Projeto1.ipynb

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      "name": "Projeto1.ipynb",
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NV6oAan22ApP"
      },
      "source": [
        "# Análise de dados NHANES 2017-2018\n",
        "Esta análise consiste do estudo de dados demográficos, de medidas corporais e pressão sanguínea obtido através do programa NHANES (National Health and Nutrition Examination Survey) conduzido nos Estados Unidos da América durante os anos de 2017-2018. <br>\n",
        "Fonte: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ohmwc1NciUO2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94815b95-0504-40b2-9f2f-2b8e1f464ffd"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__siKY1k4Yhh"
      },
      "source": [
        "## Tratamento inicial dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLDNJkeE_W8-"
      },
      "source": [
        "Inicialmente, serão importados os dados demográficos dos pacientes examinados. O dataset DEMO_J possui diversas colunas, porém apenas quatro delas serão mantidas, são elas:\n",
        "\n",
        "Coluna | Significado | Tipo | Unidade\n",
        "-----|---|---|-----\n",
        "RIAGENDR | Gênero | Categórico | -\n",
        "RIDAGEYR| Idade | Numérico | Anos\n",
        "DMDEDUC2 | Escolaridade | Categórico | -\n",
        "DMDMARTL | Estado Civil | Categórico | - \n",
        "\n",
        "Devido ao custo de armazenamento, os dados categóricos estão codificados em uma representação numérica. A coluna SEQN será designada como índice visto que é composta por valores únicos cujo propósito é identificar o paciente durante o atendimento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHnGTOEbi01Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "5134f599-b72e-40e2-8bc1-96b59600260c"
      },
      "source": [
        "DEMO = pd.read_sas('/content/gdrive/My Drive/11º Período/Ciencia de Dados/DEMO_J.XPT', index='SEQN')\n",
        "DEMO = DEMO[['RIAGENDR', 'RIDAGEYR', 'DMDEDUC2', 'DMDMARTL']]\n",
        "display(DEMO.head())\n",
        "print('Linhas = {}\\nColunas = {}'.format(DEMO.shape[0], DEMO.shape[1]))"
      ],
      "execution_count": 12,
      "outputs": [
        {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RIAGENDR</th>\n",
              "      <th>RIDAGEYR</th>\n",
              "      <th>DMDEDUC2</th>\n",
              "      <th>DMDMARTL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>93703.0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93704.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93705.0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93706.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93707.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         RIAGENDR  RIDAGEYR  DMDEDUC2  DMDMARTL\n",
              "SEQN                                           \n",
              "93703.0       2.0       2.0       NaN       NaN\n",
              "93704.0       1.0       2.0       NaN       NaN\n",
              "93705.0       2.0      66.0       2.0       3.0\n",
              "93706.0       1.0      18.0       NaN       NaN\n",
              "93707.0       1.0      13.0       NaN       NaN"
            ]
          },
          "metadata": {
            "tags": []
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          "output_type": "stream",
          "text": [
            "Linhas = 9254\n",
            "Colunas = 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQ423LWRAsqP"
      },
      "source": [
        "Em seguida, traremos o dataset com as medidas corporais. Novamente, serão consideradas apenas algumas colunas de BMX_J, sendo elas:\n",
        "\n",
        "Coluna | Significado | Tipo | Unidade\n",
        "-----|---|---|-----\n",
        "BMXWT | Peso | Numérico | kg\n",
        "BMXHT| Altura | Numérico | cm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2PwtM2Ti4HO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "aee3422a-e77b-4165-81b5-2dcbcc63b83d"
      },
      "source": [
        "BMX = pd.read_sas('/content/gdrive/My Drive/11º Período/Ciencia de Dados/BMX_J.XPT', index='SEQN')\n",
        "BMX = BMX[['BMXWT', 'BMXHT']]\n",
        "display(BMX.head())\n",
        "print('Linhas = {}\\nColunas = {}'.format(BMX.shape[0], BMX.shape[1]))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BMXWT</th>\n",
              "      <th>BMXHT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>93703.0</th>\n",
              "      <td>13.7</td>\n",
              "      <td>88.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93704.0</th>\n",
              "      <td>13.9</td>\n",
              "      <td>94.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93705.0</th>\n",
              "      <td>79.5</td>\n",
              "      <td>158.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93706.0</th>\n",
              "      <td>66.3</td>\n",
              "      <td>175.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93707.0</th>\n",
              "      <td>45.4</td>\n",
              "      <td>158.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         BMXWT  BMXHT\n",
              "SEQN                 \n",
              "93703.0   13.7   88.6\n",
              "93704.0   13.9   94.2\n",
              "93705.0   79.5  158.3\n",
              "93706.0   66.3  175.7\n",
              "93707.0   45.4  158.4"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Linhas = 8704\n",
            "Colunas = 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFNIsFgBD58F"
      },
      "source": [
        "Por último, mas não menos importante, o dataset com as medidas de pressão arterial. De BMX_J, estudaremos apenas:\n",
        "\n",
        "Coluna | Significado | Tipo | Unidade\n",
        "-----|---|---|-----\n",
        "BPXSY1 | Sístole1 | Numérico | mmHg\n",
        "BPXDI1| Diástole1 | Numérico | mmHg\n",
        "BPXSY2 | Sístole2 | Numérico | mmHg\n",
        "BPXDI2| Diástole2 | Numérico | mmHg"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuorMjUVtHGu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "31ecfad5-3df1-4616-e709-c1dc0fc7173b"
      },
      "source": [
        "BPX = pd.read_sas('/content/gdrive/My Drive/11º Período/Ciencia de Dados/BPX_J.XPT', index='SEQN') \n",
        "BPX = BPX[['BPXSY1', 'BPXDI1', 'BPXSY2', 'BPXDI2']]\n",
        "display(BPX.head())\n",
        "print('Linhas = {}\\nColunas = {}'.format(BPX.shape[0], BPX.shape[1]))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BPXSY1</th>\n",
              "      <th>BPXDI1</th>\n",
              "      <th>BPXSY2</th>\n",
              "      <th>BPXDI2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93705.0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93706.0</th>\n",
              "      <td>112.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93707.0</th>\n",
              "      <td>128.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         BPXSY1  BPXDI1  BPXSY2  BPXDI2\n",
              "SEQN                                   \n",
              "93703.0     NaN     NaN     NaN     NaN\n",
              "93704.0     NaN     NaN     NaN     NaN\n",
              "93705.0     NaN     NaN     NaN     NaN\n",
              "93706.0   112.0    74.0   114.0    70.0\n",
              "93707.0   128.0    38.0   128.0    46.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Linhas = 8704\n",
            "Colunas = 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXaMwNCjFoWg"
      },
      "source": [
        "Devido ao fato de que cada paciente é unicamente identificado pelo seu número de sequência, e que não necessariamente ele esteja presente em todos os três datasets, será feito um inner join. A ideia é ter um dataframe final com todas as colunas de interesse."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7r7e7XRtPrm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "7eea6d94-8ad6-4316-e63f-8d961dca146b"
      },
      "source": [
        "df = pd.merge(DEMO, BMX, left_index=True, right_index=True, how='inner')\n",
        "df = pd.merge(df, BPX, left_index=True, right_index=True, how='inner')\n",
        "display(df.head())\n",
        "print('Linhas = {}\\nColunas = {}'.format(df.shape[0], df.shape[1]))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RIAGENDR</th>\n",
              "      <th>RIDAGEYR</th>\n",
              "      <th>DMDEDUC2</th>\n",
              "      <th>DMDMARTL</th>\n",
              "      <th>BMXWT</th>\n",
              "      <th>BMXHT</th>\n",
              "      <th>BPXSY1</th>\n",
              "      <th>BPXDI1</th>\n",
              "      <th>BPXSY2</th>\n",
              "      <th>BPXDI2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>93703.0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.7</td>\n",
              "      <td>88.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93704.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.9</td>\n",
              "      <td>94.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93705.0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>79.5</td>\n",
              "      <td>158.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93706.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.3</td>\n",
              "      <td>175.7</td>\n",
              "      <td>112.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93707.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.4</td>\n",
