Sesi 10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96d7f4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import Math,Latex\n",
    "\n",
    "from IPython.core.display import Image\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe9bee5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes = True)\n",
    "\n",
    "sns.set(rc={\"figure.figsize\":(5,5)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79cdcab",
   "metadata": {},
   "source": [
    "# Uniform Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e76a1c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a667e7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10000\n",
    "start = 10\n",
    "width = 20\n",
    "\n",
    "data_uniform = uniform.rvs(size=n,loc=start,scale=width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0110eba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 50.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data=data_uniform,\n",
    "                 bins=100,\n",
    "                 kde=True,\n",
    "                 color=\"skyblue\"\n",
    "#                  hist_kws={\"linewidth\":15,\"alpha\":1}\n",
    "                )\n",
    "\n",
    "ax.set(xlabel=\"Uniform Distribution\",ylabel=\"Frequency\")\n",
    "plt.xlim(0,50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caea6fae",
   "metadata": {},
   "source": [
    "# Normal Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0722dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "data_normal = norm.rvs(size=10000,loc=0,scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0450a6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184a17d2940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data=data_normal,\n",
    "                 bins=100,\n",
    "                 kde=True,\n",
    "                 color=\"skyblue\")\n",
    "\n",
    "ax.set(xlabel=\"Normal Distribution\",ylabel=\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5bdd7a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0017734168573659537"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data_normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "356f35b6",
   "metadata": {},
   "source": [
    "# Gamma Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d25b4bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import gamma\n",
    "\n",
    "data_gamma = gamma.rvs(a=1,size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e93f258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184a2226a00>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zE6AIgQyFI1wiik5UgatpGux2e+R+bm4uNC2q6d9eKzzCvZgpBQDI1ARHuEQUlagCt3///tiyZQuEEPD7/fj1r3+d9HO4p3vhXlziZqkCbTrgY4tGIrqAqAL30UcfxfPPP49Dhw6hpKQEf/nLX/Doo4+aXZup4jGlAJze4ns8wMAlou5FNS/Qt29fvPDCC/B4PNB1HdnZ2WbXZbpg5EOzi3ufrHDg+g30d/RwnzARpYWoAvf5558/5+M/+tGP4lqMlbpOKfR8dGoTodUKHOES0YVEFbiffvpp5Lbf78cHH3yA0aNHm1aUFfROI9yLmX4VQqDQpjBwieiCogrcxx9/vMv9+vp6PPLII6YUZJXOc7gX+3lXkV1gf6sBQ0ooKdRNjYjiq0eTjn379kVdXV28a7GULiUEEJeALLQp0AGcDHKUS0TnF/McrpQSe/fuRVFRkWlFWUGXQLyu/djHHnqjJr9EgS0+70lEqSfmOVwgtC53/vz5phRklaAhY75a7/nk20K9cZsCEkPi9J5ElHp6NIebCuI5wlWEQJFNoMnPHWdEdH5RBe4PfvCDbi+t8z//8z9xK8gqupQ9m8A+jz52BYfadH5wRkTnFVXgXn311fjss89wxx13wGazobKyEsFgEGVlZWbXZ5p4jnCB0DzuPjdwSgcK7QIGt/oS0RmiCtyPPvoIL730ElQ1NOt544034o477sCkSZNMLc5MuiEvepdZZ31soTfb32bgEkPgCidDl4i6iuq36uPHj8Pn80Xut7W1wev1mlaUFXQJKIhf4uZpAjYBnPAb8LB7GBGdQ1Qj3FtuuQXf+9738J3vfAdSSrz++uuYOXOm2bWZKigl1DjOKQgh0Mcu0Mq1uER0HlEF7ty5czF8+HC8++67cDgc+Ld/+zdcf/31ZtdmqtAIN7762BV85dN5UUkiOqeoM6dv374YMmQIHnjgAdhsyb2635ASRpw/NAMAl12BBODhpdOJ6ByiCtxXXnkFv/jFL/Cb3/wGra2t+Od//mesXbvW7NpME/6tP96BG95x5tY5wiWis0UVuC+++CJefvllZGdno6ioCOvWrcMLL7xgdm2mCQduvHaaheVqAqoA3JzHJaJziCpwFUXp0nS8f//+kSViySjQsVwr3iNcIQSyVQG3zlUKRHS2qAI3Pz8fBw4ciOw2W79+PfLy8kwtzEzh1rXxXBYWlq0JtOuhXg1ERJ1FtUph4cKFmDt3Lr744guMHTsWDocDq1atMrs20wTidD2zMKVjqKwoAjmqgESoc1ixnVt8iei0qALX6/WisrISR44cga7r+PrXv57UKxXC1zOLx5SCU1PwmcdAu99AvlNFTseOswa/gWJ78k67EFH8RTWl8LOf/QyqqmLQoEEYOnRoUoct0HlKIT48AQOeoAFvUMKuCDgUoMHHeVwi6iqqzLnyyivx2muv4csvv0RLS0vkT7I6fQFJc94/RxVoYKtGIjpDVFMKb731Fqqrq7s8JoTAgQMHTCnKbJdnKBheYIc9aM4OhWxNoMkj0RaUyNI4j0tEIVEF7p49e3r8BZ544gmcOHECy5cvx44dO/D444/D5/NhypQpmDdvHgDgwIEDeOSRR9DW1oa/+7u/w5IlS6BpUZXWIw5FoH+miuOnzAncHO30PO7XNc7jElFIt1MKjz76aOT28ePHY37znTt34tVXXwUQ+uBt4cKFWLVqFTZs2IC9e/di69atAICHH34YixcvxsaNGyGlTOpdbACQpQooABr9XBpGRKd1G7h79+6N3L733ntjeuOWlhasXLkS999/PwBg9+7dGDhwIC677DJomoby8nJUV1ejrq4OXq8XJSUlAICKioqzpi+SjdLROYzzuETUWbe/t8tOXa9kjB2wFi9ejHnz5uHYsWMAgIaGBrhcrsjzxcXFqK+vP+txl8uF+vr6mL4WABQVZV/4oE4+b2hHZpYdTrsCoUvALiO3Hao467FYngeAyzUV/9fkRWGfbKgJvuSOy5WT0K9vFZ5naknF84x6orS7a5qd6Y9//CP69++P0aNHY926dQAAwzC6vIeUEkKI8z4eq+Zmd9RXWAhtVFDQ3uaHQ9fg0w20B4zIbakqZz0Wy/MOVYHXryMogS21p1CSoybs6g8uVw4aG1sT8rWtxPNMLcl8nt39oOg2cA3DwMmTJyGlhK7rkdth+fn553zdhg0b0NjYiGnTpuHkyZNob29HXV1dl/4LjY2NKC4uRr9+/dDY2Bh5vKmpCcXFxdGeW6/l6Pi7yasDOfzgjIguELiffvopvvWtb0VC9oYbbog8192ysOeffz5ye926dXj//fexZMkSTJw4EbW1tbj00ktRVVWF22+/HQMGDIDD4cCuXbtw7bXXorKyEqWlpfE4t4RyKIAm2KqRiE7rNnAPHjwYty/kcDiwfPlyzJ49Gz6fD+PGjcPkyZMBACtWrMCiRYvgdrsxYsSIpL98DxD6gZSlgpfcIaIIIWP9NKyXinUO93NdwfFTPhQ4T8/Bhm87Os3RXszzn7uDOOaX+MEABxLVxyaZ58JiwfNMLcl8nt3N4cb7sl7USVbH3uFGLg8jIjBwTZXZ8VkZG9kQEcDANZUqBDJVoJ47zogIDFzT5agKGn1GzBtHiCj1MHBNlq0J+CVwkqsViNIeA9dkpzuHMXCJ0h0D12QZCmAXXKlARAxc0wkhUOxQ0OCXkYtNElF6YuCazKkpsKkCxwMSn7YbDF2iNMbAtUBGR8Y2ec25wgQRJQcGrgXCH5yd4koForTGwLWApgg4FTayIUp3DFyLZKkCrUEJgxsgiNIWA9ci2SqgAzgRYOASpSsGrkXCncPq2ciGKG0xcC1iF4BNAF8xcInSFgPXIkII5GqCI1yiNMbAtVCOJuDWgTZe54woLTFwLRRej8tRLlF6YuBaKEsV0AQbkhOlKwauhRQh4LIrHOESpSkGrsX6OxU0ByR8UV5hmIhSBwPXYgMcoW/5MY5yidIOA9diLkdoHveYl4FLlG4YuBazqQr6ORR86eOUAlG6YeBayKkp+MxjwKkKtAQl2rkelyitMHAt5gkYyOz4rnMelyi9MHATIEsVsAvgSwYuUVph4CaAEAL9nQo/OCNKMwzcBLnEqaBV51UgiNIJAzdBLulYj8tpBaL0wcBNkAKbQKYK/I3TCkRpg4GbIKqq4PIMFXVeAzqvc0aUFhi4CRBejysABCTwFbuHEaUFBm6CeAIG7JBQAHzh4bQCUTpg4CaQIgTybQK1Hh2S0wpEKY+Bm2AFNgVtOnCcl08nSnkM3AQrsIUuu3PUJ6EoIsHVEJGZGLgJlmtXkWcTOOTW8bmXoUuUyhi4vUCeJuDWJVp8eqJLISITMXB7gT720D9Dk5+rFYhSGQO3F8hQBTIVBi5RqmPg9hKFNhFarcDQJUpZDNxeIl8LfVj213bO4xKlKgZuL2FTBPI1gb+2cRMEUapi4PYiLkdoEwR7KxClJgZuL1JoE7AJ4K9tnFYgSkUM3F5EFQJfz1RR4zHgNzjKJUo1DNxeZni2ioAE/trO1QpEqYaB28u4HAr62AQOuHUIAW71JUohDNxeaESOhpagxM4TQfZXIEohDNxe6IosBZoA/ubV4QlwaoEoVTBwexlFEbCrCvraFZwMAj5+eEaUMhi4vUj4WmdHfRL9nKF/mq94VV+ilKElugDqyhMwYKgKnGpo59lXvtASMf5DESU/jnB7sb52AR3A/lZuhCBKBaYG7jPPPIOysjKUlZXhySefBADs2LED5eXlmDhxIlauXBk59sCBA6ioqMCkSZPwyCOPIBgMmllaUsjsGOXubQ0iyP4KREnPtMDdsWMHtm3bhldffRV//vOfsW/fPlRVVWHhwoVYtWoVNmzYgL1792Lr1q0AgIcffhiLFy/Gxo0bIaXE2rVrzSotqQxwKvAYwOE2zuUSJTvTAtflcmHBggWw2+2w2WwYNGgQjhw5goEDB+Kyyy6DpmkoLy9HdXU16urq4PV6UVJSAgCoqKhAdXW1WaUllVxNoNgusKc1CIOjXKKkZlrgDhkyJBKgR44cweuvvw4hBFwuV+SY4uJi1NfXo6GhocvjLpcL9fX1ZpWWVIQQKMnV0Kpzuy9RsjP9w+/Dhw9j1qxZmD9/PlRVxZEjRyLPSSkhhIBhGBBCnPV4LIqKsmM6/vOGdmRm2eG0KxC6BOwyctuhirMeS9jzqsA3XBnY3XYSn7QZ+NbleVBj3HnmcuXEdHyy4nmmllQ8T1MDd9euXZgzZw4WLlyIsrIyvP/++2hsbIw839jYiOLiYvTr16/L401NTSguLo7pazU3u2FEuUkgtFVWQXubHw5dg0830B4wIrelqpz1WMKed2rYdcyNAlXiK4+BnUdP4apsNepzdbly0NjYGtP3MhnxPFNLMp9ndz8oTJtSOHbsGH7yk59gxYoVKCsrAwCMGjUKNTU1qK2tha7rqKqqQmlpKQYMGACHw4Fdu3YBACorK1FaWmpWaUnHEzDggESeJvBBSwCHPQb7KxAlIdNGuKtXr4bP58Py5csjj02fPh3Lly/H7Nmz4fP5MG7cOEyePBkAsGLFCixatAhutxsjRozAzJkzzSotKQkhcEWWio9PBnDEHcSQDHuiSyKiGJkWuIsWLcKiRYvO+dz69evPemzYsGH405/+ZFY5KaHAriBPE/ib10DAkFATXRARxYQ7zZLMwEwFQQns5e4zoqTDwE0yOZqCApvA7lNBdhIjSjIM3CR0uVOFXwK7OcolSioM3CSUpQkMzlSwr1VHm85RLlGyYOAmqWvzNUgAH50KQlEEl4kRJQG2WU1SuZqCYdkqDrh1FDp0ZGkKrnCKqDdEEJH1OMJNYiU5KjQBfNoa5LXPiJIAAzdJKYpAlk3BqDwNxwMSrUEGLlFvx8BNQuFrn+1r1VHoUGETQK3HgGT7RqJejYGbpDwBA56ggaABXJah4FRQ4igvOEnUqzFwU0BfhwKHAnzQwiblRL0ZAzcFKEJgYIaK4wGJz9iknKjXYuCmiCKbQJFN4KNTQegc5RL1SgzcFCGEwPX5Gtw6cMDNLb9EvREDN4VclqniEoeCT1p1+LkBgqjXYeCmCKem4HOvRB+7