Correlação_Regressão_Linear.ipynb

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        "<a href=\"https://colab.research.google.com/github/vaguiarfinance/AprendizadoDNC/blob/main/Correla%C3%A7%C3%A3o_Regress%C3%A3o_Linear.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Correlação\n",
        "\n",
        "## Regressão Linear Simples"
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm"
      ]
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        "dataset = {\"tempo\":[1,3,4,4,6,8,10,10,11,13],\n",
        "           \"vendas\":[91,110,106,116,119,129,139,143,138,159]\n",
        "}\n",
        "\n",
        "dataset = pd.DataFrame(dataset)\n",
        "dataset.head()"
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              "   tempo  vendas\n",
              "0      1      91\n",
              "1      3     110\n",
              "2      4     106\n",
              "3      4     116\n",
              "4      6     119"
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      "source": [
        "from pandas.io.sql import DatabaseError\n",
        "ax = sns.lmplot(x=\"tempo\", y=\"vendas\", data=dataset)\n",
        "ax.fig.set_size_inches(8,4)\n",
        "ax.fig.suptitle(\"Reta de regressão - Tempo X Venda\", fontsize=16, y=1.02)\n",
        "ax.set_xlabels(\"Tempo de experiência\", fontsize=14)\n",
        "ax.set_ylabels(\"Vendas\", fontsize=14)"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "AyFG2QTrteD5",
        "outputId": "31bb8b58-d68e-438b-bb44-6b49a854f16c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           tempo    vendas\n",
              "tempo   1.000000  0.976883\n",
              "vendas  0.976883  1.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c77f9993-3b4b-4b40-a227-fe5e5e8794e3\">\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>tempo</th>\n",
              "      <th>vendas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>tempo</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.976883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vendas</th>\n",
              "      <td>0.976883</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c77f9993-3b4b-4b40-a227-fe5e5e8794e3')\"\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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              "    <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",
              "\n",
              "\n",
              "    <div id=\"df-89ba93fd-37a7-4c7d-8828-19a348388789\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-89ba93fd-37a7-4c7d-8828-19a348388789')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-89ba93fd-37a7-4c7d-8828-19a348388789 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-c77f9993-3b4b-4b40-a227-fe5e5e8794e3 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-c77f9993-3b4b-4b40-a227-fe5e5e8794e3');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = dataset.vendas\n",
        "X = sm.add_constant(dataset.tempo)"
      ],
      "metadata": {
        "id": "NCKAkUk4tqS-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resultado_regressão = sm.OLS(Y,X).fit()"
      ],
      "metadata": {
        "id": "XA8_VenWuEsx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(resultado_regressão.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icK9sUkouOuE",
        "outputId": "a53cf1b0-28af-4261-89f0-7c0e48ad3870"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                 vendas   R-squared:                       0.954\n",
            "Model:                            OLS   Adj. R-squared:                  0.949\n",
            "Method:                 Least Squares   F-statistic:                     167.1\n",
            "Date:                Tue, 01 Aug 2023   Prob (F-statistic):           1.22e-06\n",
            "Time:                        18:28:32   Log-Likelihood:                -28.355\n",
            "No. Observations:                  10   AIC:                             60.71\n",
            "Df Residuals:                       8   BIC:                             61.32\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         90.0000      3.075     29.265      0.000      82.908      97.092\n",
            "tempo          5.0000      0.387     12.925      0.000       4.108       5.892\n",
            "==============================================================================\n",
            "Omnibus:                        0.928   Durbin-Watson:                   3.224\n",
            "Prob(Omnibus):                  0.629   Jarque-Bera (JB):                0.616\n",
            "Skew:                          -0.060   Prob(JB):                        0.735\n",
            "Kurtosis:                       1.790   Cond. No.                         17.0\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
            "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"Y_previsto\"] = resultado_regressão.predict()\n",
        "dataset.head()"
      ],
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      },
      "execution_count": null,
      "outputs": [
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              "   tempo  vendas  Y_previsto\n",
              "0      1      91        95.0\n",
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              "   tempo  vendas  Y_previsto  residuo\n",
              "0      1      91        95.0      4.0\n",
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              "\n",
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              "          document.querySelector('#df-4dafa146-5a0e-4453-a108-3b91802fe876 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-4dafa146-5a0e-4453-a108-3b91802fe876');\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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CASE_base de Aprovação dos Alunos"
      ],
      "metadata": {
        "id": "iORhCFTby23o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alunos = pd.read_excel(\"base_aprovacao_alunos.xlsx\")\n",
        "alunos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GPJdAzd5yvgx",
        "outputId": "56df9335-cef6-434f-d242-e7a61285154e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         UF  ano_2015  ano_2016\n",
