aquisicao_clinicas/hipoteses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Realizando os testes de hipóteses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando as bibliotecas.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import library as lib\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set_theme(style=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bibliotecas para testes de hipoteses.\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import f_oneway, sem, t, ttest_ind, chi2_contingency, norm, contingency\n",
    "from statsmodels.stats.proportion import proportion_confint, proportions_ztest\n",
    "import statsmodels.stats.api as sms\n",
    "import pingouin as pg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregando nossos datasets.\n",
    "df_trial_metricas = pd.read_csv(r'..\\dados\\df_trial_metricas.csv')\n",
    "df_funcionalidade_metricas = pd.read_csv(r'..\\dados\\df_funcionalidade_metricas.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_clinica</th>\n",
       "      <th>id_canal</th>\n",
       "      <th>data_inicio_trial</th>\n",
       "      <th>data_fim_trial</th>\n",
       "      <th>num_dias_trial</th>\n",
       "      <th>total_atividades_realizadas</th>\n",
       "      <th>solicitou_credenciamento</th>\n",
       "      <th>credenciamento_aprovado</th>\n",
       "      <th>credenciamento_status</th>\n",
       "      <th>realizou_assinatura</th>\n",
       "      <th>data_assinatura</th>\n",
       "      <th>status_assinatura</th>\n",
       "      <th>num_dias_para_assinatura</th>\n",
       "      <th>nom_canal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9825</td>\n",
       "      <td>0</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>4</td>\n",
       "      <td>173</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>active</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Paid - meta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9984</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>2023-08-31</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paid - google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9986</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>2023-09-14</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paid - google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9988</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>2023-08-31</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Não aderiu ao plano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paid - google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9690</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-08-17</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Não aderiu ao plano</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_clinica  id_canal data_inicio_trial data_fim_trial  num_dias_trial  \\\n",
       "0        9825         0        2023-08-21     2023-08-25               4   \n",
       "1        9984         1        2023-08-24     2023-08-31               7   \n",
       "2        9986         1        2023-08-24     2023-09-14              21   \n",
       "3        9988         1        2023-08-24     2023-08-31               7   \n",
       "4        9690         2        2023-08-17     2023-08-24               7   \n",
       "\n",
       "   total_atividades_realizadas  solicitou_credenciamento  \\\n",
       "0                          173                      True   \n",
       "1                           14                      True   \n",
       "2                            4                      True   \n",
       "3                            1                     False   \n",
       "4                            1                     False   \n",
       "\n",
       "   credenciamento_aprovado credenciamento_status  realizou_assinatura  \\\n",
       "0                     True             Concluído                 True   \n",
       "1                     True             Concluído                False   \n",
       "2                     True             Concluído                False   \n",
       "3                    False   Não aderiu ao plano                False   \n",
       "4                    False   Não aderiu ao plano                False   \n",
       "\n",
       "  data_assinatura status_assinatura  num_dias_para_assinatura      nom_canal  \n",
       "0      2023-08-25            active                       4.0    Paid - meta  \n",
       "1             NaN               NaN                       NaN  Paid - google  \n",
       "2             NaN               NaN                       NaN  Paid - google  \n",
       "3             NaN               NaN                       NaN  Paid - google  \n",
       "4             NaN               NaN                       NaN          Other  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trial_metricas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_clinica</th>\n",
       "      <th>id_funcionalidade</th>\n",
       "      <th>num_qtde_vezes</th>\n",
       "      <th>nom_funcionalidade</th>\n",
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       "      <td>medical_record</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8994</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>revenue_management</td>\n",
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       "      <th>3</th>\n",
       "      <td>8994</td>\n",
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       "      <td>1</td>\n",
       "      <td>scheduling</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8998</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>anamnese</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   id_clinica  id_funcionalidade  num_qtde_vezes  nom_funcionalidade\n",
       "0        8994                  5               1   budget_management\n",
       "1        8994                  2               2      medical_record\n",
       "2        8994                  9               1  revenue_management\n",
       "3        8994                  1               1          scheduling\n",
       "4        8998                  3               2            anamnese"
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df_funcionalidade_metricas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def teste_chi2(dataframe: pd.DataFrame, coluna1: str , coluna2: str) -> None:\n",
    "    \"\"\"Calcula o teste de hipoteses qui-quadrado - variáveis categóricas Associação : proporção : +2 categorias : 2 amostras.\n",
    "    Args: *vars_cat\n",
    "        var1 : pandas series : variável categórica do dataframe.\n",
    "        var2 : pandas series : variável categórica do dataframe.        \n",
    "    \"\"\"\n",
    "        \n",
    "    # var1, var2 = vars_cat\n",
    "    table = pd.crosstab(dataframe[coluna1], dataframe[coluna2])\n",
    "    alpha = 0.05\n",
    "    \n",
    "    print('***** Realizando a teste de hipóteses qui-quadrado *****')\n",
    "    print('* Premissas:')\n",
    "    print('- As observações devem ser contagens ou frequências.')\n",
    "    print('- Cada observação deve pertencer a uma única categoria.')\n",
    "    print('- A amostra deve ser relativamente grande, com pelo menos 5 observações em cada célula e, no caso de poucos grupos, pelo menos 10.') \n",
    "    print('- Os dados devem vir de uma amostra aleatória.') \n",
    "    print('- As categorias das variáveis devem ser mutuamente exclusivas.')\n",
    "    print('')\n",
    "    \n",
    "    # Hipóteses\n",
    "    print('----- Hipóteses -----')\n",
    "    print(f'H0 : as variáveis {coluna1} e {coluna2} são independentes. As proporções são estatisticamente iguais.')\n",
    "    print(f'Ha : as variáveis {coluna1} e {coluna2} são dependentes. As proporções são estatisticamente diferentes.')\n",
    "    print(f'Nível de significância de {alpha}.')\n",
    "    print('---------------------')\n",
    "    print('')\n",
    "        \n",
    "    # Executando o Teste qui-quadrado\n",
    "    chi2, p_valor, _, _ = chi2_contingency(table)\n",
    "    print(f\"Valor do teste qui-quadrado: {chi2}\")\n",
    "    print(f\"P_valor: {p_valor}\")\n",
    "    print('')\n",
    "    print('----- Conclusão -----')\n",
    "    if p_valor < alpha:\n",
    "        print(f\"- p_valor: {p_valor:.10f} < alpha: 0.05 => Rejeitamos a hipótese nula. Há evidência suficiente, ao nível de significância de {alpha}, para concluir que as variáveis {coluna1} e {coluna2} são dependentes.\")\n",
    "        print(f\"- Tamanho do Efeito - Cramér´s V: {contingency.association(table,method='cramer'):.10f}\")        \n",
    "    else:\n",
    "        print(f\"- p_valor: {p_valor:.10f} > alpha: 0.05 => Não rejeitamos a hipótese nula. Há evidência suficiente, ao nível de significância de {alpha}, para concluir que as variáveis {coluna1} e {coluna2} são independentes.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def difmedia_2amostras(**kargs):\n",
    "    \"\"\"Realiza o teste de hipóteses T para diferença das médias entre dua amostras.\n",
    "    kargs:\n",
    "         var1: pandas series : variável categórica do dataframe.\n",
    "         var2: pandas series : variável categórica do dataframe.\n",
    "         var3: string : nome data variável alvo do dataframe.    \n",
    "    \"\"\"\n",
    "    alpha = 0.05\n",
    "    l_keys = list(kargs.keys())\n",
    "    l_values = list(kargs.values())\n",
    "    \n",
    "    print('***** Realizando o teste de hipóteses t : Média - 2 amostras *****')\n",
    "    print('* Premissas:')\n",
    "    print('- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.')\n",
    "    print('- Grupos com variâncias diferentes.')\n",
    "    print('')\n",
    "\n",
    "    # Hipóteses\n",
    "    print('----- Hipóteses ----------------------------------------------------------------')\n",
    "    print(f'H0 : A média do {kargs[\"var\"]} entre {l_keys[0]} e {l_keys[1]}, são iguais.')\n",
    "    print(f'Ha : A média do {kargs[\"var\"]} entre {l_keys[0]} e {l_keys[1]}, são diferentes.')\n",
    "    print(f'Nível de significância de {alpha}.')\n",
    "    print('--------------------------------------------------------------------------------')\n",
    "    print('')\n",
    "\n",
    "    # Teste de hipóteses T - média - 2 amostras\n",
    "    t = pg.ttest(l_values[0],l_values[1], paired=False, correction = True).round(15)\n",
    "    \n",
    "    print('----- Resultados -----')\n",
    "    print(f\"Valor do estatística T: {t['T'].iloc[0]}\")\n",
    "    print(f\"P_valor: {t['p-val'].iloc[0]}\")\n",
    "    print(f'Intervalo de confiança para diferença das médias: {t[\"CI95%\"].iloc[0]}')\n",
    "    print('')\n",
    "    print('----- Conclusão -----')\n",
    "    if t['p-val'].iloc[0] < alpha: \n",
    "        print(f\"- p_valor: {t['p-val'].iloc[0]} < alpha: 0.05 => Rejeitamos a hipótese nula. Existe diferença estatisticamente significativa das médias do {kargs['var']} entre {l_keys[0]} e {l_keys[1]}.\")\n",
    "    else:\n",
    "        print(f\"- p_valor: {t['p-val'].iloc[0]} > alpha: 0.05 => Não rejeitamos a hipótese nula. Não existe diferença estatisticamente significativa das médias do {kargs['var']} entre {l_keys[0]} e {l_keys[1]}.\")\n",
    "    # Intervalo de confiança \n",
    "    if t['CI95%'].iloc[0][0] <= 0 <= t['CI95%'].iloc[0][1]: print(f'- Intervalo de confiança {t[\"CI95%\"].iloc[0]} passa pelo zero. As amostras são estatisticamente iguais.')\n",
    "    else:\n",
    "        print(f'- Intervalo de confiança {t[\"CI95%\"].iloc[0]} não passa pelo zero. As amostras são estatisticamente diferentes.')\n",
    "        \n",
    "    # Médias dos grupos\n",
    "    print(f'- Média do {kargs[\"var\"]} para {l_keys[0]}: {l_values[0].mean()}')\n",
    "    print(f'- Média do {kargs[\"var\"]} para {l_keys[1]}: {l_values[1].mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def variancia(**kargs):\n",
    "    \"\"\"Realiza o teste de hipóteses Bartlett de igualdade de variâncias.\n",
    "    kargs:\n",
    "         dv : string : nome da variável dependente do dataframe.\n",
    "         fator : string : nome data variável independente do dataframe.\n",
    "         data : nome do dataframe.\n",
    "    \"\"\"\n",
    "    alpha = 0.05\n",
    "    print('***** Realizando o teste de hipóteses Bartlett de igualdade de variâncias. *****')\n",
    "    print('* Premissas:')\n",
    "    print('- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.')\n",
    "    print('')\n",
    "    # Hipóteses\n",
    "    print('----- Hipóteses -------------------------------------------------------------------------')\n",
    "    print(f'H0 : As variâncias de todas as amostras da {kargs[\"fator\"]} são iguais.')\n",
    "    print(f'Ha : As variâncias para um par ou mais das amostras da {kargs[\"fator\"]} são diferentes.')\n",
    "    print(f'Nível de significância de {alpha}.')\n",
    "    print('-----------------------------------------------------------------------------------------')\n",
    "    print('')\n",
    "    # Teste de Bartlett\n",
    "    bartlett = pg.homoscedasticity(data=kargs['data'], dv=kargs['dv'], group=kargs['fator'], method=\"bartlett\", alpha=.05)\n",
    "\n",
    "    print('----- Resultados -----')\n",
    "    print(f\"Valor do estatística T: {bartlett['T'].iloc[0]}\")\n",
    "    print(f\"P_valor: {bartlett['pval'].iloc[0]}\")\n",
    "    print('')\n",
    "    print('----- Conclusão -----')\n",
    "    if bartlett['pval'].iloc[0] < alpha: \n",
    "        print(f\"- p_valor: {bartlett['pval'].iloc[0]} < alpha: 0.05 => Rejeitamos a hipótese nula. As variâncias das amostras da {kargs['fator']} são diferentes.\")\n",
    "    else:\n",
    "        print(f\"- p_valor: {bartlett['pval'].iloc[0]} > alpha: 0.05 => Não rejeitamos a hipótese nula. As variâncias das amostras da {kargs['fator']} são iguais.\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anova(**kargs):\n",
    "    \"\"\"Realiza o teste de hipóteses ANOVA ou ANOVA Welch para diferença entre as médias de duas amostras e um fator.\n",
    "    kargs:\n",
    "         dv : string : nome da variável dependente do dataframe.\n",
    "         fator : string : nome data variável independente do dataframe.\n",
    "         data : nome do dataframe.\n",
    "         var_iguais : boolean : True => teste ANOVA, False => teste ANOVA Welch.\n",
    "    \"\"\"\n",
    "    alpha = 0.05\n",
    "    if kargs['var_iguais']:\n",
    "        variancias = 'iguais'\n",
    "        teste = 'ANOVA'\n",
    "        # Teste de hipóteste ANOVA oneway\n",
    "        anova = pg.anova(dv=kargs[\"dv\"], between=kargs[\"fator\"], data=kargs[\"data\"], detailed=False)\n",
    "    else:\n",
    "        variancias = 'diferentes'\n",
    "        teste = 'ANOVA WELCH'\n",
    "        # Teste de hipóteste ANOVA Welch\n",
    "        anova = pg.welch_anova(dv=kargs[\"dv\"], between=kargs[\"fator\"], data=kargs[\"data\"])        \n",
    "    \n",
    "    print(f'***** Realizando o teste de hipóteses {teste} : Médias : +2 amostras : 1 fator *****')\n",
    "    print('* Premissas:')\n",
    "    print('- Amostras aleatórias.')\n",
    "    print('- Grupos independentes.')\n",
    "    print('- Variável dependente é continua.')\n",
    "    print('- Variável independente é categórica.')\n",
    "    print('- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.')\n",
    "    print(f'- Grupos com variâncias {variancias}.')\n",
    "    print('')\n",
    "\n",
    "    # Hipóteses\n",
    "    print('----- Hipóteses -----------------------------------------------------------------------')\n",
    "    print(f'H0 : As médias da {kargs[\"dv\"]} entre as amostras da {kargs[\"fator\"]} são iguais.')\n",
    "    print(f'Ha : As médias da {kargs[\"dv\"]} entre as amostras da {kargs[\"fator\"]} são diferentes.')\n",
    "    print(f'Nível de significância de {alpha}.')    \n",
    "    print('---------------------------------------------------------------------------------------')\n",
    "    print('')\n",
    "    \n",
    "    print('----- Resultados -----')\n",
    "    print(f\"Valor do estatística F: {anova['F'].iloc[0]}\")\n",
    "    print(f\"P_valor: {anova['p-unc'].iloc[0]}\")\n",
    "    print('')\n",
    "    print('----- Conclusão -----')\n",
    "    if anova['p-unc'].iloc[0] < alpha: \n",
    "        print(f\"- p_valor: {anova['p-unc'].iloc[0]} < alpha: 0.05 => Rejeitamos a hipótese nula. Existe diferença estatisticamente significativa das médias da {kargs['dv']} entre as amostras da {kargs['fator']}.\")\n",
    "    else:\n",
    "        print(f\"- p_valor: {anova['p-unc'].iloc[0]} > alpha: 0.05 => Não rejeitamos a hipótese nula. Não existe diferença estatisticamente significativa das médias do {kargs['dv']} entre as amostras da {kargs['fator']}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def posthoc(**kargs):\n",
    "    \"\"\"Realiza o teste de hipóteses Tukey para explorar a diferença entre as médias de vários grupos (pares).\n",
    "    kargs:\n",
    "         dv : string : nome da variável dependente do dataframe.\n",
    "         fator : string : nome data variável independente do dataframe.\n",
    "         data : nome do dataframe.\n",
    "    \"\"\"\n",
    "\n",
    "    alpha = 0.05\n",
    "    if kargs['var_iguais']:\n",
    "        print('***** Post-Hoc ANOVA - Realizando o teste de hipóteses Tukey para explorar a diferença entre as médias de vários grupos (pares). *****')\n",
    "        print('* Premissas:')\n",
    "        print('- Amostras aleatórias.')\n",
    "        print('- Grupos independentes.')\n",
    "        print('- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.')\n",
    "        print('- Grupos com variâncias iguais.')\n",
    "        print('- Tamanhos de amostras de todos os grupos são iguais.')   \n",
    "        \n",
    "        # Teste de hipóteste tukey\n",
    "        data = pg.pairwise_tukey(data=kargs[\"data\"], dv=kargs[\"dv\"], between=kargs[\"fator\"]) \n",
    "        data.rename(columns={'p-tukey':'p-valor'}, inplace=True) \n",
    "    else:\n",
    "        print('***** Post-Hoc ANOVA - Realizando o teste de hipóteses Não paramétrico - Games Howell para explorar a diferença entre as médias de vários grupos (pares). *****')\n",
    "        print('* Premissas:')        \n",
    "        print('Não assume variâncias, tamanhos amostrais e amostras normais.')\n",
    "        \n",
    "        # Teste de hipóteste Games Howell\n",
    "        data = pg.pairwise_gameshowell(data=kargs[\"data\"], dv=kargs[\"dv\"], between=kargs[\"fator\"])\n",
    "        data.rename(columns={'pval':'p-valor'}, inplace=True) \n",
    "              \n",
    "    print('')\n",
    "    # Hipóteses\n",
    "    print('----- Hipóteses ---------------------------------------------------------------------------------')\n",
    "    print(f'H0 : As médias da {kargs[\"dv\"]} entre os pares das amostras da {kargs[\"fator\"]} são iguais.')\n",
    "    print(f'Ha : As médias da {kargs[\"dv\"]} entre os pares das amostras da {kargs[\"fator\"]} são diferentes.')    \n",
    "    print(f'Nível de significância de {alpha}.')    \n",
    "    print('-------------------------------------------------------------------------------------------------')\n",
    "    print('')    \n",
    "    print('----- Resultados -----')\n",
    "    print('- Pares de amostras com médias estatisticamente iguais - p_valor > alpha: 0.05 - Não rejeitamos H0')\n",
    "   # print('--------------------------------------------------------------------------------------------------------')\n",
    "    display(data[data['p-valor'] > alpha][['A','mean(A)','B','mean(B)','p-valor']])\n",
    "    print('')\n",
    "    print('- Pares de amostras com médias estatisticamente diferentes - p_valor < alpha: 0.05 - Rejeitamos H0')\n",
    "   # print('--------------------------------------------------------------------------------------------------------')\n",
    "    display(data[data['p-valor'] < alpha][['A','mean(A)','B','mean(B)','p-valor']])\n",
    "   # print('--------------------------------------------------------------------------------------------------------')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pearson_corr(dataframe: pd.DataFrame, coluna1: str, coluna2: str) -> None:\n",
    "    \n",
    "    # Plotando o heatmap\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    sns.heatmap(dataframe[[coluna1,coluna2]].corr(), annot=True, cmap='mako_r', fmt=\".2f\",  linecolor='white', linewidths=.5)\n",
    "    plt.title(\"Matriz de Correlação\")\n",
    "    plt.show()\n",
    "    alpha = 0.05\n",
    "    print('')\n",
    "    # Hipóteses\n",
    "    print('----- Hipóteses ---------------------------------------------------------------------------------')\n",
    "    print(f'H0 : p = 0 => Não existe correlação significativa entre as variáveis.')\n",
    "    print(f'Ha : p ≠ 0 => Existe correlação significativa entre as variáveis.')    \n",
    "    print(f'Nível de significância de {alpha}.')    \n",
    "    print('-------------------------------------------------------------------------------------------------')\n",
    "    print('') \n",
    "    # Executando o teste de Pearson entre as variaveis numericas\n",
    "    pearson_coef, p_value = stats.pearsonr(dataframe[coluna1], dataframe[coluna2])\n",
    "\n",
    "    print('----- Resultados -----')\n",
    "    print(f'Coeficiente de Pearson p: {pearson_coef}')\n",
    "    print(f\"P_valor: {p_value}\")\n",
    "    print('')\n",
    "    print('----- Conclusão -----')\n",
    "    if p_value < alpha: \n",
    "        print(f\"- p_valor: {p_value:.30f} < alpha: 0.05 | coeficiente de Pearson: {pearson_coef:.4f} ≠ 0 => Rejeitamos a hipótese nula e concluimos que existe uma correlação estatisticamente significativa entre as variáveis {coluna1} e {coluna2}.\")\n",
    "    else:\n",
    "        print(f\"- p_valor: {p_value:.30f} > alpha: 0.05 | coeficiente de Pearson: {pearson_coef:.4f} = 0 => Não rejeitamos a hipótese nula e concluimos que não existe uma correlação estatisticamente significativa entre as variáveis {coluna1} e {coluna2}.\")\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testes de hipóteses podem ser extremamente úteis para validar as suposições e descobrir se há diferenças significativas entre grupos de dados, como canais de aquisição, atividades realizadas durante o trial, entre outros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Testar a Eficácia dos Canais de Marketing na Conversão\n",
    "    * Hipótese Nula (H0): Não há diferença significativa na taxa de conversão entre diferentes canais de marketing.\n",
    "    * Hipótese Alternativa (HA): Há uma diferença significativa na taxa de conversão entre diferentes canais de marketing.\n",
    "\n",
    "Teste Estatístico: Teste de Qui-Quadrado.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: #359fab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_747ab\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_747ab_level0_col0\" class=\"col_heading level0 col0\" >nom_canal</th>\n",
       "      <th id=\"T_747ab_level0_col1\" class=\"col_heading level0 col1\" >realizou_assinatura</th>\n",
       "      <th id=\"T_747ab_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_747ab_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_747ab_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_747ab_row0_col0\" class=\"data row0 col0\" >Organic</td>\n",
       "      <td id=\"T_747ab_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_747ab_row0_col2\" class=\"data row0 col2\" >39</td>\n",
       "      <td id=\"T_747ab_row0_col3\" class=\"data row0 col3\" >58.208955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_747ab_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_747ab_row1_col0\" class=\"data row1 col0\" >Organic</td>\n",
       "      <td id=\"T_747ab_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_747ab_row1_col2\" class=\"data row1 col2\" >28</td>\n",
       "      <td id=\"T_747ab_row1_col3\" class=\"data row1 col3\" >41.791045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_747ab_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_747ab_row2_col0\" class=\"data row2 col0\" >Other</td>\n",
       "      <td id=\"T_747ab_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_747ab_row2_col2\" class=\"data row2 col2\" >161</td>\n",
       "      <td id=\"T_747ab_row2_col3\" class=\"data row2 col3\" >71.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_747ab_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_747ab_row3_col0\" class=\"data row3 col0\" >Other</td>\n",
       "      <td id=\"T_747ab_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_747ab_row3_col2\" class=\"data row3 col2\" >63</td>\n",
       "      <td id=\"T_747ab_row3_col3\" class=\"data row3 col3\" >28.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_747ab_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_747ab_row4_col0\" class=\"data row4 col0\" >Paid - google</td>\n",