gNcA9nKUS9TrMHBTiCdgQEVoPncPG9sQ9ToM3BQ0MEOFBLDzRDDRpRBRJwzcFORUBb6Zp6HWa+BQi4/dxIh6CQZuiromR0WhTWDT0TYcajfwuVcydIkSjIGbohQhcGOBhraggYOnAuwmRtQLMHBTWB+7guuLM9Dgl2jyM3CJEo2Bm+JK+2ciWxX4rE3HKbZwJEooBm6KCn9QpioCQ7NUQABvNQW47ZcogRi4Kahzv9yDJ7zI0AQGZ6po8ku828KlYkSJwsBNUeF+ueEtvkV2BaNyVRxsM7Cfu9CIEoKBm0auL7Dh8gwF77YEccxvcH0ukcUYuGnCqSmo8Ur0dyjI0gTeaAzgw5Yg1+cSWYiBm0Y8AQN+XWJkrg0AsN8dRKuP0wtEVmHgpqEMVWBYtgqfARxq07lygcgiDNw0lWdTMNApcDIo8XZzgNdCI7KAlugCKHEKbQqEkKhpN+AUQYzO1yBEaD6387yuwWbmRHHBwE1zlzhV5GoCu1t1aELHdXkqVDXUzNwTMJBhU3CFUzB0ieKAgUu4Pl+D35DY07E+94YCEVnHCwBwqgmsjih1MHAJQgh8O18DEMQetw4hgCyVS8WI4o2BSx3ztUokdHe36rjEoaDYlujKiFILAzfNhfsuSAkMzlQxtlBAUYLY36ojYAgM1jh3SxQvDFyCJ2DAoYaCt91vYGCWhha/xJc+A2jXMTyHc7hE8cB1uBQR/qDMpwNfy1TQzy7Q6JfY3NHWMdx7gVuBiXqGI1w6JyEE+jsUOFWJGo+BQHMQg7I1BHTJpWJEPcTApW5d4lRxeYaCd44H0ez342vOjl+KuFSMKGacUqALujJbw9RiGwISONRunPf6aJxyIOoeR7gUlUucKkblShxoDeLTNh1+Q+LaXBX5ttDPbEUR3J1GdAEMXIqaQxEYmqnguA78zWvgiMfA1zMUlOSo6ONUuTuN6AIYuBQTIQQudSooLbRh96kgDrh11HgM9HcEkaMpyBQc1RKdDwOXLuhcc7IZqsB1eRquyVHxabuBA24dx3w6bALo7wAGZyngRjWirvihGXUrvBPtqE9CdMrd8IdjWXYVOXYVY/vYMSxbhVMBvvAa+H2dDx+eDMKjc8RLFMYRLl2QJ2DAUE//bA6HcLvfQL5ThTdoQKoKiuwKMgSgQ8CtS3zSqmOvW8eVWSquyVaRpXH1AqU3Bi71SPgDMmfw7F+S+jhVFCtAriZQ7w9NNxx06xicpWBUjoZcTbDBOaUlBi6ZwhMwIKTE1zNUTOhjw/+1BHHIreNwmx9fy1SQpSnIUoBsu8olZJQ2GLhkKqemoCEAfC3HhiKnis9ag6jzGvAbBgSAPE2HO1tFP7tAoU1AEZx2oNTFwCXTheeADUOir13gyhw7Gn066r0G3LrEey1BAIAKoMAmUGQXcNkV9LErKLALaB0hzFEwJTsGLllOCIE8W2jZWIFTQ0BKHPcbUKVEkz+0oeJQW2gDhQCQpQnkaAKDMlX0sQkU2gU6L/flVmJKFgxcSjjDkMhTBUbm2WAYoeVnH54I4oTfgKEInAwYaPIZOOY9HcJFdoEcVSDXJuA1BDLbW3GpKmEXoTXCNkhOT1Cvw8ClXuHMpWYZmoCUAgVODT7dgF0ROBUw0Owz4JeAEOgYDQMSANxBfHzGezoUIE8TKLQrKLIJXOJUkKcJCMEP6SgxGLjUa3S31EwIAacqUGATyNAUjMzTQqEpgI9bgnBm2HCizY+2gAG7piIgJdoDEkEAn7XpONiRrw4FKLIrGN7xQZ2D0xFkIQYuJZ0zR8MOVSDXrkD6FdgBFDhV+HQD7YqBAqcGb1CHFAoavEE0+Q3Uew186Q2tknDZQ43WC2wC+ZpAlipgVwCtY6MHR8IUT70qcF977TX8+te/RjAYxA9/+EPcddddiS6JeqnuRsNnCo+O+zlV5KoCeQ4VrbpEvUfHqaDE7lYdZ8aqAsCmAJmqgEMJdUqzC8CpCDjV0Dxx6LaAUwGcqoAqz5435gYP6qzXBG59fT1WrlyJdevWwW63Y/r06bjhhhswePDgRJdGKUYRAk4h4bIJDM2xAUKiLSiRrQLtQYmABOo8BiCAoATadQm3LiGlhFcH9G7eWwWgKYBNAJoiEM5YuyKQq4X+h1M7Mlgi9MMgTIT/iFDgq53+1oSApgCNJ3zweI3IY6G/O46VgCJOv04R4ffjtElv0WsCd8eOHfjWt76F/Px8AMCkSZNQXV2Nn/70p1G9PpalQYoikK0oMJwqchwKnIaAXRWR2zZFnPVYb3/+fK/J0hTAqfbKmuP5fPg8e/L6gCGRYw9tunAEDWTZVRRn6rApAgEj1FQ916FCEQLegI4Mm4oWvw4pAa8h0eaX0FQBA4BfD11sM2DIyP2gISEB+CXQJmUkhAUAA6HnBEIBHB4DGxJnjLo77rnbov7vPPLfOzqCOPx3R1CH/yhCRJ4LBbyI3FbE6eCOVbh+Kc/z+JnHy9OPOwJt8PlOr0oJO9fPDnGe2+d6Tfhm5699Zn2dn7/EEVoTHi+9JnAbGhrgcrki94uLi7F79+6oX19QkBXT1ysAgAJnTK9JWnmORFdgjXQ5T0pavaY9o2EYXX71kVLyVyEiSim9JnD79euHxsbGyP3GxkYUFxcnsCIiovjqNYH77W9/Gzt37sTx48fh8XjwxhtvoLS0NNFlERHFTa+Zw+3bty/mzZuHmTNnIhAI4Lvf/S6uueaaRJdFRBQ3QspzfUZHRETx1mumFIiIUh0Dl4jIIgxcIiKLMHCJiCySloH72muvYerUqZg4cSJ+97vfJboc0zzzzDMoKytDWVkZnnzyyUSXY6onnngCCxYsSHQZptm8eTMqKiowZcoULF26NNHlmKaysjLy3+wTTzyR6HLiT6aZr776Sk6YMEGeOHFCtrW1yfLycnn48OFElxV327dvl9/73vekz+eTfr9fzpw5U77xxhuJLssUO3bskDfccIP8+c9/nuhSTPHFF1/IsWPHymPHjkm/3y/vvPNOuWXLlkSXFXft7e3yuuuuk83NzTIQCMjvfve7cvv27YkuK67SboTbuUlOZmZmpElOqnG5XFiwYAHsdjtsNhsGDRqEL7/8MtFlxV1LSwtWrlyJ+++/P9GlmGbTpk2YOnUq+vXrB5vNhpUrV2LUqFGJLivudF2HYRjweDwIBoMIBoNwOFKrP0baBe65muTU19cnsCJzDBkyBCUlJQCAI0eO4PXXX8e4ceMSW5QJFi9ejHnz5iE3NzfRpZimtrYWuq7j/vvvx7Rp0/DSSy8hLy8v0WXFXXZ2NubOnYspU6Zg3LhxGDBgAL75zW8muqy4SrvATbcmOYcPH8Y999yD+fPn42tf+1qiy4mrP/7xj+jfvz9Gjx6d6FJMpes6du7ciWXLluHll1/G7t278eqrrya6rLg7ePAgXnnlFbz99tt45513oCgKVq9eneiy4irtAjedmuTs2rULd999Nx566CHcdtttiS4n7jZs2IDt27dj2rRpePrpp7F582YsW7Ys0WXFXZ8+fTB69GgUFhbC6XTi5ptvjql1abLYtm0bRo8ejaKiItjtdlRUVOD9999PdFlxlXaBmy5Nco4dO4af/OQnWLFiBcrKyhJdjimef/55VFVVobKyEnPmzMFNN92EhQsXJrqsuJswYQK2bduGU6dOQdd1vPPOOxgxYkSiy4q7YcOGYceOHWhvb4eUEps3b8bIkSMTXVZc9ZrmNVZJlyY5q1evhs/nw/LlyyOPTZ8+HXfeeWcCq6KeGDVqFH784x9jxowZCAQCGDNmDG6//fZElxV3Y8eOxf79+1FRUQGbzYaRI0fivvvuS3RZccXmNUREFkm7KQUiokRh4BIRWYSBS0RkEQYuEZFFGLhERBZh4NJF+dOf/oR//Md/xNSpU3HzzTfjRz/6ET755JNEl3Vef/vb33DVVVdh2rRpmDZtGsrLyzF9+nRs2LAhcsyvfvUr/PnPf+72fZ555hm8+eab53yu8+uvvPJKHD9+PKYad+/ejcWLFwMA9uzZgzlz5sT0euq90m4dLsXPU089hQ8++AC//OUvMWDAAADAzp07MWvWLKxbtw6XXHJJgis8N6fTicrKysj9uro63H333VBVFZMmTcLcuXMv+B7vvfceBg8efM7nonl9d/76179G+nuMHDkSTz/99EW9H/UeDFzqkaamJrzwwgvYtGlTl63Ro0ePxoIFC+DxeAAAb7/9Np577jn4/X4cP34ct956Kx544AG89957eOqpp9C/f3/U1NQgIyMD9913H9asWYOamhpMnDgRCxcujPo4wzCwbNkyfPLJJ2hra4OUEkuXLsW11157wXMZMGAA5syZg9WrV2PSpElYsGABhgwZgnvvvRdPP/00Nm3aBJvNhoKCAjz++OPYtGkT9u7diyeffBKqquKtt95CS0sLjh49ivHjx6O5uTnyegD45S9/iT179sAwDDzwwAOYMGEC1q1bh40bN+K5554DgMj9f/3Xf8XTTz+N1tZW/OIXv8Ctt96Kxx57DFVVVWhtbcWSJUtw8OBBCCFw44034sEHH4SmaZFNAtu3b0dDQ0NkowT1MgltDklJa9OmTfK2227r9hjDMOT3v/99WVNTI6UM9SK+6qqrZHNzs3z33XflVVddJfft2yellPLee++N9O9tbm6WI0aMkF999VXUx3300Udy9uzZUtd1KaWUzz33nJw1a9ZZNR09elSWlJSc9finn34qR40aJaWU8uc//7n8zW9+I7/88kv5zW9+U/p8PimllKtXr5abNm2SUkr5/e9/X77++uuR43/4wx9G3iv8eimlHDp0qHzuueeklFIeOnRIXn/99bK5uVm+8sor8r777ou8pvP9zrffffddWVZWJqWUcv78+fKxxx6ThmFIn88n77nnnsh7Dx06VK5Zs0ZKKeWePXvk1VdfLb1eb7f/PmQ9jnCpR+QZGxTdbjfuuusuAEB7ezumTJmCBx98EP/5n/+JLVu2oKqqCp999hmklJHR76WXXorhw4cDAC6//HLk5OTAbrejsLAQWVlZOHnyZNTHfeMb30BeXh7+8Ic/4OjRo3jvvfeQlZUV9fkIIeB0Ors81rdvXwwbNgy33XYbSktLUVpaet7OZN2NpMPbqYcOHYpBgwbh448/jrquzv7yl7/g97//PYQQsNvtmD59Ol544YXI9te///u/BwCMGDECfr8f7e3tKddPNtnxQzPqkWuuuQY1NTU4ceIEgFAv08rKSlRWVuIf/uEf4Ha70d7ejttuuw379u3D8OHDMX/+fGiaFglru93e5T017dw//6M5bsuWLZg1axaAUPDE2jNiz549GDp0aJfHFEXBiy++iMcffxz5+flYtmzZeS9VlJmZed73VpTT/5sZhgFN0yCE6PJDKxAIXLDGM1uLGoaBYDAYuR8O1/AxZ/5QpMRj4FKP9O3bFzNnzsTcuXO7XEmirq4OH330ERRFQW1tLdxuNx544AHcdNNNeO+99+D3+2EYRtzr2b59OyZMmIAZM2bg6quvxptvvgld16N6bU1NDVatWoV77rmny+MHDx7ELbfcgkGDBmHWrFm4++67sWfPHgCAqqpdwq474d61+/btwxdffIFRo0ahsLAQhw8fhs/nQyAQwMaNGyPHn++9x44dixdffBFSSvj9fqxduxbf/va3o6qBegdOKVCPzZs3D+vXr8dDDz0Ej8eD1tZW5OXlYerUqbjrrrvgcDgwfvx4TJkyBXa7HUOHDsXgwYNRW1t71qj1Yk2fPh0PPfQQysvLEQwGMWbMGLzxxhswDKPLCBMAvF4vpk2bBiA0+nQ4HHjwwQcxfvz4LscNGzYMU6ZMwe23347MzEw4nU4sWrQIAHDTTTfhqaeeimpkevToUdx6660QQuCpp55Cfn4+xowZg+uuuw5TpkyBy+XCDTfcgEOHDgEASkpK8Oyzz+KnP/0pfvCDH0TeZ9GiRVi6dCnKy8sRCARw4403pvSlhVIRu4UREVmEUwpERBZh4BIRWYSBS0RkEQYuEZFFGLhERBZh4BIRWYSBS0RkEQYuEZFF/h/h9gOZKtI+bwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data=data_gamma,\n",
    "                 bins=100,\n",
    "                 kde=True,\n",
    "                 color=\"skyblue\")\n",
    "\n",
    "ax.set(xlabel=\"Gamma Distribution\",ylabel=\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee685058",
   "metadata": {},
   "source": [
    "# Exponential Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ae01f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import expon\n",
    "\n",
    "data_expon = expon.rvs(scale=1,loc=0,size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58ad06a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184a2264eb0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data=data_expon,\n",
    "                 bins=30,\n",
    "                 kde=False,\n",
    "                 color=\"skyblue\")\n",
    "\n",
    "ax.set(xlabel=\"Exponential Distribution\",ylabel=\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b7dc72",
   "metadata": {},
   "source": [
    "# Poisson Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11b646bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import poisson\n",
    "\n",
    "data_poisson = poisson.rvs(mu=3,size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18df6255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184a214abb0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data=data_poisson,\n",
    "                 bins=100,\n",
    "                 kde=True,\n",
    "                 color=\"skyblue\")\n",
    "\n",
