{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactions and ANOVA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: This script is based heavily on Jonathan Taylor's class notes https://web.stanford.edu/class/stats191/notebooks/Interactions.html\n", "\n", "Download and format data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "execution": { "iopub.execute_input": "2021-02-02T06:51:51.356785Z", "iopub.status.busy": "2021-02-02T06:51:51.346501Z", "iopub.status.idle": "2021-02-02T06:51:51.809467Z", "shell.execute_reply": "2021-02-02T06:51:51.810637Z" } }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:51.815940Z", "iopub.status.busy": "2021-02-02T06:51:51.814293Z", "iopub.status.idle": "2021-02-02T06:51:52.682646Z", "shell.execute_reply": "2021-02-02T06:51:52.684085Z" } }, "outputs": [], "source": [ "from urllib.request import urlopen\n", "import numpy as np\n", "np.set_printoptions(precision=4, suppress=True)\n", "\n", "import pandas as pd\n", "pd.set_option(\"display.width\", 100)\n", "import matplotlib.pyplot as plt\n", "from statsmodels.formula.api import ols\n", "from statsmodels.graphics.api import interaction_plot, abline_plot\n", "from statsmodels.stats.anova import anova_lm\n", "\n", "try:\n", " salary_table = pd.read_csv('salary.table')\n", "except: # recent pandas can read URL without urlopen\n", " url = 'http://stats191.stanford.edu/data/salary.table'\n", " fh = urlopen(url)\n", " salary_table = pd.read_table(fh)\n", " salary_table.to_csv('salary.table')\n", "\n", "E = salary_table.E\n", "M = salary_table.M\n", "X = salary_table.X\n", "S = salary_table.S" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look at the data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:52.689175Z", "iopub.status.busy": "2021-02-02T06:51:52.687845Z", "iopub.status.idle": "2021-02-02T06:51:52.986496Z", "shell.execute_reply": "2021-02-02T06:51:52.987784Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Salary')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "symbols = ['D', '^']\n", "colors = ['r', 'g', 'blue']\n", "factor_groups = salary_table.groupby(['E','M'])\n", "for values, group in factor_groups:\n", " i,j = values\n", " plt.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i-1],\n", " s=144)\n", "plt.xlabel('Experience');\n", "plt.ylabel('Salary');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit a linear model:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:52.993184Z", "iopub.status.busy": "2021-02-02T06:51:52.991846Z", "iopub.status.idle": "2021-02-02T06:51:53.028157Z", "shell.execute_reply": "2021-02-02T06:51:53.029288Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: S R-squared: 0.957\n", "Model: OLS Adj. R-squared: 0.953\n", "Method: Least Squares F-statistic: 226.8\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 2.23e-27\n", "Time: 06:51:53 Log-Likelihood: -381.63\n", "No. Observations: 46 AIC: 773.3\n", "Df Residuals: 41 BIC: 782.4\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 8035.5976 386.689 20.781 0.000 7254.663 8816.532\n", "C(E)[T.2] 3144.0352 361.968 8.686 0.000 2413.025 3875.045\n", "C(E)[T.3] 2996.2103 411.753 7.277 0.000 2164.659 3827.762\n", "C(M)[T.1] 6883.5310 313.919 21.928 0.000 6249.559 7517.503\n", "X 546.1840 30.519 17.896 0.000 484.549 607.819\n", "==============================================================================\n", "Omnibus: 2.293 Durbin-Watson: 2.237\n", "Prob(Omnibus): 0.318 Jarque-Bera (JB): 1.362\n", "Skew: -0.077 Prob(JB): 0.506\n", "Kurtosis: 2.171 Cond. No. 33.5\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "formula = 'S ~ C(E) + C(M) + X'\n", "lm = ols(formula, salary_table).fit()\n", "print(lm.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Have a look at the created design matrix: " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.034173Z", "iopub.status.busy": "2021-02-02T06:51:53.032680Z", "iopub.status.idle": "2021-02-02T06:51:53.042105Z", "shell.execute_reply": "2021-02-02T06:51:53.043145Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[1., 0., 0., 1., 1.],\n", " [1., 0., 1., 0., 1.],\n", " [1., 0., 1., 1., 1.],\n", " [1., 1., 0., 0., 1.],\n", " [1., 0., 1., 0., 1.]])" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm.model.exog[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or since we initially passed in a DataFrame, we have a DataFrame available in" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.048057Z", "iopub.status.busy": "2021-02-02T06:51:53.046419Z", "iopub.status.idle": "2021-02-02T06:51:53.068406Z", "shell.execute_reply": "2021-02-02T06:51:53.069569Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Unnamed: 0 S X E M\n", "0 0 13876 1 1 1\n", "1 1 11608 1 3 0\n", "2 2 18701 1 3 1\n", "3 3 11283 1 2 0\n", "4 4 11767 1 3 0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm.model.data.frame[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Influence statistics" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.093115Z", "iopub.status.busy": "2021-02-02T06:51:53.091589Z", "iopub.status.idle": "2021-02-02T06:51:53.162822Z", "shell.execute_reply": "2021-02-02T06:51:53.163765Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==================================================================================================\n", " obs endog fitted Cook's student. hat diag dffits ext.stud. dffits\n", " value d residual internal residual \n", "--------------------------------------------------------------------------------------------------\n", " 0 13876.000 15465.313 0.104 -1.683 0.155 -0.722 -1.723 -0.739\n", " 1 11608.000 11577.992 0.000 0.031 0.130 0.012 0.031 0.012\n", " 2 18701.000 18461.523 0.001 0.247 0.109 0.086 0.244 0.085\n", " 3 11283.000 11725.817 0.005 -0.458 0.113 -0.163 -0.453 -0.162\n", " 4 11767.000 11577.992 0.001 0.197 0.130 0.076 0.195 0.075\n", " 5 20872.000 19155.532 0.092 1.787 0.126 0.678 1.838 0.698\n", " 6 11772.000 12272.001 0.006 -0.513 0.101 -0.172 -0.509 -0.170\n", " 7 10535.000 9127.966 0.056 1.457 0.116 0.529 1.478 0.537\n", " 8 12195.000 12124.176 0.000 0.074 0.123 0.028 0.073 0.027\n", " 9 12313.000 12818.185 0.005 -0.516 0.091 -0.163 -0.511 -0.161\n", " 10 14975.000 16557.681 0.084 -1.655 0.134 -0.650 -1.692 -0.664\n", " 11 21371.000 19701.716 0.078 1.728 0.116 0.624 1.772 0.640\n", " 12 19800.000 19553.891 0.001 0.252 0.096 0.082 0.249 0.081\n", " 13 11417.000 10220.334 0.033 1.227 0.098 0.405 1.234 0.408\n", " 14 20263.000 20100.075 0.001 0.166 0.093 0.053 0.165 0.053\n", " 15 13231.000 13216.544 0.000 0.015 0.114 0.005 0.015 0.005\n", " 16 12884.000 13364.369 0.004 -0.488 0.082 -0.146 -0.483 -0.145\n", " 17 13245.000 13910.553 0.007 -0.674 0.075 -0.192 -0.669 -0.191\n", " 18 13677.000 13762.728 0.000 -0.089 0.113 -0.032 -0.087 -0.031\n", " 19 15965.000 17650.049 0.082 -1.747 0.119 -0.642 -1.794 -0.659\n", " 20 12336.000 11312.702 0.021 1.043 0.087 0.323 1.044 0.323\n", " 21 21352.000 21192.443 0.001 0.163 0.091 0.052 0.161 0.051\n", " 22 13839.000 14456.737 0.006 -0.624 0.070 -0.171 -0.619 -0.170\n", " 23 22884.000 21340.268 0.052 1.579 0.095 0.511 1.610 0.521\n", " 24 16978.000 18742.417 0.083 -1.822 0.111 -0.644 -1.877 -0.664\n", " 25 14803.000 15549.105 0.008 -0.751 0.065 -0.199 -0.747 -0.198\n", " 26 17404.000 19288.601 0.093 -1.944 0.110 -0.684 -2.016 -0.709\n", " 27 22184.000 22284.811 0.000 -0.103 0.096 -0.034 -0.102 -0.033\n", " 28 13548.000 12405.070 0.025 1.162 0.083 0.350 1.167 0.352\n", " 29 14467.000 13497.438 0.018 0.987 0.086 0.304 0.987 0.304\n", " 30 15942.000 16641.473 0.007 -0.705 0.068 -0.190 -0.701 -0.189\n", " 31 23174.000 23377.179 0.001 -0.209 0.108 -0.073 -0.207 -0.072\n", " 32 23780.000 23525.004 0.001 0.260 0.092 0.083 0.257 0.082\n", " 33 25410.000 24071.188 0.040 1.370 0.096 0.446 1.386 0.451\n", " 34 14861.000 14043.622 0.014 0.834 0.091 0.263 0.831 0.262\n", " 35 16882.000 17733.841 0.012 -0.863 0.077 -0.249 -0.860 -0.249\n", " 36 24170.000 24469.547 0.003 -0.312 0.127 -0.119 -0.309 -0.118\n", " 37 15990.000 15135.990 0.018 0.878 0.104 0.300 0.876 0.299\n", " 38 26330.000 25163.556 0.035 1.202 0.109 0.420 1.209 0.422\n", " 39 17949.000 18826.209 0.017 -0.897 0.093 -0.288 -0.895 -0.287\n", " 40 25685.000 26108.099 0.008 -0.452 0.169 -0.204 -0.447 -0.202\n", " 41 27837.000 26802.108 0.039 1.087 0.141 0.440 1.089 0.441\n", " 42 18838.000 19918.577 0.033 -1.119 0.117 -0.407 -1.123 -0.408\n", " 43 17483.000 16774.542 0.018 0.743 0.138 0.297 0.739 0.295\n", " 44 19207.000 20464.761 0.052 -1.313 0.131 -0.511 -1.325 -0.515\n", " 45 19346.000 18959.278 0.009 0.423 0.208 0.216 0.419 0.214\n", "==================================================================================================\n" ] } ], "source": [ "infl = lm.get_influence()\n", "print(infl.summary_table())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or get a dataframe" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.168772Z", "iopub.status.busy": "2021-02-02T06:51:53.167184Z", "iopub.status.idle": "2021-02-02T06:51:53.175562Z", "shell.execute_reply": "2021-02-02T06:51:53.176498Z" } }, "outputs": [], "source": [ "df_infl = infl.summary_frame()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.181363Z", "iopub.status.busy": "2021-02-02T06:51:53.179847Z", "iopub.status.idle": "2021-02-02T06:51:53.204995Z", "shell.execute_reply": "2021-02-02T06:51:53.206217Z" } }, "outputs": [ { "data": { "text/html": [ "
