Hypothesis_Testing_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from PIL import Image, ImageGrab\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='green'>Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to conduct a <font color='red'>2 Tailed T-Test</font> on 2 Independent samples with Numerical Data\n",
    "We need to check whether the mean of both samples are different and\n",
    "Is there any significance difference between the two samples?\n",
    "\n",
    "**Step 1**\n",
    "\n",
    "Make two Hypothesis one contradicting to other\n",
    "\n",
    "Null Hypothesis is want we want to prove\n",
    "+ Null Hypothesis: $\\mu_1 = \\mu_2$\n",
    "+ Alternative Hypthosis: $\\mu_1 \\neq \\mu_2$\n",
    "\n",
    "\n",
    "\n",
    "**Step 2**\n",
    "\n",
    "Decide a cut-off value\n",
    "+ Significance 5%\n",
    "+ alpha = 0.05\n",
    "+ As it is a two-tailed test\n",
    "+ alpha/2 = 0.025\n",
    "\n",
    "\n",
    "\n",
    "**Step 3**\n",
    "\n",
    "Collect evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unit A</th>\n",
       "      <th>Unit B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.8090</td>\n",
       "      <td>6.7703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.4376</td>\n",
       "      <td>7.5093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.9157</td>\n",
       "      <td>6.7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.3012</td>\n",
       "      <td>6.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4488</td>\n",
       "      <td>7.1522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.3871</td>\n",
       "      <td>6.8110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.8755</td>\n",
       "      <td>7.2212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0621</td>\n",
       "      <td>6.6606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.6840</td>\n",
       "      <td>7.2402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.8236</td>\n",
       "      <td>7.0503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unit A  Unit B\n",
       "0  6.8090  6.7703\n",
       "1  6.4376  7.5093\n",
       "2  6.9157  6.7300\n",
       "3  7.3012  6.7878\n",
       "4  7.4488  7.1522\n",
       "5  7.3871  6.8110\n",
       "6  6.8755  7.2212\n",
       "7  7.0621  6.6606\n",
       "8  6.6840  7.2402\n",
       "9  6.8236  7.0503"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing Files\n",
    "\n",
    "cutlets = pd.read_csv('C:\\\\Users\\\\Riyaz-PC\\\\Exceler Solutions\\\\Assignments\\\\3. Hypothesis Testing\\\\Cutlets.csv')\n",
    "cutlets.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unit A</th>\n",
       "      <th>Unit B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.019091</td>\n",
       "      <td>6.964297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.288408</td>\n",
       "      <td>0.343401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.437600</td>\n",
       "      <td>6.038000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.831500</td>\n",
       "      <td>6.753600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.943800</td>\n",
       "      <td>6.939900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.280550</td>\n",
       "      <td>7.195000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.516900</td>\n",
       "      <td>7.545900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unit A     Unit B\n",
       "count  35.000000  35.000000\n",
       "mean    7.019091   6.964297\n",
       "std     0.288408   0.343401\n",
       "min     6.437600   6.038000\n",
       "25%     6.831500   6.753600\n",
       "50%     6.943800   6.939900\n",
       "75%     7.280550   7.195000\n",
       "max     7.516900   7.545900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unit A    0\n",
       "Unit B    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets[cutlets.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unit A</th>\n",
       "      <th>Unit B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unit A, Unit B]\n",
       "Index: []"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cutlets[cutlets.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35 entries, 0 to 34\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Unit A  35 non-null     float64\n",
      " 1   Unit B  35 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 692.0 bytes\n"
     ]
    }
   ],
   "source": [
    "cutlets.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (7,4))\n",
    "plt.subplot(121)\n",
    "plt.boxplot(cutlets['Unit A'])\n",
    "plt.title('Unit A')\n",
    "plt.subplot(122)\n",
    "plt.boxplot(cutlets['Unit B'])\n",
    "plt.title('Unit B')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (7,4))\n",
    "plt.subplot(121)\n",
    "plt.hist(cutlets['Unit A'], bins = 15)\n",
    "plt.title('Unit A')\n",
    "plt.subplot(122)\n",
    "plt.hist(cutlets['Unit B'], bins = 15)\n",
    "plt.title('Unit B')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23d79373b60>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5,4))\n",
    "labels = ['Unit A', 'Unit B']\n",
    "sns.distplot(cutlets['Unit A'], kde = True)\n",
    "sns.distplot(cutlets['Unit B'],hist = True)\n",
    "plt.legend(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(cutlets[\"Unit A\"], line = 'q')\n",
    "plt.title('Unit A')\n",
    "sm.qqplot(cutlets[\"Unit B\"], line = 'q')\n",
    "plt.title('Unit B')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4**\n",
    "\n",
    "Compare Evidences with Hypothesis using t-statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_value= 0.47223947245995\n"
     ]
    }
   ],
   "source": [
    "statistic , p_value = stats.ttest_ind(cutlets['Unit A'],cutlets['Unit B'], alternative = 'two-sided')\n",
    "print('p_value=',p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare p_value with '$\\alpha$ '(Significane Level)\n",
    "\n",
    "If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence\n",
    "\n",
    "If p_value is = '$\\alpha$ ' we reject Null Hypothesis\n",
    "\n",
    "interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significnace=0.025, p=0.472\n",
      "We fail to reject Null hypothesis\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.025\n",
    "print('Significnace=%.3f, p=%.3f' % (alpha, p_value))\n",
    "if p_value <= alpha:\n",
    "    print('We reject Null Hypothesis there is a significance difference between two Units A and B')\n",
    "else:\n",
    "    print('We fail to reject Null hypothesis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hence, We fail to reject Null Hypothesis because of lack of evidence, there is no significant difference between the two samples**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='green'>Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to conduct a <font color='red'>Anova Test</font> on 4 Independent samples with Numerical Data\n",
    "We need to check whether the mean of any of these samples are different or the same?\n",
    "\n",
    "**Step 1**\n",
    "\n",
    "Make two Hypothesis one contradicting to other\n",
    "\n",
    "Null Hypothesis is want we want to prove\n",
    "+ Null Hypothesis: $\\mu_1 = \\mu_2$ = $\\mu_3$ = $\\mu_4$\n",
    "+ Alternative Hypthos: Atleast One of them is Differente\n",
    "\n",
    "\n",
    "\n",
    "**Step 2**\n",
    "\n",
    "Decide a cut-off value\n",
    "+ Significance 5%\n",
    "+ alpha = 0.05\n",
    "\n",
    "\n",
    "\n",
    "**Step 3**\n",
    "\n",
    "Collect evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Laboratory 1</th>\n",
       "      <th>Laboratory 2</th>\n",
       "      <th>Laboratory 3</th>\n",
       "      <th>Laboratory 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>185.35</td>\n",
       "      <td>165.53</td>\n",
       "      <td>176.70</td>\n",
       "      <td>166.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>170.49</td>\n",
       "      <td>185.91</td>\n",
       "      <td>198.45</td>\n",
       "      <td>160.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>192.77</td>\n",
       "      <td>194.92</td>\n",
       "      <td>201.23</td>\n",
       "      <td>185.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>177.33</td>\n",
       "      <td>183.00</td>\n",
       "      <td>199.61</td>\n",
       "      <td>176.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>193.41</td>\n",
       "      <td>169.57</td>\n",
       "      <td>204.63</td>\n",
       "      <td>152.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>179.45</td>\n",
       "      <td>197.00</td>\n",
       "      <td>181.51</td>\n",
       "      <td>161.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>191.37</td>\n",
       "      <td>166.36</td>\n",
       "      <td>214.21</td>\n",
       "      <td>154.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>166.81</td>\n",
       "      <td>169.60</td>\n",
       "      <td>183.43</td>\n",
       "      <td>163.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>158.81</td>\n",
       "      <td>175.36</td>\n",
       "      <td>191.60</td>\n",
       "      <td>152.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>165.88</td>\n",
       "      <td>198.68</td>\n",
       "      <td>208.43</td>\n",
       "      <td>161.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Laboratory 1  Laboratory 2  Laboratory 3  Laboratory 4\n",
       "0        185.35        165.53        176.70        166.13\n",
       "1        170.49        185.91        198.45        160.79\n",
       "2        192.77        194.92        201.23        185.18\n",
       "3        177.33        183.00        199.61        176.42\n",
       "4        193.41        169.57        204.63        152.60\n",
       "5        179.45        197.00        181.51        161.12\n",
       "6        191.37        166.36        214.21        154.02\n",
       "7        166.81        169.60        183.43        163.25\n",
       "8        158.81        175.36        191.60        152.79\n",
       "9        165.88        198.68        208.43        161.98"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing Files\n",
    "\n",
    "labtat = pd.read_csv('C:\\\\Users\\\\Riyaz-PC\\\\Exceler Solutions\\\\Assignments\\\\3. Hypothesis Testing\\\\LabTAT.csv')\n",
    "labtat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Laboratory 1</th>\n",
       "      <th>Laboratory 2</th>\n",
       "      <th>Laboratory 3</th>\n",
       "      <th>Laboratory 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>178.361583</td>\n",
       "      <td>178.902917</td>\n",
       "      <td>199.913250</td>\n",
       "      <td>163.68275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.173594</td>\n",
       "      <td>14.957114</td>\n",
       "      <td>16.539033</td>\n",
       "      <td>15.08508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>138.300000</td>\n",
       "      <td>140.550000</td>\n",
       "      <td>159.690000</td>\n",
       "      <td>124.06000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>170.335000</td>\n",
       "      <td>168.025000</td>\n",
       "      <td>188.232500</td>\n",
       "      <td>154.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>178.530000</td>\n",
       "      <td>178.870000</td>\n",
       "      <td>199.805000</td>\n",
       "      <td>164.42500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>186.535000</td>\n",
       "      <td>189.112500</td>\n",
       "      <td>211.332500</td>\n",
       "      <td>172.88250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>216.390000</td>\n",
       "      <td>217.860000</td>\n",
       "      <td>238.700000</td>\n",
       "      <td>205.18000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Laboratory 1  Laboratory 2  Laboratory 3  Laboratory 4\n",
       "count    120.000000    120.000000    120.000000     120.00000\n",
       "mean     178.361583    178.902917    199.913250     163.68275\n",
       "std       13.173594     14.957114     16.539033      15.08508\n",
       "min      138.300000    140.550000    159.690000     124.06000\n",
       "25%      170.335000    168.025000    188.232500     154.05000\n",
       "50%      178.530000    178.870000    199.805000     164.42500\n",
       "75%      186.535000    189.112500    211.332500     172.88250\n",
       "max      216.390000    217.860000    238.700000     205.18000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Laboratory 1    0\n",
       "Laboratory 2    0\n",
       "Laboratory 3    0\n",
       "Laboratory 4    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat[labtat.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Laboratory 1</th>\n",
       "      <th>Laboratory 2</th>\n",
       "      <th>Laboratory 3</th>\n",
       "      <th>Laboratory 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Laboratory 1, Laboratory 2, Laboratory 3, Laboratory 4]\n",
       "Index: []"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat[labtat.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 120 entries, 0 to 119\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Laboratory 1  120 non-null    float64\n",
      " 1   Laboratory 2  120 non-null    float64\n",
      " 2   Laboratory 3  120 non-null    float64\n",
      " 3   Laboratory 4  120 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 3.9 KB\n"
     ]
    }
   ],
   "source": [
    "labtat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (16,9))\n",
    "plt.subplot(221)\n",
    "plt.boxplot(labtat['Laboratory 1'])\n",
    "plt.title('Laboratory 1')\n",
    "plt.subplot(222)\n",
    "plt.boxplot(labtat['Laboratory 2'])\n",
    "plt.title('Laboratory 2')\n",
    "plt.subplot(223)\n",
    "plt.boxplot(labtat['Laboratory 3'])\n",
    "plt.title('Laboratory 3')\n",
    "plt.subplot(224)\n",
    "plt.boxplot(labtat['Laboratory 4'])\n",
    "plt.title('Laboratory 4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zM1M7d+7U1KlTNX78eO3fv7/a+0tKSlJeXp51qcm+AAAAgMakVq7ge3h4qGPHjpKkPn366PPPP9frr7+u0aNH68KFC8rNzbW5ip+Tk6Pg4OBK93f5m2zz8/NrI0wAAADA5dXJm2xLS0tVXFysPn36qGnTpjZvsj148KCysrJ4ky0AAABQB2qc4CclJWnr1q06evSo9u7dq6SkJKWlpenee++Vn5+fJk2apMTERG3evFkZGRmaOHGioqOjmUEHABqY5ORk9e3bVz4+PgoMDNSIESN08OBBmzqDBg2SxWKxWaZMmeKkiAGgcarxLTqnTp3Sfffdp5MnT8rPz089e/bUhg0b9Jvf/EaS9Nprr8nNzU2jRo1ScXGxYmNj9eabb9Y4cACAY23ZskXx8fHq27evLl68qCeeeEJDhgzR/v371bx5c2u9yZMna+7cudb1Zs2aOSNcAGi0apzgL1y48Irbvby8lJKSopSUlJoeCgDgROvXr7dZX7x4sQIDA5WRkaEBAwZYy5s1a3bF56wAAHWrTu7BBwC4vry8PElSQECATfm//vUvtWrVSt27d1dSUpJ++umnSvdx+XtPmFQBAGquVmbRAQA0LqWlpZo2bZr69eun7t27W8vHjh2riIgIhYaGas+ePXrsscd08OBBrVixosL98N4TAKh9JPgAgCqLj4/Xvn37tG3bNpvyBx54wPrnHj16KCQkRLfffrsOHz6sDh06lNtPUlKSEhMTrev5+fkKCwuru8ABoBEgwQcAVElCQoLWrFmjrVu3qm3btlesGxUVJUk6dOhQhQn+5e89AQDUHAk+AMAuxhg9+OCDWrlypdLS0hQZGXnVNpmZmZKkkJCQOo4OAFCGBB8AYJf4+HgtXbpUH330kXx8fJSdnS1J8vPzk7e3tw4fPqylS5fqjjvuUMuWLbVnzx5Nnz5dAwYMUM+ePZ0cPQA0HiT4AAC7zJ8/X9IvL7P6tUWLFmnChAny8PDQp59+qnnz5qmoqEhhYWEaNWqUnnrqKSdECwCNFwk+AMAuxpgrbg8LC9OWLVscFA0AoDLMgw8AAAC4EBJ8AAAAwIWQ4AMAAAAuhAQfAAAAcCEk+AAAAIALIcEHAAAAXAgJPgAAAOBCSPABAAAAF8KLrgAAwBW1e3ytw4959PlhDj8m4Cq4gg8AAAC4EBJ8AAAAwIWQ4AMAAAAuhHvw0WA44x5QAACAhoYr+AAAAIALqXGCn5ycrL59+8rHx0eBgYEaMWKEDh48aFNn0KBBslgsNsuUKVNqemgAAAAAl6lxgr9lyxbFx8drx44d2rhxo0pKSjRkyBAVFRXZ1Js8ebJOnjxpXV588cWaHhoAAADAZWp8D/769ett1hcvXqzAwEBlZGRowIAB1vJmzZopODi4pocDAAAAcAW1fg9+Xl6eJCkgIMCm/F//+pdatWql7t27KykpST/99FNtHxoAAABo9Gp1Fp3S0lJNmzZN/fr1U/fu3a3lY8eOVUREhEJDQ7Vnzx499thjOnjwoFasWFHhfoqLi1VcXGxdLygoqM0wAQAAAJdVqwl+fHy89u3bp23bttmUP/DAA9Y/9+jRQyEhIbr99tt1+PBhdejQodx+kpOTNWfOnNoMDQAAAGgUau0WnYSEBK1Zs0abN29W27Ztr1g3KipKknTo0KEKtyclJSkvL8+67N+/v7bCBAAAAFxaja/gG2P04IMPauXKlUpLS1NkZORV22RmZkqSQkJCKtzu6ekpT09P63p+fn5NwwQAAAAahRon+PHx8Vq6dKk++ugj+fj4KDs7W5Lk5+cnb29vHT58WEuXLtUdd9yhli1bas+ePZo+fboGDBignj171vgDAAAAAPh/apzgz58/X9IvL7P6tUWLFmnChAny8PDQp59+qnnz5qmoqEhhYWEaNWqUnnrqqZoeGgAAAMBlauUWnSsJCwvTli1banoYAAAAAHao9XnwAQAAADgPCT4AAADgQkjwAQB2SU5OVt++feXj46PAwECNGDFCBw8etKlz/vx5xcfHq2XLlmrRooVGjRqlnJwcJ0UMAI0TCT4AwC5btmxRfHy8duzYoY0bN6qkpERDhgxRUVGRtc706dP18ccfa/ny5dqyZYtOnDihkSNHOjFqAGh8avVNtgAA17V+/Xqb9cWLFyswMFAZGRkaMGCA8vLytHDhQi1dulSDBw+W9MuMal26dNGOHTt08803OyNsAGh0uIIPAKiWvLw8SVJAQIAkKSMjQyUlJYqJibHW6dy5s8LDw5Wenl7hPoqLi5Wfn2+zAABqhgQfAFBlpaWlmjZtmvr166fu3btLkrKzs+Xh4SF/f3+bukFBQdaXIF4uOTlZfn5+1iUsLKyuQwcAl8ctOkA90+7xtQ4/5tHnhzn8mGjY4uPjtW/fPm3btq1G+0lKSlJiYqJ1PT8/nyQfAGqIBB8AUCUJCQlas2aNtm7dqrZt21rLg4ODdeHCBeXm5tpcxc/JyVFwcHCF+/L09JSnp2ddhwwAjQq36AAA7GKMUUJCglauXKlNmzYpMjLSZnufPn3UtGlTpaamWssOHjyorKwsRUdHOzpcAGi0uIIPALBLfHy8li5dqo8++kg+Pj7W++r9/Pzk7e0tPz8/TZo0SYmJiQoICJCvr68efPBBRUdHM4MOADgQCT4AwC7z58+XJA0aNMimfNGiRZowYYIk6bXXXpObm5tGjRql4uJixcbG6s0333RwpADQuJHgAwDsYoy5ah0vLy+lpKQoJSXFAREBACrCPfgAAACACyHBBwAAAFwICT4AAADgQkjwAQAAABdCgg8AAAC4EBJ8AAAAwIWQ4AMAAAAuhAQfAAAAcCEk+AAAAIALqfGbbJOTk7VixQp9/fXX8vb21i233KIXXnhBnTp1stY5f/68HnnkES1btszm1eVBQUE1PTwAAHBB7R5f6+wQgAarxlfwt2zZovj4eO3YsUMbN25USUmJhgwZoqKiImud6dOn6+OPP9by5cu1ZcsWnThxQiNHjqzpoQEAAABcpsZX8NevX2+zvnjxYgUGBiojI0MDBgxQXl6eFi5cqKVLl2rw4MGSpEWLFqlLly7asWOHbr755pqGAAAAAOD/V+v34Ofl5UmSAgICJEkZGRkqKSlRTEyMtU7nzp0VHh6u9PT02j48AAAA0KjV+Ar+r5WWlmratGnq16+funfvLknKzs6Wh4eH/P39beoGBQUpOzu7wv0UFxeruLjYul5QUFCbYQIAAAAuq1av4MfHx2vfvn1atmxZjfaTnJwsPz8/69K1a9daihAAAABwbbWW4CckJGjNmjXavHmz2rZtay0PDg7WhQsXlJuba1M/JydHwcHBFe4rKSlJeXl51mX//v21FSYAAADg0mqc4BtjlJCQoJUrV2rTpk2KjIy02d6nTx81bdpUqamp1rKDBw8qKytL0dHRFe7T09NTvr6+1sXHx6emYQIAAACNQo3vwY+Pj9fSpUv10UcfycfHx3pfvZ+fn7y9veXn56dJkyYpMTFRAQEB8vX11YMPPqjo6Ghm0AEAAABqWY0T/Pnz50uSBg0aZFO+aNEiTZgwQZL02muvyc3NTaNGjbJ50RUAAACA2lXjBN8Yc9U6Xl5eSklJUUpKSk0PBwAAAOAKan0efAAAAADOQ4IPAAAAuBASfAAAAMCFkOADAAAALoQEHwAAAHAh1UrwU1JS1K5dO3l5eSkqKkq7du26Yv158+apU6dO8vb2VlhYmKZPn67z589XK2AAAAAAlatygv/+++8rMTFRs2bN0u7du9WrVy/Fxsbq1KlTFdZfunSpHn/8cc2aNUsHDhzQwoUL9f777+uJJ56ocfAAAAAAbFU5wX/11Vc1efJkTZw4UV27dtWCBQvUrFkzvfPOOxXW3759u/r166exY8eqXbt2GjJkiMaMGXPVq/4AgPpl69atGj58uEJDQ2WxWLRq1Sqb7RMmTJDFYrFZhg4d6pxgAaARq1KCf+HCBWVkZCgmJub/7cDNTTExMUpPT6+wzS233KKMjAxrQv/dd99p3bp1uuOOO2oQNgDA0YqKitSrV68rvrRw6NChOnnypHV57733HBghAECq4ptsT58+rUuXLikoKMimPCgoSF9//XWFbcaOHavTp0/r1ltvlTFGFy9e1JQpU654i05xcbGKi4ut6wUFBVUJEwBQB+Li4hQXF3fFOp6engoODnZQRACAitT5LDppaWl67rnn9Oabb2r37t1asWKF1q5dq2eeeabSNsnJyfLz87MuXbt2reswAQC1IC0tTYGBgerUqZOmTp2qM2fOODskAGh0qnQFv1WrVnJ3d1dOTo5NeU5OTqVXbJ5++mmNGzdO999/vySpR48eKioq0gMPPKAnn3xSbm7l/42RlJSkxMRE6/oPP/xAkg8A9dzQoUM1cuRIRUZG6vDhw3riiScUFxen9PR0ubu7V9jm8l9s8/PzHRUuALisKiX4Hh4e6tOnj1JTUzVixAhJUmlpqVJTU5WQkFBhm59++qlcEl820BtjKmzj6ekpT09P6zoDPgDUf/fcc4/1zz169FDPnj3VoUMHpaWl6fbbb6+wTXJysubMmeOoEIFGr93jax1+zKPPD3P4MRu7Kt+ik5iYqLfffltLlizRgQMHNHXqVBUVFWnixImSpPvuu09JSUnW+sOHD9f8+fO1bNkyHTlyRBs3btTTTz+t4cOHV3pFBwDQ8LVv316tWrXSoUOHKq2TlJSkvLw863Ls2DEHRggArqlKV/AlafTo0frxxx81c+ZMZWdnq3fv3lq/fr31wdusrCybK/ZPPfWULBaLnnrqKf3www9q3bq1hg8frr/+9a+19yngFM64CgCg4Th+/LjOnDmjkJCQSutc/ostAKDmqpzgS1JCQkKlt+SkpaXZHqBJE82aNUuzZs2qzqEAAPVEYWGhzdX4I0eOKDMzUwEBAQoICNCcOXM0atQoBQcH6/Dhw3r00UfVsWNHxcbGOjFqAGh8qpXgAwAany+++EK33Xabdb1sMoTx48dr/vz52rNnj5YsWaLc3FyFhoZqyJAheuaZZ7hCDwAORoIPALDLoEGDKp0cQZI2bNjgwGgAAJWp83nwAQAAADgOCT4AAADgQrhFBwAAwEmYkQ51gSv4AAAAgAshwQcAAABcSI0T/K1bt2r48OEKDQ2VxWLRqlWrbLZPmDBBFovFZhk6dGhNDwsAAACgAjVO8IuKitSrVy+lpKRUWmfo0KE6efKkdXnvvfdqelgAAAAAFajxQ7ZxcXGKi4u7Yh1PT08FBwfX9FAAAAAArsIh9+CnpaUpMDBQnTp10tSpU3XmzJkr1i8uLlZ+fr51KSgocESYAAAAQINX59NkDh06VCNHjlRkZKQOHz6sJ554QnFxcUpPT5e7u3uFbZKTkzVnzpy6Dg0AgAaHaRUBXE2dJ/j33HOP9c89evRQz5491aFDB6Wlpen222+vsE1SUpISExOt6z/88IO6du1a16ECAAAADZ7Dp8ls3769WrVqpUOHDlVax9PTU76+vtbFx8fHgRECAAAADZfDE/zjx4/rzJkzCgkJcfShAQAAAJdX41t0CgsLba7GHzlyRJmZmQoICFBAQIDmzJmjUaNGKTg4WIcPH9ajjz6qjh07KjY2tqaHBgAAAHCZGif4X3zxhW677Tbretm98+PHj9f8+fO1Z88eLVmyRLm5uQoNDdWQIUP0zDPPyNPTs6aHBgAAAHCZGif4gwYNkjGm0u0bNmyo6SEAAAAA2Mnh9+ADAAAAqDsk+AAAAIALIcEHAAAAXAgJPgAAAOBCSPABAAAAF0KCDwAAALgQEnwAAADAhZDgAwAAAC6EBB8AAABwIST4AAAAgAshwQcAAABcCAk+AAAA4EJI8AEAdtm6dauGDx+u0NBQWSwWrVq1yma7MUYzZ85USEiIvL29FRMTo2+//dY5wQJAI0aCDwCwS1FRkXr16qWUlJQKt7/44ov629/+pgULFmjnzp1q3ry5YmNjdf78eQdHCgCNWxNnBwAAaBji4uIUFxdX4TZjjObNm6ennnpKd955pyTp3XffVVBQkFatWqV77rnHkaECQKPGFXwAQI0dOXJE2dnZiomJsZb5+fkpKipK6enplbYrLi5Wfn6+zQIAqBkSfABAjWVnZ0uSgoKCbMqDgoKs2yqSnJwsPz8/6xIWFlancQJAY0CCDwBwmqSkJOXl5VmXY8eOOTskAGjwSPABADUWHBwsScrJybEpz8nJsW6riKenp3x9fW0WAEDNkOADAGosMjJSwcHBSk1NtZbl5+dr586dio6OdmJkAND4MIsOAMAuhYWFOnTokHX9yJEjyszMVEBAgMLDwzVt2jQ9++yzuvbaaxUZGamnn35aoaGhGjFihPOCBoBGiAQfAGCXL774Qrfddpt1PTExUZI0fvx4LV68WI8++qiKior0wAMPKDc3V7feeqvWr18vLy8vZ4UMAI1SjW/R4c2GANA4DBo0SMaYcsvixYslSRaLRXPnzlV2drbOnz+vTz/9VNddd51zgwaARqjGCT5vNgQAAADqjxrfosObDQEAAID6o05n0anumw0BAAAAVE+dPmRb3TcbFhcXq7i42LpeUFBQNwECAAAALqZezoN/+avLu3bt6uyQAAAAgAahThP86r7Z8PJXl+/fv78uwwQAAABcRp0m+NV9s+Hlry738fGpyzABAAAAl1Hje/B5syEAAABQf9Q4wefNhgAAAED9UeMEv+zNhpUpe7Ph3Llza3ooAAAAAFdRL2fRAQAAAFA9JPgAAACACyHBBwAAAFwICT4AAADgQkjwAQAAABdCgg8AAAC4EBJ8AAAAwIXUeB58AA1fu8fXOvyYR58f5vBjAgDQGHAFHwAAAHAhJPgAAACACyHBBwAAAFwICT4AAADgQkjwAQAAABdCgg8AAAC4EBJ8AAAAwIWQ4AMAAAAuhAQfAAAAcCEk+ACAWjN79mxZLBabpXPnzs4OCwAalSbODgAA4Fq6deumTz/91LrepAlfNQDgSIy6AIBa1aRJEwUHBzs7DABotEjwXUS7x9c6OwQAkCR9++23Cg0NlZeXl6Kjo5WcnKzw8HBnhwUAjQYJPgCg1kRFRWnx4sXq1KmTTp48qTlz5qh///7at2+ffHx8ytUvLi5WcXGxdT0/P9+R4QKAS6rzh2x54AoAGo+4uDjdfffd6tmzp2JjY7Vu3Trl5ubqgw8+qLB+cnKy/Pz8rEtYWJiDIwYA1+OQWXS6deumkydPWpdt27Y54rAAACfz9/fXddddp0OHDlW4PSkpSXl5edbl2LFjDo4QAFyPQ27R4YErAGicCgsLdfjwYY0bN67C7Z6envL09HRwVADg2hxyBb/sgav27dvr3nvvVVZWliMOCwBwsL/85S/asmWLjh49qu3bt+uuu+6Su7u7xowZ4+zQAKDRqPMr+FV94Eoq/9BVQUFBXYcJAKgFx48f15gxY3TmzBm1bt1at956q3bs2KHWrVs7OzQAaDTqPMGPi4uz/rlnz56KiopSRESEPvjgA02aNKnCNsnJyZozZ05dhwYAqGXLli1zdggA0Og55BadX7vaA1dS+Yeu9u/f78AIAQAAgIbL4Ql+2QNXISEhldbx9PSUr6+vdansVh4AAAAAtur8Fp2//OUvGj58uCIiInTixAnNmjWLB64AAC6Bt4gDqI+qdQU/JSVF7dq1k5eXl6KiorRr165K6x4/flz33HOPOnTooP79+2vTpk1q3ry5Pv/882oHDQAAAKBiVU7w33//fSUmJmrWrFnavXu3evXqpdjYWJ06darC+u+++67atm2roUOHauvWrTp8+LCWLFmiNm3a1Dh4AAAAALaqfIvOq6++qsmTJ2vixImSpAULFmjt2rV655139Pjjj5er/8477+js2bPavn27mjZtKklq165dzaIGAAAAUKEqJfgXLlxQRkaGkpKSrGVubm6KiYlRenp6hW1Wr16t6OhoxcfH66OPPlLr1q01duxYPfbYY3J3d6+wDfPgA67PGfcuH31+mMOPCQCAo1XpFp3Tp0/r0qVLCgoKsikPCgpSdnZ2hW2+++47ffjhh7p06ZLWrVunp59+Wq+88oqeffbZSo+TnJwsPz8/69K1a9eqhAkAAAA0WnU+TWZpaakCAwP11ltvqU+fPho9erSefPJJLViwoNI2zIMPAAAAVE+VbtFp1aqV3N3dlZOTY1Oek5Oj4ODgCtuEhISoadOmNrfjdOnSRdnZ2bpw4YI8PDzKtfH09JSnp6d1PT8/vyphAgAAoBFr7LeBVukKvoeHh/r06aPU1FRrWWlpqVJTUxUdHV1hm379+unQoUMqLS21ln3zzTcKCQmpMLkHAAAAUH1VvkUnMTFRb7/9tpYsWaIDBw5o6tSpKioqss6qc99999k8hDt16lSdPXtWDz/8sL755hutXbtWzz33nOLj42vvUwAAAACQVI1pMkePHq0ff/xRM2fOVHZ2tnr37q3169dbH7zNysqSm9v/+3dDWFiYNmzYoOnTp6tnz55q06aNHn74YT322GO19ymuoLH/RAMAAIDGpcoJviQlJCQoISGhwm1paWnlyqKjo7Vjx47qHAoAAABAFdT5LDoAAAAAHIcEHwAAAHAhJPgAAACAC6nWPfgAANRHzphYAcCV8ffS8biCDwAAALgQruADaDQcfRWJKXMBAM7AFXwAAADAhZDgAwAAAC6EBB8AAABwIST4AAAAgAshwQcAAABcCAk+AAAA4EKYJrMO8EIHAAAAOAtX8AEAAAAX4rAEPyUlRe3atZOXl5eioqK0a9cuRx0aAOBgjPkA4DwOSfDff/99JSYmatasWdq9e7d69eql2NhYnTp1yhGHBwA4EGM+ADiXQxL8V199VZMnT9bEiRPVtWtXLViwQM2aNdM777zjiMMDAByIMR8AnKvOH7K9cOGCMjIylJSUZC