Assignment_Hypothesis_Testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2447d230",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1aeac79d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from PIL import ImageGrab\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9f63f8",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c90c3b23",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1FXNzc7EG3AzDjDlhks5G3b88ynWqRN70p+u653Vh95+X/1TJ3v1k6fXnlfy8V5lyYnL5dzlZukjc+6P71auqK4x+e5UfVk7VJhFhCbohwijpUbUhgfy3srISqo1hN+7o1UbV5iLyn67rlmEYlmEY1srKiuf46UXUfpE3YVHJ3GvDFNEGvTZdCfLXb394XROm3igbIvTq337lU8nBMMzty0g+kQXUOWWJMMsKgOBP6TKZjPKL8P/zf/5PpCd9lUrF82MVv/YBg39FPj8/r1x7OUjS6fRAv7gfxlfuo6JH1dpyGb+14jvBH/7wh55boZqmiVqthlqthmPHjiGdTu/oumdxyUaz2XQth8hkMo5X4ip7K5fL9tNUv/RxO8HMzEyoTBGAeulTXLz99tuOf/ulnWMYhgnCyAayqpyyRNhlBcePH+8ZbBiGgf/8z/9U/jY1NYXz58/7pkUS0XUd9XrdN2h77rnnPL/spTIGzfLycuhJLuzSAMIwjIF8vELouj60r9xHQY+0ltaPYX90ePbs2Z4fS8pQrtmwS23iaKtqgwJ5OcHk5KTSt4jXhvUfhPgKvh/eeOONQLm8NU1DpVIZyPpkKlv1gWM2m0WlUgkV7HPqLYZhbMI8vq3X645X4pqmBdpXe3193fF6Tdd1z1e1IqrXdn7LCkhG+Rp6/Vav161MJuOQP5PJKJcTeFGpVKxcLmel02mXXGHLarfbVrFYdJRFMsn6EeUG1PvUE/JrVb9lCJZlWaZpWsVi0TIMw/UalF71FotFZV+bpmmtrKxYhmEo903P5XK+fd2rXbR3fCaTiUXnQahUKi579dqfXmSYeiTIxsX60+m0VSwWrXa77TivVxvb7bZrvPvpWn4NLb72Nk0z8it2IPySEdM0Iy0JoqUFqiUBXq/xVXV5+Skv/0HjNJfLeS6pCNsfKjkLhYKrblrGIdoHsbKy4rJFmfX1dSuXy7n6X9M037JV7VtZWbEymYxLn1RWoVCwKpVKoPIYhrk9SFiWZXmHucOlWq3i0KFDjmOFQgFvvPGG5zWNRgOzs7OOY8VikVO1MMwQWVxcdDzN1DQNy8vLrrcWnU4Hn332GZ544gnH8UE/0WcYhmHGk5FdWgDAlbsR2H4lzzDMeCHvijUxMYE777zTlUnh22+/xZUrV1zXR12KwTAMw9za/NuwBfCi0Wi4cjcahjEyO/0wDBMcORA1TdP11NUPVRo8hmEYhhnZJ7J//OMfXcdU29cyDDP6vPjii5GvzeVyA812wTAMw4wvIxnItlot177mmqbxZMYwYwqlWQr7ZXqxWOyZjYFhGIa5fRnJpQVyih5d15VPaFXs2rUL6XTa3tNd07Shpx1iGGY7zdL+/ftx9uxZnDp1CqZp2uOUSKfT0HUde/fuxdNPPz1SW9UyDMMwo8dIZy1gGIZhGIZhGC9GcmkBwzAMwzAMw/SCA1mGYRiGYRhmLOFAlmEYhmEYhhlLOJBlGIZhGIZhxhIOZBmGYRiGYZixhANZhmEYhmEYZizhQJZhGIZhGIYZSziQZRiGYRiGYcYSDmQZhmEYhmGYsYQDWYZhGIZhGGYs4UCWYRiGYRiGGUs4kGUYhmEYhmHGEg5kGYZhGIZhmLGEA1mGYRiGYRhmLOFAlmEYhmEYhhlLOJBlGIZhGIZhxhIOZBmGYRiGYZixhANZhmEYhmEYZizhQJZhGIZhGIYZSziQZRiGYRiGYcaS2zqQbTabyOfzmJiYQCKRQCKRwPT0NDY2NoYtGsMwI8bq6ioSiQRWV1eHLcpI0Gg00Gw2hy3G0EilUkgkEkPXwajIMUharRYSiQQmJiaGLcqO0ul00Gg00Gq1hi3KSDPwQJaCxOnp6YHV0Wq1HMGo6k82hGazib1796JcLmNiYgKGYUDXdWxubuKJJ57oOVltbGwo65mYmEA+n2fDY5hbjO+//97x39uZRqOB2dlZPPTQQ8MWZWiYpgkA+PHHH1mOAXP9+nUAQLfbHbIkO8vJkycxOzuLfD4/bFFGmoEGshsbG7bhbW5uDuxJ5/Xr1+16DMNQ/t11112Oa/70pz+h2+0il8vh2rVrOH36NK5du4ZcLgcA+POf/+xb55UrVwAAmqY56ul2uyiXy0in07f0HTIz3jSbTSQSCczNzQ1VDroBvd0Y93bfcccdALb9HzNYxt1WgnK7tDMMFLfcfffdwxVkxPm3QRb+97//HQCQy+VQLpfxySef4MCBA4OsEqdPnw503traGgDg8OHDjuOHDx9GuVy273J7sXv3bkednU4Hc3Nz2NzcxJ/+9CdUq9WAkjPMzjEqT29utycsxLi3e2pqCpZlDVuM24Jxt5Wg3C7tDMPRo0dx9OjRYYsx8gzsiWyn08Ha2hp0Xcfvf/97ABipoI6eJPzwww+O419++SUAIJPJRCo3mUzif//v/w3gZrDMMAzDMAzDxM/AAtmzZ88C2A4IJycnkU6n0e12RyaYzWazAIC3337bPtZoNPDqq68CAF588cXIZd955539CaeAFrtns1l0Oh2USiX7VUwqlXIs2yiVSvYHAKlUynO9b6fTweLiIqanpx0fu6n6SFW/WEepVPKUfXV1FXNzc3YddH6n01GeXyqVHDLJf41Gw3H+xsZG4PJTqZT9wcDi4qJ9Tb922Wg0PF/V9/pQgdoqtmtjYwPZbNbuY7+11/l83v7Yo9FoIJvN2u3KZrOuJS6JRAKzs7MAgFqt5tCtyMbGhqMfUqkUFhcXQ+tGBfWXKJNf/4ptmpiYQDabdZ0XF81m01Hf3Nxcz7o6nQ7y+bw9Jkj38nVh2x2XXcftP6i8VCrlOF6tVu0P4lqtluNj2l4f0oZpb1SofFV/0jhStZe+ifBaiiOOFRqrfnLLPk7ld8PYShQ5gtos4OzXZrPpsI9+idLOMPPPINpJPiKIf+4Hkke1RlbUgd/c3Q+DmodKpZJnu4CbS98Cf1tlDQjDMCwA1tbWlmVZlrWysmIBsDKZTOx11et1C4AVpjmmaVqaplkArJWVFatQKFgALE3TrPX19Z7XF4tFC4BlGIbrt/X1dQuApet6qHb4QW1Mp9NWOp22/1/XdbvtlUrF1rt4HrXRq/26rluGYTjOLxaLgeoXrykUCi656XdN0yzDMGz5vHSXy+VsmYrFolUoFFxykk2pzi8Wiw752u22o3yqm64jHcrtDUu73bbLluusVCr2b/V63fGbaZq2fgiyRdKRYRi2DjRNc5VPOqU2ka7Fa0zTdJyv6hexP2i8inrNZDKx2XShUHDZAv2p+pf6yjAMl83HiehLyN7E+lVjY2try9Z1LpezisWilcvl7GOijGHbHZddx+0/vHwu+cVMJmO3X+4zlX8N296o0NhS+SqS0c8viXqQ7UJlLzLtdts+3zAM28dR3blcziFrEFuJIkcYm7Usd7+S34gjhAjSTtHewsw/g2gn+XNN06xCoWD7Rjom9k2/eMUZVB/ZULFYtAzDiDW+GuQ8tLW15Zr3VHWr+lTFQAJZmpzFSY+OqSb6fokSyFrWzcla7CxxwvfDy8BERxXnJCu2UdM0R0BERq2a3MWARC4vnU67Ais6XzYwv/rFQE3uW03TXHogIwbgGPR0XNd1RzlUt6xr8bhcLw0mOejw0lMcePW7GIzJ8pDuxAmMnJPYpna7bU92clAhTgK5XM6+TrRF2SF46ZQg5xP3WJXxG7d0Qyjbm2U5x27QMRsEVUDRbrcdY0zuQ9K/HKDRJKpy+n7tHoRdD8J/+AWyFGiIfSMG2f22NypeDxnE+UnVL2QXYnvE88UxKQZPsk2QfuTxKI5V2dZ7zW1R5Ahrs3K/DsIvBBkTYeefuNvZbrft62S/Q3J4+dQoqOIMmidVNyhxMuh5iMpR3djSb0FvCgYSyJLzkwfrIAI8y3IauXg3R39ed2riUwLVZGNZlqdhkoHJT7S8Jrs42yh3ruiEVboNewOhOt+vfsu6aXiyE/ZC5WDIblS6o/JFaAJWyaO6mRLbFvROLwwkvxgEkeziky4RmqjDvAWQZaeyVXfjXs61VyAb1mai4jd5Uf96jSVqd1xjjXTi9ZRAVR9NKl5PQqh/5XEZpN1x2nXc/qNXICvfjMr19NvefqDgTgxExHEij0eSQR67quCR8ArAKQhSobqpFevxIqwcUWxWnO/ivHEUCRrIBp1/BtFO8vEqXYtyxKUjVSArvl0ZBnHNQ1SObO/iA62gDCRrwbvvvgsAeOaZZxzHn3/+eWxubuLNN9+016jGTa1W63kO5ZDtdrtYWVnBu+++i83NTczNzeH06dNIJpP2eb3odrvKOhcWFgAA8/PzIVvQm6mpKce/Jycn7f9X6VXXdZimiatXr2JmZqZn+YZhoFareZ4v1w9sr9ELmukBAPbt24darYYrV67YmSz88nPeuHHDdYw+pqvVap797iXTL37xi8CyBmX//v0AgDNnztjHms0mTNPECy+8AGBb1k6nY9sYrWnas2dPz/Ife+wxAMDnn3+u/P3hhx92Hbv33ntDtOAmZAPPPvsslpeXHTa2U1D/Pv3008rfn3rqKdRqNZw7dy6WcXbx4kUA6jEE3LRZEfr3d999p1ynR2vJvvnmm8ByDNquB+0/gG1/QDauqkekn/ZGIZvNolwu49KlS7ZMp06dAgC89tprqNVq+Ne//mX7Jfre4/HHH1eW98ADD7iO3X///a5jjUYD3W4Xuq4rbeXrr78GgMjrLIPK0Y/N7t69eyi+QCTo/DOIdn700UcAtv2637rc69evD0xPu3btgqZp2NzcxOLiIn73u9+5xtogiWseevrpp7GwsIBqtYo//OEPdhv+8Y9/AIA9ZwYh9kC22Wxic3MTuq67DI4m+s3NTbRarYF0tBUgJczBgwfR7XZRqVSQzWbx9NNP2ymzxGD2//7f/wvAP4ebYRiO9FutVgsffvghFhYWsLCwgLvuumvo6TP8gsxOp4PPPvvMzosLANeuXYtdhkajYQcKAHDu3DnXOYZhYGFhAe+8844jMBEnABV006BiJ/NcTk5O2pN+s9nE1NQUPv30UwDbg/+uu+5CrVbD2bNn7cXv3W4XmUxG6YiazSY+/fRTO8CniW4nWF5eRiaTQa1Wg67rMAwDR44cGdgNqIx4E+nlJ1QTdxyoJv9e+AVhAFx5rIMwKnYd9iY1KjvV3p///Ocol8s4deqUbc9nzpyBYRiYmZmBpmlYW1vDG2+8AQA4f/48APeDmaiYpunb1p3KGToImx1FBtHOcrns+zvlWR4EyWQSH3/8MX75y19iaWkJS0tLyOVyOHz4cOAbzTAMah6iJACbm5v2nAj0foChIvZAlqLpGzdu+CZbP3v27FACvI2NDZimCV3XbcUlk0msra3ZSqVg9pNPPgEA/OpXvwpc/uTkpB2ELSws4M9//vPQA1kvFhcXsbS0NNA6qtUq8vl8oByBU1NTdiCYSqXwwgsv4Pvvv7edhphhQiTIzctOkclksLS0hE8//RRTU1M4f/48NE3DzMyM7dwuXLiAbDZrB7l79+51lNFsNnHw4MEdCR68mJycxOXLl1GtVvHmm2/ak8GFCxewvLw88PpHJc9tUIrFYuxvX0bJrneCnWqv/OaEbiifeuopANtPXtfW1uyHLWfOnIGmacongVGQH34Mi0HY7CgyiHbW6/WBBI1BmZmZwVdffYWTJ0/inXfeQblcRrlcth/OxcFOzEMvv/wyDh06ZN9U0hvMdDod6kFn7Om3KJqmV+7yH9Fr56xBQU8e5XQak5OTOH/+vL1N7dzcHMrlMjRNsx1fGOjx+zCDET9KpRKWlpaQTqexvr4Oa3u9NCzLgmEYsdTRaDRw6NAhAEClUkG73bbrKBaLrvOr1SpM00Qmk4GmaVhYWMDS0hIef/xx1Ot1z800Rmk7YHq1S6+gzpw5Y7+SnJqagqZp9gRK54j21el0sHfvXpimiWKxCNM0bZ3V6/WdbAqA7dewly9fRr1eh6ZpKJfLO5JCT5wkvNIHUc7nuJ9gRXniMIin5aNk1zvBTrU3mUzauzA2Gg37hvLRRx8FcPPG8tKlS/bboDjfRAzijVcUdvINzzAZRDu//fbb2MsMSzKZxPz8PK5du4aVlRUAwKFDh2IZRzs1D9Hct7a2hk6nYz8Iff7550OVE2sg22g0YJomNE1zBEbiHwV29Pp1p3nwwQcBbDspeYKcmprCZ599Zj+ZBYDjx49HWn9CGy2M6haO9Gr/rbfeGthua7SU4Pjx48hmsz31+N577wEAlpaWcPnyZdtmqtWq8u43nU4DAD788MN4Be8DejVZq9XsJz3iE9dsNgvTNNFoNFCr1Vx3nlevXkW324VhGJifnx/4erSgk+rMzAyOHz8OINx6z6ConC8tJaE1ijJ0IyA/0Y4K+QZxjTPR6XTwwQcfeF5TrVYj5TtVtXsU7XqQDKO99PT14sWL9lsTeuJKk+upU6fwz3/+EwDw5JNP9l3nrl27AMAe/2GJK9Dv12YHzSi385FHHgFwc64aFY4ePWo/gLp+/Xrf5e3UPJRMJu3Np86ePWs/CA378DDWQPb9998H4P2xBLD95JMUTtH3TrJnzx5omoZut4vXX3/dZeBnz551PEU9ceJE6CdQrVYL//Ef/wHAXxejgLyzWbVaDfTBXBjkj7iazSZOnDjhOm/fvn0Agk9oL7/8MoDtPpInBkr6PowNOKjPaR0cPekBttfnAcAf//hHAN4fkMh22el08NJLL8UmI90YmKbpmjgajQamp6cdN5qdTse++bnvvvtik4N8waVLl1y/vfLKKwC2E23L/bu4uIharQZN00KtpfKDfINpmo6NH8Rtp2UOHDgAXdc9/QklBpfxa/eo2vWgGEZ7aaL84IMPHG9NgJtr3c+cOWPf1AT5GLMXyWQSuVwOAPDSSy+5xh1tICE/4PGzlShEtdlBMw7tfO655wBsr7tVbZxRrVZj2zTGi1KpZG9sQrRaLVtv99xzT2x1DXoeAoD/9b/+F4DtDy1N04RhGOED58D5DQJAaU165f6iVAxxpVQJm0dWzDsHqJP9rqysuHLKie3ySr8lJmiOM99erzb6/UZpMcTUJGK+WErqLKaIks/vVb+qDjFfrJiMWqxDTk8j6o/+SMdyTjvLcuZoVW3sIKdJoeNB04RFQbQvOdWOnK9SHitinj4xYbqmaY5E6iJ+aaj80mzRdWLiasty9httQkBjI+4ckuIYo8T5Yt/Iid3lcRpn8nHLcvadmDaNxolKz2K+Ti9/Erbdcdt13P6jV/qtXmndZMK2Nw7E9Ity+aI8Xumb/HROepBtRcypKdq0KIts071sJYocYW22V7/GgV87o8w/ljWYdsr+XU69GeemBCp5xBzact1yKquo7OQ8ZFmWo4+ipGeNLZANk9uMkgrHFVD02iXC65pcLudwIOl02ioUCo4ccLRxgOxgKLG26s8wjNgdr2oHKBG//IRkYLKDrFQqDqeayWSser1uDx7x/F71Uy5IuT/r9boj4Xo6nbYqlYptL6KeVDuJqG4OZMQdiUjGTCajHBBx5/lTIdq3yqmRrF43cqZpOnafod2OaPcwuUzVrkMEjQ2VA5ET/Yvlyv2m67pVKBQGkldW3L1NTqJvWduOW7QBXdetXC43sD5cX193jQvTNG1dqhw19VkvfxKm3XHaddz+wyu/K03yXpM5tVdFmPbGgRg4yb5RDJy86vd7cOOXE7vdbjt2LhNt2ms+9LOVqHKEsVmvHLdx49XOXnO81/xjWYNpJ/lHOUheWVmJ1Ud6ySPP3el0eiAxx07MQ+K1QLTc5QnLus0+jWVGFtpve2try/WFcKvVQjqdRrfbVf7OMAzDMMz4sbq6imPHjiGXy0XKihN71gKG6RdV8mQx11/UJP8MwzAMw4wWlMXq8OHDka4fyM5eDBOFXC6HcrmMPXv2IJPJ2MHr119/jWq1am9isZO7mDAMwzAMMxgo7aau65Fz83Igy4wMy8vL+PnPf4733nvPsVGDrut4/PHH8eKLLw41CTXDMAzDMPFB20NTJp8o8BpZhmEYhmEYZizhNbIMwzAMwzDMWMKBLMMwDMMwDDOWcCDLMAzDMAzDjCUcyDIMwzAMwzBjCQeyDMMwDMMwzFjCgSzDMAzDMAwzlnAgyzAMwzAMw4wlHMgyDMMwDMMwY8nQAtlms4lGo7Hj9XY6HTQaDbRaLeVv+XweExMTSCQSSKVS9nnDkndUWF1dRSKRwOrqquO4n142NjYwPT2NRCKBRCKBUqlk/+anayY8jUYDzWZz2GKgWq0ikUggm80OWxRPvGy51Wqh0Wig0+kMSTJGZpB+IpVKIZFIjMS4GRY0XvP5/FDlaLVaSCQSmJiYGKoczHgytED2oYcewuzs7I4HhydPnsTs7Kxy4L7++usol8sAAMMwAABnz54FMDx5R4Xvv//e8V/CSy+tVgtPPPEENjc3kU6nYRgGTpw4Yf/up2smHI1GA7Ozs3jooYeGLQq++eYbAMB33303XEF88LLlfD6P2dlZnDx5chhiMQoG6SdM0wQA/Pjjj7GUN47QeB32Q4Tr168DALrd7lDlYMaToQWymqYBAO64444drfeuu+4CANx9992u38hhnj9/HqdPn8a1a9dw9OhRAMOTNwzZbBaJRGJHg20vvXz44YcAgEwmg8uXL+P06dO4ceOG/bufrplwkO6pLwZJs9lEIpHA3NzcwOvaacgnkI8YV26lPurHTwzDH44aO2kL9NR8lLmVxsYgoLdV4tvTceDfhlWxGNTsJEePHu3pCKemplzHhiVvGIbxFKyXXh5++GHf31W6ZsIxNTUFy7J2pK5b+elVtVpFtVodthh9cyv2URQ/McpvBXaKnbSFcXiaeiuOjTiR31KNC/yxF8MwDMMwDDOWDCSQLZVK9kL6RCKB6elp15MO+l1em9NsNpHNZu3XFPKf+Eogn8/bi/UbjYb9Kok+NlEt4lctbqdr5H+LH6x4yQvAVXcqlUI+n3edu7q6irm5Ocd5pVJJ+XFJKpWyF75Xq1X7o6mJiQnk83nHNaVSCYlEArVaDQAwOzur/MAqCKR/Ud9+r+ZkvZAsCwsLAICFhQW7LHqt00vXVI74oZjKhoCbHwlks1l0Oh1bvxMTE67+j1qmaM/UZ14EtQVg+2O4oPbgB8mbSqUcx8nWV1dX0Wq1HB/NTE9PY2NjI1Q9iUQCs7OzAIBareYYlyrkD3V61SmPfbL1qOv3wtoy+ROVTQQduyrbET9UEtsv25X88ZmqLb10E6aPBjHGerG6uuqo02t8BPUTKqL4Q/HDVJWPVdURRHdBEX08lSfbl9+re9UHjGHHq4og7SR7EOulv6jLOsSxMTExgcXFRc/+iGtskN2obMTvI9ZOp9P3GAvbFiBa/ONFo9HwnLcp5opbP51OB4uLi65YMcocCCtmMpmMBcAyDMMqFotWsVi0DMOwMpmM4zwAFgCrXq/bx7a2tixN0ywAVi6Xs6+lcw3DsAqFgn0+/ZbL5SwAlqZplmEYdhmaplmmaTrqLRaLdlliOXI9JL+fvJZlWZVKxf4tnU476hfrSKfTDhnl+mToN2qbruuOa9LptEMGsV6SwzAMq1KpBO06q16v2+Wr6gPg0IlKLySLruuOcgzDsEzT7Knrdrtt10XHC4WCXV4ul1PKnE6nHddpmmbLFEeZ4v8DcNhhWFuwLMvRrzROxLra7XakfhMhW89kMg45qN0ArPX19cD1GIahtGOxbVSnruuh6iTdaZpmFQoFq1gs2r5E0zRra2srsJyyToLaMp0jHw8zdr1sR2w/jRGVXa2srPSlmyB9NIgx1guxTnHci75abkcvn+xFUH8o24Ofj42quyCQLxDbSf9WzUEqaNyJ54cZr7Idh2lnoVBQ9pVhGIHHrThexXHTa56Mc2ysr6979rvYR14yiDFOFDsJ6wOjxD9ebG1tec7bNNfFqR8x1lP51bBzYKyB7NbWlmdDXRVLAZBl3QyCZWdODZSdptjwXC5nN1w0Ijng8Bq4okxB5TVN0zE5iVQqFYdT0TTNdQ7pC4DLSOm4rA/RAOSAwEtPQVENsna7bfeLapJX6cWy1I5VdZ0MXSf3m9inYl2iA/QKePotU/xNDFbFgRbGFqh8wzBcg5UcQpAJWyWvqt00JkWnRvUEGauqulTjJ2qd7Xbb0jRN6XhJ3171eRHFlr0C2TBj1892xLplO1lZWbEnkH51E7SP4hxjvSgUCkqbaLfbjps6GT+f3Ite/jCKjw2ru16IwYuo13a7bRWLRUf9YQNZywpuC/LvUdrZT1/5jRuxP8QxE/fYaLfbtgxyeeKNqNxusl9RtrD6i9KWKPFPL/zm7Tj1Q/KJclvW9jwq/haUWANZ8c69Z8WKRpOxylBHek088tNe8RqvARpHIEvOOcpduNwG2WGqHCzhFez0E8hS32ma5ivnoANZGswqqE9FfYsO0OvpYj9lqiZtGrRRbYECGlXZFBCrJnUvegWyuq67AmYx8A5D0IkxTJ0UxKls3bJu6jvo04Wotux13AvV2PWzHb+bHcu6OSZEvUXRTa8+GsQY6wX5dpVvokk87uAoaCAbxseG1V0vaNIO8uZsJwPZKO2MK5BV+UXyr+JcP4ixQb5Z7A8at2RPcnBI9YjjNqz+orQlSvzTi17zdhz6oYcAmqYpn7p6zWd+xJq1YNeuXdA0DZubm1hcXMTvfvc7JJPJwNd7ffXY60s61Zfx9957b+B6o/L5558DAJ588snIZezbtw+1Wg1XrlzBgQMHXL8/8MADrmP3339/5Pq8uHjxIgB4rkEjOQdJo9FAt9uFruvKdThff/01AO+chyr99Vum6mvpVCpl56AkwtjC2toagO11Wl46lcvvh1Qq5RqHk5OTsZXfb50fffQRgO31YX7rj69fvx5I7p2y5V5jV7YdUXaVbLquwzRNXL16FTMzMwDi180gxliYOqldIslkEo8//jjW1tZw8eJF5TmDJKiP7Vd3KjY3NwEA+/fvD3zNoBlEO8Og8rm/+MUvsLS0ZOsLiH9sAMCvfvUrrK2t4cKFC/YYpbzFr732Gmq1Gs6cOYM33njDrts0TaTTadvfRdFfP23ZyfgnDv2Q781ms8r4cGZmBpqmodvtotFoBPIHsQayyWQSH3/8MX75y19iaWkJS0tLyOVyOHz4cCBhMpkM1tbWUK1WHY6eOvm+++6LU9y+uXTpEgDgzjvvDHxNo9GwJ1oAOHfuXOxy9cMgguSwmKZpLzpXocoBPIwyRaLYgp88O5ETdtSgnKFehM3hHLctD3rsqm6QiLh1M+jxIPLll18CgOtjRJGHH37YvsEbdeLSnfgxTpgHPjvFTtpIUFTjI86xsXv3bgDbHy8tLy8D2M5hDGwHWBSjtFotTE5O4tNPPwUAPP/880pZw+ov7nEeN3Ho54svvgDg7593794d6mFD7HlkZ2Zm8NVXX+HkyZN45513UC6XUS6XUalUen5xStH+oUOHcOHCBdx///04d+4carUa0un0yG17GSZvXrVaRT6fH4tce8PGMAycPn165MsUidKv1g7lfh0X6vX6jj+NC8IojN24dTPo8SAyrrkpvYhLd6Oe03QnbaQf4hwbk5OTSKfT2NzcRLPZxNTUFM6cOYNMJgMA2Lt3L9bW1nDp0iVMTk7aQdyjjz7qKiuK/kbVBxJx6GcQ+Z0Hkn4rmUxifn4e165dw8rKCgDg0KFDPV9FvPbaa9A0DblcDtVqFQsLC7h27RqKxeJIDijj/22Z+MMPP/ie12g0cOjQIQBApVJBu92Gtb0+GcViceByhoFeewyTa9eujUWZIkFtQWTY20KOGt9++22s5cVhy6MyduPWzaDHg8hjjz0GAL4pdegpzTjsqhaX7sSAJUy6odCpiSKykzYSlHQ67ToW99j49a9/DWD7FXiz2US328XevXsB3AzILly4AGB7mZiu68rlEFH0F3dbBkG/+tm3bx8A/xtcesMZ9An0wDdEOHr0qD3J037KKhqNBkzTRDabxfLyMm7cuAHLsnDt2jXMz8+P5KsXWqvyySef+J5HryOPHz/uuS4kTqIMhgcffBAAcObMGddvnU4HH3zwQd9y9WLXrl0Atl/JxLWt5CDKVBHUFoCbzpi28R1H4pzkHnnkEQDAe++9F0t5cdryTo9dmX50o+qjnRoPIvfccw+A7fWgqpu3Tqdj95XqyVa/xBUcDEJ3uq4DuLnO0A/yG5999pnrN7oR8CLMeO23nf3eoKvyn77//vsAgOnpaftY3GODoHjl3Llz9qtxssupqSnouo5qtWrnhKankUQU/cXtA/vF7yFAv/qhJaJeS4lojbGmaYF39Is1kC2VSnbSbKLVatnRNTk0FfSh2JkzZ8bmSdXhw4cBbK9rkQ12Y2MDi4uLjmPyHUiz2cSJEydik4fudOhuKAx79uyBpmkwTdMhNyVAFxfZD4pkMolcLgcAeOmll1x2QEn9wyR6HkSZKsLYwssvvwwAOHHihOtcSqI/qlul0lMk0zRjG6fPPfccgO07fNWmANVq1TWW/BiELQ967HoRRTd+fbRT40FkcnLSnswymYyjzk6ng2effRbdbheGYcS6ZXU//lDFIHT3wgsvANh+GynrZXV11eEHKIh7++237TmWbNorKIgyXqO2kwIcmu+jsnfvXodfbDQa9tpR8rNA/GODoGCsVqvho48+cgVUjz/+OLrdLt5++20AwM9+9jPH9VH0F7cPjAq9PVE9BCD61U82m7X9s7zxSLPZxJEjRwBsPzwITOD8BgGgFBKAO7GzV6oOMTUKJdyV/9LptJXJZDzzyKpSRXil2Ygz/ZZlORMBeyXBF3NO0kYPYk5NVRu86hPb4JVqhcpNp9Oh8pGKOVLF5OCUoDmMnFHTb8mJ00mnYp46r7ydXsRdpldanyC2oDqXdN0rMb4XvdJveaVg6aU3L0S7oDb2W6doe6oE2aoUM35EsWWVPwk7dnvZThS7iqIbvz4axBjrhZhiSzU/eCVA76feXv4wio8Nq7teeG0UQf/2skWyaXEDIS9/G2W8RmmnmEOazg2aCjLIJjR+mwjENTYI0T/LZch1qoiiv7BtiRL/9EIcp+KmCHHrR84bLM9/YVOaxr6zV6VScXWgalKW86KZpqlUoDhYAWceQ1Kmqnwa9HIn+OX681O8Xy5Luc26rlu5XM41GYgJ0dPptFWpVOwOldtA9amcIt0wqAy4Uqk4ducIm/dxfX3d0ZZMJmOZpmnrU67TSy/k1LwCsl5OQNzpStSp7BiD5l2NUqaXfNSPKicdxBbEc0VHpWmalclkQu3GJsor60C1o4qIV97mXsgbC6hyO0apk8aIHPCsrKyE2uWFCGvLqsTdolxBxm4v2/HLL0m2oLKVsLrx6yP6Pe4x1ot2u+3Y3Yh0WSwWPfvXT5dB8POHpMuwPjaM7oJA5cl6KRQKLr3U63WXTW9tbXnOI1R+lPEapZ2FQsGxo1qYnaVIDtokQ4wH/B7IxD02qEz6XdapGOz5BVxR9BemLVHinyBsbW05dkDzC5T70c/W1pajn0nesPOfZVlWwrJG49PpUqmEhYUFZDIZ5WvVxcVFLC0tef7OMAzDMAzD3F4M/GOvsNDidxn6mtXrd4ZhGIZhGOb2YmSeyDabTezduxfdbheZTMaxW8UHH3yAzc1NpNNpnD59eiQzGDAMwzAMwzA7y8gEssD213x/+ctfcObMGccOHoZhYN++fXjuuec4iGUYhmEYhmEAjFggyzAMwzAMwzBBGbk1sgzDMAzDMAwTBA5kGYZhGIZhmLGEA1mGYRiGYRhmLOFAlmEYhmEYhhlLOJBlGIZhGIZhxhIOZBmGYRiGYZixhANZhmEYhmEYZizhQJZhGIZhGIYZS8Y2kO10Omg0Gmi1WsMWhfl/TExMYGJiYthiuGi1WkgkEkilUsMWJTTjLHtQVldXkUgksLq6OmxRRoJx6PONjQ1MT08jkUggkUigVCoNW6RApFIpJBIJnjfGnHw+j0QigWq16jh+q/dvs9lEIpFANpuNdG02m7XH7Nzc3AAkHA5jG8iePHkSs7OzyOfzwxbFFzKciYmJW3ZwEd1uF91ud9hiuLh+/ToAOLY9HhfGWfagfP/9947/3u6Mep+3Wi088cQT2NzcRDqdhmEYOHHixLDFCgTplHTMjCc0l37zzTeO47d6//74448AgO+++y70tQcPHsTa2hp0XYdhGLh06RKazWbMEg6HsQ1k77rrLgDA3Xff7ThOdyyjcLfR6XSwtrYGYDvIO3ny5JAlYphgTExMIJFIDFsMZgT58MMPAQCZTAaXL1/G6dOncePGjSFLtQ09OGg0GgOth94ijMuTaGZw7JTN9UOj0YBpmtB1HZ999pk9ZqempoYtWiyMbSB79OhRWJblerVAdyyjADn8XC4HAHZQyzCjzig+WWdGi4cffnjYIriI8qQqCvz2gCF2yubiIJVKIZlMDluM2BnbQHYcePfddwEAv//976HrOkzTvGUe5TMMwzAMwwyb2ANZv8fsXguVaeF2s9lEo9FwLEjOZrPK4K9arSKRSDjWyCYSCczOzgIAarWaXYb8ilT+UCGVSmFxcTGO5tu0Wi17Ddnk5CQymQwARP6gRdRRtVq1X/2K7W82m8jn8/aCd9Kfqi9If6urq2i1Wsjn83aZ09PT2NjY8JSlVCrZdUxMTKBUKqHT6fjKT7JRHbT8Q36iDtz82CWbzaLT6aBUKtnXpVIph2yiLKlUKrB+5+bmlB8LEPTqMIxdhLGrUqnkOHd6etpTll51UluoTr/+kPt6YmLCYSNUFiGOIdmOwtat+tign9dxcnnUFrn+fseFKC/5ok6n4xozqvLED9nka8LYq0hctkNsbGwo9Si3p1QqIZFIYGFhAQCwsLBgXxPmBr2XDQK9X93LH5aSbLVaDQAwOzsb+kM0sidRrnw+7/i2odFoeOpAXs7WzzxD9aiWyJF/9PqwluoU9Ul97Nc2EdGv9mNj8nyeSqWU9XY6HSwuLrrqDDK3BCVI/8oyyXoQ56wwNhelbtlXRNW/V1xEvkeeb8mnT0xMOMZ12D6S4zpxrsjn8/Y1sh/PZrPhvyeyYsYwDAuAVa/XXb/V63ULgGUYhvKaXC5nAbA0TbMMw7A0TbP/bZqm45pisegqyzAMK51OO8qgP2JlZcUCYOm6bhWLRatYLFqZTMbSdT1WPZB8KysrlmVZ1tbWli1XFGQd6bpupdNpu23r6+sWAAuAfVzXdfuY3B8kXyaTsfUsX7O+vu4ph3w+6V1lUpVKxf5N13VHP1GbRMhO0um0fV46nXbIVqlUbFnE80Sdy+WJspEdZDIZpb6pPJUOVAS1q3a7bZdtGIZVLBatQqFgt81LFyq9irZAdYr6arfbjvO3trbsvqZ+EHVqWZZVKBRcfUx/W1tbkesW20F1i/YKwCoWi4F0TW2h8VQoFKxisWjlcjlL13WHnHGNC9EXra+v22XI5Yl1+5Un2muhUAjU52FtJwjUj176qVQq9rk05uh36kfDMFz+2YsgNijqzcsm5PNJNiqb2mIYhqMNXv1OPkq0p0wmYx+jft3a2vLUgdiP/c4z7XbbllUeS6I/ldthmqZrnikUCq7xLNqzXD61m2ysWCxahmF4+kovRDmpP8QxQIg2Ic7b4rWyjPS7bB/99i8hjjWSSRyz9Xo9sM1FqVvWh1iHKobyguxVFRfR3Kaab6lO0mM/fSTHdeI1lUrF0jTN1U5d113l+TFSgSw1mhogGpPs7FWBrF8dBCkqjJKiQI5OdPB0LGhwJCLrSIYGhzyh0ICRnRDpjwxKvI4ML51OO64RB6QcLJBe5cnXNE37N1WASb+JA1+cyMXBJLZHNdGKk4dcjywbOXyVLagmg14EtSvSu2zPoq2L7fUKakQ7l+uk/pOdvF+wLB9T1Rl33e1229GfYQJZuq7XWIprXLTbbUfAJY8BKk/Wo1yeeI040YvyefV5WNvpBQX58hizrJtjSZZNlCNMfxFBbTBsIEv4zT/ideLvFDioHphQH8nzSS/54phnqE9FH2dZzpsPuX6SV9QlBaSiLKI9i36ZbhBl3x8W0b/K8lcqFYfc1E5x7qcyxN9EwgSyUfpXnANlmXK5nKMcP5uLs27RlwQNZAm/uEieb+XA2rL66yP5OrIxVRAs2qVsN36MVCCruuPr5UjCBrJewUuceDkDujOO8uREfPoYBvFuS4T0p7rzEZ2QSgY5IBXrka+hCdGrP1T9KJYlDyo/B2lZ6v71ko3aI5dDMofpp6B2RU5NhWoS8pKdAieV0yEdiQE9BS1Bnwj5BbJh66Y2eLXba1Lyg66JclMoyhRmXHjdyEUtz7JuThDimPLq87C20wvqRy+9e/VL1EA2jA3uZCBL413l1yxL/VAiqHz9zDNefkh8oyHbGwVBQcaF6sbIy47DEnSuE9+sqHTVy3cHCWTD9q/4NDzImwY/m4tiW15zn2V5xz29CBrIquym3z5SxXVewbrYRvmG3Y9/wwih+gr23nvvjbUOwzBQq9Xw7LPPYnl5GZOTk7GWDwD/+Mc/AADPP/+84/gzzzyDpaUllMtl/OEPf4j09eCvf/3rUOfPzMwAADY3N5W/q75i9NIJrQXav3+/Zz0yH330EQDgyJEjyt8Nw8DCwoJdtoycHkSUTZUUmj6qu3r1qqdMxJEjR1Cr1fDee+85yqKP9A4fPux7vdyOXnbVaDTQ7Xah67py3d7XX38NAIHWB1EGjFqt5qk7MQ/plStXAMBeq90PYeu+ePEiAHV/AcC+ffs8y/GCrvntb3+LO++8s2dfy0QZF+SLdu/e7bLLe+65J3R5wPZ43tzc7LnGNE7bIagfn376aeXvTz31FGq1Gs6dO4f5+fnA5XoRpw3GCfmoZrPpu572+vXrgeeLOOYZ8rNnzpyxjzWbTZimiRdeeAHA9hjsdDq2bdE6yj179vQs/7HHHgMAfP755/axXbt2QdM0bG5uYnFxEb/73e8izVNU5pNPPul7Ho37bDarrGdmZgaapqHb7aLRaIQe50D4/r169SoA2N+39EPYumlds67rytRY1GeD4sCBA65j/faRKq67//77AWz7P7nM++67D4DTLnsxUoHsTrC8vIxMJoNarWYnBj5y5EiknTK8KJfLANwB39TUlB1onT17NtY6iVarhUuXLrkSRcdJmMF96dIlAN43JHHnsUulUoETyVP/1Go1tFotTE5O2h/p6boeymmGsSvTNO2PRVTIuZH98CtH0zT7/7/44gsAN/Mvx0HQuglyXnEwPz+PL774Amtra5idnYWu63jllVfw9NNPK53toMdFvxNe0AA0LtsRA2cv2R944IFAZQVlEDYYJ+S3vbjjjjsClxXHPDM5OenIdjM1NYVPP/0UwHZAc9ddd6FWq9lzSbPZRLfbRSaTUY6BZrOJTz/91E4dRjc/IslkEh9//DF++ctfYmlpCUtLS8jlcjh8+HAof0h+/8477/Q9j2zCzzfs3r079I2uiqD9++WXXwJArGmqwtrWKO3qt5N9BER7eHnbBbKTk5O4fPkyqtUq3nzzTfup0oULF7C8vNx3+RsbG3YOTtWuY5Q4/NSpU7EGsp1OB88++2xsxhQXo5yPNJlMIpfLoVwu4+zZszh69CjOnj0LIPxTozB2ZRgGTp8+HUsbLMsKdN4gch0GrXtQVKtVvPjii/jb3/6GtbU1HDt2DO+++y5Onz5tT0KjOi6iEpftDCPf9qjn26zX65Ge+KmIa57JZDJYWlrCp59+iqmpKZw/fx6apmFmZsYOfi5cuIBsNmsHuXv37nWU0Ww2cfDgwcA3+DMzM/jqq69w8uRJvPPOOyiXyyiXy6hUKoHnrKB+fydtImj/DiJHcJy2tdOM+rgFBphH9ocffhhU0bGQzWZx+fJl1Ot1aJqGcrncVwob4pNPPrH/n5yX+EcDfG1tLdYta2myzmQy2NragrW9/nkgwUYYuQ3DAOBtD/RkSPUEbyegV1+0nID++8wzz0QqL4hdXbt2rQ+JnQTti3379gGI10mHtV/VE6B+mZmZQbVahWmaSKfT2NzcxOuvv27/vpPjIgrUH4888kig8+OyHXFS9UpvRE+mwrwh8COKDe7kxgPffvtt7GX2O8/84he/AHDzFfWZM2fw+OOPA9h+m6Vpmr30gM4R3wR2Oh3s3bsXpmmiWCzCNE3b/uv1ume9yWQS8/PzuHbtGlZWVgAAhw4dCjzme/l9IohN0NPdME/EVQTtX3p9H1farzB1E3HOEf2yk30UldgDWXLI//rXv1y/kWPcCYIawszMDI4fPw7AvW9zWDqdju2k5ElT/NN1HQDsp39xQE+cqtXqwLadS6fTAG4arYiXc6bXlmKAL0Jyk3PeaQ4cOGCvCWs0Gvaygn51qLKrXbt2Adh+PdzvdobUF7R7XC9o3VHY3eVUE1fYuh988EEAzrV+RKfTwQcffBBKJhWTk5N46623ADhl3olxEYRr164pJ0bqj5/+9Ke+18dpO0QvP0SBkfyELyphbNDPZoLkrA0TONCc9d577wW+hgh6cxZ1nqH1h7VazV46IPZHNpu1baJWq7nWdV69ehXdbheGYWB+fj7SEpijR4/agen169cDXdPL7xO9bILWhmuaFnn8hu1fcb17mJt1lc2FrVsc56q633///cDyxMVO9FG