{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checkpoint 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reminder: \n",
    "\n",
    "- You are being evaluated for compeletion and effort in this checkpoint. \n",
    "- Avoid manual labor / hard coding as much as possible, everything we've taught you so far are meant to simplify and automate your process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be working with the same `states_edu.csv` that you should already be familiar with from the tutorial.\n",
    "\n",
    "We investigated Grade 8 reading score in the tutorial. For this checkpoint, you are asked to investigate another test. Here's an overview:\n",
    "\n",
    "* Choose a specific response variable to focus on\n",
    ">Grade 4 Math, Grade 4 Reading, Grade 8 Math\n",
    "* Pick or create features to use\n",
    ">Will all the features be useful in predicting test score? Are some more important than others? Should you standardize, bin, or scale the data?\n",
    "* Explore the data as it relates to that test\n",
    ">Create at least 2 visualizations (graphs), each with a caption describing the graph and what it tells us about the data\n",
    "* Create training and testing data\n",
    ">Do you want to train on all the data? Only data from the last 10 years? Only Michigan data?\n",
    "* Train a ML model to predict outcome \n",
    ">Define what you want to predict, and pick a model in sklearn to use (see sklearn <a href=\"https://scikit-learn.org/stable/modules/linear_model.html\">regressors</a>.\n",
    "* Summarize your findings\n",
    ">Write a 1 paragraph summary of what you did and make a recommendation about if and how student performance can be predicted\n",
    "\n",
    "Include comments throughout your code! Every cleanup and preprocessing task should be documented.\n",
    "\n",
    "Of course, if you're finding this assignment interesting (and we really hope you do!), you are welcome to do more than the requirements! For example, you may want to see if expenditure affects 4th graders more than 8th graders. Maybe you want to look into the extended version of this dataset and see how factors like sex and race are involved. You can include all your work in this notebook when you turn it in -- just always make sure you explain what you did and interpret your results. Good luck!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Data Cleanup </h2>\n",
    "\n",
    "Import `numpy`, `pandas`, and `matplotlib`.\n",
    "\n",
    "(Feel free to import other libraries!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in the \"states_edu.csv\" dataset and take a look at the head of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/states_edu.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should always familiarize yourself with what each column in the dataframe represents. Read about the states_edu dataset here: https://www.kaggle.com/noriuk/us-education-datasets-unification-project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this space to rename columns, deal with missing data, etc. _(optional)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Exploratory Data Analysis (EDA) </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chosen Outcome Variable for Test: *8th grade math*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many years of data are logged in our dataset? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1113"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AVG_MATH_8_SCORE\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 1113 years/states of data in the set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare Michigan to Ohio. Which state has the higher average outcome score across all years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "276.1666666666667\n",
      "282.25\n"
     ]
    }
   ],
   "source": [
    "michiganScores = df[df.STATE == \"MICHIGAN\"][\"AVG_MATH_8_SCORE\"]\n",
    "michiganAvg = np.average(michiganScores[michiganScores.isna() == False])\n",
    "\n",
    "ohioScores = df[df.STATE == \"OHIO\"][\"AVG_MATH_8_SCORE\"]\n",
    "ohioAvg = np.average(ohioScores[ohioScores.isna() == False])\n",
    "\n",
    "print(michiganAvg)\n",
    "print(ohioAvg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ohio has the highest average outcome score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the average for your outcome score across all states in 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "281.2641509433962\n"
     ]
    }
   ],
   "source": [
    "nationalScores = df[df.YEAR == 2019][\"AVG_MATH_8_SCORE\"]\n",
    "nationalAvg = np.average(nationalScores[nationalScores.isna() == False])\n",
    "\n",
    "print(nationalAvg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the maximum outcome score for every state. \n",
    "\n",
    "Refer to the `Grouping and Aggregating` section in Tutorial 0 if you are stuck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATE\n",
       "ALABAMA                 269.0\n",
       "ALASKA                  283.0\n",
       "ARIZONA                 283.0\n",
       "ARKANSAS                279.0\n",
       "CALIFORNIA              277.0\n",
       "COLORADO                292.0\n",
       "CONNECTICUT             289.0\n",
       "DELAWARE                284.0\n",
       "DISTRICT_OF_COLUMBIA    269.0\n",
       "DODEA                   293.0\n",
       "FLORIDA                 281.0\n",
       "GEORGIA                 281.0\n",
       "HAWAII                  281.0\n",
       "IDAHO                   287.0\n",
       "ILLINOIS                285.0\n",
       "INDIANA                 288.0\n",
       "IOWA                    286.0\n",
       "KANSAS                  290.0\n",
       "KENTUCKY                282.0\n",
       "LOUISIANA               273.0\n",
       "MAINE                   289.0\n",
       "MARYLAND                288.0\n",
       "MASSACHUSETTS           301.0\n",
       "MICHIGAN                280.0\n",
       "MINNESOTA               295.0\n",
       "MISSISSIPPI             274.0\n",
       "MISSOURI                286.0\n",
       "MONTANA                 293.0\n",
       "NATIONAL                285.0\n",
       "NEBRASKA                288.0\n",
       "NEVADA                  278.0\n",
       "NEW_HAMPSHIRE           296.0\n",
       "NEW_JERSEY              296.0\n",
       "NEW_MEXICO              274.0\n",
       "NEW_YORK                283.0\n",
       "NORTH_CAROLINA          286.0\n",
       "NORTH_DAKOTA            293.0\n",
       "OHIO                    290.0\n",
       "OKLAHOMA                279.0\n",
       "OREGON                  285.0\n",
       "PENNSYLVANIA            290.0\n",
       "RHODE_ISLAND            284.0\n",
       "SOUTH_CAROLINA          282.0\n",
       "SOUTH_DAKOTA            291.0\n",
       "TENNESSEE               280.0\n",
       "TEXAS                   290.0\n",
       "UTAH                    287.0\n",
       "VERMONT                 295.0\n",
       "VIRGINIA                290.0\n",
       "WASHINGTON              290.0\n",
       "WEST_VIRGINIA           274.0\n",
       "WISCONSIN               289.0\n",
       "WYOMING                 289.0\n",
       "Name: AVG_MATH_8_SCORE, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = df.groupby(\"STATE\")\n",
    "states[\"AVG_MATH_8_SCORE\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Feature Engineering </h2>\n",
    "\n",
    "After exploring the data, you can choose to modify features that you would use to predict the performance of the students on your chosen response variable. \n",
    "\n",
    "You can also create your own features. For example, perhaps you figured that maybe a state's expenditure per student may affect their overall academic performance so you create a expenditure_per_student feature.\n",
    "\n",
    "Use this space to modify or create features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>PRIMARY_KEY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>ENROLL</th>\n",
       "      <th>TOTAL_REVENUE</th>\n",
       "      <th>FEDERAL_REVENUE</th>\n",
       "      <th>STATE_REVENUE</th>\n",
       "      <th>LOCAL_REVENUE</th>\n",
       "      <th>TOTAL_EXPENDITURE</th>\n",
       "      <th>INSTRUCTION_EXPENDITURE</th>\n",
       "      <th>...</th>\n",
       "      <th>GRADES_1_8_G</th>\n",
       "      <th>GRADES_9_12_G</th>\n",
       "      <th>GRADES_ALL_G</th>\n",
       "      <th>AVG_MATH_4_SCORE</th>\n",
       "      <th>AVG_MATH_8_SCORE</th>\n",
       "      <th>AVG_READING_4_SCORE</th>\n",
       "      <th>AVG_READING_8_SCORE</th>\n",
       "      <th>INSTRUCTION_EXPENDITURE_PROPORTION</th>\n",
       "      <th>FEDERAL REVENUE PROPORTION</th>\n",
       "      <th>FEDERAL_REVENUE_PROPORTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1992_ALABAMA</td>\n",
       "      <td>ALABAMA</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2678885.0</td>\n",
       "      <td>304177.0</td>\n",
       "      <td>1659028.0</td>\n",
       "      <td>715680.0</td>\n",
