Regressão_Linear_e_Não_Linear.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Regressão Linear e Não Linear.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "JDqcI_WTMYX9",
        "qLpPMAO_4hth"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPQEYeeP4aXA"
      },
      "source": [
        "#Regressão"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDqcI_WTMYX9"
      },
      "source": [
        "##Importar e Instalar Libs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XH_5aMftYVys"
      },
      "source": [
        "https://plotnine.readthedocs.io/en/stable/generated/plotnine.stats.stat_function.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6_uiEgo2z6_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b1f4450-6424-4ebe-fc61-2b82dd71ad75"
      },
      "source": [
        "!pip install pandas 'plotnine==0.8.0'\n",
        "!pip install pyforest\n",
        "!pip install dplython"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.1.5)\n",
            "Requirement already satisfied: plotnine==0.8.0 in /usr/local/lib/python3.7/dist-packages (0.8.0)\n",
            "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.7/dist-packages (from plotnine==0.8.0) (1.19.5)\n",
            "Requirement already satisfied: mizani>=0.7.3 in /usr/local/lib/python3.7/dist-packages (from plotnine==0.8.0) (0.7.3)\n",
            "Requirement already satisfied: descartes>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from plotnine==0.8.0) (1.1.0)\n",
            "Requirement already satisfied: matplotlib>=3.1.1 in /usr/local/lib/python3.7/dist-packages (from plotnine==0.8.0) (3.2.2)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from plotnine==0.8.0) (0.5.1)\n",
            "Requirement already satisfied: statsmodels>=0.12.1 in /usr/local/lib/python3.7/dist-packages (from plotnine==0.8.0) (0.12.2)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from plotnine==0.8.0) (1.7.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.1->plotnine==0.8.0) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.1->plotnine==0.8.0) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.1.1->plotnine==0.8.0) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=3.1.1->plotnine==0.8.0) (1.15.0)\n",
            "Requirement already satisfied: palettable in /usr/local/lib/python3.7/dist-packages (from mizani>=0.7.3->plotnine==0.8.0) (3.3.0)\n",
            "Requirement already satisfied: pyforest in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
            "Requirement already satisfied: dplython in /usr/local/lib/python3.7/dist-packages (0.0.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from dplython) (1.15.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from dplython) (1.1.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from dplython) (1.19.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->dplython) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->dplython) (2.8.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRcfn9VWLfEo",
        "outputId": "f68d9c50-e6ab-48dd-8f4b-225a186f1dcc"
      },
      "source": [
        "!pip install pandas plotnine ==0.11.0"
      ],
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[31mERROR: Invalid requirement: '==0.11.0'\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "AOISFgdOLRD4",
        "outputId": "e61eb0bc-3c20-44c2-b18f-6ef641844835"
      },
      "source": [
        "plotnine.__version__"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-a1af8aa30a61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplotnine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'plotnine' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzK7n1250A7V"
      },
      "source": [
        "from pyforest import *\n",
        "from plotnine import *\n",
        "from dplython import *\n",
        "from scipy.stats import norm\n",
        "from sklearn.preprocessing import PowerTransformer\n",
        "import statsmodels.formula.api as smf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wD1nyGoI4rBN"
      },
      "source": [
        "np.set_printoptions(suppress=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLpPMAO_4hth"
      },
      "source": [
        "##Importar Base de Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpltA_xG3Ei_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ea744f70-b8b9-4aed-e263-3a794a6b9f5d"
      },
      "source": [
        "bebes=pd.read_csv('/content/drive/MyDrive/Dataset_Excel_CSV/bebes.csv')\n",
        "bebes.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comprimento</th>\n",
