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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5a5210d",
   "metadata": {},
   "source": [
    "Import `numpy`, `pandas`, the `pyplot` module from `matplotlib`, `seaborn`, and the `stats` module from `scipy`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "529c5f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb564f37",
   "metadata": {},
   "source": [
    "# Comparison of two group means"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c1dd12",
   "metadata": {},
   "source": [
    "Load the `mi.csv` data file located in the `data` directory of the course repository:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00130518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Age</th>\n",
       "      <th>OwnsHouse</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>Sex</th>\n",
       "      <th>LivesWithPartner</th>\n",
       "      <th>LivesWithKids</th>\n",
       "      <th>BornInCity</th>\n",
       "      <th>Inbreeding</th>\n",
       "      <th>BMI</th>\n",
       "      <th>CMVPositiveSerology</th>\n",
       "      <th>...</th>\n",
       "      <th>VaccineWhoopingCough</th>\n",
       "      <th>VaccineYellowFever</th>\n",
       "      <th>VaccineHepB</th>\n",
       "      <th>VaccineFlu</th>\n",
       "      <th>SUBJID</th>\n",
       "      <th>DepressionScore</th>\n",
       "      <th>HeartRate</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>HourOfSampling</th>\n",
       "      <th>DayOfSampling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>94.9627</td>\n",
       "      <td>20.13</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "      <td>36.8</td>\n",
       "      <td>8.883</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.83</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>79.1024</td>\n",
       "      <td>21.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "      <td>37.4</td>\n",
       "      <td>9.350</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>117.2540</td>\n",
       "      <td>22.18</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62</td>\n",
       "      <td>36.9</td>\n",
       "      <td>8.667</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.17</td>\n",
       "      <td>No</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>94.1796</td>\n",
       "      <td>18.68</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.883</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26.17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>105.1250</td>\n",
       "      <td>29.01</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67</td>\n",
       "      <td>36.7</td>\n",
       "      <td>8.550</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age OwnsHouse  PhysicalActivity     Sex LivesWithPartner LivesWithKids  \\\n",
       "1  22.33       Yes               3.0  Female               No            No   \n",
       "2  28.83       Yes               0.0  Female              Yes            No   \n",
       "3  23.67       Yes               0.0  Female              Yes            No   \n",
       "4  21.17        No               0.5  Female               No            No   \n",
       "5  26.17       Yes               1.5  Female               No            No   \n",
       "\n",
       "  BornInCity  Inbreeding    BMI CMVPositiveSerology  ...  \\\n",
       "1        Yes     94.9627  20.13                  No  ...   \n",
       "2        Yes     79.1024  21.33                 Yes  ...   \n",
       "3        Yes    117.2540  22.18                  No  ...   \n",
       "4         No     94.1796  18.68                  No  ...   \n",
       "5        Yes    105.1250  29.01                  No  ...   \n",
       "\n",
       "   VaccineWhoopingCough  VaccineYellowFever  VaccineHepB  VaccineFlu  SUBJID  \\\n",
       "1                   Yes                  No          Yes          No       2   \n",
       "2                   Yes                  No          Yes          No       3   \n",
       "3                    No                  No          Yes          No       4   \n",
       "4                    No                  No          Yes          No       5   \n",
       "5                   Yes                  No          Yes          No       8   \n",
       "\n",
       "   DepressionScore  HeartRate Temperature HourOfSampling DayOfSampling  \n",
       "1              0.0         66        36.8          8.883            40  \n",
       "2              0.0         66        37.4          9.350            40  \n",
       "3              0.0         62        36.9          8.667            40  \n",
       "4              1.0         64        36.0          9.883            40  \n",
       "5              0.0         67        36.7          8.550            81  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/mi.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc036b2",
   "metadata": {},
   "source": [
    "Question: anything missing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a648a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(816, 43)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f0a8116",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>OwnsHouse</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Inbreeding</th>\n",
       "      <th>BMI</th>\n",
