M5-T01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Especialització de DataScience de la IT ACADEMY\n",
    "### *Exercicis d'Hipotesis Testing.*\n",
    "### *Sprint5 - Tasca M5 T01 - Exercici 1*\n",
    "#### Descarrega el dataset adjunt de dades oficials de la UEFA i selecciona un atribut del conjunt de dades. Calcula el p-valor i digues si rebutja la hipòtesi nul·la agafant un alfa de 5%. \n",
    "\n",
    "#### Alumne: Jorge Prieto Rey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Country</th>\n",
       "      <th>LgRk</th>\n",
       "      <th>MP</th>\n",
       "      <th>W</th>\n",
       "      <th>D</th>\n",
       "      <th>L</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>...</th>\n",
       "      <th>Pts</th>\n",
       "      <th>Pts/G</th>\n",
       "      <th>xG</th>\n",
       "      <th>xGA</th>\n",
       "      <th>xGD</th>\n",
       "      <th>xGD/90</th>\n",
       "      <th>Last 5</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Top Team Scorer</th>\n",
       "      <th>Goalkeeper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>ENG</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>2.43</td>\n",
       "      <td>86.1</td>\n",
       "      <td>26.8</td>\n",
       "      <td>59.3</td>\n",
       "      <td>1.60</td>\n",
       "      <td>W W W W D</td>\n",
       "      <td>52739</td>\n",
       "      <td>Kevin De Bruyne - 15</td>\n",
       "      <td>Ederson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>ENG</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>89</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>2.39</td>\n",
       "      <td>84.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>51.4</td>\n",
       "      <td>1.43</td>\n",
       "      <td>W W W D W</td>\n",
       "      <td>53367</td>\n",
       "      <td>Mohamed Salah - 22</td>\n",
       "      <td>Alisson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>ESP</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>2.30</td>\n",
       "      <td>73.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>27.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>W W L W D</td>\n",
       "      <td>40624</td>\n",
       "      <td>Karim Benzema - 27</td>\n",
       "      <td>Thibaut Courtois</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bayern Munich</td>\n",
       "      <td>GER</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>97</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>2.26</td>\n",
       "      <td>88.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>W W L D D</td>\n",
       "      <td>33176</td>\n",
       "      <td>Robert Lewandowski - 35</td>\n",
       "      <td>Manuel Neuer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris S-G</td>\n",
       "      <td>FRA</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>2.24</td>\n",
       "      <td>71.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>W D D D W</td>\n",
       "      <td>41188</td>\n",
       "      <td>Kylian Mbapp� - 25</td>\n",
       "      <td>Keylor Navas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk            Squad Country  LgRk  MP   W  D  L  GF  GA  ...  Pts  Pts/G  \\\n",
       "0   1  Manchester City     ENG     1  37  28  6  3  96  24  ...   90   2.43   \n",
       "1   2        Liverpool     ENG     2  36  26  8  2  89  24  ...   86   2.39   \n",
       "2   3      Real Madrid     ESP     1  37  26  7  4  80  31  ...   85   2.30   \n",
       "3   4    Bayern Munich     GER     1  34  24  5  5  97  37  ...   77   2.26   \n",
       "4   5        Paris S-G     FRA     1  37  25  8  4  85  36  ...   83   2.24   \n",
       "\n",
       "     xG   xGA   xGD  xGD/90     Last 5 Attendance          Top Team Scorer  \\\n",
       "0  86.1  26.8  59.3    1.60  W W W W D      52739     Kevin De Bruyne - 15   \n",
       "1  84.6  33.1  51.4    1.43  W W W D W      53367       Mohamed Salah - 22   \n",
       "2  73.0  45.8  27.2    0.73  W W L W D      40624       Karim Benzema - 27   \n",
       "3  88.1  37.1  51.0    1.50  W W L D D      33176  Robert Lewandowski - 35   \n",
       "4  71.6  38.1  33.4    0.90  W D D D W      41188       Kylian Mbapp� - 25   \n",
       "\n",
       "         Goalkeeper  \n",
       "0           Ederson  \n",
       "1           Alisson  \n",
       "2  Thibaut Courtois  \n",
       "3      Manuel Neuer  \n",
       "4      Keylor Navas  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resultados de la liga \n",
    "df = pd.read_csv('Lligues europees UEFA.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rk                   int64\n",
       "Squad               object\n",
       "Country             object\n",