/xB7IkkMul8uOjlhcXMSxY8firs4FPWlQGUKj0cD09LQrye+5c+cA3Oww4GZidFVCai/Onj1rf5Dh16G0UJ+e/sWJPFmqljdEhT40kxM5V6tVHDp0SHnN0aNHAWzbg5z8fWNjw17v9+KLL8YmZ1hoC2H6II36JyhB7YqWMgDASy+95LJPShQfZKJ++eWXAQAnTpxwBTaUyFu8udi/fz80TYNpmp7JukVo4lLdtISte8+ePXbdYkJ4Sr7t9YGUHxMTE65NO/75z38CUK/5HOS4CIJpmpibm3P0ealUgmma0DRN+QGlSJy2Q7zyyisAtnUh9+Pi4iJqtRo0TfP8GCwsYWzwJz/5CYDtYELs52q16htY09OjCxcuBJbrueeeA7B906PaoKJarbo2MqCndqpAO8w8EwTSC/nKRx991P7t5z//OQDgj3/8IwDvBwKy/Xc6Hbz00kuu80qlkmtTEVpfDtwM8npBH8qWy2XlRiqkz2w2a9uEmCQf2LYJ8sl0ExCFsP07OTlp+79CoeDSxeLioqNv/WwubN3JZNKuW9ZHPp/vudZ2EOxEH/VN4PwGARHzgFGSWzF3IHzSb6lSmXiljfBLQ0HliUl2LcuZv0xO0CyngRBzoAWF0tl4pdogxBRSQROJ90pRRHWLCbh1Xbc0TbP1L9IrjYeq7WK+SrqWys5kMvZvMmL+O1USdDn9ld8mAF6yyXoKkouVkPPaBe0T1fW97ErWoSoJvZh2JciGCKJe/TaGUG1MoUpGb1nunIW6rjt0GkfdNEYpVU+YdE5eSfW98rvGMS6CpvZTlUd1yuMmjP2HtZ0gyAnK5WT5fimAouSRDWODYo5h0b7ExO0you7oPFFO+s0rYb44b4i6kVMIiUnrxU0RLCucPwirMzn9mjiXqPpfnI/FTTQ0TVMm1xfzB8s6CJs2UrXZhmpDBDmPqexLVPVG3RAhaP+qNgCQN0RQya+yuTjqpn8POv1Wr+vj6CPL8vchvfysitgDWcvaHlyiE0qn03Z+MlXHkcGrAkByCnKj/PImyonWxfrq9brjN13XrUKh4JkzMpQyod6FTEXY3JmkI68kwe12256kSQ5K3CwG8wS1z2u3Fho4qnpyuZzDiVO/kV5VrK+vO/SuaZqVyWSUufd6bUjgl0+T9Co69CC7qok7lEUhqF1Z1s2+Eh2BrutWLpdz6aOX7OIOZ6JevexElpPOV+UPpAmP9KJK6B2m7vX1dUebaaMCamOYwIgSk4sOP5fLKSfyuMYFyek1ZlT9JE48W1tbLr+osn+/Pg9jO0FZWVlRluflx+RdC8MS1Abb7bbDBulmxLIsZd8RlUrFsfOWWC6VpQrQSS5xcxfDMKyVlRXlON7a2nLsmCTabxh/0AsxaFbZHo1Br/y88lghPVK+VLnMSqXiumGK2tdyWWRbsv63trYcMpLuvXyJ10OjOPtX1pt4vqqdXjYXpW4/X0GBZBgoOFTZjyr3t4qwfeQX1/nl1/WT1YuEZY3IVw8MM0Sy2SzW1tZCfZnLML0olUpYWFiINVMFwzAMc5OBZS1gmHGh1WphbW0t0FpFhmEYhmFGBw5kmduev/zlLwC8dy5hGIZhGGY04UCWue2pVqvQNM3OsMAwcUFfqMeVi5VhGIZxwmtkGYZhGIZhmLGEn8gyDMMwDMMwYwkHsgzDMAzDMMxYwoEswzAMwzAMM5ZwIMswDMMwDMOMJRzIMgzDMAzDMGMJB7IMwzAMwzDMWMKBLMMwDMMwDDOWcCDLMAzDMAzDjCWxB7L5fB6JRAKrq6txFx2KVquFRCKBiYmJocoRhVarhUajgU6nM2xRdpzV1dWRsB8mGM1mE4lEAtls1vVbtVrF3NwcEokEEokEUqkU8vk8Wq3WECQdDNVqFYlEAvl8ftiixAq1S9WvowD7ifDErbNbqQ+azSYajUZs5W1sbGB6etr2faVSKbayGTexB7I0SX3//fdxFx2K69evAwC63e5Q5YhCPp/H7OwsTp48OWxRdhyym2HbDxOMH3/8EQDw3XffOY6XSiUcOnQItVoNhmHAMAyYpolyuYx0Oo1msxm5zomJCSQSCUxPT/cjeix88803AHBLBefAzXbJ/ToqsJ8IT9w6u5X64KGHHsLs7GwswWyr1cITTzyBzc1NpNNpGIaBEydOxCAl48VYLy2gCe1Wg/Zlv+uuuxzHs9ksEolErHeODDMoisUi2u02Tp8+jdOnT6PdbiOdTqPb7WJhYSFSmRsbG/bN6ebmJjY2NuIU+baBnqTPzc0NW5ShQk8V+YnZ7Y2maQCAO+64o++yPvzwQwBAJpPB5cuXcfr0ady4caPvchlvxjqQHcenrUGoVquwLAtHjx51HB/VpyMMIzM/P4/5+Xkkk0n7WDKZxNraGgCgVqtFWjrz97//HQCQy+UAAJ988kkM0t5+0JP0251b4Wki0z83btyAZVmYmpqKrcyHH344trIYf8Y6kGUYZryYnJy0///q1auhru10OlhbW4Ou6/j9738PYPumj2EYhrl9GWgg22w27dfhtKbN77X46uqqY4G018ch9AEJQef7vXYvlUpIpVJ2uWFfJfV6FUftlNf+pVIp+4OzarVqt29iYgL5fF75VIo+mKNJulQqIZFIoFarAQBmZ2c9F5GL7SSdh53sV1dXXR/plEoll6z0QV02m0Wn0wmlY9k25ubmIi+ZkHVMMkxMTDhkEBfgT0xMYHFx0fOpYKfTQT6fd+gym816ythsNkOdL38MkEqlsLi46DjHbymJ10dW9JHO6uoqms2moz/k67PZrL08h+zRa60n6YPOT6VSfQeR99xzT6jzz549C2D7ld3k5KS9TMFLDlEXrVbLIf/09LTnsoSwfSlD13jpkvp1Y2PD7kfVn+pD1bD95iXf7OwsgO0n42KdKuS+99MdsG3bQfxHEKL4iY2NjZ46ajQaSCQS9hKXhYUFRx0iQf1hLzqdDhYXFx3j3ss/j4pvjVIezV/NZhPVatXuB/GDyKBjjPSuqs/vw/KNjY1QS2dIDtFGwvYBzdMqmxLjgjBjOIgu+ymz0Wg4+jabzXp+vyDrgPSrst+w82eQ+dAXK2YMw7AAWOl02gJgaZpmGYZh6bpuAbAAWPV63XFNu922zwdgGYZhGYZhaZpml7G1tWWfXygU7HrE8w3DsM+r1+v271R2Op121FMoFAK3i8ozDMO33XLbqK5cLmcBsHRdd8ieTqc9yyoWi5ZlWValUnHoI51O2+2tVCr2dZlMxpaxWCxaxWLRMgzDymQygdsp95usZ5VORL0G0bHYNyp9iG0PgkrHsgyFQkFZn0o3W1tbtq5zuZxVLBatXC5nHxN1blmWtb6+7pC/l72vrKzYslA/ZTIZS9d1x3leNiXqUO6TYrFot0vTNLsfxaFeqVTsPi4UCnb9qrFmWdvjUxyLsi36jQsZse1hoXaQfFSWl33LuiA5xb5ZX193XBO2L6kOsf2ky5WVFZdM7XbbLqvdblumaTr8l2EYDtsVCdtvfnpUjXOxDdQuXdcD686yLMcYJNsWfUO73Q4ko2VF8xM0zr3mEap/a2vL0R4q3zAMh88K4w/9ME3TloPqEvtZbseo+NYo5dE5sj8mfYUZY9SfqrbSNap+oLpVY1CFqu6wfUDztMqmTNO0zwkzhnvpMo4yZZ+uaZotLyHGaHS+qAdRb2Hnz6DzoW//BT4zIKKh53I5h+MSO0OEjDWdTjsU2G63Pa+xrJvGp0IcgJqmORRNHU+TSRD6DWTlQSV2tjwhyIFsrzqoPNJhP2ia5jI0KlsMIiwruo5poOdyOftYu922B19YZ+ulYxogoj2q2iQPWtKz3C/UZ+KEaFmWPfDkcqg9cqBF/d7L9voJZL0CBwpKVc6K+kwuk8agXJ5Yl9+kvrKyYt9UeQWEvTBN0+UH6JiXLmVdiO0V2yRfE6YvVYEs6VE1Fuk3v5tL1eQdpd/86OXPouhOLFPuD7omzLiO4ifoJl6sv91u22XJgQ2100uuMP7Qj3q9bqXTac+bWk3TXOePgm+N2gcqn0uEGWMU9Mrzvzj2VTEAyS3X4YVfIBu2D7xsKsoY7qXLOMok+cVgVb5x8JoDTNO0crmco+6w82fQ+dCPgQWyKkctPo0QHQA1RDW5iU+DvIJEFaIRqpwNGXrQCbXfQFZ1Z+jl3KMEsuLd4yBQGWcUHdM1suOW64kSyKp0rHLEfm2iScor0KA+kyc3FV594hd8qeSLEsiqHJtl3Zw4vZ5UqCYAvz5WBXIy4oSTyWQCT/4quWUHS45X1R/iU0VZ1+JEGASvvlS1X/RZXpO1l/1QPbLMUfotSHt6BbJhdEdtU/Wv6kYkiHxx+Qlqj2w/vQJZL7wm6yio/MEo+Nao5YlvZcPgNcZUY0kM0uR+IFsLU3+vQDZMDOFlU1HGcC9d9lOmao5TBb9i3NbLv0SZP4POh378GwaE6ou9ZDIJwzBQq9Vw9epVTE1NodFooNvtQtd1zMzMKK95/PHHsba2hosXLyrP6YXqS8RUKgXTNEOXFZUHHnjAdez++++Prfxdu3ZB0zRsbm5icXERv/vd7xxfjPfLvn37UKvVcOXKFRw4cMD1e1AdX7x4EQA8E61TPVFQ6ZhkOHz4sOs3+vBIbBPV/d133ynXodGaI8qz6QfZ6ubmpuM4jYFnn30Wy8vLjg+g4mL37t3Kcj/66CMA22uq/NbZXb9+HZOTk/aaJl3XlX382GOPBZZp22dF49133wUAPPPMM47jzz//PDY3N/Hmm2962lQqlXKNhbA69+pLFclkEtlsFuVyGR9++CHm5+cB3PxYTdM07N+/X3ntSy+9BAB4++23HTKH7be4CKM7MSOF1xgO6nPj9hNkp59//nnga/zo5Q/DIM6JqvltWL613/J+/etfB64L8B5jNJYuXbpk296pU6cAAK+99hpqtRr+9a9/2f1Aa+kff/zxUPX7EUcM0c8Y9tJlP2WqYrR7773XdYw+yk2n0z19S5T5M475cGCBbC+oIV9++SUAuD5GEXn44YdtJ8moSSaT+Pjjj/HLX/4SS0tLWFpaQi6Xw+HDhyMF/41Gw3ZkAHDu3Lk4xY01iB+EDH6TMeDO8QtsD9JLly71DHKXl5eRyWRQq9Wg6zoMw8CRI0d2dBelcrns+7ucT9FvfA6aZrOJzc1NZTBNAeHm5iZarVZsQVzQvvTiySefRLlcxjvvvGMHsjTBZrNZ5U3m6uoqNjc3YRiGZ3AUtt+GgV+OYMrXGZSofqLZbOLTTz+102t9/fXXkcoh4vKHnU4Hn332Ga5cuWIfu3btWl+yycTtWwflq4OOsZ///Ocol8s4deqU7SPPnDkDwzAwMzMDTdOwtraGN954AwBw/vx5AO6b3lFhEGN4kH6BYrQwD8bCzJ9xzIdDC2QJzuMXHzMzM/jqq69w8uRJvPPOOyiXyyiXy6hUKoGNolqtIp/P37I5eoNSLBbtAKQXnU4Hzz77bOCnHZOTk7h8+TKq1SrefPNNe9BfuHABy8vL/YgdmHq9HukGJwphgxeZf/zjHwC2cz36fYV89uxZV+7lsITtSy8OHDgAXddhmiaazSampqbsp0hPPvmk6/xWq4VXX30Vmqb52sBO9ltU+nny3i/NZhMHDx6M7W1bnP5wcXERS0tLMUg13oQdY3SzeubMGQDbfdztdvHUU08BgP3Glm5kz5w5A03TYs0JGyeDGMOD9AtRYrQw82cc8+HQ8sg++OCDAG6+8vFLZfLFF18AUD8F22mipJDZSZLJJObn53Ht2jWsrKwAAA4dOhQoPU+j0cChQ4cAAJVKBe12G9b2OmoUi8VY5ez3CcmgCSMfOeVMJoOtrS1bZ70m9Gw2i8uXL6Ner0PTNJTLZWUqkx9++CGs+D359ttvQ53fz1OjGzdu9LWzDb2N6Xa7tpMT/4g///nPkesgovalikwmA2A7EBeXFaiethYKBXS7XRw/ftz3qXLYfhsGcW7XG2Ycdjod7N27F6ZpolgswjRNu+/q9XrouuP0h6VSCUtLS0in01hfX3fYlWEYoWXzI27fGnd5YccYLUnsdrtoNBr49NNPAQCPPvooAGDv3r0AgEuXLtlLFXfy7VZYBjGGB+kXgsRoMlFsJuh8qGJggSwFnyKtVsueeH7yk58AuJlLkl4NynQ6HftOjAxXVe6g2bVrF4BtOeUO7XQ6sb8e8iOo0R49etR2ktevX+95Pr06O378uOfrz36hGxjqU5FOp4MPPvgg9jrDQPJVq9XAA5dsulqtRnoKMDMzg+PHjwNwrh165JFHAAD/+te/XNfQ656wUJnvvfdeoPPJ7k3TVI6z999/P5IcQWk0GjBNE5qmOSY88Y+evtHTz37oty9F6NXm2tqaY1mBzMbGhr3Rg9dTjLD9FpQ4/VY6nQZwc4vOfojiJ65evYputwvDMDA/Px9qmYlq4o3TH9JShLfeeqvvNbVexO1bB+Wro4wxevp68eJFnD9/3vHElZ7Ynjp1Cv/85z8BqN96DJtBjOFB+QWRXjGaSJT5U8ZrPvRjYIHs2tqaI+F/p9NBoVAAsL24l5zM5OSk/eQik8k4FEWvIMg5yUZPQdqlS5cG1QybZDIJXdcBAH/961/t481mE3v27NmRD8f27dsHALhw4YLrt1KpZCduJmgNEhAu+bz8KqHZbOLEiRNRRHaxZ88eaJoG0zQdCY87nQ7m5uYCfUwzSOiVcLfbxeuvv+4ajJRAWoV8rpy0mq6fnp52BFydTsee6O677z77+E9/+lMA2+ufxHGxuLiIY8eOhWzZNs899xyA7clElUi8Wq06+oWehlB7xDbm8/mea7NarRYmJiYwMTERKcikQNnvCcvk5KQtIy1D6JcgfdmLqakppNNpmKaJ1157DQBcHx12Oh389re/BeA/GYXtt17Qa0ivG5QovPzyywCAEydOuBKfUzL1oE9Y+vETqgcN9BGdDD1tUgVrRJz+UH67Uq1W+17GQsTtWwftq8OMMQpWP/jgA5w5c8bxIdfk5CR0XceZM2fsftyzZ09fsg2CuMfwoMqUEf1roVBwxRiLi4u2bw87f4aZD32JnO/AgyDJdv0SrgM3E1nTv70Sact5LHVdt1NhiKkz/OQMk89SzEtKSYmpTZR6xiv9lqoerzQdXmlN5E0e0um0fY4om6w/VeopFWJ+RDGRMdUnyxRVx2IOPjHJNiV0VrXdDz8d+/Wzl/7FHL+iPkUbFqG+FxM6UyJ5Sn8ili3bkLi5gFf+S3ljEeoXr/RbfumwRP2rEr3LqVNEfcjjuVd9oo2EzR1rWTdT7/RK2UVtEtM79ZJNZbth+jJIHeK4VKWeEpOZyxsjGIbhaHfYfuuFOO6oT4O2y2vck12KY1tMnB40Qb3c3iB+QhwvlE+2UChYmqZ5btwhzj1iAnvLCu8P/RDzxVLSejG1kjw+RsW3RimvV5qvsGOMEDdNkO1ItLuw48CyeqffUuHVB34p3cKO4SAp0+Is0ystn2oOCLIhQq/5M8x86EfsgayYS3B9fd3RWL8cku122yoUCg5jpUDNrzHkqOh8ynNGCvLKgecVePZiZWXFEVxQQmEyXrl9dK6q3eTcZIPyy1VaqVQcO4eI+fMqlYpD3+l0OtTEYVnbhiwmu06n01alUrENXCyP8vVF0bHKNkzTtPstStJulY79cltSn6l0RImeZXssFArKXU/IGYt2Qbs2qZKdizrWdd0qFApKOzdN09Uf1OcqJ0VOrdfNC8kgO5yVlRWlHFtbWy45qF/JsamgHY288tr2kpHq6oUq33SvzQfofLmcMH3Za3cxMeeqancicRJS/cl5SsP2mx9yYnuxDb3apdKd2CZxIqUb/SC5l2XC+gkat2JwSnOIV3u2trYcuxbJN+tB/WEvZP+cyWSser2unDtGxbdGKa9Xru2wY4wQH1zJ/lwMOqPYmSrfatQ+8JtXSNagYzho3vIoZarkoz5V+XN5bIl1eJ0bZP4MMx96kbCsIX5iyjAMc4szMTGBbrcL0zQHkjOYYRjmdmZoWQsYhmFudarVqr3Gn4NYhmGY+OFAlmEYZkDQR15HjhwZriAMwzC3KLy0gGEYZgA0m0089NBD0HV9R9PzMQzD3E5wIMswDMMwDMOMJby0gGEYhmEYhhlLOJBlGIZhGIZhxhIOZBmGYRiGYZixhANZhmEYhmEYZizhQJZhGIZhGIYZSziQZRiGYRiGYcYSDmQZhmEYhmGYsYQDWYZhGIZhGGYs4UB2RGg0Gmg2mzteb6vVQqPRQKfTcf3WbDaRzWaRSCSQSCQwNzdn/zYseceFarWKRCKBfD4/bFF8yefzSCQSqFarQ5Oh1WohkUgglUoNTQZmeNBYyWazwxaFGRA8X8RDs9nksaLglghkJyYm7GBL9ScPoE6n4wrQ/AbZ9PQ0JiYmBiZ/o9HA7OwsHnrooYHV4UU+n8fs7CxOnjzp+u3gwYNYW1uDruswDAOXLl1Cs9kcqrzjwjfffANgO0gbZUg+kjcOpqenXWNwYmICc3NzyoD5+vXrAADTNGOTgRkfyPa+++674QrCDASeL+Ljxx9/BMBjRWbogSwFlI1GI3IZ3W4XAGAYhvLvjjvucJz/7LPP4syZM1hfX8f6+jouXbqEgwcPKsteXFzE5uYm/uu//iuyfL0g+TRNG1gdXtx9990AgLvuustxvNFowDRN6LqOzz77DKdPn8aNGzcwNTU1VHmZ0WdzcxMAkE6n7TE4MTGBWq2GQ4cOjfxT6lGCnsCIb0P6YXV1FYlEAqVSKZbyghB3G5jRgx4mqeD5YjQYxtjfqXr/bWAlByTOO4vTp0/3PKfZbKJWq6FQKODAgQMAgD/96U84duwYGo0GZmZm7HM3NjawtLTkOHcQTE1NwbKsgZXvR7Va9X2tnEqlkEwmHceGKS8zPrz11luO8bS6uopjx46hXC7jySefHOiYulWgJzBx8f3338daXhDibgMzetDDJBU8X4wGwxj7O1Xv0J/I7jTkVH/2s5/Zxx544AEAwMWLF+1jnU4Hv/nNb5BOp/HGG2/srJAMcwty9OhR5HI5AMDf//73IUvDMAzD3AoMJJDd2NhANpu1XzdMTEwgn8871guWSiUkEgnUajUAwOzsrL2ebqcffRPi6/Vnn30WAPDuu+/2VWan00E+n3es45XX5Pp97LKxsYG5uTnP9b+irlKplL2Wt1qt2msVSf+qD7rkj30ajQYSiQRmZ2cBALVaza5rdXW1p7xy3YlEAtPT0yiVSo76O50OFhcXXeepng5TfdlsFp1OB6VSCalUypbBz14ajYZjPXQqlXLZopeuqWyV3vqlVCr1bDvJ4vXE3OuVDX2k5zf+hsX9998PINybmLD9srq6Gvj8IOOTiFuv8seUExMTto0D8ByH8ivcIP6WxvXCwgIAYGFhQfkRp6gTGmM09qIs/wraBrluasv09DQ2NjY8y4+7T8L4JbF+rz4kNjY2HGWmUiksLi46zvFbZuf1kQ99KLe6uopWq2X7c9mOVXpV1UMyNJtNV9tU19A4I8T+pXN7zRdkv7