{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regression diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "When we fit a linear regression model to a particular data set, many problems may occur. Most common among these are the following {cite:p}`James2021`:\n",
    "\n",
    "1. Outliers and high-leverage points\n",
    "2. Non-linearity of the response-predictor relationship\n",
    "3. Non-constant variance of error terms (heteroscedasticity)\n",
    "4. Non-normally distributed errors\n",
    "5. Correlation of error terms\n",
    "6. Collinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "As an example of how to identify these problems, we use data about the prestige of canadian occupations to estimate a regression model with `prestige` as the response and `income` and `education` as predictors. Review this [site](https://vincentarelbundock.github.io/Rdatasets/doc/carData/Prestige.html) to learn more about the data.\n",
    "\n",
    "*This tutorial is mainly based on the excellent [statmodels documentation](https://www.statsmodels.org/dev/examples/notebooks/generated/regression_plots.html#Partial-Regression-Plots-(Crime-Data)) about regression plots.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    " ## Python setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:48.684009Z",
     "iopub.status.busy": "2021-11-08T19:26:48.682866Z",
     "iopub.status.idle": "2021-11-08T19:26:49.638188Z",
     "shell.execute_reply": "2021-11-08T19:26:49.639067Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from patsy import dmatrices\n",
    "\n",
    "from statsmodels.compat import lzip\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rc(\"figure\", figsize=(16, 8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Import data\n",
    "\n",
    "We use the function `.datasets.get_rdataset` to load the dataset from the <a href=\"https://vincentarelbundock.github.io/Rdatasets/\">Rdatasets package</a>. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# import data\n",
    "df = sm.datasets.get_rdataset(\"Duncan\", \"carData\", cache=True).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:49.739445Z",
     "iopub.status.busy": "2021-11-08T19:26:49.738348Z",
     "iopub.status.idle": "2021-11-08T19:26:49.828161Z",
     "shell.execute_reply": "2021-11-08T19:26:49.828970Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "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>type</th>\n",
       "      <th>income</th>\n",
       "      <th>education</th>\n",
       "      <th>prestige</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accountant</th>\n",
       "      <td>prof</td>\n",
       "      <td>62</td>\n",
       "      <td>86</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pilot</th>\n",
       "      <td>prof</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>architect</th>\n",
       "      <td>prof</td>\n",
       "      <td>75</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <td>prof</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chemist</th>\n",
       "      <td>prof</td>\n",
       "      <td>64</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            type  income  education  prestige\n",
       "accountant  prof      62         86        82\n",
       "pilot       prof      72         76        83\n",
       "architect   prof      75         92        90\n",
       "author      prof      55         90        76\n",
       "chemist     prof      64         86        90"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show head of DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot numerical data as pairs\n",
    "sns.pairplot(df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Findings:\n",
    "\n",
    "- Positive association between prestige and eduction as well as between prestige and income\n",
    "- Relationships seem to be linear in both cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:49.832765Z",
     "iopub.status.busy": "2021-11-08T19:26:49.831662Z",
     "iopub.status.idle": "2021-11-08T19:26:49.844450Z",
     "shell.execute_reply": "2021-11-08T19:26:49.845237Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# estimate the model and save it as lm (linear model)\n",
    "lm = ols(\"prestige ~ income + education\", data=df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:49.849037Z",
     "iopub.status.busy": "2021-11-08T19:26:49.847763Z",
     "iopub.status.idle": "2021-11-08T19:26:49.866489Z",
     "shell.execute_reply": "2021-11-08T19:26:49.865671Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               prestige   R-squared:                       0.828\n",
      "Model:                            OLS   Adj. R-squared:                  0.820\n",
      "Method:                 Least Squares   F-statistic:                     101.2\n",
      "Date:                Tue, 05 Apr 2022   Prob (F-statistic):           8.65e-17\n",
      "Time:                        19:18:25   Log-Likelihood:                -178.98\n",
      "No. Observations:                  45   AIC:                             364.0\n",
      "Df Residuals:                      42   BIC:                             369.4\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -6.0647      4.272     -1.420      0.163     -14.686       2.556\n",
      "income         0.5987      0.120      5.003      0.000       0.357       0.840\n",
      "education      0.5458      0.098      5.555      0.000       0.348       0.744\n",
      "==============================================================================\n",
      "Omnibus:                        1.279   Durbin-Watson:                   1.458\n",
      "Prob(Omnibus):                  0.528   Jarque-Bera (JB):                0.520\n",
      "Skew:                           0.155   Prob(JB):                        0.771\n",
      "Kurtosis:                       3.426   Cond. No.                         163.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# print regression results\n",
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regression diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Outliers and high-leverage points\n",
