{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models V: Categorical Predictors (2-levels)\n",
    "\n",
    "So far we've been using `ols` to perform *univariate* and *multiple* regression with *continuous* predictor variables. But often you'll also be working with **categorical** predictor variables. In this notebook we'll discuss how to build models using:\n",
    "- a categorical variable with 2 levels \n",
    "- a categorical variable with 2 levels + a continuous variable\n",
    "- a categorical variable with 2 levels, a continuous variable, and an interaction term\n",
    "\n",
    "## Slides for reference\n",
    "\n",
    "[Modeling Data V (slides)](https://stat-intuitions.com/lectures/wk5/1.html)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Let's return to the credit card dataset we used in Notebook `02_models`\n",
    "\n",
    "This is a *smaller* version of that dataset with observations from 76 different people with the following columns:\n",
    "\n",
    "| Variable   | Description                     |\n",
    "|------------|---------------------------------|\n",
    "| Income     | in thousand dollars            |\n",
    "| Limit      | credit limit                    |\n",
    "| Rating     | credit rating                   |\n",
    "| Cards      | number of credit cards          |\n",
    "| Age        | in years                        |\n",
    "| Education  | years of education              |\n",
    "| Gender     | male or female                  |\n",
    "| Student    | student or not                  |\n",
    "| Married    | married or not                  |\n",
    "| Ethnicity  | African American, Asian, Caucasian |\n",
    "| Balance    | average credit card debt        |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (76, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Income</th><th>Limit</th><th>Rating</th><th>Cards</th><th>Age</th><th>Education</th><th>Gender</th><th>Student</th><th>Married</th><th>Ethnicity</th><th>Balance</th></tr><tr><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>20.918</td><td>1233</td><td>128</td><td>3</td><td>47</td><td>18</td><td>&quot;Female&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Asian&quot;</td><td>16</td></tr><tr><td>10.842</td><td>4391</td><td>358</td><td>5</td><td>37</td><td>10</td><td>&quot;Female&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Caucasian&quot;</td><td>1216</td></tr><tr><td>29.705</td><td>3351</td><td>262</td><td>5</td><td>71</td><td>14</td><td>&quot;Female&quot;</td><td>&quot;No&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Asian&quot;</td><td>148</td></tr><tr><td>76.348</td><td>4697</td><td>344</td><td>4</td><td>60</td><td>18</td><td>&quot;Male&quot;</td><td>&quot;No&quot;</td><td>&quot;No&quot;</td><td>&quot;Asian&quot;</td><td>108</td></tr><tr><td>30.622</td><td>3293</td><td>251</td><td>1</td><td>68</td><td>16</td><td>&quot;Male&quot;</td><td>&quot;Yes&quot;</td><td>&quot;No&quot;</td><td>&quot;Caucasian&quot;</td><td>532</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>107.841</td><td>10384</td><td>728</td><td>3</td><td>87</td><td>7</td><td>&quot;Male&quot;</td><td>&quot;No&quot;</td><td>&quot;No&quot;</td><td>&quot;African American&quot;</td><td>1597</td></tr><tr><td>27.47</td><td>2820</td><td>219</td><td>1</td><td>32</td><td>11</td><td>&quot;Female&quot;</td><td>&quot;No&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Asian&quot;</td><td>0</td></tr><tr><td>15.741</td><td>4788</td><td>360</td><td>1</td><td>39</td><td>14</td><td>&quot;Male&quot;</td><td>&quot;No&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Asian&quot;</td><td>689</td></tr><tr><td>16.751</td><td>4706</td><td>353</td><td>6</td><td>48</td><td>14</td><td>&quot;Male&quot;</td><td>&quot;Yes&quot;</td><td>&quot;No&quot;</td><td>&quot;Asian&quot;</td><td>1255</td></tr><tr><td>14.084</td><td>855</td><td>120</td><td>5</td><td>46</td><td>17</td><td>&quot;Female&quot;</td><td>&quot;No&quot;</td><td>&quot;Yes&quot;</td><td>&quot;African American&quot;</td><td>0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (76, 11)\n",
       "┌─────────┬───────┬────────┬───────┬───┬─────────┬─────────┬──────────────────┬─────────┐\n",
       "│ Income  ┆ Limit ┆ Rating ┆ Cards ┆ … ┆ Student ┆ Married ┆ Ethnicity        ┆ Balance │\n",
       "│ ---     ┆ ---   ┆ ---    ┆ ---   ┆   ┆ ---     ┆ ---     ┆ ---              ┆ ---     │\n",
       "│ f64     ┆ i64   ┆ i64    ┆ i64   ┆   ┆ str     ┆ str     ┆ str              ┆ i64     │\n",
       "╞═════════╪═══════╪════════╪═══════╪═══╪═════════╪═════════╪══════════════════╪═════════╡\n",
       "│ 20.918  ┆ 1233  ┆ 128    ┆ 3     ┆ … ┆ Yes     ┆ Yes     ┆ Asian            ┆ 16      │\n",
       "│ 10.842  ┆ 4391  ┆ 358    ┆ 5     ┆ … ┆ Yes     ┆ Yes     ┆ Caucasian        ┆ 1216    │\n",
       "│ 29.705  ┆ 3351  ┆ 262    ┆ 5     ┆ … ┆ No      ┆ Yes     ┆ Asian            ┆ 148     │\n",
       "│ 76.348  ┆ 4697  ┆ 344    ┆ 4     ┆ … ┆ No      ┆ No      ┆ Asian            ┆ 108     │\n",
       "│ 30.622  ┆ 3293  ┆ 251    ┆ 1     ┆ … ┆ Yes     ┆ No      ┆ Caucasian        ┆ 532     │\n",
       "│ …       ┆ …     ┆ …      ┆ …     ┆ … ┆ …       ┆ …       ┆ …                ┆ …       │\n",
       "│ 107.841 ┆ 10384 ┆ 728    ┆ 3     ┆ … ┆ No      ┆ No      ┆ African American ┆ 1597    │\n",
       "│ 27.47   ┆ 2820  ┆ 219    ┆ 1     ┆ … ┆ No      ┆ Yes     ┆ Asian            ┆ 0       │\n",
       "│ 15.741  ┆ 4788  ┆ 360    ┆ 1     ┆ … ┆ No      ┆ Yes     ┆ Asian            ┆ 689     │\n",
       "│ 16.751  ┆ 4706  ┆ 353    ┆ 6     ┆ … ┆ Yes     ┆ No      ┆ Asian            ┆ 1255    │\n",
       "│ 14.084  ┆ 855   ┆ 120    ┆ 5     ┆ … ┆ No      ┆ Yes     ┆ African American ┆ 0       │\n",
       "└─────────┴───────┴────────┴───────┴───┴─────────┴─────────┴──────────────────┴─────────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import polars as pl\n",
    "from polars import col\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "\n",
    "# Load data\n",
    "df = pl.read_csv('./data/credit-mini.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Predictor w/ 2 levels\n",
    "\n",
    "Let's use `ols` to explore the example discussed in class in more detail: **do students have a different balance than non-students?**  \n",
    "\n",
    "We'll fit 2 models:\n",
    "- a *compact* model that only includes the intercept\n",
    "- an *augmented* model that includes a **categorical predictor** for `Student` which has **2 levels** (`Yes` and `No`)\n",
    "\n",
    "We can tell `ols` to treat a variable as **categorical** by wrapping it in `C()`\n",
    "\n",
    "Then we'll test whether it's *worth it* to add `Student` as a predictor to the model by comparing them like we have before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2.028075e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.0</td>\n",
       "      <td>1.721872e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.062040e+06</td>\n",
       "      <td>13.159573</td>\n",
       "      <td>0.000523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid           ssr  df_diff       ss_diff          F    Pr(>F)\n",
       "0      75.0  2.028075e+07      0.0           NaN        NaN       NaN\n",
       "1      74.0  1.721872e+07      1.0  3.062040e+06  13.159573  0.000523"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compact Model\n",
    "c_model = ols('Balance ~ 1', data=df.to_pandas())\n",
    "c_results = c_model.fit()\n",
    "\n",
    "# Augmented Model\n",
    "# Treat \"Student\" as a categorical variable\n",
    "a_model = ols('Balance ~ C(Student)', data=df.to_pandas())\n",
    "a_results = a_model.fit()\n",
    "\n",
    "# Compare models - worth it?\n",
    "anova_lm(c_results, a_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's worth it!  \n",
    "\n",
    "Let's inspect our augmented model's **design matrix** to try to understand how `ols` is representing our **categorical variable** `Student`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 6 rows\n",
    "a_model.exog[:5, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at our DataFrame, we can see that by default `C()` has converted: `No = 0` and `Yes = 1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Balance</th><th>Student</th></tr><tr><td>i64</td><td>str</td></tr></thead><tbody><tr><td>16</td><td>&quot;Yes&quot;</td></tr><tr><td>1216</td><td>&quot;Yes&quot;</td></tr><tr><td>148</td><td>&quot;No&quot;</td></tr><tr><td>108</td><td>&quot;No&quot;</td></tr><tr><td>532</td><td>&quot;Yes&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 2)\n",
       "┌─────────┬─────────┐\n",
       "│ Balance ┆ Student │\n",
       "│ ---     ┆ ---     │\n",
       "│ i64     ┆ str     │\n",
       "╞═════════╪═════════╡\n",
       "│ 16      ┆ Yes     │\n",
       "│ 1216    ┆ Yes     │\n",
       "│ 148     ┆ No      │\n",
       "│ 108     ┆ No      │\n",
       "│ 532     ┆ Yes     │\n",
       "└─────────┴─────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select('Balance', 'Student').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it's helpful to see the *entire* design matrix, especially as we move onto categorical variables with more than 2 levels in future notebooks. \n",
    "\n",
    "We've provided a helper function you can use that takes a model as input. You can see each observation plotted alongside how we've *encoded* `Student` in the design matrix (beige = 1; black = 0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 626
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helpers import plot_design_matrix\n",
    "\n",
    "plot_design_matrix(a_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's inspect the `.summary()` to look at the *parameter estimates* for the model `Intercept` and `C(Student)[T.Yes]`\n",
    "\n",
    "What do the *intercept* $\\hat{\\beta}_0$ and *slope* $\\hat{\\beta}_1$ here represent?  \n",
    "\n",
    "To answer this question we need to understand what it means to think about *levels* of a category in terms of the *slopes* of lines that we can estimate. And how the 0s and 1s in the design matrix relate to this..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Balance   R-squared:                       0.151\n",
      "Model:                            OLS   Adj. R-squared:                  0.140\n",
      "No. Observations:                  76   F-statistic:                     13.16\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):           0.000523\n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept           463.2368     78.252      5.920      0.000     307.317     619.156\n",
      "C(Student)[T.Yes]   401.4474    110.664      3.628      0.001     180.944     621.951\n",
      "=====================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# slim=True just removes some extra information we don't currently need to save space\n",
    "print(a_results.summary(slim=True)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Categorical Coding Schemes\n",
    "\n",
    "Let's think about about what it means to calculate a *slope* between 2 levels of a categorical variable.  \n",
    "\n",
    "We typically define the slope as the \"*change in y for 1-unit change in x*\" - this makes sense when both variables are continuous - e.g. how much a person's `Balance` (y) increases for every additional year they're alive `Age` (x).\n",
    "\n",
    "But what is a \"unit change in x\" if x is a **categorical variable** like `Student`?  \n",
    "\n",
    "Its the *difference* between going from the mean of *first* level `No` to the mean of the *second* level `Yes`!\n",
    "\n",
    "**The mean difference *is* the regression slope $\\hat{\\beta_1}$**\n",
    "\n",
    "### Treatment (Dummy) Coding\n",
    "\n",
    "While there are actually many ways to *encode* a categorical variable as columns of our **design matrix**, this intuitive idea is the default in both Python and R and is called **treatment (dummy) coding**.  \n",
    "\n",
    "It uses the intercept to estimate the *mean* of one of the levels of your categorical variable as a **reference** level, and encodes the other levels as *mean differences* from that reference level.  \n",
    "\n",
    "In the case of a variable with just 2 levels - this is just the mean difference between both levels, i.e. between `Student=No` and `Student=Yes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student: Yes = 864.684, No = 463.237\n",
      "Mean Difference (Yes - No) = 401.44737\n"
     ]
    }
   ],
   "source": [
    "# Get mean balance for each level separately\n",
    "student_yes = df.filter(col('Student')=='Yes').select('Balance').mean()[0,0]\n",
    "student_no = df.filter(col('Student')=='No').select('Balance').mean()[0,0]\n",
    "\n",
    "print(f'Student: Yes = {student_yes:.3f}, No = {student_no:.3f}')\n",
    "print(f\"Mean Difference (Yes - No) = {student_yes - student_no:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            463.236842\n",
       "C(Student)[T.Yes]    401.447368\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# beta 0 = mean of No\n",
    "# beta 1 = mean difference\n",
    "a_results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make this more concrete, let's add a new column to our DataFrame called `Student_Dummy` that represents `Student` just like our **design matrix** does:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge\n",
    "Use `when` and `lit` that we've imported for you below to create a new column in `df` called `Student_Dummy` that encodes `Yes = 1` and `No = 0` for the `Student` variable.\n",
    "\n",
    "*Hint: You can use `.alias()` at the end of your `when` statement to give your new column a name.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Income</th><th>Limit</th><th>Rating</th><th>Cards</th><th>Age</th><th>Education</th><th>Gender</th><th>Student</th><th>Married</th><th>Ethnicity</th><th>Balance</th><th>Student_Dummy</th></tr><tr><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i32</td></tr></thead><tbody><tr><td>20.918</td><td>1233</td><td>128</td><td>3</td><td>47</td><td>18</td><td>&quot;Female&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Asian&quot;</td><td>16</td><td>1</td></tr><tr><td>10.842</td><td>4391</td><td>358</td><td>5</td><td>37</td><td>10</td><td>&quot;Female&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Caucasian&quot;</td><td>1216</td><td>1</td></tr><tr><td>29.705</td><td>3351</td><td>262</td><td>5</td><td>71</td><td>14</td><td>&quot;Female&quot;</td><td>&quot;No&quot;</td><td>&quot;Yes&quot;</td><td>&quot;Asian&quot;</td><td>148</td><td>0</td></tr><tr><td>76.348</td><td>4697</td><td>344</td><td>4</td><td>60</td><td>18</td><td>&quot;Male&quot;</td><td>&quot;No&quot;</td><td>&quot;No&quot;</td><td>&quot;Asian&quot;</td><td>108</td><td>0</td></tr><tr><td>30.622</td><td>3293</td><td>251</td><td>1</td><td>68</td><td>16</td><td>&quot;Male&quot;</td><td>&quot;Yes&quot;</td><td>&quot;No&quot;</td><td>&quot;Caucasian&quot;</td><td>532</td><td>1</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 12)\n",
       "┌────────┬───────┬────────┬───────┬───┬─────────┬───────────┬─────────┬───────────────┐\n",
       "│ Income ┆ Limit ┆ Rating ┆ Cards ┆ … ┆ Married ┆ Ethnicity ┆ Balance ┆ Student_Dummy │\n",
       "│ ---    ┆ ---   ┆ ---    ┆ ---   ┆   ┆ ---     ┆ ---       ┆ ---     ┆ ---           │\n",
       "│ f64    ┆ i64   ┆ i64    ┆ i64   ┆   ┆ str     ┆ str       ┆ i64     ┆ i32           │\n",
       "╞════════╪═══════╪════════╪═══════╪═══╪═════════╪═══════════╪═════════╪═══════════════╡\n",
       "│ 20.918 ┆ 1233  ┆ 128    ┆ 3     ┆ … ┆ Yes     ┆ Asian     ┆ 16      ┆ 1             │\n",
       "│ 10.842 ┆ 4391  ┆ 358    ┆ 5     ┆ … ┆ Yes     ┆ Caucasian ┆ 1216    ┆ 1             │\n",
       "│ 29.705 ┆ 3351  ┆ 262    ┆ 5     ┆ … ┆ Yes     ┆ Asian     ┆ 148     ┆ 0             │\n",
       "│ 76.348 ┆ 4697  ┆ 344    ┆ 4     ┆ … ┆ No      ┆ Asian     ┆ 108     ┆ 0             │\n",
       "│ 30.622 ┆ 3293  ┆ 251    ┆ 1     ┆ … ┆ No      ┆ Caucasian ┆ 532     ┆ 1             │\n",
       "└────────┴───────┴────────┴───────┴───┴─────────┴───────────┴─────────┴───────────────┘"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from polars import when, lit\n",
    "\n",
    "# Solution\n",
    "df = df.with_columns(\n",
    "    when(col('Student') == 'Yes')\n",
    "    .then(lit(1))\n",
    "    .otherwise(lit(0))\n",
    "    .alias('Student_Dummy')\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the new column you made to create a figure and build our intuitions visually.  \n",
    "First, we'll use `sns.barplot` to show the mean `Balance` of each level of `Student`  \n",
    "Then, we'll ask `sns.regplot` to estimate and plot a regression line between our `Student_Dummy` and `Balance` variables; seaborn uses `ols` behind-the-scenes to do this!\n",
    "\n",
    "You'll see below that the **slope** of `sns.regplot` going from `0` to `1` **is the same as the difference between bars**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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etGgR+vXrV2dZjUaDuXPnqo+r1/6u6fnnn1e3X3rppZCzED/yyCNISUkBAKxduxaKogQtP2PGDFx77bUB90+bNg2dOnUCANhsNrUrdzRVL4llMpkwadKkqNdfLTk52e9xOJO1RVt6ejoeeeSRgPtrfoZuuOEGjBs3LmB53zWzA32GanrmmWdgMpnq3JeZmYkrr7xSfWw0GvHHP/4xYF2zZ8+G0WgEAJw4cQJWq7XOOqs/Y3a7HWvXrg1Y3+uvv65OrnXttdciIyMj9AsKoLS0VN1OSkqK+vJW//znP9XtRYsW4fLLLw9YdsSIEbjrrrvUx//4xz9qldm4cSPy8vIAAGazOeh5Ny0tDU8++WTIGGN9biYiai6YTBNRTEyZMqXWOMiavvzyS7hcLgBVyVk4y85cccUVMJvNAFDn7ME1ZWdn480338RTTz2Fhx56CPfeey9++ctfqv8sFgsAoKSkBOfOnQtZX33ceOONQfcPGjSo3uVPnz5da39BQQH++9//AgAyMjJw1VVXhYwxLi4Oo0ePBgBYLBYcOXIkaHnfhL4ukiRhyJAh6uNwZriORElJCXbv3g2gaqx19WeiMdT8XNaV+DW2mTNnBp1NumfPnn5rHzf0M1STyWTCddddF3adY8eORYcOHQKWjYuLQ+/evQEAQoiAn4/qcd8AsHLlyoD1rVq1St2+8847g8YZiu/vN9ylsMJls9n81iu/4447Qh7ju0TXvn37as38v3XrVnV7+vTpIc+7N910k3ojI5CmODeHIqp6V0btX7TPQUTUNnACMiKKiWCtLdWqkyGgKun95S9/GVbdkiQBqGohrKys9Esiqq1fvx6PP/44Dhw4EGbEQHFxMTIzM8MuH65LLrkk6P6aF78DBw4Mu3z1xEC+fN9XIUTY7+vJkyfV7XPnzuHSSy8NWHbw4MEh60tLS1O3q29aRMv69eshyzKA6C+JVVPN5DkpKalRn68uoT5DAJCSkqImWg39DNWUlZUFvV4fdp2hnj/cGKZPn44uXbrg/Pnz2LdvHw4fPlzrs7dnzx58//33AICOHTtixowZIZ87mMTERHXbZrM1qK6aDh48qH5uExISgv6NVRs6dCji4+NRWVkJWZZx8OBBv14A1TfOgKqENpSEhAQMGjQI3377bcAyjXluJiJqSZhME1FMBGuVqnb+/Hl1e9++fWF3N/VVVlZW64Jt6dKl+P3vfx9xXY3V4lizm3BNOp3/qTqS8nWtG+v7vp4/fx5/+9vfwgnTT6iuzKFiBOCXfNV3fdtAqmfxliSpwclTKDVvBLRr165Rn68u4bzfvp+Lhn6GGvv5w41Bp9NhwYIFePrppwFUtU6vWLHCr8y//vUvdXvevHkhk/5QfH+/FRUVUBQlal29fWfvzszMDKtejUaDzMxMdahEzfXEfevs1q1bWHFkZmYGTaYb69xMRNTSsJs3EcVEoLGVvqLRWun1ev0ef/HFF36J9FVXXYVXX30VBw4cQHFxMZxOp1/XP99xpaHGCddXdWtNY5WvqTHe15oaGmNDuN1ubNy4EUBVD4guXbo06vP5jvfWarVo3759oz5fXZr6M9Scnv8Xv/iFmnT++9//htvtVvfZ7Xa89dZb6uOGdvEGgB49eqjbiqLgxIkTDa6zmm9LdySJpm/Zmjf9fOsMd7hDqOduinMIEVFLwJZpImq2fC/oVqxYgV//+tcNrvPPf/6zun333Xf7TfZTl1iMf21svu/rDTfcgA8//DCG0UTftm3b1N9bY3fxBoC9e/eq28OGDQs53jQcjXXjpjXq0aMHJk+ejI0bN6KkpATr1q1Tx+y/88476mfhqquuCjhxXyRqzjGwd+/eqK2b7Dv2uObY52B8y/p2Q69Zp91uj7i+ujTGuTlS4XYtD1daWlq9eiwRUdvGZJqImq309HR1+4cffmhwfbIs46uvvgJQ1TXyD3/4Q8hjcnNzG/y8zU2039fmprqLNwBcf/31jfpc58+fx65du9THV199dZ3lfLsWh9MiF+0x5K3dXXfdpfZGWLlypZpM+3bxjkarNFCVvPfs2VOdmO3NN9/E/Pnzo1K37/CXc+fOQQgRshVfURS/yRFr9ozwrTPc81moyRabwzmkPsNTgunevTuTaSKKGLt5E1Gz5TtZTvWFckMUFxerXUA7duwYctz20aNHa40/rEssuzTXx6hRo9Tt77//Xl02pzmqz3tbnUxnZmZi6NChUY7I30svveQ3ntd3SSlfvq2FJSUlIes9fPhww4NrQ66//np1qbUvvvgCubm5+OGHH7Bjxw4AVe//TTfdFLXn+7//+z91+8svv4za72vIkCHQarUAqrpnh1PvwYMH1ZZkrVbrN0s+AL+/gT179oSsz2azhZytP9rnZiKilorJNBE1W1OnTlUnIcrJycGnn37aoPp8J/NxOBwQQgQtX9earXWJi4tTt6M9kVZj6Nmzp1+31JoTNjUnkb63hw4dwtmzZwE0fhfvr7/+Gn/5y1/Ux5MmTcLIkSPrLNuzZ09123d25brs378/rOWo6H/0er3aOqwoClavXu3XKn3LLbdEdXm0hQsXquuuCyEwf/78ev3tb9q0ye9xQkIChg8frj5evXp1yDp8l/0aOXJkrfHOEyZMULc3bNjgt052Xd5++2112atAon1urg8ujUVEzQGTaSJqtjIyMvDzn/9cfbxo0SLk5+eHdayiKH6z2AJVY+Kqly2yWCxql++67Nq1K+xk2neJp3Dji7WHH35Y3X7hhRfw5Zdfhn1sYWFhY4RUp0jf248//ljdbsxk+vPPP8fMmTPVLttms9kvsa7JN8les2ZNwHJerzcm409bg1/84hdqT4ZVq1b5vc++azFHQ1JSEl555RX18XfffYeZM2eGPc7Z7XbjqaeeqnNt7oULF6rbf/vb33Do0KGA9Rw4cAAvv/yy+njRokW1ykydOhUZGRkAqsZM+/7t11RSUoIlS5aEjD/a52YiopaKyTQRNWvLli1D586dAVQlUyNGjMB7770XcIKm/Px8vPDCC+jfvz/efvttv30ajQbTpk1THy9YsADffPNNrTreeecdTJs2DbIshzWjru+6th999JHfbMLN1c9//nNMnDgRQFUCN336dDz77LMBkwGbzYa1a9di4sSJuPfee5sszkjf2+ou3gkJCX4tctHg9Xrx1Vdf4cYbb8T06dPVFj5JkvDGG2/U6l7r6+abb1Z7RuzevRu//e1v1fWEq+Xl5WHGjBn4+uuvozKJWVvTu3dv9TN95swZFBQUAKj6DI0YMSLqzzd37lw8+OCD6uONGzfikksuwZo1awJO9FVaWorXXnsN/fr1wxNPPFHneexnP/uZ+llyu92YOnUqtm7dWqvc5s2bce2116ot4pdddhluueWWWuW0Wi2efPJJ9fFrr72GxYsX1/pbOnXqFKZOnYr8/HwYDIaQrz+a52YiopaKE5ARUbPWuXNnrFu3DtOmTUNxcTEKCgowd+5cdOzYEVdccQXS09OhKApKSkpw5MgRnDp1Kmj37ccffxzr1q2Dw+HAmTNnMGrUKIwePRpZWVlwu93YvXu32sX2rrvuQnZ2dtAWbAC47rrrYDabYbfbcfDgQQwYMADjx49HSkqK2lI2ZcoUTJkyJXpvTANptVq88847mDx5Mg4cOAC3243f/va3ePLJJzFq1Ch069YNBoMBZWVlyM7OxtGjR9WL9htvvLHJ4ozkvb1w4YK63u2UKVPqlZDWnCHY6XSivLwchYWF+O677+BwOPz2Z2RkYNWqVZg8eXLQert3745Fixbh73//OwDg2Wefxdq1azF27FjExcXh5MmT2LVrF9xuN6655hp07twZ//73vyOOv627++67sXnzZr+fRbtV2tdf/vIXdOzYEY888ggURcHZs2cxf/58LFy4ECNHjkRGRgaSkpJQUlKC3NxcfPfdd343UXxn2q5mMBiwdu1ajBs3DhcvXkRhYSEmTpyIIUOGqOOf//vf/+LgwYPqMR07dsTatWsDrqF9xx13YP369fjggw/UuFevXo0JEyYgOTkZp0+fxldffQWv14sRI0agb9++ePPNN4O+9mifm4mIWiIm00TU7I0YMQL79+/HnXfeqV4oFxUV+c3aXFN6ejr69u1b6+cDBw7E2rVrceutt8Jut0MIga+//hpff/21X7m7774bL774IqZOnRoyvqSkJKxYsQKLFi2Coig4deoUTp065