day_2_notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import scipy.stats as scs\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_distributions(distr, continuous, X, title, x_label, x_lim):\n",
    "    title_save = '_'.join(title.lower().split())\n",
    "    \n",
    "    if continuous: \n",
    "        y = [distr.pdf(k) for k in X]\n",
    "        plt.plot(X, y, color='r')\n",
    "\n",
    "    else: \n",
    "        y = [distr.pmf(k) for k in X]\n",
    "        plt.bar(X, y, color='r')\n",
    "\n",
    "    plt.xlabel(x_label)    \n",
    "    plt.xlim(x_lim)\n",
    "    plt.title('Example: {}'.format(title))\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('question_{}_pdf.png'.format(title_save))\n",
    "    plt.show()\n",
    "\n",
    "    y_cdf = [distr.cdf(k) for k in X]\n",
    "    if continuous: \n",
    "        y = [distr.pdf(k) for k in X]\n",
    "        plt.plot(X, y_cdf, color='r')\n",
    "\n",
    "    else: \n",
    "        y = [distr.pmf(k) for k in X]\n",
    "        plt.bar(X, y_cdf, color='r')\n",
    "\n",
    "    plt.xlabel(x_label)\n",
    "    plt.xlim(x_lim)\n",
    "    plt.title('Example: {}'.format(title))\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('question_{}_pdf.png'.format(title_save))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BINOMIAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TcH09wEPAQuCZEfFXwBMj4guZ+erRnqivr7+1qttMo9Hj2DvQZIy9MUxbX1//mNvbaVuPtU7/nR+LVgJqA3AKcFVEzAc2N/VtAs6rd+nNAI4ANmXmI9OmiPhZK+Ekafw0Dpu5V1vf3dsnoRJp/7USUOuARRGxEegClkbEGUBvZq6PiDXATVTHs87KzB0TV64kqVOMGlCZuQdYOaR5a1P/WmDtPtafvd/VSdIEc7ZZLj+oK0kqkgElSSqSASVJKpIBJUkqkgElSSqSASVJKpIBJUkqkgElSSqSASVJKpIBJUkqkgElSSqSASVJKpIBJUkqkgElSSqSASVJKpIBJUkqkgElSSqSASVJKtKot3yPiGnARcBcYCewLDN7m/qXAyuAXcC5mXlNRMwGrgQOAf4dOC0zH5yA+iVJbaqVGdQSoDszFwBnAqsHO+ogWgUcCywGLoiIGfVyl2XmQuBOqgCTJKllrQTUccC1AJl5CzCvqe9oYENm7szMbUAvMAd4B3BFPfs6HPj5uFYtSWp7o+7iA2YC25oe746I6Zm5a5i+fmBWZg5ExHTgO0A38P5Wimk0elqrug059s50QGPv6tq7bWBgzM81Xu2dsq3x0Mm/82PRSkBtB5p/mtPqcBqurwe4DyAzHwaeHREvBi4HThjtifr6+lupue00Gj2OvQMd6Ngbw7QNbm+kvvFqH8/nKHlbE6HTf+fHopWA2gCcAlwVEfOBzU19m4DzIqIbmAEcAWyJiIuAL2bmjVSzqj1jqkqSCtA4bOZebX13b5+ESjpTKwG1DlgUERuBLmBpRJwB9Gbm+ohYA9xEdTzrrMzcUbddHBHvowqnt0xQ/ZKkNjVqQGXmHmDlkOatTf1rgbVD1tkKnDgO9UmSOpQf1JUkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFamVO+pKmkTedlydyoCSpDHyj4bHxqgBFRHTgIuAucBOYFlm9jb1LwdWALuAczPzmoh4GvDpevtdwJszMyegfklSm2rlGNQSoDszFwBnAqsHOyJiNrAKOBZYDFwQETOAvwE+npknAucDF4xz3ZKkNtdKQB0HXAuQmbcA85r6jgY2ZObOzNwG9AJzgHcC/1wvMx3YMW4VS5I6QivHoGYC25oe746I6Zm5a5i+fmBWZt4DEBEBXEg1CxtVo9HTUtHtyLF3pv0d+0jr7Wt7Y11nMp+jnbZ1oMt2slYCajvQ/NOcVofTcH09wH0AEfECqmNXf97q8ae+vv5WFms7jUaPY+9ArY69MUxbX1//iO37s85kPkc7basVnf47Pxat7OLbAJwEEBHzgc1NfZuAhRHRHRGzgCOALXU4fRR4SWbeOqaKJEmitRnUOmBRRGykOiNvaUScAfRm5vqIWAPcRBV2Z2Xmjoj4W+AQ4LJqLx+ZmSsmZgiSpHY0akBl5h5g5ZDmrU39a4G1Q9aZOy7VSZI6lpc6kiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyftBSdI48T5R48uAkgrhm5v0aO7ikyQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFWnUK0lExDTgImAusBNYlpm9Tf3LgRXALuDczLymqe/twOzMPHO8C5cktbdWLnW0BOjOzAURMR9YDbwMICJmA6uAeUA3cHNE3EA1M1sLHANcPRGFS5LaWyu7+I4DrgXIzFuowmjQ0cCGzNyZmduAXmAOVVhdDpw3vuVK0tTTOGzmI190dQ173UXtrZUZ1ExgW9Pj3RExPTN3DdPXD8zKzF8C10fEaWMpptHoGcvibcWxd4iurkc9bAAMDIy4+Eg/m7G2j+e2Hovn6IRtddTv/X5qJaC2A80/yWl1OA3X1wPct7/F9PX17++qU1qj0ePYO0RjmLbB8Y/UN9b28dzWVKu31G3t6zk6yVhDuZVdfBuAkwDqY1Cbm/o2AQsjojsiZgFHAFvGVIEkScNoZQa1DlgUERuBLmBpRJwB9Gbm+ohYA9xEFXZnZeaOiStXktQpRg2ozNwDrBzSvLWpfy3VGXvDrXvpgRQnSepcflBXklQkA0qSVCQDSpJUpFZOkpAkTZDhPrTbd/f2SaikPAaUNAF805EOnLv4JElFMqAkSUUyoCRJRTKgJElFMqAkSUXyLD5JKpBnghpQ0gHxTUSaOO7ikyQVyYCSJBXJXXySNIV00m5lA0oaRSe9IUglcRefJKlIzqAkqU2022x/1ICKiGnARcBcYCewLDN7m/qXAyuAXcC5mXlNRDwJ+BzwOOAuYGlmPjgB9Uvjpt3+c0tTXSu7+JYA3Zm5ADgTWD3YERGzgVXAscBi4IKImAG8D/hcZi4Evk0VYFIRGofN3OtLamdT9Xe+lV18xwHXAmTmLRExr6nvaGBDZu4EdkZELzCnXuf8epmv1N9/ZNyqlmojzXqcDUmj29f/kxL+D7USUDOBbU2Pd0fE9MzcNUxfPzBrSPtg22i6Go2eFhZrT459Pw0M7L29fbXvzzqdvK2pVm+p25pq9Y7W9xhpZRffdqD5HWRaHU7D9fUA9w1pH2yTJKllrQTUBuAkgIiYD2xu6tsELIyI7oiYBRwBbGleB3gpcNO4VSxJ6ghdA8NM45o1ncU3B+gCllKFT29mrq/P4nszVdidn5lXR8STgcuoZk/3AK/NzAcmbhiSpHYzakBJkjQZvJKEJKlIBpQkqUgGlCSpSJN6Lb7RLqPUriLiGOCDmXliRDwTuBQYoDoD8vTM3DOZ9U2EiDgY+DTwdGAGcC5wJx0wdoCIOAhYCwSwm+pkoy46Z/yHAbcBi6gui3YpHTBugIj4Nr/+XOgPgb8DPkr1c7g+M/96smqbaBHxHuBU4BCq9/p/YQyv/WTPoEa8jFK7ioi/BC4BuuumDwPvrS8L1QW8bLJqm2CvA+6tx/lS4ON0ztgBTgHIzGOpLgX2YTpk/PUfJ38H/Kpu6ohxA0REN0Bmnlh/LQUuBl5LdcWdYyLiqMmscaJExInA86kuhXcCcDhjfO0nO6AedRklYN6+F28L3wde3vT4eVR/VUB1WagXP+YVPTa+CJzd9HgXnTN2MvMfqT6OAfA7wM/pnPFfSPWmfFf9uFPGDdXeocdHxPUR8fWIOB6YkZnfz8wB4DrgRZNb4oRZTPW52XXAl4FrGONrP9kBNexllCarmMdCZl4NPNzU1FX/okLrl4WacjLz/szsj4ge4H8C76VDxj4oM3dFxGXAx6h+Bm0//og4DejLzOuamtt+3E0epAroxcBK4DN126B2Hv+TqCYdr6Ia+5VUVyJq+bWf7IDa12WUOkXz/te2vixURBwO3Ah8NjM/RweNfVBmvgF4FtXxqMc1dbXr+N8ILIqIbwBHApcDhzX1t+u4B30PuCIzBzLze1R/kD+xqb+dx38vcF1mPpSZCezg0YE06tgnO6D2dRmlTvHtel8ttPFloeqri1wPvDszP103d8TYASLiz+sDxlD9Bb0HuLXdx5+Zx2fmCZl5InA78HrgK+0+7iZvpD62HhFPAR4PPBARvxsRXVQzq3Yd/83ASyKiqx77bwBfG8trP9m709ZR/XW1kV9fRqnTvBNYGxGHAP+XatdPO/rvwKHA2RExeCzqL4A1HTB2gC8Bn4mIbwIHA2+nGnMnvPZDdcrvPMDfA5dGxM1UZ669keqPkyuBg6jO4vvXSaxvwtQ3rz2e6pqt04DTqc5ibPm191JHkqQiTfYuPkmShmVASZKKZEBJkopkQEmSimRASZKKZEBJLYqIS+srIxQhIk6NiPePssw3mj530tx+TkScM1G1SeNhsj8HJWk/ZeZ6YP1k1yFNFANKbaueOXyI6gORW6g+LPz3wG8CTwEuzcz31bOil1BdguY/U3148i31J/1XA39MdaHTg4Bv1NteSvWB0wGq20i8NTPvj4ifAf8IHAP8jOoWI6uA3wZOy8zBC2USEXOAKzPzOfXjU4BlwCuATwJ/CDwZuAN4Tf39tcA9VFcGvxI4MTNPi4hX1fU8jup2Jm/MzI31U705Ij5Sf/+OzPzGkJ/TS4D3U32A+IfA8sy8d2w/bWn8uYtP7e5ZwAvra+C9Bvh8Zs4HngO8PSKeVC/3fKpgmAOcEhHPqR8/F/gDqgtePhOg7jsLOKEOlweAv6q382TgK5n5XKpbqvxJfWuBc6iuHvGIzLwD2BMRf1g3vRq4oq7lofo2NM+kCtST6mUCeF1mLhrcTn1ftZXAH2fmXKpQHrysEsD9dT1vAK6IiBlN6zaADwCL62WuAz7Yyg9WmmgGlNpdZua2+psLgZ9ExLuobhh3CNX1wQA2ZmZ/Zj4I/IBqNnUi8KXMfDgz+4D/VS97AvDlplnGp3j0LRO+Uv/7Y+DrTd8fOkx9VwCvjojHNW33m8BFEXF6XefvAU+ol787M380ZIB7gD8BFtfHpE5rWh6qWeNgIN4N/H5T3zHA04AbI+J24K3180mTzoBSuxu8SR4RsZpqd9uPqe7oew/VNSChutLyoIG6faCpH6p7WMHe/2+6aNpdnpkPDbPOSK4EXkm1G/G6zNwREafW7Q9S3Z7hm011/GroBiLiCVTXO3tGveyaEeoerL35di8HATdn5pGZeSTwR1QzR2nSGVDqJIuA/5GZX6TaVfZUqjfokXwV+K8RMSMiDqU6TgXVcahTI2LwtgnLqW4jMmaZeRfwb1S75K6om18MXJWZn6G6HcELRqnzWVRhen5dx8uHLP9nABExj+oWB/+vqe9fgQUR8az68dlU9y+SJp0BpU5yAfDZiNhCtSvrVqpZx7Ay85+owmgL1dlyd9btd9Tb+peI2Ep1jOi9B1DXZ4EGv77T6FrgNRGxmepOxBv2VSfwHapbWWwFvgv0Ud21d9ATIuLb1Lcaz8xHZlCZ+TOqK2xfVT/fUVQnW0iTzquZS5KK5AxKklQkA0qSVCQDSpJUJANKklQkA0qSVCQDSpJUJANKklSk/wCOIyZAFN8CiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 100\n",
    "p = .4\n",
    "\n",
    "continuous = False\n",
    "distr = scs.binom(n, p)\n",
    "X = np.arange(0, 60, 1)\n",
    "title = 'Something'\n",
    "x_label = 'random variable'\n",
    "x_lim = (0, 60)\n",
    "\n",