    "\n",
    "In a regression analysis, single observations can have a strong influence on the results of the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "For example, in the plot below we can see how a single outlying data point can affect a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDE CODE\n",
    "df_outlier = pd.DataFrame(\n",
    "        { 'observation': pd.Categorical([ \"A\", \"B\", \"C\", \"D\", \"E\" ]),\n",
    "          'x': np.array([1, 2, 3, 4, 5],dtype='int32'),\n",
    "          'y': np.array([2, 4, 6, 8, 2],dtype='int32')}\n",
    "        )\n",
    "\n",
    "sns.lmplot(x=\"x\", y=\"y\", data=df_outlier, ci=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We can spot outliers by using graphs like the scatterplot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='x', y='y', data=df_outlier);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We just saw that outliers are observations for which the response $y_i$ is unusual given the predictor $x_i$. \n",
    "\n",
    "In contrast, observations with high **leverage** have an unusual value for $x_i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDE CODE\n",
    "df_leverage = pd.DataFrame(\n",
    "        { 'observation': pd.Categorical([ \"A\", \"B\", \"C\", \"D\", \"E\" ]),\n",
    "          'x': np.array([1, 2, 3, 4, 20],dtype='int32'),\n",
    "          'y': np.array([2, 4, 6, 8, 30],dtype='int32')}\n",
    "        )\n",
    "\n",
    "sns.lmplot(x=\"x\", y=\"y\", data=df_leverage, ci=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "For example, the observation with a value of $x=20$ has high leverage, in that the predictor value for this observation is large relative to the other observations. The removal of the high leverage observation would have a substantial impact on the regression line. In general, high leverage observations tend to have a sizable impact on the estimated regression line. Therefore, it is important to detect influential observations and to take them into consideration when interpreting the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Again, we could use simple graphs to identify unusual observations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='x', y='y', data=df_leverage);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA44AAAHoCAYAAAAGxbH6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAV3UlEQVR4nO3df4zkd13H8de7dwWuAjFckR8X8GgOg0CbagoJCLSFNp4YwAgxgtgrURCj14qoaFCoiQYxipRGo8AfbUOwaEBjtRy2EH5EauWq1YIWuJSWcC20XPlVegWu/frHzIXtsvvevWNnvzd7j0cyaXb2u5P37qefm3nufGe2hmEIAAAALOeEsQcAAADg2CYcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABobT6Sg08++eRh+/btMxoFAACAMV1//fVfHobhkYuvP6Jw3L59e/bu3bt2UwEAAHDMqKpbl7reqaoAAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANDaPPYAx6NLLrkk+/btG3uMVdm/f3+SZNu2bSNPsrIdO3Zk9+7dY48BAAAbjnAcwb59+3LDJ/8v9530iLFHWdGme76WJPnit47t/1U23XPX2CMAAMCGdWzXwAZ230mPyMEnPX/sMVa05aarkuSYn/XwnAAAwNrzGkcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABobR57gLVyySWXJEl279498iSM4YR7v579+w+NPQYAAGxIGyYc9+3bN/YIjKju/04OHjw49hgAALAhOVUVAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAZuzAgQO54IILcuDAgbFHOSrCEQAAYMYuu+yy3Hjjjbn88svHHuWoCEcAAIAZOnDgQPbs2ZNhGLJnz565fNZx89gDrJX9+/fn4MGDufDCC8ceZUX79u3LCd8exh4DAABYB5dddlnuv//+JMl9992Xyy+/PK95zWtGnurIrPiMY1W9qqr2VtXeO++8cz1mAgAA2DCuueaaHDp0KEly6NChXH311SNPdORWfMZxGIa3J3l7kpxxxhnH7NNk27ZtS5JcfPHFI0+ysgsvvDDX3/ylsccAAADWwTnnnJOrrroqhw4dyubNm3PuueeOPdIR8xpHAACAGdq1a1dOOGGSXps2bcp555038kRHTjgCAADM0NatW7Nz585UVXbu3JmtW7eOPdIR2zBvjgMAAHCs2rVrV2655Za5fLYxEY4AAAAzt3Xr1rztbW8be4yj5lRVAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFqbxx5grezYsWPsERjRcMKJ2bJly9hjAADAhrRhwnH37t1jj8CI7n/Iw7Nt26PGHgMAADYkp6oCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABASzgCAADQEo4AAAC0hCMAAAAt4QgAAEBLOAIAANASjgAAALSEIwAAAC3hCAAAQEs4AgAA0BKOAAAAtIQjAAAALeEIAABAa/PYAxyvNt1zV7bcdNXYY6xo0z0HkuSYn3XTPXcledTYYwAAwIYkHEewY8eOsUdYtf37DyVJtm071qPsUXP1cwUAgHkiHEewe/fusUcAAABYNa9xBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABoCUcAAABawhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEAACgJRwBAABo1TAMqz+46s4kt85uHL5PJyf58thDsCrWan5Yq/lhreaHtZoP1ml+WKv5MQ9r9cPDMDxy8ZVHFI4c26pq7zAMZ4w9ByuzVvPDWs0PazU/rNV8sE7zw1rNj3leK6eqAgAA0BKOAAAAtITjxvL2sQdg1azV/LBW88NazQ9rNR+s0/ywVvNjbtfKaxwBAABoecYRAACAlnAEAACgJRznQFX9XlV9oqq+XlV3VtWVVfXUFb5me1UNS1x2rtfcx6OqumiJn/kXV/iaU6vqI1V1sKr2V9UbqqrWa+bjVVXdsswe+Zdljren1klVPaeq/mm6H4aqOn/R52u6126b7psPV9VTVnG7Z1bV9VV1b1XdXFWvntk3cZzo1qqqTqyqN1fV/1TVN6vq9qp6d1U9foXbPGuZvfakmX9DG9Qq9tSlS/y8/30Vt2tPrbFVrNVSe2Ooqr9sbtOeWmOreWy+Ee+rhON8OCvJXyV5ZpLnJjmU5JqqesQqvnZnkscsuHxoRjPyXZ/OA3/mpy53YFU9PMnVSb6U5GlJLkjy20l+c/ZjHveelgeu048nGZL83QpfZ0/N3kOTfDLJhUkOLvH530ny2iS7M1nHO5JcXVUPW+4Gq+oJSa5K8vEkP5bkTUkuqaoXr+3ox51urU7KZF/98fS/L0ryuCR7qmrzKm77KXngXvvsGs18PFppTyXJNXngz/v53Q3aUzOz0lo9ZtHlBdPrV7rvSuyptXRWVn5svvHuq4ZhcJmzSyb/qNyX5AXNMdszeRB8xtjzHk+XJBcl+eQRHP+rSb6eZMuC634/yf5M37zKZd3W7vVJvprkpGU+b0+Nsy53Jzl/wceV5PYkr19w3ZYk30jyK83tvDnJZxdd984k1479PW6Uy+K1WuaYJ0/30anNMWdNjzl57O9pI16WWqcklyb55yO8HXtqhLVa4ph3JPn0CsfYU7Nfqwc8Nt+o91WecZxPD8vk2eKvrOLY91XVHVX1b1X1khnPxcQp01NMPldVV1TVKc2xz0jysWEYFv5W8QNJHptJqLAOpqcG/1KSdw3DcM8Kh9tT43pCkkcn+dfDV0z3z0cz+c3vcp6x8GumPpDkjKo6ca2HZFkPn/53Nfdfe6ent36wqs6e5VAkSZ41/bftM1X1jqr6oRWOt6dGVlUPTfLzmcTjathTs7P4sfmGvK8SjvPp4iQ3JLm2OebuJL+V5OcyOd3kg0neU1Uvn/l0x7frkpyf5KeSvDKTfzQ+XlVblzn+0ZmcprrQlxZ8jvVxbib/yL+zOcaeOjYc3hdL7Ztuzyy31zYnOXltRqNTVQ9K8udJrhyG4QvNobdncjbGi5P8bCan/3+wqp4z+ymPW3uSnJfkeZmcWvf0JB+qqgc3X2NPje9lSR6c5LIVjrOnZm/xY/MNeV+1mtcYcAypqrckeVaSZw3DcN9yxw3D8OVM7qAP21tVJ2dyvvW7Zjvl8WsYhvcv/Hj65gI3J9mV5C3Lfdmij2uZ65mdVyb5xDAMNyx3gD11zFlq36y0Z+y1kUxf0/iuJD+Y5IXdscMwfDqTB7aHXVtV2zP5xc1HZzTicW0YhisWfHhjVV2f5NYkP53kfd2XLvrYnlpfr0zyj8Mw3NkdZE/N1gqPzTfUfZVnHOdIVf1Fkpcmee4wDDcfxU1cl+SJazsVnWEY7k7yqSz/c/9ivvc3T4dPD1r8GydmYHo61ouy+lN9FrKn1t/hdyleat90e2a5vXYoyYG1GY2lTKPxb5OcluR5wzAczc/bXltHwzDcluQL6X/m9tSIqur0JGfk6O67EntqTTSPzTfkfZVwnBNVdXEmpyQ8dxiGm47yZk7P5HQF1klVPSTJk7L8z/3aJM+eHnfYuUluS3LLbKdj6vwk30pyxQrHLeX02FPr7XOZ3LGee/iK6f55dibvQreca5Ocs+i6c5PsHYbhO2s9JBPT1+S8J5NoPHsYhvbPEzVOj722bqZnU2xL/zO3p8b1qkweJ1xzlF9/euyp78sKj8035H2VU1XnwPRv8/xikp9J8pWqOvybiLunz2ilqt6U5OnDMDxv+vGuJN9J8l9J7s/k7Zp/Lcnr1nf640