VcmISGhWSXTQFUX6l27duGBBx7Aa6+9Bq/XC7vdji1btgQ8xmQy4fLLL2+yGCN5bz/55BP1Yr2+XbzDnSE4IyMDd9xxB+6//36kpqaGdcxzzz2HM2fOYMOGDQCqZlaumTDPmDEDb7zxBu67776I4qYqN9xwAzp06KB2IzYajX7dkRvDb37zG1x99dV47LHH1BZkl8ulTn5WF7PZjPnz5+OJJ56oc/+AAQOwc+dO3HzzzThw4ACAqonGfBPoapdddhneeecd9O7dO2ica9euxYIFC9Qkubi4GO+++65fmZEjR+KDDz7AY489FrSuatE8NxMRtURMpomoRejevTu+/PJL7N69G++++y62b9+Oc+fOoaysDDqdDmlpaejbty+GDx+OKVOmYPz48eoEOTXNmjULR44cwfPPP49NmzYhNzcXOp0OXbp0wZgxYzB//nyMHTs2ovjuuusuDBo0CC+//DL27NmD/Px8NVlvzkwmE/7xj3/g4Ycfxr///W9s2bIF2dnZKCkpgaIoSE5ORq9evTBkyBBcc801uPbaa9Vx500l3Pe2+gJeo9Fg+vTpDX5eSZKQkJCApKQkpKSkICsrC8OHD8cVV1yB8ePHq7MuhysuLg6ffvop1q5dizVr1uC7776DxWJBx44dMWTIEMyfPx8/+clPWtzs8M2JwWDAzJkz1cnHZs+ejXbt2jX6844ePRpbtmzBt99+i88++wxbtmzB2bNnUVxcDIfDgeTkZHTp0gWXX345xo8fjzlz5tTZKu0rKysL+/fvx3vvvYf3338f33zzDYqKigBAbf39yU9+ghtvvDGsz4zBYMB//vMf3H777Xj11Vexe/duFBcXIy0tDf369cOtt96KefPmhdXF21c0z81ERC2NJJr7lR4REVEIDocD7du3h91ux5VXXum39jO1HUII9OrVS52Z+YsvvsCkSZNiGxQREbVanICMiIhavM2bN6uTPl1//fUxjoZiZfPmzWoi3aNHD1xzzTWxDYiIiFo1JtNERNTi+Y7RbOz1pan5evHFF9XthQsXsss8ERE1KnbzJiIiohbv448/xqxZswBUTUp35swZv3XKiYiIoo0zQBAREVGLc/LkSfzjH/+ALMvIzs7GZ599pu5bvHgxE2kiImp0bJkmIiKiFmfbtm2YMGFCrZ+PGTMGW7ZsiXhWaiIiokhxzDQRERG1aAaDAf369cMTTzyBL774gok0ERE1CbZMExEREREREUWILdNEREREREREEWIyTURERERERBQhJtNEREREREREEWIyTURERERERBQhJtNERPVw5swZSJIESZLQo0ePkOVLS0vx5JNP4oorrkBqaiq0Wq16/OrVq2uVP3HiBO655x5ccsklSExMVMtKkoQzZ85E/fUQUfh69OjBv0dqUr7fAU1l9erV6nPOnz+/yZ6XqCXRxToAopbszJkz6Nmzp9/POnbsiPz8fOh04f15ybKMrl27orCw0O/np0+fDitJo9rGjx+Pr776qs59RqMRycnJSEpKQnp6OoYNG4bLLrsMEydORPfu3RslnjNnzmDs2LE4d+5cWOU//fRTzJ07F06ns1HiIWqLaq5LzcVMWiZ+7xJRc8JkmijKioqK8Nlnn2HmzJlhld+4cWOtL3RqPC6XC0VFRSgqKkJOTg527doFANBoNJgyZQp+9atf4brrrovqcy5cuFBNpE0mEyZNmoSMjAxotVoAwIABA9SylZWVmDdvnppId+7cGVdddRU6dOigtkgkJSVFNT4iopaM37tEFCtMpokaweuvvx72l/rrr7/eyNG0bSNGjMDIkSPVx4qiwGKxoLy8HN9//z3Onj2r/vzzzz/H559/jttuuw0vvfQSkpOTG/z8hYWF2LRpE4CqVvGDBw+ib9++Act/8sknKC0tBQBccskl2LdvH0wmU4PjICJqzfi9S0SxwGSaKIoGDhyIo0eP4pNPPkF5eTlSUlKClrdYLFi3bp3fsRRd06ZNw9KlSwPuLywsxBtvvIEXX3wReXl5AIA33ngD33//PXbs2AGz2VzncT169Airm+h3332nbl999dVBE+ma5W+55RYm0kTNEMdJNx/83iWiWOIEZERRdNtttwGo6kr89ttvhyz/zjvvqN15b7/99kaNjerWqVMnLF68GMeOHcPcuXPVn3/33XdR+Z2UlZWp2507d456eSKitozfu0QUS0ymiaLo1ltvVSdACacbWXUZnU6HW2+9tVFjo+ASEhLw9ttvY8aMGerP3n//fWzbtq1B9Xo8HnVbowl9yo20PBFRW8bvXSKKJV6pEUVRx44dce211wIAvv76a5w8eTJg2dOnT6uTX1133XXo0KFDxM8nhMCHH36IefPmISsrC8nJyYiLi0NmZiZuuOEGrFmzBl6vN6y6jh07huXLl2POnDno168fEhMTodfr0aFDBwwfPhz3339/2N3hxo8fry6nUZ2MlpaW4tlnn8WIESPQvn17mEwm9OrVC3feeSeOHDkS8WtvDJIkYc2aNUhMTFR/9vTTT9dZNtjSWNu2bVP3LViwQP35mjVr/JY3kSQJS5cuxdKlS9XHa9asUcsvWLCgVvlgyf3mzZuxaNEiXHLJJWjXrh2MRiO6dOmCqVOn4q9//SscDkdY70HN5VcOHjyIX//61xg0aBDatWsHSZJwww031Hl8SUkJnnvuOUyePBmZmZmIi4tDSkoKBg4ciHvuuQf79+8PGYPv+1HdRd/r9eL1119XJ28zGo3o3LkzbrjhBnz66ach66zp0KFD+O1vf4srrrgCnTp1gsFgQEJCAvr164ef/vSnWLlyJSwWS8h6ovF6I1VUVIRVq1Zh3rx5GDZsGNq1awe9Xo+UlBT0798fCxYswMaNG8OuTwiBjz76CLfeeiv69euHpKQkGAwGpKen45JLLsHkyZPxhz/8Afv27YOiKA2O3+PxYOPGjfjNb36DCRMmoEuXLoiLi4PJZELXrl0xbdo0vPDCC7DZbA1+rsYSztJYjX0ebEnn/4KCAixbtgwjR45Ep06doNVqQ3bHDldTf+8WFxfjmWeewbhx49C5c2cYjUa0b98ew4YNw+LFiyPuNu5wOPD8889j9OjRSEtLQ3x8PLKysnDHHXfgm2++iTi+arE4NxG1SYKI6u306dMCgPrP4XCId955R338xBNPBDx26dKlarl3331XOBwOv7pOnz4d9LkPHjwohg4d6ndMXf/69esnvv/++6B1zZ07N2Q9AIQkSeK+++4TXq83aH3jxo1Tj9m6davYuXOnyMjICFivVqsVr7zyStA6I+H7/EuWLIn4+HvvvdfvNZeUlNQq4/u77969u9++rVu3hvV+Vse3ZMmSsMtv3bq1Viy5ubli/PjxIY/t0qWL2L59e9DX7lteCCGWLFkitFptrbpmzZpV69i//vWvIjk5OeRn6I477hAulytgDL7vx5IlS0ReXp648sorg9a7YMECIcty0NcmhBBlZWXipz/9qZAkKeT7lZ6eHrSuaL3eSLzwwgt1/j7q+jdx4kRRXFwctL7CwkIxevTosD9/X3zxRYPiz83NFWlpaWE9V/v27cWmTZsa9Hy+av5dNkT37t3VegKdqxvzPNiSzv8fffSRSE1NrVVfcnJyWK+1plh+765cuTLk37xWqw3rfRJCiMOHD4s+ffoEfc8ff/xxIUTtc3Mw0To3rVq1Si0/b968kM9L1BZxAjKiKLv++uuRkpKC8vJy/Pvf/1Zb2Wp64403AACpqamYOXNmRGuebt++HTNnzkRFRQWAqu5qw4cPR79+/aDX63HmzBns3LkTTqcTJ06cwJVXXondu3f7LcHkKzc3V61n4MCB6Nu3L1JSUqDValFUVIR9+/YhPz8fQgisWLECLpcLf//738OK9ciRI3jkkUdgs9nQsWNHXH311UhLS0N+fj62bNkCh8MBWZaxaNEiDBo0CKNHjw77fWgsc+fOxUsvvQQAEEJgx44dmDVrVtjHZ2Rk4J577gEAHD9+HJs3bwYA9O/fH9dcc41f2eqZxqvLb968GcePHwcAXHPNNejfv3+tun0dO3YM11xzDQoKCgBUtSwPHToUl1xyCcxmM/Lz87F9+3ZYrVacP38ekydPxmeffea33m4gf/7zn/H73/8eANC7d2+MHDkSZrMZZ86cgV6v9yt7//33Y8WKFerjtLQ0jBo1Cl26dIHT6cSBAwdw5MgRCCHwr3/9C+fPn8f69etDdmW32Wy49tprceTIEZjNZlx99dXIzMyE1WrF1q1bUVRUBABYtWoV+vXrh4cffjhgXefPn8fEiRNx4sQJ9WcpKSkYM2YMOnfuDI/Hg7Nnz+Lbb7+F1WoNus53Y73eUM6fPw9ZlgEAvXr1woABA9ChQwfExcWhvLwchw8fxvfffw8A2LJlCyZNmoQ9e/bAaDTWqkuWZUyfPh3ffvut+rNBgwZh0KBBSElJgdPpRGFhIQ4ePKh+vhqqsrISJSUlAKrOfZdccgm6d++OhIQEuN1unD59Gnv27IHT6URxcTGmTZuGr776CldeeWVUnj9WonkebEnn/6+//hpLly6Fx+NBWloaxo4di/bt26OoqAgHDhyI8F0MrCm+d//yl79g8eLF6mOj0Yhx48ahW7duKCsrw9atW1FaWgpZlrFixQqcPXsW77//fp1xAMCpU6cwadIkXLhwQf3ZpZdeimHDhkGWZXzzzTfIzs7GU089hbS0tLDjjNW5iajNil0eT9Ty1XWHXAgh7r77bvVndbUE7tixQ92/cOFCIYQI+w55QUGBSE9PV8vdcsstIi8vr1a5wsJCMXv2bLXc4MGDA94p/+1vfyveeecdYbFY6tyvKIr4+OOPRYcOHdT6duzYEfB98W2ZMBqNQqvViueee054PB6/crm5uWLQoEFq2QkTJgSsMxINbZmurKwUOp1OreORRx6pVSZYy7SvSO/sz5s3Ty2/atWqkHEOGDBALT9p0iRx4sSJWuUsFotYtGiRWq5z586ivLy8zjp9P4M6nU4kJyeLDz/8sFY5p9Opbq9cuVI9JiEhQfzjH/+os7Vjy5Ytfi1zzz77bJ0x+LZMG41G9b2r2UOgsrJS3HLLLX7PbbPZ6qzT4/GIMWPGqGVNJpP461//Ktxud62yLpdLfPzxx+KGG26os65ov95IrFy5Urz00kt1/s1XO3jwoBg+fLj6vE899VSd5T788EO/z8SePXsC1nnkyBHx8MMPi7179zYo/jNnzoh7771X7N27N2BPAovFIh566CE1tr59+4bV6yCUWLZMR+s82NLO/zqdTkiSJJ566qlaf2u+55BIxOJ79+uvv/brEXLttdeKgoKCWq9n8eLFfvU999xzAV/HhAkT1HLt2rUTGzdurFVm7dq1wmQyCYPBENZnN9rnJrZME4XGZJqoAQJ9qe/atUv92S9+8Ytax911113q/q+//loIEf6X+h133BG0bl9er9fvC/utt95q0Ovds2ePWtdNN90UsJzvxRQA8c9//jNg2cOHD6tdbiVJEufPn29QjDWfvz7JtBBC9OjRQ63jjjvuqLW/OSTTTz75pN/FXc2L9Jpuv/12tfwzzzxTZxnf35skSeKrr74KWmdFRYVISUkRQFX3xlDljx49KuLi4gQAkZaWJiorK2uVqdnt/ZZbbglYn8PhEJmZmSE/46+++qrfBX6o7u6BNMbrbQzl5eWiU6dOaqJcVyL14IMPqu/Jq6++2iRxRcL3BtCGDRsaXF8sk+lonQdb4vn/D3/4Q4Oet6ZYfO+OHTtWLTNq1KigXaN/9atfqWWTkpJERUVFrTIbN270O88GOx+99dZbfjEG+uw2xrmJyTRRaEymiRog0Je6EEIdB5WcnOz3c4fDoX7h9e3b1+/nob7Ui4qK1Ja65ORkYbVaQ8a4e/dutc6ZM2c27AULobaEpqWlBSzjezE1ePDgkHWOHDlSLf/JJ580OMZoJNNDhgxR65g9e3at/bFOpt1ut+jYsaMAIDQajThz5kzIuvPz89UL9kC/F9/PYLAL5morVqxQy995550hywshxMKFC9Vj3n///Vr7fZNpg8FQqwWopt/85jdq+QcffLDOMv3791fLPPzww2HFWZfGeL2N5f/+7//U5z106FCt/b7JxUcffdRkcYVr7969anwPPPBAg+uLZTIdjfNgSzz/Z2RkhLzJF6mm/t49evSoX5lvv/02aHw2m020b99eLf/yyy/XKuM7Tv3mm28O+ZqvvvrqkJ/dxjg3MZkmCo1jpokayW233YYlS5bAYrFg3bp1+OlPfwoAWLduHcrLy9Uykfjyyy/hcrkAADNmzEBCQkLIY6644gqYzWbY7Xbs3LkzZPns7Gzs378fJ0+ehMVigcvl8htXVj3DcUlJCc6dO4fMzMyg9fmu3RzIsGHD1FlLA82M29R831ur1RrDSOq2f/9+dbzw6NGj0b1795DHdOnSBf3798exY8dw5MgRlJeXB51R9+abbw5Z54YNGyIqDwATJ07EP//5TwDAzp07MWfOnIBlr7rqKnTq1ClofcOGDVO36/r8nD17Vh2HDgC//OUvw4qzLo39eiNRVFSEPXv24NixYygrK0NlZaXf36rvbL3//e9/MXjwYL/ju3Xrpm7/85//xPTp09UlhpqCx+PB3r17cfDgQRQWFsJqtfrNPu37d/ff//63yeJqDNE4D7bE8/+NN97YpJ+pxvje3bp1q7o9ZMgQXHbZZUHLx8fH45ZbblHn3di6dSsWLlzoV8Z3VYZw4rn99tuxY8eOoGWa07mJqC1hMk3USG677TYsXboUQgi8/vrr6pd69RqXkiRF/KW+e/dudTs7OzvspKB6ApTqC+74+PhaZdavX4/HH388oklhiouLQ15M1byAr4vv5CrhLEfUFHwv5JOSkmIYSd18PwvFxcVhfxaqLyiFEMjPzw+aTF9++eURxfH666/jo48+CnlMXl6eun3u3LmgZaPx+dmzZ4+63bdvX3Tt2jVknYE09usNx9GjR/Hwww/js88+UycjC6W4uLjWz37yk59gyZIlUBQFn332GQYOHIgFCxbguuuuw6WXXtpoExI5HA4sW7YML7/8cp1x1SXccs1VND7HLfH8H845JJoa43vX9z0ZM2ZMWMeMGTNGTaa/++47v335+fm4ePGi+viKK64IWd+oUaNClmkO5yaitojJNFEj6dmzJ6666irs2LEDmzZtUmfs3LRpEwDg6quvrrU+cSjnz59Xt/ft24d9+/ZFHFdZWVmti6mlS5eqszZHIpwW2+Tk5JBlfGeG9ng8EcfRGHwvZtu1axfDSOrm+1k4ceKE3wzV4SorKwu6P9QarDabze8zUD1TbjRjiMbnx3e23F69ekUQnb+meL2hbNy4EbNmzVJbKMNV199q//798dxzz+GBBx6AEAI//PADHn30UTz66KNISkrC6NGjMX78eMyePRv9+vVrUNzVysrKMHHixIhbmptj75BIRONz3BLP//VZx7khGuN71zfxDacHEAC/56h5I8i3PrPZHNZM3aFuWjSHcxNRW8V58Ika0bx58wAAXq8Xb775Jt588021G2P1vkhEo9XWtxslAHzxxRd+F1JXXXUVXn31VRw4cADFxcVwOp0QVfMrQAiBcePGqWUVRQn5fIGWBWnOKisr/e7Yh+pmHAuN8VmoyWQyxTyGaHx+fC8yw+kaG0hTvN5gLl68iJ/+9KdqIt2zZ088++yz2LVrF86fPw+73Q5FUdS/1SVLlqjHBvpbve+++7B9+3ZMnTrVryW6oqICGzduxCOPPKIu6Xb48OF6x17tnnvuURNpo9GIhQsX4uOPP8YPP/ygdvOujv/06dMh428povE5bonn/1DnkMYQ7e9dm82mbtfVql8X33I1bzr41mc2myOury6xPjcRtWVsmSZqRHPnzsW9994Lh8OB119/XR17ZjKZ8JOf/CTi+ny/UFesWIFf//rXDY7xz3/+s7p99913q+OnAmnpLUTh2L9/v1/32XC62DU138/Cfffdh+XLl8c0BqCqC3k4LXBNLTExUd32vZCNVKxf76uvvqpeNA8bNgzbt28PenMg3L/Vq666Cp9//jmKi4vx1VdfYceOHdixYwf++9//qgnTli1bcMUVV+CLL74Iu6trTfn5+XjrrbcAAFqtFps2bcLYsWMbHH9bwfN/eKL9vev7N1ZZWRnWMb7lfM8/Neuz2+0R11eXWJ+biNoytkwTNaKkpCTMmjULQNUEOgcPHgQA3HDDDfUah5uenq5u//DDDw2OT5ZlfPXVVwAAjUaDP/zhDyGPyc3NbfDzNnfvvPOOuq3RaOqdPDSmaH8W6iMlJQVGozHmcYTi+175tnZGKtavd/Pmzer27373u5Ct7GfPno2o/vbt2+PGG2/EihUr8O233+LChQt48cUX0b59ewBVY51rTqQUiS1btqiJzbRp04Im0kDk8bd2PP+HJ9rfu75d1cN9/b6f3eq/n7rqs9vtKCkpCVlfqPHMsT43EbVlTKaJGtntt98e1s/C4TtRycaNG+sdU7Xi4mK43W4AQMeOHUOObzt69GiLnwgolOLiYnWyGgCYOnVq0Em6YsX3s/DVV19FPIY2WkaOHKluR+Mz2Rh8exZkZ2f7deGPVCxfr++Y2UsuuSRoWVmWsWvXrgY9X/v27XHvvffi448/Vn/2/fff49SpU/WqL5L4AWD79u31ep7Wiuf/8EXze9d3tYCvv/46rGN8//Zqzv6dkZHh9177TpAYSDhlWsK5mKg1YjJN1MimTJniN+a2c+fOmDx5cr3qmjp1qrrMSE5ODj799NMGxeY7RtLhcPgtgVKXf/zjHw16vuZOCIF58+b5dQV+7LHHYhhRYGPGjFGTfJvNhldffTUmccyYMUPdfvnll+F0OmMSRzDdu3fHgAED1Md/+9vf6l1XLF+v799rqO6hH330EQoLC6PyvKNHj/abhM93QrdIRBK/3W73u6lFPP9HIprfuxMnTlS3Dxw4oLZ0B+JwONThDDWPrzZ+/Hh1+9///nfIGNasWROyTEs4FxO1RkymiRqZVqvFjh071NlXt2/fDq1WW6+6MjIy8POf/1x9vGjRIuTn54d1rKIofrOIAlVLsVR3e7NYLGqXv7rs2rWrRV1MRcpms+Hmm2/2W6vzZz/7WbPs4g1UTd503333qY8fffTRiCaIqm9CVNPChQvVpD4vLw//7//9v5AX5dWKi4vDXtqpoR544AF1+7nnngu5ZmsgsXy9vjORr1u3LmC5ixcv4v777w8rnnCUlZX53WCq7wzNvvGvX78+6IRHDz74YNQ+o60Fz//hi+b3bv/+/f2GJNx7771BV514/PHHUVRUBKCqy/mtt95aq8ydd96pbr/99ttBz0dvv/12WGuEt5RzMVFrw2SaqAn06dMHw4cPx/Dhw9GnT58G1bVs2TJ07twZQNWEPiNGjMB7770XcGbV/Px8vPDCC+jfvz/efvttv30ajQbTpk1THy9YsADffPNNrTreeecdTJs2DbIshz2baUtRWFiIv/zlLxg4cKDfWOkrrrgCr732WgwjC+3BBx9Uu8tarVZ1Jt7qrps1lZSU4LXXXsPll1/uN/FQQyQnJ/tNfrZq1SrMnDkTx48fr7O8EAK7d+/GL3/5S3Tv3h0OhyMqcYQyf/58XHnllQCqlh269tpr8fe//73Oi2K3241PPvkEs2fPrrUvlq/Xt+XpmWeeqbNF67vvvsO4ceNw7ty5kH+rN910E6ZPn45333034ARHubm5uPnmm9XPVN++fet9Dps4caI6e/HJkycxf/58dd3zahUVFbj77rvx8ssvt7pzTTTw/B++aH7vPvPMM2oyvmPHDtx4441qwlzN7Xbjsccew3PPPaf+bMmSJXXObTBlyhR1ZnQhBG644QZ1+S5fb731FhYsWACDwRAyxpZyLiZqbTibN1EL07lzZ6xbtw7Tpk1DcXExCgoKMHfuXHTs2BFXXHEF0tPToSgKSkpKcOTIEZw6dSro3enHH38c69atg8PhwJkzZzBq1CiMHj0aWVlZcLvd2L17tzpp01133YXs7OygLRjNzYYNG/xa4BRFQUVFBcrLy3H06NE6J6S644478MILLyAuLq4pQ41YQkICPv74Y0yaNAmnT59WE5HFixdj9OjRyMjIgCRJKC0txbFjx3DixAn1onvChAlRi2P+/Pk4deoUnnrqKQBVrY4bNmzAoEGDMGjQICQlJaGyshL5+fk4cOBArQSqKeh0Orz99tuYOHEifvjhB9jtdtxzzz147LHHcNVVV6Fz587weDw4e/Ysvv32W1RUVAScDTdWr3f+/Pl4/vnnkZ2dDZfLhdtuuw3Lli3DkCFDEBcXhyNHjmD//v0AgCFDhmDq1Kn405/+FLA+RVGwYcMGbNiwAXq9HoMGDUJWVhaSk5NhtVpx9uxZ7NmzR/3MaLVavPjii/WOPzU1FQ899BCefPJJAMB//vMffPbZZ7jiiiuQkZGBgoICbNu2DZWVldBqtfj73/9er6WMwjV06NCwy3bp0sWv10qs8PwfG6NHj8YzzzyDxYsXAwA++eQTdOvWDRMmTEBmZibKysqwbds2v++a2bNnB+whIkkSVq5cidGjR+PixYsoLS3F1KlTMXToUAwdOhSyLOObb77BiRMnAFTN3u7bEymQlnAuJmp1BBHV2+nTpwUA9Z/D4ah3XQ6Hw6+u06dPBy1/5swZcc011/gdE+xfenq6+Pzzz+us66OPPhJmszno8XfffbdwOp1i3Lhx6s+2bt1aZ33hlPG1ZMkStfySJUtClg/F9/nD/afRaMS0adPEpk2bwnoO39999+7dA5ZbtWqVWm7evHkh6503b55aftWqVWHFUlJSIubOnSskSQrrtaakpIjVq1fXWZdvuUi9/fbbokuXLmG/5yNHjhROp7NWPZF+HrZu3aqWHzduXNCyJSUlYvbs2WHFl5GR0SSvNxInTpwQvXr1Cvo8Y8aMEXl5eSHfxxkzZoQde8eOHcVHH33UoNiFEMLr9Yrbb7895Ofzww8/DPtvLFy+n5NI/9V8/u7du6v7Ap2rG/M82JrO/5GK5ffua6+9JpKSkoK+V1qtVvz6178WXq835PMfPHgw6N+zJEni0UcfFUJEdm6O1rkp0u8voraILdNELVT37t3x5ZdfYvfu3Xj33Xexfft2nDt3DmVlZdDpdEhLS0Pfvn0xfPhwTJkyBePHj1cnr6lp1qxZOHLkCJ5//nls2rQJubm50Ol06NKlC8aMGYP58+eHXMampTAYDEhKSkJycjLS09MxbNgwDB8+HNdccw0yMzNjHV69tGvXDu+88w6OHDmCtWvXYtu2bTh9+jRKSkqg0WiQkpKCPn364LLLLsOkSZMwefLkRml1v+mmmzBr1iy89dZb2LhxI/bt24eLFy/CZrMhPj4eGRkZGDBgAK6++mpMmzYNWVlZUY8hlHbt2uGDDz7Avn378Oabb2Lbtm3Iy8tDWVkZTCYTunbtiqFDh+Laa68NuSZtLF5vVlYWDhw4gL/97W/44IMPcOLECbjdbnTq1AmDBw/Grbfeirlz5wb8W/f18ccf48CBA9i8eTP27t2LY8eOIS8vD5WVlTAajejQoQMuvfRSTJs2Dbfeemu9lhWqSavVYs2aNZg7dy5eeeUV7N27F2VlZUhNTUW3bt0wa9Ys3HHHHejSpQvOnDnT4OdrrXj+j40777wTs2bNwquvvorPPvsM2dnZKC0tRWJiIjIzMzFp0iTccccdGDhwYFj1XXrppTh8+DD+8Y9/4J133lF7nVS/9wsXLlSHp0SiJZyLiVoLSYgwZycgIiIiIiIiIgCcgIyIiIiIiIgoYkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCLEZJqIiIiIiIgoQkymiYiIiIiIiCKki3UA1Hp4vV4UFhYCADp16gSdjh8vIiIiIiJqndgyTVFTWFiIzMxMZGZmqkk1ERERERFRa8SmQyIiIiKiFuBQXjke+/AI8svskAXQNTUOLo9AucONSpcMIQQkSVLLJxh1SDbpYdRLyCtzQitJyEg1YdnswRjcNTmGr4SodWAyTURERETUAqw/XAC3V4HDoyA9yQiNpIHJAJgMJshK1c91GgkaSYJOW/V/tVSzHhcqXHB7Faw/XMBkmigK2M2biIiIiKiZs7u92HOyBBaHB1qNhATj/9rEhBCQFUCvlRCn18Kg0/gl0kBVK7VWI8Hi8GD3yWLY3d6mfglErQ6TaSIiIiKiZq7Y6oYiAJdXhtmg9evO7VUEhBBBj5ckCWaDFi6vDEUAJTZ3Y4dM1OoxmSYiIiIiauacXhkAoAj4tTrLioCiBE+kq2kkCdVFHR456jEStTUcM01ERERE1MzF6bQAAI0EKD+2QitCwOP1Ao6KqkISoGj/11YmmZIgabTqY0UIaH7Mw036//2ciOqHyTQRERERUTPXPtEAjQQYdVrYXF4oigKvAsBRAWXri2o5p88xcZPvgxSfCqBqXLXdLSPBqINGIyEtwdC0L4CoFWI3byIiIiKiZs5s0GFU7zQkm/SQFQGL0xNynLQvm8sLWRFINukxulcazAa2qRE1FJNpIiIiIqIWYPrgzjDoNDDqJJTbvVAQXjKtCIEyuwemH2f6nj64cyNHStQ2MJkmIiIiImoBBmckIyPFhGSTAV5FQYnNHTKhVoTAhQonPLKCtAQDuqWZMSgjqYkiJmrdmEwTEREREbUAigBuvaIbkkw6dEgwwuGRUWRxBiyfX+7A2ZJK2N0yOifHIcWsx/2TsvyW1SKi+mMyTURERETUAhRZneiaasLCcb0Rp9dAAwkuWQlYvtLlhcOjQCNJMBm0eHTaAPTpmNCEERO1bm02mS4qKsKnn36KJ554Atdddx3at28PSZIgSRLmz58fcX2ff/455syZg65du8JoNKJr166YM2cOPv/887DrsNvt+POf/4yRI0eiXbt2SEhIwIABA/DQQw8hNzc37Hq+//57LFq0CH369IHJZEKHDh0wduxY/POf/4TX6434tRERERFRbJXb3XC4G7A2dPhzlRFRmCQRyTSArUiw7i3z5s3D6tWrw6pHCIFFixbhlVdeCVjm7rvvxssvvxz0OU+ePInp06fjxIkTde5PTk7Gm2++iWnTpgWNZ+XKlbjnnnvgcrnq3D9q1Ch8+umnSEtLC1pPfeTl5SEzMxMAcO7cOXTt2jXqz0FERETU1jg9MgosTgghkFtqx3ObTqDc7kGR1QmDy4qO39V9HVoyYiG8xmTIioIuKSa0izfgj3MuZes0UZS02ZZpX5mZmZgyZUq9jv3d736nJtLDhg3D2rVr8c0332Dt2rUYNmwYAOCVV17B448/HrAOm82GGTNmqIn0XXfdhc2bN+Prr7/G008/jYSEBFgsFsydOxeHDh0KWM/GjRtx9913w+VyIT09HS+++CL27t2Lzz77DHPmzAEA7NmzB3PmzIGiBO4SRESNw+72IrfEjuwLVuSW2GF3s6cIEREFpygCF60uCCEghMCqXWdQ4fDios0Fs0GHjsnGgMdmpJjQPc2MeKMOBRYnyu0eLP8yO6IltYgosDbbMr1kyRKMGDECI0aMQHp6Os6cOYOePXsCCL9lOicnBwMGDIDX68Xw4cOxfft2mEwmdb/dbse4ceOwf/9+6HQ6HD9+HL17965Vz9KlS/H73/8eAPCnP/0Jixcv9tu/e/dujB07Fl6vFxMmTMCWLVtq1eH1ejFgwADk5OQgKSkJ3333Xa3nuueee/D3v/8dALBmzRrcfvvtIV9jJNgyTVSbEAKH8y1Yf6gAe06VQPE542okYFTvNEwf3BmDM5I5IQwREdVSVOGEzVV18/VEoRXLv8jGhQonZAF0SjZCslugbH2xzmPjJt8HTXwqFCGQV+aAVpKQkWrCstmDMbhrclO+DKJWqc22TP/+97/HjBkzkJ6eXu86li9fro5Bfumll/wSaQAwm8146aWXAFQluytWrKhVh8fjwQsvvAAAGDBgAB588MFaZUaPHo0777wTALB161Z8++23tcp8+OGHyMnJAQA88sgjdSbtf/7zn5GamqpuE1Hjyimy4ZdvHsBjHx7BthMXcaHChbwyO3JL7cgrs+NChQvbjl/EYx8ewS/fPICcIlusQyYiomakwulRE2kA2JZdBI8s4PQqSDbpoEF4N2E1koRUsx4Ojwy3V8H6wwWNFTJRm9Jmk+mGEkJg3bp1AID+/ftj1KhRdZYbNWoU+vXrBwD46KOPanWr2bZtG8rLywFUtYhrNHX/SnwnRfvggw9q7f/oo4/qLOvLbDbjpptuAgAcOXIEP/zwQ53liKjhDuSW4ZEPDiH7ghX5PybQNpcXeq0GJr0Weq0GNpcXuaV25JdVdf1+5INDOJBbFuvQiYioGXB5ZZTY3Opjp0fGwdxyWJ0eaCUJZoM2ovoSjDpoNRIsDg92nyzmUCOiKGAyXU+nT59Gfn4+AGDcuHFBy1bvz8vLw5kzZ/z27dixo1a5ugwfPhzx8fEAgJ07d9baX11Pv3790KlTp5CxBKqHiBoup8iGZRuOobTSjfxyB2QBpCcZ0SPNjPSkOHRINCI9Ke7Hx0bIomot0NJKN5ZtOMYWaiKiNk5RBIoqXH6NMGWVHigCcMsK4gwaSGG2SleTfkzAXV4ZioBfok5E9aOLdQAt1bFjx9Tt/v37By3ru//YsWPq2OxI6tHpdOjduzcOHTrkdwxQNYFZXl5evWKJRPVzBFJQwC5DREIILP8iG+V2DwosTpgNWqQnxUFTx3hoSZKQGKdHvFGHCxVOFFic0EgSln+Zjb/eMoxjqImI2qiLNhc8NdaPdslVy2IpAmF3765JI0nq3B0OTwOW2SIiAEym6+3cuXPqdqiJtqon5ap5nO/j+Ph4pKSkhKzn0KFDuHjxIlwuF4zGqtkb8/Ly1DuXDYklFN9jiahuh/MtyC21o8Tmgl6rCZhI+9JIEtKT4pBX5kCJzY3cEjuO5FdwchgiojbIYveg0lW7C7ZRW9WtWyMBSj0XjVaEgObHrySTPrJu4kRUG7t515PValW3ExKCr9VX3T0bqGpFrqueUHUEqydasRBRw60/XAC3V4HDoyDVrA+ZSFfj5DBEROT0yCi11939OjVeD40EGLQaON0KRIQJtRACdrcMo04LjUZCWoIhGiETtWlsma4np9OpbhsMwU9G1S3IAOBwOOqsJ1QdweqJViyhhGrJLigowMiRIyOqk6g1sbu92HOyBBaHB1qNhARjZKfYBKMOxRq33+QwZgNP00REbYFXVmqNk/YVp9diSLcUfHOqDFaXF3a3jPgIviNsLi9kRSDZpMfoXmn8fiGKAv4V1VNcXJy67XYHn8DB5XKp2zWXz6quJ1QdweqJViyhcN1oouCKrW4oomoGVrNBG/GY57omhzG342maiKi1UxSBwgonvIoStNz4rI44cLYccToNLA4vTAZtWKOnFSFQZvfApNfCoNNg+uDO0QmcqI1jN+96SkxMVLdDdZeurKxUt2t2w66uJ5wu14HqiVYsRNQwTq/P5DD1nDyMk8MQEbUtQghcsDrh9gZPpAEgKz0BnVNMSDEb4FUUlNjcIcdPK0LgQoUTHllBWoIB3dLMGJSRFK3wido0JtP15NtKG2qWa9/u0TUn8aqup7KyUl1vOlQ9HTp08OuuHa1YiKhh4nQ+k8ME6KYXCieHISJqW4ptbjjc4d08lSQJC8b0QJJJhw4JRjg8Mi5aA/dKrHR5kVfmgN0to3NyHFLMetw/KYurRRBFCZPpeho4cKC6ffz48aBlffcPGDCgXvV4vV6cPHmyzjoSEhLUxLghsRBRw7RPNEAjAUadFna3HHDcWyCcHIaIqG0pt7thdXoiOqZbOzMWjuuNFLMe6Ylx0AZJjIttbmglCRkpJrSLN+DRaQPQpyN7JhJFC5PpeurZsye6dOkCAPjqq6+Clt2+fTsAICMjAz169PDbd9VVV6nbwerZv3+/2kV7zJgxtfZX13PixAkUFhYGrMf3Oeqqh4jqz2zQYVTvNCSb9JAVAVsdS5sEw8lhiIjaDpvLi9LK0HPm1GVg5yQ8OKUferSPR/sgN167pJiQkWpCVqdE/HHOpRjWLbW+4RJRHZhM15MkSZg1axaAqtbePXv21Fluz549amvwrFmzanWrGT9+PJKTq9aSXbNmTcCWrNWrV6vbs2fPrrX/hhtuqLOsL7vdjnfeeQdAVYt4VlZWneWIqP6mD+4Mg04Dk16DMrsn7O7enByGiKjtcHpkXLS6QhcMols7M56YMQC/uKpXwDKje6dh2ezB+Ostw9giTdQImEw3wH333Qedrqrl6N5776211JTD4cC9994LANDpdLjvvvtq1WEwGPCrX/0KAHDs2DH85S9/qVVm9+7dWLlyJQBg3LhxGDFiRK0ys2fPRu/evQEAf/zjH9Uu4b4WL16MsrIydZuIom9wRjK6tTMjLcEIj6zgQoUzZELNyWGIiNqO6u+GSIcC1UWSJPTsYA64///G98bgrskcI03USNpsH8KdO3ciJydHfVxcXKxu5+Tk1GrdnT9/fq06srKy8NBDD+GZZ57B/v37MWbMGDz88MPo3bs3Tp48iWeffRYHDhwAUJW89u3bt85YFi9ejLfffhvZ2dn4zW9+g5ycHNx8880wmUzYunUrli1bBq/XC5PJhBUrVtRZh16vx4svvoiZM2eioqICY8aMwe9+9zuMHDkSZWVlePXVV/H+++8DqOoSftttt0XwbhFRuCRJwv2Ts/DIB4egCIECixN5ZQ6kmvVIMOr8LmiEqOoKXmb3wCMrnByGiKiVUxSBQosTstLwRJqIYk8S0bgt1gLNnz8fa9asCbt8oLdJURTcdddd+Ne//hXw2DvvvBOvvPIKNJrAHQFycnIwbdo0/PDDD3XuT0pKwn/+8x/MmDEjaJyvvvoqfvnLXwZcb3rkyJFYv3492rdvH7Se+sjLy1MnQjt37hzXpaY27UBuGZZtOIZyuwclNjccHhlaTdU60lXLX1VNNiYrAia9FmkJBqSY9Xh02gCOaSMiaoWEqFpLOtyZu8NVXHQBj/zq7jr3/ec//0GnTp2i+nxE9D/s5t1AGo0GK1euxPr16zFr1ix06dIFBoMBXbp0waxZs7Bhwwa89tprQRNpAOjTpw8OHDiAZ599FsOHD0dKSgrMZjP69euH+++/H4cOHQqZSAPAXXfdhW+//RZ33XUXevXqhbi4OKSlpeGqq67CP/7xD+zatatREmki8jesWyr+OOdSZKUnIiPVhG7tzEgw6uCRFTg8MjyyggSjDt3amTk5DBFRG3DR5op6Ik1EsdVmW6Yp+tgyTVSbEAJH8ivw6eHz2HOyBL49+zQaCaN7pWH64M4YlJHErt1ERK1Uud1d75m7Q2HLNFHstNkx00RETUGSJAzumozBXZNhd3vVLt/VXbu5/BURUevWkCWwiKh541UcEVETMRt0MLfjaZeIqK2IxhJYRNR8ccw0EREREVGURXMJLCJqnphMExERERFFkcwlsIjaBCbTRERERERRIoTAhQonPLIS61CIqJExmSYiIiIiipKLNhecHi6BRdQWMJkmIiIiIoqCsko3bE5vrMMgoibCZJqIiIiIqIGsTg/K7FwCi6gtYTJNRERERNQAlS4vl8AiaoOYTBMRERER1ZPTI6OIiTRRm8RkmoiIiIioHlxeGYUWriVN1FYxmSYiIiIiipBHVnDB4oLCRJqozdLFOgAiIiIiopbE7VVQaHHCq3AtaaK2jMk0EVETsbu9KLa64fTKiNNp0T7RALOBp2EiopbE6ZFxocIJWWGLNFFbx6s4IqJGJITA4XwL1h8qwJ5TJfC99tJIwKjeaZg+uDMGZyRDkqTYBUpERCE5PVVjpNm1m4gAJtNERI0mp8iG5V9kI7fUDrdXgcXhgcsrQxFVibRRp8W24xfxdU4JurUz4/7JWejTMSHWYRMRUR3sbi8uVLg42RgRqZhMExE1ggO5ZVi24RjK7R6U2FxweBRoNRLMBi00kgRFCNhcXlgcHpj0Gjg9Mh754BAenTYAw7qlxjp8IiLy4fLKKGIiTUQ1cDZvIqIoyymyYdmGYyitdCO/3AFZAOlJRvRIMyM9KQ4dEo1IT4r78bERsgDyyx0orXRj2YZjyCmyxfolEBHRj7yctZuIAmAyTUQURUIILP8iG+V2DwosTpgNWnRNNSExTl9rTLQkSUiM06NrqglmgxYFFifK7R4s/zKbrR9ERM2AEAIXrC7O2k1EdWIyTUQURYfzLcgttaPE5oJeq0F6Uhw0ISYW00gS0pPioNdqUGJzI7fEjiP5FU0UMRERBVJkdcHlkWMdBhE1U0ymiYiiaP3hAri9ChweBalmfchEuppGkpBq1sPhkeH2Klh/uKCRIyUiomBKbC5UuryxDoOImjEm00REUWJ3e7HnZAksDg+0GgkJxsjmeEww6qDVSLA4PNh9shh2Ny/iiIhiweLwwOLwxDoMImrmmEwTEUVJsdUNRVTN+mo2aCNeN1qSqmb7rl4+q8TmbqRIiYgoELvbixKbK9ZhEFELwGSaiChKnN6qcXVV60hHlkhXq1o2q2rbwXF6RERNqnoJLCKicDCZJiKKkjidFgCgkVDvJVQUIaD5MQ836bXRCo2IiELgElhEFCkm00REUdI+0QCNBBh1WtjdcsTLWwkhYHfLMOq00GgkpCUYGilSIiLyJSsChRVOLoFFRBFhMk1EFCVmgw6jeqch2aSHrAjYIpwF1ubyQlYEkk16jO6VBrMhsgnMiIgockIIXKhwwu1lIk1EkWEyTUQURdMHd4ZBp4FJr0GZ3RN2d0FFCJTZPTDptTDoNJg+uHMjR0pERFWJtAtOzlFBRPXAZJqIKIoGZySjWzsz0hKM8MgKLlQ4QybUyo+tIh5ZQVqCAd3SzBiUkdREERMRtV0XbS4uQ0hE9cZkmogoiiRJwv2Ts5Bi1qNzchzsbhl5ZQ5YnZ5aY6iFELA6Pcgrc8DultE5OQ4pZj3un5QV8bJaREQUmRKbCzYnE2kiqj8OyCMiirI+HRPw6LQBWLbhGDSShBKbGxcqXCjWuGE2aH9c/qpqsjFZETDpteiYYkKKWY9Hpw1An44JsX4JREStWrndDYvDE+swiKiFYzJNRNQIhnVLxR/nXIrlX2QjV2+H26vA4vDA5ZV/XIcaSDDqkGzSw6DToFuaGfdPymIiTUTUyCqcHpRWumMdBhG1AkymiYgaSZ+OCfjrrcNwJL8Cnx4+jz0nS6D49PTWaCSM7pWG6YM7Y1BGErt2ExE1skqXF8VWV6zDIKJWgsk0EVEjkiQJg7smY3DXZNjdXpTY3HB4ZJj0WqQlGLj8FRFRE3F6ZBQxkSaiKOJVHBFREzEbdDC342mXiKipubwyCi3OWhNBEhE1BGfzJiIiIqJWyyMruGBxhVymkIgoUkymiYiIiKhV8soKCi1OeBUl1qEQUSvEZJqIiIiIWh1FESiscMIjM5EmosbBZJqIiIiIWhUhqhJpt5eJNBE1HibTRERERNSqFFldcHrkWIdBRK0ck2kiIiIiajWKbS5UuryxDoOI2gAm00RERETUKpTb3ahweGIdBhG1EUymiYiIiKjFszo9KK10xzoMImpDmEwTERERUYvmcMsotjGRJqKmxWSaiIiIiFosj6ygyOqEECLWoRBRG8NkmoiIiIhaJEURuFDhhKwwkSaipsdkmoiIiIhapIs2F9eSJqKYYTJNRERERC1OaaWbS2ARUUwxmSYiIiKiFsXm8qLczgnHiCi2mEwTERERUYvh9Mi4aHXFOgwiIibTRERERNQyeGUFRRUuztxNRM0Ck2kiIiIiavaEELhgdcGrcMIxImoemEwTERERUbN30eqCyyPHOgwiIhWTaSIiIiJq1srtbtg4czcRNTNMpomIiIio2apwelBayZm7iaj5YTJNRERERM1SpcuLYs7cTUTNFJNpIiIiImp2HG4ZRUykiagZYzJNRERERM2KyyvjQoWTS2ARUbPGZJqIiIiImg23V0GhxQmFiTQRNXNMpomIiIioWfDKCi5UOCErTKTDIYTAqYuVAff/fVsODuWVs4WfqJHoYh0AEREREZGsCBRYnPDISqxDaRFyS+34167TOJObD32AMhsOFeDrfC+yOiXigcn90KdjQpPGSNTaMZkmIiIiophSFIHCCibS4TpaUIEXvsxGfpkT3konMgOUszg8KCuxo7DChdxSO56aNQjDuqU2aaxErRm7eRMRERFRzAghcMHqhMsjxzqUFiG31I7nv8jG2RI77B4ZwXrEC1TdqKh0e5FzwYZHPzyMnCJbk8VK1NoxmSYiIiKimCmyuuBwM5EOhxACf92ag7wyO9xyVRYtBSmv12ggARACcHkVnC6uxFOfHuUYaqIoYTJNRERERDFx0epCpcsb6zBajBMXrDheYIXHW5UMawDotIHTaa1GgkGvgVaqaqV2exX891w5DudbmiZgolaOyTQRERERNbkSmwtWpyfWYbQoHx7Ih8srQ6CqRVqnkyAFbZsGJEjQ6zTQAFAE4PTIWPP12aYIl6jVYzJNRERERE2q3O6GxcFEOhJOj4z9Z8ug/DhIWishZCJdTYIEraaqrKwIbP/hIuxu9gggaigm00RERETUZCqcHpRWumMdRotTYHHA7pLx41BpaIJ0766L9sfyshCwOb3IK7NHO0SiNofJNBERERE1iUqXF8VWV6zDaJGKrf+7ASEh/Fbp/x0jQSOhavA0gEILfw9EDcVkmoiIiIgancMto4iJdFTUdy5u30m8Rb1rIaJqTKaJiIiIqFE5PTIuVDi5JFMDtE8w+j2O9L1Uy//YoN05yRSNsIjaNCbTRERERNRo3F4FFyqcUJhIN0jnlDiYDVpUD5WO9P2URVVbtFaSkBCnQ9d2TKaJGorJNBERERE1Cq9clUjLChPphorTazG8Ryo06qzc4bdOCyHU34FWI2Fs3w4wG3SNFitRW8FkmoiIiIiiTlEECiuc8MhKrENpNWYPy4BRp4UGVeOmvYoImVALIeBRFCiiqlU6Tq/FvNHdmyReotaOyTQRERERRZUQAhesTri9TKSjqV96IgZ0SoROV9U6rYiqhDoQWRFwywpkpWqotF4nYWhmCgZ3TW6iiIlaNybTRERERBRVF60uONxyrMNodSRJwj0T+6BrqhmGHwdPB2uX9ioKhAAkCTDqNOjZPh6PzxgISYpsWS0iqhuTaSIiIiKKmmKbCzaXN9ZhtFrd2pnxwOQsdE8zw6zXQhMiL9ZoJMQbdOjTMQHLZg9Gn44JTRMoURvAmQeIiIiIKCpKK92ocHhiHUarN7BzEh6bPhD/2nkaZ845ApZLNumRkhaPrE4JeGByPybSRFHGZJqIiIiIGqys0o1yuzvWYbQZ3dqZsWTmQOw/asYrO+ouM/3SLvjJVYMwKCOJXbuJGgGTaSIiIiJqEIvdgzIm0k1OkiT07GAOuP//xvdGp06cbIyosXDMNBERERHVm8XhQUmlK9ZhEBE1OSbTRERERFQvVqcHJTYm0kTUNjGZJiIiIqKIVbq8KLaxazcRtV1MpomIiIgoIg63jCKrC0IEW+WYiKh1YzJNRERERGFzemRcqHAykSaiNo/JNBERERGFxe1VcKHCCYWJNBERk2kiIiIiCs0rKyi0OCErTKSJiAAm00REREQUgqwIFFic8CpKrEMhImo2mEwTERERUUCKIlBY4YRHZiLd3AghcOpiZcD9f9+Wg0N55RzfTtRIdLEOgIiIiIiaJyEELlidcHnkWIdCNeSW2rFq1xmcP18QsMyek6U4cPEIurUz4/7JWejTMaEJIyRq/dgyTURERES1CCFQZHXB4WYi3dwcLajAc5tO4ExxJYptroDlzpc7kF9mR/YFKx754BAO5JY1YZRErR+TaSIiIiKq5aLNhUqXN9ZhUA25pXb886uTKLd7cMHqhBykB3f7BANkAeSXO1Ba6cayDceQU2RrumCJWjkm00RERETk56LVBZuTiXRzI4TAql1nUOHw4qLNBZNeiw6JhoDl4406dE01wWzQosDiRLndg+VfZnMMNVGUMJkmIiIiIlWJzQWr0xPrMKgO2RdsKCh3oNzuhk6jQVqCARpIQY/RSBLSk+Kg12pQYnMjt8SOI/kVTRQxUevGZJqIiIiIAFS1SFscTKSbq23ZRfDIAk6vgmSTLmQiXU0jSUg16+HwyHB7Faw/HHjSMiIKH5NpIiIiojZOCIELFU62SDdjTo+Mg7nlsDo90EoSzAZtrTJenQkV7QfVeXyCUQetRoLF4cHuk8Wwu9mNn6ihuDQWERERURtWvY60k8tfNWtllR4oAnDLCuIMGkg1WqVdpg4o7DsLXmMSJMWDxNITfvulHxNwl1eGIoASmxvmdkwFiBqCf0FEREREbZSiCJy3OOD2KrEOhUJwyVU3OxSBWt27balZKOo5FUKrBwBc7DEFBmcZ4mrUoZEkKD/OPebgzROiBmMyTURERNQGCSFwwepkIt1CGLVV3bo1EqCgKiMWQqDYpUFZnxl+ZYVWj4K+sxCvAL5zfStCQPNjHm7S1+4mTkSR4ZhpIiIiojbootUFh5utky1FarweGgkwaDVwuhV4FQXnLU6Ueeq+nJcNiThXKUER/0u87W4ZRp0WGo2EtITAS2oRUXjYMk1ERETUAO9tOhTrECJW6ZZhZ4t0i5OkEShWFLg8Ms6U2NUu24F4ZIFSix1ajQSnV4HLoyBBJ6GdJGPDtqNNEzS1aT+ZcmmsQ2hUbJkmIiIiakMcHoWJdAvVPy0OXqFABkIm0gklx9FB64RWI0EIgUqPAoNWgk4DDOlobpJ4iVo7JtNEREREbYTLq8DGiadaJCEE8q1ulDtD3AgRAu3ydqDjqQ3QSFXHWVwKZAEkGDRIM+mQkahvmqCJWjl28yYiIiJqAzyyQAXHSLdIXkVg0+kKHL7oCFpOkl1IP/kZ4i2nAAAuWYHNKUMWQLJRC7NOg6k9kyFJUtB6iCg8TKaJiIiIWjmvImBxMZFuiWxuGR9ml+O8zRO0nM5Zjs4/fASDs1T9mdWtQGuWkGTUwKzTYEafFHSMZ6s0UbQwmSYiaiJ2txfFVjecXhlxOi3aJxpgNvA0TESNSxFVibRAiEG21OwU2Dz4ILsMNnfwrt0my1mkn1wPrez0+3mqUQdtnBZpJh2m9kxmIk0UZbyKIyJqREIIHM63YP2hAuw5VeI3YYxGAkb1TsP0wZ0xOIPd7ogo+hQhUO6U1eWRqOU4WuzAZyct8Ib41WUlAt59H0Cq42ZJn1QjLu/ZDhmJen7HEDUCJtNERI0kp8iG5V9kI7fUDrdXgcXhgcsrQxFVibRRp8W24xfxdU4JurUz4/7JWejTMSHWYRNRKyGEgNUtQ2Yi3aIoQmB7rg17CyqDltNKwJSeSegd58RnAXodTOiRhKQkridN1FiYTBMRNYIDuWVYtuEYyu0elNhccHgUaDUSzAYtNJIERQjYXF5YHB6Y9Bo4PTIe+eAQHp02AMO6pcY6fCJqBaxuBW6ZiXRL4vIq+DjHglPlrqDl4vUazM5KQUaiAZUVzqBliajxMJkmIoqynCIblm04htJKNwosTui1GqQnGZFg1Pl1sxM/JtRldg/yyx1QhMCyDcfwxzmXsoWaiBqk0i3DJXMt6Zak1OHF+yfKUOoMPlFcp3gdZmelIsmobaLIiCgQrjNNRBRFQggs/yIb5XYPCixOmA1adE01ITGu9ng1SZKQGKdH11QTzAYtCixOlNs9WP5lNgS7ZRJRPTm8CuxeJtItyalyF14/UhIykR7YPg63XpLGRJqomWAyTUQURYfzLcgttaPE5vqxRToOmhCTvmgkCelJcdBrNSixuZFbYseR/IomipiIWhOXV4GNa0m3GEII7D1fifeOl8EVpEu+BGB8t0TM6J0Mvca/h1NRpTfgcVvOVOBchZs3aIkaCbt5ExFF0frDBXB7FTg8CtKTjCET6WoaSUKqWY8LFS64vQrWHy7A4K7JjRwtEbUmTq8CKxPpFsOjCGw8ZcH3xcHHPBu1Eq7vm4JeKUa/n5fYvdiRZ4Wl3Ir4AMeeLHPhjLsU7Uw6XMulsYiiji3TRERRYnd7sedkCSwOD7QaCQnGyO5XJhh10GokWBwe7D5ZDLs7cGsDEZEvJtIti9UtY+33pSET6XZxWtw2KK1WIp1vdePzUxYUO7xBeyKUubwoc8q4UOnBu8dLcdYSfGIzIooMk2kioigptrqhCMDllWE2aCNe01OSqmb7rl4+q8TmbqRIiag1cXiYSLck561uvH64BAWVnqDleqUYcPugNKSZ/G/Mlti92HbWikqvggpX8DXEEw0aKEKgzCnD5lHwaU45ikI8LxGFj8k0EVGUOL1VF7NV60hHlkhXq1o2q2rb4eHFMREF5/AosPFc0WIcuejAm0dLYfMEnyDuis7xuLFfKow6/0t1IQR25FnhkKvGxus1EhKDTEZm1GrQLk4Lg0aCxSXD7lWw8bSFY6iJooRjpomIoiROV3VBo5EQtKUgGEUIVM8tY9JztlYiCszpZSLdUihCYOtZK/YX2oOW00nAtb2TcUl7U537Cyu9KHfJsHsUaCQgwaCB5Ax+81aSJCQbNSh1yrC5FZRovMi3etA1yVDv10NEVZhMExFFSftEAzQSYNRpYXN5IYSIqKu3EAJ2t4wEow4ajYS0BF7oEFHdOEa65XB6Faz7oRxnLMGH7iToNZjTLxWdEwJPEna8xAlZATyyQKJBE/Z3jCRJiNdrYHEp8CrAwSI7k2miKGA3byKiKDEbdBjVOw3JJj1kRcDmimwCMZvLC1kRSDbpMbpXGswG3u8kotpcMhPplqLY4cXrR0pCJtJdEvSYNzgtaCLtlhXkVrjh9CqQJECvjWw4kVErQSNVrUOeU+aCS+Za5EQNxWSaiCiKpg/uDINOA5NegzK7J+zu3ooQKLN7YNJrYdBpMH1w50aOlIhaIo9cNekUNX85ZU68caQEZc7gv69BHUy4ZWA7JBiCD+2p9AgICHiFgEEr1WuSS4NWgkepqsfmZjJN1FBs9iAiiqLBGcno1s4Mp0dGfrkDFyqcSE+KCzohmSIELlQ44ZEVdEwxoVuaGYMykpowaiJqCVxMpFsEIQT2nq/EV+dsQctJACZ0T8TwTuawEmOvLH6sH6jnHJfQAPD+eI/XI3MSMqKGYss0EVEUSZKE+ydnIcWsR+fkONjdMvLKHLA6PbVmTxVCwOr0IK/MAbtbRufkOKSY9bh/UlbELQ5E1Lo5vEykWwKPLPBJjiVkIh2nlXDTgFSM6Bwf9vle92O3bkkC6psGK/hfIh5pN3Eiqo0t00REUdanYwIenTYAyzYcg0aSUGJz40KFC8UaN8wG7Y/LX1VNNiYrAia9Fh1TTEgx6/HotAHo0zEh1i+BiJqRSk/V7M3UvFW4ZHyQXYYLlcHny0gzaXFjv1SkxkV2GR6vlyBBgk6S4JKVek1y6ZYF4nRVE5clGNimRtRQTKaJiBrBsG6p+OOcS7H8i2zk6u1wexVYHB64vPKP61ADCUYdkk16GHQadEsz4/5JWUykiciP1S3D6WUi3dzlWd34MLs85E2PPqlGzOidXGv96HAYtBp0SzLgVLkLTq8Cjyxg0IWfTLtkAUUAJp0GfVKMMGqZTBM1FJNpIqJG0qdjAv566zAcya/Ap4fPY8/JEig+ffM0Ggmje6Vh+uDOGJSRxK7dRKQSQsDqVjjjcgtwsMiOTacr/M7vdRndJR5XZyY06FzfPy0OZytc0Gsl2L0K9FoJ4dQmhEClR4FBK0GnAYZ0NNc7BiL6HybTRESNSJIkDO6ajMFdk2F3e1Fic8PhkWHSa5GWYODyV0RUixACFW4FbibSzZqsCGw5a8V3F+xBy+k0wLReyRjQ3tTg5+wUr0OKUQuvIlDhkmFzK0gMMYJaCAGLS4EsgCSjBmkmHTISAy/BRUTh41UcEVETMRt0MLfjaZeIAlNEVZLkCdXMSTHl8Cj46Idy5FYEXz860aDBnH6p6BQfneRVkiRc3TURn5+yQDEANrcMq0tGoAFCLlmBzSlDFkCyUQuzToOpPZPZE4ooSnhVR0RERNQMMJFuGS7aPXj/RDksIWZX75qoxw19UxAfYv3oSKWZdRjfPRHbzlqhkQCXO3BibHUr0JolJBk1MOs0mNEnBR2jlNgTEZNpIiIiophjIt0yZJc68WmOJeTvaUhHEyb3SIJW0zgtwBmJBlzbKxk78qywuAIn66lGHbRxWqSZdJjaM5mJNFGUMZkmIiIiiiEm0s2fEAJf51diZ17w9aMlAJN6JGJYurnRu1KnmXWY1TcFZws92L+37jJ9Uo24vGc7ZCTq2bWbqBEwmSYiIiKKESbSzZ9bVrD+pAXZpa6g5Uw6CTf0TUG3ZGMTRVY1hrpDfODL+Qk9kpCUZGiyeIjaGibTRERERDHARLr5szi9eD+7HBft3qDlOph1mJOVgpQ4XloTtSX8iyciIiJqYoKJdLOXa3Hhox/K4fAG/x1lpRoxvU8yDFpNE0VGRM0Fk2kiIiKiJmZ1K0ykm7EDhXZ8ebYCoX5FY7omYExGPMcjE7VRTKaJiIiImpDNLcMlK7EOg+ogKwJfnqnAf4scQcvpNRKm905Gv7S4JoqMiJojJtNERERETcTukeHwMpFujio9Mj7KLkee1RO0XLJRizlZXK+ZiJhMExERETUJh1dBpYeJdHN0odKDD06UocId/PfTLcmAWX1TYNZzfDQRMZkmIiIianQurwKbW451GFSH4yVOrD9ZjlAdBi5LN2Ni90RoNRwfTURVmEwTERERNSKPrKCCiXSzI4TAjjwbdudXBi2nkYDJPZIwNN3cRJERUUvBZJqIiIiokXgVAYuLiXRz45IVrM+x4IcyV9ByZp0GN2SlIDPJ0ESREVFLwmSaiIiIqBHIPybSXACreSlzevHBiXIUO7xBy6WbdZjTLxVJRm0TRUZELQ2TaSIiIqIoU0RVIq0IptLNyRmLC+uyy+GUg/9e+qfFYVqvZOi1HB9NRIExmSYiIiKKIiEEKlwyZCbSzYYQAt8W2rHlrDVkT4GxmQkY1SUeksREmoiCYzJNREREFEVWtwKPwkS6ufAqAptOV+DwRUfQcgaNhJl9k9EnNa6JIiOilo6L5EWJ2+3GypUrce2116Jz584wGo1ISEhAv379cMcdd2DPnj1h1fP5559jzpw56Nq1K4xGI7p27Yo5c+bg888/DzsWu92OP//5zxg5ciTatWuHhIQEDBgwAA899BByc3Pr+xKJiIgoBJtbhkvmWtLNhc0tY+3R0pCJdIpRi58PasdEmogiIgnBPkgNde7cOUyfPh2HDx8OWu7+++/Hc889V2e3ISEEFi1ahFdeeSXg8XfffTdefvnloN2OTp48ienTp+PEiRN17k9OTsabb76JadOmBY21PvLy8pCZmQmg6j3p2rVr1J+DiIiouXlv0yEAQKVHht3DRLq5KLB58EF2GWzu4L+T7kkGzMpKgUnXMtuYKitK8dmaZ+rc97NFjyMpuV0TR0T0Pz+ZcmmsQ2hULfOs0Yx4vV6/RPrSSy/F6tWrsXv3bmzatAlPPPEE4uPjAQDLly/HX/7ylzrr+d3vfqcm0sOGDcPatWvxzTffYO3atRg2bBgA4JVXXsHjjz8eMBabzYYZM2aoifRdd92FzZs34+uvv8bTTz+NhIQEWCwWzJ07F4cOHYrae0BERNSWVU82xkS6+Tha7MCb35eETKSHdzLjpgGpLTaRJqLYYst0A73//vv4yU9+AgAYPXo0duzYAa3WfwmFb7/9FqNHj4bH40FqaiqKioqg0/1vuHpOTg4GDBgAr9eL4cOHY/v27TCZTOp+u92OcePGYf/+/dDpdDh+/Dh69+5dK5alS5fi97//PQDgT3/6ExYvXuy3f/fu3Rg7diy8Xi8mTJiALVu2RO19ANgyTUREbY/LK2P1+oOctbuZUITA9lwb9hZUBi2nlYApPZNwaUdzE0XWOIQQOFN4Ed++V3djTedrf43Le3ZB10Q9J1SjmGDLNAW1a9cudfuRRx6plUgDwOWXX44ZM2YAAMrKynD8+HG//cuXL4fXW7XW4UsvveSXSAOA2WzGSy+9BKCqJXzFihW1nsPj8eCFF14AAAwYMAAPPvhgrTKjR4/GnXfeCQDYunUrvv3223BfJhEREdVgdXpwvtzJRLqZcHkVvH+iPGQiHa/X4JaB7Vp8Il1i92LdD+XYfs4asMzJMhfeP1GKN74vQVGlpwmjI2obmEw3kNvtVrd79eoVsJxvS7LL5VK3hRBYt24dAKB///4YNWpUncePGjUK/fr1AwB89NFHqNmhYNu2bSgvLwcAzJs3DxpN3b/a+fPnq9sffPBBwHiJiIiobkIIXLS6cNHqqvV9TLFR6vDi9SMlOFXuClquU7wO8wanISPR0ESRNY58qxufn7Kg2OGFzS0HLFfm8qLMKeNCpQfvHi/FWUvw94eIIsNkuoGysrLU7VOnTgUsd/LkSQCAJEno27ev+vPTp08jPz8fADBu3Ligz1W9Py8vD2fOnPHbt2PHjlrl6jJ8+HB1DPfOnTuDPh8RERH588gK8ssdsDrZytdcnCp34fUjJSh1Bk4qAWBg+zjcekkaEg21exG2JCV2L7adtaLSq6DCJQftGZFo0EARAmVOGTaPgk9zytlCTRRFTKYb6JZbbkFSUhIA4Nlnn4Us1z6RHzhwAOvXrwcA3HzzzWp5ADh27Ji63b9//6DP5bvf97hI6tHpdGorec06iIiIKDC724v8MgfcXk401hwIIfDN+Uq8d7wMLjl4D4Hx3RIxo3cy9JqWPW5YCIEdeVY4ZAU2twy9RkKiMfDNAaNWg3ZxWhg0UtUkeV4FG09b2KOCKEp0oYtQMB06dMDq1avxs5/9DLt27cKIESNw3333ISsrCzabDbt27cJzzz0Ht9uNoUOH4vnnn/c7/ty5c+p2qAm7qif3qnmc7+P4+HikpKSErOfQoUO4ePEiXC4XjEZjOC8VeXl5QfcXFBSEVQ8REVFLU+H0oNjKLrLNhVcR+PyUBd8XO4OWM2olzOyTgt6p4V3rNHeFlV6U/zhzvEYCEgwaSM7gNwgkSUKyUYNSpwybW0GJxot8qwddk1p2V3ei5oDJdBTMnj0b+/fvx/PPP49//etfmDdvnt/+9PR0/P73v8fdd9+tdrGuZrX+b9KIhISEoM/je6zNZquznlB11FVPuMm0bzJPRETUVljsHpRUMpFuLqxuGR+eKEdBiO7K7eK0mNMvFWmm1nO5e7zECVkBPLJAokET9gzdkiQhXq+BxaXAqwAHi+xMpomigN28o8Dj8eDNN9/EJ598Ume3mQsXLmDt2rXYtm1brX1O5//uqBoMwU9qvkmvw+Gos55QdYSqh4iIiP6ntNLNRLoZOW914/XDJSET6Z7JBtw2KK1VJdJuWUFuhRtOrwJJAvTayLqsG7USNBLg8CrIKXPBJXO4AlFDtZ4zTIxUVlZi2rRp2L59O7RaLX7zm99gwYIF6NWrF5xOJ/bu3Ysnn3wSO3fuxMyZM7F8+XL8+te/Vo+Pi4tTt31nBq+L7yzgNZfPqq4nVB2h6gmmZtfymgoKCjBy5Miw6yMiImrOim0uVDg4WVNzcfiiAxtPWRBieDRGdo7HuG4J0LSydZUrPQICAl4hYNBKEa8bLUkSDFoJHqWqHptbgdHEdjWihmAy3UBLlizB9u3bAQArV6706+JtMBgwefJkTJgwAVOmTMHWrVvxwAMPYMKECbj00qoFzBMTE9XyNbtu11RZ+b91E2t2566uJ1QdoeoJJtSYbiIiotaihIl0s6EIgW25VuwrsActp5WA63ol45IO4TcUtCTeH+8iCAHU9z6BBoD3x5sRnlB3JYgoJN6OagAhBFatWgWgaomsmmOlq+l0Ojz11FMAAEVR1GMA/wQ11ARfvi3DNccvV9dTWVmprjcdqp4OHTqEPV6aiIiorSirdMPCRLpZcHoVvHu8LGQinaDX4GeXpLXaRBoAdD9265YkoL5psIL/JeKRdhMnotqYTDfAhQsXUFpaCgAYNmxY0LKXX365un38+HF1e+DAgXX+vC6++wcMGOC3L9x6vF6vuuZ1zTqIiIjaOovdgzJ76CFT1PiKHV68fqQEZyzBfx9dEvSYNzgNnRP0TRRZbMTrJUiQoJMkuGUR8fJWQgi4ZQG9pqqLeIKBaQBRQ/GvqAF0uv/1kvd6vUHLejz/u8Pte1zPnj3RpUsXAMBXX30VtI7q7uQZGRno0aOH376rrrpK3Q5Wz/79+9Vu3mPGjAn6fERERG1JhZOzdjcXOWVOvHGkBGVOOWi5QR1MuGVgOyQYAq+13FoYtBp0SzIgTqeBEJF303bJAooATDoN+qQYYdQyDSBqKP4VNUC7du2QlJQEANi9e3fQhNo3we3Zs6e6LUkSZs2aBaCqRXnPnj11Hr9nzx61xXnWrFm1Jp0YP348kpOTAQBr1qwJeLdy9erV6vbs2bMDxktERNSWWOxcR7o5EEJgd74N758ohztIsigBmNg9EdN6JUGnaTvdlfunxUGrqeqibfcqYbdOCyFQ6VFg0ErQaYAhHc2NHClR28BkugE0Gg2mT58OADh//jyefvrpOsuVlZXh4YcfVh/PmDHDb/99992ntlbfe++9tZarcjgcuPfeewFUtWrfd999tZ7DYDDgV7/6FQDg2LFj+Mtf/lKrzO7du7Fy5UoAwLhx4zBixIhwXiYREVGrVmJzsUW6GfDIAp/kWLD9XPDJVOO0Eub2T8WIzvERz2jd0nWK1yHFqIVZr4EiAJtbgQgxgloIAYtLgSyABIMGaSYdMhJbd5d4oqbCZLqBnnjiCZjNVXf3li5diuuvvx7vv/8+Dhw4gN27d2P58uUYOnQojh49CgC45pprMGXKFL86srKy8NBDDwGo6oY9ZswYvP3229i/fz/efvttjBkzBvv37wcALF68GH379q0zlsWLFyMrKwsA8Jvf/AYLFy7E1q1bsWfPHvzxj3/ElClT4PV6YTKZsGLFisZ4O4iIiFoMIQSKKpycbKwZqHDJ+M/REhwrcQYtl2bS4vbBaeiZ0jYnUJUkCVd3TYRJq0GCQQuPImB1Be4K75IVlDpluBWBZKMWZp0GU3smt7mbEESNRRKRzl5AtXz55Ze45ZZbUFxcHLTcxIkT8d577yE1NbXWPkVRcNddd+Ff//pXwOPvvPNOvPLKK9BoAt8DycnJwbRp0/DDDz/UuT8pKQn/+c9/arWOR0NeXp46y/i5c+e4lBYRETVbiiJwweqEwx18TG44Nu46EYWI2q48qxsfZpfD7lGCluuTasSM3skw6tgWlG91Y9tZKxyyApe1DOkHXqu73NBfQGtOQYJBA7NOgxl9UtA9uW3eiKDY+MmUS2MdQqPiOtNRMGnSJBw/fhwrV67EZ599hu+//x7l5eXQ6XTo1KkTRowYgVtvvRXXX399wDuBGo0GK1euxI033ohXXnkF+/btQ3FxMdq3b48RI0Zg4cKFuO6660LG0qdPHxw4cAB/+9vf8O677yInJwdutxuZmZmYNm0afv3rX6N79+7RfguIiIhaDLdXwYUKJzxy8OSNGt/BIjs2na6AEqJpZ3SXeFydmcAW1R9lJBpwba9k7MizwuIKPPlaqlEHbZwWaSYdpvZMRsd4du8miia2TFPUsGWaiIiau0qXFxetLihRvPxhy3TkFCGw5awV3xYGXz9apwGm9UrGgPatd/3ohhBC4GzhRex/r/ZcOQDQ5dpf4/KeXZCRqOeNCIoJtkwTERERtQJllW6uId0MODwK1v1QjrMVwX8XiQYN5vRLRSe2pgYkSRI6xAe+nJ/QIwlJSYYmjIiobWEyTURERK3eRasLVicnGou1i3YP3j9RDkuQSbMAICNRj9l9UxDfBtaPJqKWi8k0ERERtWpMpJuH7FInPs2xwBNigPSlHU2Y0iMJ2ja0fjQRtUxMpomIiKjVKrI6YXN6Yx1GmyaEwNf5ldiZF3z9aAnApB6JGJZu5vheImoRmEwTERFRq8REOvbcsoL1Jy3ILnUFLWfSSZjVl8s2EVHLwmSaiIiIWhUhBC5aXbC5mEjHksXpxfvZ5bhoD/576GDWYU5WClLieFlKRC0Lz1pERETUagghcKHCBbubiXQs5Va48VF2GRze4OOjs1KNmN4nGQatpokiIyKKHibTRERE1CooikBhhRNOT/CZoqlxHSi048uzFQgxzxjGdE3AmIx4jo8mohaLyTQRERG1eLIiUGBxwO1VYh1KmyUrAl+eqcB/ixxBy+k1Eqb3Tka/tLgmioyIqHEwmSYiIqIWzSMrKLQ44ZGZSMdKpUfGuuxynLMGX4Is2ajFnKwUdIzXN1FkRESNh8k0ERERtVhOj4wLFU7IofoUU6O5UOnBByfKUOEOfjOjW5IBs/qmwKzn+Ggiah2YTBMREVGLZHd7caHCBSGYSMfK8RInNpy0wBPiZsZl6WZM7J4IrYbjo4mo9WAyTURERC2KEAIVDi9KKoOvXUyNRwiBHXk27M6vDFpOIwGTeyRhaLq5iSIjImo6TKaJiIioRZAVgQqHBxVOD7t1x5BLVrA+x4IfyoLfzDDrNLghKwWZSYYmioyIqGkxmSYiIqJmTVEESu1uWJ1edumOsTKnFx+cKEexI/g63h3NOtzYLxVJRm0TRUZE1PSYTBMREVGz5fLKKKpwcabuZuCMxYV12eVwysFvaPRvF4freifBoOVEY0TUujGZJiIiombJ6vSg2OZma3SMCSHwbaEdW85aEeo3cXVmAkZ3iYckcaIxImr9mEwTERFRs3PR6oLVGXzNYmp8XkVg0+kKHL7oCFrOoJEws28y+qTGNVFkRESxx2SaiIiImpVyu5uJdDNgc8v4KLsc+bbgv4sUoxZz+qWgg1nfRJERETUPTKaJiIio2XB6ZJRWumMdRptXYPPgw+wyWN3Bx6p3TzZgVt8UmHQcH01EbQ+TaSIiImoWZEWgqIJrR8fa0WIHPjtpgTfEAOnLO5kxsXsiNBwfTURtFJNpIiIiahaKrE54Fc7aHSuKENh+zoa95yuDltNKwJSeSbi0o7mJIiMiap6YTBMREVHMldvdcLjlWIfRZrm8Cj7OseBUefCeAfF6DWZnpSAj0dBEkRERNV9MpomImojd7UWx1Q2nV0acTov2iQaYDTwNE5VVulFm5zjpWCl1ePH+iTKUOoPfzOgUr8PsrFQkGbVNFBkRUfPGqzgiokYkhMDhfAvWHyrAnlMlUHzGIGokYFTvNEwf3BmDM5K5Liu1OV5ZwUWbiy3SMXSq3IWPfyiHSw4+QHpgWhyu7Z0MvYbnKSKiakymiYgaSU6RDcu/yEZuqR1urwKLwwOXV4YiqhJpo06Lbccv4uucEnRrZ8b9k7PQp2NCrMMmahJ2txcXrS7ISohZrqhRCCGwr9CObWetCPUbGN8tESM7m3nDj4ioBibTRESN4EBuGZZtOIZyuwclNhccHgVajQSzQQuNJEERAjaXFxaHBya9Bk6PjEc+OIRHpw3AsG6psQ6fqNEIIVBm96Cc3bpjxqsIbDxlwZFiZ9ByBq2E6/ukoHeqsYkiIyJqWZhMExFFWU6RDcs2HENppRsFFif0Wg3Sk4xIMOr8WnbEjwl1md2D/HIHFCGwbMMx/HHOpWyhplbJKyu4YHXB5WG37lixumV8eKIcBZWeoOXaxWkxp18q0ky8VCQiCkQT6wCIiFoTIQSWf5GNcrsHBRYnzAYtuqaakBinr9VFUpIkJMbp0TXVBLNBiwKLE+V2D5Z/mQ0h2PWVWpdKlxf55Q4m0jF03urG64dLQibSPZMNuG1QGhNpIqIQmEwTEUXR4XwLckvtKLG5fmyRjoMmxDhDjSQhPSkOeq0GJTY3ckvsOJJf0UQREzW+0ko3LlQ4OT46ho5cdODNo6WweYKv4z2yczx+0j8VcTpeIhIRhcIzJRFRFK0/XAC3V4HDoyDVrA+ZSFfTSBJSzXo4PDLcXgXrDxc0cqREjU8IgaIKJ8dHx5AiBLacrcD6kxYEm7BbKwEzeidjQvfEsM9bRERtHfvvEBFFid3txZ6TJbA4PNBqJCQYIzvFJhh1KNa4YXF4sPtkMexuL9ehphZLVgQuVDjhZLfumHF6Faz7oRxnLMFvZiToNZjdLwVdEgxNFBkRUevAqzQioigptrqhCMDllWE2aCNeRkaSqmb7rl4+q8TmhrkdT9PU8nhkBYUWJzxy8C7F1HiKHV58cKIMZc7gNzO6JOgxOysFCQZtE0VGRNR68CqNiChKnN6qi9aqdaTr102yatmsqm0HW/SohRFCwOLwoMzu4SR6MXSyzImPcyxwB+vXDWBQ+zhM7ZUMnYbduomI6oPJNBFRlMTpqlp2NFLVOMWaFCHglQUUIaCRJOi0Uq2ku2pf1bZJz5YiajkcbhnFNhdbo2NICIG95yvx1Tlb0HISgAndEzG8kzniHjRERPQ/TKaJiKKkfaIBGgkw6rSwubwQQkCSqpKMcocHla7aLc3xRi1STHqYDFoIAdjdMhKMOmg0EtI4fpFaAEURKLa5YHN5Yx1Km+aRBT47ZcGxEmfQcnFaCdf3TUHPFGMTRUZE1HoxmSYiihKzQYdRvdOw7fjFH7u6umFzVc3OrQgBWREQACAASFWtQ1anQKVLhkGnQYJRC1kRSDbpMbpXGicfo2bP7VVwoYJjo2OtwiXjg+wyXKgMfkMjzaTFjf1SkRrHcwsRUTTwbEpEFEXTB3fG1zkl0GklFFa4oJUAWVR135bw41jqH3tVKkJAlqu6fMuKApvLi3iDFgadBtMHd47p6yAKpdLlxUWrq84hDdR08qxufJhdDnuI9aP7pBoxo3cyjFw/mogoaphMExFF0eCMZKTGG/BDkRWKIiAD0ADQazXQaAAJ/xufKCCgKIBXVuBGVY7tlhW0izdgUEZSjF4BUWhllW6Uce3omDtYZMem0xXqpIWBjO4Sj6szEzg+uhUSQqAoSI+ELWcqcHnPBHRN1PP3T9QImEwTEUWZVPPCtvr6RfhsVz+u3s/GPWohSivdKGciHVOKENhy1opvC+1By+k0wLReyRjQ3tREkVFTKrF7sSPPCku5FfEBypwsc+GMuxTtTDpc2zMZHeP1TRojUWvHvj5ERFF0ON+CUrsbBo0EjUaCUaeBJEnwKArcsgKPzz+3rMCjKJCkqnIajQSDRoPSSjeO5FfE+qUQ1VLGRDrmHB4F7xwrC5lIJxo0+NklaUykW6l8qxufn7Kg2OGFzR14GcUylxdlThkXKj1493gpzlpcTRglUevHZJqIKIrWHy6A26vAowDpSUbE6bUwaDUwaDXQSBIEACGqGqI1kqTui9NrkZ5khEcRcHsVrD9cEOuXQuSHXbtj76Ldg9ePlOBsRfDfQ0aiHvMGpaETWyFbpRK7F9vOWlHpVVDhkoPOW5Bo0EARAmVOGTaPgk9zylFU6WnCaIlaN3bzJiKKErvbiz0nS2BxeKDVSGhnNqCdGXB6FJQ73HUujZVg1CHZpEecvureZoXDC4vDg90ni2F3ezmjNzUL5XYm0rGWXerE+hwL3CEGSA/paMLkHknQajg+tjUSQmBHnhUOWYHNLUOvkZBo1AYsb9RqYIjTwuJSYHHJ0EjAxtMW/PySNI6hJooCXqUREUVJsdUNRQAurwyzQateqJgMWpgMJiiKgFcRUETVDN66H7uC+zIbtHB5ZSgCKLG5YW7H0zTFFsdIx5YQAl/nV2Jnni1oOQnApB6JGJZuZpLUihVWelHukmH3KNBIQIJBA8kZ/PctSRKSjRqUOmXY3ApKNF7kWz3ommRooqiJWi9epRERRYnTW9XyrIgfl8CqoWpMdPCLHo0kqTPzOjyBx8ERNYVimwsVDnYJjRW3rGD9SQuyS4OPczXpJMzqm4LuycYmioxi5XiJE7Ly/9m78/i66jr/46/vOXfPcrO06RZKS0tpKYhFRBAERAUplSrKb9xxRkGdEZRRdEZBQRRHcQZHcBlG0VFxGR32HQVEhIosUloaSktLaWmbNnty93O+vz9OGlra3DTNzb1Z3s/HA5vmfu/pJ6a9Oe/7XT6Q9yw1EWe/3zgxxlAVdujK+hT84CR4hWmRkVOYFhEpkVgoWGrnGA64924wax18HA8PvnRPZLS19mTozQzeckdGV1emwP+t7WRHqvj3YEo8xLsPq6Muplu6iS7n+WzqzpEp+BgDYXd4KxCirsExkC74rOvIkvV8oq6OTxIZCb3yioiUyJSaCI6BaMilN1vAWjus5ZbWWlI5j+poCMcxNFZr1kDKz/ctO3qz9GUVpCtlU3eOm9d2kC4Uf1Pu0PooZ85PKhBNEn15i8VSsJaIa4a9nN8YQ8Q15P3gOr05n2hcf3dERkL/gkRESiQRCXHcvEaS8TCeb+kdZhjpzRbwfEsyHub4Qxp1+JiUXSbvsaUzrSBdQU9tT/GbNe1DBukTZlXxrgV1CtKTSMEL/k5YG+yRPxBO//MhWCouIiOjV2ARkRI688gZREIO8bBDRyq/38u9fWvpSOWJh10iIYczj5wxypWK7KkzlWNrV4a851e6lEnJ8y33vNDFvRu6KXZgd9gxvPPQOk48qEYHjU0yof5l3cYE7RUPhN//fBj+MnER2ZumPURESujIWUlmNyQGZvi2d2eYVhvb54Fku/jWsr07CDFNdXFmNyY4YlZtGauWyazg+bT2ZMnowLuKSeV9bl7bwUs9xQ97S0Zdzl5QR5P6R09KVWGDwRAyhqznH9BWopxniYWCg8uqI5pTExkp/SsSESkhYwwXvW0BdYkwM5IxUjmPzR1pejJ57Ktmqa219GTybO5Ik8p5zEjGqEuEueitCzTjJGXRly2wpTOtIF1BrX15/ueZnUMG6dm1ET58RKOC9CQWcR1m10aIhRysHf4y7axn8S3EQw7z66LaIiBSApqZFhEpsflN1Xxx6SKuvHMNjjG09ebY3p1lp5MjEXH7218Fh415viUedmmqi1OXCPPFpYuY31Rd6S9BJjhrLW19ObW9qrCWtgx3ru8iX2xdN7BkWoK3HFyDO0RrPZn4FjbGeLE7S9g1pAo+Ydfs1/5pay19eZ+Iawg5cFRTYtRrFZkMFKZFREbBktn1fOPs13D1fWvZFE6RK/h0pfNkC15/H2qojoZIxsNEQg6zGxNc9NYFCtIy6nIFn9aeDLmC9kZXirWWhzf38siWvqLjHANvm1PLa6cp+EhgelWIuqhLwbd0Zz16cz41Q+ygttbSlfXxLNRGHRrjIWbVaIWDSCkoTIuIjJL5TdVc+/4lrNrSze3PvMyK9W17HCzkOIbjD2nkzCNncMSsWi3tllHXmy2wsyd7wH3QZeSyns8d67p4viNbdFwi5PDOBXUcVKsWefIKYwxvaq7h7he68CPQm/PoyXoM9jZs1vPpzXh4Nthznwg5nD43qZ83IiWiMC0iMoqMMRzZnOTI5iSpXIG23hzpvEc87NJYHVH7KykLay3tfTm6tKy7ojozBf7vuU52pou3HmtKhDj7sHqSUbdMlcl40pgIccrBNTz4Yg+OgWxu8GDck/NxE4baqEMi5LBsvg6wEykl3cWJiJRJIhIi0aCXXSkvndY9NrzYleXm5zvJDNE/emFDjDPm1RLR4VBSxKyaCG8/JMmfNvfQlR38TZf6aAg35tIYD3H63KSCtEiJ6a5ORERkAsrkPbrTefpy3l4nyUv5WGt5cnuKP2zsGbI38EkHVXPczCotwZX90pgIsfzQOl7clufxv+x7zPz6KK+b28CsmrD+XomMAoVpERGRCcJaS0+2QHc6rwPGxoCCb7lvQzcrd6SLjos4hmXzkxzaECtTZTJRGGOYWjX47fyb59RSq333IqNGYVpERGSc2xWiu1J58p5C9FjQl/O4aW0nW3qL71Ovi7qcfVgdUxNafisHJl6d5Ixz/wUAA9THX/m7VF2TrFBVIpODwrSIiMg41pst0NGXU4geQ7b15rlxbQc9ueLfk4OTEZYfWkc8pP3RcuAcx6WqtgGAeMihOqKD60TKRWFaRERkHPJ9y86+LL2Z4idDS3k9uzPNXeu7GOKcMV43PcGpB9fgaB+rlEA85BAPObiO/j6JlJPCtIiIyDiTyXvs6MlqNnoM8a3lTy/1suLlvqLjXAOnza3lNU2JMlUmE1ksFLS8UogWqQyFaRERkXHCWkt3ukB7KqcTuseQbMHntnVdrO/MFh1XFXZ414I6ZtXoQCgZGccYaiKOWqiJVJjCtIiIyDiQznns7NVs9FjzUnuKn61qoz1TvI/39KoQ71pQT21U+1llZGIhh6qwoy0CImOAwrSIiMgYliv4tPflSOW0N3qs+evGdq64fQ292eJB+vDGGG+flySspbgyAo4x1EYcwpqNFhkzFKZFRETGoILn05nO05MpaEn3GGOt5XdPbOa/HnoBf4hvzSmzqzl2RhVGs4gyAvH+2Wj9PRIZWxSmRURExhDft3Sm83Sl8wrRY1Cu4PMf963l3me3Fx0XcQ1nza9jXn20TJXJROT2743WbLTI2KQwLSIiMgZYa+nqD9HeUNOdUhE7e7N8+ZbVtGzrKTquPuby7sPqaYzrNksOXCLkkNBstMiYpld5ERGRCrLW0pMt0NmXp+DrcLGxas3Wbr58y2ra+nJFx81NRjjr0DpiIc0kyoEJOYaaiEtIe+xFxjyFaRERkQpRv+jx4d7V2/j3+9aS94qvGDh2RoKTZ9folGU5YFVhl0RYb8SIjBcK0yIiZZLKFdjZkyNT8IiFXKbUREhE9DI8WXWl8uoXPcZ5vuW6h17gt09sLjrONfD2Q5IcMTVepspkogn3z0a7mo0WGVd0FyciMoqstTyzpYs7Vm5lxQtte5z86xg4bl4jZx45gyNnJbUvbpLwfcvO3iy9WbW6Gst6Mnm+dsca/rqxo+i4xuoIS+dUM7M6UqbKZCIxBLPRcc1Gi4xLCtMiIqNkXWsvV9+3lk3tKXIFn650nmzBw7dBkI6GXB5s2cEj69qY3ZDgorctYH5TdaXLllGULXi0dmtZ91i3qS3FJbesYnNHuui4RTNquPysxTzx9MbyFCYTSsR1qA47mo0WGccUpkVERsFTmzq48s41dKbytPVmSed9XMeQiLg4xuBbS2+2QFc6TzzskMl7/OuNK/ni0kUsmV1f6fKlxHIFn85UTrPR48CKF9r4+h1r6Mt5RcedvngaF711AREdNCbD5BhDdcQhqnZXIuOewrSISImta+3lyjvX0N6XY2tXhrDrMK02SnU0tMdSbtsfqDtSebZ0pvGt5co71/CNs1+jGeoJIu/5dKRy9GYUosc6ay2/euwlfvzwBortYncMfOLkebz76FnamiHDlgg7JEJqdyUyUShMi4iUkLWWq+9bS2cqz9auDImIy7Ta2D5P9zXGUBMLUxUNsb07w9auDI4xXP37tVz7viW62RrHPN/SmcrRnSnogLFxIJP3+Pa9a7m/pbXouJpYiEvPXMQxcxrKVJlMFFrSLTIxKUyLiJTQM1u62NSeoq032z8jve8gvTvHGKbVxtjckaatN8emthSrtnRzZHOyTFVLqVhr6U4X6Ezn8HyF6PGgtTvDl29dzdrtvUXHHdyQ4GvvPIJZ9TqxW/aflnSLTGwK0yIiJXTHM1vJFXzSeZ9ptdH97jfrGEN9Isz27iy5gs8dz2xVmB5nerMFOvpyOlxsHFm1pYuv3LqajlS+6LjjD2nki0sXUhXVbZPsPy3pFpn49FNBRKREUrkCK9a30ZXO4zqG6mHeeFdHQ+x0cnSl8zy6fiepXEF9qMeBTN6jrS9HNl/8wCoZW+58Zivf+f3zFIZYQfCBN8zm70+Ys99vjIlEXEN1WD2jRSYD3aWJiJTIzp4cvg3aHyUi7rBnI4wJTvve1T6rrTdHokEv02NVOufRlc6TyulwsfHE8y3ff3A9Nz21pei4aMjh86cfxpsXNpWpMhnvtKRbZPLRXZqISIlkCsHMZNBH+sBmJIK2WcHHac10jjkFz6c3W6AnU9By7nGoK53nituf5clNnUXHNdVE+eryxSyYVlOewmTcqwo7xLWkW2TSUZgWESmRWMgFgtY5/gGe4Oxby66VgfGwW6rSpAS60nna+3I6nXuc2rCzj0tuXsXWrkzRcUfMrOWysxbTUBUpU2UynumUbpHJTWFaRKREptREcAxEQy692aAl0nBmKay1pHIe1dEQjmNorNbN/Fjg+5YdvVn6slrOPV79ed1OrryzZcjVHmceOYML3zKfsJbpyhAiriERcvR3RWSSU5gWESmRRCTEcfMaebBlB13pPL3ZAjWx8H4/vzdbwPMtyXiY4w9p1OFjY0Am77GjJ6sl3eOUtZZfrNjETx7ZWHScY+BTb57P8tfO1DJdKSrqOiTCDiHNRIsICtMiIiV15pEzeGRdG/GwQ0cqT1U0tF/7p31r6UjliYddIiGHM4+cUYZqZTCeb+lM5ejOFLSse5xK5z2+eXcLD63dWXRcbSzEV95xOEtm15epMhmPYqGgzZWWc4vI7hSmRURK6MhZSWY3JMjkPbZ0ptnenWFabaxooPatZXt3hrzn01QXZ3ZjgiNm1ZaxatnFWktXOk9nKn/A+96l8rZ1Z7j05lWs39FXdNwhU6q44p2LmZGMl6kyGW80Ey0ixWijh4hICRljuOhtC6hLhJmRjJHKeWzuSNOTye81w2mtpSeTZ3NHmlTOY0YyRl0izEVvXaClpmW2K0S/1J6mvS+nID2OPb25k0/+4skhg/SJ86dwzfuWKEjLPkVdh/pYiNqoqyAtIoPSzLSISInNb6rmi0sXceWda3CMoa03x/buLDudHImI29/+KjhszPMt8bBLU12cukSYLy5dxPym6kp/CZOG71u6M3m60nk8XwF6vLv16Ze55v51Q34vP3zcwXz4jQcfcAs7mbi0nFtEhkNhWkRkFCyZXc83zn4NV9+3lk3hFLmCT1c6T7bg9fehhupoiGQ8TCTkMLsxwUVvXaAgXUZdqTwdKc1CTwR5z+faB9Zx29Nbi46LhRz+5YyFnLRgapkqk7HOYIi4hrBriLpGb7CIyLAoTIuIjJL5TdVc+/4lrNrSze3PvMyK9W3sPmHmOIbjD2nkzCNncMSsWi3tLhPPt+zoyZLKqdXVRNCZynHZbc+ycnNX0XHTaqN8bfkRzNMbVgKEHUNV2CXs6nVXRA6cwrSIyCgyxnBkc5Ijm5OkcgXaenOk8x7xsEtjdUTtr8osnQtaXRV8tbqaCNa39nLJLavY3p0tOu6o5iRfecfh1CXUu32yc4yhKuwQC+nYIBEZOd3FiYiUSSISItGgl91K6ejL0ZHKVboMKZE/rt3BN+9qIVMo/sbIWUfN5FNvnkfIVXia7GIhh6qwo6XcIlIyuqsTEZEJb0dPlp5MvtJlSAn41vI/j2zk5ys2FR3nOoYLT53PO46aWabKZKwKOYZqLekWkVGgMC0iIhOWtZbWnix9We2PnghSuQLfuKuFP69rKzouGQ9z2VmHc1RzXXkKkzHJAFVhl3hYqxJEZHQoTIuIyITk+Zbt3Rkyea/SpUgJvNyZ5pKbV7GxLVV03LypVVzxziOYXhsrU2UyFkXdYEm3WlyJyGhSmBYRkQknW/Bo7c6S93TQ2ETw5KYOvnrbs3Rniq8wOHnBVD7/9sOIh90yVSZjjZZ0i0g5KUyLiMiE0pXO096Xw6p/9LhnreWmp17m+w+u26Ot3L78wwlz+MAbZqvF3CSlU7pFpBIUpkVEZELwfMvOXu2PnihyBZ/v/uF57ly1rei4eNjli0sXcsL8KWWqTMaKsGOIuA4R1xDScm4RqQCFaRERGdestfRkC3T25dU/eoJo78vxlVtXs/rl7qLjZtbFuGL5EcydUlWmyqSSDAyE54hr1OJKRCpOYVpERMat3myBjr6c9kZPIGu393DpzavZ0ZstOu51s+u4dNnh1MbDZapMKsXtX8IdcY2W8ReR9Xx6cz55zxJ2DdURh6j6q4uMKoVpEZEySeUK7OzJkSl4xEIuU2oiJCJ6GT4QuYLPjt4sWZ3UPaHc39LKt+55jlyh+JsjZx89i0+ePE8nNU9wBkNVxCGufdCDstayuSfP060p1ndksbxyuIDBMK8+ylFNCZprwnojQmQU6C5ORGQUWWt5ZksXd6zcyooX2vY4RMkxcNy8Rs48cgZHzkrqRmc/dWfytPXqgLGJxPMt1/95A7967KWi48Ku4TNvXcAZR0wvU2VSKYmQQzzsaCl3Ea19ee7e0EV7ukDBh3TBJ+9brAVjgj3lLW0Z1nVkaIiHePvcJE1VWskhUkoK0yIio2Rday9X37eWTe0pcgWfrnSebMHDt0GQjoZcHmzZwSPr2pjdkOCity1gflN1pcses/z+A8Z6dcDYhNKbLXDlnWtY8UJ70XH1iTBfXb6YxTOTZapMKiEWckiE1B96KC92Zbl9XSepQrC0O+dZHEP/XnLwgUzBJ5X3ibiGvG/5bUs7y+bXcXAyWunyRSYMhWkRkVHw1KYOrrxzDZ2pPG29WdJ5H9cxJCIujjH41tKbLdCVzhMPO2TyHv9640q+uHQRS2bXV7r8MSeT99jRo77RE83mjhSX3LyaTe2pouMWTKvmiuVHMLVGIWCiiroOibCjU7n3Q2tfntvXddKb9+nKergGklGH6Kv2lFtryXqWvrxPR8bDj8Lt6zo5Z2GDZqhFSkRhWkSkxNa19nLlnWto78uxtStD2HWYVhulOhra60anN1ugI5VnS2ca31quvHMN3zj7NZqh7uf7lra+HD2ZfKVLkRL768Z2rrh9zZArDU5d2MTFpy0gGnbLVJmUU9gxVIVdwq5C9P6w1nL3hi5ShSBIRxxDMursc5uQMYZYyBB1