              "      <td>158.4</td>\n",
              "      <td>128.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         RIAGENDR  RIDAGEYR  DMDEDUC2  DMDMARTL  ...  BPXSY1  BPXDI1  BPXSY2  BPXDI2\n",
              "SEQN                                             ...                                \n",
              "93703.0       2.0       2.0       NaN       NaN  ...     NaN     NaN     NaN     NaN\n",
              "93704.0       1.0       2.0       NaN       NaN  ...     NaN     NaN     NaN     NaN\n",
              "93705.0       2.0      66.0       2.0       3.0  ...     NaN     NaN     NaN     NaN\n",
              "93706.0       1.0      18.0       NaN       NaN  ...   112.0    74.0   114.0    70.0\n",
              "93707.0       1.0      13.0       NaN       NaN  ...   128.0    38.0   128.0    46.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Linhas = 8704\n",
            "Colunas = 10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5szSuFAHtMx"
      },
      "source": [
        "Por questões de uma melhor compreensão do conteúdo do dataframe, as colunas terão novos rótulos e os dados de colunas categóricas serão decodificados de uma representação numérica para strings com significado em linguagem natural.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFseK5tstyDy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "2dc2124f-8932-4bee-ca18-48871843c90b"
      },
      "source": [
        "df.columns = ['Gender','Age','Education','Marital','Weight','Height','Systole1','Diastole1','Systole2','Diastole2']\n",
        "df.Gender = df.Gender.replace({1.0: 'Male', 2.0: 'Female'})\n",
        "df.Education = df.Education.replace({1.0: '-9th', 2.0: '9-11th', 3.0: 'High School', 4.0: 'Some College', 5.0: 'Graduate+', 7.0: 'Refused', 8.0: 'Don\\'t Know'})\n",
        "df.Marital = df.Marital.replace({1.0: 'Married', 2.0: 'Widowed', 3.0: 'Divorced', 4.0: 'Separated', 5.0: 'Never Married', 6.0: 'Living With Partner', 77: 'Refused', 99: 'Don\\'t Know'})\n",
        "display(df.head())\n",
        "print('Linhas = {}\\nColunas = {}'.format(df.shape[0], df.shape[1]))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Marital</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Systole1</th>\n",
              "      <th>Diastole1</th>\n",
              "      <th>Systole2</th>\n",
              "      <th>Diastole2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>93703.0</th>\n",
              "      <td>Female</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.7</td>\n",
              "      <td>88.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93704.0</th>\n",
              "      <td>Male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.9</td>\n",
              "      <td>94.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93705.0</th>\n",
              "      <td>Female</td>\n",
              "      <td>66.0</td>\n",
              "      <td>9-11th</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>79.5</td>\n",
              "      <td>158.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93706.0</th>\n",
              "      <td>Male</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.3</td>\n",
              "      <td>175.7</td>\n",
              "      <td>112.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93707.0</th>\n",
              "      <td>Male</td>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.4</td>\n",
              "      <td>158.4</td>\n",
              "      <td>128.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Gender   Age Education  ... Diastole1  Systole2  Diastole2\n",
              "SEQN                             ...                               \n",
              "93703.0  Female   2.0       NaN  ...       NaN       NaN        NaN\n",
              "93704.0    Male   2.0       NaN  ...       NaN       NaN        NaN\n",
              "93705.0  Female  66.0    9-11th  ...       NaN       NaN        NaN\n",
              "93706.0    Male  18.0       NaN  ...      74.0     114.0       70.0\n",
              "93707.0    Male  13.0       NaN  ...      38.0     128.0       46.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Linhas = 8704\n",
            "Colunas = 10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrW_jfiMHEmV"
      },
      "source": [
        "No geral, devido a discrepância entre os dados de crianças e adultos, a análise que se segue considerará apenas as pessoas examinadas que tenham mais de 18 anos de idade. Adicionalmente, converteremos os dados de altura de centímetros (cm) para metros (m) a fim de integrar uma nova coluna ao dataframe: o índice de massa corporal ou BMI."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6C5xoSdjzFm1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "9b1d1f56-b73b-4a36-c844-2d79e09fd8a1"
      },
      "source": [
        "df = df[df['Age']>=18.0].copy()\n",
        "\n",
        "df['Height'] = df['Height']*0.01\n",
        "df['BMI'] = df['Weight']/(df['Height']**2)\n",
        "display(df.head())\n",
        "print('Linhas = {}\\nColunas = {}'.format(df.shape[0], df.shape[1]))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Marital</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Systole1</th>\n",
              "      <th>Diastole1</th>\n",
              "      <th>Systole2</th>\n",
              "      <th>Diastole2</th>\n",
              "      <th>BMI</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>93705.0</th>\n",
              "      <td>Female</td>\n",
              "      <td>66.0</td>\n",
              "      <td>9-11th</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>79.5</td>\n",
              "      <td>1.583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.725268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93706.0</th>\n",
              "      <td>Male</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.3</td>\n",
              "      <td>1.757</td>\n",
              "      <td>112.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>21.476821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93708.0</th>\n",
              "      <td>Female</td>\n",
              "      <td>66.0</td>\n",
              "      <td>-9th</td>\n",
              "      <td>Married</td>\n",
              "      <td>53.5</td>\n",
              "      <td>1.502</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>138.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>23.714497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93709.0</th>\n",
              "      <td>Female</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Some College</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>88.8</td>\n",
              "      <td>1.511</td>\n",
              "      <td>120.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>38.894128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93711.0</th>\n",
              "      <td>Male</td>\n",
              "      <td>56.0</td>\n",
              "      <td>Graduate+</td>\n",
              "      <td>Married</td>\n",
              "      <td>62.1</td>\n",
              "      <td>1.706</td>\n",
              "      <td>108.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>21.337009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Gender   Age     Education  ... Systole2  Diastole2        BMI\n",
              "SEQN                                 ...                               \n",
              "93705.0  Female  66.0        9-11th  ...      NaN        NaN  31.725268\n",
              "93706.0    Male  18.0           NaN  ...    114.0       70.0  21.476821\n",
              "93708.0  Female  66.0          -9th  ...    138.0       78.0  23.714497\n",
              "93709.0  Female  75.0  Some College  ...    118.0       66.0  38.894128\n",
              "93711.0    Male  56.0     Graduate+  ...     94.0       68.0  21.337009\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Linhas = 5533\n",
            "Colunas = 11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKR76Yv9U6Q3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "1c6141d8-7e83-4ee5-810c-0d2d8d26896c"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Systole1</th>\n",
              "      <th>Diastole1</th>\n",
              "      <th>Systole2</th>\n",
              "      <th>Diastole2</th>\n",
              "      <th>BMI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5533.000000</td>\n",
              "      <td>5444.000000</td>\n",
              "      <td>5444.000000</td>\n",
              "      <td>4899.000000</td>\n",
              "      <td>4.899000e+03</td>\n",
              "      <td>5128.000000</td>\n",
              "      <td>5.128000e+03</td>\n",
              "      <td>5434.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>49.816555</td>\n",
              "      <td>82.468993</td>\n",
              "      <td>1.663623</td>\n",
              "      <td>125.984895</td>\n",
              "      <td>7.165462e+01</td>\n",
              "      <td>126.246490</td>\n",
              "      <td>7.210686e+01</td>\n",
              "      <td>29.691481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.649364</td>\n",
              "      <td>22.980417</td>\n",
              "      <td>0.100669</td>\n",
              "      <td>19.728263</td>\n",