       "      <td id=\"T_747ab_row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "      <td id=\"T_747ab_row4_col2\" class=\"data row4 col2\" >271</td>\n",
       "      <td id=\"T_747ab_row4_col3\" class=\"data row4 col3\" >74.655647</td>\n",
       "    </tr>\n",
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       "      <th id=\"T_747ab_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
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       "      <td id=\"T_747ab_row6_col1\" class=\"data row6 col1\" >False</td>\n",
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       "      <td id=\"T_747ab_row7_col1\" class=\"data row7 col1\" >True</td>\n",
       "      <td id=\"T_747ab_row7_col2\" class=\"data row7 col2\" >62</td>\n",
       "      <td id=\"T_747ab_row7_col3\" class=\"data row7 col3\" >21.985816</td>\n",
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       "      <td id=\"T_747ab_row8_col0\" class=\"data row8 col0\" >Site ou Indicação Interna</td>\n",
       "      <td id=\"T_747ab_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "      <td id=\"T_747ab_row8_col2\" class=\"data row8 col2\" >22</td>\n",
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       "      <td id=\"T_747ab_row9_col0\" class=\"data row9 col0\" >Site ou Indicação Interna</td>\n",
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      "text/plain": [
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      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_2cat(dataframe=df_trial_metricas, coluna1='nom_canal', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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//v04OjoSGBhIxYoVgYLrLF0ZT/615u9jNBqZNGkSCxcuJD09HT8/P+rVq3fD57CweEePHm1+H10pLi7uusdfeW/z67tw4cI1X08XLlzA3d2dlStXMmnSJGJiYvDw8CA0NBQHB4cix53vxx9/5PPPP2fYsGG0aNHCXJ6UlES5cuUsknDA/H6/mYS6KIpyLypVqsSCBQuYOXMm3377LV988QVubm48/fTTvPLKKxgMBubOnctnn33GmjVrWLFiBba2tnTo0IHRo0fj7u5e6Lmv956Aov39+Leufh3czGtcRKQsU/IlchVnZ2fq1q37j451d3cvdCzFhQsXgL9bPgASEhIs9snLyyMhIQFPT0/zN9sXL14stK4r67mam5sbQIHJJuLj44mIiKBBgwYMHTqUEydOMH/+fBo0aICdnR0ZGRl88803FteSH/eNruVKrq6uNGnShNdff73Q7Ve2FBYHZ2dnDAYDvXr14oEHHiiw/d8kP66urhgMhmveh6K2Bv0bV97PK2elO378OImJiQQHB9O3b1+Cg4P58ccfqVGjBlZWVmzcuJHVq1ff1LlmzpzJ/PnzGT16NPfee6+5ZbZr1643He/rr79OkyZNCmy/1gf+a3F1daVatWpMmDCh0O2VKlVix44dDBs2jB49etCnTx/zFxsfffSRueWwKPbt28ebb75Jp06dePbZZwvEnZCQQF5enkUClp9MXvl+yB8ble9GE40Upqj3ol69ekydOpXs7Gx27tzJ4sWL+fzzzwkJCeH//u//8PHx4Z133mHUqFFERESwatUqZs2aRbly5f7xWLWi/P24ln/y3KSmphbba1xEpLRpwg2RYhQeHs7Zs2fZvXu3RfnKlSuxtbU1dwcC+O233yy6y/z666/k5ubSvHlzqlevjre3t3lChXxnzpxhz549NGzY8Jox1KhRg3Llypm75ORbsWIFzz//PLm5uezcuZN7772Xpk2bmpOh3377Dfj72+3w8HB2795doBvgypUr8fb2pmrVqoWev0mTJpw8eZLq1atTt25d8893333Ht99+W6Dl4Hryv3G/HhcXF2rVqsWJEycszlezZk2mTJlinkXtn3BycqJOnTr8/PPPFh8aU1JS2LBhA40aNfrHdRdV/jnWrVtnUT5hwgTGjh3LiRMnSExMpGfPngQGBpqfs6vvZ1Hs3LmTwMBAHn30UfOH/fPnz3P06NEi11OjRg28vLyIjo62uB8+Pj5MnDixQKvtjTRp0oSYmBi8vLws6tu0aROzZ8/G2tqa3bt3YzQaGTRokDnxysvLY/PmzUV+Ds6fP2+eJGTs2LGFxpGbm8uqVassyvO70ubfJxcXF2JjYy32uZkE8MpjbnQv5s+fT7t27cjOzsbOzo7mzZvz7rvvApdnHN29ezctWrRg3759GAwGQkNDefXVVwkKCjLP2vpPFOXvR2Hv83/63BTna1xEpLSp5UukGHXp0oVFixYxYMAAXnrpJSpVqsS6detYunQpAwcONLcKAMTExPDiiy/Ss2dPYmJimDRpEq1bt6Zp06bA5Znzhg8fzpAhQ3jooYdISEhg6tSpuLu7m6e1L4y1tTWDBg1izJgxeHl50b59e06ePMnUqVPp3r07rq6u1KtXj++//57atWvj6+vLrl27mDlzJgaDwdwtsHfv3qxcuZJevXoxcOBAPDw8WLFiBVu3bmXcuHHXTIx69erFd999R69evXj22WcpV64cP/30E998880Nx5pczc3Njd27d7NlyxZq1ap1zf0GDx7M888/b36u8mdL27t3L/3797+pc15tyJAh9OnTh+eff56nn36anJwcZs6cSXZ2NgMGDPhXdRdFSEgIHTt2ZPz48WRmZhIaGspvv/3G+vXrmTp1KtWrV8fFxYXPP/8cGxsbbGxsWL16tXl5g5uZCa5evXpMnz6dmTNnUr9+faKiopgxYwbZ2dlFrsfa2ppXX32Vt99+G2tra9q1a0dycjLTp0/n/Pnz1+w+eC1dunRhwYIF9O7dmxdeeAE/Pz82b97MrFmz6N69u8WXGmPGjOHRRx8lKSmJhQsXEhERAVxuXSmsS2i+/HuZmprKe++9VyDZ9PX1NY+nGjlyJOfPnyckJITt27cza9YsOnfubB7/2K5dO2bMmMGMGTMICwtj3bp1/2g69KLci2bNmjFhwgQGDBhA9+7dsba25uuvv8bOzo527drh7++Pg4MDr7/+OoMGDaJ8+fJs3ryZw4cP07Nnz5uO6crYbvT3Iz9h3LJlCwEBAeYlL9atW8f7779P+/bt2bFjR4GlDgpTnK9xEZHSpuRLpBg5Ojry5ZdfMnHiRD755BNSU1PN36Rf3V3ogQcewM3NjVdeeQUnJyc6d+7Mq6++at7epUsXnJ2dmTFjBgMGDMDFxYXWrVszePDgQseVXalbt244OTkxZswYFixYQOXKlXnuued47rnnAPjggw949913zd+SV6tWjdGjR7Ny5Up27NgBXB7L8tVXXzFx4kTee+89cnJyCAkJYfr06eY1fArj4+PD119/zcSJE3nnnXfIysqiWrVqhT4HN9KtWzcOHDjAc889x/vvv0+FChUK3a9Vq1bMmTOHqVOn8tJLL2Fra0vt2rWZN2/ev15It3nz5sybN49PP/2UwYMHY2dnR+PGjfnwww+LdYKF6xk/fjxTp07lf//7HwkJCQQEBPDpp5/SoUMHAKZPn85HH33Eyy+/jLOzM6GhoSxYsIDnnnuOHTt2mNdZupF+/fqRkJDAF198wbRp0/Dz8+Phhx/GYDAwY8aMa47zu9pjjz2Gs7Mzs2fPZvHixTg5OdGwYUMmTJhw04t7Ozk5sXDhQiZOnMj48eNJSUnB39+fIUOGmLsGNm3alLfffpt58+axatUqypcvT9OmTZk6dSoDBgxg586d5oluChMXF2eeMOL5558vsH3gwIEMGjSIGTNm8OmnnzJ//nzi4+OpVKkSgwcPtvgypF+/fsTHxzNnzhxycnJo27YtY8eO5cUXX7yp6y7KvQgJCeHzzz9n2rRpDB48mLy8POrUqcPcuXOpUaMGAHPnzmXixImMHTuW5ORkqlWrxpgxY+jSpctNxXOlovz9cHFxoXfv3ixevJiNGzeyadMmHn30UU6fPs3y5cv5+uuvCQ8P59NPPzUv5XAtrq6uxfYaFxEpbQaTRqqKlLj27dvTpEkTPvjgg1t6nq+//prPP/+c119/nfvvv/+WnktERERErk9jvkTuUPHx8QQFBdG6dWtee+01i2m6pfQZjUZyc3Nv+CN3lry8vBve86snpRARkTuHuh2K3KHWr1/PO++8g7u7O6+99ho2Nnq7lyVvvvkmy5cvv+F+R44cKYFopKTcc889N1zLrkmTJnz55ZclFJGIiJQkdTsUESkF0dHRBZYbKMw/XfZAyqYjR47ccFFgZ2dn85gtERG5syj5EhERERERKQEa8yUiIiIiIlIClHyJiIiIiIiUACVfV+nevTvdu3cv7TBEREREROQOo+nPrhITE1PaIYiIiIiIyB1ILV8iIiIiIiIlQMmXiIiIiIhICVDyJSIiIiIiUgKUfImIiIiIiJQAJV8iIiIiIiIlQMmXiIiIiIhICdBU8yIiIiJyx8rLyyMnJ6e0w5A7lK2tLdbW1kXeX8mXiIiIiNxxTCYTsbGxJCYmlnYocofz8PDA19cXg8Fww32VfImIiIjIHSc/8apQoQJOTk5F+mAscjNMJhPp6enExcUB4Ofnd8NjylTyNWPGDP744w++/PJLc9nhw4cZO3YsBw4cwNPTk169etGzZ0/zdqPRyNSpU1myZAkpKSmEh4fz9ttvU7ly5dK4BBEREREpZXl5eebEy8vLq7TDkTuYo6MjAHFxcVSoUOGGXRDLzIQbCxcuZPLkyRZlCQkJ9O7dmypVqrB06VIGDBjAhAkTWLp0qXmf6dOns2jRIt59912+/vprjEYjffv2JTs7u4SvQERERETKgvwxXk5OTqUcifwX5L/OijK2sNRbvs6fP8+oUaPYtm0b1apVs9j2zTffYGtry5gxY7CxsSEgIICoqChmzpzJo48+SnZ2NnPnzmXo0KG0bdsWgI8//pjWrVvzyy+/0KlTp5K/IBEREREpE9TVUErCzbzOSr3l6+DBg9ja2rJy5UrCwsIstu3YsYMmTZpgY/N3jtisWTNOnTrFxYsXiYiIIC0tjebNm5u3u7m5UatWLf78888SuwYRERERESkdJpOptEMoslJPvtq3b8+UKVMKHaMVGxuLr6+vRVmFChUAiImJITY2Fig4uK1ChQrmbSIiIiIiJaV9+/a88cYbAERHRxMcHMyyZctKOarSNWXKFIKDg4u93uzsbMaNG8f3339f7HXfKqWefF1PZmYmdnZ2FmX29vYAZGVlkZGRAVDoPllZWSUTZDEw3kbZ+n/Rzdwf3cuyTffyznGz90f3s+zSvbxz6N4UVKFCBRYvXmweHvNf9dhjj7F48eJirzcuLo7//e9/5ObmFnvdt0qpj/m6HgcHhwITZ+QnVU5OTjg4OACXs9783/P3yZ955HZgZTAw/1AEsenppR2KXMXXyYletUKKvL/uZdmle3nnuNl7CbqfZZXu5Z3jn9zL/wI7Ozvq169f2mGUOl9f3wK92f6rynTy5evra543P1/+Yx8fH3OWGxcXR5UqVSz2uRVNm7dSbHo6Z1JTSzsMKQa6l3cO3cs7i+7nnUP3Uv6t9u3b06FDB44cOcLu3bt58MEHee2115g0aRJr164lJSWF0NBQXn31VYu5BeLj45kyZQobNmzgwoULODk5ER4ezvDhw6lUqVKB80RHR3P33Xfz/vvv06VLF3r06MH27dsLjemLL76gadOmZGVlMXv2bL7//nvOnj2Ln58fXbt2pW/fvlhZWZnjb9KkCR988IH5+GXLljF8+HB+/fXXQmO5loiICKZOncqOHTtISUnB09OT++67j6FDh5obNzZt2sQnn3zCsWPHsLGxoXHjxgwdOpSAgAAATp8+zbhx49i9ezeZmZmEhITQv39/7rrrLuByt8OpU6dy5MgRAHr06EGVKlWoWrUqixYt4tKlS9SuXZs333yTevXqmWNbu3Ytc+fO5fDhw+Tk5FCpUiV69OhBt27dzM8twPDhw5k6dSrr1q2jR48eABZLV23bto2ePXuan+Nly5YxcuRIRo8ezeTJk8nJyWHRokVUr16dOXPmsHLlSk6fPo2VlRUhISG88sorNGvWrMjP6fWU6eQrPDycr7/+mry8PPOc+Vu3bqV69ep4eXnh6uqKi4sL27ZtMydfycnJHDp0iO7du5dm6CIiIiJShi1cuJDevXvz3HPP4ezszDPPPMPFixd59dVXqVChAkuXLqVv377Mnj2b5s2bYzKZ6NevH0lJSQwdOpTy5ctz5MgRJk+ezKhRo5gzZ84Nzzlq1ChSr/jiICMjg8GDB+Pt7U3dunUxmUy88MIL7Nmzh4EDBxISEsK2bduYPHkyZ86c4d133y3W5yAuLo5u3bpRv359PvjgA+zs7Pjtt9+YN28eFSpU4Pnnn+fMmTP079+fRx99lMGDB5OcnMykSZN4/vnnWbNmDQD9+vWjQoUKfPTRR9jY2PDFF1/w4osv8vPPP1O1atVCz7169WoCAgIYOXIkJpOJDz/8kEGDBrFu3Tqsra3ZsGEDAwYMoGfPngwaNIjMzEwWLVrEmDFjqFOnDqGhoUydOpWBAwfy4osvcu+9997Utefl5TF37lzGjh1LQkICAQEBfPTRR3z11VcMGTKE4OBgzp8/z7Rp03j55ZfZsGFDsfSsK9PJ16OPPsrs2bMZMWIEffv2Zd++fcyfP5/Ro0cDl5tyu3fvzoQJE/D09MTf35/x48fj6+t70zdARERERP47KlasyNChQ4HLyxtFRETwzTffmGffbtOmDT169DCvMRsXF4ejoyPDhg2jcePGADRt2pTTp08XeTxTYGCg+XeTycSgQYMwGo1MmzYNJycnNm7cyObNm5k0aRIPPPAAAC1btsTBwYFPPvmEnj17UrNmzWJ7Do4ePUpoaCiffPIJLi4uALRo0YJNmzaxbds2nn/+efbt20dmZib9+vXDx8cHuNw77ddffyU9PZ2MjAxOnDhh0dJVr149pk6det11d3Nzc5kzZ475vGlpaQwbNozDhw9Tp04dIiMj6dy5MyNGjDAf06BBA5o2bcq2bdsICwsjNDQUgCpVqlCrVq2bvv4XXnjBYjxeXFwcr776qrn1DC7PJTFo0CCOHDlSLF1Iy3Ty5eXlxezZsxk7diydO3fG29ub119/nc6dO5v3eemll8jNzWXkyJFkZmYSHh7OnDlzsLW1LcXIRURERKQsy//gDrBlyxa8vb2pXbu2xeQN7dq146OPPiIpKQkfHx+++OILTCYT0dHRREVFceLECXbt2nXdJONaJk+ezLp165g9e7Z51u/t27djY2NDx44dLfZ96KGH+OSTT9i+fXuxJl+tWrWiVatW5OTkEBkZSVRUFEePHiU+Ph4PDw8AwsLCsLe3p2vXrnTs2JE2bdrQtGlTc/dAZ2dnAgMDeeutt/jjjz9o1aoVbdq0Yfjw4dc9d2BgoDnxAsyJXf6Een379gUuJ2UnT57k9OnT7N+/H+AfPd+FufI1ADBx4kTgcvfSEydOEBUVxfr164v1nGUq+bqy32q+evXqXffbBGtra1577TVee+21WxmaiIiIiNxBnJyczL8nJiZy4cIFateuXei+Fy5cwN3dnZUrVzJp0iRiYmLw8PAgNDTUYtK3ovrxxx/5/PPPGTZsGC1atDCXJyUlUa5cOfNwm3ze3t4ApKSk3PS5rsdoNDJp0iQWLlxIeno6fn5+1KtXzzy7OEClSpVYsGABM2fO5Ntvv+WLL77Azc2Np59+mldeeQWDwcDcuXP57LPPWLNmDStWrMDW1pYOHTowevRo3N3dCz331V348sezGY1G4HICNGrUKNauXYvBYKBq1armFsfiWtfrytcAwP79+xk9ejT79+/H0dGRwMBAKlasWKznLFPJl4iIiIhISXN1daVatWpMmDCh0O2VKlVix44dDBs2jB49etCnTx9zS81HH33Ezp07i3yuffv28eabb9KpUyeeffZZi23u7u4kJCRYzHcAf084V65cOXNZXl6exbHp/2AG0JkzZ5qH9Nx77724uroC0LVrV4v9ruxGuHPnThYvXsznn39OSEgI//d//4ePjw/vvPMOo0aNIiIiglWrVjFr1izKlSvHqFGjbjougKFDh3LixAnmz59PgwYNsLOzIyMjg2+++eaGx/6T5yY1NZW+ffsSHBzMjz/+SI0aNbCysmLjxo2sXr36H11DYcr0Ol8iIiIiIrdakyZNiImJwcvLi7p165p/Nm3axOzZs7G2tmb37t0YjUYGDRpkTrzy8vLYvHkz8HeLzfWcP3+eAQMGUKNGDcaOHVtoHLm5uaxatcqifOXKlQA0atQIABcXF2JjYy32uZkE8MpjAgMDefTRR82J1/nz5zl69Kj5eubPn0+7du3Izs7Gzs6O5s2bmyf+OHfuHLt376ZFixbs27cPg8FgniUyKCiIc+fO3XRMV8Z277330rRpU/Oavr/99hvw93N9dQsh/PPn5sSJEyQmJtKzZ08CAwPNLXFXn/PfUsuXiIiIiPyndenShQULFtC7d29eeOEF/Pz82Lx5M7NmzaJ79+7Y2tqaxziNGTOGRx99lKSkJBYuXEhERARwuXXlyjFMV8vOzmbAgAGkpqby3nvvWSQ4cHkSi/zxVCNHjuT8+fOEhISwfft2Zs2aRefOnc0TdrRr144ZM2YwY8YMwsLCWLduHVu3br3p665Xrx7Tp09n5syZ1K9fn6ioKGbMmEF2drZ57FWzZs2YMGECAwYMoHv37lhbW/P1119jZ2dHu3bt8Pf3x8HBgddff51BgwZRvnx5Nm/ezOHDh+nZs+dNx3RlbN9//z21a9fG19eXXbt2MXPmTAwGgzm2/IRxy5YtBAQEEBYWRrt27Vi3bh3vv/8+7du3Z8eOHaxYseKG56tevTouLi58/vnn2NjYYGNjw+rVq/n222+Bv8ei/VtKvkRERETkP83JyYmFCxcyceJExo8fT0pKCv7+/gwZMsTcNbBp06a8/fbbzJs3j1WrVlG+fHmaNm3K1KlTGTBgADt37jTP9leYuLg484QRzz//fIHtAwcOZNCgQcyYMYNPP/2U+fPnEx8fT6VKlRg8eDC9e/c279uvXz/i4+OZM2cOOTk5tG3blrFjx/Liiy/e1HX369ePhIQEvvjiC6ZNm4afnx8PP/wwBoOBGTNmkJycTEhICJ9//jnTpk1j8ODB5OXlUadOHebOnUuNGjUAmDt3LhMnTmTs2LEkJydTrVo1xowZQ5cuXW4qnit98MEHvPvuu+ZWtmrVqjF69GhWrlzJjh07gMutXL1792bx4sVs3LiRTZs28eijj3L69GmWL1/O119/TXh4OJ9++ilPPfXUdc/n6urK9OnT+eijj3j55ZdxdnYmNDSUBQsW8Nxzz7Fjxw7at2//j68nn8FUXKPH7hD5i7X9+uuvJXreD3bs0oKRZVBlFxfeaNzwpo7RvSybdC/vHP/kXoLuZ1mke3nn+Kf38lbJzMzk5MmTVK9e/R9NiCFyM27m9aaWLxERERGRO0heXt4NZ+czGAyFjpmSW0vJl4iIiIjIHeSee+7h7Nmz192nSZMmfPnllyUUkeRT8iUiIiIicgf57LPPbrgosLOzcwlFI1dS8iUiIiIicgcJDg4u7RDkGrTOl4iIiIiISAlQ8iUiIiIiIlIClHyJiIiIiIiUACVfIiIiIiIiJUDJl4iIiIiISAlQ8iUiIiIiIlIClHyJiIiIiJQQo8l0W527R48eBAcHF/rz4Ycf3vD4bdu2ERwcTHR09D8J+Y6jdb5EREREREqIlcHA/EMRxKanl+h5fZ2c6FUr5B8d+3//93+MGDGiQLmjo+O/Des/R8mXiIiIiEgJik1P50xqammHUWQODg54e3uXdhh3BHU7FBERERGRfyQpKYmRI0fSunVrateuTfPmzRk5ciQZGRmF7n/q1Cn69OlDo0aNaNCgAX369OHIkSPm7SkpKbz11ls0a9aMRo0a0bNnT/bv319Sl3PLKfkSEREREZF/5I033uDQoUNMnTqV1atXM3z4cFasWMHixYsL3X/w4MH4+PiwdOlSlixZgpWVFQMHDgTAZDLx3HPPcebMGWbMmME333xD/fr1eeqppzh06FBJXtYto26HIiIiIiJyTd9//z2rV6+2KGvUqBGzZ8+mZcuWhIeHExwcDEClSpVYsGABR48eLbSu06dP06JFC/z9/bG1tWXcuHGcOHECo9HItm3b2LNnD1u3bsXDwwO4nKzt2rWLL774gg8++OCWXmdJUPIlIiIiIiLX1L59e4YOHWpR5uDgAMDTTz/NunXrWL58OadOnSIyMpLo6Ghq1KhRaF2vvvoq48aNY9GiRTRp0oTWrVvTqVMnrKysOHjwICaTiXbt2lkck52dTVZW1q25uBKm5EtERERERK7J2dmZqlWrFig3Go3069ePY8eO0alTJ+6//35q167NW2+9dc26unXrRseOHdm4cSNbtmzh008/5bPPPmPFihUYjUZcXFxYtmxZgePs7OyK9ZpKi5IvERERERG5aYcPH+a3337jm2++ISwsDICcnBxOnz5N5cqVC+x/6dIlpk2bxvPPP0+XLl3o0qUL58+fp02bNmzfvp2goCBSU1PJyckhMDDQfNzIkSMJCQmhe/fuJXZtt4qSLxERERERuWnly5fHxsaGn3/+GU9PTxITE/n888+5cOEC2dnZBfZ3d3dnw4YNnD59miFDhphbuWxtbalTpw7+/v6Ehoby6quvMmLECPz8/Fi0aBHLli1jzpw5pXCFxU/Jl4iIiIhICfJ1crojzunj48MHH3zAlClTWLhwId7e3rRt25ZevXqxbt26Avvb2Ngwa9YsPvzwQ3r16kVGRgahoaHMnDmTKlWqADB37lzGjx/PK6+8QkZGBgEBAUydOpXmzZsXe/ylQcmXiIiIiEgJMZpM9KoVUmrntjIYbuqYL7/88rrbH3zwQR588MEC5cOHDwegadOmFut4BQQEMHPmzGvW5+npyfvvv39TMd5OtM6XiIiIiEgJudnk5045t1ym5EtERERERKQEKPkSEREREREpAUq+RERERERESoCSLxERERERkRKg5EtERERERKQEKPkSEREREREpAUq+RERERERESoCSLxERERERkRKg5EtERERERKQE2JR2ACIiIiIi/xVGowkrK8Ntce433niD5cuXX3efI0eO/Nuw/lOUfImIiIiIlBArKwNffr2NuLjkEj1vhQpu9Hiy6U0dM2LECIYMGWJ+3KpVK958803uv//+4g7vP0PJl4iIiIhICYqLSyb6XGJph3FDrq6uuLq6Fijz9vYupYhufxrzJSIiIiIiN23ZsmXcc889vPfeezRq1Ij+/fuzbds2goODiY6ONu93dZnJZGLWrFncfffdhIWF8fDDD7Ny5crSuowSpZYvERERERH5R06fPk1cXBwrVqwgMzOT+Pj4Gx7z8ccf88MPP/D2229To0YN/vzzT9555x1SUlLo1q1bCURdepR8iYiIiIjIP9a/f38qV64MXG7lup709HTmz5/PpEmTaNu2LQBVqlTh7NmzzJkzR8mXiIiIiIjItVSrVq3I+0ZGRpKVlcWQIUOwsvp7BFRubi7Z2dlkZmbi4OBwC6IsG5R8iYiIiIjIP3ajZCkvL8/8u8lkAmDy5MnUqFGjwL52dnbFG1wZowk3RERERESkWNja2gKQmppqLjt16pT59xo1amBjY8O5c+eoWrWq+Wfjxo3MmTPHojXsTnRbtHzl5uYybdo0VqxYQWJiIrVq1eK1116jfv36ABw+fJixY8dy4MABPD096dWrFz179izdoEVEREREClGhgtsde86goCCcnJyYOXMmr7zyClFRUcybN8+83dXVlSeffJJPPvkEFxcXGjZsyLZt2xg/fjz9+vUrkRhL022RfH322WcsWbKEDz74gMqVKzNr1iz69u3LTz/9hK2tLb1796Z9+/aMHj2aPXv2MHr0aJydnXn00UdLO3QRERERETOj0XTTix0X57mtrAy39BwuLi6MHz+eCRMmcP/99xMSEsKwYcMYMGCAeZ/hw4dTrlw5PvnkE+Li4vDz8+Oll16ib9++tzS2suC2SL7Wrl1Lp06daNWqFQBvvPEGS5YsYc+ePZw8eRJbW1vGjBmDjY0NAQEBREVFMXPmTCVfIiIiIlKm3Ork51ae+8iRIxaPu3TpQpcuXQrs16FDBzp06HDNY21sbBg4cCADBw78V/Hcjm6LTpVeXl6sX7+e6Oho8vLyWLx4MXZ2doSEhLBjxw6aNGmCjc3feWSzZs04deoUFy9eLMWoRURERERE/nZbtHyNGDGCl19+mbvvvhtra2usrKyYMmUKVapUITY2lqCgIIv9K1SoAEBMTAzly5cvjZBFREREREQs3BbJV2RkJK6urkybNg0fHx+WLFnC0KFDWbBgAZmZmQWmpLS3twcgKyurNMIVEREREREpoMwnXzExMQwZMoT58+fTuHFjAOrWrUtkZCRTpkzBwcGB7Oxsi2Pyky4nJ6cSj1dERERERKQwZX7M1969e8nJyaFu3boW5WFhYURFReHr60tcXJzFtvzHPj4+JRaniIiIiIjI9ZT55MvX1xcoOLvK0aNHqVatGuHh4ezcudNi5eytW7dSvXp1vLy8SjRWERERESk7TCZTaYcg/wE38zor890O69WrR6NGjRg2bBijRo3C19eXFStWsGXLFr766isqVarE7NmzGTFiBH379mXfvn3Mnz+f0aNHl3boIiIicofZPeVT7FxdqN3rWQBSz0ZzeOFCUk5H4ehdgeAnn8QzOKTQY3PS0jjy9VdcPLAfazs7/Jo1J+DhRzBYWX4Xnh4Xx9Yx79B+6nRzWVZSEvtnfk5KdDS+jcMJ6d4Dg+HytOFn1q8nPe48wU88eWsu+jZka2sLQHp6Oo6OjqUcjdzp0tPTgb9fd9dT5pMvKysrPvvsMyZPnszw4cNJSkoiKCiI+fPnExYWBsDs2bMZO3YsnTt3xtvbm9dff53OnTuXcuQiIiJyJ4n9czuXDuzHr3lzAHIz0tk1+WO864VRu1dvYrZuYd9n02kx5j3s3NwKHB/x1UKyk5Np/NrrZKekcGD2bGxdXana4R7zPpnx8eyZOgVjTo7FsadWr8LWxZXwYcPZM/VTLu7bi3dYfYy5uZxZt5ZGQ1+7tRd/m7G2tsbDw8M8FMXJycmcrIoUF5PJRHp6OnFxcXh4eGBtbX3DY8p88gXg7u7OqFGjGDVqVKHb69Wrx+LFi0s4KhEREfmvyElL49jSb3GrVs1cdm7LFqzt7Qnp1h2DlRUBDz3MpQP7SY6KovxVY9UBLu4/QJ0+fXCp6A+Ab5MmJEQcNidfcXt2c3jBl9i7uxc4Nj02Fu/6DXCpWBH3GjVIi43FOwzObfoDrzp1sXf3uCXXfTvLH7py9dwAIsXNw8PD/Hq7kdsi+RIREREpTUe/XYJf02ZkJSWayxKOHME7rL5Ft8Emb468Zh12Ls7EbtuGZ0gIuekZXDp4gAoNGpq3X9y/n4CHHsbJx5ddkyZYHOvg6UnKmdPk5eSQdu6cudXr9K9raTRkaPFd6B3EYDDg5+dHhQoVyLmqJVGkuNja2hapxSufki8RERGR64iPOEzisaM0e/sdIhYtMJdnXLyAe/XqHP7yCy7s24uDlxdBXR/HIzCw0HqCn+rGwXlzWP/SIDCZ8AwNpXqnB83ba/Xoefl8V00yBlD1nnvZ+fFEzv7+G57BIVRo2JBzmzbhVbuOWr1uwNra+qY+HIvcSkq+RERERK4hLyeHwwsXEPLU01jb2Vluy8ri1Kqfqdz+buoPepnzf25n1ycf02L0uzh4ehaoK/18LG5Vq1GjUyeykpKIWLSIqNWrqH7/AzeMw8nHh1bjPiAnNRU7NzeMebmcXruWRkOGEP3bRqJWr8Leoxy1ez+LY/nyxXb9IlK8yvxU8yIiIiKl5eQP3+NWtSpetesU2Gawssa1chUCHnoYtypVqPloV5x8fIjZuqXAvunnz3Ps2yXUeuYZ3GsEUKFBQ2p27cqp1aswXrFczvUYrKzME3nEbN6MV61amIwmIpctpdHQ1/GuX58jX3/17y5YRG4ptXyJiIiIXEPsn9vJTk5m/UsDATDm5AIQt2sXblWr4XTVIHvnCj5kJiQUqCflzGlsXVwsugi6Vq5CXmYmuWlphc6OeC3GvFyi1q6h0atDSDpxAicfXxzKlaN8nbqc/OnHf3CVIlJSlHyJiIiIXEOjIa9huqJl6tiypQDU7PIo5zb9QcLRoxb7p52PxTe8SYF67D08yElNJTs52ZxopcfGYm1vj62r603FFLNlC16htbD38AArA5iMAJiMeaBFhUXKNHU7FBEREbkGRy8vnCpUMP/YONhj42CPU4UK+Le5i9Sz0Rz/fiXpcXEcX/kdGRcu4Nu0GQC5mZlkp6QA4Fa9Bs5+fhycN5fUc2dJOHqEY0u/pXK79je1/pQxL4/Ta9dQreP/AZdbz1LPxZBw7ChnN23CvUZA8T8JIlJslHyJiIiI/AOOXl40eOkVLu7by9bRo7iwby/1B76EQ7lyAESt+YXt748FwMramvqDXsLK3p4d4z/iwNy5+ISHU+Ohh27qnLFbt1IuOORyq9dfMQQ+0pl9n00n8dhRgp54olivUUSKl7odioiIiBRR7V7PWjz2CAyk6Yi3Ct034MGHCHjw7+TKoZwnYS+8eMNzeAYH02HGrEK3VWzZkootW1qUVenQgSodOtywXhEpfWr5EhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBGjCjf+wuN272Pf5ZxZlFRo2pF6/F4nbvYvIFcvJSkjApVJlgp98ErcqVQutJyc9nWPfLuHivr2YTCbK161L0ONPYuvkZLlfWhpbRr1F+Btv4li+PAB5WVnsnz2LhKNH8AwJpU6fvljb2QFwcf8+zv7+O2H9B9yCqxcRERERKVlKvv7D0mJiKF8vjNDuPcxlVra2pJ47y4E5swnp1h2PwEBOr13LnilTaDl2LNZ29gXqiVi4gIwLcdQf9DIYIGLhQg5/+QX1+r1g3icnLY0906aY1zvJd/aP38lJSaHJ8BEcnDeXs3/8TpX2dwNw4ocfCOnW/RZdvYiIiIhIyVK3w/+wtJgYXCpWxN7d3fxj6+TEpUOHcParSMXmLXDyrkBg5y5kJyeRdi6mQB15WVnE7dpJ8FNP41a1Km5VqhL0+BNc2LObvJwcABIjj7F93HvkZWUVjCE2lnLBwTj7+uIZEkJ6bCwAFw/sx97dHbcqVW7tkyAiIiIiUkKUfP2HpcXE4OTjU6Dc1tmZtJhzJEZGYjIaObd5E9YOjjh6exesxGAgbMAgXCtVtig2GY3kZWUCcOngQfxatKRev4Jrmzh4epISHY0xL5eUM2dw8PQE4OQPP1C904PFcJUiIiIiImWDuh3+R5lMJtLOx3Lp0EFO/fwTJqOJCo0aEfDQw/g2Dufivr3sGP8hBisrMBioP/AlbJ2dC9RjbWdH+Tp1LMrOrPsVl0qVsHNxBSDg4UcAyLh4scDx/q1bE7t9G+sG9MfF3x//1m24dPAAtm6uavUSERERkTuKkq//qMz4eIzZ2VjZ2FD3+X5kXLzIkcVfY8zJodp9HclKSib4yadxr1GD6I0bOPS/eTQd8RZ2bm7XrffM+nWc37mDBi+9XKQ47Fxcafb2O2SnpGD/V90nfvyB4CefIm7PHiKXLsHa3oHQns8oGRMRERGR25q6Hf5HOXp5cdekydR6pjeulatQoUFDgh5/grO//8axZUtx8fencrt2uFWtSmj3Hljb23Nu86br1nlmw3qOLP6aoMcex6tW7SLHYjAYzInXpUOHsHV2wdnXj8NfzKfOc/2ofv8DHJw3919dr4iIiIhIaVPy9R9m6+yMwWAwP3b29cOYk0Ny1ClcK1UylxusrHCpVInM+Phr1hX1y2qOfLWIml0epcrdHf5xTCd//J4aDz5IWmwMBisr3KpUwatOHdLOnSU3I/0f1ysiIiIiUtqUfP1HXTp4gI2DXyEv++8ZCFOjz2Dr7IKDRznSYixnNkw/fx4Hr/KF1nVuy2aOLf2WoMeeoOq99/3jmOIPH8bGyRm3KlUxGAyYTCbg8uQdAH89FBERERG5LWnM13+Ue0AgVra2HPriC2p0epCMixc4tvRbqt53Hw6eXhz63zzcqlXDvUYAZ//4ncxLl6jYvDkAuZmZGHNysHN1JSctjSNfLcKveXN8wsPJSkoyn8PO1fXyhB1FdOLHHwh67HEAnCr4YMrL4/yunWReuoSTr2+BRZtFRERERG4nSr7+o2wcHGjw8isc/WYx28e9h7WDA5Vat6HqvfdhMBjIy8rk5M8/kZWQgGvlyjQcPMQ82UbUml+I2bKZVuM+4NKhg+RlZRGzZQsxW7ZYnKPl2PdxLF94a9nV4o9EYOPggFvVqgBY29sT0q0HR75ahI2DA7V7P1u8T4CIiIiISAlT8vUf5lLRn4avDC50m3+r1vi3al3otoAHHyLgwYcA8A1vgm94kyKdz7F8eTrMmFXoNs/gEDyDQyzKfMPD8Q0PL1LdIiIiIiJlncZ8iYiIiIiIlAAlXyIiIiIiIiVAyZeIiIiIiEgJUPIlIiIiIiJSApR8iYiIiIiIlAAlXyIiIiIiIiVAyZeIiIiIiEgJUPIlIiIiIiJSArTIssht7tzmTRz63/yCGwwGOnw+0/wwMfIYB+fNpeXY969ZV15ODse+XcL5nTsAqFC/AUGPPY61vT0AqWfPEvHVIlJOR2Hv4UGNTg/i26QpAFlJSeyf+Tkp0dH4Ng4npHsPDAYDAGfWryc97jzBTzxZTFctIiIicvtR8iVym/NpHI5X7Trmx6a8PHZ+PJHydeuZy1LPRrNvxudY2dpet66TP3xP4rGjNBj4EiZMHJo/j8gVywl+4kmMOTnsmTYF7/oNqPXMMyQcPcrB+fNwquCDW7VqnFq9ClsXV8KHDWfP1E+5uG8v3mH1MebmcmbdWhoNfe2WPQciIiIitwMlXyK3OWs7O6zt7MyPT/78E5hM1OzcBYDo3zZy7NslOHp7k5uRcd26Lh7Yj3/rNrhVqwaAf5u7OPv7bwCkxsSQeekSAQ89jI2DA07eFYjesJ6Eo0dwq1aN9NhYvOs3wKViRdxr1CAtNhbvMDi36Q+86tTF3t3jlly/iIiIyO1CY75E7iA5aWlErV5FYOdHza1clw4coHavZ6lyd4cbHm/r7Mz5XTvJSUsjJy2NC7t341q5inkbwLk//sBkNJJ4/DhpsbHm7Q6enqScOU1eTg5p587h4OmJMTeX07+upVrHjrfoikVERERuH2r5ErmDRG/cgL2HBz6NGpnLwvoPAC6PDbuRmo8+xr7Pp7NxyKsAuPj7EzZgIACOXl4EPNKZY8u+5djSJZiMRmp0ehDP0FAAqt5zLzs/nsjZ33/DMziECg0bcm7TJrxq11Grl4iIiAhKvkTuGCaTibN//E61+/55K1N6XBwOnp7U6vUsprw8jny9iKNLvqFWj54Y83JJj43Fv3UbKrZsScLRoxxfsQKPoGA8g4Nx8vGh1bgPyElNxc7NDWNeLqfXrqXRkCFE/7aRqNWrsPcoR+3ez+JYvnwxXvmdo6iTp2RcvMjWMe8QNmAQnsHBhdZlMpk48f1Kzv62EWNeHj4NGxH05FNY/9Uimhl/icMLF5Bw5Cj2Hu4EPtIZn8bhAKTFxrB/1kwy4+Op3P5uAh58yFzvsW+XYO9RjiodbtySKiIiIpaUfIncIZKjTpGVkGj+AH2zcjMyOPzl/2j46mDcq9cAoFbPXuyY8BEBDz3Exf37SY46RbNRozEYDLhVqUpaTAxRq1eZEwCDlRV2bm4AxGzejFetWpiMJiKXLaXZqNGc3/EnR77+ivoDBxXPRd9hijJ5CkDEooXkZWVdt66o1auI3riBus89j7W9AwfmzOLkD98T2LkLxrw8dk+ZgqN3eZqOfIuEo0c4MHcOzn4VcfH3J3L5MjwCa1KxZUt2TpxIhQYNca1UieyUFOL27qHZW6NuyfWLiIjc6TTmS+QOcengQTxq1jSPzbpZabGx5GVl4VKpsrnMtUplMJnIjE8g5fRpXPwrmaePB3CtXJnM+EsF6jLm5RK1dg3VOv4fySdP4uTji0O5cpSvU5fE45H/KL7/Ams7O+zd3c0/Mdu2WkyeAhCzbSu5mZnXrcdkNBK1dg01uz6GZ0go7tWrU+PBh0iOigLg0oH9ZCXEU6d3H5x9fanU5q6/7s1xgL8mSwnDrUpVXPz8SI+NASDql9VUvqudxQQvIiIiUnRKvkTuEMknT+ARGPiPj7f3cAcgLeacuSwtNhYAx/LlsXN3t9gGkB4bi4NXwS6EMVu24BVaC3sPD7AygMkIgMmYBybTP47xv6SwyVOyU1OJXLaU0O7dr3ts6rlz5KSmUqF+fXOZX9NmNHzl8li+hCNHKBcSio2jo3l7WP8BVGrTBvhr8pTTp8lJTyf94gUcPD3JTk0hbs9u/P/aR0RERG6euh2K3CFSz53Dt2mzmzomLzub3IwM7N3dcSjniVftOhz+8ktCu/cAk4nDCxfgEx6Onasrfk2bcmrVzxxb+i3+bdqQdPw4Z//4nbAX+1vUaczL4/TaNTR8ZTAArpWrkHouhoRjR4nbvRv3GgHFds13ssImTzm25Bv8mjXHpaL/dY/NuHgBWydnEo8fJ3LF8suJWIOG1OxyOZHLuHgRBy8vji1bSuy2rdi6uFDjwYeoUL8BADU6PcieqVOIXLEcv6bNcK8RQOTyZVS6q61avYroRuP34nbvInLFcrISEnCpVJngJ5/ErUrVQusymUxELl/GuU2XZxr1b9WawM5dMFhZfn+ak5bGllFvEf7Gm+ZxlXlZWeyfPYuEo0fwDAmlTp++5nt4cf8+zv7+u3lSHhERufWUfIncIbKTk7F1crqpY87v+JND/5tPhxmzAKjTpy9Hv13CnimfgMGAd/361Hz0MQAcy3vT8JVXOfbtt0Rv3HB5Yo6ez1iMUQKI3bqVcsEhl1u9uDxLYuAjndn32XQcvLyo0/e5f3+xd7jCJk+5dPgQiZGRNBv1zg2Pz8vMIi87m8jlywh67HFMRhMRCxdgMhoJeepp8rKyiNmyGZ/G4YQNGEjCkSPsn/E54cOG41atGh4BgbQZP5HczEzsXFzITk3l/K6dNHtrFMdXfkfMls24+FeiVq/e2Lm43MJn4vZ1vfF7qefOcmDObEK6dccjMJDTa9eyZ8oUWo4di7WdfYG6Tq9dQ+z27YS92B9jXh4H587BztWVqvfeZ94nJy2NPdOmkJ2SYnHs2T9+JyclhSbDR3Bw3lzO/vE7VdrfDcCJH34gpNv1W1FFRKR4KfkSuUO0nzr9utsrtmhJxRYtr1tm6+xM7Wd6XbMOj4BAwoe9cf3ztGxJxZaW56nSoYNmx7sJV0+ekpedTcSCBYQ8/XSRWp4M1lYYc7IJfuJJygVdngyl5mOPcWD2LIKfeBKDlRW2zi6EPN0Ng5UVblWqknjsGGd//828wLaVjY05sTq9dg2V2txF6tloYrZsptnbozj+3Xec+H4lIU89fWuehNvc9RY/P7NxA85+FanYvAUAgZ27EL1hPWnnYszP/5VO//orAQ89hEdgzb/2f5TjK1eYk6/EyGMcnDcXaweHAsemxcZSLjgYZ19fPENCSP+rK/HFA/uxd3fHrUqV4r50ERG5Do35EhEpY66ePCX51EkyLl5g34zPWf/SQNa/dHnttT1TPuHwwi8LHG/vfnn8npOvn7nM2ccXY04O2amp2Lm74+TjY9FtzcnHh8yEhAJ15aSlEbdzB5XuaktiZCTu1Wtg4+iEV506mjyliK4ev2fr7ExazDkSIyMxGY2c27wJawdHHL29CxyblZhIVkI8HjWDzGUegYFkXrpEVlIicPn14teiJfX6vVjgeAdPT1KiozHm5ZJy5gwOnp4AnPzhB6p3evDWXLCIiFyTWr5ERMqYqydPcatWnRbvjrXYZ/NbIwjt0ROv0FoFjnetXAWDjQ2p0Wewr1UbgLSYGKwdHLB1dsa9Rg1O/vQjJqPRnIClxcbi4OVVoK7Ta9fg3+YurO3sMBisMP01YYopz6jJU4ro6vF7vo3DubhvLzvGf3j5+TcYqD/wpUJnKs1KSgIwd+MFzMs5ZCYkYO/uQcDDjwCX13+7mn/r1sRu38a6Af1x8ffHv3UbLh08gK2bq1q9RERKwW3T8rVixQruv/9+6tatywMPPMDPP/9s3hYdHU2/fv1o2LAhrVq1YvLkyeTl5ZVitCIi/1zquXM4+/3damVtZ4dThQoWPwD2HuXMH8RzMzPN431sHB3xb9WaI19/RdKJ4yQeP86x5Uvxb9kKK2trfMObgMlExKKFpMfFcWbDei4dOIB/69YWceSkpXF+x59UuusuANyqVSPh6BGST58mdttWTZ5SBPnj9yq3a28uy0lLIyspmeAnnyb8jTfxa9acQ/+bR3ZycoHj87Ivr+dmZfP3d6X5v5tyc294fjsXV5q9/Q6tP5pAs7dGYevszIkff6BGpweJ27OHzW+NYNt775J8+vS/vVQRESmC2yL5+u677xgxYgTdunXjxx9/pFOnTgwePJjdu3eTk5NDnz59APj666955513+Oqrr5g2bVopRy0i8s/8k8lTotb8wvb3/24dC3rscbxq12H3lE/ZM/VTvGrVJvCv9cJsHB1p8PKrpMXGsnX0KM6s+5W6zz1fYLa907+u/avV6/IkEB6BgVRs3oJdkyaQnZJCjQcf+pdXeucrbPHzY8uW4uLvT+V27XCrWpXQ7j2wtrfn3OZNBY7PX2bAeEWilf+7VRFnnjQYDNj/laRfOnQIW2cXnH39OPzFfOo814/q9z/AwXlz//E1iohI0ZX5bocmk4lPPvmEnj170q1bNwBefPFFduzYwfbt2zl79iznzp3jm2++wd3dnaCgIC5dusRHH33ECy+8gJ2mRRaR28yNJk8BzDNU5gt48CECrkiGrGxsCH7iSYKfeLLQ410qVqTx0Neue46Ahx4uUFaz62PU7PrYDeOTywpb/DzldJRFS5jBygqXSpXIjI8vcLyDRzkAspOSzNPHZyf/1RXxr7F9N+Pkj98T9MSTpMXG/DXZShWcfX3ZN+MzcjPSsXG8uaRfRERuTplv+Tp58iRnz57lwQctBwbPmTOHfv36sWPHDmrXro37Ff8JNWvWjNTUVA4fPlzS4YqIiJgVtvi5vbsHaTExFmXp588XumC5vYcHDp6eJEb+PblJYmQkDp6e2Lt73FQs8YcPY+PkjFuVqhgMhr/H7xn/WgRdQ/hERG652yL5AkhPT6dPnz40b96cxx57jHXr1gEQGxuLr6+vxTEV/hoPEXPVf24iIiIl6erxewAVW7Xm7B+/E7N1C+lxcRxbtpTMS5eo2Lw5YDl+D6DSXW2JXL6U+CNHiD9yhMjly6j811pdNyN/rBeAUwUfTHl5nN+1k7O//4aTr+9Nd3UVEZGbV+a7HaampgIwbNgwBg4cyNChQ1m9ejX9+/dn3rx5ZGZm4vZXX/Z89vaXxydkZWWVeLwiIiL5Chu/5xseTl5WJid//omshARcK1em4eAh5slTotb8QsyWzbQa9wEAVe+9j+zkZPZ9Ph2DlRUVW7aiSod7biqO+CMR2Dg44Fb18rg+a3t7Qrr14MhXi7BxcKB272eL4WpFRORGynzyZfvXYOM+ffrQuXNnAEJDQzl06BDz5s3DwcGB7Oxsi2Pyky4nfYsnIiKl6Frj9/xbtca/VetCt109fs9gZUXQ408Q9PgT1z2XY/nyBcYC5vMMDsEzOMSizDc8HN/w8EL3FxGRW6PMdzv08fEBICgoyKI8MDCQ6OhofH19iYuLs9iW/zj/WBERERERkdJW5pOv2rVr4+zszN69ey3Kjx49SpUqVQgPD+fQoUPm7okAW7duxdnZmZCQkKurExERERERKRVlPvlycHCgb9++TJs2jR9++IHTp0/z2WefsWnTJnr37k2HDh3w9vbmlVdeISIigrVr1zJp0iSeffZZTTMvIiIiIiJlRpkf8wXQv39/HB0d+fjjjzl//jwBAQFMmTKFpk2bAjB79mxGjx7N448/jru7O08//TT9+/cv5ahFRERERET+dlskXwC9e/emd+/ehW6rWrUqc+fOLeGIREREREREiq7MdzsUERERERG5Eyj5EhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBCj5EhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBCj5EhERERERKQFKvkREREREREqAzb+tYOPGjaxevZqLFy/i5eXF3XffTYcOHYojNhERERERkTvGv0q+5s+fz+zZs7nnnnsIDQ3lwoULDB8+nAMHDvDKK68UU4giIiIif/N1cirtEOQquiciRVPk5CstLQ1nZ2eLsqVLlzJz5kxq1aplLmvbti1vv/22ki8REREpdkaTiV61Qko7DCmE0WTCymAo7TBEyrQiJ1/33HMPL7zwAk899RS2trYAeHt7s3r1avz8/HB3d+fChQusXbsWHx+fWxawiIiI/HdZGQz8uHo/8fFppR2KXMHT05kH7qtb2mGIlHlFTr7mzJnDhAkTmD9/Pi+//DIPP/ww77zzDq+88gozZszA8Nc3HYGBgYwfP/6WBSwiIiL/bRFHYok+l1jaYcgVKlX0UPIlUgRFTr5CQ0OZM2cOmzdvZsKECcyZM4chQ4awbNkyzpw5Q3x8PJ6enlSuXPlWxisiIiIiInJbuukJN1q0aMGyZctYuXIlY8aMoWLFigwdOpSwsLBbEZ+IiIiIiMgd4abX+crIyCA1NZWHHnqIVatW0b59e/r168fAgQM5ceLErYhRRERERETktlfk5CsqKoonn3yShg0bEh4ezsMPP8zx48fp3bs3a9asoVq1anTt2pW33nqL8+fP38qYRUREREREbjtFTr5GjhxJuXLlzF0OW7RoYZ5O3tXVlaFDh/LTTz+Rm5tLx44db1W8IiIiIiIit6UiJ18HDx6kZ8+ehIaGUrNmTfr3709UVBSZmZnmfXx9fXn//fdZvHjxLQlWRERERETkdlXkCTfCwsL45JNPSEtLw87OjpUrVxIUFISDg0OBfYOCgoo1SBERERERkdtdkVu+PvzwQypUqMCbb77Ja6+9RkpKClOmTLmVsYmIiIiIiNwxitzyVaFCBT799NNbGYuIiIiIiMgd66anmhcREREREZGbp+RLRERERESkBCj5EhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBCj5EhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBCj5EhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBCj5EhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBCj5EhERERERKQFKvkREREREREqAki8REREREZESYFPaAchlvk5OpR2CFEL3RURERESKy22VfJ08eZIuXbrw1ltv0aVLFwAOHz7M2LFjOXDgAJ6envTq1YuePXuWcqQ3x2gy0atWSGmHIddgNJmwMhhKOwwRERERuc3dNslXTk4OQ4cOJT093VyWkJBA7969ad++PaNHj2bPnj2MHj0aZ2dnHn300VKM9uZYGQz8uHo/8fFppR2KXMXT05kH7qtb2mGIiIiIyB3gtkm+pkyZgouLi0XZN998g62tLWPGjMHGxoaAgACioqKYOXPmbZV8AUQciSX6XGJphyFXqVTRQ8mXiIiIiBSL22LCjT///JPFixfzwQcfWJTv2LGDJk2aYGPzdw7ZrFkzTp06xcWLF0s6TBERERERkWsq88lXcnIyr7/+OiNHjsTPz89iW2xsLL6+vhZlFSpUACAmJqbEYhQREREREbmRMp98vfPOOzRo0IAHH3ywwLbMzEzs7Owsyuzt7QHIysoqkfhERERERESKokyP+VqxYgU7duzg+++/L3S7g4MD2dnZFmX5SZeTpggXEREREZEypEwnX0uXLuXSpUu0bdvWonzUqFH89NNP+Pr6EhcXZ7Et/7GPj09JhSkiIiIiInJDZTr5mjBhApmZmRZl9957Ly+99BIPPfQQ3333HV9//TV5eXlYW1sDsHXrVqpXr46Xl1dphCwiIiIiIlKoMj3my8fHh6pVq1r8AHh5eeHj48Ojjz5KamoqI0aMIDIykmXLljF//nz69etXypGLiIiIiIhYKtPJ1414eXkxe/ZsTp48SefOnZk6dSqvv/46nTt3Lu3QRERERERELJTpboeFOXLkiMXjevXqsXjx4lKKRkREREREpGhu65YvERERERGR24WSLxERERERkRKg5EtERERERKQEKPkSEREREREpAUq+RERERERESoCSLxERERERkRKg5EtERERERKQEKPkSEREREREpAUq+RERERERESoCSLxERERERkRKg5EtERERERKQEKPkSEREREREpAUq+RERERERESoCSLxERERERkRKg5EtERERERKQEKPkSEREREREpAUq+RERERERESoCSLxERERERkRKg5EtERERERKQEKPkSEREREREpAUq+RERERERESoCSLxERERERkRKg5EtERERERKQEKPkSEREREREpAUq+RERERERESoBNaQcgIiIiUpbFnjvErm2LLMp8K9amYdOnOHtmD8cOryczIwk3Dz9q1X0AD89KN6xz/67l2Du6ERR6t7ksKyuVg3u+5+KF49jZOREY3JZKVRsCYDIZ2bdrOefPHcLNoyINmjyBvb0LACnJ59m7cykt276IwWAoxisXkeKm5EtERETkOlJT4qjgG0LdBg+by6ysbIi/eIr9u1ZQt8EjlPOqQtSJbfy5+X+06zgUGxv7a9Z3/OjvnInaSWBIO3OZyWRi19ZFmExGmrZ6lsyMZPbtXIqNjT2+/rWJizlC/MVTtLirH0cOreH4kd+oVe9+AI5FrCcwuK0SL5HbgJIvkWLm6+RU2iFIIXRfROSfSk25gKtbBewdXC3Ks7JSCQxpi3+V+gAEhrTjZOQmUpMvFNr6lZOTyf5dy7l04QQOju4W25ISz5EQf5q29w7GydkTd4+K1AhqzYljf+DrX5vU1At4eFbGxa0C3j41OR8TAVxu9UpPi8fHL/TWXLyIFCslXyLFyGgy0atWSGmHIddgNJmw0jfDInKTUpPjKO8dUKDcz7+O+fe8vBxORW7Gzt4ZFzfvQuvJSE8gLy+Xlu36s2/XUsttafHY2Tnj5OxpLnN18+XoobUYjXk4OrpzLnofeXm5JCWeMydvkREb1OolchtR8iVSjKwMBn5cvZ/4+LTSDkWu4unpzAP31S3tMETkNmMymUhLvciFuGNEHtkIGPGtWIegWndjZXX5Y9TFuONs3zQfgPqNu16zy6Gbux/hLXoUus3OwYWcnEzycrOxtrEDIDMjCZPJSG5OJr7+tYk6uZ3VK0fj6ORO01bPkpocR1raJbV6idxGlHyJFLOII7FEn0ss7TDkKpUqeij5EpGblpmRSF5eDlZWNjRs8iTp6Qkc2vcDRmMuteo9AICrmw+t2vUnLjaCfbuW4ejsSTnPyjd1Ho9ylXBwdOXgvh+oVa8TWZkpnIzcBIDRmIeVlQ3N2zxHVmYqdvZOGAxW7P7zGwKD25KYEM3+3SswGY3UCnsA7wqBxf48iEjx0FTzIiIiItfg6FSODg+8Sb2GXXDz8MO3Yi1C6z7A6ZN/YjIZAbB3cMHNw4/AkHZ4eQdy+uT2mz6PtbUtDZo8yaULJ/jl+3fZ+tssqlQPB8DG9u+WNHsHFwwGK1JTLpCWcgEfv1D27VxGUOjd1G/clb07lpCXl1M8Fy8ixU4tXyIiIiLXYWdnOWGPi6s3RuPlsVcGgxXuHhXN21zdvElJvvCPzuNRrhLt7htKVmYKtnZOXIyLxM7OqdBujJFHLo/1ysnJIC31At4+NbG2tgUgLfUibu5+/ygGEbm11PIlIiIicg0Xzh9jzQ9jycvNNpclJ8Vga+fEmVM7OXLwF4v9kxLP4eJa+IQb15Odnc6WjTPJzkrH3sEVKytr4mKP4lm+eoF9U1Mukpoch0/FWhi4PNGGyWS6/K/RCKabPr2IlBAlXyIiIiLXUM6zCtbWtuzbvYLUlAvExR4l4sAqatRsTZXq4Vy6cIKTkZtJS73I0cO/kpgQTfXAFsDlGRCzMlOKdB47Oydy87KJOLiK9LR4zpzaQXTUTmoEtS6w7/EjGwj4a4ZDWztHnJy9OHNqBzHR+wFwdvEqvidARIqVuh2KiFyD1gYrm3RfpCTZ2NoT3vIZDu37iU0bPsPGxp4q1cKpUbMVBoOBhk2f5uihNRw5+Auubj40adELB0c3AGKi97Nv1zLu7/xekc7VIPwJDuz5jt9/nYKjUzkaNHkSj3KW64WlpV4iJfk89Ro9ai6r2+Bh9u1ahslopF6jLubZEkWk7FHyJSJSCK3ZVrZpzTYpSa5uPjRt1bvQbT5+Ifj4Ff63olLVhlSq2rDQbc1a9y1Q5uLqXWj5lZxdvGjVfoBFmZd3DdrdN/S6x4lI2aDkS0SkEFqzrezSmm0iInK7UvIlInINWrOtbNKabSIicrvShBsiIiIiIiIlQMmXiIiIiIhICVDyJSIiIiIiUgI05ksA+HPzF9jZOxN2xdS1APEXT7F351La3Tek0OPS0xLY8MvEQrc1a90Hz/LVSUu9xIE9K0lMOIOjUzlCat9LBd9gAHJzs9nz5zfEXzyJl3cA9cMfw9raFoC42COcObWDRs26FeOVioiIiIiUDiVfwrnofVw4fxT/Kg0sypOTYtm1/Wusra/9MnF0cufu/xtmUXZo/8+kp13Cw7MKeXk5bN80H1c3H1rc1Y/kxHPs3r6Ypq2excOzEmdO7SA7K40WbV9g786lnDm1g2oBzQE4FrGeOvUfKv4LFhEREREpBep2+B+XnZ1OxIFVuJfztyg/fXI7W36bib29y3WPNxissHdwNf+kpyVw/txBwhp1xcrKmrjYI+RkpxPWuCuubj74V2mAf5X6nDy+CYC01It4eVfHxdUbL+8apKZcACAu9igODq64e1S8NRcuIiIiIlLC1PL1HxexfxX+leuTmZliUX7h/DHCGj5Kbm4WxyLWFb2+g6upXK0xLq7ewOVuic6u5bG1dTDv4+rmw4ljvwPg4OhOwqUojMY8UpJi8PSqBkBkxHpq13/wX16diMjffJ2cSjsEuYruiYj819wWyVdiYiKTJk1iw4YNpKamEhwczJAhQ2jcuDEAW7ZsYfz48Rw/fhw/Pz8GDRrEAw88UMpRl30XLxwn/tIpWt89iAN7Vlpsyx9nFR21q8j1xV+KIjH+DA3CnzCX2ds7k5WZgslkwmAwAJCZkUR2VjoAVao15tyZvaz67h1c3X2oUr0JF84fw87eWa1eIlJsjCYTvWqFlHYYUgijyYTVX/8/iIjc6W6L5Gvw4MFcuHCBSZMm4eXlxZdffkmfPn1Yvnw5JpOJfv360bt3b8aPH8+GDRt4/fXX8fT0pHnz5qUdepmVl5fDgd0rqR32oHmCi3/rzMk/8a1YCwdHN3OZt28Qh/b9yLGIdQQG30Vy0nnORO3EaMwDwM7emdZ3DyI7Kw17h8tdHCMj1lMrrBPnzx3m8IFV2NjYUbdhZyVjIvKPWRkM/Lh6P/HxaaUdilzB09NZC2aLyH9KmU++oqKi2LRpE4sWLaJRo0YAvPXWW/z+++98//33XLp0ieDgYF599VUAAgICOHToELNnz1bydR3HItbjXq4i3j41i6U+ozGP87ERhDXqalFub+9C/fDH2bdzGZERG3ByLke1Gs04eXyLeR+DwWBOvC7ERWJr54SLqzfb/5hHk1a9yUhLYO/OpbS5e1CxxCoi/00RR2KJPpdY2mHIFSpV9FDyJSL/KWU++SpXrhwzZ86kbt2//zgbDAYMBgPJycns2LGDDh06WBzTrFkzxo4da9HVTSzFRO8jKzOV1SvHAGA05gIQe/Yg9z309k3Xlxh/BpMxj/IVAgpsq+AbzN33v0FWVir29s6cPvknjk4ehdYTGbGeWvUeIDXlAgYrK9w9KuLi6s2u7V+Rk5NpMXZMREREROR2UuaTLzc3N+666y6LstWrVxMVFcWbb77J8uXL8fX1tdheoUIFMjIySEhIwNPTsyTDvW00a90Ho9FofhxxcDUAIbXv+0f1JSZE4+ZRsUAXxtTkOA7u+4EmLXvh4OAKXF6/y6t8jQJ1XIw7jq2tI+4eFUlOjAGTCQBTfpx/PRYRERERuR2V+eTrart27WL48OHce++9tG3blszMTOzs7Cz2yX+cnZ1dGiHeFhydylk8trGxB8DZxeuGx+bl5ZCbk4n9X8kUQEryeVxcKxQ8j3M5UpPjOHZ4HZWqNuTsmb0kXIoqdP2uyIj1hNb9PwCcXLwwmozEnD1IRnoCzi7e2No53tQ1ioiIiIiUJbfVOl9r167l2WefpX79+kyYMAEAe3v7AklW/mNHR31YvxViovfz688fWpRlZ6UWmhxZW9vSsFk34s4f5fdfpxAXe4TwFs8U6HZ46cIJbGztzeuN2djYUaf+wxzc+z2nT/5JvUZdbtn1iIiIiIiUhNum5WvBggWMHTuWjh078uGHH5pbt/z8/IiLi7PYNy4uDicnJ1xdXQurSgoR1ujRQssrVW1IpaoNb1gW3uKZa9ZdzrMyrdr1v+75vbxr4OVt2RWxYqW6VKykgdgiIiIicme4LVq+Fi1axLvvvku3bt2YNGmSRTfDxo0bs337dov9t27dSsOGDbGyui0uT0RERERE/gPKfMvXyZMnGTduHPfccw/9+vXj4sWL5m0ODg706NGDzp07M2HCBDp37szGjRtZtWoVs2fPLsWoRURERERELJX55Gv16tXk5OSwZs0a1qxZY7Gtc+fOfPDBB0yfPp3x48fzv//9j0qVKjF+/Hit8SUiIiIiImVKmU++XnjhBV544YXr7tOmTRvatGlTQhGJiIiIiIjcPA2KEhERERERKQFKvkREREREREqAki8REREREZESoORLRERERESkBJT5CTdE5MYyM5I5tO9HLl04gZW1DX7+dQmufQ/W1rbEXzzFof0/kZZyAScXL0LrdKR8hcAb1rl/13LsHd0ICr27SOcxmYzs27Wc8+cO4eZRkQZNnsDe3gWAlOTz7N25lJZtX8RgMNyy50FERESkLFPLl8htzmQysWvbV+Tl5dCsTV8ahD9BXOwRjh76laysVHZsXUBF/7q0vnsQfv512Ll1IRkZSdet8/jR3zkTtbPI5wGIizlC/MVTtLirH7a2Dhw/8pv52GMR6wkMbqvES0RERP7TlHyJ3ObSUi+SmHCGeg274Ormg2f5agSFtudc9F4SLp3GYLCiRlBrnJw9CQxui5WVDYnxZwqtKycnk13bvuLE0d9wcHQv8nkAUlMv4OFZGRe3Cnj71CQt9fKC6CnJ50lPi8fHL/TWPhEiIiIiZZySL5HbnL29C+EtnsHewcWiPDcnCzs7R3Ky04k9exCTyUTsuUPk5mbj6uZTaF0Z6Qnk5eXSsl1/nJzLFfk8AI6O7qSmxJGXl0tS4jlz8hYZsUGtXiIiIiJozJfIbc/WzhFvn5rmxyaTkagT2/DyrkE5r2pUrdGUXdu/xmAwYDIZqdewCy6u3oXW5ebuR3iLHjd9HgBf/9pEndzO6pWjcXRyp2mrZ0lNjiMt7ZJavW7C9cbVHdr3I6eOb7HYv1a9TlQLaFagntzcbA7v+5HYc4cwYcLPvw6hdf8PGxv7Avv+ufkL7OydCWv0qPnYPX9+Q/zFk3h5B1A//DGsrW0BiIs9wplTO2jUrNstuHoREZE7m5IvkTtMxIHVJCWeo2W7F8nLzSY9LYGaoe2o4BtC7LmDHNr34+XugddIwP7JeQCsrGxo3uY5sjJTsbN3wmCwYvef3xAY3JbEhGj2716ByWikVtgDeBdhwo//ovxxdbZ2jjRr05ec7Az27VqOwWBFaN2OpCTHEVzrHipVbWg+prBkCuDwvh9JSjxHk5a9wGBg365lHN7/M3UbPGKx37nofVw4fxT/Kg3MZWdO7SA7K40WbV9g786lnDm1g2oBzYHL4/fq1H+o2K9dRETkv0DdDkXuIBEHVnPq+BbCGj+Gq5sPJ479DpioGdIed4+KBNe6B/dylQq0nvzb81zJ3sEFg8GK1JQLpKVcwMcvlH07lxEUejf1G3dl744l5OXl/Kvz36luNK4uLeUCbh4VsXdwNf9Y29gVWpfByoZaYZ1wL+ePu0dFKldtRMKlKIt9srPTiTiwCvdy/gXi8PKujourN17eNUhNuQBAXOxRHBxccfeoeAuuXkRE5M6nli+RO8TBvT9w+uR2whp3xc+/NgBJiedwdfO12M/dw4+U5LhiPU9hIo9cHuuVk5NBWuoFvH1qmruupaVexM3d7x/HcKe63ri6nJxMMjOTcXYpX6S66tR/0Px7eloC587sxbN8dYt9Ivavwr9yfTIzUyzKHRzdSbgUhdGYR0pSDJ5e1QCIjFhP7SvqFRERkZujli+RO8Cxw+s4fXI79cMfp2KleuZyewdXc6tFvtSUizg6lbu6in91nqulplwkNTkOn4q1MHB5og2TyXT5X6MRTP/o9He8642rS0u5ABg4fmQD637+iN9/nUp01K4b1rl3x7ds+GUiWVmp1AxpZy6/eOE48ZdOEXhFWb4q1RqTkZ7Iqu/eITMzhSrVm3Dh/DHs7J3V6iUiIvIvqOVL5DaXmhxH5JENBAS1wdOrKllXtGJUrtaYrb/N4mTkJnz8QjkfE8GF88do1b4/AHl5OeTmZGLv4PqvznP18cePbCDgrxkObe0ccXL24sypHTj8tZ+zi1dxXPod78pxdUkJZwFwdvWmakBz4i+e5MCe77CxdcC3Yq1r1lEjqA1VajTlyMHV/Ln5C1q2exGjMY8Du1dSO+xBc2vklezsnWl99yCys9LMrXCREeupFdaJ8+cOc/jAKmxs7KjbsLOSseu43uQp+dJSL/H7r1Po+PA716zHZDJxMnITUSe2kpOTiY9fKLXDOpnH++XmZnF430+cjzmMlZUNVQOaERDUxnz8kYO/EHVyO07OntRv/DgurpdbT7OyUtmycSat7x5U6OtARESKn5Ivkdvc+ZjDmExGIo9sIPLIBott93d+j4ZNn+Lo4V85euhXnF3LE96ih3mcVkz0fvbtWsb9nd/71+fJl5Z6iZTk89T7a+Y8gLoNHmbfrmWYjEbqNepyzXFK8rf8cXX1w5/A1c0HF9cKVPALwc7OCQA3d1/SUi9x+uS26yZfrm4VAGgQ/iS//vwh8RdPcSEuEvdyFS1a2a5mMBjMideFuEhs7ZxwcfVm+x/zaNKqNxlpCezduZQ2dw8qxqu+c9xo8hSAjPREdmz5EqMx97p1nTn1J8cOr6Nug4dxdffl8P6f2PPnEho37w7A/t0rSEo4R8Nm3cBkYu/ObzEYrKlRsyXJSTFEndhG09Z9OHNqJ0cO/kKjZk8DcPLYH1St0UyJl4hICVLyJXKbCwi+i4Dgu6653ccv9JpTvVeq2tBi5rwrNWvd96bOk8/ZxYtW7QdYlHl516DdfUNveKxcVti4OoPBYE688rm4enPpwokCxxuNuZyPOUL5CgHY2joAlydCsbNzIjs7nZjofWRlprJ65Rjz/gCxZw9y30NvF6gvMmI9teo9QGrKBQxWVrh7VMTF1Ztd278iJyfTfA75W/7kKXf/3xvmJDYotD2HD6witG5HYs8d4sDu74rU6nzq+Faq12xJxcphANRr1JV1P39EasoF7OyciIneT9NWz+LpVRWA4Nr3cXj/T9So2ZLUlIu4uFXA3aMi2X5pHN7/EwDZWWnEnjtM67sH3qJnQERECqPkS0SkDLlyXJ2ffx1z+dFDa0mIP03TVs+ay5KTYnB2LWwCDgP7di6lboOHzR/YM9ITyc5Ox8XVm2at+2A0Gs17RxxcDUBI7fsK1HQx7ji2to64e1QkOTEGrhy7B+bHYulGi5JfiD1CUK27cXYpz7Y/5l63rvT0BDzKVTI/dnBwxc7eicT4M7i4Xm7ZvHK7m5sPWZkppKcl4OjoTnpaAjk5mSQlnsXB0QOAE8f+oGqNJmr1EhEpYUq+RETKiOuNq6vgF8Lxo79x4tgf+PiFcjEukrOn95iTsSvH71lZWVOlejhHDq3BwdEda2tbDu79AR+/kAJLA8Dfa4UVNhYvMmI9oXX/DwAnFy+MJiMxZw+SkZ6As4s3tnaOt+KpuO3daFHyug07AxTacnk1e3tnMjOSzY9zc7PJyc4gOzsdewdnAIuZMDMykoDLSwmU86qCV/nqrPlhLHZ2jjRu0ZPsrHRizx1Sq5eISClQ8iUiUkbccPxek/zxe2txdCpH/caPUc6rClBw/F5QrXsA2L39a/LycvCpWIta9R64qXguXTiBja29eR0wGxs76tR/mIN7v8fGxp6wxl3/5RX/d1y9KPnN8POvy/Gjv+HpVRVH53LmroNGYy6OTuXwKFeZQ/t+IqxxV0zGPI5FrAPAZMwDoEGTJ6id1QkbWwesrKw5cvAXqtZoQmZGEnt2fEtOdjo1Q9pZLLQtIiK3hpIvEZEy4obj9yqG4lOxaOP3rK1tCK37f+ZWq+sJu2JylCt5edcwt9Tkq1ipLhUr1b1hnfK3qydPuVmBIe1IT0/gt1+nYGVlReVq4bi5+2Fjc3msXVjjruza/hVrf3wfG1t7gmvfS2L8GWxs7c112NlfbiHLzk4n5uxBWt89kF3bFlGxUj18/Wvzx69T8fIOwMHRrXguWkRECqXkS0RE5BYp6qLk12NjY0fDJk+Sk5MJgK2tA2t/fB9HJw/gcnfR1u0HkpWVio2NA+lp8YABB0f3AnWdjNxEleqXx3olXDpNaJ3/w9HRHScXL5ISzir5EhG5xbTIsoiIyC1Q1EXJbyTiwCqio3Zha+uAra0DiQnR5OZmUs6rCiaTke2b5pGcFIu9vQvW1jZciD2Cu0fFArNQ5mRnEBN9gKo1mlwuMBgw/bXiuclkNP8uIiK3jpIvERGRYlbY5Cn5PzdiNOaSlZmCyXR5Rkl7BzeORawnMSGapISz7N3xLVWqN8HOzgmDwQprazuOHPyFtNSLxJ47xLGI9YV2X73c6hVunuHQ3cOf6KhdxF88SWrKBS2YLSJSAtTtUEREpJgVdVHywiRcOs22P+bS9t4hODmXo1pAMzLSE/hz8xcYMOBfpT7Bte8171+n/kPs3/0df6yfjr29M7XCHiiw8HZOdgbnovfT+oo1+GrVu5/d2xdz9vRuatX9P3M3RhERuXWUfImIiBSzoi5K7uVdo0AydnWZwWBFrXoPXHO2SnsHVxo3737d89jaOdL23lctylzdfGjT4aUbxigiIsVH3Q5FRERERERKgJIvERERERGREqDkS0REREREpAQo+RIRERERESkBSr5ERERERERKgJIvERERERGREqDkS0REREREpAQo+RIRERERESkBSr5ERERERERKgJIvERERERGREqDkS0REREREpAQo+RIRERERESkBSr5ERERERERKgJIvERERERGREqDkS0REREREpAQo+RIRERERESkBSr5ERERERERKgJIvERERERGREqDkS0REREREpAQo+RIRERERESkBSr5ERERERERKgJIvERERERGREnBHJF9Go5FPP/2U1q1bU79+fZ577jnOnDlT2mGJiIiIiIiY3RHJ1/Tp01m0aBHvvvsuX3/9NUajkb59+5KdnV3aoYmIiIiIiAB3QPKVnZ3N3Llzeemll2jbti0hISF8/PHHxMbG8ssvv5R2eCIiIiIiIsAdkHxFRESQlpZG8+bNzWVubm7UqlWLP//8sxQjExERERER+ZvBZDKZSjuIf+OXX35h0KBB7N27FwcHB3P5yy+/TGZmJjNmzLip+urWrUteXh5+fn7FHep1paZlkZdnLNFzyo1ZW1vh4mx/U8foXpZNupd3jn9yL0H3syzSvbxz/NN7+W/4+fmxYMGCEj2nyL9lU9oB/FsZGRkA2NnZWZTb29uTlJR00/XZ29uXylixkv6DJbeO7uWdQ/fyzqL7eefQvRSR29Vtn3zlt3ZlZ2dbtHxlZWXh6Oh40/Xt2LGj2GITERERERHJd9uP+crvHhgXF2dRHhcXh4+PT2mEJCIiIiIiUsBtn3yFhITg4uLCtm3bzGXJyckcOnSI8PDwUoxMRERERETkb7d9t0M7Ozu6d+/OhAkT8PT0xN/fn/Hjx+Pr68u9995b2uGJiIiIiIgAd0DyBfDSSy+Rm5vLyJEjyczMJDw8nDlz5mBra1vaoYmIiIiIiAB3wFTzIiIiIiIit4PbfsyXiIiIiIjI7UDJl4iIiIiISAlQ8iUiIiIiIlIClHyJiIiIiIiUACVfIiIiIiIiJUDJl4iIiIiISAlQ8iUiIiIiIlIClHyJiIj8B1y9rKeW+RQRKXlKvkSkyPRhTeT2ZDKZMBgMAFy8eBHA/FjkevR3X6R42ZR2APLfkv8B4OjRo9ja2gJQvXr1Uo5Kiir/w9rChQtJS0vj+eefL+WIpCTlv3937NhBYmIi2dnZ3H///aUdltzAlYnX7Nmz2bBhAy4uLowfPx5XV9dSjk7KmvzXS3JyMra2tjg6OmI0GrGy0vf1IsVB7yQpMXl5eRgMBjZu3MhLL73EnDlz2LlzZ2mHJTdpxYoVrFq1is6dO5d2KFKCrnz/jh49mtzcXAYPHsyiRYv0zXgZl594LVmyhN9++42PP/6YiIgIXnvtNeLj40s5OilL8hOvNWvWMGDAALp3786JEyewsrLCaDSWdngidwQlX3LL5Xdxsba25vz580yYMIH33nuPd999l65du5KamsqxY8dKOUq5lvwP1iaTCaPRyI4dO4iIiMDDwwO4/KFc7lxxcXGkpqZibW1NfHw8//vf//j0009xcXGhbt26tG7dmt9++620w5RCnD17lnPnzgGQnZ3Nli1bePHFF0lOTuaee+4hJyeHAQMGcPz4cXJycko5WikLDAYDmzdvZubMmYwYMQJvb2+6d+/O8ePHlYCJFBMlX3JLZWdnM3/+fE6ePAmAu7s7FStWpGHDhhgMBoxGI9u2bWPOnDmlHKkU5sruSnFxcVhZWfH666/Trl07evToQXZ2NtbW1uTm5pZypHIr5ObmMn/+fF555RVSUlLw9PSkUqVKrFu3junTpzNhwgQ8PDwYPXo0qamppR2uXCEzM5NNmzbx008/8eOPP3LixAkCAgJwdHRkzZo1PPDAA0yYMIETJ07w5Zdf6j0sZn/88Qe9evWiXLlyVK9enXvvvZcnnniCgwcPquuhSDHQu0huKVtbW1544QXs7Ox47733cHBwID09nbfeegsAKysrMjIysLHR8MOyKD/x+vLLL3nnnXcYM2YMc+fO5Y033qBKlSo8//zzZGdn6/7doWxsbHjssceoVq0aI0eOJCcnB0dHR8aPH8/UqVOpWrUqhw8fxt/fX6+BMsbBwYGaNWvy448/Mn78eJycnBgwYACVK1dm586dVK9enZ07dxIeHk7fvn1xdHQs7ZCllFzdbdjV1RVXV1fWrl1LzZo1eeutt/Dz82PYsGHqpipSDJR8yS2T32ri4uJCUlISJ0+eZOrUqXzyySdERkbSv39/lixZwuzZs7nnnntKO1y5hp9++omffvqJ8ePHExkZSXx8PE5OTowYMQJ3d3deeuml0g5RboH87kVeXl48+eSTlCtXjpEjRzJw4EA6derEc889x6RJk3j33Xd55plncHBwKOWIBSw/SIeGhtKgQQPq16/P2rVrOX36NOnp6URHRzN79mzGjRvHq6++SqVKlUoxYiltBoOBLVu28MUXX/DLL7/w4osv0qZNG7Zs2ULLli2JiIggKCiIMWPG4OnpWdrhitz2DCaNlJZbID/xioiIIDk5mTp16nDy5ElmzZpF1apVGTBgAPPnz8dgMFCrVi1atmxp0cVNSl/+7Fbz58/Hx8eH9PR0fvrpJz755BPmzZtHkyZNaNy4MRcvXsTHx6e0w5VilP9e3Lt3L8uXL+eRRx7B3d2dL7/8kpSUFMaOHcu6devIysqiYsWKhIeH6/1bBlx5D3bu3ImVlRXVqlXj3LlzfPvtt3h5eTFw4EB+/PFHDAYDoaGhmm32Pyz/9bJv3z5eeeUV7r//ftavX89dd93F66+/zt13302LFi34/fffeeedd2jbtm1phyxyR1DyJbfMxo0bee+997C1taV27dq88MILpKenM2/ePLy9vRk+fHhphyjXkZOTg62tLWvWrGHChAlUqFCBL7/8EoCePXvSt29f2rRpU8pRyq2yceNG5s6dS2xsLBUrVuSVV17BxcWFr776ivPnzzNu3DhNU15GLViwgB9//JGwsDCWL1/OkiVLiIqKYs2aNcTExODn58cbb7yBk5NTaYcqpWznzp1s3LiRFi1a0KxZM44fP87gwYN57LHHeOSRR1izZg2VK1emcePGpR2qyB1D3Q7lljh58iRLlixhzpw5/PTTT+Tl5bFw4UKcnJx45plniImJ0QyHZUxsbKx55sIlS5YwcuRIfvnlFypWrEjdunVp3rw5e/bsYdWqVSQkJBAYGFjKEcutcvLkScaPH8/w4cNZvXo14eHhLFu2jPT0dJ566im8vLw4ffp0aYcphdi5cyerV69m7ty5+Pj4UKdOHVxdXWnQoAHPPfcc7dq145lnnlHi9R+W/537mTNnWL58OYsWLeL8+fMABAQEMHHiRObNm8fs2bPp3LmzEi+RYqaWLyl2iYmJTJkyhR07dvDhhx8SEhJCamoqo0aNwt7enmeffRZfX19cXFxKO1T5S2xsLFOmTKFRo0bk5eXx1Vdf0bFjR7777jteeOEFbG1tiYyM5M8//8TJyYmXX36ZkJCQ0g5bbpHY2FjGjh3L6NGjzWM8hg4dSkxMDG+//TYBAQGaYKOMuLq759GjR9mwYQNGo5Ht27czc+ZMFi9ezNatW5kyZUopRiplyfbt2xk/fjwLFy5k1qxZ/PLLL0ycONH8pVpkZCSJiYlKvERuAbV8SbHIz+FTUlLw8PDg6aefJigoiJ9//pnjx4/j4uJino7aZDIp8SpDkpKS8PHxoXbt2hw4cICVK1cybtw4nn/+eV577TWWLFlCZmYmTz/9NLNnz2bixIlKvO4w+e/frKws0tLScHR0JC0tjYiICPMU5I888gjx8fFMnjxZiVcZcWXidfbsWeLj4ylfvjwLFixg+fLlzJ07FxsbG9LT06lRo0YpRytlxbFjx5gzZw4NGzbEzs6OAQMG0KpVK15//XUiIiIACAwMpHHjxlpAXeQWUMuX/Gv5HwDWrFnD/Pnzyc3NZfLkyaSlpbFgwQK8vLz4v//7PwIDA8nLy8Pa2rq0Q5a/JCYmsmjRIlq1aoWtrS3btm3jp59+onnz5vTv3x97e3s2btzIpEmT6N69O4899lhphyzFLP/9u379epYuXUpcXBw9evTgyJEjbN68mQ4dOuDo6MgPP/zAhx9+yGeffcbLL79MlSpVSjv0/7QrE685c+awZMkSAgICeOSRR6hWrRrdu3enW7du5OXl8dtvv/HRRx9Rs2bNUo5ayoIzZ84wefJk8vLyeOaZZ2jQoAEAY8eOZcuWLSxevBhnZ+dSjlLkzqWWL/nXDAYDmzZtYubMmbz11lu4ubnx+OOP4+LiQq9evYiJieH7778nPT1ds6GVMQ4ODri7u/PBBx8wbdo0unbtylNPPUViYiLfffcdOTk53HXXXQwbNowWLVqUdrhyCxgMBnbu3MnkyZN5/vnnGThwIBs2bDCv4+bk5MTZs2d57bXXSE9P58SJE2q5LmVXJl6xsbGcPXuWqVOn8sgjj/DFF1+Yx9zm5eVhMBiYMGGCEq//sPzv2Hfs2MHatWuJjo42T7iyYcMGDhw4AMCIESP49NNPlXiJ3GLqOyL/Sv6HgN9//51evXpRrlw5AgMD8fPz4+GHH+brr7/mwQcfxNvbWwO8y5CdO3dy/PhxUlNTOXjwINHR0QQGBnL8+HE6depETk4OBw4cIDMzk6eeekqJ1x0uKiqKli1bUq9ePQC8vb0ZMGAA48aNo2PHjmzevJm9e/fyww8/MGHCBK31U4quTLwWLVrExo0bcXFxoWLFiuZp4xcsWMAjjzzCq6++WpqhShmQ/3rZuHEjEyZM4MUXX2TgwIF8/PHHPPvss/zvf/9j5cqVGI1G6tWrp6UHREqAWr7kXzlz5gwAHh4euLm5sWbNGoKCghg1ahReXl688sor1KpVSzPjlSEbN27knXfeISMjg8jISBISEoiLi8PV1ZXVq1ezfft2Hn/8capWrcqlS5fIyMgo7ZClmF3d29zBwYFTp06ZH4eGhtKxY0fi4+MBqFKlCo0aNWL69OkEBweXZKhylfzE648//uDHH3+kbt26XLx4kTVr1pCens4999zDE088werVq0lKStKYnf+o7Oxsc+KVlJTEvHnz+PTTT3Fzc6NOnTrUrl2b9PR0Xn75ZZKTk83LRqh3isitpzFfctPy/6AfP36cvn378vTTT/Pcc88BMHDgQN5++20uXLjA3Llzefrpp2nUqFEpRyz59u3bx9ChQ/nwww/N/fwBXnvtNdasWUOfPn3Izs7Gxsbm/9u784Aoy7WP498ZFlFBBCU3Fl89ihmimAkumaJliiettNJy95hpHhc8rqm4oOaCggqVUsdyKStPvJqYG0qmoZimZLiASySggiKLyDK8f3iYN6vTcjIG5Pf5RxmeGS4Yhnl+z33f103dunV54okncHJysmDFcq+Vvn4PHTrEuXPnyMrKon///gwZMoSWLVsyYsQIUlNTmTlzJosXL6Z169aWLll+ZO/evcyaNYtFixbRsWNHNm/eTFxcHB06dKBr1644OjqSm5ur6WOVVE5ODtu2baNbt24YjUbs7OxYsmQJ7u7u7Nq1i4ULF+Lo6MjTTz9NTEyMfldEypimHcrvZjAY2L17N1FRUTz22GNs2bKFjIwMpk6dyqlTp1i1ahWxsbEEBQUpeJUzFy5coGvXrvj4+JhDltFoZOHCheTl5WFra8sDDzzA7t276d27t4LXfah0mvDixYsZPHgwu3fv5vLly8ybN4/IyEjCwsJIS0tj5syZtG7d+ietzMXyOnXqhKOjI2vWrKFjx44899xzGI1Gdu7cibW1NX/96191Ml2J2djYcObMGT799FNsbGyIiIggPz+f119/nUOHDuHk5ERcXByurq7k5+drSYBIGdPIl/xu2dnZDBs2jClTptCmTRtu3rzJsGHDePrpp+nVqxd79uzB1dWVtm3bWrpU+bfSE+iwsDBSU1NZuHCh+TaTyQTAmDFj6NSpE/3796ewsBAbGxsLVy33mslkwmQyMWnSJLp3706PHj0ACAoKIi0tjTfeeIOCggJycnJwdnZW8CqHioqKsLa2prCwkJ49e/Lggw8SFhYGwCeffEK7du2oU6eOhasUSzGZTBiNRhITExk7diz169fnrbfewsbGhpEjR5KdnY2vry8xMTGMGzeObt26WbpkkUpHa77kNynN6MnJyaSmpmJra0vDhg0BqFGjBmPHjiU8PJyoqCieeeYZ2rZtq7UG5UjpCbSfnx9fffUV8fHx5uBVUlKC0WikSZMmODo6Aih43aeys7PNe3T9cK+uoKAgsrKyzK/t0hFPBa/yx9ramqKiImxsbNi+fTtnz55l2LBhwJ292BS8Kq/Sv+VZWVk4OTmxdu1a3N3dmT17Nqmpqaxdu5bevXvj6enJrFmz6Natm96nRSxA4Ut+E4PBwJdffsmkSZNwd3fHw8ODuXPnUlBQAIDRaKRDhw7s2LGD+Ph4832kfGnZsiX+/v5s2rSJuLg4jEYjVlZWbNu2jZiYGLy8vCxdotxjpSdX586dY/To0SQnJ9OjRw9mz55NUlISAF9//TW5ubkYjXfeEvTaLd+sra0pKCjAxsaGqKgorl+/Tlpamk6kK7nSroZDhw5l9erVnD17lpkzZwLw1ltvsXPnTho0aEBAQACPPPKI+T4iUrY07VD+o9u3b1NUVITBYOD777/nrbfeombNmsyYMYP09HRWrFjByZMnef7551m3bh2hoaHs27ePRx991NyyWsqf9PR03n33XbZv385DDz2Ek5MTx44dIyQkhKZNm1q6PPkT7Nu3jx07dnD+/Hns7OyYMWMGJ0+eZOnSpfTs2ZO4uDgmTpyIv7+/pUuV/6B0Cuj169fNI5OlUxBFAL755hvCw8Pp0aMHN27c4NixY3Tp0oXu3buzbNkyjh8/zquvvkrHjh0tXapIpabwJT8rKSmJpUuXkp+fj7W1NZcuXcLd3Z26devSr18/c7jatGkT9vb2NGjQACsrK+bMmUNoaChubm4W/g7klxQWFvLtt9+SmJhI/fr1adSoEfXr17d0WfInOH/+PMOHDyc0NBR7e3sSEhLYvHkzCxYsoKCggKysLGxsbGjRooXWeJUjP3wuSv9/7Ngx1qxZQ1BQEA888MDPHiuVR2pqKkajkTp16pCamsrQoUPp3bs3r7zyCpmZmRw8eJD9+/fj6+tL3759yc7ONreUFxHL0SUz+Ylz584xadIkBg0ahK+vL5mZmbz77rts3bqVrl27cuTIEYxGI15eXvTv35+vvvqKTz75hJMnT7Jw4UIFrwrAxsYGb29vjVBWArm5uTRt2pQWLVoA4Orqytdff8306dOZOnXqXa3kdQJvWUlJSXz//fd06tQJg8FgDlUGg4HTp08TGBjI7Nmz7wpeoOetsnr77bf58ssvWbNmDfXq1aNTp07885//pFevXri5udGxY0cKCwuJjY2lQ4cO1KtXz9Iliwga+ZIfuX37NlOmTMHPz48XXnjhrs8FBgYSFxdHly5dcHBwoFevXjRv3hyA06dP4+zsjIuLiyXKFpH/ICcnh0GDBtG/f3/69esHwPr16zl48CDVqlVj+vTpODk56QTewm7dusWaNWu4cuUKPXr0oEOHDsD/j2qdPn2a3Nxc7bsmd5k1axbnzp0jJCSEunXrsnTpUvbt20d4eDju7u5cv36dgoICNWIRKUfUcEPuUqVKFeBOYwa4s6aguLgYuNOK3MfHhyeffJLMzEzs7OzM9/P09FTwEilnTCYT9vb2DB06lIMHDxIcHMyBAwf44IMPeOaZZ6hduzZ2dnYKXhZmMpmoWrUqQ4YMoVq1akRHR/+kcZGnp6eCl5iVvi/PnTuXhg0bMmHCBNLS0pg0aRKPP/44gwcP5tKlSzg5OSl4iZQzCl9iVlJSQl5eHklJSSQnJwN3umqVvvk7ODjw/fff06ZNG+bOnUujRo0sWa6I/IrS7oWPPfYYw4cP58aNG+zfv5+QkBBq1arFiRMnyM/Pt3CVUvo8bd++ndOnT3Ps2DE++ugj9u/fbz5Gk1Tkh6ysrMwBbMGCBTRq1IiJEyeSlpbGuHHjCAgIID093cJVisjP0bRD+YkNGzawe/duxowZQ5s2bcybNsbHxxMaGkpoaKimKYmUY6Wv2Z8THR3NqVOn2LNnD8uXL8fT07OMq5NSV65cwcHBgapVq3LixAlmzpzJli1bSElJYf/+/Xz33Xc8/vjj2rBe/qPCwkLzvoyzZs3i5MmThIeHm9d3qRmLSPmjkS/5iR49etC4cWMiIiL47LPPSElJ4ciRI8yePZuhQ4fi7OysP+Yi5cSPr58VFxdjNBq5du0aBw8e/MnxHTp0oHHjxrzxxhsKXhaUkpLCpk2bzM+ftbU1VlZWAHh4eODv78/333/PO++8w6FDhyxZqpQDpb8naWlppKSkkJOTA9xpnlS63+bcuXNp1qwZaWlp5vvpvVqk/NHIl/ysjIwMtm7dygcffICHhwdGo5Fnn32Wrl276kqaSDlw69YtqlatCvz06nZqaiqTJk3iueeeo3fv3pYqUX5FdnY2Fy5cICkpiXbt2rF69WqaNGnC008/jb29PStXriQtLY0JEyZQu3ZtS5crFrZ7927Wrl2LwWDgL3/5C97e3uYmOtrzTaTiUPiSX3Tz5k2qVatGfn4+9vb2Cl4i5cD169eJjIykdevW5o2RS1+bhYWFzJ07Fw8PD0aMGGHhSuXHTCaTuX08wD//+U+++uorunfvTkZGBikpKZw5c4Z27doRFRXFG2+8gbu7u4WrFksr3QImJCQER0dHPv/8cw4fPkxAQIC5M6aIVAy6TCK/qEaNGgBUq1YN0BQGkfIgLy8PKysrDh06hJ2dHe3btzfvC2Vtbc24cePMIyW6YFJ+lJSUmNfiHTx4EKPRyKBBg3B2duaLL77A19eXVq1a8c0335Cdnc3KlSsVvCqp9PR0rl69ipeXF3BnywgXFxdzo6vOnTtz8OBBvvnmG4UvkQpG4Ut+k/+0eF9EypbJZKJBgwY8+OCDfPzxx3zwwQdYWVnh6+trDlkKXuVT6XOxfv163nvvPTw9PXn77beJiIjAZDJx6NAhSkpK6Nevn6aQVWJJSUlMnTqVhQsXkpubS/Xq1alXrx5Vq1bl+PHjPPjgg9SsWRMfHx8uXbpkXg+m17pIxaAzahGRCqD0BMtoNBIbG8uqVavo0KEDBQUF7Nmz52fbkutkrPyJiYlh27ZtbNq0iQ4dOpCcnMzf/vY3nnrqKXx8fDhy5Ija/1di6enpTJ06lQkTJpCbm8uoUaPM04cbNWrE+++/z7p164iOjmbt2rV07NjxrmmsIlL+ac2XiEg5l56eztmzZ/Hz88NoNLJw4UK8vLzo3bs3KSkp7Nmzh8TERJ566inatWtn6XLlBxISEsjLy6O4uJh27dqxa9cuUlJS6NatG5s3b6Z3797Mnj2boqIi1q5dC9zZU1Eqp8LCQi5cuIDRaGTKlCkEBwczffp0HB0dmTdvHkeOHOHMmTNcu3aNv/71rzz66KOWLllEfieNfImIlHPx8fHUrVuX/Px8TCYTLi4u7N69m5ycHFxdXfH39ychIYHo6GgyMzMtXa78W2xsLIGBgWzZsoWxY8fy3nvv4e3tTbNmzTh+/DguLi785S9/wcfHB5PJRFZWloJXJVZcXIyNjQ1NmjRh3bp1PPfcc9SpU4dWrVpRpUoVxo8fT7NmzZg8eTLz589X8BKpoBS+RETKuYCAAGrXrs3MmTPZtm0b3t7eNGjQgA8//JDCwkJKSkpwdnZmwIABODs7W7pcAQ4cOMCKFStYvHgxixYtYsGCBWzevBkbGxt8fX15//33sbKy4vDhwxw/fpzVq1fj6upq6bLFQkpKSrCysuLWrVvAnSmGHh4efPrpp/j6+hIeHk5GRgYrVqwgIyMDW1tbC1csIv8tregVESmnftg+vmbNmvj6+hIXF4efnx9ubm4kJiby/PPPU1BQYL4qLpZ36NAhJkyYwObNm/mf//kfAB566CHc3NwwGAwYjUZefPFFVq1ahb29PUFBQTzwwAMWrlospfR1/sUXX/Dee+/RsmVL/Pz8aNGiBRs2bGDMmDEkJibSpEkT/va3v1GrVi1Llywif4DCl4hIOVR6QrZnzx62b9+Ok5MTU6dOpUqVKsTFxdGxY0cmTpxIbm4uAK6urupuWE4UFBQAcPHiRXP42rBhA0aj0bx9R8+ePXnkkUewtbXF0dHRYrWK5RkMBuLj45k1axaBgYHY2dnh4+PDxYsXzWs6P/zwQ+bMmcPDDz9s6XJF5A9Sww0RkXJq//79LF++nNGjR7NmzRrq1atHSEgI0dHRREdHExAQQEBAgKXLlJ8RExNDcHAwU6ZM4dy5cxw/fpyVK1dia2tLcXExVlZWli5RypEPPviArKwsRo4cSXFxMQaDgR07dnDp0iVq1aqFm5sbfn5+li5TRO4BjXyJiJQTJpPJvKdeQUEBUVFRBAUFUbt2bRo1akR2djavvvoqYWFh3L5927zhqpQ/Xbp0oaSkhJkzZ1K9enV27twJ3HletV5HSkepS/91cHDgnXfeoVevXtSvXx+A6OhoRo0axUMPPXTXfUSkYlP4EhEpB5KSkoiIiKBKlSo0a9aMgQMHAncC2Y4dO/D398fV1ZXhw4czePBg3n33XWxsbCxctfwSf39/rKysmDt3Lnv27KFr164KXmIOUbGxsWzfvp22bdvi5eVFr169WLVqFS+//DIFBQUcPXrUPK1YwUvk/qFuhyIiFpacnMzkyZNp2LAhLVq0IDQ0lH379hEUFETTpk1JSEige/fu3L59m549ezJx4kQFrwriscce47XXXmPKlCls377d0uVIOWAwGNi/fz+vv/46DRo04F//+hefffYZTZs2xd3dnfHjxzN27FiaNm3Ktm3buHbtmoKXyH1EI18iIhZ07tw5Zs+ezcCBA+nTpw8AqampnDhxgs6dO5OUlMSuXbuIjIxkw4YNzJkzh0ceeURXwiuQLl26EBISgoeHh6VLkXIgMzOTzZs3ExERgb29PYcPHyYzM5OzZ8/y/PPPM2rUKG7evElMTAwhISGMHj3a0iWLyD2kkS8REQu5desWo0aNok6dOubgVXq7yWTCZDLRuHFjwsLCSE9PZ+7cueaNVRW8KpZOnTopfFViP+xt5uTkhKOjI+fPn+ejjz5ixIgRNG/enH/961/Mnz+f7OxsatSoQcuWLVm/fj1169a1YOUicq+p26GIiAXt3LmT5cuXM2rUKHr37s3GjRv58MMPiYiIoG7duuYRLnXIE6mYfriP16lTpygsLOSFF16gsLCQ6dOnExkZyalTp1i5ciUTJkygadOmli5ZRP5EmnYoImJBTzzxBNbW1ixcuJD9+/dz/fp1Vq1aRd26de8KXApeIhVTafAKDg5m4MCBVKlSBWdnZ5KTkzEajYSHh7Nr1y7+/ve/K3iJVAIKXyIiFubv74/RaGTKlCmMGjWKBg0aUFRUpMAlUkFdvHiRy5cv065dO0pKSti6dStDhgzhueeeAyA/P5/169fzxBNPcPToUcaNG0fnzp0tW7SIlAmFLxGRcqBz584sWrSIxYsXY29vT79+/Sxdkoj8F4qLi7l69SpVqlQhKysLR0dH7O3tyc/PB6CwsBA7OzvS09MZO3Ysffv2vWvPLxG5v6nhhohIOdGlSxcmTpzI6tWrSU9PR0tyRSqWK1eusGnTJurUqYOXlxd9+vQhJiaGgIAAli9fzuHDh7GxseHEiROkpKRQWFhovq+Cl0jloIYbIiLlTEZGBrVq1bJ0GSLyOx0+fJh33nkHX19fevTowdGjRwkNDWXJkiXcuHGD4OBg2rZty8mTJxk3bhxdunSxdMkiUsYUvkRERET+oNJpgxs3bmTnzp20adOG/v37c+zYMRYsWMCyZcto0qQJ6enpFBUV4enpqamGIpWQph2KiIiI/EEGg4F9+/bxv//7vzRs2JBdu3YRFRWFl5cXM2bM4JVXXiE2NpbGjRvj6elpvo+IVC4a+RIRERH5A0pKSsjNzWXSpEkMGjSI9u3bExcXR3R0NPXq1aNv374cO3YMe3t7/Pz8LF2uiFiQuh2KiIiI/AEGgwF7e3ucnJw4deoUfn5++Pr6kp2dTVBQEFZWVuY9vjTVUKRy07RDERERkd+pdOJQYmIi+/btIzc3l1atWpGRkcGhQ4cAaNy4MQ0bNqRjx45UqVIF0FRDkcpO0w5FREREfofS0av9+/czf/587OzsaNasGf369WPv3r1cvnyZ/Px8Ll68yLRp07SBsoiYadqhiIiIyG9QVFSEtbU1BoOB8+fP89FHH7F27Vo8PDyYNGkSe/fuJSAggGrVqpGWloaTkxPNmze3dNkiUo5o2qGIiIjIr8jMzOQf//gHFy5cAGD9+vVcuHCBrKwsAIKDg8nMzGTdunUYjUY6dOig4CUiP6HwJSIiIvIrioqKqF27NosWLeLatWu89NJLtGjRgl27dnHmzBmqVKnCvHnzKCkp4fbt25YuV0TKKa35EhEREfkN0tPTWb9+PWfOnGH+/PncvHmTjRs34uDgwJNPPkmzZs3UzVBEfpFGvkRERER+gzp16vDSSy/RtGlTXnvtNRwdHRkwYABXr15l27Zt5ObmomvaIvJLNPIlIiIi8itKm23A/4+AJSUlMWfOHLKysigpKaFJkyYWrlJEyjuFLxEREZH/ICkpicaNGwM/DWCRkZEkJycTHh6Ora2tJcsUkQpC4UtERETkZ+Tn5zNgwAC8vLyYO3cucHcAS0tLIy8vj0aNGlmyTBGpQBS+RERERP7txw0zkpOTmTZtGt7e3syYMQO4O4CJiPwearghIiIi8m8Gg4GjR49y6tQpABo1asSSJUs4duwYISEhAApeIvJfU/gSERER+YEDBw4wevRocwBzd3dnyJAhbNy4kddff93C1YlIRabwJSIiIpVa6QqMy5cvk5eXx7hx4xg9ejSBgYHmAObi4sLQoUPx9/e3ZKkiUsFp3FxEREQqNYPBQGxsLKtWrcLV1ZWUlBTCwsIwGo1MmDCBbt26ERUVRVhYGK1bt9ZGyiLyX1PDDREREanUzp07x5gxY1iwYAGNGzcmKiqK8PBwoqOjOXHiBJcvX6Zhw4a0b9/e0qWKSAWnkS8RERGp1G7evEnLli15+OGHMZlMDB48mPPnz7Nx40ZeffVVS5cnIvcRrfkSERGRSs3FxYUTJ06we/dujMY7p0b16tWjqKjIwpWJyP1GI18iIiJSaZlMJtzc3BgxYgRbt27l4sWLtGzZkq1btzJ9+nRLlyci9xmt+RIREZFK4ZcaZWRkZJCQkMA777xD/fr18ff3p1u3bmVcoYjc7xS+RERE5L5XGryOHDnC0aNHcXNzw8PDAy8vr7uOK51qaG1tra6GInLPac2XiIiI3NdKQ9S+ffuYM2cOLi4uREZGEhUVRUFBwV3HWltbY219Z1WGgpeI3GsKXyIiInJfys3NBe6EqLy8PHbs2EFERAQeHh5YWVkxYsQIDh06RHp6OpoIJCJlQeFLRERE7jsFBQWsXr2a7du3c+3aNapWrYqLiwvBwcEsXryY0NBQbGxsWLlyJXZ2dhrlEpEyofAlIiIi95Xs7GxsbW158sknCQ8PZ+jQoVy/fp0mTZpw5coVhg4dSv369bl69SpWVlbk5eVZumQRqSQUvkREROS+UFJSwq1bt/jHP/5Beno6Tk5OZGdnYzQa+eqrr3jyySfx8/MjKiqKl19+mQkTJjBixAjq1atn6dJFpJJQt0MRERG5r+Tk5HDlyhXOnTtH69atSU5OJiwsjP79+xMQEMDZs2fJzMykRo0aPPjgg+pqKCJlRuFLREREKrzU1FS++eYbbGxsaN26NQkJCYwfP57Jkyfz1FNPERMTw/r162nevDnOzs6MHDnS0iWLSCVkbekCRERERP6IpKQkxo8fT/Pmzbl16xaurq60a9eO0NBQZsyYgclkok+fPlSvXp3IyEgGDx5s6ZJFpJLSyJeIiIhUWOnp6YwYMYLBgwfTt29fbty4QbVq1UhJSaFRo0Z8++23jB8/nkGDBvHiiy9SUFCAra2tphqKiEWo4YaIiIhUWCkpKfj6+tK3b19MJhMff/wxEyZM4MUXX2T48OEABAcHs2bNGi5fvoyNjQ2gDZRFxDI08iUiIiIV1unTpxkwYAAvv/wy27dvp1atWvj4+NC+fXs+//xz7O3tGT58OJmZmTg7O1u6XBGp5LTmS0RERCosT09PgoKC2LVrFz4+PowePRoHBwfs7OxISEggKysLgBo1ali4UhERjXyJiIjIfSQnJwd7e3vi4+OZMWMGQUFBtGvXztJliYgAGvkSERGR+4DJZOLAgQOsW7eOxo0bs3fvXqZNm6bgJSLlika+RERE5L6Qk5PDgQMHKC4uxs3NDW9vb3U1FJFyReFLRERERESkDKjVvIiIiIiISBlQ+BIRERERESkDCl8iIiIiIiJlQOFLRERERESkDCh8iYiIiIiIlAGFLxERERERkTKg8CUiIhYRFxeHp6cncXFxli5FRESkTCh8iYiIiIiIlAGFLxERERERkTKg8CUico/4+/sTFhbG66+/Tvv27fH29mb48OFcuHDBfMwXX3zBgAEDePjhh/H19SUwMJDU1FTz57ds2UKLFi2Ij4/n2WefpUWLFnTv3p29e/eSnJzM4MGDadmyJY8//jiffvrp764xJyeHefPm8eijj9KqVSueffZZ9u3bZ/58fn4+y5Yt44knnsDLy4vWrVszdOhQvv32W/MxU6dOZciQIXz88cd0794dLy8vevfuTWxs7F1f68iRIwwfPpxHHnkELy8v/P39WblyJSaT6XfXLSIicj9Q+BIRuYfeffddkpOTWbhwIfPnzychIYEpU6YA8MknnzBs2DDq1atHSEgI06ZN49ixYzz//PNkZGSYH6OoqIjAwEBeeOEFIiIiqFq1KpMmTWLUqFF07tyZN954gwceeIApU6aQlpb2m2srLi5m2LBhbN26lZdffpnw8HAaNWrEmDFjiI+PB2Dy5Ml8/PHHjBw5krfffptp06Zx9uxZAgMDKSkpMT9WQkICkZGR/P3vf2f16tVYWVkxduxYsrKyAEhMTGTIkCHUrFmT5cuXExERQZs2bVi1ahXR0dH34kctIiJS4VhbugARkftJjRo1CA8Px8rKCoBLly6xcuVKrl+/ztKlS+nYsSPLli0zH9+6dWt69uxJZGQkkydPBsBkMjFq1Cj69esHwM2bN5kwYQKDBw9m6NChADg4OPDss8+SkJBA3bp1f1NtsbGxfP3116xevZpu3boB4Ofnx3fffceXX36Jt7c3ubm5vPbaa/Ts2ROAtm3bkpOTw6JFi7h27RouLi4AZGdns2XLFtzd3QGoVq0aL730El9++SXdu3cnMTGR9u3bs2TJEozGO9f5OnTowN69e4mLiyMgIOAP/ZxFREQqIoUvEZF7qEWLFubgBZiD0dmzZ7l69SqBgYF3He/u7o6Pjw+HDx++63YfHx/z/2vVqgVAy5YtzbfVrFkTuBPMfqujR49iY2ODv7+/+Taj0cj7779v/jgyMhKA9PR0zp8/z4ULF4iJiQGgoKDAfJyzs7M5eP3w+7x16xYAffr0oU+fPty+fZvz589z8eJFvv32W4qLiyksLPzNNYuIiNxPFL5ERO6hqlWr3vVx6ahPaSCrXbv2T+5Tu3ZtTp06dddt9vb2v/rYv9eNGzeoWbOmuaaf8/nnn7NgwQKSk5OpXr06zZo1o1q1agB3TTv8cS0GgwHAvJ4rPz+fefPmERUVRVFREa6urvj4+GBtbX3X44iIiFQmCl8iImWgdKTq2rVrP/nc1atXcXJy+tNrcHBw4MaNG5SUlJjDEsCpU6coKSnBwcGBMWPG0K1bN958803c3NwwGAxs2LCBzz///Hd9reDgYD777DNWrFhB+/btzQGuXbt29/R7EhERqUjUcENEpAzY2tri4uLCtm3b7rr9u+++4/jx47Ru3fpPr6FNmzYUFhbe1ZWwpKSEadOm8eabb5KQkMDt27cZOXIk7u7u5oBWGrx+z4jV0aNH8fX1pVu3bubglZCQQGZmprodiohIpaWRLxGRMmAwGJg4cSLTpk0jMDCQp556iuvXr7Nq1SocHR3NjTT+TJ07d8bHx4epU6cyfvx43NzciIqKIikpiXnz5uHo6Ii1tTVLlixh2LBhFBQUsGXLFnMr+ry8vN/8tby9vYmOjmbTpk00btyYxMREIiIiMBgM5nVhIiIilY3Cl4hIGXnmmWeoXr06b775JmPGjMHe3p5HH32UiRMnmrsI/pmsrKxYs2YNS5cuJTQ0lFu3buHp6cnbb7+Nt7c3AMuWLWPVqlW88sorODo60qpVK9577z0GDhxIfHw8np6ev+lrTZ06lcLCQlasWEFBQQGurq688sornDt3jr1791JcXPxnfqsiIiLlkqFEK59FRERERET+dBr5EhGp4IqLi391PZbBYLirBb6IiIiUPY18iYhUcAMHDvzJPmE/1qBBA/bu3VtGFYmIiMjPUfgSEangkpOTyc3N/cVjbG1tf/N6LREREflzKHyJiIiIiIiUAe3zJSIiIiIiUgYUvkRERERERMqAwpeIiIiIiEgZUPgSEREREREpAwpfIiIiIiIiZUDhS0REREREpAwofImIiIiIiJQBhS8REREREZEy8H+TdS7VDGIf/gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(dataframe=df_trial_metricas, coluna1='nom_canal', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando a teste de hipóteses qui-quadrado *****\n",