tV/VmSK5N8PpPfEP1Bkh/I9PUHi9cpybuTvDHJpVX1R0l+JMnvJvnDYfpWWszO9E1xfjnJFcMwfGPR5+ypkUzf8GHH9MMTkjx++tv1u4Zh+HxVvTXJ66vqpiSfyeSdiO/OZD8dvo3Lk2QYhvOmV/11kl+ffu3fJPmJTH5p8NIZfzsbWrdWmfwC7O8zeRv6FyQZFtx/fe3wm4ItXquq+o1MHhB/KsmDkrw8k/u/Y+Pt6OfQCut0VybvCP7eTEJieyZ/AuCOJP+w4DbsqXWw0r9/02NOSvILSf50qccK9tTsrfTYfBiGYUPeV439tq4uK18yOad5qctFC465NMktCz7eleR/k3wzkz/3sDfJy8f+Xjb6JZNnrW5L8u1M/qTGe5M8ebl1ml53aiavMbg3kzvtN8af4liv9Tp7upeevsTn7Knx1uWsZf7Nu3T6+crkge7t033zkSRPXXQbH07y4UXXnZnkPzN5hvlzSV499vc675durfLdP2Gz1OX85dYqk9cN78vkb9jdleRjSZ4/9vc6z5cV1mlLJu/aeMf0vuvW6fWPW3Qb9tTIa7XgmFdkcuriY5e5DXtq9uu0msfmG+6+qqYDAgAAwJK8xhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACAlnAEgKmqemRV3V5Vb1hw3WlVdW9VvWTM2QBgTDUMw9gzAMAxo6p+MsmVSc5MckOSvUn+YxiGV4w5FwCMSTgCwCJV9dYkL0zykSTPTnL6MAx3jzoUAIxIOALAIlX14CT/neSJSZ45DMN1I48EAKPyGkcA+F7bkzwuyZDklHFHAYDxecYRABaoqhOTXJvks0muS3JRktOGYfj8mHMBwJiEIwAsUFV/kuRlSU5L8rUk70+yJcnZwzDcP+ZsADAWp6oCwFRVnZnktUnOG4bhq8Pkt6vnJ/nRJK8bczYAGJNnHAEAAGh5xhEAAICWcAQAAKAlHAEAAGgJRwAAAFrCEQAAgJZwBAAAoCUcAQAAaAlHAAAAWsIRAACA1v8D6O6cx4mSjwEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='x', data=df_leverage);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Influence plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "> Influence plots are used to identify influential data points. \n",
    "\n",
    "> They depend on both the residual and leverage i.e they take into account both the $x$ value and $y$ value of the observation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "We can use **influence plots** to identify observations in our independent variables which have \"unusual\" values in comparison to other values. \n",
    "\n",
    "Influence plots show the (externally) studentized residuals vs. the leverage of each observation:\n",
    "\n",
    "Dividing a statistic by a sample standard deviation is called **studentizing**, in analogy with standardizing and normalizing. The basic idea is to: \n",
    "\n",
    "1. Delete the observations one at a time.\n",
    "2. Refit the regression model each time on the remaining n–1 observations. \n",
    "3. Compare the observed response values to their fitted values based on the models with the ith observation deleted. This produces unstandardized deleted residuals. \n",
    "4. Standardising the deleted residuals produces studentized deleted residuals (also known as externally studentized residuals)\n",
    "\n",
    "In essence, externally studentized residuals are residuals that are scaled by their standard deviation. If an observation has an externally studentized residual that is larger than 3 (in absolute value) we can call it an outlier. However, values greater then 2 (in absolute values) are usually also of interest.\n",
    "\n",
    "**Leverage** is a measure of how far away the independent variable values of an observation are from those of the other observations. High-leverage points are outliers with respect to the independent variables. \n",
    "\n",
    "\n",
    "In statsmodels `.influence_plot` the influence of each point can be visualized by the `criterion` keyword argument. Options are Cook's distance and DFFITS, two measures of influence. Steps to compute Cook’s distance:\n",
    "\n",
    "1. Delete observations one at a time.\n",
    "2. Refit the regression model on remaining (n−1) observations\n",
    "3. Examine how much all of the fitted values change when the ith observation is deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:49.870389Z",
     "iopub.status.busy": "2021-11-08T19:26:49.869263Z",
     "iopub.status.idle": "2021-11-08T19:26:50.266422Z",
     "shell.execute_reply": "2021-11-08T19:26:50.265901Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sm.graphics.influence_plot(lm, criterion=\"cooks\")\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "To identify values with high influence, we look for observations with:\n",
    "\n",
    "1. big blue points (high Cook's distance) and \n",
    "1. high leverage (X-axis) which additionally have \n",
    "2. high or low studentized residuals (Y-axis). \n",
    "\n",
    "There are a few worrisome observations with big blue dots in the plot:\n",
    "\n",
    "- `RR.engineer` has large leverage but small residual. \n",
    "- Both `contractor` and `reporter` have low leverage but a large residual. \n",
    "- `Conductor` and `minister` have both high leverage and large residuals, and, therefore, large influence.\n",
    "\n",
    "A general rule of thumb is that observations with a **Cook’s distance** over $4/n$ (where n is the number of observations) are possible outliers with leverage.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In addition to our plot, we can use the function `.get_influence()` to assess the influence of each observation and compare them to the cricital Cook's distance :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Critical Cooks distance: 0.08888888888888889\n",
      "Index(['minister', 'reporter', 'conductor'], dtype='object') \n",
      " [0.56637974 0.09898456 0.22364122]\n"
     ]
    }
   ],
   "source": [
    "# obtain Cook's distance \n",
    "lm_cooksd = lm.get_influence().cooks_distance[0]\n",
    "\n",
    "# get length of df to obtain n\n",
    "n = len(df[\"income\"])\n",
    "\n",
    "# calculate critical d\n",
    "critical_d = 4/n\n",
    "print('Critical Cooks distance:', critical_d)\n",
    "\n",
    "# identification of potential outliers with leverage\n",
    "out_d = lm_cooksd > critical_d\n",
    "\n",
    "# output potential outliers with leverage\n",
    "print(df.index[out_d], \"\\n\", \n",
    "    lm_cooksd[out_d])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Solutions\n",
    "\n",
    "If you have unusual observations in your data (unusual depends on your use case and domain knowledge) here some approaches to deal with them:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "1. **Drop** the identified unusual observations.\n",
    "\n",
    "1. Analyse your data with **robust methods** like bootstrapping (sampling with replacement).\n",
    "\n",
    "1. **Trim** the data (delete a certain amount of scores from the extremes)\n",
    "\n",
