{
 "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": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:27:55.746983Z",
     "iopub.status.busy": "2026-07-29T12:27:55.745850Z",
     "iopub.status.idle": "2026-07-29T12:27:56.468209Z",
     "shell.execute_reply": "2026-07-29T12:27:56.466475Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:27:56.470933Z",
     "iopub.status.busy": "2026-07-29T12:27:56.470468Z",
     "iopub.status.idle": "2026-07-29T12:27:58.108585Z",
     "shell.execute_reply": "2026-07-29T12:27:58.107542Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.graphics.api import abline_plot, interaction_plot\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "\n",
    "np.set_printoptions(precision=4, suppress=True)\n",
    "pd.set_option(\"display.width\", 100)\n",
    "\n",
    "\n",
    "def download_file(url, mode=\"t\"):\n",
    "    local_filename = url.split(\"/\")[-1]\n",
    "    if os.path.exists(local_filename):\n",
    "        return local_filename\n",
    "    with requests.get(url, stream=True, timeout=30) as r:\n",
    "        with open(local_filename, f\"w{mode}\") as f:\n",
    "            f.write(r.text)\n",
    "    return local_filename\n",
    "\n",
    "\n",
    "url = \"https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table\"\n",
    "salary_table = pd.read_csv(download_file(url), sep=\"\\t\")\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": "2026-07-29T12:27:58.112267Z",
     "iopub.status.busy": "2026-07-29T12:27:58.111963Z",
     "iopub.status.idle": "2026-07-29T12:27:58.424295Z",
     "shell.execute_reply": "2026-07-29T12:27:58.423429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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], 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": "2026-07-29T12:27:58.431706Z",
     "iopub.status.busy": "2026-07-29T12:27:58.430836Z",
     "iopub.status.idle": "2026-07-29T12:27:58.485541Z",
     "shell.execute_reply": "2026-07-29T12:27:58.484553Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           2.23e-27\n",
      "Time:                        12:27:58   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": "2026-07-29T12:27:58.491608Z",
     "iopub.status.busy": "2026-07-29T12:27:58.491251Z",
     "iopub.status.idle": "2026-07-29T12:27:58.501064Z",
     "shell.execute_reply": "2026-07-29T12:27:58.497521Z"
    }
   },
   "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": 5,
     "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": "2026-07-29T12:27:58.504289Z",
     "iopub.status.busy": "2026-07-29T12:27:58.503118Z",
     "iopub.status.idle": "2026-07-29T12:27:58.521999Z",
     "shell.execute_reply": "2026-07-29T12:27:58.520313Z"
    }
   },
   "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>Intercept</th>\n",
       "      <th>C(E)[T.2]</th>\n",
       "      <th>C(E)[T.3]</th>\n",
       "      <th>C(M)[T.1]</th>\n",
       "      <th>X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Intercept  C(E)[T.2]  C(E)[T.3]  C(M)[T.1]    X\n",
       "0        1.0        0.0        0.0        1.0  1.0\n",
       "1        1.0        0.0        1.0        0.0  1.0\n",
       "2        1.0        0.0        1.0        1.0  1.0\n",
       "3        1.0        1.0        0.0        0.0  1.0\n",
       "4        1.0        0.0        1.0        0.0  1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.model.data.orig_exog[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We keep a reference to the original untouched data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:27:58.524377Z",
     "iopub.status.busy": "2026-07-29T12:27:58.524109Z",
     "iopub.status.idle": "2026-07-29T12:27:58.537113Z",
     "shell.execute_reply": "2026-07-29T12:27:58.535710Z"
    }
   },
   "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>S</th>\n",
       "      <th>X</th>\n",
       "      <th>E</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13876</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11608</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18701</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11283</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11767</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       S  X  E  M\n",
       "0  13876  1  1  1\n",
       "1  11608  1  3  0\n",
       "2  18701  1  3  1\n",
       "3  11283  1  2  0\n",
       "4  11767  1  3  0"
      ]
     },
     "execution_count": 7,
     "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": "2026-07-29T12:27:58.539462Z",
     "iopub.status.busy": "2026-07-29T12:27:58.539198Z",
     "iopub.status.idle": "2026-07-29T12:27:58.590530Z",
     "shell.execute_reply": "2026-07-29T12:27:58.588874Z"
    }
   },
   "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": "2026-07-29T12:27:58.593249Z",
     "iopub.status.busy": "2026-07-29T12:27:58.592983Z",
     "iopub.status.idle": "2026-07-29T12:27:58.599933Z",
     "shell.execute_reply": "2026-07-29T12:27:58.599112Z"
    }
   },
   "outputs": [],
   "source": [
    "df_infl = infl.summary_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:27:58.603286Z",
     "iopub.status.busy": "2026-07-29T12:27:58.603003Z",
     "iopub.status.idle": "2026-07-29T12:27:58.627001Z",
     "shell.execute_reply": "2026-07-29T12:27:58.622966Z"
    }
   },
   "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>dfb_Intercept</th>\n",
       "      <th>dfb_C(E)[T.2]</th>\n",
       "      <th>dfb_C(E)[T.3]</th>\n",
       "      <th>dfb_C(M)[T.1]</th>\n",
       "      <th>dfb_X</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "      <th>student_resid</th>\n",
       "      <th>dffits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.505123</td>\n",
       "      <td>0.376134</td>\n",
       "      <td>0.483977</td>\n",
       "      <td>-0.369677</td>\n",
       "      <td>0.399111</td>\n",
       "      <td>0.104186</td>\n",
       "      <td>-1.683099</td>\n",
       "      <td>0.155327</td>\n",
       "      <td>-0.721753</td>\n",
       "      <td>-1.723037</td>\n",
       "      <td>-0.738880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.004663</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.006733</td>\n",
       "      <td>-0.006220</td>\n",
       "      <td>-0.004449</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.031318</td>\n",
       "      <td>0.130266</td>\n",
       "      <td>0.012120</td>\n",
       "      <td>0.030934</td>\n",
       "      <td>0.011972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.013627</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.036876</td>\n",
       "      <td>0.030514</td>\n",
       "      <td>-0.034970</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.246931</td>\n",
       "      <td>0.109021</td>\n",
       "      <td>0.086377</td>\n",
       "      <td>0.244082</td>\n",
       "      <td>0.085380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.083152</td>\n",
       "      <td>-0.074411</td>\n",
       "      <td>0.009704</td>\n",
       "      <td>0.053783</td>\n",
       "      <td>0.105122</td>\n",
       "      <td>0.005338</td>\n",
       "      <td>-0.457630</td>\n",
       "      <td>0.113030</td>\n",
       "      <td>-0.163364</td>\n",
       "      <td>-0.453173</td>\n",
       "      <td>-0.161773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.029382</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.042425</td>\n",
       "      <td>-0.039198</td>\n",
       "      <td>-0.028036</td>\n",
       "      <td>0.001166</td>\n",
       "      <td>0.197257</td>\n",
       "      <td>0.130266</td>\n",
       "      <td>0.076340</td>\n",
       "      <td>0.194929</td>\n",
       "      <td>0.075439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 10,
     "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": "2026-07-29T12:27:58.629330Z",
     "iopub.status.busy": "2026-07-29T12:27:58.628896Z",
     "iopub.status.idle": "2026-07-29T12:27:58.995280Z",
     "shell.execute_reply": "2026-07-29T12:27:58.994545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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(\n",
    "        x,\n",
    "        resid[group.index],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\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": "2026-07-29T12:27:59.002160Z",
     "iopub.status.busy": "2026-07-29T12:27:59.001880Z",
     "iopub.status.idle": "2026-07-29T12:27:59.080748Z",
     "shell.execute_reply": "2026-07-29T12:27:59.079420Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           8.23e-26\n",
      "Time:                        12:27:59   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": "2026-07-29T12:27:59.085161Z",
     "iopub.status.busy": "2026-07-29T12:27:59.084883Z",
     "iopub.status.idle": "2026-07-29T12:27:59.153347Z",
     "shell.execute_reply": "2026-07-29T12:27:59.151613Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           1.67e-55\n",
      "Time:                        12:27:59   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": [
    "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": "2026-07-29T12:27:59.156211Z",
     "iopub.status.busy": "2026-07-29T12:27:59.155920Z",
     "iopub.status.idle": "2026-07-29T12:27:59.176146Z",
     "shell.execute_reply": "2026-07-29T12:27:59.174428Z"
    }
   },
   "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>Intercept</th>\n",
       "      <th>C(E)[T.2]</th>\n",
       "      <th>C(E)[T.3]</th>\n",
       "      <th>C(M)[T.1]</th>\n",
       "      <th>C(E)[T.2]:C(M)[T.1]</th>\n",
       "      <th>C(E)[T.3]:C(M)[T.1]</th>\n",
       "      <th>X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 14,
     "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": "2026-07-29T12:27:59.179779Z",
     "iopub.status.busy": "2026-07-29T12:27:59.178671Z",
     "iopub.status.idle": "2026-07-29T12:27:59.192667Z",
     "shell.execute_reply": "2026-07-29T12:27:59.190972Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.  0.  1.  0.  0.  1.]\n",
      " [ 1.  0.  1.  0.  0.  0.  1.]\n",
      " [ 1.  0.  1.  1.  0.  1.  1.]\n",
      " [ 1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 1.  0.  1.  0.  0.  0.  1.]\n",
      " [ 1.  1.  0.  1.  1.  0.  2.]\n",
      " [ 1.  1.  0.  0.  0.  0.  2.]\n",
      " [ 1.  0.  0.  0.  0.  0.  2.]\n",
      " [ 1.  0.  1.  0.  0.  0.  2.]\n",
      " [ 1.  1.  0.  0.  0.  0.  3.]\n",
      " [ 1.  0.  0.  1.  0.  0.  3.]\n",
      " [ 1.  1.  0.  1.  1.  0.  3.]\n",