              "0  Rondonia        77        85\n",
              "1      Acre        79        81\n",
              "2  Amazonas        83        85\n",
              "3   Roraima        81        82\n",
              "4      Para        73        76"
            ],
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              "      <td>81</td>\n",
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              "      <td>Amazonas</td>\n",
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              "      <td>85</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Roraima</td>\n",
              "      <td>81</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Para</td>\n",
              "      <td>73</td>\n",
              "      <td>76</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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.lmplot(x=\"ano_2015\", y=\"ano_2016\", data=alunos)\n",
        "ax.fig.set_size_inches(8,4)\n",
        "ax.fig.suptitle(\"Reta de Regressão 2015 x 2016\")\n",
        "ax.set_xlabels(\"2015\")\n",
        "ax.set_ylabels(\"2016\")\n",
        "ax;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "FWhu8jxuzb8M",
        "outputId": "adf9c933-44c9-4a86-df6b-ee5f71c70a5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alunos.corr()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "lC2DzpOs0W-G",
        "outputId": "0ba26a4e-ae19-4e28-bc02-bc1fbc50325e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-8d6a2a5b8151>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  alunos.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ano_2015  ano_2016\n",
              "ano_2015  1.000000  0.894957\n",
              "ano_2016  0.894957  1.000000"
            ],
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-3ab13ab0-4bf7-4fe4-b759-19b81642f053 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-3ab13ab0-4bf7-4fe4-b759-19b81642f053');\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": "code",
      "source": [
        "Y = alunos.ano_2016\n",
        "X = sm.add_constant(alunos.ano_2015)"
      ],
      "metadata": {
        "id": "FOFHr9zk0Y4x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resultado_regressao = sm.OLS(Y,X).fit()"
      ],
      "metadata": {
        "id": "oN9sHrD60oNv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(resultado_regressao.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fF2XVYUkYGVT",
        "outputId": "788ed476-1ba9-4be9-aa14-f34b97a65613"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:               ano_2016   R-squared:                       0.801\n",
            "Model:                            OLS   Adj. R-squared:                  0.793\n",
            "Method:                 Least Squares   F-statistic:                     100.6\n",
            "Date:                Tue, 01 Aug 2023   Prob (F-statistic):           3.02e-10\n",
            "Time:                        18:34:32   Log-Likelihood:                -58.476\n",
            "No. Observations:                  27   AIC:                             121.0\n",
            "Df Residuals:                      25   BIC:                             123.5\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         11.3143      6.880      1.645      0.113      -2.855      25.483\n",
            "ano_2015       0.8681      0.087     10.030      0.000       0.690       1.046\n",
            "==============================================================================\n",
            "Omnibus:                        8.744   Durbin-Watson:                   1.616\n",
            "Prob(Omnibus):                  0.013   Jarque-Bera (JB):               11.774\n",
            "Skew:                           0.487   Prob(JB):                      0.00278\n",
            "Kurtosis:                       6.085   Cond. No.                     1.30e+03\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, 1.3e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alunos[\"Y_previsto\"] = resultado_regressao.predict()\n",
        "alunos.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qacCmzDjXgwy",
        "outputId": "a00f23fe-c5bd-403f-dea9-4f34fd7fa846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         UF  ano_2015  ano_2016  Y_previsto\n",
              "0  Rondonia        77        85   78.159571\n",
              "1      Acre        79        81   79.895812\n",
              "2  Amazonas        83        85   83.368294\n",
              "3   Roraima        81        82   81.632053\n",
              "4      Para        73        76   74.687089"
            ],
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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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            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alunos[\"residuo\"] = alunos[\"Y_previsto\"] - alunos[\"ano_2016\"]\n",
        "alunos.head()"
      ],
      "metadata": {
        "colab": {
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        "id": "1fzFgueIYbHZ",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         UF  ano_2015  ano_2016  Y_previsto   residuo\n",
              "0  Rondonia        77        85   78.159571 -6.840429\n",
              "1      Acre        79        81   79.895812 -1.104188\n",
              "2  Amazonas        83        85   83.368294 -1.631706\n",
              "3   Roraima        81        82   81.632053 -0.367947\n",
              "4      Para        73        76   74.687089 -1.312911"
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-95ddb71c-7cae-4ce7-9681-7063483c50a0 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-52b9b43d-9b01-4761-899c-7de9e8a92c8d 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-52b9b43d-9b01-4761-899c-7de9e8a92c8d');\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": "markdown",
      "source": [
        "## Regressão Linear Multipla"
      ],
      "metadata": {
        "id": "3I5z3-5oLhj7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importe o arquivo formato xlsx\n",
        "limite = pd.read_excel(\"Limite_Credito.xlsx\")\n",
        "limite.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QN-XLIi8LoG-",
