From 62b9d7174f4896a479550d8838001208eded7182 Mon Sep 17 00:00:00 2001
From: suissemaxx <mail@tv07.com>
Date: Sun, 12 Jun 2022 17:25:23 +0200
Subject: [PATCH] Fixed typos, improved graphs

Fixed several typos and improved grammar
Made graphs more consistent by adding figure sizes and setting tight layouts
Made smaller adjustments to code to improve legibility (no functionality changed)
---
 .gitignore                                    |   3 +-
 .../04-stats-and-probability/notebook.ipynb   | 302 ++++++++++--------
 2 files changed, 165 insertions(+), 140 deletions(-)

diff --git a/.gitignore b/.gitignore
index 457413e..956d271 100644
--- a/.gitignore
+++ b/.gitignore
@@ -352,4 +352,5 @@ MigrationBackup/
 # Ionide (cross platform F# VS Code tools) working folder
 .ionide/
 4-Data-Science-Lifecycle/14-Introduction/README.md
-.vscode/settings.json
\ No newline at end of file
+.vscode/settings.json
+*.ipynb
diff --git a/1-Introduction/04-stats-and-probability/notebook.ipynb b/1-Introduction/04-stats-and-probability/notebook.ipynb
index 3739c52..208eee5 100644
--- a/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -5,13 +5,12 @@
    "metadata": {},
    "source": [
     "# Introduction to Probability and Statistics\n",
-    "|\n",
     "In this notebook, we will play around with some of the concepts we have previously discussed. Many concepts from probability and statistics are well-represented in major libraries for data processing in Python, such as `numpy` and `pandas`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,24 +24,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "## Random Variables and Distributions\n",
-    "\n",
-    "Let's start with drawing a sample of 30 variables from a uniform distribution from 0 to 9. We will also compute mean and variance."
+    "Let's start with drawing a sample of 30 values from a uniform distribution from 0 to 9. We will also compute mean and variance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample: [1, 1, 0, 5, 6, 3, 7, 5, 1, 6, 5, 6, 7, 0, 3, 6, 2, 4, 2, 8, 1, 5, 7, 10, 8, 5, 7, 10, 6, 8]\n",
-      "Mean = 4.833333333333333\n",
-      "Variance = 7.938888888888889\n"
+      "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"
      ]
     }
    ],
@@ -62,18 +59,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -93,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -247,13 +245,13 @@
        "[1034 rows x 6 columns]"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t',header=None,names=['Name','Team','Role','Height','Weight','Age'])\n",
+    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
     "df"
    ]
   },
@@ -261,14 +259,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> We are using a package called **Pandas** here for data analysis. We will talk more about Pandas and working with data in Python later in this course.\n",
+    "> We are using a package called [**Pandas**](https://pandas.pydata.org/) here for data analysis. We will talk more about Pandas and working with data in Python later in this course.\n",
     "\n",
     "Let's compute average values for age, height and weight:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +278,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -298,14 +296,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]\n"
+      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
      ]
     }
    ],
@@ -315,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -323,8 +321,8 @@
      "output_type": "stream",
      "text": [
       "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118081\n",
-      "Standard Deviation = 2.305818310517566\n"
+      "Variance = 5.316798081118074\n",
+      "Standard Deviation = 2.3058183105175645\n"
      ]
     }
    ],
@@ -339,30 +337,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In addition to mean, it makes sense to look at median value and quartiles. They can be visualized using **box plot**:"
+    "In addition to mean, it makes sense to look at the median value and quartiles. They can be visualized using a **box plot**:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1000x200 with 1 Axes>"
+       "<Figure size 720x144 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.figure(figsize=(10,2))\n",
-    "plt.boxplot(df['Height'],vert=False,showmeans=True)\n",
-    "plt.grid(color='gray',linestyle='dotted')\n",
+    "plt.boxplot(df['Height'], vert=False, showmeans=True)\n",