       "      <td>2653798.0</td>\n",
       "      <td>1481703.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>731634.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.558333</td>\n",
       "      <td>0.113546</td>\n",
       "      <td>0.113546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1992_ALASKA</td>\n",
       "      <td>ALASKA</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1049591.0</td>\n",
       "      <td>106780.0</td>\n",
       "      <td>720711.0</td>\n",
       "      <td>222100.0</td>\n",
       "      <td>972488.0</td>\n",
       "      <td>498362.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122487.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.512461</td>\n",
       "      <td>0.101735</td>\n",
       "      <td>0.101735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992_ARIZONA</td>\n",
       "      <td>ARIZONA</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3258079.0</td>\n",
       "      <td>297888.0</td>\n",
       "      <td>1369815.0</td>\n",
       "      <td>1590376.0</td>\n",
       "      <td>3401580.0</td>\n",
       "      <td>1435908.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>673477.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.422130</td>\n",
       "      <td>0.091431</td>\n",
       "      <td>0.091431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1992_ARKANSAS</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1711959.0</td>\n",
       "      <td>178571.0</td>\n",
       "      <td>958785.0</td>\n",
       "      <td>574603.0</td>\n",
       "      <td>1743022.0</td>\n",
       "      <td>964323.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>441490.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.553248</td>\n",
       "      <td>0.104308</td>\n",
       "      <td>0.104308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1992_CALIFORNIA</td>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26260025.0</td>\n",
       "      <td>2072470.0</td>\n",
       "      <td>16546514.0</td>\n",
       "      <td>7641041.0</td>\n",
       "      <td>27138832.0</td>\n",
       "      <td>14358922.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5254844.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.529091</td>\n",
       "      <td>0.078921</td>\n",
       "      <td>0.078921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PRIMARY_KEY       STATE  YEAR  ENROLL  TOTAL_REVENUE  FEDERAL_REVENUE  \\\n",
       "0     1992_ALABAMA     ALABAMA  1992     NaN      2678885.0         304177.0   \n",
       "1      1992_ALASKA      ALASKA  1992     NaN      1049591.0         106780.0   \n",
       "2     1992_ARIZONA     ARIZONA  1992     NaN      3258079.0         297888.0   \n",
       "3    1992_ARKANSAS    ARKANSAS  1992     NaN      1711959.0         178571.0   \n",
       "4  1992_CALIFORNIA  CALIFORNIA  1992     NaN     26260025.0        2072470.0   \n",
       "\n",
       "   STATE_REVENUE  LOCAL_REVENUE  TOTAL_EXPENDITURE  INSTRUCTION_EXPENDITURE  \\\n",
       "0      1659028.0       715680.0          2653798.0                1481703.0   \n",
       "1       720711.0       222100.0           972488.0                 498362.0   \n",
       "2      1369815.0      1590376.0          3401580.0                1435908.0   \n",
       "3       958785.0       574603.0          1743022.0                 964323.0   \n",
       "4     16546514.0      7641041.0         27138832.0               14358922.0   \n",
       "\n",
       "   ...  GRADES_1_8_G  GRADES_9_12_G  GRADES_ALL_G  AVG_MATH_4_SCORE  \\\n",
       "0  ...           NaN            NaN      731634.0             208.0   \n",
       "1  ...           NaN            NaN      122487.0               NaN   \n",
       "2  ...           NaN            NaN      673477.0             215.0   \n",
       "3  ...           NaN            NaN      441490.0             210.0   \n",
       "4  ...           NaN            NaN     5254844.0             208.0   \n",
       "\n",
       "   AVG_MATH_8_SCORE  AVG_READING_4_SCORE  AVG_READING_8_SCORE  \\\n",
       "0             252.0                207.0                  NaN   \n",
       "1               NaN                  NaN                  NaN   \n",
       "2             265.0                209.0                  NaN   \n",
       "3             256.0                211.0                  NaN   \n",
       "4             261.0                202.0                  NaN   \n",
       "\n",
       "   INSTRUCTION_EXPENDITURE_PROPORTION  FEDERAL REVENUE PROPORTION  \\\n",
       "0                            0.558333                    0.113546   \n",
       "1                            0.512461                    0.101735   \n",
       "2                            0.422130                    0.091431   \n",
       "3                            0.553248                    0.104308   \n",
       "4                            0.529091                    0.078921   \n",
       "\n",
       "   FEDERAL_REVENUE_PROPORTION  \n",
       "0                    0.113546  \n",
       "1                    0.101735  \n",
       "2                    0.091431  \n",
       "3                    0.104308  \n",
       "4                    0.078921  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"INSTRUCTION_EXPENDITURE_PROPORTION\"] = df[\"INSTRUCTION_EXPENDITURE\"] / df[\"TOTAL_EXPENDITURE\"]\n",
    "df[\"FEDERAL_REVENUE_PROPORTION\"] = df[\"FEDERAL_REVENUE\"] / df[\"TOTAL_REVENUE\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature engineering justification: \n",
    "\n",
    "The instruction expenditure proportion was created to see if there was a correlation between states that devoted a larger proportion of their expenditure to education and increased test scores.\n",
    "\n",
    "The federal revenue proportion was to determine if an underlying feature such as proportion of money a state can raise from levying taxes (relative to total revenue) equates to poorer test scores. Essentially, do 'poorer' states create conditions to cause lower test scores. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Visualization</h2>\n",
    "\n",
    "Investigate the relationship between your chosen response variable and at least two predictors using visualizations. Write down your observations.\n",
    "\n",
    "**Visualization 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='INSTRUCTION_EXPENDITURE_PROPORTION', ylabel='AVG_MATH_8_SCORE'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"INSTRUCTION_EXPENDITURE_PROPORTION\", y=\"AVG_MATH_8_SCORE\", alpha=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a weak correlation between an increase in instructional expenditure and average 8th grade math score. As spending on instruction increases, so too do test scores. There are likely a couple of reasons for this -- one is obvious, that spending more on education leads to education doing better in comparison. However, this also could indicate that states that are able to allocate more to education and less to welfare/development/whatnot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='FEDERAL_REVENUE_PROPORTION', ylabel='AVG_MATH_8_SCORE'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XMm549xgO9ocRiMoQFQ2axnD21Al48s3DePPAcXT6YxiMSOj0RzGnxYewpCIkypjXVouBiIS+EJ9WBElCyfKSj0C4VELXsSCozeRqXu+xISTKFZFLpQvGxwPUB4WHZnoI06xY0I5Gjx3dgRjEoT23JtY48EFnEOu2HQTPczhzSi0m1jjQHYhhx+FBQ2T90+VnjOqybNYRmigO+Trulqr/xsJ9ks7VfFKts2JyIQfm8kN9UHhIyIzqEjKXk0QBpssmGJuKvvxhLwDgvJMajWPHBqMQZRV3LZuLWRO9RhlmRJAklCw9hRQIl6r/xsJ9ks7VvNxUm2B8LEJ9YA5yZCaKRqIra1x86R/a+JSDvidYfNDT4LEP/RWeXIYZt2azjtBE4Sik426p+m8s3CeVmgM5MJcf6oPiUNbHW2vXrsX8+fPh9XrR1NSEZcuWYc+ePUnn7N+/H5dffjkaGxvh8/lw5ZVXoru7O+mcgYEBrFixAj6fD7W1tbj++usRCoVKmcq4IFFEGpVVDEZOrC5hYMaABzAvLC2kQG+0slK9bvZYPrFWgwixkALhash3vJBrX4wFwXi1Q31QHMo607N582asWrUK8+fPh6Io+O53v4sLL7wQu3fvhtvtRjgcxoUXXogzzjgDmzZtAgB873vfw2c+8xls3boVPK+P2VasWIHOzk688MILkGUZX/rSl/CVr3wF69evL2d6Y45Grx1nt0/A+tcPIiZr4MCBgUFWNLgdAkKiAp7jTAlLCynQG62sVK+fM3UCAODNA8eNY2e3TwDHcXjzwMCobtJmYq0mEWIhBMLVlO9YJ9++GAuC8WqH+qA4VJSmp7e3F01NTdi8eTPOP/98/PWvf8XSpUtx/Phx41md3+/HhAkT8Ne//hVLlizB+++/jzlz5uCNN97AOeecAwB4/vnncckll+DIkSNobW0dtV7S9Jjn/hf3Y93WgxAVFeAAMMBm4TG72Que44wP2IXTM3/APvRyh+GqO/zNnK0T7WhlpXr9/WMBgGM4paXmxLFOP8A4nNLqSyqnzmUz3KSzibWQOZaCqKRi3baD2NrRb7ofE6m2fMcyheiLfO8HIn+oD0anqjU9fr8fAFBXVwcAEEURHMfBbj8xonU4HOB5Hq+88gqWLFmCLVu2oLa21hjwAMCSJUvA8zy2bduGyy+/fEQ9oihCFE9M9wYCgWKlNKboDYp488AATmn1wWO3QJRV2K36DE/cWdmMGLM3KGLL/n7UuU/oGeL/bu3ox7J5k0z/FTNaWeed1DDidY/dgpiiGv+3WXi47Zah2asTxxo8dkiKhrcPD+LU1pqsYi1kjqXCaRNww3nTsWzepKwFwtWY71ilUH2Rz/1AFAbqg8JTMUvWNU3DLbfcgo9//OOYO3cuAGDhwoVwu924/fbbEYlEEA6H8S//8i9QVRWdnfqUX1dXF5qampLKslgsqKurQ1dXV8q61q5di5qaGuOnra2tuMmNERLdcB1WATUuGxxWIclZ2YxTayFddUcr60BfZMTroqwPeOLi6/gxDhzAnXgdGN1NOl2s1eYcnEgujrvVnO9Yo9B9QQ7M5Yf6oHBUzKBn1apV2LVrF373u98ZxxobG/Hkk0/if//3f+HxeFBTU4PBwUGcddZZhp4nF9asWQO/32/8HD58uBAplJxsRLiFoFDCukIK9Bhj0FK4QPeGYtA0hlqXZURd9iHBNYP+ZLc7EEUwJkPVNICdeB0w5yZtNseYrKJzMAILzxVdhFhsMXFi+b1BEQNhCRaey7pPSyF6Hm/CahLAEkR6KuLx1urVq7Fhwwa89NJLmDx5ctJrF154Ifbv34++vj5YLBbU1taiubkZ06frz6Wbm5vR09OTdI2iKBgYGEBzc3PK+ux2e9Ijs2ojlUgxnQi3kM9+CyWsK7RotitwwgX6pCYPPuwJoTsQQ63Lhv9+6SPwHNAXjBl1hUQFdoFHUJSxeW8PFBXQhqRtLpsAf1Q2BNlm3aQz5ahqGnpDIjoHY4jJKlpqnHj27aNFeS5fbDFxYvlBUcHxsD5rMMFlw/GIhKisYNZEHya4bBnbqRSi5/EqrCYBLEGkp6wzPYwxrF69Gs888ww2bdqEadOmpT23oaEBtbW12LRpE3p6evDZz34WALBo0SIMDg5i+/btxrmbNm2CpmlYsGBB0XMoB6lcOte/fhDrth4sunNnodxw8y0nsQ3mtdViok93gX5tfz+6AzFM9Dkwr60WPM+hb8jPIrGu2S0+ABwkhQFgEDhA4DlwAA4PRJJiMuMmnSnHwwNRHOiLAACmN3jQVucqmqtqsR1cE8uPygp6gjH0BPXB3JQ6J8C4Ee2Xqp1K4TQ7nt1sx4JrNUEUg7Ku3vra176G9evX47nnnsPs2bON4zU1NXA6dUOmRx55BKeccgoaGxuxZcsWfOMb38DKlSvx//1//59x/tKlS9Hd3Y0HHnjAWLJ+zjnnmF6yXk2rt1K5dEZlFa982AsOwCcSXJGL6dxZKDfcXMpJ51R6oC+M97sCmNPiRXu9J6mORKE1APxow2580BWAledgEXgIPAdZZZAUFTOaPPjmklmY0eQZ4YSaS6y3PrEDisbQXOMoat8U28E1sXyP3YJtH/XrOijojwoXTKtHSFQgyipW/cPMEe1XqjhLVUc1MBZcqwkiE9l+f5d1puf++++H3+/H4sWL0dLSYvw88cQTxjl79uzBsmXLcMopp+Cuu+7Cv/7rv+Lf/u3fkspZt24dTj75ZFxwwQW45JJL8IlPfAL//d//Xep0SkIqkWI6EW4xRaSFEtYVUjTrtlugaQxuW/JT2+FCawAIxmRw4GC3WmCzCBB4XneZ5oCopKHWNfJLItdYFY2htdaZZN5YjL4ptpg4sXxRVqEM6Z2sAg9FZYjJ+muKxlK2X6niLFUd1QAJYAkimbJqesxMMt199924++67M55TV1c3bowIE0WK8Q8yu1UAAwM3TIRbTOHiB50BHOyPYFqjO2lvrUIQF8bWe2xgDMb/4/nG26A3FEsa4IRFBTYLD0lNvq8S2yFetsOit5msahB4vc0kRRcy1zgtppykh8eVilT9NTymXNpnf08I4ICZCbMpdW4bBJ7DscFo0qxSoe6DxFw8dgssQyvbAMAicHBYBVN11bltUDWG948F9Pbz2k1fm0ushWr3asTsfUoQ44WKEDIT5kklUgyLChxWHmBcVq7IudAXimHNUzvx9uFBSIoGm4XHvLZa/HT5GajL84skSSQbU3B8aJuLOrcNHrvFEKG67QJ4DthxaBCyqkFjAM8BVoFDs8+JQFSGzZIsOl5yykQ8m+CqPBCRIKsaZEXTRcwMiMgKvHYrzp/VWDAn6UKKSiOSgkdfPYBn3j5qrERq8NhxxVmTceU5k/HcjqPoDsTQ6Y/hw54gWmucaPDa4Y9KBbkPhudS77HhUL+uVWqvcyMkKqPm1ReK4dt/eBc7Dh+HpDJwAKwWDpMnuFDjsODS01sLcr+OdzHveBVxE8Ro0KCnComLEbd29KPTH4XLJgwd47D94IBxrBjCxTVP7cRrHf1wWS2oddkQlVS81tGP2596Bw9eNz+vsuPC0zq3HTFZRc/QiiuHjYfPaTW+wACgLyTBbuERlRSA46Ax3VTQaRXQ4LEZAs54O8iqZpTdWuuEy8YjLCqQVM3w6mnyOnDFWZMztllijK21TgRishFXOqfbVP2VS9+s33YI618/iGBMgctmARjQE4xh3daDeOvgAAYiEqbUOeGw8egajOGjvhCisorPz28r2H2QmIvLakGT1wFAN1EzI5Zd89ROvLqvDxpjsPCAqgGSwnCoL4x/OLmpoPdrodq9GsnlPiWI8QANeqqQTC6dfaHJRRMuftAZwNuHB+GyWuBz6loJq1OXhb19eBB7u4M5P+pKdJF12y3oC4vw2PU6BkISZjbq5W7e2wswwOe0whrk0eB1wMJzUFQNHM+h1m0DA5LcoRkDvvPUu0kOtU0+J3iehyiruHphO3xOa9KjotFizMbpthCuqr1BES/u6UVM1uCxW41HVzzPISop2H5oEKdNqkGTz4kmnxMzG1V0+6OwCDyWzZtUsL/uU+UCwFReH3QGsP3gIADAZhFg4fXBqqyo0BgQjCkIS0pRYx3rMzwAuWMTRCZo0FPFNHjsIz68Uh0rFAf7I5AUXeSbiNMmYDAi4aPecM6DnrjwtLXWiYikQFEZXDYBHKdP1YtDItmegAiAoclrh6IyuO0W8BwHgecRkRRYBf3fRNHynq6gUXYiPocVnaKC9nq3ca7ZGEeU44+if2hpfDry6ZuBsGSIrxPdoW0WHhGJQVYYbAJnHHdYBbTUukzFlQvDczFT/sH+CCRVBQeA5/RYeQ6wWniIioaBiFySWMc6+d6nBDGWoUHPOCMfYWN7vQs2C4+opBozPIC+KZ7NwmNaozuruhN/HwiJCIsyjg0CE2scsAic4YJs4TnYh4SuPqf+WEdSGSyCvo1EfKYnfs1woepoolaAYcv+fgSiEmpctrQzPunK6QuJ0BgDx424pGDUuW3wOqxgiI4QX/PgYLNwGQXciWTqh2zuiWyva693wSYIkIZ0VPGBjzyk7alzWVMKjIspxi2F0DdTHdnWb+b84YLzxD3yyiXiJkE1USnQoGecUAhh48ktPsxrq8VrHf0A9BmeqKQiIis4d3p92lme4XXbLTysAgdFYwjEFBzsDyMqqVA0BlVjmOCyotFjx1F/FAAwpd6VJJIFgA3vHoOsaBiMSGCc7hZT67QiEJVx6enJQtV0otbeYAx1bhu+tu4tHBuMQlYZrAKHSbVOLD+7DSvPnZrUNsPLcdkEvN8ZQLdfd3/+6cYPiiYWbfTasXh2Iw70hxCMySPE16e0eEd1jc7UDzFZM31P5Hovndziw9nttdi8txeSooIJPDQNUDQNHrsFS+ZMTOq3Yopxy+0IzcCyqj+beBu9dpwzdQLWbT1kbKwLAA6LgBUL20s66CBBNVFpVMzeW0RxKZQ77U+Xn4Fzp9eDgekDDjCcO70eP11+hum6j/mjeK2jH0cHozg6GMHxsARRUeGw8rBbeQyEJRzzR9HkdaDJ64DLakkSya5Y0I5Gjx2iounGghwHC89BVFQ0eGwphaqpHGobPXbs6Qri6PEIFI1BGJoxOnI8inVbD6Zsm8Ry3jk8qLs/1zhw5pTaojv+xnNv8joQk1XEFBVNXgdWLGw35RqdqR+yuSfyuZd+uvwMfOKkBtgtAmSVQWUME9w2XH/e9BH9VkxH5XI7Qmdbf07xcvrMX9xAUv+9tF6049kVm6hMaKZnHFBIYWOdx4YHr5uPvd1BfNQbHtWnZ3jdUVnVp9mtFhwPywjE5CRH5CavHcGYApUx/PjyuZjR6BkhQu0NitAYMG/KBF33A/2jPCKpYEBKMexwUSvA8OM/vY+opALgYBX0gZOicdA0hoik4qW9vSPaJl7OeSc14vvP7UJ7vdvQTriGPIOKJRZ12gTcvHgm/umcNuzvCYEh2acnk2g3Uz+ERQUaY6buiXzvpTqPDQ+v/Bj2dgfxzuFB+JxWnN0+IeVjn2KJcUsh9M1UR1yMb7b+bOPtDYp488BxnNJSA4/dgpiswjH0eGv7wePoC4klme0hQTVRidBMzzigGO60syZ6cdHc5lGFy8Prjjv5Om0CRFWFqumPlHiOg8b0x1txZ+XBsJzSUTZeZoPHjlqXDTUum+EAPFo+8fIADoGobGw0GtcAx3UmGhj8USVjWTzHjfjQLoXjb4PHjgXT67Fwer1p1+hM/aBozHDyHi3+Qt1LsyZ68U/ntOGiU5tTfvEV01G53I7Qgaii69NM1p9tvInnO6wCal02OKxCyd2oyRWbqERo0DMOSBQ2JmLGnbY3KGJPV9AwwxuNxPM/6AzgncOD0DRm1G23CrAInC5+5nnwnC5kjQtbBZ5LKYxOLDcxn6isYjAiYTAi4dhgFKqmP3ZLFe/wMvRl9/qy6SFjYWMQxIMznJlTtQFjDIwxHBuMYjAiGV4/qdo02zbMpo3NUue2wSJw6ByMIDYkbI33Q1woni7+4eXkei9lA2MMoqLiYH/YaFsz9Zhpm1LkkKkOn9MCh0XAwf4wegLRjPfO8LJisgr/0P1m5vxi5WeGSomDIBKhx1vjgFzcabMVICaePxiRcXBAFycLPAdV01dandmmP8Zw2Sw4FAzDIvBgDJBUDTwAr9OCsJgsjE4Xx+mTa/Dk9sOISipisgZJ1VcE2S0Cvv2HdzGtwY1PnNSQVjR6dvsEWAUeMVnVnZlVQFL0JdQCzxv1JLo4x6/jOA7bOvqxpyuIoKjAKnCocVrhc1rhtgmGq3ChRZy5lheRFDy34yi6/DF0+qNw9ITQUuuA2ybgWDSGCW4neI5DX0gc1bG42E7HEUnBY68dwNNvHcWR4xFIqobdnQFMa3Cj2WfHYFROWU+2Qt9iuzWPJp5/vzOE4xEJHDg4rTwm1jjgtVtHiPDjZWUjTK4UN+pKiYMgEqFBzzghW3fabB1dE88/NhjF8bAEjgM8Dn1Dz0BUxnvH/Jg10QvGGJxWAVYLD2blEYjqGp6YrMEq8EnC6HRx1DhtANOXrIuKPuuiDWk0Q6KCY/5okoPz8DLWv34Qmga01TnR6Y8hIqpQGcBxHCZP0FdvASzldWAcXHYB4AC7lYckq/BHFURlFR+bWme0aaFdcXMtL35dW50LTquAY/4oDvRF0OS149zp9VA0lpVjcTGdjtdvO4R1Ww8hKMqocVgRllVEJRX7ekKQFC2tu3S2bVMKt+ZUdTR67NjdGYCkaHDbBIgKQ0RWcWQggvNOasxcf4IwmYFlFCZXiht1pcRBEHFo0DNOyMadNhfhZPx8gecwEJEMcbKkMNR6LeDAQWUMV8ybhOfeOYbpjZ4kkWWXP4awJONfL52DBdPqM8YhKRrePTKIk5q8UDQGp6YhLKrGBrY2Qd+DrKXGqYtGkSwaddstiMkaOABzJ9Vj7iQgEJXRF4rBwvNYu/x0THDZRrg4x69jGoOiMbhsFtRZBUQkBarGMLPJbQipQ6JSUBFnrqLQ4ddN9Dkwo8mDLn8MVoHD2uWnAzDnqBynWE7HvUERm/f2Iqaohuu0e2hvuZisoN5tS+kunUvblMKtOZ14XlQ0eIf0NqqmITJk1yAqWkoRfi7C5Epxo66UOAgiDml6xhnphK6J5COcDA15yFh4XZ/DhsTJTpsARdUQiOluy8NFlq21TnjsVtQ6baPGYRva3ZsxXYNjF3gwMFgE3tAGKSqDVeARiMoIRJNFo6Ks6st4Of3/DquAJp8DM5t8+o71LHXd8es0MEiqZjgjO6y6c7TbZjHap9AizlzLS3VdvL0VjRnuvKPdE6nI9bp0DIQlBKK6/sNmOfHR5LAKsAoConLqPPNp60LnkKmOuHgeOJGf/ijVAoHj4I/Ko+aXrTC5FPmZoVLiIAga9IxB8hXOZitA1B8tMfSFRHgcVn3QMWQ0yA0TJ5/a6ksS1GYqO10cIVEBz+vuyxwHiIq+tYGsqAAD5AR3Zt+Q1iaxDAZAUlVIioa4hUlMVtE5GIGF54b269JzOjoYMYTKdqsABqY7IAu84RgtKRosvO6IHM8hHntfSDSEp4l5Asiqj9K1RW8oBk1L7QYdd8G18FxBxKSFFmQP54S4XG9TVdMgKSpEWQEDQ40ztWNztQhmh+cXR1a1tPkVog+L3W8EUU3Q460xRKGEs2YFiIn1dQViGIxImOhzYILLit6gCFXTNT1xcfKCaXXYedQ/QlDb5EktUM3kgMwBeO/YIDQGqJqu52EAeI7heFQecmeWcOnprQB0TY+qMfQEYvioL4yIpA9CXtzTC5/DAkVliCkqJvocWPPUuxAVDXu7gwjGdKGyz2FFjcsKm4UHD13T0x+S9C9npqHBY0dIPJFDRFLAc8COQ8fB8xzsFn2neKdVQJPXjnue/yCrPhrRFlYB73cF0B0Y6QY9XLjdFxIRlRXMmujDBJctazFpqVx1G712fHJWIzp6Q+gPifoKOQAaA9w2AYtmNKSMt1oEs/H8PuoNIyjK0DQGDO0t53VYcP6sxrTu2bn0IbkhE8RIaNAzhiikcNaMADGxvnlttdjdqX8Jex0WTHDbEJVUaBrAeN21+bTJNSkFtTEpvUA1MY53Dg/ieETCxBoHBJ7DR71hqExDopyTA2Dh9Q0sGzz2pDKfeOMwDvVHoDINHjsPgENY0oWyLpuAGY0eRGQFr3X0wzW0hNtu5SEpmr5cWFFx9pQJmD+tHq9/1A9J0RASFfgcVkyqdeLjMxuSRMx9IRETaxwIxhTEZFV3b/Y6dA2G15F1HyW1xZGhtvA5MKfFh7CkphVuu2w89nSFcHhAn13LVkxaaEH2aDm+tr8Pr+3rh8p0LySbBXA79D3SMl0HVL5gdsWCdsgqw9NvHTFmXpq8Dlxx1qS0761c+7CU/UYQ1QLH4urPcUwgEEBNTQ38fj98Pl+5w8mJ3qCI7zz1Lng+2TCvb+gv5ruXn57TX7x9ITGtw2+q+o4NRiEqKu66bC4AGK7NcWFw4vkxWTUEtf/++TMzxrenK4jvP7cLDquACW4bXv+oHxz0R1iDEd37ROA4MACnTaqBMmR6GM+7Nyjin9e/hT3dQTisAlw2C1RNw7HBKDQGTHBZcc7UOrxzZBAxWUNEVDDBbYPLZkFElKEyYEajB267gLsTxL8cBzCGEY7RibnGZBUxWcVgRMaHPUGc2lqTtAN2tn20pyuAO597D/YhbU5iOTFZf8TnsAkj7gNRVrHqH2ZiRpoNVVNRrPtqtPoUTdeF6Y99bAiJiqn60t2vlUZfSMS+nhA4YER/ZGpzs31Y6n4jiHKR7fc3aXrGCMVyPzXr8Jt4Ps9xYCzZtdmMoHY04g7IcTdhq8DDOiRitgs8XHYLOA6wW/gR7swDYQlRWfcNcgzN4qhDWhiB192gQzEZiqqXpTIGfkgnY7fqE6Ju+wmhcrxdZk30pnWMjucaF5967PoO43EBdJzs+4gDl8YNOpPbr6Ixw7naLKV21U10227yOTDR58zKTbhaBLMNHjsWTq/HghSu2pna3GwfkhsyQaSGHm9VOXGh4/Gw/hdcX0hM+uu/EGLOeB2Jfz0nikcTP4DNuMR67BaIQ8LgkKgknT+8rvjvAIzr3XaLIVRWNX21lqhq0ABYhkYqnYMRWATeKDcuIj06GIWkaHBYBQg8B0AfoPEcBwZ9O4yYooIbEmMrmoaopIIxYDAiwsLzOD7k+JzuiycxVwvPYSAkwWbloKj6qp24ADomqxBlFeGhx2sAw56u4JCQGmnageF4WIZF4Iy2jw6VE5FU+JwWgGHUfkls5+F1JZIoUo/fV1FZRffQDF2hRcLZ3ldmSHX/VjKFaINitCNBjAVo0FOlxEWKr+7rQ0dfGGFRgaoyqJqGTr/T0HnkI+bMJITMVjw6mqusyybgoZc7jLocVh6WoU1IRUWDyyaA54C+YAyAAw1uOw72hyEqGhhjOB7RB0Zuq4BtHw1AVFS01Djx7NtHjXhTiUgBQBnySvmgKwB/RIai6bM8vQHROIcxoD8swmEVcPtT72J6g9vQ8KRy/D2jrQYPvdyBkKgPmAB9RmlKvQvHwyI6/VEEojJERYOqamiuceLujR8gLKk4PjTIm+CywW0XYOE5xGQNhwYiCEsK3HYLrDyHiKTCbRcQjKkQFRUaY/jY1Dqc1T4Bf3u/O2W/JLZzUFSS6vI6LCnF0HGR+tHBKBwCj65gDDFZQ0uNw2jfQgljCylKrlYhbyHaoFrE3QRRamjQU6XERYrBmIJgTIbA8QDHYLcK6A7EICkaZjZ58hJzjiaEzEk8msZVdnhdO48O4uhgFJNqnThtUi0CMRl9QX12hTHd98dq0QXLTpsAjek+OiFJhXNIf9PgtY+Id7iI1GsXIKkcbIKgC68Zg8BhaEk8MwYsfMKS8GBMxtHBaEZR6I5Dg0kDHkB/nNYXFGHxOdAdjIHn9EdtCs/h6GDUcHjuCcYAAE6rgMGoPthwWfUceY5DMCajzm1DWFIwEJbgsglwWHh4nVb0hSQAHJbObUnZL+u2HTTaOSorSXXVuKwpxdBxkfqh/gg0pi+tntboQpPHXhRhbKFEydUs5C1EG1SLuJsgSgkJmVF9QuZEseeHPUFw0L88Y7IKBob2OjcYY7hr2dxRd0EfrQ4zQkgz4tHE8oa7ysY9bBxWwRD+bvuoH7LCYLVwWDCtHg6rYNT97YtPRn9Iwq/+vg88z8FlE8AB2H7oOCRZg83K49wZDUnXDI93X08I/oiM3247qJvf8RzePHh8yFARGIhI8NgEQ0Ar8DwY9NmaGqcVPAfMbPImiaXjfNAZwFUPbkVI1DVEPKdvb6GoGlTG4LZZcEqLD7UuXW/xzpFByAobetwG419VY2BgUBSGkKSgzmWFy25FTFZ1DxtVX6c/b0otfE7riHyBZKfl4X2wbUgMDgAMDAum1afsD0B/FLd5Ty8YGBZNr0eNy5b2figU+YiSx4qQtxDC7GoRdxNELpCQeRwQFynaBN15OO7uahV4KCqD224BP/TlnW8dZoSQ2bo8D3eVHe6aHBcq6y7OzPgSjtfNmK5ZUDSGBo8dtUNfwIwBXqcVjGHENcPjXTi9HlMb3IY7dByHVRgSYuubpPIcB47XV4VZhgTPcfNFm8ClFIUe7I9AVHTjRAsPY+BjEXhomr57uNtuMeKO5yqp+sapNosu0JZUDbKiwWo54TQNnNAFMcYgCLpwOy7OTsx3eL8k9kG8jeN1xds5VX/E+4TndTuAxNuqmMLYfETJY0XIWwhhdrWIuwmiFNCgpwqJixQlVf9ijru7JjoR5ytWLLTLbabyhrsm260CLILu4mwRTqy2Sqx7eHlmrskUU/x6SdESBhj6IINj+uMtZUg0Hd9mI9GBOZH2ehfsFn0HeTVhhKCo2pBRoWCImRPjtgk8bILuCySrGmwCD6uFh6xoRr0AjBVgHMcBTC/DTP+kyzd+3zisQsr+iMcJ6DNCDpP1lZNqcWkmCKK00KCnComLFEOivhIqKisIRGXEZBUeuwUhUcbCFEth05HKpj5ex0BYRF9IhKRo6AuJGAiLpsuOl7unK4iBsITTJ9eMKK87EMUpLV7MnzrBeI3nODitPIIxGTzHpax7eHw8pz+yicgKPHYLeI5Luoaxkds+JJYREhXUe2wIiTKisoI6lw3ikOBan6nRZ3fi2164h9p57qQa9IekpHJPbvHh7PYJYGBDgwoGUVaNwej8qRMQEmX0hUQIHAeHhUdIlOGy82j06TGERBlNXjtqnFbEVF2wHJFVDEYkRGUFPqcVLhsPu0V/BJeufxL7Nl2+IVFGg9uOkKhgICzik7Ma8clZjUl9NRCWIPAcrAKfsr5U7VsszGyrUIj7N5v6CnVdpmuKsZ3EWNmiYqzkQRQf0vSg+jQ9ABCVVKzbdhCv7uvDR31hhET9y35ahlVFwxltdUu8jq0dJ15fOH301S+pVpa5bRZjBkTRGCKSaixFr3Pb4LQKsAocREXDgf7I0BeV/ijLZuExqdaF5WdPxspzpxp1D4/PbtFXfMV3rHbZBJw1ZQI4jsObBwZGzTEYU4yYapxW+KMyGGOQVIawKBtCYq/Dgil1J3KJydqIcgdCEv7lyR3Y+lE/JEV/i9W6rLhu0VSsWNCOp98+YvRdMKYYs0tuuwCrwIPnONS5bbBbeHT5Y+gLxRAeerTntltw2qQaLJpRD4DD9oMDI/pn+FYU8fiumDcZT799ZES+dW599Vb8egAj7i+nVdC34RiKzeuw4Oz2CQCANw8cL/oKqWxXY+V6/+ZaXz7XZbomXV/m08bVurJtOGMlDyJ3sv3+pkEPqnPQEycuUkzlDDwaD73cYaxuGb6kNXF1S7ZCyHi5wZgyNJuha1LqPfoX5SdnNSIsqtjS0Y+JPkdS3R6bBR90BxCRVHjsVqgqQ0RW4LVbcf1501KuuhkeX+Lvz759NOscAYz4f7x94//+5b1ObN7bO2q5e7uDeOfwIHxOK85un5DUfr/4v714flc3Gr12I+7eoIiL507EVQva0R+S8PyuTrz0YS88diusAo+wqMAflXDRqc34xpJZaftntL5Nl+/w/v2Pv+3FX97rQqPXgQaPHYGYjO5AFAun1+Mr588w3b6FwOz9Opxchby51pfLdZmuAVDwNs41t0pjrORB5E6239+0ZL3KafDYcxIo9gZFbNnfjzr3ievj/27t6MeyeZOSjmfzqGzL/n547FZ0+mNwWi3GyrKQqKClxok3DhwHGDDR50iqQ1I07DziB8Dgc9gM7YjVwkNSVLy0tzcprnRtEP89nxzT/T+e4zuH/abKnTXRm3IFXbyMSROcxrmttU7YLDzePeLHVQv02Zd3jyTX0+i1oy9kwc6jfsMgcXjsZvPOlGO8nHeP+DFpgmtEOR29YfSHJNPtmy/Z9OVwcnmP5FpfLtdlumbz3l4AKGgb59OWlcRYyYMoLaTpGacUa3XLaCvLrAKfdqsEm8BBUvVHOIlbNdgsPMAB/qiSVVzFzjGfcs2UkWs9hcp7tHIODO1WX4oVUuXaDqMUbZ/pmlQr6czEUYzcKo2xkgdRWmjQM8YYLuhLJ/Ar1uqWeLkhUQFwYul44soyn9NiTEUDwGBEwpGBCI5HZNgEARwHY3UToK9WAgNqnJakbRRGEy4mbqFgJkezItJ82643KOJ4WDK2kkhXRqZ6BJ4ztsMwE19MVvWtOXh96wgz7TdanlMb3CVZIWW2veLnDs8rF5GrmT5OVW4u90Y2KxvNlFeI3LKhXCJiWqFH5AI93hojDBf02S08rAKXVmhbLJt6t13fLmL3sQAklUFRVdisAmwCj0avHSFRNnQKf3znGHYcOo5AVIbCGDhwmOCyQhA4hERZX6LNYGh6zp/VOGK7ilTCxcS2iG+h0OmP4pQW35CAOjnHXESk50ytw9/e78qq7YbX0xcSEZVUzG72oNZlT1nG8D46HpGwtzsAp9WC//jbh2n7OR6fqmnoDYnoHIwhJqto9jmw5ql3k7b3SCf8HO0emd3sLepWB9m0V6r74uz2zCL2TGTKfckpE/Hs20cLsj2LmXYGUNA2LtR7v9wiYtpqg8gFmukZI8Qt93meQ2utE8f8UbzW0Y+jg1G01jrB8xw27urEum0HjWtWLGjH0rktYIyh0x8FYyxvm/r12w6hLyRiYo0DNU4LLAIPUdZnbSbVOo3yVyxoR0xSMRCRoA09zrJbdKGux25Fk9eBmKwipqho8jqwYqF+zfA8U+WVeM68tlpM9DnQHYjhncODKXPMVGa61wCWddsNL6utzgVwDIcGomnLGN5HhwciAOMwpc6ZsZ/j8R0eiOJAXwQAML3BA3DAax39OOZPf18kMto9Uox7KJf2StVP618/iHVbD2a8VzKRLjcAGe/BXNok0zXFaONClGnmvVhsinn/EWMTWr2F6l69BYy03I/KKl4f2sbBZuHwsWHbOAy34C+UTf3wOGKyipisYjAig4HhrstObIvxQWcAV/96G5gGOO36jucWnkcgqp9734qzAH2iBzObPCO2UUi3tQBjSHnOscEoREVNiiFVzIllptqOYXh9QPqVT5naJrEsUVbxtX+YaeSZir6QiH3dIdz34j4jntH6+baLTsaPN+yGojE01zgAIOP2Hpm2ZhjtHin0VgfZtFeqc6Oyilc+7AUH4BMnNRqi+Fy2oUjMLd39lev2LJnqGn5NMbaTyLXMStvmg7baGL/QNhTjkOGCvqRtHDTdGA9IL/ArlE398Dji20201jqHtnY4ce7B/ggkRYPbYYHdIsDC67ei0yZAUjQMhmUsmF6fZCSXj/i3wWMfEcNoZZoRkZptu0z1KBrDBFfmD+sGjx0T3LakbTNG6+cDfWEoGkNrrRMOqzDq9h6ZhJ+j5VnorQ6yaa9U54qyqu8rxsFol/j12YpcE3Mr9PYsmerK5rVcybXMShMRF6NtiLEJDXqqnN6giI7eEERZNYSESVsy8JyxhUAuAr8TrsqBlGLFRBEjYwwsg3AYOOHa217vgs3CIyqpSedGJRU2C49pje4RsZgR9gIs6ZyYrMI/JPhNlXsmsbPDqs9AHRuMYjAiGYOEVO34QWcAf9nVhb3dwZRtOBCWYOHTC3EBhq37+/GXXV3Y1tE/qkA5KqvG/l7p+nm40DjxvuA4XSAek9Ws7guzQvl8SdXXUVnFscEoNI0libhT9aHdKoANTRWa3aYjE/Hl0R/1hTP243gRz5KImKhWTAuZL7nkEjz++OOoqakBANx999246aabUFtbCwDo7+/Heeedh927dxclUCKZiKTg0VcP4Jm3jxqPYhgDJte5cGqrDx67BUejUUxwO5O2ZDAr8IuLFF/5UHfkDUsK3HYLpg85Pl8+bxKeGRJzBkUFx4ecfWOyikBURqc/ijktPoQlFb3BGBo9dtzz/AdJgsfTJ9di20f9AGC4I0dkBedOr0/pbZNKuDhc2Ouy6ULqnkAMB/vDCERliIoGTWP42LQ6uG2WpPxSiZ2DMQV7uwOwW3Rn5aCowCrou6v7nFa4bQIuPb0VDR47+kIxrHlqJ94+PAhJ0TcLnddWi58uPwMOGz9SiCsrmDXRhwkuGwIxGb3BGOpcNqxa9xaODkYhqwxWgUNzjQPT6t1gQJJA+fTJtXhy+yHEZA0cOEQkBZKqob3eNaKfUwmNXTYBB4MR2ATdD0jfS4vHigXtGe+LbIXy+ZLY16rG0BcUcXQwgmBMgVXgcftT76J9yBVbVllKwbrDygOMQ0hUwHNczmLdx147gKffOmoMqgSeg83C4eTmGqMfx5t4lkTERLViWtMjCAI6OzvR1NQEAPD5fNixYwemT9ddL7u7u9Ha2gpVVTMVU5FUo6bnoZc78OhrHyEYU+CyWcAYw2BE/6urucaBaQ3uEVsyZGPBn+iq3B+SwHMcVKahwWOH12FBncuGgYiEOrcdXYEoDvXrYtm2WhckTUN3IIYJLhtmNnnAQX/m3uBNdl/+5KxG7DziTzlgqEvzl+LwrQV6gyNX9PQFYwiLKrqDMfCcvpGm12ExBis3nDc9ycnVbROwuzNgxGwV9Bkop41HX0jSdz+XVVgEQdfOTK3DL754Fpw2ATc+9gZe6+iHy2oZMXBbML0+yS12MCJiT1fIWD3nsgngALzXGcBASILKGHgAGk64P09rcOO0SbVGm9U4rdjbFdL3BeMApgGypqHRY0eTzzGin4e3V08ght6QCCvPg+Ohz4RYBKxY2I6bF88Y9X6I57Lz6CCODkYxqdaZFF8hnXDjsT/xxmF0+aPQGAMDB5dNgMYYBI5DRFYxqdaJOS2+pD6c2eQZ2iIj9TYdZgdmD73cgUdfPYCgKMNltQAcEBmyY2jyOYx+zLbcsUC+23wQRCEomiPz8LER6Z/LR29QxIt7ehGTNXjsVkOkKfD6l3W924Y1l5yCWRO9OQn84lP5brsFnf4YHFbBcFUOiwoa3Da8fXgQp7bWwGO3oD8kwWPXn+0PRCUsmFaPlhonRFnFjedNx3+/1IEGb7L7MgC8e8SPu5efjoGwhI96w5jW6E45w5OI0ybghvOmY9m8SSOEvfGyJUXD4eN+zGmpQa3LasTfFxKxtaMf553UMMLJ9ez2OhwbjCIYk2Gz8LBZeOzrCcFls6DOKiAiKVA1hplN+uxLWFJwsD+Mtw8PwmW1wOfU87c69SfG2w8NIhBTkupo8jnB8zxEWcWqf5iJWpcVP9rwPmKyBgZ9BZuF5yAP6W0sAodAVF+63+CxQ1RUvHvEj1Nba1DntkGUVdituidSvMwZw8TQqdprWqMHHrsl6frtBwfQF5psymU4OuSu7bJaEBYVIz6gsE64TpuAy86chM17e1HjsODIYAwCrw9iI5I+u6hv/KqA4zijD0VFxbcvPtm4l/pCk3MW627e24uYoia9z3iOg6jo77NvXHDSiDYfLyTeWyQiJqoF0vRUIQNhCcGYDA7cCOdiQQAUDYZgNxeB3wlXZcFwUY6Xr2gMGtP1IFaBN8SxNgtvuC7HZFUXDvMcBiPyqILHWRO9uGhu86gDnkRSCXuNdhD0ndnddgtqXSe2szgh8I2kFTurGtN1RcNyd1h100S3zWLEHRdjD/+rVhdjqzgekdIKcWtdNgAcgjF56A8IBoHnAOgzPADAQTdzjAtxbYIu8rYJ+hd/zVBuiWVmEhonttfw67NxGc5WKJ8PA2EJispQ67ZDYyf6g+cAlTHYEu65eJ7DBev5iHUDUX32NO4sDuiDUw4corKasc3HCyQiJqoJ04MejuPAxT+NE47lw9q1azF//nx4vV40NTVh2bJl2LNnT9I5XV1duOaaa9Dc3Ay3242zzjoLTz31VNI5AwMDWLFiBXw+H2pra3H99dcjFArlFVslo+9wbQUDG9W5eDjZOPFKqmq4KMfLt/AceE7/EpBVzRDHSopmuC47rELWrr1m3ZCHx2kROHQORtATiOLIQBj+iARpaBCW2DYA0BuKQdMYal2WUR1wE3NXNQ1hUQEHDpLKjLgzi7EFQ++RLu94P3IcBzAM1cWgavo3NoP+JR8X4kqqLvKWVDaizLiQWxedp3YkBpCT+HS4aLWQQvl0DI95+L2oMUDgOEgJ91y+MQy/z+rcNmMGT1JO3EuyqoGBocZpLahgt1zOxgQxnsjq8dbKlStht+uj+Vgshptuuglut77KRhSzf6Nu3rwZq1atwvz586EoCr773e/iwgsvxO7du41yr732WgwODuKPf/wjGhoasH79elx55ZV48803MW/ePADAihUr0NnZiRdeeAGyLONLX/oSvvKVr2D9+vVZx1QNNHrtWDy7EQf6QwjGUjsXD/+rKxv31ESRYvzxlaRohqZHVDXMa6vFQESCzcKj3mMzND3tdW6ERMW0a28mh+V0bsjx157bcRRHjkdwqD8CbUgDY+E5OK0CzpxSi5Aooy/Ew2UV8H6Xrveoddnw3y99BJ4D+oKxlDEBuvmcy2bBscEolKHBiN0qYH9PCCsWthubWM5rq8VrHanF2HFNT6o64v1z7ox67Dw6CFll0BIGMxwAVWPwOCyGQDksKka794X4JCG3wyLg9qfeRVhU4LZZMK3BjY9NqxvhSJwp73R/qacSreYrlE9HqvuU54BgVB5xL3odFkRkFfUeW14xZHpvfHJWIz7qDSMoytA0pmt6JAVehyXl+6xQOZfS2ZggxhOmhcxf+tKXTBX4yCOP5BxMb28vmpqasHnzZpx//vkAAI/Hg/vvvx/XXHONcV59fT1++tOf4oYbbsD777+POXPm4I033sA555wDAHj++edxySWX4MiRI2htbR213moUMkclFY++9lHSqpIGjx1XnDUZK8+dOuLDcrgQdTThaVyk+Oo+ffVWSFTgsetfph+f2YAr5k3G028fwdaOfgRjCgaGVm/psxeWjGLaRMHjum0H08YFYNTXDvaHMRCSoT8gAgROX12zaEYDzjupAVs7+rGvJ4TjEQkTfQ5jRVlfUESDxwYGjIgJANZtO4jHXz+kOyBD3+ndaRWMlU43L54JABgISbj9qXdSirGdNmFUoef9L+7H/2w5gN5gDHLCxJSV5+C08WjyOpIEyontnijkdlh5DEQkCBwPjTHUe2yISArAOJzS6jPaL1Pemb5gh/eh3cLnJZRPR6r7NB6zorGke3HK0OqtfGPI9N5YsaAdj752AE+/dWTY+2wSVp47rSCDkmzfmwRBnCDb7++KcmTet28fTjrpJOzcuRNz584FAFx44YWw2Wz4zW9+g9raWvz+97/H9ddfj3feeQczZ87Eww8/jG9961s4fvy4UY6iKHA4HHjyySdx+eWXj6hHFMWkmalAIIC2traqGvTE6QuJ2N8TSnIuHk4+7qlxITTH6Tqh4WLFRKE0kNmdeLioOlc35Jis6o+DNA1vHxoEMLSLu6aBA+CyWcBxwPobF4Ixhjufew92q4DWWueI3L998ckp8+oNirj19zugqhpqXfogwTEk+k3VZnu7g2nF2OnE5PH8ZU3D3q4QFFWDIHDQVAZB4DG72QvGWEqBcqJDM89x+LAnCA6cIThXNA2yyiBwqR2J0+U9GsNzKaQT7mj3aTzm4fdiPjGYfW/0hUTs6wmBAwoqXK40Z2OCqDaKtnrLDH/4wx/wuc99LqdrNU3DLbfcgo9//OPGgAcAfv/73+Pzn/886uvrYbFY4HK58Mwzz2DmTP0v7a6uLmMZfRyLxYK6ujp0dXWlrGvt2rX44Q9/mFOclUb8MUsm4kLUxC99QH9M0emPGs7CuZQ//PVszs0UV09AfwTT5HWkeE0EwOC0WqANiVl5bmjgo+pC37Ck4qPeMKY2uMFx3Ii44rkzBsxuHimgjgtoW2tdSSJWnuNSttmsid60Qux0bRjP323X8/A4rBB4DhpjiEjKUB5aSrFsg8eO/pAeo8eui3nddn1goy+5V8D0gCHKapKYO1Peo5GqvwvtwpzuPk0Xcz4xmH1vFDLPXOonCKIwZLV6S1EU7Nq1C3v37k06/txzz+GMM87AihUrcg5k1apV2LVrF373u98lHf/e976HwcFB/O1vf8Obb76JW2+9FVdeeSV27tyZc11r1qyB3+83fg4fPpxzWdVAIdxTiyGyTBQixxK2CkgUFKcWG1v0lUQcA8/pj1gAXQPDcZwhZJ7W6DZy7wuJ8Ce4KveFRGiMIZ0WvxSOs6MJxhNF05mvZ4awWFT0/c6sFt4QSDPAcJROJyA/8ZPaeTuRYrkyl8Plt9zOwtnUX2qhcz71VaIouxAxVWJeRHaYnunZtWsXPv3pTxsDhMsuuwz3338/rrzySuzatQs33ngj/vSnP+UUxOrVq7Fhwwa89NJLmDx5snF8//79+M///E/s2rULp556KgDgjDPOwMsvv4z77rsPDzzwAJqbm9HT05NUnqIoGBgYQHNzc8r67Ha7IcgeD+TjnloskWVEUvDcjqPo8sfQ6Y/C0RNCS60DTR47BqNykm4nVczx13wOCwYiMtjQYMZq4RFTVMPVOSIp4Dlgx6Hj4HkOdgsPdUgDMsFlw083fpAyn1I4zo4mGA+JsimB8YZ3j0FWNAxGZX2EwwG1TitsFiAqanjjwAA4cEnuy3EB+av7+tDRF0YoJkPTAJ7n4HGccN5ObJfh94LDesJXKK6pyefeKIfLb7mdhc3UX2qhcz71VaIouxAxVWJeRG6Ynum5/fbbMXPmTDz33HP4whe+gGeffRaLFy/GZz7zGRw5cgR333130oDFDIwxrF69Gs888ww2bdqEadOmJb0eiegiUp5PDlMQBGia/lfxokWLMDg4iO3btxuvb9q0CZqmYcGCBVnFM5ZZsaAdS+e2gDE29KiAGULNTKzfdggbd3WC5zl941Cew8ZdnVi37WBe8cTLbatzYXqDBwBwoC+CQwNRI65MMcdfO6XFB59DH7vzHAeHRcC50+vx0+VnGPX0hURMrHHAYRXgj8oYiEiwW3icOaU2Yz65tlk2xOuYVOuE12EBg+6jM6nWaaquFQva0eixQ1Q0WHj9MZ+F5yEqunGlx2EBBwAc0/9lHADOaP+jQ4aMMVlDQFQQU1QEYzKODkZHtMvwe+HoYBSvdfTjmD9asHujFG1eCXVmU3+x3oPpyKe+UsdqhkLEVIl5EblhWsjc1NSEv/71rzjzzDPh9/sxYcIEPPbYY0mrqrLla1/7GtavX4/nnnsOs2fPNo7X1NTA6XRClmXMmTMHLS0t+Ld/+zfU19fj2WefxW233YYNGzbgkksuAQAsXboU3d3deOCBB4wl6+ecc47pJevVuHorV7IRfRZLZJmq3Jisossfg1Xg8O+fPzOtWHp4ffHXBqMSBsNykpB4eD3+iIQ3Dg5AUwG7jceCafWGU3OmfAop1k3HaILxdMRzVDQGt03Q17oz/XHW3p5QSvfmuBCc53UBtKbpj1NUTTdIrHFawXPAzCYvrAKHu5efDsaQ1JYxWcW2j/ohKwxWC2e6LbNtj1K6/JajztHqL7XQOZ/6KlGUXYiYKjEv4gRFEzL39fUZy79ramrgdruxcOHC3CMFcP/99wMAFi9enHT8kUcewcqVK2G1WvHnP/8Z3/nOd/CZz3wGoVAIM2fOxGOPPWYMeABg3bp1WL16NS644ALwPI/ly5fjF7/4RV6xjVWyEWQWS2SZqlzH0OqqVOVmijnTa6nq4cDB7dBXOMWGBL6j5VMsEWsh6kjMMVFwLaua4Zod34YD0GfD4kLwJq/dcJ3WGIOF5/T9vzgOinZCEB53WE5sy0RX5mzastjtkQ/lqHO0+kstdM6nvkoUZRcipkrMi8gd04MejuMQDAbhcDjAmC4YjUajCAQCSedlM1NiZpLppJNOGuHAPJy6urqKNCLsDYoYCJd/T5pc40gUWSZel0nkaaauXMrNVH66OofXk+gkbLUUxsXXTIz53AejXTswZFrY6Y+gvd5jHE/nSh0XgoPBEECr2glBeHz12HAhNWO6M3JvKAa3zQJJUcHAEI4psNv4jG3ZG9SXe/Nc9su9830Pmbm+WO/TQpSbz3ul1PWVOtZEzH4G5BJTOfMiCk9WjsyzZs1K+j3uiBz/neO4qtxlvdBUiugt3ziyEXnm6vg8WrmZyj+7fcIIx+HEOjM5CU8qsJNwqhjzEfqO1p59oRjWPLUTbx8eRDAmQ9WAD7tDWDCtHqKqISTKI9ybUwnBPXYL+kIiOACKpg+CREUdIaROFITLqr7/mqYxqIyhnrdBY2xEW0YkBY+9diCFgeboxn753rtmri+mSL9Q5ZZaaJ1PfeUQhY/W1oWIqdxid6KwmB70/P3vfy9mHGOKuOitzm1Ha60TgZhsvGFK6bBaiDjiYsqtHf3o9EfhsgkpRZ7Z1mW23Ezlr3/9oOE4nK7O4fVMqnWivc4FRWOm6s2G4THuPDqIo4N6nadNqs2q/UdrzzVP7cRrHf1wWS1o9DpwPCxhICLh1f19OLt9ApbObUlyb06X66v7+iApGoLQd5fnOQ5ehwWTap3G6q14PH0hCXaLgKisL69mYHBaBYiKhncOD2Jmk2eEAHfd1kMIijJcVgvAAT3BGNZtOwirwGdsg3zvXTPXF+t9Wuhys32v5Es+9ZU6VjNtXYiYSp0XUTwqypG5XBRSyFwpordCx5FJ5FkIx+fRHj8MLz8qq3jlw15wSO04PLzOYjoJp4oxH6HvaO15wyemYfXjb4MDZ2yICQCDYQkq0/Bf156DBdPq0+aeyHABdSohdaJz9L6eEJjGYBH07R94Dmivd0FjwF2XzU0Skd/6+x14vzMAu+WEpigm615Cc1p8IwTrZvMf7d41c/1wYXa2deRTd673W6mF1vnUV4pYs23rQsRUbrE7MZKiOzIfPXoUTz31lGFQOHv2bFxxxRWYNGlS9tGOQSpF9FboOLIVDJuty4x4NFX5oqyCAwdwLKXj8Ghi6EKLVofHmI/Qd7T23N0ZhKToTs2JuB0WDEb0VWyJ5CoEHx6P226BojK4bBYIPAfLkHO022ZBWFKR+OfTQFhCIKrHkSiwtgo8JEWDPyqnbYN8710z1wMoyvu0mO//Ugut86mvFLFm29aFiKncYncif7Ia9PzqV7/CrbfeCkmSjBFVIBDAbbfdhn//93/H1772taIEWU1UiuitlHEUu65E5+aJNU44rALsVgEMDBwD7NYTOols64wLIAGGgbA8qtg2k2AyMcZ0ouneUAyalt4JOl5Wpvac0+KFReARGnKuFngeqqYhFJNhEXQn6kKSyjla4IWMztF1bht8TiuODkYhKZqRv6xqYGCocVpHdZruC4lw2/S+dlgF031r5n6MC7MLfc9Wyvt/PEBtTeSC6UHPn/70J3z961/HLbfcgm9961toadHFkJ2dnbj33nvxjW98A1OnTk1aSj4eqRTRWynjKGZdmZybHVYeYBxCogKe47KqMy6AfOXDPuzvC2EgJEFjDHaLLn4cLrbNJJhkYCljdFkt6BwSTWsaw/aDA+gOxFCbwQl6tPZccspE7DoWAM8BAxEZQVGBhQNUpguR69w2vLS3F20TXAUTzefiHN3oteOTsxrxUW8YQVGGpjGA09vR67Dg/FmNafvIbRdGuGj7nFa4bRZcevrofWv2fizGPVsp7//xALU1kQumNT2LFy/GJz7xCfz4xz9O+fodd9yBV155BS+++GIh4ysJhTYnjEoq1m07iK0dJ74gF04v/eqtUsZRrLoeerkDG3d1osZpQ19QxDF/FKKiodnnwBVnTQLAYfvBgazrjJcbjCk4cjwKSVHBAXBaLeB4wOuwYOW50wwxZPz8Orc97Uqo4TFO9NoxrcENRWP4qC+M4xEJE30OzGnxISypxvWpxK3p2lNWNfzt/W547Fbs7QqiJxiDygCB0zdnndXsNQYhhRTNx+N5dV8fPuoLIyQq8NgtmJZiu4rEax597QCefutIVqu3Hnq5A3969xjCkopgTDdT1BjDx6bW4RdfPMvU/WTmfizWPVsp7//xALU1ke33t+lBj8/nwxtvvJHknJzInj17MH/+/BG+PdVAsRyZK0X0Vso4ClmXWefmbOtMFObu7QrheESCwJ943lTjsEDWmCG2zSR6jbsbO2xC2hj7QyLufO492IcMGBOvz0boPTyOqKzi1Q97Icoa7FbeEHQXUzSfi3N0X0j36eEwuk9PKkF4TFYRkVTDHbpQAvxszsmFSnn/jweorccvRRMyq6oKq9Wa9nWr1UoePcOoFNFbKeMoZF1mnZuzrTNRmCupGgAGgefBGKBoGniOAwcYYlsgvejVcDf2OdLGCHDgOG5EjNkKvfd0BUeIpTmOQ63bBlFRDUF3MUXzufRvNtcM7/O4m7TLphVcgJ9LfNlQKe//8QC1NWEW0xuOnnrqqXjuuefSvv7ss88aO6ETRCFIFComkk6o2BsUsacraDxKyVSuwHM4HpZg4fUNOFWNQRvagkFjLElsmykOn9NiPO6KE5VVHBuMQtUYBiO6SDqbPMy2R6JYmgMgKhpisloWIWemtk98bbQ+yqbPzfa32TgJghj7mJ7pWbVqFW6++WbY7XZ85StfgcWiX6ooCv7rv/4Ld9xxB371q18VLVBi/GFWqJiNA25cGN0diKHTH4PGNGiMQVE08NA1PRFZHSG2zRQHoGt6VI2hLyjiqD+KUEyBhefw7T+8i2kNblgFDn1BMWMeubSHyybgYDACm8Dj3SN+MDA4rDxWLGgvyV++owm8468FYwqOR/RZszq3DR67JWUfmenzXByPK8UlnSCI8mJ60HPddddh586dWL16NdasWYMZM2aAMYaOjg6EQiF8/etfx8qVK4sYKjEeMeOEmo0DbvzcKXVOOGw8Oo9HEVAVCBwHgefACxwah8S2iXWYieOJNw6jyx81hMVOm76r+TF/FO4hzQ9j+TlBD48DQ0uvrTwPcPoSfjB99qoUZGp7AMZrMVlFTzAGAHDY9NVY6fpotLbOxfG4UlzSCYIoL1k7Mm/duhWPP/44PvzwQwDArFmz8IUvfCHvHdfLSbGEzEThSCdUzMaVNZ0wutsfhcaAL31iGmqd1oxi20xx3Pr7HYhKKo4cj0LgdW+emKxvzHlSkxdWgcO3Lz7ZtAB4tPbY1x3CfS/ug8MqwGO3QJRV2K36QKsU7t+Z2j4m6/o+h1WA227B6x/162aSAACGj02rHzXOVG2di+NxpbikEwRReIruyLxw4cKqHuAQ1Uk6oWI2rqzphNEttS50+qOYWu/G7GZvznEoKsMElxWHBiJwWvRHJlaBR0RSjH8Zw6h1mKHBY0d/SK/T57XCZjmx0znPcSVx/87U9j0BfVanyetARFKGnJwFcENePaKsjiq4TtXWuTgeV4pLOkEQ5ce0kPnDDz/EF7/4xZRL0v1+P6666ip0dHQUNDiiMshG/FlqoWg2wtdEp19/RDJmI8wIf1PllXjshGux7rYcFhUomgZZ1QwX42zFxbmIfmOyis7BCCw8VzQhczwu4ISrcVRWMTjUpoEhp2ifU9fjMOibk8Zk1XBxtg85LAs8h+MRaVQBdKacgcx9mMs1BEGMTUzP9Nx7771oa2tLOX1UU1ODtrY23Hvvvbj//vsLGiBRPrIVCJdDKJqNK2suTr+p8jpn6gQAwJsHjiflevrkWjy5/RBCMQWiooGPcLDwQOsEZ0rX4nSYbcvE3FVNQ29IROdgDDFZRUuNE8++fbSg7Z8qLsYY3jsWgKRo4MAlCakZA9a/fhAxWUNUUiEqMiy87tXjj8rY2x2A02rBf/ztw7QC6OH55+LCS869BEHEMT3o2bx5M37729+mff3KK6/EVVddVZCgiMogF4FwOYSiZkTG8Rj7QiIm1jgMp9/uQAwfm1qXVlCcKq91Ww8BHMMpLTVJudY4rQDj4EzYZ0rVOIAhK9FyNm0ZL/OJNw6jKxCD3cJjeoMHDV57wds/VVzvHQsgLCqwCSOF1Byn/5+DLupmADSNIRhTICkRgHGYUudErcueVgCdKn+z/Z2qnbK5hiCIsYfpQc+hQ4fQ1NSU9vWGhgYcPny4IEER5ac3KGLL/n7UuU/oKuL/bu3ox7J5k5LEpWbPLQZOm4AbzpuOZfMmpXVljcfY4HXg5GFOvwxAWFJGzIikystjtyCmqMb/bRYeDR47REXFu0f8OLW1BnVuG0RZBThgMCzBIvBYNm+SqRmXbNvSaRNw2ZmTsHlPLxo8djTXOAxtj8BzBWv/VHG57RZIigabwGP+tDpgaPPXkKjgtf19AIBTWn1JIuuBsIRgTIZV4OFrsI7IcfPeXoBh1PxH6+/hmLlHCIIY+5jW9NTU1GD//v1pX9+3bx+tfBpDxMWfPkeyC7fPYUVEUg2n4mzPLSYNHjtmN3szCp7jMTqsAmpdtiHfl9QxpspLHNIBceAMTRAA2ARhaACgr9qqcdlQ47ShpdYFRWOm2yCXthwIS1A0htZapzHgGe2abEnXFhw4fXU8A2pcNsMROhCVEYjK8DmsRns4rPqyfVXTtUepcgxEFV0TZCL/TP2djlyuIQhi7GB60HP++efjl7/8ZdrXf/GLX+C8884rSFDESKpBIFwuoaiZtskUYzoxbapr7EODCl27cmKAIakqbBYekprsAJGv67KZcurcNlgEDp2DkaSBWC7O1eleY4yBMZZ03G4VwMCAoZR7AjH0BGLoC4lJQubheaRysU732mBEwpGBCDr9kXEnOo73xZ6uALlIp4EctolsMf14a82aNVi0aBE+97nP4dvf/rax8egHH3yAe+65B3/5y1/w2muvFS3Q8Uo1CITLJRTNpm1SxXg8IqUV0zptQsprQqICh0UAOIaQqIDnOARiMsKignlttRiISOgL8QV1Xc5UTtxhussfQ6c/CkdPCC21DjR57BiMyqadq9OJhy+fNwnPvH0UW/b3o9Mfw2BEQqc/auwUb7PwCMcUvLi3F7KqgQGwcBzOndmA+VPr8Lf3u1LmASCjw/Uf3zmGHYeOIxCVoTAGDhymN7jBl8h0sZzE++mVD/Ud7cOSArfdgukZdrQfb5DDNpErWZkTbtiwAV/+8pfR39+fdLy+vh4PPfQQPvvZzxY8wFJQyeaED73cYYg6h385FFsgHJVUrNt2EFs7TnywLJye+oMlm3MLRbZtMzzG3qCIqKRidrPHENMOvz5VXme31wFg2H7weFKuV8ybjKffPpJ3G2TTlvE2qHHa0BcUccwfhahoaPY58Pn5bUnXZGovAClfq3PZMBCRUOe2w2UT8H5nAN2BGCa4bJjZ5IGmMbw51A48p4uXOTDUue24ZlE7rAKfMg8AaXMEgMv+8xXs7wuBBwdB4CBw+sqwj89owIPXzc/6Xqkm4v0UjCnoD0ngOQ4q09DgscPrsJTkvV/plPNzkagssv3+ztqRORqN4vnnn8e+ffvAGMOsWbNw4YUXwuVy5Rx0uanUQU+lOMmmcyHO99x8yKdthrsZm7k+VV7pci1UG4xWTjqH6S5/DFaBw79//kxTTsYxWQUY4LAlt8XRwQh2Hwvg1NaaJGO/Y4NRiLKKb/7jLPxy0z580BWAhedhEfStPBSVQVJUnNLiw79//kwASJtHqhw/6Azg6l9vA9MAp12AwHOw8DwCUVmfkbpxIWZNzN/gsRKJ95OsadjXEwKHE87eAMPMIWfv8ewiXSmfi0RlUHRHZqfTicsvvzyn4IjsqBQn2XQuxPmemw/5tM1wN2Mz16fKK12uhWqD0cpJ5zDdWus05UYNxN2TRQAMTT5H0muJ4uzhcXX6oxiMyAjGZOOLWeD183iOQVJV+KP6TEUm4XCqHA/2RyApGmpdNliFE7JDp03AYETCR73hMTvoifeT224xXKwBwGbRHb1tAofwkKB7vH6xV8rnIlGdmBYyb9myBRs2bEg69pvf/AbTpk1DU1MTvvKVr0AUSUxWSKpBIFwu8m2bYrZtqdqtUGLzdMLi0cTZUxvc8DqsYGCQVe3EdYqmr+ZyWnJqx/Z6F2wWHlFJTToeHdIQTWt0Z11mJZCN4F5SVcPJW9E0hEUFmsZwPCJndNuu5PdsoSjF5+J4aMfxiumZnrvuuguLFy/Gpz/9aQDAzp07cf3112PlypU45ZRTcO+996K1tRU/+MEPihXruKMaBMLlIt+2KUbblrrdCik2B0YKi0cTZ89u9mLx7EYc6A8hGJOhMX0lV0RW4LVbcf6sxpza8eQWH+a11eK1Dl076LQJiEoqIrKCc6fXV90sT66Ce5dNwLHjUcgqg6Ix8BwQiClomzDSbbsa3rOFopifi+OpHccrpmd6duzYgQsuuMD4/Xe/+x0WLFiABx98ELfeeit+8Ytf4Pe//31RghzPrFjQjqVzW8AYQ6c/CsZY0Z1k4867PM+htdYJnuewcVcn1m07WLQ6cyHftil025aj3bLJIdO56V776fIzMpYfv7bJ69ANHxUVTV4HVixsz+se/enyM3Du9HowMAxGJDAwnDu9Hj9dfkbOZZaLbO+LeF+AAaoGqEzfz03gOehPGkdeXy3v2UJRrM/F8daO4xHTQmaHw4EPP/wQbW1tAIBPfOITWLp0Kf71X/8VAHDgwAGcdtppCAaDxYu2SFSqkDmRahAIl4t826YQbVvudiuU2DxXcXZfSMT+nhAYgJlNnoLlurc7iI96w5jW6K66GR4g9/uiNyji1t/vQFRUcOh4FIC+uaus6tu3JgqaGUPVvWcLRSE/F8v9HiZyo2hC5okTJ+Kjjz5CW1sbJEnCW2+9hR/+8IfG68FgEFarNUMJRD5Ug0C4XOTbNoVo23K3W6HE5rmKs4t1f86a6K3KwU6cXO+LgbAutK9123HoeBQumwUCz4Hj2AhBM4Cqe88WikLed+V+DxOlwfTjrUsuuQTf+c538PLLL2PNmjVwuVxJDszvvvsuZsyYUZQgidJRbvF0tULtRqQi1/silaAZ0EXiFp6DNLSyq95jo3uvQFA7jg9MD3p+9KMfwWKx4JOf/CQefPBBPPjgg7DZTtwEDz/8MC688MKiBEkkU8yVBXGR4EBYRF9IhKRo6AuJGAiLOG1SDfpDI7drKEZs5V49kW39mdpt4fT6gv6FWO62yZZKirfUseR6X8SvC4uKvsmtrCIQlRGVdXfmkCgb12dTRyX1RbYUO/ZSvoeJ8pG1OaHf74fH44EgJCvZBwYG4PF4jIHQkSNH0NraCp43Pa4qG9Wg6QFKt7JguCOw3cLDwnOQVQZR0VLWW6jYyr16Ip/6i+1KXe62yZZKirecseR6X8Sve3Wfvh1FaGgANC3FdhSj1VFJfZEtpYy9HM7yRH4U3ZHZLD6fDzt27MD06ZVvCV4tg55SW6/HRYLP7+rESx/2Zqy3ULGV216+EPUXS3Re7rbJlkqKtxJiyfW+iF/HcQBjyHh9ujoqIf9cKUfspVo4QuRPtt/fRZuGKdJYatzSGxSxZX8/6ty6cM9m4dHgsaPObcfWjv6iTPnq5dvw7hF/xnoLFVs5cixG/Q0ee0YX4nLGVioqKd5KiSXX+yJ+3ayJ3lGvT1VHpeSfC+WKvRjvYaIyqPxnTwSAEysLfI6R2yZEElZxlKPeQsVWrhwrpf5MVHJsqaikeCsplnJQzflXc+xEZUKDniqhXCsLzNRrNrZ0QsT4cYAZ5cRkFf6IpAs4E8oxI2bMVfBYyas3ChWb2bbJdF422ykkxhuTVXQORkZso1BsgWoubZdvTJUk6C9H/oWikt+TRHWS9YajRHko15YUZuvNdI7LJuChlztGCBEvnzcJz7x9NOk4A8PuYwGIyom9nBwWAVfOn4xnh51baDF1udrYDPnGZrZtMp3HwHLaTkHVNPSGRHQOxhCTVbTU6NsopOr/YghUs2m7fO+hShT0lzL/QlPJ70miOinaoIfjuNFPIrIibrG+taMfnf4oXDah6FtSmK030znrth00hIittU4EYjI27urEto5+DESkpOPvHQ0gLCmwCjw4cGBgAMew49Ag/DF5RBkADDFj3EI+0zmFyLVc5BOb2bbJdB6ArNo3HtcTbxxGVyAGu4XH9AYPGrz2tP2fbX+ZxWzb5XsPFeIeLGQ5cUqVfzGo5PckUX0UbfWW1+vFO++8Q6u3ikC5VhaYqXf4Oems3Y8ORrD7WACnttYYDqgxWcXLH/YCAD42tQ4MgMMqoD8sjjg3XlfcHr7QVvyVvHoj29jM2utnOi8mqwADHDYh++0UntgBRWNornHAYdVnC1L1v5ny8iVT2+W7DUGhtjEo5nYIxcy/2FTye5IoH2VZvZVq3LR79260t9NIvBiUa2WBmXqHn5NOiGgTBEiKBptwYkZQlFUAGJrhAWpdNjisQspzgeKIqbPJtVxkG5vZtsl0XiCqIBCTs27fgbAERWNorXUaAx4gdf+bKS9fMrVdvvdQNQj6i5l/sank9yRRPZge9IiiiH/5l3/B+eefj5/+9KcAgB//+MfweDzwer246qqrEAgEjPPb2tpGGBgOZ+3atZg/fz68Xi+ampqwbNky7Nmzx3j9wIED4Dgu5c+TTz5pnHfo0CFceumlcLlcaGpqwm233QZFUUw3AjE6hRYHS6oKm4WHpJ4YMNuHvhQZWNIXZKpzgdzE1NnmVSmCTrOkipcxBlFRcbA/rM/YDDG8bTK1oc9pMfQUw18zs53C8OtCogyeA0KimnQ8G4FqofsmMdZ0Qnqz1ycSiMkQeA7HI5ndzM2UU+5FCwRR7ZjW9