      "* Premissas:\n",
      "- As observações devem ser contagens ou frequências.\n",
      "- Cada observação deve pertencer a uma única categoria.\n",
      "- A amostra deve ser relativamente grande, com pelo menos 5 observações em cada célula e, no caso de poucos grupos, pelo menos 10.\n",
      "- Os dados devem vir de uma amostra aleatória.\n",
      "- As categorias das variáveis devem ser mutuamente exclusivas.\n",
      "\n",
      "----- Hipóteses -----\n",
      "H0 : as variáveis nom_canal e realizou_assinatura são independentes. As proporções são estatisticamente iguais.\n",
      "Ha : as variáveis nom_canal e realizou_assinatura são dependentes. As proporções são estatisticamente diferentes.\n",
      "Nível de significância de 0.05.\n",
      "---------------------\n",
      "\n",
      "Valor do teste qui-quadrado: 27.633890295108593\n",
      "P_valor: 1.4795704151988149e-05\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.0000147957 < alpha: 0.05 => Rejeitamos a hipótese nula. Há evidência suficiente, ao nível de significância de 0.05, para concluir que as variáveis nom_canal e realizou_assinatura são dependentes.\n",
      "- Tamanho do Efeito - Cramér´s V: 0.1676657113\n"
     ]
    }
   ],
   "source": [
    "# Realizando o teste qui-quadrado.\n",
    "teste_chi2(dataframe=df_trial_metricas, coluna1='nom_canal', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nom_canal</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>nobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic</td>\n",
       "      <td>28</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paid - google</td>\n",
       "      <td>92</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Paid - meta</td>\n",
       "      <td>62</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Site ou Indicação Interna</td>\n",
       "      <td>25</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   nom_canal  frequencia  nobs\n",
       "0                    Organic          28    67\n",
       "1                      Other          63   224\n",
       "2              Paid - google          92   363\n",
       "3                Paid - meta          62   282\n",
       "4  Site ou Indicação Interna          25    47"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intervalo de confiança \n",
    "grpby_canal_conversao = lib.grpby_2cat(dataframe=df_trial_metricas, coluna1='nom_canal', coluna2='realizou_assinatura', plot=True)\n",
    "\n",
    "freq = grpby_canal_conversao.loc[grpby_canal_conversao['realizou_assinatura']][['nom_canal','frequencia']]\n",
    "nobs = grpby_canal_conversao.groupby(by=['nom_canal'], as_index=False)['frequencia'].sum().rename(columns={'frequencia':'nobs'})\n",
    "freq_nobs = pd.merge(freq, nobs, on='nom_canal', how='left')\n",
    "freq_nobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intervalo de confiança 95% para Organic: [29.981%, 53.601%]\n",
      "Intervalo de confiança 95% para Other: [22.237%, 34.013%]\n",
      "Intervalo de confiança 95% para Paid - google: [20.870%, 29.819%]\n",
      "Intervalo de confiança 95% para Paid - meta: [17.152%, 26.820%]\n",
      "Intervalo de confiança 95% para Site ou Indicação Interna: [38.926%, 67.457%]\n"
     ]
    }
   ],
   "source": [
    "# Itervalo de confiança.\n",
    "for index, row in freq_nobs.iterrows():\n",
    "    lower, upper = proportion_confint(count=row['frequencia'], nobs=row['nobs'], alpha=.05)\n",
    "    print(f'Intervalo de confiança 95% para {row['nom_canal']}: [{lower*100:.3f}%, {upper*100:.3f}%]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nom_canal</th>\n",
       "      <th>realizou_assinatura</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Organic</td>\n",
       "      <td>True</td>\n",
       "      <td>28</td>\n",
       "      <td>41.791045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other</td>\n",
       "      <td>True</td>\n",
       "      <td>63</td>\n",
       "      <td>28.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Paid - google</td>\n",
       "      <td>True</td>\n",
       "      <td>92</td>\n",
       "      <td>25.344353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paid - meta</td>\n",
       "      <td>True</td>\n",
       "      <td>62</td>\n",
       "      <td>21.985816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Site ou Indicação Interna</td>\n",
       "      <td>True</td>\n",
       "      <td>25</td>\n",
       "      <td>53.191489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   nom_canal  realizou_assinatura  frequencia  porcentagem\n",
       "1                    Organic                 True          28    41.791045\n",
       "3                      Other                 True          63    28.125000\n",
       "5              Paid - google                 True          92    25.344353\n",
       "7                Paid - meta                 True          62    21.985816\n",
       "8  Site ou Indicação Interna                 True          25    53.191489"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpby_canal_conversao.loc[grpby_canal_conversao['realizou_assinatura']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Impacto do Credenciamento na Conversão\n",
    "    * Hipótese Nula (H0): O credenciamento da clínica não tem impacto significativo na taxa de conversão para assinaturas pagas.\n",
    "    * Hipótese Alternativa (HA): O credenciamento da clínica tem impacto significativo na taxa de conversão para assinaturas pagas.\n",
    "    \n",
    "Teste Estatístico: Teste T para amostras independentes ou Teste de Qui-Quadrado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e06e8_row0_col3 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e06e8_row1_col3 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e06e8_row2_col3 {\n",
       "  background-color: #6dd3ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e06e8_row3_col3 {\n",
       "  background-color: #347fa4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e06e8\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e06e8_level0_col0\" class=\"col_heading level0 col0\" >credenciamento_aprovado</th>\n",
       "      <th id=\"T_e06e8_level0_col1\" class=\"col_heading level0 col1\" >realizou_assinatura</th>\n",
       "      <th id=\"T_e06e8_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_e06e8_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e06e8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_e06e8_row0_col0\" class=\"data row0 col0\" >False</td>\n",
       "      <td id=\"T_e06e8_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_e06e8_row0_col2\" class=\"data row0 col2\" >621</td>\n",
       "      <td id=\"T_e06e8_row0_col3\" class=\"data row0 col3\" >88.587732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e06e8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_e06e8_row1_col0\" class=\"data row1 col0\" >False</td>\n",
       "      <td id=\"T_e06e8_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_e06e8_row1_col2\" class=\"data row1 col2\" >80</td>\n",
       "      <td id=\"T_e06e8_row1_col3\" class=\"data row1 col3\" >11.412268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e06e8_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_e06e8_row2_col0\" class=\"data row2 col0\" >True</td>\n",
       "      <td id=\"T_e06e8_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_e06e8_row2_col2\" class=\"data row2 col2\" >92</td>\n",
       "      <td id=\"T_e06e8_row2_col3\" class=\"data row2 col3\" >32.624113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e06e8_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
       "      <td id=\"T_e06e8_row3_col0\" class=\"data row3 col0\" >True</td>\n",
       "      <td id=\"T_e06e8_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_e06e8_row3_col2\" class=\"data row3 col2\" >190</td>\n",
       "      <td id=\"T_e06e8_row3_col3\" class=\"data row3 col3\" >67.375887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25df12c9fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_2cat(dataframe=df_trial_metricas, coluna1='credenciamento_aprovado', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(dataframe=df_trial_metricas, coluna1='credenciamento_aprovado', coluna2='realizou_assinatura', orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando a teste de hipóteses qui-quadrado *****\n",
      "* Premissas:\n",
      "- As observações devem ser contagens ou frequências.\n",
      "- Cada observação deve pertencer a uma única categoria.\n",
      "- A amostra deve ser relativamente grande, com pelo menos 5 observações em cada célula e, no caso de poucos grupos, pelo menos 10.\n",
      "- Os dados devem vir de uma amostra aleatória.\n",
      "- As categorias das variáveis devem ser mutuamente exclusivas.\n",
      "\n",
      "----- Hipóteses -----\n",
      "H0 : as variáveis credenciamento_aprovado e realizou_assinatura são independentes. As proporções são estatisticamente iguais.\n",
      "Ha : as variáveis credenciamento_aprovado e realizou_assinatura são dependentes. As proporções são estatisticamente diferentes.\n",
      "Nível de significância de 0.05.\n",
      "---------------------\n",
      "\n",
      "Valor do teste qui-quadrado: 313.3368061990816\n",
      "P_valor: 4.095776577602947e-70\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.0000000000 < alpha: 0.05 => Rejeitamos a hipótese nula. Há evidência suficiente, ao nível de significância de 0.05, para concluir que as variáveis credenciamento_aprovado e realizou_assinatura são dependentes.\n",
      "- Tamanho do Efeito - Cramér´s V: 0.5671039892\n"
     ]
    }
   ],
   "source": [
    "# Realizando o teste qui-quadrado.\n",
    "teste_chi2(dataframe=df_trial_metricas, coluna1='credenciamento_aprovado', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credenciamento_aprovado</th>\n",
       "      <th>realizou_assinatura</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>621</td>\n",
       "      <td>88.587732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>80</td>\n",
       "      <td>11.412268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>92</td>\n",
       "      <td>32.624113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>190</td>\n",
       "      <td>67.375887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credenciamento_aprovado  realizou_assinatura  frequencia  porcentagem\n",
       "0                    False                False         621    88.587732\n",
       "1                    False                 True          80    11.412268\n",
       "3                     True                False          92    32.624113\n",
       "2                     True                 True         190    67.375887"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpby_credenciamento_conversao = lib.grpby_2cat(dataframe=df_trial_metricas, coluna1='credenciamento_aprovado', coluna2='realizou_assinatura', plot=True)\n",
    "grpby_credenciamento_conversao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credenciamento_aprovado</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>nobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>80</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>190</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credenciamento_aprovado  frequencia  nobs\n",
       "0                    False          80   701\n",
       "1                     True         190   282"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# realizando o Teste T para proporção binomial - 2 amostras.\n",
    "freq = grpby_credenciamento_conversao.loc[grpby_credenciamento_conversao['realizou_assinatura']][['credenciamento_aprovado','frequencia']]\n",
    "nobs = grpby_credenciamento_conversao.groupby(by=['credenciamento_aprovado'], as_index=False)['frequencia'].sum().rename(columns={'frequencia':'nobs'})\n",
    "freq_nobs = pd.merge(freq, nobs, on='credenciamento_aprovado', how='left')\n",
    "freq_nobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17.78031543]\n",
      "[1.00413486e-70]\n"
     ]
    }
   ],
   "source": [
    "z_stat,pvalue = proportions_ztest(count=[freq_nobs.loc[freq_nobs['credenciamento_aprovado']]['frequencia'],freq_nobs.loc[~freq_nobs['credenciamento_aprovado']]['frequencia']],\n",
    "                                   nobs=[freq_nobs.loc[freq_nobs['credenciamento_aprovado']]['nobs'],freq_nobs.loc[~freq_nobs['credenciamento_aprovado']]['nobs']])\n",
    "print(z_stat)\n",
    "print(pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intervalo de confiança 95% para Credenciamento aprovado False: [9.059%, 13.766%]\n",
      "Intervalo de confiança 95% para Credenciamento aprovado True: [61.904%, 72.848%]\n"
     ]
    }
   ],
   "source": [
    "# Itervalo de confiança.\n",
    "for index, row in freq_nobs.iterrows():\n",
    "    lower, upper = proportion_confint(count=row['frequencia'], nobs=row['nobs'], alpha=.05)\n",
    "    print(f'Intervalo de confiança 95% para Credenciamento aprovado {row['credenciamento_aprovado']}: [{lower*100:.3f}%, {upper*100:.3f}%]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credenciamento_aprovado</th>\n",
       "      <th>realizou_assinatura</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>80</td>\n",
       "      <td>11.412268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>190</td>\n",
       "      <td>67.375887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credenciamento_aprovado  realizou_assinatura  frequencia  porcentagem\n",
       "1                    False                 True          80    11.412268\n",
       "2                     True                 True         190    67.375887"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpby_credenciamento_conversao.loc[grpby_credenciamento_conversao['realizou_assinatura']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Número de Atividades e Conversão\n",
    "    * Hipótese Nula (H0): O número de atividades realizadas durante o trial não influencia significativamente a conversão para assinaturas pagas.\n",
    "    * Hipótese Alternativa (HA): O número de atividades realizadas durante o trial influencia significativamente a conversão para assinaturas pagas.\n",
    "\n",
    "Teste Estatístico: Correlação de Pearson para verificar a relação entre o número de atividades e a taxa de conversão, ou um teste T para comparar a média de atividades entre os grupos que converteram e não converteram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3a847_row0_col2 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3a847_row1_col2 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3a847\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3a847_level0_col0\" class=\"col_heading level0 col0\" >realizou_assinatura</th>\n",
       "      <th id=\"T_3a847_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_3a847_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3a847_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_3a847_row0_col0\" class=\"data row0 col0\" >False</td>\n",
       "      <td id=\"T_3a847_row0_col1\" class=\"data row0 col1\" >713</td>\n",
       "      <td id=\"T_3a847_row0_col2\" class=\"data row0 col2\" >4.932679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3a847_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_3a847_row1_col0\" class=\"data row1 col0\" >True</td>\n",
       "      <td id=\"T_3a847_row1_col1\" class=\"data row1 col1\" >270</td>\n",
       "      <td id=\"T_3a847_row1_col2\" class=\"data row1 col2\" >17.325926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25df936ca70>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_difmedia_2amostras(dataframe=df_trial_metricas, coluna1='realizou_assinatura', coluna2='total_atividades_realizadas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_difmedia_2amostras(dataframe=df_trial_metricas, coluna1='realizou_assinatura', coluna2='total_atividades_realizadas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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np+PNN99EhQoVMHPmTOn+seX9zpFseQ9u0qQJ5syZg379+qF9+/Zo27YtoqOjpTYiIyMxd+5cdOvWDZ07d0abNm3QsmXLfMd0PFwHHhgYiPj4eAC23YOF9fDPny3vJeRYTG6pwNzc3BAWFiZt169fHz169MBrr72GLVu2SAN9kpOTAeCRH6k9WDf1oJUrV2Lp0qVITk6Gv78/QkND4eLigrS0NKt2Hzeg6J9//sH777+Po0ePQqVSoWbNmqhduzaA+3MXpqSk5Dl7gr+/P4QQSE9PzzO5TU5Oxq5du/KswXs4Jq1Wa7Utl8ttmjvR1dXV6nwAMHXqVOmPigclJibm216OlJQUVK1aNdf+nOuQmpqa5/MyMzPx0Ucf4fvvv4fJZEKVKlUQEREBpVJZqLkgLRYLPvvsM6xfvx46nQ6VKlVCeHg4NBpNgdpp0qQJKlasiJ07dyI8PBw7d+5EVFTUIwfcpKSkwMfHx2qfUqnMtS+/ezGntvbh51WoUMFq25Z75umnn4bFYsGGDRuwePFiLFiwAEFBQRg3bhyefvrpAlwN5Lpvk5OTIYRAw4YN8zw+MTERderUwaFDhzB9+nRcuXIFbm5uqF27tnQvFuT/+eTJk5g6dSqio6PRu3dvq8fyO0dKSgqEELmu6cPlKLbcO1WqVMG6deuwfPlyfPvtt1izZg08PT3Rr18/vP3223n+4WDrtXqUB392U1NTYbFY8MUXX+CLL77IdWxOrI76+bJYLBgzZgwSExPx7bffWt0HtrzfOZIt78GDBw+Gm5sbNm/ejNmzZ2PWrFl46qmnMHnyZDRt2hQRERFYvnw5Vq1ahZUrV2L58uXw9/fHG2+88dgaYBcXF6vth993HXWfP0pevzfyey8hx2JyS4Xm7++P999/H6NGjcLHH38s9a54enoCyB4kk9cPe+XKlXPt2759O2bOnInx48ejV69e0i/+UaNG4ffff7dqNykpyaqnOD4+HnFxcYiIiMCQIUOgUqnw7bffok6dOlAqlbh06ZLVPLdeXl64c+dOrhhu374NIHfCksPDwwPNmzeXBgo8SKl0/I9UzuudMGFCnnVxXl5eNrfl5eUlvb4H5feaP/74Y/z444+YN28emjdvLv0iaNasmc3nzkvOL66pU6eiU6dO8PDwAACpTtBWcrkc3bt3x44dO/DGG2/gyJEjj61j8/b2zvV/n5NY5bDlXsy5Xnfv3rW6F3P+IMlh6z3TrVs3dOvWDWlpaTh8+DC++OILjB8/Ho0aNULFihVtvBq5eXh4wNXVFWvWrMnz8WrVquGff/7B8OHD0bFjRyxbtgxVq1aFTCbD+vXrrXqh8xMXF4cRI0agQYMGeOedd6wes+Uc3t7ekMvluf5/Hr6mtt474eHhWLhwIQwGA06dOoWNGzdi6dKlqF27Nrp27WrXtbKVm5sbZDIZXn311Tz/yM9Jwhz18zVr1iwcOnQIy5YtyxWnLe93165dA4Bcg9ge9SnWo1gsFpveg+VyOfr374/+/fvj7t27OHDgAJYuXYq33noLR44cgVqtRqtWrdCqVSvo9XocO3YMa9aswbRp01C/fv3H9sA/ir33ec4fQjkDgnPY8kmcLe8l5FicLYEcokuXLmjVqhV27NiBmJgYAEDjxo0BAPfu3UNYWJj0lZSUhM8//zzXLysg++NiT09PDB48WHoDyMjIwKlTp6Q33PDwcKhUKuljpBwrVqzA2LFjkZaWhqtXr6JPnz4ICwuTkoeDBw8CuP/GHRkZiZ9//tmqx8JsNmPnzp0ICwuz+ljpQTkzGNSpU0d6TaGhoVi1ahV++umnAl23vAa0PKxmzZrw8/NDXFyc1XWsWLEi5syZg/Pnz9vcfmRkJE6fPi19RJdj27ZtqFChgvQL8eHnnTp1Ck2aNJEGbwHAuXPnkJSUVKjZEk6dOoUnn3wSvXv3lpKThIQEXLx40apdW67Ts88+i1u3bmHRokVQKBS5ylYe1KxZMxw8eBB6vV7ad+jQIauPgG25FyMiIqDVavHDDz9Ytf/wvWnLPfP2229Lgyw9PDzQtWtXDBs2DCaTqUC983mJioqCTqeDEMLqHrp48SIWLVoEk8mEc+fOISsrC0OGDMETTzwh/TLP+YVvS49WRkYG3nzzTWi1Wnz++ee5/tiz5RwajQYRERHYs2eP1Tn3799v1ZYt986qVavQrl07GAwGqNVqNGvWDB999BEA4MaNG3ZfK1u5u7ujbt26uHLlilVbTz31FBYsWCB9TO+In6/vvvsOK1aswOjRo9G6detcj9vyfpdTsnDr1i3pmJSUlHxnl3jYvXv3bHoP7tu3L6ZNmwYA8PPzQ69evdC/f3+kpqYiPT0dn3zyCXr37g0hBFxcXNCuXTtpUZpH/f/lx9b7/OH3nJxr8+AnjkajEWfPns33nLa8l5BjseeWHObdd99Fjx49MG3aNGnaph49euC9995DfHw8QkNDcfXqVcydOxdVqlRB9erVc7URHh6Or7/+GjNnzkS7du2QmJiIr776Cnfu3JF6KH19ffHyyy9j1apVUKvViIqKwpkzZ/DNN99g0qRJ8PPzQ1BQENavX4/AwEB4enri0KFDUk9MTkIzYsQIHDx4EC+//LLUy7Bu3Tpcv34dX3755SNfZ86I8KFDh+Kll16CRqPBxo0bsXfvXsyfP79A18zT0xPnz59HTEzMI3shFAoFRo8ejffffx8KhQLt2rVDamoqFi9ejISEBNSrV++x7Z8+fRpHjx5F3bp1MXDgQGzbtg2vvvoqRowYAW9vb2zduhXHjh3D9OnTpTf0h+MKDw/H7t278fXXX6NWrVqIjY3FkiVLIJPJrBLEggoPD8fixYuxfPlyNGjQANeuXcOyZctgMBis2rXlOgUHB0u1bV27dn3sxO7Dhw/H3r17MWjQIAwePBhJSUmYN2+eVQ2uLfeim5sbhg0bhnnz5sHFxQVNmzbFgQMHciW3ttwzTZs2xZQpU/DJJ5+gdevWSE1NxcKFC1G9enXp41x7tWnTBpGRkRg2bBiGDRuGWrVq4ezZs5g/fz5atWoFX19f1KtXD0qlErNmzcJrr70Gg8GALVu24JdffgFgWw/VuHHjcPnyZcycORPx8fFWU50V5BxjxozBK6+8ghEjRuDFF1/E1atXsXTpUqtz2XLvNG3aFLNnz8bw4cMRHR0NhUKBb775Bmq1OleddEGuVUGMGTMGQ4YMwdixY9GjRw9pNpQzZ85g2LBh0mspzM/Xb7/9hvfeew9RUVFo2bIlzpw5Y/WHQd26dW16vwsJCUGlSpWwaNEiuLu7QyaTYdmyZbk+5s+Pre/BkZGRWLFiBfz9/REREYGEhASsXLkSUVFR8PX1RdOmTbFy5UpMmjQJPXr0gNFoxJdffglvb280bdq0QDHlsPUezPnEbMeOHahfvz6qVq2KiIgIrF27FtWqVYOXlxfWrFmDzMxMq1KUvNjyXkIOVmxD18gp5DdqdubMmSI4OFisXbtWCJE9GnfhwoWiQ4cOol69eqJ169ZiypQp4t69e9JzHhztarFYxOeffy5at24twsLCRMeOHcVHH30kNm7cKIKDg8WlS5ek47788ktptoYuXbqIr7/+WmrzwoULIjo6WjRo0EBERUWJfv36iYMHD4ouXbqIkSNHSsedP39eDB48WDRo0EBERESIV155RZw4cSLf63Du3DkxaNAgERERIRo0aCBeeOEFsXfvXulxW0cdb9++XTRr1kyEhoaKEydO5JoF4EE7d+4Uzz33nAgNDRVRUVHijTfeELGxsY+N8+jRo6Jt27aiXr16Ytu2bUIIIf755x8xatQo0bhxY1G/fn3x4osvWsWeV1z37t0TY8aMEVFRUaJBgwaiW7duYvXq1eK9994TLVq0ECaTya7ZErKyssTUqVNFixYtRHh4uOjcubOYP3++WLBggQgNDRUpKSkFuk45I55/+eUXq/15xXbu3DkRHR0twsPDRbt27cS2bdtE8+bNpZH/tt6LQgixZs0a0aFDBxEaGioGDBggNmzYkCu+/O6ZnHaefvppER4eLqKiosSoUaNyzWyRn0f9jGZkZIjp06eL1q1bi3r16on27duLOXPmiMzMTOmY3bt3i2eeeUaEhYWJli1bihEjRoiYmBgREhIijex/3GwJec2kkvOV8zNuyzmEEOLIkSOid+/eIiwsTHTt2lXs37/farS6rffOoUOHRN++fUXDhg1F/fr1Rf/+/UVMTMxjr6Et1+phj7v///e//4l+/fqJ8PBw0ahRI/Hyyy9bvc/Y8/P14Gj+nO8f9ZVzH9ryfnfmzBnx4osvitDQUNG2bVuxcuVK8d577xV4tgRb3oONRqOYP3++6NixowgNDRXNmjUT//d//yfNpCNE9s/+c889J8U8ePBgq/e9vGZLyG9GA1vuwVu3bonevXuLevXqiSlTpgghhLh69ap47bXXRHh4uGjevLn47LPPxOLFi3PNlvDwtSrIewk5hkwIB1RPE5WQuXPn4qeffsL//d//oUWLFiUdDhEREZUwJrdUZiUkJEgfqV28eDFXTR6VHLPZnG+Npkwms5p6ix5NCJFrIEteFAqFXVOHEdmC9yGVFay5pTJr06ZNWLp0KQICAnKtLkUl6z//+U+uQWsPi4qKyncpZsr23Xff5Zp5IC9r1qxBkyZNiiEiKo9iYmLw8ssv53vcjBkz0KtXr2KIiChv7LklIof7888/850M3c3NzWr6LHq0e/fuIS4uLt/jatSo8diBdESFkZ6ejqtXr+Z7XJUqVR45rSBRcWByS0REREROg/PcEhEREZHTYHJLRERERE6j3Ce30dHRiI6OLukwiIiIiMgByv1sCTdv3izpEIiIiIjIQcp9zy0REREROQ8mt0RERETkNJjcEhEREZHTYHJLRERERE6DyS0REREROQ0mt0RERETkNJjcEhEREZHTYHJLRERERE6j1C3isGzZMhw+fBhr16595DH37t3DtGnTcPDgQchkMjzzzDOYMGECXFxcijFS+yw+cQLDd+167DFyAJaH9rkplfDUahHs7Y0ANzf8Lz4ed3Q6KOVyNAgIgL+7O26kpaGKpyf8XF0BQNqu5O4OL60WN9PSEOjhgXt6Pf53/Tru6vWo5O6OdjVqQG80QiGTobq3N/qGhUEpl8NkseCb33/HtZQUVPXywlO+vjgRH49rKSlIzMiAi1IJuUyGyp6ekAFoWKkSAtzcAABJej18//3/SNLr4e/qiohKlSCXyaR2/05Ohvj3eYHu7tLjD7MIgdM3b+KOTmfVjqPZch57Yymu15Df+XL2J2Zk4K5OBx8XF9zT6+Hn6ooANze74iru11aa8VoQUXlQ2t/rSlVyu379esybNw+NGzd+7HEjR46EXq/HqlWrkJqaiv/7v/+DTqfDJ598UkyR2kc2dapNxz2c2AJAhsmEjPR03ExPt9qfZbHgyI0b0vaJAq64diYhAT9cvixty2UyjPrxRzSvUgX/i4tDSmYmLEJA2NieQiaDUi6HSi6H0WKBDICrSgV3tRoh/v6o6eODTefPI/nfdgFABsBLo0FkUBAmtWyJ9jVqSO3tv3oVMw8fxp937sBgsUAtlyPE3z/XcYVly3nsjaW4XkN+5+tcqxZ+vHwZZ27dQnJWFkxmMwQA2b//Z94aDeoHBhYoruJ+baUZrwURlQdl4b1OJoSwNW8pMgkJCZgyZQqOHz+OwMBA+Pv7P7Ln9vTp0+jbty927dqFWrVqAQAOHz6MwYMH48CBA6hYsWKBzt2hQwcAwL59+wr3IvJha2Jb2uT04NpDjuzESSGToZKHB+7qdEg3Gh95vLtKhUAPDyzr1g3ta9TA/qtXMXTHDqRlZcHPxQUapRJZJhPu6vXw0Gik4wrLlvMAsCuW4noN+Z3vZno60g0GaBQKZJnNMFssVn9EyQEo5HK4q9Xwc3W1Ka7ifm2lGa8FEZUHZeW9rlT03P7xxx9QqVTYtm0bFi1ahPj4+Ecee/LkSVSoUEFKbAEgKioKMpkMp06dwtNPP10cIRfI4hMnSjoEu1kK8bePAKBRKGC0WHBXp4PeZLJ6XIbs5Ff82zOsN5mQmpmJmYcPo3W1aph5+DDSsrIQ5OEB2b8fd7ioVAhSKhGfloaZhw+jbfXqhfooxCJEvueZcegQIJMVOBZb2nbEa7DlfEazGSaLJbsXPq//U5kMAoDRbEZaVla+cdny2qYfOIRGfpVhyDIjM9OErAf+zfkyGMwwGi0wGMwwGCzSttGY/a/JZJH+vf8lrLbNZvHvV/b3JpMFFkv2vux/LbBYAIvlwW1htc9iyb4uFgv+/VdACDyw/8Ht7GMe/PfqvXvINJmhksmRgiwIkZVzYXHHnIVuH29FkIeH9BwAyPlveHj7wX3Wx+V97IPyet7D+/N63BYF6Qcpyi6Tku+OISqvBFKzsmCyCMhlMtxBJlReMjzRT4ugJz0c/jutMEpFctu+fXu0b9/epmMTEhJQqVIlq31qtRre3t64WcCP5ItLfjW2pVlhklsgO8FVyuXQm0wwP9BWTmIL3E9wzUJAKZfjzzt38M3vv+PPO3fg5+IiHSc9VyaDr4sL/rxzB6dv3kSjypXtju/0zZv5nudcYiIAFDgWW9p2xGvI8dvNm/j73j1UdneHVqmUzpNpMkEll8NLq4XJYoFKLof53954uVwuJS0qmQJCJ+BtdMHlc8lY9s1p+CvdkJpqQGpqltW/12+n4tjl25AZlbhk1MOcJbK/DAIWA2AxAvvMifAeuKDQr6ssMedZVAToYcalhOTiDYaIqIiY/y1WNGcIJPxogMdTrg7/nVYYpSK5LQi9Xg+1Wp1rv0ajQVZWVh7PoJIikN3bI/83ebX1OQaLBddSUmCwWKBR5n2LapVK3MvMxB2drlAx3tHp8j1PltkMAAWO5Y5OB5MQcFOroZLLpRINmUwGuUwGT40Gt3U6pP573+bVY/hwD+HDvXgP/ptwRwc/uRv8VC7ZfzULwJRpgfkO4HHTFeZUgawUAUuGANKz35QMGdn/mjMELJkAIMNd6AEAw7DfhitoX8kKERE5B59QFQDH/V52hDKX3Gq1WhgMhlz7s7Ky4PrvLAFUOkhlB//+a8vniTIAKrkc1by8oJbLkWUywUWlynVcpskEtVwO/0L+n/u7uuZ7Ho1CAQC5jlHJ5VDI5TCYzQhwdUWgu3t2D/S/H5H7qVxRy80brjI11DJFduZugfRxuM5ghItBCcMdC66Iew99jJxXtCLX/vR0I65dS0FcXDpOxd7CuePJQFoKTCmAMdkCc2ahLo9DyeUyqNVyaDQKqNUKqFRy6V+VSgGlUgalMvt7lUoOpVIOhSJ7X85jCkX29wpF9mNyefZ+uVwmbSsU2Y/L5bIHvmC1LZPdP14my74/H/z+/r7s5wH39z98XFxqGlaf+Q1alQLqf2cQkZ4vA0zCAqMw47VGEXjCy8sqhofbytkGsuPL6fDP+9w532cf/2DMD34qkiOvTwkf/kQhPwU5vCg/lSxo3ERUeBdu38b4n36Cq1IldfaovGRQ+2TPKuuo38uOUOaS28DAQOzdu9dqn8FgQHJyMgICAkooqsdb9PTTZbY0IWf6KHvJABgtFrgolValCQIAhJBKEoDsmRZMFgvqBQSgb1gY1pw9i7MJCQhSKq1+mQkhkKTXI7xiRUQ8VKJSUBGVKiHE3/+R50nOzESjSpWgUirx19278HdxgQoKyIUcwgwIswWJGXqEeFaAZ6YWV64k//tcwFNoodGr8Me/pQk5pRc5jydkZCDEzw/VPL1hMj3+Gt+9q8eFC0m4fDkZV6+m4OrVFFy5koLExLz+Qi58UaLSRYZK/u7w9FTD3V0Nd3fVv19quLmp4OqqxNbLsbht0MHbQwO5GpCrZJCrAJkSSDJmolYFb8zv0RVajRIaTXbyWtqSkgeTxwe3ATyQJAMKhfyhhPh+Ig0A13fdRezdO6jg5grIAAEBC7JryW+kpaF+hQoY36dpidehERHZq6GoiLV3fsPZhAT4eKiL7PeyI5S55DYyMhKzZ8/GtWvXUK1aNQBATEwMAKBRo0YlGdojDYuMLJfJrQyAwWyGQiaDn6trrtkScsoWcrgolfDSajGpZUso5XJMatkSQ3fsQHxaGnxdXKBVKpFpMiFJr4enRoNJLVsWOlmQy2TSeW6kpaGyhwc8NRpYhIA+04Q6nhUwMrwpjEYLpscfxvWkdLgolZBBhgyDAXf1ergolRgWFgmjIfdH9NFh9fH+Lz8jLjUNXhqNNLI0JSsL7moVhjRqZPUahBCIj0/HuXN3cOHCXVy4kIQLF5IekcTaTuYmIFwF5O6AcLFAaAXg8sCXFlC4yuDqroSPtwYftmuPZlWrPLbNBtf98f4vPyPDkPXva1NIr81LrcJbbaPg71f8c0/nlbDmJKI5vcFyuVxKXrO3c3p27+8viFcjG2Dojh34JzElz3v1jcaNmdgSUZn24O/Lovy97AilYiqwB02aNAnx8fHSVGBmsxlJSUnw8PCAVquFEAL9+vVDVlYWPvjgA+h0Orz77rto0qQJZsyYUeDzFddUYEDZmA5MLpPBW6t17Dy3ajXcVapSPc/tob+v4YuYX3E18R6yDGaYDQIV3dzRPywckf8Wxh+9Hoflp07h6r17MAoLVDI5avj4YEijRo9NBB/3vMhKlREbm4Rff03AqVMJ+PXXxAInslqtAlWreiAoyANBQe4wuprwm+4m7ioyYHEX0LjLUNPPF62eeAKH/vkHsXfuIM1gyDXPrYdajdr+/vm+Hltfm61tFMTDH9PLZPi3bOF+yYJSqfg3Qb2fvBY0WbVHWZj7kYiosMrCe12pT27j4uLQoUMHzJgxA7169QIA3L17F1OnTsWhQ4eg0WjQpUsXvPPOO9BoNAU+X3EmtwBXKCstK5QJIZCZaYJeb5L+NVsE/khIRJI+Ez5aLeoGVMhzhbLzibdxL/PRx+Tlwedl3TXj5nk9Dh+KR0zMTeh0pnyfDwB+floEB/ugZk1v1KjhhZo1vVCjhhcCA92kutD84szZf1evR3KmHl4aLVKyMuGtdYGfi4vNr8cR1+RRcmpHc77PrsmVQ6lU/FuT+2AtrrxUlTqU9lV7iIgcobS/15W65La4FXdySyVLpzMgI8OEjAwDTKb7sxEUJaPRgmPHbuDAgTgcOhSHv/9Ozfc5Tzzhgbp1/VC3rh9q1/ZFnTp+CAgo+SJ9R3pwIJVCIft3cJnigWQ2e8BZaUpeiYio9CtzNbdEBZWVZUJ6ugHp6UYYjdkT+Bc1s9mCkycTsHPnFfz4499ITn70NHUqlRz16vmhYcOKaNgwABERFeHvX/y1qkUpZwS/XC6TZkuwnimh6MsGiIiofGByS04rI8OAlJQs6HSmYkloAeDcuTvYtu0ydu26gtu39Y887sknvdG6dRW0ahWEhg0rQqt1nh/FBwdwaTQKaDRKKZlVqxUlHR4RETk55/mNSoTsWtr0dCNSUjKRmWkulqTWYDBj9+6rWL/+As6cuZ3nMWq1Aq1aBaFNmypo1aoKKld2L/K4ikvO3KtabXYiq9UqmcgSEVGJYXJLTiM1NQvJyVkwGIonqb15Mx3ffPMn/vvfP5GUlHu1BIVChhYtgvDMMzXRseMTcHfPvbJeWXQ/mVXCxSV7DlutVlksMxIQERHlh8ktlXlZWSbcuaOHXm+EpRhWg716NQULF57G7t1XYTbnTqIjIgLw7LNPonPn6vD11RZ9QEUsZ+CXSiWHq6sKLi7ZSS2TWSIiKo2Y3FKZZbEI3LunR3JyVp5JpqNdv56GxYt/w9atl3L1DGs0CnTvXgv9+9dB3bp+RR5LUcupm3VxUUoJrUbDtwsiIir9+NuKyqT0dAOSkjKRlWUq8qm8bt5Mx5IlZ7B588Vcy+QGBbmjf/866N07GN7eBZ9nuTTJSWhdXZVwc1PD1VXFWQyIiKjMYXJLZYoQArdv65Caaijyulq93oSlS3/DV1+dg9FoXe9QrZonRoyIwDPP1CjTH88/mNC6u2vg6spyAyIiKtuY3FKZYTJZkJCQAZ3OWOS9tQcPxmHq1P8hLi7dan9QkDuGD2+AZ599skz3aubMN+vuroa7uwoqFWc2ICIi58DklsqEzEwjEhJ0yMoyF+l5EhJ0mDHjOHbvvmq1v2JFVwwb1gC9ej1VZqe4kskApVIOd3c1PDxU0GpVJR0SERGRwzG5pVIvLc2A27d1MJmKbioEi0Vgw4YL+OyzU8jIMEr7FQoZXn01FCNGNICra9lMBnN6aT09NXB3V7HsgIiInBqTWyrVkpL0SErKLNL62qQkPSZOPISDB+Os9kdEBGDq1OYICfEtsnMXJYVCBjc3FTw91XB1dY45domIiPLD5JZKrTt3dEhOzirSxDYm5ibGjj2AxESdtM/LS41x4yLRp08w5HJZkZ27KMhkgEIhh4eHGl5emjJbQkFERGQvJrdUKiUl6Ys0sTWbLVi69CwWLjxtdY4OHZ7ARx+1gJ+fS5Gct6jkJLVeXmp4emo4QIyIiMotJrdU6iQnZxZpKcLt2zqMH38AR4/elPapVHKMHx+Jl1+uC5ms7PTW5gwS8/LSwMtLw3paIiIq95jcUqmSlmbA3bv6IktsY2Pv4vXXf7IqQ6ha1QNz57ZDWJh/kZyzqGQntWp4e2uZ1BIREf2LyS2VGunpBiQmZhTZUrpHj97A8OH7rGZD6Ny5Oj7+uCU8PMrOgCuFQgYPj+ykljW1RERE1pjcUqmg0xmQmKgrssR227bLePfdQ9JKYwqFDO++2wT9+9cpM2UIcnn27Ae+vlpoNPzRJSIiygt/Q1KJMxrNSEwsmnlshRD48svfMXv2SWmfi4sS8+a1Q9u2VR1+vqIgkwEajRK+vlq4u5edHmYiIqKSwOSWSpQQArdv62AwOD6xNZstmDEjBmvXnpf2+fhosXz5fxAeXsHh5ysKSqUc3t4aeHtry9y0ZERERCWByS2VqHv3Mq1qYB3FbLZgwoSD2LHjirSvalUPfPllJ1Sv7uXw8zladgmCEr6+LixBICIiKgD+1qQSo9MZcO9eJoSDy2yFEJgy5X9WiW1oqD+WLfsP/P1L//y1arUcfn6uZWqQGxERUWnB5JZKhNlswe3bmQ4fQCaEwIwZMdi06aK0r1mzSli0qCPc3FQOPZej5QwY8/d34SIMREREdmJySyUiu87W5PB2588/jdWr/5C2GzYMwOLFHeHqWroTW6VSDj8/F3h5aUo6FCIiojKNyS0Vu5SULKSnGx1ejvDll79j8eLfpO169fywfHmnUp3YymT4t7fWlXPWEhEROQCTWypWWVmmIlmBbP36C5g164S0/eST3vjyy86lum5VoZDBx0cLX9/SXwdMRERUVjC5pWJ1967e4fPZ7t59FR9+eFTafuIJD6xc2QW+vlqHnseRNBoFKlRwgatr6U2+iYiIyiImt1Rs0tMN0OkcW2d74cJdTJp0UNoODHTDqlVdERDg6tDzOEpOGUKFCq4cNEZERFQEmNxSsbBYBJKSMh1ajpCUpMfw4fuQmWkGAHh6qrFyZRcEBbk77ByOpFDI4O2tga+vS5lZ8peIiKisYXJLxSI5ORNZWY7rtTUaLRg16mfEx6cDyJ5Ga+7cdqhZs3Qu0KBSyVGhgiuXzyUiIipiTG6pyBmNZiQnZzl0doRPPolBTMwtaXvcuMZo2TLIcSdwIK1WiYoVXbnSGBERUTHgb1sqcklJmQ4dRLZ580WsXXte2u7evRZeey3UYe07ikwGuLqqULGiG5RKeUmHQ0REVC4wuaUipdcbkZ5ucFh7v/2WiClT/idt16vnh2nTWpS6Gla5HHB3VyMgwA1yeemKjYiIyJkxuaUilZTkuCV2797VY8SIfTAas3uB/fy0WLiwA7Ta0nUby+Uy+Ppy/loiIqKSULqyAnIqaWkG6PWOGUQmhMB77x3B7dt6AIBSKcP8+e1RuXLpmhlBoZChQgVXeHpyGV0iIqKSwOSWikT21F+OW4ns22//wr59/0jbEyZEoXHjQIe07SgKhQwVK7pxRgQiIqISxOSWikRamgEGg9khbf3zTyqmTz8mbbdsGYQBA+o6pG1HUSrlqFjRFW5uTGyJiIhKEpNbcjghBFJSHDP1l8lkwYQJB6WVzby81Jg+vWWpGqSlVstRsaIbXFxUJR0KERFRucfklhwuu9fWMbW2X375O06fTpS2P/igOSpWdHNI246g0SgQGOjGOWyJiIhKCU6+SQ6V02trccC0tn/8cQcLFvwqbffoUQtPP12z8A07CBNbIiKi0ofJLTlURoYRWVmFr7XNzDRh/PiDMJmyaxsqVXLDe+81LXS7jsLEloiIqHRicksOlZyc6ZAZEubMOYnLl5MBZK/0NXNm61IzvZZaLWdiS0REVEoxuSWHSU83IDOz8L22v/9+x2p53YEDQ9G0aaVCt+sIarUclSq5M7ElIiIqpZjcksNk19oWrtfWbLbggw/+J820ULOmF95+u6EDois8lYo9tkRERKVdqUhuLRYL5s+fj1atWqFBgwZ4/fXXcf369Ucef/fuXYwdOxZNmzZFkyZNMHr0aCQkJBRjxPQwnc4xq5H9978Xce7cHWl7ypTmpSKZVCqzE1utltN9ERERlWalIrldvHgxNmzYgI8++gjffPMNLBYLBg8eDIPBkOfxb7/9Nm7cuIGVK1di5cqVuHHjBoYPH17MUdODHNFre/euHp99dlLa7tatZqkoR8hObF05jy0REVEZUOLJrcFgwIoVKzBy5Ei0bdsWtWvXxty5c3Hr1i3s2bMn1/GpqamIiYnB66+/jjp16qBu3boYMmQIfv/9dyQnJxf/CyBkZhqRkVH4XtvZs08iNTX7Dxp3dxUmTowqdJuFpVDIEBDgCldXrjxGRERUFpR4chsbG4uMjAw0a9ZM2ufp6Ym6devixIkTuY7XarVwc3PD1q1bkZ6ejvT0dHz//feoUaMGPD09izN0+ldycuF7bU+evIUtW/6StkeNaoiAANfChlYo2YmtG9zdmdgSERGVFSVezHjr1i0AQKVK1h8/BwQESI89SK1WY+bMmXj//ffRuHFjyGQyBAQEYN26dZDLSzxXL3eMRjMyMoyFasNksmDq1KPSdp06vujXr05hQysUuVwGf39XeHgwsSUiIipLSjwb1Ov1ALKT1gdpNBpkZWXlOl4IgQsXLiAiIgLr16/H6tWrUblyZQwbNgzp6enFEjPdl5ZmKHSv7bp153Hx4j1pe8qU5lAqS+7WlMtl8PNzgZdX6ZhXl4iIiGxX4j23Wq0WQHbtbc73AJCVlQUXF5dcx+/evRvr1q3Dzz//DHd3dwDA0qVL0a5dO3z77bd49dVXiyVuyv5DIy3NIE3bZY+EhAx8/vn9JXb79AlGRESAA6Kzj1wug6+vFj4+2vwPJiIiolKnxHtuc8oREhMTrfYnJiaiYsWKuY4/efIkatSoISW2AODl5YUaNWrg2rVrRRssWcnIMMJotBSqjTlzTkKnyx6M5u2twdixjR0Rml1kMsDLSwNf39x/VBEREVHZUOLJbe3ateHu7o7jx49L+1JTU3H+/HlERkbmOj4wMBDXrl2zKlnQ6XSIi4tD9erViyNk+ldhSxJiY5OwbdtlaXvMmEbw9S2ZHlOZDPDwUMPfn4ktERFRWVbiya1arUZ0dDRmz56Nffv2ITY2FqNHj0ZgYCA6deoEs9mM27dvIzMzEwDQs2dPANlz3cbGxiI2NhZjxoyBRqNBr169SvCVlC8Ggxk6XeEGkn322UmppOHJJ73Rp0+wAyKzj4uLEgEBbpDJZCUWAxERERVeiSe3ADBy5Ej06dMHkydPxksvvQSFQoGvvvoKKpUKN2/eRMuWLbFr1y4A2bMobNiwAUIIvPLKKxg4cCBUKhU2bNgADw+PEn4l5Ud6euF6bWNibuLAgThpe8yYxlAoSuZ21GgUqFjRDXI5E1siIqKyTiZEYYYDlX0dOnQAAOzbt6+EIyk7hBD4559UZGWZ7X7+iy/uwJkztwEADRsGYMOGZ0qk11SlkqNyZfdSscQvERERFV6p6LmlsqWwA8l++umalNgCwNixjUsksc1ZfYyJLRERkfNgcksFlppq/4pkJpMFn312Stpu164qGjcOdFRoNstZpMHNjYs0EBERORMmt1QgWVkm6PUmu5+/ZctfuHo1BUB2gjlmTPFP/SWTZU87xkUaiIiInA+TWyqQwgwk0+tNWLDgtLTds+eTCA72cVRoNnNzU8HPj1N+EREROSMmt2Qzi6VwK5KtXfsHEhN1AAC1WoG33opwYHS20Wg45RcREZEzY3JLNtPpjDCZ7BtIlpycheXLf5e2o6ProHJl98c8w/GUSjkqVnSBUsnbnoiIyFnxtzzZLLskwb7nrl79B9LSDACyVwIbOrS+AyPLX/YAMhdotapiPS8REREVLya3ZBOz2YKMDPtWJEtLM2DduvPS9sCBofD2Lr7BXDIZ4OWlgacnB5ARERE5Oya3ZBOdzmT3QLINGy4gNTW719bdXYUBA+o4MrR8ubmp4O/PAWRERETlgd2z12dlZeHPP/+EwWBAziJnFosFer0eJ0+exLhx4xwWJJW89HT7BpLpdEasXPmHtB0dXadYe1A1GgUHkBEREZUjdiW3x48fx6hRo5CSkpLn425ubkxunYjJZIFOZ19Jwn//exH37mUCAFxclHjllXqODO2xclYg4wAyIiKi8sOu5Hbu3Lnw8fHBRx99hG3btkEul6NXr144ePAgvv76a3zxxReOjpNKkE5ntKskwWAw46uv7s+Q8OKLIfD1LZ7yALkc8PV1gYsLB5ARERGVJ3Ylt3/++SemTZuG//znP0hLS8M333yDNm3aoE2bNjAajViyZAmWL1/u6FiphGRk2FeSsGXLX9K8tiqVHK+9FubgyPImkwHu7mr4+GiL5XxERERUetj1ea3FYkHFihUBANWqVcNff/0lPda5c2ecP3/+UU+lMia7JKHgy+0ajRYsX35W2u7dOxgVK7o6MrRH0miUqFCheM5FREREpYtdye0TTzyBP//8EwBQo0YN6PV6XLlyBQBgMpmQkZHhuAipRGVk2FeSsHPnZcTHpwPIrn19/fXi6bVVKuUICHCBQsE6WyIiovLIrgyge/fumD17NtatWwdfX1+Ehobio48+wv79+7Fo0SI8+eSTjo6TSog9JQlmswVLl97vte3RoxaqVPFwcGS5yeUy+PlxoQYiIqLyzK7kdvDgwejbty/OnDkDAJgyZQouXLiAYcOG4cqVK5gwYYJDg6SSYTJZoNcXvCRhz55ruHo1eyYNmQwYMqToVyPLrrNVwcuLCzUQERGVZ3YNKLt69SomTpwobYeFhWHv3r24cuUKatasCXd3d4cFSCXHnpIEIQSWLj0jbXfpUgM1a3o5OrRcWGdLREREgJ09t/369cPWrVut9rm7uyM8PJyJrROxpyTh6NEbiI1NkrbfeCPcwVHlplDI4O+vZZ0tERER2ZfcqlQq+Pj4ODoWKkWMRrNdJQmrVt1fjaxVqyDUru3nyLBykckALy8NXF3VRXoeIiIiKhvsKksYNWoUPv30U6SlpaF27dpwdc39cXDlypULHRyVHHtKEi5fTsaBA3HS9quvhjo6rFxcXJTw8yuehSGIiIio9LMruf3ggw9gNpsxfvz4Rx5z4cIFu4OikpeRYSxwScKaNffnN37qKW+0aFG0f+AolXJUqOAKmUxWpOchIiKissOu5HbatGmOjoNKEaPRjMzMgpUk3LuXia1b7y/m8cor9Yo06ZTJAB8fLTQau25hIiIiclJ2ZQbPPfeco+OgUkSvNxW4JGHjxj+RmWkGkJ10du9eqyhCk7i5qeDtzWm/iIiIyJpdye2JEyfyPSYyMtKepqkU0OkKVpJgMJixbt39koR+/WpDqy26HlWVSg5/f5YjEBERUW52ZSADBgyATCaDeCADejjRYM1t2WSxCOh0BStJ2L37Km7f1gPITjz79atTFKEByF6FzNfXBWq1osjOQURERGWXXcntmjVrcu3T6XQ4efIkvv/+eyxYsKDQgVHJ0OsLNkuCEAKrV9+f/qt791rw9y+62QtcXZVchYyIiIgeya7kNioqKs/9bdu2haurK5YsWYJly5YVKjAqGQWttz15MgF//HFX2n7llbpFERaA7NkROO0XERERPY7Dl3Rq3LgxYmJiHN0sFROdzlig41etOid937RppSJbtIGzIxAREZEtHJ7c7t+/H25ubo5ulopBVpYJBoPF5uP/+ScV+/b9I22/+mq9oggLQPZiDZwdgYiIiPJjVzfYyy+/nGufxWLBrVu3EB8fj9dff73QgVHx0+lMVoME87N27XlpVoXq1T3Rpk3VIolLoZBxsQYiIiKyiV3JbV4JkFwuR3BwMIYOHYrevXsXOjAqfnq97VOA6XRGbNlyf9GGl1+uB7nc8cmnTAZ4e2tYjkBEREQ2sStjWLt27WMfN5vNdgVDJcdstkCvt30KsB07riA9Pbs+181NhZ49nyySuDQaJXx8OIiMiIiIbGNXzW2HDh0QGxub52Nnz55F8+bNCxUUFT+93vaSBCEENmy4P49xz55Pws1N5fCYFAoZ/P21RdIjTERERM7J5p7bHTt2wGTK7tmLj4/Hnj178kxwjx49CqOxYCPuqeTpdEZYbBxLdubMbVy4kCRtv/RSbYfHI5MBnp4auLqqHd42EREROS+bk9vff/8dq1evBpC9GtnixYsfeezAgQMLHxkVGyFEgaYA+/rr+3/UREUF4qmnfBwek1qtgK+v1uHtEhERkXOzObkdO3YsXn75ZQgh0LFjRyxcuBB16lgvs6pQKODu7g53d3eHB0pFJzPTBJPJtpKEpKRM7Np1Vdouil5buVwGPz8XKBQOn6mOiIiInJzNya1arUZQUBAAYN++fQgICIBK5fg6Syp+Bam33bLlLxgM2QMG/f1d0LFjNYfH4+6ugrs7yxGIiIio4OyaLSEoKAhnz57F8ePHYTAYpMQo++NtHU6dOoX//ve/Dg2Uio5OZ9sUYBaLwDff3C9J6NMnGGq1wqGxqNVcYpeIiIjsZ1dyu379ekybNu2R8922bNmy0IFR8TAazcjKsm3qtsOH43H9ehqA7NKBF18McWgscrkMPj4uUKkcmzATERFR+WFXUeO6devQunVrHD9+HK+99hpeeOEF/Pbbb/j888+h0WjQo0cPR8dJRUSvN8Fisa0k4cGBZG3bVkXlyo6trXZ1VcLLi0vsEhERkf3sSm7j4uLQr18/eHl5ITQ0FKdOnYJWq0Xnzp0xZMgQrFmzxtFxUhGxtSThxo10/PLLdWm7Xz/HDiRTKlmOQERERIVnV3KrUqmg1WZP01StWjVcu3ZNmtu2UaNG+Pvvvx0WIBUdIYTNq5Jt3Pin1MP7xBMeaNEiyGFxcIldIiIichS7kts6derg559/BgDUqFEDFosFZ86cAQDcunXLcdFRkcrMNMFszr/b1mAwY9OmP6Xtvn1rO3TVMK1WCR8fzmlLREREhWdXcjtw4ECsWrUK7777LlxdXdGhQwdMmDABM2fOxCeffIJGjRrZ3JbFYsH8+fPRqlUrNGjQAK+//jquX7/+yOONRiPmzJkjHR8dHY0LFy488nh6NFunANu79xru3s0EkL24Qq9eTzkshuw5bbWQybjELhERERWeXcltx44dsXTpUtSqVQsA8OGHH6J69er45ptvULNmTbz33ns2t7V48WJs2LABH330Eb755htYLBYMHjwYBoMhz+M/+OADbNmyBdOnT8fmzZvh6+uL119/HWlpafa8lHJNr7et3va//73fa9u1a3WH9rJ6eKi5xC4RERE5jEzYOnt/ETAYDGjatCnGjRuHfv36AQBSU1PRqlUrfPzxx+jWrZvV8devX8d//vMfLF26FG3btpWO79mzJz7++GM0a9aswDF06NABQPbCFOWJyWTBtWsp+ZYlXL+eio4dv5W2169/Go0bBzokBpVKjipVPDj1FxERETmM3SN4hBC4cOECdDpdnh9tR0ZG5ttGbGwsMjIyrJJST09P1K1bFydOnMiV3B45cgQeHh5o3bq11fH79++392WUW5mZtk0B9u23F6Xva9XyRqNGFR1yfpkM8PHRMrElIiIih7IruT179ixGjRplNXhMCAGZTCb9a0sdbM7zK1WqZLU/ICAgz4FpV69eRdWqVbFnzx4sX74cCQkJqFu3LiZNmiSVSJBtsuttH3+MyWTB5s1/SdvPPx/ssNpYFxfOaUtERESOZ1dyO2PGDCiVSsyYMQOBgYGQy+0q3YVerwcAqNXWNZcajQYpKSm5jk9PT8e1a9ewePFiTJgwAZ6enliyZAn69euHXbt2wc/Pz644yiOdzpjvMQcOxOH27ez/I5VKjmefdcwfEHK5DL6+HERGREREjmdXcvvHH3/gs88+Q8eOHQt18py5cg0Gg/Q9AGRlZcHFJfeE/kqlEunp6Zg7d67UUzt37ly0adMG3333HQYPHlyoeMqLrCwTjEZLvsc9OP1Xx47V4OvrmEUWOIiMiIiIiopdXa5+fn5QKApfK5lTjpCYmGi1PzExERUr5q7tDAwMhFKptCpB0Gq1qFq1KuLi4godT3lhyxRgCQkZOHDg/jV9/vlgh5xbpeJKZERERFR07Epu+/Xrh2XLlkGn0xXq5LVr14a7uzuOHz8u7UtNTcX58+fzHJAWGRkJk8mE33//XdqXmZmJ69evo1q1aoWKpTzJzMy/3nbz5r+kAWdBQe5o1qxyoc8rlwO+vi5QKu0rYyEiIiLKj11lCdeuXcPly5fRokULPPXUU1YlBQAgk8mwevXqfNtRq9WIjo7G7Nmz4evri6CgIMyaNQuBgYHo1KkTzGYzkpKS4OHhAa1Wi8aNG6N58+aYOHEiPvzwQ3h7e2P+/PlQKBR49tln7Xkp5Y4QAjrd45fctViE1SwJffoEO2RFMldXFQeRERERUZGyO7mtXbu2tP3wR9wFmTp35MiRMJlMmDx5MjIzMxEZGYmvvvoKKpUKcXFx6NChA2bMmIFevXoBABYsWIDZs2djxIgRyMzMRMOGDbFmzRr4+vra81LKHb3emO8UYEeP3kB8fDqA7MFfvXsXfkUypZLlCERERFT0SnQRh9KgvC3ikJSkx507+sceM2rUfvzww98AgHbtqmLp0v8U6pwyGeDn5+KwAWlEREREj8Lix3ImvynAkpL02LfvH2n7+edDCn1OrVbp0CV7iYiIiB7F5rKEOnXqYOPGjQgPD0ft2rUfO0epTCbD+fPnHRIgOY7JZEFWlvmxx2zdekmaJqxCBRe0aVOlUOdUKGTw93fhnLZERERULGxObocPHy5NzzV8+HAmK2VQfkvuCiGwadP9gWS9ez9VqJkNZDLA01MDFxeV3W0QERERFYTNye2IESOk7996660iCYaKlk5nfOwUYKdPJ+LKlfsrw/XpU7i5bdVqBXx9WY5ARERExceu2RIA4Pr16zAYDKhVqxbS0tIwb948xMfHo0uXLujZs6cDQyRH0esfPwXY5s1/Sd83aVIJVat62n0uuVwGPz8XKBQs6yYiIqLiY1fmceDAAXTt2hXffvstAOD999/HN998g4SEBLzzzjvYtGmTQ4Okwstvyd2MDCN27boibRe219bdXQV3dy6xS0RERMXLruR2yZIlaNmyJYYPH47U1FT89NNPGDJkCL777jsMGTIEa9ascXScVEj5Lbn7449/S4s7eHio0amT/Su+KRQyzo5AREREJcKu5DY2NhavvPIK3N3dcfDgQZjNZnTu3BkA0KJFC1y7ds2hQVLh5bfk7ubN9weSdetWE1qtfRUrMhng5aWBRmN3xQsRERGR3exKbjUaDUym7F6+w4cPw8/PT1qx7M6dO/D0tL9WkxwvvyV3r15NwcmTCdJ27972lySoVHJ4e7PXloiIiEqGXd1rDRs2xIoVK5Camooff/wRzz33HADg3LlzWLhwIRo2bOjQIKlw8lty97vv7g8kCw72QWion13nkckAb29toaYPIyIiIioMu7KQd999F7du3cLYsWMRFBSEN998EwAwdOhQZGVlYdy4cQ4NkgonM9P8yHpbk8mC7767JG337h1s9xzGWq0SXl4au55LRERE5Ah29dxWrVoVu3btwt27d+Hv7y/tX7RoEerWrQu1mqPkS5PswWR5P3bkSDwSE3UAsksKevSoadc55PLsQWRc3IOIiIhKkt2fH8tkMri6ukrbP/74I06fPo2bN286JDByDLPZgszMR9fbPji3bfv2T8DX18Wu87i6Kjn1FxEREZU4u5LbK1eu4D//+Q+WL18OAJg3bx7efvttfPLJJ+jRowdOnTrl0CDJfo+bAiwpSY/9+/+Rtnv3fsqucygU2Qs2EBEREZU0u5Lb2bNnQ6lUokOHDjAYDNiwYQO6du2KkydPolWrVpg3b56DwyR76fUmWB6xdsO2bVekhR0CAlzRokWQXefw8FBz6i8iIiIqFexKbk+ePImxY8ciLCwMMTExSEtLw4svvgh3d3f07dsX586dc3ScZKdHLbkrhLCa2/a55560a5YDpVJudykDERERkaPZldwajUZpLtuDBw/CxcUFjRo1AgCYzWYolezFKw0MBjOMRnOej507dxcXL96Ttnv1KnhJQvbUXxpO/UVERESlhl1ZSXBwMPbs2YPbt2/jhx9+QMuWLaFUKmE0GrF+/XoEB9u/CAA5Tmam6ZHz2z7Ya9u4cUVUr+5V4PY1GiUXbCAiIqJSxa7kduTIkfj222/RunVrpKSk4PXXXwcAdO7cGceOHcPw4cMdGiTZ51FTgGVmmrBjxxVp255eW7lcBl9fLeRyTv1FREREpYdd9QMtWrTA9u3b8fvvv6N+/foICsoeiPTKK6+gadOmCAkJcWiQVHBCCOj1xjwf++mna0hLMwDInsKrS5caBW7fxYVTfxEREVHpY3dxbNWqVVG1alWrfa+88goAID09He7u7oWLjAolK8sEkyn/koSuXWvAzU1VoLazp/5iOQIRERGVPnYltwaDAatXr0ZMTAwMBoM0j6oQAjqdDpcuXcKZM2ccGigVjF6f95K7cXFpOHbs/kIbvXsXvD7a3V0NrbZgCTERERFRcbAruf3000+xbt06BAcHIykpCRqNBr6+vrh48SKMRiNGjBjh6DipgDIzjXnW227deknaX726Jxo2DChQu9lTf7HXloiIiEonuwaU7dmzBwMHDsS2bdsQHR2N0NBQbNq0CXv27EFQUBAsj1o1gIqFxSKg1+eeAsxiEdiy5f5yu717PwWZzPYBYTlTf6lUCofESURERORodiW3SUlJaN26NYDsacF+//13AEDFihUxZMgQ7Nq1y3ERUoFlZhrznALs+PGbiI9PB5A928GzzxZslgS1WsGpv4iIiKhUsyu59fDwgMGQPdq+WrVquHnzJtLTs5Om6tWr4+bNm497OhWx7CV3cye3mzff77Vt3boKKlZ0tblNuVwGHx9O/UVERESlm13JbePGjbF27Vro9XpUq1YNLi4u2Lt3LwDg9OnTnCmhhOW15G5qahb27Plb2i7o3LYuLkp4emoKGxoRERFRkbIruR0xYgR+++03DBkyBEqlEv369cN7772HXr164fPPP0fnzp0dHSfZyGSyICsrd73trl1Xpf0+Plq0a1c11zGPolDIOIiMiIiIygS7ZksICQnB7t27cfFi9nypY8eOhbu7O3799Ve0b98eQ4YMcWiQZLtHlyTcn9u2R49aUKttHxTm7q6Giwun/iIiIqLSz+5FHCpUqIAKFSoAAGQyGd54441cx1gsFrz66qv48MMPUb16dbuDJNvpdLmnAPvrr3s4e/aOtF2QkgRO/UVERERliV1lCbYSQiAmJgYZGRlFeRr616OW3H1w+q969fxQu7avTe1x6i8iIiIqa4o0uaXildeSu0ajBVu3XpK2C7IimUaj5NRfREREVKYwuXUiOp0p15K7Bw5cR1JSJoDseWq7datpU1tyefYgMk79RURERGUJk1snotebctXbfvvt/YFkHTs+AS8v26bzcnVVwt1d7cjwiIiIiIock1snYTJZkJlpPb9tQkIGDhyIk7aff962kgSFQgY/PxeHxkdERERUHJjcOonMzNxTgH333SVpX1CQO5o2rZxvOzIZ4OmpgUZj90QaRERERCWGya2TeHgKMItFWM1t27t3sE31s0qlHD4+HERGREREZROTWyfx8JK7MTG38M8/aQCyB4f16vVkvm1kT/2lhVLJ24KIiIjKpiLNYmQyGSpXrgy1mgOTilJWlglGo8Vq36ZNf0rft2wZhEqV3PNtJ3vqL9sGnBERERGVRnYVVt64cSPfYypXrgy5XI79+/fbcwoqgIenAEtJycKePdekbVsGkuVM/SWTceovIiIiKrvsSm7bt2+fbxJ04cIFuwKigtPrrettt2+/DIPBDADw9dWibduq+bbBqb+IiIjIGdiV3E6fPj1XcqvT6XDy5EkcP34c06dPd0hwlD+z2WJVbyuEwKZN9weS9ez5JNTqxy+fq1Bk99oSERERlXV2Jbe9evXKc3///v0xY8YMbN++HW3bti1MXGSj7IUb7nfb/vHHXcTGJknbffrkX5Lg4aGGVqsqkviIiIiIipPDB5S1b98ev/zyi6ObpUfQ602wPDCW7MEVyRo2DECtWt6PfT6n/iIiIiJn4vDk9syZM1AqC9YhbLFYMH/+fLRq1QoNGjTA66+/juvXr9v03G3btiEkJARxcXH5H+yEdDqj9L1eb8L27Zel7eefD3nsc7On/tJApXp82QIRERFRWWFXWcI777yTa5/FYsGtW7dw4sQJ9OnTp0DtLV68GBs2bMDMmTMRGBiIWbNmYfDgwdi+fftjpxGLj4/Hhx9+WOD4ncXDU4D9+OPfSE/PTnbd3FTo0qX6Y5+vVivg7c1eWyIiInIediW3x48fz7VPJpPB3d0dr7/+Ot544w2b2zIYDFixYgXGjRsn1enOnTsXrVq1wp49e9CtW7c8n2exWDB+/HjUq1cPx44ds+dllHkPTwH2YElCt2414er66DpauVwGHx+tTauWEREREZUVdiW3jpy7NjY2FhkZGWjWrJm0z9PTE3Xr1sWJEycemdwuXboURqMRI0aMKLfJ7YNTgF29moITJ25Jj+U3kEyrVcDDg1N/ERERkXOxK7nNcfnyZcTExCAtLQ0+Pj5o1KgRatasWaA2bt3KTsgqVapktT8gIEB67GFnz57FihUr8O233yIhIcG+4Ms4k8l6CrCNG2Ol74ODfRAW5v/I5+b02nLBBiIiInI2diW3QghMmTIFmzZtsvpYXCaT4bnnnivQPLd6vR4ActXWajQapKSk5Dpep9Nh3LhxGDduHKpXr15uk1udzgiLJfvaZ2WZ8N13l6TH+vat/djE1c1NCTc39toSERGR87Eruf3yyy+xefNmjBw5Ej169ECFChWQmJiI77//HkuWLEFwcDBeffVVm9rSarMHNBkMBul7AMjKyoKLi0uu46dNm4YaNWqgb9++9oTuNHS6+yUJP/zwN5KTswBkrzT27LO1Hvm87AUbcl9XIiIiImdgV3L77bffYvDgwXjzzTelfVWqVMHw4cNhNBrx3//+1+bkNqccITExEU888YS0PzExESEhuaey2rx5M9RqNSIiIgAAZnP2MrPdunXDG2+8UaDBbGWV2WyBTne/JOGbb+6XJDzzTM3HLqPr4aGGRlOoahQiIiKiUsuuLOfmzZto2rRpno81adIEK1assLmt2rVrw93dHcePH5eS29TUVJw/fx7R0dG5jt+zZ4/V9pkzZzB+/HgsX74cwcH5r8blDLIXbsieAuzPP5Pw66+J0mN9+9Z+5PO4YAMRERE5O7uS26CgIPz5559WMxzkiI2Nha+vr81tqdVqREdHY/bs2fD19UVQUBBmzZqFwMBAdOrUCWazGUlJSfDw8IBWq0W1atWsnp8z6Kxy5crw9va25+WUORkZRmlVso0b/5T2h4b6IzQ074FkXLCBiIiIygO7Vijr1q0bFixYgN27d0sDyoQQ2LVrFxYuXIinn366QO2NHDkSffr0weTJk/HSSy9BoVDgq6++gkqlws2bN9GyZUvs2rXLnlCdjsUikJGRvVCDTmfE999bDyR7FJVKDi8vTZHHR0RERFSSZOLB6Q5sZDAYMHToUBw9ehRKpRI+Pj64d+8ezGYzoqKisHz5cmg0ZSOR6tChAwBg3759JRyJbdLTDbh1KwMWi8CmTRcxefJhAIC7uwqHDvXNc+EGmQwICHBjcktEREROz66yBLVajZUrV+LAgQM4ceIEUlJS4OXlhcjISLRp08bRMdIDHpwC7MGBZM8+++QjVyTTapXw9OTUX0REROT87EpuT5w4gbCwMLRp0yZXMpuQkIBNmzZhxIgRDgmQ7hNCQKfLLkn4/fc7OHfujvTYiy/mnlkC4IINREREVL7YVXM7YMAAvPTSS7hx40aux27duoVFixYVOjDKTa83wmTK7rV9cEWyhg0DEBKS9yA+V1flY6cGIyIiInImdiW3QHYPba9evXDs2DFHxkOPodOZYLEIpKUZsGPHFWn/owaSKRQyTv1FRERE5Yrdye2cOXPQuHFjDB48GKtWrXJgSPQoObMkbNt2CXp99iIO3t4adOlSPc/j3dxUcHHJuw6XiIiIyBnZndy6ublh4cKFGDRoEGbOnIkJEybAYDCwtrOI6PVGGI0WCCHwzTf357Z97rkn81xxjMvsEhERUXlkd3KbY/To0ZgzZw727NmDl156CSkpKY6Iix6SM0vCsWM3cfHiPWn/iy/mLkmQyQAvLw3Uai7YQEREROVLoZNbAHjmmWewZs0a3L59G6NGjXJEk/SQjIzsMoS1a89L+1q1CkKNGl65juUyu0RERFRe2ZXcRkZGws3NzWpfeHg4Nm3ahJo1azokMLovK8sEg8GM69dTsX//P9L+AQPq5jo2e5ldLRQKh/zdQkRERFSm2DXP7dq1a/PcX7FiRXz99ddITEwsVFBkLSPDCCEE1q+/gJz15KpX90SrVlVyHavRKLkSGREREZVbNie3W7duRZs2beDj44OtW7fme3xQUFBh4qJ/CZE99Vd6uhHffvuXtH/AgLqQy60H78nlMvj6anPtJyIiIiovbE5uJ02ahP/+97/w8fHBpEmTHnusTCZDz549CxsbIXsgmdFowdatfyEtzQAAcHdXoWfPp3Id6+6u4oINREREVK7ZnNzu27cPFSpUkL6n4pGWZoDJZMHatRekfX36BMPd3Xr+WqVSzqm/iIiIqNyzObl9sMyAJQfFw2SyICPDiMOH43H1avYUazIZEB1dx+o4mQzw8dFy6i8iIiIq92xObt955x2bG5XJZJg+fbpdAdF9aWkGWCzCavqvdu2eQNWqnlbHabVKeHtzEBkRERGRzcnt8ePHbW6Uq5Q5RlqaAZcvp+DgwThp38svW0//JZfL4Oen5TUnIiIiQgGS2/379xdlHPQQnc4Ag8GM9evv99o+9ZQ3mjatZHWch4carq4cREZEREQEOGiFsgfpdDocPHjQ0c2WO2lpRqSkZGHLFuvpvx7soVWr5fDz4yAyIiIiohx2LeIQHx+PDz74ADExMTAYDHkec+HChTz3U/7M5uyBZJs3X4ROl73srpeXGj16PCkdI5cDPj4uUCq5EhkRERFRDruS2xkzZuDXX3/F888/j19//RUuLi5o0KABjhw5gosXL2LBggWOjrNcSUszQK83YuXKP6R9zz8fAheX+/9dWq0Knp4sRyAiIiJ6kF3dfidOnMDo0aMxefJk9OrVCxqNBuPHj8fmzZsRGRnJeXALKT3dgO3br+DWrQwAgEolx8sv15MeVyg4iIyIiIgoL3YltxkZGQgJCQEA1KxZE+fPZw96UigU6NevH44dO+a4CMuZzEwjdDoTvvjirLSvZ88nUbGiq7Tt7q6Gi4sqr6cTERERlWt2JbcBAQG4c+cOAKBatWpISUnB7du3AQDe3t64e/eu4yIsZ9LSjNi79xquXLm/aMOgQWHS49krkWlLKjwiIiKiUs2u5LZNmzaYN28eTp8+jaCgIAQGBmLFihVIT0/H5s2bUbFiRUfHWS5YLAKpqVlYtuyMtK9Tp+qoUcMLwP2VyFQqrkRGRERElBe7ktuRI0fC09MTn3/+OQBg9OjRWL16NSIjI7F9+3YMHDjQoUGWFykpWfjf/27g7Nk70r7XXw+XvtdouBIZERER0ePYNVuCj48PNm3ahMTERABAjx49ULlyZfz2228IDw9HVFSUQ4MsD8xmC1JSMrF8+f1a2+bNKyMszB9A9kpkvr4cREZERET0OHYltzkCAgKQlpaGxMREhIeHIyIiAgoFPzK3R0pKFn77LRGHD8dL+4YMud9r6+amhLs7p/4iIiIiehy7VwA4fvw4nn/+eURFRaF79+7466+/MG7cOMycOdOR8ZULJpMFyclZWL78d2lfaKi/tNSuQiGDry9XIiMiIiLKj13J7dGjRzFo0CBotVqMGzcOQggAQEhICNasWYOVK1c6NEhnl5ycicuX7+HHH/+W9g0ZEg6ZTAaZDPD21kCjKVQnOxEREVG5YFdyO2/ePHTo0AFr167FK6+8IiW3b7zxBgYPHoxNmzY5NEhnZjSakZpqwFdfnYPFkn0dq1f3RMeOTwDInvrL25tTfxERERHZwq7k9sKFC+jduzcA5Brg1KJFC8THx+f1NMrDvXuZiI9Px5Ytf0n7Bg8Oh0Ihh0wGeHlpoFDYXT1CREREVK7YlTV5eHhIizY87ObNm/Dw8ChUUOVFVpYJaWkGLF36G4xGCwAgIMAVzz5bC0D2srvstSUiIiKynV3JbYcOHTB37lz8/vv9AVAymQy3bt3C0qVL0bZtW0fF59Tu3cvEtWup2LTporRvyJBwqNWKf2tttZDLOfUXERERka3sGqU0duxYnDlzBi+88AL8/bPnYR0zZgxu3bqFSpUqYcyYMQ4N0hllZZmQkWHEokX3e20rV3bDiy+GAADUagU8PblgAxEREVFB2JXczp49G1OnTsXFixdx7NgxJCcnw8PDAwMGDECvXr3g4sJpq/KTlJSJixfvYevWS9K+YcMioFYrIJdnL7PLXlsiIiKigrErud22bRu6du2KF154AS+88IKjYyoXDAYzFiw4bTVDwnPPPQkAUKuV8PDggg1EREREBWVXzW1ERASOHTvm6FjKlfPn72L37qvS9ltvRUCplEMul8HHh8vsEhEREdnDrp7bkJAQrFixAj/++CNq164NV1dXq8dlMhmmT5/ukACd1WefnZS+Dw72wdNP1wQAaLUK9toSERER2cmu5Pann35CQEAAjEaj1YwJOdjr+HjHjt3A3r3/SNujRjWEXC6DXC7j1F9EREREhWBXcrt//35Hx1GuTJ58WPo+LMwfHTpkr0bm4qKEuzt7bYmIiIjsxaWvitnPP/+Dffvu99q+/XYjyGQ5vbac+ouIiIioMJjcFrNp0+4PxIuMDESLFpUBZPfaurmx15aIiIioMJjcFrNLl+5J37/9dkOp19bLi722RERERIVlV80t2W/hwo6YPfsE2rWrisaNAwFkz5DAWlsiIiKiwisVPbcWiwXz589Hq1at0KBBA7z++uu4fv36I4//66+/MGTIEDRp0gTNmjXDyJEjcePGjWKM2H7du9fCgQN9MXBgKABwhgQiIiIiByoVye3ixYuxYcMGfPTRR/jmm29gsVgwePBgGAyGXMfeu3cPAwcOhFarxdq1a/HFF18gKSkJgwcPRlZWVglEXzgaDXttiYiIiBylxJNbg8GAFStWYOTIkWjbti1q166NuXPn4tatW9izZ0+u4/fu3QudTodPP/0UwcHBCA0NxaxZs3D58mX8+uuvJfAK7JezGhkREREROUaJJ7exsbHIyMhAs2bNpH2enp6oW7cuTpw4kev4Zs2aYfHixdBq7yeFcnn2y0hNTS36gB1Io1HAzU1V0mEQEREROY0SH1B269YtAEClSpWs9gcEBEiPPahKlSqoUqWK1b7ly5dDq9UiMjKy6AJ1MJksu9aWq7kREREROU6J99zq9XoAgFptXXeq0WhsqqFdu3Yt1q1bh3HjxsHX17dIYiwKSqUc7u7stSUiIiJypBLvuc0pLzAYDFalBllZWXBxcXnk84QQ+Pzzz7FkyRK8+eabGDBgQJHH6kienmr22hIRERE5WIn33OaUIyQmJlrtT0xMRMWKFfN8jtFoxPjx47F06VK88847ePvtt4s6TIfjDAlEREREjlfiyW3t2rXh7u6O48ePS/tSU1Nx/vz5R9bQTpgwAT/88APmzJmDV199tZgiJSIiIqLSrsTLEtRqNaKjozF79mz4+voiKCgIs2bNQmBgIDp16gSz2YykpCR4eHhAq9Viy5Yt2LVrFyZMmICoqCjcvn1baivnGCIiIiIqn0q85xYARo4ciT59+mDy5Ml46aWXoFAo8NVXX0GlUuHmzZto2bIldu3aBQDYsWMHAODTTz9Fy5Ytrb5yjiEiIiKi8kkmhBAlHURJ6tChAwBg3759JRwJERERERVWqei5JSIiIiJyBCa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktERERETkNZ0gEAgMViwcKFC7Fp0yakpaUhMjIS77//PqpWrZrn8ffu3cO0adNw8OBByGQyPPPMM5gwYQJcXFyKOfKCm7BrF2adOGHXc+UAZADEv/+q5XJU8/ZG86pVUdXbGwJAelYWMo1GNA4KAgAcj4/HP8nJyDSZ4KPVIrRiRXhrtbiZloaK7u748+5dXEhMhEouRy0/Pzzh5YVq3t4I8fNDcmYm/F1dEVGpEuQyWa54LELg9M2buKPTPfY4W+S0lZiRgbs6HfxcXRHg5mZXmxYhcOrGDRyPiwMANKlSBY0qV7Y7NiIiIio7ZEIIUdJBLFy4EOvWrcPMmTMRGBiIWbNmIS4uDtu3b4darc51/IABA6DX6zF16lSkpqbi//7v/xAZGYlPPvmkwOfu0KEDAGDfvn2Ffh35kU2dWuTncBQZADeVCr4uLgjx98ekli3RvkYN6fH9V69i5uHD+PPOHRgsFqjl8jyPs0VOW2du3UJyVhbMFgsUcjm8NRrUDwwsUJv7r17FhJ9+wrnERBgtFgDZfwTUCwjAp//5T4FjIyIiorKlxJNbg8GApk2bYty4cejXrx8AIDU1Fa1atcLHH3+Mbt26WR1/+vRp9O3bF7t27UKtWrUAAIcPH8bgwYNx4MABVKxYsUDnL67ktiwltg8KcHWFTCaDh0aDZd26oX2NGth/9SqG7tiBtKws+Lm4QKNUIstkwl293uo4W+S0dVenQ7rBAAFALpPBbLFALpPBXa2Gn6urTW3uv3oVA777Dgnp6QAApTy76sb0b5Ib6O6ONc89xwSXiIjIiZV4zW1sbCwyMjLQrFkzaZ+npyfq1q2LE3l8fH/y5ElUqFBBSmwBICoqCjKZDKdOnSqWmAtqwq5dJR2C3e7q9ajk5oa0rCzMPHwYJosFMw8fRlpWFoI8POCiUkEuk8FFpUKQh4d0nMWGv5ksQmDm4cNIzcyEyWKBAKCSy6GUy6FRKCAAGM1mm9q0CIEZhw7hTkYGZDIZ1AoFFHI5FP+2BQC3dTrMsDE2IiIiKptKPLm9desWAKBSpUpW+wMCAqTHHpSQkJDrWLVaDW9vb9y8ebPoAi0Ee2tsSwOzEEg2GODr4oI/79zBN7//jj/v3IGfiwtkD9WwymQy6bjTNvxfnL55E3/euQM3tRqZJhOUcvn9NmUyKOVyGMxmuKpU+bZ5+uZNnEtMhAWwbufftlQKBSwWC84lJNgUGxEREZVNJZ7c6vV6AMhVW6vRaJCVlZXn8XnV4T7qeCq8LJMJWqUSBosF11JSYLBYoFHmPRYx57g7Ol2+7d7R6WD4t/wgZ5Dcg2QALMguU8ivzTs6HbLMZul5D8sZiJdlNtsUGxEREZVNJZ7carVaANm1tw/KysrKc/YDrVab69ic411dXYsmyHJOo1Qi02TKnp3BywtquRxZJlOex+Yc52/D/4W/qyvUcjksQkjJ54MEsm9QixD5tunv6iqVH+RVdJCTPGsUCptiIyIiorKpxJPbnBKDxMREq/2JiYl5Dg4LDAzMdazBYEBycjICAgKKLtBCGB8ZWdIh2E0hk8FbrUaSXo8Qf3/0DQtDiL8/7ur1eHgsohBCOi7iodKRvERUqoQQf39kGAzQKpXZdbc5bQoBk8UCtUIBndGYb5sRlSohNCAAcsC6nX/bMprNkMvlCK1Y0abYiIiIqGwq8eS2du3acHd3x/Hjx6V9qampOH/+PCLzSAojIyNx69YtXLt2TdoXExMDAGjUqFHRB2yHT59+uqRDsJufiwtuZmTAU6PBpJYtoZTLMallS3hoNIhPS4POaIRFCOiMRsSnpUnH2TKnrFwmw6SWLeGp1WbXyQIwWiwwWSzIMpshA6BSKGxqUy6T4Z1WreDv5gYhBAxmM8wWC8z/tgUAFVxd8Y6NsREREVHZVOLJrVqtRnR0NGbPno19+/YhNjYWo0ePRmBgIDp16gSz2Yzbt28jMzMTAFC/fn00bNgQo0ePxtmzZ3Hs2DG8//776NmzZ4GnAStOYsqUkg6hQGQA3FUqaJVKhFesiKUPTMXVvkYNLOvWDeEVKyLDYMDN9HRkGAy5jrNFTluNK1eGj1YrTQOmkMvho9WiceXKNrfZvkYNrH3uOTQIDIRSLofRYoHRYoFKLkeDwEBOA0ZERFQOlPg8twBgNpvx2WefYcuWLcjMzJRWKKtSpQri4uLQoUMHzJgxA7169QIA3L17F1OnTsWhQ4eg0WjQpUsXvPPOO9BoNAU+d3Eu4gBwhbJH4QplRERE5AilIrktScWd3BIRERFR0SnxsgQiIiIiIkdhcktEREREToPJLRERERE5DSa3REREROQ0mNwSERERkdNgcktERERETqPcTwUWFhYGs9ksLQNMRERERKVPpUqVsG7dunyPK/c9txqNBkqlsqTDICIiIiIHKPc9t0RERETkPMp9zy0REREROQ8mt0RERETkNJjcEhEREZHTYHJLRERERE6DyS0REREROQ0mt0RERETkNJjcEhEREZHTYHJLRERERE6DyS0REREROQ0mt0RERETkNJjcEhEREZHTYHJLRERERE6DyS0RUTkhhCjpEIjIyZWG9xmZKA1RlAOXLl1CcnIyqlatCm9vb2g0mpIOqdyzWCyQy/n3HTmvs2fPwmw2w2QyITIysqTDKbeEEJDJZCUdBlGRKI3vM8qSDqA8+Omnn/DZZ5+hZs2acHV1hYeHB0aMGAFfX9+SDq1cOXLkCI4fPw4PDw+0aNECdevWLemQyo2TJ08iIyMDOp0OXbt2LelwyoUDBw5g+vTpaNOmDU6dOoWQkBBMnz69pMMqFx6+35nYFg++zxS/0vo+w26rIpaZmYnt27djzpw5WLRoEaKjo6FSqTB16lTcu3evpMMrN44ePYqPP/4YFStWhNFoxIABA7B///6SDqtcOHz4MCZNmoQLFy5g2bJlePfddxEfH1/SYTm1rKwsrF27FhMnTsS7776L9evX4+LFi5g8ebJ0DD+0Kxq830sGr3vxK83vM0xui8GdO3cQFxcHAAgPD8dLL72EypUrY/ny5TAYDCUcXfkQGxuL3r17o3///hg2bBg+/PBDTJ8+HQcOHACQXaJAjiWEgMlkwrfffouRI0fijTfewH//+19kZGRg2bJlSEhIkI4jx8i5lhqNBpUrV5Y+HdJqtVi9ejXOnz+P9957DwDYm+hgtt7vfK9xLL7PFL+y8D7D5LaIabVavPDCCzh06BBiY2Mhk8nwxBNPoH379khISEBycnJJh1guKBQKXLp0Sdp+5plnMHr0aIwfPx4XLlxg7a2D5dQYKpVKVKtWTXqDU6vVmDlzJpKTk7Fw4UIATLIcKSUlRfq+WrVqmDhxIvR6PQDAzc0Na9aswfnz53H69OmSCtEpFeR+53uN4/B9pmSUhfcZ/pQVg9atW8Pb2xs7d+5EbGws5HI5IiMjkZaWJvXokuNdvnwZly9fRnJyMnr37o1ff/0Vn3/+ufT4M888g/79+yMmJqYEo3ROt2/fhsVigcViQUhICJYtWyb9Iefi4oJPP/0Up0+fxt69e0s2UCfyyy+/YNy4cZg8eTIWLFiA559/Hl26dMErr7yCzMxMAIC7uztCQkKgUChKOFrnwvu9ZPC6F7+y8j7DAWXFwNfXF/369cP69euxevVqtGjRAnK5HLdu3UJQUFBJh+eU9u7di4ULFyIoKAhqtRr169fHqlWr8NZbbwEARo0aBSD7L/8H/wqlwjtw4AAWL16Mp556Cnq9HpMnT8bff/+N7t27Y/v27fD29oZWq0XLli3h4uJS0uE6hUuXLuHDDz/EzJkzodfr8euvv2LgwIGYOXMmhBDo0aMHpk6diitXruDs2bN48803Szpkp8H7vWTwuhe/v/76Cx9++CFmzJiBzMzMUv0+w+S2mFSqVAmDBg3CkSNHsGPHDnh7e2PWrFmoWLFiSYfmdNLT07Fq1SpMmzYNwcHBuHTpEmbMmIEbN25g/vz5GDhwIG7cuAE/Pz8cOXIEc+bMKemQncbff/+N6dOnY9q0aahQoQJ27tyJp59+Gjt37oQQAi+//DKio6ORmpqKX375BX379i3pkJ2CyWRCs2bNEBUVBSEEmjdvDpVKhcmTJ+Orr76Cr68vDhw4gJs3b2Lu3LmoWrVqSYfsFHi/l4zr169j2rRpmD59Oq97MbJYLGjZsiWaNGlS6t9nmNwWIx8fH3Tr1g2dO3eW6oTI8YQQsFgs8PHxgVqtRt26dTFjxgxMnjwZP//8M7777jv8+OOPMBqN6NOnD2rWrFnSITsNrVaLJk2aIDIyEkIIDB8+HHK5HM8++yx27NiBp556ClevXkV8fDwWLVqE6tWrl3TIZdrff/+NjIwMuLu74+TJk9i5cyeeeeYZqFQqDBkyBEajEatXr8bw4cMBZCfBfN9xHBcXF0RERPB+L0bJyckQQiAqKorXvZjExsbi9u3bUCqVOHz4MHbv3o2uXbuW6vcZvsuVAJVKVdIhOKXz58/D398fAQEBqF+/PubMmYMpU6bAy8sLVapUwdtvv43Vq1eje/fueO6550o6XKdy9uxZxMfHQyaT4ccff0SjRo3w7LPPAgDefPNNWCwWfPzxx5g5cybkcjkX0HCAvXv3Yv78+fD09ERQUBDee+89fPfdd3B3d0ebNm2gVqsRFhYmzQgCoMR/4TiL2NhYXLlyBR06dMDp06exfv169O/fHwDv96K0f/9+7Nu3D4MGDUJMTAw2bNiAfv36AeB1Lyr79u3DZ599Bk9PT/Tu3Rvjxo3D3r174erqWqrfZ/i/TmVezrQkK1euxKhRo3Dnzh1ER0fD19cXixYtkmpq69Spg4yMDCQlJZVkuE5n3759+L//+z/s378fZ86cQZMmTbB+/Xps375dOqZTp05QKpXSLxr+wimchIQErFixAnPnzsW6detw5coVXLt2DREREdi8eTN++eUXANklOomJidDpdJwKyQEenHZqyZIl+P333zFr1izs3r0bW7dulY7j/e54+/fvx4IFC9C3b1/UrFkTM2bMwLZt26wGi/G6O5bBYMAPP/yA6dOn4+uvv0abNm3g6uqKRo0alfr3mZJPr4kK6cEpXi5fvoxJkybh448/lgYWvPfeexg9ejRiY2ORlJQEV1fXEozWuSQnJ2PDhg2YO3cunnzySWzZsgWxsbHo378/1q9fj7S0NPTr1w9//PEHrl69irS0NHh4eJR02GWeSqVCVlYWPD09cefOHVy7dg0HDx6ESqXC7du38c4776B169b47bffsGDBAt7zDpJTThYSEoI///wTW7duRVhYGN5++23MmjULFosFvXr14v3uYFeuXMGcOXPw5Zdf4vz58xg+fDg8PT3h7++P2bNnQ6/Xo3v37rzuReD69etITU1FWloa3nrrLWmgXkJCAiZOnIh27drh9OnTpe59RiZKS5pNVAhCCMydOxc9e/bEkiVLkJSUJC0BuHr1aly8eBFCCIwfPx61a9cu4WidR2pqKgYOHIj3338f9evXh16vx8CBAzFr1izEx8fjvffeQ1RUFH777TfMnTsXwcHBJR2yUzAYDDh69CjatGmDY8eO4fbt2+jevTsOHDiA/fv3o2/fvvDw8IBGo0GFChVKOlyns3fvXmzZsgUdOnTAmTNn4O/vj2vXruHMmTOoX78+Ll68iDlz5vB+d5CEhAS8+eabaNiwIW7cuIGRI0fijz/+QEJCAn788UfcvXsXrVq1wrlz53jdHWzdunW4efMmMjIyEBYWht69e+OXX37Bzz//jO7du6NSpUpQq9Wl7n2GyS05jYsXL6J69epQq9UYO3YskpKSMGvWLPj7+yMrKwsymQxqtbqkw3QqQgjs3r0btWrVQq1atXDz5k0MHToU69evh4+PD44fP46qVavC1dUV3t7eJR1uufD6669jzJgxqFOnTkmH4rQSExPxzTffYOTIkZg9ezbWr1+PsWPHIjo6Gunp6TAYDNKqTeQY586dw7BhwzBo0CC88sorMJlMuHDhAjZv3oy+ffsiKCgIRqOR193Bbt68iYULF+Ly5csYOnQo2rVrBwAYPHgwBg8ejKZNm5ZwhHljQQo5jeDgYCl5nTNnDipWrIhBgwbh7t270Gg0TGyLgEwmQ8eOHfHUU09BqVTCYDDAZDLBx8cHW7ZswZdffgmtVsvEtggZjUZcunQJ58+fx88//4ykpCT4+/uXdFhOLWee8t27d+PQoUPo06cPYmJisGvXLri7uzPBKgKhoaF4//33UbduXQDZg5bCwsLwzz//ICsrCx4eHrzuRaBSpUoYMmQIPDw8sG/fPvzyyy/48ccfkZCQgGrVqpV0eI/EmltyOjkjZGfOnIn3339fWjWFisaDfzRotVqEhYVhx44dWLduHWbOnMlfOEXMbDbjf//7H3744Qe4uLjg448/LnUfETobHx8fWCwWzJ8/H++//z6aNWuGrVu3olGjRiUdmlPr2LEjzGYzDhw4ALlcjszMTNy+fRuBgYElHZpTq1atGj744APs2rULa9euhZeXF2bNmoVKlSqVdGiPxLIEckqcAqZkxMfHo0OHDqhRowYWLlyIWrVqlXRI5YLBYIDRaITFYuFAmmISGxuL5ORk6WPZ0jC3Z3mg1+uxbds2bNmyBX5+fnjrrbdYglOMDAYDAJT6T0KZ3BKRwxgMBkyZMgWvv/46F8egckEIYTVjCxWPzMxMCCG4tC7licktETmUwWAo9X/VExGR82JyS0REREROg0WJREREROQ0mNwSERERkdNgcktEREREToPJLRERERE5DSa3RORQ9oxRLW3jWktbPEREZDsmt0TkMPv27cPEiRML9JxNmzbhk08+KdBzjh8/jpCQEBw/frxAz7Mnni1btiAkJARxcXEOjS0kJAQLFiwoVKz2xFeWDBgwAAMGDJC2HXXN8hMXF4eQkBBs2bKlyM9FRI7H5VSIyGFWrVpV4OcsWbIEUVFRjg/GTg/H07ZtW2zcuBEBAQE2Pb9evXrYuHEjnnzyyaIKsdzauHEjl1olonwxuSUiegxfX1/4+vrafLy7uzsaNGhQdAGVY7yuRGQLliUQkUMMGDAAMTExiImJkT6WT0xMxDvvvIM2bdogPDwcffr0wb59+6TntG/fHvHx8fjuu++sPlo/ceIEBg0ahMjISISGhqJ9+/ZYsGABLBZLoWKMjY3FiBEj0LRpU9SrVw+tWrXCtGnTkJmZ+ch4HvzYf/v27QgJCcHFixet2t27dy9CQkJw/vz5PMsSYmJi8OKLL6J+/fro3Lkz/ve//+WKLS4uDhMmTEDLli1Rr149NGvWDBMmTMC9e/ekYywWCxYvXoy2bduifv36GDZsGFJSUnK1dfHiRQwdOhQNGzZEw4YNMXz4cFy/ft3qmNWrV6NLly4ICwtDq1at8MEHHyA9Pb1A13PAgAEYN24cRo4ciQYNGmDgwIEAgKysLHz66ado06YNQkND0b17d+zatcvquZmZmZgzZw46deqE0NBQNGzYEAMHDsSFCxceeb4HyxImTZqEkJCQPL9yyglsPceePXvQo0cPhIeH47nnnkNsbGyuc+d37wDAkSNH8MILLyAiIgKRkZF48803cfny5QJdUyIqPPbcEpFDTJkyBePHj5e+r1ChAvr06QONRoPRo0fDx8cHW7ZswfDhw/Hpp5+iR48eWLhwIYYMGYK6deti2LBhCAgIQGxsLF599VV06dIFc+fOhRAC27dvx8KFC1GzZk0888wzdsWXmJiI/v37o0GDBpg5cybUajUOHjyIlStXIiAgAEOGDMkzngd17NgRrq6u2LlzJ4KDg6X9O3bswFNPPYW6devmqrX9448/8Nprr6Fp06aYP38+4uLiMGbMGKtj9Ho9Xn75Zfj4+GDKlCnw8PDA6dOnsXDhQmi1Wnz44YcAgFmzZmHNmjV48803Ub9+fezevRtz5syxauvq1avo27cvatasiU8++QQmkwlLlizBSy+9hO+//x5+fn7YsWMHZs2ahYkTJyIkJARXrlzBJ598Ar1eX+D65927d6NHjx5YsmQJLBYLhBAYPnw4fv31V4wcORK1atXCTz/9hNGjR8NgMKBnz54AgAkTJuDkyZMYM2YMnnjiCVy7dg2ff/45xo4di507d0Imkz32vMOGDUPfvn2lbbPZjHfffRd6vR6tWrWy+Rz79+/HyJEj0b17d4wfPx4XLlyQ7uMcttw7169fx7Bhw9C7d2+MGTMGqamp+OyzzzBkyBD89NNPkMvZl0RUXJjcEpFDPPnkk3B3dweQ/fHxrFmzkJSUhB9//BFBQUEAgDZt2uDVV1/Fp59+im7duqFu3bpQq9Xw9fWVPnKOjY1F8+bNMWvWLCkhaNGiBfbv34/jx4/bndxevHgRderUweeffy7F2bx5cxw5cgTHjx+XktqH43mQi4sLOnfujF27dmH06NEAgIyMDPz8888YPnx4nuddtmwZ/Pz8sGTJEqhUKgCAj4+P9HwA+PvvvxEYGIhPPvkEVatWBQA0bdoUZ86cQUxMDAAgNTUVa9euxcCBAzFixAgAQKtWrZCYmIhDhw5JbS1cuBAuLi5YtWqV9DqbNWuGjh074ssvv8TEiRMRExODKlWqoH///pDL5YiKioKrq2uevcD5UalUmDp1KtRqNYDs3stDhw5h7ty5ePrpp6U49Xo9Zs+ejW7dusFisSAjIwOTJ0+WjomKikJ6ejpmzpyJO3fuoEKFCo897xNPPIEnnnhC2v74449x48YNrF+/HhUqVIDBYLDpHIsWLUJ4eDhmzZolxQrA6o8GW+6ds2fPIjMzE0OHDkXFihUBAIGBgdi3bx90Op30PCIqekxuiahIxMTEICIiQkpsc/To0QPvvPMOrly5kuegq549e6Jnz57IysrC1atXce3aNVy4cAFmsxlGo9HueFq2bImWLVvCaDTi0qVLuHbtGi5evIikpCR4e3vb3M6zzz6L7777DmfPnkV4eDj27dsHg8GAHj165Hn8qVOn0K5dOymxBYBOnTpBoVBI23Xq1MGGDRtgsVjw999/49q1a7h06RKuXLkCk8kEAPjtt99gNBrRrl07q/a7du1qldweO3YMUVFR0Gq10nPd3d3RuHFjqRyiadOm2LhxI3r16oWOHTuiTZs26N69e769pXmpWbOmlNgCwNGjRyGTydCmTRvp/EB2yce2bdvw119/oU6dOvjqq68AAAkJCbh69Sr+/vtv/PzzzwAAg8FQoBg2bdqENWvWYPr06QgPDwcAqNXqfM+RmZmJP/74A6NGjbJqr2vXrlbJrS33Tv369aHRaNCnTx906dIFrVu3RpMmTaR4iKj4MLkloiKRkpIi9UI+yN/fH0B2T2ReMjMz8dFHH+H777+HyWRClSpVEBERAaVSWaj5Zy0WCz777DOsX78eOp0OlSpVQnh4ODQaTYHaadKkCSpWrIidO3ciPDwcO3fuRFRU1CNH8aekpMDHx8dqn1KpzLVv5cqVWLp0KZKTk+Hv74/Q0FC4uLggLS1NagdAruc93MOZnJyMXbt25apxBSANjHv66adhsViwYcMGLF68GAsWLEBQUBDGjRsn9XLays3NLdf5hRBo2LBhnscnJiaiTp06OHToEKZPn44rV67Azc0NtWvXhqurK4CCzTN88uRJTJ06FdHR0ejdu7fVY/mdIyUlBUKIXNf04XIUW+6dKlWqYN26dVi+fDm+/fZbrFmzBp6enujXrx/efvttu/5wICL7MLkloiLh5eWF27dv59qfs+/hhCLHxx9/jB9//BHz5s1D8+bNpWSkWbNmhYpn+fLlWLVqFaZOnYpOnTrBw8MDANCnT58CtSOXy9G9e3fs2LEDb7zxBo4cOSLVxObF29sbd+7csdqXk1jl2L59O2bOnInx48ejV69eUhI6atQo/P777wDuX6+7d++iZs2a0nOTk5Ot2vbw8EDz5s2lwV0PUirvv+V369YN3bp1Q1paGg4fPowvvvgC48ePR6NGjaSP1e3h4eEBV1dXrFmzJs/Hq1Wrhn/++QfDhw9Hx44dsWzZMlStWhUymQzr16+36oXOT1xcHEaMGIEGDRrgnXfesXrMlnN4e3tDLpfn+v95+Jraeu+Eh4dj4cKFMBgMOHXqFDZu3IilS5eidu3a6Nq1q82vi4gKhxXuROQwDw6aiYyMxOnTpxEfH291zLZt21ChQgVUq1Yt13OA7I/xmzRpIg3eAoBz584hKSmpULMlnDp1Ck8++SR69+4tJScJCQm4ePGiVbu2DPx59tlncevWLSxatAgKhQKdOnV65LHNmjXDwYMHodfrpX2HDh2yKrE4deoUPD09MXjwYCmxzcjIwKlTp6TYIiIioNVq8cMPP1i1n/Mxe46oqChcunQJderUQVhYGMLCwhAaGopVq1bhp59+AgC8/fbbUo2wh4cHunbtimHDhsFkMiExMTHf1/84UVFR0Ol0EEJI5w8LC8PFixexaNEimEwmnDt3DllZWRgyZAieeOIJqVczJ+m0pec2IyMDb775JrRaLT7//HOrxB2ATefQaDSIiIjAnj17rM65f/9+q7ZsuXdWrVqFdu3awWAwQK1Wo1mzZvjoo48AADdu3CjwdSQi+7HnlogcxtPTE6dPn8bRo0fx6quvYtu2bXj11VcxYsQIeHt7Y+vWrTh27BimT58uJZGenp44f/48YmJiEB4ejvDwcOzevRtff/01atWqhdjYWCxZsgQymcwqQSyo8PBwLF68GMuXL0eDBg1w7do1LFu2DAaDwardh+PJS3BwsFQn27Vr18cOFho+fDj27t2LQYMGYfDgwUhKSsK8efOsanDDw8Px9ddfY+bMmWjXrh0SExPx1Vdf4c6dO/Dy8gKQ/fH/sGHDMG/ePLi4uKBp06Y4cOBAruQ2ZxaBoUOH4qWXXoJGo8HGjRuxd+9ezJ8/H0B2ze2UKVPwySefoHXr1khNTcXChQtRvXp11K5d2+5rDGQPGoyMjMSwYcMwbNgw1KpVC2fPnsX8+fPRqlUr+Pr6ol69elAqlZg1axZee+01GAwGbNmyBb/88gsAQKfT5XuecePG4fLly5g5cybi4+OtpjoryDnGjBmDV155BSNGjMCLL76Iq1evYunSpVbnsuXeadq0KWbPno3hw4cjOjoaCoUC33zzDdRqda46aSIqYoKIyEGOHj0q2rZtK+rVqye2bdsm/vnnHzFq1CjRuHFjUb9+ffHiiy+KvXv3Wj1n+/btolmzZiI0NFScOHFC3Lt3T4wZM0ZERUWJBg0aiG7duonVq1eL9957T7Ro0UKYTCZx7NgxERwcLI4dO2ZzbFlZWWLq1KmiRYsWIjw8XHTu3FnMnz9fLFiwQISGhoqUlJQ849m8ebMIDg4W169ft2pvxYoVIjg4WPzyyy9W+/OK7dy5cyI6OlqEh4eLdu3aiW3btonmzZuL+fPnCyGEsFgs4vPPPxetW7cWYWFhomPHjuKjjz4SGzduFMHBweLSpUtSW2vWrBEdOnQQoaGhYsCAAWLDhg254jt37pwYNGiQiIiIEA0aNBAvvPBCruu+Zs0a8fTTT4vw8HARFRUlRo0aJeLi4my+nkIIER0dLaKjo3Ptz8jIENOnTxetW7cW9erVE+3btxdz5swRmZmZ0jG7d+8WzzzzjAgLCxMtW7YUI0aMEDExMSIkJESsW7cuz/aDg4OlaxYcHPzIr4kTJ9p8DiGEOHLkiOjdu7cICwsTXbt2Ffv37xfBwcFi8+bNQgjb751Dhw6Jvn37ioYNG4r69euL/v37i5iYmAJdUyIqPJkQhRihQURERERUirAsgYjKNLPZnG+Npkwms5p6ix5NCAGz2ZzvcQqFgjMAEFGpxJ5bIirTcpbMfZyoqCisXbu2mCIq27Zs2ZJr5oG8rFmzBk2aNCmGiIiICobJLRGVaX/++We+k/67ublZTZ9Fj3bv3j3ExcXle1yNGjW46hYRlUpMbomIiIjIaXCeWyIiIiJyGkxuiYiIiMhpMLklIiIiIqfB5JaIiIiInAaTWyIiIiJyGkxuiYiIiMhpMLklIiIiIqfx/z2OfO4YeZa7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_2variaveis(dataframe=df_trial_metricas, coluna_x='total_atividades_realizadas', coluna_y='realizou_assinatura', logistic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_atividades_realizadas</th>\n",
       "      <th>realizou_assinatura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_atividades_realizadas</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>realizou_assinatura</th>\n",
       "      <td>0.204041</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             total_atividades_realizadas  realizou_assinatura\n",
       "total_atividades_realizadas                     1.000000             0.204041\n",
       "realizou_assinatura                             0.204041             1.000000"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlação Pearson entre as variáveis.\n",
    "df_trial_metricas[['total_atividades_realizadas','realizou_assinatura']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----- Hipóteses ---------------------------------------------------------------------------------\n",
      "H0 : p = 0 => Não existe correlação significativa entre as variáveis.\n",
      "Ha : p ≠ 0 => Existe correlação significativa entre as variáveis.\n",
      "Nível de significância de 0.05.\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Coeficiente de Pearson p: 0.2040412670161802\n",
      "P_valor: 1.066720333734921e-10\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.000000000106672033373492101577 < alpha: 0.05 | coeficiente de Pearson: 0.2040 ≠ 0 => Rejeitamos a hipótese nula e concluimos que existe uma correlação estatisticamente significativa entre as variáveis total_atividades_realizadas e realizou_assinatura.\n"
     ]
    }
   ],
   "source": [
    "pearson_corr(dataframe=df_trial_metricas, coluna1='total_atividades_realizadas', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fc6fe_row0_col2 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_fc6fe_row1_col2 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fc6fe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fc6fe_level0_col0\" class=\"col_heading level0 col0\" >realizou_assinatura</th>\n",
       "      <th id=\"T_fc6fe_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_fc6fe_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fc6fe_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_fc6fe_row0_col0\" class=\"data row0 col0\" >False</td>\n",
       "      <td id=\"T_fc6fe_row0_col1\" class=\"data row0 col1\" >713</td>\n",
       "      <td id=\"T_fc6fe_row0_col2\" class=\"data row0 col2\" >4.932679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc6fe_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_fc6fe_row1_col0\" class=\"data row1 col0\" >True</td>\n",
       "      <td id=\"T_fc6fe_row1_col1\" class=\"data row1 col1\" >270</td>\n",
       "      <td id=\"T_fc6fe_row1_col2\" class=\"data row1 col2\" >17.325926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25dfb9da3c0>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_difmedia_2amostras(dataframe=df_trial_metricas, coluna1='realizou_assinatura', coluna2='total_atividades_realizadas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "realizou_assinatura_total_atividades = df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']]['total_atividades_realizadas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "nao_realizou_assinatura_total_atividades = df_trial_metricas.loc[~df_trial_metricas['realizou_assinatura']]['total_atividades_realizadas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses Bartlett de igualdade de variâncias. *****\n",
      "* Premissas:\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "\n",
      "----- Hipóteses -------------------------------------------------------------------------\n",
      "H0 : As variâncias de todas as amostras da realizou_assinatura são iguais.\n",
      "Ha : As variâncias para um par ou mais das amostras da realizou_assinatura são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "-----------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística T: 1198.1473838393365\n",
      "P_valor: 1.541432696911616e-262\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 1.541432696911616e-262 < alpha: 0.05 => Rejeitamos a hipótese nula. As variâncias das amostras da realizou_assinatura são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Verificar as variancias populacionais dos grupos.\n",
    "variancia(data=df_trial_metricas, dv='total_atividades_realizadas', fator='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.541542181791409, 18.24495202630168)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intervalo de confiança diferença duas médias - variâncias diferentes.\n",
    "cm = sms.CompareMeans(sms.DescrStatsW(realizou_assinatura_total_atividades), sms.DescrStatsW(nao_realizou_assinatura_total_atividades))\n",
    "cm.tconfint_diff(usevar='unequal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses t : Média - 2 amostras *****\n",
      "* Premissas:\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "- Grupos com variâncias diferentes.\n",
      "\n",
      "----- Hipóteses ----------------------------------------------------------------\n",
      "H0 : A média do total_atividades_realizadas entre realizou_assinatura e nao_realizou_assinatura, são iguais.\n",
      "Ha : A média do total_atividades_realizadas entre realizou_assinatura e nao_realizou_assinatura, são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística T: 4.169249467080342\n",
      "P_valor: 4.094085528e-05\n",
      "Intervalo de confiança para diferença das médias: [ 6.54 18.24]\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 4.094085528e-05 < alpha: 0.05 => Rejeitamos a hipótese nula. Existe diferença estatisticamente significativa das médias do total_atividades_realizadas entre realizou_assinatura e nao_realizou_assinatura.\n",
      "- Intervalo de confiança [ 6.54 18.24] não passa pelo zero. As amostras são estatisticamente diferentes.\n",
      "- Média do total_atividades_realizadas para realizou_assinatura: 17.325925925925926\n",
      "- Média do total_atividades_realizadas para nao_realizou_assinatura: 4.9326788218793824\n"
     ]
    }
   ],
   "source": [
    "# Teste de hipóteses T - variâncias diferentes.\n",
    "difmedia_2amostras(realizou_assinatura=realizou_assinatura_total_atividades, nao_realizou_assinatura=nao_realizou_assinatura_total_atividades, var='total_atividades_realizadas')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Funcionalidades Usadas e Conversão\n",
    "    * Hipótese Nula (H0): O uso de funcionalidades específicas (como prontuário, agendamento, gestão financeira) não influencia significativamente a conversão para assinaturas pagas.\n",
    "    * Hipótese Alternativa (HA): O uso de funcionalidades específicas influencia significativamente a conversão para assinaturas pagas.\n",
    "\n",
    "Teste Estatístico: Teste de Qui-Quadrado para verificar a associação entre o uso de funcionalidades e a conversão."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_clinica</th>\n",
       "      <th>id_funcionalidade</th>\n",
       "      <th>num_qtde_vezes</th>\n",
       "      <th>nom_funcionalidade</th>\n",
       "      <th>realizou_assinatura</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8994</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>budget_management</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8994</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>medical_record</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8994</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>revenue_management</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8994</td>\n",
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       "      <td>scheduling</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8998</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>anamnese</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>2294</th>\n",
       "      <td>11295</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>11295</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2296</th>\n",
       "      <td>11296</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2297</th>\n",
       "      <td>11301</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>11302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2299 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_clinica  id_funcionalidade  num_qtde_vezes  nom_funcionalidade  \\\n",
       "0           8994                  5               1   budget_management   \n",
       "1           8994                  2               2      medical_record   \n",
       "2           8994                  9               1  revenue_management   \n",
       "3           8994                  1               1          scheduling   \n",
       "4           8998                  3               2            anamnese   \n",
       "...          ...                ...             ...                 ...   \n",
       "2294       11295                  0               2  patient_management   \n",
       "2295       11295                  1               1          scheduling   \n",
       "2296       11296                  1               1          scheduling   \n",
       "2297       11301                  1               1          scheduling   \n",
       "2298       11302                  1               1          scheduling   \n",
       "\n",
       "      realizou_assinatura  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "2294                False  \n",
       "2295                False  \n",
       "2296                False  \n",
       "2297                False  \n",
       "2298                False  \n",
       "\n",
       "[2299 rows x 5 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funcionalidade_conversao = pd.merge(df_funcionalidade_metricas, df_trial_metricas[['id_clinica','realizou_assinatura']], on='id_clinica', how='left')\n",
    "funcionalidade_conversao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7fb90_level0_col0\" class=\"col_heading level0 col0\" >nom_funcionalidade</th>\n",
       "      <th id=\"T_7fb90_level0_col1\" class=\"col_heading level0 col1\" >realizou_assinatura</th>\n",
       "      <th id=\"T_7fb90_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_7fb90_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th id=\"T_7fb90_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7fb90_row0_col0\" class=\"data row0 col0\" >anamnese</td>\n",
       "      <td id=\"T_7fb90_row0_col1\" class=\"data row0 col1\" >False</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7fb90_row1_col0\" class=\"data row1 col0\" >anamnese</td>\n",
       "      <td id=\"T_7fb90_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row1_col2\" class=\"data row1 col2\" >87</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_7fb90_row2_col0\" class=\"data row2 col0\" >bnpl</td>\n",
       "      <td id=\"T_7fb90_row2_col1\" class=\"data row2 col1\" >False</td>\n",
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       "      <th id=\"T_7fb90_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
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       "      <td id=\"T_7fb90_row3_col2\" class=\"data row3 col2\" >6</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
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       "      <td id=\"T_7fb90_row4_col1\" class=\"data row4 col1\" >False</td>\n",
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       "      <td id=\"T_7fb90_row5_col0\" class=\"data row5 col0\" >budget_management</td>\n",
       "      <td id=\"T_7fb90_row5_col1\" class=\"data row5 col1\" >True</td>\n",
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       "      <td id=\"T_7fb90_row5_col3\" class=\"data row5 col3\" >30.952381</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
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       "      <td id=\"T_7fb90_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "      <td id=\"T_7fb90_row6_col2\" class=\"data row6 col2\" >12</td>\n",
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       "      <th id=\"T_7fb90_level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
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       "      <td id=\"T_7fb90_row7_col1\" class=\"data row7 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row7_col2\" class=\"data row7 col2\" >18</td>\n",
       "      <td id=\"T_7fb90_row7_col3\" class=\"data row7 col3\" >60.000000</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7fb90_row8_col0\" class=\"data row8 col0\" >documents_and_files</td>\n",
       "      <td id=\"T_7fb90_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "      <td id=\"T_7fb90_row8_col2\" class=\"data row8 col2\" >82</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
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       "      <td id=\"T_7fb90_row9_col1\" class=\"data row9 col1\" >True</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_7fb90_row10_col0\" class=\"data row10 col0\" >medical_record</td>\n",
       "      <td id=\"T_7fb90_row10_col1\" class=\"data row10 col1\" >False</td>\n",
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       "      <th id=\"T_7fb90_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_7fb90_row11_col0\" class=\"data row11 col0\" >medical_record</td>\n",
       "      <td id=\"T_7fb90_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row11_col2\" class=\"data row11 col2\" >117</td>\n",
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       "      <th id=\"T_7fb90_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_7fb90_row12_col0\" class=\"data row12 col0\" >patient_management</td>\n",
       "      <td id=\"T_7fb90_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "      <td id=\"T_7fb90_row12_col2\" class=\"data row12 col2\" >73</td>\n",
       "      <td id=\"T_7fb90_row12_col3\" class=\"data row12 col3\" >57.936508</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_7fb90_row13_col0\" class=\"data row13 col0\" >patient_management</td>\n",
       "      <td id=\"T_7fb90_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row13_col2\" class=\"data row13 col2\" >53</td>\n",
       "      <td id=\"T_7fb90_row13_col3\" class=\"data row13 col3\" >42.063492</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_7fb90_row14_col0\" class=\"data row14 col0\" >payment_book</td>\n",
       "      <td id=\"T_7fb90_row14_col1\" class=\"data row14 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row14_col2\" class=\"data row14 col2\" >2</td>\n",
       "      <td id=\"T_7fb90_row14_col3\" class=\"data row14 col3\" >100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_7fb90_row15_col0\" class=\"data row15 col0\" >revenue_management</td>\n",
       "      <td id=\"T_7fb90_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "      <td id=\"T_7fb90_row15_col2\" class=\"data row15 col2\" >150</td>\n",
       "      <td id=\"T_7fb90_row15_col3\" class=\"data row15 col3\" >72.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_7fb90_row16_col0\" class=\"data row16 col0\" >revenue_management</td>\n",
       "      <td id=\"T_7fb90_row16_col1\" class=\"data row16 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row16_col2\" class=\"data row16 col2\" >58</td>\n",
       "      <td id=\"T_7fb90_row16_col3\" class=\"data row16 col3\" >27.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_7fb90_row17_col0\" class=\"data row17 col0\" >scheduling</td>\n",
       "      <td id=\"T_7fb90_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "      <td id=\"T_7fb90_row17_col2\" class=\"data row17 col2\" >545</td>\n",
       "      <td id=\"T_7fb90_row17_col3\" class=\"data row17 col3\" >75.276243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_7fb90_row18_col0\" class=\"data row18 col0\" >scheduling</td>\n",
       "      <td id=\"T_7fb90_row18_col1\" class=\"data row18 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row18_col2\" class=\"data row18 col2\" >179</td>\n",
       "      <td id=\"T_7fb90_row18_col3\" class=\"data row18 col3\" >24.723757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_7fb90_row19_col0\" class=\"data row19 col0\" >user_management</td>\n",
       "      <td id=\"T_7fb90_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "      <td id=\"T_7fb90_row19_col2\" class=\"data row19 col2\" >54</td>\n",
       "      <td id=\"T_7fb90_row19_col3\" class=\"data row19 col3\" >46.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fb90_level0_row20\" class=\"row_heading level0 row20\" >19</th>\n",
       "      <td id=\"T_7fb90_row20_col0\" class=\"data row20 col0\" >user_management</td>\n",
       "      <td id=\"T_7fb90_row20_col1\" class=\"data row20 col1\" >True</td>\n",
       "      <td id=\"T_7fb90_row20_col2\" class=\"data row20 col2\" >63</td>\n",
       "      <td id=\"T_7fb90_row20_col3\" class=\"data row20 col3\" >53.846154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25dfca0f260>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_2cat(dataframe=funcionalidade_conversao, coluna1='nom_funcionalidade', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(dataframe=funcionalidade_conversao, coluna1='nom_funcionalidade', coluna2='realizou_assinatura', orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "grpby_funcionalidades_conversao = lib.grpby_2cat(dataframe=funcionalidade_conversao, coluna1='nom_funcionalidade', coluna2='realizou_assinatura', plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando a teste de hipóteses qui-quadrado *****\n",
      "* Premissas:\n",
      "- As observações devem ser contagens ou frequências.\n",
      "- Cada observação deve pertencer a uma única categoria.\n",
      "- A amostra deve ser relativamente grande, com pelo menos 5 observações em cada célula e, no caso de poucos grupos, pelo menos 10.\n",
      "- Os dados devem vir de uma amostra aleatória.\n",
      "- As categorias das variáveis devem ser mutuamente exclusivas.\n",
      "\n",
      "----- Hipóteses -----\n",
      "H0 : as variáveis nom_funcionalidade e realizou_assinatura são independentes. As proporções são estatisticamente iguais.\n",
      "Ha : as variáveis nom_funcionalidade e realizou_assinatura são dependentes. As proporções são estatisticamente diferentes.\n",
      "Nível de significância de 0.05.\n",
      "---------------------\n",
      "\n",
      "Valor do teste qui-quadrado: 76.92707229506287\n",