    "plot_distributions(distr, continuous, X, title, x_label, x_lim)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# POISSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f3+/CJUmTWy8jqIXArMycB5wCLGk3RMRuwMnAs4AFwJkRMbNuezzwNmD7fhctSZr8egmog4DLADJzJbBvo20/YHlmbsjMtcBqYK+ImAWcC7y+z/VKkqaIEU/xAbOBtY3790fEjMzc2KVtPTAH+ARwVmb+R0T0XEyrNdTzuiUZ1LphfGrv52M0+xptv2NVVwn9lNrXRNbk63Tw9BJQ64Dm3pleh1O3tiHgXuBg4EkR8V5g54i4ODOPHumB1qxZ31vVBWm1hgaybti62lujeJzOx+hHX+3aS6urn/2U2Ndo3yjH8vltyVR5nZZmtMdLLwG1HDgK+FJEHACsarRdB3ygPqU3E9gDuC4zHxg2RcRvewknSZKaegmoS4H5EbECmAYcHxGLgdWZuSwizgauofo869TMvGfsypUkTRUjBlRmbgIWdSy+qdG+FFi6he132+bqNGqtXWZvuX0LbWtuXdffYiRpK/hDXUlSkQwoSVKRDChJUpEMKElSkXr5Fp8kFc8vBE0+jqAkSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElFmjHSChExHTgHmAtsAE7IzNWN9hOBk4CNwPsz8+sR8QTgs3X/04DXZmaOQf2S1HetXWZv87Zrbl3Xx0qmtl5GUAuBWZk5DzgFWNJuiIjdgJOBZwELgDMjYibwD8AnMvMw4AzgzD7XLUma5HoJqIOAywAycyWwb6NtP2B5Zm7IzLXAamAv4G3AN+p1ZgD39K1iSdKUMOIpPmA2sLZx//6ImJGZG7u0rQfmZOZtABERwFlUo7ARtVpDPRVdmkGteyT9el793D/Nvkbb71jVVUI/pfZVYk0l9zWWfQ6CXgJqHdDcO9PrcOrWNgTcCRARz6b67OqVvX7+tGbN+l5WK0qrNVR03a1RbNt8Xv3qp199tfd7aXX1s58S+xrtG2Xpz28s+hqt0t9jtmS0x0svAbUcOAr4UkQcAKxqtF0HfCAiZgEzgT2AG+tw+hhweGb+ZlQVTkF+QCtJvQXUpcD8iFhB9Y284yNiMbA6M5dFxNnANVSfZ52amfdExD8BOwDnV2f5yMw8aWyegiRpMhoxoDJzE7CoY/FNjfalwNKObeb2pTpJ0pTlD3UlSUUyoCRJRTKgJElFMqAkSUUyoCRJRTKgJElFMqAkSUXq5Ye6kqRt4Kwwo+MISpJUJANKklQkA0qSVCQDSpJUJANKklQkA0qSVCQDSpJUJANKklQkf6jbJ/4gT5L6y4CSpAEwFf8I9hSfJKlIBpQkqUgGlCSpSAaUJKlIfklCkqaYQfnChSMoSVKRDChJUpGm/Cm+QRnqStJUM2JARcR04BxgLrABOCEzVzfaTwROAjYC78/Mr0fEo4EvAI8AbgGOz8y7x6B+SdIk1cspvoXArMycB5wCLGk3RMRuwMnAs4AFwJkRMRN4D/CFzDwY+DFVgEmS1LNeTvEdBFwGkJkrI2LfRtt+wPLM3ABsiIjVwF71NmfU63yzvv3RfhXtaTlJmvx6CajZwNrG/fsjYkZmbuzSth6Y07G8vWwk01qtoR5WAzZv7m29Llpj0FerNVRcTX3vq8Cait3vJdbUz75KrKnUvib7sT7GejnFtw5oJsf0Opy6tQ0Bd3Ysby+TJKlnvQTUcuAIgIg4AFjVaLsOODgiZkXEHGAP4MbmNsDzgWv6VrEkaUqYtnmEoV7jW3x7AdOA46nCZ3VmLqu/xfdaqrA7IzMviYhdgfOpRk+3Acdk5h/G7mlIkiabEQNKkqSJ4EwSkqQiGVCSpCIZUJKkIo3rXHzbMm3SeNa3JRGxPfBZYHdgJlV9yxrti4HXAGvqRSdlZo53ncOJiB/z4G/TfpWZxzfaSt7vxwHH1XdnAXsDu2XmnXX72VQzmayv13lRZq5lAkXE/sCHMvOwiHgScB6wmeobrm/IzE2NdR8BXAjsQvUcjs3MNQ/vdXx01L438HHgfqrX66sy83cd6w97XI23jtr3Ab4G/P+6+VOZ+cXGuiXv94uB3eqm3YGVmXl0Y91pwL/z4HP7fma+czzrret42Hsi8DP6eLyP92SxD0ybVH9lfQnwInjItEn7Ur0RXRsRV9azVJTgFcDtmfnKiPhTqimcljXa96F6Af9wQqrbgoiYBZCZh3VpK3q/Z+Z5VAc8EfFJ4LPtcKrtAyzIzNvGv7qHi4i/A14JtL+1+hHg3Zn5nYg4l+p4v7SxyeuAVZl5ekQcDbwbePN41tzWpfaPAW/KzOsj4iTgHcDixvrDHlfjrUvt+wAfycwlw2xS7H5vh1FE7ARcDby1Y5O/AH6UmUeNZ51ddHtPvJ4+Hu/jfYrvIdMmUb0ptj0wbVL9F3B72qRSfBk4rXF/Y0f7M4F3RsS1ETHuf82MYC7wJxFxRUR8u/7joK30/Q5APcXW0zPz041l04EnA5+OiOUR8eoJK/BBvwBe3Lj/TOC79e1vAs/rWP+B18Qw7eOps/ajM/P6+vYM4J6O9bd0XI23bvv9yIj4XkT8c0R0TlNT8n5v+3vg45n5nx3Lnwk8LiKujoh/jYgY8wq76/ae2NfjfbwDquu0ScO09TpF0rjIzLsyc319oP9vquRvuhhYBDwHOCgiXjDeNW7B3cBZVBP6LgIuGpT93vAuqhds0yOpTkG9AjgceH1ETGi4ZuYlwH2NRdMys/1bjm77dlumBRsTnbW33xgj4kDgjTx8Ps0tHVfjqst+vw7428w8BPgl8N6OTYrd7wARsQvwXOqzBx3+EzgzM59NNc/phWNdYzfDvCf29Xgf74DalmmTihERj6cacn8+M7/QWD4N+KfMvC0z7wW+ATxjgsrs5ufAhZm5OTN/DtwOPKZuG4T9/ijgqZl5dUfT3cDHMvPuzFwPfJvqr/qSbGrc7rZvi54WLCJeBpwLHNnls4ItHVcT7dLG6fZLefjrsej9DvwN1RUh7u/S9gPgqwCZeS3VaGraeBbX1uU9sa/H+3gH1LZMm1SEenaMK4B3ZOZnO5pnAzdGxI71gfIcoKTPol5NfZmUiHgsVb3t0wZF7/faIcC3uix/CtVnZtvVH9geBPxoXCsb2Y8j4rD6drdpv4qdFiwiXkE1cjosM3/ZZZUtHVcT7fKI2K++/Vwe/nosdr/Xnkd1Cqyb9wJvAYiIucDNjVHLuBnmPbGvx/t4D8cvBeZHxArqaZPqb7+1p006m6rg6cCpmdl5znsivQvYCTgtItrnXZcCj8zMT0fEu6j+ktgAXJWZ/zpBdXbzz8B5EXEt1bdrXg2cHBGDsN8Bguo0TXXnocfMRcBKqlMkF2TmTyeoxuG8DVgaETsA/4/qVAgRcQXwAuBTwPn1/5t7gWMmqtCmiNgOOBu4GfhK/THHdzPzvRFxAdXpnIcdV40zIhPtdcAnIuJe4LdU07EVv98bHnLMw0Nq/yBwYUQcSfW5z3HjXl2l23vim4Gz+3W8O9WRJKlI/lBXklQkA0qSVCQDSpJUJANKklQkA0qSVCQDShpBRJxXT1pbhIh4YUS8b4R1vtP4PUpz+ekRcfpY1Sb104RMSyJp29Wz6C8bcUVpwBlQmnTqkcOHge2oZsV4F9WPSh8FPBY4LzPfU4+KDgd2Bv4bcEVmvr6eDWQJ1Q8Lb6n7+U7d9/FUP77dTDU7wRsz866I+C3wf4D9qX4Y+lmqWeL/DDguM9sTaFLPF3hRZu5Z3z8KOAF4CdUPGf8S2BW4AXh5ffsy4Dbgj8BFVLM7HBcRL63reQTVJQ9enZkr6od6bUS05897a2Z+p2M/HQ68D9ge+BVwYmbevnV7Wxo7nuLTZPUU4DmZeSzVm/y/ZOYBwJ7AWyLi0fV6B1IFw17AURGxZ33/GcDTgZcCTwKo204FDq3D5Q88OAnprsA3M/MZVJct+evMPBg4nXpamrbMvAHYFBF/WS86mmrCzwOBezNzXv2Yj+LBaWECeEVmzm/3U8/mvgh4QWbOpQrl5kz6d9X1HEs188DMxrYtqhkJFtTrXA58qJcdK40XA0qTVbYvXJiZZwE3R8Tbqa5xtAPVTOgAKzJzfWbeTTW1zM7AYcBXMvO+eoLU9rRVhwJfa4wyPk01z1tbe+6031BNXNu+vVOX+i4Ejq4v4Nbu93vAORHxhrrOJwM71uvfmpm/7niCm4C/BhbUn0kd11gfqlFjOxBvBZ7aaNsfeAJwdURcTzXn3pO71ClNGANKk9Uf2zciYgnV6bbfUF318zaquSDhodc42lwv39xohwev/dX5eplG4zR5PZN95zbDuYhqxuoXAJdn5j0R8cJ6+d3A54DvNer4Y2cHEbEj1WS/T6zXPXuYutu1Ny/psB1wbWbunZl7A39FNXKUimFAaSqYD/xjZn6Z6lTZ46jeoIfzLeB/RMTM+qqmh9fLvwO8MCJ2ru+fSDVB8FbLzFuAf6M6Jde+ns/zgC9l5ueoLkPw7BHqfApVmJ5R1/HijvX/JzxwscchHrxEOMD/BeZFxFPq+6dRXdtJKoYBpangTODzEXEj1amsH1CNOrrKzK9ShdGNVN+W+1m9/Ia6r+9GxE1UnxF1Xrhya3weaPHgFUiXAi+PiFVUVytdvqU6gZ9QXWL7JuCnwBrgzxvtO0bEj6mu53RMZjYvRvhbqlntv1Q/3j5UX7aQiuFs5pKkIjmCkiQVyYCSJBXJgJIkFcmAkiQVyYCSJBXJgJIkFcmAkiQV6b8AAkKOkuAFxvEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 10\n",
    "\n",
    "continuous = False\n",
    "distr = scs.poisson(mu)\n",
    "X = np.arange(0, 20, 1)\n",
    "title = 'Something'\n",
    "x_label = 'random variable'\n",
    "x_lim = (0, 20)\n",
    "\n",
    "plot_distributions(distr, continuous, X, title, x_label, x_lim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NORMAL DISTRIBUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 10\n",
    "sigma = 2\n",
    "\n",
    "continuous = True\n",
    "distr = scs.norm(mu, sigma)\n",
    "X = np.arange(0, 20, .1)\n",
    "title = 'Something'\n",
    "x_label = 'random variable'\n",
    "x_lim = (0, 20)\n",
    "\n",
    "plot_distributions(distr, continuous, X, title, x_label, x_lim)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPONENTIAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 5\n",
    "\n",
    "continuous = True\n",
    "distr = scs.expon(0, mu)\n",
    "X = np.arange(0, 20, .1)\n",
    "title = 'Something'\n",
    "x_label = 'random variable'\n",
    "x_lim = (0, 20)\n",
    "\n",
    "plot_distributions(distr, continuous, X, title, x_label, x_lim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PRACTICE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wait for a bus can be represented by an exponential distribution with a mean of 10 minutes. Find the probability that you will have to wait between 5 and 15 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weight of a bumblehorse is 50 lbs on average, with a standard deviation of 10 lbs. What is the probability that a bumble horse is between 39.5 and 59.2 pounds? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My website usually receives 20 people per minute. What is the probability that at least 15 people will show up in the next minute?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I built 600 toasters. 