    "1. **Windsorizing** (substitute outliers with the highest value that isn’t an outlier)\n",
    "\n",
    "1. **Transform** the data (by applying a mathematical function to scores - like a log transformation to reduce positive skew)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "A note on data transformations:\n",
    "\n",
    "Be aware that transforming the data helps as often as it hinders model performance (Games & Lucas, 1966). According to Games (1984):\n",
    "\n",
    "- Because of the central limit theorem, the sampling distribution will be normal in samples > 40 anyway.\n",
    "- Transforming the data changes the hypothesis being tested (e.g. when using a log transformation and comparing means you change from comparing arithmetic means to comparing geometric means)\n",
    "- In small samples it is tricky to determine normality one way or another.\n",
    "- The consequences for the statistical model of applying the \"wrong\" transformation could be worse than the consequences of analysing the untransformed scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Non-linearity and heteroscedasticity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "One crucial assumption of the linear regression model is the linear relationship between the response and the dependent variables. We can identify non-linear relationships in the regression model residuals if the residuals are not equally spread around the horizontal line (where the residuals are zero) but instead show a pattern, then this gives us an indication for a non-linear relationship. \n",
    "\n",
    ":::{note}\n",
    "We can deal with non-linear relationships via basis expansions (e.g. polynomial regression) or regression splines ([see {cite:t}`Kuhn2019`](http://www.feat.engineering/numeric-one-to-many.html#numeric-basis-functions))\n",
    ":::\n",
    "\n",
    "Another important assumption is that the error terms have a constant variance (homoscedasticity). For instance, the variances of the error terms may increase with the value of the response. One can identify non-constant variances in the errors, or heteroscedasticity, from the presence of a funnel shape in a residual plot. \n",
    "\n",
    "When faced with this problem, one possible solution is to use [weighted regression](https://www.statsmodels.org/dev/examples/notebooks/generated/wls.html). This type of regression assigns a weight to each data point based on the variance of its fitted value. Essentially, this gives small weights to data points that have higher variances, which shrinks their squared residuals. When the proper weights are used, this can eliminate the problem of heteroscedasticity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Partial Regression Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Since we are doing multivariate regressions, we cannot just look at individual bivariate plots (e.g., prestige and income) to identify the type of relationships between response and predictor. \n",
    "\n",
    "Instead, we want to look at the relationship of the dependent variable and independent variables *conditional* on the other independent variables. We can do this through using **partial regression plots**, otherwise known as **added variable plots**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "With partial regression plots you can:\n",
    "\n",
    "1. Investigate the relationship between a dependent and independent variables conditional on other independent varibales.\n",
    "2. Identify the effects of the individual data values on the estimation of a coefficient. \n",
    "3. Investigate violations of underlying assumptions such as linearity and homoscedasticity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "If we want to identify the relationship between `prestige` and `income`, we would proceed as follows (we name the independent variable of interest $X_k$ and all other independent variables $X_{\\sim k}$):\n",
    "\n",
    "<br/>\n",
    "\n",
    "1. Compute a regression model by regressing the response variable versus the independent variables excluding $X_k$:\n",
    "    - response variable: `prestige`\n",
    "    - $X_k$: `income`\n",
    "    - $X_{\\sim k}$: `education`\n",
    "    - Model($X_{\\sim k}$): `(prestige ~ education)`  \n",
    "\n",
    "<br/>\n",
    "\n",
    "2. Compute the residuals of Model($X_{\\sim k}$): \n",
    "    - $R_{X_{\\sim k}}$: residuals of Model($X_{\\sim k}$): \n",
    "\n",
    "<br/>\n",
    "\n",
    "3. Compute a new regression model by regressing $R_{X_{\\sim k}}$ on $X_{\\sim k}$. \n",
    "    - Model($X_k$): $R_{X_{\\sim k}}$ ~ `income`\n",
    "\n",
    "<br/>\n",
    "\n",
    "4. Compute the residuals of Model($X_k$): \n",
    "    - $R_{X_{k}}$: residuals of Model($X_{k}$): \n",
    "\n",
    "<br/>\n",
    "\n",
    "5. Make a partial regression plot by plotting the residuals from $R_{X_{\\sim k}}$ against the residuals from $R_{X_{k}}$:\n",
    "    - Plot with X = $R_{X_{k}}$ and Y = $R_{X_{\\sim k}}$\n",
    "\n",
    "<br/>\n",
    "\n",
    "For a quick check of all the regressors, you can use `plot_partregress_grid`. These plots will not label the \n",
    "points, but you can use them to identify problems and then use `plot_partregress` to get more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval_env: 1\n",
      "eval_env: 1\n",
      "eval_env: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sm.graphics.plot_partregress_grid(lm)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We observe a positive relationship between prestige and income as well as between prestige and education. The relationship seems to be linear in both cases.\n",
    "\n",
    "Next, let's take a closer look at the observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval_env: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.graphics.plot_partregress(\n",
    "                             endog='prestige', # response\n",
    "                             exog_i='income', # variable of interest\n",
    "                             exog_others=['education'], # other predictors\n",
    "                             data=df,  # dataframe\n",
