tu1kJ0dVU8v02I0z5C0ITkT6qtw6ke8tklR+n/1Nqu9vX7IftkS6q+nWKA/pxWNZVlp2G2hra1NBQYFaW1uVn5+f6ssBAAAp1u2zNPuhdRHLre0V0lfvujQoYXNrZTyVo45SMdc42Z832vEvr/AOqjnOXSd8eqZmp3a1tGtCcZ4WVk5U9pD0W1uzH5pJ4Vf20+1BA/o/05yTpBsAAAwKNfXNuuGpjTHjnls0S5WTR0qKPA861i/5kZLcVCS/qRDv9+ZUuO9zbV3ToJrjPNjmVg+2z4v+zTTnZE83AAAYFJw2Yoo1osijnhFFl1d4jVfGL6/wJvAJBoZ4v7d4ZGZ4/A9IpMjJvr19IN1WQgfb55V6SuoHWyUDBj6SbgAAMCg4bcTkZERRrJXxptYO3bJyiwrzsnSo/bj/9XRcoYvne3NDXyb7/cFg+7yBQh+2AP1d+m32AAAACOO8CUWKlXtkeHriJPdXxiUFJdxST/IZrYHbQJSq0U6DbY7zYPu8wEBG0g0AAAaFt3YdVKzm2T6rJ04yXxk/cLhTq2r36OnXGqImQZFY6lmRdKOzdyTJ6CIeSapGOw22Oc6D7fMCAxnl5QAAYFBoaj3mKC7WPGipZ2X8/he2JXxtySi3tg2WueQlw3NcjevvmFsNDBysdAMAgLQVuMK7ZfdBo/e0HO2SFH0etM3NBWPThwJOxDuXPBEpm6Ntei/SZG4Pc6uBgYOkGwAApKXqrY2a/dA63fDURn31V7V6ZuNuo/cVB6yEVk0t02M3zpC3IHi1MBl9qexk3y0me8yTVdYe6XvzFuQmraP2gaOdrsb1dyl7uAHAMcrLAQBA2onURdxE6Yjg8uPQEUUHDne6UlIeqtjlsudUdRG39fVop8FYbm0/3AjdPuBNw674wEBG0g0AAAaMbp8VM4mLtsJr4sRxX6/XAkcUrardE+eRo/Pmp19jsb4c7ZSqveSpxtxqoP8j6QYAAAOCaUOwWCu8sfz+nT367JTSiD83XSm9Z94UlYzIUcmwHN3x23fU1Bb5mpKx93awrfza5dZLVm6RR8Fbt9O93Jq51UD/xp5uAADQ7zlpCJboyu3RzhNRf27awOrLF5VrwfQxuuiMEi2/ukIehd9761FyksHB2GgrFXvJASAWVroBAEDKJFoubqkneVyxpk6XV3iVmeFJeOV2VIwy73hWVFOx9zbwOiNJx5Vfyq0B9Dck3QAAICWqtzZq+eq6oLJrb36ull/trFw8tCGYyXztaM4dXxQzJp4kOhXJYNXUMi2+uFxPbWgIGm+W4ZEWzSlP25Vfyq0B9Cck3QAAoM9Vb23ULWFWYJvaOnTLyi16PKAU2GlDsGgr0SZGFw41iosnie7rZLB6a6OeWN/Q63WfJT2xvkHnji9K28QbAPoLkm4AANCnun2WvvG796LGLP3de47LxQPjIq1EZ3ikaGOpne5x7m8rqoHl+iXDchx9z26dN/Dhg8n2AQBIdyTdAACgT23c0axD7cejxhxsP66NO5p10eklcY+CCrcSffBop/712bcjnncg73EO1909lsDv2c3zlhXk6uppZVr9TmPMbvMAkO7oXg4AAPpUTX2zozi7XFwK3/1bipws2yvRC6aPUeXkkcoYoAl1LJG6u5swvR9OztvY2qEn1jcYdZsHgHRH0g0AAPqY6S7rU3FujIKyu6BHs2JNnbqj1Z8b6vZZqqlv1qraPaqpb/YfM9LriZ4rUnd3M/G9M57zWif/cut7BoCBgPJyAADQpyonleiRV+qN4gIl2v07Vhd0KbgLeiAne5P7utza5HNFE/o998V5I33PAJCOSLoBAECfmjV5pArzsqLu6y7My9KsMAlZIo3LAkeTOYmLlESHS5btcuvQNVy73LrXuU6WW5uu1odj2t09nEjfc7LPK5nfDwAY6CgvBwAASRdYVr25oUX/cc3UqPEPXnu26w3NWo50Oo6LtGc53N7keMutpcTKrU27u4eTyPecyHkl8/sBAAMdK90AACCpqrc2avnquqCVTW9+rr5ycblW1e5VU1tnwOs5Wn71p5PS3bp4WLZRXMvRLq2q3aOS4Tlavvr9sEm0pZ4mbivW1PlHbsVbbm0psXLrWN3dw/FIWnxxeULfczznDWR6P/ojRqEBcIKkGwAAJE311kbdsnJLr9eb2nrKrX/8hRkqGpbdJ8lLab7Zyuyjf46931zqnSwnWm4d7/vt7u5LVm6RR+Zt0Z5c36BzxxfFnXjHe16bt2BoXOdNNSfbDQBAorwcAAAkSbfP0jd+917UmG8+/54uKC/2j/RK6mphkppl28lyouXWibw/Unf3SF+nG2Xt8ZzXVhZmrvpA4GS7AQDYWOkGAABJsXFHc9RmaZJ0sP24Nu5o1kWnx9dB24kDR5Ozh9hOluMtt/aoZ/RZokloaHf3A4c7df8L2yLGJ1rWHum8pSNydfBop2599u2w34NHkeeq92fR9uyH224AADZWugEAQFLU1De7GpeoRFeiQ3kUvGJrl1vbPzM9huReEmp3d18wfYxKRuQYvSfRsvjQ81ZOHqmrzhmtxReX9/oe3NhLniqx9uwHPsQAgEAk3QAAIElM13sTr/sO7I5eU98ctmTaXol2Q6RkOVK5dVlBT+M4b35wIjwqPyfquDCTzxWJ6UMGtx9GSD1l2E+sb+h1Zy1JT6xvSHoZdiLfWySmDyfceIgBIL1QXg4AAJKiclKJHnkldlOyykmJlZabNrbKzPDo6mllYedlO+WN0jgrXLn1BeXFWlvXpFW1oclm5NXtRBt2XVBeHHMeelFelut7q0328n/jd+8lrQw7WY3OUvkQA8DARtINAMAgd6TjhL7+67e1++AxjS8aqv/8p3M1PDfxXxFmTR4ZM+krzMuSJK2q3RNX93K7sVXoOqbd2CpwFbnbZ2n1O85XWO0919/9X9N04Gin0XXa5daxrnNfW+/rdPq5EpGM3nIb62Pv5T/Uflwb65t10Rnu7uVP5vcWa8++W3vzAaQfkm4AAAaxqx/ZoHc/bvP/+YOmw5q6/H90zth8rb5tTkLHzszw6MFrzw47MizQF3+yyf/3TlYknTa2imeOdmAZebwJYqzrVMh1mnyu5avf14jcLB04EvkhwOaGFqPkN9FGaqFqdhwwjnMz6U52o7NoI9Lc3psPIL2wpxsAgEEqNOEO9O7Hbbr6kQ0Jn6Nqapkev3FGr73MhUN7nvuHJoVORi85bWwVz15bb0FuwqvKJsl+4HWafK6mtk598b826au/qtUNT23U7IfW9frOTD/va9sPuLr32XkbOXf0RaOzSHv23fjnBED6YqUbAIBB6EjHiYgJt+3dj9t0pONEwqXmoXucS4bn6I7f1ErHTvSKdbIi6bSxVclws27eN84cr4wMjyYU52lh5URlD0lsjaKpzew67bh4Hg6EK5823Vv8yCvb/X/vxt7nyskjg44ZLc5NfdXoLNKefVa4AUTCSjcAAIPQ1379tqtxsQSOlMrweNTUFnlmtumKpNPGVl2d3UbxKzft1i9qdun+F7bps995JeFO2y1HzOaD23HxNOIKLFO3V6vj6dbupNIgklmTRvr36kdSlJelWZPcTbr7stFZ6Ig0Em4A0ThOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHHwbF/O1vf9OCBQtUUlKi/Px8zZ49W6+88kpQzO7duzVv3jzl5eWptLRU//Zv/6YTJ3o/EQcAAM590HTY1Tgn3FqRtJPKSOlO6Bzt/3rdeddyN5LQ4mHZjuJifa5IQh9W2N3anR5DCk7enbL38kfzwLVnu56oOv3nAQD6iuOk++jRo5o2bZoeffTRsD9/+OGH9cMf/lCPP/64Nm3apGHDhumKK65QR8ep/3D+3d/9nU6cOKF169bprbfe0rRp0/R3f/d3ampqkiR1d3dr3rx56urq0uuvv66f//znevrpp3XvvffG+TEBAEDg7GLThKdgaPQVy3iUDDMr844VZze2knrvDg7X2KqtI3pTsXDcSEK9BUMdxUX7XCbshxXxdmt3a+9zz17+3vPKH0/S3men/zwAQF9xvEnryiuv1JVXXhn2Z5Zl6fvf/77uvvtuLViwQJL0i1/8QqNGjdLzzz+v66+/XgcOHNCHH36on/zkJzrnnHMkSQ8++KB+/OMfa+vWrfJ6vXrppZdUV1enP/3pTxo1apSmT5+u+++/X3fddZeWL1+u7GyzJ8YAAKBHuNnFJr5+6RnuX4yLfbbsxlahny3cHO2powv03p7o+9jDCUxC49mHbDIvuzBkXnakz2XCLp+Op1t7oIG499nJPw8A0FdcbaTW0NCgpqYmXXbZZf7XCgoKNHPmTNXU1Oj666/XyJEjdeaZZ+oXv/iFZsyYoZycHD3xxBMqLS3VeeedJ0mqqanR2WefrVGjRvmPc8UVV2jJkiV6//33de6557p52QAApLVIs4tN5Oa433P1gOEeZ9M40+Ru/Mg8x9caKNEkNJpwaWivBnTDcnTHb9/RvjazOdGJXq+be5/7Eo3OAPQ3rv6X1C4PD0yW7T/bP/N4PPrTn/6ka665RiNGjFBGRoZKS0tVXV2toqIi/3HCHSPwHKE6OzvV2XnqP85tbc6fZAMAkG6izS42kYxE07SLuGmcZJbcJbLqK8WfhJrMyz5oMC87I8Oje/+uQrc+azYnOt7rDU3eB6JUJPsAEEmfjwyzLEu33nqrSktLtWHDBg0dOlT/9V//pfnz5+uNN95QWVl8ZT8PPPCAVqxY4fLVAgAwsCVaYnzgSJej+G6fFXuF0fQJgBsjowOMKzLbWx0q0SQ03sZx4bYElBXkavHF5Vr9TmPM8unzJhQpwyM52YreV3ufjf45AYA04WrS7fV6JUn79u0LSp737dun6dOnS5LWrVunP/zhDzp48KDy8/MlST/+8Y+1du1a/fznP9c3vvENeb1ebd68OejY+/btCzpHqKVLl+r222/3/7mtrU3jxo1z7bMBADAQJbpSfeiYedIdKUkMTQYPHDUsLzeMiyYwubPiSOLdSELjGWUVaUtAU2uHnlzfoEe/cK6KhuVETVrf2nXQUcIt9c3eZ9N/TgAgXbiadJeXl8vr9erll1/2J9ltbW3atGmTlixZIklqb2+XJGVkBDdOz8jIkM/nkyRVVlbq29/+tvbv36/S0lJJ0tq1a5Wfn6+Kioqw587JyVFOjnkZGgAAg0Gi+3I/bmnXqto9MVcjIyWJjSdHbj0W0LHare7lscTbPC6QG0moPcqqqdVsL3a0LQHWyfj7X9imV++6NOqDgKbWY0bX96+fm6wzvSP6ZMU52sOE0H9OACBdOE66jxw5ou3bt/v/3NDQoNraWhUXF2v8+PH62te+pm9961s644wzVF5ernvuuUejR4/WNddcI6knoS4qKtJNN92ke++9V0OHDtVTTz2lhoYGzZs3T5I0d+5cVVRUaOHChXr44YfV1NSku+++W7feeiuJNQAADsRTYhxo1TuNWnVy7FSk1chY+8Yt9YzcurzC25PQudi9PJJEmsfdM2+KSkbkuJaE2qOslqw024sda0uAaTf1lqNmVQojh2VrwfQxRrFOBVYalAzL0fLV0R8mBP1zAgBpwnHS/eabb+qSSy7x/9ku6b7pppv09NNP684779TRo0e1ePFiHTp0SLNnz1Z1dbVyc3uetJeUlKi6ulrf/OY3demll+r48eP69Kc/rVWrVmnatGmSpMzMTP3hD3/QkiVLVFlZqWHDhummm27Sfffd58ZnBgBg0IinxDiSSKuRJvvGA5NEt7uXh0qkeVxRXpa+fFG560mfk1FW8e4BD1Vs2IjONM4pp5UGiY5mA4D+ynHS/bnPfU5WlE1RHo9H9913X9QE+fzzz9f//M//RD3PhAkT9OKLLzq9PAAAEMDN7uORViNNy5jtuHj2ODuRSPM4l3u3BamaWqZLzxqlZ2p2aldLuyYU52lh5URlDwnecufW9+PNNzuOaZwTiVQaJHM0GwCkQp93LwcAAH3HjVnLgcKtRpqWMdtxTvc4O5VI0nbIYHRXvMKt/P7Xqw29Vrrd+n7s40R7AFGWhNFgiY6pc/ufWQBItYzYIQAAoC90+yzV1DdrVe0e1dQ3q9uFunA78XJ7h+xr2w/4r7MoL9voPS1Hu7Sqdo82N7Tonnk9jVFDr6svu4VHkoyVVnvlNzQBtkv2q7c2+l+z94BLiX0/9nE8EY7jMTyOU/FWGniUnIcAAJBqrHQDANAPVG9t1PLVdWpqC9jvm5+r5Vcn1jk7WhOvRDzyyqmmqsXDsoze8+if6/1/b8+bfv7tPdp3+NRKeemIbK1YMDWp3cJjcXul1aQbeWjJvpM94NG4dRwn4nlo0VfzwQEgFTxWtA3aA1hbW5sKCgrU2trqnwcOAEB/VL21Ubes3BLx54+7MEbJjfFZyRD6ICDDIy2aU66lV4UfEWrKXlmWnD1oyPBIf73/yl77rBNRU9+sG57aGDPuuUWzepW1B3b/TqSbulvHMWH6eQMxpxvAQGSac7LSDQBACnX7LH3jd+9FjVn6u/cSHqNUNbVMl1d4/YnX/rZOffvFbXEfLxKnq+mhsT5LemJ9gyQllHhHWuGNxWf1dHx3c093It3IMzM8rlyLW8cxYbInfVR+jv7fP07XgSOdfTIfHABSiaQbAIAU2rijWYfaj0eNOdh+XBt3NOui00sSOldg4rXhb58kdKxIioZlGzdWi+bJ9Q26Y+5ZCa04hz5o+Gtjmx77y46Y79t7yKwbu6mSYWYjuUzj+juTueTLr/50wv88A8BAQSM1AABSqKa+2dW4aAIbtf1uy8dG77lm+mj94Prpuu2SyUbx98yboucWzdIPrp+uWz9n9p5wLElPv9YQ9/tt9oOGBdPH6EjnCaP31H50MOHzBjFdwE3iQm8ymvRFY1caeAuC98d7C3J7zXkHgHTHSjcAACllmvwkliTFu6d7bNFQLZg+RjX1zXrklfqY8d6Cof7V9J9siL2qHM1LdU1a/FmzxL0v9yw7deBIp6txToW7932xhzq00qC/3RcA6Csk3QAApFDlpBKjZLZyknkpbmgCevBop2599u240vaZ5T0JdDwzn4uHJ1oubZacmSaV44uHGR3PNM6UaTf0ZMynthvKhd57e1RZsled+3IvOQD0VyTdAACk0KzJI1WYlxV1X3dhXpZmGSYu4RLQDE/86+QZnp7ENzPDo6unlfmbnIVz9bSyoFVMb35iSeTnp5wWM8ZJUvmp0uFG5zWNM2XSWMybhPnU8YwqAwC4jz3dAACkUGaGRw9ee3bUmAevPdsoKbIT0NDV6ES279odtbt9lla/0xg1dvU7jUF7he1kM14eyxN1D3KspFLqSSrt927e2WJ0XtM4U3ZjsUi3wVJy5lNvbmiJWplgSWps7dDmBnc/LwAgGEk3AAAp9vbu6I27Yv1cip6AJqKprSdpi5XASb0TODvZ9Ci+HmEP/s8H+uqvanXDUxs1+6F1qt4anPQ7TSr3GHYlN43r7xIZVQYAcA9JNwAAKdR1wqenNkTv0v3UhgZ1nfBFjTFJiuPx6ocHJMWfwEXqYu2UXS4emHg7vabRhUON4k3jTNkPRCKxy7zd7iieyr3kAIBTSLoBAEihZ2p2xiz/9lk9cdEka7Xy0LGemduJJHBVU8v06l2X+keJXTjJ+d7lcOXiTq/JdC602/OjU1XmbZf3R6oy8Kh38zsAgPtIugEASKFdLe2uxCVrtdJeYU80gQuclz1tXGFc1xKanF5QXqzCvKyo7ynKy/Jf06xJI5WXnRk1flh2pmZNcrfbdqrKvO3yfql3eb/952TsJQcABCPpBgAghcYV5bkSFysplnq6mDs18mRS62YCVzwssVFiTpLT0CKC7CHRf/WJ9fN4pLLMO1J5v7cgN+njwgAAPRgZBgBACrk1xspOipes3CKPgpNNOw1+5IYZKhqWrf2HO/Toug/1t/1HY573eMBWcjuBCx1J5g0zEzuakcOyjeIisZPTzQ0tUUetSdKh9uPa3NCiyskjjeIPBsS7JVUjw2xVU8t0eYU3aHb7BeXFrHADQB8h6QYAIIWcjLH67FmlUWOcJMWbdzQbJd1neUf0OkeiCVzz0S7j2EChyanTsm27E3sspnGmTB6IJLvM2y7vBwD0PZJuAABSyO0xVqZJ8YSRw4yOFy4u0QTuwFHnSW245NRp2XbLkU6jeNM4J9yqEgAADDwk3QAApFAyxliZJMWW4URv0zgn3v+4zfF7wiWnTsu2iw3L2k3jnKLMGwAGJ5JuAABS6KLTS/TjP9cbxblpzyGz1WbTOCeGxuggbjt/fKEWXjgxYnLqtGzbW2D24MI0Lh6UeQPA4EP3cgAAUmjWpJFGY6/cHmM1odisa7plWVpVu0c19c3++diJ+swEs4Zhcyu8WjB9jConj4y4GuykO/d5E4qMznui2+f6Z7Z1+yzV1Dcn7fgAgP6HlW4AABLU7bPiLhnOzPDoMxOLtLZuf8SY8ycWuV6C/IWZE3T/C9tixj2zcbee2bhbUs8cbjf2H08Zne9qXNXUMl161ig9U7NTu1raNaE4TwsrJ/Ya/7VpR7PR8Rb+dLP/7936zJJUvbWx155uN48PAOifSLoBAAjRdcIXM4GzJZpIdZ3w6U9REm5J+lPdfnWd8Lk6Q/rNBrOu6YGaWju0ZOWWhOc7t7SbdS83jQt3D/7r1YZe9+C/t3zs7ELl3meu3tqoJSu39Np77tbxAQD9F+XlAAAEeODFOp159x91/wvb9IuaXbr/hW068+4/6oEX63rF2olUYLInnUqkqrc2xjzf0681xGxVZp2Mc9P/b8tHjt9jX+eKNXUJlUU77ToejZN70N51wtmFquczW0rsM3f7LK1YUxf2Prv1nQIA+i+SbgAATnrgxTo9sb53EmxJemJ9Q1Di7VYi9dL7TUbXZhpnynQEWShLUmNrhzbHsVJus7uORyqY96inWsDni76f3Ok9+MzE+PfFJ/KZNze09HooEMiN7xQA0H+RdAMAoJ4y7yfXR19NfnJ9g7pO+CS5l0i1dZqtvprGmRpbZNZILZL9h+Pvam53HZfUK/G2u5AfO96tL/5kk776q1rd8NRGzX5oXa/KAaf34MZZE+K+ZklqaovvM5t+V4l8pwCA/oukGwAAST9/3azM++ev9yTmbiVSU7wjjI5jGmfquhljE3p/pNJv0+7ckbqO253cD7UfD3o9XLm403tQ+9Eho/hIWo50xvU+N8vpAQADD43UAACQjEt7Nze0aNHFk11LpK49d6xWvRN77/e15yaWJIeaGecIMo96xnFdUN577JfTpnJVU8t0eYXX3/m9ZHiO7vhNbdjzWifPvWJNnS6v8Cozw+P4HiS6klw8LDuu99nl9E2tHWEf7ET7TgEAAx8r3QAASDrceTx2UECc6b7kWIlUhuEoMNM4U2/EsX/YvoJl8yt6jTCLt6lcZoZHlZNHasH0McrweNTUFnk1ObRc/ILy4pgzzgvzsvz3INGVZG/B0LjeF6ucXgr/nQIA0gNJNwAAkg53dDuKsxOpSCXplswSqU0NZrOjTeNMvV5/wPF7vAW5YUdbJdJULrAc/bXtnxhdh5MV68BvP9aDkmhMHqBEE6mcPtJ3CgBIH5SXAwAgyRNzR3ePo53Htap2j0pH5OqtXdFXi9/efdAgmTJNAd1dBd1zsN0o7qLJxfrHz4xX6YiepDMzw6Nun+UvCS8d0dNl3LShWeXkU2Xt4crRTdgr1psbWnrt/Q51sP24/7z2g5JbVm5xdD6P3FmJDi2nD/xOAQDpi6QbAAD1lA5v3Xs4ZtzO5mP66q9qjY755IYG3TH3LGUPiVxYVjl5pB55ZXvMYwUmq67wmCV6p43I1YLpY/x/DpcoFw6NXuJtC1yhtsvRnUymDt37nIyu4IV5WUGJfLQ96fGwy+kBAIMHSTcAAJIum1KqP23b7+oxLUv6+es7tejiSRFjZowvMjqWaZypMYVm+5MD4yIlyoeOme2Ht1eoo5WjRxJu77PTRmr2eaOdY2hWph69eYYOHO1kJRoA4Ar2dAMAIOn9vW1JOe4bO6OXoK/cuMvoOKZxpkz3J9tx8STKttCmcrHma4cTbu+z02Z2pnO9MzI8WjB9jL8kHQCARLDSDQCAFFcyaSIvOzPqz9/YadYg7Y2dzVFXzE0E7sXebNiY7W/7DuuzZ5bGlShL4VeoTcu9b7vkdJ0xanjEFWd7j/aSlVvkUfA9TOS8iY4WAwAgEEk3AACSxhXFNw4qlutizNfOzYqelDuNiyTepmU7DxyVZJ6IFg7NCio394bZE21aFn7R6SUx9z/bXcFDP1si5010tBgAAIFIugEAaa/rhE/P1OzUrpZ2TSjO08LKib2am1k+98+bl52pC88oiRozPMfsP8WmceHE07TMVtfYKsk8EX30izOU4fFE7c5tl4U3tXaEvabQhmmxmHYFP29CkTI8UpjJZX4Znp44AADcQtINAEhrD7xYp6c2NAQlWt9+cZsWzSnX0qsq/K9t+eig6+deOGt8zD3BpnuG491bnMhebElqPtIpyTxRnjUp9j7oWKO7TGechx4z1qr4W7sORk24pZ6E/K1dB+kwDgBwDY3UAABp64EX6/TE+oZeiZbPkp5Y36AHXjzVyTov2/3n0KvfaVR3jCxvfPEwo2NZlrSqdo9q6ptjHjNQvHuxbT6r51cFO1GWek8MD7d/uj9iTzcAIBVIugEAaanrhE9PbWiIGvPUhgZ1neipK79uRvS91/FobO3Q5obo3cs/VTrc6FgrN+3WV39Vqxue2qjZD61T9dZGo/clmkCeO6HQ//f2/mlvQXCpebjO4tGYjO5asabO0cMFE+zpBgCkAuXlAIABJ7ALd6T9u8/U7DQqJX6mZqdunjNJF55eorzsTLV3dUeMz8vK0FM3fUYHjnTqw32H9cgr9TGvNVbSuznGSLFwmlo7tGTlFqNEN9EE8tppY4L+bLp/OhrT0V2bG1pcLfN2ey85AAAmSLoBAANKuC7cZWE6Ve9qaTc6nh2XmeHRwlnj9cT6yKvjCysn6KLTexqj1dQ3GyXdsZLevYeOGV1nIEunVoMvr/BGTXhjJZqxfPjJEV2iUUGvmeyfjiZVZd5OR4wBAOAGyssBAP1W1wmffrJhh+5dtVU/2bBDa97ZqyUrt/RaJbVXfgNLricU5xmdw47r9lla/U70ku3APdp2MhspPfOo52FArFXT0XGOKgtcDY4m2l5sE2/ucr4SH0sqy7zdKpEHAMAUK90AgH4pXNfxSMKt/C6snKhvv7gt5niohZUTJZk1HAsseXZr1XTWxJF6VLFXzCMxWQ2ONMvaxNAE54OHk+oybzdK5AEAMMVKNwCg34nUdTya0JXf7CEZWjSnPOp7Fs0p98/rjqfk2Y1V04zMxBI909XgqqllevWuS/Xcoln6wfXT9YULxhm979OjC3q91u2zVFPfHFc3denU6nukd8UzMswpu0R+wfQx/ocoAAAkAyvdAICUC2yMVpyXHXVfdSyBSbE9hzt0xTzDo15zuuMteU501fTAyTnYTsWzGhy4F7u729Kzmz+K+Z6S4TlBfzbdUw8AAHqQdAMAUipcEpeI0KR46VUVumPuWXqmZqd2tbRrQnGeFlZO9K9w2+yS52jXEWmPdiKNxeLZt+xG0y/TZD8wrnpro5as3NJrhbrRQTd1yXxkWKwmcQAADAQk3QCAlImUxMUj2spv9pAM3TxnUtT3Z2Z4dPW0sqir7FdPK3M9CTTpLp7hUdBKvdeFleX397Y6irMT5Wgl4aaJcqpGhgEAkAok3QCAlIiVxDnhxsqvaffyO6umuJp4mzRke+SGGSoalu1q06+PD5pVFthxThvNRZOqkWEAAKQCSTcAICVMkjhTbqz8uplUOlU1tUyLLy7XUxsaZAVk3Z6Te8+vOsf9vdI5hr8B2HFNrWbzxE3iUjkyDACAvkbSDQBIiURXMS+aPFL/+JlxCa38BjZw+6DxsNF7Gg+ZJZ9OVG9tDFvW7rOkJ9Y36NzxRa40KQv8vJbH7PsqGd6T+LYc7TKKN4k7b0JRr5L5UBmenjgAAAY6km4AQEokuoo5fVyhFkwfE/f7423g9vZHB3XteWPjPm+obp+l23/zTtSY23/zTsJNxeL9vOOK8yRJxSFdzCMxiXtr18GY4+B8Vk8ce7oBAAMdc7oBAClhNxCLN42cVR5/MmY3cIunvN1nubEL/ZTXPzyg9q7uqDHtXd16/cMDcZ8jkc974eklkiRvvtlDEpM49nQDAAYTkm4AQErYDcQkxZd4x5mtu9nAzQ3/veVjV+NCJfJ5PZI+M7GnG7z9kCSaSCPVQrGnGwAwmJB0AwBSpmpqmR67cYa8MZK5cDY1tBjHdvss1dQ3a1XtHj39WkNCDdxG5GTF/d5w11PXaDa666OD7XGdK5GGdZakN05+z/ZDEo96P++wXzPtHh+rysEj8wQeAID+jj3dAICUqppapssrvP4GX69s26/n39kb832WYZl3vHuZI2lqS+w48V5P5wlfXOdLtET79R0HdNEZPSXm9kOS0Os36R4f2MStdESu7pk3Rbc++3bEMWmJjH8DAKA/IekGAKRcZobH3zCrqbXDKOnOHxp7xdney+xmKXkiW7oTuR5vvlkjs1CJlmjvPRjcrT30IYlJ9/hwDxrKCnK1+OJyrX6n0XECDwDAQELSDQDoV9o6jrsSl6y9258c7tCq2j2OR5Ulej0zJ8XXOM4u5W5q7Yjr3KMLh/Z6LfAhSSyRHjQ0tXboyfUNevQLM1Q0LNs4gQcAYKAh6QYA9CumZeOx4hLZyxzN6zta9PqOnn3OZQ5WZRO9nhtnTYzrffZe7FtWbonr/Yl0iY/2oMFSTyn5/S/U6dW7LiXRBgCkLRqpAQD6lcLcbFfi+mLcVFNrh5as3KLqrY0xYxO9ntqPDiX0/rglkAvHetBgSWps7dBmB03xAAAYaFjpBgC4LrRplpOS4Zb2Llfi+mLclL1au2JNnS6v8Eb9jIlez2vbP4nr+7RXm+NVU9+sOZ86zTg+8N5/uO+I0XuYxw0ASGck3QAAV0VqmmVahv3eHrMRWq9uPxB1b3Wie5lNBa7WRtvnnOj1PPJKvf/v+7Ks/Z2PDxnHxtuZnXncAIB0Rnk5ACCmIx0ntOjnb+iK76/Xop+/oSMdJ8LG2U2zQpMuJ2XYQ7PM/tO0dW+bvvqrWt3w1EbNfmhdr2Pbe5ml8HOlw72eiNdOPgSoqW9Wt693Wh3tepzqy7L2vGyz+xHp3kfDPG4AwGBA0g0AiOrqRzZo6vL/0dpt+/VB02Gt3bZfU5f/j65+ZENQXKymWVJPGXa4hDSQt6B3t+xYIiWh9lxpb0HwSmphXlbQdbnhkVe2R30IEO16nHLyfSa6ivyZCbEbqcXTmZ153ACAwYKkGwAQ0dWPbNC7H7eF/dm7H7cFJd5uNc2aNq7Q8XVGS0Krppbp1bsu1XOLZukH10/XL//3TOUMSe5//qKtRIdez8JZ4+M6h+n3aZe1x+uM0uExY+IpYfcW5OqxG2cwjxsAkPbY0w0ACOtIx4mICbft3Y/bdKTjhIbnDjEuY44Vd/CoWSO1UKZ7q//a2Kamts64zuHkWqI1WAucc/3WroMJnSvW95mZ4dHV08r0xPqGuI6/6t29uqRiVELXYLvtksk6Y9QI5nEDAAYVkm4AQFhf/dVbxnE/+fJM4zLmWHF1jdET/VhCE8B4m3slyvQhwITivITOE+v77PZZWv1O7L3fkXx8sD3ha7BddPppUb8LAADSEeXlAICw3vnIrIu4HWeXMUdauzRtmnW0M3yTNlOBCWA8zb1iueEz4/SD66frtksmG8XHWgVeWDlR8Sz4mn6fiXYvzxmSGTPGrXsPAEA6IukGAITVecLnKM6kW7hJ06zS/Bwnlxl0jsDELp7mXiaOdp7QguljdNHpZrOrY60CZw/J0OenlDq6BiffZ6Ldy88emx8zxq17DwBAOiLpBgCEVTrCLPkNjIvUndtJ06xzxxU5u1CdSuzumTdFmxtatKp2j55+rSEpJeV7Dx2T5N7qbrfP0hs7o+/rDj2Hk+8z0e7lF002e7jgxr0HACAdsacbABDWyBHZqj8Qez/vyBHZQX+umlqmyyu82tzQov2HOxw3zWo9dtzxtXoLcnX1tDLd/8K2pO/dbu86rlW1e1Q6Ilf3zJuiW599Wx4Fjx9zsrq7sb5Zh9qjf2ZL0jevOkul+bmOv8/zJhQpwyPFmCwW/eSGEr33AACkI5JuAEBYRzu7444L7M7tVPGw7NhBkv71c5N0pjdfpSNydfBol259dktcpeT/fuVZGlWQq8df2a5t+47EjK9rOqqv/qpWUs9K9uKLy7X6ncagZN9bkKtl8yuMVndrdhwwus7WY8e16GKzfeSB3tp1MP6EW9Kmnc2ac6bZareU2L0HACAdkXQDAMIzTdRc3jTtLRhqFDfnjFJVTh6pbp+l2Q+ti/syPB5pwfQxerj6r47f29TaoSfXN+jRL8xQ0bDsOFd344/r9llhV5UDX//Q4EFCNJbbm+IBABhkSLoBAGGVFQ7V+42HjeJMRUoSA11QXqzCvKyoJdeFeVn+vdKJdud+Y2eLSvNz4yprt+dx3/9CnV6969K4yqhnlhfrkVfM4gKFG4VWdrLMPnTlPRH5Q7NcOQ4AAIMVSTcAwC8wKR5bbNaA65IzS4ziIiWJpmXYgQJT20S7c79Ut18v1e2P+/2m87gjyfCYJeqBcfYotNBF6MbWDj2xvsHxNURz6GiXq8cDAGCwIekGAEgKnxSbeOWDT/TFWeUxjx0uSWxq7dCSlVuCultvbmiJ2VjsYPtxf5KbaHdut8Sb/B842ukoLlmj0CJpaktuYzoAANIdI8MAAP6kOJ6S5FjviZYk2q+tWFOn7pPdvkyTVzsu1uiuvhJv8m/6Pjsu0XJ6p0YXmW8fAAAAvTlOutevX6/58+dr9OjR8ng8ev7554N+blmW7r33XpWVlWno0KG67LLL9OGHH/Y6zgsvvKCZM2dq6NChKioq0jXXXBP08927d2vevHnKy8tTaWmp/u3f/k0nTpxwerkAMKh1+yzV1DdrVe0e1dQ3+xPb0JhEVk5jdTmPlSQGlmdLzpPQzAyPls2vkNS71Zgn5H+TwXQedyRO530nWk5/2yWT9YPrp+ubV51lFH/hJLPtAwAAIDzH5eVHjx7VtGnT9C//8i+69tpre/384Ycf1g9/+EP9/Oc/V3l5ue655x5dccUVqqurU25uzy9I//3f/61FixbpP/7jP3TppZfqxIkT2rp1q/8Y3d3dmjdvnrxer15//XU1NjbqS1/6krKysvQf//EfCXxcABg8TPdQJ7pyOvm0YVF/7nTl+rwJRfJ4onfN9nh64mxVU8v02I0zen1ee3TXpWeN0jM1O7WrpV1t7cf1/Dt7ja4plljzuE0ax9kPDZas3GI07zvRcvqLTj/N3/X90T/Xx2xYN4vxXwAAJMRx0n3llVfqyiuvDPszy7L0/e9/X3fffbcWLFggSfrFL36hUaNG6fnnn9f111+vEydO6Ktf/aq+853v6Oabb/a/t6Kiwv/3L730kurq6vSnP/1Jo0aN0vTp03X//ffrrrvu0vLly5WdbTbDFQAGKyd7qBNdOT1vfFHUnztduX5jZ0vMMVWW1RN30emnVmGrppbp8gpvxCT35jmTJEk/2bDDtaQ72jxuJ43j7IcGy1e/r6a2U3u8R+XnaPnVnw6Kt1fGm1o7HFUneE5er71inpnh0YPXnq1bVm6J+J4Hrz07ro7sAADgFFf3dDc0NKipqUmXXXaZ/7WCggLNnDlTNTU1kqQtW7Zoz549ysjI0LnnnquysjJdeeWVQSvdNTU1OvvsszVq1Cj/a1dccYXa2tr0/vvvu3nJAJB2nO6hTnTl9ONDx6L+3Gn5dE19s9F5w8VlZnhUOXmkFkwfo8rJI8MmjMXDEntwW1lepB9cP13PLZqlV++6NGLCHW6PvP3Qo3prY4SjRyqQPyVaOX0kkVbkq6aW6fEbZ8ibnxMU783P0eMBD2YAAED8XE26m5qaJCkoWbb/bP9sx44dkqTly5fr7rvv1h/+8AcVFRXpc5/7nFpaWvzHCXeMwHOE6uzsVFtbW9BfADAYOd1DnWgjsl3N7b1eC9xLvrmhRffMmyIpckoZmAx2d/vMzttyNOpe9Ui8BQk2BvN4oib1Th96SKeS9NBO4U1t4ZN0e2XcWxD8wKSsIFdfubhcZSGvewtyg6obQo/12jc+r+cWzfI/THjtG58n4QYAwCV9PjLM5+v5Zeqb3/ymrrvuOknSz372M40dO1a//e1v9ZWvfCWu4z7wwANasWKFa9cJAAOV0z3U0fYUm+g4HtxILVJZ9eKLy7X6ncawe64DE7zWjujjwmxr3mnUmnca/cc3nfdtP2SIdx9785Hoc6udPPSw91ZHa2RnqSdJv7zC22uVOlI5/Z1VU2LuJQ9kVwgAAAD3uZp0e71eSdK+fftUVnbqF599+/Zp+vTpkuR/PXAPd05OjiZNmqTdu3f7j7N58+agY+/bty/oHKGWLl2q22+/3f/ntrY2jRs3LsFPBAD9i0ljLqd7qKXIjchMTApopBZtL/mT6xv06BdmqGhYdtTr/+Sw2dzq0OOH7lWPJPAhQzwd24fnRv9Pp9OHHiaN7AKT9ECRkmWSaAAA+g9Xk+7y8nJ5vV69/PLL/iS7ra1NmzZt0pIlSyRJ5513nnJycvTBBx9o9uzZkqTjx49r586dmjBhgiSpsrJS3/72t7V//36VlpZKktauXav8/PygZD1QTk6OcnJywv4MANJB9dZGLV9dF1SC7M3P1fKrKxw12gptqGULXTl9dN2H+tv+ozGvq6a+Wfeu2qpxRUP1k1d3Riyr9ki6/4U6vXrXpVFXXYflOP9Pk338cCvC4STykOFM7/CoP3f60KOpNfqeeJtpHAAA6F8c/2Zz5MgRbd++3f/nhoYG1dbWqri4WOPHj9fXvvY1fetb39IZZ5zhHxk2evRo/xzu/Px83XLLLVq2bJnGjRunCRMm6Dvf+Y4k6R/+4R8kSXPnzlVFRYUWLlyohx9+WE1NTbr77rt16623klgDGJSqtzaG7TLd1NahW1ZuCWp6ZTKC6p554cuPA1dIn9u8WzJIuj8+1KFf1OyKGRdaVh3JdeeO1fO1zruL28d/+rUGlYzIiVlWHfqQ4a+NbXrsLztinuf88dHncTt96NFyNHq5us00DgAA9C+Ok+4333xTl1xyif/Pdkn3TTfdpKefflp33nmnjh49qsWLF+vQoUOaPXu2qqur/TO6Jek73/mOhgwZooULF+rYsWOaOXOm1q1bp6KinrEzmZmZ+sMf/qAlS5aosrJSw4YN00033aT77rsv0c8LAANOt8/SN373XtSYpb97L2iFN9rc6qunlen+F7bFHGXVFbJX2y2xyq8vPKNEedmZau+K7/z3v7DN//ex9noHPmQoGZZjlHTHasQW+NAjksDGccXDzR4mm8YBAID+xWNZsaahDkxtbW0qKChQa2ur8vPzU305ABC317Yf0Bf/a1PMuF/+75lBc6ul3nvADx7t0q3P9t7LbK8FB+6JvvnpzXr5r5+48AmCPbdoVsz9xpFW9p0K97kiSeR7DueBF+v01IYGBTZWz/BIi+aUa+lVp7ZK1dQ364anNsY8nsn3BgAA+o5pzunqyDAAgPte234g7rjAudUXlBfr/hfMR1mNyM2K84rDC53H3RcijegKZ5/h3u7XPjwQc1RZ9dZGPbk+OOGWJMuSnlzfEDQCzC5Hj6avvzcAAOAekm4A6Of2HOw9BzueOKfzuyu87lUJhZvHHUm3z9Ltv3nHtXOHfq5I3twV/ee2H/+lXl/9Va1ueGqjZj+0rtcMbadzujMzPLp6WvRV+KunlcX83gAAQP9E0g0A/Z5pshU9zukoq6Lh2Ybnjc1bkGtU4i1Jr394IO793NHE+vx/bWxzfEx7VFlg4u304Ua3z9Kv3/w46nl+8+bHMVfqAQBA/+TqyDAAgPtMU61YcU5HWb29+6DhmXvzSPrFv1yglvaumF3EQ/3mrY/iPm80sT7/4c4Tjo8ZblSZ04cbG3c061D78aixB9uPa+OOZqO95AAAoH9hpRsA+jvTfpcx4uy9w5FS39A9139tOmx+jSEWX1yuOZ86TQumj1Hl5JGOSqNrP4o/2Q/HdC/5yDhX9kNXrp0+3KipbzaKN40DAAD9C0k3APRzpvlqrDh7lJXUuxA93J7rIx3RV18jXcNXLg7uzu3UkAz3/tN0ai55hTY3tERtgJZhXMYfnr1y7fThhnu1DAAAoD+ivBwA+rkxxXlxx4WODLu8whtxfnfoPOuRw8y6l08oytFnz/JqQnGeFlZOVPaQxJLmWZOK1dBs1jwullNzyetiziUflpvYfxLtlevAOd0eBafK4R5uVE4q0SOv1Mc8fuUkSssBABiISLoBoB8KTJYLh5olvxeGJGXVWxt7Jdd2svnqXZcGJePh9lyXjBgq6VDM854zrlj3LZhqdI0m5k7x6rk3ojcWi+aeeVNUMiLn5FzyTt367Nu91ojtBmiBzd0umDhSa+v2Oz6fRz3JfWD5etXUMuOHG7Mmj1RhXlbUfd2FeVmaxYxuAAAGJJJuAOhnwiXLseRlZwYlZdVbG7Vk5RajZDMS033Ybo+yeivOPd128vvli8qVmeFRt8/S7IfWRRzdFdoA7aYLJ+o//rjNeAu9fU4p/Ci0qqllurzCG/PhRmaGRw9ee7ZuWbkl4nkevPZsRoYBADBAsacbAPoRO1l2knBLkicgH3M6JzqS4uFmK+ymcabimYwVLvl1Orore0iGzh7jbDa5x9PTNC7SA4zMDI8qJ4+M2VCuamqZHr9xhrz5OUGve/Nz9LjhqDUAANA/sdINAP1EtGQ5lqOd3f6RUk6SzcooJcvrP/jE6NzrP/hE+junVxxZUZ7zLuLhyradju7qOuHT1j3OZnX7LOnJ9Q06d3xRwomx6co4AAAYWEi6AaAPhDY0C5dMxUqWY6mp70m6nSabkbQcNetebhonmX0PxcPMku4ln52ks8ryIx7H6eiuZ2p2xrXKLgWXqSfCXhkHAADpg6QbAJIsWkOzeFZmI+vJGJ0mm4ECk2LTLuQFho3eTL+HQ+1dRscrGZ6jBdPHRPy5PbqrqbUjbPVAaAO0HQeOGp03lGnlAAAAGJxIugEgiZw0NDNNliOxR0qdN6FIGZ7oe6MzPD1xodfqtIGbJC29ckqv10JXtJ10ETctL28+0qVVtXuiNihzMrprf1tiDz0Sf2gCAADSEUk3ACRJrIZmod2zY63MRhPYvfytXQdjlkn7rJ44e2U20sMBE0OzMoP+HC55z/DI+Hs4aLjS/eO/nJptHW7FXHI2uuu0EcFNzJxK9KEJAABITyTdAJAkThuaRVuZjSUnoBTc6Z7uRBq4SdKmnc2ac+ZpkiIn79EeAoR+D6Z7ugNFG4Vm2qAs3u3Y4eZ0AwAA2BgZBgBJEk9DM3tl1lvgbNX0YPtx/+grp3u6E23gZo8eSzR5t7+H0nznK8axRqGZjO4aYbg3Pdy5w83pBgAAkFjpBoCkibehWejK7If7juiRV7bHPI6dtDptIJboXuTmo52SEk/e/d9DnFl7tIZmJl3TMzwkzQAAwH0k3QAQB5MkzmnyGyhwdFRNfbNR0m0nrU4biCW6F/m17c2S4k/eez0EONKZ4PV8EnRf1tY1GXVNL8iJb6U7dE86AABAIJJuAHDIdPSV0+Q3kni6kTtpIJZIAzdJOtTeM6c7nuTd/uT3zJvif4ixZVdLHFdxyiOvnGqwVpiX5b++QOH2gB/qMGvgFoqRYQAAIBqSbgBwwMkIMKkn+V18cbme2tAgK+BNHo+0aE55r6Zf4cTTjdw+t0kDsUQauElSblbP8UyS99CHB96CXF09rUz3v7AtodL0SMIl3FL4rukZnsTanDAyDAAAhEPSDQCGYo0Ak3qXGVdvbdST6xvCdvN+cn2Dzh1fFDPxjqchmy2wTD2aSCvjJs44bZj/XLFW9h+5YYaKhmUHzO/u0q3PxjeqLFGhK9SVk0calfFHwsgwAAAQDkk3ABgyaRQWmMSZdPM22Qscb0M2p0JXxp/d2KBNO1tjvm/k8FPzrZ2UtXf7LM1+aF1KEu5A9sOKWZNGKmdIhjpP+By9n5FhAAAgGpJuAIgisGHaB42Hjd7TeOiYJOdzuiM5b0JRzLJvj4L3dMcrcGX8yb+YrfrubG4P+rNpWXui3c7dYj+s6PZZ6up2nnBLjAwDAACRkXQDQAThGqaZePujg7r2vLEJlYUHeqOhJeZqsHUy7qIzSoJeN+myHkl7l1kCGi7OpKw91XugQ1eon6nZGbTv3kS4FXwAAIBAJN0AEEakhmkm7PeUDMuJGmfb39ahVbV7gpLiwGT5lW37jY7zev2BoKTbtMt6JKMLc9UQsoodKS4eqd4DbSl4hXpXS+zPKklzK0Zp3jlljh9iAACAwYmkGwBCmOzFjmZc0dCevzHMxb794l/9f192spv36ncaHa+w7zlZ1i7F7rL+6BeCG5qFSx7nnHGaXquPPb5rzhmnObpOW6Kjytw2oTjPKG5mebEWTB+T5KsBAADpgqQbAEIkutf4eHdPCnngSKfj9za2duiJ9Q1xnrdbklmX9due2xI8uis/V8uvDl4BT3YDt0RHlQWK9LAi1nzzwEZ2Cysn6tsvbos5D31h5cQErhQAAAw2JN0AECLRvcara/fotkvP6PPy6ff3tEkye2gQmlg2tXXolpVb9HjAnPEDh80eGoSLM91Lbnc7X766Tk1tzr732aeP1D+cPy7o+HdWTfGf98DhTt3/wraoxwhsZJc9JEOL5pRHfeixaE65sockNs8bAAAMLiTdABAi0WT5wJEuST3dxGOttLrp+MnO24k8NFj6u/f8K7/vN8YeFyapV1x8e8mdf0lfuXiy5nwquLQ9sIHb77d8bHScptZTZflLr6qQJD21oSHovmV4ehJu++cAAACmSLoBIESie41zs3pWQt/adbDPEm5Jysvu+Vd6Ig8NDrYf18Ydzbro9JKgPeLRONlL/ljASnq0eBPnT4w+F7vlaJfRcULjll5VoTvmnqVnanZqV0u7JhTnaWHlRFa4AQBAXPgNAgBC2HuNJeNeaEFmjO+Zl93XI7HOPzn6yn5oEG9P7Zr6ZklSlmGSaceZ7CVfsaZO3SefRCTasO7ZTbui/rwoL9voOOHisodk6OY5k3Tfgqm6ec4kEm4AABA3fosAgDDsvcbeAuerxmePLZTU9yOxTj9tuKTEHxrYKfJpw8ySVjsu1l5yS6f2UJvExxJrxNfBdrOVbtM4AACAeFBeDgARVE0t0+UVXn9jrrq9rUadxYtPJqF9ORIrtKu2/dAgdG+1icpJPbO+xxSZjdCy40xX9l/b/on2H+7Qh/sOO7quUGUF0eegFxs+NDCNAwAAiAdJNwBEEdiYa9OOZqP3vL37oP7h/HFRR2LZfy7My9Kh9uP+1yONvsrLzlR7V3fEc4brqh360KBkeI7+9ZdvqfXYiYjHKczL0qyTn7fQsDzbjjNd2X/klXqjuFg+bom+59xbMNToOKZxAAAA8SDpBgCZjbj6oMlsZTYwLtKKs/dkN+/ApDjS6Cv79Yertznuqh340ECSHrruHN2yckvEa3/w2rP9n/ugYSOyrXtatap2j0qG5cibn6t9bclf2Zek3QejJ912pUG0lf6ygp7vFgAAIFlIugEMevGNuDIXuuIcmtQHJsW20GRZks4dX6TThu/RvsOnkuHThmfr3JON20yv5fEbZ2j56vfV1HZqvrY3P0fLr/500Od9d88ho2OuebdRa95tlNSzUm5JvVb2k2FCcfTy98BKg3DX4pG0bH5F2PnhAAAAbiHpBjCoORlxdaZ3hN7afSjmMc/0juj1Wrgk2o3r3He4K+woLlu4FfxYDwFsBw539jpeLK0nS+ULQsrmk+Hys0bFjIlUaeDmQxUAAIBoSLoBDFqxRlx51DPi6vIKrzIzPDp3fJGe3fxRzOM6WXlO9DqlnmsNvE5brBX8WA8BhudkOr5W+3vLHZKhX/7vmTpwpFMf7juiR17Z7vhYsbQcMyt/N33IAAAAkAyMDAMwaDkdcdVy1GzlN1xct89STX2zVtXuUU19s39WtRvXqZDrlE6tjIe+z17Br97aGPO8Z4zKN77GQJakprZOZXg8WjB9jC46vSSu48TSYrjnXDpVabBg+hhVTh5Jwg0AAPoMK90ABi3TEVd23NY9bUbxoXGJ7hn/+GD0edTBcSMdr+BHEq1bugn7e0vW6LQRufwnDAAA9H+sdAMY0BJZQTYdcWXH/c2we3lgnBsrzv9jEBMY53QFP5LGQ9G7