Exe_Econometrico.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/viniventur/Exercicio-Econometrico/blob/main/Exe_Econometrico.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Exercício Econométrico - AB1 - Econometria 2\n",
        "## Por: [Vinícius Ventura](https://github.com/viniventur/)\n",
        "\n",
        "Outputs obtidos nas questões 1 e 2, no Stata e no R, respectivamente.\n",
        "\n",
        "[Link para o trabalho no RPubs](https://rpubs.com/viniventur/exereconometrico)  \n",
        "[Link para o trabalho em PDF](https://github.com/viniventur/Exercicio-Econometrico/blob/main/Exe-Econometrico-PDF.pdf)"
      ],
      "metadata": {
        "id": "8pHrrefMSeIN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Questão 1"
      ],
      "metadata": {
        "id": "P6Y5cuG5pc9j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 1 Letra a)"
      ],
      "metadata": {
        "id": "P6k1S9I1c5aK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importando os pacotes principais"
      ],
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        "id": "Nz9i91v6S7uT"
      }
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      "execution_count": null,
      "metadata": {
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      "source": [
        "# Pacotes de gerenciamento do sistema operacional, como manipulação de arquivos e diretórios, gerenciamento de processos, obtenção de informações do sistema, etc.\n",
        "import os\n",
        "from google.colab import drive\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Pacotes para manipulação e tratamento de dados\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Pacotes para análise gráfica\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "from matplotlib.offsetbox import AnnotationBbox, OffsetImage\n",
        "\n",
        "# Pacotes para análise econométrica\n",
        "import statsmodels.api as sm\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importando os dados com o pacote wooldridge"
      ],
      "metadata": {
        "id": "MB829vmBUYB5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wooldridge\n",
        "\n",
        "import wooldridge\n",
        "df = wooldridge.data('fertil2')\n",
        "df"
      ],
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      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wooldridge\n",
            "  Downloading wooldridge-0.4.4-py3-none-any.whl (5.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from wooldridge) (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->wooldridge) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->wooldridge) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas->wooldridge) (1.22.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->wooldridge) (1.16.0)\n",
            "Installing collected packages: wooldridge\n",
            "Successfully installed wooldridge-0.4.4\n"
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f34c37c1-8c34-4231-a7bd-69a7dc61073c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f34c37c1-8c34-4231-a7bd-69a7dc61073c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Manipulação dos dados"
      ],
      "metadata": {
        "id": "06bILE88U7PF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(df)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "Hf5X47FwU6Sh",
        "outputId": "c7fba227-8b25-4b85-c0a2-880cc3885e0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      mnthborn  yearborn  age  electric  radio   tv  bicycle  educ  ceb  \\\n",
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              "\n",
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              "0          NaN  ...    NaN    576      1        12       0        0         0   \n",
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              "\n",
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              "[4361 rows x 27 columns]"
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              "<p>4361 rows × 27 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05b2b061-d4dd-49b6-8046-4b1687c51216')\"\n",
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              "      \n",
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              "    .colab-df-convert {\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-05b2b061-d4dd-49b6-8046-4b1687c51216');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 3
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    },
    {
      "cell_type": "code",
      "source": [
        "# Excluindo variáveis que não serão utilizadas\n",
        "\n",
        "variaveis = ['children', 'age', 'agesq', 'educ', 'electric', 'urban']\n",
        "\n",
        "df = df.drop(columns=df.columns.difference(variaveis))\n",
        "df"
      ],
      "metadata": {
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          "height": 424
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              "      age  electric  educ  children  agesq  urban\n",
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              "<p>4361 rows × 6 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Renomeando variáveis\n",
        "\n",
        "df = df.rename(columns={'children': 'cria' ,'electric': 'elet' ,'urban': 'urb'})\n",
        "df"
      ],
      "metadata": {
        "colab": {
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        "id": "p9kGdwiuVkRL",
        "outputId": "602eba24-e253-4478-f48b-b1991bb5c84f"
      },
      "execution_count": null,
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        {
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            "text/plain": [
              "      age  elet  educ  cria  agesq  urb\n",
              "0      24   1.0    12     0    576    1\n",
              "1      32   1.0    13     3   1024    1\n",
              "2      30   1.0     5     1    900    1\n",
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              "4359   26   0.0     0     1    676    0\n",
              "4360   36   0.0     3     5   1296    0\n",
              "\n",
              "[4361 rows x 6 columns]"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reorganização do dataframe\n",
        "\n",
        "df = df[['cria', 'age', 'agesq', 'educ', 'elet', 'urb']]\n",
        "df"
      ],
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "3Hn9oqEErC1q",
        "outputId": "733cca9a-b5ee-443b-c90c-ff2d147bb2f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      cria  age  agesq  educ  elet  urb\n",
              "0        0   24    576    12   1.0    1\n",
              "1        3   32   1024    13   1.0    1\n",
              "2        1   30    900     5   1.0    1\n",
              "3        2   42   1764     4   1.0    1\n",
              "4        2   43   1849    11   1.0    1\n",
              "...    ...  ...    ...   ...   ...  ...\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Regressão"
      ],
      "metadata": {
        "id": "IMtqLJh8WBxW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Resolvendo o BO com a variável elet que aparentemente tinha valores infinitos e/ou NAs\n",
        "df.replace([np.inf, -np.inf], np.nan, inplace=True) #substitui infinitos por na\n",
        "df.dropna(inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PIdg5H9hfubD",
        "outputId": "2dad4ec5-5975-4d67-ec09-27e188f68048"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      cria  age  agesq  educ  elet  urb\n",
              "0        0   24    576    12   1.0    1\n",
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              "          const element = document.querySelector('#df-51747025-b86e-481e-9267-2e8a9193d0e3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# variáveis\n",
        "y = df.cria\n",
        "x = df[['age', 'agesq', 'educ', 'elet', 'urb']]\n",
        "x = sm.add_constant(x)"
      ],
      "metadata": {
        "id": "Tyws-K46Vc4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1º regressão\n",
        "reg = sm.OLS(y, x).fit()\n",
        "print(reg.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ryTTOM_kf47",
        "outputId": "c4cce805-9bb3-407a-e664-f545a7438283"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   cria   R-squared:                       0.573\n",
            "Model:                            OLS   Adj. R-squared:                  0.573\n",
            "Method:                 Least Squares   F-statistic:                     1170.\n",
            "Date:                Sun, 11 Jun 2023   Prob (F-statistic):               0.00\n",
            "Time:                        05:14:15   Log-Likelihood:                -7806.3\n",
            "No. Observations:                4358   AIC:                         1.562e+04\n",
            "Df Residuals:                    4352   BIC:                         1.566e+04\n",
            "Df Model:                           5                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -4.2225      0.240    -17.580      0.000      -4.693      -3.752\n",
            "age            0.3409      0.017     20.652      0.000       0.309       0.373\n",
            "agesq         -0.0027      0.000    -10.086      0.000      -0.003      -0.002\n",
            "educ          -0.0752      0.006    -11.948      0.000      -0.088      -0.063\n",
            "elet          -0.3100      0.069     -4.493      0.000      -0.445      -0.175\n",
            "urb           -0.2000      0.047     -4.301      0.000      -0.291      -0.109\n",
            "==============================================================================\n",
            "Omnibus:                      203.155   Durbin-Watson:                   1.883\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              715.135\n",
            "Skew:                           0.014   Prob(JB):                    5.13e-156\n",
            "Kurtosis:                       4.984   Cond. No.                     1.07e+04\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.07e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print em csv para exportar para o excel\n",
        "print(reg.summary().as_csv())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1yzHOfAX690",
        "outputId": "3ff15aa4-b10e-4bba-b7f7-dcd71adf600b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                      OLS Regression Results                      \n",
            "Dep. Variable:   ,cria            ,  R-squared:         ,   0.573 \n",
            "Model:           ,OLS             ,  Adj. R-squared:    ,   0.573 \n",
            "Method:          ,Least Squares   ,  F-statistic:       ,   1170. \n",
            "Date:            ,Sun, 11 Jun 2023,  Prob (F-statistic):,  0.00   \n",
            "Time:            ,05:14:15        ,  Log-Likelihood:    , -7806.3 \n",
            "No. Observations:,  4358          ,  AIC:               ,1.562e+04\n",
            "Df Residuals:    ,  4352          ,  BIC:               ,1.566e+04\n",
            "Df Model:        ,     5          ,                     ,         \n",
            "Covariance Type: ,nonrobust       ,                     ,         \n",
            "     ,   coef   , std err ,    t    ,P>|t| ,  [0.025 ,  0.975] \n",
            "const,   -4.2225,    0.240,  -17.580, 0.000,   -4.693,   -3.752\n",
            "age  ,    0.3409,    0.017,   20.652, 0.000,    0.309,    0.373\n",
            "agesq,   -0.0027,    0.000,  -10.086, 0.000,   -0.003,   -0.002\n",
            "educ ,   -0.0752,    0.006,  -11.948, 0.000,   -0.088,   -0.063\n",
            "elet ,   -0.3100,    0.069,   -4.493, 0.000,   -0.445,   -0.175\n",
            "urb  ,   -0.2000,    0.047,   -4.301, 0.000,   -0.291,   -0.109\n",
            "Omnibus:      ,203.155,  Durbin-Watson:     ,   1.883 \n",
            "Prob(Omnibus):, 0.000 ,  Jarque-Bera (JB):  , 715.135 \n",
            "Skew:         , 0.014 ,  Prob(JB):          ,5.13e-156\n",
            "Kurtosis:     , 4.984 ,  Cond. No.          ,1.07e+04 \n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.07e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 1 letra b)"
      ],
      "metadata": {
        "id": "AcbJdTkxcpoo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Correlação"
      ],
      "metadata": {
        "id": "dUTo5G-Ncu2a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Analisando correlação entre as variáveis\n",
        "\n",
        "matrizcor = df.corr()\n",
        "matrizcor.round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "X1gtertmWnuQ",
        "outputId": "b2366221-ed67-4f3a-bb4c-5cbc75bd55fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       cria   age  agesq  educ  elet   urb\n",
              "cria   1.00  0.73   0.71 -0.37 -0.07 -0.13\n",
              "age    0.73  1.00   0.99 -0.31  0.04 -0.07\n",
              "agesq  0.71  0.99   1.00 -0.31  0.04 -0.08\n",
              "educ  -0.37 -0.31  -0.31  1.00  0.30  0.23\n",
              "elet  -0.07  0.04   0.04  0.30  1.00  0.28\n",
              "urb   -0.13 -0.07  -0.08  0.23  0.28  1.00"
            ],
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              "      <td>-0.07</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.23</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(matrizcor, annot = True, cmap='coolwarm', fmt='.2f')"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### FIV"
      ],
      "metadata": {
        "id": "Yn0MXAnzr_QO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# criando classe para o VIF para fazer a tabela mais facilmente\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "class VIF:\n",
        "    def __init__(self, data):\n",
        "        self.data = data\n",
        "        self.viftab = None\n",
        "\n",
        "# Estava referenciando a variável no segundo argumento quinta kk, por isso deu erro\n",
        "\n",
        "    def vif(self):\n",
        "        viftab = []\n",
        "        for i, col in enumerate(self.data.columns):\n",
        "            vif = variance_inflation_factor(self.data.values, i)\n",
        "            viftab.append((col, vif))\n",
        "        viftab = pd.DataFrame(viftab)\n",
        "        viftab = viftab.drop(0, axis=0)\n",
        "        viftab = viftab.rename(columns={0: 'Variável', 1: 'VIF'})\n",
        "        vif_mean = viftab['VIF'].mean()\n",
        "        viftab = viftab.append({'Variável': 'Média', 'VIF': vif_mean}, ignore_index=True)\n",
        "        self.viftab = viftab"
      ],
      "metadata": {
        "id": "ze5y2L82r8mc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif = VIF(x)\n",
        "vif.vif()\n",
        "vif1 = pd.DataFrame(vif.viftab)\n",
        "vif1.round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "H-DUummA0l-a",
        "outputId": "57bb9c63-03e0-4ac9-ec9d-692c5ae7798b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Variável    VIF\n",
              "0      age  42.48\n",
              "1    agesq  42.38\n",
              "2     educ   1.26\n",
              "3     elet   1.19\n",
              "4      urb   1.12\n",
              "5    Média  17.69"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4cc4bd9-a260-4ae5-beed-04e5c785f9a2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Variável</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>age</td>\n",
              "      <td>42.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>agesq</td>\n",
              "      <td>42.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>educ</td>\n",
              "      <td>1.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>elet</td>\n",
              "      <td>1.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>urb</td>\n",
              "      <td>1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Média</td>\n",
              "      <td>17.69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4cc4bd9-a260-4ae5-beed-04e5c785f9a2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "      border: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a4cc4bd9-a260-4ae5-beed-04e5c785f9a2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a4cc4bd9-a260-4ae5-beed-04e5c785f9a2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Regressão sem o _agesq_"
      ],
      "metadata": {
        "id": "d5sORulG2Tog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# redefinindo variáveis\n",
        "y = df.cria\n",
        "x2 = df[['age', 'educ', 'elet', 'urb']]\n",
        "x2 = sm.add_constant(x2) \n",
        "x2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wu4Kuqks2hr8",
        "outputId": "05c3c60f-e81d-43cf-fe62-478e9f956486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      const  age  educ  elet  urb\n",
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              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-ed25ccad-314a-407f-ad3e-6ded9af49fe2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# regressão nova\n",
        "reg2 = sm.OLS(y, x2).fit()\n",
        "print(reg2.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-eGlzya2SAg",
        "outputId": "1ffb5abb-95b3-4ef8-8310-2053678c39f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   cria   R-squared:                       0.563\n",
            "Model:                            OLS   Adj. R-squared:                  0.563\n",
            "Method:                 Least Squares   F-statistic:                     1404.\n",
