Hypothesis Testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16e5f058",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29d2e28e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15f3218",
   "metadata": {},
   "source": [
    "## Quetion 1\n",
    "### A F&B manager wants to determine whether there is any significant difference in the diameter of the cutlet between two units. A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validity of the assumptions.\n",
    "\n",
    "**Minitab File : Cutlets.mtw**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce9112a6",
   "metadata": {},
   "source": [
    "## Solution :\n",
    "**We are going to conduct a 2 tailed t-Test 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",
    "**Make two Hypothesis one contradicting to other\n",
    "Null Hypothesis is want we want to prove**\n",
    "\n",
    "* **Null Hypothesis:** μ1 = μ2\n",
    "* **Alternative Hypthosis:** μ1 ≠ μ2\n",
    "\n",
    "**Decide a cut-off value**\n",
    "\n",
    "* Significance 5%\n",
    "* alpha = 0.05\n",
    "\n",
    "**As it is a two-tailed test**\n",
    "\n",
    "* alpha/2 = 0.025"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc435a4",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff69ef7a",
   "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>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": [
    "cutlets = pd.read_csv('D:\\Assignment ExcelR\\Hypothesis Testing\\Cutlets.csv')\n",
    "cutlets.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5d3c62",
   "metadata": {},
   "source": [
    "### Applying Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56b5cdf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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": "markdown",
   "id": "8f81b45c",
   "metadata": {},
   "source": [
    "### Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4109c0fb",
   "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": "markdown",
   "id": "78459a90",
   "metadata": {},
   "source": [
    "### Checking the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb11e76d",
   "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: 688.0 bytes\n"
     ]
    }
   ],
   "source": [
    "cutlets.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75e182f",
   "metadata": {},
   "source": [
    "### Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d6fe156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (9,6))\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": 7,
   "id": "79046e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (9,4))\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": 8,
   "id": "0039f52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\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)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5a0505",
   "metadata": {},
   "source": [
    "### Plotting Q-Q plot to check whether the distribution follows normal distribution or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e579d70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
   "id": "3df56917",
   "metadata": {},
   "source": [
    "### Compare Evidences with Hypothesis using t-statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e2727f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p_value 0.4722394724599501\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",
   "id": "335903ef",
   "metadata": {},
   "source": [
    "### Compare p_value with 'α' (Significane Level)\n",
    "**If p_value ≠ 'α' we failed to reject Null Hypothesis because of lack of evidence**\n",
    "\n",
    "**If p_value is = 'α' we reject Null Hypothesis**\n",
    "\n",
    "### Interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be9dec92",
   "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",
   "id": "45d522a1",
   "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",
   "id": "aa81a5cc",
   "metadata": {},
   "source": [
    "## ========================================================================"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f7fdff",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "### A hospital wants to determine whether there is any difference in the average Turn Around Time (TAT) of reports of the laboratories on their preferred list. They collected a random sample and recorded TAT for reports of 4 laboratories. TAT is defined as sample collected to report dispatch.\n",
    "   \n",
    "### Analyze the data and determine whether there is any difference in average TAT among the different laboratories at 5% significance level.\n",
    "**Minitab File: LabTAT.mtw**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a4c07f",
   "metadata": {},
   "source": [
    "## Solution :\n",
    "**We are going to conduct a ANOVA Test on 4 Independent samples with Numerical Data**\n",
    "\n",
    "**We need to check whether the mean of any of these samples are different or the same?**\n",
    "\n",
    "**Make two Hypothesis one contradicting to other**\n",
    "\n",
    "**Null Hypothesis is want we want to prove**\n",
    "\n",
    "* **Null Hypothesis:** μ1 = μ2 = μ3 = μ4 \n",
    "* **Alternative Hypthosis:** Atleast One of them is Differente\n",
    "\n",
    "**Decide a cut-off value**\n",
    "\n",
    "* Significance 5%\n",
    "* alpha = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0355e8",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b276fba2",
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat = pd.read_csv('D:\\Assignment ExcelR\\Hypothesis Testing\\LabTAT.csv')\n",
    "labtat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72ca644",
   "metadata": {},
   "source": [
    "### Applying Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "069d2177",
   "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>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dac16b9f",
   "metadata": {},
   "source": [
    "### Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ef17082",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c0da28",
   "metadata": {},
   "source": [
    "### Checking the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ecb70b78",
   "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": "markdown",
   "id": "a0af7501",
   "metadata": {},
   "source": [
    "### Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0962e8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (10,8))\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": 17,
   "id": "85b8e157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 18,