       "LgRk                 int64\n",
       "MP                   int64\n",
       "W                    int64\n",
       "D                    int64\n",
       "L                    int64\n",
       "GF                   int64\n",
       "GA                   int64\n",
       "GD                   int64\n",
       "Pts                  int64\n",
       "Pts/G              float64\n",
       "xG                 float64\n",
       "xGA                float64\n",
       "xGD                float64\n",
       "xGD/90             float64\n",
       "Last 5              object\n",
       "Attendance           int64\n",
       "Top Team Scorer     object\n",
       "Goalkeeper          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>1.60</td>\n",
       "      <td>52739</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>26</td>\n",
       "      <td>8</td>\n",
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       "      <td>89</td>\n",
       "      <td>24</td>\n",
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       "      <td>33.1</td>\n",
       "      <td>51.4</td>\n",
       "      <td>1.43</td>\n",
       "      <td>53367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>31</td>\n",
       "      <td>49</td>\n",
       "      <td>85</td>\n",
       "      <td>2.30</td>\n",
       "      <td>73.0</td>\n",
       "      <td>45.8</td>\n",
       "      <td>27.2</td>\n",
       "      <td>0.73</td>\n",
       "      <td>40624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>97</td>\n",
       "      <td>37</td>\n",
       "      <td>60</td>\n",
       "      <td>77</td>\n",
       "      <td>2.26</td>\n",
       "      <td>88.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>33176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "      <td>36</td>\n",
       "      <td>49</td>\n",
       "      <td>83</td>\n",
       "      <td>2.24</td>\n",
       "      <td>71.6</td>\n",
       "      <td>38.1</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0.90</td>\n",
       "      <td>41188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk  LgRk  MP   W  D  L  GF  GA  GD  Pts  Pts/G    xG   xGA   xGD  xGD/90  \\\n",
       "0   1     1  37  28  6  3  96  24  72   90   2.43  86.1  26.8  59.3    1.60   \n",
       "1   2     2  36  26  8  2  89  24  65   86   2.39  84.6  33.1  51.4    1.43   \n",
       "2   3     1  37  26  7  4  80  31  49   85   2.30  73.0  45.8  27.2    0.73   \n",
       "3   4     1  34  24  5  5  97  37  60   77   2.26  88.1  37.1  51.0    1.50   \n",
       "4   5     1  37  25  8  4  85  36  49   83   2.24  71.6  38.1  33.4    0.90   \n",
       "\n",
       "   Attendance  \n",
       "0       52739  \n",
       "1       53367  \n",
       "2       40624  \n",
       "3       33176  \n",
       "4       41188  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Seleccionamos las columnas con valores númericos unicamente\n",
    "dfNum=df.select_dtypes(np.number)\n",
    "dfNum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>LgRk</th>\n",
       "      <th>MP</th>\n",
       "      <th>W</th>\n",
       "      <th>D</th>\n",
       "      <th>L</th>\n",
       "      <th>GF</th>\n",
       "      <th>GA</th>\n",
       "      <th>GD</th>\n",
       "      <th>Pts</th>\n",
       "      <th>Pts/G</th>\n",
       "      <th>xG</th>\n",
       "      <th>xGA</th>\n",
       "      <th>xGD</th>\n",
       "      <th>xGD/90</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>98.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>49.5000</td>\n",
       "      <td>10.3163</td>\n",
       "      <td>36.3061</td>\n",
       "      <td>13.4490</td>\n",
       "      <td>9.4082</td>\n",
       "      <td>13.4490</td>\n",
       "      <td>50.8776</td>\n",
       "      <td>50.8776</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>49.7347</td>\n",
       "      <td>1.3702</td>\n",
       "      <td>48.6531</td>\n",
       "      <td>48.6459</td>\n",
       "      <td>-0.0051</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>24239.8571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.4341</td>\n",
       "      <td>5.7069</td>\n",
       "      <td>1.1524</td>\n",
       "      <td>5.7475</td>\n",
       "      <td>2.9034</td>\n",
       "      <td>5.1235</td>\n",
       "      <td>16.1029</td>\n",
       "      <td>13.6346</td>\n",
       "      <td>26.5019</td>\n",
       "      <td>16.1664</td>\n",
       "      <td>0.4439</td>\n",
       "      <td>12.4539</td>\n",
       "      <td>9.8125</td>\n",
       "      <td>19.9925</td>\n",
       "      <td>0.5514</td>\n",
       "      <td>14874.1718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>34.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>-56.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0.5300</td>\n",