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dfb_Interceptdfb_C(E)[T.2]dfb_C(E)[T.3]dfb_C(M)[T.1]dfb_Xcooks_dstandard_residhat_diagdffits_internalstudent_residdffits
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40.0293820.0009170.042425-0.039198-0.0280360.0011660.1972570.1302660.0763400.1949290.075439
\n", "
" ], "text/plain": [ " dfb_Intercept dfb_C(E)[T.2] dfb_C(E)[T.3] dfb_C(M)[T.1] dfb_X cooks_d standard_resid \\\n", "0 -0.505123 0.376134 0.483977 -0.369677 0.399111 0.104186 -1.683099 \n", "1 0.004663 0.000145 0.006733 -0.006220 -0.004449 0.000029 0.031318 \n", "2 0.013627 0.000367 0.036876 0.030514 -0.034970 0.001492 0.246931 \n", "3 -0.083152 -0.074411 0.009704 0.053783 0.105122 0.005338 -0.457630 \n", "4 0.029382 0.000917 0.042425 -0.039198 -0.028036 0.001166 0.197257 \n", "\n", " hat_diag dffits_internal student_resid dffits \n", "0 0.155327 -0.721753 -1.723037 -0.738880 \n", "1 0.130266 0.012120 0.030934 0.011972 \n", "2 0.109021 0.086377 0.244082 0.085380 \n", "3 0.113030 -0.163364 -0.453173 -0.161773 \n", "4 0.130266 0.076340 0.194929 0.075439 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_infl[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now plot the residuals within the groups separately:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.211311Z", "iopub.status.busy": "2021-02-02T06:51:53.209866Z", "iopub.status.idle": "2021-02-02T06:51:53.487055Z", "shell.execute_reply": "2021-02-02T06:51:53.488091Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Residuals')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UJiIiAQXKCEqFisJEmkk2m+Wk15/ECy+8UOuhSINSoJRQLFQUJtJsln5xKTt27mDpF5fWeihSRZs3b6at7TAef/zxxP+2AqUM+aHyMgoTaS7ZbJZly5cx+P5Bli1flpoqJY37G1122V+zf38bXV1XJP63FShliofKjNZWhYk0laVfXMrg6YMwFQbeOJCKKiWXy3HhhQu54oqrufDChakIlc2bN7N161bgIbZu3Zp4laJAqUAUKl+65RaFiTSNqDrpP6cfgP5z+pu+SonCZOPGHPv372DjxlwqQuWyy/4a+BBwJvDBxKsUBUqF2traWLx4scJEmsaB6uTw8MDhzV2lxMOkt/du4HB6e+9u+lA5WJ18Njzy2cSrFAWKSIrlVyeRZq1ShodJ1GBpYtOHysHqZGp4ZCpJVykKFJEUG1adRJqwSikeJpHmDZWh1UkWeAPwAklXKQoUkZQqVp1EmqlKKR0mkeYMlaHVyXXAjvA+2SpFgSKSUkWrk0iTVCnlh0mkuUJleHVyJ9AR3idbpShQRFKoVHUSafQqpfIwiTRPqAyvThxYF94nW6UoUERSqGR1EmnwKiWTybBu3cP09mYoP0wiE+ntDc5v1P2NClcnBy8bTrpKMXev7ASzFuCP3P0PY/rkGuvs7PTu7u5aD0OkJqafMJ2dz+0s+/3Tjp9Gz7M9VRxRdZSuUHJABugC8pcC7KOj41LmzGlj7doVDblU4LTT/oStW88BvgYsAb4L/JKgKskCpwDvA74JfIxZs37Cli2Pjfg3zWyTu3cWeq2sCsXMMmZ2hJkdBjwJPG1m/6vM7yQidabn2R7cvexbI4YJBOvG1q5dwZw5bXR0XApDWrzmgIXA1eF9/Getxg8TgKee2gp8HTDgDgpdNgy3h69/naee2jKmzyv3J69Tw4pkHnA/cDzw/jF9sojIOCgcKlGY5AiueIo/b44wARgY6MXdufzyvyb4v/vP5L3jM8AEPvCBJbg7AwO9Y/q8cgOlzczaCAJltbvnCGZ0RETqXjxU2tvnAwsIwiNYKR/c54AFtLfPb4owiWSzWe64I5o7mZr3alClfOc7dyZy4UW5gfJNghg/DNhgZicADT2HIiLp0tbWxqpVt9PR8SSwlyBEDq6UD57vpaPjSVatur0pwgTgmmuuY2DAGV6dRD7DwIBz7bXXjfmzygoUd7/B3ae5+1954Fng3DF/uojIOMnlcsyffxl9facB9xGESA5YHt5PBO6jr+805s+/rKEvFY6MXJ1EkqtSRgwUM7u62A34mzF9sojIOCl8tVehSfnmWX8C5VQnkWSqlFIVyuElbiIidW3kMCk0Kd88oXLPPfcSXD9VrDqJTAUuY+XKVWP6vIrXoTQLrUMRaX6lwyQ6tg+4lGAtyorwvvGv9po+fSY7dz5T9vunTTuRnp7tI75npHUoreV8iJkdSvAj3Czg0Oi4u3+w7JGKiIyzaKX8/v07KB4mcHBS/tLw9RUcXCk/g0wmw+LFi8d9/GNVKhySVu5VXt8BXgucDzwMTAdertagRESS0NXVxXnnzaGl5T3AKxQOk0gUKlHovEJLy3s477w5dHV1jeewG1a5gfI6d/88sNfdlwPvAt5YvWFVzswuMLOnzWybmV1T6/GISH1wB/dfEPzAUixMIvFQmYX7L0jprMColBso0azU783sNOBIYEZVRjQKZjYB+GfgncCpwPvM7NTajkpEai2TybB+/Qbc30TQt6q89vXB+07B/U2sX7+hYZtDjrdyA+VmMzsK+DywhqCf1/VVG1Xlzga2uft2d+8naKF5cY3HJCI1tmDBAiZNOhJ4FVhL4eaQ0TqUuInh+19l0qQjWbBgQdXH2gzKXdh4i7v/zt0fdveZ7v4ad/9GtQdXgWnA87HnPeExEUmxlStXsmfPH4BVFA6TYs0hCd+/ij17/sDKlSurPtZmUO5VXn9X6Li7/0Oywxk1K3Bs2C+fZraEoIczxx9/fLXHJCI11tXVxXe/u5qNG7vy2tfnr0Pp4uDVXdHlwfvo6Ohizpy3a1K+TOX+5LU3dhsgmKuYUaUxjUYPcFzs+XRgV/6b3P1md+90984pU6aM2+BEpDZKdxrObw7ZfB2Hx9OoFjaa2URgjbufn/yQKmdmrQS7xswFdgI/BbrcfWuxc7SwUSQ9ogWOGzbso6+vjeDfxfkT9NHixgm0t+d429smNmWYZLNZ/uzPzuXHP/4Rr33tays+f8wbbBXQAcwc5bmJc/f9wEeBfwWeAu4aKUxEJF1G7jQcad6Ow3FLl17Pjh0vsnRp8tdVlbtj4xNm9nh42wo8DXw18dGMgbvf7+6vd/eT3P1/13o8IlI/CncaLqT5Og7HZbNZli1bzuDgOpYtW57IHihx5VYo7wYuDG9/Cfyxu3890ZGIiFRB6X3l8zVPc8h8S5dez+DgYuBMBgYuT7xKGXEOxcyOHulkd38p0dGMI82hiDS/ysMkrrkm5rPZLDNnzuLVV7cSdBfO0t5+Gtu3b61oLmUscyibgO7wfjfBxPevwsebyh6BiEgNRM0he3szVBYmcLA55MNNsVL+YHUStbKfmniVUtZVXmb2DYKruu4Pn78TeIe7/8/ERjLOVKGIND9VKIHh1cmBVyquUpK4yutPojABcPcHgLeXea6ISE0UXodSjuYJEyhUnUSSrVLKDZTfmtnfmtkMMzvBzD4H/FciIxARqaLKQ6W5wiS6squ//9MFX+/v/3RiV3yVGyjvA6YA9wLfA14THhMRqXvlh0pzhQkUqk6ywBuAKECSq1K0BbCIpMbIcyrNFyaF504+SdBh+QPAl6J3lj2XMuo5FDP7Sni/1szW5N/K/lYiInWgeKXSfGECxaqT5cC68D7ZKqXUOpSz3H2TmRWcgHf3h8f06TWkCkUkvYZWKpmwq3BzhQnA9Okz2bnzmdiRQ4APATcCHwFuBfoPvDpt2okl96EfdYXi7pvC+4ejG/A48LtGDhMRSbd4pdLaOqMpwwSgp2c77o67s2vXLg499DCCfRIBPk97+x+RzWYPvKdUmJRSbi+vH5nZEeHK+Z8Dy8zsS6XOExGpV1Go3HLLl5oyTPLV08LGn7n7mWZ2BXCcu19nZo+7++mJjWSc6ScvEUmLelvY2GpmU4H3ErTqFBGRBjG0OolfNlybhY3/QLDXyK/d/admNpOgp5eIiNSx4QsbrwdeDO9rsLDR3Ve6++nufmX4fLu7v2fMny4iIlU1vDrJv2w4uSql3En515vZOjPbEj4/3cz+dsyfLiIiVVO4Ogn2Q4HLSbpKKfcnr38BrgVyAO7+OLBoTJ8sIiJVVbg6icLl0yRdpZQbKB3u/ljesf1j+mQREamqNWtW09//FcCAGQR1wMHLhmEhcAJg9Pd/hdWrvzemz6uk2/BJgAOY2aUEcSciInUqWtg4fFFjZOjixnFZ2AhcBXwTeIOZ7QQ+AXx4TJ8sIiLjYrz2Q6mo27CZHUYQQn3AQne/I5FR1IAWNopIGhRf1HjgHRUtbhxLt+EjzOxaM/u6mf0F0EtwicA2gkWOIk0pl8uxfPlycrlcrYciMibFq5PIOO2HYmargd8BjwJzgaMI2lV+3N03j/nTa0gVihSTy+W4cP6FrHtoHXPPncvaVWubvs+TNKfS1cmBd1Z/PxRgprt/wN2/SbBDYyfw7kYPE5FiojDZ+MxG9v/NfjY+s5EL51+oSkUaUuHqJH/HRhivy4YP/Ffk7gPAM+7+8pg+UaROxcOk96JeeAp6L+pVqEjDGnrZcHSbQRAmJww5Ph6XDb/JzP4Q3l4GTo8em9kfxvTJInVkWJjcS9C97l6FijSu+H4oQy8ffmjYXihVv2zY3Se4+xHh7XB3b409PmJMnyxSJwqGySDBxfGDKFSkaRz8CezMxPdCgQovG24mmpQXGCFM3gu0EvSDuIvgn16XQMeaDuacOEcT9dJwhk/QV74XCiSzH4pI0ykZJoT370WVijS8utmxsRmpQkm3ssIkTpWKNLB627FRpKlkMhnWPbSO3neVESYwvFJ5Vy/rHlpHJpMZpxGLjF5dtl5pJqpQ0i2Xy/Guee9i3b+vY3DKYNB0tViYxO0HVkDL7hbm/ulcvv+976tCkbpWN61XasHMvmBmO81sc3j7q9hr15rZNjN72szOjx0/y8yeCF+7wcysNqOXRmJmMJnyw4TwfQuByeH5InVuPFuv1F2ghL7s7meEt/sBzOxUgmb+s4ALgBvNbEL4/puAJcDJ4e2CGoxZGkgmk2H9j9Yz+N7B8sMk0gqD7x1k/Y/W6ycvqWvDd2wsbLx3bKwHFwN3uvs+d3+GoEHl2WY2FTjC3R/14Pe724B5NRynNIB58+ZhGKyg8q3iwp+9DGPevHnJD04kIaWrk8j47tg43j5qZo+b2bfM7Kjw2DTg+dh7esJj08LH+ceHMbMlZtZtZt27d++uxrilQXzqU58KLvttJbh6q9xQia72ag3mYT71qU9VbYwiY1W49Urh23ju2JgoM3vQzLYUuF1M8PPVScAZBF3M/ik6rcCf8hGODz/ofrO7d7p755QpU8b+RaRhffWrX2XyMZODbnXG8FAZADaH95EoTAzIweRjJvPVr351nEYsUrn81iulbmNtvVLpr8eJcPd3lPM+M/sX4L7waQ9wXOzl6cCu8Pj0AsdFiuro6OC5Xz3H8Scfz3/xX8GmDHcRXBpswErgWeAXwAKCf6JEYdIPk/dN5rlfPUdHR0eNvoFI/am7n7zCOZHIJcCW8PEaYJGZTTSzEwkm3x9z9yzwspmdE17ddTmwelwHLQ0pCpXJ+yZDP0FYrCAIjngvr7vC4woTkRHVXaAA14eXAD8OnAt8EsDdtxL8p/0k8APgqrClPsCVwC0EE/W/Bh4Y91FLQxoSKvuAFwl+BnsvMDG8z4XH9ylMREaihY0iwJ49e5gybQq5Y3PQxdAfg/cDGWj7TRu7d+7myCOPrNEoRWqvoRY2ioy3XC7HwssW0npC6/AwIXzeBa0ntLLwsoVqCilShAJFUi3eJLJvft/BMMm/yqsV+ub3qdOwyAgUKJJaQzoOX9I7NExWEuzYuJIhodJ7idrXixSjQJFUKhkm8au8FCoiZVGgSCodaF9/UZEwiV/lVShULlL7epF8ChRJpa6uLuaeO5eONR3BVVz5YVJox8YoVPYHG2zNPXcuXV1dNRi9SH3SZcOSWr29vcFK+YnhSnmn9I6NWtwoKafLhkXy5HI55i+cz94j98JLHFzMWGrHxhzwEuw9ci/zF87XHIpIjAJFUimTyfDg+gd5deBVOJbC60/yhetROBZeHXiVB9c/qDkUkRgFiqTSggULmDRpUvBT1kiVSb6oUtkPkyZNYsGCBdUaokjDUaBIKq1cuZI9e/ZUtv1vJNwGeM+ePaxcubIKoxNpTAoUSaXoKq/21e2j2rGxfXW7rvISyaNAkVRqa2tj1YpVdPy+AzJUtmNjBjp+38GqFatoa2ur4ihFGosCRVIpusqrd1IvtFHeNsDRpcNt0DupV1d5ieRRoEgqRSvl+y7uCybZWxgaKvnNIaMwaQHeC30X92mlvEgeBYqk0pCV8k6wzW8UKvsY2hxyHwfDJNwOWCvlRYZToEgqtbW1sXbVWuacOIeOe2OhYsCNBJXJeeH9jeHxKEzu7WDOiXNYu2qt5lBEYhQoklrDQiX6uetogv8y1of3R4fH9ytMREaiQJFUi0Jl9gmzabmpJWgCGfX1+kR4fwgwCC03tTD7hNkKE5EiFCgigJnhR3vwX0TUJDJqX+9AC/jRjpnVcpgidU