linearreggsession2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages=np.random.normal(30,5,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33.52966068, 29.36988273, 35.65190289, 40.76328159, 25.53999865,\n",
       "       31.21273133, 27.4459091 , 31.34452137, 32.57832342, 32.45222505,\n",
       "       33.29277706, 31.16300364, 28.00450753, 29.18926143, 40.05620436,\n",
       "       22.56680032, 27.43514261, 35.61887038, 20.8160631 , 25.70667489,\n",
       "       30.93690931, 31.20008186, 37.25955583, 36.67956068, 33.18575547,\n",
       "       35.93316548, 31.95189026, 33.06789913, 25.67158489, 33.68624051,\n",
       "       25.23108377, 34.20843215, 22.48369684, 25.05291446, 23.57636513,\n",
       "       31.30611369, 30.70637041, 30.24169851, 42.73613779, 25.57314596,\n",
       "       28.84165366, 30.94465791, 30.11919689, 26.97901749, 31.68730168,\n",
       "       23.73164993, 30.30813241, 32.72614031, 34.8595187 , 36.79046925,\n",
       "       36.34661237, 30.16472071, 37.03570187, 34.60261272, 34.7660806 ,\n",
       "       25.24334151, 29.4086031 , 24.07245482, 24.44299213, 17.5163399 ,\n",
       "       24.54661493, 21.89268465, 35.19114275, 26.91499662, 28.39932888,\n",
       "       23.43754393, 27.25726457, 18.88433881, 40.05758751, 24.13424434,\n",
       "       34.71970517, 28.30449328, 30.46737516, 32.25713899, 38.80715607,\n",
       "       31.59727228, 39.29319148, 36.49803686, 23.67048855, 28.3415729 ,\n",
       "       25.50571805, 39.65144451, 32.61619533, 23.15549763, 29.27789758,\n",
       "       37.56151166, 26.3269701 , 38.68202255, 27.21676488, 31.71312058,\n",
       "       24.65014043, 26.21251188, 28.57806515, 18.65487835, 37.23757089,\n",
       "       33.44402769, 26.47436789, 37.76106753, 31.03913778, 26.54041378])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "purchasing=np.random.normal(500,22.24,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 3.,  3., 12., 17., 11., 21., 12., 12.,  7.,  2.]),\n",
       " array([17.5163399 , 20.03831969, 22.56029947, 25.08227926, 27.60425905,\n",
       "        30.12623884, 32.64821863, 35.17019842, 37.69217821, 40.214158  ,\n",
       "        42.73613779]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1001b4df28>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VRYI8gLu+mbKPdhD0d39lsd/pYefr74eCPICUbF/yDr44ngaZWZzTpiy7ZFCb7TMVpSMq0Ctxx3Wmvk2Zs6ouJoE+GAgQ8EavWPU53cTDWFVH0gsyGX7Z6MjjzKJsxl49EY1G/fkqnaOaBErcSR2aD0JAq+CbM2kEuhh0U2gNelIKs6g/URUpyxiejzaOV69akq3Mvv9aLv/8HIKBIAazEUsMurESnZQSIURfVyMuxO9vtzJgGWxmxt97HYff3ojf7SV/xlhSh8ZmsZnRZmba/dex918fUXv0NBnD8xm5cDqGHhhEjSWT3YzJHptZPImu9kw976/4iJNlp1lw6xxyC7Kw2Ab2DKVOBXohxDGgCQgA/tbTeYQQ3wF+BWRKKc+I0C30aWAh4ATukVJ+EuuK95aioiLsdjtarRadTkdpaSm1tbXceeedHDt2jKKiIl599VVSU1PbnLt48WJ++tOfAvCDH/yAu+++u7er3y9pDXpSiwcx7u6FEJToLCY02th1U5hTbIy7fTZ+rxedyaAWNbXSXNeMz+NFq9dhtpnRG/vX96buTAOP3fMTKiuqAVj9+jv89/8+zMQZl/RxzfpWV/56ZkspJ5wT5AuA+cCJVsddCwwL/3sA+H0sKtqX3nnnHbZt20bLXP8nn3ySuXPncvDgQebOncuTTz7Z5pza2loef/xxNm/ezJYtW3j88cepq6vr7ar3awarGYPdEtMg30Jn0mNKsqog30p9VT0vPvICT939FL++91dsf2c7bmf8DlK351TZ6UiQb7Hkj0tpqm/qoxrFh+7+Bf0WeJizcxgAFgEvyZBNQIoQIrebr3NBK95cw4LL7mB80VUsuOwOVry5psdea9myZZHWeUdpiletWsW8efNIS0sjNTWVefPmsXLlyh6rk6J0h9vpZsUfV1B5vBIAn8fH0qffwN3cvwJ9e33yWo0IjfkMYJ0N9BJYLYTYKoR4AEAIsQiokFJuP+fYPKCs1ePycFkUIcQDQohSIURpdXX1uU93yYo31/D4o09xqqISKSWnKip5/NGnYhLshRDMnz+fyZMn8/zzodTHlZWV5OaG7l05OTlUVla2Oa+iooKCgoLIY5WmWIlnPreP8v1lUWUyKGmobuijGl2c3IJsBg0+Ox1VCMFnvnor9mRbH9aq73V2MHamlLJCCJEFrBFC7AP+i1C3zUWRUj4PPA+hFAgXex2AZ375J9wuT1SZ2+XhmV/+ietumtedS7Nhwwby8vKoqqpi3rx5jBw5Mup5IYQa2Vf6PYPZwNAJJXyyZmukTKvTkpKd0oe16rqU9GR+9sL32fzuJ5wuq2TODVeQmdu95IaJoFOBXkpZEf6/SgixFJgFFAPbw0EuH/hECDENqAAKWp2eHy7rMadPVnWpvCtaUgtnZWVx8803s2XLFrKzszl16hS5ubmcOnWKrKysds979913I4/Ly8u56qqrul0fRekJRrOR+ffOp6m2kYNbD2JPs3PLt2/BHKN8Pb0pNSOFa26b09fViCsX7LoRQliFEPaWrwm14j+WUmZJKYuklEWEumcmSSlPA8uBL4qQS4EGKeWpnnsLkDOobaA9X3lnORyOyM5SDoeD1atXM3bsWG688UYWL14MdJymeMGCBaxevZq6ujrq6upYvXo1CxYs6FZ9FKUnJaUncedjd/HIy4/yjee+ScnkYRhUioWE0JkWfTawNNxy1wGvSCnPN6r4FqGplYcITa+8t7uVvJD/fPjLPP7oU1HdNyazkf98+Mvdum5lZSU333wzAH6/n89+9rNcc801TJ06lTvuuIMXX3yRwYMH8+qrrwJQWlrKH/7wB1544QXS0tL44Q9/yNSpUwH40Y9+RFpaWrfqo4QEfH78bg9I0JmNcb3Yqb+x2C1g7+taKLGWMGmKV7y5hmd++SdOn6wiZ1AW//nwl7vdP98bVJrirvE63JRt3MXx97YhBAyeNYH8S8fG/YIn5azmhmYC/iB6ox5LP+waiicDLk3xdTfN6xeBXemeppPVHF17tlFwZE0pyYNzSC/J78NaKZ0RDAapKq9m8RMvU3awglFTR/DZh+4gNbN/Dfj2RyorktKvVO080rZsV9syJf401TXz2wef5fDOo3jdXrZ/sJP/+9nfcDQ6+7pqCS+uA308dCv1pER/fz0hdcigNmVpQ9Sm6/2Bx+Whrio6M+ne0v34z8kmqsRe3AZ6k8lETU1NwgZDKSU1NTWYTKpvuSvSSvLJHFsceZx1yZB2g78SfwwmA4ZzcudkFWQiNGodSk+L28FYn89HeXk5bnf/WoLdFSaTifz8fPR6lW+lK3xOdzinvEBr0KG3qJtlf+D1ePn0/R0s/vnL+H1+zFYTD/726xSNGqwWHV6kzg7Gxm2gVxQl8XhcHlzNLlwON2abGVuyFZ2aHnvRBtysG0VR4oPX7aW5wUHZgTLSc9NJyUzGFs41YzQbMZqNpGT2cSUHGBXoFUWJqbKD5Tzz4HMEwls/zlg4nZu+eiPWJLUrVl+J28FYRVH6n6a6Zl793euRIA+w8a3NuON48/WBQAV6RVFiJhgM0lTbdpMPzznZZZXepQJ9ggi4PTgrqzmzbQ+uqjOhXDCK0sssdjMzFk6PKmvdR6/0DdVHnwACXh81O/dRt/sAADXbdpM+YTRpY0eg0akfsdJ79AY9s2+fhTXZSum6reQMzua6Ly0kKT2pr6s2oKkokACCPj91ew5GldXu3EfK8CEq0F8kT7OLuhNVnNp1lJzRg0krysGoEnB1ii3Fxqxbr2Da/CnojXqMZmNfV2nAU1EgIUg4Zz2EDEr6foVE/+Rze9m78mMOrvsEgEPvbGPorHFcsuhy9CpodYpWq8WWorpr4oXqo08AGp0OW1F0vpekoYNVa/4i+d1eDr27LarsyAc78XtUThalf1KRIAFojQayL52ENTcbx8lKrPm52AoGoTXELrWCz+kCCUKrQWcaAK3acz4OSamS0AG4ml3UVdWz4/2d5JUMonhMEbZU1XKPdyrQJwidyUTy8CEklRSh0Wpjdl0ZDOKuaeD4qg9x1dSTVDSIgrmXYrBZYvYa8UZnMjBk5lgOv78jUlY0YxT6Ab6tXjAYZH/pAf76P3+LlA2bNIwv/OBzWJPVYqh4NuACfdDrxdvYQNDnxZiajsZgQGgSowdLCIGIYZAH8LvcHHpjDf7wPOjGoxWUrdvM4AWXJWzLXm8yMOaGGWSNLOTkjiPkji0ia3jBgO+fd9Q7eOvFt6PKDn5yEFezSwX6ODegAn3A66Vhz04CbhcAjhPHSL1kAjpLYv6S+t0ehEagNbRtiQa8XoK+ABqdFq2x45ZqwOuPBPkWjccrkIFAB2ckBqPNTP7EEvImDFWZFcMkMmrFa4tgINgHtVG6olNNWSHEMSHETiHENiFEabjsKSHEPiHEDiHEUiFESqvjHxNCHBJC7BdCLOipyndVwOmIBHkApMRRdpyg3993leoBfo+XxmNllK/5gIr1G3HX1EW9R5/DScPhMnzNDjz1jXibHB32P2v0OoQ2+tfEnJ4KIjE+BV2ICvJnWZOszL7zqqiyQUMHYbb3z2mnzY0OaqvrcDpcFz64n+tKi362lPJMq8drgMeklH4hxC+Ax4BHhBCjgbuAMcAgYK0QYriUss+bgDLYtuWRiC1Td3UNFes+ijw+dqqKIbctxGDX4Xd7aDxWQcDt4cj7WyAo0dssFN0wB6HTgZRo9LpIt4zWqKfw6hmcWLsRGQiiM5sYvOAylQN+ANLqtEyaM5GMvAxKV5WSPzyfyVdPwp5q7+uqdVllRRXP/vTPHNx9mHHTxvCVh79IelZaX1erx1x0142UcnWrh5uA28JfLwKWSCk9wFEhxCFgGrDxomsZIzqbHaHTIVu1bi15BQk1DTHg87VZPCWDQRwVpzCMLEEGApgzUjnyxtkfn6/ZyakNWzHnZFK5eTtJQwrInzUNncWEVq8nuaSQ0QU5BH1+tAYdOrMK8gOVJcnCyKkjGDaxBI1W0y8/8dTV1PODrz5J+dGTAHywahONdU18/zffxp6gqRo6+/lbAquFEFuFEA+08/yXgJZRmjygrNVz5eGyPqfR60m9ZAKmnFwMaemkjL4EnTWxfrBCaNBZ24456K0ts2QEflfbTILu2noMSaHvReORMprKTkWe0+p1GGwWTKlJ6K2WhBm8Vi6eVqftl0EewOPyRoJ8i+1bduP1ePuoRj2vs03ZmVLKCiFEFrBGCLFPSvk+gBDi+4AfeLkrLxy+YTwAUFhY2