            "Date:                Sun, 11 Jun 2023   Prob (F-statistic):               0.00\n",
            "Time:                        05:14:16   Log-Likelihood:                -7856.6\n",
            "No. Observations:                4358   AIC:                         1.572e+04\n",
            "Df Residuals:                    4353   BIC:                         1.576e+04\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.9997      0.097    -20.698      0.000      -2.189      -1.810\n",
            "age            0.1767      0.003     64.780      0.000       0.171       0.182\n",
            "educ          -0.0756      0.006    -11.872      0.000      -0.088      -0.063\n",
            "elet          -0.3034      0.070     -4.347      0.000      -0.440      -0.167\n",
            "urb           -0.1713      0.047     -3.648      0.000      -0.263      -0.079\n",
            "==============================================================================\n",
            "Omnibus:                      226.076   Durbin-Watson:                   1.895\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              846.219\n",
            "Skew:                          -0.072   Prob(JB):                    1.76e-184\n",
            "Kurtosis:                       5.154   Cond. No.                         130.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Novo VIF\n",
        "\n",
        "vif2 = VIF(x2)\n",
        "vif2.vif()\n",
        "vif2 = pd.DataFrame(vif2.viftab)\n",
        "vif2.round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TcYRxarU3IUS",
        "outputId": "25bdadf7-bdeb-406c-841c-ef26fb4ab410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Variável   VIF\n",
              "0      age  1.13\n",
              "1     educ  1.26\n",
              "2     elet  1.19\n",
              "3      urb  1.11\n",
              "4    Média  1.17"
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80f74f5a-1fa7-4889-b509-fea839336f17 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80f74f5a-1fa7-4889-b509-fea839336f17');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparando os modelos\n",
        "\n",
        "from statsmodels.iolib.summary2 import summary_col\n",
        "\n",
        "table = summary_col([reg, reg2], stars=True, float_format='%.2f',\n",
        "                     model_names=['COM agesq', 'SEM agesq'],\n",
        "                     info_dict={'R-squared': lambda x: \"{:.2f}\".format(x.rsquared)})\n",
        "print(table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7ThuY8wpdEQ",
        "outputId": "aca0efb2-8cb3-49e1-a6ba-1e427a7974a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==================================\n",
            "               COM agesq SEM agesq\n",
            "----------------------------------\n",
            "R-squared      0.57      0.56     \n",
            "R-squared Adj. 0.57      0.56     \n",
            "age            0.34***   0.18***  \n",
            "               (0.02)    (0.00)   \n",
            "agesq          -0.00***           \n",
            "               (0.00)             \n",
            "const          -4.22***  -2.00*** \n",
            "               (0.24)    (0.10)   \n",
            "educ           -0.08***  -0.08*** \n",
            "               (0.01)    (0.01)   \n",
            "elet           -0.31***  -0.30*** \n",
            "               (0.07)    (0.07)   \n",
            "urb            -0.20***  -0.17*** \n",
            "               (0.05)    (0.05)   \n",
            "R-squared      0.57      0.56     \n",
            "==================================\n",
            "Standard errors in parentheses.\n",
            "* p<.1, ** p<.05, ***p<.01\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 1 letra c)"
      ],
      "metadata": {
        "id": "RyPWBxyl6t7z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pacote\n",
        "import statsmodels.stats.diagnostic as smd"
      ],
      "metadata": {
        "id": "8Sfg7qbc6j1Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Teste Breusch-Pagan - Heterocedasticidade"
      ],
      "metadata": {
        "id": "BcI596XUj1-5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Teste BP\n",
        "bptest = smd.het_breuschpagan(resid=reg2.resid, exog_het=x2)\n",
        "bphet = pd.DataFrame({'lm': [bptest[0]], 'lm_valorp:': [bptest[1]]})\n",
        "bphet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "WLdJcLfPkQWw",
        "outputId": "05146e90-91af-4950-b303-64232160417a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            lm     lm_valorp:\n",
              "0  1041.464351  3.684149e-224"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e3a3de7-6083-46a0-985d-771c84aa7aa9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>lm</th>\n",
              "      <th>lm_valorp:</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1041.464351</td>\n",
              "      <td>3.684149e-224</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e3a3de7-6083-46a0-985d-771c84aa7aa9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e3a3de7-6083-46a0-985d-771c84aa7aa9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e3a3de7-6083-46a0-985d-771c84aa7aa9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Teste White - Heterocedasticidade"
      ],
      "metadata": {
        "id": "iR70A917lPyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Teste White\n",
        "whitetest = smd.het_white(resid=reg2.resid, exog=x2)\n",
        "white = pd.DataFrame({'lm': [whitetest[0]], 'lm_valorp:': [whitetest[1]]})\n",
        "white"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "cIvCxa8wlMXG",
        "outputId": "ace8f302-4df5-4094-ed93-9878ff9d8be4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            lm     lm_valorp:\n",
              "0  1124.687371  2.831002e-233"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3cc311ff-027d-4cd9-911b-68970e6b0def\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>lm</th>\n",
              "      <th>lm_valorp:</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1124.687371</td>\n",
              "      <td>2.831002e-233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cc311ff-027d-4cd9-911b-68970e6b0def')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3cc311ff-027d-4cd9-911b-68970e6b0def button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3cc311ff-027d-4cd9-911b-68970e6b0def');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Corrigindo a heterocedasticidade"
      ],
      "metadata": {
        "id": "BFwLQx1RnNuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(reg2.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNcImvS9n2nd",
        "outputId": "b72ce12d-cbe6-49f5-a976-438a9ded4458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   cria   R-squared:                       0.563\n",
            "Model:                            OLS   Adj. R-squared:                  0.563\n",
            "Method:                 Least Squares   F-statistic:                     1404.\n",
            "Date:                Sun, 11 Jun 2023   Prob (F-statistic):               0.00\n",
            "Time:                        05:14:16   Log-Likelihood:                -7856.6\n",
            "No. Observations:                4358   AIC:                         1.572e+04\n",
            "Df Residuals:                    4353   BIC:                         1.576e+04\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.9997      0.097    -20.698      0.000      -2.189      -1.810\n",
            "age            0.1767      0.003     64.780      0.000       0.171       0.182\n",
            "educ          -0.0756      0.006    -11.872      0.000      -0.088      -0.063\n",
            "elet          -0.3034      0.070     -4.347      0.000      -0.440      -0.167\n",
            "urb           -0.1713      0.047     -3.648      0.000      -0.263      -0.079\n",
            "==============================================================================\n",
            "Omnibus:                      226.076   Durbin-Watson:                   1.895\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              846.219\n",
            "Skew:                          -0.072   Prob(JB):                    1.76e-184\n",
            "Kurtosis:                       5.154   Cond. No.                         130.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Regressão com robust\n",
        "reg3 = reg2.get_robustcov_results(cov_type='HC1', use_t=None)\n",
        "print(reg3.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrY_g3FFnM-_",
        "outputId": "03391fb6-1282-4cb7-8ab1-628cce65d952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   cria   R-squared:                       0.563\n",
            "Model:                            OLS   Adj. R-squared:                  0.563\n",
            "Method:                 Least Squares   F-statistic:                     759.4\n",
            "Date:                Sun, 11 Jun 2023   Prob (F-statistic):               0.00\n",
            "Time:                        05:14:16   Log-Likelihood:                -7856.6\n",
            "No. Observations:                4358   AIC:                         1.572e+04\n",
            "Df Residuals:                    4353   BIC:                         1.576e+04\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:                  HC1                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -1.9997      0.095    -21.066      0.000      -2.186      -1.814\n",
            "age            0.1767      0.003     51.652      0.000       0.170       0.183\n",
            "educ          -0.0756      0.006    -11.847      0.000      -0.088      -0.063\n",
            "elet          -0.3034      0.065     -4.672      0.000      -0.431      -0.176\n",
            "urb           -0.1713      0.046     -3.739      0.000      -0.261      -0.081\n",
            "==============================================================================\n",
            "Omnibus:                      226.076   Durbin-Watson:                   1.895\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              846.219\n",
            "Skew:                          -0.072   Prob(JB):                    1.76e-184\n",
            "Kurtosis:                       5.154   Cond. No.                         130.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Questão 2"
      ],
      "metadata": {
        "id": "1lQL7l8CpXoc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 2 letra a) Importando dados"
      ],
      "metadata": {
        "id": "ABk4jJtUoj8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmXwkwHYhTwy",
        "outputId": "5fd78462-a6f9-42a3-fd39-0c8c227175ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importando o arquivo\n",
        "\n",
        "dfq2 = pd.read_excel(\"/content/drive/MyDrive/Atividades keuler python/Atividade AB1/data_prova.xls\")\n",
        "dfq2"
      ],
      "metadata": {
        "id": "2rRLDziZo8yF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "057fd9fd-700b-48c9-bf41-3b86ec1bf79d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ano  uf   codmun                  munic  analf  pobre      rpc   agua  \\\n",
              "0      2000  11  1100015  ALTA FLORESTA D'OESTE  15.84  35.59   371.15  57.13   \n",
              "1      2000  11  1100023              ARIQUEMES  11.77  21.55   530.87  73.54   \n",
              "2      2000  11  1100031                 CABIXI  17.50  36.47   342.46  58.61   \n",
              "3      2000  11  1100049                 CACOAL  12.14  25.25   456.63  78.60   \n",
              "4      2000  11  1100056             CEREJEIRAS  13.90  33.57   511.47  73.98   \n",
              "...     ...  ..      ...                    ...    ...    ...      ...    ...   \n",
              "11125  2010  52  5222005             VIANÓPOLIS   7.97   5.85   664.33  86.89   \n",
              "11126  2010  52  5222054         VICENTINÓPOLIS  12.68   6.62   651.07  98.60   \n",
              "11127  2010  52  5222203               VILA BOA  15.88  23.61   376.72  85.25   \n",
              "11128  2010  52  5222302          VILA PROPÍCIO  19.25  19.77   401.84  77.94   \n",
              "11129  2010  53  5300108               BRASÍLIA   3.47   4.93  1715.11  98.13   \n",
              "\n",
              "         lixo  esgag_inad  poprur   poptot   popurb  \n",
              "0       70.33       27.52   14192    26533    12341  \n",
              "1       90.61       21.14   19385    74503    55118  \n",
              "2       76.57       27.36    4846     7518     2672  \n",
              "3       90.22       14.28   22170    73568    51398  \n",
              "4       70.04       24.82    3361    18207    14846  \n",
              "...       ...         ...     ...      ...      ...  \n",
              "11125   99.32        0.38    3378    12548     9170  \n",
              "11126   98.78        9.04    1050     7371     6321  \n",
              "11127   99.21        1.98    1233     4735     3502  \n",
              "11128  100.00        6.63    3641     5145     1504  \n",
              "11129   98.87        0.72   87950  2570160  2482210  \n",
              "\n",
              "[11130 rows x 13 columns]"
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              "      <td>2000</td>\n",
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              "      <td>33.57</td>\n",
              "      <td>511.47</td>\n",
              "      <td>73.98</td>\n",
              "      <td>70.04</td>\n",
              "      <td>24.82</td>\n",
              "      <td>3361</td>\n",
              "      <td>18207</td>\n",
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              "      <td>7.97</td>\n",
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              "      <td>664.33</td>\n",
              "      <td>86.89</td>\n",
              "      <td>99.32</td>\n",
              "      <td>0.38</td>\n",
              "      <td>3378</td>\n",
              "      <td>12548</td>\n",
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              "      <td>12.68</td>\n",
              "      <td>6.62</td>\n",
              "      <td>651.07</td>\n",
              "      <td>98.60</td>\n",
              "      <td>98.78</td>\n",
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              "      <td>1050</td>\n",
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              "      <td>19.25</td>\n",
              "      <td>19.77</td>\n",
              "      <td>401.84</td>\n",
              "      <td>77.94</td>\n",
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              "</table>\n",
              "<p>11130 rows × 13 columns</p>\n",
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              "\n",
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              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3bf3028a-6ca6-4423-afb2-cfaf9362cf26');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Filtando para AL e ano 2010"
      ],
      "metadata": {
        "id": "Tw4bl_wmitaJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "al = dfq2[(dfq2.ano == 2010) & (dfq2.uf == 27)]\n",
        "al"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "mqFVTjnoiszf",
        "outputId": "e56ae2cf-881b-425a-99ea-0e7a1944d8d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun                   munic  analf  pobre     rpc   agua  \\\n",
              "7214  2010  27  2700102             ÁGUA BRANCA  30.92  52.87  213.00  61.78   \n",
              "7215  2010  27  2700201                  ANADIA  36.16  47.59  239.53  61.62   \n",
              "7216  2010  27  2700300               ARAPIRACA  22.45  26.65  423.28  94.68   \n",
              "7217  2010  27  2700409                 ATALAIA  33.57  42.73  237.33  82.49   \n",
              "7218  2010  27  2700508  BARRA DE SANTO ANTÔNIO  27.94  44.24  247.81  91.16   \n",
              "...    ...  ..      ...                     ...    ...    ...     ...    ...   \n",
              "7311  2010  27  2709103               TAQUARANA  37.95  49.78  215.16  74.19   \n",
              "7312  2010  27  2709152         TEOTÔNIO VILELA  28.87  42.79  244.39  83.78   \n",
              "7313  2010  27  2709202                  TRAIPU  42.69  63.39  178.79  37.18   \n",
              "7314  2010  27  2709301      UNIÃO DOS PALMARES  31.62  36.82  313.92  90.57   \n",
              "7315  2010  27  2709400                  VIÇOSA  33.47  50.85  251.25  83.52   \n",
              "\n",
              "       lixo  esgag_inad  poprur  poptot  popurb  \n",
              "7214  97.94       36.29   14276   19377    5101  \n",
              "7215  99.15       11.06    8475   17424    8949  \n",
              "7216  97.38       12.38   32525  214006  181481  \n",
              "7217  95.31       18.19   21865   44322   22457  \n",
              "7218  93.66        2.74     988   14230   13242  \n",
              "...     ...         ...     ...     ...     ...  \n",
              "7311  95.94       34.31   11706   19020    7314  \n",
              "7312  98.38        8.06    6367   41152   34785  \n",
              "7313  82.18       38.60   17675   25702    8027  \n",
              "7314  97.46        5.68   14707   62358   47651  \n",
              "7315  97.40        5.87    7094   25407   18313  \n",
              "\n",
              "[102 rows x 13 columns]"