        "outputId": "4d84b759-dd80-4358-d955-9b47041d9389"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
              "0     70            10736     5214                      400   \n",
              "1     68             5000     5000                      380   \n",
              "2     44             5800     5800                      500   \n",
              "3     72             4300     4300                     2000   \n",
              "4     75             4400     4400                     3000   \n",
              "\n",
              "   LimitedoChequeEspecial  \n",
              "0                     500  \n",
              "1                     600  \n",
              "2                     800  \n",
              "3                    1000  \n",
              "4                    1000  "
            ],
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              "\n",
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              "      <th>1</th>\n",
              "      <td>68</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
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              "      <td>600</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>44</td>\n",
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              "      <td>5800</td>\n",
              "      <td>500</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>72</td>\n",
              "      <td>4300</td>\n",
              "      <td>4300</td>\n",
              "      <td>2000</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75</td>\n",
              "      <td>4400</td>\n",
              "      <td>4400</td>\n",
              "      <td>3000</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc50e691-06c3-4049-9e37-846b446e7b2d')\"\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",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8b121842-94da-4f59-81b6-23269af40de1\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b121842-94da-4f59-81b6-23269af40de1')\"\n",
              "              title=\"Suggest charts.\"\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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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8b121842-94da-4f59-81b6-23269af40de1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-bc50e691-06c3-4049-9e37-846b446e7b2d 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-bc50e691-06c3-4049-9e37-846b446e7b2d');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "limite.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tIpcvJoAMNXd",
        "outputId": "8b6ef1e5-1fed-445a-e4d4-cb36dd546d4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Idade  RendimentoTotal   Salario  \\\n",
              "Idade                    1.000000         0.066396  0.063227   \n",
              "RendimentoTotal          0.066396         1.000000  0.960551   \n",
              "Salario                  0.063227         0.960551  1.000000   \n",
              "LimitedeCreditoImediato  0.064149         0.031041  0.081792   \n",
              "LimitedoChequeEspecial   0.075883         0.858120  0.888839   \n",
              "\n",
              "                         LimitedeCreditoImediato  LimitedoChequeEspecial  \n",
              "Idade                                   0.064149                0.075883  \n",
              "RendimentoTotal                         0.031041                0.858120  \n",
              "Salario                                 0.081792                0.888839  \n",
              "LimitedeCreditoImediato                 1.000000                0.226990  \n",
              "LimitedoChequeEspecial                  0.226990                1.000000  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Idade</th>\n",
              "      <th>RendimentoTotal</th>\n",
              "      <th>Salario</th>\n",
              "      <th>LimitedeCreditoImediato</th>\n",
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              "  <tbody>\n",
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              "      <th>Idade</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.066396</td>\n",
              "      <td>0.063227</td>\n",
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              "      <td>0.075883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RendimentoTotal</th>\n",
              "      <td>0.066396</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960551</td>\n",
              "      <td>0.031041</td>\n",
              "      <td>0.858120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Salario</th>\n",
              "      <td>0.063227</td>\n",
              "      <td>0.960551</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.081792</td>\n",
              "      <td>0.888839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LimitedeCreditoImediato</th>\n",
              "      <td>0.064149</td>\n",
              "      <td>0.031041</td>\n",
              "      <td>0.081792</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.226990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LimitedoChequeEspecial</th>\n",
              "      <td>0.075883</td>\n",
              "      <td>0.858120</td>\n",
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              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-73eebe08-4278-4585-893c-3c394af95d67 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-e0fa9786-eb9e-45fd-bf93-01b2d1e12377 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-e0fa9786-eb9e-45fd-bf93-01b2d1e12377');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(limite);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "id": "d9f27agRMPQ-",
        "outputId": "f4e77ab7-6722-488c-c899-53103003ac4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x1250 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "DvGobZmFMUHp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#modelo MRLS\n",
        "function = 'LimitedoChequeEspecial~Idade+RendimentoTotal+Salario+LimitedeCreditoImediato'\n",
        "model = smf.ols(formula=function, data=limite).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDh_4GdaMUGa",
        "outputId": "5c10f110-b754-4680-e55b-064fe7050a85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.816\n",
            "Model:                                OLS   Adj. R-squared:                  0.799\n",
            "Method:                     Least Squares   F-statistic:                     47.65\n",
            "Date:                    Tue, 01 Aug 2023   Prob (F-statistic):           2.92e-15\n",
            "Time:                            22:21:53   Log-Likelihood:                -445.61\n",
            "No. Observations:                      48   AIC:                             901.2\n",
            "Df Residuals:                          43   BIC:                             910.6\n",
            "Df Model:                               4                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept               -2851.7932   1889.470     -1.509      0.139   -6662.272     958.685\n",
            "Idade                       3.7798     26.772      0.141      0.888     -50.212      57.772\n",
            "RendimentoTotal             0.0753      0.116      0.649      0.519      -0.158       0.309\n",
            "Salario                     0.4770      0.157      3.031      0.004       0.160       0.794\n",
            "LimitedeCreditoImediato     1.0602      0.436      2.429      0.019       0.180       1.940\n",
            "==============================================================================\n",
            "Omnibus:                        2.177   Durbin-Watson:                   1.791\n",
            "Prob(Omnibus):                  0.337   Jarque-Bera (JB):                1.346\n",
            "Skew:                          -0.098   Prob(JB):                        0.510\n",
            "Kurtosis:                       3.797   Cond. No.                     1.06e+05\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, 1.06e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Remover a variável RendimentoTotal que tem o maior P-value\n",
        "#Ainda temos variáveis acima de 5%\n",
        "function = 'LimitedoChequeEspecial~RendimentoTotal+Salario+LimitedeCreditoImediato'\n",
        "model = smf.ols(formula=function, data=limite).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75ujHTPeMb5z",
        "outputId": "a4c40535-5247-4d42-dc7c-4108ab9df14f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.816\n",
            "Model:                                OLS   Adj. R-squared:                  0.803\n",
            "Method:                     Least Squares   F-statistic:                     64.97\n",
            "Date:                    Tue, 01 Aug 2023   Prob (F-statistic):           3.34e-16\n",
            "Time:                            22:22:10   Log-Likelihood:                -445.62\n",
            "No. Observations:                      48   AIC:                             899.2\n",
            "Df Residuals:                          44   BIC:                             906.7\n",
            "Df Model:                               3                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept               -2632.8485   1067.330     -2.467      0.018   -4783.910    -481.787\n",
            "RendimentoTotal             0.0758      0.115      0.662      0.512      -0.155       0.307\n",
            "Salario                     0.4767      0.156      3.063      0.004       0.163       0.790\n",
            "LimitedeCreditoImediato     1.0642      0.431      2.471      0.017       0.196       1.932\n",
            "==============================================================================\n",
            "Omnibus:                        2.073   Durbin-Watson:                   1.776\n",
            "Prob(Omnibus):                  0.355   Jarque-Bera (JB):                1.245\n",
            "Skew:                          -0.075   Prob(JB):                        0.537\n",
            "Kurtosis:                       3.775   Cond. No.                     6.08e+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, 6.08e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Remover a variável RendimentoTotal que tem o maior P-value\n",
        "#Ainda temos variáveis acima de 5%\n",
        "function = 'LimitedoChequeEspecial~Salario+LimitedeCreditoImediato'\n",
        "model = smf.ols(formula=function, data=limite).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "id": "5EJmQVKhMekn",
        "outputId": "8ad245ac-3c0e-4452-bf2f-bfd6cee49ac2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.814\n",
            "Model:                                OLS   Adj. R-squared:                  0.806\n",
            "Method:                     Least Squares   F-statistic:                     98.47\n",
            "Date:                    Tue, 01 Aug 2023   Prob (F-statistic):           3.66e-17\n",
            "Time:                            22:22:22   Log-Likelihood:                -445.86\n",
            "No. Observations:                      48   AIC:                             897.7\n",
            "Df Residuals:                          45   BIC:                             903.3\n",
            "Df Model:                               2                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "===========================================================================================\n",
            "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------------------\n",
            "Intercept               -2636.5605   1060.626     -2.486      0.017   -4772.770    -500.351\n",
            "Salario                     0.5757      0.042     13.582      0.000       0.490       0.661\n",
            "LimitedeCreditoImediato     1.0153      0.422      2.408      0.020       0.166       1.865\n",
            "==============================================================================\n",
            "Omnibus:                        1.010   Durbin-Watson:                   1.779\n",
            "Prob(Omnibus):                  0.604   Jarque-Bera (JB):                0.330\n",
            "Skew:                           0.014   Prob(JB):                        0.848\n",
            "Kurtosis:                       3.405   Cond. No.                     3.83e+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, 3.83e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    }
  ]
}