              "      <th>idade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.07</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>65.63</td>\n",
              "      <td>21.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.63</td>\n",
              "      <td>22.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66.73</td>\n",
              "      <td>22.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.37</td>\n",
              "      <td>23.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comprimento  idade\n",
              "0        63.07  19.00\n",
              "1        65.63  21.00\n",
              "2        65.63  22.50\n",
              "3        66.73  22.50\n",
              "4        66.37  23.25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sc8uyKsR4YtO"
      },
      "source": [
        "##Sumário"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8E9ltDp3b75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "f7d63452-f23f-4511-e036-1b8866ef9495"
      },
      "source": [
        "bebes.describe().transpose()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>comprimento</th>\n",
              "      <td>74.0</td>\n",
              "      <td>66.921622</td>\n",
              "      <td>12.852386</td>\n",
              "      <td>31.90</td>\n",
              "      <td>56.47</td>\n",
              "      <td>70.4</td>\n",
              "      <td>75.8075</td>\n",
              "      <td>87.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>idade</th>\n",
              "      <td>74.0</td>\n",
              "      <td>25.312162</td>\n",
              "      <td>12.975168</td>\n",
              "      <td>2.15</td>\n",
              "      <td>12.50</td>\n",
              "      <td>26.0</td>\n",
              "      <td>33.0000</td>\n",
              "      <td>60.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count       mean        std    min    25%   50%      75%    max\n",
              "comprimento   74.0  66.921622  12.852386  31.90  56.47  70.4  75.8075  87.63\n",
              "idade         74.0  25.312162  12.975168   2.15  12.50  26.0  33.0000  60.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObvYtsWw5K7Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "eb785883-5215-43d0-bc0f-e6231c947db2"
      },
      "source": [
        "ggplot(bebes, aes('idade', 'comprimento')) + \\\n",
        "  geom_point(color = \"#333333\", alpha = 0.6, size = 2) + \\\n",
        "  geom_smooth(aes('idade', 'comprimento'),\n",
        "              method='lm',se=False, color= \"#FDE725FF\") + \\\n",
        "  geom_smooth(aes('idade','comprimento'),\n",
        "              color = \"#440154FF\", se = False) + \\\n",
        "  labs(x = \"Idade em semanas\", y = \"Comprimento em cm\") + \\\n",
        "  theme_bw()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8763202521745)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfkikMLt2a-m"
      },
      "source": [
        "X=bebes['idade']\n",
        "y=bebes['comprimento']"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "UV_WHATQpasl",
        "outputId": "7345cb35-c2aa-4370-a2ad-61d9580969eb"
      },
      "source": [
        "X=sm.add_constant(X)\n",
        "model=sm.OLS(y,X)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import statsmodels.api as sm\\nimport pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import statsmodels.api as sm\\nimport pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXKCIT5QpkpY"
      },
      "source": [
        "model = smf.ols(formula='comprimento ~ idade', data=bebes)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZtI46nf29d6"
      },
      "source": [
        "resultados = model.fit()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GdKoeJI3FgT",
        "outputId": "ca711f62-4025-4cea-ae7f-179cb6b36c75"
      },
      "source": [
        "print(resultados.summary())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:            comprimento   R-squared:                       0.903\n",
            "Model:                            OLS   Adj. R-squared:                  0.901\n",
            "Method:                 Least Squares   F-statistic:                     667.7\n",
            "Date:                Thu, 12 Aug 2021   Prob (F-statistic):           3.72e-38\n",
            "Time:                        16:26:16   Log-Likelihood:                -207.26\n",
            "No. Observations:                  74   AIC:                             418.5\n",
            "Df Residuals:                      72   BIC:                             423.1\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept     43.1004      1.034     41.665      0.000      41.038      45.163\n",