1zc3NTTEyM0tPTK2xTXFys4uJi63p+fr7Nf6uitPinKrcBgNpQnTHr1+2MMbUZjkNUdcy/fLzPy8uTVP2+Y8wH4CzVGbfqaryv8wT/9OnTunTpkoKCgmzKg4KC9PXXX1fYJjk5WXPmzClXHhYWVicxAkBd8JtXs/YFBQXy8/OrlVgcpapjPuM9AFdRkzG/sLCw1uKQ6uk0mUlJSUpMTLSu5+bmKiIiQllZWbXyZZefn6+wsDAdO3ZMvr6+Nd6fq6Kf7EM/2Yd+sk9ZP+3fv1+hoaHODqfOXT7el5aW6uzZs2rZsqUsFotd++Dcsg/9ZD/6yj70k/0q66u8vDyFh4crJCSkVo9X5wl+q1at5O7urpycHJvynJwcBQcHV9jG09NTnp6e5cr9/Pxq9QTy9fXlhLQD/WQf+sk+9JN92rRpIze3hjeTcVXH/IrGe39//2odm3PLPvST/egr+9BP9qusr2p7vK/zbw8PDw/16dNHqamp1rLS0lKlpqYqOjq6rg8PAHAgxnwAcD6H3KKTmJio8ePH68Ybb9RNN92kefPmqaioSBMnTnTE4QEADsSYDwDO5ZAEf/To0frxxx81c+ZMZWdnq3fv3lq/fn25h7AcxdPTU7NmzarwNiD8P/STfegn+9BP9nGFfnL0mO8KfeYI9JP96Cv70E/2c3RfWUwDmIctPz9ffn5+ysvL4x4vAAAAuIS6ynEb3hNcAAAAACpFgg8AAAC4EBJ8AAAAwIWQ4AMAAAAuxKUS/K1bt2r48OEKDQ2VxWLRqlWrKq07ZcoUWSwWzZs3z6b87Nmzuvfee+Xr6yt/f39NmjSp1l8f7Gz29NOBAwf0u9/9Tn5+fmrevLn69u2rrKws6/bz588rPj5eLVu2VIsWLTRq1KhyL7ZpyK7WR4WFhUpISFDbtm3l7e2trl27asGCBTZ1XL2PJCk5OVl9+/aVj4+PAgMDNWLECB08eNCmjj39kJWVpWHDhqlZs2YKDAzUjBkzdPHiRUd+lDp1tX46e/asHnzwQXXq1Ene3t4KDw/XQw89pLy8PJv9uHo//RrjuX0Yz+3HuG4fxnX71dbYLkl33313rfaVSyX4RUVF6tWrl1JSUq5Yb+XKldqxY0eFr4G/99579dVXX2njxo1as2aNtm7dqgceeKCuQnaKq/XT4cOHdeutt6pz585KS0vTnj179PTTT8vLy8taZ/r06fr444+1fPlybdmyRSdOnNDIkSMd9RHq3NX6KDExUevXr9c///lPHThwQNOmTVNCQoJWr15trePqfSRJW7ZsUXx8vHbs2KGNGzeqpKREQ4YMUVFRkbXO1frh0qVLGjZsmC5cuKDt27dryZIlWrx4sWbOnOmMj1QnrtZPJ06c0IkTJ/Tyyy9r3759Wrx4sdavX69JkyZZ99EY+unXGM/tw3huP8Z1+zCu26+2xnZJtd9XpgHIy8szkkxeXp7dbSSZlStXlis/fvy4adOmjdm3b5+JiIgwr732mnXb/v37jSTz+eefW8v+/e9/G4vFYn744YeafIR6q6J+Gj16tPnDH/5QaZvc3FzTtGlTs3z5cmvZgQMHjCSTnp5eV6E6TUV91K1bNzN37lybshtuuME8+eSTxpjG10dlTp06ZSSZLVu2GGPs64d169YZNzc3k52dba0zf/584+vra4qLix37ARzk8n6qyAcffGA8PDxMSUmJMaZx9lMZxnP7MJ7bj3Hdfozr9qvO2P7hhx8aSebbb7+11qmNvnKpK/hXU1paqnHjxmnGjBnq1q1bue3p6eny9/fXjTfeaC2LiYmRm5ubdu7c6chQnaa0tFRr167Vddddp9jYWAUGBioqKsrmp8yMjAyVlJQoJibGWta5c2eFh4crPT3dCVE73i233KLVq1frhx9+kDFGmzdv1jfffKMhQ4ZIarx9VPazY0BAgCT7+iE9PV09evSweQlSbGys8vPz9dVXXzkwese5vJ8qq+Pr66smTX55H2Fj7KcrYTy/OsbzqmFcrxjjuv2qM7bv2rVLkhQYGGitUxt91agS/BdeeEFNmjTRQw89VOH27Oxsmw6WpCZNmiggIEDZ2dmOCNHpTp06pcLCQj3//PMaOnSoPvnkE911110aOXKktmzZIumXfvLw8JC/v79N26CgoEbTT2+88Ya6du2qtm3bysPDQ0OHDlVKSooGDBggqXH2UWlpqaZNm6Z+/fqpe/fukuzrh+zs7HJvOC1bd8W+qqifLnf69Gk988wzNreTNLZ+uhrG86tjPK8axvXyGNftV92xvaJnOGqjr5pUu2UDk5GRoddff127d++WxWJxdjj1VmlpqSTpzjvv1PTp0yVJvXv31vbt27VgwQINHDjQmeHVG2+88YZ27Nih1atXKyIiQlu3blV8fLxCQ0Ntrmo0JvHx8dq3b5+2bdvm7FDqtav1U35+voYNG6auXbtq9uzZjg2ugWA8tw/jedUwrpfHuG6/+ja2N5or+J999plOnTql8PBwNWnSRE2aNNH333+vRx55RO3atZMkBQcH69SpUzbtLl68qLNnzyo4ONgJUTteq1at1KRJE3Xt2tWmvEuXLtZZF4KDg3XhwgXl5uba1MnJyWkU/fTzzz/riSee0Kuvvqrhw4erZ8+eSkhI0OjRo/Xyyy9Lanx9lJCQoDVr1mjz5s1q27attdyefggODi53BaNs3dX6qrJ+KlNQUKChQ4fKx8dHK1euVNOmTa3bGlM/XQ3juX0Yz+3HuF4e47r9ajK2X/5Lh1Q7fdVoEvxx48Zpz549yszMtC6hoaGaMWOGNmzYIEmKjo5Wbm6uMjIyrO02bdqk0tJSRUVFOSt0h/Lw8FDfvn3LTYn1zTffKCIiQpLUp08fNW3aVKmpqdbtBw8eVFZWlqKjox0arzOUlJSopKREbm62f33c3d2tV8waSx8ZY5SQkKCVK1dq06ZNioyMtNluTz9ER0dr7969NsnYxo0b5evrWy4xaaiu1k/SL1d3hgwZIg8PD61evdpmlhOpcfSTvRjP7cN4bj/G9f+Hcd1+tTG233TTTZKkH3/80VpWK31V7cdzHcjeWXQKCgrMl19+ab788ksjybz66qvmyy+/NN9//32F9S+fdcEYY4YOHWquv/56s3PnTrNt2zZz7bXXmjFjxtTWR6kXrtZPK1asME2bNjVvvfWW+fbbb80bb7xh3N3dzWeffWbdx5QpU0x4eLjZtGmT+eKLL0x0dLSJjo521keqdVfro4EDB5pu3bqZzZs3m++++84sWrTIeHl5mTfffNO6D1fvI2OMmTp1qvHz8zNpaWnm5MmT1uWnn36y1rlaP1y8eNF0797dDBkyxGRmZpr169eb1q1bm6SkJGd8pDpxtX7Ky8szUVFRpkePHubQoUM2dS5evGiMaRz99GuM5/ZhPLcf47p9GNftVxtj+9mzZ40kM3jw4FrtK5dK8Ddv3mwklVvGjx9fYf2KvhDOnDljxowZY1q0aGF8fX3NxIkTTUFBQS19kvrBnn5auHCh6dixo/Hy8jK9evUyq1atstnHzz//bP785z+ba665xjRr1szcdddd5uTJk7Ua55EjR4wk89JLL9Xqfu1xtT46efKkmTBhggkNDTVeXl6mU6dO5pVXXjGlpaXWfTiij5ytoj6SZBYtWmStY08/HD161MTFxRlvb2/TqlUr88gjj1inEHMFV+unys43SebIkSPW/bh6P/0a47l9Gsp4bo+6HvMZ1+3DuG6/2hjby3Lc3/zmN7XaVy6V4MP5Fi1aVG7u6epyZoJ/Jf/5z3/MrFmzzLlz55wdSjlvvfWWGTBggAkMDDQeHh6mXbt2ZsKECTZJIgDUFsb8+uPChQumS5cu9bIPUbm6ynEbzT34QG3Zvn275syZU+4Bo/rgyy+/VGRkpB599FHNnz9ff/jDH/Tvf/9bffv21YkTJ5wdHgA0OPV5zP+1N954w/rwNNBopskEKlNUVKTmzZs7NQZjjM6fPy9vb+8a7efNN98sVzZixAjdeOONevfdd/X444/XaP8A0NC50phf5tSpU5o7d64ee+wxzZw5s1b2iYaNK/hwuAsXLmjmzJnq06eP/Pz81Lx5c/Xv31+bN2+utM1rr72miIgIeXt7a+DAgdq3b1+5Ops2bVL//v3VvHlz+fv7684779SBAwds6syePVsWi0X79+/X2LFjdc011+jWW2+VJO3Zs0cTJkxQ+/bt5eXlpeDgYP3xj3/UmTNnbNrPmDFDkhQZGSmLxSKLxaKjR49K+mUavmeeeUYdOnSQp6en2rVrpyeeeELFxcU2cbRr106//e1vtWHDBt14443y9vbW3//+dw0cOFC9evWqsA86deqk2NjYq3fwZcqmDazvV58AuCbG/Lof8x9//HF16tRJf/jDH+yqD9fHFXw4XH5+vv7xj39ozJgxmjx5sgoKCrRw4ULFxsZq165d6t27t039d999VwUFBYqPj9f58+f1+uuva/Dgwdq7d691/thPP/1UcXFxat++vWbPnq2ff/5Zb7zxhvr166fdu3dbk9wyd999t6699lo999xzMsZI+mVaqu+++04TJ05UcHCwvvrqK7311lv66quvtGPHDlksFo0cOVLffPON3nvvPb322mtq1aqVJKl169aSpPvvv19LlizR//zP/+iRRx7Rzp07lZycrAMHDmjlypU2MRw8eFBjxozRn/70J02ePFmdOnVSixYtNHnyZO3bt8/mTXiff/65vvnmGz311FN29fGZM2d06dIlZWVlae7cuZKk22+/3b7/QQBQixjzf1FXY/6uXbu0ZMkSbdu2jRe/4f+p1Tv66wgP2TYc9jxwdfHiRVNcXGxTdu7cORMUFGT++Mc/WsvKHrjy9vY2x48ft5bv3LnTSDLTp0+3lvXu3dsEBgaaM2fOWMv++9//Gjc3N3PfffdZy2bNmmUkVThV3q+nACvz3nvvGUlm69at1rKXXnqp3OwmxhiTmZlpJJn777/fpvwvf/mLkWQ2bdpkLYuIiDCSzPr1623q5ubmGi8vL/PYY4/ZlD/00EOmefPmprCwsFyMFfH09LQ+qd+yZUvzt7/9za52AFAVjPnOHfNLS0vNTTfdZP189fVBZVSOh2zhMtzd3eXh4SHpl1epnz17VhcvXtSNN96o3bt3l6s/YsQItWnTxrp+0003KSoqSuvWrZMknTx5UpmZmZowYYICAgKs9Xr27Knf/OY31nq/NmXKlHJlv74X8vz58zp9+rRuvvlmSaowrsuVHScxMdGm/JFHHpEkrV271qY8MjKy3M+vfn5+uvPOO/Xee+9ZrzJdunRJ77//vkaMGGH3faP//ve/tW7dOr3yyisKDw9XUVGRXe0AoLYx5v+iLsb8xYsXa+/evXrhhReuGi8aFxJ8OMWSJUvUs2dPeXl5qWXLlmrdurXWrl2rvLy8cnWvvfbacmXXXXed9R7I77//XtIv9yterkuXLjp9+nS5BLeit82dPXtWDz/8sIKCguTt7a3WrVtb61UU1+W+//57ubm5qWPHjjblwcHB8vf3t8Z5pRgk6b777lNWVpY+++wzSb/8FJ2Tk6Nx48ZdNYYyt912m+Li4pSYmKjly5drzpw5+t///V+72wNAbWLMr/0xPz8/X0lJSZoxY4bCwsKuGi8aFxJ8ONw///lPTZgwQR06dNDChQu1fv16bdy4UYMHD7a+EryuVTRzwe9//3u9/fbbmjJlilasWKFPPvlE69evl6QqxWXvPZCVzZ4QGxuroKAg/fOf/5T0S38FBwcrJibG7hh+rUOHDrr++uv1r3/9q1rtAaAmGPMrj0Gq/pj/8ssv68KFCxo9erSOHj2qo0eP6vjx45Kkc+fO6ejRo7pw4YLdnwOuhYds4XAffvih2rdvrxUrVtgMjLNmzaqw/rfffluu7JtvvrE+RBURESHplweYLvf111+rVatWV/2Z89y5c0pNTdWcOXNsphir6NiVDeYREREqLS3Vt99+qy5duljLc3JylJuba43zatzd3TV27FgtXrxYL7zwglatWqXJkyfL3d3drvYV+fnnn8vN6gAAjsCYf2XVHfOzsrJ07tw5devWrdy25557Ts8995y+/PLLcg8xo3HgCj4crmzQKrvfUJJ27typ9PT0CuuvWrVKP/zwg3V9165d2rlzp+Li4iRJISEh6t27t5YsWWIzFeS+ffv0ySef6I477qhWTJI0b968cnXLvjgun3ay7DiXt3n11VclScOGDbtqHGXGjRunc+fO6U9/+pMKCwvtmvrs4sWLOnfuXLnyXbt2ae/evbrxxhvtPj4A1BbG/Kurzpj/0EMPaeXKlTbL3//+d0nShAkTtHLlykpvC4Lr4wo+6sQ777xj/anz1x5++GH99re/1YoVK3TXXXdp2LBhOnLkiBYsWKCuXbuqsLCwXJuOHTvq1ltv1dSpU1VcXKx58+apZcuWevTRR611XnrpJcXFxSk6OlqTJk2yTpnm5+en2bNnXzVeX19fDRgwQC+++KJKSkrUpk0bffLJJzpy5Ei5un369JEkPfnkk7rnnnvUtGlTDR8+XL169dL48eP11ltvKTc3VwMHDrROXzZixAjddtttdvff9ddfr+7du2v58uXq0qWLbrjhhqu2KSwsVFhYmEaPHq1u3bqpefPm2rt3rxYtWiQ/Pz89/fTTdh8fAKqCMd/xY/4NN9xQrl7ZcwrdunXTiBEj7D4+XFCtzslTR5gms+EomzKtsuXYsWOmtLTUPPfccyYiIsJ4enqa66+/3qxZs8aMHz/eREREWPf16+m+XnnlFRMWFmY8PT1N//79zX//+99yx/70009Nv379jLe3t/H19TXDhw83+/fvt6lTNmXajz/+WK798ePHzV133WX8/f2Nn5+fufvuu82JEyeMJDNr1iybus8884xp06aNcXNzs5k+raSkxMyZM8dERkaapk2bmrCwMJOUlGTOnz9v0z4iIsIMGzbsin354osvGknmueeeu2K9MsXFxebhhx82PXv2NL6+vqZp06YmIiLCTJo0qdz0bgBQGxjznTfmV4RpMhueuspxLcZc9vtUPZSfny8/Pz/l5eXJ19fX2eEADvH6669r+vTpOnr0qMLDw50dDgCgDjHmN051leOS4AP1kDFGvXr1UsuWLa/4OncAQMPHmN941VWOyz34QD1SVFSk1atXa/Pmzdq7d68++ugjZ4cEAKgjjPmoKyT4QD3y448/auzYsfL399cTTzyh3/3ud84OCQBQRxjzUVdI8IF6pF27duWmbQMAuCbGfNQV5sEHAAAAXAgJPgAAAOBCGsQtOqWlpZKkvLw8J0cCAHXPGKOCggKFhobKza1xXYcpLS3ViRMn5OPjI4vF4uxwAKBOleW2ZblubWkQCf7JkycliXlhATQqx44dU9u2bZ0dhkOdOHFCYWFhzg4DABzq5MmT8vf3r7X9NYgEv0WLFpJ++bJjHnwAri4/P19hYWHy8fFxdigOV/aZGe8BNAZl431ZrltbGkSCX/Yzra+vLwM+gEajMd6iwngPoDGq7fG+cd3cCQAAALg4EnwAAADAhZDgAwAAAC6EBB8AYJf58+erZ8+e1vvjo6Oj9e9//9u6/fz584qPj1fLli3VokULjRo1Sjk5OU6MGAAaJxJ8AIBd2rZtq+eff14ZGRn64osvNHjwYN1555366quvJEnTp0/Xxx9/rOXLl2vLli06ceKERo4c6eSoAaDxaRCz6AAAnG/48OE263/96181f/587dixQ23bttXChQu1dOlSDR48WJK0aNEidenSRTt27NDNN9/sjJABoFHiCj4AoMouXbqkZcuWqaioSNHR0crIyFBJSYliYmKsdTp37qzw8HClp6dXup/i4mLl5+fbLACAmuEKPhqMdo+vdfgxjz4/zOHHBOqzvXv3Kjo6WufPn1eLFi20cuVKde3aVZmZmfLw8Cj3JsagoCBlZ2dXur/k5GTNmTOnjqMG7MP3DFwFV/ABAHbr1KmTMjMztXPnTk2dOlXjx4/X/v37q72/pKQk5eXlWZdjx47VYrQA0DhxBR8AYDcPDw917NhRktSnTx99/vnnev311zV69GhduHBBubm5Nlfxc3JyFBwcXOn+PD095enpWddhA0CjwhV8AEC1lZaWqri4WH369FHTpk2Vmppq3Xbw4EFlZWUpOjraiRECQOPDFXwAgF2SkpIUFxen8PBwFRQUaOnSpUpLS9OGDRvk5+enSZMmKTExUQEBAfL19dWDDz6o6OhoZtABAAcjwQcA2OXUqVO67777dPLkSfn5+alnz57asGGDfvOb30iSXnvtNbm5uWnUqFEqLi5WbGys3nzzTSdHDQCNDwk+AMAuCxcuvOJ2Ly8vpaSkKCUlxUERAQAqwj34AAAAgAshwQcAAABcCLfoAFfAS08AAEBDwxV8AAAAwIVUK8FPSUlRu3bt5OXlpaioKO3atcuudsuWLZPFYtGIESOqc1gAAAAAV1HlBP/9999XYmKiZs2apd27d6tXr16KjY3VqVOnrtju6NGj+stf/qL+/ftXO1gAAAAAV1blBP/VV1/V5MmTNXHiRHXt2lULFixQs2bN9M4771Ta5tKlS7r33ns1Z84ctW/fvkYBAwAAAKhclRL8CxcuKCMjQzExMf9vB25uiomJUXp6eqXt5s6dq8DAQE2aNMmu4xQXFys/P9+6FBQUVCVMAAAAoNGqUoJ/+vRpXbp0SUFBQTblQUFBys7OrrDNtm3btHDhQr399tt2Hyc5OVl+fn7WpWvXrlUJEwAAAGi06nQWnYKCAo0bN05vv/22WrVqZXe7pKQk5eXlWZf9+/fXYZQAAACA66jSPPitWrWSu7u7cnJybMpzcnIUHBxcrv7hw4d19OhRDR8+3FpWWlr6y4GbNNHBgwfVoUOHcu08PT3l6elpXc/Pz69KmAAAAECjVaUE38PDQ3369FFqaqp1qsvS0lKlpqYqISGhXP3OnTtr7969NmVPPfWUCgoK9PrrryssLKz6kQMuipdrAQCAmqjym2wTExM1fvx43Xjjjbrppps0b948FRUVaeLEiZKk++67T23atFFycrK8vLzUvXt3m/b+/v6SVK4cAAAAQM1VOcEfPXq0fvzxR82cOVPZ2dnq3bu31q9fb33wNisrS25uvCAXAAAAcIYqJ/iSlJCQUOEtOZKUlpZ2xbaLFy+uziEBAAAA2KHGl9qTk5PVt29f+fj4KDAwUCNGjNDBgwdt6pw/f17x8fFq2bKlWrRooVGjRpV7UBcAAABAzdU4wd+yZYvi4+O1Y8cObdy4USUlJRoyZIiKioqsdaZPn66PP/5Yy5cv15YtW3TixAmNHDmypocGAAAAcJlq3aLza+vXr7dZX7x4sQIDA5WRkaEBAwYoLy9PCxcu1NKlSzV48GBJ0qJFi9SlSxft2LFDN998c01DAAAAAPD/q/WnYfPy8iRJAQEBkqSMjAyVlJQoJibGWqdz584KDw9Xenp6hfsoLi5Wfn6+dSkoKKjtMAEAAACXVKsJfmlpqaZNm6Z+/fpZp8HMzs6Wh4eHdXrMMkFBQcrOzq5wP8nJyfLz87MuXbt2rc0wAQAAAJdV41t0fi0+Pl779u3Ttm3barSfpKQkJSYmWtd/+OEHknwAABoRZ7z0D3AVtZbgJyQkaM2aNdq6davatm1rLQ8ODtaFCxeUm5trcxU/JydHwcHBFe7L09NTnp6e1vX8/PzaChMAAABwaTW+RccYo4SEBK1cuVKbNm1SZGSkzfY+ffqoadOmSk1NtZYdPHhQWVlZio6OrunhAQAOYs+0yIMGDZLFYrFZpkyZ4qSIAaBxqvEV/Pj4eC1dulQfffSRfHx8rPfV+/n5ydvbW35+fpo0aZISExMVEBAgX19fPfjgg4qOjmYGHQBoQMqmRe7bt68uXryoJ554QkOGDNH+/fvVvHlza73Jkydr7ty51vVmzZo5I1wAaLRqnODPnz9f0i9XbX5t0aJFmjBhgiTptddek5ubm0aNGqXi4mLFxsbqzTffrOmhAQAOdLVpkcs0a9as0lswAQB1r1Zu0aloKUvuJcnLy0spKSk6e/asioqKtGLFCgZ/AGjgLp8Wucy//vUvtWrVSt27d1dSUpJ++uknZ4QHAI1Wrc6iAwBoHCqaFlmSxo4dq4iICIWGhmrPnj167LHHdPDgQa1YsaLC/RQXF6u4uNi6zqQKAFBzJPgAgCqrbFrkBx54wPrnHj16KCQkRLfffrsOHz6sDh06lNtPcnKy5syZU+fxAkBjUutvsgUAuLayaZE3b95sMy1yRaKioiRJhw4dqnB7UlKS8vLyrMuxY8dqPV4AaGy4gg8AsIsxRg8++KBWrlyptLS0ctMiVyQzM1OSFBISUuH2y997AgCoORJ8AIBdrjYt8uHDh7V06VLdcccdatmypfbs2aPp06drwIAB6tmzp5OjB4DGgwQfAGCXq02L7OHhoU8//VTz5s1TUVGRwsLCNGrUKD311FNOiBYAGi8SfACAXYwxV9weFhamLVu2OCgaAEBleMgWAAAAcCEk+AAAAIALIcEHAAAAXAgJPgAAAOBCSPABAAAAF0KCDwAAALgQEnwAAADAhZDgAwAAAC6EBB8AAABwIST4AAAAgAshwQcAAABcCAk+AAAA4EJI8AEAAAAXQoIPAAAAuBASfAAAAMCFkOADAAAALoQEHwAAAHAhJPgAAACACyHBBwAAAFwICT4AAADgQkjwAQAAABdCgg8AAAC4EBJ8AIBdkpOT1bdvX/n4+CgwMFAjRozQwYMHbeqcP39e8fHxatmypVq0aKFRo0YpJyfHSREDQONEgg8AsMuWLVsUHx+vHTt2aOPGjSopKdGQIUNUVFRkrTN9+nR9/PHHWr58ubZs2aITJ05o5MiRTowaABqfJs4OAADQMKxfv95mffHixQoMDFRGRoYGDBigvLw8LVy4UEuXLtXgwYMlSYsWLVKXLl20Y8cO3Xzzzc4IGwAanRpfwd+6dauGDx+u0NBQWSwWrVq1ymb7hAkTZLFYbJahQ4fW9LAAACfLy8uTJAUEBEiSMjIyVFJSopiYGGudzp07Kzw8XOnp6U6JEQAaoxpfwS8qKlKvXr30xz/+sdKfYYcOHapFixZZ1z09PWt6WACAE5WWlmratGnq16+funfvLknKzs6Wh4eH/P39beoGBQUpOzu7wv0UFxeruLjYup6fn19nMQNAY1HjBD8uLk5xcXFXrOPp6ang4OCaHgoAUE/Ex8dr37592rZtW432k5ycrDlz5tRSVAAAyUEP2aalpSkwMFCdOnXS1KlTdebMGUccFgBQBxISErRmzRpt3rxZbdu2tZYHBwfrwoULys3Ntamfk5NT6UWepKQk5eXlWZdjx47VZegA0CjU+UO2Q4cO1ciRIxUZGanDhw/riSeeUFxcnNLT0+Xu7l5hm8t/si0oKKjrMAEAV2GM0YMPPqiVK1cqLS1NkZGRNtv79Omjpk2bKjU1VaNGjZIkHTx4UFlZWYqOjq5wn56