2BIqkW32jL23x4x+Go1YqDt7k21hIZgQJFUisKkw3bN9A30Fe8fX0sVPoG+tiwfYNCRaQABYqkUtlhElGoiJSkQJFUitrX9+0vI0wi8VDZ36f29SJ5FCiSSmpfL5I8BYqkktrXiyRPgSKpNKT1yija16v1ishwChRJpaKr5EvRanmRohQokloVh4rCRGREChRJtbJDRWEiUpICRVKvZKgoTETKokARYYRQUZiIlK0mgWJmC8xsq5kNmlln3mvXmtk2M3vazM6PHT/LzJ4IX7vBwi59ZjbRzFaEx39iZjPG+etIkxgWKvsUJiKVqFWFsgWYD2yIHzSzU4FFwCzgAuBGM5sQvnwTsAQ4ObxdEB7/EPA7d38d8GXgH6s+emlaQ9rZ/5Pa1YtUoiaB4u5PufvTBV66GLjT3fe5+zPANuBsM5sKHOHuj7q7A7cB82LnLA8f3w3MNfUYlzEyM6zV1K5epAL1NocyDXg+9rwnPDYtfJx/fMg57r4f2ANMLvTHzWyJmXWbWffu3bsTHro0g6hp5CM7HmHg4wM8suMRNYEUKVPVAsXMHjSzLQVuF490WoFjPsLxkc4ZftD9ZnfvdPfOKVOmjPwFJHXie6P0XtILE6H3kl7tgSJSpkq7GJXN3d8xitN6gONiz6cDu8Lj0wscj5/TY2atwJHAS6P4bEmxYWES22Cr95JeNt4bhIrmU0SKq7efvNYAi8Irt04kmHx/zN2zwMtmdk44P3I5sDp2zuLw8aXA+nCeRaQsRcMk0qpKRaQctbps+BIz6wHeCnzfzP4VwN23AncBTwI/AK5y94HwtCuBWwgm6n8NPBAevxWYbGbbgKuBa8bti0jDKxkmEYWKSEmW1n/Md3Z2end3d62HITVUdpjEaaGjpJyZbXL3zkKv1dtPXiLjYlRhAqpUREagQJFUymQyrHtoHb0XVRAmkVbovaiXdQ+t0xbAIjEKFEklbbAlkjwFiqSSNtgSSZ4CRVJLG2yJJEuBIqmmDbZEkqNAkdQrGCoDwObwXmEiUpaqtV4RaSRRqFw4/0I2rNpA30AfPAc8Be0T2pkzU2EiUooqFJFQW1sbq1asouP3HZADPgHkoOP3HaxasUphIlKCAkUklMvlmL9wPn1H9UEXMBHogr6j+pi/cL4WMYqUoEARoYxuw1oZL1KSAkVST92GRZKhQJFUU7dhkeQoUCS1Km4QqVARGZECRVJJ3YZFkqdAkVRSt2GR5ClQJJXUbVgkeQoUSSV1GxZJngJFUkvdhkWSpUCRVFO3YZHkKFAk9UqGisJEpCwKFBFGCBWFiUjZFCgioWGhsk9hIlIJBYpITDxUWm9oVZiIVEAbbInkiUIlk8nQ1dWlMBEpkwJFpIC2tjYWL15c62GINBT95CUiIolQoIiISCIUKCIikggFioiIJEKBIiIiiVCgiIhIImoSKGa2wMy2mtmgmXXGjs8wsz4z2xzevhF77Swze8LMtpnZDWZm4fGJZrYiPP4TM5tRg68kIpJ6tapQtgDzgQ0FXvu1u58R3j4cO34TsAQ4ObxdEB7/EPA7d38d8GXgH6s3bBERKaYmgeLuT7n70+W+38ymAke4+6Pu7sBtwLzw5YuB5eHju4G5UfUiIiLjpx7nUE40s5+Z2cNmNic8Ng3oib2nJzwWvfY8gLvvB/YAkwv9YTNbYmbdZta9e/fu6oxeRCSlqhYoZvagmW0pcLt4hNOywPHufiZwNZAxsyOAQhWHRx81wmtDD7rf7O6d7t45ZcqUSr5OqmWzWd5w0km88MILtR6KiNSxqvXycvd3jOKcfcC+8PEmM/s18HqCimR67K3TgV3h4x7gOKDHzFqBI4GXxjB0yXPdNdfwwvbtfOGaa/jGt79d6+GISJ2qq5+8zGyKmU0IH88kmHzf7u5Z4GUzOyecH7kcWB2etgaIuvhdCqwP51kkAdlslu/ecQcPAZnbb1eVIiJF1eqy4UvMrAd4K/B9M/vX8KW3AY+b2c8JJtg/7O5RtXElcAuwDfg18EB4/FZgspltI/iZ7Jpx+hqpcN011/D+gQHOBC4bGOAL1+h/XhEpzNL6j/nOzk7v7u6u9TDqWjab5fXHHccvBwaYSjDBdcqECfyyp4fXvva1tR6eiNSAmW1y985Cr9XVT15SX6LqZGr4fCqqUkSkOFUoUlB+dXLgOKpSRNJMFYpULL86iahKEZFiVKHIMMWqkwOvoypFJK1UoUhF8quTLPAGILpgWFWKiBSiQJEhonUnn48dux54MbyPfB6tSxGRoRQoMkSh6mQ5sC68V5UiIsUoUGSIe+65h5sIGqQZMANYBJwJLAROiL12E7Dy7rtrM1ARqTsKFBniyEmThjxvgQM/f30emFDi/SKSXgoUGWJ7Tw/ujrvziSuv5MOHHDJkYeP/OOQQPvmRjxx4z/aenpH+nIikiC4bloKy2SyzZs5k66uvAnAu8COCfQFOa29n6/btumRYJIV02bBU7PqlS1k8OMhUhl7lNRW4fGCA65curen4RKT+qEKRYfKrk1kEV3m9A9iKqhSRNFOFIhXJr04WE1zldTmqUkSkOFUoMkSh6mQrHGhffxqqUkTSTBWKlK1QdRK/yktViogUowpFhpg5fTrP7NwJwKHAdhjWvv4koC98fuK0abp0WCRFVKFI2aJ1KPlrUCL5a1EUJiISUYUiw8TnUYq1r9f8iUg6qUKRisTnUQrR/ImIFKJAkSGy2SzLly3j0/39B48xdD8UgE/397N82TK1rxeRAxQoMkSh6qTQfiiqUkQkn+ZQZIj4VV6RQ4F/B2Zz8OquiK7yEkkXzaFI2eLdhuNXe53J8E7DuspLROJUoUhR+Vd76eouEVGFIqOSP5+ieRMRGYkqFCmo2FoUVSki6aYKRSpWbC2KqhQRKUYVigyjlfIiUowqFKmIVsqLyGioQpEhSlUnB96HqhSRNFKFImUrVZ1EVKWISL6aBIqZ/V8z+4WZPW5m95rZpNhr15rZNjN72szOjx0/y8yeCF+7wcwsPD7RzFaEx39iZjPG/xs1j9Vr1vCV/n4MSt6+0t/P91avrtlYRaS+1KpC+SFwmrufDvwSuBbAzE4FFhHsPHsBcKOZTQjPuQlYApwc3i4Ij38I+J27vw74MvCP4/UlmlH+SvlSN62UF5FITQLF3f/N3feHT/8DmB4+vhi40933ufszwDbgbDObChzh7o96MOlzGzAvds7y8PHdwNyoehERkfFTD3MoHwQeCB9PA56PvdYTHpsWPs4/PuScMKT2AJMLfZCZLTGzbjPr3r17d2JfQEREoLVaf9jMHgQKXf7zOXdfHb7nc8B+4I7otALv9xGOj3TO8IPuNwM3Q3CVV9HBi4hIxaoWKO7+jpFeN7PFwLuBuX7w2uUe4LjY26YDu8Lj0wscj5/TY2atwJHAS2P+AiIiUpFaXeV1AfAZ4CJ37429tAZYFF65dSLB5Ptj7p4FXjazc8L5kcuB1bFzFoePLwXWe1oX14iI1FDVKpQSvg5MBH4Yzp//h7t/2N23mtldwJMEP4Vd5e4D4TlXAt8G2gnmXKJ5l1uB75jZNoLKZNG4fQsRETlAK+VFRKRsI62UT22gmNlu4NlRnn4M8NsEh9MI9J3TQd85HcbynU9w9ymFXkhtoIyFmXUXS+hmpe+cDvrO6VCt71wP61BERKQJKFBERCQRCpTRubnWA6gBfed00HdOh6p8Z82hiIhIIlShiIhIIhQoFTCzb5nZi2a2pdZjGS9mdpyZPWRmT5nZVjP7eK3HVG1mdqiZPWZmPw+/89/XekzjwcwmmNnPzOy+Wo9lPJjZjnCPpc1mlopFaWY2yczuDvejesrM3pro39dPXuUzs7cBrwC3uftptR7PeAi3Dpjq7v9pZocDm4B57v5kjYdWNWF7n8Pc/RUzawMeAT7u7v9R46FVlZldDXQSbBXx7lqPp9rMbAfQ6e6pWYNiZsuBje5+i5kdAnS4+++T+vuqUCrg7htIWeNJd8+6+3+Gj18GnuLg1gFNyQOvhE/bwltT/8vLzKYD7wJuqfVYpDrM7AjgbQTtqnD3/iTDBBQoUoFwe+UzgZ/UeChVF/78sxl4Efihuzf7d/4K8GlgsMbjGE8O/JuZbTKzJbUezDiYCewGloU/bd5iZocl+QEKFCmLmf0RcA/wCXf/Q63HU23uPuDuZxBslXC2mTXtT5xm9m7gRXffVOuxjLPZ7v5m4J3AVeFP2s2sFXgzcJO7nwnsBa5J8gMUKFJSOI9wD3CHu6+q9XjGU/iTwI+AC2o7kqqaDVwUzincCZxnZrfXdkjV5+67wvsXgXuBs2s7oqrrAXpi1fbdBAGTGAWKjCicoL4VeMrdv1Tr8YwHM5tiZpPCx+3AO4Bf1HRQVeTu17r7dHefQbD9w3p3v6zGw6oqMzssvMiE8GefvwSa+upNd38BeN7MTgkPzSXYKiQxtdoPpSGZ2XeBPweOMbMe4Dp3v7W2o6q62cD7gSfCOQWAz7r7/bUbUtVNBZab2QSCf3Td5e6puJQ2RY4F7g33Y2oFMu7+g9oOaVx8DLgjvMJrO/Dfk/zjumxYREQSoZ+8REQkEQoUERFJhAJFREQSoUAREZFEKFBERCQRChSRKjKzY80sY2bbwxYfj5rZJbUel0g1KFBEqiRcFPo9YIO7z3T3swgWDk7Pe5/Wg0lT0DoUkSoxs7nA37n72wu89gGC7r6HAocBlwLfImjg1wsscffHzewLwCvu/v/C87YAUWv5HxA06jwT+CVwubv3VvM7iYxEFYpI9cwC/nOE198KLHb384C/B37m7qcDnwVuK+PvnwLcHJ7zB+AjYxyvyJgoUETGiZn9c7gL5E/DQz9092h/nT8DvgPg7uuByWZ2ZIk/+by7/zh8fHv4N0RqRoEiUj1biXVzdferCBryTQkP7Y291wqc78B+hv53emje6/nvF6kZBYpI9awHDjWzK2PHOoq8dwPw3wDM7M+B34b7zuwgDCUzezNwYuyc42N7gr+PYKtikZrRpLxIFZnZVODLwFsIdsvbC3wDaCfYz/yj4fuOBpYRBEZ8Ur4dWA28Bvgpwc9a7wz//P0EQfSnwK+A92tSXmpJgSLSgMLtmO9z96bdSVIaj37yEhGRRKhCERGRRKhCERGRRChQREQkEQoUERFJhAJFREQSoUAREZFEKFBERCQR/x/sh8D7HROXvQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid = lm.resid\n", "plt.figure(figsize=(6,6));\n", "for values, group in factor_groups:\n", " i,j = values\n", " group_num = i*2 + j - 1 # for plotting purposes\n", " x = [group_num] * len(group)\n", " plt.scatter(x, resid[group.index], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", "plt.xlabel('Group');\n", "plt.ylabel('Residuals');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will test some interactions using anova or f_test" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.493661Z", "iopub.status.busy": "2021-02-02T06:51:53.492034Z", "iopub.status.idle": "2021-02-02T06:51:53.593820Z", "shell.execute_reply": "2021-02-02T06:51:53.594863Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: S R-squared: 0.961\n", "Model: OLS Adj. R-squared: 0.955\n", "Method: Least Squares F-statistic: 158.6\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 8.23e-26\n", "Time: 06:51:53 Log-Likelihood: -379.47\n", "No. Observations: 46 AIC: 772.9\n", "Df Residuals: 39 BIC: 785.7\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "===============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", "Intercept 7256.2800 549.494 13.205 0.000 6144.824 8367.736\n", "C(E)[T.2] 4172.5045 674.966 6.182 0.000 2807.256 5537.753\n", "C(E)[T.3] 3946.3649 686.693 5.747 0.000 2557.396 5335.333\n", "C(M)[T.1] 7102.4539 333.442 21.300 0.000 6428.005 7776.903\n", "X 632.2878 53.185 11.888 0.000 524.710 739.865\n", "C(E)[T.2]:X -125.5147 69.863 -1.797 0.080 -266.826 15.796\n", "C(E)[T.3]:X -141.2741 89.281 -1.582 0.122 -321.861 39.313\n", "==============================================================================\n", "Omnibus: 0.432 Durbin-Watson: 2.179\n", "Prob(Omnibus): 0.806 Jarque-Bera (JB): 0.590\n", "Skew: 0.144 Prob(JB): 0.744\n", "Kurtosis: 2.526 Cond. No. 69.7\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "interX_lm = ols(\"S ~ C(E) * X + C(M)\", salary_table).fit()\n", "print(interX_lm.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do an ANOVA check" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.600415Z", "iopub.status.busy": "2021-02-02T06:51:53.598710Z", "iopub.status.idle": "2021-02-02T06:51:53.671144Z", "shell.execute_reply": "2021-02-02T06:51:53.672411Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 41.0 4.328072e+07 0.0 NaN NaN NaN\n", "1 39.0 3.941068e+07 2.0 3.870040e+06 1.914856 0.160964\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: S R-squared: 0.999\n", "Model: OLS Adj. R-squared: 0.999\n", "Method: Least Squares F-statistic: 5517.