      "P_valor: 2.003545134135274e-12\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.0000000000 < alpha: 0.05 => Rejeitamos a hipótese nula. Há evidência suficiente, ao nível de significância de 0.05, para concluir que as variáveis nom_funcionalidade e realizou_assinatura são dependentes.\n",
      "- Tamanho do Efeito - Cramér´s V: 0.1829237586\n"
     ]
    }
   ],
   "source": [
    "# Realizando o teste qui-quadrado.\n",
    "teste_chi2(dataframe=funcionalidade_conversao, coluna1='nom_funcionalidade', coluna2='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nom_funcionalidade</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>nobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anamnese</td>\n",
       "      <td>87</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bnpl</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>budget_management</td>\n",
       "      <td>91</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>credit_check</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>documents_and_files</td>\n",
       "      <td>33</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>medical_record</td>\n",
       "      <td>117</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>patient_management</td>\n",
       "      <td>53</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>payment_book</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>revenue_management</td>\n",
       "      <td>58</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>scheduling</td>\n",
       "      <td>179</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>user_management</td>\n",
       "      <td>63</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     nom_funcionalidade  frequencia  nobs\n",
       "0              anamnese          87   283\n",
       "1                  bnpl           6     7\n",
       "2     budget_management          91   294\n",
       "3          credit_check          18    30\n",
       "4   documents_and_files          33   115\n",
       "5        medical_record         117   393\n",
       "6    patient_management          53   126\n",
       "7          payment_book           2     2\n",
       "8    revenue_management          58   208\n",
       "9            scheduling         179   724\n",
       "10      user_management          63   117"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intervalo de confiança \n",
    "freq = grpby_funcionalidades_conversao.loc[grpby_funcionalidades_conversao['realizou_assinatura']][['nom_funcionalidade','frequencia']]\n",
    "nobs = grpby_funcionalidades_conversao.groupby(by=['nom_funcionalidade'], as_index=False)['frequencia'].sum().rename(columns={'frequencia':'nobs'})\n",
    "freq_nobs = pd.merge(freq, nobs, on='nom_funcionalidade', how='left')\n",
    "freq_nobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intervalo de confiança 95% para anamnese: [25.366%, 36.118%]\n",
      "Intervalo de confiança 95% para bnpl: [59.792%, 100.000%]\n",
      "Intervalo de confiança 95% para budget_management: [25.668%, 36.237%]\n",
      "Intervalo de confiança 95% para credit_check: [42.470%, 77.530%]\n",
      "Intervalo de confiança 95% para documents_and_files: [20.428%, 36.963%]\n",
      "Intervalo de confiança 95% para medical_record: [25.250%, 34.292%]\n",
      "Intervalo de confiança 95% para patient_management: [33.444%, 50.683%]\n",
      "Intervalo de confiança 95% para payment_book: [100.000%, 100.000%]\n",
      "Intervalo de confiança 95% para revenue_management: [21.790%, 33.979%]\n",
      "Intervalo de confiança 95% para scheduling: [21.581%, 27.866%]\n",
      "Intervalo de confiança 95% para user_management: [44.813%, 62.879%]\n"
     ]
    }
   ],
   "source": [
    "# Itervalo de confiança.\n",
    "for index, row in freq_nobs.iterrows():\n",
    "    lower, upper = proportion_confint(count=row['frequencia'], nobs=row['nobs'], alpha=.05)\n",
    "    print(f'Intervalo de confiança 95% para {row['nom_funcionalidade']}: [{lower*100:.3f}%, {upper*100:.3f}%]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nom_funcionalidade</th>\n",
       "      <th>realizou_assinatura</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>anamnese</td>\n",
       "      <td>True</td>\n",
       "      <td>87</td>\n",
       "      <td>30.742049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bnpl</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>85.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>budget_management</td>\n",
       "      <td>True</td>\n",
       "      <td>91</td>\n",
       "      <td>30.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit_check</td>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>documents_and_files</td>\n",
       "      <td>True</td>\n",
       "      <td>33</td>\n",
       "      <td>28.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>medical_record</td>\n",
       "      <td>True</td>\n",
       "      <td>117</td>\n",
       "      <td>29.770992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>patient_management</td>\n",
       "      <td>True</td>\n",
       "      <td>53</td>\n",
       "      <td>42.063492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>payment_book</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>revenue_management</td>\n",
       "      <td>True</td>\n",
       "      <td>58</td>\n",
       "      <td>27.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>scheduling</td>\n",
       "      <td>True</td>\n",
       "      <td>179</td>\n",
       "      <td>24.723757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>user_management</td>\n",
       "      <td>True</td>\n",
       "      <td>63</td>\n",
       "      <td>53.846154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     nom_funcionalidade  realizou_assinatura  frequencia  porcentagem\n",
       "1              anamnese                 True          87    30.742049\n",
       "2                  bnpl                 True           6    85.714286\n",
       "5     budget_management                 True          91    30.952381\n",
       "6          credit_check                 True          18    60.000000\n",
       "9   documents_and_files                 True          33    28.695652\n",
       "11       medical_record                 True         117    29.770992\n",
       "13   patient_management                 True          53    42.063492\n",
       "14         payment_book                 True           2   100.000000\n",
       "16   revenue_management                 True          58    27.884615\n",
       "18           scheduling                 True         179    24.723757\n",
       "19      user_management                 True          63    53.846154"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpby_funcionalidades_conversao.loc[grpby_funcionalidades_conversao['realizou_assinatura']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Duração do Trial e Conversão\n",
    "    * Hipótese Nula (H0): A duração do período de trial não tem impacto significativo na taxa de conversão.\n",
    "    * Hipótese Alternativa (HA): A duração do período de trial tem impacto significativo na taxa de conversão.\n",
    "\n",
    "Teste Estatístico: Teste T para duas amostras independentes, comparando as durações de trial entre clínicas que converteram e não converteram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_47e14_row0_col2 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_47e14_row1_col2 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_47e14\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_47e14_level0_col0\" class=\"col_heading level0 col0\" >realizou_assinatura</th>\n",
       "      <th id=\"T_47e14_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_47e14_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_47e14_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_47e14_row0_col0\" class=\"data row0 col0\" >False</td>\n",
       "      <td id=\"T_47e14_row0_col1\" class=\"data row0 col1\" >713</td>\n",
       "      <td id=\"T_47e14_row0_col2\" class=\"data row0 col2\" >9.423562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_47e14_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_47e14_row1_col0\" class=\"data row1 col0\" >True</td>\n",
       "      <td id=\"T_47e14_row1_col1\" class=\"data row1 col1\" >270</td>\n",
       "      <td id=\"T_47e14_row1_col2\" class=\"data row1 col2\" >6.466667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25dfb7d0290>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_difmedia_2amostras(dataframe=df_trial_metricas, coluna1='realizou_assinatura', coluna2='num_dias_trial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_difmedia_2amostras(dataframe=df_trial_metricas, coluna1='realizou_assinatura', coluna2='num_dias_trial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Média e variância da duração do tempo de Trial.\n",
    "grpby_conversao_duracao_trial = lib.grpby_difmedia_2amostras(dataframe=df_trial_metricas, coluna1='realizou_assinatura', coluna2='num_dias_trial', plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "converteu_duracao_trial = df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']]['num_dias_trial']\n",
    "nao_converteu_duracao_trial = df_trial_metricas.loc[~df_trial_metricas['realizou_assinatura']]['num_dias_trial']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses Bartlett de igualdade de variâncias. *****\n",
      "* Premissas:\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "\n",
      "----- Hipóteses -------------------------------------------------------------------------\n",
      "H0 : As variâncias de todas as amostras da realizou_assinatura são iguais.\n",
      "Ha : As variâncias para um par ou mais das amostras da realizou_assinatura são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "-----------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística T: 305.0680173597413\n",
      "P_valor: 2.5920974956059793e-68\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 2.5920974956059793e-68 < alpha: 0.05 => Rejeitamos a hipótese nula. As variâncias das amostras da realizou_assinatura são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Verificar as variancias populacionais dos grupos.\n",
    "variancia(data=df_trial_metricas, dv='num_dias_trial', fator='realizou_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.647257627184542, -1.2665338641665564)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intervalo de confiança diferença duas médias - variâncias diferentes.\n",
    "cm = sms.CompareMeans(sms.DescrStatsW(converteu_duracao_trial), sms.DescrStatsW(nao_converteu_duracao_trial))\n",
    "cm.tconfint_diff(usevar='unequal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses t : Média - 2 amostras *****\n",
      "* Premissas:\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "- Grupos com variâncias diferentes.\n",
      "\n",
      "----- Hipóteses ----------------------------------------------------------------\n",
      "H0 : A média do num_dias_trial entre realizou_assinatura e nao_realizou_assinatura, são iguais.\n",
      "Ha : A média do num_dias_trial entre realizou_assinatura e nao_realizou_assinatura, são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística T: -3.432768718087284\n",
      "P_valor: 0.000622599020828\n",
      "Intervalo de confiança para diferença das médias: [-4.65 -1.27]\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.000622599020828 < alpha: 0.05 => Rejeitamos a hipótese nula. Existe diferença estatisticamente significativa das médias do num_dias_trial entre realizou_assinatura e nao_realizou_assinatura.\n",
      "- Intervalo de confiança [-4.65 -1.27] não passa pelo zero. As amostras são estatisticamente diferentes.\n",
      "- Média do num_dias_trial para realizou_assinatura: 6.466666666666667\n",
      "- Média do num_dias_trial para nao_realizou_assinatura: 9.423562412342216\n"
     ]
    }
   ],
   "source": [
    "# Teste de hipóteses T - variâncias diferentes.\n",
    "difmedia_2amostras(realizou_assinatura=converteu_duracao_trial, nao_realizou_assinatura=nao_converteu_duracao_trial, var='num_dias_trial')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Tempo Médio para Conversão e Atividades Realizadas\n",
    "    * Hipótese Nula (H0): O tempo médio para conversão não é influenciado pelo número de atividades realizadas.\n",
    "    * Hipótese Alternativa (H1): O tempo médio para conversão é influenciado pelo número de atividades realizadas.\n",
    "    \n",
    "Teste Estatístico: Correlação de Pearson para analisar a relação entre o tempo para conversão e o número de atividades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.311111111111112"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']]['num_dias_para_assinatura'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
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       "<table id=\"T_c0ebb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c0ebb_level0_col0\" class=\"col_heading level0 col0\" >total_atividades_realizadas</th>\n",
       "      <th id=\"T_c0ebb_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_c0ebb_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
       "    </tr>\n",
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       "      <th id=\"T_c0ebb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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       "      <td id=\"T_c0ebb_row0_col1\" class=\"data row0 col1\" >53</td>\n",
       "      <td id=\"T_c0ebb_row0_col2\" class=\"data row0 col2\" >11.396226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_c0ebb_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_c0ebb_row1_col1\" class=\"data row1 col1\" >53</td>\n",
       "      <td id=\"T_c0ebb_row1_col2\" class=\"data row1 col2\" >8.962264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_c0ebb_row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "      <td id=\"T_c0ebb_row2_col1\" class=\"data row2 col1\" >20</td>\n",
       "      <td id=\"T_c0ebb_row2_col2\" class=\"data row2 col2\" >7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_c0ebb_row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "      <td id=\"T_c0ebb_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_c0ebb_row3_col2\" class=\"data row3 col2\" >7.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_c0ebb_row4_col0\" class=\"data row4 col0\" >4</td>\n",
       "      <td id=\"T_c0ebb_row4_col1\" class=\"data row4 col1\" >13</td>\n",
       "      <td id=\"T_c0ebb_row4_col2\" class=\"data row4 col2\" >8.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_c0ebb_row5_col0\" class=\"data row5 col0\" >5</td>\n",
       "      <td id=\"T_c0ebb_row5_col1\" class=\"data row5 col1\" >8</td>\n",
       "      <td id=\"T_c0ebb_row5_col2\" class=\"data row5 col2\" >15.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_c0ebb_row6_col0\" class=\"data row6 col0\" >6</td>\n",
       "      <td id=\"T_c0ebb_row6_col1\" class=\"data row6 col1\" >10</td>\n",
       "      <td id=\"T_c0ebb_row6_col2\" class=\"data row6 col2\" >5.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_c0ebb_row7_col0\" class=\"data row7 col0\" >7</td>\n",
       "      <td id=\"T_c0ebb_row7_col1\" class=\"data row7 col1\" >5</td>\n",
       "      <td id=\"T_c0ebb_row7_col2\" class=\"data row7 col2\" >9.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_c0ebb_row8_col0\" class=\"data row8 col0\" >8</td>\n",
       "      <td id=\"T_c0ebb_row8_col1\" class=\"data row8 col1\" >5</td>\n",
       "      <td id=\"T_c0ebb_row8_col2\" class=\"data row8 col2\" >8.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_c0ebb_row9_col0\" class=\"data row9 col0\" >9</td>\n",
       "      <td id=\"T_c0ebb_row9_col1\" class=\"data row9 col1\" >5</td>\n",
       "      <td id=\"T_c0ebb_row9_col2\" class=\"data row9 col2\" >11.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_c0ebb_row10_col0\" class=\"data row10 col0\" >10</td>\n",
       "      <td id=\"T_c0ebb_row10_col1\" class=\"data row10 col1\" >7</td>\n",
       "      <td id=\"T_c0ebb_row10_col2\" class=\"data row10 col2\" >10.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_c0ebb_row11_col0\" class=\"data row11 col0\" >11</td>\n",
       "      <td id=\"T_c0ebb_row11_col1\" class=\"data row11 col1\" >8</td>\n",
       "      <td id=\"T_c0ebb_row11_col2\" class=\"data row11 col2\" >18.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_c0ebb_row12_col0\" class=\"data row12 col0\" >12</td>\n",
       "      <td id=\"T_c0ebb_row12_col1\" class=\"data row12 col1\" >3</td>\n",
       "      <td id=\"T_c0ebb_row12_col2\" class=\"data row12 col2\" >5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_c0ebb_row13_col0\" class=\"data row13 col0\" >13</td>\n",
       "      <td id=\"T_c0ebb_row13_col1\" class=\"data row13 col1\" >5</td>\n",
       "      <td id=\"T_c0ebb_row13_col2\" class=\"data row13 col2\" >9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_c0ebb_row14_col0\" class=\"data row14 col0\" >14</td>\n",
       "      <td id=\"T_c0ebb_row14_col1\" class=\"data row14 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row14_col2\" class=\"data row14 col2\" >8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_c0ebb_row15_col0\" class=\"data row15 col0\" >15</td>\n",
       "      <td id=\"T_c0ebb_row15_col1\" class=\"data row15 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row15_col2\" class=\"data row15 col2\" >3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_c0ebb_row16_col0\" class=\"data row16 col0\" >16</td>\n",
       "      <td id=\"T_c0ebb_row16_col1\" class=\"data row16 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row16_col2\" class=\"data row16 col2\" >11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_c0ebb_row17_col0\" class=\"data row17 col0\" >17</td>\n",
       "      <td id=\"T_c0ebb_row17_col1\" class=\"data row17 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row17_col2\" class=\"data row17 col2\" >2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_c0ebb_row18_col0\" class=\"data row18 col0\" >18</td>\n",
       "      <td id=\"T_c0ebb_row18_col1\" class=\"data row18 col1\" >4</td>\n",
       "      <td id=\"T_c0ebb_row18_col2\" class=\"data row18 col2\" >19.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_c0ebb_row19_col0\" class=\"data row19 col0\" >19</td>\n",
       "      <td id=\"T_c0ebb_row19_col1\" class=\"data row19 col1\" >3</td>\n",
       "      <td id=\"T_c0ebb_row19_col2\" class=\"data row19 col2\" >4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_c0ebb_row20_col0\" class=\"data row20 col0\" >20</td>\n",
       "      <td id=\"T_c0ebb_row20_col1\" class=\"data row20 col1\" >4</td>\n",
       "      <td id=\"T_c0ebb_row20_col2\" class=\"data row20 col2\" >17.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_c0ebb_row21_col0\" class=\"data row21 col0\" >22</td>\n",
       "      <td id=\"T_c0ebb_row21_col1\" class=\"data row21 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row21_col2\" class=\"data row21 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_c0ebb_row22_col0\" class=\"data row22 col0\" >23</td>\n",
       "      <td id=\"T_c0ebb_row22_col1\" class=\"data row22 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row22_col2\" class=\"data row22 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_c0ebb_row23_col0\" class=\"data row23 col0\" >24</td>\n",
       "      <td id=\"T_c0ebb_row23_col1\" class=\"data row23 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row23_col2\" class=\"data row23 col2\" >2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_c0ebb_row24_col0\" class=\"data row24 col0\" >26</td>\n",
       "      <td id=\"T_c0ebb_row24_col1\" class=\"data row24 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row24_col2\" class=\"data row24 col2\" >5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_c0ebb_row25_col0\" class=\"data row25 col0\" >27</td>\n",
       "      <td id=\"T_c0ebb_row25_col1\" class=\"data row25 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row25_col2\" class=\"data row25 col2\" >5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_c0ebb_row26_col0\" class=\"data row26 col0\" >28</td>\n",
       "      <td id=\"T_c0ebb_row26_col1\" class=\"data row26 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row26_col2\" class=\"data row26 col2\" >8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_c0ebb_row27_col0\" class=\"data row27 col0\" >30</td>\n",
       "      <td id=\"T_c0ebb_row27_col1\" class=\"data row27 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row27_col2\" class=\"data row27 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_c0ebb_row28_col0\" class=\"data row28 col0\" >31</td>\n",
       "      <td id=\"T_c0ebb_row28_col1\" class=\"data row28 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row28_col2\" class=\"data row28 col2\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_c0ebb_row29_col0\" class=\"data row29 col0\" >32</td>\n",
       "      <td id=\"T_c0ebb_row29_col1\" class=\"data row29 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row29_col2\" class=\"data row29 col2\" >5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_c0ebb_row30_col0\" class=\"data row30 col0\" >34</td>\n",
       "      <td id=\"T_c0ebb_row30_col1\" class=\"data row30 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row30_col2\" class=\"data row30 col2\" >3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_c0ebb_row31_col0\" class=\"data row31 col0\" >35</td>\n",
       "      <td id=\"T_c0ebb_row31_col1\" class=\"data row31 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row31_col2\" class=\"data row31 col2\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_c0ebb_row32_col0\" class=\"data row32 col0\" >36</td>\n",
       "      <td id=\"T_c0ebb_row32_col1\" class=\"data row32 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row32_col2\" class=\"data row32 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_c0ebb_row33_col0\" class=\"data row33 col0\" >37</td>\n",
       "      <td id=\"T_c0ebb_row33_col1\" class=\"data row33 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row33_col2\" class=\"data row33 col2\" >9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_c0ebb_row34_col0\" class=\"data row34 col0\" >38</td>\n",
       "      <td id=\"T_c0ebb_row34_col1\" class=\"data row34 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row34_col2\" class=\"data row34 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_c0ebb_row35_col0\" class=\"data row35 col0\" >40</td>\n",
       "      <td id=\"T_c0ebb_row35_col1\" class=\"data row35 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row35_col2\" class=\"data row35 col2\" >5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_c0ebb_row36_col0\" class=\"data row36 col0\" >46</td>\n",
       "      <td id=\"T_c0ebb_row36_col1\" class=\"data row36 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row36_col2\" class=\"data row36 col2\" >11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_c0ebb_row37_col0\" class=\"data row37 col0\" >49</td>\n",
       "      <td id=\"T_c0ebb_row37_col1\" class=\"data row37 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row37_col2\" class=\"data row37 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_c0ebb_row38_col0\" class=\"data row38 col0\" >50</td>\n",
       "      <td id=\"T_c0ebb_row38_col1\" class=\"data row38 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row38_col2\" class=\"data row38 col2\" >6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_c0ebb_row39_col0\" class=\"data row39 col0\" >51</td>\n",
       "      <td id=\"T_c0ebb_row39_col1\" class=\"data row39 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row39_col2\" class=\"data row39 col2\" >5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_c0ebb_row40_col0\" class=\"data row40 col0\" >52</td>\n",
       "      <td id=\"T_c0ebb_row40_col1\" class=\"data row40 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row40_col2\" class=\"data row40 col2\" >4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_c0ebb_row41_col0\" class=\"data row41 col0\" >55</td>\n",
       "      <td id=\"T_c0ebb_row41_col1\" class=\"data row41 col1\" >2</td>\n",
       "      <td id=\"T_c0ebb_row41_col2\" class=\"data row41 col2\" >6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_c0ebb_row42_col0\" class=\"data row42 col0\" >57</td>\n",
       "      <td id=\"T_c0ebb_row42_col1\" class=\"data row42 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row42_col2\" class=\"data row42 col2\" >11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_c0ebb_row43_col0\" class=\"data row43 col0\" >60</td>\n",
       "      <td id=\"T_c0ebb_row43_col1\" class=\"data row43 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row43_col2\" class=\"data row43 col2\" >24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_c0ebb_row44_col0\" class=\"data row44 col0\" >63</td>\n",
       "      <td id=\"T_c0ebb_row44_col1\" class=\"data row44 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row44_col2\" class=\"data row44 col2\" >4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_c0ebb_row45_col0\" class=\"data row45 col0\" >69</td>\n",