enty2CQC1rM4T/Hvuucf65x49eqhnz57q0KGD0tLSdPvtt1fYhp9sq67d42sdfsyjzw9z+DEBOE98fLyWLl2qjz76SD4+Ptb76v38/OTt7S0/Pz9NmjRJiYmJCggIkK+vrx588EFFR0czgw4AOJDD58Fv3769WrVqpUOHDlVa5/KfbPfv3+/ACAEAFZk/f77y8vI0aNAghYSEWJf333/fWue1117Tb3/7W40aNUoDBgxQcHCwVqxY4cSoAaDxcfg8+MePH9eZM2cUEhJSaZ3Lf7JlVgUAcD5jzFXreHl5KSUlRSkpKQ6ICABQkRon+IWFhTZX448cOaLMzEwFBAQoICBAc+bM0ahRoxQcHKzDhw/r0UcfVceOHRUbG1vTQwMAAAC4TI0T/C+++EK33XabdT0xMVGSNH78eM2fP1979uzRkiVLlJubq9DQUA0ZMkTPPPMMD1UBAAAAdaDGCf6gQYOu+LPthg0banoIAAAAAHZy+EO2AAAAAOoOCT4AAADgQkjwAQAAABdCgg8AAAC4EBJ8AAAAwIWQ4AMAAAAuhAQfAAAAcCEk+AAAAIALIcEHAAAAXAgJPgAAAOBCSPABAAAAF9LE2QEAAID6rd3ja50dAoAq4Ao+AAAA4EJI8AEAAAAXQoIPAAAAuBASfAAAAMCFkOADAAAALoQEHwAAAHAhTJMJAADgJI6egvTo88Mcejw4B1fwAQAAABdCgg8AsMvWrVs1fPhwhYaGymKxaNWqVTbbJ0yYIIvFYrMMHTrUOcECQCNGgg8AsEtRUZF69eqllJSUSusMHTpUJ0+etC7vvfeeAyMEAEjcgw8AsFNcXJzi4uKuWMfT01PBwcEOiggAUBGu4AMAak1aWpoCAwPVqVMnTZ06VWfOnLli/eLiYuXn59ssAICaIcEHANSKoUOH6t1331VqaqpeeOEFbdmyRXFxcbp06VKlbZKTk+Xn52ddwsLCHBgxALgmbtEBANSKe+65x/rnHj16qGfPnurQoYPS0tJ0++23V9gmKSlJiYmJ1vX8/HySfACoIa7gAwDqRPv27dWqVSsdOnSo0jqenp7y9fW1WQAANVOtBD8lJUXt2rWTl5eXoqKitGvXrkrrvv322+rfv7+uueYaXXPNNYqJiblifQCAazh+/LjOnDmjkJAQZ4cCAI1KlRP8999/X4mJiZo1a5Z2796tXr16KTY2VqdOnaqwflpamsaMGaPNmzcrPT1dYWFhGjJkiH744YcaBw8AcJzCwkJlZmYqMzNTknTkyBFlZmYqKytLhYWFmjFjhnbs2KGjR48qNTVVd955pzp27KjY2FjnBg4AjYzFGGOq0iAqKkp9+/bV//7v/0qSSktLFRYWpgcffFCPP/74VdtfunRJ11xzjf73f/9X9913n13HPH78uMLCwpSXl8fPt5Vw9Kuu4Vp4dXn9kp+fLz8/v3o35qWlpem2224rVz5+/HjNnz9fI0aM0Jdffqnc3FyFhoZqyJAheuaZZxQUFGT3MerrZ2/s+I5xHYz39UvZmHfs2DG1bdu21vZbpYdsL1y4oIyMDCUlJVnL3NzcFBMTo/T0dLv28dNPP6mkpEQBAQFVixQA4FSDBg3Sla4JbdiwwYHRAAAqU6UE//Tp07p06VK5qzFBQUH6+uuv7drHY489ptDQUMXExFRap7i4WMXFxdb1goKCqoQJAAAANFoOnUXn+eef17Jly7Ry5Up5eXlVWu/yeZG7du3qwCgBAACAhqtKCX6rVq3k7u6unJwca9nWrVv13nvv6b///a8sFotWrVpl08YYo5kzZ8rX11dJSUnq2LGjvL29r3icpKQk5eXlWZf9+/dXJUwAAACg0apSgu/h4aE+ffooNTXVWlZQUKC8vDzdfffdFbZ58cUX9dJLL+nixYv6v//7P4WEhCg2Nlbnz5+v9DiXz4vs4+NTlTABAACARqvKt+gkJibq7bff1pIlS3TgwAGtXr1a7u7uevnllyVJ8+bNsz6Ea4zRX//6V5WUlOj//u//FBMTo5deekknTpzQsmXLaveTAAAAAKjaQ7aSNHr0aP3444+aOXOmsrOz1bt3b61fv9764O3p06d18uRJSb/MkVz2gOz//M//2Oxn/vz5mjBhQoXH4CFbAAAAoHqqnOBLUkJCghISEirc9uyzz2rEiBGSpOzsbEnSiRMnbN5k+Pvf/14Wi6XS/ScnJ2vOnDnVCQ0AAABo1Bw6i469eMgWAAAAqJ46TfCDg4MlyWbWnbL1sm0V4SFbAAAAoHrqNMGPjIxUcHCwzaw7+fn52rlzp6Kjo+vy0AAAAECjVK178H+tsLBQhw4dsq4fOXJEmZmZCggIUHh4uKZNm6Znn31W1157rSIjI/X0008rNDTUep8+AAAAgNpT4wT/iy++0G233WZdT0xMlCSNHz9eixcv1qOPPqqioiI98MADys3N1a233qr169df8U22AAAAAKqnxgn+oEGDZIypdLvFYtHcuXM1d+7cmh4KAAAAwFXUy1l0AAAAAFQPCT4AAADgQkjwAQAAABdCgg8AAAC4EBJ8AAAAwIWQ4AMAAAAuhAQfAAAAcCEk+AAAAIALIcEHAAAAXAgJPgDALlu3btXw4cMVGhoqi8WiVatW2Ww3xmjmzJkKCQmRt7e3YmJi9O233zonWABoxEjwAQB2KSoqUq9evZSSklLh9hdffFF/+9vftGDBAu3cuVPNmzdXbGyszp8/7+BIAaBxa+LsAFxRu8fXOjsEoEqccc4efX6Yw4+JmomLi1NcXFyF24wxmjdvnp566indeeedkqR3331XQUFBWrVqle655x5HhgoAjRpX8AEANXbkyBFlZ2crJibGWubn56eoqCilp6c7MTIAaHy4gg8AqLHs7GxJUlBQkE15UFCQdVtFiouLVVxcbF3Pz8+vmwABoBHhCj4AwGmSk5Pl5+dnXcLCwpwdEgA0eCT4AIAaCw4OliTl5OTYlOfk5Fi3VSQpKUl5eXnW5dixY3UaJwA0BiT4AIAai4yMVHBwsFJTU61l+fn52rlzp6Kjoytt5+npKV9fX5sFAFAz3IMPALBLYWGhDh06ZF0/cuSIMjMzFRAQoPDwcE2bNk3PPvusrr32WkVGRurpp59WaGioRowY4bygAaARIsEHANjliy++0G233WZdT0xMlCSNHz9eixcv1qOPPqqioiI98MADys3N1a233qr169fLy8vLWSEDQKNEgg8AsMugQYNkjKl0u8Vi0dy5czV37lwHRgUAuBwJPgAADQgvU0RN8GLDxoGHbAEAAAAXQoIPAAAAuBASfAAAAMCFkOADAAAALoQEHwAAAHAhJPgAAACACyHBBwAAAFwI8+ADcArmYgYAoG5wBR8AAABwIST4AAAAgAshwQcAAABcSJ0n+LNnz5bFYrFZOnfuXNeHBQAAABolhzxk261bN3366af/76BNeLYXAAAAqAsOybSbNGmi4OBgRxwKAAAAaNQccg/+t99+q9DQULVv31733nuvsrKyHHFYAAAAoNGp8yv4UVFRWrx4sTp16qSTJ09qzpw56t+/v/bt2ycfH58K2xQXF6u4uNi6XlBQUNdhAgAAAC6hzhP8uLg465979uypqKgoRURE6IMPPtCkSZMqbJOcnKw5c+bUdWgAGhlHv1yLF2sBAJzB4dNk+vv767rrrtOhQ4cqrZOUlKS8vDzrsn//fgdGCAAAADRcDk/wCwsLdfjwYYWEhFRax9PTU76+vtalslt5AAAAANiq8wT/L3/5i7Zs2aKjR49q+/btuuuuu+Tu7q4xY8bU9aEBAACARqfO78E/fvy4xowZozNnzqh169a69dZbtWPHDrVu3bquDw0AAAA0OnWe4C9btqyuDwEAqCdmz55dbpKETp066euvv3ZSRADQ+PBKWQBAreLt5QDgXIy6AIBaxdvLAcC5HD6LDgDAtfH2cgBwLq7gAwBqTVXfXn75m8vz8/MdGS4AuCQSfABAranq28sb+pvLHf12ZACwB7foAADqzNXeXn75m8uPHTvm4AgBwPWQ4AMA6szV3l5++ZvLfX19HRwhALgeEnwAQK3h7eUA4Hzcgw8AqDW8vRwAnI8EHwBQa3h7OQA4H7foAAAAAC6EBB8AAABwIST4AAAAgAshwQcAAABcCAk+AAAA4EJI8AEAAAAXQoIPAAAAuBDmwQcAuIx2j691dggA6gFnjAVHnx/m8GNWhiv4AAAAgAshwQcAAABcCAk+AAAA4EJI8AEAAAAXQoIPAAAAuBASfAAAAMCFME0mAAAA6gzT1zoeV/ABAAAAF0KCDwAAALgQEnwAAADAhZDgAwAAAC7E5R+y5cEOAAAANCZcwQcAAABcCAk+AAAA4EJI8AEAAAAXUq0EPyUlRe3atZOXl5eioqK0a9euK9Zfvny5goKCZLFY5Obmpk6dOl21DQCg4arq9wQAoPZUOcF///33lZiYqFmzZmn37t3q1auXYmNjderUqQrrb9++XaNHj9aZM2f07LPP6oEHHtC3336rmJiYStsAABquqn5PAABqV5UT/FdffVWTJ0/WxIkT1bVrVy1YsEDNmjXTO++8U2H9119/XX5+fpoyZYqefPJJLViwQH379tWlS5cqbQMAaLiq+j0BAKhdVZom88KFC8rIyFBSUpK1zM3NTTExMUpPT6+wzfbt25Wfn6+YmBhr2dChQ/Xtt99W2iY/P1/5+fnW9R9++MFaXlWlxT9VuQ0A1IbqjFm/bpeXl1eb4ThE2ffEww8/bPP5Bw4cqM8++0x//vOfbeoXFxeruLjYul7Wprp9x5gPwFmqM26VtSktLa3VWKqU4J8+fVqXLl1SUFCQTXlQUJC+/vrrCttkZ2ertLTUpk1QUJCKi4uVnZ1dYZvf/e532rJlS7nysLCwqoQLAE7lN69m7cPDw2slDme49957Kyz38/Ozqz3jPYCGpiZj/rFjx2p1zK+XL7pavXq1zb+Czp07p549e2rfvn12fzmUKSgoUNeuXbV//375+PjUdqguwZX6qK4+iyv1UV1piH3k6JjtPZ4xRoWFhQoJCZGbW8Oa7OzkyZPq3LmzNm7cqJtuusla/vTTT+s///mPNm3aZFP/8iv4paWlOnfunAICAmSxWBwW99XUh/O7PsRQX9W3vqlP8dSnWOqb+tA3eXl56t69u7p06VKr+61Sgt+qVSu5u7srJyfHpjwnJ0fBwcEVtgkODtaJEyds2uTk5MjT07PSNr6+vvL19bVZl365ovPrcnuU/UOhTZs2VW7bWLhSH9XVZ3GlPqorDbGPHB1zQ+yjqvLy8pK7u7sKCwttPmNubq7dn7s+/nJRH/7f1YcY6qv61jf1KZ76FEt9Ux/6puy4TZrU7jX3Kl0a8vDwUJ8+fZSammotKy0tVWpqqqKjoytsc8stt8jX19emzSeffKILFy5U2gYA0DBV53sCAFC7qvzPhcTERI0fP1433nijbrrpJs2bN09FRUWaOHGiJOm+++5TmzZtlJycLEl6+OGH9eGHH2rBggVq06aNjh07pl27dql58+bWNgAA13G17wkAQN2qcoI/evRo/fjjj5o5c6ays7PVu3dvrV+/3voQbVZWls09o7fccouWLVum+Ph4JSUlyWKx6Nprr9W7775b7mHdynh6emrWrFny9PSsarg1attYuFIf1dVncaU+qisNsY8cHXND7KPquNr3RENUH/7f1YcY6qv61jf1KZ76FEt9Ux/6pq5isBhjTK3uEQAAAIDTNKzpGQAAAABcEQk+AAAA4EJI8AEAAAAXQoIPAAAAuJB6k+CnpKSoXbt28vLyUlRUlHbt2nXF+nPmzFGLFi1ksVhksVj09NNPl6tz4MAB/e53v5Ofn5+aN2+uvn37Kisry7r9/Pnzio+PV8uWLdWiRQuNGjWq3Eu8GrKtW7dq+PDhCg0NlcVi0apVq2y2FxYWKiEhQW3btpW3t7e6du2qBQsW2NSpj31UlXPl7bffVv/+/XXNNdfommuuUUxMjLV+cnKy+vbtKx8fHwUGBmrEiBE6ePCgTXt7Pn9WVpaGDRumZs2aKTAwUDNmzNDFixdr/4M7wdX66OzZs3rwwQfVqVMneXt7Kzw8XA899JDy8vJs9uPMPqrq2FJm2bJlslgsGjFixBXr2XMelTHGKC4ursK/j658HjUUVT1XcnNzFR8fr5CQEHl6euq6667TunXrqnzcq43VvzZlyhRZLBbNmzfPpvzs2bO699575evrK39/f02aNEmFhYVVjqW+sadvnPVdX9XzZd68edaxMiwsTNOnT9f58+erfXxX/Y6vqfr23V6Vv99l0tLSdMMNN8jT01MdO3bU4sWLq3xcmXpg2bJlxsPDw7zzzjvmq6++MpMnTzb+/v4mJyenwvr/+c9/jJubmxk8eLD529/+ZiQZd3d3s3fvXmudQ4cOmYCAADNjxgyze/duc+jQIfPRRx/Z7HPKlCkmLCzMpKammi+++MLcfPPN5pZbbqnzz+so69atM08++aRZsWKFkWRWrlxps33y5MmmQ4cOZvPmzebIkSPm73//u3F3dzcfffSRtU5966Oqnitjx441KSkp5ssvvzQHDhwwEyZMMH5+fub48eMmNjbWLFq0yOzbt89kZmaaO+64w4SHh5vCwkJr+6t9/osXL5ru3bubmJgY8+WXX5p169aZVq1amaSkpDrvC0e4Wh/t3bvXjBw50qxevdocOnTIpKammmuvvdaMGjXKug9n9lFVz5cyR44cMW3atDH9+/c3d9555xXr2nMelXn11VdNXFxcub+Prn4eNQRVPVeKi4vNjTfeaO644w6zbds2c+TIEZOWlmYyMzOrfOyrjdVlVqxYYXr16mVCQ0PNa6+9ZrNt6NChplevXmbHjh3ms88+Mx07djRjxoypciz1zdX6xlnf9VU9X/71r38ZT09P869//cscOXLEbNiwwYSEhJjp06dXOwZX/I6vDfXtu93ev99lvvvuO9OsWTOTmJho9u/fb9544w3j7u5u1q9fX6Xj1osE/6abbjLx8fHW9UuXLpnQ0FCTnJxcYf3f//73ZtiwYdZ1Sebaa681f/rTn6xlo0ePNn/4wx8qPWZubq5p2rSpWb58ubXswIEDRpJJT0+vyceplyo6qbp162bmzp1rU3bDDTeYJ5980hhTP/uoqufK5S5evGh8fHzMkiVLym07deqUkWS2bNlijLHv869bt864ubmZ7Oxsa5358+cbX19fU1xcXK3PWJ9d3kcV+eCDD4yHh4cpKSkxxji3j6pzvly8eNHccsst5h//+IcZP378VRP8y1XWR19++aVp06aNOXnyZLm/j43tPKqPqnquzJ8/37Rv395cuHChVuOoLAE4fvy4adOmjdm3b5+JiIiwSfD3799vJJnPP//cWvbvf//bWCwW88MPP9RqfM5UUd8467u+qudLfHy8GTx4sE1ZYmKi6devX7Vj+DVX+Y6vC/Xpu92eBP/RRx813bp1sykbPXq0iY2NrdKxnH6LzoULF5SRkaGYmBhrmZubm2JiYpSenl5hm/T0dJv6knT99ddb65eWlmrt2rW67rrrFBsbq8DAQEVFRdn8LJKRkaGSkhKb/XTu3Fnh4eGVHtfV3HLLLVq9erV++OEHGWO0efNmffPNNxoyZIik+tdH1TlXLvfTTz+ppKREAQEB5baV3VZSts2ez5+enq4ePXrYvMAnNjZW+fn5+uqrr6r+Ieu5y/uosjq+vr5q0uSX9+g5q4+qe77MnTtXgYGBmjRpUrWOW1Ef/fTTTxo7dqxSUlIUHBxcrk1jO4/qm+qcK6tXr1Z0dLTi4+MVFBSk7t2767nnntOlS5dqPb7S0lKNGzdOM2bMULdu3cptT09Pl7+/v2688UZrWUxMjNzc3LRz585aj6e+cNZ3fXXOl1tuuUUZGRnW23i+++47rVu3TnfccUe1YrBHQ/uOrysN7bu9ohw3Nja2yv9PnJ7gnz59WpcuXSr3hsOgoCBlZ2dX2CY7O7tcfX9/f2v9U6dOqbCwUM8//7yGDh2qTz75RHfddZdGjhypLVu2WPfh4eEhf39/u4/rat544w117dpVbdu2lYeHh4YOHaqUlBQNGDBAUv3ro+qcK5d77LHHFBoaWu4vT2lpqaZNm6Z+/fqpe/fukuz7/BWdi2XrrnYeVdRHlzt9+rSeeeYZPfDAA9YyZ/VRdc6Xbdu2aeHChXr77berdczK+mj69Om65ZZbdOedd1bYrjGdR/VRdc6V7777Th9++KEuXbqkdevW6emnn9Yrr7yiZ599ttbje+GFF9SkSRM99NBDFW7Pzs5WYGCgTVmTJk0UEBDg0uePs77rq3O+jB07VnPnztWtt96qpk2bqkOHDho0aJCeeOKJasVgj4b2HV8XGuJ3e2XHzs/P188//2z3fprUdmD1QWlpqSTpzjvv1PTp0yVJvXv31vbt27VgwQINHDjQmeHVG2+88YZ27Nih1atXKyIiQlu3blV8fHyFCbAreP7557Vs2TKlpaXJy8vLZlt8fLz27dunbdu2OSm6+u9qfZSfn69hw4apa9eumj17tmODqwUFBQUaN26c3n77bbVq1apa+6ioj1avXq1Nmzbpyy+/rK1QUQ+UlpYqMDBQb731ltzd3dWnTx/98MMPeumllzRr1qxaO05GRoZef/117d69WxaLpdb26woa0nd9WlqannvuOb355puKiorSoUOH9PDDD+uZZ56pcJKQ2tDYvuMr0pi/252e4Ldq1Uru7u7lnl7Oycmp8KdsSQoODi5XPzc311q/VatWatKkibp27WpTp0uXLtb/ycHBwbpw4YJyc3Nt/hV3peO6kp9//llPPPGEVq5cqWHDhkmSevbsqczMTL388suKiYmpd31UnXOlzMsvv6znn39en376qXr27GmzLSEhQWvWrNHWrVvVtm1ba7k9nz84OLjczAll8bnSeVRZH5UpKCjQ0KFD5ePjo5UrV6pp06bWbc7qo6qeL4cPH9bRo0c1fPhwa1lZAtGkSRMdPHhQHTp0qPR4lfXRpk2bdPjw4XJXi0aNGqX+/fsrLS2t0ZxH9VV1xpaQkBA1bdpU7u7u1rIuXbooOztbFy5ckIeHR63E9tlnn+nUqVMKDw+3ll26dEmPPPKI5s2bp6NHjyo4OFinTp2yaXfx4kWdPXvWpc8fZ33XV+d8efrppzVu3Djdf//9kqQePXqoqKhIDzzwgJ588km5udXuDRUN8Tu+tjXU7/aKctycnBz5+vrK29vb7v04/RYdDw8P9enTR6mpqday0tJSpaamKjo6usI20dHRNvUl6b///a+1voeHh/r27VtuWqRvvvlGERERkqQ+ffqoadOmNvs5ePCgsrKyKj2uKykpKVFJSUm5QcXd3d2a1NS3PqrOuSJJL774op555hmtX7/e5h5VY4wSEhK0cuVKbdq0SZGRkTbt7Pn80dHR2rt3r82X68aNG+Xr61vuS6chulofSb9cuR8yZIg8PDy0evXqcr+OOKuPqnq+dO7cWXv37lVmZqZ1+d3vfqfbbrtNmZmZCgsLq/A4V+ujxx9/XHv27LHZryS99tprWrRokSTXP4/qu+qMLf369dOhQ4es46X0y3dMSEhIrSX3kjRu3Lhy509oaKhmzJihDRs2SPrl/MnNzVVGRoa13aZNm1RaWqqoqKhai6W+cdZ3fXXOl59++qnC71vplzGktjXE7/ja0tC/2yvKcTdu3Fj1/ydVeiS3jixbtsx4enqaxYsXm/3795sHHnjA+Pv7W59eHjdunHn88cet9f/zn/8Yd3d3M336dOu0Q25ubmb58uXm+++/N8b8Mp1Y06ZNzVtvvWW+/fZb6zRDn332mXU/U6ZMMeHh4WbTpk3miy++MNHR0SY6OtqxH74OFRQUmC+//NJ8+eWXRpJ59dVXzZdffmnto4EDB5pu3bqZzZs3m++++84sWrTIeHl5mTfffNO6j/rWR1U9V55//nnj4eFhPvzwQ3Py5EnrUlBQYKZOnWr8/PxMWlqazbaffvrJ2v5qn79sKq0hQ4aYzMxMs379etO6dWuXmd7wan2Ul5dnoqKiTI8ePcyhQ4ds6ly8eNEY49w+qur5cjl7ZtGx5zy6nCqZJtNVz6OGoKrnSlZWlvHx8TEJCQnm4MGDZs2aNSYwMNA8++yzVT721cbqy10+i44xv0yTef3115udO3eabdu2mWuvvdYlpsm8Wt8467u+qufLrFmzjI+Pj3nvvffMd999Zz755BPToUMH8/vf/77aMbjid3xtqG/f7Vf7//T444+bcePGWeuXTZM5Y8YMc+DAAZOSktJwp8k0xpg33njDhIeHGw8PD3PTTTeZHTt2WLcNHDjQjB8/3qb+rFmzjKRyy6/rLVy40HTs2NF4eXmZXr16mVWrVtns4+effzZ//vOfzTXXXGOaNWtm7rrrLnPy5Mm6/JgOtXnz5iv20cmTJ82ECRNMaGio8fLyMp06dTKvvPKKKS0tte6jPvZRVc6ViIiICvugsvNHklm0aJG1vT2f/+jRoyYuLs54e3ubVq1amUceecQ6RWRDd7U+quwck2SOHDli3Y8z+6iqY8uv2ZPg23MeVdTm8qnSXPk8aiiqeq5s377dREVFGU9PT9O+fXvz17/+1foP26q42lh9uYoS/DNnzpgxY8aYFi1aGF9fXzNx4kRTUFBQ5VjqG3v6xlnf9VU5X0pKSszs2bNNhw4djJeXlwkLCzN//vOfzblz56p9fFf9jq+p+vbdfrX/T+PHjzcDBw4s16Z3797Gw8PDtG/f/orfJ5WxGFMHvw0BAAAAcAqn34MPAAAAoPaQ4AMAAAAuhAQfAAAAcCEk+AAAAIALIcEHAAAAXAgJPgAAAOBCSPABAAAAF0KCDwAAALgQEnwAAADAhZDgAwAAAC6EBB8AAABwIST4AAAAgAv5/wC6eUcyZw0RlQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (9,6))\n",
    "plt.subplot(221)\n",
    "plt.hist(labtat['Laboratory 1'])\n",
    "plt.title('Laboratory 1')\n",
    "plt.subplot(222)\n",
    "plt.hist(labtat['Laboratory 2'])\n",
    "plt.title('Laboratory 2')\n",
    "plt.subplot(223)\n",
    "plt.hist(labtat['Laboratory 3'])\n",
    "plt.title('Laboratory 3')\n",
    "plt.subplot(224)\n",
    "plt.hist(labtat['Laboratory 4'])\n",
    "plt.title('Laboratory 4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23d79090e60>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\n",
