{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "9c4785f9-5e5a-422c-b63c-a2a15a2a4302",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e92204b-3b8c-4cc2-afef-52c8ddff0223",
   "metadata": {},
   "outputs": [
    {
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       "<p>300 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "              x          y\n",
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       "1      0.334448  20.234114\n",
       "2      0.668896  20.468227\n",
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       "298   99.665552  89.765886\n",
       "299  100.000000  90.000000\n",
       "\n",
       "[300 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "x = np.linspace(0, 100, 300)\n",
    "a = 0.7\n",
    "b = 20\n",
    "y = a*x + b\n",
    "data = {'x': x, 'y': y}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a161a0de-1d15-4626-908d-bdc8d7a41150",
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   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              x          y  x_in_std  y_in_std\n",
       "0      0.000000  20.000000 -1.723407 -1.723407\n",
       "1      0.334448  20.234114 -1.711880 -1.711880\n",
       "2      0.668896  20.468227 -1.700352 -1.700352\n",
       "3      1.003344  20.702341 -1.688824 -1.688824\n",
       "4      1.337793  20.936455 -1.677296 -1.677296\n",
       "..          ...        ...       ...       ...\n",
       "295   98.662207  89.063545  1.677296  1.677296\n",
       "296   98.996656  89.297659  1.688824  1.688824\n",
       "297   99.331104  89.531773  1.700352  1.700352\n",
       "298   99.665552  89.765886  1.711880  1.711880\n",
       "299  100.000000  90.000000  1.723407  1.723407\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_x = df['x'].mean()\n",
    "sd_x = df['x'].std()\n",
    "mean_y = df['y'].mean()\n",
    "sd_y = df['y'].std()\n",
    "\n",
    "df1 = df.assign(x_in_std = (df['x'] - mean_x)/sd_x)\n",
    "df2 = df1.assign(y_in_std = (df['y'] - mean_y)/sd_y)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a1bea50-93c7-4048-9f3c-60fcfb856a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9966666666666667\n"
     ]
    }
   ],
   "source": [
    "print((df2['x_in_std']*df2['y_in_std']).sum()/df2.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "733ff687-3998-4a8f-b352-10e2dd2c31b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>300 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              x          y\n",
       "0      0.000000  20.000000\n",
       "1      0.334448  20.234114\n",
       "2      0.668896  20.468227\n",
       "3      1.003344  20.702341\n",
       "4      1.337793  20.936455\n",
       "..          ...        ...\n",
       "295   98.662207  89.063545\n",
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       "297   99.331104  89.531773\n",
       "298   99.665552  89.765886\n",
       "299  100.000000  90.000000\n",
       "\n",
       "[300 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b91677c0-fe1c-4fd0-92b8-29a01e9d7761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = df['x'].corr(df['y'])\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a731df9a-71a1-402d-9cba-7bbd514e469d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.688824</td>\n",
       "      <td>1.688824</td>\n",
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       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>99.331104</td>\n",
       "      <td>89.531773</td>\n",
       "      <td>1.700352</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>99.665552</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
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       "      <td>90.000000</td>\n",
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       "      <td>1.723407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              x          y  x_in_std  y_in_std\n",
       "0      0.000000  20.000000 -1.723407 -1.723407\n",
       "1      0.334448  20.234114 -1.711880 -1.711880\n",
       "2      0.668896  20.468227 -1.700352 -1.700352\n",
       "3      1.003344  20.702341 -1.688824 -1.688824\n",
       "4      1.337793  20.936455 -1.677296 -1.677296\n",
       "..          ...        ...       ...       ...\n",
       "295   98.662207  89.063545  1.677296  1.677296\n",
       "296   98.996656  89.297659  1.688824  1.688824\n",
       "297   99.331104  89.531773  1.700352  1.700352\n",
       "298   99.665552  89.765886  1.711880  1.711880\n",
       "299  100.000000  90.000000  1.723407  1.723407\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7eac3ee7-41fc-4561-89a2-ccba424642db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10c653190>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize = (5, 5))\n",
    "ax.scatter(df2['x_in_std'], df2['y_in_std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d953c3f-a76c-4d70-b5d5-4ca3f25a366f",
   "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>family</th>\n",
       "      <th>father</th>\n",
       "      <th>mother</th>\n",
       "      <th>midparentHeight</th>\n",
       "      <th>children</th>\n",
       "      <th>childNum</th>\n",
       "      <th>sex</th>\n",
       "      <th>childHeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>75.5</td>\n",
       "      <td>66.5</td>\n",
       "      <td>73.66</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>934 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    family  father  mother  midparentHeight  children  childNum     sex  \\\n",
       "0        1    78.5    67.0            75.43         4         1    male   \n",
       "1        1    78.5    67.0            75.43         4         2  female   \n",
       "2        1    78.5    67.0            75.43         4         3  female   \n",
       "3        1    78.5    67.0            75.43         4         4  female   \n",
       "4        2    75.5    66.5            73.66         4         1    male   \n",
       "..     ...     ...     ...              ...       ...       ...     ...   \n",
       "929    203    62.0    66.0            66.64         3         1    male   \n",
       "930    203    62.0    66.0            66.64         3         2  female   \n",
       "931    203    62.0    66.0            66.64         3         3  female   \n",
       "932    204    62.5    63.0            65.27         2         1    male   \n",
       "933    204    62.5    63.0            65.27         2         2  female   \n",