JVTL5oQAq3RhK2wGCllTKchxguNTkvG+JE0HSsjEE5sZspIxZQR+liqMxsxpaUgtNqobitrXg6uqprI4+by06SOKKY7Ah4vfo+PgMeLzmREZzHid3shKNEYdOjOMwicSIKBAO4mFwFfAJ1Bh8luUfuk9iGdXofRbIzKqJmRnYYmgRswnQr0UsqK8P9VQoilhLpi3hdC3ANcD8yVZ0fzKoCCVqfnh8vOvebzwPMQ2krwYt/AxRAaDYn8Z6azWii+eQGe2no0ej2GJFuku0VoNGhNBopumE3FO5vxNjWTVFxA2ugSjv77ncg1kku6d/MNeH1U7z7M0VWbQEpShxeSd9l4Dq/4ENeZetJGDKZ4/vSEno8PEAgEqD58ivXPvIm70YktI4l537mN1LyMvq4armYXfq8fjVYzoKZHarUa7v/O5/nDk/9HwB/AYNTz1cfuQZtAXbjnuuA7E0JYAY2Usin89XzgJ0KIa4CHgVlSSmerU5YDrwghfkNoMHYYsCX2VVc6IoRAbzGjt7Q/G0JrMGDJyaT4pqtDnXJCULfvCEiJ0GnJnDAKS1Z6t+rg93g5tnpTJAfPoGlj2f23twmE0wic2X0EodEwZOFl6GK4gjfeeB0ePE0uZn9jEbUnqtj11hbeeXYZ1z52F+Y+3HGp8UwDy595kyPbDpNdnMMt37mN9PyMhG7VtnC53Bw/VMav/u/HNDY0YU+2sexvbzPikpK+rlqP6cwtLBtYGu6P0wGvSClXhgdZjYS6cgA2SSn/Q0q5WwjxKrCHUJfO1+Nhxo0SreVm0CJj3IhIV43WoEfTzQ2bg14/MhgK8kKrJeAPRIJ8i7qDJwh6pkKCBvqAL0DDqVo2v7ye5jMN5IwsYPY3FrHu6aV9Ovfc1eRk6W9e58i2wwCcPFjB4v/6M1955uvY0/rfDJquMplMvL9yIyv+sQajyYDb5WHIiMEDu0UvpTwCjG+nvMPbn5TyZ8DPulc1pTdpdFo0utjNh9Ya9eitJnwONzIQQGfUgxBRWTbNmalt5ugnEo/DxepfvxYalwBO7ytj27KPmHjL5X06PuT3+SNBvkVTbRNed+IORraWnGrn8ece5mcP/ZYzlbUUFA/iv379IClpiZszP3FvYUqf0ltMjP7cQg6v2ICrug7nmXqK503j6JotICV6i4mS62ZGzcf3u734XG5c1Q2YM5PRm03o+nF+Ga/DEwnyLU7vLWPGF+dhTuq7sQmh0ZCRn8GZ8rPLYvRGPXpjYn6yOpdWp2XYmKE8veTn+H1+9AY9qenJfV2tHqUCvdIjhEaDJSOFkbdfjQwGw11BgvRRxQS8PnRGA3rr2SAf8Pmp3n2EA8vej5SNuHkW9vwsdCYjBqspLqd1Bnx+PA43fo8v8qnF63CjN+nRmw1odFqCrdIGpBdlozf37c3LlmLj1u/dwUvf/wuuZhc6vY6bHroVk23grI/QajWkZaRc+MAEIeIh9eqUKVNkaWlpX1dD6QNeh4uA14cQgo+f/SdB79nFbFqjntF3zmPPq+uY9MAiLHH2hxkIBKjaX857zy3D7/ahNxu5/MsL2fX2Fk7vL+Oqry8iGAiy4YW38Xt82DOTmf+9O0jOSe3rqhPwB3A2OvE4PRjMBsw284Bp0ScSIcRWKeWUCx2nWvT9RNDnJ+jzEfT50Bj0aA3Gft+/7W1ysuuVlThO1TDu3uujgjxAwONDo9Pid3k49PZGRt8+J64yZnqaXGz44wr87tAgs8/lYctf1zLls7M5va+Md59dxu2/+Q9u++X9+H2hcYq+7LJpTavTYk+zD4jBV0UF+n4h6PPRdLyc6o+3hXPR6MmbewXGtPhq4XZF0B+gfOMOHKdCi7Sc1fUkD86h4fjC4fPfAAAgAElEQVTpyDEpxYNoPh163lXbGNUFEg+C/gCe5uiEWM66Jgytxh2cdc1kDs3t7aopSpT+3SQcIII+P9Wl2yIzVoI+H5WbtuJ3ey5wZmz5nG4ajlVwYt1m6g+V4XO2TaXQWQGfn6aK6sjjE+99wpD508mZPBJLZio5k0ZQNHcqJ977FICsS4bGVWseQqkh7FnRN9vUwiyazzQAoDPqsaoWsxIHVIu+Hwj6/RCMHkvxNjaB7L252AGvj9Mf76Jq6x4AqrfvJ31sCQWzppw3n31HdEY96SOLaAy34L1NTnb97W0mfe1WhEZLwOvjwL82IDQaCq6cQN70MWh0backBgMBfA43PpcHnckQ+tdLqRVMSRZmP3gLH/7pLWqOniazJI+pn5vDh39eSVphFpfduwCjrf0pq8FAIGqKpdflwVnXzIkdR0kryCS9IDNuunmU/k8F+n5Ao9ejNRkjuWsArINyENre+/EFvD6qt+2LKqvZfZhBM8ZfVKAXGg3Z40pw1zZw+tMD6EwGhl4zA53RGJlSOeaOuQQDwfDslfbfa/PpWkr/uBy/24vQaBh1y5XkTiiJ6X65Hb4HIUjKTmX2t25GBiVCq0Gr1zL3wVvQaDSY2gnU7iYXVYdOcvCj3WSX5DHk0pGYkyyc3HOCt371WmSv2qLJw5h599WYkywDfgtDpftUoO8HtEYjeXOvoGrzJ3jqG7EMyiZz8vheCWZRYjxBS281UzxvOoVXTgIBOrMJTasB5gvlvPc2u9i1ZH1krroMBtm79H0yRhT0+PfG63QT8AUQGoHJHh3Q9R3c+AI+P3vf2c7H/3gPgMMf7eXwpr3M/eYiPvzb+qjv77GtB5m06FKc9Q5yhsdF8lelH1OBvh8QGoEhOYncK2cgpURotb0e5LUGPZnjh1P16dlWfdqoIWi6WQ+tQX/R70UGgziq66LLAkEC3p7dMcxZ18Tml9Zweu8JUvIymPGla0jKvXD2Q4/DzY4Vm6PKKg9UEPD68bnajrcE/EG2LvuQBf95M8bwmgO/z483PCWyv+UI8ri8ICTGOBtrGQhUoO9HtD38B+J3unFW1+KpbyRpcB46a2jjEQgF5NxLx2EvHET94TKSinKx5+fEtD9cBoJ4m51U7z2G0AgyRhZhsHWc0lej15ExcjDVe45Fygx2S2jhUg/xONxsWrya07uPA1B3oor1v32da3/4ecydyADZXuoDoRWMu2YKm5a8FylLHZSO1+km6A9Gtnl01DdTumwjJ3YeI29UAdNvmYk1tWv7KbiaXdSdrGXfxj3kjSwgf2RBj2eu9Lg8VJ6o4q2XVqPRCK675xoy8zMwDJA01fFABXoFCM2oOb7yAxwnqwA4ueETht40F1teduQYndlEytB8Uobm90gdPE1OSv/3NQLhrphj60uZ8rXbMHaQ5