              "      <td>1.356110e+01</td>\n",
              "      <td>20.282001</td>\n",
              "      <td>1.313658e+01</td>\n",
              "      <td>7.439997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>32.600000</td>\n",
              "      <td>1.383000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>14.203401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>66.500000</td>\n",
              "      <td>1.589000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>6.400000e+01</td>\n",
              "      <td>24.576145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>78.900000</td>\n",
              "      <td>1.659000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>7.200000e+01</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>7.200000e+01</td>\n",
              "      <td>28.457344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>65.000000</td>\n",
              "      <td>94.500000</td>\n",
              "      <td>1.736000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>8.000000e+01</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>8.000000e+01</td>\n",
              "      <td>33.489376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>242.600000</td>\n",
              "      <td>1.977000</td>\n",
              "      <td>228.000000</td>\n",
              "      <td>1.360000e+02</td>\n",
              "      <td>236.000000</td>\n",
              "      <td>1.360000e+02</td>\n",
              "      <td>86.160237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Age       Weight  ...     Diastole2          BMI\n",
              "count  5533.000000  5444.000000  ...  5.128000e+03  5434.000000\n",
              "mean     49.816555    82.468993  ...  7.210686e+01    29.691481\n",
              "std      18.649364    22.980417  ...  1.313658e+01     7.439997\n",
              "min      18.000000    32.600000  ...  5.397605e-79    14.203401\n",
              "25%      33.000000    66.500000  ...  6.400000e+01    24.576145\n",
              "50%      51.000000    78.900000  ...  7.200000e+01    28.457344\n",
              "75%      65.000000    94.500000  ...  8.000000e+01    33.489376\n",
              "max      80.000000   242.600000  ...  1.360000e+02    86.160237\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzwtlcMK99EC"
      },
      "source": [
        "O método *describe* acima revela que:\n",
        "* A contagem de dados totais por coluna não é igual, isso implica na presença de NaNs. \n",
        "* A idade mínima é 18 anos, confirmando que a seleção de adultos foi de fato realizada. Enquanto a idade máxima é 80 anos.\n",
        "* Como a média dos pesos é maior que a mediana, então a distribuição deve ser enviesada positivamente e possuir outliers à direita, o que na verdade já é possível perceber pelo peso máximo de 242.6kg. \n",
        "* O formato da distribuição dos pesos parece ser propagada ao índice de massa corporal, já que são diretamente proporcionais e a distribuição das alturas dá indícios de simetria devido às diferenças entre os quartis.\n",
        "* As duas medidas de pressão sistólica possuem exatamente os mesmos 1º, 2º e 3º quartis. Entretando diferem em média. Será que essa diferença é estatisticamente significativa? A mesma afirmação e questionamento valem para as duas medidas de pressão diastólica."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nggfbgE0FwkW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a19717a9-dbe4-4bb6-8f0e-9b4a8a54a6c9"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender         0\n",
              "Age            0\n",
              "Education    268\n",
              "Marital      268\n",
              "Weight        89\n",
              "Height        89\n",
              "Systole1     634\n",
              "Diastole1    634\n",
              "Systole2     405\n",
              "Diastole2    405\n",
              "BMI           99\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsV8VNNyJeUB"
      },
      "source": [
        "Visualizando a contagem de NaNs por coluna, podemos concluir que mesmo para os casos onde há uma maior quantidade de dados ausentes, este número não é tão expressivo em comparação à quantidade total de pacientes adultos examinados, ou seja 5533. Sendo assim, quando necessário, o tratamento de dados ausentes será feito através de sua remoção."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcc0vhs61FEb"
      },
      "source": [
        "## Analisando as medidas de pressão sistólica"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIfqEJRTXLLB"
      },
      "source": [
        "#### Comparação entre todas as amostras de pressão sistólica 1 e 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWkrP8TZQ4yl"
      },
      "source": [
        "As colunas sístole1 e sístole2 se tratam de medições realizadas uma após a outra, sendo separadas por um curto intervalo de tempo. Este procedimento serve para evitar erro humano durante o exame."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJmkOCZpJnhn"
      },
      "source": [
        "O primeiro passo se dará pela remoção de NaNs. Criaremos um novo dataframe que seja livre de dados não valorados de sístole 1 ou 2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uXeRkW0Efav",
        "outputId": "eb48e2a0-0203-4d38-9b2b-532a9068ddc1"
      },
      "source": [
        "df1 = df[(df['Systole1'].notna()) & (df['Systole2'].notna())]\n",
        "print('Nº de linhas removidas = {}'.format(df.shape[0] - df1.shape[0]))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de linhas removidas = 722\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcdGhvwBTsfK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "fa1bf4ec-1c4d-453f-895b-bf24fe32d270"
      },
      "source": [
        "fig, axes = plt.subplots(1,2, figsize=(10,4), sharex=True)\n",
        "sns.histplot(data=df1, x='Systole1', binwidth=5, ax=axes[0])\n",
        "sns.histplot(data=df1, x='Systole2', binwidth=5, ax=axes[1])\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAEGCAYAAADLxYlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcZklEQVR4nO3df7RddXnn8fdHUOpSawRCJg3Y0JHCuHREmirUtuNIbQ3TGlos6OpIpDhpZ7RTx+kPGOvYds1a6nSmVm2LixEhdFEVRCZpF9hSKjrTJY4BGVABiTFA0kBiithqawf6zB/nm3II957c3Ny997m579daZ529v3vvs597zs6TZ3/3r1QVkiRJGs5Thg5AkiRpqbMgkyRJGpgFmSRJ0sAsyCRJkgZmQSZJkjSwI4cO4FAce+yxtXr16qHDkNSjW2+99WtVtXzoOA6V+Utaeiblr0VdkK1evZotW7YMHYakHiW5b+gYFoL5S1p6JuWvzg5ZJjk5ye1jr28keUuSo5PcmOTe9v6cNn+SvC/J1iR3JDmtq9gkaRLzl6S+dVaQVdU9VXVqVZ0KfB/wLeA64CLgpqo6CbipjQOsBU5qrw3AJV3FJkmTmL8k9a2vk/rPBL5SVfcB64CNrX0jcHYbXgdcWSO3AMuSrOwpPkmajflLUuf6KsheC3y4Da+oql1t+EFgRRteBTwwtsyO1vYESTYk2ZJky549e7qKV5L2WbD8JUmz6bwgS/I04NXANftPq9GDNA/qYZpVdWlVramqNcuXL/oLrSRNsYXOX+5QSppNHz1ka4HbquqhNv7Qvq789r67te8EThhb7vjWJklDWdD85Q6lpNn0UZC9jse7+wE2A+vb8Hpg01j7+e1qpdOBR8YODUjSEMxfknrR6X3IkjwDeCXwc2PN7wKuTnIhcB9wbmu/HjgL2MroiqYLuoxNkiYxf0nqU6cFWVV9Ezhmv7a9jK5a2n/eAt7UZTySNFfmL0l9WtR36tfCWrvuHHbt3jvjtJXHHcMNm67tOSJJS415SEuVBZn+0a7deznlgnfOOO3uyy/uORpJS5F5SEtVX/chkyRJ0iwsyCRJkgZmQSZJkjQwCzJJkqSBeVL/YcYrlCRJWnwsyA4zXqEkSdLi4yFLSZKkgVmQSZIkDcyCTJIkaWAWZJIkSQOzIJMkSRqYBZkkSdLALMgkSZIGZkEmSZI0MAsySZKkgVmQSZIkDcyCTJIkaWAWZJIkSQPz4eKSpEVv7bpz2LV774zTVh53DDdsurbniKSD02lBlmQZ8EHgBUABPwvcA3wUWA1sB86tqoeTBHgvcBbwLeANVXVbl/FJkg4Pu3bv5ZQL3jnjtLsvv7jnaKSD1/Uhy/cCn6iqU4AXAXcBFwE3VdVJwE1tHGAtcFJ7bQAu6Tg2SZpVkmVJPpbk7iR3JTkjydFJbkxyb3t/Tps3Sd6XZGuSO5KcNnT8khaXzgqyJM8Gfhi4DKCq/r6qvg6sAza22TYCZ7fhdcCVNXILsCzJyq7ik6QDcIdSUm+67CE7EdgDXJ7k80k+mOQZwIqq2tXmeRBY0YZXAQ+MLb+jtUlSr9yhlNS3LguyI4HTgEuq6sXAN3l8bxKAqipG55bNWZINSbYk2bJnz54FC1aSxnSyQ2n+kjSbLguyHcCOqvpsG/8YowLtoX17ju19d5u+EzhhbPnjW9sTVNWlVbWmqtYsX768s+AlLWmd7FCavyTNprOCrKoeBB5IcnJrOhP4ErAZWN/a1gOb2vBm4Px2cuzpwCNje6KS1KdOdiglaTZd34fsF4CrkjwN2AZcwKgIvDrJhcB9wLlt3usZ3fJiK6PbXlzQcWySNKOqejDJA0lOrqp7eHyH8kuMdiTfxZN3KN+c5CPAS3GHUtJB6rQgq6rbgTUzTDpzhnkLeFOX8UjSQXCHUlJvvFO/JM3AHUpJffJZlpIkSQOzIJMkSRqYBZkkSdLALMgkSZIGZkEmSZI0MAsySZKkgVmQSZIkDcyCTJIkaWAWZJIkSQOzIJMkSRqYBZkkSdLALMgkSZIGZkEmSZI0MAsySZKkgVmQSZIkDcyCTJIkaWAWZJIkSQOzIJMkSRqYBZkkSdLALMgkSZIGZkEmSZI0sE4LsiTbk9yZ5PYkW1rb0UluTHJve39Oa0+S9yXZmuSOJKd1GZskTWL+ktSnPnrI/mVVnVpVa9r4RcBNVXUScFMbB1gLnNReG4BLeohNkiYxf0nqxRCHLNcBG9vwRuDssfYra+QWYFmSlQPEJ0mzMX9J6kTXBVkBf5rk1iQbWtuKqtrVhh8EVrThVcADY8vuaG1PkGRDki1JtuzZs6eruCXJ/CWpN0d2/Pk/WFU7kxwH3Jjk7vGJVVVJ6mA+sKouBS4FWLNmzUEtK0kHwfwlqTed9pBV1c72vhu4DngJ8NC+rvz2vrvNvhM4YWzx41ubJPXO/CWpT50VZEmekeRZ+4aBHwW+AGwG1rfZ1gOb2vBm4Px2tdLpwCNjhwYkqTfmL0l96/KQ5QrguiT71vOHVfWJJJ8Drk5yIXAfcG6b/3rgLGAr8C3ggg5jk6RJzF+SetVZQVZV24AXzdC+FzhzhvYC3tRVPJI0V+avpWPtunPYtXvvrNNXHncMN2y6tseItFR1fVK/JElTa9fuvZxywTtnnX735Rf3GI2WMh+dJEmSNDALMkmSpIFZkEmSJA3MgkySJGlgFmSSJEkDsyCTJEkamAWZJEnSwCzIJEmSBuaNYXXIJt3p2rtcS0uPOUE6eBZkU2oxJbRJd7r2LtfS0tNVTvjqtm2cesbLZ5y2/f77OWXenywNz4JsSlnkSNITPQaz5sWtbz+v32CkBeY5ZJIkSQOzIJMkSRqYBZkkSdLALMgkSZIGZkEmSZI0MAsySZKkgVmQSZIkDcz7kC0hk26qCN5YUZKkoViQLSGTbqoI3lhRkqShdH7IMskRST6f5I/b+IlJPptka5KPJnlaaz+qjW9t01d3HZskTWL+ktSXPs4h+0XgrrHxdwPvqarnAQ8DF7b2C4GHW/t72nySNCTzl6RedFqQJTke+FfAB9t4gFcAH2uzbATObsPr2jht+pltfknqnflLUp+67iH7HeBXgH9o48cAX6+qR9v4DmBVG14FPADQpj/S5n+CJBuSbEmyZc+ePV3GLmlpW/D8JUmzmVNBluRlc2nbb/qPA7ur6tZ5xjajqrq0qtZU1Zrly5cv5EdLOgxNU/5yh1LSbObaQ/b+ObaNexnw6iTbgY8w6up/L7Asyb6rO48HdrbhncAJAG36s4G9c4xPkmYzNfnLHUpJs5l424skZwA/ACxP8taxSd8JHDFp2aq6GLi4fc7LgV+qqp9Jcg3wGkZJbj2wqS2yuY1/pk3/86qqg/2DJAnMX5IWlwP1kD0NeCajwu1ZY69vMEo68/GrwFuTbGV0jsVlrf0y4JjW/lbgonl+viSB+UvSIjKxh6yqPgV8