g8dy4HCnVtXu0eaGFt0zr6e7upvryy+93+Ti0QAAAJKDZQIAA1aiK8ixVmA96hntZY+UGma4smrHubXiHNhlPBo7zukKfiQ5WfE/l83wSPe/sM3/57KCXC2+uLzX/HF7Tnk83c5NvxcAAIBUIukGMCA56ToeSeBIqdCkz06BA0dKneUdri0G3cvP8g6XZL7i/PRrDSoZkROxwVdhXlbMcwbGOV3Bj2TK6Hy9Vh99NTyS0IKDptYOPbm+QY9+4VwVDcsJami2tq6p18MTE6bfCwAAQCqRdAMYcNxaQZYij5Tyhlkxnza2UM9u/jjm9U0bWyjJfMU5dEU49LyL5kzSq9ubYx5n0ZxJksxX8H0+S6tq90RM9o92ON/TneHpnXBLp+7L/S9s06t3XRp0rtDu4o2HjunB6g9insv+vAAAAP0ZSTeAAcfJnmWT2dimI6XeM2yk9t6eNv2TzFecA4VbqZ99xmnKyvToeHfkAuysTI9mn3GapNgr+JakY8e79cWfbPK/Hi7Z/2Cf2R72M0qH6bZLz9CBw51BDxBCRbsvgXPMu32W/vNPH6rzhC/isXKGZPg/LwAAQH9GIzUAA45be5YDmYyU2mvYWMyOs1ecnTQPsxPkFWvqgprCZWVG/9d16M/tFfxR+cGJv12Sfaj9eNDr4Rq7Hek8YXTNnpOjwUpG5BjFx7ovmRke/eD66VFjfnD9dMZ+AQCAAYGkG8CA49ae5UAmXdB3HDhqdCw7zl5xlpx17Q7tLr6xvjnm+K72rm5trA9Xgh78OUKT7dCowGR/yqjhRtdrxxXnZRvFm8RVTS3T4zfOkDc/OJH35ufqcYP9+gAAAP0F5eUABpzp4wpdjave2qjlq98P6obtzc/R8qs/HZTcDcs2e04ZGBdpz7gJe0X49R0HjOJf33FAF51RIilyo7loHcJDy78XnDtOq96NPZZrwbnjJEl/bTIrv/9rU5vmfCp2abhp2T8AAEB/RtINYMBZuXGncdyiiydHjane2qhbVm7p9XpTW6duWbklaFW1rDBP7zceiXnessK8oD+HJo+x9j7b7JX6vQcNy9pPxkVrNGfCTvY/2GeWRH+wr02XTinVR4bXaRonBe/1BgAAGIgoLwcw4Nhl14nGdfssfeN370WN+cbv3vOXW19RMcrovLHizvLmy5sfea+3Rz2Nzez54KMLhxqd146L1WguFjvZX/t+7FXuwLgJxXkxIuUoDgAAIB2w0g1gwDkWY3+zadzG+uaIe5xth9qPa2N9sy46o0QleWaNwkLjqrc29iovL8zL8o/RijUf/MLJJXr0z/Uxz3vh5J7ScicN5EIV5mX5k/1PDnfGiFZQ3MLKifr2i9vCjgyzZXh64gAAAAYLVroBDAiBjc5GDjdLfs85OS87khrDvdJ23C/f2GUUHxhn760OXXluPZnsF5zsJm7zFuQGjQuTpM+UF8dsxOY5GSfFN6os8Di20/LNjmPHZQ/J0KI55VFjF80pV/YQ/tMDAAAGD1a6AfR74VaKTdhNxSIzbcjVE7fNsFGYHRdtb7W9yp07JEO//N8zdeBIZ8RGYW/tOhhzf7Yl6ZmanSoZkaOS4Tny5udoX1un433dB9uP+xupzZ0ySlt2H4r5nrlTTpXTL72qp1v7Uxsagla8Mzw9Cbf9cwAAgMGCpBtAvxapC3csw3IyNWtS9AZclZNH6pFXtsc8lt3Iq73LZ3RuOy7W3mpLPQ3bMk7OuY5k78F2o/MGNmeLVL5uwi5P//SYAqP40LilV1Xojrln6ZmandrV0q4JxXlaWDmRFW4AADAokXQD6LcS6cKdlRk7wZs1aaQK87Ki7usuysvyJ+/eghwdjLEH3I6TzPdWv7b9QNSRWLUfHzI6TqDA8vVY+9ZD2eXpLe1dRvHh4rKHZOjmOZMcnRcAACAdkXQD6LcS6cJ9KKBMOpLMDI8evPbssCPDbA9ce7Y/CZ4+tlDbDEaGTT+5l9x0b3XgantZQa6Wza8I2tNtXgZ/ir3KPTQrU4/ePEMHjnaqZFiO7vjtO9rX1hH2QYZHPXvK7UZqJcMMG8cZxgEAAAxG1PoB6LcS6cItSU2tsedBV00t0+M3zpA3pGlYWUFu0IxuSRpfaDbqyo67oLxYZQWRR4OFv+YOLVm5RdVbG/2vTRwZ34gtS1Jja4cyMnrK1y86o0TLr+7ZUx16TeG6pncdN+sSbxoHAAAwGJF0A+i3EunCLUkHjpiVR1dNLdNr37hUzy2apR9cP13PLZqlV++6NGS1WdpiWOZtx2VmeLRsfvgkNxJ7BXrFmjr/fPAvzJxg+O7wAh9eVE0t02M3zpC3IPi7Ddc1/anXdhgd3zQOAABgMKK8HEC/Za8UN7WGL4eO5ZDhnmSpJ0GOVoouSUc7TxgdKzDOTnKddF+3V6jt8vjajw4ZvS+S0IcXVVPLdHmFV5sbWqLuJd+x/6jR8U3jAAAABiOSbgD9lr1SvGTllri6cFvxZOpRlIww3OMcEhea5H6477AeeaU+5nHsFWqTMvlwQvdoBzJ5yJBhuDxvGgcAADAYUV4OoF+LVA5torXDfKXbxLhis73V4eLsJHfB9DG66PTTjI5z4HCnVtXu0Zu7WhxdpxR+j7ZThcOyXI0DAAAYjFjpBtDvha4Ur35nj17e9knM97m80K3zxhdLir1C3RMXmUnZfIYneO62U96wXdCdGTUiR3UG3dpHGVYAAAAADEYk3QAGhMBy6Dd3mq38ZnrMV3i7fVbMPc7/8/5eo2P9z/t7demU0sjXZVA274vjicE100frkrNKI16/Ux8fNNuDbhoHAAAwGJF0A3BF1wmfnqnZqV0t7ZpQnKeFlROVPSQ5O1imjS3UM9ptFGeiemujlq+uU1PbqeTRm5+r5VcHrxT/5W8HjI5nEhepwVqGJ76EW5KumzFWcz5lVrpu4pjhKDDTOAAAgMGIpBtAwh54sU5PbWgISha//eI2LZpTrqVXVbh+voOGXclN4qq3NuqWlVt6vd7U1qFbVm4JmtXdcdyse3m4uHAr6aFl8wcOdyZUUp7hYGXfxJBMs4cmpnEAAACDEUk3gIQ88GKdnljf0Ot1nyX/624n3ofaj7sS1+2z9I3fvRc1Zunv3tPlFV5lZnhUkDNEh47FXtUtyAn+V2v11sZeK9plAXuu7bL5VbV7Yh47mpodB3TgaKdr5eWXVZTqvzbsNIoDAABAeCxPAIhb1wmfntrQO+EO9NSGBnWd8Ll6Xo/him6suI07mmMm5gfbj2vjjmZJUvEIsw7qgXHVWxu1ZOWWXjO6m1o7tGTlFlVvbfS/FjpP26lHXqnXV39Vqxue2qjZD60LOnY8LjlzlKtxAAAAgxFJN4C4PVOzM+b+Y5/VExeq22eppr5Zq2r3qKa+Wd0ONjLPDDN3Op64mvpmo+PYcblZmUbxdly3z9KKNXVhG6XZr61YU+f/7HZXczeKxO2k/sV398b9Pc+aNFKFedHHgRXlZWnWpOjzvgEAAAYzyssBxG1nc3tccbHKrWMyzRtjxjk8kGE23HasS6tq9+jA4c5eK9yhR21s7dDmhhZVTh5p1NXclP3e2557O+jBiJPvOTPDowevPTvsnnfbA9eenXAZOwAAQDpjpRtA3CzLLC0MjHNSbh3Jpp1mK9Sx4ionlRgdx44bZrjS/X7jEX31V7XGTdH2Hz71Xdhdzb0FiZWa20IXtp18z/b1PH7jDHnzg6+nrCA3qMkcAAAAwmOlG0Dchuea/SvEjotVbu1RT7m13bgsMtOV1ehxsyb3lE9H29ddmJelWScbnZUMzzE8rzOhe7lDu5pv3HFAz23+2JVzOfuew1+PW43aAAAABgNWugHEzTTnsuM2N7QYl1tHY3f7jiVWnF0+Hc1/XDNVmxtatKp2j97bc8jovE5keKTzJhSFvbbKySO1YPoYXVButiJvyvR7jnQ9dik8AAAAYmOlG0DcivLMVn7tuMAy6mjCxQXOuS7Mid7cyzZjfO9kNlTV1DJ95eJyPbm+IWgF3qOeUVj3v7At6oOCRPks6a1dB6M+IAgt7XaL6f0AAABA/Ei6AcRt5LBsR3GmI7FC48I1XjOxcuNOLbp4ctSY6q2NvRJuqWc1eG3dfkfni1es5Pe8CUUJN1YLJ9ERZQAAAIiN8nIAcXO6ch1rJJZHPQ26LggY9RWp8ZqJN3YejPrzaHvM+1Ks5PeNhhbXrzH0ewYAAEBykHQDiNv6v33iKM4eiSX1bnFm/3nZ/Ar/fuFEk+Jh2dG7jcfaY55s4R4yhFOz44Dr5756Whn7sgEAAPoASTeAuO05ZDanOzAu0kgsb0GuHgsZQZVoUnzNuWOi/jyVe5rDPWSIHe2e1e80qjt0nhgAAABcx55uAHFrPRZ51Fa0ONMRVIkmxUMyoj9X7Is9zWUFubp6WplWv9MY9ADBW5CrZfMrjOZczywv1iOvuHtddvdy007wAAAAiI/jle7169dr/vz5Gj16tDwej55//vmgn1uWpXvvvVdlZWUaOnSoLrvsMn344Ydhj9XZ2anp06fL4/GotrY26Gfvvvuu5syZo9zcXI0bN04PP/yw00sFkGSZMZLaaHEmI6gSTYr3tUVP2mPtMY/XmIIc/eD66Xpu0Sy9etelWnpVhV6961I9t2hW0OsmCbck+brNVqS/ccWZ+sH103XbJdGbx9noXg4AAJB8jpPuo0ePatq0aXr00UfD/vzhhx/WD3/4Qz3++OPatGmThg0bpiuuuEIdHb1/ubvzzjs1evToXq+3tbVp7ty5mjBhgt566y195zvf0fLly/Xkk086vVwASTRqhNnIMNO4UIkmxW9/FL2RWrQ95onwFgzt9TAhkTnXv39nj1HcX/cd1oLpY3TR6acZxdO9HAAAIPkcJ91XXnmlvvWtb+nv//7ve/3Msix9//vf1913360FCxbonHPO0S9+8Qvt3bu314r4H//4R7300kv67ne/2+s4v/zlL9XV1aWf/vSn+vSnP63rr79e//f//l9973vfc3q5AJJo/jm9H5olEhcq0aTYZH040h7zsoJcfeXicpUVOE9MPzVqhOP3RHO084SjuHi6xAMAACA5XN3T3dDQoKamJl122WX+1woKCjRz5kzV1NTo+uuvlyTt27dPixYt0vPPP6+8vLxex6mpqdHFF1+s7OxTM4CvuOIKPfTQQzp48KCKiop6vaezs1OdnZ3+P7e1tbn50YC01e2zYu6tjqS0YKirceHYSXE8c7rHFZmdt2pqmS49a5SeqdmpXS3tmlCcp4WVE5U9JEN3Vk3xfz9/qmvSmnebYh4vP8/ddhmj8s0SfzvOflixZOWWXvO9nTVwAwAAQKJc/c2wqannl9FRo0YFvT5q1Cj/zyzL0pe//GXdcsstOv/887Vz586wxykvL+91DPtn4ZLuBx54QCtWrHDjYwCDRvXWxl7JbJmDBl/7244Zncc0LpLQxmsb6w/ouTc+jvm+nQfatap2T8yHCeG+h/96tcH/PdjNxv781/1G17u/tTN2kAPnji/Syk27jeJskR5WOGngBgAAgMT1effyH/3oRzp8+LCWLl3q6nGXLl2q22+/3f/ntrY2jRs3ztVzAOmkemujlqzc0qsEu6m1Q0tWbuk1viucNe82Gp1rzbuNWnLJGXFeaQ97T7QkvWB43ufe+EjPvfGRpMgPE5x8D6El6JGYxpkaXWi2Yh8aZ9olHgAAAMnj6pxur9crqad8PNC+ffv8P1u3bp1qamqUk5OjIUOG6PTTT5cknX/++brpppv8xwl3jMBzhMrJyVF+fn7QXwBO6fZZqqlv1qraPXpt+wEtX/1+2D3P9msr1tTFnOP88UGzFWzTOFPDsjMdv8dOoqu3nkrYu32WVqypM/4eioZmh4nsrfXYca2q3aOa+mZXZmFPH1cYd1wiDdwAAACQOFdXusvLy+X1evXyyy9r+vTpknpWnDdt2qQlS5ZIkn74wx/qW9/6lv89e/fu1RVXXKFf//rXmjlzpiSpsrJS3/zmN3X8+HFlZWVJktauXaszzzwzbGk5gOjClU9HY8lsjnOW4WM70zhT184Yq9/X7nX0nsAk+vIKrzIzPNrc0BL1Own9Hlo7zeaSP7v5Iz27OfoKuxPPbtplHHfznElxnwcAAADuc/yr8JEjR1RbW+ufq93Q0KDa2lrt3r1bHo9HX/va1/Stb31Lq1ev1nvvvacvfelLGj16tK655hpJ0vjx4zV16lT/X5/61KckSZMnT9bYsWMlSV/4wheUnZ2tm2++We+//75+/etf6wc/+EFQ+TgAM3b5tNMmZFLsOc6nnzbc6DimcaYuPL1EOUPiy+TtJFoyn1OdyDzrcCvsTu1qaXc1DgAAAH3H8Ur3m2++qUsuucT/ZzsRvummm/T000/rzjvv1NGjR7V48WIdOnRIs2fPVnV1tXJzzfc4FhQU6KWXXtKtt96q8847TyUlJbr33nu1ePFip5cLDGrRyqdNxJrjnGGY95rGOZFI0XZTW08SXTLMbH64HVc4NMvxuSz1dAwPXGF3akJx7ykPicQBAACg7zhOuj/3uc/JsiL/uuvxeHTffffpvvvuMzrexIkTwx7vnHPO0YYNG5xeHoAAscqnozGZ49zVbXYs0zhTr28/oK4Tvrjf33LkZHdx0/z3ZFxxntme7lCm5fqRLKycqG+/uE3RtodneHriAAAA0L/0efdyAMnVdcLnnzfddsxsD3I4V08ri7kqO644T2/tPhTzWONcXoH9/70Ve1xYNM1Hu7Sqdo8+3HfEKP7AyST9wNGuhM4bb5l69pAMLZpTrifWN0SMWTSnXNlxltwDAAAgeUi6gTTywIt1empDQ9QVUVOr32nUnVVToibe180Yq+cNGppdN2Ns4hcU4KOWowm9/8d/rncUf+Bwp1bV7tH6v32S0HljletHs/SqCknqdX8zPD0Jt/1zAAAA9C8k3UCaeODFuqgroU6ZlEPPnDRSHkXfX+05GeemzgRKy53K8Ej3v7AtoWN41DO7O1a5fixLr6rQHXPP8lcyTCjO08LKiaxwAwAA9GMk3UAa6Drh01Mb3Eu4bbHKod/adTBmQzPrZFw8e5kjibdzeTwSrRqw6wTumTdFmxtatP9wh0pH9CTg8TRVyx6SwVgwAACAAYSkG0gDz9TsdKWkPFSscmi7C3gspnGmhmZnunq8cDI8iSfcUs8K99XTynT/C9uCmtq5Mb8bAAAA/R9JN5AGTOczz60YpXnnlKlkWI7u+O072tfWEXal2rQc+oBhYzDTOFMlw832Rl84eaT+6TPj9OG+w3rkldj7uG+7ZLLOGDVCBw53JlRS/vmzTtPV08eodESuDh7t0q3Pbun1Pdvzux+7cQaJNwAAQBpjIyCQBkznM88sL9aC6WN00RklWn51T+Ot0AJn+8/L5lfELH9uOWLWzds0zlRZodl87XPG5vd83tNPM4q/6PTTtGD6GJWMMDt+JPvaOrVg+hhdUF6s+18IPyfdfm3Fmjp1J6NMAQAAAP0CSTeQBhZWTpQnxvZgT8gc56qpZXrsxhnyFgSvGnsLco1XX1NVXl481CwptuMuKC9WWUFuxLHcHgXPJU+ky3igWHPSA+d3AwAAID1RXg6kgcwMj4ZmZaq9qztiTF5WZq+V66qpZbq8wht3g6/RRUNdjTPV2mk2f9yOy8zwaNn8Ci1ZuaVXt/VwK/t2kt7UGr78PpaLTu9pGmc6lzve+d0AAADo/1jpBtLA5oaWqAm3JB3t6g67opqZ4VHl5JFaMH2MKiePdNRR+8JJJa7GmTK9wsA4Jyv7dpLu5FyBCvOyJZmvmLu1sg4AAID+h5VuIA2kakV11uSRKszL0qH2yCvPhXlZmuXiuDBJqpxUYtQYrTIk2Y+2st/ts4Jev7zCq8dunKEVa+qiloiH89fGNkmxV8zdmt8NAACA/oukG0gDqVpRzczw6MFrz9YtK7dEjHnw2rPjmkcdzWfKi3uViYfynIwLZa/sB6re2tgrubZHer1616X+ZPwXr+/UW7sPxbw+u+rAaVk7AAAA0g/l5UAacNoozE1VU8v0+I0z5M0Pbm7mzc/R40kah/XWroMx91pbJ+NCdfss1dQ3a1XtHtXUN+vFdxu1ZOWWXqvZ9kivtXVN/vL7KWX5Rtc3Kv/Uww03GtYBAABg4GKlG0gDqV5RrZpapkvPGqVnanZqV0u7JhTnaWHlRGUPSc5zvb0HzeaS98SdWtUOt6Kd4Qm/Ym6p57tbsaZOl1d4lZnh0bnjCrVy0+6Y5z13XGHQnxNtWAcAAICBi6QbSBP2impoUuk9WSadzBXV6q2NWrbqfe073Ol/7cn1O7RiwaeTct7ajw8Zx113/jj/NS5ZuaVXgh1tRHbgSK/KySNVMtxsVFm4uHBl7QAAAEh/JN1AGunrFWepJ5kNt6d73+FO3bJyi2sl5oGNzsznfnv8712xpi6u8V/SqQZ0/1PXZBT/P3VN+uxZpXGeDQAAAOmEpBtII+HKp//r1YakrXR3+yzd/pt3osbc8Zt3/OXZ8Qr3uUxYlqVVtXt04HCn4/cGshvQvftxq1G8aRwAAADSH0k3kCYilU/bDcGS0bTr9e0HjOaDv779gOZ86rS4zhHpc5lYuWm30R7sSEJHelmGV2EaBwAAgPRH0g2kgWjl04ENwUbkZOnA0U7XGnn99s2PjOPiSboTLQtPRLgGdAU5Zv/KNI0DAABA+uM3Q6CfCNyz7DQp3tzQErV82m4I9sWfbPK/VuZCg7Utu3uP5EokLlSsz5VM4RrQDcvNMnqvaRwAAADSH0k30A+E27PsJCm2G3054UbZuelCebwL6vF8rkTcM2+KSkbkRHzocUH5SK3dtj/mcS4op0s5AAAAeiSvpTEAI/ae5dAVXTsprt7aGPMYJcPMRlkFsku2V6ypU3e0uVlRZBl2RTeNC2U3MOsrJSNytGD6GFVOHhm2yuCmCyfKE+MBgsfTEwcAAABIJN1ASsXaiy0ZJsVxriQHzqGOR86QTFfjQl1QXqzCvL4r1Y6V5GcPydDiOeVRYxbPKU/qiDYAAAAMLPxmCKSQ6V7sWElxU4L7nsOVcXf7LNXUN2tV7R7V1DeHTfwzYi37OoxLFY96yvntLuXRLL2qQl+5uLxXyXyGR/rKxeVaelVFci4SAAAAAxJ7uoEUMt2zHCvu7TgbldlCV3hN95jPmlysrXvbYh5/1uTYyWw4mxtadKj9eFzvNRWuS3ksS6+q0B1zz9IzNTu1q6VdE4rztLByIivcAAAA6IWkG0gSk27kpnuWY8Xta4tvpTt0DrXkbN73qBFDjc5jGhcqkUZqedmZQTPEywpydfW0Mq1+pzHoYUK4LuUmsodk6OY5k+K+PgAAAAwOJN1AEpiuFF9QXqyyglw1tXaE3dcdLikOZ1icc6EtBa/wms77vrzCq8wMj0pGmDVwM40LlUgjNY+kX948s9dc8jurpsQ9mg0AAABwilpIwGVOupFnZni0bH7PHuDQtM9J2fPfTx+T6GVLcr7HvHS4WTJtGhfKfigRT0p8tKtbPsvq1Y08M8Ojyskjo3YpBwAAANxC0g24KJ5u5FVTy/TYjTPkLQhe1fUW5BrP0M6IM3G0V67t63G6x/yEz2cUbxonBTdw29zQonvmTfFfq1OP/6U+aiM4AAAAINkoLwdc5GSluHLySP/rVVPLdHmFN+6y540NzXFdb+j1ON1j/vu39xjF//7tPfrsmaUx4yKV5S++uLzXXmwTr9U367X6Zv9x4tm7DQAAACSCpBtwUSLdyO2y53jsOXgsrveFXo/TPeYfG563rrFVq2r3RH2YEK2B25PrG/ToF2aoaFi29h/u0Jp39upP2/Y7+IThG8EBAAAAyUZ5OeAit7qRO2UlWDltX4/TPeZjC80+x9/2HdVXf1WrG57aqNkPrQva1y6ZleXf/0KdLigv1oLpY3RVHElzpPJ+AAAAIJlIugFDgXuNI+0RjtX4y6OeMudY3cidGlMUXxIf7nqc7DH/+xljHZ8zXEM5pw3cygrjG0EWehwAAAAg2SgvBwyYjgCzV4qXrNwS8Vgm3cidqiwv0Y//vMPRe6J1RzfdYz4kw/lzu3Cjx5yW5dsPN5zu8Q49DgAAAJBsrHQDMTgZASb1JKyLLy5XaF6d4ZEWX1yelP3EGZnOk/hY3dFNRms1tcWXvPYaPeawLN9+uBHvowu3y/sBAACASFjpBqKItdc4dMVW6knSn1zf0Os9liU9ub5B544vcj3xPnCk0yjutktO1xmjhjvujh7J27sTK9OOt4GbdOrhxlMbGmS6RTvccQAAAIBkYqUbiMLpXuNYSbql5DTyMl25vej0kqgr107tazNL9iOJt4GbdOrhhpOEO9xxAAAAgGQi6QaicLrXOFaSLiWnkVeqGrjlZcdXLJNoA7doDzdsoXl1rHJ6AAAAIBkoLweicLrX2HSPc7x7oSMJbODmkYKS0WSu8FaMzteqd/Y6eo8bDdxMHm74LOmeeVNUMiLHtXJ6AAAAwCmSbiAKp3uNWwz3VpvGOWGvFId2WfeG6bLultJ85w3JYl2P3cAtGtOHFsXDc7Rg+hjH1wgAAAC4haQbiMLpCnLxsGyj45rGOWW6UuyW0hE5RnHfvGqKSvPdW3H+xDDpNo0DAAAAkoWkG4jByQpy8VDDpNswLh4mK8WuMWxiVlGWr4vOKHHttO/vbXU1DgAAAEgWkm7AgOkK8kvbmoyO99K2Jn12SmkyLrVPNbUeczXO1LHj3a7GAQAAAMlC0g0YMllBfneP2cqqaVw8un1Wn5WX1358yDjuuvPHuXbez0wcqZfq9hvFAQAAAKlE0g24KD83y9U4p6q3NvYqgy9LYiM103njbs8lv+nCifqPP26TFeWwHk9PHAAAAJBKzOkGXPS/Lyp3Nc6J6q2NWrJyS69RWk2tHVqycouqtza6fs5PDpt1YTeNM5U9JEOL50T/DhfPKVf2EP4VBwAAgNTiN1LARUMMkzzTOFPdPksr1tSF7Wtmv7ZiTZ3rK87Fw8xW7E3jnFh6VYW+cnG5QivnMzzSVy4u19KrKlw/JwAAAOAU5eWAizY1tBjHzfnUaa6dd3NDS68V7kCWpMbWDm1uaHG1s/n2/UddjXNq6VUVumPuWXqmZqd2tbRrQnGeFlZOZIUbAAAA/QZJN+Aq05Vkd1ec9x82m0dtGmcqJ8ssuTWNi0f2kAzdPGdS0o4PAAAAJILlIMBFlZPMZlGbxpkqHZHrapypvGyz53amcQAAAEC64TdhIEQiI7dmTR6pwrwsHWo/HjGmMC9Ls1ws8ZakC8qLVVaQq6bWjrBr6B5J3oKez+Km8SOHuhoHAAAApBuSbiBAoiO3MjM8evDas3XLyi0RYx689mzX52ZnZni0bH6FlqzcIo+Ci9ftMy2bX+H6eT9oPOJqHAAAAJBuKC8HTnJr5FbV1DJ95eJyhaa3HvV01U7GvGz7vI/dOEPeguAScm9Brh67cUZSznvseLercQAAAEC6YaUbUOyRWx71jNy6vMIbc7W4emujnljfEPY4T6xv0Lnji5KaeF9e4Y27PN6poVmZrsYBAAAA6YakG5B7I7e6fZa+8bv3op5r6e/eM0re45WZ4XF1LFg0RblmybRpHAAAAJBuKC8H5N7IrY07mqM2UZOkg+3HtXFHs/G19Wc7Dx5zNQ4AAABINyTdgNwbufX69gNGxzGNi0e3z1JNfbNW1e5RTX2zun3uzgQP5HM5DgAAAEg3lJcDcm/k1u7mo0bnM41zKlr39WTs9c7NNHtuZxoHAAAApBuSbkDujdx6b0+r0flM45ywu6+HPjRoau3QLSu39Jof7mQUWiQFQ7NcjQMAAADSDctPwElujNw62mU2Gss0zlSs7uuSeu01dzoKLZxPjnS5GgcAAACkG1a6gQCJjtwalZ9jlGCOys9J9FKDxOq+Ho7TUWjhjCrI0da9ZnEAAADAYMRKNxDCHrm1YPoYVU4e6SgZvePzZ7oaZ8q0+3qowFFo8ZhVXuJqHAAAAJBuSLoBF108pVRDYiTpQzI8unhKqavnNe2+Hkm8SfsNF4x3NQ4AAABINyTdgIsyMzy6efbEqDE3z56YcNfwUBeUF6swL/5mZfEm7b/ctMvVOAAAACDdkHQDLur2WVr9TvTGZKvfaUzK7OyuE86nYXvU08U81ii0SF56v8nVOAAAACDdkHQDLjJpaJbIHupINtY3q91hR3Qno9AiOdxxwtU4AAAAIN2QdAMuamo95mqcqZodBxy/x8kotEjOLBvhahwAAACQbhgZBrio5ajZPGrTOFOmxerXTB+tS84qNRqF1u2zYo5O+8fzxmlNjHJ6Ow4AAAAYjEi6ARcVDzebR20aZyo/16yJ2pSyfC2YPiZmXPXWRq1YUxdUKl9WkKtl8yuCVsYvPL1EedmZUUvbh2Vn6sLTGRkGAACAwYnycsBF3nyzLuCmcabajh13La56a6OWrNzSa296U2uHlqzcouqtp1a2MzM8+t4/Tot6vP/3j9Nc79YOAAAADBQk3Uh73T5LNfXNWlW7RzX1zUnpHG67oLxYZQXRE+pEuoUnW7fP0oo1dWHL1e3XVqypC/oOq6aW6fEbZ2jUiODVe29+jh5PcM84AAAAMNBRXo60Zlom7ZbMDI+Wza/QkpVbJAXvtXajW3gkhXnZrsTF6r5u6VT39crJI/2vV00t0+UV3ph7wAEAAIDBhpVupC0nZdKBEl0Zr5papsdunKFRISXkbnQLj6Q4z2xPd6y4/YejjzuLFpeZ4VHl5JFaMH2MKiePJOEGAAAAxEo30lSsMmmPesqkL6/wBiWHbq6MW5Yv6M8+ny9CZOIOGe7pjhVXOsJsr7lpHAAAADDYsdKNtOSkTNoW78p4qOqtjbpl5RbtOxw8Fmzf4S7d4uA4TiTSNT1wZd/ns+TNz1WkNWqP+veedAAAAKC/YaUbaclpmXS8K+Ohun2WvvG796Ke8xu/ey/mcZyKt2t6uJX9wrws/2fuqz3pAAAAQLpipRtpyWmZdDwr4+FsrG/WofboJdyH2o9rY32z0fWZOm9CkWLlwRmenjhbpJX91pPXXxCy/zuZe9IBAACAdMVKN9KSPbqrqbUj7Oq1Rz1JpF0mnUgDsUCv1X9idJzX6j/RRWeUGMWaeGvXQcXq9+azeuIqJ480WtnPHZKhX/7vmTpwpJNu5AAAAECcHK90r1+/XvPnz9fo0aPl8Xj0/PPPB/3csizde++9Kisr09ChQ3XZZZfpww8/9P98586duvnmm1VeXq6hQ4dq8uTJWrZsmbq6gve/vvvuu5ozZ45yc3M1btw4Pfzww/F9QgxK9uguSb32J4crk3argdjeQ2bJu2mcKacPDUxW9pvaOpXh8dCNHAAAAEiA46T76NGjmjZtmh599NGwP3/44Yf1wx/+UI8//rg2bdqkYcOG6YorrlBHR88v+H/961/l8/n0xBNP6P3339d//ud/6vHHH9e///u/+4/R1tamuXPnasKECXrrrbf0ne98R8uXL9eTTz4Z58fEYGSP7vIWxB7dZa+MJ9pArKzQLHk3jTPl9KGBWyv7AAAAAKJzXF5+5ZVX6sorrwz7M8uy9P3vf1933323FixYIEn6xS9+oVGjRun555/X9ddfr6qqKlVVVfnfM2nSJH3wwQd67LHH9N3vfleS9Mtf/lJdXV366U9/quzsbH36059WbW2tvve972nx4sXxfE70A90+S5sbWrT/cEeflStXTS3TpWeN0jM1O7WrpV0TivO0sHKisocEP2+yV8aXrNySUAOx4jzDLuKGcaacltMzGgwAAADoG67u6W5oaFBTU5Muu+wy/2sFBQWaOXOmampqdP3114d9X2trq4qLT60g1tTU6OKLL1Z2drb/tSuuuEIPPfSQDh48qKKiol7H6OzsVGdnp//PbW1tbnwkuMTN+deJnve/Xm0Ie157ZTw03uvgOouHZceMcRJnyulDA6dJOgAAAID4uNq9vKmpSZI0atSooNdHjRrl/1mo7du360c/+pG+8pWvBB0n3DECzxHqgQceUEFBgf+vcePGxf054C635l/3xXmrppbp1bsu1XOLZukH10/Xc4tm6dW7LjV+MHCovSt2kIM4J5yU0zvd8w4AAAAgPikdGbZnzx5VVVXpH/7hH7Ro0aKEjrV06VK1trb6//roo49cukokIlaXbKln/nV3rNbbLp/XinLezAyPKiePjKuBWPFww/JywzinqqaWad0dn9PCWeM154wSLZw1Xuvu+FzYhwZOknQAAAAA8XG1vNzr9UqS9u3bp7KyU7+w79u3T9OnTw+K3bt3ry655BJdeOGFvRqkeb1e7du3L+g1+8/2OULl5OQoJyc5iQzi52T+deXkkX12XiXpvN58sz3QpnFOPfBinZ7a0OAfH7bhQ+mXm3Zr0ZxyLb2qold81dQyXV7h7fO99gAAAMBg4epKd3l5ubxer15++WX/a21tbdq0aZMqKyv9r+3Zs0ef+9zndN555+lnP/uZMjKCL6OyslLr16/X8ePH/a+tXbtWZ555Ztj93Oi/UtUlu6nN7HimcaYuKC9WYV5W1JiivKyk7JV+4MU6PbG+ode8bp8lPbG+QQ+8WBf2fYms7AMAAACIznHSfeTIEdXW1qq2tlZST/O02tpa7d69Wx6PR1/72tf0rW99S6tXr9Z7772nL33pSxo9erSuueYaSacS7vHjx+u73/2uPvnkEzU1NQXt1f7CF76g7Oxs3XzzzXr//ff161//Wj/4wQ90++23u/Kh0XdS1SW75Uhn7CAHcW5yt5C+R9cJn57a0BA15qkNDeo64UvC2QEAAABE4ri8/M0339Qll1zi/7OdCN900016+umndeedd+ro0aNavHixDh06pNmzZ6u6ulq5uT1J1dq1a7V9+3Zt375dY8eODTq2ZfWkIwUFBXrppZd066236rzzzlNJSYnuvfdexoUNQKnqkp1IF/FERpttbmjRofbjUWMOtR93vaz9mZqdvVa4Q/msnrib50xy7bwAAAAAonOcdH/uc5/zJ8fheDwe3XfffbrvvvvC/vzLX/6yvvzlL8c8zznnnKMNGzY4vTz0M3aX7FtWbgn7c0vJ6ZLtLRgaV1yio82aWo8Zndc0ztTO5nZX4wAAAAC4I6Xdy4FksVfYoykLWWF3Y7TZgSNmo8BM48yZFq0no7gdAAAAQCQk3Ugqe3RXJB45HxnW7bNUU9+sVbV7VFPfHHHs17L5Fb1mUAeeN3CF3a3RZoeOGc7pNowzNX1soatxAAAAANzh6sgwIJTbI8OclH/bc6hN4t26TtMiebf7g48uynM1DgAAAIA7SLqRVG6ODLPLv0PXmu3y78dunBE28TaZQ+3WdVZOKtEjr9THPE7lpBKj85myy+mjPTgILacHAAAAkHyUlyOp3BoZlkj5t8kcareuc9bkkcrLzowaMyw7U7Nc7FwuOS+nBwAAANA3SLqRVBeUF6swLytqTGFeVswVWCfl3/GwV4qjJa2mK8XZQ6L/3yorxs/jZZfThzaQKyvIDVsFAAAAACD5KC9HypmsvbpZph6OvVK8ZOUWeRTc49u+PpOV4lTN6baZltMDAAAA6BusdCOpTJLQgyeT0GjcKv+Oxl4p9oasFHsdrBSnak53IJNyegAAAAB9g5VuJFXjIbPkMlacXf7d1NoRdl+3Rz3Jcbjy726fZbzym+hKcctRs1FgpnEAAAAABjaSbiTV2x8dNI679ryxEX8eb/m3kxFjgeeKt/S7eHiOq3EAAAAABjbKy5FU4Val441zWv5tjxgLbcBmjxir3tpoeHXmvPlm5e2mcQAAAAAGNla6kVTlI4e5Gmda/h1rxJhHPSPGLq/wurrn+bwJRfJ4JCvKUwSPpycOAAAAQPpjpRtJtbByomLltBmenjhTJo3Ckj1iLJI3GlqiJtxST0L+hsvnBQAAANA/kXQjqbKHZOjzU0qjxnx+SmnM2dZOJXvEWCSv7zjgahwAAACAgY2kG0nV7bO0dU9b1Jite9rU7TPd/W2mMCfL1ThTe1raXY0DAAAAMLCRdCOpYpV5S8kp8177132uxpnyuRwHAAAAYGAj6UZSparMe2fzUVfjTHkss6ZspnEAAAAABjaSbiRV6Qiz0VimcaaGZmW6GmfKZzgkzTQOAAAAwMBG0o2kuqC8WGUFuYq0ruuRVFbQM/bLTXM/7XU1zlTzkU5X4wAAAAAMbCTdSKrMDI+Wza+IuK5rSVo2v8LVWdmSNLYoz9U4Ux3Hu12NAwAAADCwkXQjLdkr7NEkY4W9q9usRZppHAAAAICBjaQbSdXts7RiTV3En3skrVhT5/rIMHuFPVpZezJW2L35ZnvTTeMAAAAADGwk3UiqWCPDLCVnZJgkVU0t02M3zui14l1WkKvHbpyhqqllrp9z5qQSV+MAAAAADGxDUn0B6D+6fZY2N7Ro/+EOlY7oKb1OdCU4VSPDbFVTy3R5hdf1zxXJTRdO1H/8cZusKAv3Hk9PHAAAAID0R9I9CIVLrtfWNWnFmrqgVemyglwtm1+R0IpwqkaGBcrM8Khy8sikHT9Q9pAMLZ5TrifWN0SMWTynXNlDKDIBAAAABgOS7kGmemtjr+S6MC9Lh9qP94ptau3QkpVbEirFthuaNbV2hO1g7pHkTUJDs1RaelWFJOmpDQ0K3Kqe4ZEWzSn3/xwAAABA+vNYVrRC2IGrra1NBQUFam1tVX5+fqovp1+o3tqoJSu3RBzfFY6dFL9616Vxl2Tb55UUdG77aMnaX51qXSd8eqZmp3a1tGtCcZ4WVk5khRsAAABIE6Y5Jyvdg4TdRdzpE5bARmfxlmjbDc1CV9i9LpSv92fZQzJ085xJqb4MAAAAAClE0j1IxOoiHkuijc76uqEZAAAAAPQHJN2DRKJJsxuNzvqyoRkAAAAA9Ack3YNEvElzOjY6AwAAAIC+QlenQcLuIu6kmNuOXTa/gjJwAAAAAIgDSfcgkZnh0bL5PaOqQtNn+8+FeVlBr3sLctO2szgAAAAA9AXKyweRWF3EaXQGAAAAAO5iTvcg1O2zSK4BAAAAIAHM6UZEdBEHAAAAgL7Bnm4AAAAAAJKEpBsAAAAAgCShvBx9JlV7ydnDDgAAACBVSLrRJ6q3Nvbqml52smt6MkeSpeq8AAAAACBRXo4+UL21UUtWbglKfCWpqbVDS1ZuUfXWxrQ6LwAAAADYSLqRVN0+SyvW1CncXDr7tRVr6tTtc3dyXarOCwAAAACBSLoHoW6fpZr6Zq2q3aOa+uakJp6bG1p6rTQHsiQ1tnZoc0NLWpwXAAAAAAKxp3uQ6es9zvsPR05844nr7+cFAAAAgECsdA8iqdjjXDoi19W4/n5eAAAAAAhE0p1CfVnmnao9zheUF6usIFeRBnR51LPSfkF5cVqcFwAAAAACUV6eIn1d5u1kj3Pl5JGunTczw6Nl8yu0ZOUWeaSgpN9OiJfNr3B9bnaqzgsAAAAAgVjpToFUlHmnco9z1dQyPXbjDI3Kzwl6fVR+jh67cYZrDxlCKwcur/DqsRtnyFsQXELuLch19bwAAAAAEAkr3X0sVpm3Rz1l3pdXeF1dhe0fe5xDP497ny9a5cCrd12qzQ0t2n+4Q6UjekrKWeEGAAAA0BdY6e5jqRpllco9zvbKflNb8Ofe1+bOyn6syoG1dU2qnDxSC6aPUeXkkSTcAAAAAPoMSXcfS1WZt73HWYq83pyMPc7JbuCWqgZxAAAAAGCCpLuPpbLM295b3Zd7nJO9sp+qygEAAAAAMMGe7j5ml3k3tXaEXZ31qCcJTtYoq6qpZbq8wttne5yTvbKfygZxAAAAABALSXcf6w+jrDIzPK6OBYsm2Sv7/aNBHAAAAACER3l5CqSizDtV7JX9aBJp4JbKBnEAAAAAEAsr3SnS12XeqZKZ4dHV08r0xPqGiDFXTyuL+3P3h8oBAAAAAIiEle4Ussu803mUVbfP0up3oo8EW/1OY0LdxQdT5QAAAACAgYWVbiRVrO7i0qnu4onsMx8slQMAAAAABhaSbiRVX3YX78sGcQAAAABggvJyJBXdxQEAAAAMZiTdSKoLyotVmJcVNaYoL4vu4gAAAADSEkk3kq7rhC/qzztj/BwAAAAABiqSbiTVxvpmtXd1R41p7+rWxvrmProiAAAAAOg7JN1Iqtd3HHA1DgAAAAAGEpJuJNXeg8dcjQMAAACAgYSkG0k1unCoq3EAAAAAMJCQdCOpLpxc4mocAAAAAAwkJN1IqlmTR8YcGVaYl6VZk0f20RUBAAAAQN8h6UZSZWZ49E/nj40a80/nj1VmhqePrggAAAAA+g5JN5Kq22dp9TuNUWNWv9Oobp/VR1cEAAAAAH2HpBtJtbmhRY2tHVFjGls7tLmhpY+uCAAAAAD6Dkk3kmr/4egJt9M4AAAAABhISLqRVCXDclyNAwAAAICBhKQbyWXaH40+agAAAADSEEk3kurAkU5X4wAAAABgICHpRlKVjsh1NQ4AAAAABhKSbiTVBeXFKivIjVg97pFUVpCrC8qL+/KyAAAAAKBPkHQjqTIzPFo2v0JS723b9p+Xza9QZgabugEAAACkH8dJ9/r16zV//nyNHj1aHo9Hzz//fNDPLcvSvffeq7KyMg0dOlSXXXaZPvzww6CYlpYWffGLX1R+fr4KCwt1880368iRI0Ex7777rubMmaPc3FyNGzdODz/8sPNPh36hamqZHrtxhrwFwSXk3oJcPXbjDFVNLUvRlQEAAABAcg1x+oajR49q2rRp+pd/+Rdde+21vX7+8MMP64c//KF+/vOfq7y8XPfcc4+uuOIK1dXVKTe3J+n64he/qMbGRq1du1bHjx/XP//zP2vx4sV69tlnJUltbW2aO3euLrvsMj3++ON677339C//8i8qLCzU4sWLE/zISIWqqWW6vMKrzQ0t2n+4Q6UjekrKWeEGAAAAkM48lmVZcb/Z49Hvf/97XXPNNZJ6VrlHjx6tO+64Q//f//f/SZJaW1s1atQoPf3007r++uu1bds2VVRU6I033tD5558vSaqurtZVV12ljz/+WKNHj9Zjjz2mb37zm2pqalJ2drYk6Rvf+Iaef/55/fWvfzW6tra2NhUUFKi1tVX5+fnxfkQAAAAAAHoxzTld3dPd0NCgpqYmXXbZZf7XCgoKNHPmTNXU1EiSampqVFhY6E+4Jemyyy5TRkaGNm3a5I+5+OKL/Qm3JF1xxRX64IMPdPDgwbDn7uzsVFtbW9BfCK/bZ6mmvlmraveopr5Z3b64n7sAAAAAAKJwXF4eTVNTkyRp1KhRQa+PGjXK/7OmpiaVlpYGX8SQISouLg6KKS8v73UM+2dFRUW9zv3AAw9oxYoV7nyQNFa9tVEr1tSpsbXD/1pZQa6Wza9gbzUAAAAAuCxtupcvXbpUra2t/r8++uijVF9Sv1O9tVFLVm4JSrglqam1Q0tWblH11sYUXRkAAAAApCdXk26v1ytJ2rdvX9Dr+/bt8//M6/Vq//79QT8/ceKEWlpagmLCHSPwHKFycnKUn58f9BdO6fZZWrGmTuEKye3XVqypo9QcAAAAAFzkatJdXl4ur9erl19+2f9aW1ubNm3apMrKSklSZWWlDh06pLfeessfs27dOvl8Ps2cOdMfs379eh0/ftwfs3btWp155plhS8sR2+aGll4r3IEsSY2tHdrc0NJ3FwUAAAAAac5x0n3kyBHV1taqtrZWUk/ztNraWu3evVsej0df+9rX9K1vfUurV6/We++9py996UsaPXq0v8P5lClTVFVVpUWLFmnz5s167bXXdNttt+n666/X6NGjJUlf+MIXlJ2drZtvvlnvv/++fv3rX+sHP/iBbr/9dtc++GCz/3DkhDueOAAAAABAbI4bqb355pu65JJL/H+2E+GbbrpJTz/9tO68804dPXpUixcv1qFDhzR79mxVV1f7Z3RL0i9/+Uvddttt+vznP6+MjAxdd911+uEPf+j/eUFBgV566SXdeuutOu+881RSUqJ7772XGd0JKB2RGzvIQRwAAAAAILaE5nT3Z8zpDtbtszT7oXVqau0Iu6/bI8lbkKtX77pUmRmevr48AAAAABhQUjKnG8705bzszAyPls2vkNSTYAey/7xsfgUJNwAAAAC4yNU53TCXinnZVVPL9NiNM3qd18ucbgAAAABICsrLU8Celx36xdtrzI/dOCOpCXC3z9LmhhbtP9yh0hG5uqC8mBVuAAAAAHDANOdkpbuPxZqX7VHPvOzLK7xJS4QzMzyqnDwyKccGAAAAAJzCnu4+xrxsAAAAABg8SLr7GPOyAQAAAGDwIOnuY8zLBgAAAIDBg6S7j11QXqyygtxeY7tsHvV0Mb+gvLgvLwsAAAAAkAQk3X2MedkAAAAAMHiQdKeAPS/bWxBcQu4tyE36uDAAAAAAQN9hZFiKVE0t0+UVXuZlAwAAAEAaI+lOIeZlAwAAAEB6o7wcAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkGZLqCxjMun2WNje0aP/hDpWOyNUF5cXKzPCk+rIAAAAAAC4h6U6R6q2NWrGmTo2tHf7XygpytWx+haqmlqXwygAAAAAAbqG8PAWqtzZqycotQQm3JDW1dmjJyi2q3tqYoisDAAAAALiJpLuPdfssrVhTJyvMz+zXVqypU7cvXAQAAAAAYCAh6e5jmxtaeq1wB7IkNbZ2aHNDS99dFAAAAAAgKUi6+9j+w5ET7njiAAAAAAD9F0l3HysdketqHAAAAACg/yLp7mMXlBerrCBXkQaDedTTxfyC8uK+vCwAAAAAQBKQdPexzAyPls2vCNtITerZ071sfgXzugEAAAAgDZB0AwAAAACQJCTdfcweGRaJR4wMAwAAAIB0QdLdxxgZBgAAAACDB0l3H2NkGAAAAAAMHiTdfYyRYQAAAAAweJB09zFGhgEAAADA4EHS3cfskWGSeiXe9p8ZGQYAAAAA6YGkOwWqppbpsRtnyFsQXELuLcjVYzfOUNXUshRdGQAAAADATUNSfQGDVdXUMl1e4dXmhhbtP9yh0hE9JeWscAMAAABA+iDpTqHMDI8qJ49M9WUAAAAAAJKE8nIAAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkoSkGwAAAACAJCHpBgAAAAAgSUi6AQAAAABIEpJuAAAAAACShKQbAAAAAIAkIekGAAAAACBJSLoBAAAAAEgSkm4AAAAAAJKEpBsAAAAAgCQh6QYAAAAAIElIugEAAAAASBKSbgAAAAAAkmRIqi8gWSzLkiS1tbWl+EoAAAAAAOnGzjXt3DOStE26Dx8+LEkaN25ciq8EAAAAAJCuDh8+rIKCgog/91ix0vIByufzae/evRoxYoQ8Hk+qLwcntbW1ady4cfroo4+Un5+f6suBy7i/6Y37m964v+mPe5zeuL/pjfvbP1mWpcOHD2v06NHKyIi8czttV7ozMjI0duzYVF8GIsjPz+dfGGmM+5veuL/pjfub/rjH6Y37m964v/1PtBVuG43UAAAAAABIEpJuAAAAAACShKQbfSonJ0fLli1TTk5Oqi8FScD9TW/c3/TG/U1/3OP0xv1Nb9zfgS1tG6kBAAAAAJBqrHQDAAAAAJAkJN0AAAAAACQJSTcAAAAAAElC0g0AAAAAQJKQdCNh69ev1/z58zV69Gh5PB49//zzvWK2bdumq6++WgUFBRo2bJg+85nPaPfu3f6fd3R06NZbb9XIkSM1fPhwXXfdddq3b18ffgpEE+seHzlyRLfddpvGjh2roUOHqqKiQo8//nhQDPe4f3rggQf0mc98RiNGjFBpaamuueYaffDBB0ExJvdu9+7dmjdvnvLy8lRaWqp/+7d/04kTJ/ryoyCMWPe3paVF/+f//B+deeaZGjp0qMaPH6//+3//r1pbW4OOw/3tn0z+/2uzLEtXXnll2H+Hc3/7L9N7XFNTo0svvVTDhg1Tfn6+Lr74Yh07dsz/85aWFn3xi19Ufn6+CgsLdfPNN+vIkSN9+VEQhsn9bWpq0sKFC+X1ejVs2DDNmDFD//3f/x0Uw/3t/0i6kbCjR49q2rRpevTRR8P+vL6+XrNnz9ZZZ52lP//5z3r33Xd1zz33KDc31x/z9a9/XWvWrNFvf/tb/eUvf9HevXt17bXX9tVHQAyx7vHtt9+u6upqrVy5Utu2bdPXvvY13XbbbVq9erU/hnvcP/3lL3/Rrbfeqo0bN2rt2rU6fvy45s6dq6NHj/pjYt277u5uzZs3T11dXXr99df185//XE8//bTuvffeVHwkBIh1f/fu3au9e/fqu9/9rrZu3aqnn35a1dXVuvnmm/3H4P72Xyb//7V9//vfl8fj6fU697d/M7nHNTU1qqqq0ty5c7V582a98cYbuu2225SRcerX/C9+8Yt6//33tXbtWv3hD3/Q+vXrtXjx4lR8JAQwub9f+tKX9MEHH2j16tV67733dO211+of//Ef9fbbb/tjuL8DgAW4SJL1+9//Pui1f/qnf7JuvPHGiO85dOiQlZWVZf32t7/1v7Zt2zZLklVTU5OsS0Wcwt3jT3/609Z9990X9NqMGTOsb37zm5ZlcY8Hkv3791uSrL/85S+WZZnduxdffNHKyMiwmpqa/DGPPfaYlZ+fb3V2dvbtB0BUofc3nN/85jdWdna2dfz4ccuyuL8DSaT7+/bbb1tjxoyxGhsbe/07nPs7sIS7xzNnzrTuvvvuiO+pq6uzJFlvvPGG/7U//vGPlsfjsfbs2ZPU64Uz4e7vsGHDrF/84hdBccXFxdZTTz1lWRb3d6BgpRtJ5fP59MILL+hTn/qUrrjiCpWWlmrmzJlBpW1vvfWWjh8/rssuu8z/2llnnaXx48erpqYmBVcNpy688EKtXr1ae/bskWVZeuWVV/S3v/1Nc+fOlcQ9HkjssuLi4mJJZveupqZGZ599tkaNGuWPueKKK9TW1qb333+/D68esYTe30gx+fn5GjJkiCTu70AS7v62t7frC1/4gh599FF5vd5e7+H+Diyh93j//v3atGmTSktLdeGFF2rUqFH67Gc/q1dffdX/npqaGhUWFur888/3v3bZZZcpIyNDmzZt6tsPgKjC/X/4wgsv1K9//Wu1tLTI5/PpV7/6lTo6OvS5z31OEvd3oCDpRlLt379fR44c0YMPPqiqqiq99NJL+vu//3tde+21+stf/iKpZ69Kdna2CgsLg947atQoNTU1peCq4dSPfvQjVVRUaOzYscrOzlZVVZUeffRRXXzxxZK4xwOFz+fT1772NV100UWaOnWqJLN719TUFPQLu/1z+2foH8Ld31AHDhzQ/fffH1SWyP0dGCLd369//eu68MILtWDBgrDv4/4OHOHu8Y4dOyRJy5cv16JFi1RdXa0ZM2bo85//vD788ENJPfextLQ06FhDhgxRcXEx97gfifT/4d/85jc6fvy4Ro4cqZycHH3lK1/R73//e51++umSuL8DxZBUXwDSm8/nkyQtWLBAX//61yVJ06dP1+uvv67HH39cn/3sZ1N5eXDJj370I23cuFGrV6/WhAkTtH79et16660aPXp00Aop+rdbb71VW7duDVohQfqIdX/b2to0b948VVRUaPny5X17cUhYuPu7evVqrVu3LmjvJwaucPfY/j3rK1/5iv75n/9ZknTuuefq5Zdf1k9/+lM98MADKblWOBfp39H33HOPDh06pD/96U8qKSnR888/r3/8x3/Uhg0bdPbZZ6foauEUK91IqpKSEg0ZMkQVFRVBr0+ZMsXfvdzr9aqrq0uHDh0Kitm3b1/YUjj0L8eOHdO///u/63vf+57mz5+vc845R7fddpv+6Z/+Sd/97nclcY8Hgttuu01/+MMf9Morr2js2LH+103undfr7dXN3P4z97d/iHR/bYcPH1ZVVZVGjBih3//+98rKyvL/jPvb/0W6v+vWrVN9fb0KCws1ZMgQ/5aB6667zl+ayv0dGCLd47KyMkmK+XvW/v37g35+4sQJtbS0cI/7iUj3t76+Xo888oh++tOf6vOf/7ymTZumZcuW6fzzz/c3t+X+Dgwk3Uiq7OxsfeYzn+k1/uBvf/ubJkyYIEk677zzlJWVpZdfftn/8w8++EC7d+9WZWVln14vnDt+/LiOHz8e1CVVkjIzM/1P4LnH/ZdlWbrtttv0+9//XuvWrVN5eXnQz03uXWVlpd57772g/+ivXbtW+fn5vX4RRN+KdX+lnhXuuXPnKjs7W6tXrw6aLCFxf/uzWPf3G9/4ht59913V1tb6/5Kk//zP/9TPfvYzSdzf/i7WPZ44caJGjx4d9fesyspKHTp0SG+99Zb/5+vWrZPP59PMmTOT/yEQUaz7297eLklRf8fi/g4QqezihvRw+PBh6+2337befvttS5L1ve99z3r77betXbt2WZZlWb/73e+srKws68knn7Q+/PBD60c/+pGVmZlpbdiwwX+MW265xRo/fry1bt06680337QqKyutysrKVH0khIh1jz/72c9an/70p61XXnnF2rFjh/Wzn/3Mys3NtX784x/7j8E97p+WLFliFRQUWH/+85+txsZG/1/t7e3+mFj37sSJE9bUqVOtuXPnWrW1tVZ1dbV12mmnWUuXLk3FR0KAWPe3tbXVmjlzpnX22Wdb27dvD4o5ceKEZVnc3/7M5P+/oRTSvZz727+Z3OP//M//tPLz863f/va31ocffmjdfffdVm5urrV9+3Z/TFVVlXXuuedamzZtsl599VXrjDPOsG644YZUfCQEiHV/u7q6rNNPP92aM2eOtWnTJmv79u3Wd7/7Xcvj8VgvvPCC/zjc3/6PpBsJe+WVVyxJvf666aab/DE/+clPrNNPP93Kzc21pk2bZj3//PNBxzh27Jj1r//6r1ZRUZGVl5dn/f3f/73V2NjYx58EkcS6x42NjdaXv/xla/To0VZubq515plnWv/v//0/y+fz+Y/BPe6fwt1XSdbPfvYzf4zJvdu5c6d15ZVXWkOHDrVKSkqsO+64wz9yCqkT6/5G+v+2JKuhocF/HO5v/2Ty/99w7wkd+8j97b9M7/EDDzxgjR071srLy7MqKyuDFjYsy7Kam5utG264wRo+fLiVn59v/fM//7N1+PDhPvwkCMfk/v7tb3+zrr32Wqu0tNTKy8uzzjnnnF4jxLi//Z/HsizL7dVzAADw/2/fDgkAAAAABP1/7QobvDAIAODpBgAAgI3oBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgInoBgAAgEkSp7/Bi3WIFQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Чи можете ви здогадатися, чому точки вирівнюються у вертикальні лінії таким чином?\n",