       "      <td>28.2000</td>\n",
       "      <td>26.8000</td>\n",
       "      <td>-40.4000</td>\n",
       "      <td>-1.0900</td>\n",
       "      <td>5870.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.2500</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-17.7500</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>39.1000</td>\n",
       "      <td>41.3000</td>\n",
       "      <td>-13.5750</td>\n",
       "      <td>-0.3650</td>\n",
       "      <td>12355.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.5000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>46.4500</td>\n",
       "      <td>47.7500</td>\n",
       "      <td>-1.1000</td>\n",
       "      <td>-0.0300</td>\n",
       "      <td>20491.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73.7500</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>59.0000</td>\n",
       "      <td>17.2500</td>\n",
       "      <td>61.7500</td>\n",
       "      <td>1.6950</td>\n",
       "      <td>55.5500</td>\n",
       "      <td>54.7000</td>\n",
       "      <td>12.8000</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>32382.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.0000</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>97.0000</td>\n",
       "      <td>89.0000</td>\n",
       "      <td>72.0000</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>2.4300</td>\n",
       "      <td>88.1000</td>\n",
       "      <td>73.3000</td>\n",
       "      <td>59.3000</td>\n",
       "      <td>1.6000</td>\n",
       "      <td>73150.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rk     LgRk       MP        W        D        L       GF       GA  \\\n",
       "count  98.0000  98.0000  98.0000  98.0000  98.0000  98.0000  98.0000  98.0000   \n",
       "mean   49.5000  10.3163  36.3061  13.4490   9.4082  13.4490  50.8776  50.8776   \n",
       "std    28.4341   5.7069   1.1524   5.7475   2.9034   5.1235  16.1029  13.6346   \n",
       "min     1.0000   1.0000  34.0000   3.0000   3.0000   2.0000  23.0000  24.0000   \n",
       "25%    25.2500   5.2500  36.0000   9.0000   7.0000  10.0000  39.0000  40.0000   \n",
       "50%    49.5000  10.0000  37.0000  13.0000   9.0000  13.0000  47.0000  50.0000   \n",
       "75%    73.7500  15.0000  37.0000  17.0000  11.0000  17.0000  60.0000  59.0000   \n",
       "max    98.0000  20.0000  37.0000  28.0000  16.0000  26.0000  97.0000  89.0000   \n",
       "\n",
       "            GD      Pts    Pts/G       xG      xGA      xGD   xGD/90  \\\n",
       "count  98.0000  98.0000  98.0000  98.0000  98.0000  98.0000  98.0000   \n",
       "mean    0.0000  49.7347   1.3702  48.6531  48.6459  -0.0051   0.0009   \n",
       "std    26.5019  16.1664   0.4439  12.4539   9.8125  19.9925   0.5514   \n",
       "min   -56.0000  18.0000   0.5300  28.2000  26.8000 -40.4000  -1.0900   \n",
       "25%   -17.7500  37.0000   1.0000  39.1000  41.3000 -13.5750  -0.3650   \n",
       "50%    -1.0000  46.0000   1.3000  46.4500  47.7500  -1.1000  -0.0300   \n",
       "75%    17.2500  61.7500   1.6950  55.5500  54.7000  12.8000   0.3550   \n",
       "max    72.0000  90.0000   2.4300  88.1000  73.3000  59.3000   1.6000   \n",
       "\n",
       "       Attendance  \n",
       "count     98.0000  \n",
       "mean   24239.8571  \n",
       "std    14874.1718  \n",
       "min     5870.0000  \n",
       "25%    12355.5000  \n",
       "50%    20491.0000  \n",
       "75%    32382.0000  \n",
       "max    73150.0000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfNum.describe().round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>P-value</b>\n",
    "\n",
    "<p>What is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.</p>\n",
    "\n",
    "By convention, when the\n",
    "\n",
    "<ul>\n",
    "    <li>p-value is $<$ 0.001: we say there is strong evidence that the correlation is significant.</li>\n",
    "    <li>the p-value is $<$ 0.05: there is moderate evidence that the correlation is significant.</li>\n",
    "    <li>the p-value is $<$ 0.1: there is weak evidence that the correlation is significant.</li>\n",
    "    <li>the p-value is $>$ 0.1: there is no evidence that the correlation is significant.</li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shapiro-Wilk Test\n",
    "Tests whether a data sample has a Gaussian distribution.\n",
    "\n",
    "- $H_0 = $ the sample has a Gaussian distribution\n",
    "- $H_1 = $ the sample does not have a Gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shapiro-Wilk Test\n",
    "from scipy.stats import shapiro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test-Statistic = 0.850 , p-value = 0.059\n",