KskVVXXffFdSVTcDN7fhbcBLZpjn74Cfnu86JGmc+UvSYjLXO/UfleRSYPX4MlX1ii6CkqQFZP6SNPXmWpBdA3yA0f14HusuHElacOYvSVNvrgXZo1V1SaeRSFI3zF+Spt5cC7I/SvLvgOuAb+9rrKq/6iQqSVo45q8p8tVt2zj1jJfPOn37/fdzSn/hSFNjrgXZ+vb+y2NtBXzPwoYjSQvO/DVFHgNOueCds07f+vbz+gtGmiJzKsiq6sSuA9HcTdrDdO9SeiLzl6TFYE4FWZLzZ2qvqisXNhzNxaQ9TPcupScyf0laDOZ6yPL7x4a/AzgTuA0woUmaduYvSVNvrocsf2F8PMkyRo8OkaSpZv6StBjMtYdsf98EPC9D0mJk/lpiPO9Wi8FczyH7I0ZXJcHoobz/DLi6q6AkaaGYv+R5t1oM5tpD9t/Ghh8F7quqHR3EI0kLzfwlaeo9ZS4ztYf03g08C3gO8PddBiVJC8X8JWkxmOshy3OB3wJuBgK8P8kvV9XHOoxNU8RzMLRYmb8kLQZzPWT5NuD7q2o3QJLlwJ8BJrQlwnMwtIiZvyRNvTkdsgSesi+ZNXsPYllJGpL5S9LUm2sP2SeS/Anw4TZ+HnB9NyFJ0oIyf0maehMLsiTPA1ZU1S8n+SngB9ukzwBXdR2cJM2X+UvSYnKgHrLfAS4GqKqPAx8HSPLCNu0nOo1OkubP/CVp0TjQeRQrqurO/Rtb2+pOIpKkhWH+krRoHKggWzZh2tMXMhBJWmDmL0mLxoEKsi1J/s3+jUneCNzaTUiStCDMX5IWjQOdQ/YW4LokP8PjCWwN8DTgJyctmOQ7gE8DR7X1fKyq3pHkROAjwDHtM19fVX+f5CjgSuD7GF2Wfl5VbZ/XXyVJ5i9Ji8jEHrKqeqiqfgD4DWB7e/1GVZ1RVQ8e4LO/Dbyiql4EnAq8KsnpwLuB91TV84CHgQvb/BcCD7f297T5JGlezF+SFpO5Psvyk1X1/vb68zkuU1X1N230qe1VwCt4/A7ZG4Gz2/C6Nk6bfmaSzGVdkjQb85ekxaDTu1UnOSLJ7cBu4EbgK8DXq+rRNssOYFUbXgU8ANCmP8LosMD+n7khyZYkW/bs2dNl+JKWMPOXpD51WpBV1WNVdSpwPPASOPRnUFfVpVW1pqrWLF++/JBjlKSZmL8k9amX57lV1deBTwJnAMuS7LuY4HhgZxveCZwA0KY/m9HJsZI0GPOXpD50VpAlWZ5kWRt+OvBK4C5Gie01bbb1wKY2vLmN06b/eVVVV/FJ0mzMX5L6NteHi8/HSmBjkiMYFX5XV9UfJ/kS8JEk/wX4PHBZm/8y4A+SbAX+Cnhth7FJ0iTmL0m96qwgq6o7gBfP0L6N0fkY+7f/HfDTXcUjSXNl/pLUty57yCRJOmytXXcOu3bPfKrgyuOO4YZN1/YckRYzCzJJkuZh1+69nHLBO2ecdvflF/ccjRa7Xq6ylCRJ0uzsIdNgJnX3g13+kqSlw4JMg5nU3Q92+UuSlg4PWUqSJA3MgkySJGlgFmSSJEkDsyCTJEkamAWZJEnSwCzIJEmSBmZBJkmSNDALMkmSpIFZkEmSJA3MgkySJGlgFmSSJEkD81mW6tRXt23j1DNePuO07fffzyn9hiNJ0lSyIFOnHoNZHyC+9e3n9RuMJElTykOWkiRJA7MgkyRJGpgFmSRJ0sA6K8iSnJDkk0m+lOSLSX6xtR+d5MYk97b357T2JHlfkq1J7khyWlexSdIk5i9Jfeuyh+xR4D9W1fOB04E3JXk+cBFwU1WdBNzUxgHWAie11wbgkg5jk6RJzF+SetVZQVZVu6rqtjb818BdwCpgHbCxzbYROLsNrwOurJFbgGVJVnYVnyTNxvwlqW+93PYiyWrgxcBngRVVtatNehBY0YZXAQ+MLbajte0aayPJBkZ7oDz3uc/tLGZJAvPXUue9FNWXzguyJM8ErgXeUlXfSPKP06qqktTBfF5VXQpcCrBmzZqDWlaHj7XrzmHX7r0zTlt53DHcsOnaniPS4cj8Je+lqL50WpAleSqjZHZVVX28NT+UZGVV7Wpd+rtb+07ghLHFj29t0pPs2r131iR59+UX9xyNDkfmL0l96vIqywCXAXdV1W+PTdoMrG/D64FNY+3nt6uVTgceGTs0IEm9MX9J6luXPWQvA14P3Jnk9tb2n4B3AVcnuRC4Dzi3TbseOAvYCnwLuKDD2CRpEvOXpF51VpBV1f8GMsvkM2eYv4A3dRWPJM2V+UtS33y4+IAmnZju1TuSJC0dFmQDmnRiulfvSJK0dFiQSZLUI2/bo5lYkEmS1CNv26OZdPksS0mSJM2BBZkkSdLALMgkSZIGZkEmSZI0ME/qlyQdNO+jKC0sCzJJ0kHzPorSwvKQpSRJ0sAsyCRJkgZmQSZJkjQwCzJJkqSBWZBJkiQNzIJMkiRpYBZkkiRJA/M+ZJKkGXnzV6k/FmSSpBl581epPx6ylCRJGpg9ZJpaX922jVPPePmM0zxcIkk6nHRWkCX5EPDjwO6qekFrOxr4KLAa2A6cW1UPJwnwXuAs4FvAG6rqtq5i68uk8y/AouJAHgMPl2gw5jBJfeqyh+wK4HeBK8faLgJuqqp3Jbmojf8qsBY4qb1eClzS3he1SedfgEWFNOWuYInnMM2fPfw6WJ0VZFX16SSr92teB7y8DW8EbmaUzNYBV1ZVAbckWZZkZVXt6io+SZrEHKZDYQ+/DlbfJ/WvGEtQDwIr2vAq4IGx+Xa0tidJsiHJliRb9uzZ012kkvRkh5TDzF+SZjPYVZZtT7LmsdylVbWmqtYsX768g8gk6cDmk8PMX5Jm03dB9lCSlQDtfXdr3wmcMDbf8a1NkqaJOUxSJ/ouyDYD69vwemDTWPv5GTkdeMRzLyRNIXOYpE50eduLDzM6+fXYJDuAdwDvAq5OciFwH3Bum/16RpeLb2V0yfgFXcUlSXNhDpPUpy6vsnzdLJPOnGHeAt7UVSzSPge6N9zK447hhk3X9hiRppU5TNNoUg4zfy1u3qlfS8qB7g139+UX9xiNJB2cSTnM/LW4+SxLSZKkgVmQSZIkDcyCTJIkaWCeQ6bDjs+QkyQtNhZkOuz4DDlJi9WkHUpwp/JwZkEmSdKUmLRDCe5UHs48h0ySJGlgFmSSJEkDsyCTJEkamOeQSZJ0GJh0QYCPVZp+FmSSJB0GJl0QcMPbz7NYm3IWZNIY9zAlHY4mFWs+A3M6WJBJY0xakqQhWJBJc2TvmSSpKxZk0hzZeyZJ6ooFmbQA7D2TJB0KCzJpAdh7Jkk6FBZkUsfsPZO0WK1ddw67du+ddbo5bOFYkEkds/dM02zSf7jb77+fU3qOR/2btNO4/f77edU7rpp1WXPYwrEgkwY0KRGCe5/q3q7de2fdYdj69vN6jkZDmLTTeKBtYFIO+8udD/Bdq06YcZq57cksyA6Re5c6FJMSIXh3bUnT7UDFnEcH5m6qCrIkrwLeCxwBfLCq3jVwSAfk3qW65OHOxWUx5jBJ02FqCrIkRwC/B7wS2AF8LsnmqvrSsJFJ06mLQwWewDt/05rDDvSb2pOvIcw3f8H8c9i056+pKciAlwBbq2obQJKPAOuABUtmk36oSRvApGkmMw1lvocKJh0GPZQTeOf772vak+RB6DSHzfc/mkm9+GBPvoYx3/wF889h8z2q0NeOaqrqkD9kISR5DfCqqnpjG3898NKqevN+820ANrTRk4F7DmG1xwJfO4TlF5rxTGY8ky2VeL67qpZ38LmHZC45zPzVK+OZzHgm6z1/TVMP2ZxU1aXApQvxWUm2VNWahfishWA8kxnPZMYz/cxf/TGeyYxnsiHieUqfKzuAncD4MY3jW5skLQbmMEnzNk0F2eeAk5KcmORpwGuBzQPHJElzZQ6TNG9Tc8iyqh5N8mbgTxhdMv6hqvpix6tdkEMHC8h4JjOeyYxnQAPksGn7fo1nMuOZbMnHMzUn9UuSJC1V03TIUpIkaUmyIJMkSRrYkijIkpyc5Pax1zeSvCXJryfZOdZ+VsdxfCjJ7iRfGGs7OsmNSe5t789p7UnyviRbk9yR5LSe4vmtJHe3dV6XZFlrX53kb8e+qw/0FM+sv1GSi9v3c0+SH+spno+OxbI9ye2tvdPvJ8kJST6Z5EtJvpjkF1v7INvPhHgG234OZ9OQw8xf84rH/PX4us1hB1JVS+rF6GTbB4HvBn4d+KUe1/3DwGnAF8ba/itwURu+CHh3Gz4LuAEIcDrw2Z7i+VHgyDb87rF4Vo/P1+P3M+NvBDwf+L/AUcCJwFeAI7qOZ7/p/x34z318P8BK4LQ2/Czgy+07GGT7mRDPYNvPUnkNlcPMX/OKx/z1+PrMYQd4LYkesv2cCXylqu7re8VV9Wngr/ZrXgdsbMMbgbPH2q+skVuAZUlWdh1PVf1pVT3aRm9hdC+lXszy/cxmHfCRqvp2VX0V2Mro0TW9xJMkwLnAhxdynRNi2VVVt7XhvwbuAlYx0PYzWzxDbj9LyCA5zPx18PFMsKTyV4vHHHYAS7Egey1P3Ajf3LomP7Svq7RnK6pqVxt+EFjRhlcBD4zNt6O19elnGe2h7HNiks8n+VSSH+oxjpl+o6G/nx8CHqqqe8faevl+kqwGXgx8linYfvaLZ9y0bD+Hm2nKYYNvfxNMy/Zn/tqPOWxmS6ogy+hmja8GrmlNlwD/FDgV2MWoC3cwNeoXnYr7kCR5G/AosO8prbuA51bVi4G3An+Y5Dt7CGWqfqMxr+OJ/yn28v0keSZwLfCWqvrG+LQhtp/Z4pmi7eewMs05zPw1o6n5ffYzSP4Cc9gkS6ogA9YCt1XVQwBV9VBVPVZV/wD8Dxa4y3iOHtrXDdved7f2wR7DkuQNwI8DP9P+gdC61ve24VsZnfPwvV3HMuE3GvL7ORL4KeCjY3F2/v0keSqjxHFVVX28NQ+2/cwSz1RtP4ehacth5q8JzF9PWrc5bIKlVpA9Ya9gv+PRPwl84UlLdG8zsL4Nrwc2jbWf3640OR14ZKxbtzNJXgX8CvDqqvrWWPvyJEe04e8BTgK29RDPbL/RZuC1SY5KcmKL5/90HU/zI8DdVbVjLM5Ov592zsdlwF1V9dtjkwbZfmaLZ9q2n8PQtOUw89fkeMxfj6/DHHYg1eEVA9P0Ap4B7AWePdb2B8CdwB2MfvyVHcfwYUbdnv+P0fHwC4FjgJuAe4E/A45u8wb4PUZV+J3Amp7i2crouP3t7fWBNu85wBdb223AT/QUz6y/EfC29v3cA6ztI57WfgXw8/vN2+n3A/wgo678O8Z+m7OG2n4mxDPY9nO4v4bOYeavecVj/np8HeawA7x8dJIkSdLAltohS0mSpKljQSZJkjQwCzJJkqSBWZBJkiQNzIJMkiRpYBZk6kWStyX5YnuEyO1JXnqQy78hyXfNYb6bk6w5wDxvTrI1SSU59mDikLQ0TVkOuyrJPUm+kNEjmZ56MLFoOlmQqXNJzmB01+PTquqfM7ox4QOTl3qSNwAHTGZz9Bctht4fMC9p8ZnCHHYVcArwQuDpwBsX6HM1IAsy9WEl8LWq+jZAVX0NOCXJ/9w3Q5JXJrkuyRFJrmh7fncm+Q9JXgOsAa5qe6ZPT3JmRg95vbPtIR61/0qT/GiSzyS5Lck17ZllVNXnq2p7L3+5pMPBtOWw66thdIf/4/v4EtQtCzL14U+BE5J8OcnvJ/kXwCcZJbTlbZ4LgA8xegjvqqp6QVW9ELi8qj4GbGH0XLFTGd1d+QrgvDbPkcC/HV9hOxT5a8CPVNVpbfm3dv2HSjosTWUOa4cqXw98oos/Wv2yIFPnqupvgO8DNgB7GD3Udj2jx4r86yTLgDOAGxg9G+x7kry/PVPsGzN85MnAV6vqy218I/DD+81zOvB84C+S3N7W990L+odJWhKmOIf9PvDpqvpfh/gnagocOXQAWhqq6jHgZuDmJHcySi4/B/wR8HfANVX1KPBwkhcBPwb8PHAu8LPzWGWAG6vqdQsQvqQlbtpyWJJ3AMtbDDoM2EOmziU5OclJY02nAvdV1V8Cf8moW/7yNu+xwFOq6trWflpb5q+BZ7Xhe4DVSZ7Xxl8PfGq/1d4CvGzfPEmekeR7F/Yvk7QUTFsOS/JGRgXf66rqHxbuL9WQ7CFTH54JvL916z8KbGXU9Q+jq4WWV9VdbXwVcHmSfTsLF7f3K4APJPlbRocGLgCuSXIk8DngA+MrrKo9Sd4AfHjsZNlfA76c5N8DvwL8E+COJNdXlVcpSZrNVOWwNu99wGeSAHy8qn5z4f5cDSGjizSkYST5XeDzVXXZ0LFI0sEyh2mhWJBpMEluBb4JvHLf5eSStFiYw7SQLMgkSZIG5kn9kiRJA7MgkyRJGpgFmSRJ0sAsyCRJkgZmQSZJkjSw/w+nQs6Tm04pEgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXHrQhGP-s8J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "216a372b-726c-4b6f-a4ef-f686a847311b"
      },
      "source": [
        "print('\\t\\tPearson corr: {:.4f}'.format(df1['Systole1'].corr(df1['Systole2'])))\n",
        "sns.scatterplot(data=df1, x='Systole1', y='Systole2');"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\tPearson corr: 0.9662\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gvl3JbOdXYx2"
      },
      "source": [
        "Através do histograma vemos que ambas as distribuições são muito semelhantes, e enviesadas positivamente como dito anteriormente. Devido a estas características devemos salientar a presença predominante de outliers à direita. O scatterplot revela que as medições de sístole são altamente correlacionadas linearmente. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esf8t4cTWcJG"
      },
      "source": [
        "O passo seguinte se dará pela remoção de outliers das colunas sístole1 e sistole2 utilizando o método de Tukey para dados univariados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YM5nhAovTw-x",
        "outputId": "b1e41c4b-6ab8-4e2d-b23c-d0c1ea2ff65f"
      },
      "source": [
        "q1 = df1['Systole1'].quantile(q=0.25)\n",
        "q3 = df1['Systole1'].quantile(q=0.75)\n",
        "iqr = q3 - q1\n",
        "lines_before = df1.shape[0]\n",
        "df1 = df1[(df1['Systole1'] > (q1-1.5*iqr)) & (df1['Systole1'] < (q3+1.5*iqr))]\n",
        "print('Nº de linhas removidas = {}'.format(lines_before - df1.shape[0]))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de linhas removidas = 141\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r21a1FYIUfGw",
        "outputId": "ac77b95a-9bb4-43d2-e8f6-db4614db4c8f"
      },
      "source": [
        "q1 = df1['Systole2'].quantile(q=0.25)\n",
        "q3 = df1['Systole2'].quantile(q=0.75)\n",
        "iqr = q3 - q1\n",
        "lines_before = df1.shape[0]\n",
        "df1 = df1[(df1['Systole2'] > (q1-1.5*iqr)) & (df1['Systole2'] < (q3+1.5*iqr))]\n",
        "print('Nº de linhas removidas = {}\\nNº de linhas restantes = {}'.format(lines_before - df1.shape[0], df1.shape[0]))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de linhas removidas = 58\n",
            "Nº de linhas restantes = 4612\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "TwSNZirt4Z98",
        "outputId": "98a965e7-8ff7-44e7-ed9e-9c3a700df6db"
      },
      "source": [
        "print('\\t\\tPearson corr: {:.4f}'.format(df1['Systole1'].corr(df1['Systole2'])))\n",