+    "plt.grid(color='gray', linestyle='dotted')\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -375,24 +375,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 9,
    "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x576 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df.boxplot(column='Height',by='Role')\n",
+    "df.boxplot(column='Height', by='Role', figsize=(10,8))\n",
     "plt.xticks(rotation='vertical')\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -400,33 +402,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Note**: This diagram suggests, that on average, height of first basemen is higher that height of second basemen. Later we will learn how we can test this hypothesis more formally, and how to demonstrate that our data is statistically significant to show that.  \n",
+    "> **Note**: This diagram suggests, that on average, the heights of first basemen are higher than heights of second basemen. Later we will learn how we can test this hypothesis more formally, and how to demonstrate that our data is statistically significant to show that.  \n",
     "\n",
     "Age, height and weight are all continuous random variables. What do you think their distribution is? A good way to find out is to plot the histogram of values: "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "df['Weight'].hist(bins=15)\n",
+    "df['Weight'].hist(bins=15, figsize=(10,6))\n",
     "plt.suptitle('Weight distribution of MLB Players')\n",
     "plt.xlabel('Weight')\n",
     "plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -436,75 +440,80 @@
    "source": [
     "## Normal Distribution\n",
     "\n",
-    "Let's create an artificial sample of weights that follows normal distribution with the same mean and variance as real data:"
+    "Let's create an artificial sample of weights that follows a normal distribution with the same mean and variance as our real data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([187.05660174, 181.77292853, 183.09148457, 198.30703945,\n",
-       "       201.51640234, 213.21564624, 221.00562653, 218.30263433,\n",
-       "       234.16968198, 187.40138853, 199.34286071, 205.52705493,\n",
-       "       251.03651986, 189.64156046, 222.23536452, 211.37502445,\n",
-       "       205.07287496, 207.90248813, 180.66579133, 226.86092236])"
+       "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])"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "generated = np.random.normal(mean,std,1000)\n",
+    "generated = np.random.normal(mean, std, 1000)\n",
     "generated[:20]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.hist(generated,bins=15)\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.hist(generated, bins=15)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.hist(np.random.normal(0,1,50000),bins=300)\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.hist(np.random.normal(0,1,50000), bins=300)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -512,29 +521,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since most values in real life are normally distributed, it means we should not use uniform random number generator to generate sample data. Here is what happens if we try to generate weights with uniform distribution (generated by `np.random.rand`):"
+    "Since most values in real life are normally distributed, we should not use a uniform random number generator to generate sample data. Here is what happens if we try to generate weights with a uniform distribution (generated by `np.random.rand`):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "wrong_sample = np.random.rand(1000)*2*std+mean-std\n",
+    "plt.figure(figsize=(10,6))\n",
     "plt.hist(wrong_sample)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -549,16 +561,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 15,
    "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 = 201.73 ± 0.94\n",
+      "p=0.90, mean = 201.73 ± 1.08\n",
+      "p=0.95, mean = 201.73 ± 1.28\n"
      ]
     }
    ],
@@ -574,7 +586,7 @@
     "\n",
     "for p in [0.85, 0.9, 0.95]:\n",
     "    m, h = mean_confidence_interval(df['Weight'].fillna(method='pad'),p)\n",
-    "    print(f\"p={p:.2f}, mean = {m:.2f}±{h:.2f}\")"
+    "    print(f\"p={p:.2f}, mean = {m:.2f} ± {h:.2f}\")"
    ]
   },
   {
@@ -588,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +708,7 @@
        "Third_Baseman      73.044444  200.955556     45"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -709,12 +721,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's test the hypothesis that First Basemen are higher then Second Basemen. The simplest way to do it is to test the confidence intervals:"
