{
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/saketkc/pyFLGLM/blob/master/Chapters/03_Chapter03.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukvcEJ8tg5ZT",
        "colab_type": "text"
      },
      "source": [
        "## Chapter 3 - Normal Linear Models: Statistical Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9TmaUlbdL3ra",
        "colab": {},
        "outputId": "291201e9-8dfc-48da-b41a-ee5d78535b1d"
      },
      "source": [
        "import warnings\n",
        "\n",
        "import pandas as pd\n",
        "import proplot as plot\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "from patsy import dmatrices\n",
        "from scipy import stats\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "%pylab inline\n",
        "\n",
        "\n",
        "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
        "plt.rcParams[\"font.weight\"] = \"bold\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ag4cBCkpL3rn",
        "colab": {},
        "outputId": "ad6e7ee4-2b49-4e9d-aa7d-ba092b3de783"
      },
      "source": [
        "stats.f.ppf(0.95, 2, 27)  # 0.95 quantile of F dist. with df1=2, df2 =27"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.3541308285291986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3uWctdHFL3rv",
        "colab": {},
        "outputId": "4593778d-7d59-452f-d26e-611055b57da3"
      },
      "source": [
        "1 - stats.f.pdf(3.364131, 2, 27)  # right tail probability of non-central F"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9602934531255635"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ufmy7_yHL3r3",
        "colab": {},
        "outputId": "1f870f2e-c053-412a-b222-2a493ce94d7e"
      },
      "source": [
        "house_df = pd.read_csv(\"../data/Houses.tsv.gz\", sep=\"\\t\")\n",
        "house_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>case</th>\n",
              "      <th>taxes</th>\n",
              "      <th>beds</th>\n",
              "      <th>baths</th>\n",
              "      <th>new</th>\n",
              "      <th>price</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3104</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>279.9</td>\n",
              "      <td>2048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1173</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>146.5</td>\n",
              "      <td>912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3076</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>237.7</td>\n",
              "      <td>1654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1608</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>2068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1454</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>159.9</td>\n",
              "      <td>1477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>96</td>\n",
              "      <td>990</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>97</td>\n",
              "      <td>3030</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>196.5</td>\n",
              "      <td>1730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>98</td>\n",
              "      <td>1580</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>132.2</td>\n",
              "      <td>1370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>99</td>\n",
              "      <td>1770</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>88.4</td>\n",
              "      <td>1560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>100</td>\n",
              "      <td>1430</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>127.2</td>\n",
              "      <td>1340</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    case  taxes  beds  baths  new  price  size\n",
              "0      1   3104     4      2    0  279.9  2048\n",
              "1      2   1173     2      1    0  146.5   912\n",
              "2      3   3076     4      2    0  237.7  1654\n",
              "3      4   1608     3      2    0  200.0  2068\n",
              "4      5   1454     3      3    0  159.9  1477\n",
              "..   ...    ...   ...    ...  ...    ...   ...\n",
              "95    96    990     2      2    0  176.0  1060\n",
              "96    97   3030     3      2    0  196.5  1730\n",
              "97    98   1580     3      2    0  132.2  1370\n",
              "98    99   1770     3      2    0   88.4  1560\n",
              "99   100   1430     3      2    0  127.2  1340\n",
              "\n",
              "[100 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7F_SYtLaL3r_"
      },
      "source": [
        "**Data Description**: Observations on recent homesales in Gainesville, Florida. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "prZZKWlpL3sA",
        "colab": {},
        "outputId": "f4d82883-3e60-4388-d7e9-fe50df2b5173"
      },
      "source": [