      " [ 1.  0.  1.  1.  0.  1.  3.]\n",
      " [ 1.  0.  0.  0.  0.  0.  4.]\n",
      " [ 1.  0.  1.  1.  0.  1.  4.]\n",
      " [ 1.  0.  1.  0.  0.  0.  4.]\n",
      " [ 1.  1.  0.  0.  0.  0.  4.]\n",
      " [ 1.  1.  0.  0.  0.  0.  5.]\n",
      " [ 1.  0.  1.  0.  0.  0.  5.]\n",
      " [ 1.  0.  0.  1.  0.  0.  5.]\n",
      " [ 1.  0.  0.  0.  0.  0.  6.]\n",
      " [ 1.  0.  1.  1.  0.  1.  6.]\n",
      " [ 1.  1.  0.  0.  0.  0.  6.]\n",
      " [ 1.  1.  0.  1.  1.  0.  6.]\n",
      " [ 1.  0.  0.  1.  0.  0.  7.]\n",
      " [ 1.  1.  0.  0.  0.  0.  8.]\n",
      " [ 1.  0.  0.  1.  0.  0.  8.]\n",
      " [ 1.  0.  1.  1.  0.  1.  8.]\n",
      " [ 1.  0.  0.  0.  0.  0.  8.]\n",
      " [ 1.  0.  0.  0.  0.  0. 10.]\n",
      " [ 1.  1.  0.  0.  0.  0. 10.]\n",
      " [ 1.  0.  1.  1.  0.  1. 10.]\n",
      " [ 1.  1.  0.  1.  1.  0. 10.]\n",
      " [ 1.  1.  0.  1.  1.  0. 11.]\n",
      " [ 1.  0.  0.  0.  0.  0. 11.]\n",
      " [ 1.  1.  0.  0.  0.  0. 12.]\n",
      " [ 1.  0.  1.  1.  0.  1. 12.]\n",
      " [ 1.  0.  0.  0.  0.  0. 13.]\n",
      " [ 1.  1.  0.  1.  1.  0. 13.]\n",
      " [ 1.  1.  0.  0.  0.  0. 14.]\n",
      " [ 1.  0.  1.  1.  0.  1. 15.]\n",
      " [ 1.  1.  0.  1.  1.  0. 16.]\n",
      " [ 1.  1.  0.  0.  0.  0. 16.]\n",
      " [ 1.  0.  0.  0.  0.  0. 16.]\n",
      " [ 1.  1.  0.  0.  0.  0. 17.]\n",
      " [ 1.  0.  0.  0.  0.  0. 20.]]\n"
     ]
    },
    {
     "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(interM_lm.model.exog)\n",
    "interM_lm.model.exog_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:27:59.195022Z",
     "iopub.status.busy": "2026-07-29T12:27:59.194764Z",
     "iopub.status.idle": "2026-07-29T12:27:59.431594Z",
     "shell.execute_reply": "2026-07-29T12:27:59.429893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'standardized resids')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAAINCAYAAACNlF21AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAPYQAAD2EBqD+naQAAU9NJREFUeJzt3Xl4U2X+Pv77NOnG0gKFsjUURG1xAUURGTYFHVDrUgSLI8IPCqOFr8jmAiOOIFh0BnRcKH6kLCMiINDBRkcHFJlxYxFZXJGCQNmhpS2Upm36/P4oCU3bNCcnT5aT3K/rygVNTk6fpEnOnfd5FkUIIUBEREQkSZi/G0BERETBheGCiIiIpGK4ICIiIqkYLoiIiEgqhgsiIiKSiuGCiIiIpGK4ICIiIqkYLoiIiEgqo78b4GtVVVU4duwYmjZtCkVR/N0cIiIi3RBCoKSkBO3atUNYmPP6RMiFi2PHjsFkMvm7GURERLp15MgRJCQkOL095MJF06ZNAVQ/MTExMX5uDRERkX4UFxfDZDLZj6XOhFy4sJ0KiYmJYbggIiLSwFW3AnboJCIiIqkYLoiIiEgqhgsiIiKSiuGCiIiIpGK4ICIiIqkYLoiIiEgqhgsiIiKSiuGCiIiIpGK4ICIiIqkYLohIl8rKyrBs2TJYLBZ/N4WIamG4ICJdWrhwIUaPHo2FCxf6uylEVIsihBD+boQvFRcXIzY2FkVFRVxbhEinSktLYUo0oaCoAHHN4nD498No1KiRv5tFFPTUHkNZuSAi3Vm0aBEKCwuBh4GCggK8/fbb/m4SEdXAygUR6Yq9amEqAO4HsAGIy2f1gsgXWLkgoqBkr1r0vXRFX1YviAINKxdEpBt1qhY2rF4Q+QQrF0QUdOpULWxYvSAKKKxcEJEuOK1a2LB6QeR1rFwQUVBxWrWwYfWCKGCwckFEAc9l1cKG1Qsir2LlgoiChsuqhQ2rF0QBgZULIgpoqqsWNqxeEHkNKxdEFBRUVy1sWL0g8jtWLogoYLldtbBh9YLIK1i5IFKBy3YHNrerFjasXhD5FcMFhTQu2x24SktLMTdzLkRXAbRw884tANFVYG7mXJSWlnqlfUTkHE+LUMjist2BbcmSJUhPT4ehqQFhke5/D6qyVMFaYkV2djbGjBnjhRYShR61x1CjD9tEFFDsJfdHgIL3qkvokydP9nez6JL+/ftjxowZqKqq0ryPsLAw9O/fX2KriEgNVi4oJHHZbiIi9wV1h85Dhw5h06ZN1d86iTTgst1E/sOO1MFPV+Hi22+/RUpKCm699Vbceeed2L17t7+bRDpUb0dBdgAk8hl2pA5+ugoXeXl5eOyxx7B161Z/N0XXQv1bA5ftJvIfW7hHOBjmg5iuwsUjjzyCe++9F2Fhumq2Zt4KAaH8raHB4Y2sXpBOmc1mmDqYYDab/d0Ul+zh/mGG+WAW9Edpi8WC4uJih4teeCMEhPq3Bi7bTcHGbDYjdUgq8gvykTokNaADhkO4v4JhPpgFfbjIzMxEbGys/WIymfzdJFW8FQJC+VuDqkmZWL0gHbEFC+uVVuBJwHqlNaADBjtSh46gDxfTp09HUVGR/XLkyBF/N0kVb4SAUP/WwGW7KZjUDBZiqAAiADFUBGzAYEfq0BL04SIyMhIxMTEOl0DnrRAQyt8a3JpKmh945CWy+lHVCRaGSzcYAjdgsCN1aAn6cKFH3ggBof6tgct2UyCQ0Y/KabCwCcCAwY7UoUdX4eL48ePYtGkTvvzySwDAd999h02bNuHAgQN+bpk83goBofytQdMCWPzAI8lk9KNyGSxsAixgsCN16NFVuNi7dy/mzZuHxYsXY+DAgfj3v/+NefPm2cNGMPBGCAj1bw1ctpsCgaf9qFQHC5sACRjsSB2auLZIAKmz3kVtGte/WLBgAaY9PQ1igpM3dwGgvKVg/t/mB93CXS6fU1e45ojHzGYzMsZnIGthFlJSUvzdHL/wdC0bt4NFTVZAWavAsN+AnPU5Pv8buPz8sQniz6FgEtRriwQrb5QOQ/1bw6pVq1BwpgCG/QaEvxle52J8wwjD3w0wvmGs93bDfgPOnj6LVatW+fuh6JKe5mDwJk/6UXkULAC/VjDYkTp0sXIRIFR/w3bzG0+of2vIy8vDkiVLnC7bvW3bNnz++ecYMGAAbrnllnq3CQsLw5gxY9C5c2dvNjXoOBwUUwWUHP99e/Ynp+9tFe9lj4NFTX6oYKj+/LEJ0s+hYKL2GMpwESC8EQLcPiUQYqcASktLYTJ1QkFBCeLiYnD48IGQeNy+UO9B0c/leX9x+t528V6WGixsfPg30HxKMsQ+h/SGp0V0xFulQw6/bFj181MAIBcFBWdD5nF7mx7nYPAWTzpTZ4zPQGVEJUSqpGABVP8NUgUqIyqRMT5D0k7rx47UoY3hIgB4IwRw+GXDSktLMXfuyxBiFICBEGIU5s59Oegft7fpcQ4Gb/KkH1XWwiwYy41QchTAKqlBVkDJUWAsNyJrYZakndal6fPHJoQ+h4IZw4WfeSsE8FtDwy5XLWZcumaGLqoXZrMZiabAXP1Sr3MweIunnalTUlKQsz4Hhv0GKGslBAwfnhJx1ZHa1YUdqfWPfS78zO0OTzYNnK/l8MuGXe5rcS+AxTVuGYu4OHPA9r0wm80YkpqK5pWVKDQasT4ncPotaOofEOR9MGT1o5LS98LHz7WrjtRqsCN1YGKHTicCKVx4KwQsWbIE6enpMDQ1ICzS/eJUlaUK1hIrsrOzMWbMGA0NC2wLFizAtGnPQIhfAVxR45YDUJQkzJ//SsD1VLcFixSrFSuEwAhFgdlgCIiAoec5GLxFdmdqPscUKBgunAikcOGtEMBvDc45r1rYBF71omawWC0EwgFUAEgLgIChx2/VvuCNIZisDlEgYLhwIpDCBUOA7zmvWtgEVvWivmBh4++Aofc5GLzFm0Mw3XrOg+g5pcDBcOFEIIUL8i3XVQubwKheNBQsbPwVMPQ+B4M3eaMfVU2qnvsgeS4p8HCeC6Ja6o4Qccb/I0fUBAsACAewWgikWK0Ykuq7kRd6n4PBW3wxBNPlKBIGCwoArFxQSFBftbDxX/VCbbCoydcVDFYu6ufLztScBZX8gadFnGC4CE2u+1rU5p++F1qChY2uA0aQHBR93Y+K67eQrzFcOMFwEXrcr1rY+LZ64UmwsNFlwAiSYOEvtr9BZUQljOVGPofkVexz4UNmsxkmU6L0892BPBujnqjva1Gb7/peyAgWgO/7YHg8iySDhcdsf4OEFgl8DilgsHLhIbPZjNTUIaisbA6jsRA5OeulvLkDeTZGPdFetbDxfvVCVrCoSRcVDAYLIt1h5cIHbMHCak0BcBBWawpSU4d4/G2x5sHmIODzkQDBZNWqVSgoOAWDwYzw8CvdvhgMZpw9e9KraxxMyMhA88pKrJAULIDqCsYKIdC8shITMrw/8sLtCgaDBVFQY+VCo5rBQojVwKV5ExUlDQaDWXMFI5BnY9QjPUxUFgyVC5tgmIPBbDYjY3wGshZmBVzbiPxN9TFUhJiioiIBQBQVFWneR25urjAaw4WipAqgXACixqVcKEqqMBrDRW5urtv7DTcaRaqiiHLHnYpyQKQqigg3Gt3eLwW+hv727l78/VrJzc0VxnCjULooAjMh8EKNy0wIpYsijOGB+Tq2tR2NEbBtDAa5ubmiQ0ICn18dUnsMZbhwU8PBQnvAUHNw8fdBg7xLRsAIlNdIvQFDJ8FC6aIIzPBOW3lQvfw6jwf8/jol9zFcOOFJuFAXLNwPGO4cVALl4EHe4UnACLTXhi8O1rL4IgzxoOr4+r4QYK9XUofhwgmt4cK9YKE+YGg5mHh6ELl48aJYunSpKCsrc/u+5H3+eE14ix5OM/jiNA4PqvW/rgP1dUvOMVw4oSVcaAsWrgOGv76lzp8/XwAQCxYscOt+5DvBVM3Kzc0VCabAPBXQYLCQFDB4UGV/smDCcOGEu+HCs2DhPGD46/z6hQsXRIsW8QKIFnFxrcWFCxc0P5fkXeyH412qgoWHAYMHVb6Ogw3DhRPuhAs5waJuwJg5c6bfRgbMnz9fKIpRAJuEohhZvQhwPDh5h1vBQmPA4EE1uCpwVI3hwgm1T4zcYFEzYDwgAEXceunNJGPHat+Ul6sW6Zfumi61ejFz5kxhMFSHJ5KHZXW5NAULNwMGD6rB1XeILmO4cELtE5OQ0EEA8QK4IClY2C4XBNBKtEeYzJ2KC4CIB0SHhASnj+ly1SLv0t3ypFUvZs6cKQBFAK0EoIR8wJDdaZYdAuXwKFioDBg8qAbXqCdyxHDhRKhWLupWLWwXz6sXl4PF/ZfC0/0hHzC80WmWQxk9IyVYuAgYPKgG13wtVBfDhROh2ueibtXCdvGseuEYLMrtjzWUA8aFCxdEfIsWIhoQrePipHaa5SRM2kgNFk4CBg+q6oNFLiASlOp/g/W5CFYMF06E4mgR51ULz6oX9QeLy485VAPG/PnzhVFRxCZAGBWFnWYDQIIpQaAxBGZICha2ywwINIaIaxUXNNO3a+VOsDACAo2q/2XA0BeGCydCcZ4L51UL7dWLhoNF6AYMW9Ui/dKTkA751QtynzcrF2GGMGEIC5MSLPR8UO2QkCDiUd3/y1WwUJKqg5mS5DpgqOlPRr6j9hjKJddVSElJQU7OehgMZihKGqrXnFSj4VVSU1JSsD4nB2aDAWmK4sZe1a94WVpairlzX4YQowBc4WSrKyDEKMyd+zJKS0td/v7nn38eL744B8B9AD4AnK7jGX7p9vvw4otz8Pzzz7vct94tWrQIBYWFmHHp5xkAzhYU4O233/Zns/zKbDYj0WSC2Wz2WxvcXhLelRoru8Y1a464qiqsEHJWtAWq3zkrhEDzykpMyMiQtFfveisrC4VGI0Y4+SwzA0gFYE0CxEMAIqr/tSZVX1/fq6MCwAhFQaHRiLeysrzYepLOR2EnYITa2iKuqxbuVS/UVSxCs4JRu2phu4Ry9SLQOqF6Y7SIjFOcwVC5EML5Z5lDxaK+KdbrqWDo9TkIdjwt4kQorYrquq9F7UvDfS+0BQs5AUMP66HY+lrk1XrweQjNvheBOnzWG/NcyAwYej+o1n4uGgwWTgKG3p+DYMZw4YSn4UIIVwHD/WBRc78yZ2NUX7VwXb1QFywuCmCpAMqkB4xAXw/FWdUiVKsXgT7xlzdm6ORokctsz8WtaoJFPQHjVgRGpYvqYrhwQka4EMJZwNAeLGruV8aHsvtVC+fVC/UVi+oAACyQWsHw5tBOWZxVLUKxeqGXKcu9sbYI57m4rPpzAwJqgkWN5xlJEACC+hSqnjFcOCErXAhRO2Bc8DhY1Nyvp+Vk96sW9Vcv1AeLC6J6RtNoAbQWDc9s6l7ACPShna6qFqFUvdDbehqqAoYX1hQJ5OdEBl9MsU7+wXDhhMxwIcTlgAHESwkWNfertSOc9qpF3eqFwRAuqqf0djUN+nwBVC+IVv1vQ9ULWxhpJQyGcJePJdCHdrqqWoRK9UKv62k0eCD0wmqogfxcyOCLKdbJfxgunJAdLoSofjMlJHSQ/ibQOhtjdna2ACAMhtYiPLyz/QJ0EGEwifAalzCYBNDBYTuDobUAILKzs1VWLmxVi8sLojVcvVBfuah94A60A7TaqkWwVy/0/m293gOihwc4vVVxZPDFFOvkX2qPoYoQQvh28Kt/FRcXIzY2FkVFRYiJifF3c7wiLy8PS5YsQVVVlf26bdu2YfPnn+MxAM1qbHsOwNsAbh8wALfccov9+rCwMIwZMwadO3dWMa/FAgDPAPgV1XNpHACQBOAVAJNrbVsBYBiADzFz5nOYPXu208dRWlqKTiYT7i0owOIa148FYI6Lw4HDh9GoUSMXz0bDzGYzMsZnIGthVoPzhTizYMECPDNtGn4VwuksIjUdAJCkKHhl/nxMnlz7udEns9mMIampSLFasVq4N9eDO3O2eJvZbEbqkFRYr7RCpAooOdXzWOSs196uhp6bQHrsMjg8f0MFYJCw0xrziXjydyB5VB9DfRJ1Aog3KheBTsZIBucVjNpVi8unVupWL7T1tfDW0E7btyw0hqZvR+5WLYKxehFsIyQ8fU0422cgj5yRxdtTrCeYOENnIOBpESf0FC5knW6RNZKh/oBh62tRd0E0x74X7gULbw/tdCjfztBWflXb10Lr8x3ognVuh9zcXJFgkrs4XKDO+SGTLxaHI/9juHBCL+FCVkdR2SMZHAPGOSdVi9rVi3NuBQshvDu0U8b5da1Vi2CpXnBWSvcF2myl3sA+F8GP4cIJPYQLmUNcvTGS4XLAuNZJ1aJ29eI6t4KFN4d2yhoZYOs029pgEJ3Dw92+tDYYBFDdaVaP1CxSpeUS7ItUae2krSccLRLc2KHTiUDv0Gk2m5GaOgRWawqEWI3qzpMNL4DmjLMOkc6401Fy+vTpmDfvbwBGAchuYMt0AP/Es89OQ2Zmpqp219dJ0gwgA2HIQhVsj97dzpGqOpyp7EBWX6dZd9XsNKs3nnTidCbYOjiGMo86d7ITZ0Bjh04nArlyIXtacXf7BLhTvaiejlvdgmiA+uXc66taVK9NoAiglTBCcVjcSG31whuzMYa6YO1zQXJ4Y4p18j8uua4z9VcsagqHEKthtaYgNXWIy+WrS0tL8fLcuRgl1A2RBKoHkY4SAi/Pndvg0uu2Zdyrqxau9n4FAPXLuddesrx6mWYFVtwH4HdYcR9SodiXZ1azpLnb36IMgBgqYL3SitQhqX5dKjyQpaSkYH1ODswGA9KcLLOtBisWwcntZe5ZsQgqugsXe/bswYQJEzB06FDMmjUL586d83eTPOY6WNioDxi1D9JqqTlYL1q0CIWFBZe2VrfXgoKzDe4TqBuIagYLgQ8ANILABw4Bw1Ug0lyeZcBQxdOAwWAR3FQHDAaLoKOrcLFt2zb07NkTVVVVuPfee/Hpp5+iT58+uHjxor+bppn6YGHjOmBoqVrYuDpY26oWQqipWlzeqxCuqxc1A1HdYGF7XsLrBAxngcjjSX0YMFTRGjAYLEKDy4DBYBGcvH6CRqIBAwaI++67z/5zYWGhaNy4sXjjjTdU7yOQ+lw03MfC1cV5HwxvjmSQtSBabTX7Wtj6WCgNTjleLhTcb++DUbvvBYfE+V4or6dBrnljinXyvaAbLVJWVoYmTZogOzsbo0aNsl9///33w2q1qv5WGSijRdyvWNSn/lEk3hrJUFpaCpOpEwoK7gVUjT+pbSzi4sw4fPhAndEoS5YsQXp6OpqFheFclYDzqcZrujyVeLMwBeeqqpCdnY34+PignYbY0+nKvU3NKBJWLEKXN6ZYJ99SewzVTbjYt28fkpKSsGnTJgwcONB+/f/7f/8Pmzdvxo8//ljv/SwWCywWi/3n4uJimEwmv4YLOcHCRtswVS1sAcBgaI2wsCZu37+q6jys1pPIzs7GmDFjHG7Ly8vD9OnTsXbteghxL4A1UPe8VAB4CIqSi6FDhyAzMxO33X4b8gvygScBRLjdTOfKAfwDSGiRgCOHj0jcsTq2D+bKiEoYy40B+4EcSutpBLuysjKsWrUKDz/8MCIjI6XsUy+vY6qf2nBh9GGbPFJeXg4Adb7xNmrUyH5bfTIzMzFr1iyvts0dcoMFcLkPRhpSU4eoChhav/32798fM2bM8Lgi0r9//zrX//zzz8jJ+ReqKxbuPC/hqA4iacjJ+RdGjhyJrIVZ1d+OciRXLnIUGMoNyFqYJWGH7qn5jQ+pgDWnuh9IIH4w2/pgDElNRVqNgMFgoT8LFy7E1KlTUVhYKG2hPVsfjECuwJHndFO5yM/Ph8lkgtlsxj333GO/Pj09HXv27MH27dvrvV+gVS5MpkTk55cBOAjAsxU9HZUC6ISEhCgcOXLI6VaB+K3BG6eIAMg7NeLnUyL1dkwNkNM0DalZwVghBEYwWOjK5dOgJYiLi6n3dCaFHrWVC92MFmnfvj3i4uKwe/duh+t37dqFbt26Ob1fZGQkYmJiHC7+lJX1FozGQijKCEDzzAC1VUBRRsBoLERW1ltOt3L49vskAmIUhLxKjuMoGgDujbF3JhCDBaCLkSw1R5F0AhgsdObykPNcVUPJiWrSTbhQFAWPPvooFi9ejLNnzwIANm7ciJ07d2LkyJF+bp16KSkpyMlZD4PBDEVJg+cBQ12fizoHqQj/H5y8d4pIUsAI1GBho6OAEZWQwGChI45DzgeqGkpOVJNuwgUAvPjii+jYsSOSkpLwhz/8Affddx9mz56Nfv36+btpbpEXMDQGCy99+zWbzUi8dOpKjYyMCaisbA4hVsDzYGETDiFWoLKyOTIyJrg/S6BNoAcLG50EjENHjjBY6EjdifLUTYRHZKObPhc17dmzBydPnsS1116Ldu3auXXfQBmKCnj6zd3DYFGThAOp7fx688pKFBqNqr6lyq9cAM6eF7cm1NJLsKhJB30wSB+cDzl3PpScQkfQ9bmoqWvXrrjzzjvdDhaBRnsFQ2KwADz+9luz495BAClWK4akut6PL08R6WUaYk5XTv7mfHp/Vi9IPV2Gi2Di/gFWcrCw0Xhwqj2nQSMAq4XwQ8Bw/bwE+jTEqv5mFQC+B1BZz20MGOShhqf3VzeNPxHAcBEQ1B9gvRQsbNw8ODmbLCkcvg4Y6icScxow9BAsAGA7gA2X/q1PAAaMsrIyLFu2zGFIeCDTW3tlcr0oIasXpA7DhQQyPoxcH2