VFvNjL6livZp9Vy5sAJbNlpjLl9NoYOBkCjX8uF3+NFaAQ6ox6DtXN5foL+AKf3HI8qczc48Lm9Fwz0JruFqbdfwfsvrIyUFU4qwWAyMnruBGyZyRzcsJvk3FRKZoxm7bPLmf2VhZhsZtzNLlY9u5xjn4Zy1FQfPU31sUpu/N7tnR60DfgD7P5gF289uzxSNuKy0dzwzUVYenDgt7ayjice+FUkyd22D3bw4799n6z8zB57TSWaCvQKAH6XOxLkAZCSkx9+ypAbruq1rQNPfrwnEuQB/E4PVTsOUTCzTU69CGOSlTG3X4Xf60Oj1WKwXriu7iYnW15YQd3R0IyfnHFDmHDnbIyt+tplUOJqdFD28X78Hh9FM0ZjSrIihCA5N52Gk2c3adEadJ36FKHRaiieNpz0omyOfryfrKGDyC4ZhCm8ufbwy8eQN7qQ0wdPsv+DnVzz0C0khadv+jy+SJBvUbHnBD6vj86monM1Onn3pbVRZfs/2sOCL1/bY4FeSsl7Sz+IBHmAYCDIR29t5qYHru+R11TaUoG+HwkGAgQ9XmQwGOqnNxljtp2bbGcxUtDno7dSZEgpCfra9q0HvL52jo7WMq2ys69TXro/EuQBTu84Qv2MMWSPKYqUuRsdrP6fv+F1hNYK7Fv1MQt++AVsWSnMuO9a1v/mNbwONxqdlkvvWYDB3LluNaPVTGaxmczinHaft6baGTptBEOmDo/62QohMFiMeJ1n+/Izi3MwmAwEA0GcjQ58bh96ox6T3YyugzTTgXbSI8tgz/2MhRDYUtp2L9m72OWkdI9aMNVPBP0BXJXVHF+xhmPLVlK+5j18zY6YXV9vs6C3R3c9ZE4c1WuteSEEg6aPiUrroNFpyZk4PKavEwwEqTt2uk153fHKqMcV2w9HgjyEcsvvX/sJwUCQlLx0rnv8i1z/P/dy48/vI2/ckJiPC5x7AzfZzVx17/zwkzD3gWu5/LNX8f5La9m17lPqTtbyt0de5IVvPEf57uMEfG1v3Ca7mRm3XB5VVji2CGMPJ1m7/PoZ2FsF+5SMZCbPntijr6lEU0nN+gm/08Wx5aui9nk1ZaQxaNZlMRuk9TU7qd6+H09dA2ljS7DmZMZkNarX4SLo8yO0GnRGQ4ezbAI+P56GZso+3BFaKHXZJRiSbWjbWSjVHad2HGHLCyuiymZ9705SCrIijw+s/5Rtr74XdcyQmWOZdNfsdhdu9QaPw42ryYWz0cGp/eW8+5fVkefyRhcyds4E3npmGUarifv+39ewtbMq19nopHxvGbve20HB6EJGzxyDtZ0WdyxJKWmsaeTg9kNoNBqGjhtKcnr3c783NTSj02kxd3J8JRGppGadEPB5kYEAAoHQatHo43e6WtDvjwryAO6auph2rehtFnJmjEcGgmi7ucNUC0+jg73/WIWruh6h1VA4eypZ40ravYFo9TosGSkMu34mQNSc+lhKG5rLyOsu5fA7n6LRaRl1/Qws5wSe/Ikl7F6+EV94zECj1TD86kkxD/KuBgfVxyqpKz9D4cShWFNtHXYDGa0mjFYTOoOON59YEvVcxZ4TXPmFuUDohuDvYIqpJcnC8OkjGDqlBG0vbX4ihCA5I5kpcyfH5HrNjQ52bdnLW39fgz3Fxme+fivZBVnoY/Q7m4gG7Hcm4PXSsG83fkczAIbUNOxDh6HVx+eUL41Oh0anC6VDCDNnZcQ8R7tGo4EYXTPg9XFi/ce4quuB0PTJ42s3kza88LyfFHoqwLcwWs2UzJ3I4BmjATBYTW2mPZqSLMz/0ec5/P5O/B4fw64ajzm147w1Aa8fr8uD1qDrdH+9q8nJut//i7LtRwH46OV1XPvd2xk8cWiHYy/ORgdNNY3nHZuxpdsv2JXU1SDvdrjRaDUY4mCV7t5PDvDbR38febxj0x5+98bPSM9O68NaxbcBGeillLirqyJBHsBbV4u/qQltWvc2xe4pGqOBQbMv5/SHW/A7XRjTUsmeMfmi0g/0loDPT/PpM23KPfVNmFL6NtmXVqdDm9Txr79Gq8WalsS4my4PLVI7T2B1NTrZtWIzFduPkJKfwZS7rsKWceEdizwOdyTIAyBh0yvryRqai6WdqZrBQJC97+3k9MEKJt9wKR/8dV3kucJLijlTdoaMwkxu+O5tMZsb73K4qDhQwbuvvovZZmb+F+eTmpPa4WBvT3M0OVn16rqoMq/Hy95PDzLzmul9Uqf+YEAGeqTE72hqU+xzNGOM10Cv1WLKSKfgmjlIGURotHGXzfFcWoOe5OI83LWNZwuFwBTjvTllUOJ1uECC3mqK6SeCYDCIp8lFc3UDBqsRk90SlajM5/ay9R/vcnTjXgCaquqpK6vmmv/6zAXn1beXdtnn9kXtq9ua2+Fm/4bdnD50kkEjC7jxkTs4vu0wOcPyGDQyH2eDk9t++FmSMmOXvvrkoZM8//Dzkcd7Nu7he3/5HikxfI2u0Om1pLSzAU1qRuLu9xoLnfqLEEIcE0LsFEJsE0KUhsvShBBrhBAHw/+nhsuFEOIZIcQhIcQOIcSknnwDF0NoNJgystqUx2uQbyE0Ap3ZhN5iiYsgL6XE1+zEXdeIt9lJ4JzpkVq9jvzLJ5A6rBAAvc3MiNvmxnSFr8/loWr3UT7+/ZtsfvZ1yjftxtuN9MnncpxpZOXjL7H+qX+w8scvUfryOjzNzsjzfo+P41v2R53TXN0Q6ds/H5PdQsqg6O6GcQuntsmd00Jv1JFeGFpk9M6Lq3h/8Vo8Tg9p+Rks+cFL1JSd6TBb5sXwurx88PoHUWU+j48DpQdi9hpdZTQZuf2BG7G0ep/Dxg4hf4jKB3Q+XWnRz5ZStv4c/iiwTkr5pBDi0fDjR4BrgWHhf9OB34f/jyt6exLWwiKcJysQGoGtoAitceDsmRkLnrpGDry6Bm+TA6HTUjR/BinDCqNm1RhsZkpuuIKgLwAilKgsluMK7vpmtv91VeTxvmUbsKQnkzGysNvX9rm97Fj6QdQ0y/JPDjL62mkYbWeDsTU9iaaq+shjoRFoddqz+8h2wJJs5cYffI7daz+h5kQVI68cR+7Igg4/keiNBmbcOYuyXcdprKqnobKOgkuKsKXa+MJT92O0mrrUh+73+XE2Oqk+UYU9PQlbihVLq1XIQiuwprT9VNJeWW/KyE3nN//8KUf2HcOWZCOnIIvkGH9KTDTd6bpZBFwV/nox8C6hQL8IeEmGpoNsEkKkCCFypZSnulPRWNPo9Vhy8zBlZkcex2rx0UDgd7k5tvIjvE2hufzSH+DYyo+4pDCnzfRJnckIPXQPrdp9tE1ZxdZ9pJXkdXuGTMAfwFHTtovPWd9MamHoE6EpycKMe+ez9tevR7pixt04g7LtR6g8UM6Emy7Dlpnc4eCnNdXGlFtmEvAHOrVna1JGMp954l48zW50Bh0GixHTRbbiq09U86fv/AGfJ7QobdL8ySy47+wqWb1Bz5zPzGHneztxhz8lZQ/OpjAGN9Hu0Gq1pGamMDlzQp/Woz/pbKCXwGohhAT+KKV8HshuFbxPAy1JUfKAslbnlofL4irQQ6gLR2uI38HMeBYMBHFW1UaVyWCQgMcHvTjOas9t292WNCgzJp8aDBYjxTNGRy2m0uq1pLaaby+EIL04l5t/cT+NlXWYkiwcLz3Apr+uAQll2w5x88/vO+9OUxqtpkvjCtYUW7fnvjsbnaz4/b8iQR7gk9VbufLOWVHpEFKyUvjOi9/h6M6jmG1mBpUMwn6e2UdKfOpsoJ8ppawQQmQBa4QQUTtQSCll+CbQaUKIB4AHAAoLu95CkMEgMuAPpQPQaBBaXcynGiod0+p1JBUNov7giUiZxqBH28vT71IG55A2LI/agxUA2HLSGDRlRIcZL7tCo9FQOHUEAZ+fIxt2YUq2MvGOWW36wXUGHTqDDa1Rx3v/+y9O7joWec7v9lF1qILiaSO7XZ9YCvgDNIS3PGzN2egifdDZx1qtluSMZCbMVq3n/qxTgV5KWRH+v0oIsRSYBlS2dMkIIXKBloxYFUBBq9Pzw2XnXvN54HkIrYztSqVlMIivqYHmE0dBhvK+2IuHozVbVPdLL9EaDRRePZ2gz0/jsZMYU5MoXjgTXSfnkMeKwWZm3Gfn4XeFcgDpLUYMttgl6DLazAy/ehKDLx2F9gJJ04L+YLtz6DsaXO1LZruZiVdP5J2X10eVpWRdeFqo0v9cMNALIayARkrZFP56PvATYDlwN/Bk+P9l4VOWA98QQiwhNAjbEOv+eRnw01x2NLJnqgwEaD5xhKShIxFxvLo10RhsFobccCVBfwAhBDqLqU9utAarudNphi+GRqPB3EGq5BauRgcfvriSS66bTvmOI/jDXSLpg7NJyYu/2Vw6vY7pN8xAZ9Cxfd02UnPTuObLC7EmX3yXkN/nx9HoxOsKbYxiTbKh0/dNugglWmda9NnA0vAfsA54RUq5UgjxMfCqEOI+4DhwR/j4t4CFwCHACdwb60rLYBCC0Vn4gl4PMd/rTrmgeJjmCaFc9gGvD41Oi87Y+WyWsVJz9DQndxxFBoMsePgOzhw9jTXNTubQQRe8SfQVa7KVy2+5gknzpqAz6DB1IsVzRwKBAMf2HOf577+Aq9mNxW7hKz+/n6LRg3t8pbNyYRcM9FLKI0CbhOBSyhpgbjvlEvh6TGrXAaHRoNEbCPrOzlXWWWwg1C/UQOSqb6L0+X/hqK5HaDQMWzCNghlj0PdiN1LL7JxTu45zes8JUvMzSS/KJnfM4Iu+pqvRAUJg7sGuH61Oiy0GKYOb6x38+fHFuJpDs3OcTU7+/PhivvfHh2KSwEzpnn65Mlbo9NiLh9FcdpSAy4nOZseaX4xG1y/fjtINfo+XA29twtGSTycY5MDbm8gZN7RXA33euGI0Wg3BQBAZlNSeqGLibVd0uFH4+Xgcbir2nmDLax+g0Wi49K5Z5JTkYbDEx6en9gT8AZpqo6eiNpxpINDO6t/+wu1y09zkRAC2JBvGOMjzc7H6ZWQUQqA1mbEXDwv11gihgvwAFfD6aSyvblPurG3E0ol8M7FiSrJy7Q8+yyf/fB+f28voBVNIzc+44KKp9tSdqmHFU/+MPF72s7/zmae+TEZh29XcF8vV5MTr8iIBg8nQ7R2mdHodWQWZVJWd/VnkFuegN/TPv8uGukZe+dMbLH35LbRaDZ994FZuvOsakvs4R9PF6td9HRqdHo1er4L8AKYzGcgcFd09IjQarFmpvVsPg470ohyu+OoNXP7AdZzeV87yH71E6T/eDXXBdJIMSnav/bRN+f4Pdsasro56B/9+djm/vedX/O6eX/HGr17DUd984RPPIynNzlee+DIFI0IT7gaPLOTLP72v386531G6h1f/sgyf14fb5eHPT7/C8UNlFz4xTqkIqfRrWr2O4qsm4m50UrnjEMYkK2Nvuwp9H3VzBP0B/v3jl/C5QuNH+9ZvQ2g0TL7jSnQdbLjSmtAIUvMy2pSnDIrdzJ2yfSfY/cGuyONDpQfYv2U/k+Z3L198Vn4mX/vFAwQCQbRaTbtbCDqanFSXV7N5TSmFwwsYM30USXF2Mwj4A7y/emOb8k3vlTJuyug+qFH3qUCvxITP4UJKidag73AHqZ5itFsYc+uVjLzhstDeqlZzTBZMXQxnXXMkyLc4VrqfS66f3qlADzDiirHsWvsJDafrAEgvyKR4YknM6ni81YKuSB13HGXC1RND+xF0Q3vBvUUwEGTnR7v58/+8FCkbeskQvvbz++Oq5a/VaZk8Yxxr//1+VPn4qWP6qEbdpwK90i0Bnx9XVS1HV36It9FB6ogiCmZNRt/L27vpTUb0cTDV02Rv+76TslPRdqF70Zpi47af3E1jVT1CI7BnJLebn/5ijZoxmo1vfBhVNvbKS7od5C+kuaGZf734VlTZ4Z1HcDa54irQA1x61RRmXj2dDWs3I4Rg/k2zGTE2djfb3qYCvdItAbeHff9YhQzPrqjZdQid0UD+lZPRDMDFMnqzkfGLLmP78o9Ahrb/m/HFeRhtXZujbkm2xjS4t5ZRmMm8Ly3g/SXvEgwEmXHL5eSPyO/WNX1eH64mF3qjHnMHSdYkEGwn13487Ft9rpS0ZL7306/zzf+6HwSYLSbsST27t25PUoFe6RZPfVMkyLeoP3SCnOmXYNAPvE2bjVYTYxZMZtiVl+B1uDElWeIuBYLFbmH6ohmMmxPKX2OymdB3o7utqa6Jta+sZ/sHO8guzOLWb95CRl56m08ItmQr1929gJeefCVSNnhEAdZuzvjpKUnJdpKS4+uTxsVSgV7pFr29bavTlJE6IFvzLQwWEwaLCeJ4oZBOr8N+noyaneVxe3j7/1bx4fKPAKirrOOZb/0/Hv7Td0k65/1rtVomzhpPdkEWG1duZvCIQiZeOS7uum0SkQr0SqcFvD78TjdN5acxpSZjTE1CZzIw6PIJnPxoO0iJwW6hcO40dJ1cKOR3e/E2u2g4fhJrdjrmVHuv9+/HE4/DTdOZRo59cpDMohyyhubEbQoFAI/Dw6fvbIsqa6prxtHkbBPoAax2C8PGD2XY+KG9VUUFFejjSjAYJOjxgpQIrQatse8