+    "> Чи можете ви здогадатися, чому точки утворюють вертикальні лінії таким чином?\n",
     "\n",
-    "Ми спостерігали кореляцію між штучно створеною концепцією, такою як зарплата, і спостережуваною змінною *зріст*. Давайте також подивимося, чи корелюють дві спостережувані змінні, такі як зріст і вага:\n"
+    "Ми спостерігали кореляцію між штучно створеною концепцією, такою як зарплата, і спостережуваною змінною *зріст*. Давайте також перевіримо, чи корелюють дві спостережувані змінні, такі як зріст і вага:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,7 +852,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "На жаль, ми не отримали жодних результатів — лише дивні значення `nan`. Це пов’язано з тим, що деякі значення в нашій серії є невизначеними, представленими як `nan`, що призводить до того, що результат операції також стає невизначеним. Якщо подивитися на матрицю, можна побачити, що проблемною колонкою є `Weight`, оскільки самокореляція між значеннями `Height` була обчислена.\n",
+    "На жаль, ми не отримали жодних результатів — лише деякі дивні значення `nan`. Це пов’язано з тим, що деякі значення в нашій серії є невизначеними, представленими як `nan`, що призводить до того, що результат операції також стає невизначеним. Подивившись на матрицю, ми можемо побачити, що проблемною колонкою є `Weight`, оскільки самокореляція між значеннями `Height` була обчислена.\n",
     "\n",
     "> Цей приклад демонструє важливість **підготовки даних** та **очищення**. Без належних даних ми не можемо нічого обчислити.\n",
     "\n",
@@ -1026,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Висновок\n",
     "\n",
-    "У цьому зошиті ми розглянули, як виконувати базові операції з даними для обчислення статистичних функцій. Тепер ми знаємо, як використовувати надійний апарат математики та статистики для перевірки деяких гіпотез, а також як обчислювати довірчі інтервали для довільних змінних на основі вибірки даних.\n"
+    "У цьому нотатнику ми навчилися виконувати базові операції з даними для обчислення статистичних функцій. Тепер ми знаємо, як використовувати надійний апарат математики та статистики для доведення деяких гіпотез, а також як обчислювати довірчі інтервали для довільних змінних на основі вибірки даних.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Відмова від відповідальності**:  \nЦей документ був перекладений за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, будь ласка, майте на увазі, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ на його рідній мові слід вважати авторитетним джерелом. Для критичної інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникають внаслідок використання цього перекладу.\n"
+    "\n---\n\n**Відмова від відповідальності**:  \nЦей документ було перекладено за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, зверніть увагу, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ мовою оригіналу слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникли внаслідок використання цього перекладу.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:15:13+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T18:02:01+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "uk"
   }
diff --git a/translations/uk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/uk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index a625a5fd..65e57989 100644
--- a/translations/uk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/uk/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -155,9 +155,9 @@
     "* BMI — індекс маси тіла  \n",
     "* BP — середній артеріальний тиск  \n",
     "* S1 до S6 — різні показники крові  \n",
-    "* Y — якісна міра прогресування хвороби протягом одного року  \n",
+    "* Y — якісна оцінка прогресування хвороби протягом одного року  \n",
     "\n",
-    "Давайте вивчимо цей набір даних за допомогою методів ймовірності та статистики.  \n",
+    "Давайте вивчимо цей набір даних за допомогою методів ймовірності та статистики.\n",
     "\n",
     "### Завдання 1: Обчисліть середні значення та дисперсію для всіх значень  \n"
    ],
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -535,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -844,8 +844,8 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Висновок:\n",
-    "* Найсильніша кореляція Y спостерігається з ІМТ та S5 (рівень цукру в крові). Це виглядає логічно.\n"
+    "Висновок:  \n",
+    "* Найсильніший кореляційний зв'язок із Y мають ІМТ та S5 (рівень цукру в крові). Це виглядає логічно.\n"
    ],
    "metadata": {}
   },
@@ -853,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -883,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -914,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Відмова від відповідальності**:  \nЦей документ був перекладений за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, будь ласка, майте на увазі, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ на його рідній мові слід вважати авторитетним джерелом. Для критичної інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникають внаслідок використання цього перекладу.\n"
+    "\n---\n\n**Відмова від відповідальності**:  \nЦей документ було перекладено за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, зверніть увагу, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ мовою оригіналу слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникли внаслідок використання цього перекладу.\n"
    ]
   }
  ],
@@ -940,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:27:16+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T18:02:30+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "uk"
   }
diff --git a/translations/ur/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/ur/1-Introduction/04-stats-and-probability/assignment.ipynb
index 487950ef..9d63b1ee 100644
--- a/translations/ur/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/ur/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -149,16 +149,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "اس ڈیٹاسیٹ میں کالمز درج ذیل ہیں:  \n",
-    "* عمر اور جنس خود وضاحتی ہیں  \n",
-    "* BMI جسمانی وزن کا اشاریہ ہے  \n",
-    "* BP اوسط بلڈ پریشر ہے  \n",
-    "* S1 سے S6 مختلف خون کے پیمائشیں ہیں  \n",
-    "* Y بیماری کی ترقی کا ایک سال کے دوران معیاری پیمانہ ہے  \n",
+    "اس ڈیٹاسیٹ میں درج ذیل کالم شامل ہیں:\n",
+    "* عمر اور جنس خود وضاحتی ہیں\n",
+    "* BMI جسمانی ماس انڈیکس ہے\n",
+    "* BP اوسط بلڈ پریشر ہے\n",
+    "* S1 سے S6 مختلف خون کے پیمائش ہیں\n",
+    "* Y بیماری کی ترقی کا ایک سال کے دوران معیاری پیمائش ہے\n",
     "\n",
-    "آئیے اس ڈیٹاسیٹ کا مطالعہ احتمال اور شماریات کے طریقوں سے کریں۔\n",
+    "آئیے اس ڈیٹاسیٹ کو احتمال اور شماریات کے طریقوں سے مطالعہ کریں۔\n",
     "\n",
-    "### کام 1: تمام اقدار کے لیے اوسط اور واریانس کا حساب لگائیں  \n"
+    "### کام 1: تمام اقدار کے لیے اوسط اور تغیر کا حساب کریں\n"
    ],
    "metadata": {}
   },
@@ -186,7 +186,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### کام 3: عمر، جنس، BMI اور Y متغیرات کی تقسیم کیا ہے؟\n"
+    "### کام 3: عمر، جنس، بی ایم آئی اور وائی متغیرات کی تقسیم کیا ہے؟\n"
    ],
    "metadata": {}
   },
@@ -200,9 +200,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### کام 4: مختلف متغیرات اور بیماری کی ترقی (Y) کے درمیان تعلق کا تجزیہ کریں\n",
+    "### کام 4: مختلف متغیرات اور بیماری کی پیشرفت (Y) کے درمیان تعلق کا ٹیسٹ کریں\n",
     "\n",
-    "> **اشارہ** تعلق میٹرکس آپ کو سب سے زیادہ مفید معلومات فراہم کرے گا کہ کون سی قدریں ایک دوسرے پر منحصر ہیں۔\n"
+    "> **اشارہ** تعلق کا میٹرکس آپ کو سب سے زیادہ مفید معلومات فراہم کرے گا کہ کون سی قدریں ایک دوسرے پر منحصر ہیں۔\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### کام 5: اس مفروضے کا تجربہ کریں کہ ذیابیطس کی ترقی کی شدت مردوں اور عورتوں کے درمیان مختلف ہے۔\n"
+    "### کام 5: اس مفروضے کا تجربہ کریں کہ ذیابیطس کی ترقی کی شدت مردوں اور عورتوں کے درمیان مختلف ہے\n"
    ],
    "metadata": {}
   },
@@ -227,7 +227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ڈسکلیمر**:  \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا غیر درستیاں ہو سکتی ہیں۔ اصل دستاویز کو اس کی اصل زبان میں مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ ہم اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے ذمہ دار نہیں ہیں۔\n"
+    "\n---\n\n**ڈسکلیمر**:  \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے پوری کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا عدم درستگی ہو سکتی ہیں۔ اصل دستاویز کو اس کی اصل زبان میں مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے لیے ہم ذمہ دار نہیں ہیں۔\n"
    ]
   }
  ],
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-01T23:21:02+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:08:45+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "ur"
   }
diff --git a/translations/ur/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/ur/1-Introduction/04-stats-and-probability/notebook.ipynb
index 6a69d34d..2669f3e2 100644
--- a/translations/ur/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/ur/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# احتمال اور شماریات کا تعارف\n",
-    "اس نوٹ بک میں، ہم ان تصورات کے ساتھ کھیلیں گے جن پر ہم نے پہلے بات کی ہے۔ احتمال اور شماریات کے بہت سے تصورات ڈیٹا پروسیسنگ کے لیے پائتھون کی بڑی لائبریریوں جیسے `numpy` اور `pandas` میں اچھی طرح سے پیش کیے گئے ہیں۔\n"
+    "# احتمال اور شماریات کا تعارف  \n",
+    "اس نوٹ بک میں، ہم ان تصورات کے ساتھ تجربہ کریں گے جن پر ہم پہلے بات کر چکے ہیں۔ احتمال اور شماریات کے کئی تصورات ڈیٹا پروسیسنگ کے لیے پائتھون کی بڑی لائبریریوں، جیسے کہ `numpy` اور `pandas` میں بخوبی پیش کیے گئے ہیں۔  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,22 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## بے ترتیب متغیرات اور تقسیمات  \n",
-    "آئیے 0 سے 9 تک کی یکساں تقسیم سے 30 اقدار کا نمونہ نکالنے سے شروع کرتے ہیں۔ ہم اوسط اور تغیر بھی حساب کریں گے۔  \n"
+    "## تصادفی متغیرات اور تقسیمات  \n",
+    "آئیے 0 سے 9 تک کی یکساں تقسیم سے 30 قدروں کا نمونہ نکالنے سے شروع کرتے ہیں۔ ہم اوسط اور واریانس بھی حساب کریں گے۔  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ہم یہاں ڈیٹا کے تجزیے کے لیے ایک پیکیج [**Pandas**](https://pandas.pydata.org/) استعمال کر رہے ہیں۔ ہم اس کورس میں آگے چل کر Pandas اور Python میں ڈیٹا کے ساتھ کام کرنے کے بارے میں مزید بات کریں گے۔\n",
+    "ہم یہاں ڈیٹا کے تجزیے کے لیے [**Pandas**](https://pandas.pydata.org/) نامی پیکیج استعمال کر رہے ہیں۔ اس کورس میں آگے چل کر ہم Pandas اور Python میں ڈیٹا کے ساتھ کام کرنے کے بارے میں مزید بات کریں گے۔\n",
     "\n",
-    "آئیے عمر، قد اور وزن کے اوسط اقدار کا حساب لگاتے ہیں:\n"
+    "آئیے عمر، قد اور وزن کے اوسط نکالیں:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,19 +143,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "اب آئیے قد پر توجہ مرکوز کریں، اور معیاری انحراف اور تغیر کا حساب لگائیں:\n"
+    "اب آئیے قد پر توجہ مرکوز کرتے ہیں، اور معیاری انحراف اور واریئنس کا حساب لگاتے ہیں:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "میڈین ویلیو اور کوارٹائلز کو دیکھنا بھی معنی خیز ہے، اس کے علاوہ اوسط کو دیکھنا۔ انہیں ایک **باکس پلاٹ** کے ذریعے بصری بنایا جا سکتا ہے:\n"
+    "میڈین کی قیمت اور چوتھائی حصوں کو دیکھنا بھی معنی خیز ہے۔ انہیں ایک **باکس پلاٹ** کے ذریعے تصور کیا جا سکتا ہے:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ہم اپنے ڈیٹاسیٹ کے ذیلی حصوں کے باکس پلاٹس بھی بنا سکتے ہیں، مثال کے طور پر، کھلاڑی کے کردار کے مطابق گروپ کیے گئے۔\n"
+    "ہم اپنے ڈیٹاسیٹ کے ذیلی حصوں کے باکس پلاٹس بھی بنا سکتے ہیں، مثال کے طور پر، کھلاڑی کے کردار کے مطابق گروپ بندی کر کے۔\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -402,26 +250,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **نوٹ**: یہ خاکہ ظاہر کرتا ہے کہ اوسطاً، پہلے بیس مین کی قد دوسرے بیس مین کی قد سے زیادہ ہوتی ہے۔ بعد میں ہم سیکھیں گے کہ اس مفروضے کو مزید رسمی طور پر کیسے جانچ سکتے ہیں، اور یہ کیسے ثابت کر سکتے ہیں کہ ہمارے ڈیٹا میں شماریاتی اہمیت موجود ہے تاکہ یہ دکھایا جا سکے۔\n",
+    "> **نوٹ**: یہ خاکہ ظاہر کرتا ہے کہ اوسطاً، پہلے بیس مین کی لمبائی دوسرے بیس مین کی لمبائی سے زیادہ ہوتی ہے۔ بعد میں ہم سیکھیں گے کہ اس مفروضے کو مزید رسمی طور پر کیسے جانچا جا سکتا ہے، اور یہ ظاہر کرنے کے لیے کہ ہمارے ڈیٹا کا شماریاتی طور پر اہم ہونا کیسے ثابت کیا جا سکتا ہے۔\n",
     "\n",
-    "عمر، قد اور وزن سب مسلسل بے ترتیب متغیرات ہیں۔ آپ کیا سوچتے ہیں کہ ان کی تقسیم کیسی ہے؟ اس کا پتہ لگانے کا ایک اچھا طریقہ یہ ہے کہ ان اقدار کا ہسٹوگرام بنایا جائے:\n"
+    "عمر، قد اور وزن سب مسلسل بے ترتیب متغیرات ہیں۔ آپ کے خیال میں ان کی تقسیم کیسی ہوگی؟ اس کا پتہ لگانے کا ایک اچھا طریقہ یہ ہے کہ ان اقدار کا ہسٹوگرام بنایا جائے:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -440,24 +286,25 @@
    "source": [
     "## نارمل ڈسٹریبیوشن\n",
     "\n",
-    "آئیے وزن کے ایک مصنوعی نمونے بناتے ہیں جو نارمل ڈسٹریبیوشن کی پیروی کرتا ہے اور ہمارے حقیقی ڈیٹا کے جیسا اوسط اور ویرینس رکھتا ہے:\n"
+    "آئیے وزن کے ایک مصنوعی نمونے کو تخلیق کرتے ہیں جو ہمارے اصل ڈیٹا کے اوسط اور ویرینس کے مطابق نارمل ڈسٹریبیوشن کی پیروی کرتا ہو:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "چونکہ حقیقی زندگی میں زیادہ تر اقدار عام طور پر تقسیم شدہ ہوتی ہیں، ہمیں نمونہ ڈیٹا پیدا کرنے کے لیے یکساں بے ترتیب نمبر جنریٹر استعمال نہیں کرنا چاہیے۔ اگر ہم یکساں تقسیم کے ساتھ وزن پیدا کرنے کی کوشش کریں (جو `np.random.rand` کے ذریعے پیدا کیا گیا ہے)، تو یہ ہوتا ہے:\n"
+    "چونکہ حقیقی زندگی میں زیادہ تر اقدار عام تقسیم شدہ ہوتی ہیں، ہمیں نمونہ ڈیٹا پیدا کرنے کے لیے یکساں بے ترتیب نمبر جنریٹر استعمال نہیں کرنا چاہیے۔ یہاں یہ ہوتا ہے اگر ہم یکساں تقسیم کے ساتھ وزن پیدا کرنے کی کوشش کریں (جو `np.random.rand` کے ذریعے پیدا کیا گیا ہے):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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QAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMAhkAAAaBDAAAg0AGAIBBIAMAwCCQAQBgEMgAADAIZAAAGAQyAAAMexbIVXVDVX2uqh6uqiN79TkAALCb9iSQq+qSJP8+yfcmuS7Jm6rqur34LAAA2E17dQT59Uke7u7f7u6vJHl/kpv36LMAAGDX7Nujn3tVksfG45NJ/tp8QVUdTnJ48fAPq+pzezQLe+/yJF9c9xCcN+wHtrMn2M6e4Fn1E0nWtyf+wpkW9yqQ6wxr/ZwH3UeTHN2jz2eFqupEd2+uew7OD/YD29kTbGdPsN35tif26hSLk0muGY+vTvLEHn0WAADsmr0K5P+Z5GBVXVtVL0lya5J79+izAABg1+zJKRbd/UxV/VCSX0pySZK7uvuBvfgszgtOlWGyH9jOnmA7e4Ltzqs9Ud197lcBAMBFwpX0AABgEMgAADAIZJ63qnpNVX1q/PflqvqRqvrJqvpsVf1mVf1CVb1y3bOyGl9nT/z4Yj98qqo+XFXftO5ZWY2z7Ynx/I9WVVfV5WsckxX5Or8j/mVVPT7Wv2/ds7IaX+93RFX9cFV9rqoeqKp/tdY5nYPMTiwuJ/54ti4A85okH1n8z5k/kSTd/c51zsfqbdsTv9/dX16svz3Jdd391nXOx+rNPdHdX6iqa5K8J8m3JPmr3e1CEReRbb8jfjDJH3b3T613KtZp2554dZJ3Jbmxu5+uqiu6+6l1zeYIMjt1fZL/3d1f6O4Pd/czi/WPZut7r7n4zD3x5bH+imy7UBAXjWf3xOLxv0nyY7EfLlbb9wPMPfFPktzZ3U8nyTrjOBHI7NytSd53hvW3JPnFFc/C+eE5e6Kq3l1VjyV5c5J/sbapWKdn90RV3ZTk8e7+jfWOxBpt/3fjhxanYt1VVZeuayjWau6Jb07yN6vqY1X1q1X1HWucyykWvHCLi788keS13f3kWH9Xks0k/6BtrIvK2fbE4rnbk7ysu+9Yy3CsxdwTSf4gyS8n+bvd/aWqeiTJplMsLh7bf0dU1ZVJvpitvyb8eJL93f2Wdc7Iap1hT3w6yUeSvCPJdyT5uSSvXldPOILMTnxvkk9si+NDSd6Q5M3i+KL0p/bE8F+T/MMVz8P6zT3xF5Ncm+Q3FnF8dZJPVNWfX+N8rNZzfkd095Pd/dXu/lqS/5jk9WudjnXY/u/GySQf7C0fT/K1JGv7n3kFMjvxpjz3T+k3JHlnkpu6+4/WNhXrtH1PHBzP3ZTksyufiHV7dk9092919xXdfaC7D2TrH8Jv7+7fWeeArNT23xH7x3N/P8mnVz4R6/acPZHkvyX520lSVd+c5CXZ+ivDWjjFghekql6e5LFs/dnjS4u1h5O8NMnvLl72Ud9YcPE4y574+Wx9u8nXknwhyVu7+/H1TckqnWlPbHv+kTjF4qJxlt8R/znJX8nWKRaPJPnH3X1qXTOyWmfZEy9Jcle29sVXkvxod39kbTMKZAAA+BNOsQAAgEEgAwDAIJABAGAQyAAAMAhkAAAYBDIAAAwCGQAAhv8PCCPnhqb/Rl0AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## اعتماد کے وقفے\n",
     "\n",
-    "اب ہم بیس بال کھلاڑیوں کے وزن اور قد کے لیے اعتماد کے وقفے کا حساب لگائیں گے۔ ہم اس کوڈ کا استعمال کریں گے [اس اسٹیک اوور فلو بحث سے](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "آئیے اب بیس بال کھلاڑیوں کے وزن اور قد کے لیے اعتماد کے وقفے کا حساب لگاتے ہیں۔ ہم اس کوڈ کا استعمال کریں گے [اس اسٹیک اوور فلو بحث سے](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -595,12 +436,12 @@
    "source": [
     "## مفروضہ کی جانچ\n",
     "\n",
-    "آئیے ہمارے بیس بال کھلاڑیوں کے ڈیٹا سیٹ میں مختلف کرداروں کا جائزہ لیتے ہیں:\n"
+    "آئیے اپنے بیس بال کھلاڑیوں کے ڈیٹا سیٹ میں مختلف کرداروں کا جائزہ لیتے ہیں:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,38 +545,38 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "آئیے اس مفروضے کا تجربہ کریں کہ پہلے بیس مین دوسرے بیس مین سے لمبے ہوتے ہیں۔ اس کا سب سے آسان طریقہ یہ ہے کہ اعتماد کے وقفوں کا تجربہ کریں:\n"
+    "آئیے اس مفروضے کا تجربہ کریں کہ فرسٹ بیس مین سیکنڈ بیس مین سے لمبے ہوتے ہیں۔ اس کا سب سے آسان طریقہ یہ ہے کہ اعتماد کے وقفوں کا تجربہ کریں:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -750,22 +591,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ہم دیکھ سکتے ہیں کہ وقفے ایک دوسرے سے متصادم نہیں ہیں۔\n",
+    "ہم دیکھ سکتے ہیں کہ وقفے آپس میں اوورلیپ نہیں کرتے۔\n",
     "\n",
-    "فرضیہ کو ثابت کرنے کا ایک زیادہ شماریاتی درست طریقہ **Student t-test** استعمال کرنا ہے:\n"
+    "ایک شماریاتی طور پر زیادہ درست طریقہ مفروضے کو ثابت کرنے کا یہ ہے کہ **اسٹوڈنٹ ٹی-ٹیسٹ** استعمال کریں:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,9 +621,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ttest_ind` فنکشن کے ذریعے واپس کیے گئے دو نتائج یہ ہیں:\n",
-    "* p-value کو اس بات کے امکان کے طور پر سمجھا جا سکتا ہے کہ دو تقسیمات کا اوسط ایک جیسا ہو۔ ہمارے معاملے میں، یہ بہت کم ہے، جس کا مطلب ہے کہ اس بات کے مضبوط شواہد موجود ہیں کہ پہلے بیس مین زیادہ لمبے ہیں۔\n",
-    "* t-value نارملائزڈ اوسط فرق کی درمیانی قدر ہے جو t-test میں استعمال ہوتی ہے، اور اسے دی گئی اعتماد کی قدر کے لیے ایک حد کی قدر کے خلاف موازنہ کیا جاتا ہے۔\n"
+    "`ttest_ind` فنکشن کے ذریعے واپس کیے گئے دو نتائج یہ ہیں:  \n",
+    "* p-value کو اس بات کے امکان کے طور پر سمجھا جا سکتا ہے کہ دو تقسیمات کا اوسط ایک جیسا ہے۔ ہمارے معاملے میں، یہ بہت کم ہے، جس کا مطلب ہے کہ اس بات کے مضبوط شواہد موجود ہیں کہ پہلے بیس مین زیادہ لمبے ہیں۔  \n",
+    "* t-value نارملائزڈ اوسط فرق کی درمیانی قدر ہے جو t-test میں استعمال ہوتی ہے، اور اسے دی گئی اعتماد کی قدر کے لیے ایک حدی قدر کے خلاف موازنہ کیا جاتا ہے۔  \n"
    ]
   },
   {
@@ -796,19 +637,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,21 +667,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## تعلق اور شیطانی بیس بال کارپوریشن\n",
+    "## تعلق اور ایول بیس بال کارپوریشن\n",
     "\n",
-    "تعلق ہمیں ڈیٹا کے سلسلوں کے درمیان تعلقات تلاش کرنے کی اجازت دیتا ہے۔ ہمارے مثال کے طور پر، فرض کریں کہ ایک شیطانی بیس بال کارپوریشن ہے جو اپنے کھلاڑیوں کو ان کے قد کے مطابق تنخواہ دیتی ہے - جتنا کھلاڑی لمبا ہوگا، اتنی ہی زیادہ رقم وہ حاصل کرے گا۔ فرض کریں کہ ایک بنیادی تنخواہ $1000 ہے، اور قد کے مطابق $0 سے $100 تک کا اضافی بونس دیا جاتا ہے۔ ہم MLB کے حقیقی کھلاڑیوں کو لیں گے اور ان کی خیالی تنخواہیں حساب کریں گے:\n"
+    "تعلق ہمیں ڈیٹا کے سلسلوں کے درمیان روابط تلاش کرنے کی اجازت دیتا ہے۔ ہمارے اس مثال میں، فرض کریں کہ ایک ایول بیس بال کارپوریشن ہے جو اپنے کھلاڑیوں کو ان کے قد کے مطابق ادائیگی کرتی ہے - جتنا کھلاڑی لمبا ہوگا، اتنی ہی زیادہ رقم وہ حاصل کرے گا۔ فرض کریں کہ ایک بنیادی تنخواہ $1000 ہے، اور قد کے مطابق $0 سے $100 تک کا اضافی بونس دیا جاتا ہے۔ ہم MLB کے حقیقی کھلاڑیوں کو لیں گے اور ان کی خیالی تنخواہیں حساب کریں گے:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +695,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "چلو اب ان سلسلوں کی کوویریئنس اور کورلیشن کا حساب لگاتے ہیں۔ `np.cov` ہمیں ایک **کوویریئنس میٹرکس** دے گا، جو کوویریئنس کو متعدد متغیرات تک بڑھانے کا ایک طریقہ ہے۔ کوویریئنس میٹرکس $M$ کا عنصر $M_{ij}$ ان پٹ متغیرات $X_i$ اور $X_j$ کے درمیان کورلیشن ہے، اور قطر کے اقدار $M_{ii}$ متغیر $X_{i}$ کا ویرینس ہے۔ اسی طرح، `np.corrcoef` ہمیں **کورلیشن میٹرکس** دے گا۔\n"
