diff --git a/1-Introduction/04-stats-and-probability/assignment.ipynb b/1-Introduction/04-stats-and-probability/assignment.ipynb
index a6f81476..4bc3180b 100644
--- a/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -1,252 +1,958 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Introduction to Probability and Statistics\r\n",
-    "## Assignment\r\n",
-    "\r\n",
-    "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
-   ],
-   "metadata": {}
-  },
-  {
-   "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",
-    "df.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
-       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
-       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
-       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
-       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
-       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\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>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>59</td>\n",
-       "      <td>2</td>\n",
-       "      <td>32.1</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>157</td>\n",
-       "      <td>93.2</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.8598</td>\n",
-       "      <td>87</td>\n",
-       "      <td>151</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
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-       "      <td>87.0</td>\n",
-       "      <td>183</td>\n",
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-       "      <td>70.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.8918</td>\n",
-       "      <td>69</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
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-       "      <th>2</th>\n",
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-       "      <td>30.5</td>\n",
-       "      <td>93.0</td>\n",
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-       "      <td>4.0</td>\n",
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-       "      <td>85</td>\n",
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-       "      <td>135</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Jmackalister/Data-Science-For-Beginners/blob/main/1-Introduction/04-stats-and-probability/assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Introduction to Probability and Statistics\n",
+        "## Assignment\n",
+        "\n",
+        "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
+      ],
+      "metadata": {
+        "id": "rGTTaCyh4qCl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "df = pd.read_csv(\"https://raw.githubusercontent.com/Jmackalister/Data-Science-For-Beginners/main/data/diabetes.tsv\",sep='\\t')\n",
+        "df.head()"
+      ],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
+              "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
+              "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
+              "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
+              "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
+              "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-30c85aba-808e-438e-a0a9-81aba0543f8a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>AGE</th>\n",
+              "      <th>SEX</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>BP</th>\n",
+              "      <th>S1</th>\n",
+              "      <th>S2</th>\n",
+              "      <th>S3</th>\n",
+              "      <th>S4</th>\n",
+              "      <th>S5</th>\n",
+              "      <th>S6</th>\n",
+              "      <th>Y</th>\n",
+              "    </tr>\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>59</td>\n",
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+              "      <td>101.0</td>\n",
+              "      <td>157</td>\n",
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+              "      <td>38.0</td>\n",
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+              "      <td>4.8598</td>\n",
+              "      <td>87</td>\n",
+              "      <td>151</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
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+              "      <td>48</td>\n",
+              "      <td>1</td>\n",
+              "      <td>21.6</td>\n",
+              "      <td>87.0</td>\n",
