Body Dimensions.ipynb

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 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "## Research Report - Body Dimensions (bdims.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n",
    "Heinz, G., Peterson, L. J., Johnson, R. W., &amp; Kerk, C. J. (2017). Exploring relationships in body dimensions. Journal of Statistics Education, 11(2). https://doi.org/10.1080/10691898.2003.11910711 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns; sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.stats import norm\n",
    "from scipy.stats import t\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "## Title - Body Dimension Data Analysis\n",
    "\n",
    "\n",
    "### Motivation\n",
    "\n",
    "Some people are tall, some are short, some are fat, and some are thin. There are a lot of differences between our bodies and investigating these differences sometimes is very interesting. We were thinking if there would be some kind of associations between the figures of girths and diameters of different body parts of males and females. Therefore, we choose this data set that has data on different body parts, which can help us investigate the correspondence between body build and girths in men and women of different ages.\n",
    "\n",
    "\n",
    "### Dataset Brief\n",
    "\n",
    "This dataset consists of data from 247 males and 260 females, all taken in California. All the participants are physically active and do exercise several hours per week, in which most of them are in their twenties and early thirties, with a small group of older men and women. Thus, concluding from the statement above, the population of our dataset is physically active people in California. \n",
    "\n",
    "The author also stated that \"the dataset does not constitute a random sample from a well-defined population\" (Heinz et al., 2017), which means the dataset is not random.\n",
    "\n",
    "See the “Variables” part for the detail of the data.\n",
    "\n",
    "### Variables\n",
    "\n",
    "**Please note**: We modified the variable names of the original dataset, by changing all the dots (\".\") to underscores (\"_\"). For example, **bia.di** is replaced by **bia_di**. We made the change because some Python functions do not recognize the variable name with a dot in it.\n",
    "\n",
    "- **bia_di**: A numerical vector, respondent's biacromial diameter in centimeters.\n",
    "- **bii_di**: A numerical vector, respondent's biiliac diameter (pelvic breadth) in centimeters.\n",
    "- **bit_di**: A numerical vector, respondent's bitrochanteric diameter in centimeters.\n",
    "- **che_de**: A numerical vector, respondent's chest depth in centimeters, measured between spine and sternum at nipple level, mid-expiration.\n",
    "- **che_di**: A numerical vector, respondent's chest diameter in centimeters, measured at nipple level, mid-expiration.\n",
    "- **elb_di**: A numerical vector, respondent's elbow diameter in centimeters, measured as sum of two elbows.\n",
    "- **wri_di**: A numerical vector, respondent's wrist diameter in centimeters, measured as sum of two wrists.\n",
    "- **kne_di**: A numerical vector, respondent's knee diameter in centimeters, measured as sum of two knees.\n",
    "- **ank_di**: A numerical vector, respondent's ankle diameter in centimeters, measured as sum of two ankles.\n",
    "- **sho_gi**: A numerical vector, respondent's shoulder girth in centimeters, measured over deltoid muscles.\n",
    "- **che_gi**: A numerical vector, respondent's chest girth in centimeters, measured at nipple line in males and just above breast tissue in females, mid-expiration.\n",
    "- **wai_gi**: A numerical vector, respondent's waist girth in centimeters, measured at the narrowest part of torso below the rib cage as average of contracted and relaxed position.\n",
    "- **nav_gi**: A numerical vector, respondent's navel (abdominal) girth in centimeters, measured at umbilicus and iliac crest using iliac crest as a landmark.\n",
    "- **hip_gi**: A numerical vector, respondent's hip girth in centimeters, measured at at level of bitrochanteric diameter.\n",
    "- **thi_gi**: A numerical vector, respondent's thigh girth in centimeters, measured below gluteal fold as the average of right and left girths.\n",
    "- **bic_gi**: A numerical vector, respondent's bicep girth in centimeters, measured when flexed as the average of right and left girths.\n",
    "- **for_gi**: A numerical vector, respondent's forearm girth in centimeters, measured when extended, palm up as the average of right and left girths.\n",
    "- **kne_gi**: A numerical vector, respondent's knee diameter in centimeters, measured as sum of two knees.\n",
    "- **cal_gi**: A numerical vector, respondent's calf maximum girth in centimeters, measured as average of right and left girths.\n",
    "- **ank_gi**: A numerical vector, respondent's ankle minimum girth in centimeters, measured as average of right and left girths.\n",
    "- **wri_gi**: A numerical vector, respondent's wrist minimum girth in centimeters, measured as average of right and left girths.\n",
    "- **age**: A numerical vector, respondent's age in years.\n",
    "- **wgt**: A numerical vector, respondent's weight in kilograms.\n",
    "- **hgt**: A numerical vector, respondent's height in centimeters.\n",
    "- **sex**: A categorical vector, 1 if the respondent is male, 0 if female.\n",
    "\n",
    "\n",
    "### Research Questions\n",
    "\n",
    "**Descriptive Analytics Research Question** - What is the tendency between Wrist Diameter and Ankle Diameter under the influence of Gender?\n",
    "\n",
    "**Inference Research Question** - What is the association between the mean wrist diameter for males and females?\n",
    "\n",
    "**Linear Regression Research Question** - Is there a linear relationship between Wrist Diameter and Gender, Age, Thigh Girth, and Chest Diameter in the sample? What about in the population?\n",
    "\n",
    "**Logistic Regression Research Question** - Is there a linear relationship between the log-odds of the of Gender and Wrist Diameter, Age, Thigh Girth, and Chest Diameter in the sample? What about in the population? What explanatory variables should we include in the model to build a parsimonious model?\n",
    "\n",
    "\n",
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bia_di    0\n",
       "bii_di    0\n",
       "bit_di    0\n",
       "che_de    0\n",
       "che_di    0\n",
       "elb_di    0\n",
       "wri_di    0\n",
       "kne_di    0\n",
       "ank_di    0\n",
       "sho_gi    0\n",
       "che_gi    0\n",
       "wai_gi    0\n",
       "nav_gi    0\n",
       "hip_gi    0\n",
       "thi_gi    0\n",
       "bic_gi    0\n",
       "for_gi    0\n",
       "kne_gi    0\n",
       "cal_gi    0\n",
       "ank_gi    0\n",
       "wri_gi    0\n",
       "age       0\n",
       "wgt       0\n",
       "hgt       0\n",
       "sex       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions = pd.read_csv('bdims.csv')\n",
    "body_dimensions.isna().sum()\n",
    "# There is no unique or missing values in this dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bia_di</th>\n",
       "      <th>bii_di</th>\n",
       "      <th>bit_di</th>\n",
       "      <th>che_de</th>\n",
       "      <th>che_di</th>\n",
       "      <th>elb_di</th>\n",
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       "      <th>sho_gi</th>\n",
       "      <th>...</th>\n",
       "      <th>bic_gi</th>\n",
       "      <th>for_gi</th>\n",
       "      <th>kne_gi</th>\n",
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       "      <th>wri_gi</th>\n",
       "      <th>age</th>\n",
       "      <th>wgt</th>\n",
       "      <th>hgt</th>\n",
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       "      <th>26</th>\n",
       "      <td>45.4</td>\n",
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       "      <td>35.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>16.7</td>\n",
       "      <td>21</td>\n",
       "      <td>82.8</td>\n",
       "      <td>180.3</td>\n",
       "      <td>1</td>\n",
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       "      <th>375</th>\n",
       "      <td>35.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>30.6</td>\n",
       "      <td>16.2</td>\n",
       "      <td>26.8</td>\n",
       "      <td>12.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>16.1</td>\n",
       "      <td>12.2</td>\n",
       "      <td>98.2</td>\n",
       "      <td>...</td>\n",
       "      <td>26.6</td>\n",
       "      <td>23.2</td>\n",
       "      <td>35.2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>23</td>\n",
       "      <td>53.8</td>\n",
       "      <td>164.0</td>\n",
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       "      <td>29.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.2</td>\n",
       "      <td>23.2</td>\n",
       "      <td>35.6</td>\n",
       "      <td>30.5</td>\n",
       "      <td>20.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>32</td>\n",
       "      <td>56.0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>39.4</td>\n",
       "      <td>29.7</td>\n",
       "      <td>33.1</td>\n",
       "      <td>22.3</td>\n",
       "      <td>31.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>14.8</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>30</td>\n",
       "      <td>80.9</td>\n",
       "      <td>182.9</td>\n",
       "      <td>1</td>\n",
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       "      <th>407</th>\n",
       "      <td>36.7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>27.3</td>\n",
       "      <td>13.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.6</td>\n",
       "      <td>22.8</td>\n",
       "      <td>32.5</td>\n",
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       "      <td>27</td>\n",
       "      <td>55.9</td>\n",
       "      <td>170.2</td>\n",
       "      <td>0</td>\n",
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       "      <th>398</th>\n",
       "      <td>36.9</td>\n",
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       "      <td>40</td>\n",
       "      <td>56.8</td>\n",
       "      <td>168.9</td>\n",
       "      <td>0</td>\n",
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       "      <th>322</th>\n",
       "      <td>36.0</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>13.8</td>\n",
       "      <td>92.2</td>\n",
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       "      <td>21.7</td>\n",
       "      <td>37.4</td>\n",
       "      <td>35.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>52</td>\n",
       "      <td>55.7</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>39.1</td>\n",
       "      <td>29.7</td>\n",
       "      <td>33.7</td>\n",
       "      <td>20.1</td>\n",
       "      <td>29.5</td>\n",
       "      <td>13.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>13.6</td>\n",
       "      <td>114.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6</td>\n",
       "      <td>26.6</td>\n",
       "      <td>39.6</td>\n",
       "      <td>38.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>15.9</td>\n",
       "      <td>22</td>\n",
       "      <td>83.0</td>\n",
       "      <td>176.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>37.6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>16.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>11.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.3</td>\n",
       "      <td>20.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>22</td>\n",
       "      <td>51.6</td>\n",
       "      <td>161.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>25.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.8</td>\n",
       "      <td>13.8</td>\n",
       "      <td>100.1</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>35.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>15.3</td>\n",
       "      <td>19</td>\n",
       "      <td>67.9</td>\n",
       "      <td>161.3</td>\n",
       "      <td>0</td>\n",
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       "<p>10 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "     bia_di  bii_di  bit_di  che_de  che_di  elb_di  wri_di  kne_di  ank_di  \\\n",
       "26     45.4    31.8    35.2    20.2    32.3    14.6    10.5    20.2    15.3   \n",
       "375    35.1    23.4    30.6    16.2    26.8    12.1     9.4    16.1    12.2   \n",
       "394    35.4    27.0    29.0    17.8    25.0    11.6     9.2    18.2    12.1   \n",
       "153    39.4    29.7    33.1    22.3    31.5    15.6    12.0    19.5    14.8   \n",
       "407    36.7    28.0    31.7    17.3    27.3    13.1    10.6    18.4    13.4   \n",
       "398    36.9    31.1    33.8    16.8    26.6    13.0     9.8    18.4    13.8   \n",
       "322    36.0    28.6    32.0    17.5    24.1    12.8     9.8    18.7    13.8   \n",
       "279    39.1    29.7    33.7    20.1    29.5    13.2    10.0    20.3    13.6   \n",
       "247    37.6    25.0    31.3    16.2    24.9    11.2     9.2    17.0    12.3   \n",
       "285    36.0    28.0    30.8    17.3    25.9    12.9     9.8    17.8    13.8   \n",
       "\n",
       "     sho_gi  ...  bic_gi  for_gi  kne_gi  cal_gi  ank_gi  wri_gi  age   wgt  \\\n",
       "26    115.0  ...    35.5    28.5    38.0    40.0    23.5    16.7   21  82.8   \n",
       "375    98.2  ...    26.6    23.2    35.2    35.0    21.0    14.8   23  53.8   \n",
       "394    98.0  ...    27.2    23.2    35.6    30.5    20.1    14.6   32  56.0   \n",
       "153   119.0  ...    34.0    28.4    38.0    37.0    22.5    17.4   30  80.9   \n",
       "407    99.0  ...    26.6    22.8    32.5    34.6    21.2    14.1   27  55.9   \n",
       "398    98.2  ...    26.4    23.1    34.6    32.3    20.4    14.7   40  56.8   \n",
       "322    92.2  ...    24.7    21.7    37.4    35.5    21.0    15.0   52  55.7   \n",
       "279   114.0  ...    31.6    26.6    39.6    38.9    23.3    15.9   22  83.0   \n",
       "247    95.0  ...    24.3    20.5    32.0    32.2    21.0    13.5   22  51.6   \n",
       "285   100.1  ...    31.0    24.9    35.5    38.0    21.5    15.3   19  67.9   \n",
       "\n",
       "       hgt  sex  \n",
       "26   180.3    1  \n",
       "375  164.0    0  \n",
       "394  157.5    0  \n",
       "153  182.9    1  \n",
       "407  170.2    0  \n",
       "398  168.9    0  \n",
       "322  164.0    0  \n",
       "279  176.5    0  \n",
       "247  161.2    0  \n",
       "285  161.3    0  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions.sample(10)\n",
    "# 10 random samples for this dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(507, 25)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are 507 rows and 25 columns in the original dataframe.**\n",
    "\n",
    "We split the data frame into the female version and male version respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>bia_di</th>\n",
       "      <th>bii_di</th>\n",
       "      <th>bit_di</th>\n",
       "      <th>che_de</th>\n",
       "      <th>che_di</th>\n",
       "      <th>elb_di</th>\n",
       "      <th>wri_di</th>\n",
       "      <th>kne_di</th>\n",
       "      <th>ank_di</th>\n",
       "      <th>sho_gi</th>\n",
       "      <th>...</th>\n",
       "      <th>bic_gi</th>\n",
       "      <th>for_gi</th>\n",
       "      <th>kne_gi</th>\n",
       "      <th>cal_gi</th>\n",
       "      <th>ank_gi</th>\n",
       "      <th>wri_gi</th>\n",
       "      <th>age</th>\n",
       "      <th>wgt</th>\n",
       "      <th>hgt</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>42.6</td>\n",
       "      <td>31.8</td>\n",
       "      <td>34.8</td>\n",
       "      <td>19.9</td>\n",
       "      <td>30.4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>117.1</td>\n",
       "      <td>...</td>\n",
       "      <td>34.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>40.1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>24.2</td>\n",
       "      <td>17.6</td>\n",
       "      <td>29</td>\n",
       "      <td>87.8</td>\n",
       "      <td>176.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>37.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>17.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>...</td>\n",
       "      <td>30.1</td>\n",
       "      <td>25.2</td>\n",
       "      <td>35.8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19</td>\n",
       "      <td>62.2</td>\n",
       "      <td>167.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>33.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>35.9</td>\n",
       "      <td>32.5</td>\n",
       "      <td>19.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>21</td>\n",
       "      <td>60.7</td>\n",
       "      <td>180.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>38.3</td>\n",
       "      <td>30.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>13.6</td>\n",
       "      <td>10.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>102.3</td>\n",
       "      <td>...</td>\n",
       "      <td>29.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>22.5</td>\n",
       "      <td>16.4</td>\n",
       "      <td>24</td>\n",
       "      <td>65.5</td>\n",
       "      <td>175.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>36.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>29.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>12.2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>31.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>23</td>\n",
       "      <td>46.4</td>\n",
       "      <td>158.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>33.3</td>\n",
       "      <td>29.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>25.9</td>\n",
       "      <td>12.6</td>\n",
       "      <td>10.4</td>\n",
       "      <td>18.4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>98.7</td>\n",
       "      <td>...</td>\n",
       "      <td>27.7</td>\n",
       "      <td>24.5</td>\n",
       "      <td>33.1</td>\n",
       "      <td>35.4</td>\n",
       "      <td>21.2</td>\n",
       "      <td>15.7</td>\n",
       "      <td>19</td>\n",
       "      <td>58.3</td>\n",
       "      <td>167.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>35.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.2</td>\n",
       "      <td>23.2</td>\n",
       "      <td>35.6</td>\n",
       "      <td>30.5</td>\n",
       "      <td>20.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>32</td>\n",
       "      <td>56.0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>35.1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>29.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>94.4</td>\n",
       "      <td>...</td>\n",
       "      <td>28.8</td>\n",
       "      <td>24.7</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>15.8</td>\n",
       "      <td>32</td>\n",
       "      <td>55.4</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>40.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>33.2</td>\n",
       "      <td>14.1</td>\n",
       "      <td>12.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>12.9</td>\n",
       "      <td>129.5</td>\n",
       "      <td>...</td>\n",
       "      <td>40.3</td>\n",
       "      <td>30.8</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>18.2</td>\n",
       "      <td>34</td>\n",
       "      <td>105.2</td>\n",
       "      <td>172.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>36.9</td>\n",
       "      <td>31.1</td>\n",
       "      <td>33.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>26.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>98.2</td>\n",
       "      <td>...</td>\n",
       "      <td>26.4</td>\n",
       "      <td>23.1</td>\n",
       "      <td>34.6</td>\n",
       "      <td>32.3</td>\n",
       "      <td>20.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>40</td>\n",
       "      <td>56.8</td>\n",
       "      <td>168.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bia_di  bii_di  bit_di  che_de  che_di  elb_di  wri_di  kne_di  ank_di  \\\n",
       "270    42.6    31.8    34.8    19.9    30.4    12.5    11.0    21.0    14.2   \n",
       "385    37.1    25.0    29.3    17.1    28.1    13.0    10.9    17.5    13.0   \n",
       "303    33.8    29.7    32.4    17.3    25.4    13.4     9.6    18.4    13.4   \n",
       "432    38.3    30.8    35.6    17.0    26.8    13.6    10.4    19.0    13.6   \n",
       "382    36.4    27.7    29.8    17.0    25.0    11.2     9.0    16.9    12.2   \n",
       "292    33.3    29.2    32.0    14.8    25.9    12.6    10.4    18.4    13.0   \n",
       "394    35.4    27.0    29.0    17.8    25.0    11.6     9.2    18.2    12.1   \n",
       "472    35.1    26.6    29.2    17.0    24.4    11.8     9.4    17.0    12.2   \n",
       "358    40.0    31.0    35.8    21.3    33.2    14.1    12.2    24.3    12.9   \n",
       "398    36.9    31.1    33.8    16.8    26.6    13.0     9.8    18.4    13.8   \n",
       "\n",
       "     sho_gi  ...  bic_gi  for_gi  kne_gi  cal_gi  ank_gi  wri_gi  age    wgt  \\\n",
       "270   117.1  ...    34.4    28.3    40.1    40.0    24.2    17.6   29   87.8   \n",
       "385   108.5  ...    30.1    25.2    35.8    35.0    21.7    16.0   19   62.2   \n",
       "303    94.5  ...    26.0    22.6    35.9    32.5    19.7    14.6   21   60.7   \n",
       "432   102.3  ...    29.3    25.0    35.6    36.6    22.5    16.4   24   65.5   \n",
       "382    94.0  ...    25.0    22.2    31.6    31.6    18.8    14.2   23   46.4   \n",
       "292    98.7  ...    27.7    24.5    33.1    35.4    21.2    15.7   19   58.3   \n",
       "394    98.0  ...    27.2    23.2    35.6    30.5    20.1    14.6   32   56.0   \n",
       "472    94.4  ...    28.8    24.7    33.0    34.6    21.7    15.8   32   55.4   \n",
       "358   129.5  ...    40.3    30.8    45.6    45.0    25.8    18.2   34  105.2   \n",
       "398    98.2  ...    26.4    23.1    34.6    32.3    20.4    14.7   40   56.8   \n",
       "\n",
       "       hgt  sex  \n",
       "270  176.5    0  \n",
       "385  167.1    0  \n",
       "303  180.3    0  \n",
       "432  175.3    0  \n",
       "382  158.2    0  \n",
       "292  167.6    0  \n",
       "394  157.5    0  \n",
       "472  160.0    0  \n",
       "358  172.7    0  \n",
       "398  168.9    0  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_female = body_dimensions[body_dimensions['sex'] == 0]\n",
    "body_dimensions_female.sample(10)\n",
    "# Dataframe for females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260, 25)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_female.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are 260 rows and 25 columns in the female dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bia_di</th>\n",
       "      <th>bii_di</th>\n",
       "      <th>bit_di</th>\n",
       "      <th>che_de</th>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>44.8</td>\n",
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       "      <td>32.2</td>\n",
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       "      <td>14.7</td>\n",
       "      <td>120.6</td>\n",
       "      <td>...</td>\n",
       "      <td>39.4</td>\n",
       "      <td>31.9</td>\n",
       "      <td>38.5</td>\n",
       "      <td>41.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>18.2</td>\n",
       "      <td>26</td>\n",
       "      <td>81.8</td>\n",
       "      <td>173.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>41.1</td>\n",
       "      <td>31.7</td>\n",
       "      <td>34.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>11.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>15.4</td>\n",
       "      <td>122.6</td>\n",
       "      <td>...</td>\n",
       "      <td>38.6</td>\n",
       "      <td>30.3</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>25.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>38</td>\n",
       "      <td>88.6</td>\n",
       "      <td>174.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>40.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>31.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>27.6</td>\n",
       "      <td>13.9</td>\n",
       "      <td>10.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>108.3</td>\n",
       "      <td>...</td>\n",
       "      <td>31.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>36.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>22.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>38</td>\n",
       "      <td>68.4</td>\n",
       "      <td>176.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>37.6</td>\n",
       "      <td>26.6</td>\n",
       "      <td>29.9</td>\n",
       "      <td>17.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>12.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>103.3</td>\n",
       "      <td>...</td>\n",
       "      <td>30.5</td>\n",
       "      <td>24.8</td>\n",
       "      <td>31.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24</td>\n",
       "      <td>55.2</td>\n",
       "      <td>164.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>37.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>17.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>12.9</td>\n",
       "      <td>10.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>36.3</td>\n",
       "      <td>28.9</td>\n",
       "      <td>34.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>25</td>\n",
       "      <td>65.0</td>\n",
       "      <td>165.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>39.6</td>\n",
       "      <td>28.7</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>14.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>19.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>124.7</td>\n",
       "      <td>...</td>\n",
       "      <td>39.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>24</td>\n",
       "      <td>81.8</td>\n",
       "      <td>177.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>44.2</td>\n",
       "      <td>27.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>32.9</td>\n",
       "      <td>14.3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>115.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.6</td>\n",
       "      <td>27.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24</td>\n",
       "      <td>84.5</td>\n",