\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 1.67e-55\n", "Time: 06:51:53 Log-Likelihood: -298.74\n", "No. Observations: 46 AIC: 611.5\n", "Df Residuals: 39 BIC: 624.3\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "=======================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "---------------------------------------------------------------------------------------\n", "Intercept 9472.6854 80.344 117.902 0.000 9310.175 9635.196\n", "C(E)[T.2] 1381.6706 77.319 17.870 0.000 1225.279 1538.063\n", "C(E)[T.3] 1730.7483 105.334 16.431 0.000 1517.690 1943.806\n", "C(M)[T.1] 3981.3769 101.175 39.351 0.000 3776.732 4186.022\n", "C(E)[T.2]:C(M)[T.1] 4902.5231 131.359 37.322 0.000 4636.825 5168.222\n", "C(E)[T.3]:C(M)[T.1] 3066.0351 149.330 20.532 0.000 2763.986 3368.084\n", "X 496.9870 5.566 89.283 0.000 485.728 508.246\n", "==============================================================================\n", "Omnibus: 74.761 Durbin-Watson: 2.244\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1037.873\n", "Skew: -4.103 Prob(JB): 4.25e-226\n", "Kurtosis: 24.776 Cond. No. 79.0\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 41.0 4.328072e+07 0.0 NaN NaN NaN\n", "1 39.0 1.178168e+06 2.0 4.210255e+07 696.844466 3.025504e-31\n" ] } ], "source": [ "from statsmodels.stats.api import anova_lm\n", "\n", "table1 = anova_lm(lm, interX_lm)\n", "print(table1)\n", "\n", "interM_lm = ols(\"S ~ X + C(E)*C(M)\", data=salary_table).fit()\n", "print(interM_lm.summary())\n", "\n", "table2 = anova_lm(lm, interM_lm)\n", "print(table2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The design matrix as a DataFrame" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.677743Z", "iopub.status.busy": "2021-02-02T06:51:53.676139Z", "iopub.status.idle": "2021-02-02T06:51:53.701212Z", "shell.execute_reply": "2021-02-02T06:51:53.702439Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Intercept C(E)[T.2] C(E)[T.3] C(M)[T.1] C(E)[T.2]:C(M)[T.1] C(E)[T.3]:C(M)[T.1] X\n", "0 1.0 0.0 0.0 1.0 0.0 0.0 1.0\n", "1 1.0 0.0 1.0 0.0 0.0 0.0 1.0\n", "2 1.0 0.0 1.0 1.0 0.0 1.0 1.0\n", "3 1.0 1.0 0.0 0.0 0.0 0.0 1.0\n", "4 1.0 0.0 1.0 0.0 0.0 0.0 1.0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interM_lm.model.data.orig_exog[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The design matrix as an ndarray" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.707294Z", "iopub.status.busy": "2021-02-02T06:51:53.705717Z", "iopub.status.idle": "2021-02-02T06:51:53.715088Z", "shell.execute_reply": "2021-02-02T06:51:53.716309Z" } }, "outputs": [ { "data": { "text/plain": [ "['Intercept',\n", " 'C(E)[T.2]',\n", " 'C(E)[T.3]',\n", " 'C(M)[T.1]',\n", " 'C(E)[T.2]:C(M)[T.1]',\n", " 'C(E)[T.3]:C(M)[T.1]',\n", " 'X']" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interM_lm.model.exog\n", "interM_lm.model.exog_names" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:53.721101Z", "iopub.status.busy": "2021-02-02T06:51:53.719676Z", "iopub.status.idle": "2021-02-02T06:51:54.007018Z", "shell.execute_reply": "2021-02-02T06:51:54.008283Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'standardized resids')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "infl = interM_lm.get_influence()\n", "resid = infl.resid_studentized_internal\n", "plt.figure(figsize=(6,6))\n", "for values, group in factor_groups:\n", " i,j = values\n", " idx = group.index\n", " plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", "plt.xlabel('X');\n", "plt.ylabel('standardized resids');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like one observation is an outlier." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:54.013434Z", "iopub.status.busy": "2021-02-02T06:51:54.012051Z", "iopub.status.idle": "2021-02-02T06:51:54.107539Z", "shell.execute_reply": "2021-02-02T06:51:54.108736Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: S R-squared: 0.955\n", "Model: OLS Adj. R-squared: 0.950\n", "Method: Least Squares F-statistic: 211.7\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 2.45e-26\n", "Time: 06:51:54 Log-Likelihood: -373.79\n", "No. Observations: 45 AIC: 757.6\n", "Df Residuals: 40 BIC: 766.6\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 8044.7518 392.781 20.482 0.000 7250.911 8838.592\n", "C(E)[T.2] 3129.5286 370.470 8.447 0.000 2380.780 3878.277\n", "C(E)[T.3] 2999.4451 416.712 7.198 0.000 2157.238 3841.652\n", "C(M)[T.1] 6866.9856 323.991 21.195 0.000 6212.175 7521.796\n", "X 545.7855 30.912 17.656 0.000 483.311 608.260\n", "==============================================================================\n", "Omnibus: 2.511 Durbin-Watson: 2.265\n", "Prob(Omnibus): 0.285 Jarque-Bera (JB): 1.400\n", "Skew: -0.044 Prob(JB): 0.496\n", "Kurtosis: 2.140 Cond. No. 33.1\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\n", "\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: S R-squared: 0.959\n", "Model: OLS Adj. R-squared: 0.952\n", "Method: Least Squares F-statistic: 147.7\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 8.97e-25\n", "Time: 06:51:54 Log-Likelihood: -371.70\n", "No. Observations: 45 AIC: 757.4\n", "Df Residuals: 38 BIC: 770.0\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "===============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", "Intercept 7266.0887 558.872 13.001 0.000 6134.711 8397.466\n", "C(E)[T.2] 4162.0846 685.728 6.070 0.000 2773.900 5550.269\n", "C(E)[T.3] 3940.4359 696.067 5.661 0.000 2531.322 5349.549\n", "C(M)[T.1] 7088.6387 345.587 20.512 0.000 6389.035 7788.243\n", "X 631.6892 53.950 11.709 0.000 522.473 740.905\n", "C(E)[T.2]:X -125.5009 70.744 -1.774 0.084 -268.714 17.712\n", "C(E)[T.3]:X -139.8410 90.728 -1.541 0.132 -323.511 43.829\n", "==============================================================================\n", "Omnibus: 0.617 Durbin-Watson: 2.194\n", "Prob(Omnibus): 0.734 Jarque-Bera (JB): 0.728\n", "Skew: 0.162 Prob(JB): 0.695\n", "Kurtosis: 2.468 Cond. No. 68.7\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\n", "\n", " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 40.0 4.320910e+07 0.0 NaN NaN NaN\n", "1 38.0 3.937424e+07 2.0 3.834859e+06 1.850508 0.171042\n", "\n", "\n", " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 40.0 4.320910e+07 0.0 NaN NaN NaN\n", "1 38.0 1.711881e+05 2.0 4.303791e+07 4776.734853 2.291239e-46\n", "\n", "\n" ] } ], "source": [ "drop_idx = abs(resid).argmax()\n", "print(drop_idx) # zero-based index\n", "idx = salary_table.index.drop(drop_idx)\n", "\n", "lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit()\n", "\n", "print(lm32.summary())\n", "print('\\n')\n", "\n", "interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit()\n", "\n", "print(interX_lm32.summary())\n", "print('\\n')\n", "\n", "\n", "table3 = anova_lm(lm32, interX_lm32)\n", "print(table3)\n", "print('\\n')\n", "\n", "\n", "interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit()\n", "\n", "table4 = anova_lm(lm32, interM_lm32)\n", "print(table4)\n", "print('\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Replot the residuals" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:54.113779Z", "iopub.status.busy": "2021-02-02T06:51:54.112285Z", "iopub.status.idle": "2021-02-02T06:51:54.422729Z", "shell.execute_reply": "2021-02-02T06:51:54.423847Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'standardized resids')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BNakVUdJECgozey3wduCzwXONTpsiLRSGxete9jpOufsM+G4YEmXDohUfgO9ewSl3n8HrXva62IbE/Goi1OtVRd1qItShqgJKv1ebN2+O5e9JLUmtiJIkygH/VuD/BO5z9/1mthrY29ZWSSQ2m4afXgFzfw+VhkWbfQDmriutF1Pzq4lQeVVx57Zt3WhaW01MTFD4XgH+uf66BQrseWoP2+7uvc+hVcKw0Jzf7aEb7hKoWCwyMrKJ6eki+fwuFoZEuaOk0xsZGkoxOXlvrP4D5XI5Ll69mv1HjiwICoAccMngIPsPtOfGMxE5YVE33JnZpJlNVFva11yppbGQAFhKPr+L6enSdnG6h6JaNRHqh2sVIklQtaIwsyuDbzcA5wOfCB6/DTjo7n/Q/uYtTq9WFI2HRLl4VRb1qonj66GqQqQTFlVRuPuD7v4gcKm7b3L3yWDJAK9vV2Olumw2y9TUg+TzWRoLCShVFqXt43DDXb1qIqSqQqT7ovR6Oi+4gA2Ama0Czmtfk/pXvbGOMpkMw8NXkk5nIPJIN6GjpNOl7eNww92eiQnuKhQwqLvcVSjwmT17utZWkX4XJShuA75oZl80sy9S6vF0azsb1a/qjXWUSpVOGw0NpUinNxI9LOJ12gngwMwM7h55OTAz0+0mi/StSL2ezGwpcFHw8Fvu3uifsx2VxGsU4Tn7qSNHeFOdc/K90utJROJjsb2efjX4ugH4NeDCYPm14DlpoUbGOopeWSgkRKR5tXo9fcDd32dmOyq87O7+O+1t2uIlraKY3wMoak+f2pWFQkJEoltsr6f3BV/fUWGJbUgk0WLHOqpeWSgkRKR1ogwz/m4zO9NKtpvZ18zsLa3YuZldbWbfNrMnzOy9FV5/o5n92MweDpY/asV+46TZsY7Kw2Jw8DrgFQwOXqeQEJGWidLr6Xfc/XngLcCLKA0z/sfN7tjMTgW2AddQGo32bWZWaVTaaXd/dbD8l2b3GzdRxjqqJwyLc8+dAZ7l3HNnFBIi0jKRRo8Nvl4L7HD3R8qea8blwBPufsDdC8CngOtb8L6JUa2aCDUygupzzz3H4cPfB/by3HPP8oMf/KDFrRWRfhUlKB4ys89TCorPmdkyYK4F+14BPFX2eCZ4br7XmtkjZvaAmV1c7c3M7EYz22dm+w4fPtyC5rVfK8c6GhvbytzcZuBSjh27gbGxrQ23p94Nf1JbsVhkfHw8VuNpibREvRudKIXJZcBZweOfAX6hkZulqrzvbwDbyx7/e0oTI5WvcyZwRvD9tcDjUd77Na95jcfdoUOH/OzTTvND4F5jOQR+zuCg53K5mu912mlnOxwKNjvkg4Pn1NymkltvusnPPuUUv23LlmZ/vL5TKBR8/VVX+TkDA77+qqu8UCh0u0kiDQH2eZVjapSKwildQ7gleHw6cFoLMmoGeEnZ4wuAQyft2P15d/9J8P39QMrMzm3BvruulWMdnagmTvSbarSqCE+DTc3N9fSEQe1QLBbZNDJCcXqag7OzFKenS49VWUivqJYg4QJ8hNJF58eCx2cDX623XYT3HQAOAKuAJcAjwMXz1jmfE/d6XA58L3xca0lCRbFqxQqnFMKRllUrVlR8n4XVhC+qqrj1ppv81iVL3MFvXbJEVUVEYSVxXTrtR4IP/wj4dem0KgtJFJqsKH7Z3W8GjgTB8qPgwN4Ud58F3gV8DngM+DsvzaD3TjN7Z7DaRuBRM3sEuBt4a/ADJV6rxjpaWE2EolcV8y+q9/o0pK1SXknsyufLJ6JlVz6vyqLDdI2ojeodoICvAKcCXwsenwd8vZGDXKeXJFQUrVC9mmisqiivJsJFVUVtlSqJ+Ysqi84pFAp+1XVX+cDpA37Vdfq8F4MmK4q7gfuAF5nZ/w38E/DBtqSWNKR6NRGqX1U0e8NfP6pWScynyqIzisUiIxtGmH5ymtlbZpl+cpqRDfq8W6lmUJjZKcCTwB3Af6M0DNGvu/unO9A2qSGXy7FjxziFwh011ysU7mDHjvGqB/xW3PDXT6KGREhh0V7lIZFfn4elkF+fV1i0WN1hxs3sf7r7azvUnpZI2qCAi7Fly23ccw8UCn9ed90lS25jdNTYtu3Ok56vNx2ppiE9WaMhUe4osDGdJjU0xL2Tk7prvgUWhMRA2YuzkL4vzdCqISZ36/OOYlGDApb5vJn972bWiruxpUUmJvZQKNxF/fnhjELhLvbs+cyC92jlDX/9IJvN8uDUFNkGQwJKlUU2ny9tH4OpaJOuZkgADKiyaKUoFcULlO6dmKXU88koDTN+Zvubtzj9UFE0q141cXw9VFWEVFHEQ92QKKfKIrKmKgp3X+bup7j7Enc/M3gc25CQaFp5w1+/SKVSpYP80BAb0+kGJqJVSLRKQyEBqixaJMqpJ+lBeyYmuKtQiHDiCu4qFPjMnj1da2ucNBoWConWaTgkQgqLpiko+lSrbvjrR1HDQiHRWtlslqm9U+TXNRASoQHIr8sztVfXiBZDQSGyCPXCQiHReplMhuG1w6Qn0qUrpo2YhfREmuG1w2Qymba0r5dVDQozO6fW0slGisRRtbBQSLRHKpVicvckQ6uGSN/XQFjognbTalUUDwH7gq+Hgf8FPB58/1D7myYSf/PD4gUUEu3UcFgoJFqialC4+yp3X01p0L4Rdz/X3X8GuA7Y3akGSnLkcjkufEX/TXxUHhYrBwYUEm0WOSwUEi0T5RrFL3lpLggA3P0B4Mr2NUmSauyDYxx8+iBjH+y/rrRhWNy5fbtCogPqhoVCoqWi3HD3OWAa+ASluRF+C3iDu1/V/uYtjm6467xcLsfqV67mSOYIg58c5MC3dYOetF/FLrMKiUVpdgiPt1EaWvy+YDkveE7kuLEPjjH3C3OwHI79/LG+rCqk8xZUFkcVEu1Qt6I4vqLZGR5MSxp3qig663g1ceMRWAa8AIN/rapCOiesLKb2TjG8dlghsQhNVRRm9itm9q/AvwaPf9HM/rLFbZQEO15NLAueWKaqQjorrCy2b9uukGiDKNcovkJpStIJd780eO5Rd7+kA+1bFFUUnbOgmgipqhBJlGavUeDuT8176ljTrZKesKCaCKmqEOkZUYLiKTP7FcDNbImZvQd4rM3tkgTI5XLsGN9B4YpCxdcLVxTYMa7pVEWSLkpQvBO4GVgBzACvDh5Ln6taTYRUVYj0hChBMejub3f3n3X3F7n7bwG6UtTn6lUTocIVBXbsVFUhkmRRguJJM/ukmQ2WPXd/1bWlL3zgv36Aoz93tHo1EVoGR37uCO//r+/vRLNEpA2i9Hr6OvDXwO8Cv+nu3zGzr4c9oOJIvZ7aq1gssuzsZRz9t6hzvMHS05fywo9eULdFkZiq1espyvQf7u5/aWaPAJNm9p8oDeUhfSi8senUlafCeqL9Bs3CqfedysiGEfVxF0mgKKeeDMDd/xkYBn4fuKidjZJ40lSUIv0pSlBcG37j7jngV4Gr29aiBCsWi4yPj/fsgVBTUYr0p1oz3P1W8O3bzOz2cAFuASqex+pnxWKRkZFNjI7ezsjIpp4MC01FKdKfalUUpwdfl1VZJBCGxPR0kdnZg0xPF3syLDQVpUh/ijx6bFt2bnY18BfAqcB2d//jea9b8Pq1QB74bXf/Wr337WSvp/KQyOd3AUuBo6TTGxkaSjE5eW/PHRgbulahkBBJhFq9nqoGhZndXetN3f2WJht1KqV5uN9M6Y7vrwJvc/d/LVvnWuD3KAXFLwN/4e6/XO+9OxUUlUMipLBQSIgkx2IHBXwoWE4DLgMeD5ZX05pBAS8HnnD3A+5eAD4FXD9vneuBj3vJl4GzzGx5C/bdtNohAbCUfH5X/56GUkiI9IyqQeHu4+4+DrwcWOvuH3L3D1HqIvvqFux7BVA+Ku1M8Fyj63Rc/ZAI9WlYKCREekqU7rEv5uSL12cEzzXLKjw3/zxYlHVKK5rdaGb7zGzf4cOHm25cNdFDIhQ9LHK5HBe+4sJEjYukqShFel+UoPhj4OtmttPMdgJfAz7Ygn3PAC8pe3wBcGgR6wDg7h9z9zXuvua8885rQfMWajwkQtHCYuyDYxx8+mDiRlstD4uBuwcUEiK9xt2rLpSC5FeA8yldL7geOL/WNlEXSpc/DwCrgCXAI8DF89b5NeABSpXFFcC/RHnv17zmNd4OO3fu9IGBcxyed/BFLM/7wMA5vnPnzgXvfejQIT9t2WnOf8AHzxz0XC7Xlp+hnQqFgu/cudMLhUK3myIiDQL2eZVjas2Kwt3ngD9z92fcfU+wtOS8iLvPAu8CPkdpIqS/c/f9ZvZOM3tnsNr9QZg8QWlgwi2t2PdiZTIZhoevJJ3OANEHxCs5Sjpd2r7SDWfH53ZYntw5HFKpFJs3b1YlIdJjoowe+wHgG8Bur7dyTLSze+ziTj/V7iq7YN5pzTctIh3W7JzZtwOfBo6a2fNm9oKZPd/SFiZIKlU62A8NpUinN1K/sqh/P8WCmeI0M5yIxEjdoHD3Ze5+irsvcfczg8dndqJxcRU9LOqHRLWZ4jTftIjERZSKAjM728wuN7M3hEu7GxZ3YVhcfvkcpRvH54fFUeBaLr98ruad2VXnnVZVISIxUTcozGwU+BKli84fCL6+v73NSoZUKsUrfu4C7KyvQOoaToTFUUhdg531FV75qpdUDYl6806rqhCROIhSUbwb+CXgu+6+FrgUaN8dbQmSy+X4+Cc+jv/2v8FLvxyExQulry/9Mv7b/8bHP/Hxqgf6qtVESFWFiMRAlKA44u5HAMxsqbt/C3hle5uVDMcP9GcBmZ+WwuKUFaWvmZ/CWdUP9PWqiVAnq4pen3hJRBYnSlDMmNlZwGeAL5jZHqrcHd1PFhzoT6UUDuteKH09tfR0tQN93Woi1KGqoh8mXhKRxWloPgozuxL4d8A/eGnE11jqxDDjW35vC/d8/R4Kb67/MSz5whJGLxtl293bjj93wcsu4OnvPR15fyteuoKZ784sqq31nHxvSJZ0OtOzw6OLSGWLnY/inFpv6u4/bEHb2qITQRGnA30z+nHiJRFZqJn5KPYFXw9TmmTo8eD7h1rdyKSZ+e5MQ2NbJSckoNeHRxeRxtSaj2KVu6+m1B12xN3PdfefAa4DdneqgdIe/T7xkohEF+Vi9i+5+/3hA3d/ALiyfU2SdtPESyLSiChB8ZyZ/WczW2lmLzOzPwR+0O6GSXu0c+IlEelNUYLibcB5wH2Uusi+KHhOEqbdEy+JdFMSZ4hMiiiDAv7Q3d/t7pcGy7vj3ONJqstms0xNPUg+nyV6SISWks+Xts9ms+1onkhTkjpDZBJEmY/iFcB7gJWUZqUDwN1/ta0ta0Inuscm0eIrClCXWYmz43O6ZI4w+EnN5bIYzc5H8Wng68B/Bn6/bJEWa/cQGo3PpRFSSEi89cIMkXEWJShm3f0j7v4v7v5QuLS9ZX2mWCwysmGE0ZtHGdkwEqOwUEhIvM0fTkejLrdelKCYNLMtZrbczM4Jl7a3rI+EITH95DSzt8wy/eR0TMIiWSGhQQ37k2aIbL8o1yierPC0BzfjxVKSrlGUh0R+fb50FWgW0velGVo1xOTuybYdoGtfs0heSIxsGGFq7xTDa4fb+rlJfCyYbz6keecb1tQ1iuAO7flLbEMiSSqGBMAA5Nfnu1hZJDMkOlWRSXxohsjOiDR6rJldArwKOC18zt0/3sZ2NSUJFUXVkCjXlcoiWaPHdrMik+6qWk2EVFU0pKmKwszeB3woWNYCW4F1LW1hn4kUEtCVymJgYGWyQwI69rlJd2mGyM6JcjF7IzAMPOPu7wB+kcbv1pJA5JAIdTgstm+/M9khEVJY9LQ4zhDZy6IExU/dfQ6YNbMzgWcBXaNYhIZDItTBsNi8eXPyQyKksOhZcZshstdF6fX0l8AfAG8F/iPwE+DhoLqIpbheoxgfH2f05lFmb5ldXE12FAbuHmD7tu1s3ry55e1LgkWFra5Z9JxemTgsThY1w12VN1oJnOnu32hR29oirkGx6IoCdLBDn59IOzV7MXsq/N7dD7r7N8qfk+hSqRSTuycZWjVE+r40zEbcUAc5IBjUcO8U+XUNhgSUTkOtyzO1d0qDGlah0VelmqpBYWanBXdgn2tmZ5fdlb0SeHHHWthjGg4LhcRxmUyG4bXDpCcaCNnQLKQn0gyvHSaTybSlfUmn0VelmloVxX+gNDf2RcHXcNkDbGt/03pX5LBQSJxEFVn7hL2I5v79XNd6CeVyOS688CJVNDFUa87sv3D3VcB73H112V3Zv+juH25mp0Fl8gUzezz4enaV9Q6a2TfN7GEzi99FhybUPegt4uDWD2MdqSJrjziMvjo2tpWDB59lbGxrx/cttUXpHvuMmS0DCKZE3W1mlzW53/cCU+7+cmAqeFzNWnd/dbWLLElW9aC3yJDYNDLC7aOjbBrp7a6gqshaKw6jr+ZyOXbsGGduboodO8ZVVcRMlKD4v9z9BTN7PXAVMA58pMn9Xh+8D8HXX2/y/RJrwUHv6OJDojg9zcHZWYrT030VFoO7BytWZIO7BxUSEcRh9NWxsa3MzW0GLuXYsRtUVcRMlPsovu7ul5rZfwO+6e7Z8LlF79Ts/3P3s8oe/8jdF5x+Ckau/RHgwF+5+8eivH9cu8fWstjRT8tDYlc+z1JKQ/ttTKdJDQ1x72RvHySLxSIvv+hCnvanmH07x8d6GvhbWGEv4fFvfaenf/5mxWH01Vwux+rVF3PkyH5gOZBjcPASDhzYrzGaOqjZGe6eNrO/An4TuN/MlkbZzsz+0cwerbBc30DbX+fulwHXADeb2Rtq7O9GM9tnZvsOHz7cwC7iIfwLefu27U2FBJTu5duVz/dFZfHcc8/x46ef5fVPQfoTlCqyT8Drn4LnDx3mBz/4QbebGGtxGH31RDWxPHhmuaqKmIlSUaSBqylVE4+b2XLg593984veqdm3gTe6ey54vy+6+yvrbPN+4Cfu/qf13j+JFUWjqoVEuX6oLG7bsgXuuYethQIjAzB1CgzPweQs3LFkCTY6yp3b1EmvkjiMvrqwmjj+iqqKDmt2Poq8u+9298eDx7lmQiIwAYRjUGym1OX2JGZ2etlF9NOBtwCPNrnfmpLSPS9KSEDvVxa5XI7xHTu4o1AgRSkcthdKX1PAHYUC4zs0IFw1cRh9dWE1EVJVESdRTj21wx8Dbzazx4E3B48xsxeb2f3BOj8L/JOZPQL8C/BZd/+HdjYqCd3zooZEqJfDYuvYGJvn5o4fYlKU/uoI66blwA3HjrF1TDeQzReH0VfDnk6Fwh2V9124Qz2gYqKhsZ6SYjGnnk6UwFMMDr4pliVvoyFRrtdOQ+VyOS5evZr9R44s+Fv0pPWASwYH2X9Ak9eU2/J7W7jn6/dQeHPtoABY8oUljF42yra7W3sKb8uW27jnHigU/rz6vpfcxuiosW3bnS3dtyxU69QT7t5zy2te8xpv1E033epLltzq4L5kya2+ZcttDb9Hu+3cudPPGRjw58F9Ecvz4OcMDPjOnTu7/aM07dabbvJblyyJ9HPfumSJ37ZlS7ebHCsrXrrCKfUmjLSseOmKlu7/0KFDftppZzscqvPPd8gHB8/xXC7X0v3LQsA+r3JMVUVBcrrnqaI4YfUFF/Dk09GHmV61YgUHZjTMdFxEqSZCqio6o9nusT0vKd3zUqlU6SA/NMTGdJqjEbfrtZAAODAz01CVqZCIl4mJPRQKdwFWdykU7mLPns90qaUCukaRyO55jVQWvRgSItJ6qihqSGL3vKiVRb+FRFK6N4skTV8HRZK759ULi34LCUhG92aRJOrroKheTYTiW1VA9bDox5DQ6KMi7dO3QVGvmgjFuaqAhWHxAv0XEqDRR0XaqW+Don41EYp3VQEnh8XKgYG+C4n5oR/3cBdJmr4Nil7rnheGxZ3bt/dVSEByujeLJFXfd4+VZEti92aROFL3WOlZSezeLJI0CgpJrLh1b9Z9HNKrFBSSWHHr3qz7OKRX6RqFJFL1axML1uzItYo4DFOfy+V4/evX8s///EVdl5GG6RqF9Jy4dW+Ow30cqmikXVRRSCJdcMFqnn76ycjrr1ixipmZA21pSxyGqY9DRSPJpopCes7MzIGGhhlvV0hAPO7jiENFI71LFYVIE+JwH0ccKhpJPlUUERWLRcbHxykWi91uiiREHO7jiENFI71NFUWgWCwysmGEqb1TDK8dZnJ3fw2DIY2r3/Oq/X/Zx6Gikd6giqKOMCSmn5xm9pZZpp+cZmTDiCoLqSkO93HEoaKR3tf3FUV5SOTX52EAmIX0fWmGVg2pspCK4nAfRxwqGukdqiiqqBgSAAOQX59XZSFVxeE+jjhUNNIf+raiqBoS5VRZSBXdvo8jDhWN9BZVFPNECglQZSFVdfs+jjhUNNI/+i4oIodESGEhMdRrE29JvPVVUDQcEiGFhcRMtysa6S99FRTZbJapvVPk1zUQEqEByK/LM7V3imw225b2iYjEUV8FRSaTYXjtMOmJNMw2uPEspCfSDK8dJpPJtKV9IiJx1JWgMLPfMLP9ZjZnZhWvsgfrXW1m3zazJ8zsvc3uN5VKMbl7kqFVQ6TvayAsIvZ+0hAgItKLulVRPApsAL5UbQUzOxXYBlwDvAp4m5m9qtkdNxwWDYTEyIYRRm8e1XUMEekpXQkKd3/M3b9dZ7XLgSfc/YC7F4BPAde3Yv+Rw6LBkNAQICLSi+J8jWIF8FTZ45nguYrM7EYz22dm+w4fPlz3zeuGxSJCIr8+D0vVQ0pEekvbgsLM/tHMHq2wRK0KrMJzVW8jd/ePufsad19z3nnnRdpB1bBYbEhoCBAR6UFtCwp3f5O7X1Jh2RPxLWaAl5Q9vgA41Op2LgiLo02GREhhISI9Is6nnr4KvNzMVpnZEuCtwEQ7dlQeFgN3DzQfEiGFhYj0gG51j11vZjPAa4HPmtnngudfbGb3A7j7LPAu4HPAY8Dfufv+drUpDIvt27a3JiRCCgsRSbi+HT12MRY9BAhoJFoRiTWNHtsiGgJERPqRgqJMLpfjogsv5Jlnnqn4uoYAEZF+pKAos3VsjGcPHmTr2FjF19s9BIiISBwpKAK5XI7xHTuYmptjfMeOqlVFu4YAERGJKwVFYOvYGJvn5rgUuOHYsapVBbR+CBARkThTrydK1cTFq1ez/8gRlgM54JLBQfYfOFBznuGavaAUEiKSIOr1VEdYTYSzDy+nflUBzQ8BIlJOw9RLXPV9UITXJu4oFE56/o5Coea1itBihwARKVcsFtk0MsLto6NsGtGNmRIvfR8U86uJUNSqAhofAkSkXBgSxelpDs7OUpyeVlhIrPT1NYr51yYWvE60axWhYrFINpslk8koJCSS8pDYlc+zFDgKbEynSQ0Nce+k/uCQztA1iiqqVROhRqoKKFUWmzdv1n9siaRSSAAsBXbl86osJDb6tqKoV00cX4/GqgqRKKqFRDlVFtJJqigqqFdNhBqtKkTqiRISoMpC4qNvg2LPxAR3FQoY1F3uKhT4zJ6o8y2JVBc1JEIKC4mqnd2r+zYoDszM4O6RlwMzM91usiRcoyERUlhIPe3uXt23QSHSadlslgenpsg2EBKhpUA2ny9tr2HqpUwnulcrKOQ43RncXplMhiuHh8mk0xxtcNujQCadLm2vYeolML9KXUZ7qk8FhQAnxq0avXlUU7a2SSqVKvVeGhpiYwNhod5PUkknu1crKOSkwQ1nb5nV/N5t1GhYKCSkknrXu1odFgqKPrdgBNylkF+fV1i0UdSwUEhIJd3oXq2g6GNVh0kfUFi0W72wUEhIJd3qXq2g6FM159IAhUUHVAsLhYRU0s3u1QqKPlQ3JEIKi7abHxYvoJCQyrrZvVpB0Wcih0RIYdF25WGxcmBAISEVdbN7tYKiTK/fR9BwSIQUFm0XhsWd27crJKSibnavVlAEisUiIyObGB29nZGRTT15MMxms0ztnSK/roGQCA1Afl2eqb26M7hdNEy91NOt7tUKCk6ExPR0kdnZg0xPF3syLDKZDMNrh0lPlM3vHdUspCfSDK/VncEi3dSN7tV9HxTlIZHP7wKWkc/v6smwWDC/d9SwmNU84CJx0unu1X0dFAtD4sRN8AqLgEJCJJY62b26K0FhZr9hZvvNbM7MKs6oFKx30My+aWYPm1n9SbAbUD0kQgoLhYRIvHWqe3W3KopHgQ3AlyKsu9bdX11tir7FqB8SoT4OC4WESCJ0ont1V4LC3R9z9293Y9/RQyLUh2GhkBBJlHZ3r477NQoHPm9mD5nZjc2+WeMhEeqjsDiqkBBJonZ2r25bUJjZP5rZoxWW6xt4m9e5+2XANcDNZvaGGvu70cz2mdm+w4cPV1wnm80yNfUg+XyW6CERWko+X9q+1+4jKA+LgbsHFBIichJz9+7t3OyLwHvcve6FajN7P/ATd//TeuuuWbPG9+1b+JaLrygAjpJOb2RoKMXk5L09eRAtFotks1kymUxP/nwiUp2ZPVTtWnBsTz2Z2elmtiz8HngLpYvgi5ZKlQ7yQ0Mp0umN0MBN8L0eEqA7g0Wksm51j11vZjPAa4HPmtnngudfbGb3B6v9LPBPZvYI8C/AZ939H5rdd+Nh0R8hISJSTVdPPbVLtVNP5aKdhlJIiEh/SOSpp3arX1koJEREoI+DAmqFhUJCRCTU10EBlcLiBYWEiEiZvg8KODksBgZWKiRERMo0On1NzwrDQvcRiIicTEFRJryPQERETtCpJxERqUlBISJC6d6q8fHxnhr0s1UUFCLS94rFIiMbRhi9eZSRDSMKi3kUFNIy+otMkigMieknp5m9ZZbpJ6cVFvMoKKQlisUim0ZGuH10lE0j+k8myVAeEvn1eVgK+fV5hcU8CgppWhgSxelpDs7OUpyeVlhI7C0IibAP6IDCYj4FhTSlPCR25fMsA3bl8woLibWqIRFSWJxEQSGLNj8kwvF3l6KwkPiqGxIhhcVxCgpZlGohEVJYSBxFDomQwgJQUMgi1AuJkMJC4qThkAgpLBQU0pioIRFSWEhcZLNZpvZOkV/XQEiEBiC/Ls/U3imy2Wxb2hdnCgqJrNGQCCksJA4ymQzDa4dJT6RhtsGNZyE9kWZ47TCZTKYt7YszBYVEls1meXBqimwDIRFaCmTz+dL2ffgXmXRfKpVicvckQ6uGSN/XQFjMQvq+NEOrhpjcPdmXI0srKCSyTCbDlcPDZNLpBRPH1nMUyKTTpe378C8yiYeGw0IhASgopAGpVIp7JydJDQ2xsYGwOApsTKdJDQ2Vtu/T/2wSD5HDQiFxnIJCGtJoWCgkJI7qhoVC4iQKCmlY1LBQSEicVQ0LhcQCCgpZlHphoZCQJFgQFkcVEpUoKGTRqoWFQkKSpDwsBu4eUEhUoKCQpswPixdQSEjyhGGxfdt2hUQFCgppWnlYrBwYUEhIIqVSKTZv3qzf2woavZFdpKIwLLLZLJlMRv/ZRHqIgkJaJvyLTER6i049iYhITQoKERGpSUEhIiI1KShERKQmc/dut6HlzOww8N1ut6OKc4Hnut2IGtS+5qh9zVH7mtNM+17m7udVeqEngyLOzGyfu6/pdjuqUfuao/Y1R+1rTrvap1NPIiJSk4JCRERqUlB03se63YA61L7mqH3NUfua05b26RqFiIjUpIpCRERqUlC0gZm9xMz2mtljZrbfzN5dYZ03mtmPzezhYPmjDrfxoJl9M9j3vgqvm5ndbWZPmNk3zOyyDrbtlWWfy8Nm9ryZ3TpvnY5+fmb2N2b2rJk9WvbcOWb2BTN7PPh6dpVtrzazbwef5Xs72L4/MbNvBf9+95nZWVW2rfm70Mb2vd/Mni77N7y2yrbd+vzuLWvbQTN7uMq2nfj8Kh5TOvY76O5aWrwAy4HLgu+XAf8LeNW8dd4I/H0X23gQOLfG69cCDwAGXAF8pUvtPBV4hlIf7659fsAbgMuAR8ue2wq8N/j+vcB/r9L+7wCrgSXAI/N/F9rYvrcAA8H3/71S+6L8LrSxfe8H3hPh378rn9+81/8M+KMufn4Vjymd+h1URdEG7p5z968F378APAas6G6rGnY98HEv+TJwlpkt70I7hoHvuHtXb6B09y8BP5z39PXAePD9OPDrFTa9HHjC3Q+4ewH4VLBd29vn7p9393Am6C8DF7R6v1FV+fyi6NrnFzIzA34T+GSr9xtVjWNKR34HFRRtZmYrgUuBr1R4+bVm9oiZPWBmF3e2ZTjweTN7yMxurPD6CuCpssczdCfs3kr1/6Dd/PwAftbdc1D6jwy8qMI6cfkcf4dShVhJvd+FdnpXcGrsb6qcNonD5zcEfN/dH6/yekc/v3nHlI78Dioo2sjMzgD+B3Cruz8/7+WvUTqd8ovAh4DPdLh5r3P3y4BrgJvN7A3zXrcK23S0i5yZLQHWAZ+u8HK3P7+o4vA5/iEwC/xtlVXq/S60y0eAC4FXAzlKp3fm6/rnB7yN2tVExz6/OseUqptVeK6hz1BB0SZmlqL0D/q37r57/uvu/ry7/yT4/n4gZWbndqp97n4o+PoscB+l8rTcDPCSsscXAIc607rjrgG+5u7fn/9Ctz+/wPfD03HB12crrNPVz9HMNgPXAW/34IT1fBF+F9rC3b/v7sfcfQ746yr77fbnNwBsAO6ttk6nPr8qx5SO/A4qKNogOKd5D/CYu99ZZZ3zg/Uws8sp/Vv8oEPtO93MloXfU7ro+ei81SaAG6zkCuDHYYnbQVX/kuvm51dmAgin9NsM7KmwzleBl5vZqqBCemuwXduZ2dXAfwLWuXu+yjpRfhfa1b7ya17rq+y3a59f4E3At9x9ptKLnfr8ahxTOvM72M4r9f26AK+nVNp9A3g4WK4F3gm8M1jnXcB+Sj0Qvgz8SgfbtzrY7yNBG/4weL68fQZso9Rb4pvAmg5/hmlKB/5/V/Zc1z4/SoGVA4qU/kL7XeBngCng8eDrOcG6LwbuL9v2Wkq9VL4TftYdat8TlM5Nh7+DH53fvmq/Cx1q3/8T/G59g9KBa3mcPr/g+Z3h71zZut34/KodUzryO6g7s0VEpCadehIRkZoUFCIiUpOCQkREalJQiIhITQoKERGpSUEhIiI1KShEKgiGdX7SzM4JHp8dPN5speHN72/gvY4FQ1C/OHj8D8EYVfvN7KNmdmrw/O1m9q/B2EdTZvay4PkLg+1/EjweCtbryI1xIrqPQqQKM7sD+N/c/UYz+ytKw0n/T0pDY1/XwPv8xN3PKHt8prs/H9xtuwv4tLt/yszWUhrOPW9mNwFvdPdNld4nGBju7939khb8qCI1qaIQqe7PgSusNGnS66k8aF3D/MRgbgOU5gfw4Pm9fmKoja4OCy5STkEhUoW7F4HfpxQYt3ppLP+TmNmAmf2Rme0zs8+b2W+Y2UVm9mdm9tJq721mn6M0gNsLlKqK+X6X6sOCi3SUgkKktmsojQFU7RTPi4EfAb8E/CHwdkoH/oPu/r1qb+ruV1GatWwp8Kvlr5nZbwFrgD9ptvEirTDQ7QaIxJWZvRp4M6WpYP/JzD41f50gDD4UPPwqlWcYq8jdj5jZBKXZxr4Q7PNNlALnSnc/2kz7RVpFFYVIBcGF5o9QOuX0PUp/3f9pC973jLL5AwYojer5reDxpcBfURoWvNK8AiJdoYpCpLL/A/ieu38hePyXwG8DVzb5vqcDE2a2lNKk9/8v8NHgtT8BzgA+HUy18T13X9fk/kSapu6xIg0wszfSZPfYJvat7rHSFTr1JNKYAnBJIzfcAc+X33DXqPCGO+D7weMhYBJ4bjHvJ9IoVRQiIlKTKgoREalJQSEiIjUpKEREpCYFhYiI1KSgEBGRmv5/sORgqhJ33ukAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n", "\n", "plt.figure(figsize=(6,6))\n", "resid = resid.reindex(X.index)\n", "for values, group in factor_groups:\n", " i,j = values\n", " idx = group.index\n", " plt.scatter(X.loc[idx], resid.loc[idx], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", "plt.xlabel('X[~[32]]');\n", "plt.ylabel('standardized resids');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Plot the fitted values" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:54.428888Z", "iopub.status.busy": "2021-02-02T06:51:54.427426Z", "iopub.status.idle": "2021-02-02T06:51:54.740526Z", "shell.execute_reply": "2021-02-02T06:51:54.741690Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Salary')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lm_final = ols('S ~ X + C(E)*C(M)', data = salary_table.drop([drop_idx])).fit()\n", "mf = lm_final.model.data.orig_exog\n", "lstyle = ['-','--']\n", "\n", "plt.figure(figsize=(6,6))\n", "for values, group in factor_groups:\n", " i,j = values\n", " idx = group.index\n", " plt.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i-1],\n", " s=144, edgecolors='black')\n", " # drop NA because there is no idx 32 in the final model\n", " fv = lm_final.fittedvalues.reindex(idx).dropna()\n", " x = mf.X.reindex(idx).dropna()\n", " plt.plot(x, fv, ls=lstyle[j], color=colors[i-1])\n", "plt.xlabel('Experience');\n", "plt.ylabel('Salary');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management,M and education,E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:54.746797Z", "iopub.status.busy": "2021-02-02T06:51:54.745359Z", "iopub.status.idle": "2021-02-02T06:51:55.187636Z", "shell.execute_reply": "2021-02-02T06:51:55.188807Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "U = S - X * interX_lm32.params['X']\n", "\n", "plt.figure(figsize=(6,6))\n", "interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],\n", " markersize=10, ax=plt.gca())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Minority Employment Data" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:55.194082Z", "iopub.status.busy": "2021-02-02T06:51:55.192469Z", "iopub.status.idle": "2021-02-02T06:51:55.959963Z", "shell.execute_reply": "2021-02-02T06:51:55.961090Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'JPERF')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "try:\n", " jobtest_table = pd.read_table('jobtest.table')\n", "except: # do not have data already\n", " url = 'http://stats191.stanford.edu/data/jobtest.table'\n", " jobtest_table = pd.read_table(url)\n", "\n", "factor_group = jobtest_table.groupby(['MINORITY'])\n", "\n", "fig, ax = plt.subplots(figsize=(6,6))\n", "colors = ['purple', 'green']\n", "markers = ['o', 'v']\n", "for factor, group in factor_group:\n", " ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "ax.set_xlabel('TEST');\n", "ax.set_ylabel('JPERF');" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:55.966167Z", "iopub.status.busy": "2021-02-02T06:51:55.964605Z", "iopub.status.idle": "2021-02-02T06:51:55.991029Z", "shell.execute_reply": "2021-02-02T06:51:55.992056Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: JPERF R-squared: 0.517\n", "Model: OLS Adj. R-squared: 0.490\n", "Method: Least Squares F-statistic: 19.25\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 0.000356\n", "Time: 06:51:55 Log-Likelihood: -36.614\n", "No. Observations: 20 AIC: 77.23\n", "Df Residuals: 18 BIC: 79.22\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 1.0350 0.868 1.192 0.249 -0.789 2.859\n", "TEST 2.3605 0.538 4.387 0.000 1.230 3.491\n", "==============================================================================\n", "Omnibus: 0.324 Durbin-Watson: 2.896\n", "Prob(Omnibus): 0.850 Jarque-Bera (JB): 0.483\n", "Skew: -0.186 Prob(JB): 0.785\n", "Kurtosis: 2.336 Cond. No. 5.26\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "min_lm = ols('JPERF ~ TEST', data=jobtest_table).fit()\n", "print(min_lm.summary())" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:55.996682Z", "iopub.status.busy": "2021-02-02T06:51:55.995025Z", "iopub.status.idle": "2021-02-02T06:51:56.219956Z", "shell.execute_reply": "2021-02-02T06:51:56.221094Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6,6));\n", "for factor, group in factor_group:\n", " ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "ax.set_xlabel('TEST')\n", "ax.set_ylabel('JPERF')\n", "fig = abline_plot(model_results = min_lm, ax=ax)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:56.226204Z", "iopub.status.busy": "2021-02-02T06:51:56.224543Z", "iopub.status.idle": "2021-02-02T06:51:56.255618Z", "shell.execute_reply": "2021-02-02T06:51:56.256620Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: JPERF R-squared: 0.632\n", "Model: OLS Adj. R-squared: 0.589\n", "Method: Least Squares F-statistic: 14.59\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 0.000204\n", "Time: 06:51:56 Log-Likelihood: -33.891\n", "No. Observations: 20 AIC: 73.78\n", "Df Residuals: 17 BIC: 76.77\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "=================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "---------------------------------------------------------------------------------\n", "Intercept 1.1211 0.780 1.437 0.169 -0.525 2.768\n", "TEST 1.8276 0.536 3.412 0.003 0.698 2.958\n", "TEST:MINORITY 0.9161 0.397 2.306 0.034 0.078 1.754\n", "==============================================================================\n", "Omnibus: 0.388 Durbin-Watson: 3.008\n", "Prob(Omnibus): 0.823 Jarque-Bera (JB): 0.514\n", "Skew: 0.050 Prob(JB): 0.773\n", "Kurtosis: 2.221 Cond. No. 5.96\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "min_lm2 = ols('JPERF ~ TEST + TEST:MINORITY',\n", " data=jobtest_table).fit()\n", "\n", "print(min_lm2.summary())" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:56.261930Z", "iopub.status.busy": "2021-02-02T06:51:56.260243Z", "iopub.status.idle": "2021-02-02T06:51:56.515612Z", "shell.execute_reply": "2021-02-02T06:51:56.516777Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6,6));\n", "for factor, group in factor_group:\n", " ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n", " slope = min_lm2.params['TEST'], ax=ax, color='purple');\n", "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n", " slope = min_lm2.params['TEST'] + min_lm2.params['TEST:MINORITY'],\n", " ax=ax, color='green');" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:56.521831Z", "iopub.status.busy": "2021-02-02T06:51:56.520247Z", "iopub.status.idle": "2021-02-02T06:51:56.548480Z", "shell.execute_reply": "2021-02-02T06:51:56.549536Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: JPERF R-squared: 0.572\n", "Model: OLS Adj. R-squared: 0.522\n", "Method: Least Squares F-statistic: 11.38\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 0.000731\n", "Time: 06:51:56 Log-Likelihood: -35.390\n", "No. Observations: 20 AIC: 76.78\n", "Df Residuals: 17 BIC: 79.77\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 0.6120 0.887 0.690 0.500 -1.260 2.483\n", "TEST 2.2988 0.522 4.400 0.000 1.197 3.401\n", "MINORITY 1.0276 0.691 1.487 0.155 -0.430 2.485\n", "==============================================================================\n", "Omnibus: 0.251 Durbin-Watson: 3.028\n", "Prob(Omnibus): 0.882 Jarque-Bera (JB): 0.437\n", "Skew: -0.059 Prob(JB): 0.804\n", "Kurtosis: 2.286 Cond. No. 5.72\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "min_lm3 = ols('JPERF ~ TEST + MINORITY', data = jobtest_table).fit()\n", "print(min_lm3.summary())" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:56.554303Z", "iopub.status.busy": "2021-02-02T06:51:56.552838Z", "iopub.status.idle": "2021-02-02T06:51:56.805483Z", "shell.execute_reply": "2021-02-02T06:51:56.806452Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6,6));\n", "for factor, group in factor_group:\n", " ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "fig = abline_plot(intercept = min_lm3.params['Intercept'],\n", " slope = min_lm3.params['TEST'], ax=ax, color='purple');\n", "fig = abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['MINORITY'],\n", " slope = min_lm3.params['TEST'], ax=ax, color='green');" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:56.811636Z", "iopub.status.busy": "2021-02-02T06:51:56.810079Z", "iopub.status.idle": "2021-02-02T06:51:56.839801Z", "shell.execute_reply": "2021-02-02T06:51:56.840942Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: JPERF R-squared: 0.664\n", "Model: OLS Adj. R-squared: 0.601\n", "Method: Least Squares F-statistic: 10.55\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 0.000451\n", "Time: 06:51:56 Log-Likelihood: -32.971\n", "No. Observations: 20 AIC: 73.94\n", "Df Residuals: 16 BIC: 77.92\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "=================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "---------------------------------------------------------------------------------\n", "Intercept 2.0103 1.050 1.914 0.074 -0.216 4.236\n", "TEST 1.3134 0.670 1.959 0.068 -0.108 2.735\n", "MINORITY -1.9132 1.540 -1.242 0.232 -5.179 1.352\n", "TEST:MINORITY 1.9975 0.954 2.093 0.053 -0.026 4.021\n", "==============================================================================\n", "Omnibus: 3.377 Durbin-Watson: 3.015\n", "Prob(Omnibus): 0.185 Jarque-Bera (JB): 1.330\n", "Skew: 0.120 Prob(JB): 0.514\n", "Kurtosis: 1.760 Cond. No. 13.8\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "min_lm4 = ols('JPERF ~ TEST * MINORITY', data = jobtest_table).fit()\n", "print(min_lm4.summary())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:56.845520Z", "iopub.status.busy": "2021-02-02T06:51:56.844049Z", "iopub.status.idle": "2021-02-02T06:51:57.104249Z", "shell.execute_reply": "2021-02-02T06:51:57.105289Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,6));\n", "for factor, group in factor_group:\n", " ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n", " marker=markers[factor], s=12**2)\n", "\n", "fig = abline_plot(intercept = min_lm4.params['Intercept'],\n", " slope = min_lm4.params['TEST'], ax=ax, color='purple');\n", "fig = abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['MINORITY'],\n", " slope = min_lm4.params['TEST'] + min_lm4.params['TEST:MINORITY'],\n", " ax=ax, color='green');" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.110427Z", "iopub.status.busy": "2021-02-02T06:51:57.108833Z", "iopub.status.idle": "2021-02-02T06:51:57.127141Z", "shell.execute_reply": "2021-02-02T06:51:57.128347Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 18.0 45.568297 0.0 NaN NaN NaN\n", "1 16.0 31.655473 2.0 13.912824 3.516061 0.054236\n" ] } ], "source": [ "# is there any effect of MINORITY on slope or intercept?\n", "table5 = anova_lm(min_lm, min_lm4)\n", "print(table5)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.132887Z", "iopub.status.busy": "2021-02-02T06:51:57.131569Z", "iopub.status.idle": "2021-02-02T06:51:57.148656Z", "shell.execute_reply": "2021-02-02T06:51:57.149654Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 18.0 45.568297 0.0 NaN NaN NaN\n", "1 17.0 40.321546 1.0 5.246751 2.212087 0.155246\n" ] } ], "source": [ "# is there any effect of MINORITY on intercept\n", "table6 = anova_lm(min_lm, min_lm3)\n", "print(table6)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.154290Z", "iopub.status.busy": "2021-02-02T06:51:57.152655Z", "iopub.status.idle": "2021-02-02T06:51:57.169681Z", "shell.execute_reply": "2021-02-02T06:51:57.170659Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 18.0 45.568297 0.0 NaN NaN NaN\n", "1 17.0 34.707653 1.0 10.860644 5.319603 0.033949\n" ] } ], "source": [ "# is there any effect of MINORITY on slope\n", "table7 = anova_lm(min_lm, min_lm2)\n", "print(table7)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.175243Z", "iopub.status.busy": "2021-02-02T06:51:57.173785Z", "iopub.status.idle": "2021-02-02T06:51:57.190601Z", "shell.execute_reply": "2021-02-02T06:51:57.191620Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 17.0 34.707653 0.0 NaN NaN NaN\n", "1 16.0 31.655473 1.0 3.05218 1.542699 0.232115\n" ] } ], "source": [ "# is it just the slope or both?