AB_adicionales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fase 4b: Prueba de AB Testing Adicional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importamos las librerías que necesitamos\n",
    "# Tratamiento de datos\n",
    "# -----------------------------------------------------------------------\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Imputación de nulos usando métodos avanzados estadísticos\n",
    "# -----------------------------------------------------------------------\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "# Librerías de visualización\n",
    "# -----------------------------------------------------------------------\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Configuración\n",
    "# -----------------------------------------------------------------------\n",
    "pd.set_option('display.max_columns', None) # para poder visualizar todas las columnas de los DataFrames\n",
    "\n",
    "# Evaluar linealidad de las relaciones entre las variables\n",
    "# ------------------------------------------------------------------------------\n",
    "from scipy.stats import shapiro, kstest\n",
    "from itertools import combinations\n",
    "from scipy.stats import mannwhitneyu\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "\n",
    "# Evaluar linealidad de las relaciones entre las variables\n",
    "# y la distribución de las variables\n",
    "# ------------------------------------------------------------------------------\n",
    "#from scipy.stats import shapiro, kstest, poisson, chisquare, ttest_ind, levene, bartlett, sem, ppf\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>unnamed_0</th>\n",
       "      <th>age</th>\n",
       "      <th>attrition</th>\n",
       "      <th>businesstravel</th>\n",
       "      <th>dailyrate</th>\n",
       "      <th>department</th>\n",
       "      <th>distancefromhome</th>\n",
       "      <th>education</th>\n",
       "      <th>educationfield</th>\n",
       "      <th>employeenumber</th>\n",
       "      <th>environmentsatisfaction</th>\n",
       "      <th>gender</th>\n",
       "      <th>hourlyrate</th>\n",
       "      <th>jobinvolvement</th>\n",
       "      <th>joblevel</th>\n",
       "      <th>jobrole</th>\n",
       "      <th>jobsatisfaction</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>monthlyrate</th>\n",
       "      <th>numcompaniesworked</th>\n",
       "      <th>overtime</th>\n",
       "      <th>percentsalaryhike</th>\n",
       "      <th>performancerating</th>\n",
       "      <th>relationshipsatisfaction</th>\n",
       "      <th>stockoptionlevel</th>\n",
       "      <th>totalworkingyears</th>\n",
       "      <th>trainingtimeslastyear</th>\n",
       "      <th>worklifebalance</th>\n",
       "      <th>yearsatcompany</th>\n",
       "      <th>yearssincelastpromotion</th>\n",
       "      <th>yearswithcurrmanager</th>\n",
       "      <th>datebirth</th>\n",
       "      <th>remotework</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>684</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Desconocido</td>\n",
       "      <td>1620</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Research Director</td>\n",
       "      <td>3</td>\n",
       "      <td>Desconocido</td>\n",
       "      <td>19537.0</td>\n",
       "      <td>6462</td>\n",
       "      <td>7</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1972</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>699</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2590</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Manager</td>\n",
       "      <td>3</td>\n",
       "      <td>Desconocido</td>\n",
       "      <td>19999.0</td>\n",
       "      <td>5678</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1971</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>532</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>3190</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>19232.0</td>\n",
       "      <td>4933</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>1981</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>359</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Research Director</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>17169.0</td>\n",
       "      <td>26703</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>19</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1976</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>1319</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>1</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>4859.0</td>\n",
       "      <td>7739</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1977</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unnamed_0  age attrition businesstravel  dailyrate  \\\n",
       "0          0   51        No  travel_rarely        684   \n",
       "1          1   52        No  travel_rarely        699   \n",
       "2          2   42        No  travel_rarely        532   \n",
       "3          3   47        No  travel_rarely        359   \n",
       "4          4   46        No  travel_rarely       1319   \n",
       "\n",
       "                 department  distancefromhome  education    educationfield  \\\n",
       "0   Research & Development                  6          3       Desconocido   \n",
       "1   Research & Development                  1          4     Life Sciences   \n",
       "2   Research & Development                  4          2  Technical Degree   \n",
       "3   Research & Development                  2          4           Medical   \n",
       "4   Research & Development                  3          3  Technical Degree   \n",
       "\n",
       "  employeenumber  environmentsatisfaction  gender  hourlyrate  jobinvolvement  \\\n",
       "0           1620                        1    Male        51.0               3   \n",
       "1           2590                        3    Male        65.0               2   \n",
       "2           3190                        3    Male        58.0               3   \n",
       "3        Unknown                        1  Female        82.0               3   \n",
       "4        Unknown                        1  Female        45.0               4   \n",
       "\n",
       "   joblevel              jobrole  jobsatisfaction maritalstatus  \\\n",
       "0         5   Research Director                 3   Desconocido   \n",
       "1         5             Manager                 3   Desconocido   \n",
       "2         5             Manager                 4       Married   \n",
       "3         4   Research Director                 3       Married   \n",
       "4         4     Sales Executive                 1      Divorced   \n",
       "\n",
       "   monthlyincome  monthlyrate  numcompaniesworked overtime  percentsalaryhike  \\\n",
       "0        19537.0         6462                   7       No                 13   \n",
       "1        19999.0         5678                   0       No                 14   \n",
       "2        19232.0         4933                   1       No                 11   \n",
       "3        17169.0        26703                   3       No                 19   \n",
       "4         4859.0         7739                   2       No                 12   \n",
       "\n",
       "   performancerating  relationshipsatisfaction  stockoptionlevel  \\\n",
       "0                3.0                         3                 0   \n",
       "1                3.0                         1                 1   \n",
       "2                3.0                         4                 0   \n",
       "3                3.0                         2                 2   \n",
       "4                3.0                         4                 1   \n",
       "\n",
       "   totalworkingyears  trainingtimeslastyear  worklifebalance  yearsatcompany  \\\n",
       "0               10.0                      5              3.0              20   \n",
       "1               34.0                      5              3.0              33   \n",
       "2               22.0                      3              3.0              22   \n",
       "3               10.0                      2              3.0              20   \n",
       "4               10.0                      5              3.0              19   \n",
       "\n",
       "   yearssincelastpromotion  yearswithcurrmanager  datebirth remotework  \n",
       "0                       15                    15       1972        Yes  \n",
       "1                       11                     9       1971        Yes  \n",
       "2                       11                    15       1981        Yes  \n",
       "3                        5                     6       1976         No  \n",
       "4                        2                     8       1977         No  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# abrimos el csv\n",
    "df = pd.read_csv(\"dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B TESTING - EDAD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* H0 = No existe relación entre la edad de los empleados y la rotación\n",
    "\n",
    "* H1 = Existe una relación entre la edad de los empleados y la rotación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "35    84\n",
       "34    83\n",
       "31    83\n",
       "29    78\n",
       "36    75\n",
       "32    66\n",
       "30    64\n",
       "38    64\n",
       "33    61\n",
       "40    60\n",
       "27    54\n",
       "37    54\n",
       "28    53\n",
       "42    50\n",
       "45    48\n",
       "41    46\n",
       "26    46\n",
       "39    45\n",
       "43    40\n",
       "46    38\n",
       "44    34\n",
       "50    31\n",
       "24    27\n",
       "47    27\n",
       "25    27\n",
       "49    25\n",
       "55    25\n",
       "51    20\n",
       "54    20\n",
       "53    20\n",
       "48    20\n",
       "52    19\n",
       "56    17\n",
       "21    16\n",
       "22    16\n",
       "23    15\n",
       "58    15\n",
       "20    11\n",
       "59    10\n",
       "19     9\n",
       "18     8\n",
       "60     5\n",
       "57     5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscamos en la columna 'age' los valores únicos para encontrar a los miembros de cada grupo de control.\n",
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group_age\n",
       "A    897\n",
       "B    717\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creamos una nueva columna llamada Group en la que agrupamos a los empleados en función de sus edades.\n",
    "df['Group_age'] = np.where(df['age'] >= 35, 'A', 'B')\n",
    "df['Group_age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos a los empleados de los grupos A y B en función de si han dejado la empresa o no.\n",
    "attrition_A_A = df[df['Group_age'] == 'A']['attrition']\n",
    "attrition_B_A = df[df['Group_age'] == 'B']['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>800</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>555</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition   No  Yes\n",
       "Group_age          \n",
       "A          800   97\n",
       "B          555  162"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Para ver la relación entre estas variables, creamos una tabla de contingencia.\n",
    "tabla_contingencia_age = pd.crosstab(df['Group_age'], df['attrition'])\n",
    "tabla_contingencia_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos para los gráficos de quesitos\n",
    "etiquetas = tabla_contingencia_age.columns.tolist()\n",
    "\n",
    "# Obtener los colores de la paleta \"mako\"\n",
    "colores_mako = sns.color_palette(\"mako\", len(etiquetas))\n",
    "\n",
    "# Crear los subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Gráfico del grupo A\n",
    "tamaños_A = tabla_contingencia_age.loc['A']\n",
    "ax1.pie(tamaños_A, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax1.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax1.set_title('Grupo A')\n",
    "\n",
    "# Gráfico del grupo B\n",
    "tamaños_B = tabla_contingencia_age.loc['B']\n",
    "ax2.pie(tamaños_B, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax2.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax2.set_title('Grupo B')\n",
    "\n",
    "# Título principal del gráfico\n",
    "fig.suptitle('Satisfacción con respecto a la edad', fontsize=16)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observaciones: Observamos que en el grupo A, donde los empleados tienen 35 años o menos, la tasa de rotación es del 10.8%, mientras que en el grupo B, empleados mayores de 35 años, la tasa de rotación es del 22.6%, lo que significa que el porcentaje de empleados que se han ido en el grupo A es menor al del grupo B, donde las edades son mayores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizamos los datos de las variables de la tabla de contingencia.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "#Gráfico de barras para grupo A.\n",
    "sns.barplot(x=tabla_contingencia_age.columns, y=tabla_contingencia_age.loc['A'], ax=axes[0])\n",
    "axes[0].set_title('Grupo A')\n",
    "axes[0].set_xlabel('Rotación')\n",
    "axes[0].set_ylabel('Frecuencia')\n",
    "axes[0].set_ylim(0, 850);\n",
    "\n",
    "#Gráfico de barras para grupo B.\n",
    "sns.barplot(x=tabla_contingencia_age.columns, y=tabla_contingencia_age.loc['B'], ax=axes[1])\n",
    "axes[1].set_title('Grupo B')\n",
    "axes[1].set_xlabel('Rotación')\n",
    "axes[1].set_ylabel('Frecuencia')\n",
    "axes[1].set_ylim(0, 850);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para la columna 'rotacion_grupo_A', los datos no siguen una distribución normal, p-value: 0.00000\n",
      "...................................................................\n",
      "Para la columna 'rotacion_grupo_B', los datos no siguen una distribución normal, p-value: 0.00000\n"
     ]
    }
   ],
   "source": [
    "#Antes de realizar la prueba, comprobamos si la distribución de los datos es normal o no.\n",
    "df['rotacion_grupo_A'] = ((df['Group_age'] == 'A') & (df['attrition'] == 'Yes')).astype(int)\n",
    "df['rotacion_grupo_B'] = ((df['Group_age'] == 'B') & (df['attrition'] == 'Yes')).astype(int)\n",
    "\n",
    "#Hacemos el de Shapiro-Wilk para comprobar la normalidad.\n",
    "statistic, p_value_a = stats.shapiro(df['rotacion_grupo_A'])\n",
    "if p_value_a > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos no siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "\n",
    "print('...................................................................')\n",
    "\n",
    "statistic, p_value_b = stats.shapiro(df['rotacion_grupo_B'])\n",
    "if p_value_b > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos siguen una distribución normal, p-value: {p_value_b:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos no siguen una distribución normal, p-value: {p_value_b:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El valor del p_value es de 1.5057859354321485e-10\n",
      "......................................\n",
      "Rechazamos la hipótesis nula.\n",
      "Existe una diferencia significativa entre la rotación del grupo A y del grupo B.\n"
     ]
    }
   ],
   "source": [
    "# Como las distribuciones no son normales, realizamos el test de Mann Whitney.\n",
    "\n",
    "#Usamos los datos filtrados.\n",
    "rotacion_grupo_A = df.loc[df['Group_age'] == 'A', 'rotacion_grupo_A']\n",
    "rotacion_grupo_B = df.loc[df['Group_age'] == 'B', 'rotacion_grupo_B']\n",
    "\n",
    "\n",
    "statistic, p_value = mannwhitneyu(rotacion_grupo_A, rotacion_grupo_B)\n",
    "\n",
    "print(f'El valor del p_value es de {p_value}')\n",
    "print('......................................')\n",
    "\n",
    "#Alfa\n",
    "alfa=0.05\n",
    "#Comparamos el valor p con el nivel de significancia.\n",
    "if p_value > alfa:\n",
    "    print(\"No podemos rechazar la hipótesis nula.\")\n",
    "    print(\"No existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")\n",
    "else:\n",
    "    print(\"Rechazamos la hipótesis nula.\")\n",
    "    print(\"Existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión, El p-valor es mucho menor que el nivel de significancia alfa (0.05), lo que significa que se rechaza la hipótesis nula. Es decir, existe una relación entre la edad de los trabajadores y la rotacioón de la empresa, siendo los empleados más jóvenes los que tienen mas probabilidad de dejar la empresa.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para intentar retener a los trabajadores mas jóvenes la empresa podría implementar programas de desarrollo y retención específicos como oportunidades de formación y desarrollo profesional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B TESTING: DISTANCIA AL TRABAJO\n",