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              "      <td>99.15</td>\n",
              "      <td>11.06</td>\n",
              "      <td>8475</td>\n",
              "      <td>17424</td>\n",
              "      <td>8949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7216</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700300</td>\n",
              "      <td>ARAPIRACA</td>\n",
              "      <td>22.45</td>\n",
              "      <td>26.65</td>\n",
              "      <td>423.28</td>\n",
              "      <td>94.68</td>\n",
              "      <td>97.38</td>\n",
              "      <td>12.38</td>\n",
              "      <td>32525</td>\n",
              "      <td>214006</td>\n",
              "      <td>181481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700409</td>\n",
              "      <td>ATALAIA</td>\n",
              "      <td>33.57</td>\n",
              "      <td>42.73</td>\n",
              "      <td>237.33</td>\n",
              "      <td>82.49</td>\n",
              "      <td>95.31</td>\n",
              "      <td>18.19</td>\n",
              "      <td>21865</td>\n",
              "      <td>44322</td>\n",
              "      <td>22457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7218</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700508</td>\n",
              "      <td>BARRA DE SANTO ANTÔNIO</td>\n",
              "      <td>27.94</td>\n",
              "      <td>44.24</td>\n",
              "      <td>247.81</td>\n",
              "      <td>91.16</td>\n",
              "      <td>93.66</td>\n",
              "      <td>2.74</td>\n",
              "      <td>988</td>\n",
              "      <td>14230</td>\n",
              "      <td>13242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7311</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709103</td>\n",
              "      <td>TAQUARANA</td>\n",
              "      <td>37.95</td>\n",
              "      <td>49.78</td>\n",
              "      <td>215.16</td>\n",
              "      <td>74.19</td>\n",
              "      <td>95.94</td>\n",
              "      <td>34.31</td>\n",
              "      <td>11706</td>\n",
              "      <td>19020</td>\n",
              "      <td>7314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7312</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709152</td>\n",
              "      <td>TEOTÔNIO VILELA</td>\n",
              "      <td>28.87</td>\n",
              "      <td>42.79</td>\n",
              "      <td>244.39</td>\n",
              "      <td>83.78</td>\n",
              "      <td>98.38</td>\n",
              "      <td>8.06</td>\n",
              "      <td>6367</td>\n",
              "      <td>41152</td>\n",
              "      <td>34785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7313</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709202</td>\n",
              "      <td>TRAIPU</td>\n",
              "      <td>42.69</td>\n",
              "      <td>63.39</td>\n",
              "      <td>178.79</td>\n",
              "      <td>37.18</td>\n",
              "      <td>82.18</td>\n",
              "      <td>38.60</td>\n",
              "      <td>17675</td>\n",
              "      <td>25702</td>\n",
              "      <td>8027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7314</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709301</td>\n",
              "      <td>UNIÃO DOS PALMARES</td>\n",
              "      <td>31.62</td>\n",
              "      <td>36.82</td>\n",
              "      <td>313.92</td>\n",
              "      <td>90.57</td>\n",
              "      <td>97.46</td>\n",
              "      <td>5.68</td>\n",
              "      <td>14707</td>\n",
              "      <td>62358</td>\n",
              "      <td>47651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7315</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709400</td>\n",
              "      <td>VIÇOSA</td>\n",
              "      <td>33.47</td>\n",
              "      <td>50.85</td>\n",
              "      <td>251.25</td>\n",
              "      <td>83.52</td>\n",
              "      <td>97.40</td>\n",
              "      <td>5.87</td>\n",
              "      <td>7094</td>\n",
              "      <td>25407</td>\n",
              "      <td>18313</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>102 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e73ca83e-94b3-461b-854b-7a67f90eefa9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e73ca83e-94b3-461b-854b-7a67f90eefa9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e73ca83e-94b3-461b-854b-7a67f90eefa9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 2 letra b) Análise mais detalhada para variável analf"
      ],
      "metadata": {
        "id": "wa-Ek4iGiUQN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Estatística descritiva geral\n",
        "al.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ScOpiweqiNC2",
        "outputId": "c5cbf483-642c-4af3-8053-2c21c07305b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ano     uf        codmun       analf       pobre         rpc  \\\n",
              "count   102.0  102.0  1.020000e+02  102.000000  102.000000  102.000000   \n",
              "mean   2010.0   27.0  2.704783e+06   32.560392   45.935490  251.215490   \n",
              "std       0.0    0.0  2.738851e+03    5.949083    9.895573   79.124345   \n",
              "min    2010.0   27.0  2.700102e+06   11.860000   15.570000  151.620000   \n",
              "25%    2010.0   27.0  2.702430e+06   29.430000   40.605000  204.572500   \n",
              "50%    2010.0   27.0  2.704758e+06   32.335000   46.935000  233.440000   \n",
              "75%    2010.0   27.0  2.707082e+06   37.317500   51.485000  269.020000   \n",
              "max    2010.0   27.0  2.709400e+06   43.890000   67.570000  792.540000   \n",
              "\n",
              "             agua        lixo  esgag_inad        poprur         poptot  \\\n",
              "count  102.000000  102.000000  102.000000    102.000000     102.000000   \n",
              "mean    70.909020   94.608137   20.248922   8065.039216   30593.078431   \n",
              "std     19.264509    6.569923   15.028983   6796.627403   93445.981869   \n",
              "min     21.470000   66.930000    1.330000    553.000000    2866.000000   \n",
              "25%     61.660000   92.875000    8.102500   3151.000000    8444.500000   \n",
              "50%     76.090000   96.820000   15.010000   5653.000000   17076.500000   \n",
              "75%     85.107500   98.807500   31.320000  11796.000000   25352.250000   \n",
              "max     97.250000  100.000000   60.860000  32525.000000  932748.000000   \n",
              "\n",
              "              popurb  \n",
              "count     102.000000  \n",
              "mean    22528.039216  \n",
              "std     93321.530481  \n",
              "min      1171.000000  \n",
              "25%      4068.000000  \n",
              "50%      7054.000000  \n",
              "75%     14423.500000  \n",
              "max    932129.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6135d263-0846-4b3d-ae68-ad2e2ab15bba\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>102.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.020000e+02</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>102.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2.704783e+06</td>\n",
              "      <td>32.560392</td>\n",
              "      <td>45.935490</td>\n",
              "      <td>251.215490</td>\n",
              "      <td>70.909020</td>\n",
              "      <td>94.608137</td>\n",
              "      <td>20.248922</td>\n",
              "      <td>8065.039216</td>\n",
              "      <td>30593.078431</td>\n",
              "      <td>22528.039216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.738851e+03</td>\n",
              "      <td>5.949083</td>\n",
              "      <td>9.895573</td>\n",
              "      <td>79.124345</td>\n",
              "      <td>19.264509</td>\n",
              "      <td>6.569923</td>\n",
              "      <td>15.028983</td>\n",
              "      <td>6796.627403</td>\n",
              "      <td>93445.981869</td>\n",
              "      <td>93321.530481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2.700102e+06</td>\n",
              "      <td>11.860000</td>\n",
              "      <td>15.570000</td>\n",
              "      <td>151.620000</td>\n",
              "      <td>21.470000</td>\n",
              "      <td>66.930000</td>\n",
              "      <td>1.330000</td>\n",
              "      <td>553.000000</td>\n",
              "      <td>2866.000000</td>\n",
              "      <td>1171.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2.702430e+06</td>\n",
              "      <td>29.430000</td>\n",
              "      <td>40.605000</td>\n",
              "      <td>204.572500</td>\n",
              "      <td>61.660000</td>\n",
              "      <td>92.875000</td>\n",
              "      <td>8.102500</td>\n",
              "      <td>3151.000000</td>\n",
              "      <td>8444.500000</td>\n",
              "      <td>4068.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2.704758e+06</td>\n",
              "      <td>32.335000</td>\n",
              "      <td>46.935000</td>\n",
              "      <td>233.440000</td>\n",
              "      <td>76.090000</td>\n",
              "      <td>96.820000</td>\n",
              "      <td>15.010000</td>\n",
              "      <td>5653.000000</td>\n",
              "      <td>17076.500000</td>\n",
              "      <td>7054.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2.707082e+06</td>\n",
              "      <td>37.317500</td>\n",
              "      <td>51.485000</td>\n",
              "      <td>269.020000</td>\n",
              "      <td>85.107500</td>\n",
              "      <td>98.807500</td>\n",
              "      <td>31.320000</td>\n",
              "      <td>11796.000000</td>\n",
              "      <td>25352.250000</td>\n",
              "      <td>14423.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2.709400e+06</td>\n",
              "      <td>43.890000</td>\n",
              "      <td>67.570000</td>\n",
              "      <td>792.540000</td>\n",
              "      <td>97.250000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>60.860000</td>\n",
              "      <td>32525.000000</td>\n",
              "      <td>932748.000000</td>\n",
              "      <td>932129.000000</td>\n",
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              "</table>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6135d263-0846-4b3d-ae68-ad2e2ab15bba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6135d263-0846-4b3d-ae68-ad2e2ab15bba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Estatística descritiva para analfabetismo\n",
        "pd.DataFrame(al.analf.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "gXApAxQpjwuI",
        "outputId": "c5f19953-d7bc-4a97-a0a8-df37a00f7877"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            analf\n",
              "count  102.000000\n",
              "mean    32.560392\n",
              "std      5.949083\n",
              "min     11.860000\n",
              "25%     29.430000\n",
              "50%     32.335000\n",
              "75%     37.317500\n",
              "max     43.890000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47f1fc9c-aef5-4fe1-a1b7-32be13abff60\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>analf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>102.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>32.560392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.949083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.860000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>29.430000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>32.335000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>37.317500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>43.890000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47f1fc9c-aef5-4fe1-a1b7-32be13abff60')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-47f1fc9c-aef5-4fe1-a1b7-32be13abff60 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-47f1fc9c-aef5-4fe1-a1b7-32be13abff60');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(al.analf, bins=7, align='left', histtype='bar', edgecolor='black')\n",
        "plt.title(\"\")  # Título do histograma\n",
        "plt.xlabel(\"Taxa de Analfabetismo\")  # Rótulo do eixo x\n",
        "plt.ylabel(\"Frequência\")  # Rótulo do eixo y\n",
        "\n",
        "plt.show()  # Exibe o histograma"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "KS0ndY_XEAga",
        "outputId": "d1539000-88fc-44fc-a934-e34e55a18d78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "percentis = [0, 5, 50, 100]\n",
        "\n",
        "perce = np.percentile(al.analf, percentis)\n",
        "pcts = pd.DataFrame({\"Percentis\": percentis, \"Valores\": perce})\n",
        "round(pcts, 2)"
      ],
      "metadata": {
        "id": "UTNEMB67DuJk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "113ad1c7-f5b4-423d-83a8-df7438294237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Percentis  Valores\n",
              "0          0    11.86\n",
              "1          5    22.47\n",
              "2         50    32.33\n",
              "3        100    43.89"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b4a966af-1c6d-4f81-bd84-01baef483647\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Percentis</th>\n",
              "      <th>Valores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>11.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>22.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>32.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100</td>\n",
              "      <td>43.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4a966af-1c6d-4f81-bd84-01baef483647')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b4a966af-1c6d-4f81-bd84-01baef483647 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b4a966af-1c6d-4f81-bd84-01baef483647');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questão 2 letra c) Criação de variável categórica para analfabetismo"
      ],
      "metadata": {
        "id": "BlFgZW5HkAy3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percentis = [0, 5, 50, 100]\n",
        "\n",
        "perce = np.percentile(al.analf, percentis)\n",
        "pcts = pd.DataFrame({\"Percentis\": percentis, \"Valores\": perce})\n",
        "round(pcts, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "IFNtCZq3j8YV",
        "outputId": "d9edafcc-3f7e-450b-ffb6-f5169158f555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Percentis  Valores\n",
              "0          0    11.86\n",
              "1          5    22.47\n",
              "2         50    32.33\n",
              "3        100    43.89"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ccc1d195-93fd-4c73-8d90-a303a171ba55\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Percentis</th>\n",
              "      <th>Valores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>11.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>22.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>32.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100</td>\n",
              "      <td>43.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccc1d195-93fd-4c73-8d90-a303a171ba55')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ccc1d195-93fd-4c73-8d90-a303a171ba55 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ccc1d195-93fd-4c73-8d90-a303a171ba55');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "perce[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQ4MqBI5CKiR",
        "outputId": "36526d70-d435-4c8b-8543-9644ba12e166"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.469"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criação da variável sitanalf\n",
        "al['sitanalf'] = ''\n",
        "al.loc[al['analf'] <= perce[1], 'sitanalf'] = \"bom\"\n",
        "al.loc[(al['analf'] > perce[1]) & (al['analf'] <= perce[2]), 'sitanalf'] = \"regular\"\n",
        "al.loc[(al['analf'] > perce[2]), 'sitanalf'] = \"ruim\"\n",
        "al"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5n7HYJoBBjY2",
        "outputId": "af837c35-695b-4ff8-b64b-186c28c33108"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun                   munic  analf  pobre     rpc   agua  \\\n",
              "7214  2010  27  2700102             ÁGUA BRANCA  30.92  52.87  213.00  61.78   \n",
              "7215  2010  27  2700201                  ANADIA  36.16  47.59  239.53  61.62   \n",
              "7216  2010  27  2700300               ARAPIRACA  22.45  26.65  423.28  94.68   \n",
              "7217  2010  27  2700409                 ATALAIA  33.57  42.73  237.33  82.49   \n",
              "7218  2010  27  2700508  BARRA DE SANTO ANTÔNIO  27.94  44.24  247.81  91.16   \n",
              "...    ...  ..      ...                     ...    ...    ...     ...    ...   \n",
              "7311  2010  27  2709103               TAQUARANA  37.95  49.78  215.16  74.19   \n",
              "7312  2010  27  2709152         TEOTÔNIO VILELA  28.87  42.79  244.39  83.78   \n",
              "7313  2010  27  2709202                  TRAIPU  42.69  63.39  178.79  37.18   \n",