        "sns.scatterplot(data=df1, x='Systole1', y='Systole2').set(xlim=(72,228), ylim=(72,236));"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\tPearson corr: 0.9522\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6qMFa67WHnNzrPnpba53na78HzNFaf+hrb8drIG70zfeQr/9fwH1a60O6jwoKCnRRUdGhThEE4LvFFHZU1nHJvP8LudHecE4vY1ePN9lsMFvLawFvDsGM0QOod3tobGqhR3K0EU8AuHNUX8Jsikc/2Iqr2cO0kdlGfCHwGi9eP5xJvkS0gqx4rvlhT4or6vBoCFPQLy2W5NhISg+46BJtx9XcQr3bw86qeuOcvmmxpMREUnrQe06ju5ltFQ24WrzXWr29kkk/6MmdyzZI7aPTDKXUGq11gdWxw70p/PkQxzRQeIRreQO4APhQKdUHb8Z0FbAc+KevttIZQA7w2RHOLQjtYrMpRuWm0W/auVTUukiNPXxF0PZcToFbLFzNHooralmw0vv0ffeofhxoaObeN7xJYX+9arBpDqUgMsxmBIQzEqx3H1XUNvHI+MHUu1vokexk936XYTyykpzcfEEOtyz9zLiZzx0/iIMNzaZzfvWjbG5d2pb/MHtMLh99XW7EFKYV5pDVxck7R/CdCKc+h0teu+C7TqyUWgycDyQrpfbgjUs8Czzr26bqBq7V3leVzUqppcCXQAtws+w8Eo42NpuiV0pMhwPL7bmcAl+uHXabkQHsavawr8HNQ+99ZYxJi3ea5shIiOJ3r7YFmqcWWu8+SomJ5MqF3jDbP38xwjAy4CuIF5SJXFxRZ3rjGD0og/t8AWT/OTOWb2bu+MEUlXh3Mc1buY2zs886ou9EOPXpaPKaHe/OofN8XR8BT2mtm9sbo7We2M6h9rKkHwQe7Mh6BKEzsAoq//mKfKNiqT8f4G//aXMd3T2qH7VNLUwtzAYgKiIsKLCsmTG6P7Pf3oKr2UPJvnrT8fgoO7+/pD8PvrPFuMa0whxq3c1GobrmFs9hC+J5NCFvJB0piNfglmcvwUxHdx/9DbADT/jak3x9N3bGogThWGMVc1hw9RA82mPa+x/nDGfCsEzq3a0M6R7P9sp6FnxYbIyZNSaXyef0NALLDruN+8fmcc/F/aioc5OXEUdSTKTp+KwxuTx+9RC+2F1t5ArcNyYvpFDdoQrihSnrInnB7URnhKndVYrfCUF0NKN5mNb6Wq31St/PZLy7hwThlMAqj2HDnmp+9+oG5q0oZsHKYuatKObWpeupa2plwcpi3C3aCBj7x8xcvpmdVfWmvj+8uYmE6EgWrCymoanVyAsIHONu0SxYWcwzq3bwm8I+3Lc8tFDdoQriJUVH8OBPBxp9b60vZdaY0KJ54T5xNn9QuUdS9DH4doWTiY6+KbQqpXprrbcD+LaRynuncMpQXuMyuXWiIsOpaEf7wP+5prG5nfwBQvq0RzO1MBt3q7Yc0+rLOega62D3gXrcLTpE5yAj3mlkIy/+tIQnrh7K2t0H0Rr+9p8d/Or83jw16UwONjSTHBPB3z/eEVIQ797/6c+SKSMkqCy0S0eNwu/w7hjagTdPIQu4vtNWJQjHmPR4hymfwC+LOfnsHsbbgP9pO97p/W+TFh9p6aLpEmXOCHbYbYTZFAtWFvPkz4e2O+bqv39mZCdbaS40uFtYsLItnlHnbjW1q+qamPHmZgB+95M+fLJzPx9urTJdJy3ewZlZXTrnSxROCTpqFFbh3Sba19f+unOWIwjfjeAgcUeeggPHhNtUiFtnW0Udb64rNZ62AZZ8vouHxw9mzriBxEbaLUtUDO4ez7SR2aZ8gW8PNjK1MBt7mLIcE+Zbq6vZQ21ji6XmwgNjBzK1MJswBb1Toln03x2A92b/5yvy8WiPYXDsNuvrJDqlhIVwaDpqFD7RWg8FNvg7fBnOh0xuE4RjwXdJTAseM+fygSFunaiIMK4qyDRKW/t3Bm0urWbW21v461X5IZoF724sIzkm0tge6t+x9OLqEkr2NTJtZDavFO0JceuckeA0rlsd5Jbyay784sUiY84//nQg44d246zeKdgURNoV5/VOI3NKFHurXdhsihlvbg65zpDMBHqlxnbOX4RwSnBIo6CUSsNbg8iplBpCmy5zHBDVyWsThA7xXYrdBY+JiggtJdEtIYrbA3IK/Hv7n5p0JmCtWTBtZHaImtl9b202sqA9Gkudg4QAl1O0w7wWK82Fe17fyFOTzuSu1zcbc7wz7VwGd09kcHdvNra1noLsNhIOzeF2H/0EeATohje72f9zK3BP5y5NENonUBvhm331IU/5/mJ3hxozsSCDJVPO8gZ44yJ57KrBpt06mtCgcJ/UGMJtijnjBtLS6n3DCBzTKzkmZExiVAT90mKZWphNTGRYiBbC/WPzeGn1TqMdboPZY/OMc8Js1jkHBxuaTe2aRjfrdx/gvU1l1DW1sODqIabryG4joSMcLqP5BeAFpdTlWutlx2hNgnBIgl0/7WkSpMQ42h3z4vVn0v+MBK57rq1UxOwxeTw9aSjfVjcRHRFO16BA8qCMOCaOyDJKVDvsNmaPzeXFG4bz7UEXCVF2KmtclnoKvwsIYD80biDTR+ZQ727F5ssvOCc7lT5pCdgUNLV4yD0j1qhempUUbVkfKfDtoiArnm0V9cZbir9437vTzqVcSlgIR0BH8xS6KaXilJe/K6XWKqUu6tSVCUI7BLt+/JoEwXvyA1Ulg8dEhtuZsTy4DMQmPCjuXLaRqYu/4JXPS5g9pu2Jfcp5vUOC0TPe3ExVrZvpS9bxyxfXkJbgNK0lWE/B1ezVQvDnOsxbUczvXt1g5DHMW1HMncs20tDUyh2vrufOZRt5dlWxpYpa4NvFbRf1C3Fb3fvGJmpczZzVK5leKTFiEIQO0dFA8/Va68d8MplJeDOaXwTe77SVCUIAgTuFGptbTU/NZdUuPtuxj2evG0ZlTRMJ0XaeX7WTc7KTKPpmP3trXKTGRjKxIIMhPZJpbGqhvLbJ0iVT62ph3oR8UmIjeeXzXQzsFs2iycMpr3UR77Sbyk34x6DwuaAcNLe2UlHdwPOTh1NZ6yIhKoJXivaEjAkuqhdcfqK8tol/3DDCWPu6kiqe9amoJcdEsrq4ghvPyWbc0EzS452U1zRa/j57q10M7n60/haE04GOGgX/P+FLgEVa680dkOMUhKPC4dxFgzLiuCgvneuf/9xwnfxp3EC+Lq8zCsn5n67//P5XlOxr5NWbzrJ0OYUpxdQlX+Cw2/jbz4ewcU89M5a3zWFVbmJLWY2hUfDw+EEMyEg0uaWsxgQX1Ytz2E3txCg7P3/mU1MpjLtf20DJvkaykpzc9KNsrgm4xlM/P9Py90mTMhbCEdJR99EapdT7eI3Cv5RSsYDnMGME4ahg5S669cd9DunW2VFVb6os6q8S+vDlg5k/MR97mC2kVMTMS3NxRNiYWpjNjef2IlyFGQbBP0dguQn/Df+Voj3G8W0VdSFunOAxMy/N5e0NpaY5KmvbDMbssXm88F9vNrJ/LU98VMzoQV4xwtGDMkLdWMs38cBleSG/j0hrCkdKR98UbgDygR1a6walVBIwufOWJQhtWOkaBGoSYFERNNxms3SnrNpexbwVxUwbmc1HX1WESFGe3y/V0Ebo2zV0J5Gr2UO3BG+5iX5dY3nwnS0m11B71+2fFmcqUTFzdK5RomLRJyXMGpPL/In5JMVE0tTSQn73pJD8CJvvEc6qAmrJvkaqG5pFWlP43nTUKLyCVwthHYDWeh+wr7MWJQiBBOsajBvazaRZsGRKqCuoV0q0pTvFr33g0VhKUZ7bJxXw3nSTY6zLWJTsb+TxD4uZWpjNgQa3aa3tXTc+yn7IEhXxTjtXLVwDwKLrh4XkJcxb6dVSDpwz+BrVrmbu/98tpj7JSxCOlCMpnT0ZmKeUegV4TmstpS6ETsFK1+DRK/MNF1KYDVOhuuZWD3PH5WGzhVHf1EK0I5xYRxi/v6Q/lXVNRrmJ5JhI6nzaB06fFoLfuHizhHNJj4+ib9cYUmIjqW9yM3N0LrPe3mx6Yn9xdQngLVJ396h+7GtwG9ewAdMKc0Ke8g/Wu40SFb2CSlTcPaof7laPcbzB3Wr5trFrXz3grYD6wGV5pnjJo1fmExGuDGMheQnCd6VDRkFr/W/g30qpeGCi7/Nu4GngH4cS2xGEI6G9khUX9e9qyEbGOsJx2sOMrZ6rs+K5oiCTmcvbcgHmjh+Ewx5mKjcxa0wuS1btomRfIw67jVt/3MfQMTindxd27XcxOSBYPXtsHl/sqjJcMpldorjrtQ0md1FTq8d0jb9elc/LRbtM5SVeLtrF2PwMwy1116h+jOyfxlm9U4iOCKOp1cMvFrXlPjw9qcDyTeDcnGQKeiSSGus1lEMzE00ymoBIawrfG6W1PvxZgC+O8HO821G/BV4CzgEGaq3Ptzj/WWA0UKG1zgs6dhveTOkUrXWVbyfTY3gD2Q3AdVrrtYdbU0FBgS4qKurQ+oWTgx2VdVwy7/9CbojvBJSs2LD7AFcuXG2cM2/iEKO6qZ9pI7MtE7785Sb87b9cmc+vXlrLy1PO4lrfbp7A85+5dhg3vOA1FAWG8fG+OVhdw78zyB8Ibm/30ZTzehmxDas5bv1xX+5ctqHDtZwE4UhQSq3RWhdYHeuoHOfreCukvgiM1lrv9R16WSnV3l35eWABsChoru7ARcCugO6L8VZhzQFG4HVXjejI2oSTn5YWD5vLqimrdhFmU+2WrPAbhW+rzYHnxqaWkDHB8pT+eTITnYZ0pl8b4a9X5VPZTt7CgXq3V6OgvpmU2Eg+2PytoVlgDwsNKpfsa6ShqcV4u0iLd/Dnf30dkqeQkxrLnMsHEhURbjlHRoJDnvqF48LhCuINA3YD87TWHyqlrgWeUkqVAPdprfe3Z2201h8rpXpYHPoLcAfwZkDfWLz5DxpYrZRKUEqla63LjvxXEk4mWlo8vLG+1PCP3zWq72FLVmQkOk3nJEZHhIxpT56ytLrRcONMH5nDrn31/PHdr406QcHnJ8dGmnIOZl6ay4w3N1Gyr7HdtWYkOvnty+tMbwqVdW7Tm8JXe2uNYLXVHIlRkfRKiWm3oJ8gdBaHy1N4CnD7DMJ5wEN4n/yrgYVHejGl1FigVGu9PuhQBl7j42ePr084xdlcVm3KJ9BgWbJCaw+f7dzHW+u/pam5lT9fMYhpI7OZWphNYnQ4D40baLSnj8xmwBnxplwGq5yCx1Zsw93qdZ8u/M/2kLyF2WPyeHZVsWkX0JP/Kea2i/oxtTCbnikxIdf4w+gBPPTulkPmKdw/No94RxhTC7OJiwzj/rF5IesM62gGkSAcZQ7nPgrTWu/3fb4KWOgrjLdMKbXuSC6klIrCW1n1e9VMUkpNAaYAZGZmfp+phBOAsiBXUHOrZvFnuyz1BgJ97LPH5PHmulJK9jXy1vpSbj4/2xTwfeCyPN5e/60xz+CMeGYs3xzixgn33X03lNbApyU8c20BVXVuEqPseLTm/S/blMv8ugaB6mx3j+rH1AuycbV40BpqXc2U7Gs0/Y6uZg8D0uOMUhi79tXz6L/bdifNGN3fNIdf96BHsrwlCMeewz2PhCml/IZjJLAy4FhHt7P66Q30BNYrpb7BW457rU+zoRQIrNDSzdcXgtZ6oda6QGtdkJKScoRLEE400uOdxlMyQM/kaEMHYMHKYh7/sJgDDW62V9aFZPAGZvgGF7e7941NnN8v1Zgn1mkPySlw2G30T28TnNlaUcd/t+9n+pJ1XPPs53yx66BpbVa6Bg+99xWNzR5jrY3uVtMY/3WcEWH8ZvE6ymuauPdN81pnv73FNMeBBrfkFwjHjcPd2BcD/1FKVQGNwP8BKKWy8bqQOozWeiOQ6m/7DEOBb/fRcmCqUmoJ3gBztcQTTky+r+ylP+9g14EGymtcpMU5eOinA7n79Y24mj2UHWzglgv78Jd/bw14kh7Aks92mYTsN+85yA97dyEnNZ8u0XZeWxtaqC4zMcrw11c3uEPmveXCPria2/IDkmMieeKj7cYcS4v2MHf8IIor6vBoyEx0Wgaj/a4eh91G79SYkOtMK8xhR0UdACXtaD8EziH5BcLx5HB6Cg8qpVYA6cD7um3/qg34zaHGKqUWA+cDyUqpPcBMrfUz7Zz+Dt7tqMV4t6RKCY0TkKMhe5mV5OQ3hTmmxKu5lw8y9AWyu8awZ3+jUcLCpqB7FwcXD0w38o17taMAACAASURBVBKykpz8+vxsk66B1bbPppZWw32UEhtJeY3LNK/TbqOkqs4IPM8ak0tKTJtxiQhXNLdqwy3Vnm7DOb2TyUhwEh0RTpdoO39+/+uQPIV5E4awZMoInPZwFnxYHDLHyL6p/LB3kuw0Eo47Hc5TOBGRPIVjS0dyCA435uYLsnlmVfv5A3/72VBuCSh+B3DnqL4mTYL25vDv/fcbiaFZiVz7rHfn0JIpI7juuc9Dxky9IJtH3t9qtB8ZP5ipi/1VUs/kV/9YY4zxC+b41+LfjRQZDrcu3Qh4cwx+O7KP8eYTbDi/qarj3U17TXNMH5nDxXlpEkMQjhnfO09BOD0Jdvvsqw/dy98nNYaq2ia2lteSHu8kNz2O8PA2n3p5jctUkqJv11h+0LMLPz2zO41NLURFhrNt70HOzkmmX1oMSsEluV0ZPyyTqtomUmIj+aaqLmSOYF2DPqkx/KBXEjmpMd5gblUNdps2tBC0hh/07MKAjATDBbVszR5SYiJNeQthNsX8ifmkxDrYH/T7llW7WPRJCQsnFXCgwU1ClFe3IbdbgnFOyb5GUmLtvDzlLMqqXcZ34n/y988RHEiXwLJwoiBGQbDEylU05/JBZCU5jd01fnnKwLr+D1yWx2WDMwzDEPx07c8KDtzBM3tMLncsW0/JvkbmTxzEWb2TTdoID48fzOSze/DoB1st3UX+dQSWqJg7fhDbKxtN8pSzxuTyStEuikqqjTmqG92mvIXd+715Cw67jSctNAoONLhRwPQl64wxgWQlOamsbebGRWst3xS6xjmMQLofKVwnnEiI+0iwpD1X0cJJBUx50evLXzBxCLcHlZdw2G28eP1w9tW7SY93EhsZxuQXPmf0oAyUgmE9Epnx5iajDd4Cb6MHZfD4h8Us/sUI4+bup72SFY+MH8xX5bUM75HIlBfXdGjM3PGDjcqoVu6jwHZWkpNpI/vw+wBX0P1j89Dawx3LNhlj/nplPpvKaghTMDQrkV8GrSXQxfZd4jKCcLQR95FwxFhpGLiaPdjDlFF+oaax2fKcT3fu55H3txpP7FcPzzJ249xzcV+uKshsVytgX727wyUrtlbUsmClt35QR8cEy166Wjzttt0tGnuYMgWnm1tbqXGZ5/hyb43xtvHw+MGHLNNhsylG5abRT0pYCCcoYhQES6Iiwi132kRFhBnlFz7buc/ynEzfdkpXs4fiijrTE3t6QhS/C3i7cDWbtQJSY0M1DNorWRGojdDRMc6IcFM7WBYzsH1FQTfueHWD5duG1Tq8v2+t5XUD3UM2m5ISFsIJiyTTnyZ4PJodlXV8sr2KHZV1eDyHdhu6W1uZVmguNzGtMAet2+Zxt3i4e1Q/c2mISweQ6LQzZ9xA5k8cQrzDbrpB7qyqJzEqgpsv8JakmFqYTWJUBI5wG/Mn5qO15pYLzaUjukRF8Mj4waYyFneP6sdra70lK5at2ROy1uSYyJDyEbPG5Jp0DGaNMctizhxtbndPjLJ86vfrGvi/E/86wJvb8MefDjRdV/IOhJMJiSmcBnwXP/aOyjomP/+Z4fvXGj7dUcmkH/Q0lZu49cd9aPVo6t2tpMZEYA8PM5WNfuCyPOav3GYEp++6uC9aE7IlM0xhBHjvubgfNa4Wo+zDtvKD/HjAGaag8a0/7sNz/+8bYwdSVpKT2y7qx9byWrSGtzeUcv/YXGoaW6l3txAdEY5Ht5Ia52RV8T5j3uvO7k2lb5fTo+9/xYheKcbvGxsZxl8DtsKCOZbRr2ssD7//lamshcNu473p5+LRiHtIOGE5VExBjMJpwHfJL7AyJIFB5sB5/PkB7QWeH796KDf/07sbJzjnwH9OcMA3MOfg6UkF/OIQ1/XnCzz5n2JDQOeBywYyf+XWkBv2H/6nP79/Y3PImKwkJzefn22Uy3DYbTw0biBoTDkHgbuerPIWJGgsnAxIoPk0p72gcaBGQTA2m+LCvqn844YR7K1xkR7noKnZYzlP3hnxzJ84BJQ3X+DG83obOQhPf7yd+qYWXpg8nIpal6UGQZ/UGIZkJvDXq7wlK55btZN+abGG0H1ZO+vvl9amSfDhlr38adwgKuuaSImJZPf+BsvCdHHOCOaMG0hUZDhrv6li7uWDqahtomtcJOtKqkKE76eNzDHWnhwTyUPvbDHeTvw5By9MHo5Gy1uBcEogRuE0IFj4Hg6/N76lxcPyjd+aylH8/RprmcjtlXXMee9r/nnjMCaOyDLlIMy8NJeMRAc/+7s3lyFYg8CfYxBYsmLWmFwyE538+iXv1tGnJ4XmCzjsNjweuHPZRkPtLDBP4YHL8kw5Ff4xlbUuZr+9xSiVcW2QVsLfP97urZiK1yV1oKGZqYu/ML0pBGojHGhwkxIbKUFj4ZRB3EenCIcqVHco3WN/YbrgQnURYTYeevdLw8cOMLR7PHVNzSRERbK/vpku0XYiwzVKhRvF7SY9GyppuWCiVwdZKRiYEc/+OhdlNU14NAzvkcgffKI1gWMWXT/cWJfTbuPLslpDBtNvOF5aXcKG0pp2y1785cp8o2SG/4beKyWGX764pt0xgWUuAt1egecEuq3EXSScjIj76BTncIFkq73xmYlRvL+l3DQmMCicleTkpvOymfV22434mWvPpLJOc4cvOcubjZzH0qJtFJVU8/D4QZZungZ3q3EDfmR8HuFhYSbtg2mFOby4uq2YnavZQ1m1y8gafmxCPu4WjylfwN3iobLOWwpbKeuchG0VdSHlJG67qM8hx2hgzuUDcUaE802VdUXTHknRTC3Mxqa8RfME4VRCjMIpwDf76o2bO3hvXLcuXUe/gEBy8N74HZV1IWPufWOTUZhu9KAMwyD4j9ttYcbTur9vxvJNzB0/mKKSL+gaF5pj4LDbSIhq25Z6RmK0UcLCP8e8lduM6waO8R932sN48J0tIfMGjwk+3uLxhJSTSImNNLWDx2i8LimgXYnOpJgIbl263mgfKmAvCCcbYhROAToSSA52L7U3xi9sb1V0rrzWXNwO4OOvK4h3hjNn3EBatWbm6FzT28XM0bnsrXExb+IQGptaONjQHDLHsjV7yOriNHQNeqVE8/yqncZ1K2pDC/G5mts0CN5aX8qsMbkm99IDl+VR72oxbur+N5KaxmaTDGbgNtfZQXkMdpvi7lH92NfgxqO9CXFdoiI4GCDWc7iAvSCcbIhROAU4XCDZyr309CTroHGwsH2gRsEZCU7L0tF+IfvpI7P5f8WVph08i/67g6mFfbjJV4J69pgBIXNMH5nDwYa2wnT3j81jX707YF1hlms9MyuRqYXZ5KbHUe9uNrmXwmyKtzaUhugajM3PMK7zx5/m8fzkYZTXNJEQZed/1+/hD6NzaWxuJTXWQWNzCyX7G0yuLq8mszkrWorZCacSktF8CtAjKZpHr8xvN4vWyr1075sbmXP5INMYK2H7QMF5rbUpx8DV7GHWW5sNWcylRXsY2T+NO15dz53LNnLHq+sZ2T+NrXtrjDEpsY6QOR5bsQ13qzbaf3hzE1PO621c124jJDt59phc5q/YyoKVxXyzr55739jszZVYWcy8FcXc/dpGbjy3N8+s2sGClcU8s2oHE4Zlmn6/e17fxBe7DjJ9yTp++eIazu+XzsCMBM7qlUyvlBgiw8KMyqz+MY9+sJXSAw2W37MgnArIm8IpgFUguVu8k42lBymrdtElOsJSSyAjwWEUt1MonvyomLsv6W/kGLy2ZjfDe3Rh/sQhdI2LpLzG2o3jn7Os2sVnO/bx7HXDqKhpMnIORvROMqQ0m1o8li6oLF/w1r+2sDCvrkFqnIP/t7Wc9IQonpp0JgcbmkmIsvPRlr3celE/KmubLHMfXM0etpa3BZr7do3ljwE5Bv5z+qXFsmTKCMscg8o669+3Z3J0u2ME4WRHjMIpQmAguaXFwxvrS005BrPG5PLER20Zv9NH5pAcE0mPZO+Yb6rquCgv3cgx8O/jv3FRW/7AUxb6AoFF5AZlxHFRXrpJC2HmpbnEOsK4/RVvaYwXJhdYuqAe8ZWL8K9t9742XYPpI3OodbVw7bOfm65bOCCd6UvWMbXQWiYzMNA8tTCbAwGxAP85mV2i6Z1qHQ9ozy2XlRQtMQThlKXT3EdKqWeVUhVKqU0BfQ8rpb5SSm1QSr2ulEoIOHa3UqpYKfW1UuonnbWu04HNZdWGQQDv0+3M5W1uHr/LprlVs373Ad7bVEZlbZNRswhg9KAMy51G912aG+TGyTOKyE05r7dpDr97KUx5dwp5b95hh3RBWbmTHluxjb5psaaCePdc3I/S/d7CdG+tLw1xLz1wWR69kqONPn8wOthdFnaI/wGHc8sJwqlIZ74pPA8sABYF9H0A3K21blFKzQHuBu5USg0AJgC5wBnAv5VSfbTWrZ24vlOW9nYWRQbIZHrdK7Xc9or3zWDehHzTmMjwUJdMyb5G4p12I6CbHhdJfFQ4Y/Mz8Gho8WjL67pbPUaegsNi3kAXlL8dqGuQGBVB6UGXKeB7y4V96N4lmvkT80mLd1BR02QKNCc47RxocPPI+MFtBfE8rUy9INsotHc4GUzRPhBORzrNKGitP1ZK9Qjqez+guRoY7/s8FliitW4CdiqlioHhwCedtb5TmaRo63yBHsnRpnasI9w4p2u82VXS0/eUHTzHlr01zFvhdcm8cP0wk8rY/InW+/rDlDL6spKs5z2crsHst780vV385d9beX7ycCY/b52d3J7yWnBuw+F2Don2gXC6cTxjCtcDL/s+Z+A1En72+PpCUEpNAaYAZGZmdub6Tlo8aG6/qC+PvP+18WR956h+OMJtRi5Al6gIbDbNP38xgqraJsIULJw0FFDsr28mMcrO7y/pbySNOew2ZoweQNHOfbxw/TAO1DdjQ9EnNYZz+6SiFBxoaOK+S3O5L6B09uwxuSxbs9sINEeEe5/y/Ups/lyGJz9uu1H74x/+dmYXa12Dylpv0NgqO7k95TW/u0hcQYJgzXExCkqp3wMtwEtHOlZrvRBYCN7aR0d5aacEqbGRJMVEmNwpXaLtLPy42BCtn3XpAMoOuo3kLW/p6BxmLDcHp2+9MIeaplZsCvIyYgmzKePtwKr89J2j+plcNs4IGz/ITja2djrsXr2EQDfOsrW7mHv5YL7ZV48zIpy31+/m4fGD+XhbFVpDVW2Tdaa0MwIAp90Wcrw95bWR/VL5Ye8kcQUJQjsc8zwFpdR1wGjgZ7qtGl8p0D3gtG6+PqGDBCqrVdW6+eu/t5rkKl/85Bt+M7IPUwuzufHcXlQ3NrPgw21GAPj2i/oZBgG8T9VPfFRMfvdE+naN4ZzsZDweQoLPgUHi0YMymPPeV2zZW8vuA418VV7L1vK6kL3+f3z3K9ytHiN/YGT/NJTSAbkN6bjczcbxqIgwHrjMHEieeWkuZdXefAG7TTF9pFl5LSk6grlBeRiPXplvykMAjkiNThBOB47pm4JSahRwB/AjrXVDwKHlwD+VUo/iDTTnAJ8dy7WdzARnLD82YTBXFWQyb+U2k4vGn3nssHsFZK4enmW4cW6/qI/pqTo93sFVBZlcE1Baes4464J3/iBxrCMs5Lr3j82zHJMR7zT0EhZ9UkK3xCgem5BPQpSd51ftZFReunH8b//ZwY3n9AzROpj0gx5eOc/oSB79YKspe/lv/9nBvIn5Rh5G8JvBd1GjE4TTgU4zCkqpxcD5QLJSag8wE+9uo0jgA+W9k6zWWt+ktd6slFoKfInXrXSz7DzqOMEZywlREdy5bKP5if7tzUaQ1dXsYWdVvSkQGxwAHje0m3Fz98+xo6rukEHijIQofhegvOZq9rDnQIPlmF0HGk0B36SYCK5++lOjPSAjwXS82tXM/f+7xTRHpD2MBSuLmT9xCAca3CHF7yLCbO0GiTtSRFAQTkc6zX2ktZ6otU7XWtu11t201s9orbO11t211vm+n5sCzn9Qa91ba91Xa/1uZ63rVKS8xsWUc7J4ecpZzJ/oLTMd/HSeGBVhqJlNLcwmKiLMdE7pwQZuubCP4W4Js4UGapcW7QnJB5g1pk3svmRfaKlpqzH3j23LbfDP8e2BeqM9O2BO//lZSVGmOaYV5rD3oPdls+xgA9MKc0KON7ea1xL8nVm9wVTUutoZIQinB5LRfArQPcnB7v0xhorY9JHmDF+/lvDvghTRApXJHOE2bEoZwek+XWNDnvAPNLhJT3CYAtgOu42fDc+kpqmVHhbbWK3GKKV5aNxAKmvdJETZeWn1Tm48J9twH/3f13t5ePxgb0XXWAduTzO/f/3LkOJ28yYMYcmUETjt4Uxbsjbk+Ki8tHa/s++iRicIpwOivHYK8NnOfVwToHgWLCjf3p79QGWyJ382lN8GuFOsROkfuGwg81duDVFJ87ulrMe0CfcEjglUL5s9Jpd4Zxi//Mc6I9N4eI8uXP7kJzjsNuaOH0SYsnHbK9b+/+8SH5CYgnA6I8prJxCHks08kjGA0VdV5zbd8MuqXWzbW8MLk72SlonREbxStCekGFzJ/gZv8LapBQ0hcyz6pIS/X1PAgQY3XeMcNLpbTDd3/zyBBfEWfVLCE1cPpbKuCWdEOJU1LssxuenxzJ+YT1JMJK9+vosRvZJDAs+PXz2ErnEOBp0RT3i4jf7p1kHj75J5LNnKgmCNGIVjyNF8oo0IV0z9p1dQfukvzzK5Qi7ok8yI3skmUfpgbQSH3UbPpGimv+yd465RfS1dPxtLq5nznrcw3RM/G3rYbOQDDW7W7DpoBH2DXVn+MbHOcK5+eo3RTk+MNgWWu8ZFMrxnkum7OFRm8XfJPJZsZUEIRdxHx5AdlXVcMu//Qm6Q700/F4/G8u2hvTF3/KQvybEOGpta6JsWw7fVLjweqG9qoUdyNA//awsjeqUYT/FvrS9lbH6G4bL5w+gBvL52t3HOwIx4dlTWhYjf9EqJYWNpNQCrt1dy3dm92FpeayiR9U6J5s8fbDW2ugZXY517+UDcrdpUsXX2mDyKvqlk6Zoyo/34R9uMMbPH5HFpXhpOp/3Y/eUIwmmEuI9OEKx2vCRGRbB210HueX2j5dtD2cFGy10yKbGRRuC4ICueKwsymbHcLIP55MdtN+dphTlkJDgMF02rx0NhvzRj2+m0kdm8UrQnROj+ioJuhlLZ3aP64WpuNRWmu+fifkwYlkm9uxWt4b2NZfzxpwOpqnMbOQc/OyuLhZPO5ECDt3xGrcvNpYO7kRofjU1BfFS4EVhOjXVgD9dERso/TUE4Hsj/vGOI1Y6XKwq6GQYBQvfL28NDSzg47DYUbUXmrvlhL0MHwT9HcF7CvJXbmDt+MAtWel00CyYO4Y/vtI3xaCz3+vt3dbqaPexrcPPQe1+FZCcHF5n76Zndmb5kndEe2D3BKKLn73tq0pnGWrzB5MFMW9w25h3JFxCE44IYhWOIvz5/YHygT2qs5dtDWXUjW8triYkM57EJg3Haw9lf30yXaDsHG93sb2gyisx52ilZHVyOete+tlwAjzaPWbZmD9MKc0zZyNMKc3hxdYlxjkcTopq2bM0esro4jUJ7GfEOImzKaPdMjmbOe1+HrG1LWa2p3ehuMbUral1iFAThOCBG4RhiteNFe7RlTsENLxQFFKrLZvrythyD2WPySI2N5I/veJ/an722oEPlqIdkJjDn8oE4I8KJtivTmLJqFy8X7eKxq4aw8dtqRvTswr1vbDTtWIqLDAvZcjp9ZA4HG9yGi2nmpbk89N4Ww231wGV5RISbg+gOu41aV4up7YwIN7UlX0AQjg/HvCDe6Y5/x4u/KFtYmLmY2xUF3UzKZKMHZRixAmhTQGtpbdtCGhURFlIQbual5qzg6SNzsCmMonOJUZEhYyYMywQFC1YWU1xey4RhmabjPZNjQlTTglXSglXU7n1jE7PGmDOaZwdlNM8em8ei/+4w2lLSWhCOH/KmcJwpq3bx7sYyo9hbrMNuctF0T3BauoaaWjzMmziExqYWKuvcpjnS4h08v2onowdlGEHjz3bsY3D3BOZPzKdrnIPqpua2MU0tREWG89qa3fTt6i2FkRzr4OmPt5sCz1v21lquJVAlzcpt5W7xmArTdYt30rdrDHurXaTFO+jfNY6CrETJFxCEEwAxCseZ9HgHFw9MNwLFc8blmlw0VvkDDruN5NgIQ9fglZtGmOYIzksYlBHHxBFZXP/856bqpVcO626MyUpyctOPsvn1P9e2m9vQXs7BoVTT/DkHwfkAg7snMjigWLrkCwjCiYHkKRxljjRjeXtFHXcuW8c1P+xFY1MLPVOi+d2r642n/Nwz4qhvambX/kYjNyAlNhKn3UZyrIMD9c10S3Ty82c+Nd2ss5Kc/GncIMprmkiPdxiJbH4CS00AlpKWWUlOfndRP74qr/UGkRMctGrFrABltUCRHaucgwcuy2PMwDPYU914RFncgiB0HpKncIz4LhnLNS43lw/NNJ7YZ47ub9IkyEpy8uvzs025AfddmktidNubwmMT8k03c78WwmTfm8Gcywdaun0CNWWCJS39c9we8PZxy4V9cEbYTC6lxZ+WcP/YPGpdLXSNc5DbNZa+aWbX0L+/rpAaQ4JwkiCB5qNIezX6v/FtBbWi1QOz3m4LJPdKjTHpGIwelBGidnbfW5sJt7W5cVJjI41ALoRqIURFhJuOg/dNIfiefKg5XM0e/vLvrXSJiuTxD4tZsLKYxz8sZmtFHQ57GP8z6AwKenTB6bQzuHsiP8lLZ3D3RPZUNx7xdyIIwvFDjMJRpCM1+gNlM3dU1lHjcpMYFcHNF3h1Dqobmk1zWInSu5o9VNa6mTdxCHPGDcSmMMlVBmshPP3xdmaOzjXtALrv0lxyUmOMvrfWlzJ7TPtz+K+r0SG6BQ0BOQbf5TsRBOHEQdxHR5HD1ei3dC9dkc/ks3sYOsZP/ty66FxwOy0+0shlcNhtzLl8INNH5lDvbg3RQthQWkPE2l08NelM9la7cEaEs+i/O7jz4v48d90wquqaSI6JROtWFl0/3PD9W5XbtttsIboFj1015Dt/J4IgnFjIm8JRxJ+xHPgkHbjn3sq99FV5jUnYvqLGxcxLc01P8LPGmJ/yZ43J5S8ffG2a585lG6lramXBymIe/N8tITkIVxZkMuPNTUaewpXDspjz7hYmPv0pv1m8jolPf8r1L6yl9EADv1m8jq/Kakzr8AeVlU3xzKodLFhZzDOrdnDTj7KJtLcfGzjcdyIIwolFZ2o0PwuMBiq01nm+vi7Ay0AP4BvgSq31AeUVbH4MuARoAK7TWq/trLV1Foer0V9e46JPagw3ntfbyA040NDED3p24bpzenLAV8biuVU7jZyDrnEOln+xh6cmncnB+mYSou2U1zTibtFGnkJUZDhPf7w9RNfgqUlncrChmYQoO2t2VjHn8kFU1jaRFBPJnv0NFJVUm9bvavawvcorcVlR5+bjryuMdTgjwvn7x9u56Ue9TWt5ftVOukTZ6Z+e8J2+E0EQTiw60330PLAAWBTQdxewQmv9J6XUXb72ncDFQI7vZwTwN9+fJx2HqtGfHhfJxBFZpnyCueMHccmgM4ydRP78gIfe2UJZtYunJp1JTlqc6fisMblMPqenaZ6Zl+bS3NJqXOtAg5uNe6p55P2tgE/I3m432u3lPzjC214et1bUMW3xF6bjEfawkLVmJDq/83ciCMKJRae5j7TWHwP7g7rHAi/4Pr8AXBbQv0h7WQ0kKKXSO2ttx5LAwHJlndvY4w/eJ/Piijr+8OamkNIRv7+kP1MLs1EQUlpi5vLN7KyqN1dFfWszKT4/vf9mHRGm2m3nnhEX4mIKPMcbeDa7jx64LI8nP9rGDef0YmphNjee24sVW/bS0qqNwLnHc/LmvQiCcOwDzV211mW+z3uBrr7PGcDugPP2+PrKCEIpNQWYApCZmdl5Kz0KBAeWH71ycMhOHI+23uWztaKWBSuLuf2iPofNMfD3tXi0SdLytyNzTO2Zlw7gsQn5JEbZOdjQzKJPSkL0E2aNyeXxq4eQHBPJextLTe6jepfbpMGQleTkpvOymfD0aslBEIRThOO2+0hrrZVSR/xYqbVeCCwEb0bzUV/YUSQ4sJweH7oTJ0xZ7y7y6xhkJUVbHrfKMUiKiWDB4jaNgl0HGk06B86IMG76hzdUM31ktqV+Qnaq182z5pv9LC4q5blP2mz1golDmPX2FlMORWCOhT8HoZ9oIQjCScux3n1U7ncL+f6s8PWXAgGVcOjm6zupKa9xmXIQahqbmVZodtn0TonmzlH9TH23XNiH19buAaD0YEPImNsv6kvvlOiQnUG7q+qM9uwx5kqkM0fnUhKQMLa0aA9/vmIw00Z61zZ9ZDYLrh5i7Aqy2QjJbQjWYGgvh0JyEATh5OVYvyksB64F/uT7882A/qlKqSV4A8zVAW6mk5a0OIepuN2Cq4fwctEuk8smLT6SVg1TzuuFR4NNgTMgs7iuqZW31peaxry36VuuO7snj4wfTL27heiIcBrczWQmxTC1MBubglhHGPdeMoD9DW4jL2FErxRj3ohwRYtHm8pnPHplvnE83hnBsrW7TO6j2kZ3h3IoJAdBEE5eOq0gnlJqMXA+kAyUAzOBN4ClQCZQgndL6n7fltQFwCi8W1Ina60PW+nuRCyIF8iG3Qe4cuFq46bpr1YaWFBu0fXDuebZ9ovV+UV2AvWXn/jZUH790tqQMfMnDOEXL64x2o+MH8zUxV9YFq77y5X53BLg2vKP8ctgWiXaPXfdMNbtPmgYOX9l1cDfR2IKgnDic1wK4mmtJ7ZzaKTFuRq4ubPWciwJrJJa3djMJbldGT8sk6raJlJiI/mkuMKUWVxe02TpgslJjTWCxFu+PciiycMpr3XRNdZBVb31GFeLh6mF2YBXJrM1IPC8+NMSbr+oH1+X16I1bKuoa9f10yslxjK/oLzGFRKcXvxpf5IhewAADbBJREFUCS9MHo5GSw6CIJwCSJmLo0jw0/X8iYM4q3eyScdg9phcqhuauHPZRgCW/vIsSxdMmE8Bzd+OjYpk3gqv5OXT11jLb27z7Vjyby91RISZ5vhqb60RWG5PGyHQ9WOVX2AVnE6JjZTAsiCcIkiZiyMguJhd8J784N1GyTHOECnNxz8qJi0uijnjBrLg6iHEOsIsy1jsr28y2tNH5vBK0R5jjsf+/TWzx5olLkPOWbGN0gMNxvFgec6k6AjmXj7oiMpPSMkKQTj1EZGdDtIRrYRPtlcx8elPjTHzJuQzbck6o50e72DSWVnGPn9/RnOdq5m9NU1GoDklJpLUOAcHG9ykxETy+zc2Gepnfp6bfCZhysaBhmYSo+zcuWxj6DnXFeCMCEOhePB/v+TcPqmG2+e1tXuYPzGfLtGRR1R+wu8ek5IVgnDyIiI7R4H2tBIC9+T7dQv856T4dA78bSuNguKKOstqpHPHD+bOZRuZWujNJwjEYbdR7/IwdbE3qDx/4hDLc7pERzC4eyI7KuvYWlHHhtKaoOOhMpmHQ0pWCMKpjbiPOkh7ugD765sMl1JTSyt3B+QcvL52V4hGQWDewtTCbKIiwizn3eXLKQjWOfDnICz8eLtxvlUuw7TCHJp9GXDi9hEEoaPIm0IHsdIFyEpyUnrQxc+f+cxwB9364z6GrkFSlJ2UWDsLJ53JgYZmzkhw4LSHGVs6HXYbM0YPICvJScm+RmNeh91GfvcE5lw+kOiIcBx2DN2DpJhIKmsa2FpRZ5xvlcvwctEuRuWlAVKpVBCEjiMxhQ5iFVNYOKmAKS8WtZtjsGDiEEPjGOD2i/qw4MPikPP/elU+v325bd7pI3MAmPPe1yFzOuw2Hh4/CJuycdsr64x8gd8U5nDvG5skX0AQhMMiMYWjgM2muLBvKv+4YQR7a1ykxzloDSr7AF730PAeXZg/cQjxUXYSoyKMALCrxWNd/K68LqQw3eVndjOdMzAjnqd+PpS0eAe56fHYbIr+6W1P/pmJUQzNTJQ3AUEQvhdiFDpIS4uH5Ru/NT2N//GnA02un/R4b1mLGxcVmZ76F31SYhgGq9yAFo8nZO9/4Auct0ZSaHA3OOArAWBBEL4vEmjuIJvLqg2DAN6n93te38jMSwcYReV+f0n/EO2Dx1Zs44oC71O/lUbB/WPzGJgRH6JbEJhTIEFhQRCOFfKm0EHKqkN3HyVGRVBV12xsKZ02MtvSPdQvLY45lw/EGRFO8V5/yYomusZFkpcWiyPSzjvTxBUkCMLxR4xCB0mPd4a4fq4o6MaMANU0j7Z2D0VHhvHrl9Ya7cuGZjK8V5JpfnEFCYJwIiBGoYPkpsfxwGV5pphC75QYEqMiGDe0G8pX8vruUf146L2vAmod5VFe3cjUwmzCFAzNSkBrb/Zz1zh5CxAE4cRCjEIHCQ+3cdngDHJSY9hb7SIt3kG4TZn0Evx5Cv+8cQRl1S7S4hzUNTXzy3+sNY4/cFkeM97cbJSwlq2jgiCcSEig+QgID7cxuHsiP8lLZ3D3RCLDw0ICy49+sJU4h53/GXQGidERhkHwH7/3jU2MHpRhtG9duo5vAhTRBEEQjidiFL4HlXXWugZVvgqn7ZXGUMrcFvlKQRBOFMQofA/8pS8CCdQkaO94cA6CyFcKgnCicFyMglLqFqXUZqXUJqXUYqWUQynVUyn1qVKqWCn1slIq4nis7Ug4XKE5q+OSgyAIwonMMa99pJTKAFYBA7TWjUqppcA7wCXAa1rrJUqpJ4H1Wuu/HWquE0Gj+XD6AsHHMxOj2HWgQXIQBEE4bpyItY/CAadSqhmIAsqAQuBq3/EXgPuAQxqFE4HD6QtYHZccBEEQTlSOuftIa10KPALswmsMqoE1wEGtdYvvtD1AhtV4pdQUpVSRUqqosrLyWCxZEAThtOGYGwWlVCIwFugJnAFEA6M6Ol5rvVBrXaC1LkhJSemkVQqCIJyeHI9A84XATq11pda6GXgNOBtIUEr53VndgNLjsDZBEITTmuNhFHYBZymlopRSChgJfAl8