        "pd.concat([house_df.mean(), house_df.std()], axis=1).rename(\n",
        "    columns={0: \"mean\", 1: \"sd\"}\n",
        ")"
      ],
      "execution_count": null,
      "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>mean</th>\n",
              "      <th>sd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>case</th>\n",
              "      <td>50.500</td>\n",
              "      <td>29.011492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>taxes</th>\n",
              "      <td>1908.390</td>\n",
              "      <td>1235.825663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>beds</th>\n",
              "      <td>3.000</td>\n",
              "      <td>0.651339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baths</th>\n",
              "      <td>1.960</td>\n",
              "      <td>0.567112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new</th>\n",
              "      <td>0.110</td>\n",
              "      <td>0.314466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>155.331</td>\n",
              "      <td>101.262213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>1629.280</td>\n",
              "      <td>666.941702</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           mean           sd\n",
              "case     50.500    29.011492\n",
              "taxes  1908.390  1235.825663\n",
              "beds      3.000     0.651339\n",
              "baths     1.960     0.567112\n",
              "new       0.110     0.314466\n",
              "price   155.331   101.262213\n",
              "size   1629.280   666.941702"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cVI347euL3sJ",
        "colab": {},
        "outputId": "1b16fd44-38ca-45bc-a617-6ba23f41fbeb"
      },
      "source": [
        "fit = smf.ols(formula=\"\"\"price ~ size + new\"\"\", data=house_df).fit()\n",
        "print(fit.summary())\n",
        "model_residuals = fit.resid\n",
        "sd_model_residuals = model_residuals.std()\n",
        "print(\"Residual SE: {}\".format(np.round(sd_model_residuals, 2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.723\n",
            "Model:                            OLS   Adj. R-squared:                  0.717\n",
            "Method:                 Least Squares   F-statistic:                     126.3\n",
            "Date:                Mon, 22 Jun 2020   Prob (F-statistic):           9.79e-28\n",
            "Time:                        23:41:14   Log-Likelihood:                -539.05\n",
            "No. Observations:                 100   AIC:                             1084.\n",
            "Df Residuals:                      97   BIC:                             1092.\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept    -40.2309     14.696     -2.738      0.007     -69.399     -11.063\n",
            "size           0.1161      0.009     13.204      0.000       0.099       0.134\n",
            "new           57.7363     18.653      3.095      0.003      20.715      94.757\n",
            "==============================================================================\n",
            "Omnibus:                       12.906   Durbin-Watson:                   1.483\n",
            "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               29.895\n",
            "Skew:                           0.370   Prob(JB):                     3.22e-07\n",
            "Kurtosis:                       5.574   Cond. No.                     6.32e+03\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 6.32e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "Residual SE: 53.33\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aizw69QsL3sQ"
      },
      "source": [
        "**Interpretation**: \n",
        "    \n",
        "    - $H_0: \\beta_1 = \\beta_2 = 0$ The F-statistic with df1 = 2, df2 = 100-3 = 97 is  126.3 with a low p-value which is not surprising since the null is too strict. In some sense the probability of both the coefficients of size and new being 0 is low.\n",
        "    \n",
        "    - Coeff of new: Having \"adjusted\" for size the coefficient of new is still significant\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VQtwXVGRL3sS"
      },
      "source": [
        "95%CI for the mean selling price of new homes at the mean size of the new homes, 2354.73 square feet: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YEiqmGoEL3sU",
        "colab": {},
        "outputId": "2bde257d-8ff5-445b-9a6e-08e05c89b211"
      },
      "source": [
        "prediction = fit.get_prediction({\"size\": 2354.73, \"new\": 1})\n",
        "prediction.summary_frame()"
      ],
      "execution_count": null,
      "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>mean</th>\n",
              "      <th>mean_se</th>\n",
              "      <th>mean_ci_lower</th>\n",
              "      <th>mean_ci_upper</th>\n",
              "      <th>obs_ci_lower</th>\n",