   "id": "2d15dfaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4YDlFUeIraolfShkCHgI2A8eBp6SUR4UQnxNCfK632CbgLFAB/Ar4fO/2POANIcQhem4gXpNSvti777vAjUKI08CNve8VRYmwg9UdPPCHvUzLsvPkA2tIs5mHPSbbYeX+tWX4wzqP7zxPMKw6/SlKoolmUz9Syk30JPe+237R53cJPDjAcYeAJYOcsxW4PrKRKorSV21HN595bC+Zdgu///RK0keQ9C/IcVi5e1kRj+86z5bjjWyYnxfFSBVFGS3V/VZRlEsEQjoP/nEf/mCY392/gqyU0XfUm5vvYEVpOu+cbqGqrSsKUSqKMlYq8SuKconvvXKCA9UdfO8jC5mRnTLm82yYn4szycRz+2vRpRpYoyiJQiV+RVEu2nGmlV+/c45PrC5hw4LxNdFbTQY2LMijwe3jvcr2CEWoKMp4qcSvKAoAXn+IrzxzkNKMZL5+y5yInHN+voOS9GRePd6IPxiOyDkVRRkflfgVRQHgx1tOUdPezffvWkSyOTL9foUQ3LIgD68/xM5zbRE5p6Io46MSv6IoHK938+j2Su5dWcSK0vSInrsoPZlZOXbePt1MIKSG9ylKvKnEryhTnJSS72w8ijPJxD/fHJkm/v6um51NVyDMrnOtUTm/oigjpxK/okxx2042s+tcG/94w8wRTdIzFsUZNqZl2nj3TCshNamPosSVSvyKMoXpuuS/XjlBSUYy96yM7iqWa2dk4uoOsvloY1SvoyjK0KI6c5+iKInt+YO1nGjw8D/3LsEU5eV0Z+emkG4z81+vnBj3PP4fW6WW2laUsVKJX1GmqEBI5wevnmJevoPbxjhmf3rV06Mq/4G0NB6ryUE7sZGyZP+AZc4U3zWmWBRFGRmV+BUlAQUbGvC++y7Bqir07m6MOTlYy8tJXrECYYzMn+0Tu6uoae/m/35wAZo28Ip7kXZVhos/1WbxeksqnylWTf6KEg/qGb+iJBCp63i2bKH14YfxHz+OKT8f6/z5SL8fzyuv0PrIIwQbx58wg2GdX7x5hlVl6Vw5MzMCkY+M3aizOs3DO20O/HpsbjYURbmUqvErSoKQ4TAdTz2F/8QJkpYuJeWmm9CSki7u9508ifuFF2j77W9J/9SnMOXmjvlaLx2qp97l4/9+cAFCxDYBX5fZwdttTna2p3B1hjum11YURdX4FSUhSClxv/AC/hMnSFm/Hucdd1yS9AGss2eT/ulPI0wm2h57jFD72Oa/l1LyyFtnmZlt5+pZWZEIf1TK7d3kWgK81eqM+bUVRVGJX1ESQteOHXTv34/t6quxrVkzaDljejrpn/oUhMO4nnkGGR79/PfvnmnlWL2bv72yLGbP9vsSAtaluzjqSaY1oBodFSXWVOJXlDgLNjTg2bIFy5w52K+9dtjyxowMHLffTrCmhs5t20Z9vUfeOkum3cIdiwvGEG1kXJnuRiLY3uaIWwyKMlWpxK8ocSTDYVx/+QtaUhLO228f8fP2pPnzsS5ahHf7dkItLSO+3skGD2+eauZTV5RgNRnGGva45VqDzLB187ZK/IoScyrxK0ocde3eTaixEccHPoBms43q2JSbbkKYTLg3bUJKOaJjfvfuOawmjY+vKhlLuBG1Lt1NVbeVWl90pglWFGVgKvErSpyEOzvpfOMNzDNmYJk9e9THG+x27NdeS+DMGfynTw9b3usPsfFAHR9YmB+1OflHY2WqB4Dd7fY4R6IoU4tK/IoSJ51vvIEMhXBs2DDmIXXJK1ZgSEvrOdcwtf5Nh+vxBsLcvaJoTNeKtAxziBnJ3ezuSIl3KIoypajEryhxEKiupnvfPpKXLsWYOfYJdITBgO2qqwjV1dH5xrYhyz69t4ayTBvLS9LGfL1IW5nm4WxXEi2qd7+ixIxK/IoSBy0//RloGrarrhr3uZIWLcKQlkbLww8PWus/29zJ7so27lpeGPMJe4ayore5f4+q9StKzKjErygxFjh/HtcLL/Q00zvG36tdGAzY1q7Fd/gw3fv2DVjmmfdq0AR8eGnhuK8XSfnWIIVWP7vbVeJXlFhRiV9RYqz10d8ijEZsa9dG7JxJixZhcDpp+93vLtsXCus8u6+Ga2dnk+OwRuyakbIqzcPxziTcwfgNL1SUqUQlfkWJoVBzM66//hXnnXdiSIlcLVeYzaTecw+eLVsJVFVdsu/t0y00uv3ctTwxOvX1tzLVg0Sw16V69ytKLKjErygx1Pb4H5HBIBmfvj/i50772MfAYKD9z3++ZPtzB2pJSzZx3ZzsiF8zEkqS/GSZA6q5X1FiRCV+RYkR3e+n46mnsF93HebS0oif35STTcq11+L6y1/RAwEA/KEwW483cdPcXMzGxPxzFwJWpnZy2JOML5w4HQ8VZbJKzG8CRZmEPJs3E25vJ+1j90btGql33024vZ3OLVsAeOd0C53+EBsWjH0J31hY7OwkJDWOdSbHOxRFmfTU4FlFiZH2Pz2BubR0yNX3+tt9rm3EZVt3VYGxkLmZOZz41e+pyJjPM+/VYDVpVLV18addVcOfJE7m2LuxaDoHXHaujncwijLJRbXGL4RYL4Q4KYSoEEJ8bYD9QgjxP737DwkhlvZuLxJCvCGEOC6EOCqE+EKfY74jhKgVQhzofd0Szc+gKJHgO3aM7gMHSLv3HoQWxT87TaP1qvWkHD+IoamB4/VuynMdGKN5zQgwa5J5KV0cdI9uvQJFUUYvat8GQggD8DNgAzAXuFcIMbdfsQ3AzN7XA8DDvdtDwD9JKcuB1cCD/Y79kZRyce9rU7Q+g6JESvsTTyCsVpwf/GD0r3XFdT2/bN1MdzDM/AJn1K8ZCYscnTT4zbR2+uMdiqJMatGsBqwEKqSUZ6WUAeBJ4I5+Ze4Afi977ARShRB5Usp6KeU+ACmlBzgOxG/xcEUZh7DLheuFF3F+4LaITNgznEBWLp0z55G7extmg2BG9sQYJrfY4QXgVFNnnCNRlMktmom/AKju876Gy5P3sGWEEKXAEmBXn80P9T4aeFQIMeDE40KIB4QQe4UQe5ubm8f4ERRl/FzPPYf0+Ui7N3qd+vprXXMd2W11XGvswGRI7Gb+C3KtQXIsAU43euIdiqJMatH8RhhoXE7/icSHLCOEsAPPAl+UUrp7Nz8MTAcWA/XADwa6uJTyESnlcinl8qysrFGGriiRIaWk/amnsS5aiHVu/ydd0XNo+jKCwsCNtftjds1IWOzwcqa5k1BYj3coijJpRbNXfw3Qd6qwQqBupGWEECZ6kv4fpZR/uVBAStl44XchxK+AFyMbtqJEju/wYQJnzpD77/82+oPPbyerrWvExVOrvBd/316bzXu5c1hx4HW8ldmgaZwpvmv0McTYIkcnm5vTqGztmjCPKBRloolmjX8PMFMIUSaEMAP3ABv7ldkIfLK3d/9qwCWlrBc9y4f9Bjgupfxh3wOEEHl93n4QOBK9j6Ao49Px178iLBYcGzbE9LoH3TYqZs3F0NmF4Vx9TK89HvNSujBogtNNqrlfUaIlaolfShkCHgI209M57ykp5VEhxOeEEJ/rLbYJOAtUAL8CPt+7fS3wCeC6AYbtfU8IcVgIcQi4FvjHaH0GRRkP3e/H/dImUm68MaLz8g+nJWCkxmfBWF6ItJgwHayI2bXHy2qQlGQkc0o951eUqInqBD69Q+029dv2iz6/S+DBAY57h4Gf/yOl/ESEw1SUqOh8/XV0txvnB++M6XUPunrGwi9M9xOcV4bp6Dl8H4jcSoDRNjPLzuZjjXT6Q9gtao4xRYm0idHdV1EmoI6//hVjbi621atjet2DbhsZpiAF1gDBBdMQ/iDGitqYxjAe07J6nu2fbVbD+hQlGlTiV5QoCDY24X1nO8477kAYYrfOfEjCIbeNRU4vQkC4LB+ZZMF49FzMYhiv/NQkLEaNsy3e4QsrijJqKvErShS4X9gIuo7zzv5zVkVXhTeJbt1wcTIcDBrB8hJMJ6sQoWBMYxkrgyYozbBxtlklfkWJBpX4FSXCpJR0PPccSYsXYykri+m1D7hsaEjmp7yfNENzSxG+AClHD8Q0lvGYlmWjpdOPu3ti3KwoykSiEr+iRJj/1CkCFWdw3nF7zK990G1jpq0bm/H9CXBC0wuQFhPOve/EPJ6xKsvs6aComvsVJfJU4leUCHO/+BIYDKTcfHNMr+sKGjjblcQiZ79kaTQQnF1M6r53IRyOaUxjlZ+ahNWkqQ5+ihIFKvErSgRJKXFv2oRtzRqM6ekxvfZhT08teZHj8lpyaF4Zxk439hOHYhrTWGmi9zm/qvErSsSpxK8oEdR94ADB2loct94a82sf8ySTbAgzLdl32b7QjELCZgupe96OeVxjNS3LTps3QEdXIN6hKMqkohK/okSQe9PLCLOZlBtviPm1j3mSmGPvRhto6iuzEfeilT3N/frEaO6fpp7zK0pUqMSvKBEiw2Hcr7yM/eqrMdhju8BMGxbq/RbK7YMv6tOxfB0mVzu2iuNRjUXoYQxhH0IPjes8uU4rSSaDGtanKBGm5sNUlAjp2r2bcHMLjltvGb5whB0nA4C5KYMnfveiFegGI859O/DOmh+R6xpC3Ti6KknpqiLJ10yyvxlz6P159kMGK15rHi77dFqd8wmYHCM+tyYEZZk2zrWoDn6KEkkjSvxCiGeBR4GXpZRqoWxFGYDrpZfQkpOxX3NNzK99VGRi1cKUDfB8/wI9yUZn+SKc+3dSd8/fjek6WjhAZscB8lp2kNu6g3TXUQQQFka6LVm4bGX4zOnomglNhjAHXdi7aylu3EJR41aaUxdTm331iG8ASjNtHKt34+oO4kwyjSlmRVEuNdIa/8PA/cD/CCGeBn4npTwRvbAUZWKRgQCeV1/DfsP1aFZrzK9/lExm27sxDLi01ftcS9dQ9PufYqmrwp9fPOx5jcFO0t3HSXcdJbdtF9lt72EMd6MLIy2pC6nNuhqXfRrepHykGHxqYou/jdy23WS3v0e6+xjn8zbQkrpw2OuXZiQDcL7Vy8LC1GHLK4oyvBElfinlFmCLEMIJ3Au8JoSopmcp3cellGp6LWVK63xne89KfHHoze/GTLVwsNbePGxZ1+JVFP3+pzj376Apr5AkXxPJvkaS/U3v/+5rJKn3Z0p3zfvH2ko5U3gnDRlX0JixgpDRxvSqp0cUo9+Szvm89TRkrGR67fNMr30OW3cd53NvAjF4V6M8ZxJmg8b51i6V+BUlQkb8jF8IkQH8DfAJYD/wR2AdcB9wTTSCU5SJwv3SSxicTmxr1sT82sd6n++XD/F8HyCpu4F879uI7GRK33yMa4z/jVG/9NFAWJjotmbTZc2hNXUB5wrvoNU5jzbHXPyWjHHH6jenc6z0PoobXyOvdRemkIeKwg8PmvwNmqAoPYnK1vh28JNSUttZS723Hn/YT7IxmTJnGWnWtLjGpShjMdJn/H8B5gB/AD4gpazv3fVnIcTeaAWnKBOB3tWF5/XXcX7gAwizOebXPyoyMcsQM5K7B9zv8J4jr2UHK4/+BwJJXX4OrgMGTmfcjid7Nl3WXLqsOXRbs/GbUkEM87xgvIRGVe7NBIwOShpfQ9b8lTOFHxw0+Zdk2HjjRBO+YBirKXYrHUopOdh8kGdOPcM7te/Q6mu9rEypo5QbS27krll3kWfPi1lsijIeI63x/1pKuanvBiGERUrpl1Iuj0JcijJhdG7bhuzujsukPdBT459NO8Z+edMUdFNW9xJpnacJGO0cmfFZzueuJzgX5hz4POe9K2grXh+XmAEaMtcgpE5x01YCjQ6qc28csFxphg0JVLV1MSsnJSaxHW4+zH/v/W/2Ne3DbrJzVeFVLM9dTlFKEVaDFU/Aw5mOM2yv285vjvyG3x79LR+a8SH+Yek/4LQ4YxKjoozVSBP//wE29du2A1ga2XAUZeJxv/wyxqwskpcvi/m1OzFRiZOPyhPA+zV1p6eC6bV/RdNDVOVcT0P6KipK7+3ZaZf4M3NI3beDtqvjl/gB6rPWYgm6yG/dQbcli5a0xZeVKUpPQhM9Hfyinfh9IR8/2fcTHj/+OBnWDL6x6hvcMf0Okk3Jl5W9svBKPjX/U9R31vObI7/hmVPPsLVqK/++9t+5qvCqqMapKOMxZOIXQuQCBUCSEGIJ73+zOIDL/xIUZYrRvV4633qb1I98BGGIXTP0BSdIRwrBXNkKZAKQ2XGQabUb6bJmU1H4YXyWzEsPEgLXktVkbnsZ4fchLbEfhdBXZd56rIFWyuo34U26vLncYjSQ50yisnXoPgzjVeOp4R/e+AdOt5/m3jn38oWlX8Bmsg17XJ49j2+u/iYfnvlhvrX9Wzy49UE+v+jzfG7R5xDRfmyiKGMwXI3/ZuBTQCHwwz7bPcA3ohSTokwYnW+9hfT7Sbn5prhc/4RIxyB1ZtIOZJLRcZjptc/jspVxquhudINlwONcS9aQ/drzOI7uw7X0itgG3Z/QqCj8IAvOPMLM6mc4Pu3ThI2X1itKM5LZda6NkK5j1EY/4ejTp4YefVDtqebJE08ipeTeOfcyM20mm871b+Qc3F2z7qI8o5zHb3mc/9j5H/z84M9p7GrkW6u/hUGL/Q2hogxlyL8gKeVjUsprgU9JKa/t87pdSvmXGMWoKAnLvflVDBkZJC+LfTM/wCnSKcWFhTCOzrNMq30ed3IJJ4vvHTTpA3TOXkAo2YZz344YRju4kNHOmYIPkhRoZeHpn122vyTDRkiX1HUMPkHRWJ3tOMvjxx4nyZjEZxZ8hplpM8d8LqvRyv9Z+394YOEDPHv6Wb65/Zvoas4zJcEM19T/N1LKx4FSIcSX+u+XUv5wgMMUZUrQu7vpfPNNnHfcHpdm/jCCCtK4lipMoosZNS/gs2RyqvijSG2YxjyjEffCFTgO7OpZtCcBaqVuexmNacuYXfk4VXk309pngp+SPhP5FKdH7iljlbuKJ088SUZSBh8v/zh28/jXWBBC8PdL/h6zZuanB36K0+Lkqyu+qpr9lYQxXJvZhQdcdiBlgJeiTFmdb7/d05v/5pvjcv0qHPiEkTmyhTLzTjQZ5nTRXYQNI3tm71q6BpPHha0icSbhrM65gW5rFqsOfxtNf39esBSriQybmcoIrtTX1NXEkyeexGlx8om5n4hI0u/rgYUP8Im5n+CPx//IH479IaLnVpTxGLJaIKX8Ze/Pf4tNOIoycXg2v4ohNZXkFSvicv2T9Ewec7PxHeyGFiryPohvFJPsuBf0LtqzfwfeWfOiFeaohA0W9sz7Fte89xBzz/yaIzP/v4v7SjJsnGxwI6Uc93Vcfhd/PP5HjJqRj8/9+IC99sdLCMGXl3+Zus46fvDeD5iZNpM1+bGf4ElR+htRLxkhxPeEEA4hhEkIsVUI0SKE+JtoB6coiUr3++l84w1SbrwBYYzPIpcnRTrzqWKWaT8doXxanaNbcU9PttE5Z2HCPOe/oC77airzNjDvzCM4Os9e3F6UnoQ3EKbNGxjX+X0hH388/kcC4QAfL/84qZbUcUY8OE1o/Oe6/2Sacxr//NY/0+htjNq1FGWkRto99iYppRu4DagBZgFfiVpUipLgvNu3o3d1kXJz/MbBnyKdfzf9DolGdXDpmGbccy1dg7WhBkt9dRQiHLv3yr9K2GBlyYkfXNx24dl+VdvYh/VJKdl4ZiOt3a3cPftucmw54451ODaTjR9e80P8YT/feOcbhPVw1K+pKEMZaeK/sB7mLcATUsq2KMWjKBOCZ/NmNKcT26qVcbm+CzMzDGdZajhBfXAuQTm2pmrXktUAOPfvjGR44+a3ZHB0+gMUNL9FXvN2AHIcVixGbVyJf2f9Tk60neCGkhsoc5ZFKtxhlTnL+PrKr7O7YTePHXssZtdVlIGMNPG/IIQ4ASwHtgohsoDIj6tRlAlADwTwvP4GKddfjzDFZ43406TyDeMf8egOmkMzxnyeYEY2XcXTEy7xA5ws+TiepEKWnPg+Qg+hCUFhWhLVY0z8593n2XJ+C3PS57A6b3WEox3enTPu5Pri6/nZ/p9x1nV2+AMUJUpGlPillF8D1gDLe5fg9QJ3RDMwRUlUXTt2oHs8OOI0aQ+AzVhLuVZNXXABkvENxXMtWY3t9DEMHleEoosM3WBm/5wvk9p5hunVzwI9zf0Nbh9dgdCozuUNenn21LOkWdO4ffrtcRlaJ4Tgm6u/SZIpiX/d/q9qfL8SN6OZAqsc+KgQ4pPAR4D4fespShy5N7+KlpJCchyW4O2hc5VxO2f1fLzh/HGfzbWkZ7Ec58HdEYgtsmpyrqMxfTkLT/8UU9BNcXoyuoSD1SO/SZFS8sKZF+gOdfORWR/BaozfFMWZSZl8ZflXONh8kOcrno9bHMrUNtJe/X8A/htYB6zofQ27Kp8QYr0Q4qQQokII8bUB9gshxP/07j8khFjau71ICPGGEOK4EOKoEOILfY5JF0K8JoQ43ftTLYitxIwMBvFs3UrKddeixWEJXgCnoYYirYktwavouzDPWHWXziCQmoFzf2L17gdACPbN+QrWYAfl5x6jKK2nL8P+6vYRn+K5iuc41X6K64qvI9eWG61IR+wD0z/AwqyF/GTfT+gMdMY7HGUKGuk4pOXAXDmKAbRCCAPwM+BGekYC7BFCbJRSHutTbAMws/e1Cni492cI+Ccp5T4hRArwnhDitd5jvwZslVJ+t/dm4mvAV0cal6KMVfufn8J/5gy6y4VIttH+56fiEIUk03SSk3oh3nAeUDv+U/Yu2pP+7lZEIICM0w3NYNqdczmfexOzKx/nZMnHybSb2Xe+Y0TH1nhq+O7u71LiKInLc/2BaELj6yu/zr0v3csjhx7hS8svmxRVUaJqpE39R4DR3iqvBCqklGellAHgSS7vF3AH8HvZYyeQKoTIk1LWSyn3AUgpPcBxelYJvHDMhW6xjwF3jjIuRRkz3/HjCLMZy/Tpcbl+itaIU2vnV+FbmUVHxM7rXrIGg99HyvGDETtnJB2e+SCGsI+5Z39DcXoy+6vah53IJ6yH+Zd3/gUhBHfMuCOhpsydnzmfO2fcyR+O/4FKV2W8w1GmmJEm/kzgmBBisxBi44XXMMcUAH0HB9fwfvIecRkhRCmwBNjVuylHSlkP0Psze4SfQVHGReo6/hMnMM+YEbfe/Dmmk3RIO1tCy8klctPXesoXEbZYcSRicz/gtk+jsuA2ZlU9yfwUL63ewLDD+v504k/sa9rH11Z+LaqT9IzVF5Z+AYvBwvf3fj/eoShTzEib+r8zhnMPdHvd/xZ9yDJCCDvwLPDF3gmERn5xIR4AHgAoLi4ezaGKMqBgbS26x4O1vDwu17cKFw5DI78O3k4x3gg83X+fNJvxLFiGc/9Oau77+zFNBjQeb4ZOXvy9uePlActUZs/ln+te4rr2H/F7HuLhXZtZNmPgyXA6fB08fPBhZqTOIBAORL22P9yyv4NZk7eGLVVb+N6e7w06r8Bds+4aT2iKcpmRDud7E6gETL2/7wH2DXNYDVDU530hUDfSMkIIEz1J/4/9lgBuFELk9ZbJA5oGifkRKeVyKeXyrKysYUJVlOH5jx8HTcMyc+zLto5HpvEsutT4ZWg9MyLYzH+Ba8kazB2tJFVWRPzckdBuTWd39kqubN/JdFMj55sG/vqSUvLS2ZcQCG6ddmtCNfH3tzJvJSnmFLZVb4vIGgSKMhIj7dX/d8AzwC97NxUAzw1z2B5gphCiTAhhBu4B+j8e2Ah8srd3/2rAJaWsFz1/qb8Bjg+w9O9G4L7e3+8D1JgYJeqklPhOnMBcVoaWlBTz6wvCpBvPUxMupVmkMl12RPwa7oUrkEJLzN79vbYWXoeOxleTnqWqeeD5C460HOGM6wzXFV+H0+KMcYSjY9SMrCtYR7WnWk3qo8TMSJ/xPwisBdwAUsrTDPNsXUoZAh4CNtPTOe8pKeVRIcTnhBCf6y22CTgLVAC/Aj7fu30t8AngOiHEgd7XLb37vgvcKIQ4Tc+Ige+O8DMoypgFKioIt7bGrZk/1VCDUQR4O9SzEuAMRj6cbaRCjlS8M8sTcha/C9xmJ+/mXsENge0kt9cRCF66vyvYxebKzRTYC1ieO+yI44SwJHsJDrODN6vfVLV+JSZG+ozfL6UMXGgyE0IYufx5/WWklJvoSe59t/2iz++SnpuK/se9wyADlKWUrcD1I4xbUSLCs2ULAJbZs+Ny/UzjWXy6ndflQhz4yaR71OeYXjX8c2hjmQ3r5qPMOPRbZGpk16ePlNcLrmV10y6+YHiWIy2fZXre+zPgvXr+VXxhH7dNuw1NjGZ+svi5UOvfdG4TZ1xnmJE69imYFWUkRvqX8aYQ4htAkhDiRuBp4IXohaUoicXz2hZMhYUYHI6YX9si3KQYmmkNlXGadGbQHtGOfX2F5pQAYDxZFaUrjF+XycaJGTdwi7Ybf1X9xe1nOs5wqPkQa/PXxmTVvUhanL0Yp9mpav1KTIw08X8NaAYOA5+lpxb/zWgFpSiJJFhXh+/YMSxxaubPNJ5FSkFdaDo1OJgehY59F+iZTsKZTkwnzkftGpFQOe8GfMLMVTUvAhAMB3np7EtkWDO4svDKOEc3ekbNyLrCddR21nLGdSbe4SiT3Eh79ev0dOb7vJTyI1LKX41mFj9Fmcg8W7YCxOX5viBMhrGSjnABp8hFF4IZUejY11doTjGGc/XgC0T1OuMRsKbwqv16rvLvxOZq4M2aN+nwd3DrtFsxaiN9gplYFmctJsWUwo66xO1cqUwOQyb+3t723xFCtAAngJNCiGYhxL/GJjxFiT/Pli1YZs7AmJER82s7DPUYRYDWUBlnSAWIao0fepr7RVjHWFET1euM176ymwlIE4aDz7GjbgdLspdQ6iyNd1hjZtAMrMhbwTnXORq8DfEOR5nEhqvxf5GeHvYrpJQZUsp0eubSXyuE+MdoB6co8RZqb6dr717sN9wQl+unG6oISgtuPYcKkUa67CYdX1SvGS7KRk+2YDyRuM/5ATLy7fwxfC2PhatJNli4oTg+/48iaVnOMkyaiZ31iTuyQpn4hkv8nwTulVKeu7BBSnkW+JvefYoyqXW+/gboOinxSPwygNNQR3uoCNA4Q2pUJu65jKYRml2M6VQ1hBN3zficNMkvnYUctlp4QLeTZIr9/AqRlmRMYkn2Eo60HMET8MQ7HGWSGi7xm6SULf03SimbgfhMVq4oMeTZsgVjfh7WuXNjfm1zuApN6LSHi+nCSK1IYbqM/Pj9gYRmFyO6/RiqGmNyvbHoCnnwZ73FdF8y91UeJMnbGu+QImJV3iqklOyu3x3vUJRJarjEP1TvnsTt+aMoEaB7vXi3byfl+hviMu2rOXQWv27Dq2dwNkbP9y8IzShEGjSMCdy7/5Vzr4AI01h7LwBzjr0S54giI82axpz0ObzX+B6BsPqaVSJvuMS/SAjhHuDlARbEIkBFiZfOt99BBgJxaeYXITdGvZ72cDEgqOhN/DFp6gewmAhNy8d0ogoScADPqfZTHG87zlzH1dQHZnIkdy1lZ97G2tUR79AiYk3+GnxhHweaDsQ7FGUSGjLxSykNUkrHAK8UKaVq6lcmNc+WLRhSU0letjTm1za79yCQtIV6VpasEKlkSy+OGDa0heaUoLW50Zo7YnbNkQjpAV4++zJZSVlcX7oagGdTbkNIndnHJ0etvzClkHx7Pnsb96oJfZSImxhzWipKjMlAgM4338R+3XUIY+zHhZvduwmJNHyyZ5GZM6TFrJn/gtDsnpuOROvdX9G5G1fAxW3TbyPVZiDdrrPflUtV6WqmVbyFxTeqFbwT1vKc5bR0t/Be43vxDkWZZFTiV5QBeHfvQfd44tLMrwWaMXafJWCcBoAHE43CFpUV+YYinTbC+ZkJ9ZzfHWzmfNdBluUsoyilZ0XvkmydqiaN4/NuwaAHmXX81ThHGRnzMuZhNVh56uRT8Q5FmWRU4leUAXi2vIZITsZ2xZqYX9vk2QdA0FAKcHHinplRWJFvOME5xRhqmhCdXTG/dn+6DHPYtRWzlsT1xe+v01WcrePq0qgx5FFdvILpp7dhCsQ/3vEyGUwsylrEa1Wv0do9OUYsKIlBJX5F6UfqOp1bX8e+bh2a1Rrz65s9+whZitG1FAAqSANgGq6YxxKaU4KQYDxZHfNr93fW+x6eUAtzHddgNb7//6Ukq2eugaomjZPl6zGFfEw7vS1OUUbWstxlhPQQf634a7xDUSYRlfgVpR/foUOEmptJuTEOvfmDbRi7zxJ0vN+h8IxIJU92YiM4xJHRoeemozttcX/O7w62cKZzL3nWWeRYp12yLy9dx2iQnG/W6EgvpiF3LjNPbkULx/6/V6RlJmWyIncFz5x6Bl0m7mRKysSiEr+i9OPZuhWMRuxXXRXza5s9BwAIpLyf+CtIZUYcmvkBEILQnBKMZ2ogGIpLCLoMc8S1FZNmodxx+cp7RgMUZOicb+r5Ojs5dwNJPhcl5ybHYjd3z76b2s5a3q17N96hKJOESvyK0oeUEs9rW7CtXInB6Yz59U2efYTNeeiWPADasdAqkqO+It9QgnOKEcEwxrN1cbn+We8+3KFm5jmuwawNPC1vSZZObatGKAxNOXNoTytm9vHNMAlqydcXXU+6NV118lMiRiV+RekjcPYsgcpK7DdcP3zhCBMhD8auUwQcyy5ui9WKfEMJl+YhLaa49O7vCDRwpnM3edaZ5FinD1quJFsnFBbUt2kgBCfmrifF00h+zcEYRhsdJoOJO6bfwds1b6tOfkpEqMSvKH14XtsCQMr1sU/8Js9+BJJgn2b+M6SiSUlZHBM/RgOhGYU9z/n12E0mE9IDHHS9itVgZ67jmiHLFmf31OzPN/d8pdUWLaPTlsmcYy8n5MyDo3XnjDsJyRAvnn0x3qEok0DsZyZRlATm2boV68KFmHJyYn5ts2c/YVMWYUvhxW0VIo0CPCQRHvZ4054TEY0nuGLOxd9Dc4oxHT2HVteMXpgd0esM5pj7TbrDHlalfwiTZhmybKpN4kzuGc/PXJCagVPlN7F075/IaK6gNXtmTGKOlmmp01iYuZDnKp7jk3M/GZe1I5TJQ9X4FaVXsKEB3+HD8ZmbP9yF0Xu8p7bf+6UuJbFbincYwVlFSE30zN0fA3XdJ6nznWSGfQVp5rwRHVOc/X4HP4DKaWvxW+w9tf5J4I4Zd1DRUcGx1mPxDkWZ4FTiV5Renq1bAUiJw/N9Y+dhBGECjiUXtzVJCx3CGrOleIeUbCVcnIPxePSf83uCrRx1byPNlMd02/IRH1ecpdPWqeHp7nkfNlo4Pet68usOkeKKT8fESNpQtgGLwaLG9CvjphK/ovTq3LoV87RpWKZNG75whJk7D6IbHIStZRe3HQs5gBiuyDeMUHkphqZ2RGv0JhIKyBD7OzZhFCYWpd6MECP/iro4kU+z4eK2MzOvIWwwMfPk1ojHGmsp5hRuKLmBTec24Q/74x2OMoGpxK8oQNjlwrt7T1w69SFDGDuPELQvhD6J7mjIiUHqlMRhxr6BBMtLADBFqdYvpeSd0Gm6wx4Wp27AarCP6vjCTB1NyJ7n/L0C1hTOl66i5NwOTAFvpEOOuTtn3Ikn4OH1qtfjHYoyganEryhA55tvQigUn2b+rtNoejfBlEWXbD8WclCMGwuJMRZdpqUQzs/AeKwyKuc/FK6hVnZQ7rhyxM/1+zIZIT9Dv9iz/4KKWddjDAcoO/NOpEKNm5W5K8m35fNcxXPxDkWZwFSvfkWhZxifMTsb64IFMb+2yXMQKUwEbeUXt0kJx8IOVpBYz6aD5aVYt76HcHuRDlvEzns23MwhvYbpWhZFSfPHfJ6SLJ09p43oOmi9+d+VVkSTI5cZx17mlNV2SavKmJVeMf5zjIEmND4w/QP86vCvaOpqIjs5NiMslMlF1fiVKU/3+eh85x3s11+H0GL8JyElps6DPUm/z5C1uoDAJc0xX4p3OKHyUoCITuZzLFzHu+Ez5AgHqwzTxjVUrThbJxASNLRfeo6KvLnY/J3kt8V/saHxunXarehS5+Vzk2O0ghJ7KvErU5733XeR3d1xGcan+eswBFsI2i9t5j/a1fOnGbc5+gehZ6cSznBiOhaZxF+lt/Jz/xs4RRLXGGdjGGdtvDjrwkQ+hku216UX4zXbmFk/8YfClTnLmJ8xn5fOvhTvUJQJSjX1K1OeZ8tWtJQUbCtWxPza5s4DAD0d+/o41qVhQqcYd8xjumCwCYH0tBSMZ2oxbT8C5pF9hfSdDOiCRt3NT/xbSBImrjXOwSzG/3WUkSKxWSVVzRpr+lxSCo0zueUsrNqLw9uG25Y+7mvF023Tb+O7u79LRXsFM9JmxDscZYJRNX5lSpOhEJ2vv479mmsQZnPMr2/yHCRkLUOaUi/ZfqzLwCyDBxOJN91sOC8DISWGxrYxn6NRd/Pf/lcIS50vWm7EJoaemW+khICSrPAlPfsvOJczi7AwMK3xZESuFU83l96MQRh46Zyq9SujpxK/MqV17dtHuKMjLsP4RMiFwVdJMOXS2r4u4XiXxlxj/Gr7Q5GpdqTVjKF+bAvGNOguvu9/haAM82Xregq0tIjGV5yt0+TS8Pou3R4wWanJKKWk+QyGcHyWGI6UzKRM1uSv4aWzL6FPghUIldiKauIXQqwXQpwUQlQIIb42wH4hhPif3v2HhBBL++x7VAjRJIQ40u+Y7wghaoUQB3pft0TzMyiTW+fWrQizGfuV62J+bVPnoZ5FeeyLL9le5Rd06oK5xsQYv38ZIQjnZqA1t0No+DUE+qrXO/h+b03/K9b1FEY46QOUXliwZ4Ba/9mcWZjDAQpbz0X8urF227TbqPfWs69xX7xDUSaYqCV+IYQB+BmwAZgL3CuEmNuv2AZgZu/rAeDhPvt+B6wf5PQ/klIu7n1timjgypQhpcTz2hZsV1yBZovc0LSRMnkOEjZlELYUXLL9WG/HvnmGxKzxQ29zf1jvSf4jVKd38H3fK0gp+UoUavoXFGX1TORT2WS4bF+LIxeP1TEpmvuvLbqWJGOSWrFPGbVodu5bCVRIKc8CCCGeBO4A+narvQP4vZRSAjuFEKlCiDwpZb2U8i0hRGkU41OmOP+JEwTr6sh88POxv7jux+Q9jj913cVFeS441mXAKiRlBi+JNPjslN7w/ps0yTyThreuluqc4ZvNDwW7eS14DAHcaJrLab2R03rjgGWz2t4bPhgx+I2aGShIWUHleR0y+51LCM7mzGbR+T04vO24bdG5+YiFZFMyNxTfwKuVr/L1VV/HYohMPwll8otmU38BXPK9VdO7bbRlBvJQ76OBR4UQA/7lCiEeEELsFULsbW5uHk3cyhTheW0LaBr2a6+N+bVN3hMIGbhsGB/01PhnJ+sYReJ17LtIE7hzbDiavD2dEobQZAnwavBob9Kfh1MkRz28UmcH1R4HIf3yOQEqs2cSFhrTmiZ+rf+2abfhCXp4u+bteIeiTCDRTPwDzcLR/xtiJGX6exiYDiwG6oEfDFRISvmIlHK5lHJ5VlbWMKdUpiLP1q0kL12KMT32Q7tMnQeRmpWQbdYl28MSjndrzE1O/A5brlwbxqCOvbV70DJNlgB/nNaAhuAm0zycIikmsZU6XYR0AzUex2X7AiYrtRkllDRVoE3wTn4r81aSmZSpmvuVUYlm4q8Bivq8L4TL5h8dSZlLSCkbpZRhKaUO/IqeRwqKMiqB6mr8J09ij8Pc/Egdk+cQQdt86Dd2/ZxP4NPFhEj8nqxkwgaBs6FzwP2N1gCPT2vAIHuSviNGSR96avwAlS7ngPvP5szp6eTXVhmzmKLBqBnZULaBt2rewuVP0M6gSsKJZuLfA8wUQpQJIczAPcDGfmU2Ap/s7d2/GnBJKeuHOqkQou/qHR8EjgxWVlEG43n1NYC4zNZn8J1HC7suW5QHep7vA8xLHl1v+XiQBg1PVjLOBm/P4gJ9NFj9/LGsAaMu+JuzuTFN+gAOS4CMpC4qO1IH3N/syMVrsVPSfCamcUXDbdNuI6gH2Vy5Od6hKBNE1BK/lDIEPARsBo4DT0kpjwohPieE+FxvsU3AWaCCntr7xV5WQogngB3AbCFEjRDiM727vieEOCyEOARcC/xjtD6DMnm5N2/GOm8e5sLCmF/b5DmARCNov3xBoGNdGsmapMSSwM/3+3Dl2jH5wyR3vD9ovsUS4ImyRky64BPnckkPmOISW6mzg0pXav97kh5CcD5rOjkddVgDXTGPLZLK08spc5apKXyVEYvqlL29Q+029dv2iz6/S+DBQY69d5Dtn4hkjMrUE6ytxXfoEFn/9KW4XN/UeYhQ8gyk4fKe6ce6NMqTdbSxr1MTU+6cZHQBznovXWlJdJiC/KmsESHh4+dySYtT0oee5/zvNeTT0p1EVvLl/RDOZ81gbs1BipvPcKog9qsyRooQgtum3cb/7v9f6jrryLfnxzskJcGpufqVKcfd28zvuPnmmF9bC7Rg9NfQlX3XZfuCOpzs1vho1sTpcKabDHRm9jT3n1rg5E9ljQQ1yd+cjV9N/4KLz/k7UgdM/J1JTlrtWZQ2V3Aqf/5lwyoTxdOnnh5x2f/a81+sKxjdZFR3zbr836IyuanEr0w5ns2bscwtx1xcHPNrmzoPAgz4fP+MTyMgBXMnwPP9vly5NooON/OWvYFOo87Hz+WQ44vuugfnWr3DlpHSi8UQ4HCTnUzLwOXPZ81g6bkdOLvacNkyIh1mzKRZ0yhKKeJw82HW5q8d19LGyuSnEr8ypQTr6+k+cICsL34xsic+v31ExUy+NwkLJ3p9BT1dW953zFcAzGNe+x5wDz5ELtG4cpMpOAKzzwWYE86joNsa75CAngp8bnIb9d7Bh2tWZ5axuHIXJU0VHCqbuIkfYEHmAjad20RjVyO5ttx4h6MkMLVIjzKleF7r7c1/802xv7gMYNQbCRqKBtx9OOzEIYIUahMn6QO8UeTlaLHg+iMa0z2x7b0/nDxbGx3+FLqCA7dABExW6tKKKGk5i5jgi93MzZiLJjQONx+OdyhKglOJX5lS3K9sxjJ7Npaysphf2xSuRaATGCTxHwk5WWDsSNRHzQM64fDydk4H1cUWUt1hrJ5AvEO6RL69BYA6b+agZc5nzcAa7CanozZWYUVFsimZGakzONJyRK3YpwxJNfUrk1b7n5+65H3Y7aZ73z7s11132b5YMIWr0bEQ1i6fSbJTGjgTtnODeeD56xNRuznIxsIWCrosFCRlIKnCWd+Jz5E4c8ZnJXdg1ELUdWYwI3XgucEa0goJGMwUtZyjIW3gm7KJYkHmAk61n+K8+zxlztjf3CoTg6rxK1OG71jP+lDWuf0XiYwBqWMK1xA0FIK4/M/uaMiJRLDAMDFmX9ORPF/YggZ8qCoLzGa8GUmk1g88i1+8GIQkL7ltyBq/rhmoTS8hv60KTZ9YHSv7m5U2C7PBrJr7lSGpxK9MGb5jxzBmZ2OMw9oNRr0JjQBBw8AjCY6EeqaWnW+cGIn/3SwXtTY/62szcAR7Gg478uxYO4NYPP44R3epfHsLbT4H3aHBRxrUZJZiDgcmfHO/yWCiPL2c423HCYaD8Q5HSVAq8StTQtjjIVhVFZ/aPj3N/BKNoGHgyVUOhZyUal4cWuKP4a9L8vN2TgdzO2zMd9kvbnfl2pBAav3wQ+1i6eJz/s7Be+03OgvwGy0UtZyLVVhRsyBrAf6wn9Mdp+MdipKgVOJXpgT/8eMgJdZ582J/cSkxhasIaXkgLp/URko4HEplgbEj9rGNUkjobCxswRYysL720mFyIasRb7oVZ4I192cndWAUoSGb+6Wm9Tb3n5/wK/aVOkqxm+yquV8ZlEr8ypTgO3oUY1YWxuzsmF9bky4M0jNob/5aPYl2aWbBBGjm35npptUa5LaaTJJ0w2X7O/LsJHkCWBKod79Bk+Ta2oes8UPPmH6THiK3oyZGkUWHJjTmZ87ndMdpuoMTa2ioEhsq8SuTXtjjIXD+PJY4NvMDg4/f732+n+iJ32UKsT3bxRxXMtM6Bx6v78qz9zb3J1atP9/eQqvPgS80+DTCzc48fEbrpGnu16XOsbZj8Q5FSUAq8SuTnu/IEZCSpAXxWYjFHK4mpGUgtcsX5YGexG8lzAxDYiXL/rbktQFwQ/3gM+ElanN/vq0FENR5B6/1S6FRm1FKfns1hgneMS43OZespCwONR+KdyhKAlKJX5n0fIcPY8zLi0tvfiG7MehNg9b2oef5/lyjC6NI3KV4j9pcnHB2cUWzE2dw6Ok/XBea+zsTp7k/J7kDgwhT1zn4c37oae436iHy2id2c78QgvmZ86n2VNPh64h3OEqCUYlfmdRCra0Ea2vjVts3hWsQDN7MH5CCk+EUFiZwM38IncdzK0n1G1nT7Bi2fEduT0//RKr1GzSdXFsbtcMk/mZHDj6jlYK28zGKLHoWZPb8mz/cojr5KZdSiV+Z1HyHD4MQWOfPj8v1TeFqdJFMWAzcPH4i7CCIltDj999Ka6bO2s0N9ekY5fBfGaEkI940K6l1iZP4AQrsLbT6nIPO2w+A0KhLLyavvRoxwSfzSbWm9qzY13IYKRO3NUmJPZX4lUlLSkn34cOYSkowOJ1xCCCEKVxHwFA86FrvhxK8Y19A6GzMqmVGl51Zo1iAp72gp7nf6k6cyXyKUpoAqO0c+pFPXXoxpnCQbFd9LMKKqkVZi2jpbqG2c2JPTKRElkr8yqQVamgg3NISt2Z+o96AIDTk8/39wTQKtC6ytcRJkH1tS2uk3RTgQ01FCEa+epArz44UJFStPyupA4shQLVn6CGdjan5BDXjpGjun5cxD6Nm5GDzwXiHoiQQlfiVSav70CEwGOI2W585XIXESEgbeG10KWF/KI0lCTpxj1+EeTGzjjneFOZ6h3+231fIYqQzM4nUOk/PB00AmoBCezPVnVlDhqRrRhrSCslvq0qY2MfKYrQwN30uR1qOqCl8lYtU4lcmJanr+I4cwTJjBlpychwCkL2L8hSAuHyiG4DzejLt0swSY3uMgxuZ19MbcZmCo67tX9Cen4KlK0RyR+K0ZhSlNOMNJtHuTxmyXG16CUnBbtI7m2MUWfQsyl6EP+znRNuJeIeiJAiV+JVJqWv3bnS3G2ucmvkNeiua7BqymX9fMA2AJabES/w+LcxLmXXM63Qyu2t0tf0LXLl2dE2QWuuJcHRjV9j7nL/aM/Rz/vq0InShUdA68Zv7Sx2lpFpSOdB8IN6hKAlCJX5lUnL99TmExYJ1zpy4XL9nUR7RswzvIPaH0kgTAUq1rhhGNjKvpzXiMYb4UNPg8Q9HN2m4s5N7ZvHTE6PJ3GHuxmnuHDbxh4xmmpx5Pc/5J3hzvxCCRVmLOOc6p8b0K4BK/MokFO704n71Vazz5yNMg0/RGk09i/JkI4V10DL7Q6ksMbYP1uE/bkJC59WMBuZ2OpjRPXST+HA6ClIw+cNkVnZEJrgIKEppos6bSVgf+uuvNr2EFJ8bR3dHbAKLokXZiwBUJz8FUIlfmYQ8mzcju7tJWrIkLtfX9E6Msn3IZv4m3UKtnpyQzfy7HK20mwKsb80b97nc2cmEjYKCw4nzrLwopZmQbqShK23IcnXpxUiYFM39qZZUypxlHGw+qMb0KyrxK5OP669/xVxaiqlw7M3U4zHcojwA+4OpAAnXo18ieSWjnnxfEgs7U8d/PoOGK9dO3vEWtJA+/gAjoMDegkAfdlifz5xMa0r2pBjWB7Akewkd/g7OuM7EOxQlzlTiVyaVQFUVXXv34vzgBxFxakM3hasJCwe6NvikQftDaSQRYrYhcTq+ARyzualK6mJDa96YevIPpCPfjskfJqsiMVo3zIYQubb2YRM/9DT3p3lbSfYlznwEYzUnfQ42k429DXvjHYoSZyrxK5OK67nnQQicd9wel+sL6ceo1xM0FA9Zbl8ojYUJuDDPyxl1OIMm1riGntN+NDyZyfiTTRQcaYrYOcerJKWR5u5UXH7LkOXq0nv+P+a3T/xav1Ezsjh7MafbT+PyJ+ZMkUpsqMSvTBpS13E99xy2K67AlDvwpDnR1rMojyRgKBm0jEc3UhG2J9zz/SqLl8MpLm5sy8U0gjn5R0wT1M3NJPdkGwZ/KHLnHYdSZwMAx1qGvsHpTHLiSkqdFM/5AZblLEMiea/xvXiHosSRSvzKpNG1ezfBujqcd94ZtxhM4fM9i/JogyeUA6FUJIKlCTZxz6sZDZh1jWvbcyJ+7roFWRhCOrkn2yJ+7rFIs3hwmL0cHybxA9RmlJDlbsQc9MUgsuhKtaQyK20W+5v2E57gixApY6cSvzJptP/5z2hOJyk33hCfAGQQU7h2yEV5AHaH0jETTqiFeToNIXY6W7iiIxN72Bjx87cVOehKtVB0sDHi5x4LIaDU0cDp9nQC4aG/BuvSSxBI8tqrYxRddC3PWY436FUz+U1hUU38Qoj1QoiTQogKIcTXBtgvhBD/07v/kBBiaZ99jwohmoQQR/odky6EeE0Icbr359BjcpQpIdTSgmfLVlLvvAPNOvjY+WgyhesQhAkO0cwPsCuYwWJjB1aRGL3cAd5JbSaoSa6LQm0fACGoWZhN5tmOhFmxr9TRQEg3cKotY8hy7bYMuszJPXP3TwLTU6eTZkljT8OeeIeixEnUEr8QwgD8DNgAzAXuFUL0Xy1lAzCz9/UA8HCffb8D1g9w6q8BW6WUM4Gtve+VKa7jL3+FYJDUj340bjGYwufRsRDSBk+erbqZ0+EUVplaYxjZ0HQkr6c1MqPLTonPFrXrVC/KQQAFhxKjk1+evRWrMTjsc36EoC69mNyOWrRwYvRRGA8hBMtyllHlqaLRmxgtMEpsRbPGvxKokFKelVIGgCeBO/qVuQP4veyxE0gVQuQBSCnfAgZ6IHgH8Fjv748Bd0YjeGXikLpOx9NPk7xiBZZp0+IURBhTuLqnN78Y/M9qdzAdgFWmxHjWDXDM5qLR4uO6tijV9nt1pSfRVuToae5PgElkDEIyO72V462Zw84oXJdeglEPkeOqi01wUbYkewkmzcSO+h3xDkWJg2gm/gKg70Oxmt5toy3TX46Ush6g9+eAg3GFEA8IIfYKIfY2NyfOrGFK5Hnf3UGwujqutX2j3oBGkKBx6GF8u4LpOESQOQZ3jCIb3uvpjaSEjKxwD93kHQnVi7JJaenGWZcY4+LnZrbQGbBQ4x56IaImRy5Bg2nSNPcnmZJYmr2UIy1HaPA2xDscJcaimfgH6t3U/756JGXGREr5iJRyuZRyeVbW0AtyKBNbx5+fxJCWRspNN8YtBnP4PBIjQW3waW6lhF2hDFYY2zAkyPz8bUY/+1Lauao9G3Mkh/ANom5eFmGDSJhOfnMyWtCEztGWob8jpGagPq2Q/PYqkInTN2M8VuWvQkrJH479Id6hKDEWzb/0GqDvnKWFQP92spGU6a/xwuOA3p+J8cBQiYtgYxOe19/A+aEPopnN8QlC6phCVT0r8YnBe8RX6ck06EkJ9Xx/W1rPn8+17cPPYhcJIauRhjkZFBxpTogpfJNNIcqcHRxpzh726UNtegnWoI8Mz+RoQUy1pDI/cz7PnHoGdyBxWqCU6Itm4t8DzBRClAkhzMA9wMZ+ZTYCn+zt3b8acF1oxh/CRuC+3t/vA56PZNDKxNLx9NMQDpN2991xi8HYXYGGb8hJe6CnmR9ImMQfRvJmWhMLOlPJCsZuJETNohzM3SGyTydGP4eF2Y00ddlo8A7dsbEhtRBdaJOmuR9gTf4aukJdPHXyqXiHosRQ5Afs9pJShoQQDwGbAQPwqJTyqBDic737fwFsAm4BKoAu4P4LxwshngCuATKFEDXAt6WUvwG+CzwlhPgMUAXcFa3PoCQ2PRCg/YknsF99NeaSoZNuNJk8+5FoPTX+IewKZpCvdVOkdUcljlP66J7VVqR00WEKcn2dadTHjkfz9DR8djNFBxppKI/c1MBjtTC7iedOzeFgYy559sEXsAkZzTQ58yhoO89hKYecq2GiyLXlsjZ/LX88/kc+MfcTWAxDT2GsTA5RfagnpdwkpZwlpZwupfzP3m2/6E369Pbmf7B3/wIp5d4+x94rpcyTUpqklIW9SR8pZauU8nop5czen4lRbVBizv3iS4RbW0m/75PxC0JKTJ79BA0FIEyDFgvLnol7VplaEyZfHEjrJDmkMdOTHNPrSk1QvTiHnNNtCTGm324OMj2tjYNNI2nuLybF5ybFPVzD5MRx//z7aelu4bnTz8U7FCVG1Mx9yoQkpaTt97/HMnMGyWvWxC0Og68KQ7B12EV5joWddEoTq4yJ0czvNYQ57ehiQbsdg4z9nUjVkhyEhKIDidHJb3F2Iy3dNuo6U4YsV5fWu2hPzYEYRBUbK3NXsiR7Cb889Eu6Q9FpjVISi0r8yoTUtXsP/hMnSPvkJ+O2/C6A2bMXiYGgoWjIcm8HMtGQCTN+/0haJ7qARe32uFy/Kz2J5rJUivc3JMSY/vlZzWhC52DT0J0cfRYbbfZMCiZR4hdC8IWlX6C5u5knTjwR73CUGIjaM35FGYv2P4+sk1H7E08gkpOR/sCIj4k4KTG59xCylSPl0J3j3gxmsdjYTqoWjFFwg5NIDqR1UtBlIcsfp5EQQNXSHJY9e5Kssx00T4/vzNs2c5AZaW0cbMxhw7QzQz6OqU0vZkHVPqzdHfiSUmMWYzQty1nGuoJ1/Obwb/jIrI/gMA89r4EysakavzLhhNra8J88SfLy5QjT4M/Vo83gq8QQbCXgWD5kubqwlVNhB1ebEmMYWF2SnxZrkEVt8antX9AwJ5NAkpHifYkxgcyi7CbafMnUeIZr7u/pSJpXezAWYcXMPyz5B9wBN7878rt4h6JEmUr8yoTTtWMHaBrJK1bENQ6zew9SGAmmLBmy3NvBnslhrjYnRuI/kN6JSRfMdUVvXv6R0I0a1YtyyD3RitkbiGssAPOzmjAInQONuUOWcyen0mnPoqB6f4wii43yjHI2lG7g8eOP09LdEu9wlChSiV+ZUMKdnXTt20fSwoUYHHFsjpQ6ZvdegrZ5SMPQveLfDGRRqnkpMXTFKLjBBYTOMaeX8g4bFj3+f/5VS3PRdEnhwfjPw5VsCjE7o5UDjbmE9SHa+oWgtnAx2Y0nMAZ9sQswBh5c8iCBcICfH/h5vENRoij+f/mKMgpd774L4TC2K6+MaxyG7rNooXaCwzTzd0oDe0LpXJUgtf3jTi8Bg4xbp77+OrOSaS12UPJefUJ08lueV4c7YOF0e/qQ5eoKl2DQQ+TWHxmy3ERT4ijhY+Uf45lTz3Co+VC8w1GiRCV+ZcLQu7ro2rMH67x5GDOiv6DMUC408wfsi4YstyOYSQiNq03xr9ECHEzvJN1vpKgrcSZqqVyRh73NR9aZ9niHQnlGC8mmAHvrB19zAaAlcwZ+i538msnV3A/w4OIHyUrO4j92/gchfeIvQ6xcTiV+ZcLw7tqFDASwXXVVfAOROmb3ewTtC8CQNGTRNwNZpIoAC42uGAU3uFZzkGqbn0VtKYgB18eKj/ryTHx2E2W747/krVGTLM1p4EhzNl3BIQY9aRp1BQvJqz2MmGTJ0Way8dUVX+VE2wmePPFkvMNRokAlfmVC0P1+unbtwjJ7Nqac6K4bPxxj12m0sIuAY+jOhSEpeCeYyVpTC0YR/2bsg2kehIQFHfHt1NefNGicX5ZH9ul2ktviP4HM8rx6wlLjQOPQ/87qCpdgDnaR1XQ6RpHFzo0lN7K2YC0/PfBTmroSo7VKiRyV+JUJoWvPHmR3d/xr+4DZtRMpLATtC4cstz+UikuaE6I3v47kUJqXGZ4kUkKJN33H+WW5SE1Quif+U+EWpHjIs3vY25A/ZLnG3LmEDOZJ2dwvhOBfVv4LwXCQ/9z5n8gE6H+hRI5K/ErC030+vNu3Y54+HXPh0AvhRD+YIGbPewQcS0Ab+jn5y4E8kgmxLgHG71ekdOM1hVnUNvQY9Xjxp1ion5tJ0YFGDIFwvMNhRV4d1W4njUOs2Bc2WmjMm9szi98kTIxFjiIeWvIQr1e/zl9O/yXe4SgRlHi3/orSj3f7dmRXFyk33BDvUDB1HkTo3QQcQ68PEJCCV33ZrKCOhtbOGEU3uINpHmxBjRmeofskxNO5FXkUHGmm4HATVcuG7lwXsWu2egfcnm46i8ZMXjubxdr8y5u6m3vXBtMtM/lo1wFS26vpSB96vYaJ6L5597G9bjv/tee/WJqzlDJnWbxDUiJAJX4loYU7O+nasQPrvHmY8odueo0Fs2snujGVkG3OkOW2BzPxCjNX6jUximxwncYQpx3drGpxYEigTn39tRc5cOXaKNtVR9XS3Lgue5tsClDqbOBkWxGrco9j1PQByx1PK0cKQUH1exM28T996ukh96/JW8Oh5kN89rXP8un5n8agGUZ87rtmqVXTE5Fq6lcSmvfNN5HhMPbrrot3KIiQB1PnEQKOlSCG/tN5JZCHQ/pZSPyb+Q+nepFxXJBnxITg7OoCHM1dZFfEf2jf/Ixz+MIWznQMfsPpNdlpyp5DUdXeSdncD+CwOLh9+u3Ue+vZWrU13uEoEaASv5KwQm1tdL33HklLl2LMzIx3OJjdexCECTiHbub3SgNvBrJYSy1G4psMJJID6R6KvBYy47ggz0jVzs+i22Fh+rvxbykpsLeQavFwpHXo5u3qkhWkeBpJba+OUWSxNzt9NstzlrOzfieHmw/HOxxlnFTiVxJW5+uvgxDYr7463qEAPc38IUsBYevQHQzfCGTjx8CVMv7JqzrZT5slFPcFeUZKGjTOri4gs9JFaq0nrrEIAfMyKmnsSqele/DpoWuLlqILA0VVu2MYXezdXHozxSnFbDyzkRpP/P9tK2OnEr+SkALnz+M7fBjbmjXxnZO/lxZoxOg7N2xtH3p68+dr3cymLQaRDe1gugdzWFAe5wV5RuP80hwCVmNC1Ppnp1VjFCGOtAxe6w9Y7DTmllN0fvI29wMYNAN3z76bFHMKT518Cpc//pNSKWOjEr+ScGQ4jPull9CczoQYtw9g7tiORPQ83x9Cq25mVzCd9eb6uHej82k6x5xdzOuwYZYT5089bDFyfnkeecdasLXGd0IfqzHIjNRaTnUUEggP3he6umQFNm8LaW2VsQsuDpJNydwz5x4CeoA/n/wz/pA/3iEpYzBxvg2UKaNrzx5CjY041q9HMyfAc2kZwtKxnaB9AdKUNmTRF/z5hNG4zRL/6WePpXoJaZLF7Yk5dn8o51bloxsE03bEv9Y/L7OSkG7kRFvRoGXqChejawaKzu+JYWTxkZ2czYdnfphGbyNPnnySYDgY75CUUVLD+ZSEEvZ46Hz9dczTp2MpL493OACYPIfQwm4CqUOvCCgl/MVfwBJjO2WGLs7FKL7BHEjzkN1tIq87/jdPxe+Nfka+joIUivc34k21Ekq69KsqVuP8AXKSO8hJbuNwyzTmZ55DG6ApJ2i20ZA7j6KqvRxa8pFhR31MdDPTZnLnjDv5a8Vfeeb0M9w96+5RDfNT4mty/+tUJhzPa68hQyEct9yCiOM47r4sHW+jG1N7FuUZwt5QGtW6jQ9Z4l9LbbQGqE8OsLg9sRbkGY2mGWkIKclJgKF9i7LO4ArYOe/OHbRMdckKkrvayGg5G8PI4mdB1gJuKbuF0+2neb7ieXQ58FwHSuJRiV9JGJ5t2/AdPIht7dqEGL4HIIKtGL1H8TvXghi6RvOMv4gUEeQGc2OMohvcgTQPBh3mJdiCPKMRSDbRWuQgvcqFqSu+zcnTnPXYTV0cbJ4+aJm6wsWEDSaKK3fGMLL4Wp67nOuKr+NI6xGV/CcQlfiVhBBqb6f+m9/CmJOTMMP3ACwd2wEIpK4bslyjbmFrIJsPWmqxivh++YWEzpFUL7PdNpLDE7v5tWlmGgjiXuvXhGRh1lnqvJk0dTkHLBMyJVFTtJTiyt1oU+i597qCdVxbdC2HWw7z7KlnCevxX2tBGZpK/ErcSSlp+M6/EXa5cH7oQwhjgnQ9kTqWju2EbOXo5qFbIJ7xFaEjuNsS/0lcTjq68Bl1Fk+QsftDCSaZaCt2kl7txuyNbzItTz+PSQsOWeuvnLYOc7CLgurJt2LfUK4svJIbS27keNtxnj71NCE9FO+QlCGoxK/EnfvFl/Bs3kzWQw9hyh38GWqsGTuPoIXa8KcOPaTQp8Mz/kKuNjVTYIj/evIH0jtxBoyUeq3xDiUiGmekIYUg53R850WwGELMTT/PmY4CunwDt6Q05czGa8ug9Oz2GEcXf2vy17ChbAOn2k/x5xN/Vr39E5hK/EpcBaqrafiP/yBp8WIyPvPpeIdzCWv7lp5OfSmLhiz3QpuRDmnm49bzMYpscC2WAJV2H0va7BO2U19/IauR1hIHaTUeLJ74jhtfkHUWieB0zSCtKUKjsuwKchqOkeRtjW1wCWBF7go+MO0DnHGd4YkTT+ANDrz6oRJfKvErcaN7vdQ8+BAIQf73v5c4TfyA5qvF5D2OP+0aEIPHFZbwh0YT8w0ulhnj3/t8X7oHTYfFbRNv7P5QGmemEzZpFBxtievseA5zNzNSazhTZ8MfHPjrs3LaFQgkped2xDi6xLAkZwkfnPFBzrvP88CrD6gZ/hKQSvxKXEgpqfvGv+CvqKDgBz/AXDT45CjxYG3bghRm/KlDdzTc0mGgOqDxqaRz8VxFFoCApnMorZNytw3bBO/U11/YbKBxZjopLd2kNHXFNZal2acJhzUqBqn1d9mzaMqZ09PcP4mn8B3KgqwF3DX7Lo63Hef+zffT0t0S75CUPlTiV+Ki9Ve/xrN5M9n/9E/Y162NdziXECE3ZvdOAs7VSOPgHeR0Cb9qMFNm0bnG1BTDCAd2JNWL3yBZ3jq5avsXtJQ68dlMFBxrQYTjN3IiI8lDfmY3p2vsBEMD3+2dm7YWe2czmc2nYxxd4piTPoefXf8zajw1fOqVT1HfOfpJnJToiGriF0KsF0KcFEJUCCG+NsB+IYT4n979h4QQS4c7VgjxHSFErRDiQO/rlmh+BiXy3Js20fyjH+G49VbSP31/vMO5jKX9TYQM4Uu/Ychyr3UYOOPT+GxeAEOca/sSyXvpbnK6zRR0WeIbTLRogrq5mVi8Qcp2x3dK5DnFHoIhjbN1A8+TUFu0lIApiWmn34xxZIllTf4aHrnxEdq62/jkK5+k0lUZ75AUopj4hRAG4GfABmAucK8QYm6/YhuAmb2vB4CHR3jsj6SUi3tfm6L1GZTI87z+OrX//FWSly0j7//8R8LMzneRHsTSvo2gbT66ZfBpYUMSfllvZrpV58bU+I9bPp3soSkpyLLWiTtT30h4cmy4s5KZ9WYV5s5A3OLIcAbITvNxqjqF4AAj18JGC5XT1lJUvRdL99R+xr04ezGPrn+UQDjAfa/cx8m2k/EOacqLZo1/JVAhpTwrpQwATwJ39CtzB/B72WMnkCqEyBvhscoE0/nOdmq/8EWsc+dS+ItfoCUlxTuky5jdu9DC7mFr+8+3Gjnn1/h8XmDAudtjbUt6I5awmNAz9Y1U3bxMtJDO/FfOxDWO8hIPvoCB3acG7vx5Zua1aHqY6RVTu9YPPc3+v13/W0yaifs338/B5oPxDmlKi2biLwD6zmZS07ttJGWGO/ah3kcDjwohhl4uTUkInW+/Q81DD2GeNo3iR36JwZ6ACUqGsLa8RMhaQsjWv3HqfV1heLjexBJbmGud8a/ttxn97HW0sag9ZUItvztWfruZ01cVU3C0hdzj8es0lpXqJ9Pp5/WDxgFr/Z2OHOrz5jPt9JtTaia/wUxzTuOxDY+Raknl7179O3bWT52pjRNNNL8lBqoH9e/iOliZoY59GJgOLAbqgR8MeHEhHhBC7BVC7G1ubh5RwEp0dDzzDNWf+xzmkhKKH/0NhtTUeIc0IHPHDgzBFnyZH2CoLvqPNppoDWn8Y0Eg7j35AV7NaEAiWdEyOTv1DaRibSGuXBsLNlVg6o5PUhUC5pe5cHdrvHti4Fr/qTk3kuRzUVy5K8bRJaYCewGPrX+MAnsBD255kDeq3oh3SFNSNBN/DdB3jFYh0L9HzmBlBj1WStkopQxLKXXgV/Q8FriMlPIRKeVyKeXyrKyscX0QZWyklDT9+MfUf/Nb2NasoeSPj2PMyIh3WAOTIaytLxGylhK0Lxy0WKVP8FiTidvSgyywxX9BEq8W4o20Jla6MkgNmuIdTsxIg8aB22dh9gaZ+2r8FkDOSgswMz/MG4dM+Ae4/2jKnUt7WjGzj78CagEbALKSs/jtzb9lVtos/nHbP/LS2ZfiHdKUE80ZU/YAM4UQZUAtcA/wsX5lNtLTbP8ksApwSSnrhRDNgx0rhMiTUl4YF/JB4EgUP4MyRhcW3encupXUuz5C7r/+K8KUuInJ3LEdQ7AVT+7HB63tSwnfrbFg1eCL+fHrWNbXtvRGfIYwt7Tm48cT73Biyp1n58zaIma+U019eQZNs+JzU3nz0iA/fdHK9mNGrlvUr81fCE6Wr2f1u4+QX3OQuqIlcYkxXp4+9fSg+26dfiueoIevvf013q59m2U5y0Z9/rtm3TWe8KasqCV+KWVICPEQsBkwAI9KKY8KIT7Xu/8XwCbgFqAC6ALuH+rY3lN/TwixmJ6m/0rgs9H6DMrw2v/81GXbAufP0/HMM+heLynr12OZN5+Ov/w1DtGNkB4kqWUTIWsZIdv8QYs912Zkl8fA1wv9ZCTAPUxQ6Lya3sC8TiclPhunpljiBzh1dTHZFW0see4Ub312Cd3O2K9PUJKtU14UZtthE2vKQySZL91fU7yMzkNZzD3yAnWFi4d8jDSVWAwWPjbnYzxz6hleOvsS/pCfKwquiHdYU0JUewJJKTdJKWdJKadLKf+zd9svepM+vb35H+zdv0BKuXeoY3u3f6K37EIp5e19av9KnMlAAM9rr9H2298ijEYy/vZvsa1Zk3hD9vqxdLyFFmqjO+v2Qb+UGwKCH9aYWW4P85HMxFh5bIezhQ5TkFtaBh92ONnpRo29d5UjwpKlz5yI28Q+Ny8N0B0QvHHo8jtCqRk4Nv8DpLVXUVCzLw7RJS6TwcTds+9mbsZctlRt4Y2qN5BTdLbDWEqcydGVCc138iTuTZvQOzpIWryYlA0b0KwJujrc+fdXThPSj7X7rwS1PEItHdB6+apqYQn/4llOWDfzbcMOtKr4r8CnI9mUWUdxdzLzvAOvDz9VdKUncfD2mSx/5gTlWyo5dvO0mMdQkCFZNiPE20eNrJ4dIj3l0uR1vnQ1c45tYt6h56ktWALa5B99MVIGzcCHZn4Is2bm7dq3CckQNxTfkPAVholM/etTxiXc0UH7E0/Q8ac/oZlMpN9/P84PfjBxk34/1uABBAG6zSsGre3/qns6+0LpfMN2nMIEWHYXYK+jjXqLj1ta8yf1hD0jVT8vi7Mr85m+s5a8o/EZxbN+WRABbNo7wHMgTePowjtxuuooO/tOzGNLdJrQ+MD0D7A8Zzk76nawuXKzqvlHkarxK2MiAwFaH3uMlp/+FAD7jTdiW706oVbYG45Bb8MSOkHAOJOwlj5gmTcDWTzim8Zt5jpusyTGU6Uwkr9kV1PgS2KVK0FHScTB8RvLSK3vZMlfT+JPMdNWHNuWkFSb5OoFIbYcMLFuXojS7EsfO9QULaM5aybzD/6V6uLlhMzJMY0v0Qkh2FC2AYNmYFf9LsIyzC1lt6iafxSoGr8yat7duzn7oQ/R/IMfYp4+ncyHHsK+bt2ESvpIneTAu0gsdJsG7k1cEbLzL50LmGPw8C+2YzEOcHDbU5upt/j4cFMRmqrtX6QbNXbfM5fuVCsrnjiGvTn2q/hduyCII0nnhV0m9MtmLREcWPZRLP5O5h3eGPPYJgIhBDeV3MQV+VfwXuN7vHDmBXQ1DDLiJtA3tRJvodZWmr73fVzPP4+poIDCn/+c0ASdHMkSOoFRb8FrvhIpLl3U5lxLF80k8XWxBDNB/jG4g/rWxGjiDwqd57JqKOuysdSjJq3sL5hsYufH57PuNwdY9ccjvPOZxfhTzMMfGCFmE6xfHuSpty3sPW1g5axLZ3bsSC/l7IyrmHlqK9UlK2nLjH1/hEQnhOD64usxakbeqnmLkAxx54w70YSqp0aK+i+pDEtKScezz3LmlltxbdpExmc/y7QXXyDlumvjHdqYaHoHScH3CGoFBAyXf/G2YuU7Yi0+jHxL7iCTxEj6ANvSmmg1B7irqVg92x9Ed5qV3R+bh7kryJrfH8Li8cf0+stmhCnLCfPibjPuARodDi35CN3WVJbv+p2ayncQQgiuKbqG64qv40jLEZ499SxhPf7TY08WKvErQwqcP0/V/Z+m/l++iWXGDKY991ey//GLCbnAzojIELbAO0iMeC1rL+vQVxtO4ltiHR1Y+KbcQSnuOAV6Ob8IszGrlvJOB3O9jniHk9Bc+Sns+vh8ktwB1v72EEkdvphdWxPwkXUBAiHYuOvy1oaQKYn3Vn0Sp6uOhfsHn+BGgXUF67ip5CaOtx3n6VNPE9ITYyjtRKea+qeYgSbcGYgMh/G++y6d27YhDAYct91G0rJldO19j66970U5yuhJanwGo95Cp/kapLi0c9XRkIMveJbgR/Cv8l1m0x6nKAf2UmYdbmOQLzTNUrX9EWgrcbLjE/NZ9cejrP3tIXZ8cgHejIFvWIvfG13HzXTb4Yu/p50cYMGp9cu5YXGQzfvMLJ0eZm7xpbXVhvwFnJp9I7NOvkZzzmxqi0Y/a91UsTp/NQbNwMvnXubPJ//M3bPuxmRIgBm0JjCV+JXLhFpb6XjmGUJ1dVjKy3HccgsGx8SvYZpcu7C2b8VnnEvQWHpxu5TwF38B/9VVTpbm59tyO4V0xi/QATSZfGzKrGNNRwYzuqfOYjzj1VHoYMd9C1j9h8Os+80B9t5dTmtpakyufc2CEAfPGfnLDhNlOWGSLu1KwuHFHyKjpYKV7/6GN27MpMNdG/kgSifHTHgrcldgEAZePPsiT5x4gnvm3IPZELu+G5ONaupXLpJS0rVvH62/+AXh9nZS776btHvumRRJ39BVga3+dwSTZl7Si78ubOWhzqX8n655rDC18bhjZ8IlfYA/5Z5Hk4K7G0viHcqE4861s/3Ti/HbTKz+wxFK9vRfKyw6jAa4a10AT5fgqXfM9B+WrhtMbL/qIQIWO+ve/F9svsR5rJSIluYs5c4Zd3LefZ4/Hf8T/nBs+25MJirxKwDo3d24nn4ad2+P/cz/7//DOm9evMOKCM1fi73mZ+jGdLyFnwdhwC81ft9dwoddazkQTOWfk4/zP/Z9pGmJ19lqT0or+x3t3NFcSHpI1XLGwpuRxDufWUzz9DQWbjrDwo2nMQSj31msOEvnluVBjpw3sv345Q2s/iQnb1/zD2jhIFcfeZlk39Rbb2E0FmYt5EMzP0S1p5rHjz2OO6BulsZCJX6FQHU1LQ8/jO/4cew33EDaJz+JwTk5poHV/A2knP8RCAOdxV+gS7PzuK+Y2zrW8aPu2aw0tfGsczv3WqsxJOBjc68W4g95lZR0J7O+derOyR8JIauR3ffM5fS6Ikr2N3DVL/fjrIt+or1qfoi5RSFe3G2iuuXyr1x3aiFvXfdPmPQg1x1+kdTOlqjHNJHNy5zHXbPvot5bz9+9+ne4/K54hzThqMQ/xXXt3duzqI6mkfG3f4v9yisRk2QecYOvipTz3wN0dmR8mX9vKGTDkWR+0DWHMkMXj6Ts4cf2/eQaErfJ8A95lXiMQT5TNx2D6tA3fprgxPWlvPvJBRgCYdb95iAz3q7i8tl2IkcI+OhVAVKSJH943UznAKNDO9KLeX3+reiagWuPvERJ02kuezagXDQnfQ53z76bivYKPr3507T52uId0oQyOb7hlVGTwSCu55/H/cILmMvKyHjgAUwFBfEOK2KM7oMkV34fl27mLv+3+Pj56WxqN7LGEebRlN084tjLClN7Qq+Q+q6zhR2pLdzZXEiJb4Ce48qYtZal8ub/t5T68gzKXz/PrLerSW6P3nwNyRb4xLUBPN2C3221EBxgVJonOY2tC26jzZ7Nyoq3WX3qDayB2M8+OFHMSpvF/17/v1S5q/j0K5+muWtiTiYWD6pX/xQUdrno+POfCdbWYrvqKuzXXhv3Wv7uc+O/Y5cS6kKCnNAbbOAVjshSPh/4R7JMyTyQ3MICkw+zlKS66jgXgZijqcHcze/zzjHTm8JtzZPnhiyRBJNM7PtIObXzW1n8/Clmbq+ltdhB/ewMwhZDxK9XnK1z71UBHn/DzB/esPDJ6/wYey+z+1wbWW1eAE7mrmOl+QRrW4+S3V7D3rTZ7EmbRWAEvdjLMqbWDeIV+Vfw8xt+zoNbH+T+zffz65t+Ta4tN95hJTxV459iApWVtP7yl4Sam0m95x5Srr8+7kl/vBrCRl7odvD7ziCrgj/jVvEyW+UVvGB4iC87fXzO1soyczdmMTGaTv0izP8WncIgBZ+rnaGa+KOscU4GJ68ppmlaKunVbsrfqCS7og0Rjvwc8QvLwty5JsjxagN/3GYmNED/Qik0dmXM5bel66lMzmVt61E+d/ZF1rQcJTkUu4mIJooVuSt45MZHaO1u5VOvfIrazigMi5xkVI1/ipBS0v6Hx2l77DEM6emk338/xqyseIc1Zp26xu5gMjsCNlrDYT5nfIHPGl8kIJJ41/gpdGM5y5l4s3zpSB4pOEOtpZsvn59DZtAy/EFT0Ggn3BmObtSon5tJW7GDvOOt5J1oI6PSTcPsdNoLUwZdsnksrigPoUt4fqeZ37wquO/6gfuYtJtTeL5gLdm+dta2HuHK1iNc0XqMkymFHEydTnVSVkTjmsgWZy/mVzf9igdee4D7Xr6Pn9/wc2alzYp3WAlLTIU1j5cvXy737t0b7zDiRu/upv7b38a98QUss2fj/NCH0KzWeId1icGa+rPa3p8lMAzsJ4fXRQl7yUUKyUPaS/ydaSN20UVbqJiawGJCJNZnG40/5lbyakYD9zaUjLoX/ym9IUpRTT221m7yj7WQ7PLTnWKmcVY6rlzbgIk203b5zVn72qJhr7G3Po+nT5STmdTN9UU7SbMOPX9Eut/NYlcF812VWPUg7SY7h51lHHGU0mnqmYXysqb+STKBz2DumnXXJe9Ptp3k81s+jzfk5YdX/5ArCib35x+KEOI9KeXyAfepxD+5BWpqqfn7v8d/4gSZf/8QhrT0hGzaHyrxd2NkK8W8JKbTKGwUyja+bNzITYbtJGtePOFMaoOL6NIn9tr0z2fV8JfsGm5qzeVjDSWjnpZXJf4Ik5LUuk5yTrVh9QbpdphpmJWOO+fSG4CxJn6AivY0Hj+ygEBYY23+YcrTq4atxBv1ELM91SxwVVLc3YSOoNKWw2FHGaGSWehan/4JUyzxAzR4G3hw64Oc6TjDN1d/k4/M+kgcIos/lfinaOL3bNtG3Ve/BrpO/ve/R8o114x4rv5YGyjxt+kG3nV1sZUSuoSJaznBPxqeZ57xKAYRwhPOojE4G7eeBxP4ObhEsjGrlr9k17CuPZPP1E1HG8PnUYk/SnRJWp2HnFPtWLouvwEYT+IH6PBZ+N2hOdR2ZpFna+GK/KPkJHeM6NjUQCfz3eeY76rEEeoiYDRzPnM6Z3Nm47alT8nED+ANevmnN/+J7bXb+cz8z/APS/9hyi3rO1TiV8/4JyEZDtP8v/9L6y9+iWXOHAp/8mPMJRNnqtdW3cArPgfbAzZA5zPa29xneIV8QxUSjfZwEU3BWXTLib8evY7kj7mVbMloZG3H2JO+EkWaoL3QQXt+ysUbgLK9DXQ5LTRNT4Pp5p4l+cYo1ern9mnvcqythN0Nc3j29NXMTK1mWc5p0q1DTzDUYbbzTuYCtmfMo7iriTW+aqY1nmJmw3EanPmcMjtozJs35foC2Ew2fnrdT/l/u/4fvznyG867z/Mfa/8Du9ke79ASgkr8k0yopYXaL3+Frp07cX7kw+R+85sJ9zx/MC1hA6/4HbwbsGEmyFfNr3EPG3FobQSlhYZQOc3BGYSYoEsC9+PVQvyisIJDKR2sb8njo43FKuknsr43ALUeciraKd3XQPB0G51zMukqcYJhbLVKIWBexnlmptayr2kmB5unc7qjiAJ7M/MzzlHqaMSgDT7KQAqN87ZctOLpmIJ+pjWeYGbDca7a9mNcznxOz76B82Vr0KfQqnZGzcg3V3+TEkcJP3zvh9z70r386JofMSNtRrxDizvV1D+JeHftpu4rXyHscpH7r/9K6oc/dFmZRGzqr/ULvluh827ARiZuvmp+mVu0N0jCQ7fuoCk4k7ZwCXIS3adWJHn4RWEFbcYAH28o4fr28Y89Vk39MSYlznov+Wc7MHf4CCcZ8czOoGtaGm3XlI7qVOdavZe87w6ZOd5WzJGWMjqDyZi1INOc9UxPraXQ3jLoTUDfzn1CD1MkjMw68Spp7dV0JadzfN4tVE5bO2luAAZr6u9vb8NevvLWV/AGvXxr9bf4wPQPRDmy+FPP+Cd54tf9fpp/9GPaHnsMc3ExBT/5MdY5cwYsm0iJv9Yv+HWjiRdbjcwUNXzN/AJrxU6MhGjWZnHecCWay81Efn7fX7cW4vmsWl7JqCcjaOZzNTOZGaFldlXij4/MZDOWBi8pJ5qxNHWhmw20XFdK69UlhG0jW1Spf+K/QJdQ7cmmoqOAc648AroJkxakOKWJac56ih2NWAzvD1sdsFe/lOTUH2XukRfIbDkzqW4ARpr4AZq7mvnym19mX9M+1peu55urv4nTMjnWJBmISvyTOPF3HzlK/de/hv90Ban33kPOV76Clpw8aPlESPxVfsGjDSY2tWlcpR3mq0mbmBU+QhgjdYalnDesw6vlAJcO55vIdCTvpDbzdE41HkOQqzqyuaehmGQ9cq0YKvHHR9/OfaaWLlJOtJBU60E3aXSsyKf1qhL8+UPf3A2W+PsK6xrVnVlUunI5586lO2RFQyff3kKZs4EyRz0L8vrNONi3c5+UZDccY97hjb03AGm9NwDrJuwNwGgSP0BID/HokUd5+MDDpFvT+fe1/87agrVRii6+VOKfhIk/7PHQ/OOf0P7EExgy0sn/z//EftVVwx4Xz8Rf6RP8usHEG+1hPmJ4hwctr5Cj16EbnZxhNdWGVQTFpTWWiZ74Q+jscrayKbOeGmsXM7rs/E19KWW+yHcyUok/Pgbq1d81LZWMbedJ3VuHFtTpnJVO69WleOZlDdgRcCSJvy8pobErjXOuPM658+jw9/x7KkxxMy+zmXlZTeTavIiyAXr1D3ADcGLuBs5NW4dunFjLPo828V9wrPUY33j7G5xxnWF96Xq+vPzL5NhyIhxdfKnEP4kSvwyHcb3wAk0/+AHhllbS7r2XrC9+AYPDMaLj45H4z3QLftVg5nBHB580buUTpq3YZCchSzH+jBsIOFawu3LgdbUnauJvNwZ4N7WZLemNtJkCFPqSuK2lgPT24KjH5yuJbajhfAZvgLR3a8h4uwpTh49ARhKtVxbTsbKAsP39JDvaxH/Z9Xx2zrlzqfMWUOVOBSDd2sW86SbmFYcpzdEv73coJdmNx3tuAJor8FlTODX7Rs7MvIaQefBWw0Qy1sQP4A/7efTwo/zmyG/QhMbnFn2Oj5d/HIthcsyWqRL/JEj8Uko8W7bQ/JOfEKg4g3X+fHK//W2SFswf1Xlilfh1CdvdBv7cpGHqOsYnjFu4VtuPAIL2RfjTbyCUPOviMKORzNyX6DoNIQ7a23k3tYWjNhdSwByvg1ta8ljYmYpAqFr5JDSicfxhHcehJjLePI/tbDu6QeCZn03HygI8czM51xGZlQHLMmy4/WaOtWRxtCWL0+0ZhHVBskUyrzjMgtIQM/P1i4sDASAlmc2nKT/6Ern1RwmYkjgz81oqZl2LLzmxh8yOJ/FfUO2p5nu7v8e2mm1kJ2XzmQWf4cOzPjzhbwBU4p/AiV/3+3Fvepm23/8e//HjmMvKyPrCP5By001jmoEv2onfHYIX24y83dLK8tAu7jFuo1A0EzakEEhdRyD1KnRz5mXHTcTEryOpsXRxzO5mf0o7p5Ld6AIyA2bWdmRxhSuT3MClQw9V4p98RjuBj6XOQ9quWlL31mH0BAimmKmem0l9eSZtRY5xzQnQv3Ofr+AKTtUYOFJl4HiVAV9QYDX33AQsLA0zqyB8yU1Aalslc46+TGH1PqQQ1BYu4cysa2nOnp2QcwFEIvFfsLt+Nz878DP2Ne0jOzmbe+fcywdnfJCMpIk5I6hK/BMs8Usp8Z86hfvFl+h49lnCbW1YZs4g/VP347zjdoRx7B3CopH4/Tq86zbwTqubzK493KrtYJF2FoBA0mwC6VcTTFkCYvC4J0LiDwmdGks3p5LdHLe5OZnsxmvsWV6t0JfMEk8qiz1pTOu2DzoeXyX+yWfMM/eFdVKOtZC2qwbb0WYMYYk/2UTD7HQa5mTSWuYkbBrd8sBDzdUfCsPpOo1DlUaOnjfQHRBYTZLy4jALSsLMzA9j7X36YPM0Mb3iTUrPvIMl4MVry6SqdCXnS1fjceaPKqZoimTih57v3l0Nu/jVoV+xu2E3Rs3IjSU3csf0O1iZuxLTBOoEGbfEL4RYD/wEMAC/llJ+t99+0bv/FqAL+JSUct9Qxwoh0oE/A6VAJXC3lLJ9qDgmQuKXgQDdhw/j3f4u7s2bCZw5AwYD9quvJv2TnyB51SpEBO64I5X46wOCfe4QdR2VpPmOslYcYq52HoBOcwla6koCjuVIU/qIzpdoid+nhak3d1Nt7eJckpdz1k6qrV2EtJ6/l6yAhXKvgzm9r4zQyJoFVeKffMY7ZS9AdZ2L7NPt5J5sJftUG6ZAGF0TdBSk0FripLXUSVuRg7B56BuBkS7SEwrDmfqem4Aj5w10+QWakBRn6cwsCDOrQKcwQ8csAxRWv0dJ5U5yGo4hpMRjz6a+YCEN+QtoyZpB2Bi/JvFIJ/6+znac5alTT7GxYiOeoIcUcwrXFF7DVYVXsTx3OZlJl7dcJpK4JH4hhAE4BdwI1AB7gHullMf6lLkF+Ht6Ev8q4CdSylVDHSuE+B7QJqX8rhDia0CalPKrQ8WSaIlfBoMEa2vxnTiB79hxfEeP0rVvH7K7G4QgeflyHLdsIOWmmzBmRLaZabSJX0roCEOl10dbZz2B7nrMgRpmy9OUiyqMQieMgXbzTKzOuYRTlqBbRj8ZTTwSv1cL0WL202ry02Ly02z2U2fpps7cTZs5cLFcUthAabeNMp+N0m4bM7pTyBjjcrkq8U8+kUj8fTv3aSGdjMoOMs+5yDjvwlnnQZMgBXjTk3Dn2HBn23Dn2OjMSqLbaUU39jz2G8vqfGEdKhs1TtUaOF2nUdOiIREYDZK8NJ3CzJ7XDFs7i117KW48RHbjCQx6CF1odKQV0Zo5nY60ItzOAtzOfEKm2MwWGs3Ef4E/7GdH3Q5eO/8a26q34Q70dEQudZSyOHsxs9NmMyttFjPSZpBmSYtIBS0S4pX41wDfkVLe3Pv+6wBSyv/Xp8wvgW1Syid6358ErqGnNj/gsRfKSCnrhRB5vcfPHiqWSCZ+KSUEg+iBIDIYQAb6vIJBZCCA3tVF2OUm7Hahu92EXW6CDfUE6+oI1tYRamwEvXfmLaMRy/TpJC9fTvLqVdhWrsTgjOykEqFgkECgm6Cvm5YXNhIOhwjpPa+wHsIfDhAI+QiHu5ChLsLhLrSQG7veSpreSq5owyG6Lp7Ph5UWUykkTcfpmNbTSc8wvj/0wRJ/Zttewkh0AWEhCQuJjiQkJLqQhOnZFtR0/ELHr4Xxa+//9GlhPMYgbkOITmMQjyGE2xjEYwjiM1w6+5lZ18jzJ5Hf51XoTyI7YI3YVLoq8U8+kU78/RkCYdKr3aRVuXE0eXE0erG1+y4p47eZ6HZaEJk2Qg4z4WRTz6tkLmF7ErrFhDQbe3+a0M1GdLMJaTKApiENPS8MGl6/4Ey9gaqWnpuA2hYNX/D9f//JFkm+vZt15hMslieZE6igzHcGi/7+zXKXyY43KR1vcjrdyWn4kp2EzDZC5mSCliRC5mTCBjNhgxFdM6BrJnSDkbBmRNeM6AYjjGBRnVgk/r5CeojjrcfZ27iXvY17OdJyhDbf+99dVoOVXFsuebY88ux5ZFgzcFqcOMwOUswpOMwOrEYrFoMFk8GExWDBrJkxG8wXt0dKvBbpKQCq+7yvoadWP1yZgmGOzZFS1gP0Jv/sSAY9nOaf/ITWX/xydAcZjRizszDnF2BbuQJTQQGmgkIss2djmTkDzRK9prLnD9Qy6y83U671/Occ6S2FCzvtIgOvOZuzxjlYzOk4bTnYkvPRTenYev8oQ8OcZzyOWyr5zbxd4z6PSRekhE2khIzYwyayuyykhE2kB81kBi1kBCxkBi2khI1qqJ2ScMJmA83T02ie/n4Pe0MgTEqTF3trN0ku/8WXo7ET25kAhq4gQgKcHPX1zv3HfdgWTWdhWU//FV1Cq1tQ367R5hG0ugWtHgsveBbzpG8JvqBAQ6dQNDNbVDNT1FIQaiHP10peRyv54hTOPhWHkQiYknj+rv8ddezRZtSMLMhawIKsBdw//34AWrpbONV2irOuszR4G6jz1tHgbeDN6jdp97ejy8HXWOhrVtosnr392WiGf1E0E/9A36D9mxcGKzOSY4e+uBAPAA/0vu3sbSnIBFpGc56IORrVs0fhc7mBusiecvTi9/8reibjZwL1uSaawT/X3V8f0wnPAW+PPZ5+3PDNu0d70IT+f3WEI4NVPMb6uQZdkjWaib8G6NveVcjlmWSwMuYhjm0UQuT1aepvGujiUspHgEf6bhNC7B2s6WMiU59r4piMnwnU55poJuPnmoyfCaLzuca2huTI7AFmCiHKhBBm4B5gY78yG4FPih6rAVdvM/5Qx24E7uv9/T7g+Sh+BkVRFEWZVKJW45dShoQQDwGb6RmS96iU8v9v72xj5aqqMPy8aZFSqtACfsWa4kdFxFBpQGoK1o8oEhTwhyRiUhGREqNWRII2Jo36o1BDTTTaEEQwVgwEqgZRkQ+pxtxW+3HbixWoCkpFjFijrYQ0uvyx99hzD2emt3jHO3vO+ySTu2ftPWf2O3vuWnPO7FnrAUnLcv9a4E7Sjv5dpJ/zXdTrsfnQq4BbJF0M/B74/+7uMMYYYwqmrwXOI+JOUnCv2tZW2gF8eKKPzfYngbc8yyldd/AhRWJd5TCMmsC6SmMYdQ2jJuiDrlZk7jPGGGNMop/f8RtjjDFmwBiqwC/pBkl/ljRWsc2R9GNJD+e/syt9n5K0S9KDkt4+NbM+OF10rZb0a0nbJa2XdHSlb+B1NWmq9F0hKSQdW7ENvCborkvSR/LcH8jZJzv2YnVJWiBpRNI2Sb+UdFqlb+B1SZor6T5JO/O6fCzbi/YZPXSV7jMadVX6i/MbvTT11WdExNDcgDOBU4Cxiu0a4Krcvgq4OrdPBEaBw4Hjgd8A06ZawyHoehswPbevLk1Xk6Zsn0va1PkocGxJmnqs1ZuAu4HD8/3nD4muu4B35PbZpCyaxegCXgScktvPJaUJP7F0n9FDV+k+o1FXvl+k3+ixVn31GUN1xh8RG4B67tdzgZty+ybgvIr92xHxdET8jvTLgtMYQJp0RcRdEdFJnDdCynUAhejqslYAa4ArGZ+wqQhN0FXXZcCqiHg6j+nknihdVwDPy+2jOJBrowhdEfF45KJgEfEPYCcpa2jRPqObriHwGd3WCwr1Gz009dVnDFXg78K4FL9AJ8Vvt3TBJfIB4Ae5XawuSe8CdkfEaK2rWE2Z+cAZkjZKul/Sqdleuq7lwGpJfwC+AHRSvhWnS9I84HXARobIZ9R0VSnaZ1R1DYvfqK1VX31GX3/ON+D8z2mBBwFJK0gp89d1TA3DBl6XpJnACtLlyGd0N9gGXlOF6cBs4HTgVFIeipdRvq7LgI9HxG2S3gN8DXgrhemSNAu4DVgeEX9X9+pqReuq2Iv2GVVdJB3F+42G92BffUYbzvifUErti8an+J1ISuGBRtJS4BzgwshfAFGurpeTvrMalfQIad5bJL2QcjV1eAy4PRKbgH+T8m+XrmspcHtu38qBS47F6JJ0GMnhrouIjpbifUYXXcX7jAZdxfuNLmvVX58x1ZsbJvtGKulb3YC0mvEbda7J7dcwfpPEbxmwjR8H0XUW8CvguNq4YnTVNdX6HuHAJp1iNHVZq2XAZ3N7PulSnYZA105SiWxISbU2l7ReeQ2+AXyxZi/aZ/TQVbTP6KarNqYov9FjrfrqM6Zc+CS/iDcDjwP7SZ+MLgaOAe4BHs5/51TGryDtinyQvDt5EG9ddO3Kb4Zt+ba2JF1Nmmr9//0HLkVTj7V6DvBNYAzYArx5SHQtBjZnR7QRWFiSrjz/ALZX/o/OLt1n9NBVus9o1FUbU5Tf6LFWffUZztxnjDHGtIg2fMdvjDHGmIwDvzHGGNMiHPiNMcaYFuHAb4wxxrQIB35jjDGmRTjwGzPkSNp7CGNXSrqiT/P4dD+OW3uO79UrIxpjxuPAb4yZFCRNO8iQQwr8SkzYR0l6NzDhDznGtBUHfmNaiKR35gIgWyXdLekFle6TJd2b69Ffkscr13Mfk7RD0gXZviTXE/8WsCPbviNpc64j/qFsWwUcIWmbpHXZdnk+3pik5dk2L9cm/wopcclnJK2pzPsSSdc26JkFXA58fvJfLWOGCyfwMWbIkbQ3ImbVbLOBv0VESPog8OqI+ISklcD5pOIgRwJbgdcDi0hpRM8i5Qz/Rba/Cvg+cFKkMqFImhMRf5V0RB73xoh4sjoPSQuBG/PziJT5733AHlIa0jdExIikI0lZzU6IiP2Sfg5cGhE7anrWABvyfO+IiJMm6/UzZtjwGb8x7eQlwI8k7QA+ScoB3uG7EfFURPwFuI9UfGcxcHNE/CsingDuJ1UNA9jUCfqZj0oaJdV8nwu8suH5FwPrI2JfROwlFfs5I/c9GhEjABGxD7gXOEfSCcBhDUF/AfCKiFj/rF4JY1qGA78x7eRLwJcj4rXApcCMSl/9MmDQXA60w75OQ9ISUmneRRFxMukMfEbDYyZ0vMz1wPuBi4CvN4xfBCzM1dl+BsyX9JMexzem1TjwG9NOjgJ25/bSWt+5kmZIOgZYQrpcvwG4QNI0SccBZwKbuhx3T0T8M5+hn17p259LkJKPd56kmfly/vnAT5smGhEbSVcO3ksqFlTv/2pEvDgi5pGuJDwUEUt6qjemxUyf6gkYY/rOTEmPVe5fC6wEbpW0m3RJ/vhK/ybS9/YvBT4XEX+UtJ50Zj1KugJwZUT8KQf3Kj8ElknaTqoeNlLpuw7YLmlLRFwo6UYOfHi4PiK2SprXRcMtwIKI2HMowo0xz8Sb+4wxA4+kO4A1EXHPVM/FmNLxpX5jzMAi6WhJDwFPOegbMzn4jN8YY4xpET7jN8YYY1qEA78xxhjTIhz4jTHGmBbhwG+MMca0CAd+Y4wxpkU48BtjjDEt4j/Gp4VHDYm4+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9491315c",
   "metadata": {},
   "source": [
    "### Plotting Q-Q plot to check whether the distribution follows normal distribution or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "78fade10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
   "id": "3bf1c3b9",
   "metadata": {},
   "source": [
    "### Compare Evidences with Hypothesis using t-statictic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70394585",
   "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",
   "id": "0395e772",
   "metadata": {},
   "source": [
    "### Compare p_value with 'α' (Significane Level)\n",
    "**If p_value ≠ 'α' we failed to reject Null Hypothesis because of lack of evidence**\n",
    "\n",
    "**If p_value is = 'α' we reject Null Hypothesis**\n",
    "\n",
    "### interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "291653df",
   "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",
   "id": "8b58d88a",
   "metadata": {},
   "source": [
    "### Hence, We fail to reject Null Hypothesis because of lack evidence, there is no significant difference between the samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91fa5759",
   "metadata": {},
   "source": [
    "## ========================================================================"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "588c2f10",
   "metadata": {},
   "source": [
    "## Quetion 3\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d0cae64",
   "metadata": {},
   "source": [
    "## Solution :\n",
    "**We are going to conduct a Test of Independence using Chi-Square  test with Contingency table**\n",
    "\n",
    "**We need to check whether the proportion of any of these samples are different or the same?**\n",
    "\n",
    "**Make two Hypothesis one contradicting to other**\n",
    "\n",
    "**Null Hypothesis is want we want to prove**\n",
    "\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",
    "**Decide a cut-off value**\n",
    "\n",
    "* Significance 5%\n",
    "* alpha = 0.05\n",
    "\n",
    "**As it is a one-tailed test**\n",
    " \n",