    "                             obs_labels=True # show labels\n",
    "                             );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval_env: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same plot for education\n",
    "fig = sm.graphics.plot_partregress(\"prestige\", \"education\", [\"income\"], data=df)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "The partial regression plots confirm the influence of `conductor`, `minister` on the partial relationships between prestige and the predictors income and education. The influence of `reporter` is less obvious but since the observation was considered critical according to Cook`s D, we also drop this case.\n",
    "\n",
    "Note that influential cases potentially bias the effect of income and education on prestige. Therefore, we will drop these cases and perform a linear regression without them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['accountant', 'pilot', 'architect', 'author', 'chemist', 'minister',\n",
      "       'professor', 'dentist', 'reporter', 'engineer', 'undertaker', 'lawyer',\n",
      "       'physician', 'welfare.worker', 'teacher', 'conductor', 'contractor',\n",
      "       'factory.owner', 'store.manager', 'banker', 'bookkeeper',\n",
      "       'mail.carrier', 'insurance.agent', 'store.clerk', 'carpenter',\n",
      "       'electrician', 'RR.engineer', 'machinist', 'auto.repairman', 'plumber',\n",
      "       'gas.stn.attendant', 'coal.miner', 'streetcar.motorman', 'taxi.driver',\n",
      "       'truck.driver', 'machine.operator', 'barber', 'bartender',\n",
      "       'shoe.shiner', 'cook', 'soda.clerk', 'watchman', 'janitor', 'policeman',\n",
      "       'waiter'],\n",
      "      dtype='object') \n",
      " [ True  True  True  True  True False  True  True False  True  True  True\n",
      "  True  True  True False  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True]\n"
     ]
    }
   ],
   "source": [
    "# make a subset and flag as TRUE if index doesn't contain ...\n",
    "subset = ~df.index.isin([\"conductor\", \"minister\", \"reporter\"])\n",
    "\n",
    "print(df.index, \"\\n\", subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               prestige   R-squared:                       0.897\n",
      "Model:                            OLS   Adj. R-squared:                  0.891\n",
      "Method:                 Least Squares   F-statistic:                     169.0\n",
      "Date:                Tue, 05 Apr 2022   Prob (F-statistic):           6.13e-20\n",
      "Time:                        19:18:31   Log-Likelihood:                -157.02\n",
      "No. Observations:                  42   AIC:                             320.0\n",
      "Df Residuals:                      39   BIC:                             325.2\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -7.2414      3.391     -2.136      0.039     -14.099      -0.383\n",
      "income         0.8773      0.113      7.775      0.000       0.649       1.106\n",
      "education      0.3538      0.092      3.861      0.000       0.168       0.539\n",
      "==============================================================================\n",
      "Omnibus:                        1.483   Durbin-Watson:                   1.577\n",
      "Prob(Omnibus):                  0.476   Jarque-Bera (JB):                0.744\n",
      "Skew:                          -0.291   Prob(JB):                        0.689\n",
      "Kurtosis:                       3.293   Cond. No.                         156.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# compute regression without influential cases\n",
    "lm2 = ols(\"prestige ~ income + education\", data=df, subset=subset).fit()\n",
    "\n",
    "print(lm2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "Dropping the influential cases confirms our believe. The new regression model without the influential cases is superior to the initial one. To see this, compare the regression summary above with our initial summary (e.g., see $R^2$, adjusted $R^2$ and the F-statistic)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### CCPR plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "The Component-Component plus Residual (CCPR) plot provides another way to judge the effect of one regressor on the response variable by taking into account the effects of the other independent variables. They are also a good way to see if the predictors have a linear relationship with the dependent variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "A CCPR plot consists of a partial residuals plot and a component: \n",
    "\n",
    "1. The partial residuals plot is defined as $\\text{Residuals} + \\hat\\beta_{i} X_i \\text{ }\\text{ }$ versus $X_i$, where\n",
    "  \n",
    "    - Residuals = residuals from the full model\n",
    "    - $\\hat\\beta_{i}$ = regression coefficient from the ith independent variable in the full model\n",
    "    - $X_i$ = the ith independent variable\n",
    "\n",
    "2. The component adds $\\hat\\beta_{i}$ $X_i$ versus $X_i$ to show where the fitted line would lie. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    " A significant difference between the residual line and the actual distribution of values indicates that the predictor does not have a linear relationship with the dependent variable. \n",
    "\n",
    "*Care should be taken if $X_i$ is highly correlated with any of the other independent variables (see multicollinearity). If this is the case, the variance evident in the plot will be an underestimate of the true variance.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:52.749496Z",
     "iopub.status.busy": "2021-11-08T19:26:52.748867Z",
     "iopub.status.idle": "2021-11-08T19:26:53.057570Z",
     "shell.execute_reply": "2021-11-08T19:26:53.058362Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sm.graphics.plot_ccpr(lm, \"education\")\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "As you can see the relationship between the variation in $y$ explained by education conditional on income seems to be linear, though you can see there are some observations that are exerting considerable influence on the relationship (see section leverage). \n",
