Modulo_1/teste_hipoteses_python.ipynb

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    "# Bootcamp: Cientista de Dados: Teste de Hipótese\n",
    "\n",
    "- Data: junho de 2022.\n",
    "\n",
    "## Autor\n",
    "\n",
    "Feito por [Alexsander Lopes Camargos](https://github.com/alexcamargos). Entre em contato!\n",
    "\n",
    "[![GitHub](https://img.shields.io/badge/-AlexCamargos-1ca0f1?style=flat-square&labelColor=1ca0f1&logo=github&logoColor=white&link=https://github.com/alexcamargos)](https://github.com/alexcamargos)\n",
    "[![Twitter Badge](https://img.shields.io/badge/-@alcamargos-1ca0f1?style=flat-square&labelColor=1ca0f1&logo=twitter&logoColor=white&link=https://twitter.com/alcamargos)](https://twitter.com/alcamargos)\n",
    "[![Linkedin Badge](https://img.shields.io/badge/-alexcamargos-1ca0f1?style=flat-square&logo=Linkedin&logoColor=white&link=https://www.linkedin.com/in/alexcamargos/)](https://www.linkedin.com/in/alexcamargos/)\n",
    "[![Gmail Badge](https://img.shields.io/badge/-alcamargos@vivaldi.net-1ca0f1?style=flat-square&labelColor=1ca0f1&logo=Gmail&logoColor=white&link=mailto:alcamargos@vivaldi.net)](mailto:alcamargos@vivaldi.net)\n",
    "\n",
    "## Licença\n",
    "\n",
    "[MIT License](https://choosealicense.com/licenses/mit/)\n",
    "\n",
    "# Hipóteses Estatísticas (Teste de hipótese)\n",
    "\n",
    "O teste de hipóteses fornecem ferramentas que nos permitem rejeitar ou não rejeitar uma hipótese estatística através da evidência fornecida pela amostra. É uma metodologia estatística que nos auxilia a tomar decisões sobre uma ou mais populações baseado na informação obtida da amostra. Nos permite verificar se os dados da amostra trazem evidência que apoiem ou não uma hipótese estatística formulada.\n",
    "\n",
    "Ao tentarmos tomar decisões, é conveniente a formulação de suposições ou de conjeturas sobre as populações de interesse, que, em geral, consistem em considerações sobre parâmetros (_μ_,_σ2_,_p_) das mesmas. Essas suposições, que podem ser ou não verdadeiras, são denominadas *Hipóteses Estatísticas*.\n",
    "\n",
    "Em muitas situações práticas o interesse do pesquisador é verificar a veracidade sobre um ou mais parâmetros populacionais (_μ_,_σ2_,_p_) ou sobre a distribuição de uma variável aleatória.\n",
    "\n",
    "Exemplos:\n",
    "- A produtividade média de café, no estado de Minas Gerais, é de 31,6 sacas de 60 kg por hectare;\n",
    "- A proporção de peças defeituosas na unidade de fabricação X é de 10%;\n",
    "- A propaganda produz efeito positivo nas vendas;\n",
    "- Os métodos de ensino produzem resultados diferentes de aprendizagem.\n",
    "\n",
    "Um dos primeiros trabalhos sobre testes foi publicado em 1710 por [John Arbuthnot](https://pt.wikipedia.org/wiki/John_Arbuthnot) e um dos primeiros procedimentos estatísticos que chega perto de um teste, no sentido moderno foi proposto por [Karl Pearson](https://pt.wikipedia.org/wiki/Karl_Pearson) em 1900. Esse foi o famoso teste do [Qui-quadrado (Chi-squared)](https://pt.wikipedia.org/wiki/Qui-quadrado), utilizado para comparar uma distribuição de frequência observada com uma distribuição teoricamente assumida. A ideia de testar hipóteses foi posteriormente codificada e elaborada por [Ronald Aylmer Fisher](https://pt.wikipedia.org/wiki/Ronald_Fisher) (1925), que considerou os dados como um vetor de variáveis aleatórias que pertenciam a uma distribuição de probabilidades.\n",
    "\n",
    "# Teste de hipótese aplicados a churn de clientes\n",
    "\n",
    "Saber por que um cliente abandona ou deixa de comprar o seu produto é fundamental para garantir um crescimento sustentável a longo prazo para a sua empresa. No entanto, e em meio a tantas métricas que compõe essa categoria de análise, o churn talvez seja a mais básica, e também a mais fundamental como ponto de partida por representar o número total de clientes cancelados. Parece complexo, mas não é.\n",
    "\n",
    "![Como reduzir o churn dentro da sua empresa](churn.jpg)\n",
    "\n",
    "## O que significa churn?\n",
    "\n",
    "Para colocarmos de maneira simples: _churn_ diz respeito aos casos onde um cliente ou utilizador para de comprar ou usar o produto ou serviço de uma empresa. Ou seja, representa o número total de clientes cancelados. Pense assim: de toda a sua carteira de clientes, ao longo dos últimos meses, quantos deles realmente desistiram do seu produto ou serviço? Esse é o seu churn.\n",
    "\n",
    "Na prática, o churn de clientes atrasa o crescimento da empresa de forma direta por isso, é uma métrica muito importante. Além disso, como já sabemos, é muito mais barato trabalhar para reter clientes atuais do que para conquistar novos clientes. Portanto, é uma perda que, na maioria das vezes, poderia ter sido evitada.\n",
    "\n",
    "As empresas devem definir um método para calcular o churn e fazer isso de maneira frequente. Monitorar o churn permite identificar possíveis problemas operacionais e técnicos e desenhar melhores estratégias de fidelização.\n",
    "\n",
    "## Existe uma fórmula certa para calcular churn?\n",
    "\n",
    "Existem algumas formas diferentes que podem ser usadas para calcular o churn de uma empresa. A maneira mais comum é dividir o número total de clientes que a empresa tem no começo de um determinado período pelo número de clientes perdidos durante esse mesmo tempo. A taxa de churn pode ser representada de outras formas, como, por exemplo, pelo número total de clientes que a empresa perdeu num determinado período, como também pela porcentagem de clientes perdidos num período comparado com o número total de clientes que a empresa possuí.\n",
    "\n",
    "# Data Acquisition\n",
    "Os dados originais são do [IBM Developer](https://developer.ibm.com/), e reporta um problema tipico de empresas de telecom. O dataset completo pode ser encontrado [aqui](telecom_customer_churn.csv). Apesar de não existir uma descrição exata, o nome das colunas permite entendermos bem o problema.\n",
    "\n",
    "## Pacotes e bibliotecas utilizadas\n",
    "- [Matplotlib](https://matplotlib.org/) — Biblioteca para plotagem de gráficos.\n",
    "- [pandas](https://pandas.pydata.org/) — Biblioteca para manipulação de dados.\n",
    "- [seaborn](https://seaborn.pydata.org/) — Biblioteca para visualização de dados.\n",
    "- [SciPy](https://scipy.org/) - Biblioteca para cálculos matemáticos.\n",
    "\n",
    "## Importando as bibliotecas"
   ]
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   "source": [
    "# matplotlib — Biblioteca para plotagem de gráficos\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# pandas — Biblioteca para manipulação de dados\n",
    "import pandas as pd\n",
    "\n",
    "# seaborn — Biblioteca para visualização de dados\n",
    "import seaborn as sns\n",
    "\n",
    "# scipy — Biblioteca para cálculos estatísticos\n",
    "from scipy import stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Salvando os gráficos junto (inline) com o notebook.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "# Configurando a exibição dos gráficos para o notebook.\n",
    "sns.set_theme(style='whitegrid', context='notebook')\n",
    "\n",
    "# Tamanho padrão dos gráficos.\n",
    "sns.set(rc={'figure.figsize': (12, 6)})"
   ]
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   "source": [
    "## Importando o dataset"
   ]
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   "source": [
    "DATA_PATH = r'datasets\\telecom_customer_churn.csv'"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
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       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>6385</th>\n",
       "      <td>0702-PGIBZ</td>\n",
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       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>101.70</td>\n",
       "      <td>364.55</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>5702-SKUOB</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.60</td>\n",
       "      <td>93.45</td>\n",
       "      <td>No</td>\n",
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       "      <th>6888</th>\n",
       "      <td>3191-CSNMG</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>80.70</td>\n",
       "      <td>239.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>7100-FQPRV</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>71.90</td>\n",
       "      <td>3173.35</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4784</th>\n",
       "      <td>1622-HSHSF</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>...</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.55</td>\n",
       "      <td>19.55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4093</th>\n",
       "      <td>2476-YGEFM</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>76.00</td>\n",
       "      <td>2215.25</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>4695-WJZUE</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>88.55</td>\n",
       "      <td>179.25</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>3129</th>\n",
       "      <td>7080-TNUWP</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>70</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>95.00</td>\n",
       "      <td>6602.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3975</th>\n",
       "      <td>2860-RANUS</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>85.80</td>\n",
       "      <td>1727.5</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>6016</th>\n",
       "      <td>4250-ZBWLV</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>68</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>108.45</td>\n",
       "      <td>7176.55</td>\n",
       "      <td>Yes</td>\n",
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       "<p>10 rows × 21 columns</p>\n",
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       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "6385  0702-PGIBZ    Male              0      No        Yes       4   \n",
       "2497  5702-SKUOB  Female              0     Yes         No       4   \n",
       "6888  3191-CSNMG  Female              0     Yes        Yes       3   \n",
       "190   7100-FQPRV    Male              0     Yes        Yes      43   \n",
       "4784  1622-HSHSF  Female              0      No         No       1   \n",
       "4093  2476-YGEFM  Female              0      No         No      29   \n",
       "2184  4695-WJZUE  Female              1      No         No       2   \n",