    "labels = ['Lab 1', 'Lab 2','Lab 3', 'Lab 4']\n",
    "sns.distplot(labtat['Laboratory 1'], kde = True)\n",
    "sns.distplot(labtat['Laboratory 2'],hist = True)\n",
    "sns.distplot(labtat['Laboratory 3'],hist = True)\n",
    "sns.distplot(labtat['Laboratory 4'],hist = True)\n",
    "plt.legend(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ISKYuXYKnnrJMK4+JgaZNLa05NwUdgO0nLqdp0bmZAURExbH9xOU7KsOmLTt3ssRPevcEBQWxfPnynChJRERE0pHtgcOrV0OPHpY1dJyc4IMPYNAgcHRMc+n5axkHnTu57lb5YoCyiIiI5J67neGUrYHDN27Am2/Cp59aju+7D777DmrXzvD5vh6uWaojq9fdSmFHRETEjt3tDKeUgcO39qukDBye1rX2f8/Zs8cypTxlUd/QUPjkEyha9LbvUT/YhwAvVyKj4tIdt2MC/L0sIe1O5JtFBUVERCRn3e0Mp8wGDoNl4HByUjKMGwf161uCjp8fLFsGkydnGnQAHB1MDOtomUF9a3tTyvGwjiF3vN6Owo6IiIgdynJQMWc8fjYrA4c5fZqYJg9ZxuMkJEDHjpZByO3aZaveNlUDmNa1Nv5eqbuq/L1cU7ce3QF1Y4mIiNih7MxwalihRLrXZDYguP2h3xm1ajJe8bGWFpwJEyxbQNzhdg9tqgbwSIi/fa6gLCIiIjkrJ2Y4ZTQg2CM+luFrpvPkgXUAxFSvRbH530Plytkv9BaODqYMw9edUjeWiIiIHcqJGU4pA4dvblepd3o/K75+nScPrCPZ5MDXzbvitn1bjgSd3KKwIyIiYofSCyo3M2GZlXW7GU43Dxx2Tk5k0MbZfD/vLcpEnyfcy49nO39E6Umf4OjinPMfIAepG0tERMQOpQSV3nN2YYJUA5WzM8OpTdUAZjf2pGTv/1HlzD8AzK/akulPvM7gp+vd1cDhvKKwIyIiYqdSZjjdus6Of1bX2TEMmDGDJgMGwI0bJHl589fbH1HmiadYnQMDh/OKwo6IiIgdu+MZTufPQ8+esHSp5fjhhynyzTfUu+ee3C86hynsiIiI2Llsz3BautQSdM6fB2dn+Ogj6NcPHArmUF+FHREREbG4ft2yOOC0aZbjqlVh7lyoVs22dd2lghnRREREJGft3GnZrDMl6PTvDzt2FPigAwo7IiIihVtyMoweDQ0awOHDULo0rFkD48eD653tMp7fqBtLRESksDp5El54ATZtshw/9RTMmAE+d7a7eH6llh0REZHCxjBgzhyoUcMSdIoVg1mz4Mcf7S7ogFp2RERECpcrV6B3b/jhB8txo0bw7bdQvrxt68pFatkREREpLNauherVLUHH0RE++AA2bLDroANq2REREbF/8fHwzjswbpylC6tSJUs3Vv36tq4sTyjsiIiI2LMDB6BLF9izx3L88suW0FOsmG3rykPqxhIREbFHZjNMmgR16liCTsmSsGiRZbZVIQo6oJYdERGRAifZbNx+r6uICHjxRVi1ynLcti18/TX4+9umYBtT2BERESlAVu6PSLOLecDNu5gvXAi9esGlS5ZFAceOhddeA1PB2KE8NyjsiIiIFBAr90fQe84ujFvOR0bFMfDrzdx/agGBC+ZZTtaqBd99B1Wq5Hmd+Y3CjoiISD6QWddUstlgxJKDaYIOQI2zh5m4ZCyBVyMwTCZMb7wB779v2bFcFHZERERsLdOuKWD7icupXgdwNCcTuvVH+m6eRxHDzBmPUlyd/iX3d340T+vP7zQbS0RExIZSuqZuDTKRUXH0nrOLlfsjADh/LfXrZa9EMP+7Nxiw6TuKGGYWV2lG25c+42hInTyrvaBQy46IiIiN3K5rygBMwIglB3kkxB9fj//fgdwweHrfrwz77XOKJdwg2rko77R+jV9CHgL47zqxUtgRERGxkfS6pm5mABFRcWw/cZn6wT7c6xRP2I/jaPvPFgD+CKzKgPYDOOPliwnw97KM9ZHUFHZERERs5Nauqdtd5/jrGhbPeA3XC+dIcCjC+CZd+bz+45gdHEkZxjysY0jq9XYEUNgRERGxmax0ObkkxlP/0w9g1gxcgZjgioS2G8iGYoHWa/xvGcwsqSnsiIiI5IH0ppbXD/YhwMuVyKi4dMfthJw/zmfLxhNw/qTlRGgoxT75hK9d3W6/grKkYjIMI73fb6ESHR2Nl5cXUVFReHp62rocEREpgG63Ts7tppYD9J6zC8AaeEyGmf/tWMSgjbNxSU4CPz/Ldg/t2uXpZ8rvsvr9rbCDwo6IiNydrISZW79sU9phpnWtDWC93z/6IuOWj6fxqb2WCzp2hC+/BF/fXP4UBY/CTjYo7IiIyJ3KaAsHE5aWGu+iTly9npjuvSkzqDYNaQHAsclfU+6dgThfi8IoWhTThAmWfa4K8b5Wt5PV72+N2REREblDma2TA2QYdFKuiYiKY+fek9QfP4zK335reaFePUxz5kDlyjldcqGksCMiIpINN4/NuXgt/rbr5GRF3X8PULXdqxDxLzg4wFtvwXvvgZNTDlUsCjsiIiJZlN7YnDtVJDmJsM1z6b3tJxwNMwQHw7ffQuPGOVCp3ExhR0REJAsyGptzJ8pf+pcJS8dRI/IIAOZu3XH4bBJo3GiuUNgRERHJxO3G5tyOCfAq6kTU/4/bMQyDLrtX8M7ar3BLiueqazFOfDiOWgNfzvGa5T8KOyIiIpnIbA+r9KTMn/roiWoATJq3mf4/juGRo9sB2FGhFjEzvqT5w7VzslRJh8KOiIhIJrK6h9XNUm3hsGwZrb8MxXT+PMlOzpwe/C61R7yJYxHHXKhWbqWwIyIicotbV0MuWcwlS/e9274KJT1c/ltBOe4GvPYaTJtmaempWhXH776jXPXquVq/pKawIyIicpP0Zlz5e7rg/f9jb9Ibt5OyOGCPxsH/7VG1cyd06QKHD1uO+/eHUaPANfPNPyVnKeyIiIj8v4xmXJ2Ljv9v3ypI9XrK2JxhHUMsQSc5GT75xLJWTlISlC4N33wDLVvmev2SPoUdERERMl8NOWVmlWsRRyKjb2r1uXlszqlT8MIL8PvvlheffBJmzIASJfLiI0gGFHZERETIfMaVgWXrh+961sbBwZR6d3MTMGcOhIZCdDQUKwaffQbdu2tfq3xAYUdERAq1lMHIK/ZHZOn6i7HxPFbznv9OXLliGYT8/feW44YNLcGnfPlcqFbuhMKOiIgUGrfOsroSm8AHy7K3/YOvx00DjNetg27d4N9/wdERhg2DN9+EIvp6zU/01xARkULhbve1SplxVT/YB+Lj4d13YexYMAyoWBG++w7q18/ZoiVHKOyIiIjdu9t9rVLNuDp00DKlfM8ey8levWD8eMs4HcmXHGxdgIiISG66032tbubv5cq0LrVos+4nqFvXEnRKloRFi+DzzxV08jm17IiIiF27k32tUnRrGETbqgHUd43HsedLsGqV5YW2beHrr8HfPwcrldyilh0REbFrd7KvVYq2VQNouHcjjjWqW4KOqytMngzLlinoFCBq2REREbuWavZUFpmAYFeDB0a9YWnBAahVyzIIuUqVnC1Qcp3CjoiI2LX6wT4EeLkSGRWXpXE7JqDmmb+Zs2EyDqdPWhYFfOMNeP99cHbO5WolN6gbS0RE7Jqjg4lhHUOA/2ZVZXitOZm3/vyRn+cNwf30SQgMhLVr4aOPFHQKMLXsiIiI3WtTNYBpXWunWWcnwMuVd9tXobi7C7EHDvHAe2F47NlpefH552HqVPD2tk3RkmMUdkREpFBoUzWAR0L8U62gbN3XauZM6NsXYmPBy8sScjp3tnXJkkMUdkRExC7dujVE/WAfHB1MNKxw0w7kFy/Cyy/DwoWW46ZNYfZsCAqyTdGSKxR2RETE7qS3NUSAlyvDOobQpmqA5cTq1dCjB0REgJMTfPABDBpk2eNK7IoGKIuIiF1J2Rri1oUEI6Pi6D1nF6v/PA79+kHr1pagc999sG0bDBmioGOn1LIjIiJ243ZbQxhAyPnjVGjfB86ftJwMDYVPPoGiRfOwSslrCjsiIlLgpYzP2Xz0QrpbQ5gMMz13LGLwxtm4JCeRUNIX529mQrt2NqhW8prCjoiIFGjpjc+5WUD0BcYtm0Cj8L0ArKn4AEkzPqdti+p5WabYkMKOiIgUWCnjczJaGbnDoY2MXDUFr/hYrju58H6LXnxfozXzgu7J0zrFthR2RESkQLrd+ByP+FiGr5nOkwfWAbA7oBJhHQZxyuceArws09Cl8FDYERGRAmn7icvpdl3VO72fCUvHUyb6PMkmB6Y0eJpJjZ8n2dHylTesYwiODpltHCH2RGFHREQKpPPXUgcdp+RE+m2eR+9tP+FomAn38iOswyB2lbHsUp5mnR0pNBR2RESkQPL1cLX+c/lL/zJx6ViqRx4FYH7Vloxo+TIxLkXp07wijSuWtK6gLIWPTRcVHD16NPXq1cPDwwNfX186derE4cOHra9fvnyZ119/nXvvvRc3NzfKli1L3759iYqKSvWc8PBw2rdvT9GiRfH19WXw4MEkJSXl9ccREZE8VD/YhwBPF7r8tZxls/pRPfIoV12L0fuxoQxuH0asS1ECvFzp/0hlGlYooaBTiNm0ZWfDhg2EhoZSr149kpKSeOutt2jVqhUHDx7E3d2ds2fPcvbsWcaOHUtISAinTp3i1Vdf5ezZs/z0008AJCcn0759e/z9/dmyZQsRERF069YNJycnRo0aZcuPJyIiuSTZbLDrz8N8s+hDKu/YAMDvQTUZ1D6Mcx4lSYk1Gp8jACbDMDKasZfnLly4gK+vLxs2bKBp06bpXjN//ny6du1KbGwsRYoUYcWKFXTo0IGzZ8/i5+cHwPTp0xkyZAgXLlzA2dk50/eNjo7Gy8uLqKgoPD09c/QziYhIzlq5P4I1Y75m6E9jKXX9KvGORRjTrAdf1X0Uw2TpsND4nMIhq9/f+WrMTkr3lI9PxlMCUz5QkSKW0rdu3Uq1atWsQQegdevW9O7dmwMHDlCrVq00z4iPjyc+Pt56HB0dnVMfQUREctHqHce48Epfxv21HIC/SwbR79HBHC5VDoCejcvRMsRf43MklXyzEajZbCYsLIzGjRtTtWrVdK+5ePEiH3zwAS+//LL1XGRkZKqgA1iPIyMj033O6NGj8fLysv4EBgbm0KcQEZHckrzjT+5t15wX/j/ofFn3MR7rPsEadEzA8v2RCjqSRr4JO6Ghoezfv5/vv/8+3dejo6Np3749ISEhDB8+/K7e68033yQqKsr6c/r06bt6noiI5KLkZBg9GlOjhgRdPE1kMR+6PPshHz7ci/gi/w1VMICIqDi2n7hsu1olX8oX3Vh9+vRh6dKlbNy4kTJlyqR5/dq1a7Rp0wYPDw8WLlyIk5OT9TV/f3+2b9+e6vpz585ZX0uPi4sLLi4uOfgJREQkV5w8CS+8AJs24QAsu7cxb7XuQ5SbR4a33Lr+johNW3YMw6BPnz4sXLiQtWvXEhwcnOaa6OhoWrVqhbOzM7/88guurq6pXm/YsCH79u3j/Pnz1nNr1qzB09OTkJCQXP8MIiKSCwwD5syBGjVg0ybw8ODImCmEPjb0tkEHUq+/IwI2DjuhoaHMmTOHuXPn4uHhQWRkJJGRkdy4cQP4L+jExsby1VdfER0dbb0mOTkZgFatWhESEsILL7zAnj17WLVqFe+88w6hoaFqvRERKYiuXIHnnrO06ERHQ6NGsGcP5Qf0JsDbjYxG45hA+15Jumw69dxkSv9/sjNnzqRHjx6sX7+e5s2bp3vNiRMnKFeuHACnTp2id+/erF+/Hnd3d7p3785HH31knbGVGU09FxHJJ9auhe7d4d9/MRwd+fuVAVztO5A6FUqx89QV1hyM5OvNJzFBqg1AU75NpnWtrenmhUhWv7/z1To7tqKwIyJiY/Hx8M47MG4cGAbhJe7h9XYD2FP6XgAcTGC+6dvq1mOtq1M4Fch1dkREpBA6cAA6d4a9ewGYW6MNH7boyXVnN+sl5lv+szzlWOvqSFYo7IiIiG2YzTB5MrzxBsTHY5QsyRtt+zK/dO0s3Z6yrs5b7bUlhNxevllnR0RECpGzZ6FtW+jXz9KF1bYtO5dsyHLQAa2rI1mnsCMiInlr4UKoXh1WrwZXV0vrzrJlnHH1uqPHaV0dyYy6sUREJG/ExEBYGHz1leW4Vi347juoUgW48/VxtK6OZEYtOyIikvu2bYOaNS1Bx2SCoUMt5/4/6CSbDcxmA283p9s/5yZaV0eySi07IiKSe5KS4MMPLT/JyVC2LMyeDc2aWS9ZuT+CEUsOEhGV9e6olOHIwzpqcLJkTmFHRERyx9Gj0LUr/PGH5bhLF8v4HG9v6yUr90fQe84uMlvw7dZ1dfy1ro5kg8KOiIjkLMOAr7+2zLSKjQUvL5g2jeRnn2P7icucP3mGku4umA2DoT/vu23Q8XZzYkqX2tQr58POU1c4fy0OXw9Xrasj2aKwIyIiOefiRXjlFViwwHLcrBnMns3KaCdGfLw2W11VAFdvJOJgMuFcxIGGFUrkQsFSGGiAsoiI5IxVqzCqV4cFCzAXceLU4PdIWLWGT/+J59U5u7IddFJoarncLbXsiIhItiWbDUuX1LU4fB3NBI39gNKzv8AEHCkRSFjHQRxwqIBp2OpMx+NkRlPL5W4p7IiISIZuDjUl3V3ABL8dOsei3We5HJtAlfPHmbhkLKUvhgMwq3YHRj/0IvFOLgB3FXRMWAYia2q53C2FHRERSdftpoSbDDO9ti9i0O+zcUlO4oK7N4PbhrG+Qt0ceW9NLZecpLAjIiJp3G5KeED0BcYtm0CjcMsu5asrNWBom9e5XPTOtntIj6aWS05S2BERkVSSzQYjlhxMN+h0OLSRkaum4BUfy3UnF95v0Yvva7S2rIqcA1KmmjcoX0ItOpJjsh12du3ahZOTE9WqVQNg8eLFzJw5k5CQEIYPH46zs3OOFykiIrkjvTE5W45eTNN15REfy/A103nywDoAdgdUJqzDQE763JMjdaTEmo+erEbjiiVz5JkiKbIddl555RWGDh1KtWrVOH78OM899xyPP/448+fP5/r160ycODEXyhQRkZyW1W0a6p3ez4Sl4ykTfZ5kkwOTGz7DZ42eI8kx5zoH1G0luSnb/0v9559/qFmzJgDz58+nadOmzJ07l82bN/Pcc88p7IiIFABZ2abBKTmRfpvn0XvbTzgaZsK9/AjrMIhdZarc9fv3e7gi9YNLcDEmXisiS67LdtgxDAOz2QzAr7/+SocOHQAIDAzk4sWLOVudiIjkuNuNyUlR/tK/TFw6luqRRwGYX7Ulw1u+TKxL0Wy91617WgWoBUdsINthp27dunz44Ye0bNmSDRs2MG3aNABOnDiBn59fjhcoIiI5a9vxSxl3XRkGXXev4O21X+GWFM9V12K82boPK+57MMvP93F34vGa99AyxJ86QcW1p5XYXLbDzsSJE+nSpQuLFi3i7bffpmLFigD89NNPNGrUKMcLFBGRnLNyfwRDf96X7mslY6/w8YpJPHxsBwC/B9VkUPswznmkP2A4JdS0uM8PTGTYJaU9rcTWTIZh3O1K3gDExcXh6OiIk5NTTjwuT0VHR+Pl5UVUVBSenp62LkdEJFcs3xvBa3N3pfvaw0f/4OMVkyh5PYp4xyJ80qwHX9d9FMOUegvFt9tVwdfTRa00ki9k9fv7jobSX716lZ9++oljx44xePBgfHx8OHjwIH5+ftxzT85MQxQRkZyzfO9Z+sz7K815t4Q43ln3JV12rwTgUKlyhHUcxOFS5VJdl7J1w0sPBivgSIGT7bCzd+9eHn74Yby9vTl58iS9evXCx8eHBQsWEB4ezuzZs3OjThERyaJb187ZcfIyE387kua6ahFHmLh0LBUunwHgy7qPMaZZd+KLpL9emrZukIIq22FnwIABvPjii3zyySd4eHhYz7dr147OnTvnaHEiIpI9WVk7x8GcTO9tPxG2eS5O5mQii/kwsP0ANperme71mkElBV22w86OHTuYMWNGmvP33HMPkZGROVKUiIhkX1bWzikTdY7xS8dR/9+DACy7tzFvte5DlJvlP17fbleFksWcuRybgE8xF/w9NTZHCr5shx0XFxeio6PTnP/nn38oVapUjhQlIiLZk+naOYbB4wfW8f6aaXgk3OCasxvDW77Kz1VbWPe1CtCYHLFT2Q47jz76KO+//z4//vgjACaTifDwcIYMGcKTTz6Z4wWKiEjmtp+4nGHXlWdcDKNWTaHD378DsOOeEPp3GMC/3v6prtOYHLFXDplfktq4ceOIiYnB19eXGzdu0KxZMypWrIiHhwcjR47MjRpFRCQT56+lH3QantrDqq9C6fD37yQ6ODK2SVee6zw6VdBxMMHUzrU1JkfsVrZbdry8vFizZg2bNm1i7969xMTEULt2bVq2bJkb9YmISBb4erimOnZOSmTQxtm8vGMhAMeLlyas4yD2BlROc+/k52vRrrqCjtivO96y9sEHH+TBB7O+fLiIiOSe+sE++Hu6EhkdR+ULJ/l0yViqXDgJwNwabfiwRU+uO7ulukezrKSwyFLYmTRpUpYf2Ldv3zsuRkREsidlTZ01ByOJjUvgxT8XM3T9LFySE7nk5snQtn1ZU6lBmvv6t6xEnxaVNEZHCoUsbRcRHByctYeZTBw/fvyui8pr2i5CRAqim9fU8b12ibHLJ9L0pGWV5LXl6zKkbT8uFCue6h615og9ydHtIk6cOJFjhYmIyN27eU2d1oe3MHrVZHxuRBNXxJkPm/dkTq121inlxYs68V7H+7VmjhRadzxmR0REbCNlTZ2i8dd577cveHbfGgD2+1WgX4dBHCsZmOr6K9cT8fd01e7jUmhlKewMGDCADz74AHd3dwYMGHDba8ePH58jhYmIiMWte10djIjG/+Bu5i0dR7mrEZgxMeOBJxnfpAuJjk7pPiOjqekihUGWws5ff/1FYmKi9Z9FRCT3JZsNJq89yszNJ7h6w/LvYEdzMq9v+Z75W36giGHmX89SDGw/gD/KVrvts26dmi5SmGQp7Kxbty7dfxYRkdyxcn8EQxfs4+r1ROu5oCtnmbhkHLUiDgOwMOQhhj3yKtGuxTJ8jgnw97KM1REprLK9gvJLL73EtWvX0pyPjY3lpZdeypGiREQKs+V7I3h1zq7/go5h8Mye1Syf2ZdaEYeJdnGnb8fB9O84KNOgA9oGQiRLU89v5ujoSEREBL6+vqnOX7x4EX9/f5KSknK0wLygqeciYmsp43JWHYjgm62nSPk3c/HrUYxeNZk2/2wFYFtgVQZ0GMBZT9/bPM1C08zF3uXo1POUBxqGgWEYXLt2DVfX//p/k5OTWb58eZoAJCIimbt5vZybNT2+kzErPsUv5jIJDkUY36Qrn9d/HLOD422f16xySV5tVlHTzEX+X5bDjre3NyaTCZPJROXKafdWMZlMjBgxIkeLExGxdzevl5PCJTGeoRtm8eLOJQAcKRFIWMdBHPCrkKVnvtqsoqaZi9wky2Fn3bp1GIZBixYt+Pnnn/Hx+W+wm7OzM0FBQZQuXTpXihQRsUfJZoPhvxxMFXRCzh1n4pKxVL4UDsCs2h346KEexDllPptKg5FF0pflsNOsWTPAsppyYGAgDg7ZHtssIiL/L9lsMOSnvURGW7quTIaZ/21fxOCNs3E2J3HB3ZvBbcNYX6Futp6rwcgiaWV7BeWgoCCuXr3K9u3bOX/+PGazOdXr3bp1y7HiRETs0a3TygOiLzBu2QQahe8FYHWlBgxt8zqXi3pl+ZkajCySsWyHnSVLltClSxdiYmLw9PTEZPrvvyBMJpPCjojIbazcb5lWnqLjwQ18uHoqXvGxXHdyYcTDL/ND9VbWfa1uZgJeb1GRB8qX4Hx0HJdjE/Ap5qI9r0Qyke2wM3DgQF566SVGjRpF0aJFc6MmERG7kjKtPDLqBh8sOwSAR3wsI9ZM54kDloVadwdUJqzDQE763JPhc6Z0rkW76hobKZJd2Q47Z86coW/fvgo6IiKZSG+7B4D6p/czfuk4ykRfINnkwOSGz/BZo+dIckz/X8nqohK5O9kOO61bt+bPP/+kfPnyuVGPiIhdSG+7B6fkRMI2zaX3tp9wwCDcy4+wDoPYVaZKus9oW9Wfbg3LqYtK5C5lO+y0b9+ewYMHc/DgQapVq4aTU+oddh999NEcK05EpCBavjeC1+buSnWu/KV/mbh0LNUjjwIwv2pLhrd8mViX9FvJ/T1dmNy5tkKOSA7I9nYRt5tybjKZSE5Ovuui8pq2ixCRnJBsNpj02xEm/Xbkv7VzDIMuu1fwztqvcEuK54qrB2+1DmXFfQ9m+BwTMK1rbXVbiWQix7eLSHHrVHMREUm/26pk7BU+WjGJlsd2APB7UE0GtQ/jnEfJDJ9TvKgTo5+opqAjkoOyHXZERCS19LqtWhzdzicrPqXk9SjiHZ34uFkPZtbtiGHKuHX8qdpl+Pip6uq6EslhdxR2YmNj2bBhA+Hh4SQkJKR6rW/fvjlSmIhIfndzt1UKt4Q43ln3JV12rwTgUKlyhHUcxOFS5W77LH9PFwUdkVyS7bDz119/0a5dO65fv05sbCw+Pj5cvHiRokWL4uvrq7AjInYvZUr5jI3HuJ7w3zjFahFHmLh0LBUunwHgy7qPMaZZd+KLOGf4rJRoM/zR+xV0RHJJtsNO//796dixI9OnT8fLy4tt27bh5ORE165d6devX27UKCJicykLA645GMmPf/5LTHyS9TUHczKv/vEz/Td9h5M5mchiPgxsP4DN5Wpm+lx/raEjkuuyHXZ2797NjBkzcHBwwNHRkfj4eMqXL88nn3xC9+7deeKJJ3KjThERm8hoYcAUZaLOMX7pOOr/exCAZfc25q3WfYhy80hz7c3bPVyMicfXQ9s8iOSFbIcdJycn6/RzX19fwsPDqVKlCl5eXpw+fTrHCxQRyUs3b+2w+ehFlu+PTNVVZWUYPH5gHe+vmYZHwg2uObsxvOWr/Fy1Rbr7WoG2exCxlWyHnVq1arFjxw4qVapEs2bNeO+997h48SLffvstVatWzY0aRUTyxMr9EYxYcpCIqLjbXucZF8PIVVPo+PfvAOy4J4T+HQbwr7d/utdrOrmIbWU77IwaNYpr164BMHLkSLp160bv3r2pVKkSX3/9dY4XKCKSF1buj6D3nF1ktspqw1N7GL90PAExl0h0cOTTxs8zrcHTJDs4prnWBPR7uBKvP1xJXVUiNpTtFZTtkVZQFincks0GD3689rYtOs5JiQzaOJuXdywE4Hjx0oR1HMTegMoZ3jNV3VYiuSrXVlAWEbE3209cvm3QqXzhJJ8uGUuVCycBmFujDR+0+B83nF3TvV7dViL5S7bDTnBwMKYMBt8BHD9+/K4KEhHJS8lmg9lbT6b7mskw02PnEoaun4VLciKX3DwZ0rYfv1Z6IN3r3Z0deblpefq0ULeVSH6S7bATFhaW6jgxMZG//vqLlStXMnjw4JyqS0Qk16W3n1UK32uXGLt8Ik1P/gXAuvJ1eKNtGBeKFU9zrbebEy82LqeQI5JPZTvsZLRw4JQpU/jzzz/vuiARkdyUMrV89YEIZm45le41rQ9v4aOVn1E87hpxRZz5sHlP5tRql2pKeTEXR56tG0jLEH+tlSOSz+XYAOXjx49Ts2ZNoqOjc+JxeUoDlEUKh8ymlrvHX+e9377g2X1rANjnV4GwDoM4VjLwv2vUVSWSb+T5AOWffvoJHx+fnHqciEiOymxqea0zfzNh6TjKXY3AjIkZDzzJ+CZdSHR0AtRVJVKQ3dGigjcPUDYMg8jISC5cuMDUqVNztDgRkZyQbDYY/svBdIOOozmZ17d8T58tP1DEMPOvZykGdBjI9sD/Fkl9t30VejQOVsgRKaCyHXY6deqU6tjBwYFSpUrx0EMPcd999+VUXSIiOWby2iNERqftugq6cpaJS8ZRK+IwAAvub86wR17lmou79ZoAL1cFHZECLtthZ9iwYblRh4hIrli+N4IJvx5JfdIweGbvGob99jnuiXFEu7jzTqvX+CWkWarLTMCwjiEKOiIFnEN2bzhz5gyTJk2iT58+DBgwgBkzZnDlypU7evPRo0dTr149PDw88PX1pVOnThw+fDjVNXFxcYSGhlKiRAmKFSvGk08+yblz51JdEx4eTvv27SlatCi+vr4MHjyYpKSkO6pJROxDstlgwpp/CJ27K9X54tejmL5oFJ+snIR7YhzbAqvS5qXP0gSd4kWdmNa1thYGFLED2WrZmTp1KgMGDCAhIcE66jk6OpoBAwbw5Zdf8vzzz2MYBrt376ZWrVqZPm/Dhg2EhoZSr149kpKSeOutt2jVqhUHDx7E3d3SjNy/f3+WLVvG/Pnz8fLyok+fPjzxxBNs3rwZgOTkZNq3b4+/vz9btmwhIiKCbt264eTkxKhRo7L7+xCRAi7ZbDB57VFmbDyWZrfypsd3Mnb5RHxjr5DgUIRxTbvyRb3HMd+0r5X2sxKxP1meer5s2TIee+wxwsLCGDhwIAEBlv/aiYiIYMyYMUyePJm1a9cydepU7rvvPt57771sF3PhwgV8fX3ZsGEDTZs2JSoqilKlSjF37lyeeuopAP7++2+qVKnC1q1badCgAStWrKBDhw6cPXsWPz8/AKZPn86QIUO4cOECzs7Omb6vpp6L2IeMFgl0SYxn6IZZvLhzCQBHSgQS1nEQB/wqpHmG9rMSKThyfOr5mDFjGDp0KB9++GGq8wEBAYwfP56iRYvyyCOP4O/vz+jRo++o6KioKADrFPadO3eSmJhIy5Ytrdfcd999lC1b1hp2tm7dSrVq1axBB6B169b07t2bAwcOpNvCFB8fT3x8vPW4IK4NJCKpLd8bwWu3dFkBVDl/nIlLxnLvxXAAZtXuwOiHXiTeySXVdQFergzrGKJuKxE7lOWws2vXLmbMmJHh6y+88AKjRo1iw4YNlC1bNtuFmM1mwsLCaNy4MVWrWqZ8RkZG4uzsjLe3d6pr/fz8iIyMtF5zc9BJeT3ltfSMHj2aESNGZLtGEckfUlZBPn8tjpLuLvxx4hKfrT2a6hqTYabX9oUM2vgtzuYkzrsXZ3C7MDaUr5PqOi+3IkztUocG5Uuo20rETmU57CQnJ+Pk5JTh605OTri5ud1R0AEIDQ1l//79bNq06Y7uz44333yTAQMGWI+jo6MJDAy8zR0iYksp4SYy6gabj15kzaHzRN1Iu59VioDoC4xfNp6G4fsAWF2pAUPbvM7lol5prv34yeo0rlgy12oXEdvLcti5//77Wbx4Mf3790/39UWLFnH//fffURF9+vRh6dKlbNy4kTJlyljP+/v7k5CQwNWrV1O17pw7dw5/f3/rNdu3b0/1vJTZWinX3MrFxQUXF5d0XxOR/CNlsPHMzSe4eptwc7MOhzYyctUUvOJjiXVy5f2He/FD9Vap9rUCcDDB5Oc120qkMMjy1PPQ0FDefvttpk6dmmpad1JSElOmTOGdd97htddey9abG4ZBnz59WLhwIWvXriU4ODjV63Xq1MHJyYnffvvNeu7w4cOEh4fTsGFDABo2bMi+ffs4f/689Zo1a9bg6elJSEhItuoRkfxj5f4I6ny4hgm//pOloOMRH8v4peOY/MsneMXHsjugMu17fMoPNVqnCToAk5+vRbvqCjoihUG2NgIdNGgQ48ePx8PDgwoVKmAYBsePHycmJoa+ffsyYcKEbL35a6+9xty5c1m8eDH33nuv9byXlxdubm4A9O7dm+XLlzNr1iw8PT15/fXXAdiyZQtg6V6rWbMmpUuX5pNPPiEyMpIXXniB//3vf1meeq7ZWCL5S0aDjTNS//R+xi8dR5noCySbHJjc8Fk+a/QsSY5pG6+LF3Vi9BPV1KIjYgey+v2d7V3Pt23bxrx58zhyxLIiaaVKlXj++edp0KBBtos0pfNfWwAzZ86kR48egGVRwYEDBzJv3jzi4+Np3bo1U6dOTdVFderUKXr37s369etxd3ene/fufPTRRxQpkrVeOoUdkfxj+d6z9Jn3F+Ys/JvJKTmRsE1z6b3tJxwwCPfyI6zDIHaVqZLmWu1WLmJ/ci3s2COFHZH8ITstOhUunWbC0nFUj7TMwppftSXDW75MrEvRVNdpkUAR+5Xj6+yIiOSmlBadTBkGXXev4O21X+GWFM8VVw/ebNOHlfc2TvfyKVokUKTQU9gREZtbuT+C1+ZmHnRKxl7h4xWTePjYDgA2lqvFoHZhnPcokeZajc0RkRQKOyJiU8lmg+G/HMz0uhZHt/PJik8peT2KeEcnPnqoB7PqdMQwpZ5U6u3mxIuNy2lsjohYKeyIiE1NXnuEyOi4DF93S4jjnXVf0mX3SgAOlSpHWMdBHC5VDm83J7o3CqJ+cAkuxsTj6+FK/WAfhRwRSeWOwk5SUhLr16/n2LFjdO7cGQ8PD86ePYunpyfFihXL6RpFxE6t3B/BhF+PZPh6tYgjTFw6lgqXzwDwRb1OHO07lFfvL4O/p4KNiGRNtsPOqVOnaNOmDeHh4cTHx/PII4/g4eHBxx9/THx8PNOnT8+NOkXEziSbDUYsSb/7ysGcTO9tPxG2eS5O5mQii/kwsP0Aurz1Ir002FhEsinLKyin6NevH3Xr1uXKlSvWhf8AHn/88VQrHYuI3M72E5eJiErbfVXmaiQ/zH2Twb9/i5M5mWX3NqZtzyl0eeslzaoSkTuS7Zad33//nS1btuDs7JzqfLly5Thz5kyOFSYi9u3Xg5GpTxgGTxxYy4g10/FIuME1ZzeGt3yVn6u2YGqX2traQUTuWLbDjtlsJjk5Oc35f//9Fw8PjxwpSkTs28r9EXy1+aT12OvGNUaunkqHv38HYMc9IfTvMIB/vf3p37KyWnRE5K5kuxurVatWTJw40XpsMpmIiYlh2LBhtGvXLidrExE7dOtYnYan9rDy6z50+Pt3Eh0cGdPkBZ7rPJp/vf3x93ShT4uKNqxWROxBtlt2xo0bR+vWrQkJCSEuLo7OnTtz5MgRSpYsybx583KjRhGxE8lmg1mbTxARFYdzUiKDNs7m5R0LATjmcw/9Owxkb0Bl6/XDH71fs61E5K5lO+yUKVOGPXv28P3337N3715iYmLo2bMnXbp0STVgWUTkZiv3RzBiyUEiouKofOEkny4ZS5ULJwGYW6MNH7T4HzecXa3Xv9S4nFY/FpEccUfr7BQpUoSuXbvmdC0iYqdW7o+g95xdYJh5cecShq6fhUtyIheLejGkbV9+q/hAmnseCfG3QaUiYo+yFHZ++eWXLD/w0UcfveNiRMT+JCSZeWvhfkpdu8TY5RNpetKyB9ba8nV5o10/LroXT3W9CfD3siwYKCKSE7IUdjp16pSlh5lMpnRnaolI4ZNsNpi89iifbzzGg/t+56OVn1E87ho3irgwskVP5tRsC6a043EMYFjHEI3VEZEck6WwYzabc7sOEbEjK/dHMHTBPhKvRDHst895Zt+vAOzzq0BYx0EcKxGY4b0aqyMiOU0bgYpIjkg2G2w/cZnVByKYueUUtc8cYsLScQRdjcSMiWkNnmLig51JdHS67XM0VkdEctodhZ3ffvuNCRMmcOjQIQCqVKlCWFgYLVu2zNHiRCR/Sgk256/FUdLdhR0nLzNry0mu3kjE0ZxM2Jbv6bPlB4oYZv71LMWADgPZHlj1ts/UWB0RyS3ZDjtTp06lX79+PPXUU/Tr1w+Abdu20a5dOyZMmEBoaGiOFyki+UPKOJyZm09w9UZimteDrpxl4pJx1Io4DMDCkId4r1Vvrrm4Z+n5GqsjIrnBZBiGkZ0bypQpw9ChQ+nTp0+q81OmTGHUqFEFcn+s6OhovLy8iIqKwtPT09bliOQbKS04kVE32Hz0Isv3R3I9IZ1JCIbBM3vXMOy3z3FPjCPaxZ13Wr3GLyHNsvQ+AV6uDOsYorE6IpItWf3+znbLztWrV2nTpk2a861atWLIkCHZfZyI5FM3LwJ4O8WvR/HRys9ofWQbAFvLVmNg+/6c9fS97X0ero6M6FiVAG836gf7qEVHRHJNtsPOo48+ysKFCxk8eHCq84sXL6ZDhw45VpiI2E7KIoCZNfs2Pb6Tscsn4ht7hQSHIoxt+gJf1H8cw3T7bfdMwJinaqglR0TyRLbDTkhICCNHjmT9+vU0bNgQsIzZ2bx5MwMHDmTSpEnWa/v27ZtzlYpIrkrpsjp75TrDlx68bdBxSYxn6IZZvLhzCQBHSgQS1nEQB/wqZPo+xYs6MfqJago6IpJnsj1mJzg4OGsPNpk4fvz4HRWV1zRmRwq7rHZZAYScO87EJWOpfCkcgJl1OvJRsx7EO7nc9j4T0O/hSrz+cCV1WYlIjsi1MTsnTpy4q8JEJH/JapeVyTDTa/tCBm38FmdzEufdizO4XRgbytfJ0vtM6VyLdtVL333BIiLZpEUFRQqxZLPBiCW377ICCIi+wLhlE2gUvheA1ZUaMLTN61wu6pXpe2imlYjYWrbDjmEY/PTTT6xbt47z58+n2UpiwYIFOVaciOSu7ScuZ9p11fHgBj5cPRWv+FhinVx5/+Fe/FC9Vbr7WgF4uRbhkRA/Glcqhb+nq2ZaiYjNZTvshIWFMWPGDJo3b46fnx+mDP6FJyL53/lrGQcdj/hY3l89jccPrgdgd0BlwjoM5KTPPdZr3J0d+V+TYOoHl+BiTDy+Hgo3IpL/ZDvsfPvttyxYsIB27drlRj0ikod8PVzTPV//9H7GLx1HmegLJJscmNzwWT5r9CxJjpZ/ZXi7OfFi43L0aaHBxiKS/2U77Hh5eVG+fPncqEVE8kjKNPNVByIwmSBlTqZTciL9N33Hq9t+xgGDU97+9O8wkF33VNEigCJSYGU77AwfPpwRI0bw9ddf4+bmlhs1iUguymiaeYVLp5m4ZCzVzh0D4MdqLRnx8MvEuhTVIoAiUqBlO+w888wzzJs3D19fX8qVK4eTk1Oq13ft2pVjxYlIzlq+N4LX5t7y/1HDoOtfy3l73de4JcVzxdWDN9v0YeW9jQHNphKRgi/bYad79+7s3LmTrl27aoCySAGyfO9Z+sz7K9W5krFX+HjFJB4+tgOAjeVqMbhdP855lATg3fZV6NE4WF1WIlKgZTvsLFu2jFWrVvHggw/mRj0ikgtW7o/gtbmpg87DR//g4xWTKHk9inhHJz5u1oOZdTum2teqpIeLgo6IFHjZDjuBgYHaUkGkAElZODCFW0Ic76z7ki67VwJwqFQ5+nUcxD+lyqW5N6PZWiIiBcnttyZOx7hx43jjjTc4efJkLpQjIjlt2/FL1sHI1SKOsPSbftag83m9x3ms24Q0QceEZaxO/WCfPK5WRCTnZbtlp2vXrly/fp0KFSpQtGjRNAOUL1++nGPFicidSzYbTF57lBkbjuFgTubVP36m/6bvcDInE1GsBAPb92dLuZpp7kvptBrWMURdWCJiF7IddiZOnJgLZYhITkhZP2fNwUh+/PNfYuKTKHM1kglLx1PvjKUra+m9D/J261Ci3DzSfYa/Zl+JiJ25o9lYIpL/pFk/xzB44sBaRqyZjkfCDa45uzHskVdZcH+LNPtamYAejcrR6n5/LRgoInbnrnY9j4uLIyEhIdU5DV4WyVsp3VUTfv3Hes7rxjVGrp5Kh79/B+DPe6oQ1mEg/3r7p/uMKZ1r0a566TypV0Qkr2U77MTGxjJkyBB+/PFHLl26lOb15OTkHClMRDK3cn8Ew385QGR0vPVco5O7GbdsAgExl0h0cGRi485Mb/AUyQ6Oae73LurER09UU5eViNi1bIedN954g3Xr1jFt2jReeOEFpkyZwpkzZ5gxYwYfffRRbtQoIulYuT+C3nN28f/bWuGclMigjbN5ecdCAI4XL01Yx0HsDaic4TOmPF+bxpVK5kG1IiK2k+2ws2TJEmbPns1DDz3Eiy++SJMmTahYsSJBQUF89913dOnSJTfqFJGbJJsNhv9y0Bp0Kl84yadLxlLlwkkAvqvZhg+b/48bzhmvkxPg5UqDCiVyv1gRERvLdti5fPmydddzT09P61TzBx98kN69e+dsdSKSrslrjxAZHYfJMNNj5xKGrp+FS3IiF4t6MaRtX36r+MBt7zehqeUiUnhkO+yUL1+eEydOULZsWe677z5+/PFH6tevz5IlS/D29s6FEkXkZiv3RzDh1yP4XrvE2OUTaXrSsg3E2vJ1GdK2HxeKFb/t/cWLOjFa43REpBDJdth58cUX2bNnD82aNWPo0KF07NiRyZMnk5iYyPjx43OjRhH5fylbP7Q5vJnRKydTPO4acUWc+bB5T+bUapdmSvnNvN2ceLFxOfq0qKQWHREpVEyGYRiZX5axkydPsmvXLipWrEj16tVzqq48FR0djZeXF1FRUZo6L/naH3tOEt61F0/v/xWAfX4VCOswiGMlA9O93sfdicdr3kPLEK2fIyL2J6vf33e1zg5AuXLlKFeu3N0+RkQys3UrVZ95ngf+PYUZE9MaPMXEBzuT6OiU7uX9W1ZSK46ICNnYCHTr1q0sXbo01bnZs2cTHByMr68vL7/8MvHx8RncLSJ3LDERhg2DBx/E/d9T/OtZiuc6j2ZMs+63CTqV6deysoKOiAjZCDvvv/8+Bw4csB7v27ePnj170rJlS4YOHcqSJUsYPXp0rhQpUmgdOQJNmsD774PZjLlLV3qEfcGOwKoZ3uLv6UKfFhXzsEgRkfwty2Fn9+7dPPzww9bj77//ngceeIAvvviCAQMGMGnSJH788cdcKVKk0DEM+PJLqFUL/vgDvL1h3jwc5nzLoKfrA//tTp7C9P8/wx+9Xy06IiI3yXLYuXLlCn5+ftbjDRs20LZtW+txvXr1OH36dM5WJ1IYXbwITzwBvXpBbCw89BDs2QPPPUey2cDLzZkXG5ejuLtzqtv8vVyZ1rW2ppSLiNwiywOU/fz8OHHiBIGBgSQkJLBr1y5GjBhhff3atWs4OaU/fkBEsmjVKujRAyIjwckJRo4kuf8Atp+6ypolB1i0+yyXY//bfFezrUREMpflsNOuXTuGDh3Kxx9/zKJFiyhatChNmjSxvr53714qVKiQK0WK2L0bN2DIEPjsM8txSAh89x0ri/gxYsx6IqLi0r3tSmwiX28+ST0FHRGRDGW5G+uDDz6gSJEiNGvWjC+++IIvvvgCZ+f/mtG//vprWrVqlStFiti13buhbt3/gs7rr8Off7KyiB+95+zKMOgA1r2xRiw5SLL5rpbMEhGxW1lu2SlZsiQbN24kKiqKYsWK4ejomOr1+fPnU6xYsRwvUMRuJSfD+PHw9tuW6eX+/jBzJrRpY10pOSvxxQAiouLYfuIyDbWxp4hIGtleVNDLyyvd8z4+PnddjEihcfo0dOsG69dbjjt1gi++gJIlAdh2/NJtW3TSc/5a9q4XESks7noFZRHJpu+/h1dfhagoDHd3jr89kv1tnqTkFeDqRX47dI4f/sz+zEZfD9ecr1VExA4o7IjklagoCA2F774D4Gr12vR6pB87okrAD3vu+LEmLNPO6werdVVEJD0KOyJ5YeNGeOEFCA8HBweOvhJGW/emJDre3f8FU+ZfDesYotlYIiIZyPJsLBG5AwkJ8OabloUBw8OhfHmSN/7OC4Ht7jrogBYSFBHJCrXsiOSWv/+GLl1g1y7L8Ysvwqefsv18AhFR2+74se4ujjxXN1ALCYqIZJHCjkhOMwyYNg0GDbIsFujjQ/L0GWyv3Zzzx6L5J/LaXT3+8651aVypZA4VKyJi/xR2RHLSuXPw0kuwfLnl+JFHWDf0Y9764zIRO++8NQf+G4jcQGvpiIhki8KOSE5ZsgR69oQLF8DFBfNHH/FZSFsmrD5614/WQGQRkTunAcoidys21rJuzqOPWoJO9epsmreCRterMmHt3Qcd0EBkEZG7oZYdkbuQvH0HCc89j9uJY5bj/gOY1vJFxm48ddfP1o7mIiI5Q2FH5E4kJ/NP/7cpP2UsbuZkIoqVYGD7/mx1rolxF0En9KEKVPb3wNfDVQFHRCSHKOyIZFPyseOce+xpKh+wTClfeu+DvN06lCg3j7t+9oOVSmkzTxGRHKawI5KJZLPB9hOXOR99A9fv5/LgpyMoHX+da85uDHvkVRbc3wJMd9cCoy0fRERyj8KOyG2s3B/BiCUHuR55gZGrptD68CYAdtwTQv8OA/jX2/+u30MzrUREcpdNZ2Nt3LiRjh07Urp0aUwmE4sWLUr1ekxMDH369KFMmTK4ubkREhLC9OnTU10TFxdHaGgoJUqUoFixYjz55JOcO3cuDz+F2KuV+yPoPWcXwXu2sfLrPnQ4vIlEB0c+adqNZzuPzpGgA5ppJSKS22zashMbG0uNGjV46aWXeOKJJ9K8PmDAANauXcucOXMoV64cq1ev5rXXXqN06dI8+uijAPTv359ly5Yxf/58vLy86NOnD0888QSbN2/O648jdiTZbDBq4W7eWvslvXYsAuCYzz2EdRjEvoBKd/Xsfg9XpH5wCS7GxGsgsohIHjAZhmHYuggAk8nEwoUL6dSpk/Vc1apVefbZZ3n33Xet5+rUqUPbtm358MMPiYqKolSpUsydO5ennnoKgL///psqVaqwdetWGjRokKX3jo6OxsvLi6ioKDw9PXP0c0nBtHvFJly6v0CVCycBmFOzLSOb9+SGs+sdPzPAy5VhHUPUgiMikkOy+v2drxcVbNSoEb/88gtnzpzBMAzWrVvHP//8Q6tWrQDYuXMniYmJtGzZ0nrPfffdR9myZdm6dWuGz42Pjyc6OjrVjwgAZjN8+inVOrWkyoWTXCzqRc8n3+Wd1qF3FXT6t6zEpiEtFHRERGwgXw9Q/uyzz3j55ZcpU6YMRYoUwcHBgS+++IKmTZsCEBkZibOzM97e3qnu8/PzIzIyMsPnjh49mhEjRuRm6VIQnT0LPXrAmjU4Ar9VqMeQtn256F48y49wMIH5prZSteaIiNhevg8727Zt45dffiEoKIiNGzcSGhpK6dKlU7XmZNebb77JgAEDrMfR0dEEBgbmRMlSUC1YAL16weXLxDm58GHznsyp2TZbU8r7t6xE74cqsvPUFc5fi9N4HBGRfCLfhp0bN27w1ltvsXDhQtq3bw9A9erV2b17N2PHjqVly5b4+/uTkJDA1atXU7XunDt3Dn//jGfKuLi44OLiktsfQQqCa9egXz+YOROAfX4VCOs4iGMlsh5+b2290aKAIiL5S74NO4mJiSQmJuLgkHpYkaOjI2azGbAMVnZycuK3337jySefBODw4cOEh4fTsGHDPK9ZCpitW6FrVzh+HMNk4uvGz/BRg+dIdHS67W3+ni48X78s5Uq6q/VGRKQAsGnYiYmJ4ejR/3aFPnHiBLt378bHx4eyZcvSrFkzBg8ejJubG0FBQWzYsIHZs2czfvx4ALy8vOjZsycDBgzAx8cHT09PXn/9dRo2bJjlmVhSuCSbDXb8cw7vCR9z75eTMJnNRHj70a9df7YHVs30/nfbV6FH42CFGxGRAsSmYefPP/+kefPm1uOUcTTdu3dn1qxZfP/997z55pt06dKFy5cvExQUxMiRI3n11Vet90yYMAEHBweefPJJ4uPjad26NVOnTs3zzyL5k3Wrh2txnLx4nU3Lt/D296O4L+IfABbc35xhj7zKNRf3LD2vpIeLgo6ISAGTb9bZsSWts2OfUrZ6iIiKA8Pg2b2ree+3L3BPjCPKxZ13Wr3GkpBm2XrmvF4NNCZHRCSfyOr3d74dsyNyN1K2ejAAn+tRfLTyM1od2QbAlrLVGdi+PxGepbL8PG3UKSJScCnsiN1I6bKKjLrBB8sOYQDNju9kzPKJ+MZeIcGhCGOaduPL+p0wTFlfT1MbdYqIFGwKO2IXUnVZAS6J8QxfP5Meu5YC8E+JsoR1HMRBv/LZfra/FgYUESnQFHakwLu5ywog5NxxPl0yhkqXTgMws05HPmrWg3in7K2t5O3mxJQutWlQvoRadERECjCFHSnQks0GI5YcxAAczMn02rGQgRvn4GxO4rx7cQa1C2Nj+TrZemZKrPnoyWo0rlgyx2sWEZG8pbAjBVLK+JzNRy8QERVH6ejzjFs2gYbh+wBYVakBQ9u8zpWiXtl+trqtRETsi8KOFDi3js959OAGPlw9Fc/4WGKdXBnx8Mv8WP2RLO1rFeDlyrvtq1Dc3UX7WYmI2CmFHcnXbl4U0NfDlSuxCYTOtYzP8YyLYcSa6Tx+cD0AuwMqE9ZhICd97snwedrqQUSk8FHYkXzh1lBTP9iHNQcjU7XgADiYwAAeCN/HuGXjKRN9gWSTA581epbJDZ8lyTHt/6R93J14t8P9+Hsq3IiIFEYKO2Jzt3ZLAXgXdeLq9cQ01zomJTL49+945Y+fccDglLc//TsMZNc9VdJcmxJpRj1eTeNvREQKMYUdyTMZtd7cPG08RXpBp8LF03y6dCxVzx0D4MdqLRnx8MvEuhRN9/000FhEREBhR/JIeq03/p4uxCWZ0wSdNAyDF/5axtvrvsY1KYErrh4MbfM6q+5tlO7lfZpXpHHFkuqyEhERQGFH8sCti/6liIyOz/TeUjFX+GTFRJof3wnAxnK1GNQujPMeaTfjTNm/qv8jlRVyRETESmFHctXNi/5lV8sjf/Dxik8pcSOaeEcnPnqoB7PqdEx3XyvtXyUiIhlR2JFctf3E5VRdV1nhlhDHu2u/pPOelQAcKlWOfh0H8U+pctZrHExgvilBaXyOiIhkRGFHctX5a9kLOtUj/mHikrGUv3IWgBn1n2BckxdIKOIE/NeCM/n5WloIUEREskRhR3LUrTOuShbL2uabDuZkem/7ibDNc3EyJxNRrAQDOgzgUJW6JNw0M0stOCIikl0KO5LulPD0Wkkyuy6jGVfeRZ2Iup6Y7rgdE1Al/hKjF35CjVMHAFh6XxM+e6o//Z9twCMh/lmqTUREJCMKO4VcegElIJ3Wk8yuy2jG1bnoeOs5E6R63WQYPHFgLR+v/4IisTEkuRdj75APKfF8F5aXL2ENNQ0rpJ15JSIiklUmwzDuZKKMXYmOjsbLy4uoqCg8PT1tXU6eySigpLSbTOta+7ZBJuW6KZ1r8cGyQxkORDYBXkWdcC3iSGS05RqvG9cYv3YaD+/faLnowQfh22+hXLm7/2AiIlIoZPX7Wy07diY7XVIZTQk3sASUEUsO0uI+v0yve2fxfi7Hpl3x+Obrrl5P5LuetXFwMGH+9VfqDhuAy7kIKFIERoyAIUPA0fGOPrOIiMjtKOzYkax2SUHmU8INICIqjm+3nsz0utsFnZtdvhJNxx+mwPjxlhOVK8OcOVCvXpbuFxERuRNpV2eTAimlq+nWYBIZFUfvObtYuT8i1fmsTgk/dfl6jtRX+cJJWnTr+F/QefVV2LVLQUdERHKdwo4dyKxLCixdUsk3rcLn6+GapWcH+aS/yeatfNydSW+OlMkw89KOxSyZ3R/3wwehVCn45ReYNg3c3bP0bBERkbuhsGMHstoltf3EZeu5+sE+BHi5phtQwDIWJ8DLlRcalsvSdR8+VtV6nML32iVm/ziM99Z+gUtSIrRvD/v2QceO2fh0IiIid0dhxw5ktUvq5uscHUwM6xgCkCbI3LzPlHMRhyxd1656ANO61sbfy9Ji1ObwZlZ93YcmJ/8i2dUVpk6FJUvAzy8bn0xEROTuKezkM8lmg63HLrF49xm2HruUquspI1ntkrr1ujZVUweUFP5ertZp59m9btNr9dh+bC7TF42meNw1jNq1cfzrL+jdG0xaDFBERPKeZmPlI9mZTXWzlC6pyKi4DFcp9veyTEO/VZuqAVlapThL123dimPXrvgeP24JNkOHYho+HJyds/mbEBERyTlaVJD8sahgVhf4y+x+uGWV4izef1cSE+HDDy0/ZjMEBcHs2dC0ae68n4iICFn//lY3Vj5wJ7OpbpXVrqYcd+QINGkC779vCTpdu8KePQo6IiKSb6gbKx/Izmyq2+0TldUuqRxhGPDVVxAWBrGx4O1tmU7+3HM5/14iIiJ3QWEnH7iT2VQZcXQw5f7GmRcvQq9esGiR5fihhyzdVoGBufu+IiIid0DdWPnAnc6msomVK6FaNUvQcXKCMWPgt98UdEREJN9S2MkHsrrAX3qzqfLMjRvw+uvQti1ERkJICGzfDoMGgYP+ZyQiIvmXvqXygawu8JcrY2+y4q+/oE4dmDzZcvz66/Dnn1Czpm3qERERyQaFnXzCZrOpbic5GT75BB54AA4dAn9/WLECJk0CN7e8r0dEROQOaIByPpKns6kyEx4O3brBhg2W406d4IsvoGTJvK9FRETkLijs5DN5MpsqM/PmWbZ3iIqy7Ez+6afw0kva7kFERAokhR35z9WrEBoKc+dajh94AObMgYoVbVqWiIjI3dCYHbHYsAGqV7cEHUdHGD4cNm1S0BERkQJPLTuFXUICvPeeZSCyYUCFCvDtt9Cwoa0rExERyREKO4XZoUPQpYtlajlYxuVMnAgeHjYtS0REJCepG6swMgyYMgVq17YEHR8f+Plny15XCjoiImJn1LJT2ERGWlpwVqywHLdqBTNnQunStq1LREQkl6hlpzD55RfLvlYrVoCLi2VK+YoVCjoiImLX1LJTGMTGwoAB8PnnluOUWVf332/bukRERPKAWnbs3Y4dUKuWJeiYTJaNO7dvV9AREZFCQ2HHXiUlwYcfWqaQHzkCZcrAr7/CmDGWLiwREZFCQt1Y9ujECXjhBdi82XL8zDMwfToUL27bukRERGxALTv2xDDgm2+gRg1L0PH0tCwQ+P33CjoiIlJoqWXHXly+DK++CvPnW44ffNASdMqVs2lZIiIitqaWHXvw66+WKeXz50ORIjByJKxfr6AjIiKCWnYKtrg4ePttGD/ecly5Mnz3HdSta9u6RERE8hGFnYJq3z7Lvlb79lmOX30Vxo4Fd3fb1iUiIpLPqBuroDGbLZt11qtnCTqlSsGSJTBtmoKOiIhIOtSyU5CcPQs9esCaNZbj9u0tm3f6+dm0LBERkfxMLTsFxc8/WwYhr1kDbm6WlpwlSxR0REREMqGWnfzu2jXo2xdmzbIc165tGYR83302LUtERKSgUMtOfrZlC9SsaQk6JhO89RZs3aqgIyIikg1q2cmPEhPhgw8s6+WYzRAUZFkgsEkTW1cmIiJS4Cjs5DdHjkDXrpadycHyz5Mng5eXbesSEREpoNSNlV8YBnz5JdSqZQk63t4wb56lRUdBR0RE5I6pZSc/uHABevWCxYstx82bWzb0DAy0bV0iIiJ2QC07trZyJVSvbgk6Tk4wZoxlrysFHRERkRyhlh1buXED3njDMh4HICTEMqW8Zk2bliUiImJv1LJjC3/9BXXq/Bd0+vaFP/9U0BEREckFCjt5KTkZPvkEHngADh0Cf39LN9ann1pWRRYREZEcp26svBIeDt26wYYNluNOneCLL6BkSZuWJSIiYu/UspMX5s2zDELesMGyM/mXX8KCBQo6IiIieUAtO7np6lXo08cy8Bgs3Vdz5kDFijYtS0REpDCxacvOxo0b6dixI6VLl8ZkMrFo0aI01xw6dIhHH30ULy8v3N3dqVevHuHh4dbX4+LiCA0NpUSJEhQrVownn3ySc+fO5eGnyMCGDVCjhiXoODrCsGGwaZOCjoiISB6zadiJjY2lRo0aTJkyJd3Xjx07xoMPPsh9993H+vXr2bt3L++++y6urq7Wa/r378+SJUuYP38+GzZs4OzZszzxxBM5Wmey2WDrsUss3n2GrccukWw2Mr44IQGGDrUsDBgeDuXLw++/w/DhUEQNaSIiInnNZBjGbb65847JZGLhwoV06tTJeu65557DycmJb7/9Nt17oqKiKFWqFHPnzuWpp54C4O+//6ZKlSps3bqVBg0aZOm9o6Oj8fLyIioqCk9Pz1SvrdwfwYglB4mIirOeC/ByZVjHENpUDUj9oEOHoEsXy9RygJ49YcIE8PDIUh0iIiKSdbf7/r5Zvh2gbDabWbZsGZUrV6Z169b4+vrywAMPpOrq2rlzJ4mJibRs2dJ67r777qNs2bJs3br1rmtYuT+C3nN2pQo6AJFRcfSes4uV+yMsJwwDpkyB2rUtQcfHB37+2TIQWUFHRETEpvJt2Dl//jwxMTF89NFHtGnThtWrV/P444/zxBNPsOH/p29HRkbi7OyMt7d3qnv9/PyIjIzM8Nnx8fFER0en+rlVstlgxJKDpNfslXJuxJKDJJ+NgPbtLQOR4+KgVSvYtw9yuCtNRERE7ky+HURiNpsBeOyxx+jfvz8ANWvWZMuWLUyfPp1mzZrd8bNHjx7NiBEjbnvN9hOX07To3MwA7v9zA+Yxz+J45RK4uFgWDOzTBxzybYYUEREpdPLtt3LJkiUpUqQIISEhqc5XqVLFOhvL39+fhIQErl69muqac+fO4e/vn+Gz33zzTaKioqw/p0+fTnPN+WsZBx23hDhGrZzMlws+wOnKJcsaOjt3WrZ9UNARERHJV/LtN7OzszP16tXj8OHDqc7/888/BAUFAVCnTh2cnJz47bffrK8fPnyY8PBwGjZsmOGzXVxc8PT0TPVzK18P13TuhBpnD7NsVl8671kJwNn/hcL27XD//dn+jCIiIpL7bNqNFRMTw9GjR63HJ06cYPfu3fj4+FC2bFkGDx7Ms88+S9OmTWnevDkrV65kyZIlrF+/HgAvLy969uzJgAED8PHxwdPTk9dff52GDRtmeSZWRuoH+xDg5UpkVBwG4GhOpve2+YRtmksRw0xEsRKMfHYon84YCA6mu3ovERERyUWGDa1bt87AMvwl1U/37t2t13z11VdGxYoVDVdXV6NGjRrGokWLUj3jxo0bxmuvvWYUL17cKFq0qPH4448bERER2aojKirKAIyoqKhU51fsO2uUG7LUqDRoofFHmRDDsMy7Mpbc18So3u97Y8W+s3f82UVEROTuZPT9fat8s86OLWVlnZ2eCz7j2b2ree+R3mxr1JZhj96fdp0dERERyTNZXWdHYYfMf1nJZoMdf58l+uRpPO6tRP1gHxzVdSUiImJTWQ07+XbqeX7i6GCiQcg9EHKPrUsRERGRbMq3s7FEREREcoLCjoiIiNg1hR0RERGxawo7IiIiYtcUdkRERMSuKeyIiIiIXVPYEREREbumsCMiIiJ2TWFHRERE7JrCjoiIiNg1hR0RERGxawo7IiIiYtcUdkRERMSuaddzwDAMwLJVvIiIiBQMKd/bKd/jGVHYAa5duwZAYGCgjSsRERGR7Lp27RpeXl4Zvm4yMotDhYDZbObs2bN4eHhgMplsXc4diY6OJjAwkNOnT+Pp6Wnrcgo1/S3yF/098g/9LfIXe/h7GIbBtWvXKF26NA4OGY/MUcsO4ODgQJkyZWxdRo7w9PQssP+jtTf6W+Qv+nvkH/pb5C8F/e9xuxadFBqgLCIiInZNYUdERETsmsKOnXBxcWHYsGG4uLjYupRCT3+L/EV/j/xDf4v8pTD9PTRAWUREROyaWnZERETErinsiIiIiF1T2BERERG7prAjIiIidk1hx86cPHmSnj17EhwcjJubGxUqVGDYsGEkJCTYurRCa+TIkTRq1IiiRYvi7e1t63IKlSlTplCuXDlcXV154IEH2L59u61LKpQ2btxIx44dKV26NCaTiUWLFtm6pEJr9OjR1KtXDw8PD3x9fenUqROHDx+2dVm5TmHHzvz999+YzWZmzJjBgQMHmDBhAtOnT+ett96ydWmFVkJCAk8//TS9e/e2dSmFyg8//MCAAQMYNmwYu3btokaNGrRu3Zrz58/burRCJzY2lho1ajBlyhRbl1LobdiwgdDQULZt28aaNWtITEykVatWxMbG2rq0XKWp54XAmDFjmDZtGsePH7d1KYXarFmzCAsL4+rVq7YupVB44IEHqFevHpMnTwYse+AFBgby+uuvM3ToUBtXV3iZTCYWLlxIp06dbF2KABcuXMDX15cNGzbQtGlTW5eTa9SyUwhERUXh4+Nj6zJE8kxCQgI7d+6kZcuW1nMODg60bNmSrVu32rAykfwlKioKwO6/IxR27NzRo0f57LPPeOWVV2xdikieuXjxIsnJyfj5+aU67+fnR2RkpI2qEslfzGYzYWFhNG7cmKpVq9q6nFylsFNADB06FJPJdNufv//+O9U9Z86coU2bNjz99NP06tXLRpXbpzv5e4iI5CehoaHs37+f77//3tal5Loiti5AsmbgwIH06NHjtteUL1/e+s9nz56lefPmNGrUiM8//zyXqyt8svv3kLxVsmRJHB0dOXfuXKrz586dw9/f30ZVieQfffr0YenSpWzcuJEyZcrYupxcp7BTQJQqVYpSpUpl6dozZ87QvHlz6tSpw8yZM3FwUANeTsvO30PynrOzM3Xq1OG3336zDoQ1m8389ttv9OnTx7bFidiQYRi8/vrrLFy4kPXr1xMcHGzrkvKEwo6dOXPmDA899BBBQUGMHTuWCxcuWF/Tf9HaRnh4OJcvXyY8PJzk5GR2794NQMWKFSlWrJhti7NjAwYMoHv37tStW5f69eszceJEYmNjefHFF21dWqETExPD0aNHrccnTpxg9+7d+Pj4ULZsWRtWVviEhoYyd+5cFi9ejIeHh3UMm5eXF25ubjauLvdo6rmdmTVrVob/Mtef2jZ69OjBN998k+b8unXreOihh/K+oEJk8uTJjBkzhsjISGrWrMmkSZN44IEHbF1WobN+/XqaN2+e5nz37t2ZNWtW3hdUiJlMpnTPz5w5M9Ou+YJMYUdERETsmgZziIiIiF1T2BERERG7prAjIiIidk1hR0REROyawo6IiIjYNYUdERERsWsKOyIiImLXFHZERETErinsiNi59evXYzKZuHr1qq1LyRaTycSiRYty7HnlypVj4sSJOfY8Wzl58iQmk8m67UhB/fuK5CWFHZECzGQy3fZn+PDhti4xU8OHD6dmzZppzkdERNC2bds8reXy5cuEhYURFBSEs7MzpUuX5qWXXiI8PDxP60jRo0cP60amKQIDA4mIiKBq1ao2qUmkINJGoCIFWEREhPWff/jhB9577z0OHz5sPVesWDH+/PNPW5RGQkICzs7Od3x/Xm9ce/nyZRo0aICzszPTp0/n/vvv5+TJk7zzzjvUq1ePrVu3Ur58+TytKT2Ojo7a1Fckm9SyI1KA+fv7W3+8vLwwmUypzt28q/rOnTupW7cuRYsWpVGjRqlCEcDixYupXbs2rq6ulC9fnhEjRpCUlGR9PTw8nMcee4xixYrh6enJM888w7lz56yvp7TQfPnllwQHB+Pq6grA1atX+d///kepUqXw9PSkRYsW7NmzB7BsXDtixAj27NljbY1K2Rjy1m6sf//9l+effx4fHx/c3d2pW7cuf/zxBwDHjh3jsccew8/Pj2LFilGvXj1+/fXXbP0u3377bc6ePcuvv/5K27ZtKVu2LE2bNmXVqlU4OTkRGhpqvTa9LrGaNWumakkbP3481apVw93dncDAQF577TViYmKsr8+aNQtvb29WrVpFlSpVKFasGG3atLEG2OHDh/PNN9+wePFi6+9m/fr1abqx0rNp0yaaNGmCm5sbgYGB9O3bl9jYWOvrU6dOpVKlSri6uuLn58dTTz2Vrd+VSEGjsCNSSLz99tuMGzeOP//8kyJFivDSSy9ZX/v999/p1q0b/fr14+DBg8yYMYNZs2YxcuRIAMxmM4899hiXL19mw4YNrFmzhuPHj/Pss8+meo+jR4/y888/s2DBAuuX8dNPP8358+dZsWIFO3fupHbt2jz88MNcvnyZZ599loEDB3L//fcTERFBREREmmcCxMTE0KxZM86cOcMvv/zCnj17eOONNzCbzdbX27Vrx2+//cZff/1FmzZt6NixY5a7n8xmM99//z1dunRJ02ri5ubGa6+9xqpVq7h8+XKWf98ODg5MmjSJAwcO8M0337B27VreeOONVNdcv36dsWPH8u2337Jx40bCw8MZNGgQAIMGDeKZZ56xBqCIiAgaNWqU6fseO3aMNm3a8OSTT7J3715++OEHNm3aRJ8+fQD4888/6du3L++//z6HDx9m5cqVNG3aNMufS6RAMkTELsycOdPw8vJKc37dunUGYPz666/Wc8uWLTMA48aNG4ZhGMbDDz9sjBo1KtV93377rREQEGAYhmGsXr3acHR0NMLDw62vHzhwwACM7du3G4ZhGMOGDTOcnJyM8+fPW6/5/fffDU9PTyMuLi7VsytUqGDMmDHDel+NGjXS1A0YCxcuNAzDMGbMmGF4eHgYly5dyuJvwzDuv/9+47PPPrMeBwUFGRMmTEj32sjISAPI8PUFCxYYgPHHH39k+KwaNWoYw4YNy7Ce+fPnGyVKlLAez5w50wCMo0ePWs9NmTLF8PPzsx53797deOyxx1I958SJEwZg/PXXX4Zh/Pf3vXLlimEYhtGzZ0/j5ZdfTnXP77//bjg4OBg3btwwfv75Z8PT09OIjo7OsFYRe6MxOyKFRPXq1a3/HBAQAMD58+cpW7Yse/bsYfPmzdaWHIDk5GTi4uK4fv06hw4dIjAwkMDAQOvrISEheHt7c+jQIerVqwdAUFAQpUqVsl6zZ88eYmJiKFGiRKpabty4wbFjx7Jc++7du6lVqxY+Pj7pvh4TE8Pw4cNZtmwZERERJCUlcePGjWwPLDYM47avZ2cM0q+//sro0aP5+++/iY6OJikpyfr7LFq0KABFixalQoUK1nsCAgI4f/58tmq+1Z49e9i7dy/fffed9ZxhGJjNZk6cOMEjjzxCUFAQ5cuXp02bNrRp04bHH3/cWpOIPVLYESkknJycrP9sMpkAUnUDjRgxgieeeCLNfSljb7LC3d091XFMTAwBAQGsX78+zbXe3t5Zfq6bm9ttXx80aBBr1qxh7NixVKxYETc3N5566ikSEhKy9PxSpUpZg1t6Dh06RJEiRQgODgYsXVS3BqPExETrP588eZIOHTrQu3dvRo4ciY+PD5s2baJnz54kJCRYg8XNfxOw/F0yC1yZiYmJ4ZVXXqFv375pXitbtizOzs7s2rWL9evXs3r1at577z2GDx/Ojh07svU3ESlIFHZEhNq1a3P48GEqVqyY7utVqlTh9OnTnD592tq6c/DgQa5evUpISMhtnxsZGUmRIkUoV65cutc4OzuTnJx82/qqV6/Ol19+yeXLl9Nt3dm8eTM9evTg8ccfByxf+CdPnrztM2/m4ODAM888w3fffcf777+fatzOjRs3mDp1Ko8//jheXl6AJRzdPBMuOjqaEydOWI937tyJ2Wxm3LhxODhYhkb++OOPWa4nRVZ+N7eqXbs2Bw8ezPBvCVCkSBFatmxJy5YtGTZsGN7e3qxduzbdsCtiDzRAWUR47733mD17NiNGjODAgQMcOnSI77//nnfeeQeAli1bUq1aNbp06cKuXbvYvn073bp1o1mzZtStWzfD57Zs2ZKGDRvSqVMnVq9ezcmTJ9myZQtvv/22dUp8uXLlOHHiBLt37+bixYvEx8enec7zzz+Pv78/nTp1YvPmzRw/fpyff/6ZrVu3AlCpUiXroOg9e/bQuXNna6tVVo0cORJ/f38eeeQRVqxYwenTp9m4cSOtW7fGwcGBTz/91HptixYt+Pbbb/n999/Zt28f3bt3x9HR0fp6xYoVSUxM5LPPPuP48eN8++23TJ8+PVv1pPxu9u7dy+HDh7l48WKq1qOMDBkyhC1bttCnTx92797NkSNHWLx4sXWA8tKlS5k0aRK7d+/m1KlTzJ49G7PZzL333pvt+kQKCoUdEaF169YsXbqU1atXU69ePRo0aMCECRMICgoCLN0rixcvpnjx4jRt2pSWLVtSvnx5fvjhh9s+12QysXz5cpo2bcqLL75I5cqVee655zh16hR+fn4APPnkk7Rp04bmzZtTqlQp5s2bl+Y5zs7OrF69Gl9fX9q1a0e1atX46KOPrAFj/PjxFC9enEaNGtGxY0dat25N7dq1s/U7KFmyJNu2baN58+a88sorBAcH06xZM5KTk9m9e7d1nBPAm2++SbNmzejQoQPt27enU6dOqcbe1KhRg/Hjx/Pxxx9TtWpVvvvuO0aPHp2tegB69erFvffeS926dSlVqhSbN2/O9J7q1auzYcMG/vnnH5o0aUKtWrV47733KF26NGDpPlywYAEtWrSgSpUqTJ8+nXnz5nH//fdnuz6RgsJk3G0HsYiInfrqq6947bXX+OGHH9KsZCwiBYdadkREMtCzZ0++//57Dh06xI0bN2xdjojcIbXsiIiIiF1Ty46IiIjYNYUdERERsWsKOyIiImLXFHZERETErinsiIiIiF1T2BERERG7prAjIiIidk1hR0REROyawo6IiIjYtf8DN1CQY3uEesQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(labtat['Laboratory 1'], line = 'q')\n",
    "plt.title('Laboratory 1')\n",
    "sm.qqplot(labtat['Laboratory 2'], line = 'q')\n",
    "plt.title('Laboratory 2')\n",
    "sm.qqplot(labtat['Laboratory 3'], line = 'q')\n",
    "plt.title('Laboratory 3')\n",
    "sm.qqplot(labtat['Laboratory 4'], line = 'q')\n",
    "plt.title('Laboratory 4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4**\n",
    "\n",
    "Compare Evidences with Hypothesis using t-statictic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_value = 2.1156708949992414e-57\n"
     ]
    }
   ],
   "source": [
    "test_statistic , p_value = stats.f_oneway(labtat.iloc[:,0],labtat.iloc[:,1],labtat.iloc[:,2],labtat.iloc[:,3])\n",
    "print('p_value =',p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare p_value with '$\\alpha$ '(Significane Level)\n",
    "\n",
    "If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence\n",
    "\n",
    "If p_value is = '$\\alpha$ ' we reject Null Hypothesis\n",
    "\n",
    "interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significnace=0.050, p=0.000\n",
      "We reject Null Hypothesis there is a significance difference between TAT of reports of the laboratories\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "print('Significnace=%.3f, p=%.3f' % (alpha, p_value))\n",
    "if p_value <= alpha:\n",
    "    print('We reject Null Hypothesis there is a significance difference between TAT of reports of the laboratories')\n",
    "else:\n",
    "    print('We fail to reject Null hypothesis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hence, We fail to reject Null Hypothesis because of lack evidence, there is no significant difference between the samples**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# <font color='green'>Question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to conduct a <font color=\"red\">Test of Independence</font> using Chi-Square $\\chi_2$ test with Contingency table\n",