      "With a confidence level of 95.0%\n",
      "Sample come from a Gaussian distribution (fail to reject H0)\n"
     ]
    }
   ],
   "source": [
    "sample =  df['Attendance'].sample(frac=.1, random_state=1)\n",
    "# normality test\n",
    "stat, p = shapiro(sample)\n",
    "print(\"Test-Statistic = %.3f , p-value = %.3f\"%(stat,p))\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "print(\"With a confidence level of {}%\".format((1-alpha)*100))\n",
    "if p > alpha:\n",
    "\tprint('Sample come from a Gaussian distribution (fail to reject H0)')\n",
    "else:\n",
    "\tprint('Sample does not come from a Gaussian distribution (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd66f978>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (18, 5)\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "df['Attendance'].plot.hist(ax=axes[0])\n",
    "df['Attendance'].plot.kde(ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Sprint5 - Tasca M5 T01 - Exercici 2*\n",
    "#### Amb el mateix dataset selecciona dos altres atributs del conjunt de dades. Calcula els p-valors i digues si rebutgen la hipòtesi nul·la agafant un alfa de 5%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Pts vs. Attendance</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the  Pearson Correlation Coefficient and P-value of 'Pts' and 'Attendance'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.5792612831808652  with a P-value of P = 4.1381860089628327e-10\n",
      "With a confidence level of 95.0%\n",
      "The correlation between points and number of attendance is statistically significant\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['Pts'], df['Attendance'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "print(\"With a confidence level of {}%\".format((1-alpha)*100))\n",
    "if p > alpha:\n",
    "\tprint('The correlation between points and number of attendance is statistically significant')\n",
    "else:\n",
    "\tprint('the correlation between points and number of attendance is not statistically significant')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Conclusion:</h4>\n",
    "<p>Since the p-value is $<$ 0.001, the correlation between points and number of attendance is statistically significant, although the linear relationship isn't extremely strong (~0.579).</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd8b29b0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "sns.regplot(x=\"Pts\", y=\"Attendance\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Sprint5 - Tasca M5 T01 - Exercici 3*\n",
    "#### Continua amb el conjunt de dades adjunt i selecciona tres atributs del conjunt de dades. Calcula el p-valor i digues si rebutja la hipòtesi nul·la agafant un alfa de 5%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>ANOVA: Analysis of Variance</h3>\n",
    "<p>The Analysis of Variance  (ANOVA) is a statistical method used to test whether there are significant differences between the means of two or more groups. ANOVA returns two parameters:</p>\n",
    "\n",
    "<p><b>F-test score</b>: ANOVA assumes the means of all groups are the same, calculates how much the actual means deviate from the assumption, and reports it as the F-test score. A larger score means there is a larger difference between the means.</p>\n",
    "\n",
    "<p><b>P-value</b>:  P-value tells how statistically significant our calculated score value is.</p>\n",
    "\n",
    "<p>If our price variable is strongly correlated with the variable we are analyzing, we expect ANOVA to return a sizeable F-test score and a small p-value.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.anova import AnovaRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ENG', 'ESP', 'GER', 'FRA', 'ITA'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Country</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENG</td>\n",
       "      <td>39538.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ESP</td>\n",
       "      <td>22937.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRA</td>\n",
       "      <td>20314.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GER</td>\n",
       "      <td>21025.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ITA</td>\n",
       "      <td>17062.450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country    Attendance\n",
       "0     ENG  39538.700000\n",
       "1     ESP  22937.600000\n",
       "2     FRA  20314.000000\n",
       "3     GER  21025.055556\n",
       "4     ITA  17062.450000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_test = df[['Country','Squad','Attendance']]\n",
    "df_group_test1 = df_group_test.groupby(['Country'],as_index=False).mean()\n",