    "\n",
    "We can quickly look at more than one variable by using `plot_ccpr_grid`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:53.064018Z",
     "iopub.status.busy": "2021-11-08T19:26:53.063427Z",
     "iopub.status.idle": "2021-11-08T19:26:53.519520Z",
     "shell.execute_reply": "2021-11-08T19:26:53.520910Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sm.graphics.plot_ccpr_grid(lm)\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Residuals vs fitted plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Residual plots are a useful graphical tool for identifying non-linearity as well as heteroscedasticity. The residuals of this plot are those of the regression fit with all predictors. \n",
    "\n",
    "You can use [seaborn's residplot](https://seaborn.pydata.org/generated/seaborn.residplot.html) to investigate possible violations of underlying assumptions such as linearity and homoskedasticity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitted values\n",
    "model_fitted_y = lm.fittedvalues\n",
    "\n",
    "#  Plot\n",
    "plot = sns.residplot(x=model_fitted_y, y='prestige', data=df, lowess=True, \n",
    "                     scatter_kws={'alpha': 0.5}, \n",
    "                     line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})\n",
    "\n",
    "# Titel and labels\n",
    "plot.set_title('Residuals vs Fitted')\n",
    "plot.set_xlabel('Fitted values')\n",
    "plot.set_ylabel('Residuals');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "This plot shows how residuals are spread along the ranges of predictors. It’s a good sign if we observe relatively equal (randomly) spreaded points along the dotted horizontal line where the residuals are zero. \n",
    "\n",
    "The red line indicates the fit of a locally weighted scatterplot smoothing (lowess), a local regression method, to the residual scatterplot. **Local regression** is a different approach for fitting flexible non-linear functions, which involves computing the fit at a target point $x_0$ using only the nearby training observations {see cite:t}`James2021` for more details). \n",
    "\n",
    ":::{note}\n",
    "lowess creates a smooth line through the scatter plot to help us investigate the relationship between the fitted values and the residuals.\n",
    ":::\n",
    "\n",
    "We can interpret the result of our lowess fit as follows: The fit is almost equal to the dotted horizontal line where the residuals are zero. This is an indication for a linear relationship. Note that in the case of non-linear relationships, we typically observe a pattern which deviates strongly from a horizontal line.  \n",
    "\n",
    "Regarding **homoscedasticity**, the residuals seem to spread equally wide with an increase of x. This is an indication of homoscedasticiy. Next, we use the Breusch-Pagan Lagrange Multiplier test to confirm our assumption."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Breusch-Pagan Lagrange Multiplier test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "> The Breusch-Pagan Lagrange Multiplier test can be used to identify heteroscedasticity. \n",
    "\n",
    "The test assumes homoscedasticity (this is the null hypothesis $H_0$) which means that the residual variance does not depend on the values of the variables in x."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "*Note that this test may exaggerate the significance of results in small or moderately large samples {cite:p}`Greene2000`. In this case the F-statistic is preferable.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If one of the test statistics is significant (i.e., p <= 0.05), then you have indication of heteroscedasticity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Lagrange multiplier statistic', 0.5752191351127306),\n",
       " ('p-value', 0.7500543806429694),\n",
       " ('f-value', 0.27191134322318933),\n",
       " ('f p-value', 0.7632527707017838)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = ['Lagrange multiplier statistic', 'p-value', 'f-value', 'f p-value']\n",
    "test = sm.stats.het_breuschpagan(lm.resid, lm.model.exog)\n",
    "lzip(name, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In our case, both p-values are above 0.05, which means we can accept the null hypothesis. Therefore, we have an indication of homoscedasticity. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Single Variable Regression Diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "The `plot_regress_exog` function is a convenience function which can be used for quickly checking modeling assumptions with respect to a single regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "It gives a 2x2 plot containing the: \n",
    "\n",
    "1. dependent variable and fitted values with prediction confidence intervals vs. the independent variable chosen, \n",
    "2. the residuals of the model vs. the chosen independent variable, \n",
    "3. a partial regression plot, and a \n",
    "4. CCPR plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-08T19:26:53.550486Z",
     "iopub.status.busy": "2021-11-08T19:26:53.526103Z",
     "iopub.status.idle": "2021-11-08T19:26:54.334123Z",
     "shell.execute_reply": "2021-11-08T19:26:54.335208Z"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eval_env: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sm.graphics.plot_regress_exog(lm, \"income\")\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If we are faced with non-linear relationships between the dependent and independent variables, we can adjust the linear model via different methods (for more details, [see {cite:t}`Kuhn2019`](http://www.feat.engineering/numeric-one-to-many.html)):\n",
    "\n",
    "- Basis expansions (polynomial features) \n",
    "- Regression splines (especially natural cubic splines)\n",
    "- Locally weighted scatterplot smoothing (loess)\n",
    "- Generalized additive models (GAMs)\n",
    "\n",