IOVfKp2hvEVoL2UxDC+JcoczLg7q9UKhX5g9xBjn0/uw5Tb5Dx4YsVM4VCwQJgAbAMw7AMw7A0TbMAWJqmWe1227Isy6pUKo7f0um0fX6lUglVJ9UXhHq9bgGwCoWCfWxlZcUCYNXrdcuyLKtYLFoAQssh0263LV3XLQBWLpezisWiVSgUrHQ6ba2srLhkkttQqVRsvRUKBatYLNrlaZrm0hWVkcvlLACWruuWYRj28XQ67ZKRfi8Wi5ZlWdbW1pZlGIaVTqcd9RiGYa2vr/vKa1mWXbfY//RvqsM0TbvfSUaqTzxP1k86nbbPE/9f7k9Zf6J9Ub2GYSjl1nXdKhaLVrFYdNRFdhsUsiG5nna7bZdrGIZtE6KdyGVkMhllHVQO9YvYZtFmMpmMfWxra8tRhtz/cUA6p/EkotJLEHsK2i8quxXtUSTo+LSs8HrtxdbWlqu8XC5n6bpul+U1DsV2BPW3NK6pvTTuDMNwjJ2trS37etJJLpezj4X1iUHaQDah67pjfJKsso0Pqk/C+qUgfWhZN+cX0YYzmYyl67pLV17jhsaIbMOijxB1J9rA+vq6rUtZr17+gMYd9ZlXXxQKBdcYoz8qO+h8QT5arEu2N6/2+tlK0H4KQhj/YlnR5uR2u+3oPzlO8qrLi0GN/V52HbTeoOPDj9gDWZqcxQlGnDDkCcJv8AZFNYgKhYLS+VmWZTvM9fV1a3193dI0zTYoGnRewUMYqINUQZaI10AnAxIHGxm5OEkRVIasZ9FAZZ14BTJejtNPXnFikWUuFouOQDidTrv6nPSlaZpnfZqmOa4Tg1VRH6ZpejrDSqXiaK/YVlmn5IDCBnpegSwdl21CDHCpfWIbZLnoN1FXom2Ypulqs0qenQ5kqT6x/V72FKVfNE1z9TdNZPJYCjo+o+i1FxRwefkowm8cWlZ4f0v259Xf1D+qYMDL7/SiVxtIJpqoRR1TP8sT/iD6JKxfCtqH5Ht76a2fQFbWnWgDKp9Msos3zqIMNAeKMovBmIxXoCrKLv++vr6u9OmWdVPnABz9G8VWgvZTEML4F8uKNieL7RD1L7Y9rG2L18c19oPada96g5bjR+yBrBdeE3icgazqTzUJbm1t2cYtDlga/Lqu96VUwqvNMqqBToNDZbBk6LLOVANGvkY2pjgDWQrE+nmSrQrcxPpUd9DksEV90JMq2VGrIFtQlU0BY5i7Q8vyDmTJIaigCViU2Uun5OjFc+mYqv8t66aeROe/k4Gs6IhFGbzsKc5+UTnpoOMzil6jyKOiVxDohVfb/CYV8jleN/HkQ8KO76CBrMrvijdzIoPoEz9UfiloH6quVdFPIKvSndeDBbE8OegjGVQ20G63ewZsKnqN716Blfh7FFsJ2k/94FVHlDnZb67zmleCEPfYD2rXvQLZoOX4sWNZCx577DEAwOeffx572aZp4q677rK/rm+1Wvjwww9x4sQJ1Go1vP7661heXrbPn5qaUn6tn81mYZomtra2kEwmUSqVcOLECXS7Xei6jrfffjvUl9bU5nK5jPvuuw9Hjx4NfK3fl743btzwvZY+XhOhtYmDhNIu7d+/P3IZhmGgVqvh6tWrji/eiampKdexVCrlykFKdvbkk0/2rHNtbQ3A9ho+WrMtE0eO00ajYduSal3v119/DcCZe/b555/H5uYmTp065VgfR2u3xfZ99NFHALbXg/mtG75+/TomJyf7aksQXnrpJXtMXrp0yf6yuVKpBKo/zn7Zt28farUarly5Yo/hoONzEHoleX7729/izjvvVNp6P0Txt6Tj7777TtnOQeQcFlFlSPHS507busovBe1DuvbZZ5/F8vLyQMaeSnf33nsvAGD37t0uv3nPPfcAuOmzZR5++GHXsWQy6dCDyheHgcb3008/rfz9qaeeQq1Ww7lz5zA/P+/4LYytDHqsiXWI/kUk6JxMa1F1XVfql8Z13EQZ+3HZdRzlDCyQbTab+PTTT+3UCzRJDwK54ZOTk5ifn8d9992HQ4cOoVwu4w9/+IPL8EVWV1extraGlZUVTE1NoVQqYWFhAcViEY899hj++Mc/4oknnkC9Xg88EGZmZrCysoJjx47h2LFjePXVV5HL5fDcc8/17KyZmRlomoZarYZWq2Wf3+l0cObMGQA3ndEoIH4c46dnkU6ng88++wxXrlyxj127di0WeS5dugQAuPPOOwNfQ4vSVcSZA9E0Td+6KLcvcPOmYG1tDf/5n/+JZDKJVquFzc1N6LqudJrlctm3fjmv8qAQJ0ld15HNZvH73/8+tKOK0i+NRsORheTcuXOuc8KOzzj1Oj8/jy+++AJra2uYnZ2Frut45ZVX8PTTTwcePyJx+lu/GwfAnXN6mAzC1oP6paB9uLy8jEwmg1qtZm8uc+TIkaHuztRvMN3vzYw4X3jJogr+ohD3WAOC+Zd+GNYuh2HGflx2HUc5sQeyzWYTBw8eHIlderLZLA4dOgQAnk/4gG2ZX331VRiGYT+Veeedd5BOp+07weXlZei6jn/+85+h7uiOHj2K/fv34y9/+Quq1SqWlpZQLpdx/vz5nne02WwW5XIZ6XQauVwOd911F9555x10u10UCoUdeaoWlLC5IhcXF7G0tDQgafzzGnph7VCuQ8MwAuU8BradPN2xnj17FtlsFmfPngUAZDIZ5TVhbrYGSVxyhOmXarWKfD4fuP/DjM+49VqtVvHiiy/ib3/7G9bW1nDs2DG8++67OH36dOAJdhD+tlgsup6AjSpx90lYvxSkDycnJ3H58mVUq1W8+eabdrBw4cIFx5vC24mdzi0cx1ijcsL4l3EjzNiPy67jKCfW9FudTgd79+6FaZooFoswTRPW9jpc1Ov1OKsKjdedeafTwfPPPw8A+O///m/7uGmaDgOnoDHK0ojJyUksLy/jq6++Qi6XQ7fb9dxJjGi1WnYQ+/jjj2NpaQkLCwvQNA2VSmXkctuKk0mvlCalUglLS0tIp9NYX1+3bcSyLBiGEYs8VM4PP/wQ+JqdSk8V9qnzkSNHAACnTp0CcHNZwTPPPKM8/9tvv40s2ygStF8ajYZ941qpVNBut227KhaLntcFHZ+D0OvMzAyq1SpM00Q6ncbm5iZef/31QNcOyt8O8u1Z3MTZJ1H9UtA+zGazuHz5Mur1OjRNQ7lcVqZUCuOzdhry7Q8++GBf5QSZL7788ksAzjdU/dYZdawB0f1LFOJ6MxmWKGM/qF0PspxYA9mrV6+i2+3CMAzMz8+HemI4iEmClKBpmufTz7/+9a/Y3NzExx9/HPk1Q1CSyaR9h9HrCcqHH34IAHj55ZdRrVbtAXP58uWhvpLyQ9d1ALCfGHpBr2Heeuutge3uRLb3ySef9Dw3nU4DuKnzQbFr1y4A230fJienuLyg2Wzaywpkm37kkUcAAO+99148Ag+ZsP1Cr/qOHz+ObDYbejx7jc+d0Ovk5CTeeustAOrAXTWx9eNvVRMWBSfVanUguZPjnJwH0Sf9+qVefUjMzMzg+PHjAJyv6KlN//rXv1zXUFC3U3zxxReuY7SkCQB+8pOfKK8L8zCg13xB66D37t0buMwgBO0nmX79SxDEOUIl2/vvv993HYMa+1527VdvlHJUDGRDBFkRnU4HL730kvLcffv2AQAuXLgQqa58Pq9MRtxoNOw93UkpMuIWtPLrKU3THGWSUZGzCcLc3Jxr4TQlbKZB7AUt6j516tRAJpVB8MILLwAAXnvtNccg7HQ6WF1ddd1dyU8eqtWq7/qcMBw+fBjA9ho6OWjc2NhwJFt++eWXAQAnTpxwnUsbMERNRi2STCbtna1eeukll6OiBN9yMv5kMmkvI6C3B6Rrkeeeew7A9jon2rxCpFqtBk4yTYnVh7U5CRC9X+QtEZvNJk6cOOE6L+j4jFOvxMTEhCt5+z//+U8AzjWD5Je8JjYgnL8lv0Lr7EUOHDgAXdfR7Xbx+uuvK31qlJvoIG0IyyD6hAjql4L0YaPRwPT0tGNMdzodO2i+77777OM//elPAWz7LFFPi4uLOHbsWKS2RGVtbc2RsL/T6aBQKADYftsl3zTRE2v6NiEIr7zyCoBtXyOP78XFRdRqNWia5vkxWFCCjrWgBPUvUaAP6gC4NkzI5/M914T7EefYD2PXfvWGKceXyPkOFIi568Rk75SnFYq0EWJqDkpyHyYVkCoZs5g/zyv9EiW/ViUktqyb6ZuKxaJVr9ftesKkcxGTc8vJtcU0Fl7pScRk5+KfYRhWLpdzyUK/+yWiH2T6LTEXqtgf9G+qQ8zLSAmq6Tw5l6pffXIb5Harkm332hABUCdD90rz40WQDRFEufySlFuWM1+unx2K56mSdsvpVbz6X+zDsPjZoYqgCdN79YuYz1FM6C3aldjOoOPTssLrtRdy4n1xoxOvJPVicnTLiuZvxWTrYoJyUYf0uziGxQTtUfBqg2X1TinkZRtx90lYvxSkD0WblH2QnCdU7E95IwKyY6/0W35+Ooxe5fbKCfm9NpqQc5zqum7rym98y5sD9Koriq2EGWt+hPUvojxh5mRxDMr67yf9VpxjP6xde9Ubphw/Ys8ja5qmYzcI2q2BctCpnEulUnHs/hAm3xsl2xcHhKZpViaT8c13SDuD+AWmNCmQUsPmoSPZxCAlk8m4ylHlxKRk0WIHy45alp/qUQ1OctLyoPHKDem3MYRfDk9Vm9PptFUoFBxGWalUHIFDJpOx6vW6PVDFNqiS/4tQQKJyFnI9uq5buVzOM1gMa0dekL698jGKO1SJcnkFf6Iz6OXE6vW6Y+cbumZlZcXlGEh3cqAedSMIy1Jv5OGHuPOOijD9Qm0Xba9Sqdj2LLYz6PiUyw6i117IflLTNE+7bLfbrrzXXuUE8bdbW1uOQEXuYypTNYaj5mX1a4PfWLGsm/akIs4+sazwfilIH8o2qeu6yx+KZcr2S/ao0hEF81797KdX1ZgTb2zX19dduggzX9K5vXI+r6ysKH2hqi6/9lqW2lbCjLVehPEvlhVtTrYsd657caMOCm6jEOfYD2PXfvWGKceLhGXt0KfaTCjm5uZQq9WwsrLiym/Z6XTw7LPPev7OMAzDMGGheWecMlcwzEDWyDLx8T//5/90HRMXmat+ZxiGYRiGuR3YsZ29mHAcOXIEtVoNv/nNb5DNZu1dQL7//nusra3BNE3kcrmBffXPMAzDMAwz6nAgO6Jks1nce++9+Nvf/oZqtWonYNY0Dbt378Yf//jHkU3DxTAMw4wf9BX/KO3exjC94DWyDMMwDMMwzFjCa2QZhmEYhmGYsYQDWYZhGIZhGGYs4UCWYRiGYRiGGUs4kGUYhmEYhmHGEg5kGYZhGIZhmLGEA1mGYRiGYRhmLOFAlmEYhmEYhhlLOJBlGIZhGIZhxpIdD2Sr1SoSiQTvSnWL02w20Wg0Bl4H29JgGbXx2mq1kEgkMDExMWxRlJB8qVTKcbzT6aDRaKDVagUua3V1FYlEAqurq646Go0GOp1OLDLvFKVSCalUyu6/jY2NvsqjsprNZkwSMsNmXG2bGS47Hsh+8803AIDvvvtup6tmdpCHHnoIs7OzAw1mf/zxRwBsS4Nk1Mbr9evXAcDesnnUIPlM03QcP3nyJGZnZ5HP5wOX9f333zv+S+TzeczOzuLkyZN9SrtzVKtVLCwswDRNGIYBXdfx97//va8yScfkB5jxZxxtmxk+sQey9JRsbm4u7qLHSobbHU3TAAB33HHHkCXZeaLaH9vtrQvtXX/33Xf3XRaVQWUS9AS3VCr1XUfcvPfeewCAlZUVnD59GpcvX0a1WvW9JpvNIpFIDPzNDrNz9LJRL9tmGD/+Le4CR+HueBRkuN25cePGsEUYGlHtj+321uXo0aM4evRoLGVVq1VlECg/uR1FHnjggcDnjspbACY+etmol20zjB/8sRfDMAzDMAwzlsQayCYSCczOzgIAarUaEomE/aei0+kgn89jYmICiUQC09PTnh8ANJtN5PN5e4E/fYAiv3YKK4N4XSKR8PwYg15zyfKtrq5ienravj6VSiGfzyvL6fVaZWJiItRHLJ1Ox/EBBb2Wlu9og+oOuPlxz+rqKprNpn2N+PFKPp+3P7KoVqt2/4nr/+g6lR6azSay2ax93cTEhKfOqJ2inaRSqch37Z1OB4uLiw5dTE9Po1QqKT8woDpVqD7G6cf+gl4Xxub8aLVaDr1OTEx42gUQbrwC0fo5iD17tYWu8zu/VCq5bFWWWV7eQTYj6nx6ejqUDdK4UtVLehLb7Pc6ncYf1d9oNJBIJLCwsAAAWFhYcJQFRPdvXgS1wbm5OSQSCdRqNQDA7Oxsz4/1qI/ka/x858bGhi0P2ZnfB0OlUqmv/qQ6w9i3bNuqOuUxRn0oftDWawkS9aX8EVwqlbL1Xq1WHR/eiXqVdbm4uOjSpfyhrfwhn3xNEBsF3LYt6yaM75bbG9Q+gvQTM2JYMWIYhpVOpy0AlqZplmEY9h9RLBYtAJau65amaRYAyzAMS9d1C4AFwFpfX3eUu76+bv+WTqdd59fr9VAyqMhkMhYAa2VlxfVbu92262q32/YxqofaYBiG3SZN06ytrS1HOdT2YrGolIHKCoJYP7VTlId0EkZ3ooyZTMbSNM0uW5SL/p3L5ey+pLLltsjlVyoVW+ZCoWAVi0Vb9yqdtdtth05FHVN7e/UtsbW15SqL2kLlUf/K7VCh6s+o9hfkuig2F0QXuq677EJuY5jxalnR+jmIPdfrdZeM4rW5XM633XS9pmnK3wuFggXAKhQKlmVZlmmaLj2JcsljWSWfqEfZDsTzqXzRHlV10Dl0fGtry9EnVI5hGHY7wvo3L8LaYKFQcI1ZwzCsTCbjWUelUlFeYxiGValU7PNkPan05ye/YRhWsVi0CoWCrbte9iO2y08Hsh5J/1RnsVh06aHdbjvkINnS6bSj38hmvHwK6UD2vSSv7LfpeKFQsNsl61LuL5IhnU7bZVA/qfQfxEZF2WWb78d3i+3tZR9B+okZPWINZC2r9yAjh06GZJqm/RsZnGxgNAmK51rWTaPzGmRBAxzLujnxqoybfhProQEvt6HdbjsGjqrtcQSyoq7EAWyappXL5WyZwupO7h/VxCY6Ay/HrwpkKSjVNM0lD+lY7jOvdopyBu1nMdiRdeYVCIUNZC0rmv0FuS6KzXnhNXHX63XHsSjjNc5+lu1ZDhTDBLFy21UBOP1GwVi9XrfS6bQrKFhZWVEGxGEDWVU/tNtte3wGCWTlOlT+Jax/8yKqDXoFV370uob0IwfoYtAj9zHpSAyeSH6yoyAyUqAj2qoYiMryeOlehGxKlk2m30BWlo/qVfl0kh2Ao79FO9c0zaFnUf/ijYdl9Z4DvWy7H98d1D6C9hMzegwtkNV13RUkmaYZKpgT7wrDyKBCfPLnFfSJg5LOVTk9sSzx97gCWfEJiixrULx0RzKqghCCnI3fgFcFsuQwVU+FLOvmpC7WS+WonjR6BQcqyEmpnpZYlncAMkqBbBSbU0FP6YMEvVHGa9h+DmPPcj9R8BQ0iLWsm8GYfA3ZSNCbAZJD1EuYQLbX02GvST1KIBvWv3kR1QYHGciq7IzsQtYF3WCpoIA+jC3JqAJlL18b5FoV/QayKn35PZGm8sSgT7RzlW+mMeb1oCRMINuv7w5qH0H7iRk9hvaxVyqVQjKZdBybnJwMVcbMzAwAYHNzs295ksmkvd7nww8/tI93Oh2sra1B0zTs378fwPZ6n263C13XbRnksh5//HEAwMWLF/uWTebq1asAgHQ6HVpnRC/d7d69u2fZv/71r0PV+dFHHwHYXl9VKpVcfwTl4qR1grquY2pqylXeY489FrhuWm+XzWZddgds64NSho1iup84be7KlSsAgEwmE7j+MOM1bD9Hted8Po9yuYxcLofl5eXA1z3zzDMA4Fr39o9//ANAcL0YhgHgpvxhoX7y2mxi3759kcpVEca/eTFsv+eFKhPC/fff7zpG8tOaUPnvwoULABB6rbkI+aTPP//cPrZr1y5omobNzU3lelP52nK57NoEI05U+qJvIA4fPuz6jcYk+Q0ZlW/+xS9+ASCeublf3x3UPoL2EzN6xJ5+a5C0Wi1cunTJTtIeN08++STK5TLeeecdzM/PAwDOnj0LwDmIvvzySwBw7d4j8vDDD2NtbW0gclL9qkHtxaB1F4Zyuez7u5x71k/PQfniiy8AqB0YsXv3bttpjhpx2hzpYtC5GoP2cxR7Bm4GoqqJ1I+pqSnoug7TNLGxsYEDBw4AgK275557znVNp9PBZ5995pjMr127FqpeL/xsMk6C+jcvhu334sI0TfujIxVhcv02m018+umndlqpr7/+2nVOMpnExx9/jF/+8pdYWlrC0tIScrkcDh8+7LghmJmZwcrKCo4dO4Zjx47h1VdfRS6Xw3PPPRf5gUUc9GOf8sYgUdgp3x20n5jRYywC2U6ng2effXbgQcaBAwfsCa7ZbGJqagqnTp0CsD0JEMPO1xim/p3SXRjq9fqOO4Zxz0kZp83tlC6C9nPUtv3Xf/0XnnjiCRw7dgyPPvpoqID2hRdewMLCAj755BMcOHAAzWYTpmkqnwovLi5iaWkpkoyjRFD/5sWw/V5cGIaB06dP91VGs9nEwYMHAwdqMzMz+Oqrr3Dy5Em88847KJfLKJfLqFQqjifyR48exf79+/GXv/wF1WoVS0tLKJfLOH/+fOgbtluFnfTdQfuJGS3GIo8sBWKZTAZbW1uwttf2wrKs2Oui14r/+Mc/HK/d6KkNcPMVkN+rB7+nXv1OCEHqJ3ZSd0H59ttvQ50fx5Mvek3rp/tLly4BUO9GNuzXTP3anEgQXcRB0H4OY88iBw4cQKFQAAAcPHgw1PVPP/00gJtPdWlZwfPPP+84r1QqYWlpCel0Guvr647xQ0sL+kX1FG9QBPFvXsRpg8OkX3/S6XSwd+9emKaJYrEI0zRtm6jX657XJZNJzM/P49q1a1hZWQEAHDp0yLWUYXJyEsvLy/jqq6+Qy+XQ7XZx8OBBpRyjyg8//ABge7lQv/Tru8MStJ+Y0WFggWxcr92Am2tkqtVqqLvSKDLQ+rm1tTXHazeRe+65B8D2+h+VcXc6HZw5cwYA8Oijj9rHH3zwQQCwfxORc/750at+kai6GwSPPPIIgJvbVfZi165dALZfT6na+f777weu+7777gMAz9eetH5O0zSHnsgRf/bZZ65raNL2IuoYUF0X1eZU9NJFv4Tt5zD2LPPGG28gnU7DNE38+7//e+DrJicnkU6n0e12sbGxYetCXid67tw5AMBbb70VKNgLg58/6HQ6+OCDDyKV6xcYB/FvXsRpg2EIe+PrhehP+lkHf/XqVXS7XRiGgfn5+Uiv/Y8ePWrfCNFacZlkMmmv/Raf/FI7Njc3XcFsp9OJde4Ngmru+uSTTwAA09PTymvC3LxF9d1xEKSfmOETeyBLrxK9go9+kAetV1LzfmSYmpqyJ8bXXnsNgHsB/OTkpP1kI5PJOOqgV/nk6MSB9