    "\n",
    "* H0 = No hay una diferencia significativa entre la distancia que recorren hasta el trabajo los empleados y la rotación.\n",
    "\n",
    "* H1 = Hay una diferencia significativa entre la distancia que recorren hasta el trabajo los empleados y la rotación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distancefromhome\n",
       "2     217\n",
       "1     203\n",
       "10     91\n",
       "9      85\n",
       "8      81\n",
       "7      80\n",
       "3      79\n",
       "5      62\n",
       "6      61\n",
       "4      61\n",
       "16     35\n",
       "24     35\n",
       "12     32\n",
       "11     31\n",
       "25     30\n",
       "15     30\n",
       "13     30\n",
       "18     30\n",
       "29     29\n",
       "23     29\n",
       "26     28\n",
       "14     27\n",
       "20     22\n",
       "19     22\n",
       "17     22\n",
       "21     21\n",
       "22     21\n",
       "28     19\n",
       "27     17\n",
       "38      7\n",
       "47      7\n",
       "35      7\n",
       "42      6\n",
       "36      6\n",
       "31      6\n",
       "48      5\n",
       "46      5\n",
       "45      5\n",
       "41      4\n",
       "37      4\n",
       "30      4\n",
       "32      4\n",
       "44      3\n",
       "33      3\n",
       "34      2\n",
       "43      2\n",
       "49      2\n",
       "39      1\n",
       "40      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscamos en la columna 'distancefromhome' los valores únicos para encontrar a los miembros de cada grupo de control.\n",
    "df['distancefromhome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group_distance\n",
       "B    929\n",
       "A    685\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creamos una nueva columna llamada Group en la que agrupamos a los empleados en función de la distancia que recorren hasta el trabajo\n",
    "df['Group_distance'] = np.where(df['distancefromhome'] >= 10, 'A', 'B')\n",
    "df['Group_distance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos a los empleados de los grupos A y B en función de la distancia al trabajo.\n",
    "attrition_A = df[df['Group_distance'] == 'A']['attrition']\n",
    "attrition_B = df[df['Group_distance'] == 'B']['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>attrition</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_distance</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>560</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>795</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition         0    1\n",
       "Group_distance          \n",
       "A               560  125\n",
       "B               795  134"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Para ver la relación entre estas variables, creamos una tabla de contingencia.\n",
    "tabla_contingencia_distance = pd.crosstab(df['Group_distance'], df['attrition'])\n",
    "tabla_contingencia_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos para los gráficos de quesitos\n",
    "etiquetas = tabla_contingencia_distance.columns.tolist()\n",
    "\n",
    "# Obtener los colores de la paleta \"mako\"\n",
    "colores_mako = sns.color_palette(\"mako\", len(etiquetas))\n",
    "\n",
    "# Crear los subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Gráfico del grupo A\n",
    "tamaños_A = tabla_contingencia_distance.loc['A']\n",
    "ax1.pie(tamaños_A, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax1.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax1.set_title('Grupo A')\n",
    "\n",
    "# Gráfico del grupo B\n",
    "tamaños_B = tabla_contingencia_distance.loc['B']\n",
    "ax2.pie(tamaños_B, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax2.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax2.set_title('Grupo B')\n",
    "\n",
    "# Título principal del gráfico\n",
    "fig.suptitle('Satisfacción con a la distancia recorrida al trabajo', fontsize=16)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observaciones: Observamos que en el grupo A, donde los empleados recorren más de 10 km hasta llegar al trabajo, la tasa de rotación es del 18.2%, mientras que en el grupo B,empleados que recorren menos de 10 km, la tasa de rotación es del 14.4%, lo que significa que la rotación de empleados en el grupo A es mayor al del grupo B, donde la distancia es menor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizamos los datos de las variables de la tabla de contingencia.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "#Gráfico de barras para grupo A.\n",
    "sns.barplot(x=tabla_contingencia_distance.columns, y=tabla_contingencia_distance.loc['A'], ax=axes[0])\n",
    "axes[0].set_title('Grupo A')\n",
    "axes[0].set_xlabel('Rotación')\n",
    "axes[0].set_ylabel('Frecuencia')\n",
    "axes[0].set_ylim(0, 800);\n",
    "\n",
    "#Gráfico de barras para grupo B.\n",
    "sns.barplot(x=tabla_contingencia_distance.columns, y=tabla_contingencia_distance.loc['B'], ax=axes[1])\n",
    "axes[1].set_title('Grupo B')\n",
    "axes[1].set_xlabel('Rotación')\n",
    "axes[1].set_ylabel('Frecuencia')\n",
    "axes[1].set_ylim(0, 800);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para la columna 'rotacion_grupo_A', los datos siguen una distribución normal, p-value: 1.00000\n",
      "...................................................................\n",
      "Para la columna 'rotacion_grupo_B', los datos siguen una distribución normal, p-value: 1.00000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Rimer\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: Input data has range zero. The results may not be accurate.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#Antes de realizar la prueba, comprobamos si la distribución de los datos es normal o no.\n",
    "df['rotacion_grupo_A'] = ((df['Group_distance'] == 'A') & (df['attrition'] == 'Yes')).astype(int)\n",
    "df['rotacion_grupo_B'] = ((df['Group_distance'] == 'B') & (df['attrition'] == 'Yes')).astype(int)\n",
    "\n",
    "#Hacemos el de Shapiro-Wilk para comprobar la normalidad.\n",
    "statistic, p_value_a = stats.shapiro(df['rotacion_grupo_A'])\n",
    "if p_value_a > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos no siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "\n",
    "print('...................................................................')\n",
    "\n",
    "statistic, p_value_b = stats.shapiro(df['rotacion_grupo_B'])\n",
    "if p_value_b > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos siguen una distribución normal, p-value: {p_value_b:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos no siguen una distribución normal, p-value: {p_value_b:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El valor del p_value es de 1.0\n",
      "......................................\n",
      "No podemos rechazar la hipótesis nula.\n",
      "No existe una diferencia significativa entre la rotación del grupo A y del grupo B.\n"
     ]
    }
   ],
   "source": [
    "# Como las distribuciones no son normales, realizamos el test de Mann Whitney.\n",
    "\n",
    "#Usamos los datos filtrados.\n",
    "rotacion_grupo_A = df.loc[df['Group_distance'] == 'A', 'rotacion_grupo_A']\n",
    "rotacion_grupo_B = df.loc[df['Group_distance'] == 'B', 'rotacion_grupo_B']\n",
    "\n",
    "\n",
    "statistic, p_value = mannwhitneyu(rotacion_grupo_A, rotacion_grupo_B)\n",
    "\n",
    "print(f'El valor del p_value es de {p_value}')\n",
    "print('......................................')\n",
    "\n",
    "#Alfa\n",
    "alfa=0.05\n",
    "#Comparamos el valor p con el nivel de significancia.\n",
    "if p_value > alfa:\n",
    "    print(\"No podemos rechazar la hipótesis nula.\")\n",
    "    print(\"No existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")\n",
    "else:\n",
    "    print(\"Rechazamos la hipótesis nula.\")\n",
    "    print(\"Existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión: Dado que el p-valor (0.039) es menor que el nivel de significancia alfa (0.05), rechazas la hipótesis nula y concluimos que hay una diferencia significativa en la rotación de personal entre los dos grupos. Esto significa que la distancia al trabajo tiene un impacto significativo en la rotación de personal, teniendo los empleados que viven más lejos mas probabilidad de dejar la empresa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "para intentar revertir esta situación la empresa podría considerar políticas de apoyo para empleados que viven lejos, como opciones de teletrabajo o ayuda para el transporte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B TESTING : TRABAJO REMOTO\n",
    "\n",
    "* H0 = No hay relacion significativa entre los empleados que teletrabajan y la rotación\n",
    "\n",
    "* H1 = Hay relacion significativa entre los empleados que teletrabajan y la rotación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "remotework\n",
       "Yes    1000\n",
       "No      614\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscamos en la columna 'remotework' los valores únicos para encontrar a los miembros de cada grupo de control.\n",
    "df['remotework'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group_remote\n",
       "A    1000\n",
       "B     614\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creamos una nueva columna llamada Group en la que agrupamos a los empleados en función de si teletrabajan o no.\n",
    "df['Group_remote'] = np.where(df['remotework'] == \"Yes\", 'A', 'B')\n",
    "df['Group_remote'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos a los empleados de los grupos A y B en función de si han dejado la empresa o no.\n",
    "attrition_A = df[df['Group_remote'] == 'A']['attrition']\n",
    "attrition_B = df[df['Group_remote'] == 'B']['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "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>attrition</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_remote</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>833</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>522</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition       0    1\n",
       "Group_remote          \n",
       "A             833  167\n",
       "B             522   92"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Para ver la relación entre estas variables categóricas, creamos una tabla de contingencia.\n",
    "tabla_contingencia_R = pd.crosstab(df['Group_remote'], df['attrition'])\n",
    "tabla_contingencia_R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos para los gráficos de quesitos\n",
    "etiquetas = tabla_contingencia_R.columns.tolist()\n",
    "\n",
    "# Obtener los colores de la paleta \"mako\"\n",
    "colores_mako = sns.color_palette(\"mako\", len(etiquetas))\n",
    "\n",
    "# Crear los subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Gráfico del grupo A\n",
    "tamaños_A = tabla_contingencia_R.loc['A']\n",
    "ax1.pie(tamaños_A, labels=etiquetas, autopct='%1.1f%%', startangle=140,  colors=colores_mako)\n",
    "ax1.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax1.set_title('Teletrabajo SI')\n",
    "\n",
    "# Gráfico del grupo B\n",
    "tamaños_B = tabla_contingencia_R.loc['B']\n",
    "ax2.pie(tamaños_B, labels=etiquetas, autopct='%1.1f%%', startangle=140,  colors=colores_mako)\n",
    "ax2.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax2.set_title('Teletrabajo NO')\n",
    "\n",
    "# Título principal del gráfico\n",
    "fig.suptitle('Rotación según teletrabajo', fontsize=16)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBSERVACIONES: Observamos que en el grupo A, donde los empleados teletrabajan, la tasa de rotación es del 16.7%, mientras que en el grupo B, empleados que no teletrabajan, la tasa de rotación es del 15%, lo que significa que el porcentaje de empleados que se han ido en el grupo A, que son los que teletrabajan es mayor que los que no teletrabajan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizamos los datos de las variables de la tabla de contingencia.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "#Gráfico de barras para grupo A.\n",
    "sns.barplot(x=tabla_contingencia_R.columns, y=tabla_contingencia_R.loc['A'], ax=axes[0])\n",
    "axes[0].set_title('Grupo A')\n",
    "axes[0].set_xlabel('Rotación')\n",
    "axes[0].set_ylabel('Frecuencia')\n",
    "axes[0].set_ylim(0, 850);\n",
    "\n",
    "#Gráfico de barras para grupo B.\n",
    "sns.barplot(x=tabla_contingencia_R.columns, y=tabla_contingencia_R.loc['B'], ax=axes[1])\n",
    "axes[1].set_title('Grupo B')\n",
    "axes[1].set_xlabel('Rotación')\n",
    "axes[1].set_ylabel('Frecuencia')\n",
    "axes[1].set_ylim(0, 850);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrition       0    1\n",
      "Group_remote          \n",
      "A             833  167\n",
      "B             522   92\n",
      "\n",
      "Chi-cuadrado: 0.709269108604536\n",
      "p-valor: 0.39968655694139343\n",
      "\n",
      "No hay evidencia de una diferencia significativa en las tasas de rotación entre los grupos.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "contingency_table_R = pd.crosstab(df['Group_remote'], df['attrition'])\n",
    "print(contingency_table_R)\n",
    "\n",
    "# Realizar la prueba de proporciones\n",
    "chi2, p_value, _, _ = chi2_contingency(contingency_table_R)\n",
    "\n",
    "print(f\"\\nChi-cuadrado: {chi2}\")\n",
    "print(f\"p-valor: {p_value}\")\n",
    "\n",
    "# Imprimir el resultado de la prueba\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"\\nHay una diferencia significativa en las tasas de rotacion entre el Grupo de A  y el Grupo B.\")\n",
    "   \n",
    "else:\n",
    "    print(\"\\nNo hay evidencia de una diferencia significativa en las tasas de rotación entre los grupos.\")\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión: Dado que el p-valor (0.4) es mayor que el nivel de significancia alfa (0.05), aceptamos la hipótesis nula. Esto significa que no hay suficiente evidencia estadística para afirmar que existe una relación significativa entre el teletrabajo y la rotación de empleados en la empresa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se recomienda mantener la opción de teletrabajo como parte de la política laboral, ya que no afecta negativamente la retención de empleados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B TESTING : GÉNERO\n",
    "\n",
    "* HO = No hay una diferencia significativa entre el género de los empleados y la rotación.\n",
    "\n",
    "* H1 = Hay una diferencia significativa entre el género de los empleados y la rotación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Male      971\n",
       "Female    643\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscamos en la columna 'Gender' los valores únicos para encontrar a los miembros de cada grupo de control.\n",
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group_gender\n",
       "A    971\n",
       "B    643\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creamos una nueva columna llamada Group en la que agrupamos a los empleados en función de su género.\n",
    "df['Group_gender'] = np.where(df['gender'] == \"Male\", 'A', 'B')\n",
    "df['Group_gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos a los empleados de los grupos A y B en función de si han dejado la empresa o no.\n",
    "attrition_A_G = df[df['Group_gender'] == 'A']['attrition']\n",
    "attrition_B_G = df[df['Group_gender'] == 'B']['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>808</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>547</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition      No  Yes\n",
       "Group_gender          \n",
       "A             808  163\n",
       "B             547   96"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Para ver la relación entre estas variables categóricas, creamos una tabla de contingencia.\n",
    "tabla_contingencia_G = pd.crosstab(df['Group_gender'], df['attrition'])\n",
    "tabla_contingencia_G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>83.21</td>\n",
       "      <td>16.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>85.07</td>\n",
       "      <td>14.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition        No    Yes\n",
       "Group_gender              \n",
       "A             83.21  16.79\n",
       "B             85.07  14.93"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculamos los porcentajes.\n",