       "      <th>CMVPositiveSerology</th>\n",
       "      <th>FluIgG</th>\n",
       "      <th>MetabolicScore</th>\n",
       "      <th>LowAppetite</th>\n",
       "      <th>TroubleConcentrating</th>\n",
       "      <th>TroubleSleeping</th>\n",
       "      <th>HoursOfSleep</th>\n",
       "      <th>Listless</th>\n",
       "      <th>UsesCannabis</th>\n",
       "      <th>RecentPersonalCrisis</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Education</th>\n",
       "      <th>DustExposure</th>\n",
       "      <th>Income</th>\n",
       "      <th>HadMeasles</th>\n",
       "      <th>HadRubella</th>\n",
       "      <th>HadChickenPox</th>\n",
       "      <th>HadMumps</th>\n",
       "      <th>HadTonsillectomy</th>\n",
       "      <th>HadAppendicectomy</th>\n",
       "      <th>VaccineHepA</th>\n",
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       "      <th>VaccineHepB</th>\n",
       "      <th>VaccineFlu</th>\n",
       "      <th>SUBJID</th>\n",
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       "      <th>DayOfSampling</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>94.9627</td>\n",
       "      <td>20.13</td>\n",
       "      <td>No</td>\n",
       "      <td>0.464319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>No</td>\n",
       "      <td>PhD</td>\n",
       "      <td>No</td>\n",
       "      <td>(1000-2000]</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "      <td>36.8</td>\n",
       "      <td>8.883</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.83</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>79.1024</td>\n",
       "      <td>21.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-0.049817</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.05</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Active</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Baccalaureat</td>\n",
       "      <td>No</td>\n",
       "      <td>(2000-3000]</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66</td>\n",
       "      <td>37.4</td>\n",
       "      <td>9.350</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>117.2540</td>\n",
       "      <td>22.18</td>\n",
       "      <td>No</td>\n",
       "      <td>0.332944</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Active</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Baccalaureat</td>\n",
       "      <td>Current</td>\n",
       "      <td>(2000-3000]</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62</td>\n",
       "      <td>36.9</td>\n",
       "      <td>8.667</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.17</td>\n",
       "      <td>No</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>94.1796</td>\n",
       "      <td>18.68</td>\n",
       "      <td>No</td>\n",
       "      <td>0.404886</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>No</td>\n",
       "      <td>PhD</td>\n",
       "      <td>No</td>\n",
       "      <td>(3000-inf]</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.883</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26.17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>105.1250</td>\n",
       "      <td>29.01</td>\n",
       "      <td>No</td>\n",
       "      <td>-0.303782</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Baccalaureat</td>\n",
       "      <td>No</td>\n",
       "      <td>[0-1000]</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67</td>\n",
       "      <td>36.7</td>\n",
       "      <td>8.550</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age OwnsHouse  PhysicalActivity     Sex LivesWithPartner LivesWithKids  \\\n",
       "1  22.33       Yes               3.0  Female               No            No   \n",
       "2  28.83       Yes               0.0  Female              Yes            No   \n",
       "3  23.67       Yes               0.0  Female              Yes            No   \n",
       "4  21.17        No               0.5  Female               No            No   \n",
       "5  26.17       Yes               1.5  Female               No            No   \n",
       "\n",
       "  BornInCity  Inbreeding    BMI CMVPositiveSerology    FluIgG  MetabolicScore  \\\n",
       "1        Yes     94.9627  20.13                  No  0.464319               0   \n",
       "2        Yes     79.1024  21.33                 Yes -0.049817               1   \n",
       "3        Yes    117.2540  22.18                  No  0.332944               2   \n",
       "4         No     94.1796  18.68                  No  0.404886               0   \n",
       "5        Yes    105.1250  29.01                  No -0.303782               1   \n",
       "\n",
       "   LowAppetite  TroubleConcentrating  TroubleSleeping  HoursOfSleep  Listless  \\\n",
       "1            0                     0              1.0          9.00         3   \n",
       "2            0                     0              1.0          7.05         3   \n",
       "3            0                     0              1.0          6.50         3   \n",
       "4            0                     0              2.0         10.00         3   \n",
       "5            0                     0              1.0          9.00         0   \n",
       "\n",
       "  UsesCannabis RecentPersonalCrisis Smoking Employed     Education  \\\n",