DV3ZYLxj4J4NXXxwcaO2FomUgE50EBEpIWstV9+3ls5Unq1dGRIRl+b6ODWxvQ9/McZQEwvTXB8nEXHZ2pWhM5Xn6t+v1X5goC9bYHNHWkF6grHW8tvHX+Jfb3ymaJA2wPlvmsuXli5UkJ6AXGOojbjUxUIK0sOwuSdPe7pAb84fmJEeKhQbEwRu10BvzqctXWBLj15XRUpBM9MiIiX0zJYuNrWnaOvN9s9Ix4Y8QMcxhmm1MTZ3pGnrzbGpLcWqLd0c2Tw594bmCj7tfTlSOe2NnmhyBZ//uG8t9z67vei4qojLl85cxHGHNJapMikXQ9DmKhZSm6sD8XRrioIPOc/uV5DexfS3F+vK+hT84DrNtZFRrlZk4lOYFhEpoTue2Uqu4JPO+0yrje73SbSOMdQnwmzvzpIr+NzxzNZJF6Z939KRytGdKWhmfgLa2Zvly7espmVbT9FxzfVxvrb8CGY3JspUmZSLTugemazns74jS7rgB4dYDnNGP9p/OFm64LOuI0vW89WHWmSEFKZFREoklSuwYn0bXek8rmOojg7vJbY6GmKnk6MrnefR9TtJ5QqTpg91KldgR08Wz1eInojWbO3my7espq0vV3TcsXPqueTMw6mOTY6/95OFTugujd6cj8WS9y0Rd/gz+8aYgZO9LZbenE80rjAtMhL6aSUiUiI7e3L4FrIFj0TE3etGx/ctBd/iW4tjDCHH4Ox2c2lMcNr3rvZZbb05Eg0T/2W6Kx2ceC4T072rt/Hv960l7xV/o+T/HdPMeW86RIFrAom4weFiOqG7NHb9G7L97RUPhAMU7J7XE5EDN/Hv0kREyiRT8AD6+0gHdzrWWtJ5j650nr6st9dzqqIuyXiYeDgI30HbrOCxdH7v8RNNW2+WrrQOwpmIPN9y3UMv8NsnNhcdF3YNnz3tME47fFqZKpPR5hpDdcQhoiXEJbXroDbT30f6QPj9z9/9eiJy4BSmRURKJBYKThx2DPjWksl7tPYEe6B9a/F8iwWwgAlOK+7JWPqyHpGQQ1NNtH/WOrhefAKfYGytpbUnS98QbZFkfOrJ5PnaHWv468aOouMaqyN89azFLJpRW6bKZDS5/YdcRUMK0aOhOuJgMIQdQ6bgY60d1lJvay05zxILBQeXVUf0fRIZKYVpEZESmVITCQ6FCbl0pfP0Zgt4uy3tZveleRY8C17/km/fWrZ0prEWkvEwjmNorJ6YJ632Zgt09OXIewc6tyJj2aa2FJfcsorNHemi4xbNqOHysxYzpTpapspktIQcQyKkED3aoq7DvPooLW0ZUnmfrGeJhfY/TGc9i28hHnKYXxfV4WMiJaAwLSJSIolIiOPmNXLPqm209ng4xuD5FkOwrM6y99I8A1jfkvMtrhOE6lg4yvGHNE64w8cyeY/2vhyZSbB8fbJa8UIbX79jDX254t/j0xdP46K3LiCi8DWuxUIOMdfRcuEyOqopwbqODBHX0Jf3ie7nQWTWWvryPhHXEHKC64jIyE2sOzURkQpbesR0fv3YSxig0B+kdy3tHljivbvdfl/wLa4xdKTyLD1iepkqHn3WWtr6cnRrb/SEZa3l1399iR/9acNef8V35xj4+MnzeM/Rs9RjeJwyQCLsEAupxVUlNNeEaYiHyPuWjoxHV9Yfst+0tZaurI9noTbq0BgPMasmXMaqRSYuhWkRkVGw6xCxoc5KffXj/q7+yhPkHjVX8GntyZAraEn3RJXJe3z73rXc39JadFx1NMSlyxbx+jkNZapMSi0WcqhSn+iKMsbw9rlJftvSjh+FrqxHe8YL9qq/apbaWkvWC2akPQvJqEsi5HD63KTezBIpEYVpEZESunPVNqqi7qAnVO9+/2L3kbQtUB11ufOZbbymuW5UaiyXnkyett7cK28QyISzoyfLpbesYu323qLjDm5IcMU7F9Ncr6Wl45FaXI0tTVVhls2v4/Z1nTgm6D/dlfVxTPC9cgi2FOX690hHXENtNOj1vWx+HU1VmpUWKRWFaRGREknlCjy6biftvblXlne/ylC50hD0l35k3Q5Sufnjct903vPpSOXozeik7ols1ZYuvnLrajpSxZfvH3dIA19auoiq6Pj7uzzZxUIO8ZCjED0GHZyMcs7CBu7e0EXYKVDwIV3wyfuWgg3euH3l+weN8RCnz00qSIuUmH6yiYiUyM6eHH05j7xvwYADwV7p/mS9rxxt+v/H9LfLskDet6RyPm29ORIN4+dluuD5dKSCU8ytZqMntLue2cp3/vA8ea/49/n9xx7E358wF1dhbNwwGBJhoz3R40BTVZgPLW5kS0+ev7WmWN+Rxe72k8YYw/y6KEc1JZhVE9bSbpFRMH7u0kRExrhMwaMrncdai7UQdh1cJ9g/7fkWbx8B0zUG1zE4Bjwfcp6PxdKVzpMeJ6dee76lI5WjJ6MQPdF5vuX7D67npqe2FB0XDTlcfPphnLqwqUyVSSloT/T4Y4yhuTZCc22ErOfTm/PJe5awG/SRVvsrkdGlMC0iUiLWh76sR/8kc39P6SAoO64hhN1jmbcxwSzQLo555fTv3mxh6NPLKsz3g9Dflc5rX/Qk0JXO89Xbn+WpTZ1Fx02tjnLFOxezYFpNeQqTEQs7huqI9kSPd1HXIRpXeBYpJ4VpEZFSMa8ESgt7nchtMBSd8Olf5j0wxIzNgGqtpTtToDOVw/PHZo1SWht29nHJzavY2pUpOu6ImbVcdtZiGqoiZapMRqoq7JAIu5UuQ0RkXFKYFhEpEWMMVVGXbCGYnfZ9GM4KO7+/e5QhaCM0Fvtj9WTydKby5D21upos/rxuJ1fe2TLktoOlR07n0285lLCWlY4LBqiJuloGLCIyAgrTIiIlEgu51MXDdKTyGGvxfIvj7LmUezCWYLwhCOXJeJj4GJotSuUKtPfl1C96ErHW8ou/bOInf95YdJxj4FNvns/y187UAUfjhGsMtVEt6xYRGSmFaRGREplSEyERDRF2DFlr8a2l4EHILR6oLZaCF4zHBPsXq6IujdWVWyprrSWd9+jLeqRzHgVfIXoySec9vnX3c/xx7Y6i42pjIb78jsM5enZ9mSqTkdIhYyIipaMwLSJSIolIiOPnNdKdzrOpPYXrGDxryReC5d6vnqW2WHw/OCHZYgn1j59RF+P4eVMq0mM6V/DpSufpyxZ0qNgkta07w6U3r2L9jr6i4+ZOqeKK5YuZWRcvU2UyEhHXUBXWbLSISCkpTIuIlNCZR87gkXVtVEVDpHJef0CGvO9jfPaYDfJt0BHUMYaQCcZVR0NUR8OceeSMstadyXsDIVomr6c3d3L5rc/Smc4XHXfi/Cn86xkLiUfGzlYE2TfXBC2SItobLSJScgrTIiIldOSsJLMbEmTyHi91pPAthA1YTP8MNANHdjv9PaYN4ANhB6bVxpjdmOCIWbVlqbcvW6AznSc7Tnpay+i57emX+e7964Y8of3Dxx/Mh48/WMuEx4GqsEs8ZLSXXURklChMi4iUkDGGi962gH+9cSW+jbO1K4Njgl7TuYK/x02ttZZoyMXr3189IxmjLhHmorcuGNWb312trbrTOpVboOD5XPvAem59+uWi42Ihh385YyEnLZhapsrkQEVcQ3XYxdWSbhGRUaUwLSJSYvObqvni0kVceecaHGNo682Rznu4riEWemVZbKbgkfN84uHgsLG6RJgvLl3E/KbqktZjrSVb8EnnPDIFj0zex2o/tACdqRyX3fYsKzd3FR03vTbGFe9czLyppf27KaVlgOqISyykJd0iIuWgMC0iMgqWzK7nG2e/hqvvW8umcGrgYK9swcO3QTuhmmiYZDxMJOQwuzHBRW9dUNIgnc559GTzpLKeDhOTvaxv7eWSW1axvTtbdNxRzUkue8dikolwmSqTAxF2DDURzUaLiJSTwrSIyCiZ31TNte9fwqot3dz+zMusWN/G7ttRHcdw/CGNnHnkDI6YVVuSpd25gk9PJk9fVu2sZHAPrd3Bv93VQmaIvuFnHTWTT715HiEdXjWmVYVdEmF9j0REyk1hWkRkFBljOLI5yZHNSVK5wsCS711Lu0vR/spaS2+2QE+mQEYHiUkRvrX8zyMb+fmKTUXHuY7h02+Zz7LXzCxTZXIgYiGHRMjRbLSISIUoTIuIlEkiEiLRULqX3YLn050p0JPJD3kCs0gqV+Abd7Xw53VtRcfVxcNcdtbhvKa5rjyFybAYIB5yiClEi4hUnMK0iMg4k8l7dKfz9KontOynlzvTXHLzKja2pYqOmze1iiveeQTTa2Nlqkz2164QHQ87aksmIjJGKEyLiJRJKldgZ0+OTMEjFnKZUjO8Zd7pnEdHKqel3DIsT23q4PLbnqU7U/zNl5MXTOXzbz+MeNgtOk7KLx5ySChEi4iMOQrTIiKjyFrLM1u6uGPlVla88KoDyAwcNy84gOzIWclBDyBTiJYDYa3lpqde5vsPrmOoXQD/cMIcPvCG2aPa31yGx2CIh4yWc4uIjGEK0yIio2Rda2/QGqt9362xoiGXB1t28Mi6NmY3JLjobXu2xlKIlgOVK/h89w/Pc+eqbUXHxcMuX1y6kBPmTylTZTIUx5j+PdFGM9EiImOcwrSIyCh4alMHV965hs5UnrbeLOm8j+sYEhEXxxj8/hO4u9J54mGHTN7jX29cyReXLmLh9FqFaDlg7X05vnLrala/3F103IxkjK+98wjmTqkqU2UylET/cm6tEBARGR8UpkVESmxday9X3rmG9r4cW7syhF2HabVRqqOhPW6Sd7W06kjl2dyRouDF+Mqtq/nnty1gdkOigl+BjFdrt/dw6c2r2dGbLTru6Nl1fHnZ4dTGw2WqTIpxjKEm4hBRP28RkXFFr9oiIiVkreXq+9bSmcqztStDIuLSXB+nJhbea7bJGEN1NMT02iixsMvW7gxdqTw/eWQj1qrVlQzPH9a0cuGv/zZkkD776Fl8892vUZAeIyKuQ33MVZAWERmHNDMtIlJCz2zpYlN7irbebP+MdGyvfY/WWnwLnm8HQnNjdYRtXdkghHekeX57HwumV+/rjxDZg+dbrv/zBn712EtFx4Vdw2feuoAzjphepsqkGANUR1xiIYVoEZHxSmFaRKSE7nhmK7mCTzrvM602uleQ9nxLwffhVRPPDoZkPMTO3hx5z/Lg2laFaRlSb7bAlXeuYcUL7UXH1SfCfHX5YhbPTJapMikm7BhqIq5O6RYRGecUpkVESiSVK7BifRtd6TyuEyzhBrC+h00Hh0FZa7Hebkk6VotxgpmpRMTFNYaeTJ6/beogk/eIqeevDGJzR4pLbl7NpvZU0XELplVzxfIjmFoTLVNlUkx12CUe1my0iMhEoDAtIlIiO3ty+BayBY9ExB3YI23T3WTu+84+n+O8+UJI1AFBX9lYxCHn+fgWOlJ5ZiQVpmVvf93YzhW3r6E3Wyg67i0Lm/jcaQuI6k2ZinONoTbqEtJstIjIhKG3Rkto586dfOtb3+KEE05g+vTpRKNRZs6cyRve8AYuvvhiHn300SGvcffdd3P22WfT3NxMNBqlubmZs88+m7vvvnu/60ilUlx11VUce+yxNDQ0UF1dzaJFi/jc5z7Hpk2bRvIlikgRmULQyiroI31gN8wOBr9/4jpbUGss2ZO1lt8+/hL/euMzRYO0AT524ly+uHShgvQYEA8Fh4wpSIuITCyamS6R3/72t3zyk5+kra1tj89v3bqVrVu38thjj/H8889z88037/P51lo+8YlPcN111+3x+S1btnDTTTdx0003cf755/PDH/6waP/J9evXc+aZZ/Lcc8/t8fmWlhZaWlr40Y9+xC9/+UuWLl16YF+oiAwqFgpCi2PAP8DTuH0su+63oyGFIHlFruDzH/et5d5ntxcdVxVx+dKZizjukMYyVSaDMRhqo2p5JSIyUSlMl8DPfvYz/v7v/x7f92lqauKTn/wkJ554Ig0NDWzbto3169dz2223EQ4P3obkkksuGQjSS5Ys4fOf/zzz5s1j/fr1fOtb3+Kpp57iuuuuY+rUqXzta1/b5zV6e3tZtmzZQJA+77zzeO9730s8HueBBx7gG9/4Bl1dXZxzzjk8+uijvOY1ryn9/xkik9iUmgiOCUJwb7aAtbbom1+vZrFkcj6JiIvjGOoTal0kgZ29Wb5y62rWbO0pOq65Ps7Xlh/B7Eb1Ka801xiSUR0yJiIykRmrZqYjsmbNGpYsWUI2m+VNb3oTt912G8nkvk9LzeVyRCKRvT6/bt06Fi1aRKFQ4JhjjuGhhx4iHo8PPJ5KpTj55JN5/PHHCYVCtLS0MG/evL2uc9lll3H55ZcD8K1vfYuLL754j8cfffRRTjrpJAqFAm9+85u5//77R/Kl72Xz5s0cdNBBALz00ks0NzeX9Poi48E37lrDgy072NSeYlptlJpYGL+vo+ieadO/Z7ovV2Bnb46ZyThvOKSB8086pHyFy5i1Zms3X751NW29uaLjXj+nnkvOXERNTG/ClNs9f95zNVjYCfZHH+h2DxGRieI9p03syTutOxqhCy64gGw2y5QpU7jxxhsHDdLAPoM0wNVXX02hEOx9u+aaa/YI0gCJRIJrrrkGgEKhwHe+8529rpHP5/nP//xPABYtWsRnP/vZvcYcf/zxfPSjHwXggQce4Iknnhj6CxSRYTnzyBlEQg7xsENHKr/fy719LF3pArGQS9g1nLKgaZQrlfHg3me385nf/G3IIP3/jmnmyncdqSA9BkRdh6SCtIjIpKAwPQItLS384Q9/AOBTn/oUU6ZMGfY1rLXccsstACxcuJDjjjtun+OOO+44DjvsMABuvvlmXr2g4MEHH6SzsxOAc889F8fZ97f2Ix/5yMDHN95447DrFZHijpyVZHZDgsbqKHnPZ3t3Bv/VTaVfxcfS1puj4PvUJcLMqI9z6LSqMlUsY5HnW374x/X8210t5L3B//6EXcO/nLGQT5w8T8uJKyziGqojLrVRd1jbO0REZPxSmB6B3/72twMfn3POOQMfd3R08Pzzz+91GNm+bNiwgS1btgBw8sknFx276/HNmzezcePGPR7705/+tNe4fTnmmGOoqgpu0h9++OEh6xOR4THGcNHbFgShOBkjlfPY1pUddHw6X2BbV5Z03mNqdZTaeIi/f+Mc3YxPYr2ZAl+66Rn+9/HNRcc1VkX4zt+9ltMOn1amyuTVEpEQ02pjNMZDJKMh4iHdVomITCZ61R+BFStWAJBMJlm0aBE33HADRx11FA0NDSxYsIApU6ZwyCGHcPnll9Pb27vPa6xZs2bg44ULFxb983Z/fPfnDec6oVBoYL/1q68xlM2bNxf9b+vWrcO6nshENb+pmi8uXURDVYRZdXHcIrm4vS+PawzTamLUJcJ8/OR5zG7Q4VGT1aa2FP/4yyd5bGNH0XELp9fwgw8ezaIZtWWqTF6tJhZmejJGVTSkJd0iIpOUTvMegWeffRaAOXPmcMEFF/C9731vrzEbNmzgsssu43e/+x333HMPM2fO3OPxl156aeDjoQ7s2nW416uft/vvq6qqqKurG/I6K1euZMeOHWSzWaLRaNHx+/rzRaS4JbPr+cbZr+Hq+9ayMddNZpBx02pihGuizKiP8/dvnKMgPYmteKGNr9+xhr5c8f7ipx0+jX9+2wIimgWtmNp4mCnV+/ezU0REJi79JB6B9vZ2INg7/b3vfY+6ujp++MMf0traSiaT4a9//StnnHEGAKtWreKcc87B9/09rtHT80qbk+rq6qJ/3q7l2cBeM927rjPUNYa6joiUzvymaq59/xIuPn3w1SKvnV3HP7/tML585iIF6UnKWsuvHtvEl25aVTRIOwY+eco8vvD2wxSkKyipIC0iIv00Mz0CfX19AGSzWVzX5a677trjALFjjjmG22+/nWXLlnHXXXfxyCOPcOONN/Ke97xnYEwm88p81WCnfe+y+wxyOp3e47Fd1xnqGkNdp5hXz4a/2tatWzn22GP3+3oik4ExhoUzagZ9/H3HHsSUpqHfBJOJKZv3+Pa9a/lDS2vRcdXREJcuW8Tr5zSUqTLZl7pEhIaqoX/OiojI5KAwPQKxWGwgUJ9zzjn7PInbcRyuuuoq7rrrLgB+9atf7RGmY7HYwMe5XPHWJ9nsK4cYvbp91q7rDHWNoa5TjPpGi4iUzo6eLJfesoq124uvEDq4IcEV71xMc71WLlRSfSJCvYK0iIjsRuvERqCm5pXZpl3Lufdl8eLFzJo1C4C//vWvg15jqCXXu4I77L2ce9d19mfZdrHriIjI6Fv9chef+MUTQwbp4w5p4Nr3L1GQrrCGKgVpERHZm8L0COx+INf+Hh7W2rrnUr7dn7d5c/E2KLsvs371YWC7rtPX1zfQb3qo60ydOnW/Dx8TEZHSuOuZrfzz/z5NRypfdNz7jz2IK5YfQVVUi8gqqbEqSl1CQVpERPamMD0CixcvHvjY84qfvrrr8VBoz5uiww8/fODjlpaWotfY/fFFixYd0HUKhQLr16/f5zVERGT0eL7l2vvXcdW9a8l7dtBx0ZDDJWcu4mNvOgTXUculSmqsjpJMhCtdhoiIjFEK0yNw0kknDXy8K6AO5oUXXgAYWO69y9y5cwfaZf3xj38seo2HHnpo4Bpz5szZ47ETTzxx4ONi13n88ccHlnmfcMIJRf88EREpja50ni/830pufGpL0XFTq6P853tfy6kLm8pUmQxmSk2UZFxBWkREBqcwPQJnnXUW4XDwg/bGG28cdNwf//hH2traAHjTm960x2PGGJYvXw4EM8orVqzY5zVWrFgxMOO8fPlyjNlztuKUU04hmUwC8D//8z9Yu+9Zj5/+9KcDH7/rXe8atGYRKQ1rLS1buwd9/JePbeK5bT2D/puV8W/Dzj7+8YYneXJTZ9FxR8ys5QcfPJoF0wY//V3KY2pNlNqYgrSIiBSnMD0CjY2NfOxjHwPgvvvu49e//vVeY3p6evjMZz4z8PuPf/zje435zGc+M7D8+4ILLtirXVU6neaCCy4AgmXiu19vl0gkwoUXXgjAmjVr+Pa3v73XmEcffZQf//jHAJx88sm8/vWv34+vUkQO1LrWXj71y6e46p7nBh3z9KYurr5vLV+9fQ2b2lNlrE7K4c/rdvKpXz7F1q5M0XFLj5jOt885Sm2XKswYQ1NtjBoFaRER2Q8K0yN0+eWXM3v2bAA+9KEPccEFF/DAAw/wxBNP8NOf/pRjjz2Wv/3tbwB88pOf3GeAXbBgAZ/73OeAYBn2CSecwG9+8xsef/xxfvOb33DCCSfw+OOPA3DxxRdz6KGH7rOWiy++mAULFgDw+c9/no9//OM88MADrFixgm984xucdtppFAoF4vE43/nOd0r8/4SI7O6pTR38640rWbu9h+3dgwep7T0Ztndn2Lizj3+/9zmeLTKLLeOHtZafr3iRS29ZTTo/+JkajoELTp3PZ09bQCSkH8mVZIxhak2Uah34JiIi+8lYrS0csTVr1nDWWWexbt26Qcf8wz/8Az/84Q8HloW/mu/7nHfeeVx//fWDXuOjH/0o1113HY4z+A3XunXrWLp0Kc8///w+H6+treWGG25g2bJlg17jQG3evHnglPGXXnpJfall0lrX2su/3riS9r4cW7syRPM9NDz2w32OzZ74j3RSTcH3mVodpS4R5rOnHcbsBrVCGq/SeY9v3f0cf1y7o+i42liIL7/jcI6eXV+mymQwxhiaaqIHfHL67+5dWeKKREQmhvec9ppKlzCq9DZ4CSxatIi//e1vXHXVVbzhDW+goaGBSCRCc3Mzf/d3f8f999/Pj3/840GDNIDjOPz4xz/mjjvuYPny5cycOZNIJMLMmTNZvnw5d955Jz/60Y+KBmmA+fPn89RTT/HNb36TY445hrq6OhKJBIcddhgXXXQRK1euHJUgLSIBay1X37eWzlSerV0ZEhGX6cnBW9DFwyGmJ6PEwy47erN0pwv85JGN2kM9Tm3rznDhr54aMkjPaUzw/Q8crSA9BhhjmFZ74EFaREQmL81MS8loZloEVm7u5Es3rWJLRwrPQnN9HFKdZO77zj7HO2++EJOow8eyrSuL239j/89vO4wF06vLW7yMyNObO7ns1mfpShfvH33C/Eb+9YyFJCIKb5W2K0iP9HuhmWkRkX2b6DPT+kkuIlJCdzyzlVzBJ533mVYbxTEGfz+e52BIxkPs7M2R9ywPrm1VmB5Hbnv6Zb57/zo8v/j70x8+7mA+/MaDcYz6R1eaMYbptTHiEbfSpYiIyDilMC0iUiKpXIEV69voSudxHTPsg4wSERfXGHoyef62qYNM3iMW1o3+WJb3fK59YB23Pb216LhYyOELZyzk5AVTy1SZFOM6hmm1Mf37EhGREVGYFhEpkZ09OXwL2YJHIuJijMFaS6bIac7tqRyJsEcs7GAwxCIOOc/Ht9CRyjMjqZv9saozleOy255l5eauouOm1Ua5YvkRzG/SSoOxIOw6TE/GCLs6NkZEREZGYVpEpEQyhSA0+xYcY8jkPVp7sni9WaYN8py+bIHenixh16GxKoKDwe8/yiJbGDyES2Wtb+3lkltWsb07W3TcUc1JvvKOw6lLqH/0WBALu0yrjeE6WmYvIiIjpzAtIlIisVAwi+yYIAh3d+aDPbT+4LumC77F93x8a9nekyHiOuy6z4+GNCs9Fv1x7Q6+eVcLmULx3fBnHTWTT715HiHNgI4JyXiYhqoIRvvVRUSkRBSmRURKZEpNBMeAawztqRwh16Hg+YSLnEkVcgw5CwUvGNSTL1AXD+M4hvrE4O30pPx8a/nZIy/ysxUvFh3nOoYLTp3PWUfNLFNlUkw07DKlOqI3p0REpOQUpkVESiQRCfGGQxr41V9ewlrIF3xcxxAqsqTUMYaQa/B8S74/UOcKPkc1J3U40hiSyhX4t7ue4+F1O4uOS8bDXHbW4RzVXFeewmRQjjHUV0VIxvWmlIiIjA6FaRGREjpsWg2F3dojuQ5DLis1xuA4Fq9/i3TBWg6ZqsOqxoqXO9NcestqNuzsKzpu3tQqrlh+BNOTsTJVJoMJuw7TamNEQlpiLyIio0dhWkSkhNZu791jJtrzIUTx3sMWu8e26pBj2LCzFwY9tkzK5clNHXz1tmfpzhSKjjtpwRS+8PaFxLWaoOKqoyGmVEdxdMiYiIiMMoVpEZESSeUKrHihjWjYoS8XhOKCbwf2Q++Lby2FgsUCYQcKPkRCDn/b1Kk+0xVkreXmv73M9x5Yh1/8vRD+/oQ5fPANs3WwVYUZY2hIREjqrAERESmTiofpTCbDE088wbZt20ilUixfvpza2tpKlyUiMmy7+kx7viUZD5Mt+BhjoUjGKvjB4yHH4DqGeMTB8636TFdQ3vP57h/WccczW4uOi4ddvrh0ISfMn1KmymQwkZDDlOqo3nwSEZGyqliYfumll7jkkkv4zW9+Qz6fH/j8M888w+GHHz7w+x//+Mf813/9F8lkknvvvVfv/IvImLV7n+l42GVqTZTt3Vm87OD7NkOOwboOYddhSnWE3kxhoOWS+kyXX3tfjstuXc2ql7uLjpuRjPG1dx7B3ClVZapM9sWY4NT7ZDys+wMRESm7ioTpxx57jKVLl9LR0YG1r6yf29cPwrPOOot/+qd/Ip/Pc++993L66aeXs1QRkf22e59p31qiIZfZDXFSTnrQ51RFQ1TVxIiFDWDwseozXSFrt/dw6c2r2dGbLTpuyew6vrzscJ0SXWHRsMvU6qgOGRMRkYop+0+grq4uli9fTnt7O9OnT+f73/8+zzzzzKDjp06dyhlnnAHAHXfcUa4yRUSGbVef6WjIJZXz+t8sNMTCg7/UNiQi/Y8bLJZMzifiOuozXWb3t7Ty6V//bcggffaSWXzr3a9RkK6waNhlhk7rFhGRCiv7zPQ111zD9u3bmTJlCo8++iizZ88e8jlve9vbuOWWW3jsscfKUKGIyIFJREIcN6+RB1t20JXO05stUBPb/9CVynl41lITC/Pag+q0/7MMfGu5/uEN/PKxl4qOCzmGi956KGccOaNMlclgwq7D9NqYTusWEZGKK3uYvu222zDG8M///M/7FaQBFi9eDMD69etHszQRkRE788gZPLKujXjYoSOVpyq6fy+zPpaudIFYyCXsGk5Z0DTKlUpftsDX71zDihfai46rT4S5/KzFHDErWabKZDCuY5hWG8NVkBYRkTGg7GH6+eefB+Ckk07a7+fU1dUB0N1d/EAYEZFKO3JWktkNCTJ5jy2dabZ3Z5gaKt5bycfS1puj4Ps01sSYUR/n0Gk62Go0be5IcenNq3mxPVV03IJp1Xz1rMU01cbKVJkMxpggSGtpt4iIjBVl/4mUTgcH8VRV7f+NYm9vLwCxmG5mRGRsM8Zw0dsWUJcIMyMZI5Xz2NY1+D7cdL7Atq4s6bzH1OootfEQf//GOTqZeBQ9vrGdf7zhqSGD9FsWNvGff/daBekxwDGGphq1vhIRkbGl7GF66tSpQNAaa3898cQTAMyYob1qIjL2zW+q5otLF9FQFWFWXRy3SC5u78vjGsO0mhh1iTAfP3kesxsS5St2ErHW8tsnNvMvNz5Db7Yw6DgDnP+muXxx6UKiCm8VFwk5zKyL7/eWCRERkXIpe5g+9thjAbjrrrv2a7zneVx33XUYYzjxxBNHszQRkZJZMrueb5z9GhZMq2FakZnNaTUxptVGmTO1is+edhiHz6gtY5WTR67g8617nuMHD67HL7Lqviri8vV3HcF7j52t1QFjQG08zKy6uJZ2i4jImFT2n07ve9/7sNZy/fXX89RTTxUd6/s+n/jEJ3j22WcB+OAHP1iOEkVESmJ+UzXXvn8JF5++cNAxr51dxz+/7TC+fOYizUiPkrbeLBf979+4Z/X2ouOa6+N87/1Hc9whjWWqTAZjjKGpNsaU6qje1BARkTGr7GH63e9+N2984xvJZrO85S1v4Xvf+x6tra0Djxtj2L59Oz//+c855phjuP766zHG8Pa3v51TTjml3OWKiIyIMYaFM2oGffx9xx7EgunVCgyjZM3Wbj5xw5Os2dpTdNzr59TzvfcvYXaj3tCoNMcYptfGqNaybhERGeMq8pPq5ptv5qSTTqKlpYULL7yQCy+8cOBG8uijjyaXyw2MtdZy5JFHcsMNN1SiVBERGafufXY7/37vc+S94qepn/O6Zs4/6RC1WxoDdrW+0kFjIiIyHlRkE9KUKVN4/PHH+ad/+iei0SjW2oH/stnswMehUIjzzz+fRx55ZKA9loiISDGeb/nhH9fzb3e1FA3SYdfwL28/jE+eMk9BegxwHcP0pIK0iIiMHxVbQ5VIJLjmmmu47LLLuOeee3j88cdpbW3F8zwaGxtZsmQJZ5xxBjNnzqxUiSIiMs70ZPJ8/Y41PLaxo+i4xqoIX12+mEU68G1MCDkO05PqIS0iIuNLxTckNTY28v73v5/3v//9lS5FRETGsU1tKS65ZRWbO9JFxy2cXsNXly9mSnW0TJVJMWE3CNJhV0FaRETGl4qHaRERkZFa8UIbX79jDX05r+i40w6fxj+/bYFmQMeIsOswIxkjpCAtIiLjkMK0iIiMW9Zafv3Xl/jRnzZQ7Jgxx8DHT57He46epZPTx4ho2GV6bUz71UVEZNwq+1vBTz31FK7rEo/H2bJly5Djt2zZQiwWIxQKDfSbFhERyeY9rryzhf8eIkhXR0N84+wjOed1zQrSY0Qs7DJDQVpERMa5sofp3/zmN1hrWbZsGbNmzRpy/KxZszjrrLPwfZ9f//rXZahQRETGuh09WT79m7/xh5bWouNmNyT4/geW8Po5DWWqTIaSiISYkYzhKEiLiMg4V/Yw/eCDD2KM4Ywzztjv55x55pkA/P73vx+tskREZJxYtaWLT/ziCdZu7y067rhDGrj2/Utork+UqTIZSlU0xLTaqFYIiIjIhFD2PdMvvfQSAIcffvh+P+ewww4DYPPmzaNSk4iIjA93PbOVq3//PAW/2MJueN+xB/EPJ8zVMuIxpDoWoqkmVukyRERESqbsYbqtrQ2AWGz/f6BGo0H7ktbW4sv5RERkYvJ8yw8eXM+NTxU/ayMScrj4tMN4y6KmMlUm+6MmFmZqjVqRiYjIxFL2Zd719fUAbNq0ab+fs2tGura2dlRqEhGRsasrnecL/7dyyCA9tTrKd9/7WgXpMaY2riAtIiITU9nD9K7l3bfeeut+P+emm24CXlnuLSIik8OGnX384w1P8uSmzqLjFs+s5QcfPJoF02rKU5jsl2Q8zJRqBWmRcsh6Pm3pAtt687SlC2Q9v9IliUx4ZV/mvXTpUh544AF+9rOfce655/KmN72p6PiHHnqIn//85xhjWLZsWZmqFBGRSvvzup1ceWcL6bxXdNzSI6Zz4VsOJRIq+/vDUkRdIkJDVaTSZYhMaNZaNvfkebo1xfqOLHa3RoEGw7z6KEc1JWiuCevgP5FRUPY7j49//ONMmTIFz/NYunQp11xzDZlMZq9xmUyG7373u5x55pl4nkd9fT2f/OQny12uiIiUmbWWn694kUtvWV00SDsGPvXm+Xz2tAUK0mOMgrTI6Gvty/Pz1W3833PttLRl6M55tGc82tLBr905j5a2DP/3XDs/X91Ga1++0iWLTDhln5murq7ml7/8JUuXLiWVSvGZz3yGL37xixxzzDHMmDEDYwwvv/wyjz/+OKlUCmst4XCYX/3qV9ozLSIywaXzHt+6+zn+uHZH0XG1sRBfXnY4Rx9cX6bKZH8pSIuMvhe7sty+rpNUwac355PzLI6BiGtwDPhApuCTyvtEXEPet/y2pZ1l8+s4OKmtFyKlUvYwDfDWt76Ve+65hw9+8INs3bqVvr4+HnrooT3GWBssU5k1axY///nPOeWUUypQqYiIlMu27gyX3ryK9Tv6io6b05jga+88gpl18TJVJvtLQVpk9LX25bl9XSe9eZ+urIdrIBl1iLpmj6Xc1lqynqUv79OR8fCjcPu6Ts5Z2EBTVbiCX4HIxFGRMA3w5je/mfXr1/Ozn/2MO+64g6eeeoqdO3cCMGXKFI4++mje8Y538MEPfnCgNZaIiExMKzd3ctmtz9KZLr4M8YT5jfzrGQtJRCr240sGoSAtMvqstdy9oYtUIQjSEceQjDr73A9tjCEWMkRdQ1c2GO8YuGdDFx9c3Kg91CIlUNG7kVgsxvnnn8/5559fyTJERKSCbnv6Zb57/zo83xYd9+HjDubDbzwYRzeAY46CtEh5bO7J054u0JvzB2akhwrFxgSBuz3j0ZvzaXMKbOnJ01yrf7MiI6W39kVEpCIKns+1D6zn1qdfLjouFnL4whkLOXnB1DJVJsOhIC1SPk+3pij4kPPsfgXpXYwxVIUdurI+BT+4jsK0yMgpTIuISNl1pnJcftuzPL25q+i4abVRvrb8COY1VZepMhmOZDysIC1SJlnPZ31HlnTBxzEQdYe3SifafzhZuuCzriNL1vOJuuqEIDISYyJMd3d309PTg+cV7yUKMHv27DJUJCIio2V9ay+X3LKK7d3ZouOOak7ylXccTl1CYW0sSsbDNFbrTBORcunN+Vgsed8SedVhY/vDGDNwsrfF0pvzicYVpkVGomJh+r777uP73/8+f/rTn+jo6Niv5xhjKBQKo1yZiIiMlofW7uDf7mohU/CLjjvrqJl86s3zCGnWZExSkBYpv7wXnCthLTgHeHSEAxTsntcTkQNXkTB94YUX8r3vfQ94pQWWiIhMXL61/OyRF/nZiheLjnMdwwWnzueso2aWqTIZLgVpkcoI9y/rNv19pA+E3//83a8nIgeu7GH6l7/8Jddeey0QnOb9zne+k9e97nU0NDTgOJqBEBGZaFK5Av9213M8vG5n0XHJeJjLzjqco5rrylOYDJsOGxOpnOqIg8EQdgyZgo+1dlhLva215DxLLBQcXFYd0X23yEiVPUz/13/9FwAHHXQQ999/P/PmzSt3CSIiUiYvd6a59JbVbNjZV3TcvKlVXPHOI5heGytTZTJcCtIilRV1HebVR2lpy5DK+2Q9Syy0/2E661l8C/GQw/y6qA4fEymBsofplStXYozhK1/5ioK0iMgE9uSmDr5627N0Z4qfdXHygql8/u2HEQ+7ZapMhktBWmRsOKopwbqODBHX0Jf3ie7nQWTWWvryPhHXEHKC64jIyJU9TOfzeQCWLFlS7j9aRETKwFrLzX97me89sA5/iGMx/v6Nc/jgcbOHfSqtlI+CtMjY0VwTpiEeIu9bOjIeXVl/yH7T1lq6sj6ehdqoQ2M8xKyacBmrFpm4yr6+Y86cOQD09vaW+48WEZFRlvd8/v2+tVxzf/EgHQ+7XLF8MR86/mAF6TFMQVpkbDHG8Pa5SRIhh2TUJedb2jPewB7q3VlryRR82jMeOd+SjLokQg6nz03qdVekRMoeps8++2wA/vCHP5T7jxYRkVHU3pfjs//7NHc+s63ouBnJGNe+fwknzJ9SpsrkQChIi4xNTVVhls2vozrsUB9zcYyhK+uzM+3RlfXoyQa/Br/3cYyhPuZSHXZYNr+OpirNSouUStnD9Gc/+1lmz57Nd77zHVpaWsr9x4uIyChYu72HT/7iSVa93F103JLZdXz/A0czd0pVmSqTA1GvIC0yph2cjHLOwgamVYWpj7k0xkPEQg6ehZwPnoVYKFjSXR9zmV4V5pyFDRycVFs7kVIq+57pZDLJ3XffzVlnncUJJ5zAFVdcwfve9z7q6+vLXYqIiJTAAy2tfOue58gWinc+PXvJLD55yjxcR8sLxyrXMUypjlIVLfvtgYgMU1NVmA8tbmRLT56/taZY35HF8spSb2MM8+uiHNWUYFZNWEu7RUZB2X9aHnLIIQCkUik6Ojq44IILuPDCC5kyZQqJRPGTBY0xrF+/vhxliojIEHxr+cmfN3LDXzYVHRdyDBe99VDOOHJGmSqTA1EdDdFYHdWbHSLjiDGG5toIzbURsp5Pb84n71nCbtBHWu2vREZX2cP0xo0b9/i9tRZrLa2trUM+V++oiYiMDX3ZAl+/cw0rXmgvOq4+EebysxZzxKxkmSqT4XKMYUpNlGrNRouMa1HXIRpXeBYpp7L/5Dz33HPL/UeKiEgJbe5IcenNq3mxPVV03KFN1VyxfDFNtbEyVSbDFXIcpiWjREPq8S0iIjJcZQ/TP/nJT8r9R4qISIn8dWM7V9y+ht5soei4Uxc28bnTFhALK6SNVZGQw/TaGCEtAxURETkgWtMlIiJDstbyuye38F9/XF+0f7QBPvamubz39Qdpa84YloiEaKqJ4mh/tIiIyAFTmBYRkaJyBZ+rf7+We1ZvLzquKuLypTMXcdwhjWWqTA5EMh6msVrtcUREREZKYVpERAbV1pvly7euZs3WnqLjmuvjfG35EcxuLN6VQSpHB42JiIiUVsV/onZ0dPD000+zc+dO0uk01hZZPwh8+MMfLlNlIiKTW8u2bi69ZTVtvbmi414/p55LzlxETSxcpspkuMKuQ1OtDhoTEREppYqF6QcffJCvfOUrPPzww/v9HGOMwrSISBnc9+x2vn3vc+S94m9wnvO6Zs4/6RD1Jh7DtD9aRERkdFQkTP/gBz/gggsuGOgxLSIiY4PnW/77Ty/wv49vLjou7Bo++7YFnLZ4epkqkwNRHQ0xtSaqw+BERERGQdn7YaxZs4YLL7wQay1HHnkkN998M3fccQcQzDyvX7+exx9/nB/+8IccffTRAJx44omsXr2aF154odzliohMGr2ZAl+66Zkhg3RjVYTv/N1rFaTHuNp4mKbamIK0iIjIKCn7zPQ111yD53lMnTqVP/3pT9TU1LB69eqBx+fOncvcuXM5+uijOe+88/iXf/kXrrrqKi644AJ+//vfl7tcEZFJYVN7iktuXsXmjnTRcQun1/DV5YuZotOgx7T6RIT6qkilyxAREZnQyj4z/cc//hFjDBdeeCE1NTVFxxpj+OY3v8mpp57KAw88wPXXX1+mKkVEJo8VL7TxTzc8OWSQPu3waXzn716rID2Gmf4TuxWkRURERl/Zw/TmzcHywV1LuIE9lqDl8/m9nnP++edjreUXv/jF6BcoIjJJWGv59WOb+NJNq+jLeYOOcwx88uRD+MLbDyMSKvuPDdlPkZDDrLo4tTpVXUREpCzKvsw7k8kAMHPmzIHPVVVVDXzc0dFBU1PTHs+ZP38+AM8++2wZKhQRmfiyeY9/v28tv1/TWnRcdTTEpcsW8fo5DWWqTA5EMh6moSqi/dEiIiJlVPYphoaG4Iasr69v4HNTp04duAFYu3btXs/ZuXMnAJ2dnaNfoIjIBLejJ8tnfvP0kEF6dkOC739giYL0GOYYw/RkjMZqndgtIiJSbmUP0wsXLgTg+eefH/hcIpHg0EMPBeDWW2/d6zm7Pjd16tQyVCgiMnGt2tLFJ37xBM9t7yk67rhDGrj2/Utork+UqTIZLtcJgnQiUpEulyIiIpNe2cP0iSeeiLWWhx56aI/Pn3322Vhr+e53v8v1119PX18fO3bs4Nvf/jbXXXcdxhhOPfXUcpcrIjJh3LVqG5/97dN0pPY+m2J37z/2IK5YfgTVUYW0sSrkOMxIxomF3UqXIiIiMmmVPUwvW7YMgFtuuWVg/zTAZz/7WRoaGsjn85x33nnU1tYyffp0vvCFL1AoFIjFYvzLv/xLucsVERn3PN9y7QPruOqe58h7dtBxkZDDl5Yu4mNvOgTX0ZLhsSrsOsysi+kwOBERkQor+7TDG97wBn7yk59QKBTo6OhgxowZADQ2NnLPPffw//7f/2PDhg17PKepqYmf/exnLFq0qNzlioiMa93pPFfc/ixPbOosOm5qdZQr3rmYBdOKtyyUygqCdFxvdoiIiIwBFVnDd+655+7z86973etoaWnh/vvvZ/Xq1RQKBQ499FBOP/10Egnt2xMRGY4NO/u49JZVvNyZKTpu8cxaLj9rMQ3qTTymhRyH6cmYgrSIiMgYMeY2xIXDYU4//XROP/30SpciIjJu/XndTq68s4V0fvD+0QBLj5jOhW85VEuGxzjHGKYlo4RdfZ9ERETGijEXpkVE5MBZa7nhL5u4/s8bi45zDPzjKfN515KZaqk0xhljmFYbIxrSYWMiIiJjicK0iMgEkc57fOvu5/jj2h1Fx9XGQnx52eEcfXB9mSqTkZhaEyUeUZAWEREZa0YtTL+69VWpnHTSSaNyXRGR8Wxbd4Yv37yadTt6i46b05jga+88gpl18TJVJiPRWB1VizIREZExatR+Qp9yyiklXzpojKFQKJT0miIi493KzZ1cduuzdKaL948+YX4j/3rGQhIRhbPxoC4RIRkPV7oMERERGcSo3lFZO3g/UxERGbnbV77Mf/5hHZ5f/PX2w8cdzIffeDCO9kePC9WxkE5XFxERGeNGLUw/8MADo3VpEZFJr+D5fO+B9dzy9MtFx8VCDl84YyEnL5hapspkpBKREFOro5UuQ0RERIYwamH65JNPHq1Li4hMap2pHJff9ixPb+4qOm5abZSvLT+CeU3VZapMRioadmmqieqEdRERkXFAG+dERMaR9Tt6ueTmVWzvzhYdd1Rzkq+843DqEloqPF6EXYfptTEcR0FaRERkPFCYFhEZJx56fgf/dlcLmbxfdNxZR83kU2+eR8h1ylSZjFTIcZiRjOEqSIuIiIwbCtMiImOcby0/e+RFfrbixaLjXMdwwanzOeuomWWqTErBdQzTkzG9+SEiIjLOVDRM9/T08Pvf/56nn36anTt3kk6ni54Abozhxz/+cRkrFBGprHTO4xt3tfDwup1FxyXjYS57x+EcdVBdeQqTknBMEKQjIQVpERGR8aYiYdr3fa644gr+/d//nb6+vv16jrVWYVpEJpWXO9NcestqNuws/jo5b2oVVyw/gunJWJkqk1IIuw7TahWkRURExquKhOmPfOQj3HDDDVhrcV2XxsZGWltbMcbQ3NxMR0cHvb29QDAbPWXKFBKJRCVKFRGpiKc2dXD5bc/SnSkUHXfSgil84e0LiYfdMlUmpRANu0yv1R5pERGR8azsb4ffc889/OIXvwCCUN3a2srvf//7gcdffPFFuru7aWlp4dOf/jSO41BfX89dd93Fhg0byl2uiEhZWWu56aktXPy7lUMG6b9/4xy+suxwBelxpjoaYqYOGxMRERn3yh6mf/KTnwCwePFirr/+eurr6/fZT3PBggVcffXV3HTTTaxfv56lS5fS1VW8p6qIyHiW93z+/b61XHP/OvzBj48gHnb56lmL+dDxB6sf8ThTGw/TVBvT901ERGQCKHuYXrFiBcYY/umf/mm/xi9btoxzzz2XF198ke9+97ujXJ2ISGW09+X47P8+zZ3PbCs6bkYyxrXvX8KJh04pU2VSKtWxEFOqo5UuQ0REREqk7GG6tbUVCGaed3HdV5YoZrPZvZ7znve8J1j6eNNNo1+giEiZrd3ewz/e8CSrXu4uOm7J7Dq+/4GjmTulqkyVSakkIiGmKkiLiIhMKBVrjdXQ0DDwcU1NzcDHra2tHHTQQXuMbWpqAmDjxo1lqU1EpFz+vKGbH65YS7bgFx33ztfO5B9PmadexONQLOwyrTaqpd0iIiITTNnvyqZNmwZAe3v7Hp+LRCIArFy5cq/nbNq0CYBMJlOGCkVERp8F2madwH8+/HLRIB1yDJ87bQEXvuVQBelxaNep3QrSIiIiE0/Z78yOPPJIAJ599tmBz4VCIZYsWQK8ckDZ7v7rv/4LgIMPPrgMFYqIjC7fibDt0OV0znxD0XH1iTD/8f+OYumRM8pUmZRSLOwyozaGo1O7RUREJqSyh+lTTjkFa+0e7bAAPvjBDw7si/7whz/MHXfcwW9/+1ve8Y53cM8992CMYfny5eUuV0SkpHLROjYf/j5SdfOKjju0qZoffOBojpiVLFNlUkpV0RAzkgrSIiIiE5mx1hZpwFJ6GzZsYN68eUSjUTZu3Diw7LtQKHDcccfx5JNP7rUczlrLwQcfzJNPPkl9fX05y5Vh2Lx588B+95deeonm5uYKVyQyNmzbto0PfOADpGoPZvu8M/FDsaLjT13YxOdOW0BM/aPHpepYiKaa4t9jmVh+d+/eW9RERATec9prKl3CqCr7zPTcuXN54YUXWLVqFbW1tQOfD4VC3HfffXzgAx8gFAphrWVXzj/zzDP505/+pCAtIuOStZbOaUezdcG7igZpA5z3prl8aelCBelxKhkPK0iLiIhMEhU5zXvOnDn7/Hx9fT0///nP+f73v8/zzz9PoVBg/vz5e5z8LSIynmTyHlfc+yJts08pOq4q4vKlMxdx3CGN5SlMSq42HqZR7a9EREQmjYq1xiqmpqaGo48+utJliIiM2NW/X8uda9qKjmmuj3PF8sUc3Kj+0eNVdSzEFAVpERGRSWVMhunOzk7y+