       "3129  7080-TNUWP    Male              0     Yes         No      70   \n",
       "3975  2860-RANUS  Female              1      No         No      20   \n",
       "6016  4250-ZBWLV    Male              0      No         No      68   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  ...  \\\n",
       "6385          Yes           Yes     Fiber optic                   No  ...   \n",
       "2497          Yes            No              No  No internet service  ...   \n",
       "6888          Yes            No     Fiber optic                   No  ...   \n",
       "190           Yes           Yes             DSL                  Yes  ...   \n",
       "4784          Yes            No              No  No internet service  ...   \n",
       "4093          Yes           Yes     Fiber optic                   No  ...   \n",
       "2184          Yes           Yes     Fiber optic                   No  ...   \n",
       "3129          Yes           Yes     Fiber optic                   No  ...   \n",
       "3975          Yes           Yes     Fiber optic                   No  ...   \n",
       "6016          Yes           Yes     Fiber optic                   No  ...   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "6385                   No                   No                  Yes   \n",
       "2497  No internet service  No internet service  No internet service   \n",
       "6888                   No                   No                  Yes   \n",
       "190                    No                  Yes                   No   \n",
       "4784  No internet service  No internet service  No internet service   \n",
       "4093                   No                   No                   No   \n",
       "2184                  Yes                   No                   No   \n",
       "3129                  Yes                   No                  Yes   \n",
       "3975                   No                   No                   No   \n",
       "6016                  Yes                  Yes                  Yes   \n",
       "\n",
       "          StreamingMovies        Contract PaperlessBilling  \\\n",
       "6385                  Yes  Month-to-month              Yes   \n",
       "2497  No internet service  Month-to-month               No   \n",
       "6888                   No  Month-to-month              Yes   \n",
       "190                   Yes        One year              Yes   \n",
       "4784  No internet service  Month-to-month              Yes   \n",
       "4093                   No  Month-to-month              Yes   \n",
       "2184                  Yes  Month-to-month              Yes   \n",
       "3129                   No        One year              Yes   \n",
       "3975                  Yes  Month-to-month              Yes   \n",
       "6016                  Yes        One year               No   \n",
       "\n",
       "                  PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "6385           Electronic check         101.70        364.55   Yes  \n",
       "2497               Mailed check          19.60         93.45    No  \n",
       "6888           Electronic check          80.70        239.45    No  \n",
       "190     Credit card (automatic)          71.90       3173.35    No  \n",
       "4784               Mailed check          19.55         19.55    No  \n",
       "4093    Credit card (automatic)          76.00       2215.25    No  \n",
       "2184           Electronic check          88.55        179.25   Yes  \n",
       "3129  Bank transfer (automatic)          95.00        6602.9    No  \n",
       "3975    Credit card (automatic)          85.80        1727.5   Yes  \n",
       "6016           Electronic check         108.45       7176.55   Yes  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando os dados.\n",
    "dados = pd.read_csv(DATA_PATH)\n",
    "dados.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nossa base de dados tem 7043 linhas e 21 colunas.\n"
     ]
    }
   ],
   "source": [
    "# Descobrindo as dimensões do dataset.\n",
    "print(f'Nossa base de dados tem {dados.shape[0]} linhas e {dados.shape[1]} colunas.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
      "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
      "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
      "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
      "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Visualizando os nomes das colunas.\n",
    "print(dados.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Para melhor visualização, e para podermos consultar os dados no futuro, caso seja necessário, vamos criar um dataframe auxiliar contendo as informações sobre o tipo dos dados e se existe ou não informações faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Explorando o dataset.\n",
    "dados.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Convertendo o tipo das colunas MonthlyCharges e TotalCharges para numérico.\n",
    "dados.loc[:, 'TotalCharges'] = pd.to_numeric(dados['TotalCharges'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Convertendo as variáveis categóricas.\n",
    "dados[['gender',\n",
    "       'Partner',\n",
    "       'Dependents',\n",
    "       'PhoneService',\n",
    "       'MultipleLines',\n",
    "       'InternetService',\n",
    "       'OnlineSecurity',\n",
    "       'OnlineBackup',\n",
    "       'DeviceProtection',\n",
    "       'TechSupport',\n",
    "       'StreamingTV',\n",
    "       'StreamingMovies',\n",
    "       'Contract',\n",
    "       'PaperlessBilling',\n",
    "       'PaymentMethod']] = dados[['gender',\n",
    "                                  'Partner',\n",
    "                                  'Dependents',\n",
    "                                  'PhoneService',\n",
    "                                  'MultipleLines',\n",
    "                                  'InternetService',\n",
    "                                  'OnlineSecurity',\n",
    "                                  'OnlineBackup',\n",
    "                                  'DeviceProtection',\n",
    "                                  'TechSupport',\n",
    "                                  'StreamingTV',\n",
    "                                  'StreamingMovies',\n",
    "                                  'Contract',\n",
    "                                  'PaperlessBilling',\n",
    "                                  'PaymentMethod']].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Nulo</th>\n",
       "      <th>Percentual</th>\n",
       "      <th>Tamanho</th>\n",
       "      <th>Unicos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>category</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>float64</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>6530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7043</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Tipo  Nulo  Percentual  Tamanho  Unicos\n",
       "customerID          object     0         0.0     7043    7043\n",
       "gender            category     0         0.0     7043       2\n",
       "SeniorCitizen        int64     0         0.0     7043       2\n",
       "Partner           category     0         0.0     7043       2\n",
       "Dependents        category     0         0.0     7043       2\n",
       "tenure               int64     0         0.0     7043      73\n",
       "PhoneService      category     0         0.0     7043       2\n",
       "MultipleLines     category     0         0.0     7043       3\n",
       "InternetService   category     0         0.0     7043       3\n",
       "OnlineSecurity    category     0         0.0     7043       3\n",
       "OnlineBackup      category     0         0.0     7043       3\n",
       "DeviceProtection  category     0         0.0     7043       3\n",
       "TechSupport       category     0         0.0     7043       3\n",
       "StreamingTV       category     0         0.0     7043       3\n",
       "StreamingMovies   category     0         0.0     7043       3\n",
       "Contract          category     0         0.0     7043       3\n",
       "PaperlessBilling  category     0         0.0     7043       2\n",
       "PaymentMethod     category     0         0.0     7043       4\n",
       "MonthlyCharges     float64     0         0.0     7043    1585\n",
       "TotalCharges       float64    11         0.0     7043    6530\n",
       "Churn               object     0         0.0     7043       2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando o tipo dos dados para cada coluna e se existe algum valor nulo.\n",
    "null_data = pd.DataFrame({'Tipo': dados.dtypes,\n",
    "                          'Nulo': dados.isna().sum(),\n",
    "                          'Percentual': round(dados.isna().sum() / dados.shape[0], 2),\n",
    "                          'Tamanho': dados.shape[0],\n",
    "                          'Unicos':dados.nunique()})\n",
    "null_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Criando gráficos de barras para melhor visualizar todas as colunas categóricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1800 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Configurando a exibição múltipla dos gráficos.\n",
    "total_plots = len(dados.select_dtypes(include=['category']).columns)\n",
    "total_column = 3\n",
    "total_rows = total_plots // total_column\n",
    "fig, axs = plt.subplots(nrows=total_rows, ncols=total_column, figsize=(5*total_column, 5*total_rows), constrained_layout=True)\n",
    "\n",
    "# Para cada coluna categórica, criamos um gráfico de barras.\n",
    "for index, column in enumerate(dados.select_dtypes(include=['category']).columns):\n",
    "    row = index // total_column\n",
    "    position = index % total_column\n",
    "    plot = sns.countplot(x=column, data=dados, ax=axs[row][position])\n",
    "    plot.set_title(f'Coluna Categórica: {column}')\n",
    "    plot.set_xlabel('')\n",
    "    plot.set_xticklabels(plot.get_xticklabels(), rotation=45, ha='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def test_normal_shapiro_wilk(data, alfa=0.05):\n",
    "    \"\"\"Aplica o Teste de Shapiro-Wilk para verificar se os dados são normalmente distribuídos.\"\"\"\n",
    "\n",
    "    statistic_w, p_value = st.shapiro(data)\n",
    "\n",
    "    if len(data) > 5_000:\n",
    "        if statistic_w < alfa:\n",
    "            return False\n",
    "\n",
    "    if p_value < alfa:\n",
    "        return False\n",