+    "Let's test the hypothesis that First Basemen are taller than Second Basemen. The simplest way to do this is to test the confidence intervals:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -738,14 +750,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can see that intervals do not overlap.\n",
+    "We can see that the intervals do not overlap.\n",
     "\n",
-    "More statistically correct way to prove the hypothesis is to use **Student t-test**:"
+    "A statistically more correct way to prove the hypothesis is to use a **Student t-test**:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -768,34 +780,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Two values returned by the `ttest_ind` functions are:\n",
-    "* p-value can be considered as the probability of two distributions having the same mean. In our case, it is very low, meaning that there is strong evidence supporting that first basemen are taller\n",
-    "* t-value is the intermediate value of normalized mean difference that is used in t-test, and it is compared against threshold value for a given confidence value "
+    "The two values returned by the `ttest_ind` function are:\n",
+    "* p-value can be considered as the probability of two distributions having the same mean. In our case, it is very low, meaning that there is strong evidence supporting that first basemen are taller.\n",
+    "* t-value is the intermediate value of normalized mean difference that is used in the t-test, and it is compared against a threshold value for a given confidence value."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simulating Normal Distribution with Central Limit Theorem\n",
+    "## Simulating a Normal Distribution with the Central Limit Theorem\n",
     "\n",
-    "Pseudo-random generator in Python is designed to give us uniform distribution. If we want to create a generator for normal distribution, we can use central limit theorem. To get a normally distributed value we will just compute a mean of a uniform-generated sample."
+    "The pseudo-random generator in Python is designed to give us a uniform distribution. If we want to create a generator for normal distribution, we can use the central limit theorem. To get a normally distributed value we will just compute a mean of a uniform-generated sample."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -805,7 +818,9 @@
     "    return sum(sample)/sample_size\n",
     "\n",
     "sample = [normal_random() for _ in range(100)]\n",
+    "plt.figure(figsize=(10,6))\n",
     "plt.hist(sample)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -815,12 +830,12 @@
    "source": [
     "## Correlation and Evil Baseball Corp\n",
     "\n",
-    "Correlation allows us to find inner connection between data sequences. In our toy example, let's pretend there is an evil baseball corporation that pays it's players according to their height - the taller the player is, the more money he/she gets. Suppose there is a base salary of $1000, and an additional bonus from $0 to $100, depending on height. We will take the real players from MLB, and compute their imaginary salaries:"
+    "Correlation allows us to find relations between data sequences. In our toy example, let's pretend there is an evil baseball corporation that pays its players according to their height - the taller the player is, the more money he/she gets. Suppose there is a base salary of $1000, and an additional bonus from $0 to $100, depending on height. We will take the real players from MLB, and compute their imaginary salaries:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -834,19 +849,19 @@
    "source": [
     "heights = df['Height']\n",
     "salaries = 1000+(heights-heights.min())/(heights.max()-heights.mean())*100\n",
-    "print(list(zip(heights,salaries))[:10])"
+    "print(list(zip(heights, salaries))[:10])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's now compute covariance and correlation of those sequences. `np.cov` will give us so-called **covariance matrix**, which is an extension of covariance to multiple variables. The element $M_{ij}$ of the covariance matrix $M$ is a correlation between input variables $X_i$ and $X_j$, and diagonal values $M_{ii}$ is the variance of $X_{i}$. Similarly, `np.corrcoef` will give us **correlation matrix**."