    "We need to check whether the proportion of any of these samples are different or the same?\n",
    "\n",
    "**Step 1**\n",
    "\n",
    "Make two Hypothesis one contradicting to other\n",
    "\n",
    "Null Hypothesis is want we want to prove\n",
    "+ Null Hypothesis: There is no association or dependency between the gender based buyer rations across regions\n",
    "+ Alternative Hypthosis: There is a significant association or dependency between the gender based buyer rations across regions\n",
    "\n",
    "\n",
    "\n",
    "**Step 2**\n",
    "\n",
    "Decide a cut-off value\n",
    "+ Significance 5%\n",
    "+ alpha = 0.05\n",
    "\n",
    "As it is a one-tailed test\n",
    "+ alpha = 1-0.95 = 0.05\n",
    "\n",
    "\n",
    "\n",
    "**Step 3**\n",
    "\n",
    "Collect evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>East</th>\n",
       "      <th>West</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Observed Values</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Males</th>\n",
       "      <td>50</td>\n",
       "      <td>142</td>\n",
       "      <td>131</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Females</th>\n",
       "      <td>435</td>\n",
       "      <td>1523</td>\n",
       "      <td>1356</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 East  West  North  South\n",
       "Observed Values                          \n",
       "Males              50   142    131     70\n",
       "Females           435  1523   1356    750"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing Files\n",
    "\n",
    "buyer = pd.read_csv('C:\\\\Users\\\\Riyaz-PC\\\\Exceler Solutions\\\\Assignments\\\\3. Hypothesis Testing\\\\BuyerRatio.csv', index_col = 0)\n",
    "buyer.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = [[50,142,131,70],\n",
    "        [435,1523,1356,750]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying Chi-Square $\\chi_2$ contingency table to convert observed value into expected value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.595945538661058 \n",
      " 0.6603094907091882 \n",
      " 3 \n",
      " [[  42.76531299  146.81287862  131.11756787   72.30424052]\n",
      " [ 442.23468701 1518.18712138 1355.88243213  747.69575948]]\n"
     ]
    }
   ],
   "source": [
    "stat, p, dof, exp = stats.chi2_contingency(buyer)\n",
    "print(stat,\"\\n\", p,\"\\n\", dof,\"\\n\", exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chi2ContingencyResult(statistic=1.595945538661058, pvalue=0.6603094907091882, dof=3, expected_freq=array([[  42.76531299,  146.81287862,  131.11756787,   72.30424052],\n",
       "       [ 442.23468701, 1518.18712138, 1355.88243213,  747.69575948]]))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chi2_contingency(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "observed = np.array([50, 142, 131, 70, 435, 1523, 1356, 750])\n",
    "expected = np.array([42.76531299,  146.81287862,  131.11756787, 72.30424052, 442.23468701, 1518.18712138, 1355.88243213, 747.69575948])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4**\n",
    "\n",
    "Comparing Evidence with Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics =  1.5959455390914483 \n",
      " P_Value =  0.8095206646905712\n"
     ]
    }
   ],
   "source": [
    "statistics, p_value = stats.chisquare(observed, expected, ddof = 3)\n",
    "print(\"Statistics = \",statistics,\"\\n\",'P_Value = ', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare p_value with '$\\alpha$ '(Significane Level)\n",
    "\n",
    "If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence\n",
    "\n",
    "If p_value is = '$\\alpha$ ' we reject Null Hypothesis\n",
    "\n",
    "interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significnace=0.050, p=0.810\n",
      "We fail to reject Null hypothesis\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "print('Significnace=%.3f, p=%.3f' % (alpha, p_value))\n",
    "if p_value <= alpha:\n",
    "    print('We reject Null Hypothesis there is a significance difference between TAT of reports of the laboratories')\n",
    "else:\n",
    "    print('We fail to reject Null hypothesis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We fail to reject Null Hypothesis because of lack evidence. Therefore, there is no association or dependency between male-female buyers rations and are similar across regions. Hence, Independent samples**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'>Question 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to conduct a <font color=\"red\">Test of Independence</font> using Chi-Square $\\chi_2$ test with Contingency table\n",
    "We need to check whether the mean of any of these samples are different or the same?\n",
    "\n",
    "**Step 1**\n",
    "\n",
    "Make two Hypothesis one contradicting to other \n",
    "\n",
    "Null Hypothesis is want we want to prove\n",
    "+ Null Hypothesis: $\\mu_1 = \\mu_2$ = $\\mu_3$ = $\\mu_4$\n",
    "+ Alternative Hypthosis: Atleast One of them is Differente\n",
    "\n",
    "\n",
    "\n",
    "**Step 2**\n",
    "\n",
    "Decide a cut-off value\n",
    "+ Significance 5%\n",
    "+ alpha = 0.05\n",
    "\n",
    "\n",
    "\n",
    "**Step 3**\n",
    "\n",
    "Collect evidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Phillippines</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Malta</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Defective</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Defective</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Defective</td>\n",
       "      <td>Defective</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Defective</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Defective</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Phillippines   Indonesia       Malta       India\n",
       "0   Error Free  Error Free   Defective  Error Free\n",
       "1   Error Free  Error Free  Error Free   Defective\n",
       "2   Error Free   Defective   Defective  Error Free\n",
       "3   Error Free  Error Free  Error Free  Error Free\n",
       "4   Error Free  Error Free   Defective  Error Free\n",
       "5   Error Free  Error Free  Error Free  Error Free\n",
       "6   Error Free   Defective  Error Free  Error Free\n",
       "7   Error Free  Error Free  Error Free  Error Free\n",
       "8   Error Free  Error Free  Error Free  Error Free\n",
       "9   Error Free  Error Free  Error Free  Error Free"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing Files\n",
    "\n",
    "centers = pd.read_csv('C:\\\\Users\\\\Riyaz-PC\\\\Exceler Solutions\\\\Assignments\\\\3. Hypothesis Testing\\\\Costomer+OrderForm.csv')\n",
    "centers.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Phillippines</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Malta</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "      <td>Error Free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>271</td>\n",
       "      <td>267</td>\n",
       "      <td>269</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Phillippines   Indonesia       Malta       India\n",
       "count           300         300         300         300\n",
       "unique            2           2           2           2\n",
       "top      Error Free  Error Free  Error Free  Error Free\n",
       "freq            271         267         269         280"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Phillippines    0\n",
       "Indonesia       0\n",
       "Malta           0\n",
       "India           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Phillippines</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>Malta</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Phillippines, Indonesia, Malta, India]\n",
       "Index: []"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers[centers.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300 entries, 0 to 299\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Phillippines  300 non-null    object\n",
      " 1   Indonesia     300 non-null    object\n",
      " 2   Malta         300 non-null    object\n",
      " 3   India         300 non-null    object\n",
      "dtypes: object(4)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "centers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phillippines\n",
      "Error Free    271\n",
      "Defective      29\n",
      "Name: count, dtype: int64 \n",
      " Indonesia\n",
      "Error Free    267\n",
      "Defective      33\n",
      "Name: count, dtype: int64 \n",
      " Malta\n",
      "Error Free    269\n",
      "Defective      31\n",
      "Name: count, dtype: int64 \n",
      " India\n",
      "Error Free    280\n",
      "Defective      20\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(centers['Phillippines'].value_counts(),'\\n',centers['Indonesia'].value_counts(),'\\n',centers['Malta'].value_counts(),'\\n',centers['India'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[271, 267, 269, 280], [29, 33, 31, 20]]\n"
     ]
    }
   ],
   "source": [
    "contingency_table = [[271,267,269,280],\n",
    "                    [29,33,31,20]]\n",
    "print(contingency_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics =  3.858960685820355 \n",
      " P_Value =  0.2771020991233135 \n",
      " degree of freedom = 3 \n",
      " Expected Values =  [[271.75 271.75 271.75 271.75]\n",
      " [ 28.25  28.25  28.25  28.25]]\n"
     ]
    }
   ],
   "source": [
    "stat, p, df, exp = stats.chi2_contingency(contingency_table)\n",
    "print(\"Statistics = \",stat,\"\\n\",'P_Value = ', p,'\\n', 'degree of freedom =', df,'\\n', 'Expected Values = ', exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "observed = np.array([271, 267, 269, 280, 29, 33, 31, 20])\n",
    "expected = np.array([271.75, 271.75, 271.75, 271.75, 28.25, 28.25, 28.25, 28.25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4**\n",
    "\n",
    "Compare Evidences with Hypothesis using t-statictic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistic =  3.858960685820355 \n",
      " p_value = 0.4254298144535761\n"
     ]
    }
   ],
   "source": [
    "test_statistic , p_value = stats.chisquare(observed, expected, ddof = df)\n",
    "print(\"Test Statistic = \",test_statistic,'\\n', 'p_value =',p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the data\n",
    "\n",
    "Compare p_value with '$\\alpha$ '(Significane Level)\n",
    "\n",
    "If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence\n",
    "\n",
    "If p_value is = '$\\alpha$ ' we reject Null Hypothesis\n",
    "\n",
    "interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significnace=0.050, p=0.425\n",
      "We fail to reject Null hypothesis\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "print('Significnace=%.3f, p=%.3f' % (alpha, p_value))\n",
    "if p_value <= alpha:\n",
    "    print('We reject Null Hypothesis there is a significance difference between TAT of reports of the laboratories')\n",
    "else:\n",
    "    print('We fail to reject Null hypothesis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We fail to reject Null Hypothesis because of lack of evidence.**"
   ]
  }
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