CIz3nXMt8OZxWJsgCMJpzfGIKXwKvAqsBTb61rAQuBO4VSlVDCQBzxzrtQmCIJzuHJfdR1rrmcDMoO4dwPDjsBxBEATBh2Q0C4IgCAZiFARBEAQDMQqCIAiCgRgFQRAEwUCMgiAIgmBwzAviHU2UUpVAyfecJhmoOgrLORbIWjsHWWvnIGvtHI7GWrO01pYlIU5qo3A0UEoVtVct8ERD1to5yFo7B1lr59DZaxX3kSAIgmAgRkEQBEEwEKPgLbFxsiBr7RxkrZ2DrLVz6NS1nvYxBUEQBKENeVMQBEEQDMQoCIIgCAannVFQSt2ilNqslNqklFqslHIopXoqpT5VShUrpV5WSkUcp7U9q5SqUEptCujropT6QCm1zfdnoq9fKaXm+da8QSk19ARY68NKqa9863ldKZUQcOxu31q/Vkr95HivNeDYbUoprZRK9rVPuO/V1/8b33e7WSk1N6D/hPpelVL5SqnVSql1Ptnc4b7+4/a9KqW6K6U+VEp96fv+pvv6T7j/W4dY67H7v6W1Pm1+8Oo+7wScvvZS4DrfnxN8fU8CvzpO6zsPGApsCuibC9zl+3wXMMf3+RLgXUABZwGfngBrvQgI933+/+2dW4hVZRTHf/80By8VmGlq0mR5IYpMjRQLyUtZRNODdKGLVlAJERoUlBL1VhT10AUfyks2WJli1ktmNAaiSY7OTGSZpaUzYymVWpForh7Wd47HYWacgfHsjbN+MMy+fOfs/16ctdf+vm/vtV4s0Xo5UAdU4GVYfwR6ZKk1bR8GfIq/ADkgx3a9AVgHVKT1gXm1K7AWuLnEljVZ2xUYDIxNy+cAO5Ltcudb7Wgtm291u54CXkOidyr92QdoBqbghX8AlgK3ZyHMzL4Efm+xuQrXBCdrqwLeMWcTXs50cHmUtq7VzNaa2bG0ugkvq1rQ+p6ZHTGzXcBOylg7ow27ArwKPAWUPm2RO7sCc4AXzOxIavNbida82dWAc9PyeUBTWs7MrmbWbGa1afkwsB2/Qcydb7WltZy+1a2Cgpk1Ai/jJUGbgYPAFuDPEoPvxX8weWGQmTWn5X3AoLQ8FNhT0i5vuh/E77Ygh1olVQGNZlbXYlfutAIjgevTEOd6Sdek7XnUOhd4SdIe3NeeTttzoVVSJXA18BU5960WWks5rb7VrYJCGjOswrtZQ4C+wIxMRXUC8/5i7p8hljQfOAZUZ62lNST1AZ4Bns1aSwfpCfTHhzKeBD6QpGwltckcYJ6ZDQPmkaOyupL6ASuBuWZ2qHRf3nyrLa3l8K1uFRSAacAuM9tvZkeBVcAkvHtYKE16EdCYlcBW+LXQdU3/C0MHjfiYeIFc6JY0G7gVuCc5GuRP66X4jUGdpN1JT62kC8mfVvC7v1VpOGMzcBxPipZHrbNwvwJYwYmhjEy1Sjobv8hWm1lBXy59qw2tZfOt7hYUfgEmSOqT7rSmAt8CXwAzU5tZwEcZ6WuNNbgmOFnbGuD+9KTEBOBgSVc4EyTNwMfobzOzf0p2rQHuklQh6RJgBLA5C40AZtZgZgPNrNLMKvGL7lgz20cO7QqsxiebkTQS6IVnycyVXRNNwOS0PAX4IS1nZtfk628D283slZJdufOttrSW1bfKNauelz/geeA74BtgGT5rPzwZcid+d1ORkbbl+FzHUfxC9RBwPvA57lzrgP6prYA38KcNGoDxOdC6Ex/f3Jb+Fpa0n5+0fk96OiVLrS327+bE00d5tGsv4N30m60FpuTVrsB1+DxdHT4WPi5ruyZNBtSX/DZvyaNvtaO1bL4VaS6CIAiCIt1t+CgIgiBohwgKQRAEQZEICkEQBEGRCApBEARBkQgKQRAEQZEICkGQkDQ/ZaasT1k+r+3k52dLGtKBdjWS2i28LumxlPmymME1CMpBz1M3CYIzH0kT8bdFx5rZkXQh7mwK9dn4uwRNp2jXETYAnwA1XfBdQdBhoqcQBM5g4ICdyER6ABgtaXWhgaTpKZd9D0lL5DU5GuQ1OmYC44Hq1MvoLWmqpK2pzSJJFS0PKulGSRsl1UpakXLeYGZbzWx3Wc48CEqIoBAEzlpgmKQdkt6UNBlPfzJa0gWpzQPAImAMns74CjO7ElhsZh8CX+N5acbgb6UuAe5MbXriyeKKpN7IAmCamY1Nn3/idJ9oELRHBIUgAMzsL2Ac8DCwH3gfz4ezDLg3VbqaiKcs/gkYLum1lJPmUCtfOQpPvrgjrS/Fi9KUMgEvkrJB0rZ0vIu79MSCoJPEnEIQJMzsP3wMv0ZSA36RfgT4GPgXWGFed+MPSVcBNwGPAnfgOe47i4DPzOzuLpAfBF1C9BSCAJA0StKIkk1jgJ/NrAmfOF4ALE5tBwBnmdnKtL1Qw/cwXkIRPDlZpaTL0vp9wPoWh90ETCq0kdQ3ZUENgsyIoBAETj9gqbxgej0+rPNc2lcN7DGz7Wl9KN6b2IZnLy1UF1sCLEzbhc9BrEi9juN4/e8iZrYff2JpeTrmRmA0gKTHJe3F8+PXS3qry884CFohsqQGwSmQ9Dqw1cxyU0UsCE4XERSCoB0kbQH+BqYXHlcNgjOZCApBEARBkZhTCIIgCIpEUAiCIAiKRFAIgiAIikRQCIIgCIpEUAiCIAiK/A+3rFrpf0bKGAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ES9va8A3zMzt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "41345fa3-85f1-44d7-84b6-215cda5a0d80"
      },
      "source": [
        "sns.histplot(df1['Systole1'] - df1['Systole2'], binwidth=2);"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2rYnw-yapPI"
      },
      "source": [
        "Visualmente, a distribuição das diferenças entre as medidas de sístole 1 e 2 parecem ser normalmente distribuídas. Entretanto, conclusões visuais nem sempre são adequadas. Para isso executaremos um teste de Shapiro-Wilk para testar a normalidade mais consistentemente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsdUP-DW0DJ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6db789e5-6517-4d90-9f67-2f5f193bb071"
      },
      "source": [
        "stats.shapiro(df1['Systole1'] - df1['Systole2'])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9797497391700745, 3.774910698293721e-25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0v3vE3VF265"
      },
      "source": [
        "O teste de Shapiro-Wilk revelou um p-value igual a 3.77e-25 que é menor que o nível de significância adotado para o teste, ou seja, 0.05. Rejeitamos a hipótese nula de que a distribuição das diferenças é aproximadamente normal. Portanto, usaremos um teste de Wilcoxon para verificar se as medidas pareadas de sístole são estatisticamente iguais."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaRynyoPmSK3",
        "outputId": "a34a5b79-9ec2-4ec3-986f-4eaf9d0bc70a"
      },
      "source": [
        "stats.wilcoxon(df1['Systole1'], df1['Systole2'])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WilcoxonResult(statistic=3413963.0, pvalue=6.002447073273366e-06)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4vBqF7gGp2b"
      },
      "source": [
        "O teste de Wilcoxon revelou um p-value menor que o nível de significância do teste que é 0.05. Rejeitamos a hipótese nula de que as medidas de sístole1 e sístole2 são iguais."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SejGUQIXXVjX"
      },
      "source": [
        "#### Comparação da pressão sistólica 1 por sexo e idade\n",
        "Sabe-se que uma pressão sistólica alta é um forte de indicador de doenças como hipertensão arterial. Além disso, é de conhecimento comum que este tipo de problema de saúde se agrava conforme a idade aumenta. Será que há uma diferença significativa entre a pressão sistólica de homens e mulheres?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFQWbfkedd2E"
      },
      "source": [
        "Para que possamos analisar melhor a relação da pressão sistólica com o sexo e a idade, criaremos uma nova coluna no dataframe de forma a transformar a idade numérica em intervalos categóricos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "yRB6p4tHxpit",
        "outputId": "74598811-d014-4aa9-b184-7089e5a53eb1"
      },
      "source": [
        "df['Age_Intervals'] = pd.cut(df['Age'], [18,35,50,65,80], include_lowest=True, labels=['18-35','36-50','51-65','66+'])\n",
        "sns.countplot(data=df, x='Age_Intervals', hue='Gender').set(ylim=(0,1000));"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bm91JLaof_Wx"
      },
      "source": [
        "O gráfico de barras acima mostra que as classes de interesse estão bem representadas, pois para todo sexo e para toda idade há uma quantidade significativa e quase uniforme de pacientes. Dito isso, consideraremos que na análise que segue não deverá haver viés relacionado à má representação."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "mkgD2yFeM8Nc",
        "outputId": "d6214a8b-ee7c-4c43-852e-bd7df0fe0339"
      },
      "source": [
        "sns.boxplot(data=df, x='Age_Intervals', y='Systole1', hue='Gender');"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gf3uLYNPeOz7"
      },
      "source": [
        "O gráfico de caixas revela uma tendência de crescimento da pressão sistólica a medida que a idade aumenta. Confirmando visualmente nossas suposições iniciais. Homens entre 18 e 50 anos tendem a ter pressão sistólica maior que mulheres dentro da mesma faixa etária. Já na faixa dos 51 aos 65 anos, ambos os sexos parecem ter pressões sistólicas semelhantes. Contudo, a situação parece se inverter para pessoas maiores de 66 anos, homens apresentam medidas de sístole menor que mulheres. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MkLN9OsiIba"
      },
      "source": [
        "Para confirmar com mais propriedade as conclusões feitas com o box-plot, serão realizados testes de hipóteses. Para cada faixa etária, serão testadas a normalidade da pressão sistólica para cada sexo, e só depois testaremos a igualdade entre os sexos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmBhQO2vMU4S",
        "outputId": "ccc00ba1-852a-4f15-d6aa-eb833fbb794e"
      },
      "source": [
        "for age in ['18-35','36-50','51-65','66+']:\n",
        "  print('Age {}:'.format(age))\n",
        "  male_systole1 = df.loc[(df['Gender']=='Male') & (df['Age_Intervals']==age),'Systole1'].dropna()\n",
        "  female_systole1 = df.loc[(df['Gender']=='Female') & (df['Age_Intervals']==age),'Systole1'].dropna()\n",
        "  print('Male: Shapiro = {}'.format(stats.shapiro(male_systole1)))\n",
        "  print('Female: Shapiro = {}'.format(stats.shapiro(female_systole1)))\n",
        "  print(stats.mannwhitneyu(male_systole1, female_systole1), end='\\n\\n')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Age 18-35:\n",
            "Male: Shapiro = (0.9706166386604309, 2.071271071324432e-10)\n",
            "Female: Shapiro = (0.9366715550422668, 6.89477936922304e-17)\n",
            "MannwhitneyuResult(statistic=137484.5, pvalue=5.2302963907869326e-45)\n",
            "\n",
            "Age 36-50:\n",
            "Male: Shapiro = (0.957950234413147, 1.5460249747079047e-10)\n",
            "Female: Shapiro = (0.9224153757095337, 3.998170740981261e-16)\n",
            "MannwhitneyuResult(statistic=102391.0, pvalue=2.624607408347283e-10)\n",
            "\n",
            "Age 51-65:\n",
            "Male: Shapiro = (0.9610551595687866, 4.158907159629299e-12)\n",
            "Female: Shapiro = (0.9630112648010254, 3.428596165658271e-12)\n",
            "MannwhitneyuResult(statistic=222603.0, pvalue=0.3903659507847398)\n",
            "\n",
            "Age 66+:\n",
            "Male: Shapiro = (0.9648102521896362, 1.1646388020647436e-10)\n",
            "Female: Shapiro = (0.9817180037498474, 2.3296238396142144e-06)\n",
            "MannwhitneyuResult(statistic=134508.5, pvalue=9.198412222441116e-07)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xS3K4IMIjIPM"
      },
      "source": [