KxZswZPPPEEvvjFL+Kxxx7DoUOHsGHDBvzXf/0XeJ7H97//fdxxxx34xS9+YbryzZs3Y9WqVZg/fz4URcF3v/tdXHjhhdi9ezfcbjfa2trQ2dmZdM1///d/495778XSpUsBAKqq4tJLL0VzczNee+01dHZ24tprr4XVasVPfvIT07EQqSmWODgsKpjXVouBiIS+EA+fw4qQqMBhEQCOISQq4Dku7bnZiqmH/3U4Wl6VJugcjVTxnjN1AiRFw4Z3O3HkeASSquH9zgCmNbjR5HPAH5WS2mY00SiArAWlw8t0WQW83xVAdyAGnuOwu9OP/rCIU1p8iEiqKYFqsfqm0WvHOVMnYN3WQ4gpJwZjDouAFQvbR51hSNV+xyMS9nYH4LRa8B9/+9BUrJW+aIEgqhnTmp4pU6bg4YcfxpIlS9DR0YGTTjoJTz/9NC677DIAwAsvvIAbb7wRBw4cyDmY3t5eNDU1YfPmzTj//PNTnjNv3jycddZZ+PWvfw0A2LhxIz796U/j2LFjmDhxIgDggQcewO23347e3t6k/cHSUW2anlJSCDfUdNbuV8ybjKffPpJ0/Oz2OgAM2w8eH/Xc4fbw2VjIj5ZXtTnYpor3/WMBBEX9r3anVR/IRSQVAsdjSr0Ln5/fNqJtMrUhgLy2U9ja0Y99PSEcj0iY6HNgVpMXe3uC6A7EMMFlw8wmj6nyitk397+4D+u2HURM1gwhvcPKY8WCdty8eOao1w9vv96giKikYnazB7Uuu+lYy7UdAm3DQFQbRduGwuVy4YMPPsCUKVMAADabDW+//TZOPfVUAPqjqFNPPRXhcDjn4Pft24eTTjoJO3fuxNy5c0e8vn37dpxzzjl49dVXce655wIAvv/97+OPf/wjduzYYZz30UcfYfr06Xjrrbcwb968EeWIoghRPDHNHAgE0NbWRoOeYRRa2JhOiJjqeDbnmq3HbF63XXQy7nn+g4oVdA4nVT4xWcXmPT0IiQrq3Ta47LpOIyIpiMkqZjd78Z9XnZVTG+YqKN3TFcCdz70Hu1VIEi8fG4xCVFTcddlczJrozTrXeEz59k1i2fFNPh1WASFRybrsvpCIfd0h3PfiPjis2Ym/h5dTqYsWCKISKJqQecqUKdiyZQsA4I033gDHcXj99deN17dt24ZJkyblELKOpmm45ZZb8PGPfzzlgAcAfv3rX+OUU04xBjwA0NXVZczwxIn/3tXVlbKctWvXoqamxvhpa2vLOe6xTKnEwamOZ3Ou2XrM5nWgL1zRgs7hpMpHlFVojIExgE+wj3BYBVgEDlFJy5hHpjbMXVCq6/FS9SnPcTDz51cxxbaJZTusgiGkz6XsBo8dE9w2KCrLK9ZKXrRAENWIaU3PTTfdhJUrV+Khhx7C9u3b8W//9m/47ne/iw8++AA8z+P+++/Ht771rZwDWbVqFXbt2oVXXnkl5evRaBTr16/H9773vZzriLNmzRrceuutxu/xmR4imURhY+KHnxlx8EC4cv9KHC2vqQ3unPIejWK1S6p82NAPOEBLGE1IigYwoMZpAcCwpytYkn6K527hOSPOmKxClFWEhx6jmGnXxFw9dgtEWYV9aDYmX7Ftrvd7scqr9PcRQZilku5l04OeW265BU1NTdiyZQu+/OUv44tf/CJOO+00fP/730ckEsE3v/lN/Ou//mtOQaxevRobNmzASy+9hMmTJ6c85w9/+AMikQiuvfbapOPNzc1JM04A0N3dbbyWCrvdDrudPkRGI1thY7WIf0fLa3azt6CCzmK3S1r3Y0kD0xiOR+KrcThEZAVumwV2C497nt9T9H4anntfSERY0gcooZgCUdGgaQwfm1YHt230j6N8xcajlV3Ifs+1vGp5HxHEaFTivZyVT89VV12FX/7yl/jiF78IAFi8eDFeeuklvPnmm7jzzjvB8yeKe/XVV5N0M6lgjGH16tV45plnsGnTJkybNi3tub/+9a/x2c9+Fo2NjUnHFy1ahJ07d6Knp8c49sILL8Dn82HOnDnZpEekYMWCdiyd2wLGGDr9UTDG0rqhxt1ceZ5Da60TPM9h465OrNt2sAyRZ2a0vLLJezRK0S7xeA8PRHGgLwIAOGmiB1MaXAAYBqMyYoqKJq8DJ7f40BeSStJPw3OfUudEWFRwZCACUdHgsAqYWONAX0jMrn5On73iwCX8nr/PaiH7Pdfyqul9RBCZqMR7uWiOzD6fDzt27MjoyPy1r30N69evx3PPPYfZs2cbx2tqauB0nhA67tu3D7NmzcKf//xnXHzxxUllqKqKM888E62trbjnnnvQ1dWFa665BjfccIPpJeu0emt08hUHV5r4N85oeeUr6Cxlu6RzPz42GEUoJuMrn5yBqfUu3PP8npLFk0pg/fKHvdAYcGZbDWqcum4mF9fjfMXGmSi0kNdsedX6PiKI4ZTqXi6LI3MqzIyl7r//fvj9fixevBgtLS3GzxNPPJF03sMPP4zJkyfjwgsvHFGGIAjYsGEDBEHAokWLcPXVV+Paa6/FXXfdVbBciPzFwZUm/o0zWl75CjpL2S7p3I8bPHbYrQKm1rsBcCWNJ5XAGgAEjoPdIhhx5uJ6nK/YOBOFFvKaLa9a30cEMZxKvZfLusu62Ummn/zkJxlnbdrb2/HnP/+5UGERKRhNiFZMgWkh4yw1ZsSshYrZTF2MIemcXMTE+cSTznU7F9fjQorMcyGXfsvmfdTgsSM61D+RIvQPQRSTSnqvJlLWQQ9R+ZgVohVTYFrIOEtNJjHrklMm4tm3jxYsZrPC2UUz6rHh3WM42B9GICpnLSbOJ550rttmxcKV4Bqcy72WzfsouX8UiIpuP/CxqYXtH4IoJpXwXk1F0R5vEWODrIVoRRKYFjzOEpJOzAqg4DGbEc6uWNCORo8d3YFYfmLiHONZsbAdKxa05ywWLrTYOFtyudeyuSaxfyRFhcPCY6LPgb6QVBH3M0GYpdzv1VSUVchcKZCQOTXZCNFKJTDNN85ykihmZQxFjTmTcDbeXorG4LLpuppsxMSFiidfsXA5XINzudeyvSaxf9w2AfYS9A9BFJNivlerSshMVDbZCNFKJTDNN85ykihmLXbMmYSz8bobPHajnwpZt9l48hULl8M1OJd+y/aaxP6pKVH/EEQxqSSH76INeoLBYFXM8hDpSRSiJTJciNYbFHE8LMEicKOeW844K4lixtwbFLGnK4i+UGqfrELVPVo9Y5Fc2i7ba6rxfiaIasG0Km7evHngEvbwScdbb72VV0BE5TCaEM1lE/DQyx1JbrvpdpQu5gi/UgVzmShGzNmKZXOtu1JF46Ugl7bL9ppqvJ8JolowPehZtmyZ8X/GGNauXYubbroJdXV1xYiLqBDigrOtHf3o9EfhsgmGEG3dtoPYuKsTdW47WmudcNoE7O0O4NBAFFFZSzq3nHFWKoWOOS6WjfdHICYbX5w3nJc865pP3dnUMxbJpe2yvaYa72eCqAZyFjJ7vV688847Y+IRFgmZR2e4EC2TOFOUVXztH2ZiZpOn5H+VlkPcmi+FiDlXMXe2dVeLaLwU5NJv2V5TjfczQZSSbL+/yfSBMEWDx570oRsXW7bWOpPO8zms6BQVTHCV50N6eJzVQCFiztgf/ij6Q1LKOrKtO9d6xiK59Fu211Tj/UwQlQz59BApyUYMG5VVDEYkxGR13IotSynqTVXXcPFrvE/6QmJO/ZEun2KLbKtBHF0NMRIEkRqa6SGSyEYMe3b7BKx//SBisgYO3NDWAjxWLCi+A3OlUEpRb6a6CuXkO1o+xRLZVoM4uhpiJAgiM6YHPb/4xS+SflcUBY8++igaGhqSjn/9618vTGREWchGpMpxHMCGvJc5NmS+zAEYfZXfWKGUot7R6lqxoB3bOvrx+oEBCBwHh4WH12k1nHzNxGMmn2KIbKtBHF0NMRIEkRnTg56f/exnSb83Nzfjf/7nf5KOcRxHg54qpjcoYsv+ftS5T+gI4v9u7ejHsnmTkhyY3zwwgFNafSM2GN1+cAB9ocljfrYnm/YqRV2MARoDzmybMMLJ10w8ZvNx2gTccN50LJs3qSAi21K2Y65UQ4wEQYyO6UHPRx99VMw4iAogG5Fq4rk2C2+4xvIcN24EraUU9ZqpC0BSn2QbT7b5FEpkWw3i6GqIkSCI0TEtZP7Upz6FwcHBIoZClJu4SPXYYBRHBsLwR/Qv0lQi1VK4xhZKMJpvOeUS9SbWDbBR6zITT6a2KJcTcDU4EFdDjOmoNOF1pcVDjC9Mz/S8+OKLkCTa82Usw3EMhwci6OgLAwAEnoPPYUFrjROfObM16S/ZYrrGFkowmm855RL1pqub54C+oJixrnTxXHDyRDz79tGMbVEuJ+BqcCCuhhiHU2nC60qLhxif0JJ1wmDNUztxdDAKu5WHVeCgMoaBsISorKYUqa5Y0I6lc1vAGEOnPwrGWEFcY+OCUZ7n0FrrBM9z2LirE+u2HSxpOWauL2Ub9IVENHhsGetKFw/Hcabaolj5jEa56s2GaogxkUK9j8ZqPMT4xLQjM8/z2LRp06jbTpx++ukFCayUkCMz8EFnAFf/ehs4cPA5rVA1DarGEJFUcByw/saFmDXRm/LaQrrGFsrxN99ysr2+lG3w7YtPBmPIWFdiPIwh67YolxNwNTgQV0OMleacXWnxEGOHojoyX3DBBUg1RuI4DowxcBwHVVWzKZKoEA72RyApGmpdujZB4HkIvN63gxEJH/WG0w56CukaWyjBaL7llEvUa6ZuxoDZzan7IlU8e7qCWbdFuZyAq8GBuBpirDThdaXFQ4xfshr0bNu2DY2NjcWKhSgj7fUu2Cw8opIKq/PEU8+opMJm4TGt0V2SOBIFo4kfgtkKRvMtp1BxpKM3KGIgnHq2oNB1FzsXovKotD6vtHiI8UtWg54pU6agqampWLEQZeTkFh/mtdXitY5+AIDTJiAqqYjICs6dXp92lqfQFEowmm855XQeLnTd1SjCJfKj0vq80uIhxi8FFTIPDAwUsjiixPx0+Rk4d3o9GBgGIxIYGM6dXo+fLj+jpHEUSjCabznFEK6aFXMWuu5qE+ES+VNpfV5p8RDjE9NC5n/4h3/AM888g9ra2hGv/fWvf8VDDz2E//3f/0U0Gi10jEWHhMzJ7O0O4qPeMKY1uks2w5OKQglG8y2nUHHkIuYstGi2GkS4RGGptD6vtHiI6qZoQua///3vSb8fPHgQDz/8MB577DEcP34cS5cuxW9+85vsIyYqjlkTvWUd7MQplGA033LK6TxcaNFsNYhwicJSaX1eafEQ44usND2SJOHpp5/GQw89hFdffRVLlizBkSNH8Pbbb+O0004rVowEkReZRMOlpJrFnJXShuOJSmvzSouHIHLB9KDnn//5n/H444/jpJNOwtVXX40nnngC9fX1sFqtEARy0yQqj0pzgK1GMWelteF4oNLavNLiIYh8MC1kvv/++/HVr34Vf/3rX7Fq1SrU19cXMy6CyJtKdICtNjFnJbbhWKfS2rzS4iGIfDA96Pmf//kfvP7662hpacHnP/95bNiwgYwIiYqlNyhiy/5+1Ll1/YDNwqPBY0ed246tHf1l2+zQaRNww3nTcffy03HnZ07F3ctPxw3nTa/Iv5grtQ3HMpXW5pUWD0Hki+lBzxe/+EW88MIL2LlzJ04++WSsWrUKzc3N0DQNu3fvLmaMBJE1cdGwz2FNOu5zWBGRVPSHyrt5boPHjtnN3op8pBWn0ttwLFJpbV5p8RBEvmTt0zNt2jT88Ic/xIEDB/Db3/4Wy5cvx9VXX43Jkyfj61//ejFiJIisSRQNJ1IJouHeoIg9XcGMfyWbOafYZNOG8RmBbRn++q+EnCqdQty3hWznSn4fEUQuZLV6KxGO43DRRRfhoosuwsDAAH7zm9/gkUceKWRsBJEzlSgaNiMIrSTRqJk2jEgKHnvtAJ5+66jxJdvgseOKsyZh5bnT4LQJFZVTpZPPfVuMdq7E9xFB5ENBHJnr6upwyy234J133ilEcQRRECpNNGxGEFppotHR2nD9tkNYt/UQeoIxOCwCHFYBPcEY1m07aMRcaTlVOrnet8Vq50p7HxFEPph2ZL7rrrtGL4zj8L3vfS/voEoNOTKPbSrBAdaMGzNjyNqxuVSkasPeoIhbf78D73cGYB8a8ABATFYhKirmtPjwr5eegnue31OROVU62dy3ubh9FzMegigVRXNk/sEPfoDW1lY0NTUh3TipWgc9xNimEhxgzbgxA8jasblUpGrDgbCEQFTXetgsJyaNrQIPSdHgj8o40Bep2JwqnWzu21zcvosZD0FUKqYHPUuXLsWmTZtwzjnn4Mtf/jI+/elPg+cLul8pQYxZzLgxM4aqcmyuc9vgc1pxdDAKSdGMmR5Z1cDAUOO0YmqDq6pyqlaq2e2bIEqJ6VHLn/70J+zfvx8LFizAbbfdhkmTJuH222/Hnj17ihkfQVQM+ayKiQtCB8Ii+kIiJEVDX0hEtz+GGY2ejOcMhEUsnF5fcX9lN3rt+OSsRjgsAkKijIioICIpCIkyHFYe589qxOxmX1XlVOmkuwer7d4hiHJhWtMznJdeegmPPPIInnrqKZx22mn429/+BqfTOfqFFQhpeohMFGpVTFRSsW7bQWzt6EcwpmAgrD/SmuCyweuwYNGMelwxbzKefvsItnacqGvh9Mpd6RSVVDz62gE8/daRtKu3EvOuhpwqETP3ILUzMR7J9vs750FPNBrFk08+ifvuuw87d+5EV1dX1Q4YaNBDZOKhlzuwcVcn6tz2EUt2bzhvetbl9YVE/PdL+7G1ox8Tfc6UZVabaLQvJGJfTwgcgBlNnpQxV1tOlUQ29yC1MzGeyPb7O2tRzpYtW3DjjTeiubkZv/zlL3Hdddfh2LFjNFggxiTFsOFnDNjfE8ZEnzNtmdXg2JxIg8eOhdPrsSDDo5Rqy6lSyPYepHYmiPSYHvTcc889mDNnDi677DJ4PB68/PLLeOONN/C1r30NtbW1RQyRIMpHMWz4ydqfyAa6XwiicJge9HznO99BJBLBlVdeCY7j8Oijj+LWW28d8ZMNa9euxfz58+H1etHU1IRly5alFEZv2bIFn/rUp+B2u+Hz+XD++ecjGo0arw8MDGDFihXw+Xyora3F9ddfj1AolFUsRPVTjG0OCm3D3xsUMRCWYOE5svbPETNbXowlaCsIgigcppesn3/++eA4Du+9917acziOy6ryzZs3Y9WqVZg/fz4URcF3v/tdXHjhhdi9ezfcbjcAfcBz8cUXY82aNfjlL38Ji8WCd955J2m5/IoVK9DZ2YkXXngBsizjS1/6Er7yla9g/fr1WcVDVCfF3OagUDb8w2PsC4mIygpmTfRhgstG1v4mMLPlxViEtoIgiMKRs5C5GPT29qKpqQmbN2/G+eefDwBYuHAh/vEf/xE/+tGPUl7z/vvvY86cOXjjjTdwzjnnAACef/55XHLJJThy5AhaW1tHrZeEzNVNoYXGwynEqpjhMQ5GROzpCsFpE9DotdNKGxM89HIHHn31AIKiDJfVAnD6QMjrsGDludMK0teVCq3MIojUFM2ROVt8Ph927NiB6dPNfxD5/X4A+l5eANDT04Nt27ZhxYoVOPfcc7F//36cfPLJ+H//7//hE5/4BAB9Jqi2ttYY8ADAkiVLwPM8tm3bhssvv3xEPaIoQhRPTIsHAoGcciTKz3CRJwDj360d/Vg2b1Lefwk7bQJuOG86ls2blNOqmFQxNvmc4Hkeoqxi1T/MTLviidDpDYrYvLcXMUWFx241jBB5jkNMVvHS3t6C9HWlku89SBCETtEslbOdQNI0Dbfccgs+/vGPY+7cuQCAjo4OAPoWGDfeeCOef/55nHXWWbjgggvw4YcfAgC6urrQ1NSUVJbFYkFdXR26urpS1rV27VrU1NQYP21tbdmmR1QIpRR55roqJlOMisZQ66IvsNHItOUFBw7+qDwuBL20Mosg8qNi9pFYtWoVdu3ahd/97nfGMU3TAABf/epX8aUvfQnz5s3Dz372M8yePRsPP/xwznWtWbMGfr/f+Dl8+HDe8RPlIRuRZzGEzmbKrjQhajHbIVdGiym+5QUASIpmHE/c8qKU7ViJbUgQxOgU7fFWNqxevRobNmzASy+9hMmTJxvHW1paAABz5sxJOv+UU07BoUOHAADNzc3o6elJel1RFAwMDKC5uTllfXa7HXY7/aU0FjAj8iym0NlM2ZUiRC1mOxQ7pviWFx/1hhEUZWgaS9L0nD+rsSTtWIltSBCEeco608MYw+rVq/HMM89g06ZNmDZtWtLrU6dORWtr64hl7Hv37kV7ezsAYNGiRRgcHMT27duN1zdt2gRN07BgwYLiJ0GUnRUL2rF0bgsYY+j0R8EYw9K5LVixQL9H1m87hI27OsHzHFprneB5Dht3dWLdtoN512227NFiLAXFbIdSxLRiQTtWLGxHk9eBmKIiJqto8jr04yVqx0psQ4IgzFO0mR4zy9dXrVqF9evX47nnnoPX6zU0ODU1NXA6neA4DrfddhvuvPNOnHHGGTjzzDPx2GOP4YMPPsAf/vAHAPqsz8UXX4wbb7wRDzzwAGRZxurVq/GFL3zB1MotovrJJPIsptA5m7LLLUQtheC72DE5bQJuXjwD/3TO5FG3vKiEeAmCqDyKNugxI2S+//77AQCLFy9OOv7II49g5cqVAIBbbrkFsVgM3/zmNzEwMIAzzjgDL7zwAmbMmGGcv27dOqxevRoXXHABeJ7H8uXL8Ytf/KJguRDVQYPHPuJLJy4ibq1N3gzX57Ci0x9Ff0jK+Ysql7JTxVgKitkOpY6J2pAgiFwp2qBn48aNmDRpUsZzzK7w+s53voPvfOc7aV+vq6sjI0IiJYki4sQvpLiIGGDY0xXMaeZltLIrySm3EmOtxJgyUW3xEgQxEtODnsHBQTz++OO4+eabAeguyIlbQQiCgAcffNDYhyvuo0MQ5SSdiLg3GEOjx457nt+TsyC1UgTKZqjEWCsxpkxUW7wEQYzEtJD5wQcfxCuvvGL8/sc//hE8zxteNzt37sTPf/7zYsRIEHmRSkTc6LGjLyTlLUitBIGyWSox