              "7314  2010  27  2709301      UNIÃO DOS PALMARES  31.62  36.82  313.92  90.57   \n",
              "7315  2010  27  2709400                  VIÇOSA  33.47  50.85  251.25  83.52   \n",
              "\n",
              "       lixo  esgag_inad  poprur  poptot  popurb sitanalf  \n",
              "7214  97.94       36.29   14276   19377    5101  regular  \n",
              "7215  99.15       11.06    8475   17424    8949     ruim  \n",
              "7216  97.38       12.38   32525  214006  181481      bom  \n",
              "7217  95.31       18.19   21865   44322   22457     ruim  \n",
              "7218  93.66        2.74     988   14230   13242  regular  \n",
              "...     ...         ...     ...     ...     ...      ...  \n",
              "7311  95.94       34.31   11706   19020    7314     ruim  \n",
              "7312  98.38        8.06    6367   41152   34785  regular  \n",
              "7313  82.18       38.60   17675   25702    8027     ruim  \n",
              "7314  97.46        5.68   14707   62358   47651  regular  \n",
              "7315  97.40        5.87    7094   25407   18313     ruim  \n",
              "\n",
              "[102 rows x 14 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>munic</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "      <th>sitanalf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7214</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700102</td>\n",
              "      <td>ÁGUA BRANCA</td>\n",
              "      <td>30.92</td>\n",
              "      <td>52.87</td>\n",
              "      <td>213.00</td>\n",
              "      <td>61.78</td>\n",
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              "      <td>19377</td>\n",
              "      <td>5101</td>\n",
              "      <td>regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700201</td>\n",
              "      <td>ANADIA</td>\n",
              "      <td>36.16</td>\n",
              "      <td>47.59</td>\n",
              "      <td>239.53</td>\n",
              "      <td>61.62</td>\n",
              "      <td>99.15</td>\n",
              "      <td>11.06</td>\n",
              "      <td>8475</td>\n",
              "      <td>17424</td>\n",
              "      <td>8949</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7216</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700300</td>\n",
              "      <td>ARAPIRACA</td>\n",
              "      <td>22.45</td>\n",
              "      <td>26.65</td>\n",
              "      <td>423.28</td>\n",
              "      <td>94.68</td>\n",
              "      <td>97.38</td>\n",
              "      <td>12.38</td>\n",
              "      <td>32525</td>\n",
              "      <td>214006</td>\n",
              "      <td>181481</td>\n",
              "      <td>bom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700409</td>\n",
              "      <td>ATALAIA</td>\n",
              "      <td>33.57</td>\n",
              "      <td>42.73</td>\n",
              "      <td>237.33</td>\n",
              "      <td>82.49</td>\n",
              "      <td>95.31</td>\n",
              "      <td>18.19</td>\n",
              "      <td>21865</td>\n",
              "      <td>44322</td>\n",
              "      <td>22457</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7218</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700508</td>\n",
              "      <td>BARRA DE SANTO ANTÔNIO</td>\n",
              "      <td>27.94</td>\n",
              "      <td>44.24</td>\n",
              "      <td>247.81</td>\n",
              "      <td>91.16</td>\n",
              "      <td>93.66</td>\n",
              "      <td>2.74</td>\n",
              "      <td>988</td>\n",
              "      <td>14230</td>\n",
              "      <td>13242</td>\n",
              "      <td>regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7311</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709103</td>\n",
              "      <td>TAQUARANA</td>\n",
              "      <td>37.95</td>\n",
              "      <td>49.78</td>\n",
              "      <td>215.16</td>\n",
              "      <td>74.19</td>\n",
              "      <td>95.94</td>\n",
              "      <td>34.31</td>\n",
              "      <td>11706</td>\n",
              "      <td>19020</td>\n",
              "      <td>7314</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7312</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709152</td>\n",
              "      <td>TEOTÔNIO VILELA</td>\n",
              "      <td>28.87</td>\n",
              "      <td>42.79</td>\n",
              "      <td>244.39</td>\n",
              "      <td>83.78</td>\n",
              "      <td>98.38</td>\n",
              "      <td>8.06</td>\n",
              "      <td>6367</td>\n",
              "      <td>41152</td>\n",
              "      <td>34785</td>\n",
              "      <td>regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7313</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709202</td>\n",
              "      <td>TRAIPU</td>\n",
              "      <td>42.69</td>\n",
              "      <td>63.39</td>\n",
              "      <td>178.79</td>\n",
              "      <td>37.18</td>\n",
              "      <td>82.18</td>\n",
              "      <td>38.60</td>\n",
              "      <td>17675</td>\n",
              "      <td>25702</td>\n",
              "      <td>8027</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7314</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709301</td>\n",
              "      <td>UNIÃO DOS PALMARES</td>\n",
              "      <td>31.62</td>\n",
              "      <td>36.82</td>\n",
              "      <td>313.92</td>\n",
              "      <td>90.57</td>\n",
              "      <td>97.46</td>\n",
              "      <td>5.68</td>\n",
              "      <td>14707</td>\n",
              "      <td>62358</td>\n",
              "      <td>47651</td>\n",
              "      <td>regular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7315</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709400</td>\n",
              "      <td>VIÇOSA</td>\n",
              "      <td>33.47</td>\n",
              "      <td>50.85</td>\n",
              "      <td>251.25</td>\n",
              "      <td>83.52</td>\n",
              "      <td>97.40</td>\n",
              "      <td>5.87</td>\n",
              "      <td>7094</td>\n",
              "      <td>25407</td>\n",
              "      <td>18313</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>102 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9920afb2-3dc1-47f0-8619-5e2fc5eb73c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9920afb2-3dc1-47f0-8619-5e2fc5eb73c8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9920afb2-3dc1-47f0-8619-5e2fc5eb73c8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questão 2 letra d) Análise da sifanalf"
      ],
      "metadata": {
        "id": "jfTrXOZIH4Cx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Top 5 melhores\n",
        "al[(al.sitanalf == 'bom')].head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sHqrAmWYIAS7",
        "outputId": "c4cbb22e-e369-47c1-cb17-75dc77a8e594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun                  munic  analf  pobre     rpc   agua  \\\n",
              "7216  2010  27  2700300              ARAPIRACA  22.45  26.65  423.28  94.68   \n",
              "7260  2010  27  2704302                 MACEIÓ  11.86  15.57  792.54  80.17   \n",
              "7264  2010  27  2704708       MARECHAL DEODORO  21.90  32.01  431.43  97.25   \n",
              "7296  2010  27  2707701              RIO LARGO  18.28  24.26  369.11  77.58   \n",
              "7305  2010  27  2708600  SÃO MIGUEL DOS CAMPOS  21.28  28.11  360.82  96.16   \n",
              "\n",
              "       lixo  esgag_inad  poprur  poptot  popurb sitanalf  \n",
              "7216  97.38       12.38   32525  214006  181481      bom  \n",
              "7260  97.74        2.32     619  932748  932129      bom  \n",
              "7264  94.74        4.89    2585   45977   43392      bom  \n",
              "7296  88.92        5.50   12534   68481   55947      bom  \n",
              "7305  99.31        1.33    2011   54577   52566      bom  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>munic</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "      <th>sitanalf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7216</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700300</td>\n",
              "      <td>ARAPIRACA</td>\n",
              "      <td>22.45</td>\n",
              "      <td>26.65</td>\n",
              "      <td>423.28</td>\n",
              "      <td>94.68</td>\n",
              "      <td>97.38</td>\n",
              "      <td>12.38</td>\n",
              "      <td>32525</td>\n",
              "      <td>214006</td>\n",
              "      <td>181481</td>\n",
              "      <td>bom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7260</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2704302</td>\n",
              "      <td>MACEIÓ</td>\n",
              "      <td>11.86</td>\n",
              "      <td>15.57</td>\n",
              "      <td>792.54</td>\n",
              "      <td>80.17</td>\n",
              "      <td>97.74</td>\n",
              "      <td>2.32</td>\n",
              "      <td>619</td>\n",
              "      <td>932748</td>\n",
              "      <td>932129</td>\n",
              "      <td>bom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7264</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2704708</td>\n",
              "      <td>MARECHAL DEODORO</td>\n",
              "      <td>21.90</td>\n",
              "      <td>32.01</td>\n",
              "      <td>431.43</td>\n",
              "      <td>97.25</td>\n",
              "      <td>94.74</td>\n",
              "      <td>4.89</td>\n",
              "      <td>2585</td>\n",
              "      <td>45977</td>\n",
              "      <td>43392</td>\n",
              "      <td>bom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7296</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2707701</td>\n",
              "      <td>RIO LARGO</td>\n",
              "      <td>18.28</td>\n",
              "      <td>24.26</td>\n",
              "      <td>369.11</td>\n",
              "      <td>77.58</td>\n",
              "      <td>88.92</td>\n",
              "      <td>5.50</td>\n",
              "      <td>12534</td>\n",
              "      <td>68481</td>\n",
              "      <td>55947</td>\n",
              "      <td>bom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7305</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2708600</td>\n",
              "      <td>SÃO MIGUEL DOS CAMPOS</td>\n",
              "      <td>21.28</td>\n",
              "      <td>28.11</td>\n",
              "      <td>360.82</td>\n",
              "      <td>96.16</td>\n",
              "      <td>99.31</td>\n",
              "      <td>1.33</td>\n",
              "      <td>2011</td>\n",
              "      <td>54577</td>\n",
              "      <td>52566</td>\n",
              "      <td>bom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00e05d2c-0f91-4ac9-a628-7447d28989d0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-00e05d2c-0f91-4ac9-a628-7447d28989d0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-00e05d2c-0f91-4ac9-a628-7447d28989d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Top 5 piores\n",
        "al[(al.sitanalf == 'ruim')].head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NS-KAwh1DkUM",
        "outputId": "adefc328-fda0-4fae-8f48-1af7bed7ab9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun       munic  analf  pobre     rpc   agua   lixo  \\\n",
              "7215  2010  27  2700201      ANADIA  36.16  47.59  239.53  61.62  99.15   \n",
              "7217  2010  27  2700409     ATALAIA  33.57  42.73  237.33  82.49  95.31   \n",
              "7221  2010  27  2700805       BELÉM  33.33  37.05  309.50  62.16  92.54   \n",
              "7222  2010  27  2700904  BELO MONTE  38.11  60.98  187.77  40.13  99.16   \n",
              "7224  2010  27  2701100  BRANQUINHA  41.58  40.52  221.93  81.68  97.83   \n",
              "\n",
              "      esgag_inad  poprur  poptot  popurb sitanalf  \n",
              "7215       11.06    8475   17424    8949     ruim  \n",
              "7217       18.19   21865   44322   22457     ruim  \n",
              "7221       30.81    2679    4551    1872     ruim  \n",
              "7222       17.30    5859    7030    1171     ruim  \n",
              "7224       17.15    3910   10583    6673     ruim  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>munic</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "      <th>sitanalf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700201</td>\n",
              "      <td>ANADIA</td>\n",
              "      <td>36.16</td>\n",
              "      <td>47.59</td>\n",
              "      <td>239.53</td>\n",
              "      <td>61.62</td>\n",
              "      <td>99.15</td>\n",
              "      <td>11.06</td>\n",
              "      <td>8475</td>\n",
              "      <td>17424</td>\n",
              "      <td>8949</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700409</td>\n",
              "      <td>ATALAIA</td>\n",
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              "      <td>237.33</td>\n",
              "      <td>82.49</td>\n",
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              "      <td>21865</td>\n",
              "      <td>44322</td>\n",
              "      <td>22457</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7221</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700805</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>33.33</td>\n",
              "      <td>37.05</td>\n",
              "      <td>309.50</td>\n",
              "      <td>62.16</td>\n",
              "      <td>92.54</td>\n",
              "      <td>30.81</td>\n",
              "      <td>2679</td>\n",
              "      <td>4551</td>\n",
              "      <td>1872</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7222</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700904</td>\n",
              "      <td>BELO MONTE</td>\n",
              "      <td>38.11</td>\n",
              "      <td>60.98</td>\n",
              "      <td>187.77</td>\n",
              "      <td>40.13</td>\n",
              "      <td>99.16</td>\n",
              "      <td>17.30</td>\n",
              "      <td>5859</td>\n",
              "      <td>7030</td>\n",
              "      <td>1171</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7224</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2701100</td>\n",
              "      <td>BRANQUINHA</td>\n",
              "      <td>41.58</td>\n",
              "      <td>40.52</td>\n",
              "      <td>221.93</td>\n",
              "      <td>81.68</td>\n",
              "      <td>97.83</td>\n",
              "      <td>17.15</td>\n",
              "      <td>3910</td>\n",
              "      <td>10583</td>\n",
              "      <td>6673</td>\n",
              "      <td>ruim</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-773e6556-087c-41e1-9f69-3efcf0bda52c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-773e6556-087c-41e1-9f69-3efcf0bda52c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-773e6556-087c-41e1-9f69-3efcf0bda52c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questão 2 letra e) Criando variável de faixa populacional"
      ],
      "metadata": {
        "id": "wx4wFSORINTD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "al['fxpop'] = ''\n",
        "al.loc[al['poptot'] <= 5000, 'fxpop'] = 1\n",
        "al.loc[(al['poptot'] > 5000) & (al['poptot'] <= 20000), 'fxpop'] = 2\n",
        "al.loc[(al['poptot'] > 20000) & (al['poptot'] <= 50000), 'fxpop'] = 3\n",
        "al.loc[(al['poptot'] > 50000) & (al['poptot'] <= 100000), 'fxpop'] = 4\n",
        "al.loc[al['poptot'] > 100000, 'fxpop'] = 5\n",
        "al.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lNddJN2YIL4_",
        "outputId": "069b77b3-77d1-4d62-9e90-5997acf3554e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun                   munic  analf  pobre     rpc   agua  \\\n",
              "7214  2010  27  2700102             ÁGUA BRANCA  30.92  52.87  213.00  61.78   \n",
              "7215  2010  27  2700201                  ANADIA  36.16  47.59  239.53  61.62   \n",
              "7216  2010  27  2700300               ARAPIRACA  22.45  26.65  423.28  94.68   \n",
              "7217  2010  27  2700409                 ATALAIA  33.57  42.73  237.33  82.49   \n",
              "7218  2010  27  2700508  BARRA DE SANTO ANTÔNIO  27.94  44.24  247.81  91.16   \n",
              "\n",
              "       lixo  esgag_inad  poprur  poptot  popurb sitanalf fxpop  \n",
              "7214  97.94       36.29   14276   19377    5101  regular     2  \n",
              "7215  99.15       11.06    8475   17424    8949     ruim     2  \n",
              "7216  97.38       12.38   32525  214006  181481      bom     5  \n",
              "7217  95.31       18.19   21865   44322   22457     ruim     3  \n",
              "7218  93.66        2.74     988   14230   13242  regular     2  "