    "\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def test_equal_variances(data_01, data_02, alfa=0.05):\n",
    "    \"\"\"Aplica o teste de Levene para determinar se os dados têm a mesma variância.\"\"\"\n",
    "\n",
    "    statistic, p_value = st.levene(data_01, data_02)\n",
    "\n",
    "    if p_value < alfa:\n",
    "        return False\n",
    "\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def hypothesis_testing(data_01, data_02):\n",
    "    \"\"\"Aplica o melhor teste de hipótese baseado nas características dos dados.\"\"\"\n",
    "\n",
    "    if test_normal_shapiro_wilk(data_01) and test_normal_shapiro_wilk(data_02):\n",
    "        if test_equal_variances(data_01, data_02):\n",
    "            return st.ttest_ind(data_01, data_02)\n",
    "\n",
    "        return st.ttest_ind(data_01, data_02, equal_var=False)\n",
    "\n",
    "    return st.mannwhitneyu(data_01, data_02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Tipos de contratos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def summary_contract(data):\n",
    "    \"\"\"Resume as informações da coluna Contract.\"\"\"\n",
    "\n",
    "    contracts_type = data['Contract'].value_counts()\n",
    "\n",
    "    contract_per_month = contracts_type['Month-to-month']\n",
    "    contract_one_year = contracts_type['One year']\n",
    "    contract_two_year = contracts_type['Two year']\n",
    "\n",
    "    contract_per_month_percentage = round((contract_per_month / data.shape[0]) * 100, 2)\n",
    "    contract_one_year_percentage = round((contract_one_year / data.shape[0]) * 100, 2)\n",
    "    contract_two_year_percentage = round((contract_two_year / data.shape[0]) * 100, 2)\n",
    "\n",
    "    print(f'Percentual de clientes com contrato por mês: {contract_per_month_percentage}%')\n",
    "    print(f'Percentual de clientes com contrato de um ano: {contract_one_year_percentage}%')\n",
    "    print(f'Percentual de clientes com contrato de dois anos: {contract_two_year_percentage}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de clientes: 7043\n",
      "Percentual de clientes com contrato por mês: 55.02%\n",
      "Percentual de clientes com contrato de um ano: 20.91%\n",
      "Percentual de clientes com contrato de dois anos: 24.07%\n"
     ]
    }
   ],
   "source": [
    "# Descobrindo a percentagem dos relacionamentos existentes para todos os clientes.\n",
    "print(f'Total de clientes: {dados.shape[0]}')\n",
    "\n",
    "summary_contract(dados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de clientes churn: 1869\n",
      "Percentual de clientes com contrato por mês: 88.55%\n",
      "Percentual de clientes com contrato de um ano: 8.88%\n",
      "Percentual de clientes com contrato de dois anos: 2.57%\n"
     ]
    }
   ],
   "source": [
    "# Descobrindo a percentagem dos relacionamentos existentes para os clientes churn.\n",
    "client_churn = dados[dados['Churn'] == 'Yes']\n",
    "print(f'Total de clientes churn: {client_churn.shape[0]}')\n",
    "\n",
    "summary_contract(client_churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de clientes: 7043\n",
      "Total de clientes não churn: 5174\n",
      "Total de clientes churn: 1869\n",
      "\n",
      "Percentual de clientes com contrato por mês: 42.91%\n",
      "Percentual de clientes com contrato de um ano: 25.26%\n",
      "Percentual de clientes com contrato de dois anos: 31.83%\n"
     ]
    }
   ],
   "source": [
    "# Descobrindo a percentagem dos relacionamentos existentes para os clientes não churn.\n",
    "client_not_churn = dados[dados['Churn'] == 'No']\n",
    "print(f'Total de clientes: {dados.shape[0]}')\n",
    "print(f'Total de clientes não churn: {client_not_churn.shape[0]}')\n",
    "print(f'Total de clientes churn: {client_churn.shape[0]}\\n')\n",
    "\n",
    "summary_contract(client_not_churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "a = client_not_churn['Contract'].value_counts().sort_values(ascending=False)\n",
    "b = client_not_churn['Contract'].value_counts(normalize=True).sort_values(ascending=False).values * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2220 (43%)', '1647 (32%)', '1307 (25%)']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[f'{value[0]} ({value[1]:.0f}%)' for value in zip(a, b)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def compare_plot(df, ax_plot, variable, title, palette='pastel', x_label=''):\n",
    "    \"\"\" Cria um gráfico de barras para comparação visual de valores.\"\"\"\n",
    "\n",
    "    sns.countplot(x=variable, data=df, ax=ax_plot, palette=palette)\n",
    "\n",
    "    ax_plot.set_title(title)\n",
    "    ax_plot.set_xlabel(x_label)\n",
    "    ax_plot.set_xticklabels(ax_plot.get_xticklabels(), rotation=45, ha='right')\n",
    "\n",
    "    abs_values = df[variable].value_counts().sort_values(ascending=False)\n",
    "    rel_values = df[variable].value_counts(normalize=True).sort_values(ascending=False).values * 100\n",
    "    bar_label = [f'{value[0]} ({value[1]:.0f}%)' for value in zip(abs_values, rel_values)]\n",
    "\n",
    "    ax_plot.bar_label(container=ax_plot.containers[0], labels=bar_label, fontsize=10.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de barras para visualizar os tipos de contratos.\n",
    "fig, (ax_1, ax_2, ax_3) = plt.subplots(nrows=1, ncols=3, constrained_layout=True)\n",
    "\n",
    "# Todos os clientes.\n",
    "compare_plot(dados, ax_1, 'Contract', 'Todos os clientes')\n",
    "\n",
    "# Clientes churn.\n",
    "compare_plot(client_churn, ax_2, 'Contract', 'Clientes churn')\n",
    "\n",
    "# Clientes não churn.\n",
    "compare_plot(client_not_churn, ax_3, 'Contract', 'Clientes não churn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Teste de Hipótese de tipos de contrato - Clientes Churn\n",
    "\n",
    "- H₀ — Clientes com contratos curtos permanecem ativos por mais tempo.\n",
    "- H₁ — Clientes com contratos longos permanecem ativos por mais tempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes com contratos curtos: 14.02 meses\n",
      "Tempo médio de permanência para clientes com contratos longos: 48.62 meses\n"
     ]
    }
   ],
   "source": [
    "# Clientes churn com contratos curtos.\n",
    "churn_sort = client_churn[client_churn['Contract'] == 'Month-to-month']\n",
    "churn_sort_mean_tenure = churn_sort['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes com contratos curtos: {churn_sort_mean_tenure:.2f} meses')\n",
    "permanancia = ''\n",
    "# Clientes churn com contratos longos.\n",
    "churn_log = client_churn[client_churn['Contract'] != 'Month-to-month']\n",
    "churn_log_mean_tenure = churn_log['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes com contratos longos: {churn_log_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpretação do teste de hipotesete: Alfa = 0.05\n",
      "Rejeitamos H₀, os tempos médios de permanência para clientes com contratos curtos e longos são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Aplicando o teste de hipótese para tipos de contratos.\n",
    "grupo_01 = churn_sort['tenure']\n",
    "grupo_02 = churn_log['tenure']\n",
    "statistic, p_value = hypothesis_testing(grupo_01, grupo_02)\n",
    "\n",
    "ic = .95    # Intervalo de confiança de 95%.\n",
    "alfa = (1 - ic)\n",
    "print(f'Interpretação do teste de hipótese: Alfa = {alfa:.2f}')\n",
    "\n",
    "# Interpretação do resultado do teste de hipótese.\n",
    "if p_value < alfa:\n",
    "    print('Rejeitamos H₀, os tempos médios de permanência para clientes com contratos curtos e longos são diferentes.')\n",
    "else:\n",
    "    print('Não rejeitamos H₁, os tempos médios de permanência para clientes com contratos curtos e longos são iguais.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Teste de Hipótese de tipos de contrato - Clientes Ativos\n",
    "\n",
    "- H₀ — Com contratos curtos os clientes permanecem ativos por mais tempo.\n",
    "- H₁ — Com contratos longos os clientes permanecem ativos por mais tempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes não churn com contratos curtos: 21.03 meses\n",
      "Tempo médio de permanência para clientes não churn com contratos longos: 50.00 meses\n"
     ]
    }
   ],
   "source": [
    "# Clientes não churn com contratos curtos.\n",
    "not_churn_sort = client_not_churn[client_not_churn['Contract'] == 'Month-to-month']\n",
    "not_churn_sort_mean_tenure = not_churn_sort['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn com contratos curtos: {not_churn_sort_mean_tenure:.2f} meses')\n",
    "\n",
    "# Clientes não churn com contratos longos.\n",
    "not_churn_log = client_not_churn[client_not_churn['Contract'] != 'Month-to-month']\n",
    "not_churn_log_mean_tenure = not_churn_log['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn com contratos longos: {not_churn_log_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpretação do teste de hipotesete: Alfa = 0.05\n",
      "Rejeitamos H₀, os tempos médios de permanência para clientes não churn com contratos curtos e longos são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Aplicando o teste de hipótese para tipos de contratos.\n",
    "grupo_01 = not_churn_sort['tenure']\n",
    "grupo_02 = not_churn_log['tenure']\n",
    "statistic, p_value = hypothesis_testing(grupo_01, grupo_02)\n",
    "\n",
    "ic = .95    # Intervalo de confiança de 95%.\n",
    "alfa = (1 - ic)\n",
    "print(f'Interpretação do teste de hipótese: Alfa = {alfa:.2f}')\n",
    "\n",
    "if p_value < alfa:\n",
    "    print('Rejeitamos H₀, os tempos médios de permanência para clientes não churn com contratos curtos e longos são diferentes.')\n",
    "else:\n",
    "    print('Não rejeitamos H₁, os tempos médios de permanência para clientes não churn com contratos curtos e longos são iguais.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Identificando fatores que possam influenciar o tempo de permanência.\n",
    "\n",
    "Existe algum fator que pode influenciar o tempo de permanência dos clientes?\n",
    "\n",
    "Talvez, o cenário ideal, seja buscar clientes que tenham parceiros, visto que tendo duas pessoas utilizando os serviços, caso uma não use a outra pode usar, evitando assim o cancelamento.\n",
    "\n",
    "## Proporção de clientes ativos com parceiros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentagem de clientes ativos com parceiros: 52.82%\n",
      "Porcentagem de clientes churn com parceiros: 35.79%\n"
     ]
    }
   ],
   "source": [