ZOf/ATAtgMSE49Xq1Xs3bs3sIyTk5P24CoUCg69tFotLC4uupxLUN0NElp7WKvVlB8zVKtVLC4u2v9OJpN2O+UE1vShT1Cy2Sw0TYNpmq6yms0mjhw5AgA4fvy44zpyxG+//bZ9TafTwdzcnKdjjWp/ftdFtTkV+/fvt3UhJ5in5Pz9ELafo9izCD1VXFtbC/WRDD19/Y//+A+YpgnDMDyDEnrCJLah3+U6e/bssftB1AfZV9gPZeiJqSowJoL4Ny/itMEg0JM4+girX5LJJHK5HADgpZdeco0x2iAk6EMFVRD50ksvuc4rlUrIZrMuu6aniHSDMDc35xqPNE/ouu5oB/37r3/9q3282Wxiz549saxLDcPevXsdNwaNRsP2zbJtBbFRmai+OyxB+wm4uXGD1wYdzA4ziFQIlEJDTGBP9EqZBEUaDUoPo+u6naSYErRT2pAwMvRCzKvnlYpHTDVD7emVoFlsC10jJpOm/w+CKkG0KoF8WN0FSWnllf5HRJaDoBQ3otx+ibdV7aR/h0m/ZVnu3IeyzlSpZ8Q8ipRQm+r3SvEj6iis/fldF9XmVIj9EGRDhDDjVS4/aj8H3RDBspwbfwTdFEJMHwaP9FNivlja2EFMPyfbeNg8sqp+EOtT2ZfX+BPtQ0w479UmP//mRVQbjJJ+S9Ql+UexzV4+xrK80zzJGzqoNonpZT9ivlhxUwVN05SbtIj6lvUl+hxxwxDZ/mXbFMsUNyLQNM0uxyv9lkpffv2j0qXcN/Ic5uVPe9mol21H8d1h7SNoP4llh02xyAyGgQSyckJvcdIiY/FKvk1GLpdHARgZMiVJ9woS/GTohTjB+eX0a7fbjl1haDDLibJV14iDmQZT2ICbksXLE4uYNy+s7oLkUqQAzi/vpConLFGv1+2dw2S5VXrb2tpy9KWYoJ6cWlC2traUOvNrCyXFF21pa2vLdq5euyVFsb9e10WxOb92iXXRJCjmi4wyXuXyg/ZzEHv2y/UqJusPihgAeOmvUqm4+r9er9uToRj4iDkv5TK8xtX6+rqrfNM07bLkSd1v/G1tbTl2SFPdZAX1b15EsUHSc9CbDKJSqTh2gxJtk+xEVSbZrVdO3WKx6NC5rutWLpcLHGjLtkp+nHIiy+NFtiF5ty5RLlGv8niU2yj7dSpDpRd5sw8R8gN++bpVu4sBsDfDEHXhlUPasvxttJdth/Hdfu31so8g/STK6fcwh9k5EpY1xK9+RpiJiQl0u12YpjnU1CcMwzBxw/6N6YdGo4HZ2VkAGOqHwwwDjEnWgp2mWq3aa73YyTMMcyvB/o1hmFsJDmQV0EcQtIicYRjmVoH9G8MwtxK8tECi2WzioYcegq7rO57GhGEYZpCwf2PioNVqQdd1aJqGGzduDFsc5jaHA1mGYRiGYRhmLOGlBQzDMAzDMMxYwoEswzAMwzAMM5ZwIMswDMMwDMOMJRzIMgzDMAzDMGMJB7IMwzAMwzDMWMKBLMMwDMMwDDOWcCDLMAzDMAzDjCUcyDIMwzAMwzBjCQeyDMMwDMMwzFjCgSzDMAzDMAwzlnAgyzAMwzAMw4wlHMgyDMMwDMMwY8n/D7lql4DjSjEGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.BmpImagePlugin.DibImageFile image mode=RGBA size=690x246 at 0x216251C1400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageGrab.grabclipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55144837",
   "metadata": {},
   "source": [
    "## We are going to conduct a <font color='red'>2 tailed t-Test</font> on 2 Independent samples with Numerical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf4b0cf",
   "metadata": {},
   "source": [
    "### We need to check whether the mean of both samples are different and\n",
    "### Is there any significance difference between the two samples?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8882e7e5",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 1\n",
    "#### Make two Hypothesis one contradicting to other \n",
    "#### Null Hypothesis is want we want to prove"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e133fa9c",
   "metadata": {},
   "source": [
    "+ **Null Hypothesis:** $\\mu_1 = \\mu_2$\n",
    "+ **Alternative Hypthosis:** $\\mu_1 \\neq \\mu_2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7922ad69",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 2\n",
    "### Decide a cut-off value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ba417ff",
   "metadata": {},
   "source": [
    "+ **Significance 5%**\n",
    "+ **alpha = 0.05**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55074425",
   "metadata": {},
   "source": [
    "### As it is a two-tailed test\n",
    "+ **alpha/2 = 0.025**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2843a58",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 3 \n",
    "### Collect evidence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bec8e3f",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ff76d27",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets = pd.read_csv('..\\Documents\\EXcelR Study and Assignment Material\\Data Science Assignments\\Hypothesis testing 3\\Cutlets.csv')\n",
    "cutlets.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935a4abf",
   "metadata": {},
   "source": [
    "### Applying Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "109efd28",
   "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>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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770a7a99",
   "metadata": {},
   "source": [
    "### Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9a66bc6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unit A    0\n",
       "Unit B    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b9d278",
   "metadata": {},
   "source": [
    "### Checking for Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e999587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets[cutlets.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "07149e9f",
   "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>Unit A</th>\n",
       "      <th>Unit B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unit A, Unit B]\n",
       "Index: []"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutlets[cutlets.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0097e8c0",
   "metadata": {},
   "source": [
    "### Checking the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "87b56f2e",
   "metadata": {
    "scrolled": true
   },
   "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": "0c4948ef",
   "metadata": {},
   "source": [
    "### Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "313452e4",
   "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": 35,
   "id": "b695939e",
   "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.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": 67,
   "id": "e3007bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21629013580>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360ed2e1",
   "metadata": {},
   "source": [
    "### Plotting Q-Q plot to check whether the distribution follows normal distribution or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f14c708",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ELhdt3eEvf4EO18Onn8K114ZCcfvtl84QJQ3iXiNw91equqUySBGpmWqLvK1YASedBAMGQPv2YfnI++5TEshRVXUNPeHuPzazN4nRReTunZMamYjUSuVrAbus/3vBFrjjDhg9GvbfHyZOhJ//PGQMyVlVdQ1dFd2fl4pARKRuxJsvMGfYTPrfNCRcIPjZz+Dee6Fly7TEKJmlqq6hD6KHg929qOINGFzdgc2sg5ktqXDbZGZXV9qnv5kti27zzazLHv01IrLbfIE2FDGD3jxS0huaNYO5c+HPf1YSkK8k0h48I8a2s6t7k7uvcfeu7t4VOAbYCjxVabe3gZOibqY7gQkJxCMiVSifL9CQHdzAKFbSidN5gd81vxcWL4YTTkhvgJJx4iYCM7s8uj7QocKv9mVm9jawrIafcxrwn6g18RV3n+/un0ZPXwVa1/C4IlLJyJFw+j7zWMzRjOJGZnMm3RqvIu/B62FvLSUiu6vqGsFfgWeBu4EbK2zf7O4ba/g5/Qizkqvyi+jzdmNmA4GBAG1Uu1YkvpIS+r9wA/23T2Z9gzx+WDqTZXnnh/kCmukrcSQ0s9jMGgCHUCFxuHtCy0qYWSPgfSDf3TfE2ecUYCxwvLt/UtXxNLNYJIayMpg0CYYPh02b4Lrr4JZbYN990x2ZZIhazSyu8OYrgduBDUBZtNmBRIePng0sqiIJdAYeAc6uLgmISAzLlsGgQbBgAZx4IowbB506pTsqySKJlKG+GuiwByfpi4jTLWRmbYDpwE/d/d+1PL5Ibtq8OcwEvv9+OPBAmDwZLr1UpSGkxhIZNfQu8HltDm5mTQmjjqZX2DbIzAZFT28DDgbGRkNM1ecjUslus4SnOEybxta8I2H0aCaW/pwujddQ0PAyJQGplURaBOuAl83sGUIpagDcfXR1b3T3rYQTfcVt4ys8/iXwy4SjFckxlWcJW9HbHHzZlVA2i7esC79mKq/SC9ZXmD2si8JSQ4m0CIqB54FGQLMKNxFJsvJZwnvzJb/hd6ykE98vm8u1NppuXhiSQKR8tTGRmqq2ReDuI1IRiIjsrrgYTmYOYxnMkaxmKn0Zxh95z2NPuYm3CplIVRIZNdQSuIGwSlnj8u3ufmoS4xKRDRt4sul1/M+WKayjHefwDM9yDgANGkBp6e5v0TQbqY1EuoYKgNVAO2AE8A7wehJjEsltpaVhCGiHDvTe9jj3NLyFfFZ8lQSaNg3XA7ROsNSVRBLBwe7+J2BHtBbBz4FjkxyXSG5atAh69YLBg6FbNxosX8Zhk+/kkLwmu6wHPHas1gmWupPIqKEd0f0HZnYuYZawagKJ1KXPP4dbb4WHHoIWLWDKFLj4YjCjf8fYJ/hdVhsT2QOJJIK7zOwA4FrgAWB/YFhSoxLJFe7wxBMwbBh8+CFcfnno32nePN2RSQ5JZNTQP6KHnwOnJDcckRyydi1ccQU8/zx06wZPPw09eqQ7KslBiYwa+jOxl6r8eVIiEqnvtm2De+4Jt332gTFjwjWBBg3SHZnkqEQuFv8DeCa6vUjoGvpvMoMSqbdmz4ajjoIRI6BPH1i9GoYMgQYNql9wXiRJEukamlbxuZk9BryQtIhE6qP334drroHHH4f27UNCOOPrxf+qXHBeF4QlyRJpEVTWHtC0FZFElJbCAw9Ax44wY0ZoCSxbtksSgPgLzqtkhKRCtYnAzDZHC89vNrNNwN+B4ckPTSTLvf469OwJQ4eGuQHLl8Ntt1EwrfFuXUDxSkOoZISkQiJdQyowJ1ITn30WfsqPGwff/GboDrrwQjCL2wV00EHwSYwVP1QyQlKhykRgZk2A/kD5ckeFwJPu/mWyAxPJOu7w17/CtddCSUloCdxxB+y//1e7xOsCatIklIio+JpKRkiqxO0aMrOjgFXACYT6QkXAD4B/mVlzM7srJRGKZIPVq+H00+GSS0K9h8JC+N//3SUJQPyuno0bVTJC0qeqFsEY4Ffu/nzFjWZ2OrAcWJHMwESywhdfhJ/t994bFoofNw5+9au4cwLatAndQbG2q2SEpEtVF4tbVU4CAO7+AqH+UJ+kRSWSDWbNgvz8kAj69QutgkGDqpwYNnKkqoZK5qkqEexlZvtU3mhmjQmVSLfGeI9I/bd+PfTtC+eeG2YGz5kDjz4KhxxS7Vv791cXkGSeqhLBo8A0M2tbviF6/ATw/5IblkgG2rkTRo+GI4+EZ54JP+OXLoWTT67RYfr3h3fegbKycK8kIOkW9xqBu99lZlcCc82svDG7BbjP3R9ISXQimWLBgtDts2wZnHNOmCT27W+nOyqROlHl8FF3fxB40MyaRc83pyQqkUyxcSPceCNMnAitW8O0aaFGkFm6IxOpMwmVmHD3zTVNAmbWwcyWVLhtMrOrK+1jZjbGzN4ys2Vm1q0mnyGSNO4weTJ06ACTJoW5AatWwf/8j5KA1Du1qTWUEHdf4+5d3b0rcAywFXiq0m5nE2oXtQcGAuOSFY9IwlasgJNOggEDQoG4RYvgvvtgv/1qdBhVE5VskbREUMlpwH/cvfII6t7Aox68CjQ3s1YpiklkV1u2wPDh0LVrSAYTJ8I//wmdO9f4UOWlJIqKQuOivJSEkoFkokSKzjU1s1vNbGL0vL2ZnVfDz+kHPBZj+6HAuxWer4+2VY5hoJkVmllhSUlJDT9aJAEzZ0KnTmFi2E9/GuYE/PKX4ed8LaiaqGSTRP6V/xnYDvSKnq8HEi4vYWaNgB8CU2O9HGNbrNXQJrh7d3fv3rJly0Q/WqR6xcVwwQXQuzc0awZz54ZrAnv470zVRCWbJJIIvuPu9xJmE+PuXxD7BB7P2cAid98Q47X1wGEVnrcG3q/BsUVqZ8eO8Ov/yCPDmsGjRsHixXDCCXVy+HhVQ1VNVDJRIongy6gKqQOY2XcILYREXUTsbiGAmcCl0eihY4HP3f2DGhxbpObmzYOjjw7XA844A1auhBtugL33rrOPUCkJySaJJILfAs8Bh5lZAWHd4hsSOXg0Ee0MYHqFbYPMbFD0dBawDngLmAgMTjx0kRoqKQkjgU48Ef7733BdYMaMUOehjqmUhGQTc9+tS373ncwOBo4ldAm96u4fJzuweLp37+6FhYXp+njJRmVlod9/+HDYtCnMCbj11lAttI4UFIQLwcXFoftn5Eid9CWzmNkb7t491mtVrUfQrfwG5AEfEPrv22jil2SNZcvg+ONDaejvfheWLIF77qk2CcSbAxBru4aKSraL2yIwszlVvM/d/dTkhFQ1tQgkIZs3w+23w/33w4EHhglhl16a0KzgystJQujfv+wy+Mtfdt/epEnsZSbz8kJROZFMUKsWgbufUsUtLUlApFruMH16GA00ejT84hewZg0FDS+jbTtL6Bd+vDkAEybE3h4rCYCGikr2qHbx+mj9gcHA8YSRQ/OA8e6+LcmxidTMunUwZEhYMKZLF5g6FXr1irtg/L/+tesv/PLtlU/25UpLaxaOhopKtkhk1NCjQD7wAPAgYSF7rUcgmWP79nB1Nj+fHS/N5c4DR7P30kLaXtSrVr/w4y0wFm/7wQdrqKhkt2pbBEAHd+9S4fkcM1uarIBEamTOHBg8GFavpqhnX05f9kfe+rQ1UPtf+KWl4USe6DWC++8PjzVqSLJVIi2CxdFkLwDM7HvAv5IXkkgCNmwINYFOPRW+/BJmzeKkDVN5a1vrXXarzS/88jH/lecAjB0bf26AVh2TbFbtPAIzWwV0AMovfbUBVgFlhNFDNS/NuAc0aijHlZaGs+