    "tabla_contingencia_G = pd.crosstab(df['Group_gender'], df['attrition'], normalize='index') * 100\n",
    "tabla_contingencia_G.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos para los gráficos de quesitos\n",
    "etiquetas = tabla_contingencia_G.columns.tolist()\n",
    "\n",
    "# Obtener los colores de la paleta \"mako\"\n",
    "colores_mako = sns.color_palette(\"mako\", len(etiquetas))\n",
    "\n",
    "# Crear los subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Gráfico del grupo A\n",
    "tamaños_A = tabla_contingencia_G.loc['A']\n",
    "ax1.pie(tamaños_A, labels=etiquetas, autopct='%1.1f%%', startangle=140,  colors=colores_mako)\n",
    "ax1.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax1.set_title('Grupo Masculino')\n",
    "\n",
    "# Gráfico del grupo B\n",
    "tamaños_B = tabla_contingencia_G.loc['B']\n",
    "ax2.pie(tamaños_B, labels=etiquetas, autopct='%1.1f%%', startangle=140,  colors=colores_mako)\n",
    "ax2.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax2.set_title('Grupo Femenino')\n",
    "\n",
    "# Título principal del gráfico\n",
    "fig.suptitle('Rotación según género', fontsize=16)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBSERVACIONES: Observamos que en el grupo A que son los empleados hombres de la empresa, la tasa de rotación es del 16.8% mientras que en el grupo B que son las mujeres empleadas, la tasa de rotación es del casi 15%, lo que significa que el los hombres tienen mas probabilidad de dejar la empresa que las mujeres, aunque la diferencia es poco significariva."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a9KV58+ZVdt3uZ95WZdeCtWHCb47K3QRgHdem2kqtp7ZS66H616asc8gBAACgthLIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAAKC2BfLOnTtHnTp1ltsGDhxYnJ8/f37xeevWraNZs2bRr1+/mDFjRs4mAwCrQa0HgBIN5OPHj49p06ZVbI888khx/NBDDy0+nnbaafHAAw/E3XffHWPHjo0PPvggDjnkkJxNBgBWg1oPACtXPzLacMMNK+1fdNFFsfnmm8dee+0Vc+bMiZtuuinuuOOO2HfffYvzI0eOjG7dusUzzzwTu+++e6ZWAwCrSq0HgGowh3zhwoXxxz/+MY499thiKNuECRNi0aJF0atXr4rXdO3aNTp16hTjxo1b6XUWLFgQc+fOrbQBAPmp9QBQooH8/vvvj9mzZ8fRRx9d7E+fPj0aNmwYLVu2rPS6tm3bFudWZvjw4dGiRYuKrWPHjmu97QDAV1PrAaBEA3kastanT5/o0KHD17rOkCFDiiFw5dvUqVOrrI0AwJpT6wGghOaQl3v33XfjH//4R9x7770Vx9q1a1cMbUs96Uv3nKeVV9O5lWnUqFGxAQClQ60HgBK9Q54WcGnTpk0ceOCBFce6d+8eDRo0iDFjxlQcmzRpUkyZMiV69OiRqaUAwJpQ6wGgBO+QL1mypCjS/fv3j/r1///mpDlhAwYMiEGDBkWrVq2iefPmcfLJJxcF2qqrAFB9qPUAUKKBPA1fSz3hacXVZV1++eVRt27d6NevX7Giau/eveOaa67J0k4AYM2o9QCwYnXKysrKogZLj0JJPfBp0ZfU815Vup95W5VdC9aGCb85KncTgHVcm2ortZ7aSq2H6l+bSmIOOQAAANQ2AjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQG0M5O+//3786Ec/itatW0eTJk1iu+22i+eff77ifFlZWQwdOjTat29fnO/Vq1e89dZbWdsMAKw6tR4ASjCQz5o1K/bYY49o0KBBPPjgg/H666/HpZdeGhtssEHFa0aMGBFXXXVVXHfddfHss89G06ZNo3fv3jF//vycTQcAVoFaDwArVz8yuvjii6Njx44xcuTIimObbrpppR7zK664Is4+++w4+OCDi2O33XZbtG3bNu6///44/PDDs7QbAFg1aj0AlOgd8r/+9a+xyy67xKGHHhpt2rSJnXbaKW688caK85MnT47p06cXQ9fKtWjRInbbbbcYN27cCq+5YMGCmDt3bqUNAMhDrQeAEg3k//rXv+Laa6+NLl26xEMPPRQnnHBCnHLKKXHrrbcW51OBTlIv+dLSfvm5ZQ0fPrwo5OVb6pUHAPJQ6wGgRAP5kiVLYuedd44LL7yw6DE//vjj47jjjivmkK2pIUOGxJw5cyq2qVOnVmmbAYBVp9YDQIkG8rSa6tZbb13pWLdu3WLKlCnF5+3atSs+zpgxo9Jr0n75uWU1atQomjdvXmkDAPJQ6wGgRAN5WnV10qRJlY69+eabsckmm1Qs+pKK8ZgxYyrOp3liaQXWHj16rPP2AgCrR60HgBJdZf20006Lb33rW8Uwth/+8Ifx3HPPxQ033FBsSZ06deLUU0+NCy64oJh7lor2OeecEx06dIi+ffvmbDoAsArUegAo0UC+6667xn333VfMBTv//POLIpwefXLkkUdWvGbw4MExb968Ys7Z7Nmzo2fPnjF69Oho3LhxzqYDAKtArQeAlatTlh4AWoOlYW9pBda06EtVzjHrfuZtVXYtWBsm/Oao3E0A1nFtqq3UemortR6qf23KOoccAAAAaiuBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIIP6a/qF8+bNi7Fjx8aUKVNi4cKFlc6dcsopVdE2ACAz9R4ASiyQv/jii3HAAQfE559/XhTqVq1axUcffRTrrbdetGnTRoEGgBpAvQeAEhyyftppp8VBBx0Us2bNiiZNmsQzzzwT7777bnTv3j0uueSSqm8lALDOqfcAUIKBfOLEiXH66adH3bp1o169erFgwYLo2LFjjBgxIn7xi1+s8nXOPffcqFOnTqWta9euFefnz58fAwcOjNatW0ezZs2iX79+MWPGjDVpMgCQod6r9QBQxYG8QYMGRXFO0pC1NK8sadGiRUydOnW1rrXNNtvEtGnTKrYnn3yyUs/8Aw88EHfffXcxf+2DDz6IQw45ZE2aDABkqvdqPQBU4RzynXbaKcaPHx9dunSJvfbaK4YOHVrMKfvDH/4Q22677Wpdq379+tGuXbvljs+ZMyduuummuOOOO2Lfffctjo0cOTK6detWDJnbfffd16TpAMA6rvdqPQBU4R3yCy+8MNq3b198PmzYsNhggw3ihBNOiA8//DBuuOGG1brWW2+9FR06dIjNNtssjjzyyIre9wkTJsSiRYuiV69eFa9NQ9w6deoU48aNW+n10nC6uXPnVtoAgHz1Xq0HgCq8Q77LLrtUfJ6GsI0ePXpNLhO77bZb3HLLLbHVVlsVQ9jOO++8+Pa3vx2vvvpqTJ8+PRo2bBgtW7as9DVt27Ytzq3M8OHDi+sAAF9PVdR7tR4A1sJzyKtCnz59Kj7ffvvti6K9ySabxJ/+9KdiNdc1MWTIkBg0aFDFfuo1TwvQAADrnloPAFUQyHfeeecYM2ZMMVwtzSlLq6SuzAsvvBBrIvWQb7nllvH222/Hd77znVi4cGHMnj27Us95Wnl1RfPQyjVq1KjYAIDVt7brvVoPAGsQyA8++OCK4te3b99YGz777LN455134sc//nHxjNO0umv6oyA9AiWZNGlSMe+sR48ea+X9AaC2W9v1Xq0HgDUI5L/61a9W+PnXccYZZ8RBBx1UDF1LjzlJ103POT3iiCOKR6oMGDCgGJLWqlWraN68eZx88slFgbbqKgCsHVVd79V6AKjiOeTpEShLliwp5oEt7dlnny2K7NKLwHyZ9957ryjIH3/8cWy44YbRs2fP4jEn6fPk8ssvL55/mnrN04qqvXv3jmuuuWZNmgwAZKj3aj0AVHEgHzhwYAwePHi5Av3+++/HxRdfXBTqVTFq1KgvPd+4ceO4+uqriw0AWLeqot6r9QBQxc8hf/3114tFX5aVFn9J5wCA6k+9B4ASDORpsZe0Auqy0vNF69fP+iQ1AKCKqPcAUIKBfP/99y+eATpnzpyKY+mRJb/4xS+KR5gAANWfeg8Aa9cadW9fcsklseeeexYrpqZha8nEiROjbdu28Yc//KGq2wgAZKDeA0AJBvKNNtooXn755bj99tvjpZdeiiZNmsQxxxxTrKKanicKAFR/6j0ArF1rPAGsadOmcfzxx1dtawCAkqLeA0AJBvK33norHnvssZg5c2bxjNKlDR06tCraBgBkpt4DQIkF8htvvDFOOOGE+MY3vhHt2rWLOnXqVJxLnyvQAFD9qfcAUIKB/IILLohhw4bFWWedVfUtAgBKgnoPACX42LNZs2bFoYceWvWtAQBKhnoPACUYyFNxfvjhh6u+NQBAyVDvAaAEh6xvscUWcc4558QzzzwT22233XKPPjnllFOqqn0AQCbqPQCUYCC/4YYbolmzZjF27NhiW1pa5EWBBoDqT70HgBIM5JMnT676lgAAJUW9B4ASnENebuHChTFp0qT44osvqq5FAEBJUe8BoIQC+eeffx4DBgyI9dZbL7bZZpuYMmVKcfzkk0+Oiy66qKrbCABkoN4DQAkG8iFDhsRLL70Ujz/+eDRu3LjieK9eveKuu+6qyvYBAJmo9wBQgnPI77///qIQ77777sWiLuVS7/k777xTle0DADJR7wGgBO+Qf/jhh9GmTZvljs+bN69SwQYAqi/1HgBKMJDvsssu8fe//71iv7wo//73v48ePXpUXesAgGzUewAowSHrF154YfTp0ydef/31YsXVK6+8svj86aefXu45pQBA9aTeA0AJ3iHv2bNnTJw4sSjO2223XTz88MPFkLZx48ZF9+7dq76VAMA6p94DQAneIU8233zzuPHGG6u2NQBASVHvAaDEAnn5c0hXplOnTmvaHgCgRKj3AFCCgbxz585furrq4sWLv06bAIASoN4DQAkG8hdffLHS/qJFi4pjl112WQwbNqyq2gYAZKTeA0AJBvIddthhhY9G6dChQ/zmN7+JQw45pCraBgBkpN4DQAmusr4yW221VYwfP74qLwkAlBj1HgAy3iGfO3dupf2ysrKYNm1anHvuudGlS5cqahoAkJN6DwAlGMhbtmy53CIvqUh37NgxRo0aVVVtAwAyUu8BoAQD+aOPPlqpQNetWzc23HDD2GKLLaJ+/TV+tDkAUELUewBYu9aomu69995V3xIAoKSo9wBQgou6DR8+PG6++ebljqdjF198cVW0CwDITL0HgBIM5Ndff3107dp1uePbbLNNXHfddVXRLgAgM/UeAEowkE+fPj3at2+/3PE0ryytvgoAVH/qPQCUYCBPq6s+9dRTyx1Pxzp06FAV7QIAMlPvAaAEF3U77rjj4tRTT41FixbFvvvuWxwbM2ZMDB48OE4//fSqbiMAkIF6DwAlGMjPPPPM+Pjjj+PEE0+MhQsXFscaN24cZ511VgwZMqSq2wgAZKDeA0AJBvL0TNK0uuo555wTb7zxRjRp0iS6dOkSjRo1qvoWAgBZqPcAUIJzyJde7OWTTz6JzTffvCjOZWVlVdcyAKAkqPcAUEKBPA1f22+//WLLLbeMAw44oGKl1QEDBqzxnLKLLrqo6IlPc9XKzZ8/PwYOHBitW7eOZs2aRb9+/WLGjBlrdH0AYPWo9wBQgoH8tNNOiwYNGsSUKVNivfXWqzh+2GGHxejRo1f7euPHjy+edbr99tsv9z4PPPBA3H333TF27Nj44IMP4pBDDlmTJgMAq0m9B4ASDOQPP/xwMads4403rnQ8zSt79913V+tan332WRx55JFx4403xgYbbFBxfM6cOXHTTTfFZZddVqzs2r179xg5cmQ8/fTT8cwzz6xJswGA1aDeA0AJBvJ58+ZV6ikvl+aXre5CL2mI2oEHHhi9evWqdHzChAnFY1aWPt61a9fo1KlTjBs3bqXXW7BgQcydO7fSBgDUnHqv1gNQqwP5t7/97bjtttsq9tNcsCVLlsSIESNin332WeXrjBo1Kl544YUYPnz4CheQadiwYbRs2bLS8bZt2xbnViZdq0WLFhVbx44dV7k9AEDp13u1HoBa/dizVIjTIi/PP/988VzSwYMHx2uvvVb0mD/11FOrdI2pU6fGz372s3jkkUeKZ5pWlfRc1EGDBlXsp15zhRoAak69V+sBqNV3yLfddtt48803o2fPnnHwwQcXQ9rS4isvvvhi8UiUVZGGqM2cOTN23nnnqF+/frGlhVyuuuqq4vPUM56K/+zZsyt9XVp1tV27diu9bhpC17x580obAFBz6r1aD0CtvUOe5nl997vfjeuuuy5++ctfrvEbpx73V155pdKxY445ppg3dtZZZxU93Wll1zFjxhSPP0kmTZpUrPTao0ePNX5fAOCrqfcAUIKBPBXNl19++Wu/8frrr1/0vC+tadOmxTNIy4+n55ymIWmtWrUqer9PPvnkojjvvvvuX/v9AYCVU+8BoESHrP/oRz8qHlGytl1++eXxve99r+gx33PPPYuha/fee+9af18AQL0HgJJc1O2LL76Im2++Of7xj38UzwtNPd1LS88SXROPP/54pf20+MvVV19dbADAuqXeA0AJBfJ//etf0blz53j11VeLxVmStNjL0tIjUQCA6ku9B4ASDORdunSJadOmxWOPPVbsH3bYYcUqqWmFVACgZlDvAaAE55CXlZVV2n/wwQeLR6AAADWHeg8AJbyo28oKNgBQ86j3AFACgTzNF1t2zpg5ZABQs6j3AFCCc8hTD/nRRx8djRo1Kvbnz58fP/3pT5dbddWjSgCg+lLvAaAEA3n//v2Xez4pAFCzqPcAUIKBfOTIkWuvJQBASVDvAaAaLOoGAAAArBmBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAAKC2BfJrr702tt9++2jevHmx9ejRIx588MGK8/Pnz4+BAwdG69ato1mzZtGvX7+YMWNGziYDAKtBrQeAEg3kG2+8cVx00UUxYcKEeP7552PfffeNgw8+OF577bXi/GmnnRYPPPBA3H333TF27Nj44IMP4pBDDsnZZABgNaj1ALBy9SOjgw46qNL+sGHDip70Z555pijgN910U9xxxx1F8U5GjhwZ3bp1K87vvvvumVoNAKwqtR4AqsEc8sWLF8eoUaNi3rx5xXC21JO+aNGi6NWrV8VrunbtGp06dYpx48at9DoLFiyIuXPnVtoAgPzUegAosUD+yiuvFHPGGjVqFD/96U/jvvvui6233jqmT58eDRs2jJYtW1Z6fdu2bYtzKzN8+PBo0aJFxdaxY8d18F0AACuj1gNAiQbyrbbaKiZOnBjPPvtsnHDCCdG/f/94/fXX1/h6Q4YMiTlz5lRsU6dOrdL2AgCrR60HgBKcQ56knvEtttii+Lx79+4xfvz4uPLKK+Owww6LhQsXxuzZsyv1nKeVV9u1a7fS66Xe97QBAKVBrQeAEr1DvqwlS5YUc8NSwW7QoEGMGTOm4tykSZNiypQpxbwzAKB6UusBoATukKchZ3369CkWb/n000+LVVYff/zxeOihh4o5YQMGDIhBgwZFq1atimeXnnzyyUWBtuoqAFQPaj0AlGggnzlzZhx11FExbdq0oihvv/32RYH+zne+U5y//PLLo27dutGvX7+iJ713795xzTXX5GwyALAa1HoAWLk6ZWVlZVGDpUehpD8A0qIvqee9qnQ/87YquxasDRN+c1TuJgDruDbVVmo9tZVaD9W/NpXcHHIAAACoDQRyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAKhtgXz48OGx6667xvrrrx9t2rSJvn37xqRJkyq9Zv78+TFw4MBo3bp1NGvWLPr16xczZszI1mYAYNWp9QBQooF87NixRQF+5pln4pFHHolFixbF/vvvH/Pmzat4zWmnnRYPPPBA3H333cXrP/jggzjkkENyNhsAWEVqPQCsXP3IaPTo0ZX2b7nllqL3fMKECbHnnnvGnDlz4qabboo77rgj9t133+I1I0eOjG7duhWFfffdd8/UcgBgVaj1AFBN5pCnopy0atWq+JiKdepJ79WrV8VrunbtGp06dYpx48at8BoLFiyIuXPnVtoAgNKg1gNACQbyJUuWxKmnnhp77LFHbLvttsWx6dOnR8OGDaNly5aVXtu2bdvi3MrmqrVo0aJi69ix4zppPwDw5dR6ACjRQJ7ml7366qsxatSor3WdIUOGFL3v5dvUqVOrrI0AwJpT6wGghOaQlzvppJPib3/7WzzxxBOx8cYbVxxv165dLFy4MGbPnl2p5zytvJrOrUijRo2KDQAoHWo9AJTYHfKysrKiQN93333x6KOPxqabblrpfPfu3aNBgwYxZsyYimPpUSlTpkyJHj16ZGgxALA61HoAKNE75GnoWlpV9S9/+UvxfNLyuWJpPliTJk2KjwMGDIhBgwYVi780b948Tj755KJAW3UVAEqfWg8AJRrIr7322uLj3nvvXel4etzJ0UcfXXx++eWXR926daNfv37Fqqq9e/eOa665Jkt7AYDVo9YDQIkG8jSM7as0btw4rr766mIDAKoXtR4AqsEq6wAAAFCbCOQAAACQgUAOAAAAGQjkAAAAkIFADgAAABkI5AAAAJCBQA4AAAAZCOQAAACQgUAOAAAAGQjkAAAAkEH9HG8KUK77mbflbgJ8qQm/OSp3EwCAGsodcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAM6ud4UwAAoPbofuZtuZsAX2rCb46KHNwhBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAKhtgfyJJ56Igw46KDp06BB16tSJ+++/v9L5srKyGDp0aLRv3z6aNGkSvXr1irfeeitbewGA1aPWA0CJBvJ58+bFDjvsEFdfffUKz48YMSKuuuqquO666+LZZ5+Npk2bRu/evWP+/PnrvK0AwOpT6wFg5epHRn369Cm2FUk95ldccUWcffbZcfDBBxfHbrvttmjbtm3Ru3744Yev49YCAKtLrQeAajiHfPLkyTF9+vRi6Fq5Fi1axG677Rbjxo1b6dctWLAg5s6dW2kDAEqPWg9AbVeygTwV6CT1ki8t7ZefW5Hhw4cXxbx869ix41pvKwCw+tR6AGq7kg3ka2rIkCExZ86cim3q1Km5mwQAVCG1HoCaomQDebt27YqPM2bMqHQ87ZefW5FGjRpF8+bNK20AQOlR6wGo7Uo2kG+66aZFMR4zZkzFsTRHLK3A2qNHj6xtAwC+PrUegNou6yrrn332Wbz99tuVFneZOHFitGrVKjp16hSnnnpqXHDBBdGlS5eiaJ9zzjnFc0z79u2bs9kAwCpS6wGgRAP5888/H/vss0/F/qBBg4qP/fv3j1tuuSUGDx5cPL/0+OOPj9mzZ0fPnj1j9OjR0bhx44ytBgBWlVoPACUayPfee+/iGaQrU6dOnTj//POLDQCoftR6AKiGc8gBAACgJhPIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyqBaB/Oqrr47OnTtH48aNY7fddovnnnsud5MAgCqm3gNQ25R8IL/rrrti0KBB8atf/SpeeOGF2GGHHaJ3794xc+bM3E0DAKqIeg9AbVTygfyyyy6L4447Lo455pjYeuut47rrrov11lsvbr755txNAwCqiHoPQG1UP0rYwoULY8KECTFkyJCKY3Xr1o1evXrFuHHjVvg1CxYsKLZyc+bMKT7OnTu3Stu2eMF/qvR6UNWq+v/za4vfJWrj71L5NcvKyqr82tXR6tZ7tR6qV61P/D5R236f5q5irS/pQP7RRx/F4sWLo23btpWOp/3/+7//W+HXDB8+PM4777zljnfs2HGttRNKUYvf/jR3E6BGWJu/S59++mm0aNEiarvVrfdqPfw/aj2U/u/TV9X6kg7kayL1rqc5aOWWLFkSn3zySbRu3Trq1KmTtW18eQ9S+kNq6tSp0bx589zNgWrL71L1kHrLU4Hu0KFD7qZUS2p99eTfJ6gafpdqVq0v6UD+jW98I+rVqxczZsyodDztt2vXboVf06hRo2JbWsuWLddqO6k66R8V/7DA1+d3qfS5M77m9V6tr978+wRVw+9Szaj1Jb2oW8OGDaN79+4xZsyYSr3gab9Hjx5Z2wYAVA31HoDaqqTvkCdpSFr//v1jl112iW9+85txxRVXxLx584pVWAGAmkG9B6A2KvlAfthhh8WHH34YQ4cOjenTp8eOO+4Yo0ePXm7hF6q3NPQwPXt22SGIwOrxu0R1pd7XfP59gqrhd6lmqVPmmSsAAACwzpX0HHIAAACoqQRyAAAAyEAgBwAAgAwEcgAAAMhAIGedOfroo6NOnTpx0UUXVTp+//33F8eBlUvrb/bq1St69+693LlrrrkmWrZsGe+9916WtgGUU+thzan1tZNAzjrVuHHjuPjii2PWrFm5mwLVSvpDduTIkfHss8/G9ddfX3F88uTJMXjw4Pjtb38bG2+8cdY2AiRqPawZtb52EshZp1KvX7t27WL48OErfc2f//zn2GabbYpnK3bu3DkuvfTSddpGKFUdO3aMK6+8Ms4444yiOKee9AEDBsT+++8fO+20U/Tp0yeaNWtWPLf5xz/+cXz00UcVX3vPPffEdtttF02aNInWrVsXv4vz5s3L+v0ANZNaD2tOra99BHLWqXr16sWFF15Y9PCtaMjNhAkT4oc//GEcfvjh8corr8S5554b55xzTtxyyy1Z2gulpn///rHffvvFscceG7/73e/i1VdfLXrR991336JQP//88zF69OiYMWNG8buUTJs2LY444ojia9544414/PHH45BDDimKPEBVU+vh61Hra5c6Zf4rsQ7nlc2ePbuYR9ajR4/Yeuut46abbir2f/CDHxT/YBx55JHx4YcfxsMPP1zxdWmIzt///vd47bXXsrYfSsXMmTOLO0uffPJJcZcpFep//vOf8dBDD1W8Jv0RnHrZJ02aFJ999ll07949/v3vf8cmm2ySte1AzabWQ9VQ62sPd8jJIs0tu/XWW4sevKWl/T322KPSsbT/1ltvxeLFi9dxK6E0tWnTJn7yk59Et27dom/fvvHSSy/FY489VgxhK9+6du1avPadd96JHXbYoehpT8PYDj300LjxxhvN7QTWOrUe1pxaX3sI5GSx5557FitIDhkyJHdToFqqX79+sSWpV/yggw6KiRMnVtrSH7fpdy0NH33kkUfiwQcfLO5WpWGkW221VTE3DWBtUevh61Hra4f/918YMkiPRNlxxx2LfyzKpV7Ap556qtLr0v6WW25Z/EMDLG/nnXcuhrOlhZHKC/eKVm5Nd6DSNnTo0GI423333ReDBg1a5+0Fag+1HqqGWl9zuUNONmlITZpHdtVVV1UcO/3002PMmDHx61//Ot58881iqFtazCKtNAms2MCBA4s5Zmkxl/HjxxdD19Ics2OOOaYY/pken5IWWEqLwEyZMiXuvffeYv5m+qMYYG1S66FqqPU1l0BOVueff34sWbKkUu/fn/70pxg1alRsu+22Re9eek1aJAZYsQ4dOhR3l1JBTo9FSX8An3rqqdGyZcuoW7duNG/ePJ544ok44IADijtQZ599dvGIofToFIC1Ta2Hr0+tr7mssg4AAAAZuEMOAAAAGQjkAAAAkIFADgAAABkI5AAAAJCBQA4AAAAZCOQAAACQgUAOAAAAGQjkwDpTVlYWl112WTz//PO5mwIArCXqPaw6gRyoUueee27suOOOKzw3fPjwGD16dOywww7rvF0AQNVR76FqCORQTR199NFRp06dYmvQoEFsuummMXjw4Jg/f/4qff3jjz9efO3s2bOrtF1nnHFGjBkzZrnjTzzxRNxzzz3FltoLAHw19R5qtvq5GwCsue9+97sxcuTIWLRoUUyYMCH69+9fFN2LL744W5uaNWtWbMvac88944UXXsjSJgCoztR7qLncIYdqrFGjRtGuXbvo2LFj9O3bN3r16hWPPPJIcW7BggVxyimnRJs2baJx48bRs2fPGD9+fHHu3//+d+yzzz7F5xtssEFR1FMPfJKGmKXXtmzZMlq3bh3f+9734p133qn0vu+9914cccQR0apVq2jatGnssssu8eyzz65wCNuSJUvi/PPPj4033rhobzqX3qNcakt6/3vvvbdo03rrrVcMcRs3btw6+AkCQOlT76HmEsihhnj11Vfj6aefjoYNGxb7aTjbn//857j11luLnuotttgievfuHZ988klR0NO5ZNKkSTFt2rS48sori/158+bFoEGDioVY0lC0unXrxg9+8IOi0CafffZZ7LXXXvH+++/HX//613jppZeK9yo/v6x03UsvvTQuueSSePnll4s2fP/734+33nqr0ut++ctfFsPfJk6cGFtuuWXxB8AXX3yxln9qAFC9qPdQw5QB1VL//v3L6tWrV9a0adOyRo0alaVf57p165bdc889ZZ999llZgwYNym6//faK1y9cuLCsQ4cOZSNGjCj2H3vsseJrZs2a9aXv8+GHHxave+WVV4r966+/vmz99dcv+/jjj1f4+l/96ldlO+ywQ8V+es9hw4ZVes2uu+5aduKJJxafT548ubj+73//+4rzr732WnHsjTfeWKOfDQDUFOo91GzukEM1loZ8pR7mNHwszSc75phjol+/fsWQszTPbI899qh4bVpY5Zvf/Ga88cYbX3rN1JOdeqs322yzaN68eXTu3Lk4PmXKlOJjer+ddtqpGL72VebOnRsffPBBpXYkaX/Zdmy//fYVn7dv3774OHPmzFX6OQBATabeQ81lUTeoxtJ8rjQ0Lbn55puLuVg33XRT7Lrrrmt8zYMOOig22WSTuPHGG6NDhw7F0LRtt902Fi5cWJxv0qRJrA1Lr8Sa5pglKxsWBwC1iXoPNZc75FBDpLlfv/jFL+Lss8+OzTffvJhb9tRTT1WcTz3oaZGXrbfeutgvn3u2ePHiitd8/PHHxRyzdI399tsvunXrFrNmzVquZzv1mqe5aV8l9binIr90O5K0X94OAGDVqfdQswjkUIMceuihUa9evbj22mvjhBNOiDPPPLNY4fT111+P4447Lj7//PMYMGBA8drUK556pv/2t7/Fhx9+WCzeklZgTSut3nDDDfH222/Ho48+Wiz4srQ0vC2t9JpWeU2F9l//+lexYMzKVklNbUiPZbnrrruK4v/zn/+8KPA/+9nP1snPBABqGvUeag5D1qEGqV+/fpx00kkxYsSImDx5cjEE7Mc//nF8+umnxaNKHnrooaIIJxtttFGcd955RcFMc9GOOuqouOWWW2LUqFHF41PSsLWtttoqrrrqqth7770r3iP1tD/88MNx+umnxwEHHFCsjJp6v6+++uoVtilda86cOcXr0xyx9Nq0WmuXLl3W2c8FAGoS9R5qjjppZbfcjQAAAIDaxpB1AAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAACIde//A57NVdS0/18bAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizamos los datos de las variables de la tabla de contingencia.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "#Gráfico de barras para grupo A.\n",
    "sns.barplot(x=tabla_contingencia_G.columns, y=tabla_contingencia_G.loc['A'], ax=axes[0])\n",
    "axes[0].set_title('Grupo A')\n",
    "axes[0].set_xlabel('Rotación')\n",
    "axes[0].set_ylabel('Frecuencia')\n",
    "axes[0].set_ylim(0, 90);\n",
    "\n",
    "#Gráfico de barras para grupo B.\n",
    "sns.barplot(x=tabla_contingencia_G.columns, y=tabla_contingencia_G.loc['B'], ax=axes[1])\n",
    "axes[1].set_title('Grupo B')\n",
    "axes[1].set_xlabel('Rotación')\n",
    "axes[1].set_ylabel('Frecuencia')\n",
    "axes[1].set_ylim(0, 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrition       0    1\n",
      "Group_gender          \n",
      "A             808  163\n",
      "B             547   96\n",
      "\n",
      "Chi-cuadrado: 0.8569492443636865\n",
      "p-valor: 0.3545938344382915\n",
      "\n",
      "No hay evidencia de una diferencia significativa en las tasas de rotación entre los grupos.\n"
     ]
    }
   ],
   "source": [
    "contingency_table_G = pd.crosstab(df['Group_gender'], df['attrition'])\n",
    "print(contingency_table_G)\n",
    "\n",
    "# Realizar la prueba de proporciones\n",
    "chi2, p_value, _, _ = chi2_contingency(contingency_table_G)\n",
    "\n",
    "print(f\"\\nChi-cuadrado: {chi2}\")\n",
    "print(f\"p-valor: {p_value}\")\n",
    "\n",
    "# Imprimir el resultado de la prueba\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"\\nHay una diferencia significativa en las tasas de rotacion entre el Grupo de A y el Grupo B.\")\n",
    "  \n",
    "else:\n",
    "    print(\"\\nNo hay evidencia de una diferencia significativa en las tasas de rotación entre los grupos.\")\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión: Dado que el p-valor (0.35) es mayor que el nivel de significancia alfa (0.05), aceptamos la hipótesis nula. Esto significa que no hay suficiente evidencia estadística para afirmar que existe una relación significativa entre el género de los empelados y la rotación en la empresa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por lo que la empresa  debería de continuar promoviendo la igualdad de género y revisar otras posibles áreas de mejora en la diversidad e inclusión."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B TESTING: RELACIONES INPERSONALES\n",