HF1tIKWk+Wc2hpesie5qmDh9M4dxLyZ4ymqyJIwj6AgidFr2lc/3RMhCk7nA5+/55drPn7EkjKZ43rVdXtcaLYDDI8W2HWfP/lkXKiqcMY85Xrou77p8WGq2GtNw0nE1nt1cUQmDq5O+A0jv69YKpRBL0+XGWn6R81XpOrFhN5aZS/K7Y7X3aXX6Xm/J3P47auLruwHH8Hi8Bt5eaPUdorqhCQKczWPpcbo6s2hRVVvnJPoJeXwdnJDZ3o5ONr7wTVXa09CBe14X3n+0rthQbdz50e9QWhvO/MA9THKdrGIhUiz5OBLxeqjZvjTx2V52hbs9+0seP7faWeDEhIeBpG3CCPh97X36LoDe0MbgpLYmRn7m2090vfren7TUDwXaO7L+8bi/OumaObt5Hcm4auSMLMLczo0ZCmw3WIdTSj2eDhuTyg78+Ru3pOuxpdix2c4czb5S+oVr0ccLX1HYJurvqDEF/fLRutUYDGeNHRJUZU+wEvP5IkAdw1zbiqKrp9DVzJ4+KKrPmpPX6gquedubIaV575AW2vr6B9c8uZ+WvXms3LYLJZmL8ddOiyjKLc9rdzCSe6PQ6kjOSKR5bRMagdCytupkC/gD+fpzYLFGoFn2c0NvbztE1ZaTFTR4fjU5L1vjhGGwWavcewZyRQvaUMVRsaJuXpXXgPx+tXkf+zAmY05M5s/sItrxM8qaPxZBArUF3k5OPX30vaquEmmOVOGqb2wyyanU6xsyZSOqgdA5s2E3W0FxGXnlJj82n70k+n4/6qgbW/uMdgoEgV985m9TsVAxdTPCmxEZ8RBEFjUFPxuTx1GzbhQwEMKalkjp2ZNwEegCd2UT6mKGkDitEaLVodFrSx5RwZuehyDFakxFbXuezLBqsJnImjSRz7BA0Ol18dFO14mpw0FBxBgQkD8pot8vlfKSEgK9ti7aj9L0mu5khU0dQOH4oWp22z1I5dFfjmUZ+evcv8IXHWza9vYUfvvQoWfmZMXuNhtpGDu0+yuG9R5k6axJZgzKwxumgdV+LnygywGn1emyF+Vhys5FBiUanjatZNy2EEGhbTZ20ZKcx6vPXUVm6G73VQvbU0V2eHik0Im52qmrN1eBg7ZN/xxnOs25NT2LuI3d1Kdib7GbG3zCdd577V6TMlpmM/QIplHX9NL1vi41vb4kEeQgnClu6gdu+eXPUcc2NDhwNDurPNJCVn4ktxYq+E9uBNtY38/v/+Qul74emdr76x2V88yf3M/OaS9Fq46uxEA/6929Tggm1aPvPj8TncFF/qAy/y03elZPRmY2dnj/fHxzfvDcS5AEcNY2UlR5g+NyJnb6GEIL8S4q57vt3sXfddpJz0xg5ezyWlP7XHdMVhnY26dCfU+ZocrJi8SpW/X1t6Byjnkf+9yGKR114Vy630xUJ8i1eee4Nxl86lpR0tcH5udRgrHJRfA4XB15dzfHVG6n44FN2/ekNHBVVfV2tmHKcs2NSqKyxy9cxWk3kjixk1gMLmXjTDKwx2Lov3k2bNzkqtYHZZuaKGy+LOsbtcEWCPIDX4+OvT/2dpvq23/dzBQNt8+P4PL64zJsTD/pP81GJK94mJ64z9VFlFRu2YcnJ6PSCqd4Q9AeQQYn2IrpChl5xCYfe3XZ2IFXAkMvHXnRdtAMoLURSWhLf/79H2P7BToKBIBNmjSPpnJQLLkfbdSI1lbUEOjG91mwzMXR0MYf3HI2U3fCFa7D148RjPanTv/1CCC1QClRIKa8XQswFniL0qaAZuEdKeUgIYQReAiYDNcCdUspjMa+50rfaaznFUWsqGAjgqmvm8Lqt+Nxehs6ehC0rBV0X9v20picx57t3sOtfGxEIxt44A0sM8sMMBBqthpSMZGbdPLPDY+wpNpLSkmhs9Snp0nlTsXRi1lVyahKP/e5bfPD2Rg7sOsKshTMYMX5Yv926sKeJzn7UEUI8BEwBksKB/gCwSEq5VwjxNWCalPKe8NfjpJT/IYS4C7hZSnnn+a49ZcoUWVpa2s23ovQmn8PF/iUrcbf6Ix1602xShxX2Ya3OctU3896TrxBoGRAUcPmDt5NS0PV9V71ONyDienPu/igYDFJdcYYlz7zGqeOVTJ0ziXl3zunSjlNSSvw+f7eyb/ZnQoitUsopFzquU7c/IUQ+cB3wM+ChcLEEWrIWJQMnw18vAn4c/vo14FkhhJCq8yyh6K1mRty5gJp9x3DX1JM5bjjG1PjJ1li159jZIA8g4cg7nzL+rrld7sYxxFFXVCLRaDRkF2Tx5f++F5/HhyXJgl7ftZ+NEGLABvmu6Ox39XfAw0DrW+39wFtCCBfQCFwaLs8DygCklH4hRAOQDpyJSY2VuKG3WciJ0z002+ui0ZsM/XZeeiKz2MyQQIvk4tEFZ90IIa4HqqSUW8956tvAQillPvAX4DddeWEhxANCiFIhRGl1dXVXTlWUC0ovycPcqj9da9AzZM6kuFuQpZzlcXmoP9NAY92FZ90oXXPBPnohxBPAFwA/YCLUXfMOMFJKOTR8TCGwUko5WgixCvixlHKjEEIHnAYyz9d1o/ro44ff7cHX7MRxsgpLTiaGJGu7i5kCPj++Zic1uw+jt1lIKSnAYIuvVYnuRge1hyvwubxkjS7CaDejUYtp4lJjXRNvPP8vtqwtJS07jXsf+xyFw/JVt8wFdLaPvtODseGLXgV8F7iJUAC/TEp5QAhxH6HW/a1CiK8Dl7QajL1FSnnH+a6rAn18CPr8nNl5gFMfnc1fkz3tEjInjkJ7zmpFR2UN+/62AhkM/f4YkqyM+vx1A3rTEOXi+Lw+XvvDMlb9/ey+BAajnl/88yekZqb0Yc3iX2cD/UUtmJJS+oEvA68LIbYTavF/L/z0i0C6EOIQoYHbRy/mNZTeF/B6Ob1lR1RZVelugh7fOcf5OPnhtkiQB/A2OnQePLkAAA6NSURBVHCc7lzWSkVpzdnk5JP3tkeVeT0+qipUl26sdGmIW0r5LvBu+OulwNJ2jnEDt8egbkpvk7TZ6FsGg21WG0opo4J862MVpav0RgODinOoPhk9XyM1M7WPapR4VAoEJUKj15FcEj0P3l6Uh+acKW86o4FBM8ZFl1lNWHMzeryOSuKx2Mx89sE7SAknehNCcOO9C6NSKCjd06U++p6i+ujjh8/ppv7AURqPn8Sen0vqqCHtpjQIeLx4Gpqp+mQferuFzHHD0Nssnd5GMJH4PT78Xh8Gs1HN6rlIUkoaahtxNrkwmY2YrKZOrZAd6HpkMLanqEAfX2QwSNDnR6PXITTn/9Ang0EQYkAGeABHXRM73vyIuhNV5E8qYfhV4+N2I28l8cR0ZawysAiNJirn/IWOHahcjU7WPfVPGivrAKg9UYWrrplJd8xqk5JX6TvBYJCG2ka8Hi96g4HkVDvaAfbJSwV6ZUDxNLtorqqjseIMGcMLMCVZ+P/t3Xtw3NV1wPHv2V3tw5ZlPSzJsiRLtmWBwa9imzQhBEgaEhIGN5M2EAJ10gelHSYQ8nRnOnl0SOiUQsp0hgwktAlJx2ESIIyTmdRT28UeMI1lDBg7GL+ELfQw6GHJ1kr7OP1jf1JWD9trWbtr3T0fz452r/Zxr3+jo6vzu79zi6a4J2s8Ojwa5EcceXE/K9d/wKlA39/Tz+s7XqfjSAdrP76WyrrKGbX599uHT/Cdex7iZMd7lJaXsPHhL3HZiiUFFewt0JuCMXwmyr5nd3Did2+Otq37y5uoWbl4Sn+Z+AJ+EMbsBxt27ATiQM8AT3z9CTqOdgCw69e7uOMf72DFtStmRLqu970+Hvzyo5z0lv72dp/iu/c/wqNPf5fyAlrVU7h/d5uCE4/GxgR5gH3P7WRoYHBK71cUDnLFx9aNPhYRrr7zT5zK0ff39I8G+RFbntrCQO9Annp0YeLxBG2t7WPaUmmc2Fle4Sab0ZuCMdk6//jg8JTfLzgrxJU3rWPxNVfQ39FDWUM1odlh5wunifdvJggU+Wloqqf10PHRtvKqMkIOpdYyYTN6UzACoSJKFlSMaVt07Yop5+gBQsURShfMo/6qpRRXlDiVmweYUzaHmiU1Y9pu3HAjs2fInrel5XPZ+PC9LGyqA2B+XRXf/PevUnIJldTOBVteaQpKtO80R154jd7jXdStaab6ykZCM+jEYj709/Sz/6X9dBztYM2Na6ioqZhRJ2MhlZuPD8fwFwUoLS+ZEecXMmHr6I05i2Q8SSIeIxAKOvMDbwqTraM35ix8AR++gG0LaAqH5eiNMcZxFuiNMcZxlroxORE7E2XwvT56j7Qxt6GGWVVlkxZLM8ZMPwv0BSIZj6cKlQWLcr6dXmI4TvvuA5zYkdq56sTOvcxft4yF160hkGFNHWPM1FnqpgDETg/S8dIrtP5mOydb9hEfjOb08xNDw7zz0tidqzp2/57EcGFdnWhMvtiMPkuSiQQai6GqiM+fcTXI6RYfjHL8ty8QfS9VfGuou5fhUwMs+NA6/MHc9EnRiVelqqZuxpissxl9FiTjcQbbO2j/3x20b93Ouy17iEdzO4tO78tIkB/Rf+wEyVjiLK+Yfv5gEVWrmse0VVzeiG/chuPGmOywGX0WJGMxul/9Q6piuKeHU2++RemVy/AFcvtfLj4f+HyQNqP2h4PkslRJIBRk4fVrmFNfTc9bxyldXEv50oUXVXrAGJO5jGf0IuIXkVdEZLP3WETkARE5KCIHROSLae2PisghEXlNRK7KVucvVfGB0xPahrq7J2y8nQu+YBGVa5YTLCnGF0zNoGuuWUsgnNsgWzQrTNWKJpau/xDVq5spmm0rbozJlQuZXt4LHABGqgF9HqgHLlfVpIhUee03AUu92/uAx7yvBSNQPLHgU6i8HMnDRgciPuY2NRAqLaFodgR/JIw/GMzbzlC5XvFjjMkw0ItIHfBJ4AHgfq/574DbVTUJoKpdXvt64CeaKqKzS0RKRaRGVdvHv6+rfEVFlK9aSc8b+9F4nGBZGSWXLc152gYg2t1L6+atoydD5zYvYv7Vq3Pej1xIxOIM9Z+hreVNAqEgNauaCJUU5oblxqTLNPJ8H/gaMCetbQlwq4h8CjgJfFFV3wJqgeNpzzvhtRVOoA8EiCyoITyvIrXqxu/P2QqXdPFolI4X94xZ8dJ38CiVq69I5ekdE+0dYMdDm0h6KbLD/9PCB798K+GSmVFS15hsOe/f7yJyM9Clqi3jvhUCol7ltCeAJy/kg0XkLhHZLSK7T548eSEvnRF8Ph/+cJhAJJKXIA+gSSV+ZuLuScmYe+vXk4kEh7ftGQ3yAMMDg5w80JrHXhlzacgkUXsNcIuIHAM2AR8WkZ+Smqk/4z3nWWCld7+NVO5+RJ3XNoaqPq6qa1V1bWVl5RS7b87FHwpS2rxoTFsgEsYfcfBEqIImJu4glZykzZhCc95Ar6obVbVOVRuB24CtqnoH8Bxwg/e064CD3v3ngb/wVt/8MdBXSPn5S4nP76d8eTNVV68iXFHKnEX1NN7yEQIOBnpfwM/iG64ac5I5EA5SfWVj/jplzCXiYs4OPgj8TES+BAwAf+21/wb4BHAIOAN84aJ6aC5KIByiYnkzpUsbkYAfv8MXKUXK53DtV2+jdcdr+MNBGj6wnGCxOxt1GzNVtsOUcY4mkyBiq22M82yHKVOw8nWNgDGXKvuJMMYYx1mgN8YYx1mgN8YYx1mO3hhHJRIJTvee5vgbrURKZlHVUE1xWXG+u2XywAK9MY7q6+zlh/f9gOHBIQCqGqu5/Z82UFxqwb7QWOrGcapKMhbnUlhGa3InNhTjhU3bR4M8QNexTjoP27WLhchm9A6LDw7R/3Yb/a1tzK6pYm5Tg5NXxZqJkokEg6fOTGg/c2riXgnGfTajd1QiFqOr5XXad/yOgbffofPlvbRtf5l4dOj8LzYzXmhWmPetf/+YtqJwkIYVi87yCuMym9E7KhmL03vw6Ji2020daDxOqvCocd2CpbV89tt3suvZF4nMmcV1n7uB2ZafL0gW6F2l4PP7SKbVokckdTMFIVwcYclVS6m9rB6/30eRg3sQzHTR6BCBgJ9AljclskDvKH8oSOWa5XTu2jvaVrasCZ/DRc3M5MK2P+8l51RfPwf2HeTnTz1HfUMtt3/h01TPz165dgv0jvIF/MxtamTW/CpOv9NJpKqCUGkJ/qAFemPySVXZtbOFr/z9N0fbfv3sFjZtfpx5VRVZ+UwL9A4LhEMEwiEi88ry3RVjjKe3u48nH/uvMW1dne/SevRE1gK9rboxxpgc8vl9RCITF0SEwtlbJGGB3hhjcmhuaQn3feNu/H7/aNuy5c0sqK3O2mda6sYYY3KsedkSnt/2FFv/eyf1Cxewas1yyrOYYrVAb4wxORaZFaa+oZYNf3NrTj7PUjfGGOM4C/TGGOO4jAO9iPhF5BUR2Tyu/VERGUh7HBKRn4vIIRF5WUQap6+7xhhjLtSFzOjvBQ6kN4jIWmD8GYS/AnpUtQl4BPjni+qhMcaYi5JRoBeROuCTwA/T2vzAvwBfG/f09cCPvfu/AD4iYgVWjDEmXzKd0X+fVEBPq5DFPcDzqjp+J4Na4DiAqsaBPiA7l3sZY4