    "ax.set(xlabel=\"Poisson Distribution\",ylabel=\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "148f3ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 4, 5, 3, 3, 4, 2, 2, 1, 2, 0, 2, 1, 2, 3, 5, 3, 2, 4, 4, 1,\n",
       "       3, 4, 5, 3, 3, 2, 4, 4, 7, 1, 4, 2, 1, 1, 3, 5, 9, 0, 2, 4, 1, 2,\n",
       "       5, 5, 3, 6, 5, 4, 2, 4, 3, 3, 3, 0, 2, 2, 3, 2, 1, 7, 4, 5, 2, 5,\n",
       "       6, 8, 4, 3, 4, 2, 0, 5, 5, 3, 3, 7, 4, 4, 4, 3, 1, 2, 4, 2, 5, 2,\n",
       "       1, 2, 1, 4, 4, 3, 3, 1, 6, 5, 3, 3])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_poisson[100:200]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb8a0b33",
   "metadata": {},
   "source": [
    "# Binomial Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf7a00f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import binom\n",
    "\n",
    "data_binom = binom.rvs(n=10,p=0.8,size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2607ba2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184a229a1c0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data=data_binom,\n",
    "                 kde=False,\n",
    "                 color=\"skyblue\")\n",
    "\n",
    "ax.set(xlabel=\"Binomial Distribution\",ylabel=\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a7dce7",
   "metadata": {},
   "source": [
    "# Bernoulli Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "216a673a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import bernoulli\n",
    "\n",
    "data_bern = bernoulli.rvs(size=10000,p=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "72acd99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184a351b0d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.displot(data=data_bern,\n",
    "                 kde=False,\n",
    "                 color=\"skyblue\")\n",
    "\n",
    "ax.set(xlabel=\"Bernoulli Distribution\",ylabel=\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb3fcb07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1,\n",
       "       1, 1])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bern[:200]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2917e8e9",
   "metadata": {},
   "source": [
    "# Confidence Intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55e6a621",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec8665fb",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1025 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0      52    1   0       125   212    0        1      168      0      1.0   \n",
       "1      53    1   0       140   203    1        0      155      1      3.1   \n",
       "2      70    1   0       145   174    0        1      125      1      2.6   \n",
       "3      61    1   0       148   203    0        1      161      0      0.0   \n",
       "4      62    0   0       138   294    1        1      106      0      1.9   \n",
       "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
       "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
       "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
       "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
       "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
       "\n",
       "      slope  ca  thal  target  \n",
       "0         2   2     3       0  \n",
       "1         0   0     3       0  \n",
       "2         0   0     3       0  \n",
       "3         2   1     3       0  \n",
       "4         1   3     2       0  \n",
       "...     ...  ..   ...     ...  \n",
       "1020      2   0     2       1  \n",
       "1021      1   1     3       0  \n",
       "1022      1   1     2       0  \n",
       "1023      2   0     2       1  \n",
       "1024      1   1     3       0  \n",
       "\n",
       "[1025 rows x 14 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d69aea3a",
   "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",