       "\n",
       "     childHeight  \n",
       "0           73.2  \n",
       "1           69.2  \n",
       "2           69.0  \n",
       "3           69.0  \n",
       "4           73.5  \n",
       "..           ...  \n",
       "929         64.0  \n",
       "930         62.0  \n",
       "931         61.0  \n",
       "932         66.5  \n",
       "933         57.0  \n",
       "\n",
       "[934 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('family_heights.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b28c4530-8be7-4104-8b32-c9f5717d31e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10c6d9730>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize = (5, 5))\n",
    "ax.scatter(data['midparentHeight'], data['childHeight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9a7b45aa-2545-46fc-b78d-e0ecb4635167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.32094989606395924)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = data['midparentHeight'].corr(data['childHeight'])\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bf5000b7-f0ea-47b6-b5c3-72392fdb5ed1",
   "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>family</th>\n",
       "      <th>father</th>\n",
       "      <th>mother</th>\n",
       "      <th>midparentHeight</th>\n",
       "      <th>children</th>\n",
       "      <th>childNum</th>\n",
       "      <th>sex</th>\n",
       "      <th>childHeight</th>\n",
       "      <th>midp_in_std</th>\n",
       "      <th>child_in_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.2</td>\n",
       "      <td>3.452802</td>\n",
       "      <td>1.803190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>69.2</td>\n",
       "      <td>3.452802</td>\n",
       "      <td>0.685637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3.452802</td>\n",
       "      <td>0.629760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3.452802</td>\n",
       "      <td>0.629760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>75.5</td>\n",
       "      <td>66.5</td>\n",
       "      <td>73.66</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.5</td>\n",
       "      <td>2.470762</td>\n",
       "      <td>1.887006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>64.0</td>\n",
       "      <td>-1.424110</td>\n",
       "      <td>-0.767180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>-1.424110</td>\n",
       "      <td>-1.325957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>-1.424110</td>\n",
       "      <td>-1.605345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>66.5</td>\n",
       "      <td>-2.184220</td>\n",
       "      <td>-0.068710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>57.0</td>\n",
       "      <td>-2.184220</td>\n",
       "      <td>-2.722897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>934 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    family  father  mother  midparentHeight  children  childNum     sex  \\\n",
       "0        1    78.5    67.0            75.43         4         1    male   \n",
       "1        1    78.5    67.0            75.43         4         2  female   \n",
       "2        1    78.5    67.0            75.43         4         3  female   \n",
       "3        1    78.5    67.0            75.43         4         4  female   \n",
       "4        2    75.5    66.5            73.66         4         1    male   \n",
       "..     ...     ...     ...              ...       ...       ...     ...   \n",
       "929    203    62.0    66.0            66.64         3         1    male   \n",
       "930    203    62.0    66.0            66.64         3         2  female   \n",
       "931    203    62.0    66.0            66.64         3         3  female   \n",
       "932    204    62.5    63.0            65.27         2         1    male   \n",
       "933    204    62.5    63.0            65.27         2         2  female   \n",
       "\n",
       "     childHeight  midp_in_std  child_in_std  \n",
       "0           73.2     3.452802      1.803190  \n",
       "1           69.2     3.452802      0.685637  \n",
       "2           69.0     3.452802      0.629760  \n",
       "3           69.0     3.452802      0.629760  \n",
       "4           73.5     2.470762      1.887006  \n",
       "..           ...          ...           ...  \n",
       "929         64.0    -1.424110     -0.767180  \n",
       "930         62.0    -1.424110     -1.325957  \n",
       "931         61.0    -1.424110     -1.605345  \n",
       "932         66.5    -2.184220     -0.068710  \n",
       "933         57.0    -2.184220     -2.722897  \n",
       "\n",
       "[934 rows x 10 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_x = data['midparentHeight'].mean()\n",
    "sd_x = data['midparentHeight'].std()\n",
    "mean_y = data['childHeight'].mean()\n",
    "sd_y = data['childHeight'].std()\n",
    "\n",
    "data1 = data.assign(midp_in_std = (data['midparentHeight'] - mean_x)/sd_x)\n",
    "data2 = data1.assign(child_in_std = (data['childHeight'] - mean_y)/sd_y)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8a61f71c-b6e7-43a8-a9b3-650087c331a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10dd99400>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize = (5, 5))\n",
    "ax.scatter(data2['midp_in_std'], data2['child_in_std'])\n",
    "l = np.linspace(-4, 4, 100)\n",
    "ax.plot(l, 1*l, color = 'green', linewidth = 3)\n",
    "ax.plot(l, r*l, color = 'red', linewidth = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55c0c6e-d82b-4798-b133-9c2738327211",
   "metadata": {},
   "source": [
    "##  Building a predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "62b923c1-1b69-4264-818f-dda67f1d42fe",
   "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>family</th>\n",
       "      <th>father</th>\n",
       "      <th>mother</th>\n",
       "      <th>midparentHeight</th>\n",
       "      <th>children</th>\n",
       "      <th>childNum</th>\n",
       "      <th>sex</th>\n",
       "      <th>childHeight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>75.5</td>\n",
       "      <td>66.5</td>\n",
       "      <td>73.66</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>934 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    family  father  mother  midparentHeight  children  childNum     sex  \\\n",
       "0        1    78.5    67.0            75.43         4         1    male   \n",