+    "Let's now compute covariance and correlation of those sequences. `np.cov` will give us a so-called **covariance matrix**, which is an extension of covariance to multiple variables. The element $M_{ij}$ of the covariance matrix $M$ is a correlation between input variables $X_i$ and $X_j$, and diagonal values $M_{ii}$ is the variance of $X_{i}$. Similarly, `np.corrcoef` will give us the **correlation matrix**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -856,43 +871,46 @@
       "Covariance matrix:\n",
       "[[  5.31679808  57.15323023]\n",
       " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.15323023054467\n",
+      "Covariance = 57.153230230544736\n",
       "Correlation = 1.0\n"
      ]
     }
    ],
    "source": [
-    "print(f\"Covariance matrix:\\n{np.cov(heights,salaries)}\")\n",
-    "print(f\"Covariance = {np.cov(heights,salaries)[0,1]}\")\n",
-    "print(f\"Correlation = {np.corrcoef(heights,salaries)[0,1]}\")"
+    "print(f\"Covariance matrix:\\n{np.cov(heights, salaries)}\")\n",
+    "print(f\"Covariance = {np.cov(heights, salaries)[0,1]}\")\n",
+    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Correlation equal to 1 means that there is a strong **linear relation** between two variables. We can visually see the linear relation by plotting one value against the other:"
+    "A correlation equal to 1 means that there is a strong **linear relation** between two variables. We can visually see the linear relation by plotting one value against the other:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "plt.figure(figsize=(10,6))\n",
     "plt.scatter(heights,salaries)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -905,20 +923,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9835304456670811\n"
+      "Correlation = 0.9835304456670837\n"
      ]
     }
    ],
    "source": [
     "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100\n",
-    "print(f\"Correlation = {np.corrcoef(heights,salaries)[0,1]}\")"
+    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
    ]
   },
   {
@@ -930,41 +948,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation = 0.9384710733057905\n"
+      "Correlation = 0.9363097848296155\n"
      ]
     }
    ],
    "source": [
     "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100+np.random.random(size=len(heights))*20-10\n",
-    "print(f\"Correlation = {np.corrcoef(heights,salaries)[0,1]}\")"
+    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 25,
    "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "plt.figure(figsize=(10,6))\n",
     "plt.scatter(heights, salaries)\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -974,12 +995,12 @@
    "source": [
     "> Can you guess why the dots line up into vertical lines like this?\n",
     "\n",
-    "We have observed the correlation between artificially engineered concept like salary and the observed variable *height*. Let's also see if the two observed variables, such as height and weight, also correlate:"
+    "We have observed the correlation between an artificially engineered concept like salary and the observed variable *height*. Let's also see if the two observed variables, such as height and weight, correlate too:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -989,7 +1010,7 @@
        "       [nan, nan]])"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1002,7 +1023,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Unfortunately, we did not get any results - only some strange `nan` values. This is due to the fact that some of the values in our series are undefined, represented as `nan`, which causes the result of the operation to be undefined as well. By looking at the matrix we can see that `Weight` is problematic column, because self-correlation between `Height` values has been computed.\n",
+    "Unfortunately, we did not get any results - only some strange `nan` values. This is due to the fact that some of the values in our series are undefined, represented as `nan`, which causes the result of the operation to be undefined as well. By looking at the matrix we can see that `Weight` is the problematic column, because self-correlation between `Height` values has been computed.\n",
     "\n",
     "> This example shows the importance of **data preparation** and **cleaning**. Without proper data we cannot compute anything.\n",
     "\n",
@@ -1011,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1021,7 +1042,7 @@
        "       [0.52959196, 1.        ]])"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1034,30 +1055,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The is indeed a correlation, but not such a strong one as in our artificial example. Indeed, if we look at the scatter plot of one value against the other, the relation would be much less obvious:"
+    "There is indeed a correlation, but not such a strong one as in our artificial example. Indeed, if we look at the scatter plot of one value against the other, the relation would be much less obvious:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "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.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -1067,7 +1091,7 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "In this notebook, we have learnt how to perform basic operations on data to compute statistical functions. We now know how to use sound apparatus of math and statistics in order to prove some hypotheses, and how to compute confidence intervals for random variable given data sample. "
+    "In this notebook we have learnt how to perform basic operations on data to compute statistical functions. We now know how to use a sound apparatus of math and statistics in order to prove some hypotheses, and how to compute confidence intervals for arbitrary variables given a data sample. "
    ]
   }
  ],
@@ -1076,7 +1100,8 @@
    "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
   },
   "kernelspec": {
-   "display_name": "Python 3.8.8 64-bit (conda)",
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -1089,10 +1114,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  },
-  "orig_nbformat": 4
+   "version": "3.8.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }