regressao_linear.ipynb

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        "<a href=\"https://colab.research.google.com/github/miller00315/ia_studies/blob/main/regressao_linear.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "from numpy import *\n",
        "from math import sqrt\n",
        "\n",
        "class LinearRegression:\n",
        "    def __init__(self, x: float, y: float) -> float:\n",
        "      self.x = x\n",
        "      self.y = y\n",
        "      self.__correlation_coefficient = self.__correlation_coefficient_()\n",
        "      self.__inclination = self.__inclination_()\n",
        "      self.__interception = self.__interception_()\n",
        "\n",
        "\n",
        "    def __correlation_coefficient_(self) -> float:\n",
        "      covariation = cov(self.x, self.y, bias=True)[0][1]\n",
        "\n",
        "      x_variance = var(self.x)\n",
        "\n",
        "      y_variance = var(self.y)\n",
        "\n",
        "      return covariation / sqrt(x_variance * y_variance)\n",
        "\n",
        "    def __inclination_(self) -> float:\n",
        "      standard_desviation_x = std(self.x)\n",
        "\n",
        "      standard_desviation_y = std(self.y)\n",
        "\n",
        "      return self.__correlation_coefficient * (standard_desviation_y / standard_desviation_x)\n",
        "\n",
        "    def __interception_(self) -> float:\n",
        "      x_mean = mean(self.x)\n",
        "\n",
        "      y_mean = mean(self.y)\n",
        "\n",
        "      return y_mean - x_mean * self.__inclination\n",
        "\n",
        "    def prediction(self, value: float):\n",
        "\n",
        "      return self.__interception + (self.__inclination * value)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = array([1,2,3,4,5])\n",
        "\n",
        "y = array([2,4,6,8,10])\n",
        "\n",
        "\n",
        "linear_regretion = LinearRegression(x, y)\n",
        "\n",
        "prediction = linear_regretion.prediction(62.5)\n",
        "\n",
        "prediction"
      ],
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              "125.0"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.formula.api as sm\n",
        "import scipy.stats as stats\n",
        "import seaborn as sns"
      ],
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        "id": "MozWvHw7MWua"
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      "execution_count": 102,
      "outputs": []
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      "source": [
        "base = pd.read_csv('mt_cars.csv')\n",
        "\n",
        "base.shape"
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        "outputId": "eb1df8e1-a160-43fb-ded3-ef9831e5ad60"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 12)"
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          "metadata": {},
          "execution_count": 103
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      "execution_count": 44,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          Unnamed: 0   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
              "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
              "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
              "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
              "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
              "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
              "\n",
              "   carb  \n",
              "0     4  \n",
              "1     4  \n",
              "2     1  \n",
              "3     1  \n",
              "4     2  "
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              "      <td>18.61</td>\n",
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              "      <td>Hornet 4 Drive</td>\n",
              "      <td>21.4</td>\n",
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              "      <th>4</th>\n",
              "      <td>Hornet Sportabout</td>\n",
              "      <td>18.7</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.15</td>\n",
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        "base = base.drop(['Unnamed: 0'], axis = 1)\n",
        "\n",
        "base.head()"
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            "text/plain": [
              "    mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb\n",
              "0  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4     4\n",
              "1  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4     4\n",
              "2  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4     1\n",
              "3  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3     1\n",
              "4  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3     2"
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              "      <th>mpg</th>\n",
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              "      <th>drat</th>\n",
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              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.620</td>\n",
              "      <td>16.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.875</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>108.0</td>\n",
              "      <td>93</td>\n",
              "      <td>3.85</td>\n",
              "      <td>2.320</td>\n",
              "      <td>18.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21.4</td>\n",
              "      <td>6</td>\n",
              "      <td>258.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.215</td>\n",
              "      <td>19.44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18.7</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.440</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a91f7420-bd21-4cb4-8053-29b01934640f')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a91f7420-bd21-4cb4-8053-29b01934640f 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-a91f7420-bd21-4cb4-8053-29b01934640f');\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",
              "\n",
              "\n",
              "<div id=\"df-3518a026-f107-4882-bcda-e3cceaa46712\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3518a026-f107-4882-bcda-e3cceaa46712')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3518a026-f107-4882-bcda-e3cceaa46712 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = base.corr()\n",
        "\n",
        "sns.heatmap(correlation, cmap=\"coolwarm\", annot=True, fmt='.2f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "uheGX-iaUzQs",
        "outputId": "9ca47d6f-055d-475f-fd63-6764b466ead6"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_pairs =[('mpg', 'cyl'), ('mpg', 'disp'), ('mpg', 'hp'), ('mpg', 'wt'), ('mpg', 'drat'), ('mpg', 'vs')]\n",
        "\n",
        "n_plots = len(columns_pairs)\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(nrows=n_plots, ncols = 1,figsize = (6,4 * n_plots))\n",
        "\n",
        "\n",
        "for i, pair in enumerate(columns_pairs):\n",
        "  x_col, y_col = pair\n",
        "\n",
        "  sns.scatterplot(x=x_col, y = y_col, data = base, ax=axes[i])\n",
        "\n",
        "  axes[i].set_title(f'{x_col} vs {y_col}')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2ajyOmV7VzLx",
        "outputId": "0afa9091-6980-42c2-9264-c1e0c45cf528"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x2400 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = sm.ols(formula='mpg ~ drat + vs', data=base)\n",
        "\n",
        "model = model.fit()\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "fcv2182FY4ao",
        "outputId": "1f793bf1-6a73-4fd2-f4ce-a8cdf5803383"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                    mpg   R-squared:                       0.628\n",
              "Model:                            OLS   Adj. R-squared:                  0.603\n",
              "Method:                 Least Squares   F-statistic:                     24.53\n",
              "Date:                Sun, 03 Dec 2023   Prob (F-statistic):           5.82e-07\n",
              "Time:                        21:04:03   Log-Likelihood:                -86.536\n",
              "No. Observations:                  32   AIC:                             179.1\n",
              "Df Residuals:                      29   BIC:                             183.5\n",
              "Df Model:                           2                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept     -1.8253      4.903     -0.372      0.712     -11.853       8.202\n",
              "drat           5.4365      1.421      3.826      0.001       2.530       8.343\n",
              "vs             5.4013      1.507      3.583      0.001       2.318       8.484\n",
              "==============================================================================\n",
              "Omnibus:                        0.243   Durbin-Watson:                   1.890\n",
              "Prob(Omnibus):                  0.886   Jarque-Bera (JB):                0.440\n",
              "Skew:                           0.059   Prob(JB):                        0.803\n",
              "Kurtosis:                       2.438   Cond. No.                         29.0\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>mpg</td>       <th>  R-squared:         </th> <td>   0.628</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.603</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   24.53</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Sun, 03 Dec 2023</td> <th>  Prob (F-statistic):</th> <td>5.82e-07</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>21:04:03</td>     <th>  Log-Likelihood:    </th> <td> -86.536</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    32</td>      <th>  AIC:               </th> <td>   179.1</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    29</td>      <th>  BIC:               </th> <td>   183.5</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td>   -1.8253</td> <td>    4.903</td> <td>   -0.372</td> <td> 0.712</td> <td>  -11.853</td> <td>    8.202</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>drat</th>      <td>    5.4365</td> <td>    1.421</td> <td>    3.826</td> <td> 0.001</td> <td>    2.530</td> <td>    8.343</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>vs</th>        <td>    5.4013</td> <td>    1.507</td> <td>    3.583</td> <td> 0.001</td> <td>    2.318</td> <td>    8.484</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 0.243</td> <th>  Durbin-Watson:     </th> <td>   1.890</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.886</td> <th>  Jarque-Bera (JB):  </th> <td>   0.440</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.059</td> <th>  Prob(JB):          </th> <td>   0.803</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.438</td> <th>  Cond. No.          </th> <td>    29.0</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}    &       mpg        & \\textbf{  R-squared:         } &     0.628   \\\\\n\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.603   \\\\\n\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     24.53   \\\\\n\\textbf{Date:}             & Sun, 03 Dec 2023 & \\textbf{  Prob (F-statistic):} &  5.82e-07   \\\\\n\\textbf{Time:}             &     21:04:03     & \\textbf{  Log-Likelihood:    } &   -86.536   \\\\\n\\textbf{No. Observations:} &          32      & \\textbf{  AIC:               } &     179.1   \\\\\n\\textbf{Df Residuals:}     &          29      & \\textbf{  BIC:               } &     183.5   \\\\\n\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{Intercept} &      -1.8253  &        4.903     &    -0.372  &         0.712        &      -11.853    &        8.202     \\\\\n\\textbf{drat}      &       5.4365  &        1.421     &     3.826  &         0.001        &        2.530    &        8.343     \\\\\n\\textbf{vs}        &       5.4013  &        1.507     &     3.583  &         0.001        &        2.318    &        8.484     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:}       &  0.243 & \\textbf{  Durbin-Watson:     } &    1.890  \\\\\n\\textbf{Prob(Omnibus):} &  0.886 & \\textbf{  Jarque-Bera (JB):  } &    0.440  \\\\\n\\textbf{Skew:}          &  0.059 & \\textbf{  Prob(JB):          } &    0.803  \\\\\n\\textbf{Kurtosis:}      &  2.438 & \\textbf{  Cond. No.          } &     29.0  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# salva o modelo treinado\n",
        "\n",
        "model.save('treined_model_2.pickle')"
      ],
      "metadata": {
        "id": "WWXS0ByOaTv-"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# carrega o mddelo trinado\n",
        "import statsmodels.api as sma\n",
        "\n",
        "loaded_model = sma.load('treined_model_2.pickle')\n"
      ],
      "metadata": {
        "id": "0eM6oGOJas60"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "residue = loaded_model.resid\n",
        "\n",
        "plt.hist(residue, bins=20)\n",
        "plt.xlabel('Residue')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Residue histogram')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "BLGrXE2RbddZ",
        "outputId": "0084c05c-8d9c-4d3b-955a-779e78eb5bb9"
      },
      "execution_count": 79,
      "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": [
        "stats.probplot(residue, dist='norm', plot=plt)\n",
        "\n",
        "plt.title('Q-Q Plot de resíduos')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "PDRNj8I5cKQX",
        "outputId": "d3bd30e4-5b37-4473-a2f2-272bb76aea75"
      },
      "execution_count": 80,
      "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": [
        "# Teste de shapiro\n",
        "# h0 - hipótese nula - dados estão normalmente distribuídos\n",
        "# pval <= 0.05 rejeito a hipotese nula, (não estão normalmente distrbuídos)\n",
        "# pval > 0.05 não é possível rejeitar a h0\n",
        "\n",
        "stat, pval = stats.shapiro(residue)\n",
        "\n",
        "print(f'Shapiro-Wilk estatística: {stat:.3f} p-value: {pval:.3f}')\n",
        "\n",
        "if(pval > 0.05):\n",
        "  print(\"Não dá para rejeitar a hipotese nula\")\n",
        "else:\n",
        "  print('Já era a nula')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ckCBmqHZc2np",
        "outputId": "9d53dcca-6a5a-4ade-82a6-ade14a6a966e"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shapiro-Wilk estatística: 0.981 p-value: 0.822\n",
            "Não dá para rejeitar a hipotese nula\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "  Vamos testar com uma amostra aleatória e verificar o resultaod\n",
        "'''\n",
        "\n",
        "import random\n",
        "\n",
        "amo = base.sample() # vamos pegar uma amostra\n",
        "\n",
        "mpg = amo.loc[:, 'mpg'];\n",
        "\n",
        "print(f'mpg vale {mpg}')\n",
        "\n",
        "amo = amo.drop('mpg', axis = 1)\n",
        "\n",
        "amo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "hJdec0XgdRKf",
        "outputId": "3f57e522-e463-48b6-dfaa-a3b7e477fb99"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mpg vale 22    15.2\n",
            "Name: mpg, dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    cyl   disp   hp  drat     wt  qsec  vs  am  gear  carb\n",
              "22    8  304.0  150  3.15  3.435  17.3   0   0     3     2"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
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              "      <th>22</th>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.435</td>\n",
              "      <td>17.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "\n",
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              "        document.querySelector('#df-6c875e38-bfee-4f46-85ec-964b31ccb8cd 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-6c875e38-bfee-4f46-85ec-964b31ccb8cd');\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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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model.predict(amo) # Vamos tentar predizer e comparar com o modelo anteriro"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAB_Y_lgkIgb",
        "outputId": "2d2eb838-4460-4c29-a5df-d45fbf0e0ac2"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22    15.299814\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    }
  ]
}