    "# Porcentagem de clientes não churn com parceiros\n",
    "not_churn_partner = client_not_churn[client_not_churn['Partner'] == 'Yes']\n",
    "not_churn_partner_percent = not_churn_partner['tenure'].shape[0] / client_not_churn['tenure'].shape[0]* 100\n",
    "print(f'Porcentagem de clientes ativos com parceiros: {not_churn_partner_percent:.2f}%')\n",
    "\n",
    "# Porcentagem de clientes churn sem parceiros.\n",
    "churn_partner = client_churn[client_churn['Partner'] == 'Yes']\n",
    "churn_partner_percent = churn_partner['tenure'].shape[0] / client_churn['tenure'].shape[0] * 100\n",
    "print(f'Porcentagem de clientes churn com parceiros: {churn_partner_percent:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes não churn com parceiros: 45.79 meses\n",
      "Tempo médio de permanência para clientes churn sem parceiros: 13.18 meses\n"
     ]
    }
   ],
   "source": [
    "# Tempo médio de permanência de clientes não churn com parceiros.\n",
    "not_churn_partner_mean_tenure = not_churn_partner['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn com parceiros: {not_churn_partner_mean_tenure:.2f} meses')\n",
    "\n",
    "# Tempo médio de permanência de clientes churn sem parceiros.\n",
    "churn_partner = client_churn[client_churn['Partner'] == 'No']\n",
    "churn_partner_mean_tenure = churn_partner['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes churn sem parceiros: {churn_partner_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Teste de Hipótese clientes com/sem parceiros — Clientes Churn\n",
    "\n",
    "- H₀ — clientes sem parceiro ficam mais tempo\n",
    "- H₁ — clientes com parceiro ficam mais tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes churn com parceiros: 26.59 meses\n",
      "Tempo médio de permanência para clientes churn sem parceiros: 13.18 meses\n"
     ]
    }
   ],
   "source": [
    "# Clientes churn com parceiros.\n",
    "churn_partner_yes = client_churn[client_churn['Partner'] == 'Yes']\n",
    "churn_partner_yes_mean_tenure = churn_partner_yes['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes churn com parceiros: {churn_partner_yes_mean_tenure:.2f} meses')\n",
    "\n",
    "# Clientes churn sem parceiros.\n",
    "churn_partner_no = client_churn[client_churn['Partner'] == 'No']\n",
    "churn_partner_no_mean_tenure = churn_partner['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes churn sem parceiros: {churn_partner_no_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpretação do teste de hipotesete: Alfa = 0.05\n",
      "Rejeitamos H₀, os tempos médios de permanência para clientes churn com parceiros e sem parceiros são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Aplicando o teste de hipótese para tipos de contratos.\n",
    "grupo_01 = churn_partner_yes['tenure']\n",
    "grupo_02 = churn_partner_no['tenure']\n",
    "statistic, p_value = hypothesis_testing(grupo_01, grupo_02)\n",
    "\n",
    "ic = .95    # Intervalo de confiança de 95%.\n",
    "alfa = (1 - ic)\n",
    "print(f'Interpretação do teste de hipótese: Alfa = {alfa:.2f}')\n",
    "\n",
    "if p_value < alfa:\n",
    "    print('Rejeitamos H₀, os tempos médios de permanência para clientes churn com parceiros e sem parceiros são diferentes.')\n",
    "else:\n",
    "    print('Não rejeitamos H₁, os tempos médios de permanência para clientes churn com parceiros e sem parceiros são iguais.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Teste de Hipótese clientes com/sem parceiros — Clientes Ativos\n",
    "\n",
    "- H₀ — clientes sem parceiro ficam mais tempo\n",
    "- H₁ — clientes com parceiro ficam mais tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes não churn com parceiros: 45.79 meses\n",
      "Tempo médio de permanência para clientes não churn sem parceiros: 28.36 meses\n"
     ]
    }
   ],
   "source": [
    "# Clientes não churn com parceiros.\n",
    "not_churn_partner_yes = client_not_churn[client_not_churn['Partner'] == 'Yes']\n",
    "not_churn_partner_yes_mean_tenure = not_churn_partner_yes['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn com parceiros: {not_churn_partner_yes_mean_tenure:.2f} meses')\n",
    "\n",
    "# Clientes churn sem parceiros.\n",
    "not_churn_partner_no = client_not_churn[client_not_churn['Partner'] == 'No']\n",
    "not_churn_partner_no_mean_tenure = not_churn_partner_no['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn sem parceiros: {not_churn_partner_no_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpretação do teste de hipotesete: Alfa = 0.05\n",
      "Rejeitamos H₀, os tempos médios de permanência para clientes não churn com parceiros e sem parceiros são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Aplicando o teste de hipótese para clientes com/sem parceiros.\n",
    "grupo_01 = not_churn_partner_yes['tenure']\n",
    "grupo_02 = not_churn_partner_no['tenure']\n",
    "statistic, p_value = hypothesis_testing(grupo_01, grupo_02)\n",
    "\n",
    "ic = .95    # Intervalo de confiança de 95%.\n",
    "alfa = (1 - ic)\n",
    "print(f'Interpretação do teste de hipótese: Alfa = {alfa:.2f}')\n",
    "\n",
    "if p_value < alfa:\n",
    "    print('Rejeitamos H₀, os tempos médios de permanência para clientes não churn com parceiros e sem parceiros são diferentes.')\n",
    "else:\n",
    "    print('Não rejeitamos H₁, os tempos médios de permanência para clientes não churn com parceiros e sem parceiros são iguais.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Se clientes com parceiros permanecem por mais tempo, será que esta análise se aplica para clientes com dependentes? Vamos aos testes!\n",
    "\n",
    "## Proporção de clientes com dependentes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentagem de clientes não churn com dependentes: 34.48%\n",
      "Porcentagem de clientes churn com dependentes: 17.44%\n"
     ]
    }
   ],
   "source": [
    "# Porcentagem de clientes não churn com dependentes.\n",
    "not_churn_dependent_yes = client_not_churn[client_not_churn['Dependents'] == 'Yes']\n",
    "not_churn_dependent_yes_percent = not_churn_dependent_yes['tenure'].shape[0] / client_not_churn['tenure'].shape[0]* 100\n",
    "print(f'Porcentagem de clientes não churn com dependentes: {not_churn_dependent_yes_percent:.2f}%')\n",
    "\n",
    "# Porcentagem de clientes churn com dependentes.\n",
    "churn_dependent_yes = client_churn[client_churn['Dependents'] == 'Yes']\n",
    "churn_dependent_yes_percent = churn_dependent_yes['tenure'].shape[0]/ client_churn['tenure'].shape[0] * 100\n",
    "print(f'Porcentagem de clientes churn com dependentes: {churn_dependent_yes_percent:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes não churn com dependentes: 41.35 meses\n",
      "Tempo médio de permanência para clientes churn com dependentes: 22.03 meses\n"
     ]
    }
   ],
   "source": [
    "# Tempo médio de permanência de clientes não churn com parceiros.\n",
    "not_churn_dependent_yes_mean_tenure = not_churn_dependent_yes['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn com dependentes: {not_churn_dependent_yes_mean_tenure:.2f} meses')\n",
    "\n",
    "# Tempo médio de permanência de clientes churn com parceiros.\n",
    "churn_dependent_yes_mean_tenure = churn_dependent_yes['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes churn com dependentes: {churn_dependent_yes_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Teste de Hipótese para clientes com/sem dependentes — Clientes Churn\n",
    "\n",
    "H₀ — clientes com pagamento não automático ficam mais tempo\n",
    "H₁ — clientes com pagamento automático ficam mais tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes não churn com dependentes: 41.35 meses\n",
      "Tempo médio de permanência para clientes churn sem dependentes: 17.12 meses\n"
     ]
    }
   ],
   "source": [
    "# Clientes não churn com dependentes.\n",
    "not_churn_dependent_yes = client_not_churn[client_not_churn['Dependents'] == 'Yes']\n",
    "not_churn_dependent_yes_mean_tenure = not_churn_dependent_yes['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn com dependentes: {not_churn_dependent_yes_mean_tenure:.2f} meses')\n",
    "\n",
    "# Clientes churn sem dependentes.\n",
    "churn_dependent_no = client_churn[client_churn['Dependents'] == 'No']\n",
    "churn_dependent_no_mean_tenure = churn_dependent_no['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes churn sem dependentes: {churn_dependent_no_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpretação do teste de hipotesete: Alfa = 0.05\n",
      "Rejeitamos H₀, os tempos médios de permanência para clientes não churn com dependentes e sem dependentes são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Aplicando o teste de hipótese para clientes com/sem dependentes.\n",
    "grupo_01 = not_churn_dependent_yes['tenure']\n",
    "grupo_02 = churn_dependent_no['tenure']\n",
    "statistic, p_value = hypothesis_testing(grupo_01, grupo_02)\n",
    "\n",
    "ic = .95    # Intervalo de confiança de 95%.\n",
    "alfa = (1 - ic)\n",
    "print(f'Interpretação do teste de hipótese: Alfa = {alfa:.2f}')\n",
    "\n",
    "if p_value < alfa:\n",
    "    print('Rejeitamos H₀, os tempos médios de permanência para clientes não churn com dependentes e sem dependentes são diferentes.')\n",
    "else:\n",