TxTpkxROxEpiZcfvqXSJcgk9e4Y3B43bEnve9n24TU5/vnwAvGNK2jdWN7apDQSIYjF4OVKF3IAZp6ogz1FREQOVNn2THuex6pVq3j88cdpbW3d6/FsNstll13GQQcdRGNjI9OnT6e6upp3v/vdrF69ulxlioiUVHUYvrSoB6eQ2eux5LbH+cd5vVSHK1CYlEQ8BI2akBYREZmURj1MW2u5/PLLmTJlCkcddRRveMMbmDFjBscffzx/+ctfAMjn85xxxhlcccUVvPzyywOHj6XTaW6++WaOPfZY/vCHP4x2qSIio2JW3Gfa+jvB+gAYv0DTC3cx5aWHcLX4ZtxKhGBKFLSASkREZHIa9WXe5513Hj/5yU8A2L0L11/+8hfOOOMMVqxYwX//93/z4IMPAtDQ0MChhx5KoVDg2WefJZ1Ok06n+cAHPsBzzz1HMqmeqyIy/iS6N9K4+U90Tnsd09fdSqxvW6VLkhGoCkGjDu0WERGZ1EZ1ZvqPf/wj119/PQCxWIz3vOc9fO5zn+M973kP8Xicrq4uvvOd73D99dcTDoe57rrr2LFjB48++ih//etf2blzJ5/97GcB2LFjBz/96U9Hs1wRkVGV3PYEB636mYL0OFcdVpAWERGRUZ6Z3jUj3dTUxP3338+iRYsGHmtpaeHUU0/lv//7v/F9n4svvpiPfexjezw/Ho9z1VVXsWrVKu655x7uuOMOPv3pT49mySIio8YArrf33mkZP6rD0KA90iIiIsIoz0w/+uijGGO46KKL9gjSAAsXLuSiiy7C8zwAPvShDw16nXPPPRdAB5GJiEhFGKA+qiAtIiIirxjVmemXXw4ahRx//PH7fHz3z8+fP3/Q6xx66KEAtLe3l7A6ERGRobkGpsQguu/uZiIiIjJJjWqY7uvrwxhDQ0PDPh+vq6sb+DgaHfzt/lgs2JyWy+VKWp+IiEgxETc4sTtUtkaSIiIiMl6M+mneAGaQviGDfV5ERKTSEv09pPWjSkRERPalLGFaRERkPNFBYyIiIjIUhWkREZHdJCPBfyIiIiLFlCVMf//736epqWmvz7e2tg58/NWvfnXQ5+8+TkREZLQ0RINZaREREZGhlCVM/+AHPxj0sV37pi+//PJylCIiIrIXAzTEoErrtURERGQ/jfr5pNbakvwnIjIeWQvrugd//OYXDet7gnFSGYag9ZWCtIiIiAzHqN46PPDAA6N5eRGRMW1jL/zoOYctbYO/b7m60/D8cy5Nccs5c31mJcpYoOAYmKoe0iIiInIARjVMn3zyyaN5eRGRMWt1B1y7xqU7B50ZaBhkXFsWXBdyvuG/n3P5wDyPQ2vLWuqk5fYH6YiCtIiIiByAUV/mLSIy2WzsDYJ0Rxa2pYsv4a4LB4/vyEBXDm5Y77IlVb5aJ6uwA9PiCtIiMnFkPZ+2dIFtvXna0gWynl/pkkQmPO0QExEpIWuDpd3duSAgx91gP643yPi4C7EotOWgPRvMlv5ug8OFh/v0n88oJRZxgxlpV///isg4Z61lc0+ep1tTrO/IYnnl3VuDYV59lKOaEjTXhAcO/RWR0lGYFhEpoZYueDllaM8Fs59TY0Cm+HOMgcYItGahMw+RtGFDLxxSU5aSJ5W4C42xYK+0iMh41tqX5+4NXbSnCxR8SBd88r7F2uDnStgxtLRlWNeRoSEe4u1zkzRVqfefSClpmbeISAndv9Uh50PWg2SY/Z5dNgZqQpDzIGdhRatenkutKhSsElCQFpHx7sWuLL9taWd7X56OjEdbukCm4OMaiDjByptMIVj23ZHx2N6X57ct7bzYla106SITiu7WRERKJF2AJ3caevJBYEsMc+1P3A2e15eHVR2GzGBrw2XYasLBjLRWOYrIeNfal+f2dZ305n06Mh6+tSSjDlPiLsmoS000+DX4vYNvLR0Zj968z+3rOmnty1f6SxCZMBSmRURKpD0LlmB2Oe4OP7gZAzEH8v1nxnTrfqck6iJQH610FSIiI2et5e4NXaQKPl1Zj4hjaIi5xELOXnuijTHEQg4NMZeIY+jKeqQKPvds6MIWOxlTRPabwrSISIlk+0Owz4EvJTaGgeNjspqZHhEDNEahNlLpSkRESmNzT572dIHeXLCkOxndO0S/mjGGZNTBNdCbC5Z+b+nRu7UipaAwLSJSItH+V1QH8A/wTX9rgxAIEFXbpgNmCPZH66wdEZlInm5NUfAh51mqwkMH6V2MMVSFHXKepeAH1xGRkVOYFhEpkYZoEOIiLqS94v2l98VayPjBKeDGQK2C4AExBPuj4+pXISITSNbzWd+RJV3wcQxEh9nfL+oaHBOc+r2uI6s+1CIloDAtIlIi8RAcPcVSEw5mplOF4T0/7QXPqwrD4jpLTDPTB6QhNvzD30RExrrenI/FkvctEdcMu2+0MYaIa4L2WVh6cwrTIiOlMC0iUkKnzvCJOMES7a78/s9O+xZ6CsGsdsTAcU26yTkQjdGgBZaIyEST94IfKNbu+wbet5aCb8l7wa/+Pn4AObzyc2nX9UTkwOmWQ0SkhBYmYWbCkvUM29LQmoGpQ9yvWAvtOSj4MDUG0+KWudXlqXciaYhqj7SITFzh/mXdxgQHXUJwunfet6Tyluw+wnE0ZEiEDGEnmMn2eaXTRHiYy8RFZG+amRYRKSFj4GOH+dRGgmCc8YJAPZi0B63Z4OTuhihUh+E9c331Qx6mXf/fiYhMVNURB0MQjHOeJef5tGc8OjI+Gc/Htxavf3bas8HMdKbg05EJxuU8n5xnB4J1dUQxQGSk9K9IRKTE5lTDpxZ51Edherx4v+nOfPD41BgkI/CBeR6zEuWrdSJQkBaRySDqOsyrjxIPORR8S3vGI98fnD0/2C5kbX+LRRv83vPB65+9bs94FHxLPOQwvy5K1FUMEBkp/SsSERkFi+vhC6/xmFtjaYoNPq4xCk1ROKjKct5hHofWlq/GiaBeQVpEJpGjmhJYa7H0B2X/lT3QuxZ57+v3A2MJloYf1aR3bUVKQXumRURGyZxquOJon79s8vnen/c95og6y0lzPeZWF5/Blr3VR6FGQVpEJpFZ1SEynsXwyr7p3b161/Srf+8AGc8ys1oRQKQUNDMtIjKKjIF5RWablx9sOaRGQXo4DDAlpiAtIpPPlt4CsZDBP8CDuK2FWMjwcu8wezeKyD4pTIuIyLjhGGiKq4+0iExOT7em8EbQOXHX8vCnW1Mlq0lkMlOYFhEZRdbCuu7BH7/5RcP6nv3vRz2ZhRyYFg96eIuITDZZz2d9R4bO7MhmlbuyBdZ1ZMiOJJWLCKA90yIio2ZjL/zoOYctbYO/b7m60/D8cy5Nccs5c32d5D2IsBPMSKstqohMVr05n6xng1O7Cba8hJzg410neb+aMcGKHgMU+g8g8yxkPUtvzica17yayEjoX5CIyChY3QHfXOmyoccU7TPdlg36TG/uM/z3cy7PF5nFnqwUpEVEIO9ZUgV/IDQHr4kGg8E1hpBjcB2D64Dr9P/eBI+DGXgNtRbSBZ+8pyVRIiOlMC0iUmIbe+HaNS4dWdiWLr6Euy4cPL4jA105uGG9yxZtZRugIC0iErBYsoVXfqDs6+DKIDYH8Xmvx3b7ZKZgsXud9S0iw6UwLSJSQtYGS7u7c0FAjrsU7TMdd4M+01EX2rPQm4ffbXC0h5ogSE+NKUiLiOzLcH9O6OeKSOkpTIuIlFBLF7ycMrTnXgmDQ7W9MgYaI8Het848bE8bNvSWp96xatf/dyH9lBIRAYIZ52jolR8ow83Gu4+PhQabvxaR4dBtiohICd2/1SHnQ9aDZHj/+0cbAzUhyHmQs7CidfK+PO9a2q0gLSLyirBrSIQMpv9AseDQsf2L1NYGB5cZgp838ZBDWMt+REZMtyoiIiWSLsCTOw09+eD01OH2Qo67wfP68rCqw5DxRqfOsUx7pEVE9q064hBxHNzd3qXdn0C9K0jv4hpDxDVURxQDREZK/4pGyec//3mMMQP/Pfjgg0M+5+677+bss8+mubmZaDRKc3MzZ599Nnffffd+/7mpVIqrrrqKY489loaGBqqrq1m0aBGf+9zn2LRp0wi+IhEZSns2WEaX84JgvL+z0rsYAzEH8v2tP7vzJS9xTIu4QR9pBWkRkb1FXYf5DTGSURcI3nzd1erKt3avUB2EaIvX30rL6X9tTUZdDq2PEXUVA0RGSn2mR8HTTz/N1Vdfvd/jrbV84hOf4Lrrrtvj81u2bOGmm27ipptu4vzzz+eHP/whpsjd+fr16znzzDN57rnn9vh8S0sLLS0t/OhHP+KXv/wlS5cuHd4XJCL7Jdsfgn1euWkZLmNeOSQmO4lmpqNusEf6QP9/ExGZDI5qSrCuI0PUNeQ8i+sEs9O7Zp6N7V/LbV/ZI72r17TvQ9Q1xEKGo5oSlfoSRCYUvSVVYr7vc95551EoFGhqatqv51xyySUDQXrJkiX86le/4rHHHuNXv/oVS5YsAeC6667j0ksvHfQavb29LFu2bCBIn3feefzhD3/gkUce4etf/zrV1dV0dXVxzjnnsHLlyhF+lSKyL9H+V1QH9lhSNxy77oMgCJiTQTwUnHiuIC0iUlxzTZiGeIhkzMV1ABus5nGd/tfQXW/I9gdo1+lf7WODj5Mxl8Z4iFk14cp+ISIThMJ0iX33u9/lr3/9KwsXLuSjH/3okOPXrVvHt771LQCOOeYY/vznP/Pe976X17/+9bz3ve/l4Ycf5phjjgHgm9/8JuvXr9/ndb797W/T0tICwLe+9S2uu+46Tj31VI4//ni++MUvcu+99xIKhUilUnzmM58pzRcrIntoiAZBOOJC2juwtiUZP9g3bAzUToJ7narQ/p14LiIiYIzh7XOTJEIOdbHQwKxz2DFBeDaGkGNwjdnj88ZAXSxEIuRw+txk0ZWOIrL/FKZL6KWXXhqYPf7BD35AJBIZ8jlXX301hUIBgGuuuYZ4PL7H44lEgmuuuQaAQqHAd77znb2ukc/n+c///E8AFi1axGc/+9m9xhx//PED4f6BBx7giSee2P8vTET2SzwER0+x1ISDmelUYXjPT3vB86rCsLjOEpvgM9M1YWgs0oNbRET21lQVZtn8OqrDDg3xECHHoeC/8mZu1Al+NUDBh5ATjKsOOyybX0dT1SR4p1akTBSmS+gf//Ef6e3t5dxzz+WUU04Zcry1lltuuQWAhQsXctxxx+1z3HHHHcdhhx0GwM0337zXARMPPvggnZ2dAJx77rk4zr6/rR/5yEcGPr7xxhuHrE9Ehu/UGT4RJ1ii3ZXf/9lp30JPIbgBihg4rskf3UIrrDYC9dFKVyEiMj4dnIxyzsIGplWFqe9fuh0Pu1gMHgaLIR4OPl8fc5leFeachQ0cnNQLr0gp6QCyEvnf//1fbr/9dhoaGrjqqqv26zkbNmxgy5YtAJx88slFx5588sk899xzbN68mY0bNzJ37tyBx/70pz/tMW4wxxxzDFVVVfT19fHwww/vV40iMjwLkzAzYcl6hm1paM3A1CECtbXQngtmEKbGYFrcMre6PPVWQnUY6oZeuCMiIkU0VYX50OJGtvTk+VtrivUdWSyv/MAxxjC/LspRTQlm1YS1tFtkFChMl0BnZyef/vSngWBf89SpU/freWvWrBn4eOHChUXH7v74mjVr9gjT+3udUCjEvHnzWLly5R7PEZHSMQY+dpjPN1e6eBZ2ZKA1B42DjE970J0NgnRDNAia75nrT9g9xPFQ8HWKiMjIGWNoro3QXBsh6/n05nzyniXc30da7a9ERpfCdAl8/vOfZ9u2bbzxjW/cr0PHdnnppZcGPm5ubi469qCDDtrn83b/fVVVFXV1dUNeZ+XKlezYsYNsNks0uv93tZs3by76+NatW/f7WiIT2Zxq+NQij2vXuDgE/acH05YF14UpUaiJwAfmecyaoB1LYv1fp4iIlF7UdYjGFZ5FyklheoQefvhhfvSjHxEKhYbsA/1qPT09Ax9XVxdf01lVVTXwcW9v7z6vM9Q19nWd4YTp3QO9iBS3uB4+OM/jqmeCGerBGAOeDWaoPzDb49Da8tVYTmEHpujUbhEREZlA9PbVCORyOc4//3ystVx00UUceeSRw3p+JpMZ+Hiok793D73pdHqf19mf08OLXUdESmd1B/xivUtVqL/H5yCshZCBRAhu3eTyfHf5aiwXxwR7wdVHWkRERCYSzUyPwJVXXsmaNWuYPXs2X/nKV4b9/FjslZ4wuVyu6Nhs9pV1oq9un7XrOkNdY6jrDOXVy8tfbevWrRx77LHDuqbIRLSxF65d49KRDfZMR4uEyMYodJtgnGfhhvUu5x02sZZ6N0YhpLduRUREZIJRmD5ALS0tfOMb3wCC/tC7L5/eXzU1NQMfv3rp9qv19fUNfPzq5dy7rjPUNYa6zlCG2tctIsFM84+ec+jOBQE57gbLm71BxsddiEWhLRfsrXYN/G6Dw4WHT4xDyJKR4NAxERERkYlGtzgH6OqrryaXy3HIIYeQSqX49a9/vdeYVatWDXx8//33s23bNgDe8Y53UFVVtUc4Hepwr91nhV+9d7m5uZm//OUv9PX10dnZWfQQsl3XmTp16rD2S4vI/mnpgpdThvZcsE94agzIFH+OMdAYgdYsdOYhkjZs6IVDaoo/b6yLu0GYFhEREZmIFKYP0K7l0i+88ALve9/7hhx/xRVXDHy8YcMGqqqqOPzwwwc+19LSUvT5uz++aNGiPR47/PDD+b//+7+Bcccdd9w+r1EoFFi/fv0+ryEipXH/VoecD1kPpkaDoDxEm2kgGFcTCvpN5yysaHU4pMYf9XpHS8iBhtjQ40RERETGK+1iq6C5c+cyc+ZMAP74xz8WHfvQQw8BMGvWLObMmbPHYyeeeOLAx8Wu8/jjjw8s8z7hhBMOpGQRKSJdgCd3GnrywWFbiWG+XRl3g+f15WFVhyEz2NrwMW7XgWPFDl4TERERGe8Upg/QT3/6U6y1Rf/b/VCyBx54YODzu8KwMYbly5cDwYzyihUr9vlnrVixYmBmevny5Xu13zrllFNIJpMA/M///A/W7nse7Kc//enAx+9617sO6OsWkcG1Z4NZ6JwXBOPh7nk2BmIO5PsnpLvzJS9x1O0K0mH9dBEREZEJTrc7FfaZz3yGUCiYvrrgggv2aleVTqe54IILAAiFQnzmM5/Z6xqRSIQLL7wQgDVr1vDtb397rzGPPvooP/7xjwE4+eSTef3rX1/KL0NEgGx/CPY58DZQuy8Lz46zmWlDEKSjbqUrERERERl9CtMVtmDBAj73uc8BwTLsE044gd/85jc8/vjj/OY3v+GEE07g8ccfB+Diiy/m0EMP3ed1Lr74YhYsWADA5z//eT7+8Y/zwAMPsGLFCr7xjW9w2mmnUSgUiMfjfOc73ynL1yYy2UT7X1EdwN+fjdL7YG0QSmF8hVJDcGr5eKpZREREZCR0ANkY8PWvf53W1lauv/56nnrqKd773vfuNeajH/0oX/va1wa9Rk1NDXfccQdLly7l+eef57rrruO6667bY0xtbS033HADr33ta0v9JYgI0BANQmXEhb5CfzAexgy1tZDxX1kiXhsetVJLriGmFlgiIiIyuWhmegxwHIcf//jH3HHHHSxfvpyZM2cSiUSYOXMmy5cv58477+RHP/oRjlP82zV//nyeeuopvvnNb3LMMcdQV1dHIpHgsMMO46KLLmLlypUsW7asTF+VyOQTD8HRUyw14WBmOlUY3vPTXvC8qjAsrrPExsksb2MUqhSkRUQqKuv5tKULbOvN05YukPXGb0cIkfHC2MFOqxIZps2bNw/0wH7ppZf26KNdaS8/fEulS5BJYk0nXPWMy9Z0MNM8Iw5kuij8+fp9jrfH/QPEk/gWdmSDGemmKHzsMG9c9JluiEL1OJpBlz3NPHF5pUuYEH5378pKlyCTlLWWzT15nm5Nsb4ji92tGaPBMK8+ylFNCZprwnsdYCtSDu857TWVLmFUaS5BRKSEFiZhZsKS9Qzb0tCagalDvGVpbdBfuuAHB3hNi1vmVpen3pGoV5AWEamY1r48d2/ooj1doOBDuuCT9+3AFqOwY2hpy7CuI0NDPMTb5yZpqtKLtkgpaZm3iEgJGQMfO8ynNhIE44wXBOrBpD1ozQYnd++a5X3PXH/YbbXKrS4CNbonExGpiBe7svy2pZ3tfXk6Mh5t6QKZgo9rIOKAayBTCJZ9d2Q8tvfl+W1LOy92ZStdusiEojAtIlJic6rhU4s86qMwPV78ELLOfPD41BgkI/CBeR6zEuWr9UDURaA2UukqREQmp9a+PLev66Q379OR8fCtJRl1mBJ3SUZdaqLBr8HvHXxr6ch49OZ9bl/XSWtfvtJfgsiEoTAtIjIKFtfDF17jMbfG0hQbfFxjNNgjfVCV5bzDPA6tLV+NByKpIC0iUjHWWu7e0EWq4NOV9Yg4hoaYSyzk7LUn2hhDLOTQEHOJOIaurEeq4HPPhi50ZJJIaWjPtIjIKJlTDVcc7fOXTT7f+/O+xxxRZzlprsfc6uG10aqE2kgQpkVEpDI29+RpTxfozQVLupPRvUP0qxljSEYd2jMevTmfNqfAlp48zXpnVGTENDMtIjKKjIF5RWablx9sOaRm7AfpmnCwvFtERCrn6dYUBR9ynqUqPHSQ3sUYQ1XYIedZCn5wHREZOYVpEREpqiYcnNwtIiKVk/V81ndkSRd8HANRd3jvwkZdg2OCU7/XdWTVh1qkBBSmRURkUNUK0iIiY0JvzsdiyfuWiGuG3TfaGEPENUH7LCy9OYVpkZFSmBYRkX2qDgftukREpPLyXnBomLUHfgPv9D9/9+uJyIFTmBYRkb0oSIuIjC3h/mXdxsCBzin7vHJGR3iYy8RFZG8K0yIisoeqkIK0iMhYUx1xMBjCjiHn2WG3t7LWkvMsYSdYIl4dUQwQGSn9KxIRkQFVIWgs0hdbREQqI+o6zKuPEg85+Bayw1ymnfUsvoV4yGF+XZSoqxggMlL6VyQiIgAkFKRFRMa0o5oShByIuIa+vL/fs9PWWvryPhHXEHKC64jIyClMi4gIiRBMUZAWERnTmmvCNMRDVEccPAtd2aEDtbWWrqyPZ4Ol4o3xELNqwmWqWGRiU5gWEZnk4iFo1B5pEZExzxjD2+cmSYQcklGXnG9pz3hkCnuHamstmYJPe8Yj51uSUZdEyOH0uclht9USkX0LVboAERGpnHgIpkRfOd1VRETGtqaqMMvm13H7uk4cE/Sf7sr6OCZY/u0QnNqd698jHXENtVGHRMhh2fw6mqo0Ky1SKgrTIiKTVNxVkBYRGY8OTkY5Z2EDd2/oIuwUKPiQLvjkfUvBBq/rsZBDPOQQcqAxHuL0uUkFaZESU5gWEZmE4m6wR1pBWkRkfGqqCvOhxY1s6cnzt9YU6zuyWF5Z6m2MYX5dlKOaEsyqCWtpt8goUJgWEZlkYgrSIiITgjGG5toIzbURsp5Pb84n71nCbtBHWu2vREaXwrSIyCQSUZAWEZmQoq5DNK7wLFJO+hcnIjJJhB2YGgNHQVpERERkxBSmRUQmgZADTXFwFaRFRERESkJhWkRkgnMNNMUUpEVERERKSWFaRGQCc0wwIx3Sq72IiIhISen2SkRkFFkL67oHf/zmFw3re4JxpWYIDhsL65VeREREpOR0mreIyCjZ2As/es5hS9vgaXZ1p+H551ya4pZz5vrMSpTuz2+IBW2wRERERKT0NF8hIjIKVnfAN1e6bOgxtGYGH9eWhdYsbO4z/PdzLs8XmcUejroIVOntUhEREZFRozAtIlJiG3vh2jUuHVnYli6+hLsuHDy+IwNdObhhvcuW1Mj+/Oow1EZGdg0RERERKU5hWkSkhKwNlnZ354KAHHeDk7QHE3ehKQpRF9qz0JuH321wDngPdU0YGqIH9lwRERER2X8K0yIiJdTSBS+nDO254OCvqTEwQ7SkMgYaI8GJ25152J42bOgd/p9dF4F6BWkRERGRslCYFhEpofu3OuR8yHqQDA8dpHcxBmpCkPMgZ2FF6/BenhuiWtotIiIiUk4K0yIiJZIuwJM7DT35oL9zYpgHgMXd4Hl9eVjVYch4Qz9nV/ur6vABlSwiIiIiB0hhWkSkRNqzYAlml+Pu/s9K72IMxBzI+8Hvu/NDP6chNvzQLiIiIiIjpzAtIlIi2f4Q7BPMMB8IY4JADsFS8WIao2p/JSIiIlIpCtMiIiUS7X9FdQD/AE/jtjZYug3BCd+DaYhClZZ2i4iIiFSMwrSISIk0RIMgHHEh7RXvL70v1kLGD04BNwZqBwnL9VHtkRYRERGpNIVpEZESiYfg6CmWmnAwM50qDO/5aS94XlUYFtdZYvuYma6LBL2kRURERKSyFKZFREro1Bk+ESdYot2V3//Zad9CTyGY1Y4YOK7J32tMXUTtr0RERETGCoVpEZESWpiEmQlLQyQ4lbs1M3Sgthbac1DwoS4M0+KWudV7jkkqSIuIiIiMKQrTIiIlZAx87DCf2ghMjUHGCwL1YNIetGaDk7sb+vdCv2euv0dbrdpIEKZFREREZOxQmBYRKbE51fCpRR71UZgeL95vujMfPD41FgTmD8zzmJV45fHqcLC8W0RERETGFoVpEZFRsLgevvAaj7k1lqbY4OMao9AUhYOqLOcd5nFo7SuPVYWC2WoRERERGXtClS5ARGSimlMNVxzt85dNPt/7877HHFFnOWmux9zqPWewEyFoLBLCRURERKSyFKZFREaRMTCvdvDHlx9sqa/Z83NxN5ixFhEREZGxS8u8RUTGkJgLU2LF91mLiIiISOUpTIuIjBExNziITEFaREREZOxTmBYRGQMUpEVERETGF4VpEZEKU5AWERERGX8UpkVEKijiKEiLiIiIjEcK0yIiFdQQVZAWERERGY8UpkVEKshRkBYREREZlxSmRURERERERIZJYVpERERERERkmEKVLkBEZKJrqK3mPz7zkUEfExEREZHxR2FaRGSUua7D1PraSpchIiIiIiWkZd4iIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMCtMiIiIiIiIiw6QwLSIiIiIiIjJMoUoXICIyWaQL0J6FrA9RBxqiENersIiIiMi4pNs4EZFRZC20dMH9Lzs82Wawuz1mgKOnWE6d4bMwCcZUqkoRERERGS6FaRGRUbKxF370nMPLKUPOh5485DzwCfbYRFx4tNXwxE6XmQnLxw7zmVNd6apFREREZH8oTIuIjILVHXDtGpfuHLTnIOuBYyDuBr/6FvoKQcCOupD1DN9c6fKpRR6L6ytdvYiIiIgMRQeQiYiU2MbeIEh3ZGFbOljqPTUKByVgagwao8GvByWCz1sbjOvIBs/b2Fvpr0BEREREhqIwLSJSQtYGS7u7c7AjE8xEz4hDVXjvPdHGBJ+fEYeYG4zvzsGPn3Owdt/XFxEREZGxQWFaRKSEWrrg5ZShPQdhJ5iBHupgMWOgKRaMb8/BlpThua7y1CsiIiIiB0ZhWkSkhO7f6pDzgz3SyX3MRg/GmGB81oOcH1xHRERERMYu3a2JiJRIugBP7jT05INDxhLDPOIxEQqe15OHJ3Ya0oXRqVNERERERk5hWkSkRNqzYAnaX8Xd4feNNv2nfee84DodudGoUkRERERKQWFaRKREsn7wq08ww3wgHBM8HyDjlaIqERERERkNCtMiIiUS7X9FdQj6SB8I377ywhxzS1GViIiIiIyGYe7oExGRwTREwQARF/oKQZus4Sz1thbSHlT1752uj4xaqSIiMsFkPZ/enE/es4RdQ3XEIepq3kxkNClMi4iUSDwER0+xPNoaHEKWKgR9pPdXqhDMTNeE4ehGS1yv0CIiUoS1ls09eZ5uTbG+I4vllWVRBsO8+ihHNSVorgljhnuQh4gMSbdqIiIldOoMnyd2ukRd6MoHJ3Tvz/2Lb4PxURciTnAdERGRwbT25bl7Qxft6QIFH9IFn7xvB1ZFhR1DS1uGdR0ZGuIh3j43SdNw3uEVkSFp7YeISAktTMLMhKUhAnkfWjPB8u1irIUdmWB8QwRmJSyHJctTr4iIjD8vdmX5bUs72/vydGQ82tIFMgUf1wRvyLoGMgWftnSBjozH9r48v21p58WubKVLF5lQFKZFRErIGPjYYT61EZgaC07k3pqGvvzeodra4PNb08G4qTGojcBHD/OH3VZLREQmh9a+PLev66Q379OR8fCtJRl1mBJ3SUZdaqLBr8HvHXxr6ch49OZ9bl/XSWtfvtJfgsiEoTAtIlJic6rhU4s86qMwPR4E7B1ZeCkVzEC3ZYNfX0oFnzcmGFcfDZ43p7rSX4GIiIxF1lru3tBFquDTlfWIOIaGmEss5Oy1J9oYQyzk0BBziTiGrqxHquBzz4Yu7FBLpkRkvyhMi4iMgsX18IXXeMytscyIw8xEcEp3wQ9moQt+8PuZCZgRh0NqLF94jcfi+kpXLiIiY9Xmnjzt6QK9uWBJdzK6d4h+NWMMyaiDa6A3Fyz93tKj2WmRUtABZCIio2RONVxxtM9zXfCHrQ5P7jTsPhfgmODU7lNn+ByWHF4bLRERmXyebk1R8CHn2f0K0rsYY6gKO3RlfQp+cJ3mWvVfFBkphWkRkVFkDCysg4V1PukCdOSCmemYG/SRVvsrERHZH1nPZ31HlnTBxzEQdYf3DmzUNTgmOPV7XUeWrOerD7XICOk2TkSkTOIhhWcRETkwvTkfiyXvWyKuGXbfaGMMEdcE7bOw9OZ8onGFaZGR0L8gEREREZExLu8FG4WsPfAbeIdXOkvsup6IHDiFaRERERGRMS7cv6zbGPAP8Bo+r5zPER7mMnER2ZvCtIiIiIjIGFcdcTAYwo4h59lht7ey1pLzLGEnWCJeHVEMEBkp/SsSERERERnjoq7DvPoo8ZCDbyE7zGXaWc/iW4iHHObXRXX4mEgJ6F+RiIiIiMg4cFRTgpADEdfQl/f3e3baWktf3ifiGkJOcB0RGTmFaRERERGRcaC5JkxDPER1xMGz0JUdOlBba+nK+ng2WCreGA8xqyZcpopFJjaFaRERERGRccAYw9vnJkmEHJJRl5xvac94ZAp7h2prLZmCT3vGI+dbklGXRMjh9LnJYbfVEpF9U8dTEREREZFxoqkqzLL5ddy+rhPHBP2nrZMugwAARrhJREFUu7I+jgmWfzsEp3bn+vdIR1xDbdQhEXJYNr+OpirNSouUisK0iIiIiMg4cnAyyjkLG7h7Qxdhp0DBh3TBJ+9bCjZofxULOcRDDiEHGuMhTp+bVJAWKTGFaRERERGRcaapKsyHFjeypSfP31pTrO/IYnllqbcxhvl1UY5qSjCrJqyl3SKjQGFaRERERGQcMsbQXBuhuTZC1vPpzfnkPUvYDfpIq/2VyOhSmBYRERERGeeirkM0rvAsUk76FyciIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMC0iIiIiIiIyTArTIiIiIiIiIsOkMD1CTz75JFdeeSVnnHEGBx10ENFolOrqahYsWMBHPvIR/vSnPw3renfffTdnn302zc3NRKNRmpubOfvss7n77rv3+xqpVIqrrrqKY489loaGBqqrq1m0aBGf+9zn2LRp03C/RBEREREREXkVY621lS5ivDr55JN56KGHhhz3oQ99iB/96EdEIpFBx1hr+cQnPsF111036Jjzzz+fH/7whxhjBh2zfv16zjzzTJ577rl9Pp5MJvnlL3/J0qVLh6x7uDZv3sxBBx0EwEsvvURzc3PJ/4wD9fLDt1S6BBGRMWfmicsrXcKE8Lt7V1a6BBGRMek9p72m0iWMKs1Mj8CWLVsAmDlzJp/+9Kf53e9+x2OPPcajjz7Kf/zHfzBr1iwAfv7zn/ORj3yk6LUuueSSgSC9ZMkSfvWrX/HYY4/xq1/9iiVLlgBw3XXXcemllw56jd7eXpYtWzYQpM877zz+8Ic/8Mgjj/D1r3+d6upqurq6OOecc1i5Uj/4RUREREREDpRmpkdg2bJlfPjDH+bd7343ruvu9fjOnTs54YQTWLt2LQAPPfQQb3rTm/Yat27dOhYtWkShUOCYY47hoYceIh6PDzyeSqX4/+3de5yUZf3/8fd1z2nPJ2DlGKAokKY/TVFTAzygJohWHioLFbRIVMzMskLT7KHlCbUy46CVmuZXJdCQVDBBiqMoBiQIJgddll0Wdnd2Tvf1++OGaZc9DszussPr+Xjw2Nm5r/uaz8yyu/Pe67qva/jw4Vq2bJn8fr/Wrl2rI444olE/d9xxh372s59Jkn75y1/qlltuaXB88eLF+uIXv6h4PK6RI0fqjTfeOKDnvy9GpgGga2FkOj0YmcbBIJJwVR11FUtYBXxGeUFHIR/jZuhcjEyjWXPmzNGll17aZJCWpO7du+v+++9Pfv7888832e7BBx9UPB6XJD3yyCMNgrQk5eTk6JFHHpEkxeNxPfTQQ436iMVimjp1qiRp6NChuvnmmxu1OfXUUzV+/HhJ0vz587V8+fJWniEAAAAOVtZafbwrqjnrd+qxFdv1x9Xl+vOaHfrj6nI9tmK75qzfqY93RcXYGdA+CNPtbMSIEcnbGzZsaHTcWqtZs7xR0yFDhuiUU05psp9TTjlFgwcPliS99NJLjX4oLliwQDt37pQkjRs3To7T9Je2/nTzF154oa1PAwAAAAeRspqY/vj+Dv3fugqt3VGnXdGEKuoS2hH2Pu6KJrR2R53+b12F/vj+DpXVxDq7ZCDjEKbbWTQaTd5uKuBu3Lgxee318OHDW+xr7/HNmzdr06ZNDY7VXzW8pX5OPPFE5ebmSpIWLlzYcvEAAAA46HxUFdFf1lbo05qYKusS2hGOqy7uymekoCP5jFQXd7UjHFdlXUKf1sT0l7UV+qgq0tmlAxnF39kFZLo333wzeXvIkCGNjq9Zs6bF4/XVP75mzRoNHDgw5X78fr+OOOIIvfvuuw3OaYvNmze3eHzbtm0p9QcAAIDUlNXENGf9TlXHXFVFEvIZqTDkKOQzDXZ8sdYqkrCqibmqrEvIDUlz1u/UJUNKVJob6MRnAGQOwnQ7cl1X99xzT/LzSy+9tFGbjz/+OHm7tQW79i7ute959T/Pzc1VUVFRq/28++672r59uyKRiEKhUIvtm3p8AAAAdCxrreZurFJt3AvSQceoMOQ0uW2qMUZZfqOQz6gq4rV3jPTqxipdcXS3FrdaBdA2TPNuRw8++KCWLFkiSbr44ot14oknNmqze/fu5O28vLwW+9s7PVvytsFqqp/W+mitHwAAABycNu+OqSIcV3XUTY5ItxaKjfECt89I1VFv6veW3Vw/DaQDI9Pt5M0339QPf/hDSVJpaal++9vfNtmurq4ueTsYDLbYZ/0R5HA43GQ/rfXRWj8t2Xc0fF/btm3TsGHD2twfAAAA2m5VWa3irhRN2DYF6b2MMcoNOKqKuIq7Xj99C1p/zwigZYTpdvD+++/r4osvVjweVygU0nPPPafDDjusybZZWVnJ2/UXK2tKJPK/RSP23T5rbz+t9dFaPy05mPaNBgAAOJREEq42VEYUjrtyjBTypTZNO+QzcowUjrtaXxlRJOGyDzVwgPgOSrONGzdq1KhRqqyslM/n0zPPPNPi6tr5+fnJ261Nua6pqUne3nc6995+2jJtu6V+AAAAcPCpjrqysoq5VsF9FhtrC2OMgj6jmGtlZVUdddupUuDQQZhOo61bt+rss8/W1q1bZYzRjBkzdPHFF7d4Tv3R3tZWy64/zXrfxcD29lNTU5Pcb7q1fnr06NHmxccAAADQeWIJK0mydv/fwDt7zq/fH4D9R5hOk/Lycp1zzjn68MMPJUmPPPKIvvWtb7V63mc/+9nk7bVr17bYtv7xoUOH7lc/8XhcGzZsaLIPAAAAHJwCe6Z1GyPt75iyu+f8+v0B2H+E6TSoqqrSueeeq3//+9+SpHvuuUfXXXddm84dOHCgevfuLanhntRN+cc//iFJ6tOnjwYMGNDg2Omnn5683VI/y5YtS07zPu2009pUIwAAADpXXtCRkVHAMYomrKxNbWTZWqtowirgeFPE84LEAOBA8V10gGpra3XBBRdoxYoVkqQf//jHuvXWW9t8vjFGY8eOleSNKP/zn/9sst0///nP5Ijz2LFjG10nM2LECBUWFkqSnnzyyWZ/wD7xxBPJ261NQQcAAMDBIeRzdERxSNl+R66VIilO044krFwrZfsdDSoKsfgYkAZ8Fx2AaDSqiy++WIsWLZIk3Xjjjfr5z3+ecj+TJ0+W3+8trH799dc32q4qHA7r+uuvlyT5/X5Nnjy5UR/BYFA33HCDJGnNmjW67777GrVZvHixpk+fLkkaPny4TjrppJRrBQAAQOc4rjRHfkcK+oxqYm6bR6ettaqJuQr6jPyO1w+AA8fWWAfga1/7mubNmydJOvPMMzV+/HitXr262fbBYFBHHXVUo/uPOuooff/739c999yjZcuW6bTTTtOtt96qI444Qhs2bNC9996rlStXSpJuueUWHXnkkU32f8stt+jZZ5/Vf/7zH/3gBz/Q+vXrdfnllys7O1vz58/XL37xC8XjcWVnZ+uhhx468BcAAAAAHaZvfkAl2X7FXKvKuoSqIm6r+01ba1UVcZWwUkHIUbdsv/rkBzqwaiBzGZvqBRdISnVLgv79+2vTpk1NHnNdV9dcc41mzJjR7Pnjx4/X448/LsdpfkLB+vXr9aUvfUkffPBBk8cLCgr01FNPafTo0SnV3habN29OrjL+8ccfH1T7Um9dOKuzSwCAg07v08d2dgkZ4fl573Z2CTiElNXE9Je1FaqOuaqKJOQzUm7AUWif7bKstYokvBHphJUKQz7lBRxdMqREpbmEaXSMr446trNLaFdM8z5IOI6j6dOn6+WXX9bYsWPVu3dvBYNB9e7dW2PHjtUrr7yiadOmtRikJWnQoEFauXKl7r33Xp144okqKipSTk6OBg8erJtuuknvvvtuuwRpAAAAtL/S3IBGDypSXsBRcZZPjjGqirgqDydUFUlod8T76H3uyjFGxVlekB49qIggDaQRI9NIG0amAaBrYWQ6PRiZRmcoq4lp7sYqVYTjirtSOO4q5lpZ621/FXCMsv2O/I7ULduvcwcWEqTR4TJ9ZJprpgEAAIAupjQ3oG8e3U1bdsf0TlmtNlRGZPW/MTJjjAYVhXRcaY765AdSvjwRQOsI0wAAAEAXZIxR34Kg+hYEFUm4qo66iiWsAj5vH2m2vwLaF2EaAAAA6OJCPkehbMIz0JH4jgMAAAAAIEWEaQAAAAAAUkSYBgAAAAAgRYRpAAAAAABSRJgGAAAAACBFhGkAAAAAAFJEmAYAAAAAIEWEaQAAAAAAUkSYBgAAAAAgRYRpAAAAAABSRJgGAAAAACBF/s4uAAAAAMCBiSRcVUddxRJWAZ9RXtBRyMe4GdCeCNMAAABAF2St1ebdMa0qq9WGyoisbPKYkdERxSEdV5qjvvkBGWM6sVIgMxGmAQAAgC6mrCamuRurVBGOK+5K4birmGtlrWSMFHCM1u6o0/rKOpVk+3XewEKV5gY6u2wgoxCmAQAAgC7ko6qI5qzfqdq4N7U7mrByjBT0GTlGciXVxV3VxlwFfUYx1+ovays0elCR+heGOrt8IGNwIQUAAADQRZTVxDRn/U5Vx1xV1iXkWqvCkKPu2T4VhnzKD3kfvc8dudaqsi6h6pirOet3qqwm1tlPAcgYhGkAAACgC7DWau7GKtXGXVVFEgo6RiVZPmX5nUbXRBtjlOV3VJLlU9AxqookVBt39erGKllrm3kEAKkgTANABwnHpS010oe7vY/heGdXBADoSjbvjqkiHFd11JXPSIWhxiF6X8YYFYYc+YxUHXW1IxzXlt2MTgPpwDXTANCOrJXWVklvbHW0YodR/bEAI+mE7lZn9nI1pNBbMAYAgOasKqtV3JWiCdumIL2XMUa5AUdVEVdx1+unb0GwnasFMh9hGgDayaZqado6R1trjaKutDsmRRPewjCOpKBPWlxmtLzcp945VhMGuxqQ19lVAwAORpGEqw2VEYXjrhwjhXyp/QU2tGdxsnDc1frKiCIJl32ogQNEmAaAdvB+pfToGp92RaWKqBRJSI6Rsn3eR9dKNXEvYId8UiRhdO+7Pk0amtDRxZ1dPQDgYFMddWVlFXOtgj6T8r7Rxpjkyt5WVtVRV6FswjRwIPgOAoA021TtBenKiPRJ2Jvq3SMk9cuRemRJ3ULex3453v3Weu0qI955m6o7+xkAAA42sYR3oZC1+/8G3tlzfv3+AOw/wjQApJG13tTuXVFpe503Et0rW8oNNL4m2hjv/l7ZUpbPa78rKk1f54iFVgEA9QX2TOs2e/aR3h+u/ve7KJDiNHEAjRGmASCN1lZJW2uNKqJSwPFGoFubiWeMVJrlta+ISltqjdZVdUy9AICuIS/oyMgo4BhFEzbl7a2stYomrAKON0U8L0gMAA4U30UAkEZvbHMUdb1rpAubGI1ujjFe+0hCirpePwAA7BXyOTqiOKRsvyPXSpEUp2lHElaulbL9jgYVhVh8DEgDvosAIE3CcWlFudHumLfIWE6KSzzm+L3zdsek5eWGfagBAA0cV5ojvyMFfUY1MbfNo9PWWtXEXAV9Rn7H6wfAgSNMA0CaVEQkK2/7q2xf6vtGmz2rfUcTXj+V0faoEgDQVfXND6gk26+8oKOElaoirQdqa62qIq4S1psq3i3brz75gQ6qGMhshGkASJPInhVhXHkjzPvDqbewTF0iHVUBADKFMUbnDSxUjt9RYcinqGtVUZdQXbxxqLbWqi7uqqIuoahrVRjyKcfv6NyBhSlvqwWgaewzDQBpEtrz50lH3j7S+8Ott+VJli8dVQEAMklpbkCjBxVpzvqdcoy3/3RVxJVjvOnfjrw/ykb3XCMd9BkVhBzl+B2NHlSk0lxGpYF0IUwDQJqUhCQjKeiTauLeNlmp/PHfWimckHL3XDtdHGy3UgEAXVj/wpAuGVKiuRurFHDiirtSOO4q5lrF9/zuyfI7yvY78jtSt2y/zh1YSJAG0owwDQBpku2XTuhutbjMW4SsNu7tI91WtXFvZDo/IJ3QzSqbn9AAgGaU5gb0zaO7acvumN4pq9WGyois/jctyhijQUUhHVeaoz75AaZ2A+2At2oAkEZn9nK1vNynkE+qinkrdLfl/YtrvfYhnxR0vH4AAGiJMUZ9C4LqWxBUJOGqOuoqlrAK+Lx9pNn+CmhfhGkASKMhhVLvHKtIwuiTsFRWJ5VmtRyorZW210kxV+qZLfXJsRpc2HE1AwC6vpDPUSib8Ax0JL7jACCNjJEmDHZVEJR6ZHkrcm8LSzUxLzTXZ613/7aw165HllQQlMYPdlPeVgsAAAAdi5FpAEizAXnSpKEJPbrGJ5+RKqLS9ojkRL19pB3jTesOJ7yPIZ83Il0Q9M4bkNfZzwAAAACtIUwDQDs4uli69diEpq1zFKo1irrS7pgUTezZh1reqt35Ae8a6T45VuMHuwRpAACALoIwDQDtZECedNcJrtZVSa9vc7Si3Kj+TG/HeKt2n9nL1eDC1LbRAgAAQOciTANAOzJGGlIkDSlyFY5LlVHv+ugsn7ePNNtfAQAAdE28jQOADpLtJzwDAABkClbzBgAAAAAgRYRpAAAAAABSRJgGAAAAACBFhGkAAAAAAFJEmAYAAAAAIEWEaQAAAAAAUkSYBgAAAAAgRYRpAAAAAABSRJgGAAAAACBFhGkAAAAAAFJEmAYAAAAAIEWEaQAAAAAAUuTv7AIA4FARjksVESniSiFHKglJ2fwUBgAA6JJ4GwcA7chaaW2V9MZWRyt2GNl6x4ykE7pbndnL1ZBCyZjOqhIAAACpIkwDQDvZVC1NW+doa61R1JV2x6RoQnLlXWMT9EmLy4yWl/vUO8dqwmBXA/I6u2oAAAC0BWEaANrB+5XSo2t82hWVKqJSJCE5Rsr2eR9dK9XEvYAd8kmRhNG97/o0aWhCRxd3dvUAAABoDQuQAUCabar2gnRlRPok7E317hGS+uVIPbKkbiHvY78c735rvXaVEe+8TdWd/QwAAADQGsI0AKSRtd7U7l1RaXudNxLdK1vKDTS+JtoY7/5e2VKWz2u/KypNX+fI2qb7BwAAwMGBMA0AabS2Stpaa1QRlQKONwLd2sJixkilWV77iqi0pdZoXVXH1AsAAID9Q5gGgDR6Y5ujqOtdI13YxGh0c4zx2kcSUtT1+gEAAMDBiwXIACBNwnFpRbnR7pi3yFhOij9hc/ySE/UWJVtebhSOsw81AKBtIglX1VFXsYRVwGeUF3QU8vGHWaA98TYNANKkIiJZedtfZftS3zfa7FntO5rw+qmMEqYBAM2z1mrz7phWldVqQ2VEVv9bcMPI6IjikI4rzVHf/IBMqr+UALSKt2kAkCYR1/voyhuZ3h+O8c6XpLpEOqoCAGSispqY5m6sUkU4rrgrheOuYq6Vtd4fZwOO0doddVpfWaeSbL/OG1io0txAZ5cNZBTCNACkSWjPbDpH3j7S+8O1/1vMIsuXjqoAAJnmo6qI5qzfqdq4N7U7mrByjBT0meQfZevirmpjroI+o5hr9Ze1FRo9qEj9C0OdXT6QMbiQAgDSpCQkGUlBnxROKOXtraz1zgv6vBHq4mC7lAkA6MLKamKas36nqmOuKusScq1VYchR92yfCkM+5Ye8j97njlxrVVmXUHXM1Zz1O1VWE+vspwBkDMI0AKRJtl86obtVfsAbYa6Np3Z+bdw7Lz8gndDNcr00AKABa63mbqxSbdxVVSShoGNUkuVTlt9pdE20MUZZfkclWT4FHaOqSEK1cVevbqySTfWvvQCaRJgGgDQ6s5eroCOFfFJVrO2j06712od8UtDx+gEAoL7Nu2OqCMdVHXXlM1JhqHGI3pcxRoUhRz4jVUdd7QjHtWU3o9NAOhCmASCNhhRKvXOsSoJSzJXK6loP1NZK2+u89iVBqU+O1eDCjqkXANB1rCqrVdyVogmr3EDrQXovY4xyA46iCau46/UD4MARpgEgjYyRJgx2VRCUemR5K3JvC0s1TYxSW+vdvy3steuRJRUEpfGD3ZS31QIAZLZIwtWGyojCcVeOkUK+1H5RhPYsThaOu1pfGVEkwQwo4EBxRR4ApNmAPGnS0IQeXeOTz0gVUWl7RHKi3j7SjvGmdYcT3seQT+qZ7QXpSUMTGpDX2c8AAHCwqY66srKKuVZBn0l532hjTHJlbyur6qirUDbjasCBIEwDQDs4uli69diEpq1zFKo1irrS7pgUTezZh1pSrt9bbCzoeFO7xw92CdIAgCbFEt70Jmu9P8ruD0dS3DbsD8D+I0wDQDsZkCfddYKrdVXS69scrSg3qv/WxTHeqt1n9nI1uFBM7QYANCuwZ1q32bOP9P5w9b/fNYEUp4kDaIwwDQDtyBhpSJE0pMhVOC5VRr3ro7N83j7SbH8FAGiLvKAjI6OAY1QXd2WtTWmqt7VW0YRNbqOVF2SKN3CgeBsHAB0k2094BgDsn5DP0RHFIa3dUafamKtIwirL3/YwHUlYuVbK9jsaVBRSyEeYBg4U30UA0EHCcWlLjfThbu9jON7ZFQEAupLjSnPkd6Sgz6gm5o1Ot4W1VjUxV0Gfkd/x+gFw4BgjAYB2ZK20tkp6Y6ujFTsaXjNtJJ3Q3btmegjXTAMAWtE3P6CSbL9irlVlXUJVEVeFoZb3m7bWqiriKmGlgpCjbtl+9ckPdGDVQOYiTANAO9lULU1b52hrM6t5B33S4jKj5eU+9c6xmsBq3gCAFhhjdN7AQv1lbYXckFQVSaiiLqHcgKPQPttlWWsVSXgj0gkrFYZ8yvE7OndgYcrbagFoGmEaANrB+5XSo2t82hX19pmOJLzVu+vvM10T9wJ2yCdFEkb3vuvTpKEJHV3c2dUDAA5WpbkBjR5UpDnrd8ox3v7TVRFXjvGmfzvy/mgb3XONdNBnVBBylON3NHpQkUpzGZUG0oUwDQBptqnaC9KVEWl7nRRwpB4hKcffcCq3tVJtXKqKSZ+EpYT1zrv12AQj1ACAZvUvDOmSISWau7FKASeuuCuF465irlXcer9rsvyOsv2O/I7ULduvcwcWEqSBNCNMA0AaWetN7d4V9YJ0tk/qkdX09dDGSLkBL2SX1XntfUaavs7RnSe4XEMNAGhWaW5A3zy6m7bsjumdslptqIzI1luZwxijQUUhHVeaoz75AaZ2A+2AMA0AabS2Stpaa1QR3TMi3UyQrs8YqTRL2hb2poSHao3WVXn7UwMA0BxjjPoWBNW3IKhIwlV11FUsYRXweftIs/0V0L4I0wCQRm9scxR1vWuke4TavkK3MVJhQNoekaKu18+QIrd9iwUAZIyQz1Eom/AMdCS+4wAgTcJxaUW50e6Yt8hYTop/rszxe+ftjknLyw37UAMAABzECNMAkCYVEcnK2/4q25f6vtFmz2rf0YTXT2W0PaoEAABAOhCmASBNIntmZbvyRpj3h2O88yWpLpGOqgAAANAeCNMAkCahPT9RHXn7SO8P1/7vB3OWLx1VAQAAoD0QpgEgTUpCkpEU9EnhhLdNViqs9c4L+rwR6uJgu5QJAACANCBMA0CaZPulE7pb5Qe8EebaFBcQq4175+UHpBO6WWWz3wIAAMBBizANAGl0Zi9XQUcK+aSqWNtHp13rtQ/5pKDj9QMAAICDF2EaANJoSKHUO8eqJCjFXKmsrvVAba20vc5rXxKU+uRYDS7smHoBAACwfwjTAJBGxkgTBrsqCEo9srwVubeFpZomRqmt9e7fFvba9ciSCoLS+MFuyttqAQAAoGNxRR4ApNmAPGnS0IQeXeOTz0gVUWl7RHKi3j7SjvGmdYcT3seQT+qZ7QXpSUMTGpDX2c8AAAAArSFMA0A7OLpYuvXYhKatcxSqNYq60u6YFE3s2YdaUq7fW2ws6HhTu8cPdgnSAAAAXQRhGgDayYA86a4TXK2rkl7f5mhFuVH9md6O8VbtPrOXq8GFYmo3AABAF0KYBoB2ZIw0pEgaUuQqHJcqo9710Vk+bx9ptr8CAADomngbBwAdJNtPeAYAAMgUrOYNAAAAAECKCNMAAAAAAKSIMA0AAAAAQIoI0wAAAAAApIgwDQAAAABAigjTAAAAAACkiE1aAKCDhONSRUSKuFLIkUpCbJUFAADQVfE2DgDakbXS2irpja2OVuwwsvWOGUkndLc6s5erIYWSMZ1VJQAAAFJFmAaAdrKpWpq2ztHWWqOoK+2OSdGE5Mq7xibokxaXGS0v96l3jtWEwa4G5HV21QAAAGgLwjQAtIP3K6VH1/i0KypVRKVIQnKMlO3zPrpWqol7ATvkkyIJo3vf9WnS0ISOLu7s6gEAANAaFiADgDTbVO0F6cqI9EnYm+rdIyT1y5F6ZEndQt7Hfjne/dZ67Soj3nmbqjv7GQAAAKA1hGkASCNrvandu6LS9jpvJLpXtpQbaHxNtDHe/b2ypSyf135XVJq+zpG1TfcPAACAgwNhGgDSaG2VtLXWqCIqBRxvBLq1hcWMkUqzvPYVUWlLrdG6qo6pFwAAAPuHMA0AafTGNkdR17tGurCJ0ejmGOO1jySkqOv1AwAAgIMX79YAIE3CcWlFudHumLfIWE6KSzzm+L3zdsek5eVG4Xj71AkAAIADR5gGgDSpiEhW3vZX2b7U9402e1b7jia8fiqj7VElAAAA0oEwDQBpEnG9j668Eeb94RjvfEmqS6SjKgAAALQHwjQApEloz09UR94+0vvDtf/7wZzlS0dVAAAAaA+EaQBIk5KQZCQFfVI4oZS3t7LWOy/o80aoi4PtUiYAAADSgDANAGmS7ZdO6G6VH/BGmGtTXECsNu6dlx+QTuhmlZ3iAmYAAADoOIRpAEijM3u5CjpSyCdVxdo+Ou1ar33IJwUdrx8AAAAcvAjTAJBGQwql3jlWJUEp5kplda0Hamul7XVe+5Kg1CfHanBhx9QLAACA/UOYBoA0MkaaMNhVQVDqkeWtyL0tLNU0MUptrXf/trDXrkeWVBCUxg92U95WCwAAAB2LK/IAIM0G5EmThib06BqffEaqiErbI5IT9faRdow3rTuc8D6GfFLPbC9ITxqa0IC8zn4GAAAAaA1hGgDawdHF0q3HJjRtnaNQrVHUlXbHpGhizz7UknL93mJjQceb2j1+sEuQBgAA6CII0wDQTgbkSXed4GpdlfT6Nkcryo3qz/R2jLdq95m9XA0uFFO7AQAAuhDCNAC0I2OkIUXSkCJX4bhUGfWuj87yeftIs/0VAABA18TbOADoINl+wjMAAECmYDVvAAAAAABSRJgGAAAAACBFhGkAAAAAAFJEmAYAAAAAIEUshQMAAAB0cZGEq+qoq1jCKuAzygs6CvkYNwPaE2EaAAAA6IKstdq8O6ZVZbXaUBmRlU0eMzI6ojik40pz1Dc/IGNMJ1YKZCbCNAAAANDFlNXENHdjlSrCccVdKRx3FXOtrJWMkQKO0doddVpfWaeSbL/OG1io0txAZ5cNZBTCNAAAANCFfFQV0Zz1O1Ub96Z2RxNWjpGCPiPHSK6kurir2piroM8o5lr9ZW2FRg8qUv/CUGeXD2QMLqQAAAAAuoiympjmrN+p6piryrqEXGtVGHLUPdunwpBP+SHvo/e5I9daVdYlVB1zNWf9TpXVxDr7KQAZgzANAAAAdAHWWs3dWKXauKuqSEJBx6gky6csv9PommhjjLL8jkqyfAo6RlWRhGrjrl7dWCVrbTOPACAVhGkAAACgC9i8O6aKcFzVUVc+IxWGGofofRljVBhy5DNSddTVjnBcW3YzOg2kA2EaAAAA6AJWldUq7krRhFVuoPUgvZcxRrkBR9GEVdz1+gFw4AjTAAAAwEEuknC1oTKicNyVY6SQL7WtrkJ7FicLx12tr4woknDbqVLg0EGYBgAAAA5y1VFXVlYx1yroM41GpV1rFXetYgnvo7vPddHGmOTK3lZW1VHCNHCg2BoLAAAAOMjFEl44tlZy9uRoa71wXRuziiQaLyoW8hvl+I0Cjhe+HUlx27A/APuPMA0AAAAc5AJ7pnWbPftIxxJWu6IJxV3JyspaycoL28ZIRlJd3CoSN/I7UkHQJ3fP+fX7A7D/CNMAAADAQS4v6MjIG2WujSUUibtyJbnWC9CSF6CNkWS9wC0rGWNlXamyLi4rKSfgkzFGeUGu9gQOFGEaADrIs+ulv34s7YpKBUHpwn7SZYM6uyoAQFcQ8jk6ojik1dvDirveVG93T4h29oxEa+9g855AbeW1SVjJ7mkfcIwGFYUU8hGmgQNFmM5Q//3vf/Xwww/r5Zdf1n//+1+FQiENGjRIl156qb773e8qJyens0sEDgnV1dLFb0k1CV+D+3fEpKlrvX+5voRePEPKy+ukIgEAXcKxPbL1r63VkryA7Mgbid4bmrXPZdDGeEHb7gnURlJNLKFje2R3cOVAZiJMZ6CXX35Z3/jGN1RVVZW8r7a2VkuXLtXSpUs1bdo0vfLKKzr88MM7sUog8130d6ks4mu1XU3Cp1ELpNJQQi+d0/51AQAygyvJ2EYZOsna5CB1Q1wuDaQF8zsyzKpVq3TppZeqqqpKeXl5uvvuu/X222/r9ddf1zXXXCNJWrdunS644AJVV1d3crVA5jrzlbYF6frKIj6d+Uo7FQQA6PLe3R5WbqDh75bW1uTe93huwKd3y8JprQs4VDEynWEmT56s2tpa+f1+zZs3T6eeemry2JlnnqkjjzxSP/jBD7R27Vo98MADmjJlSidWC2Smi/4u1bmpBem96lyfLvo7I9QAgIYiCVcbKiOqix/Y/tB1cVfrKyOKJFyumwYOEN9BGWTp0qVasGCBJGn8+PENgvReN998s4YOHSpJeuihhxSLxTqyRCDjVVenPiK9r7KIT0wcAQDUVx11ZWUV3hOmU52pvbd9OO71Ux09sFAOgDCdUV566aXk7auuuqrJNo7j6Fvf+pYkqbKyMhm+AaTHxW+lp58vL0xPPwCAzBBLWEUTNrmCtyQFHMnv/G81733/OcY7Hqj3jt+1UjRhFUu0NkEcQGsI0xnkrbe8d/G5ubn6/Oc/32y74cOHJ28vXMg7diCd9l21e39Vx9PTDwAgMwR8RrV7RqWtJN+evbCMjHzGyO8Y+RwjnyP5nD2fG++4ZOQz/7t+Ohx3FfCxChlwoLhmOoOsWbNGkjRo0CD5/c1/aYcMGdLonLbYvHlzi8c//vjj5O1t27a1ud+O8ElZeWeXgEPA7E1SfFf6QvDvliQ0ZkDaugMacVv5uY622bH9084uAYeA6lhcNRXbFXetF4qNZEzbA7G1VvE9q3tXO0bVlZKqiQJoX63lh87Ss2fPFvNSWxlrLXM8MkBdXZ2ys709Ay+44ALNmTOnxfZ5eXmqqanRKaecosWLF7fpMVL5gQ0AAAAAB6OPP/5Yffv2PeB+mOadIXbv3p28nZeX12r73NxcSWJ7LAAAAADYD8ztyBB1dXXJ28FgsNX2oVBIkhQOt32fwfrTuJurYe3atTrssMPUo0ePtEydADLJtm3bNGzYMEnSkiVL1KtXr06uCACQKfgdA7Rdz54909IPaSdDZGVlJW9Ho9FW20ciEUlKTg1vi7ZMhRg0aFCb+wMOZb169UrL9CIAAPbF7xigYzDNO0Pk5+cnb7dl6nZNTY2ktk0JBwAAAAA0RJjOEFlZWerevbuk1lfNq6ysTIbpfv36tXttAAAAAJBpCNMZZOjQoZKk9evXKx6PN9tu7dq1jc4BAAAAALQdYTqDnH766ZK8KdzLly9vtt2bb76ZvH3aaae1e10AAAAAkGkI0xnkoosuSt6eOXNmk21c19Uf/vAHSVJRUZFGjhzZEaUBAAAAQEYhTGeQYcOG6YwzzpAkTZ8+XYsXL27U5v7779eaNWskSTfeeKMCgUCH1ggAAAAAmYCtsTLM1KlTddpppykcDmvUqFG67bbbNHLkSIXDYf35z3/W448/Lkk66qijdPPNN3dytQAAAADQNRlrre3sIpBes2fP1hVXXKFdu3Y1efyoo47Syy+/zJ7QAAAAALCfCNMZ6qOPPtLUqVP18ssva/PmzQoGgxo0aJAuueQSTZo0STk5OZ1dIgAAAAB0WYRpAAAAAABSxAJkAAAAAACkiDANAAAAAECKCNMAAAAAAKSIMA0AAAAAQIoI0wAAAAAApIgwDQAAAABAigjTAAAAAACkiDANAAAAAECKCNMAAAAAAKSIMA0AKVqwYIGMMcl/l19+eavnXHnllcn2AABccMEFMsbIcRwtXLiwTecsXLhQjuPIGKPRo0e3c4UAWkOYBoAD9Nxzz+m9997r7DIAAF3I7373OxUUFMhaqwkTJqiurq7F9pFIRBMmTJC1VgUFBXrsscc6qFIAzSFMA8ABstbq9ttv7+wyAABdSN++fXXvvfdKktatW6ef/exnLba/8847tW7dOknSL3/5S/Xt27fdawTQMsI0AByA7t27S5JefPFFrVixopOrAQB0Jd/+9rc1fPhwSdJ9992nlStXNtlu1apV+uUvfylJGjFihK699toOqxFA8wjTAHAAbrzxRoVCIUlidBoAkBJjjKZNm6bs7GzF43FdffXVisfjDdokEgmNHz9e8Xhc2dnZmjZtGutvAAcJwjQAHIC+ffsmRwjmzJmjf/3rX/vd1/bt2/WTn/xExx9/vIqKipSVlaUBAwbom9/8ZpsXpwEAdC2DBg3SnXfeKUl655139Ktf/arB8QceeEDLly+XJN1111064ogjksc2b96sH/3oRzrhhBNUXFysrKwsfeYzn9Fll12m+fPnt/i4iURCTzzxhM4991z17NlTwWBQRUVFOvLII3XWWWfpF7/4hf7973+n+dkCmcVYa21nFwEAXcmCBQs0cuRISdLMmTN17rnn6ogjjlA4HNaoUaP06quvNjrnyiuv1JNPPinJu8Z6X/PmzdMll1yiXbt2Nfu41113nR5++GE5Dn8HBYBMkkgkdOqpp2rp0qUKhUJatWqVBg8erA0bNuhzn/ucwuGwTjrpJC1evFg+n0+SNH36dF1//fUKh8PN9jt+/Hg99thj8vv9De6vrq7Wl770Jb311lst1vWVr3xFzz///IE/QSBD8Y4MAA5Qr169NHHiREleKE51FPmdd97RmDFjtGvXLgUCAU2ePFnz58/XkiVL9Lvf/U4DBw6UJP3617/Wj370o7TXDwDoXD6fT9OnT1cgEFAkEtH48ePluq6uueYahcNhBQIBzZgxIxmkZ8yYoQkTJigcDuuYY47RI488ooULF2rFihX6v//7P33pS1+S5AXuW2+9tdHj3XHHHckgPXr0aD3zzDNatGiRli9frrlz5+ree+/VGWecwXRyoDUWAJCS+fPnW0lWkp05c6a11tqysjKbm5trJdmRI0c2OmfcuHHJc/Z10kknWUnW5/PZV199tdHxiooK+9nPftZKso7j2NWrV6f9OQEAOt+UKVOSvyvOOuus5O3bb7892ea///2vzcnJsZLsuHHjbCwWa7Kv2267Lfl7Y926dQ2O9evXz0qyX/3qV1usZ8eOHQf8nIBMxsg0AKRBjx49NGnSJEnS/PnzW71Wba8lS5Zo6dKlkqQJEyZo1KhRjdoUFxfr8ccflyS5rqvf/OY3aaoaAHAw+fGPf6yjjz5akvT6669Lko455hjddtttyTZTp05VbW2tevfu3eQU7r1+9rOfqU+fPnJdV3/4wx8aHPvkk08kSWeccUaL9ZSUlOz3cwEOBYRpAEiTW265Rfn5+ZKkn/70p20657XXXkveHj9+fLPtTjvtNA0dOrTROQCAzBEMBhtM5947/TsYDCbbzJo1S5I0ZswYZWVlNduX3+/XqaeeKklavHhxg2O9evWSJD377LOqra1N63MADiWEaQBIk27dumny5MmSpEWLFjW5ENm+Vq9eLcl7A3X88ce32Pbkk0+WJH3wwQeKRqMHViwA4KA0bNgw9e3bV5K3Y8SwYcOSx6qqqrR+/XpJ0u9+9zsZY1r8t3fxsL0j0XuNGzdOkvT2229r4MCBmjRpkl588UVt3769I54ikDEI0wCQRt/73vdUVFQkSZoyZUqr7SsqKiR5U+mam6q3V8+ePSV5q4FXVlYeWKEAgC6nrKxsv87bd/T5pz/9qa6++moZY1RWVqZf//rX+vKXv6zDDjtMn/vc53T77bfr008/TUfJQEZr+Z0bACAlRUVF+t73vqcpU6ZoyZIlmjNnjkaPHt3qeW1ZMdWykyEAHNISiUTy9uTJk1u8PKi++tPEJSkQCGj69Om6+eab9cwzz+iNN97QsmXLFI1GtXr1aq1evVoPPPCA/vSnP2ns2LFpfQ5AJiFMA0CaTZ48WVOnTtWOHTs0ZcoUXXDBBc223bu4y44dOxSPx1scnd47SmCMUXFxcXqLBgAc9Lp165a8XVtbq2OOOeaA+vvsZz+ru+66S3fddZfC4bAWLVqkp59+Wn/4wx9UXV2tr33ta9qwYUPyGmsADTHNGwDSLD8/X7fccoskaeXKlXrppZeabbv3jVA0GtXKlStb7HfJkiWSpCOPPLLRKAMAIPP16NFDffr0keQtRpnOGUvZ2dk6++yzNWPGDP3qV7+SJIXDYc2ZMydtjwFkGsI0ALSDSZMmqbS0VJJ0++23N/uG5+yzz07enj59erP9LV68WP/+978bnQMAOLRceOGFkqQPP/wwucBYup111lnJ2+Xl5e3yGEAmIEwDQDvIzc3VrbfeKkl677339MorrzTZbtiwYTrppJMkSdOmTdPf//73Rm2qqqr07W9/W5LkOI4mTpzYTlUDAA52t9xyi0KhkCTpO9/5jpYtW9Zi+1deeUXvvvtu8vOKigr99a9/bXFUe968ecnbAwcOPMCKgczFNdMA0E4mTpyo++67T9u2bWvxL/uPP/64Tj75ZEWjUV1wwQW6/vrrNWbMGOXl5WnlypW655579OGHH0qSvv/97x/wNXIAgK5r4MCBeuyxx3TVVVepoqJCp512mr75zW9q9OjR+sxnPqN4PK7NmzdryZIlev7557VhwwbNnj1bxx57rCRp165dGjt2rAYMGKAvf/nLOvnkk9W/f3/5/X5t27ZNs2fP1rRp0yR5W3ONGTOmM58ucFAzluVhASAlCxYs0MiRIyVJM2fO1JVXXtls20cffVTXX399g/ua+rE7b948XXLJJdq1a1ezfV133XV6+OGH5ThMKgKATDZgwAB99NFH6t+/vzZt2tRkm2effVbXXntti783JG9G02uvvZb8vbVp06Y2jTb36dNHs2fP1vHHH59y/cChgndkANCOrrnmGvXr16/VdqNGjdL69et122236f/9v/+ngoIChUIhfeYzn9E3vvENvfXWW3r00UcJ0gAASdJll12mTZs26Z577tGIESNUWlqqQCCgnJwcHX744RozZoweeOABbdq0KRmkJal///5655139Ktf/Urnn3++Bg8erKKiIvn9fnXv3l3Dhw/XfffdpzVr1hCkgVYwMg0AAAAAQIoY4gAAAAAAIEWEaQAAAAAAUkSYBgAAAAAgRYRpAAAAAABSRJgGAAAAACBFhGkAAAAAAFJEmAYAAAAAIEWEaQAAAAAAUkSYBgAAAAAgRYRpAAAAAABSRJgGAAAAACBFhGkAAAAAAFJEmAYAAAAAIEWEaQAAAAAAUkSYBgAAAAAgRYRpAAAAAABSRJgGAKALWrBggYwxMsZowYIFnV0OAACHHH9nFwAAQHurqanRU089pVmzZmnVqlUqLy+X3+9XaWmpDjvsMB133HEaMWKEhg8frl69enV2udgPxpgm78vLy1NhYaFKS0t1/PHHa9iwYfrKV76ibt26dUKVAIBMYqy1trOLAACgvSxZskSXXXaZNm3a1Grbww47TJ988kmD+0aMGKE333xTw4cPP6hGgBcsWKCRI0dKkubPn68RI0Z0bkEtGDBggD766CONGzdOTzzxRLs8RlNhujlZWVm6/PLL9atf/Urdu3dvl3oAAJmPkWkAQMZav369zjnnHO3atUuSdOGFF+qrX/2qjjrqKAWDQZWXl2vVqlX6+9//rvnz53dytUiHE088UTNnzkx+HolEVFlZqQ8++EALFy7Uiy++qHA4rCeeeEJz587Viy++qFNOOaUTKwYAdFWEaQBAxvrxj3+cDNIzZszQVVdd1ajNOeeco+9///vavn27nnvuuY4uEWmWm5urY445ptH9Z599tiZOnKjy8nJNnjxZTz31lD755BNdeOGFWrp0qfr3798J1QIAujIWIAMAZKREIqE5c+ZI8kYrmwrS9fXo0UPXXXddR5SGTtS9e3f96U9/0ne+8x1J0vbt23XjjTd2clUAgK6IMA0AyEjbt29XbW2tJGnQoEEpn3/llVfKGKM333xTkvTmm28mV8/e+2/AgAHJ9ps2bUre39p1wQMGDJAxRldeeWWzbcLhsO6++24dd9xxys3NVbdu3XTaaafp97//vVzXbfPzsNbq+eef11e+8hX169dPWVlZKi4u1rBhw3TXXXdp586drb4Ge5/nzp07NWXKFB199NHKzc1VUVGRvvjFL+qpp55q8vwRI0bIGKOPPvpIkvTkk082eg0761rvhx56SP369ZMk/fWvf9X777/fqM3eGu+4444W+9r7PJt6Lvuuum6t1fTp03X66aerW7duKigo0LBhw/THP/6xwXnRaFSPPfaYTjnlFJWUlCg/P1+nnXZai7Mnmvo/+MILL2jUqFEqLS1Vbm6ujjvuOD3yyCOKxWLJ86y1evrppzVixAiVlpYqJydHJ5xwgh577DE1tbTOww8/nHycf/3rXy2+NpL0la98RcYYFRUVKRwOt9oeALoKpnkDADJSMBhM3l6zZk0nVpK6rVu36qyzztLatWuT99XW1urtt9/W22+/rRdeeEE33XRTq/1s375dF198sRYtWtTg/kgkoqVLl2rp0qX69a9/rVmzZunkk09usa+1a9fq/PPPb7SQ21tvvaW33npLixcv1qOPPtr2J9nJQqGQJk6cqNtuu03WWs2aNUtHH310uz5mLBbT2LFjNXv27Ab3L126VN/61re0bNkyTZ06VZWVlbrooov0j3/8o0G7vV//9evX67bbbmv18b773e/qt7/9bYP73n33Xd1www1asGCBnnvuOcXjcV1xxRV6/vnnG7RbuXKlJk6cqBUrVujxxx9vcOyb3/ymbr31VtXV1WnmzJkt/t8pLy9PzhC5/PLLlZ2d3WrdANBVMDINAMhIJSUlyetgV61apXvvvTelEd27775b7733nk488URJ3lTx9957r8G/efPmpb3ueDyu0aNHJ4P0qFGj9OKLL2rZsmV64YUXdPbZZ2vu3Ln6yU9+0mI/NTU1Gj58uBYtWqRgMKhvf/vbmjVrllasWKG33npLd999t7p166ZPP/1U559/fnL0uCm1tbW68MILtWPHDv3kJz/RggULtGzZMv3+979X3759JUm//vWv9eqrrzY4b+bMmXrvvffUu3dvSdLYsWMbvYb1FwvraGeffXby9ltvvdXuj/fTn/5Us2fP1je+8Q29/PLLWr58uZ555hkNHjxYkjfi+9prr+nKK6/U22+/rYkTJ2revHlavny5pk+fnnwdp0yZ0uRIen2PPfaYfvvb3+pLX/qSXnjhBS1fvlwvvfRSMvi+8MILmjlzpm655RY9//zz+vrXv645c+Zo+fLl+vOf/6whQ4ZIkn7/+99r7ty5DfouLi7WxRdfLEn685//3OJo81NPPaVoNCpJuvrqq/fjVQOAg5gFACBD3XfffVZS8l///v3tpEmT7FNPPWXXr1/fpj6GDx9uJdnhw4e32G7jxo3Jx5k5c2aLbfv3728l2XHjxjU69vDDDyf7ufbaa5s8/+qrr27wvObPn9+ozaRJk6wkW1hYaJcuXdpkP5s2bbK9evWykuwVV1zR6Pi4ceOSj1FUVGRXr17dqM0HH3xgs7KyrCR74YUXpvx802Vvna19neqLRCLWcRwryR5++OHN9nn77be32E9L/0fmz5/f4Gv10EMPNWqzbds2W1BQYCXZHj16WGOMffHFFxu1W7VqVbLeG264odHx+v8HJdnJkyc3alNTU2MHDBhgJdnu3btbY0yzNeXn5zf7dX3jjTeSj/PUU08188pYe9xxx1lJ9phjjmm2DQB0VYxMAwAy1k033dRgNOyjjz7So48+qm984xsaNGiQevbsqcsvv1yzZ89u8trQzrB3Wu5hhx2mBx98sMk2U6dOVY8ePZrto7y8XNOmTZMk3XnnncnR9X31799fP/3pTyVJzz77bPIa86bceeedTU6DHjRokC666CJJHTO6m07BYFD5+fmSpMrKynZ/vJNPPrnJxc569uyZHOndvn27LrvssuRrWt+xxx6r008/XVLrr3W/fv30y1/+stH9OTk5GjdunCTv/0lbamrqsUaMGJFci6C52QUrVqzQqlWrJDEqDSAzEaYBABnLcRxNnz5df/vb33TOOefIcRr+2vv000/17LPP6sILL9SwYcO0YcOGTqrUs3Xr1uT13ZdeeqlycnKabJeXl6dLL7202X5effVV1dXVJftpyRe/+EVJ3vW8y5cvb7KNMUZf//rXm+3j85//vCQvkLa0oNnBKC8vT5K0e/fudn+syy+/vNljxx57bPL2ZZdd1my74447TpL04YcftvhYX/7ylxUIBNLyWE19XY0xyYD8+uuv67///W+j8/eG7EAgoCuuuKLFegGgKyJMAwAy3nnnnad58+apvLxcs2fP1u23367Ro0ersLAw2WbZsmU644wztG3btk6r87333kvePumkk1psO2zYsGaPLVu2LHm7V69ejVbQrv+v/p7Mn3zySZP9de/eXd26dWv28UpKSpK3OyKUptPeegsKCtr9sY466qhmjxUVFaXUrrXXOZ2P1dzjXXXVVfL7/bLW6sknn2xwLBKJ6Omnn5YkjRkzpsWZFADQVRGmAQCHjOLiYo0ePVp33HGHZs+erU8//VQzZsxQcXGxJGnbtm3Jac+dof5U49LS0hbbHnbYYc0eKysr26/Hb26ad3Mj5HvVH/FPJBL79didIRKJJENi/T8ItJeWXsf6r2Fb2rW2mF46H0tq+uvas2dPXXDBBZKkJ554osGlErNmzVJFRYUkafz48S3WCgBdFVtjAQAOWaFQSFdddZV69+6t8847T5K3yvHjjz/eaEp4R6gfRowxbW67r73BJxgMNjt1uyl7V+Y+VKxatSr5Ou5dURupmTBhgmbNmqUPP/xQ//jHPzR8+HBJ/5vi3bt3b5177rmdWSIAtBvCNADgkHfuueeqX79++vjjj1VZWakdO3akPC21fvhubdSwpqamyfvrj45++umnLfbR0ujz3inZ0WhU3bp1U69evVrs61D197//PXl778Je9RljZK3d76/noeD8889Xnz59tGXLFs2cOVPDhw/Xli1bkq/tuHHj5PP5OrlKAGgfTPMGAEBK7uErNQzGrY0Q77V3VWip5ZWhd+zYofLy8iaPfe5zn0veXrp0aYuP19Lx448/Pnm7PfbCTlVbX8OOVFdXp8cee0ySV9/YsWMbtWnLSt+u6+qDDz5onyK7AJ/PpyuvvFKS9Pzzz6u6ulpPPvlkcnbEVVdd1YnVAUD7IkwDAA55tbW1+ve//y3JW4iq/ghxVlaWJO/62pYUFxcnF2yqvwDYvp555plmj/Xu3VtDhw6VJP3lL39ROBxusl1NTY2ee+65Zvs5//zzkys5P/jgg4rH4y3W3t7a+hp2pJtuukmbN2+WJF100UXJ172+gQMHSmr56/nKK6+oqqqqfYrsIsaPHy9jjGpqavTss8/qiSeekOStFH/kkUd2bnEA0I4I0wCAjFRdXa2TTz5Zc+bMaXGaruu6uv7665MLUV144YUNRlL3TpH+8MMPW92Leu82U7NmzWpym601a9ZoypQpLfYxceJESd7K2jfffHOTbW666aYWp3n36dMnOSK4atUqffvb324xUJeVlSX3pW4Pe1/Dzt56TPL2Vr7iiiuSo9KHHXaYpk6d2mTbvdf//utf/9KiRYsaHd+2bZtuuOGG9iu2ixg4cKDOOussSdJPfvKT5Eg9e0sDyHRcMw0AyFhLlizRmDFj1KdPH1100UU69dRT1b9/f+Xn52vnzp1auXKlZsyYkdySqrCwUHfddVeDPr7whS9o5syZKisr0/e+9z1dccUVyS21AoGA+vfvn2z73e9+V3/9618VDoc1YsQI3XHHHTr++ONVXV2t1157TVOnTlVpaan8fr+2b9/eZM0TJ07UzJkztXLlSv32t7/Vxo0b9Z3vfCd5TfdvfvMbzZs3TyeddFKLU73vv/9+vf3221q9erVmzJihf/7zn7r22mv1+c9/Xnl5edq5c6fef/99vfbaa3rllVf0uc99ThMmTDjQl7xJX/jCFzR//nwtXbpU99xzj84//3zl5uZKkrKzs9WnT5+0PVZNTY1Wr16d/DwSiWjnzp364IMPtHDhQr3wwgvJEf/evXvrpZdeUr9+/Zrs69prr9VvfvMbxeNxjRkzRlOmTNHpp5+uaDSqRYsW6f7771c8HteRRx55SE/1lryFyF577bXk9mr5+fm65JJLOrkqAGhnFgCADBQOh23Pnj2tpDb9O/LII+2yZcsa9bN79257+OGHN3lO//79G7W/4YYbmn2Mfv362ffff9/279/fSrLjxo1rsvYtW7bYwYMHN9vPqFGj7Kuvvpr8fP78+U32s2PHDnveeee16fmPHDmy0fnjxo1r9nnWN3PmzGQ/GzdubHR88+bNtqSkpMnHHT58eIt9t1Vbv86SbFZWlr366qtteXl5q/0+8MADzfZTXFxs33zzTTt8+PBmn8v8+fNb/TpZ2/pruNftt9+ebLevjRs3Jo/NnDmz2T7SXZO11kYiEdu9e/dk+2uuuabF9gCQCZjmDQDISFlZWdqyZYsWLVqkn/3sZzr//PN1+OGHKzc3Vz6fTwUFBRoyZIguu+wyPf3001q9erU+//nPN+onLy9Pb7/9tm688UYNHTq01T2Xp06dqqefflpf/OIXVVBQoOzsbA0ePFg//OEPtXLlSn32s59ttfbevXtr5cqV+vnPf65jjjlG2dnZKioq0imnnKLf/OY3+tvf/qZgMNhqPyUlJfrb3/6m119/XVdddZWOPPJI5eXlye/3q6SkRCeddJKuu+46vfLKKw1Wtk63Pn36aMmSJRo/frwGDRqUvIa6I+Tl5al37946/vjjNX78eD3++OPasmWLpk+fnlz1vCU33XST5s6dq3PPPVfFxcUKhUIaOHCgrrvuOr3zzjvJqf2HumAw2GAkmineAA4FxtpWLgADAAAAWnHGGWdo4cKFGjp0aHJBPwDIZIxMAwAA4ID85z//0cKFCyV5q3sDwKGAMA0AAIADct9990nyLq8YN25cJ1cDAB2D1bwBAACQknA4rC1btqi2tlazZ8/W9OnTJXmrenfv3r2TqwOAjsE10wAAoFP95z//UTQaTfm80tJSlZaWtkNFaM2CBQs0cuTIBvf17dtXq1atUklJSSdVBQAdi5FpAADQqUaNGqWPPvoo5fNuv/123XHHHekvCG1mjFGvXr105pln6u677yZIAzikEKYBAACQkhEjRojJjQAOdUzzBgAAAAAgRazmDQAAAABAigjTAAAAAACkiDANAAAAAECKCNMAAAAAAKSIMA0AAAAAQIoI0wAAAAAApIgwDQAAAABAigjTAAAAAACkiDANAAAAAECKCNMAAAAAAKSIMA0AAAAAQIoI0wAAAAAApIgwDQAAAABAigjTAAAAAACkiDANAAAAAECKCNMAAAAAAKSIMA0AAAAAQIoI0wAAAAAApOj/AzpDKYrSkWemAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 555,
       "width": 489
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_order = ['No', 'Yes']\n",
    "\n",
    "# Create grid\n",
    "grid = sns.FacetGrid(data=df.to_pandas(),height=5) ;\n",
    "\n",
    "# Add a barpplot; we use map_dataframe so we can control hue separately for this layer\n",
    "grid.map_dataframe(sns.barplot, 'Student', 'Balance', hue='Student',palette='deep', order=x_order, alpha=.5);\n",
    "\n",
    "# Add a regression using our new column\n",
    "grid.map_dataframe(sns.regplot, 'Student_Dummy', 'Balance')\n",
    "\n",
    "# Aesthetics\n",
    "grid.set(ylim=(0,1025));\n",
    "grid.figure.suptitle(\"Treatment/Dummy Code =\\n Mean Difference as a Linear Model\", y=1.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see this by estimating a new model using our newly created column `Student_Dummy` as a predictor and comparing its output to our original *augmented model*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept        463.236842\n",
       "Student_Dummy    401.447368\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_model = ols('Balance ~ Student_Dummy', data=df.to_pandas())\n",
    "dummy_results = dummy_model.fit()\n",
    "\n",
    "dummy_results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            463.236842\n",
       "C(Student)[T.Yes]    401.447368\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting parameter estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our model our parameters represent:\n",
    "\n",
    "- $\\hat{\\beta_0} = StudentNo_{mean}$ (reference level)\n",
    "- $\\hat{\\beta_1} = StudentYes_{mean} - StudentNo_{mean}$\n",
    "\n",
    "By default `ols` will use **alphabetically sort** the levels of your categorical predictor and use the **first one** as the reference level. In our case this is `No` as as `N` comes before `Y` alphabetically.\n",
    "\n",
    "But we can be more explicit and even control what group is the reference category. Let's switch it to `Student = Yes`\n",
    "\n",
    "We can do this by passing a second argument to `C()` in addition to our column name. We can use `Treatment(reference='category_level')` to tell `ols` that the reference category is `Yes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Balance   R-squared:                       0.151\n",
      "Model:                            OLS   Adj. R-squared:                  0.140\n",
      "No. Observations:                  76   F-statistic:                     13.16\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):           0.000523\n",
      "================================================================================================================\n",
      "                                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------------------\n",
      "Intercept                                      864.6842     78.252     11.050      0.000     708.765    1020.604\n",
      "C(Student, Treatment(reference='Yes'))[T.No]  -401.4474    110.664     -3.628      0.001    -621.951    -180.944\n",
      "================================================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Set the reference group (intercept) to 'Yes'\n",
    "ref_yes = ols(\"Balance ~ C(Student, Treatment(reference='Yes'))\", data=df.to_pandas())\n",
    "\n",
    "print(ref_yes.fit().summary(slim=True)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the intercept is the mean of `Student = Yes` and the slope is `No - Yes`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "864.6842105263158"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-401.44736842105266"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_no - student_yes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting the design matrix we can see that the coding has simply been flipped from our *augmented model* above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No = 1\n",
    "# Yes = 0\n",
    "ref_yes.exog[:5, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Originally we had\n",
    "# No = 0\n",
    "# Yes = 1\n",
    "a_model.exog[:5, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And just to confirm, we can see that the t-statistic and p-value that `ols` gives us is that same as running an independent t-test using `scipy`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Independent samples t-test t(74.0) = -3.628, p = 0.00052\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "results = ttest_ind(\n",
    "    df.filter(col('Student') == 'No').select('Balance').to_numpy(),\n",
    "    df.filter(col('Student') == 'Yes').select('Balance').to_numpy())\n",
    "\n",
    "print(f\"Independent samples t-test t({results.df[0]}) = {results.statistic[0]:.3f}, p = {results.pvalue[0]:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or in APA style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Independent samples t-test t(74.0) = -3.628, p < .001\n"
     ]
    }
   ],
   "source": [
    "print(f\"Independent samples t-test t({results.df[0]}) = {results.statistic[0]:.3f}, p < .001\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical (2-level) and Continuous Predictors\n",
    "\n",
    "In the previous notebook `02_models` we saw that `Income` was also a meaningful predictor of `Balance`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = sns.lmplot(data=df, x='Income', y='Balance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's expand our model to account for this relationship when looking at the difference between Students and non-Students.  \n",
    "\n",
    "Specifically let's estimate a *multiple regression* that asks: **do students have difference Balances than non-students when accounting for Income?**\n",
    "\n",
    "$$\n",
    "\\hat{Balance}_{i}= \\beta_0 + \\beta_1 Student + \\beta_2 Income\n",