    "In particular, GAMs extend general linear models to have nonlinear terms for individual predictors. GAM models can adaptively model separate basis functions for different variables and estimate the complexity for each. In other words, different predictors can be modeled with different levels of complexity {cite:p}`Kuhn2019`. \n",
    "\n",
    "In case of heteroscedasticity, you have the following options to get realsitic estimates of the standard errors:\n",
    "\n",
    "- Use heteroskedasticity-consistent standard error estimators for OLS regression (see [statsmodels](https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLSResults.get_robustcov_results.html)\n",
    "\n",
    "- Use weight least squares (see [statsmodels](https://www.statsmodels.org/dev/examples/notebooks/generated/wls.html)) \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Non-normally distributed errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "It can be helpful if the residuals in the model are random, normally distributed variables with a mean of 0. This assumption means that the differences between the predicted and observed data are most frequently zero or very close to zero, and that differences much greater than zero happen only occasionally. \n",
    "\n",
    "Note that non-normally distributed errors are not problematic for our model parameters but they may effect significance tests and confidence intervals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Jarque-Bera test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "The Jarque–Bera (JB) test is a goodness-of-fit test of whether sample data have the [*skewness*](https://en.wikipedia.org/wiki/Skewness) and [*kurtosis*](https://en.wikipedia.org/wiki/Kurtosis) matching a normal distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The null hypothesis ($H_0$) is a joint hypothesis of: \n",
    "\n",
    "1. the skewness being zero and \n",
    "2. the kurtosis being 3. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Samples from a normal distribution have an:\n",
    "\n",
    "1. expected skewness of 0 and \n",
    "2. an expected *excess* kurtosis of 0 (which is the same as a kurtosis of 3). \n",
    " \n",
    "Any deviation from this assumptions increases the JB statistic. \n",
    "\n",
    "Next, we calculate the statistics but you can also find the results of the Jarque-Bera test in the regression summary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Jarque-Bera', 0.5195278349686433),\n",
       " ('Chi^2 two-tail prob.', 0.7712336390906139),\n",
       " ('Skew', 0.1549318677987544),\n",
       " ('Kurtosis', 3.425518480614924)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = ['Jarque-Bera', 'Chi^2 two-tail prob.', 'Skew', 'Kurtosis']\n",
    "test = sm.stats.jarque_bera(lm.resid)\n",
    "\n",
    "lzip(name, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The p-value is above 0.05 and we can accept $H_0$. Therefore, the test gives us an indication that the errors are normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Omnibus normtest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Another test for normal distribution of residuals is the Omnibus normtest (also included in the regression summary). The test allows us to check whether or not the model residuals follow an approximately normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Our null hypothesis ($H_0$) is that the residuals are from a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Chi^2', 1.2788071145675917), ('Two-tail probability', 0.5276070175778638)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = ['Chi^2', 'Two-tail probability']\n",
    "test = sm.stats.omni_normtest(lm.resid)\n",
    "lzip(name, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The p-value is above 0.05 and we can accept $H_0$. Therefore, the test gives us an indication that the errors are from a normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "### Solution\n",
    "\n",
    "Note that the assumption of normality can be (at least partly) relaxed if the sample size N is large enough; the errors need not follow a normal distribution because of the [Central Limit Theorem (CLT)](https://en.wikipedia.org/wiki/Central_limit_theorem). In other words, the assumption of normality is in most of the cases not crucial with large enough N (usually if N ≥ 50)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Correlation of error terms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Another important assumption of the linear regression model is that the error terms are uncorrelated. If they are not, then p-values associated with the model will be lower than they should be and confidence intervalls are not reliable {cite:p}`James2021`.\n",
    "\n",
    "Correlated errors occur especially in the context of time series data. In order to determine if this is the case for a given data set, we can plot the residuals from our model as a function of time. If the errors are uncorrelated, then there should be no evident pattern. This means if the errors are uncorrelated, then the fact that $ϵ_i$ is positive provides little or no information about the sign of $ϵ_{i+1}$.\n",
    "\n",
    "Correlation among the error terms can also occur outside of time series data. For instance, consider a study in which individuals’ heights are predicted from their weights. The assumption of uncorrelated errors could be violated if some of the individuals in the study are members of the same family, or eat the same diet, or have been exposed to the same environmental factors {cite:p}`Field2013`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Durbin-Watson test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "A test of autocorrelation that is designed to take account of the regression model is the **Durbin-Watson test** (also included in the regression summary). It is used to test the hypothesis that there is no **lag one autocorrelation** in the residuals. This means if there is no lag one autocorrelation, then information about $ϵ_i$ provides little or no information about $ϵ_{i+1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "If there is no autocorrelation, the test-statistic is around 2. \n",
    "\n",
    "- This statistic will always be between 0 and 4. \n",