            "idade          0.9411      0.036     25.841      0.000       0.868       1.014\n",
            "==============================================================================\n",
            "Omnibus:                       21.203   Durbin-Watson:                   0.278\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               29.159\n",
            "Skew:                          -1.218   Prob(JB):                     4.66e-07\n",
            "Kurtosis:                       4.877   Cond. No.                         62.7\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HzhtzyyG3H9b"
      },
      "source": [
        "from scipy.stats import shapiro"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_pGm8Qr4cXA"
      },
      "source": [
        "W, p_value=shapiro(resultados.resid)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGD94IBKMv8V",
        "outputId": "3c116118-3751-4d9e-f8bc-affa3e2a33d6"
      },
      "source": [
        "print(f'W {W}')\n",
        "print(f'p value: {p_value:.9f}')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W 0.9094864130020142\n",
            "p value: 0.000060124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "a_5nJY5vdRQP",
        "outputId": "5115db9f-38cd-4171-da33-db855bfedc8f"
      },
      "source": [
        "ggplot(bebes >>\\\n",
        "  mutate(residuos=resultados.resid),\n",
        "         aes(x='residuos')) + \\\n",
        "  geom_histogram(aes(y = '..density..'),\n",
        "                 color = \"#7F7F7F\", \n",
        "                 fill = \"#E5E5E5\", \n",
        "                 bins = 30,\n",
        "                 alpha = 0.6) + \\\n",
        "  stat_function(fun=norm.pdf,\n",
        "                args={'loc':np.mean(resultados.resid),\n",
        "                      'scale':np.std(resultados.resid)},                \n",
        "                size = 2,\n",
        "                color=\"#FDE725FF\") +\\\n",
        "   labs(title=\"Gráfico\",\n",
        "        x = \"Resíduos\", \n",
        "        y = \"Frequência\")  +\\\n",
        "   theme(panel_background= element_rect(\"white\"),\n",
        "        panel_grid = element_line(\"#F2F2F2\"),\n",
        "        panel_border = element_rect(np.NaN),\n",
        "        legend_position = \"bottom\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8763201177341)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzenN-ydkbOu",
        "outputId": "8a5fe8d4-c6b4-43f3-f569-2573d3c6c5b7"
      },
      "source": [
        "pt = PowerTransformer(method='box-cox', standardize=False)\n",
        "pt.fit(bebes['comprimento'].values.reshape(-1, 1))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PowerTransformer(copy=True, method='box-cox', standardize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDtSrYQCUueq",
        "outputId": "2068e7fc-edc1-4c89-fb20-88fbb03336c2"
      },
      "source": [
        "lambda_bc=pt.lambdas_\n",
        "lambda_bc"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.65905101])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Nxjb_iI-mwTL",
        "outputId": "aa07a626-dbd5-4b7c-9603-b07ad4f9352d"
      },
      "source": [
        "bebes.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comprimento</th>\n",
              "      <th>idade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.07</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>65.63</td>\n",
              "      <td>21.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.63</td>\n",
              "      <td>22.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66.73</td>\n",
              "      <td>22.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>66.37</td>\n",
              "      <td>23.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   comprimento  idade\n",
              "0        63.07  19.00\n",
              "1        65.63  21.00\n",
              "2        65.63  22.50\n",
              "3        66.73  22.50\n",
              "4        66.37  23.25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gIAcgJQj94o"
      },
      "source": [
        "bebes['comprimento_bc'] = (bebes['comprimento'] ** lambda_bc - 1)/ lambda_bc"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3YzaO_dm4X4"
      },
      "source": [
        "modelo_bc = smf.ols(formula='comprimento_bc ~ idade', data=bebes)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rak7BPExnDvL"
      },
      "source": [
        "resultados_bc=modelo_bc.fit()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPCvxxlXnGDi",