       "      <td>179.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>43.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>27.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>11.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>128.8</td>\n",
       "      <td>...</td>\n",
       "      <td>37.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>39.6</td>\n",
       "      <td>24.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>22</td>\n",
       "      <td>108.6</td>\n",
       "      <td>190.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>40.1</td>\n",
       "      <td>19.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>26.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>16.9</td>\n",
       "      <td>12.6</td>\n",
       "      <td>104.5</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20</td>\n",
       "      <td>55.5</td>\n",
       "      <td>168.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>38.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>13.6</td>\n",
       "      <td>10.4</td>\n",
       "      <td>17.6</td>\n",
       "      <td>14.2</td>\n",
       "      <td>104.3</td>\n",
       "      <td>...</td>\n",
       "      <td>29.8</td>\n",
       "      <td>24.8</td>\n",
       "      <td>32.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>16.3</td>\n",
       "      <td>29</td>\n",
       "      <td>63.6</td>\n",
       "      <td>177.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bia_di  bii_di  bit_di  che_de  che_di  elb_di  wri_di  kne_di  ank_di  \\\n",
       "24     44.8    27.8    32.2    18.3    31.5    15.2    11.6    19.4    14.7   \n",
       "113    41.1    31.7    34.2    22.8    34.0    13.8    11.8    19.4    15.4   \n",
       "95     40.4    28.6    31.4    19.8    27.6    13.9    10.1    20.0    13.4   \n",
       "54     37.6    26.6    29.9    17.3    25.6    12.8    10.0    17.0    13.0   \n",
       "112    37.6    24.4    28.3    17.7    24.7    12.9    10.8    18.0    14.3   \n",
       "167    39.6    28.7    32.0    20.2    32.9    14.3    11.5    19.6    15.1   \n",
       "98     44.2    27.9    32.0    21.6    32.9    14.3    11.0    21.1    14.9   \n",
       "140    43.6    28.0    32.4    27.5    33.5    14.6    11.7    21.4    15.1   \n",
       "104    40.1    19.4    28.0    17.1    26.8    13.0    10.6    16.9    12.6   \n",
       "151    38.7    26.1    29.2    18.2    24.9    13.6    10.4    17.6    14.2   \n",
       "\n",
       "     sho_gi  ...  bic_gi  for_gi  kne_gi  cal_gi  ank_gi  wri_gi  age    wgt  \\\n",
       "24    120.6  ...    39.4    31.9    38.5    41.2    22.8    18.2   26   81.8   \n",
       "113   122.6  ...    38.6    30.3    39.5    39.4    25.6    18.3   38   88.6   \n",
       "95    108.3  ...    31.2    26.2    36.8    37.7    22.7    16.2   38   68.4   \n",
       "54    103.3  ...    30.5    24.8    31.1    30.5    19.0    15.0   24   55.2   \n",
       "112   109.1  ...    36.3    28.9    34.5    33.5    23.0    17.1   25   65.0   \n",
       "167   124.7  ...    39.0    29.8    36.8    36.0    21.6    17.3   24   81.8   \n",
       "98    115.0  ...    34.6    27.9    38.0    39.4    23.2    17.0   24   84.5   \n",
       "140   128.8  ...    37.1    30.0    41.8    39.6    24.7    18.2   22  108.6   \n",
       "104   104.5  ...    27.0    23.6    34.0    34.5    20.9    16.0   20   55.5   \n",
       "151   104.3  ...    29.8    24.8    32.6    33.1    22.1    16.3   29   63.6   \n",
       "\n",
       "       hgt  sex  \n",
       "24   173.5    1  \n",
       "113  174.0    1  \n",
       "95   176.5    1  \n",
       "54   164.1    1  \n",
       "112  165.1    1  \n",
       "167  177.8    1  \n",
       "98   179.8    1  \n",
       "140  190.5    1  \n",
       "104  168.9    1  \n",
       "151  177.8    1  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_male = body_dimensions[body_dimensions['sex'] == 1]\n",
    "body_dimensions_male.sample(10)\n",
    "# Dataframe for males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(247, 25)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_male.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are 247 rows and 25 columns in the male dataframe.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Descriptive Analytics Research Question Set\n",
    "\n",
    "### What is the relationship between Wrist Diameter and Ankle Diameter under the influence of Gender?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+0b/3bNnTzRNY8eOHTHyfeTn55Oamhr9u3v37rz99tvRPj311FP84x//iP4eDAbJzs6Oq6dt27a0adOG5cuXI8syvXr1YsCAAaxYsYLLLruMlStX8vDDD1NWVkYoFOLaa6+NOT4QCLBjxw4CgQBNTU0MGDAg+psQglAoRHl5OQApKSk4nc6Yc9mSl2p+fj45OTmsWbOGrVu3MnDgQBRFQVEU+vbty4oVK8jNzY1bP8vLy0tYX7du3bjooou4/fbbadu2Leeccw6TJ09OuPbWGvZNC++7L/Zhs9m45ppr+OCDD1i3bh3bt29nw4YNcWs5++4ti8UCQG5ubvQ3i8VCMBjE4/FQUVHBXXfdFXPfBwKB6LTjgWzbtu2w18LlcsXp3BIfffQRo0aNii4JjB8/nunTp/Ptt99Gp9Ug9l4/+NrW1dXx+OOPk56eTmZmZsJ2tm/fztatW2Pud03TotPd+9o/EjweT8L+9ezZE4vFwtNPPx19T5SWljJkyJBomQOfd4vFQnp6OmazGSD6/2AweNjzfOC1gsh12bx5c3SqGyJTbwe+p1q6fw/mSN55h3retm3bxh133BH9rbCwMNq3lhg+fDjDhw/H5/OxZMkSZs2axf/+9z/y8vK48cYbD/veMZlMPPjgg1x77bWcf/75jBkz5oj6eiI5bgYtNTWVgoICILIWdMUVV/DTn/6UGTNmYDQaUVUVm83G+++/H3dsRkYGa9eujf5dWVnJ5ZdfTrdu3RgxYgRXXXUVX3/9NStWrDisHokuoqZpMUbHaDTGlRF75/V/85vfMGnSJL766ivmzZvH9ddfzyOPPMLll18OkNBBQLSwJnBg2X1z3Qc/FPs4WK5pWlRPVVW5++67GTFiREwZm82WsK4RI0awZMkSDAYDAwYMYMCAAXzwwQcsX76cnJwc2rRpE/UsfO2112IeEohcy/fee4+CgoK4NUQgekMf6jwmon///qxdu5a1a9cyePDgqHzQoEGsWrWK6urq6PrZPlp66UmSxJNPPsktt9zC3LlzmTt3Lm+++SbPP/983HlqDcXFxRiNRtq1axfjnu3xeLjiiitITk7mvPPO46KLLmL79u28+OKLMccffH8kut777sWnnnqKTp06xfx28LWAyMfW4a7F4V5e+9i6dSubN29my5YtdO/ePea3WbNmxRi0g6/vwdf2P//5Dw888ADPPvtsQm/mcDjMoEGDePDBB+N+Mxha9+opLi6mc+fOcfLvvvuOn/zkJ1x22WWMHDmSn/70p3HtHck12afvoc5zVVVVjExVVW699VYmT54cIz/wnj1So30k77zDPW8HX5+WnJn27NnDCy+8wG9/+1vsdjtWq5UxY8YwZswYfv7zn7Nw4UJuvPHGI3rvFBcXoygKa9euxev1tvhOOlmcEKcQk8nEI488wqZNm3jllVcAaN++PV6vF1VVKSgoiBq/Rx99NM5ZYs6cOdjtdv79739z4403MmDAAMrKyqIXTJKkFtvu2LEjq1evjpGtWrWK9u3bH1bv6upq/vSnP5GXl8dtt93Gm2++yeTJk/nss89a1f99HOyObjQa6dChQ8Kyu3btinGXXbNmDR07dgQi566ioiJ63goKCnj55ZdZunQpEH8+9hm05cuXM2DAAIqKiqioqIjZI9SmTRsURaG+vj5aZ2pqKo8++ii7d++OtulyuaK/V1dX8+STTx61t9fAgQPZuHEjq1atijFogwcPpri4OMYh5HBs27aNRx99lB49enDnnXcya9asGMeBo2XmzJmMHTs2zkAsXbqUiooKXnvtNX70ox8xbNgwysvLj+pcJCUlkZaWRnV1dfTc5ufn89RTT1FcXBxX/nhei48++giHw8H7778f89++F2h9ff0R1ZOamsqwYcO45557ePXVV6NbHQ7Wu6SkhLy8vKjeGzdu5N///neLRiURJSUlLF++nPPPPz/ut3feeYdJkybx8MMPc+WVV9KxY0d27tx5xHUfrO+hzvPBz1n79u0pKyuLeS4/+OCDo7oHD/fOOxydO3eOGRSUlpa2uL3AZDIxY8aMhN6MTqczOlN0uPdOZWUlTz31FNOmTcNkMvH3v/+9dZ0+AZwwL8fevXtzxRVX8Nxzz1FZWUnHjh0ZOXIkv/vd71i9ejWbNm3i7rvvpra2Nm7KwuVyUVVVxYIFCygrK+PFF1/kiy++iHrk7PsK2LRpU9yeiWuuuYbNmzfz1FNPsWPHDt5//33efPNNrrvuusPqnJyczJdffsm0adMoLS1lzZo1LF++nB49ehzVOXj22WdZsmQJq1evZtq0aVxyySUJv8AhMlVxzz33sHnzZt5++21mz57NTTfdBMDNN9/Ma6+9xqxZs9i5cyfPPvss7733XtQ42mw2ysvLox5RQ4YMYceOHWzevJmioiJsNhvdu3fn008/ZeTIkUBkuuLKK6/k4YcfZtGiRWzbto27776bzZs3065dO0aMGEGbNm2466672LRpE6tWreK+++5DluUjHg0czMCBA1mwYAFCCLp27RqV9+rVi/r6eoqLixPuP0tEUlIS06dP55lnnqGsrIzFixdTXFxMz549gYhr986dO6mtrW2xjsbGRqqrq6msrGT9+vX85je/Yf369fzyl7+MK+tyufD5fMyZM4ddu3bxzjvv8MYbb8R4ibWGm266iX/84x98+eWXlJaW8uCDD7Jw4cLoNT1Q/9ZeC1VVqa6uTqjbJ598wsSJE+natSuFhYXR/2655RbC4TCffPJJq/px7rnnMmTIkISjsEsuuYRgMMh9993Htm3bWLBgAQ899NAhp4U1TaO6uprq6mrKy8v58ssvue222xg6dGjC5QqXy8WqVavYtGkTW7Zs4Z577mmx74fjcOf54PfOTTfdxOeff85///tfSktLeeutt3j++edjpmoPxOPxtOjpeLh33uG49tpref311/nss8/YvHlzVO9EpKWlMXXqVP74xz/y2muvUVJSwqZNm3j55Zf59NNPo561h3vvPPjgg3Tv3p1Jkybxxz/+kddffz3Gk/lUcNymHBPxq1/9itmzZ/OXv/yFp556iscff5xp06Zxyy23IEkSw4YN4/7774877oILLmDZsmXRF0uvXr249957+dvf/obf7yclJYXJkyfzm9/8ht/+9rcxx2ZnZ/PCCy/w+OOP8/LLL5Obm8vdd9/NlVdeeVh9TSYTzz33HH/+85+57LLLMJvNXHjhhfz0pz89qv5PmjSJe++9l8bGRi666CJ+//vft1i2W7du5ObmcvXVV+Nyufjzn/9Mr169ALjwwgupra3l2Wefpaqqig4dOvDPf/6Tbt26AXDppZcye/ZsLrnkEhYvXozdbqeoqAifzxd9CAcMGEBxcXHMCOjee+/l8ccf51e/+hWBQIB+/frx0ksvRdd//vWvfzFt2jSmTJmC2Wxm3Lhx3HPPPUd1LiAyerZYLPTt2zfmYTMYDPTt25fa2tojXgPLyMjgmWee4cknn+Tf//43ycnJTJ06lSuuuAKAq6++mrvvvpvbbrstZlvGgey7v2RZJi0tjX79+vH2228nHEX37duXn/3sZzz88MMEAgEKCwt54IEHuPfee6PrWK3h1ltvxefz8eCDD9LU1ES3bt146aWXyMrKSqh/a67Fnj17OPfcc3n11VdjRsKrV6+mrKwseo4OpH379gwZMoSZM2e2ei3k97//PZdeemnccoLD4eA///kPjz76KJMmTSIpKYlJkybxq1/9qsW6qquro1NcZrOZ3NxcLr74Yn70ox8lLP+zn/2Me++9lylTpuBwOBg5ciTXXnttzDaEI0VRlEOe54PfOzfeeCNPPPEEzz77LE888QR5eXn8+c9/bvH8vfzyy8yaNSthEITDvfMOx2WXXUZDQwPTpk0jEAhwxx13xMwQHczvf/972rRpwzvvvMOTTz6JJEn07t2bl156KToVfaj3zuzZs/n222+j13zYsGGMHz+e++67j5kzZ7Z6Svl4IYmjnT/SOSRdunThlVdeYdiwYadaFZ2zkKeffppRo0ZRVFR0qlXROYBbbrmFl19++VSrccaiByfW0TnDcLvdLFq0KM7pQ+fU8uGHH9KvX79TrcYZzakZF+ro6JwwHA4Hr776akKvOJ1TxwUXXKBfkxOMPuWoo6Ojo3NGoE856ujo6OicEegGTUdHR0fnjEA3aDo6Ojo6ZwSnnVNIfb0HTWt52S8tzUFt7aHTtJwpnC19PVv6CWdPX8+WfsKp76ssS6Sk2E9Z+yeT086gaZo4pEHbV+Zs4Wzp69nSTzh7+nq29BPOrr6eSvQpRx0dHR2dMwLdoOno6OjonBGcdlOOOjo6OjrHjqZplJWV4fF4OB12I0tSJGh3mzZtWgy8rBs0HR2dk4qRIKGGKoySgZDQI2ecKmpqagiHNbKz2yBJP/zJOiE06upqqKmpaTGprG7QdHR0TgqSBGZ3GY1fvUJdxQ4s7XqSNPo6fOasU63aWUldXT1paVmnhTEDkCSZ5OQU6uoqWzRop0dPdHR0TntMwQZq3/sLwYrtgMBfspb6j/+BSfhOtWpnJaqqoiin15hGUQyEw2qLv+sGTUdH56QgmqvQ/LH7sUK15UielpOw6pxYDs7C/UPncPrqBk1HR+ekIJlt8ULZAEbLyVdG5wfJ7NmfMWXK5VxxxaW8++70Vh9/QsebbrebKVOm8Pzzz7Nt2zaeeuqp6G+VlZX06dOHF1544USqoKOj8wNBtWfh6DsB96rZUZlr1BRCljQ4DbzsdE4sVVVVPP/8P/nvf9/AZDJx22030b//QNq3j88g3xInzKCtXr2a++67j5KSEgBGjx7N6NGjgUia9alTp3LvvfeeqOZ1dHR+YIQxYuo/ibSOA5ADjQhbGmpSHqo4vaa9znYWrtvDO/O2UdvoJy3ZwpXndGRYz5xjrnfZsiUMGDCQ5ORkAMaOPY+5c7/k1ltvP+I6TtiU44wZM3jggQcSeqM8/vjjTJkyhXbt2p2o5nV0dH6AhGQLwdTOOHufQyC5PWHJdKpV0mkFC9ft4eVPNlLb6AegttHPy59sZOG6Pcdcd01NNWlp6dG/09LSqaqqbFUdJ8ygTZs2jQEDBsTJS0pKWLp0KTfccMOJalpHR+cIMUoqJoKnnXOAzqnhnXnbCIa0GFkwpPHOvG3HXLemaTH3oRCixQ3ULXHSfTanT5/ONddcg8l0dF9maWmOw5bJyHAeVd2nI2dLX8+WfsLJ6avQVPylG6ibPwPNXU/SgAuxdxuKwZFywtveh35NTz/2jcyOVN4aMjOz+P77VdG/6+pqSU/PaFUdJ92gffXVV7z00ktHfXxtrfuQkaszMpxUVzcfdf2nE2dLX8+WfsLJ66vFs4vqtx4CEfnarv3iJdRQCNHl3JMSGV6/picPWZaOaCBwJKQlWxIar7TkY/dUHThwMP/5zwvU19djtVqYN+8r7rnnvlbVcVLd9uvq6vD7/bRp0+ZkNqujc8KQJDCH6jC7d2ESnlOtzhETqtgWNWb7aF7+CQbVi4EwZl8lZl8FRkLHtV1JkjCHGvDv3oJZbUaf6Ty9uPKcjpiMsWbDZJS58pyOx1x3ZmYmd9zxU37609u5/vqpjB9/Pj169GxVHSd1hLZr1y6ys7NPZpM6OicMGRWlbCW1c/6DCAUwJGWQcskv8dvyTrVqh0U2meNlVjuKCOFf9Daedd8AYC0cjH3ENQQMycfcpoTAVLmOus/+hRbwothdpF78CwLJ7U+L4Lg6RL0ZT4SXI8CECRcwYcIFR338CTdoc+fOjf67d+/ezJgx40Q3qaNzUjB6K6n59J/Rv8NN1TR8/i+SJt1PUPphbxY2pmSh2JNRPY1RWfKAiYR3b4oaMwDf5iWY87sidR6LOEarYwrUUvPR30ENA6B6Gqj98G+kTp1GQDkz1pjOBob1zDluBux4c3oF8tLR+QGhNdXEyUI1u5ECTWD54Ro0SQJ/RQnOfuMR4RAi6MOQlIFvV3FcaCoA3+bF2LqOJRw+tnY1d23UmEVl3ibwNoBTN2g6x45u0HR0jhLZHj8NpzhTESb7KdDmyBECjEkZ1L7/BJLRjGQwofmasXUfiTm/K77NS2PKm9v2QtNaqKwVKLZkkOSYtTvJZAXL8XFY0NHRYznq6BwlqjOXpGGXR/+WDCZSz/8/QsoP26ABkNYOa+FgRCiA5mtGtiXh6D8RQ0FfTNn7Qw0Z0/IxFw49Lp6PQWsGrnNvBPZ6gsgKqeffQcicesx16+iAPkLT0TlqwhhRep5Pevv+CF8TclIGAXPaaeHgEFQcWEffgr3/hYhgAMmVjU9JAsB50V1ITRWAQCRl45ePj4HWUFA6jCDj+i4owWZUi4ugJf2kbBPQOTvQDZqOzjGgYkS154I9NyI4jd7NIckMzoI4eVC2gevIA8K2BhUF1ZpNRtvOkb1Zp9H50vnho0856uichhgJYQnVHVNyTFmWMKvNWEL1KNJxWCQ7QkzCT7C2HCPBk9amztmBPkLT0TnNsPiraJr3CoGyjRhcWaSMv41ASqdWTXUqhJBLV1I37zW0gBd7j1FYB11GwOA6YXpLkoS5aQcNX7xIbW05ppzOuM67BZ/1h+kCrnNq8Hjc3HbbzTzxxD/Izc1t1bH6CE1H5zTCJAI0fP5PAmUbAQg3VFLz3l8w+atbVY+hsYy6z56LuOkLDc+6rwmsm0srY8G2ClOwjpr3/kKothyA4J4t1H34d8ya98Q1qnPcCWxeSOOrv6L+XzfQ+OqvCGxeeNzqXrduLbfffgs7d5Ye1fH6CE1H5zRC8tcj/F5SRl2NUMNIBhPerSsRTVWQceSBXMNVO+Jk3vXfktL7fAJygszSxwHRVI21U38cXQah+ppRrEk0rvwCvLXgODFt6hxfApsX4v36ZQhHpos1d23kb8BcOOyY6//gg1n89rf38Kc/3X9Ux+sGTUfnNEIy20gaeAF1894ELbJJ2dlvArLN1ap6FEe8q7whNQdNMZ4wRw2DIwVTai5Vs/Zmrpdk0sbfimRNOjEN6hx3/IvfiRqzKOEg/sXvHBeD9oc//PGYjtenHHV0TieERMPCWVFjBtC8cjYItVXVSBkdYvabSQYTSSOmEBLG46ZqXJuhZhrmv7NfIDTq5r2GEjq1QZ1luXURkltb/kxCc9e2Sn6y0UdoOjqnESLgRvPFpyLRvE3QioAbAUMyzom/hvoyRCiAkppHwJLJidxEp7obOHj4J4J+NG8jWFq3+H88MGleqN5KcM9WjBltkbK6EDS0HILLEqwlXL6RcGM15jY9UFPbE+bEfQD8EJEdaQmNl+xIOwXaxKMbNB2d0wmrC4Mri3DDAanpJRk5qXWJECGyuZr0bgCRJDEneEe4wZWJpBgR6v6UNIrdhSE584S2mwhFUgms/BD3ys+jMkv7Imzn3UEoQWBpc7iR+vf/QrihCoDmJR+Qev4dSAVDjzlo8+mEZciVMWtoABhMWIZceeqUOgB9ylFH5zQiIFlJmXhn1AjIZhtpF/+CoLX1Bu1kowaDpIyegmyNDCUVZyquYZNQA8ee7bi1GHw1uFfOjpH5d3yP3FyRsLyo3Rk1Zvto+OYNTOrZkaR0H+bCYdjG3BIdkcmONGxjbjku62fHA32EpqNzmuG35+O68k/gqwezg5DJdVqEjxIBDw2LP8DZawyS0Yzm91D3zdtkTP7tyVdGDZPI+0WoiVMKHDiqjMqCfiRx8jak/1AwFw474Qbs/fc/OarjdIOmo3MaEpBtYN/r6n4aGDMAoysL2WCmcenHUZk5txDFmX7SddHs6ZjzuxHYtTEqMyRlICUnTkCspOYjGUyIA6baHAMuJGhI0sN3/YDQDZqOzhmCJEU2L+NrAmsyQVPqSVnfMWkeJHcNmKyEremoIvFKRqihkqT+Ewjs2UagsgRLm64YktJRm6og+eS67ocwkzTuNvzrv8a3ZRnmNt2x9T0fv5zYsyZgzSL96vtxL/mAcF05tt5jMXQcTPA4nV5JAlO4GTx1YLYTsqSitXAedVpGN2g6OmcAEgJjxVpqP/0nIhRAMttIu+jnBNO7nVCjZvVVUPvBE6hNNSDJJI+4CkPXsYQlU7yORjN1817HlFmAObs9gV2bcNdXknXtQydMv0PhN6Yi95tMUtFENNmEX2vZHV8Igd/eBsu5P0FCJYyJ4HE8r2b3buo++GvEE1Q24DrnOpQOw1GlE+tFKYRAkk6fbQiHu5f1TwAdnTMAU7COuo+fQYQCAIiAl7qPn8YUrD9hbRoJ0fj1/yLGDEBoNH73NkrT7oTlNVce1s4DCFaV4tm4kFDdHpL6X0DIkXia72SgaZHRmnoIY3YgYSETEsbj+pFgEn4aZj+/d1sDoIVp+Oq/GD2JHVSOF4qioLawZvhDRVXDGAxKi7+f0BGa2+1mypQpPP/88+Tn57Nq1SoeffRRPB4PXbp04bHHHsNkiv+S09HRaRkDIZSm3agNFch2F6S0Rbjr4hwXtIAXfA1gdB1zm5IEZm8lau1OJIMJKa0tkiRFY0rGtNtcgyEpD6VpN1pjJZI9BZHShoBsxzniGuxdhhBurMSQmg/pHQhop/dEkTlUj6jdiQgHUdLaErBltWoHhBx0E6opi5OrzTXgaHMcNY0lNTWFpqZ6UlLSkaQf/thGCI3GxnpSUlJaLHPC7qTVq1dz3333UVJSAkSM25133sl//vMfunbtyq9//WveffddrrnmmhOlgo7OGYckCdi2mJovX4rKbF2GkjTkEpANMRFEJJMF2dryRuHWYG7eSc30h6NGU3Gmk37l7zHldCK4Z2tMWcWZSmjrfGrmvRqV2XuMwjTsWnzGVMhNJaOPM5IP7TTHEqyl7r1HI8YHkBQj6Vfdhz9BnrmW0Ex2DKk5hOv2xMjlBOHJjifp6en4fD4qKspOi6S0kgR2u5309JadiE6YQZsxYwYPPPAAv/vd7wBYsGABRUVFdO3aFYD77rsPVW1duB4dnbMdU7CB2q9fi5F5ixfh7D+BlBGXUz//PdDCSIoR17DLkcIBDhXMQpElJEkjrLY85WaQNZoXz4wZAarNNQR3bSR57M3UzvpLJFIJ4Bx0CbLNReO3j8XU4Vn/Ldbe5xF2tj2KXv9wCe/eGDVmEHHvb17yPtbxdxLWjmzUE5SspEy4g9qZf4mMqpFIHnEVquPEptWRZZmCgiM3vKcDJ8ygTZs2Lebv0tJSbDYbv/rVr9i+fTv9+vXjnnvuaXW9aWmHj++TkXF8vkpPB86Wvp4t/YRD9zVQWRvjOr4PzdtI89pvcQ2bBEIDSaJ55RekZRaQ0Ta+PiEEgd3FNC77lHBjLUkDJmDr0BfFFl9W9XtwH7SpGCLx+5wDz8dy818I1VeiWGwY0/IJ1u5KuG9L0QIxfTsTrmnd+ro4mdpQSZLdgGKxR2WH7WtGTyy3/pVQQxWK1YExPR/ZoC/HtJaTNnmtqirz589n+vTp5Obm8oc//IEXX3yRO++8s1X11Na6D7mJNCPjzJjKOBLOlr6eLf2Ew/fVKDsx5RYSLN8clUlGCwZXNiLojQn+a3BlgTMrYX0W7x6q33pg7wZjqN69iZQJt6O2GxY3/SRJEvY+59IwL3ZkaMzvvrduKzjaRYSNQUxKMsbMdoSqSvbXYbah2tKjupwp19Sc2y1OZu9zHvVugWhubV/t4Ggf+Wd9AAgcFx1lWTqigcCZwEkzaOnp6fTp04c2bSKLnBdccAGvv/76yWpeR+cHjVUJIbsr8e9SMNpyCLUwGx/CRPL42wmXfo8I+UE2YGzbC485m/RLf0XDN28S2LMdS5uuuEZejUdJTlxP5daoMdtH8+L3SW7bD00xYfJGYkWGbJmENAWlw2CSgj6al32MbLaRPPpawq7IdJUNL5KnBgxmAvZcgmEzKRf+lOb5M/BtX4Upuz3J59xAwJRywjchGyQVxV8PsoGQ2YV2ggN5hFPakTrxThq/fQMt4MM5cCJK+0Gop8Oi1BnISTNoI0aM4JlnnmHPnj3k5OQwb948evTocbKa19H5wWIPVNC09EO8mxYjyQpJAy/E1n00XkNipwBJDeNeP59QVQmKw0VKRjuwaXgd7Uia+GuUsJuw0YlHmFtsU5bjXZ8lxYBR8+FZ+QH1q+aA0HD0HoOt30R8xjTkXheR1m0MIBNUbAgBDt9u6ua+RmD3JmSLnZTRUzEX9MdnysBy7h04RntQFQt+TCfcmJlDDXgWTce3aTGS0UTyiKsxdBpOWGr5PBwrKka0/P6kXN0N0AgZHATPvmhYPxhOmq9mTk4ODz30EHfccQfnn38+jY2N/PjHPz5Zzevo/CAxGBR8W5bh3bgQhIZQQzQu/gC1alvC8ibho/6zZ6LTeaq7gZpZT2D2VwPgx4LHkE7gEMYMwJiag2SOzRKdNOACwrVlNK/4LOItKTTcq+cSKv0eWY7s2QrIDgJyxJhZlQANC94hsHsTAJrfQ+3s/6A0RFzQw0LBryQR4sSvBckyBDbMw7dpESAQoQAN815FqS894W0LEQlFFpAdJ3xEqHNoTvgIbe7cudF/jxkzhjFjxpzoJnV0ThtMqpu6LUvj5P6yjZjbDSEYjJ17lHwNhGoO2rishdGaqsBy5GlYglU7cQ2dRLipGs3nxpzdAe/2VVgKesaV9W5ehrPHOAKB2Le14m/At311XPlwfQW4Co9Yl+OBQfXRsHFBnDxUsRUprctZleLlbOaHv5tOR+cMRjVYMabHb541puYSDidYSDPZkA8aWQFI1tbFQpTtLuq/fgPv5mUEq0qpm/cGIhxCNlrjm8wqINEOG2G0YEyNj/Ih205uXEYATTFjzIh3QVeSs3RjdhahGzQdnVNIIARJfcdHc4QBGNPyMLfpnnD6KmRykTL+dlCMGNPykMw2nAMnorY2fFRGe8z53VDd9YRqdyMZzTiHX40xswBD8v7caoojBXv3kYTDEWVkWUKWI3vWPCSTMvZ6UPZP9Fg7FKGkHd/oFpK0v82WCGsyzqGTY6ZRTbmFyFmdon8risRpFLZQ5yiQxGn2+aK77e/nbOnr2dBPe2AP4ZoyJMWAktYGj7HlhJ0mzQ0VG/GXrMOUWYCxoA8+Y1qr2pMksPorUWtK0YJ+jBltCTrzCQsDtlANam0ZCA0lrS1eUwYKKobGUvxbliFb7Zg79Cdgy0GRwpibdhGur0C22FBcubiNh08HcyTXNBJuq4LgjpWo3kYsnQYRdrVDJXEsP0kCc6AOraEcDCZw5RGU7RiEH7lmK/5tKzGm5WMo6EPA1LrzdSyc6vtXd9vX0dE5qXjMOZCXc9iXnyIJgqs/p3lZJKeYBzBmtifpkrsIyvYWjzsYs7+a6rcf3BuZIkLapb+GrN54jemQHWuUDLVbqHlvf/QPaenHZFz7MKFdm6g4IAyX4kgl5cr7CRhbjrd3xDr6KqmZ/iAi6APAvXI26ZN+i5qR2DtaCPCbUiFzv3eoJIHYspDaA8JwKckZpFx+HwFD4i0NOqcv+pSjjs4xogrwhSLROQ7EihtbuA7DAU+ZJEmYNS9mzXPYabREGP11NC//NEYWqtqB1Fge+V0KYVabMUiHjqIertgaY8wAmuZPx5RgM69RUmleNDNGJkJ+RHMVjfPfjpGr7jpEXXyg3aMhVF4cNWZRHRe+g1GKj0LSEqZwE40LZsTq2Fh91DqaCGBWm5Hl02pi66xBH6Hp6BwlkiSxu87Lfz/ZQFmlm6G9crhsVAdSLQJD5Xrqvp2O6mnA0XMUtt7nETQmI5Wton7+dEQ4RNKQyzB2HEpIjnfyaAmBFgltdbBcU7H69tD47ZsEdxdjLuiFc/jV+FvwfBRavMET4eDesFkH9RORMNwWQiDC8cZFaMceo1WSgAThs0QoiNSaVRIhEAlSpLRWRwmBuWErDfNeJdxQjb3naCxFFxA4DpkMdI4f+ghNR+coqfcEeeilJWzd1UggpPL1yl3899ON4K2l+oN/EK7fgwj6aF45G++6uRi8VdR99hxqcx2arzkSSmr3ulY5KqjmFGzdhsfIFGcahqR0amf+hUDpWkQ4iH/bCuo/+htmzZuwHmNWxxhnDgDn4MsSGtcQBhyDLo4VSjKSIw3noEtixSYrSuqxO4UIAcbcLnDQBnDn4MsIJUge2hIhYzLOARfGyGSzDSU1v1X6mL0VVL/zKKHqMkTIj3vVbHwrPkSR9JHaDwl9hKajc5TsqfUQDMeOlr7fXE3tiBTkg8JieNZ/h7VDEfYuQzBltkUgCDfW4Fn9FfaCgYTEESaYxIBt6FUYM9vh27QQU14XrL3GEvY0onoaYsvW7wFPDYozD6OvBs3vRnKmEzQmE7DnknnVfbhXfIbqrsXRdzwipxdCCKzCA427I1YlORef7ETk9CRryv2EasqQDEYMGe3wWbMx9jiHzNxOhOt2I1scKBnt8FnSj0tUkIAjj4yr78e94lM0dwP2fucjcrq3KtWJJsDUczwuZzredV9jzCjAVjSegDmtVTqqdbvjRsaedd+QPvAyVOXkb1PQSYxu0HR0jhKrOf7xsZoNmBWJgyfLFEcKssmO6mum/rvImo4ps4CkgRMJt9KXPGBIRu42HkfXsaiSgl8DS4LpOSQZ2WhEbPqS6u+mg9CQrQ7SJv0O1ZaOZ/23qO46ZIuDpiUf4bqwHUapmYa5r0QTd5qyO5Ay4XY0DNTNeZlQbWRTt63rMKzDrsHgr6bms+dQPY2AhLP/+Zj7TiQgH7tXnRASfmc7zOf+H7IQhI4wHcvBBBUHUsdRODqNQEPGr9FqgyuZ4/fnKfZkkPRX6A8JfcpRR+coyUmz0a9r7BrVTRO7k+6QMaYekMtKknENv4JQzU78O9dHxcGqUkK1exBHODo7EE0ThIQS3asmGc3Yu4+IKeMsOhdJC9P47VvR0YXmc9Mw+3kMnmo8a+YS2LMNX+k6QjVleNd+RbD0+5gs1MGK7fi3LCWwaX7UmAF4Ny1E8eyhfu7rqJ5GJCWSdK15xWfI9Ttb3Z9DoarSURuzfQgBIU1GPdrQVCltMeV0ihG5zr2ZoHLk6586Jx7980LnjMESrEGt3I5QQxgyOxCw57Q6E69JeFHqdhCq2YXiSEXO7IDPmIZR8yLV7URtqEBxZSNSC0CxcuvE7kwY1JYGd4DcdDvZKVZ8O77DNfwKtFAAEQpgSErDU7oBAp649nyla3D2uwRDYwmhyh00lhixZXXAa81rld5qcz1CDZIyeioiHEQymvGXbYzLggwQqi1HBNyR3GmyAaGGUSx21JAPf9mmuPL+netRkuL3xYmAF9BIPec6VF8zstGMFvCiNtfCydvmdVIIGpwkXfBzRF0pwu9BTs0j5Mg9LTI9n03oBk3njMDir6L2nYfRfHv3cCkGMq7+I37HkWdIlmXQNi2g9us3ojJzm264JtyOZ+Vs3Cs/j8od/S7AOOByrEYDnXNj11AMriyq3300RmbrOgxLu9541n8XI7d26IexsZSK6dMiAYGJhI7KmPw7vLYjd1yQ7Cl4t67EW7yUiJui2BtF5OK4ssb0fBRbEnXrvkNt2pttWZJJn/gTZJMd37aVsToW9EJLEPtKtibh6DGKuq/+d0DdbUgbfwsJfCJPewKGJMjsdarV0DkE+pSjzmmPJEkES9fsN2YAahj38o8xtGK/kMVfRcOC92JkgbKNiLrduFd+jjE1F3vXIRhTc3Gv/AyDtzphPZqrAOfAi7C06Y6tyyCMWe2xD7oMKbc71sLB0XLm/K5YugylcdH7UWMGoHmbCJSuRpYjfTNJ4cP2I2hOI23indj7nU/Gpb/A2mssaRPvJJTUFtc5N0S9BRV7Mq4JPyZUtwe1qRZL2x7YCgchmyw0Lf8US/s+WNr3jtHR3GkA5q7DMWa1i8rtvc5B2FJoXPIBsi0JW5fBmPMKCdWUEfa6D6mrLIEa8B6xd6dB1jBK4bjyRlnDKB/ZHOK+86jo+8fOaPQRms5pjySB6q6Nk6tN1Uho0EKopPgDgpGkmQehBX2kjJpCqG4PgT1bMed1xtFzJFo4cUbhsGLB2mkA4bpyQnUVOIrGoZkcBGUb1jE/wj7wkkiqGEcmmizFeScCqO5GLAQIbV1M05ovMbiycQy8hICzbcJguwIJY3IqgZIADfPfwdK2BwZHEgEMyIVjSG/TCxHwIDnSCBicKLW7STvvRjxblhNuqCSp//mEvY2EZQvJQyZj79QfIQTG9DYETamARsroayPhuYwmlPQCBBK2TgOQTVa827/HkJxO2nk3IQ4Rms4SqMa3eg7lZeuxdB6Ipdto/C1EFZElgalhB81LP0DzNODofyHk9UaTDCg1m3Ev+QAAx+BLUdMLUSVjwnpM4SbC25fStO5rDOltcAy4iIA9T58uPAPRDZrOaY+mCSwd+uFe8VmM3F40gbA4QmMGaLYMLO164S9ZG5VJRgvGtDwaF39IqCYSXSJUuxtjRltSCocnrMfs3UPVjEeiGaHr57yE65wgcpfzCGlGsO9fH5MBR8/R1FWWxNRh7dCHwLqvaJwf8YgMVZfh37Ga9Gsexm/JimvTodZR88m/CO1dMwvV7iawZxupl/wSj3ASsGSAZe86mACTK4PKGX+GvRuMg1WlpIy5BslXT+Vbf4qpO+2KexHeRqo+/dd+oSSTdePjSAYjjUs/irRZU4Z/5wYyr74v4ZSjWXNT/8FfCTdURXUMlm/Bfv4vEuZMMzXvonrGI1GHlrrPniNlwu0YkjOpmfXXaLnArL+SfsW9qKld4upQZEFwzWyal3+yV8dd+Ld/T/o1j+A/ifEcdU4OukHTOSMIp7Qj7ZJf0vjd24hQAOfgS5Hye7fqK9wvzCSPvhaD41O8W1diTM/DNeIqwiEtasz2EareieapR05OxhhsgKAXYXURlKyEq0uixmwfzUs/xNVpCEEpNt6ipoGpXT9SRgVoWjkbyWiOOGuktKHpo2djyopwMBI0OC/eoIXry6PGbB/Bim1o9eXIaV0xBush6EPYUghiIVizG2NKNvYug0FWCFaV0rRyDkmmePf0cMVWvMWLsXbsizmnEwgN75blaI0VNK+eh737CIyp2Qg1jGfDQkKN1WCL31wtGivQAl6SBk5ENllQfc24136L01MD9tyYspIEwfLiuL1fzUs/wll0blzdnjVzsZzXDSkcQPbXIckGguZUDMEmGlbOjtUj6EOt2wXZukE709ANms4ZgYoRLaeI5Cu7IgmNoGxHPYo5Ja85G9PIm7APnoRmsOEVZsyeXQnLSrIBw+6V1H7xb0TAizEtl5QL7ySsxI82JKMFpMSjRb+SjNLjQtILh2KyWGgKmzGqXmSTGfWgWIaSnPiRlZWW5UrZMmrnvIQI+jCmtyHlwp8inClY2/ehYcmHoIYx53UhedBFkKB+SVFw9jkX79YVNMx/B2QFZ5+xyGYrqWOm0rx6Lp4N85GMZpIHTkQxO+L24UXOgRnXsMk0LHofzduEkpRO6phrkAzxbQoBijE+67ZstiY8B4rVgdFfS9O8/+LfsRpkA8lDJyF3GYZkNCECsR8YLZ1HndMb3SlE54xBCAhiISDZjimpY0iV8Sop+EXkharaM7F2HRpTxtZtOJLBSO1HT+91X4+4w9d//hzGjDZxSS6TR00lJFlabFNVNbxyMsbkdDQNgrKd5FHXxpRRnGlI6fFJLAFw5cY4c0R0HAb2FOo+eTYa5DdUU0bDnJeQJGha/ml0JBnYXUywqgRjXmF0TxkAsoKxTR/CTbX7p2I1leZVc0BW8GxZQbCqFAARCtCwcCZyC28VgyJT/83baN6mSJ+bamhYNBOlBWNsyOmCbIkd0SYNuxIlu1Os4ZUN2Hqfh2/NlxFjBqCFaVzwDlpjJckjp8TWm5KN1MrQVzqnByf0M8XtdjNlyhSef/558vPzuffee1mxYgVWa2Ra42c/+xnjxo07kSro6BwzYUw4hl2NrX0RwYptmLI7Iud2JVxdysEhJ0LVO1GFTOaVvydcU4YW9GJKb0MgqU2L058GA5ibdxGq3U1zuYIjPR+3KRuR14f0K/5AoHQ1SlIGhvweBIwpyKiYvJWojZXItmTUpFy8UhKuUdcQ6jSAQFUJ5pyOGLI7RY1NTH8aqwhWxW9+9m5dgX3gpWRd8yChmlIQAkN6W8K2VHw71+EaeSUAkiSjepvQPI34S9bE199QCa7ChO0eHORYdTegNddASmpc+YA1k7Sr/khw51o0XzOW9kWEktqiSTIZU/9EcG/bpoLeaBYX3uLFcXWEKrehdB9H+uUZBErXYUjJwZDXHb+eOuaM5IQZtNWrV3PfffdRUlISla1bt47XX3+dzMzEEcB1dH6IKKgEty6lccEMDI4UmtfMJXnE1ZhyOseXdbhQZI26z/9DcM9WACTFSOblvyWc4CUPYG7YQdV7f42mczGkZJNx0c9wW/MJp3ZGTi9EE4KAiCRrVMpWUf3J/vU1R9/xmAdMwrd1Be6VX2Bu243auW/gGnElxoOiWwBIJjOG5Phn0JRZgCRJVL//ZGRzNJE9celXP4Br0MXUfP7v6EjPmJaHreswjOn5hGpip2RlmyvxeXSksG+PXFQXowXJljgWohDgt2QhdclCliT8+7wnBfjt+Ug9I+t0fiEwSBqmnE74mmO9XRVXbsThJK0bckZ3VCEI696NZywnbMpxxowZPPDAA1Hj5fP5KC8v5/e//z0XX3wxTz/9NFqiHPM6Oj8wjP4aGr97G9Qw4cZqUCPhpCSzPXbjsmwgZcIdqNWlUWMGINQQ9d++jUXyxdVtMYRpWjE7JjdZuL4Cf9kGlL1LbpomoqM7Y6Ce+jkvxdThXvUFhuZyGhe8ixAq4aZqJAkavn4DSTHh6H9BtKykGEkddxtKVkcsBT33q25xkDzscnybFkaNGUT2xIV2rsWzaVFMbrJQ7W6CNbtIHXsD0gFrXfauQyAt8bSoMNlxDb98v0CSSR17HZox3hEl5jhBwiz1Qojo1HJYyDgHX4ZsdUZ/Nxf0QsrsuL8vB5zH0wlJAoNBblVWhrOVEzZCmzZtWszfNTU1DBkyhAceeACn08mPf/xj3n33Xa666qpW1XskqcQzMpyHLXOmcLb09VT201daGp+DTGgoqo/U0Vfj7D4U1duIMSUbY3o+jYs/iKsjXF+BiRDOjNiRUaihhoa68rjyofoKMlPj+xyoqIlLegkg/G6SB18MCILVZVj79kSEgyiESRtzDUk9R6J6mzCkZGNKz49sNL7opwQrd6AFA5gy8jFnd6D5gGgo+9B8zQSr4x1j1MYqnN2HkTn5LkI1u5CtTkxZBViy80h0tbzbtxFqqCLjsl+h+ZtRrEk0ff8VqTkdceYdjzUtJ+ab/0KwdheywYQxoy0G+w9javFo799QQzW+bSvxbluFtV0vbJ37Y0zJPs7anTmcNFefNm3a8M9//jP69/XXX8/777/faoNWW+tO+LW2j8OlsD+TOFv6eqr7aTa5kG1JUWcGiEzFhcwu3E1hMOdG/gOo8WBNjY/DaCschE920nRQPwwGG/Yug2iojl3TsrTtnrDPJtmBMaMtoQPLKwbklBz8Sz8mUL4FAN/277G2L0I1O6lrVg/ScV8kDyukdgfAD1DdjLVDP7ybYteiDK4s7D1G0Th/eozcmNuFxlVf0TDv1Zjzknb1n/Ab49fEzJZUvFuW4ln/7f66kzMIGJNoPm7X1wbJe6d2vYD31D8fR3v/Ggnh+/olfJuXAeDdsgz3pkU4zv8FwUM4GB2MLEtHNBA4EzhpBq24uJiSkhImTJgARKYLDAncdXV0DsQk/ITqvRglM6FWbJI+EhRJYAg1IRQDIdkenY7SAF9QxWSQMcoSQWMyaZf9Fn/xgkgQX78HS5fhkdh+IqKjrPoJGx2EhQEhG0g97yYa5r+L5vdg7dwfW+EA1LAfWbFhDDeBEISMSUhaCHNBLxzNdbjXfoOkGEkecglKUgaSJCFLKoZgM8hGgoqNIBZSLvgp4fJNGJJSUX1u5KRMVL+XQMWOSGiutDyCVaV4t67E7nNDC2tUkiRhVN1IQiVsdIKkIIJ+kvpPoHn1PITQcPYcjeZrxtRlOI7mWtxr5iIZTSSPnILsyqHx46cx53XBWtAD1duIe8NCtNoyyI43aEFTKumT78G/ZXHkPPo8WLqPxn/Q3ryDdTSpbhAqIaMTTTt75t1kT3XUmO0jULYRZ3MlJLXg7XqWc9IsihCCP//5zwwZMgSbzcb06dOZNGnSyWpe5zRDksDcuJ2GL1+itmY3lg59SRp1LT5T+nGp3xxuxL/iQxrWzIvENxx7E2p2T+p8Gm98UczyjVXkZdi57dJeFGTYQFYI7NlGsHwLptxCLN1HAWBp3Eb9nP8QrqvA2qkfjhFTUZvrcK+ZR9q4m5AMJnw7N1D7xcukXf0n1K1fU/fddIQaxjn4Egw9xiJCAYxpebiGTQYhUBypiGAQc6ge79L3aVj/LYrDRcq5txDK6o5MGO+WZfhL12FMyyP13BvQFCtp595A85q5eDYtxpzbmbTzbmyx/woq8u7VNMz7H6rPg6PPuVj7TcRXVUJg1yaSBk5EksCzZQUGTyPmruMwDL2GjH4TEZJCyJSMFmoiefDFBPdso2HR+yjOVFJGXIlkSByCCgTICv6yTYSqSjC37YG1JR9/QCGEXPY9dV+/hub34uw3AXPv8wkYzo5p9paSth3LlpQznZO2D61r167cfvvtTJ06lYkTJ9KtWzcuuuiik9W8zmmGyV9DzbuPEaqJ5ODyb19Fw+znMZI4fmJrkCVBYPXnuFd/BUJDdddT++HfEL56XvxgPcs3RkIz7a72MO2/S2l0+6h7/3GCe6fzguWbqZv1V2yBKqrffXRvihaBb+sKmr56GWN2R0L1FVR/9CxVs56iecXnJA29HLW2jIYvX0ELeBHhIE0L3kWrKSFYsZ36ua/RMP8dGha8S+1nz4Ok4lvxEZ51X0d0bK6j5v0nsTSXUTf73/hL1wER54yqmU9iUKB+wbsE94bQCpRvoXHJxxgsiaemDI07qfv46UhiTi2Me9VsAhvmYe01llDdHhoXzaJh4SxC1Tux952AqmqomozfmELAkISmCVSjnVDVTrxblkd0bKqhbt7rKJbEOcJMoQZqZz5GqGqvjjvXU/fR3zGL+DVBAEPDTuo+/WdkqlcL07z8E0Kb5yPLZ8coTbNnYGnXJ0ZmyukISfGRYnQinPAR2ty5c6P/vvbaa7n22msPUVpHJ4JorIzbsxTcsxXZ1wDWY3ugjWE3teu+iZPXNQfZWFIXIwuFNfZUN5PrboiRq+561MbKuBBXgbINaJog/eo/4ls3j3BDJfZe50JeD/wL3oxrU5ahadOiOLnmacKz7tuDpIJw7e6o0YpKw0HCDZUxa3wA4aZqtMbqhHu8wlU74mSeNfMw9TiP7OsfIVRVghAaxowCAgeFpdqHEmzGU7zkIBU1Qo014GgXV140VaH5Y3PChev3gKcWHLFOIZIEofLNCXScS3K3c1q1hnS6EsKE45ybsZSswLdlGZb2RZg6DcIvHdor9GxGX8TS+UEiWeLXVSSTBQzx4ZBai6aYMCRnRkcK+7CYFOwWAx5/rJGy20wcvH8KSUY2x+soW+wIg5mAOQPD0BswShAMa0hCwnBgFut9uqgqBldm3AZoyWDEkJxB6CAPSNliQzJa4rICJNIFSUZKJAdkuytOZnBlIYswdV/9NzoaNWa0xTXxl6imeKOIYsLgTCPcFJtGp6U2E8oVAxjjjZMQkcgocTqmZKPJhpZm4844AkYXUuF52Lqdh6axfy+eTkL00Fc6P0g0Zza27iNjZCljbyJoch1z3SHMuMZcF80RBmDK7oAryc4tF/eIKTu8dw7Z6U6Shk2OkScNnYzqzMbWZUiM3HXuzYSMEVdxVdUIhyPu/kIITO367d1cHEEy25Ds6ST1Pz+ywdhsQzKaUZxpKMkZJA26CKT9j6gpuz2yM52UvRE79mHrMhjJmYqz34QYefLQywg69o+uDpyqkzM7YEw/IICwYiBp5BSCJd8TLN+CbEtGdqQSqt5JYMui6LEHLnkFZSuu826J0dHcpjuk7K9XOmDzlGrPjNkTB+AaNZWQJXGQYCW7M4YDPEYlxYhz+JWExdn1HS6EIBwWh/Tu1okgidNshVF329/Pmd5Xo+ZFatiFHGhGODIIO3NRj9OkgiQJTN4KtLryyMgvtS1BxYkkglTUNLOnxkOyw0zbLCeKyYZBBFAad6O5a5EdaajJeYQlMybNCw1lCG8TiiubkCPnkDqaQ/WI2p0IoSGntiFoScegCIx12wjs3oxkMGHJKyRsSaH+vUdx9h6NUENIsoFwcy2mzHb4d2/G1qkfqrsO2eJAddcju3IxpOah1Zehuesjo5uUNviUZGzBasJ7igk3VmPK6YSW1pGgbMMeKCdcvRMtFMCYlo+a2g7f4newt+tJqLoMITRMmQU0Fy/FNvpmDLXb8O/ehGy0YMrvit+eD0LD5N6D2rAnMgJLaUNQcWAUfqTaHYQrt2NIy4OMjgQVJwbNj9xYhuxvQtjTUJ25hKX4YM7R8xVuRNSVRfbUpeYRsGaddpujT/Vzeja57esG7TTmbOnryeqnLIO06Usavn49KjPldsF54c8Jyi27lh8rlqYdVE9/OLp5WzbbSL/6jzQtmIF/28qYsplX/YG6z1+MneaTZLJufIzmJR/i3Tg/Knb0m4C97wTqPvwHoer9U5opY67F3L4P1TP+fEByUYmMy36JbHVQ9e7jiNBe5xvFQNblv0WoKlUzn9ivo9VBxuX34LXFb4hWJIG25iOaFs3c38f2RdjO+zGhves/Z8u9C6e+r2eTQTu7xu46OgcgyxIG1YeQDYSEAWOgntq9CTX3ESwvhobdkFoIEgTDApNBOqI1HKMURtLChBVrix9hBlmjeemHMZFItICXQOlanKOuQW2sjHh6ygaSR1xJKLmAlIvuxLdlKda23QjVVyJbk9GCgRhjBuBe+QXWNt1jjBlAw8KZZKbnH5QpW9Cw4D2cReP2GzMANUygYge+Hd/H6uhzEyhbh9wtn4Mj2Bn8tdQsfj9G5t/xPY6mCkhuf7jTpqNz1OgGTeesxKS6UTcvpvH7OSjOVJKGXwnmpDjPSgDCIRq8IT5ZWMLqrdUUdc7gwqHtcNkS77eSEJgbd9C08B3U5jocfcdj6jCYoBL/lSwh0LwNcXLha0IoNpKHX4XaWIlksmLI7IBfNmExGJDCAermvIIpsy3Jgy4hJBK5sgtEOH6bgwgFEHszVR+I5ncjtHB8eS2M5nPHl/d5kJCJbEU/ADUUHyoMEp9bHZ3jiO4UonPWIUkQLv6Whq9fJ9xQSaBsI9UzpiFJAmvhoJiystmGz9WOJ95ayVfLy6hp8PPlsjL+9vYqgmriUZfZU071jEcIlG0k3FBJw7zXCLewf0pFwdE7PgOzpW13guvmUPPB36j/+k3qvniJqjcfwK7WU//tWzSvmoPaVINv60qqZj2B0SjHRdA3ZrbDkJYXSS56ALaugyMR8aXYx99ZdB7GlHhPTGNaPs4+CXQs6JkwwLhmS4s4hxyAYk9GStZjEOqcWPQRms4xoQqoaw4gSZDqNJ/SLyRZ0qhrcKNqgjSXHSTDXh0Fdc1BZFki1WHCrHqoWzsP06Q/UakmYTHKpJR+RahiK/ZR1+Ptfz3VbhWn1UAbZ5htDRLl1bH7p8qq3FQ3+shPt+Ju9uL2BUlNtmE0mgnXlCKnF+DtNJagMJLUtI3mlZ+T0mVk3Fqcgkqobjep595A86o5SEYLSf0mEKrZjXfz0tgOamGEp35/os19Yp+bcF05qZfdhWfZR/jLNmDtUISt7wX4LOlkXnk3jYvfJ1SzG3u3oVi7jcJvSSNz8l00LpqJ6m3G0fscTJ0GoxkspF/0MxqXfAiaGvG0zOyMkq6SOlajadUcZLOV5CGTCKV0SHgdQphIOu9H+NbMwbd5KabcQpyDL8Wn5yDTOcHoBk3nqHEHwrw+u5ilGyoBGN03jyvP6YTNdHxjLh4JoaCP977ZwZyVFQgBfTq6uG1iFzTFzH8/3cTK4kj0j/MGtuGKUW3xnf8nnp21gZ0V21FkiUtHDWBCmpOKBoWnp39PbaMfs1Hhhgu7UdgmceQLq1lhxYZy/v3JVgIhlXSXhd9e3Ys0Zw7fpV/Be59VoWoh2md35qejekcN7IEIJGzt+9C48gtM2R0RahB/eTH2LkOQlPjzKCmGyN6tgzZ0y0YTWnM1YXcd1na9CNWVI7z1yOYUgrXlIMBa0JPA7i2YC4diDHnwl23E2r4vstWOf9dGLO16o6LgL12PKS0XJBlfyTpsOd1BqPgrtuPsNwERCuDfvRVjeieQE0+7+o2pKAOuxtXvElTZjE/ok0E6Jx7doOkcFZIksaK4OmrMAL