    "$$\n",
    "\n",
    "We'll also estimate another *univariate* regression that only looks at the relationship between Balance and Income:\n",
    "\n",
    "$$\n",
    "\\hat{Balance}_{i}= \\beta_0 + \\beta_1 Income\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge\n",
    "\n",
    "1. Estimate the two models above\n",
    "2. Use `anova_lm` to compare both models. Is the multiple regression *worth it*? \n",
    "3. Use `anova_lm` to compare the multiple regression to `a_results`, your original augmented model (univariate with only `Student` as a predictor). Is the multiple regression *worth it*?\n",
    "4. Create a new dataframe called `df_models` that includes the following columns:\n",
    "\n",
    "- `Balance` the original balance variable\n",
    "- `Student` the original student variable\n",
    "- `Income` the original income variable\n",
    "- `balance_pred_si` the `.fittedvalues` attribute of the multiple regression\n",
    "- `resid_si` the `.resid` attribute of the multiple regression\n",
    "- `balance_pred_i` the `.fittedvalues` attribute of the univariate regression\n",
    "- `resid_i` the `.resid` attribute of the univariate regression\n",
    "- `balance_pred_s` the `.fittedvalues` from the univariate `a_results` we estimated above\n",
    "- `resid_s` the `.resid` from the univariate `a_results` we estimated above\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "# Student only\n",
    "s_model = ols('Balance ~ C(Student)', data=df.to_pandas())\n",
    "s_results = s_model.fit()\n",
    "\n",
    "# Income only\n",
    "i_model = ols('Balance ~ Income', data=df.to_pandas())\n",
    "i_results = i_model.fit()\n",
    "\n",
    "# Student + Income\n",
    "si_model = ols('Balance ~ C(Student) + Income', data=df.to_pandas())\n",
    "si_results = si_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.0</td>\n",
       "      <td>1.721872e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73.0</td>\n",
       "      <td>1.374135e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.477369e+06</td>\n",
       "      <td>18.473293</td>\n",
       "      <td>0.000052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid           ssr  df_diff       ss_diff          F    Pr(>F)\n",
       "0      74.0  1.721872e+07      0.0           NaN        NaN       NaN\n",
       "1      73.0  1.374135e+07      1.0  3.477369e+06  18.473293  0.000052"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# S+I vs S\n",
    "anova_lm(s_results, si_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.0</td>\n",
       "      <td>1.685200e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73.0</td>\n",
       "      <td>1.374135e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.110658e+06</td>\n",
       "      <td>16.525165</td>\n",
       "      <td>0.00012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid           ssr  df_diff       ss_diff          F   Pr(>F)\n",
       "0      74.0  1.685200e+07      0.0           NaN        NaN      NaN\n",
       "1      73.0  1.374135e+07      1.0  3.110658e+06  16.525165  0.00012"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# S+I vs I\n",
    "anova_lm(i_results, si_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Balance</th><th>Student</th><th>Income</th><th>balance_pred_si</th><th>resid_si</th><th>balance_pred_i</th><th>resid_i</th><th>balance_pred_s</th><th>resid_s</th></tr><tr><td>i64</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>16</td><td>&quot;Yes&quot;</td><td>20.918</td><td>727.826282</td><td>-711.826282</td><td>526.484832</td><td>-510.484832</td><td>864.684211</td><td>-848.684211</td></tr><tr><td>1216</td><td>&quot;Yes&quot;</td><td>10.842</td><td>672.269098</td><td>543.730902</td><td>471.318923</td><td>744.681077</td><td>864.684211</td><td>351.315789</td></tr><tr><td>148</td><td>&quot;No&quot;</td><td>29.705</td><td>371.643114</td><td>-223.643114</td><td>574.593491</td><td>-426.593491</td><td>463.236842</td><td>-315.236842</td></tr><tr><td>108</td><td>&quot;No&quot;</td><td>76.348</td><td>628.823915</td><td>-520.823915</td><td>829.963033</td><td>-721.963033</td><td>463.236842</td><td>-355.236842</td></tr><tr><td>532</td><td>&quot;Yes&quot;</td><td>30.622</td><td>781.332328</td><td>-249.332328</td><td>579.614049</td><td>-47.614049</td><td>864.684211</td><td>-332.684211</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 9)\n",
       "┌─────────┬─────────┬────────┬─────────────┬───┬────────────┬────────────┬────────────┬────────────┐\n",
       "│ Balance ┆ Student ┆ Income ┆ balance_pre ┆ … ┆ balance_pr ┆ resid_i    ┆ balance_pr ┆ resid_s    │\n",
       "│ ---     ┆ ---     ┆ ---    ┆ d_si        ┆   ┆ ed_i       ┆ ---        ┆ ed_s       ┆ ---        │\n",
       "│ i64     ┆ str     ┆ f64    ┆ ---         ┆   ┆ ---        ┆ f64        ┆ ---        ┆ f64        │\n",
       "│         ┆         ┆        ┆ f64         ┆   ┆ f64        ┆            ┆ f64        ┆            │\n",
       "╞═════════╪═════════╪════════╪═════════════╪═══╪════════════╪════════════╪════════════╪════════════╡\n",
       "│ 16      ┆ Yes     ┆ 20.918 ┆ 727.826282  ┆ … ┆ 526.484832 ┆ -510.48483 ┆ 864.684211 ┆ -848.68421 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆ 2          ┆            ┆ 1          │\n",
       "│ 1216    ┆ Yes     ┆ 10.842 ┆ 672.269098  ┆ … ┆ 471.318923 ┆ 744.681077 ┆ 864.684211 ┆ 351.315789 │\n",
       "│ 148     ┆ No      ┆ 29.705 ┆ 371.643114  ┆ … ┆ 574.593491 ┆ -426.59349 ┆ 463.236842 ┆ -315.23684 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆ 1          ┆            ┆ 2          │\n",
       "│ 108     ┆ No      ┆ 76.348 ┆ 628.823915  ┆ … ┆ 829.963033 ┆ -721.96303 ┆ 463.236842 ┆ -355.23684 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆ 3          ┆            ┆ 2          │\n",
       "│ 532     ┆ Yes     ┆ 30.622 ┆ 781.332328  ┆ … ┆ 579.614049 ┆ -47.614049 ┆ 864.684211 ┆ -332.68421 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆            ┆            ┆ 1          │\n",
       "└─────────┴─────────┴────────┴─────────────┴───┴────────────┴────────────┴────────────┴────────────┘"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "df_models = df.select(\n",
    "    col('Balance'),\n",
    "    col('Student'),\n",
    "    col('Income'),\n",
    "    balance_pred_si = si_results.fittedvalues.to_numpy(),\n",
    "    resid_si = si_results.resid.to_numpy(),\n",
    "    balance_pred_i = i_results.fittedvalues.to_numpy(),\n",
    "    resid_i = i_results.resid.to_numpy(),\n",
    "    balance_pred_s = a_results.fittedvalues.to_numpy(),\n",
    "    resid_s = a_results.resid.to_numpy(),\n",
    ")\n",
    "df_models.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting Parameter Estimates\n",
    "\n",
    "Now that we know the multiple regression is *worth it*, let's interpret the parameter estimates.\n",
    "\n",
    "Let's build some visual intuitions by plotting the predictions from the *multiple regression* and each separate *univariate* regression to see what's changing. In the figure below:\n",
    "\n",
    "- The scatterplot points represent the *raw data* separated by the levels of `Student`\n",
    "- In **solid black line** is the relationshion between `Income` and `Balance` if we *ignore* `Student`  \n",
    "- The **dashed colored lines** represent the relationship between `Balance` and `Student` if we ignore `Income`  \n",
    "- The **solid colored lines** represent the relationship between `Balance` and `Income` if we account for difference in the *intercepts* for each level of `Student`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 622.125x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 609
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create grid\n",
    "grid = sns.FacetGrid(data=df_models.to_pandas(), hue=\"Student\", height=5.5, aspect=1);\n",
    "\n",
    "# Plot data\n",
    "grid.map(sns.scatterplot, \"Income\", \"Balance\",alpha=.5);\n",
    "\n",
    "# Plot our predictions from student only model\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_s\", ls='--');\n",
    "\n",
    "# Plot our predictions from income only model\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_i\", ls='-', color='black', lw=2);\n",
    "\n",
    "# Plot our predictions student + income\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_si\");\n",
    "\n",
    "# Aesthetics\n",
    "grid.set(ylabel='Balance');\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the parameter estimates...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            207.855285\n",
       "C(Student)[T.Yes]    404.633047\n",
       "Income                 5.513813\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si_results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "$\\hat{\\beta_0}$ is supposed to be the *mean* of `Student = No`...but its not\n",
    "\n",
    "$\\hat{\\beta_1}$ is supposed to be the *mean difference* between `Student = Yes` and `Student = No`...but its not\n",
    "\n",
    "$\\hat{\\beta_2}$ is supposed to be the *slope* of `Income`...but it doesn't match our univariate regression `i_results`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model with just `Student` shows that the $\\hat{\\beta}_0$ was the mean of `Student = No`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            463.236842\n",
       "C(Student)[T.Yes]    401.447368\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And our model with just `Income` shows that the $\\hat{\\beta}_1$ was the slope of `Income`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    411.959178\n",
       "Income         5.474981\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What's going on?**\n",
    "\n",
    "\n",
    "Remember what we learned in notebooks `02_models` and `03_models`:\n",
    "\n",
    "In the GLM, we interpret each parameter estimate **assuming other parameter estimates = 0**\n",
    "\n",
    "Because of this:\n",
    "\n",
    "$\\hat{\\beta_0}$ is the *mean* of `Student = No` **when `Income = 0`**\n",
    "\n",
    "$\\hat{\\beta_1}$ is the *mean difference* between `Student = Yes` and `Student = No` **when `Income = 0`**\n",
    "\n",
    "$\\hat{\\beta_2}$ is the *slope* of `Income` **when `Student = 0 (No)`**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge\n",
    "\n",
    "Can you make the parameter estimates **more interpretable** using an approach we learned in class and demonstrated in `02_models`? Think about how to change what value the regression \"fixes\" `Income` to from `0` to something more useful...\n",
    "\n",
    "1. Fit a new more interpretable multiple regression called `si_interp_model` using `Student` and `Income` as predictors and save the results to `si_interp_results`\n",
    "2. Compare your parameter estimates from `.summary()` or `.params` to the estimates from the original multiple regression. What do you notice?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Balance   R-squared:                       0.322\n",
      "Model:                            OLS   Adj. R-squared:                  0.304\n",
      "No. Observations:                  76   F-statistic:                     17.37\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):           6.75e-07\n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept           461.6440     70.383      6.559      0.000     321.371     601.917\n",
      "C(Student)[T.Yes]   404.6330     99.538      4.065      0.000     206.254     603.012\n",
      "center(Income)        5.5138      1.283      4.298      0.000       2.957       8.071\n",
      "=====================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "si_interp_model = ols('Balance ~ C(Student) + center(Income)', data=df.to_pandas())\n",
    "\n",
    "si_interp_results = si_interp_model.fit()\n",
    "\n",
    "print(si_interp_results.summary(slim=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing intutions\n",
    "\n",
    "Hopefully you remembered that you can *center* a predictor to change what the *fixed* value is when intepreting *other parameters*. It doesn't change the estimate for what you're centering (`Income`), but it always changes the intercept and other categorical parameters!\n",
    "\n",
    "Let's visualize the effect of centering a predictor - we'll annotate the same plot as earlier illustrating the shift in coefficient interpretation:  \n",
    "\n",
    "1. We'll add a vertical line for `Income = mean(Income)` and a horizontal line for the mean of `Student = No`- **this is our model intercept**\n",
    "2. The vertical distance between this point and where it intersect the *blue* line is $\\beta_1$ - **mean difference between Yes and No when Income is at its mean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 390,
       "width": 389
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create grid\n",
    "grid = sns.FacetGrid(data=df_models.to_pandas(), hue=\"Student\", height=4)\n",
    "\n",
    "# Plot data\n",
    "grid.map(sns.scatterplot, \"Income\", \"Balance\",alpha=.5)\n",
    "\n",
    "# Plot our predictions student + income\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_si\")\n",
    "\n",
    "# Plot Income mean\n",
    "grid.map(plt.axvline, x=df_models['Income'].mean(), color=\"black\", ls='--');\n",
    "\n",
    "# Plot mean for Student = 0\n",
    "grid.map(plt.axhline, y=student_no, color=\"black\", ls='--');\n",
    "\n",
    "# Aesthetics\n",
    "grid.set(ylabel='Balance');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll also notice that **centering does not change model predictions**. It only changes how we *interpret* the parameter estimates from our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 432,
       "width": 580
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(\n",
    "    x=si_results.fittedvalues, # predictions fro un-centered model\n",
    "    y=si_interp_results.fittedvalues, # predictions for centered model\n",
    ")\n",
    "ax.set(xlabel='Uncentered Model Predictions', ylabel='Centered Model Predictions');\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In notebook `03_models` we learned that we can use `.predict()` to generate predictions from our model by fixing one or more of our predictors at certain values.\n",
    "\n",
    "Let's do that now to get our predicted estimates of the average `Balance` for each level of `Student` when `Income` is fixed at it's mean of 46.03 - **this is what our parameter estimate for `Student` is**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 1-row dataframes to pass into .predict()\n",
    "student_no_x =pl.DataFrame({\n",
    "    'Income': df['Income'].mean(),\n",
    "    'Student': 'No'\n",
    "})\n",
    "\n",
    "student_yes_x =pl.DataFrame({\n",
    "    'Income': df['Income'].mean(),\n",
    "    'Student': 'Yes'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "student_no_prediction = si_interp_results.predict(student_no_x.to_pandas())\n",
    "\n",
    "student_yes_prediction = si_interp_results.predict(student_yes_x.to_pandas())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student (No): 463.237\n",
      "Student (Yes): 864.684\n",
      "Student (No prediction): 461.644\n",
      "Student (Yes prediction): 866.277\n",
      "Student (Yes prediction - No prediction): 404.633\n"
     ]
    }
   ],
   "source": [
    "print(f\"Student (No): {student_no:.3f}\")\n",
    "print(f\"Student (Yes): {student_yes:.3f}\")\n",
    "\n",
    "print(f\"Student (No prediction): {student_no_prediction[0]:.3f}\")\n",
    "print(f\"Student (Yes prediction): {student_yes_prediction[0]:.3f}\")\n",
    "\n",
    "print(f\"Student (Yes prediction - No prediction): {student_yes_prediction[0] - student_no_prediction[0]:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can verify that:\n",
    "\n",
    "- $\\beta_0$ = $\\hat{balance}_{\\text{student\\_no}}$ when $Income = Income_{mean}$\n",
    "- $\\beta_1$ = $\\hat{balance}_{\\text{student\\_yes}}$ - $\\hat{balance}_{\\text{student\\_no}}$ when $Income = Income_{mean}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            461.644003\n",
       "C(Student)[T.Yes]    404.633047\n",
       "center(Income)         5.513813\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "si_interp_results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "When we **add** a categorical predictor to a model with a continuous predictor, we estimate different *intercepts* but the same *slope* for each level of the categorical variable. \n",
    "\n",
    "Intuitively, this is like accounting for differences in the means of each group when considering the continuous relationship between two predictors. \n",
    "\n",
    "Alternatively, we can think about it like accounting for the continuous relationship between another variable when considering the mean difference between groups.\n",
    "\n",
    "**Centering** our continuous predictor makes our estimates more interpretable. It also makes sure we compare levels of our categorical predictor along a **meaningful value** of our continuous predictor!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactions (2-levels and Continuous Predictor)\n",
    "\n",
    "Our previous *multiple regression* tested whether students and non-students have a different `Balance` when accounting for the relationship between `Balance` and `Income`.\n",
    "\n",
    "However, we did not test whether **the relationship between `Balance` and `Income` was different for students and non-students**\n",
    "\n",
    "We can extend our previous model to test this by adding **an interaction term** to capture this relationship  \n",
    "Specifically we add `Student` x `Income` to our model to **estimate separate slopes** for students and non-students\n",
    "\n",
    "\n",
    "$$\n",
    "\\hat{Balance}_{i}= \\beta_0 + \\beta_1 Student + \\beta_2 Income + \\beta_3 Student \\times Income\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge \n",
    "\n",
    "1. Estimate the model above and call it `six_model` and save the results to `six_results`.\n",
    "2. Compare it to `si_results` from earlier using `anova_lm()`. Is the addition of the interaction term *worth it*?\n",
    "3. Create 2 new columns in `df_models` called `balance_pred_six` and `resid_six` that include the `.fittedvalues` and `.resid` from this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.0</td>\n",
       "      <td>1.374135e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72.0</td>\n",
       "      <td>1.368314e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58201.972255</td>\n",
       "      <td>0.306256</td>\n",
       "      <td>0.581701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid           ssr  df_diff       ss_diff         F    Pr(>F)\n",
       "0      73.0  1.374135e+07      0.0           NaN       NaN       NaN\n",
       "1      72.0  1.368314e+07      1.0  58201.972255  0.306256  0.581701"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "six_model = ols('Balance ~ C(Student) * Income', data=df.to_pandas())\n",
    "six_results = six_model.fit()\n",
    "\n",
    "anova_lm(si_results, six_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Balance</th><th>Student</th><th>Income</th><th>balance_pred_si</th><th>resid_si</th><th>balance_pred_i</th><th>resid_i</th><th>balance_pred_s</th><th>resid_s</th><th>balance_pred_six</th><th>resid_six</th></tr><tr><td>i64</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>16</td><td>&quot;Yes&quot;</td><td>20.918</td><td>727.826282</td><td>-711.826282</td><td>526.484832</td><td>-510.484832</td><td>864.684211</td><td>-848.684211</td><td>746.752889</td><td>-730.752889</td></tr><tr><td>1216</td><td>&quot;Yes&quot;</td><td>10.842</td><td>672.269098</td><td>543.730902</td><td>471.318923</td><td>744.681077</td><td>864.684211</td><td>351.315789</td><td>698.87892</td><td>517.12108</td></tr><tr><td>148</td><td>&quot;No&quot;</td><td>29.705</td><td>371.643114</td><td>-223.643114</td><td>574.593491</td><td>-426.593491</td><td>463.236842</td><td>-315.236842</td><td>360.557752</td><td>-212.557752</td></tr><tr><td>108</td><td>&quot;No&quot;</td><td>76.348</td><td>628.823915</td><td>-520.823915</td><td>829.963033</td><td>-721.963033</td><td>463.236842</td><td>-355.236842</td><td>648.864509</td><td>-540.864509</td></tr><tr><td>532</td><td>&quot;Yes&quot;</td><td>30.622</td><td>781.332328</td><td>-249.332328</td><td>579.614049</td><td>-47.614049</td><td>864.684211</td><td>-332.684211</td><td>792.859379</td><td>-260.859379</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 11)\n",
       "┌─────────┬─────────┬────────┬─────────────┬───┬────────────┬────────────┬────────────┬────────────┐\n",
       "│ Balance ┆ Student ┆ Income ┆ balance_pre ┆ … ┆ balance_pr ┆ resid_s    ┆ balance_pr ┆ resid_six  │\n",
       "│ ---     ┆ ---     ┆ ---    ┆ d_si        ┆   ┆ ed_s       ┆ ---        ┆ ed_six     ┆ ---        │\n",
       "│ i64     ┆ str     ┆ f64    ┆ ---         ┆   ┆ ---        ┆ f64        ┆ ---        ┆ f64        │\n",
       "│         ┆         ┆        ┆ f64         ┆   ┆ f64        ┆            ┆ f64        ┆            │\n",
       "╞═════════╪═════════╪════════╪═════════════╪═══╪════════════╪════════════╪════════════╪════════════╡\n",