    "\n",
    "* HO: No hay una relación significativa entre la satisfacción con las relaciones interpersonales y la rotación de los empleados.\n",
    "\n",
    "* H1: Hay una relacion significativa entre la satisfacción con las relaciones interpersonales y la rotación de los empleados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "relationshipsatisfaction\n",
       "3    504\n",
       "4    468\n",
       "2    339\n",
       "1    303\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscamos en la columna 'RelationshipSatisfaction' los valores únicos para encontrar a los miembros de cada grupo de control.\n",
    "df['relationshipsatisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group_relation\n",
       "A    972\n",
       "B    642\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creamos una nueva columna llamada Group en la que agrupamos a los empleados en función de sus niveles de satisfacción con las relaciones interpersonales.\n",
    "df['Group_relation'] = np.where(df['relationshipsatisfaction'] >= 3, 'A', 'B')\n",
    "df['Group_relation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos a los empleados de los grupos A y B en función de si han dejado la empresa o no.\n",
    "attrition_A_RS = df[df['Group_relation'] == 'A']['attrition']\n",
    "attrition_B_RS = df[df['Group_relation'] == 'B']['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_relation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>84.67</td>\n",
       "      <td>15.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>82.87</td>\n",
       "      <td>17.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition          No    Yes\n",
       "Group_relation              \n",
       "A               84.67  15.33\n",
       "B               82.87  17.13"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculamos los porcentajes.\n",
    "tabla_contingencia_RS = pd.crosstab(df['Group_relation'], df['attrition'], normalize='index') * 100\n",
    "tabla_contingencia_RS.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos para los gráficos \n",
    "etiquetas = tabla_contingencia_RS.columns.tolist()\n",
    "\n",
    "# Obtener los colores de la paleta \"mako\"\n",
    "colores_mako = sns.color_palette(\"mako\", len(etiquetas))\n",
    "\n",
    "# Crear los subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Gráfico del grupo A\n",
    "tamaños_A = tabla_contingencia_RS.loc['A']\n",
    "ax1.pie(tamaños_A, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax1.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax1.set_title('Grupo A')\n",
    "\n",
    "# Gráfico del grupo B\n",
    "tamaños_B = tabla_contingencia_RS.loc['B']\n",
    "ax2.pie(tamaños_B, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax2.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax2.set_title('Grupo B')\n",
    "\n",
    "# Título principal del gráfico\n",
    "fig.suptitle('Satisfacción con las relaciones interpersonales', fontsize=16)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observaciones: Observamos que en el grupo A, donde los empleados tienen unos niveles de satisfacción altos, la tasa de rotación es del 15.3%, mientras que en el grupo B, la tasa de rotación es del 17.1%, lo que significa que el porcentaje de empleados que se han ido en el grupo A es menor al del grupo B, donde los niveles de satisfacción son más bajos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a9KV58+ZVdt3uZ95WZdeCtWHCb47K3QRgHdem2kqtp7ZS66H616asc8gBAACgthLIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAAKC2BfLOnTtHnTp1ltsGDhxYnJ8/f37xeevWraNZs2bRr1+/mDFjRs4mAwCrQa0HgBIN5OPHj49p06ZVbI888khx/NBDDy0+nnbaafHAAw/E3XffHWPHjo0PPvggDjnkkJxNBgBWg1oPACtXPzLacMMNK+1fdNFFsfnmm8dee+0Vc+bMiZtuuinuuOOO2HfffYvzI0eOjG7dusUzzzwTu+++e6ZWAwCrSq0HgGowh3zhwoXxxz/+MY499thiKNuECRNi0aJF0atXr4rXdO3aNTp16hTjxo1b6XUWLFgQc+fOrbQBAPmp9QBQooH8/vvvj9mzZ8fRRx9d7E+fPj0aNmwYLVu2rPS6tm3bFudWZvjw4dGiRYuKrWPHjmu97QDAV1PrAaBEA3kastanT5/o0KHD17rOkCFDiiFw5dvUqVOrrI0AwJpT6wGghOaQl3v33XfjH//4R9x7770Vx9q1a1cMbUs96Uv3nKeVV9O5lWnUqFGxAQClQ60HgBK9Q54WcGnTpk0ceOCBFce6d+8eDRo0iDFjxlQcmzRpUkyZMiV69OiRqaUAwJpQ6wGgBO+QL1mypCjS/fv3j/r1///mpDlhAwYMiEGDBkWrVq2iefPmcfLJJxcF2qqrAFB9qPUAUKKBPA1fSz3hacXVZV1++eVRt27d6NevX7Giau/eveOaa67J0k4AYM2o9QCwYnXKysrKogZLj0JJPfBp0ZfU815Vup95W5VdC9aGCb85KncTgHVcm2ortZ7aSq2H6l+bSmIOOQAAANQ2AjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQAYCOQAAAGQgkAMAAEAGAjkAAABkIJADAABABgI5AAAAZCCQAwAAQG0M5O+//3786Ec/itatW0eTJk1iu+22i+eff77ifFlZWQwdOjTat29fnO/Vq1e89dZbWdsMAKw6tR4ASjCQz5o1K/bYY49o0KBBPPjgg/H666/HpZdeGhtssEHFa0aMGBFXXXVVXHfddfHss89G06ZNo3fv3jF//vycTQcAVoFaDwArVz8yuvjii6Njx44xcuTIimObbrpppR7zK664Is4+++w4+OCDi2O33XZbtG3bNu6///44/PDDs7QbAFg1aj0AlOgd8r/+9a+xyy67xKGHHhpt2rSJnXbaKW688caK85MnT47p06cXQ9fKtWjRInbbbbcYN27cCq+5YMGCmDt3bqUNAMhDrQeAEg3k//rXv+Laa6+NLl26xEMPPRQnnHBCnHLKKXHrrbcW51OBTlIv+dLSfvm5ZQ0fPrwo5OVb6pUHAPJQ6wGgRAP5kiVLYuedd44LL7yw6DE//vjj47jjjivmkK2pIUOGxJw5cyq2qVOnVmmbAYBVp9YDQIkG8rSa6tZbb13pWLdu3WLKlCnF5+3atSs+zpgxo9Jr0n75uWU1atQomjdvXmkDAPJQ6wGgRAN5WnV10qRJlY69+eabsckmm1Qs+pKK8ZgxYyrOp3liaQXWHj16rPP2AgCrR60HgBJdZf20006Lb33rW8Uwth/+8Ifx3HPPxQ033FBsSZ06deLUU0+NCy64oJh7lor2OeecEx06dIi+ffvmbDoAsArUegAo0UC+6667xn333VfMBTv//POLIpwefXLkkUdWvGbw4MExb968Ys7Z7Nmzo2fPnjF69Oho3LhxzqYDAKtArQeAlatTlh4AWoOlYW9pBda06EtVzjHrfuZtVXYtWBsm/Oao3E0A1nFtqq3UemortR6qf23KOoccAAAAaiuBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIIP6a/qF8+bNi7Fjx8aUKVNi4cKFlc6dcsopVdE2ACAz9R4ASiyQv/jii3HAAQfE559/XhTqVq1axUcffRTrrbdetGnTRoEGgBpAvQeAEhyyftppp8VBBx0Us2bNiiZNmsQzzzwT7777bnTv3j0uueSSqm8lALDOqfcAUIKBfOLEiXH66adH3bp1o169erFgwYLo2LFjjBgxIn7xi1+s8nXOPffcqFOnTqWta9euFefnz58fAwcOjNatW0ezZs2iX79+MWPGjDVpMgCQod6r9QBQxYG8QYMGRXFO0pC1NK8sadGiRUydOnW1rrXNNtvEtGnTKrYnn3yyUs/8Aw88EHfffXcxf+2DDz6IQw45ZE2aDABkqvdqPQBU4RzynXbaKcaPHx9dunSJvfbaK4YOHVrMKfvDH/4Q22677Wpdq379+tGuXbvljs+ZMyduuummuOOOO2Lfffctjo0cOTK6detWDJnbfffd16TpAMA6rvdqPQBU4R3yCy+8MNq3b198PmzYsNhggw3ihBNOiA8//DBuuOGG1brWW2+9FR06dIjNNtssjjzyyIre9wkTJsSiRYuiV69eFa9NQ9w6deoU48aNW+n10nC6uXPnVtoAgHz1Xq0HgCq8Q77LLrtUfJ6GsI0ePXpNLhO77bZb3HLLLbHVVlsVQ9jOO++8+Pa3vx2vvvpqTJ8+PRo2bBgtW7as9DVt27Ytzq3M8OHDi+sAAF9PVdR7tR4A1sJzyKtCnz59Kj7ffvvti6K9ySabxJ/+9KdiNdc1MWTIkBg0aFDFfuo1TwvQAADrnloPAFUQyHfeeecYM2ZMMVwtzSlLq6SuzAsvvBBrIvWQb7nllvH222/Hd77znVi4cGHMnj27Us95Wnl1RfPQyjVq1KjYAIDVt7brvVoPAGsQyA8++OCK4te3b99YGz777LN455134sc//nHxjNO0umv6oyA9AiWZNGlSMe+sR48ea+X9AaC2W9v1Xq0HgDUI5L/61a9W+PnXccYZZ8RBBx1UDF1LjzlJ103POT3iiCOKR6oMGDCgGJLWqlWraN68eZx88slFgbbqKgCsHVVd79V6AKjiOeTpEShLliwp5oEt7dlnny2K7NKLwHyZ9957ryjIH3/8cWy44YbRs2fP4jEn6fPk8ssvL55/mnrN04qqvXv3jmuuuWZNmgwAZKj3aj0AVHEgHzhwYAwePHi5Av3+++/HxRdfXBTqVTFq1KgvPd+4ceO4+uqriw0AWLeqot6r9QBQxc8hf/3114tFX5aVFn9J5wCA6k+9B4ASDORpsZe0Auqy0vNF69fP+iQ1AKCKqPcAUIKBfP/99y+eATpnzpyKY+mRJb/4xS+KR5gAANWfeg8Aa9cadW9fcsklseeeexYrpqZha8nEiROjbdu28Yc//KGq2wgAZKDeA0AJBvKNNtooXn755bj99tvjpZdeiiZNmsQxxxxTrKKanicKAFR/6j0ArF1rPAGsadOmcfzxx1dtawCAkqLeA0AJBvK33norHnvssZg5c2bxjNKlDR06tCraBgBkpt4DQIkF8htvvDFOOOGE+MY3vhHt2rWLOnXqVJxLnyvQAFD9qfcAUIKB/IILLohhw4bFWWedVfUtAgBKgnoPACX42LNZs2bFoYceWvWtAQBKhnoPACUYyFNxfvjhh6u+NQBAyVDvAaAEh6xvscUWcc4558QzzzwT22233XKPPjnllFOqqn0AQCbqPQCUYCC/4YYbolmzZjF27NhiW1pa5EWBBoDqT70HgBIM5JMnT676lgAAJUW9B4ASnENebuHChTFp0qT44osvqq5FAEBJUe8BoIQC+eeffx4DBgyI9dZbL7bZZpuYMmVKcfzkk0+Oiy66qKrbCABkoN4DQAkG8iFDhsRLL70Ujz/+eDRu3LjieK9eveKuu+6qyvYBAJmo9wBQgnPI77///qIQ77777sWiLuVS7/k777xTle0DADJR7wGgBO+Qf/jhh9GmTZvljs+bN69SwQYAqi/1HgBKMJDvsssu8fe//71iv7wo//73v48ePXpUXesAgGzUewAowSHrF154YfTp0ydef/31YsXVK6+8svj86aefXu45pQBA9aTeA0AJ3iHv2bNnTJw4sSjO2223XTz88MPFkLZx48ZF9+7dq76VAMA6p94DQAneIU8233zzuPHGG6u2NQBASVHvAaDEAnn5c0hXplOnTmvaHgCgRKj3AFCCgbxz585furrq4sWLv06bAIASoN4DQAkG8hdffLHS/qJFi4pjl112WQwbNqyq2gYAZKTeA0AJBvIddthhhY9G6dChQ/zmN7+JQw45pCraBgBkpN4DQAmusr4yW221VYwfP74qLwkAlBj1HgAy3iGfO3dupf2ysrKYNm1anHvuudGlS5cqahoAkJN6DwAlGMhbtmy53CIvqUh37NgxRo0aVVVtAwAyUu8BoAQD+aOPPlqpQNetWzc23HDD2GKLLaJ+/TV+tDkAUELUewBYu9aomu69995V3xIAoKSo9wBQgou6DR8+PG6++ebljqdjF198cVW0CwDITL0HgBIM5Ndff3107dp1uePbbLNNXHfddVXRLgAgM/UeAEowkE+fPj3at2+/3PE0ryytvgoAVH/qPQCUYCBPq6s+9dRTyx1Pxzp06FAV7QIAMlPvAaAEF3U77rjj4tRTT41FixbFvvvuWxwbM2ZMDB48OE4//fSqbiMAkIF6DwAlGMjPPPPM+Pjjj+PEE0+MhQsXFscaN24cZ511VgwZMqSq2wgAZKDeA0AJBvL0TNK0uuo555wTb7zxRjRp0iS6dOkSjRo1qvoWAgBZqPcAUIJzyJde7OWTTz6JzTffvCjOZWVlVdcyAKAkqPcAUEKBPA1f22+//WLLLbeMAw44oGKl1QEDBqzxnLKLLrqo6IlPc9XKzZ8/PwYOHBitW7eOZs2aRb9+/WLGjBlrdH0AYPWo9wBQgoH8tNNOiwYNGsSUKVNivfXWqzh+2GGHxejRo1f7euPHjy+edbr99tsv9z4PPPBA3H333TF27Nj44IMP4pBDDlmTJgMAq0m9B4ASDOQPP/xwMads4403rnQ8zSt79913V+tan332WRx55JFx4403xgYbbFBxfM6cOXHTTTfFZZddVqzs2r179xg5cmQ8/fTT8cwzz6xJswGA1aDeA0AJBvJ58+ZV6ikvl+aXre5CL2mI2oEHHhi9evWqdHzChAnFY1aWPt61a9fo1KlTjBs3bqXXW7BgQcydO7fSBgDUnHqv1gNQqwP5t7/97bjtttsq9tNcsCVLlsSIESNin332WeXrjBo1Kl544YUYPnz4CheQadiwYbRs2bLS8bZt2xbnViZdq0WLFhVbx44dV7k9AEDp13u1HoBa/dizVIjTIi/PP/988VzSwYMHx2uvvVb0mD/11FOrdI2pU6fGz372s3jkkUeKZ5pWlfRc1EGDBlXsp15zhRoAak69V+sBqNV3yLfddtt48803o2fPnnHwwQcXQ9rS4isvvvhi8UiUVZGGqM2cOTN23nnnqF+/frGlhVyuuuqq4vPUM56K/+zZsyt9XVp1tV27diu9bhpC17x580obAFBz6r1aD0CtvUOe5nl997vfjeuuuy5++ctfrvEbpx73V155pdKxY445ppg3dtZZZxU93Wll1zFjxhSPP0kmTZpUrPTao0ePNX5fAOCrqfcAUIKBPBXNl19++Wu/8frrr1/0vC+tadOmxTNIy4+n55ymIWmtWrUqer9PPvnkojjvvvvuX/v9AYCVU+8BoESHrP/oRz8qHlGytl1++eXxve99r+gx33PPPYuha/fee+9af18AQL0HgJJc1O2LL76Im2++Of7xj38UzwtNPd1LS88SXROPP/54pf20+MvVV19dbADAuqXeA0AJBfJ//etf0blz53j11VeLxVmStNjL0tIjUQCA6ku9B4ASDORdunSJadOmxWOPPVbsH3bYYcUqqWmFVACgZlDvAaAE55CXlZVV2n/wwQeLR6AAADWHeg8AJbyo28oKNgBQ86j3AFACgTzNF1t2zpg5ZABQs6j3AFCCc8hTD/nRRx8djRo1Kvbnz58fP/3pT5dbddWjSgCg+lLvAaAEA3n//v2Xez4pAFCzqPcAUIKBfOTIkWuvJQBASVDvAaAaLOoGAAAArBmBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAAKC2BfJrr702tt9++2jevHmx9ejRIx588MGK8/Pnz4+BAwdG69ato1mzZtGvX7+YMWNGziYDAKtBrQeAEg3kG2+8cVx00UUxYcKEeP7552PfffeNgw8+OF577bXi/GmnnRYPPPBA3H333TF27Nj44IMP4pBDDsnZZABgNaj1ALBy9SOjgw46qNL+sGHDip70Z555pijgN910U9xxxx1F8U5GjhwZ3bp1K87vvvvumVoNAKwqtR4AqsEc8sWLF8eoUaNi3rx5xXC21JO+aNGi6NWrV8VrunbtGp06dYpx48at9DoLFiyIuXPnVtoAgPzUegAosUD+yiuvFHPGGjVqFD/96U/jvvvui6233jqmT58eDRs2jJYtW1Z6fdu2bYtzKzN8+PBo0aJFxdaxY8d18F0AACuj1gNAiQbyrbbaKiZOnBjPPvtsnHDCCdG/f/94/fXX1/h6Q4YMiTlz5lRsU6dOrdL2AgCrR60HgBKcQ56knvEtttii+Lx79+4xfvz4uPLKK+Owww6LhQsXxuzZsyv1nKeVV9u1a7fS66Xe97QBAKVBrQeAEr1DvqwlS5YUc8NSwW7QoEGMGTOm4tykSZNiypQpxbwzAKB6UusBoATukKchZ3369CkWb/n000+LVVYff/zxeOihh4o5YQMGDIhBgwZFq1atimeXnnzyyUWBtuoqAFQPaj0AlGggnzlzZhx11FExbdq0oihvv/32RYH+zne+U5y//PLLo27dutGvX7+iJ713795xzTXX5GwyALAa1HoAWLk6ZWVlZVGDpUehpD8A0qIvqee9qnQ/87YquxasDRN+c1TuJgDruDbVVmo9tZVaD9W/NpXcHHIAAACoDQRyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAKhtgXz48OGx6667xvrrrx9t2rSJvn37xqRJkyq9Zv78+TFw4MBo3bp1NGvWLPr16xczZszI1mYAYNWp9QBQooF87NixRQF+5pln4pFHHolFixbF/vvvH/Pmzat4zWmnnRYPPPBA3H333cXrP/jggzjkkENyNhsAWEVqPQCsXP3IaPTo0ZX2b7nllqL3fMKECbHnnnvGnDlz4qabboo77rgj9t133+I1I0eOjG7duhWFfffdd8/UcgBgVaj1AFBN5pCnopy0atWq+JiKdepJ79WrV8VrunbtGp06dYpx48at8BoLFiyIuXPnVtoAgNKg1gNACQbyJUuWxKmnnhp77LFHbLvttsWx6dOnR8OGDaNly5aVXtu2bdvi3MrmqrVo0aJi69ix4zppPwDw5dR6ACjRQJ7ml7366qsxatSor3WdIUOGFL3v5dvUqVOrrI0AwJpT6wGghOaQlzvppJPib3/7WzzxxBOx8cYbVxxv165dLFy4MGbPnl2p5zytvJrOrUijRo2KDQAoHWo9AJTYHfKysrKiQN93333x6KOPxqabblrpfPfu3aNBgwYxZsyYimPpUSlTpkyJHj16ZGgxALA61HoAKNE75GnoWlpV9S9/+UvxfNLyuWJpPliTJk2KjwMGDIhBgwYVi780b948Tj755KJAW3UVAEqfWg8AJRrIr7322uLj3nvvXel4etzJ0UcfXXx++eWXR926daNfv37Fqqq9e/eOa665Jkt7AYDVo9YDQIkG8jSM7as0btw4rr766mIDAKoXtR4AqsEq6wAAAFCbCOQAAACQgUAOAAAAGQjkAAAAkIFADgAAABkI5AAAAJCBQA4AAAAZCOQAAACQQf0cbwoAANQe3c+8LXcT4EtN+M1RkYM75AAAAJCBQA4AAAAZCOQAAACQgUAOAAAAGQjkAAAAkIFADgAAABkI5AAAAJCBQA4AAAAZ1M/xpgDlup95W+4mwJea8JujcjcBAKih3CEHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAKhtgfyJJ56Igw46KDp06BB16tSJ+++/v9L5srKyGDp0aLRv3z6aNGkSvXr1irfeeitbewGA1aPWA0CJBvJ58+bFDjvsEFdfffUKz48YMSKuuuqquO666+LZZ5+Npk2bRu/evWP+/PnrvK0AwOpT6wFg5epHRn369Cm2FUk95ldccUWcffbZcfDBBxfHbrvttmjbtm3Ru3744Yev49YCAKtLrQeAajiHfPLkyTF9+vRi6Fq5Fi1axG677Rbjxo1b6dctWLAg5s6dW2kDAEqPWg9AbVeygTwV6CT1ki8t7ZefW5Hhw4cXxbx869ix41pvKwCw+tR6AGq7kg3ka2rIkCExZ86cim3q1Km5mwQAVCG1HoCaomQDebt27YqPM2bMqHQ87ZefW5FGjRpF8+bNK20AQOlR6wGo7Uo2kG+66aZFMR4zZkzFsTRHLK3A2qNHj6xtAwC+PrUegNou6yrrn332Wbz99tuVFneZOHFitGrVKjp16hSnnnpqXHDBBdGlS5eiaJ9zzjnFc0z79u2bs9kAwCpS6wGgRAP5888/H/vss0/F/qBBg4qP/fv3j1tuuSUGDx5cPL/0+OOPj9mzZ0fPnj1j9OjR0bhx44ytBgBWlVoPACUayPfee+/iGaQrU6dOnTj//POLDQCoftR6AKiGc8gBAACgJhPIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyEMgBAAAgA4EcAAAAMhDIAQAAIAOBHAAAADIQyAEAACADgRwAAAAyqBaB/Oqrr47OnTtH48aNY7fddovnnnsud5MAgCqm3gNQ25R8IL/rrrti0KBB8atf/SpeeOGF2GGHHaJ3794xc+bM3E0DAKqIeg9AbVTygfyyyy6L4447Lo455pjYeuut47rrrov11lsvbr755txNAwCqiHoPQG1UP0rYwoULY8KECTFkyJCKY3Xr1o1evXrFuHHjVvg1CxYsKLZyc+bMKT7OnTu3Stu2eMF/qvR6UNWq+v/za4vfJWrj71L5NcvKyqr82tXR6tZ7tR6qV61P/D5R236f5q5irS/pQP7RRx/F4sWLo23btpWOp/3/+7//W+HXDB8+PM4777zljnfs2HGttRNKUYvf/jR3E6BGWJu/S59++mm0aNEiarvVrfdqPfw/aj2U/u/TV9X6kg7kayL1rqc5aOWWLFkSn3zySbRu3Trq1KmTtW18eQ9S+kNq6tSp0bx589zNgWrL71L1kHrLU4Hu0KFD7qZUS2p99eTfJ6gafpdqVq0v6UD+jW98I+rVqxczZsyodDztt2vXboVf06hRo2JbWsuWLddqO6k66R8V/7DA1+d3qfS5M77m9V6tr978+wRVw+9Szaj1Jb2oW8OGDaN79+4xZsyYSr3gab9Hjx5Z2wYAVA31HoDaqqTvkCdpSFr//v1jl112iW9+85txxRVXxLx584pVWAGAmkG9B6A2KvlAfthhh8WHH34YQ4cOjenTp8eOO+4Yo0ePXm7hF6q3NPQwPXt22SGIwOrxu0R1pd7XfP59gqrhd6lmqVPmmSsAAACwzpX0HHIAAACoqQRyAAAAyEAgBwAAgAwEcgAAAMhAIGedOfroo6NOnTpx0UUXVTp+//33F8eBlUvrb/bq1St69+693LlrrrkmWrZsGe+9916WtgGUU+thzan1tZNAzjrVuHHjuPjii2PWrFm5mwLVSvpDduTIkfHss8/G9ddfX3F88uTJMXjw4Pjtb38bG2+8cdY2AiRqPawZtb52EshZp1KvX7t27WL48OErfc2f//zn2GabbYpnK3bu3DkuvfTSddpGKFUdO3aMK6+8Ms4444yiOKee9AEDBsT+++8fO+20U/Tp0yeaNWtWPLf5xz/+cXz00UcVX3vPPffEdtttF02aNInWrVsXv4vz5s3L+v0ANZNaD2tOra99BHLWqXr16sWFF15Y9PCtaMjNhAkT4oc//GEcfvjh8corr8S5554b55xzTtxyyy1Z2gulpn///rHffvvFscceG7/73e/i1VdfLXrR991336JQP//88zF69OiYMWNG8buUTJs2LY444ojia9544414/PHH45BDDimKPEBVU+vh61Hra5c6Zf4rsQ7nlc2ePbuYR9ajR4/Yeuut46abbir2f/CDHxT/YBx55JHx4YcfxsMPP1zxdWmIzt///vd47bXXsrYfSsXMmTOLO0uffPJJcZcpFep//vOf8dBDD1W8Jv0RnHrZJ02aFJ999ll07949/v3vf8cmm2ySte1AzabWQ9VQ62sPd8jJIs0tu/XWW4sevKWl/T322KPSsbT/1ltvxeLFi9dxK6E0tWnTJn7yk59Et27dom/fvvHSSy/FY489VgxhK9+6du1avPadd96JHXbYoehpT8PYDj300LjxxhvN7QTWOrUe1pxaX3sI5GSx5557FitIDhkyJHdToFqqX79+sSWpV/yggw6KiRMnVtrSH7fpdy0NH33kkUfiwQcfLO5WpWGkW221VTE3DWBtUevh61Hra4f/918YMkiPRNlxxx2LfyzKpV7Ap556qtLr0v6WW25Z/EMDLG/nnXcuhrOlhZHKC/eKVm5Nd6DSNnTo0GI423333ReDBg1a5+0Fag+1HqqGWl9zuUNONmlITZpHdtVVV1UcO/3002PMmDHx61//Ot58881iqFtazCKtNAms2MCBA4s5Zmkxl/HjxxdD19Ics2OOOaYY/pken5IWWEqLwEyZMiXuvffeYv5m+qMYYG1S66FqqPU1l0BOVueff34sWbKkUu/fn/70pxg1alRsu+22Re9eek1aJAZYsQ4dOhR3l1JBTo9FSX8An3rqqdGyZcuoW7duNG/ePJ544ok44IADijtQZ599dvGIofToFIC1Ta2Hr0+tr7mssg4AAAAZuEMOAAAAGQjkAAAAkIFADgAAABkI5AAAAJCBQA4AAAAZCOQAAACQgUAOAAAAGQjkwDpTVlYWl112WTz//PO5mwIArCXqPaw6gRyoUueee27suOOOKzw3fPjwGD16dOywww7rvF0AQNVR76FqCORQTR199NFRp06dYmvQoEFsuummMXjw4Jg/f/4qff3jjz9efO3s2bOrtF1nnHFGjBkzZrnjTzzxRNxzzz3FltoLAHw19R5qtvq5GwCsue9+97sxcuTIWLRoUUyYMCH69+9fFN2LL744W5uaNWtWbMvac88944UXXsjSJgCoztR7qLncIYdqrFGjRtGuXbvo2LFj9O3bN3r16hWPPPJIcW7BggVxyimnRJs2baJx48bRs2fPGD9+fHHu3//+d+yzzz7F5xtssEFR1FMPfJKGmKXXtmzZMlq3bh3f+9734p133qn0vu+9914cccQR0apVq2jatGnssssu8eyzz65wCNuSJUvi/PPPj4033rhobzqX3qNcakt6/3vvvbdo03rrrVcMcRs3btw6+AkCQOlT76HmEsihhnj11Vfj6aefjoYNGxb7aTjbn//857j11luLnuotttgievfuHZ988klR0NO5ZNKkSTFt2rS48sori/158+bFoEGDioVY0lC0unXrxg9+8IOi0CafffZZ7LXXXvH+++/HX//613jppZeK9yo/v6x03UsvvTQuueSSePnll4s2fP/734+33nqr0ut++ctfFsPfJk6cGFtuuWXxB8AXX3yxln9qAFC9qPdQw5QB1VL//v3L6tWrV9a0adOyRo0alaVf57p165bdc889ZZ999llZgwYNym6//faK1y9cuLCsQ4cOZSNGjCj2H3vsseJrZs2a9aXv8+GHHxave+WVV4r966+/vmz99dcv+/jjj1f4+l/96ldlO+ywQ8V+es9hw4ZVes2uu+5aduKJJxafT548ubj+73//+4rzr732WnHsjTfeWKOfDQDUFOo91GzukEM1loZ8pR7mNHwszSc75phjol+/fsWQszTPbI899qh4bVpY5Zvf/Ga88cYbX3rN1JOdeqs322yzaN68eXTu3Lk4PmXKlOJjer+ddtqpGL72VebOnRsffPBBpXYkaX/Zdmy//fYVn7dv3774OHPmzFX6OQBATabeQ81lUTeoxtJ8rjQ0Lbn55puLuVg33XRT7Lrrrmt8zYMOOig22WSTuPHGG6NDhw7F0LRtt902Fi5cWJxv0qRJrA1Lr8Sa5pglKxsWBwC1iXoPNZc75FBDpLlfv/jFL+Lss8+OzTffvJhb9tRTT1WcTz3oaZGXrbfeutgvn3u2ePHiitd8/PHHxRyzdI399tsvunXrFrNmzVquZzv1mqe5aV8l9binIr90O5K0X94OAGDVqfdQswjkUIMceuihUa9evbj22mvjhBNOiDPPPLNY4fT111+P4447Lj7//PMYMGBA8drUK556pv/2t7/Fhx9+WCzeklZgTSut3nDDDfH222/Ho48+Wiz4srQ0vC2t9JpWeU2F9l//+lexYMzKVklNbUiPZbnrrruK4v/zn/+8KPA/+9nP1snPBABqGvUeag5D1qEGqV+/fpx00kkxYsSImDx5cjEE7Mc//nF8+umnxaNKHnrooaIIJxtttFGcd955RcFMc9GOOuqouOWWW2LUqFHF41PSsLWtttoqrrrqqth7770r3iP1tD/88MNx+umnxwEHHFCsjJp6v6+++uoVtilda86cOcXr0xyx9Nq0WmuXLl3W2c8FAGoS9R5qjjppZbfcjQAAAIDaxpB1AAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAADIQCAHAACADARyAAAAyEAgBwAAgAwEcgAAAMhAIAcAAIAMBHIAAACIde//A3OwVdRFx5GSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizamos los datos de las variables de la tabla de contingencia.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "#Gráfico de barras para grupo A.\n",