9NN31dLfSmm6BJE/baK1wrjqUmv/A18Uvqo1qNGqrgLKAdcFJ0awecA5wHnF9XQYpUa9Ei6NUrdAUdfXSYI3DnnWH8JvEvztbmF75ILkl0ZvGBhOJwX11TcPdFSYwrLrUIctDnn4eZwA89BC1ahGGhF1+825yAeOP/dXIX2cMWgZndCSwDxgB/iG731WmEIrG4w+OPhzkBDz4YFo9fs4YC+secE6D6PiK1k8iooR8TSlF/mexgRL6ydi1ccUUoEd2tGzz9NPToEXdOAHx90VYnfpGaSeQawXKgeZLjEAm2bQulIY46ChYuhDFj4LXXoEcPQCt/iSRDIi2CuwlDSJdTYR0Cd/9h0qKS3DR7dmgFvPUW9OvHtO+P5tr7WlF81ddj87Xyl0jdSyQR/AUYBbxJGDIqUrc++ACGDQvXA9q3h9mzKfjojJhdQAcdFLu2j8o5iNReIongY3cfk/RIJPeUloaRQLfcEiaFjRgRVgpr3Jib28buAmrSJPacAJVzEKm9RK4RvGFmd5tZr0prFIjU3uuvQ8+ecNVVzN3Ri/bbl9N20m0UTGsMxO/q2bhRI4NE6loiLYKjo/tjK2xzQKWopeY++yzMBB4/nq0HfJNfN3qcKdsuBAwqjABq0yZ0B1XWpo1GBonUtWpbBFqPQPZUQQG0zXMusQJKDu5I2fiHYehQejZbzZQvf0xYATUoHwGkxd9FUieRFgFmdi6hFHXj8m3ufkeygpL6o6AA7vvlav607QpO4yUWlvWkd+NnuaLH0ayMc+WpuPjrX/yq6CmSfInMLB4P/AQYQvjpdiFhDWMRIP76vnzxBRsH38LCbZ3pxiIGMY7jmM+CbUdz883xR/qUb1dFT5HUSORi8XHufinwqbuPAHoR6g6JxF24fc71syA/nyGbRvI3+tGR1TzMIMoItZ+Li9X9I5IpEkkEX0T3W83sW8AOQgXSaplZczN70sxWm9kqM+tV6fUDzOzvZrbUzFaY2YCahS/pVnmm76Gs5y9b+3LKfefCPvvQ75A5XMajfMQhu7yv/KKvRgCJpF8iieAfZtYc+D2wCHgHeCzB498PPOfuHYEuhHUMKroCWBmtgHYy8Acza5TgsSUDlA/zbMBOhjGa1XTkXJ7hJn4HS5dy/h9OrvJXv7p/RNIvkVFDd7r7Z+4+jXBtoKO731bd+8xsf+BE4E/Rcb50988qHx5oZmYG7AdsBHbW7E+QdGrTBnoxnzc4htFcy8ucTCdW8te830CjRvrVL5IF4iYCM+thZt+s8PxS4AngTjM7KIFjfxsoAf5sZovN7BEz27fSPg8CRwLvE0pYXOXuu5WxMLOBZlZoZoUlJSUJfLTUpbgXgz/5hBfa/Yr5fJ+D2EgfpnM+f+ejpu126efXr36RzFZVi+Bh4EsAMzsRuAd4FPgcmJDAsRsC3YBx7n40sAW4sdI+PwCWAN8CugIPRi2JXbj7BHfv7u7dW7ZsmcBHS12JeTH4V86CX0+Gjh05fN6fWXnOdfzgsFU8bX3IyzP94hfJMlXNI2jg7hujxz8BJkTdQ9PMbEkCx14PrHf3hdHzJ9k9EQwA7vGwTNpbZvY20BF4LdE/QJKr8sXgTqxg3BeX02vCPDjuOBg3jk6dO7MyfSGKyB6qqkXQwMzKE8VpwEsVXqt2Ipq7fwi8a2YdKhyj8vmiONqOmR0CdADWJRC3pEj5xeCmbOEehrOEruSzgl/yCMybB507pzdAEdljVZ3QHwNeMbOPCUNI5wGY2eGE7qFEDAEKopFA64ABZjYIwN3HA3cCk83sTcJkteHu/nGt/hJJijZtoHPRTB5gCHkUM4kB3MC97JfXIrExZyKS8eImAncfaWYvAq2A2f71Kvd7EU7w1XL3JUDlxZLHV3j9feDMmgQsKVRUxLyDh3JY0UyWk8/xzONfHE/TpnC/Jn2J1BtVdvG4+6sxtv07eeFIRtixA0aPhjvu4DBgUb97+fH8q1n37t7kqeaPSL2TUNE5ySFz58Lll8PKldC7N4wZQ7c2bXgr3XGJSNKol1eCkhIYMABOOgm2bIGZM2HGDK0BKZIDlAhyXVkZTJwIHTrAlCkwfDisWAHnn5/uyEQkRdQ1lMuWLg3dQAsWwAknwLhxkJ+f7qhEJMXUIshFmzfDNdfAMcfA2rUweTK88oqSgEiOUosgl7jD9Olw1VXw3nuhdsTdd8NBiZSOEpH6Si2CXLFuHZx7LvTtCy1awPz58PDDSgIiokRQ723fHgb+5+eHkhCjR0NhIfTqVf17RSQnKBHUUwUFcNE357C6cRe45RaKOp8Hq1bBsGHQsOEu+8UsMS0iOUOJoB6aNnYDDS67hMc2nMre7OBsZtFp+VQKXmm9y37x1htWMhDJLfZ1CaHs0L17dy8sLEx3GJmptBQmTGDTFb+hsW9lFMP5HTexjSZAWB3snXe+3r1t23Dyr6zyfiKS/czsDXevXPsN0Kih+mPRIhg0CF5/ndc5lcGM5d902GWX8pLS8Z5Xt11E6id1DWW7TZvCcNAePcIZvKCAX7R5YbckALtXi4hXPUJVJURyixJBtnKHxx+Hjh3hgQdCa2D1arj4Ykb+zmjadNfdmzZll3WEITxPZD8Rqd+UCLLR2rVw1lnQrx+0agULF8JDD0Hz5kAoET1hQujrNwv3sdYRTnQ/EanfdLE4m2zbBqNGhdnA++wDd90FgwdDgwbpjkxEMpwuFtcHzz8fTvpvvRVaAqNHh9aAiMgeUtdQpvvgA7joIjjzzNB/M3s2PPaYkoCI1BklgkxVWhouAnfsCE89BSNGwLJlcMYZ6Y5MROoZdQ1lotdfD6OAFi0KLYGHHoLDD093VCJST6lFkEk++wyuuAK+973QJfT44/Dcc0oCIpJUSU0EZtbczJ40s9VmtsrMdit5aWYnm9kSM1thZq8kM56M5R4K/HTsCOPHw5AhYU7Aj38crgtEVCBORJIh2V1D9wPPuXtfM2sE7DJ9ycyaA2OBs9y92My+keR4Ms+aNWE00EsvQc+eMGsWdOu2227lBeK2bg3PywvEgcb9i8ieSVqLwMz2B04E/gTg7l+6+2eVdrsYmO7uxdE+HyUrnozzxRdw663QuXO4FjBuXFgsJkYSALj55q+TQLmtW8N2EZE9kcyuoW8DJcCfzWyxmT1iZvtW2ucI4EAze9nM3jCzS2MdyMwGmlmhmRWWlJQkMeTUmHPDsxTt/1246y6m7/1jpo1cHS4OVzExTAXiRCRZkpkIGgLdgHHufjSwBbgxxj7HAOcCPwBuNbMjKh/I3Se4e3d3796yZcskhpxk69dT3LMvp/z+HLbubMQpvMSPtvw/Lr3+kGr7+1UgTkSSJZmJYD2w3t0XRs+fJCSGyvs85+5b3P1jYC7QJYkxpcfOnfDHP8KRR/KNwme4iZF0YSkvcwqQWBePCsSJSLIkLRG4+4fAu2ZWXg/5NGBlpd2eBk4ws4Zm1hT4HrAqWTGlxYIF0L07XHMNnHginXwld3MTO2i0y27VdfGoQJyIJEuyRw0NAQqiEUPrgAFmNgjA3ce7+yozew5YBpQBj7j78iTHlBobN8KNN8LEidC6NUybBn36UNbOIMaqYIl08fTvrxO/iNS9pCYCd18CVK52N77SPr8Hfp/MOFLKHR59FK67Dj79FK69Fm6/HfbbDwhdORWHgYK6eEQkvTSzuC6tWAEnnww/+xkccUQYFnrffV8lAVAXj4hkHiWCGoo5u3fLltAN1LUrLF8OjzwC8+aFOQIx9O8fFocvKwv3SgIikk4qOlcDsWb3zvj5THpfPZT9Pi6CAQPg3nuhRYv0BioiUgNqEdRAxdm9h1HMU1zA1C97897n+4UWwKRJSgIiknWUCGqguBgasoPruZdVHMkZPM/13MtROxbD8cenOzwRkVpR11AN/Ogb8/jthsv5LiuYQW+GMoZ3aUNeXrojExGpPbUIElFSAgMGMHXDiTSz/3I+M+nDDN6ljYZ+ikjWUyKoSllZGAHUsSNMmQLDh7Ng4grezDtfQz9FpN5Q11A8y5aFiqALFsCJJ8LYsZCfTz+g3y/SHZyISN1Ri6CyzZvDbOBu3WDtWpg8GV5+GfLz0x2ZiEhSqEVQzh2mT4erroL33gsTBu6+Gw46KN2RiYgklVoEAOvWwXnnQd++YR7A/Pnw8MNKAiKSE3I7EWzfHob85OfD3LkwejQUFkKvXumOTEQkZXK3a2jOHLj88rB4fN++YeGY1q3THZWISMrlXotgwwa45BI49VTYsQNmzYKpU5UERCRn5U4iKC2FceOgQwd44gm45ZZQKfTss9MdmYhIWtWLRBCzNHRFixaFfv/Bg8Ow0GXL4M47oUmTNEQrIpJZsj4RlJeGLioKI0CLisLzggLg889h6FDo0SO8MGUKvPhimCksIiJAPUgEFUtDl9u61Vlw9eNw5JHw4INhhvCaNaEWhFl6AhURyVBZP2qouHjX54ezloe4gjM/fj50Az39dGgRiIhITFnfImjTJtzvwzZ+y+28yVF8j4X89sAx8NprSgIiItXI+kQwciR0bvxv3uQobmcET9GHbk1Wc8QDQ6BBg3SHJyKS8ZKaCMysuZk9aWarzWyVmcWcsmtmPcys1Mz61vQz+veHG8e2YX3j9pzJbH6T9xh3TGyl0tAiIglK9jWC+4Hn3L2vmTUCmlbewcwaAKOA/6vth1w0oDEMmMUptY9TRCRnJa1FYGb7AycCfwJw9y/d/bMYuw4BpgEfJSsWERGJL5ldQ98GSoA/m9liM3vEzPatuIOZHQr0AcZXdSAzG2hmhWZWWFJSkryIRURyUDITQUOgGzDO3Y8GtgA3Vtrnf4Hh7l5a1YHcfYK7d3f37i1btkxKsCIiuSqZ1wjWA+vdfWH0/El2TwTdgb9ZmOTVAjjHzHa6+4wkxiUiIhUkLRG4+4dm9q6ZdXD3NcBpwMpK+7Qrf2xmk4F/KAmIiKRWskcNDQEKohFD64ABZjYIwN2rvC4gIiKpkdRE4O5LCN0/FcVMAO7+s2TGIiIisWX9zGIREdkz5u7pjqFGzKwEKEp3HNVoAXyc7iASlC2xKs66pTjrVjbEmefuMYddZl0iyAZmVujulbvEMlK2xKo465birFvZEmc86hoSEclxSgQiIjlOiSA5JqQ7gBrIllgVZ91SnHUrW+KMSdcIRERynFoEIiI5TolARCTHKRHUATO70MxWmFmZmcUdQmZm75jZm2a2xMwKUxlj9PmJxnmWma0xs7fMrHKhwKQzs4PM7HkzWxvdHxhnv7R8n9V9PxaMiV5fZmbdUhVbDeM82cw+j76/JWZ2W5rinGRmH5nZ8jivZ8r3WV2cGfF91oq767aHN+BIoAPwMtC9iv3eAVpkcpxAA+A/hPUkGgFLgU4pjvNe4Mbo8Y3AqEz5PhP5foBzgGcBA44FFqbhv3UicZ5MKPSYln+PFeI4kVCyfnmc19P+fSYYZ0Z8n7W5qUVQB9x9lYcKqxktwTh7Am+5+zp3/xL4G9A7+dHtojfwl+jxX4ALUvz5VUnk++kNPOrBq0BzM2uVgXFmBHefC2ysYpdM+D4TiTNrKRGklgOzzewNMxuY7mDiOBR4t8Lz9dG2VDrE3T8AiO6/EWe/dHyfiXw/mfAdJhpDLzNbambPmll+akKrsUz4PhOVDd/nbpJdhrreMLMXgG/GeOlmd386wcN8393fN7NvAM+b2eroV0adqYM4Lca2Oh9jXFWcNThM0r/PGBL5flLyHVYjkRgWEerP/NfMzgFmAO2THVgtZML3mYhs+T53o0SQIHc/vQ6O8X50/5GZPUVovtfpiasO4lwPHFbheWvg/T085m6qitPMNphZK3f/IOoC+CjOMZL+fcaQyPeTku+wGtXG4O6bKjyeZWZjzayFu2da8bRM+D6rlUXf527UNZQiZravmTUrfwycCcQcfZBmrwPtzaxdtKBQP2BmimOYCVwWPb4M2K0lk8bvM5HvZyZwaTTa5Vjg8/KurhSqNk4z+6ZZWCfWzHoSzgefpDjORGTC91mtLPo+d5fuq9X14Qb0Ifxq2Q5sAP4v2v4tYFb0+NuEkRtLgRWErpqMizN6fg7wb8Kok3TEeTDwIrA2uj8ok77PWN8PMAgYFD024KHo9TepYiRZmuO8MvrulgKvAselKc7HgA+AHdG/z19k6PdZXZwZ8X3W5qYSEyIiOU5dQyIiOU6JQEQkxykRiIjkOCUCEZEcp0QgIpLjlAgkLczs4ApVGj80s/eix5+Z2coUx3KBmXWq8PwOM6vxxDwza1tFZcp8M3vJzP5tZv8xsxFmVuf//1X1t5jZy1ZF1VnJXUoEkhbu/om7d3X3rsB44I/R465AWV1/nplVNYv+AuCrk6e73+buL9ThZzchTIq6x92PAI4izIK+qq4+o4ILSOLfIvWTEoFkogZmNtHC2gmzoxMpZvYdM3suKjI3z8w6RtvzzOzFqFb9i2bWJto+2cxGm9kcYFSs95vZccAPgd9HLZLvRO/rGx2jh5nNjwqJvWZmzaJf/vPMbFF0O66av+di4F/uPhvA3bcSJh9dH33G7WZ2XfnOZrbczNpGj2dE8a6wCoX1zOy/ZjYyiutVMzukur+lIjM708wWRPFPNbP9ou33mNnK6Lu8r+b/6SQbKRFIJmoPPOTu+cBnwI+i7ROAIe5+DHAdMDba/iChTHFnoAAYU+FYRwCnu/u1sd7v7vMJv9avj1oo/yl/Y1Sa4XHgKnfvApwOfEGofXSGu3cDflLp82LJB96ouCH6nCZm1rya9/48irc7MNTMDo627wu8GsU1F/hVVX9LRWbWArgl+l66AYXANWZ2EGH2eX70Xd5VTWxST6jonGSit919SfT4DaBt9Iv1OGBqVM4FYJ/ovhfwP9Hj/0dY2KbcVHcvreb98XQAPnD31+HromIWahs9aGZdgVJCsqmKEbtaZqyqmpUNNbM+0ePDCEnyE+BL4B/R9jeAMxI4VrljCd1H/4q+i0bAAmATsA14xMyeqXB8qeeUCCQTba/wuBRoQmi9fhZdR6hOxZPului+Ju8vF+8EPoxQq6lLdNxt1RxnBWF1q68PbPZt4GN3/8zMdrJr67xxtM/JhFZIL3ffamYvl78G7PCv68OUUrP/lw143t0v2u2FUCztNEKRuiuBU2twXMlS6hqSrBD9Gn/bzC6Er9ax7RK9PJ9w4gLoD/yzhu/fDDSL8bGrgW+ZWY/oPc2ii84HEFoKZcBPCctCVqUAOL7C6J0mhO6k30avv0NYAhEL6/G2i7YfAHwaJYGOhF/y1Yn3t1T0KvB9Mzs8+symZnZE1Go6wN1nAVcTLtxLDlAikGzSH/iFmZVXHC1fenEoMMDMlhFOzPFG48R7/9+A681ssZl9p3xnD0s8/gR4IHrP84Rf5GOBy8zsVUK30Baq4O5fEC7i3mxm/wY+Jlw8Loh2mQYcZGZLgMsJFUMBngMaRn/XnYQTeHVi/i2V4ikBfgY8Fh37VaAjIYH8I9r2CqHlIzlA1UdFUszMLgBGA6e4e1GawxFRIhARyXXqGhIRyXFKBCIiOU6JQEQkxykRiIjkOCUCEZEcp0QgIpLj/j/9Tv0qmmNKJAAAAABJRU5ErkJggg==\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": "6ad9a9f9",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 4 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "919dba32",
   "metadata": {},
   "source": [
    "### Compare Evidences with Hypothesis using t-statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c90d6c0f",
   "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": "61ebbe32",
   "metadata": {},
   "source": [
    "### Compare p_value with '$\\alpha$ '(Significane Level)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d357f9e",
   "metadata": {},
   "source": [
    "### If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence \n",
    "### If p_value is = '$\\alpha$ ' we reject Null Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c93930",
   "metadata": {},
   "source": [
    "### interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3bea7c93",
   "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": "ec5a2d3e",
   "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": "56329f5f",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4af3830b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.BmpImagePlugin.DibImageFile image mode=RGBA size=704x366 at 0x216201C4FA0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageGrab.grabclipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2f59fa",
   "metadata": {},
   "source": [
    "## We are going to conduct a <font color='red'>ANOVA Test</font> on 4 Independent samples with Numerical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a44374",
   "metadata": {},
   "source": [
    "### We need to check whether the mean of any of these samples are different or the same?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa3744e",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 1\n",
    "#### Make two Hypothesis one contradicting to other \n",