\n", "table8 = anova_lm(min_lm2, min_lm4)\n", "print(table8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## One-way ANOVA" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.196484Z", "iopub.status.busy": "2021-02-02T06:51:57.195007Z", "iopub.status.idle": "2021-02-02T06:51:57.429184Z", "shell.execute_reply": "2021-02-02T06:51:57.430217Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "try:\n", " rehab_table = pd.read_csv('rehab.table')\n", "except:\n", " url = 'http://stats191.stanford.edu/data/rehab.csv'\n", " rehab_table = pd.read_table(url, delimiter=\",\")\n", " rehab_table.to_csv('rehab.table')\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "fig = rehab_table.boxplot('Time', 'Fitness', ax=ax, grid=False)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.435114Z", "iopub.status.busy": "2021-02-02T06:51:57.433503Z", "iopub.status.idle": "2021-02-02T06:51:57.468491Z", "shell.execute_reply": "2021-02-02T06:51:57.469501Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df sum_sq mean_sq F PR(>F)\n", "C(Fitness) 2.0 672.0 336.000000 16.961538 0.000041\n", "Residual 21.0 416.0 19.809524 NaN NaN\n", " Intercept C(Fitness)[T.2] C(Fitness)[T.3]\n", "0 1.0 0.0 0.0\n", "1 1.0 0.0 0.0\n", "2 1.0 0.0 0.0\n", "3 1.0 0.0 0.0\n", "4 1.0 0.0 0.0\n", "5 1.0 0.0 0.0\n", "6 1.0 0.0 0.0\n", "7 1.0 0.0 0.0\n", "8 1.0 1.0 0.0\n", "9 1.0 1.0 0.0\n", "10 1.0 1.0 0.0\n", "11 1.0 1.0 0.0\n", "12 1.0 1.0 0.0\n", "13 1.0 1.0 0.0\n", "14 1.0 1.0 0.0\n", "15 1.0 1.0 0.0\n", "16 1.0 1.0 0.0\n", "17 1.0 1.0 0.0\n", "18 1.0 0.0 1.0\n", "19 1.0 0.0 1.0\n", "20 1.0 0.0 1.0\n", "21 1.0 0.0 1.0\n", "22 1.0 0.0 1.0\n", "23 1.0 0.0 1.0\n" ] } ], "source": [ "rehab_lm = ols('Time ~ C(Fitness)', data=rehab_table).fit()\n", "table9 = anova_lm(rehab_lm)\n", "print(table9)\n", "\n", "print(rehab_lm.model.data.orig_exog)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.474322Z", "iopub.status.busy": "2021-02-02T06:51:57.472917Z", "iopub.status.idle": "2021-02-02T06:51:57.579637Z", "shell.execute_reply": "2021-02-02T06:51:57.580718Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Time R-squared: 0.618\n", "Model: OLS Adj. R-squared: 0.581\n", "Method: Least Squares F-statistic: 16.96\n", "Date: Tue, 02 Feb 2021 Prob (F-statistic): 4.13e-05\n", "Time: 06:51:57 Log-Likelihood: -68.286\n", "No. Observations: 24 AIC: 142.6\n", "Df Residuals: 21 BIC: 146.1\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "===================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "Intercept 38.0000 1.574 24.149 0.000 34.728 41.272\n", "C(Fitness)[T.2] -6.0000 2.111 -2.842 0.010 -10.390 -1.610\n", "C(Fitness)[T.3] -14.0000 2.404 -5.824 0.000 -18.999 -9.001\n", "==============================================================================\n", "Omnibus: 0.163 Durbin-Watson: 2.209\n", "Prob(Omnibus): 0.922 Jarque-Bera (JB): 0.211\n", "Skew: -0.163 Prob(JB): 0.900\n", "Kurtosis: 2.675 Cond. No. 3.80\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "print(rehab_lm.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Two-way ANOVA" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:57.597468Z", "iopub.status.busy": "2021-02-02T06:51:57.591824Z", "iopub.status.idle": "2021-02-02T06:51:58.121631Z", "shell.execute_reply": "2021-02-02T06:51:58.122941Z" } }, "outputs": [], "source": [ "try:\n", " kidney_table = pd.read_table('./kidney.table')\n", "except:\n", " url = 'http://stats191.stanford.edu/data/kidney.table'\n", " kidney_table = pd.read_csv(url, delim_whitespace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Explore the dataset" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:58.128014Z", "iopub.status.busy": "2021-02-02T06:51:58.126563Z", "iopub.status.idle": "2021-02-02T06:51:58.142757Z", "shell.execute_reply": "2021-02-02T06:51:58.143767Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Days Duration Weight ID\n", "0 0.0 1 1 1\n", "1 2.0 1 1 2\n", "2 1.0 1 1 3\n", "3 3.0 1 1 4\n", "4 0.0 1 1 5\n", "5 2.0 1 1 6\n", "6 0.0 1 1 7\n", "7 5.0 1 1 8\n", "8 6.0 1 1 9\n", "9 8.0 1 1 10" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kidney_table.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Balanced panel" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:58.148752Z", "iopub.status.busy": "2021-02-02T06:51:58.147145Z", "iopub.status.idle": "2021-02-02T06:51:58.434507Z", "shell.execute_reply": "2021-02-02T06:51:58.435810Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "kt = kidney_table\n", "plt.figure(figsize=(8,6))\n", "fig = interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n", " colors=['red', 'blue'], markers=['D','^'], ms=10, ax=plt.gca())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have things available in the calling namespace available in the formula evaluation namespace" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:58.441388Z", "iopub.status.busy": "2021-02-02T06:51:58.439706Z", "iopub.status.idle": "2021-02-02T06:51:58.553656Z", "shell.execute_reply": "2021-02-02T06:51:58.554843Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 56.0 29.624856 0.0 NaN NaN NaN\n", "1 54.0 28.989198 2.0 0.635658 0.59204 0.556748\n", " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 58.0 46.596147 0.0 NaN NaN NaN\n", "1 56.0 29.624856 2.0 16.971291 16.040454 0.000003\n", " df_resid ssr df_diff ss_diff F Pr(>F)\n", "0 57.0 31.964549 0.0 NaN NaN NaN\n", "1 56.0 29.624856 1.0 2.339693 4.422732 0.03997\n" ] } ], "source": [ "kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit()\n", "\n", "table10 = anova_lm(kidney_lm)\n", "\n", "print(anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',\n", " data=kt).fit(), kidney_lm))\n", "print(anova_lm(ols('np.log(Days+1) ~ C(Duration)', data=kt).fit(),\n", " ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n", " data=kt).fit()))\n", "print(anova_lm(ols('np.log(Days+1) ~ C(Weight)', data=kt).fit(),\n", " ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n", " data=kt).fit()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sum of squares\n", "\n", " Illustrates the use of different types of sums of squares (I,II,II)\n", " and how the Sum contrast can be used to produce the same output between\n", " the 3.\n", "\n", " Types I and II are equivalent under a balanced design.\n", "\n", " Do not use Type III with non-orthogonal contrast - ie., Treatment" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:58.560227Z", "iopub.status.busy": "2021-02-02T06:51:58.558481Z", "iopub.status.idle": "2021-02-02T06:51:58.620544Z", "shell.execute_reply": "2021-02-02T06:51:58.621767Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df sum_sq mean_sq F PR(>F)\n", "C(Duration, Sum) 1.0 2.339693 2.339693 4.358293 0.041562\n", "C(Weight, Sum) 2.0 16.971291 8.485645 15.806745 0.000004\n", "C(Duration, Sum):C(Weight, Sum) 2.0 0.635658 0.317829 0.592040 0.556748\n", "Residual 54.0 28.989198 0.536837 NaN NaN\n", " sum_sq df F PR(>F)\n", "C(Duration, Sum) 2.339693 1.0 4.358293 0.041562\n", "C(Weight, Sum) 16.971291 2.0 15.806745 0.000004\n", "C(Duration, Sum):C(Weight, Sum) 0.635658 2.0 0.592040 0.556748\n", "Residual 28.989198 54.0 NaN NaN\n", " sum_sq df F PR(>F)\n", "Intercept 156.301830 1.0 291.153237 2.077589e-23\n", "C(Duration, Sum) 2.339693 1.0 4.358293 4.156170e-02\n", "C(Weight, Sum) 16.971291 2.0 15.806745 3.944502e-06\n", "C(Duration, Sum):C(Weight, Sum) 0.635658 2.0 0.592040 5.567479e-01\n", "Residual 28.989198 54.0 NaN NaN\n" ] } ], "source": [ "sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',\n", " data=kt).fit()\n", "\n", "print(anova_lm(sum_lm))\n", "print(anova_lm(sum_lm, typ=2))\n", "print(anova_lm(sum_lm, typ=3))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:58.626812Z", "iopub.status.busy": "2021-02-02T06:51:58.625171Z", "iopub.status.idle": "2021-02-02T06:51:58.685322Z", "shell.execute_reply": "2021-02-02T06:51:58.686575Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " df sum_sq mean_sq F PR(>F)\n", "C(Duration, Treatment) 1.0 2.339693 2.339693 4.358293 0.041562\n", "C(Weight, Treatment) 2.0 16.971291 8.485645 15.806745 0.000004\n", "C(Duration, Treatment):C(Weight, Treatment) 2.0 0.635658 0.317829 0.592040 0.556748\n", "Residual 54.0 28.989198 0.536837 NaN NaN\n", " sum_sq df F PR(>F)\n", "C(Duration, Treatment) 2.339693 1.0 4.358293 0.041562\n", "C(Weight, Treatment) 16.971291 2.0 15.806745 0.000004\n", "C(Duration, Treatment):C(Weight, Treatment) 0.635658 2.0 0.592040 0.556748\n", "Residual 28.989198 54.0 NaN NaN\n", " sum_sq df F PR(>F)\n", "Intercept 10.427596 1.0 19.424139 0.000050\n", "C(Duration, Treatment) 0.054293 1.0 0.101134 0.751699\n", "C(Weight, Treatment) 11.703387 2.0 10.900317 0.000106\n", "C(Duration, Treatment):C(Weight, Treatment) 0.635658 2.0 0.592040 0.556748\n", "Residual 28.989198 54.0 NaN NaN\n" ] } ], "source": [ "nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',\n", " data=kt).fit()\n", "print(anova_lm(nosum_lm))\n", "print(anova_lm(nosum_lm, typ=2))\n", "print(anova_lm(nosum_lm, typ=3))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 1 }