+              "      <td>183</td>\n",
+              "      <td>103.2</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.8918</td>\n",
+              "      <td>69</td>\n",
+              "      <td>75</td>\n",
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+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>72</td>\n",
+              "      <td>2</td>\n",
+              "      <td>30.5</td>\n",
+              "      <td>93.0</td>\n",
+              "      <td>156</td>\n",
+              "      <td>93.6</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.6728</td>\n",
+              "      <td>85</td>\n",
+              "      <td>141</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
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+              "      <td>1</td>\n",
+              "      <td>25.3</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>198</td>\n",
+              "      <td>131.4</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>4.8903</td>\n",
+              "      <td>89</td>\n",
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+              "    <tr>\n",
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+              "      <td>23.0</td>\n",
+              "      <td>101.0</td>\n",
+              "      <td>192</td>\n",
+              "      <td>125.4</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.2905</td>\n",
+              "      <td>80</td>\n",
+              "      <td>135</td>\n",
+              "    </tr>\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30c85aba-808e-438e-a0a9-81aba0543f8a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </svg>\n",
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+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-30c85aba-808e-438e-a0a9-81aba0543f8a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-30c85aba-808e-438e-a0a9-81aba0543f8a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "</svg>\n",
+              "  </button>\n",
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+              "  .colab-df-quickchart {\n",
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+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
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+              "\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-52252daf-b4e1-41b4-b799-a0d24dffca46 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "metadata": {
+        "id": "HJs0xKfP4qCt",
+        "outputId": "12d0c557-36f5-4601-cb1a-df9506971412",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        }
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "In this dataset, columns as the following:\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 study this dataset using methods of probability and statistics.\n",
+        "\n",
+        "### Task 1: Compute mean values and variance for all values"
+      ],
+      "metadata": {
+        "id": "NXyoxwjU4qC1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "source": [
+        "# Calculate the mean and variance of all numeric columns\n",
+        "\n",
+        "mean = df.mean()\n",
+        "variance = df.var()\n",
+        "\n",
+        "# Create a new DataFrame with the results\n",
+        "results = pd.DataFrame({'Mean': mean, 'Variance': variance})\n",
+        "\n",
+        "# Display the result\n",
+        "print(results.round(3))"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "       Media  Varianza\n",
+            "AGE   48.518   171.847\n",
+            "SEX    1.468     0.250\n",
+            "BMI   26.376    19.520\n",
+            "BP    94.647   191.304\n",
+            "S1   189.140  1197.717\n",
+            "S2   115.439   924.955\n",
+            "S3    49.788   167.294\n",
+            "S4     4.070     1.665\n",
+            "S5     4.641     0.273\n",
+            "S6    91.260   132.166\n",
+            "Y    152.133  5943.331\n"
+          ]
+        }
+      ],
+      "metadata": {
+        "id": "ZxVqeOXV4qC5",
+        "outputId": "4bf7a7c3-c22f-48f3-bbde-49f4c10f7005",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
+      ],
+      "metadata": {
+        "id": "l9jvD6Ou4qC7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "TFRfrvyKIUW8",
+        "outputId": "512eaa2a-63df-45cf-c437-efab647ecfb0"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 3 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Replace values 1 and 2 with \"men\" and \"women\"\n",