    "df_group_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "ENG    20\n",
       "ESP    20\n",
       "FRA    20\n",
       "GER    18\n",
       "ITA    20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_group_test.groupby('Country').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Squad</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Manchester Utd</td>\n",
       "      <td>73150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>59636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>58370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>56234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Barcelona</td>\n",
       "      <td>53933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>53367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Manchester City</td>\n",
       "      <td>52739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Newcastle Utd</td>\n",
       "      <td>51443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Marseille</td>\n",
       "      <td>51082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Atl�tico Madrid</td>\n",
       "      <td>48039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Milan</td>\n",
       "      <td>42388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Inter</td>\n",
       "      <td>41995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Dortmund</td>\n",
       "      <td>41800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Betis</td>\n",
       "      <td>41749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>41748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Paris S-G</td>\n",
       "      <td>41188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Roma</td>\n",
       "      <td>40745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>40624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Everton</td>\n",
       "      <td>38446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>36991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Squad  Attendance\n",
       "57   Manchester Utd       73150\n",
       "3           Arsenal       59636\n",
       "95         West Ham       58370\n",
       "87        Tottenham       56234\n",
       "9         Barcelona       53933\n",
       "50        Liverpool       53367\n",
       "56  Manchester City       52739\n",
       "65    Newcastle Utd       51443\n",
       "58        Marseille       51082\n",
       "7   Atl�tico Madrid       48039\n",
       "60            Milan       42388\n",
       "40            Inter       41995\n",
       "25         Dortmund       41800\n",
       "11            Betis       41749\n",
       "4       Aston Villa       41748\n",
       "69        Paris S-G       41188\n",
       "76             Roma       40745\n",
       "72      Real Madrid       40624\n",
       "30          Everton       38446\n",
       "21          Chelsea       36991"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_group_test2 = df_group_test.groupby(['Squad'],as_index=False).mean().sort_values('Attendance', ascending=False)\n",
    "df_group_test2.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "sns.boxplot(x=\"Country\", y=\"Attendance\", data=df, ax=ax)\n",
    "sns.swarmplot(x=\"Country\", y=\"Attendance\", data=df, color='black', alpha = 0.5, ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Since ANOVA analyzes the difference between different groups of the same variable, the groupby function will come in handy. Because the ANOVA algorithm averages the data automatically, we do not need to take the average before hand.</p>\n",
    "\n",
    "<p>To see if differents 'Country' impact 'Attendance', we group the data.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Country</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENG</td>\n",
       "      <td>52739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENG</td>\n",
       "      <td>53367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ESP</td>\n",
       "      <td>40624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GER</td>\n",
       "      <td>33176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FRA</td>\n",
       "      <td>41188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ITA</td>\n",
       "      <td>42388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ITA</td>\n",
       "      <td>41995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GER</td>\n",
       "      <td>41800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ESP</td>\n",
       "      <td>53933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FRA</td>\n",
       "      <td>5870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Attendance\n",