    "sns.barplot(x=tabla_contingencia_RS.columns, y=tabla_contingencia_RS.loc['A'], ax=axes[0])\n",
    "axes[0].set_title('Grupo A')\n",
    "axes[0].set_xlabel('Rotación')\n",
    "axes[0].set_ylabel('Frecuencia')\n",
    "axes[0].set_ylim(0, 90);\n",
    "\n",
    "#Gráfico de barras para grupo B.\n",
    "sns.barplot(x=tabla_contingencia_RS.columns, y=tabla_contingencia_RS.loc['B'], ax=axes[1])\n",
    "axes[1].set_title('Grupo B')\n",
    "axes[1].set_xlabel('Rotación')\n",
    "axes[1].set_ylabel('Frecuencia')\n",
    "axes[1].set_ylim(0, 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para la columna 'rotacion_grupo_A', los datos no siguen una distribución normal, p-value: 0.00000\n",
      "...................................................................\n",
      "Para la columna 'rotacion_grupo_B', los datos no siguen una distribución normal, p-value: 0.00000\n"
     ]
    }
   ],
   "source": [
    "#Antes de realizar la prueba, comprobamos si la distribución de los datos es normal o no.\n",
    "df['rotacion_grupo_A'] = ((df['Group_relation'] == 'A') & (df['attrition'] == 'Yes')).astype(int)\n",
    "df['rotacion_grupo_B'] = ((df['Group_relation'] == 'B') & (df['attrition'] == 'Yes')).astype(int)\n",
    "\n",
    "#Hacemos el de Shapiro-Wilk para comprobar la normalidad.\n",
    "statistic, p_value_a = stats.shapiro(df['rotacion_grupo_A'])\n",
    "if p_value_a > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos no siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "\n",
    "print('...................................................................')\n",
    "\n",
    "statistic, p_value_b = stats.shapiro(df['rotacion_grupo_B'])\n",
    "if p_value_b > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos siguen una distribución normal, p-value: {p_value_b:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos no siguen una distribución normal, p-value: {p_value_b:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El valor del p_value es de 0.33382509249152137\n",
      "......................................\n",
      "No podemos rechazar la hipótesis nula.\n",
      "No existe una diferencia significativa entre la rotación del grupo A y del grupo B.\n"
     ]
    }
   ],
   "source": [
    "# Como las distribuciones no son normales, realizamos el test de Mann Whitney.\n",
    "\n",
    "#Usamos los datos filtrados.\n",
    "rotacion_grupo_A = df.loc[df['Group_relation'] == 'A', 'rotacion_grupo_A']\n",
    "rotacion_grupo_B = df.loc[df['Group_relation'] == 'B', 'rotacion_grupo_B']\n",
    "\n",
    "\n",
    "statistic, p_value = mannwhitneyu(rotacion_grupo_A, rotacion_grupo_B)\n",
    "\n",
    "print(f'El valor del p_value es de {p_value}')\n",
    "print('......................................')\n",
    "\n",
    "#Alfa\n",
    "alfa=0.05\n",
    "#Comparamos el valor p con el nivel de significancia.\n",
    "if p_value > alfa:\n",
    "    print(\"No podemos rechazar la hipótesis nula.\")\n",
    "    print(\"No existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")\n",
    "else:\n",
    "    print(\"Rechazamos la hipótesis nula.\")\n",
    "    print(\"Existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión: Dado que el p-valor (0.33) es mayor que el nivel de significancia alfa (0.05), aceptamos la hipótesis nula. Es decir, No hay evidencia suficiente para concluir que la rotación de personal entre los grupos es significativamente diferente. Esto sugiere que, en este caso, la satisfacción de las relaciones interpersonales no influyen significativamente en la rotación de personal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aunque no haya evidencia de que influya en la rotación, la empresa debe fomentar un ambiente de trabajo positivo y colaboración ya que siempre es más ameno un buen ambiente laboral, pero debe centrar los esfuerzos de retención en factores más influyentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B TESTING: IMPLICACION EN EL TRABAJO\n",
    "\n",
    "* H0: No hay una relación significativa entre la rotación de los empleados y la implicación en el trabajo.\n",
    "\n",
    "* H1: Hay una relación significativa entre la rotación de los empleados  y la implicación en el trabajo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "jobinvolvement\n",
       "3    955\n",
       "2    406\n",
       "4    164\n",
       "1     89\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscamos en la columna 'JobInvolvement' los valores únicos para encontrar a los miembros de cada grupo de control.\n",
    "df['jobinvolvement'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group\n",
       "A    1119\n",
       "B     495\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creamos una nueva columna llamada Group_in en la que agrupamos a los empleados en función de sus niveles de satisfacción.\n",
    "df['Group'] = np.where(df['jobinvolvement'] >= 3, 'A', 'B')\n",
    "df['Group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos a los empleados de los grupos A y B en función de si han dejado la empresa o no.\n",
    "attrition_A_JI = df[df['Group'] == 'A']['attrition']\n",
    "attrition_B_JI = df[df['Group'] == 'B']['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>968</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>387</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition   No  Yes\n",
       "Group              \n",
       "A          968  151\n",
       "B          387  108"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Para ver la relación entre estas variables categóricas, creamos una tabla de contingencia.\n",
    "tabla_contingencia_JI = pd.crosstab(df['Group'], df['attrition'])\n",
    "tabla_contingencia_JI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>86.51</td>\n",
       "      <td>13.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>78.18</td>\n",
       "      <td>21.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition     No    Yes\n",
       "Group                  \n",
       "A          86.51  13.49\n",
       "B          78.18  21.82"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculamos los porcentajes.\n",
    "tabla_contingencia_JI = pd.crosstab(df['Group'], df['attrition'], normalize='index') * 100\n",
    "tabla_contingencia_JI.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos para los gráficos de quesitos\n",
    "etiquetas = tabla_contingencia_JI.columns.tolist()\n",
    "\n",
    "# Crear los subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Gráfico del grupo A\n",
    "tamaños_A = tabla_contingencia_JI.loc['A']\n",
    "ax1.pie(tamaños_A, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax1.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax1.set_title('Grupo A')\n",
    "\n",
    "# Gráfico del grupo B\n",
    "tamaños_B = tabla_contingencia_JI.loc['B']\n",
    "ax2.pie(tamaños_B, labels=etiquetas, autopct='%1.1f%%', startangle=140, colors=colores_mako)\n",
    "ax2.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax2.set_title('Grupo B')\n",
    "\n",
    "# Título principal del gráfico\n",
    "fig.suptitle('Implicación en el trabajo', fontsize=16)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observaciones: Observamos que en el grupo A, donde los empleados tienen unos niveles de implicación altos, la tasa de rotación es del 13.5%, mientras que en el grupo B, la tasa de rotación es del 21.8%, lo que significa que el porcentaje de empleados que se han ido en el grupo A es menor al del grupo B, donde los niveles de implicación son más bajos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizamos los datos de las variables de la tabla de contingencia.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "#Gráfico de barras para grupo A.\n",
    "sns.barplot(x=tabla_contingencia_JI.columns, y=tabla_contingencia_JI.loc['A'], ax=axes[0])\n",
    "axes[0].set_title('Grupo A')\n",
    "axes[0].set_xlabel('Rotación')\n",
    "axes[0].set_ylabel('Frecuencia')\n",
    "axes[0].set_ylim(0, 90);\n",
    "\n",
    "#Gráfico de barras para grupo B.\n",
    "sns.barplot(x=tabla_contingencia_JI.columns, y=tabla_contingencia_JI.loc['B'], ax=axes[1])\n",
    "axes[1].set_title('Grupo B')\n",
    "axes[1].set_xlabel('Rotación')\n",
    "axes[1].set_ylabel('Frecuencia')\n",
    "axes[1].set_ylim(0, 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para la columna 'rotacion_grupo_A', los datos no siguen una distribución normal, p-value: 0.00000\n",
      "...................................................................\n",
      "Para la columna 'rotacion_grupo_B', los datos no siguen una distribución normal, p-value: 0.00000\n"
     ]
    }
   ],
   "source": [
    "#Antes de realizar la prueba, comprobamos si la distribución de los datos es normal o no.\n",
    "df['rotacion_grupo_A'] = ((df['Group'] == 'A') & (df['attrition'] == 'Yes')).astype(int)\n",
    "df['rotacion_grupo_B'] = ((df['Group'] == 'B') & (df['attrition'] == 'Yes')).astype(int)\n",
    "\n",
    "#Hacemos el de Shapiro-Wilk para comprobar la normalidad.\n",
    "statistic, p_value_a = stats.shapiro(df['rotacion_grupo_A'])\n",
    "if p_value_a > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_A', los datos no siguen una distribución normal, p-value: {p_value_a:.5f}\")\n",
    "\n",
    "print('...................................................................')\n",
    "\n",
    "statistic, p_value_b = stats.shapiro(df['rotacion_grupo_B'])\n",
    "if p_value_b > 0.05:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos siguen una distribución normal, p-value: {p_value_b:.5f}\")\n",
    "else:\n",
    "    print(f\"Para la columna 'rotacion_grupo_B', los datos no siguen una distribución normal, p-value: {p_value_b:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El valor del p_value es de 2.6704991581250297e-05\n",
      "......................................\n",
      "Rechazamos la hipótesis nula.\n",
      "Existe una diferencia significativa entre la rotación del grupo A y del grupo B.\n"
     ]
    }
   ],
   "source": [
    "# Como las distribuciones no son normales, realizamos el test de Mann Whitney.\n",
    "\n",
    "#Usamos los datos filtrados.\n",
    "rotacion_grupo_A = df.loc[df['Group'] == 'A', 'rotacion_grupo_A']\n",
    "rotacion_grupo_B = df.loc[df['Group'] == 'B', 'rotacion_grupo_B']\n",
    "\n",
    "\n",
    "statistic, p_value = mannwhitneyu(rotacion_grupo_A, rotacion_grupo_B)\n",
    "\n",
    "print(f'El valor del p_value es de {p_value}')\n",
    "print('......................................')\n",
    "\n",
    "#Alfa\n",
    "alfa=0.05\n",
    "#Comparamos el valor p con el nivel de significancia.\n",
    "if p_value > alfa:\n",
    "    print(\"No podemos rechazar la hipótesis nula.\")\n",
    "    print(\"No existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")\n",
    "else:\n",
    "    print(\"Rechazamos la hipótesis nula.\")\n",
    "    print(\"Existe una diferencia significativa entre la rotación del grupo A y del grupo B.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión: Dado que el p-valor (0.000026) es menor que el nivel de significancia alfa (0.05), rechazamos la hipótesis nula. Es decir, si hay evidencia suficiente para concluir que la rotación de personal entre los grupos es significativamente diferente. Esto sugiere que, en este caso, la impliacion en el trabajo influye significativamente en la rotación de personal, siendo los empleados menos implicados los que tienen una tasa de rotación mayor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para mejorar en este aspecto la empresa debe desarrollar iniciativas para aumentar el compromiso y la implicación de todos los empleados, como programas de reconocimiento y participación en decisiones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B TESTING : HORAS EXTRAS\n",
    "\n",
    "* H0: No hay relación significativa entre la rotación de empleados y las horas extra que hacen cada uno.\n",
    "\n",
    "* H1: Hay relación significativa entre la rotación de empleados y las horas extra que hacen cada uno."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "overtime\n",
       "No     1358\n",
       "Yes     256\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Buscamos en la columna 'overtime' los valores únicos para encontrar a los miembros de cada grupo de control.\n",
    "df['overtime'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group_ot\n",
       "B    1358\n",
       "A     256\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creamos una nueva columna llamada Group_ot en la que agrupamos a los empleados en función de si echan o no horas extra.\n",
    "df['Group_ot'] = np.where(df['overtime'] == \"Yes\", 'A', 'B')\n",
    "df['Group_ot'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividimos a los empleados de los grupos A y B en función de si han dejado la empresa o no.\n",
    "attrition_A_OT= df[df['Group_ot'] == 'A']['attrition']\n",
    "attrition_B_OT = df[df['Group_ot'] == 'B']['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_ot</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>178</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1177</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition    No  Yes\n",
       "Group_ot            \n",
       "A           178   78\n",
       "B          1177  181"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Para ver la relación entre estas variables categóricas, creamos una tabla de contingencia.\n",
    "tabla_contingencia_OT= pd.crosstab(df['Group_ot'], df['attrition'])\n",
    "tabla_contingencia_OT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>attrition</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group_ot</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>69.53</td>\n",
       "      <td>30.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>86.67</td>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "attrition     No    Yes\n",
       "Group_ot               \n",
       "A          69.53  30.47\n",
       "B          86.67  13.33"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculamos los porcentajes.\n",
    "tabla_contingencia_OT = pd.crosstab(df['Group_ot'], df['attrition'], normalize='index') * 100\n",