    "- The closer to 0 the statistic, the more evidence for positive serial correlation. \n",
    "- The closer to 4, the more evidence for negative serial correlation.\n",
    "\n",
    "\n",
    "As a rough rule of thumb {cite:p}`Field2013`: \n",
    "\n",
    "- If Durbin–Watson is less than 1.0, there may be cause for concern. \n",
    "- Small values of d indicate successive error terms are positively correlated. \n",
    "- If d > 2, successive error terms are negatively correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.458332862235202"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.durbin_watson(lm.resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "### Solution\n",
    "\n",
    "We can get consistent estimates of the standard errors with [Newey-West standard errors](https://en.wikipedia.org/wiki/Newey%E2%80%93West_estimator). See [statsmodels documentation](https://www.statsmodels.org/dev/generated/statsmodels.stats.sandwich_covariance.cov_hac.html) for implementation details. \n",
    "\n",
    "Furthermore, you can use a [generalized least squares (GLS)](https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.GLS.html) model instead of the ordinary least squares (OLS).\n",
    "\n",
    "In the case of correlated errors, you may also use a multilevel model (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs).\n",
    "\n",
    "Multilevel models are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., nested data). The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level).\n",
    "\n",
    "See [statsmodels' documentation](https://www.statsmodels.org/stable/mixed_linear.html) to learn how to implement multilevel models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Collinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Collinearity refers to the situation in which two or more predictor variables collinearity are closely related to one another. The presence of collinearity can pose problems in the regression context, since it can be difficult to separate out the individual effects of collinear variables on the response. \n",
    "\n",
    "It is possible for collinearity to exist between three or more variables even if no pair of variables has a particularly high correlation. We call this situation **multicollinearity**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Correlation matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "A simple way to detect collinearity is to look at the **correlation matrix** of the predictors. An element of this matrix that is large in absolute value indicates a pair of highly correlated variables, and therefore a collinearity problem in the data. \n",
    "\n",
    "Unfortunately, not all collinearity problems can be detected by inspection of the correlation matrix since it is possible for collinearity to exist between three or more variables even if no pair of variables has a particularly high correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect correlation\n",
    "# Calculate correlation using the default method ( \"pearson\")\n",
    "corr = df.corr()\n",
    "# optimize aesthetics: generate mask for removing duplicate / unnecessary info\n",
    "mask = np.zeros_like(corr, dtype=bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "# Generate a custom diverging colormap as indicator for correlations:\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "# Plot\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, annot=True,  square=True, annot_kws={\"size\": 12});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Variance inflation factor (VIF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Instead of inspecting the correlation matrix, a better way to assess **multicollinearity** is to compute the variance inflation factor (VIF). Note that we ignore the intercept in this test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- The smallest possible value for VIF is 1, which indicates the complete absence of collinearity. \n",
    "- Typically in practice there is a small amount of collinearity among the predictors. \n",
    "- As a rule of thumb, a VIF value that exceeds 5 indicates a problematic amount of collinearity and the parameter estimates will have large standard errors because of this. \n",
    "\n",
    "Note that the function `variance_inflation_factor` expects the presence of a constant in the matrix of explanatory variables. Therefore, we use `add_constant` from statsmodels to add the required constant to the dataframe before passing its values to the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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>VIF Factor</th>\n",
       "      <th>Feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.59</td>\n",
       "      <td>const</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.10</td>\n",
       "      <td>income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.10</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VIF Factor    Feature\n",
       "0        4.59      const\n",
       "1        2.10     income\n",
       "2        2.10  education"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose features and add constant\n",
    "features = add_constant(df[['income', 'education']])\n",
    "# create empty DataFrame\n",
    "vif = pd.DataFrame()\n",
    "# calculate vif\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(features.values, i) for i in range(features.shape[1])]\n",
    "# add feature names\n",
    "vif[\"Feature\"] = features.columns\n",
    "\n",
    "vif.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "We don't have a problematic amount of collinearity in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "### Solution\n",
    "\n",
    "A simple solution would be to remove some of the highly correlated features. Furthermore, you could manually combine some features (e.g. adding them together) or use a method which automatically combines features, such as principal components analysis or partial least squares regression."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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