              "      <th>obs_ci_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>290.963953</td>\n",
              "      <td>16.245718</td>\n",
              "      <td>258.720701</td>\n",
              "      <td>323.207205</td>\n",
              "      <td>179.270051</td>\n",
              "      <td>402.657856</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         mean    mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  \\\n",
              "0  290.963953  16.245718     258.720701     323.207205    179.270051   \n",
              "\n",
              "   obs_ci_upper  \n",
              "0    402.657856  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "12UkW0OVL3se"
      },
      "source": [
        "**Interpretation**:  If the model truly holds, the 'mean_ci_lower' and 'mean_ci_upper' reflects\n",
        "the 95% confidence interval where the population mean will lie. While 'obs_ci_lower' and 'obs_ci_upper' reflects the 95% CI where the future (predicted) will lie.    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HuSIZaHEL3sf"
      },
      "source": [
        "### TODO: Derivation of the CI for mean and future y\n",
        "\n",
        "Also see [CrossValidated](https://stats.stackexchange.com/questions/16493/difference-between-confidence-intervals-and-prediction-intervals/271232#271232)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JB-lBIYIL3sh",
        "colab": {},
        "outputId": "c33fd8de-1002-420a-eeef-f09bb9e22fd3"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(fit.fittedvalues, fit.get_influence().resid_studentized_internal)\n",
        "ax.set_xlabel(\"Fitted values\")\n",
        "ax.set_ylabel(\"Studentized Residual\")\n",
        "ax.set_title(\"Figure 3.4\")\n",
        "fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 480,
              "width": 640
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_KPcZriIL3sn",
        "colab": {},
        "outputId": "183f401e-f63f-4c4c-8c0f-af371ebb13b3"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(house_df[\"size\"], fit.get_influence().resid_studentized_internal)\n",
        "ax.set_xlabel(\"Size\")\n",
        "ax.set_ylabel(\"Studentized Residual\")\n",
        "fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 480,
              "width": 640
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "z8pgspgwL3s0",
        "colab": {},
        "outputId": "1f26d2aa-ffc5-4da6-dda8-7ac284cf53d9"
      },
      "source": [
        "cooks_distance = fit.get_influence().cooks_distance[0]\n",
        "plt.plot(cooks_distance)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x2b8494d3ac88>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 480,
              "width": 640
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4GpgzGHFL3s7"
      },
      "source": [
        "Observation 64 seems influential. Let's remove it and refit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fvkDYD0hL3s8",
        "colab": {},
        "outputId": "e4e1b5fa-fe99-4dcd-e77a-23265a5f4921"
      },
      "source": [
        "fit = smf.ols(\n",
        "    formula=\"\"\"price ~ size + new\"\"\", data=house_df.loc[cooks_distance < 1]\n",
        ").fit()\n",
        "print(fit.summary())\n",
        "model_residuals = fit.resid\n",
        "sd_model_residuals = model_residuals.std()\n",
        "print(\"Residual SE: {}\".format(np.round(sd_model_residuals, 2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.772\n",
            "Model:                            OLS   Adj. R-squared:                  0.767\n",
            "Method:                 Least Squares   F-statistic:                     162.5\n",
            "Date:                Mon, 22 Jun 2020   Prob (F-statistic):           1.52e-31\n",
            "Time:                        23:41:15   Log-Likelihood:                -524.21\n",
            "No. Observations:                  99   AIC:                             1054.\n",
            "Df Residuals:                      96   BIC:                             1062.\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept    -63.1545     14.252     -4.431      0.000     -91.444     -34.865\n",
            "size           0.1328      0.009     15.138      0.000       0.115       0.150\n",
            "new           41.3062     17.327      2.384      0.019       6.913      75.700\n",
            "==============================================================================\n",
            "Omnibus:                        8.263   Durbin-Watson:                   1.480\n",
            "Prob(Omnibus):                  0.016   Jarque-Bera (JB):                7.876\n",
            "Skew:                           0.640   Prob(JB):                       0.0195\n",
            "Kurtosis:                       3.520   Cond. No.                     6.42e+03\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 6.42e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "Residual SE: 48.48\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uUXu0OeGL3tD"