w5r/MurxSRm4EuVW0Rkeu9tgXAnwPXT/WDReQu4C7v4YCIvDnV98qTecC7+e5EjhXamG287pvpY27I5Elyvi3mROR7wJ1AHAgDJcCQd4t6T1sIHFHVJhH5LfAtVX1JRAJAB1Cpju1lJyK7VXVtvvuRS4U2Zhuv+wplzOdN3ajqRlWtU9VG4DZgq6qWqep8VW302s94J18Bngc2ePf/zHu+U0HeGGNmkmxcGfsj4CkROQR0k/rlYIwxJk8uKNCr6nZg+yTtxWn3o6Ty9657PN8dyINCG7ON130FMebz5uiNMcbMbFYCwRhjHGeBPgMiUi8i20Rkv4i8ISL3eu3lIrJFRN7yvjqxldM5xvstEWkTkb3e7RP57ut0EZGwiPyfiLzqjfnbXvsir5THIa+0hxM7bJ9jvP8pIkfTjvHqfPd1Oo0v5eLq8R3PUjcZEJEaoEZV94jIHKAF+FPg80C3qj4oIt8AylT163ns6rQ4x3g/Awyo6kN57WAWeFdvz1bVAREpAnaSKvtxP/CMqm4SkR8Ar6rqY/ns63Q4x3jvBjar6i/y2sEsEZH7gbVAiareLCJP4+DxHc9m9BlQ1XZV3ePd7ydV86eWseUefkwqGM545xivszRlpDhfkXdT4MOkSnmAW8f4bON11vhSLt4vOyeP73gW6C+QV43zj4CXgeq0EhAdQPb2AsuTceMFuEdEXhORJ11JVY3w/qzfC3QBW4DDQK9XygPgBA79whs/XlUdOcYPeMf4ERHJ3kamuTe+lEsFDh/fdBboL4CIFAO/BO5T1VPp3/MuCnNqRjTJeB8DlgCrgXbgX/PYvWmnqglVXQ3UAVcDl+e5S1k1frwishzYSGrc64ByYManImFsKZd89yUfLNBnyMtj/hL4mao+4zV3evnskbx2V776N90mG6+qdnrBIQk8QSoYOkdVe4FtwPuBUq+UB6QCYlveOpYlaeP9uJe2U1UdAv4Dd47xNcAtInIM2EQqZfNvFMDxBQv0GfFyeT8CDqjqw2nfSi/3sAH4Va77lg1nG+/ILzXPp4B9ue5btohIpYiUevcjwEdJnZvYRqqUB7h1jCcb7+/TJi5CKl/txDE+SymXz+Ho8R3PVt1kQEQ+COwAXucP+b1/IJW3fppUUbdW4DOq2p2XTk6jc4z3s6TSNgocA/52kjLVM5KIrCR1Ms5PagL0tKp+R0QWk5oBlgOvAHd4s90Z7Rzj3QpUAgLsBe5OO2nrBK8K71e8VTdOHt/xLNAbY4zjLHVjjDGOs0BvjDGOs0BvjDGOs0BvjDGOs0BvjDGOs0BvjDGOs0BvjDGOs0BvjDGO+39yes3bL3CeewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(ages,purchasing,hue=ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "purchasing=ages*2+ages**2+ages**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10019b4e10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(ages,purchasing,hue=ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from pylab import *\n",
    "\n",
    "def de_mean(x):\n",
    "    xmean = mean(x)\n",
    "    return [xi - xmean for xi in x]\n",
    "\n",
    "def covariance(x, y):\n",
    "    n = len(x)\n",
    "    return np.dot(de_mean(x),de_mean(y))/n-1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84638.82122530241"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covariance(ages,purchasing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5685691994391844"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.kurtosis(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 3.,  3., 12., 17., 11., 21., 12., 12.,  7.,  2.]),\n",
       " array([17.5163399 , 20.03831969, 22.56029947, 25.08227926, 27.60425905,\n",
       "        30.12623884, 32.64821863, 35.17019842, 37.69217821, 40.214158  ,\n",
       "        42.73613779]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=sp.expon.pdf(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([97.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([2.75379229e-19, 2.47030308e-09, 4.94060616e-09, 7.41090924e-09,\n",
       "        9.88121232e-09, 1.23515154e-08, 1.48218185e-08, 1.72921216e-08,\n",
       "        1.97624246e-08, 2.22327277e-08, 2.47030308e-08]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rXLzQ0EvSmHWL1H0NeH2SPUk2A+8CNie5H3iQ4X9b3HYGv1L0AQa/TvT+qvrsisbjomaS1DZf0UtS4wy9JDXO0EtS4wy9JDXO0EtS4wy9JDXO0EtS4wy9JDXufwHDApL9jJdTQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.67295611824981"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.kurtosis(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}