C9HCxsVB6cXE3z7LuA4f4MpXUCRrlOgkU5gP8ZAEQD/zVU/1yfAAsYCxcuxOjRo7Fw4UK/tkMtvbVXFnWLErJ6QeowXEgg68PI+QHWR8HCxsXBSc36EYAvAob2qc9rBgz8A37tCJkxPgOVEZUQqS7+ZjsAXASA3Op/v2tgWwMgUgUqIyqRMT5DZnPdYjtgAdG6OCDprb0yua5a2LB6Qa4xXHhI9odR3QNsqW+DhY2TgKE2WNh4L2B4vqaKLWAktEjw6wiLrIVZMJYboeQ00NHUXrWonncAGNVw9eLSdOXGcqNfpiu30dtETHprryzqqhY2rF6Qa7ociuoJ2UNRFyxYgGnTnoEQn0BRBmP+/FekzMFvG6ZZWdkcRmOhywOoqYPJ64t1ZS3McitY1OTuuhIND1P13WJtvuIyHH4N4D8GAPtQ/eF/AMDVwCAr0KvWtgEyLLXukMbAHsqot/bKdPlz7Fe4DhcAcACKkiTt8470I6iHogaKy2l/JKpnsRspLc3bvsEnJESpOoCq+vbrrhrffkf/f6M1BwtAZgUj+IIF4GIki0PVwvbBfwXqrV4ESLAA9DcRk97aK4t7VQsbVi/IBRFiioqKBABRVFTk8b7mz58vFMUogDwBCAHkCUUxigULFkhoqftyc3OFMdwolC6KwEwIvODBZSaE0kURxnCjmDlzpgg3GkWqoojy6geq+VIOiFRFEeFGo8jNzVX3mIzhQlFSBXBBKEqqMBrDVd1Xj2x/QyTj8t/wjxCAocbrTNhfb4BBYNDlvxmSIYzh6p5bb7pw4YJo0SJeAOm12pwu4uJaiwsXLvi1fbXprb0y1f0cU3vx7+cdNSw3N1d0SEiQ/lmg9hjKcKHRhQsXRNOmzQUwptYbboxo2rS53z6MpAQMLwULTwMGEB/UwcJm7dq1AgoEkiDwLASiDfW8zi6/3hBtqN4uCQIKxNq1a/39EBo4YAXmAUlv7ZXFeahSewn+8KVHubm5ItxoFPGA6s9ZtRgunJAVLtLT0xv8Npmenu5xW3Nzc0WCyf3k6VHAqBEsbMk3HhAXJAUL2+UCIOIB0SEhQf1zkdAh6IOFENUHujBAGACBVs6qFrWqF62qtw8D/H4gdH3ACqwDkt7aK1N2drYAIAyG1iI8vLPbF4OhtQAgsrOz/f1Q6BJbsEhVFHHBzS9yaqg9hrJDpwZr167FsGEPAxgJILueLdIB/BMffPA+hg4dqul32Dr4VUZUwlhudPv8uaz1KdwdHaKGu507Q0lpaSk6mUy4t6AAgwEMg62vRX2vM5t0AMvxAaz4BIA5Lg4HDh/2WydE150D5XQGNJvNmJCRgbeysjx6DfmqvYEoLy8PS5YsQVVVleZ9hIWFYcyYMejcubPElpEW9X1ey/68VX0MlRJldMTTykVubq4ICwtT9W0yLCxMU1p0qDzMcKwkaN6PqwrGTOe/p2YS9nWfi1Bj+ybZ2mAQcS5fZ46vt7iwMNHaYPDrN0n1ZXbPqgGyyr6+ai+RtzX0OS3zc5enRZzwJFzk5uYKg9EgENbQOfAa58INRmEwGtz6Y9YbCBo48GvanxvBouZ+PA0YDBau7d+/X8yYMUNMnTpVREU1UfE6u/x6i45uKqZOnSpmzJgh9u/f75f2q+8cqL0vg8yyry/aS+Rtaj6fZX3+Mlw4oTVc2Hvxx7s6B17rXHi8+t77DQYBbwUMN/brScBgsHCP+z34/X/wc79zoPvVgPpeg1pfW75oL5G3ufO5LONzmOHCCS3hwh4sklz13K+nehFtEEhyHTBkVRjc2r+G/WkJGAwW7tHeg9+/Bz9vByLZZV89Bjiimvzxecxw4YS74cLhoHyn2qpFrerFnQ0fxGX1jXDrsXjYl8OXSdn2O7WMnNEjPc474O1AJLvsq9cAR2Tjr0oyw4UT7oQLh4Oxy/kGXFQvnq3/YK5p2KiEgIHGnk225MtzfLLarAd6nXfAm4HIG2FWjwGOyMaffeAYLpxQ+8TUOeg7nSXR9YeRfRbFWqFA5nwU7pBVBfBF72RZ1Ra90OO8A94MRN4o++o1wJE6wV7l9PfoPc5z4YTaMboOC4EBwKsG4OIoNDzfgDPpQPRyYPKlBSP+AcRFx6GoqMizVUwDYB0Jb46rrneujgB4zN6kx3kHlixZgvT0dBgMrREW1sTt+1dVnYfVehLZ2dkYM2aM/XpP5lhp6DXorfaS/3k6P1CgC4R5h9QeQxkunHA4sF0pgFwAShygXJqYSFy6IAyAscY9KwFUAQqqLwAgSgFxFrgXUPYrUPZV3yCuFp4vjx4AB9uaL/gVQmCEt4KFTQA8ZrrMG4FIxoeosw9NPQY4cs3hMyNVQMkJvs+IRJMJZfn5OAhA5hR5pQA6AYhKSMChI0ca3Jbhwgl3Zui0p+AOlUAbXA4LZwH8DABXAXgAdY58+BeA34AuAOIuXS0AnADCfq9eiFZKsKjxK/19sLUdDJpXVqLQaPResLAJgMdM3iHz2xlngw0NoVLlZOUigLk7/XedF+1+AKsVoOo+AB8A9f55KwAMA8I+BNIEcOXlF3lss1icLT1bfbolQuIDKwfwDyChRQKOHG44eXqLjOmY3Zq2PAg/PEJdIHx4kr6EWpXT3+Gb4cIJLWuL2CsYrSuBY66ChU2NgNFOwHiy+vwfAPfX/HAlSN5AstZDIf0KhLIv6UeoVjm9edrQFbXH0DANbQo5KSkpmP7sdCBfbbDApds/qN4+X8H0Z6cjJSUFKSkpyFmfA8N+A5S1SvVZFE8EyRtHU7AAAAMghgpYr7QidUgqzGazV9tJ3vVWVhYKjUaMUBRUSNpnBYARioJCoxFvZWVJ2iv5m+rPjCD8jEhJScH6nByYDQakaXiv+KKax3ChgtlsRmbmPCjK/VAXLGyqA4ai3I/MzHn2F7W0gBHqwcImCD88QpWnH5q18ZRIcHL7MyMIPyO0vld89Z5guHDBbDYjNXUIrNYUCLEG6oOFTTiEWAOrNQWpqUPkBQwGC0caPzzMZjMSTaag+LAJFrICBoNFcGKV8zJ33yu+fE8wXDTAMVishvvBwiYcQqyWFzAYLOrn5oeH7bxlWX4+hqQGx4dNsNBD2Zd8j1XOutS+V3z9nmC4cEJesLCRFDCCJFgAQMb4DFRGVEKkSurYClR/eKQKVEZUImN8htPNanaIOgggxWplwAgwgV72Jd/yd5UzkLl6r/jjPcFw4URGxgRUVjaHECvgebCwCYcQK1BZ2RwZGRPs16oOGEEULAAga2EWjOVGKDkSOrbaWAElR4Gx3IishfV33qvd07oRgNVCMGAEoEAu+5Lv+LvKqQfO3iv+ek8wXDiRlfUWjMZCKMoIQGK/dUUZAaOxEFlZbznc4jJgBFmwACR2bLVR8Rw5G8IVDgaMQBWoZV/yHX9WOfWk9nulFP57TzBcOJGSkoKcnPUwGMwAHoLnAaMCwEMwGMzIyVlf7x/Z6cE2CIOFjS9HzrgaG86AEbgCsexLvuOvKqce1XyvdAL89p5guGjA5YDxETwLGLZg8ZHTYOHwO2sebMuDN1jY+GLkjNpJZxgwAleglX3Jdzg/kHts75WohAS/vSc4Q6cKnnXurICipDVYsXD6O4N4db/6eGuGTi2z2fGAFbi8sVAe6YOUvhdBHiy8jTN0SlTzFImipEF9BUNbsLD/zvU5SGiREDJvAG+MnNE6TS4rGIErUMq+5HucH0g/WLlwg3sVDO3BItTJWi/An/Pvk/fJWCiP9InrEPkPFy5zwpNwAagNGAwWnvJ0pUN/rxxIRN7FFZT9g6dFvMT1KRIGCxk8GTkje9luniIhCjyhOj+QXrByoVH9FQwGC9kcvp2kCig5rj8kuGw3UejwtMpJ7mHlwsvqVjBKGSy8oOa3E/wDqj4kuGw3UegIxfmBdEGEmKKiIgFAFBUVSdlfbm6uMBrDBRAvjMZwkZubK2W/5Cg3N1ckmBJUP7+5ubki3GgUqYoiygEhPLiUAyJVUUS40ci/L1GAys3NFcZwo1C6KAIzIJQuijCG8z0rm9pjKE+LSGA2m5GRMQFZWW8xHQcQjhYhCi2hOD+Qr3G0iBPeCBcUuDwJGAwWRPpjNpuRMT4DWQs5RNkbGC6cYLgIPZyhk4hIDnboJLqEy3YTEfkWwwWFBC7bTUTkOwwXFDK4bDcRkW8wXFBI4bLdRETep8twcfHiRfz+++8oKyvzd1NIh2oHjFIwWBARyaSrcJGXl4dJkyYhMTERnTp1wrfffuvvJpFOcdluIiLv0VW4yM3NRWJiIj777DN/N4WCgC1gRCUkMFgQEUlk9HcD3DFp0iQAQH5+vn8bQkEjJSWFoYKISDJdVS6IiIgo8Pm1cnHmzBmcP3++wW0SEhJgNGpvpsVigcVisf9cXFyseV9ERETkml/Dxbx587B27doGt9myZQsSExM1/47MzEzMmjVL8/2JiIjIPbpcWyQ/Px8mkwmbN2/Gbbfd1uC29VUuTCYT1xYhIiJyk9q1RXTVoVOLyMhIREZG+rsZREREIUNXHTrPnz+P33//3T5a5MSJE/j9999x7tw5/zaMiIiI7HQVLj755BPcdtttGD58OBITE/Hss8/itttuw7Jly/zdNCIiIrpESp+LkpISfPrpp7jiiivQvXt3Ge3yGrXni4iIiMiR2mOopspFTk4Ohg8fDgCoqqrCgAEDMHLkSPTo0QPvv/++thYTERFRUNAULmbPno3nnnsOAPDVV1/hxIkTOHXqFNatW4d58+ZJbSARERHpi6Zw8euvv+LKK68EAHz++edITU1FkyZNMGjQIOzfv19qA4mIiEhfNIWLNm3a4JtvvoHVasXatWsxcOBAANXzT7Rr105qA4mIiEhfNM1zMXHiRNx9991o2bIloqOjMWjQIADAmjVr7H0xiIiIKDRpCheTJk1Cjx49cOjQIQwePBhRUVEAgPj4eKSlpUltIBEREemLLqf/9gSHohIREWkjffrvxYsXq/7lY8eOVb0tERERBRfVlQvb6BAAEELgwIEDAICWLVsCqF4+HQCuuOIK5OXlyW6nNKxcEBERaSN9Eq39+/fbL+np6Rg4cCAOHDiA06dP4/Tp0zhw4AAGDhzIqgUREVGI09TnonPnzvjiiy9gMpkcrj9y5Ahuv/32gJ7rgpULIiIibbw6/fexY8ec3nb06FEtuyQiIqIgoSlc9OvXD2PHjsWhQ4fs1x06dAjp6eno37+/tMYRERGR/mgKF++88w6KiorQqVMntG7dGvHx8ejYsSPOnz/v1qgSIiIiCj6aJtHq0KEDvv32W3zzzTf46aefAADXXHMNevXqJbVxREREpD+awoVNr169GCiIiIjIgepwsWLFCgDAiBEj7P93ZsSIEZ61ioiIiHRL9VDUNm3aAABOnDhh/78zJ06c8LxlXsKhqERERNpIn/67ZmAI5PBARERE/qVptEhtJSUlWLt2LXbu3Cljd0RERKRjmsJFTk4Ohg8fDgCoqqrCgAEDMHLkSPTo0QPvv/++1AYSERGRvmgKF7Nnz8Zzzz0HAPjqq69w4sQJnDp1CuvWrcO8efOkNpCIiIj0RVO4+PXXX+2rpH7++edITU1FkyZNMGjQoIBeV4SIiIi8T1O4aNOmDb755htYrVasXbsWAwcOBADk5+ejXbt2UhtIRERE+qJpEq2JEyfi7rvvRsuWLREdHY1BgwYBANasWWPvi0FEREShSVO4mDRpEnr06IFDhw5h8ODBiIqKAgDEx8cjLS1NagOJiIhIX1RPohUsOIkWERGRNmqPoZrnufj1118xc+ZMPPLII/br1q1bh7KyMq27JCIioiCgKVxs3rwZN954I7777jusXLnSfv2OHTvw1ltvSWscERER6Y+mcDF9+nRkZWXh448/drj+0UcfxaJFi6Q0jIiIiPRJU7jYu3cvhg4dCgBQFMV+fWJiIg4dOiSnZURERKRLmsJFkyZNcPLkSQCO4WLHjh1o27atnJYRERGRLmkKF8OGDcOUKVNw7tw5AIAQAl999RXGjh3LoahEREQhTlO4mDdvHi5evIiWLVuiqqoKsbGx6NOnDzp37oxZs2bJbiMRERHpiKZJtJo0aYJPP/0U27dvx44dO1BVVYXu3bujV69esttHREREOqMpXHTp0gU///wzevTogR49eshuExEREemYptMiJ06cQFFRkey2EBERURDQFC4efPBBZGVlyW4LERERBQFNp0UKCwsxffp0rFq1Ctdccw0iIiIcbl+2bJmMthEREZEOaQoX0dHRDmuKVFZWSmsQERER6ZumcLFixQrZ7SAiIqIgoXlVVCIiIqL6MFwQERGRVAwXREREJBXDBREREUnFcEFERERSqR4tkpKSonqnZrNZU2OIiIhI/1RXLpKTk+2XVq1a4aOPPkJ+fj7atm2Ltm3bIj8/Hx999BFatWrlzfYSERFRgFNdufj73/9u///w4cPx4osv4rnnnnPYZs6cOfjxxx/lta4en332Gb7++msYjUb06dMHffv29ervIyIiIvcoQgjh7p3i4+Oxb98+NGvWzOH6c+fOISkpCSdPnpTVPruqqircfPPNiIuLQ69evVBaWorFixfjT3/6ExYuXKh6P8XFxYiNjUVRURFiYmKkt5OIiChYqT2Gapqhs6KiAj/88AP69OnjcP3evXtRUVGhZZcuKYqCJUuW4IYbbrBf98c//hGDBg1CRkYGrr/+eq/8XiIiInKPpnAxevRoDBs2DNOnT0ePHj0ghMCOHTuQmZmJMWPGyG4jgOpwUTNYALAHiqNHjzJcEBERBQhN4eKVV15BfHw85s6di1OnTgGoPlUyZcoUTJs2TWoDG7Js2TJER0ejR48eTrexWCywWCz2n4uLi33RNCIiopClqc9FTWfPngUAxMXFuX3flStXYtu2bQ1u89xzz6Fly5Z1rt+8eTMGDRqEV199FRMmTHB6/xdeeAGzZs2qcz37XBAREbnHq30uatISKmxatmyJjh07NrhNeHh4neu+/vpr3HfffXj22WcbDBYAMH36dEyZMsX+c3FxMUwmk6b2EhERkWuaKhdVVVV4/fXXkZ2djQMHDuDChQsAgGnTpmHixIno0KGD9IbafPPNNxg0aBCeeOIJzJ071+37c7QIERGRNmqPoZqm/54/fz5ef/11PPHEEygtLbVff/311+PFF1/UsktVtm7disGDB2PixImaggURERF5n6bKxZVXXolVq1bh5ptvhqIosO3i6NGjuOGGG3D69GnpDT1//jwSEhIQGRmJhx9+2OG24cOH49Zbb1W1H1YuiIiItPFqn4sjR47g2muvBVA9RNQmOjoaJSUlWnbpktFoxAsvvFDvbU2bNvXK7yQiIiL3aQoXnTp1wo4dO9C3b1+HcPHBBx+gS5cu0hpXU1RUFCZNmuSVfRMREZE8msLF1KlT8eijj2LOnDkAqtf7+OSTT/DGG28gOztbagOJiIhIXzSFi3HjxqGyshLPPPMMqqqqcMcdd6B169Z47bXX8Mgjj8huIxEREemIx5NoHT9+HFVVVWjXrp3DKZJAxQ6dRERE2nh1KGpycjImTJiAiooKtG3bFu3bt7cHi+TkZG0tJiIioqCg6bTIr7/+ivPnz2Pv3r1Yt24dWrVq5XAbERERhS5NlQsA2LFjByorK3HzzTdj165dEptEREREeqY5XLRp0wZffPEF7rjjDvTu3Rtr1qyR2S4iIiLSKY8WLouIiEB2dja6deuGESNGYM+ePbLaRURERDrl8aqoADBx4kRcd911eOihh2TsjoiIiHRMU7j47bff6lw3YMAAfP/999i9e7fHjSIiIiL90hQurrzyynqvN5lMMJlMHjWIiIiI9E11uBg6dCgAYO3atfb/O7N27VrPWkVERES6pTpctGzZst7/ExEREdXk8fTfesPpv4mIiLTx6vTfRERERM6oPi2SkpKieqdms1lTY4iIiEj/VFcukpOT7ZdWrVrho48+Qn5+Ptq2bYu2bdsiPz8fH330kcM6I0RERBR6VFcu/v73v9v/P3z4cLz44ot47rnnHLaZM2cOfvzxR3mtIyIiIt3R1KEzPj4e+/btQ7NmzRyuP3fuHJKSknDy5ElZ7ZOOHTqJiIi08WqHzoqKCvzwww91rt+7dy8qKiq07JKIiIiChKYZOkePHo1hw4Zh+vTp6NGjB4QQ2LFjBzIzMzFmzBjZbSQiIiId0RQuXnnlFcTHx2Pu3Lk4deoUgOpTJVOmTMG0adOkNpCIiIj0RVOfixMnTqBNmzYAgLNnzwIA4uLi5LbMS9jngoiISBu1x1BNlYt27dqhqqoKgH5CBREREfmGpg6d7dq1w9GjR2W3hYiIiIKApnAxadIkPPnkkygoKJDdHiIiItI5TX0uEhMTcfjwYYSFhaF169aIiIhwuP3333+X1T7p2OeCiIhIG6/2uZg5c6bmhhEREVFw0xQuxo4dK7sdREREFCS45DoRERFJpalyAQDfffcd1q1bh8OHD6OystLhtlWrVnncMCIiItInTZWLNWvWoG/fvti3bx/ee+89GI1G7Nq1C6tXr+baIkRERCFOU+Vizpw5ePfdd/Hggw9CURSsWLECVVVVmDx5MoenEhERhThNQ1GjoqJQWFiI6OhoRERE4Ny5c2jUqBFOnz6N5ORk+5TggYhDUYmIiLTx6pLrFosF0dHRAKpn6/zll18AAGVlZbBYLFp2SUREREFCc4dOmwceeACjRo1CWloaNmzYgAEDBshoFxEREemUpsrF5s2b7f/PzMzE4MGD8cknn6Bbt25YvHixtMYRERGR/mjqc6Fn7HNBRESkjVf7XBARERE5o7rPRZMmTVTv9Pz585oaQ0RERPqnOlzUnHVz9+7deOmllzBu3Dj06NEDALB9+3a88847mDFjhvxWEhERkW5o6nPRr18/TJ06Fffff7/D9Rs2bMCCBQuwZcsWaQ2UjX0uiIiItPFqn4tdu3bh9ttvr3P97bffju+//17LLomIiChIaAoXzZo1w7p16+pcv3btWjRv3tzjRhEREZF+aZpEa9asWRg3bhxyc3PRo0cPCCGwY8cO5ObmIjs7W3YbiYiISEc0hYvRo0cjOTkZr732GlauXAkAuOaaa/C///0Pt956q9QGEhERkb5onv67V69e6NWrl8y2EBERURDweG2RsrKyOtdFRUV5ulsiIiLSKU0dOvfv348777wTjRo1QnR0dJ2Lt/z+++/485//jMTERMTFxaFfv3749NNPvfb7iIiIyH2aKhfp6emIiorC6tWrfTo65G9/+xv69euHWbNmwWg0YuHChUhJScHOnTtx/fXX+6wdRERE5JymSbSaNGmCgwcPolWrVt5ok2pVVVWIjIzEokWLkJ6eruo+nESLiIhIG7XHUE2Vi/bt26OyslJz42S4cOEC/u///g+NGzfGwIED/doWIiIiukxTn4uMjAw89dRTuHDhguz2uPSf//wHRqMRTZo0wezZs7FmzRp07NjR6fYWiwXFxcUOFyIiIvIeTeHi1VdfxXvvvYfmzZsjMTERHTt2dLiolZ6eDqPR2OAlLy/P4T533nknysrKcPz4cUyaNAn3338/tm/f7vR3ZGZmIjY21n4xmUxaHjIRERGppKnPxeLFixu8fezYsar2U1VVhaqqqga3MRobPnNz00034eabb8bbb79d7+0WiwUWi8X+c3FxMUwmE/tcEBERucmrfS7UhgdXwsLCEBamqXhiZ7Va0VA+ioyMRGRkpEe/g4iIiNTz7MjuQxcuXMDDDz+MXbt2wWKx4NSpU3j++eexd+9ejBgxwt/NIyIioks0z9D53XffYd26dTh8+HCdkSOrVq3yuGG1NW7cGGlpaRg/fjx27dqFRo0a4cYbb8SmTZvQr18/6b+PiIiItNFUuVizZg369u2Lffv24b333oPRaMSuXbuwevVqVFRUyG6j3QMPPICvv/4apaWlOHPmDDZu3Ijbb7/da7+PiIiI3KepcjFnzhy8++67ePDBB6EoClasWIGqqipMnjwZBQUFsttIREREOqJptEhUVBQKCwsRHR2NiIgInDt3Do0aNcLp06eRnJyMs2fPeqOtUnCGTiIiIm3UHkM1nRaxWCz2BcratWuHX375BUD1Cqk1h30SERFR6PF4yfUHHngAo0aNQlpaGjZs2IABAwbIaBcRERHplKbKxebNm+3/z8zMxODBg/HJJ5+gW7duLifYIiIiouCmKVx88cUX9v9HR0fjb3/7G7788kssXrwYCxculNU2IiIi0iFNHToVRXE6K2ZDtwUCdugkIiLSxqsdOp356aef0LJlS5m7JCIiIp1xq0NnmzZt6v0/UL0IWUFBAR5//HE5LSMiIiJdcitc/P3vfwcAPProo/b/24SHh6Njx47o2bOnvNYRERGR7rgVLmwLhLVs2RKDBw/2SoOIiIhI3zT1uejWrRsWLFhg//mNN95AQkIC+vfvjyNHjkhrHBEREemPpnAxdepUJCQkAACOHj2Kp59+GpMnT0ZcXBymTp0qtYFERESkL5qGorZq1Qr79+9HbGwssrOz8eGHH2LDhg04ceIEunbtilOnTnmjrVJwKCoREZE2Xh2KWllZidLSUgDAxo0bMXDgQADVC5p5c8l1IiIiCnya1hbp378/xo0bh379+uHDDz9EZmYmAODrr79G7969pTaQiIiI9EVT5eKtt95CdHQ01q5di4ULF6JTp04AgCVLluD555+X2kAiIiLSF019LvSMfS6IiIi08cv030REREQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCSVbsNFXl4e3nzzTXz55Zf+bgoRERHVYPR3A7SwWCx48MEH8dtvv2HcuHHo06ePv5tEREREl+gyXEyZMgV/+MMf/N0MIiIiqofuwkVOTg4+//xzfPfddwwYREREAUhX4eLIkSPIyMjAxx9/jEaNGqm6j8VigcVisf9cXFzsreYRERER/BwuPvvsM/z8888NbvPoo48iNjYWVqsVf/rTnzB58mR0795d9e/IzMzErFmzPG0qERERqeTXcHH8+HH88ssvDW5TUVEBAHjvvfewd+9eDBs2DG+++SYA4MyZM9i9ezfefPNNTJgwAYqi1Ln/9OnTMWXKFPvPxcXFMJlMEh8FERER1eTXcDFixAiMGDFC1bYdO3bEiBEjsG/fPvt1FosFhYWF+OWXXyCEqDdcREZGIjIyUlqbiYiIqGGKEEL4uxFa3XDDDbjtttvw2muvqb5PcXExYmNjUVRUhJiYGO81joiIKMioPYbqdhItIiIiCky6Gi1S2/Dhw3HVVVf5uxlERERUg65Pi2jB0yJERETa8LQIERER+QXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVAwXREREJBXDBREREUnFcEFERERSMVwQERGRVEZ/N8Adzz//PFauXOlw3dVXX42PP/7YTy0iIiKi2nQVLk6dOoWrrroKb7zxhv26yMhIP7aIiIiIatNVuACAxo0b48orr/R3M4iIiMgJ3fW5+PLLL9G1a1f07dsXM2bMQElJib+bRERERDXoqnLRokULPPPMM+jfvz+OHj2K5557DmazGTt27EBERES997FYLLBYLPafi4uLfdVcIiKikKQIIYS/fvkzzzyDdevWNbjN559/jg4dOgAArFYrDAaD/bb8/Hx06tQJ2dnZGDlyZL33f+GFFzBr1qw61xcVFSEmJsaD1hMREYWW4uJixMbGujyG+jVcnDp1ymUlITExEeHh4U5vv+qqqzBkyBC8/PLL9d5eX+XCZDIxXBAREblJbbjw62mR+Ph4xMfHa76/xWLBiRMn0KxZM6fbREZGckQJERGRD+mmQ2d5eTmeeuopnD17FgBQWlqK8ePHw2q1Ii0tzc+tIyIiIhvddOgMDw9HQkICunbtiqqqKhQWFqJbt2747LPPcMUVV/i7eURERHSJX/tcaHXy5EnExsYiKirK7fuqPV9EREREjnTR50Kr1q1b+7sJRERE5IRu+lwQERGRPjBcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0RERCQVwwURERFJxXBBREREUjFcEBERkVQMF0TkVWVlZVi2bBksFou/m0JEPsJwQURetXDhQowePRoLFy70d1OIyEcUIYTwdyN8qbi4GLGxsSgqKkJMTIy/m0MU1EpLS2FKNKGgqABxzeJw+PfDaNSokb+bRUQaqT2GsnJBRF6zaNEiFBYWAg8DBQUFePvtt/3dJCLyAVYuiMgr7FULUwFwP4ANQFw+qxdEesbKBRH5lb1q0ffSFX1ZvSAKFaxcEJF0daoWNqxeEOkaKxdE5Dd1qhY2rF4QhQRWLohIKqdVCxtWL4h0i5ULIvILp1ULG1YviIIeKxdEJI3LqoUNqxdEusTKBRH5nMuqhQ2rF0RBjZULIpJCddXChtULIt1h5YKIfEp11cKG1QuioMXKBRF5zO2qhQ2rF0S6wsoFEfmM21ULG1YviIISKxdE5BHNVQsbVi+IdEPtMdTowzZJY7FY8P3336NRo0a4/vrroSiKv5tEFLJWrVqFgjMFMFgMCHvT/WJolaUKZ0vOYtWqVRgzZowXWkhEvqa7cLFixQpMnDgRJpMJjRo1gtFoxNq1a9G6dWt/N40oJPXv3x8zZsxAVVWV5n2EhYWhf//+EltFRP6kq9MimzZtwqBBg7By5UqkpaUBALZt24aYmBgkJyer2gdPixAREWmj9hiqq3DRr18/xMTEwGw2a94HwwUREZE2QTdapKysDN988w3uvvtunD59Glu2bMFvv/3m8n4WiwXFxcUOFyIiIvIev/a52Lt3L44ePdrgNv369UOjRo1w9uxZVFZW4ttvv8XcuXPRuXNn/PTTT0hKSsL69eud9rnIzMzErFmzvNF8IiIiqodfT4vMnz8fGzdubHCbpUuXom3btjh9+jTi4+Nx1VVXYdu2bWjWrBlKSkpw66234sYbb8SKFSvqvb/FYoHFYrH/XFxcDJPJxNMiREREbtLFUNSpU6di6tSpqrZt2bIlmjZtivvuuw/NmjUDADRt2hSpqalYuXKl0/tFRkYiMjJSRnOJiIhIBd30uVAUBYMHD8bBgwcdrj948CDatm3rp1YRERFRbbqa52L27Nno1asXpk6dit69e2Pr1q1Ys2YNPvzwQ383jYiIiC7RTeUCAJKTk7F9+3ZUVlbin//8Jy5evIjt27fjrrvu8nfTiIiI6BJdzXMhA+e5ICIi0kYXHTr9wZalON8FERGRe2zHTld1iZALFyUlJQAAk8nk55YQERHpU0lJCWJjY53eHnKnRaqqqnDs2DE0bdo0qFZTtc3fceTIkaA83RPMjy+YHxvAx6dnwfzYgOB+fN56bEIIlJSUoF27dggLc95tM+QqF2FhYUhISPB3M7wmJiYm6N4kNQXz4wvmxwbw8elZMD82ILgfnzceW0MVCxtdjRYhIiKiwMdwQURERFIxXASJyMhI/PWvfw3aqc6D+fEF82MD+Pj0LJgfGxDcj8/fjy3kOnQSERGRd7FyQURERFIxXBAREZFUDBdEREQkVcjNc6FH5eXl+Oc//4n//Oc/KCoqQteuXTF58mS0a9fO6X0+/fRT/PWvf61z/aZNm9CkSRNvNtdtEydOxLZt2xyu69+/P15++eUG75eTk4Ply5ejqKgIf/jDH/DMM88E3Fj1d999F2+99Va9t33yySdo1qxZnetPnz6Ne++9t871r7zyCvr16ye7iW4pLS3F+++/j5UrV6JVq1ZYtWpVnW3Ky8vx+uuv49NPP4XRaMSQIUMwduzYBiet03Ifb8jLy8Pbb7+N//3vfxg/fjweffRRh9stFguWL1+OjRs3ori4GN26dcPkyZPRtm1bp/v86KOP8OKLL9a5fvPmzYiOjpb+GJypqqrCp59+isWLF+Po0aP4+OOP0aJFC4dtxo8fj507dzpcN2DAALz00ksN7nvdunX45z//iZKSEvTu3RtPP/00mjZtKv0xNKSwsBDLly/H+vXrcdNNN+HVV191uH3ZsmVYtGhRnfspioL//Oc/9bb3+PHjSE1NrXP9/Pnz0bt3b3mNd6GkpATvvPMOtmzZAqvViltuuQVPPvlknfkmdu7ciVdffRWHDx/G1VdfjWeffRadO3ducN9a7qMGw4UODBkyBO3atcOwYcMQExODN998EzfeeCN27tyJ9u3b13uf06dP47fffsNHH33kcL0vP8zU+umnn3D11Vdj/Pjx9utqf+jVtnTpUjz++ON4+eWX0alTJ8yePRubN2/Gl19+2eCscb42cOBAXHXVVQ7XTZw4EeXl5fUGC6D6ALZ161asWrUKiYmJ9uuTkpK82VRVkpOTcccdd6BFixbYtWtXvduMHj0aX331FV555RVcuHABU6ZMwaFDhzBnzhyn+9VyH9k+/PBDTJ06Fenp6cjLy8PRo0frbPPAAw8gMTERDz30EJo2bYrXX38d3bt3x86dO50GjNOnT+PAgQP48MMPHa73dS/+tLQ0lJSUoFu3bli/fj3Ky8vrbPPTTz+hS5cueOyxx+zXxcXFNbjfd955B0888QReeeUVJCYm4oUXXsCWLVuwZcsWn4XDU6dO4cYbb8TQoUNhNBrx888/19nmzjvvRHJyssN1EyZMgKIoToOQ7b34wQcfOEy+WHs/3tanTx/88Y9/RHp6OgDgpZdewpo1a7B161Y0btwYALBnzx706dMH6enp+NOf/oRly5ahV69e2LVrl9Mvolruo5qggFdSUuLws8ViEXFxceLll192ep93331XtG7d2ttNk2LgwIHimWeeUb291WoVbdu2FX/5y1/s1x08eFAoiiL+9a9/eaOJ0pw4cUIYjUbx5ptvOt3myJEjAoD4+eeffdgydc6dOyeEEOIvf/mLSEpKqnP7nj17BACxefNm+3XvvPOOiIiIEAUFBfXuU8t9vKG4uFhUVVUJIYRo3769yMzMrLNN7fdiWVmZaNasmZg/f77T/S5dulS0b99ebmM1sP3tNm7cKACI48eP19mmf//+Du8rVyorK0V8fLz461//ar/ut99+EwCE2Wz2uM1qlZeXi9LSUiGEEI888ogYNGiQy/scPXpUGAwGsWjRIqfbHDx4UAAQv/32m7S2alH7dXfixAkBQKxbt85+3ZAhQ0T//v3tP1dWVorExEQxbdo0p/vVch+1AucrHjlV+zRGeHg4IiMj6/3mUVNxcTHuuusu3HXXXXj22Wdx+vRpbzbTIx9++CFuu+02pKWlYenSpQ2uuPfTTz/h+PHjDqcOOnbsiOuvvx6bNm3yRXM1W758OcLDw/HII4+43HbChAkYMGAAHnvsMezevdsHrXPN1bS/n332GWJiYhxO39x///0oLy/Hf//7X2n38QY16w3Vfi9GRESoei8WFhZi8ODBuOuuuzB9+nScOXPG4/a6S82UzUD16cb+/ftj+PDhWL58eYPvxb179+LUqVMO78Urr7wS11xzjU/fi+Hh4W5XZZctW4aoqCg8/PDDLrd9/PHHMXDgQDz++OP44YcftDZTs9qvu0aNGiEsLMzhdffZZ585/B0MBgPuueeeBv8OWu6jFsOFDi1btgwnTpyo97y8jaIoGDZsGDIyMjBu3Dhs3boV1157LY4dO+bDlqrTsmVLjBw5Es899xz69OmDZ555ps657poOHToEAHXKdu3atbPfFqiWLFmCYcOGOT0lYtO7d2+MGTMGTz/9NBRFwc0334xPPvnEN430wKFDh9CmTRuHU1OtWrVCeHi407+NlvsEisWLF+PMmTNISUlxuo2iKHjooYcwYcIEjB07Fl9//TWuu+46nDx50octVSc+Ph6jRo3CzJkz0atXL0ydOhWjR492ur1e34tCCCxZsgRpaWku+2n169cPo0ePxrRp02C1WnHjjTdi48aNPmpp/ebNm4fGjRtjwIABAICioiIUFRW59XfQch93sM+Fznz11VeYMGEC5syZg27dujndbujQoQ7fju+55x5cc801eOmll/Dmm2/6oqmqLV++3H7++Y477kCXLl1w5513YtKkSbj55pvrbF9RUQGg7jnr6OhoXLx40fsN1ujLL7/Er7/+isWLFze4XZs2bfDf//7XfrAdPHgwzp8/j2nTpmHw4MG+aKpmFRUV9fYliIqKsv/dZNwnEPz3v//FxIkTkZmZieuuu87pdsOHD8eoUaPsP99zzz1ITk5GZmYmXnvtNR+0VL13333X4b2YlJSEu+66C5MmTcINN9xQZ/uG3ouB/LfbsmUL8vLysGLFiga3a9++PTZv3mx/L951110oKSnBU0895bTPkbetXr0aL7/8Mt577z3Ex8cD0PZ38PbfjpULHdm6dSvuvvtuPPnkk5g+fXqD29Z+wURGRqJv374BU16vqXZbb7vtNhgMBqdttXX2LCgocLj+7NmzLjuf+VN2djaSk5PRp0+fBrczGo11OqXecccd+Pnnn12W3/2tRYsWdf4u5eXlKCkpcfq30XIff/v666+RkpKCadOm4amnnmpw29qv76ioKPTp00cX78UBAwZAUZSgfC9ed911uPXWWxvcLjw8vN734o8//ojKykpvNrFe69evx8iRI5GVlYW0tDT79bGxsTAYDG79HbTcxx0MFzqxbds2DBo0CI8//jgyMzM17ePEiRP2nsWB7MyZM7BarU7b2rVrVxiNRmzfvt1+XXl5OXbv3o0bb7zRV810S0lJCT744AOMGzdO0/1PnDiByMhIGI2BXWzs3r07jh075nD6zTbM2NnfRst9/Ombb77B4MGD8cQTT9Q7xFQNvbwXT506BSGE07Z269YNBoPB4b1YVlaGPXv2BOTfDgDOnTuHdevW6e69+K9//QsPP/ww3njjjTptDw8Px3XXXefwdwCqv5A6+ztouY9bPO4SSl63fft2ERsb2+CICrPZLHr27GnvVfzaa6+JU6dO2W9ftWqVUBRFLF261NvNdcvBgwfFkiVLhNVqFUIIceHCBTFs2DDRvHlzh5ECGRkZ4umnn7b//NBDD4kbbrjB/njnzZsnGjVqJI4ePerbB6DS22+/LSIiIsTp06fr3Hbs2DHRs2dPsWXLFiGEEKtXrxZ79uyx3753714RHx8vRo0a5avmuuRstMjFixdF+/btxWOPPSaEEKKiokIMHjxY3HTTTfZtKisrRc+ePcUHH3yg+j6+5my0yNatW0VMTEyDIyo2bNggevbsaR+98Oqrr4ozZ87Yb3/vvfeEoiji3Xffld9wFZyNFsnLyxPLli2zj5g5f/68GDJkiIiLi7OPNBFCiD//+c9i+vTp9p+HDBkiunfvLs6fPy+EEGLOnDmicePG9Y5G8QVXo0XeeustERUVVe9IpCNHjoiePXuKL7/8UgghxPvvvy9++OEH++27d+8WLVu2FOnp6fIb3oANGzaIyMhI8fbbbzvd5vXXXxexsbH2UWZffPGFMBgMIjc3177NokWLHJ4bNffRiuFCB66//nphNBpFz549HS6zZ8+2b7N06VIBQBQWFgohhFi5cqVITEwUV199tejQoYOIjY0VCxYs8NMjcO78+fMiIyNDNG/eXFx//fWiadOm4qabbhLbt2932K5///7i/vvvt/985swZ0bdvX9G0aVPRqVMn0bx5c7FhwwYft169W265RaSlpdV7m224W05OjhBCiB07doiePXuK9u3bi6SkJBERESH+/Oc/1xmO5g/p6en2tkVFRdlfi2fPnrVv8+233wqTySTatWsn4uLixLXXXuswlK+iokIAEG+88Ybq+/jC77//bn88ERERwmQyiZ49e4pJkybZt+nSpYsIDw+v81586aWX7Nu88847AoD977VixQrRoUMHkZSUJEwmk2jWrJn4xz/+4dPHJkT1QbVnz56iS5cuAoDo3r276Nmzp9i4caMQonoo7mOPPebwXuzRo4f47rvvHPbTu3dv8eCDD9p/PnXqlOjdu7do2rSp6Nixo2jRooWUg5O77r77btGzZ0/RsmVLERsbK3r27Cn69etXZ7vu3buLRx55pN592IbR2tq/bds20aNHD5GQkCCSkpJEZGSkePzxx+1Byleio6NFkyZN6rzuan5ZtFqt4rHHHhORkZHi6quvFpGRkWLWrFkO+/nLX/4i4uLi3LqPVlwVVQd2795db0fFVq1a2WdSO336NPLy8tCjRw8YDAYA1T2i8/LyoCgKOnbsaL8+EF28eBH79+9HmzZt0KpVqzq3//TTTzAajbj66qsdrj948CCKi4uRnJwcsMsmCyGwbds2dOzYEa1bt65zu8Viwffff4+kpCQ0b97cfv3x48dRWFiITp06BczkZz/++CNKSkrqXH/TTTchPDzc/rPVasXPP/8Mo9GIpKSkOkM8v/32W3Tq1Mnh+XB1H28rKyurt5Nes2bN7JMm7dq1C2VlZXW2iY+PxxVXXAHg8nvxlltusZ+vF0Jg//79MBgMSExM9Mt78ciRI/VODHbVVVc5nGMvLS1FXl5eg+/F8PDwOpPDHThwACUlJX57L+7cubNOn6SwsDDccsst9p9t78VOnTrZO0PWZHsvJicnO4zoOnbsGM6dO4crrrgCUVFRXnsMzmzdurXeIcEmk6nORIqnTp1Cfn4+OnXq5PB5AgD5+fk4efIkbrrpJtX30YrhgoiIiKRih04iIiKSiuGCiIiIpGK4ICIiIqkYLoiIiEgqhgsiIiKSiuGCiIiIpGK4ICIiIqkYLojIrzZs2IDvvvuuzvXff/89cnJy/NAiIvIUwwUR+dWBAwdwxx13OMweeezYMdxxxx04cOCAH1tGRFpxhk4i8ishBG6//XZER0fj3//+N4QQGDRoEKxWKzZt2uTzacCJyHOBvX4zEQU9RVGwdOlSdO3aFe+88w7Onz+PHTt2YM+ePQwWRDrFcEFEftepUyfMnz8fU6ZMQUVFBZYvX46EhAR/N4uINOJpESIKCBaLBW3btkVsbCzy8vLsK4oSkf7w3UtEAWHGjBlo0qQJioqK8I9//MPfzSEiD7ByQUR+9/nnn2PQoEHYtGkTjhw5gnHjxuH7779HcnKyv5tGRBowXBCRXxUWFqJr16545JFHMG/ePADAkCFDcPToUXz99dcwGAx+biERuYvhgoj8avjw4di3bx++/fZbREREAABOnTqF6667DpMmTcKMGTP83EIichf7XBCR3/zyyy8AgPfee88eLAAgPj4ey5Ytw6+//oqSkhJ/NY+INGLlgoiIiKRi5YKIiIikYrggIiIiqRguiIiISCqGCyIiIpKK4YKIiIikYrggIiIiqRguiIiISCqGCyIiIpKK4YKIiIikYrggIiIiqRguiIiISCqGCyIiIpLq/we+RSuSzXSyIgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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(\n",
    "        X[idx],\n",
    "        resid[idx],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\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": "2026-07-29T12:27:59.434675Z",
     "iopub.status.busy": "2026-07-29T12:27:59.434108Z",
     "iopub.status.idle": "2026-07-29T12:27:59.516431Z",
     "shell.execute_reply": "2026-07-29T12:27:59.515578Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           2.45e-26\n",
      "Time:                        12:27:59   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:                Wed, 29 Jul 2026   Prob (F-statistic):           8.97e-25\n",
      "Time:                        12:27:59   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": "2026-07-29T12:27:59.519791Z",
     "iopub.status.busy": "2026-07-29T12:27:59.519485Z",
     "iopub.status.idle": "2026-07-29T12:27:59.972679Z",
     "shell.execute_reply": "2026-07-29T12:27:59.971544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'standardized resids')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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(\n",
    "        X.loc[idx],\n",
    "        resid.loc[idx],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\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": "2026-07-29T12:27:59.979142Z",
     "iopub.status.busy": "2026-07-29T12:27:59.978662Z",
     "iopub.status.idle": "2026-07-29T12:28:00.318581Z",
     "shell.execute_reply": "2026-07-29T12:28:00.316916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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(\n",
    "        X[idx],\n",