      },
      "source": [
        "$R^2$ sees a considerable improvement. Next we check for an interaction:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Z97b9KweL3tF",
        "colab": {},
        "outputId": "74e39067-5347-4e11-e30f-3cb124a97b91"
      },
      "source": [
        "fit = smf.ols(formula=\"\"\"price ~ size + new + size:new\"\"\", data=house_df.loc[cooks_distance<1]).fit()\n",
        "print(fit.summary())\n",
        "model_residuals = fit.resid\n",
        "sd_model_residuals = model_residuals.std()\n",
        "print(\"Residual SE: {}\".format(np.round(sd_model_residuals, 2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.782\n",
            "Model:                            OLS   Adj. R-squared:                  0.775\n",
            "Method:                 Least Squares   F-statistic:                     113.7\n",
            "Date:                Mon, 22 Jun 2020   Prob (F-statistic):           2.52e-31\n",
            "Time:                        23:41:16   Log-Likelihood:                -521.95\n",
            "No. Observations:                  99   AIC:                             1052.\n",
            "Df Residuals:                      95   BIC:                             1062.\n",
            "Df Model:                           3                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept    -48.2431     15.686     -3.075      0.003     -79.385     -17.102\n",
            "size           0.1230      0.010     12.536      0.000       0.104       0.142\n",
            "new          -52.5122     47.630     -1.102      0.273    -147.070      42.046\n",
            "size:new       0.0434      0.021      2.109      0.038       0.003       0.084\n",
            "==============================================================================\n",
            "Omnibus:                       14.139   Durbin-Watson:                   1.508\n",
            "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               16.134\n",
            "Skew:                           0.807   Prob(JB):                     0.000314\n",
            "Kurtosis:                       4.144   Cond. No.                     1.76e+04\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.76e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "Residual SE: 47.39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gGCTKcVWL3tL"
      },
      "source": [
        "The increase in $R^2$ is minimal."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jFB0AUiqL3tM"
      },
      "source": [
        "### Simpson's paradox\n",
        "\n",
        "Consider adding beds as another explanatory variable.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UpJuTg7GL3tN",
        "colab": {},
        "outputId": "99caab91-8728-4b0d-8540-2bb57fccc673"
      },
      "source": [
        "stats.pearsonr(house_df['beds'], house_df['price'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.3939570220583582, 5.006768706208471e-05)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mQtIq3wzL3tT"
      },
      "source": [
        "Thus, beds is modestly correlated with price. Let's include it in the model and see how the fit changes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jd1Z-PArL3tV",
        "colab": {},
        "outputId": "e851566c-143e-4a7f-d6cb-84f709d3d884"
      },
      "source": [
        "fit = smf.ols(formula=\"\"\"price ~ size + new\"\"\", data=house_df).fit()\n",
        "print(fit.summary())\n",
        "model_residuals = fit.resid\n",
        "sd_model_residuals = model_residuals.std()\n",
        "print(\"Residual SE: {}\".format(np.round(sd_model_residuals, 2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.723\n",
            "Model:                            OLS   Adj. R-squared:                  0.717\n",
            "Method:                 Least Squares   F-statistic:                     126.3\n",
            "Date:                Mon, 22 Jun 2020   Prob (F-statistic):           9.79e-28\n",
            "Time:                        23:41:16   Log-Likelihood:                -539.05\n",
            "No. Observations:                 100   AIC:                             1084.\n",
            "Df Residuals:                      97   BIC:                             1092.\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept    -40.2309     14.696     -2.738      0.007     -69.399     -11.063\n",
            "size           0.1161      0.009     13.204      0.000       0.099       0.134\n",
            "new           57.7363     18.653      3.095      0.003      20.715      94.757\n",
            "==============================================================================\n",
            "Omnibus:                       12.906   Durbin-Watson:                   1.483\n",