1kqMKRPVFi9BEMmY3oZiwYIF+H//7/9hyZIlAACv14t33nnHcFx+5plncNddd+Htt98uXrRFgrahGB/0hcQhAzuGe57fA57nkv467wuJYIzh7uWnZ/1Xe7zsanDKrcRYKzGmTFRbvAQxVinaNhQdHR2YPXu28fvs2bNhs514hn3GGWcYLskEUYnEBbB7uoIFF6SWS1ybC5UYayXGlIlqi5cgCB3Tg55wOAy/329s2fDmm2+OeD3uoEwQlUZvUMRAWP/LvJIFqYlx0pcqQRBEYTE96Jk+fTreeustY1+s4bz55psjzAUJotykc9A9Z2od/va+7gtVCYJUcvolCIIoPqaFzJdffjnuuOMOdHd3j3itq6sLd955Z8odzQminKRz0AVYRQlSyemXIAii+JgWMgeDQSxYsABHjhzBNddcg1mzZgEA9uzZg9/+9reYNGkSXn/9dXi93qIGXAxIyDw26Q2K+M5T72YULAMouyDVTJz0qIsgCGIkRRMye71evPrqq1izZg0ef/xxDA4OAgBqa2tx1VVX4Sc/+UlVDniIsYsZB93Zzd6yDyjI6ZcgCKI0ZOXIPGHCBDzwwAO4//770dvbCwBobGw0tc8WQZSaShYsJ1KJcZKgmiCIsYjpQc8555yDG264AVdddRV8Ph+ampqKGRdB5E21OOhWUpwkqCYIYixjWsh8xhln4Nvf/jZaWlpwzTXX4MUXXyxiWARRGKrFQbdS4iRBNUEQYxnTQmYAiEQi+P3vf49HH30UL7/8MqZNm4Yvf/nLuO6660bdXLSSISHz2KdaHHTLGScJqgmCqDay/f42PdMDAC6XCytXrsSLL76IvXv34gtf+AL+67/+C1OnTsWll16Kp59+OufACaKYNHjsFSFaHo1yxhkXVPsc1qTjPocVEUkd2sKDIAiieslq0JPIjBkz8OMf/xgHDhzA448/jq1bt+Kf/umfChkbQRAlJFFQnUilCb8JgiByJedBDwC8+OKLWLlyJVauXAlVVXHjjTcWKi6CIEpMXFA9EBbRFxIhKRr6QiIGwiIWTq+v+FkygiCI0chqyToAHDlyBI8++igeffRRdHR04LzzzsOvfvUr/NM//ROcTufoBRAEUbHEhdNbO/rR6Y/CZRMqUvhNEASRC6YHPb///e/x8MMP4//+7//Q1NSE6667Dl/+8pcxc+ZM45xdu3al3ZuLIIjKx2kTcMN507Fs3qSqEH4TBEFkg+lBz9VXX41LL70UzzzzDC655BLwvP5kLBgM4vHHH8dDDz2E7du3Q1XVogVLEERpaPDYabBDEMSYw/Sg58iRI0mGhC+99BJ+/etf46mnnkJrayuuuOIK3HfffUUJkiCI1JBzcmmh9iaI6sb0oKepqQldXV149NFH8etf/xqBQABXXnklRFHEs88+izlz5hQzToIgEiDn5NJC7U0QYwPTq7c+85nPYPbs2Xj33Xfx85//HMeOHcMvf/nLYsZGEEQayDm5tFB7E8TYwPSgZ+PGjbj++uvxwx/+EJdeeikEgf66IYhy0BsUsWV/P+rcuu7GZuHR4LGjzm3H1o5+9IXEcoc4pqD2Joixg+lBzyuvvIJgMIizzz4bCxYswH/+53+ir6+vmLERBJECck4uLdTeBDF2MD3oWbhwIR588EF0dnbiq1/9Kn73u9+htbUVmqbhhRdeQDAYLGacBEEMQc7JpYXamyDGDlk7Mrvdbnz5y1/GK6+8gp07d+Jb3/oW7r77bjQ1NeGzn/1sMWIkCCIBck4uLdTeBDF2yGsbitmzZ+Oee+7BkSNH8PjjjxcqJoIgRmHFgnYsndsCxhg6/VEwxsg5uYhQexPE2IBjjLFyB1Fust2aniAqhb6QSM7JJYTamyAqi2y/v7Pee4sgiMqBnJNLC7U3QVQ3NOghCKJkkKMxQRDlhAY9BEEUHXI0JgiiEshLyEwQBGEGcjQmCKISoEEPQRBFhRyNCYKoFGjQQxBEUSFHY4IgKgUa9BAEUVTI0ZggiEqBBj0EQRQVcjQmCKJSoNVbBEEUnbhz8daOfnT6o3DZBHI0Jgii5NCghyCIouO0CbjhvOlYNm8SORoTBFE2aNBDEETJIEdjgiDKCWl6CIIgCIIYF5R10LN27VrMnz8fXq8XTU1NWLZsGfbs2ZN0zuLFi8FxXNLPTTfdlHTOoUOHcOmll8LlcqGpqQm33XYbFEUpZSoEQUD35NnTFSTvHYIgKpKyPt7avHkzVq1ahfnz50NRFHz3u9/FhRdeiN27d8Ptdhvn3XjjjbjrrruM310ul/F/VVVx6aWXorm5Ga+99ho6Oztx7bXXwmq14ic/+UlJ8yGI8QptM0EQRDVQ1kHP888/n/T7o48+iqamJmzfvh3nn3++cdzlcqG5uTllGX/961+xe/du/O1vf8PEiRNx5pln4kc/+hFuv/12/OAHP4DNRh4gBFFs4ttM1LntaK11IhCTsXFXJwDghvOmlzk6giAInYrS9Pj9fgBAXV1d0vF169ahoaEBc+fOxZo1axCJRIzXtmzZgtNOOw0TJ040jl100UUIBAJ47733UtYjiiICgUDSD0EQuUHbTBAEUS1UzOotTdNwyy234OMf/zjmzp1rHL/qqqvQ3t6O1tZWvPvuu7j99tuxZ88ePP300wCArq6upAEPAOP3rq6ulHWtXbsWP/zhD4uUCUGML+LbTLTWOpOO+xxWdPqj6A9JtGKLIIiKoGIGPatWrcKuXbvwyiuvJB3/yle+Yvz/tNNOQ0tLCy644ALs378fM2bMyKmuNWvW4NZbbzV+DwQCaGtryy1wghjnJG4zkTi4oW0mCIKoNCri8dbq1auxYcMG/P3vf8fkyZMznrtgwQIAwL59+wAAzc3N6O7uTjon/ns6HZDdbofP50v6IQgiN2ibCYIgqoWyDnoYY1i9ejWeeeYZbNq0CdOmTRv1mh07dgAAWlpaAACLFi3Czp070dPTY5zzwgsvwOfzYc6cOUWJmyCIZFYsaMfSuS1gjKHTHwVjjLaZIAii4uAYY6xclX/ta1/D+vXr8dxzz2H27NnG8ZqaGjidTuzfvx/r16/HJZdcgvr6erz77rv45je/icmTJ2Pz5s0A9CXrZ555JlpbW3HPPfegq6sL11xzDW644QbTS9YDgQBqamrg9/tp1ocg8qAvJNI2EwRBlIxsv7/LOujhOC7l8UceeQQrV67E4cOHcfXVV2PXrl0Ih8Noa2vD5ZdfjjvuuCMpuYMHD+Lmm2/Giy++CLfbjeuuuw533303LBZzkiUa9BAEQRBE9VFVg55KgQY9BFEZ9AZFDIRppoggCHNk+/1dMau3CIIYv5CjM0EQpaAiVm8RBDG+iTs68zyH1loneJ7Dxl2dWLftYLlDIwhiDEGDHoIgygo5OhMEUSpo0EMQRFmJOzr7HNak4z6HFRFJRX9IKlNkBEGMNWjQQxBEWUl0dE6EHJ0Jgig0NOghCKKskKMzQRClglZvEQRRduLOzVs7+tHpj8JlE8jRmSCIgkODHoIgyo7TJuCG86Zj2bxJ5OhMEETRoEEPQRAVQ4PHToMdgiCKBg16CIIgiLwhN22iGqBBD0EQBJEz5KZNVBO0eosgCILIGXLTJqoJGvQQBEEQOUFu2kS1QYMegiAIIifITZuoNmjQQxAEMUboDYrY0xUs2QwLuWkT1QYJmQmCIKqccomJ427aG3d1AtBneAIxGQNhEUvnttAqLqLioJkegiCIKqecYuIVC9qxdG4LGGPo9EfBGCM3baJioZkegiCIKma4mBiA8e/Wjn4smzepqDMu5KZNVBM000MQBFHFVIqYuMFjx+xmLw14iIqGBj0EQRBVDImJCcI8NOghCIKoYuJi4oGwiL6QCEnR0BcSMRAWsXB6Pc28EEQCpOkhCIKocuKi4a0d/ej0R+GyCSQmJogU0KCHIAiiyiExMUGYgwY9BEEQY4QGj50GOwSRARr0EARB5ElvUMRAmGZYCKLSoUEPQRBEjpTLCZkgiNyg1VsEQRA5Uk4nZIIgsocGPQRBEDkw3AnZZuHR4LGjzm3H1o7+km36SRCEeWjQQxAEkQOV4oRMEIR5aNBDEASRA+SETBDVBw16CIIgcoCckAmi+qDVWwRBEDlCTsgEUV3QoIcgCCJHyAmZIKoLGvQQBEHkCTkhE0R1QJoegiAIgiDGBTToIQiCIAhiXECDHoIgCIIgxgU06CEIgiAIYlxAgx6CIAiCIMYFNOghCIIgCGJcUNZBz9q1azF//nx4vV40NTVh2bJl2LNnT8pzGWNYunQpOI7Ds88+m/TaoUOHcOmll8LlcqGpqQm33XYbFEUpQQYEQRAEQVQLZR30bN68GatWrcLWrVvxwgsvQJZlXHjhhQiHwyPO/fnPfw6O40YcV1UVl156KSRJwmuvvYbHHnsMjz76KL7//e+XIgWCIAiCIKoEjjHGyh1EnN7eXjQ1NWHz5s04//zzjeM7duzApz/9abz55ptoaWnBM888g2XLlgEANm7ciE9/+tM4duwYJk6cCAB44IEHcPvtt6O3txc228hN/0RRhCiKxu+BQABtbW3w+/3w+XzFTZIgCIIgiIIQCARQU1Nj+vu7ojQ9fr8fAFBXV2cci0QiuOqqq3Dfffehubl5xDVbtmzBaaedZgx4AOCiiy5CIBDAe++9l7KetWvXoqamxvhpa2srcCYEQRAEQVQaFTPo0TQNt9xyCz7+8Y9j7ty5xvFvfvObOPfcc3HZZZelvK6rqytpwAPA+L2rqyvlNWvWrIHf7zd+Dh8+XKAsCIIgCIKoVCpm761Vq1Zh165deOWVV4xjf/zjH7Fp0ya8/fbbBa3LbrfDbqd9cgiCIAhiPFERMz2rV6/Ghg0b8Pe//x2TJ082jm/atAn79+9HbW0tLBYLLBZ9jLZ8+XIsXrwYANDc3Izu7u6k8uK/p3ocRhAEQRDE+KSsgx7GGFavXo1nnnkGmzZtwrRp05Je/853voN3330XO3bsMH4A4Gc/+xkeeeQRAMCiRYuwc+dO9PT0GNe98MIL8Pl8mDNnTslyIQiCIAiisinr461Vq1Zh/fr1eO655+D1eg0NTk1NDZxOJ5qbm1PO1kyZMsUYIF144YWYM2cOrrnmGtxzzz3o6urCHXfcgVWrVtEjLIIgCIIgDMo603P//ffD7/dj8eLFaGlpMX6eeOIJ02UIgoANGzZAEAQsWrQIV199Na699lrcddddRYycIAiCIIhqo6wzPblYBKW6pr29HX/+858LERJBEARBEGOUihAyEwRBEARBFBsa9BAEQRAEMS6gQQ9BEARBEOMCGvQQBEEQBDEuoEEPQRAEQRDjAhr0EARBEAQxLqBBD0EQBEEQ4wIa9BAEQRAEMS6gQQ9BEARBEOMCGvQQBEEQBDEuoEEPQRAEQRDjAhr0EARBEAQxLqBBD0EQBEEQ4wIa9BAEQRAEMS6wlDsAgiDS0xsUMRCWUO+xocFjL3c4BEFUMfR5QoMegqhIIpKC9dsOYcv+fkQkFS6bgEUz6rFiQTucNqHc4REEUUXQ58kJ6PEWQVQg67cdwsZdneB5Dq21TvA8h427OrFu28Fyh0YQRJVBnycnoEEPQVQYvUERW/b3o85tR4PHDpuFR4PHjjq3HVs7+tEXEssdIkEQVQJ9niRDgx6CqDAGwhIikgqfw5p03OewIiKp6A9JZYqMIIhqgz5PkqFBD0FUGHVuG1w2AYGYnHQ8EJPhsgmo99jKFBlBENUGfZ4kQ4MegqgwGr12LJpRj4GwiL6QCEnR0BcSMRAWsXB6/bhddUEQRPbQ50kytHqLICqQFQvaAQBbO/rR6Y/CZROwdG6LcZwgCMIs9HlyAo4xxsodRLkJBAKoqamB3++Hz+crdzgEYdAXEtEfGt++GgRBFIax+HmS7fc3zfQQRAXT4LGPmQ8ngiDKC32ekKaHIAiCIIhxAg16CIIgCIIYF9CghyAIgiCIcQENegiCIAiCGBfQoIcgCIIgiHEBDXoIgiAIghgX0KCHIAiCIIhxAQ16CIIgCIIYF9CghyAIgiCIcQENegiCIAiCGBfQNhQA4tuPBQKBMkdCEARBEIRZ4t/bZrcRpUEPgGAwCABoa2srcyQEQRAEQWRLMBhETU3NqOfRLusANE3DsWPH4PV6wXHcqOcHAgG0tbXh8OHDY35X9vGSK+U5thgveQLjJ1fKc2xRqDwZYwgGg2htbQXPj67YoZkeADzPY/LkyVlf5/P5xvRNmch4yZXyHFuMlzyB8ZMr5Tm2KESeZmZ44pCQmSAIgiCIcQENegiCIAiCGBfQoCcH7HY77rzzTtjt9nKHUnTGS66U59hivOQJjJ9cKc+xRbnyJCEzQRAEQRDjAprpIQiCIAhiXECDHoIgCIIgxgU06CEIgiAIYlxAgx6CIAiCIMYF43bQc99992Hq1KlwOBxYsGABXn/99YznP/nkkzj55JPhcDhw2mmn4c9//rPxmizLuP3223HaaafB7XajtbUV1157LY4dO5ZUxtSpU8FxXNLP3XffXZT84hQyTwBYuXLliBwuvvjipHMGBgawYsUK+Hw+1NbW4vrrr0coFCp4bokUOs/hOcZ/7r33XuOcSu/P9957D8uXLzfi/PnPf55TmbFYDKtWrUJ9fT08Hg+WL1+O7u7uQqaVdUyJmMlz7dq1mD9/PrxeL5qamrBs2TLs2bMn6ZzFixeP6M+bbrqp0KmNoNC5/uAHPxiRx8knn5x0zljo01TvP47jsGrVKuOccvRpNnk++OCDOO+88zBhwgRMmDABS5YsGXE+Ywzf//730dLSAqfTiSVLluDDDz9MOqfSP3NHy7Ok36FsHPK73/2O2Ww29vDDD7P33nuP3Xjjjay2tpZ1d3enPP/VV19lgiCwe+65h+3evZvdcccdzGq1sp07dzLGGBscHGRLlixhTzzxBPvggw/Yli1b2Mc+9jF29tlnJ5XT3t7O7rrrLtbZ2Wn8hEKhqsmTMcauu+46dvHFFyflMDAwkFTOxRdfzM444wy2detW9vLLL7OZM2eyL37xi1WVZ2J+nZ2d7OGHH2Ycx7H9+/cb51R6f77++uvsX/7lX9jjjz/Ompub2c9+9rOcyrzppptYW1sb+7//+z/25ptvsoULF7Jzzz23WGkWJc+LLrqIPfLII2zXrl1sx44d7JJLLmFTpkxJ6q9PfvKT7MYbb0zqT7/fX6w0GWPFyfXOO+9kp556alIevb29SeeMhT7t6elJyvGFF15gANjf//5345xS92m2eV511VXsvvvuY2+//TZ7//332cqVK1lNTQ07cuSIcc7dd9/Nampq2LPPPsveeecd9tnPfpZNmzaNRaNR45xK/8wdLc9SfoeOy0HPxz72MbZq1Srjd1VVWWtrK1u7dm3K86+88kp26aWXJh1bsGAB++pXv5q2jtdff50BYAcPHjSOtbe3p3zzFoti5Hndddexyy67LG2du3fvZgDYG2+8YRzbuHEj4ziOHT16NMdMMlOK/rzsssvYpz71qaRjld6fiaSLdbQyBwcHmdVqZU8++aRxzvvvv88AsC1btuSRTXqKkedwenp6GAC2efNm49gnP/lJ9o1vfCOXkHOmGLneeeed7Iwzzkh73Vjt02984xtsxowZTNM041ip+zSfPBljTFEU5vV62WOPPcYYY0zTNNbc3Mzuvfde45zBwUFmt9vZ448/zhirjs/c4QzPMxXF+g4dd4+3JEnC9u3bsWTJEuMYz/NYsmQJtmzZkvKaLVu2JJ0PABdddFHa8wHA7/eD4zjU1tYmHb/77rtRX1+PefPm4d5774WiKLknk4Fi5vniiy+iqakJs2fPxs0334z+/v6kMmpra3HOOecYx5YsWQKe57Ft27ZCpJZEKfqzu7sbf/rTn3D99dePeK2S+7MQZW7fvh2yLCedc/LJJ2PKlCk515tvTIXA7/cDAOrq6pKOr1u3Dg0NDZg7dy7WrFmDSCRSsDqHU8xcP/zwQ7S2tmL69OlYsWIFDh06ZLw2FvtUkiT89re//f/bu++gKM43DuBfDjwQjiIgTYoKYkFxIlEEEzCjMWI0iBrBaNSoiMaCBQPoRKMkyGSINYlxEgRbxIo6ZmwIJNhHA8EGCqKO8S4aFMFYaM/vD+f2x7IHHM12z2dmZ7j3ffetd7cve/vuYtKkSZKHRr+oMW2Odj5+/Bjl5eXC+7KwsBAqlUqUp7m5Oby9vYU8X4fv3JpqtlOTljqG6twDR//9919UVlbC1tZWFG5ra4vc3FyN+6hUKo3pVSqVxvRPnz5FZGQkxowZI3qQ2uzZs9GrVy9YWlri5MmTiI6OhlKpxIoVK5rYKqmWaufgwYMxYsQIdOjQAQUFBVi4cCECAgJw6tQp6OvrQ6VSwcbGRpSHgYEBLC0ta+2vpngR47lx40aYmppixIgRovBXfTybI0+VSgW5XC754qmrv5qiJdpZU1VVFebMmYN+/fqhe/fuQvgnn3wCFxcXODg4ICcnB5GRkcjLy8OePXuapdyaWqqt3t7eSEpKQufOnaFUKrF06VK8++67uHjxIkxNTd/IMd27dy+Ki4sxceJEUfiLHNPmaGdkZCQcHByECYV6POr6vnodvnNrqtnOmlryGKpzk56WVl5ejtGjR4OIsG7dOlHcvHnzhL89PT0hl8sRFhaG5cuXvza3HA8JCRH+7tGjBzw9PeHq6oqMjAwMGDDgJdas5WzYsAFjx46FkZGRKPxNGE9dNGPGDFy8eBHHjx8XhU+dOlX4u0ePHrC3t8eAAQNQUFAAV1fXF13NRgsICBD+9vT0hLe3N1xcXLBjxw6NZyvfBAkJCQgICICDg4Mo/HUa07i4OCQnJyMjI0PyXfMmqa+dLX0M1bmft6ytraGvry9ZqfDPP//Azs5O4z52dnZapVcP1s2bN3H06FHRDFUTb29vVFRU4MaNGw1vSD1asp3VdezYEdbW1sjPzxfyuHv3rihNRUUF7t+/X2c+jdXS7czMzEReXh6mTJlSb11etfFsjjzt7OxQVlaG4uLiZiu3qXVqipkzZ+LAgQNIT0+Ho6NjnWm9vb0BQHhvN7eWbquahYUF3N3dRZ/RN2lMb968idTUVK0/o0DLjGlT2hkfH4+4uDgcOXIEnp6eQrh6v/o+o6/6d65abe1UexHHUJ2b9Mjlcnh5eeHYsWNCWFVVFY4dOwYfHx+N+/j4+IjSA8DRo0dF6dWDde3aNaSmpsLKyqreumRnZ0Mmk0lOTTaHlmpnTbdv30ZRURHs7e2FPIqLi3H+/HkhTVpaGqqqqoQvnObU0u1MSEiAl5cXevbsWW9dXrXxbI48vby80KpVK1GavLw83Lp1q9HlNrVOjUFEmDlzJlJSUpCWloYOHTrUu092djYACO/t5tZSba3p0aNHKCgoENrxpoypWmJiImxsbPDhhx/Wm7Ylx7Sx7fz2228RExODQ4cOia7LAYAOHTrAzs5OlGdJSQnOnDkj5Pk6fOcCdbcTeIHH0CZdBv2aSk5OJkNDQ0pKSqLLly/T1KlTycLCglQqFRERffrppxQVFSWkP3HiBBkYGFB8fDxduXKFlixZIlriXFZWRh999BE5OjpSdna2aDnds2fPiIjo5MmTtHLlSsrOzqaCggLasmULtW3blsaPH//atLO0tJQiIiLo1KlTVFhYSKmpqdSrVy/q1KkTPX36VMhn8ODB9NZbb9GZM2fo+PHj1KlTpxZfPtmc7VR7+PAhGRsb07p16yRlvg7j+ezZM8rKyqKsrCyyt7eniIgIysrKomvXrmmdJ9Hz5c3Ozs6UlpZG586dIx8fH/Lx8Xmt2jl9+nQyNzenjIwM0efz8ePHRESUn59Py5Yto3PnzlFhYSHt27ePOnbsSH5+fi3WzpZq6/z58ykjI4MKCwvpxIkTNHDgQLK2tqa7d+8Kad6EMSV6vmrI2dmZIiMjJWW+jDFtaDvj4uJILpfTrl27RO/L0tJSURoLCwvat28f5eTkUGBgoMYl66/yd2597XyRx1CdnPQQEa1du5acnZ1JLpdTnz596PTp00Kcv78/TZgwQZR+x44d5O7uTnK5nDw8POi3334T4goLCwmAxk19z4jz58+Tt7c3mZubk5GREXXt2pViY2NFk4VXvZ2PHz+mQYMGUdu2balVq1bk4uJCoaGhogMkEVFRURGNGTOGFAoFmZmZ0WeffSb6EL/q7VRbv349tW7dmoqLiyVxr8N41va+9Pf31zpPIqInT57Q559/Tm3atCFjY2MKCgoipVLZks1s9nbW9vlMTEwkIqJbt26Rn58fWVpakqGhIbm5udGCBQta/D49LdHW4OBgsre3J7lcTu3ataPg4GDKz88XlfkmjCkR0eHDhwkA5eXlScp7WWPakHa6uLhobOeSJUuENFVVVfTll1+Sra0tGRoa0oABAyTtfdW/c+tr54s8huoREWl/Xogxxhhj7PWkc9f0MMYYY0w38aSHMcYYYzqBJz2MMcYY0wk86WGMMcaYTuBJD2OMMcZ0Ak96GGOMMaYTeNLDGGOMMZ3Akx7GGGOM6QSe9DDGGGNMJ/Ckh7FaTJw4EXp6epItPz+/1rjBgwcL+7dv314Ib926Ndq3b4/Ro0cjLS1NVM6NGzc05qWnp4fTp08DAJKSkoQwmUwGe3t7BAcH49atWxrr3qVLFxgaGkKlUkni+vfvjzlz5jSqT6rXzczMDL1798a+fftEaarXtfpmZGQEABg2bJion6rLzMyEnp4ecnJyGtQvNfMrLi6Gnp4eMjIyAPy/j9UPnKyrP6qPW/UtLi6u3v6pWWcrKysMGjQIWVlZovKq94m7uzuWL18OTTfH37hxI3r37g1jY2OYmprC398fBw4cEKXJyMgQlWlra4uRI0fi+vXronQnT57EkCFD0KZNGxgZGaFHjx5YsWIFKisrRenqGuPqdde09e/fX+jDVatWNbp8IyMj3Lx5UxQ+fPhwTJw4sd4xYKwuPOlhrA6DBw+GUqkUbeondGuK27Ztm2j/ZcuWQalUIi8vD5s2bYKFhQUGDhyIb775RlJWamqqJD8vLy8h3szMDEqlEn///Td2796NvLw8fPzxx5J8jh8/jidPnmDUqFHYuHFjM/fI86daK5VKnDt3Dv369cOoUaNw4cIFURp1Xatv6oPY5MmTcfToUdy+fVtj3m+//TY8PT2FsPr6xcDAAKmpqUhPT2+2NqrHrfo2a9YsrfdX1/nw4cN49OgRAgICUFxcLMSHhoYK74vo6GgsXrwYP/30kyiPiIgIhIWFITg4GDk5OTh79izeeecdBAYG4vvvv5eUmZeXhzt37mDnzp24dOkShg0bJkwoUlJS4O/vD0dHR6SnpyM3Nxfh4eH4+uuvERISIplw1TbGe/bsEfrj7NmzorYqlUrs2bNHY380tHw9PT0sXrxY6/5mTGtNeN4YY2+0CRMmUGBgYIPj1FxcXGjlypWS8MWLF5NMJqPc3Fwi+v/D9rKysmrNKzExkczNzUVha9asIQCSByhOnDiRoqKi6ODBg+Tu7i7Jy9/fn8LDw+use20AUEpKivC6pKSEANDq1avrrGt15eXlZGtrSzExMaLw0tJSUigUwlPtG9IvoaGh1KdPHyH8wYMHoocV1pVXzf6obdy0oamcEydOEAA6dOiQxvKIiHr16kVBQUHC61OnThEAWrNmjaSMefPmUatWrejWrVtERJSenk4A6MGDB0KarVu3EgDKzc2lR48ekZWVFY0YMUKS1/79+wkAJScnC2HajHFtbVWr3oeNKT8iIoJkMhlduHBBCA8MDJQ8OJixhuIzPYy9YOHh4SAiyc9CDXH37l2kpKRAX18f+vr6QnhpaSl27tyJcePG4f3338fDhw+RmZnZHNWWqKioQEJCAgBALpdrvZ+BgQHGjx+PpKQk0X/4O3fuRGVlJcaMGdPgunz11Ve4cOECdu3a1eB9W1rr1q0BAGVlZZI4IkJmZiZyc3NFfbht2zYoFAqEhYVJ9pk/fz7Ky8uxe/durco8cuQIioqKEBERIUk3bNgwuLu7S85QqjV2jKtrTPn9+vXD0KFDERUV1agyGasNT3oYq8OBAwegUCiErfrPSTXjFAoFYmNj683T0tISNjY2uHHjhijc19dXkl91Dx8+hEKhgImJCWxtbZGeno4ZM2bAxMRESJOcnIxOnTrBw8MD+vr6CAkJEQ5azWXMmDFQKBQwNDTE3LlzhWuVNNW1+hYQECDET5o0CQUFBfj999+FsMTERIwcORLm5uaivOrrFwBwcHBAeHg4Fi1ahIqKiia3MTIyUlJmYyaPxcXFiImJgUKhQJ8+fYTwH3/8UehDPz8/VFVVYfbs2UL81atX4erqqnGi4eDgADMzM1y9elVjmUqlEvHx8WjXrh06d+4spOvatavG9F26dJHkpc0Ya6sx5QPA8uXLcejQoRabtDPdZPCyK8DYq+y9997DunXrhNfVJxg144DnExptEBH09PREYdu3b6/1wAAApqam+PPPP1FeXo6DBw9i69atkmuDNmzYgHHjxgmvx40bB39/f6xduxampqZa1a0+K1euxMCBA3H9+nXMnTsXa9askbRbXdfq1GcfgOcHOl9fX2zYsAH9+/dHfn4+MjMzsWzZMkl59fWLWmRkJNavX48NGzY0+gCttmDBAslFs+3atdN6f19fX8hkMvz333/o2LEjtm/fDltbWyF+7NixWLRoER48eIAlS5bA19cXvr6+ojxIw4XNdXF0dAQR4fHjx+jZsyd2794tmjQ1JD9txrihGtqebt26Yfz48YiKisKJEyeaVDZjajzpYawOJiYmcHNza3BcXYqKinDv3j3hgmg1JyenOvOTyWRCfNeuXVFQUIDp06dj8+bNAIDLly/j9OnTOHv2LCIjI4X9KisrkZycjNDQ0AbXVRM7Ozu4ubnBzc0NiYmJGDJkCC5fvgwbGxuNda3N5MmTMWvWLPzwww9ITEyEq6sr/P39Jenq6xc1CwsLREdHY+nSpRg6dKgozszMDMDzM1A1FRcXS84uWVtbN2ps1bZv345u3brBysoKFhYWknhzc3Mh/x07dsDNzQ19+/bFwIEDAQDu7u44fvw4ysrKJGd77ty5g5KSEri7u4vCMzMzYWZmBhsbG9EEV53uypUrkomVOrxbt26iMG3GWFuNKV9t6dKlcHd3x969extcLmOa8M9bjL1gq1evhkwmw/Dhw5uUT1RUFLZv3y6cUUlISICfnx/++usvZGdnC9u8efOa/ScutT59+sDLy0vjarT6jB49GjKZDL/++is2bdqESZMmSc5+NdSsWbMgk8mwevVqUbilpSWsra1x/vx5UXhJSQny8/MlE4imcnJygqurq8YJT00KhQLh4eGIiIgQzoaEhITg0aNHWL9+vSR9fHw8WrVqhZEjR4rCO3ToAFdXV8kZvUGDBsHS0hLfffedJK/9+/fj2rVrdV5H1ZQxbmr5Tk5OmDlzJhYuXChZ2s5YY/CZHsYa6dmzZ5L74BgYGMDa2lp4XVpaCpVKhfLychQWFmLLli345ZdfsHz5csmZhKKiIkl+FhYWwv1tanJyckJQUBAWL16MlJQUbN68GcuWLUP37t1F6aZMmYIVK1bg0qVL8PDwAADcu3dPcs8ae3t70U8w2pozZw6CgoLwxRdfCD8BEZHGewTZ2NhAJnv+v5ZCoUBwcDCio6NRUlJS6z1YGtIvRkZGWLp0KWbMmCGJmzdvHmJjY2Fra4u+ffuiqKgIMTExaNu2LUaMGCFKqx636oyNjYUzRs0tLCwMMTEx2L17N0aNGgUfHx+Eh4djwYIFKCsrw/Dhw1FeXo4tW7Zg9erVWLVqFZycnLTK28TEBOvXr0dISAimTp2KmTNnwszMDMeOHcOCBQswatSoen8O1DTG2mpq+dHR0fj5559RWFiI4ODgBpXNmMTLWjbG2KuuviXrACRb586dhTQuLi5CuFwuJ2dnZxo9ejSlpaWJ8lIv/dW0bdu2jYhqXwauXtocFxdHMpmMVCqVxvp27dqV5s6dS0TPl0xrKqvmEnJNUGM5MxFRVVUVdenShaZPny7Utbb2KJVK0b4nT54kADRkyBBJWY3tl4qKCurWrZtoybo6fM2aNdSjRw8yNjYmR0dHCg4OpsLCQtH+1cet+hYWFlZv/2izzL62WwaEhYWRh4cHVVZWCmEJCQnk5eVFRkZGZGJiQu+++y7t379ftJ+mJeua/PHHH/TBBx+QmZkZyeVy8vDwoPj4eKqoqBCl02aM62urpmX/TSk/NjaWAPCSddZkekQNvLqMMcYYY+w1xNf0MMYYY0wn8KSHMQYAiI2NldybRtM9dnTZtGnTau2jadOmvezqMcbqwT9vMcYAAPfv38f9+/c1xrVu3brBF7C+ie7evYuSkhKNcerl4oyxVxdPehhjjDGmE/jnLcYYY4zpBJ70MMYYY0wn8KSHMcYYYzqBJz2MMcYY0wk86WGMMcaYTuBJD2OMMcZ0Ak96GGOMMaYT/gc+Ly3wnT6WywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"FEDERAL_REVENUE_PROPORTION\", y=\"AVG_MATH_8_SCORE\", alpha=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also showed a weak correlation between states who require a greater proportion of federal revenue resulting in lower test scores. However, this correlation is lower than I would've expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Data Creation </h2>\n",
    "\n",
    "_Use this space to create train/test data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"FEDERAL_REVENUE_PROPORTION\", \"INSTRUCTION_EXPENDITURE_PROPORTION\"]].dropna()\n",
    "y = df.loc[X.index][\"AVG_MATH_8_SCORE\"]\n",
    "y.fillna(y.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Prediction </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ML Models [Resource](https://medium.com/@vijaya.beeravalli/comparison-of-machine-learning-classification-models-for-credit-card-default-data-c3cf805c9a5a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create your model here\n",
    "model = LinearRegression() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose some metrics to evaluate the performance of your model, some of them are mentioned in the tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.037270978145216516"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R^2 Value\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have copied over the graphs that visualize the model's performance on the training and testing set. \n",
    "\n",
    "Change `col_name` and modify the call to `plt.ylabel()` to isolate how a single predictor affects the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Behavior On Training Set')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_name = \"FEDERAL_REVENUE_PROPORTION\"\n",
    "\n",
    "f = plt.figure(figsize=(12,6))\n",
    "plt.scatter(X_train[col_name], y_train, color = \"red\")\n",
    "plt.scatter(X_train[col_name], model.predict(X_train), color = \"green\")\n",
    "\n",
    "plt.legend(['True Training','Predicted Training'])\n",
    "plt.xlabel(col_name)\n",
    "plt.ylabel(\"Federal Revenue Proportion\")\n",
    "plt.title(\"Model Behavior On Training Set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Behavior on Testing Set')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_name = \"FEDERAL_REVENUE_PROPORTION\"\n",
    "\n",
    "f = plt.figure(figsize=(12,6))\n",
    "plt.scatter(X_test[col_name], y_test, color = \"blue\", alpha = 0.6)\n",
    "plt.scatter(X_test[col_name], model.predict(X_test), color = \"black\")\n",
    "\n",
    "plt.legend(['True testing','Predicted testing'])\n",
    "plt.xlabel(col_name)\n",
    "plt.ylabel('Federal Revenue Proportion')\n",
    "plt.title(\"Model Behavior on Testing Set\")"
   ]
  },
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   "source": [
    "<h2> Summary </h2>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Overall I found that the model that I made was, quite frankly, terrible at predicting the math score based on the federal revenue proportion. I created the new data point that I hoped would prove that lower-income states on average produce poorer test results. This was done by finding the proportion of revenue that was Federal (rather than Local or State). As a result, this created a very weak proportion that was unable to create a reliable model. One reason for this may be that my method of determining a low-income state was not accurate. Another possible explanation is that this correlation is not as great as I thought, and year-to-year revenues were not reliable for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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