    "        S[idx],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\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": "2026-07-29T12:28:00.321285Z",
     "iopub.status.busy": "2026-07-29T12:28:00.321025Z",
     "iopub.status.idle": "2026-07-29T12:28:00.669093Z",
     "shell.execute_reply": "2026-07-29T12:28:00.667437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAINCAYAAAD2sJ95AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAPYQAAD2EBqD+naQAAVwRJREFUeJzt3Xd4VGXe//FPCgkEUmihd6QaqhQ1CoIC0u3YEGXBFQuWtaCL2IP+1lVRV3B5eMTHLiogCLhIFZEqTUSkB0MNkFBC6vn9ce9kMiSQZDLJmTN5v65rLmbm3DP5xhjmw33u+3uCLMuyBAAA4BDBdhcAAABQHIQXAADgKIQXAADgKIQXAADgKIQXAADgKIQXAADgKIQXAADgKIQXAADgKKF2FxBocnJylJSUpMjISAUFBdldDgAAjmFZlk6ePKm6desqOPj88yuEFx9LSkpSgwYN7C4DAADHSkxMVP369c97nPDiY5GRkZLMf/ioqCibqwEAwDlSU1PVoEGD3M/S8yG8+JjrVFFUVBThBQAALxS27IIFuwAAwFEILwAAwFEILwAAwFFY8wIAgJ/IyclRRkaG3WWUmgoVKigkJKTE70N4AQDAD2RkZGj37t3Kycmxu5RSFRMTo9q1a5eoFxrhBQAAm1mWpQMHDigkJEQNGjS4YIM2p7IsS2fOnNHhw4clSXXq1PH6vQgvAADYLCsrS2fOnFHdunUVERFhdzmlplKlSpKkw4cPKzY21utTSIEX7QAAcJjs7GxJUlhYmM2VlD5XOMvMzPT6PQgvAAD4ifJwTTxffI+EFwAA4CiEFwAAyoHFixfr4Ycf1pQpU/Id27Ztmx5++GE999xzZV+YFwgvAACUA+vWrdN7772nRx99VKmpqR7HJk2apPfee09Tp061qbriIbwAAFBO1KxZU+3atdOnn36a+1xaWpo++eQTDRkyxMbKiofwAgBAOTJy5EhNmzYt9/GXX36pJk2aqFOnTjZWVTyEFwAAypFbbrlFv/76q7Zs2SJJmjp1qkaOHGlzVcVDeAEAoByJjIzUzTffrGnTpmn79u1as2aNbr/9drvLKhY67AIAUM7cc889uu6662RZloYOHaqqVavaXVKxEF4AAChn4uPjVb16dU2aNEkLFiywu5xiI7wAAFAOTZkyRZs2bVLv3r3tLqXYCC8AAJRDPXr0UI8ePewuwyuEFwAAyoFevXpdcG3L1VdfrZo1a5ZhRd4jvAAAUA506tTpgr1cLrnkEl1yySVlWJH32CoNAAAchfACAAAchfACAAAchfACoNx48UUpONj8CcC5WLALoFx48UXp2WfNfdef48fbVw8A7zHzAiDg5Q0uLs8+ywwM4FSEFwABraDg4kKAAZyJ8AIgYF0ouLgQYADnIbwACEhFCS4uBBgElIULpTZtzJ8BivACIOAUJ7i4EGAQECxLevpp6bffzJ+WVSZfdv/+/Vq8eLF27txZJl+P8AIgoHgTXFwIMHC877+X1qwx99esMY9L2WOPPaaLLrpITz31lDp06KBhw4YpKyurVL8m4QVAwChJcHEhwMCxLMvs/w8JMY9DQszjUpx9+fLLL/XOO+/oxx9/1KpVq7Rp0yZ9//33euutt0rta0qEFwABwhfBxYUAA0dyzbpkZ5vH2dmlPvsyffp0XXPNNercubMkqUmTJho2bJg++OCDUvuaEuEFQICYMMG/3w8oFsuSTp8u+u3UKbPGxTXr4hISYp4/daro71WMmZoNGzaoY8eOHs917NhRW7duVWZmpi/+SxSIDrsAAsLzz/tu5kWSxo3z3XsBxXbmjFSlSsnfJztbWr9eiows+mtOnZIqVy7S0BMnTqhatWoez1WvXl05OTlKTU1V9erVi1NtkTHzAiAgjB8vvfCC795v4kTpkkukxx6Tvv1WOnHCd+8NBIqwsDClpaV5PHfmzJncY6WFmRcAAcN1raKSzMB07iylpkp//CGtW2du//ynFBQkdegg9expbldcIVWt6oOigYJERJgZkKJYuFAaOrTwcTNnSldfXbSvXUSNGzdWYmKix3P79+9XtWrVFFmc2Z5iIrwACCglCTAvvOB+/Z9/SkuXmtuSJdL27dIvv5jbG2+YMNO+vQkyPXpIV14pnTN7DngvKKhop24sS3r5ZbO2xbVQtyAhIWbc4MHmvX2kX79++vjjj5WZmakKFSrIsix9/fXX6tevn8++RkGCLKuMOtiUE6mpqYqOjlZKSoqioqLsLgcol44elVq3Nn8WVd7gUpCkJGnZMhNkliyRfv/d83hQkNSunWeYKaXT/QhAZ8+e1e7du9WkSRNVrFix6C9csEAqTlCYP1/q27f4BZ7HkSNH1LFjR7Vv31533HGHvvvuO82ePVurV69Wy5YtC3zNhb7Xon6GEl58jPAC2OvUKTMzvmqVFBVlTgEVprDgUpADBzzDzLZt+ce0a2eCTM+eJszUqFG8r4Hyw6vwYllSt25mQe6FZl1cQkKkTp3ML4cPZ1+SkpL0xhtv6Pfff1fDhg310EMPqUWLFucdT3jxQ4QXwD4ZGWZWfMECcwrnxx+lGTMufArJm+BSkIMH3WFm6VJp69b8Yy6+2L1m5sorpZo1S/51ERi8Ci/FnXVx8fHsS3ERXvwQ4QWwR06OdMcd0qefmvWGP/wgde9ujp2vgZ2vgktBDh0yYca1ZubXX/OPadvWM8zExpZOLfB/xQ4vrlmXdevM//xFFRxsVqX7ePalOHwRXliwC8DxLEt65BETXEJDpa++cgcXqeBFvKUZXCSpVi3pppvMTZKOHPE8zbRliwk0v/4qvfuuGdOmjWeYqVWr9OqDw2VkSPv2FS+4SGZ8YqJ5fXh46dRWBggvABwvIUGaNMnc/+CDgmfSXUFlwgTT0K40g0tBataUbrjB3CSzmDhvmNm82Zxq2rpV+te/zJjWrd0LgHv0kGrXLtua4cfCw03r/yNHiv/a2FhHBxeJ00Y+x2kjoGxNnSqNGmXuv/mmNHasreV47ehRafly95qZjRvzj2nVyr0AuEcPqU6dsq4SpcXr3UYOxGkjAOXaN99I995r7o8b59zgIpmdSNddZ26SlJxswoxrzczGjWZH07Zt0pQpZkyLFu7TTD16SHXr2lQ8UMa4PAAAR1q6VLr1VnMKf+RI038rkFSvbpqmvvGGaYx39KhpkPrII1LHjmat5fbt0vvvS7fdJtWrZ8LM6NHSJ5+YJnson1580azLDeQrozPzAsBxNmwwW6LT080H/OTJtm2cKDPVqklDhpibJB0/braCu9bMbNhgLmnwxx/Sv/9txjRv7p6V6dlTql/fltJRhvLurHP9Wdbru8oC4QWAo+zcaRbkpqaaHTmffGJ2GJU3VatKgwaZm2QuHOkKM0uXmr5lO3aY29SpZkyzZu4g07On1KCBPbWjdBTUEiBQA4ztv/JZWVmaM2eOtm3bpttuu00NGzbMN+bPP//UDz/8oOPHj6thw4bq37+/ws9ZKX3q1Cl9++23OnTokOLi4tS7d+987+OrMQDscfCg6a116JC5rtDs2VKlSnZX5R9iYqSBA81NklJSTJhxrZlZt84Ev507pWnTzJgmTTzXzDRqZE/tKLnz9TKSAjTAWDb6/PPPrYYNG1pXXXWVJclavHhxgWPCw8OtwYMHWw899JDVrl07q2HDhta+fftyx+zfv99q0qSJ1alTJ+svf/mLVatWLeuWW26xcnJyfD6mMCkpKZYkKyUlxbv/KAAKdOKEZXXoYFmSZTVpYllJSXZX5CwpKZY1d65lPfGEZXXtalkhIea/Zd5b48aWNWKEZX3wgWXt3m13xeVLWlqatXXrVistLa3Yr33hhfw/y4JuL7xQCoV74ULfa1E/Q20NL8uWLbMSExOtxMTE84aXtm3bWqNGjcp9fPbsWatevXrWM888k/vcHXfcYXXq1MlKT0+3LMuyfv31VyskJMT66quvfD6mMIQXwPfS0iyrRw/zF3BsrGX98YfdFTlfaqplzZtnWU8+aVnduhUcZho1sqy77rKsadMsa9cuyyrGv+NQTN6Gl6IGl9IKMOnp6dYnn3xiXXHFFZYkKyEhodDX+CK82Lrb6IorrlD9QlaQVapUyeMUUWhoqEJDQ1X5v5cKz87O1jfffKO77rpLYWFhkqQ2bdooPj5eX375pU/HACh72dlmN83SpVJkpLksS/PmdlflfJGRZu3QxInSzz+bBcDz50tPPSVdeqlZR7R3rzR9unTPPVLTplLjxtLw4ea0065d5uMQ9rnQqaLzefZZ3+5CmjVrlmbPnq0XXnhB9erV890bF8L2NS+Fef/99zVq1CjdfffdatSokZYtW6ZLL71UDz74oCRp3759On36dL5Lb7ds2VKrVq3y6ZiCpKenKz09PfdxalEuYQugSCxLuu8+088lLEyaNctsE4bvRUaa9USu6/WdOiX99JN7AfDq1aYb/f/9n7lJZsFv3gXATZsG/q4vf+FNcHHx5RqYm266STe5roFRhvw+vJw9e1ZpaWk6fPiwIiMjlZycrOjoaGVmZkoyC2wlKTo62uN1MTExucd8NaYgCQkJev7557399gBcwLPPmm2/wcHmukVXXWV3ReVHlSpSnz7mJkmnT5sw41oAvHq1uUTORx+Zm2R6zbiCTM+eZncTYcY7liWdOVPwsYkTpZdeKtn7P/usubzRU0/lPxYR4f8/N78OL1lZWbr++us1YsQIJSQk5D7XsWNHPfnkk3r//fdzTx+dO+ORkpKSe8xXYwoybtw4Pfroo7mPU1NT1YD9h0CJTZrk/gv6vfek66+3t57yrnJl6ZprzE0yH6wrV7r7zKxaZRrjffyxuUmm42/e3UwXXeT/H4r+4swZEyBL00svFRyCTp0yP29/5tcddg8cOKCDBw/qqjz/3AoNDVV8fLzWrVsnSWrYsKEqVqyoHTt2eLx2x44datGihU/HFCQ8PFxRUVEeNwAl8+mn7lb/L75ousbCv0RESL17m5/P8uWmz8wPP5hTEVdeaU7zJSWZPjyjR0stW5qZmdtuM5c3+P131szAe34dXurWrauIiAitXLky97mcnBytXr1aF110kSQTZgYOHKiPPvpI2dnZkqRdu3Zp6dKluv6//1Tz1RgApW/BArMoVJIeeEB65hl760HRRERIvXpJL7xgTi2dOCEtWmROT/ToYcLMgQMmmP71r+Yik3XrSsOGmQ7J27YRZvKKiDAzIOfe/v53336dv/89/9eIiPDt1ygNtl5VevPmzZo7d65SU1OVkJCgUaNGqWnTpoqPj1d8fLwkafLkyRo7dqxuuukmNW3aVAsXLtT27dv1448/qlWrVpKk3bt367LLLlPLli3VtWtXffHFF2rTpo3mzJmj4OBgn44pDFeVBry3erX5ADx92nyoffyxWe8C50tLM6eWXGtmVq40l3fIq1YtzwXArVqVn9NMxbmqdEkW6+b1wgu+bVxXv359PfDAA3qqoIU0efjiqtK2hpd169YVuA356quv1tVXX537eNu2bVq4cKGSk5PVqFEjXX/99fm+qeTkZH3++ee5nXGvv/76fIHDV2MuhPACeGfbNik+3lxNuU8f6dtvzb/WEZjOnjVh1bVmZuVK81xesbHuMNOjh9SmTeCGmeKEF6nkAcbXwUUqR+ElEBFegOLbv1+67DKze6VLF3O6obQXK8K/pKe7w8zSpdKKFfnDTM2aJsS4Ak2bNoEzM1fc8CJ5H2B8GVy2bdum1q1b53u+b9++mj9/foGvIbz4IcILUDzHjklXXCFt3WoWdS5fbj6kUL6lp0tr1niGmbQ0zzE1apjFwa7TTG3bOjfMeBNepOIHmNKYcSkuX4QXv94qDSCwnT5tLiS4davZibJgAcEFRni4OY0YH28WlWZkmDDjWjOzYoV09Kj09dfmJknVq3uGmYsvdm6YKSpXEClKgPGH4OIrhBcAtsjMlG66yax1qFrVBBeuaozzCQuTLr/c3J5+2oSZdevca2ZWrDDrpb75xtwkqVo1d5jp0UNq1y4ww0xRAkwgBReJ8ALABjk55no58+ZJlSpJc+aYKX+gqMLCzDWYLr1UGjfOhOG8YebHH80pyZkzzU0yIfncMBMSYtu34FMXCjCBFlwkwguAMmZZ0t/+ZlrKh4RIX35pFusCJVGhgtS9u7k99ZQJM+vXu9fMLF9uLj45a5a5SVJMjAkzrgXA7ds7O8wUFGACMbhIhBcAZey116Q33jD3p02TBgywtx4EpgoVpG7dzO3JJ6WsLBNmXGtmXF2BZ882N0mKjjaLx11rZjp0cF6YcQWVCROk558PzOAisdvI59htBJzftGnSyJHm/j/+IT32mL31oPzKypI2bHCfZlq+XDrn0naKisofZkJL6Z/8rh04jRs3VqVKlUrni/iJtLQ07dmzh63S/oTwAhRs9mzpuuvMepcnnpBefdXuigC37Oz8YSYlxXNMZKQ7zPToIXXq5Lswk5mZqR07dqhu3bqKjo72zZv6qeTkZB0+fFgtWrRQyDlTW4QXmxBegPyWLzddc8+ele6+W/qf/wncTqkIDNnZ0saN7jUzy5aZ00x5RUaardyuNTOdOpnTVd6wLEv79u1TZmam6tatW6zO7k5hWZbOnDmjw4cPKyYmRnXq1Mk3hvBiE8IL4GnTJrMoMiVFGjTI9OQoral3oLRkZ5v/l11rZpYtMwuA86pSxWzldp1m6ty5eGEmIyNDu3fvVk5Ojg8r9z8xMTGqXbu2ggr4FwzhxSaEF8Bt926zk+jgQfMv1AULnHHFWqAwOTnS5s3u00zLlpmt2XlVruwZZi65pPAwk5OTo4yMjNIp2g9UqFAh36mivAgvNiG8AMbhw+Yv7h07TKfTZctMnw0gEOXkSFu2eIaZ5GTPMRER7jDTo4e5jpcdFx998UX/3Y1EeLEJ4QUwuzauuspsTW3USPrpJ6luXburAspOTo7066/uNTNLl5rLGeRVqZIJM641M126mMsilKZzr4Xkb31gCC82IbygvEtPl/r3N1eGrlnTdDpt0cLuqgB75eSYa3i51swsWVJwmLnsMneY6drVt2HmfBdx9KcAQ3ixCeEF5Vl2tjRsmDRjhlm8uGSJWbQIwJNlSb/95g4yS5ZIR454jqlY0Vz+wLVmpls378NMYVef9pcAQ3ixCeEF5ZVlSWPGSJMnm/P4330n9e5td1WAM1iWtG2bO8gsXSodOuQ5pmJFc/mDvGHmnB5vBSosuLj4Q4AhvNiE8ILy6rnnzALAoCDp88/NFaMBeMeypN9/dweZJUvMrr28wsPdYaZHD3P/3Oa8RQ0uLnYHGMKLTQgvKI/+9S/p/vvd9++7z956gEBjWdL27Z5rZg4c8BwTFmYCjGvNzJIlJrwUl50BhvBiE8ILypsvvjDrXCzLzL5MmGB3RUDgsyzThiDvmpmkJN+9v10BhvBiE8ILypOFC83OosxMs97lnXdo+w/YwbKknTtNiHn7bdMNuKTsCDBF/QwNvIsnACgTa9dKQ4ea4HLTTdKkSQQXwC5BQVLz5uZUki+Ci2TWynhz2qksMPPiY8y8oDzYvt001zp61Owomju39JtrAShccLCZhfGVoCDTo6asMPMCoFQkJZkrRB89anq4fPMNwQXwF88/79/v5yuEFwBFdvy41LevtHevdNFFppdLZKTdVQFwGT/erFXxBbu3TV8I4QVAkZw5Iw0aZC4+V6eO9P33Umys3VUBOJcvAow/BxeJ8AKgCDIzpVtukVaskGJipAULpMaN7a4KwPmUJMD4e3CRCC8ACmFZ0qhR0pw5phX5t99KcXF2VwWgMN4EGCcEF4nwAqAQTz4pTZ8uhYSYhnTx8XZXBKCoihNgnBJcJMILgAv4xz+k//f/zP2pU82aFwDOUpQA46TgIhFeAJzH9OnS44+b+6++Ko0YYWs5AErgQgHGacFFIrwAKMCcOdLIkeb+Y4+5QwwA5yoowDgxuEiEFwDnWLFCuvlmKTtbuvNO6bXXaPsPBApXgAkKcm5wkbg8gM9xeQA42ZYt0hVXSCdOmAsuzpwpVahgd1UAygsuDwCgWPbuNd1zT5yQLrtM+vJLggsA/0R4AaAjR8z1ipKSpLZtTS+XiAi7qwKAghFegHLu5Elzimj7dqlhQ2n+fKlaNburAoDzI7wA5Vh6unT99dLatVL16qbtf/36dlcFABdGeAHKqZwc6a67pIULpcqVzRWiW7WyuyoAKBzhBSiHLEsaO1b6/HOzKPebb6SuXe2uCgCKhvAClEMvvSS9847p9fDhh9I119hdEQAUHeEFKGcmT5aefdbcf+stadgwe+sBgOIivADlyIwZ0pgx5v7f/y49+KC99QCANwgvQDmxaJF0++1mvcvo0YVfZRYA/BXhBSgH1q+Xhg6VMjLM1uh//YvrFQFwLsILEOD++EPq1880o+vZU/r4YykkxO6qAMB7hBcggB04YNr+HzkidewozZolVaxod1UAUDKEFyBAnThhZlz27JGaNZPmzZO40DmAQEB4AQJQWpo0eLC0aZNUu7b0/fdSrVp2VwUAvkF4AQJMVpZ0663S8uVmpmX+fKlpU7urAgDfIbwAAcSypHvvNWtbwsOlb7+V2re3uyoA8C3CCxBAnn5amjZNCg6WPvtMuvJKuysCAN8jvAAB4o03pIkTzf0pU0xfFwAIRIQXIAB89JH06KPm/iuvSH/5i731AEBpIrwADjdvnnT33eb+ww9LTz1lazkAUOoIL4CDrVwp3XCD2WF0++3S66/T9h9A4CO8AA61das0YIDp6dKvn3uhLgAEOv6qAxxo3z6pb1/p+HGpWzdpxgwpLMzuqgCgbBBeAIc5etQEl/37pdatpblzpcqV7a4KAMoO4QVwkFOnpIEDpW3bpPr1pQULpOrV7a4KAMoW4QVwiIwM6cYbpVWrpGrVzPWKGjSwuyoAKHuEF8ABcnKkESPMTEtEhDlV1Lq13VUBgD0IL4CfsyzpkUekTz+VQkOlr76Sune3uyoAsE+o3QVs375d77//vrZt26aEhATFxcXlG5OVlaUPP/xQixYtUkREhEaOHKlu3bp5jNm0aZOmTJmiQ4cOKS4uTmPHjlVMTEypjAHKUkKCNGmSuf/BB2ZbNACUZ7bOvCQkJGjQoEEKCQnR3LlzlZycnG/M2bNn1atXL/3jH/9Qjx491KNHDz3xxBNav3597pjVq1erW7duysnJ0aBBg7RgwQLFx8crLS3N52OAsjR1qvTMM+b+m2+aRnQAUO5ZNvrzzz+tnJwcKzEx0ZJkLV68ON+Y8ePHWzVq1LCOHDmS+1x2draVmpqa+7hXr17W4MGDcx8fP37cqly5svX222/7fExhUlJSLElWSkpKkV8DFOTrry0rONiyJMsaN87uagCg9BX1M9TWmZe6desqqJBe5v/7v/+rO++8UzVq1Mh9Ljg4WJGRkZLMzMzSpUt1/fXX5x6PiYlR7969NX/+fJ+OAcrK0qXSrbeahbojR0ovv2x3RQDgP/x6we6xY8e0f/9+dejQQRMnTtRNN92ksWPHau3atblj9u3bp+zsbNWvX9/jtQ0aNNDu3bt9OqYg6enpSk1N9bgBJbFhgzR4sJSeLg0dKk2ezPWKACAvvw4vZ86ckSQ99dRTSkpK0k033aTQ0FB1795ds2fPliRlZGRIkiIiIjxeGxERkXvMV2MKkpCQoOjo6NxbAxpvoAR27jQLclNTpSuvlD75xOwwAgC4+fVfi65dPt27d9ek/263uPnmm3XgwAElJCRo8ODBuWOOHTvm8drk5OTcY74aU5Bx48bp0UcfzX2cmppKgIFXDh40bf8PHZLat5dmz5YqVbK7KgDwP34981KlShU1b95czZo183i+adOmOnz4sCSpXr16ql69ujZu3OgxZsOGDWrfvr1PxxQkPDxcUVFRHjeguFJSpGuvNTMvTZpI8+ZJ0dF2VwUA/smvw4sk3XPPPZo9e3buWpJTp05p1qxZio+PlyQFBQXpzjvv1NSpU3O3Wv/nP//R+vXrNXz4cJ+OAUrD2bPSkCFmrUtsrGn7X6eO3VUBgB8ro91PBVq4cKE1YMAAq3fv3pYkq3v37taAAQOsjz/+OHdMRkaGdfPNN1s1atSwrrrqKis2Nta68sorraNHj+aOOXnypHXVVVdZ1atXty699FKrYsWK1gsvvODxtXw1pjBslUZxZGVZ1nXXme3QkZGWtX693RUBgH2K+hkaZFmWZVdw2r9/vzZs2JDv+RYtWqhFixYez/3xxx/au3evGjZsmO+Yy6ZNm3To0CG1bdtWdevWLdUx55Oamqro6GilpKRwCgkXZFnSvfdK//63