            "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               29.895\n",
            "Skew:                           0.370   Prob(JB):                     3.22e-07\n",
            "Kurtosis:                       5.574   Cond. No.                     6.32e+03\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 6.32e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "Residual SE: 53.33\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "h77-B-HfL3tb",
        "colab": {},
        "outputId": "42da1f1c-a1d2-4993-d423-37caf52635ae"
      },
      "source": [
        "fit = smf.ols(formula=\"\"\"price ~ size + new + beds\"\"\", data=house_df).fit()\n",
        "print(fit.summary())\n",
        "model_residuals = fit.resid\n",
        "sd_model_residuals = model_residuals.std()\n",
        "print(\"Residual SE: {}\".format(np.round(sd_model_residuals, 2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  price   R-squared:                       0.724\n",
            "Model:                            OLS   Adj. R-squared:                  0.715\n",
            "Method:                 Least Squares   F-statistic:                     83.97\n",
            "Date:                Mon, 22 Jun 2020   Prob (F-statistic):           9.68e-27\n",
            "Time:                        23:41:16   Log-Likelihood:                -538.78\n",
            "No. Observations:                 100   AIC:                             1086.\n",
            "Df Residuals:                      96   BIC:                             1096.\n",
            "Df Model:                           3                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept    -25.1998     25.602     -0.984      0.327     -76.020      25.620\n",
            "size           0.1205      0.011     11.229      0.000       0.099       0.142\n",
            "new           54.8996     19.113      2.872      0.005      16.961      92.838\n",
            "beds          -7.2927     10.159     -0.718      0.475     -27.458      12.872\n",
            "==============================================================================\n",
            "Omnibus:                       13.929   Durbin-Watson:                   1.496\n",
            "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               41.568\n",
            "Skew:                           0.277   Prob(JB):                     9.41e-10\n",
            "Kurtosis:                       6.110   Cond. No.                     8.80e+03\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 8.8e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n",
            "Residual SE: 53.19\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IXDug4RcL3th"
      },
      "source": [
        "Including beds leads to a reduced $R^2$!! Thus, once `size` and `new` are in the model, it does not help to add beds. Although the marginal effect of beds is positive (as shown by the modest positive correlation), the estimated partial effect of beds is negative. This illustrates Simpson's paradox."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EY-7Vq1mL3ti"
      },
      "source": [
        "### Partial Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zD1riIqTL3tj",
        "colab": {},
        "outputId": "7ef068b9-0203-40f7-d5a7-6fb4b8237411"
      },
      "source": [
        "regress_sizenew_on_price = smf.ols(formula=\"\"\"price ~ size + new\"\"\", data=house_df).fit()\n",
        "regress_sizenew_on_bed = smf.ols(formula=\"\"\"beds ~ size + new\"\"\", data=house_df).fit()\n",
        "\n",
        "stats.pearsonr(regress_sizenew_on_price.resid, regress_sizenew_on_bed.resid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.07307200731424236, 0.4699872574144045)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QLk_AjrVL3tp"
      },
      "source": [
        "Partial correlation between selling price and beds. Found using 1) finding the residuals for predicting selling price using size and new 2) finding the residual for predicting beds using size and new and 3) correlating the residuals in 1 and 2."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tySFXbiEL3tq"
      },
      "source": [
        "### *TODO: Testing contrats as a general linear hypothesis*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-nV65TljL3ts",
        "colab": {},
        "outputId": "03942122-877e-4580-d17d-5852b1adc5df"
      },
      "source": [
        "from statsmodels.stats.libqsturng import psturng, qsturng\n",
        "\n",
        "qsturng(0.95, 10, 190)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.52786052558441"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Av7u42RPL3ty",
        "colab": {},
        "outputId": "e1e46eb7-701f-445c-f5f0-6b0b27c4723c"
      },
      "source": [
        "stats.t.ppf(1-0.05/(2*45), 190)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.3113791139604563"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    }
  ]
}