       "1           No                   No   Never       No           PhD   \n",
       "2           No                   No  Active      Yes  Baccalaureat   \n",
       "3          Yes                   No  Active      Yes  Baccalaureat   \n",
       "4           No                   No   Never       No           PhD   \n",
       "5           No                   No   Never      Yes  Baccalaureat   \n",
       "\n",
       "  DustExposure       Income HadMeasles HadRubella HadChickenPox HadMumps  \\\n",
       "1           No  (1000-2000]         No         No           Yes       No   \n",
       "2           No  (2000-3000]         No         No           Yes       No   \n",
       "3      Current  (2000-3000]         No         No           Yes       No   \n",
       "4           No   (3000-inf]         No         No           Yes       No   \n",
       "5           No     [0-1000]         No         No            No       No   \n",
       "\n",
       "  HadTonsillectomy HadAppendicectomy VaccineHepA VaccineMMR VaccineTyphoid  \\\n",
       "1               No                No          No         No             No   \n",
       "2               No                No          No         No             No   \n",
       "3               No                No          No         No             No   \n",
       "4               No                No          No         No             No   \n",
       "5               No                No          No         No             No   \n",
       "\n",
       "  VaccineWhoopingCough VaccineYellowFever VaccineHepB VaccineFlu  SUBJID  \\\n",
       "1                  Yes                 No         Yes         No       2   \n",
       "2                  Yes                 No         Yes         No       3   \n",
       "3                   No                 No         Yes         No       4   \n",
       "4                   No                 No         Yes         No       5   \n",
       "5                  Yes                 No         Yes         No       8   \n",
       "\n",
       "   DepressionScore  HeartRate  Temperature  HourOfSampling  DayOfSampling  \n",
       "1              0.0         66         36.8           8.883             40  \n",
       "2              0.0         66         37.4           9.350             40  \n",
       "3              0.0         62         36.9           8.667             40  \n",
       "4              1.0         64         36.0           9.883             40  \n",
       "5              0.0         67         36.7           8.550             81  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in Jupyter-lab, pandas is set to display dataframes with a limited number of columns\n",
    "pd.options.display.max_columns = None\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3512f950",
   "metadata": {},
   "source": [
    "Show a summary table for these data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7a7d087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>Inbreeding</th>\n",
       "      <th>BMI</th>\n",
       "      <th>FluIgG</th>\n",
       "      <th>MetabolicScore</th>\n",
       "      <th>LowAppetite</th>\n",
       "      <th>TroubleConcentrating</th>\n",
       "      <th>TroubleSleeping</th>\n",
       "      <th>HoursOfSleep</th>\n",
       "      <th>Listless</th>\n",
       "      <th>SUBJID</th>\n",
       "      <th>DepressionScore</th>\n",
       "      <th>HeartRate</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>HourOfSampling</th>\n",
       "      <th>DayOfSampling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "      <td>816.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.485711</td>\n",
       "      <td>2.751804</td>\n",
       "      <td>91.904255</td>\n",
       "      <td>24.208958</td>\n",
       "      <td>0.203601</td>\n",
       "      <td>0.932598</td>\n",
       "      <td>0.512255</td>\n",
       "      <td>0.355392</td>\n",
       "      <td>1.119771</td>\n",
       "      <td>7.499246</td>\n",
       "      <td>1.290441</td>\n",
       "      <td>576.877451</td>\n",
       "      <td>0.544526</td>\n",
       "      <td>59.209559</td>\n",
       "      <td>36.431985</td>\n",
       "      <td>9.214806</td>\n",
       "      <td>185.485294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.854402</td>\n",
       "      <td>3.565008</td>\n",
       "      <td>12.936172</td>\n",
       "      <td>3.181184</td>\n",
       "      <td>0.232411</td>\n",
       "      <td>0.893942</td>\n",
       "      <td>1.674008</td>\n",
       "      <td>1.408535</td>\n",
       "      <td>0.931400</td>\n",
       "      <td>1.017186</td>\n",
       "      <td>2.055716</td>\n",
       "      <td>518.489935</td>\n",
       "      <td>1.333918</td>\n",
       "      <td>9.206104</td>\n",
       "      <td>0.318461</td>\n",
       "      <td>0.378376</td>\n",
       "      <td>84.971737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.727000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>-0.430491</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>35.700000</td>\n",
       "      <td>8.433000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.830000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>84.077225</td>\n",
       "      <td>21.770000</td>\n",
       "      <td>0.065082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>36.200000</td>\n",