       "1        1    78.5    67.0            75.43         4         2  female   \n",
       "2        1    78.5    67.0            75.43         4         3  female   \n",
       "3        1    78.5    67.0            75.43         4         4  female   \n",
       "4        2    75.5    66.5            73.66         4         1    male   \n",
       "..     ...     ...     ...              ...       ...       ...     ...   \n",
       "929    203    62.0    66.0            66.64         3         1    male   \n",
       "930    203    62.0    66.0            66.64         3         2  female   \n",
       "931    203    62.0    66.0            66.64         3         3  female   \n",
       "932    204    62.5    63.0            65.27         2         1    male   \n",
       "933    204    62.5    63.0            65.27         2         2  female   \n",
       "\n",
       "     childHeight  \n",
       "0           73.2  \n",
       "1           69.2  \n",
       "2           69.0  \n",
       "3           69.0  \n",
       "4           73.5  \n",
       "..           ...  \n",
       "929         64.0  \n",
       "930         62.0  \n",
       "931         61.0  \n",
       "932         66.5  \n",
       "933         57.0  \n",
       "\n",
       "[934 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ddf5dc38-9746-468c-a0d7-e22403f95100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>family</th>\n",
       "      <th>father</th>\n",
       "      <th>mother</th>\n",
       "      <th>midparentHeight</th>\n",
       "      <th>children</th>\n",
       "      <th>childNum</th>\n",
       "      <th>sex</th>\n",
       "      <th>childHeight</th>\n",
       "      <th>predicted_height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.2</td>\n",
       "      <td>70.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
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       "      <td>female</td>\n",
       "      <td>69.2</td>\n",
       "      <td>70.100000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>70.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>70.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>75.5</td>\n",
       "      <td>66.5</td>\n",
       "      <td>73.66</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>73.5</td>\n",
       "      <td>70.415789</td>\n",
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       "      <th>...</th>\n",
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       "      <th>929</th>\n",
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       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>64.0</td>\n",
       "      <td>65.156579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>65.156579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>203</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.64</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.156579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>66.5</td>\n",
       "      <td>64.229630</td>\n",
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       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>204</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.0</td>\n",
       "      <td>65.27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>57.0</td>\n",
       "      <td>64.229630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>934 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    family  father  mother  midparentHeight  children  childNum     sex  \\\n",
       "0        1    78.5    67.0            75.43         4         1    male   \n",
       "1        1    78.5    67.0            75.43         4         2  female   \n",
       "2        1    78.5    67.0            75.43         4         3  female   \n",
       "3        1    78.5    67.0            75.43         4         4  female   \n",
       "4        2    75.5    66.5            73.66         4         1    male   \n",
       "..     ...     ...     ...              ...       ...       ...     ...   \n",
       "929    203    62.0    66.0            66.64         3         1    male   \n",
       "930    203    62.0    66.0            66.64         3         2  female   \n",
       "931    203    62.0    66.0            66.64         3         3  female   \n",
       "932    204    62.5    63.0            65.27         2         1    male   \n",
       "933    204    62.5    63.0            65.27         2         2  female   \n",
       "\n",
       "     childHeight  predicted_height  \n",
       "0           73.2         70.100000  \n",
       "1           69.2         70.100000  \n",
       "2           69.0         70.100000  \n",
       "3           69.0         70.100000  \n",
       "4           73.5         70.415789  \n",
       "..           ...               ...  \n",
       "929         64.0         65.156579  \n",
       "930         62.0         65.156579  \n",
       "931         61.0         65.156579  \n",
       "932         66.5         64.229630  \n",
       "933         57.0         64.229630  \n",
       "\n",
       "[934 rows x 9 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_avg_ht(h):\n",
    "    return(data[abs(data['midparentHeight'] - h) <= 0.5]['childHeight'].mean())\n",
    "\n",
    "# Let's build our predictor\n",
    "predicted_data = data.assign(predicted_height = data['midparentHeight'].apply(find_avg_ht))\n",
    "predicted_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "953fd4d5-39b4-4ded-a1e2-c03abba70ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10de31850>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RhE5ig70O9aYXlRyKv+rblv7f/za7ItVhVietBXvGrveZleNZRW96x4tjuilZ45b0ilcyN0bHY09IvUTBWvCghftvGBmkIfcTi8weGJGB0E1RsOt8ksF2/r6bEiN2VJKdLNi7ocqsh945y7S7lfq4LVnj1tSVlzI3bsH9m/tvWEmJkvuJRTuGt7VA3GI2RcEjHZkpDDenOvTK8mrB3srxBnZpIkYQRsjItxi1F393ksY2Pu6d/dvVOr4XkjVuXc9YytzwyER2NMbPe27LMMaR+Q1kXEBcZvawUo7VOt09/Vv6ePUe031v6tuGnrqqm63jqI/38oINNNEgxRE/eNXTOUHM7CFbvtP6BS2zx/srdgpBVxleHtaThvRs4dqx4wFZewBDBoBFpny+iZ7+cI3pfo9d3plG9G8fel01YJ8lG/cLVXIZeFSL62jPHmBqEQCLdGpaz9X9ghTDBtxFcVKRAdfRPjBkAFjkQEmZq/sFOfUPcIbaSUUGXEd7wJABEOB0PH6n/gHOYceRkQM6Su2L62gPxJHFCfGoumt2Tuzezp6BnAWBA0g59oa3R3+HUTtC8MISZ1yon5VGH6/eTdsOlFDbRtk0+vIulJWeonv87i1y6bm5a2hzQRFlpibRsRP6y8sN66SJ/acu2lSjftGSK3rXStlnd+ExUdaBouOmMU9a5SrnzuUcOFpKDeukU35uFp3RpgF9u/VgrTpwm05bvEkkIo6WipG5Jk6upx/Eqk73XXwK/fOrrbrxa1biGkFt4OwRB8Sj6q7ZOXH2DI7VUgdG88MgOz2FisrKqz9TXKxlXaDZlX3KTX1MJVTswM/HSzrXllyJvlZWj811vrZ3y1rfMTp3rou67bgO3VrkVBmwmnC7shxOr9YNbPezIPZRP2R5OEs/6ejjeZ39IozAazFBUG6O6IsY5pvD7JxYf0vrgesWPVrm0Pc7Cj3JPq+F+loxWuceVGT6WRD7qF91CqJBDzIwZAlAPLpmy5xToDqsSyjXim9HmezsQcKonwWxj/pdpyBOsQYVuN8nAPHomi1zTvGIcq3CZsTM+lkQ+6jfdfIjhVO8A0MWYuLRNTtMdQXuycPE8roHsU7AGTBkISYeXbPDVFfgnjxMLK97EOsEnAFDFmL8VmX165ziEeVascRI2M7RqJ8FsY8GsU7AGTBkIcaKtAUvMC/eUEAvfLSWXvjoR1q8vkB8psD/57xwnOSU/6q3xRIzCROu1Rlt6ntaB/Za1JKJ8Qr1tVIkRoJqzPT62Zgruog1pej+47dysBZBrBNwBrwW4wAzl17e/uh739eKJ+I4o2ev6S7+HzSXYLNYqjrpKVRcVl4rHic6joxlOSIBiSNj2jTKorITEVfjyBSiA6djFUc29PRmNOu7XZbPKYh9zO86gZrA/T7B0HPp5Zv1rqogTCsEIQ7NSMJEqd+1vVtQdkaq55k91u85Si8v3ODKeY04vy1d3ClfKrOHlWNPvL4n5edkxjSzx8GiUrp3xnKpeKwgup0HsU7gJDBkQNyk/Z5dIB5idvA7Ds3veB+7chxmcFV/HD9YWgWYp4FvnPql6X5v3n429Ts1jxLx+oD4BHFkoPqXuF38jEPjh+Trize7Fu/DIw3Oe/jE+6vEX37vpoOAFSyrR8seNMa2wu94LAAUkDQ4jnErDibW8TRW14jM6qeVl5GFMUecLy8rrzgIKLnynMKJhGXhaVA393MLxGOBoIARWRzjVhxMLONplBx4VhwdjOrHRuyvn9c0Ygy/5895uyy81sNrPuxU4RRe0wt73FNQ6wUSDxiyOIanw3jx3y6xjqfh6UQeiUVcqh9PH/JIzAjebmWakY3Z0lEDhNOEXXi5iB1TZGHHDLMlJt7O+8USxGOBoICpxTiGp8PGDe3iyGsxFvE0iucYx7nJjsSi45cUrzO1N943Ww7UGonprVfdfn77Wh5sWp59DH/2s57NaepiC+tcKq7onl8d2yfWMQ+X0IGiMmpYN0P88Ij2nOPjyZwH78d15rXFj37YRUdKy6lTfj267oxWdHb7RjXORc9r0aoG3GODO9Fv3lpRK5mz8n5Yn1Y0e+VPjj0C1fVSe56q/68cg/eN1qlTO9bAUzH+gNdiAmAUR9anbQOat2YvRfcCjsd66Rene+56bzdmiuPDfnFmy1rxS9HxUTLc1LcNnduhUa16RJelFZeVlES12k4Goxiv6FgmDjL+7VsrTMu8uFNjWvjjPs0RbbSh0YojM4sH01pr1NNY0zo/uzFaVvoI99uS4+U1rgnXUVkPRexYuID7PagB/wpdumm/cCPnR1rf9nn06bq9hlNvd/aXd4ZwUxMq1vy8dwv697Kdvtcj2vAoMVh/nreOJs5b71s9ZDTg2FBc3KmpGOVsKSimP81b54rWl5t9hIPd563eGyhdNGAMDBkwhKeIOo2ZYzh6sRrv5GYMUizh/IZBk09RYrA+e/giOv+5+bTnSJmvdTF7SCh9hafo3Ioti2UfQcxbMEEcGTCE1xBk14/8iEGKJUEzYuoYLG5/P42YUhczlL7iZmxZLPsIYt7CDZw9EhTZOCYr8U5WQGwR+dr+XtU1r16Ga9ffjz6CfhlOMCJLUGTjmKzEO1kBsUXka/t7VVc3Y8v86CPol+EEhixBiJZpueHsNqaxSeyR17FJPU8kXdxM+eRoXSSHXd7lRhGxRInB+vkZrSgrzd/bNElyH+5TMteVPRorKiKm/SqWfUQd8xYrSaOgSCfFA3D2SAD0XI71ZDui8co92Q2PNBlHBL3vKd54Wp5sfsP1694yh1buKJT2yJO5lnbqQZJ1UfoJo6TyijjsV3bVG4y8FqPrpfZaZLxzz99NRD05KIFOlFfQ5oJ61Lz+EYpEkml/US7d8cb/0chL+8NzUgWcPYBhyqfdh4+Jm5pvbrORGe/LZXBZbqKkfGI5FTtweAB7mqmJPhd+CPF+/FcNf09xKQ+aEeO6WjFifH7t8+q4cmwl/kvdTqxF9r1EXZR+wvB1jb42sepXelzbu6Vmvfi9YsT07hXn9eTrwwZqDxGVUmrKcTq16QGqk3Gc6maWUptGe2nuyGF0drtTYtYe8QRGZHGMrMzGgocupOlLt9LET9ZR8fHymLsnc0aPG/+fuUyJnnu6nYwVPVvVp65j51oOnvaCey/sIFJeKZk9urfIpW7jPpL+ftN66bTvaJkr58LTrC9e15MKikqr2/OC5xdayrii9BOGYxfvfXMZHSo5brlfybjfN8hOpf8b1psKispo/OwfagiLGtVLS7POO0kaNmLmTjvKk7jwWB2qm3EEYQAkbw/gtRjHyLpCr9h+iLq1yNU1Yup9ucy+HRq5Ws9z2jcSoxD+5Sv7LFbqw0Yruj5a9eOHgvpzlnLx24gpD8cHLz2txkNrzMzvLZXjpns+hyIkJyfRVT1biPe8dmPFBT66nyQnJekaMa39rbrfHyw+QakpyeIHgJ4R0zpO9LHMztN+/+fpRDnPUyVLTE5mES3dtJb6duhk4TiJDaYW4xgrMht+S3IM69Pa1hSf3fr47dZulMtyy35/66ZuU7vtq3zPSb+KZf/1rv/zmpg8bMz41b3lpRaPk9hgRBbHeCGz4bZ7st1ci07rE2u3dpZ+UY8Y8g0cCNo2yqZF/mSkqtWmdttX+Z6TPhjL/uudJM0hskNmKqeSCytHiehXRLSRiDpw+gUiquvpEWHI4hjFfVlvyk6Z2lLWCKzsGwuvxToZKVRUarxmZ7c+nBGdxTW9nl7UW8szyrg++vIu9I+l26TLz8lMocPH9KeFra6RqdvUrA+ZXRf+y4l8i8v068fbta5jLPuv1WPJU7/KwcMaSUlNKJycRURfq97zNHk9IurDk8WeHRWGLMSYyVEYqRprTW0p+2q5tPP7Xq1yaenG/XROh0ZSkh9muR5H/2eV7sORS0lPSaYi0n4ARqokXL5YX0D/Xr5DPCj7tG1Iw89pI9b81HXiYz3z4WoxZcejHTYUWekpdPt57Uz1ypzC9czJTKWh//cp7Tt6nCooiXKzUum6Xq2oaf1M+m7HIbFPu0Z1quVGuG4DOjemeWv2SZUv5qIkOKN1ffp2m/EIYdzQrrp9yCzUIbpPcf/4YkOBoRFjOFs97xvdd4yOrezJMjIsW8NTxf06NKJ3l+3U3ddIkkjmWPYkjVi1QN6dXnH4SEn27qEfOyOm5uuq7V/577XYtm1b2rp1a63P77nnHnrllVfo2LFj9NBDD9Fbb71FpaWldNlll9Grr75KTZs2la4QvBblkJWj0JNwYRmUCdd0rzW1JTPVxy7a12tIqMjG2/AxRv/ne8PFeYX2edm0qaD2mlFKMr+STUUxM1KTqVRjnx4tc2jfkbLA5HtUy430at3A0XSrHfiaPqvRH9TX7MG3vzM0Surrb3XKeMwVnYUmnNU4yPlr9tYYVbNNz0qrOQK0EgfmjcxLHYpEKvuw0W8O5UmclJRre0rS3+lEHnmZccTSNKMn2e/37dtH5eUnO8iqVato4MCBtHDhQrrwwgvp7rvvpg8++IBef/11cfD77ruPkpOTafHixa5XPJHRm5KLlqMwm7qbpCNbwb+OX16wgSbOW2epXjJyGEGRbgGVDO7WlIaf3bZ6lK0Ha5H99XPj0eurN/Smy3uY9zs9Tbinruqmuz169L/gxz2Go+khPZrRwC5NbQlnui28ye1x3iltqU5GmakhC6cRY35GRDPJnKuJ6D8UKBmXBx54gGbPnk3r168XB2zcuDHNmDGDfv7zn4vtP/74I3Xu3JmWLFlC55xzjqsVT1RkY8N4TcYo/scsfqffswtod6H1EYHTuCAQO2Rjo3jUe9rjc0wNUzOJfmdnRBY0CSIrqPt8/cwCmjvyAaqfdZQiFKHN++tRm4ZFPL9AmWmsGs7Tbo0pnPSoWg8zozsRrQxOHFlZWRlNnz6dHnzwQUpKSqJvv/2Wjh8/TgMGDKjep1OnTtS6dWtDQ8ZTkPxSVxw4jw1jSQ27cTH8mR0jJlMujFhwkI2N4r4k82tXpt/pGR1eH/RCgkjWOHqFus8fOpZH50yYrrnfP0ec43p8ZmzpIGnIeD/3sf1zZebMmXTo0CG65ZZbxPvdu3dTeno61a/PXjon4fUx3qbHhAkThMVVXq1atbJbpYRANo5FNk7KSfyOH+UC9zG7LlZi7uzE5/G6oJWRk98SRFbwOz4zdvzD5f1iZMimTp1KgwcPpubNmzuqwKhRo8SwUXlt377dUXnxjmwci2yclJP4Ha/KbVQnnS7omOe4DkAOs+tiJebOyr48EuM8kaMur0w07PYxgiCB4118WtCoW+Vib0Qfz+LJbBky9lycN28e/frXv67+LD8/X0w38ihNzZ49e8Q2PTIyMsTcp/oF7EtbKHIUPFUjs59e/A6n/LGDWblmshwcOLxk1CU0afiZto6fyPA1+8dtZ0knYTa6Vmq4L8m4Osj0OyVu7LHLO4k1LKtGTKmPme+F1elKr3BT1ib4fGVgzLyNI7NlyKZNm0ZNmjShK664ovqzM844g9LS0mj+/PnVn61du5a2bdtGffv2dae2oDrehYm+OdTxLjxVI7Of1iI/fzZuqPUHDBMxKZe3Gd2uz/yse3UsFWfmB/LwNTu/Y2N69tru4hobPTytxEbx9WClADNk+h2/XvrF6TSifwfbjhj8PZ6OdHO60o/7VYFDY26c+qVwCgl/5vuvqlzsr65y7Li66r23cXGWr3RFRYUwZDfffDOlpp70FeH1rdtvv104f7A7Pjt/3HrrrcKIyXosAmvyJ3pyFIrru+x+esdg9/xoWY9YMuWmPrrGLC0lKRAPqmjYvZpHHLGEM6BMkrjuVvuAAj9cFR0vvVg9meNbOaYZPJLjacloG2x3utJLZK6HH7I23lG3ysV+ZdVfb9NT2XK///jjj0WgM4+2OnbsWGObEhD9z3/+s0ZAtNHUYjRwvyfX412cxMXwd1mK44uNBfT64i1UZJKlwYn7vd53S8rK6Q8f/EArdxym3Kw0GnF+ezrv1Eo3Zb3MHux1aSTr4QbsMs6qyDO+3CocC3hNhqezuO7c3ovW7aNXP+N8c/bhacJ7L+pAeXUzKK9OBq3adZjmrd5DR0qPU+f8XPr5GS3p3FPyTK87f5cbuOBoqaU+IBMywWmtFj96ieb1djMeS88Vn70T1e0fxB846nvJrqxNIlIYizgyL4AhCyYsc/HLKUul99dyJ5Ytww1XZKv1jaZeZiodPXbCMO+e2cNm8foCMWXkFD9ds2N5zRIBtKc1oEcGXMWqe7DW/rsPl0h9V3Y/q8e3Qu/WDejzdfq5DrXWlqJHIG65VMfaNVt9Huv38PqGO3WMxQjNOTzjMI+IXmS1syonBf5/liulJ4I7frkP1xmGDHjiHhy9P8/7j/9gjdR3eT929nCyluLUnbn/qXnUKb+uSIOkdiZT8iHK5Khk1Wc3iKVrtl1ZHbM6epPD0G3eI6IbeZFE9dk3nHSNiK6STMGU2O74c326zsGcTAaBQ3EjtuPSreTeO1Akp2R8sKjM8aK3jNuzHmysmuRk0uTPaxoxhifi+XN13ZTzi37483kYwXUz+qEq6x7vFnrnYYRMHfXKDZZzAxuxa6OMmJr3qzzwYhM+E6tr7iZ+XmeMyEKGF8N2ZRGa5+/Zgf7sto0oOSWplmOAInNhtqiqnnbjssfNWm0pgSzvy9/mX3b1MtKooEjbQUHUe+N+WrKpQHyD1xTY4UPR/RrWpxVNnLfeVH5EKzP+o/9eqfkd5bPfvfMd1UlPpTPbNtSVo5E5Jo/u2DDq7Tv68s7VMiXNcjNoS0GRuFbs3NKqQR0a1C2fbulX6Wru1KmH29vqgnmlnE5n3eMYlat8Nm7WDzSwS76tfuyOswdPJ94vsR8bsxLb04zK9bm8Wz5NXbyl1nZncjH+YnadlfvZ7nU2A84eCT5s15N5sSPPoUy7qV2f/zxvnTAmbhBdD616s/u7ukfXSU8x9bR0glUjqdVWnFleawqTZUpW7Sw0zSvIdeA4L97Xbt9w4hxjJAEjW+7IAR3ptwNOtXRcvXaL7oPmfEpEF0nuey8RvUxW0bpvuK7qugdvqtV/JxZZe4CpxQQetvN37pq+zNCIRR+DbzIWtNQietqN/7plxNT14AeYXr2jf5aZGbG+7RrSBR3tZxy3+ytQaSs+F60pTH6/coe5EVPq8MnqvY76xrzV+vlQzeDrcJfOcWSdFlgyyEofVmRltNqNP+ft8li5d9a7du8qffW2fm3FA569YMNoxILgxAJDFgJkpmd4u5UUN8qUnwzqY/BUzvgPVkvtx3/dRCmfH/xusXTzAVq7O/aKC8q58IjCqykR2b7B2/6zYqfj42kdx4rTgmwf5r5lpuzN2/VFV49W6Wf1qPorIwipcKrrU25zVu0OqAenPA2z0l3dzyowZHEk3cL7WSnTilSLVXkYO3IesvVw88HPZe0uPCkjFEv42F6n15PpG7zNjcBxrePIOgnpfd+pjEttzqoyXDOrZEf475WcK0aqjkTPk9/3bhD5UTJMQ3Y/q8DZI0GH7XaH+GGS0ABy19vN6Z7oshQnIZ56tPN9s76Vl72bFjx8H2Wnl1JxWQYNeOH/qH3TAmpS7yAlJXFarTZCuPKkEftap1QZQ36VZUcPv6fcYsX2g8Wu7mcVGLIQ4EXsid04lTBJaAC56+1mzJJWWbzuc2WPfPrvSvN1OJm6KH1r7fihlJ5aIRx8mJysY/Tl43dUv6/kCXY5IqJLDYyYDPbiyOI9biwozwVMLYYAL2JPrEq1WJWHUfazCsu4NK2Xbli+mysJIt1UToaLJVo7ttfLIjJ9QybmTlbGRes47OxgZsSs9GHuW4oRM2dHVXzYxSQPO1wMJCKWErqbiIptB0PHc9xYkKR1YMgCAC8Is/vq+yt2ir/RC96y0i1WFoutSrVENGQ69FDvZ/U5fdM5bWjslV0Nz/WO/sYSHlbgssYNrTxerOE2ZVdxt41z9DF+cWZLmr3yJ82+ZSavo9RNps310nbJOv0Y9eHyir1UUtaajp+oQ+UVTaqNWM3RV+33J/naohfjpqrvvOooPZX63tUjjHFjQZPWwdRiSGLDFCmI6H3zHcSeKFItZnFkjFrOhb/HDzaz9E16dTbiT/M3iGNx+bO+26V7rr1aN5CqtxEsEaPEG8m2g5twjBufx2vDG9hKCyXLn+dvkIpV4naPPv/cqBgxreBtlpF58brTNcs0cnZITiqns9r9INaz+nfsQYO6aUv2lB7PpfTUQlIc3tLECMlrWLXgMLeA45Jk75ewM6rqXnInts8aCIj2ESW+JPoCKL/NtLSbvM7ssXHfEZqzak+tfdR1YqzUW6nzTweLacUOVhBPouKyE/TvZcYu36/e0Jsa1EnXPVcu96WP19Irn9qTSkmKqmt0hpM+bRrSur1HaPvBEjG3z5Ity7YeFJlENu4rEm7TbsBGlDMeTPxkLb280Jnsixla10ivHyr7G11zBbUemRqeZfjtWytqfX5Z1y9o7JWTqXl9zsqi0LJqPeuaWkZM1CXmg5Z+RPQ/X+7zMFPmorQOZFwCjl1tLr/r1FSsJyXpuu7L1JuP0+/Z+aZu7808lkqx28YyGl1WUM6Tjb0T6Rk7583I9EN+TOhdL6N21Mr4wEbsteHPiP/X3F15864wZjydmJzUtHKLS7cAP+3ky2pFRNvi7j4PE8jsEXCCGF8iUyd+mBnFn8nGLcnEbkmdv4N7324bm7WTVZQ6OEl0bPe8Zfuh0fUyasfoc+LpRB6JVf5fqyTmAZH/sOzEmcLouDkSi0SsFNY6Lu/zeASGzCeCGF/iZTyR3eOY7cuJjYOgteZGHYyceryAj+n1NY8+J14T4+lE/cEHP9q3E9EiSk3mKV53OVDcU4zKjOahTm7/IC7v83gEhswnghhf4nU8kZ3jmO3rRp2daq25gVKm4iDD001ew8d0ci48ujqn/UoaevpndFHHL+iSzpdwAAURsffalKpkvOXV59Ql/xBNv320ZOm76ESFdYVks4WS9XvepY9/OFt3X+WzY2XtXHH0COJ9Ho/AazEKGWcKs8VMmTKUKRdO7Kp37zWqk05ntGkQswVYmTqJGK+kZNpTqL2PMuffs1V9mrpokzheqwZZ1Ck/Ryg/f7x6j5AgyUxLpmPHjeOAuC4VFRF65+tt4nslxyuoXV42jb68ixDelK2zEQ2yUkW9eC1Hz3Gm2lnlUAkt23aA9hw+JlV/Wbj+3Vvk0piZ39OW/cXUtlE2fTLyAvp+52HRh/LqZNBv31pGBS6kkVLIyUwV14j7gFH7qdfI9hSWin3qph+iOQ88IEZWKZo/hVlZ+Y4apQzqFqHLulqZJmxG6anfUCTi5hpZBzqrXTvq9+zTRPQYXdpVe211+4HmtP3AV1RQtNOxQ5VZ/1TaN+xxZH4DZw+LrvBm0hFWpFYUbyZG7yK4Ie1gRe5CySquB7toX39my+rEvep6K7c6S4rMX7PXcR5BLXdwtev8lJv6SLejDFptbVcxmSzqn3Gme71zvOD5BbR1P+tguYvSBzgEQKv9tDxVl4waTk1zDnnoQZhU5b3I/SvFstfikZJsqptZrLFvByLaUEP+Jz2lhMZdMZku77mYUpMraM2uNnTz38ZSelqjGv3ODakkoxRdeh6fgOC16IWL7PJtBw0f8vzgmbd6ryU3W7MHpVMXXTPDdGf/k8bM7IZT10krzotveNbQYkmRWBBtzJwanOi2NnJLd4ustGQx0rS73Q24D7AxM/sBVlLWhDLT9sXAFf7f1S74fA3ObncK1c/WMk4nUZ5inR5/h1KTj9OiRyZSw7q7qxw2eK0r1/b1dHoPwpDZB4bMZRfZJvXSad/RMtujDCM3W572O2fCPN0M5HZddLncTmPmGNaZi/tx/GBRroxLvLo+nz18UbUaM0/B8FRV17FzPc/oruaNm/vQodLj4vg8DavUp+BIKY3/YI3l8tTndsHzCz0diflFZupR+tOwidS60W7atj+fHvzXSFox9jrRB77avIG6NL+Y6qbvEIYjOZnXblZXrRc1sui+bgc2PFtr3Zc5GQdoyehfU3Z6mdimroPyBOO1rzunj9G8X5yGTHgVqgH3e3fsAdbIJF1k9xypvIHsonazjVZI5YevkYyG0Xfdkrvo0jxXWs5EqQ/XW10fXhOLpRFjbn7j61ojiKt6thAPkD/NX09Hjp2wVJ7XMjR+M/OeB+j0VhuqDUGXZlvpsq7DaN+RZ6hJzi7q26FyxHUSboP21e+8D0qunMaMvi8LSxtS17Hv0V+Hj9dc21KMmN794jRkwu49aMX93kq5oCYwZAFwcffKRdeK5EpePeuJc6Pr47d0i6KIrEwB9W7dgD5bF/1gluNTm98LMgsfup3a5u3RNEaN662MwWhLhn8Y9nc2Vry29dgV06ht3i7aUtCMnv7gViorr50PUf19t+5xr0I14H7vDBiyALi4e+Wia0VawU4bRH/Hb+kWRXGX13o45VP/U/NsG7JF69Wpk8IDT8HN+e0D1Kz+yQBbxUDpBRfzZ8FYYOD1zrrV7/T6JButsbPuMS1N/X237nGvQjXgfu8MxJFJSi2w27mTKWwjuQavpB6sSCtUyrrIjcr06iNzPK9RT9UEoT6xIjW5jNY8eTV9N+4matHwACUnU/UrJaXyr9Foy+0MGvaM2Fc1PrGb6USrf1pRqpYtU4ZEkXHxGxgySZmUJ6/qZipTwF50STakVtyWaVFkYeas2kWXd28mJa1QKesiL2eiVR8ZKYdYwVM1QaqPUTyXEey1qObM5ktp49NDaNMzla81T11F4wf9hdb+4RrKyjgRgKlBOxypZcQYO5lO9O4XpSw7zWNXKkl9XHU5bpQLagJDVoVeRgV+r6y5sJs6uypH9zl+z5+zK7hZGU6OL+vqy15SnKiVs47PXrmLstNTaj3glDqr48gUWRe1ZEs0zUzqw+WxQfcbZaqG3cr5/L3C6fOnTkaqiCPTgttxzfjB1LhupX4JG7B3fvOH6hEWv7LSy2n4BR8FZOTJ58EB/G1NHi2pVfux6KUIsbZ8X3A/5P7bzML9opSlNzKzU6YMbt3bQB+43/uU2cPJ8e3EwvFnP+/dgrIzUk2lFYScycb9Qq6EvRAbZKcLZxBWlDarj1exV92a51D97HSR9eLjH3bRvqPHDTMlsDvzJ6t3S9Xlgo68lubPmpjSkn/+xen09baD1Zk9Rl/ekLLSz6ATFQcoKXLccI0rCE4aXIePfthCg7q1UX3KmeN5lM9OQLx++oPtRLx694Wd+0X5Dmd0OVBURg3r1uzbXkglGZ0D0AdxZAlGEOJV3JY3UfPPEedUuyfrZfJQB66ys4dMXfg7DeqkGYY/eE3ta1On6uEfDvgJ8vriwTR50UOIhwKuAhmXBMNPuQhlTW7iJ+ssGzF+5Bk997QWw2WmamTjhrhd2Ig1rJMek4zz5tfGPyNm9ydteQXRk7PvhRwJ8A2438cJfsWrOEkNpRgOdsgwyt2otRjOxopHXXpTNVbP8+qezWna4i3V07B+sPfIlkAZMdkpyze/vLz6/4iHAn4AQxYn+BGv4nQ9LF+Vy08r1596uxZstPSyIVg9TzaKbAij68AKBPuLnGV10YOzyL9z92hqUm8vZaYdIxMHRs/ZUpBP/1g6pDp11VtfXUw/PHWD2Ka3NsdwQLIC4qGAH8CQBczZwy4yciYN66QJdWcjyRI9os/5hrPbiIe+VSPGdRgzpGutxfXcrHT6/aBOdOBoqZjma5KTKYZGBUWl9NmavTRn9S76cvMBSktOojPaNKQ+bRtSs/pZtc5DqSc7TdTLTKEjx8pN66TIxZSeqKAXfn66GAqyYKeSv7H/HxcaqmLbYanLWeStOnzw/hUVRLsK8+inQ43p1r+NoZITObVSjHHqJ04JFV2+Or8hByhbkSNx4qChyNqor5HVvuxmWSAYwNnDRxkXL+ovk73eap20ztlONgh+PES7G2u1l+L+ryfhonceWvWUgd3zWSNNq0y17IcbWTf+e//vqFWDvdVGwUtvQ0XpWFwnZc40iehwcTYNePFlOlAiFyah5DeMNmTq/IZa11YLO/eH2fS1lb7sZlnAe+C1GBIZF78MmWydzGRgrBAdt+aWq35SlQaaW/IxStuwVA2v3blxg3w/9lqqm1nqieGqHC0l05pd91HbRq9QWkoFFZawsfqLtLEywyy/IcfCzbrvfMf3mJ5mX8SFvuxmWSA2wJCFSMbFrXOQlWGRrZOMDIwVmqmO5aarvhcOGiJDS1KlOoATmtfbRosevcc0RZQdlOnBvs9MpgUP30oDXvrU0vV3mzVPDapW7nYjPMRKHzHry26WBWIH3O89kHFx8lDz0v2dEQGeFh9iZnWSkYGxgvpYTmU11HjxS4zLtHvuDbP20rLHrxcppBaPvkdk4vDCiLHbe4fHZtPeouY08l/LfTVizDMfsm6Ze+EhVvqIWV92sywQPODsEQAZF7/L1fuuF7IsyrHi1U17zVNDKDPNXcNVXn5yTZL/lp5IoYv++ArtPtqyep9tB0vIb9jBxs3wEDt9hL+j5UxitywQDmDIAiDj4ne5et/1QpZFOVY8umlzLkQ3pxCVSf8zxr9Oh47lGe7bukEWrd3NyXedw16jnLpJzxlGD06t5WZ4iJ0+sqWgqNYUIk9pD+tjPTVWPPbReAVTiwGQcXEDKzIssnVyUwYl+lh2JTr0ynYbs4wjsTJiRaXppkaMmXh9L8vXX+8aLR11iUgJ9udhPcXfr0YPkPr+aJUjjxtyJlb6iEg1lp1GE+etrzWFyCEpf5q3TnjDypYFaZVwAUMWJbWgh4yMS7cW2ouRsZBrsCrDQhJ1kpVBMTsjrfOXaXMruJlxX51xROvcMlOP0qTh4+nD394r/rptxBTYiHUb957UudfNTLV1/bWuEbP6p8P07daD4i/3A7P2HdC5Ca3YfojeX7FTxCny9J4avt5jruiim+jZSHpFvY9R3fWWNJXPVREIpmVBWiVcYGqxCna1ZXdrvRgx3q644+rFKq3aWVj9HfV2swwVbp4Du7jLuss/MKCjaZ0Ud3m9dtHKyGHl/HOz02rFaNXJSKG0lGRLcWTM4g37a02BsXHp3iJHXBtZ543o+r7z9df04QMPUP2sQ5QWJVLZpdlWx9nnldEXeyDyk/TA0Vy6btIrtOVAfdPvZqQm07W9T66V8ajDTsybcs4cYnLPm8tqtNXTH64R15pd7FfuqOzjato0yqIffioU0kF68Vjs+j7+A21nEKP+oeTVNIr9yq+aOpw4b53u+fHpHCw+TiMHnEpvfb3dsCzEkYUPuN/biHFRskd8vr6APlu3r1ZZyi+/2/q1rU59FItfd1bjsnjq6KqeLaT2NcpmEr24ztkw+Nd87cwN3xPR6aJ1ysuT6OI//pm2Hm6vebxXb+hNDeqkizLqZ8yl8zr+utrhYdLMF6hZ2+upWf3NdFa7/tXXaP/RujTwpZdrTcWxxtrx8gj95p/LTc9z9IA6dPvF11BK8hEqPZ5BqclFnoy21PA5nSjPoQ9XraYtBcViGszKTZnkIObtpr5taHC3ZuIa/XHuGtdiBtX3DWPUL1+9oRdd3qO5o2wcs1f+JPT3ZPr8kB7NkdkjJCCOzAJexLjEOhbFTlyWWhrFe5J046DYhVy/7azNfnOZ6ik5pSzu5mbu6Wueupoy02Krsswu9LsP30YtGky1HVvnJOZN6QNuxwzKtr1b9wlPZ6pHhMHo88ApiCOzgBcxLrGORbESJxP7xWz9BxTbKV5j0mo7itjrnnUyymjVuGtqlGVkxJrU+UnUgY1YrGCDW1yaRhc+/yHl5/4/R7F1dmLeovuA2zGDsm3v1n1ix5kExA9YI/M4xiVWsShWjxO7xWyeTtRGmSpkY9Y6dxP9dKQl3dT3Q5F9/YazZovPraKUycasfmZBrWlGNloLf38vZaYdF09RI+Vlt1HyHm4/2ISG/t8LVFjakF4b3tO29IxdtBwavIgZtILTc1ccQ3gKMzrTCxw44h8YMo9jXGIViyJ7HM4+/8zPusdwMbuH4VbFgCz8/f3i/3aMl16Z3465hR5771bacvBU+mpzV1o7/mpKTYnEdOpQTJ9GiI4dTxNBzJyFg6pGB89dV9OpIFZ9RcuhwYuYQSu4ce56jiFw4Ih/YMgkJFC0JCrsfMdvGRfW1loy6pIakjPecrX0npzGyW24zGevm1adHcMNI2kVXgM85bHZoi9MHHa6kKXRcyqQuYZ2SKoKcn78is6Un1tb+oZh5x32TvRqjWxPYWlM7hMzwVUQn8SlIbOqd6SeltBDL8YlKFMZImlwh0b07rKduvuwGzqvhUTrp9ltP+P9OGXS+xQUvDCURiguVN3GviX+cgxVclQbKu23+3CJyKTRsG6GcCNnr0VX60JEN57VmgqOltHy7YdEbFh0H1BiBt30WuTjcp9kj0CtdTI37hO9PmjXoYPLW7ppv3Ae4TPo2z6PzunQyJX72A+dwkQh7rwWneiBmWmNuX08t7Cqw2V0TrLnY77ffUT0CiUiyh21Yvup9LNXJ4pgYo5jk9Vcs6LHFk1majIdO8EBadb7AF/T+2YspxMaHSk6NlBNSjJRvcya8Wuc1qrkeLmhZp3T+8Tte09Pe46vx7PXOJuOD8JzIowkpPu9Hb0jN77r5y8tJ3phsvpg0W0QvV9qclm1k8a2/Rw39zgN6nYTEX1OiQjfUYoRcwIH7/JDddoXW8grlD5gRctOj5EDOlLbvGz6ZPUemr1yl+5+F3dqTCPO7+DoPnFyv+qVZ3b+HI9o10C6WddEojDRDJmTuK6gxYTJ4jT2h0/lx/GDxbSSbBt89vBFdMHzC6v3e3TQ32hE//fEr3IFpUfF0qkiVlQrLlcLWp78yyxa34nu/Mc4OnairqPjWIl/cwL3gR+eHEQXPr9ASBU5IT8nkxb+7kLqOnauYZ9U9zs7uH2/Vmr5LaDdhcaek5zLcvGjl1h6BoT12RI2exA3a2RW4rqi58+dfNdPnMb+8He5jNvPby/dBry/st/jV0yi28+rGcwca5ymh5JFaecH3rmPZi0b5PnxqmPpYnBerCPm1IgxbAi4LLM+qe53dnD7fq3U8jNva/5BYfUZENZnS9iIm4BoJ3FdQYsJk8WN2B+lDNlz4/2Tk8rpi0eHCyOmFYPlVVyWkgmk9HgylZ1IpgNH69Cod4fVGCV5xe7DeXT39NExMWJB0hHzqiwnfdft+9XKfW31GRDWZ0vYiJsRmZO4rqDFhMniRuyPUkb0uWWnFdLrt42n5vX30U+H8mjK/wbRuCv+QU1yjtLYIaW+eQFO/vwaenbubTW2jb3qXVeycqgNYsnxNLr/3XupTnIa7T3SQMShVURifNKS1M1IpUFdmxp6rJrpiC1a705dZMty0nfdvl+t3NdWnwFhfbaEjbgxZE7iujjJrZFXFsPbeb8g4TT2h8+Jy4huv/kPjaB2ebuqR1UtGxTQWe1+JD/hc5yiYcSYLk/MFCmpsjPKLI0Eq7POi4Bloov+OLk6YDkIcPD6ifIKKjxmLGpZNyOFxl/dnd5bvtNyX+A+wDpiH/+w25U1skcGdabpX24zHCGr+50dZOLt2NOwoiIi1qjM1p4qtfwypdbIrMa7ydQVqbOcEzdTi0baRWbxKpypXWZen/cLErJ6YXpc0rlJ9YK70n6KEfMW/kFwiKOMqLgsn3YeyKWiUn5o154i5HZfvKE7nfb4e5pGTLma/7dgOfV68nXaczhLJOLlKUgOgjabdjx2PJU6jJ5NXcfODpQRYw4UHTc1YsraDWuB8fW0CvefrPQUobfnlKt6NqOBEz8zneblYzoJypfRKWNvzxunfikcLdhr0Ky8cUPNtfFY782qQ4ZMXdnIsfIAsE/ceC06iddgMUBZCQhZ2RP7bCMiFkjkNQSefvmBhextx79tKiiiT1bv1fxeUrXrbz0e3xHROopEKjWjvHWg2EFELTTdkqNd+f++5HI6UZEupUem5T69dvxQSk+tqHE+ylpbnz/8jQ6UuCfI6SetGmbRjgMl0hlB9OLIHnz7u1qabmakpSQJySJZGRm7buwy9zo5cHH3Oo7sobe/oyKDto0OhwGUeO73TuK6Fq8vEL/ezHjz9rOp36nmsvP24UDYEzozwMel9cK6NNxD1517BSUlldKJ8hS6/58P0Mdr+tda4+EW+e9vHqKuzdfG2FU+YluyJHrqbemoAeLaGpWVl72bFjx8H2Wnl1JxWQZd/PzLVFCcT0HAbErbLThF1Z3929POQyW19OTU8HX5Yn0B/Xv5DmHQzmzTgKb+b5PhtGPTeumUlJQs5fnntru5konj3jeX0aGS446P6VVmD74/T3t8jqGhdxqWEI8knPu9GsspamT7qKcPez0jRlWfpxkaM+78le7MbKxOZnZITy6nSTe9SJHIi3SgqB5t3NeSbvnbGCo+nkPv3TOSujZ3aZVfmv2OJEuip96U6V6jstho9XjyXQoisTBiDKfA6tGyPt15QQfTe+f80xqLF8MPdLO1Mytra267m3N9k5OSdI2Y1WNyef1OyRMvN+EfmWaX2mlYQiITl4bMKpwLzs397E0nmnndnajaz2iasaYRU8MjrkZ1j1Cjumvoh6duoLITbPxObosNTXls4Kq78eINBVTInhrAFDtt7pVbuJvlhsHFXTbcwG85nbACQxYIF9nOkvvx2tkRnW28xmeeY08xXBmV6fxiCBux3a635csLN7hSTiJgp8296vNuluv//eteuIHfcjphBZOxvqvL/r7KsUMGvf1OIaJe5G83Oq3KEzFSNX3IXnANq/7ur2HEZNo8UeDz97oNnPRf5TqZUT8rTeo8vLiXwqAOzWuSZstsTsMSEhkYsigXWT2sSE3wwu7URZvoifdXib/8Xt+IPW+hptk6Rmyj55kttMllSUwqKSuhMTPfoV9N/ZHGzPyeDhytS2NmzqBfTf1Q/C0py63VLq8v3kyPDe4kPk9kY8Yef25cuh4tczTb0apUCjs78LoYe/JWOjywBI35jIHIOSl5Ld2WN3ISehOkUBmnYQmJjGWvxZ07d9IjjzxCc+bMoeLiYjrllFNo2rRpdOaZZ4rtt9xyC73xxhs1vnPZZZfR3LlzY+a1aJcRf/9a01WdZTim3NRHqgx5KZiyKsNUbiGf4NaoNbLD/Fs4ZvkGT8IHKxAjLr02i6ZNoyzafqCkVrtw7FO0xImaOhkpVFxabvthL9qFY8kofmlcN53GX91N03W8QXYaTZB0HdcLXRnWpxVNnLdeKlv/W19vN3S8sXIvxaNUih2pqESm0Av3+4MHD1KvXr3ooosuorvvvpsaN25M69evpw4dOoiXYsj27NkjjJtCRkYGNWjQINCGzEwORSbGw1oZf+Jb37ReytVhN/q01BNVhm8R+2ER0TNEtIpiCz8QfhL/kzViZvBNfHGnpmIxPq9OhrCT7FizpaBI6gEKnMdtGUmNyD4gOM5ySI/m9Nu3lhvKuHgZLxUG8Up1qIxRKAQgb9zvn3vuOWrVqlUNI9WuXe3hMhuu/PxgxOnIdiz+lWQEb3/o0k66HU5dRs08hY2r3d1rlrHRQv2S6bQx79Pa8e9SRtrIqoBiv/hO/FtSVu6KEWOm/m8zPXxZzbZV4syAM/gRzqOUgV3ydR/o3Na8j5bBsjKSZcPBZX34/S5H95ITnKhDx4qToTLALSz1pFmzZokpxOuuu46aNGkiRmdTpkyptd+nn34qtp922mli5LZ/f+VcuxalpaXC6qpfQZRDUWI8zMpY8NAI4d5+Vrs1VTkKK93d+fOaZRjH8yi89NEv6LQxs+iyrl9QeuovfDZivNZVGV/Ech1uodW2bsSZgZoxVHq40dY5mali9OPGvQSAp4Zs06ZN9Nprr9Gpp55KH330kTBS999/f401sUGDBtHf//53mj9/vhjBffbZZzR48GAq58R3GkyYMEEMHZUXj/jCGOPB2xYY5Cnkz3n7yTLuqYr70p9SPFGRTK99PkzIpoy9crLF38fmeQbV+1XCHobpBkaMvRLdl/7QalvIWriLUXu60da9WjcQoyHESwE/sDS1WFFRIUZkzzzDazMkRmSrVq2iSZMm0c033yw+GzZsWPX+3bt3px49eoj1Mx6lXXLJJbXKHDVqFD344IPV73lEFmtj5kaMxykNj1cbMS19LjYWvJ33q4QNBp/387WcNRTD8v8+v1rkGezbYQU1r8/OFfIoZZRXJFNqikx82VVENLPq//vYqbnqL4/AvqoeiXkh/aHVtkGRteA0WJxBJOwYtacbbd2/KnUb4qVA4EdkzZo1oy5dai7Sdu7cmbZt44wT2rRv357y8vJowwbtwFVeT+NFPPUr1rgR43HjufcZCkoq23i/k/yRiB6uNTIrjyTTXz+rlCzhKcVXb3iW7LC5oBl1GvMfGjb5GXr8nV/S8fKTWeE37GpKi9b1oHe+uZIqIkUqI0ZVRovX+45W/a1pxBiW/nALrbb1O85M5OfLyaTLujal7i1yqF+HhpSbmSJ1LtNuOjNQIQXcjj1b1a8OfZjy+UZatG5ftYs9yxM5aWv19ZO5l/g+6NiknlhPkyU6LMDKd0H8Y2lE1q9fP1q7dm2Nz9atW0dt2rTR/c6OHTvEGhkbwaCixHgYeRyaxXikpGyXOlbt/f5ISzeNpE9WP1Er4zsbsdeGP2PrAXPr3x6nz9afQ3f0b0eTP0+hpdSDpn97o6YHWUqy9V/HLP3BrtRueS1Gt60SG6SV0T4W8GOSk+D+8ytra5KZaSlUWlEh2t2oP8WSxvXSqevYubprV2zEhp7eTGSvj/ZSlPFaVMsBydxLPFvwq2lfSbvGh8GtHoRoRDZy5EhaunSpmFrkEdaMGTNo8uTJdO+994rtR48epYcffljss2XLFrFOdtVVV4lYM44lCzLsDswP9ehfk/xezl3YWGrFaL+9Ryrob4uvpnGz7hJ/2YidXBezFyPWOq9CSFdwvfmhqgc/vMz0mvS4tndLcgNeXwkSGSbedEYjDvbmZDd2PifuN7EYmXGMHh9Lj5U7Cg0dMFgPi/sB9xPOEh8tYWLGvNV7a/QhvXtJ67jcVkb9TwkLiHZGkfkuSBwsB0TPnj1brGtx/Bi73vP61ogRI8S2kpISuvrqq2n58uV06NAhat68OV166aU0fvx4atqUc+0FOyDaWYxHZXCyOewwUZnpQoGnSn45ZWmNz85pv5LeumM02aW8YgGlJF9kKpdiV1bDDRkWo+O7Vb4MddJT6P5LTqEmOVnUMDuNbp72tel3eJrxsI7opXJOnz18EfX/40IpeZOs1CS6pHNTmv19zVReMvDxmkooHJuVodSZFQVETF/dDHro7RVCuNPONeR76Y0vttDET9ZRMc9rW/gu41XfBeHBMxmXIUOGiJcWWVlZwpsxzNiP8citcqk3ig/rUMuI6cmhN6lnV42ab+iWlJLcX8q12q6shlvu8XrHj6X7PYsd9mjZQByfU2zJoGfE1OfEP4hkjUvJiQid3qqBLUOmTIM6QakzGzHlOvAPLDMjZnQN+V7q1iJX14gZfdfLvgviD4ST24JHX+dVTROeV+Xdx5k6BhNRIwMjtkE6V9zeI3am25Rvc11SPJW4cNs9Prq8WLvfK8dzM6zAqov59oP+u6RzOyiOFXMsTttpXTMn/S8M8iwgGEDGxTKVSXpPws4bTTR+HzSr+svG7gPNkZgaXrTmNS1lUfurzV3pp0N5lJ9boLnWwBPC7N2Ymqx2rW9ZZcSu8Vziwm33+OjyYu1+rxzPzbACqy7mQXBJ57Rgdqd0ta6Zk/4XBnkWEAwwInNkxPRg47KTo+qI6H+mRkxtzHi+/58jzqEXr+tNL31yj6aKML/nj56Y+ZhYCyOaIbLQV7rKnzRiXkpcuOUer3f8WLnfRx9fNqwgPyfDtE15fVX2HKzurxUq4KStkqoSDHNuS6tGzKgPOel/YZBnAcEAhkw6XuWwpfyIlbxUleXe2vFX/3SYlm8/RBT5Gd0zfTTtPlxTdp3f8+f9O94tHDqIfklEF1ZPJ6rPgdcPWIZDz6OHP+ftWgvt6nZgbzy1NA1v15POkMVIYkNGWscpimv54G75op34HGd8uZVaNsgy/B6HHTx+uXGb9mqVS19u2k/X9mph6r7O9eBz5TUlO23K3objhjq7Fkq97X5P6xqyswfL9ZzWtK5m2WYSKzLyLNx3+dqFMb4MsXE+ei16jZ9ei+zKO+3zWTT9zrsoJYmn7oiG/3US3dp/KA3qdh0RLbZR6kQiesC2xAPfsHUziLq2WCkcQHjtbPuBM2jMEG1pDq2YG35A6mui1Y7J0SojGkV6gt3Mo/dVMpmoyU5PEfVQy4zIxALpZdhn/a19R8pMj8seiccrIprnbyWzu1o/7MLTGtP8NXtNcwrKwO7tz0bJrGi1P8vZFJXWdprg83vxF6eL7+vFW3VrkWNa38zUZDpm0EesnoNWX47GaRwZx77N+m5XKOPLEBvno4xLPBsy7lgDOzen5OTa6aI4G0ZpeXPKTq+UL7EGZ/L4i+leZhIwV/bIpwFd8g2lKfSkOMxQSuI1OsZKGRwv9PtBlb+KedFdT3pFMRqsWdU2r46UxIZZm6jlX7g8zlDx9ZYDVYKQEVH2n+drO9gEXWYlWo7kYFEZ3TtDW2ZFuXZchp6MCRvy0e+tpHeX8ZS3u0TXwey6XdypMY04v4MliRW77RFEjCRzgl73WANDZgExpI8kCyPGaOU9ZGOWYp6hyNaIjB8yncbMMfz1yvf7j+MH68a0OY27UmJyuDvIuFxr1cvNuB+nbcJ1OXfCPNpzxNrUrh80M2kTN9qVy+j37ALHbvp6KHVY8NCFhllEZPqyGWGOLwtz3YNsD7BGJuJVPtM0Yur3vN26yWfLV+mwYYQb0hdO466UmBwrRiy6XlbifrxuEz5GGIwYuSCzIivV4pURU9eB5X28lnFxs5/FmjDXPcjAkLF3VNuLpRL+8qjMGg8ayKKcxA3pCz9jaZR6uRn347RNwhZb5IbMitdSLTLIxuE5kXEJc3xZmOseZGDIhKGSG2pVGroOkiOxh6uy25vjhvSFn7E0Sr3cjPtx2iZhiy1yQ2bFLakWJxNaHIfndcxcmOPLwlz3IBOnhqysKjD4N1V/jaeYIpK3buV+G6ryJfYjolZVf/dWrYXdV/W3WNqIuSUj4zTuSonJMYqPMquXm3E/TtuEj9G0nvloOAiYtYkb7crbONZMBjuL5kodOA7PaV82I8zxZWGue5CJQ0P2e3b2Zv84Inq56m921efapCR/Z6imrGzj/SrJrQp03lb1t3GVQ8dfqv5ae4Dyou4V3fMN9zGVkTGIubEST/XLs1pbepCp6yUT+6UVM6QVT6PIgRhxa7929MYXm+mOv39DI99aLjS2lFgcPsaTV7HidfB5bLBxLJRMPJVZu3L5TwzxJi5PXQeW9zG7bhy+wDkd7cZN2W2PIBDmugeZOPNaZGP1vMF2o+m+pGpDpuW1WPmZ+01lFrOlxGuZy8jYjyPjY6ifKRybVFxWbni2RvXSiiHS298snkYvjoylS7btL6lVR3VclVKXoOiCyapQ68UTWYk9Moq9+tc3O2rE8zF1M1LpaOkJW/XXqoObcWTxGIsV5rrHkgR0vy+rGnmVm6xdFRuMmCqNWbQh89KIGcVs/bx3C3rmmh6W3ZS1YonYkLFHGS/Gt26YRZd2yafP1+2jqYvteY+9PKwnDenZwlGMjNm+laKgm221PMdmMXbi6vzGKJ5IL05MjVm7vnJDL8rNTq+Ot+vbPk+UN/JtZcZBn5v6tqHLuuSLwgqOlhrGAyqSSIvW76NP1xVYOk9ZZNojqIS57rEiAQ3Zn6qmEZ3GdbGMx+lVhos7Fd/c3Sne4kmcxJ3Z1RBTf48x21d4itrsnZXrQdZi4oKEVzpxeuVqaeJpwXlArUim+N3PQbhJwDiyjS7tx0aLp+EiVX/dN2JBiCdxEnemVzcr5ySzr5MUUBwzFVYjZnb9jXL02e1XXjkh+N3PQWIQRzIuHVzeL77jSdwo166GGGJk5IluK7O1FbvXQHFC4CnJ6ByUTpwQ0CdALIijERln0EhxJdNGIsSTuFGuXQ0x3i/IcTKN6gTHbV/dTsraV/QIh5XF+XPe7qRfKZp4PNWnht/bXcfyu5+DxCCORmTpVZk0nneUaSNWC7DKVA4/hCIGD1ROhCuDUb2VRffKrAsR6tmyPuXnZol1pD2F+sfXQ1nXUKaZlGPzdF60B140vF05p4Z10ulAUZnva2Ts6XhOuwZ09Nhx2lBQTGUnjByGYoPSxt1b5NKYmd/T5oIi+nbbIc1rpazm8kjts4cvEvFzRum51NcgGjZWA7vk6/Ylq/eHWT+P7kt6wDECGBFHzh5qF3zWASuPGok9aBqkHGuXWOUXNqN3EWSOb1Tv5dsO6rpBs/wGu2FblTPh/ZVf6DKSL1rHZaJdwNXlR6qkXzgMgBLUa7F7yxxauaPQ0ndZJWH297tN84La6dd27w+9fi7rtQhX9cSlMPG8FtXwr9FXqxw7OlRNJ6YHUlrBzBCYHd+o3rIXVjFoMrCK8IQq7Sm7sjEyxzgoWZ9oWLfrxetOxpFd8PwC2rq/hIKIkaZW43rplo2YFaz2a6f3h1MjCMmTxKQwsQ2ZNfx2Eeapv3MmzNOdkrPr7i4Lp6V68Rc9ae+RUho/+wfDqUHed/Gjl4j/u3FsrWmvtOQkW5nrpw4/gy7s0rS6jY4eO0Hdxn1EXnHfRadQnzYN6L5/LqcjFoOJuYarnxokMmFET5vxdKKX9bbar926P6xOD/p9XwL/SUD3e/v47SLM6XqMjIddd3dZeB0pOSlJrCsZ1UPZV8Z93i58fLvyK59u2FfjgTbyX8vJCxRX9JEDO1J6WoplI6Zc0xlfbhX/5zpzbNZVPVuIv8/NXeNBre33a7fuj+jzNDM+ft+XIDzEkbOHffx2EbZ7fDfrY6WsoLpKR0uIbDvo/pRitCu6k7bQkzKRlUJxC7Nz8Ov+8Pu+BOEBI7IAuAjbPb6b9bHiEh9U9/loCZHWDbJcP0a0K7qTdtCTMpGVQnGLLQVFgbw//L4vQXiAIQuAtILd4zuVblFQyrZSD7eOrVU+r8PZgSVE1Ey8vhe5yT9uPUusx6idC+y2g5GUSfR5yJCTmWIqn6LHxHnrhVNF0O4Pv+9LEB5gyAIgrWD3+DLfMyNJVbaZDAuvSQw4rXH1moRd2Ri9eihljhva1XKZjeumC0eV5z5cQy98tJYWbygQjhQ9WrrjMDSkRzM6s11Den3xZnri/VU0ddGmakWBYX2sSd8wLNszZ9UuTdkWrvfALk0slZecnEy3n2csn6KHEoOmJ6si08/GXNHFUIomjPclCA/wWgxQvIoTF2U7cWRaZXNZj773vak7vvJdJvrYZq7/7F7P29XHiK6Lnfg0rbCCZ6/pThPmrPHEBT+pKtatyEKsm9Z39K6xnoSNUULfT9fuMZVPsZsQ2EgaRiuEwK37xu/7EvgH3O9t4ncGAbvHt5rZo3mD7FplW40LUwKjozNBcNaILzfup38v3yECmvl9l2Y5dKC4rLpujNl5VmcMOVwiMoA0rJtBDTPTaOoXm+jz9SxB4hwWE62oiNBHq/eQ1/Rt15CWbj5gKSaqpKycBrz0Ke08ZG7Q/zysp/AIjL7e5ZEIzfhyu/T3rfSzg0WldO+M5Z7Hefl9XwJ/gCEDlrATk+ZHHA/X89wJ82y76KvhGjcV63FJIr2Wl5il3NJrSzZKHR+fI72Gd/5prFZeE0i0gLCCODJgCTtxYX7E8fCx3DBiSv05Ls5rI6Ycy2i6T68teWQly497jmh+DokWEO/AkAHHsTixjOOJ95ih6PPTizXTYvvB4pg6TSDOCwQFGDLgOBYnlnE88R4zFH1+erFmWhjt64VES16dDFf3A8AuyOxhY1HZy4Vnr8pWHAD4F36rBlnUKb+m84WMrIzeGgg7c/A6jLrOfB7K8fgBq8RMnaxDNnVqWo8OlJSZnic7PDzz4WrhvMBl5WWnUEGxO1IrLHmSlJQck+lFI/TkTLjdxn9gnrKKm65jk3rC/V2rPfl61MtMo2t6t6CdB0uoef1M6tehMZ0jkSrKsNJu7hej+wWOI/EHDJlFN18vXYG9KnvCh6sNXbKVY+gpBBvBrtcXPL+wRp1Z34u9FdVl8MPYqFxZF/RF5C4HWMaGvTB8xGh6Lz01me7s347++vlmwzIy01LoV9O+0mxPvZCKGV9tF+EJdvtWwdFSV/eLxf0CV/74BF6LFuQiGK8kJbySq2AjZvYQjD5HmfgtJX5o8uebXZFx0TpPq3FUYcXsQSpzDfXa8w4JIzjJZt/yyhvSq/sFkjDhA+73FpBxIzZz03biauyVGzNPJ3YaM0cqOFZ9DEaZeslJS6U3v9lG2w4UU72MVLq+T2tq1TBbTCdGj8Scoq4D173zE3Mp3mEV8CWjLhEjL6fX0K7KtiLNY7ffmqk/u+1+b+d+QahAOJG1B5halHQjZjdtI9SuxlZ/fVpxY7ZSNq9HyT4Ao4+hPs5FXZtq/hp3W8ZFXYcPv/+JEoH9RWVCxkfvulq5htHw12R+pirSPFb7reINqTUd7WUKKTv3i1f3GAgG8Fr0UQ7F6neslm3FdVt9DP71yobKKG+ely7VnINwxfZDlCgYtaWda+h2HYzwwhvSrbqqc1kiVCC+wYjMAzkUr75jtWwrrttqSY/oKRitNRwv3eD/vqRScDJRMGpLO9fQ7TqYwf0iOk2Zl56AsnXlfsQv7r+c2NnNskGwwIhMMvMBryOwgrIXkhJeZV5g123ZZ0lSVTJflvSInoLhNRCePlJLfXgh45JoyFxXK9dQq3yZ73LfdiqFYlX92QlW+x733z/NWyeSSEMSJj6BIZPMfMDSIuOGeiMp4VXmBXYgGHG+ubSHUqrecoryuVrqw6jOoDZ2r6vVa6iGr5TMd7lvh8nBwWrfU/qvso4HSZj4A4bMwly/l+sBXpU96vIuIg7J6P7kYzwwoKOhdItW3jy9OtfJSNF8wLj5iGD9Ma3y+Nh8vvzrWoaM1GQhq6KFG+FlXDbXx8l11buG/J4/Z/f53Oy0Wt/jEUiv1g3E9vo62+263vuNXt8z6r8Hi4/TyAGnxnQ9D8QGuN9HkaiZPWav/Il++9YKW1IfWnWWzezxyY97XF8T44ezes2GUySdKK+gfy/bTmt2HxEGqnN+Lv38jJZ07il54jtLN+6nxRv30U+HjtGx4+U09wd3ZV1evaE3NaiT7ui6qq+h0qY8YpOJj+L2WLppv3B+4Md63/Z5zrJ6BASl77Fjh0w/4v47pEdzZPYICYgjA6EIbpU9rixO44HsyNnIYDdWywzER/kfnA28AzIuIBAOJ06PaxWn0iF25GxkUGK13AZSKv72XxAMYMiApw4nTo7rBLvxQF7GESlly8TpWS3Trf3Cil/9FwQDGDLga3Cr0XGdYDceyMs4Ii6b17N4KpCnwXhNkv/ye3Vog9Uy3dwvzPjVf4H/YI0MBEbmQn3cvLoZ9NDbK2hPYamlpMRurZFZkbORXSN7YkgXunfGcleT1vqV7zDIQKYlfoCzR0hvAjeOr+XdxmWovfj4CcfyGn4ZKpnj6nnj6WFkEJRj7z5cQgeKyqhh3coAd2XNRF2vg0WlwuAwese2InWjeC0+NfsH3ZydTgyOWTu9ekMvurxHc0tlAhAEkDTYBn5rFblxfC3tsT98sEbEMxWVaYtRxuIc7Zwbf84yJEZaamrydcrTOraCEl+ljqHjevFxZ323S/o7HL9WVKrdvrz/h6t2GSaedpK0ls93QJcmupI3rAWXnJyEqTUQt2BEFhCtIjeOb0e3yuoxYnluVkZkIwd0pPsuPqXWaMbqqE5dr1+fX2lEtbZzeRxc2zavjshPyam93EArTs8tzTmsE4GwAfd7C/C0E/9ij0imZwri8Xk6UeuhK4OX52j33Iy+p/WQfuvrbZaObYSy/9T/abenkubora+30+BuzcRft7DqlGHlunvZhwHwExiyAMTiuHF8J7pVsseI5blZiedyowytMo3aUzkmt7tbcWd24pxkr3uixJOBxASGLACxOG4c3y3dKrfP0e65uaHrFovYKTf1wuzEOVk9frzHk4HEBIYsALE4bhzfLd0qt8/R7rm5oesWi9gpt9qd19vsrF9ZPX4ixJOBxAOGLADpbdw4vhPdquhjaGWesJuNwu65WUldpZTRs1V9mrpoEz3x/irxl9/LZsHXKtOoPZVjcrs7TbHF37/v4lNtfVf2uiNFE4hn4H6vSm/D3m3R8UGxSG/jxvEV3SonXot8jE9W767lqq7noi7jsq+c213Tl2luj+icm1GbaNW9W4sc6jp2bo31oqc/XEOXdG5iObhZKZPbc3JVe+pdE253pZ5WcaNvWbnuSNEE4hWMyAKS3saN4+vpVvHbOjqaW+pjMPxAjnZeYAMWrVWmpRrtR+oq3qbEUEUPEvk9f87b9UZmrIodrdWltAe3p8w1Ueqpdwz+XEsjza2+ZaY5x8eF6z2IZxBHFkWiZvZgrMqXyGSjcENmpEbqqqi68/Rh9EgsGi72hycH0Yrth6Qye0S3uew1qc4eUniMDhwtpYZ10ik/N6t6f6/7lnLdN+8vEu3aq1UDalb/5PEBCBtIUQVipgtmpPHktU4Ur4Vx5gozxlzRmW4/v73l8gEA/oGAaBAzt2yj73od2iDrfu6mmzwAIFjA2QM4dss2+q7XoQ2y7uduucnHw/Q1APEGDBmo4e5uxcNPWd8ycuk2K1emDCN4/Y+9E83WyHi/IOB3YmoA4pG4NGROfvG68WvZ71/cR4+doJH/Wk7bDpZQ6wZZNPH6XpSVnmJYJ1l3d6uu426HNmi1rZn7OW9nN3WZshi1JhpXuKCottyNmUON1v6j31tJ7y7bWaseigeokWeh330qqHUBwJazx86dO+mRRx6hOXPmUHFxMZ1