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              "      <th>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>munic</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "      <th>sitanalf</th>\n",
              "      <th>fxpop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7214</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700102</td>\n",
              "      <td>ÁGUA BRANCA</td>\n",
              "      <td>30.92</td>\n",
              "      <td>52.87</td>\n",
              "      <td>213.00</td>\n",
              "      <td>61.78</td>\n",
              "      <td>97.94</td>\n",
              "      <td>36.29</td>\n",
              "      <td>14276</td>\n",
              "      <td>19377</td>\n",
              "      <td>5101</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700201</td>\n",
              "      <td>ANADIA</td>\n",
              "      <td>36.16</td>\n",
              "      <td>47.59</td>\n",
              "      <td>239.53</td>\n",
              "      <td>61.62</td>\n",
              "      <td>99.15</td>\n",
              "      <td>11.06</td>\n",
              "      <td>8475</td>\n",
              "      <td>17424</td>\n",
              "      <td>8949</td>\n",
              "      <td>ruim</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7216</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700300</td>\n",
              "      <td>ARAPIRACA</td>\n",
              "      <td>22.45</td>\n",
              "      <td>26.65</td>\n",
              "      <td>423.28</td>\n",
              "      <td>94.68</td>\n",
              "      <td>97.38</td>\n",
              "      <td>12.38</td>\n",
              "      <td>32525</td>\n",
              "      <td>214006</td>\n",
              "      <td>181481</td>\n",
              "      <td>bom</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700409</td>\n",
              "      <td>ATALAIA</td>\n",
              "      <td>33.57</td>\n",
              "      <td>42.73</td>\n",
              "      <td>237.33</td>\n",
              "      <td>82.49</td>\n",
              "      <td>95.31</td>\n",
              "      <td>18.19</td>\n",
              "      <td>21865</td>\n",
              "      <td>44322</td>\n",
              "      <td>22457</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7218</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700508</td>\n",
              "      <td>BARRA DE SANTO ANTÔNIO</td>\n",
              "      <td>27.94</td>\n",
              "      <td>44.24</td>\n",
              "      <td>247.81</td>\n",
              "      <td>91.16</td>\n",
              "      <td>93.66</td>\n",
              "      <td>2.74</td>\n",
              "      <td>988</td>\n",
              "      <td>14230</td>\n",
              "      <td>13242</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questão 2 letra f) Tabulação cruzada entre sitanalf e fxpop\n"
      ],
      "metadata": {
        "id": "f3GKK-vQKCh-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cross = pd.crosstab(al['sitanalf'], al['fxpop'])\n",
        "popcross = cross.apply(lambda r: r/r.sum(), axis=1) * 100\n",
        "popcross = popcross.round(2)\n",
        "popcross"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "OSIw9rJkKByh",
        "outputId": "bc67182c-5036-4287-87c0-2a73a7f49d8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fxpop        1      2      3      4      5\n",
              "sitanalf                                  \n",
              "bom       0.00  16.67  16.67  33.33  33.33\n",
              "regular   2.22  55.56  31.11  11.11   0.00\n",
              "ruim      9.80  58.82  31.37   0.00   0.00"
            ],
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              "\n",
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              "      <th>fxpop</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sitanalf</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>bom</th>\n",
              "      <td>0.00</td>\n",
              "      <td>16.67</td>\n",
              "      <td>16.67</td>\n",
              "      <td>33.33</td>\n",
              "      <td>33.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>regular</th>\n",
              "      <td>2.22</td>\n",
              "      <td>55.56</td>\n",
              "      <td>31.11</td>\n",
              "      <td>11.11</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ruim</th>\n",
              "      <td>9.80</td>\n",
              "      <td>58.82</td>\n",
              "      <td>31.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c2211a12-015e-4ecf-bb34-5e526670f03b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questão 2 g) Estatística descritiva da variável pobre por faixa populacional\n"
      ],
      "metadata": {
        "id": "3RCbxtEcKasB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pobrepop = al.groupby('fxpop')['pobre']\n",
        "pobrepop = pd.DataFrame(pobrepop.describe())\n",
        "pobrepop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "Bzx8TCJaKftf",
        "outputId": "229fb5cf-c58e-4095-b5c0-502bd1bf545e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count       mean        std    min      25%    50%     75%    max\n",
              "fxpop                                                                   \n",
              "1        6.0  45.330000  10.980789  37.05  38.9775  42.57  44.490  66.88\n",
              "2       56.0  48.279107   9.060028  23.89  44.0675  48.71  53.205  67.57\n",
              "3       31.0  46.238710   7.806200  32.01  41.5750  45.62  51.060  63.39\n",
              "4        7.0  33.455714   6.425239  24.26  29.4150  33.79  36.820  43.67\n",
              "5        2.0  21.110000   7.834743  15.57  18.3400  21.11  23.880  26.65"
            ],
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              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fxpop</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>1</th>\n",
              "      <td>6.0</td>\n",
              "      <td>45.330000</td>\n",
              "      <td>10.980789</td>\n",
              "      <td>37.05</td>\n",
              "      <td>38.9775</td>\n",
              "      <td>42.57</td>\n",
              "      <td>44.490</td>\n",
              "      <td>66.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>56.0</td>\n",
              "      <td>48.279107</td>\n",
              "      <td>9.060028</td>\n",
              "      <td>23.89</td>\n",
              "      <td>44.0675</td>\n",
              "      <td>48.71</td>\n",
              "      <td>53.205</td>\n",
              "      <td>67.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31.0</td>\n",
              "      <td>46.238710</td>\n",
              "      <td>7.806200</td>\n",
              "      <td>32.01</td>\n",
              "      <td>41.5750</td>\n",
              "      <td>45.62</td>\n",
              "      <td>51.060</td>\n",
              "      <td>63.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>33.455714</td>\n",
              "      <td>6.425239</td>\n",
              "      <td>24.26</td>\n",
              "      <td>29.4150</td>\n",
              "      <td>33.79</td>\n",
              "      <td>36.820</td>\n",
              "      <td>43.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>21.110000</td>\n",
              "      <td>7.834743</td>\n",
              "      <td>15.57</td>\n",
              "      <td>18.3400</td>\n",
              "      <td>21.11</td>\n",
              "      <td>23.880</td>\n",
              "      <td>26.65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c75d1e98-7d73-43ab-a6c9-28839953e1ef')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c75d1e98-7d73-43ab-a6c9-28839953e1ef button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c75d1e98-7d73-43ab-a6c9-28839953e1ef');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adição do coeficiente de variação\n",
        "pobrepop['CV'] = (pobrepop['std']/pobrepop['mean'])*100\n",
        "pobrepop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "dMqxcn7KQU-2",
        "outputId": "213a9a16-f656-49e9-d871-6857049eeef4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count       mean        std    min      25%    50%     75%    max  \\\n",
              "fxpop                                                                      \n",
              "1        6.0  45.330000  10.980789  37.05  38.9775  42.57  44.490  66.88   \n",
              "2       56.0  48.279107   9.060028  23.89  44.0675  48.71  53.205  67.57   \n",
              "3       31.0  46.238710   7.806200  32.01  41.5750  45.62  51.060  63.39   \n",
              "4        7.0  33.455714   6.425239  24.26  29.4150  33.79  36.820  43.67   \n",
              "5        2.0  21.110000   7.834743  15.57  18.3400  21.11  23.880  26.65   \n",
              "\n",
              "              CV  \n",
              "fxpop             \n",
              "1      24.224109  \n",
              "2      18.765940  \n",
              "3      16.882392  \n",
              "4      19.205206  \n",
              "5      37.113895  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-39527361-a2bc-4766-af92-ffe070553b74\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>CV</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fxpop</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>1</th>\n",
              "      <td>6.0</td>\n",
              "      <td>45.330000</td>\n",
              "      <td>10.980789</td>\n",
              "      <td>37.05</td>\n",
              "      <td>38.9775</td>\n",
              "      <td>42.57</td>\n",
              "      <td>44.490</td>\n",
              "      <td>66.88</td>\n",
              "      <td>24.224109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>56.0</td>\n",
              "      <td>48.279107</td>\n",
              "      <td>9.060028</td>\n",
              "      <td>23.89</td>\n",
              "      <td>44.0675</td>\n",
              "      <td>48.71</td>\n",
              "      <td>53.205</td>\n",
              "      <td>67.57</td>\n",
              "      <td>18.765940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31.0</td>\n",
              "      <td>46.238710</td>\n",
              "      <td>7.806200</td>\n",
              "      <td>32.01</td>\n",
              "      <td>41.5750</td>\n",
              "      <td>45.62</td>\n",
              "      <td>51.060</td>\n",
              "      <td>63.39</td>\n",
              "      <td>16.882392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>33.455714</td>\n",
              "      <td>6.425239</td>\n",
              "      <td>24.26</td>\n",
              "      <td>29.4150</td>\n",
              "      <td>33.79</td>\n",
              "      <td>36.820</td>\n",
              "      <td>43.67</td>\n",
              "      <td>19.205206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
              "      <td>21.110000</td>\n",
              "      <td>7.834743</td>\n",
              "      <td>15.57</td>\n",
              "      <td>18.3400</td>\n",
              "      <td>21.11</td>\n",
              "      <td>23.880</td>\n",
              "      <td>26.65</td>\n",
              "      <td>37.113895</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39527361-a2bc-4766-af92-ffe070553b74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-39527361-a2bc-4766-af92-ffe070553b74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-39527361-a2bc-4766-af92-ffe070553b74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pobreg = sns.regplot(x=al.pobre,\n",
        "            y=al.poptot,\n",
        "            data=al,\n",
        "            fit_reg=False)\n",
        "pobreg.set_title('População x Pobreza - Alagoas')\n",
        "pobreg.set_xlabel('Taxa de Pobreza')\n",
        "pobreg.set_ylabel('População')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "gBmax_FayVgI",
        "outputId": "38391574-593a-46bb-af8a-8fb396dd5a25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'População')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RRx9V26tXXfsLIfDKK6/U8oqBgIAApKSkeD0qntKs7P3330eHDh0wefJkXH/99V6Phx9+GEFBQeecs6uu77vU1FQ4HA689dZb6jJFUfD66697lYuNjUWfPn2wePFir/C0d+9efPfdd+rnzrPvivsFgDlz5lTpGT5z5ozXz0FBQejUqVOVqV2IasOeLiI/6d27N9LS0vDmm2+ioKAAw4YNw9atW7F48WKMGTMGl112mVf5Ll264Pbbb8e2bdsQHR2NBQsW4NSpU15BZvjw4YiPj8ftt9+ORx55BDqdDgsWLEDr1q2RmZl5zvr87W9/w2effYZrrrkGo0aNwrFjxzBv3jx0797dKwRcdtlluOWWW/Dqq6/i8OHDGDFiBBRFwQ8//IDLLrsM99xzD4YPHw6j0Yirr74ad911F4qLi/HWW28hKioKWVlZ6rZat26N6dOnY/bs2RgxYgT+7//+DwcPHsQbb7yBCy+8EOPHjz9nnf/9739j165dWL16NYKDg3HBBRdgxowZePLJJ3H99dd7HWAbor71a9OmDf7zn//gt99+Q5cuXfDxxx9j165dePPNN2uc+sJDlmW8/fbbGDlyJHr06IGJEyeibdu2OHHiBNauXYuQkBAsX74ce/bswb/+9S8MHToUOTk5eP/99722M378eCQlJaFjx454+OGHceLECYSEhOB///tflbFdjXXy5EmsXbsW9913X7XrTSYTUlNTsXTpUrz66qvVtkFd33djxozBwIED8dBDD+HIkSNISkrCl19+iby8PADep9aff/55jBw5EsnJybj99tvVKSNCQ0Mxa9Ysr32/9957CA0NRffu3bF582Z8//336nQhHt27d8ell16K/v37IyIiAtu3b8enn36Ke+65pzHNR39F/hm/T9Syea4W3LZt2znLORwOMXv2bJGYmCgMBoOIi4sT06dPF2VlZV7lEhISxKhRo8TKlSvFBRdcIEwmk0hKShJLly6tss0dO3aIQYMGCaPRKOLj48VLL71Up6sXFUURzzzzjEhISBAmk0n07dtXfPXVVyItLU0kJCR47cPpdIrnn39eJCUlqVf9jRw5UuzYsUMt8+WXX4oLLrhABAQEiPbt24v//Oc/YsGCBVXqIYR7CoakpCRhMBhEdHS0mDJlisjPzz9n2+3YsUPo9Xpx7733VqnbhRdeKNq0aXPObXiuIqyuDSurS/2GDRsmevToIbZv3y6Sk5NFQECASEhIEK+99lq99vvzzz+La6+9VrRq1UqYTCaRkJAgbrzxRrF69Wqv59f08Ni/f79ISUkRQUFBIjIyUtxxxx1i9+7dVaZXaIwXX3xRAFDrVp1FixYJAGLZsmVCiMa973Jzc8Xf//53ERwcLEJDQ8WECRPExo0bBQCxZMkSr7Lff/+9GDx4sDCbzSIkJERcffXVVa4Uzs/PFxMnThSRkZEiKChIpKamigMHDoiEhASRlpamlvv3v/8tBg4cKMLCwoTZbBZJSUni6aefVqcBIaorSYhKfatEdN5p3749evbsia+++srfVanixx9/xGOPPeY1azqRVr744gtcc801+PHHHzF48GB/V4fonDimi4ga5ZJLLkFGRgZ+/fVXf1eFWrjKtzByuVyYM2cOQkJC0K9fPz/ViqjuOKaLiBokNzcXCxYsAOAeJF/d4G8iX7r33ntRWlqK5ORk2Gw2fPbZZ9i0aROeeeaZKlNwEJ2PGLqIqEFcLhdeffVV5OfnY/z48Zwokprc5ZdfjhdffBFfffUVysrK0KlTJ8yZM4cD2qnZ4JguIiIiIg1wTBcRERGRBhi6iIiIiDTAMV0aUhQFJ0+eRHBwsE/vt0dERERNRwiBoqIitGnTBrLc8P4qhi4NnTx5EnFxcf6uBhERETXA8ePH0a5duwY/n6FLQ8HBwQDcv7SQkBA/14aIiIjqwmq1Ii4uTj2ONxRDl4Y8pxRDQkIYuoiIiJqZxg4N4kB6IiIiIg0wdBERERFpgKGLiIiISAMMXUREREQaYOgiIiIi0gBDFxEREZEGGLqIiIiINMDQRURERKQBhi4iIiIiDXBG+mZOUQT2nbQir8SOCIsRPdqEQJZ5M20iIqLzDUNXM7bpyGnMXX8UR3OK4XAJGHQSOkYFYcqwjri4U6S/q0dEREQV8PRiM7XpyGk88fkeZGRZEWjSIyrYhECTHhlZRXji8z3YdOS0v6tIREREFTB0NUOKIjB3/VEU25yICQlAgEEHWZYQYNAhJsSEYpsLc9cfhaIIf1eViIiIyjF0NUP7TlpxNKcY4RZjlTueS5KEMIsBR3OKse+k1U81JCIiosoYupqhvBI7HC4Bo676X59JJ8OhCOSV2DWuGREREdWEoasZirAYYdBJsLuUatfbXAoMsoQIi1HjmhEREVFNGLqaoR5tQtAxKgj5JQ4I4T1uSwiBghIHOkYFoUebED/VkIiIiCpj6GqGZFnClGEdEWTSIdtqQ6nDBUURKHW4kG21Icikw5RhHTlfFxER0XmEoauZurhTJJ65phe6xQajxOZETrENJTYnusUG45lrenGeLiIiovMMJ0dtxi7uFImLOrTijPRERETNAENXMyfLEnq1C/V3NYiIiKgWPL1IREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREREQaYOgiIiIi0gBDFxEREZEGGLqIiIiINMDQRURERKQBhi4iIiIiDTB0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREREQa8GvocrlceOqpp5CYmAiz2YyOHTviX//6F4QQahkhBGbMmIHY2FiYzWakpKTg8OHDXtvJy8vDuHHjEBISgrCwMNx+++0oLi72KvPLL79gyJAhCAgIQFxcHJ577rkq9Vm6dCmSkpIQEBCAXr164ZtvvvFaX5e6EBEREVXHr6HrP//5D+bOnYvXXnsNGRkZ+M9//oPnnnsOc+bMUcs899xzePXVVzFv3jxs2bIFgYGBSE1NRVlZmVpm3Lhx2LdvH1atWoWvvvoKGzZswJ133qmut1qtGD58OBISErBjxw48//zzmDVrFt588021zKZNmzB27Fjcfvvt+PnnnzFmzBiMGTMGe/furVddiIiIiKol/GjUqFHitttu81p27bXXinHjxgkhhFAURcTExIjnn39eXV9QUCBMJpP46KOPhBBC7N+/XwAQ27ZtU8t8++23QpIkceLECSGEEG+88YYIDw8XNptNLfPYY4+Jrl27qj/feOONYtSoUV51GTRokLjrrrvqXJfaFBYWCgCisLCwTuWJiIjI/3x1/PZrT9fFF1+M1atX49ChQwCA3bt348cff8TIkSMBAMeOHUN2djZSUlLU54SGhmLQoEHYvHkzAGDz5s0ICwvDgAED1DIpKSmQZRlbtmxRywwdOhRGo1Etk5qaioMHDyI/P18tU3E/njKe/dSlLpXZbDZYrVavBxEREf016f2588cffxxWqxVJSUnQ6XRwuVx4+umnMW7cOABAdnY2ACA6OtrredHR0eq67OxsREVFea3X6/WIiIjwKpOYmFhlG5514eHhyM7OrnU/tdWlsvT0dMyePbsOLUFEREQtnV97uj755BN88MEH+PDDD7Fz504sXrwYL7zwAhYvXuzPavnM9OnTUVhYqD6OHz/u7yoRERGRn/i1p+uRRx7B448/jptvvhkA0KtXL/z+++9IT09HWloaYmJiAACnTp1CbGys+rxTp06hT58+AICYmBjk5OR4bdfpdCIvL099fkxMDE6dOuVVxvNzbWUqrq+tLpWZTCaYTKa6NQYRERG1aH7t6SopKYEse1dBp9NBURQAQGJiImJiYrB69Wp1vdVqxZYtW5CcnAwASE5ORkFBAXbs2KGWWbNmDRRFwaBBg9QyGzZsgMPhUMusWrUKXbt2RXh4uFqm4n48ZTz7qUtdiIiIiGrko4H9DZKWlibatm0rvvrqK3Hs2DHx2WeficjISPHoo4+qZZ599lkRFhYmli1bJn755RcxevRokZiYKEpLS9UyI0aMEH379hVbtmwRP/74o+jcubMYO3asur6goEBER0eLW265Rezdu1csWbJEWCwWMX/+fLXMxo0bhV6vFy+88ILIyMgQM2fOFAaDQezZs6dedTkXXr1IRETU/Pjq+O3X0GW1WsX9998v4uPjRUBAgOjQoYP4xz/+4TW1g6Io4qmnnhLR0dHCZDKJK664Qhw8eNBrO2fOnBFjx44VQUFBIiQkREycOFEUFRV5ldm9e7e45JJLhMlkEm3bthXPPvtslfp88sknokuXLsJoNIoePXqIr7/+2mt9XepyLgxdREREzY+vjt+SEBWmf6cmZbVaERoaisLCQoSEhPi7OkRERFQHvjp+896LRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREREQaYOgiIiIi0gBDFxEREZEGGLqIiIiINMDQRURERKQBhi4iIiIiDTB0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREREQaYOgiIiIi0gBDFxEREZEGGLqIiIiINMDQRURERKQBhi4iIiIiDTB0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAb+HrhMnTmD8+PFo1aoVzGYzevXqhe3bt6vrhRCYMWMGYmNjYTabkZKSgsOHD3ttIy8vD+PGjUNISAjCwsJw++23o7i42KvML7/8giFDhiAgIABxcXF47rnnqtRl6dKlSEpKQkBAAHr16oVvvvnGa31d6kJERERUHb+Grvz8fAwePBgGgwHffvst9u/fjxdffBHh4eFqmeeeew6vvvoq5s2bhy1btiAwMBCpqakoKytTy4wbNw779u3DqlWr8NVXX2HDhg2488471fVWqxXDhw9HQkICduzYgeeffx6zZs3Cm2++qZbZtGkTxo4di9tvvx0///wzxowZgzFjxmDv3r31qgsRERFRtYQfPfbYY+KSSy6pcb2iKCImJkY8//zz6rKCggJhMpnERx99JIQQYv/+/QKA2LZtm1rm22+/FZIkiRMnTgghhHjjjTdEeHi4sNlsXvvu2rWr+vONN94oRo0a5bX/QYMGibvuuqvOdalNYWGhACAKCwvrVJ6IiIj8z1fHb7/2dH355ZcYMGAAbrjhBkRFRaFv375466231PXHjh1DdnY2UlJS1GWhoaEYNGgQNm/eDADYvHkzwsLCMGDAALVMSkoKZFnGli1b1DJDhw6F0WhUy6SmpuLgwYPIz89Xy1Tcj6eMZz91qUtlNpsNVqvV60FERER/TX4NXb/++ivmzp2Lzp07Y+XKlZgyZQruu+8+LF68GACQnZ0NAIiOjvZ6XnR0tLouOzsbUVFRXuv1ej0iIiK8ylS3jYr7qKlMxfW11aWy9PR0hIaGqo+4uLjamoSIiIhaKL+GLkVR0K9fPzzzzDPo27cv7rzzTtxxxx2YN2+eP6vlM9OnT0dhYaH6OH78uL+rRERERH7i19AVGxuL7t27ey3r1q0bMjMzAQAxMTEAgFOnTnmVOXXqlLouJiYGOTk5XuudTify8vK8ylS3jYr7qKlMxfW11aUyk8mEkJAQrwcRERH9Nfk1dA0ePBgHDx70Wnbo0CEkJCQAABITExETE4PVq1er661WK7Zs2YLk5GQAQHJyMgoKCrBjxw61zJo1a6AoCgYNGqSW2bBhAxwOh1pm1apV6Nq1q3qlZHJystd+PGU8+6lLXYiIiIhq5KOB/Q2ydetWodfrxdNPPy0OHz4sPvjgA2GxWMT777+vlnn22WdFWFiYWLZsmfjll1/E6NGjRWJioigtLVXLjBgxQvTt21ds2bJF/Pjjj6Jz585i7Nix6vqCggIRHR0tbrnlFrF3716xZMkSYbFYxPz589UyGzduFHq9XrzwwgsiIyNDzJw5UxgMBrFnz5561eVcePUiERFR8+Or47dfQ5cQQixfvlz07NlTmEwmkZSUJN58802v9YqiiKeeekpER0cLk8kkrrjiCnHw4EGvMmfOnBFjx44VQUFBIiQkREycOFEUFRV5ldm9e7e45JJLhMlkEm3bthXPPvtslbp88sknokuXLsJoNIoePXqIr7/+ut51OReGLiIioubHV8dvSQgh/NvX9tdhtVoRGhqKwsJCju8iIiJqJnx1/Pb7bYCIiIiI/goYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBvSNefKOHTuQkZEBAOjevTv69evnk0oRERERtTQNCl05OTm4+eabsW7dOoSFhQEACgoKcNlll2HJkiVo3bq1L+tIRERE1Ow16PTivffei6KiIuzbtw95eXnIy8vD3r17YbVacd999/m6jkRERETNXoNueB0aGorvv/8eF154odfyrVu3Yvjw4SgoKPBV/VoU3vCaiIio+fHrDa8VRYHBYKiy3GAwQFGUBleGiIiIqKVqUOi6/PLLcf/99+PkyZPqshMnTmDatGm44oorfFY5IiIiopaiQaHrtddeg9VqRfv27dGxY0d07NgRiYmJsFqtmDNnjq/rSERERNTsNejqxbi4OOzcuRPff/89Dhw4AADo1q0bUlJSfFo5IiIiopaiQQPpqWE4kJ6IiKj58dXxu8GTo549exbr169HZmYm7Ha71zpOG0FERETkrc6h63//+x+GDx+O4OBg/Pzzz7jqqqtQUlKCs2fPIiIiAqdPn4bFYkFUVBRDFxEREVEldR5Iv2vXLgwfPhwAMG3aNFx99dXIz8+H2WzGTz/9hN9//x39+/fHCy+80GSVJSIiImqu6hy6BgwYALPZDMAdwB566CHIsgydTgebzYa4uDg899xzeOKJJ5qsskRERETNVZ1PL95111346quvALgnQZVld16LiopCZmYmunXrhtDQUBw/frxpakpERETUjNU5dI0fPx533nkndu7cib59+2Lbtm3o3Lkzhg0bhhkzZuD06dN477330LNnz6asLxEREVGz1KApI7Zv346ioiJcdtllyMnJwa233opNmzahc+fOeOedd9CnT58mqGrzxykjiIiImh9fHb85T5eGGLqIiIiaH7/e8PrYsWM4fPhwleWHDx/Gb7/91uDKEBEREbVUDQpdEyZMwKZNm6os37JlCyZMmACXy4Xdu3ejtLS00RUkIiIiagkaNCP9zz//jMGDB1dZftFFF+HWW2/FxRdfjJycHLRp0wYbN25sdCWJiIiImrsGhS5JklBUVFRleWFhISRJwtq1a3Hy5EleyUhERERUrkGnF4cOHYr09HS4XC51mcvlQnp6OoYPHw6LxQKDwYDp06f7rKJEREREzVmDrl7cv38/hg4dirCwMAwZMgQA8MMPP8BqtWLNmjXs4aoBr14kIiJqfvx69WL37t3xyy+/4MYbb0ROTg6Kiopw66234sCBAwxcRERERNXgPF0aYk8XERFR8+Or43eDBtJ7lJSUIDMzE3a73Wv5BRdc0JjNEhEREbU4DQpdubm5mDhxIr799ttq11ccYE9EREREDRzT9cADD6CgoABbtmyB2WzGihUrsHjxYnTu3Blffvmlr+tIRERE1Ow1qKdrzZo1WLZsGQYMGABZlpGQkIArr7wSISEhSE9Px6hRo3xdTyIiIqJmrUE9XWfPnkVUVBQAIDw8HLm5uQCAXr16YefOnb6rHREREVEL0aDQ1bVrVxw8eBAA0Lt3b8yfPx8nTpzAvHnzEBsb69MKEhEREbUEDTq9eP/99yMrKwsAMHPmTIwYMQIffPABjEYjFi1a5Mv6EREREbUIPpmnq6SkBAcOHEB8fDwiIyN9Ua8WifN0ERERNT/nxTxdHhaLBf369fPFpoiIiIhapDqHrgcffLDOG33ppZcaVBkiIiKilqrOoevnn3+uUzlJkhpcGSIiIqKWqs6ha+3atU1ZDyIiIqIWrUFTRhARERFR/TRoIP1ll112ztOIa9asaXCFiIiIiFqiBoWuPn36eP3scDiwa9cu7N27F2lpab6oFxEREVGL0qDQ9d///rfa5bNmzUJxcXGjKkRERETUEvl0TNf48eOxYMECX26SiIiIqEXwaejavHkzAgICfLlJIiIiohahQacXr732Wq+fhRDIysrC9u3b8dRTT/mkYkREREQtSYNCV2hoqNfPsiyja9eu+Oc//4nhw4f7pGJERERELUmDQtfChQt9XQ8iIiKiFq1RN7zevn07MjIyAADdu3dH//79fVIpIiIiopamQaHrjz/+wNixY7Fx40aEhYUBAAoKCnDxxRdjyZIlaNeunS/rSERERNTsNejqxUmTJsHhcCAjIwN5eXnIy8tDRkYGFEXBpEmTfF1HIiIiomZPEkKI+j7JbDZj06ZN6Nu3r9fyHTt2YMiQISgpKfFZBVsSq9WK0NBQFBYWIiQkxN/VISIiojrw1fG7QT1dcXFxcDgcVZa7XC60adOmwZUhIiIiaqkaFLqef/553Hvvvdi+fbu6bPv27bj//vvxwgsv+KxyRERERC1Fg0LXhAkTsGvXLgwaNAgmkwkmkwmDBg3Czp07cdtttyEiIkJ91NWzzz4LSZLwwAMPqMvKysowdepUtGrVCkFBQbjuuutw6tQpr+dlZmZi1KhRsFgsiIqKwiOPPAKn0+lVZt26dejXrx9MJhM6deqERYsWVdn/66+/jvbt2yMgIACDBg3C1q1bvdbXpS5ERERENWnQ1Ysvv/yyTyuxbds2zJ8/HxdccIHX8mnTpuHrr7/G0qVLERoainvuuQfXXnstNm7cCMB9OnPUqFGIiYnBpk2bkJWVhVtvvRUGgwHPPPMMAODYsWMYNWoUJk+ejA8++ACrV6/GpEmTEBsbi9TUVADAxx9/jAcffBDz5s3DoEGD8PLLLyM1NRUHDx5EVFRUnepCREREdE7Cz4qKikTnzp3FqlWrxLBhw8T9998vhBCioKBAGAwGsXTpUrVsRkaGACA2b94shBDim2++EbIsi+zsbLXM3LlzRUhIiLDZbEIIIR599FHRo0cPr33edNNNIjU1Vf154MCBYurUqerPLpdLtGnTRqSnp9e5LnVRWFgoAIjCwsI6P4eIiIj8y1fH7wbf8NrlcuF///sf/v3vf+Pf//43Pv/8c7hcrnpvZ+rUqRg1ahRSUlK8lu/YsQMOh8NreVJSEuLj47F582YA7hts9+rVC9HR0WqZ1NRUWK1W7Nu3Ty1TedupqanqNux2O3bs2OFVRpZlpKSkqGXqUpfq2Gw2WK1WrwcRERH9NTXo9OKRI0dw1VVX4cSJE+jatSsAID09HXFxcfj666/RsWPHOm1nyZIl2LlzJ7Zt21ZlXXZ2NoxGozr5qkd0dDSys7PVMhUDl2e9Z925ylitVpSWliI/Px8ul6vaMgcOHKhzXaqTnp6O2bNn17ieiIiI/joa1NN13333oWPHjjh+/Dh27tyJnTt3IjMzE4mJibjvvvvqtI3jx4/j/vvvxwcffICAgICGVOO8N336dBQWFqqP48eP+7tKRERE5CcN6ulav349fvrpJ6+rE1u1aoVnn30WgwcPrtM2duzYgZycHPTr109d5nK5sGHDBrz22mtYuXIl7HY7CgoKvHqYTp06hZiYGABATExMlasMPVcUVixT+SrDU6dOISQkBGazGTqdDjqdrtoyFbdRW12q47myk4iIiKhBPV0mkwlFRUVVlhcXF8NoNNZpG1dccQX27NmDXbt2qY8BAwZg3Lhx6r8NBgNWr16tPufgwYPIzMxEcnIyACA5ORl79uxBTk6OWmbVqlUICQlB9+7d1TIVt+Ep49mG0WhE//79vcooioLVq1erZfr3719rXYiIiIjOqSGj72+55RbRo0cP8dNPPwlFUYSiKGLz5s2iZ8+eIi0trcGj+itevSiEEJMnTxbx8fFizZo1Yvv27SI5OVkkJyer651Op+jZs6cYPny42LVrl1ixYoVo3bq1mD59ulrm119/FRaLRTzyyCMiIyNDvP7660Kn04kVK1aoZZYsWSJMJpNYtGiR2L9/v7jzzjtFWFiY11WRtdWlLnj1IhERUfPjq+N3g0JXfn6+GD16tJBlWRiNRmE0GoUsy2LMmDGioKCgwZWpHLpKS0vF3XffLcLDw4XFYhHXXHONyMrK8nrOb7/9JkaOHCnMZrOIjIwUDz30kHA4HF5l1q5dK/r06SOMRqPo0KGDWLhwYZV9z5kzR8THxwuj0SgGDhwofvrpJ6/1dalLbRi6iIiImh9fHb/rdcNrRVHw/PPP48svv4Tdbkd8fDzS0tIgSRK6deuGTp06NVWHXIvAG14TERE1P746ftdrIP3TTz+NWbNmISUlBWazGd988w1CQ0OxYMGCBleAiIiI6K+gXgPp3333XbzxxhtYuXIlvvjiCyxfvhwffPABFEVpqvoRERERtQj1Cl2ZmZm46qqr1J9TUlIgSRJOnjzp84oRERERtST1Cl1Op7PKRKYGgwEOh8OnlSIiIiJqaeo1pksIgQkTJnhN+FlWVobJkycjMDBQXfbZZ5/5roZERERELUC9QldaWlqVZePHj/dZZYiIiIhaqnqFroULFzZVPYiIiIhatAbdBoiIiIiI6oehi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREREQaYOgiIiIi0gBDFxEREZEGGLqIiIiINMDQRURERKQBhi4iIiIiDTB0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREREQaYOgiIiIi0gBDFxEREZEGGLqIiIiINMDQRURERKQBhi4iIiIiDTB0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaUDv7woQNXeKIrDvpBV5JXZEWIzo0SYEsiz5u1pERHSeYegiaoRNR05j7vqjOJpTDIdLwKCT0DEqCFOGdcTFnSL9XT0iIjqP8PQiUQNtOnIaT3y+BxlZVgSa9IgKNiHQpEdGVhGe+HwPNh057e8qEhHReYShi6gBFEVg7vqjKLY5ERMSgACDDrIsIcCgQ0yICcU2F+auPwpFEf6uKhERnScYuogaYN9JK47mFCPcYoQkeY/fkiQJYRYDjuYUY99Jq59qSERE5xu/hq709HRceOGFCA4ORlRUFMaMGYODBw96lSkrK8PUqVPRqlUrBAUF4brrrsOpU6e8ymRmZmLUqFGwWCyIiorCI488AqfT6VVm3bp16NevH0wmEzp16oRFixZVqc/rr7+O9u3bIyAgAIMGDcLWrVvrXRf6a8grscPhEjDqqv8ImXQyHIpAXold45oREdH5yq+ha/369Zg6dSp++uknrFq1Cg6HA8OHD8fZs2fVMtOmTcPy5cuxdOlSrF+/HidPnsS1116rrne5XBg1ahTsdjs2bdqExYsXY9GiRZgxY4Za5tixYxg1ahQuu+wy7Nq1Cw888AAmTZqElStXqmU+/vhjPPjgg5g5cyZ27tyJ3r17IzU1FTk5OXWuC/11RFiMMOgk2F1KtettLgUGWUKExahxzYiI6HwlCSHOm0Enubm5iIqKwvr16zF06FAUFhaidevW+PDDD3H99dcDAA4cOIBu3bph8+bNuOiii/Dtt9/ib3/7G06ePIno6GgAwLx58/DYY48hNzcXRqMRjz32GL7++mvs3btX3dfNN9+MgoICrFixAgAwaNAgXHjhhXjttdcAAIqiIC4uDvfeey8ef/zxOtWlNlarFaGhoSgsLERISIhP2460pSgCaQu3IiOrCDEhJq9TjEIIZFtt6BYbjMUTB3L6CCKiZs5Xx+/zakxXYWEhACAiIgIAsGPHDjgcDqSkpKhlkpKSEB8fj82bNwMANm/ejF69eqmBCwBSU1NhtVqxb98+tUzFbXjKeLZht9uxY8cOrzKyLCMlJUUtU5e60F+HLEuYMqwjgkw6ZFttKHW4oCgCpQ4Xsq02BJl0mDKsIwMXERGpzpvQpSgKHnjgAQwePBg9e/YEAGRnZ8NoNCIsLMyrbHR0NLKzs9UyFQOXZ71n3bnKWK1WlJaW4vTp03C5XNWWqbiN2upSmc1mg9Vq9XpQy3Fxp0g8c00vdIsNRonNiZxiG0psTnSLDcYz1/TiPF1EROTlvJkcderUqdi7dy9+/PFHf1fFZ9LT0zF79mx/V4Oa0MWdInFRh1ackZ6IiGp1XvR03XPPPfjqq6+wdu1atGvXTl0eExMDu92OgoICr/KnTp1CTEyMWqbyFYSen2srExISArPZjMjISOh0umrLVNxGbXWpbPr06SgsLFQfx48fr0NrUHMjyxJ6tQvFsC6t0atdKAMXERFVy6+hSwiBe+65B59//jnWrFmDxMREr/X9+/eHwWDA6tWr1WUHDx5EZmYmkpOTAQDJycnYs2eP11WGq1atQkhICLp3766WqbgNTxnPNoxGI/r37+9VRlEUrF69Wi1Tl7pUZjKZEBIS4vUgIiKivyjhR1OmTBGhoaFi3bp1IisrS32UlJSoZSZPnizi4+PFmjVrxPbt20VycrJITk5W1zudTtGzZ08xfPhwsWvXLrFixQrRunVrMX36dLXMr7/+KiwWi3jkkUdERkaGeP3114VOpxMrVqxQyyxZskSYTCaxaNEisX//fnHnnXeKsLAwkZ2dXee61KawsFAAEIWFhQ1tMiIiItKYr47ffg1dAKp9LFy4UC1TWloq7r77bhEeHi4sFou45pprRFZWltd2fvvtNzFy5EhhNptFZGSkeOihh4TD4fAqs3btWtGnTx9hNBpFhw4dvPbhMWfOHBEfHy+MRqMYOHCg+Omnn7zW16Uu58LQRURE1Pz46vh9Xs3T1dJxni4iIqLmp0XO00VERETUUjF0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREREQaYOgiIiIi0gBDFxEREZEGGLqIiIiINMDQRURERKQBhi4iIiIiDTB0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGtD7uwJEWlIUgX0nrcgrsSPCYkSPNiGQZcnf1fKZlv76iIiaM4Yu+svYdOQ05q4/iqM5xXC4BAw6CR2jgjBlWEdc3CnS39VrtJb++oiImjueXqS/hE1HTuOJz/cgI8uKQJMeUcEmBJr0yMgqwhOf78GmI6f9XcVGacmvT1EE9vxRiPWHcrHnj0IoivB3lYiIGoQ9XdTiKYrA3PVHUWxzIiYkAJLkPt0WIOsQEyIj22rD3PVHcVGHVs3yVFxLfn2bjpzGG+uO4mB2EewuBUadjK4xwbj7UvbeEVHzw54uavH2nbTiaE4xwi1GNZB4SJKEMIsBR3OKse+k1U81bJyW+vo2HTmNaZ/swpZjZ5BfYkNxmQP5JTZsOXYG0z7Z1ax774jor4mhi1q8vBI7HC4Bo676t7tJJ8OhCOSV2DWpj69Pl51vr88XFEUg/dsM5BbZoCgCOlmGQS9DJ8tQFIHcIhvSv83gqUYialZ4epFavAiLEQadBLtLQYCsq7Le5lJgkCVEWIxNXpemGOzuj9fX1FdJ7jlRiEOniiEBMOhlSHBvW5LcPzucCg6dKsaeE4XoHRfms/0SETUlhi5q8Xq0CUHHqCBkZBUhJkT2OgUnhEBBiQPdYoPRo01Ik9bDM9i92OZEuMUIo06G3aWog92fuaZXg4KX1q9Pi6skd2UWwOFSoJclNXB5SJCgkyU4XAp2ZRa0qNDFKT+IWjaGLmrxZFnClGEd8cTne5BttSHMYoBJJ8PmUlBQ4kCQSYcpwzo26cGtKQe7N/T11eUAX7lMYakdT36x1+fBsTLhqUZNTSFVKtcCcMoPopaPoYv+Ei7uFIlnrumlHtQKFQGDLKFbbLAmB7X6DHbv1S603tuv7+urywG+ujIlDhcURSA+wtKkV0n2jQuDXpbhcimQdaJK753LJaCXZfRtZr1cNQXdpuoFbUydiMj3GLroL+PiTpG4qEMrvxxg6jLYvbCRg93r+vrqcoAHUKWMtcwBa6kDOlnCWbsLQaY/vz58ERwr6tU2FF1jgrDvpBUORYFeliEBEACcigIBoGtMEHq1bdx+tOSe/uIIDmQXweEUMOglJMUEY/Kwjpi/4Ve/TPnB3jUibTF00V+KLEuNDgQNodVg99peX11Oc76x7igAUaWMTnaPrlKEQG5RGQKNgV49UL4IjhVfx/SR3TDtk13IO2uHq8JVirIkISLIiOkjuzWbHhnP9Bd5Z+0QnpdiB7Ycy8P+LCskSIgIbJpe0HPVyV+9a0R/VZwygkgDnsHu+SUOCOE9zYFnsHvHqKAmH8xfl9OcB7OLcCC7qEoZvSxDliXIkgSbU0GZQ/F6vq+vkry4UyT+e2MfDEqMQJjFgECTHmEWAwYlRuC/N/ZpNoHAa/oLIaCTJehl98UAihDIL3GgoNQOg676ANkUU35UDt8BBh1kWUKAQYeYEBOKbS7MXX+UU3KcB3hHhpaFPV1EGjgfBvMDdTvNaXcpgECVMgFGGSa9DqV2JyRJglNRALh77epylWRDxg7585Swr+w5UYiD2eXTX8h/Xl3q+dnuUuBSgFPWMoRbTAgweF+B2hRTfvhijCHHgjU9nv5teRi6iDTi78H8QN1Ocxp1MgRElTISJLQONuF4nguKEHAqAooi6hQcG3Pw8NcpYV/5+XgBnIoCvU6qEnAUQD3deOasA9YyJ0x6Ga2DAxBk0jfZlCaNHWPIMNA0KgbZ43kleGvDUZy1u3j6twVh6CLSkL97buoyp1dSTDAAgQPZxVXKBBp1CDTpIEkSXC4FOcW2WoPjX33skOQ5GyQABQJCuCd5FQJwuNwXBQCALLkPuqV2F07klyAy2AS7U1QJs77oYarrGMO8YjvWH8o9b660bMkqB1lrmQOKEGgbZkaAwf07agn3U/2rY+gi0pg/e27qcprz7ks7AkCNZcItRvx7TE+Emo21Hvib+mbcvjrFVXk73WKCkZFd5JNg3Cc+DDpZgt0lIEGguhE5EoCY0ABYS50oczjhcAmcLrJjYGI47r600zmn8WhID1Nt4Tu3qAyKAvzrq30QAMwGHTpGBeGuoR2a5ErL2n6PLflUpqIIfLg1E3PWHIbNoSAy2AhFAfJLbBACOFlQhrbhknq1cFNeXFHferfU30lTYugi+oup62lOX5wKbczYodq+1H0VQCpvRxECLiGgkwBZkht96qyozKHO8VrTEGhJco+hax9pQZldQYndHbweSU1SZ9z3ZQ/TucJ3VkEpSuwudznJXbcyh4Jf/ijAI5/+ArtTUa+0FBAosyvu06eyjDCLvt5hoGL7lzpckCQJkUEmjO7TBkM7t0ZhqR3zN/yqrpclCXERFjw8vAsu6dy6TvtoCr4IHZ5pRLYey4fDpUAnA05FlAcsCXqd++fKVwv78krhhuDp5YaTROVLqajJWK1WhIaGorCwECEhTXuVGlFtGjIjfX0PLOsP5eLhT3YjKthUY09YTrENL9zQG8O6/HkAre1LvaYAkl/eW1fXAFJ5O3aXghP5pXApAnqdhDahZhj1srrduvbweTidCq6fvxmHsq2wuYTX1BcVyZL76tC24WYEmfRV2kVRBNIWbkVGltWrhwkAFKHgREEZ2oaZ8cw1vdAjNqTOvXRe7Vw+Ri//rB0KAL0MyOXzo7kUUX4fTAkOl0DnqCCUOhTkFtlgc7rUU6ZGnQy9TsarY/uqv89zvYc87Z9fYofdKWBzulCxiSwGGQoAnSRBEYDD5d6XAGDUy3joyi64Y2jHP9tCo94XX4QOz2svKLGj2OaCLLvHTboUAUkCFAHoPaeUhUBCRCDMRvdpxlKHCyU2J+bfMqDef6z44rU39rPXHHvJfHX8Zk8X0V9UXU5zNvZUaEPmJ6utR+ffY3r65BRX5VOfAPBHfikU4T6IuoTAmbM2tG8ViJgQE47nl+Cej35GgE5CmVNAloD4VoE19rhsOnIaz608iD1/FLgTSY39XIBeJ8PlEsgqLEXbMLP7xt4V2qWmHsNimxO5RTaUOZzIyHLg1ne2QJKlOvfSVRxjeKbYhie/2IMz5eucCgBFUeuiCMCluNstt9iGwhKnOgWGJLvHqJU5XBAOF9Zk5Ki3jfL0UlUOJxd1aIW5648iv8SOEpv74ozKmbSkwrQkOsndTlL52DebQ8HzKw/B4RIYUqlHrLG9L3UJio3pcaz43gs1G1FsK4UM94UWkg5wukT570BAX962nquFz3VxRXVhMCokAKk9YnBJp8hGhxtfDBf4q/eSsadLQ+zpalma419rWlMUgVsXbMXek4UIDdDDoNMhwChDggQhBLKtNnSLDcbiiQMhy9I5e3Q85duFm3GqsBRBAQZ1gHFF5+oFqGjPH4W4673tCDTp4VQEsgvLUOpwn1bz7FWWJSS2CoRLCBzPK4FLETDqZSiKKL/yUFTb4+I5MOedteOszQm9ztNT4/661ZX3ZHi+fD0D6z3rZNk9W/2yqZcAAN7b/Dte+v4QWgUaYTHpIEFCsc2JE+UhUZbcB2h3O6HaXrrawsD7P/2OGcv2Vgk+HnrZ/TuTpPJ6C8Cgl9UbkjsVRX19ehkICTCgxOGCSS8jKjigSo/IpCEd8MbaI8gvccDhUtTQ5fmVVz4ySQCMegmyJMMlBBxO90UIFfdl1MmIDqm6r/qcej1XKLioQ6ta358V3881qfjeEwL4Pe8sZMk9Bx4A9epgz8UVkiQhPsICWZbUsZfPXNPL66Kcylc72l0KcqzunkgACLcY0L1NaKPCTcV6N+Sz56se6uo09fcxe7qI/Kil/rXm6y+un349g8JSO4rKnCgscUAnAya9DmEWI2xOpcqVeXUZA+YJPxGBjbulkmfaBLtLQVZBWXlPArzGX7kUgaIy91QOnlDjcCkw6GTo4J7yweZQ8OKqQ+gWG4JLOrf26g1oHWQqD3KeaCLU/7rH75Tvq0LA8Ow3p8iGd378FRsOn0ZGlhVFZQ6ctTkQYNCjdbARuUV2KMJ9GtSlKFCEe7Zrvc79/Iq9dLX1QCiKwEdbM2sMXMCfoS6gfOwXhLtHRpZEeUhwl5PhDpRFNiecLnc4CjQ5YQo0evWILNmaiVKHCw6XexyXopS3fQ11EADsTgGdrMCl/HlBgiKAs3YX7E4FiuIOKwEGqUGD+2vrxZo0pMM535+hZj0ysqx4b/Pv6JcQ7vX5qfjZ+i33rDplhyQBJr2MUocCg+zejiS52yLCYkBeiQMSJFjLHDDqZHVcJQCkLdxa7dWOTkUgq6DM/f6Q3b22pXYF+09aG3WFaWOmGmnKi2qa0/cxQxe1eL4OEi31knnPF9eRU0UodSi1nj6ry/Y87RQTYkJBiQM2p4IShwtl1jJ0jw3G9JHdvNqqLl/qnp6dxt5SyXPqM8dqKz+lKMPuSQ4VzgbmFNm8coCnh0qWJOggAToFdqeCF747hIsSW2H5L1nYf9IKi1EHs1EHk16HModLvY2SKN9GTQHHpNchOsSE/BI7Xlx1CBaDDItRD4NOhsOp4KzN6Q5yojy4ORU4y7elABDujg2UOlwocygwG3W1Xu2276QVp6xl52wvjzLnn6f8nNW8CAXuF6mU93q5BJBVUAZrqROtg00IKr+zQLa1DC4BKArUazprO+0iatineyC/e11WYSnahpphNurqdbFGmNmAN9adOxR8tDUTdqeCcEvV92exzYkcq7u39KXvD8GokxEVYsLYgfFo38ridTGAogh3z5xeQkSgCa2DA3AivxQO9XSiACBgc7lD1KQhHRAXYVG/v3769YzXd5BLERWudix19xqXB3IJEiAU2F0KQnR6FJQ68Ma6hoWbxtzOzBcT8lanuX0fM3RRi+brv4CaegoEX6pP2Kx4OszpEu7TPQB++aMAk97dXuX0WV32Xbmdwi1GlDkUOFwKCksdCDUbcFGHVl7PO9eXuoD7r3n31W1G5J21V3uKp6DEjnbhFpw+a8OePwprfN3dYoIRFGBAVmEZdBV6GBT38a7CfqtyOBUIzwFNkiBB4GhuMa6fvxmZZ84iv9SBwlKUnzbRw+5UahxEX5FBB8SGmuAS7isGbU4FEoAimw2u8lOaANRtuVxVtyHUMkCRzeEOfjX0QDidCpbtPomv95xEUZmj1vo1lABQanfhj/wStAo0wWLUAcI975u1tOH7VXslFcBV/rsrcyj4Pa8EAQb3JLMWg67a1175u0FAoKjMicggU42hIMdqA6oJ/J5Tva7y3lKbw4WzNgdOF9swY9le6GQJRp0ERUhwuFxQFHc4PVFQBqdLICokAG3CAnDKaoPd5e6xM+pl9GwTirsv9f6uqu6z5f7dlV/t6BLqd50EqfxUrDvW5hTZIEvAtt/y8OHWTIy/KKFe3xN1meevpol8Gzshb8XXX5+gfL58H3swdFGL1RR/ATXVX2u+Vp+w6fkSzztrR5nDBVf5VVM1nT6ri+raSZIkmI06mKGDQS/j19yzVdqppi/1ir0IOtn9l3uJQ8Hx/BK0Dg5QpzvILSqDzangeN5ZPLr0lxpft6d9sgtLy3tPAJfi7t2rjae3yj1+6c8gVWxz4tfcYoSaDSgqcwKSe2C5Z4qFYpsTZ23OGrcHuIPS8fxSKArgKj/nWFbe69iQW+5ZS51oHSSq7YF4a8NRvLL6MIpt1SS3JqAIAZfLfbsjnQTodDIidI27tZFUftWn59ZVFZU63FeiRgYb63SxRn6JvXwcVBmMelmdF8vDVB4WYkICkG21qe9Pz7xmLuE+xYvy8XU6WYZOFrA5BZTyICRLgEEnl4cjBU4FOFVkg0tRUOpQYHe6P3+yBMSFmzF5WIcqn9fqPlt6WS4fCyeVjw8UgASvsW+Ae6wfhDs0zll9GKV2JzYcPl3nP0obczuzxvSSVfy91Tconw/fxxXxhtfUIjXVDX3r8tear29OXF+eA0pGlhWBJj2igk0INOnVsLnpyGmv8vtOWnHkVBGcLgGXcF+pJkvuK6l05VfAeU6f1bW9GtpOni/1IJMO2VYbSh0uWMscOJ5XghK7O3C1CTWjVVAAjDodbE4F+WftyCm2If+sHTan+zZGEYGmGl93xfYJNRvcc1GhfCxV+QGv8jGj4o+ipuUCCDTpEWo2wGSQoZSf/lOEQLHNiegQk7ovWfrzy1dU2I6neaUKe/HUqyGcioJSu6vKDdXf2nAUz357QLPABfz5OgXcYd6lCJwqKkNIgB51yLpVSHBPJSHL3sskADpZgkGW4BLuaS06tA5UX3tN3w0Wox768tNyuUW2KtPY2lwKDDoJNw+M93p/ltjdp3FF+Qt0j6vzDIqv+srcSyUYdDroy+t++qwDJXYXJMnd+xcbakZBqRNPfrG3yue1us+W576orvLB94C7989Z6Y4Hnisk9bL7QowXVx2q8/eEh2eev26xwSixOZFTbEOJzYluscHn/EPW8wdVfokDla/f8/SSVXyPVlbd95rnD+kcaxmKq/mD5nz4Pq6MoaueXn/9dbRv3x4BAQEYNGgQtm7d6u8qUTXq0yNVHxX/WqtOU9ycuD4aEjbzSuwoLT/tp5er3h9QLh+LdDyvpM7t1Zh2qvylfrLAfYWexahDu3ALQszuqxbjI8wINOoRF2HBf67thbgICwKNOsRHWGp83U6n4tU+oRYDLEZ9eQ+E+8Bk0stoG2pyX0UI92zscREB1QaDylnIWuoAJKB1cAB0kgSn4plc1InCUgcUARh0EuJbWZDYOhABBlmdgNRDalAEqUpf3juWW2z36oFwOhW8vvaoGuQkQB243dT+DAASIgKN6kUDrYPr/3kx6WW0DjGpc6qpPYYVQjTKx86N6Blb68UaAQYZpvIUVOZwosz+53u3Yij4+8B4r/fnmbPuCxqMOhmyJLmntfBcNlHpDeKZY8xDV2H/MSEmtG8VhMTWgYgINNb4ea3us+W5L6osSeqcak5FVAjx7t4wlC93n+oUsDsVhJZfBVyfP0ov7hSJxRMHYv4tA/DCDb0x/5YBWDxx4DnPHFT3B5WiCJQ6XMi22s7ZS9aooOzH7+PqMHTVw8cff4wHH3wQM2fOxM6dO9G7d2+kpqYiJyfH31WjSpqqR6qxf601tYaEzQiL0X36CtUfeD0TXyqi7u3V2HbyfKk/kpqEYJMebcPMSIwM9DrlI0kSwgONyLGWobDUffoxIvDcpxmW/5Ll1T6eg5VOliGEBJ0swekSsLnKB8vLEmJCAxBqNiEi0FBtXT1708vu0zZldgVBJj3ahpthNsjqgd99+kZG62ATgk3usBcbaoZcPvGnZ1sCQh0Y3xjusWkCHVoHevVALP8lC9by8VtSxRdQQ+rydRiTAEQFmxASYIBOkmBzumDU62CQJVT3cZUqPPSy5/8SLEadO8SUX7XpCa+yJJUHDoEAgw5BJncw96jpu0GSJHdYliW4FKDE7qwxFFQMHdOu7IJWgSb3LP2oFKArNV75r0TliU2yBJgM7gsvPIGtps9rTZ+tIJMebcICIMuSOqmqp/3cf0wBDkVAJ0kItRjhdLnDmavS57Ouf5R65vAb1qU1erULrdOYqYb2kjU2KPvr+7g6DF318NJLL+GOO+7AxIkT0b17d8ybNw8WiwULFizwd9WokqbqkWrMX2taaEjY7NEmBPGtAiGEgFLpL0UB9yzqBp0OZoOuzu3li3aSZQkRQUboZBkhAYYqYari6zlRUFKn111dOU9ACjC4J550CQGnS0GI2YBAkw6B5bOABwf8eSrSw6Bz9w7oZHd9/5zE0r3d9q0CER0agDCzATP+1h0DE8Nhcwr1YBlk0qNVkLHKacqG0Mvu3h+jXlJDSa92Yfj0rmSvg9mJgpIa91FNE0OW3KfrPMGmoQyy+/kWo4yIQKN6wHQp7rnJTAbdOaeLgFQesGQJtyYnoEfb0ArzewmYjXq0b2VBYqtAtAs3IyEiELFhAQg0er9vz/XdEGTSIzLY5L5S1CXOGQo8oePWixKQFBuMEocLKL+heaWaqyqGMiH+vEOBjD97oiqq7vN6rs9Wsc2FtmFmzPy/Hrg1OQF6WYIsea6YFTAb3Hc9MOrcM/17xsTVZb++0pBeMl8E5fMFB9LXkd1ux44dOzB9+nR1mSzLSElJwebNm6t9js1mg81mU3+2Wut3KosarjFX2dSmrvcu9IeGDFaVZQkPD++CSe9uh82hADpFDRCeUxVGvVTvvxh90U51fT1twyyNKhdk0iPQpENhiXtszZOjuqN1sBFPfrFXHTDsmbzSMwt7qyAjgk0GmPQSfs8rQWn5mJzKB7Eyh4IebUNxTd+2iA0NqDII2VA+V5NOAiKDAiBL7sHmnqkl6spzgs3lEpDL6/doalfo9d71aRtm8ZqMtSYB5VNVKEIg0KhDjtXmdQVmfQb362VAgTsMRoWY1c9jqMWAMqeCwjInQs0GlNm9x+VUvMhAwp+Txj45qjsAYM+JQjzx+R6cKChF27AAyJLnteq8Jiut+L6t7bvB7hQYmBiOR1KTUFDqqPWKPk8Imv75Hpy1OeF0Ke4B63CfXtbLkvc0F8J9Fapn8lMhAIPePWlwZTX9cViXz5aiuK+o3XvCPXbRoJMRYHC/3hKbs3xi3/rt11fqe6eLc30PeILy6SK7GpTPl+/j6jB01dHp06fhcrkQHR3ttTw6OhoHDhyo9jnp6emYPXu2FtWjShpzlU1dVLx9yvk0I31Dw+YlnVvjoSu74MVVh2B3KpAU98zjBp0ORr17uoeGtFdj26mur+fqC2Lx2c9/NKochPuKt+5tQjC6TxvIsuR1YLO7FPcFBjqgbZgZwQF/nm6MDDIhM6/EPXhact/DsLr3WnUHS70EhJgNEEIgMsg9rYZc3j0hROVRKjVThIDT+WcwqTwHmsfVF8Ri9vJ9KCh1uAe3VxjJ7wliEoD+8eGYcmlHdX4pi0nvvrrVMzGpEOrpQE+nUXUXHHhm3zcbZESFBKiniD0Bp3tsMELNRvyaW4zAAANKbE44ysO+59Y/gSYdXAIIMxswfWQ39f3TOy4M/7iqG574fA9OWe11+pzX5bvh7ks7qTcar4uLO0Ui/ZpeSP82A/uzimB3uW+YHqCXEWoxqGEeAJwCkMvHgOllCQ5FgVEveV9Rgdr/OKztsyXLEu6+tFP5VZouhFlk962anC4UlDpgLO8Vre9+/cHXQdmfeBugOjp58iTatm2LTZs2ITk5WV3+6KOPYv369diyZUuV51TX0xUXF8fbAGmo8g19DfL5O1Oxr/x5Obyr2gPKucZO/Hg4Fy98dwjH80rKT0fo/N5edX09vi7nUXFeoON5JXjrh19xtprn6mT3WKUzxfZa32uV50YqLLXjyS/2ltdJj6yCMvd9DFF18LWHTnZPV+C57U9ogAHRoQEYOzAefx8Yf84DjufqxZquigy3GPD63/upPSYV50UC3Kd7Cs46EG4xIDzQiF/+KMDH244ju7BUHTcUF2HB9f3bodjmxLubf3dPKhporLa9K4aHMLMBv/xRgCXbjrvnxQJqncqgIZ/zpvhuUBSBD7dmYsnWTGSXTzZr1MnoGBWESzpF4tu92cg8cxYKALNeRqfoYAztHIkPtmQ26PNaFzW9zqber6815nvNF3x1GyCGrjqy2+2wWCz49NNPMWbMGHV5WloaCgoKsGzZslq3wXsv+sdf8R6JjTmgnI/tVdfX4+ty9a1LY3r1Km73rN2FYpsTsgREBhkhhITc4jJ1/FNkkAlGvYzS8vsN3pLcvt43NK5uni4JQPtIC54eU/8DWG03ia5ve9f3fdiQ921Tvddr2m5Ny5v6j0N/7dfX/Flfhi4/GDRoEAYOHIg5c+YAABRFQXx8PO655x48/vjjtT6foYu0dD6Gp8ao6+vxdbnG1KUxr/F4XglW7M3Cr7ln4VAEFEWBS3jmppJ8csDxzEj/8/ECBBp0GNErBr3bhTXJ+6SlvR99zV/t09x+L/6qL0OXH3z88cdIS0vD/PnzMXDgQLz88sv45JNPcODAgSpjvarD0EVE9VH5ANMtJhgZ2UXN5gBJ1FL46vjNgfT1cNNNNyE3NxczZsxAdnY2+vTpgxUrVtQpcBER1Vd1V3mdL7czIaL6Y0+XhtjTRURE1Pz46vjNyVGJiIiINMDQRURERKQBhi4iIiIiDTB0EREREWmAoYuIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQY4I72GPPPQWq1WP9eEiIiI6spz3G7sfPIMXRoqKioCAMTFxfm5JkRERFRfRUVFCA1t+K24eBsgDSmKgpMnTyI4OBiS9Ne4Sa3VakVcXByOHz/OWx/5GNu2abBdmw7btumwbZuGp10zMzMhSRLatGkDWW74yCz2dGlIlmW0a9fO39Xwi5CQEH4RNBG2bdNguzYdtm3TYds2jdDQUJ+0KwfSExEREWmAoYuIiIhIAwxd1KRMJhNmzpwJk8nk76q0OGzbpsF2bTps26bDtm0avm5XDqQnIiIi0gB7uoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChixotPT0dF154IYKDgxEVFYUxY8bg4MGDXmXKysowdepUtGrVCkFBQbjuuutw6tQpP9W4+Zg7dy4uuOACdcLD5ORkfPvtt+p6tqtvPPvss5AkCQ888IC6jG3bMLNmzYIkSV6PpKQkdT3btXFOnDiB8ePHo1WrVjCbzejVqxe2b9+urhdCYMaMGYiNjYXZbEZKSgoOHz7sxxo3D+3bt6/yvpUkCVOnTgXgu/ctQxc12vr16zF16lT89NNPWLVqFRwOB4YPH46zZ8+qZaZNm4bly5dj6dKlWL9+PU6ePIlrr73Wj7VuHtq1a4dnn30WO3bswPbt23H55Zdj9OjR2LdvHwC2qy9s27YN8+fPxwUXXOC1nG3bcD169EBWVpb6+PHHH9V1bNeGy8/Px+DBg2EwGPDtt99i//79ePHFFxEeHq6Wee655/Dqq69i3rx52LJlCwIDA5GamoqysjI/1vz8t23bNq/37KpVqwAAN9xwAwAfvm8FkY/l5OQIAGL9+vVCCCEKCgqEwWAQS5cuVctkZGQIAGLz5s3+qmazFR4eLt5++222qw8UFRWJzp07i1WrVolhw4aJ+++/XwjB92xjzJw5U/Tu3bvadWzXxnnsscfEJZdcUuN6RVFETEyMeP7559VlBQUFwmQyiY8++kiLKrYY999/v+jYsaNQFMWn71v2dJHPFRYWAgAiIiIAADt27IDD4UBKSopaJikpCfHx8di8ebNf6tgcuVwuLFmyBGfPnkVycjLb1QemTp2KUaNGebUhwPdsYx0+fBht2rRBhw4dMG7cOGRmZgJguzbWl19+iQEDBuCGG25AVFQU+vbti7feektdf+zYMWRnZ3u1b2hoKAYNGsT2rQe73Y73338ft912GyRJ8un7lqGLfEpRFDzwwAMYPHgwevbsCQDIzs6G0WhEWFiYV9no6GhkZ2f7oZbNy549exAUFASTyYTJkyfj888/R/fu3dmujbRkyRLs3LkT6enpVdaxbRtu0KBBWLRoEVasWIG5c+fi2LFjGDJkCIqKitiujfTrr79i7ty56Ny5M1auXIkpU6bgvvvuw+LFiwFAbcPo6Giv57F96+eLL75AQUEBJkyYAMC33wd6H9WRCIC752Dv3r1eYziocbp27Ypdu3ahsLAQn376KdLS0rB+/Xp/V6tZO378OO6//36sWrUKAQEB/q5OizJy5Ej13xdccAEGDRqEhIQEfPLJJzCbzX6sWfOnKAoGDBiAZ555BgDQt29f7N27F/PmzUNaWpqfa9dyvPPOOxg5ciTatGnj822zp4t85p577sFXX32FtWvXol27durymJgY2O12FBQUeJU/deoUYmJiNK5l82M0GtGpUyf0798f6enp6N27N1555RW2ayPs2LEDOTk56NevH/R6PfR6PdavX49XX30Ver0e0dHRbFsfCQsLQ5cuXXDkyBG+ZxspNjYW3bt391rWrVs39fStpw0rX1XH9q2733//Hd9//z0mTZqkLvPl+5ahixpNCIF77rkHn3/+OdasWYPExESv9f3794fBYMDq1avVZQcPHkRmZiaSk5O1rm6zpygKbDYb27URrrjiCuzZswe7du1SHwMGDMC4cePUf7NtfaO4uBhHjx5FbGws37ONNHjw4CrT8Rw6dAgJCQkAgMTERMTExHi1r9VqxZYtW9i+dbRw4UJERUVh1KhR6jKfvm99POCf/oKmTJkiQkNDxbp160RWVpb6KCkpUctMnjxZxMfHizVr1ojt27eL5ORkkZyc7MdaNw+PP/64WL9+vTh27Jj45ZdfxOOPPy4kSRLfffedEILt6ksVr14Ugm3bUA899JBYt26dOHbsmNi4caNISUkRkZGRIicnRwjBdm2MrVu3Cr1eL55++mlx+PBh8cEHHwiLxSLef/99tcyzzz4rwsLCxLJly8Qvv/wiRo8eLRITE0Vpaakfa948uFwuER8fLx577LEq63z1vmXookYDUO1j4cKFapnS0lJx9913i/DwcGGxWMQ111wjsrKy/FfpZuK2224TCQkJwmg0itatW4srrrhCDVxCsF19qXLoYts2zE033SRiY2OF0WgUbdu2FTfddJM4cuSIup7t2jjLly8XPXv2FCaTSSQlJYk333zTa72iKOKpp54S0dHRwmQyiSuuuEIcPHjQT7VtXlauXCkAVNtevnrfSkII0ej+OCIiIiI6J47pIiIiItIAQxcRERGRBhi6iIiIiDTA0EVERESkAYYuIiIiIg0wdBERERFpgKGLiIiISAMMXUREFUyYMAFjxozxax1mzZqFPn36+LUOROR7DF1EpAlJks75mDVrlr+r6DOXXnqp+roCAgLQvXt3vPHGG/6uFhH5md7fFSCiv4asrCz13x9//DFmzJjhdfPeoKAgf1Srydxxxx345z//iZKSErz77ruYOnUqwsPDMXbs2CbZn91uh9FobJJtE5FvsKeLiDQRExOjPkJDQyFJkvrz2bNnMW7cOERHRyMoKAgXXnghvv/+e/W5Bw4cgMViwYcffqgu++STT2A2m7F//34AwLZt23DllVciMjISoaGhGDZsGHbu3HnOOrlcLjz44IMICwtDq1at8Oijj6LyndEURUF6ejoSExNhNpvRu3dvfPrpp7W+XovFgpiYGHTo0AGzZs1C586d8eWXXwIAMjMzMXr0aAQFBSEkJAQ33ngjTp06VWUb8+fPR1xcHCwWC2688UYUFhaq6zynQZ9++mm0adMGXbt2BQAcP34cN954I8LCwhAREYHRo0fjt99+U59XXS9j+/bt1fa4/fbb1dfatWtXvPLKK7W+ViKqG4YuIvK74uJiXHXVVVi9ejV+/vlnjBgxAldffTUyMzMBAElJSXjhhRdw9913IzMzE3/88QcmT56M//znP+jevTsAoKioCGlpafjxxx/x008/oXPnzrjqqqtQVFRU435ffPFFLFq0CAsWLMCPP/6IvLw8fP75515l0tPT8e6772LevHnYt28fpk2bhvHjx2P9+vX1eo1msxl2ux2KomD06NHIy8vD+vXrsWrVKvz666+46aabvMofOXIEn3zyCZYvX44VK1bg559/xt133+1VZvXq1Th48CBWrVqFr776Cg6HA6mpqQgODsYPP/yAjRs3IigoCCNGjIDdbgfg7nH0PI4cOYJOnTph6NChANwBs127dli6dCn279+PGTNm4IknnsAnn3xSr9dKRDVo9G25iYjqaeHChSI0NPScZXr06CHmzJnjtWzUqFFiyJAh4oorrhDDhw8XiqLU+HyXyyWCg4PF8uXLaywTGxsrnnvuOfVnh8Mh2rVrJ0aPHi2EEKKsrExYLBaxadMmr+fdfvvtYuzYsTVud9iwYeL+++8XQgjhdDrFe++9JwCI1157TXz33XdCp9OJzMxMtfy+ffsEALF161YhhBAzZ84UOp1O/PHHH2qZb7/9VsiyLLKysoQQQqSlpYno6Ghhs9nUMu+9957o2rWrV7vYbDZhNpvFypUrveqoKIq45pprRP/+/UVJSUmNr2Xq1Kniuuuuq3E9EdUdx3QRkd8VFxdj1qxZ+Prrr5GVlQWn04nS0lK1p8tjwYIF6NKlC2RZxr59+yBJkrru1KlTePLJJ7Fu3Trk5OTA5XKhpKSkyjY8CgsLkZWVhUGDBqnL9Ho9BgwYoJ5iPHLkCEpKSnDllVd6Pddut6Nv377nfE1vvPEG3n77bdjtduh0OkybNg1TpkzBa6+9hri4OMTFxallu3fvjrCwMGRkZODCCy8EAMTHx6Nt27ZqmeTkZCiKgoMHDyImJgYA0KtXL69xXLt378aRI0cQHBzsVZeysjIcPXrUa9kTTzyBzZs3Y/v27TCbzery119/HQsWLEBmZiZKS0tht9t5JSWRjzB0EZHfPfzww1i1ahVeeOEFdOrUCWazGddff716Ssxj9+7dOHv2LGRZRlZWFmJjY9V1aWlpOHPmDF555RUkJCTAZDIhOTm5yjbqo7i4GADw9ddfewUgADCZTOd87rhx4/CPf/wDZrMZsbGxkGXfj+YIDAz0+rm4uBj9+/fHBx98UKVs69at1X+///77+O9//4t169Z5va4lS5bg4Ycfxosvvojk5GQEBwfj+eefx5YtW3xed6K/IoYuIvK7jRs3YsKECbjmmmsAuMNDxcHfAJCXl4cJEybgH//4B7KysjBu3Djs3LlT7aXZuHEj3njjDVx11VUA3APKT58+XeM+Q0NDERsbiy1btqhjmpxOJ3bs2IF+/foBcPdAmUwmZGZmYtiwYfV6TaGhoejUqVOV5d26dcPx48dx/Phxtbdr//79KCgoUMenAe7B9idPnkSbNm0AAD/99BNkWVYHzFenX79++PjjjxEVFYWQkJBqy2zevBmTJk3C/PnzcdFFF3mt27hxIy6++GKvsWOVe8iIqOE4kJ6I/K5z58747LPPsGvXLuzevRt///vfoSiKV5nJkycjLi4OTz75JF566SW4XC48/PDDXtt47733kJGRgS1btmDcuHFep82qc//99+PZZ5/FF198gQMHDuDuu+9GQUGBuj44OBgPP/wwpk2bhsWLF+Po0aPYuXMn5syZg8WLFzfotaakpKBXr15qaNy6dStuvfVWDBs2DAMGDFDLBQQEIC0tDbt378YPP/yA++67DzfeeKN6arE648aNQ2RkJEaPHo0ffvgBx44dw7p163Dffffhjz/+QHZ2Nq655hrcfPPNSE1NRXZ2NrKzs5Gbm6u24fbt27Fy5UocOnQITz31FLZt29ag10lEVTF0EZHfvfTSSwgPD8fFF1+Mq6++GqmpqWpvEwC8++67+Oabb/Dee+9Br9cjMDAQ77//Pt566y18++23AIB33nkH+fn56NevH2655Rbcd999iIqKOud+H3roIdxyyy1IS0tTT6d5ets8/vWvf+Gpp55Ceno6unXrhhEjRuDrr79GYmJig16rJElYtmwZwsPDMXToUKSkpKBDhw74+OOPvcp16tQJ1157La666ioMHz4cF1xwQa0TrFosFmzYsAHx8fG49tpr0a1bN9x+++0oKytDSEgIDhw4gFOnTmHx4sWIjY1VH55xZHfddReuvfZa3HTTTRg0aBDOnDlT5YpJImo4SYhKk9IQERERkc+xp4uIiIhIAwxdRERERBpg6CIiIiLSAEMXERERkQYYuoiIiIg0wNBFREREpAGGLiIiIiINMHQRERERaYChi4iIiEgDDF1EREREGmDoIiIiItIAQxcRERGRBv4fil8Ns8lPSJEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alsem_mcz = al[(al.poptot <= 900000)]\n",
        "alsem_mcz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OUg0-PAEyBZR",
        "outputId": "fff767d4-8460-4e76-b99b-18c16d0fcd2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun                   munic  analf  pobre     rpc   agua  \\\n",
              "7214  2010  27  2700102             ÁGUA BRANCA  30.92  52.87  213.00  61.78   \n",
              "7215  2010  27  2700201                  ANADIA  36.16  47.59  239.53  61.62   \n",
              "7216  2010  27  2700300               ARAPIRACA  22.45  26.65  423.28  94.68   \n",
              "7217  2010  27  2700409                 ATALAIA  33.57  42.73  237.33  82.49   \n",
              "7218  2010  27  2700508  BARRA DE SANTO ANTÔNIO  27.94  44.24  247.81  91.16   \n",
              "...    ...  ..      ...                     ...    ...    ...     ...    ...   \n",
              "7311  2010  27  2709103               TAQUARANA  37.95  49.78  215.16  74.19   \n",
              "7312  2010  27  2709152         TEOTÔNIO VILELA  28.87  42.79  244.39  83.78   \n",
              "7313  2010  27  2709202                  TRAIPU  42.69  63.39  178.79  37.18   \n",
              "7314  2010  27  2709301      UNIÃO DOS PALMARES  31.62  36.82  313.92  90.57   \n",
              "7315  2010  27  2709400                  VIÇOSA  33.47  50.85  251.25  83.52   \n",
              "\n",
              "       lixo  esgag_inad  poprur  poptot  popurb sitanalf fxpop  \n",
              "7214  97.94       36.29   14276   19377    5101  regular     2  \n",
              "7215  99.15       11.06    8475   17424    8949     ruim     2  \n",
              "7216  97.38       12.38   32525  214006  181481      bom     5  \n",
              "7217  95.31       18.19   21865   44322   22457     ruim     3  \n",
              "7218  93.66        2.74     988   14230   13242  regular     2  \n",
              "...     ...         ...     ...     ...     ...      ...   ...  \n",
              "7311  95.94       34.31   11706   19020    7314     ruim     2  \n",
              "7312  98.38        8.06    6367   41152   34785  regular     3  \n",
              "7313  82.18       38.60   17675   25702    8027     ruim     3  \n",
              "7314  97.46        5.68   14707   62358   47651  regular     4  \n",
              "7315  97.40        5.87    7094   25407   18313     ruim     3  \n",
              "\n",
              "[101 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-21ed3f06-55b2-41c1-b694-0f0ea7dcb95d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>munic</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "      <th>sitanalf</th>\n",
              "      <th>fxpop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7214</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700102</td>\n",
              "      <td>ÁGUA BRANCA</td>\n",
              "      <td>30.92</td>\n",
              "      <td>52.87</td>\n",
              "      <td>213.00</td>\n",
              "      <td>61.78</td>\n",
              "      <td>97.94</td>\n",
              "      <td>36.29</td>\n",
              "      <td>14276</td>\n",
              "      <td>19377</td>\n",
              "      <td>5101</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700201</td>\n",
              "      <td>ANADIA</td>\n",
              "      <td>36.16</td>\n",
              "      <td>47.59</td>\n",
              "      <td>239.53</td>\n",
              "      <td>61.62</td>\n",
              "      <td>99.15</td>\n",
              "      <td>11.06</td>\n",
              "      <td>8475</td>\n",
              "      <td>17424</td>\n",
              "      <td>8949</td>\n",
              "      <td>ruim</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7216</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700300</td>\n",
              "      <td>ARAPIRACA</td>\n",
              "      <td>22.45</td>\n",
              "      <td>26.65</td>\n",
              "      <td>423.28</td>\n",
              "      <td>94.68</td>\n",
              "      <td>97.38</td>\n",
              "      <td>12.38</td>\n",
              "      <td>32525</td>\n",
              "      <td>214006</td>\n",
              "      <td>181481</td>\n",
              "      <td>bom</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700409</td>\n",
              "      <td>ATALAIA</td>\n",
              "      <td>33.57</td>\n",
              "      <td>42.73</td>\n",
              "      <td>237.33</td>\n",
              "      <td>82.49</td>\n",
              "      <td>95.31</td>\n",
              "      <td>18.19</td>\n",
              "      <td>21865</td>\n",
              "      <td>44322</td>\n",
              "      <td>22457</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7218</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700508</td>\n",
              "      <td>BARRA DE SANTO ANTÔNIO</td>\n",
              "      <td>27.94</td>\n",
              "      <td>44.24</td>\n",
              "      <td>247.81</td>\n",
              "      <td>91.16</td>\n",
              "      <td>93.66</td>\n",
              "      <td>2.74</td>\n",
              "      <td>988</td>\n",
              "      <td>14230</td>\n",
              "      <td>13242</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7311</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709103</td>\n",
              "      <td>TAQUARANA</td>\n",
              "      <td>37.95</td>\n",
              "      <td>49.78</td>\n",
              "      <td>215.16</td>\n",
              "      <td>74.19</td>\n",
              "      <td>95.94</td>\n",
              "      <td>34.31</td>\n",
              "      <td>11706</td>\n",
              "      <td>19020</td>\n",
              "      <td>7314</td>\n",
              "      <td>ruim</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7312</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709152</td>\n",
              "      <td>TEOTÔNIO VILELA</td>\n",
              "      <td>28.87</td>\n",
              "      <td>42.79</td>\n",
              "      <td>244.39</td>\n",
              "      <td>83.78</td>\n",
              "      <td>98.38</td>\n",
              "      <td>8.06</td>\n",
              "      <td>6367</td>\n",
              "      <td>41152</td>\n",
              "      <td>34785</td>\n",
              "      <td>regular</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7313</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709202</td>\n",
              "      <td>TRAIPU</td>\n",
              "      <td>42.69</td>\n",
              "      <td>63.39</td>\n",
              "      <td>178.79</td>\n",
              "      <td>37.18</td>\n",
              "      <td>82.18</td>\n",
              "      <td>38.60</td>\n",
              "      <td>17675</td>\n",
              "      <td>25702</td>\n",
              "      <td>8027</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7314</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709301</td>\n",
              "      <td>UNIÃO DOS PALMARES</td>\n",
              "      <td>31.62</td>\n",
              "      <td>36.82</td>\n",
              "      <td>313.92</td>\n",
              "      <td>90.57</td>\n",
              "      <td>97.46</td>\n",
              "      <td>5.68</td>\n",
              "      <td>14707</td>\n",
              "      <td>62358</td>\n",
              "      <td>47651</td>\n",
              "      <td>regular</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7315</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709400</td>\n",
              "      <td>VIÇOSA</td>\n",
              "      <td>33.47</td>\n",
              "      <td>50.85</td>\n",
              "      <td>251.25</td>\n",
              "      <td>83.52</td>\n",
              "      <td>97.40</td>\n",
              "      <td>5.87</td>\n",
              "      <td>7094</td>\n",
              "      <td>25407</td>\n",
              "      <td>18313</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>101 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21ed3f06-55b2-41c1-b694-0f0ea7dcb95d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pobregsem_mcz = sns.regplot(x=alsem_mcz.pobre,\n",
        "            y=alsem_mcz.poptot,\n",
        "            data=alsem_mcz,\n",
        "            fit_reg=False)\n",
        "pobregsem_mcz.set_title('População x Pobreza - Alagoas (sem Maceió)')\n",
        "pobregsem_mcz.set_xlabel('Taxa de Pobreza')\n",
        "pobregsem_mcz.set_ylabel('População')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "nZ0z3aq-yCO4",
        "outputId": "b48b6298-ee3f-4931-fc51-6a7207bacc8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'População')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alsem_ara_e_mcz = al[(al.poptot <= 200000)]\n",
        "alsem_ara_e_mcz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "T1Xy2OrbxE17",
        "outputId": "4923267f-5c24-48f8-d148-b247c89a068e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun                   munic  analf  pobre     rpc   agua  \\\n",
              "7214  2010  27  2700102             ÁGUA BRANCA  30.92  52.87  213.00  61.78   \n",
              "7215  2010  27  2700201                  ANADIA  36.16  47.59  239.53  61.62   \n",
              "7217  2010  27  2700409                 ATALAIA  33.57  42.73  237.33  82.49   \n",
              "7218  2010  27  2700508  BARRA DE SANTO ANTÔNIO  27.94  44.24  247.81  91.16   \n",
              "7219  2010  27  2700607     BARRA DE SÃO MIGUEL  23.71  23.89  422.78  90.49   \n",
              "...    ...  ..      ...                     ...    ...    ...     ...    ...   \n",
              "7311  2010  27  2709103               TAQUARANA  37.95  49.78  215.16  74.19   \n",
              "7312  2010  27  2709152         TEOTÔNIO VILELA  28.87  42.79  244.39  83.78   \n",
              "7313  2010  27  2709202                  TRAIPU  42.69  63.39  178.79  37.18   \n",
              "7314  2010  27  2709301      UNIÃO DOS PALMARES  31.62  36.82  313.92  90.57   \n",
              "7315  2010  27  2709400                  VIÇOSA  33.47  50.85  251.25  83.52   \n",
              "\n",
              "        lixo  esgag_inad  poprur  poptot  popurb sitanalf fxpop  \n",
              "7214   97.94       36.29   14276   19377    5101  regular     2  \n",
              "7215   99.15       11.06    8475   17424    8949     ruim     2  \n",
              "7217   95.31       18.19   21865   44322   22457     ruim     3  \n",
              "7218   93.66        2.74     988   14230   13242  regular     2  \n",
              "7219  100.00        1.50    1053    7574    6521  regular     2  \n",
              "...      ...         ...     ...     ...     ...      ...   ...  \n",
              "7311   95.94       34.31   11706   19020    7314     ruim     2  \n",
              "7312   98.38        8.06    6367   41152   34785  regular     3  \n",
              "7313   82.18       38.60   17675   25702    8027     ruim     3  \n",
              "7314   97.46        5.68   14707   62358   47651  regular     4  \n",
              "7315   97.40        5.87    7094   25407   18313     ruim     3  \n",
              "\n",
              "[100 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5f52797-7b74-45a5-95a2-4ff0c3470b0a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>munic</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "      <th>sitanalf</th>\n",
              "      <th>fxpop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7214</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700102</td>\n",
              "      <td>ÁGUA BRANCA</td>\n",
              "      <td>30.92</td>\n",
              "      <td>52.87</td>\n",
              "      <td>213.00</td>\n",
              "      <td>61.78</td>\n",
              "      <td>97.94</td>\n",
              "      <td>36.29</td>\n",
              "      <td>14276</td>\n",
              "      <td>19377</td>\n",
              "      <td>5101</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700201</td>\n",
              "      <td>ANADIA</td>\n",
              "      <td>36.16</td>\n",
              "      <td>47.59</td>\n",
              "      <td>239.53</td>\n",
              "      <td>61.62</td>\n",
              "      <td>99.15</td>\n",
              "      <td>11.06</td>\n",
              "      <td>8475</td>\n",
              "      <td>17424</td>\n",
              "      <td>8949</td>\n",
              "      <td>ruim</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700409</td>\n",
              "      <td>ATALAIA</td>\n",
              "      <td>33.57</td>\n",
              "      <td>42.73</td>\n",
              "      <td>237.33</td>\n",
              "      <td>82.49</td>\n",
              "      <td>95.31</td>\n",
              "      <td>18.19</td>\n",
              "      <td>21865</td>\n",
              "      <td>44322</td>\n",
              "      <td>22457</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7218</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700508</td>\n",
              "      <td>BARRA DE SANTO ANTÔNIO</td>\n",
              "      <td>27.94</td>\n",
              "      <td>44.24</td>\n",
              "      <td>247.81</td>\n",
              "      <td>91.16</td>\n",
              "      <td>93.66</td>\n",
              "      <td>2.74</td>\n",
              "      <td>988</td>\n",
              "      <td>14230</td>\n",
              "      <td>13242</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7219</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700607</td>\n",
              "      <td>BARRA DE SÃO MIGUEL</td>\n",
              "      <td>23.71</td>\n",
              "      <td>23.89</td>\n",
              "      <td>422.78</td>\n",
              "      <td>90.49</td>\n",
              "      <td>100.00</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1053</td>\n",
              "      <td>7574</td>\n",
              "      <td>6521</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7311</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709103</td>\n",
              "      <td>TAQUARANA</td>\n",
              "      <td>37.95</td>\n",
              "      <td>49.78</td>\n",
              "      <td>215.16</td>\n",
              "      <td>74.19</td>\n",
              "      <td>95.94</td>\n",
              "      <td>34.31</td>\n",
              "      <td>11706</td>\n",
              "      <td>19020</td>\n",
              "      <td>7314</td>\n",
              "      <td>ruim</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7312</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709152</td>\n",
              "      <td>TEOTÔNIO VILELA</td>\n",
              "      <td>28.87</td>\n",
              "      <td>42.79</td>\n",
              "      <td>244.39</td>\n",
              "      <td>83.78</td>\n",
              "      <td>98.38</td>\n",
              "      <td>8.06</td>\n",
              "      <td>6367</td>\n",
              "      <td>41152</td>\n",
              "      <td>34785</td>\n",
              "      <td>regular</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7313</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709202</td>\n",
              "      <td>TRAIPU</td>\n",
              "      <td>42.69</td>\n",
              "      <td>63.39</td>\n",
              "      <td>178.79</td>\n",
              "      <td>37.18</td>\n",
              "      <td>82.18</td>\n",
              "      <td>38.60</td>\n",
              "      <td>17675</td>\n",
              "      <td>25702</td>\n",
              "      <td>8027</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7314</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709301</td>\n",
              "      <td>UNIÃO DOS PALMARES</td>\n",
              "      <td>31.62</td>\n",
              "      <td>36.82</td>\n",
              "      <td>313.92</td>\n",
              "      <td>90.57</td>\n",
              "      <td>97.46</td>\n",
              "      <td>5.68</td>\n",
              "      <td>14707</td>\n",
              "      <td>62358</td>\n",
              "      <td>47651</td>\n",
              "      <td>regular</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7315</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2709400</td>\n",
              "      <td>VIÇOSA</td>\n",
              "      <td>33.47</td>\n",
              "      <td>50.85</td>\n",
              "      <td>251.25</td>\n",
              "      <td>83.52</td>\n",
              "      <td>97.40</td>\n",
              "      <td>5.87</td>\n",
              "      <td>7094</td>\n",
              "      <td>25407</td>\n",
              "      <td>18313</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5f52797-7b74-45a5-95a2-4ff0c3470b0a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c5f52797-7b74-45a5-95a2-4ff0c3470b0a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c5f52797-7b74-45a5-95a2-4ff0c3470b0a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pobregsem_ara_e_mcz = sns.regplot(x=alsem_ara_e_mcz.pobre,\n",
        "            y=alsem_ara_e_mcz.poptot,\n",
        "            data=alsem_ara_e_mcz,\n",
        "            fit_reg=False)\n",
        "pobregsem_ara_e_mcz.set_title('População x Pobreza - Alagoas (sem Arapiraca e Maceió)')\n",
        "pobregsem_ara_e_mcz.set_xlabel('Taxa de Pobreza')\n",
        "pobregsem_ara_e_mcz.set_ylabel('População')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "QnaT4vCwRAIF",
        "outputId": "94ebce1a-63aa-4b1e-c67b-c64adc640b47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'População')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Questão 2 h) Box plot de pobreza por faixa populacional"
      ],
      "metadata": {
        "id": "RHeko_PGMcxX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "boxplot = sns.boxplot(x='fxpop', y='pobre', data=al)\n",
        "boxplot.set_xlabel('Faixa populacional')\n",
        "boxplot.set_ylabel('Taxa de Pobreza')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "_XVi8XhGMcXE",
        "outputId": "4fc5ddf3-5d0f-4bff-9d6a-cadcfd279288"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Taxa de Pobreza')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 2) i) Criação de variável de urbanização"
      ],
      "metadata": {
        "id": "RXkUl9pvSbr0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "al['urb'] = (al.popurb/al.poptot)*100\n",
        "al['rur'] = (al.poprur/al.poptot)*100\n",
        "al.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "NcqTxMeVSbYD",
        "outputId": "a3c7c07d-89db-4b0f-f277-a34e49ff201d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ano  uf   codmun                   munic  analf  pobre     rpc   agua  \\\n",
              "7214  2010  27  2700102             ÁGUA BRANCA  30.92  52.87  213.00  61.78   \n",
              "7215  2010  27  2700201                  ANADIA  36.16  47.59  239.53  61.62   \n",
              "7216  2010  27  2700300               ARAPIRACA  22.45  26.65  423.28  94.68   \n",
              "7217  2010  27  2700409                 ATALAIA  33.57  42.73  237.33  82.49   \n",
              "7218  2010  27  2700508  BARRA DE SANTO ANTÔNIO  27.94  44.24  247.81  91.16   \n",
              "\n",
              "       lixo  esgag_inad  poprur  poptot  popurb sitanalf fxpop        urb  \\\n",
              "7214  97.94       36.29   14276   19377    5101  regular     2  26.325025   \n",
              "7215  99.15       11.06    8475   17424    8949     ruim     2  51.360193   \n",
              "7216  97.38       12.38   32525  214006  181481      bom     5  84.801828   \n",
              "7217  95.31       18.19   21865   44322   22457     ruim     3  50.667840   \n",
              "7218  93.66        2.74     988   14230   13242  regular     2  93.056922   \n",
              "\n",
              "            rur  \n",
              "7214  73.674975  \n",
              "7215  48.639807  \n",
              "7216  15.198172  \n",
              "7217  49.332160  \n",
              "7218   6.943078  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25292664-8e86-48be-9887-8e27a40492e7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ano</th>\n",
              "      <th>uf</th>\n",
              "      <th>codmun</th>\n",
              "      <th>munic</th>\n",
              "      <th>analf</th>\n",
              "      <th>pobre</th>\n",
              "      <th>rpc</th>\n",
              "      <th>agua</th>\n",
              "      <th>lixo</th>\n",
              "      <th>esgag_inad</th>\n",
              "      <th>poprur</th>\n",
              "      <th>poptot</th>\n",
              "      <th>popurb</th>\n",
              "      <th>sitanalf</th>\n",
              "      <th>fxpop</th>\n",
              "      <th>urb</th>\n",
              "      <th>rur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7214</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700102</td>\n",
              "      <td>ÁGUA BRANCA</td>\n",
              "      <td>30.92</td>\n",
              "      <td>52.87</td>\n",
              "      <td>213.00</td>\n",
              "      <td>61.78</td>\n",
              "      <td>97.94</td>\n",
              "      <td>36.29</td>\n",
              "      <td>14276</td>\n",
              "      <td>19377</td>\n",
              "      <td>5101</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
              "      <td>26.325025</td>\n",
              "      <td>73.674975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700201</td>\n",
              "      <td>ANADIA</td>\n",
              "      <td>36.16</td>\n",
              "      <td>47.59</td>\n",
              "      <td>239.53</td>\n",
              "      <td>61.62</td>\n",
              "      <td>99.15</td>\n",
              "      <td>11.06</td>\n",
              "      <td>8475</td>\n",
              "      <td>17424</td>\n",
              "      <td>8949</td>\n",
              "      <td>ruim</td>\n",
              "      <td>2</td>\n",
              "      <td>51.360193</td>\n",
              "      <td>48.639807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7216</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700300</td>\n",
              "      <td>ARAPIRACA</td>\n",
              "      <td>22.45</td>\n",
              "      <td>26.65</td>\n",
              "      <td>423.28</td>\n",
              "      <td>94.68</td>\n",
              "      <td>97.38</td>\n",
              "      <td>12.38</td>\n",
              "      <td>32525</td>\n",
              "      <td>214006</td>\n",
              "      <td>181481</td>\n",
              "      <td>bom</td>\n",
              "      <td>5</td>\n",
              "      <td>84.801828</td>\n",
              "      <td>15.198172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7217</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700409</td>\n",
              "      <td>ATALAIA</td>\n",
              "      <td>33.57</td>\n",
              "      <td>42.73</td>\n",
              "      <td>237.33</td>\n",
              "      <td>82.49</td>\n",
              "      <td>95.31</td>\n",
              "      <td>18.19</td>\n",
              "      <td>21865</td>\n",
              "      <td>44322</td>\n",
              "      <td>22457</td>\n",
              "      <td>ruim</td>\n",
              "      <td>3</td>\n",
              "      <td>50.667840</td>\n",
              "      <td>49.332160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7218</th>\n",
              "      <td>2010</td>\n",
              "      <td>27</td>\n",
              "      <td>2700508</td>\n",
              "      <td>BARRA DE SANTO ANTÔNIO</td>\n",
              "      <td>27.94</td>\n",
              "      <td>44.24</td>\n",
              "      <td>247.81</td>\n",
              "      <td>91.16</td>\n",
              "      <td>93.66</td>\n",
              "      <td>2.74</td>\n",
              "      <td>988</td>\n",
              "      <td>14230</td>\n",
              "      <td>13242</td>\n",
              "      <td>regular</td>\n",
              "      <td>2</td>\n",
              "      <td>93.056922</td>\n",
              "      <td>6.943078</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25292664-8e86-48be-9887-8e27a40492e7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-25292664-8e86-48be-9887-8e27a40492e7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-25292664-8e86-48be-9887-8e27a40492e7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 2 letra j) Regressão com pobre como variável dependete\n"
      ],
      "metadata": {
        "id": "-rfly6tHTXBO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definindo as variáveis dependentes e independetes\n",
        "y = al.pobre\n",
        "x = al.urb\n",
        "x = sm.add_constant(x)"
      ],
      "metadata": {
        "id": "DoL_C0enTeX3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg = sm.OLS(y, x).fit()\n",
        "print(reg.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrKJ0iTsTWmU",
        "outputId": "f5888bf5-7fa0-4141-e81b-3609c15e78d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  pobre   R-squared:                       0.552\n",
            "Model:                            OLS   Adj. R-squared:                  0.547\n",
            "Method:                 Least Squares   F-statistic:                     123.1\n",
            "Date:                Sun, 11 Jun 2023   Prob (F-statistic):           4.00e-19\n",
            "Time:                        05:14:45   Log-Likelihood:                -337.10\n",
            "No. Observations:                 102   AIC:                             678.2\n",
            "Df Residuals:                     100   BIC:                             683.4\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         64.2892      1.781     36.102      0.000      60.756      67.822\n",
            "urb           -0.3233      0.029    -11.095      0.000      -0.381      -0.265\n",
            "==============================================================================\n",
            "Omnibus:                        0.802   Durbin-Watson:                   2.029\n",
            "Prob(Omnibus):                  0.670   Jarque-Bera (JB):                0.825\n",
            "Skew:                          -0.206   Prob(JB):                        0.662\n",
            "Kurtosis:                       2.841   Cond. No.                         165.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questão 2 k) Gerando os resíduos e fazendo o teste de Jarque-Bera"
      ],
      "metadata": {
        "id": "n7My92kQUeQA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# k.1) Gerando os resíduos\n",
        "resid = reg.resid\n",
        "plt.hist(resid, bins=8, align='left', histtype='bar', edgecolor='black')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "RUA4b1ViUlTz",
        "outputId": "6b854c13-e44c-49e0-c444-eb6cd036e332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 6.,  7., 14., 24., 26., 18.,  6.,  1.]),\n",
              " array([-16.41437623, -12.12628864,  -7.83820106,  -3.55011347,\n",
              "          0.73797411,   5.02606169,   9.31414928,  13.60223686,\n",
              "         17.89032445]),\n",
              " <BarContainer object of 8 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sm.qqplot(resid, line='s');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "JVXIBLfDkFBD",
        "outputId": "546dc4a9-a6e0-4a96-b3d3-e43b8416de30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "jb_value, p_value, skew, kurtosis = sm.stats.jarque_bera(resid)\n",
        "jb = np.array([jb_value, p_value, skew, kurtosis])\n",
        "jb[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCbnUPTUWZuQ",
        "outputId": "0ddb3ed9-06d7-49c4-9cd5-c2fcbb1c29dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8252003188849905"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# k.2) Teste de Jarque-Bera\n",
        "jbresult = pd.DataFrame({\"Estatística de Jarque-Bera\": jb[0], \"Valor p\": jb[1]}, index=[0])\n",
        "jbresult"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "WWHKY3DqVROe",
        "outputId": "745c590e-b44d-4f52-ce70-2204c83c3cce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Estatística de Jarque-Bera   Valor p\n",
              "0                      0.8252  0.661927"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ced85c2-52e4-4009-a5af-e2d475b982c5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Estatística de Jarque-Bera</th>\n",
              "      <th>Valor p</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8252</td>\n",
              "      <td>0.661927</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ced85c2-52e4-4009-a5af-e2d475b982c5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9ced85c2-52e4-4009-a5af-e2d475b982c5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9ced85c2-52e4-4009-a5af-e2d475b982c5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    }
  ]
}