       "│ 16      ┆ Yes     ┆ 20.918 ┆ 727.826282  ┆ … ┆ 864.684211 ┆ -848.68421 ┆ 746.752889 ┆ -730.75288 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆ 1          ┆            ┆ 9          │\n",
       "│ 1216    ┆ Yes     ┆ 10.842 ┆ 672.269098  ┆ … ┆ 864.684211 ┆ 351.315789 ┆ 698.87892  ┆ 517.12108  │\n",
       "│ 148     ┆ No      ┆ 29.705 ┆ 371.643114  ┆ … ┆ 463.236842 ┆ -315.23684 ┆ 360.557752 ┆ -212.55775 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆ 2          ┆            ┆ 2          │\n",
       "│ 108     ┆ No      ┆ 76.348 ┆ 628.823915  ┆ … ┆ 463.236842 ┆ -355.23684 ┆ 648.864509 ┆ -540.86450 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆ 2          ┆            ┆ 9          │\n",
       "│ 532     ┆ Yes     ┆ 30.622 ┆ 781.332328  ┆ … ┆ 864.684211 ┆ -332.68421 ┆ 792.859379 ┆ -260.85937 │\n",
       "│         ┆         ┆        ┆             ┆   ┆            ┆ 1          ┆            ┆ 9          │\n",
       "└─────────┴─────────┴────────┴─────────────┴───┴────────────┴────────────┴────────────┴────────────┘"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "df_models = df_models.with_columns(\n",
    "    balance_pred_six = six_results.fittedvalues.to_numpy(),\n",
    "    resid_six = six_results.resid.to_numpy(),\n",
    ")\n",
    "df_models.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting Parameter Estimates\n",
    "\n",
    "Let's use the `df_models` you updated to update our previous figure to see what's happening visually:\n",
    "\n",
    "We've reduced the opacity of the previous models' predictions and overlayed the interaction model's predictions using **solid colored lines**.\n",
    "\n",
    "As you can tell, now not only the *intercepts* are different for each level of `Student`, but the **slopes are also different**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 622.125x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 609
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create grid\n",
    "grid = sns.FacetGrid(data=df_models.to_pandas(), hue=\"Student\", height=5.5, aspect=1);\n",
    "\n",
    "# Plot data\n",
    "grid.map(sns.scatterplot, \"Income\", \"Balance\",alpha=.25);\n",
    "\n",
    "# Plot our predictions from student only model\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_s\", ls='--', alpha=.5);\n",
    "\n",
    "# Plot our predictions from income only model\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_i\", ls='-', color='black', lw=1.5, alpha=0.5);\n",
    "\n",
    "# Plot our predictions student + income\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_si\", ls='-.', alpha=.5);\n",
    "\n",
    "# Plot our predictions student x income\n",
    "grid.map(sns.lineplot, \"Income\", \"balance_pred_six\", lw=3);\n",
    "\n",
    "# Aesthetics\n",
    "grid.set(ylabel='Balance');\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's inspect the parameter estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Balance   R-squared:                       0.325\n",
      "Model:                            OLS   Adj. R-squared:                  0.297\n",
      "No. Observations:                  76   F-statistic:                     11.57\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):           2.82e-06\n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                  176.9471    108.096      1.637      0.106     -38.539     392.434\n",
      "C(Student)[T.Yes]          470.4184    155.351      3.028      0.003     160.732     780.105\n",
      "Income                       6.1811      1.765      3.502      0.001       2.662       9.700\n",
      "C(Student)[T.Yes]:Income    -1.4298      2.584     -0.553      0.582      -6.580       3.721\n",
      "============================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(six_results.summary(slim=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait we forgot to **center**! Let's do that now to make these estimates more interpretable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Balance   R-squared:                       0.325\n",
      "Model:                            OLS   Adj. R-squared:                  0.297\n",
      "No. Observations:                  76   F-statistic:                     11.57\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):           2.82e-06\n",
      "====================================================================================================\n",
      "                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Intercept                          461.4512     70.721      6.525      0.000     320.472     602.430\n",
      "C(Student)[T.Yes]                  404.6055    100.014      4.045      0.000     205.231     603.980\n",
      "center(Income)                       6.1811      1.765      3.502      0.001       2.662       9.700\n",
      "C(Student)[T.Yes]:center(Income)    -1.4298      2.584     -0.553      0.582      -6.580       3.721\n",
      "====================================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "six_model_cent = ols('Balance ~ C(Student) * center(Income)', data=df.to_pandas())\n",
    "six_results_cent = six_model_cent.fit()\n",
    "\n",
    "print(six_results_cent.summary(slim=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's verify that:\n",
    "\n",
    "- $\\beta_0$ = $\\hat{balance}_{\\text{student\\_no}}$ when $Income = Income_{mean}$\n",
    "- $\\beta_1$ = $\\hat{balance}_{\\text{student\\_yes}}$ - $\\hat{balance}_{\\text{student\\_no}}$ when $Income = Income_{mean}$\n",
    "\n",
    "just like we did before by generating a single **marginal prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student (No): 463.237\n",
      "Student (Yes): 864.684\n",
      "Student (No prediction): 461.451\n",
      "Student (Yes prediction): 866.057\n",
      "Student (Yes prediction - No prediction): 404.606\n"
     ]
    }
   ],
   "source": [
    "# Create 1-row dataframes to pass into .predict()\n",
    "student_no_x =pl.DataFrame({\n",
    "    'Income': df['Income'].mean(),\n",
    "    'Student': 'No'\n",
    "})\n",
    "\n",
    "student_yes_x =pl.DataFrame({\n",
    "    'Income': df['Income'].mean(),\n",
    "    'Student': 'Yes'\n",
    "})\n",
    "\n",
    "# Generate predictions\n",
    "student_no_prediction = six_results.predict(student_no_x.to_pandas())\n",
    "student_yes_prediction = six_results.predict(student_yes_x.to_pandas())\n",
    "\n",
    "print(f\"Student (No): {student_no:.3f}\")\n",
    "print(f\"Student (Yes): {student_yes:.3f}\")\n",
    "\n",
    "print(f\"Student (No prediction): {student_no_prediction[0]:.3f}\")\n",
    "print(f\"Student (Yes prediction): {student_yes_prediction[0]:.3f}\")\n",
    "\n",
    "print(f\"Student (Yes prediction - No prediction): {student_yes_prediction[0] - student_no_prediction[0]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                           461.451226\n",
       "C(Student)[T.Yes]                   404.605544\n",
       "center(Income)                        6.181137\n",
       "C(Student)[T.Yes]:center(Income)     -1.429850\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "six_results_cent.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify that:\n",
    "\n",
    "- $\\beta_2$ = $Balance \\sim Income$ when $Student =0$\n",
    "- $\\beta_3$ = the difference between $Balance \\sim Income$ when $Student =0$ vs $Student = 1$\n",
    "\n",
    "we'll use the same approach we did in `03_models` - we'll set `Student = 0` and `Student = 1` and provide the original values for `Income` to generate **marginal predictions** for `Balance`. \n",
    "\n",
    "Then we'll estimate the slope of the relationship between these **marginal predictions** and our original values of `Income` using 2 *univariate* regressions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get only student = yes or student = No\n",
    "student_0_data = df.filter(col('Student') == 'No').select(['Income', 'Student']) \n",
    "student_1_data = df.filter(col('Student') == 'Yes').select(['Income', 'Student']) \n",
    "\n",
    "# Use values to get model predictions for Balance separately for students and non-students\n",
    "student_0_predictions = six_results.predict(student_0_data.to_pandas())\n",
    "student_1_predictions = six_results.predict(student_1_data.to_pandas())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income slope for Student = No (0): 6.1811\n",
      "Income slope for Student = Yes (1): 4.7513\n",
      "Difference in slopes: -1.4298\n"
     ]
    }
   ],
   "source": [
    "# Create a new dataframe of Predicted_Balance ~ Income when Student = No\n",
    "marginal_data_student_0 = pl.DataFrame({\n",
    "    'Income': student_0_data['Income'].to_numpy(),\n",
    "    'Predicted_Balance': student_0_predictions.to_numpy()\n",
    "})\n",
    "\n",
    "# Run univariate OLS to slope\n",
    "marginal_student_0_params = ols('Predicted_Balance ~ Income', \n",
    "                                marginal_data_student_0.to_pandas()).fit().params\n",
    "\n",
    "# Same for student = Yes\n",
    "marginal_data_student_1 = pl.DataFrame({\n",
    "    'Income': student_1_data['Income'].to_numpy(),\n",
    "    'Predicted_Balance': student_1_predictions.to_numpy()\n",
    "})\n",
    "\n",
    "marginal_student_1_params = ols('Predicted_Balance ~ Income', \n",
    "                                marginal_data_student_1.to_pandas()).fit().params\n",
    "\n",
    "print(f\"Income slope for Student = No (0): {marginal_student_0_params['Income']:.4f}\")\n",
    "print(f\"Income slope for Student = Yes (1): {marginal_student_1_params['Income']:.4f}\")\n",
    "\n",
    "print(f\"Difference in slopes: {marginal_student_1_params['Income'] - marginal_student_0_params['Income']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can see the interaction term reflects this difference-in-slopes while `center(Income)` reflects the slope of `Student = No`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                           461.451226\n",
       "C(Student)[T.Yes]                   404.605544\n",
       "center(Income)                        6.181137\n",
       "C(Student)[T.Yes]:center(Income)     -1.429850\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "six_results_cent.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "When we **multiply** a categorical predictor in a model with a continuous predictor, we estimate different *intercepts* and *different slopes* for each level of the categorical variable. \n",
    "\n",
    "Intuitively, this is like fitting a separate *univariate* regression to each level of the categorical variable and comparing the *difference-in-slopes* of both regressions.\n",
    "\n",
    "Alternatively, we can think about it testing whether the *mean difference* between levels of our categorical variable *increase* or *decrease* as we move along the continuous predictor.\n",
    "\n",
    "**Centering** our continuous predictor makes our estimates more interpretable. It also makes sure we compare levels of our categorical predictor along a **meaningful value** of our continuous predictor!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "## Challenge\n",
    "\n",
    "In the **Parameter Interpretation** section of `03_models` we had you answer a **response challenge** about a model you built using the advertising dataset:\n",
    "\n",
    "$$\n",
    "sales_i = \\beta_0 + \\beta_1 tv_i + \\beta_2 radio_i\n",
    "$$\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"./figs/03_plot.png\" width=\"60%\" alt=\"Figure 1\">\n",
    "</div>\n",
    "\n",
    "Specifically we said:\n",
    "\n",
    "> Notice in the plot above that the slopes of the predicted lines **are not** changing - it looks like only the intercept is shifting up or down.  Can you provide an explanation why? What is our model failing to capture and how might we fix this? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your tasks\n",
    "\n",
    "Using what you learned about interactions in *multiple regression*:\n",
    "\n",
    "1. Estimate the model using the formula above (same as in `03_models`)\n",
    "2. Estimate a *new* model that captures what this model is missing\n",
    "3. Compare this new model to the original model to see if it's *worth it*\n",
    "4. Make a new figure like the one above that visualizes the predictions from your *new* model\n",
    "5. Interpret the parameter estimates from `.summary()` and provide a natural-language explanation of what they mean\n",
    "6. Fit another like you just did, but this time **center** each predictor\n",
    "7. How are the parameter estimates from this model different to the uncentered one?\n",
    "8. Compare the correlations between predictors in the centered and uncentered models using the provided function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Variable   | Description                     |\n",
    "|------------|---------------------------------|\n",
    "| tv     | TV ad spending in $1000 of  dollars            |\n",
    "| radio     | Radio ad spending in $1000 of  dollars            |\n",
    "| newspaper     | Newspaper ad spending in $1000 of  dollars            |\n",
    "| sales     | Sales generated in $1000 of  dollars            |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tv</th><th>radio</th><th>newspaper</th><th>sales</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>230.1</td><td>37.8</td><td>69.2</td><td>22.1</td></tr><tr><td>44.5</td><td>39.3</td><td>45.1</td><td>10.4</td></tr><tr><td>17.2</td><td>45.9</td><td>69.3</td><td>9.3</td></tr><tr><td>151.5</td><td>41.3</td><td>58.5</td><td>18.5</td></tr><tr><td>180.8</td><td>10.8</td><td>58.4</td><td>12.9</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 4)\n",
       "┌───────┬───────┬───────────┬───────┐\n",
       "│ tv    ┆ radio ┆ newspaper ┆ sales │\n",
       "│ ---   ┆ ---   ┆ ---       ┆ ---   │\n",
       "│ f64   ┆ f64   ┆ f64       ┆ f64   │\n",
       "╞═══════╪═══════╪═══════════╪═══════╡\n",
       "│ 230.1 ┆ 37.8  ┆ 69.2      ┆ 22.1  │\n",
       "│ 44.5  ┆ 39.3  ┆ 45.1      ┆ 10.4  │\n",
       "│ 17.2  ┆ 45.9  ┆ 69.3      ┆ 9.3   │\n",
       "│ 151.5 ┆ 41.3  ┆ 58.5      ┆ 18.5  │\n",
       "│ 180.8 ┆ 10.8  ┆ 58.4      ┆ 12.9  │\n",
       "└───────┴───────┴───────────┴───────┘"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import polars as pl\n",
    "from polars import col\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.formula.api import ols\n",
    "df = pl.read_csv('./data/advertising.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Estimate model from previous notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "model_c = ols('sales ~ tv + radio', data=df.to_pandas())\n",
    "results_c = model_c.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Estimate new model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "model_a = ols('sales ~ tv + radio + tv:radio', data=df.to_pandas())\n",
    "results_a = model_a.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Compare both models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "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>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>197.0</td>\n",
       "      <td>556.913980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>196.0</td>\n",
       "      <td>174.483383</td>\n",
       "      <td>1.0</td>\n",
       "      <td>382.430597</td>\n",
       "      <td>429.590463</td>\n",
       "      <td>2.757681e-51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid         ssr  df_diff     ss_diff           F        Pr(>F)\n",
       "0     197.0  556.913980      0.0         NaN         NaN           NaN\n",
       "1     196.0  174.483383      1.0  382.430597  429.590463  2.757681e-51"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "anova_lm(results_c, results_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Figure of model predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "# Save to variable just to make coding easier\n",
    "tv = df['tv'].to_numpy()\n",
    "zeros = np.repeat(0, len(tv))\n",
    "twenty = np.repeat(20, len(tv))\n",
    "forty = np.repeat(40, len(tv))\n",
    "\n",
    "# We use all values in the TV column\n",
    "# And just a bunch of 0s for radio\n",
    "y_radio_0 = results_a.predict({'tv': tv, 'radio': zeros, 'tv:radio':tv * zeros})\n",
    "y_radio_20 = results_a.predict({'tv': tv, 'radio': twenty, 'tv:radio':tv * twenty})\n",
    "y_radio_40 = results_a.predict({'tv': tv, 'radio': forty, 'tv:radio':tv * forty})\n",
    "\n",
    "grid = sns.relplot(\n",
    "    data=df,\n",
    "    kind='scatter',\n",
    "    x='tv',\n",
    "    y='sales',\n",
    "    hue='radio',\n",
    "    size='radio',\n",
    "\n",
    ")\n",
    "grid.set(xlabel='TV Spending ($1000)', ylabel='Sales ($1000)');\n",
    "grid.legend.set_title('Radio Spending ($1000)');\n",
    "grid.legend.set_bbox_to_anchor((.45, .8));\n",
    "\n",
    "# Add them to the plot\n",
    "grid.ax.plot(tv, y_radio_0, color='gray');\n",
    "grid.ax.plot(tv, y_radio_20, color='gray');\n",
    "grid.ax.plot(tv, y_radio_40, color='gray');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Interpret parameter estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.968\n",
      "Model:                            OLS   Adj. R-squared:                  0.967\n",
      "Method:                 Least Squares   F-statistic:                     1963.\n",
      "Date:                Tue, 18 Feb 2025   Prob (F-statistic):          6.68e-146\n",
      "Time:                        16:12:39   Log-Likelihood:                -270.14\n",
      "No. Observations:                 200   AIC:                             548.3\n",
      "Df Residuals:                     196   BIC:                             561.5\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      6.7502      0.248     27.233      0.000       6.261       7.239\n",
      "tv             0.0191      0.002     12.699      0.000       0.016       0.022\n",
      "radio          0.0289      0.009      3.241      0.001       0.011       0.046\n",
      "tv:radio       0.0011   5.24e-05     20.727      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                      128.132   Durbin-Watson:                   2.224\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1183.719\n",
      "Skew:                          -2.323   Prob(JB):                    9.09e-258\n",
      "Kurtosis:                      13.975   Cond. No.                     1.80e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.8e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "print(results_a.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Fit a model with centered predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.968\n",
      "Model:                            OLS   Adj. R-squared:                  0.967\n",
      "No. Observations:                 200   F-statistic:                     1963.\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):          6.68e-146\n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   13.9470      0.067    208.737      0.000      13.815      14.079\n",
      "center(tv)                   0.0444      0.001     56.673      0.000       0.043       0.046\n",
      "center(radio)                0.1886      0.005     41.806      0.000       0.180       0.198\n",
      "center(tv):center(radio)     0.0011   5.24e-05     20.727      0.000       0.001       0.001\n",
      "============================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.28e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "model_a_cent = ols('sales ~ center(tv) * center(radio)', data=df.to_pandas())\n",
    "results_a_cent = model_a_cent.fit()\n",
    "\n",
    "print(results_a_cent.summary(slim=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) How are the parameter estimates from centered and un-centered models the same or different?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Uncentered predictor slopes assume other predictors = 0*  \n",
    "*Centered predictor slopes assume other predictors = their respective means*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8) Compare the correlations between centered and un-centered models\n",
    "\n",
    "Use the provided function `plot_predictor_correlations` to visualize the correlation matrix of the predictors for both the centered and uncentered models.\n",
    "\n",
    "Then answer the question below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 536
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helpers import plot_predictor_correlations\n",
    "\n",
    "# Example Usage\n",
    "# plot_predictor_correlations(my_model)\n",
    "\n",
    "# Solution\n",
    "ax = plot_predictor_correlations(model_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 434,
       "width": 686
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "ax = plot_predictor_correlations(model_a_cent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do you think the change in correlation will affect our estimates and inferences?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your response here*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "201b",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}