        "Como todos os testes de Shapiro revelaram p-value menor que o nível de significância (0.05), concluímos que as medidas de sístole não seguem distribuição normal em nenhum dos casos. Analisaremos os testes de Mann-Whitney para dados não pareados.\n",
        "* 18-35 anos: p-value < 0.05. Rejeitamos a hipótese nula de que as pressões sistólicas são iguais para homens e mulheres.\n",
        "* 36-50 anos: p-value < 0.05. Rejeitamos a hipótese nula.\n",
        "* 51-65 anos: p-value > 0.05. Não rejeitamos a hipótese nula, as medidas de sístole entre homem e mulheres são estatisticamente iguais.\n",
        "* 66+ anos: p-value < 0.05. Rejeitamos a hipótese nula. <br>\n",
        "\n",
        "As conclusões feitas a partir do box-plot foram confirmadas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zf3oOzTzX4Hf"
      },
      "source": [
        "## Analisando as medidas de pressão diastólica"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJ91dCEfX7EC"
      },
      "source": [
        "#### Comparação entre todas as amostras de pressão diastólica 1 e 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1d7afF8BtGRK"
      },
      "source": [
        "As colunas diastole1 e diastole2 também se referem a medições realizadas uma após a outra, separadas por um curto intervalo de tempo. <br>\n",
        "Assim como para sístole, iniciaremos removendo dados não valorados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZ9dA_V6n5ZD",
        "outputId": "42be5da7-6731-4bd3-fe80-a9b5dc7afc25"
      },
      "source": [
        "df2 = df[(df['Diastole1'].notna()) & (df['Diastole2'].notna())]\n",
        "print('Nº de linhas removidas = {}'.format(df.shape[0] - df2.shape[0]))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de linhas removidas = 722\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j2EF6jwYCka",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "85e75d71-3bc0-4172-f4f3-2592b961486a"
      },
      "source": [
        "fig, axes = plt.subplots(1,2, figsize=(10,4), sharex=True)\n",
        "sns.histplot(data=df2, x='Diastole1', binwidth=4, ax=axes[0])\n",
        "sns.histplot(data=df2, x='Diastole2', binwidth=4, ax=axes[1])\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWnaVzEgG7r6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "45af71a3-050f-41fa-f775-f8de469155a2"
      },
      "source": [
        "print('\\t\\tPearson corr: {:.4f}'.format(df2['Diastole1'].corr(df2['Diastole2'])))\n",
        "sns.scatterplot(data=df2, x='Diastole1', y='Diastole2');"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\tPearson corr: 0.8838\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CosB7_dUuRSG"
      },
      "source": [
        "Observamos através do histograma que ambas as distribuições são simétricas a menos de um pequeno conjunto de outliers próximos à 0mmHg. O scatterplot enfim reforça a presença destes e mais outros outliers que tentaremos remover no passo seguinte utilizando a razão entre as duas variáveis para aplicar a técnica do z-score robusto."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKpipT2PoelL",
        "outputId": "b409433c-cf0d-4593-fa4e-3e9b381e4a74"
      },
      "source": [
        "threshold = 3.5\n",
        "\n",
        "diastole_ratio = df2['Diastole1']/df2['Diastole2']\n",
        "mad = abs(diastole_ratio - diastole_ratio.median()).median()/0.6745\n",
        "df3 = df2[abs(diastole_ratio - diastole_ratio.median())/mad < threshold]\n",
        "print('Nº de linhas removidas = {}\\nNº de linhas restantes = {}'.format(df2.shape[0] - df3.shape[0], df3.shape[0]))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de linhas removidas = 117\n",
            "Nº de linhas restantes = 4694\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "QFjLJaEIqSwz",
        "outputId": "91faeeea-5c66-4a6f-c37e-d9b51a36ced4"
      },
      "source": [
        "print('\\t\\tPearson corr: {:.4f}'.format(df3['Diastole1'].corr(df3['Diastole2'])))\n",
        "sns.scatterplot(data=df3, x='Diastole1', y='Diastole2');"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\tPearson corr: 0.9428\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwmrKr3TvlMe"
      },
      "source": [
        "A remoção dos outliers usando uma abordagem bivariada removeu quase todas as amostras de diástole iguais a 0mmHg. Entretando, outliers próximos a reta de regressão foram mantidos mesmo estando visivelmente longe o suficiente dos pontos centrais da distribuição conjunta. A dificuldade em lidar com estes outliers persiste mesmo se o limiar do z-score robusto for menor que 3.5."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_1RmTev7XqS",
        "outputId": "84323866-0170-419f-c036-73bcdb71fd0d"
      },
      "source": [
        "threshold = 2\n",
        "\n",
        "df4 = df2[abs(diastole_ratio - diastole_ratio.median())/mad < threshold]\n",
        "print('Nº de linhas removidas = {}\\nNº de linhas restantes = {}'.format(df2.shape[0] - df4.shape[0], df4.shape[0]))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de linhas removidas = 525\n",
            "Nº de linhas restantes = 4286\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "uEWpv7-47asi",
        "outputId": "d61441a2-e453-46a5-ac48-008d027b6dc0"
      },
      "source": [
        "print('\\t\\tPearson corr: {:.4f}'.format(df4['Diastole1'].corr(df4['Diastole2'])))\n",
        "sns.scatterplot(data=df4, x='Diastole1', y='Diastole2');"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\tPearson corr: 0.9621\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POJwa3P37Wvz"
      },
      "source": [
        "Para tentar contornar este problema, iremos utilizar o método multivariado Elliptic Envelope. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_BHwqHnx24m",
        "outputId": "1c9bccbd-6903-483d-85e6-e5816a6547ce"
      },
      "source": [
        "from sklearn.covariance import EllipticEnvelope\n",
        "\n",
        "df5 = df.copy()\n",
        "df5 = df5.drop(columns='Age_Intervals')\n",
        "df5 = df5.dropna()\n",
        "print('Nº de linhas removidas pelo dropna = {}\\nNº de linhas restantes = {}'.format(df.shape[0] - df5.shape[0], df5.shape[0]))\n",
        "\n",
        "df5['Gender'] = df5['Gender'].astype('category')\n",
        "df5['Education'] = df5['Education'].astype('category')\n",
        "df5['Marital'] = df5['Marital'].astype('category')\n",
        "\n",
        "df5['Gender'] = df5['Gender'].cat.codes\n",
        "df5['Education'] = df5['Education'].cat.codes\n",
        "df5['Marital'] = df5['Marital'].cat.codes\n",
        "\n",
        "detector = EllipticEnvelope(contamination=0.2)\n",
        "detector.fit(df5)\n",
        "scores = detector.predict(df5)\n",
        "lines_before = df5.shape[0]\n",
        "df5 = df5[scores != -1]\n",
        "print('Nº de linhas removidas pelo Elliptic Envelope = {}\\nNº de linhas restantes = {}'.format(lines_before - df5.shape[0], df5.shape[0]))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nº de linhas removidas pelo dropna = 1014\n",
            "Nº de linhas restantes = 4519\n",
            "Nº de linhas removidas pelo Elliptic Envelope = 904\n",
            "Nº de linhas restantes = 3615\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "viRKENg20IQv",
        "outputId": "5f14510f-3981-480e-d733-4f7e794654ce"
      },
      "source": [
        "print('\\t\\tPearson corr: {:.4f}'.format(df5['Diastole1'].corr(df5['Diastole2'])))\n",
        "sns.scatterplot(data=df5, x='Diastole1', y='Diastole2').set(xlim=(0,140), ylim=(0,140));"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\tPearson corr: 0.9260\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0TL-MqvmDrO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "f8b9d8bd-289f-4260-8693-8b4fb5cc93be"
      },
      "source": [
        "sns.histplot(df5['Diastole1'] - df5['Diastole2'], binwidth=2).set(xlim=(-25,25));"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXbAEvDz9qsM"
      },
      "source": [
        "Vamos executar um teste de Shapiro-Wilk para verificar se a distribuição das diferenças acima possui distribuição normal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5Tb1FPaOkGE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3bc1fcb-542d-43c2-9511-ff1833890097"
      },
      "source": [
        "stats.shapiro(df5['Diastole1'] - df5['Diastole2'])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9769898653030396, 9.556062212899221e-24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PPFfQQkB5QM"
      },
      "source": [
        "O teste de Shapiro-Wilk revelou um p-value igual a 9.55e-24 que é menor que o nível de significância de 5%. Portanto, rejeitamos a hipótese nula de que a distribuição das diferenças é aproximadamente normal e por isso devemos usar um teste de Wilcoxon para verificar se as medidas pareadas de diástole são estatisticamente iguais."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBN4RFNL96ga",
        "outputId": "5a1ec679-16a9-48dc-c882-1ac3ce9b8268"
      },
      "source": [
        "stats.wilcoxon(df5['Diastole1'], df5['Diastole2'])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WilcoxonResult(statistic=2243937.5, pvalue=0.4912980853628047)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-vEsGQgB9mA"
      },
      "source": [
        "O teste de Wilcoxon revelou um p-value maior que o nível de significância de 5%. Consequentemente, aceitamos a hipótese nula de que as medidas de diástole1 e diástole2 são iguais."
      ]
    }
  ]
}