+    "آئیے اب ان سلسلوں کی کوویریئنس اور تعلق کا حساب لگائیں۔ `np.cov` ہمیں ایک **کوویریئنس میٹرکس** دے گا، جو کوویریئنس کو متعدد متغیروں تک بڑھانے کا ایک طریقہ ہے۔ کوویریئنس میٹرکس $M$ کا عنصر $M_{ij}$ ان پٹ متغیروں $X_i$ اور $X_j$ کے درمیان تعلق کو ظاہر کرتا ہے، اور قطر کے عناصر $M_{ii}$ متغیر $X_{i}$ کی ویرینس کو ظاہر کرتے ہیں۔ اسی طرح، `np.corrcoef` ہمیں **تعلق میٹرکس** فراہم کرے گا۔\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -886,24 +725,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ایک تعلق جو 1 کے برابر ہو مطلب یہ ہے کہ دو متغیروں کے درمیان ایک مضبوط **خطی تعلق** موجود ہے۔ ہم ایک قدر کو دوسری کے خلاف پلاٹ کرکے خطی تعلق کو بصری طور پر دیکھ سکتے ہیں:\n"
+    "دو متغیروں کے درمیان ایک مضبوط **لکیری تعلق** کا مطلب ہے کہ ارتباط 1 کے برابر ہے۔ ہم ایک قدر کو دوسری کے مقابلے میں پلاٹ کرکے لکیری تعلق کو بصری طور پر دیکھ سکتے ہیں:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -918,19 +755,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "آئیے دیکھتے ہیں کہ کیا ہوتا ہے اگر تعلق غیر خطی ہو۔ فرض کریں کہ ہماری کارپوریشن نے قد اور تنخواہوں کے درمیان واضح خطی انحصار کو چھپانے کا فیصلہ کیا، اور فارمولے میں کچھ غیر خطی عنصر جیسے `sin` شامل کر دیا:\n"
+    "آئیے دیکھتے ہیں کہ کیا ہوتا ہے اگر تعلق خطی نہ ہو۔ فرض کریں کہ ہماری کارپوریشن نے اونچائیوں اور تنخواہوں کے درمیان واضح خطی انحصار کو چھپانے کا فیصلہ کیا، اور فارمولا میں کچھ غیر خطی عنصر جیسے `sin` شامل کر دیا:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -943,19 +780,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "اس صورت میں، تعلق تھوڑا کم ہے، لیکن یہ اب بھی کافی زیادہ ہے۔ اب، تعلق کو مزید کم واضح کرنے کے لیے، ہم شاید تنخواہ میں کچھ اضافی بے ترتیبی شامل کرنا چاہیں گے۔ آئیے دیکھتے ہیں کیا ہوتا ہے:\n"
+    "اس صورت میں، تعلق تھوڑا کم ہے، لیکن یہ اب بھی کافی زیادہ ہے۔ اب، تعلق کو اور بھی کم واضح بنانے کے لیے، ہم تنخواہ میں کچھ بے ترتیب متغیر شامل کرکے کچھ اضافی بے ترتیبی شامل کرنا چاہ سکتے ہیں۔ آئیے دیکھتے ہیں کیا ہوتا ہے:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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A0CEgAwBAh4AMAAAdAjIAAHQIyAAA0GEfZNji3vCbD+XTX3zq9P2Xv/CKvP9nf6LHEQFAv6wgwxa2NBwnyae/+FTe8JsP9TQiAOifgAxb2NJwfK46AGwF5wzIVfWeqnqiqj7fqV1RVR+vqkcGt5d3Hvuxqnqoqh6uqiNV9QOD+o2D+49W1Turqi7MPwkAAFZvJSvI703yqiW1O5I82Fq7LsmDg/upqouT3JfkH7bWrk/yiiQnB695V5Lbk1w3+LP0cwIAQO/OGZBba59KsvT3ra9Jcu/g43uTzAw+/s+S/GFr7XOD1/55a22hqq5O8uzW2kOttZbkfZ3XAADA2FhtD/LzWmuPJ8ng9qpB/YeTtKo6UFW/X1W/PKhPJznWef2xQW1ZVXV7VR2qqkNPPvnkKocIAACjW+tt3i5O8teT/LUk307yYFV9NslfLPPcNuyTtNbuSXJPkuzevXvo8wAAYK2tdgX5a4O2iQxunxjUjyX5t621P2utfTvJR5P8+KB+Tef11yQ5vsq/GwAALpjVBuQPJ7lt8PFtST40+PhAkh+rqksHF+z9zSR/NGjD+GZV3TTYveJNndcAAMDYWMk2b/cneSjJzqo6VlVvTnJ3kpur6pEkNw/up7X29STvSPLvk/xBkt9vrX1k8KnekuTdSR5N8sUkH1vbfwoAAJy/c/Ygt9ZuHfLQK4c8/74sbvW2tH4oyYtGGh0AAKwzJ+kBAECHgAywQtddddlIdQA2JgEZYIU+/ouveEYYvu6qy/LxX3xFPwMC4IJY632QATY1YRhg87OCDAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHRf3PQDYKm5+xyfzyBPfOn3/uqsuy8d/8RX9DQgAWJYVZFgHS8NxkjzyxLdy8zs+2c+AAIChBGRYB0vD8bnqAEB/BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkIGxc8lEjVQHgLUkIANj59df++IsjcI1qAPAheYkPWDszOyaTpLsP3A0x0/MZ/vUZPbu2Xm6DgAXkoAMjKWZXdMCMQC90GIBAAAdAjIAAHQIyLCFDdsUwmYRAGxlAjJsYQtttDoAbAUCMgAAdAjIAADQISDDOnAyHABsHAIyrIPvDmnqHVYHAPojIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeADOtg25DvtGF1AKA/fjzDOnh6yG5uw+oAQH8EZFgHw7Y7tg0yAIwfARkAADoEZAAA6Li47wHAWrv5HZ/MI0986/T96666LB//xVf0NyAAYEOxgsymsjQcJ8kjT3wrN7/jk/0MCADYcARkNpWl4fhcdQCApQRkAADoOGdArqr3VNUTVfX5Tu2Kqvp4VT0yuL18yWuuraq/rKpf6tRurKojVfVoVb2zqmpt/ykAAHD+VrKC/N4kr1pSuyPJg62165I8OLjf9RtJPrak9q4ktye5bvBn6ecE1tmlQ47yG1YHgK3gnD8FW2ufSvLUkvJrktw7+PjeJDOnHqiqmSRfSvJwp3Z1kme31h5qrbUk7+u+BujHP77lx3LRkt/lXFSLdQDYqla7TPS81trjSTK4vSpJquqyJG9N8qtLnj+d5Fjn/rFBDejRzK7pvONnXpLpqclUkumpybzjZ16SmV2+PQHYutZ6H+RfTfIbrbW/XNJivFy/8dBDdqvq9iy2Y+Taa69d0wECZ5rZNS0QA0DHagPy16rq6tba44P2iScG9ZcleW1V/XqSqSRPV9VfJfntJNd0Xn9NkuPDPnlr7Z4k9yTJ7t27hwZpAABYa6ttsfhwktsGH9+W5ENJ0lr7G621Ha21HUn+5yT/uLX2zwdtGN+sqpsGu1e86dRrAABgnKxkm7f7kzyUZGdVHauqNye5O8nNVfVIkpsH98/lLUneneTRJF/MM3e5gPM2PTU5Uh0AYKlztli01m4d8tArz/G6ty25fyjJi1Y8MliFv/UjV+a+g48tWwcAWAmbnbKpfOQPHx+pDgCwlIDMpvL1b58cqQ4AsJSADAAAHQIyAAB0CMgAANAhIAMAQIeADAAAHQIyAAB0CMgAANAhIAMAQIeAzKYyNbltpDoAwFICMpvK008/PVIdAGApAZlN5S++szBSHQBgKQEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOi4uO8BsLHNHp7L/gNHc/zEfLZPTWbvnp2Z2TXd23guv3Rbvv7tk8vWAQBWwgoyqzZ7eC77HjiSuRPzaUnmTsxn3wNHMnt4rrcx/cpPXZ9tE3VGbdtE5Vd+6vqeRgQAbDQCMqu2/8DRzJ888wCO+ZML2X/gaE8jSmZ2TWf/a1+c6anJVJLpqcnsf+2Le13VTpLLLpkYqQ4A9EdAZtWOn5gfqb5eDn3lqfzpN/4qLcmffuOvcugrT/U6niT5tb93QyYuOnNle+Kiyq/9vRt6GhEAMIyAzKptn5ocqb4e7pw9kvsOPpaF1pIkC63lvoOP5c7ZI72NKVlc2f5nrztzZfufva7/lW0A4JlcpMeq7d2zM3s/+LmcXGina9smKnv37OxtTO8/+NjQ+l0z/a7WzuyaFogBYAOwgsz5aee4v86G/fU9DwsA2EAEZFZt/4GjOfn0mdHz5NOt14v0AADOl4DMqo3jRXqXLNni7Vx1AIClBGRWbRwv0ts2sfyX9LA6AMBSUgOrtnfPzmUP5ejzIr1vfXdhpDoAwFICMudnzC7SAwA4XwIyq+YiPQBgMxKQWbVxvEivhlyLN6wOALCUgMyqjeNFem1Ii8ewOgDAUgIyq7Z3z85Mbps4oza5baLXi/QAAM6XgMyqzeyazk/fOJ2JQf/CRFV++kbHKQMAG5uAzKrNHp7Lb392LguD/oWF1vLbn53L7OG5nkcGALB6AjKrtv/A0cyfPHN/4fmTC3axAAA2NAGZVRvHXSwAAM6XgMyqTV26baQ6AMBGICCzarZUAwA2IwGZVfvG/MmR6gAAG4GAzKqN40EhAADnS0Bm1RwUAgBsRhf3PQA2rlMHguw/cDTHT8xn+9Rk9u7Z6aAQAGBDE5A5LzO7nJwHAGwuWizYVGrEOgDAUgIym8qwHebsPAcArJSADAAAHQIym8rU5JDT/YbUAQCWEpDZVN726uuz7aIzO463XVR526uv72lEAMBGYxcLNhVbzwEA5+ucAbmq3pPkJ5M80Vp70aB2RZL/J8mOJF9O8jOtta9X1c1J7k5ySZLvJtnbWvvE4DU3JnlvkskkH03yP7TWXDvFmrP1HABwPlbSYvHeJK9aUrsjyYOtteuSPDi4nyR/luSnWms3JLktyf/Vec27ktye5LrBn6Wfkw1o9vBcXn73J/KCOz6Sl9/9icwenut7SAAA5+WcK8ittU9V1Y4l5dckecXg43uTfDLJW1trhzvPeTjJD1TVs5JckeTZrbWHkqSq3pdkJsnHzmPsW8rs4bmxaxuYPTyXfQ8cyfzJhSTJ3In57HvgSJL0PjYAgNVa7UV6z2utPZ4kg9urlnnOTyc53Fr7TpLpJMc6jx0b1JZVVbdX1aGqOvTkk0+ucoibx6kgOndiPi3fD6J9r9buP3D0dDg+Zf7kQvYfONrTiAAAzt8F2cWiqq5P8k+S/INTpWWeNrT/uLV2T2ttd2tt95VXXnkhhrihjGsQnTsxP1IdAGAjWG1A/lpVXZ0kg9snTj1QVdck+Z0kb2qtfXFQPpbkms7rr0lyfJV/95ZzfEjgHFZfLzXk/OZhdQCAjWC1AfnDWbwIL4PbDyVJVU0l+UiSfa21T5968qAN45tVdVNVVZI3nXoN57Z9anKk+noZtgeJvUkAgI3snAG5qu5P8lCSnVV1rKrenMWt3G6uqkeSnNraLUn+uyT/cZL/sar+YPDnVH/yW5K8O8mjSb4YF+it2N49O7NtYsnhFxOVvXt29jQiAIDNayW7WNw65KFXLvPcu5LcNeTzHEryopFGx/ctXZW1SgsAcEE4anoD2H/gaE4+fWYiPvl06/0iPQCAzUhA3gDG9SI9AIDNSEDeAMb1Ir3LL902Uh0AYCMQkDeAvXt2ZnLbxBm1yW0TvV+k93d/7OqR6gAAG8E5L9Kjf6eObR63o6Z/9wvLn3I4rA4AsBEIyBvEzK7p3gPxUnqjAYDNSIsFq3bpJRMj1QEANgIBmVX71ncXRqoDAGwEAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyKza5Lblv3yG1QEANgJJhlX76RuvGakOALARCMismqOmAYDNyFHTG8Ts4bnsP3A0x0/MZ/vUZPbu2dn70dOOmgYANiMryBvA7OG57HvgSOZOzKclmTsxn30PHMns4blex7V9anKkOgDARiAgbwD7DxzN/Mkzj2+eP7mQ/QeO9jSiRXv37MzktokzapPbJrJ3z86eRgQAcP60WGwA49rKcKrFY9xaPwAAzoeAvAFsn5rM3DJheBxaGWZ2TQvEAMCmosViA9DKAACwfqwgbwBaGQAA1o+AvEFoZQAAWB9aLAAAoENABgCADgEZAAA6BGQAAOhwkd4GMXt4zi4WAADrQEDeAGYPz2XfA0dOHzc9d2I++x44kiRCMgDAGtNisQHsP3D0dDg+Zf7kQvYfONrTiAAANi8BeQM4vswx02erAwCwegLyBrB9anKkOgAAqycgbwB79+zM5LaJM2qT2yayd8/OnkYEALB5uUhvAzh1IZ5dLAAALjwBeYOY2TUtEAMArAMtFgAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0OEkvQ1i9vCco6YBANaBgLzEOAbR2cNz2ffAkcyfXEiSzJ2Yz74HjiRJ72MDANhstFh0nAqicyfm0/L9IDp7eK7Xce0/cPR0OD5l/uRC9h842tOIAAA2LwG5Y1yD6PET8yPVAQBYPQG5Y1yD6PapyZHqAACsnoDcMa5BdMdzlv/7h9UBAFi9cwbkqnpPVT1RVZ/v1K6oqo9X1SOD28s7j+2rqker6mhV7enUb6yqI4PH3llVtfb/nPOzd8/OTG6bOKM2uW0ie/fs7GlEiw5+6esj1QEAWL2VrCC/N8mrltTuSPJga+26JA8O7qeqfjTJ65NcP3jN/15VpxLnu5LcnuS6wZ+ln7N3M7um8/Zbbsj01GQqyfTUZN5+yw297xSx0NpIdQAAVu+c27y11j5VVTuWlF+T5BWDj+9N8skkbx3U/2Vr7TtJ/qSqHk3y0qr6cpJnt9YeSpKqel+SmSQfO+9/wRqb2TXdeyBeaqJq2TA8MX6L8AAAG95qe5Cf11p7PEkGt1cN6tNJvtp53rFBbXrw8dI6K3Dry54/Uh0AgNVb64v0llvSbGepL/9Jqm6vqkNVdejJJ59cs8FtVHfN3JA33nTt6RXjiaq88aZrc9fMDT2PDABg81ntSXpfq6qrW2uPV9XVSZ4Y1I8l6S5rXpPk+KB+zTL1ZbXW7klyT5Ls3r1bo20WQ7JADABw4a12BfnDSW4bfHxbkg916q+vqmdV1QuyeDHe7w3aML5ZVTcNdq94U+c1AAAwNs65glxV92fxgrznVtWxJL+S5O4kH6iqNyd5LMnrkqS19nBVfSDJHyX5XpKfa62dOpruLVncEWMyixfnjd0FegAAUG3MtwrbvXt3O3ToUN/DAABgk6mqz7bWdi+tO0kPAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6BGQAAOgQkAEAoENABgCADgEZAAA6Lu57AONm9vBc9h84muMn5rN9ajJ79+zMzK7pvocFAMA6EZA7Zg/PZd8DRzJ/ciFJMndiPvseOJIkQjIAwBahxaJj/4Gjp8PxKfMnF7L/wNGeRgQAwHoTkDuOn5gfqQ4AwOYjIHdsn5ocqQ4AwOYjIHfs3bMzk9smzqhNbpvI3j07exoRAADrzUV6HacuxLOLBQDA1iUgLzGza1ogBgDYwrRYAABAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAh4AMAAAdAjIAAHQIyAAA0CEgAwBAR7XW+h7DWVXVk0m+0vc4xshzk/xZ34PYIMzVaMzXaMzXypmr0Ziv0ZivlTNXz/QftdauXFoc+4DMmarqUGttd9/j2AjM1WjM12jM18qZq9GYr9GYr5UzVyunxQIAADoEZAAA6BCQN557+h7ABmKuRmO+RmO+Vs5cjcZ8jcZ8rZy5WiE9yAAA0GEFGQAAOgRkAADoEJDHWFVNVdUHq+oLVfXHVfUTVfWSqjpYVX9QVYeq6qV9j3McVNXOwZyc+vMXVfXzVXVFVX28qh4Z3F7e91jHwVnma//g6+0Pq+p3qmqq77H2bdhcdR7/papqVfXcHoc5Ns42X1X131fV0ap6uKp+veehjoWzfC96r19GVf3C4Ovn81V1f1X9gPf54YbMl/f5FdCDPMaq6t4k/6619u6quiTJpUk+kOQ3Wmsfq6r/Iskvt9Ze0ec4x01VTSSZS/KyJD+X5KnW2t1VdUeSy1trb+11gGNmyXztTPKJ1tr3quqfJIn5+r7uXLXWvlJVz0/y7iQ/kuTG1poN+DuWfG39UJJ/lOTvtta+U1VXtdae6HWAY2bJfP1mvNefoaqmk/x/SX60tTZfVR9I8tEkPxrv889wlvk6Hu/z52QFeUxV1bOT/KdJ/s8kaa19t7V2IklL8uzB0/6DLH6hc6ZXJvlia+0rSV6T5N5B/d4kM30Naoydnq/W2r9urX1vUD+Y5JoexzWOul9bSfIbSX45i9+XPFN3vt6S5O7W2neSRDheVne+vNcv7+Ikk1V1cRYXjY7H+/zZPGO+vM+vjIA8vn4oyZNJ/kVVHa6qd1fVZUl+Psn+qvpqkn+aZF+PYxxXr09y/+Dj57XWHk+Swe1VvY1qfHXnq+u/TvKxdR7LuDs9V1X16iRzrbXP9Tuksdb92vrhJH+jqj5TVf+2qv5aj+MaV935+vl4rz9Da20ui3PxWJLHk3yjtfav431+WWeZry7v80MIyOPr4iQ/nuRdrbVdSb6V5I4srsL8Qmvt+Ul+IYMVZhYNWlFeneS3+h7LRjBsvqrqHyX5XpL39zGucdSdq6q6NIvtAv9Tv6MaX8t8bV2c5PIkNyXZm+QDVVU9DW/sLDNf3uuXGPQWvybJC5JsT3JZVb2x31GNr3PNl/f5sxOQx9exJMdaa58Z3P9gFgPzbUkeGNR+K4kLN870nyf5/dba1wb3v1ZVVyfJ4Navdc+0dL5SVbcl+ckkb2guUujqztULs/hD53NV9eUs/ory96vqP+xxfONm6dfWsSQPtEW/l+TpJC5s/L6l8+W9/pn+TpI/aa092Vo7mcX5+U/ifX6YYfPlfX4FBOQx1Vr70yRfraqdg9Irk/xRFvut/uag9reTPNLD8MbZrTmzXeDDWfxBk8Hth9Z9ROPtjPmqqlcleWuSV7fWvt3bqMbT6blqrR1prV3VWtvRWtuRxfD344PvWxYt/V6czeJ7Vqrqh5NcksRFjd+3dL681z/TY0luqqpLB799eGWSP473+WGWnS/v8ytjF4sxVlUvyeIV8pck+VKS/yrJ9Un+lyz+uvKvkvy3rbXP9jXGcTL4tfdXk/xQa+0bg9pzsrjzx7VZfLN4XWvtqf5GOT6GzNejSZ6V5M8HTzvYWvuHPQ1xbCw3V0se/3KS3XaxWDTka+uSJO9J8pIk303yS621T/Q2yDEyZL7+erzXP0NV/WqSv5/F1oDDSf6bJD8Y7/PLGjJfD8f7/DkJyAAA0KHFAgAAOgRkAADoEJABAKBDQAYAgA4BGQAAOgRkAADoEJABAKDj/wceBaX6Xh706QAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1000,17 +835,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,16 +858,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "بدقسمتی سے، ہمیں کوئی نتائج نہیں ملے - صرف کچھ عجیب `nan` ویلیوز۔ اس کی وجہ یہ ہے کہ ہماری سیریز میں کچھ ویلیوز غیر متعین ہیں، جو `nan` کے طور پر ظاہر ہوتی ہیں، اور اس کی وجہ سے آپریشن کا نتیجہ بھی غیر متعین ہو جاتا ہے۔ میٹرکس کو دیکھ کر ہم دیکھ سکتے ہیں کہ `Weight` مسئلہ پیدا کرنے والا کالم ہے، کیونکہ `Height` ویلیوز کے درمیان خود-تعلق کا حساب لگایا گیا ہے۔\n",
+    "بدقسمتی سے، ہمیں کوئی نتائج نہیں ملے - صرف کچھ عجیب `nan` قدریں۔ اس کی وجہ یہ ہے کہ ہماری سیریز میں کچھ قدریں غیر متعین ہیں، جو `nan` کے طور پر ظاہر ہوتی ہیں، اور اس کی وجہ سے آپریشن کا نتیجہ بھی غیر متعین ہو جاتا ہے۔ میٹرکس کو دیکھ کر ہم دیکھ سکتے ہیں کہ `Weight` مسئلہ پیدا کرنے والا کالم ہے، کیونکہ `Height` کی خود سے تعلق کا حساب لگایا گیا ہے۔\n",
     "\n",
     "> یہ مثال **ڈیٹا کی تیاری** اور **صفائی** کی اہمیت کو ظاہر کرتی ہے۔ مناسب ڈیٹا کے بغیر ہم کچھ بھی حساب نہیں لگا سکتے۔\n",
     "\n",
-    "آئیے `fillna` میتھڈ استعمال کرتے ہیں تاکہ گمشدہ ویلیوز کو بھر سکیں، اور تعلق کا حساب لگائیں:\n"
+    "آئیے `fillna` طریقہ استعمال کرتے ہیں تاکہ گمشدہ قدریں بھری جا سکیں، اور تعلق کا حساب لگائیں:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,32 +890,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "بے شک ایک تعلق موجود ہے، لیکن ہمارے مصنوعی مثال کی طرح اتنا مضبوط نہیں۔ حقیقت میں، اگر ہم ایک قدر کو دوسری کے خلاف بکھرے ہوئے پلاٹ پر دیکھیں، تو تعلق بہت کم واضح ہوگا:\n"
+    "درحقیقت ایک تعلق موجود ہے، لیکن یہ ہمارے مصنوعی مثال کی طرح اتنا مضبوط نہیں ہے۔ واقعی، اگر ہم ایک قدر کے مقابلے میں دوسری قدر کے اسکیٹر پلاٹ کو دیکھیں، تو تعلق بہت کم واضح ہوگا:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1098,7 +931,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ڈسکلیمر**:  \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا غیر درستیاں ہو سکتی ہیں۔ اصل دستاویز کو اس کی اصل زبان میں مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ ہم اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے ذمہ دار نہیں ہیں۔\n"
+    "\n---\n\n**ڈسکلیمر**:  \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے پوری کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا عدم درستگی ہو سکتی ہیں۔ اصل دستاویز کو اس کی اصل زبان میں مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے لیے ہم ذمہ دار نہیں ہیں۔\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-01T23:16:10+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:08:31+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "ur"
   }
diff --git a/translations/ur/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/ur/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 14e41a8d..8977bfb7 100644
--- a/translations/ur/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/ur/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,16 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "اس ڈیٹاسیٹ میں کالمز درج ذیل ہیں:  \n",
-    "* عمر اور جنس خود وضاحتی ہیں  \n",
-    "* BMI جسمانی وزن کا اشاریہ ہے  \n",
-    "* BP اوسط بلڈ پریشر ہے  \n",
-    "* S1 سے S6 مختلف خون کے پیمائشیں ہیں  \n",
-    "* Y بیماری کی ترقی کا ایک سال کے دوران معیاری پیمانہ ہے  \n",
+    "اس ڈیٹاسیٹ میں درج ذیل کالم شامل ہیں:\n",
+    "* عمر اور جنس خود وضاحتی ہیں\n",
+    "* BMI جسمانی ماس انڈیکس ہے\n",
+    "* BP اوسط بلڈ پریشر ہے\n",
+    "* S1 سے S6 مختلف خون کے پیمائش ہیں\n",
+    "* Y بیماری کی ترقی کا ایک سال کے دوران معیاری پیمائش ہے\n",
     "\n",
-    "آئیے اس ڈیٹاسیٹ کا مطالعہ احتمال اور شماریات کے طریقوں سے کریں۔\n",
+    "آئیے اس ڈیٹاسیٹ کو احتمال اور شماریات کے طریقوں سے مطالعہ کریں۔\n",
     "\n",
-    "### کام 1: تمام اقدار کے لیے اوسط اور واریانس کا حساب لگائیں  \n"
+    "### کام 1: تمام اقدار کے لیے اوسط اور تغیر کا حساب کریں\n"
    ],
    "metadata": {}
   },
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -477,7 +477,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### کام 2: جنس کے لحاظ سے BMI، BP اور Y کے لئے باکس پلاٹس بنائیں\n"
+    "### کام 2: جنس کی بنیاد پر بی ایم آئی، بی پی اور وائی کے لیے باکس پلاٹس بنائیں\n"
    ],
    "metadata": {}
   },
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -529,7 +529,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### کام 3: عمر، جنس، BMI اور Y متغیرات کی تقسیم کیا ہے؟\n"
+    "### کام 3: عمر، جنس، بی ایم آئی اور وائی متغیرات کی تقسیم کیا ہے؟\n"
    ],
    "metadata": {}
   },
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -595,14 +595,14 @@
     "نتائج:\n",
     "* عمر - معمول کے مطابق  \n",
     "* جنس - یکساں  \n",
-    "* بی ایم آئی، Y - کہنا مشکل ہے  \n"
+    "* بی ایم آئی، وائی - کہنا مشکل ہے  \n"
    ],
    "metadata": {}
   },
   {
    "cell_type": "markdown",
    "source": [
-    "### کام 4: مختلف متغیرات اور بیماری کی ترقی (Y) کے درمیان تعلق کی جانچ کریں\n",
+    "### کام 4: مختلف متغیرات اور بیماری کی ترقی (Y) کے درمیان تعلق کا ٹیسٹ کریں\n",
     "\n",
     "> **اشارہ** تعلق میٹرکس آپ کو سب سے زیادہ مفید معلومات فراہم کرے گا کہ کون سی قدریں ایک دوسرے پر منحصر ہیں۔\n"
    ],
@@ -847,7 +847,7 @@
    "cell_type": "markdown",
    "source": [
     "نتیجہ:\n",
-    "* Y کے ساتھ سب سے مضبوط تعلق BMI اور S5 (بلڈ شوگر) کا ہے۔ یہ معقول لگتا ہے۔\n"
+    "* Y کا سب سے مضبوط تعلق BMI اور S5 (بلڈ شوگر) کے ساتھ ہے۔ یہ معقول لگتا ہے۔\n"
    ],
    "metadata": {}
   },
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -879,7 +879,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### کام 5: اس مفروضے کا تجربہ کریں کہ ذیابیطس کی ترقی کی شدت مردوں اور عورتوں کے درمیان مختلف ہے\n"
+    "### کام 5: اس مفروضے کا تجربہ کریں کہ ذیابیطس کی پیش رفت کی شدت مردوں اور عورتوں کے درمیان مختلف ہے۔\n"
    ],
    "metadata": {}
   },
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -907,7 +907,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "نتیجہ: p-value قریب 0 (عام طور پر، 0.05 سے کم) ہماری مفروضہ پر اعلی اعتماد کی نشاندہی کرے گا۔ ہمارے معاملے میں، کوئی مضبوط ثبوت نہیں ہے کہ جنس ذیابیطس کی ترقی کو متاثر کرتی ہے۔\n"
+    "نتیجہ: p-value کا صفر کے قریب ہونا (عام طور پر، 0.05 سے کم) ہماری مفروضے پر زیادہ اعتماد کی نشاندہی کرے گا۔ ہمارے معاملے میں، اس بات کا کوئی مضبوط ثبوت نہیں ہے کہ جنس ذیابیطس کی پیش رفت کو متاثر کرتی ہے۔\n"
    ],
    "metadata": {}
   },
@@ -920,7 +920,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**ڈسکلیمر**:  \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا غیر درستیاں ہو سکتی ہیں۔ اصل دستاویز کو اس کی اصل زبان میں مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ ہم اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے ذمہ دار نہیں ہیں۔\n"
+    "\n---\n\n**ڈسکلیمر**:  \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا عدم درستگی ہو سکتی ہیں۔ اصل دستاویز، جو اس کی اصل زبان میں ہے، کو مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے لیے ہم ذمہ دار نہیں ہیں۔\n"
    ]
   }
  ],
@@ -946,8 +946,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-01T23:27:34+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:09:04+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "ur"
   }
diff --git a/translations/vi/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/vi/1-Introduction/04-stats-and-probability/assignment.ipynb
index fab01166..9b5edfc3 100644
--- a/translations/vi/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/vi/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,10 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Giới thiệu về Xác suất và Thống kê  \n",
-    "## Bài tập  \n",
+    "## Giới thiệu về Xác suất và Thống kê\n",
+    "## Bài tập\n",
     "\n",
-    "Trong bài tập này, chúng ta sẽ sử dụng tập dữ liệu của các bệnh nhân tiểu đường được lấy [từ đây](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).  \n"
+    "Trong bài tập này, chúng ta sẽ sử dụng tập dữ liệu của các bệnh nhân tiểu đường được lấy [từ đây](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -154,10 +154,10 @@
     "* Tuổi và giới tính đã rõ ràng\n",
     "* BMI là chỉ số khối cơ thể\n",
     "* BP là huyết áp trung bình\n",
-    "* S1 đến S6 là các phép đo máu khác nhau\n",
+    "* S1 đến S6 là các chỉ số đo lường máu khác nhau\n",
     "* Y là thước đo định tính về mức độ tiến triển của bệnh trong một năm\n",
     "\n",
-    "Hãy nghiên cứu tập dữ liệu này bằng các phương pháp xác suất và thống kê.\n",
+    "Hãy cùng nghiên cứu tập dữ liệu này bằng các phương pháp xác suất và thống kê.\n",
     "\n",
     "### Nhiệm vụ 1: Tính giá trị trung bình và phương sai cho tất cả các giá trị\n"
    ],
@@ -224,7 +224,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
    ]
   }
  ],
@@ -250,8 +250,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:48:52+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:42:38+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "vi"
   }
diff --git a/translations/vi/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/vi/1-Introduction/04-stats-and-probability/notebook.ipynb
index 2256ccdd..9b9c6a9f 100644
--- a/translations/vi/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/vi/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# Giới thiệu về Xác suất và Thống kê\n",
-    "Trong tài liệu này, chúng ta sẽ thực hành một số khái niệm đã được thảo luận trước đó. Nhiều khái niệm từ xác suất và thống kê được thể hiện rất tốt trong các thư viện chính dành cho xử lý dữ liệu trong Python, chẳng hạn như `numpy` và `pandas`.\n"
+    "Trong notebook này, chúng ta sẽ thực hành một số khái niệm đã được thảo luận trước đó. Nhiều khái niệm từ xác suất và thống kê được thể hiện rất tốt trong các thư viện chính dành cho xử lý dữ liệu trong Python, chẳng hạn như `numpy` và `pandas`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +30,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Chúng ta đang sử dụng một gói gọi là [**Pandas**](https://pandas.pydata.org/) ở đây để phân tích dữ liệu. Chúng ta sẽ nói thêm về Pandas và cách làm việc với dữ liệu trong Python sau trong khóa học này.\n",
+    "> Chúng ta đang sử dụng một gói có tên là [**Pandas**](https://pandas.pydata.org/) ở đây để phân tích dữ liệu. Chúng ta sẽ thảo luận thêm về Pandas và cách làm việc với dữ liệu trong Python ở các phần sau của khóa học này.\n",
     "\n",
     "Hãy tính giá trị trung bình cho tuổi, chiều cao và cân nặng:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -342,19 +194,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -370,24 +220,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Chúng ta cũng có thể tạo các biểu đồ hộp cho các tập con của tập dữ liệu, ví dụ, được nhóm theo vai trò của người chơi.\n"