+        "df['SEX'] = df['SEX'].replace({1: 'men', 2: 'women'})\n",
+        "\n",
+        "# Create a figure with three subplots\n",
+        "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+        "\n",
+        "# Plot the boxplots for BMI, BP, and Y depending on gender\n",
+        "sns.boxplot(x='SEX', y='BMI', data=df, ax=axes[0])\n",
+        "sns.boxplot(x='SEX', y='BP', data=df, ax=axes[1])\n",
+        "sns.boxplot(x='SEX', y='Y', data=df, ax=axes[2])\n",
+        "\n",
+        "# Set the titles for the subplots\n",
+        "axes[0].set_title('BMI')\n",
+        "axes[1].set_title('BP')\n",
+        "axes[2].set_title('Y')\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
+      ],
+      "metadata": {
+        "id": "I7N3sUbQ4qDB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "source": [
+        "# Create a figure with four subplots\n",
+        "fig, axes = plt.subplots(2, 2, figsize=(10, 10))\n",
+        "\n",
+        "# Create histograms for the variables Age, Sex, BMI, and Y.\n",
+        "axes[0, 0].hist(df['AGE'])\n",
+        "axes[0, 1].hist(df['SEX'])\n",
+        "axes[1, 0].hist(df['BMI'])\n",
+        "axes[1, 1].hist(df['Y'])\n",
+        "\n",
+        "# Set the titles for the subplots\n",
+        "axes[0, 0].set_title('Age')\n",
+        "axes[0, 1].set_title('Sex')\n",
+        "axes[1, 0].set_title('BMI')\n",
+        "axes[1, 1].set_title('Y')\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()"
+      ],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "metadata": {
+        "id": "yKaqwzQb4qDD",
+        "outputId": "3027725f-b6ea-4802-a925-679ff84e6c8d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 853
+        }
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4: Test the correlation between different variables and disease progression (Y)\n",
+        "\n",
+        "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
+      ],
+      "metadata": {
+        "id": "d7M0nVE54qDG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the correlation between columns\n",
+        "corr = df[['AGE','BMI','BP','Y']].corr()\n",
+        "\n",
+        "# Create a heatmap to show the correlation\n",
+        "sns.heatmap(corr, annot=True)\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "-y55KMmPBtqx",
+        "outputId": "05353b4b-bdca-4019-beb0-68c962c7aafe",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 435
+        }
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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5A19fX9jZ2eHrr79Gv379cP78eU3GRkRERPWg1tMEAJCeno4dO3Zg69atKCgowDvvvAOJRIJDhw6hU6dOdRUjERFR/eI0QfW8vb3h5OSEuLg4hISEIDU1FevWravL2IiIiLRDYNMEta4M/Prrr5g1axamTp2Kl156qS5jIiIi0i5WBqoXERGBwsJCuLq6wt3dHevXr0d2Nr93nIiIqLGrdTLQq1cvhIaGIi0tDZMnT8aePXtgZ2en+JakwsLCpw9CRETUGAhsmkDtqwmMjY0xfvx4RERE4MqVK5g7dy6WL18OKysrDB8+vC5iJCIiql8ymea2RuCZLy0EHnxfwYoVK3Dv3j388MMPmoqJiIiI6pFalxbWRFdXFyNHjsTIkSM1MRwREZF2NZK/6DVFI8kAERFRk1K7r+1pMp5rmoCIiIgaP1YGiIiIVHGagIiISOAElgxwmoCIiEjgWBkgIiJS1UhuFqQpTAaIiIhUCWyagMkAERGRKl5aSERERELCygAREZEqThMQEREJnMCSAU4TEBERCRwrA0RERKp4aSEREZGwyWW8moCIiIgEhJUBIiIiVQJbQMhkgIiISJXA1gxwmoCIiEjgWBkgIiJSJbAFhEwGiIiIVHHNABERkcAJLBngmgEiIiKBY2WAiIhIlcC+wpjJABERkSpOExAREZG2bNiwAY6OjjA0NIS7uzuioqKe2D8kJAROTk5o1qwZHBwcMGfOHJSVlal1TFYGiIiIVGnp0sK9e/fC398fmzZtgru7O0JCQuDl5YWEhARYWVlV6b97924EBARg27Zt6N27NxITEzFu3DiIRCKsXr261sdlZYCIiEiVXKaxTSKRoKCgQGmTSCTVHnb16tWYNGkS/Pz80KlTJ2zatAlGRkbYtm1btf3PnTuHPn364P3334ejoyMGDhyI995776nVBFVMBoiIiOpQcHAwzMzMlLbg4OAq/crLyxEdHQ1PT09Fm46ODjw9PREZGVnt2L1790Z0dLTiwz85ORnHjh3DkCFD1IqR0wRERESqNDhNEBgYCH9/f6U2sVhcpV92djakUimsra2V2q2trXHt2rVqx37//feRnZ2NV199FXK5HJWVlZgyZQo+//xztWJsMMlAM7u+2g6BHipNPavtEOihaW6faTsEeuhPC3dth0D1SK7BqwnEYnG1H/6acOrUKSxbtgwbN26Eu7s7bty4gdmzZ+PLL7/EggULaj1Og0kGiIiIhMzCwgK6urrIyMhQas/IyICNjU21+yxYsAAffvghJk6cCADo2rUriouL8dFHH2HevHnQ0andagCuGSAiIlIlk2tuqyUDAwO4uroiPDz8URgyGcLDw+Hh4VHtPiUlJVU+8HV1dQEAcjVunMTKABERkSq5dm465O/vD19fX7i5uaFnz54ICQlBcXEx/Pz8AAA+Pj6wt7dXLED09vbG6tWr8corryimCRYsWABvb29FUlAbTAaIiIhUaek+A2PGjEFWVhYWLlyI9PR0uLi4ICwsTLGo8M6dO0qVgPnz50MkEmH+/PlISUmBpaUlvL29sXTpUrWOK5KrU0eoQ3oG9toOgR7iAsKGgwsIG46J5VJth0CPcU89WKfjFy/+QGNjGS/8XmNj1RVWBoiIiFQJ7LsJmAwQERGp0tI0gbbwagIiIiKBY2WAiIhIlZauJtAWJgNERESqOE1AREREQsLKABERkQpNfjdBY8BkgIiISBWnCYiIiEhIWBkgIiJSJbDKAJMBIiIiVby0kIiISOAEVhngmgEiIiKBY2WAiIhIhVxglQEmA0RERKoElgxwmoCIiEjgWBkgIiJSxTsQEhERCRynCYiIiEhIWBkgIiJSJbDKAJMBIiIiFXK5sJIBThMQEREJHCsDREREqjhNQEREJHBMBoiIiIRNaLcj5poBIiIigWNlgIiISJXAKgNMBoiIiFQJ627EnCYgIiISOlYGiIiIVAhtASGTASIiIlUCSwY4TUBERCRwalUGDh8+XKt+w4cPf6ZgiIiIGgSBLSBUKxkYOXLkU/uIRCJIpdJnjYeIiEjrhLZmQK1pAplM9tSNiQAREdGz27BhAxwdHWFoaAh3d3dERUXV2Pf111+HSCSqsg0dOlStY3LNwBNMneKLG4nnUVSQhHMRv6CHm0uNfTt1ehn79m7GjcTzqCxPwayZE6v0MTExxqqvFyHp+gUU5t/A2dM/w83VuQ7PQHguxV7B9E+D0G/4B+jSZzDCz5zTdkiC8PqHXgiO2ICNCd8j8NAyODq3r9V+Pbx7I/TWfkzb/H91HGHTZD1uEFwubEKP5D3ofGQ5jF1q/rlbvNMP7qkHlbYeyXuU+uhZmKHtmhl4JWYL3JJ+gNP3CyBuY1vXp9EwyTS4qWHv3r3w9/dHUFAQYmJi4OzsDC8vL2RmZlbb/+DBg0hLS1Ns8fHx0NXVxejRo9U6rlrTBGfOnKlVv//85z9qBdEQjR49HF+vDMK06QGIuvgXZs2ciGNHv0enLv9BVtb9Kv2NmjXDzeQ7+PHAEaxa+UW1Y27+5mt07uyEcX6zkJqWgQ/efxPHw/agq3M/pKam1/EZCUNpaRmc2rfFqKED8fHnS7QdjiC4DeuNd+b7Ytf8zbj51w14jh+Kj3fOw4L+s1F4v6DG/Vq1tsToz32QeOGfeoy26Wg5vA9eCPLDzYBvUByTCJtJw9Bh90Jc7jsTlffzq92nsqAYcX1nKh7L5cql8Je3BUBeWYlEv+WQFpXA5qPh6Lj3C8S9NguyUkmdnk9Do61pgtWrV2PSpEnw8/MDAGzatAlHjx7Ftm3bEBAQUKV/y5YtlR7v2bMHRkZGdZsM/FuOAKr+I/pXU1kzMGf2JGzZuhvf7twHAJg2PQBDBr8Bv3HvYsXKDVX6X4q+jEvRlwEAy5Z8XuV5Q0NDvDlqCN58azzORlwAACz+cjWGDh2AKZN9sDBoRR2ejXD09eiBvh49tB2GoAyYOAxn94Tj3P5TAIBd8zaja//u6PNOf4T971C1+4h0dDAxZBYOr9mHl3p2RDNTo/oLuImw/cgbmbtPIHvvSQDAzc++QYs3XGH5Xn+krf+p+p3kQEVWXrVPGba1RXM3J8S9PhuliXcBALcCvkH3y9vQalRfZO3+vS5Oo+HS4AJCiUQCiUQ5mRKLxRCLxUpt5eXliI6ORmBgoKJNR0cHnp6eiIyMrNWxtm7dinfffRfGxsZqxajWNIG5uTkcHBywYMECXL9+Hbm5uVW2nJwctQJoiPT19dG9ezeEnzyraJPL5Qg/GYFevVyfaUw9PV3o6emhrEz5H0RZaRn69OaHFzVOuvp6eLFLW1z9M07RJpfLcfXPOLTr/nKN+3nPfhuF9wsQse9kfYTZ5Ij09WDcrR0Kzj76uUMuR/7ZODR3dapxP11jQ7hEfQOXS5vx8vYANHvZ4dGYBvoAAJmkXGlMWXkFmvfooPFzEJLg4GCYmZkpbcHBwVX6ZWdnQyqVwtraWqnd2toa6elPrx5HRUUhPj4eEydWnaZ+GrWSgbS0NHz11VeIjIxE165dMWHCBJw7dw6mpqZKJ/k0EokEBQUFSltNlQZtsLBoCT09PWRmZCu1Z2Zmwcba8pnGLCoqRmTkJcz7fDZsba2ho6OD999/E716ucLG1vrpAxA1QCbmzaGrp4uCbOWydEFWPkwtW1S7T3u3Dnj1nf7YGbCpHiJsmvRaNodIT7fKX/kV2XnQr+HnXpaUgmT/DUj0C0bSjBBAR4ROh5fBwLbVg+dvpEByLwsOgWOha2YMkb4ebKePgtjOAvrW5nV7Qg2QXKa5LTAwEPn5+Urb43/9a8rWrVvRtWtX9OzZU+191UoGDAwMMGbMGBw/fhzXrl1Dt27dMGPGDDg4OGDevHmorKys1TjVZUlyWaHawTc2vn6zIBKJcPd2DEqKbmLm9PHYs/cQZDKBXdBKgiU2NsSENTOxM3ATinKb/nu+ISmKTkT2j6dQ8vctFJ7/B9cnrEDl/QJYjR0IAJBXSpE44SsYtrOD29Xv0CPpB5j27oK88GjB3Y0PgEYXEIrFYpiamiptqlMEAGBhYQFdXV1kZGQotWdkZMDGxuaJ4RYXF2PPnj2YMGHCM53uM19N8MILL2DhwoX4/fff8fLLL2P58uUoKKh5sdDjqsuSRDrNnzUUjcvOzkFlZSWsrC2U2q2sLJGekfXM4yYn30Z/z7dh2qI9HNv2gEefYdDX18fN5DvPGzKRVhTlFkJaKYWphXJF0NTSDAXVzE1bvWgDCwcrzNgSgE039mDTjT3o9eZ/4Ozphk039sDyBVbJaqMypxDySmmVKoC+RYsa1wSokldKURx/E+I2jz5kSq4kI37AXFxyGosYlwlI+OBL6Jk3h+ROxhNGIk0xMDCAq6srwsPDFW0ymQzh4eHw8PB44r779++HRCLB2LFjn+nYz5QMSCQS7N69G56enujSpQssLCxw9OjRKqsaa1JdlvTvwsSGoKKiAjExcejf71VFm0gkQv9+r+L8+ejnHr+kpBTp6Zlo0cIMAwe8hsO/HH/uMYm0QVpRidvxyejYu6uiTSQSoWPvrkiKSazSPy0pBUED/bF4yP8ptsu/X0JC5N9YPOT/kJNW9UodqkpeUYniuCSYvtrtUaNIBLNXu6EwOqF2g+jowKjjC6jIyK3ylLSwBJU5BRC3sYWxczvkHq/5OvemSpPTBOrw9/dHaGgovv32W1y9ehVTp05FcXGx4uoCHx+faqcYtm7dipEjR6JVq1bPdL5qXU0QFRWF7du3Y8+ePXB0dISfnx/27dtX6ySgMVmzNhTbt65BdEwcLl78C7NmToKxcTPs+HYvAGD7trVITU3DvPnLATxYdNip04MFUwYG+rC3s4Gzc2cUFRUjKekWAGDggNcgEomQkJiE9u0csXz5AiQkJCnGpOdXUlKKO/dSFY9TUjNwLTEJZqbNYWtjpcXImq4TW45g/KrpuHUlCTdjb8BzwlAYGInx5/4/AADjV81AbkYOflqxG5WSCqQ+XKn+r9KCEgCo0k5Plrb5F7QLmYniyzdQ9Nd12Ezyho6RGFl7HizKbLt2FirS7+Nu8PcAAPs5o1EUk4iym+nQNTOG7dQRENtbKl0l0HKYByruF6A8JRtGHV/Ai4snIDcsCvmnL2vlHLVKS7O3Y8aMQVZWFhYuXIj09HS4uLggLCxMsajwzp070NFR/js+ISEBERER+O233575uGolA7169cILL7yAWbNmwdX1war6iIiIKv2awncT7N9/GJYWLfHFwk9gY2OJy5f/xtBhY5GZ+WBR4QsOdkpz/XZ21oi++OiFmDt3KubOnYrTp8/hjQEPrvc0NTPF0i8D0Lq1LXJy8nDwp2NYsPCrWq+1oKeLv3Yd42d+pni8Yt1mAMCIwZ5YOn+utsJq0i4dOYfmLU0xYs4YmFq2wN2rt7DWdykKHy4qbGlv0aAWCDcVOYf/hH4rU7T+v/egb9kCJX/fxLUPvkTlw5+72N4CeOx3lK6ZCdqsnAZ9yxaozC9CcVwy/h7xOUqv31P00bc2xwtf+EHfwgwVmXnI3n8KKSH76/3chG7GjBmYMWNGtc+dOnWqSpuTk9Nzv8dEcjVGUM1Gqh3wGe8zoGdgr/Y+VDdKU88+vRPVi2lunz29E9WLieWN//4pTYl76sE6HT9rwGsaG8vyxGmNjVVX1KoMcNU7EREJgbpz/Y2dWsnAv+7fv69YpHD37l2EhoairKwM3t7e6Nu3r0YDJCIiqm9CSwbUuprgypUrcHR0hJWVFTp06IDY2Fj06NEDa9aswTfffIN+/frh0KFDdRQqERER1QW1koFPP/0UXbt2xZkzZ/D6669j2LBhGDp0KPLz85Gbm4vJkydj+fLldRUrERFR/ZCLNLc1AmpNE1y8eBEnT55Et27d4OzsjM2bN2PatGmKhYUzZ85Er1696iRQIiKi+sJpgifIyclR3BLRxMQExsbGMDd/dM9qc3NzFBbyFqNERESNidoLCFXvFNiQ7hxIRESkCXKZsD7b1E4Gxo0bp/iChbKyMkyZMkXxvcmq39dMRETUGAltmkCtZMDX11fpcXVfiODj4/N8EREREVG9UisZ2L59e13FQURE1GDIG8lVAJryTDcdIiIiasqENk3wTF9hTERERE0HKwNEREQqeDUBERGRwAntW7eZDBAREakQWmWAawaIiIgEjpUBIiIiFUKrDDAZICIiUiG0NQOcJiAiIhI4VgaIiIhUcJqAiIhI4IR2O2JOExAREQkcKwNEREQqhPbdBEwGiIiIVMg4TUBERERCwsoAERGRCqEtIGQyQEREpIKXFhIREQkc70BIREREgsLKABERkQpOExAREQkcLy0kIiIiQWFlgIiISIXQLi1kZYCIiEiFXK65TV0bNmyAo6MjDA0N4e7ujqioqCf2z8vLw/Tp02FrawuxWIyXX34Zx44dU+uYrAwQERE1EHv37oW/vz82bdoEd3d3hISEwMvLCwkJCbCysqrSv7y8HAMGDICVlRV+/PFH2Nvb4/bt22jRooVax2UyQEREpEJbCwhXr16NSZMmwc/PDwCwadMmHD16FNu2bUNAQECV/tu2bUNOTg7OnTsHfX19AICjo6Pax+U0ARERkQq5XKSxTSKRoKCgQGmTSCRVjlleXo7o6Gh4enoq2nR0dODp6YnIyMhq4zx8+DA8PDwwffp0WFtbo0uXLli2bBmkUqla58tkgIiIqA4FBwfDzMxMaQsODq7SLzs7G1KpFNbW1krt1tbWSE9Pr3bs5ORk/Pjjj5BKpTh27BgWLFiAVatWYcmSJWrFyGkCIiIiFZq8HXFgYCD8/f2V2sRisUbGlslksLKywubNm6GrqwtXV1ekpKRg5cqVCAoKqvU4TAaIiIhUaHLNgFgsrtWHv4WFBXR1dZGRkaHUnpGRARsbm2r3sbW1hb6+PnR1dRVtHTt2RHp6OsrLy2FgYFCrGBtMMvCGdTdth0APTXP7TNsh0EMbL32l7RDooUVu87UdAj3GvY7H18Z9BgwMDODq6orw8HCMHDkSwIO//MPDwzFjxoxq9+nTpw92794NmUwGHZ0HM/+JiYmwtbWtdSIAcM0AERFRg+Hv74/Q0FB8++23uHr1KqZOnYri4mLF1QU+Pj4IDAxU9J86dSpycnIwe/ZsJCYm4ujRo1i2bBmmT5+u1nEbTGWAiIioodDWpYVjxoxBVlYWFi5ciPT0dLi4uCAsLEyxqPDOnTuKCgAAODg44Pjx45gzZw66desGe3t7zJ49G599pl6Fl8kAERGRCg2uH1TbjBkzapwWOHXqVJU2Dw8PnD9//rmOyWkCIiIigWNlgIiISIXQvsKYyQAREZEKfmshERERCQorA0RERCpk2g6gnjEZICIiUiEHpwmIiIhIQFgZICIiUiHT5o0GtIDJABERkQqZwKYJmAwQERGp4JoBIiIiEhRWBoiIiFTw0kIiIiKB4zQBERERCQorA0RERCo4TUBERCRwQksGOE1AREQkcKwMEBERqRDaAkImA0RERCpkwsoFOE1AREQkdKwMEBERqeB3ExAREQmcwL60kMkAERGRKl5aSERERILCygAREZEKmYhrBoiIiARNaGsGOE1AREQkcKwMEBERqRDaAkImA0RERCp4B0IiIiISFFYGiIiIVPAOhERERALHqwmIiIhIUFgZICIiUiG0BYRMBoiIiFQI7dJCThMQERGpkGtwU9eGDRvg6OgIQ0NDuLu7Iyoqqsa+O3bsgEgkUtoMDQ3VPiaTASIiogZi79698Pf3R1BQEGJiYuDs7AwvLy9kZmbWuI+pqSnS0tIU2+3bt9U+rtrJwK1btxAaGooNGzYgPj5e7QM2Jt6+w/DtuR345frPWHt4DZxcXq6xb59BvbHu6FociN+PnxN+wsaw9Xjjzf419p+1bAaO3/0VoyaMrIPIm77XP/RCcMQGbEz4HoGHlsHRuX2t9uvh3Ruht/Zj2ub/q+MI6VLsFUz/NAj9hn+ALn0GI/zMOW2H1OS4fzgAcyPWIihhByYfWgx753a12q+rtweW3NqN9zf719hn+NLxWHJrNzzGD9JUuI2KTKS5TSKRoKCgQGmTSCTVHnf16tWYNGkS/Pz80KlTJ2zatAlGRkbYtm1bjbGKRCLY2NgoNmtra7XPV61k4I8//kDnzp0xefJkzJw5E927d8euXbvUPmhj8Jr3f/DRgo/wfcj3mD5kJpL/uYml3y2BWSuzavsX5hXih3V78fFIf0wZOA2/7TuBuav84fpa9yp9ew/qjQ7dOyA7PbuuT6NJchvWG+/M98Uva/fjy6Gf4d4/t/Hxznlo3sr0ifu1am2J0Z/7IPHCP/UUqbCVlpbBqX1bzJs7TduhNEldhvXC4Plj8cfag9g4dB7S/7mDcTsDYPyU90GL1hYY9Pn7uHXhao19Onq5weGV9ihIz9F02I2GTINbcHAwzMzMlLbg4OAqxywvL0d0dDQ8PT0VbTo6OvD09ERkZGSNsRYVFeHFF1+Eg4MDRowYgb///lvt81UrGViwYAEGDBiAlJQU3L9/H5MmTcKnn36q9kEbgzcnjULYD7/it30ncOf6Hfw3cB0kZRJ4jRlYbf+481dwLuwc7t64i7TbaTi07WckX72Jzj06K/VrZdMK0xZPxVezVqCyQlofp9LkDJg4DGf3hOPc/lNIu3EPu+ZtRnlpOfq8U3MlRqSjg4khs3B4zT5k36253Eaa09ejB2Z95AvP1/poO5Qmqc/EIbi05w/E7D+NrBspODxvKypKJXB957Ua9xHpiDA6ZDpOrjmAnBreB82tzTHsC1/sn70B0kr+jtKEwMBA5OfnK22BgYFV+mVnZ0MqlVb5y97a2hrp6enVju3k5IRt27bh559/xq5duyCTydC7d2/cu3dPrRjVSgbi4+OxbNky2NrawtzcHCtXrkRmZibu37+v1kEbOj19PbzU9SXERMQq2uRyOf46G4tOrh1rNYZLHxc4tGuN+AuPplJEIhE+DfkEP276EbcT72g6bEHQ1dfDi13a4uqfcYo2uVyOq3/GoV33mqdxvGe/jcL7BYjYd7I+wiSqU7r6urDr0gZJfz76/SKXy5H0Zzwcur9U4379Zr+J4vsFiN53qtrnRSIRRq+ZhojNR5F5PUXTYTcqmqwMiMVimJqaKm1isVgjcXp4eMDHxwcuLi547bXXcPDgQVhaWuKbb75Raxy1Li0sKCiAhYWF4rGRkRGaNWuG/Px8tGrVSq0DN2SmLU2hq6eLvKxcpfbc7Fw4tG9d435GzY2w++Iu6BvoQyaVYd38DYg5+5fi+XemjYZUKsOhbT/XWexNnYl5c+jq6aIgO1+pvSArHzbt7Kvdp71bB7z6Tn8sHsJ1AtQ0GD18HxSpvA+KsvJh0c6u2n1edHOC6zuvY8OQz2sct+9Ub8gqpYjcHqbReBsjuRbuM2BhYQFdXV1kZGQotWdkZMDGxqZWY+jr6+OVV17BjRs31Dq22vcZOH78OMzMHs2by2QyhIeHKy0mHD58+BPHkEgkVRZPyOQy6Iga98UNpUWlmDZoOgyNmuGVV10wecEkpN9OQ9z5K2jftT1Gjh+B6UNmajtMQREbG2LCmpnYGbgJRbmF2g6HSCsMjA3x9pqpOBS4BSU1vA/surSBh98gbBxac7JAdcvAwACurq4IDw/HyJEjATz6jJ0xY0atxpBKpbhy5QqGDBmi1rHVTgZ8fX2rtE2ePFnx/yKRCFLpk+eZgoODsWjRIqW2ts3bob1ZzeWt+lSQUwBppRQtLM2V2s0tzJGrUi14nFwuR+qtNABA8j/JcGjvgDEzxiDu/BV07dkFLSxaYNf5nYr+unq6mLRgIkZOGAnf3uPq5FyamqLcQkgrpTC1UF7IaWpphoKsvCr9rV60gYWDFWZsCVC0iXQepPybbuzBgv6zkXUno8p+RA1ZycP3gYnK+8DE0gxF1bwPWr5oDXMHK4zd8omi7d/3waIb32Ft/7l4sacTjFuZ4pNz6xR9dPV0MXjeWPQePxirXp1dNyfTQGnrpkP+/v7w9fWFm5sbevbsiZCQEBQXF8PPzw8A4OPjA3t7e8UCxMWLF6NXr15o37498vLysHLlSty+fRsTJ05U67hqJQMymWZ+PIGBgfD3V76k5a1OozUytiZUVlTi+pXreKWPCyKPP1jBKRKJ4PKqCw7vOFzrcXR0dKBvoA8A+P1AOGIi/lJ6ftmuJQg/cBK/7ftNc8E3cdKKStyOT0bH3l0R+9tFAA9em469u+LkzqqlzbSkFAQNVP63NvKTd2Fo3Ax7Fm1HTlrTWu9CwiCtkCI1/iba9u6Mq79dAvDgfdC2d2dc2Fn190l2Uir+O1B5sbfnJ+9AbGyIo4t2Ij/tPmIPRiApQvly8XE7AxD7UwRi9p+uu5NpoLSVDIwZMwZZWVlYuHAh0tPT4eLigrCwMMWiwjt37kBH51EVPTc3F5MmTUJ6ejrMzc3h6uqKc+fOoVOnTmod95luR3z//n3FGoG7d+8iNDQUZWVl8Pb2Rt++fZ+6v1gsrrJ4oqFNERwM/QmfrJ6LxLjrSIhNwKgJI2HYTIzf9p0AAPzfmrnITr+P7V/tAACMmf4OrsddR+rtNOgb6KNn/x54483+WPf5egAPLj0szFMuz1VWSJGblYt7ycJeqKOuE1uOYPyq6bh1JQk3Y2/Ac8JQGBiJ8ef+PwAA41fNQG5GDn5asRuVkgqkJt5V2r+0oAQAqrSTZpWUlOLOvVTF45TUDFxLTIKZaXPY2lhpMbKm4c8tx/DWqilIvZKMe7FJ6D1hMAyMDBH98IP7rVVTUZCRgxMr9qJSUoHMROXV5WUFxQCgaC/NK0JpXpFSH2mlFIVZechOTquHM6J/zZgxo8ZpgVOnTik9XrNmDdasWfPcx1QrGbhy5Qq8vb1x9+5dvPTSS9izZw8GDRqE4uJi6OjoYPXq1fjxxx8Vcx2N2elfzsCspRl85o6FuWVLJP+ThHkfLkBedh4AwNLeCjL5oxtNGhoZYsbS6bCwtUB5WTnu3riLFbNX4vQvZ7R0Bk3XpSPn0LylKUbMGQNTyxa4e/UW1vouReHDxVQt7S0glz/LTUBJk+KvXcf4mZ8pHq9YtxkAMGKwJ5bOn6utsJqM+CPnYdzSFG/MeRsmli2QdvU2vvVdjuLsAgBAC/tWkMuFdod9zRHabxCRXI3fmoMHD4aenh4CAgLw3Xff4ciRI/Dy8kJoaCgAYObMmYiOjsb58+fVDsTLYbDa+1DdeEHXRNsh0EMbL32l7RDooUVu87UdAj1mya3ddTr+2hfGamys2Xca/s351KoMXLx4ESdPnkS3bt3g7OyMzZs3Y9q0aYr5i5kzZ6JXr151EigREVF9EVpNRa2J+pycHMW1jiYmJjA2Noa5+aMV9+bm5igs5OVbREREjYnaCwhFItETHxMRETV2QqsMqJ0MjBs3TnElQFlZGaZMmQJjY2MAqPFbmIiIiBoToS0gVCsZUL3h0NixVRdY+Pj4PF9EREREVK/USga2b99eV3EQERE1GDKBzYA/002HiIiImjKhrRloWLf9IyIionrHygAREZEKLiAkIiISOJnA0gFOExAREQkcKwNEREQqhLaAkMkAERGRCmFNEjAZICIiqkJolQGuGSAiIhI4VgaIiIhU8A6EREREAsdLC4mIiEhQWBkgIiJSIay6AJMBIiKiKng1AREREQkKKwNEREQqhLaAkMkAERGRCmGlApwmICIiEjxWBoiIiFQIbQEhkwEiIiIVXDNAREQkcMJKBbhmgIiISPBYGSAiIlLBNQNEREQCJxfYRAGnCYiIiASOlQEiIiIVQpsmYGWAiIhIhQxyjW3q2rBhAxwdHWFoaAh3d3dERUXVar89e/ZAJBJh5MiRah+TyQAREVEDsXfvXvj7+yMoKAgxMTFwdnaGl5cXMjMzn7jfrVu38Mknn6Bv377PdFwmA0RERCrkGtzUsXr1akyaNAl+fn7o1KkTNm3aBCMjI2zbtq3GfaRSKT744AMsWrQIbdu2VfOIDzAZICIiUqHJaQKJRIKCggKlTSKRVDlmeXk5oqOj4enpqWjT0dGBp6cnIiMja4x18eLFsLKywoQJE575fJkMEBER1aHg4GCYmZkpbcHBwVX6ZWdnQyqVwtraWqnd2toa6enp1Y4dERGBrVu3IjQ09Lli5NUEREREKjR5NUFgYCD8/f2V2sRi8XOPW1hYiA8//BChoaGwsLB4rrGYDBAREanQ5E2HxGJxrT78LSwsoKuri4yMDKX2jIwM2NjYVOmflJSEW7duwdvbW9Emkz1IY/T09JCQkIB27drVKkZOExAREamQaXCrLQMDA7i6uiI8PPxRHDIZwsPD4eHhUaV/hw4dcOXKFcTGxiq24cOHo1+/foiNjYWDg0Otj83KABERUQPh7+8PX19fuLm5oWfPnggJCUFxcTH8/PwAAD4+PrC3t0dwcDAMDQ3RpUsXpf1btGgBAFXan6bBJAPhGXHaDoEe+tPCXdsh0EOL3OZrOwR6KOjSEm2HQPVIW99NMGbMGGRlZWHhwoVIT0+Hi4sLwsLCFIsK79y5Ax0dzRf1RXK5vEF8G4Oegb22Q6CHmAw0HL8YGGo7BHqIyUDDom/xbNfT15av41saG+vbWwc0NlZd4ZoBIiIigWsw0wREREQNhaxhFM3rDZMBIiIiFcJKBThNQEREJHisDBAREal4lq8ebsyYDBAREanQ1qWF2sJpAiIiIoFjZYCIiEiFJr+oqDFgMkBERKSCawaIiIgEjmsGiIiISFBYGSAiIlLBNQNEREQC10C+w6/ecJqAiIhI4FgZICIiUsGrCYiIiAROaGsGOE1AREQkcKwMEBERqRDafQaYDBAREakQ2poBThMQEREJHCsDREREKoR2nwEmA0RERCqEdjUBkwEiIiIVQltAyDUDREREAsfKABERkQqhXU3AZICIiEiF0BYQcpqAiIhI4FgZICIiUsFpAiIiIoHj1QREREQkKKwMEBERqZAJbAEhkwEiIiIVwkoFOE1AREQkeKwMEBERqRDa1QSsDBAREamQQa6xTV0bNmyAo6MjDA0N4e7ujqioqBr7Hjx4EG5ubmjRogWMjY3h4uKC7777Tu1jMhkgIiJSIZfLNbapY+/evfD390dQUBBiYmLg7OwMLy8vZGZmVtu/ZcuWmDdvHiIjIxEXFwc/Pz/4+fnh+PHjah2XyQAREVEDsXr1akyaNAl+fn7o1KkTNm3aBCMjI2zbtq3a/q+//jpGjRqFjh07ol27dpg9eza6deuGiIgItY7LZICIiEiFJqcJJBIJCgoKlDaJRFLlmOXl5YiOjoanp6eiTUdHB56enoiMjHxqzHK5HOHh4UhISMB//vMftc6XyQAREZEKuQb/Cw4OhpmZmdIWHBxc5ZjZ2dmQSqWwtrZWare2tkZ6enqNsebn58PExAQGBgYYOnQo1q1bhwEDBqh1vkwGnmDqFF/cSDyPooIknIv4BT3cXGrs26nTy9i3dzNuJJ5HZXkKZs2cWKWPiYkxVn29CEnXL6Aw/wbOnv4Zbq7OdXgGTYf1uEFwubAJPZL3oPOR5TB2aV9jX4t3+sE99aDS1iN5j1IfPQsztF0zA6/EbIFb0g9w+n4BxG1s6/o0mgT3DwdgbsRaBCXswORDi2Hv3K5W+3X19sCSW7vx/mb/GvsMXzoeS27thsf4QZoKlwBcir2C6Z8God/wD9Clz2CEnzmn7ZAEJTAwEPn5+UpbYGCgxsZv3rw5YmNjcfHiRSxduhT+/v44deqUWmPUOhlITU1VN75GbfTo4fh6ZRC+XLIaPdwH4XLcPzh29HtYWraqtr9Rs2a4mXwHn89fhrS0jGr7bP7ma3h69sU4v1lw6e6JE7+fxvGwPbCzs6nLU2n0Wg7vgxeC/HBv9T7Ee32Ckn9uocPuhdBrZVbjPpUFxYhxHq/Y/uo5Wen5l7cFQPyiNRL9liN+4FxI7mWh494voNNMXNen06h1GdYLg+ePxR9rD2Lj0HlI/+cOxu0MgHEr0yfu16K1BQZ9/j5uXbhaY5+OXm5weKU9CtJzNB224JWWlsGpfVvMmztN26E0GppcQCgWi2Fqaqq0icVVf9dYWFhAV1cXGRnKnyEZGRmwsan5c0JHRwft27eHi4sL5s6di7fffrvaysOT1DoZ6Ny5M3bv3q3W4I3ZnNmTsGXrbny7cx+uXr2OadMDUFJSCr9x71bb/1L0ZXwWuAT79h2GRFJe5XlDQ0O8OWoIAgOX4mzEBSQl3cLiL1fjRtItTJnsU9en06jZfuSNzN0nkL33JEqv38PNz76BrFQCy/f617yTHKjIylNsldn5iqcM29qiuZsTbgVsRvHlGyhLSsWtgG+gY2iAVqP61sMZNV59Jg7BpT1/IGb/aWTdSMHheVtRUSqB6zuv1biPSEeE0SHTcXLNAeTcrX5FdHNrcwz7whf7Z2+AtFJaV+ELVl+PHpj1kS88X+uj7VAaDW1cWmhgYABXV1eEh4c/ikMmQ3h4ODw8PGofu0xW7ZqEJ6l1MrB06VJMnjwZo0ePRk5O087c9fX10b17N4SfPKtok8vlCD8ZgV69XJ9pTD09Xejp6aGsTPkFKistQ5/ePZ4r3qZMpK8H427tUHA27lGjXI78s3Fo7upU4366xoZwifoGLpc24+XtAWj2ssOjMQ30AQCyx5M2uRyy8go079FB4+fQVOjq68KuSxsk/RmvaJPL5Uj6Mx4O3V+qcb9+s99E8f0CRO87Ve3zIpEIo9dMQ8Tmo8i8nqLpsIkaFX9/f4SGhuLbb7/F1atXMXXqVBQXF8PPzw8A4OPjozTFEBwcjBMnTiA5ORlXr17FqlWr8N1332Hs2LFqHbfWycC0adMQFxeH+/fvo1OnTvjll1/UOtDjqltZqe61mHXJwqIl9PT0kJmRrdSemZkFG2vLZxqzqKgYkZGXMO/z2bC1tYaOjg7ef/9N9OrlChtb66cPIFB6LZtDpKeLiqw8pfaK7DzoW7aodp+ypBQk+29Aol8wkmaEADoidDq8DAa2D6Z4ym6kQHIvCw6BY6FrZgyRvh5sp4+C2M4C+tbmdXtCjZiReXPo6umi6LEqCwAUZeXDpIbX4kU3J7i+8zoOBWypcdy+U70hq5QicnuYJsMlei7aus/AmDFj8PXXX2PhwoVwcXFBbGwswsLCFIsK79y5g7S0NEX/4uJiTJs2DZ07d0afPn1w4MAB7Nq1CxMnVl239iRq3Y64TZs2OHnyJNavX48333wTHTt2hJ6e8hAxMTFPHSc4OBiLFi1SahPpmECk++R5x8bO128Wtmxehbu3Y1BZWYm//rqCPXsPoXv3btoOrUkpik5EUXTio8eXEtDt9H9hNXYg7q38AfJKKRInfIW2q6fD7ep3kFdKkX82Dnnh0YBIpMXImxYDY0O8vWYqDgVuQUluYbV97Lq0gYffIGwc+nk9R0f0ZNq8HfGMGTMwY8aMap9TXRi4ZMkSLFmy5LmPqfZ3E9y+fRsHDx6Eubk5RowYUSUZqI3AwED4+yuvKDZv1XDKs9nZOaisrISVtYVSu5WVJdIzsp553OTk2+jv+TaMjJrB1LQ50tMzsfv7/+Fm8p3nDbnJqswphLxSWqUKoG/Rokq1oCbySimK429C3ObRApySK8mIHzAXus2NINLXQ2VOATofWY7iuCQNRt+0lOQWQlophYmF8sJNE0szFFXzWrR80RrmDlYYu+UTRZtI50GytejGd1jbfy5e7OkE41am+OTcOkUfXT1dDJ43Fr3HD8aqV2fXzckQkRK1PslDQ0Mxd+5ceHp64u+//4al5bOVzMVicZWVlKIG9BdZRUUFYmLi0L/fqzh8+MEtHUUiEfr3exUb/7f9uccvKSlFSUkpWrQww8ABryEgcOlzj9lUySsqURyXBNNXuyE37OH9uUUimL3aDek7jtVuEB0dGHV8AXnhVatW0sISAIC4jS2Mndvh3sofNBV6kyOtkCI1/iba9u6Mq79dAvDgfdG2d2dc2Plblf7ZSan478BPldo8P3kHYmNDHF20E/lp9xF7MAJJEfFKfcbtDEDsTxGI2X+67k6G6CnkAvuiolonA4MGDUJUVBTWr18PH5+mv/p9zdpQbN+6BtExcbh48S/MmjkJxsbNsOPbvQCA7dvWIjU1DfPmLwfwYNFhp04vAwAMDPRhb2cDZ+fOKCoqRlLSLQDAwAGvQSQSISExCe3bOWL58gVISEhSjEnVS9v8C9qFzETx5Rso+us6bCZ5Q8dIjKw9JwEAbdfOQkX6fdwN/h4AYD9nNIpiElF2Mx26ZsawnToCYntLZO3+XTFmy2EeqLhfgPKUbBh1fAEvLp6A3LAo5J++rJVzbCz+3HIMb62agtQrybgXm4TeEwbDwMgQ0Q8/uN9aNRUFGTk4sWIvKiUVyEy8p7R/WUExACjaS/OKUJpXpNRHWilFYVYespPTQJpRUlKKO/ceXR6ekpqBa4lJMDNtDlsbKy1G1nDJGtA6tvpQ62RAKpUiLi4OrVu3rst4Goz9+w/D0qIlvlj4CWxsLHH58t8YOmwsMjMfLCp8wcEOMplM0d/OzhrRFx/9dTR37lTMnTsVp0+fwxsDRgMATM1MsfTLALRubYucnDwc/OkYFiz8CpWVlfV7co1MzuE/od/KFK3/7z3oW7ZAyd83ce2DLxWXC4rtLYDHXgtdMxO0WTkN+pYtUJlfhOK4ZPw94nOUXn/0waRvbY4XvvCDvoUZKjLzkL3/FFJC9tf7uTU28UfOw7ilKd6Y8zZMLFsg7eptfOu7HMXZBQCAFvatIJfLnjIK1bf4a9cxfuZniscr1m0GAIwY7Iml8+dqK6wGTWiVAZG8gSzj1zOw13YI9NCfFu7aDoEe+sXAUNsh0ENBl55/kRZpjr5F2zodv7O15n4P/p1xQWNj1RX1V/8RERE1cZwmICIiEjihTRPwi4qIiIgEjpUBIiIiFZwmICIiEjhOExAREZGgsDJARESkgtMEREREAsdpAiIiIhIUVgaIiIhUCO222kwGiIiIVMgENk3AZICIiEhFA/nannrDNQNEREQCx8oAERGRCk4TEBERCRynCYiIiEhQWBkgIiJSwTsQEhERCRzvQEhERESCwsoAERGRCqEtIGQyQEREpEJolxZymoCIiEjgWBkgIiJSwWkCIiIigeOlhURERAIntMoA1wwQEREJHCsDREREKoR2NQGTASIiIhWcJiAiIiJBYWWAiIhIhdCuJmBlgIiISIVcg/+pa8OGDXB0dIShoSHc3d0RFRVVY9/Q0FD07dsX5ubmMDc3h6en5xP714TJABERUQOxd+9e+Pv7IygoCDExMXB2doaXlxcyMzOr7X/q1Cm89957+OOPPxAZGQkHBwcMHDgQKSkpah1XJG8gqyT0DOy1HQI99KeFu7ZDoId+MTDUdgj0UNClJdoOgR6jb9G2Tsdv1uxFjY1VWnq71n3d3d3Ro0cPrF+/HgAgk8ng4OCAmTNnIiAg4Kn7S6VSmJubY/369fDx8an1cblmgIiISIUm/06WSCSQSCRKbWKxGGKxWKmtvLwc0dHRCAwMVLTp6OjA09MTkZGRtTpWSUkJKioq0LJlS7Vi5DQBERFRHQoODoaZmZnSFhwcXKVfdnY2pFIprK2tldqtra2Rnp5eq2N99tlnsLOzg6enp1oxsjJARESk4lkW/tUkMDAQ/v7+Sm2qVQFNWL58Ofbs2YNTp07B0FC9KUYmA0RERCo0OU1Q3ZRAdSwsLKCrq4uMjAyl9oyMDNjY2Dxx36+//hrLly/H77//jm7duqkdI6cJiIiIVMjlco1ttWVgYABXV1eEh4cr2mQyGcLDw+Hh4VHjfitWrMCXX36JsLAwuLm5PdP5sjJARETUQPj7+8PX1xdubm7o2bMnQkJCUFxcDD8/PwCAj48P7O3tFWsOvvrqKyxcuBC7d++Go6OjYm2BiYkJTExMan1cJgNEREQqtHXN/ZgxY5CVlYWFCxciPT0dLi4uCAsLUywqvHPnDnR0HhX1//e//6G8vBxvv/220jhBQUH44osvan3cBnOfgcZOIpEgODgYgYGBdbIwhNTD16Ph4GvRcPC1oJowGdCQgoICmJmZIT8/H6amptoOR/D4ejQcfC0aDr4WVBMuICQiIhI4JgNEREQCx2SAiIhI4JgMaIhYLEZQUBAX5TQQfD0aDr4WDQdfC6oJFxASEREJHCsDREREAsdkgIiISOCYDBAREQkckwEiIiKBYzJAREQkcEwGniIyMhK6uroYOnRolefKy8uxcuVKdO/eHcbGxjAzM4OzszPmz5+P1NRURb9x48ZBJBJV2QYNGlSfp9Loqf4cW7VqhUGDBiEuLk7R59/nzp8/r7SvRCJBq1atIBKJcOrUKaX+hw4dqqczaLrUeW1EIhHMzMzQp08fnDx5UotRC4NcLoenpye8vLyqPLdx40a0aNEC9+7d00Jk1JAwGXiKrVu3YubMmThz5ozSB7xEIsGAAQOwbNkyjBs3DmfOnMGVK1fw3//+F9nZ2Vi3bp3SOIMGDUJaWprS9sMPP9T36TR6j/8cw8PDoaenh2HDhin1cXBwwPbt25XafvrpJ7W+zpPUV5vXZvv27UhLS8Off/4JCwsLDBs2DMnJyVqKWBhEIhG2b9+OCxcu4JtvvlG037x5E59++inWrVuH1q1bazFCahDkVKPCwkK5iYmJ/Nq1a/IxY8bIly5dqnguODhYrqOjI4+Jial2X5lMpvh/X19f+YgRI+o63Cavup/j2bNn5QDkmZmZcrlcLgcgnz9/vtzU1FReUlKi6DdgwAD5ggUL5ADkf/zxh6IdgPynn36qh+ibttq+No//rFNSUuQA5Js2barHSIVrx44dchMTE3lycrJcJpPJ+/XrJx81apS2w6IGgpWBJ9i3bx86dOgAJycnjB07Ftu2bYP84T2afvjhBwwYMACvvPJKtfuKRKL6DFWQioqKsGvXLrRv3x6tWrVStLu6usLR0REHDhwA8OD7v8+cOYMPP/xQW6EKTk2vzeOaNWsG4MF0G9U9X19fvPHGGxg/fjzWr1+P+Ph4pUoBCRuTgSfYunUrxo4dC+BBCTQ/Px+nT58GACQmJsLJyUmp/6hRo2BiYgITExP07t1b6bkjR44onvt3W7ZsWf2cSBPy+M+xefPmOHz4MPbu3QsdHeV/yuPHj8e2bdsAADt27MCQIUNgaWmpjZAFo7avDQCUlJRg/vz50NXVxWuvvaaFaIVp8+bNiI+Px8cff4zNmzfzPUEKTAZqkJCQgKioKLz33nsAAD09PYwZMwZbt26tcZ+NGzciNjYW48ePR0lJidJz/fr1Q2xsrNI2ZcqUOj2Hpujxn2NUVBS8vLwwePBg3L59W6nf2LFjERkZieTkZOzYsQPjx4/XUsTCUZvX5r333lMkCwcOHMDWrVvRrVs3LUYtLFZWVpg8eTI6duyIkSNHajscakD0tB1AQ7V161ZUVlbCzs5O0SaXyyEWi7F+/Xq89NJLSEhIUNrH1tYWANCyZcsq4xkbG6N9+/Z1G7QAqP4ct2zZAjMzM4SGhmLJkiWK9latWmHYsGGYMGECysrKMHjwYBQWFmojZMGozWuzZs0aeHp6wszMjH+Vaomenh709Pirn5SxMlCNyspK7Ny5E6tWrVL6S/7y5cuws7PDDz/8gPfeew8nTpzAX3/9pe1wBU0kEkFHRwelpaVVnhs/fjxOnToFHx8f6OrqaiE6YavutbGxsUH79u2ZCBA1MEwPq3HkyBHk5uZiwoQJMDMzU3rurbfewtatW3H27FkcPXoUb7zxBoKCgtC3b1+Ym5sjMTERv/76a5UPH4lEgvT0dKU2PT09WFhY1Pn5NCWP/xxzc3Oxfv16FBUVwdvbu0rfQYMGISsrC6ampvUdpiCp89oQUcPCZKAaW7duVZQyVb311ltYsWIFEhMTER4ejpCQEGzfvh2BgYGQyWRo06YNBg8ejDlz5ijtFxYWpphG+JeTkxOuXbtWp+fS1Dz+c2zevDk6dOiA/fv34/XXX6/SVyQSMdmqR+q8NkTUsIjk/14rR0RERILENQNEREQCx2SAiIhI4JgMEBERCRyTASIiIoFjMkBERCRwTAaIiIgEjskAERGRwDEZICIiEjgmA0RERALHZICIiEjgmAwQEREJ3P8DDc2BmafMxPIAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a figure with three subplots\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
+        "\n",
+        "# Plot the boxplots for BMI, BP, and Y depending on gender\n",
+        "sns.scatterplot(x='Y', y='BMI', data=df, ax=axes[0])\n",
+        "sns.scatterplot(x='Y', y='BP', data=df, ax=axes[1])\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "gC5Fqp19FZo0",
+        "outputId": "4cc0e3ee-eb91-4fcc-81d5-3abebf656e39",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 465
+        }
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x500 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "dh-DxaqH4qDK"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
+      ],
+      "metadata": {
+        "id": "lHAZDxcY4qDL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "sHpUiNUU4qDN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.groupby('SEX').agg({ 'Y' : 'mean'})"
+      ],
+      "metadata": {
+        "id": "9el94npsGWFw",
+        "outputId": "ec34d5d5-7ffb-4506-c279-54b01d456344",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        }
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              Y\n",
+              "SEX            \n",
+              "1    149.021277\n",
+              "2    155.666667"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ba0e7ef9-5525-4cbb-a432-cadcc10a4640\" class=\"colab-df-container\">\n",
+              "    <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>Y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SEX</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>149.021277</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>155.666667</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba0e7ef9-5525-4cbb-a432-cadcc10a4640')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ba0e7ef9-5525-4cbb-a432-cadcc10a4640 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ba0e7ef9-5525-4cbb-a432-cadcc10a4640');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-7b2bd5a9-e355-4e51-8c06-89f90e115e3a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7b2bd5a9-e355-4e51-8c06-89f90e115e3a')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-7b2bd5a9-e355-4e51-8c06-89f90e115e3a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
       ]
-     },
-     "metadata": {},
-     "execution_count": 13
     }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\r\n",
-    "In this dataset, columns as the following:\r\n",
-    "* Age and sex are self-explanatory\r\n",
-    "* BMI is body mass index\r\n",
-    "* BP is average blood pressure\r\n",
-    "* S1 through S6 are different blood measurements\r\n",
-    "* Y is the qualitative measure of disease progression over one year\r\n",
-    "\r\n",
-    "Let's study this dataset using methods of probability and statistics.\r\n",
-    "\r\n",
-    "### Task 1: Compute mean values and variance for all values"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
-    "\r\n",
-    "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.8",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.8 64-bit (conda)"
+  ],
+  "metadata": {
+    "orig_nbformat": 4,
+    "language_info": {
+      "name": "python",
+      "version": "3.8.8",
+      "mimetype": "text/x-python",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "file_extension": ".py"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3.8.8 64-bit (conda)"
+    },
+    "interpreter": {
+      "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
   },
-  "interpreter": {
-   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
\ No newline at end of file