       "      <td id=\"T_c0ebb_row45_col1\" class=\"data row45 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row45_col2\" class=\"data row45 col2\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_c0ebb_row46_col0\" class=\"data row46 col0\" >70</td>\n",
       "      <td id=\"T_c0ebb_row46_col1\" class=\"data row46 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row46_col2\" class=\"data row46 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_c0ebb_row47_col0\" class=\"data row47 col0\" >72</td>\n",
       "      <td id=\"T_c0ebb_row47_col1\" class=\"data row47 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row47_col2\" class=\"data row47 col2\" >4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_c0ebb_row48_col0\" class=\"data row48 col0\" >82</td>\n",
       "      <td id=\"T_c0ebb_row48_col1\" class=\"data row48 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row48_col2\" class=\"data row48 col2\" >6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_c0ebb_row49_col0\" class=\"data row49 col0\" >110</td>\n",
       "      <td id=\"T_c0ebb_row49_col1\" class=\"data row49 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row49_col2\" class=\"data row49 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_c0ebb_row50_col0\" class=\"data row50 col0\" >119</td>\n",
       "      <td id=\"T_c0ebb_row50_col1\" class=\"data row50 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row50_col2\" class=\"data row50 col2\" >8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_c0ebb_row51_col0\" class=\"data row51 col0\" >134</td>\n",
       "      <td id=\"T_c0ebb_row51_col1\" class=\"data row51 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row51_col2\" class=\"data row51 col2\" >5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_c0ebb_row52_col0\" class=\"data row52 col0\" >164</td>\n",
       "      <td id=\"T_c0ebb_row52_col1\" class=\"data row52 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row52_col2\" class=\"data row52 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_c0ebb_row53_col0\" class=\"data row53 col0\" >171</td>\n",
       "      <td id=\"T_c0ebb_row53_col1\" class=\"data row53 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row53_col2\" class=\"data row53 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_c0ebb_row54_col0\" class=\"data row54 col0\" >173</td>\n",
       "      <td id=\"T_c0ebb_row54_col1\" class=\"data row54 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row54_col2\" class=\"data row54 col2\" >4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_c0ebb_row55_col0\" class=\"data row55 col0\" >175</td>\n",
       "      <td id=\"T_c0ebb_row55_col1\" class=\"data row55 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row55_col2\" class=\"data row55 col2\" >6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_c0ebb_row56_col0\" class=\"data row56 col0\" >193</td>\n",
       "      <td id=\"T_c0ebb_row56_col1\" class=\"data row56 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row56_col2\" class=\"data row56 col2\" >12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_c0ebb_row57_col0\" class=\"data row57 col0\" >264</td>\n",
       "      <td id=\"T_c0ebb_row57_col1\" class=\"data row57 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row57_col2\" class=\"data row57 col2\" >2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c0ebb_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_c0ebb_row58_col0\" class=\"data row58 col0\" >599</td>\n",
       "      <td id=\"T_c0ebb_row58_col1\" class=\"data row58 col1\" >1</td>\n",
       "      <td id=\"T_c0ebb_row58_col2\" class=\"data row58 col2\" >7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25dfb637080>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_difmedia_2amostras(dataframe=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], coluna1='total_atividades_realizadas', coluna2='num_dias_para_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_2variaveis(dataframe=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], coluna_x='total_atividades_realizadas', coluna_y='num_dias_para_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_atividades_realizadas</th>\n",
       "      <th>num_dias_para_assinatura</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_atividades_realizadas</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.068078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_dias_para_assinatura</th>\n",
       "      <td>-0.068078</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             total_atividades_realizadas  \\\n",
       "total_atividades_realizadas                     1.000000   \n",
       "num_dias_para_assinatura                       -0.068078   \n",
       "\n",
       "                             num_dias_para_assinatura  \n",
       "total_atividades_realizadas                 -0.068078  \n",
       "num_dias_para_assinatura                     1.000000  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']][['total_atividades_realizadas','num_dias_para_assinatura']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----- Hipóteses ---------------------------------------------------------------------------------\n",
      "H0 : p = 0 => Não existe correlação significativa entre as variáveis.\n",
      "Ha : p ≠ 0 => Existe correlação significativa entre as variáveis.\n",
      "Nível de significância de 0.05.\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Coeficiente de Pearson p: -0.06807824865064005\n",
      "P_valor: 0.2649603206365412\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.264960320636541180050471666618 > alpha: 0.05 | coeficiente de Pearson: -0.0681 = 0 => Não rejeitamos a hipótese nula e concluimos que não existe uma correlação estatisticamente significativa entre as variáveis total_atividades_realizadas e num_dias_para_assinatura.\n"
     ]
    }
   ],
   "source": [
    "pearson_corr(dataframe=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], coluna1='total_atividades_realizadas', coluna2='num_dias_para_assinatura')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Impacto da Fonte de Aquisição no Tempo para Conversão\n",
    "    * Hipótese Nula (H0): O tempo para conversão não é significativamente diferente entre as fontes de aquisição.\n",
    "    * Hipótese Alternativa (HA): O tempo para conversão é significativamente diferente entre as fontes de aquisição.\n",
    "    \n",
    "Teste Estatístico: ANOVA para comparar o tempo para conversão entre diferentes fontes de aquisição."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_2f54c_row0_col2 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2f54c_row1_col2 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_2f54c_row2_col2, #T_2f54c_row3_col2 {\n",
       "  background-color: #3bafad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_2f54c_row4_col2 {\n",
       "  background-color: #d4f1dc;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2f54c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2f54c_level0_col0\" class=\"col_heading level0 col0\" >nom_canal</th>\n",
       "      <th id=\"T_2f54c_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_2f54c_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2f54c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_2f54c_row0_col0\" class=\"data row0 col0\" >Organic</td>\n",
       "      <td id=\"T_2f54c_row0_col1\" class=\"data row0 col1\" >28</td>\n",
       "      <td id=\"T_2f54c_row0_col2\" class=\"data row0 col2\" >11.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f54c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_2f54c_row1_col0\" class=\"data row1 col0\" >Other</td>\n",
       "      <td id=\"T_2f54c_row1_col1\" class=\"data row1 col1\" >63</td>\n",
       "      <td id=\"T_2f54c_row1_col2\" class=\"data row1 col2\" >7.841270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f54c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_2f54c_row2_col0\" class=\"data row2 col0\" >Paid - google</td>\n",
       "      <td id=\"T_2f54c_row2_col1\" class=\"data row2 col1\" >92</td>\n",
       "      <td id=\"T_2f54c_row2_col2\" class=\"data row2 col2\" >9.684783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f54c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_2f54c_row3_col0\" class=\"data row3 col0\" >Paid - meta</td>\n",
       "      <td id=\"T_2f54c_row3_col1\" class=\"data row3 col1\" >62</td>\n",
       "      <td id=\"T_2f54c_row3_col2\" class=\"data row3 col2\" >9.693548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f54c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_2f54c_row4_col0\" class=\"data row4 col0\" >Site ou Indicação Interna</td>\n",
       "      <td id=\"T_2f54c_row4_col1\" class=\"data row4 col1\" >25</td>\n",
       "      <td id=\"T_2f54c_row4_col2\" class=\"data row4 col2\" >7.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25df935e690>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_difmedia_2amostras(dataframe=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], coluna1='nom_canal', coluna2='num_dias_para_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_difmedia_2amostras(dataframe=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], coluna1='nom_canal', coluna2='num_dias_para_assinatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses Bartlett de igualdade de variâncias. *****\n",
      "* Premissas:\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "\n",
      "----- Hipóteses -------------------------------------------------------------------------\n",
      "H0 : As variâncias de todas as amostras da nom_canal são iguais.\n",
      "Ha : As variâncias para um par ou mais das amostras da nom_canal são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "-----------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística T: 1.142223749433616\n",
      "P_valor: 0.8875163763693666\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.8875163763693666 > alpha: 0.05 => Não rejeitamos a hipótese nula. As variâncias das amostras da nom_canal são iguais.\n"
     ]
    }
   ],
   "source": [
    "# Verificar as variancias populacionais dos grupos.\n",
    "variancia(data=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], dv='num_dias_para_assinatura', fator='nom_canal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses ANOVA : Médias : +2 amostras : 1 fator *****\n",
      "* Premissas:\n",
      "- Amostras aleatórias.\n",
      "- Grupos independentes.\n",
      "- Variável dependente é continua.\n",
      "- Variável independente é categórica.\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "- Grupos com variâncias iguais.\n",
      "\n",
      "----- Hipóteses -----------------------------------------------------------------------\n",
      "H0 : As médias da num_dias_para_assinatura entre as amostras da nom_canal são iguais.\n",
      "Ha : As médias da num_dias_para_assinatura entre as amostras da nom_canal são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "---------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística F: 0.8483763311044628\n",
      "P_valor: 0.49563315910278916\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.49563315910278916 > alpha: 0.05 => Não rejeitamos a hipótese nula. Não existe diferença estatisticamente significativa das médias do num_dias_para_assinatura entre as amostras da nom_canal.\n"
     ]
    }
   ],
   "source": [
    "# Variância estatisticamente iguais. \n",
    "# Teste de hipóteste ANOVA oneway\n",
    "anova(dv='num_dias_para_assinatura', fator='nom_canal', data=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], var_iguais=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Post-Hoc ANOVA - Realizando o teste de hipóteses Tukey para explorar a diferença entre as médias de vários grupos (pares). *****\n",
      "* Premissas:\n",
      "- Amostras aleatórias.\n",
      "- Grupos independentes.\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "- Grupos com variâncias iguais.\n",
      "- Tamanhos de amostras de todos os grupos são iguais.\n",
      "\n",
      "----- Hipóteses ---------------------------------------------------------------------------------\n",
      "H0 : As médias da num_dias_para_assinatura entre os pares das amostras da nom_canal são iguais.\n",
      "Ha : As médias da num_dias_para_assinatura entre os pares das amostras da nom_canal são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "-------------------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "- Pares de amostras com médias estatisticamente iguais - p_valor > alpha: 0.05 - Não rejeitamos H0\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>mean(A)</th>\n",
       "      <th>B</th>\n",
       "      <th>mean(B)</th>\n",
       "      <th>p-valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>Other</td>\n",
       "      <td>7.841270</td>\n",
       "      <td>0.468379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Organic</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>Paid - google</td>\n",
       "      <td>9.684783</td>\n",
       "      <td>0.890543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Organic</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>Paid - meta</td>\n",
       "      <td>9.693548</td>\n",
       "      <td>0.909509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>Site ou Indicação Interna</td>\n",
       "      <td>7.960000</td>\n",
       "      <td>0.679814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other</td>\n",
       "      <td>7.841270</td>\n",
       "      <td>Paid - google</td>\n",
       "      <td>9.684783</td>\n",
       "      <td>0.817085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Other</td>\n",
       "      <td>7.841270</td>\n",
       "      <td>Paid - meta</td>\n",
       "      <td>9.693548</td>\n",
       "      <td>0.859105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Other</td>\n",
       "      <td>7.841270</td>\n",
       "      <td>Site ou Indicação Interna</td>\n",
       "      <td>7.960000</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paid - google</td>\n",
       "      <td>9.684783</td>\n",
       "      <td>Paid - meta</td>\n",
       "      <td>9.693548</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Paid - google</td>\n",
       "      <td>9.684783</td>\n",
       "      <td>Site ou Indicação Interna</td>\n",
       "      <td>7.960000</td>\n",
       "      <td>0.948843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Paid - meta</td>\n",
       "      <td>9.693548</td>\n",
       "      <td>Site ou Indicação Interna</td>\n",
       "      <td>7.960000</td>\n",
       "      <td>0.956228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A    mean(A)                          B   mean(B)   p-valor\n",
       "0        Organic  11.750000                      Other  7.841270  0.468379\n",
       "1        Organic  11.750000              Paid - google  9.684783  0.890543\n",
       "2        Organic  11.750000                Paid - meta  9.693548  0.909509\n",
       "3        Organic  11.750000  Site ou Indicação Interna  7.960000  0.679814\n",
       "4          Other   7.841270              Paid - google  9.684783  0.817085\n",
       "5          Other   7.841270                Paid - meta  9.693548  0.859105\n",
       "6          Other   7.841270  Site ou Indicação Interna  7.960000  0.999999\n",
       "7  Paid - google   9.684783                Paid - meta  9.693548  1.000000\n",
       "8  Paid - google   9.684783  Site ou Indicação Interna  7.960000  0.948843\n",
       "9    Paid - meta   9.693548  Site ou Indicação Interna  7.960000  0.956228"
      ]
     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- Pares de amostras com médias estatisticamente diferentes - p_valor < alpha: 0.05 - Rejeitamos H0\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>mean(A)</th>\n",
       "      <th>B</th>\n",
       "      <th>mean(B)</th>\n",
       "      <th>p-valor</th>\n",
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       "  <tbody>\n",
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       "Empty DataFrame\n",
       "Columns: [A, mean(A), B, mean(B), p-valor]\n",
       "Index: []"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Posthoc Anova - Verificar quais pares de amostras tem as médias estatisticamente diferentes.\n",
    "posthoc(dv='num_dias_para_assinatura', fator='nom_canal', data=df_trial_metricas.loc[df_trial_metricas['realizou_assinatura']], var_iguais=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Abandono durante o Trial\n",
    "    * Hipótese Nula (H0): O número de atividades realizadas durante o trial não influencia significativamente as clínicas que abandonam o produto durante o trial e aquelas abandonam o produto após o trial.\n",
    "    * Hipótese Alternativa (HA): O número de atividades realizadas durante o trial não influencia significativamente as clínicas que abandonam o produto durante o trial e aquelas abandonam o produto após o trial.\n",
    "    \n",
    "Teste Estatístico: Teste T para duas amostras independentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_atividades_churn_durante_trial = df_trial_metricas.loc[~df_trial_metricas['realizou_assinatura']]['id_clinica']\n",
    "total_atividades_churn_apos_trial = df_trial_metricas.loc[df_trial_metricas['realizou_assinatura'] & (df_trial_metricas['status_assinatura'] != 'active')]['id_clinica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_clinica</th>\n",
       "      <th>total_atividades_realizadas</th>\n",
       "      <th>tipo_churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9984</td>\n",
       "      <td>14</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9986</td>\n",
       "      <td>4</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9988</td>\n",
       "      <td>1</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9690</td>\n",
       "      <td>1</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9991</td>\n",
       "      <td>2</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>11291</td>\n",
       "      <td>0</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>10459</td>\n",
       "      <td>7</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>9011</td>\n",
       "      <td>0</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>11146</td>\n",
       "      <td>0</td>\n",
       "      <td>Durante o Trial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>11031</td>\n",
       "      <td>7</td>\n",
       "      <td>Após o Trial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>742 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id_clinica  total_atividades_realizadas       tipo_churn\n",
       "1          9984                           14  Durante o Trial\n",
       "2          9986                            4  Durante o Trial\n",
       "3          9988                            1  Durante o Trial\n",
       "4          9690                            1  Durante o Trial\n",
       "7          9991                            2  Durante o Trial\n",
       "..          ...                          ...              ...\n",
       "974       11291                            0  Durante o Trial\n",
       "977       10459                            7  Durante o Trial\n",
       "978        9011                            0  Durante o Trial\n",
       "980       11146                            0  Durante o Trial\n",
       "982       11031                            7     Após o Trial\n",
       "\n",
       "[742 rows x 3 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Churn durante o Trial e após o Trial.\n",
    "df_churn = df_trial_metricas[['id_clinica','total_atividades_realizadas']].loc[~df_trial_metricas['realizou_assinatura'] |\n",
    "                                                                                (df_trial_metricas['realizou_assinatura'] & (df_trial_metricas['status_assinatura'] != 'active'))]\n",
    "\n",
    "# Definição do tipo de Churn.\n",
    "df_churn['tipo_churn'] = np.where(df_churn['id_clinica'].isin(total_atividades_churn_durante_trial), 'Durante o Trial', 'Após o Trial')\n",
    "\n",
    "df_churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_bb08a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_bb08a_level0_col0\" class=\"col_heading level0 col0\" >tipo_churn</th>\n",
       "      <th id=\"T_bb08a_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_bb08a_level0_col2\" class=\"col_heading level0 col2\" >mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bb08a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_bb08a_row0_col0\" class=\"data row0 col0\" >Após o Trial</td>\n",
       "      <td id=\"T_bb08a_row0_col1\" class=\"data row0 col1\" >29</td>\n",
       "      <td id=\"T_bb08a_row0_col2\" class=\"data row0 col2\" >34.689655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bb08a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_bb08a_row1_col0\" class=\"data row1 col0\" >Durante o Trial</td>\n",
       "      <td id=\"T_bb08a_row1_col1\" class=\"data row1 col1\" >713</td>\n",
       "      <td id=\"T_bb08a_row1_col2\" class=\"data row1 col2\" >4.932679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25df3cab9b0>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_difmedia_2amostras(dataframe=df_churn, coluna1='tipo_churn', coluna2='total_atividades_realizadas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_difmedia_2amostras(dataframe=df_churn, coluna1='tipo_churn', coluna2='total_atividades_realizadas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses Bartlett de igualdade de variâncias. *****\n",
      "* Premissas:\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "\n",
      "----- Hipóteses -------------------------------------------------------------------------\n",
      "H0 : As variâncias de todas as amostras da tipo_churn são iguais.\n",
      "Ha : As variâncias para um par ou mais das amostras da tipo_churn são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "-----------------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística T: 1240.7227445252681\n",
      "P_valor: 8.614550060923065e-272\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 8.614550060923065e-272 < alpha: 0.05 => Rejeitamos a hipótese nula. As variâncias das amostras da tipo_churn são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Verificar as variancias populacionais dos grupos.\n",
    "variancia(data=df_churn, dv='total_atividades_realizadas', fator='tipo_churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_durante_trial = df_churn.loc[df_churn['tipo_churn'] == 'Durante o Trial']['total_atividades_realizadas']\n",
    "churn_apos_trial = df_churn.loc[df_churn['tipo_churn'] == 'Após o Trial']['total_atividades_realizadas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "importlib.reload(lib)\n",
    "lib.colormap = 'mako_r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-72.04110629499601, 12.527153593927185)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intervalo de confiança diferença duas médias - variâncias diferentes.\n",
    "cm = sms.CompareMeans(sms.DescrStatsW(churn_durante_trial), sms.DescrStatsW(churn_apos_trial))\n",
    "cm.tconfint_diff(usevar='unequal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** Realizando o teste de hipóteses t : Média - 2 amostras *****\n",
      "* Premissas:\n",
      "- Dados sejam normais ou que tenhamos pelo menos 15 dados em cada grupo.\n",
      "- Grupos com variâncias diferentes.\n",
      "\n",
      "----- Hipóteses ----------------------------------------------------------------\n",
      "H0 : A média do total_atividades_realizadas entre churn_durante_trial e churn_apos_trial, são iguais.\n",
      "Ha : A média do total_atividades_realizadas entre churn_durante_trial e churn_apos_trial, são diferentes.\n",
      "Nível de significância de 0.05.\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      "----- Resultados -----\n",
      "Valor do estatística T: -1.441507780430825\n",
      "P_valor: 0.160526886069243\n",
      "Intervalo de confiança para diferença das médias: [-72.04  12.53]\n",
      "\n",
      "----- Conclusão -----\n",
      "- p_valor: 0.160526886069243 > alpha: 0.05 => Não rejeitamos a hipótese nula. Não existe diferença estatisticamente significativa das médias do total_atividades_realizadas entre churn_durante_trial e churn_apos_trial.\n",
      "- Intervalo de confiança [-72.04  12.53] passa pelo zero. As amostras são estatisticamente iguais.\n",
      "- Média do total_atividades_realizadas para churn_durante_trial: 4.9326788218793824\n",
      "- Média do total_atividades_realizadas para churn_apos_trial: 34.689655172413794\n"
     ]
    }
   ],
   "source": [
    "# Teste de hipóteses T - variâncias diferentes.\n",
    "difmedia_2amostras(churn_durante_trial=churn_durante_trial, churn_apos_trial=churn_apos_trial, var='total_atividades_realizadas')"
   ]
  }
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