    "    print('Não rejeitamos H₁, os tempos médios de permanência para clientes não churn com dependentes e sem dependentes são iguais.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Será que o o tipo de pagamento escolhido influencia o tempo de permanência do cliente? Vamos testa!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DevToRI8enZg/fy4A33//Hd26tWfMmJHmdU2b9jmnT580T8+fP5fjx/9M1+9FxFotWPANXbq8RZcub7FixTLz/K5d3zJvPz17dgX+fbt92DqPHz9mnj5//hzvvdftgeVu3gyhSZN6hIeHA3Do0AG6dWtPnz49CQsLBWDbti34+y81v+fgwf0sX77kP/dbRCS92MvY58SJx499VJyJ2JA//jjIwIF9CA29ZZ63adMvRETcYeHCZUyf/hWzZn1BREQEq1evpEiRYixc+D2vv/4G33wzB4CpUz+jcuUqLFiwlLJly7Ns2XcPfM7mzb9QunQZ3NzcmDt3DrVq1WHhwu/p3bsfkyePB+DUqZN06tSNBQuWsmDBUt5++z0Atm37jUWLluPunpWzZ88QEhLMrVs3KVWqjHn9b73VkS+/nPE0vyoRq3D06GH27t3DggVL+eabRfz442ouXrxATEwMJpPJvP3Mm7cY4F+32/vduHGDs2fPULZseeDuNjd48AfExsY+sOznn08iKSnRPO3v/z1ffDGbdu06sGnTLxiGwdq1q2jVqo15mcqVq7Bv3++EhYWl99chIpJm9jT2mTNn5mP7q+JM7FZq97IEBd2gf//edO/egVGjhhEdHQXAsWNHefvtTnTo8AbffbfgoZ8RHx/P6NHDSU5O5q+/LtG377t0796RDz54n6CgGwBs3vwrrVo1Me9l+emnNcDdPSrdu3dk5swvzOvz8RnOzZs3zdNTpkzixo0b5ukNG37Cx2dcijY891xhunfviclkIkeOHLi7u3P7djiGkUx0dDQAMTExuLi4kJyczN69e3jjjTcB6NixCy1btuafli9fSsOGjQF49dWaNGjQCIDixUsQHBwEwMmTx9mzJ5C33+7EmDEjiYiIAMBkgsTERGJjY3B2dmbx4vl06pRyj36WLO54eHiwf//+h36vIvYie/bs9O07ACcnJzJlykS+fAUICQnm9OmTJCQk0KdPT957rxt//HEQ4KHb7T+tXr2cunXrm6c3bvwZb2/fB5Zbt24tpUqVJnv2HOZ5zs7OxMXF/f/26cSWLZupWrU6rq6ZUry3Vq26rF27Kj2+AhF5xuxt/GNPY5+cOT34449DD/1O71FxJsB/35DvWb16BR9/PPBfP+dZHS5Oy16WmTO/oHbteixcuIzXXmti3ssyceJYRo8ex+LF/uzcuZ0zZ04/0B9//6U0aPAaDg4OTJ06ma5d32bhwu9p0qQ5s2dPB+DUqRN88MFH5r0sLVu2JiIigsuXL7Fw4fdcunSByMhITp48gYdHbnLnzm1ef/v2nZk9e5p5esQIH0qWLJ2iDWXKPE+xYsWBu6cnmUwOFChQkLZt3+LAgb20atWYuXO/pHPn7oSHh+Hu7s4338yhe/cOTJo0nsyZM6dYX3h4OOHhYeTO7QlAzZp1yJYt2/9/x99Qu3ZdANzcsvDWWx2ZP38pBQoUZObMqQC8/nob3nuvG9mz5yBTpkyEht6iTJnnH/juXnihEtu2bXtgvog9ee65IpQvXwG4m29nz56mXLkKxMTEULVqdWbM+Iphw0bj6+tNZGTkQ7fbf9qzJ5Dy5Suapz/99DPy5cufYpng4CACAtbTtevbKeZ37tyd0aOHs23bFho0aMyaNStp3brdA59RoUJFdu/elR5fgYhV0/jH+sc/9jT2uZutOx+Yfz8VZ0/gnxtyTEwMw4cPpnv3DvTq1cO8oYWFhTFgQB86dWqLj89w4uPjU6zHnjZkgGvXrv7rHhZ4toeL07KX5cKF89SqVReAqlWrs2dPINeuXcXJyZmiRYvh7OxM/foN2b59S4r1JSUlsWHDT7zySi0AmjZtQdWq1QEoVuzvvSynTp1g/fof6d69I59/Pon4+HgcHR1JSEjEMAzi4+NxcHBg8eJv6dw55V6WAgUKcvNmCNevX/vX7/WePXsCmTJlEt7evphMJubN+4qmTVuwdu1GRo70wdfXm8TERK5du0qpUqVZuHAZxYoVf+AQ+7VrV/D09Hpg/fPnz+WPPw7Sq1dfAAYM+Mj8vbVv35l9+343fw/z5y+lf/9BLFr0LZ07d2fZsu/4+ONBbNmyybw+L688XL58+bH9ErEHp06dZOTIjxk2bBRubm5Ur/4KgwZ9jJOTE8WLl6B06TKcPHn8odvtP12/fj3FIOZh/Pwm8MEHH+LklPKJOaVLl2Hu3IVMnjyVPXt2Ub36qxw9epiPPx7I3LlfmpfLkycP165dTZ/Oi93Q+EfjH7Cu8Y+tjX1Sk60qztLo0KEHN+SVK5dRpEhRFi5cxrvvvs/06VMA+Prr2dSr15ClS1fh6ZmHNWv+PkXEnjZkAMMw+PzzSXTr9s6/9ulZHS4+fPhQmvaylCxZiq1b724427dvITw8nNDQW3h45DK/18MjV4rD7QBnzpwmd25PnJ2dAWjUqCmOjo4YhsHChd9Qu3Y9AHLkyMk77/Ti22+/IyYmmqVLF+Hm5kaVKi/z9tudqV79VS5cOE/u3J7mPTb3K1euAr//vvtfv1eALVs2MXHiJ0ye/AUlS5YCYO/ePTRq1BS4uwcoKOg62bJlx9HRkbp1GwBQp059zpw5lWJdDg4ODwzoZsyYQmDgDqZP/5IsWdwBWLJkIYmJf1/L4ujomOI9169fIzQ0lMKFi7B//14mTZrC6tUrUizv4KAIEvt3+PAffPzxAIYNG2UewOzbt+eBC8MdHR0fut3+81oyR0eHB7a3+wUHB3H27Bk+/XQsPXp04ubNED74oDe3b4ebl0lKSmLNmlW88UY7vvtuIZ98Monw8DD++uvi/3+GEw4OpnTovdgLjX80/rnHWsY/tjr2MZkePfbRyCiN1q//8YENuWvXt+nZszcAN25cNx8G3bt3t3kDbNy4qXnPg71tyAArVy7npZeq8NxzhR/an2d7uPjRp+L8cy/L//43kMOHD5kHMTlz5iQ5ORmTKeXA5J8DlatXH9zLkpyczOTJ44mNjeXNNzsA4Ov7KeXLV8DR0ZG33urI3r17gLt/NwsWLKVjxy4sXvwtXbr0YPbsaQwdOsh8/QncPcL0qL0sZ86c4osv/Jg27UtKl/77B6ho0eLs2rUdgBMn/jR/d5UqVWbbtrt/i3v37qZEiZT/9/ny5Tf/jcHdPZxHjx5h2rQ5ZMuW3Tz/9993s23bbwCsXbuKV1+tlWI9CxfOo2vXHiQl3f0uTSYTCQkJ5teDgm5QqFChf+2XiD24efMm3t4fM3bsJKpVq5Fi/vz5c0lOTubKlcucO3eWsmXL/et2e788efJx82bIv36ml1ceVq9ebx645s7tyfTpX6W49uzXXwN45ZWaZM6cmaSkRPOAIS4uDri7febPXzAdvwmxdRr/aPxzjzWMf2x57FOgwKOzVcVZGnl7j3lgQwZwcnKib993+eILP9q2fQuAyMgIsmbNCqTc82BvG/KVK5fZsWMr7dt3/tf1WMvh4oftZYmPj8fHZzwLFiylRYtWuLllIXduT8LC/t47GBoa+sBeHQcHU4q9LImJifj4jODWrZtMnDgFJycn4uLiHrgj0D/3shw58geennmIjIwkIiKSTz6ZxKJF36ZY/lFHmJYuXUxiYgI+PiPMp3hcuHCeDz74kJ07t9O161t88YUfo0ff/VEdPtyHH3/8gS5d3iIwcCe9evVJsb7s2XOQPXsO8+Bv0aJvCQ29Rd++79KjRyfef//uj+rgwcNYuXIZXbq8ydGjR3j33b/Xc/XqFUJDQylbtjxZs2YlT558dO36FnXq/H0Tg0OHDlKnTp1/7ZeIPVi1ajlxcXFMnTrZvH3u2/c7jRs3w8srD926dWD48I8YPnw0rq6Z/nW7vd8rr9Tk0KGDD/m01Ll31KxNm7u/VS1atKJHj07cuXObEiXu5uKhQweoUePVJ/4MsT8a/2j8c481jH9seezzyiuPzlanR74qaTJ79jecO3eWDz/8H99/vxrDSPm6g4PJvCFPnTqLw4cffreWx23Is2Z9DdzdkO9p374zPXp0BO5uyE2btgDgs88+NW/IBw8eoGnT5tSr1xC4uyH/Wxvg7oY8bdrnD92QM2XKxJ07t9myZTM7dmwjODiYnj27EhMTTXh4GJ9/PomPPhp6X98ffrj4yJE/Hjhc3L59Z/OyjzpcPG/eV3z22TQ++OB9c5/u7v19+Kk49/ayTJ8+h8KFi5jnL1++lCJFitK6dVtWrlxOrVp1yJ+/AHFxcZw/f47nnivMli2b6NdvYIr1FShQ0HxHIoCvv55FYmIin37qZ26/q6srq1evoEKFSpQrV541ax7cy7Jo0bcMGTKCO3fu4OTkhIODQ4rz84OCblCwYMojTL/+usP873/uybzf1KmzHpiXN29epk2b/a/vAXjzzQ78/PN6unbtwZo1Pz90meeeK8KXX3770NcKFCjI5MlTzdODBw9L8XpkZCS3bt2kcuXKj2yHiK3r3bsfvXv3e+hrgwcPf2Cep6fXQ7fb+7Vu3ZYpUybRqFET87x8+fKzeLH/Q5dfufKnFNOOjo7MmfP3ttukSXOaNGmeYpmtWzfj4zP+ke0QuUfjH41/nsX4x17GPhUqVPznW1NQcZYOjhz5g/z5C5I7d26KFy+Bp6cXwcFBZM2alaioSLJkcTfvebDHDbljxy507NgFuPvw0mXLvkvRH3j04WI3Nzfz/N9/303evPlo0KBRuh8uvn8vyz0+PuPo0qUHPj7DWblyOaVLl2bo0FEAjBgxhnHjRhMdHcNrrzU233HtnpIlSxMcHER8fDyJiYmsWLGMPHny8e67d0+hKFiwIOPGTWb06E/4/POJxMbGUK5cBdq162Bex6FDB8ibNx958uTFyysPN2+G8Pbbnejevad5mSNHDpn3Rv6TR47MODqn/2bcvn0bBg4cSObMJtzd3dNlnUkJiYSGxwCwfPkS+vTpny7rFbEF2XNmxsUpfbZVT8+svPhiRa5dO0/Fio/+kX8S23bsoGrV6uTMmTPd1y32ReMfjX/safzzNMY+8Pf4Z/nyJbz//uPHPirO0sG+fb8THb2F/v0H8ddfF7l9+zb58xfg5ZersWnTL7Rq1YZff/2Zl1+ubrcb8uPcf7g4d25PFi361nwqBECmTJmYM+dbBg8exoQJY5k/fy758xdk9OhPzOu4/3AxYD5c3KzZ6+ZlDh06SLt27c3Tqd3LMmvW3AfmlStXnm+/XfKv7zGZTDRr1pLt27fQsGFjtmx5+EWr5cu/wLffPviwQ4AXX3yJF198yby+CRP8Urz+11+XyJ3bCy+vPA99v6OzE5Erl/9rG/+L8bVegYD1RKbT+tzv+3/p2bO3bjYgGYqLkxNfnVmWfutr4skeTrDnzIl0W+c9vWt1oGyZSum+XrE/Gv88nsY/tjX+Se+xD/w9/rl3febjqDhLB506dWP8+DF0794BFxcXRo3yxcXFhd69+zF27ChWrPieggUL4ev7aarWZ4sb8j2VK1ehcuUqD33NWg4Xp6c33+zIJ5+Mon79157KnQd1hElERKyVxj9/0/gnfWXk8Y/JMP55ZrA8yq1bkSQnP/2vbPPmX7h27Rpdu/Z4KuuPjIxk2LAPmTnz66ey/ocxDIPRo4czfPgo3NyypPv65837imrVXnng8Ps96Xla0dMWn5jI7bCYxy7n6Zn1qR05S2/u7doTEhJhnnZwMJErV/qdNpBRPKsMkvTl6Zk1XY+cPU29S3ZIsa3aK2VQ2mj88+Q0/kmd1I59wL7HP9b/P5VB1a//GqNHDyc6OuqpbMiPO+/VI0cmHP//+RXpac6cR1/onlZJCQmEht99BtDjDhen92lFT1Pvkh0ev5CIiIid0fgndexx/KOxz10qztJBjpxZcHZK/0O66b0hJyQmEx4WBTx+Q3Z0diY8YEa6fv7TkKNJfyD2scuJiIhI+nlaYx/Q+Cc1NP6xXyrO0oGzkwMr9/77A0GtRbuqDz59XURERCStbGXsAxr/iG3RQ6hFRERERESsgIozERERERERK6DiTERERERExAqoOBMREREREbECKs5ERERERESsgIozERERERERK6DiTERERERExAqoOBMREREREbECKs5ERERERESsgIozEZFUioyMpEWLFly5cgWAwMBAWrZsSaNGjZg6dap5uRMnTtC2bVsaN27MyJEjSUxMBODatWt07tyZJk2a0KdPH6KioizSDxEREbFOKs5ERFLh8OHDdOzYkYsXLwIQGxvLiBEjmD17Nhs2bODYsWNs27YNgCFDhjBq1Cg2btyIYRj4+/sD4OvrS6dOnQgICKB8+fLMnj3bUt0RERERK6TiTEQkFfz9/fHx8cHLywuAI0eOULhwYQoVKoSTkxMtW7YkICCAq1evEhsbS6VKlQBo06YNAQEBJCQksG/fPho3bpxivoiIiMg9TpZugIiILRg/fnyK6eDgYDw9Pc3TXl5eBAUFPTDf09OToKAgwsLCcHd3x8nJKcV8ERERkXtsqjiLjIykQ4cOzJkzh4IFC7J8+XIWL16MyWSifPny+Pr64uLiwokTJ/D29iYyMpIqVarg6+uLk5MT165dY8iQIdy6dYuiRYvi5+dHlixZLN0tEbFBhmE8MM9kMqV5flrlyuWe5veIpJWnZ1ZLN0Huo/GPSMZhM8XZ4cOH8fb2Nl/vceHCBebNm8fq1avJkiULw4YNY+nSpfTo0YMhQ4Ywbtw4KlWqxIgRI/D396dTp07m6z2aN2/OrFmzmD17NkOGDLFsx0TEJuXJk4ebN2+ap4ODg/Hy8npgfkhICF5eXnh4eBAZGUlSUhKOjo7m+Wl161YkyckPFnpi3Wyt2AkJibB0E546BweTTezs0PhHJGOxmWvO/nm9h4uLC2PGjMHd3R2TyUSpUqW4du2arvcQkWeiYsWKXLhwgUuXLpGUlMS6deuoXbs2BQoUwNXVlQMHDgCwZs0aateujbOzM1WqVGHDhg0p5ouIPIrGPyIZi80cOfvn9R4FChSgQIECAISGhrJkyRImTJig6z1E5JlwdXVl4sSJ9O/fn7i4OOrUqUOTJk0A8PPzw9vbm6ioKMqWLUu3bt0A8PHxYdiwYXz55Zfky5ePKVOmWLILImIDNP4RyVhspjj7N0FBQbz77ru0bduWatWqcfDgwQeW0fUef7O1U2tSwx77BPbZL3vo02+//Wb+d40aNfjxxx8fWKZMmTKsXLnygfkFChRg8eLFT7V9IpIxaPyTNvbw+/NP6pPtSEu/bLo4O3fuHO+99x5dunThnXfeAR68DuRZXO9hS39Iqb2OQH2yvNT0y5b7ZCvXe4iIWBtrGP/Y8u/Po9hSv9Qn25GW8Y/NXHP2T5GRkfTs2ZMBAwaYgwnQ9R4iIiJitzT+EbFvNlucrVy5kps3b/Ltt9/SqlUrWrVqxbRp04C713tMmDCBpk2bEhMTk+J6D39/f5o1a8b+/fsZOHCgBXsgIiIikjYa/4jYN5s7rfHe9R49evSgR48eD11G13uIiIiIPdH4RyRjsNkjZyIiIiIiIvZExZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAGbKs4iIyNp0aIFV65cASAwMJCWLVvSqFEjpk6dal7uxIkTtG3blsaNGzNy5EgSExMBuHbtGp07d6ZJkyb06dOHqKgoi/RDREREJLU0/hHJOGymODt8+DAdO3bk4sWLAMTGxjJixAhmz57Nhg0bOHbsGNu2bQNgyJAhjBo1io0bN2IYBv7+/gD4+vrSqVMnAgICKF++PLNnz7ZUd0REREQeS+MfkYzFZoozf39/fHx88PLyAuDIkSMULlyYQoUK4eTkRMuWLQkICODq1avExsZSqVIlANq0aUNAQAAJCQns27ePxo0bp5gvIiIiYq00/hHJWJws3YDUGj9+fIrp4OBgPD09zdNeXl4EBQU9MN/T05OgoCDCwsJwd3fHyckpxXwRERERa6Xxj0jGYjPF2T8ZhvHAPJPJlOb5aZUrl3ua32NNPD2zWroJ6c4e+wT22S977JOIyLOk8c+TscffH/XJdqSlXzZbnOXJk4ebN2+ap4ODg/Hy8npgfkhICF5eXnh4eBAZGUlSUhKOjo7m+Wl161Ykyckpg86W/pBCQiJStZz6ZHmp6Zct98nBwWTzP/YiIs+atYx/bPn351FsqV/qk+1Iy/jHZq45+6eKFSty4cIFLl26RFJSEuvWraN27doUKFAAV1dXDhw4AMCaNWuoXbs2zs7OVKlShQ0bNqSYLyLyX6xdu5bmzZvTvHlzJk2aBOiOaSLy9Gj8I2LfbLY4c3V1ZeLEifTv359mzZpRrFgxmjRpAoCfnx8TJkygadOmxMTE0K1bNwB8fHzw9/enWbNm7N+/n4EDB1qwByJi62JiYhg/fjyLFy9m7dq17N+/n8DAQN0xTUSeGo1/ROybzZ3W+Ntvv5n/XaNGDX788ccHlilTpgwrV658YH6BAgVYvHjxU22fiGQcSUlJJCcnExMTg5ubG4mJiTg5OT1wx7Tp06fz5ptvsm/fPmbNmmWe36VLF4YMGWLBHoiIrdD4RyRjsLniTETEWri7uzNgwACaNm1KpkyZqFq1Ks7OzrpjmoiIiDwRFWciIk/o5MmTrFq1ii1btpA1a1YGDx7Mrl27HlhOd0wTW2NrF9uLiNgLFWciIk9o586d1KhRg1y5cgF3T1WcN2/eM79jrFg/Wyt2UnvHNFumO8aKiDWy2RuCiIhYWpkyZQgMDCQ6OhrDMPjtt9+oWrWq7pgmIiIiT0RHzkREnlDNmjU5fvw4bdq0wdnZmQoVKtCrVy9ee+01vL29iYqKomzZsinumDZs2DC+/PJL8uXLx5QpUyzcAxEREbEmKs5ERP6DXr160atXrxTzdMc0EREReRI6rVFERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAnZRnK1du5bmzZvTvHlzJk2aBMCJEydo27YtjRs3ZuTIkSQmJgJw7do1OnfuTJMmTejTpw9RUVGWbLqIiIjIE9H4R8T+2HxxFhMTw/jx41m8eDFr165l//79BAYGMmTIEEaNGsXGjRsxDAN/f38AfH196dSpEwEBAZQvX57Zs2dbuAciIiIiaaPxj4h9svniLCkpieTkZGJiYkhMTCQxMREnJydiY2OpVKkSAG3atCEgIICEhAT27dtH48aNU8wXERERsSUa/4jYJydLN+C/cnd3Z8CAATRt2pRMmTJRtWpVnJ2d8fT0NC/j6elJUFAQYWFhuLu74+TklGK+iIiIiC3R+EfEPtl8cXby5ElWrVrFli1byJo1K4MHD2bXrl0PLGcymTAM46Hz0yJXLvcnbqs18PTMaukmpDt77BPYZ7/ssU8iIpag8U/a2OPvj/pkO9LSL5svznbu3EmNGjXIlSsXcPdQ/bx587h586Z5mZCQELy8vPDw8CAyMpKkpCQcHR3N89Pi1q1IkpNThpwt/SGFhESkajn1yfJS0y9b7pODg8nmf+xFRCzF0uMfW/79eRRb6pf6ZDvSMv6x+WvOypQpQ2BgINHR0RiGwW+//UbVqlVxdXXlwIEDAKxZs4batWvj7OxMlSpV2LBhQ4r5IiIiIrZE4x8R+2TzR85q1qzJ8ePHadOmDc7OzlSoUIFevXrx2muv4e3tTVRUFGXLlqVbt24A+Pj4MGzYML788kvy5cvHlClTLNwDERERkbTR+EfEPtl8cQbQq1cvevXqlWJemTJlWLly5QPLFihQgMWLFz+rpomIiIg8FRr/iNgfmz+tUURERERExB6oOBMREREREbECKs5ERERERESsgIozERERERERK6DiTERERERExAqoOBMREREREbECKs5ERERERESsgIozEZH/4LfffqNNmzY0adKEcePGARAYGEjLli1p1KgRU6dONS974sQJ2rZtS+PGjRk5ciSJiYmWaraIiIhYIYsUZ0FBQQ/MO3v2rAVaIiIZTXrmz+XLl/Hx8WH27Nn89NNPHD9+nG3btjFixAhmz57Nhg0bOHbsGNu2bQNgyJAhjBo1io0bN2IYBv7+/v+pLyJiWzT+EZHHeabFWXh4OOHh4bz33nvcvn3bPH3z5k369u37LJsiIhnM08ifX3/9lWbNmpE3b16cnZ2ZOnUqmTNnpnDhwhQqVAgnJydatmxJQEAAV69eJTY2lkqVKgHQpk0bAgIC0rGHImKtNP4RkdRyepYf9tFHH7Fr1y4AqlWr9ncjnJxo2LDhs2yKiGQwTyN/Ll26hLOzMz179iQkJIR69epRsmRJPD09zct4eXkRFBREcHBwivmenp4P3Yv+OLlyuT9RW0XSwtMzq6WbYFc0/hGR1Hqmxdm8efMAGD58OBMmTHiWHy0iGdzTyJ+kpCT279/P4sWLcXNzo2/fvmTOnPmB5UwmE4ZhPHR+Wt26FUly8oPrEutma8VOSEiEpZvw1Dk4mJ7Zzg6Nf0QktZ5pcXbPhAkTuHr1Krdv304xYClXrpwlmiMiGUh65k/u3LmpUaMGHh4eADRo0ICAgAAcHR3NywQHB+Pl5UWePHm4efOmeX5ISAheXl7/oSciYms0/hGRx7FIcebn58fixYvJlSuXeZ7JZGLz5s2WaI6IZCDpmT/16tVj6NCh3LlzhyxZsrBjxw6aNGnC119/zaVLlyhYsCDr1q2jbdu2FChQAFdXVw4cOMBLL73EmjVrqF27dnp2TUSsnMY/IvI4FinONmzYwC+//EKePHks8fEikoGlZ/5UrFiRd999l06dOpGQkMCrr75Kx44dKVasGP379ycuLo46derQpEkT4O7AzNvbm6ioKMqWLUu3bt3+cxtExHZo/CMij2OR4ixfvnwKJhGxiPTOn3bt2tGuXbsU82rUqMGPP/74wLJlypRh5cqV6fbZImJbNP4RkcexSHFWo0YNJk+eTIMGDciUKZN5vs65FpGnTfkjIpai/BGRx7FIcbZ69WqAFM/40TnXIvIsKH9ExFKUPyLyOBYpzn777TdLfKyIiPJHRCxG+SMij2OR4mz+/PkPnf/2228/45aISEaj/BERS1H+iMjjWKQ4O336tPnf8fHxHDhwgGrVqlmiKSKSwSh/RMRSlD8i8jgWewj1/UJDQ/n4448t0RQRyWCUPyJiKcofEXkcB0s3AMDDw4OrV69auhkikgEpf0TEUpQ/IvJPFr/mzDAMjh07Rq5cuSzRFBHJYJQ/ImIpyh8ReRyLX3MGdx/KqMP6IvIsKH9ExFKUPyLyOBa95uzq1askJiZSuHBhSzRDRDIg5Y+IWIryR0QexyLF2aVLl+jbty/BwcEkJyeTM2dOvvrqK4oXL26J5ohIBqL8ERFLUf6IyONY5IYgY8eO5d1332Xfvn0cOHCAPn364Ovra4mmiEgGo/wREUtR/ojI41ikOLt16xZvvPGGebpt27aEhYVZoikiksEof0TEUpQ/IvI4FinOkpKSCA8PN0+HhoZaohkikgEpf0TEUpQ/IvI4FrnmrEuXLrRv356mTZsC8PPPP9O9e3dLNEVEMhjlj4hYivJHRB7HIkfO6tSpA0BCQgLnz58nKCiI1157zRJNEZEMRvkjIpai/BGRx7HIkbNhw4bRuXNnunXrRlxcHN9//z0jRoxg7ty5lmiOiGQgyh8RsRTlj4g8jkWOnIWFhdGtWzcAXF1d6dGjByEhIZZoiohkMMofEbEU5Y+IPI7FbggSFBRknr558yaGYViiKSKSwSh/RMRSlD8i8jgWOa2xR48etG7dmlq1amEymQgMDOTjjz+2RFNEJINR/oiIpSh/RORxLFKctWvXjvLly7Nnzx4cHR3p2bMnpUqVeuL1/fbbb8ycOZPo6Ghq1qyJt7c3gYGBTJgwgbi4OJo2bcqgQYMAOHHiBN7e3kRGRlKlShV8fX1xcrLI1yAiFpDe+SMikloa/4jI41hsqyxTpgxlypT5z+u5fPkyPj4+rFixgly5ctG9e3e2bduGj48PixcvJl++fPTu3Ztt27ZRp04dhgwZwrhx46hUqRIjRozA39+fTp06pUOPRMRWpFf+iIiklcY/IvIoFrnmLD39+uuvNGvWjLx58+Ls7MzUqVPJnDkzhQsXplChQjg5OdGyZUsCAgK4evUqsbGxVKpUCYA2bdoQEBBg2Q6IiIiIpJHGPyL2yeaPZ1+6dAlnZ2d69uxJSEgI9erVo2TJknh6epqX8fLyIigoiODg4BTzPT09U1yYKyIiImILNP4RsU82X5wlJSWxf/9+Fi9ejJubG3379iVz5swPLGcymR56RySTyZSmz8uVy/2J22oNPD2zWroJ6c4e+wT22S977JOIiCVo/JM29vj7oz7ZjrT0y+aLs9y5c1OjRg08PDwAaNCgAQEBATg6OpqXCQ4OxsvLizx58nDz5k3z/JCQELy8vNL0ebduRZKcnDLkbOkPKSQkIlXLqU+Wl5p+2XKfHBxMNv9jLyJiKZYe/9jy78+j2FK/1CfbkZbxj81fc1avXj127tzJnTt3SEpKYseOHTRp0oQLFy5w6dIlkpKSWLduHbVr16ZAgQK4urpy4MABANasWUPt2rUt3AMRERGRtNH4R8Q+2fyRs4oVK/Luu+/SqVMnEhISePXVV+nYsSPFihWjf//+xMXFUadOHZo0aQKAn58f3t7eREVFUbZsWbp162bhHoiIiIikjcY/IvbJ5oszuPvckHbt2qWYV6NGDX788ccHli1TpgwrV658Vk0TEREReSo0/hGxPzZ/WqOIiIiIiIg9UHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIpIOJk2axLBhwwA4ceIEbdu2pXHjxowcOZLExEQArl27RufOnWnSpAl9+vQhKirKkk0WERERK6PiTETkP9q9ezc//PCDeXrIkCGMGjWKjRs3YhgG/v7+APj6+tKpUycCAgIoX748s2fPtlSTRURExAqpOBMR+Q/Cw8OZOnUq77//PgBXr14lNjaWSpUqAdCmTRsCAgJISEhg3759NG7cOMV8ERERkXucLN0AERFbNnr0aAYNGsT169cBCA4OxtPT0/y6p6cnQUFBhIWF4e7ujpOTU4r5aZUrl3v6NFzkETw9s1q6CSIiGZKKMxGRJ7RixQry5ctHjRo1WL16NQCGYTywnMlk+tf5aXXrViTJyQ+uS6ybrRU7ISERlm7CU+fgYNLODhGxOirORESe0IYNGwgJCaFVq1bcvn2b6OhoTCYTN2/eNC8TEhKCl5cXHh4eREZGkpSUhKOjo3m+iIiIyD265kxE5AnNnz+fdevWsXbtWj744APq16/PhAkTcHV15cCBAwCsWbOG2rVr4+zsTJUqVdiwYUOK+SIiIiL3qDgTEUlnfn5+TJgwgaZNmxITE0O3bt0A8PHxwd/fn2bNmrF//34GDhxo2YaKiIiIVdFpjSIi6aBNmza0adMGgDJlyrBy5coHlilQoACLFy9+1k0TERERG6EjZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVUHEmIiIiIiJiBVSciYiIiIiIWAEVZyIiIiIiIlZAxZmIiIiIiIgVsJvibNKkSQwbNgyAEydO0LZtWxo3bszIkSNJTEwE4Nq1a3Tu3JkmTZrQp08foqKiLNlkERERm7RgwTd06fIWXbq8xYoVywCIiYlh+PDBdO/egV69enDu3FkAQkNv8eGH/enevSO9e79tnv+wdR4/fsw8ff78Od57r5t5OjY2llGjhtG9ewfefrsTBw/uB+DQoQN069aePn16EhYWCsC2bVvw919qfu/Bg/tZvnxJ+n4JVkLjHxH7YhfF2e7du/nhhx/M00OGDGHUqFFs3LgRwzDw9/cHwNfXl06dOhEQEED58uWZPXu2pZosIiJik44ePczevXtYsGAp33yziB9/XM3FixdYuXIZRYoUZeHCZbz77vtMnz4FgLlz51CrVh0WLvye3r37MXny+AfWeePGDc6ePUPZsuUB2LbtNwYP/oDY2FjzMv7+S8mXLz8LFy5j7NiJjB8/5v/nf88XX8ymXbsObNr0C4ZhsHbtKlq1amN+b+XKVdi373fCwsKe4jfz7Gn8I2J/bL44Cw8PZ+rUqbz//vsAXL16ldjYWCpVqgRAmzZtCAgIICEhgX379tG4ceMU80VERCT1smfPTt++A3ByciJTpkzky1eAkJBgunZ9m549ewNw48Z1smXLBsCrr9akQYNGABQvXoLg4KAH1rl69XLq1q1vnt648We8vX1TLFO+/Au0afMWAAUKFCQmJob4+HicnZ2Ji4sjNjYGZ2cntmzZTNWq1XF1zZTi/bVq1WXt2lXp90VYmMY/IvbJ5ouz0aNHM2jQIPOPQHBwMJ6enubXPT09CQoKIiwsDHd3d5ycnFLMFxERkdR77rkilC9fAYDjx49x9uxpypW7O+3k5ETfvu/yxRd+tG17t5CqWbOO+Td6/vxvqF277gPr3LMnkPLlK5qnP/30M/Lly59imcqVq5A3b14AVqz4nvLlX8DFxYXOnbszevRwtm3bQoMGjVmzZiWtW7d74DMqVKjI7t27/vsXYCU0/hGxT06WbsB/sWLFCvLly0eNGjVYvXo1AIZhPLCcyWT61/lplSuXe9obakU8PbNaugnpzh77BPbZL3vsk0hGderUSUaO/Jjhw0fj5uZmnj979jecO3eWDz/8H99/v9r82vz5c/njj4PMmvX1A+u6fv06uXPnTtXnrl//IytXLmfWrLkAlC5dhrlzFwLw668BVK/+KkePHmbFiu8pWbI0773XB4A8efJw7drV/9Rna6HxT9rZ4++P+mQ70tIvmy7ONmzYQEhICK1ateL27dtER0djMpm4efOmeZmQkBC8vLzw8PAgMjKSpKQkHB0dzfPT6tatSJKTUwadLf0hhYREpGo59cnyUtMvW+6Tg4PJ5n/sRTKqw4f/YPTooYwYMYaqVasDcOTIH+TPX5DcuXNTvHgJPD29CA4OokiRosyYMYUjR/5g+vQvyZLlwe3e0dEBR0fHx36uv//3rF7tz/Tpc/DyypPitaSkJNasWYWf33SGDfuIyZOnMn365/z110Wee64Ijo5OODikvSixRtYw/rHl359HsaV+qU+2Iy3jH5s+rXH+/PmsW7eOtWvX8sEHH1C/fn0mTJiAq6srBw4cAGDNmjXUrl0bZ2dnqlSpwoYNG1LMFxERkdS7efMm3t4fM3bsJKpVq2Gev2/f73z//WIA/vrrIrdv3yZ//gKsXr2Co0ePMG3aHLJly/7QdebJk4+bN0Me+bmBgTtZvdqfWbPmPnDKI9w9avbKKzXJnDkzSUmJODo6YjI5EBcXB0BQ0A3y5y/4pN22Khr/iNgvmz5y9m/8/Pzw9vYmKiqKsmXL0q3b3Vvx+vj4MGzYML788kvy5cvHlClTLNxSERER27Jq1XLi4uKYOnWyeV6/fgPo1Kkb48ePoXv3Dri4uDBqlC8uLi4sWvSt+Vo0gEyZMjFnzrcp1vnKKzU5dOggjRo1+dfPXbz4W2JiYvjoow/M82bM+IqsWbOaj5pNnToLgBYtWtGjRyeKFStOiRKlgLu33K9R49V0+x6skcY/IrbPZDzsZGT5V/92WuPKvY/e42cN2lX1TNPh4vCAGU+5Rf9djib909Snr84se8otSh+9S3ZI9WmNkSuXP4MW/Xfu7drrtMZ08LAMEutnj/mTnoKCbjBlyiQmTZr61D5j4MC++PiMJ2fOnIAyKK0edlqjLYx9QOMfW8mftGSPPY9/7PLImYiIiDx9Hjky4+j834cSnp5ZefHFily7dp6KFSs+/g1ptGP7DqpWrW4uzERErJWKMxEREXkijs5O6bb3+u18XnDmJJFnTqbL+u5Xq117yjxfKd3XKyKS3mz6hiAiIiIiIiL2QsWZiIiIiIiIFdBpjSIiNuTmzRD69HmX5cvXAJCcnMznn0/kjz8O4ubmxiefTCZv3rz079+biIi7FyBHRUWSmJjIDz9sMK9n3DgfXn21FvXqNXzo5/j6ejNkyAji4mJp3/4N8ucvAEC5cuX54IMP6d37HfOy4eFh5MuXny+/nMe0aZ9z8OB+Xn65Gv/730AAfHyG07//R+aHDE+ZMolOnbqTN2/e9P56REREbJqKMxERG7Fv3z68vUcRGnrLPG/Tpl+IjIxkyZKV7Nq1g2nT/JgwwY8ZM74yLzNwYF/atHkLgNu3w/Hzm0hg4A5efbXWQz9n8+ZfKF26DG5ubhw+fIhGjZoyePCwFMssWLAUgPj4eN57rzsDBnxEREQEly9fYuHC7xkyZACRkZFcuXIZD4/c5sIMoH37zsyePY2xYyek23cjIiJiD3Rao4iIjVi9ejVjxoxPMW/v3t00aNAIgFdfrcXRo4dJSEgwv75162ZcXV2pXbsuANu3b6VSpcrUr//av37O8uVLadiwMQCnTp3g7NnTvPNOZz7+eCDBwUEpll21yp8XX6xMmTJlcXR0JCEhEcMwiI+Px8HBgcWLv6Vz524p3lOgQEFu3gzh+vVrT/xdiIiI2CMVZyIiNmLChAmUKlU6xbzQ0Ft4eOQyT2fLlo3bt8PN04sWzadnz97m6ZYtW9O27Vv/+hnh4eGEh4eRO7cnAE5OTjRp0oxvv11CrVp1GT9+jHnZxMREVq1aTteubwPg5uZGlSov8/bbnale/VUuXDhP7tye5nXdr1y5Cvz+++409V9ERMTe6bRGEREblpycjMlkSjHv3vSpUydxdXWlVKkyqV7ftWtX8PT0Mk936dLD/O+WLVvz5ZczSEhIwNnZmV27tvPCC5XIlevvUxa7dn3bXKwNG/YhH300jNmzp3Hp0kU6duxKpUqVAfDyysO1a1fT3F8RERF7piNnIiI2LHduT8LC/r4GLTIykuzZcwCwa9d26tVrkKb1OTg44OT09367H3/8gbCwMPO0yXR3mbvr30Hdug9f/5Ejf+DpmYfIyEgiIiL55JNJLFr0rfl1R0dH83pERETkLv0yiojYsJdfrsamTb8AsHv3TkqUKGUuro4fP0b58i+kaX358uVPcV3ZsWNHWL9+LQC//hpA+fIv4OjoaF5/hQoPX/+iRd/SpUt3EhMTcXJywsHBgfj4ePPrQUE3KFCgYJraJiIiYu9UnImI2LCGDRvj7u5Oly5vMW/e1wwcONj82o0b11Ocopga2bPnIHv2HNy8GQLA++//j4MH99O161usXbuajz76+66NISHB5Mzp8cA6Dh06QN68+ciTJy8lSpTk5s0Q3n67E61btzUvc+TIIapVq5HW7oqIiNg1XXMmImJjfv11h/nfjo6ODB48/KHLfffdin9dx8iRY/71tTff7MDPP6+na9ceeHjkYsqUmQ9dbuPGbQ+d/+KLL/Hiiy8Bd69/mzDBL8Xrf/11idy5vfDyymOel5SUhJ/fRI4dO4yTkxP9+39I5cpViIuLxdd3FJcuXcDT04tPPplE1qxZOXfuLBMnjiU+Pp7ChYvi7e2Li4vLA22597w2Nzc3ANav/5FTp07w4YdDgbuPFvj0U1+CgoLw8PBg7NiJuLu763ltIiJiETpyJiIiKdSv/xqnT58kOjrqqax/+fIl9OnTP8W83377lZiYaBYv9sfXdwKTJo37/2WX8txzhVmyZCW1atVh/vyvAZg1axrvvNObhQuXkSlTJgIC1j/wOfc/ry0xMZH58+cybdrnKZaZOvUzKleuwoIFSylbtjzLln2X4nltly5dIDIykpMnT/zr89pERETSi46ciYjYsBw5s+DslP772ebMmZWu60tITCY87G6xN2TIiAdef+21JtSr1xCAoKDrZMuWDYC9e/cwYMBHADRq1IwePTrywQcfYRjJREdHYxgGcXGxDz1qtnz5Uj799DMAzp07S3h4GO+//z8uXjwP3L3T5d69exgxwgeAjh27EBkZ+a/Paxs06OMU67//eW358uVPj69JREQyOBVnIiI2zNnJgZV7QyzdjMdqV/XBZ539k5OTE2PGjGTLlk2MGjUWgFu3bpqf45Y1a1YiIiIA6N37f/Tv35tp0/zInduTkSNTPlT7n89rK126DKVLl2HDhp/uWyYMd3d3vvlmDr//HkjhwkUZPHhYiue1NW7cLFXPa7v/ejoREZEnpdMaRUTEaowZM54VK35k9uzpXL9+DcMwUjzHzcHBhGEY+PlN4LPPvmDt2gAqV67CV1+lPNL3z+e1PUxiYiLXrl2lVKnSLFy4jGLFijNnzt3r67p2fZsFC5bSsWMXFi/+li5dejB79jSGDh3EH38cNK9Dz2sTEZH0pOJMREQs7syZ01y+/Bdwt+ApW7Y8Fy9eIHduT0JDQ4G7z3DLmjU74eFh3L4dTqVKlTGZTDRr1pIjRw6lWN8/n9f2MNmz58DR0dH8rLY6depz5sypFMvoeW0iIvIs6RdFREQs7vTpk3z11UwMwyAsLJTTp09SsmRpqlSpyubNd5/j9ssvP1O1ajWyZ89BQkICZ86cBmDPnkBKliydYn3/fF7bw7i6ulKpUmW2bdsCwN69uylRIuV69Lw2ERF5lnTNmYiIWFzTpi04ceJPunVr//+30h9E7ty5ad++M59+6kuXLm+RI0cOPvlkEg4ODvj6fsqnn44hOTmZvHnzmW/qcc/9z2t72LVi9wwf7sOECWOZP38uuXLlYsyY8ebX7n9em5dXHvPz2rp372le5siRQ7Rt+1b6fyEiIpIhqTgTERGLc3BweOjz2jJnzswnn0x8YH7Fii8yf/7SR67z/ue13dOsWUuaNWtpns6bNy/Tps1+6Puf5HltIiIi/4WKMxERsSoeOTLh6Oz8n9fTvn0bBg4cSObMJtzd3dOhZSnNmOHP8KGD0329IiKScak4ExERq+Lo7Ex4wIx0WZdv0xIk7pxPeLqsLaUPX/EkR9GihIREPIW1i4hIRqQbgoiIiIiIiFgBFWciIiIiIiJWQMWZiIiIiIiIFVBxJiLyH8ycOZPmzZvTvHlzJk+eDEBgYCAtW7akUaNGTJ061bzsiRMnaNu2LY0bN2bkyJEkJiZaqtkiIiJihVSciYg8ocDAQHbu3MkPP/zAmjVr+PPPP1m3bh0jRoxg9uzZbNiwgWPHjrFt2zYAhgwZwqhRo9i4cSOGYeDv72/hHoiIiIg1UXEmIvKEPD09GTZsGC4uLjg7O1O8eHEuXrxI4cKFKVSoEE5OTrRs2ZKAgACuXr1KbGwslSpVAqBNmzYEBARYtgMiIiJiVVSciYg8oZIlS5qLrYsXL7JhwwZMJhOenp7mZby8vAgKCiI4ODjFfE9PT4KCgp51k0VERMSK6TlnIiL/0ZkzZ+jduzdDhw7FycmJCxcupHjdZDJhGMYD7zOZTGn+rFy50v9hys+Kp2dWSzfhqbDHfqlPIiKWoeJMROQ/OHDgAB988AEjRoygefPm7N27l5s3b5pfDw4OxsvLizx58qSYHxISgpeXV5o/79atSJKT/y70bGnAmdqHNdtSnyB1/bLHPoFt9euffXJwMNn0zg4RsU86rVFE5Aldv36dfv364efnR/PmzQGoWLEiFy5c4NKlSyQlJbFu3Tpq165NgQIFcHV15cCBAwCsWbOG2rVrW7L5IiIiYmV05ExE5AnNmzePuLg4Jk6caJ7XoUMHJk6cSP/+/YmLi6NOnTo0adIEAD8/P7y9vYmKiqJs2bJ069bNUk0XERERK6TiTETkCXl7e+Pt7f3Q13788ccH5pUpU4aVK1c+7WaJiIiIjdJpjSIiIiIiIlbALoqzmTNn0rx5c5o3b87kyZOBuw+HbdmyJY0aNWLq1KnmZU+cOEHbtm1p3LgxI0eOJDEx0VLNFhEREXkiGvuI2CebL84CAwPZuXMnP/zwA2vWrOHPP/9k3bp1jBgxgtmzZ7NhwwaOHTvGtm3bABgyZAijRo1i48aNGIaBv7+/hXsgIiIiknoa+4jYL5svzjw9PRk2bBguLi44OztTvHhxLl68SOHChSlUqBBOTk60bNmSgIAArl69SmxsrPmhsW3atCEgIMCyHRARERFJA419ROyXzRdnJUuWNAfOxYsX2bBhAyaTCU9PT/MyXl5eBAUFERwcnGK+p6cnQUFBz7rJIiIiIk9MYx8R+2U3d2s8c+YMvXv3ZujQoTg5OXHhwoUUr5tMJgzDeOB9JpMpTZ9j6w+stKUHhqaWPfYJ7LNf9tgnERFLeVZjH9D4xxqpT7YjLf2yi+LswIEDfPDBB4wYMYLmzZuzd+9ebt68aX49ODgYLy8v8uTJk2J+SEgIXl5eafqsW7ciSU5OGXS29IcUEhKRquXUJ8tLTb9suU8ODiab/7EXEbGUZzn2gQfHP7b8+/MottQv9cl2pGX8Y/OnNV6/fp1+/frh5+dH8+bNAahYsSIXLlzg0qVLJCUlsW7dOmrXrk2BAgVwdXXlwIEDAKxZs4batWtbsvkiIiIiaaKxj4j9svkjZ/PmzSMuLo6JEyea53Xo0IGJEyfSv39/4uLiqFOnDk2aNAHAz88Pb29voqKiKFu2LN26dbNU00VERETSTGMfEftl88WZt7c33t7eD33txx9/fGBemTJlWLly5dNuloiIiMhTobGPiP2y+dMaRURERERE7IGKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAirORERERERErICKMxERERERESug4kxERERERMQKqDgTERERERGxAhmyOPvpp59o1qwZr732GkuWLLF0c0QkA1H+iIilKH9ErJ+TpRvwrAUFBTF16lRWr16Ni4sLHTp0oFq1apQoUcLSTRMRO6f8ERFLUf6I2IYMd+QsMDCQ6tWrkyNHDtzc3GjcuDEBAQGWbpaIZADKHxGxFOWPiG3IcEfOgoOD8fT0NE97eXlx5MiRVL/fwcH00PluLrZR5/5b+x+6bOasT7El6SctfXJ3yvIUW5K+Utsvk5vbU25J+rm/T2n5f7MX/zV/4OHfm/LHslLbL3vMH7CdDPpnnzJaBj2N8Y+tZA/YZ/7Y4/jHHrMH0jb+yXDFmWEYD8wzmVL/h5Az58P/uJtVyvXEbXqWcuVyT/Wy2er0eHoNSUdp6VPnoi2fYkvSV2r7laWZ/fXJXv3X/IGHZ5Dyx7JS2y97zB+wnQxS/qT/+MdWsgfsM3/scfxjj9kDaeuX7ezySCd58uTh5s2b5ung4GC8vLws2CIRySiUPyJiKcofEduQ4YqzV155hd27dxMaGkpMTAy//PILtWvXtnSzRCQDUP6IiKUof0RsQ4Y7rTFPnjwMGjSIbt26kZCQQLt27XjhhRcs3SwRyQCUPyJiKcofEdtgMh52ErKIiIiIiIg8UxnutEYRERERERFrpOJMRERERETECqg4ExERERERsQIqzkRERERERKyAijMREREREREroOLsGbPXm2MmJiammLaHftpDH0T+yR7/ru0xf8B++iEC9vn3rOyRp0G30n+GIiMjGTduHFWqVMHFxYXXX3/d0k1KFxEREWzYsAEnJydq165Njhw5cHZ2Jjk5GQcH26//z507R/HixS3djHRjGAYmk4mYmBgyZ85s6ebIM2SPGWTv+QP2lUHKn4xJ2WOb7Cl7wHbyxz7+emyEu7s7HTp0oFixYixYsIBPP/2UnTt3kpycbOmm/SfR0dGULVuWgwcPMm/ePEaMGEFUVBQODg4kJSVZunlpduHCBY4fPw7Ajh07mDZtmoVblD6uXLkCgMlkIiAggOnTpwPaQ5aR2GMG2Vv+gH1mkPInY1P22AZ7zB6wvfxRcfaM3AugSpUqUblyZb755hucnZ355ZdfCAgIsNo/kNTIkycPFSpUwMfHhy5duuDk5ESbNm2IjIzE0dHRpsI3MjKSZcuWsXr1ai5fvkyuXLlS7F2x1f+nW7duMWPGDGbOnAlAcHAwbm5uwN2wsqX/I3ky9ppB9pQ/YJ8ZpPzJ2JQ9tsEeswdsM39UnD0DhmHg4ODAsWPH2L17N4cOHcLDw4O+fftSuHBhDh48yPXr1y3dzDS7t6GePXuWEydOEB4eTsGCBZkwYQJVqlThnXfeITY21qYO77u7u9OoUSOcnZ1Zvnw5u3bt4sqVK5w5c4aTJ09iMpks3cQn4ubmRu3atbl8+TKLFy8mU6ZMGIbBrVu3AGzq/0jSzh4zyB7zB+wzg5Q/GZeyx3bYY/aAbeaPrjl7yhITE3FycmL79u2MGTOGRo0a4e7uzv/+9z8Abt++zahRoyhVqpR5ni24d97upk2bmDVrFjlz5qRLly7Ur1/fvMyYMWMoXbo0HTt2NC9vrf7ZvpMnTxIQEMCff/7Jjh076N69Oxs3bqRYsWIULFiQsWPHWrC1qffPfv3yyy/88ccfbNiwgcTEREqXLo2zszNZsmSha9euVKpUyXKNlafCHjPI3vIH7DODlD8Zm7JH2WNJtpw/TpZugL26cuUKBQsWxMnJibCwMObOnYuvry+1atUC7p6rfPr0aSpVqsTIkSP55JNPuHbtGvny5bPqDTksLAzDMPDw8ODWrVt8+eWXfPbZZxQuXBhnZ2fi4+O5dOkSJUuWpHr16uZzl625T/B3+1asWEFgYCA1atTg5Zdf5vbt29SrV4/WrVvTu3dvbty4QZYsWSzc2tS5P5h++uknIiMjyZw5M+XLl+f48eO4u7szfvx49u3bx5EjR8iaNauFWyzpyR4zyF7zB+wvg5Q/GZeyR9ljabaeP9Z3LM/G3TsQOWvWLI4dOwZAzpw5yZs3L87OzubXjx8/jq+vL9HR0Xh6elKwYEESExOtekOOjo5m+fLlREdHk5SUhIuLC/Hx8eY7FMXHx/PDDz+wYcMGAOrXr8+5c+c4efKkhVueOitWrGDRokU0atSIAgUK8Oqrr9K8eXNKlSrFypUruXnzJmXLlqVw4cKWbmqq3PtbWrBgAYsWLeL69es899xzNGvWjI4dO5IjRw5WrlxJw4YN+fDDD+3qjkwZmb1mkL3nD9hXBil/Mh5lj7LHWth6/qg4S2cREREATJgwgRw5cvDGG28AkDt3blavXk1kZCQArq6uPPfcc7i6uuLg4MCLL76Io6Ojxdr9OJGRkWTKlImePXvi4uLCnDlzSEpKombNmsycOZOwsDBcXFxwdXXlxo0bxMXF4eLiQtOmTSlQoIClm/9YiYmJnD9/niFDhtC0aVNq1KhBREQEFy5coEyZMmTJkgUPDw9LNzPNYmNj2bt3L1988QUffvghlStXJjIykpCQEF566SUuXbpEaGiopZsp6cgeM8je8wfsM4OUPxmLskfZY01sOX90zVk6Sk5OZsSIERQoUIA33niDggUL8vrrr5M/f37mzJnDhx9+SHx8PHnz5uXAgQP069ePhg0bWrrZjxUfH8/KlStxc3OjQoUK7Nu3jz/++INixYrx3HPPcfr0abZu3UqHDh2YO3cuPj4+1KxZE4CEhAScnZ0t3IMHPew88GnTpnHo0CG+/PJLMmfOTEhICMOGDWP69OlkypTJan887vfPfkVHR9O+fXvefvtt2rRpQ3JyMr///jubNm1i1KhRREVF2cxpCvJ49phB9pg/YJ8ZpPzJuJQ9yh5Ls6f8UXGWzhISEqhevTpRUVEcOnSIzJkz07ZtW3Lnzs1XX33F77//bj68WrlyZZu4WDQ5OZktW7awYMECYmJimD59OleuXOGXX37Bw8OD1157jaNHjxIZGUnJkiWpUaOGVffr/rZt2LCB0NBQsmXLRv78+fntt9+Ii4tj8ODB7N69m7lz5zJr1iyb2Gt0f7927NhBtmzZyJYtGzdu3GD69On07t2bunXrsnr1atatW8fs2bPJlCmThVst6c3eMsje8gfsM4OUP6LsUfZYir3lj4qzdHTvqfAff/wx27Zto23btnz88ccAvPXWWyQlJbFq1SoLtzJt7v3B37x5kwEDBuDo6EinTp1o1KgRgYGBbN682Xy3IlvYgO/37bffsmXLFl5//XUWL17M66+/TuXKlVmzZg0XLlwgKSmJ0aNHU6ZMGUs3NU0WLFjApk2bqFu3LkuXLmXUqFHcuXOHSZMmUa9ePQ4dOsT06dMpUaKEpZsq6czeMsie8wfsM4OUPxmTsse22GP2gB3ljyH/WXJysmEYhnH06FHj9OnTRnh4uJGUlGS88sorxqhRo8zLvfPOO8a+ffss1cw0u9evixcvGmfPnjWioqKMX3/91Rg6dKjh7+9vGIZhbNy40Rg3bpxx6dIlSzY1zW7dumV89NFHhmEYxvz5840+ffoYUVFRxvr1643ExEQjODjYiIiIsHAr0+7s2bNGjx49DMMwDD8/P2PgwIFGaGiocfbsWePGjRvGqVOnjBs3bli4lZLe7DGD7Dl/DMM+M0j5k/Eoe5Q91sKe8ke30k8HJpOJzZs3M378ePLly0exYsX46KOP+Omnn2jSpAlXr14lT548zJ071yofdvdvTCYTW7duZezYsRQtWpRixYoxcuRIoqOj2bt3L+fPnyd37tx069aNQoUKWbq5j5SUlJTinGkXFxdiY2Pp2bMnTk5OzJo1iyNHjrBmzRqaNWuGp6enBVubevf2Vt6TlJRE9uzZmTVrFidPnmT69Ols3bqVPXv24OvrS548eSzYWnla7DGD7Cl/wD4zSPkjyh5lj6XYc/7YxpZi5U6cOMG8efNYvXo1b775pvlcXRcXFzZt2kTp0qVp1KiRzQTTPQcPHmTatGlMnz6dJk2a8NNPPzF27Fhef/11GjZsSHBwMKVLl7b6cEpISDAH065duzh06BDJycnUrl2byMhIunfvjpOTExcvXsQwDKKjo823/LV29/6mzp07R1xcHEWLFiU5OZnVq1ebL+w9efIkycnJADbTL0kbe8wge8kfsN8MUv6Isse62Wv2gH3nj645Swc7duxgzZo1fPzxx3z//fcULlyYFStWkDt3brp06ULVqlWBh98hx5pt3LiRW7du0aJFC+bMmUOtWrWYNGkS5cqVY/z48ea9Ftbcr3PnzplDds2aNeYNtmHDhhQvXpy//vqLgIAAChUqxKlTp5gxYwalSpWydLMfa9++fdy4cYOWLVvy3Xff8e233/LCCy9QvXp1PD092b59O5cuXaJatWqsX7+eadOmWd1zPCT92GMG2UP+gH1mkPJH7lH2WG+f7DF7IGPkj05rfAL3NsigoCDy5MlDmTJlaN++PefOncPJyYk33niDGzducOTIEbJnz25+nzVvxPB3v2JjY3F1dSV37txERkayc+dOcufOzUsvvUTVqlX56aefOH78OGXLlgWsu19OTk5kyZKFnj174ujoyIYNG7hw4QLz58/H1dWVatWq8dprrxESEsJzzz1Hvnz5LN3kVAkPD2f8+PGcPHmSoKAg5s+fz86dOzl//jyxsbG8++67bN68GWdnZ5sMJnk0e8wge8wfsM8MUv5kXMoeZY+lZYT8sZ3jzFbEZDKxbds2evfuzfvvv8+NGzd4+eWXOXjwIOHh4Rw8eJCtW7fy9ttvU7p0aUs3N9VMJhObNm2iX79+jBs3juzZs9O2bVvWrFlDoUKFuH37NufPn2fRokXmcLJW9w5jFy5cmH79+lGkSBGOHz9OREQEJUqUoH379ly4cIGNGzfi7OxMtWrVbCKY7h3ofu2115gwYQI//fQTDg4OFC5cmHbt2lGiRAn++usvtmzZQqdOnejcubNNBpM8mj1mkD3lD9hnBil/RNmj7LGUjJQ/Ks7S4N4fRkhICMuWLWPMmDEUKVKEOXPmcOjQITw9PYmPj2fIkCH07t3bfDjf2t3rV2hoKCtXrqR58+bkypWLTz75hMuXL1OnTh0mTJhAt27daNu2LSVLlrRwix/NMAzzucjHjh0jMTGRli1bUrt2bcaMGcPt27d54YUXaN++PbGxsVb7EMJ/uv8UivDwcF5++WUmTZrEtm3bWLduHa6urrRp04ZixYpx5coVYmJiLNxiSW/2mEH2lj9gnxmk/MnYlD3KHkvKaPmja87SaNeuXZw6dYo///yTzz//HIDJkycTHBzMm2++SdGiRUlISKBAgQJWfz7y/fbt28fu3btJSkpi0KBBREZGsmTJEvbu3cvQoUPJmjUr0dHRFC9e3Gb6tWTJEpYuXcrLL79M48aNKVSoEAsWLCAkJIQxY8aQM2dO4uPjcXFxsXRT02TRokX88ssvZMuWjWHDhnHy5EkmT57M//73P1q3bk1iYiLR0dFky5bN0k2Vp8AeM8ge8wfsM4OUPxmXssc2+gT2mT2QgfLnadyf317t37/fqF27tvHFF18YlStXNqZOnWp+zdfX1+jdu7dx+/ZtyzUwje49y+Pw4cNGo0aNjN69exvt27c3duzYYSQmJhoRERHGjBkzjM6dOxtRUVEWbm3aHD161GjRooVx7do1IzIy0jAMw7h+/bqxe/duY8yYMcbQoUONpKQk83dgKwICAozWrVsb586dMz+vIyIiwti3b5/x8ssvG+vXr7dwC+VpsqcMsuf8MQz7zCDlT8al7LEd9pg9hpGx8kc3BEmlCxcusHLlSnr16kXnzp157bXXGDp0KCaTiQEDBjB69GjOnTtnU9W6yWRi//79TJs2jU8++YSqVasydepUfvnlFwBeeeUVevToweuvv46bm5uFW5s2Tk5OFCtWjHz58pGcnExCQgKbN28mKiqKXr164ejoaFO39r0nKSmJunXrUqxYMeLj40lOTsbHx4e2bdsyefJkihYtaukmylNibxlkz/kD9plByp+MSdljW+wxeyBj5Y/t/e9YSHBwMBEREezYsYPz589TtmxZJk+ezNq1a82H923xwsNMmTKxb98+tmzZAkCfPn3w8PBgzZo17Ny5E3d3d5577jkLtzLt8uXLx9GjR/nqq69wcHDA2dmZoKAgHBwcyJcvH15eXpZuYqolJSWZ/52QkMCmTZtISEjAxcUFBwcHcubMiZubG3Xr1qVw4cIWbKk8TfaYQfaaP2A/GaT8EWWPbbGX7IGMmz8qzv6F8f+X4p0+fZojR45QqFAh3n77bYoWLcr69eu5ePEizz//PDNnzqROnToWbm3q3evXhQsXuHjxIiVLlmT9+vV89913LFmyhEyZMtGnTx/y5s1rE09TNx5yyWRiYiLZs2dn9uzZLFu2jNGjR/P111+za9cu6tWrZ4FWpt2ff/7J+++/D4CjoyOJiYkAtGrVipIlS9KuXTt2797N0qVL+f3338mVK5clmytPgT1mkL3lD9hnBil/MjZlj7LHkpQ/uiHIQxn/f9Hn9u3bGT9+PJUrV+bQoUO8//77GIbBqVOncHBw4K233qJIkSIp3mMLfvvtN+bMmUOePHm4c+cOnTt3pkSJErRt25YBAwbQo0cPm+jP/W08e/Ys8fHxD9zm9saNG6xYsYJMmTJRv359m9m7d/v2bQYNGkSOHDmYMmUKQIqLd6dOnUpMTAw3btzggw8+oESJEpZsrqQze84ge8kfsN8MUv5kXMoe2+iLvWYPKH8A3RDkfnFxceZ/X79+3WjTpo2xb98+wzAMY8uWLcagQYOMrVu3GocOHTJ8fHyM8+fPW6qpTyQxMdH466+/jBYtWhinT582bt++bRw4cMBo2bKlsW/fPuPgwYNG6dKljatXrxqJiYmWbm6qffXVV8Zbb71ltGjRwjh06JB5flJSkuUa9YTuv0B38+bNRr169YzBgweb58XGxqZYPj4+/pm1TZ4+e84ge80fw7CfDFL+ZFzKHmWPpSl//qbTGv9fZGQk69at4/r168TGxuLm5kaJEiUoXrw4ycnJ1K1bl4oVK/LNN99QqVIlBg4caBMXH966dYtly5YBdw8Pm0wmcuTIQcmSJcmWLRuVK1emRYsWbN++nRdffJHdu3eTP39+HB0dLdzyfxcaGkp8fDwAR44cISAggGXLlrFmzRoqVapEeHg48fHxNnnB6709YfPnz2fp0qW0a9eOy5cvmw/xu7q6mvsOdy/8Fftgjxlkj/kD9ptByp+MSdmj7LEGyp+/2d7/3lMSERHBmTNn+Oyzzxg/fjwxMTFcunSJVatWmf/Iy5cvj5eXF4mJieTIkcOyDU4lBwcHfvzxR/r168fEiRPx8PAgU6ZMrFixwny+cpYsWQgPDyc5OZmsWbMCDz+X2RpcuXKFadOmkZycDICLiwuurq7cuXMHR0dHEhIS+OSTT1ixYoWFW/rkoqOj2blzJ8OHD6dv374sXboUR0dHPvroI4AUzyWxhdMvJHXsMYPsLX/A/jNI+ZPxKHuUPdZC+XOXijPuboz58uWjatWqbNmyhejoaDw9PZk4cSLz589nzJgxzJkzB19fX5o2bWoz1bphGOTMmZOOHTsSGBjItWvXcHNzo0WLFhw7dgxvb2+2bt3KggULaNy4MQ4ODua+WesffcGCBRkyZAjHjh3j119/JVOmTBQrVoydO3cSFhaGs7MzJUuWtHQz0+SfPwYODg7ExMRw/fp183TXrl3ZsWMHo0aNskQT5Smzxwyyx/wB+8sg5U/GpuxR9liS8ufhdEOQ/3fkyBFOnz5N6dKl8ff3x8PDg/fee4/4+HhWrlyJg4MD5cqVo0aNGjZzwSjAtWvXOHPmDPHx8cybN4+qVavy4Ycfcvz4cfNesZo1a1r9HZeSk5MxmUzm7/3bb79l8+bN9OvXj9OnT3Px4kUiIiIoXrw4q1at4ptvvrH60y4g5UW9gYGBuLu7kytXLo4fP46vry9ff/01ZcuWZd26dRw/fpyOHTtSqFAhC7dangZ7zCB7yR+wzwxS/ggoe6ydPWYPKH8eRcUZd289umLFClasWMHEiRPJnDkz06ZNo3Tp0jz//PNkzZqVihUrWrqZaZKUlERcXBx9+vShUqVK9O7dm+vXrzNkyBAaNGhAu3btyJo1q/kBi7YSuL///jvZsmWjRIkSbNy4kbVr1/L2229jMpm4du0a169fp3nz5jYRTHA3dB0cHFi8eDE///wzjRo1YsqUKaxdu5Zt27bxzTffUKtWLfbv38/XX39tM/2StLG3DLLX/AH7yiDljyh7lD2Wovx5hGdz3xHrde/OPGFhYcaSJUuMzp07G0eOHDEuXrxojB492mjatKn5jkW25N6dl06dOmW8++67xrRp04zQ0FDj/PnzRtu2bY2qVasa+/fvt3ArH+/+u/csX77cqFWrljFgwACjd+/exp07d4y1a9ca7777rvHrr79asJVp99dff5n//eeffxpdu3Y14uLijFmzZhm9e/c2bty4YQQHBxuXL182zpw5Y1y9etWCrZWnyR4zyF7yxzDsM4OUP2IYyh5rZ4/ZYxjKn9TI0MXZ0aNHjd69exsRERGGYRjG7du3jaVLlxpdunQxjh49ahiGYQQHB1uyiU/k7NmzxqhRo4zLly+bp99++21j/PjxRkREhBETE2OcPXvWwq1Mm61btxqff/65cf36dePmzZuGn5+fOaBWrlxp9O7d24iMjLR0M1Pl1q1bxscff2zMmDHDMAzDuHLlijFq1Chjzpw5Rs+ePY24uDhj48aNKW4hK/bJHjPIHvPHMOwng5Q/YhjKHltiL9ljGMqf1MpwNwQx7juLs3z58iQkJDB06FAiIyPJli0bNWvWxNHRkXHjxhEeHo6np6cFW5t69/crLi4OuHte8tWrVylevDiDBg1i1apVfP/99zg6Olr9wwjv9ScpKYnIyEjGjx9PYGAgzs7O5MyZk86dO1OyZEn69u1L48aN8fPzI0uWLBZu9eOdPn0aJycn6taty/Xr1/nmm2/Inz8/Fy5cYPHixXzzzTe4uLhw6tQpm+iPpJ09ZpC95Q/YZwYpfzI2ZY+yx5KUP6mXoYoz4//PLd61axdff/01gYGBzJs3D0dHR4YMGUJERAShoaEUKVKETz/91CZuFwt/9+vw4cOsXbuWggUL0rlzZ5ydnfnmm2+Ijo4mZ86cvPzyy7z66qs4OztbusmPZNx3DnhMTAzu7u74+/ub++Pg4EDevHnp2LEjVatWJSIiAnd3dwu3+tEMwyA5OZn58+fj4OBAnTp1qF+/PqdOnWLhwoUMHz6ccuXK0b9/f2bMmMHmzZvp0qWLpZst6cweM8je8gfsL4OUP6LsUfZYivIn7TLcDUG2bNnC5MmTqVOnDn/++SdvvPEGbdq0YeDAgeZnffj4+NCgQQNLNzVNtm/fzpgxYyhVqhQXL15k7ty53Llzh+XLl3Ps2DEiIiIYNWoUtWvXtnRTU83f35/NmzeTKVMmXnjhBTp16kSXLl2oXr06Q4YMAe7uWbL2h0ZCysD9/fffWbFiBd7e3uzfv5/NmzdTtGhR3nrrLfz9/XF1daVmzZo2sYdP0s4eM8ge8wfsJ4OUPwLKHltiL9kDyp8n8qzOn7SU4OBg48iRI4ZhGEZISIjxzjvvGEFBQcaRI0eMTp06GYMGDTJWrVplGIZh3Lhxwzh//rxhGCkvxLRGERER5jaePn3aaNu2rXHx4kXj/Pnzxquvvmp07NjRuHLlihEWFmbs27fP/B3YilWrVhmtWrUyDhw4YBw4cMBo0qSJ4efnZ0RERBh169Y1n69sC/75txQdHW20b9/eGD16tBEWFmb8+uuvxrBhw4yvvvrKQi2Up8keM8je88cw7CeDlD8Zl7JH2WNpyp8nY/enNV64cIGrV68CEB8fT0xMDHfu3GH79u10796dEiVK8OWXX+Lr60uePHnMt+q05lurGobB9OnT2bx5M3D3Kfcvv/wyiYmJrF+/nvnz55MrVy66devG0aNHqVKlChUqVLBwqx/NuO8ArmEYBAUFMWDAACpXrkzlypVZunQpW7duJTw8nCVLltCqVSsLtjb1jPv2GK1YsYK5c+fyxx9/8N1333Hx4kU+//xzXn75ZWrXrs21a9cICwuzcIslvdlbBtlj/oB9ZpDyJ2NT9ih7LEn58+TsvjiLiYlh1apVxMTEkD9/fkaPHo2rqytBQUE0atSI0qVLU716dVq3bm3ppqaayWSiQYMGXLlyBbj7rIi6dety584dgoODKVmyJM2aNSNr1qzkypXLwq19vPs34CVLlrBjxw5iY2NZuHAhMTExAOTMmZNSpUoRGhpK/vz5beZBhPf6tXDhQpYsWUJkZCQzZsxgy5YtzJs3j2vXruHr68urr77KkCFDyJkzp4VbLOnN3jLI3vIH7DeDlD8Zm7LH+tlr9oDy57+w++KsTp06lChRgp9//pmkpCTKlCnDqVOn2L9/Pz///DNTpkyhadOmNvWQRQA3Nze2bdtGcHAwBQsWpFq1ahw6dIhMmTJx6NAhFi1ahLe3N2XLlrV0Ux8pISHBvAH/9ttv/PTTT5QtW5bevXtTvHhxJk+eTFhYGBs3buTChQvkzp3bwi1OnaCgIKKiogA4e/Ys27ZtY82aNZQrV45MmTKxYcMGtm7dypw5c4iPjyc2NjbD353IXtljBtlL/oB9ZpDyR0DZY+3sMXtA+ZMe7Lo4M+4+x40iRYpw+PBhTp8+jWEYVKlShWbNmrFkyRIGDx7MK6+8Yummptq9w98VKlSgTJky9OvXjzt37gBQpkwZTpw4weTJk+nRowdVqlSxZFMf6/Lly+zYsYOkpCSCg4P56quvyJIlC3Fxcbi4uPDGG28QExPDgAED+O677/j000/Jnz+/pZv9WH/++SdNmzZl3bp1REZG4uzsTHx8PGfPnuXEiRO899575MqVi08//ZSlS5cyc+ZMvLy8LN1seQrsLYPsKX/APjNI+SOg7LF29pg9oPxJLxnibo3R0dF88sknuLq60qpVK1544QUcHR25ffs22bNnJzk5GQcH661Tb926xYULF3jppZcwmUwkJibi5OREQkIC48eP58aNG0ycOJEcOXJw584dDMMge/bsKQ6XW6NDhw7x9ddfkzlzZmrVqkX58uWZNGkSlSpVokuXLuZb+YaHh+Pi4oKbm5tlG5xKQUFBdOnShZIlS9KwYUOaNWtGeHg4V65c4ddff2X48OEsWLCA4OBgOnXqRMGCBS3dZHnKbDmD7DV/wD4zSPkj91P2WCd7zB5Q/qQX69win9C9OnPv3r2sWbMGuHurUTc3N0aOHImrqyvr1q1jwoQJREVFkZycDGC1wQR3z6n+7rvv8Pf35/fff8cwDJycnDAMA2dnZ/r06WN+tsehQ4fMgQvWfVEvwIsvvkjZsmUJCAggNDSUkiVL8vHHH7N//36WL19OUFAQADly5LCZYDIMg5w5c1KjRg1CQkI4fvw4GzZswM3NjQsXLnD48GG+//57VqxYwVtvvaVgsjP2lkH2mD9gvxmk/Mm4lD3KHktT/qQfuztyduTIEYYPH86sWbMoUqQIgHnPUHx8POfPn2fDhg2Eh4eTPXt2+vbtS+bMmS3b6H9x/96f0aNHYzKZeP3113nppZceWPa7774jNDSU8+fP89prr9G8efNn3dxUub9PYWFhHDlyhKSkJBYvXkzLli1p06YNFy5cYPDgwbRs2ZKuXbvaxHM8QkND8fDwME9fu3aNGTNmkDVrVsLDw6levTrFixdn7969BAYGMnz4cEqVKmXBFsvTYi8ZZI/5A/aZQcofAWWPsscylD/pz8nSDUhPd+7c4ccffyQxMdF8N5t7wWQYBi4uLpQpU4YyZcoQGxuLg4MDLi4uFm71v7v/oX0XL17kxo0b3Lp1i/j4eKpXr47JZDI/hLBLly4YhkFISIiFW/1o9/r09ddfs2/fPq5evcpPP/1EREQEK1euJHv27OTLl4+BAwdSvHhxmwimXbt2MWrUKF577TX69u2Lg4MD+fPnp1y5cpQrV45bt27xyy+/EBcXR/fu3enevbtV/93Jk7OnDLLH/AH7yyDlj4CyR9ljGcqfp8Pmi7N7eyKSkpLIli0br7/+OuHh4YwbN44PPviAnDlzpjin+t7ymTJlsnDLU+fy5cuMGzeO6dOn4+npybx589i+fTvOzs689NJLODo6mvtkGIZNXFi5bds2fv75Z5YsWcKtW7dwdHSkVatW5MiRg8WLF3P16lUWLVqEp6enpZuaKvHx8YSHh/Pdd9+Z90K2aNGCUqVKMWPGDL7++mvCwsL4448/aNGiBVmzZrVwiyU92XMG2WP+gH1lkPIn41L2KHssTfnzdFjnycaplJSUhMlkYsuWLUycOJHx48eTmJjI66+/jqOjI1999RWhoaEpzqm25nOR/ykkJIQcOXKQLVs23NzccHd355133uHKlSvMnj2b3bt3A3/3yVrPHf+niIgISpYsiZubG/nz5ycqKoru3buTN29eJkyYwJIlS2wmmADq1avH7NmzKVKkCKGhoZQvX55+/foRGRmJyWTil19+4c0332TYsGEKJjtjzxlkr/kD9pVByp+MSdmj7LEGyp+nw3b+ou8TFBRkPqS9e/duvvjiCzp37syePXtYvXo1VapUoXnz5kRGRjJ79mwSExMt3eRUu3cJ4P79++natSvBwcEULVqU7du3ExQURNasWWnSpAkJCQnky5fPwq19vPsvaYyLiwPAy8uLpKQkzp49C0CWLFkoUqQIYWFheHp6pjh32VZUr16djz/+mMDAQNzc3Pj66685c+YMf/31F6tWrcIwDAWTHbHXDLK3/IGMkUHKn4xD2aPssTbKn/Rnc6c1Xr58mfbt2/P9999TuHBhDh06RP/+/bl+/Tpubm707t0bf39/mjZtyhtvvEG2bNlwcrKdbppMJo4ePcrAgQMZMWIExYsXp2LFihw8eJC9e/dSrlw5VqxYgbe3N0WLFrV0cx/p/otfFy1axF9//UV8fDzNmjXDZDKxaNEiihcvTqZMmQgMDOS9996zcIv/mzp16pCQkIC3tzcjRoygd+/edOrUibCwMJvZYymPZ88ZZE/5Axkrg5Q/9k/Zo+yxVsqf9GUbW+19bty4QfHixTl9+jQnTpwgZ86c+Pv7ExERgZ+fH4UKFWLdunVUrlz5oXf2sVb3b8ilSpUiS5YsrF69mmbNmvHmm29SuHBhzp07x7Vr1xgxYgQ1atSwcItTb9WqVfz666/MmjWLpk2bUqRIEXx9ffnxxx+5cuUKt27dYs6cOXZxW9WGDRvi6OjIkCFD8PHxoWXLltpjZGfsMYPsOX8g42SQ8se+KXuUPdZM+ZOODBsTExNj9O/f36hSpYqxfv1648aNG0bjxo2NJUuWGDExMcbx48eN1157zTh37pylm5pqycnJhmEYxr59+4yffvrJuHbtmhEXF2c0atTIGDhw4EOXtWbbt2831q9fbxiGYSQlJRkTJkwwDh48aHz//fdGz549jZs3bxpTp041QkNDDcMwjLi4OEs296nYtm2bcfHiRUs3Q54Ce8sge8sfw1AGKX/sk7LH+mX07DEM5U96sJnnnBn/v3clISGBadOmcf36dbJkyUKvXr24du0ac+bMMT/1vlevXjRs2NDSTU6T3377jU8//ZRy5coRHx9P3759KV26NK1bt6ZIkSLMnj3b0k1MleTkZHbv3k3Pnj2ZOnUqTZs2ZebMmezZs4ds2bIxY8YMHB0dad26NZ9//jnFixdPsedMxFrZcwbZS/6AMkjsj7LHNih7JN1Yri5MvXt7TA4fPmysX7/euHbtmhEWFmZMmzbN+Oijj4wrV64YsbGxRnBwsPHXX3+leI8tuHjxotGlSxcjLCzM2LFjh9GiRQtjxIgRxpEjR4y4uDijYcOGxvHjx62+T7t27TJmzJhhGIZhbN682ShTpoyxdetW4+LFi0b9+vWN9evXG2FhYcbPP/9sNG/e3LznSMTa2XMG2Uv+GIYySOyPskfZIxmPTdyt0WQysW3bNoYOHcq5c+f4888/yZEjB2+99RZFixZl0qRJnDx5Ek9PT/PDF21pT0R8fDxOTk5ERERw6NAh+vTpQ0JCAr6+vmzcuJFff/2V559/3qr7tGPHDsaNG0fVqlUBqF+/PrNmzaJ3794EBQUxefJkfvjhBwYPHsySJUvw8/MjZ86cFm61SOrYcwbZQ/6AMkjsk7JH2SMZj9XfEMQwDEJDQ/n222+ZOXMmXl5ebN++nV69etGtWzcaN25MYmIirq6ulm5qqhn/fxj7xIkTJCcnYzKZGD58OHfu3CE6OppmzZoRFRVFUlISRYoUsXRzH2vnzp1MmTKFUaNGUbVqVa5fv85ff/1F/fr1mTlzJt26dWPhwoXMnTuX0NBQAJu8XaxkTPaWQfaWP6AMEvuk7LF+yh55Gqz2yJnx/5fCmUwmcuXKxfPPP4+3tzf9+vXj8OHD5MuXj6+++ooCBQrQp08fypQpY+EWp57JZGL79u0MGDCAVatWcfDgQUqVKsXRo0c5ffo0O3fuZOnSpbRr144KFSpYurmPtHv3bgYNGsRnn31GjRo1uHr1Kr169eKvv/4C7t69Z+bMmXTv3p2NGzfi4eGhYBKbYK8ZZE/5A8ogsT/KHmWPZGxWe+TMZDKxd+9ejh8/TpkyZXjjjTcoUKAA1apVo1SpUoSHhzNgwADu3LlDnjx5LN3cNLl16xbTpk3D19fXfFvYO3fukJiYyEsvvcSkSZMYNGiQTdwyNj4+HsMwiIiIICEhgY8++oj27dvz5ptvmpdp2LAhS5YsIUeOHJZrqEga2WsG2VP+gDJI7I+yR9kjGZvVFWf3DnsfPHgQX19fihUrxtGjR6lWrRodO3Zk7969fPHFF/z1118MGDDApoLpnntPhL//sP3JkycJDAxk2rRpvPXWW+TOndsm7uJTp04dPvvsMz766CPi4uIYNmwYLVu2NL/+448/EhgYyCeffIKzs7MFWyqSOvaeQfaUP6AMEvuh7FH2iIAVFmf3nhI/c+ZMPv/8c8qUKcOKFSs4evQosbGxlC5dmhYtWpA3b14qV65s9RtxfHw8Li4uKea5uroSHh6On58fn3/+OQDR0dFkyZIFZ2dncuXKBdjORb316tXDwcGBkSNHkiVLFvP8tWvXsmDBAj777DMFk9gMe8qgjJA/oAwS+6DsUfaIgBUWZwBhYWHs3buXXbt2UaZMGVq3bg3AH3/8gZOTE506dTIva80bcWRkJL/88guvvPIK2bNnJ1OmTBiGgYODA3PmzKFt27b069ePsmXLsmnTJj744APAuvv0b+rUqcPYsWP59NNPcXR0xMnJiUWLFvHZZ59RokQJSzdPJE3sIYMyUv6AMkjsg7LH9ih7JL1ZxUOo7+39OXXqFJkzZyZv3rzs3LmTsWPHMmjQIFq1akViYiKrVq2iUqVKlC5d2tJNfqzY2FiSkpKYMWMGISEhZM2alREjRuDi4kJCQgLOzs4kJiayYsUKnJyceO6556hWrZpV7wlLjS1btjBgwACyZ8/O/PnzFUxiE+wtgzJq/oAySGyLskfZI/KAp/8otdT59ddfjXr16hmdO3c2ZsyYYYSEhBhbt241GjdubPj7+1u6eWly+/Ztw9vb2zAMw9i4caPxwgsvGEOHDjVCQkLMy8THx1uqeU/drl27jHPnzlm6GSJpYi8ZlNHzxzCUQWJblD32Q9kj6cEqjpwdOXKETz/9lFmzZrF69WpWrlxJ27ZtadeuHYcPH+aTTz7h+++/x9PTEwcHq737PwBRUVFERUVhGAbx8fHs27ePYsWKsXLlSvLly0eTJk0oXry4pZspIvexlwxS/ojYFmWPiPyTxbb0ezWhYRjcuXOHEiVK4ODgQEREBJ07d+bXX39l1qxZ5M6dm1WrVpEnTx6rDia4e571kCFD2LFjB5kzZ2bXrl189913ZM2alffee4/z58+zc+dONm3axJEjRyzdXJEMzd4ySPkjYhuUPSLyKBbZ2o3/P7d4165dvP/++3h6etKgQQMOHz6Mh4cH3bp144UXXuDGjRtkypSJnDlzWqKZaebu7k7r1q1Zv349gYGB1KtXjzfffJMxY8YQHR1Nv379OHfuHJ9//jkJCQmWbq5IhmWPGaT8EbF+yh5lj8jjPNPiLDk5Gbh7R549e/bw+eef0717d0qXLk29evXYvn07d+7c4fjx4xw7doy3336bkiVLPssmPrGkpCQAChYsyO3bt/Hx8WHPnj3Url2bpk2bMmnSJCIiIhg7diwLFy7kpZdesnCLRTIee80g5Y+IdVP2KHtEUuuZFWcRERGMHDnSPO3m5sbx48cJCgoC7j4To2DBgly/fp1+/frx/vvvU6VKlWfVvP/M0dGR/fv3M2TIEMaMGUPv3r1Zv349R44coUGDBtStW5eJEydy+/ZtvLy8LN1ckQzHnjNI+SNivZQ9yh6RtHhmzznLmjUrHh4eVKpUibx58xIQEMD48eMZMWIEuXLlonbt2rRt25aIiAjeffddm7xw9MKFC1SsWJEKFSpQoUIFChYsyJQpU3jvvfdo0KABjRo1Inv27JZupkiGZO8ZpPwRsU7KHmWPSFo8kyNn9y5+fffdd8mfPz/BwcEAtG3blvHjx9OvXz82b95M9uzZKViwoM0E071+3TtdoWDBgiQmJnLhwgWSk5Np1KgRzz//PGvWrCFz5szkz5/fks0VybDsMYOUPyLWT9mj7BFJq2dy5OzegwXd3d3x9/dnxowZVK9enbVr19KmTRsA+vXrR2BgIDly5LDquxLdc++i3h07dnD8+HFcXFyoVKkSkZGR/PDDD5QrV45cuXJx69YtBgwYQO7cuS3dZJEMy94ySPkjYhuUPcoekbR6qs85S05O/teg8fX15ZdffmHUqFEULlyYPHny4OHh8bSa8lTs2rWLCRMmMG7cOHr37s0777xDo0aN+OGHH7hx4wY3btyge/fuNGjQwNJNFcmQ7DmDlD8i1kvZo+wReVJP5chZfHw8Li4uODg4cPDgQaKjo6lZsybwd2D5+Pjg7u7Ol19+Sf/+/Xn++eeBv/fK2IJ74ZqUlMRzzz1Hs2bNOH78OL169cLd3Z2wsDBy5sxpU30SsQcZIYOUPyLWR9mj7BH5r9L9yFlISAg//PADNWrUoEKFCvTr148mTZrQsmVL8zJJSUk4OjoCcPv2bbJnz24TG/G9Nu7duxeA48ePc/ToUa5evcpnn31GoUKFaNCgAXPmzKFkyZI20ScRe2OvGaT8EbFuyh5lj0h6SPeTm2/fvs3Ro0f57bffuHLlCvnz5ydbtmwplnF0dDQ/iPDeHXxsYUO+F06TJ08md+7ceHl5cejQIfr06UOhQoU4c+YMbm5uZM2a1by8iDxb9ppByh8R66bsUfaIpId0PXJ2b2/J6dOnmT9/Ps8//zw//fQTycnJ1K1bl6xZs1K+fHleeOEFXFxc0utjn6r79wDduXOHMWPGcPXqVZYvXw7AjBkz+OOPP3ByciIkJIT333+fRo0aWbLJIhmWvWWQ8kfENih7lD0i6SXdirN7G3J4eDg5cuTgxo0bTJs2jb1791KpUiXq1avHqlWrAOjfvz+VK1dOj499qs6fP8+cOXMICgqiTp06tGvXjhMnTjBhwgRq1qzJ4MGDgbuH+J2cnDCZTDqkL2Ih9pZByh8R26DsUfaIpKd0PXK2efNmZs+eTY4cOXjzzTf/r737CYlqC+A4/pvnODOgLhKH0ojS/IfgImZauIiZNhG0MFBrURtrYRqmqAVGJYppf9BctMmF0KJNYCAD4cYQDW0hiCQIwkAGlYsMZAyme3V6i5723oN65pu5d5y+n+3AvWc2Xzj3nHuuAoGAurq65PV6de7cOe3du1dfvnyR2+2O1y0TJhwO6+rVq6qurlZxcbFM09TRo0fldDo1MzOjoaEhlZSUqKmpye6hAvhLqjSI/gC7C+0BEC9xm5wtLS3p7t27Onv2rAzD0L1799TS0qKKigp1dnYqLy9P9fX1yszMjMftEioajeratWsKBAKqqqr6x2+GYejt27daXV3VwMCAfD6fmpub7RkogC2p0iD6A+wutAdAPMXlKP03b97oxo0bKioqUiAQkCR5PB51d3crFoupo6NDy8vLSR+mTR6PR+np6fL7/ZKk9fV1paWlyeFwaGRkREtLS2pra9OVK1fk8XhsHi2AVGoQ/QF2D9oDIN52fFrj3xfcDh06pIqKCi0sLGh2dlbr6+s6duyY2tvbdf/+fZmmqdLS0rgMONG+fv2qtbU1zc/Pa35+XpLkdDq3/m9RUZGmp6dlmqb8fr/Ky8vtHC7w20rFBtEfIPnRHtoDJNKOVs42X/ocHx/X5OSkDhw4oMuXL8vhcOjRo0dqaGhQWVmZgsGgnj17tqu+fO9wOJSZmanz589reHhY+/btk8/n2wrU6uqq8vLyFMdX9QD8olRtEP0BkhvtoT1Aou1o5WwzTH19fTp48KBGR0d1584dNTQ06MiRI+rv79968rJbwvRvp06dUkFBgQYHBzU2NqbPnz9rZmZGfX19qq6ulsvl4lQiwCap3iD6AyQn2kN7gETb0crZ2tqahoeH9fDhQ21sbOjVq1f68OGDent71d7erlgsprS0tHiP1VLZ2dmqr69XKBTS7du3VVxcrEgkoubmZgUCAY6MBWyU6g2iP0Byoj20B0i0bZ3W+P79e83NzSk/P1+lpaWKRCJqbGxUa2urpqamlJ+fr1gspu7ubpWVlWlwcNCKsVvm06dPSk9PVzQaldfrJU6AxX7nBtEfwD60h/YAVvvPbY3hcFgXL17UixcvVFtbq9evXysrK0u9vb3av3+/FhcXdeLECe3Zs0cnT55UXV2dFeO2VHZ2trKyspSTkyNJxAmw0O/eIPoD2IP20B7ADj/d1hgOh3Xr1i1duHBBNTU1evDggUKhkAzDUEZGhjIyMvTy5Us9efJEjx8/1s2bN+Xz+awau+UIE2AtGvQd/QGsQ3u+oz2AtX64rTESiaiqqkp+v189PT2KxWKqrKyU1+tVNBqV2+1WXV2dPn78qLGxMZ0+fXrrGx8A8H/RIAB2oD0A7PTDyZlhGHr69KkmJydVU1Oj58+fKycnR9evX1c4HNbQ0JAKCwtVW1srwzDkcrnYjwwgbmgQADvQHgB2+uG2RpfLpTNnzsjtdquzs1MlJSXq7++XJB0+fFi5ubl69+7dt4s4v12GMAGIFxoEwA60B4CdfnogiMvlUmVlpRobG2WapiYmJiRJs7OzCoVCOn78+LeL/LGjz6UBwE/RIAB2oD0A7LKto/QNw9DIyIhGR0fl8/k0MTGhS5cuKRgMspQPIOFoEAA70B4AVtvWR6g3nyCZpqmBgQH19PQoGAxKYikfQOLRIAB2oD0ArLatlbNNhmFoZWVFubm5PDECYDkaBMAOtAeAVX5pcgYAAAAASAzeZAUAAACAJMDkDAAAAACSAJMzAAAAAEgCTM4AAAAAIAkwOQMAAACAJMDkDAAAAACSAJMzAAAAAEgCfwLLGUaySteMDwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizando as categorias de pagamentos.\n",
    "fig, (ax_1, ax_2, ax_3) = plt.subplots(nrows=1, ncols=3, constrained_layout=True)\n",
    "\n",
    "# Todos os clientes.\n",
    "compare_plot(client_not_churn, ax_1, 'PaymentMethod', 'Todos os clientes')\n",
    "\n",
    "# Clientes churn.\n",
    "compare_plot(client_churn, ax_2, 'PaymentMethod', 'Clientes churn')\n",
    "\n",
    "# Clientes não churn.\n",
    "compare_plot(client_not_churn, ax_3, 'PaymentMethod', 'Clientes não churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo médio de permanência para clientes não churn com pagamento automático: 46.62 meses\n",
      "Tempo médio de permanência para clientes churn com pagamento manual: 14.82 meses\n"
     ]
    }
   ],
   "source": [
    "# Clientes não churn com pagamento automático.\n",
    "client_not_churn_payment_automatic = client_not_churn[\n",
    "    (client_not_churn['PaymentMethod'] == 'Credit card (automatic)') |\n",
    "    (client_not_churn['PaymentMethod'] == 'Bank transfer (automatic)')]\n",
    "\n",
    "client_not_churn_payment_automatic_mean_tenure = client_not_churn_payment_automatic['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes não churn com pagamento automático: {client_not_churn_payment_automatic_mean_tenure:.2f} meses')\n",
    "\n",
    "# Clientes churn com pagamento manual.\n",
    "client_churn_payment_manual = client_churn[\n",
    "    (client_churn['PaymentMethod'] == 'Mailed check') |\n",
    "    (client_churn['PaymentMethod'] == 'Electronic check')]\n",
    "\n",
    "client_churn_payment_manual_mean_tenure = client_churn_payment_manual['tenure'].mean()\n",
    "print(f'Tempo médio de permanência para clientes churn com pagamento manual: {client_churn_payment_manual_mean_tenure:.2f} meses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpretação do teste de hipotesete: Alfa = 0.05\n",
      "Rejeitamos H₀, os tempos médios de permanência para clientes não churn com pagamento automático e clientes churn com pagamento manual são diferentes.\n"
     ]
    }
   ],
   "source": [
    "# Aplicando o teste de hipótese para tipo de pagamento.\n",
    "grupo_01 = client_not_churn_payment_automatic['tenure']\n",
    "grupo_02 = client_churn_payment_manual['tenure']\n",
    "statistic, p_value = hypothesis_testing(grupo_01, grupo_02)\n",
    "\n",
    "ic = .95  # Intervalo de confiança de 95%.\n",
    "alfa = (1 - ic)\n",
    "print(f'Interpretação do teste de hipótese: Alfa = {alfa:.2f}')\n",
    "\n",
    "if p_value < alfa:\n",
    "    print(\n",
    "        'Rejeitamos H₀, os tempos médios de permanência para clientes '\n",
    "        'não churn com pagamento automático e clientes churn com pagamento manual são diferentes.')\n",
    "else:\n",
    "    print(\n",
    "        'Não rejeitamos H₁, os tempos médios de permanência para clientes'\n",
    "        ' não churn com pagamento automático e clientes churn com pagamento manual são iguais.')"
   ]
  }
 ],
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