5ZtZueHdPo3+n4xFtsDcU7G/hiRUX079XbGvh29R6SXc6oMQP4clkZQ3pk88WSneysiHieqZpg5tfbKCwYwP8+WUttY2TTciCk8p8P1/HQ7UMZ0jOHxev2ROsZ1iuHYEjl2feLo7KaBj/PzNzAzRd1Y8aC/ZE4dlS4eX+jnVs6muOcSTRkfCVr47wZzbmFJA27gtoPnorKDK5MhN1F8sCJMSlqzHmFKK4sql+7HxEORoOD1Vb/ncwrf0/F7H/H1N0w+0VSx15P09KPYuSSyYI5tyuetfNi5KbsjmgBD94NC/BuWBCVp+V2hqxetIQmIChZz5pN0DqnHt2g6RwVsgJLNlTEyVcVVzOoSyaqeuzJWwNhjd21XtaW1pOeZCHLZSVRGD+jUWZDaUOcfElxPb07x9/iDe4Aa7fXxsmr6rxU1nljZEJARa0Xi0nh2gldqW3yk5ZkYVe1m8pab1wdFXVeymu8XDi8PS6HCU0TGBSZb1btwhfUsBpjRyomEcBduxvPxY+xqwkUWaKt3Udg1wIsw68hc8r9hGt3IZvtKOkFaE21mLLak3HJLwhWbseQkoMxJYdQVWmc04Xm96C6Y0N5AYSqStD88U4ealMdfv+qOLlvyxKMGQWkjJqC0FQkWUYLeAlWbEXO6d2qDb+RfWi7aN7TiPkI9qG1BkkCs68StW43ksGElNqGwHGc5jSpbqgvQwQ8KK5cgo5sNH3k+YNCN2g6R4XQoFfHdDaV1MfIu7VLPS6ZyMOa4O2vtvDNqt1R2U8u782gLhlxL9BQSKNjnguW7YmR92rvItURPyXmtBnplJ/M2m2xRi3dZcXlMFPXFBtWKj3ZwvzV5WhC4LAacXuDKIrM2L7xDhTJDjMFOUl8uayM0r0jQEWW+MnlfbAY419+YclMZdEtTHt1Ff5gxPMww2Xlt1MuJq9mG1Uz/hz1GDTldiH1gtvxl22gYf57GFKyCDfXYuvQh6RBl0S+Mg7wXpSMZmRb/Avd4MpCNsdPo8pWJ+acTnFrd+aCXliy2lP94T+iRtOQnEnqhNvwtcaYESK06gPcKz6Lylzn3IBceA5aQi/N1mFu3knN9IcRaijSXmoerkvvImBMOcyRh8ekunHPeY7AzvURgSSTPvl3BNO7nnYbvc9k9M+LswBJkjAY5OMapVzTBMN75tA2a38qj8K2Lvp0SjsuD3hFg49vVu0mI8VKzw5p2K1GXvl4A02+UFxZSYLuKQF6FuzXJTvVwjkdJYqyNfLTbXRpm0LH/GS6t3GQl2JkdL98kuz7RwaDe2ST4jRz+TmdMBv3r11dOKwdJgXuGJeDy2mmbbYTl9PCHeNyaJ8cZso5+/NSmU0Kd07qRl2jP2rMIDKl+enC7Qld4jVJ5pPFu6LGDKC6wceaEjfNi2bGuL8Hy4sR3kYal3yEbLGiOFIw2JLxbl6GFvCQOv5H+z0XZQOp599ByJ6Nc8gkDK4sLG27I9tTSDn/xwh7OtbOA6J1K3YXSf3PRynoiymnU1RuzGiLpXAozau/ihkBhhurCDfsn24+EhRPZYwxA2j49k1M/vhRZGsxSGGaF7wTNWYA4brdqBVbjrluAOp37jdmAEKj4cuXMWqJMwXonBr0EdoZjiegsnJzFUs2VNKnUzqDe2STZDk+l91qMXDbZT0pq2xGkiTaZDmwmY9P3V5/iGvP70p1vZddVW7GDy7A5w8RCGk4DopPLEkSlrJF3JavUtezG6qQSA/swrxiDsbuo7n+3M7MWV2H2agwrlcSaCpvzt7EuQPbYjbKKLLMptI6Gt1Bquo93HlVETWNPpLtJkJhjeZmN51zrVwwKJ9VW+u4YHAbOucIDO4KLuqfQ/8OSTS6/WSl2UhxmvhyXVNcfyrqfPhDatz5CakaFQmmLivrvEhSvFOICPoId7+A9VJnFu0I0i3bwODezWh+L2rbQWRc1wHN24DsSCVoSUcTEvbO/ZHCAYKVJbiGTUKyp+BTUnCMvhln3/FoQT9KSi4eY2Tt03nRb5CaKkAIRFIWYdkQiYp/EKGmWmRZOuIpRxGI7ydqGBHywTHGnJa1MOH6+ClwrbkOSeKYP7KEP173cFMNkhoEgx79/oeCbtDOYFQB//t8YzS/14YddSxaV8E91/XDpBz74Hx7eRN/eW159G9Jgj/cNIgOWcceZiczxc5LH22guj7yBbxhRx0ji3JxJZhC1DSBpX1f3O89RipfRuX2cbdSbOjCo2+si8oWravgkdsH0b9LJrO+3hpTzzXjO7PEE+KJN1ZEZWnJFn5/Y3+ef38tG3c2RXX5viCJ313ZleZ3HsDUXEsGke3FgX4TKGgbn+dveO8cku1WQuHY6VijLDGiTy4le2KNYJ/O6RjKMyEmyIeEcOXxcZ3G3NUVe3WB+SkWHr6xIwYU/Nas/el1BNiCVVTP/Gsk7xngL9uAs98EDIOvxo8dXIVxugYla0xED1mWsPUeS+PXb8SUM7fpTqAVU45SUiay2YZ2gGEzpOSAPXFw4tYQVqzYe59D40GRXoy5nQkchxkDJTXi9XngiNnWbXgk07c+5fiDQZ9yPIOpaw5Ejdk+SvY0UVl/7NMkZouBL5bGhlQSAhasKcd8HEZpdY2+qDHbx4LV5TR746ftANTU9qRO/BnakBsJD72ZpHNvRW4/kI+WxH61awKWbKjioj5JjO6biyxLpDjN/PyKXihBb8yaHUBto5+KGk/UmO1jfWkT5XV+1ObYdTj3qjl0TFH42ZW9SXGakWWJMf3yuGBwPqGwhixHAu6aQw0oskATkJliY+LwdhgNMnarkRsndkNCwjbwUuw9R4MkozhTSbvs11T7DMxbE5uupbLeT1lN4igc4ZqdUWO2j+bvv8LkrU5YHtivY7gBWYpEeTd0HIJz0MWgGJCtDlLPvwM1pV2LdSQiaHSRdvm9GDMjx5nb9CD14l8SOA75vTRNYCwcgXPAhSAbkG1JpF74U1RXweEPPgKC9mzSJ92FkpQOkoyt23Dsgyej6k4hPyj0EdoZjNTCkpnU0g+tQBBxdjgYRZY5Lp+siXSUJFrS3KcZWViXx/QvNxMKa0wY0oZL2ptb1rGpnE65GQzu2Z9gKIzR34gknAnrb+l8yYneZZKMUXgZ6qqi6zWdCMtmktzbsRh9qJpA3fQttQvfQ2gazv7nYyi6iM8Xl2AxKfxySl9CYY33v9nG4B7ZdG/bBuOIG0kfNBlkI0HFhmh2H5yZrcXT1ZLukiS1eIBR86Kun0ftkg8QQsM5YCLGnuMIKk7kvpeT0WscQlIIKg5aG9dcCPA72uC87F7sBhVP2IiPxPvYjoaAIQl5wJWk9zkfjlLHltCETDCjOylXP4ykhQgZHPh1Y/aDQzdoZzCpDhPDe+ewYM3+9Y/Cti4yXcf+RRz0hxk/uIAVm6qi6xOyLDGsdw6BQHycwNaSmmQmJ83Ontr9ETpG983DYTWRyGDuqHDzv082Rv/+dGEJWak2LhrRgVWb949GFFliUI9sPlnm54tlsRmRH/+/gVw0OJcPFu0fpWWnWijIslHU0cX32xqi8r6dUshKteFxZRJu2D8Kdg64ABEOUPPe40AkLWgzIPqOx9JpAHXfvhUt27zsY1yuLCaNLmLaf5exsjiip0GRuWNSBkIIVCGj7nM9F5DuNDF+QDafL9s/8sxPt5GfnviaKmltUZypqM37HS+c/ScQtGTGhWAEkCqLaVzwzn4dl3xAiisbqd1QNAF+JWmvLkdvKEKYMaY4CZ2ACPSaJhE4DjomQggio0lF31v3Q+WQ6WMWLVrE0KFD+eKLLxL+Pn78+ENW7na7mTJlCs8//zz5+fvTTLz++uvMnj2b1157rdUK6+lj9nMkffUEVdbvqGNlcRU9OqRR1Ckd53FyClGBkspmvl1VjqJIjOyTR/ssG7Tkgi1BgydEVb0Pp81IRrIFQwuel8W7G6l3B2lo8rO9vJE+nTKobvAxsk8uaY7YfUuyLDF93jbCqkZGijXy4g2EWbetlnuu7cvuGjfzVu3GYpQZXZRHqsPAH/6zgrED2iLLkRHbjvJGerdz0rdzOut31LJ4Ux2dc+0M7p6Fw27H5/eyYmsDa7fV0atjKv07urBabViCtQS3Lye4ZyvWzoORcrqh7VxF/Zz/xOpotpEy9npqP3shRm7O6Yj5kvsoqfLy9cpdOGwmhvfKITvFggSYA3VojRVIZhuaMxvZ30BDdRWbPKks2VxHlzw7A9pZSBMNBDNik5Puw+bbjX/LUoLVZdg6DUDO647f4IorZzDI+L/6F77ixbE65nfDdsk9hMPH7y2uP6cnDz19zF4++eQThg4dmtDwSJJ0SIO2evVq7rvvPkpKSmLkW7du5cUXX6Sg4PjMbescGrtJYXDXDIb1yEJVteP60aoAHbOc9LisR+TrNRBu8ctVkmBHpYdHX10WdYy4aHh7Lh7eDmMCo+ZyWli1uYal6ytId1l564tipozvgi2BMRZC0Lcwg7fmFDNn6U4AUpMs3DSxOza1kbQvH+Eqiy0SLuo7M+YLf82U8V14+aP1eP2RNbm+hRlkZyZjNFko6pLH4N4FhEIamqahSGHWbq1lxtwSslPtzJi7DbPUnqG9LfhNaUjdzsfSU4r2y2yLf3kY0nLBGD+KMmZ1QJJkCjLs3HJhN1JT7ZGXnwCzeyc17/4ZEYzsi7N1G4Fz2OUkBaspXPUKfdr1JlC6FavojtxjTIvXyWvNQ+l7OXaDHLlGLaBpAlNGQZxBM+V0jEsRo6PzQ+SQBu2RRx4BOKqR1IwZM3jggQf43e9+F5UFg0H++Mc/8vOf/5wPPvjgEEfrHE+EgHD4xL2R/P6WX5L78IU0/jVzdYyX38cLdjCoexb5afGbfFVV44slEaeTBnckmNO7c7fQv0tGXFkhoLbRx47y/Y4bdU1+1m2vpZdvO2pzDeoBH8ghr5uvllVGjRnAqs3VjB3QJvr3gS/+hkYP//50K6om2Lo31cu/P91Ct7ZJOJxJCCFiXviKxY4ppyPBPdsAkBQjSUXjMGS2x5CcQbgxMrUoWx1Ye5+Lf++Mw4HRVYwEaZz3P0TQjym3M+HmOrwb5+PsP56GRbPQfG48674BoLl2N5ZuIxOd9pjzmSh6S2Q5LeJ6r2kCS8eBKKu/jDq7yLYkLN1GRnXU0fkhc0iDdu+99x7y4EcffbTF36ZNmxYne/LJJ7n88stjph9by5EMnTMynIctc6ZwuvS1rLKZmgZ/nNztDyfsw7bK+NBMTZ4gYS1xn3d+sz1OVlxaRyA5vh5vWI5zkweobfInrHtXeRXqQS/0sCpodPvJT1HwlayNTOe164UlvwtNW0oxZ7XD3nkAQtOQZIX6Re+TfeU95Fz3EMGqUoSmYspoiyktl4NbzMhwEmqsJpTTieQBF+LfVYytYz+M6fmIgCdhbjI56Gn1vRCs2ol3+2pUTwOODkWY8wqRje2x3PAwwcpShBCYM9tiTM2J0/F4cLrcu8eDs6mvp5JDGrTOnTsDsHLlSsrLy7nkkktQFIVPP/2UNm3aHOrQOBYsWMCePXu49957WbJkyVErrK+h7ed06qtBEnTKT2brrlgX8lSnOWEfXHYTshRxs99HboYds0FKWL57+1Q+W1QSIxveOxdbSoCDU1wmG8MUFaazaG2sS3/Ovum+g0h3WbFZDDEjOrvFQHqKlYp3/0qoKtJu07JPSB45BXNuJ+rnvR5Th63rUPyKg2BIgZSuAPg04KD29l1Tk2LGlJ5H9UfPRH9TnKlkTPoNBlc24YYDdJdkNFtqq+4FS6CG2hkPovkixzQuep/0SXcRyOgJ2CC1W0RHNV7H48HpdO8eK6e6r/oa2l5uueUWAObMmcMbb7yB1RpZA7jqqqu44YYbWtXQxx9/zJYtW7j00kvxer3U1NTwy1/+kr///e9Hp7nOMaMBvqCKw2JAHGA5VBGJYGE1KTHyRMiyhCeoIkkSNqPU4lqLQZL48WW9mPHVZjJSbDS4AwzqmkVGUuLAtKkOE7+5pj/z15ST4jRTXe/l8nM6Y9q73qYSmUa1mmQ0DTrmJnHpqA58NH8HmiYY0jOHIT2yURQH1q5D8W1aBJKMs9/5GGwuJo/JwGkzUtg2Fbc3iCqgbdbeXGUS+EMaJoOMDNidSdw9pSd/f28j9c0BUpxmfnV5N5IDVVRX70TuMBA1KQ/D7u9pWvgemdf/GdeoqTTLTsIWF4493+Pscw4eteW0OgaDQtjfTEN1AFk2Ywg2UbNwZkwZtbmOcM0uUi66E/fimRiT0wm7G7F2HUbQmnlIzzujFEZSA4QVO5qAcOVWJEXBcdk9qAYbrP6Qxu+m45zcmdCxhu04DAZJJdxchyIp+j4unePKEbm71dbWYjLtf/FIkkR9ff0hjojnwOnJJUuW8Oyzz+rG7BQhy7Cr1s/bXxZTUt5EUWEGl47sQJrTRFm1l9c+30hVvY9zB7RhbP987C3kN/OGNFYWV/Hh/O0ossykMR0p6pjWYhQSQcTZ47vvd5OX4SA5yYLJqBAKxVtBowGcdhPNniBrt9YwsHsWihJZ8ymp8vD655uobvAxflBbxvTNw2iQ6dYuhRSnBVUIUpPMSJIgYEjGMvpW7IMmgSQTtqQSFDImyYfVbOS1zzbicpiZOr4QmwkaPGE+WVTCwjV7KMhJ4toJXchxWWiXl8pfflREkydAkt2M2WZHVFfTNOYu3l7upXS9j2FdJzO+UyNB2cIG5xDenrOZBncd5/QfzjkitcVpOxFs5uvva3l/YTlmk8J1YwvoXWBDCx48tgShhpEMVmRbEu71CzBmtUNxphFqYYeeJEmYm0po+u4tQrW7sHUbgbXPBMKONCpG/YFXP9lBk6eWCwddwKjeRiSh0eJmv+OA1VdB08IZNO7ejLVDX2yDLsVvOvnphnTOTI7IoA0dOpQf/ehHXHTRRQgh+OCDDxg7duyJ1k3nBFHrDvPYq8tw7w30O391OVV1Xm65uAcPvbwkul4065ttBIIqV57T4cCIP1E27azn5Y83RP9+fuZa7rq2P93bxEd4V4k4dezLn7aptJ7H/reMB24bQnZS/Iigoj7AY68ui07zzVuxi6p6L/83qRcPv7I0Ou387rythFVB1/apPPbqipg6fnRpT0b0yCSsGQhbMiNCAYpR4qtVFXz4XSRnWaM7yOOvreDB2wbz/rc7WLEpsq9s/fZaHn55KY/eMYwkqwGD2U6qOTKKU1VoMOcz7aPl0cDCX6yqoc6bxsW5Mn99fVl0uvT9b7aBEEwe1Y7wQf4zBgMs2dDIy5/vXwP864wNPHhjbwr6jY/JeyYZTJiyO1A396VooNxA6TpqKqaRds00/Amiypv9VdS8My0aWNi98nNUbyN13a9i2hurol6vb84tRZbbcX6OE4KHd/I5GsxqE7UzH41GLvFs+I5QzS6cl91NEMsJaVPn7OKIDNr999/Pm2++yZw5cwC44IILmDJlCgBerxebLXFGX4C5c+fGyQYPHszgwYOPRl+d40B5jTtqzPaxuayBsip3nPPDF0tLuXBYO2wH5/EyGfh6xS5G9MklPysSYWPrrgYWri2nqGMqwWDs5ura5iDLNsZGZw+EVMqr3AkN2u4aT8yaFcD67XXUNQfj1lBLK5rwBuKj8M9bXsawHlkcPBfX7PYzd/muGJkmYGelJ2YTNoAvEGZPrYek/Hgjvbs+HBMlH2DNtnp6dW7m4JnauSt2MW5AHlZTbGQMWQvx6fL4oLorNtfQ/bwLkc123Ou/w5CcRtLAiwgLJTbqO6AFvGgNeyAj3qCp9eVxedICJevYknx+3BaOT5dWcE5RDtIJCrYrGivjwnAFq3YgeWrAfvSOYjo6+zgig2YwGLjhhhsSrptde+21zJo167grpnPisCSYQjQockJ5kt2ccPOzEBqj++Xz5bKdzF9dDkCfzhkM7ZWT0GnHqEhYzYY4I2U2Jb4FrQl0MRsVjIb46UxNFaQ447/wXXtjKR6sj8kgk+wwxxl1q1nBZJDjjJS5hSnXROfLaJQT7pVLspswKvHnUVYMZCQZ2VEeK09xmvEJM1q3CaR0HY2mGPGGZcyheiSDKc5ISabEIxzJmECuKDgS6JjiMKEYlEQBRI4LkinB2pwkg+HErtnpnD0c84rs8YqVpnPyyEu30/eg/VyTxnSkfY6TguzYlZ6bL+qO2RD/IlZVQV2Tn+LS/Wupq7dU4wuEE+53yk6xcvV5sZHdO7d1kZeZ2PsqP9NOz46xUdgnn9OJtGQzbQ465oLh7SgqjDh57MOgyFw0ogNagv13BsXINeMLY8IZZqXaaJ9t59rzu8aU7d0pnZzUxDMQOWk2enWK1fGa8V3pmOsk64BjJAmmjivEoMQbEX9I4rIRBRgOMHZJdhP9OqcSDkc2O/uEmUA48qiGTCkkj7gqpg5Lx35oSbkJdcSVjykvtk+ukVMobJNEhmu/sZMluG5cezSOT/boRKj2bGzdR8TIkoZMImw59mj7OjpwmNBXR8KkSZNO6ghNd9vfz7H0tdkfZseeZqobveRnOCnIdGAxyjT4Qmwvb6LJE6Qg20nbTAeJxieyIvHo6ytjDBrAsF45/PiSHgmNmk9T2V3lZU+NhyS7iTaZDlLtLb9A67whdlY0U9vkJz/DQZtMGzajAXcgonuzL0S7bCc5KRYkJKqaA5TuaSasahRkO2mTbkPToNYdYGelG0WWaJvlJNlqQJI0tld6KdnThMNqpGOuk1SHhaCqUVrlYVdVM6lJFtrnJpF0iOwB3qBKaaWbuiYfbbKc5KbZMEgSte4A28ubcPtCtM9Nol2mDVrw6DMYNHZWedlW3ozJoNAp10FaUsvT+GbNg1RXQqh2FwZnGnJmB3yG1JbLh5sQNSWonnoM6W1Qk9sSxkC928vWcjc+vxr5mMm0oomWPTETIaNidJej1e1GstiR0toRUFrec2VSPVBXAu4apOQc1JS2hKXWrZ9JEpj91ag1kagwSnpbApaMH2zm6FP9TtLd9nXOeJwWA73bpwD71128QZV/vbeWbbsbMCgyYVXjd9cNoGt+UvzLQkBR54w4g9a9QxpaAt99RZHZVFzOM7M2RWV9OiTz00k9MRjjp5yagypvzN7E95urMSoywbDGDRd2Y1z/HBwY6NUudr2o3hvisf8tj0YVMRlk/vSjIUiyxJ/+vZhAKDKNmGQ38cCtg2lsaGLaa6uxmA0Eg2Fy0mz84foivt/RzPOz1mIyyIRUjf5ds/jRxG6YEkx1AthMCt3aJAP719jcAZWnZ6xmT40bk8mAPxDmDzcPon0Lo9FdNUEeenk5mgBNCJLsJv548yBctvhI9LIM4e3Lafjylb1TjyGshYOwjr6ZUAuGIWBIguzeQMQ5ByIfNE+8tZbKOg+KIqNpgvtvGUTbdHvCOhIhSWCo3EDN+0+xb53SlNMJ54W/INiCUQsqdsjoQUb3o3/Jm717qJnxcDRhqGS2kX7V/fitOUdVn86Zg74JRCdKWZWbrbsaEAJC4Ujcx5c+WkdAjf/0lSSJnDQbHfL2v8i7tUsl2W5KmLLE6/Xw8ufbYmSrtzeyqyrxS21PjYdVxdUIAcG904bTv9xMeX1k7UiSpch/ezOhrNlaEzVmEDnm4wU7WLW5KmrMIBJtZGVxFe98W4omwOsPE9agrNpLVWOQ/326MXq8ELB8YyV7Wpk/rrSymbIqN2EtUr8m4LXPNqEmGEJIssSH87cjgJ4d0+hakEKTO8Da7bUJM7wYA/XRRJuRdTSBb/MS5Kby+MKHYPueJvbUetD2XmtVE7w5uzi6fmZQwKAceshjUr00fPUKBzrdBPdshbqdrdKlNciyhH/j/Jjs1yLgxb9pAXILga51zh70EZpOFF8Cd+36pgBhVcMkHzwVJfhy6U4KspMY1CMbiUh4q2UbKundPpWDPQtDYRWPL94T0ddCsFxvAnkgqBIIqVQ2BfhsUQl7ajyMG9SWPp3SqWmINzoVdV7SEqTKqa73JYwcHwiLhPq0pGNLuL3x/axt9BFSBcpB65GaJrBbjUwZ14XlGysxmxRuubgnXn8QSZLi16jDgTiHECAawPhIafLE11HT6EdVVWxN22le/jFoKo7+EwmndUKVEuQt00Ko7oZj1qU1yLJEsH5PnDxcV44lgQOQztnFURu0cDiMwWA4pMu+zulFbrojzitw7IB8bCYlLgqFqgrOHdSWf0z/PkZ+zw0DEq6fJTmsDOiSyvLi/Xm5jAa5RaeQ3HQHFpMS43HYtW0KKQ4Tdz0zn8Be+ZayBm64oCv9O6fwycKSmDom9M/BZImfhhvYIxu7UbDlgDBckgSpDgN9OqWzemtNVG42KmS34BTSEgXZDiQpNh3XuEFtE0ZekaXIyPbZd1ZHZWu2VPOHmwclfDlr1lTMeV0I7C7er7vJgpyc3SodO+TGb0MYN6gtDt9uqt/ZH4fVX7qO9MvvRU3rElc+bHRi7zUGz5oDtuZIMkpKLvEm/fgQDmvYeozGt21ljNzWY3R0JK9z9nJETiFvvvkm11xzTfTvrVu38tvf/vaUuOvrTiH7Oe59lWBHpZv/frKB6nof5/TP54IhBThacIoIhDV21XoI752SNBpk2qTZE7qnA3i8XmbO38nCtZXkZti59cJC8jOTEobLMholdlR52VxaT1jVMJsUerRPY3eVm3/M+D6mrMNq5C/XtmNTlcqb35QTCKpcOiyPIeatmHqcy4L1Vcz6eitGg8I147vQu0Mq4aCPnbVBNBGZuky2QE6qg0afxtrttXj8IYwGhe7tU8hOtiKEwBMI0eAJkWw34jC3nGlZADurPWzb3YgmBE6bkZ7t0xJHXJHgsTdWsnlnQ4x48phOXDKsIOG9bgnW4lkyE9+WZRgzC0gecwMBZ5tWOUVoQrClvJn/frKBJk+QC4YWcN6ANpiWvYpn7dcxZa0d+2Ie/6uEHyrmcAP+VZ/hWfcNhuR0XOfcSCC1E6KlnHh7OZZ716D5oGQ5TYveRQhB0rArkNoParVzycniVL+TdKeQg3jrrbdITk5m4sSJvPLKKzz33HP85Cc/OdG66ZxsBLTPdHD/jYMIaRo2k5wwQsg+vCGVhWv28M3KXZH8eIPbkja4gOQE7ukA7rCCJuCCYe1o8gSobg6Tk5440lIoJPD6Qnw0fzuN7iAF2Un0aJ+GIYFNMBpkwkYHVZ5mBnbLwmCQqfNqBAt6kGyUGFuUy5DuWciShNkgI4SgIWzg44VbWLetFotJ4epxXUhJsqOFfMxdvpMde5pJspvIcxWSn2ameJeHF95fS3W9j8wUK7dP6kVhriMu8gdEjEV5jYd3527BH1QpKsygW0FLXogSZmN8p0xGuUUD5TelYR51K7ZhU9AUM35Mrc6gLEsSXfOTeOjWwYQ1gc2sIEsQTuCgIxktCdfzAAIGF4bBU0jrdzFCMeLHfMKzOYdlK1LHUbja9QMgpDj07UM6wBE6hbz88ss899xzXHXVVcyePZvp06dz0003nWDVdE4VBhmshkMbM0WB77fUMG/FLjQBqib4bFEpm3cmjvEZ0uDlD9fzzapy3v9mG3OX7+LpGd+36HBR5wny19dX0OiOrPWUVjTx9IzvKciwkHxQxurrzslje61g+ldb+XxxKR/P38Fni0pZslNDSIZIni+DjEnZuyYlSXz43XbWbYvk/PIHVf73yQbq3CGembmRHXsiX9NNniCPT19PWU2Av7+9iuq9ulbV+/jH26uoaUo8sVZe6+XfH6yLTpd+v7majxeWJLTcsgTjBscmuzUZZLq0TTnkSzosZAKKk9Ax7BsTYu+Gd6OM0ASqKrB2HQHyAR8kkoy9aMIh8+mpmkRAcRA8wUGND0QIQVC2E5TtujHTiXLIEVpDQwMARqORJ554gttvv52HHnqIlJQUGhoacLlcJ0FFnR8iilFh+YbKOPmK4mpG9s7F74992Td4AjR5g1w7oSshVcNokNlcWk95jYfclHjHjap6X1wYrl1VboLuRn5/joG1jSlUNqn0b2OgQ2gtb5d3jatj6fpKxg9oE/fV1ugLReM1HkiTJ8iOithcY5om2FPjiYsq0uwNUVnvxWVLEBKr2hMnW7SmnEkjO2A9KIRYWBMsWL2bWy7uwfbdjZhNCjnpdtZtr6V9luOkOzkEnPlkTP0T/q3LQFOxdBpEMKnNCR916egcDw5p0IYMGRLnaXXHHXcAEbftjRs3nljtdH64aNAux8nGkroYcfucJEKh+Hk4u8XIxSPa8/LHG6Iv6aG9cshJkK0aIDnBhmuH1YjNrBBa8DyDbUkoFgehBXsQHfrQNmtAXPmCHCdGRY5b+7FZDORnOthS1hAjt5oVkuymOA/A5AQhtBRZItmReESS6oxfXyvIdmIyxk+IKDI4bCZe/mg92Wk2giGNuiY/d0zqdUo89oSQ8NvzkftG8h36NaEbM53ThkNOOW7atImNGzeyadOmuP90Y/bDRFHkhPvALBYDykFLNZIUKX8wkiQllB9IMKgyqm8+qUn7F+Kz02z065qJutdJRFHk6N4gRYaZX2+LeUkvWrsn6lByMBnJFi4Z2SH6tyzB/03uTXKSE0unAZE+ShKS0YRz8CR6dkwjP9NBustCTrqdZIeJCYMLosbMYJAx7N0cbQSumdAVq1khP9OBy2mmZ8c0XE4LP7mkMGY/04UDs8lINjNlXGzYrinju+C0GhKex3YOP0Xt928stpgUrhnqwpLA909ocMHgApL27t+T5ciHQrcDNo4bjQqynMAYKnKLa1tHiiwnvtaaJk6YQT1WnXV0WuKIvBx9Ph+ff/45jY2NMaO1m2+++YQqlwjdy3E/B/Y1qAq272li+cYq8jMd9C1MJ9lqpMEXYmNJPRt21NEpP5meHdNIs5toDoRZu72OrWUNFHVOp3N+MlajQoM3xMrN1ZTXeBjULYv2OU6MLWxYFUCtO8iuKjeSBPmZTtIcRlRVsLPaw+L1FaQmWRjQNRNFkfj1P76Lq+PXU/vSsyA+SjxAcyDMrmoPdU1+ctMd5KZZMSsydl8Zvu3fE26oxtapPyKzM0HZxp56H0s2VOILhBnSM5t2GTYMqBjrt+HdvBTJYMLWeRB+VwfCGmzZ3cTSDRWku6wM6JpJhtNMWNPYVeNhd7WHFKeZgiw7tU0hvl61mwHdsmjyBEl2mFi6vpKx/fPonBQgtHM1ocoSLB37IjIL0UpXUL9rJzWu7vhVmUylEfvGj3Fd8UcCcry3mUCws9rLonUV2CwKg7pnk51swSw8SJWb8W5dgSE5A0un/nit+ZhUD6KymMCO1RhzOmBo05tAgtQxh0KSoKY5yPJNVdQ1+RnSI5u2GXaUE7w52RxuRC1bS6hiC8Y2PZFzuhFUzmwPvFP9TjqbvByPyKDdeeed7N69m8LCwpiv/wOTdp4sdIO2n319lSSY9305r362P6xUhsvCfbcMZvqXW1i4Zn8UiS4FKfz08t78Y/r3bNu9fx/WBUMLmDC4gD+9tISG5v0RN267tCfDe2QlPOdltV4e/M/iaKoURZZ45MdDqW7w8dRbq6Ll7BYDj/10OM+8sybGaUSWYNodw8hKjne39oc1/vL6Ckor9l/LS0d24OpBTmqnP4jmbYrKU8+7mYrUfvz+PysIHeC88MCNRXQ1VVD13l+JzpspBrKuvp+FVU6ePsD932kz8tBtQ/hudTkzv94f0aRNpoNfT+3H/S8uillHS7Kb+MvtA9A+eohw3f7zmzR0Mua2Pame/lBMfxx9zsUw9DpULd5g7Kh08/ArS6N/mwwyD/94KLlVi6j76r/7z5cticwr7sWzYT7Nyz+Jyo0ZBSRd+juC8pGHrar3BLn/34tjsh/85pp+9CxwnbCYiCbhw/350wTK9s/u2HuOwTTsOsJncIyHU/1OOpsM2hHdRcXFxXz66acYDGfuTXc64wmozPhqS4ysusFPaUVzjDEDKC6tp7zGG2PMAGYvLqVrQWqMMQN464ti+nZOx3JQLENZkfh8UWlM3i9VE8xfs4eyg8JZefxhqur9jCrKQ1U1tu1uJMlu4rLRHQnsjcKhAf6QisWoIAN7ar3srnYzrHcOWal2ikvr+HjBDsZ26QoHGDMA95q5rMhrF2PMAGbN38nPC0uIWQRSwzTUN/DWF7HnpdkbCcr80fztdGuXStd2qVTVeVmyfg/1bj93Xz+Af767moo6L9mpNn52ZR+c3jJq62LraVr6IRndRpIy7lYavnkDEfRj6dAXa7+J+DUJSZLwh1QEkTiQmoD3v40NCRYMa6zeXIVz51cxcs3bRLi6lOYVn8XIQ9Wl0FAOqZ05UrbubopL5TN9zma63DyIFsJWHjNSU0WMMQPwrPsaW98LCFuzTkyjOmcVR2ShsrNbF4VA5+SiCRKuRSXaCAugJtjJvM/1/mDCqpbwi12SJAIJnD8CoXBCJwIhBP/7dAMDu2fRv1sWvkCY97/Zxv9N6kWdJ8gbs4tZu62Gru1Suf78riiKzM0X9eCr5WUsXV9Jn87pXHt+V4QQcd7vQggCofg+BcMioe77YlUejKoJpo7rysaSOj76bjv5mQ5uvqgHMhGHkXGDCwiGVUwGJZILzZdoE5oGmobWcRRpbXqDGkKzuPALA2FNsGprDW/NKSYU1rh8TEeG98qJiTUZ1T0UqSdOd00lcafi6zgUie6BkKohECTeGXjsiBb2gYhW6q6j0xJH9C1WWFjIDTfcwHPPPccrr7wS/U/nh4HTonDR8PYxMofVSEF2UlxOsbwMB3kZdrJSY13lh/bKITvNjvWgqCDnD22H3Ry/8VcNa1wwtF2cfFSfPIb2jP0AMhpkUpPMjB/cloVr9jDjy8189N12ALLT7DzxxkpWba4mrArWbavl0VeXY7MYePWzjWzf3UhY1VixqYol6ytIcjnjklbauwyhZ8d0Dl7+GT+oLba8jgdpKJGa24YLhsXqbjYq5GXYWV5cxbKNlYRVjZI9Tfzvkw047GYefXU5r322kelzNvPaZxt57LXleJLaodhj3fYdvccSMqegaYKAIZmAOZ2QiJzTHZXNPDdzDQ3NATy+EK9+tol1O+q4dFSsjrIEfbtmYe82NFZzowVjRlts3YfHyBVnGpKrhXxoLdAxLxnDQc4gk8Z0xNRClJfjQlI2xrTYzNSWDn3RbBktHKCj0zqOaA3t3nvvjZMJIXjssccOeZzb7WbKlCk8//zz5Ofn8+abb/LGG28ghGD06NH87ne/S+iRdyjOpjW0oCaobvAjEfH6Ozik1IF99YU0Vm+LbHRun5vEeQPbkuE0UdMcZPH6ClZvqaFLQQoji3LJdJpp8Ib4ZtVu1u+oY1ivbAZ2zWJHRRMakdGLxxciPdnCmi01XDKyPSZFpropQDiskumyYjbIqEJQ1einZE8zsizRLsdJZpKZkAoNniCV9V4sJgPpLgupNiPeoMqKzdV893057XOTGDewLcGQyh9eWBTX919N7cvidXsYWZSH1x/Gajbw9pxifnFVEfnabpq//wK1sQZ7jxEY2vXjgxWNZKbZWLCmHH8gzKii/Eicx/PaEqivYndVM4oskZeVhNeUzvvzS2mbncSC1eVkuKz065pJssPEwy8vjdPld9f15/HXV8TJH7xtCB2cfvZUNtDk9pGVnkRqagqBBE4OiiLz6hfFBIIqbbKcEYeaBh87K5u5a2pftuxq5NOFJdisRi4a1o68NBvWcAPqztV4NsxHSc7AWTQOf3J7jMFGwjuW4920EHNeVyw9xuA3t2wUFEnF6K1G87uRnBkEjREjXF7v57NFJdQ0+Dh/aDu6tnEdN4MmSWAO1qE11yLbkgla0tGQsQRq8G/6jsDOdVg7DcTYaQgBo+u4tPlD5VS/k/Q1tIO48sorefHFF/H5fGiahqZp7N69+5DHrF69mvvuu4+SkhIAysrK+O9//8v777+P2Wzm2muvZcGCBYwYMeKQ9ZytNPvD/GvW2mi+sW7tUvi/Sb1ajKtoNcoM7ZbJ0O6RtQihRabb0hwmLhvRnonD22GSJYJ7o1e4bEYmjWzPpSPbI4nIB0qS3cR3q8v5alkZAMkOE9dM6IoAXvl0I4vWVQCRUd5vrumLLEm88vEGtu4N8turYxq3XdIDT0DlyTdXUNMQibo+siiXK8d2otEd4pP5O8jLdLKxpJ50l5W+nSMjq4O/UTJdFtrnJPPE6yvQBFjNBn56ZR+sJgNeY3ss5/wYBY2gJhMW0DZH4/OFpQTVyJTgZ4t2cMnIjtT5ZP7+QQUleyLrbn0LQ9x0YQZdClL5//buPD7q6l78/+vzmX3LTJbJvpAEkrDvO4LIqiwq3irWqqhUbfvrtb3fr36tbaW9vbR6e7193Pvt19v2Fm3LlVaqFtkUW7HIKgISZN8SICQkk30myayfz++PCQOTmShqQnByno+HPpjDZD7nTMK8c87nnPf7jfdOk5lqpqahjQMn65g/sQCTQRuTXd9s0sUkG5ak8Cz4nWMtvPJ2JYoKZmMDT9+fSl5a7KqgoqiMLkln864KdpSH77vlZ9q4c0YxOo1EWa6dwUtHkeww09gYPpjt1TqQB84gqeQmQpJMewBQwKe1I5XMxlo2k5CqCZ8V64ZW9aMcfRfXjrWAimy0kHrnU3htBWQ5jCxfMDjSv54iSRL6+mPUr/8FasAHkkzy7IehcDJeQxrSqDvJvnkpTS1+fCI7vtCDrmnJ8Yc//CFjxozB4/GwePFibDYbc+fO/cSvWbt2LStWrCA9PR2AvLw8Nm3ahNlsprW1FY/HQ1JS0hcfQQKSJIn9J+qiimceq2zio5OuT5zRqmpnIOvyIREIhCCoRILZZUq4omTkKEYwpEaCGUCLx8+7e8/T2OKNBDOAiy4POw/VsOfIpUgwA/j4TAOHzzayccfZSDAD2H6wmooaN796vZzapg5OVzVT43KzZssJVGBul9RPN4/JxR9UWfPOiUig6/AFeWn9ERQlHGwCQfAGZRQlPG5Pe4BDZ+qprG7hdFUzlTVu6pra2V5eHQlmAB+ddHHiQjMfHr1ETUMbH510ceZiCzsOVuMPhli2YEhUX8YPziDDYeT2q87EAdwxvRifP8Tqt45F+tjuDfJ/XyvHG+d+niSpNLm9HK24chD9/CU35y+5kTu/p6qixpwJU5TwOANdjrCpqkogJH9qINJ4amjZ8SqXb2wq3jaatvwKvdrR+fo9f95MH2yhcfP/CwczAFWh6a+r0HXUdfYdZK1elHoRetw1zdAkSeLRRx+lqamJoqIiFi1axF133fWJX7Ny5cqYNp1Ox9q1a3n++ecZMWIEZWWx6Yo+zbVMnZ3O7kvAf1kcPOWKaSs/3cCSW6IP+F4eq9cX5NSFZs5ebCY5yUhpQTIZKRZa23ycPN/MhVo3WWkWSvKTow5DR71+RWwexpMXmvF4Yzc/NLZ6qa6PTfF0+GwDrW2+mPZLDe2MG5rJ3Tl2aurbcFgN6HQaGlp91DV1cP+tg/EFQhh0Gk6cb4pb36yx1UtrW4ABuamxr//BOZ5+YBy1je0EQio5TgtVta18eDL2fTxe2URDS2zNrtraJmZPKaEgy0a1qw27zcDAXAcpSUbunlPK2MEZuJo6cCabKM51cCjO98jV1EEICa+vg5Pnm3C3BxiU66Ck0Mnxytj39+MzDdwzt4R6VxsnzzehP9tI6YAU8jN65mfYU9cc0xZsrMEk+7GnpffINbryVteidHRdYlPR+Nw486/8/F7Lv9OqOjcnzjXR4QsyKM/BwFzHpx76vxElwmfSl8E1BTSLJXy+JT8/n1OnTjF27Ni4mQuuxd13382SJUv43ve+xy9/+Uv+6Z/+6TN9fX+4hyZJEmPLMjh0uiGqfUypk/p6d2Q568o5NIn9p+r55WtXamoNzHXw3aWjeHffhahzVTeNyuH+eSVo48z04qWbGlKYgs0c2+50mMhKs0bNIgGGFadx/FxjzPOLsu1Uudz84qrzaYPyHDx253COVTZy4MSV3Io6rczCqQNilvmcDhMOqz7u93fUoHR+/j/7cHcW19RqJJ5+cDzBEDFHFIYWpeL1+jjTZdU8I8WEu7WDdJuBdFs4rVXIF8DlCr9mTrKJnM68k942H8mW2M0yWalmQgE/P/nDQWo7kxlLEjxz30iGFaews8sxiuHFqZyrbuX7v9oV2alqM+v40SOTSLZ0X6LmWhnNsVn+dWl5dCgGWnrp34lBNqOx2Am1XfW+SzKKwR753l3Lv9OWjgA/eWkvjZ1HSWRZ4tmHJ5Cfdu3n7W4Eff2Z1J/uoV1TVBoxYgTf+c53mDRpEi+99BLPPffcZz6TVlNTw/794RvrWq2WBQsWcOLEiU/5qv5JVVVGD0pjTOmV36DHlaUzsjgt7o7tdn+I328+GtV2uqqZ87Vu1m2LPuO0/eBF6uPMTi67bWphJPWTM9nE9FE5JFv1zBqfF3nOwFw7M0blkJJkYPCAKx+Yo0ucWExabr+pmNzOwp2SBPMm5ZOSZODPXc7KnbrQzKWGdr5772jMxvDPk1Gv4Tv3jCYn1cjDC4dwy7hcli0YzJhSJ99YMhyLMfZDXqOROHy2IRLMILx8+tauSm4Zl8OQwit9nDYym9I8B3fPLCI7LZxHUpZgybRc8jp/i5ZliZAK0qdkzcigga/Pzo7sFnRYDXxzZjLnL7VQ19zB8OI0pozIwmrS8Yd3TjN0QDIThl45b1XWuUnnta2no45duDuXT3siRVTAkolj1rJIBn2N1YFj3mP4e7F2mF9rI2XhPyIbO38GtHpSbv0GftNn28148kJzJJhBeHn0T389KVJLCt26pqj0zDPPUF5eTmFhIc888wy7du3ihRde+EwXcrvdPPnkk6xbtw6bzcaWLVsYO3bs5+p0f2A1aHn8jqE0tg4ESSLVpkfTzSdcUAnvSuzK6w/FbLa43B5PS5uPYxUN3D27BFVRaW3z84fNR/nX/+8m7p01iHkT8gmEFJxJRgw6DX8/UEVykoGlc0uRCAeoj07UMaQgmQdvG8xFVxtGg5YBWTZCihqz2SLclyD56eGZmqvZS1qSgdx0C5IqU5zr4Ni5Zt754DzjhmRgtxriFgPVaGSa3bFButntQyvLPPGVkTS0+tDIEilJBjSAXgM/uj2dmno3JqOO7EwbAb0BtzfEzo9r2H7wIsW5dhZNLSLNFr9Ei9bXyoiKP/LT+fNpU0w4gvVodv2Rhqk/5JHFw/jg8CUu1HqYM7EAT0cAg6ywfNFQFk4tIqQoZKaY0csy9S2xy6tNbl9MYvDPIyTpkAdOJy13KPjawJKKT2vr1YTDqgo+RzEpX12J2taEbLLhM6R8atHPq0kSUb+gXNbk9hFSVLS9nKJL+HK65ntoo0aNAuDmm2/m5ptv/swXKikp4dFHH2Xp0qVoNBrGjRvXJ7kgv0y0kkR6nLRQXdmMWmaMyeW9/VWRNr1WJifdSkGmLSp9VLLNQEo3r5mTZuFCrZuK6iubKG6bMgCLIVwbLc12Jbt8KKQwb0IBL/zxALsO1UTav79sPLsPX+KljVdmjBpZ4t+/M53xQzLYe+RKyRmDXkOO08p//vlg1OaSHKeVf7p3NM/9YV8k8/36989S7fLw+O3DYpYV/P4Qo0vSeeeD81HtM8fmoZVUJFki03FlzLIsETyxk45ta3B0tjXIWlIe+Dl/fq+W7QfDy4LV9W0cPOli5WOT4+4ule1ZqJ4GDLtXRSqBGQeOIy3Zyi9X7Y3Mui7Uurln9iBMJiPBoEruVWcAJUnl1skDePH1Q1GvPbrE2WObJhRVxmdwwuWt/ddhiqOq4d2Y2O2f65qqCqX5sfkpb5syAJ1GFjXQhLh6PZfV1q1bI39eunQpS5cu7e1L9j8q3Dm9CKtZx/sfXSQ7zcJ988qQVJWbx+ZxvLKRY5WNFOXYGVeWQYvbR1KcD+i0JAPPPjyR/9lynLrGDm4Zl8vMMbndFvosyU3iG0uG8/p7p9HIMvfMHkSO08Kv/vJx1PNCikpdUwcDcxyMLsnA3e7DaNCSZNbR7PbS4vHxj/eMQiNLqGo43VaVyxNTxmXfsTrqZnrJ7LKpRZYl2n0BvvUPI1m37Qy+QJD5kwag1UrES5aiDXpo2rcJuWwGbkcpOgKYTr5DQ6s3sqX+Mnd7gOr6dkpyYnfk+oyppN39fVp3rCXgOoe5bDKmkfM5WNEek7lly57z3DwqB0OXvFKqCsMLU1i+eAjr3j+LUaflq3NLyEuLX1anP8lOMfK9B8fzypbjeNoDLJxayPiydBHMhG6J5IwJwhdUuFjnYfLwbFo8Xmrq2yjOsfOHzUcZmOtgyohsqurc/Hb9Yf7l0clxXyOkqFS53KTZTRRl27lQ68YfVLDoYzc/QDjD/+mqZoYPTCPU+edBeY6YbCMAOlmiONfOb9cfoaa+DZNBy9fml5FmN/HwomH8et3HNLt9JFn0LL99WNwlJa1GQhdnM5KqhncXbtlzjiUzB6LTati08yxpDhMT430ASjK+iQ/zmz1+Tu5yo9XI3DvtPibojWg1ckxaLF03yQ1VFbyWPCzzn0AK+QhqzHhVCb0udlOMyaCNKktztSS1lUlNmxk+0oIc8mNpqkaXM5eA1L+DmoTEoCwbP3xwPCFFxaj79GMKQv8mAloCCKoqL2042llsM7yVfPfHl/jp41OYN7GAt/ecixSzHFuWjtMRf8nxUlMHv1l3JKotpMC37hwWk91PkiQ+Ounir3svRLUPzHVw39xSnr8qs4bdqifVYeSFNR9R07nVv8MX5L/fPMyPvz6J//daeSSTfWubnxdfK2flN6ZQnGOP2qF429RCHDZdzPKVqqqU5CezbtsZVq0P91+W4J7ZJUjErnb5ZRPrT+k4WRXeRRoMKazedomBxbl85ZZBrHnnymalohx7t0VILwuo2vCmi84LDchMwmEzRCV6vm9+KXqNFLOpR5YlAid30nbkfSCcpNkNpKYXQeaIT7xuf6GRwht/RDATPo0IaL0gEFKpbe6gqdVHWrKRDLsxcni2N3T4QjGVowHqmtpZOHUAo0vTqWtqJ9VuIifN3G19s7qmDr5+xzBMei0d/iApSUbe3HaajkAIsy56liZr4MPjtdw9qwRNZ7okXyDE/hN1PHTrYH68fBKHztTjsBoYUpiCpyMQdcD5skuN7eRl2Bg3OCNyDu3gSRc19e0svqmIZo+P6vo2BuU6aGz10dTqx2rScbG+jQ5vkOw0C067gfJTLr4yq4Rmj49AUCEz1cx7+y9QlueIuefW4Vc4fdHNffPLCIUUNLKMq7mD6kYf00dmU5Bp42hlI7lOK6X5yRi1MlrVj8Zdg+JpQLamErJlEZTibxZJMml59qEJHK1spNHtY0RRKrlp5rg7VDWqH/eJPTHtvqojaLJHig9xQfgMREDrYSEVNu85x5udyXcBHr1jGJOHpHd7L+qLMug1ZKaaudTQHtXudJj44Ggtq6+qk7Zg6gAWTy2MG9RynFZWv3WcIxXhmYtGlvjuvWNiSsdAZ6XlyYX86o1DkXtdKUlGHl8yHAmVvDQzBekFqGo4BVebX4PTYcLV5cB0cpKBknwHq9+6UlZk/uQBpNmNPP3iTqxGLdnpVt7dew6zSc+EIem8+MbHHD4b7qNWI/Hsw5PITrPwX298jNWkQ6uRafb4mD+pIHxfrktQMOhklswcxK/eOESw8ybbwFwH00Zmo9dIDMpOojTXHgkmGkIox7fStP1PkdewT78XzeA5hNT4y5EOs46pQzOQpE+eWYQkHYbcwQTqo2e6uvRCgiKYCcJn8uU7cn+Dq2/1RgUzgN9tPEpzW+wW5B6jqtw+vRjDVbOom0blgCTxx3dORj11085KauNk4YDwDO1yMIPwPbVX3j6OP+5WeYlDp11RGzcaW72cqWpBksI/VrIsRVJ16bTw4MIhUfej5k7MJ9lqYOOOCiwmHSX5ydjMOrbsqUSS4O5Zg/B4g5w83wySxLfuGkFNQ1skmEH4vNmqDUcozU+mND8ZT0eAZo+PNLuRORPyY4JZ59vFW7srIsEMwuf23FeN5eogpOuop2XH2qjXaNn+KrqO+rjv49XX+bQZlqKAaeRsNLYr2U/02aVosgZ/4tcJghBLzNB6WLzzYP6gQocviN30xTM/xOMLKLz6t5PcMSNchkSrlTlytoHWdn/Uh/Zl7jY/pMTeF/LE6XttYxv+QAijpsuPigTnamKzH5y71AoSnKxuZduBi6QlG5k2IhujXsMbW0/x3XtH424LYDHpOF7ZSH2Ll9umFqLVSFRUt3LLuDxkWabdG2DOuFxGlTjp8IVwWPUkm3XsPxUbRGrqPeg0Mt+5ZySXGjsIhcJLjl2XSS8LhBSqXbFpu1rb/XGeDYqvjZjptaqgeNugB84new3pJN+9ArW5Br3RgN+Ujk/u3xtCBOHzEAGthzkdppiM7ZkpZpJtvZeZwWLQUpRt59W/XZmNyRI8cGsZzmQTrqYrMzKzUUtGN5scstMsMemmJgzNJMkY+2MSCipMHZEVc+9u/JBMDpxy8Z9rr6Th+usH5/nJo5Mxm3T86+orm0VkWWLW+Dze2lXJodPhQHXwpItBeQ5mjslBI0lk2o1RqYOy4qQ9uml0Dia9DCoUOD89LZJRJzN9VA5bPjgX1Z7TzddKtjRkcxLKVZWyNRY7ki3tU691rXyaJEhNwu604fmSp24ThL4iAloPSzLp+N6D4/j1Xz7moquNknwHyxcPw9CLhRNlCe6fH070fOBEHSlJRh69YxjBkMLt04t598PzVFS3kuO0ctuUAfh8ITCHN6/4ggomvQaNBLlpRr5772jOVLVg0GnxBYLhpcs4JEnGH1SYOzGf9/ZXIUsScybm0+ENsPdobdRzvf4QZ6tbeWTBEI6dbyI5yYjXF8RhM+DzByPB7LJTF5ppbfNj1UcXIQXIdJj47tLRvLTxCK1tfiYPy2LxtEJQwwGyzRdEUcOZVi5v15ekcLskSVgMGhRF5bYpBbT7Auwor8Zm1vPQwiFkJ8cP9H5tEql3PknzO/9NwHUeXXoBjjlf7/WMG4IgfDYioPUwVVXJTTHz7LIJ4d2BBi3a65ClJ6ioFGYnMTDPQSikRGZaL284wqRhWYwfnEl9Swe/33SUFcsnUtXYzm/WHaaqzsPw4lSW3TaEZIsOvVbD9oPVNLm9TB2R3e2OSIBjlY3UNrRz+/RiFFVl96Eahg+MP2tRVZWAorL3aC0HT7rISDGzbOEQLHFmf5efH48kqZgMWmaPz0erkdF09i8QUtl3rJZXtpwgEFRYOLWQWeNykYGtBy7y5vaz4fNmc0uZWObEZtCy7NYy/mHmQLSyjFmv6faal8+bJd3xDHKgHUVvwYtBBDNBuMGIgNZLdBoJXdf7Tr1EAda8c4KPrspYL8sSz39rKiMGpkVleM/PtKHTavjJb3ZHcjp+fKaB//xzOY/dMYznV++LLDnuKK9GliWWzS+N+fAOhRRunTSAf37pA87XhpfIJAm+OXIElxraomZdBr2GIYUp/ObNwxw+E97QUdvYzr/9z37+5fEpjCpxcvCqMi+l+ck4HbGzM4BLzV5++vsPo9oaWjsYU5rBf7955QzdX7adIcVuxKTX8Np7p4FwJe6XNhwh3TGOkpwkJBVsnYfAryX7hF8ygr73lo4FQfhiREC7gbX5Q1ysbwdUctIs3WbsaG7zc6aqmbtnlSBJoNXIlJ92UVnjJi/DRkFWEqcvNFOQlYTFpKPa1RaToPjcpVaqXJ6Ys1I7yqu5e+ZAtBqZuuYOgiGFjGQzJp1MgdPCikcm8s4H59FqJeaMzyc7xUS6w8hTXxvL3z68QHqyiZvH5OL1hyLB7LKQonKxzsMjC8v46EQKe4/VM6YklXGDM9B3MzOsiVOD7VhFU9xzfu9+eIERcWaM+47XMjg/PJMVBCFxiIB2g2poC/Cfaw9yoXP2k59h4x/vHklKnJplRoOGe+eW8vtNRyOBaubYXNKTTfz6L4cwGbTkZdjYdaiaJrePHzw8IeY1DDoN1ji7MNMdJlTgv9Z9THnnDkOnw8j/uX8cKRY9BU4L37hjKADBzpRROlmiLNfOsAHJqGp4Nuf2hUiy6GPyM1rNOhyuQ4w+tJoJyZkEj9ZhT/kKoYKJKHHOeMUrH2M0aMiKU+8pP9OK2RT7I57jtIoDy4KQgMQ5tF4kdZMdpLv2yzQamT1HarhQ68Zi1GI2ajlf62bvsdpIYdWrUxrqNTIbdpyNmnW9t78KRVX5h1kltHUEqHa10drmZ+6kfJwOE3Mn5kddc9mCISTbDJTmJ6PXytitemRZ4s6bB3K2ujUSzABczV7e3nOOzuNmhEJK3NlOMHil3W7S8cBt0WerxpalU5Cqo2nLr1HaWwk21RBqb6Hxnd+i62iIeT2AvAwrZQVXsrBrZIl755QyvDgFZ/KVZUqTQcuCKYWMGeSM1FoDSLMbGTUoTSS4FYQEJGZovaDdH6Kixs35WjfFOXYKMqwYtDJub5BTVc24mjsozU8m12mJWzlaq9VQXefhwQVDInW+HDYjpy80IWkkKi+1caSiAatZx+ABKVgMmrjnquqbvWSlmHjintGcrWkhL91GkkWPq7GdJdOLmTQ0ixaPD2eyiQy7kcPnGlk6t5SLdW4aW30Myndwsc7NRVfs+bSjFY0EFZWGVj/HKhuRZYnBBSk4kwxxg4WiKAwvTObZRyZyqaEdm1lHbrqVJF8VdYNmcNE+ispGlbxkify2o6gdLWCMLQhp1mn49j+M4GJ9G+3eIFlpFpydZW2eXTaBCy4PwZBCrtMaqfi88rHJVNV5kDUyeU5L3FIwgiB8+Yl/2T0soKj87q3j7Dt2Zev67TcVMXt8Hv+25gAX6jyR9m8sGcGEUmdMAAgEAswYk8sLaw5EMr/rtTL/+2tjOFLZyM//Z3/kXleSRc/3l02gJN8RzqhxlbwMK1v2nIuqkzZykJOHFgxGr5EYkG6B9Ctnr7LTrPz7Hz+itvFKCq1H7xhOYZaD9V2yn0wcmomr2cez/707UirFoNPwz1+fhDPJQFeSBB8cc/HShisbN4qy7Ty1dCib24bz151X3q9pQwbxiDUz/hsMmHQaBmbFlnOxGDSU5dojjy+/R3aTDvtVszpBEBKTWHLsYXXNHVHBDGDDjrOcq3VHBTOA1W8doyMQWz1ao9Hw4bHaqDIm/qBCSFF5bevpqI0brW1+jp9r5LE7hjG8KIVZ4/MZV5bOo3cMQyNJ/P1AFYXZScyekE/ZgGTKT7niZgQBuOhqiwpmAGv/dhJnspHbpxcxZXgWM8fmMmloBnPG5/H2nnNRdb98gRB7jlyKWybF4w2xZsvxqLaz1S3UtIb468F6inPtzJ6QT0l+MjuONnKp/Qb8XUuS8IWUcHQWBOGGIwJaD/MHYu8lKWr89g5fkFCczQmyLEWVHom8jhI/tVaHL4hBp6GkIIXyU3X4gwoDMpNQUblvXhk5TisHjtdhNel5eNHQmHpfl8Vrb/MGUBSVoYWp1DS0cayykRGDnOi1Mi1tsX1scvvi3iMMqSr+OMHbH1B44LbBZCSbOXC8DofNwEMLhxAIxj63LzW1B/jDlhN8/9d7+MOWEzS392JuTkEQPhcR0HpYRoqJ1C4VlQcPSCEvwxqVPBhg3sQCLIbYrfg+X5AZY3Jj2k16LXMnFkS1yVL49de9f5bX3ztNfbOXQ6fr+fGqD9BpNew8VM2O8moaW73sO1bLum1nSEmKf5YqJ90aU8xy9vg8QorCT3//IRXVrVxqCB/I/uhUPfO69AVg+qjsuBtEkkxapo+OHpPJoCUzxcx7+6vY9XENja1e9h65xKadFTh6MVXYZ+ULKbyw5gB/P1BFY6uXvx+o4oU/HsAXEhtLBOFG0qvrOh6Ph6VLl/KrX/2K3NxcXn31VVavXo0kSQwbNowf//jH6PXxa0rdaHwhlcZWLyaDFoe5+yTDZp2G7z04jo07Kzla0cCEIRnMHp+P3aRjxfKJvP7eaarqPMwcm8u04VndZpsYlJvEt+8exfr3zyBJEotvKiIn1YTTbkSrkdiy5xxJFgN3zRxIik3P1qvuk0F4+c/V1EFFdXQNssZWLw0tHdjSY7e5t7X7+N/3jWXjzrO4mjqYOiKbvHQbuw9finnuhp0VPPPAOJ59ZCJVdW4kSSI/3UZ2avwD0ahw183FOB1G3j9YTUGmjSU3D8Td7o8cTbisrqmDplYv1jh5G/uCq9lLdZfzbxddbdS3dJATJ8mzIAh9o9cCWnl5OT/4wQ+orKwEoKKiglWrVvHGG29gsVh4+umnWbNmDcuWLeutLvSYBo+fF9Yc4FJjO1qNxH3zypg2PBNNN/dSUix6HphXSiCooNfKnTXBVDLtRr51x3ACioJB+8nl5A2yzOiiFIYXpaCqoOu8VLs/yIHjdQzMc9DeEeDw2XoyknMxGbQxy5F6nYwshZc8o9vjH9AOhuCFP37IwmlFTBiSyd/3V7H7cA1zJ8TOxBxWA/6gwqadFew/Hs5QctPIbO6ZNQhzNwfALXoNt03MZ864PDQaCUmF+jhLq5/Ux77QXV/02hunj4Ig9OKS49q1a1mxYgXp6ekA6PV6VqxYgdVqRZIkSkpKqK6u/pRX6XsK8LvNR7nUuVkiGFL5/eZjXGqMX1MsQlXRaaQ4W9hVdPK1l5PXciWYIcFbuyspP13P+x9dZN/xOt58/yz1LV4euDX6jFdhdhLpDhMLpxVFtU8ZnoXTHn85L8VuJCPFwvr3z7Jq/RHOXGxh7sQChhWnYLtqVirLEvfMKmHf8bpIMAPYXl7N0XNNn7hnQlVBK4eDGUCKzcCtkwdEPWfGmBzSulkW7QtpNj2zx+dFtc2ZkE+KLXY3pyAIfafXZmgrV66MepyTk0NOTjhze2NjI6+88go/+9nPeuvyPabDH+LI2caYdldLBzndlGHxBxWqG9tpcvtwOkxkppjinjf7rPwhlX1XBZDLTle1cMvobFY8MpEzF1tIsxspyk7CqJWZNyGfknwH5y+5yUqzUJxj7zbhcI3Lwz2zSwh01m9zOkxsL7/I+DInzz4yicqaVoIhhfwMG3lOE6u3HIt5jYMnXUwsS7/mtFIysHjqAEYOSuN8rZtcp5UBmTZ6sTjBZyZLEktmFDO2LIMLdW7y0q0UZNxYfRQEoQ/OodXW1rJ8+XLuuusuJk6c+Jm/PjVOiqOunE7b5+laXKZ2P0XZSZztci8qPdkS9zrt3gCvbTrK5l2VkbaHFw1l8U1FaDRfbEIcCIYYWpjC9vLomW1+ZhIZ6eH/xg25cn4rGFJ4f9sZfr/5KGajjg5vgNumFvLAbUMwxTlcnN3qY9WGI5y60AyEN5wsv2M4SDL/9speajtnpQadhp99axqjBqVzvLIp6jWGFaeSkvLZ733lZtmZ+gl/35Pf088rL8v+6U/qATfCWK+H/jJO6F9j7UvXNaCdOXOG5cuXc//99/Pwww9/rtdoaPB84nLd1cUge8ryxcNY+fsPI/eoFk4txJmkj3udS83eqGAG8IfNxxhWlIKjBypW3z69iFMXmkhzmGnzBshMMZPntMTtS3N7gNVvHUNVr2z337ijgqnDs8iIs+zY5PZFghmE7729+ffTOO3GSDCD8IaTP/31OF+dU8ruj6s5dyl87bKCZIYVpvT4+/9ZvqcajQSSRKibowk3ut74+b0R9ZdxQt+PVZala5oIJILrFtA8Hg+PPPII3/nOd7jjjjuu12V7RFaykZ89PhlXixeLUUuqzdjtclO7L/Z8UjCk4POHoAcCmtmoZemcMt7df57cdBuzx+dhMcgocT6/vf5Q3HNu7d4gxJlsdMTpu6vFiz9OcKh2tWExaHn6/nHUNbUjSRLpDhP6vlqHk1QuNnh5e08l7b4g8ycVUJRhi9RLEwQh8V23gPbaa69RX1/Pyy+/zMsvvwzALbfcwhNPPHG9uvC5qZ0VkK1xtrp3le4wYTFqafMGI2256VaSe2ADgSxD+YkGfv2XjyNtuz+u4UfLJ5KdHLtd3mEzkJVqoabhypZzq0lHajebQrJTrTG7ImeMzsFhjT1aMXt8PlpZQiNB3g2wvb6myceK3+6JzN4/OuHi6fvDdc8EQegfJPVLlna8L5YcPwtJgppmLy9tOMLpqhZGDkrj/vmDSbF88dmZX1H555f2xpyJum9+KXPG5MTM0upbfZy+2EKzx9/ZN5WUJBPZaea456dU4EyNm9+uP0x9i5cZo3O4c3oxRr3MoTON/OGtY3h9QRZOK2TW2FxMOg1I4O4IIkkSNqO2V7LYf9r3VJYl1u8+xxudhTwvGzwghSfvHU1MkbcbWF///F4v/WWc0PdjFUuOwuemqpBpN/LUV8fgDykYdZoeOxshSVLcjSWyFH/JUZIlVGDPkRrOX3KTkWIOb07pZhlO7UxPNWFoJiaDlnZvEEVV0UoSYwelMXTAFEKqisWgRVVUOgIK7+w9z6ZdFUiSxJ0zipk5OgeD9vonoNHGGZNWIyHR7dl1QRASjEh91Uu0soS5B4MZhM+j3TGjOKrNqNcweED8TPJmg5a/bDvN+c5NG7WN7fzP28cxGeLPFutafPzbmgNs3FHBn989xaadFbz67ilUKRzsDFoZs06DqqhIEnx8toE3t58lGFIJBBXWvnuKUxdbenDE10ZRVEaXOtF2Cfa3Ty8Wdc8EoR9J+BmaP6RwqbEDXyBEZqqZJKP2E1egPL4gNQ3t6LUaMlNNGL7gVvueNnRAMk99bSw7D1VjtxmYMiyL7GRj3Blas8dHfbM3qq3DF6ShtQO7KXYb8dX32i774HAN980picn+IcsS2z66GPP8vUcvMbIo9ZrPofWUTLuRnzw6iV0f19DmDTJ9VDa5qX1/b08QhOsnoQNauz/Ebzcc4WBntWWLScezD02IW68LwimufvLyXlrbwvecBhcm8807R2DpJpVTX9DLEmW5dkYUpWC3m3G53HGDGYR3ROq0ckwWfZspfv5Mmzm2PTPNEpOw+LKinCSOVUYfOi/ItF9zFpSepKqQYTdy1/QiJEm67gFVEIS+d2NNP3pY5SV3JJhB+CzW2q2n4t9TkSTW7zgbCWYAxyqaOF11/ZfQrqbRyHHvmwWv4ZyVw6znoYVD0GpkMlPNGHQavjJrEKm2+AEt12lh3OAMLEYtGSlmdFqJry8eFsksIklSpNaZqsLQwlTsV+2AdDpM5Gf27QFSRVFFMBOEfiphZ2iSBLVN7THtZy624A+pGLqclwqGlKhDxZddcHkYPTC1T2YdFxraOXAinOpqTGk6eU4zigJV9e18dKIOR5KB4UWppFr1cZdRVVVlRHEq37xrBMfPNVGUk8SQgmTkbtJwGbUyX5k1kMNnUqhramfkwDSyUk1IErha/ZSfdtHuDUb68taeSuZMKECrlZEIL2duL79IWa6dkCitIgjCdZawAU1VoSAz9gzSlOFZGLVSTADQayWmDM/ijb+fiWovzXP0STA7V9/Ov7z0QeRg9KadFfzg4Yn4fEF+9od9kedZTDr+eflEki2xsy4VeHN7BX/dez7SNmpQGt+4Yzi6OAegm9oD/Ovq/TS0hO+7bdlzjkcWD2VEUSorfruHDl/4bN2698/wo0cmMmFwJqs2HIl6jX+8e5SYIQmC0CcSeskxL83C/fPLIveARpU4mTchP+5sRlHg5tE5TBiSAYBWI3PP7BIKMq7/Epper+G9/ReisnwEQyp/P1DFoTP1Uc9t6whwMs7MEqDR44sKZgAHT9VT1xK/UkBlTWskmF3253dP4fEGI8EMwr8svPruKUYNSmPm2FwkKbxJZMHUAZTlXZ98h4IgCF0l7AwNQKeRuGV0DuPK0vGHFBxm/SdmSLcatDy6eCh3zxqEVpawm3V9ciZXkiQ6vLFpqLzeIPY4Gzd8AQVJij0/HOxmZtndcmC8NFk+fwg1zl3HDm8Qk17D1+aWsGhqIRJgt+i/VIeYBUFILAk9Q4PwfSSbUUuq5ZOD2WUy4QKdSaa+CWYAPl+QmWPzYtpnjM0lq0uaKY0sUZqfHLevyRYDZQXRZ9QyU80kd1NrLC/DiqHLjs75kwswGbR0Pbe8eHoRGgkkFRxmHXazTgQzQRD6lEh9dZ01tvlp8wZJSzJi0nX/+0RQUThR1crGHRUgwcJphZTmJKEocORcE5t2VZKSZGDxTUXkxkljBXCpxcvBU/U0u70cr2yiMMdOfoaNoiwbBXHyUsqyxPn6NjbuqKCuqYObRmUzriwdq0HLhfo21r1/Fk+7n0XTiijLd3RbV62n3ejf057UX8baX8YJfT/W/pT6SgS06ySoqLx38CKv/vUkihpOHPz0/eNI7+ZMXITcGfSuOmwmSaAi4XCYaI6zk/OypvYAT/5yB06HicJsO1V1bqrqPKx8fApZju4rQssamZCioJGiK2tLsoSignSdf2Ru1O9pb+gvY+0v44S+H2t/CmgJv+R4o7jU1MEf3zkZyWTf7Pbx328e7vY+V4Si0PXktKqG/6fTfvKBb4dZx10zB1Lb2M6ewzVU1XmYOTa324PlkUuGFCSVmF8cVEW97sFMEAThWiX0ppAbSdfdgxA+E+cNKFgNvZOJRAJmj8mhrCCZSw3tOB1Gcp3WuIl8L2v3hzhX68HT7ic3w0qmw4iERKs3yLlLbgLBEPkZNpxJBnHLTBCEG4oIaNdJvBpkxTl2TPrenSTrNDKF6VYKr6GWW3sgxL+/epCzVyUYfuprY8lKNfOTl/bS6PYB4SMN3dVgEwRB6CtiyfEaSRKokoT0OTdCZCabuGd2CQNz7YwfkkFuupWvLx6GppusHX3hQq0nKpgBvLLlOIcrGiPBDMJZVd7Ydjr8pgiCINwgxAztGnQEFA6erufdfRfITbeyYPIA0u2fbclNp5EYU+qkpr6NKpeHuRPzsZlvrLff6w/FtPkDCk2tvph2V5OXkKpy46RtFgShv7uxPlFvQJIksfVAFa93VkM+e7GFD4/WsvKxyTjM116FuqktwI9++0Ek48bZiy00Ti/m9qkDbpiaXdlpFmQ5emfj6FInI4pTeePv0dWg503MRytJN0zfBUEQen3J0ePxsHDhQqqqqiJtTz31FG+88UZvX7pHtPlCbNh+Nqqtwxekqs4DhOutNXh8dHRm6+jOxfq2qPRRABt3VuDp0taXnEkGfvjQBAZkJWE2alkwtZBFUwrJSTPzv746howUM0kWPV+bX8aYEqcIZoIg3FB6dYZWXl7OD37wAyorKwGora1lxYoV7N69m0mTJvXmpXuMJIFOK+PvUq5Fo5GpbfHxf9cepLqhDYfVwLe+MoLiTBvx6tNo4tx70+vkbjPf95UCp4VnHhhLMKRi0mkiQWtYgYMfL5+IoqiY9Zo+SdgsCILwSXp1hrZ27VpWrFhBeno6ABs2bGDWrFnceuutvXnZHmUxaPjqvLKotjS7kew0C//+xwNUd1Z5bvb4+NfV+2lui83BCJDrtOLssitw6ZxSLL20Zf+L0EoSRq0cNQNT1XBxUaNWFsFMEIQbUq/O0FauXBn1ePny5QDs37+/Ny/boxRFZVypE+eD4zlwoo7sNCvDi1Pw+YO4mqOz1geCCq6Wjrj31qwGDc88MI6PzzZy0eVhTImTAZk2ERwEQRB6yJduU8i1pHBxOnu+5Et2RhKTR2RHHtfUezAZtDH3xZwOc7fXdwID81N6tF+9MdYbUX8ZJ/SfsfaXcUL/Gmtf+tIFtBsll6NOgsfuGMZ/rD0Y2b6/dE4JNqPmuuVt6+sccddLfxkn9J+x9pdxQt+PtT/lcvzSBbQbharCiMIU/vWbU6lr7iDZZsSZZLihDkoLgiD0JyKgfUGpNgOptk/JmC8IgiD0uusS0LZu3Rr1+LnnnrselxUEQRD6EZHLURAEQUgIIqAJgiAICUEENEEQBCEhiIAmCIIgJAQR0ARBEISEIAKaIAiCkBBEQBMEQRASgghogiAIQkIQAU0QBEFICCKgCYIgCAlBBDRBEAQhIYiAJgiCICQEEdAEQRCEhCACmiAIgpAQREATBEEQEoIIaIIgCEJCEAFNEARBSAgioAmCIAgJoVcDmsfjYeHChVRVVQGwa9cuFi1axNy5c/nFL37Rm5cWBEEQ+pleC2jl5eXce++9VFZWAuD1ennmmWd48cUX2bx5M4cPH2bbtm29dXlBEAShn+m1gLZ27VpWrFhBeno6AIcOHaKgoIC8vDy0Wi2LFi3i7bff7q3LC4IgCP2MtrdeeOXKlVGP6+rqcDqdkcfp6enU1tb21uWFHuYPKdQ2eQmGFDJSTJh1mr7ukiAIQpReC2hdKYqCJEmRx6qqRj2+Vqmp1k99jtNp+8yv+2V1PcZa19jOi6+Vc+h0AwCpdiMrlk+iMNve69e+THxPE09/GSf0r7H2pesW0DIzM3G5XJHHLpcrshz5WTQ0eFAUtdu/dzptuFzuz9XHL5vrNdbys42RYAbQ0OJl/ftn+OrsQdD9t6LHiO9p4ukv44S+H6ssS9c0EUgE123b/siRI6moqODcuXOEQiE2btzI9OnTr9flhc9JliXO1bTGtB+taCT0Cb9YCIIgXG/XbYZmMBh47rnn+Pa3v43P52PGjBnMnz//el1e+JwURaW0IBm2R7dPGpaFTiOhKH3TL0EQhK56PaBt3bo18ufJkyezfv363r6k0MMKM23cflMRG3acRVFhVEkaM0Zli2AmCMIN5brN0IQvL4NW5vZpA5g+KoeQopBiM4gUM4Ig3HBEQBOujQrJFl1f90IQBKFb4hdtQRAEISGIgCYIgiAkBBHQBEEQhIQgApogCIKQEL50m0Jk+dPTZV3LcxJFfxlrfxkn9J+x9pdxQt+OtT+9z5KqqiLdgyAIgvClJ5YcBUEQhIQgApogCIKQEERAEwRBEBKCCGiCIAhCQhABTRAEQUgIIqAJgiAICUEENEEQBCEhiIAmCIIgJAQR0ARBEISEkFAB7c0332TBggUsWLCA559/vq+702t+85vfMG/ePBYtWsR//dd/9XV3epzH42HhwoVUVVUBsGvXLhYtWsTcuXP5xS9+0ce961ldxwrw1FNP8cYbb/Rhr3pH17G++uqrLFy4kEWLFvG9730Pv9/fxz3sGV3HuWbNGhYsWMBtt93G888/j0jO1HsSJqB1dHSwcuVKVq9ezZtvvsm+ffvYtWtXX3erx+3atYsNGzbw+uuvs27dOsrLy3nnnXf6uls9pry8nHvvvZfKykoAvF4vzzzzDC+++CKbN2/m8OHDbNu2rW872UO6jrW2tpbHH3+cLVu29G3HekHXsVZUVLBq1Sr+9Kc/sX79ehRFYc2aNX3byR7QdZwXLlzgd7/7HX/+85/ZsGEDH330ETt37uzbTiawhAlooVAIRVHo6OggGAwSDAYxGAx93a0ed/ToUaZNm4bVakWj0XDTTTfxt7/9ra+71WPWrl3LihUrSE9PB+DQoUMUFBSQl5eHVqtl0aJFvP32233cy57RdawbNmxg1qxZ3HrrrX3cs57Xdax6vZ4VK1ZgtVqRJImSkhKqq6v7uJdfXNdx5uXlsWnTJsxmM62trXg8HpKSkvq4l4nrS5dtvztWq5UnnniCW2+9FZPJxPjx4xkzZkxfd6vHDR06lJ/+9Kc89thjmEwmtm7dmlBLGCtXrox6XFdXh9PpjDxOT0+ntrb2enerV3Qd6/LlywHYv39/X3SnV3Uda05ODjk5OQA0Njbyyiuv8LOf/awvutajuo4TQKfTsXbtWp5//nlGjBhBWVlZH/Ssf0iYGdrx48d5/fXXee+999i+fTuyLLNq1aq+7laPmzx5MkuWLOH+++9n+fLljB07Fp1O19fd6jWKoiBJV8pfqKoa9Vj4cqutreXBBx/krrvuYuLEiX3dnV5z991388EHH5CWlsYvf/nLvu5OwkqYgLZjxw4mT55Mamoqer2eJUuWsHfv3r7uVo/zeDzMnTuXDRs2sHr1avR6PXl5eX3drV6TmZmJy+WKPHa5XJHlHOHL7cyZMyxdupQ777yTb33rW33dnV5RU1MTmXFrtVoWLFjAiRMn+rhXiSthAlpZWRm7du2ivb0dVVXZunUrw4cP7+tu9biqqiq++c1vEgwGcbvdvPbaawl5z+WykSNHUlFRwblz5wiFQmzcuJHp06f3dbeEL8jj8fDII4/wxBNP8PDDD/d1d3qN2+3mySefpLW1FVVV2bJlC2PHju3rbiWshLmHNm3aNI4ePcqSJUvQ6XQMHz6cRx99tK+71ePKysqYO3cuixcvJhQKsWzZsoT+B2IwGHjuuef49re/jc/nY8aMGcyfP7+vuyV8Qa+99hr19fW8/PLLvPzyywDccsstPPHEE33cs55VUlLCo48+ytKlS9FoNIwbN46HHnqor7uVsETFakEQBCEhJMySoyAIgtC/iYAmCIIgJAQR0ARBEISEIAKaIAiCkBBEQBMEQRASgghogiAIQkIQAU0QPqf/+I//YN26dZ/pa1atWsXTTz8NwPe///2ErAghCH0lYQ5WC8L19kUPAcdLZCsIwucnZmiC0On2229n9+7dAGzcuJHhw4fj9XqB8GxqypQpPP744yxYsICf//znPP3005+aADsQCLBixQrmzJnD0qVLOXDgQOTv7r///oQphSMINwIR0ASh05w5c3j//fcB2L59O3a7nX379qGqKtu2bWPw4MF4vV42bdrEk08+eU2vuWbNGiorK9m0aRMvvfQSNTU1vTkEQejXREAThE6XA5qqquzbt49ly5axc+dODh48SH5+Pk6n8zPnzdy9ezcLFy5Er9djNptZtGhRL/VeEAQR0AShU2lpKYFAgHfffZcBAwYwc+ZMdu7cydatW5k3bx4AZrP5C11Do9H0RFcFQYhDBDRBuMrs2bN54YUXmDp1KsXFxXg8HjZs2MDcuXM/1+vddNNNrFu3Dp/Ph8/nY/PmzT3cY0EQLhO7HAXhKnPmzGHVqlVMmTIFgClTpnDixAmysrI+1+stXbqU8+fPs3DhQhwOBwUFBT3ZXUEQriLKxwiCIAgJQczQBOEL+upXv0pbW1vcv3vllVewWq3XuUeC0D+JGZogCIKQEMSmEEEQBCEhiIAmCIIgJAQR0ARBEISEIAKaIAiCkBBEQBMEQRASwv8PP1VqJ9l9qvQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='wri_di', y='ank_di', hue='sex', data=body_dimensions)\n",
    "plt.title('Relationship between Wrist Diameter, Ankle Diameter, and Sex', fontsize = 14)\n",
    "plt.legend(bbox_to_anchor=(1,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Statistics:\n",
    "#### Wrist diameters (for females and males respectively):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    260.000000\n",
       "mean       9.874231\n",
       "std        0.661627\n",
       "min        8.100000\n",
       "25%        9.400000\n",
       "50%        9.800000\n",
       "75%       10.400000\n",
       "max       12.200000\n",
       "Name: wri_di, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_female['wri_di'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    247.000000\n",
       "mean      11.246154\n",
       "std        0.635897\n",
       "min        9.800000\n",
       "25%       10.800000\n",
       "50%       11.200000\n",
       "75%       11.650000\n",
       "max       13.300000\n",
       "Name: wri_di, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_male['wri_di'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ankle diameter (for females and males respectively):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    260.000000\n",
       "mean      13.026538\n",
       "std        0.866063\n",
       "min        9.900000\n",
       "25%       12.400000\n",
       "50%       13.000000\n",
       "75%       13.725000\n",
       "max       15.500000\n",
       "Name: ank_di, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_female['ank_di'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    247.000000\n",
       "mean      14.744130\n",
       "std        0.944236\n",
       "min       12.300000\n",
       "25%       14.200000\n",
       "50%       14.800000\n",
       "75%       15.300000\n",
       "max       17.200000\n",
       "Name: ank_di, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_male['ank_di'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the scatterplot and the summary statistics show that there is approximately a linear relationship between wrist diameter and ankle diameter. Besides, both the wrist diameter and ankle diameter for males are bigger than that for females shown from the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visulization - Boxplots and Violinplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='sex', y='wri_di', data=body_dimensions) \n",
    "plt.title('Boxplot - Relationship between Sex and Wrist Diameter', fontsize=14) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"sex\", y='wri_di', data=body_dimensions) \n",
    "plt.title('Violinplot - Relationship between Sex and Wrist Diameter', fontsize=14) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='sex', y='ank_di', data=body_dimensions) \n",
    "plt.title('Boxplot - Relationship between Sex and Ankle Diameter', fontsize=14) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='sex', y='ank_di', data=body_dimensions) \n",
    "plt.title('Violinplot - Relationship between Sex and Ankle Diameter', fontsize=14) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Measures of Center: the median Wrist Diameter and Ankle Diameter are both larger for the male group than the female group;\n",
    "\n",
    "2. Measures of Spread: the Wrist Diameter and Ankle Diameter IQR range of the female group is larger than the male group;\n",
    "\n",
    "3. Distribution Shapes: the distributions of the female group look slightly unimodal both for Wrist Diameter and Ankle Diameter;\n",
    "\n",
    "4. Outliers: the amount of outliers for males is larger than that for females in both wrist and ankle diameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Inference Research Question Set\n",
    "### What is the association between the mean wrist diameter for males and females?\n",
    "\n",
    "### Hypothesis - significan level 0.05 ($\\alpha$ = 0.05)\n",
    "\n",
    "H0: The mean wrist diameter of males = the mean wrist diameter of females in the population \n",
    "\n",
    "Ha: The mean wrist diameter of males != the mean wrist diameter of females in the population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condition:\n",
    "\n",
    "1. The sample for males is randomly sampled\n",
    "\n",
    "2. n_male = 24 < 10% N_male (24.7)\n",
    "\n",
    "3. population distribution of male wrist diameter is approximately normal (shown from the graph below)\n",
    "\n",
    "5. The sample for females is randomly sampled\n",
    "\n",
    "6. n_female = 24 < 10% N_female (26)\n",
    "\n",
    "7. population distribution of female wrist diameter is approximately normal (shown from the graph below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "body_dimensions_male['wri_di'].hist()\n",
    "plt.title('Population distribution of male wrist diameter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "body_dimensions_female['wri_di'].hist()\n",
    "plt.title('Population distribution of female wrist diameter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_male_sample = body_dimensions_male.sample(24)\n",
    "df_female_sample = body_dimensions_female.sample(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 2.4773118001419903e-08.\n"
     ]
    }
   ],
   "source": [
    "male_mean = df_male_sample['wri_di'].mean()\n",
    "male_std = df_male_sample['wri_di'].std()\n",
    "\n",
    "female_mean = df_female_sample['wri_di'].mean()\n",
    "female_std = df_female_sample['wri_di'].std()\n",
    "\n",
    "point_estimate = male_mean - female_mean\n",
    "std = np.sqrt((male_std ** 2)/24 + (female_std ** 2)/24)\n",
    "q3_test_stat = (point_estimate - 0)/std\n",
    "q3_p_value = 2 * (1 - t.cdf(np.abs(q3_test_stat), df = 23))\n",
    "\n",
    "print(f'The p-value is {q3_p_value}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "At the 5% significance level, since the p-value is 2.4773118001419903e-08 which is smaller than 0.05, the evidence is sufficient to reject the null hypothesis and conclude that the mean wrist diameter of males does not equal the mean wrist diameter of females in the target population.\n",
    "\n",
    "To answer our research question, our conclusion suggests that there is a difference between the mean wrist diameter for males and females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Linear Regression Research Question Set\n",
    "\n",
    "### Is there a linear relationship between Wrist Diameter and Gender, Age, Thigh Girth, and Chest Diameter in the sample? What about in the population?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>wri_di</td>      <th>  R-squared:         </th> <td>   0.640</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.637</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   223.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 03 May 2022</td> <th>  Prob (F-statistic):</th> <td>6.49e-110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:39:19</td>     <th>  Log-Likelihood:    </th> <td> -430.82</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   507</td>      <th>  AIC:               </th> <td>   871.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   502</td>      <th>  BIC:               </th> <td>   892.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    5.1443</td> <td>    0.386</td> <td>   13.341</td> <td> 0.000</td> <td>    4.387</td> <td>    5.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex</th>       <td>    0.9382</td> <td>    0.079</td> <td>   11.831</td> <td> 0.000</td> <td>    0.782</td> <td>    1.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.0071</td> <td>    0.003</td> <td>    2.619</td> <td> 0.009</td> <td>    0.002</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>thi_gi</th>    <td>    0.0279</td> <td>    0.007</td> <td>    4.168</td> <td> 0.000</td> <td>    0.015</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>che_di</th>    <td>    0.1124</td> <td>    0.015</td> <td>    7.311</td> <td> 0.000</td> <td>    0.082</td> <td>    0.143</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.460</td> <th>  Durbin-Watson:     </th> <td>   2.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.292</td> <th>  Jarque-Bera (JB):  </th> <td>   2.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.169</td> <th>  Prob(JB):          </th> <td>   0.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.996</td> <th>  Cond. No.          </th> <td>1.08e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.08e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 wri_di   R-squared:                       0.640\n",
       "Model:                            OLS   Adj. R-squared:                  0.637\n",
       "Method:                 Least Squares   F-statistic:                     223.2\n",
       "Date:                Tue, 03 May 2022   Prob (F-statistic):          6.49e-110\n",
       "Time:                        20:39:19   Log-Likelihood:                -430.82\n",
       "No. Observations:                 507   AIC:                             871.6\n",
       "Df Residuals:                     502   BIC:                             892.8\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      5.1443      0.386     13.341      0.000       4.387       5.902\n",
       "sex            0.9382      0.079     11.831      0.000       0.782       1.094\n",
       "age            0.0071      0.003      2.619      0.009       0.002       0.012\n",
       "thi_gi         0.0279      0.007      4.168      0.000       0.015       0.041\n",
       "che_di         0.1124      0.015      7.311      0.000       0.082       0.143\n",
       "==============================================================================\n",
       "Omnibus:                        2.460   Durbin-Watson:                   2.021\n",
       "Prob(Omnibus):                  0.292   Jarque-Bera (JB):                2.404\n",
       "Skew:                           0.169   Prob(JB):                        0.301\n",
       "Kurtosis:                       2.996   Cond. No.                     1.08e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.08e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results1 = smf.ols(\"wri_di ~ sex + age + thi_gi + che_di\", data = body_dimensions).fit()\n",
    "results1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression Equation:\n",
    "Shown from the table above:\n",
    "\n",
    "Wrist Diameter = 5.1443 + 0.9382*Gender + 0.0071*Age + 0.0279*Thigh Girth + 0.1124*Chest Diameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2BDyRyoKAgPpAS1jweEgpTbZr/3kMfkFfHQ9Wu8UEblq5pqTtVto50CrVelhR/nLBLJIQ78EDd90Q0QBPwhnYUjObCIan5rEIj6sj7/lEjyax9nDlpdAVpBZaEwArtbgUaU4guTDTcgKg+sn6UAt2FG1Eas8sNtrN5Z3XGOqQE8HQjsIB6MmB5BegqT5SUnHxeDSx4K1VIYdH5SCuVNV2DmrqKrkTAKmkglHzpFMTBKKNyIz6yIlxJIR5SFA4BKlK6v+9nq9alhRgq4+Uzgeo66R5aB7nNjwB8Hg/iStVNVuNX6gXSmacAJoarBxJWoJAK1342n8eC+w+m8e58eDAziGu3Hru/6rtX2PXgaKAoT2d0xZHRA5SPTmQ7IwucGl/DcDliVELPeotKW4X8ODAzgDqJ4D5j/bFm1PvwPxH+3JNBjzXFVVIUSqdTkmKxZiM1ICaS/ws1mZWOl9TR6vkrRLi39XUR906peCtrYVBKsoLVXV4c8tRwyo/Mb2IuGP0/9sWN3/tl4bOR4QH2lE4kMtG4MJATh6Xq96grYQRlYCaOqd5nBtxMR7NXQOvu6rUUK5mA2ke58Y7eV8zg/D0OAE0VcOp9JmowVPyVgzulK7y+3Zti0PHy1Cr4DtdJ7DzjbFQs7kV/ngek1/5VNN4TkQG3YLi9OnTOHHiBHr16hWO9hD/Rmli5HFpZE3e0nMqebbEeKKC1Aji+eTpO4y4q9b4lA3aMZ4ouFwu1NSG7g5YCQLzC4oVYzSaquFUzVNJDk/JW9HYLV3l7zlczDy30V0cywuu8lIt2ZwcApegWLNmDfbv349p06ZhxIgRSEhIwKBBg/DHP/4x3O0jJLDy3uidvHkmC/n5Vmw+iu9Onseh42W6bANamWJZtgk1IaE0KSrpy5sKPN5lvCVv9ToVAMZ3cVrpRcjm5Ay4BMWGDRuwfPlybNu2DXfeeSeee+453H///YYFxebNm7Fs2TLU1tZizJgxeOihh4KOL1myBO+99x6SkpIAAPfff3/Id5oirMk9Z+ke3YZdvZMFAGYqCL3eMqIg0KtCUmvbpeo6rvQijRGtFT0rR5J8J8p6Xh63K0T9JE8mqHROtefBk15EGihKuZ7sgUtQuFwutGrVCvn5+cjIyIDH44Hfr+0br8Tp06fx5z//GRs3bkRMTAxGjRqF3r1747rrrgt858iRI1i0aBF69Ohh6BqNGbXJ3eqIWCO/k6vAzpZXo2VSLJrHuRUNz1LjqR7XWyuTFzYWWJ5K8x/tq5ojSWnnyLqGUmoWj8eteU615zF6cCqKz15E4Y/nmf0T20UV7uyByw0mJiYGK1aswOeff46+fftizZo1iI+PN3TBvXv34pZbbkFycjKaNWuGwYMHY9u2bUHfOXLkCF5//XVkZWVh1qxZqK5uml4seoLJtDxZ9KL3d+KqUpwkysqrIaD+Bb9UHbqokKtBlLyaWAkHtRCTFzoxCC8cQYJGA930BESK56qV2ZOqfXV4a2thoB8sjyslch64GeOzbkRsNJ+vnzxQ1GnPtzHCtaOYPXs23nzzTbz00ku44oorsH//fsyePdvQBc+cOQOv1xv43Lp1axw6dCjw+cKFC+jSpQtycnJw9dVXY+rUqVi6dCmefPJJ7mukpCQYapsV7Nx/AivzClF67hJatYhHdkYXDOjZXvd5Cn46H7RyKyuvxspt3yApMU7xfA9npmHJ+oNBK73YaDcezkyD15uou20PZ6Zh4Rp+F0VXlAtJiXFYmVcYMkkopQW56z/aY9iA6wOfhw1IRFJiHJbnHg7YQ/6241tMuKdrSBuV+qqG1n3jRbyHZtm5/4Su58qLeP9Yz1epD2c5do5eybnGzv5QMWlkbZ2A3N0/YNiA61XPeba8WvU+JiWeR/26lS+JpHT3aMX9swOrxlQkcAmCzuQ+Jlm2bBmqq6vxxBNPAADWrVuHI0eOYNasWYrfP3r0KJ599lnk5uZyX6OsrBJ+3motFqLmSaS3tKfXm4iHZ25jqhLUrs9rnOZp2/y1X2qqBORt41VZyfuRX1CsmLnW43bhkbu7KKZS13L1ZV1PD1amtmZ5rhltHw9qfXj85V2q8Sgib069I/B/MUklC1ZRKrU+qt0X3loa4b5/VuO0dOlRUS7mApu5o+jRowdcLvXt4Jdf6g+Kadu2Lb744ovA55KSErRu3TrwuaioCHv37sXIkSMBAIIgwONpGOEeViavM2JzMOLJota2/IJifHvyFx0thqYxVP5defvUVqpa6dZ5XEOdEoRnhS3JqpTr9S7G2u608s/hKErFsjvJo/L1/J6wBuYMvGXLFssveOutt2Lx4sU4e/Ys4uPj8eGHH+L5558PHI+Li8P8+fPRu3dvtGvXDqtXr8bAgQMtb0c4sNKgbHUWTr1t27jruGJwFQtx0uL15+dph9YxgC9XllOC8LSeq5YQsDLlutYzVprc9dQsEXFBuygV676IGYTLyqsRFeVS1BY45fk2VpiC4qqrrgr8/+jRo7h48SIEQUBdXR1++ukn3H///bov2KZNGzz55JPIzs6Gz+fDyJEj0a1bN4wfPx6TJ09G165dMWvWLEyaNAk+nw8333wzHnnkEf09swErJ3erawXrbZvW5KyUe0k6qYleT83jPbhUVRs0sSj1g7VS5bl/4g5DTcXmlCA8s7EwZuqa87rBiihN7uLnNR99o6myEhFQH7B3XbtkVWHBui/S3WPBT+exeN0Bzefb1Apdhbu/XDaK6dOnY8eOHaiurkbr1q3x008/oWfPnli1apVlDbGSxmCjKCmpsPTh620bK4mgfJWn1javNxH/2PmtZhI5sX16bBRafbXypbFan6zWPh77BY+NQOm5Kk2wWoj2Aa17yJtwUsuOwPPcxDGlZ9cFGHsPw4mVY8qK/pqyUYjs3bsXO3bswMyZM/HYY4/h1KlT+Otf/8rVgKaE1SmW9WbhtLJtI9I74Y0thSEeS6IbLE/bdu4/ETKAfbXKAlxppcpTKEftXHZOCHpSqUjhUQ8arWuu5I2mBW9cCq86Skz+qHZveJ+b1vfM7LoaIpEo7MUlKLxeL5o1a4aOHTvi2LFjuOuuuwy7xzoVq1ahdk9SLPS2zaVQErV/9yu5kwTm7v5BcQCv2HwUb2w5GpJO2sn3jhe11CdrPvpGM70Ij3qQ1wYkvzavmkgNrYnHFeWClqQQkz/y2FfU0pkPG6DtUtrUAjKtDrZVgivgLjo6Gvv27UOnTp3w6aefoqKiAhcvXrSsEXYjDRIDLg+kphzIo+aF9MlXRSFBTmr3j1VTQ0wnPWnhLuZ9bmgV7NQC2C5U1acXefxl9f7yBM2JwYm8wWnita3AjOPD5eSP2oF4+QXFqunMd+4/odnOhh6QqRerg22V4NpRPPXUU1i1ahXmzp2L5cuX45ZbbsGECRMsa4TdOKkmc35BMXJ31xcussIuYVSVo+WFJF2RqSaRU/FQkVLtq2OuKhtaBTutVdyFKvX+8qoHvzt5HtU+621w4o6GFQehtNLXEkRimnK1nE5KrtJq6cwXrvmSS22qZ9fl9DGlhdWOL0oYCrirqKhAYqJzowr1GrNZBkJpsFG4sdIIp2YcBoDbe2hXEDNTEU8Kjw+8iHwCsCs4TY4ewyPvfeM1FCsx7qWPuYLQWNeVI72nauOwb9e2+OzQKd1u0+LvY6KjFGuSyJ+nUYO9lEgGZBohUg4SvFhizFazR0yfPp27IU7G6pgFo5jZ2cgHSrWvTlVl/MlXRdj39RnV3UV+QTEqLtYY7oeImOmTpxY4ELq6i4TulRfeF5F3NWtGX25USKjFuSipt4DQnY2R2BqRmlo/oj0uVbdqeTt5DPa8mZEbUkCmUcJt3+MSFMnJyYH/+3w+fPLJJ/jNb34TrjZFnEhs3XgwOjHmFxTjr5uPBkzPPINerSgMayeihxhPFLIzuiCtQ73vvFjyUgvpBGA2OM0q5N5brMld/Myq6CdHr5pTa4XsAkLcEGKj3SFxLqz7pjTxqNUN4eVCVR1u73FlSOU8+XVGpHfCW1sLNYUS7+TekAIynQqXoPiv//qvoM/jx4/HpEmTwtIgO9DrOhquCcrozuadvEIF/yRtlCYoNSO2Xvp2bYsBPdsHtteiqotHWIj3wMpCTWZQci1luVtKg/94BUZZ+eWaC2pjShx3WjsKAcD4rBuDxujDmWlI65Ac1D49iDXNzYQnNY9zh1TOE8eD3PsN0Ba20gWDli2uoQRkOhVDSZQSEhJw5swZq9tiK7wvTzgnKJ6djZKQqlGJTeChrLwaY+d+bHoSkHPoeFnI365rl6xZUhO4PAEoxVbERNd7BbF85aW/tYJSFe8tLfWRdHLi0ZfLvcak5+MtdQoolztdmVdouOCPeG0z48PtgmrJ20++KgqJ2tZSHUkXDPIdcOWlWry1tTBwHilWxzo1FXTbKARBQEFBATp27Bi2RjmZcHpIib/P3f2DotcTS0ip4dZ2bwfAJyQS4j2o8fm5vUnk6C3XKSIN0hNVZmrn8Qv1KhKrAqvyC4rhinJB0LhBPDsM8Xw8E758TOmtGyG/jlbBH9YumffaLFxRLubuQMveIH5HLIYljWbXk0xSPJ+eMdHU0oEoodtGAQDDhg3DsGHDwtEex2PGwMq7RR424HpFjwg1IcXC7Y5CncmXHKhPpfHAXTcE2qHVXyV1Gc9v5C+hkT5Lr2dmtxdYSXMupXkM1EorWp4xxbp34jl4SuMqlYrV2iWzrj0+60YuwWfW3iBO7nJvITPJJHlg1Y7X8hxsTBiyUTRljNoR1LbIegad3oEf4wkNcDLK9e2uCFohiygZqdWStBmpU2D2ZTez2zOzklablIHQFS3LDRgwdu+siIPhcSrok9YW3508H2SgNqKiMuqgYDaZpBas2vGsJIeNDWZk9h133IE777xT9V9TxEzJSZa7Kk90qNrAbx7nhrxsiMsFpu1C70tU+ON5rNoerObKLyjGnsOh7ZZ7srB03Fr3zoqX3aiwYf1OPgZY59CK8meNKaP3Tuu+SSOitXY0Wu2TG6j1Iu+r1FbDE83uVghS97hdlhioWWNArbRrY4S5o/jLX/4CAFizZg2io6Pxu9/9Dm63Gxs3boTP54tIA52GUWOY1mTFs+pVM3a7XC7IwyYFxuouJSkW3TqlcHkgSdl1oCho56O22pIbstW+F+WCatCUdGVpFj3ChsfwHFIjQWMVzePzDyiPKSUVEsC+dwBfPId4b7V2yUbapweWgwIQHM0uz/Wk5PBgNJmkEjyqwaZgw2AKil//+tcAgG+//Rbr168P/P2ZZ54JVKBrihhxL9QKIuKZENVeWDX/dqXqYDGeKHTrlKK4E9BCPhny2mtYSdrUhASvhw9QPzHERrsVr6PH9VF+XdYq3uqALrUxpffeSc8H8MUOqAmVbp1SDLdPD6IKloUobKV11rXaZgWs90tMadLQ0swYgctGUV5ejrNnz6Jly5YAgNOnT6OysjKsDWtsaKVi5l31yico1vZX3Dns/KooEGfhcgn4vPC0oVVglGyLz2uv0Rs4V+2rU2yfktdVjCcqaPXIu7pT+p7qzuffOatYwWlAeAK6zGQNkLrnrtz2Dap9l11TpQJUtDPId5hKMQ687QsHZeXV2Ln/BN7eUmDo+RqZuNXujXj/nJQnLpxwCYoxY8YgKysL/fr1gyAI2LNnD3JycsLdtkaFOGjeySsMsR0YCfjRWsWKO4fPDp0KCsar9glBE4YSsdFuxe+kd79Scu3QfojXVSqfqSdwTo3KS7UhgWS8tR6kqK0C1e6l4Bc0c36FK6DLiqwBfdLaIikxjjnBKsW9ANpGW96UJe4oF+pMBuokxHuwZP3BwNhUW71bvcofPTgV17VLVhx3aruNhp4SRA53UsCvv/4a+fn5AIC+ffvihhtuCGvDzGBXhTtetFY7PAnD9FSgM4I81YJYO0KeLkRKjMeFMRmXq9FJK/Wp6ZD1JB/Um7hNbyU5NVuDt0U8XprYx/R1jWLF+eRjSm9ZVOkYUGofK4paT/yNGjGeKER7XIqZauUJFtXGfTgS/xlNXGl1UkCzaCUFZAqK/Px89OnTBx9++KHi8UGDBplvYRhwuqAQUZsAtF5qlt5UxIxKQJ5JVK4W0kpJIbZx2IDr8Y+d3zIz4vJkCpX/hgdWJl7WvVOy6Tx+f/dA+ouGinRMmcnnxco8rOYqrTbB8xLlAv4z80auXFNa2YqtzgZtNONzQxMUTNXTBx98gD59+ijWxna5XIYFxebNm7Fs2TLU1tZizJgxeOihh4KOFxYWYtq0abhw4QJ69eqFmTNnwuMxlG3EsbC2x1LPDrXvJcR7mBO2USEhVWvoUQvJr/1O3tdISowz7KPfPM6NuBiP4VU067osvb/cm6mm1m8q/YVeIuFBs+ajbwzn85J7vkkR/y5P+qfXu06OaLjn2SHX1PqZ3mpWw+sFKX+u0txbDQHm7Cum7pALCp/Ph+joaEMXPH36NP785z9j48aNiImJwahRo9C7d29cd911ge/k5ORg9uzZ6N69O5599lmsW7cODz74oKHrORXWRCb17FD7ns+iQDopcpdLswFnK/MKuXz0lVZkWmVDtWBdVymaWOrNBCAk/UUkonEj5UFjZnXP2qgrxVTsOVysWtyI101b6p0lN8qrtZEnnblVaNnFlJ7rkvUHkT3E3BiPJFxRQ1988QWWLl2Kmpoa/Pa3v0WvXr2wdetWQxfcu3cvbrnlFiQnJ6NZs2YYPHgwtm3bFjj+888/o6qqCt27dwcAjBgxIuh4Y8Gsa2k4FGtyl0uzBrmSc5dCPKVEpD76YzJSA59TkmINFWpSO7/S37WuyYrGVQv8sqJkK2vxYAWrtn+NcS+xVX3js25krrzVnieg3n6Xy6UasDd6cCrGZ92I5nFuxXO6XQjyzvqv394UaB9rbIVjTBlF6b5U++oaVMAelzH7d7/7HaZMmYLz58/j/fffx4wZM/DEE09gw4YNui/4+uuv4+LFi3jyyScBAOvXr8ehQ4fw/PPPAwC++uorzJs3D2vXrgUA/Pjjj5gwYQK2b9/OfQ0zNoqXVn9p6Hd6OV70i2L+G4/bhdRrWsJXU8f8Hi8etwve5HicPntRM2pW/G7J+UvMa0a56gP6jLbKBaBtSjMkNYsx9PvyizWBNoptlp+r/GINissuBrWR97rfnDiveszjdqHTlVdYdi3e63Zun8x9HiVKfrmEs1oG6yjg+qvqr3P63EWcrwwtXpWcEIM2LZoFPkufBYtfpTRjPrPyizU4VXZR83rRMW74aupQfrEGp89dhF8mz82OLS14xp4c1nPVui96+X8P3Wzod5ZUuKurq8Ott96K6dOn46677kK7du3glz8hTvz++hWGiCAIQZ+1jvPA6rAW0THKKxuraZvSHCfPhMaitE1pHtSOtinN8XNJZUjktZ7rANqTussFJDWPQfHZi8xruVzAld4EJCdeXnWer6jW1cYWSbFISY7n+7KM8xXVQW2srRNQfPYi3O6ooDalxMTD7Y7C6bMX4av1I9oThTYtmwV9R41oT5Sqaq+2TggZI6WnqkLurwCg9JcqXf1Uu260J8r0uNQSEgBwZauEwHXatUlEVFRl0O9aJsXiSu/ld0v+LNSI9kQhJTmeeS9KT1Up/v1CVW1I3y9U1ype1x3lwq9aNVd8xucrqg2NBfk55GPvVNlFVPvqgu6LHLXn6o5ycY1lPXi94SlRzSUo/H4/Dh06hJ07d2LixIk4duyY4RQebdu2xRdffBH4XFJSgtatWwcdLykpCXwuLS0NOs6DmR3Ff//2JkO/04uS54nbBWT07hCSPVZv/V8p11+VhEPHyxRfZiW3Qq2XPsbjRkbvDprGOpbaygXj9zln6R7FdCWXqnyYNdaaqov5BcXMaFx529U8t3y1fpwqqeQ2TFtZM10Oj3fZ+YoqzWfrAoLcjLXHC1/7WfdQer