+    "Chúng ta cũng có thể tạo các biểu đồ hộp của các tập hợp con trong tập dữ liệu của mình, ví dụ, được nhóm theo vai trò của người chơi.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -409,19 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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m5Mtf/nIuu+yyXHXVVRk1alTfnBkAwP+xLgAAtXRQHe/W1ta0tLRk9uzZvbavX78+O3bs6LX9+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dWVxx9/fK/P193dna6url43AAAAGAiq7njfcccd+elPf5p169a9Zl97e3tGjRqVcePG9dre2NiY9vb2yjF7hu7d+3fv25vFixfnS1/6UrWlAjAAWeAKABhsqup4b968OZ/97GfzrW99K6NHjy5V02ssWrQonZ2dldvmzZsP23MDAADAoagqeK9fvz5bt27NO97xjowcOTIjR47MmjVrcsMNN2TkyJFpbGzM9u3bs23btl736+joSFNTU5KkqanpNauc7/599zGvVldXl/r6+l43AAAAGAiqCt7vfe9789hjj+XRRx+t3E455ZTMmzev8vMRRxyR1atXV+6zYcOGbNq0Kc3NzUmS5ubmPPbYY9m6dWvlmFWrVqW+vj7Tp0/vo9MCAACA/qGqOd5jx47N2972tl7bxowZkwkTJlS2n3/++Wlra8v48eNTX1+fiy66KM3NzZk1a1aS5Mwzz8z06dNz3nnn5dprr017e3suv/zytLa2pq6uro9OCwAAAPqHg7qc2Ou57rrrMnz48MydOzfd3d2ZM2dObrrppsr+ESNGZMWKFbnwwgvT3NycMWPGZP78+bn66qv7uhQAAACouUMO3j/60Y96/T569OgsXbo0S5cu3ed9pkyZknvuuedQnxoAAAD6vYO6jjcAAABwYPp8qDkAvJ49r9Pdl8cCAPRXOt4AAABQkOANAAAABQneAAAAUJDgDQAAAAVZXA0A4FUs7AdAX9LxBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChoZK0LAGDgmLpwZeXnjUtaalgJAMDAoeMNAAAABQneAAAAUJCh5gAAB8BUCwAOlo43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBFlcDAOgjey7AtieLsQEMbTreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AHLI957WaywoA0JuONwAAABSk4w0AUCWjPACoho43AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFAAAMFVMXrqz8vHFJSw0rAeBw0vEGAACAggRvAPrU1IUre3X1AACGOsEbAAAAChK8AQAAoCCLqwFQhOHmDBX+rwOwPzreAAAAUJDgDQAAAAUJ3gAAAFCQOd4AAIWZBw4wtOl4AwAAQEGCNwAAABQkeAMAAEBB5ngDcFDMWQUAODCCNwBADez55dXGJS01rASA0gw1BwAAgIJ0vAF4XYaUAwAcGh1vAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAglxODGAI2vMSYRuXtNSwEgCAwU/HG4CKqQtXum43AEAfE7wBAACgIMEbAAAACjLHGwCgn7IeA8DgoOMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQ4A0AAAAFWdUcgNfYcyVlAAAOTVUd72XLluWkk05KfX196uvr09zcnHvvvbey/+WXX05ra2smTJiQo48+OnPnzk1HR0evx9i0aVNaWlpy1FFHZeLEibn00kvzyiuv9M3ZAAAMQFMXrqzcABh8qgrexx57bJYsWZL169fnJz/5Sc4444x86EMfyuOPP54kueSSS3L33XfnzjvvzJo1a7Jly5acc845lfvv3LkzLS0t2b59ex588MHcdtttufXWW3PFFVf07VkBAABAP1HVUPMPfvCDvX7/m7/5myxbtiwPPfRQjj322Nx88825/fbbc8YZZyRJbrnllpxwwgl56KGHMmvWrPzgBz/IE088kfvvvz+NjY2ZMWNGvvzlL+eyyy7LVVddlVGjRvXdmQEAAEA/cNCLq+3cuTN33HFHXnrppTQ3N2f9+vXZsWNHZs+eXTnm+OOPz+TJk7N27dokydq1a3PiiSemsbGxcsycOXPS1dVV6ZrvTXd3d7q6unrdAAAAYCCoOng/9thjOfroo1NXV5fPfOYz+e53v5vp06envb09o0aNyrhx43od39jYmPb29iRJe3t7r9C9e//uffuyePHiNDQ0VG7HHXdctWUDAABATVQdvP/oj/4ojz76aB5++OFceOGFmT9/fp544okStVUsWrQonZ2dldvmzZuLPh8AAAD0laovJzZq1Kj84R/+YZLk5JNPzrp16/L3f//3+djHPpbt27dn27ZtvbreHR0daWpqSpI0NTXlkUce6fV4u1c9333M3tTV1aWurq7aUgEAAKDmDnqO9267du1Kd3d3Tj755BxxxBFZvXp1Zd+GDRuyadOmNDc3J0mam5vz2GOPZevWrZVjVq1alfr6+kyfPv1QSwEAAIB+p6qO96JFi3LWWWdl8uTJeeGFF3L77bfnRz/6Ub7//e+noaEh559/ftra2jJ+/PjU19fnoosuSnNzc2bNmpUkOfPMMzN9+vScd955ufbaa9Pe3p7LL788ra2tOtoAAAAMSlUF761bt+YTn/hEfvWrX6WhoSEnnXRSvv/97+d973tfkuS6667L8OHDM3fu3HR3d2fOnDm56aabKvcfMWJEVqxYkQsvvDDNzc0ZM2ZM5s+fn6uvvrpvzwoAYJCZunBlkmTjkpYaVwJAtaoK3jfffPPr7h89enSWLl2apUuX7vOYKVOm5J577qnmaQEAAGDAOuQ53gAAAMC+Cd4AAABQkOANAAAABQneAAAAUFBVi6sBMPjsXikZAIAydLwBAACgIB1vgEFsz262a//CwGAUCsDgo+MNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdUABgCLpAEADFw63gAAAFCQ4A0AAAAFGWoOADCAmHoCMPDoeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUNLLWBQDQt6YuXFnrEgAA2IPgDTBECOQAALVhqDkAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAU5DreAAPYntfm3rikpYaVAACwL4I3wCCxZwgHhgZfvgEMDIaaAwAAQEGCNwAAABRkqDnAAGNIOQDAwKLjDQAAAAUJ3gAAAFCQ4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJDgDQAAAAUJ3gAAAFDQyFoXAABA35q6cGXl541LWmpYCQCJjjcAAAAUJXgDAABAQYaaA/RTew4VBdgffzMA+i8dbwAAAChI8AYAAICCBG8AAAAoyBxvAIBBzKXFAGpPxxsAAAAK0vEGABhidMEBDi8dbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAAChI8AYAAICCBG8AAAAoSPAGAACAgkbWugAAAGpn6sKVlZ83LmmpYSUAg5eONwAAABQkeAMAAEBBhpoD1IjhnQAAQ4OONwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQVUF78WLF+ed73xnxo4dm4kTJ+bss8/Ohg0beh3z8ssvp7W1NRMmTMjRRx+duXPnpqOjo9cxmzZtSktLS4466qhMnDgxl156aV555ZVDPxsAAADoZ6oK3mvWrElra2seeuihrFq1Kjt27MiZZ56Zl156qXLMJZdckrvvvjt33nln1qxZky1btuScc86p7N+5c2daWlqyffv2PPjgg7ntttty66235oorrui7swIAAIB+YlhPT0/Pwd75ueeey8SJE7NmzZq8+93vTmdnZ97whjfk9ttvz5//+Z8nSZ588smccMIJWbt2bWbNmpV77703f/Znf5YtW7aksbExSbJ8+fJcdtllee655zJq1Kj9Pm9XV1caGhrS2dmZ+vr6gy0foKb2dzmxPfcD9IXdf2sO5O+LyxwCvL5qcukhzfHu7OxMkowfPz5Jsn79+uzYsSOzZ8+uHHP88cdn8uTJWbt2bZJk7dq1OfHEEyuhO0nmzJmTrq6uPP7443t9nu7u7nR1dfW6AQAAwEBw0MF7165dufjii3PaaaflbW97W5Kkvb09o0aNyrhx43od29jYmPb29soxe4bu3ft379ubxYsXp6GhoXI77rjjDrZsAAAAOKwOOni3trbmZz/7We64446+rGevFi1alM7Ozspt8+bNxZ8TAAAA+sLIg7nTggULsmLFijzwwAM59thjK9ubmpqyffv2bNu2rVfXu6OjI01NTZVjHnnkkV6Pt3vV893HvFpdXV3q6uoOplQAAACoqao63j09PVmwYEG++93v5oc//GGmTZvWa//JJ5+cI444IqtXr65s27BhQzZt2pTm5uYkSXNzcx577LFs3bq1csyqVatSX1+f6dOnH8q5AADwOqYuXGnhRoAaqKrj3dramttvvz133XVXxo4dW5mT3dDQkCOPPDINDQ05//zz09bWlvHjx6e+vj4XXXRRmpubM2vWrCTJmWeemenTp+e8887Ltddem/b29lx++eVpbW3V1QYAAGDQqSp4L1u2LEly+umn99p+yy235JOf/GSS5Lrrrsvw4cMzd+7cdHd3Z86cObnpppsqx44YMSIrVqzIhRdemObm5owZMybz58/P1VdffWhnAjAI6EQBAAw+VQXvA7nk9+jRo7N06dIsXbp0n8dMmTIl99xzTzVPDQAAAAPSQS2uBsCB27OLvXFJSw0rAQCgFgRvgMPIUHIAgKHnoK/jDQAAAOyf4A0AAAAFCd4AAABQkOANAAAABQneAAAAUJBVzQH6AaudAwAMXoI3AACvsecXghuXtNSwEoCBz1BzAAAAKEjHGwCA16X7DXBodLwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoKCRtS4AYLCYunBl5eeNS1pqWAkAAP2JjjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAB2zqwpW9FpMEYP8EbwAAAChI8AYAAICCBG8AAAAoaGStCwAYjMx/BABgNx1vAAAAKEjwBgAAgIIEbwAAACjIHG+AQ2Q+NzAU7fm3b+OSlhpWAtD/Cd4AABwSIRzg9RlqDgAAAAUJ3gAAAFCQ4A0AAAAFmeMNcIDMYQQA4GDoeAMAAEBBOt4AB8ElxAD2z0ghgN/S8QYAAICCBG8AAAAoSPAGAACAggRvAAAAKEjwBgAAgIIEbwAAACjI5cQAAOgzfXG5RZchAwYbwRvgdbheNwAAh8pQcwAAAChI8AYAAICCDDUHAKA487aBoUzwBngV87oBAOhLgjcAAAOWTjowEJjjDQAAAAUJ3gAAAFCQoeYAANSc9TWAwUzHGwAAAArS8QaITgsAAOXoeAMAAEBBgjcAAAAUJHgDAABAQYI3AACH1dSFK62tAQwpgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQ0stYFANSSVXUBAChNxxsAAAAKErwBAACgIMEbAAAACjLHGwCAmrDOBjBUCN4AAPRbe4bzjUta9rodoL8TvIEhx4c1AAAOJ3O8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgoKqD9wMPPJAPfvCDmTRpUoYNG5bvfe97vfb39PTkiiuuyDHHHJMjjzwys2fPzlNPPdXrmOeffz7z5s1LfX19xo0bl/PPPz8vvvjiIZ0IAAAA9EdVB++XXnopb3/727N06dK97r/22mtzww03ZPny5Xn44YczZsyYzJkzJy+//HLlmHnz5uXxxx/PqlWrsmLFijzwwAP59Kc/ffBnAbAfUxeurNwAAOBwqvo63meddVbOOuusve7r6enJ9ddfn8svvzwf+tCHkiT/9E//lMbGxnzve9/Lueeem5///Oe57777sm7dupxyyilJkhtvvDEf+MAH8rWvfS2TJk16zeN2d3enu7u78ntXV1e1ZQMAAEBN9Okc72eeeSbt7e2ZPXt2ZVtDQ0NmzpyZtWvXJknWrl2bcePGVUJ3ksyePTvDhw/Pww8/vNfHXbx4cRoaGiq34447ri/LBgAAgGL6NHi3t7cnSRobG3ttb2xsrOxrb2/PxIkTe+0fOXJkxo8fXznm1RYtWpTOzs7KbfPmzX1ZNjDAGUYOAEB/VvVQ81qoq6tLXV1drcsAAACAqvVp8G5qakqSdHR05Jhjjqls7+joyIwZMyrHbN26tdf9XnnllTz//POV+wP0BR1wgMHF33VgoOrToebTpk1LU1NTVq9eXdnW1dWVhx9+OM3NzUmS5ubmbNu2LevXr68c88Mf/jC7du3KzJkz+7IcAAAAqLmqO94vvvhinn766crvzzzzTB599NGMHz8+kydPzsUXX5xrrrkmb37zmzNt2rR88YtfzKRJk3L22WcnSU444YS8//3vzwUXXJDly5dnx44dWbBgQc4999y9rmgOAAAAA1nVwfsnP/lJ3vOe91R+b2trS5LMnz8/t956az7/+c/npZdeyqc//els27Yt73rXu3Lfffdl9OjRlft861vfyoIFC/Le9743w4cPz9y5c3PDDTf0wekAg9GeQws3LmmpYSUAAFC9YT09PT21LqJaXV1daWhoSGdnZ+rr62tdDlDY/oK3OX8AJL6cBQ6vanLpgFjVHAAAqmG0FNCf9OniagAAAEBvgjcAAEPG1IUrTVECDjvBGwAAAAoyxxsAgEFNhxuoNR1vAAAAKEjwBgAAgIIMNQf6DZd+AQBgMNLxBgAAgIIEbwAAACjIUHNgQDEcHQCAgUbHGwAAAAoSvAEAAKAgQ82BfmnPIeUAADCQ6XgDAABAQYI3AAAAFGSoOQAAg4JpSkB/peMNAAAABQneAAAAUJDgDQAAAAUJ3gAAAFCQxdWAw2bPRW82Lmnp08cDgJL6+j0MGFoEbwAAhhxBGjicBG8AAPg/AjlQgjneAAAAUJCONwAA7IW1RIC+IngDADCkCdhAaYaaAwAAQEGCN1ATUxeu1GEAAGBIMNQcKEq4BgBgqBO8gZoSzAEAGOwMNQcAgCqYLgVUS/AGAACAggRvAAAAKEjwBgAAgIIEbwAAACjIquZAn7PgDABDzZ7vfRuXtNSwEqA/0vEGAACAggRvAAAAKMhQc+CgGVYHAAdn93uo908YGnS8AQAAoCAdbwAA6ENGhAGvJngDfcJK5gAAsHeCN1A1IRsAAA6c4A3sM0jvOTxO2AYAgIMjeAP7JGwDAMChE7wBAOAg+IIaOFCCNwAAHAZ7C+pWQIehwXW8AQAAoCDBGwAABqCpC1ca7g4DhKHmAABQiGAMJII3AAD0a+aBw8BnqDkAAAAUJHgDAABAQYaaAwDAAGHOOAxMgjcAAPQzAjYMLoI3DAH7WpTFmzoAAJQneAMAQD/gC3EYvARvAAAYwFxuDPo/wRsGqL19K+7NFgAA+h/BGwYR33gDAED/4zreAAAAUJCONwxSFmgBAID+QfAGAIAhxNQ0OPwEbxhAdLEBgAMlYEP/IXgDAMAgUfJLekEeDp7gDTW0rzdHb2YAADB4WNUcqjR14UpDvgEAgAOm4w19rL8Pw/KlAQCw2+7PBf3xMwsMJjreAAAAUJCONwAADHIHO+KtL0bK9ffRgHA4CN5wAPrizaqaNxrDwQGAw6nazyx7+6wiVMO+Cd5QA4I1ANBfHe6GAwwFgjdDUl+8MXhzAQCojs9PDFWCNxwmutwAAL8jhDOUCN4MefsKxN4AAAD6ByGdgU7whn2opkOtmw0A8Dt9vRo6DHSCNwPagXz76Y82AMDAcCCf23S/GYgEbwYlYRsAYOAYKJ/dhH4OVs2C99KlS/PVr3417e3tefvb354bb7wxp556aq3K4RBU03Uu+QdqoPzBBgCgnIO9JrkgTUk1Cd7f/va309bWluXLl2fmzJm5/vrrM2fOnGzYsCETJ06sRUlF1TJ07vmch1pHX1+Ca1/2VjMAALza/j6fVvP5tdoFd2t5eVqd94GnJsH77/7u73LBBRfkU5/6VJJk+fLlWblyZf7xH/8xCxcufM3x3d3d6e7urvze2dmZJOnq6jo8BR+iXd3/L0nvet925ff3euzPvjTnkJ7j1fZ8zv3Vsb/n3vM59va4r/fY1Zh8yZ0HdT8AAIau/X2GPNjPqQfy2bSaXLKv5979PPv6TL6v++3tuav5jL8vffEYA+E5D8Xuf/uenp79Hjus50CO6kPbt2/PUUcdle985zs5++yzK9vnz5+fbdu25a677nrNfa666qp86UtfOoxVAgAAwP5t3rw5xx577Osec9g73r/+9a+zc+fONDY29tre2NiYJ598cq/3WbRoUdra2iq/79q1K88//3wmTJiQYcOGFa33UHV1deW4447L5s2bU19fX+tyoN/zmoHqed1A9bxuoHpeN7319PTkhRdeyKRJk/Z77IBY1byuri51dXW9to0bN642xRyk+vp6/zmhCl4zUD2vG6ie1w1Uz+vmdxoaGg7ouOGF63iN3//938+IESPS0dHRa3tHR0eampoOdzkAAABQ1GEP3qNGjcrJJ5+c1atXV7bt2rUrq1evTnNz8+EuBwAAAIqqyVDztra2zJ8/P6ecckpOPfXUXH/99XnppZcqq5wPJnV1dbnyyitfM1Qe2DuvGaie1w1Uz+sGqud1c/AO+6rmu33961/PV7/61bS3t2fGjBm54YYbMnPmzFqUAgAAAMXULHgDAADAUHDY53gDAADAUCJ4AwAAQEGCNwAAABQkeAMAAEBBgncNdHd3Z8aMGRk2bFgeffTRWpcD/dbGjRtz/vnnZ9q0aTnyyCPzpje9KVdeeWW2b99e69KgX1m6dGmmTp2a0aNHZ+bMmXnkkUdqXRL0W4sXL8473/nOjB07NhMnTszZZ5+dDRs21LosGDCWLFmSYcOG5eKLL651KQOK4F0Dn//85zNp0qRalwH93pNPPpldu3blG9/4Rh5//PFcd911Wb58eb7whS/UujToN7797W+nra0tV155ZX7605/m7W9/e+bMmZOtW7fWujTol9asWZPW1tY89NBDWbVqVXbs2JEzzzwzL730Uq1Lg35v3bp1+cY3vpGTTjqp1qUMOC4ndpjde++9aWtry7/927/lrW99a/7zP/8zM2bMqHVZMGB89atfzbJly/KLX/yi1qVAvzBz5sy8853vzNe//vUkya5du3LcccfloosuysKFC2tcHfR/zz33XCZOnJg1a9bk3e9+d63LgX7rxRdfzDve8Y7cdNNNueaaazJjxoxcf/31tS5rwNDxPow6OjpywQUX5J//+Z9z1FFH1bocGJA6Ozszfvz4WpcB/cL27duzfv36zJ49u7Jt+PDhmT17dtauXVvDymDg6OzsTBLvLbAfra2taWlp6fWew4EbWesChoqenp588pOfzGc+85mccsop2bhxY61LggHn6aefzo033pivfe1rtS4F+oVf//rX2blzZxobG3ttb2xszJNPPlmjqmDg2LVrVy6++OKcdtppedvb3lbrcqDfuuOOO/LTn/4069atq3UpA5aO9yFauHBhhg0b9rq3J598MjfeeGNeeOGFLFq0qNYlQ80d6OtmT88++2ze//735yMf+UguuOCCGlUOwGDS2tqan/3sZ7njjjtqXQr0W5s3b85nP/vZfOtb38ro0aNrXc6AZY73IXruuefym9/85nWPeeMb35iPfvSjufvuuzNs2LDK9p07d2bEiBGZN29ebrvtttKlQr9xoK+bUaNGJUm2bNmS008/PbNmzcqtt96a4cN9ZwjJb4eaH3XUUfnOd76Ts88+u7J9/vz52bZtW+66667aFQf93IIFC3LXXXflgQceyLRp02pdDvRb3/ve9/LhD384I0aMqGzbuXNnhg0bluHDh6e7u7vXPvZO8D5MNm3alK6ursrvW7ZsyZw5c/Kd73wnM2fOzLHHHlvD6qD/evbZZ/Oe97wnJ598cv7lX/7FH3Z4lZkzZ+bUU0/NjTfemOS3Q2cnT56cBQsWWFwN9qKnpycXXXRRvvvd7+ZHP/pR3vzmN9e6JOjXXnjhhfzP//xPr22f+tSncvzxx+eyyy4zTeMAmeN9mEyePLnX70cffXSS5E1vepPQDfvw7LPP5vTTT8+UKVPyta99Lc8991xlX1NTUw0rg/6jra0t8+fPzymnnJJTTz01119/fV566aV86lOfqnVp0C+1trbm9ttvz1133ZWxY8emvb09SdLQ0JAjjzyyxtVB/zN27NjXhOsxY8ZkwoQJQncVBG+g31q1alWefvrpPP3006/5gspgHfitj33sY3nuuedyxRVXpL29PTNmzMh99933mgXXgN9atmxZkuT000/vtf2WW27JJz/5ycNfEDAkGGoOAAAABVmhCAAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAAChK8AQAAoCDBGwAAAAoSvAEAAKAgwRsAAAAKErwBAACgIMEbAAAACvr/ciHiWioJ+MUAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vì hầu hết các giá trị trong thực tế phân bố chuẩn, chúng ta không nên sử dụng bộ tạo số ngẫu nhiên phân bố đều để tạo dữ liệu mẫu. Đây là điều xảy ra nếu chúng ta cố gắng tạo trọng lượng với phân bố đều (được tạo bởi `np.random.rand`):\n"
+    "Vì hầu hết các giá trị trong thực tế phân phối chuẩn, chúng ta không nên sử dụng bộ tạo số ngẫu nhiên phân phối đều để tạo dữ liệu mẫu. Đây là điều xảy ra nếu chúng ta cố gắng tạo trọng lượng với phân phối đều (được tạo bởi `np.random.rand`):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## Khoảng tin cậy\n",
     "\n",
-    "Bây giờ, chúng ta hãy tính các khoảng tin cậy cho cân nặng và chiều cao của các cầu thủ bóng chày. Chúng ta sẽ sử dụng đoạn mã [từ cuộc thảo luận trên stackoverflow này](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
+    "Bây giờ, chúng ta hãy tính toán khoảng tin cậy cho cân nặng và chiều cao của các cầu thủ bóng chày. Chúng ta sẽ sử dụng đoạn mã [từ cuộc thảo luận trên stackoverflow này](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -593,14 +434,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Kiểm Định Giả Thuyết\n",
+    "## Kiểm định giả thuyết\n",
     "\n",
     "Hãy cùng khám phá các vai trò khác nhau trong tập dữ liệu cầu thủ bóng chày của chúng ta:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,17 +545,17 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
@@ -724,16 +565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -755,15 +596,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -779,7 +620,7 @@
    "metadata": {},
    "source": [
     "Hai giá trị được trả về bởi hàm `ttest_ind` là:  \n",
-    "* p-value có thể được xem như xác suất để hai phân phối có cùng giá trị trung bình. Trong trường hợp của chúng ta, giá trị này rất thấp, điều này có nghĩa là có bằng chứng mạnh mẽ cho thấy các cầu thủ chơi ở vị trí first basemen cao hơn.  \n",
+    "* p-value có thể được xem như xác suất hai phân phối có cùng giá trị trung bình. Trong trường hợp của chúng ta, giá trị này rất thấp, nghĩa là có bằng chứng mạnh mẽ cho thấy các cầu thủ ở vị trí gôn một cao hơn.  \n",
     "* t-value là giá trị trung gian của sự khác biệt trung bình đã được chuẩn hóa, được sử dụng trong kiểm định t-test, và nó được so sánh với một giá trị ngưỡng cho một mức độ tin cậy nhất định.  \n"
    ]
   },
@@ -787,26 +628,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Mô phỏng phân phối chuẩn với định lý giới hạn trung tâm\n",
+    "## Mô phỏng phân phối chuẩn với Định lý Giới hạn Trung tâm\n",
     "\n",
     "Bộ tạo số giả ngẫu nhiên trong Python được thiết kế để cung cấp cho chúng ta một phân phối đều. Nếu chúng ta muốn tạo một bộ tạo cho phân phối chuẩn, chúng ta có thể sử dụng định lý giới hạn trung tâm. Để có được một giá trị phân phối chuẩn, chúng ta chỉ cần tính trung bình của một mẫu được tạo ngẫu nhiên theo phân phối đều.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -828,19 +667,19 @@
    "source": [
     "## Mối tương quan và Công ty Bóng chày Xấu xa\n",
     "\n",
-    "Mối tương quan cho phép chúng ta tìm ra mối quan hệ giữa các chuỗi dữ liệu. Trong ví dụ minh họa của chúng ta, hãy giả sử có một công ty bóng chày xấu xa trả lương cho các cầu thủ dựa trên chiều cao của họ - cầu thủ càng cao thì càng nhận được nhiều tiền. Giả sử có một mức lương cơ bản là $1000, và một khoản thưởng bổ sung từ $0 đến $100, tùy thuộc vào chiều cao. Chúng ta sẽ lấy các cầu thủ thực sự từ MLB và tính toán mức lương tưởng tượng của họ:\n"
+    "Mối tương quan cho phép chúng ta tìm ra mối quan hệ giữa các chuỗi dữ liệu. Trong ví dụ minh họa của chúng ta, hãy giả sử có một công ty bóng chày xấu xa trả lương cho các cầu thủ dựa trên chiều cao của họ - cầu thủ càng cao thì càng được trả nhiều tiền. Giả sử có một mức lương cơ bản là $1000, và một khoản thưởng bổ sung từ $0 đến $100, tùy thuộc vào chiều cao. Chúng ta sẽ lấy dữ liệu từ các cầu thủ thực sự trong MLB và tính toán mức lương tưởng tượng của họ:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -854,12 +693,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bây giờ hãy tính toán hiệp phương sai và hệ số tương quan của các dãy số đó. `np.cov` sẽ cung cấp cho chúng ta một cái gọi là **ma trận hiệp phương sai**, đây là một sự mở rộng của hiệp phương sai cho nhiều biến. Phần tử $M_{ij}$ của ma trận hiệp phương sai $M$ là hệ số tương quan giữa các biến đầu vào $X_i$ và $X_j$, và các giá trị trên đường chéo $M_{ii}$ là phương sai của $X_{i}$. Tương tự, `np.corrcoef` sẽ cung cấp cho chúng ta **ma trận tương quan**.\n"
+    "Bây giờ hãy tính hiệp phương sai và hệ số tương quan của các dãy đó. `np.cov` sẽ cho chúng ta một **ma trận hiệp phương sai**, đây là một mở rộng của hiệp phương sai cho nhiều biến. Phần tử $M_{ij}$ của ma trận hiệp phương sai $M$ là hệ số tương quan giữa các biến đầu vào $X_i$ và $X_j$, và các giá trị đường chéo $M_{ii}$ là phương sai của $X_{i}$. Tương tự, `np.corrcoef` sẽ cho chúng ta **ma trận tương quan**.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -867,10 +706,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -887,19 +726,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -917,14 +754,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -937,19 +774,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Trong trường hợp này, mối tương quan nhỏ hơn một chút, nhưng nó vẫn khá cao. Bây giờ, để làm cho mối quan hệ ít rõ ràng hơn, chúng ta có thể muốn thêm một số yếu tố ngẫu nhiên bằng cách thêm một biến ngẫu nhiên vào mức lương. Hãy xem điều gì xảy ra:\n"
+    "Trong trường hợp này, mối tương quan nhỏ hơn một chút, nhưng nó vẫn khá cao. Bây giờ, để làm cho mối quan hệ ít rõ ràng hơn, chúng ta có thể muốn thêm một số yếu tố ngẫu nhiên bằng cách thêm một biến ngẫu nhiên vào mức lương. Hãy xem điều gì sẽ xảy ra:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -960,19 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -987,24 +822,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> Bạn có đoán được tại sao các dấu chấm lại xếp thành các đường thẳng đứng như thế này không?\n",
+    "> Bạn có đoán được tại sao các chấm lại xếp thành các đường thẳng đứng như thế này không?\n",
     "\n",
-    "Chúng tôi đã quan sát mối tương quan giữa một khái niệm được tạo ra nhân tạo như lương và biến quan sát *chiều cao*. Hãy cùng xem liệu hai biến quan sát, chẳng hạn như chiều cao và cân nặng, có tương quan với nhau không:\n"
+    "Chúng tôi đã quan sát mối tương quan giữa một khái niệm được tạo ra một cách nhân tạo như lương và biến quan sát *chiều cao*. Hãy cùng xem liệu hai biến quan sát, chẳng hạn như chiều cao và cân nặng, có tương quan với nhau không:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,16 +852,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Thật không may, chúng ta không nhận được bất kỳ kết quả nào - chỉ có một số giá trị `nan` kỳ lạ. Điều này là do một số giá trị trong chuỗi của chúng ta không được xác định, được biểu thị dưới dạng `nan`, dẫn đến kết quả của phép toán cũng không được xác định. Khi xem xét ma trận, chúng ta có thể thấy rằng cột `Weight` là vấn đề, vì tự tương quan giữa các giá trị `Height` đã được tính toán.\n",
+    "Rất tiếc, chúng ta không nhận được kết quả nào - chỉ có một số giá trị `nan` kỳ lạ. Điều này xảy ra do một số giá trị trong chuỗi của chúng ta không được xác định, được biểu thị bằng `nan`, dẫn đến kết quả của phép toán cũng không được xác định. Khi xem xét ma trận, chúng ta có thể thấy rằng cột `Weight` là vấn đề, vì sự tự tương quan giữa các giá trị `Height` đã được tính toán.\n",
     "\n",
     "> Ví dụ này cho thấy tầm quan trọng của việc **chuẩn bị dữ liệu** và **làm sạch dữ liệu**. Nếu không có dữ liệu phù hợp, chúng ta không thể tính toán được gì.\n",
     "\n",
-    "Hãy sử dụng phương thức `fillna` để điền các giá trị bị thiếu và tính toán mối tương quan:\n"
+    "Hãy sử dụng phương pháp `fillna` để điền vào các giá trị bị thiếu và tính toán sự tương quan:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +871,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,27 +887,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1083,14 +916,14 @@
    "source": [
     "## Kết luận\n",
     "\n",
-    "Trong notebook này, chúng ta đã học cách thực hiện các thao tác cơ bản trên dữ liệu để tính các hàm thống kê. Chúng ta hiện đã biết cách sử dụng các công cụ toán học và thống kê một cách hiệu quả để chứng minh một số giả thuyết, cũng như cách tính khoảng tin cậy cho các biến bất kỳ dựa trên mẫu dữ liệu.\n"
+    "Trong notebook này, chúng ta đã học cách thực hiện các thao tác cơ bản trên dữ liệu để tính các hàm thống kê. Chúng ta hiện đã biết cách sử dụng một bộ công cụ toán học và thống kê vững chắc để chứng minh một số giả thuyết, cũng như cách tính khoảng tin cậy cho các biến bất kỳ dựa trên một mẫu dữ liệu.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
    ]
   }
  ],
@@ -1113,11 +946,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:40:45+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:42:27+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "vi"
   }
diff --git a/translations/vi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/vi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 4b5dccca..106c384a 100644
--- a/translations/vi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/vi/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -6,7 +6,7 @@
     "## Giới thiệu về Xác suất và Thống kê\n",
     "## Bài tập\n",
     "\n",
-    "Trong bài tập này, chúng ta sẽ sử dụng tập dữ liệu của các bệnh nhân tiểu đường được lấy [từ đây](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
+    "Trong bài tập này, chúng ta sẽ sử dụng bộ dữ liệu của các bệnh nhân tiểu đường lấy [từ đây](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
    ],
    "metadata": {}
   },
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,17 +150,16 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Trong tập dữ liệu này, các cột bao gồm:\n",
-    "\n",
-    "* Tuổi và giới tính đã rõ ràng\n",
-    "* BMI là chỉ số khối cơ thể\n",
-    "* BP là huyết áp trung bình\n",
-    "* S1 đến S6 là các phép đo máu khác nhau\n",
-    "* Y là thước đo định tính về mức độ tiến triển của bệnh trong một năm\n",
+    "Trong tập dữ liệu này, các cột như sau:  \n",
+    "* Tuổi và giới tính là dễ hiểu  \n",
+    "* BMI là chỉ số khối cơ thể  \n",
+    "* BP là huyết áp trung bình  \n",
+    "* S1 đến S6 là các phép đo máu khác nhau  \n",
+    "* Y là thước đo định tính về mức độ tiến triển của bệnh trong một năm  \n",
     "\n",
-    "Hãy nghiên cứu tập dữ liệu này bằng cách sử dụng các phương pháp xác suất và thống kê.\n",
+    "Hãy nghiên cứu tập dữ liệu này bằng các phương pháp xác suất và thống kê.\n",
     "\n",
-    "### Nhiệm vụ 1: Tính giá trị trung bình và phương sai cho tất cả các giá trị\n"
+    "### Nhiệm vụ 1: Tính giá trị trung bình và phương sai cho tất cả các giá trị  \n"
    ],
    "metadata": {}
   },
@@ -355,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -447,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -486,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -536,8 +535,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -854,10 +853,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -884,9 +883,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -915,7 +914,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
    ]
   }
  ],
@@ -941,8 +940,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:57:35+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:42:53+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "vi"
   }
diff --git a/translations/zh/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/zh/1-Introduction/04-stats-and-probability/assignment.ipynb
index e04576ea..90e8436d 100644
--- a/translations/zh/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/translations/zh/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 概率与统计学简介\n",
+    "## 概率与统计简介\n",
     "## 作业\n",
     "\n",
     "在本次作业中，我们将使用[此处](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)提供的糖尿病患者数据集。\n"
@@ -14,10 +14,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -200,9 +200,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 任务 4：测试不同变量与疾病进展 (Y) 之间的相关性\n",
+    "### 任务 4：测试不同变量与疾病进展（Y）之间的相关性\n",
     "\n",
-    "> **提示** 相关性矩阵可以为您提供最有用的信息，帮助判断哪些值是相关联的。\n"
+    "> **提示** 相关性矩阵可以为您提供最有用的信息，帮助判断哪些值是相关的。\n"
    ],
    "metadata": {}
   },
@@ -214,7 +214,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### 任务5：检验糖尿病进展程度在男性和女性之间是否存在差异的假设\n"
+    "### 任务5：检验糖尿病进展程度在男性和女性之间是否存在差异\n"
    ],
    "metadata": {}
   },
@@ -253,8 +253,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "defe9f96b3d327a6f37d795c43ad0219",
-   "translation_date": "2025-09-02T09:49:08+00:00",
+   "original_hash": "6d945fd15163f60cb473dbfe04b2d100",
+   "translation_date": "2025-09-06T17:10:07+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
    "language_code": "zh"
   }
diff --git a/translations/zh/1-Introduction/04-stats-and-probability/notebook.ipynb b/translations/zh/1-Introduction/04-stats-and-probability/notebook.ipynb
index a97ca90e..2dc161e0 100644
--- a/translations/zh/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/translations/zh/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,12 +5,12 @@