FBYmzZ8vXXWV3VUBgH2K+hlqa3gJRIQXFNX48dJLL0nBwdKXX0p5WgwBQLlU1M9Qv1/zAgSiSZNMcJGk994juABAcRBegDL26afS2LHm/osvSqNH21sPADgN4QUoQwsWSK7Naw884L7oIgCg6AgvQBlZvVq64QYpK0saNkx66y3a/gOANwgvQBnYtk3q3186fVrq00eaPt0s1AUAFB9/fQKlbP9+E1iSk6UuXaSvvjJbowEA3iG8AKXo2DFzvaLERKllS2nuXKlKFburAgBnI7wApeT0aWngQGnrVqlePbNYt2ZNu6sCAOcjvAClIDNTuukmaeVKqWpVE1waNbK7KgAIDIQXwMdycqR77jFXhq5USZozR2rb1u6qACBwEF4AH7Is6W9/kz76SAoJMW3/L7vM7qoAILAQXgAfeu016Y03zP1p06QBA+ytBwACEeEF8JFp06SnnjL3//EPdyddAIBvEV4AH5g9Wxo1ytx/4gnpscfsrQcAAhnhBSih5culW24xC3XvvluaONHuigAgsBFegBLYtEkaNEg6e9b8+f77XK8IAEob4QXw0u7dpntuSooUHy999pkUGmp3VQAQ+AgvgBcOHzbXKzp4ULr4YrPmJSLC7qoAoHwgvADFlJoqXXuttGOH6Zq7YIHpogsAKBuEF6AY0tOl666T1q831yn6/nupbl27qwKA8oXwAhRRdrZ0xx3SokXmytDz5kktWthdFQCUP4QXoAgsS3rgAWnGDCksTJo5U+rc2e6qAKB8IrwARfD889LkyWYb9EcfSb17210RAJRfhBegEO++a8KL6/5NN9lbDwCUd4QX4AK++EJ68EFz/7nnpPvus7UcAIAIL8B5LVxoFuhaljRmjPTss3ZXBACQCC9AgdaulYYOlTIzzWmiSZNo+w8A/oLwApxj+3bThO70abMw9//+TwoJsbsqAIAL4QXI488/Tdv/o0fNVuhvvpHCw+2uCgCQF+EF+K/jx6V+/aS9e6WLLpK++06KjLS7KgDAuQgvgKQzZ6RBg6QtW6Q6dUzb/9hYu6sCABSE8IJyLzNTuuUWacUKKSbGXGixcWO7qwIAnA/hBeWaZUmjRklz5kgVK0rffivFxdldFQDgQggvKNeefFKaPt3sJvriCyk+3u6KAACFIbyg3PrHP6T/9//M/alTzZoXAID/I7ygXJo+XXr8cXP/1VelESNsLQcAUAyEF5Q7c+ZII0ea+4895g4xAABnILygXFmxQrr5Zik7Wxo+XHrtNdr+A4DTEF5QbmzZIg0cKKWlSQMGmHUuwfwGAIDj8Fc3yoW9e6W+faUTJ6TLLjM7iypUsLsqAIA3CC8IeEeOmOsVJSVJbduaXi4REXZXBQDwFuEFAe3kSal/f3Ol6IYNpfnzpWrV7K4KAFASXoUXy7I0efJkdenSRdWrV899fty4cfrzzz99VhxQEunp0vXXS2vXSjVqmOsV1a9vd1UAgJLyKry89dZbeuWVV3TnnXfq2LFjuc9fdNFFeumll3xWHOCt7GzprrukhQulypXNFaJbtrS7KgCALwRZlmUV90UtW7bUhx9+qG7duikoKEiut0hMTFTnzp11+PBhnxfqFKmpqYqOjlZKSoqioqLsLqdcsizpwQeld981i3LnzpWuucbuqgAAhSnqZ6hXMy979+5Vu3btJElBeZpkVKlSRSkpKd68JeAzL71kgktQkPThhwQXAAg0XoWXhg0b6pdffpHkGV6++eYbtWrVyjeVAV6YPFl69llz/623pGHD7K0HAOB7od686OGHH9bw4cM1ceJESdKKFSs0f/58vf766/rXv/7l0wKBopoxQxozxtwfP96cOgIABB6vwsuYMWOUnp6u++67Tzk5OYqPj1fVqlX1yiuvaARXuIMNFi2Sbr/drHcZPVp6/nm7KwIAlBavFuy65OTkaO/evcrJyVHjxo0VEhLiy9ociQW7ZW/9eqlHD+nUKbM1+osvJP5XBADnKepnqFczLy7BwcFq0qRJSd4CKJE//pD69TPBpWdP6eOPCS4AEOi8Ci9nz57Vu+++qxUrVnj0eXFZsmRJSesCCnXggGn7f+SI1LGjNGuWVLGi3VUBAEqbV+Hl/vvv19y5c3XDDTeoTZs2vq4JKNSJE2bGZc8eqVkzad48ibN0AFA+eBVevvnmGy1dulRxcXG+rgcoVFqaNHiwtGmTVLu2aftfq5bdVQEAyopXfV4qVqyoxo0b+7gUoHBZWaZ3y/LlZqZl/nypaVO7qwIAlCWvwkv//v310Ucf+boW4IIsS7r3Xmn2bCk8XPr2W6l9e7urAgCUNa9OG50+fVpjxozRjBkz1Lx5c48uu5I0efJknxQH5PX009K0aVJwsPTZZ9KVV9pdEQDADl6Fl8zMTN1www2SpOTkZJ8WBBTkjTek/zZ01vvvS0OH2loOAMBGXoWXGTNm+LoO4Lw++kh69FFz/5VXpJEj7a0HAGAvr9a8AGVl3jzp7rvN/Ycflp56ytZyAAB+wOvwsnnzZo0YMULdunVT165dNWLECG3ZssWXtaGcW7lSuuEGs8Po9tul11+XzlleBQAoh7wKL3PmzFHHjh21Z88eXXXVVerdu7f27NmjDh06aM6cOb6uEeXQ1q3SgAGmp0u/fu6FugAAePVx8Mwzz+i1117TkiVLNHHiRCUkJGjJkiV67bXX9MwzzxT5fdLS0vTBBx+oe/fuiomJ0Y8//njB8S+//LJiYmI0bty4fMemTZum9u3bq3bt2rrmmmv0yy+/lNoYlK59+6S+faXjx6Vu3aQZM6SwMLurAgD4C6/Cy2+//aaRBayaHDlypLZt21bk93nuuee0ePFiPf7440pJSVFWVtZ5xy5fvlz/+7//q+rVqystLc3j2Keffqr77rtPjz/+uFasWKFmzZqpV69eOnDggM/HoHQdPWqCy/79UuvW0ty5UuXKdlcFAPArlhfq1q1rLV26NN/zS5YsserVq1fk98nJybEsy7ISExMtSdbixYsLHJecnGw1btzYWr58udW+fXtr7NixHsfj4uKse++9N/dxVlaWVatWLWv8+PE+H1OYlJQUS5KVkpJS5NfAOHnSsrp2tSzJsurXt6x9++yuCABQlor6GerVzMvIkSN1yy236J133tHq1au1evVqvf3227r55pt1zz33FPl9zm1udz733HOPbr/9dsXHx+c7duLECW3evFm9e/fOfS4kJES9evXKPQ3lqzEoPRkZ0o03SqtXS9WqmesVNWhgd1UAAH/kVZ+XCRMmqGLFiho/frxOnDghSYqJidHjjz+uJ5980pf16e2331ZSUtJ5e8skJSVJkmqdc2W+2NhYrV+/3qdjCpKenq709PTcx6mpqYV+T/CUkyONGCEtWCBFRJhTRa1b210VAMBfeRVeQkJC9PTTT2vcuHFKSkpSUFCQ6tSpU+SZlKLatGmTnn/+ea1cuVKhoRcuNficrSihoaGyLKtUxuSVkJCg559//oK14fwsS3rkEenTT6XQUOmrr6Tu3e2uCgDgz4oVXi60GDfvjEOrVq28ryiPZcuWKSUlRV26dMl97uTJk/rtt9/0wQcfKDk5WbGxsZKko0ePerz28OHDucd8NaYg48aN06Ou9q8y/x0acL6jyBISpEmTzP0PPjDbogEAuJBihZfWRZzLv9BMRXGMHj1ad9xxh8dzV1xxheLj45WQkKCQkBDVqFFDTZs21fLlyzU0zwVvli1bphtvvFGSfDamIOHh4QoPDy/5N1sO/fvfkmtn/ZtvmkZ0AAAUplgLdjdv3lzgbdOmTXryySdVsWJF1ahRw2fFhYWFKSYmxuMWEhKi8PBwxcTE5I4bO3aspk6dqhUrVigjI0MJCQk6ePCg7r33Xp+PgW98843017+a++PGSWPH2lsPAMBBSrqtacGCBVaHDh2sypUrW88880yxtgh//PHHVnR0tBUVFWVJsipXrmxFR0dbCQkJ531NQVulc3JyrPHjx1tRUVFWSEiI1axZM2vevHmlMqYwbJUu3JIllhUebrZEjxxpWf/dMQ8AKOeK+hkaZFneneNZt26dnnzySS1dulQjR47Uc889p9q1axfrPTIyMnTmzJl8z1esWFEVK1Ys8DUnT55UaGioKlWqlO+YZVk6e/Zsgcd8PeZ8UlNTFR0drZSUFEVFRRX79YFuwwapRw8pNVUaOlT68kuzUBcAgKJ+hhb7Y2PXrl36+9//rs8//1xDhw7Vr7/+qhYtWnhVZFhYmMKK2fc9MjLyvMeCgoIKDRy+GoPi27nTLMhNTZWuvNK9wwgAgOIo1pqXsWPHqnXr1vrzzz/1008/6auvvvI6uKB8OXhQ6tNHOnRIat9emj1bOs/kGgAAF1Ss00ZBQUGqWLGi2rdvf8FxP//8c4kLcypOG+WXkiL17GlOGTVpIq1YIdWpY3dVAAB/UyqnjcayJQTFdPasNGSICS6xsabtP8EFAFASxQovb775ZimVgUCUnS3ddpu0dKkUGSnNny81b253VQAAp/PqwoxAYSxLuu8+088lLEyaNUvq2NHuqgAAgYDwglIxfrzpoBscbHYVXXWV3RUBAAIF4QU+N2mS9PLL5v5770nXX29vPQCAwFLk8LJw4cLSrAMB4tNP3a3+X3xRGj3a3noAAIGnyOHlmmuuyb3vy+sXIXAsWCANH27uP/ig+6KLAAD4UpHDS7Vq1bRr1y5JUnJycqkVBGdatUq64QYpK0saNsxcJTooyO6qAACBqMhbpW+99VZdfPHFqvPfJh3NL7DndceOHSWvDI6xbZs0YIB0+rTpojt9ulmoCwBAaShyeHnnnXd04403aseOHRo1apT+9re/lWZdcIj9+01gSU6WunSRvvrKbI0GAKC0FKtJXc+ePdWzZ0/9/PPP+utf/1paNcEhjh2T+vaVEhOlli2luXOlKlXsrgoAEOi8mtyfOnWqr+uAw5w+LQ0cKG3dKtWrZxbr1qxpd1UAgPLA65UJmzdv1ogRI9StWzd17dpVI0aM0JYtW3xZG/xUZqZ0003SypVS1aomuDRqZHdVAIDywqvwMmfOHHXs2FF79uzRVVddpd69e2vPnj3q0KGD5syZ4+sa4UdycqR77pHmzZMqVZLmzJHatrW7KgBAeRJkWZZV3Be1b99ed911lx599FGP5//5z39q+vTp2rhxo88KdJqiXs7biSxLeuwx6Y03pJAQc72iAQPsrgoAECiK+hnqVXgJCwvTkSNHFB0d7fF8SkqKYmNjlZ6eXvyKA0Qgh5dXX5Weesrcnz7d3ZAOAABfKOpnqFenjWrWrFng7MqGDRtUk1WbAWnaNHdwef11ggsAwD7F2irtMnLkSN1yyy165pln1LVrV0nSqlWr9NJLL+nee+/1aYGw3+zZ0qhR5v4TT0jnnC0EAKBMeRVeJkyYoIoVK2r8+PE6ceKEJCkmJkaPP/64nnzySV/WB5stXy7dcotZqHv33dLEiXZXBAAo77xa8+JiWZaSkpIUFBSkOnXqKIiL2QTUmpdNm6Qrr5RSUqRBg6Svv5ZCvYq7AAAUrqifoSX6KAoKClK9evVK8hbwU7t3m+65KSlSfLz02WcEFwCAf+Dyecjn8GFzvaKDB6W4OLPmJSLC7qoAADAIL/CQmipde620Y4fUuLE0f77pogsAgL8gvCBXerp03XXS+vXmOkULFkh169pdFQAAnggvkCRlZ0t33CEtWmSuDD1vntSihd1VAQCQn1dLMM+ePat3331XK1as0LFjx/IdX7JkSUnrgo+9+KI0YYL0/PPS+PGexyxLeuABacYMKSxMmjlT6tzZljIBACiUV+Hl/vvv19y5c3XDDTeoTZs2vq4JPvbii9Kzz5r7rj/zBpjnn5cmT5aCgqSPPpJ69y77GgEAKCqvwss333yjpUuXKi4uztf1wMfyBheXvAHm3XdNeJHM/ZtuKtv6AAAoLq/CS8WKFdW4cWMflwJfKyi4uDz7rLRli/Tll+bxc89J991XZqUBAOA1rxbs9u/fXx999JGva4EPXSi4uHzxhVnvMmZM4WMBAPAXXs28nD59WmPGjNGMGTPUvHnzfJcFmDx5sk+Kg3eKElzyio01610AAHACr8JLZmambrjhBklScnKyTwtCyRQ3uEjmlFFwcP5dSAAA+COvwsuMGTN8XQd8wJvg4lLQLiQAAPwRTeoCREmCi8uzz5r3AQDAn3l9neCzZ89q9erV2rdvn7KysjyOjRgxoqR1oRh8EVxcmIEBAPi7IMuyrOK+aPPmzRo0aJCOHTumkydPqnr16rlrX+rWras///zT54U6RWpqqqKjo5WSkqKoqKgy+ZrBwWbXkK8EBUk5Ob57PwAAiqKon6FenTZ65JFHdPPNNyslJUWSdPToUe3YsUOXXXaZ7r//fu8qhtdcTeb89f0AAPAlr2Zeqlatqh07dqh69eoKDg7W2bNnFRYWpm3btqlv377au3dvadTqCHbMvEi+O3X0wgucMgIA2KNUZ15OnDih6tWrS5Jq1qyppKQkSVK9evV06NAhb94SJTR+vAkeJUFwAQA4QYl3G1122WWaMGGC1q5dq3Hjxql169a+qAteKEmAIbgAAJzCq91GTz75ZO791157Tddff726dOmi+vXr6/PPP/dZcSg+VwApzikkggsAwEm8WvNSkFOnTqlKlSq+eCtHs2vNy7mKugaG4AIA8BeluualIAQX/1KUU0gEFwCAE3kVXizL0uTJk9WlS5fchbuSNG7cuHLd48XfXCjAEFwAAE7lVXh566239Morr+jOO+/UsWPHcp+/6KKL9NJLL/msOJRcQQGG4AIAcDKv1ry0bNlSH374obp166agoCC53iIxMVGdO3fW4cOHfV6oU/jLmpdzvfiiNGGCaUBHcAEA+KOifoZ6FV4qVqyo48ePq1KlSgoODlbOf3vJHz9+XLVr11Z6err3lTucv4YXAAD8Xaku2G3YsKF++eUXSVJQUFDu8998841atWrlzVsCAAAUiVd9Xh5++GENHz5cEydOlCStWLFC8+fP1+uvv65//etfPi0QAAAgL6/Cy5gxY5Senq777rtPOTk5io+PV9WqVfXKK69oxIgRPi4RAADArURN6nJycrR3717l5OSocePGCgkJ8WVtjsSaFwAAvFPUz1CvZl5cgoOD1aRJk5K8BQAAQLF4HV5+/vlnrVixQsePH893jF4vAACgtHgVXhISEvT3v/9dcXFxiomJ8XFJAAAA5+dVeHnzzTf13XffqW/fvr6uBwAA4IK86vOSlpamyy+/3Ne1AAAAFMqr8HLttdfqq6++8nUtAAAAhfL6tFFcXJy+/PJLNWvWzKPLrus4AABAafAqvDz33HNKTU3ViRMntHPnTl/XBAAAcF5ehZdPP/1U//nPf9SjRw+fFJGSkqLExEQ1adJElStXLnDMgQMHFBoaqpo1a573fU6cOKGjR4+qYcOGCgsLK9UxAADAHl6tealUqZI6depU4i++detW/eUvf1GzZs0UFxenNWvW5Bvz7rvvqkmTJurcubNatWqlVq1aacmSJR5jsrKyNHLkSNWqVUtXXnmlYmNj9eGHH5bKGAAAYC+vwkuPHj308ccfl/iLL126VN26ddOKFSsKPJ6dna3ffvtNS5YsUVJSkg4fPqwBAwZoyJAhOnLkSO64V155RXPmzNHWrVuVlJSkSZMm6e6779aGDRt8PgYAANjLq2sb3Xjjjfrqq6/Uq1cvNW/ePN+C3cmTJxfr/fbv368GDRpo8eLF6tmz5wXHHjhwQHXr1tW8efPUr18/SVL9+vU1YsQIj86+rVu3Vu/evfXOO+/4dExhuLYRAADeKfVrG91www2SpOTkZG/fwiu//vqrJKlBgwaSpIMHD+rPP/9Ut27dPMZdeumlWrdunU/HAAAA+3kVXmbMmOHrOookJSVFDzzwgAYMGKC2bdtKcoen6tWre4ytXr26jh496tMxBUlPT1d6enru49TU1GJ/XwAAoOi8WvNihzNnzmjw4MEKDw/3WEQbGmryV0ZGhsf49PR0VahQwadjCpKQkKDo6Ojcm2tGCAAAlA5HhJe0tDQNHDhQx44d08KFC1WtWrXcY/Xq1VNQUJAOHDjg8ZoDBw7kBglfjSnIuHHjlJKSkntLTEws0fcKAAAuzO/Diyu4HDlyRIsWLcrX56VKlSrq0qWLvvvuu9zn0tPTtXDhQl111VU+HVOQ8PBwRUVFedwAAEDp8XrBri+cOHFC+/fv16FDhyRJu3fvVo0aNRQbG6vY2FhlZWVpyJAh2rJliz777DMdOnQod2y9evVUtWpVSdLzzz+vgQMH6uKLL9all16qN954Q5UrV9Zf//rX3K/lqzEAAMBeXm2V9pVZs2bpmWeeyff8mDFjNGbMGJ04cULx8fEFvvaFF17Q9ddfn/t4/vz5mjRpkg4dOqS4uDg999xzaty4scdrfDXmQtgqDQCAd4r6GWpreAlEhBcAALxT1M9Qv1/zAgAAkBfhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQAAOArhBQCA8mThQqlNG/OnQxFeAAAoLyxLevpp6bffzJ+WZXdFXiG8AABQXnz/vbRmjbm/Zo157ECEFwAAygPLksaPl0JCzOOQEPPYgbMvhBcAAMoD16xLdrZ5nJ3t2NkXwgsAAIHu3FkXF4fOvhBeAAAIVAcOSLNmSbfd5jnr4uLQ2ZdQuwsAAAA+kJIirV0rrV5tAsnq1dKffxb+OtfsS58+UlBQ6dfpA4QXAACc5uxZaeNGz6Dy++/5xwUHSw0bSnv2nP+98s6+9O1baiX7EuEFAAB/lp1t+rK4QsqaNdKmTVJmZv6xTZpIXbpIXbuaW4cOUu/eUmJi/lNGeTls9oXwAgCAv7Asae9ez6Cydq10+nT+sTVrmoDiCiuXXGKey2vBAndflwtx2OwL4QUAALscOWJCQ96wcuRI/nGVK5tw4goqXbpIjRpdeJYk7w6jC826uDho9oXwAgBAWTh1Slq/3jOo7N6df1yFClK7dp6zKq1a5d/mXJi83XSLwkGzL4QXAAB8LTNT2rzZHVRWr5a2bpVycvKPbdnSM6i0by9VrFiyr++adQkOLvhrnk9wsCNmXwgvAACURE6OtGOH586fDRvMjqBz1a/veeqnc2cpJsb3NWVkSPv2FS+4SGZ8YqJ5fXi47+vyEcILAADFkZTknk1xrVdJSck/LibGM6h06SLVrVs2NYaHn3/9TGFiY/06uEiEFwAAzu/EifyN35KS8o+rWFHq1MkzrDRvbu+plwYNzC0AEV4AAJDMaZ4NGzyDyvbt+ccFB0sXX+zZT6VtW7PQFmWC8AIAKH+ys80C2nMbv2Vl5R/btKnngtqOHc3WZdiG8AIACGyWZdrj5935s359wY3fYmPzN36rUaPMS8aFEV4AAIHl8OH8jd+OHs0/rkoVE07yhpUGDfx6izAMwgsAwLlOnZLWrfMMKgVdhLBCBdM/JW9Qadmy+I3f4BcILwAAZ8jIMI3f8i6o/e23/L1MgoJMR9q8O3/at/f77b8oOsILAMD/5ORIf/yRv/Fbenr+sQ0a5G/8Fh1d5iWj7BBeAAD2+/NPz8Zva9cW3PitalXPUz9duki1a5d9vbAV4QUAULaOH8/f+O3AgfzjKlXybPzWtavZtsyC2nKP8AIAKD1pae7Gb66w8scf+ceFhJjGb3lnVdq2lUL5mEJ+/F8BAPCNrKz8jd82by648VuzZvkbv0VElH3NcCTCCwCg+CxL2r3b89TP+vXSmTP5x9aq5T7t06WL6a1SvXrZ14yAQXgBABTu0KH8jd+Sk/OPi4zM3/itfn3WqcCnCC8AAE8nT5rGb3lnVfbtyz8uLEzq0MFz50/LlubChUApIrwAQHmWkWEuSHhu4zfL8hwXFCS1bu0ZVNq1o/EbbEF4AYDyIidH2r7dc+fPhg0mwJyrYUPPUz+dOklRUWVeMlAQvwgvS5Ys0bZt2zR48GDVrVs33/GMjAz98MMPOnTokOLi4tS5c2dbxwCA37Msaf9+zzUqa9dKqan5x1arlr/xW61aZV8zUES2hpdvv/1WTzzxhCpXrqx169apVatW+cLLkSNH1KtXL2VkZKhdu3Z65JFHdMstt2jy5Mm2jAEAv3TsWP7GbwcP5h9XqZJpn583rDRpwoJaOIqt4SU8PFxff/21IiMj1aBBgwLHjBs3TpL0yy+/KCIiQuvWrVOXLl00aNAgDRgwoMzHAIDtzpyRfvnFc1Zlx47840JCpLg4z6DSpg2N3+B8lh9ITEy0JFmLFy/2eD47O9uKjIy0Xn/9dY/nL7/8cuuOO+4o8zFFkZKSYkmyUlJSivwaADivzEzL2rDBsv79b8saNcqy2re3rJAQyzInhjxvzZtb1m23Wdabb1rWihWWdfq03dUDxVLUz1C/jt+JiYk6efKk2rRp4/F8mzZttHbt2jIfU5D09HSl57nKaWpB55MBoCgsS9q1K3/jt7S0/GNr187f+K1atbKvGbCBX4cXVxCIiYnxeL5q1aq5x8pyTEESEhL0/PPPF+0bAoC8Dh7M3/jt2LH846Ki3I3fXGGlXj3WqaDc8uvwUqlSJUnSyZMnPZ5PTU1VxH+vgVGWYwoybtw4Pfroox7jz7d+B0A5lpqav/FbYmL+cWFh5jo/eXf+tGhB4zcgD78OL40aNVJYWJh2797t8fzu3bvVvHnzMh9TkPDwcIXTpAlwhoULpYcekiZNkq6+uvS+Tnp6/sZv27YV3PitTRvPBbVxcSbAADgvv47yFSpUUL9+/fTZZ5/J+u8vfVJSkhYvXqzBgweX+RgADmZZ0tNPm+6xTz+dP0h4KyfHXEl5+nTp/vtNAImKMn8+8IB53tWxtlEj6aabpNdek5YskVJSpC1bpGnTpPvuM1uYCS5AoYIsy1e/wcX3+++/a/HixTpx4oTGjRunRx55RC1atNAll1yiSy65RJK0bds2XXbZZYqPj1f37t314YcfKjY2VosWLVLof7f7leWYwqSmpio6OlopKSmKohsl4D8WLJD69XM/nj9f6tu3eO9hWeZUz7mN38453SzJXDU57xqVLl2k2NiSfQ9AgCvqZ6it4WXlypWaPn16vucHDhyogQMH5j7ev3+/Pvzww9yut8OHD1fYOf86KcsxF0J4AfyQZUndupmdO9nZpv9Jp07SqlUXXvSanOxu/OYKK4cO5R8XEZG/8VvjxiyoBYrJEeElEBFeAD907qyLS97ZlzNnTLjJO6uyc2f+14SGuhu/ucJK69Y0fgN8oKifofy2AQhsliWNH29mW7Kz3c8HB0v33msW7q5ZI/36q+dxlxYtPHf+dOhgWuwDsA3hBUBg+/57E07OlZMj7d0r/c//uJ+rU8c9o9K1qzkVVLVq2dUKoEgILwACU3q69MMP0ogRFx5Xp4707rsmrNSrVyalASgZwguAwHH8uPTdd9LMmWY9y6lThb/mwAGz4JbgAjgG4QWAs+3bJ82aZQLLsmVSVpb7WIUK5vGF9iWEhJg1MX36sDsIcAjCCwBnsSxp40YTWGbNkn75xfN427bS0KGmp8rYsYW/X3a2WRPz/ffF7/sCwBaEFwD+LzNTWr7cHVj27nUfCw6WLr9cGjLE3Jo3d/d1OXeH0fkw+wI4CuEFgH86dcqsW5k1S5o716xncalUyQSNIUOkgQOlmjU9X3u+HUbnw+wL4CiEFwD+4+BB6dtvzfqVH34wO4ZcatSQBg0ygeWaa8wi24K4+roEB5vt0EUVHMzsC+AQhBcA9tq2zb3gdtUqz8W1zZqZ9StDhkiXXWZO7xQmI8Ms4i1OcJHM+MRE83quFA/4NcILgLKVkyP9/LM7sGzf7nm8Sxd3YGnTpvizIOHh5hTQkSPFry02luACOADhBUDpS0szp4FmzZJmz5YOH3Yfq1BB6tXLBJZBg3zTb6VBA3MDEJAILwBKR3KyWWg7a5a5MOLp0+5j0dFS//4msPTrJ3ERUwDFQHgB4Dt79rhPBy1f7rlNuX59cypo6FDpyiulsDCbigTgdIQXAN6zLNMkzhVYNm3yPN6unbv/SqdO7OIB4BOEFwDFk5kpLV3qbhiXmOg+FhxsZlVcgaVJE/vqBBCwCC8ACpea6m4Y99130okT7mMREWbdypAh0oABUvXqtpUJoHwgvAAoWFKS2Rk0a5a0aJHpf+ISG2t2Bg0dKvXubTreAkAZIbwAMCxL+u03s3Zl1ixp9WrP4y1auBfcuq4bBAA2ILwA5Vl2trRypXvB7Y4dnse7d3cHllat7KgQAPIhvADlTVqa9J//mMDy7beenWjDwqSrrzaBZdAgqU4d++oEgPMgvADlwdGj0pw57oZxaWnuYzEx5srMQ4aYKypHRtpWJgAUBeEFCFQ7d7q3M//4o+eFChs2dF8/6IorTIt+AHAIwgsQKCxLWrfOveB2yxbP4x06uNevtG9PwzgAjkV4AZwsI0NassQEltmzpT//dB8LCZF69HA3jGvUyK4qAcCnCC+A06SkSPPmmcAyb55pIOdSpYq7YVz//lK1araVCQClhfACOMH+/WZmZeZMM9OSmek+VquWe3alVy+pYkW7qgSAMkF4AfyRZZk1K64Ft2vXeh5v1cq94LZrV3NNIQAoJwgvgL/IypJWrHAHll273MeCgqRLL3UHlhYtbCsTAOxGeAHsdPq0aRg3c6bpw5Kc7D4WHi5dc40JLAMHmtNDAADCC1DmDh82QWXmTBNczp51H6tWzQSVoUOlPn2kypXtqhIA/BbhBSgLf/zhvn7QTz+ZNS0uTZq4F9zGx0uh/FoCwIXwtyRQGnJypDVr3OtXtm71PN65szuwxMXRMA4AioHwAvhKerq0aJEJK7NnSwcOuI+FhkpXXWXCyuDBUoMG9tUJAA5HeAFK4vhx6bvvTGCZN086dcp9LDJSuvZas37l2mvNBRABACVGeAGKa98+d8O4pUvNFmeXunXNzMrQoVLPnmbHEADApwgvQGEsS9q0yb3g9pdfPI+3beu+4GHnzjSMA4BSRngBCpKVJS1f7l5wu2eP+1hwsHT55e4Ft82b21YmAJRHhBfA5dQpacECE1bmzDHrWVwqVTJ9V4YMMX1Yata0r04AKOcILyjfDh6Uvv3WBJaFC82OIZcaNdwN4665RoqIsK1MAIAb4QXlz++/m7Urs2ZJP//s2TCuWTP3+pXLLpNCQuyqEgBwHoQXBL6cHGnVKndg+f13z+NdurgDS5s2NIwDAD9HeEFgOntW+uEHd8O4Q4fcxypUkHr1cjeMq1fPvjoBAMVGeEHgOHZMmjvXBJb5880Vm12ioqQBA0xg6ddPio62r04AQIkQXuBse/a4tzMvWyZlZ7uP1a/v3s7co4cUFmZbmQAA3yG8wFksS9qwwb1+ZeNGz+NxcWbtypAhUqdOrF8BgABEeIH/y8w0syozZ5r1K/v2uY8FB0tXXGECy+DBUtOmdlUJACgjhBf4p5MnzbqVWbPMOpYTJ9zHIiKkvn3N7MqAAaYfCwCg3CC8wH8cOGBmVmbNMjuFMjLcx2JjpUGDTGC5+mrT8RYAUC4RXmAfy5J++8294HbVKs/jF13kXr/SvTsN4wAAkggvKGvZ2aarrWvB7R9/eB7v1s0dWFq1YsEtACAfwgtKX1qauW7QzJnmOkJHjriPhYVJvXubwDJokFSnjl1VAgAcgvCC0nH0qFloO3Om9P330pkz7mMxMWah7dChZuFtZKRNRQIAnIjwAt/ZtcucCpo5U/rxR3NNIZeGDd3XD7riCtOiHwAALxBe4D3LktatcweWLVs8j3fo4A4s7duzfgUA4BOEl/Ji4ULpoYekSZPMVmNvZWRIS5a4L3i4f7/7WEiIdOWV7oZxjRuXsGgAAPIjvJQHliU9/bTZlvz002aBbHFmQVJSpHnzTGD57jspNdV9rHJlc6HDoUOl/v2latV8Xj4AAHkRXsqD77+X1qwx99esMY/79r3wa/bvdzeMW7zYtOh3qVXLzKwMHSr16iVVrFhqpQMAcC7CS6CzLGn8eHNKJzvb/Dl+vNSnj+fsi2VJv/7qXr+ydq3n+7Rq5V6/0rWruaYQAAA2ILwEuryzLpIJMK7Zl6uvllascAeWXbvc44KCpEsvNYFlyBCpZcsyLx0AgIIQXgLZubMuLsHB0u23m+PHjrmfDw+XrrnGhJVBg8zpIQAA/AzhJZCdO+vikpMjJSeb+9WqSQMHmsDSp49UpUrZ1ggAQDE5IrwsWbJE3333nY4fP66GDRtq+PDhatSokceYnTt3atq0aTp06JDi4uI0evRoVTrnysO+GuMI55t1cQkKklq0kDZvpmEcAMBR/H7V5Wuvvab+/furQoUK6tq1qzZu3Kg2bdpow4YNuWM2bdqkjh07ateuXYqLi9O0adPUo0cPZWRk+HyMY7hmXQoKLpIJN7//Li1aVLZ1AQBQQkGWZVl2F3EhF110ka677jq99tprkiTLstSyZUsNHTo097n+/fsrOztbCxYskCQdPnxYjRo10qRJkzRq1CifjilMamqqoqOjlZKSoqioKN/9hygOyzJXZ16//vzhRTKzMp06SatW0f0WAGC7on6G+v3MS+PGjbU/TxfXU6dO6cSJE2ratKkkKSMjQwsXLtTNN9+cOyY2Nla9evXSnDlzfDrGMQqbdXHJu/MIAACH8PvwMn36dJ06dUpdunTRjTfeqA4dOujBBx/MnQnZt2+fMjMz862BadSokXb9d+uvr8YUJD09XampqR43W+Vd61IUrr4v/j0BBwBALr8PL0uXLtXKlSvVq1cv9enTRx07dtTHH3+svXv3SpLS0tIkSVXO2SUTGRmZe8xXYwqSkJCg6Ojo3FuDBg28/VZ9o6izLi7MvgAAHMavw8vZs2c1evRojRs3Tq+++qpGjx6tGTNmKDY2Vk899ZQkKTo6WpJ0/Phxj9ceO3Ys95ivxhRk3LhxSklJyb0lJiZ6++2WnGvWpbjdb4ODmX0BADiGX4eXo0eP6tSpU2rbtq3H823bttXu3bslSQ0aNFBMTIy2bNniMWbz5s2Ki4vz6ZiChIeHKyoqyuNmm4wMad8+08elOHJypMRE83oAAPycX4eXevXqKTY2Vl9//XXucydPntT333+vjh07SpKCgoI0bNgw/c///I9OnjwpSfrpp5+0evVq3XbbbT4d4/fCw80poHXrin9bs8a8HgAAP+f3W6Xnz5+vO+64Q/Xr11fTpk21cuVK1a1bV/Pnz1fNmjUlmVM9ffr00eHDh9W2bVstX75co0eP1uuvv577Pr4aUxi/2CoNAIADFfUz1O/Di2RmW9atW6fk5GQ1atRInTt3VtA5fUmys7O1YsWK3M64rVq1yvc+vhpzIYQXAAC8E1DhxUkILwAAeCdgmtQBAADkRXgBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOQngBAACOEmp3AYHGsixJUmpqqs2VAADgLK7PTtdn6fkQXnzs5MmTkqQGDRrYXAkAAM508uRJRUdHn/d4kFVYvEGx5OTkKCkpSZGRkQoKCrK7nFypqalq0KCBEhMTFRUVZXc55RI/A/vxM7AfPwP7+fPPwLIsnTx5UnXr1lVw8PlXtjDz4mPBwcGqX7++3WWcV1RUlN/9z1re8DOwHz8D+/EzsJ+//gwuNOPiwoJdAADgKIQXAADgKISXciI8PFwTJkxQeHi43aWUW/wM7MfPwH78DOwXCD8DFuwCAABHYeYFAAA4CuEFAAA4CuEFAAA4Cn1eAsTx48c1ffp0ff311+rcubPeeOONQl9z8uRJvfbaa1qxYoUiIyM1fPhw3XDDDWVQbWA6cOCApk6dqvnz52vIkCF64oknLjh+/fr1GjNmTL7nP/nkEzVt2rS0ygxY6enpmj59uv7zn/8oNTVV7du31yOPPKI6depc8HU7d+7UxIkTtX37djVs2FCPPPKIOnXqVEZVB5aTJ0/q3//+t5YuXars7Gx17dpVY8eOvWDfjqlTp2rq1Kkez8XExGj+/PmlXW5ASk9P17Rp0/Sf//xHp0+fVrt27fTggw+qYcOGF3zdokWLNHnyZB0+fFgdOnTQ008/rdjY2DKquvgILwHg8OHD6tixo2688UaFhobqt99+K/Q1lmWpf//+On36tCZMmKC9e/fqtttu07vvvqu//OUvZVB1YPnll180ZMgQ3XnnnUpJSdGuXbsKfU1qaqpWrVqlH3/8USEhIbnPF/Zhi4INHTpUjRo10s0336zIyEhNmjRJnTp10vr168/73zQpKUmXXnqpevXqpaeeekpz5sxRfHy8fv75Z7Vr166MvwPni4+PV58+fTRy5EhJ0iuvvKIvvvhCq1atUuXKlQt8zf79+5WWlqYpU6bkPlehQoUyqTcQ3XHHHWrYsKHuueceBQcH67333lOXLl20ceNG1a5du8DXLFiwQAMHDtTf//533XXXXfrnP/+p+Ph4bdiwQREREWX8HRSRBcfLyMiwzpw5Y1mWZd1+++1W3759C33N7NmzLUnWrl27cp975plnrFq1allZWVmlVmugOn36tJWZmWlZlmVdfvnl1r333lvoaxYvXmxJyn0dSubkyZMej8+ePWvFxMRYr7/++nlf8+ijj1pNmzb1+H8+Pj7euvHGG0utzkB27s/g4MGDliTrq6++Ou9rJkyYYF1++eWlXVq5cfr06XyPg4KCrE8//fS8r+nUqZN111135T5OSUmxIiIirHfeeae0yiwx1rwEgAoVKqhSpUrFes0PP/ygiy++WE2aNMl9bsiQITp06JC2bNni6xIDXkREhEJDvZvIHDp0qPr06aOHH35Ye/fu9XFl5UeVKlU8HoeFhSk8PFwZGRnnfc0PP/yg/v37e8x8DR48WAsXLiy1OgPZuT+DiIgIBQcHX/BnIEl//PGHrr76ag0ePFgvv/yyTp8+XZplBrRzZ0qWL1+u4OBgXXzxxQWOP378uNavX69BgwblPhcVFaWePXv69e8B4aWc2rt3r+rWrevxnOsxH6BlZ8iQIbr77rv1wAMPaN++fWrTpo1+/fVXu8sKCFOnTtXRo0c1cODA84453+/BiRMnlJqaWtolBryJEyeqcuXK6tWr13nHVKhQQbfddpsee+wxDRs2TJ9//rm6dOmiM2fOlGGlgWXZsmXq3r27Wrdurdtvv13ffvvtecPLvn37JKnA3wN//ixgzUs5lZmZma+7omv2JjMz046Syp1LL71UPXv2zH08aNAgXXbZZXrmmWc0c+ZM2+oKBMuWLdNDDz2khISE8/6lLfF7UJo+//xzvfrqq/r4448vuPDzb3/7m8fP4Oqrr1bz5s31/vvv6+GHHy6DSgNPXFyc3nzzTR05ckRTpkzR/fffrx9//DFfQJHc/58X9Hvgz78DzLyUU9WqVdOxY8c8nktOTpYkVa9e3Y6Syp1z/7IICgpS7969tXHjRpsqCgw//fSTBg4cqL/97W96/PHHLzj2fL8HoaGhRbqyLQr29ddfa/jw4Xrvvfd0yy23XHDsub8HsbGxateuHb8HJVC1alV1795dgwYN0syZM5WVlaV33323wLHVqlWTpAJ/D/z5s4DwUk516tRJmzZtUnp6eu5zq1atUkhIiOLi4mysrHw7ePDgeXdloHArV65Uv3799OCDD+rFF18sdHynTp20Zs0aj+dWrVqliy++2Os1TOXdzJkzdeutt+rtt9/WqFGjvHoPfg98JzQ0VDVq1NDRo0cLPN64cWNVq1Yt3+/B6tWr1bFjx7Io0SuEl3Ji//796t69u1asWCFJGjZsmHJycnL7wZw6dUqvv/66hg4d6tdp28nWrFmj7t27526j/ve//517vlmSFi9erI8++kjDhg2zq0RHW716tfr166eHHnpIL7/8coFjZs+ere7duystLU2SNHLkSC1atEjLly+XJP3222/68ssvc7f6onhmz56tYcOG6e2339bo0aMLHDNlyhT169cv9/HEiRNzF+halqVXX31Vu3btKnTGBvmlpKRo0qRJysrKyn1uxowZ2rhxo6699trc55577jndfffdkqTg4GCNGDFCU6ZM0aFDhyRJH3/8sXbt2pU7xh/xT4sAMWDAACUnJ2vnzp3KzMxU9+7dFR4erqVLl0qSzp49q1WrVun48eOSpNq1a+uzzz7TXXfdpSlTpig5OVnt2rXT5MmT7fw2HCsjI0NXXnmlJGnr1q36448/tGHDBjVu3FifffaZJPMXy6pVq3IXIjZs2FB9+/bN3Ylx8OBBPf744xo3bpw934TDjRgxQmlpaVq4cKHHLokhQ4bk/jc9fPiwVq1apezsbElmndH48eN1zTXXqFGjRtqzZ4/uvvvuApsHonDDhg1ThQoVNG3aNE2bNi33+b/+9a8aMWKEJCkxMVFr167NPRYeHq5mzZqpevXqSk5OVkhIiD755BNdccUVZV2+41WuXFlJSUmqXbu26tatq2PHjikrK0tvvvmmhg4dmjtux44d2rZtW+7jl156STt37lSTJk1Ur149HThwQFOmTPHrXkdcVTpArF+/Pt92xODgYHXt2lWS6br4yy+/qFWrVoqJickdk56erm3btikyMpKuriVgWZZWrVqV7/lKlSqpffv2kkxTuq1bt6p9+/YeW9v37NmjjIwMNW7cWGFhYWVWc6DZsGGDzp49m+/52NjY3P+3jxw5op07d6pr164KDnZPPB8/fly7d+9W/fr1/bqrqL9btWqVCvpIadCggerVqyfJzAIfOnRInTt3zj2elZWlP/74Q5UrV1aDBg0UFBRUZjUHooyMDG3fvl2RkZGqX7++RysAyXSVTktLy7eYff/+/Tpy5IhatGjh96ftCC8AAMBRWPMCAAAchfACAAAchfACAAAchfACAAAchfACAAAchfACAAAchfACAAAchQ67AAKaZVn6/PPPCzzWqVMntWjRoowrAlBShBcAAS07O1u33nqrunfvrkaNGnkci46OJrwADkSHXQABLSsrSxUqVND//d//6Y477rC7HAA+wJoXAADgKJw2AlAu/PzzzwoN9fwr75prrlH16tVtqgiAtwgvAMqFdevW6ejRox7PXXLJJYQXwIFY8wIgoLHmBQg8rHkBAACOQngBAACOwpoXAOVCQQt2W7ZsqY4dO9pUEQBvseYFQEDLycnRbbfdVuCx/v37a/jw4WVcEYCSIrwAAABHYc0LAABwFMILAABwFMILAABwFMILAABwFMILAABwFMILAABwFMILAABwFMILAABwFMILAABwFMILAABwFMILAABwFMILAABwlP8PjEqY+GzNp4cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = S - X * interX_lm32.params[\"X\"]\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "interaction_plot(\n",
    "    E, M, U, colors=[\"red\", \"blue\"], markers=[\"^\", \"D\"], markersize=10, ax=plt.gca()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethnic Employment Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:00.671831Z",
     "iopub.status.busy": "2026-07-29T12:28:00.671554Z",
     "iopub.status.idle": "2026-07-29T12:28:00.905121Z",
     "shell.execute_reply": "2026-07-29T12:28:00.903297Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'JPERF')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/jobtest/jobtest.table\"\n",
    "jobtest_table = pd.read_csv(download_file(url), sep=\"\\t\")\n",
    "\n",
    "factor_group = jobtest_table.groupby([\"ETHN\"])\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",
    "    factor_id = np.squeeze(factor)\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor_id],\n",
    "        marker=markers[factor_id],\n",
    "        s=12**2,\n",
    "    )\n",
    "ax.set_xlabel(\"TEST\")\n",
    "ax.set_ylabel(\"JPERF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:00.908190Z",
     "iopub.status.busy": "2026-07-29T12:28:00.907920Z",
     "iopub.status.idle": "2026-07-29T12:28:00.932997Z",
     "shell.execute_reply": "2026-07-29T12:28:00.931556Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           0.000356\n",
      "Time:                        12:28:00   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": "2026-07-29T12:28:00.936238Z",
     "iopub.status.busy": "2026-07-29T12:28:00.935970Z",
     "iopub.status.idle": "2026-07-29T12:28:01.120863Z",
     "shell.execute_reply": "2026-07-29T12:28:01.119225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "for factor, group in factor_group:\n",
    "    factor_id = np.squeeze(factor)\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor_id],\n",
    "        marker=markers[factor_id],\n",
    "        s=12**2,\n",
    "    )\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": "2026-07-29T12:28:01.123461Z",
     "iopub.status.busy": "2026-07-29T12:28:01.123208Z",
     "iopub.status.idle": "2026-07-29T12:28:01.150605Z",
     "shell.execute_reply": "2026-07-29T12:28:01.149801Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           0.000204\n",
      "Time:                        12:28:01   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:ETHN      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:ETHN\", data=jobtest_table).fit()\n",
    "\n",
    "print(min_lm2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.153916Z",
     "iopub.status.busy": "2026-07-29T12:28:01.153619Z",
     "iopub.status.idle": "2026-07-29T12:28:01.358385Z",
     "shell.execute_reply": "2026-07-29T12:28:01.356648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "for factor, group in factor_group:\n",
    "    factor_id = np.squeeze(factor)\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor_id],\n",
    "        marker=markers[factor_id],\n",
    "        s=12**2,\n",
    "    )\n",
    "\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm2.params[\"Intercept\"],\n",
    "    slope=min_lm2.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"purple\",\n",
    ")\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm2.params[\"Intercept\"],\n",
    "    slope=min_lm2.params[\"TEST\"] + min_lm2.params[\"TEST:ETHN\"],\n",
    "    ax=ax,\n",
    "    color=\"green\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.361005Z",
     "iopub.status.busy": "2026-07-29T12:28:01.360737Z",
     "iopub.status.idle": "2026-07-29T12:28:01.389819Z",
     "shell.execute_reply": "2026-07-29T12:28:01.388200Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           0.000731\n",
      "Time:                        12:28:01   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",
      "ETHN           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 + ETHN\", data=jobtest_table).fit()\n",
    "print(min_lm3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.392125Z",
     "iopub.status.busy": "2026-07-29T12:28:01.391866Z",
     "iopub.status.idle": "2026-07-29T12:28:01.594203Z",
     "shell.execute_reply": "2026-07-29T12:28:01.593338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "for factor, group in factor_group:\n",
    "    factor_id = np.squeeze(factor)\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor_id],\n",
    "        marker=markers[factor_id],\n",
    "        s=12**2,\n",
    "    )\n",
    "\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm3.params[\"Intercept\"],\n",
    "    slope=min_lm3.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"purple\",\n",
    ")\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm3.params[\"Intercept\"] + min_lm3.params[\"ETHN\"],\n",
    "    slope=min_lm3.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"green\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.596748Z",
     "iopub.status.busy": "2026-07-29T12:28:01.596463Z",
     "iopub.status.idle": "2026-07-29T12:28:01.623750Z",
     "shell.execute_reply": "2026-07-29T12:28:01.622272Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           0.000451\n",
      "Time:                        12:28:01   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",
      "ETHN          -1.9132      1.540     -1.242      0.232      -5.179       1.352\n",
      "TEST:ETHN      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 * ETHN\", data=jobtest_table).fit()\n",
    "print(min_lm4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.626060Z",
     "iopub.status.busy": "2026-07-29T12:28:01.625840Z",
     "iopub.status.idle": "2026-07-29T12:28:01.874711Z",
     "shell.execute_reply": "2026-07-29T12:28:01.873936Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "for factor, group in factor_group:\n",
    "    factor_id = np.squeeze(factor)\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor_id],\n",
    "        marker=markers[factor_id],\n",
    "        s=12**2,\n",
    "    )\n",
    "\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm4.params[\"Intercept\"],\n",
    "    slope=min_lm4.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"purple\",\n",
    ")\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm4.params[\"Intercept\"] + min_lm4.params[\"ETHN\"],\n",
    "    slope=min_lm4.params[\"TEST\"] + min_lm4.params[\"TEST:ETHN\"],\n",
    "    ax=ax,\n",
    "    color=\"green\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.880871Z",
     "iopub.status.busy": "2026-07-29T12:28:01.879858Z",
     "iopub.status.idle": "2026-07-29T12:28:01.895399Z",
     "shell.execute_reply": "2026-07-29T12:28:01.892879Z"
    }
   },
   "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 ETHN 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": "2026-07-29T12:28:01.898835Z",
     "iopub.status.busy": "2026-07-29T12:28:01.898563Z",
     "iopub.status.idle": "2026-07-29T12:28:01.918017Z",
     "shell.execute_reply": "2026-07-29T12:28:01.916374Z"
    }
   },
   "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 ETHN on intercept\n",
    "table6 = anova_lm(min_lm, min_lm3)\n",
    "print(table6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.920833Z",
     "iopub.status.busy": "2026-07-29T12:28:01.920558Z",
     "iopub.status.idle": "2026-07-29T12:28:01.936902Z",
     "shell.execute_reply": "2026-07-29T12:28:01.935818Z"
    }
   },
   "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 ETHN on slope\n",
    "table7 = anova_lm(min_lm, min_lm2)\n",
    "print(table7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:01.942904Z",
     "iopub.status.busy": "2026-07-29T12:28:01.942603Z",
     "iopub.status.idle": "2026-07-29T12:28:01.954423Z",
     "shell.execute_reply": "2026-07-29T12:28:01.953318Z"
    }
   },
   "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": "2026-07-29T12:28:01.958230Z",
     "iopub.status.busy": "2026-07-29T12:28:01.957924Z",
     "iopub.status.idle": "2026-07-29T12:28:02.227208Z",
     "shell.execute_reply": "2026-07-29T12:28:02.225773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/rehab/rehab.csv\"\n",
    "rehab_table = pd.read_csv(download_file(url))\n",
    "\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": "2026-07-29T12:28:02.229327Z",
     "iopub.status.busy": "2026-07-29T12:28:02.229095Z",
     "iopub.status.idle": "2026-07-29T12:28:02.257944Z",
     "shell.execute_reply": "2026-07-29T12:28:02.256273Z"
    }
   },
   "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": "2026-07-29T12:28:02.260553Z",
     "iopub.status.busy": "2026-07-29T12:28:02.260290Z",
     "iopub.status.idle": "2026-07-29T12:28:02.277877Z",
     "shell.execute_reply": "2026-07-29T12:28:02.276285Z"
    }
   },
   "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:                Wed, 29 Jul 2026   Prob (F-statistic):           4.13e-05\n",
      "Time:                        12:28:02   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": "2026-07-29T12:28:02.280317Z",
     "iopub.status.busy": "2026-07-29T12:28:02.280067Z",
     "iopub.status.idle": "2026-07-29T12:28:02.447849Z",
     "shell.execute_reply": "2026-07-29T12:28:02.445979Z"
    }
   },
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/kidney/kidney.table\"\n",
    "kidney_table = pd.read_csv(download_file(url), sep=r\"\\s+\", engine=\"python\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-29T12:28:02.450681Z",
     "iopub.status.busy": "2026-07-29T12:28:02.450396Z",
     "iopub.status.idle": "2026-07-29T12:28:02.463438Z",
     "shell.execute_reply": "2026-07-29T12:28:02.462579Z"
    }
   },
   "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>Days</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Weight</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 38,
     "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": "2026-07-29T12:28:02.466458Z",
     "iopub.status.busy": "2026-07-29T12:28:02.466190Z",
     "iopub.status.idle": "2026-07-29T12:28:02.802209Z",
     "shell.execute_reply": "2026-07-29T12:28:02.800700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kt = kidney_table\n",
    "plt.figure(figsize=(8, 6))\n",
    "fig = interaction_plot(\n",
    "    kt[\"Weight\"],\n",
    "    kt[\"Duration\"],\n",
    "    np.log(kt[\"Days\"] + 1),\n",
    "    colors=[\"red\", \"blue\"],\n",
    "    markers=[\"D\", \"^\"],\n",
    "    ms=10,\n",
    "    ax=plt.gca(),\n",
    ")"
   ]
  },
  {
   "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": "2026-07-29T12:28:02.804377Z",
     "iopub.status.busy": "2026-07-29T12:28:02.804137Z",
     "iopub.status.idle": "2026-07-29T12:28:02.961193Z",
     "shell.execute_reply": "2026-07-29T12:28:02.957260Z"
    }
   },
   "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(\n",
    "    anova_lm(ols(\"np.log(Days+1) ~ C(Duration) + C(Weight)\", data=kt).fit(), kidney_lm)\n",
    ")\n",
    "print(\n",
    "    anova_lm(\n",
    "        ols(\"np.log(Days+1) ~ C(Duration)\", data=kt).fit(),\n",
    "        ols(\"np.log(Days+1) ~ C(Duration) + C(Weight, Sum)\", data=kt).fit(),\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    anova_lm(\n",
    "        ols(\"np.log(Days+1) ~ C(Weight)\", data=kt).fit(),\n",
    "        ols(\"np.log(Days+1) ~ C(Duration) + C(Weight, Sum)\", data=kt).fit(),\n",
    "    )\n",
    ")"
   ]
  },
  {
   "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": "2026-07-29T12:28:02.963745Z",
     "iopub.status.busy": "2026-07-29T12:28:02.963457Z",
     "iopub.status.idle": "2026-07-29T12:28:03.055661Z",
     "shell.execute_reply": "2026-07-29T12:28:03.054546Z"
    }
   },
   "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)\", 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": "2026-07-29T12:28:03.061000Z",
     "iopub.status.busy": "2026-07-29T12:28:03.060724Z",
     "iopub.status.idle": "2026-07-29T12:28:03.150215Z",
     "shell.execute_reply": "2026-07-29T12:28:03.149546Z"
    }
   },
   "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(\n",
    "    \"np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)\", data=kt\n",
    ").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 (ipykernel)",
   "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": "3.14.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}