6 years later, half of the toasters had died. How many toasters do I expect to be working in 10 years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of miles that a particular car can run before its battery wears out is exponentially distributed with an average of 10,000 miles. The owner of the car needs to take a 5000-mile trip. What is the probability that he will be able to complete the trip without having to replace the car battery?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average, 5 people pass by my candy store every minute. What is the probability that 4 people will pass by in the next minute?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example:\n",
    "I toss a coin 500 times and it lands heads 263 times.   Is the coin fair?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prob of flipping 263 heads out of 500: 0.018\n"
     ]
    }
   ],
   "source": [
    "# Wrong solution num 1:\n",
    "\n",
    "n=500\n",
    "p=.5\n",
    "k=263\n",
    "\n",
    "distr = scs.binom(n, p)\n",
    "prob = distr.pmf(k)\n",
    "print(\"Prob of flipping {} heads out of {}: {:.3f}\".format(k, n, prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of 263 heads given a fair coin: 0.018\n",
      "Probability of 263 heads given a biased coin: 0.036\n"
     ]
    }
   ],
   "source": [
    "# Wrong solution num 2:\n",
    "\n",
    "n = 500\n",
    "p_fair = .5\n",
    "p_bias = 263/500\n",
    "\n",
    "fair_distr = scs.binom(n, p_fair)\n",
    "bias_distr = scs.binom(n, p_bias)     \n",
    "\n",
    "prob_fair = fair_distr.pmf(263)\n",
    "prob_bias = bias_distr.pmf(263)\n",
    "                       \n",
    "'''P(263|fair coin)'''\n",
    "print(\"Probability of 263 heads given a fair coin: {:.3f}\".format(prob_fair))\n",
    "\n",
    "'''P(263|biased coin)'''\n",
    "print(\"Probability of 263 heads given a biased coin: {:.3f}\".format(prob_bias))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example:\n",
    "Apples are 110 grams on average, with a standard deviation of 12 grams. \n",
    "\n",
    "I pick up something apple-like and find it's mass. It is 85 grams.\n",
    "\n",
    "Can I say with 95% confidence that the object is not an apple?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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