    "metadata": {},
    "source": [
     "# 概率与统计简介\n",
-    "在本笔记中，我们将实践一些之前讨论过的概念。概率与统计中的许多概念在 Python 的主要数据处理库中得到了很好的体现，例如 `numpy` 和 `pandas`。\n"
+    "在本笔记中，我们将实践一些之前讨论过的概念。概率和统计的许多概念在 Python 的主要数据处理库中得到了良好的体现，例如 `numpy` 和 `pandas`。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "## 随机变量和分布\n",
-    "我们先从0到9的均匀分布中抽取一个包含30个值的样本。我们还将计算均值和方差。\n"
+    "让我们从0到9的均匀分布中抽取一个包含30个值的样本。我们还将计算均值和方差。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
+      "Sample: [0, 8, 1, 0, 7, 4, 3, 3, 6, 7, 1, 0, 6, 3, 1, 5, 9, 2, 4, 2, 5, 6, 8, 7, 1, 9, 8, 2, 3, 7]\n",
+      "Mean = 4.266666666666667\n",
+      "Variance = 8.195555555555556\n"
      ]
     }
    ],
@@ -59,19 +59,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -91,194 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Name, Team, Role, Weight, Height, Age]\n",
+      "Index: []\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
+    "df\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> 我们在这里使用一个名为 [**Pandas**](https://pandas.pydata.org/) 的库进行数据分析。在本课程的后续部分，我们会更详细地讨论 Pandas 以及如何在 Python 中处理数据。\n",
+    "我们在这里使用一个名为 [**Pandas**](https://pandas.pydata.org/) 的库进行数据分析。稍后在本课程中，我们会详细讨论 Pandas 以及如何在 Python 中处理数据。\n",
     "\n",
-    "让我们计算年龄、身高和体重的平均值：\n"
+    "现在让我们计算年龄、身高和体重的平均值：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
+       "Height    201.726306\n",
+       "Weight     73.697292\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
+      "[180, 215, 210, 210, 188, 176, 209, 200, 231, 180, 188, 180, 185, 160, 180, 185, 197, 189, 185, 219]\n"
      ]
     }
    ],
@@ -313,16 +165,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
+      "Mean = 201.72630560928434\n",
+      "Variance = 441.6355706557866\n",
+      "Standard Deviation = 21.01512718628623\n"
      ]
     }
    ],
@@ -337,24 +189,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "除了均值之外，查看中位数和四分位数也是有意义的。它们可以通过一个**箱线图**来可视化：\n"
+    "除了平均值，查看中位数和四分位数也是有意义的。它们可以通过一个**箱线图**来可视化：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
+       "<Figure size 1000x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -375,19 +225,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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VeP14VFSU+vTp4/DIMEmaPHmyfZqXX35Zq1evVseOHfXggw+qRYsWOnPmjH744QetXLlSZ86ckSQ9+OCDevPNN3XvvfcqIyNDderU0UcffSR/f/+rWsci+/fv18CBA3Xrrbdqw4YN+vjjj3X33XcXezb3DTfcoKioKPtNy9q0aeNUO5dq0aKF+vXrp3//+9965plndP3112v48OF67733dPbsWcXExGjjxo2aO3euYmNj1bNnz8sua9iwYfrvf/+rhx9+WKtXr1aXLl1UWFionTt36r///a++/vprh/sdAADgwMxbpwMAXF9JjwyrXLmyrXXr1ra3337bZrVaHab/448/bGPGjLHVrVvX5uPjY2vatKntX//6l326jIwMm7e3t8NjwGw2m62goMDWvn17W926dW2///67zWa78MisgIAA2759+2y9e/e2+fv722rXrm177rnnbIWFhQ7z65JHhtlsNtsPP/xg69Onjy0wMNDm7+9v69mzp239+vXF1vH999+3NW7c2Obl5XVVj8P69NNPbZGRkTZfX19bVFSUbcmSJbbBgwfbIiMj7dMUPTLsX//6V4nLWLlypa1Lly42Pz8/W3BwsO22226zbd++vdh0K1assEVFRdkqVapka9asme3jjz++7CPD4uPjbR9//LGtadOmNl9fX9sNN9xQ4rocP37cFh8fb2vQoIHNx8fHFhoaarv55ptt7733nsN0Bw8etA0cONDm7+9vq1mzpu3RRx+1LV++3KlHhm3fvt02ZMgQW1BQkK1atWq2kSNH2vLy8kqcZ+rUqTZJtilTplxx2ReLiYmxtWzZssRxRY9yK/q7sFgstsmTJ9vCw8NtPj4+tgYNGtgmTJhgO3fuXLFlXvzIMJvNZjt//rztlVdesbVs2dLm6+trq1atmq1t27a2yZMn2zIzM6+6XgBAxeNhs/3v+RwAALiY++67T4sWLbrqI8hmat26tWrVqqWUlBRT2vfw8FB8fHyxU/vLk9dee01jxozRgQMH1LBhQ7PLAQDgmuCabgAAnGCxWOw3kCuyZs0abdmyRT169DCnKDdgs9n0wQcfKCYmhsANAHArXNMNAIATDh8+rFtuuUVDhw5V3bp1tXPnTr3zzjsKDQ3Vww8/bHZ55U5OTo6WLFmi1atXa+vWrVq8eLHZJQEAcE0RugEAcEK1atXUtm1b/fvf/9bJkycVEBCg/v376+WXX1aNGjXMLq/cOXnypO6++25VrVpVEydO1MCBA80uCQCAa4prugEAAAAAMAjXdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAFcR9992nsLCwUs8bGBh4bQsCAKACIHQDAOBi5syZIw8PD23atKnE8T169FBUVFQZV3V1cnNzNWnSJK1Zs8bsUgAAcAneZhcAAADKxvvvvy+r1WpoG7m5uZo8ebKkC18OAABQ0RG6AQCoIHx8fMwuAQCACofTywEAcAMff/yx2rZtKz8/P1WvXl133XWXfv31V4dpSrqm+/Tp0xo2bJiCg4NVtWpVDR8+XFu2bJGHh4fmzJlTrJ3Dhw8rNjZWgYGBqlWrlsaNG6fCwkJJ0oEDB1SrVi1J0uTJk+Xh4SEPDw9NmjTJiFUGAKBc4Eg3AAAuKjMzU6dOnSo23GKxOLx+6aWX9Mwzz+iOO+7Q3//+d508eVJvvPGGunfvrh9//FFVq1YtcflWq1W33XabNm7cqEceeUSRkZFavHixhg8fXuL0hYWF6tOnjzp27Khp06Zp5cqVmj59upo0aaJHHnlEtWrV0ttvv61HHnlEt99+u+Li4iRJrVq1+mu/CAAAyjFCNwAALuqWW2657LiWLVtKkg4ePKjnnntOL774oiZOnGgfHxcXpxtuuEFvvfWWw/CLJScna8OGDZo5c6YeffRRSdIjjzyiXr16lTj9uXPndOedd+qZZ56RJD388MNq06aNPvjgAz3yyCMKCAjQkCFD9Mgjj6hVq1YaOnRoqdYbAAB3QugGAMBFzZo1S9ddd12x4QkJCfZTupOSkmS1WnXHHXc4HBUPDQ1V06ZNtXr16suG7uXLl8vHx0cPPvigfZinp6fi4+O1atWqEud5+OGHHV5369ZNH330kdPrBgBARUHoBgDARXXo0EHt2rUrNrxatWr2gL1nzx7ZbDY1bdq0xGVc6eZpBw8eVJ06deTv7+8wPCIiosTpK1eubL9m++Jafv/99yuuBwAAFRmhGwCAcsxqtcrDw0PLli2Tl5dXsfGBgYHXrK2Slg8AAK6M0A0AQDnWpEkT2Ww2hYeHl3gq+pU0atRIq1evVm5ursPR7r1795a6Hg8Pj1LPCwCAO+KRYQAAlGNxcXHy8vLS5MmTZbPZHMbZbDadPn36svP26dNHFotF77//vn2Y1WrVrFmzSl1PUXg/e/ZsqZcBAIA74Ug3AADlWJMmTfTiiy9qwoQJOnDggGJjYxUUFKT9+/fr888/10MPPaRx48aVOG9sbKw6dOighIQE7d27V5GRkVqyZInOnDkjqXRHrf38/NSiRQstWLBA1113napXr66oqChFRUX9pfUEAKC84kg3AADl3Pjx4/XZZ5/J09NTkydP1rhx47RkyRL17t1bAwcOvOx8Xl5e+vLLL3XnnXdq7ty5euqpp1S3bl37ke7KlSuXqp5///vfqlevnsaMGaO//e1vWrRoUamWAwCAO/CwXXouGgAAqNCSk5N1++23a926derSpYvZ5QAAUK4RugEAqMDy8vLk5+dnf11YWKjevXtr06ZNOnbsmMM4AADgPK7pBgCgAhs1apTy8vLUuXNn5efnKykpSevXr9eUKVMI3AAAXAMc6QYAoAKbP3++pk+frr179+rcuXOKiIjQI488opEjR5pdGgAAboHQDQAAAACAQbh7OQAAAAAABiF0AwAAAABgkHJ5IzWr1aojR44oKChIHh4eZpcDAAAAAKhgbDab/vjjD9WtW1eenpc/nl0uQ/eRI0fUoEEDs8sAAAAAAFRwv/76q+rXr3/Z8eUydAcFBUm6sHLBwcEmV/PXWSwWrVixQr1795aPj4/Z5eAS9I9ro39cF33j2ugf10b/uDb6x3XRN67N3fonKytLDRo0sOfTyymXobvolPLg4GC3Cd3+/v4KDg52iz8+d0P/uDb6x3XRN66N/nFt9I9ro39cF33j2ty1f/7skmdupAYAAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAACg3CssLFRqaqrWrl2r1NRUFRYWml0SIInQDQAAAKCcS0pKUkREhHr16qUZM2aoV69eioiIUFJSktmlAYRuAAAAAOVXUlKShgwZoujoaKWlpemTTz5RWlqaoqOjNWTIEII3TEfoBgAAAFAuFRYWKiEhQQMGDFBycrI6duwoPz8/dezYUcnJyRowYIDGjRvHqeYwFaEbAAAAQLmUlpamAwcOaOLEifL0dIw2np6emjBhgvbv36+0tDSTKgQI3QAAAADKqaNHj0qSoqKiShxfNLxoOsAMhG4AAAAA5VKdOnUkSdu2bStxfNHwoukAMxC6AQAAAJRL3bp1U1hYmKZMmSKr1eowzmq1KjExUeHh4erWrZtJFQKEbgAAAADllJeXl6ZPn66lS5cqNjZW6enpysvLU3p6umJjY7V06VJNmzZNXl5eZpeKCszb7AIAAAAAoLTi4uK0aNEiJSQkqHv37vbh4eHhWrRokeLi4kysDiB0AwAAACjn4uLiNGjQIK1evVrLli1T37591bNnT45wwyUQugEAAACUe15eXoqJiVFOTo5iYmII3HAZXNMNAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBnArdiYmJat++vYKCghQSEqLY2Fjt2rXLYZpjx45p2LBhCg0NVUBAgNq0aaPPPvvMYZozZ87onnvuUXBwsKpWraoRI0YoOzv7r68NAAAAAAAuxKnQnZqaqvj4eKWnpyslJUUWi0W9e/dWTk6OfZp7771Xu3bt0pIlS7R161bFxcXpjjvu0I8//mif5p577tHPP/+slJQULV26VGvXrtVDDz107dYKAAAAAAAX4O3MxMuXL3d4PWfOHIWEhCgjI0Pdu3eXJK1fv15vv/22OnToIEl6+umn9eqrryojI0M33HCDduzYoeXLl+v7779Xu3btJElvvPGG+vXrp2nTpqlu3brXYr0AAAAAADCdU6H7UpmZmZKk6tWr24fdeOONWrBggfr376+qVavqv//9r86dO6cePXpIkjZs2KCqVavaA7ck3XLLLfL09NR3332n22+/vVg7+fn5ys/Pt7/OysqSJFksFlkslr+yCi6haB3cYV3cEf3j2ugf10XfuDb6x7XRP66N/nFd9I1rc7f+udr18LDZbLbSNGC1WjVw4ECdPXtW69atsw8/e/as7rzzTq1YsULe3t7y9/fXwoUL1bt3b0nSlClTNHfu3GLXgoeEhGjy5Ml65JFHirU1adIkTZ48udjw+fPny9/fvzTlAwAAAABQarm5ubr77ruVmZmp4ODgy05X6iPd8fHx2rZtm0PglqRnnnlGZ8+e1cqVK1WzZk0lJyfrjjvuUFpamqKjo0vV1oQJEzR27Fj766ysLDVo0EC9e/e+4sqVFxaLRSkpKerVq5d8fHzMLgeXoH9cG/3juugb10b/uDb6x7XRP66LvnFt7tY/RWdg/5lShe6RI0fab4BWv359+/B9+/bpzTff1LZt29SyZUtJ0vXXX6+0tDTNmjVL77zzjkJDQ3XixAmH5RUUFOjMmTMKDQ0tsT1fX1/5+voWG+7j4+MWnVXE3dbH3dA/ro3+cV30jespLCzU+vXrtXbtWgUEBKhnz57y8vIyuyyUgPePa6N/XBd949rcpX+udh2cunu5zWbTyJEj9fnnn2vVqlUKDw93GJ+bm3thoZ6Oi/Xy8pLVapUkde7cWWfPnlVGRoZ9/KpVq2S1WtWxY0dnygEAAE5KSkpSRESEevXqpRkzZqhXr16KiIhQUlKS2aUBAOCWnArd8fHx+vjjjzV//nwFBQXp2LFjOnbsmPLy8iRJkZGRioiI0D/+8Q9t3LhR+/bt0/Tp05WSkqLY2FhJUvPmzXXrrbfqwQcf1MaNG/Xtt99q5MiRuuuuu7hzOQAABkpKStKQIUMUHR2ttLQ0ffLJJ/bLv4YMGULwBgDAAE6F7rfffluZmZnq0aOH6tSpY/+3YMECSRcOr3/11VeqVauWbrvtNrVq1Urz5s3T3Llz1a9fP/ty/vOf/ygyMlI333yz+vXrp65du+q99967tmsGAADsCgsLlZCQoAEDBig5OVkdO3aUn5+fOnbsqOTkZA0YMEDjxo1TYWGh2aUCAOBWnLqm+2pudN60aVN99tlnV5ymevXqmj9/vjNNAwCAvyAtLU0HDhzQJ598Ik9PT4dw7enpqQkTJujGG29UWlqa/TGfAADgr3PqSDcAACifjh49KkmKiooqcXzR8KLpAADAtUHoBgCgAqhTp44kadu2bSWOLxpeNB0AALg2CN0AAFQA3bp1U1hYmKZMmWJ/okgRq9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnTeF43AADXmFM3UgMAAOVXXFycFi1apISEBHXv3t0+PDw8XIsWLVJcXJyJ1QEA4J4I3QAAVCBxcXEaNGiQVq9erWXLlqlv377q2bMnR7gBADAIoRu4gsLCQqWmpmrt2rUKCAhgxxSAW/Dy8lJMTIxycnIUExPDdg0AAANxTTdwGUlJSYqIiFCvXr00Y8YM9erVSxEREUpKSjK7NAAAAADlBKEbKEFSUpKGDBmi6OhopaWl6ZNPPlFaWpqio6M1ZMgQgjcAAACAq0LoBi5RWFiohIQEDRgwQMnJyerYsaP8/PzUsWNHJScna8CAARo3bpwKCwvNLhUAAACAiyN0A5dIS0vTgQMHNHHiRHl6Or5FPD09NWHCBO3fv19paWkmVQgAAACgvCB0A5c4evSoJCkqKqrE8UXDi6YDAAAAgMshdAOXqFOnjiRp27ZtJY4vGl40HQAAAABcDqEbuES3bt0UFhamKVOmyGq1OoyzWq1KTExUeHi4unXrZlKFAAAAAMoLQjdwCS8vL02fPl1Lly5VbGys0tPTlZeXp/T0dMXGxmrp0qWaNm0az7UFAAAA8Ke8zS4AcEVxcXFatGiREhIS1L17d/vw8PBwLVq0SHFxcSZWBwAAAKC8IHQDlxEXF6dBgwZp9erVWrZsmfr27auePXtyhBsAAADAVSN0A1fg5eWlmJgY5eTkKCYmhsANAAAAwClc0w0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AQAVTWFio1NRUrV27VqmpqSosLDS7JAAA3BahGwCACiQpKUkRERHq1auXZsyYoV69eikiIkJJSUlmlwYAgFsidAMAUEEkJSVpyJAhio6OVlpamj755BOlpaUpOjpaQ4YMIXgDAGAAQjcAABVAYWGhEhISNGDAACUnJ6tjx47y8/NTx44dlZycrAEDBmjcuHGcag4AwDVG6AYAoAJIS0vTgQMHNHHiRHl6On78e3p6asKECdq/f7/S0tJMqhAAAPdE6AYAoAI4evSoJCkqKqrE8UXDi6YDAADXBqEbAIAKoE6dOpKkbdu2lTi+aHjRdAAA4NogdAMAUAF069ZNYWFhmjJliqxWq8M4q9WqxMREhYeHq1u3biZVCACAeyJ0AwBQAXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAC4FW+zCwAAAGUjLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAcE+EbgAAKpC4uDgNGjRIq1ev1rJly9S3b1/17NmTI9wAABiE0A0AQAXj5eWlmJgY5eTkKCYmhsANAICBuKYbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDOBW6ExMT1b59ewUFBSkkJESxsbHatWtXsek2bNigm266SQEBAQoODlb37t2Vl5dnH3/mzBndc889Cg4OVtWqVTVixAhlZ2f/9bUBAAAAAMCFOBW6U1NTFR8fr/T0dKWkpMhisah3797KycmxT7Nhwwbdeuut6t27tzZu3Kjvv/9eI0eOlKfn/2/qnnvu0c8//6yUlBQtXbpUa9eu1UMPPXTt1goAAAAAABfg7czEy5cvd3g9Z84chYSEKCMjQ927d5ckjRkzRqNHj9b48ePt0zVr1sz+/x07dmj58uX6/vvv1a5dO0nSG2+8oX79+mnatGmqW7duqVcGAAAAAABX8peu6c7MzJQkVa9eXZJ04sQJfffddwoJCdGNN96o2rVrKyYmRuvWrbPPs2HDBlWtWtUeuCXplltukaenp7777ru/Ug4AAAAAAC7FqSPdF7NarXrsscfUpUsXRUVFSZJ++eUXSdKkSZM0bdo0tW7dWvPmzdPNN9+sbdu2qWnTpjp27JhCQkIci/D2VvXq1XXs2LES28rPz1d+fr79dVZWliTJYrHIYrGUdhVcRtE6uMO6uCP6p+zk5uaWeJ+IK8nOy9f6rfsUVDVdgX6+Ts3brFkz+fv7OzUPrh7vnbLDe8f98P5xbfRP2WDb5n7c7b1ztetR6tAdHx+vbdu2ORzFtlqtkqR//OMfuv/++yVJN9xwg7755ht9+OGHSkxMLFVbiYmJmjx5crHhK1ascKs3RkpKitkl4AroH+Pt27dPCQkJpZp3ainmmT59upo0aVKq9nD1eO8Yj/eO++L949roH2OxbXNf7vLeyc3NvarpShW6R44cab8BWv369e3D69SpI0lq0aKFw/TNmzfXoUOHJEmhoaE6ceKEw/iCggKdOXNGoaGhJbY3YcIEjR071v46KytLDRo0UO/evRUcHFyaVXApFotFKSkp6tWrl3x8fMwuB5egf8pObm6uunbt6tQ8u49m6vHPt+tft7fQdXWqODUv32gbi/dO2eG94354/7g2+qdssG1zP+723ik6A/vPOBW6bTabRo0apc8//1xr1qxReHi4w/iwsDDVrVu32Gkgu3fvVt++fSVJnTt31tmzZ5WRkaG2bdtKklatWiWr1aqOHTuW2K6vr698fYufHuLj4+MWnVXE3dbH3dA/xqtSpYo6dOjg1DyVDp6W74bzimrdRq0b1TCoMvwVvHeMx3vHffH+cW30j7HYtrkvd3nvXO06OBW64+PjNX/+fC1evFhBQUH2a7CrVKkiPz8/eXh46PHHH9dzzz2n66+/Xq1bt9bcuXO1c+dOLVq0SNKFo9633nqrHnzwQb3zzjuyWCwaOXKk7rrrLu5cDgAAAABwK06F7rfffluS1KNHD4fhs2fP1n333SdJeuyxx3Tu3DmNGTNGZ86c0fXXX6+UlBSH6yP+85//aOTIkbr55pvl6empwYMH6/XXX/9rawIAAAAAgItx+vTyqzF+/HiH53Rfqnr16po/f74zTQMAAAAAUO78ped0AwBQksLCQqWmpmrt2rVKTU1VYWGh2SUBAACYgtANALimkpKSFBERoV69emnGjBnq1auXIiIilJSUZHZpAAAAZY7QDQC4ZpKSkjRkyBBFR0crLS1Nn3zyidLS0hQdHa0hQ4YQvAEAQIVD6AYAXBOFhYVKSEjQgAEDlJycrI4dO8rPz08dO3ZUcnKyBgwYoHHjxnGqOQAAqFAI3QCAayItLU0HDhzQxIkT5enp+PHi6empCRMmaP/+/UpLSzOpQgAAgLJH6AYAXBNHjx6VJEVFRZU4vmh40XQAAAAVAaEbAHBN1KlTR5K0bdu2EscXDS+aDgAAoCIgdAMArolu3bopLCxMU6ZMkdVqdRhntVqVmJio8PBwdevWzaQKAQAAyh6hGwBwTXh5eWn69OlaunSpYmNjlZ6erry8PKWnpys2NlZLly7VtGnT5OXlZXapAAAAZcbb7AIAAO4jLi5OixYtUkJCgrp3724fHh4erkWLFikuLs7E6gAAAMoeoRsAcE3FxcVp0KBBWr16tZYtW6a+ffuqZ8+eHOEGAAAVEqEbAHDNeXl5KSYmRjk5OYqJiSFwAwCACotrugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFAGUpNzdXO3fudGqe7Lx8rd+6T9VqblKgn69T80ZGRsrf39+peQAAAAC4D0I3KpSdO3eqbdu2pZp3ainmycjIUJs2bUrVHgAAAIDyj9CNCiUyMlIZGRlOzbPr6FmNXbhVM/4vWs3qVHW6PQAAAAAVF6EbFYq/v7/TR549D56Wb1qemkddr9aNahhUGQAAAAB3xI3UAAAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDeJtdgLvJzc3Vzp07nZonOy9f67fuU7WamxTo5+vUvJGRkfL393dqHgAAAABA2SB0X2M7d+5U27ZtSzXv1FLMk5GRoTZt2pSqPQAAAACAsQjd11hkZKQyMjKcmmfX0bMau3CrZvxftJrVqep0ewAAAAAA10Tovsb8/f2dPvLsefC0fNPy1DzqerVuVMOgygAAAAAAZY0bqQEAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQp0J3YmKi2rdvr6CgIIWEhCg2Nla7du0qcVqbzaa+ffvKw8NDycnJDuMOHTqk/v37y9/fXyEhIXr88cdVUFBQ6pUAAAAAAMAVORW6U1NTFR8fr/T0dKWkpMhisah3797KyckpNu3MmTPl4eFRbHhhYaH69++v8+fPa/369Zo7d67mzJmjZ599tvRrAQAAAACAC/J2ZuLly5c7vJ4zZ45CQkKUkZGh7t2724dv3rxZ06dP16ZNm1SnTh2HeVasWKHt27dr5cqVql27tlq3bq0XXnhBTz75pCZNmqRKlSr9hdUBAAAAAMB1OBW6L5WZmSlJql69un1Ybm6u7r77bs2aNUuhoaHF5tmwYYOio6NVu3Zt+7A+ffrokUce0c8//6wbbrih2Dz5+fnKz8+3v87KypIkWSwWWSyWv7IKLqHo1PqCggK3WB93Q/+4NvqnbOTm5l72cqLLyc7L1/qt+xRUNV2Bfr5OzdusWTP5+/s7NQ+cw3vHtRX1CX3jmugf18W2reywb3D124BSh26r1arHHntMXbp0UVRUlH34mDFjdOONN2rQoEElznfs2DGHwC3J/vrYsWMlzpOYmKjJkycXG75ixQqX+8WXxq/ZkuSt9PR0Hd5mdjW4FP3j2uifsrFv3z4lJCSUat6ppZhn+vTpatKkSanaw9XhvVM+pKSkmF0CroD+cT1s28oO+wYXvni4GqUO3fHx8dq2bZvWrVtnH7ZkyRKtWrVKP/74Y2kXW6IJEyZo7Nix9tdZWVlq0KCBevfureDg4Gvalhm2HDojbd2kTp066fqG1f98BpQp+se10T9lIzc3V127dnVqnt1HM/X459v1r9tb6Lo6VZya1xW/zXY3vHdcm8ViUUpKinr16iUfHx+zy8El6B/Xxbat7LBv8P/PwP4zpQrdI0eO1NKlS7V27VrVr1/fPnzVqlXat2+fqlat6jD94MGD1a1bN61Zs0ahoaHauHGjw/jjx49LUomno0uSr6+vfH2Ln37g4+PjFhs6b29v+093WB93Q/+4NvqnbFSpUkUdOnRwap5KB0/Ld8N5RbVuo9aNahhUGUqL90754C77Ou6K/nE9bNvKDvsGuuq/MafuXm6z2TRy5Eh9/vnnWrVqlcLDwx3Gjx8/Xj/99JM2b95s/ydJr776qmbPni1J6ty5s7Zu3aoTJ07Y50tJSVFwcLBatGjhTDkAAAAAALg0p450x8fHa/78+Vq8eLGCgoLs12BXqVJFfn5+Cg0NLfFodcOGDe0BvXfv3mrRooWGDRumqVOn6tixY3r66acVHx9f4tFsAAAAAADKK6eOdL/99tvKzMxUjx49VKdOHfu/BQsWXPUyvLy8tHTpUnl5ealz584aOnSo7r33Xj3//PNOFw8AAAAAgCtz6ki3zWZzuoGS5mnUqJG++uorp5cFAAAAAEB54tSRbgAAAAAAcPUI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBvM0uAPgr9p/KUU5+gaFt7DuZY//p7W3sWybA11vhNQMMbQMAAABA2SF0o9zafypHPaetKbP2EhZtLZN2Vo/rQfAGAAAA3AShG+VW0RHumXe2VkRIoHHt5OVr6ZoNGtCjswL8fA1rZ++JbD22YLPhR+4BAAAAlB1CN8q9iJBARdWrYtjyLRaLjtWS2jSqJh8fH8PaAQAAAOB+uJEaAAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQb7MLAOC+9p/KUU5+gaFt7DuZY//p7W3sJi3A11vhNQMMbQMAAADuhdANwBD7T+Wo57Q1ZdZewqKtZdLO6nE9CN4AAAC4aoRulFv5hefkWfmw9mftkmflQMPaKSgo0JGCI9pxZoehR1L3Z2XLs/Jh5Reek1TFsHbKStER7pl3tlZEiHH9k5OXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAgHshdKPcOpJzUAHhb2jixrJp763lbxneRkC4dCSntdqqtuFtlZWIkEBF1TPuSwSLxaJjtaQ2jarJx8fHsHYAAACA0iB0o9yqG9BIOftH6bU7W6uJgUdSCwoK9O26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAAAEDZInSj3PL1qizruXoKD26mFjWMPZK633u/mldvbuiRVOu5TFnPnZSvV2XD2gAAAABQtnhkGAAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGcSp0JyYmqn379goKClJISIhiY2O1a9cu+/gzZ85o1KhRatasmfz8/NSwYUONHj1amZmZDss5dOiQ+vfvL39/f4WEhOjxxx9XQUHBtVkjAAAAAABchFOhOzU1VfHx8UpPT1dKSoosFot69+6tnJwcSdKRI0d05MgRTZs2Tdu2bdOcOXO0fPlyjRgxwr6MwsJC9e/fX+fPn9f69es1d+5czZkzR88+++y1XTMAAAAAAEzm7czEy5cvd3g9Z84chYSEKCMjQ927d1dUVJQ+++wz+/gmTZropZde0tChQ1VQUCBvb2+tWLFC27dv18qVK1W7dm21bt1aL7zwgp588klNmjRJlSpVujZrBgAAAACAyZwK3ZcqOm28evXqV5wmODhY3t4XmtqwYYOio6NVu3Zt+zR9+vTRI488op9//lk33HBDsWXk5+crPz/f/jorK0uSZLFYZLFY/soquISiU+sLCgrcYn3KSln93oqWbXTfuNvfQU5+tjwrH9be37fL6h1gWDsFBQU6UnBEW09stW9njPDL7znyrHxYOfnZslj8DWvHnbjb37S7oX9cW1l99qB06J/SOXA6Rzn5hYa2sftYpsNPIwX4eimshnH7OO7I3T57rnYdSr2HarVa9dhjj6lLly6KiooqcZpTp07phRde0EMPPWQfduzYMYfALcn++tixYyUuJzExUZMnTy42fMWKFfL3L/87v79mS5K30tPTdXib2dWUH0W/t3Xr1ulgoPHtpaSkGLr8sl4fo/3wxxEFhL+lZzLKpr23Vr5leBsB4dJX6wt1LKiu4W25A7Ztro3+KR+M/uzBX0P/XL0TedJLm437cvxST3y+o0zaeap1gUL8yqQpt+Bunz25ublXNV2p//Lj4+O1bds2rVu3rsTxWVlZ6t+/v1q0aKFJkyaVthlJ0oQJEzR27FiHZTdo0EC9e/dWcHDwX1q2K9hy6Iy0dZM6deqk6xte/qwBOPr5SJambU1X165d1bKucX8HFotFKSkp6tWrl3x8fAxrp6zWp6yE/npCH83z0owh0Wpcy9gj3d+lf6eOnToae6T7ZI7GLtqqfvf2V5sGIYa1407YtpVeWRwNyj+WKW3doZCIaDUKrWJoWxwNcl5ZffagdOgf5/18JEvanK5pQ6IVYeB+Qc65fC1P+163dmuvgMq+hrWz92SOxi3aqvad3WO/ray4275B0RnYf6ZUe6gjR47U0qVLtXbtWtWvX7/Y+D/++EO33nqrgoKC9PnnnztsjEJDQ7Vx40aH6Y8fP24fVxJfX1/5+hZ/0/j4+LjFhq4oKHh7e7vF+pSVsv69Gf335m5/BwG+gbKeq6eIai0UVdu4HXqLxaJfvX9VdEi0ob83z4JMWc+dUYBvoFv0T1lwt7/psrL/VI56zfy2zNorq6NBq8f1UHhNgrez3GVfx13RP1ev6DMhsk4VRdUzdr/g1E6pQ+Na7Le5IHf7vV3tOjgVum02m0aNGqXPP/9ca9asUXh4eLFpsrKy1KdPH/n6+mrJkiWqXLmyw/jOnTvrpZde0okTJxQScuFoUUpKioKDg9WiRQtnygEAwO3k5F+43m3mna0VEWLctSY5eflaumaDBvTorAA/A48GncjWYws229cLAICKxqnQHR8fr/nz52vx4sUKCgqyX4NdpUoV+fn5KSsrS71791Zubq4+/vhjZWVl2Q+516pVS15eXurdu7datGihYcOGaerUqTp27JiefvppxcfHl3g0GwCAiigiJNDwo0HHakltGlVzi6MNAAC4KqdC99tvvy1J6tGjh8Pw2bNn67777tMPP/yg7777TpIUERHhMM3+/fsVFhYmLy8vLV26VI888og6d+6sgIAADR8+XM8///xfWA0AAAAAAFyP06eXX0mPHj3+dBpJatSokb766itnmgYAAAAAoNzxNLsAAAAAAADcFaEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADCIt9kFuLr9p3KUk19gaBv7TubYf3p7G9slAb7eCq8ZYGgbAAAAAIALCN1XsP9UjnpOW1Nm7SUs2lom7awe14PgDQAAAABlgNB9BUVHuGfe2VoRIYHGtZOXr6VrNmhAj84K8PM1rJ29J7L12ILNhh+5BwAAAABcQOi+ChEhgYqqV8Ww5VssFh2rJbVpVE0+Pj6GtQMAAAAAKFvcSA0AAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAABcSGFhoVJTU7V27VqlpqaqsLDQ7JIAAH8BoRsAAMBFJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSAACl5G12AQCAsrf/VI5y8gsMbWPfyRz7T29vYz9uAny9FV4zwNA2AKMlJSVpyJAhGjBggD766CP99ttvql+/vqZOnaohQ4Zo0aJFiouLM7tMAICTCN0AUMHsP5WjntPWlFl7CYu2lkk7q8f1IHij3CosLFRCQoIGDBig5ORkFRYW6vTp0+rYsaOSk5MVGxurcePGadCgQfLy8jK7XACAEwjdAFDBFB3hnnlna0WEBBrXTl6+lq7ZoAE9OivAz9ewdvaeyNZjCzYbfuQeMFJaWpoOHDigTz75RJ6eng7XcXt6emrChAm68cYblZaWph49ephXqBvKzc3Vzp07nZonOy9f67fuU7WamxTo5PYtMjJS/v7+Ts0DlAXOgjMOoRsAKqiIkEBF1ati2PItFouO1ZLaNKomHx8fw9oB3MHRo0clSVFRUSWOLxpeNB2unZ07d6pt27almndqKebJyMhQmzZtStUeYBTOgjMWoRsAAMBkderUkSRt27ZNnTp1KjZ+27ZtDtPh2omMjFRGRoZT8+w6elZjF27VjP+LVrM6VZ1uD3A1nAVnLEI3AACAybp166awsDBNmTJFycnJDuOsVqsSExMVHh6ubt26mVOgG/P393f6yLPnwdPyTctT86jr1bpRDYMqA8oeZ8EZg0eGAQAAmMzLy0vTp0/X0qVLFRsbq/T0dOXl5Sk9PV2xsbFaunSppk2bxk3UAKAc4kg3AACAC4iLi9OiRYuUkJCg7t2724eHh4fzuDAAKMcI3QAAAC4iLi5OgwYN0urVq7Vs2TL17dtXPXv25Ag3AJRjhG4AAAAX4uXlpZiYGOXk5CgmJobADQDlHKEbAADAQDwHGgAqNkI3AACAgXgONABUbIRuAAAAA/EcaACo2AjdAAAABuI50ABQsfGcbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADMJzuq8gv/CcPCsf1v6sXfKsHGhYOwUFBTpScEQ7zuyQt7dxXbI/K1uelQ8rv/CcpCqGtQMAAABcjP1qVGSE7is4knNQAeFvaOLGsmnvreVvGd5GQLh0JKe12qq24W0BAAAAEvvVqNgI3VdQN6CRcvaP0mt3tlaTEGO/kft23bfq0rWLod/I7TuRrUcXbFbdno0MawMAAAC4FPvVqMgI3Vfg61VZ1nP1FB7cTC1qGHfaiMVi0X7v/Wpevbl8fHwMa8d6LlPWcyfl61XZsDYAAACAS7FfjYqMG6kBAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswsASivPUihJ2nY409B2cvLytemkFHrwdwX4+RrWzt4T2YYtGwAAAIA5nArdiYmJSkpK0s6dO+Xn56cbb7xRr7zyipo1a2af5ty5c0pISNCnn36q/Px89enTR2+99ZZq165tn+bQoUN65JFHtHr1agUGBmr48OFKTEyUtzffAeDq7ftfSB2ftLUMWvPWR3u/L4N2pABf3gcAAACAu3Bq7z41NVXx8fFq3769CgoKNHHiRPXu3Vvbt29XQECAJGnMmDH68ssvtXDhQlWpUkUjR45UXFycvv32W0lSYWGh+vfvr9DQUK1fv15Hjx7VvffeKx8fH02ZMuXaryHcVu+WoZKkJiGB8vPxMqydXUczlbBoq6YPiVazOlUMa0e6ELjDawYY2gYAAACAsuNU6F6+fLnD6zlz5igkJEQZGRnq3r27MjMz9cEHH2j+/Pm66aabJEmzZ89W8+bNlZ6erk6dOmnFihXavn27Vq5cqdq1a6t169Z64YUX9OSTT2rSpEmqVKnStVs7uLXqAZV0V4eGhrdTUFAgSWpSK0BR9YwN3QAAAADcy1+6kVpm5oVraatXry5JysjIkMVi0S233GKfJjIyUg0bNtSGDRskSRs2bFB0dLTD6eZ9+vRRVlaWfv75579SDgAAAAAALqXUF49arVY99thj6tKli6KioiRJx44dU6VKlVS1alWHaWvXrq1jx47Zp7k4cBeNLxpXkvz8fOXn59tfZ2VlSZIsFossFktpV+FPFR3hLCgoMLSdomUb2YZUduvjbvi9lc4feRfes1sOnbH/Do2Qc+7Cje5q/nJSAZUNvNHdyRxJ7vF3kJOfLc/Kh7X39+2yeht3OUNBQYGOFBzR1hNbDb1nxy+/58iz8mHl5GfLYvE3rJ2yQv9A4rPH1dE/zmO/2rXx2VM6V9v3pV7T+Ph4bdu2TevWrSvtIq5aYmKiJk+eXGz4ihUr5O9v3C/x12xJ8ta6det0MNCwZuxSUlIMXX5Zr4+7KPq9paen6/A2s6spPzYc95DkpacWby+D1rz10d4fy6Ad6fsN63TQr0yaMswPfxxRQPhbeiajbNp7a+VbhrcREC59tb5Qx4LqGt6W0egfSHz2uDr6x3nsV7s2PntKJzc396qmK1XoHjlypJYuXaq1a9eqfv369uGhoaE6f/68zp4963C0+/jx4woNDbVPs3HjRoflHT9+3D6uJBMmTNDYsWPtr7OystSgQQP17t1bwcHBpVmFq/LzkSxN25qurl27qmVd49qxWCxKSUlRr1695OPjY1g7ZbU+7mbLoTPS1k3q1KmTrm9Y3exyyo1OOecVveOEGtcKMPRGd7uPZeqJz3do6u3NdV2o0Te681JYjfJ/o7vQX0/oo3lemjEkWo1rGftt9nfp36ljp47Gfpt9MkdjF21Vv3v7q02DEMPaKSv0DyQ+e1wd/eM89qtdG589pVN0BvafcWpNbTabRo0apc8//1xr1qxReHi4w/i2bdvKx8dH33zzjQYPHixJ2rVrlw4dOqTOnTtLkjp37qyXXnpJJ06cUEjIhV9ASkqKgoOD1aJFixLb9fX1la9v8dNGfXx8DH0zFf0heHt7G9pOEXdbH3fB7610alf10T2dw/98wmvkutAqat2oRpm1V54F+AbKeq6eIqq1UFRt476osFgs+tX7V0WHRBv63vEsyJT13BkF+Aa6xXuU/oHEZ4+ro3+cx361a+Ozp3SudtlOhe74+HjNnz9fixcvVlBQkP0a7CpVqsjPz09VqlTRiBEjNHbsWFWvXl3BwcEaNWqUOnfurE6dOkmSevfurRYtWmjYsGGaOnWqjh07pqefflrx8fElBmsAAAAAAMorp0L322+/LUnq0aOHw/DZs2frvvvukyS9+uqr8vT01ODBg5Wfn68+ffrorbf+/zn7Xl5eWrp0qR555BF17txZAQEBGj58uJ5//vm/tiYAAAAAALgYp08v/zOVK1fWrFmzNGvWrMtO06hRI3311VfONA0AAAAAQLnzl57TDQAAAAAALo/QDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABjE2+wCgLKUm5urnTt3OjXPrqNnlX9sr3Zs85P1dFWn5o2MjJS/v79T8wAAgNLbfypHOfkFhrax72SO/ae3t7G70wG+3gqvGWBoGwCMRehGhbJz5061bdu2VPPePdf5eTIyMtSmTZtStQcAAJyz/1SOek5bU2btJSzaWibtrB7Xg+ANlGOEblQokZGRysjIcGqe7Lx8fbl6g/r37KxAP1+n2wMAAGWj6Aj3zDtbKyIk0Lh28vK1dM0GDejRWQFO7hs4Y++JbD22YLPhR+4BGIvQjQrF39/f6SPPFotFv586oc4d2snHx8egygAAwLUSERKoqHpVDFu+xWLRsVpSm0bV2DcA8Ke4kRoAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBvswtwZXmWQknStsOZhraTk5evTSel0IO/K8DP17B29p7INmzZAIBrg88e17f/VI5y8gsMbWPfyRz7T29vY3fXAny9FV4zwNA2ALZtro3+MRah+wr2/a+zxidtLYPWvPXR3u/LoJ0LH64AANfEZ49r238qRz2nrSmz9hIWlcXfgbR6XA+CNwzFts210T/Gco0qXFTvlqGSpCYhgfLz8TKsnV1HM5WwaKumD4lWszpVDGtH4ttsAHB1fPa4tqIj3DPvbK2IkEDj2snL19I1GzSgR2fDjwY9tmCz4UfuAbZtro3+MRah+wqqB1TSXR0aGt5OQcGFD7omtQIUVc/YPz4AgGvjs6d8iAgJNPT3ZrFYdKyW1KZRNfn4+BjWDlBW2La5NvrHWNxIDQAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwiLfZBQAAylaepVCStO1wpqHt5OTla9NJKfTg7wrw8zWsnb0nsg1bNnCp/MJz8qx8WPuzdsmzcqBh7RQUFOhIwRHtOLND3t7G7a7tz8qWZ+XDyi88J6mKYe0AQEVG6AaACmbf/0Lq+KStZdCatz7a+30ZtCMF+PKRBuMdyTmogPA3NHFj2bT31vK3DG8jIFw6ktNabVXb8LYAoCJiDwUAKpjeLUMlSU1CAuXn42VYO7uOZiph0VZNHxKtZnWMPYIW4Out8JoBhrYBSFLdgEbK2T9Kr93ZWk1CjD3S/e26b9WlaxdDj3TvO5GtRxdsVt2ejQxrAwAqOkI3AFQw1QMq6a4ODQ1vp6CgQJLUpFaAoupx2ircg69XZVnP1VN4cDO1qGHc37XFYtF+7/1qXr25fHx8DGvHei5T1nMn5etV2bA2AKCi40ZqAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGMTp0L127Vrddtttqlu3rjw8PJScnOwwPjs7WyNHjlT9+vXl5+enFi1a6J133nGY5ty5c4qPj1eNGjUUGBiowYMH6/jx439pRQAAAAAAcDVOh+6cnBxdf/31mjVrVonjx44dq+XLl+vjjz/Wjh079Nhjj2nkyJFasmSJfZoxY8boiy++0MKFC5WamqojR44oLi6u9GsBAAAAAIAL8nZ2hr59+6pv376XHb9+/XoNHz5cPXr0kCQ99NBDevfdd7Vx40YNHDhQmZmZ+uCDDzR//nzddNNNkqTZs2erefPmSk9PV6dOnUq3JgAAAAAAuBinQ/efufHGG7VkyRI98MADqlu3rtasWaPdu3fr1VdflSRlZGTIYrHolltusc8TGRmphg0basOGDSWG7vz8fOXn59tfZ2VlSZIsFossFsu1XoUyV1BQYP/pDuvjbor6hL4xXm5urnbt2uXUPLuPZir/2F5t21xJ549XcWreZs2ayd/f36l5cPXYtrk2+qd0yur3VlafPe72d5CTny3Pyoe19/ftsnoHGNZOQUGBjhQc0dYTW+Xtfc13p+1++T1HnpUPKyc/WxYLn1dXw93+pt2Nu/XP1a7DNd9KvPHGG3rooYdUv359eXt7y9PTU++//766d+8uSTp27JgqVaqkqlWrOsxXu3ZtHTt2rMRlJiYmavLkycWGr1ixwi12mH/NliRvpaen6/A2s6vB5aSkpJhdgtvbt2+fEhISSjXvsLnOzzN9+nQ1adKkVO3hz7Ftc230T+kU/d7WrVung4HGt2f0Z09Zr4/RfvjjiALC39IzGWXT3lsr3zK8jYBw6av1hToWVNfwttwB2zbX5m79k5ube1XTGRK609PTtWTJEjVq1Ehr165VfHy86tat63B02xkTJkzQ2LFj7a+zsrLUoEED9e7dW8HBwdeqdNNsOXRG2rpJnTp10vUNq5tdDi5hsViUkpKiXr16ycfHx+xy3Fpubq66du3q1DzZefn6Ou179enWXoF+vk7Ny5FuY7Ftc230T+n8fCRL07amq2vXrmpZ17h9kLL67Cmr9Skrob+e0EfzvDRjSLQa1zL2SPd36d+pY6eOxh7pPpmjsYu2qt+9/dWmQYhh7bgTtm2uzd36p+gM7D9zTbcSeXl5mjhxoj7//HP1799fktSqVStt3rxZ06ZN0y233KLQ0FCdP39eZ8+edTjaffz4cYWGhpa4XF9fX/n6Ft+Z9vHxcYsQVLSx9vb2dov1cVfu8vfmyqpUqaIOHTo4NY/FYtEfZ8+o242d6B8Xw7bNtdE/pVPWvzejP3vc7e8gwDdQ1nP1FFGthaJqO3fJkTMsFot+9f5V0SHRhv7ePAsyZT13RgG+gW7RP2XB3f6m3Y279c/VrsM1fU530TXWnp6Oi/Xy8pLVapUktW3bVj4+Pvrmm2/s43ft2qVDhw6pc+fO17IcAAAAAABM5fSR7uzsbO3du9f+ev/+/dq8ebOqV6+uhg0bKiYmRo8//rj8/PzUqFEjpaamat68eZoxY4akC0eyRowYobFjx6p69eoKDg7WqFGj1LlzZ+5cDgAAAABwK06H7k2bNqlnz57210XXWg8fPlxz5szRp59+qgkTJuiee+7RmTNn1KhRI7300kt6+OGH7fO8+uqr8vT01ODBg5Wfn68+ffrorbeMvxEFAAAAAABlyenQ3aNHD9lstsuODw0N1ezZs6+4jMqVK2vWrFmaNWuWs80DAAAAAFBuXNNrugEAAAAAwP9H6AYAAAAAwCCEbgAAAAAADHJNn9MNAADgzvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAQAXELoBAACu0r7/hdTxSVvLoDVvfbT3+zJoRwrwZZcQAIzCFhYAAOAq9W4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQBARUboBgAAuErVAyrprg4NDW+noKBAktSkVoCi6hkbugEAxuJGagAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQbzNLgAAAAC4FvIshZKkbYczDW0nJy9fm05KoQd/V4Cfr2Ht7D2RbdiyAZQdQjcAAADcwr7/hdTxSVvLoDVvfbT3+zJoRwrwZZcdKM94BwMAAMAt9G4ZKklqEhIoPx8vw9rZdTRTCYu2avqQaDWrU8WwdqQLgTu8ZoChbQAwFqEbAAAAbqF6QCXd1aGh4e0UFBRIkprUClBUPWNDN4DyjxupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNrsAd5Obm6udO3c6Nc+uo2eVf2yvdmzzk/V0VafmjYyMlL+/v1PzAAAAAMBfQe65eoTua2znzp1q27Ztqea9e67z82RkZKhNmzalag8AAAAASoPcc/UI3ddYZGSkMjIynJonOy9fX67eoP49OyvQz9fp9gAAAACgLJF7rh6h+xrz9/d3+hsYi8Wi30+dUOcO7eTj42NQZQAAAABwbZB7rh43UgMAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgTofutWvX6rbbblPdunXl4eGh5OTkYtPs2LFDAwcOVJUqVRQQEKD27dvr0KFD9vHnzp1TfHy8atSoocDAQA0ePFjHjx//SysCAAAAAICrcTp05+Tk6Prrr9esWbNKHL9v3z517dpVkZGRWrNmjX766Sc988wzqly5sn2aMWPG6IsvvtDChQuVmpqqI0eOKC4urvRrAQAAAACAC/J2doa+ffuqb9++lx3/1FNPqV+/fpo6dap9WJMmTez/z8zM1AcffKD58+frpptukiTNnj1bzZs3V3p6ujp16uRsSQAAAAAAuCSnQ/eVWK1Wffnll3riiSfUp08f/fjjjwoPD9eECRMUGxsrScrIyJDFYtEtt9xiny8yMlINGzbUhg0bSgzd+fn5ys/Pt7/OysqSJFksFlkslmu5CqYoWgd3WBd3RP+4NvqnbOTm5mrXrl1OzbP7aKbyj+3Vts2VdP54Fafmbdasmfz9/Z2aB84pKCiw/+T9YyzeP+6H94/rom9cm7vtt13telzT0H3ixAllZ2fr5Zdf1osvvqhXXnlFy5cvV1xcnFavXq2YmBgdO3ZMlSpVUtWqVR3mrV27to4dO1bichMTEzV58uRiw1esWOFWHyopKSlml4AroH9cG/1jrH379ikhIaFU8w6b6/w806dPdzhLCtfer9mS5K309HQd3mZ2Ne6N94/74f3juuib8sFd9ttyc3OvarprfqRbkgYNGqQxY8ZIklq3bq3169frnXfeUUxMTKmWO2HCBI0dO9b+OisrSw0aNFDv3r0VHBz81ws3mcViUUpKinr16iUfHx+zy8El6B/XRv+UjdzcXHXt2tWpebLz8vV12vfq0629Av18nZqXI3XG23LojLR1kzp16qTrG1Y3uxy3xvvH/fD+cV30jWtzt/22ojOw/8w1Dd01a9aUt7e3WrRo4TC8efPmWrdunSQpNDRU58+f19mzZx2Odh8/flyhoaElLtfX11e+vsU/cHx8fNyis4q42/q4G/rHtdE/xqpSpYo6dOjg1DwWi0V/nD2jbjd2om9ckLe3t/0n/WMs3j/uh/eP66Jvygd32W+72nW4ps/prlSpktq3b1/suqXdu3erUaNGkqS2bdvKx8dH33zzjX38rl27dOjQIXXu3PlalgMAAAAAgKmcPtKdnZ2tvXv32l/v379fmzdvVvXq1dWwYUM9/vjjuvPOO9W9e3f17NlTy5cv1xdffKE1a9ZIuvBt74gRIzR27FhVr15dwcHBGjVqlDp37sydywEAAAAAbsXp0L1p0yb17NnT/rroWuvhw4drzpw5uv322/XOO+8oMTFRo0ePVrNmzfTZZ585XMv06quvytPTU4MHD1Z+fr769Omjt9566xqsDgAAAAAArsPp0N2jRw/ZbLYrTvPAAw/ogQceuOz4ypUra9asWZo1a5azzQMAAAAAUG5c02u6AQAAAADA/0foBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAACAoQoLC5Wamqq1a9cqNTVVhYWFZpdUZgjdAAAAAADDJCUlKSIiQr169dKMGTPUq1cvRUREKCkpyezSygShGwAAAABgiKSkJA0ZMkTR0dFKS0vTJ598orS0NEVHR2vIkCEVIngTugEAAAAA11xhYaESEhI0YMAAJScnq2PHjvLz81PHjh2VnJysAQMGaNy4cW5/qjmhGwAAAABwzaWlpenAgQOaOHGiPD0do6enp6cmTJig/fv3Ky0tzaQKywahGwAAAABwzR09elSSFBUVVeL4ouFF07krQjcAAAAA4JqrU6eOJGnbtm0lji8aXjSduyJ0AwAAAACuuW7duiksLExTpkyR1Wp1GGe1WpWYmKjw8HB169bNpArLBqEbAAAAAHDNeXl5afr06Vq6dKliY2OVnp6uvLw8paenKzY2VkuXLtW0adPk5eVldqmG8ja7AAAAAACAe4qLi9OiRYuUkJCg7t2724eHh4dr0aJFiouLM7G6skHoBgAAAAAYJi4uToMGDdLq1au1bNky9e3bVz179nT7I9xFCN0AAAAAAEN5eXkpJiZGOTk5iomJqTCBW+KabgAAAAAADEPoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAg3mYXAAAA/prc3Fzt3LnTqXl2HT2r/GN7tWObn6ynqzo1b2RkpPz9/Z2aB3BVvH9cF30Dd0HoBgCgnNu5c6fatm1bqnnvnuv8PBkZGWrTpk2p2gNcDe8f10XfwF0QugEAKOciIyOVkZHh1DzZefn6cvUG9e/ZWYF+vk63B7gL3j+ui76BuyB0AwBQzvn7+zt9dMZisej3UyfUuUM7+fj4GFQZ4Pp4/7gu+gbughupAQAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAbxNruA0rDZbJKkrKwskyu5NiwWi3Jzc5WVlSUfHx+zy8El6B/XRv+4LvrGtdE/ro3+cW30j+uib1ybu/VPUR4tyqeXUy5D9x9//CFJatCggcmVAAAAAAAqsj/++ENVqlS57HgP25/FchdktVp15MgRBQUFycPDw+xy/rKsrCw1aNBAv/76q4KDg80uB5egf1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1j82m01//PGH6tatK0/Py1+5XS6PdHt6eqp+/fpml3HNBQcHu8Ufn7uif1wb/eO66BvXRv+4NvrHtdE/rou+cW3u1D9XOsJdhBupAQAAAABgEEI3AAAAAAAGIXS7AF9fXz333HPy9fU1uxSUgP5xbfSP66JvXBv949roH9dG/7gu+sa1VdT+KZc3UgMAAAAAoDzgSDcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEK3CQoKCvT888/rt99+M7sUALhm2LYBAMqaxWLRzTffrD179phdCq7g/Pnz+u2333To0CGHfxUFdy83SVBQkLZu3aqwsDCzS8ElLBaLIiMjtXTpUjVv3tzscoByhW2ba2P75vq++eYbffPNNzpx4oSsVqvDuA8//NCkqlBk06ZN2rFjhySpefPmateunckVQZJq1aql9evXq2nTpmaXgkvs2bNHDzzwgNavX+8w3GazycPDQ4WFhSZVVra8zS6gorrpppuUmprKjqkL8vHx0blz58wuAyiX2La5NrZvrm3y5Ml6/vnn1a5dO9WpU0ceHh5ml4T/+e233/S3v/1N3377rapWrSpJOnv2rG688UZ9+umnql+/vrkFVnBDhw7VBx98oJdfftnsUnCJ++67T97e3lq6dGmF3q5xpNsk77zzjiZPnqx77rlHbdu2VUBAgMP4gQMHmlQZJGnKlCnavXu3/v3vf8vbm++mXE1OTo5efvnlyx4N+uWXX0yqDGzbXB/bN9dVp04dTZ06VcOGDTO7FFzi1ltv1dmzZzV37lw1a9ZMkrRr1y7df//9Cg4O1vLly02usGIbNWqU5s2bp6ZNm5b42TNjxgyTKkNAQIAyMjIUGRlpdimmInSbxNPz8pfTV6RTLVzV7bffrm+++UaBgYGKjo4utvFOSkoyqTJI0t/+9jelpqZq2LBhJX5r+uijj5pUGdi2uT62b66rRo0a2rhxo5o0aWJ2KbiEn5+f1q9frxtuuMFheEZGhrp166bc3FyTKoMk9ezZ87LjPDw8tGrVqjKsBhdr3769Xn31VXXt2tXsUkzFV9wmufTIHFxL1apVNXjwYLPLwGUsW7ZMX375pbp06WJ2KbgE2zbXx/bNdf3973/X/Pnz9cwzz5hdCi7RoEEDWSyWYsMLCwtVt25dEyrCxVavXm12CbiMV155RU888YSmTJmi6Oho+fj4OIwPDg42qbKyxZFuF3Du3DlVrlzZ7DKAciM8PFxfffUVN4JycWzbAOc8+uijmjdvnlq1aqVWrVoV2znlFFnzLF68WFOmTNGsWbPsN0/btGmTRo0apSeffFKxsbHmFghJ0t69e7Vv3z51795dfn5+9pt1wTxFZ8Bd2g8V7UZqhG6TFBYWasqUKXrnnXd0/Phx7d69W40bN9YzzzyjsLAwjRgxwuwSK7yCggKtWbNG+/bt0913362goCAdOXJEwcHBCgwMNLu8Cu3jjz/W4sWLNXfuXPn7+5tdDi7Ctq18YPvmmjhF1nVVq1ZNubm5KigosN8Loej/l16icebMGTNKrNBOnz6tO+64Q6tXr5aHh4f27Nmjxo0b64EHHlC1atU0ffp0s0ussFJTU684PiYmpowqMRenl5vkpZde0ty5czV16lQ9+OCD9uFRUVGaOXMmO6YmO3jwoG699VYdOnRI+fn56tWrl4KCgvTKK68oPz9f77zzjtklVmjTp0/Xvn37VLt2bYWFhRU7GvTDDz+YVBnYtrk+tm+ui1NkXdfMmTPNLgFXMGbMGPn4+OjQoUMOZ8HdeeedGjt2LKHbRBUlVP8ZQrdJ5s2bp/fee08333yzHn74Yfvw66+/Xjt37jSxMkgXTvFr166dtmzZoho1atiH33777Q5BAubgND7XxbbN9bF9A5w3fPhws0vAFaxYsUJff/11sUe3NW3aVAcPHjSpKlwsNzdXhw4d0vnz5x2Gt2rVyqSKyhah2ySHDx9WREREseFWq7XEG3WgbKWlpWn9+vWqVKmSw/CwsDAdPnzYpKpQ5LnnnjO7BFwG2zbXx/bNtW3atEn//e9/S9w55c7y5iosLFRycrJ27NghSWrZsqUGDhwoLy8vkytDTk5OiZebnTlzRr6+viZUhCInT57U/fffr2XLlpU4vqJc0335Z7vAUC1atFBaWlqx4YsWLSr2OAqUPavVWuJG4LffflNQUJAJFQHlA9s218f2zXV9+umnuvHGG7Vjxw59/vnnslgs+vnnn7Vq1SpVqVLF7PIqtL1796p58+a69957lZSUpKSkJA0dOlQtW7bUvn37zC6vwuvWrZvmzZtnf+3h4SGr1aqpU6de8V4JMN5jjz2ms2fP6rvvvpOfn5+WL1+uuXPnqmnTplqyZInZ5ZUZjnSb5Nlnn9Xw4cN1+PBhWa1WJSUladeuXZo3b56WLl1qdnkVXu/evTVz5ky99957ki5svLOzs/Xcc8+pX79+JleHwsJCvfrqq5c9GsRNbMzDts31sX1zXVOmTNGrr76q+Ph4BQUF6bXXXlN4eLj+8Y9/qE6dOmaXV6GNHj1aTZo0UXp6uqpXry7pws27hg4dqtGjR+vLL780ucKKberUqbr55pu1adMmnT9/Xk888YR+/vlnnTlzRt9++63Z5VVoq1at0uLFi9WuXTt5enqqUaNG6tWrl4KDg5WYmKj+/fubXWKZ4O7lJkpLS9Pzzz+vLVu2KDs7W23atNGzzz6r3r17m11ahffbb7+pT58+stls2rNnj9q1a6c9e/aoZs2aWrt2rUJCQswusUJ79tln9e9//1sJCQl6+umn9dRTT+nAgQNKTk7Ws88+q9GjR5tdYoXGts21sX1zXQEBAfr5558VFhamGjVqaM2aNYqOjtaOHTt000036ejRo2aXWGEFBAQoPT1d0dHRDsO3bNmiLl26KDs726TKUCQzM1Nvvvmmw2dPfHw8X1iZLDg4WD/99JPCwsLUqFEjzZ8/X126dNH+/fvVsmVL5ebmml1imeBIt4m6deumlJQUs8tACerXr68tW7ZowYIF9o33iBEjdM8998jPz8/s8iq8//znP3r//ffVv39/TZo0SX/729/UpEkTtWrVSunp6YRuk7Ftc21F27dPP/1UP/30E9s3F1KtWjX98ccfkqR69epp27Ztio6O1tmzZyvMjqmr8vX1tffNxbKzs4vdHwHmqFKlip566imzy8AlmjVrpl27diksLEzXX3+93n33XYWFhemdd96pUF+IcKTbZOfPn9eJEydktVodhjds2NCkiiBJa9eu1Y033mh/FmeRgoICrV+/Xt27dzepMkgXjjjs2LFDDRs2VJ06dfTll1+qTZs2+uWXX3TDDTcoMzPT7BIBwGl333232rVrp7Fjx+qFF17QG2+8oUGDBiklJUVt2rThRmomuvfee/XDDz/ogw8+UIcOHSRJ3333nR588EG1bdtWc+bMMbdA6OzZs9q4cWOJ+9X33nuvSVXh448/VkFBge677z5lZGTo1ltv1ZkzZ1SpUiXNmTNHd955p9kllglCt0n27NmjBx54QOvXr3cYbrPZ5OHhUWHu5OeqvLy8dPTo0WKnWZ4+fVohISH0j8maNWumefPmqWPHjuratasGDBig8ePHa8GCBRo1apROnDhhdokVSrVq1eTh4XFV03K9vWvYs2ePVq9eXeLO6bPPPmtSVThz5ozOnTununXr2m8CtX79ejVt2lRPP/20qlWrZnaJFdbZs2c1fPhwffHFF/Lx8ZF04Yv4gQMHavbs2apataq5BVZwX3zxhe655x5lZ2crODjY4TPJw8ODzx4Xkpubq507d6phw4aqWbOm2eWUGUK3Sbp06SJvb2+NHz9ederUKbbDev3115tUGSTJ09NTx48fV61atRyG7969W+3atVNWVpZJlUGSxo8fr+DgYE2cOFELFizQ0KFDFRYWpkOHDmnMmDF6+eWXzS6xQpk7d679/6dPn9aLL76oPn36qHPnzpKkDRs26Ouvv9YzzzyjMWPGmFUm/uf999/XI488opo1ayo0NLTYzukPP/xgYnWAa9u7d6/9kWHNmzcv8RGJKHvXXXed+vXrpylTppT46DDAbIRukwQEBCgjI0ORkZFml4KLxMXFSZIWL16sW2+91eHZjoWFhfrpp5/UrFkzLV++3KwSUYINGzZow4YNatq0qW677Tazy6nQBg8erJ49e2rkyJEOw998802tXLlSycnJ5hQGu0aNGumf//ynnnzySbNLwWWcOHGixLMQWrVqZVJFeP755zVu3LhigS4vL0//+te/OEPEZAEBAdq6dasaN25sdim4hM1m06JFiy57dlVFuWyG0G2S9u3b69VXX1XXrl3NLgUXuf/++yVdOHJ3xx13ONxUqFKlSgoLC9ODDz5YoU6HAZwRGBiozZs3Fzv6s3fvXrVu3Zo7/LqA4OBgbd68mZ1TF5SRkaHhw4drx44dunT3jEvPzMVlZ64tLi5Od911l+644w6zS8ElHn30Ub377rvq2bOnateuXezs3tmzZ5tUWdni7uVl6OJTkl955RU98cQTmjJliqKjo+3XBxUJDg4u6/Kg///GDwsL0+OPP84pSi7syJEjWrduXYnfmnL3cvPUqFFDixcvVkJCgsPwxYsXq0aNGiZVhYv93//9n1asWKGHH37Y7FJwiQceeEDXXXedPvjggxJ3TmGeonvuXGrLli3253ajbC1ZssT+//79++vxxx/X9u3bS9yvHjhwYFmXh//56KOPlJSUpH79+pldiqk40l2GPD09HTbYJW3AuZGaa7jpppuUlJRU7MYoWVlZio2N1apVq8wpDJKkOXPm6B//+IcqVaqkGjVqFLsm9ZdffjGxuoptzpw5+vvf/66+ffuqY8eOki7c4Xf58uV6//33dd9995lbYAX1+uuv2/+fk5OjGTNmqH///iXunPKllXmCgoL0448/cp2wCym6UWRmZmaxG3QVFhYqOztbDz/8sGbNmmVilRWTp6fnVU3HfrW5wsPDtWzZsgp/SS2huwylpqZe9bQxMTEGVoI/c7nTyE6cOKF69erJYrGYVBkkqUGDBnr44Yc1YcKEq/7QRdn57rvv9PrrrzvcbGj06NH2EI6yFx4eflXT8aWVuWJjYzVs2DANHjzY7FLwP3PnzpXNZtMDDzygmTNnqkqVKvZxRZedFd00EkBxc+fO1fLly/Xhhx86XLZZ0RC6gYv89NNPkqTWrVtr1apVDqeMFRYWavny5Xr33Xd14MABkyqEdOEU5o0bN6pJkyZmlwIA18ypU6c0fPhwdejQQVFRUZwi60JSU1PtT54BcPXy8vJ0++2369tvv1VYWFix7VpFeWIGWw6TzJ49W4GBgfq///s/h+ELFy5Ubm6uhg8fblJlFVvr1q3l4eEhDw8P3XTTTcXG+/n56Y033jChMlxsxIgRWrhwocaPH292KZCceoQe96sALm/Dhg369ttvtWzZsmLjOEXWXEFBQdqxY4eio6MlXbhPxezZs9WiRQtNmjRJlSpVMrnCim306NGKiIgodnnMm2++qb1792rmzJnmFAYNHz5cGRkZGjp0aIW+VwVHuk1y3XXX2e/kd7HU1FQ99NBD2rVrl0mVVWwHDx6UzWZT48aNtXHjRofndFeqVEkhISHy8vIysUJIF846GDBggPLy8kq8JnXGjBkmVVYxXXq/ipJwvwrXMXjwYHXo0KHYI8OmTp2q77//XgsXLjSpMoSFhWnAgAF65plnVLt2bbPLwUXat2+v8ePHa/Dgwfrll1/UokULxcXF6fvvv1f//v0JdSarV6+elixZorZt2zoM/+GHHzRw4ED99ttvJlWGgIAAff311xX+iU0c6TbJoUOHSrzGrlGjRjp06JAJFUG68PuXVOxu2HAtiYmJ+vrrr9WsWTNJKnYjNZSt1atXm10CnLB27VpNmjSp2PC+fftq+vTpZV8Q7E6fPq0xY8YQuF3Q7t271bp1a0kXzkqMiYnR/Pnz9e233+quu+4idJvs9OnTDtfbFwkODtapU6dMqAhFGjRowFluInSbJiQkRD/99JPCwsIchm/ZsoXH6phkyZIl6tu3r3x8fBweQ1ESrqsz1/Tp0/Xhhx9yJ2wXwY0fy5fs7OwST4X18fFx6lIBXHtxcXFavXo196twQTabzf6F/MqVKzVgwABJFwIFoc58ERERWr58uUaOHOkwfNmyZWrcuLFJVUG6sM/2xBNP6J133imWeyoSQrdJ/va3v2n06NEKCgpS9+7dJV04tfzRRx/VXXfdZXJ1FVNsbKyOHTumkJAQxcbGXnY6TpE1n6+vr7p06WJ2GbiMtLQ0vfvuu/rll1+0cOFC1atXTx999JHCw8Mr/OllriA6OloLFizQs88+6zD8008/VYsWLUyqCtKFS88mTJigdevW8Tg3F9OuXTu9+OKLuuWWW5Samqq3335bkrR//37OTHABY8eO1ciRI3Xy5En7PXm++eYbTZ8+nbMQTDZ06FDl5uaqSZMm8vf3L7ZdO3PmjEmVlS2u6TbJ+fPnNWzYMC1cuNB+J0yr1ap7771Xb7/9tnx9fU2uEHBdiYmJOnr0qMOzh+EaPvvsMw0bNkz33HOPPvroI23fvl2NGzfWm2++qa+++kpfffWV2SVWeF988YXi4uJ09913O+ycfvLJJ1q4cOEVv3SEsa70aDce52auLVu2aOjQoTp06JDGjh2r5557TpI0atQonT59WvPnzze5Qrz99tt66aWXdOTIEUkX7pEwadIk3XvvvSZXVrHNnTv3iuMrys2jCd0m27NnjzZv3iw/Pz9FR0fbrykGcHm33367Vq1apRo1aqhly5bFvjVNSkoyqTLccMMNGjNmjO69914FBQVpy5Ytaty4sX788Uf17dtXx44dM7tESPryyy81ZcoU++dPq1at9Nxzz3GpAOCkc+fOydvbm0eJuZCTJ0/Kz89PgYGBZpcC2LGFMMnzzz+vcePGqWnTpmratKl9eF5env71r38VO+0PZeNqj5xyip+5qlatqri4OLPLQAl27dplv2TmYlWqVNHZs2fLviCUqH///urfv7/ZZeAyzp8/r/3796tJkyaEORfRuHFjff/998Xuu3Pu3Dm1adOGsxBMdtNNNykpKUlVq1Z1ePJMVlaWYmNjtWrVKhOrw759+zR79mzt27dPr732mkJCQrRs2TI1bNhQLVu2NLu8MsGRbpN4eXnp6NGjCgkJcRh++vRphYSEcM2wSS49te/XX39VnTp1HHZ6OMUPuLzGjRvrvffe0y233OJwpHvevHl6+eWXtX37drNLrPAuFx7Onj1LeDBZbm6uRo0aZT8dc/fu3WrcuLFGjRqlevXqafz48SZXWHF5enra7/tysePHj6tBgwY6f/68SZVBunz/nDhxQvXq1ZPFYjGpMqSmpqpv377q0qWL1q5dqx07dqhx48Z6+eWXtWnTJi1atMjsEssEX5+apOiZtZfasmWLqlevbkJFkC7cEOViQUFBSk1N5c6XLqigoEBr1qzRvn37dPfddysoKEhHjhxRcHAwp5SZ6MEHH9Sjjz6qDz/8UB4eHjpy5Ig2bNigcePG6ZlnnjG7PEg6cOBAiV/s5ufn6/DhwyZUhCITJkzQli1btGbNGt1666324bfccosmTZpE6DbBxU8z+frrrx0eS1VYWKhvvvnmitfiw1g//fST/f/bt293uISpsLBQy5cvV7169cwoDf8zfvx4vfjiixo7dqyCgoLsw2+66Sa9+eabJlZWtgjdZaxatWry8PCQh4eHrrvuOofgXVhYqOzsbD388MMmVgi4voMHD+rWW2/VoUOHlJ+fr169eikoKEivvPKK8vPz9c4775hdYoU1fvx4Wa1W3XzzzcrNzVX37t3l6+urcePGadSoUWaXV6FdTXioyI9zcQXJyclasGCBOnXq5LB/0LJlS+3bt8/Eyiqui28seOkNn3x8fBQWFsbz7U3UunVr+3510Y0hL+bn56c33njDhMpQZOvWrSXeaDAkJKRCPW6P0F3GZs6cKZvNpgceeECTJ0922OmpVKmSwsLC1LlzZxMrBFzfo48+qnbt2hV7rv3tt9+uBx980MTK4OHhoaeeekqPP/649u7dq+zsbLVo0YKzD1xAUXjw8PAgPLiokydPFjs9VpJycnJKPDsOxit6Nnd4eLg2bdpU7LIMmGv//v2y2Wxq3LixNm7c6HA9d6VKlRQSEiIvLy8TK0TVqlV19OjRYmeE/PjjjxXqLARCdxkr2tEJDw/XjTfeWOyuywD+XFpamtavX69KlSo5DA8LC+P0WBdRqVIlnvnsYi4OD99//71q1qxpckW4VLt27fTll1/azwopCtr//ve/+ULeRBaLRY0bN9aZM2cI3S6m6Kk/Rds3uJ677rpLTz75pBYuXCgPDw9ZrVZ9++23GjduXIV6nBuh2yQXP5bl3LlzxW7AERwcXNYlQRfucnkxDw8PZWdnFxtO/5jLarWWeE3qb7/95nC9EMpGXFyc5syZo+Dg4D+9qzyPczPfpfeugOuYMmWK+vbtq+3bt6ugoECvvfaatm/frvXr1ys1NdXs8iosHx8fh2uH4RqWLFmivn37ysfHx+HymZIMHDiwjKrCpaZMmaL4+Hg1aNBAhYWFatGihQoLC3X33Xfr6aefNru8MsPdy02Sm5urJ554Qv/97391+vTpYuO5e7k5PD09HU7hu/SGd0Wv6R9z3XnnnapSpYree+89BQUF6aefflKtWrU0aNAgNWzYULNnzza7xArl/vvv1+uvv66goCDdd999VzwNlr4xx+uvv66HHnpIlStX/tNHI/JIRHPt27dPL7/8srZs2aLs7Gy1adNGTz75pKKjo80urUIbM2aMfH199fLLL5tdCv7n4juWe3p6XnY69ttcw6+//qqtW7cqOztbN9xwg8MjkysCQrdJ4uPjtXr1ar3wwgsaNmyYZs2apcOHD+vdd9/Vyy+/rHvuucfsEiukqz2ScPGZCih7v/32m/r06SObzaY9e/aoXbt22rNnj2rWrKm1a9eWeE0kjHPx0Qa4pouvR73SnZZ5JCJQslGjRmnevHlq2rSp2rZtq4CAAIfxM2bMMKkyoHwpLCzU1q1b1ahRI1WrVs3scsoModskDRs21Lx589SjRw8FBwfrhx9+UEREhD766CN98skn+uqrr8wuEVfh5Zdf1sMPP6yqVauaXUqFU1BQoAULFjgcDbrnnnvk5+dndmkVjpeXl44dO6ZatWrJy8tLR48e5YsPoBR++OEH+fj42I9qL168WLNnz1aLFi00adKkYvexQNnp2bPnZcd5eHho1apVZVgNLnbgwAGlpKTIYrEoJiZGLVu2NLskXOSxxx5TdHS0RowYocLCQsXExGj9+vXy9/fX0qVL1aNHD7NLLBOEbpMEBgZq+/btatiwoerXr6+kpCR16NBB+/fvV3R0tLKzs80uEVchODhYmzdv5jneqNBCQ0P1/vvv67bbbpOnp6eOHz/ucAdZuI709HR98cUXslgsuummmxyeBQ3ztW/fXuPHj9fgwYP1yy+/qEWLFoqLi9P333+v/v37a+bMmWaXCLiU1atXa8CAAcrLy5MkeXt768MPP9TQoUNNrgxF6tevr+TkZLVr107Jycn65z//qTVr1uijjz7SqlWr9O2335pdYpm4/AUQMFTjxo3tN7OJjIzUf//7X0nSF198wVHTcoTvrMwxd+5cffnll/bXTzzxhKpWraobb7xRBw8eNLGyiunhhx/WoEGD5OXlJQ8PD4WGhsrLy6vEfzDPokWL1KVLF7322mt6//331b9/f02bNs3ssnCR3bt3q3Xr1pKkhQsXKiYmRvPnz9ecOXP02WefmVsc7H777Tf99ttvZpcBSc8884x69eqlw4cP6/Tp03rwwQf1xBNPmF0WLnLq1CmFhoZKkr766ivdcccduu666/TAAw9o69atJldXdgjdJrn//vu1ZcsWSdL48eM1a9YsVa5cWY899pgef/xxk6sDXNuUKVPsp5Fv2LBBb775pqZOnaqaNWtqzJgxJldX8UyaNEnbt2/X4sWLZbPZ9OGHHyopKanEfzBPYmKiHnzwQWVmZur333/Xiy++qClTpphdFi5is9nsjz5auXKl+vXrJ0lq0KCBTp06ZWZpFZ7VatXzzz+vKlWqqFGjRmrUqJGqVq2qF154gcdV/b/27jys5rz/H/jztGsvadGEkiUUkoy1QZYy0s1t3GMr21iGYZA9S8LgjsZtbpIlTMNYs0y2iWk0QkTxbSoRNciWUGlR5/dHd+fX0WHM4rxPnefjulxX5/05mWfXXD6d1+f9fr/eAl2/fh3Lly+HjY0NzMzMsHr1ajx8+FBhk2ISw8rKCikpKSgrK8Px48fRq1cvABVNpdXpYTyPDBOkamHg6emJ1NRUXL58GU2aNGGHUqLfkZ2dDUdHRwBAVFQU/vnPf+Kzzz5D586d1WZvkKpp3rw5mjdvjkWLFmHw4MHQ19cXHYlek5aWhu+//172IWfGjBlYuHAhHj58yD34KsLNzQ3BwcHw9PREbGwsNmzYAKDimDcrKyvB6dTb/PnzsWXLFnz11Vfo3LkzACAuLg6LFy9GUVERli1bJjihenr+/DksLCxkr/X19VGnTh08e/aMZ6qriFGjRuGTTz6BjY0NJBIJPD09AQAXLlxA8+bNBadTHhbdSnb69GlMnjwZ58+flzvrufKJaadOnbBx40Z07dpVYEoi1WZoaIgnT56gQYMGOHnyJKZPnw4A0NPTk+3rIjFiY2MxderUakX38+fP4evry2ZDAhUWFsr93tHR0YGenh7y8/NZdKuI0NBQDBs2DFFRUZg/f77s4eK+ffvQqVMnwenU2/bt27F582a5855dXFxga2uLSZMmsegW6MSJEzAxMZG9Li8vR0xMDK5fvy4b4znd4ixevBitWrVCdnY2Bg8eDF1dXQAVTVjnzJkjOJ3ysJGakvn4+KB79+5vXAK7bt06nDlzBgcPHlRyMvozjIyMkJSUxEZqSjZs2DCkpqaibdu22LVrF7KyslC3bl0cPnwY8+bNk/tFS8r1pu7lDx8+hK2tLUpLSwUlIw0NDQQHB8PQ0FA2Nnv2bAQEBMjNFPGcbtVTVFQETU1NHssnkJ6eHpKTk9G0aVO58bS0NLRp04YPfAV52/nclXhON6kCznQrWVJSElauXPnG671792Zjmxqka9euPKJKgG+++QYLFixAdnY29u/fL1tCdvnyZXz66aeC06mn5ORkABV7UlNSUpCTkyO7VrmPy9bWVlQ8QsVRleHh4XJj1tbW2Llzp+y1RCJh0a2C9PT0REdQe61bt8b69euxbt06ufH169ejdevWglIR99PXDAUFBYiNjUVWVhZKSkrkrqnL7xzOdCuZnp4erl+/Llsy9rqMjAw4OzvziakAz58/f+f3Vl2iSUQVsw0SiQSA4q7+derUwX/+8x+MHj1a2dGIaoyysjKsXbsWe/bsUfjhNDc3V1Ayio2NRb9+/dCgQQN07NgRQEUjz+zsbERHR3NbYA3Rr18/bN68GTY2NqKjqI0rV67A29sbhYWFKCgogLm5OR4/fgx9fX1YWlri1q1boiMqBWe6lczW1vatRXdycjJvBIKYmprKiobfw2VKqqGwsFDhB1MXFxdBidRXZmYmpFIpHBwccPHiRblzunV0dGBpaalWXUprA2dnZ0RHR8POzk50FLWxZMkSbN68GTNmzMCCBQswf/583L59G1FRUVi4cKHoeGrNw8MD6enp+Oabb5CamgoAGDhwICZNmoT69esLTkfv6ueff+bElpJ9+eWX6N+/PzZu3AgTExOcP38e2traGD58OKZOnSo6ntJwplvJpkyZgp9++gkJCQnVlou9fPkS7u7u6N69e7XlS/T+xcbGyr6+ffs25syZA39/f7kn2tu3b8eKFSvg5+cnKiYBePToEfz9/XH8+HGF1/lQhOivY88K5WvcuDHWrVuHfv36wcjICFevXpWNnT9/Ht99953oiEQ1Gu9rymdqaooLFy6gWbNmMDU1RXx8PJycnHDhwgX4+fnJHmLVdpzpVrIFCxbgwIEDaNq0KSZPnoxmzZoBAFJTU/HNN9+grKwM8+fPF5xSPXl4eMi+DgoKwpo1a+T2B/v4+MDZ2RmbNm1i0S3YtGnT8OzZM1y4cAEfffQRDh48iAcPHiA4OBghISGi46m1HTt2vPX6yJEjlZSEqObJycmRHRtqaGiIZ8+eAQA+/vhjBAYGioxGAPLy8nDx4kU8fPiw2l5i3tuIFNPW1pY1vLO0tERWVhacnJxgYmKC7OxswemUh0W3kllZWeHcuXOYOHEi5s6dK9v7KJFI0KdPH3zzzTc8i1MFxMfHY+PGjdXG3dzcMHbsWAGJqKrTp0/j0KFDcHNzg4aGBho2bIhevXrB2NgYK1asQL9+/URHVFuvLxUrLS1FYWEhdHR0oK+vzw+mRG/xwQcf4P79+2jQoAEaN26MkydPwtXVFQkJCbJjdkiMI0eOYNiwYcjPz4exsbHcdjSJRMJ7G9EbtG3bFgkJCWjSpAk8PDywcOFCPH78GDt37kSrVq1Ex1Oa3++zT3+7hg0bIjo6Go8fP8aFCxdw/vx5PH78GNHR0bC3txcdjwDY2dlV6/ILAJs3b+b+RhVQUFAgO5LKzMwMjx49AlCxBzUxMVFkNLX39OlTuT/5+flIS0tDly5dsGvXLtHxiFTaP/7xD8TExACo2I4WGBiIJk2aYOTIkWxCKNiMGTMwevRo5OfnIy8vT+4+xwZ3RG+2fPlyWb+qZcuWwczMDBMnTsSjR4+wadMmwemUh3u6iRSIjo7GoEGD4OjoiA4dOgAALl68iBs3bmD//v3w9vYWnFC9tW/fHsHBwejTpw98fHxgamqKFStWYN26ddi3bx9u3rwpOiK95tKlSxg+fLja7N2qDbj3Ubz4+HjEx8ejSZMm6N+/v+g4as3AwADXrl3jv4cajvc1EoXLy4kU8Pb2Rnp6OjZs2CArEvr3748JEyZwplsFTJ06Fffv3wcALFq0CH379kVkZCR0dHQQEREhNhwppKWlhXv37omOQVSjdOzYUdbMk8Tq06cPLl26xGKthps3bx7Mzc1Fx1BrJSUlKCkpgaGhoegoSsWZbiKq8QoLC5GamooGDRrAwsJCdBy1dvjwYbnXUqkU9+/fx/r162FnZ4djx44JSqbezM3NkZ6eDgsLC4wePRpff/01jIyM3vo93333HQYMGAADAwMlpaQnT56gbt26AIDs7GyEh4fj5cuX8PHx4TnQAlS9nz169AhBQUEYNWoUnJ2doa2tLfdeHx8fZcejKl7/3VNJIpFAT08Pjo6O3MIpwLZt25CYmIgPP/wQw4YNw9y5c7FmzRq8evUKPXr0wO7du2X3vNqORTfRG5w9exZhYWG4desW9u7dC1tbW+zcuRP29vbo0qWL6HhEKqmyQ2kliUSCevXqoUePHggJCZHt6yLlMjQ0RHJyMhwcHKCpqYmcnBy5s9RJrGvXrqF///7Izs5GkyZNsHv3bvTt2xcFBQXQ0NBAQUEB9u3bB19fX9FR1crr97M3kUgkPKpSMA0NDUgkErxe1lSOSSQSdOnSBVFRUTAzMxOUUr0sW7YMy5YtQ+fOnZGYmIhPPvkEUVFRmDZtGjQ0NLBu3Tp8/PHH2LBhg+ioSsGim0iB/fv3Y8SIERg2bBh27tyJlJQUODg4YP369YiOjkZ0dLToiGrrxo0bSE5OhqurK+zt7fHDDz9g5cqVePnyJXx9fTFv3jy5rrIkRmVzOxZ2qqFXr1548OAB2rVrh+3bt2PIkCGoU6eOwvdu3bpVyenIy8sLWlpamDNnDnbu3ImjR4+iT58+soaeU6ZMweXLl3H+/HnBSYlUU0xMDObPn49ly5bB3d0dQEUvnsDAQCxYsAAmJiYYP348OnTogC1btghOqx6aNGmCoKAgfPrpp7h06RI6dOiAPXv2YNCgQQCAY8eOYcKECbhz547gpMrB7uVECgQHB2Pjxo0IDw+XW0JW+bSOxDh48CBatGiBoUOHwsnJCTt27MA///lPGBgYwMrKCosXL8aqVatEx1RbeXl5+Pzzz2FhYQFra2tYW1vDwsICkydPRl5enuh4au3bb7+Ft7c38vPzIZFI8OzZs2qd5iv/kPIlJCTIZoT+/e9/4969e5g0aRI0NDSgoaGBKVOmsAmhIPHx8Th69Kjc2I4dO2Bvbw9LS0t89tlnKC4uFpSOKk2dOhVr1qxBz549YWRkBCMjI/Ts2ROrV69GQEAAOnfujNDQUJw6dUp0VLWRlZUlWxnq5uYGLS0tuSPCXFxcZP151AEbqREpkJaWhm7dulUbNzExYfEg0LJlyzBr1iwEBwcjIiICEyZMwIoVKzBt2jQAwKZNm7B27VrMnj1bbFA1lJubi44dO+Lu3bsYNmwYnJycAAApKSmIiIhATEwMzp07x2V9glhZWeGrr74CANjb22Pnzp1qs4+uJsjNzYW1tTWAiq0ABgYGcv9WzMzM8OLFC1Hx1NqSJUvQvXt3fPzxxwAqtgKMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1d/PmTRgbG1cbNzY2xq1btwBUzLw+fvxY2dHUVmlpKXR1dWWvdXR05CaytLS01GpbBme6iRSwtrZGRkZGtfG4uDh2LhUoLS0No0ePhkQigZ+fH0pKSuDp6Sm73rt3b7VZpqRqgoKCoKOjg5s3byIsLAzTpk3DtGnTsGnTJmRkZEBbWxtBQUGiYxKAzMxMWcFdVFQkOA1Ven1bDLfJqIakpCT07NlT9nr37t3o0KEDwsPDMX36dKxbtw579uwRmJAAoF27dggICJBtbQIqtjnNmjUL7du3B1CxPY0n0ChXSkoKkpOTkZycDKlUitTUVNnr//u//xMdT6k4002kwLhx4zB16lRs3boVEokE9+7dQ3x8PGbOnInAwEDR8dRWQUGBrOOyhoYG6tSpA319fdn1OnXqcJmfIFFRUQgLC4OVlVW1a9bW1li1ahUmTJiAtWvXCkhHVZWXl2PZsmXYuHEjHjx4gPT0dDg4OCAwMBCNGjXCmDFjREdUS/7+/rJZoaKiIkyYMEHWOZ73NXGePn0qd1+LjY2Fl5eX7HX79u2RnZ0tIhpVsWXLFgwYMAAffPCBrLDOzs6Gg4MDDh06BADIz8/HggULRMZUOz179pRrble5YqRqgzt1waKbSIE5c+agvLwcPXv2RGFhIbp16wZdXV3MnDkTU6ZMER1PbUkkErkb9OuvSZz79++jZcuWb7zeqlUr5OTkKDERvUlwcDC2b9+OVatWYdy4cbLxVq1aITQ0lEW3AH5+fnKvhw8fXu09I0eOVFYcqsLKygqZmZmws7NDSUkJEhMTsWTJEtn1Fy9eVDs+jJSvWbNmSElJwcmTJ5Geni4b69Wrl6wLPbv/K1dmZqboCCqF3cuJ3qKkpAQZGRnIz89HixYtYGhoKDqSWtPQ0ICJiYms0M7Ly4OxsbHsF6pUKsXz58/Vao+QqrC1tcX333//xuP0zp49iyFDhuDevXtKTkavc3R0RFhYmKzhUFJSEhwcHJCamoqOHTuymVoN8Ntvv6F+/frvfKQV/XkTJ05EUlISVq5ciaioKGzfvh337t2Djo4OACAyMhKhoaFISEgQnJSoZps0aRKCgoJgYWEhOsp7wZluIgVGjx6Nr7/+GkZGRmjRooVsvKCgAFOmTOGROoJs27ZNdAR6gz59+mD+/Pk4deqU7MNopeLiYgQGBqJv376C0lFVd+/ehaOjY7Xx8vJylJaWCkhEf1SLFi1w9epV9hhRgqVLl2LgwIHw8PCAoaEhtm/fLneP27p1K3r37i0wIVWKiYlBTEwMHj58iPLycrlr/Nym+r799lvMnDmz1hbdnOkmUkBTUxP379+HpaWl3Pjjx49hbW2NV69eCUpGf8SuXbvg4+Mj2xdJ789vv/0GNzc36Orq4vPPP0fz5s0hlUrx66+/4r///S+Ki4tx6dIlNrFRAe3atcOXX36J4cOHy810BwUF4dSpUzh79qzoiPQ7qv5/I+V49uwZDA0NoampKTeem5sLQ0PDag8bSbmWLFmCoKAguLm5wcbGptrWs4MHDwpKRu+qtt/XONNNVMXz588hlUohlUrx4sUL6Onpya6VlZUhOjq6WiFOqmv8+PHo0KFDrb2Bq5IPPvgA8fHxmDRpEubOnStrnCKRSNCrVy+sX7+eBbeKWLhwIfz8/HD37l2Ul5fjwIEDSEtLw44dO6qdR0xEFUxMTBSOm5ubKzkJKbJx40ZERERgxIgRoqMQKcSim6gKU1NTWXOupk2bVrsukUjkGqiQauNCHuWyt7fHsWPH8PTpU9y4cQNAxf5hfihVLQMGDMCRI0cQFBQEAwMDLFy4EK6urjhy5Ah69eolOh4R0R9WUlKCTp06iY5B9EYsuomqOHPmDKRSKXr06IH9+/fLFQs6Ojpo2LAh6tevLzAhkeozMzODu7u76Bj0Fl27dsWpU6dExyAi+luMHTsW3333HY91JZXFopuoCg8PDwCQHQ/CzrBERKRqeFQikbyioiJs2rQJP/74I1xcXKod47ZmzRpByYgqsOgmUqBhw4YAgMLCQmRlZaGkpETuuouLi4hYRER/irm5OdLT02FhYQEzM7O3Fm25ublKTEZ/BrfOEMlLTk5GmzZtAADXr1+Xu8aHVDXD8OHDYWxsLDrGe8Oim0iBR48eYdSoUTh27JjC6zwHmohqkrVr18LIyAgAEBoaKjYM/WUpKSnc6kRUxZkzZ0RHoCqSk5Pf+b2VE1kbNmx4X3FUAo8MI1Jg2LBhuHPnDkJDQ/HRRx/h4MGDePDgAYKDgxESEoJ+/fqJjkjvoFWrVjh27Bi7ZhORyho4cOA7v/fAgQPvMQkR0d9DQ0MDEokEUqn0d1caqMtEFme6iRQ4ffo0Dh06BDc3N2hoaKBhw4bo1asXjI2NsWLFChbdgjk4OCAhIQF169aVG8/Ly4Orqytu3boFoPoSMyJ19fz583d+b21e3qeKqh5FJZVKcfDgQZiYmMDNzQ0AcPnyZeTl5f2h4pxIHQwcOBAREREwNjb+3X8ffGClXJmZmbKvr1y5gpkzZyIgIAAdO3YEAMTHxyMkJASrVq0SFVHpWHQTKVBQUCA7j9vMzAyPHj1C06ZN4ezsjMTERMHp6Pbt2wqfjBYXF+Pu3bsCEhGptsrjEN+mckZCXWYdVMW2bdtkX8+ePRuffPIJNm7cCE1NTQAVs0CTJk3iwxCi15iYmMjua8bGxty7rUIqeyMBwODBg7Fu3Tp4e3vLxlxcXGBnZ4fAwED4+voKSKh8LLqJFGjWrBnS0tLQqFEjtG7dGmFhYWjUqBE2btwIGxsb0fHU1uHDh2VfnzhxQm6GqKysDDExMWjUqJGAZESqjfsda4atW7ciLi5OVnADgKamJqZPn45OnTph9erVAtMRqZaqD6wiIiLEBaG3unbtGuzt7auN29vbIyUlRUAiMVh0EykwdepU3L9/HwCwaNEi9O3bF5GRkdDR0eGNXaDKp6ESiQR+fn5y17S1tdGoUSOEhIQISEak2iqPQyTV9urVK6SmpqJZs2Zy46mpqSgvLxeUikj19ejRAwcOHICpqanc+PPnz+Hr64vTp0+LCUZwcnLCihUrsHnzZujo6AAASkpKsGLFCjg5OQlOpzxspEb0DgoLC5GamooGDRrAwsJCdBy1Z29vj4SEBP6/IPqTzp49i7CwMNy6dQt79+6Fra0tdu7cCXt7e3Tp0kV0PLU1ffp07NixA/PmzYO7uzsA4MKFC/jqq68wYsQInjVM9AYaGhrIycmRbQ2s9PDhQ9ja2qK0tFRQMrp48SL69+8PqVQq61SenJwMiUSCI0eOyO51tR1nuonegb6+PlxdXUXHoP+p2qCjUl5eXrUn3ERU3f79+zFixAgMGzYMiYmJKC4uBgA8e/YMy5cvR3R0tOCE6uvf//43rK2tERISIlttZWNjg4CAAMyYMUNwOiLVU/VoqpSUFOTk5Mhel5WV4fjx47C1tRURjf7H3d0dt27dQmRkJFJTUwEAQ4YMwdChQ2FgYCA