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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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "      <th>Sex1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target    Sex1  \n",
       "0   2     3       0    Male  \n",
       "1   0     3       0    Male  \n",
       "2   0     3       0    Male  \n",
       "3   1     3       0    Male  \n",
       "4   3     2       0  Female  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex1\"] = df.sex.replace({1:\"Male\",0:\"Female\"})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31896f27",
   "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",
       "        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>target</th>\n",
       "      <th>Sex1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1025 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      target    Sex1\n",
       "0          0    Male\n",
       "1          0    Male\n",
       "2          0    Male\n",
       "3          0    Male\n",
       "4          0  Female\n",
       "...      ...     ...\n",
       "1020       1    Male\n",
       "1021       0    Male\n",
       "1022       0    Male\n",
       "1023       1  Female\n",
       "1024       0    Male\n",
       "\n",
       "[1025 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dx = df[[\"target\",\"Sex1\"]].dropna().copy()\n",
    "dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e59bf2cf",
   "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>Sex1</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>226</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex1    Female  Male\n",
       "target              \n",
       "0           86   413\n",
       "1          226   300"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(dx.target,dx.Sex1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "39ae8fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7243589743589743"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_fm = 226 / (86 + 226)\n",
    "p_fm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bae50042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "312"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 86 + 226\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3395a647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02529714756803247"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se_female = np.sqrt(p_fm * ((1 - p_fm) / n))\n",
    "se_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a7103bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6747765651256307, 0.773941383592318)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_score = 1.96\n",
    "\n",
    "lcb = p_fm - (z_score * se_female)\n",
    "ucb = p_fm + (z_score * se_female)\n",
    "\n",
    "lcb,ucb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a76c90cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6747774762140357, 0.773940472503913)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "sm.stats.proportion_confint(n * p_fm,n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edfdeeb9",
   "metadata": {},
   "source": [
    "# Calculation of CI of Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0c811b52",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">chol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>261.455128</td>\n",
       "      <td>64.466781</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>239.237027</td>\n",
       "      <td>43.155535</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              chol                \n",
       "              mean        std size\n",
       "Sex1                              \n",
       "Female  261.455128  64.466781  312\n",