    "* alpha = 1-0.95 = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e459288",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e534bbaa",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buyer = pd.read_csv('D:\\Assignment ExcelR\\Hypothesis Testing\\BuyerRatio.csv', index_col = 0)\n",
    "buyer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40630d7c",
   "metadata": {},
   "source": [
    "### Creating Contingency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87d7f5a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50, 142, 131, 70], [435, 1523, 1356, 750]]\n"
     ]
    }
   ],
   "source": [
    "table = [[50,142,131,70],\n",
    "        [435,1523,1356,750]]\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ad182e",
   "metadata": {},
   "source": [
    "### Applying Chi-Square  contingency table to convert observed value into expected value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1bb40e4e",
   "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": 25,
   "id": "a87c5051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.595945538661058,\n",
       " 0.6603094907091882,\n",
       " 3,\n",
       " array([[  42.76531299,  146.81287862,  131.11756787,   72.30424052],\n",
       "        [ 442.23468701, 1518.18712138, 1355.88243213,  747.69575948]]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chi2_contingency(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2000a5eb",
   "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",
   "id": "4d527651",
   "metadata": {},
   "source": [
    "### Comparing Evidence with Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d08aeb19",
   "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",
   "id": "78d89862",
   "metadata": {},
   "source": [
    "### Compare p_value with 'α' (Significane Level)\n",
    "**If p_value ≠ 'α' we failed to reject Null Hypothesis because of lack of evidence**\n",
    "\n",
    "**If p_value is = 'α' we reject Null Hypothesis**\n",
    "\n",
    "### Interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ff425df3",
   "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",
   "id": "dd46608e",
   "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",
   "id": "7ec846aa",
   "metadata": {},
   "source": [
    "## ========================================================================"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17156b6a",
   "metadata": {},
   "source": [
    "# Quetion 4\n",
    "### Tele Call uses 4 centers around the globe to process customer order forms. They audit a certain %  of the customer order forms. Any error in order form renders it defective and has to be reworked before processing.  The manager wants to check whether the defective %  varies by centre. Please analyze the data at 5% significance level and help the manager draw appropriate inferences\n",
    "\n",
    "**Minitab File: CustomerOrderForm.mtw**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a5c0fa",
   "metadata": {},
   "source": [
    "## Solution :\n",
    "**We are going to conduct a Test of Independence using Chi-Square  test with Contingency table**\n",
    "\n",
    "**We need to check whether the mean of any of these samples are different or the same?**\n",
    "\n",
    "**Make two Hypothesis one contradicting to other**\n",
    "\n",
    "**Null Hypothesis is want we want to prove**\n",
    "\n",
    "* **Null Hypothesis:** μ1 = μ2 = μ3 = μ4\n",
    "* **Alternative Hypthosis:** Atleast One of them is Differente\n",
    "\n",
    "**Decide a cut-off value**\n",
    "\n",
    "* Significance 5%\n",
    "* alpha = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f46f5d9b",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0288e4e0",
   "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>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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers = pd.read_csv('D:\\Assignment ExcelR\\Hypothesis Testing\\Costomer+OrderForm.csv')\n",
    "centers.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "449d1049",
   "metadata": {},
   "source": [
    "### Applying Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d2554612",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354daee5",
   "metadata": {},
   "source": [
    "### Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cfd28f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Phillippines    0\n",
       "Indonesia       0\n",
       "Malta           0\n",
       "India           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a33bb93",
   "metadata": {},
   "source": [
    "### Checking the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bbb4f873",
   "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": "markdown",
   "id": "aed8c361",
   "metadata": {},
   "source": [
    "### Checking value counts in data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5a298444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error Free    271\n",
      "Defective      29\n",
      "Name: Phillippines, dtype: int64 \n",
      " Error Free    267\n",
      "Defective      33\n",
      "Name: Indonesia, dtype: int64 \n",
      " Error Free    269\n",
      "Defective      31\n",
      "Name: Malta, dtype: int64 \n",
      " Error Free    280\n",
      "Defective      20\n",
      "Name: India, 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": "markdown",
   "id": "abad631b",
   "metadata": {},
   "source": [
    "### Creating Contingency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "09516d78",
   "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": "markdown",
   "id": "46889936",
   "metadata": {},
   "source": [
    "### Calculating Expected Values for Observed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b3c933ae",
   "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": "markdown",
   "id": "12660702",
   "metadata": {},
   "source": [
    "### Defining Expected values and observed values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "118a8a3b",
   "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",
   "id": "aa20c51e",
   "metadata": {},
   "source": [
    "### Compare Evidences with Hypothesis using t-statictic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ca88d8e9",
   "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",
   "id": "bb2d227a",
   "metadata": {},
   "source": [
    "### Compare p_value with 'α' (Significane Level)\n",
    "**If p_value ≠ 'α' we failed to reject Null Hypothesis because of lack of evidence**\n",
    "\n",
    "**If p_value is = 'α' we reject Null Hypothesis**\n",
    "\n",
    "### Interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "99ff903e",
   "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",
   "id": "20635e9e",
   "metadata": {},
   "source": [
    "### We fail to reject Null Hypothesis because of lack of evidence."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030f8af3",
   "metadata": {},
   "source": [
    "# The End !!!"
   ]
  }
 ],
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   "file_extension": ".py",
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