    "#### Null Hypothesis is want we want to prove"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bb25b3",
   "metadata": {},
   "source": [
    "+ **Null Hypothesis:** $\\mu_1 = \\mu_2$ = $\\mu_3$ = $\\mu_4$\n",
    "+ **Alternative Hypthosis:** Atleast One of them is Differente"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9a4eb0",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 2\n",
    "### Decide a cut-off value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc32af6d",
   "metadata": {},
   "source": [
    "+ **Significance 5%**\n",
    "+ **alpha = 0.05**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfab2ac",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 3 \n",
    "### Collect evidence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f09ad924",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3eb9a66",
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat = pd.read_csv('..\\Documents\\EXcelR Study and Assignment Material\\Data Science Assignments\\Hypothesis testing 3\\LabTAT.csv')\n",
    "labtat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b6a881",
   "metadata": {},
   "source": [
    "### Applying Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8e7cfe34",
   "metadata": {
    "scrolled": true
   },
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2fd6ad",
   "metadata": {},
   "source": [
    "### Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3a538fe3",
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93dfa561",
   "metadata": {},
   "source": [
    "### Checking for Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a7f69437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 4)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat[labtat.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e15296cb",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Laboratory 1, Laboratory 2, Laboratory 3, Laboratory 4]\n",
       "Index: []"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labtat[labtat.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a37658",
   "metadata": {},
   "source": [
    "### Checking the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ff50b8ec",
   "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": "358da179",
   "metadata": {},
   "source": [
    "### Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6f84299b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (16,9))\n",
    "plt.subplot(221)\n",
    "plt.boxplot(labtat['Laboratory 1'])\n",
    "plt.title('Laboratory 1')\n",
    "plt.subplot(222)\n",
    "plt.boxplot(labtat['Laboratory 2'])\n",
    "plt.title('Laboratory 2')\n",
    "plt.subplot(223)\n",
    "plt.boxplot(labtat['Laboratory 3'])\n",
    "plt.title('Laboratory 3')\n",
    "plt.subplot(224)\n",
    "plt.boxplot(labtat['Laboratory 4'])\n",
    "plt.title('Laboratory 4')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "db835e36",
   "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": 86,
   "id": "8c7c65c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21629597b20>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 = ['Lab 1', 'Lab 2','Lab 3', 'Lab 4']\n",
    "sns.distplot(labtat['Laboratory 1'], kde = True)\n",
    "sns.distplot(labtat['Laboratory 2'],hist = True)\n",
    "sns.distplot(labtat['Laboratory 3'],hist = True)\n",
    "sns.distplot(labtat['Laboratory 4'],hist = True)\n",
    "plt.legend(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d06c47cb",
   "metadata": {},
   "source": [
    "### Plotting Q-Q plot to check whether the distribution follows normal distribution or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef4a3505",
   "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": "5b898ac5",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986d9a20",
   "metadata": {},
   "source": [
    "### Compare Evidences with Hypothesis using t-statictic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d0690c1",
   "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": "4a487be2",
   "metadata": {},
   "source": [
    "### Compare p_value with '$\\alpha$ '(Significane Level)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce541600",
   "metadata": {},
   "source": [
    "### If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence \n",
    "### If p_value is = '$\\alpha$ ' we reject Null Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f7dc7c",
   "metadata": {},
   "source": [
    "### interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45fc9ed4",
   "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": "8fb6edd4",
   "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": "a59dc84f",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d4fb2c0c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.BmpImagePlugin.DibImageFile image mode=RGBA size=1082x643 at 0x1C23C7BBBB0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageGrab.grabclipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d88d314",
   "metadata": {},
   "source": [
    "## We are going to conduct a <font color=\"red\">Test of Independence</font> using Chi-Square $\\chi_2$ test with Contingency table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac72de0e",
   "metadata": {},
   "source": [
    "### We need to check whether the proportion of any of these samples are different or the same?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf653886",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 1\n",
    "#### Make two Hypothesis one contradicting to other \n",
    "#### Null Hypothesis is want we want to prove"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ed3c27",
   "metadata": {},
   "source": [
    "+ **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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f04129",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 2\n",
    "### Decide a cut-off value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27825c2",
   "metadata": {},
   "source": [
    "+ **Significance 5%**\n",
    "+ **alpha = 0.05**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1c2b61",
   "metadata": {},
   "source": [
    "### As it is a one-tailed test\n",
    "+ **alpha = 1-0.95 = 0.05**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67bd43ab",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 3 \n",
    "### Collect evidence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0fc6eb",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43b5e55e",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buyer = pd.read_csv('..\\Documents\\EXcelR Study and Assignment Material\\Data Science Assignments\\Hypothesis testing 3\\BuyerRatio.csv', index_col = 0)\n",
    "buyer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e20c1b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = [[50,142,131,70],\n",
    "        [435,1523,1356,750]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea5f1a9",
   "metadata": {},
   "source": [
    "### Applying Chi-Square $\\chi_2$ contingency table to convert observed value into expected value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aabae775",
   "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": 24,
   "id": "40bf3d9d",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chi2_contingency(table) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f94bc5a",
   "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": "2510b038",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd569d6",
   "metadata": {},
   "source": [
    "### Comparing Evidence with Hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2024d72f",
   "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": "7a94a64d",
   "metadata": {},
   "source": [
    "### Compare p_value with '$\\alpha$ '(Significane Level)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c04deb7",
   "metadata": {},
   "source": [
    "### If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence \n",
    "### If p_value is = '$\\alpha$ ' we reject Null Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245b3e0f",
   "metadata": {},
   "source": [
    "### interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0296e8fb",
   "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": "a224e547",