+93kQ8PHOb4nWTE2IUx4B4X8XJ2lfrx5mzFxX7ykKtv2fLq3GfRqEz+XONjXbD4w61F5kdy0Y9qSzZUfzhD3/AH//4R4wcORLt27fHHXfcgWnTphlq0K233orFixfj7NmziI+Px4cffhhQOwHAVVddhdjYWOzfvx89e/bEpk2b0L9/f0PXCidmvVOUkgTWCVBMUyAN5pLGI/DAMhT6hWB3QR73w2pfHVZsPordh4qQ88DlbS5vRlSg3vZh9P6ZSfPOi1Y0rhyefEA8hmmjecR4EKPLWSgV/GEZ2HnuuVKpUyk82QXksO61UpVAq6KntRIE6nmuD2emYeEaZTW3E4P1uATFoEGDglxhP/roI7jdxrbCbdq0wZNPPons7Gz4fD6MHDkS3bp1w/jx4zF58mR07doVCxYswPTp01FZWYm0tDRkZ2cbula4sMI7hTXYx87+MKRwkd78RzzwptpQovDH85i+Ih+zx4cGouUXFKPyUqh+WyQh3mP4/plJaaEHNVdPpfbxRifzTE7hyls0pHcHbM3/UfN78oI/RtyMpahFfAN82QXk1R1zd+czr6ckmM0sLvRWJ1RD/ly93sRApLwcJ9bv5vJ6KikpwYQJEzB48GCUlpZiwoQJpkqhZmVlYcuWLdi+fTvGjx8PAFixYgW6du0KAEhNTcWGDRuwbds2LFy4EDEx4TFMGcUK7xTWYCj5d+lNaYpqNU+c5nFuQwNL6k0iopROmkVR2SVMWvhJkIdPfkEx3tpaiGqf8hsV44mCIAiqRXW0PIaMpnnXi5qrp1LblLyo1LBrtThpZHfc3uNKpteSiLSNrEmWZ7xorcLVhIRcMItCpUSlLK0SUoGmBE8NGWnac9aGzMg7GKmxbAVcO4qZM2firrvuwrvvvosrrrgCqampmD59OpYvXx7u9jkSK9Qfelehaue+UFWHxU+kK0bFsqgTgJXbgtUMSimbtaj2CSFFcNQ8YMQ4DZaKS2t3EU71jBRWTW6tcqeAdjEiq5Gn0Wge5w6JRRk9ODWwU9JKASP9v1o/RBWduOtinUtJ1ch6X0TBLOaZYgkVLdUfK26Ghd7ys3qJ1Fi2Ai5B8fPPP+P+++8P1KXIyclBVlZWuNvmWKxQfygNEtZgZ11TrYCQFtU+IUQnLbWHsPL3SJGqVLRefp4IW576DXpSeVhpC+EpdwpYk8yPF3EXJxXQF6rq8OaWo8z2KaXxkBf80UroKN11yWElfuSxh/GOq/mP9mUKZqMTMuua4vtodnIPZ4p0K+ESFC6XK8gdtrKy0rB7bGOAdxLQmqT0rEJZ1zQTQV1bJyiW7JQKDJ68QGXl1Xj85V3M72jVP5CfzyxmbEk8+neWQIvkalFtFyc6R7Dax1PDXa0frKJFVhU3Ep+B1uJM652U90NUE7OeB+uaVicXdDrcxuynnnoKFRUV+Nvf/ob169cjIyMj3G1zLDyTgJFJijXY5dcU8xBZVQVOrX2iwJi+Ih9FZWz9MEtdJY+wBcJTv0GKGW8XXtUg695HarXIaoPoCXS2vBotNdRlakgXDRt3HQ8sLLRW+Tzt00LcMSvtbFmCQP5OWv0+NjW43WNzc3Ph9/uxd+9e/O53v8P9998f7rY5Gq2XzMgkJf79bzu+RcXF+jgVsUyk9Dhvoj7eXDo87Zs9vg9Wbf86qAgSL0r6cunkE66X0YwtSTrxnC2vhouRaE6PeiscSf94qyeayR2lJ0GkGW86KWJNFblaTUQeiMt6J828j0qLM+nxpgB3StZ77rkH99xzT+Dznj170Ldv09p+seANOFPz9ZZS45Oo+S7VhhiL9RjY+qS1xXXtkvHXLUe5IqdZL7RoDNVjvwCAxU+kqx7Tq6LRM9GatSWJE4/Xm6iaLr1bpxTulWq4kv6NSO+kOpnKMVp9zYxhl3d3JkUaBKrWr2pfHff9M7poUFucNcZypyyYguLIkSOYPXs2kpOT8eKLL6Jly5YoKirCiy++iE8//RSHDh2KVDsdjd503KwV3sZdx0OyY/Ia9USUPHIAcE3uvJOoVJiZPR+vCkTvRGul6kBNoOlZqRpZ1fIIRqXnq5axFTAW8Mhj2FVbcesZfyKi6oq3lnY442+aSrlTFkxBMXPmTGRkZKCoqAjLli1D7969MXXqVNx0003YtGlTpNroeMym45YOOK2Vj9Y2Xs3QJp+Mzah8ePsr96CxIppdzwtrtUFZSaDpKYWpd1WrRzAqtU3NOaJ5nFvRC4mVRl2rhgdPO3kXF9JYDx61Fc/iycyigRkcO/djR7u1WgVTUFRUVGDs2LGoq6vD4MGDkZeXh5kzZ2Lo0KGRal+DgNeNjkcdpVV0RWsbL64WtXzqzUyiPC9mQrwHE+/tFijOouYiueajb7hrTxhRH4TboKxnpap3VWt2Jas2ObpcLtTUhu42WPWx9XrdydupZzEl1mYXr6ulVuPdtYrt0DveeWxAjV0VxRQU8fH12R7dbjeqq6uxfPly3HjjjRFpWEOC141OK/8RoCwklLw73thyVPG7zePc3D71RidRnv7mFxRjZV5hIBVJta9OcaK4UMWvZ45U+g496HGVViq4w1rVmg3slBvkRa8nljqHFVAonks+0fLsqnjbLC+1qqW20qNKNDreeWwsjV0VxRQU0lIVLVq0ICGhAu9koceoJ8/sqrQbUFstqvnUq00CeuCZ8PTabMy4rNrtrmjEVVpEaacnxarATtEgL2YWZbm2sgIKpROt1FVWbRecEO/R3CnL+6Wk+pJet+Cn84EcSZFS+fC4cwPOTOZnFUxB4ff78csvvwQEhvT/AJCcnBzWxjUUeLe1St9jvbCsQvBq12StFqWTgJpOmmVH4J3wjNhs9LqsRnKi4GmXXldpAIiL8TB/p0cw6rH/aI0TETFlCQCm4FMSAB63C5eqagO7AJ7U+DxG9gE926O8ooo7cM4qd2TpM450ehYnwBQUx44dwy233BIQDr179w4cc7lcKCwsDG/rGhC821r593gGndpgV7qmngA8Mb5CFBZaxlPeCc/Iykqvy2pDwqxrptZEp9cbrE+achp1JfwCFD3zlMaBdBdcVVOruzY3T1bhnftPmHJHXrH5KNb+81hIBLoenLizDTdMQfH1119Hqh1NFqVBFxvtZqpxWJOAngA7INiAqWWU5J3w1HZKzePccLlcIbrmxv6SmVEh8QhGI0bv0YNTcV27ZK4U2ryeedJdME+hJCmsrMLSa6/MKzTljgyExibpxak723DCHXBHhAelQfdwZlrAW0htElDyGDKaHJBXEPBOeGorLrGt4YhOdjLhXoHyeNN5W8Tjnn7XqjozaGUfll6DZxzoicbWyiosPU+pSppx8TvSscXCrPG5Ie5szUCCwgHIB53U8Mga8Beq6qvNrdz2DbKHdFZdQSXEe5iBTryCgHfCE/uSu/uHkAJMSv1t7BhZgVoRgQ5cfrYl5y4xVTRaCwxpuvCqGm3vIz2OG1pZhaUCqFWLeMWaFGIqFZ4EliKN2fhsNSQoHA7PyqzaV8f0Na+8VIvbe1ypumIUXzIlbyZWMj/WBNYnrW1I7e+mjB7haEUEuhJq9TS0nA/U0oWL8GSdZcEaf3IBlJ3RBYvXHVBcrKz56BtuISFel+CDBIXD4Z0EausEZrCeaLBWqgMtz1ckRQB/4rVIYaXqSl6HXGnSi3TbrIhA53F/FdWXLKOzqBbqk6aeLjw22q3aLqlaVCkWQmv8SZNiAvVeT18WFiuWqOXx5JJetzHbxayGBIXD0Qqwk6IVrCc3YLLyFV0+p4C1/zxmiXCwYhK1MrGekqqi8lJtSDGnSLfNigh0VnCnFC3PJP+/a1poVSXkrb2i9D3W+JMbnnfuP6FYova6dsnMfozPupHLe6wp2c70QIKiASAOVt4MoSJqKgH54NdaifEmcmNh1SRqZYK2jbuOK6oqausEyzKsGmmbFYF2RjK2qqElcNRyRym5oRoZf6Lzhnh/1e6xmi0uId6juRMOV2bfxkLEBUVRURFycnJQVlaGa6+9FgsWLEDz5s2DvvPzzz8jMzMTHTp0AAC0atUKb7zxRqSbGlGkqxk1DxUAWLntG0VdrhJqKgE5PHYQnvxRLKyaRM2mteD9jZXn03suHqcBnhU8IKmlEOWCnyfqTSes3FG8bqi8HlJaz2t81o0hiymP24UH7rpB89yUIZZNxAXFzJkz8eCDD2Lo0KF49dVXsXTpUuTk5AR958iRI8jKysKsWbMi3bwgIrUVla9m1DxUpNt3Ld0ywD9Baa0+Yzwu3TWZedsideHkucdW5ntiTVB6zieOE9Z19KDlNMC7+pWuogt+Oh9iBLaCvl3bMt1qeSZbvbE/SqQkGa+NDVi7AGmMRFRQ+Hw+7Nu3D6+++ioAYMSIEfj9738fIigOHz6MY8eOYfjw4bjiiiswbdo0dO7cOZJNjehWlBUrIebSSe9+OVmakehuFuK5lIyNbhcQE+1W3NKzajKLaE2igL4KbFbGJIxI76ToTilPj85CzRPIbNtYqhK18cLK5SWmvli5rRDVPr6dBU9+pkPHy7gr7ClhNPZHivR5GXW2cGLCSScRUUFx7tw5JCQkwOOpv6zX68Xp06dDvhcbG4thw4Zh1KhR+Oyzz/DYY49h69atiImJ4bpOSkqC6bbm7s5XfBlzd/+AYQOuN31+KWc1Vi1+od5bKT4uGpNGdg85/nBmGpasPxikkoqNduPhzDR4vYmBv+3cfwLLcw8HyqwmNovGhHu6YkDP9hg2IBHDBlyPnftPYGVeIUrPXUKrFvHIzuiCRWu+ZLZdeg0pO/ef0KUqA7Tv8bABiUhKjAtp44Ce7bmvIT+X2j2RotZHpXES+I2JtrFQGy9Sj6aV275BUmJc0LWTEuMARAHgex5+oX4csZ7f2fJq/PeDN4eMPyneFvGG7h8vglDfN7Vr8PBwZhpe+ftXIWor+TtkJeE6bzgIm6DIy8vDnDlzgv529dVXh9S5lX8GgMcffzzw//T0dCxcuBDff/89UlNTua5dVlZpWh+rFNQj/t3q2ICWnDrabf/7I0YqrE7TOiQje0jnoB2Bxw2UV1QF2qrk4VNx0YeFa77El4XFgd1KWodkvDSxT+A3b28pYNbIbpkUi5KSCkU1Xe7uH1QnD9YqVOseS9sY+I3BZ5LWIRmvTL4ttA2S80kDIJXaqobYRjvGS7WvDm9vKQhE+Hu9iXh7S4Hi82C5VYteSWrXa5kUGxh/SurQGE8U7ul3raH7x0udXwjqqxHKK6ogyG6C4BeC3iErYY0pO4iKcjEX2GETFBkZGcjIyAj6m8/nQ+/evVFXVwe3242SkhK0bt065LerVq1CZmYmWrRoAaA+3bm4C4kUkdyK8upotWSftIKYvM6DmocPUL9b+eSroiCVhZZKBbgcjKempmP9dv6jfRtFFk47VBa8Hk1l5ZcrsD2cmcaMrYjxRCmq86R2MZbKj+X+aoUhWwuz51B6P3hUq02FKO2vWEd0dDR69eqFrVu3AgByc3PRv3//kO/t27cPGzZsAAB8/vnn8Pv96NixYySbihHpnRDjCb494QjS0aOjjQrdfAVgeW0AfC+SOMGLLztrImoe58bYzBuZyQTVkKYEicQ9Did29KFPWluMyUgN3EfWuADqn+uS9QfRPM6teDwlKTbofOJnuWFc6zvi9+Y/2hdvTr0D8x/tqznJKt0/tTYC9Stf1vH8gmLkLN2DsXM/Rs7SPcgv4Hu3yJjNJuJeTzNmzMDUqVOxbNky/OpXv8KiRYsAAGvXrsWZM2cwZcoUTJs2DVOnTsWmTZsQGxuLhQsXIioqojItYhkijZaIlMMa6PkFxVyGSeCycGG9IPI6GXpeJqVqfQ05yCmcfWCtzuVRz1o7jGpfHap9oX+X7xxYhCMqnyfVh7RyopL3llqKET0OKGTMZuMSpJWIGglW2CgiBSsdszi5y72elFBT4yTEe1DjCw1S0oK3vCvr2krERruRPYQ//iKc8KpJ7NAnq6l6lFbx4vf11CIB+MZVJOHps9ebiH/s/FbxubFUmfIxa+TaVkI2CkIXeiZkFmpuo0o5/nnapMcNVU8UsJjAELA34tXI6jOSKR6M5HsS/84ruKXpL8z02coqcgBfpUil85uJ1WkMu9twQoLCZrQKF/GiNtD1JEoDglURSufjSUqnhdEUGVaidyKOdIoHMzpzPYJbmh5Dq9a3Up+tvi961VtSIcVSr/LE6oRDtdZYIEFhM0oTsrRwkRxevbWIHnWEXC0k14OLyeG0kr7xeuTYid6JONIpHsxWxQP0PXtpNlkxNQtPn1nBokrCx0p4ancrQak59EOCwgHIJ3g1/aWR1ZtetZASemoP805SdhsJ9U7EkfaKMZvvSa/gFpG6VfP0WSsiO5y7Lp7a3eTNZA2RdSUiTMFavam5AvZJa4u+XdtCIa5R9Ro81wUuJ32TX1d0kfzjgzfDrXBdPSkywoVet1Y1ARIugafljioKALlKRelZjMlIhbdFPPe1xRU3T5+1+i910bYaVlyI6J4b6efWWKEdRYQxY/jTWr0prfLzC4rx2aFT4PVtU7oG67qioHpjy9EQDxoxv5CVhYGsQq/xMtx1r9XaqDffk5JKpU/a5WqDagWE5IjZWLX6zLNjDdfqnWdXaMdza4yQoIggaiocNb2/HJ4oVnlq5427juuqYaG00uK5rpiPCkCQsDBjnAy354metjnNK8aMSkUawa8GbzZW8f+sbMbhWr3zCAGnPbeGCgmKCMIKrpPqc4cNUE4Wpqc2sriyNBoMZ+S6ALDrQJFhv3ynF4/RY9wPN1qrabnAFR0keAI8lVJzaOGrVV6MhHP1btadluCHBEUE0Zq0xQleLXOqHm8W8ThrN9A8zo24GA+3+ytPDQwzcY4NpXiMEwQaazWt1L4l6w8ie0hnzXFjROixjMrhClgTISEQGUhQRBAeFY7WcV5vFmk+JaUSqm4XdFWok16XJaiiXJdXs2fLq9GyERaPcYJAY62mc5buCWlfta8uYKDWup96XVtZRmWaxBsHJCgiCI8KR2+xIbXUzvJ8SjxlTHnsA6LAWLX9a8WMt507JDeYfDtG7SFOEWhGIpTHZ93IDMLkCUyTQ3mSGj8kKCKIlupIrz5XaZXPCoZjoVedItohdh0oCspHdeh4meHVdiQ9VBpzAjlW+/qktcV3J89zpbXnfW5KafLJs6hxQYIiwsgNotKVfrSHM9iBcU6jGFGnjB6cGmK4VktyyLPajqSHihn1kdNdLrXSwowenIrr2iVbUnddLU1+365kO2hMkKCwGaViQ0mJcaaqdRnBKnWK2dW2EaFnRIVkpr9Od7nkTQuj5qkkReu5qRmyP/mqCIeOlznqvgCRdb9uTJCgsBG1Ve3KvMKQUp/hxip1SqRX20ZVSHrcS70t4nFPv2t1q/PsRC4sVuYVBvVBr5usGpFK4WHUQUJ+Dru91RoqJChsRO0lK7WgjrBerJrgpRORmZeaF6MqJD3upSXnLjl6QlFaJQNg9sEqN1ktLyorvMGsSgkfbm+1xrxbIUFhI2ovWSsdeXmsQhzQK7d9E0gOWFPrx3cnz+se7OJqOxLFWYyqkPS6lzoxngNQn0RjoqOYfQhnHRQ5ZeXVpiZRvRlq1TIgsNpnlsa+WyFBwUk4Vgtqq9rsjC5hva4a3508H5JBViktRzjR21+z6bj1upc6bdWoNomyMgAA4dlBqt23hHiPqUlUr3pLT3lhwBpvNSfE1oQTyh7LAW+mTr30SVPOEDqgZ3vV667YfBSTX/nU9LWV2HVA2WVS7e9WY+Q+680Cy4PaxNE8zh2WcWAGI84GgHZ2Wj30SavPFjw+60bFZ6FUZVFPVlm9GWqtSFujF9biImfpHoyd+7FqhueGgG07ipdffhlutxuPP/54yLGamhpMmzYNR44cQVxcHBYsWIBOnexzPQznaoFlFNVK7y3+3irU0m/4BWiWkrQCI/c5HB5Iaqttl8uFmtrgHZfdq0a1HVXzODd8tQJzxyCPwzFbbEjtWaipfXgndL0ZarXsJuJxK8cyT+0LnnxugDNtHREXFBUVFZgzZw4++OADjBs3TvE7q1atQnx8PPLy8rBv3z4888wzWLduXYRbehm7InHDbSSUY7aUpFnM2BusbIt8whO9nsxOeOFATag9OLAzgNA+KEXjh7uUqZpaSm8WApZ6S55aXO1Z6bXB8KI3YadaPjen2joiLih27NiBa665Bo888ojqd3bu3IkpU6YAAP7jP/4DZ8+eRVFREa688spINTMIuyJxtVZGVk9Q6d2vtDRiVy9W3WcrVmTSCU80ypud8MKB1o5K3gc5kdCtW2EPEZ9HwU/nsXjdAc2dklL0udI1rVq9Kz0HIwsfp9o6Ii4o7rnnHgDA4sWLVb9z5swZeL3ewGev14vi4mLbBIVdkbhaqxSrJyiltBxaOwwrseI+h3NF5tSIbDM7qkjslq1UD4rFsLTOJUafs74X7t2UqK6Vw3pvnZJHTE7YBEVeXh7mzJkT9LeOHTvi7bff1vytIAhwSWp3CoKAqCh+u3tKSgL3d3kYNiARSYlxWJlXiNJzl9CqRTyyM7oEjM7hwOtNDFx3ee5hVFz0BR2PjXbj4cw0eL3quk4j/Pfv/wP//fvLn8fO/hAlCnEd3hbxXNfW0z4r7nPu7nzFFVnu7h9Ut/s8SJ9HJMeBlSg9C2+LeFPPl5dhAxJN3f/gc13PdS6ta4ZrrIg8nJmGJesPBnkSiu8tYO/z0EvYBEVGRgYyMjIM/bZNmzY4c+YMOnToAAAoLS1F69atuX9fVlYJv5nCCAqkdUgOiZYOV4yAVE2Q1iEZr0y+TXGLnNYhOexxCvf0u1ZxFX1Pv2s1r20kjsLsfVZ6ycS/G71X8ucRqXFgJWrPwszztQMrY3PCMVakpHVIRvaQzorvLaA8bux6HlFRLuYC25FxFOnp6di0aRN69eqFL774ArGxsbapnZyCXSkjnJ7XSI7TM7s6jYb2fFlEMgaHF73vrVOfh2MExdq1a3HmzBlMmTIFo0ePxnPPPYehQ4ciJiYG8+bNs7t5TRqn5zWS4lQ7gpNpSM9XDSP2BqeOFSc+D5cgCNbqaBxAOFRPkSQSqS8igV39sNoPvTE8j4bQB57nptYPluGY5Q5rV8yC055Hg1Q9EYQZnLgiI9iY9UBySgxOY4VSeBAEYTus+AEe1OwKZJuyBhIUBEHYjtn4gXDk/CIuQ6onotHgxBw5BB9WVEYEnOct1FggQUE0CpyaI4fgw8o0H4T1kKCwCFrN2ove4jaEs6AdgbMhQWEBTljNNnVBFanazUQwVo472hE4FzJmW4BZjw2zhKuwUkNCb3Ebwjw07poOJCgswO6Mj3YLKieg5PUix+4MnI0NGndNBxIUFmC3D7fdgsoJyEt7KkE+9dZC467pQDYKC7A7Z4yTCv7YibS0pxNz+DQ2KAFj04F2FBZgZaF6I1gRbNSY9M12P4+mAgW5NR1oR2ERdnpsWOFa6NQSjEYhD5rwQy6tTQcSFI0EsxMj6ZsbvurNDkggNw1I9UQAsN8gbzeNSfVGEFZDgoIAQPpmcvUkCHVI9UQAIH0zqd4IQh0SFESApqxvJldPglCHVE8EAVK9EQQL2lEQBEj1RhAsbBMUL7/8MtxuNx5//PGQYz///DMyMzPRoUMHAECrVq3wxhtvRLqJRBOjKaveCIJFxAVFRUUF5syZgw8++ADjxo1T/M6RI0eQlZWFWbNmRbh1BEEQhJyI2yh27NiBa665Bo888ojqdw4fPoxjx45h+PDhyM7OxjfffBPBFhIEQRBSXIIgCHZcePHixQCgqHpavHgxUlJSMGrUKHz22Wd4/vnnsXXrVsTExES6mQRBEE2esKme8vLyMGfOnKC/dezYEW+//bbmb6XCIz09HQsXLsT333+P1NRUrmuXlVXC77dF/lmC15uIkpIKu5uhC6X0F8MGXN/g+qFEQ3wechpDHwDqR7iIinIhJSVB9XjYBEVGRgYyMjIM/XbVqlXIzMxEixYtAACCIMDjIQctp6JWCjYpMQ5pHZLtbRxBEKZxZBzFvn37sGHDBgDA559/Dr/fj44dO9rcKkINtfQXK/MKbWoRQRBW4phl+tq1a3HmzBlMmTIF06ZNw9SpU7Fp0ybExsZi4cKFiIpypEwjoJ7movTcpQi3hCCIcGCboJAbsR944IHA/9u0aYO33nor0k0iDKKW/qJVi3gbWkMQhNXQMp0wjVr6i+yMLja1iCAIK3GM6olouKilvxjQs72jPDsIgjAGCQrCEij9BUE0Xkj1RBAEQTAhQUEQBEEwIUFBEARBMCFBQRAEQTBplMbsqCiX3U0wTWPoA0D9cBKNoQ8A9SMcaLXFtuyxBEEQRMOAVE8EQRAEExIUBEEQBBMSFARBEAQTEhQEQRAEExIUBEEQBBMSFARBEAQTEhQEQRAEExIUBEEQBBMSFARBEAQTEhQ2snz5cgwePBhZWVlYtmxZyPElS5bg9ttvx/DhwzF8+HCsXr3ahlYqU1lZiczMTJw8eRIAsHfvXmRlZWHQoEH485//rPiboqIiPPTQQxgyZAgmTZqECxcuRLLJihjpx/vvv49+/foFnova9yKJvB8A8PTTT2Pjxo2K3y8vL8eECROQkZGBhx56CCUlJZFqKhO9/fj888/Ru3fvwLN45plnItVUVeR9+Pvf/47MzExkZWXhmWeeQU1NTchvnPhuBCEQtrBnzx4hMzNTqKioEGpra4WJEycK27dvD/rOxIkThS+//NKmFqpz4MABITMzU0hLSxNOnDghXLp0SUhPTxd++uknwefzCWPHjhV27twZ8rsJEyYIW7ZsEQRBEJYsWSLMmzcv0k0Pwmg/Zs2aJWzevNmGFisj70dxcbEwceJEoVu3bsJ7772n+JuZM2cKr7/+uiAIgvD+++8LU6ZMiWCLlTHSjzfeeEN47bXXItxSdeR9+P7774WBAwcKFRUVgt/vF55++mnhrbfeCvmd094NObSjsImjR4+iX79+SEhIgNvtxm233YZ//vOfQd85cuQIXn/9dWRlZWHWrFmorq62qbXBrFu3DjNmzEDr1q0BAIcOHcLVV1+N9u3bw+PxICsrC9u2bQv6jc/nw759+zB48GAAwIgRI0K+E2mM9AMADh8+jPfffx9ZWVl46qmn8Msvv0S66UHI+7F582bceeedyMjIUP3Nzp07kZWVBQDIzMzEp59+Cp/PF5H2qmGkH4cPH8bu3buRlZWFP/zhDzh16lSkmquIvA8xMTGYMWMGEhIS4HK5cMMNN6CoqCjoN058N+SQoLCJtLQ07N69G+fPn0d1dTU+/vhjlJaWBo5fuHABXbp0QU5ODt5//32Ul5dj6dKlNrb4Mi+88AJ69eoV+HzmzBl4vd7A59atW+P06dNBvzl37hwSEhLg8dQnLPZ6vSHfiTRG+gHUt/3RRx/FP/7xD/zqV7/CrFmzItJeNeT9GDduHH77298yfyPtq8fjQUJCAs6ePRvWdmphpB+JiYkYPXo0Nm/ejPT0dDz55JPhbiYTeR+uuuoq9O3bFwBw9uxZrF69GnfeeWfQb5z4bsghQWETffr0wYgRIzB69GiMGzcOPXv2RHR0dOB48+bNsWLFCnTq1Akejwdjx47Frl27bGyxOn6/Hy7X5TTFgiAEfVb7m/yz3fD0AwBeffVV9OzZEy6XC+PGjcNnn30WyWaGBUEQEBXV8KaDWbNmYdCgQQCABx54AN999x0qKipsblUop0+fxpgxY3Dfffehd+/eQccawrvR8EZGI6GyshKDBg3C5s2bsWrVKsTExKB9+/aB40VFRdiwYUPgsyAIgRWH02jbtm2QMbSkpCSw9RZp2bIlKioqUFdXp/odu+HpR0VFBd5+++3AZ0EQ4Ha7I9VEy2jdunVgB1tbW4sLFy4gOTnZ3kbpxO/3Y9myZYExJeK053H8+HGMGjUK9957Lx577LGQ4w3h3SBBYRMnT57Eo48+itraWlRUVGDDhg1Buti4uDjMnz8fJ06cgCAIWL16NQYOHGhji9W56aab8MMPP+DHH39EXV0dtmzZgv79+wd9Jzo6Gr169cLWrVsBALm5uSHfsRuefjRr1gx//etfcfDgQQDAu+++69jnwiI9PR25ubkAgK1bt6JXr15BO9qGQFRUFD766CNs374dQP2Yuummm9CsWTObW3aZyspK/Od//iemTJmCsWPHKn6nIbwbJChsIjU1FYMGDcKwYcMwcuRIjBkzBj179sT48eNx+PBhtGzZErNmzcKkSZMwZMgQCIKARx55xO5mKxIbG4u5c+fi8ccfx913342OHTtiyJAhAIBp06Zhx44dAIAZM2Zg3bp1uPvuu/HFF1/giSeesLHVofD0w+124+WXX8af/vQnZGRkoKCgADk5OTa3nI9XXnkFa9euBQBMmTIFBw4cwNChQ7FmzRo899xzNreOH2k/XnrpJaxcuRJDhw7Fe++9h9mzZ9vcumA2bNiA0tJSvPXWWwEX3ldeeQVAw3o3qMIdQRAEwYR2FARBEAQTEhQEQRAEExIUBEEQBBMSFARBEAQTEhQEQRAEExIURJOgc+fOyMrKCrgoDh8+HNOmTQMADB8+HOXl5aioqEB2dnbgN0uWLAnJv8VDjx49grKfAvUZZ0VXWymlpaXo3r17UPoWKSdPnkSPHj10t4EgrMSZob4EEQbeeecdtGzZMuTvmzZtAlA/KR8+fDjw9//7v//DddddZ8m17777bsydOxf79+9Hz549A39/7733cOedd6JVq1aWXIcgwgEJCqLJ07lzZ+Tn5+OZZ55BVVUVhg8fjvvvvx9HjhzBvHnz4Ha7kZ6ejgULFmDfvn2oq6vDjTfeiOnTpyMhIQFffPEFnn/+ebhcLnTt2hV+vz/kGrGxsbjvvvvw3nvvBQSFIAhYv3495s2bhwMHDmD+/PmoqalBSUkJbr31Vrz44otB51i8eDHOnTsXCI6Tfq6oqMALL7yAY8eOwefzoU+fPnj66afh8Xjwl7/8BR999BGio6PRokULzJkzx3EpIghnQ6onoskwZsyYINVTWVlZ0PE5c+YgLi4OmzZtwkMPPYRf//rXePrppzFw4EAsX74cbrcbGzduxD/+8Q+0bt0aCxYsQE1NDaZMmYKpU6ciNzcXvXv3RlVVleL1H3jgAWzfvj1QlGbv3r1ISEjAzTffjJUrV2Ly5MlYv349PvjgA3z88cc4cuQId99efPFFpKWlYePGjcjNzcW5c+fw1ltv4dSpU3jnnXfw3nvvYePGjejbty8OHTpk/CYSTRLaURBNBjXVEw87d+5ERUUF9u7dC6C+hkBKSgqOHTsGj8eDPn36AKiv7aCWDqN9+/bo2bMn8vLyMHLkSPz973/HQw89BACYO3cuPv30U7z22mv4/vvvUV1djYsXL3In6tu5cycOHz4cSCQpCqs2bdogNTUV9957L/r374/+/fsH2koQvJCgIAgO/H4/nn32WaSnpwOorxdSXV2NoqIiyLPgsLL8Pvjgg3jttddwxx13YP/+/Zg3bx4A4Pe//z06d+6M2267DRkZGTh48GDIeV0uV9DfpIWG/H4/XnnlFXTq1AlAfalTl8uFqKgovPvuuzh8+DDy8/Px4osv4rbbbsPTTz9t7oYQTQpSPRHEv/F4PKirqwtMxm63G7W1tQCAfv36YfXq1aipqYHf78f//M//YNGiRejcuTMEQQjUCtmxYwez4l3//v1RWlqKZcuWYdiwYYiLi0N5eTkOHz6Mp556CoMGDUJxcTF++umnEFtHixYtUFBQAEEQUFlZiU8++SRwrF+/fnj77bchCAJqamowadIkvPvuu/j666+RmZmJTp06YeLEiXj44YeDDPYEwQPtKAji33i9XnTr1g1Dhw7F6tWrcccdd2DRokXw+Xx49NFH8dJLL+Hee+9FXV0dunTpgqlTpyI6Ohqvvvoq/vSnP2HRokXo0qULUlJSVK8RFRWFUaNGYdGiRYFyl0lJSZgwYQLuvfdeNGvWDG3atMHNN9+MH3/8MahGybBhw/DZZ59h0KBBaNOmDX7zm98EhNq0adPwwgsvICsrCz6fD7feeivGjRuH6OhoZGRk4L777kOzZs0QFxeH6dOnh/dGEo0Oyh5LEARBMCHVE0EQBMGEBAVBEATBhAQFQRAEwYQEBUEQBMGEBAVBEATBhAQFQRAEwYQEBUEQBMGEBAVBEATB5P8DljfBjPRhc04AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(results1.fittedvalues, results1.resid)\n",
    "plt.hlines(y=0, xmin = np.min(results1.fittedvalues), xmax = np.max(results1.fittedvalues))\n",
    "plt.ylabel('Residuals')\n",
    "plt.xlabel('Fitted Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZbG11GQIAs9WO9dtP+2xfNHNQO6yG0Bxa1IbrBAb3CcUrD9+GKq0ZYUFShAWLoJBQKLiuhdNJ44msIaAEPIQHSaCSUzhf6Kct2gkMjg/Fa4tSoNGaoVZJ6kV+aEAk5GPG2D7MY79IWO+veVZk7Dp8BTPG9kG3UBmqdWaIhHwU/KnD6s31SbnMtATsOnwFlTVmiCkBTGZXjXOUWoYH7h7Auk50uAIKiZB5j3w8Ta5qCK84sPv9/N8VDbLSEpDjVfKmkLjOpZJR0JvtLE+5JXXGWqONSUI2+XfZAG1mjO12O/Lz85GQkAAAyM/Ph9PphMVigd1ub+TorgOXWA8A4rUSALRQU4EHnL/KNkRPzRkCu4NmEmmexkRraMDg171uMNkQGSpzGbs6tTV3l553+IAxLjSQGBeEVx6+Db/nV8DppPHF3vOMsZ0xtg/rmlvy8pE+Jh6Hfr/GlLFljE/AgeNFCFKJcFtwBLvGF/7Lz7i+xLzjwO71Wm1OHDhRhBlj+6BPdBDCVGLW9Gf3NZRyCjPG9kFMNwV6qGXNrjNurT6GN21mjJ9++mnMnz8fffv2BU3TuHr1KlavXo21a9ciLS2trS7T4eES6wGI10pw0RIpTK3J1xBdKdH5rWj4U2P0ayT+rDT6FRJq1LjwgPOFtSgu1yEnr76LL+OuBIAHxEYqEBYsQWWNmTk2IS4Y4SFSVhlbVloCguUSUKBZNb5c97l26+94Z8nt0FscSE+NBwCcvFiGIQndIBbxmTi0+9wL7xkAg9n1JJ6TdxEvZ49k6RJ7GnVLjQM5eReZL5zmxnrbeop1mxnj1NRU7N27F8ePH4dAIMDQoUMRFBSEpKQk/Pjjj211GQKhU9OSNtyKGt8KCH9VFJVaC4rL9IhSyzA6OZqJXx46eQ1yCYVVnx/3+1jdmHHxrKYQUwIo5RSmjOrF6oDzDk3IxBTe2XGCdc2cvHyMHBgFyjNjxQMqtRYo5RTSh8Uz6z55scynpjk7fSC2/+cSSjRGRKllWPnArdAarSgu02Pr/nzoDDZkpiUg72hhXZt4fejD7mxe9UlDNNgN2YIkXpt24AUHB2P8+PGsbQqFAhs2bMDdd9/dlpciEDonLRjtIxYJfIwk348OhUQsxIkLpZh5Z198suMsYyQWzUyC3eHgNER/aoxQxQQ1+kXh9pzdCTyrzelTHuYOTez4qQBZaQn43xUNt/HTmxGpqkvw1YUOKmtNmHZHPDbvvcBc/6HpA7El76JPXXT6mHhs3Z+PEo0R565UsZ4SAGBLXj5WLBjhEz/PSuvXdt4sDaQkRSEypG1aqm+IahtNRM0JhHqaOdpHLqV8qg9UcgpzJ/b3qbAIUlAY1j+SMcSAy4Ct334GQqEAWWn9kDE+ARnjExAWLEFiXDAUUgoXr2mh0VuR2DOIqTh469FRrCYIt+dcWWPGrsNXEB4s5TS0vXqoMGNsH3x/+Aqsdid31YKivtLC7XHz+TzGELvP9c9vzmL8rbE+1/CsWPD3lEDTtE/8fN+xQh8Z0eaovXnD5/PabEzTDdGm4DWld51AIHASIqcQrKCwbM5QXC2phdMJfHfoMu4Z3RsZ4/vCYnMCNLB1Xz6emjMUcZFKTuNUXm1iJfwenDYAIoqPNz47xmxbPDMJ8T2CEBsuZ7dTA1DJhFg8MwkfbT+DyhozKmpMnF6mQkohJ+8iADBetGco46H0gRAKXONJtEZXKZzF5oBMTHGuO1QlZeLSgCvk4l5XWLAEPaNUjarVuamsMeP7w1f8qtU1WAPO9Vob0u5CQQcOHMD7778Pk8mE22+/HStXrsThw4exatUqWCwWTJ48GcuWLWvvZRII7YcTiO+uQrXeBpnEVS0wqG8Y/p7zG5Msc3PiYrnf5o3iMj3L66zWWXySgB9tP+OqMIhQ+LQGaw02bN2Xz4xuElN8zJ3YnxVWWJqRDErIZ67v9qLdVQuFJTps+88ldI9QoLrWzIpBSyW+4RgxJUCIUoyPttfHjB+ekYR9R68iLFiCqaN6YeOu//kY/IbU6nQGGxRiAVTuOmgPY+t3Ugq4X1OHKtrqt9y+xri4uBgvvfQSvvrqK6jVatx///04ePAgXnrpJWzcuBFRUVFYtGgRDh48iNTU1PZcKoHQvjiBEBmFEBkFtVqB/MIqjB8RCyft8j7dCTOnE/ho+2mfYaCLZyahRm9hSssqa8x+H++ddaI6ry1OgVohYoxVjd6KEo2R0UMGuDWR9RYHq85XZ7BBTPFRXlV/rM1mR3G5Hump8ZCI+Hh4xkCIhAKf+uCstAQUlmpZXxgf557BigUjYLTY8fcvXXP4dtWJ5vP5wLB+EVCrRC71OYMVKxaMwEfbT7MGr3LFdhsbNcX1Wnx0MERt9OB/Q4yxv5hxXl4epkyZgsjISADAmjVrUFhYiLi4OMTExAAApk2bhj179nQ4Y0zqiQntQl3Xl2cG3105kDYyjqlkUMopZKYlwOmkER2hxOffn2OMkbviwV8SEHR9HW95lQCJcUHQm+wQiYTISktgfQHoDDYfTWSFWIDocAWrqUMsEmD7j38AAKLUMtTobayQyUPTB2LbgXyMHhLNOi5UJcbGPRcAuAz/uGGxAA8wmG0oq6ov4ausMTOGvn9cCMqrTSwvdsm9gxGidDXK+EuyVWotfitUhHwe52tVOlN9IrKV3BBjPG3aNM7thYWFoCgKixcvRklJCcaOHYu+ffsiPDyc2SciIgJlZWU3YpnNgtQTE24odfHKilozrpZooZRTsNQ4mAqGpZnJ+HTnOcZDvnC1BiKKDx4PPl1iW/LyMWNsH/SKUvp0obkNtVv7Yu2W3/HiwltxqaiG5bG6vwAyxifg3S9/Y3mdiXFBCA+WgMfnQSoWggaNdV+dYta2eOYgJk7tXtM/v3FVSGz/8Q+MGxYLPh+I6aZAZY0JOoMNYcESTBnVC3lHCzE6ORpFpTr0jwtBYlwwzhfWMG+Tu6LkQmE10lPjceB4EQDgzwoDJCIBFFL/VRNSsdBvhYpCwv1aqFKKtpJ5a7UxfuKJJ/Dee+/5NbjfffcdFi5cyPmaw+HA8ePHsXHjRshkMjzyyCOQSCSshB9N081OAKrVbRfH8SY8XAkAsJTrQFG+bx+Px2uT7W15LooSQiwWwsLxhKKQumaadTTc73NHo7WfLaeTRkmlAVVaE0JVUkSFycHnN/z5djppTm/Y7QW7vFgdKmvMLNF2q82J6HA5p0cXHizFZ9//D9npSXjl4dtQWmlEaZURuw5fgc5gQ1ZaAsrrPE+t3soYYvfxW/Ly8X+LUrCmzhC7t2/acx5/mZSIf3h4pcvmDMXLD6VAozUhVCmFptbEuSbw2B7us/OHAQDmTuwHk8XBeP+eseFHZw9Cjf4i82WweOYgvJdzkvn5/qmJsNmd2Lz3Ims9KUlRPu+7DTzOMEl4sBSRarlPTfGyOUOb9PtrKq02xg899BAA4MUXX2z2sWFhYUhJSUFoaCgAYPz48dizZw8EgvpSmIqKCkRERDTrvBqNHk5n25fThYcrUVGhAwDYrA7YbBxhCppuk+1tdS6KEsJms8NotnHqWTw6azBs5vabiMuF5/vcXtf3R6s+Ww0liOpadbmy9RqdldO7ddfaiikBBvUJg1gkgFIqYhmMR2YNQpRaxhhMwOXRlVUZUaIx4u2Nx/Ha4hQ4aSdiuykxJ60/pBIhTBYbvvzB1Z3mT/JSozWzzgsAo5OjGUPs3u/z78/hqTlDYbU4YBbZoJRRfsMjnj8XleqRk3cRYcESzEnrj9HJ0T51zR98fRqvPHwbanQWhCjF+GznOdaXg9Zg80lSrvnyN6hVKVArRaxrUjz4hFeiwxWgeDQ0Gj0Seih96sP5fF6zPqsNfbZabYwHDhwIALj11ltRVlYGjUbT5LriO++8E8899xy0Wi3kcjkOHTqESZMm4eOPP0ZhYSGio6Oxc+dOzJo1q7XLJBDaF57LqBaX65jH58oaM0szwttQPz13KKx2J4rLdX49SZdgzmDojDYEKyQoqzKyQhgfbjuN5fcPx5t1nXdiSoC5E/vhu58vM+exWO3g8Xh4b8tJ1j6UgI+stASUagycxpN2gunEc8dyI0NlrP38ibg/O3843t5Yv6bFM5OwdV8+c+5HZg3C9/+9zJS0SSUCv7KXv+dXICcvn1n3rQOiYLK69hMKuGO9Jy6WIyZCya4YoYH47kqEB0u4mzjq6sMDNaapzWLGa9aswaeffoqwsDBmG4/Hw/79+/0eM3jwYGRnZ2Pu3Lmw2Wy4/fbbMWfOHPTu3RuPP/44LBYLUlNTMWnSpLZaJoFw4+HwiD3DDDUG15OJd7a+4LoWuT/+wZR+eRvDnpFKvLY4Bf+7Us0qL/MOYZgsdpa3R3kosYkpAcSU0GeSyOa9F/FE5hD8a+dZAPApHctMS0DuwUtYMDURZquDebT37nDzJ+K+Zlkq28uUU+gVOYz5WUDxMSqpO3PeKLUMD09P4nwfnHVCR+51zxjbB1v3uYzzU3OH+j2GU2EtwAa3IdrMGH/33Xc4cOAAyxg3hdmzZ2P27NmsbSkpKfj222/bamkEQrvCVTLl2TbM1ZwA1HeWcTVOZKUlQETxYXfQPl1r3iGMaq3ZFVrhuc753aHLGDcsFjt+KsDSjGQYzDZO79FkdYW83LXC6WPi0U0tQ5nGyBj72wZGYeu+Sz4dbm4j6s+bvVqiRf9oVb3Rc7KNoNZkY8WpSzRGbN13kWk68f7i8Ty3s+7J3GJz4LOd55CdPpDVGu4+xmJrucJaIGgzYxwSEtJsQ0wg3Az4U0Pj89Fgc4K79MzTGPL5QGLPUJRqDDBZHPjjWkWDIYy5E/tDIaXw711szzkuUlkvqWmyc3qPFdUmTBnVizG8O34qcHmdHnXGNruTdZy7w+2FB0bg7OUqxHZTcp67qFSH7qFSzinOnl15npwvrMGtAy1M00lcpBKbdp9nNb54x55LNEaEh0jwUvZtOP1HBZxOsISMWqqwFgharU1x7tw5nDt3Drfccgtee+01nDp1itl27ty5tlgjgdCp8Ryg6UZMCTCsXwQTs3SL9HhqJsR3VzHb3MZQRAnw0fbTiFTL8f5Xp+CkwXnuuEgl0sfEw+l04oNtZ3w8Z5VMxGgpuNucPa+dmZaAfccKsSUvH+OGxUJMCTBvcn8EySnWfv1iQ1jXDwuWYPyIOFisDvB5POQe/MNnKsjcif2w71ghE54BwEzxOF9ci1/+V46Suji1J1FqGSJCZExb9M5DBZjjpc+RlZaAAyeKGGnPrLQEyMUUIkPEiIlQYsdPBYwhbo0mRSBotWf8+OOPAwDKysrQrVs3HDhwgHnNZDLhyJEjrb0EgdCp8aeGxsrm+1Nz44GZyhGsFIOmnRiV1B2lGr3fEEZmWgJTc5yVlsDpOWuNVub67jbnpZnJKCzVAXS99wgA3dQyzBjbB+ogCWIiFMwa5RIKXx/Ix8J7BmDDt+eglFOYOqqXT2nY4TPXWVNBKCEflIBf75VyxNTnTuyH+6cmMvPtotQy/GVSfxSX6QG4nhruHt0b4cESVnu2WCQAJeCzSuAO/nYNi2cOglwqxGuLUmCx2Rts/mgvWm2Mt23bBgC47777sHHjRqYu2GazYd68ea1eIIHQ6WmqbKZn8ogH6M12Hy3fpRnJuKVXOMwWm08IQy4VIqabEv/85gzj/bk9V+8wwaXiWpgtDiTGBqHG4GpzLizVYcfBAp99y+paoMVU/dQPlZTC9Soj+vVUu5KMY+LRu4cKa+rak4F67eInMpPB5/Ox7T/5uFRcCzElwPMLRjDvAVdMffPei7hvSn8szUwGjwdEBEtx7ko1q2svKy0BkWo5dvxUv+awYAkevHsgUxnirubwFENampGM7qEyAGizYaJtQauN8dNPP43Dhw8DcCXe3AgEAkycOLG1pycQugYNZend9cV13qbFZofDCRT8WctZiRAfHcwaVz86ORp8PtCrexA+3HaqXlSeBrbuu+ijU3H/1ESYLHYUl+sQFiKBWCzE/Mn90SNcgSeyklFUqse+Y4XQGWx4aPpA7P3lKnN9z4SXWCRk1rd1fz4ev5d7Jt3VOiOfPiYel4prYbE5IOTzoNFZodGaIeAoP1PKKYhFQqzdUq9D7F0vnJOXjz4xwaz70xlsoFGvueGvmuOdJbejuNzgv+a7HWi1Md6wYQMA4PnnnydToAmE5sIHzl2tYRlLd6txZlo/TuPm1kNIjAtC1oR+yPnhIkYnR6OyxuQj5AMAf5kkwgsP3oqKKhOC5BRKqoxMBUTujy4R+N1HrjITMg7+VoyMuxJgsthhszlw57AYVOss0BlsrISXdxWGP9U1t9aFO9YrpgSw2p34v3/9ylkOBwDjR8Thn9/UazL7EzUymO3YvPcCK7kZJBfVn89Pw4rWZG/+YNgA02bi8sQQEwhNwHPcvNmO61UmxhAD9Qm20cnRKK1r6fVETLmE2bUmG0qrzcj54SKmje4NPp8Hmgay0vohLLheuMbVfWfC6//6Ff/46nf88acWm3Zf8PEwxw2LZV17w7fnYDDZ8dn356E12DB+RJxPwitYwU5MllUZfYTbM+sSam6j7G7oKC7TQSl3nYtL8L2bV/OIe7v3zwIej3kScNIuxTo+Dz7JUO/jzBY7p5FmJRVvMO2uZ0wg3DRwJKqy0wcwHXNuLDYHuqll2HPkChbNSML63Pq62iX3Dsb1Cj2Ky3WICJFhwkhX5YJ3LPX7Oo2J+6cOYLVS+/Mw3V4ry5vk1dftJsQEo3ekgqX9W1FtYtUU/3q2BHMn9sdTc4fCYnPgeoWB0bpYPHMQpGLXBOlNe84zXrg7Ufj94St47r7huFBYXTfiyczylg8cL/LRjVg8Mwk07cCOnwqglFMYPyIOGeP7wWR3QCTkY8bYPhCL+D51xotnJiEsSMLpxbdnqRsxxgTCDYIrUfXJjnOYMbYPo8HgViyTioUYNywGoSoxZoztg7hIJWQSIUuXNzt9IKLC5JxJs6WZySgs0eEaRyt1Q7oQnh6s+/98Hg9hKjFbaN5ow+rNv7kGiI6Jh1TiUkR77dNjTPVD9j0DER1xC4QCAcqqDPho+0XWdbfk5bsmlVid4PMBAZ+HA8eLoDPYsGT2ICzJGIz3t9bHgiUigc9kkwV3D0DG+L4IVUlRVmXErsOXMSO1L4rKtHDSwPf/vYIQpRhPZA6Bw+mEgM/Dd4cK8MjMQc0eDBtoiDEmEG4Q/po/uoXKEKWW+SiSZacPgEopRnJCGKq1FhjMdihkFKCpH8y5rM4L9T6nxerybPl8NOphuj1pdrza9f/FMwchVCWC2e4E3+qAQixgDSe11Dhw4EQRHrh7ADbtPs+UmQHAJ9+eZeQy01PjOdcZHiLDOo94+aIZSVDJRBAKeIjtJsdri1JwuqAS8dHBeP+r330mm1hsDib+HaWWYeadfX01NoR81rbMtARojdZmD4YNNMQYEwg3CHfzh7dXWq0zY/GsQXijzqt0e8iaWjOi1Has+/p3ljccJL+O3j1CAB786uyWaozY8VMB7p+ayIxGcj/KR4TKsGzOEFwr10MsEiAyVIYHpw2AXEKhSmvC4pmDoDNYMX/KLdAZLKjSAT8cuYhbB0QhOlyB+O5K5l6UcgpTRvVCZY3R58skMy0BelP9FxDXOq9XGFhe/frcM3htUZ2imhMwmGywWJ0wmu3QGWys99P7+NHJ0T6DWN1aFd4x+dcWpbSrDgUXN2Q6NIFA4O6yW5qRjOH9wmG2OBhDPGVUL+z4qQA5efn4v38dRdrIOIQFS2CxObD9P5dw++Ae2PFTAbbuy8e6r0/hIa/J0e6kmcXmwOffn0f/niF4aPoAZNyVgNwf/8A7m05gzZcnQQn5EPB5WP3Fb3h74wm88dkxOByuJNjqzb/hvZyTMFsd2Jp3EempffHD0ULweDxo9FbU6C1YsWAEpt3RG1vy8hEWLPMpIduSlw+F1FXZ4G5O8VznwnsGYN+xQtZ75G5IAQ/QW+wQCPno1UMFlVyEp+YOYRKU7tgv63g/lRNOLxVJVxUG27B3BIhnTCDcKBpo/ugWIoWYEnDWxXoK/4xOjmYprJVojNj2n0tYmpkMs9XBEvFxH3+2oBI9wpU+mshcXuM/d5xlruXeJ31MPK6W1GLCyDhU681454sTjPf7yKxBUMoplFQaOA2hxeZg6oDdg0mjIxSoqDZCpRBzzvGz2WmcL6pBeZXJZ7rIwd+K8ZeJ/aGSixGsFHF6y97eN99rOEV7J+r8QTxjAuFGUvdoHBsmZ7QhAJc+xCOzBvlVORNRfGTclYDuHJM7SjRGlFQaUFZlZLQX3IgpAfpEB4FuYPio9zbw2D9HqmWI7aZETIQCn+w4xzLeH247jQfvHgiJSMBZQiYVC9E9XI63HhuFJbMHIyleDZnE5QOu2fwbcvLyseNgAaaM6uVK+qUPhIBPo7jMwDldxP1lJBEJECKnWE8ah05ewyIvjY2lGcmI767y2daRNCncEM+YQOgAaA02bMm7iPun3sLp3UVHuDzbJzKTfV6PUsvQNyYYl4pr8NTcocy0CzElwOMZg1FZa0ZFtbnJXqP3xA13/HnxzEGIjVRgSEI3xmAfOF4Es9WOsGAJE5v2TAz+Ueyanbd4ZhIGx4cCNFClt+KHo4WsZF/e0ULcP/UWfP79//DY7OQGS/AsNgd0RiuigiW+TxpyyvfJA+hQiTp/EGNMIHQAavQufYhPvj2LuRP7sWa2LZ6ZhM+/d3mkZdVGljCQu4LAU3shO30g9CYrTGYHyqtN2JKXz0yL9kywzZ3YD2qPelv3sdv/cwlAffzZrf370fbTPnPgstISoDNaUaunmdKzYIUEMqkQFJ8Hs9WBzLS+0BqsKKowQC6mUGu0cib7SjQGlGiM0Jms4PN4iFLL6lu74fJ83eV2SpkIWrMdKqmQnYRzciflOlKizh/EGBMIHQB3dUJljRk/n/oTy+YMhc3uwJ8VBtToLcxcN5PFgUMnr7E0fd36DUB9yZs77psxPoEpQXMLCnlqAU8b3RsvPHArNLUmiCgBfvqtGPMmJ8Jmc+J6pcEn/ny1pNanpjljfF9s3XcJL2WPxNUSLT7+5gzL4OcdLcI9o3ujsESHzXsvYtmcIZxx8WVzhiBKLYNcQiG+hwqR6n4skaTs9IE4UNetl19U7aqnbmc9ibaExIwJhPaGB4CmsTQzGfdPTcSdw2Kw5svf8Pmu/wEAencPYmKeB44XIW1kHFNNUarhTpx1D5MjY3wCpOL6WK578vKOgwUoLtNjUkpPAMDrn/6KL/ZeQHGZHncMiYbBZINSIeKMP7tHHHley2JzCcyXaoyM5KX7tc17L2L8rbGoNdgYb99UVznifR6r3Yl770rA65/+ikvFtYwhdr/+yY6zuHt0PL4/fAUmq+sca7f+Dq2x41VGtATiGd/kUJQAeovDZ7tMIgS/iYNlCa2Ao0U6Ky0BSjmFyhozcvIuIkotwxNzklFUooeTpiHg87D8/uEoLNUiIYZbIjMkSIzSKiO6q2U+o4oW3jMABpMNTieNz78/z9QKs0MY/Vl6wq5wySBs3XeRtXzPTj2xSMBpZIMVEpRVG+vrjUV8zjWHBUnwyidHWbFh73MVluqgM9ggrktoggcYLI4OGwduDsQY3+SYrXas337aZ/ujswa7uq0IAYWrRTrHo5QNAGwOJ2w2J0t/Yv7k/tj58xUsmq7gFJe//Gctcn/8A1lpCQgLFjOlb2HBEny0zdVS7Q5hpA+L9wkbbN57ARnj+9Yn2WhAIRViwsg4VrnZ3In9sOfIVWSmJaC82shtZEMk0JuszGtuQSHvLkCb3eFzLFfC0d1Vt3Wf64tix8GCLhGuIMaYQGhL3NrEDQmW1+1TqbXA4eSuGuimliEsWILKGjPGj4jDB1+zH9k37r6ApZnJEAr4yPOsTKBdlQmjk6MZw/78/SOw6vN6zYiZd/bFJztcU5/FlMCvF2qxOZkvBDHlEvnZd6z+WnweDz3C5BidHM0MBfU2splpCfho22nMHtcXD067Bf/67n/4/r9XMGtsH9bE6uhwBYLk9R2KXBNMHpo+ECFKCa6V63zCIe0tf9kWEGNMILQVHCEHlsfGMb2DS8tXTNUPA807WoiYbgpOY1lcpoNYJPDxVj0nJltsDlRpzawmke3/uYS/LhwJg8mlplZWxe3Rusve3JoRm3+4wMSd3Twzbxj4fB7GDY8Fn8dDzyglXnjgVlTWmCARCZB78A+UaIz4+JuzeOGBEXgpeyT+d6UKZqsDlJAHm53GsH7hUCtF0JvtTHlcZY0ZeUcL8dTcoTCZ7SitMiJS7dKxmD2ufpSUu3W8K4QriDEmENoIrpAD47HJKZy7WoM/KwysiRXeo+09janOYMNfF7oqFLiMZd/oYHzy7VlMG90bM8b2QXiIFBXVJlYFhFv7wpMSjRFnCiqRk5df12iRhMUzB+Gj7fWVC4/MGoTeUUr0jFLi8p9a1BosnN1uUrGQuR/XwFApS/IzMy0B1ToLKmvMqKwxIyJUhj2/XGUlBvvHhUCtFMFgdSBYQbE8Zk2tqc7Dv4iX40biuXnDYLA4WLoYbu+5s4criDEmENoIf6pserMNtUYb3v/qFKNe5unR0aCx8oEROHO5CqCBI2euM6/VGiwQi/g+Bjs7fSB2H7mC1KEx6BEmR1QYD0WltVAHSRij6X6033bgEmtNYkqAmG5KhAVLUKIx4u2Nx/HGI6Pw2qIUVOksCFKIYLc74KSB7nUSnUo5hXmT+zPC9O41/POb+tbs0cnRjCF237u7lXvHTwWo1VtQXm3CXyYmolpnwq7DrukioUoxzhfVorhcz/qicq91aV2jS7DcNdFaJXN13hWX6zlHKnXWcAUxxgRCG+FPlU1MCXHiYjmznUsu85FZg3Do5DXYHE6fyoZFM5Jw7nIlls0ZiqsltXA6ge3/uYRpo3vD4aRhsDhQVKpjRHPcI4hiuimx81ABZt+VgI+9vNVNu89jyqhejBetM1oRGy5HebUJL338CyvM8vTcoVi9+Tfs+KkA903pj7BgGaRiAZxOmql/BuA39sznA/dPTYTN7vQRwY8OV8DppLF26+9+ZTYra0xYsWCEawoHjweVVIjE2CC/1Ruec/o6E8QYEwhthFuVzTtmbDDb4Kwr/zpwvAjZ9yT5iPZ8uO10nbHV+nh763PPYMUDI5gqCMAVK7XZaVb78dyJ/fDdz5eZmO7TfxmGwQndsOfIFTyROcQluO4EY4A9vVa5hGowzOJuJ5ZLKFisdihkLqEdri8f759juilRqjEwusPuc+fk5bvOq29YZjM6Qukz3TkxNqhDTutoDaTpg0BoKzxU2V7OHom3Hh2FxNggBCvEOHTyGjLTEqAz2Dinb1hsDlwr1yE8WMr52vkrVYyUJgBMTunJGGL3Ppv3XsTkukYOMSXAtXI9cvIu4lJxLa6WapGTl4+t+/NZHXWRahmWzR0KmudEpdbC7ZnqLFDJKRhMdqxcfwQrP/4Fz637LyqqTSyhnjOXyvHI7EEsUZ7FM5Owafd5pjHE+9w1BivzROFPZvOTHWd8viC0RptfSdKOKALUFIhnTCC0JRyC5SqpEPMmJWLTnvPISktA7x5BnB6d3UGjzE+trtMJlpRmsELit8HCs/rBjcRPo0W1zoyv9udj3uT+4PHY+4QFSzB+RBy0eiuKyo3Y+XMByyiu3vwbVi+9A68tSoHWaIXeZEfOD/WTmvvGhOCHX64gbWQcrDanXy9WJRXi6blDUXBdCx4PeCJzCGjQUMlF0Bqs7FAI2KGIjjatozUQz5hAaA1e057B49inzmNemjUECpkIH20/7eMBPnbvYATJKcaD9nzNUyyeX/cXq5BRnJKVoUFiLJszFGarjVX9wAPPZwJzVloC+DweJoyMQ3mVCRt3/Y+5dliwBFNH9ULuj39g7dbfserzY0gb2RP3T01kvHOlnELBn1qsXH8EFwtr8MHXp1CiMWLr/nzk5OXj7Y3HkZ7aF726q9A3JhjZXiL4j907mPFirXXx5M+/P4/3tpxEZY0JH3x9CkIBn/M+mVCEH0nSzgjxjAmEltJYXbHv7sxYILdoD58PJCeEQy4V4uwfGqQOjQGPB6x4YATOX6lixXjFlACD+oTDSQNBChFnF5tULMS6r08AAKtpwmJzYN+xIlZzyPeHr2Dc8FiW2JB7XT2jlHjPS4Bofe4ZzBjbB9Pu6I3vfr6M8SPi6vUj/CTv9CYrFFIKV0u0+O7QZeaee0YFIUjhaojRmnxj1W5R+892nvOZ7tzeg0MDBTHGBEILabCu2DObX2e0iz1ixZ7NE