       "      <td>8.917000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.710000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>91.862800</td>\n",
       "      <td>23.850000</td>\n",
       "      <td>0.227855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>556.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>9.233000</td>\n",
       "      <td>187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.352500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.008000</td>\n",
       "      <td>26.210000</td>\n",
       "      <td>0.363819</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>779.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69.750000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>150.107000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.769841</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>5701.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>11.217000</td>\n",
       "      <td>335.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  PhysicalActivity  Inbreeding         BMI      FluIgG  \\\n",
       "count  816.000000        816.000000  816.000000  816.000000  816.000000   \n",
       "mean    46.485711          2.751804   91.904255   24.208958    0.203601   \n",
       "std     13.854402          3.565008   12.936172    3.181184    0.232411   \n",
       "min     20.170000          0.000000   43.727000   18.500000   -0.430491   \n",
       "25%     35.830000          0.500000   84.077225   21.770000    0.065082   \n",
       "50%     47.710000          2.000000   91.862800   23.850000    0.227855   \n",
       "75%     58.352500          4.000000  100.008000   26.210000    0.363819   \n",
       "max     69.750000         49.000000  150.107000   32.000000    0.769841   \n",
       "\n",
       "       MetabolicScore  LowAppetite  TroubleConcentrating  TroubleSleeping  \\\n",
       "count      816.000000   816.000000            816.000000       816.000000   \n",
       "mean         0.932598     0.512255              0.355392         1.119771   \n",
       "std          0.893942     1.674008              1.408535         0.931400   \n",
       "min          0.000000     0.000000              0.000000         0.000000   \n",
       "25%          0.000000     0.000000              0.000000         0.000000   \n",
       "50%          1.000000     0.000000              0.000000         1.000000   \n",
       "75%          1.000000     0.000000              0.000000         2.000000   \n",
       "max          4.000000    14.000000             14.000000         3.000000   \n",
       "\n",
       "       HoursOfSleep    Listless       SUBJID  DepressionScore   HeartRate  \\\n",
       "count    816.000000  816.000000   816.000000       816.000000  816.000000   \n",
       "mean       7.499246    1.290441   576.877451         0.544526   59.209559   \n",
       "std        1.017186    2.055716   518.489935         1.333918    9.206104   \n",
       "min        3.000000    0.000000     2.000000         0.000000   37.000000   \n",
       "25%        7.000000    0.000000   300.750000         0.000000   54.000000   \n",
       "50%        7.500000    0.000000   556.500000         0.000000   58.000000   \n",
       "75%        8.000000    3.000000   779.250000         1.000000   65.000000   \n",
       "max       12.000000   14.000000  5701.000000        14.000000  100.000000   \n",
       "\n",
       "       Temperature  HourOfSampling  DayOfSampling  \n",
       "count   816.000000      816.000000     816.000000  \n",
       "mean     36.431985        9.214806     185.485294  \n",
       "std       0.318461        0.378376      84.971737  \n",
       "min      35.700000        8.433000      17.000000  \n",
       "25%      36.200000        8.917000     136.000000  \n",
       "50%      36.400000        9.233000     187.000000  \n",
       "75%      36.600000        9.550000     263.000000  \n",
       "max      37.700000       11.217000     335.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2a049f",
   "metadata": {},
   "source": [
    "Inspect the distribution of variables `Age` and `OwnsHouse`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14572a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical vs continuous => boxplot, violinplot\n",
    "sns.boxplot(x='OwnsHouse', y='Age', data=df);\n",
    "# optional\n",
    "sns.swarmplot(x='OwnsHouse', y='Age', data=df, linewidth=1, size=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c625646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " _________________________________________\r\n",
      "/ Where on Earth ALL younger people own a \\\r\n",
      "\\ house while elder people do not?        /\r\n",
      " -----------------------------------------\r\n",
      "        \\   ^__^\r\n",
      "         \\  (oo)\\_______\r\n",
      "            (__)\\       )\\/\\\r\n",
      "                ||----w |\r\n",
      "                ||     ||\r\n"
     ]
    }
   ],
   "source": [
    "!cowsay \"Where on Earth ALL younger people own a house while elder people do not?\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af5660e7",
   "metadata": {},
   "source": [
    "Isolate the house-owners group from the others, draw their respective age distributions and check they are normally distributed:"
   ]