       "0      ENG       52739\n",
       "1      ENG       53367\n",
       "2      ESP       40624\n",
       "3      GER       33176\n",
       "4      FRA       41188\n",
       "5      ITA       42388\n",
       "6      ITA       41995\n",
       "8      GER       41800\n",
       "9      ESP       53933\n",
       "13     FRA        5870"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gptest = df[['Country','Squad','Attendance']]\n",
    "grouped_test=df[['Country', 'Attendance']].groupby(['Country'])\n",
    "grouped_test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Country</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENG</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>52739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENG</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>53367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ESP</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>40624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GER</td>\n",
       "      <td>Bayern Munich</td>\n",
       "      <td>33176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FRA</td>\n",
       "      <td>Paris S-G</td>\n",
       "      <td>41188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Milan</td>\n",
       "      <td>42388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Inter</td>\n",
       "      <td>41995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>26681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GER</td>\n",
       "      <td>Dortmund</td>\n",
       "      <td>41800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ESP</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>53933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country            Squad  Attendance\n",
       "0     ENG  Manchester City       52739\n",
       "1     ENG        Liverpool       53367\n",
       "2     ESP      Real Madrid       40624\n",
       "3     GER    Bayern Munich       33176\n",
       "4     FRA        Paris S-G       41188\n",
       "5     ITA            Milan       42388\n",
       "6     ITA            Inter       41995\n",
       "7     ITA           Napoli       26681\n",
       "8     GER         Dortmund       41800\n",
       "9     ESP        Barcelona       53933"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gptest.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 9.531073835720898 , P = 1.6755280882403337e-06\n",
      "With a confidence level of 95.0%\n",
      "The dependence of the quantitative variables according to the groups of the categorical variables is statistically significant\n"
     ]
    }
   ],
   "source": [
    "# ANOVA\n",
    "f_val, p_val = stats.f_oneway(grouped_test.get_group('ENG')['Attendance'], grouped_test.get_group('ESP')['Attendance'], \n",
    "                              grouped_test.get_group('FRA')['Attendance'],grouped_test.get_group('ITA')['Attendance'],\n",
    "                             grouped_test.get_group('GER')['Attendance'])  \n",
    " \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val) \n",
    "\n",
    "alpha = 0.05\n",
    "print(\"With a confidence level of {}%\".format((1-alpha)*100))\n",
    "if p > alpha:\n",
    "\tprint('The dependence of the quantitative variables according to the groups of the categorical variables is statistically significant')\n",
    "else:\n",
    "\tprint('The dependence of the quantitative variables according to the groups of the categorical variables is not statistically significant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# qqplot graphs\n",
    "# ==============================================================================\n",
    "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(15, 10))\n",
    "\n",
    "sm.qqplot(df_gptest.loc[df_gptest.Country=='ESP', 'Attendance'], dist='norm', fit= True, line=\"45\", \n",
    "          xlabel='ESP: Theoretical Quantiles', ylabel='ESP: Ordered Quantiles',ax = axs[0, 0])\n",
    "plt.close()\n",
    "sm.qqplot(df_gptest.loc[df_gptest.Country=='ENG', 'Attendance'], dist='norm', fit= True, line=\"45\", \n",
    "          xlabel='ENG: Theoretical Quantiles', ylabel='ENG: Ordered Quantiles',ax = axs[0, 1])\n",
    "plt.close()\n",
    "sm.qqplot(df_gptest.loc[df_gptest.Country=='GER', 'Attendance'], dist='norm', fit= True, line=\"45\", \n",
    "          xlabel='GER: Theoretical Quantiles', ylabel='GER: Ordered Quantiles',ax = axs[1, 0])\n",
    "plt.close()\n",
    "sm.qqplot(df_gptest.loc[df_gptest.Country=='ITA', 'Attendance'], dist='norm', fit= True, line=\"45\", \n",
    "          xlabel='ITA: Theoretical Quantiles', ylabel='ITA: Ordered Quantiles',ax = axs[1, 1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}