        "outputId": "4d42f296-9f49-4fb3-a063-6958217580e1"
      },
      "source": [
        "print(resultados_bc.summary())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:         comprimento_bc   R-squared:                       0.962\n",
            "Model:                            OLS   Adj. R-squared:                  0.961\n",
            "Method:                 Least Squares   F-statistic:                     1822.\n",
            "Date:                Thu, 12 Aug 2021   Prob (F-statistic):           7.16e-53\n",
            "Time:                        16:26:17   Log-Likelihood:                -681.77\n",
            "No. Observations:                  74   AIC:                             1368.\n",
            "Df Residuals:                      72   BIC:                             1372.\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept   4995.1602    630.253      7.926      0.000    3738.774    6251.546\n",
            "idade        947.2298     22.189     42.689      0.000     902.997     991.463\n",
            "==============================================================================\n",
            "Omnibus:                        1.140   Durbin-Watson:                   0.813\n",
            "Prob(Omnibus):                  0.566   Jarque-Bera (JB):                0.557\n",
            "Skew:                           0.098   Prob(JB):                        0.757\n",
            "Kurtosis:                       3.377   Cond. No.                         62.7\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "qhmW79YgnSi5",
        "outputId": "f11881dc-7caf-4e41-eca6-bb458e0d41b2"
      },
      "source": [
        "pd.DataFrame({'R² OLS':resultados.rsquared,\n",
        "              'R² BoxCOX':resultados_bc.rsquared},index=[0]) "
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import statsmodels.api as sm\\nimport pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R² OLS</th>\n",
              "      <th>R² BoxCOX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.902668</td>\n",
              "      <td>0.961993</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     R² OLS  R² BoxCOX\n",
              "0  0.902668   0.961993"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_gr-IOzqUFb"
      },
      "source": [
        "Wb, p_value_bc=shapiro(resultados_bc.resid)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPK37SNura3u",
        "outputId": "af5e5bb6-f43c-4909-9f03-7dbd6b6f8070"
      },
      "source": [
        "print(f'W {Wb}')\n",
        "print(f'p value: {p_value_bc:.9f}')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W 0.9764547348022461\n",
            "p value: 0.181918442\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "wPSmQiWlpWfL",
        "outputId": "bd7dad0b-e4b3-4f1d-f6ef-f8fd660bf2cd"
      },
      "source": [
        "ggplot(bebes >>\\\n",
        "  mutate(residuos=resultados_bc.resid),\n",
        "         aes(x='residuos')) + \\\n",
        "  geom_histogram(aes(y = '..density..'),\n",
        "                 color = \"#7F7F7F\", \n",
        "                 fill = \"#E5E5E5\", \n",
        "                 bins = 30,\n",
        "                 alpha = 0.6) + \\\n",
        "  stat_function(fun=norm.pdf,\n",
        "                args={'loc':np.mean(resultados_bc.resid),\n",
        "                      'scale':np.std(resultados_bc.resid)},                \n",
        "                size = 2,\n",
        "                color=\"#FDE725FF\") +\\\n",
        "   labs(title=\"Gráfico\",\n",
        "        x = \"Resíduos\", \n",
        "        y = \"Frequência\")  +\\\n",
        "   theme(panel_background= element_rect(\"white\"),\n",
        "        panel_grid = element_line(\"#F2F2F2\"),\n",
        "        panel_border = element_rect(np.NaN),\n",
        "        legend_position = \"bottom\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8763192220913)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "q27VzAPGrgra",
        "outputId": "c0eb1653-3e9c-4d3f-844b-3f1001529b6c"
      },
      "source": [
        "resultados.predict(exog=pd.DataFrame({'idade':[52]}))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import statsmodels.api as sm\\nimport pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    92.037486\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "-EnTPY5QuV8m",
        "outputId": "13446a0e-bf12-415f-9065-479b8ce98d93"
      },
      "source": [
        "resultados_bc.predict(exog=pd.DataFrame({'idade':[52]}))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import statsmodels.api as sm\\nimport pandas as pd'); }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    54251.109775\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JhFRA9_vtRvK",
        "outputId": "deac9979-ae96-4b68-8e76-8ab2d7de654c"
      },
      "source": [
        "(((54251.109775 * lambda_bc) + 1)) ** (1/lambda_bc)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([87.14006473])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyOBqOQctmQZ"
      },
      "source": [
        "bebes['y_hat_linear']=resultados.fittedvalues\n",
        "bebes['y_hat_boxcox']=((resultados_bc.fittedvalues * lambda_bc) +1)** (1/lambda_bc)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "7niLiKvpuT-v",
        "outputId": "ef3c7e66-8d44-4c09-9de2-217a64ca95ae"
      },
      "source": [
        "DplyFrame(bebes) >> \\\n",
        "  select(bebes.idade,bebes.comprimento,bebes.y_hat_linear,bebes.y_hat_boxcox) >>\\\n",
        "  head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idade</th>\n",
              "      <th>comprimento</th>\n",
              "      <th>y_hat_linear</th>\n",
              "      <th>y_hat_boxcox</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19.00</td>\n",
              "      <td>63.07</td>\n",
              "      <td>60.981260</td>\n",
              "      <td>63.097300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.00</td>\n",
              "      <td>65.63</td>\n",
              "      <td>62.863455</td>\n",
              "      <td>65.004303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.50</td>\n",
              "      <td>65.63</td>\n",
              "      <td>64.275102</td>\n",
              "      <td>66.375858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>22.50</td>\n",
              "      <td>66.73</td>\n",
              "      <td>64.275102</td>\n",
              "      <td>66.375858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23.25</td>\n",
              "      <td>66.37</td>\n",
              "      <td>64.980925</td>\n",
              "      <td>67.044337</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   idade  comprimento  y_hat_linear  y_hat_boxcox\n",
              "0  19.00        63.07     60.981260     63.097300\n",
              "1  21.00        65.63     62.863455     65.004303\n",
              "2  22.50        65.63     64.275102     66.375858\n",
              "3  22.50        66.73     64.275102     66.375858\n",
              "4  23.25        66.37     64.980925     67.044337"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iz6Mqgb2KYOe"
      },
      "source": [
        "def poly(x, degree=1):  \n",
        "    \"\"\"\n",
        "    Polinômio\n",
        "    Esses são fatores não ortogonais, mas pode não importar se\n",
        "    nós só precisamos disso para previsões (sem interpretar o\n",
        "    coeficientes) ou visualização.\n",
        "    \"\"\"\n",
        "    d = {}\n",
        "    for i in range(degree+1):\n",
        "        if i == 1:\n",
        "            d['x'] = x\n",
        "        else:\n",
        "            d[f'x**{i}'] = np.power(x, i)\n",
        "    return pd.DataFrame(d)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "JQEuucIIOnQs",
        "outputId": "6bdc4731-9d9b-4d0a-9bfa-d08cbf9569c0"
      },
      "source": [
        "(ggplot(bebes) + \n",
        " geom_smooth(aes(x='comprimento', y='y_hat_linear'),\n",
        "             method='lm', formula='y ~ poly(x, degree=3)', se=False,color=\"yellow\") +\n",
        " geom_point(aes('comprimento', 'y_hat_linear'),\n",
        "            color = \"#FDE725FF\", alpha = 0.6, size = 2) +\n",
        " geom_smooth(aes(x='comprimento', y='y_hat_boxcox'),\n",
        "             method='lm', formula='y ~ poly(x, degree=3)', se=False,color=\"purple\") +\n",
        " geom_point(aes('comprimento', 'y_hat_boxcox'),\n",
        "            color = \"#440154FF\", alpha = 0.6, size = 2) +\n",
        " geom_smooth(aes(x='comprimento',y='comprimento'), \n",
        "             method = \"lm\", color = \"#440154FF\", size = 1.05,\n",
        "             linetype = \"dashed\") +\n",
        " labs(title=\"Comparação entre modelos\",x='comprimento',y='Fitted Values') +\n",
        " theme(panel_background = element_rect(\"white\"),\n",
        "      panel_grid = element_line(\"grey\"),\n",
        "      panel_border = element_rect(np.nan),\n",
        "      legend_position = \"bottom\"))"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8763158141341)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    }
  ]
}