   "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": "83335b97",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccbc02f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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r57ld1263aXl5mba3t4XfDww5/Y7ChAUvSurWW8UOBeNFIXsxEqRUKtnOSyaTnvfN5/O+PV1sOshEnHk9y5qmud7DbXihqqqez/EaksjLs0ql4turIXqdXxo3m03HNX49Vmyk2e/93SL61uGeYdLvfFUURXgoJTusmjcFzJQxm8369gK4vd+o5bFhONNfZGgzL8+SyaRrj7iu69wpDH49RGGkx6DaDXZEgaqqnr2H5v0HpceJ7SHlyc6bEiMzasIt/3lDl83nsXIpisKVje11Fe2tZPVYZGSCH6ytFclnWfnddDmZTApPU+r2HsNUp8jQzUgQkbTg2ZYwp7saBr+s5XI5z6nfvFESfuWhWx3i9dC7TUGpVCquU6780o/VIb/RHbxRI15p4TUiU2Z6kSzss/zg+T1u9tfEbUqfn+2P0kZpmuZ4vsjUL5nrBqVuBtEzEkEQdnicqENjKn+/giDseWFU6t0EQdhKRnRYJK8y80p/nmPj1tg1yefzrs6Q7HVesA1r0UpMxqHmOSluDn4Y9DNfZXSQ9zy/yshtqKPXM0cxjw1DPgjCa9iKNiB5joOXYxRGegyq3WDriqjn+ocJW3d6BXu6WX+Dl/+qqnqWb169zqsj2c4EkSkxbOAqrLLJppGI7Ws2mw4b7VWWePos25Du9h7DUqfIEkYQxGsNG8Pg25aw6gXZ9DXhNfz9AlRh65BIBxUvWOP1zKDlnLU9XmnhFgSJMgBiGPJBELbOFg3E86ZWBQnUhmGjvNZp4dnRoNdFnXdgcBiJIAhbuGUKW6FQ6NuaIIMUBOEZ8m56+rzSi+fY+MnJm78tmmbsNV6G32rsZeaXs+nnVUnwGgS9cup6na8ylQkbZBBtkPMcP6/0HLU8NpENgrDn+zm9Vni9Sl7PCyM9BtVuDHMQhA0IejWY2MabjL6w+a8oipAdYm2Cm46xTqzsyK4w1maQCSixsLrtJT+rl0ECON3cY5jqFFm6DYJomib0HNZ2hlU3sGkro9e8fPWSqxsd4gU4RXSIFwjx0h/WZsmsZcSmpZt8vHQLe3QPD/aZXgQZ/WqFVw5lZAvDRomkKW90sEiZZN9PxnaD4WYktsg9ceKE7fjpp58WvjaVSgnPZQ8bdh7cO++80xc5iIg++eQT27Hs/Gg2zdm1A7zI5/O+c2JjsRjF43HHdSJzv9nr3Obib21t2ebRplIp33ubsPKzOulFLpcLdU6wlX7mazwed8zh94JdB0B03mkqlaJsNit07ijmcVA++OAD2/Hzzz8vfG0sFqN0Om377dSpU8LXh5Eeg2I32Pndm5ubQ7PdHlvm2DU2vP7TdT3wVp+nT58WskPs/P+LFy9yz0smk7Zjv2182f9/8Ytf+Mrix/vvv287vv/++4Wvvffee23Hn376qfC1f//737v2YWTuMUx1Si9RVVV4LYEDBw7Yjr/99tuun99qtRxpKbN2w9zcHGmaZvvt888/F75eRodYOUV1KBaLSa3XcPz4cduxl31jsa6HRUT0xRdfCF2nKAq9++67ws/pBWfOnLH5Pea6K6I8/vjjjt9kbH+3NkpVVaE0/fnPf+64TkRfWPv73XffSckHhpehD4K0Wi3Hgjh79uzpkzRysAZ5c3OTUqmU1B7lYfHll1/ajmUcOCLiNipEjSRv4VYebCUpep0oX331le3YXCQqCKxOehGlU9fPfD1y5Ijwc9h7KooitWCe38JaJqOYx0FhFxqTcRCJnA6HTGMmjPQYFLsxMzNDqqp2jnVdp0QiEThA0Cu2trZsOpxMJj0d1ampKUew4b333gv0bNGyzeaVW93IOulegahWq2UL/iiKIrzIsResrbUuWC6LqA8Qj8e7DibK3mNY6pReMz09LdzQMxcnNbl06VLXz//mm29sx6qqStWhRNRZxNVENLAtq0PsfWUWO5VpTLN1XDcLH4vm0eHDh/vWseoG6/fIBECI+J0JogGyMGyUaNliF36XKZNgPBn6IAjP8A+L0k9NTTl6sDc3N0lVVcpkMj11otkGTBAHju1F+P7777uSiaWbBqsI586dsx0vLy/TxMSE8GcQGYZ85XHPPfdEct9RzOMg8GyLrIPI22FqEEdARG03iIjeeOMN23G1WqX5+XlKJBJSuyz0ko8//th2LNKYZc8ZlHebmppy2Cl29xe339kRTUFhe4rn5+eF7Qq708MgM6x1yqjDNkqnp6el77F7927bca/sediBaaLru72wyNT1y8vLocvUL9igU5A68e6777YdX716tRuRIiGM3b3AeDH0QRCWIIa/nxw9epTrhK2vr9P8/DxNT0/TxsZGzxsXQSK3bPCJjT4POuMwBG5Q81Vm2G03jEMeB4Ht5RGBF2xmg9LjwsLCAhUKBcfv5XKZFhcXaXJyklZWVvoyyo9Hq9VyBDBERgLxtqQdlEDIE088YTt+++23ueexvz/00EOhPJ/dknRcGNQ6Zdxhp3OIcMMNN9iOeVunh4HMqMGgDMoW5IMIO/1DBHa0LTsiDIBhZOSCIMPI2toalUol25BqE13X6dChQ3TrrbfS6upqH6QDQQirdxEMLshjYCWVSlGtVuMGlNrtNh07dqwzyq/fI2ZOnjxpa7RrmkbffPMN7ezseH6++eYbRy++3/obveKBBx6wHZfLZUfQqdFo2BpgQaYM9ILHHnus3yIAAP4PiqIE6igAAAw2/9ZvAcB1FhYWaGFhgba2tuidd95xLFjXbrdpeXmZLl68SGtra32SUg7RdRoGlVwuN5BrOfSbYc9XK8jjcGEXCR03ZmZmaHt7m+r1Om1sbFCxWHSMEFhfX6ezZ8/S9vZ236ZusoELc/pOEDY3N+nNN9/s+zTUWCxGyWTSVneePHnStjgkOxXmySefjEyeSqUycIshDyqjVKeMEoqi9FuEUDEMo98ijAw33XRTv0UAoGtGbiTIhQsX+i1CVywsLFCxWKRms0m5XM5RCa2vr/dkREiQ9UjY3s0o5nmC7hjUfL3llltCvycQJ8jwZN5ohkHsVe8HMzMztLa2RleuXKFCoeAY5VetVumRRx7pi2yNRsMRZO+WDz/8MNT7BeU3v/mN7ZjdHYI9ZkePAHkGtU4Zd2R26zJh12aJam0u3qhn0DuCTD9m1wBhF/cFYBgZ+iAI2/Mos2PDIBOLxWhpaYnOnz/vWJH/1VdfDf15bKUku2Vbq9VyzB9lV2oedIZh4SdZhiVf2cX1RLejk2UU8zgIvDyUXa/izJkztmM4tnxSqRRduHCBcrmc7fdyucxdvC9q3BYM7QY2uNAvFhYWbB0H1Wq1o9eNRsNmyzRNC3UhvXFZ7HNY6pRxg+1ICNIh+Nlnn9mOo1pokl277+uvvw79GcOycHcvYPPRbatxL06cOGE7RscVGAWGPgjC63nc2trq+r5spSzTUxpmwyoWi9Gbb75pc+za7XboC+yxDtynn34qdT3rWKuqOnQrNd9xxx22Y9boDyPDkq9seZPVcdEyN4p5HISpqSnHKDPZwBO7u8iBAwe6lmuUWVpacswr78dCsmzAolAokGEYUh92EVhrsKHfsGv1vPXWW0TktGW//e1vQ33u7Oys7ZhtUI4Kw1KnjBtso1/XdekyyY4Q+9nPfta1XDzY/JYJBosGMmKxmCNgxwbuxwW2nSTr97ABZKLoRgkB0EuGPghCRI6REux2hV7s7OxwDTCvUhYx1MVikY4dOyb8fBFisZjD4Fy+fJl7LjtPT7SXht2nvVgsSlWg7Ir7bJ4MA2waV6vVnm5THAXDkq9TU1MOh0V0iL1MmRvFPA4KG7T44x//KHwtb3eRX/ziF6HINcqwOzZcunSpp8/nObMiu8Kw7N+/3xFEG5QpMexuL2bDjg3+BHlvL9gG4/r6+kj2PA9LnTJuzMzMOMqkGQAUYWdnxzGSOuwyYsKWlWKxKFRWWq0WJRIJ4eewdZxM22CU+MlPfmI71nVdqrOYte1hj6IDoF+MRBDk/vvvtx2Xy2WhAr6xsUHz8/P0/vvvc/9nezxeeeUVz/sVi0VaXFz0fW4Q2KGNvKF+RM55ev/85z+F7s/OjW6325TNZoWuXV1ddYyUefzxx4WuHSSmpqYcPbXPPPOMtCPbjyHubgxTvvKmffk51xsbG1JlbhTzOCjs+gnVapU2NjaErn3kkUdsC36qqkoLCwuhyjeKsMOQd+/ezT2vXq/T9PQ0TUxMdLbXDQO2UZRMJgMtaBqLxSiVStl++/Of/9yVbGExMzNjq7tNh98a/An63l6wgaF2u00vvvii1D1ardbAjKhxY5jqlHGDHQV17Ngxobqq1WrRL3/5S8e9olrsmLfV9muvveZ5TaPRoEQiIbVt78MPP2w7LpfL0lt6NxqNoQ9msjaRiOjpp58Weq+dnR1aXl62/Rb2KDoA+sVIBEFSqZSjF/nRRx91dehNY3ro0CEict/v+sEHH7Qdb25ucu/ZarVoZWVFOgAyOTlJmUzGt5IqFou2CL2maa6VE7vKOq83qtVqOX6LxWIOR2Zzc5MSiYSrU9ZqtSiTyTgMZDabHdoo8ZEjR2zH1WrVMw2sFItFmp6epjvvvDMi6eQZpnxlneF2u03JZJJbPhqNBqVSqU4ZlmHU8jgoCwsLDsfo0KFDtLKy4uocNRoNmp2ddTRkxrWHjeh6/ZNIJHwD7/V63eGA79mzh3vu8vJyx+ab2+uG0Thmh7uzHQgysCN/dF0fmODgE088YTt+9NFHbcfdvLcbsViMDh8+bPttfX1daEvkVqtFq6urdOutt1ImkwldtjAZpjpl3Hj88ccdo0H27t3rGdze2dmhW2+91RbUVhSFnnvuucjkjMVi3ICN26L/xWKRNE2TCoAQEc3NzTk6PRYXF4WC/Y1GgzKZDKmqKjWiZlD5/e9/bzvWdZ327NnjORLW7Ci2ommaIwAOwLAyMlvkvvHGG3Tfffd1jtvtNh06dIj+8Ic/UDKZ7AQHTp065XDg2SHKJg888AC9+uqrtsqBvefFixcd2yCKGut2u03r6+u0vr5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      "text/plain": [
       "<PIL.BmpImagePlugin.DibImageFile image mode=RGBA size=1089x532 at 0x1E431EAC2B0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageGrab.grabclipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09fc68a6",
   "metadata": {},
   "source": [
    "## We are going to conduct a <font color=\"red\">Test of Independence</font> using Chi-Square $\\chi_2$ test with Contingency table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80133a1e",
   "metadata": {},
   "source": [
    "### We need to check whether the mean of any of these samples are different or the same?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71df53b0",
   "metadata": {},
   "source": [
    "##  <font color='red'>Step 1\n",
    "#### Make two Hypothesis one contradicting to other \n",
    "#### Null Hypothesis is want we want to prove"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fd43d48",
   "metadata": {},
   "source": [
    "+ **Null Hypothesis:** $\\mu_1 = \\mu_2$ = $\\mu_3$ = $\\mu_4$\n",
    "+ **Alternative Hypthosis:** Atleast One of them is Differente"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e661e476",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 2\n",
    "### Decide a cut-off value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e564fbde",
   "metadata": {},
   "source": [
    "+ **Significance 5%**\n",
    "+ **alpha = 0.05**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82c7331",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 3 \n",
    "### Collect evidence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2532844",
   "metadata": {},
   "source": [
    "### Importing Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "601aa655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers = pd.read_csv('..\\Documents\\EXcelR Study and Assignment Material\\Data Science Assignments\\Hypothesis testing 3\\Customer_OrderForm.csv')\n",
    "centers.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5068d51c",
   "metadata": {},
   "source": [
    "### Applying Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3fbd6e5d",
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18b771d",
   "metadata": {},
   "source": [
    "### Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2f771785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Phillippines    0\n",
       "Indonesia       0\n",
       "Malta           0\n",
       "India           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e14999ce",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Phillippines, Indonesia, Malta, India]\n",
       "Index: []"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers[centers.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf8964cf",
   "metadata": {},
   "source": [
    "### Checking the data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cfbcebc1",
   "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": "d43c2dd5",
   "metadata": {},
   "source": [
    "### Checking value counts in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "46294ffd",
   "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": "59cfaa58",
   "metadata": {},
   "source": [
    "### Creating Contingency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "847362cb",
   "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": "6cc10924",
   "metadata": {},
   "source": [
    "### Calculating Expected Values for Observed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e806293a",
   "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": "34691090",
   "metadata": {},
   "source": [
    "### Defining Expected values and observed values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a8b0fe0",
   "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": "d29b11de",
   "metadata": {},
   "source": [
    "## <font color='red'>Step 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52575f82",
   "metadata": {},
   "source": [
    "### Compare Evidences with Hypothesis using t-statictic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "01fa0190",
   "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": "cda3c1e0",
   "metadata": {},
   "source": [
    "### Plotting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06401232",
   "metadata": {},
   "source": [
    "### Compare p_value with '$\\alpha$ '(Significane Level)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a946ef87",
   "metadata": {},
   "source": [
    "### If p_value is $\\neq$ '$\\alpha$ ' we failed to reject Null Hypothesis because of lack of evidence \n",
    "### If p_value is = '$\\alpha$ ' we reject Null Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1982813",
   "metadata": {},
   "source": [
    "### interpreting p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c952c1f7",
   "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": "0b8642ec",
   "metadata": {},
   "source": [
    "## We fail to reject Null Hypothesis because of lack of evidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5864c131",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75768262",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9fb7889",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62974376",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0314f7f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01216204",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b0e3c53",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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