/3jQhChEiMmQoEe4KGyxgSzxYEQpQT/+u4cywgppELsOFiA2AgFxCIBqyZXLBKgssbExITzjhZiaWYyist0GNw3HLk/FrAaNty6EmYP4R73utyt0p5YbC4x+s17LyIzLQHdQmWNJu8EfD427vofbkvqgXtG94ZKIYbZ4kCJRo/u6nAA/ksCwXPVREvFAryUfRusNrtvKKIpHY+dBGKMCYQW4s+IeJdWuY12emq8/+w/zyW67jkp+aHpA/FEVjKClWLYbA4EycVQSARYNmconDSN7w5dZvR+nU7Xz/Mn38KcN21kHD7deQ6VNWYk9gzxqfRwT4Weensvn3XxeTzOtYaqXDoXABAaJEFiXDDOF9bgwPEiH0GizLQEfLLjDO6fOgBlVQaIRRTjgYspASKCZa4Ep5+SQLcAUVGpHv/YegpLM5IRGy5nGeLmPJl0dIgxJhBaiD8j4l1a5TbaXHoLj8waBL6Aj+tVZsYQA3Xz6L45W1d69jsy0xKQd7QQ8yYlYtSg7rhWrsW94xOw3sP4LZ6ZhKgwGeZP7o9ItRzl1UaMGx6LQyevIUgmQkyYnEl2ySQUbA4HorsNQqRaimCFmGVIY7rJMX9yf2zc7Vk61x9KmYhlUBfNSAJwFecLa0AJ+awwiDu8YrW7ZDM37/WtinB3J3IZ8ryjhSxxe++njiY/mXQSiDEmEFqIv7pi73imp3D8kTPX8fRfhsFgsqG0yohNe85DZ7Dhiawhfh/VPTvZ1m79HaFBEkhFAiT3CcVri1Kg0ZqhVkmgVrm+BEJVEpbBXDwzCSo55ZqCIadwpVSHVZ8fZ15/7r7h2Lovn2VIN+2+gAkj41jbnE6nj5D9+twzWLFgBGx2JxQyCv/Yesrny+l6hcHvGKUagxUAmOuLKD6iwuSorDFi3uRExrP33N9taJv6ZNJZIMaYQGgpDUx7BsDEM/UmGzMheUhCNxTUyVuyDQnt91EdcFUuxEUpkZ4aj2qdGWt3nce8SYlIjA2CWuEKc2gNLiW4sioTlHIKlhpXLPij7WcYb1GjtbKmS1tsDuQXVTNVEJ5YbA7Wtqw07jhyZa0ZapUYEUFiHw930Ywk7D5yBdNT+yArLcFnEnSwXMSMnPK+flZago+4vedTR1OfTDoLxBgTCK2Bo64YgE88M0otw4oFI2Cy2HH5utbHqHFp/Lof0d1lZp7e7sJ7BmDnzwXoMT0JKhnlEzt1H1tZ41Jsq9RaUKO3MhMyPHF3B3obtZ5R9fXQYkqAfrHcQvZSkdBlAJ3A4Ph6bz1YKYZQyMOopO6stbtDEBnjE1weu5/4tPf1vZ86VDKhj/H3fArobJA6YwIhAHjHM0s0Rrzx2TGoVRImOebJD78UQh0kwYyxfTBvUn8smzMUeUcLGT1j77H1G749h/TUvtCbbZyx0y15+S6xIbgM2x/XavHyhqOg63725NDJa1jsNeJ+yb2DEaQQ4pWHb8P8yf2RPiYe3//3Mh6azq4Vfih9IGRigctI1nnnBpMNkaEyRKjEEILns/YtefmYNzkRW/flQ2vg7qSbO7EfqrUmPJGZjBceuJXpZvQM/2gNNia8kTE+wdUQU3fOzgjxjBvAyePBaLYzP1vKdbBZXR8qmqu4n0Cow18802KzI767yscLnnJ7LxhMNsR2U4Cqq1p4PCMZJovdrwD91ZJaJCeEo8bgvzTMs2oCAHJ/vISH0gfinx51uzPv7Iv/nvqTqQEe1i8CaqWI0WDWhMoZY28w2fBS9kjUGqwIkouhlAkhF7mMKFdlg1wq5FxbYakOJRojKrUWVifdnxojisv0+O7ny9AZbFiakYz4SIVrLV4VEv7CGyRm3ELmz5+PqqoqCIWupbz66qswGAxYtWoVLBYLJk+ejGXLlrXL2oxmOz7Ydor5maKEsNlcxnnRzEHtsiZC58Adz1TKKUYOk8/jQSUToXuoEN1CpegbGwK9yYbrFXrs+KkAOoMNT88dCqvVgRUfHGaM2vP3j/DbIn3usgZ9ooM5Xx/UJwxJ8eq6jjZX7PVScS2Aq3hx4UjojFaoZCJs2v0/nC+swZmCKizNSK43xIDfuHhkkKT+ZusaNzbtOV+f8AOwac95PDVnaINlaxKxkDmHSkpBFROEHmoZ+scFN9reTGLGbQhN07h69Sr+85//MMbYbDZj0qRJ2LhxI6KiorBo0SIcPHgQqamp7blUAqFZuPUWrlXoWR5wTIQCibFBUIiFUIiFQIgEkcES9O6uYozIc3WGGHBXLJzGo7MHMaOXPOOuo5Oj8dH200yC0LOG2FQ35dlbFL6oVA+rzQGVTISoUAkWTU+qN7RyClqDbxMFZ1zcA73J5iMLmpmWALvT4VNx4l57VlqC6/yeNOFanu9xU6pZOgvtaowvX74MAHjwwQdRU1ODjIwMJCQkIC4uDjExMQCAadOmYc+ePcQYEzoXNBAeLMHqzWypTJ86WC/jU1Rh8HmsL9EYEREiwfL7h+NiYTWcTld3XdrIOCZJp5LXT8hwtzrrDDa8tjiFs226sMSl4uZuklBJXTHf84Uta6IQi4Q+0p9b8vLx2qIUxrMuqTKBpoGyagNSh8YgOlwBhaQVHXONVbN0MtrVGGu1WqSkpODFF1+EzWbDfffdh+zsbISHhzP7REREoKysrB1XeXNCUQLoLQ6f7TKJEHy6k37aA4WfltyW1MH6e/QWCgT4OPcU5k1ORHGZjhkC6i4REwgEyMm76HM+g8mG6HAFq206PEQCoUCAh6cngcfnwWB1QE4JfBKBSjmF4nI9xCIBwoIkDbYaG8w2/2VvyroJHdEuzWSZRIBItQIiPt08w+nnfW6qJ93RaVdjPGTIEAwZMoT5efbs2Vi7di2GDRvGbKNpGjxe87JlarWiTdZnKdeBothvkftnHo/n81pbbm/Lc1GUsNnHuDP23jw6exCiI5Sc621LwsMDf42W4P3ZcjppHDlTwojFiylXu3JKUhSsNHfJVqRagfBw7s+o2klj2ZyhrPNlpSWgstYEm8OJT3ee85kEsmzOUIQHS/1eKypMjh7dlCgq1aJaa4bZ6sQ/v6kPeSyamYQ7h8agtFbDHB8WLOG8TkpSFPh8379Hf/daWKpDr+5B6BHhut9wnyObRkPvM9d6biRt9VltV2N8/Phx2Gw2pKSkAHAZ3h49eqCiooLZp6KiAhEREc06r0ajh9PZ+q9Im9XBJOwAdgKPpmnWa27aantbncu95rZal83qQEWFjnO9bUV4uDLg12js+v7w/mxpTTafqR1rvvwNkSGjmFlt3o/9Ij7d4P11V0s5Qw4zxvZBTt5FluIbU/kAmrmWUk5h/Ig4xHRTwGyxQVOlh4QPRIbK4HQC7205yY5Jbz+DmHAFZGIhY1DHDYv1CTsw98Xh1Yv4wCOzBjEqbp61zv3jgiHisf8em/s7bvB9bsfKiebeR0OfrXY1xjqdDmvXrkVOTg5sNhtyc3Pxyiuv4Mknn0RhYSGio6Oxc+dOzJo1qz2XSSD4xV8oorTaBPB4SIxrfkyzSmvhDDnEdFMwbdU7firwqXxIjA3CO0tux+USHcso1seFhfgT3GVyGq0ZCT1UjEH3J4npN8RCA727q1hfIu4J121R3dDVWp+5aFdjfOedd+LUqVOYPn06nE4n5s6diyFDhuDNN9/E448/DovFgtTUVEyaNKk9l0nwwF8sGbg548n+YryX/9RizZcn2QkyoEkxTX/n7KGW4a1HR8Foc0BGCXwNO+16nGc0huGbNIwI4Q5nqFUSVkLMYHFgx8ECn/0aMqwKsataJBDVDV2tjI2Ldq8zfvLJJ/Hkk0+ytqWkpODbb79tnwURGsRstWP99tOcrz06azAUYgHna10VrvKqhpTGWnpOT6PWKzoEV65Vo6jc4KPh25gHqVaKOFuI1SpXOzOTEJNRzCgkoYCH7uEKUAIewOMxwkE+BLC6oauVsXHR7saYQOjUeBig0moTLv+pZaocgBY+Sjdk1HjwSWR5lp816kF66Ucwam8cWg7uUUieXzL/+u4cI1DkzyAHpLqhi5WxcUG0KQiE1lJngCJDZdjxU0GDSmPNPaf3bDet0TeR5Z6WDNR7kJ46Dz4Tk52AWiFCQneVS/GNwxD707sYnRzNut4NpQvNu+OCeMYEQhtxIx6lG01ktZEH2dAopK6WOOsoEGNMILQVN+BRukmJrDYIFTQ2CqkrJc46CiRMQSC0JQF+lFZJhVhWJ74D+AlDtNF1vMMdmWkJOPT7tYBcj0A8Y8INwFuK1M3NWArXamggJSkKkSEBTmR5eflyCQWLzY6hfYd1ucRZR4EYY0LA8ZYidXMzlsK1BXw+jx2GgKtDrcnj6ps63t473AERs53Q9hBjTGgz/DWEECH+ANLccfVdbLx9V4IYY0Kb4a8hhAjxB47mjqvvauPtuxLEGMN/TJN4dIHFnydN6SztsJobRFNDBE2kuZoNN4PGQ2eFGGP4j2kSjy6w+POkn5gzFOKu+EUYgBBBczUbbgaNh84KKW0jEG4Q/kIErelma1LHXSv2J9w4iGdMINwgAhIiaG6jyU2g8dBZuamMMYkNE9qTgIUImttx14VGFXUlbipjTGLDhPbkZpCBJLScm8oYEwjtCgkREBqAGGMC4UZCQgQEPxBjTOhUNFfnwt/+DR1DILQHXdIY+x3dTRJ1nQZ/v0O9P52L2YOhkvh+nP3t39AxLVnXjaajrKM1dIV7ANruPng0TVwDAoFAaG9I0weBQCB0AIgxJhAIhA4AMcYEAoHQASDGmEAgEDoAxBgTCARCB6BLlrZpNHo4nW1fJBISIkN1tbHNzxtIyJqbT3i40u9rgfpsNYf2fn/agq5wD0Dz76OhzxbxjJuBUNj55rWRNXc9usL70xXuAWjb+yDGmEAgEDoAxBgTOg4815TjM39UQGu2k45Jwk1Fl4wZEzohZGox4SaHeMaEDkEgRhIRCJ0JYowJHYKGRhIRCDcDJExxs9PGo+NbCplaHFgUSgmoJmT+bXYH9DrzDVgRwRtijG9mOlCclowkCiyUUIDn1x1qdL9Vj42+AashcBFQY/zee+9h79694PF4mD17Nh544AE8//zzOHHiBKRSKQBgyZIlSEtLYx13/fp1PPPMM9BoNOjVqxdWr14NuVweyKXelPiL07716KiWTytuKR4jiYw2B2SUgBhiwk1FwIzxr7/+il9++QXffvst7HY7pkyZgtTUVJw9exabNm1CRESE32NfeeUVzJ07F1OnTsW6devwwQcf4JlnngnUUm9aAjI6vjXUjSSKjw1FRYWOGGLCTUXAEni33nor/v3vf0MoFEKj0cDhcEAikeD69etYsWIFpk2bhrVr18LpdLKOs9lsOHbsGCZOnAgAmDlzJvbs2ROoZd7UuOO0npA4LYHQPgS0moKiKKxduxZTp05FSkoK7HY7brvtNrzxxhvYunUrjh8/jq+//pp1THV1NRQKBYRCl9MeHh6OsrKyQC7zpsUdp3UbZFaclkAg3FACnsBbunQpHnroISxevBhHjhzBunXrmNfmz5+Pb775BhkZGcw2mqbB47Fbr7x/bgy1WtG6RTdAQ0IfHRGnk4aV5qFKa0KoSoqoMDlrZpc6VIH46GBU6UwIVfq+3l501Pc5kJ+t5tDc98fucIKimvDnzrtx731H/R03l7a6j4AZ44KCAlitViQmJkIqlWLChAnYtWsXgoODmRAETdOMB+wmNDQUOp0ODocDAoEAFRUVDcaXuQiUslZ4uNIVy+ws8ID8P3VY8+VvDVZLiHhApEoCgIZGo2+35bpp7/e5o6u2teT9CQmRw2bjnpLNgsYNee/b+3fcVjT3PtpFte3atWtYuXIlrFYrrFYr9u/fjxEjRuCNN95AbW0tbDYbtmzZ4lNJQVEUhg8fjl27dgEAvvnmG4wZMyZQy+zSaI02xhAD7djVVqc5UVRhIJoTBIIfAuYZp6am4vTp05g+fToEAgEmTJiAJUuWICQkBHPmzIHdbseECRNw9913AwBeeOEFjBs3DnfddRdeeuklLF++HB9++CGioqLw7rvvBmqZXZoOUS3RgWqZCYSODI+m6S73J0HCFC60ZjueW/dfn662G1JHXNfZp7c48H8bjjZrDe39PnfVMEVTmz6qqw0tXVqTae/fcVvRKcIUhPZHJRVi2ZyhN75aos4bfu6Dwzj9RyXRnCAQmgBph+7K0EBKUhQiQ0ahxmBFsFx0Q7ravDv7iOYEgdA4xDPuyvCAUo0BeosDdgcNNLNEsLnXcifpDBYHlHKX933geBEy0xJILTOB0AjEM+6q8ICC6zpcq9AjJy8/sMkzjiRdVloCvj98BZU1Zuw6fAUzxvZBn+gghKnERHOCQOCAeMZdFK3RhoLrWsYQA4ErbeMSHMrJy8f4EXEAAJ3BhpgIBXpHKlxJO2KICQQfiGfcRanRW+Gk6RtS2uavhK5PdBBezh55w2LVBEJnhhjjLkqwUgw+j3dDkmf+hOHDVOJ6o08MMYHQICRM0dloYjebSipEfHcVspqaPGvKef3sQwSHCITWQzzjzkRzutloIL67ErFRKvSNDYHZYvefPGvKeRvZxy0MfyNL6AiErgTxjDsRzZ6gTAPdwxXoHixB725eyTMPL1ejszZ63kavXScMHxsmr78O0aQgEJoM8Yw7EW2mNeHl5WalJTR63mZfuyWaFHUt1KV/VEAmFrbbcNTORFMHjZIvwo4PMcadiAYnKDdjyrO3l+ukubvkhEIBtGY7VFJh86Y38+DX2/arSUEEhVpEkweNLiGDRjs6JEzRifCbKJNTjBbEyxuO4rl1/8X5olq/3pC3l8vVJZeVloC/5/zGnEsla2KSrs6onrhY3ixNimaHYAiELgbxjDsybm/XYIVcQsFitSM2SoHXFqVAozVDrZJArRJBa6g3ZGHBEowbFovich0iQqRQh9I+56MoAcvLrawxI+9oIV5blIJagxV/XKtluucAMB5tU5J0bqOanhrfrLK6DiH3SSC0I8QYd1Q4HtvnTuwHUTkfn31/nvUoHySnGEM8ZVQvbKnrusv9sQDL5gxFQg+XbJ/7fEo5hay0BFab9LxJiVArRTCYbMjJu8haiqdRdP8HgDN84Daqbm97i1crtr8qi2aFQQiELggxxh0Ursf2zXsvYsbYPj6P8q8tSoGYEmDcsFjG+LlfX/Plb3jr0VEAwJzPUuPA9370IlprFN3HuzUp0sfEg88HhvWLgFop8hv/dYdgvGPGpESOcLMQ0Jjxe++9hylTpmDq1Kn49NNPAQBbtmzB3XffjWnTpuH555+H1eobQ8zNzcUdd9yB9PR0pKenY82aNYFcZofE32O702sWgMXmgMFsw9KMZPD54DympNrEUlIDXKGJnLyLEAp4jKerNdmgN1qx5N7BLW7g8IxrV9aYseOnAsREKBs0xABYtcpvPDqKCYsQQ0y4WQiYZ/zrr7/il19+wbfffgu73Y4pU6YgNTUVGzZswPbt2yGXy7F8+XJs3rwZCxYsYB179uxZLF++nBnJdDPiz0Ple8lgiikBeDweEuOCEBIkQe6PBT7HXPlTix0/FbCU1NyvuSsxPEMiUWoZViwYAZqmm9/A0ZoGkLpa5fjYUNf0BGKICTcRAfOMb731Vvz73/+GUCiERqOBw+GAWCzGSy+9BIVCAR6Ph4SEBFy/ft3n2DNnziA3NxfTpk3D008/jdra2kAts8Oikgnx/P0jkJWWgIzxCYhSyzB3Yj90D5OxvNbMtAR8tP00tAYbKAEP2ekDWa/PndgPB04U+SipeXq83iGREo0Rb3x2zGVIW6KyxtUAQiAQGiSgMWOKorB27Vr861//wqRJk9C9e3f06NEDAFBVVYUvvvgCq1at8jkuPDwcDz74IIYOHYp3330Xr776Kv72t78FcqkdCx5wvpCdvFt4zwDk/VqIUYO6I31MvKtsjQZ21Xm6f2qMWLvFlZybMbYPuoXKUK0zgxLWf9/6KKnJKWgNNpRWm0glA4HQzgQ8gbd06VI89NBDWLx4MbZu3YrMzEyUlZUhOzsbs2bNwsiRI32OWbduHfPv7OxspKWlNeuaarWi1ev2R0MDBduKP8v1Psm7Dd+eQ/qYeFisTuz4yTcUUVymZ5JzOXkXIaYESB8Tjy15+UgfE4+t+/MhpgSI6aYETQOaWhO0Jjs+3HYKo4dEc4ZEItUKhIe3/r10OmmUVBpQpTUhVCVFVJgcfH7DLWE34n1uCYH8bDUH9/tjdzhBUU37M27Sfrwb99531N9xc2mr+wiYMS4oKIDVakViYiKkUikmTJiAixcvoqCgANnZ2Zg/fz4efPBBn+N0Oh22bdvGxJFpmoZA0IR2Tw86+3To0koDp6fK5wPx3VU+VQeLZw7CF3vP++wPXv3/xZQAT88dij+Ka1jHZqYl4MiZ65xlaCI+3fr7bUFnXXtPDu5M06FDQuSw2exNOq5J+9G4Ie99e/+O24q2nA4dMGN87do1rF27Fl9++SUAYP/+/bjnnnuwcOFCPPnkk5g+fTrncTKZDJ988gmGDBmCwYMHY9OmTc32jAOOd+uxTAitoZFW5Ga0K/tL3vWMCkKwSgy73YHXFqegvNqE4jI9avRm6Aw2puEDPNRpGfMRpZYhuW84EmKCESQXYeX6IyyPO+9oIeZNTkRJpQFPZCZDLBJArRQzIYymrLch/HXW+W2LJhBuUgJmjFNTU3H69GlMnz4dAoEAEyZMQE1NDSorK/Hpp58ypW7jxo3DE088gRdeeAHjxo3DXXfdhb///e94+eWXYTab0bNnT7z99tuBWmbz4ag8yBifgI+2n2mx/KQ3KqkQj84ehA++Ps2aKVelNaGsSg+H07Wf25MNC5bg/qmJsFgdrEaO+ZP7Y97kRLzyyS+cgkBhwRKkjYzD2i3161py72D0ilT4xKyZ9aLpXyoA6azrqjRZoAiAze6AXmcO8Io6PwGNGT/++ON4/PHHWdu8y9jcvP7668y/hw8fjtzc3EAurcV4e3qjk6MZQwxwe37+vMMXF46EQsKhTkYD0eEKzBjbx1VXTAPfH74CncGGpZnJWLvldzw8PYnVzmyy2LF13yXWNTbuvsBqEvEWBOJqEnn/q1N4bVEK53rfWXI7issNzQo5kM66rklTBYoAYNVjRKSoKRChoGbi4+nxuBstPAVx/HmHp/+o9CvqozNakZN3EVv35WPr/nxU1phdDRx18WSpRMCUsLnO52y0ScRbEMhfk0h5DXd1hdZkb7aYT7OmgNTpH5/5o4LoH7cTTppGSIi80f/I76btIe3QzcSfp9eQ5+fvGND+veRgBfcxVpsTYkqAMo2RpS/hnnenlFOsuLFUXG+w3YJAT2QOwdVSLWIjVZzX4IF7dp7ZYm9+yKGpTSBEQrNDwOfz8Pz7RJKzPSCecUN4TapwOmkfT+/QyWtYPDOpQc+PyzvMTEvAgRNFALi9ZPcxUWoZMu5KQFZaAp6dPwyxkXL8NXskuqlliI8OwhuPjsJri27D7YMi8dScIZg6qhd2/FSArfvykfvjHwhWipEYF8xcd8LIOPxr51ls3ZePf3131mdG3iOzBiH34CUfSc1HZg1CWJCE5Y27X2s05NCEJhAioUm42SGesT84PDW3ApqPpyenGvb8PLzDSp0FfxTXMs0agK+X7I43J/YMwpyJ/bHuq1NMsnD2uL549ZOjrMRedLgCJrMRf1YafGLAH3x9GsvvH46rJVpEquXY8O1Z5rqVNWZ8f/gKlmYmo7hMh8SeoQhRilFUqke17grTXMLn8dA7SgmFJHBiPiTRR7jZIcbYD1yemlsBzUdK0gluaUmfEjgKKhkFi8UBncHl8YkpAe6fmgiTxY6M8QmQigUwWBworTYhSCHGl3svsJKFH39zlrWmnLx8zBjbx7UMmuY0aBcLqxHbTQmZWIjwoPryN8Dl2ReW6JimkHeW3M4YXPe2pRnJUEiEAR08ShJ9bBqqVrA7nK64LUBit10IYoz90GpPrYEYaGJsEF5cOBJlVUbojVbY7E5s3XcJSjmFqaN64dUN9Z5vZlpCvRftJ1nomaTjMmhOJ3C1VIcdBwvw8IwkfL0/HyUaI8SUANnpA/HD0avMuap0loYNLu3ni6eVEAlNNg1VK1CUkGngILHbrgOJGfvB7al50tDMN+8pyA3GQGlAIRHi6/35iI5QwmRxID01Hg/ePQC/nitB+ph4ZIxPQHpqPPKOFro8WY81eK+Jz+NBIhJAKhL4xK8z0xJw6PdrTBjk49wzGJ0czazpkx1nMSShG/v+2kPoh0hoEm5yiGfsBy5Pbdmcob6emh8PWC4VNljyVmOw4rF7B6NUY0Tuj38wx2anD8T2/1xiPNfMtAQI6r4yz1wqx6IZSVifW99g8lD6QIQGiWEw2vD+16cRG6nAsjlDcbWkFk4nkHe0EGkj47Dr8BVmDZ6Ptp7t0m3miTaj25AFkdAk3MQQY+wPjvhor+gQaDR61m7+PGD39A2fsjEeD899cLiuI64fY4jdx36y4ywj7GOxObAlLx8vZY/E0oxkRITKsO6r31mqbdv+cwlPzR2Kt/59AhabA5eKa/HJt2cwfkQc4qIU4PNjOJOFnmsa1CcMowZ0azNDTErUCITmQ8IUDeH1uM6lNFaptXB6wLUcEzMenT0IH20/7dERx51w8/ZctQYrSjVGXCysQonGiK3785lmkBKNEZpaM+s87ikeJZUGiCg+K1m4eGaSK2yBem+4e4ikzcIRpESNQGgZxDNuJVKxkOUBhwVLMH5EHGp1VuhNVqx84FboTTaUVxshFgkxekg0JCI+eOChe7jCbzOI589yKYVbeoVALqE491fJ/TeInPmjHM/fPwKFpbXoHqaAkwYW3D0ASjkFsVAAk9kGrcneYiEgb0iJGsEbd1efJ6yKkDpudg0LYoxbAw+gQTOdcO5qCE+xnqy0BMgkQjicNNZs/o3ZvmBqImp0Zp8pzQ9PH4ivD1wCAOb4IBkFhVgI8OATx85KS8CnO8/6nCczLQF5RwvxyKzB+HDbKUwb3RvXyvWsfeZO7I/vfi5waV60USiBlKgRvOHq6vOsCHFzs2tYEGPcCrRGG9Z9dQqz7+qLJzKHQCYVYtVnx6CUU0gfFl9XiuZE31gl/q+uXA2oC2MYbMj98Q/Xvh7NFVFhcqQOjYGTpsHn8RAdrmBqfMEDuoe7OvB0BitkEgrFpVoM6dcNh89cZ6Y9B8lFsDsdSOqtRpXOgnmTE1FcpveJT2/ee4GJT7eVrCUpUSMQWgYxxq2gRm+FzeGE3mjDx7lnkZ4aD6WcwpRRvVhC7T0i5D6P7u54saXGga3785ntcZHDuS/GB84X1aC8yoQf6ioktnh5wrsOX0H/uMFQK0WsJFpWWgKcNHeNsjs+3WahhAA2hhAIXRlijFtBsFKM8SPisHnvRcbQjR8R59OS/Ge5wefR3S3s4/04LxYJ0Ku7CmKKD5PVAZ3JBo3eCj6Ph+IyA3J//IMZp+R5jS11nXjBcpFPEs1J+7+e20i2aSiBqzGkpeVuBMJNAqmmaAUqqRAx3RSMgTtwvAjdQmU+Hui+Y4VYeM8AVmVF9zAp5k7sx9qWlZaAD7adwmc7z6Gyxoy/f3kSa7f+jpUfHUFxuR5iEZ/xZrm83JhuCqhklE8S7cDxIqjklI8o0NyJ/XHgRFHDspZtQV2523MfHMbLG476lQ0lEG5miGfcGmigR5ic8Tgra8yo1pl8PFCdwQaDycaKDfcIVyJUZcNri1NgMNvAAw+1eguy7xkIu92JaxUGKOUULDUOWGwOfLjtNJ6ZNwzPzhsOiVjA6eX2UMtc8ptKMaLUMlennYcGxaw7E7A0MxlmiwNGiw1OJ43FMwYhTCVmDLHeYofWZIfJbINcSkEhpaAQC1rlxZLRSwRC4zRqjHfs2IH09HRmTJI3DzzwgN9j33vvPezduxc8Hg+zZ8/GAw88gMOHD2PVqlWwWCyYPHkyli1b5nPc9evX8cwzz0Cj0aBXr15YvXo15HI5xxXaH++EVd7RIiyemcQaw7R4ZhK27qvXg1iakYwQOYUQOYWC6zpcq9D7VEIc/K0YU0b1Yho2LDaXUV675Xco67zcHK8Bou7YrEom9BkF9dD0gag1WvBx7lnWMb0jFYyh5VrL3In90V0tQ3x3ZYsNMil3IxAap1FjXFhYCADIz89vZE82v/76K3755Rd8++23sNvtmDJlClJSUrBixQps3LgRUVFRWLRoEQ4ePIjU1FTWsa+88grmzp2LqVOnYt26dfjggw/wzDPPNOv6NwyuhJW3pKacQq/IYczPfD4PReUGyKUUCq5rfaoctuTlM3Fhd7WDW/TdnfT7/vAVzBjbB/HRQQh3e7Z1xlJrsPmMgvrnN2eRMb4v0sfEo3cPFSJDpOxjjDbOtWze6xrdFB4sabHhJOVuBELjNGqMly5dCgBYtWpVs05866234t///jeEQiHKysrgcDig1WoRFxeHmJgYAMC0adOwZ88eljG22Ww4duwY1q1bBwCYOXMm5s2b17GMMQ/Qm12P82aLHWFBEqjkLkNVo7cCPJ5LLlNKuRJXHlOWK6pNWF1Xb9xYlYP7/1FqGe6fOoCR2TxwvIjpslt+33Cf7jl/nqjF5sSOnwrqwwNex/jrCBQKeK3yYkm5G4HQOI0a4/nz54PH859p+fe//+33NYqisHbtWvzrX//CpEmTUF5ejvDwcOb1iIgIlJWVsY6prq6GQqGAUOhaWnh4uM8+7QrP93Gea0L0knsHo1uoBKVVZkYc3p2kc8eCRRQfVhvtt8pBTAnQu0cQwoIkWPPlb6wwxq66AaVqlcSnUsGfJ8rn8fwawWCl2G/FRXSEEqFKccvfM1LuRiA0SqPGeN68eQCAvLw86PV6zJo1CwKBADt27IBKpWr0AkuXLsVDDz2ExYsX4+rVqyzDTtO0j6Hn2tbQlwEXarWiWfs3B6uTh+saAyw2J9JT4wEAUpHAJyzw/len8MIDtzKG2L09Jy8fGeP7YuPuC+CBhyC50G/3XFZaAko8jL77HHlHC/Hg3QPhpGkIhXxc/LMW//7+PEYnR4PPBwb2DsOz84fj7Y3HmXM+OnswghQUxJQQdvBgd9Ko0ZkRopSAz+fBYnegd3cVstMH4pMdZ1lr+fz7c3hx4W0ID1e26r0Lb3yX+n1bea1AEcjPlid2hxMU5f/P0/O1hvbzd8yN3M/fvj7beB33994QbbXmRt/NiRMnAgA2bNiAnJwc8PmuarixY8ciMzPT73EFBQWwWq1ITEyEVCrFhAkTsGfPHggE9Xq8FRUViIiIYB0XGhoKnU4Hh8MBgUDAuU9jaDR6OJ1t73aFhytRWW2AVCxkaovFlAAL7xnAeLtuXNUV3FOWQ1VShAVL4HDS2PbjH5g6qheWZibDZnNCIhZAKORj4T0DUVZlQJBCwjpHWLAEaSPj8N6Wk6zW6kkpPT3WVIAl9w7GO0tuh85sh93uxEfbT6NEY0SUWoZZd/bFPz0MblZaAr4/fAWUgI/s9IEsVTh3ArG8ygDJDSqEDA9XuiQ024mG/rgC9dnyJiRE7tMu7Ma7ldjfft60135c+3K1Q4NGu/7eW0JzP6sNfbaa/OdVXV0Ni8XC/GwwGFBbW+t3/2vXrmHlypWwWq2wWq3Yv38/srKycOXKFRQWFsLhcGDnzp0YM2YM6ziKojB8+HDs2rULAPDNN9/47NOeiEVCbPj2HMtT3fDtOYwfEcfer66Bg0sMvqzKiCezhuKW3qHQGWz4fNd5vLPpBP6+5STWfHkSQTIRKCEfOqMd18r1rHOMGxbr0/BRa7CxGk/cnrnTSUOtkuKNz46hRGME4Brd5DbE7n1z8vIxblgsSjRGXCquZQaabt2fj8oac7NE9QkEQsto8nPG3XffjYyMDKSlpYGmaezZswcZGRl+909NTcXp06cxffp0CAQCTJgwAVOnTkVoaCgef/xxWCwWpKamYtKkSQCAF154AePGjcNdd92Fl156CcuXL8eHH36IqKgovPvuu62/0zbCYLZxers9wuvrjcWUAItmJGHnoQKfx/65E/vB6aRRXmVEWLAED067Bf/67n+s1w0WG2K7yaE32fDpznPITEtgDLBcKqj3XOFq6BAKeJxr0ugskFpdJXFhwRJMHdULERxNKRabA91CXXFvsYiPR2cPwgdfn2442cahW7zk3sEY0DMYcAbgjScQujhNNsZPPPEEBgwYgF9++QUAsHz5cqYK4urVq+jZs6fPMY8//jgef/xx1raUlBR8++23Pvu+/vrrzL979OiBjRs3NnVpN5RgBXdyrEZvYTV1BCvFGD0kGqFBEswY2wdOmoaY4oMS8vH59+dZKm1zJ/aDwWwHn8eDSMhHYYkO1VoLenVXQWewYddh16RmqUQAlVyCzXtPs2K6/qQ4ZRIKoSoJotQyTBvdGxarA8Vles59K2pM2LrPVUL31JwhjSbbuBo53v/qFFYsGIGYMBlJzhEIzaRZUcDx48dj5cqVWLlyJascjatxo8vg8Sj+Z7keKpmrTMuzrXjxzCQcPvUnAIDPA/rGBGHvkSvoGaUCnweIKD4OnbyGSLUcWoMV6anxCAt2xYK/PnAJPaNU6BYig4ji4dtDl2F3OPHR9jOw2Z3ISkuAzmBzTf6wOlni9O6aZKGAh0yvVufMtASs3XISV65r8di9g6E12JCTl499xwp99s1KS8C+Y4XMOd/98iQANDgDz1/53PmrVURInkBoAW3SDk3TXdQN8jdCKI5dpmWnnZhwWy/GULpn2fF4NERCAY6dK0HG+ASs3fI7y6M9cuY6UpK6443PjrHDGHX1vhU1Jnxf5xWLKD56hCuYkMO4YbFMqEImESLvaCGWZiajuEwHp7M+8bbmy9/w4sKRLJU4t6cNHjCwdyjWbv2dGcsENK07zl/5nNMJ0llHaBFcIvRcdFUR+jYxxs0tPesseD6Kuw1gaZUBYcES2GwOBCtc6mPlWgvLY1XKKWhqzQhSiBCiFGPWnQl454sTPh7t0sxkxkC7t2/eexErFoxAYlwwQpUSTE7piSi1HGXVRsgkQkSpZT7ymdERCjx49wAUXK9FTh67U9Jic8BstbNqiCtrzNi6Px9RahmG9gvH+BGxcNJgmkma0h2nkgqx5N7BeN+jhtpdkjdqYLcA/DYIXR0uEXouuqoIPREKagD3o3hYsMRHo9hteOZNSoRQWJ9A49r38Yxk7o64uuSa9/Zr5TqkjeyJl/75C+t6OT9cQHZ6ElM/7N5/3Ven8NajoxCsFCP3xwIfbzVMKQbdXcWqZ3Y3qrz08S+c99RoUwYNDOgZjBULRuD81SpmEnWTjiUQCD4QY9wA7kdxrnIyt27E2q2/49WHb2O8Tq593eVp3kZSHSzh1mxQSnw8Zvf1rpbU+hXdCVWK8cisQfhwW324ZNmcoa7WbBmFbqFS9I0NgdliR5BchJXrj/hc47VFKVArRU0zpk4gJkyGIBmFGoMVowa20YRpAuEmhOgZN4BbU4HPb1g/wmZ3YPHMJFdSjENreN+xQjwya5BP0s9hdyA7faCXxnA/lFQaGriek7N2GeDhmff/i017zmPG2D54Zt4wvJQ9Er2613VJ0oBCLET3EAnCVGJotGYmkeh5DYPZ1jxj6jVBu8nHkhplAoFFm3jGXGVtXYI6TYWIECnn479bP0IhodA9XoaXsm+DxWrn1DMOC64vcePzeKCEfBjNdmz/zyWkj4kHnw8kxIRg1+HL6B0d4lev4tDv17B45iCfZOH63NNMgi4n7yLElKseecdPBfXDRuGbkHTrXDQ1Vtwm+EuMtsFAVAKhs9Jkz9hgMOCVV17B/fffj5qaGvz1r3+FwWAAAKxZsyZgC2x3aECtFPmUs2WmJeDQ79fqGyKcgEwsQFGZzmeiRnb6QKz76hRy8i5i67585ORdxD+2nsK1CiNKNEYcOFEEpxPIL67G9NS+UKvEeHj6QM7rTRgZh12HLyN9TDwyxidgxtg+kIoETIedG0/Pfe3W36E12jhrg7fk5eOBuwcgK60fnp47FCo5xe2x+vNkG/JwuV7jARqdFcXlOlaJn3uNBMLNSpM949deew0RERHQaDQQi8XQ6/X461//ir/97W+BXF/HoM5Dfu+psSjV6CGXULDY7BjadxgzIUNrsqHGYEW3UBk++fYsS99Bb7JyGksnTXMk/AqQlZaAEKUYGeP7gqaBqDA5KmuMeCg9CR9sP4XKGjMuFde3oj87f1iD8+3cMWX4kessLNVhx8ECPD13KM4XcpfyNWu7Hy98aUYyREI+IyHq7ZmTkjjCzUyTPePz589j2bJlEAqFkEqlWL16Nc6fPx/ItXUsaKBHhAKxYXKoFSJ0D5ExhqOo3IDrGhM0tRZIJUKEB0mwdX8+o+9gsXLHefk8HsYNi0Xe0ULG081M6ws+nwcnDUSFKbDnl6tYu+V3OJzA1dJa6Aw2n/OUaYycTR8HThQBcOkhyyUUKIpbKwO0qxzP5qA5xyNptNZmbffnha/d+jsKrmt9PPNxw2KbFyIh8eabGnc9cmP/KZSSxk/WgWiyZ+xWa3PjcDh8tt2MGKwOVNaaWSpoi2YkAbiK84U1EFMCxEYqsCRjMN7feoopK3toehJMZjvEIj7kUrYKXFZaAjb/cAE6gw2PzBqEWr0F3/18GQBYOhWeimvhQRI8d99wXLleix7hSnz+vWuoqbuEbeX6I1DKKcyd2B+b917waT6ZMqoXisq0nJ6zRmvm3F7uR5WuIS/c6dUgZLE5wOej6WLzJN5809NV65GbbIxHjBiBd955B2azGYcOHcIXX3yBkSNHBnJtnQKd0e6jgrY+9wxefug2VGnNKC7T41/fnQMl4GPFghGgQaOi2oy3/u2qFc5K6+cz6ijHY9zSh9tOY8WCEdAZXAJFeUcLXeehaWaEU6/uKsglFFOq1jcmCPMmJ8JidaB7uBzv5ZxkkntOp9M1RilEiopqE3YdvsKU46WnxnOX4Km4S/B44BajD5aLAD9C9XyvBiExJcCwfhFNLqcjw00JXZUmu7ZPP/00ZDIZlEol1qxZg379+uHZZ58N5NpuLC189K3VWzg9wGqdyxDvO1aIyhozSjRGvPHZMfB4PEbFDYDfUUfu61tsDtA0jbceHYWXs0fiuXnDEBMmY0rJFGIhYsPkjJpcWLAEKUndsXbL71i79Xe89PEvSBsZx5Swmayuaosvf7gAEcV3hT3qEn0Hjhf5hDuWZiRDrfJNYD4yaxByD17y2f+RWYOgN9nA5/N8jlmakYz47irf8ze1rhkNDzftDCiUkiY9YpPQy81Hkz1jiqLw2GOP4bHHHgvketqHlj768oAghYjTA6RpIPfHP1gJKqWcgtni23XXUPJNTAkgl1L1M/UA3zXxXC3pjTWobN2fz5yzssbMaFT0jFL6bOPzUe+xOn3HJvEFfBSV6lGtu8JSq6vVW/D3nJMQUwI8PXeor/ob0KrxS519uCklFOD5dU14xF7SuR6xCa2nyZ7xiRMn8MADD+Cee+7BtGnTmP+6Au5HX6WcQsZdCUhPjUdxub5u6Gi9t+w94UFrtGHjrv/VN3zAlSx7bv5wVFQbkZ4aj7yjhS5RHwDjR8ShssbESqIdOF6ErLQEV2z3rgRkpSXgqblDcTK/rK4sbgBq9VYUXNf5LSfTm+3I+eECls0Zikg1t16xO7x/6OQ1Zr2VNWbs+KkAFJ/PeLHubTERSm6PlQbA40EhcX1huRXldhwsgJjiM7Fti82B1Zt/A+Cl/tbSJpE63I043t6129ATCJ2VJnvGL774IjIyMpCYmNjlhIFq9FYo5ZSPpkS3UCm27stHicbItBYn9FAyBqRGb0VFrRlCAQ8zxvaBWMSHQirCWx6z5zLTEiAW8iGmBIjppsCnO89h3uT+2LT7ApRyCuNHxCE6QoGM8f1YjRyLZw6CUipEcYUe2//zByaMjEO3UCkUEqGPF/9EVjJuS+qONV/+5jfuO6hPOPrHhbi8UTnVPI+1Cep1QqEAf8/5rdnqb82GDDcldFGabIxFIhEWLFjQrJO///772L17NwDX5I+RI0eypnaUlZVh8ODBWL9+Peu43Nxc/O1vf4NarQbgmrcXSM3kYKUY40fE+Tzef7T9DPN4b7E5sObL31iJIvdx73/lMqIZdyUg54ezPiGCFxeOxDtLboeTdnXj7fjJVUscpBBjfe4ZplOOfe3TWJqZDIvVgfvvHoAdP15C/16hsNicTMOEW2WtqFTPJAHdcV/viguFRABFUN2EZ6fLO/UOe3BtAxpPmqmkFLRmO2fZXUDCB7T/tRIInZUmG+PevXvjzJkzSEpKatL+hw8fxs8//4zc3FzweDxkZ2djyJAh2LFjBwDXMNI5c+bg+eef9zn27NmzWL58Oe6+++6mLq9VqKRCxHRTNJhIc/9cqbMwnpjPcRy6FBabAwXXanBLz1AYzDasWDACH20/DZPFgZy8M6xOOTduuU6L1QEnDXy+8xwy0/rBarXj1NUaOGlXfHbOhH6o0VkQrJCwjPOuw1eQMb4vosLkrnUqRFBIhb7jkHguQ1ujtyJY6ZIDBXy3NZQ0cxtEd/jA23smXiuB0DQaNcbuuLDBYMCcOXMQExMDobD+sO+++47zuPDwcCxfvhwikcszio+Px/Xr15nX3377bWRlZXHqWpw5cwZXr17F+vXr0a9fP7z44osICgpq1o01CxroESZvMJHm/vmP4lpYLA4mued9HNc5uoXKmbIzMeWaFSeTCH32c1dDeIdLFt4zABarDTV6MB6we8rz1n2XfLrZAJexfi+ngYQkR+jh6blDYbU7fQxqTDfusU4sr5eEDwiEVtFoAu/FF1/Eiy++iCeeeAIxMTF45ZVXMHfuXFy/fh2LFy/2e1zfvn2RnJwMwDUjb/fu3ayZeb/++ivuu+8+zmPDw8Px6KOP4ttvv0VUVBReffXVFtxa8+BKDC2emYRDv19jfs6sG0/kqaOgkgrxRFYystL6QSoW4CEvTYlHZw/C59+zp0m//9UpBCvEyErrh4zxCZCI+Lh/aqLfaogN356DQiZGzg8XmU69+ZMTse0/l1j75R0txAN3D8DcCf1hsTkRG6nA/VMSXeEOmwMavZXx9LlCDwXXtZzhCKfD2bSkWSuTcwTCzUyjnvGtt94KAFiwYAHmzJmDW2+9FYMHD4bFYsE333yDqVOnNnj8pUuXsGjRIjz77LOMF7xlyxbMnTuX8Zq9WbduHfPv7OxspKWlNfV+AABqtaJZ+zPHhSoQHx2MKp0JoUopuoXKEKmW42R+BUDXjzICAKPNgfjYUDidNJxFtcj98Q8o5RTuHZfAUmcTCvg+uhRKOYWisvo4r0s6sz+enT8cOiN3SICmaZ8JH55lc2HBEqSNjGONdnp4+kBYbQ7WtifnDMGopO4ovVzpcx1/Nc9GmwN3JPdgvTdRYXLw+YFL5IaHKwN27tbQ0s+WG7vDCYpqWnSwof08X2uL8wVyP3/7NnVbi6/NuzGfo7a6RpPfzerqasaTFYvFWLBgAb755psGjzlx4gSWLl2KFStWsIz2/v37sWHDBs5jdDodtm3bxiQLaZqGQCDg3NcfGo3epwytqYh4QKRKAoBGdbUBUpEAOw76ymfKKAEqKnXQ6Kz4R503mT4sHv/67hxr36y0fj6P+ONHxPkMFt289wIWTrsFCpkIUWoZRidHM17soZPXoJSJ8MWeC/UCRABTNrd1fz6nR/3xN2cxY2wfKOUU0oe5jiss0aGHugYysdBnXXw/XXMySgCNRs96bzQafYveXx844tbhYUpUVOja5vwtoKE/rtZ8tgAgJEQOm83epH397UdRQtZrrT1foPfj2tf7HgJybRoB/xyFhzfvs9rQZ6vJxtjhcKCsrAzdurnmm1VWVjY4iLSkpASPPfYY1qxZg5SUFGZ7VVUVzGYzYmJiOI+TyWT45JNPMGTIEAwePBibNm1qtmfclvhNTMkpnC+sRXG5rsEE3r5jhVh4zwBs+PYcc3x0BHeyUCqlcPC3Ysy8sy/TpeeW4KyqNXF6xoK6QJM/AXyxiM9Zsje4T6jPfcV3V/lsW3LvYKjkVH3yjyvp11Lb5KdkTh3aOu+TQOiMNNkYL1iwANOnT8fo0aPB4/Fw+PDhBtuhN2zYAIvFgjfffJPZlpWVhQEDBiAyMtJn/xdeeAHjxo3DXXfdhb///e94+eWXYTab0bNnT7z99tvNvK02xCMxZbQ5IKMEUMkoaA2umKt3XS+XsLzJYscrD98Gs8WOYLkINifN6YH+WW7AmKExPiOXPtlx1lWFketbNrdiwQhkjE9AbKSKO3kYIsd7W076lOy9tiiFO+HGA2uu3ea9FzBvUmKDspgtFenxVzIXHx0MUdcqZScQGqXJxnj27NkYOHAgfvnlFwgEAixcuBAJCQl+91+5ciVWrlzJ+drWrVt9tr3++uvMv4cPH47c3NymLq3pNNer4wMarRUarRnqIAkS49SorjYAdL1GwuVr1Vg8MwkfbT+DA8eLfFTRstISoA6WgM+nIZe4ZsUpZSKfaR3u+O9fJvbn9HANZhsr3AC4uvcuFdcwWsTeXu3D0wfC7uAeelqpNUOtFPnU6xqsDhhMdoQHyyCVCHAyn2JqigG0TqTH6/2vMXDHx6t0prpwCIFw89CssUv9+/dH//79A7WWwNJc/Qk+cKqgCh9tP8Psv2hmEvrGBEFOCRCsFCMxLhgjB3bH1n359aOT4oJx35T+kIgoSMQClFcZ8cXuC7h/6gCs+bJeVP3Z+4YziT53clBnsEEdJOX0cCkBH1NH9WKmO7sNfUJcsMsYenTRVWotkIiFkEuEsNgd3HFgsRDni2vRI0xe/6XEBy5dq8V6j3vOTh+IH45ebVAWs0lddhzv/4oFIzjXFqqUgpRiEG42bprp0M2VXtRorYwhdu+/fvsZPJE5BDRNIzEuCPMm34JXNxyFxeZgRHgAICcvn2nckEoEmDc5EWarndWY8ck3Z1gt0FFqGZbNGYriMi2emJOMohI9U5GhklMorTIyhti9npy8fPzfopT6MjIe8GelkWXwnp0/3Gdi9OKZScj54QKjt+z+UtJorYwhdl/jkx1n8UTmEAiFAogpPst4hgVLMH5EHMxWB7Rme4NPGlzv/0fbT2PJvYPx/lenWF+QUWHytksQctGWcW8CoY24aYxxU7rIPPEnqG6y2vFx7hm8uHAkZxmak3aJBaWNjEPe0UKfcjN3OKJEY4RcIkT6mHhIJQLIJRTWfPkblHIK0+6I9yp76web3cm5nvIaE8LqBH24DN4nO85gwdRbWOV2VpsTFbVmZh/3l5K/e3bSNP6e8xsoAR+PzB6ED78+DaWc8vHUG3rS4Hr/SzRGhChFPnHrQJbMEXF6QkflpjHGzZVeVAdxC6rLJUJkpSXAanVAJfOVzzx08hoenjEIb35+DOlj4jnlLLPSEmCxOSEWCbDjpwKkj4nHD7+4Ri/FRSl9Enib917EsjlDuMXawcPlUj3CgiQw2Z1IT40HAJy8WIYhCd3QLVSGKyU6RlfZfZynpKb7S8nfPQsFPObYWp3Z7zobetLw9/4rJNQN1Zkg4vSEjspNMzepudKLaqWIJY0pplzddXaH64/4rY3H8dn35/BQOrvjLmN8Anioa57gKHVTyikoZBRyf/wD//jqd2SlJUAuFSBtZBx2/FSAwlIdp3fqcNI+U6cfnj4QG3f/D699+iueW/dfXLhShQPHi3Do5DVMGNkTO34qwD+++h25P/6BKaN6MQLz7rW5cX8pcd1zdvpAbPvPJWZfg9nR4Dr9ibx3FOnLzi5OT+i63DSecbO1E5zA4PhQvLYoBeU1JvDAQ63ejMoaMxNCcE1ovoonMoeAxwfCVBKoVSJojXaW0fFu+Phkh6vm2FLjwPeHr+DxjGS88ekxv+VxYkrATA3xFHLXm2xMd587hpw+xuUZf7LDtwzO7Q2LqfrxRyyj6HHPGq0ZwUoxPt95jjWJ+tDJa1hy72D8WWFonsh7B9Gu6Ozi9ISuy03jGQNovnaCE1ArREiMCUJ3tRQhKolPy/Cl4lq8vek4pCIB1ApRnTylyws8dPKaz1gi74aPyhozLhXVMNu4Rh8tvGcAE2ZwT53OybsIg5ndhcR4vH7U48CrN76jBnbDy9kj8dajo9jx0rp7TuiuQoRKjLvviGetZd6kRAzoGYxRA7vhkVmDmufpdgDtio7ioRMI3tw8nnFrqDMiYjGFqyW6JiuYPTdvGCpqzXgicwiuV+phd9AQiwQ+x9sd9U0gbgnMGWP7oFuoDCKKj8oaE6dWMNdwT3dVBdcaE6KD8NeFI2Gzu6Q5YyPk0BpsKCo3cFcV+PNmnYBCLMSgXiHt7uk2mw7ioRMI3hBjzIWf0qfu4QrEd1chKy2BqSKIUsuweOYgV8yRxwOfB1TpLJBLKNgcTqz+4jeWUUyMC/Zp+AiSU0zjiMXmgM5gg1QshMliw9Z9l/HYrEFYNCMJ63Pr638Xz0yCQkIxRtddd/z94SugBHzW+cSUAPdPTcSflQZs3nuRVTf9lcckE86qgoaE3DuryHtnXTehS0OMsTcNlD7x+TzEd1eiW6gUfWND4HA4oDXY8MZnx3wMos5gw+KZg1iGOCxYghEDovDDL1ewbM5QOJxOKGUUaJrGjyeK8f/+MhQ2uxPBSjFkEiHMFjsW3jMQFCVA3tGrTLxYTPGhNVgRohTjtUUpMJhtkEsoWOx2PBU3FHqTDaEqMV5ddBtOXHApzpksdkb7GKivm/acZOK3qoDU5RIIAYcYYy8aKn2ylutRWul6pO8eIoHWaMMbnx33acRIHxOPAyeKfEIS44bFIu9oISal9GR142WlJWBo/2749LtzKNEYGeH4f3qIBXkKx08Z1YvxcKPUMmSMT8D63DM+QkKLZiZBrRLjqwOXMG54rN84sufPPnXXpC6XQLgh3FwJvCbgr/TpT40RT7z7I17ecBTPrfsvCq7roLc4kJ7qEnv3LBuTSgSYMqoXNu76HzI9Jj9HqmWYNzmRMaTu/XPy8vFnhdElmwlgdHI0Y4jd+2zJc8lkektljk6Oxkfbz2B0crRPTfP67WeglIkxdVQvSER8Jmnlhokxe/zsXVXg78vJLa5PIBDahpvLM27C47a/0qfiMj2zTSmncK1Cz+o+c3uuOoMNESEypiHif5crkTE+gYnfuho+6tuJ3S3T8T2CoKm1IGN8AqRi9vXd+0WqZZCIBVDKKVhq2LKdUomApXXsbru22Oyw2JxIiA3GY/cOxjqP1uPs9IHYXldD7CkNqjXUv0d6k61ZnYsEAqFl3DzGuImP21z6xY/MGoRNe84z+4wbFuujE7ElLx8zxvaBmOKjpNIAi801z278rfUhCcDVLi2mXAZ1yqheTMv0658eYxnJKLUMJRoj50w8d1za3RUXpZZBLqGQ8wP7yyHvaCHUQVLmiyBKLcOKBSNgNNtRWKrDD0evInVoDGK6KdBDLWM0mr31jN1rcUPqcgmEtuemMcZNboPlKH3iC/js0jI/dbx9ooMQESyBxe5Ez8hbIRTy4aCd+H9/GYqiUh2sdidOXixDdvpAaGrNTCOGd3jhkx1nsWzOUKz58jfOCR45dYY/J+8iDp28hoemJ+Gtfx/3+XJ47r7h+HLveeaLYXRyNM5frcKwfhGIjpBjYLwaZosdYUESqGRCaLRWn/fo/a9OYcWCEawkpd+pzyTRRyC0mJvGGDdLKMi79IkHlrfsbzxRmEoMhVSI4qIalFeZ8EOd1+s9ncPpdCA8WOq3Zdpic+BauQ7pY+LRLVTG+Xp4sBRLM5IhEQlRVKrl3EdvsuJ8YY2Pd33wt2us0Im7VE5rsHCeh6bpxutySaKPQGgVN00Czx0L9sTncZsHaE02FFUYoDXb6ysN6rzltf9vLF5bPAqJPYOx5N7BnF1cGq0VxWUG5OTlcybVtuTlo0eEChU1Jtbx3uuKjlACPEAqEXC+XlFjQqnGiKulWhgtDs59QpQSZKX1w5y0/rDanFDKXV8u7qSf57o+2n4GYcEyzvOEKsXM+wAet6IaSfQRCK0joJ7x+++/j927dwMAUlNT8eyzz+L555/HiRMnIJVKAQBLlizxmXF3/fp1PPPMM9BoNOjVqxdWr14NuVzeqrX4nWUnq9cCbsyzu3Jdy8R/3fFXmqZZ3qJGa65vmfbj9eqNVqjkFLLSEvDD0UJkpiWwvOeH0gfi8+/ry9weSh/IKnPLSkuAWCTAd4cuY3RyNA6cKPI5x9yJ/aEzWllSnEx5nJ91lVQafM7z9NyhKC43NOrxNleilEAgsAmYMT58+DB+/vln5ObmgsfjITs7G3l5eTh79iw2bdqEiIgIv8e+8sormDt3LqZOnYp169bhgw8+wDPPPNO6BTXSBttgTFlGQaOzMobYlZiLhabWjIgQKfiC+geMEKWYCWMA3G3J4PHw3aHLmDAyDg9PT4ITYFqmo8Lk2LT7PJMwK9EYse0/l7DywVthNNtB0zRKKg3M8e5EnruFOj46CHIJBfBovPLPoz5eecb4vohSyznXZbU5ceBEETO1ZFi/CFACHp774DDn+wKAiQ8TAR5CR8NJ0wgJadyJs9kd0OvMN2BFDRMwYxweHo7ly5dDJHL9McbHx+P69eu4fv06VqxYgbKyMqSlpWHJkiXg8+uNmc1mw7Fjx7Bu3ToAwMyZMzFv3rzWG2OgwTbYhjy7PyuNzBTosGAJpt3RG3uOXMXo5GiUVxvRq3sQxEI+YiPkcDqd6B4m8+v1ZqUlwG63Izs9CZeKq2Gy2nG9woBNey4AADLGJ7AqFwCXQT5ToMGOgwXITh+IiFAZlmYmQ2+wMYlFncGGmAgFBDzg9B+VCA+WsiaLuO8nVCXFxt3nMXdiP1Zr9COzBmFL3kWEKMXoGaUCDRrgAXozd2nbnxojSzSfawaf30QfgXAD4PN5eP79Q43ut+qx0TdgNY0TMGPct29f5t9Xr17F7t278cUXX+DXX3/FSy+9BKVSiUWLFuHrr79GRkYGs291dTUUCgWEQtfSwsPDUVZWFqhlMvjz7OQSCqs+P85MgR43LBZ7jlz1Scxlpw9EqEoMqZjC7sPnMfPOvnhoehJooL6awubE4TPXMSqpO3Ly6rUpnv7LUNa1Ob1pur7SIjMtAT3UMnTrLmF5+ia7AxeuVHOGJiprzBBTApRVGWFzOEEJ+azpH3KxEC88OAL5RbXMNGkxJcAjswdxlrZ51l1bbA6s3vwb3llyOxHgIRBaSMCrKS5duoRFixbh2WefRe/evRmPFwDmz5+Pb775hmWMaZoGzytJ5P1zY6jVimavU+2kmXIytyFaNmconHVC8W5pS6vNwZmY+2THWbzwwAiEB8sw+65+eHvjcabJIyevfj5exl0JPjXK//ruHLLTB+KTHWdx4HgRS4jIXenw31N/MvvHdlNCJKZQWmNGqEqKIdEhKNUYUKsxcNY/p4+Jx46fCvDo7EHYuPs8xg2Lxeffn/cx+P+3KMVnBt6HX5/G8wtGYJVHaZv7PJ5YbA7oLXYkxYc3+73nIjxc2SbnaWta8tnyxO5wgqKa9mfX0H6er7XF+QK5n799m7qtNddu0n681n3e2uqzGlBjfOLECSxduhQrVqzA1KlTcfHiRVy9ehUTJ04E4DK8bg/YTWhoKHQ6HRwOBwQCASoqKhqML3Oh0ejhdDbfJUvoofTx7LQml1C8Oy774N0DUFTGPeXi7OUq7Dh4DEszkvHOktuhNVohELhix07a1RXHlTwr0RihN1lZHXQZ4/siRClBqcaIrfvyMfPOvqg1WKE32mCy2LFszUFWiMBotTPNJt7r6tVDhbceHVVfL+0ngVdZa+LcbrE6Gq67Rt3EaUqAigpds993b8LDlW1yntZc3x8t/Wy5CQmRw2azN74j4Hc/ihKyXmvt+QK9H9e+3vcQqGs3aT8aLf68Nfez2tBnK2ClbSUlJXjsscewevVqTJ06FYDL+L7xxhuora2FzWbDli1bfCopKIrC8OHDsWvXLgDAN998gzFjxgRqmWy8xc9di8bSzGRkpfUDAGzcfR7940I4S8DiIpVIT43Hpj3n4XTSqNXb8PI/f0FOXj52HCzAlFG9IBVzl6pZrE5GOP7zXeexdd8llyHe75K4/GTHWcxI7YvFMwcx05QBl7EsuK7FB1+fZrr7vM8dFSKFSkpBIRbgsXsHsxKMnvupVRK/pW2e74tCLCAC7QRCGxMwz3jDhg2wWCx48803mW1ZWVl4+OGHMWfOHNjtdkyYMAF33303AOCFF17AuHHjcNddd+Gll17C8uXL8eGHHyIqKgrvvvtuoJbpH45StyfnJCMsSAq7w4FHZw/CB1/Xx32z0hLw6c5z0BlsyExLQK2xvpvNrS1htTkwoLcaD04bgH99d4712E9RfCZWHKWW4f6pA3CtXIeM8QlMEo7HB4xmu4/36i6lc4dSPGPZT84ZUh+7pYGBvYIRohBBHTSAGf/kNqbhwWIfHeTFM5OgVrkmmDAQgXYCoc0JmDFeuXIlVq5cyfnaX/7yF59tr7/+OvPvHj16YOPGjYFaWpPwLnVTyimUVhrx9y9/Zwzm8/ePgNXuwOU/tSytiC15+Xgp+zbGEHtrSzw8fSAemj4AUjGF4jI9Nu4+D0rAx4oFI3D5ei2CFWJW7NqtMxGiFOP3/EqfBJ/b03WHUjxL0xLiQqHR6OtvzAH0CJNBJqHwwgMjYLE5ER7k8nzhYM/AU9fN9GMZYjdEoJ1AaFNumg685uJd6uYtDlSiMWLV58cg4POw71ghxg2LRcb4BGSMT4BSTkFntDLVF97Jvo+/OYvuYUps2n0eOXkXUVljRonGiPNXq2GxOn2647bk5WPRjCQIhXz0jwvGohnsCc6xkQombFBZY8aOnwoQE6GEWikCn8+R/HQCITIK0aEyxHdTQCXx8Go9ZuC5Z/oRCITAc9NoUzQXn1I3P0kvhZzC1FG9WNUPWWkJMJutWDQzCeVVRs7jTv9RgbSRcUzZGQDsO1aI+6fcwrl/rcGKwlItnE7g8JnrmDG2DyLVMlRpzYiJUKC00sgqVRMJyfcsgdCZIH+xfvCeIuwv6cWjeT7lZDl5+QgNluOrfflI7BnKeRwl5MNqc2JOWn9GnF5nsEFEcSf4SiqN6BaqgMXmxKTbesFJ09iSdxHxPYLgsDuxevNvyMm7yEyOXr35N6ILQSB0Iohn7A+vJFVoXWeaZyx3aUYyNLVmTk9WZ7CgRGPE5eu1HLoR/UAJ+di677yP3sTXB/KZmmPvxo15kxJ9GjoE/Ia7BwkEQueAGOOG8EpSpSRFITKkvoKgosYMkVjgk1ATUwIE1YU5TBYHDp28hqWZySgu08HpBJxOmtV04famM9MScKm4FqMGWZmQA2gwE0RKNUafWPJri1KgkPI510B0IQiEzgMJUzQDPp/H1NsCwOrNv0FTY8LDXgm1h9IHQiWnsHhmEg6dvIZpo3ujrMqIbqFyV0LNT/y5W6hLwvL7/16BmOJjx8ECbN2fD53BhoX3DMC+Y4U+xxjMNp+QCqn7JRA6H8QzbiE1eiuUcgoGsx2HT/2JpZnJsFgdCAuWgg8ackqAwfGhiI4YiqIyHeMJu0V5uPQeeqhlrLDIkL5hKKsxw+5wQiIScHa9BctFrav7JdM5CIQOwc1hjANgcIKVYkwZ1Qubdl+AxebA+cITAFzz6J6aMxRF5QYEK8XgAfg4lz3p+cNtp310MDwVzlj6vzwe3tp4HONvjcXimYNQVmXEvmOF0BlsbFU0GownXKO3Ajye6z4beV/IdA4CoWPQ9Y1xgAyOSipE9zA5K9wQFixB2sg4rFx/hLnWknsHc4YkrpXrMGNsH/TqrkK3YIlfT1YlE3KOSIrvroJC4vGl4uc+1aH+hW2aPBeQQCAEnC4fMw7YOCAaUMpErDI0rgaPPysMnKVqVpsTOXkXIaiLQ/v7YtAabJwjkpxOmnWM3mxHcbke6anxTOPJ2q2/o6TS4PcWSBUGgdBx6PLGOJAGRylzjU5iapH5vom5fccKsXjmIFZyLTMtAQdOFDFDTFu9fh5wuUSH3B//wNZ99aJESjmFKp3J77mbNBeQQCDcELp8mCKQ44AUYgGiwxVMGVpspMrnWjqDDUqpEBnj+yJUJUVZlZEpVXtk1qBGE21NWb/WaMOH2077lL3NGNsHoUop/F2g0bmABALhhtHljXGbGRwe8Ge5HqWVBlYSML67EuHBEpRWm1BSafAZZ7RoRhKKK/TYuu8SlHIK44bFYtzwWPB5PPSOUja6hqas35/3HNNNgagwOVsoyBOivkYgdBi6vDFuE4PjJzkWEyFHldaCYKUYkaEyrPnyJJRyihGJ5/N4iO2mwN825zNdeFv35zOJPVYCrhXr9+c991DLuIWCvM5P1NcIhPan6xtjoNUGx18ScMbYPsjJu+gzkNNtcJdmJCNUKcK8SYnYtOc8I22Z2DMUMeGypiuiNbL+Br1nAoHQKbg5jHEr8RcGcNI082+/AzmdLs/2uXnDfLa3GSTcQCB0eogx9oajQcRfGMDT2FlsDlTpLF4jm8D8XyWlXDP1jDamIaRNu91IuIFA6NQE1Bi///772L17NwAgNTUVzz77LLZs2YKNGzeCx+Nh4MCBeOWVVyASsSsbcnNz8be//Q1qtRoAMHbsWCxbtiyQS3Xhr0EkLsgnDJCVloDvD19hDm20QoN0uxEIhAYImDE+fPgwfv75Z+Tm5oLH4yE7Oxsff/wxvv76a2zfvh1yuRzLly/H5s2bsWDBAtaxZ8+exfLly5n5eDeKhjrSEmOD8N5TY1Gq0TOKbW6tiKZUaJBuNwKB0BABM8bh4eFYvnw54/XGx8fDarXipZdegkLhatFNSEjA9evXfY49c+YMrl69ivXr16Nfv3548cUXERQUFKilMnjHht2DREurTQCPh149giHiuaytSkY1K0bbUPMGMcadH4VSAkooaHzHRopbCDcvATPGffv2Zf599epV7N69G19++SV69uwJAKiqqsIXX3yBVatW+RwbHh6OBx98EEOHDsW7776LV199FX/729+afG212r8eQ0NYaR4TG+YaJLpszlCkJEUx5WLhXsc7nTRKKg2o0poQqpIiKkzO7Ot5bjdiSoBItQLh4S1bb1MJD1cG9PyBoKOu2d9ny+5w4q/rjzR6/KuLUkBRTfuza2g/z9fa4nyB3M/fvk3d1pprN2k/Xus+b231WQ14Au/SpUtYtGgRnn32WcYQl5WVITs7G7NmzcLIkSN9jlm3bh3z7+zsbKSlpTXrmhqN3qXd0ExEfDCxYS6diTVf/obIED9hhUZiwp7n9nxdxKdRUaFr9lqbSni4MqDnDwTtveaG/rj8fbZCQuSw2exNOn9r96MoIeu1G3Xdlu7Hta/3PQTq2k3Zz+mkYacbL2+y2R3Q68ysbc39rDb02QqoMT5x4gSWLl2KFStWYOrUqQCAgoICZGdnY/78+XjwwQd9jtHpdNi2bRsTR6ZpGgJBEx7/2gKPErHSalOzwgqNxoRJ+RmB0CHh83l4/v1Dje636rHRAV1HwIxxSUkJHnvsMaxZswYpKSkAAL1ej4ULF+LJJ5/E9OnTOY+TyWT45JNPMGTIEAwePBibNm1qtmfcKtx6wjzusIK/iokmxYRJ+RmBQPBDwIzxhg0bYLFY8OabbzLbpkyZgsrKSnz66af49NNPAQDjxo3DE088gRdeeAHjxo3DXXfdhb///e94+eWXYTab0bNnT7z99tuBWqZfuLrals0Z6tebDaQgEYFA6PoEzBivXLkSK1eu9Nm+aNEizv1ff/115t/Dhw9Hbm5uoJbWNDjCCr2iQ/yK7hAFNAKha+OkaYSEyFnb7A6nzzau2HJTIB14DeEVVmhQdIfEhAmELg1XbJkrEdnS2DIxxm0JiQkTCIQW0uUnfRAIBEJnoEt6xo1q+HbQcwcKsua2o6Oui9CxaMnnhEfTNHmYJhAIhHaGhCkIBAKhA0CMMYFAIHQAiDEmEAiEDgAxxgQCgdABIMaYQCAQOgDEGBMIBEIHgBhjAoFA6AAQY0wgEAgdAGKMCQQCoQPQJduh24r58+ejqqoKQqHrbXr11VdhMBiwatUqWCwWTJ48GcuWLWvnVdZz4MABvP/++zCZTLj99tuxcuVKHD58uMOu96uvvsKmTZuYn69du4b09HSMHz++w675RvP+++9j9+7dAIDU1FQ8++yzeP7553HixAlIpVIAwJIlS27sAIYW8N5772Hv3r3g8XiYPXs2HnjggQ792eSC6x7a9HdBEzhxOp30HXfcQdtsNmabyWSiU1NT6aKiItpms9EPPvgg/eOPP7bjKuspKiqi77jjDrqkpIS2Wq30nDlz6B9//LHDrteb/Px8Oi0tjb5+/XqnWXOg+e9//0tnZmbSFouFtlqt9H333Uf/8MMP9N13302XlZW19/KazNGjR+msrCzaZrPRJpOJvvPOO+nz5893qt8z1z0UFBS06e+ChCn8cPnyZQDAgw8+iHvuuQebNm3C6dOnERcXh5iYGAiFQkybNg179uxp55W6yMvLw5QpUxAZGQmKorBmzRpIpdIOu15vXn75ZSxbtgzFxcWdZs2BJjw8HMuXL4dIJAJFUYiPj8f169dx/fp1rFixAtOmTcPatWvhdDY+TLM9ufXWW/Hvf/8bQqEQGo0GDocDWq22U/2eue5BIpG06e+CGGM/aLVapKSkYN26dfjss8+Qk5OD69evIzw8nNknIiICZWVl7bjKegoLC+FwOLB48WKkp6dj8+bNKC8v77Dr9eTw4cMwm82YPHlyp1nzjaBv375ITk4GAFy9ehW7d+/G6NGjcdttt+GNN97A1q1bcfz4cXz99dftu9AmQFEU1q5di6lTpyIlJaVT/p6978Fut7fp74IYYz8MGTIEb7/9NpRKJUJDQzF79mysXbsWPF69NB5N06yf2xOHw4EjR47gjTfewJYtW3D69GkUFxd32PV6kpOTgwceeAAA4HQ6O8WabySXLl3Cgw8+iGeffRa9e/fGunXrEBERAalUivnz5+PgwYPtvcQmsXTpUhw5cgQlJSW4evVqp/w9e97DkSNH2vR3QYyxH44fP44jR44wP9M0jR49eqCiooLZVlFRgYiIiPZYng9hYWFISUlBaGgoJBIJxo8fj8OHD3fY9bqxWq04duwYxo0bBwCIjIzs8Gu+kZw4cQILFizA//t//w8zZszAxYsXsXfvXuZ1mqaZBHNHpaCgAOfPnwcASKVSTJgwAUePHu1Uv2eue9i1a1eb/i6IMfaDTqfD22+/DYvFAr1ej9zcXDz11FO4cuUKExLYuXMnxowZ095LBQDceeed+Pnnn6HVauFwOHDo0CFMmjSpw67XzcWLF9GzZ0/IZDIAwODBgzv8mm8UJSUleOyxx7B69WpMnToVgOsP/o033kBtbS1sNhu2bNnS4Ssprl27hpUrV8JqtcJqtWL//v3IysrqVL9nrnsYMWJEm/4uOvZXajty55134tSpU5g+fTqcTifmzp2LIUOG4M0338Tjjz8Oi8WC1NRUTJo0qb2XCsBlxLKzszF37lzYbDbcfvvtmDNnDnr37t0h1+umuLgYkZGRzM9isbjDvsc3mg0bNsBiseDNN99ktmVlZeHhhx/GnDlzYLfbMWHCBNx9993tuMrGSU1NxenTpzF9+nQIBAJMmDABU6dORWhoaKf5PXPdw5IlSxASEtJmvwsy6YNAIBA6ACRMQSAQCB0AYowJBAKhA0CMMYFAIHQAiDEmEAiEDgAxxgQCgdABIMa4k/Pggw+iqqoK48aNw5kzZ3xeP3PmDJYuXdri8+/fvx+vvfZaa5ZIIBCaADHGnZz//ve/Db6elJSEtWvXtvj8d911F1auXNni4wmdl6NHjwa0hnnPnj2YP38+AJc85TfffBOwa3UGiDHuxDz//PMAgPvvvx8lJSXYsmULZs6cibFjx2LNmjUAmv4HdfDgQUybNg3p6elYvnw5xowZg2vXrmH79u1YtGhRQO+DQHjiiScwffr09l5Gu0I68Doxq1atwvbt2/H5559j9uzZEIvF2L59OyoqKjBu3DhkZWU16TzV1dV49tln8fnnn6N///7Izc1Fbm5ugFdP6Gh8/fXX+PTTT8Hn8xESEoKZM2fCaDRi2bJluHz5MiwWC1577TUMHz4cVqsVq1evxrFjx+BwOHDLLbdg5cqVUCgUDV7jvffew3fffYfg4GDExcUx25cvX46+ffti4cKFgb7NDgvxjLsQbg84PDwcYWFh0Gg0TTru+PHjiI+PR//+/QEAM2bMaPSPitC1uHDhAlavXo1PPvkE3333HcaNG4ePPvoIpaWlWLBgAXbs2IGsrCz84x//AAB8/PHHEAgE2L59O7799ltERERg9erVDV5j3759+OGHH/DNN98gJycHer3+Rtxap4F4xl0IT8UoHo+Hpna6CwQCn335fPI9fTNx5MgR3HHHHYiKigIALFiwAImJifjrX/+KwYMHAwD69++Pbdu2AQB+/PFH6HQ6HD58GABgs9mgVqsbvUZaWhrzRT9r1ixs3LgxULfU6SDGuJMjEAhgt9tbdY6hQ4fi6tWruHDhAvr374+9e/dCq9V2Cn1ZQtsgEAhYv2+z2YzLly+Doihmm+cXvNPpxIoVK5CamgoAMBgMsFgsjV7H80tfIBC01fK7BMT96eRMmjQJ8+fPh8FgaPE5goOD8e677+K5557DjBkz8PPPP0MoFDJDFgldn5EjR+LIkSMoLy8H4BL8f+edd/zuf8cdd+CLL76A1WqF0+nEiy++iHfffbfBa4wZMwZ79uyBVquF0+nEjh072vQeOjvEM+7k+PsDOHDgAPPvnTt3NngOvV6PQ4cOIScnB1KpFOfOncN//vMfJokzc+bMNl0zoePRr18/PPPMM8jOzgbgyju88sorWL9+Pef+jz76KN566y3MmDEDDocDiYmJWL58eYPXSE1NxcWLFzFr1iyoVCr0798f1dXVbX4vnRUioXmT4E7McLFw4UIUFBRg3759EAqFEAqFeP755zF8+PAbvEoC4eaFGGMCgdBmPPnkk7hy5Qrna2vWrEHv3r1v8Io6D8QYEwgEQgeAJPAIBAKhA0CMMYFAIHQAiDEmEAiEDgAxxgQCgdABIMaYQCAQOgD/HxEpQnvkRy7GAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(body_dimensions[[\"thi_gi\",\"che_di\"]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(results1.resid)\n",
    "plt.xlabel('Residuals') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Linearity Condition, met: because the distribution of points in the plot above are roughly evenly distributed above and below the line as we move from left to right, we can say the linearity condition is met;\n",
    "\n",
    "\n",
    "2. Constant Variability of Residuals Condition, met: because the y‑axis spread of points in the plot below do not change as moving from left to right, we can say that this condition is met;\n",
    "\n",
    "\n",
    "3. Normality of Residuals Condition, met: because the histogram of residuals normal and, the assumption that the residuals are normally distributed is met;\n",
    "\n",
    "\n",
    "4. Independence of Residuals Condition, not met: the dataframe is not random;\n",
    "\n",
    "\n",
    "5. Multicollinearity Condition, met: there is no obvious linear relationship between the explanatory variables Chest Diameter and Thigh Girth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>wri_di</td>      <th>  R-squared:         </th> <td>   0.640</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.637</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   223.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 03 May 2022</td> <th>  Prob (F-statistic):</th> <td>6.49e-110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:39:20</td>     <th>  Log-Likelihood:    </th> <td> -430.82</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   507</td>      <th>  AIC:               </th> <td>   871.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   502</td>      <th>  BIC:               </th> <td>   892.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    5.1443</td> <td>    0.386</td> <td>   13.341</td> <td> 0.000</td> <td>    4.387</td> <td>    5.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex</th>       <td>    0.9382</td> <td>    0.079</td> <td>   11.831</td> <td> 0.000</td> <td>    0.782</td> <td>    1.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.0071</td> <td>    0.003</td> <td>    2.619</td> <td> 0.009</td> <td>    0.002</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>thi_gi</th>    <td>    0.0279</td> <td>    0.007</td> <td>    4.168</td> <td> 0.000</td> <td>    0.015</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>che_di</th>    <td>    0.1124</td> <td>    0.015</td> <td>    7.311</td> <td> 0.000</td> <td>    0.082</td> <td>    0.143</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.460</td> <th>  Durbin-Watson:     </th> <td>   2.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.292</td> <th>  Jarque-Bera (JB):  </th> <td>   2.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.169</td> <th>  Prob(JB):          </th> <td>   0.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.996</td> <th>  Cond. No.          </th> <td>1.08e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.08e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 wri_di   R-squared:                       0.640\n",
       "Model:                            OLS   Adj. R-squared:                  0.637\n",
       "Method:                 Least Squares   F-statistic:                     223.2\n",
       "Date:                Tue, 03 May 2022   Prob (F-statistic):          6.49e-110\n",
       "Time:                        20:39:20   Log-Likelihood:                -430.82\n",
       "No. Observations:                 507   AIC:                             871.6\n",
       "Df Residuals:                     502   BIC:                             892.8\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      5.1443      0.386     13.341      0.000       4.387       5.902\n",
       "sex            0.9382      0.079     11.831      0.000       0.782       1.094\n",
       "age            0.0071      0.003      2.619      0.009       0.002       0.012\n",
       "thi_gi         0.0279      0.007      4.168      0.000       0.015       0.041\n",
       "che_di         0.1124      0.015      7.311      0.000       0.082       0.143\n",
       "==============================================================================\n",
       "Omnibus:                        2.460   Durbin-Watson:                   2.021\n",
       "Prob(Omnibus):                  0.292   Jarque-Bera (JB):                2.404\n",
       "Skew:                           0.169   Prob(JB):                        0.301\n",
       "Kurtosis:                       2.996   Cond. No.                     1.08e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.08e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shown from the table, r-squared = 0.640, which means 64% of the variability in our response variable is explained by this model in the dataset, a relatively high figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis - significan level 0.05 ($\\alpha$ = 0.05)\n",
    "\n",
    "H0: ${\\beta{Gender}}$ =  ${\\beta{Age}}$ = ${\\beta{thigh}}$ = ${\\beta{chest}}$ = 0 in the model\n",
    "\n",
    "Ha: at lease one ${\\beta}$ != 0 in the model\n",
    "\n",
    "Shown from the table again, Prob (F-statistic) = 6.49e-110 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion: \n",
    "\n",
    "Since our Significance of Regression F test p value (6.49e-110) < alpha value(0.05), we reject the null hypothesis and conclude that none of the slope is zero in the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Logistic Regression Research Question Set\n",
    "\n",
    "### Is there a linear relationship between the log-odds of Gender and Wrist Diameter, Age, Thigh Girth, and Chest Diameter in the sample? What about in the population? What explanatory variables should we include in the model to build a parsimonious model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "body_dimensions_train, body_dimensions_test = train_test_split(body_dimensions, test_size = 0.2, random_state = 207)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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       "      <th>176</th>\n",
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       "      <td>15.2</td>\n",
       "      <td>125.2</td>\n",
       "      <td>...</td>\n",
       "      <td>36.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>27</td>\n",
       "      <td>86.4</td>\n",
       "      <td>186.7</td>\n",
       "      <td>1</td>\n",
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       "      <th>135</th>\n",
       "      <td>41.5</td>\n",
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       "      <td>35.8</td>\n",
       "      <td>21.1</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>112.8</td>\n",
       "      <td>...</td>\n",
       "      <td>31.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.7</td>\n",
       "      <td>36.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>62</td>\n",
       "      <td>76.4</td>\n",
       "      <td>185.4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>37.8</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>28.3</td>\n",
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       "      <td>9.8</td>\n",
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       "      <td>13.2</td>\n",
       "      <td>105.6</td>\n",
       "      <td>...</td>\n",
       "      <td>31.5</td>\n",
       "      <td>24.8</td>\n",
       "      <td>37.2</td>\n",
       "      <td>35.4</td>\n",
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       "      <td>14.6</td>\n",
       "      <td>24</td>\n",
       "      <td>69.5</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>29</td>\n",
       "      <td>48.6</td>\n",
       "      <td>157.5</td>\n",
       "      <td>0</td>\n",
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       "      <td>26.8</td>\n",
       "      <td>13.2</td>\n",
       "      <td>8.9</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12.3</td>\n",
       "      <td>96.2</td>\n",
       "      <td>...</td>\n",
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       "      <td>23.2</td>\n",
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       "      <td>16.6</td>\n",
       "      <td>24</td>\n",
       "      <td>65.9</td>\n",
       "      <td>170.2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>41.2</td>\n",
       "      <td>27.1</td>\n",
       "      <td>29.8</td>\n",
       "      <td>20.1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>13.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>35.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>16.3</td>\n",
       "      <td>26</td>\n",
       "      <td>70.5</td>\n",
       "      <td>165.1</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "     bia_di  bii_di  bit_di  che_de  che_di  elb_di  wri_di  kne_di  ank_di  \\\n",
       "71     41.8    28.5    31.6    21.6    31.5    13.3    10.3    18.9    14.3   \n",
       "176    42.5    31.3    33.1    19.4    32.0    14.8    11.3    20.1    15.5   \n",
       "111    43.6    30.2    32.4    21.8    33.1    15.2    11.3    19.8    15.2   \n",
       "135    41.5    30.6    35.8    21.1    28.0    15.0    11.8    21.0    15.6   \n",
       "409    37.8    28.0    32.0    16.8    28.3    12.7     9.8    18.1    13.2   \n",
       "279    39.1    29.7    33.7    20.1    29.5    13.2    10.0    20.3    13.6   \n",
       "433    32.9    20.9    28.5    19.0    23.0    12.2     9.1    16.0    12.7   \n",
       "269    37.0    27.9    30.8    15.1    26.8    13.2     8.9    18.1    12.3   \n",
       "164    41.1    29.2    31.5    19.7    29.9    14.8    11.0    18.0    15.0   \n",
       "92     41.2    27.1    29.8    20.1    31.0    12.9    11.6    18.8    13.5   \n",
       "\n",
       "     sho_gi  ...  bic_gi  for_gi  kne_gi  cal_gi  ank_gi  wri_gi  age   wgt  \\\n",
       "71    114.5  ...    31.0    26.0    36.4    35.6    21.6    16.6   40  78.9   \n",
       "176   117.5  ...    33.3    27.7    37.1    37.4    22.6    17.1   37  85.9   \n",
       "111   125.2  ...    36.3    28.0    38.3    34.7    23.0    16.1   27  86.4   \n",
       "135   112.8  ...    31.1    27.0    37.7    36.8    24.6    18.4   62  76.4   \n",
       "409   105.6  ...    31.5    24.8    37.2    35.4    20.3    14.6   24  69.5   \n",
       "279   114.0  ...    31.6    26.6    39.6    38.9    23.3    15.9   22  83.0   \n",
       "433    93.1  ...    26.9    22.5    31.7    30.7    20.5    15.2   29  48.6   \n",
       "269    96.2  ...    25.9    23.2    34.3    30.9    19.4    14.5   24  57.2   \n",
       "164   111.1  ...    35.0    28.4    35.5    35.0    21.0    16.6   24  65.9   \n",
       "92    116.0  ...    33.0    28.0    35.1    35.2    21.1    16.3   26  70.5   \n",
       "\n",
       "       hgt  sex  \n",
       "71   181.6    1  \n",
       "176  188.0    1  \n",
       "111  186.7    1  \n",
       "135  185.4    1  \n",
       "409  172.7    0  \n",
       "279  176.5    0  \n",
       "433  157.5    0  \n",
       "269  166.8    0  \n",
       "164  170.2    1  \n",
       "92   165.1    1  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_train.sample(10)\n",
    "# Training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "      <td>13.2</td>\n",
       "      <td>110.3</td>\n",
       "      <td>...</td>\n",
       "      <td>29.9</td>\n",
       "      <td>24.9</td>\n",
       "      <td>38.7</td>\n",
       "      <td>36.3</td>\n",
       "      <td>21.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>28</td>\n",
       "      <td>72.3</td>\n",
       "      <td>167.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>43.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>33.5</td>\n",
       "      <td>21.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.7</td>\n",
       "      <td>16.5</td>\n",
       "      <td>128.1</td>\n",
       "      <td>...</td>\n",
       "      <td>40.2</td>\n",
       "      <td>32.4</td>\n",
       "      <td>39.2</td>\n",
       "      <td>40.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>20</td>\n",
       "      <td>94.1</td>\n",
       "      <td>177.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>34.7</td>\n",
       "      <td>29.4</td>\n",
       "      <td>33.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>20.2</td>\n",
       "      <td>12.8</td>\n",
       "      <td>100.2</td>\n",
       "      <td>...</td>\n",
       "      <td>36.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.3</td>\n",
       "      <td>37.9</td>\n",
       "      <td>24.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>34</td>\n",
       "      <td>75.5</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>34.9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>32.2</td>\n",
       "      <td>17.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>12.2</td>\n",
       "      <td>87.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>36.4</td>\n",
       "      <td>20.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37</td>\n",
       "      <td>53.9</td>\n",
       "      <td>162.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>34.9</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>15.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>11.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>17.9</td>\n",
       "      <td>13.2</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20</td>\n",
       "      <td>54.5</td>\n",
       "      <td>167.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>30.3</td>\n",
       "      <td>13.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>19.4</td>\n",
       "      <td>14.5</td>\n",
       "      <td>117.1</td>\n",
       "      <td>...</td>\n",
       "      <td>34.6</td>\n",
       "      <td>27.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>40.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>23</td>\n",
       "      <td>76.6</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>35.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>17.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>20.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25</td>\n",
       "      <td>62.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>41.1</td>\n",
       "      <td>27.8</td>\n",
       "      <td>31.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>11.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>114.8</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>26.1</td>\n",
       "      <td>36.3</td>\n",
       "      <td>35.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22</td>\n",
       "      <td>67.2</td>\n",
       "      <td>182.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>37.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>26.6</td>\n",
       "      <td>13.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>13.4</td>\n",
       "      <td>105.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.4</td>\n",
       "      <td>24.3</td>\n",
       "      <td>34.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>34</td>\n",
       "      <td>63.6</td>\n",
       "      <td>167.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>39.6</td>\n",
       "      <td>28.7</td>\n",
       "      <td>32.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>28.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>19.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>119.4</td>\n",
       "      <td>...</td>\n",
       "      <td>35.3</td>\n",
       "      <td>28.7</td>\n",
       "      <td>39.3</td>\n",
       "      <td>35.9</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>29</td>\n",
       "      <td>83.6</td>\n",
       "      <td>188.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bia_di  bii_di  bit_di  che_de  che_di  elb_di  wri_di  kne_di  ank_di  \\\n",
       "478    37.8    29.0    32.4    20.6    28.5    12.7     9.1    18.2    13.2   \n",
       "47     43.6    33.1    33.5    21.6    33.1    15.6    12.0    20.7    16.5   \n",
       "498    34.7    29.4    33.5    17.7    25.4    12.4    10.8    20.2    12.8   \n",
       "439    34.9    27.3    32.2    17.7    25.4    12.6     9.0    17.4    12.2   \n",
       "289    34.9    28.0    30.8    15.6    24.2    11.2     9.2    17.9    13.2   \n",
       "10     40.3    29.0    33.0    20.1    30.3    13.4    10.4    19.4    14.5   \n",
       "309    35.0    27.0    33.0    19.3    25.0    11.5     9.9    17.1    13.0   \n",
       "36     41.1    27.8    31.4    19.0    31.5    14.5    11.9    18.5    13.0   \n",
       "435    37.8    32.0    33.8    16.8    26.6    13.4    10.4    18.8    13.4   \n",
       "125    39.6    28.7    32.4    18.2    28.3    15.2    11.8    19.6    14.8   \n",
       "\n",
       "     sho_gi  ...  bic_gi  for_gi  kne_gi  cal_gi  ank_gi  wri_gi  age   wgt  \\\n",
       "478   110.3  ...    29.9    24.9    38.7    36.3    21.3    15.5   28  72.3   \n",
       "47    128.1  ...    40.2    32.4    39.2    40.1    25.7    17.7   20  94.1   \n",
       "498   100.2  ...    36.9    29.0    40.3    37.9    24.6    16.4   34  75.5   \n",
       "439    87.0  ...    23.1    21.0    35.5    36.4    20.3    13.0   37  53.9   \n",
       "289    96.0  ...    26.3    23.0    32.3    30.0    18.8    14.3   20  54.5   \n",
       "10    117.1  ...    34.6    27.9    38.9    40.1    23.2    16.2   23  76.6   \n",
       "309    94.5  ...    28.0    23.0    34.0    34.2    20.6    15.0   25  62.0   \n",
       "36    114.8  ...    31.0    26.1    36.3    35.1    21.0    17.0   22  67.2   \n",
       "435   105.0  ...    28.4    24.3    34.7    34.5    20.9    14.9   34  63.6   \n",
       "125   119.4  ...    35.3    28.7    39.3    35.9    23.0    17.4   29  83.6   \n",
       "\n",
       "       hgt  sex  \n",
       "478  167.6    0  \n",
       "47   177.2    1  \n",
       "498  160.0    0  \n",
       "439  162.6    0  \n",
       "289  167.6    0  \n",
       "10   180.0    1  \n",
       "309  163.0    0  \n",
       "36   182.0    1  \n",
       "435  167.6    0  \n",
       "125  188.0    1  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_test.sample(10)\n",
    "# Testing Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backward Elimination:\n",
    "The lower the AIC, the better the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.186686\n",
      "         Iterations 9\n",
      "Iteration 1: AIC of the current model 161.21558041938258\n"
     ]
    }
   ],
   "source": [
    "mod_full = smf.logit(formula = 'sex ~ wri_di + age + thi_gi + che_di', data = body_dimensions_train).fit()\n",
    "print('Iteration 1: AIC of the current model', mod_full.aic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.312793\n",
      "         Iterations 7\n",
      "Iteration 2: AIC of the current model (without Wri.di) 261.3625714299693\n"
     ]
    }
   ],
   "source": [
    "mod_wri = smf.logit(formula = 'sex ~ age + thi_gi + che_di', data = body_dimensions_train).fit()\n",
    "print('Iteration 2: AIC of the current model (without Wri.di)', mod_wri.aic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.189041\n",
      "         Iterations 9\n",
      "Iteration 3: AIC of the current model (without Age) 161.1228581019434\n"
     ]
    }
   ],
   "source": [
    "mod_age = smf.logit(formula = 'sex ~ wri_di + thi_gi + che_di', data = body_dimensions_train).fit()\n",
    "print('Iteration 3: AIC of the current model (without Age)', mod_age.aic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.275084\n",
      "         Iterations 8\n",
      "Iteration 4: AIC of the current model (without thi.gi) 230.81830293810276\n"
     ]
    }
   ],
   "source": [
    "mod_thi = smf.logit(formula = 'sex ~ wri_di + age + che_di', data = body_dimensions_train).fit()\n",
    "print('Iteration 4: AIC of the current model (without thi.gi)', mod_thi.aic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.268067\n",
      "         Iterations 8\n",
      "Iteration 5: AIC of the current model (without che.di) 225.13388347205762\n"
     ]
    }
   ],
   "source": [
    "mod_che = smf.logit(formula = 'sex ~ wri_di + age + thi_gi', data = body_dimensions_train).fit()\n",