       "Male    239.237027  43.155535  713"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Sex1\").agg({\"chol\":[np.mean,np.std,np.size]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f430588d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_fe = 261.4\n",
    "sd = 64.4\n",
    "n = 312\n",
    "z = 1.96"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "76418540",
   "metadata": {},
   "outputs": [],
   "source": [
    "se = sd / np.sqrt(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "485de113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(254.2539731821066, 268.54602681789333)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcb = mean_fe - (z*se)\n",
    "ucb = mean_fe + (z*se)\n",
    "\n",
    "lcb,ucb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d33d5c99",
   "metadata": {},
   "source": [
    "# Other Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd14b298",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import random\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(rc={\"figure.figsize\":(13,7.5)})\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ff15822d",
   "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>us_people_mass_pounds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192.921140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>177.681657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>196.544525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>217.552717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>175.380319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   us_people_mass_pounds\n",
       "0             192.921140\n",
       "1             177.681657\n",
       "2             196.544525\n",
       "3             217.552717\n",
       "4             175.380319"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "normal_distribution_us_male_mass_pounds = np.random.normal(loc=181,scale=24,size=6000)\n",
    "\n",
    "normal_distribution_us_female_mass_pounds = np.random.normal(loc=132,scale=22,size=6500)\n",
    "\n",
    "all_mass_values = np.concatenate((normal_distribution_us_male_mass_pounds,normal_distribution_us_female_mass_pounds),axis=0)\n",
    "\n",
    "df_ppl_mass = pd.DataFrame(data={\"us_people_mass_pounds\":all_mass_values})\n",
    "\n",
    "df_ppl_mass.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "063d8527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12500, 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ppl_mass.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "030921a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_ppl_mass[\"us_people_mass_pounds\"],color=\"darkslategrey\")\n",
    "\n",
    "plt.xlabel(\"mass [pounds]\",labelpad=14)\n",
    "plt.ylabel(\"probability of occurence\",labelpad=14)\n",
    "plt.title(\"Distribution of Mass of People in U.S.\",y=1.015,fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bbc5ed0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.4232805942338"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_mean_mass = df_ppl_mass[\"us_people_mass_pounds\"].mean()\n",
    "pop_mean_mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "833cdd67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.585190883958624"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_std_dev_mass = df_ppl_mass[\"us_people_mass_pounds\"].std()\n",
    "pop_std_dev_mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1eafb400",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_means = []\n",
    "\n",
    "n = 25\n",
    "\n",
    "for sample in range(0,300):\n",
    "    sample_values = np.random.choice(a=df_ppl_mass[\"us_people_mass_pounds\"],size=n)\n",
    "    sample_mean = np.mean(sample_values)\n",