yyik0bdo0OvPMM8V2Lm7s2LE0ZcoUOnToEPXr149ee+01OvXUU2Pi7OHkF68bv5b9/sU99OVFtHJHYa3P01KS6Hh5xLROWvV3EkdmVK6bZSzfdrCW3As/W9mIsXu6TFla56nG6FhJSURZaSlUrJLKMdrfivem330qqHUB8U+hF16LBw8epF69etFFF11Ed999NzVu3JjWr19PHTp0EC/mueeeowkTJtAbb7xB7dq1ozFjxtD3339Pq1evpszMTE8NmRMpFDdkVPyWgtEzYnZFKPVGKn6MVGXa9uJOTWuNkrRGYnakXZRjWQ2stnqMaO9Nv/uUmiDVBSQGhV4YskcffZQWL15MixYt0tzORTVv3pweeugh+t3vfic+4wo0bdqUXn/9dRo2bJhrFXczXsmtWCenZTidTuw27iNL3/G6Tm7hZtualeU3aj0yv/uUmiDVBSQOhV6438+aNUtMIV533XXUpEkTMTrjKUSFzZs30+7du2nAgAHVn3Elzj77bFqyZIlmmaWlpaKy6lespVDckFHxWwqG18SsEhZpDzfb1om0SyxQe2/63afUBKkuADgyZJs2bape7/roo4/E9OL9998vphEZNmIMj8DU8HtlWzQ8DcnGTnm1atWK7OAkXsmNWCe/pWDYscMuQZf2cLNtg3quWkl9/e5Tdo4R1PYF8Y0lQ1ZRUUG9e/emZ555RozG7rjjDhoxYgRNmjTJdgVGjRolho3Ka/t2e2q7TuKV3Ih18lsKhr0T7RJ0aQ832zbI5xrtvel3n7JzjCC3L4hfLBmyZs2aUZcuNT3AOnfuTNu2VcrM5+fni7979uypsQ+/V7ZFk5GRIeY+1a9YS6G4IaPitxQMu9hbJSzSHm62rRV5mFjRtF66pqOE330qqHUBwJEhY1f6tWvX1vhs3bp11KZNG/F/9lJkgzV//vzq7bzm9eWXX1Lfvn3JS5R4JSb6ZjOLV3LyXTfLcELdzFTq0VL+R0As6uQWbratUVlmeLH/kB7N6ItRAzS9/fzuU0GtCwCODNnIkSNp6dKlYmpxw4YNNGPGDJo8eTLde++9YntSUhI98MAD9Ic//EE4hrDb/U033SQ8Ga+++mryGidSKG7IqPgtBTPrvvN1jRnHkflRJ7dws231yqqvIekSfaxJw3trS+UkEWVHSeUY7c/v+fOXb+ht+PD3u08FtS4AOAqInj17tljX4vgxHoE9+OCDYp1MQQmIZgPHAdHnnXcevfrqq9SxY8eYZb9HZg/rmT3CgpttG+vMHjIxbrE4b6cEqS4gvoGMCwAAgFADGRcAAAAJAQwZAACAUANDBgAAINTEpYwLCCZwEgAAeAEMGYgJkP8AAHgFphZBzOQ/opPOKoKSvB0AAOwCQwY8n07kkZhWjIfyGW/n/QAAwA4wZMBTIP8BAPAaGDLgKZD/AAB4DQwZ8BTIfwAAvAaGDHgK5D8AAF4DQwY8BfIfAACvgSEDngP5DwCAlyAgGsQENlYDu+QjswcAwHVgyEDMYKPVt0Mjv6sBAIgzMLUIAAAg1MCQAQAACDUwZAAAAEIN1siigNQIAACECxgyFZAaAQCA8IGpxSogNQIAAOEEhgxSIwAAEGpgyCA1AgAAoQaGDFIjAAAQamDIIDUCAAChBoYMUiMAABBqYMggNRI3sDPOko376f0VO8VfOOcAkBggjixKaiQ6joylRhBHFnwQAwhA4pIUiUQC9bO1sLCQcnNz6fDhw5STkxPz4yOzR3hjAKM7snLVoHkGQDiRtQcYkUUBqZH4igFkY8bbWQsNP0gAiE+wRgZCDWIAAQAwZCDUIAYQAABDBkINYgABADBkINQgBhAAAEPmQiwS4pf8AzGAAAB4LTqMRUL8kv8gBhCAxAZxZA5ikRC/FCwQAwhAfCFrDzC1aFOPDBpmwY0BvKpnC/EXRgyAxACGzGYsEuKXAAAgGMCQ2YxFQvwSAAAEAxgym7FIiF8CAIBgAENmMxYJ8UsAABAMYMhsxiIhfgkAAIIBDFlULBLHHqnh93pu9Ha+AwAAwF0QR+ZCLBLilwAAwH2gRxZDPTJomAEAgH9gahEAAECogSEDAAAQamDIAAAAhBqskcUhcD4BACQSMGRxBmRlAACJBqYW4whFViY6mfHuw8fE57wdAADiDRiyOAGyMgCARAWGLE6ArAwAIFGBIYsTICsDAEhUYMjiBMjKAAASFRiyOAGyMgCARAWGLE6ArAwAIFGBIYsjICsDAEhEEBAdZ7CxGtglH5k9AAAJAwxZHAJZGQBAIoGpRQAAAKEGhgwAAECogSEDAAAQamDIAAAAJI4hGzduHCUlJdV4derUqXr7hRdeWGv7XXfd5UW9AQAAAHtei127dqV58+ZVv09NrVnEiBEj6Kmnnqp+n52dbfUQAAAAgHeGjA1Xfn6+7nY2XEbbAQAAAF/XyNavX0/Nmzen9u3b04033kjbtm2rsf3NN9+kvLw86tatG40aNYqKi4sNyystLaXCwsIaLwAAAMCTEdnZZ59Nr7/+Op122mm0a9cuevLJJ+n888+nVatWUb169eiGG26gNm3aCEO3cuVKeuSRR2jt2rX03nvv6ZY5YcIEUQ4AAABgh6RIJGJbMvjQoUPCcL300kt0++2319q+YMECuuSSS2jDhg3UoUMH3REZvxQOHz5MrVu3pu3bt1NOTo7dqgEAAAg5PEPXqlUrYWtyc3O9SVFVv3596tixozBUeiM4xsiQZWRkiJe64gxXHgAAADhy5Ih3huzo0aO0ceNG+tWvfqW5fcWKFeJvs2byWdd5WpJHYzxVye77aqucqKM0nD/OH+eP80/E849EIsKIsV0wwpIh+93vfkdXXnmlmE786aefaOzYsZSSkkK//OUvhUGbMWMGXX755dSoUSOxRjZy5Ejq378/9ejRQ/oYycnJ1LJlS81tfBET7UKqwfnj/HH+OP9EI9dgJGbLkO3YsUMYrf3791Pjxo3pvPPOo6VLl4r/Hzt2TMSX/elPf6KioiLxC+Laa6+lxx9/3Mk5AAAAAO4Zsrfeekt3Gxuuzz77zEpxAAAAQGLkWmRnEJ7GVDuFJBI4f5w/zh/nn6jn77n7PQAAAOA3oRiRAQAAAHrAkAEAAAg1MGQAAABCDQwZAACAUANDBgAAINQEypDt3LmThg8fLjKDZGVlUffu3embb77R3JeVpzmFFQdgxwsy579mzRoaOnSoiHavU6cO9enTp5aUTryeP6dEu++++0TmF97epUsXmjRpEsUDbdu2raWuzq97771XbOeEA/x/bpu6deuKZAN79uyheMHo/A8cOEC/+c1vhOoGX3dOKn7//feLBOPxgtn1V2An88GDB4ttM2fO9K2+QcNRrkU3OXjwIPXr148uuugimjNnjsgWwtpnDRo0qLXvf/7zH5FRxCz/VpiQOX9OA8bZVFhpgKVvOF3NDz/8QJmZmZQI5//ggw8KRYXp06eLG//jjz+me+65R/QDNu5h5uuvv6by8vLq9yyNNHDgQLruuuvEe0739sEHH9A777wjfsSwQb/mmmto8eLFFA8YnT+nw+PXCy+8IH68bN26VfyQ5c/effddigfMrr8C/3BXctACFZGA8Mgjj0TOO+880/127NgRadGiRWTVqlWRNm3aRCZOnBiJB2TO//rrr48MHz48Eo/InH/Xrl0jTz31VI3PevfuHXnsscci8cZvf/vbSIcOHSIVFRWRQ4cORdLS0iLvvPNO9fY1a9Zw/GdkyZIlkXhEff5avP3225H09PTI8ePHI4ly/suXLxfPvl27dolr/5///MfXOgaJwEwtzpo1i84880zxC6RJkybUq1cvmjJlSo19KioqRKb9hx9+mLp27UrxhNn587nzL3KWzbnsssvEPiyTEy/TCzLX/9xzzxX78RQkT7EsXLiQ1q1bR5deeinFE2VlZWLUedttt4lf399++y0dP36cBgwYUL1Pp06dxBTbkiVLKN6IPn8teFqRZyRSUwMzqeTp+RcXFwvh4ldeeYXy8/P9rmLwiASEjIwM8Ro1alRk2bJlkb/+9a+RzMzMyOuvv169zzPPPBMZOHBg9a+UeBqRmZ2/8issOzs78tJLL4lfZxMmTIgkJSVFPv3000jYkbn+x44di9x0002iHVJTU8Uv8jfeeCMSb/zrX/+KpKSkRHbu3Cnev/nmm+Jco+nTp0/k97//fSTezz+affv2RVq3bh0ZPXp0JB7ROv877rgjcvvtt1e/x4isJoExZDx10rdv3xqf/eY3v4mcc8454v/ffPNNpGnTpjUubjwZMrPz5/PmzvvLX/6yxj5XXnllZNiwYZF4P3/m+eefj3Ts2DEya9asyHfffRf5y1/+Eqlbt27kk08+icQTl156aWTIkCHV7xPNkEWfv5rDhw9HzjrrrMigQYMiZWVlkXgk+vzff//9yCmnnBI5cuRI9WcwZAGdWmTxTV7IVdO5c+dqj7xFixbR3r17xXQKTyfwixd9H3roIbHwH3bMzj8vL0+cs9E+8Xz+JSUlNHr0aHrppZeEJh5r3LHDw/XXXy+cAOIF7tMsh/TrX/+6+jOeSuLpJpZ7V8Nei/E2zaR1/gossDho0CAhussOX2lpaRRvaJ0/Ozixo1f9+vWrn30Me65eeOGFPtY2OARmgpk91tauXVvjM17/YBFPhtfG1GsEDK8V8ee33norhR2z809PTxeu9kb7xPP58xoRv1h4VQ0Lu/L6Ybwwbdo0sUZ4xRVXVH92xhlniIf2/PnzxcOL4bZiI9+3b1+KJ7TOX1FJ5vudM8DzOmk8eOrKnv+jjz5ay7BzaMrEiRPFjzoQoDWyr776Sqx7PP3005H169eL6RReD5o+fbrud+JpalHm/N977z0xBTd58mSxD0+t8Vz6okWLIolw/hdccIHwXFy4cGFk06ZNkWnTpol1tFdffTUSD5SXl4u1H/bgjOauu+4S2xYsWCCm2XkaNnoqNl7Pn6cTzz777Ej37t0jGzZsEOvFyuvEiROReMHo+keDqcWaBMaQMf/9738j3bp1E4v+nTp1Eg9sI+LJkMme/9SpU8V8OT/ATz/99MjMmTMjiXL+/OC65ZZbIs2bNxfnf9ppp0VefPFFXRftsPHRRx+JB9TatWtrbSspKYncc889kQYNGggD/7Of/Uy0Rzyhd/78w4U/13pt3rw5Ei8YXf9oYMhqAj0yAAAAoSYwzh4AAACAHWDIAAAAhBoYMgAAAKEGhgwAAECogSEDAAAQamDIAAAAhBoYMgAAAKEGhgwAAECogSEDAAAQamDIAAAAhBoYMgAAABRm/j9bB+cDS1g6FwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize = (5, 5))\n",
    "ax.scatter(predicted_data['midparentHeight'], predicted_data['childHeight'])\n",
    "ax.scatter(predicted_data['midparentHeight'], predicted_data['predicted_height'], color = 'yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0d230fc1-3e88-41c5-8e9f-69f9406996cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.637360896969479), np.float64(22.63624054958975))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope = r*sd_y/sd_x\n",
    "intercept = mean_y - slope*mean_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66ff9742-1adb-470a-afd3-6d7f6d2d1b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10dead5b0>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize = (5, 5))\n",
    "ax.scatter(predicted_data['midparentHeight'], predicted_data['childHeight'])\n",
    "ax.scatter(predicted_data['midparentHeight'], predicted_data['predicted_height'], color = 'yellow')\n",
    "l = np.linspace(64, 80, 100)\n",
    "ax.plot(l, slope*l+intercept, color='red', linewidth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1413e594-76ce-4ead-8e1f-fc9fed1663e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}