4nfJwpptIgbKyMkRERCAmJgYPHz6stqyPy5TEWrlyJRo1aoQhQ4YAqGjSsX//ftjY2CA6OhqtW7cWnJBIdbVt2xZffvklRo4cCSMjIyQlJcHBwQFXrlyBl5eX3IdWEqey4zwbqBG9WeXRVEBFM8jX1alTB//5z38wevRoZUcjAKWlpWjevDmOHj2qVkvJFeFMN5ECU6dORUREBPr164dWrVqxI6aK2bhxIyIjIwEAp06dwo8//ojjx49jz549CAgIwMmTJwUnJFJdaWlp6NatW7VxExMT5OXlKT8QKcRim+j3ZWZmQiqVwsHBARcvXkS9evVk13R0dGBpaSnXmJCUS1tbG0VFRaJjqAQW3UQK7N69G3v27JE73oBUR05ODuzs7AAAR48exSeffILevXujUaNG6NChg+B0RKrN2toaGRkZ1Tr9x8XFwcHBQUwoAgA8ePAAM2fOlK2yen3mjse5Eclr2LAhSktL4efnh7p168odVUWq4fPPP8fKlSuxefNmaGmpb+mpvj850Vvo6OjA0dFRdAx6AzMzM2RnZ8POzg7Hjx9HcHAwgIqlZfxQSvR248aNw9SpU7F161ZIJBLcu3cP8fHxmDFjBhYuXCg6nlrz9/dHVlYWAgMDYWNjw1VWRO9AW1sbBw8e5P1LRSUkJCAmJgYnT56Es7NztX3cBw4cEJRMuVh0EykwY8YMfP3111i/fj0/9KiggQMHYujQoWjSpAmePHkCLy8vAMCVK1f4sITod8yZMwfl5eXo2bMnCgsL0a1bN+jq6iIgIABjx44VHU+txcXF4ezZs2jTpo3oKEQ1yoABAxAVFYUvv/xSdBR6jampKQYNGiQ6hnAsuokUiIuLw5kzZ3Ds2DG0bNkS2tractfV5amcqlq7di3s7e2RlZWFVatWwdDQEABw//59TJo0SXA6ItUmkUgwf/58BAQEICMjA/n5+WjRogXCwsJgb2/PRmoC2dnZKWwGRURv16RJEwQFBeGXX35Bu3btqs2mfvHFF4KS0bZt20RHUAnsXk6kwKhRo956nTcQcUpLSzF+/HgEBgbC3t5edByiGqO4uBiLFy/GqVOnZDPbvr6+2LZtGxYsWABNTU18/vnnmD17tuioauvkyZMICQlBWFhYtT33RPRmb/s8IJFIcOvWLSWmIaqORTcR1TgmJia4evUqi26iP2D27NkICwuDp6cnzp07h0ePHmHUqFE4f/485s2bh8GDB7PLr2BmZmYoLCzEq1evoK+vX22VVW5urqBkRETvztXVFTExMTAzM0Pbtm3fulUzMTFRicnE4fJyIqpxfH19uXeL6A/au3cvduzYAR8fH1y/fh0uLi549eoVkpKS2LtCRYSGhoqOQET0lw0YMAC6uroAKj6zEWe6iRR601M5iUQCPT09ODo6wt/fH927dxeQjoKDgxESEoKePXty7xbRO9LR0UFmZiZsbW0BAHXq1MHFixfh7OwsOBkR0V/322+/4fDhw8jKykJJSYnctTVr1ghKRVSBRTeRAnPnzsWGDRvg7OwMd3d3ABVHHiQnJ8Pf3x8pKSmIiYnBgQMHMGDAAMFp1Q/3bhH9cZqamsjJyUG9evUAAEZGRkhOTuY2DRVTVlaGqKgo/PrrrwCAli1bwsfHh0v/id4iJiYGPj4+cHBwQGpqKlq1aoXbt29DKpXC1dUVp0+fFh1R7ZWUlODhw4coLy+XG2/QoIGgRMrFoptIgXHjxqFBgwYIDAyUGw8ODsadO3cQHh6ORYsW4YcffsClS5cEpSQiencaGhrw8vKSLfk7cuQIevToobZnpqqijIwMeHt74+7du2jWrBkAIC0tDXZ2dvjhhx/QuHFjwQmJVJO7uzu8vLywZMkSGBkZISkpCZaWlhg2bBj69u2LiRMnio6ottLT0zFmzBicO3dOblwqlUIikaCsrExQMuVi0U2kgImJCS5fvlztzOeMjAy0a9cOz549Q2pqKtq3b48XL14ISklE9O5+71SGSjydQRxvb29IpVJERkbC3NwcAPDkyRMMHz4cGhoa+OGHHwQnJFJNRkZGuHr1Kho3bgwzMzPExcWhZcuWSEpKwoABA3D79m3REdVW586doaWlhTlz5sDGxqba9s3WrVsLSqZcbKRGpICenh7OnTtXreg+d+4c9PT0AADl5eWyr+n9mz59OpYuXQoDAwNMnz79re/l3i2i6lhMq77Y2FicP39eVnADQN26dfHVV1+hc+fOApMRqTYDAwPZPm4bGxvcvHkTLVu2BAA8fvxYZDS1d/XqVVy+fBnNmzcXHUUoFt1ECkyZMgUTJkzA5cuX0b59ewAVe7o3b96MefPmAQBOnDiBNm3aCEypXq5cuYLU1FS0bdsWV65ceeP72IWZiGoqXV1dhaun8vPzoaOjIyARUc3w4YcfIi4uDk5OTvD29saMGTNw7do1HDhwAB9++KHoeGqtRYsWfPABLi8neqPIyEisX78eaWlpAIBmzZphypQpGDp0KADg5cuXsm7mpByampq4f/8+LC0tAQBDhgzBunXrYGVlJTgZEdFfN3LkSCQmJmLLli2yJp4XLlzAuHHj0K5dO0RERIgNSKSibt26hfz8fLi4uKCgoAAzZszAuXPn0KRJE6xZswYNGzYUHVGtPH/+XPb1pUuXsGDBAixfvhzOzs7Q1taWe6+xsbGy4wnBopuIagwNDQ3k5OTIim5jY2NcvXoVDg4OgpMREf11eXl58PPzw5EjR2QfTF+9egUfHx9ERETAxMREcEIiot+noaEht/KwsmlaVerWSI3Ly4moxuIzQyKqTUxNTXHo0CFkZGTIjgxzcnKq1l+EiOQ5ODggISEBdevWlRvPy8uDq6srjxJVsjNnzoiOoHJYdBP9j7m5OdLT02FhYQEzM7O37g3Ozc1VYjKqJJFIqv1/4R5uIqptHB0dWWgT/QG3b99WOGNaXFyMu3fvCkik3jw8PBAUFISZM2dCX19fdByVwKKb6H/Wrl0LIyMj2dcs5lSPVCqFv7+/7JzhoqIiTJgwgecME1GtMGjQILi7u2P27Nly46tWrUJCQgL27t0rKBmRajp8+LDs6xMnTshtwSgrK0NMTAwaNWokIBktWbIEEyZMYNH9P9zTTUQ1Bs8ZJqLarF69ejh9+jScnZ3lxq9duwZPT088ePBAUDIi1aShoQGgYtXb6yWNtrY2GjVqhJCQEHz88cci4qm11/vwqDvOdBMpkJiYCG1tbdkHn0OHDmHbtm1o0aIFFi9ezKNbBGExTUS12ZuOBtPW1pbrBkxEFcrLywEA9vb2SEhIgIWFheBEVBVXjf5/GqIDEKmi8ePHIz09HUDFMRRDhgyBvr4+9u7di1mzZglOR0REtZGzszO+//77auO7d+9GixYtBCQiUm3x8fE4evQoMjMzZQX3jh07YG9vD0tLS3z22WcoLi4WnFJ9NW3aFObm5m/9oy44002kQHp6Otq0aQMA2Lt3Lzw8PPDdd9/hl19+wb/+9S+EhoYKzUdERLVPYGAgBg4ciJs3b6JHjx4AgJiYGOzatYv7uYkUWLJkCbp37y5bPn7t2jWMGTMG/v7+cHJywurVq1G/fn0sXrxYbFA1tWTJEh51+D8suokUkEqlsiVLP/74o+xmbmdnh8ePH4uMRkREtVT//v0RFRWF5cuXY9++fahTpw5cXFzw448/wsPDQ3Q8IpWTlJSE4OBg2evdu3ejQ4cOCA8PB1DxuW3RokUsugX517/+xT3d/8Oim0gBNzc3BAcHw9PTE7GxsdiwYQMAIDMzE1ZWVoLTERFRbdWvXz/069dPdAyiGuHp06dyn8tiY2Ph5eUle92+fXtkZ2eLiKb2uJ9bHvd0EykQGhqKxMRETJ48GfPnz5edl7pv3z506tRJcDoiIqqt8vLysHnzZsybNw+5ubkAKpp78qxhouqsrKyQmZkJACgpKUFiYiI+/PBD2fUXL15AW1tbVDy1xgOy5PHIMKI/oKioCJqamryBExHR3y45ORmenp4wMTHB7du3kZaWBgcHByxYsABZWVnYsWOH6IhEKmXixIlISkrCypUrERUVhe3bt+PevXuyUwAiIyMRGhqKhIQEwUlJ3XGmm+gNKmcb5s6dK5ttSElJwcOHDwUnIyKi2mj69Onw9/fHjRs3oKenJxv39vbGzz//LDAZkWpaunQptLS04OHhgfDwcISHh8sdu7d161b07t1bYEKiCpzpJlIgOTkZPXv2hKmpKWcbiIhIKUxMTJCYmIjGjRvDyMgISUlJcHBwwJ07d9CsWTMUFRWJjkikkp49ewZDQ0NoamrKjefm5sLQ0FCuECcSgTPdRApMnz4do0aN4mwDEREpja6uLp4/f15tPD09HfXq1ROQiKhmMDExqVZwA4C5uTkLblIJLLqJFEhISMD48eOrjdva2iInJ0dAIiIiqu18fHwQFBSE0tJSABXdf7OysjB79mwMGjRIcDoiIvqzWHQTKcDZBiIiUraQkBDk5+fD0tISL1++hIeHBxo3bgxDQ0MsW7ZMdDwiIvqTuKebSIGxY8fiyZMn2LNnD8zNzZGcnAxNTU34+vqiW7duCA0NFR2RiIhqqbi4OCQnJyM/Px/t2rVDz549RUciIqK/gDPdRApUzjbUq1dPNtvg6OgIIyMjzjYQEdHfKj4+HkePHpW97tKlCwwMDPDf//4Xn376KT777DMUFxcLTEhERH8FZ7qJ3uKXX35BUlIS8vPz4erqCk9PT9GRiIiolvHy8sJHH32E2bNnAwCuXbuGdu3awc/PD05OTli9ejXGjx+PxYsXiw1KRER/ipboAESqpry8HBEREThw4ABu374NiUQCe3t7WFtbQyqVQiKRiI5IRES1yNWrV7F06VLZ6927d8Pd3R3h4eEAADs7OyxatIhFNxFRDcXl5URVSKVS+Pj4YOzYsbh79y6cnZ3RsmVL3LlzB/7+/vjHP/4hOiIREdUyT58+hZWVlex1bGwsvLy8ZK/bt2+P7OxsEdGIiOhvwJluoioiIiLw888/IyYmBt27d5e7dvr0afj6+mLHjh0YOXKkoIRERFTbWFlZITMzE3Z2digpKUFiYiKWLFkiu/7ixQtoa2sLTEhERH8FZ7qJqti1axfmzZtXreAGgB49emDOnDmIjIwUkIyIiGorb29vzJkzB2fPnsXcuXOhr6+Prl27yq4nJyejcePGAhMSEdFfwaKbqIrk5GT07dv3jde9vLyQlJSkxERERFTbLV26FFpaWvDw8EB4eDjCw8Oho6Mju75161b07t1bYEIiIvor2L2cqAodHR3cuXMHNjY2Cq/fu3cP9vb2PLqFiIj+ds+ePYOhoSE0NTXlxnNzc2FoaChXiBMRUc3BPd1EVZSVlUFL683/LDQ1NfHq1SslJiIiInVhYmKicNzc3FzJSYiI6O/EopuoCqlUCn9/f+jq6iq8zhluIiIiIiL6I1h0E1Xh5+f3u+9h53IiIiIiInpX3NNNRERERERE9J6wezkRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREX766SdIJBLk5eWJjkJERFSrsOgmIiKqBfz9/SGRSCCRSKCtrQ17e3vMmjULRUVFoqMRERGpNZ7TTUREVEv07dsX27ZtQ2lpKS5fvgw/Pz9IJBKsXLlSdDQiIiK1xZluIiKiWkJXVxfW1taws7ODr68vPD09cerUKQBAcXExvvjiC1haWkJPTw9dunRBQkLCW/++uLg4dO3aFXXq1IGdnR2++OILFBQUKONHISIiqjVYdBMREdVC169fx7lz56CjowMAmDVrFvbv34/t27cjMTERjo6O6NOnD3JzcxV+/82bN9G3b18MGjQIycnJ+P777xEXF4fJkycr88cgIiKq8SRSqVQqOgQRERH9Nf7+/vj222+hp6eHV69eobi4GBoaGtizZw/69u0LMzMzREREYOjQoQCA0tJSNGrUCNOmTUNAQAB++ukndO/eHU+fPoWpqSnGjh0LTU1NhIWFyf4bcXFx8PDwQEFBAfT09ET9qERERDUK93QTERHVEt27d8eGDRtQUFCAtWvXQktLSzZTXVpais6dO8veq62tDXd3d/z6668K/66kpCQkJycjMjJSNiaVSlFeXo7MzEw4OTm995+HiIioNmDRTUREVEsYGBjA0dERALB161a0bt0aW7ZsQfv27f/w35Wfn4/x48fjiy++qHatQYMGfzkrERGRumDRTUREVAtpaGhg3rx5mD59OjIyMqCjo4NffvkFDRs2BFCxvDwhIQHTpk1T+P2urq5ISUmRFfFERET057CRGhERUS01ePBgaGpqYsOGDZg4cSICAgJw/PhxpKSkYNy4cSgsLMSYMWMUfu/s2bNx7tw5TJ48GVevXsWNGzdw6NAhNlIjIiL6gzjTTUREVEtpaWlh8uTJWLVqFTIzM1FeXo4RI0bgxYsXcHNzw4kTJ2BmZqbwe11cXBAbG4v58+eja9eukEqlaNy4MYYMGaLkn4KIiKhmY/dyIiIiIiIioveEy8uJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe8Kim4iIiIiIiOg9YdFNRERERERE9J6w6CYiIiIiIiJ6T1h0ExEREREREb0nLLqJiIiIiIiI3hMW3URERERERETvCYtuIiIiIiIioveERTcRERERERHRe/L/AJ/aJD9mHqDtAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -409,19 +257,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -445,19 +291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
+       "array([183.05261872, 193.52828463, 154.73707302, 204.27140391,\n",
+       "       203.88907247, 213.74665656, 225.10092364, 171.75867917,\n",
+       "       204.3521425 , 207.52870255, 158.53001756, 240.94399197,\n",
+       "       189.9909742 , 180.72442994, 173.4393402 , 175.98883711,\n",
+       "       197.86092769, 188.61598821, 234.19796698, 209.0295457 ])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,19 +316,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -494,19 +339,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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T/OHwsDOTsXOf6/YktyfJNddcs+vCgfkzhQKAg2jqgFxV35rkd5L8VHf/VVVtu+kWY33eQPfdSe5Oko2NjfPuB4B14cMmrJapVrGoqpdmMxy/v7t/dzL81aq6YnL/FUmemYyfSXL18PCrkjw1m3IBAGC+dgzItdkq/rUkj3X3Lw53nUxyZHL9SJL7hvHDVfWyqro2yXVJHppdyQAAMD/TTLF4c5IfS/LHVfWZydjPJjme5ERV3ZbkiSS3Jkl3P1JVJ5I8ms0VMO7o7hdmXTgArJOz0zAeP37zgisBdgzI3f2/svW84iS5cZvHHEtybA91AQDAQjiTHgAADARkAAAYCMgAADDY1YlCAIDZskYyLB8dZAAAGAjIAAAwEJABAGAgIAMAwEBABoAlcujo/Q7cgwUTkAEAYGCZNwDYZzrEsNx0kAEAYCAgAwDAQEAGAICBgAwAAAMBGQAABgIyAAAMBGQ4oJyMAAC2JiADwBLyIRYWx4lC4IA7+wf48eM3v+g2ABxUOsgAsAZ0nGF2BGQAABgIyAAAMDAHGUhi7jEAnKWDDAArxFxjmD8BGQAABgIyAAAMzEGGA8ZXs7Bazl2rHJg/HWQAABgIyAAAMDDFAgBWgOlRsH90kAEAYCAgw5qxRioA7I2ADAAAA3OQ4YDQVYb15HcbZk8HGQAABgIyAKwxxyXA7gnIAAAwEJABAGAgIAMAwEBABgCAgWXeYM05OAcAdkdAhjUhCAPAbAjIALCCfCiG+TEHGQAABgIyAAAMTLGAFXP2a9XHj9/8otsAwGzoIAMAwEBABgCAgYAMAAADARmW3KGj95tnDAD7yEF6sKaEagC4OAIyrAiBFwD2hykWAAAwEJABAGCwY0CuqvdV1TNV9flh7NVV9UBVfWly+arhvruq6nRVfbGq3j6vwuGgc/AeAMxHdfeFN6j6gSR/k+TXu/v1k7H/kORr3X28qo4meVV331lV1yf5QJIbkrw2yUeTfGd3v3Ch19jY2OhTp07t/V8Da0gIBmbp7Fk4gaSqHu7ujXPHd+wgd/fHk3ztnOFbktwzuX5PkncN4/d293Pd/ZUkp7MZlgEAYCVc7Bzky7v76SSZXF42Gb8yyZPDdmcmYwAAsBJmfZBebTG25RyOqrq9qk5V1alnn312xmUAAMDFudiA/NWquiJJJpfPTMbPJLl62O6qJE9t9QTdfXd3b3T3xqWXXnqRZQAAwGxdbEA+meTI5PqRJPcN44er6mVVdW2S65I8tLcS4WCxOgUALNaOZ9Krqg8keUuS11TVmSQ/l+R4khNVdVuSJ5LcmiTd/UhVnUjyaJLnk9yx0woWAMD+O/tB3KoWcL4dA3J3/8g2d924zfbHkhzbS1EAALAozqQHAAADARkAAAYCMgAADHacgwzMlwNlgP200yo53pNABxkAAF5EQAYAzmNNdg4yARkAAAYCMgAADBykB3PmgBdgFZhOAd8gIMOS8scKABbDFAsAABjoIMOS0DEGgOWggwz7zNJJwCo59z3LexgHgYAMAAADARkAAAbmIMOC+IoSAJaTgAwA7MiHeg4SARlmzIlBgFVyscHXex3rzBxkAGDPrG7BOhGQAQBgYIoF7BOdFQBYDTrIAAAw0EGGizB2g7c7QEXHGABWk4AMMyIQA0zXQIBlZ4oFAAAMdJBhF3SJAWD96SADAMBAQAYAgIGADADMhbPrsarMQYY98uYPAOtFBxkAAAYCMgCwr0y9YNkJyAAAMBCQAYC50jFm1QjIcAHe1AHg4LGKBWxBKAaAg0tAhnwjED9+/OYL3g/Ai83j/XGn92SYNwEZANgXmg2sCgGZA2HaboQ3b4D9o1PMshKQOdAEYoDF2y4oC9AsioDMWtEpBlh/577XC9LMmoAMACwFzQuWhXWQAQBgoIMMAKwEHWb2S3X3omvIxsZGnzp1atFlsAa8eQIcXOYgs1tV9XB3b5w7booFAAAMBGQAABiYg8xKcCpoAKZl2Tf2SgcZAAAGOsgAwFrY7ttEHWV2S0BmpZz75ufNDgCYNQEZAFhL5zZVtusw78cpq3WxV4s5yAAAMNBBZl+d+wl6uykTPmkDsEr83VovAjIzt9WbxMUuw2b5NgCWkUC83gRkdjTtGsSLeJMQoAHYq93OVWb9zS0gV9VNSX45ySVJ3tvdx+f1WizGXsLpTkvxAMAq02FebXMJyFV1SZJfSfKPk5xJ8qmqOtndj87j9ZjOdr+su/0lnjbECrsArJOt/q5N232e9rmX8dvag2heHeQbkpzu7i8nSVXdm+SWJALyHO0UgLfbfqfnu9jtAYDd2elg9t0+frePu5jHrqPq7tk/adU/T3JTd/+bye0fS/IPu/vdW22/sbHRp06dmnkd09jrJ7aL7b5u9YO/3QoOF/vLcrG/XADAfC3qb/Q0r7vTN8177WYv00m/qurh7t44b3xOAfnWJG8/JyDf0N0/Pmxze5LbJze/K8kXZ17I3r0myZ8tuogVYV/tjv01Pftqd+yv6dlXu2N/Tc++2p1F7q+/292Xnjs4rykWZ5JcPdy+KslT4wbdfXeSu+f0+jNRVae2+lTB+eyr3bG/pmdf7Y79NT37anfsr+nZV7uzjPtrXmfS+1SS66rq2qr65iSHk5yc02sBAMDMzKWD3N3PV9W7k3w4m8u8va+7H5nHawEAwCzNbR3k7v69JL83r+ffJ0s9BWTJ2Fe7Y39Nz77aHftrevbV7thf07Ovdmfp9tdcDtIDAIBVNa85yAAAsJIE5ClV1b+tqq6q1yy6lmVVVf++qj5XVZ+pqo9U1WsXXdMyq6pfqKovTPbZB6vqlYuuaVlV1a1V9UhVfb2qlupI52VRVTdV1Rer6nRVHV10Pcusqt5XVc9U1ecXXcsqqKqrq+oPquqxye/hTy66pmVVVS+vqoeq6rOTffXzi65p2VXVJVX1R1X1oUXXMhKQp1BVV2fztNlPLLqWJfcL3f093f2GJB9K8u8WXM+yeyDJ67v7e5L87yR3LbieZfb5JP8syccXXcgyqqpLkvxKkn+S5PokP1JV1y+2qqX235LctOgiVsjzSX66u787yZuS3OHna1vPJXlrd39vkjckuamq3rTYkpbeTyZ5bNFFnEtAns5/TPIzSUzYvoDu/qvh5itif11Qd3+ku5+f3PzDbK4Xzha6+7HuXsaTCS2LG5Kc7u4vd/ffJrk3yS0LrmlpdffHk3xt0XWsiu5+urs/Pbn+19kMM1cutqrl1Jv+ZnLzpZP//C3cRlVdleTmJO9ddC3nEpB3UFXvTPIn3f3ZRdeyCqrqWFU9meRfRAd5N/51kv++6CJYWVcmeXK4fSYCDHNQVYeSvDHJJxdcytKaTBn4TJJnkjzQ3fbV9n4pmw3Iry+4jvPMbZm3VVJVH03yHVvc9Z4kP5vkB/e3ouV1oX3V3fd193uSvKeq7kry7iQ/t68FLpmd9tdkm/dk8yvM9+9nbctmmn3FtmqLMV0rZqqqvjXJ7yT5qXO+MWTQ3S8kecPkuJIPVtXru9t893NU1TuSPNPdD1fVWxZcznkE5CTd/batxqvqHyS5NslnqyrZ/Ar801V1Q3f/6T6WuDS221db+M0k9+eAB+Sd9ldVHUnyjiQ39gFfc3EXP1uc70ySq4fbVyV5akG1sIaq6qXZDMfv7+7fXXQ9q6C7/7KqPpbN+e4C8vnenOSdVfVDSV6e5Nur6je6+0cXXFcSUywuqLv/uLsv6+5D3X0om3+Evu+ghuOdVNV1w813JvnCompZBVV1U5I7k7yzu//fouthpX0qyXVVdW1VfXOSw0lOLrgm1kRtdoh+Lclj3f2Li65nmVXVpWdXJKqqb0nytvhbuKXuvqu7r5rkq8NJfn9ZwnEiIDNbx6vq81X1uWxOS7EU0IX9pyTfluSBydJ4/2XRBS2rqvqnVXUmyfcnub+qPrzompbJ5GDPdyf5cDYPoDrR3Y8stqrlVVUfSPKJJN9VVWeq6rZF17Tk3pzkx5K8dfJe9ZlJ14/zXZHkDyZ/Bz+VzTnIS7V8GdNxJj0AABjoIAMAwEBABgCAgYAMAAADARkAAAYCMgAADARkAAAYCMgAADAQkAEAYPD/ASvKmaTtYFHZAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -521,24 +364,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "由于现实生活中的大多数数值呈正态分布，我们不应该使用均匀随机数生成器来生成样本数据。以下是如果我们尝试使用均匀分布（由 `np.random.rand` 生成）来生成体重时会发生的情况：\n"
+    "由于现实生活中的大多数数值呈正态分布，我们不应该使用均匀随机数生成器来生成样本数据。以下是尝试使用均匀分布（由`np.random.rand`生成）生成体重时的情况：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -556,21 +397,21 @@
    "source": [
     "## 置信区间\n",
     "\n",
-    "现在让我们计算棒球运动员体重和身高的置信区间。我们将使用[这个 StackOverflow 讨论中的代码](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)：\n"
+    "现在我们来计算棒球运动员体重和身高的置信区间。我们将使用[这个 stackoverflow 讨论中的代码](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
+      "p=0.85, mean = 73.70 ± 0.10\n",
+      "p=0.90, mean = 73.70 ± 0.12\n",
+      "p=0.95, mean = 73.70 ± 0.14\n"
      ]
     }
    ],
@@ -600,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -624,8 +465,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Height</th>\n",
        "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
        "      <th>Count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -681,7 +522,7 @@
        "    <tr>\n",
        "      <th>Starting_Pitcher</th>\n",
        "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
+       "      <td>205.321267</td>\n",
        "      <td>221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -695,7 +536,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Height      Weight  Count\n",
+       "                      Weight      Height  Count\n",
        "Role                                           \n",
        "Catcher            72.723684  204.328947     76\n",
        "Designated_Hitter  74.222222  220.888889     18\n",
@@ -704,38 +545,38 @@
        "Relief_Pitcher     74.374603  203.517460    315\n",
        "Second_Baseman     71.362069  184.344828     58\n",
        "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
+       "Starting_Pitcher   74.719457  205.321267    221\n",
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
+    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "让我们测试一下假设，一垒手比二垒手更高。最简单的方法是测试置信区间：\n"
+    "让我们测试一下一垒手是否比二垒手更高。最简单的方法是测试置信区间：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
+      "Conf=0.85, 1st basemen height: 209.36..216.86, 2nd basemen height: 182.24..186.45\n",
+      "Conf=0.90, 1st basemen height: 208.82..217.40, 2nd basemen height: 181.93..186.76\n",
+      "Conf=0.95, 1st basemen height: 207.97..218.25, 2nd basemen height: 181.45..187.24\n"
      ]
     }
    ],
@@ -757,15 +598,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
+      "T-value = 9.77\n",
+      "P-value: 1.4185554184322326e-15\n"
      ]
     }
    ],
@@ -780,9 +621,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ttest_ind` 函数返回的两个值是：  \n",
-    "* p-value 可以被看作是两个分布具有相同均值的概率。在我们的例子中，p-value 非常低，这表明有强有力的证据支持一垒手更高的结论。  \n",
-    "* t-value 是 t 检验中使用的标准化均值差的中间值，它会与给定置信水平的阈值进行比较。  \n"
+    "`ttest_ind`函数返回的两个值是：  \n",
+    "* p值可以被视为两个分布具有相同均值的概率。在我们的例子中，p值非常低，这表明有强有力的证据支持一垒手更高。  \n",
+    "* t值是用于t检验的标准化均值差的中间值，它会与给定置信水平的阈值进行比较。  \n"
    ]
   },
   {
@@ -796,19 +637,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -830,19 +669,19 @@
    "source": [
     "## 相关性与邪恶棒球公司\n",
     "\n",
-    "相关性可以帮助我们发现数据序列之间的关系。在我们的示例中，假设有一家邪恶的棒球公司，它根据球员的身高来支付薪水——球员越高，薪水就越多。假设基础工资是 $1000，另外根据身高提供 $0 到 $100 的奖金。我们将使用 MLB 的真实球员数据，计算他们的虚构薪水：\n"
+    "相关性可以帮助我们发现数据序列之间的关系。在我们的示例中，假设有一家邪恶的棒球公司，根据球员的身高来支付薪水——球员越高，薪水越多。假设基础工资是1000美元，额外奖金根据身高从0到100美元不等。我们将使用MLB的真实球员数据，计算他们的假想薪水：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
+      "[(180, 1033.985209531635), (215, 1073.6346206518763), (210, 1067.9704190632704), (210, 1067.9704190632704), (188, 1043.0479320734046), (176, 1029.4538482607504), (209, 1066.837578745549), (200, 1056.6420158860585), (231, 1091.760065735415), (180, 1033.985209531635)]\n"
      ]
     }
    ],
@@ -856,12 +695,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "让我们现在计算这些序列的协方差和相关性。`np.cov` 将为我们提供一个所谓的**协方差矩阵**，这是协方差在多个变量上的扩展。协方差矩阵 $M$ 的元素 $M_{ij}$ 是输入变量 $X_i$ 和 $X_j$ 之间的相关性，而对角线上的值 $M_{ii}$ 是 $X_i$ 的方差。同样，`np.corrcoef` 将为我们提供**相关性矩阵**。\n"
+    "现在让我们计算这些序列的协方差和相关性。`np.cov` 将为我们提供一个所谓的**协方差矩阵**，这是协方差在多变量上的扩展。协方差矩阵 $M$ 的元素 $M_{ij}$ 是输入变量 $X_i$ 和 $X_j$ 之间的相关性，而对角线上的值 $M_{ii}$ 是 $X_i$ 的方差。同样，`np.corrcoef` 将为我们提供**相关矩阵**。\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -869,10 +708,10 @@
      "output_type": "stream",
      "text": [
       "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
+      "[[441.63557066 500.30258018]\n",
+      " [500.30258018 566.76293389]]\n",
+      "Covariance = 500.3025801786725\n",
+      "Correlation = 0.9999999999999997\n"
      ]
     }
    ],
@@ -891,19 +730,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -918,19 +755,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "让我们看看如果关系不是线性的会发生什么。假设我们的公司决定隐藏高度和薪水之间明显的线性依赖关系，并在公式中引入一些非线性，例如 `sin`：\n"
+    "让我们看看如果关系不是线性的会发生什么。假设我们的公司决定隐藏高度和薪资之间明显的线性依赖关系，并在公式中引入一些非线性，例如 `sin`：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670837\n"
+      "Correlation = 0.9910655775558532\n"
      ]
     }
    ],
@@ -943,19 +780,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "在这种情况下，相关性略小一些，但仍然相当高。现在，为了使关系更加不明显，我们可能想通过向薪资中添加一些随机变量来增加一些额外的随机性。让我们看看会发生什么：\n"
+    "在这种情况下，相关性略小一些，但仍然相当高。现在，为了使关系更加不明显，我们可能需要通过向工资中添加一些随机变量来增加一些额外的随机性。让我们看看会发生什么：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9363097848296155\n"
+      "Correlation = 0.948230287835537\n"
      ]
     }
    ],
@@ -966,19 +803,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -993,24 +828,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> 你能猜到为什么这些点会排列成这样的竖线吗？\n",
+    "> 你能猜出为什么这些点会排列成这样的竖直线吗？\n",
     "\n",
-    "我们已经观察到一个人为设计的概念（比如薪资）与观察变量*身高*之间的相关性。现在让我们看看两个观察变量，比如身高和体重，是否也存在相关性：\n"
+    "我们已经观察到一个人为设计的概念（如薪资）与观察变量*身高*之间的相关性。现在让我们看看两个观察变量（如身高和体重）是否也存在相关性：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
+       "array([[1.        , 0.52959196],\n",
+       "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,16 +858,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "不幸的是，我们没有得到任何结果——只有一些奇怪的 `nan` 值。这是因为我们系列中的某些值是未定义的，用 `nan` 表示，这导致操作的结果也未定义。通过查看矩阵，我们可以发现问题出在 `Weight` 列，因为已经计算了 `Height` 值之间的自相关。\n",
+    "不幸的是，我们没有得到任何结果——只有一些奇怪的 `nan` 值。这是因为我们序列中的某些值是未定义的，用 `nan` 表示，这导致操作的结果也未定义。通过查看矩阵，我们可以发现问题出在 `Weight` 列，因为 `Height` 值之间的自相关已经被计算出来了。\n",
     "\n",
-    "> 这个例子展示了**数据准备**和**清理**的重要性。没有适当的数据，我们无法计算任何内容。\n",
+    "> 这个例子展示了**数据准备**和**清洗**的重要性。如果没有合适的数据，我们无法计算任何内容。\n",
     "\n",
     "让我们使用 `fillna` 方法填充缺失值，然后计算相关性：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -1042,7 +877,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,32 +890,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "确实存在相关性，但不像我们人工示例中那样强。实际上，如果我们查看一个值与另一个值的散点图，关系就不会那么明显：\n"
+    "确实存在相关性，但不像我们的人为示例中那样强烈。实际上，如果我们查看一个值与另一个值的散点图，关系就不会那么明显：\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
+    "plt.scatter(df['Weight'],df['Height'])\n",
+    "plt.xlabel('Weight')\n",
+    "plt.ylabel('Height')\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1098,7 +931,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。\n"
+    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
    ]
   }
  ],
@@ -1121,11 +954,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.6"
   },
   "coopTranslator": {
-   "original_hash": "25bc46a63f19dd223940c5a13b1f44f4",
-   "translation_date": "2025-09-02T09:41:48+00:00",
+   "original_hash": "0499b3f3da9a5b4cd91afc2a9d088298",
+   "translation_date": "2025-09-06T17:09:54+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
    "language_code": "zh"
   }
diff --git a/translations/zh/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/zh/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
index 95267fea..37ed8e9b 100644
--- a/translations/zh/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ b/translations/zh/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## 概率与统计学简介\n",
+    "## 概率与统计简介\n",
     "## 作业\n",
     "\n",
     "在本次作业中，我们将使用[此处](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)提供的糖尿病患者数据集。\n"
@@ -14,11 +14,11 @@
    "cell_type": "code",
    "execution_count": 13,
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
     "df.head()"
    ],
    "outputs": [
@@ -150,14 +150,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "在这个数据集中，列的含义如下：\n",
-    "* Age 和 sex 不言自明\n",
-    "* BMI 是身体质量指数\n",
-    "* BP 是平均血压\n",
-    "* S1 到 S6 是不同的血液测量值\n",
-    "* Y 是疾病在一年内进展的定性指标\n",
+    "在此数据集中，列的含义如下：  \n",
+    "* Age 和 sex 不言自明  \n",
+    "* BMI 是身体质量指数  \n",
+    "* BP 是平均血压  \n",
+    "* S1 到 S6 是不同的血液测量值  \n",
+    "* Y 是疾病在一年内进展的定性指标  \n",
     "\n",
-    "让我们使用概率和统计方法来研究这个数据集。\n",
+    "让我们使用概率和统计的方法来研究这个数据集。\n",
     "\n",
     "### 任务 1：计算所有值的均值和方差\n"
    ],
@@ -354,7 +354,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "source": [
-    "# Another way\r\n",
+    "# Another way\n",
     "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
    ],
    "outputs": [
@@ -446,7 +446,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
+    "# Or, more simply, for the mean (variance can be done similarly)\n",
     "df.mean()"
    ],
    "outputs": [
@@ -485,8 +485,8 @@
    "cell_type": "code",
    "execution_count": 17,
    "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
+    "for col in ['BMI','BP','Y']:\n",
+    "    df.boxplot(column=col,by='SEX')\n",
     "plt.show()"
    ],
    "outputs": [
@@ -537,8 +537,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
+    "for col in ['AGE','SEX','BMI','Y']:\n",
+    "    df[col].hist()\n",
     "    plt.show()"
    ],
    "outputs": [
@@ -855,10 +855,10 @@
    "cell_type": "code",
    "execution_count": 26,
    "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
+    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
+    "for i,n in enumerate(['BMI','S5','BP']):\n",
+    "    ax[i].scatter(df['Y'],df[n])\n",
+    "    ax[i].set_title(n)\n",
     "plt.show()"
    ],
    "outputs": [
@@ -887,9 +887,9 @@
    "cell_type": "code",
    "execution_count": 27,
    "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
+    "from scipy.stats import ttest_ind\n",
+    "\n",
+    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
     "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
    ],
    "outputs": [
@@ -918,7 +918,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。\n"
+    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
    ]
   }
  ],
@@ -944,8 +944,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "coopTranslator": {
-   "original_hash": "1bdbefe3f2486d8e178ee242ac532d43",
-   "translation_date": "2025-09-02T09:57:54+00:00",
+   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
+   "translation_date": "2025-09-06T17:10:25+00:00",
    "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
    "language_code": "zh"
   }