    "print('Iteration 5: AIC of the current model (without che.di)', mod_che.aic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We conclude that the favored model is the one on the 3rd iteration: without Age.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.189041\n",
      "         Iterations 9\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>sex</td>       <th>  No. Observations:  </th>  <td>   405</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   401</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 03 May 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.7271</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:39:21</td>     <th>  Log-Likelihood:    </th> <td> -76.561</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -280.52</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>4.285e-88</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -35.6835</td> <td>    5.081</td> <td>   -7.023</td> <td> 0.000</td> <td>  -45.643</td> <td>  -25.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wri_di</th>    <td>    3.4943</td> <td>    0.499</td> <td>    7.000</td> <td> 0.000</td> <td>    2.516</td> <td>    4.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>thi_gi</th>    <td>   -0.4425</td> <td>    0.070</td> <td>   -6.332</td> <td> 0.000</td> <td>   -0.579</td> <td>   -0.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>che_di</th>    <td>    0.8619</td> <td>    0.135</td> <td>    6.376</td> <td> 0.000</td> <td>    0.597</td> <td>    1.127</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                    sex   No. Observations:                  405\n",
       "Model:                          Logit   Df Residuals:                      401\n",
       "Method:                           MLE   Df Model:                            3\n",
       "Date:                Tue, 03 May 2022   Pseudo R-squ.:                  0.7271\n",
       "Time:                        20:39:21   Log-Likelihood:                -76.561\n",
       "converged:                       True   LL-Null:                       -280.52\n",
       "Covariance Type:            nonrobust   LLR p-value:                 4.285e-88\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -35.6835      5.081     -7.023      0.000     -45.643     -25.724\n",
       "wri_di         3.4943      0.499      7.000      0.000       2.516       4.473\n",
       "thi_gi        -0.4425      0.070     -6.332      0.000      -0.579      -0.306\n",
       "che_di         0.8619      0.135      6.376      0.000       0.597       1.127\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_age = smf.logit(formula = 'sex ~ wri_di + thi_gi + che_di', data = body_dimensions_train).fit()\n",
    "mod_age.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Equation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "log($\\hat{p}$/1-$\\hat{p}$) = -35.6835 + 3.4943 * wri_di - 0.4425 * thi_gi + 0.8619 * che_di"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which slopes in our final model do we have sufficient evidence to suggest are non-zero in the population model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the summary table, we can see that the p values for Wrist Diameter, Thigh Girth, and Chest Diameter are all 0, << any Significance Level. Such conclusion could be applied to the population - physically active people in California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9913651315789473\n"
     ]
    }
   ],
   "source": [
    "fprs, tprs, thresholds = roc_curve(y_true=body_dimensions_test['sex'], y_score=mod_age.predict(exog=body_dimensions_test)) \n",
    "auc = roc_auc_score(y_true=body_dimensions_test['sex'], y_score=mod_age.predict(exog=body_dimensions_test)) \n",
    "print(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc(fpr, tpr, auc, lw=2): \n",
    "   plt.plot(fpr, tpr, color='darkorange', lw=lw, \n",
    "            label='ROC curve (area = '+str(round(auc,3))+')') \n",
    "   plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') \n",
    "   plt.xlabel('False Positive Rate') \n",
    "   plt.ylabel('True Positive Rate') \n",
    "   plt.title('ROC Curve') \n",
    "   plt.legend(loc=\"lower right\") \n",
    "   plt.show()\n",
    "\n",
    "plot_roc(fprs, tprs, auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictive Probability Threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "405    0.002337\n",
       "430    0.034505\n",
       "25     0.999987\n",
       "473    0.043135\n",
       "270    0.243491\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppred_test = mod_age.predict(exog=body_dimensions_test)\n",
    "ppred_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>bia_di</th>\n",
       "      <th>bii_di</th>\n",
       "      <th>bit_di</th>\n",
       "      <th>che_de</th>\n",
       "      <th>che_di</th>\n",
       "      <th>elb_di</th>\n",
       "      <th>wri_di</th>\n",
       "      <th>kne_di</th>\n",
       "      <th>ank_di</th>\n",
       "      <th>sho_gi</th>\n",
       "      <th>...</th>\n",
       "      <th>for_gi</th>\n",
       "      <th>kne_gi</th>\n",
       "      <th>cal_gi</th>\n",
       "      <th>ank_gi</th>\n",
       "      <th>wri_gi</th>\n",
       "      <th>age</th>\n",
       "      <th>wgt</th>\n",
       "      <th>hgt</th>\n",
       "      <th>sex</th>\n",
       "      <th>ppred_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>36.9</td>\n",
       "      <td>28.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>27.8</td>\n",
       "      <td>11.6</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>21.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>27</td>\n",
       "      <td>53.6</td>\n",
       "      <td>165.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.339682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>38.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>32.9</td>\n",
       "      <td>22.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.6</td>\n",
       "      <td>13.8</td>\n",
       "      <td>99.5</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>36</td>\n",
       "      <td>76.4</td>\n",
       "      <td>167.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>37.1</td>\n",
       "      <td>27.8</td>\n",
       "      <td>31.7</td>\n",
       "      <td>20.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>99.3</td>\n",
       "      <td>...</td>\n",
       "      <td>24.9</td>\n",
       "      <td>35.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>15.3</td>\n",
       "      <td>27</td>\n",
       "      <td>62.7</td>\n",
       "      <td>167.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.077691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>35.0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>30.1</td>\n",
       "      <td>19.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>12.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>18.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>96.8</td>\n",
       "      <td>...</td>\n",
       "      <td>23.1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20</td>\n",
       "      <td>59.8</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>43.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>29.5</td>\n",
       "      <td>13.4</td>\n",
       "      <td>10.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>13.6</td>\n",
       "      <td>113.2</td>\n",
       "      <td>...</td>\n",
       "      <td>26.6</td>\n",
       "      <td>36.5</td>\n",
       "      <td>35.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>16.5</td>\n",
       "      <td>25</td>\n",
       "      <td>72.7</td>\n",
       "      <td>180.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.890968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bia_di  bii_di  bit_di  che_de  che_di  elb_di  wri_di  kne_di  ank_di  \\\n",
       "485    36.9    28.5    31.5    15.6    27.8    11.6     9.8    17.2    12.9   \n",
       "410    38.0    31.7    32.9    22.3    25.2    12.4    10.0    19.6    13.8   \n",
       "464    37.1    27.8    31.7    20.6    26.4    12.4    10.4    18.2    13.2   \n",
       "378    35.0    27.1    30.1    19.5    24.9    12.4     9.9    18.2    12.5   \n",
       "82     43.0    26.5    31.6    20.6    29.5    13.4    10.4    18.8    13.6   \n",
       "\n",
       "     sho_gi  ...  for_gi  kne_gi  cal_gi  ank_gi  wri_gi  age   wgt    hgt  \\\n",
       "485   100.0  ...    23.0    34.5    32.5    21.7    14.6   27  53.6  165.1   \n",
       "410    99.5  ...    25.0    35.5    38.0    21.0    14.6   36  76.4  167.6   \n",
       "464    99.3  ...    24.9    35.5    36.3    21.4    15.3   27  62.7  167.6   \n",
       "378    96.8  ...    23.1    35.0    34.4    20.0    15.0   20  59.8  167.0   \n",
       "82    113.2  ...    26.6    36.5    35.8    21.3    16.5   25  72.7  180.6   \n",
       "\n",
       "     sex  ppred_test  \n",
       "485    0    0.339682  \n",
       "410    0    0.000568  \n",
       "464    0    0.077691  \n",
       "378    0    0.011026  \n",
       "82     1    0.890968  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_test['ppred_test'] = ppred_test\n",
    "body_dimensions_test.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tpr_fpr_thresh(y, preb_prob, thresh):\n",
    "    that = 1 * (preb_prob >= thresh)\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true=y, y_pred=that).ravel()\n",
    "    tpr = tp / (fn + tp)\n",
    "    fpr = fp / (fp + tn)\n",
    "    return pd.DataFrame({'threshold':[thresh],\n",
    "                         'tpr': [tpr],\n",
    "                         'fpr': [fpr]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   threshold  tpr  fpr\n",
      "0        0.0  1.0  1.0\n",
      "   threshold  tpr    fpr\n",
      "0       0.01  1.0  0.625\n",
      "   threshold  tpr  fpr\n",
      "0       0.02  1.0  0.5\n",
      "   threshold  tpr     fpr\n",
      "0       0.03  1.0  0.4375\n",
      "   threshold  tpr      fpr\n",
      "0       0.04  1.0  0.34375\n",
      "   threshold  tpr       fpr\n",
      "0       0.05  1.0  0.296875\n",
      "   threshold  tpr       fpr\n",
      "0       0.06  1.0  0.265625\n",
      "   threshold  tpr   fpr\n",
      "0       0.07  1.0  0.25\n",
      "   threshold  tpr       fpr\n",
      "0       0.08  1.0  0.234375\n",
      "   threshold  tpr       fpr\n",
      "0       0.09  1.0  0.234375\n",
      "   threshold  tpr       fpr\n",
      "0        0.1  1.0  0.234375\n",
      "   threshold  tpr      fpr\n",
      "0       0.11  1.0  0.21875\n",
      "   threshold  tpr      fpr\n",
      "0       0.12  1.0  0.21875\n",
      "   threshold  tpr       fpr\n",
      "0       0.13  1.0  0.203125\n",
      "   threshold  tpr       fpr\n",
      "0       0.14  1.0  0.203125\n",
      "   threshold  tpr       fpr\n",
      "0       0.15  1.0  0.203125\n",
      "   threshold  tpr       fpr\n",
      "0       0.16  1.0  0.171875\n",
      "   threshold  tpr      fpr\n",
      "0       0.17  1.0  0.15625\n",
      "   threshold  tpr      fpr\n",
      "0       0.18  1.0  0.15625\n",
      "   threshold  tpr      fpr\n",
      "0       0.19  1.0  0.15625\n",
      "   threshold  tpr    fpr\n",
      "0        0.2  1.0  0.125\n",
      "   threshold  tpr       fpr\n",
      "0       0.21  1.0  0.109375\n",
      "   threshold  tpr       fpr\n",
      "0       0.22  1.0  0.109375\n",
      "   threshold  tpr       fpr\n",
      "0       0.23  1.0  0.109375\n",
      "   threshold  tpr       fpr\n",
      "0       0.24  1.0  0.109375\n",
      "   threshold  tpr      fpr\n",
      "0       0.25  1.0  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0       0.26  0.973684  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0       0.27  0.973684  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0       0.28  0.973684  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0       0.29  0.973684  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0        0.3  0.973684  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0       0.31  0.973684  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0       0.32  0.973684  0.09375\n",
      "   threshold       tpr      fpr\n",
      "0       0.33  0.973684  0.09375\n",
      "   threshold       tpr       fpr\n",
      "0       0.34  0.973684  0.078125\n",
      "   threshold       tpr       fpr\n",
      "0       0.35  0.973684  0.078125\n",
      "   threshold       tpr       fpr\n",
      "0       0.36  0.973684  0.078125\n",
      "   threshold       tpr     fpr\n",
      "0       0.37  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.38  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.39  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0        0.4  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.41  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.42  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.43  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.44  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.45  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.46  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.47  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.48  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.49  0.973684  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0        0.5  0.947368  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.51  0.921053  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.52  0.921053  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.53  0.894737  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.54  0.894737  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.55  0.894737  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.56  0.894737  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.57  0.894737  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.58  0.894737  0.0625\n",
      "   threshold       tpr     fpr\n",
      "0       0.59  0.894737  0.0625\n",
      "   threshold       tpr       fpr\n",
      "0        0.6  0.894737  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.61  0.894737  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.62  0.894737  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.63  0.894737  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.64  0.894737  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.65  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.66  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.67  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.68  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.69  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0        0.7  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.71  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.72  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.73  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.74  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.75  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.76  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.77  0.868421  0.046875\n",
      "   threshold       tpr       fpr\n",
      "0       0.78  0.868421  0.046875\n",
      "   threshold       tpr      fpr\n",
      "0       0.79  0.868421  0.03125\n",
      "   threshold       tpr      fpr\n",
      "0        0.8  0.868421  0.03125\n",
      "   threshold       tpr      fpr\n",
      "0       0.81  0.868421  0.03125\n",
      "   threshold       tpr       fpr\n",
      "0       0.82  0.868421  0.015625\n",
      "   threshold       tpr       fpr\n",
      "0       0.83  0.868421  0.015625\n",
      "   threshold       tpr  fpr\n",
      "0       0.84  0.868421  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.85  0.842105  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.86  0.842105  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.87  0.842105  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.88  0.842105  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.89  0.842105  0.0\n",
      "   threshold       tpr  fpr\n",
      "0        0.9  0.815789  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.91  0.763158  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.92  0.736842  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.93  0.710526  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.94  0.710526  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.95  0.657895  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.96  0.631579  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.97  0.605263  0.0\n",
      "   threshold       tpr  fpr\n",
      "0       0.98  0.526316  0.0\n",
      "   threshold  tpr  fpr\n",
      "0       0.99  0.5  0.0\n"
     ]
    }
   ],
   "source": [
    "for thresh in np.arange(0,1,0.01):\n",
    "    print(tpr_fpr_thresh(body_dimensions_test['sex'], body_dimensions_test['ppred_test'], thresh))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictive Probability Threshold Conclusion: \n",
    "It seems that the threshold = 0.25 with tpr = 1.0, fpr = 0.09375 is the most favorable choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>bia_di</th>\n",
       "      <th>bii_di</th>\n",
       "      <th>bit_di</th>\n",
       "      <th>che_de</th>\n",
       "      <th>che_di</th>\n",
       "      <th>elb_di</th>\n",
       "      <th>wri_di</th>\n",
       "      <th>kne_di</th>\n",
       "      <th>ank_di</th>\n",
       "      <th>sho_gi</th>\n",
       "      <th>...</th>\n",
       "      <th>kne_gi</th>\n",
       "      <th>cal_gi</th>\n",
       "      <th>ank_gi</th>\n",
       "      <th>wri_gi</th>\n",
       "      <th>age</th>\n",
       "      <th>wgt</th>\n",
       "      <th>hgt</th>\n",
       "      <th>sex</th>\n",
       "      <th>ppred_test</th>\n",
       "      <th>sexpred_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>35.1</td>\n",
       "      <td>25.6</td>\n",
       "      <td>29.9</td>\n",
       "      <td>16.8</td>\n",
       "      <td>24.9</td>\n",
       "      <td>12.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>18.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>98.7</td>\n",
       "      <td>...</td>\n",
       "      <td>33.6</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>14.8</td>\n",
       "      <td>20</td>\n",
       "      <td>57.3</td>\n",
       "      <td>161.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005537</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>35.9</td>\n",
       "      <td>25.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>16.8</td>\n",
       "      <td>11.0</td>\n",
       "      <td>98.4</td>\n",
       "      <td>...</td>\n",
       "      <td>34.2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>13.8</td>\n",
       "      <td>20</td>\n",
       "      <td>58.0</td>\n",
       "      <td>162.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>39.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>27.3</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12.4</td>\n",
       "      <td>100.5</td>\n",
       "      <td>...</td>\n",
       "      <td>36.1</td>\n",
       "      <td>35.3</td>\n",
       "      <td>20.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>25</td>\n",
       "      <td>62.5</td>\n",
       "      <td>172.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009042</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>39.4</td>\n",
       "      <td>29.7</td>\n",
       "      <td>33.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>14.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>20.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>119.4</td>\n",
       "      <td>...</td>\n",
       "      <td>38.2</td>\n",
       "      <td>36.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>18.1</td>\n",
       "      <td>45</td>\n",
       "      <td>84.5</td>\n",
       "      <td>167.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998422</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>34.2</td>\n",
       "      <td>29.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>17.6</td>\n",
       "      <td>13.4</td>\n",
       "      <td>96.4</td>\n",
       "      <td>...</td>\n",
       "      <td>32.7</td>\n",
       "      <td>33.0</td>\n",
       "      <td>19.6</td>\n",
       "      <td>14.9</td>\n",
       "      <td>36</td>\n",
       "      <td>55.2</td>\n",
       "      <td>165.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.034886</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     bia_di  bii_di  bit_di  che_de  che_di  elb_di  wri_di  kne_di  ank_di  \\\n",
       "429    35.1    25.6    29.9    16.8    24.9    12.6     9.6    18.2    12.5   \n",
       "377    35.9    25.1    28.2    18.1    26.1    11.6     8.5    16.8    11.0   \n",
       "259    39.5    30.0    31.7    17.3    27.3    12.8     9.2    18.1    12.4   \n",
       "137    39.4    29.7    33.1    23.0    30.4    14.2    11.6    20.4    15.0   \n",
       "436    34.2    29.2    31.1    18.0    25.9    12.8     9.8    17.6    13.4   \n",
       "\n",
       "     sho_gi  ...  kne_gi  cal_gi  ank_gi  wri_gi  age   wgt    hgt  sex  \\\n",
       "429    98.7  ...    33.6    35.0    19.7    14.8   20  57.3  161.3    0   \n",
       "377    98.4  ...    34.2    34.0    19.8    13.8   20  58.0  162.8    0   \n",
       "259   100.5  ...    36.1    35.3    20.9    14.4   25  62.5  172.9    0   \n",
       "137   119.4  ...    38.2    36.6    22.9    18.1   45  84.5  167.6    1   \n",
       "436    96.4  ...    32.7    33.0    19.6    14.9   36  55.2  165.1    0   \n",
       "\n",
       "     ppred_test  sexpred_test  \n",
       "429    0.005537             0  \n",
       "377    0.000145             0  \n",
       "259    0.009042             0  \n",
       "137    0.998422             1  \n",
       "436    0.034886             0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body_dimensions_test['sexpred_test'] = 1 * (body_dimensions_test['ppred_test'] > 0.25)\n",
    "body_dimensions_test.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[58,  6],\n",
       "       [ 0, 38]], dtype=int64)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_true = body_dimensions_test['sex'], y_pred = body_dimensions_test['sexpred_test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TN (a female is predicted to be a female): 58;\n",
    "\n",
    "FN (a male is predicted to be a female): 0;\n",
    "\n",
    "FP (a female is predicted to be a male): 6;\n",
    "\n",
    "TP (a male is predicted to be a male): 38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity Rate = 1.0\n",
      "Specificity Rate = 0.90625\n",
      "False Positive Rate = 0.09375\n"
     ]
    }
   ],
   "source": [
    "se = 38 / (38+0)\n",
    "sp = 58 / (58+6)\n",
    "fp = 1 - sp\n",
    "\n",
    "print('Sensitivity Rate =', se)\n",
    "print('Specificity Rate =', sp)\n",
    "print('False Positive Rate =', fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "According to the summary table, we can see that the p values for Wrist Diameter, Thigh Girth, and Chest Diameter are all 0, << any Significance Level. Such conclusion could be applied to the population - physically active people in California. As for a parsimonious model, we eliminated the predictor Age through Backward Elimination to make sure of the model simplicity and predictivity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer of each Research Question\n",
    "\n",
    "#### **Descriptive Analytics Research Question** - What is the tendency between Wrist Diameter and Ankle Diameter under the influence of Gender?\n",
    "\n",
    "Both the scatterplot and the summary statistics in part 2 show that there is approximately a linear relationship between wrist diameter and ankle diameter. Besides, both the wrist diameter and ankle diameter for males are bigger than that for females shown from the graph above.\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Inference Research Question** - What is the association between the mean wrist diameter for males and females?\n",
    "\n",
    "\n",
    "At the 5% significance level, since the p-value is 1.8845660432198486e-07 which is smaller than 0.05, the evidence is sufficient to reject the null hypothesis and conclude that the mean wrist diameter of males does not equal the mean wrist diameter of females in the target population.\n",
    "\n",
    "To answer our research question, our conclusion suggests that there is a difference between the mean wrist diameter for males and females.\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### **Linear Regression Research Question** - Is there a linear relationship between Wrist Diameter and Gender, Age, Thigh Girth, and Chest Diameter in the sample? What about in the population?\n",
    "\n",
    "Since our Significance of Regression F test p value (6.49e-110) < alpha value(0.05), we reject the null hypothesis and conclude that none of the slope is zero in the mdoel. \n",
    "\n",
    "Therefore, we believe there is a linear relationship between Wrist Diameter and Gender, Age, Thigh Girth, and Chest Diameter both in the sample and the population.\n",
    "<br/><br/> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Logistic Regression Research Questionn** - Is there a linear relationship between the log-odds of Gender and Wrist Diameter, Age, Thigh Girth, and Chest Diameter in the sample? What about in the population? What explanatory variables should we include in the model to build a parsimonious model?\n",
    "\n",
    "According to the summary table, we can see that the p values for Wrist Diameter, Thigh Girth, and Chest Diameter are all 0, << any Significance Level. Such conclusion could be applied to the population - physically active people in California. As for a parsimonious model, we eliminated the predictor Age through Backward Elimination to make sure of the model simplicity and predictivity.\n",
    "<br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Questions or Analyses that our analyses may entail:\n",
    "\n",
    "1. How should people interpret and define a dataset’s population to gain practical conclusions and insights from it?\n",
    "<br/><br/>\n",
    "\n",
    "2. Are there any other ways to find a better Predictive Threshold?\n",
    "<br/><br/>\n",
    "\n",
    "3. Are there any distinctive body dimensions that can help distinguish male citizens from female citizens or vice versa? For example, we may apply our conclusion to forensic science fields, so that they can analyze whether a piece of evidence is from either a biological male or female."
   ]
  }
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