    "tabla_contingencia_OT.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U9TocdthhjsDAQHX+q6++ulff1abnK6+5XP/oo49W78NZZ53V/J5mZGTscbvExMTm+5TrnHHGGY5DDz1UvVdHHnmkY968efts99P6/I6+lm2RfrXHHXecup30pZXPjDwP6bMs8Vx33XXqsquuumqv23722WeqtVJTuyZ57hdccIG6P/lbzpfPdnc9h668R/trrSSk1ZF8XuR6YWFhjvPPP98xf/785s/LQw891MFXk4iIdGNiS0TUA4mtJA0TJ05U17v77rv36B+7ePFix8EHH6ySXkmEJJG48cYbHenp6W3el/TyfPLJJ9UOfVMy1TpB2LVrl+Pkk092DBw4UF1HesbeeeedjtLSUtWHdF/x/vTTTypRkD667u7uqr/t7NmzHQ8//HBzUt6RRDMyMlI9liQ2cj+ScBx++OGOpUuXtnn97k5s5bV97bXXVDIrybX0s5UETV5fSSAlGW3P2rVrVWIm760kefLejB071nHqqac63nzzTUdhYeFet9m5c6c6aBAcHKxec3m/n332WdUDVt5XSZikf3Fbr5McoAgKClKPNW7cOMdjjz2meuLur49tdya2Qh5T+sBK7BJL37591XPavXu36v8q93nPPfe0eVvp0Xvrrbc6Jk+e7PDx8VGvgbx+kmxKD+L4+PhufQ6dfY86ktg2HRiSgyADBgxQB2LkANMJJ5zgWL58+X5vS0RExuEi//TO2DAREZHzk2ndCxcuxJQpU9S6WbOSFklS4VvW2p5++um6wyEiItonrrElIiLqJFnH21aFa1lbKmuOxWWXXQajk8JiUiiqJflbim1JUiu9W5uqSxMRERkZR2yJiIg66Y8//lDVqydOnIiRI0eqFjiS6EoBLSmmJJWDf/zxR8NX05XKwJLcTps2TRXEKioqwu7du1XhMSl49vXXX+PYY4/VHSYREdF+MbElIiLqpMzMTFUBetWqVcjIyGjuSztp0iScf/75atTW6Emt+Oijj9RJpkwXFBSo6sODBg1SSbtU75bEnYiIyAyY2BIREREREZGpcY0tERERERERmRoTWyIiIiIiIjI1JrZERERERERkakxsiYiIiIiIyNSY2BIREREREZGpMbElIiIiIiIiU2NiS0RERERERKbGxJaIiIiIiIhMjYktERERERERmRoTWyIiIiIiIjI1JrZERERERERkakxsiYiIiIiIyNSY2BIREREREZGpMbElIiIiIiIiU2NiS0RERERERKbGxJaIiIiIiIhMjYktHbB3330XLi4u2LJlS5uXH3bYYZg8eTLM7tJLL4Wvr2+7l8trcMMNN8BsJO72Tv/61786fX+LFy9WnwkiIjLuNtvLywsZGRk9vs1+6KGH1OPl5+e3efnw4cNx4oknwuz29bolJyer1+C5556DmTTF3d7pqaee6vR9PvHEE/jmm296JF4im+4AiEi/o446ChdffPFe548dO7ZLiW3fvn3VgQAiIjKmmpoalZj873//0x0KGdx5552H448/fq/zZ8yY0aXE9swzz8Spp57aTdER/YOJLZEB1dfXo7GxER4eHr3yeJLAXnjhhehtFRUV8PHx6fXHJSKyuunTp+ONN97APffcg0GDBukOhzqhurpa7R+4uvbOxMuwsDDuI5ApcCoyafHOO+9g0aJF6N+/Pzw9PTFx4kS8+uqrbV73p59+wsKFC+Hn5wd/f38cdNBB+Pjjj/e4zsaNG3HsscciICAA3t7e6vpr165tczpUfHy8Gk0MDAxU17/ssstQWVnZI88zNzcXV1xxBUJCQtS0r2nTpuG9995rd4rSCy+8gFGjRqnXJDIyErW1tXjggQcwc+ZMFat8wR9yyCFYuXLlXo+1dOlSdb2m12nKlCl48cUXu+V5REVFwW637zWq++eff8LNzQ133XVX85SyiIgIrFq1qnmqkkzPajn9TS677rrr1HsfGhqqLktJSVHnjRs3Tj1Onz59cNZZZ6nXhoiIut9//vMfNDQ0dGg6qRxsffTRR5u3T/JdL7eXUd+eINvDefPmqW2BbBNk2/bFF1+0ed0PP/wQs2fPVtv+oKAgHHrooVi+fPle+xGy7ZRtqGwjTzjhBLWtamu5kUzPltFE+b1fv364/fbb1evUExITE9W2Ljg4WMV/8MEH44cfftjjOn/88Yfadso2/r777sPgwYPVdUtLS1FYWKjik+29xCvb/uOOOw47d+7c67FkZH7SpEnNr9OsWbP22pfqqhUrVqgkW/ZXWpL7l9ib9u/kd0lWZT+oaR+haXZX0z6a7Pucf/75KsYFCxaoy3bt2qWuN3LkSLUvNWDAAFx++eUoKCjolvjJeXDElrpNSUlJm2to6urq9jpPvuTkC/bkk0+GzWbDd999pxIbGaW8/vrrm68nyZB8ecl15aiyJKPbt2/Hzz//rL74mr5Q5YtcNnwPPvig+nJtSpzXrFmjNngtnX322RgxYgSefPJJbNu2DW+++aZKsp5++ukOPc/21gm1VlVVpZI6SaRl7a085ueff66+nIuLi3HzzTfvcX2JWY7CXn311WrHQTZ0suGS+GQa0FVXXYWysjK89dZbOOaYY7Bp0yZ1xF38+uuv6jpHHHFE8/OQZFSS+9aP0xZ53Lael2wk5ajwhAkT1E7NHXfcoaYQyfsmGyd5LuPHj8cjjzyiri+J+Y033qg2sPfee686T5L6luR9lp0F2QDKfYjNmzdj3bp1OPfcc1WyKwmtfEbk9ZONnGyIiYio+8g2SQ5Wyqjt3Xffvc9R2yuvvFIlI/L9f9ttt6mDybINle3M119/3aHHkySsLbLdb00Oysp25oILLlAHeCWpkwTw+++/V0lpk4cfflglRJIEy3ZItlcSm+wXHH300eo6H3zwAS655BK13ZTtoxzIlu2LJE2yPyFJehNJYOV6c+bMUcn1b7/9hueff14l9Ndee+1+n6Pcvq1taVFR0V7n5eTkqLglnptuukkl8fIay/OWJP60007b4/qyDZbnJ4msHFCQ32X7KOtV5bWR91Puc8mSJergvlzW9J7KeyyPIe+f7BPINl+SRXmtmval9kVibOt5yT6Z7MPJ/pZs2+UzIQcFZIQ3KytL7Q8ceeSRzfU65L2Qz5Lsl8m+jpDXtiV5LmPGjFFTlh0OR/M+jhwEkIEISWrloMTrr7+ufm7YsEElxESKg+gAvfPOO/LNs8/TpEmT9rhNZWXlXvdzzDHHOEaOHNn8d3FxscPPz88xZ84cR1VV1R7XbWxsbP45ZswYddum85ruf8SIEY6jjjqq+bwHH3xQxXL55ZfvcV+nnXaao0+fPvt9npdccsl+n+f111/ffP0XXnhBnffhhx82n1dbW+uYO3euw9fX11FaWqrOS0pKUtfz9/d35Obm7vGY9fX1jpqamj3OKyoqcoSEhOzxPG6++WZ1e7l+Z+3r+XzyySfN12toaHAsWLBAPXZ+fr56rjabzbF58+Y97k/e64ULF7b7OZH7aB1nW5+H9evXq+u///77nX5ORETUtqbvYvnuTkhIUN/jN910U/Pl8v3dcpu9Y8cOdf0rr7xyj/u5/fbb1fkrVqzY5+M1bXv3dTrhhBP2uU2QbefkyZMdixYtaj4vLi7O4erqqrbhsn1qqWl/oKyszBEYGOi46qqr9rg8OzvbERAQsMf5Tdv4Rx55ZI/rzpgxwzFz5kzH/sjrtr/n+eyzzzZf/5ZbblHnrVmzpvk8iVf2XYYPH978nFauXKmuJ/tHrV+X6urqvZ677FN4enru8TxOOeWUvfbDOqJp/6S9k2ynm1RUVDhGjx6tHkfikvdU9ktSUlL2uE8fHx/1Wrf3OTnvvPP2uqytfQTZP5Hrr169utPPi5wXR2yp27zyyittFhuSo7utp/HI1KKWI70yqitHGH/55Rf1t0y7lSN0MkIpR5Jl6klLTUfnduzYgbi4ODU9p/WUFBm9lKODcjS45TqU1pV+ZXqSHHGW0VEZodwXiUNGl9srwNTSjz/+qI4sykhqE3d3d3XUVM6TKbktK0GeccYZaiSzJZnmKychz0NGeuWnTCGS0eaWR01l9FNeM5mS3VmnnHJKmxWdZXpTE3kNZQRdplPLCLlUwZbXXWLpDBl5bnpObX0e5LMg78Xo0aPV85LnedFFF3X6ORER0b7J1E75fpXRL9nWDhw4cK/ryLZM/Pvf/95r2y6jmjJ19vDDD9/vY3355ZdtbmPbWrvZcpsgo52yDyHb6k8++aT5fBmplO2hzP5pvda0aR9Btomy3ZRtbssRR9kGyahsW8t62tpHkH2JjpDRXxkdbU1GUls/T3ldZeSyabqtkNlOMpIpM9RkxLVllWUZdW75ugiZ3dVEXiN5rnIfsqyn9T5Cenq6mh0ly7k6S2KSkdTWZBlZE5lZJfsIMhVcTjKrTGaYDR06tFOP1VY3hpbPW0aby8vL1bRtIc9T3iMiwcSWuo18QbeV5Mg6idZTWGSKrEwbXr9+/V7rW5sS24SEBPX3vtoOSFLb9IXfHrk/iaFJ6y/Zpstk47m/xFY2hjKtpiNk3ahMp2m9wZVpvU2XtyTTiNoiU5NkKlR0dPQe07pbXl+mAH322Wcq4ZT1NzIFS6ZcdzTJlem/HXleMmVIpn3JlGR5X+6//350VlvPU6ZtyxQmmY4t65uaph81vX9ERNQz5AClJG6y1ratugyyrZLtmBxsbEkO3ErC1Hpb1h5JdqRifmutD1wLmXL82GOPqYPXLdfxtpxyKvsIElfL5Kq9fQSZKtuW1tt8iaX1AWbZR2hrKnFbZA1vW9vStupFyOsmyXVrLfcRWu7/tLXtlMRe3jPpRpCUlLTHIIJMbW4idTBkWrXsp8n7KPsIMgV5/vz5HXpesi/TkX0EuT+Zsi0DHTKlW5aSdVZbz1Omscu0c5mSLrVLWuI+ArXExJZ6nWyMZDRV1mb+97//xZAhQ9RaETl6+X//939trrdpT9N1n3322eb1pq217j3berSwSctkSofWR2KbimLIOlZZsyLJpKwFlvglCWxK/IWcLzsAMuItRTLkJEmirJ9qXazqQDUV5cjMzFSj5LJzc6DPU9bhSLy33HIL5s6dqw5syA6MrLntzOeBiIg6P2oro4lNo7bt6a11jFIbQ9aZSiIsCZuMIstsJ9lGdLbYUdP2QxL3trZVsj60I/sHRtDWtlPWocoBZkkgZQ2u1OaQZF+2pS23nZIsx8TEqAMGUqNERs/ltZXRbkkYu4schJBiV0L2UWTgorM1Mtp6nnKgXupwyH6Q7OvJfp08Pzl4z30EaomJLfU6mcorX37Lli3bY/S09ZSgpoIC4eHhex0pbn0dOera0ZHU3jJs2DBVnKH1VGgZeW26fH+kgITsdHz11Vd77FTIaHdrcnDgpJNOUid5TBnFlSISstFr7/XrrNdee01N7Xr88cdVcn3NNdfg22+/PeCdH3meMuouI9MtpxvJtCoiIur5UVs5kNpWEUXZVsk2RUY/m0YTm6bXynd0R7ZlnSFJl4ycyoHallNtJbFtvf2XuGTKbnsHtpv2EeTgrxH3ESTZbK2z+wgyDVym/LYk70vr0XEZTT7nnHPUSQpynX766WpbLtOe2xo17wrZN5GCYjJFXUaJ5UDJSy+9dED7CDJa/vvvv6sEvGXV5abReKKW2O6Hel3TEdHW001bb7RkqoyU5ZcESpKclppuK5WQZcMlX6Ky5qK1vLw86CLNzLOzs/Hpp5/u0TJBSu7L0UZZU9yV10qqGMoU7pZary+WRHrq1Knq9+5qxyDTnORoqawFljYP8prLwYn3339/r41nZxNSeZ6tR8zldeqpFgtERPQP2Y7KqK0cDJXtVuttWVPV+5ZkxpVoWaW4O8j2QJKflt//MpVX1tS2JDOZZFsn1ZBbj9o1bU9kOqwc+JaRzbY6NOjeR5B1qC2351IrQ0bOZa3uvqZY72vbKd0XZEnPvvYR5EC43L/ctq3XpStk30T2C2S0WNZfy/7Cyy+/rOqJHMg+Qlv7QW19HokER2yp10nC2jS6KCN+kpBKsQU5oirl4ZvIxkimJktpeCl20NTXTPqzyfQWmWIrGzVphyNrS6UlkJSClzWm8qUuI8ByH+0Ve+ppUmxBdhJkKvHWrVvVhkqOrsr6YvlClqR9f6S4lIzWStl/2XmQ5FJGTWWD1DKRl9dI1qDIOiJZLytrcyQxlKPYLY+wtyc2NlYdrW9NWvVIUSzZoMhUJ5ki1NSPTt47ObIurQPkSHhTWwE52CDXkfVRMlIs72t765taPk+ZKiZTkOW5yYZe1gO1XCNEREQ9R1q0yfewjCLK9rSJFAyUGTWScElCIgdlJSGTbbAklx0pHNUZsq2TpFmmmcp2X9ZUyppN2Z7ILKgm8rfELFNwpXiQjEDKCK8USJLtkRwUl30A2R5JgSxpQSPLW2QNbWpqqip6JWtCJfnSQUYzpRiW7L9IUUmZRiyvqWznZdvauj5He9tOSexl30daB+3evRsfffSRmunVer9LpmLL85XtuoyqyvOW17oj+yJSoKmtfQQ5ICLLh2TwQT4jshZXRoGFjLDK/pfEJnFJQtu0jyDbd3mP5X2SNbVtrTVuIu+hTEt/5plnVBIu+3iyJEpeJ6K96C7LTM7VOqAtrVsHiGXLljmmTp3q8PLyUmXtn376acfbb7+t7kfKy7e+7rx58xx2u12Vjp89e/YebWjE9u3bHaeffrpq2yNl7ocNG+Y4++yzHb///vtepeTz8vLajL/147Ym5emlTH17Wrf7ETk5OY7LLrvM0bdvX4eHh4djypQp6vHaKqffsg1Ay5YFTzzxhHo+8ryk7cD333+vYpHzmnzxxReOo48+2tG/f3/1OEOHDnVcc801jqysrH0+p6a42zs1te158cUX1d9ffvnlHrdNTU1V78nxxx+/RxsFKfMvrZpa3se+PifSwqjpdZJWSNK+KTo6Wj3HttoCEBFR1+zru7ip5U3rbXZdXZ3j4YcfVq1o3N3dHUOGDHHcc889qq3L/rS37W0i3/Ot2/289dZbqpWfbPfGjx+vYm66n9Zk30G2jXLdoKAgtc359ddf97iOtMyR7Yq0+JH9jlGjRjkuvfRSx5YtW/a7jW/vcTuyr7O/7by0WzrzzDNVSyKJS/ZvZBvfOna57eeff77X/crrf9tttzkGDhyo9pHmz5+vWvBILC3b7i1ZssRx6KGHNu8jyfO/4447HCUlJQfU7qdp+3zrrbc63NzcHBs3btzj9vL6Sjupa6+9tvk82bZLLBJvy/vY1+ckPT1dtXWS10new7POOsuRmZmpri+3I2riIv/sne4SERERERERmQPX2BIREREREZGpMbElIiIiIiIiU2NiS0RERERERKbGxJaIiIiIiIhMjYktERERERERmRoTWyIiIiIiIjI1JrZERERERERkakxsiYiIiIiIyNSY2BIREREREZGpMbElIiIiIiIiU2NiS0RERERERKbGxJaIiIiIiIhMjYktERERERERmRoTWyIiIiIiIjI1JrZERERERERkakxsiYiIiIiIyNSY2BIREREREZGpMbElIiIiIiIiU2NiS0RERERERKbGxJaIiIiIiIhMjYktERERERERmRoTWyKTcjgcOPLII3HMMcfsddnixYsRGBiI9PR0LbEREREREfUmJrZEJuXi4oJ33nkHGzduxJIlS5rPT0pKwp133on//e9/CA0N1RojEREREVFvYGJLZGJDhgzBiy++iNtvv10ltDKKe8UVV+Doo4/GjBkzcNxxx8HX1xchISG46KKLkJ+f33zbL774AlOmTIHdbkefPn3U6G9FRYXW50NERERE1BUuDtkTJiJTO/XUU1FSUoLTTz8djz76KCIiIjBp0iRceeWVuPjii1FVVYW77roL9fX1WLFiBbKysjB06FA888wzOO2001BWVoY1a9ao60oiTERERERkJkxsiZxAbm6uSmQLCwvx5ZdfIjw8XCWqv/zyS/N1ZL2tjPDGxMSgvLwcM2fORHJyMoYNG6Y1diIiIiKiA8WpyEROoH///rjmmmswYcIENXq7c+dOrFy5Uo2+Np3Gjx+vrpuQkIBp06bhiCOOUFORzzrrLLzxxhsoKirS/TSIiIiIiLqEiS2Rk7DZbOokZET2pJNOwo4dO/Y4xcXF4dBDD4Wbmxt+/fVX/PTTT5g4caIqNDVu3Di1TpeIiIiIyGyY2BI5obCwMLXOdvjw4Rg9evQeJx8fn+aqyvPnz8fDDz+M7du3w8PDA19//bXu0ImIiIiIOo2JLZETuv7669V62/POOw+bN29W049lve1ll12GhoYG1SLoiSeewJYtW5CamoqvvvoKeXl5aiozEREREZHZ/DVvkYicyqBBg7B27VpVCVla/9TU1KgiUcceeyxcXV3h7++P1atX44UXXkBpaam67Pnnn1ftgYiIiIiIzIZVkYmIiIiIiMjUOBWZiIiIiIiITI2JLREREREREZkaE1siIiIiIiIyNSa2REREREREZGpMbImIiIiIiMjUmNgSERERERGRqTGxJSIiIiIiIlNjYktERERERESmxsSWiIiIiIiITI2JLREREREREZkaE1siIiIig3A4HDjyyCNxzDHH7HXZ4sWLERgYiPT0dC2xEREZGRNbIiIiIoNwcXHBO++8g40bN2LJkiXN5yclJeHOO+/E//73P4SGhmqNkYjIiJjYEhERERnIkCFD8OKLL+L2229XCa2M4l5xxRU4+uijMWPGDBx33HHw9fVFSEgILrroIuTn5zff9osvvsCUKVNgt9vRp08fNfpbUVGh9fkQEfUGF4d8WxJRt5P/WjUNDepU/ffP+kYHbK4ucHd1/efk5tb8OxERUZNTTz0VJSUlOP300/Hoo48iIiICkyZNwpVXXomLL74YVVVVuOuuu1BfX48VK1YgKysLQ4cOxTPPPIPTTjsNZWVlWLNmjbquJMLUvRoaG1FbU4/6+kbUNzSivr7hr9/VqUGd5+rqAruXO7y9PeBt94CHh0132EROi4kt0X6U1NaisLoa+VXVf/2srkbB3yf5u6KuvjmBbXmqbWzs9GM1Jbg2V1d4uLrCx90dAR4eCPD0gL/89PBAoKcngr3k5IU+f5+CPD3h6uLSI8+fiIj0yM3NVYlsYWEhvvzyS4SHh6tE9Zdffmm+jqy3lRHemJgYlJeXY+bMmUhOTsawYcO0xm4msiss2/qimhq1Xf/rZ80efxdV16Csrg7V9fWoamhAVX09AtzdUf5aXKcey83NFd52d9jtHrDb3VWyq07eHvCSBFj9/tdPuY6frycGDgjAoIEB6BPMgxNE+8LDRmR5pbW1SC4tQ0rZX6e0snLkV1epjZokr3VdSFC7Sh6r5eNJEt0Rbi4uKtEN9fXBUF9fhPr5YoivL4b6+WGwjw883Nx6MGoiIuoJ/fv3xzXXXINvvvlGjd5+9NFHWLlyZZujrwkJCWqq8hFHHKGmIkvxKfn7zDPPRFBQEKxOEtL08gqkl5erU5r6WYGMctnmV6OhC+M8XTmA3dDQiLLyGnXqLC9PGwb8neQOGhD490/5OxADQvzh7s5tPVkbR2zJEhodDmRVVDQnrykqkS1Xv8uRWGcmE5z7e3urRHdIc8Lri7GBgehrt+sOj4iI9uGhhx5Sie2OHTvU2lpvb288/fTTe11v4MCB8PHxUaOP69atw/Lly/H1118jOztbFaIaMWIErHKwOqaoGLHFxUgqLUXG38lsRw8Ud4aXHDR+MwlGIFOe+/X1+zvRbToFInRQIEYM76tGiomcHUdsySllVlQgvKAQEQUFiCgsQlxxcZeOrDoDedbZlZXqtDk3d4/L+tntmBAUhAnBQZgYHITxQUFqyjMRERlPWFiYmpI8fPhw2Gy2dqsqz58/X50eeOABNSVZEtx///vfcDYyTTi6qEglsjHFxeqnbOt6iyw78oQxNDY6kJNbqk7bd6XtNdI7dkwIJk0YiInjB6mfQYHe2mIl6ilMbMn0KurqEFVU9HciW4iIwkKnH4XtLnlVVeq0OjNT/S2rdAf7+mBicHBzwjsuMBBe7exAERFR77n++uvxxhtv4LzzzlOtf4KDgxEfH4+lS5fizTffxJYtW/D777+rKcgyjVlGavPy8jBhwgSYXX1jo9q+b8/Lx+6CAsQWFffIKGxnyJRHm6cb6msaYGTVNfXYFZ6hTk1kRHfS+IGYOOGvRHckR3XJCXBvlUx5hHZTTi625+WpZDa5tFSNSlL3bKT/WoNUgeWpac3rd8cFBWJ2SAhmh/THlD59VHErIiLqXYMGDcLatWtVJWRJXmtqatSI7LHHHgtXV1f4+/tj9erVeOGFF1BaWqoue/7559UUZrORehORhYXYlpuHbX9v76XDgNF4eLsbPrFtS2ZWiTr9ujJa/S2FrMaPGYCJEwb+nfAORIA/lyuRuXCNLZniKK1s0DZkZ2NDdo5aN8MPrT7eNhum9+urktw5ISEY7u+vOyQiIjI5Kd4UXlBg+ES2tQE/F6I4qxzOaMjgIEyeNAhzZg3HQWHDVeVmIiNjYkuGlFNZifV/J7Jbc/NQXlenOyRqR3+7HQeF9FcnGdWV1kNEREQdWUok2/k/M7OwLjtbFX4ymyF/lCIvqRjOzt3mhqmTB2PunJGYN2ekakFEZDRMbMkwVYt35OdjTUam2sgll5XpDom6QNboTgoOxqIhg7EoNBQh3ixOQURE/8itrMSazCysyczEtrz8Xm2p1xNGbKxEVmQ+rGbY0GCV4M4/eBQmjh+oipYR6cbElrSSQhC/pabht7R07UUgqAeS3D7BOCI0FIuGhKqRXSIisp74khKsSs/A6swstZzImYzZUYu07dmwsr59fHHo/NE47JCxmDxxEJNc0oaJLfW6xJJS/JqWhl9T05BRUaE7HOoFsomTolNHDAnF4aGDVZshIiJyXlJxX4oQ/pySqhJbZzUhsgFJG/+pNmx1ffv44JB5Y7BwwRhMmTRY9dcl6i1MbKnX+srKBu63tDQklJTqDoc0kk3c1L5/JblHDRmCQK7JJSJyClX19fgjI0Mls1tyci3RsWBSApCwOlV3GIbUJ9gHixaOw8nHT0Xo4CDd4ZAFMLGlHi0K8UtqGn5MTlFTjola83B1xWGhg3HqyBGY0a+f7nCIiKgLNTK25Obip+RUrMrIQJUJKhl3p6lpboj9LUl3GIYmM5PDpg3FKSdOU+ty2S+XegoTW+p2MUVF+DohEb+mpaOyvl53OGQSw/38cOqoEThu2DD4e7ClABGRkeVXVeGbxCR8m5hk6RoZ03PcEf1jgu4wTLUe98RjJ+PE46agT7Cv7nDIyTCxpW5R09Cg1sx+lZCIqKIi3eGQiXm6uWGRGsUdqaYsExGRcezKL8AX8fFYmZ6Beu5CYkahF6K+jdUdhunIqO38g0filBOmIWz6UN3hkJNgYksH3G/2y/gELEtKRokJ+8+RsY0K8FcJ7nHDhsLH3V13OERElj54/UV8AmKcrKrxgQorsyPyixjdYZjakNAgtQ732CMnwtfXS3c4ZGJMbKlLtufl4bO4eNWLroEfIeph3jYbThk5AuePHYO+rKhMRNQrsisr8RUPXu/TzCofRCyN0h2GU/DytOHwhePUKO64MSG6wyETYmJLnbI+KxvvREVjd0GB7lDIosWmjh8+DBeOG4vBvlybQ0TUE+KKi/F+dIyabsyD1/s2s84XER9G6g7D6YwfG4IzTw3D4YeOY8sg6jAmtrRf8hGRkdl3o6K5fpYMwc3FBYtCQ3HxhHEYHRCgOxwiIqcgB61lW78uK1t3KKYx0+GHiHcjdIfhtIYP64NLL5iLQ+ePhouUVybaBya21C75aKxIz8B7UdGIc+Lm6mResombP3AgLpkwDpP7sNAUEVFXbMvNw9uRUdial6c7FNMJc/FH5NvhusNwemNG9cdlF87F3DkjdYdCBsbElvYi045+S0tTCW1SaZnucIg6JKxfP5Xgzg7huhwioo7YmpuLtyKjsD0vX3copjXD5o+oN5jY9paJ4wfi8ovmYuaMYbpDIQNiYkvN6hsb8UtqKt6LikFaebnucIi6ZHrfvrhx2hRMDA7WHQoRkSHtzM/Ha7sjsCOfCe2Bmubhj5glTGx727QpoSrBnTo5VHcoZCBMbElZm5WFl3fuRnIZR2jJOaYoHzkkFP+aMhmDfHx0h0NEZAhpZWV4eVc4Vmdm6g7FaUzx9Efca0xsdZkVNgxXXDwP48cO0B0KGQATW4tLKCnBSzt3YVNOru5QiLqdu6srzhg1EpdOnIAADw/d4RARaVFcU6OmHH+TkIh67vZ1q4l2fyQuZmKr27w5I3HZRfMwemQ/3aGQRkxsLaqwuhqvR0Ti+6RklvInp+fn7o5LJozHWaNHwcPNTXc4RES9oqahQfWcl9Y95XV1usNxSuPsfkhZzKrIRiBFkw+dP0YVmRo2lAUlrYiJrcXUNjRgaVy8KgxVWV+vOxyiXjXQ2xvXTJmEo4cMYdsAInJasmu3PDUNr4VHILuyUnc4Tm2U3Q8ZTGwNRfrenn7yDFx+8TzYvdx1h0O9iImthUil48W7wpHFjRxZ3PigQNw6fTqm9uURXSJyLhEFhXh++w72ne8lw+w+yFkcpTsMakNIPz/cfN0itgiyECa2FhBTVIzntm9HeEGh7lCIDEPGa08aMRzXTZ3C9bdEZHoVdXVYvDtcraNt1B2MhQz28kbBq9G6w6B9OHT+aNz4r8PRt4+v7lCohzGxdfK1NW9FROLj2DiuoyVqR5CnJ26YOgXHD2dPPCIyp9/T0vHCjp3Ir67WHYrl9Pf0QulrsbrDoP3w8fbAlZcuwMnHT1VTlck5MbF1Utvz8vDUlm1IZT9aog4J69cPd82cgaF+frpDISLqkKyKCjy3fQfWZWXrDsWyAj08UL0kXncY1EETxw/EbTceiZEj+uoOhXoAE1snnIr08q7d+DYxCXxjiTrHw9UVV0yaiPPHjoHN1VV3OEREbapvbMSncfFqVlZVQ4PucCzN22ZD4xuJusOgTnBzc8XZp4fhkvPnwtPTpjsc6kZMbJ3ImsxMPLttB/KqqnSHQmRqYwMD8Z9ZYRgXFKQ7FCKiPUQWFuKprdsQV1yiOxQCYHNxge3tFN1hUBcMHOCPW64/ArNnDtcdCnUTJrZOoKimBv/dvgO/paXrDoXIabi5uKiR26smT4I7R2+JyACjtO9GRasT62YYi/39NDga+J6Y1aKF43D91QsRHOSjOxQ6QExsTe6XlFT8346dKKmt1R0KkVMaExiAR+bMxnB/f92hEJFFpZWX4+GNmxFRyO4GRhT4RTaqy7gfZmZ+vp645opDccIxk3WHQgeAia2J19I+s227asBORD3Ly80NN02bitNGsRceEfWubxIT8dKOXVxLa2D9v89HaV6l7jCoGxwybzTuuPko+Pl56Q6FuoCJrQlFFxXh/g0bkV5eoTsUIks5bPAg3D1rJvveElGPK6yuxpNbtuHPrCzdodB+DP6tGAVppbrDoG4S0s8P9955HKZMGqw7FOokJrYmIm/V0rg4vLo7AnWNbL9OpEN/ux0PzJ6Fmf376w6FiJzUn5lZeGLLVlVDg4xv2Npy5MRymrgzkV63l14wFxecM5t9b02Eia1JFNfU4NHNW9irjsgApJTUBePH4epJE9kWiIi6TU1DA17csRNfJybpDoU6YdTWamTsytUdBvWAGVOH4D93HIu+fXx1h0IdwMTWBLbl5uHBjZuQX12tOxQiamFicBAenjMbob7c4BHRgcmqqMB/1m9AdFGx7lCok8aF1yNlc6buMKiHBPjbcc9tx2DOQSN0h0L7wcTWwKScvzRffy8qGpx4TGRM3jYb7pk1E0cOCdUdChGZ1IbsbDy0cTM7HJjUxDgHEv9kMU9n5uICXHjuHDU9mVOTjYuJrUEVVFfjvvUbsSM/X3coRNQBF48fh2smT4KrbP2IiDpAdsHeiYpWB7F5ANu8pqS4Im5Fsu4wqBccNHMY7r3jODWKS8bDxNaAYoqKcOfa9citqtIdChF1wvyBA9TUZB93d92hEJHBldXW4uFNm7GWtTNMb1qWDTE/J+oOg3pJSH9/PPyfEzBu7ADdoVArrHpiMCvS0/GvlauY1BKZkOygXvn7SqSVlekOhYgMLL64BJf9toJJrZNw9eDutJXk5Jbixjs+w7Ifd+kOhVrhiK2BvBUZpaYj8Q0hMjc/d3c8cvBsHDyAR3OJaE+/pKTiya3bVAVkcg5hJV6I/CpWdxikwbFHTcJtNx4Bm81NdyjExNYYqhsa8NimLfg9PV13KETUTdxcXHDtlMm4YNxY3aEQkUG8ERGJtyOjdIdB3WxmpTciPo3WHQZpMmvGUDx870nw9vbQHYrlce6EZjLl+NqVfzCpJXLCquYv79qNhzdu5sgMkcXVNzbikU2bmdQ6Kxt3p61sy/ZU3HLX5ygsqtAdiuXxf6JGkYWFuOK3FexZR+TEfk5NxbUrVyGf6+aJLKm8rg63rPkTP6Wk6g6Feoobq+FbXVxCLm64bSnSM4p0h2JpTGw1+S0tDdf9sRr51dW6QyGiHhZVVIRrVv6B9PJy3aEQUS/KqqjANSv+wNbcPN2hUE9iYkvy/z27FDfc9imiYlgUThcmthp8GheHBzZs4vREIgvJrKhUO7ixxZyhQWQF0UVFuGrFH0gsLdUdCvWwRtYNor+VlFbh3/d8gQ2bk3SHYklMbHvZmxGReGHHLlY+JrKgwpoaXP/HamzP4+gNkTP7MzML161chQLOyrIEhytHbOkf1dV1uO+RZfhpeYTuUCyHiW0vkeLTL+zYqVr6EJHF19ut/hNrMjN1h0JEPWBZUhLuXrceVZyVZRmN3JumVhoaGvHMC8vxwScbdYdiKfyv2EvVUR/fshWfxsXrDoWIDKC2sRH3rNuAH5KTdYdCRN3oi/gEPLVlm9ruk3U0uPD9pra9/cE6vPDKCjQ28jPSG5jY9rC6xkbcv34jfkhO0R0KERntgNfmrfgoJlZ3KETUDeT/8vPbd3CpkQU1cCYy7cO3P+zEQ098j9raet2hOD0mtj2our4ed/y5FiszMnSHQkQGJDvA0utWTkRkXrLMiP+PrauBhzNoP9asi8ft936JsjKuu+9JTGx7SFltLW5e/Sc25uTqDoWITDDS89SWrWotPhGZy6u7w1VhSLKueia21AG7IzJx052foai4UncoTouJbQ8oksqnq1ZjV0GB7lCIyCS+TUpW0xiJyDykKOT70TG6wyDNmNhSRyWnFODO+75CeTlHbnsCE9seGaldg7jiEt2hEJHJfJmQiJd27tIdBhHth8yueGbrNhaFJKXO0ag7BDKR+MQ83P3gN6iqrtMditNhYtuNKuvr8e81a5nUElGXfRIbhyXh7H1HZGRPb92GrxOTdIdBBqp0T9QZEVFZeODRZaitY0Gp7sTEtpvUNDTgzrXrEF5YqDsUIjK5d6Oi1YmIjOd/O3eppQNETThiS12xZXsqHnv6J9XzlroHE9tuUN/YiHvXb8DW3DzdoRCRk5BR209i2QqIyEjkgNPHsXG6wyADDm4QdbVa8jMvLGfxyG7CxPYANToceGTTZqzNytYdChE5mZd27saX8Qm6wyAiAF8lJHCZALU/FZm9bKmLlv8ehZdeXak7DKfAxPYAyNEVWWfza1q67lCIyElJpeRlSVzLR6TT8tQ0PL+NVcupfR52m+4QyMS++X4n3nxvre4wTI+J7QF4cecuLOM6GyLqQTI56ekt29SONRH1vrVZWWpmFlfB0b54eLnrDoFM7qNPN+GTzzfrDsPUmNh20RsRkSzzT0S9QnaoH9u8BdvzuI6fqDfJ/7l7129EA9e/0X64c8SWusHr7/yJZT/s1B2GaTGx7QJZ8/Z2ZJTuMIjIQuoaG3H3ug1ILSvTHQqRJcQUFeOOP9exMBB1CBNb6i4vvroSv65kntEVTGw7aV1WFv5vB4+kEFHvK62txW1/rkVJTY3uUIicWm5lJW7/cy0q6tljkjrG5snElrpHY6MDT/93OdZtZPHIzmJi2wnxxSW4f8MmTkkiIm3Syytw17r1qOUoElGPqKyvx+1/rkN+dbXuUMhE3JjYUjeS3rYPP/kDIqIydYdiKkxsOyi/qkodvZUNHhGRTjvzC/DElq26wyByOnLg+v4NGxFXUqI7FDIZm6eb7hDIydTWNuDBx79HYWGF7lBMg4ltB1RX1+H/1m1DTlWV7lCIiJRfUtNUETsi6j4frN2NdexLT13g5sHElrpfQWEFHnzie9TXc5ZWRzCx7YCn/285Nr60BdPdA3SHQkTUTIrY/ZSSojsMIqfw4/JwvPfE75hR4Al3V+4eUee4ccSWekh4ZCZeeX2V7jBMgd/c+/Hh0o34Y00sGuoaEf36bsys9OGLRkSG8eSWbWwDRNQNO44vvLxC/R61LA6jw+sQ4O6hOywyERd37h1Sz/nm+534+bcI3WEYnovDwUpI7Vm7IQH3P7oMrV+hMYcPR8JoV663pWbZy39B7vJfUPN3gmEPHYLBZ56JoBlh6u/G2lqkvP8eCtatRWNdPQKnTcPwK6+CR2Bgu/cZ/8rLyF/1xx7nBUybjgn33vfXfdbVIfG1V1G0ZTPcAwMx4oqrEDB1avN1M5d9i5r8fIy4/IoeetZkFP4eHnjnyEUY5OOjOxQi08nLL8M1N3+MoqLKPc7vNyIQjccNQEbVnucTtSWsyAuR38TqDoOcmIeHG1569hyMGxOiOxTDYmLbjqTkfNxw26eorKpt8/JBk/qh7JAg5NWwaiIBRVu2AK6u8Bo4EHIkJG/VH8hatgxTnnkW3kOGIPGN11G8bRtGXX893Ly9kfzWW4CrCyY/+vg+E9u6kmKMuu765vNcbe6w+fqq37N/+hE5y5djzL9vQ/H2bSqRnfnGW3BxcUF1bg6iH38Mk598GjZv7155DUivCUFBWLLoME6hJOqEmpp63HTHp4iNz23zcnuAJ0IuGIPoqtJej43MZWa5NyI+j9YdBjm5kH5+WPLSBQgIsOsOxZC4B9SGsrJq3PfIsnaTWpEZkQeXrzIwyu7Xq7GRMQXNmoWgsDDYBw6EfdAgDD3vfLh6eaE8Lhb1lRXIW7ECwy65BAGTp8B35CiVrJbHxKAsdt9HdyWR9QgMaj41JbWiKiNDPa4kzgOOPRb1paWoL/tr5yvpjTcw9IILmdRaSFRREV5kj22iTnn2xV/bTWpFVUkNUpZEIMzNv1fjIhOyueiOgCwgJ68Mjzz9g2oHRHtjYtvOhi4ze/+l/ktzK5DzVjSmebCoFP3D0diA/LV/orGmGr5jx6IiMRGOhnoETPlnmrB98GB49O2L8tiYfd5XaWQEtlx5OXbcfJMa9a0rK2u+zHvYMJRFR6OxtgbFO3bCPSgINj9/5K9ZDVd3dwTPntOjz5OM58uERPyWlq47DCJTWPrFFvz+x/5H2BwNDkS+GY6wMjvcXJi8UDuY2FIv2bYjDW+8+6fuMAyJ3aRbWfbDTqxZF9/h69fXNCBmyW7MvGAitnmUg/O6rasyNQXh996LxrpauHl5Yeztd8I7dAjyk5PhYrPB1mr9o3tAIGqLi9u9v8Dp0xE8Zw68+vdHdXYO0j75GNFPPI7Jjz8OF1c39Dt8ESpTUrDz1lth8/fDmFv/jYaKcqR99ikmPvgw0pZ+gvy1a+E1IASjrr0OHsF9euFVIN2e3LIV4wIDMMSPs0mI2hMZnYU331vbudt8EYPxhwxFyngbylljg1pzY2JLvefTL7eqtbaHHzpOdyiGwhHbVutqF7+5uku3jfgoElMzbfByY7l3q/IaNAhTn30Wk594EiFHH4OEV15GZXpal++v7/wFCJ51ELyHDkPw7NkYd/c9qEiIR2nEX1XxXG02jLjyKsx4ZTGmPPk0/MdPQMr772PAccejIjkJhZs3Yeqzz8F3zFgkv/12Nz5TMjIpavef9RtR08Ced0RtqaqqxRPP/dylqXwJa1LRZ1UxBnhxfRvtycHElnrZMy/8qnIX+gcT2xYFJB55+kf1s6tifklE6JZKBHt4dmtsZA6yHtZrwEC1hnbo+RfAe/gwZP/4o6pY7KivR31FxR7Xl8JQ+6qK3JpXSIiaalydnd3m5SXh4ahMS1PrbSX5DZwRpkaO+8ydp6Y0k3XEl5Tg+e07dIdBZEgvL1mFjMz2Z8vsT05sIeo/TcVY1tigFhq5R029rLq6Dvc/9h3Ky1nItgn/G/5t8RurkJxScMD3k74jB57fZWG4/Z8iP2RRjQ7Vksdn5Ei4uNlQsnt380VVmRmozc+H79iOTyGpKShAfXmZWku710PV1iL5rTcx8uqr1TRlNDaqZFrI+l5HI4sMWM13Scn4ITlFdxhEhvLnunj8uDz8gO+nvLAKGW9EYbo7i0rRXxyuHLGl3icH6R5/9mewyc1fmNgCWL02Dst+3NVt91eUWY7Cd2MxxYsbPKtI/fgjlEZGojo3V621/evvCPQ95BDYvH3Qb9EipLz/rhpVLU9MQMLiV1RhKb+xY5vvY8ctN6Fw00b1e0N1FVI+eF9VTZb7LNm9C7HPPA2vAQMQOG36Xo+f/uUXCJwxAz4jRqq//caNV/dVkZKM7J9/Vn+T9Ty3bTsSS9imhEgUFlbguZd+67b7a6hrRPTr4ZhZ6cOdKUKjKxML0mPD5iRVDI9YPAo5uaV47sVfu/1+ayvrEfdaOGZePAlbXf+pZEvOqa6kBPGv/A91RUWqT61ULB5/730InDpNXT78kkuR4uKC2Oefg6O+DgHTpqn1sS1VZ2aivrJS/e7i6qoSZOmH21BRCffgIHVfoeecqyoet1SZmorC9esw5Znnms8LPvhglVhHPvCAWvs75uabe+V1IGOpbmjAves34J2jjuD6f7K8p/9vOUpKq7r9fiM+jcKkw4cjYbSrWuNO1tTAAVvS6J0P1uPgg0ZgxPC+sDIXh4XHrqVwxK13f47dEZk9+jjjTxyNyJB61HI6KBFpcN7YMbhp2j/tpois5uvvduClV1f26GMMmtQPZYcEIa+G692sSNZcpy5mPQvSZ/TIfnj1hfNgs1n3QLalR2zf/2RDjye1Ivr7eIyYNQg5M71RXFvb449HRNTSp7FxWDh4EKb1tfaRXLKmlNQCLHl7TY8/TmZEHvzzKjHqzOFIqLLOTC1ZhpO57FtUJCWqWUvS6k4q+TeRFnQF69aitqBAtb6TuhNDzj0PfmP+WYrTWvbyX5C7/BfU5OWpv+2hQzD4zDMRNCOs+TrJ772LvD/+gJuXpyrY2PeQQ5svK1i/DnmrVmH83fegtzSw4SNpFp+Yh/c/3ojLL54Hq7LsspAdu9Px4dJNvfZ4KVsy4fNjLobY9+xlSkTU02SuyOObt6qpyURWUl/foAqrHEjHg84oza1AzlvRmOYRAKtoqKmGz/DhGHHFlW1ebh80CCMuvxJTn/svJj3yGDz79Uf0Y4+hrrSk3fv0DO6DIedfiMlPPYPJ0s5u8mTEPvOMqvwvirZsQcGff2LCffdh6AUXIeG111BX+lc9gfrKCtXHfcSVbcfTU+qZ2JIBfPTZJkTFtN09wwpcrdrD7qnnfkZjY+9+CRWklaLsgwRMZFEpIuplaeXleG33gVeDJTKTtz9Yh7iE3F59zPqaBsQs2Y2Ztb6wwrJLGUWVEdjg2XPavLzvgkMQMHWqalnnPWQIhl18CRqqKlGZ0n7V9qBZsxAUFgb7wIEqMR563vlw9fJCeVysurwqIx3+kybCd9Ro9F2wADZvO2py/3qfUz/8ECFHHQ3Pvv3Qm+qsu7KPDKSx0YGn/vsLamutud7f1aobupw8PdOEqstqkbQkAjNdmNwSUe/6PC4eO/PZzJ2sYefudHz65VZtjx/xUSSmZtpYuK2Fxvo65P72699FFod36DaOxgbkr/0TjTXVqpuAkNuWJySivrxcdRqQlnfSNaA0OkpNiR5w/PHobXUO1lEhY0hNK8Qb7/4JK7LcGtuYuBx8tWyH1hgcjQ5EvB2OsFPHYldwDep5lI+IeoHsdj22eQs+OOpIeNks9/VPFlJeUYMnn/+l12dmtRbzSyJCp4egcI4/CmtrYFVFW7cg7oUX0FhbA/fAIEy47wG4++/7AL90Bgi/91401tXCzctLrd31Dh2iLgucPl2109t9z91w9fDAqOtvgKuXJ5LeeAOjrr8eOcuXI/unn2Dz98PIq/+lRop7GguEkpF8tWwHFi4Yi8kTB8FKLFUVWaogX3vLJ70+LWlfRswLRfpkT5TV1ekOhYgs4uwxo3Hr9L9aURE5o8ef/Qm/rYyGUQQN8oXnqUORXFUOZ7bh7DP3Kh4lGqqrUVdchLrSMuT+/htKw8Mx+Ykn4R4QsM/R3dr8fNUGr3DDBuT+/jsmPvxwc3LbWvrnn6G+ogL9Dj9creGd+vzzKNq6FTk//4wpTz+DnmZ3c4PjzaQefxyijho2NBhv/O9CuLtbZ9aIpaYif/ntdkMltSJpXTqCVhRikJe37lCIyEJTknfkcUoyOae1GxIMldSKosxyFL0bh8le1ikq1ZKMuHoNGAi/sWMx6trr4OLmitwVv+/zNq42d3Ub35GjVNVj7+HDkP3jj21etyojA/lrVmPIueeiNCICfhMmwN0/AH3mzlNTkxuqur9/cWscsSWjSUktxIefboSVWCaxzc4pxTsfrocR5cYXoXppMsbbue6WiHqe4+8pydX11iwuQc6rtq4ei19fBSOqqaxD/Gu7MbPRD1YnkwUbOztTrbHt28h9Jb6+BMMuvhRuXnapngPH3xXgm3/2QtLZ4HDA5mGdkTEyh08+24KkFOscyLZMYvvi4hWorjbudN/Komqkvh6JGW7WPJpLRL0ro6ICb0VG6Q6DqFt99tU2ZGa330ZGOwcQ8V4Epud5wMPVOXbBGqqrUJGcpE6iJjdH/V6Tn6emIKd+/BHKYmNVT1op9JSw+BXUFhaq0dQmkY88hOyff2r+W24j/XGrc3PVWtu//o5Q62pbk6nNsl5XKikLv/Hj1VRnecysH76HPTQUNp/eabXo7u3eK49D1FF19Q147sXftNcb6C2WqB7yx5pYbNhs/HUPjfWNiHpzN2aeOQ47/KvV0T8iop6yNDYOJwwfhuH7KeJCZAZ5+eX46NPe609/IKK/j8eIWYOQM9MbxbW1MLPyhAREPfxQ898p77+nfvZdeBhGXnU1qjIzkPf8KtSXlcLm5wffUaMw6eFH9yjoVJ2T09yHVtSVlCD+lf+hrqjo7wrKwzD+3vsQOHXP2gC1xcXI+PorTH708ebzfEePwcCTTkLMU0/AFhCA0dffgN7i4eWGnp/0TNQ5kdFZ+Pq7HTjjlBlwdk5fPEoqI156zXsoKKyAmYw6dCiSx9lQwamCRNSDDurfHy8t3HsUhMhsjFYwqiP6DPGH60mDkVZlrn0UalvoH6XITyrWHQbRXux2d7zz6sUI6e/cB7KdYx7MPrzxzp+mS2pFwupU9FtdggGyXoSIqIdszs3FivR03WEQHZDwyEzTJbWiIK0UZR8kYKKXc+9sWoXNi2tsyZiqqurw+jvO39vW1dk3dN/9tAtmlR1TgPrPUzHGzkITRNRzXtqxC1WcHUImJWvH/vfaSphVdVktkpZEYKYLk1uzs3laYoUfmdTK1TGIicuBM3N15g3d/73yO8w+0bo8vwpZb0ZjujuLShFR9/O22RBa6IpPPtusOxSiLvlpeThi443Vyq+zHI0ORLwdjrAiL9hcXHSHQ11k8+SILRmXwwEseXsNnJnTJra/roxCYpJzlLeur21A9Ou7MbPax3nfMCLqVbLrLAfMfL/LQcTSKHz2+Rbk5P5TvIXILHU03nxvLZxF5DexGBPdAD93Vtc1IzcPjtiSsW3fmYaNJiio21WuztrH7p0PjNmz9kBEfBKFSWmusLvxiCARdd1Iuy9G76xTB8xKs/+qQVBTU2+J9TfkXN77aD2KS5yrDm3SunQErSjEIC9v3aFQJ7l6OuVuNTmZ19/502nb/zjl/8BvvtvptCMPcb8lY9DGCvTz9NIdChGZTJCHJ2YUeCLz1Uikbcva6/IVq2IQEZWpJTaizkpJLcDX3+2EM8qNL0L10mSMt3PdrZm4uXPggYwvMTkfy3+PhDNydcZpSR99Zo4+dl2VsTsXLt9kqFEXIqL9kTV7YfAHPk5F1LI4YB8Hal9esgpO3gWOnMTLr69CQ0MjnFVlUTVSX4/EDDfW2DALF3en260mJ/X2B+tQW+t8RSOd7n/g0i+2oLS0Gs5Opg/mvR2DqZ7c4BFR+ybY/TH4zzJEvhOOqrKa/V4/OjYbv/1hvrYpZC1/rk/Alm0pcHaN9Y2IenM3ZpZ7w41FpQzPxcPpdqvJSeXll+OLb7bB2TjV/8CCwnJ86YRvUnvqqhsQ+9puzKzzVYVgiIiaSA/sySmuSFocjpzYwk7d9r2PNjj1SBiZv47Gq2+sgpVEfB6NCQkO+NhYnMjIXGxOtVtNTu7jz7egpNS5ahQ41f9A2RmrrnG+YfX9ifgwElOzbPBkUSkiy5PicjNrfFD+VjziVyR36T4yMos5akuGrqORmV0Cq4lflYp+q0vUQSsyKDcOM5B5VFTU4MOlG+FMnCaxTUsvwo/LI2BVMT8nYti2KgR7eOoOhYg0kN2paR4BCPghDxEfR6k2YQfiw082ctSWDDla+9lXW2FV2TEFqP88FWPsfrpDobbYmNiSuXz7/S5kOdGBQqdJbN96f63ld8JSt2XD8/tsDLP76A6FiHrRCLsvxobXI2bJbhRnlXfLfaZnFuN3jtqSwfz8ayQKCv9qUWVV5flVyHozWvWhJmNxcMSWTKauvsGpeoE7RWIbFZONVX/G6Q7DEIoyylD8bjwme3GDR+TsAj08EFbkhezXopCyufvb9Hy4dJPlDxiScchn8dMvtugOwxBkRob0oZ5Z7eMcO3JOwsEVYWRCK1fHICY2G87AKb4P33jnT90hGEpNZR3ipaiUg1OViJyRVEcNc/GHy6fpiPwmFo4earSellGEFatjeuS+iTpr5epYS66t3ZeIT6IwKc1Vra0n/RyuHLEl83E4gCVvO0cuZfrENjwyE9t3pekOw3gcQMS7EZhR4AF3V9O/zUT0t/F2fwxZV47It8NRVdzzrc1k1LaxhxJnoo6S3sqffL5ZdxiGFPdbMgZtrEA/Ty/doVheA/NaMqntu9KwwwnyKdNnPFYuItERUcviMXJ3LQLcPXSHQkQHIMTLjqlpbkheHI7s6IJee9zUtEI1TYlIpw2bkpCYnK87DMPK2J0Ll28yMNLuqzsUS2t04UFAMq/PnaBlqqkTW2lJsXZDgu4wDC9lUyb8fs1HqN1bdyhE1Ele0r6n1heV78Qj9rckLTF88MlGjtqSVh99tkl3CIZXml2BvLdjMNWTNTZ04Ygtmf0AYkZmMczM1Int519v485WB+UnFaPiwyRMsPvrDoWIOkh2UIN+zkfER5Goqz6w9j0HIiWtEH+sidX2+GRtO3anIyIqS3cYpiDfE7FSY6PeV7UAo95VL+vAiEyqsdGBL5dth5mZNrEtKa3CL79Zt29tV1SV1iB5SQTCXJncEhmZtOyaEPnXDmpRehmM4IOlG9U6R6Le9tGnHK3trIgPIjE12x2eLCrVq5jYktn9/GsEyitqYFamTWy//X4nqmvqdYdhOo4GByLfCkdYiR02Fx7PJTISWQsfVuKF3NeikbQxA0aSnFKA1WvjdYdBFhMbn4Mt21J0h2FKMT8lYNi2KgR7eOoOxTLqwPZoZG5VVXX44efdMCtTJra1dfX45vudusMwtcivYjA2uhF+7u66QyGyPGnfM8PVH7bP0xH5Vc+17zlQX3+3Q3cIZDEff8ZKyAcidVs2PL/PVrNAqOfVc1YLOYGvl+0wbQ97Uya2y3+PQlFxpe4wTC9xXRqCVhRhoBeLShHpMtbuh6EbKhD1Vjgqinq+fc+B2Lk7HcmpvVeRmawtNb0Qa9ZxlsCBKsooQ/G78ZjsxaJSPa220ZzJAFFLOXllpv3uNV1iK2u8pGgUdY/c+ELULk3GOBaVIupV0nNyaqYNqYsjkBVpnjYmy37YpTsEsohPPt/CApHdpKayDvFSVMrhpzsUp1bbqK/IH1F3+sKkrX9czViKWvoqUveRUaK01yMxw8bklqinSTGXmfV+qHk/EbG/JMJslv8eiarqOt1hkJPLzSvDbyujdIfhXBxAxLsRmFHgAXdX0+3+mQJHbMlZRERlITLafNXoTffN9ulXW3WH4JQa6xsR9UY4ZlZ4q/V+RNT9Jnv6o+8vBYj4IAK1leYsfldRWYtfVzDhoJ4vEFlfzyShJ0Qti8fI3bWqWB11r7rGRri4ch+KnMMXJhy1NVViGxOXo9Z4Uc+J+CwaExIBb5tNdyhETmOItO+JaUT8a+EoSCuF2S37kdORqedI0ZKf2c6vR6VsyoTfr/kItbPGRnfzsLMoJzmH1Wvj1ewZMzFVYmvm8tNmEv9HCkLWliLEy647FCJTk6rjM8vsyF8SjaR1znNQLiExD7sjjNWOiJzH+o2JKCxigcielp9UjIoPkzCBNTa6lYedAwPkPAcZvzZZNwTTJLa1tfVYuSZWdxiWIcVsHF+kYbSdhSaIuvLFKmvWPb/KRMQXMap/tLP5lkWkqIf88Eu47hAso6q0BslLIhDmyuS2u7gzsSUn8v3Pu01VV8M0ie2f6xNQXl6jOwxLKc2rRPab0ZjmwRYBRB0lB4NGbKlWa9bL86vgrFb/GYfiEo6qUffKyy/Hpq3JusOwFDnwFvlWOMJK7Kyx0Q3cvZjYkvMoL6/Bz7+aZ2mIaRLbX36L1B2CJdXXNiBmyW7MrPEFN3dE7evr6YnpOe5IXxyBjN25cHZ19Q34kSNr1M1kB4otfvSI/CoG42Ia1RIK6jobE1tywlFbszBFYltQWI4t21N0h2FpER9HYmqGDV5ubrpDITIUD1dXzGz0Q937yYj+MQFW8t1Pu5mEULf2qf/pVx4s0SlxbRqCVxZhIGtsdJnNk4ktOZfEpHykpBbADEyR2C7/PYo7TwYQszwRoZsq1cgUEQGTvPzRf0UxIt6LQE2ledagdJfsnFJOG6Vus31nGrKyzV813Oxy4gpRuzQFY1ljo0vcPDgAQM5n5Wpz1DkyRWL7y++chmwU6btyYPs2CyPsvrpDIdJmsN0bk+KBhFfDVWVRK/ttZbTuEMhJcFtvHBVF1Uh/PUoVwaPOcfVkYkvOZ6VJCvgaPrGNjs1GSmqh7jCoheKschS8G4spniwqRdbiY7NhZoU3il6PRcKaVN3hGKY1i1StJzoQNTX1+HOdtabyG11jfaMqgiffeSwq1XFuHobftSbqtNS0QsQn5sHoDP+/7+dfeQTXiGor6xEnRaUaOHJLzk++KKe7+8Pnm2xEfBatdvjoL5VVtdi4hdOR6cCs3ZCgPktkPPKdNyER8LZx7WhHuLobfteaqEtWroqB0Rn6f19dXQNWrjb+i2hZDiDi/UhVCVYK6BA5o1F2P4zcVoPo18NVCyza2x8mmaJExvXriijdIdA+xP+RgpC1pQhhUan9cmFiS05q5Rrj52SG/t+3bmMCSsuqdYdB+yGVYIfvqEGQB4tKkfMI9vDE9DwPZCyOQPrOHN3hGNr6TYlqKilRV5SUVGHzNnY+MLqsyHw4vkhTvbqpfUxsyVllZZciKiYbRmbo/33sXWseqVuzYP8xB0PtPrpDITog7tK+x+GHxo9TEP19vO5wTKGqqg4bNifpDoNMasXqGDQ0cHq/Gcislew3ozHNgzU22mXjemRyXisMPh3ZsIltRWUNj+CaTGFaKUo/SFAtUIjMaKKXPwauKkHEuxGoLuN6v87gdGTqKlbWNpf62gbESI2NGl8whWsDE1tyYqv+jFU9x43KsInt5q0pqGeBFtORZCBxSQTCwKlKZB6DvLwxOdEFia+GIze+SHc4piQjttXV1uvlSwfeCzkyOkt3GNQFER9HYmqGDV5ubG+zBzcmtuS88vLLsTsiA0blauQ1W2ROjkYHIt+JwIxCLzWtk8iopMrnzCoflLwZi/hVnCFyICSp5XRk6qxNW/iZMbOY5YkI3VSJvp6ssdHEwcSWnNzK1cadoWXIrKOx0YGNm9k+wuyivo3FqIg6+Lu76w6FaA+y2zHNPQC+3+UgYmkUGuo4O6Q7cDoyddamrTygZHbpu3Jg+zYLI+xs/ycaDblnTdR9Vq2NM2xdBEP+95NpSSWlVbrDoG6QvCEDAb8WYLCXt+5QiBTZ+Rq9sw4xr+9GaXaF7nCcysbNSajidGTqoPr6BmzfmaY7DOoGxVnlKHg3FlM8WWOj0ZUjtuTciooqsWN3OozIkIktpyE7l7ykYlR+nITxdm7wSB9pRzWjwBNZr0YibRvX9PWE6pp6rN/I72/qmPCoLFRWsUibs6itrEfcknDMbLD2yG2ji3EL6xB1l5UGrY5syMR2wyauuXE2VSU1SJGiUm5Mbql32VxcMBP+wCepiFoWB3Cfo0fxwCR11OatXHLkdBxAxPuRmJ7jDg+L1tho4IAtWcCadfGGnI5suG+dnNxSJCbn6w6DeoCjwYHIN8MRVmaHmwu/+annTbD7Y/CfZYh4JxxVpTW6w7EETi2lznQ/IOcU/WMChu+oUTNlrKaBR0/JAkrLqhETlwOjMVxiu56jtU4v8osYjI9zwNdm0x0KOakBXnZMSXFF0uJw5MQW6g7HUgoKK5CcWqA7DDK4wqIKxCfm6g6DelDq1izYf8zBULsPrKSeiS1ZxLYdqTAawyW2GziNzRIS1qSiz6pilYAQdRe7mxtm1vig/K14xK3gNEddjLixI2PZsi0FDu7/O73CtFKUfpCASV7WWYZUx8SWLGKrAbf1rkbrg7h9F6exWYWMpNV/moqxdj/doZDJycT2qR7+CPwxDxEfR6G+tkF3SJa2bQe/x2nf2ObHOqrLapEoNTZgjW19ncN46w6JekJEVJbK3YzE1WiZfy13SC2lvLAKGW9EYbq7dY7mUvcabvfF2PB6xC4JR1Fmue5wCFBtAIxYVIKMweFwYOt2JrZW4mh0IPKdCMwo9IK7kxeVYmJLVlFX14BdERkwElej9UAk62moa0T06+GYWeVjrA8kGVqAuwdmFHkh57UopGzO1B0OtVBRUYPYeK6fpLbFxuWguIS96q0o6ttYjIqog7+7O5xVbSMTW7KObQabjmyoPMJoWT/1roilUZiU4gpvFpWifZCK2mEu/nD9LB1R38SqkQAyHqNt7Mg4Nm/jaK2VJW/IQMCvBRjs5Q1nxMSWrGSbwZYeGSaxLS+vRmoaq5danRT8GbCuDP08vXSHQgY0zu6HIevKEfl2OKqKq3WHQ/uwbScTW2rbJvavtby8pGJUfpKM8XbnW4ZU28AldWQd8Ym5KCk1zgwcwyS2kTHZrJBISmZEHly+ysAoFpWiv4V42TE13Q0piyOQHc1WMmYQHpmJ2tp63WGQwVRU1iAyOlt3GGQAcnAyZUkEZrg6V3Iru7LuXm66wyDqFZK7yfbeKAyT2EZEGedFIf1KcyuQ81Y0pnkE6A6FNPKS9j21vqh8Jx6xv3INvplIIUAjbezIGLZuT2VhMWrmaHAg6q1whJXZ1TITZ+Fhd941xESt7TbQUlLDJLaRUVm6QyCDqa9pQMyS3SqxcZ7NHXXUFE9/BP2cj4iPIlFXzaldZrR9p7HW3pB+O3en6w6BDCjyixiMj3PA10lqbHjYneN5EHWEkQ5iGyKxbWx0ICqGU5OobZLYTM20qdE7cn7D7D6YENmAuNfCUZRepjscOgDbmNhSK/EJebpDIINKWJOKPquKMcDLDrOzeXHElqwjNi7XMEuPDJHYJqcUoKKyVncYZGAxvyQidEslgj08dYdCPUTaP4SVeCH3tWgkbTTOtBY6sKISnHZKLfvXJiQxsaX25cQWov7TVIw1eY0NG9fYkoXU1TeoWklGYIjENiLaOEPYZFzpO3Lg+V0Whtt9dYdC3d2+x80f7p9nIPIrtu9xtnW2rHZPTTKzS3gQm/arvLAKGW9EYbq7eYtKuXtxKjJZS7hB1tkaI7Hl+lrqoKLMchS9G4fJXiwq5QzkqPywjZWIfDMcFUVs3+OM4hJydYdABhHPzwJ1UENdI6JfD8fMKh9j7Kh2kqsHR2zJWnZHGGOQ0hDfFywcRZ1RU1mH+Nd2Y2ajuacqWZn0KZZ106mLI1R7J3JecVxTSX/jZ4E6K2JpFCaluMLbZEWlbJ5MbMlaImOMkctpT2ylqW9aRpHuMMhsHEDEexGYnucBD1ftH2PqIE9p31Pvi5r3ExH7S6LucKgXJCQymaG/xPOzQF0QtyIZA9aVqQOiZsERW7Ka8vIa5BeU6w5Df2LL0Vo6ENHfx2PErloEenjoDoX2Y4qXP/r+WoiIDyJRW2mM6nnUOwWkiASnIlNXycwel68yMMokNTZc3bXvXhP1upTUAuim/X9eVKwxqmiReaVszoTPz3kYYvfRHQq1Qd6XiTEOxL0ajoKUEt3hUC8rK69Bdg7fd6srKq5EQWGF7jDIxEpzK5DzVgymehq/xoYLR2zJglIMUCzS1QitfogOlCRM5R8kYqKXeasoOhs/ad9TZkf+kmgkrmM/Uyvj2kpiETHqDvU1DYiVGhu1vnCBcbnajBwdUc9gYiuJrQGGrck5VJXVIGlJBMJcmNzq/lKZYfOH51eZiPwiBo4Gtu+xOk5HJk5Dpu4U8VGkKkDo5WbQkVFORSYLSrV6YltX14CMzGKdIZCTkR6okW+HI6zICzYXHjHtbaPtfhixpRpRb4SjPL9KdzhkEPEcsbU8Fo6i7hbzSyJCt1Qi2MMThuPG/Q+ynhSrJ7Zp6UVobORoDnW/yG9iMSa6QU2HpZ7X19MT03Pckb44Ahm7OTJDe+I0VOLBDeoJ6Tty4PldFoYbragUpyKTBRUVVaKsrNq6iS2nIVNPSlqXjqAVhRjk5a07FKclrZakn3DdBymI/jFBdzhkUHn55aq1G1lTVVUtMrI4O4t6RlFmOYrejcNkL+MUlXJwxJYsKllzbqc1sU1JY2JLPSs3vgjVS5Mx3s51t91tkpc/QlYUq37CNRW1usMhg2M/W+tKSMrn7CzqUTWVdYiXolKNfjACB5fYkkWlap6OrH0qMlFPqyyqRurrkZjhZpyjuWY22Msbk+KBhFfDkZfEURjqmKxstvyxKk5Fp17hgDrQOj3PQ80m0qnRlSO2ZE0pVk5sWTiKektjfSOi3tyNmeXecGNRqS7xsdkws8IbRW/EImFNqu5wyGRy8sp0h0AWPYJP1hL9fTxG7KpFoIeHthgaOWJLFpVq6cQ2i0fwqXdFfB6NCQkOlaRRx78kprv7w+ebbER8Fq0OEhB1Vk5uqe4QSJP8gnLdIZDFpGzOhM/PeRhi99Hy+A0yfExkQSlWTWyLSypRUVGj6+HJwuJXpaLf6hKEeNl1h2J4o+x+GLm9BtGvh6M0r1J3OGRiObkcsbUqJrakQ0FKCco/SMREr96vsdHAiWFk4YPY1dV11ktsOQ2ZdMqOKUDj52mq7yrtTfoCzsjzQMarEaqdAtGB4oitdeUXVOgOgSyqqqwGSUsiEObSu8ltPUdsyaIcDiAtQ18NJSa2ZFll+ZXIfjMa0z1YVKqJu6srwhx+aPw4BVHfx6tiHETd1fKHlXGtR97zomLO9iB9HI0ORL4djrBiL9h6qcZGPbhkh6wrWWPLH22JbVYOj96TfvW1DYheshszq330Ljg3AJmuNXBVCSLfjUB1Gdv3UPdqaGhEQSGnpFpNUXGFeu+JdIv8OhZjohvg5+7e449VJ8NWRBaVprHrjbZ9+aIiHsEl44j4JAqT0lxhd3OD1Qz08sbkJBckvhqu+v4S9ZRsHtC0HE5DJiNJWpeOoBWFGOTl3aOPU+fgwRyyriKNs3S0Fo8iMpK435IxaGMF+nl6wQq8pX1PtQ9K34xF/B8pusMhC2ABKevhKD0ZjRzArV6ajPH2nlt3y8SWrKy0tMqKia2+J03UnozduXD5JgMj7b5wVrLCSNYV+32Xo0aqG+q4AabewQJS1pOfz8SWjKeyqBqpr0dihlvP1NiobeR2layrtKzagokti0mQQZVmVyDv7RhM9XS+olIj7L4YvbNOrSsuyeYUQepdTGytJ7+Q3zNkTNKTPerN3ZhZ7g23bi4qVdPQ0K33R2QmpaVWTGw5YksGVlfdgNjXdmNmva8a4TS7QA8PzCjwRNarkUjblqU7HLIoTkW2ngKusSWDi/g8GhMSHPCx2brtPhscDrjarF6SkqyqtMxiU5Gl/H9Zub5snqijIj6IxNRsd3iatKiUtDYIgz9cPklD1LI4tu8hrXLzmdhaTX4BpyKT8cWvSkW/1SUI8bJ323162nu++jKREZVYbcS2pLSK/QzJNGJ+SsCwbVUI9vCEmUyw+2Pwn2WIfCccVaU1usMhQnk5P4dWk8/iUWQS2TEFaPw8DaPtft1yf+727hsBJjKTuroGVFXXWSexZUVkMpvUbdnw/CEbw+w+MLoBXnZMSXFF0uJw5MQW6g6HqFlVFfsjWw2nIpOZlOVXIvvNaFVg8UAxsSUrK9VUGVlPYlvM9bVkPkXpZSh+LwGTvYxZVEp68M6s8UX52/GIW5GsOxyiveg6gkv6jtrLDC0iM6mvbVAFFqUd3oHsJLt7MbEl6yrVVBmZI7ZEnVBTUYt4KSrl6J6pSt1lqkcAAn/MQ8THkaivYTVGMiZZgsLk1jrYw5bMTNrhTU5zg1cXa2zYPJnYknWVWmrElhWRycwcQMS7EZhR4AF3V71VD4fbfTEuvB6xS3ajKJM7kWR8VZWcjmwVhUU8iE3mFvtbEgZvrEBfT69O39bN05xFJ4nMXECKI7ZEXRS1LB4jd9ciwN2j1x9bHjOs2As5r0UhZXNmrz8+UVdVcp2tZVRzdJ6cQMbuXLh+k4GRdt9O3Y6JLVlZiZVGbHU27iXqTimbMuH3az5C7d698njSRD7M1R9un6cj8utYOFhdnEymgiO2llFbx2UR5BxKsyuQ93YMpnp2vMaGmwcTW7KuUiutsZWCEkTOIj+pGBUfJqn2Oj1pnN0PQ9aXI/KtcFQW8eAQmRMrI1tHbW297hCIuk1ddQNipcZGvS9cOnB9Vw+9S5WIdLLUGtuGhkYdD0vUY6RPbPKSCDWa2t2kYfzUdDekLI5AdlRBt98/UW/iVGTrqK3lQWxyPhEfRGJqtjs891NUioktWVmplUZsmdiSM3I0ONRoaliJXU0ZPlBSiXFmnS8q34lH7K9J3RIjkW6VlVx3aRU1HLElJxXzUwKGbatCsIdnu9dxsTGxJesqtVLxqAauCyQnFvlVDMbFNMLP3b3L9zHFMwDBvxQg4sNINf2JyFlwxNY6OBWZnFnqtmx4/pCNYXafNi93cWdiS9ZV36Bn35UjtkQ9IHFtGoJXFmGgl71Ttxtq98H4qAbEvbYbhWmlPRYfkS6VLB5lGaynQc6uKL0Mxe8lYLJXG0WlOGJLFubaDTMXu/S4Oh6UiS1ZQU5cIWqXpmCs3W+/1/V3d0dYqR15S6KRvCGjV+Ij0qGqmomtVbAqMllBTUUt4qWolKPVtt5Nz449kRG4uuo5sMPElqgHVRRVI/31KEy3tV1UStbiznDzh/vnGYj8Mkat0yVyZg5+/VtGI7f1ZBUOIOLdCMwo8IR70w49E1uyMFdXC43YNnKNLVlIY30jot8Ix8wK7z2KSslI7rCNlYh6M1wlwESWwH09InJSUcviMCq8FgHuHmhkG1uyMFdNB3ZsOh6UI7ZkRRGfRWPC4cNRMsEbwYk1iPk5QndIRJZZd0O9j4ewyYqSN2aib24gXOcM1B0KkeW29ZoSW27uyJriVyYDK4E83YEQacK81kIc3NaTNeUnFasTkVW5WmkqMkdsiYgsipmtZTCvJSKyJldLFY9qZGJLRGRFnIpMRETk3FytNGLL4lFERNaka2NHvc/BVbZERJbkaqXE1svTXcfDEhGRZu7uLBVKRETkzFytlNjavZnYEhFZERNb6+BBbCIia3K10hpbH7uHjoclIiLNPJjYWoafn5fuEIiISAM3a43YMrElIrIiDw8tXeZIAz9fT90hEBGRBi5WSmx9mNgSEVkSpyJbh68vR2yJiKzI1UqJrbc3j+ISEVkRE1vr8OdUZCIiS3Kz0hpbbzsLShARWZGvDw9sWgWnIhMRWZOHh5uVRmw5FZmIyIqCAr11h0C9hFORiYisKcDfbqU1tjyKS0RkRYFMbC1D6mnoWmdFRET6BAbo2dZzKjIREfUKm82V01MtxMXFBb58v4mILCcgQM+MHRaPIiKiXpuaJMkOWYcfpyMTEVlOIEdsiYjImXEasvUwsSUisp6AACutsWVVTCIiywnStKEjffz8uL0nIrKaQCsltlIV081NT38jIiLSgyO21sMRWyIia3G3uWkrFKwlu5Sktm8fXx0PTUREFltzQ/owsSUispbAQH2zs7QNm4b099P10EREpEGQxo0d6cEq2ERE1tKvr58VE1t/XQ9NREQacCqy9fj5ccSWiMhK+vX1tV5iO4CJLRGRpQRxKrLl9An20R0CERH1IksmtiEhTGyJiKyEI7bWMyQ0SHcIRETUiyw5FXkA19gSEVnKAB7QtJwhocFwcdEdBRER9RZrjthyKjIRkaWKCEmrN7IWu5c7uyAQEVlIPyuO2Pbv78ejuEREFhq5I2vidGQiIuvo38+Cia2Hu41H74mILGIokxvLGsqDGkRElmCzuWotGqgtsRWcjkxEZA1DhzC5sSqO2BIRWedAppubvvSSiS0REfW4IYOZ3FgVp6ETEVnDqBF9tT6+1sSWFTKJiKyBI7bWxWnoRETWMGpkP+smtiOH683qiYio58m0pEEDA3SHQRoLiXh52nSHQUREPWzUCAsntqNH6X3yRETU8ySptdncdIdBmri4uCCUU9GJiJzeSCtPRZYFxp48iktE5NRYPIj4GSAicm5BQd4IDtJXEVl7YivT00YM43RkIiJnxnYvxMSWiMi5jdI8DVl7YivGcDoyEZFTY2JL/AwQETm3UZqnIRsisR09qr/uEIiIqAdxtI74GSAicm6jNFdENkRiO4aJLRGR03J1dTHExo7097J1cdEdBRER9ZTRnIr8V8sf2fEhIiLnXHNj93LXHQZpJp+BgSFs+URE5IzcbW6G6FevPbGVqshGeCGIiKj7TZowUHcIZBCTJw3SHQIREfWAYUODVVFg3fRHINOROU2NiMgpTZrAZIb+Mm1KqO4QiIioBxhlyZEhElsWkCIick6TJ3LElv4ybTITWyIiZzTKAOtrDZPYsoAUEZHz6RPsgwFcV0l/GzwoEH37+OgOg4iInLDVj4ES236slkhE5GQmjudoLe1pyqTBukMgIqJuJDkcpyK34OvrhdEjOWpLRORMJk/k+lra01RORyYiciojR/RDgL8dRmCIxFaETR+iOwQiIupGrIhMrU2bzBFbIiJnMmvGUBiFgRJb47woRER0YNzd3TB2dIjuMMhghg/rA39/L91hEBFRN5k1YxiMwjCJ7dRJg1VzXyIiMr+xo/ur5JaoJRcXF0yZyFFbIiJn4Olpw1QDzcQxTGLr5eWOiZy2RkTkFLi+ltpjpJ0gIiI6sIKAHh42GIVhElsRNo3rbImInMGkCUxsqW1cZ0tE5BxmGWh9reES25kGe3GIiKjzbDZXFgSkdo0e1R/edg/dYRAR0QGaFWac9bWGS2zHjx3AjR0RkRNMNfXx9tQdBhmUm5srJk3k0iMiIjMLDvLGqBHG6F9ryMRWNnbTpnCKEhGRmc2dPVJ3CGRw09jPlojI1GYasKONoRJbwbY/RETmNncOE1vaNxaQIiIyt1kGm4ZsyMTWiNk/ERF1zNAhwRg8MFB3GGRwE8YNgI8Pp6sTEZnVLAP1rzVsYjtieF8EBXnrDoOIiLpg7uwRukMgE7DZ3HDwQfysEBGZ0cjhfREc7AOjMVxiK+bMHK47BCIi6gKur6WOOmTeKN0hEBGRk0xDNmxie9ghY3WHQEREneTn64nJE9m/ljpm9qwR8PS06Q6DiIg6iYltJ/vZ+vt56Q6DiIg6mahIdXuijrB7uRtyjRYREbXPw8MNUycZswCgq1HX3iyYO1p3GERE1AlcX0udtYDTkYmITGXG1CGGnW1jyMRWHHYopyMTEZmFjNTOZn0E6qT5c0ZxlJ+IyESOPHwCjMqwW5OwaUMQ4G/XHQYREXWArK314xIS6iT5zEyfEqo7DCIi6gBvuwcOmWfcWbWGTWzlCO4h8437whER0T+MvKEjY1t4yBjdIRARUQccumCMYachGzqxFayOTERkfDabK448bLzuMMikFi4YC3ebm+4wiIhoP445wrjTkA2f2Mr0pKBAb91hEBHRPhx80AgEBHDpCHWNdEE4aCarIxMRGVlIPz9MM/jSEUMntpyOTERkfMccOUl3CGRyixaO0x0CERHtw5GLJsDFxQVGZujEVhzO6chERIYls2rY5ocO1PyDR8HLy113GERE1I6jDT4N2RSJ7dTJoegT7KM7DCIiasMRh41juxY6YJLUzpszUncYRETUhvFjB2BoaDCMzvB7I66uLli4gBUTiYiMiNOQqbuwABkRkTEdbYLRWlMktuL4YybrDoGIiFoZNbIfRo/spzsMchJSQIr964mIjNf5YJFJ6iCYIrEdNaIfJo4fqDsMIiJq4dgjJ+oOgZyIzeaG44/hDAAiIiOZM2uEaQ46miKxFScdN0V3CERE1LJ37eGcOkrd69QTp3PNNhGRgRxtkmnIwjRbj8MPHQdfX0/dYRAR0d9HcAMD2Geculf/fn5YMHeU7jCIiAiAn68n5s42T2E/0yS2np42HLXIPEcMiIicGachU08545QZukMgIiL8NbDo7u4GszBNYis4HZmISL+gIG8czN611EOmTBqMMaP76w6DiMjyjjHZQWxTJbYjhvXFtCmhusMgIrK000+argr9EPUUjtoSEek1fmyI6Yr3miqxFaefPF13CEREluXl5Y6TT5imOwxycosOHadmBhARkR5nnTYTZmO6xHb+waMQ0t9fdxhERJZ03FGT4O/npTsMcnKypovLj4iI9Ajp74+FC8bAbEyX2EobgFNOmKo7DCIiy3F1dcGZp3KKKPWOU46fBndOeSci0rIcxM2ErdfMFzGAE4+dAi9Pm+4wiIgs5ZB5ozFoYKDuMMgigoN9cNgh5hsxICIyMx8fT5xwzGSYkSkTWz8/LxzJ1j9ERL3qnDNm6Q6BLIZFpIiIeteJx06Gt7cHzMiUia0494xZalocERH1vKmTBmPCuAG6wyCLGTd2ACZNMFdVTiIis3Jzc8XpJ5v3gKJpE9vBgwJxFEdtiYh6xTlnmq86IjkHM+9kERGZyaKF49C/nx/MyrSJrbjo3DkctSUi6mFDhwRj7uyRusMgi5LKnP36+uoOg4jIqbm6uuCCsw+CmZk6sZVR2yMPH687DCIip3bWaWFwceFBRNI3Ne7UE9k7mYiopwtEDhvaB2Zm6sRWXHzewRy1JSLqIUFB3jj6CC77IL1OO2k6ggK9dYdBROS0LjxnNszO9ImtjNoecRhHbYmIesKZp8yAhzvbq5FedrsHLjpvju4wiIic0sEHjcDoUf1hdqZPbMXF53GtLRFRd+vbx4eFe8gwTjpuCgYNCNAdBhGR07nISQ4cOkViGzo4iKO2RETd7OLzD4aXl7vuMIgUm80Nl188T3cYREROZca0IZg43jnaqjlFYis4aktE1H2GhAbh+KMn6w6DaK9WFGOcYLocEZGRDmI7C6dJbNWo7cJxusMgInIKV14yX1WjJTISqc591WULdIdBROQUFswdhelTQuEsnGqv5SJWSCYiOmAyJenQ+WN0h0HUpoPChiFs+hDdYRARmZq7zQ3/uvJQOBNXZ5s6x762REQH5prLOSJGxnb1pQvA1spERF13xqkzMHhgIJyJUyW24qpLF8BuZ7ETIqKumDt7BKZOdp5pSeScxo0dgIULxuoOg4jItD3qLzrXOSohO3Vi27ePLy442/wNhomIepss5eD6RTILrgMnIuqaKy6eD29vDzgbp9winHV6GAYPcq6hdSKinnb0ogkYMayv7jCIOkS28yccy8rdRESdMWZUfxx31CQ4I6dMbD3cbbjuKudaDE1E1JM8PNxw2UXsEUrmcsl57LVMRNQZN1yz0GmL7TplYivmzRmFObOG6w6DiMgUTjtpOvr389MdBlGnBAf74KxTw3SHQURkCgsXjHHqOhpOm9iK668+TJWyJiKi9gUHeeNCJywiQdZw7pmzEOBv1x0GEZHhZ2b96wrnntFqgxOT9j+nnzIdn365VXcoRESGdd1VC+Hr46k7DKIukQIoV106H8+99JvuUIi0czgakRz3C3Iyt6K2phQengEYEHoQho06Ci4temRVlOcgMeZ7FBcmqNv4+IZg0oxL4WUPavN+t298BSWFCXudH9xvAqbOukr9npq4EmlJK9XvQ0cuwpARhzVfr7Q4BbERXyJs7s1wdeWgkw7nnD4LA0L84cycOrEVF593MH5bGY2CwgrdoRARGc7M6UNxxGHs/03mdsKxU7BidQy27UjTHQqRVqmJK5CRug4Tpp4Hb98BKCtJQ8zupbDZvBA6/K/RuqqKfGzf8D8MDJ2D4aOPUZdVlGfD1bX9tGDyjEvR6Gho/ru+thKb1z6HfgOmqb/LSzORHPczpsy6UrJr7N76JoL6joWv3yA0NjYgNvwLjJ18FpNaTfr28cF5Zx8EZ+fUU5Gbj+SyfQUR0V7c3d1wy/WLdIdB1C1uv+koFpIiyyspSkbfkEno038i7N7B6D9wmkowS0tSm6+TGPcj+vSbgFHjT4JfQCjsPn3RN2QyPDzbr7Pg7uEDT0//5lNhfgzcXN3R/+/EtrIiFz5+AxHUZ4x6PB+/Qagsz1WXyShuQPBI+AcO7YVXgNpy1aULYLfA96PTJ7ZNLSwmTRioOwwiIkM5/6yDEDq47WlnRGYzcEAArriYlb3J2gKChqOoIE4lmqK8NAMlRUno03eC+lumHRfmRsHu0w87Ny/B2t8fwNZ1LyAvZ3enHic7fSP6D5oBN9tfy1gkqa2qzEN1VRGqqwpRVZH313kV+chO34wRY4/rgWdLHTFh3AActeiv99/ZuTgcDgcsICYuB9fd+gkaGy3xdImI9il0UCDeevUi1R6NyFnINv6mOz5FRFSW7lCItJDENTH2R6QlrlRramU3X5LKYaOOVJfX1JRi/YqH4OrmgRFjjkNgn9EozItGUuyPmD77WvX3/sh62W3rX1TrZf0DhzWfL1Og05NXqd9Dhy/E4KHzsGPTqxg8bAEcjY1Ijv8Fri5uGD3xVAQGj+rBV4GayLLql58/FxPHW2OAzzJ7NOPGhODMU8Pw2VcsJEVE1iYbuttuPopJLTkd6c145y1H48obPkRd3T/rAYmsIjdrJ3Izt2HCtAvh4xei1r7GR30Dz7+LSMn6V9G3/yQMGbFQ/e7nPxilxcnITFvfocQ2K32jGo1tmdQKSWTl1ERGam02TwQEDsfG1U9i5rxbUVNdjMgdH+DghffB1Y3boJ525qlhlklqLTMVuckVl8zDsKHBusMgItLqxGOnYPoU5+1jR9Y2dEgwLjn/YN1hEGmRGPOdqkgcMmiGKtw0YPAsNXqakvh781pZFxdXVViqJW+f/moa8f401NcgN2uHKjy1L7W15WqEdvSE09UIr7dPP3WSNbiOxgZUVv41VZp6zohhfXDlpfNhJZZKbGV04p7bjoWbm6WeNhFRs759fHHNFYfoDoOox3vbjhnVX3cYRL2uoaFW5uXscZ5q8/P3SK1UPvYLGIqqv9fgNpH1se21+mkpN3snGhvrETJo5j6vlxD1rUqoveyBanq0TEVuov62xkpIbdxtbvjPHcdZbmaW5TI8mZJ80bmzdYdBRKTFv284Aj7e7FlLzk0OYN95y1E8kE2W06f/JKQk/IaC3EhUVRYiL3sX0pNWqarHTaS/rIy6ytTjyoo8pKesQX5uJAYN/Wd0L2rnx6rPbVtFo+S+ZOS3PVIxWe538LC/7k8S6cqKHBTkRSEzdT0gI8Y+/br9udM/Lr3wYIweab3X2Fpp/N8uPHcO1m1MRGw8p0EQkXUsWjgOc+eM1B0GUa8YPao/zjtzFj78dJPuUIh6zZiJpyEp9ifERnyJutoyeHgGYODQuRg++ujm6/QbMBVjJ52J1MTfER/5New+/VWf2sDgf7YP1dVFfxVkaEHa90iF5akHXbPPEeO4yK8wcfrFasqzkFHb0RNPR/SupWrEWHrsurl59MjzJ2DKpEE490zn71lr6arIrSWnFuCamz5CbS2LSxCRNaYgv/nKhQjwt+sOhajX1NbV4+obPkJKWqHuUIiIepy33UNt66X9mRVZNrEVUiH51TdX6w6DNJLqfAkx36tS940NtbB798W4qefBP2CIury2pkxdXpQfg/q6KtVgfMzE0/c5hSYrfRNidi/d4zwXVxsWHvNM89+piStVw3IhRR5kWlATKbIgR1qljL6rq1sPPGuyYqXY5584A9On/vW5JrKSyOgs3Hj7p2z3R0RO7/abj8IJx/wz7dxqLDkVuWUJ7LUbErArPEN3KKRBXV0ltm34H4KCR2PqrKvg7uGrGom72/4a0ZJjPuHb3oaLixsmh10Om80Lacl/YOem1zD7kDubm5K3xc3mhdmH3t38t0uLQg5Sej857mdMmXWlKuawe+ubCOo7VlUvbGxsQGz4Fxg7+SwmtdRtLjhnNpNasixpdXH6ydPxxTfbdYdCRNRj5h080tJJrXC1+ijG3f8+Bna7u+5QSIPUxBXw8grEeBmhDRwGu3cfBPcbB7tP3+YKgTJ6KutQ/AOHwtu3v/q9sbEOOVn730Hy9PRvPnl4+jWfX1mRq/q/Scl7SWh9/AapdStCRnFlVFgej6i71tqw9QlZ3RUXz8eggdacmkdEzi8wwI7bbzoKVmfpxFbIHPRrrzxUdxikQUFOBPwChiBi+3tY+/sD2PLn86pCYBMpZy+k0EETKYQgf0vxhH2R4gnrVz6K9Ssfwe6tb6GiLLv5MklqJWmWfnHVVYWoqsj767yKfNXMfMTY43rk+ZL1+Pt54b47j2dlWLI8Ly93PHDX8aoFBhGRs7ntpqMQFOgNq+PeDoCTjpuKOQeN0B0G9bKqqgJkpK5T62qnzroag4bOU9UBJbkU3j4h8PQKQmLsD2rasiS6qQm/q3W5tTWl7d6vNDkfP+UcTJ55OSZMPV9NN9624SVUVxWry318QzBi7AnYufk17Ny8BCPGnaDOi4n4HCPHn4jCvBhsWvOMSrSLCxN67fUg53PHzUehf79/ZgsQWdm4sQNw7VU8kE1EzuW4oydhwdxRusMwBEsXj2qppLQK19z0MXJy209YyLms+vkONWIbNvem5vOkRH1ZSZoq3CTk9+jdn6KiLFP1XZPpw381OgemHnR1hx5H1s1uWvMUQgaGtTsaK8l0fu5ujJ10FjaufhIz592qEuionR/h4IX3wdXN0svhqQtOOWEabrl+ke4wiAznkad+wMrVsbrDICI6YAMH+OPNly+CtzfbJwmO2P5NWmA8ct+J8PDgNCWr8PD0h7dvyB7nySitTBFuIonvQQtux4IjH8e8wx/CtIOuQV1tJby8+3T4caQIlJ9/KKoq89u8vLa2HMnxv2D0hNPVml6puCwnSaIdjQ2orGS/ZeqckSP64jqOTBG1SdahDRkcpDsMIqIDrhV0z23HMqltgYltC2NHh+DW64/QHQb1koCg4aiq2DNplLWvXvbgva5rc7fDw9MXlRV5ahS3b/+OV51zOBpRXpalEum2JER9i9DhC1UDc7muo7Fxj9tyUgV1hpenDQ/efQI8PDjKT9QW2Ql88D8nwNOT/0eIyLwuvWAupkwarDsMQ2Fi28qxR03CycdP1R0G9QJJJmWENCXhN5Ww5mRuRWbaBgweOr/5OrlZO1BUEI+qygLk54SrdbF9Qyar6slNonZ+jMSY75v/To77Ra2TlduUlaSr6cQ1VYUYGDpnrxgK82PUYw8e9tdj+gUMRWVFDgryopCZul5Nf95Xz1yi1m741+EYOmTvgzNE9I9RI/rhpmsP1x0GEVGXLFo4Dhedt/d+pdXxcGUbbrjmMMQn5qmm7uS8pKXOpLDLkBTzA5Ljl8NuD8boCacgZPDM5utIkaiE6GWorSlTI64DBs/CsNF7llOvri6ScsnNf9fXVyEm/DN1W5u7N/wCQjHj4Jvg4zdgr8rJsqZ34vSLVbVlIaO2oyeejuhdS1X15QlTz4ObG6eYUMc3dFbvYUfUUccfPRnhkZn4aXmE7lCIiDps3JgQ3HnL0brDMCQWj2pHXn45rrn5IxQVVeoOhYioQxu6F54+S7U1IaKOqa2rxy13fo6omH9ashERGVXfPj549YXz0bePr+5QDIlTkdvRr6+vWqfG/o9EZHSygXvsgZOZ1BJ1koe7DY/cdxKCg9j/kYiMTQrcPnr/yUxq94FZ2z5MmxKKay4/RHcYRETtkmT2iYdO4YaOqIvk/87D954Edxu7IhCRccn04/Fj91zWRntiYrsfZ50WptatEREZjSzt/s/tx2LMqP66QyEytckTB+GGfx2mOwwiojZdcM5sHHHYeN1hGB4T2w644+ajMHJ4X91hEBHt4cpL5uOQeaN1h0HkFKQjwonHTdEdBhHRHhbMHYUrLp6nOwxTYGLbAZzqR0RGc8yRE3H+2bN1h0HkVG6+9nBMZV9IIjKIkSP6qplZLi26b1D7mNh2UEh/fzzz6Gnw9fXUHQoRWZzseN9245G6wyByOjabGx5/8GS1M0lEpFNggB2PP3AK7Ha2fewoJradMGJ4Xzz+wMmqKhkRkQ6DBgTgkftPgrs7v4eIeoKvrxeeffR0DBoYoDsUIrIoKWYnFdsHhPjrDsVUmNh20tTJobj/zuPh6sopAUTUu3x8PNWyiAB/u+5QiJxacLAPnnv8DNUzkoiot91ywyJM4bKITmNi2wUL5o3GLdcv0h0GEVmI9NR+8J4TMGxoH92hEFnCwAEBeOax0+Hv56U7FCKyWEeW44+erDsMU2Ji20UnHTcVl144V3cYRGQBMkPk3juOxUFhw3SHQmQpI4b1xZMPn6qKSBIR9bRjj5yIa688VHcYpsXE9gBccv7BOPmEqbrDICInT2rvuvVoHH4o+2kT6TBx/EA8JuvabVzXTkQ957BDxuL2m49iBeQDwMT2AN187SIcOp99JImo+8m27dYbjsDRR0zUHQqRpc2cMQz33nkc62sQUY+YO3uEmpkly46o6/jqdccUwTuPw/SpobpDISInc8M1h+HEY6foDoOIACxcMAb/ZpstIupmYdOH4KF7T1TtxujAMLHtBh7uNjx6/8kYM6q/7lCIyElcc/khOP3kGbrDIKIWTjhmMv51xSG6wyAiJzF54iA89sApKpegA8fEtpv4+nji+SfOwPixIbpDISKTk8J05545S3cYRNSGc86YhfPPOkh3GERkcuPGhKjidHYWp+s2TGy7kZ+fF5574gx19IWIqCvOP/sgVZiOiIzrqssW4KTjuEyAiLpGBsKee/x0NTBG3YeJbTfz8fZUfe9mTB2iOxQiMpkzT52Bqy5doDsMIuqAW64/AosWslo5EXXOhHED8OzjZ8DXlz2yu5uLw+FwdPu9Empr63H/o99h09Zk3aEQkQlI67Bbrz9CdxhE1AmNjQ68+OoKLPthl+5QiMgEJk0YiKcfPU0NhFH3Y2Lbg+rqGvDwkz9g7YYE3aEQkYGddvJ03HjNYexdR2RS7328Ae9+uF53GERkYLJU8elHToO3t4fuUJwWE9se1tDQiMee+Ql/rInVHQoRGdCVl8zHBefM1h0GER2gZT/uwouLV6hRXCKilqZOGoynHjkVdjuT2p7ExLaXkttnXliO5b9H6Q6FiAxCmrDfduOROO7oSbpDIaJusnptnDqYLTO2iIjErBlD8cj9J7P6cS9gYttL5GX+78u/4/ufdusOhYg08/K04YF7TsDc2SN1h0JE3WzHrjTc98gyVFTW6g6FiDQ78bgpuOW6RepgNvU8Jra97JXX/8AX32zXHQYRaeLv56X61k0cP1B3KETUQ+ITcnHXA1+jsKhSdyhEpIGUzLj6skPYk76XMbHV4ItvtuHVN1dzHQ6RxfTv54dnHj0Nw4b20R0KEfWwzKxi3Hn/18jILNYdChH1Ik9PG+657VgsXDBGdyiWw8RWkw2bk/DY0z9yqhKRRYwY1gdPP3o6+vX11R0KEfWSouJKNXIbF5+rOxQi6gVBQd54/IFTVK9a6n1MbDVKSs7Hfx7+Ftk5pbpDIaIeNGXSIDzx4Clsxk5kQZWVtbj/sWXYtiNNdyhE1IOGDQ3GUw+figEhAbpDsSwmtpoVl1SqIhMRUVm6QyGiHjD/4FF44O7j4eFh0x0KEWkiVZKfeO5ntv4jclIzpw/FQ/eeCF8fT92hWBoTWwOoravHcy/8il9XRusOhYi6sXDEhefMwaUXzoWrq4vucIhIM6mrsfiNVfjyWxaQJHImJxwzGbfecAQrHxsAE1sD+XDpRrz9wTrwHSEyNx8fT9xz2zFqtJaIqKXfVkbj+f/9hurqOt2hENEBHsC+8pL5OP/s2bpDob8xsTWYVX/G4snnf0FNTb3uUIioC4YP64NH7zsJoYODdIdCRAaVnFqAhx7/HilphbpDIaIu8PBwU5WPDztkrO5QqAUmtgYUE5uN+x5dhvyCCt2hEFEnHH7oWNxxy9Gwe7nrDoWIDK6qug7//d9vagSXiMwjKNAbjz1wMvvRGxATW4PKyy/Ho0//gN0RmbpDIaL9kHU1V1+2AGefPlN3KERkMst+2ImXX1+lCkwRkbFNnTwY995xnOpLT8bDxNbAGhoa8fb76/DJF5u57pbIwEduH7jnBEyfEqo7FCIyqZi4HDz85PfIymb7PyKjHsC+5PyDccE5s1kQ0sCY2JrApi3JePL5n1FcUqU7FCJqQaYhPfSfE9Gvr6/uUIjI5MrLq1WNjXUbE3WHQkQtDBzgj/vuPJ5Tj02Aia1J5BfI1OQfsSs8Q3coRATg5OOn4oZrDoO7u5vuUIjIScgu2dIvtuDN99aq9kBEpNcRh43HrTcsgo83+9OaARNbk01N/uCTjfhg6UZu8Ig0CfC3q43cwgWshEhEPWPn7nR1MLugkEUkiXTwtnvg5usOx9FHTNQdCnUCE1sTCo/MxOPP/oTsHK7FIepN8w4eidtuPBLBQT66QyEiJ1dYVIHHnv4J23el6Q6FyFLGjx2A++46DoMHBuoOhTqJia1JVVTW4IVXVrBNAFEv8PHxxA3XLMSxR07SHQoRWWym1nsfb8DHn21WvxNRz5GiUOeeOQuXXzRPFYsi82Fia3KS2L6weAUqKmp0h0LklMKmD8Fdtx7D0v5EpLVq8jMvLEdiUr7uUIicUt8+vvjP7cdixrQhukOhA8DE1gnk5ZfhxcUrsXZDgu5QiJyGl6cNV19+CE49cRpcXFjan4j0qq9vwEefbsKHn25CfT1Hb4m6yyHzRuP2m4+Cv5+X7lDoADGxdSJr1sXjpVdXqgrKRNR1kyYMxN3/Pgahg4N0h0JEtAcZtX36/35BbHyu7lCITM3H20MdwJYuB+QcmNg64drbt95bh29/2MnKyUSd5G5zw2UXzcXZp8/k+hoiMixZb/vpl1vU+tva2gbd4RCZzlGLJuBflx+C4GAWg3QmTGydVGR0Fp5/6TckJnM9DlFHTJ44CLdefwRGjuirOxQiog5JSy/CC4t/x7YdrJxM1BGyjb/lukWYMmmw7lCoBzCxdfIjup99tVUd0a2pqdcdDpEh9e3jo6YiHXX4BN2hEBF1ya8ro/Dqm6tRVFSpOxQiw3Y3uPyiuTjlhGmckeXEmNhaQGZWMf7vlRXYsi1FdyhEhuHu7oazTg3DhefOht3uoTscIqIDUl5ejTfeXYvvf97NpUhEf5Paj8ccMVEdwA4K9NYdDvUwJrYWO6K7+PVVKC6p0h0KkVZzZ4/A9VcfhsGD2HydiJxvKdL/vfw74hPzdIdCpNWYUf1x07WHq6VGZA1MbC14RPeTz7fgy2XbOT2ZLGfI4CBcf/VCzDlohO5QiIh6dCnSsh934YOlGzk9mSzH11emHc9T1Y457dhamNhaVF5+Od77aD1++jWCU5bI6XnbPXDReXNw5qkzYLO56Q6HiKhXVFXX4ctvt6sKyuXlNbrDIerxacfHHjUJV1+2AIEBnHZsRUxsLS41rRBvvPsn/lyfoDsUoh7ZyElJ/2suY0l/IrL4bK0vtuCrZTtQXV2nOxyibjdmdH9V7Xji+IG6QyGNmNiSEhGVidff/hO7IjJ0h0LULQ4+aITqSTt2dIjuUIiIDKGwqAIffroJ3/+4G3X17H9L5jd0SDAuOf9gHHbIWLi6uugOhzRjYkt7WL8xUY3gJqUU6A6FqEtmzRiKyy6ax6O2RETtyMktxbsfbcDy3yO5HIlMWzPjovPn4IiF45nQUjMmtrQX2cjJxu6dD9cjN69MdzhEHTJtSqjqUTd1cqjuUIiITCE1vRDvfLAeq/6MBfcGyQykm4HUzDjysPEsDEV7YWJL7aqtrcePyyPw+ddbkZlVojscojaFTR+Ci86dg+lTh+gOhYjIlOITcvHm++uwcXOS7lCI2jRoYAAuPGcOjj5iAhNaahcTW+rQCO6adfH47Kutqj8ekVF60V547hxOOSYi6ia7IzLw1vvrsHN3uu5QiJTRI/vhvLMOwsIFY5jQ0n4xsaVOb/QkwV23MZHrcqjXyTqaQ+aNxoXnzMboUf11h0NE5JSiYrLxzfc7sHJ1LOrqWGSKet/UyYNxwdmzMXvWcN2hkIkwsaUuSc8owudfb8Mvv0eipqZedzjk5AID7Dj+6Mk46fgpGBASoDscIiJLKCmpwo/Lw/HtD7tUwSminm7RN3f2SJx/9kGYNGGQ7nDIhJjY0gEpLqnEN9/txLc/7ERxSZXucMjJTJowEKecMA0LDxkDD3eb7nCIiCxJZmit35SIb77bga07Ulloirr94LX0nD/hmMkYNrSP7nDIxJjYUreQUdtffotQR3UTk/N1h0Mm5uXljiMOG4dTT5jG6cZERAaTll6kDmb//FskKipqdIdDJl5adFDYMBx39GTMP3gkbDY33SGRE2BiS90uLiEXP/8agd//iEFJKUdxqWOGhAap0dljjpwIXx9P3eEQEdE+VFXX4dcVUfj2+508oE0dNnCAP447ajKOPWoi+vX10x0OORkmttRj6usbsGFTklqHu2FzEurrG3WHRAYjFQ7nzRmJU06chpnTh+oOh4iIumBXeDq++X6n6qDAbT215uHhhkPmjcHxR0/CjGlD4CKLaYl6ABNb6rUCFL+ujMLy36PUiC5Ze/rR9KmhOOyQsTh03hgEBNh1h0RERN2goLAcK1bFYs26OEREZbF7gsWNGd1fFX488rBx8PX10h0OWQATW+p1CUl5+OW3SPz2RzSKiip1h0O9lMxK6f7DJZldMAaBAd66QyIioh5UWFiBP9fHY/XaeOzYnY6GBo7kWoGfryeOPHyCGp1lnQzqbUxsSRvZyEl1xfUbE7F+UxJbCThhMjt54iA1Mrtw/hgEB/voDomIiDQoLavGug0Jaqry5m0p7I3rZAYNCMCc2SNw8EEj1IwsdjEgXZjYkmEkJuWrdgJykubwnMJkPrJsRnrPqWR2wRj07eOrOyQiIjKQyspaVXdj9do4bNySjOrqOt0hUSfZbK6YMmmwSmQPnj0CQ0ODdYdEpDCxJcOuyd2wJUmN5m7ZloKKylrdIVE7JHkNmz4EYdOHqgJQTGaJiKijrQI3b03G6nXx6qB2eTnbBxlVn2AfzJ41XCWzs2YMg7e3B4zq0ksvxXvvvYcnn3wSd999d/P533zzDU477TQw9XFeTGzJFNWVd4ZnYIMazU1CRmax7pBg9fUz06cOUZUNZ84YyiO1RETUPdv63enYvitd/YyJzUFdPacs61xONG5MSPOo7JhR/U1TzVgS208//RReXl5ITExEUFCQOp+JrfNjYkumk5dfjsjoTIRHZiEiKhPxCXnc+PUgT08bpkwcpEZkw6YNwZjRIWqDR0RE1JOjubKN3xkuiW4GIqOzuDa3B0nOOnhgIMaPG4CDwoap0VmzFnqUxLagoADx8fE46aST8Mwzz7SZ2H755Zd44IEH1PUGDhyIG2+8Ebfddpvm6OlAcHU3mU6/vr5YuEDWcI5Vf9fW1iMmLke1FpANn2wIC1ltuctC+vmpSoZjRvXD1MmhmDRxIAtBEBFRrx9UVQdU/+5xXltXj+iYbERGZyMqRrb32cgvKNcdpmkNGhigRmTHjg5RP6U1j6+PJ5yFm5sbnnjiCZx//vm46aabEBoausflW7duxdlnn42HHnoI55xzDtatW4frrrsOffr0UYkxmRNHbMkpZWYVI0KS3MgsRMZkITWtUB39pX/IqOuQ0CA1vUhOo0f2Uwmtvx97zRERkTlmcEmSKwUn5RQbn4OqKhajaqtqsSSuKpEdE4JxksQ6cV9ZSUyLi4vVCO3cuXMxceJEvPXWW3uM2F5wwQXIy8vD8uXLm29355134ocffkBERITW+KnrOAxDTmnQwEB1OurwCepv+RLLyS1DWkaRSnLT0ouQmv7XTysc8bXb3TFsSB81CtuUwI4a0U8dESciIjLrDK5+fcfg0Pljmrf1+QUVyMgqRkZGEdIzi1VdDjnJAe9qJz/A7ebm+vesq37No7GSyFr5gPXTTz+NRYsW4fbbb9/j/KioKJxyyil7nDd//ny88MILaGhoUCO+ZD7cqyVLkIIHA0L81UnWjrRuPaAS3r8TXUl8ZWOYn1+O0rIqmGFOQ2CAHSH9/RHS3+/vn3+dBvz9t5+FN2pERGSdbf1fya4vpk/Zc+pp66RXfqZnFKufmZnmSHplplVwkDcGhASo/ZmB8nPAXz8HDvBHv75+Krmlfxx66KE45phjcM8993CKsQUwsSXLk5L1cmRTTm1VaSwqrkRBYQUKiyrUz+LiKpXwlpRWo6ysWjWeLy2V86rVet+GBgcaGhu71IdXRlC97R4qpn9+usNu94CP9z/nBwV5/524+qN/Pz94ebl306tBRERkraRXyOwtGdUtLa1GRVUtKipq1IHv8r9/VjSdmv6uqFHXk987ur13d3eDh5w8bOr3ppNH80+bmmEVHOyDvsE+f//0/etnHx9VzImJa+c99dRTmD59OsaNG9d83oQJE7B27do9rid/jx07lqO1JsbElmgfbDY3dQRUTp0lR4cbGhr/SnTVz0bU//3zn5NDbcyaklZusIiIiHqf9GDvah/2qipJgCXJrUGjw6ESVA+PfxLXpqSV9JgyZYpaU/vSSy81nyfVjw866CA8+uijqnjU+vXr8fLLL2Px4sVaY6UDw+JRRERERETkdMWjmiQnJ6sR29ra2r3a/cTFxTW3+2m9FpfMhYktERERERERmRrnPRIREREREZGpMbElIiIiIiIiU2NiS0RERERERKbGxJaIiIiIiIhMjYktERERERERmRoTWyIiIiIiIjI1JrZERERERERkakxsiYiIiIiIyNSY2BIREREREZGpMbElIiIiIiIiU2NiS5Zw6aWXwsXFBU899dQe53/zzTfqfCIiIiIiMi8mtmQZXl5eePrpp1FUVKQ7FCIiIiIi6kZMbMkyjjzySAwYMABPPvlku9f58ssvMWnSJHh6emL48OF4/vnnezVGIiIiIiLqPCa2ZBlubm544okn8L///Q/p6el7Xb5161acffbZOPfcc7F792489NBDuP/++/Huu+9qiZeIiIiIiDqGiS1ZymmnnYbp06fjwQcf3Ouy//73vzjiiCNUMjt27Fi1LveGG27As88+qyVWIiIiIiLqGCa2ZDmyzva9995DVFTUHufL3/Pnz9/jPPk7Li4ODQ0NvRwlERERERF1FBNbspxDDz0UxxxzDO655x7doRARERERUTewdcedEJmNtP2RKcnjxo1rPm/ChAlYu3btHteTv2VasqzPJSIiIiIiY2JiS5Y0ZcoUXHDBBXjppZeaz7vttttw0EEH4dFHH8U555yD9evX4+WXX8bixYu1xkpERERERPvGqchkWY888ggaGxub/w4LC8Nnn32GpUuXYvLkyXjggQfUdaSIFBERERERGZeLw+Fw6A6CiIiIiIiIqKs4YktERERERESmxsSWiIiIiIiITI2JLREREREREZkaE1siIiIiIiIyNSa2REREREREZGpMbImIiIiIiMjUmNgSERERERGRqTGxJSIiIiIiIlNjYktERERERESmxsSWiIiIiIiITI2JLREREREREZkaE1siIiIiIiIyNSa2REREREREZGpMbImIiIiIiMjUmNgSERERERGRqTGxJSIiIiIiIlNjYktERERERESmxsSWiIiIiIiITI2JLREREREREZkaE1siIiIiIiIyNSa2REREREREZGpMbImIiIiIiMjUmNgSERERERGRqTGxJSIiIiIiIlNjYktEREREREQws/8HrplCG+QitjoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datos para los gráficos de quesitos\n",
    "etiquetas = tabla_contingencia_OT.columns.tolist()\n",
    "\n",
    "# Obtener los colores de la paleta \"mako\"\n",
    "colores_mako = sns.color_palette(\"mako\", len(etiquetas))\n",
    "\n",
    "# Crear los subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Gráfico del grupo A\n",
    "tamaños_A = tabla_contingencia_OT.loc['A']\n",
    "ax1.pie(tamaños_A, labels=etiquetas, autopct='%1.1f%%', startangle=140,  colors=colores_mako)\n",
    "ax1.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax1.set_title('Hacen Horas Extra')\n",
    "\n",
    "# Gráfico del grupo B\n",
    "tamaños_B = tabla_contingencia_OT.loc['B']\n",
    "ax2.pie(tamaños_B, labels=etiquetas, autopct='%1.1f%%', startangle=140,  colors=colores_mako)\n",
    "ax2.axis('equal')  # Para que el gráfico sea un círculo\n",
    "ax2.set_title('No Hacen Horas Extra')\n",
    "\n",
    "# Título principal del gráfico\n",
    "fig.suptitle('Rotación según género', fontsize=16)\n",
    "\n",
    "# Mostrar los gráficos\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBSERVACIONES: Observamos que en el grupo A que son los empleados que hacen horas extra en la empresa, la tasa de rotación es del 30.5% mientras que en el grupo B que no las hacen, la tasa de rotación es del 13.3% lo que significa que el los empleados que hacen horas extra tienen mas del doble de probabilidad de dejar la empresa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizamos los datos de las variables de la tabla de contingencia.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "#Gráfico de barras para grupo A.\n",
    "sns.barplot(x=tabla_contingencia_OT.columns, y=tabla_contingencia_OT.loc['A'], ax=axes[0])\n",
    "axes[0].set_title('Grupo A')\n",
    "axes[0].set_xlabel('Rotación')\n",
    "axes[0].set_ylabel('Frecuencia')\n",
    "axes[0].set_ylim(0, 90);\n",
    "\n",
    "#Gráfico de barras para grupo B.\n",
    "sns.barplot(x=tabla_contingencia_OT.columns, y=tabla_contingencia_OT.loc['B'], ax=axes[1])\n",
    "axes[1].set_title('Grupo B')\n",
    "axes[1].set_xlabel('Rotación')\n",
    "axes[1].set_ylabel('Frecuencia')\n",
    "axes[1].set_ylim(0, 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrition     0    1\n",
      "Group_ot            \n",
      "1           178   78\n",
      "2          1177  181\n",
      "\n",
      "Chi-cuadrado: 45.70867962674184\n",
      "p-valor: 1.3721343132620265e-11\n",
      "\n",
      "Hay una diferencia significativa en las tasas de rotacion entre el Grupo de A  y el Grupo B .\n"
     ]
    }
   ],
   "source": [
    "contingency_table_OT = pd.crosstab(df['Group_ot'], df['attrition'])\n",
    "print(contingency_table_OT)\n",
    "\n",
    "# Realizar la prueba de proporciones\n",
    "chi2, p_value, _, _ = chi2_contingency(contingency_table_OT)\n",
    "\n",
    "print(f\"\\nChi-cuadrado: {chi2}\")\n",
    "print(f\"p-valor: {p_value}\")\n",
    "\n",
    "# Imprimir el resultado de la prueba\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"\\nHay una diferencia significativa en las tasas de rotacion entre el Grupo de A  y el Grupo B .\")\n",
    " \n",
    "else:\n",
    "    print(\"\\nNo hay evidencia de una diferencia significativa en las tasas de rotación entre los grupos.\")\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En conclusión : Dado que el p-valor es extremadamente menor que el nivel de significancia alfa (0.05), rechazas la hipótesis nula. Esto significa que hay una evidencia estadística muy fuerte de que existe una relación significativa entre los empleados que hacen horas extra y la rotación de empleados, siendo los empleados que hacen horas extra mucho mas probable que abandonen la empresa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dado que hay una relación tan fuerte, la empresa debe revisar y ajustar las políticas de horas extra para evitar sobrecargar a los empleados, promoviendo un mejor equilibrio entre la vida laboral y personal."
   ]
  }
 ],
 "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}