    "    sample_means.append(sample_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dd4fda97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sample_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "39693000",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(sample_means)\n",
    "\n",
    "plt.title(\"Distribution of Sample Means ($n=25$) of People's Mass in Pounds\",y=1.015,fontsize=20)\n",
    "plt.xlabel(\"sample mean mass [pounds]\",labelpad=14)\n",
    "plt.ylabel(\"frequency of occurence\",labelpad=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "84dca603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.3008268518431"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_of_sample_means = np.median(sample_means)\n",
    "median_of_sample_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "faaec4f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.12418330193915"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_of_sample_means = np.mean(sample_means)\n",
    "mean_of_sample_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f77ea077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.4232805942338"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_mean_mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc66512d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.734407481483423"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev_of_sample_means = np.std(sample_means)\n",
    "std_dev_of_sample_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ebd00def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.717038176791725"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standard_error = pop_std_dev_mass / np.sqrt(n)\n",
    "standard_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "554f1106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024997895148220484"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail = 1 - stats.norm.cdf(1.96)\n",
    "tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3265ebf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.950004209703559"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - (tail * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b19f9ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141.95878847542738"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_95_perc_bound = mean_of_sample_means - (1.96 * standard_error)\n",
    "lower_95_perc_bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b12d5b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168.28957812845093"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_95_perc_bound = mean_of_sample_means + (1.96 * standard_error)\n",
    "upper_95_perc_bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e7eae3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kde = stats.gaussian_kde(sample_means)\n",
    "\n",
    "pos = np.linspace(np.min(sample_means),np.max(sample_means),10000)\n",
    "\n",
    "plt.plot(pos,kde(pos),color=\"teal\")\n",
    "\n",
    "shade = np.linspace(lower_95_perc_bound,upper_95_perc_bound,300)\n",
    "\n",
    "plt.fill_between(shade,kde(shade),alpha=0.45,color=\"teal\")\n",
    "\n",
    "plt.text(x=154,y=0.01,horizontalalignment=\"center\",fontsize=17,\n",
    "         s=\"95% of sample means fall within\\nthis shaded area of plus or minus\\n1.96 z-scores from mean\",\n",
    "         bbox=dict(facecolor=\"whitesmoke\",boxstyle=\"round,pad=0.1\"))\n",
    "\n",
    "plt.title(\"Distribution of Sample Means ($n=25$) of People's Mass in Pounds\",y=1.015,fontsize=20)\n",
    "\n",
    "plt.xlabel(\"sample mean mass [pounds]\",labelpad=14)\n",
    "\n",
    "plt.ylabel(\"frequency of occurence\",labelpad=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14577480",
   "metadata": {},
   "source": [
    "# Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9f92582e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86807965",
   "metadata": {},
   "source": [
    "- H0: p = 0.52\n",
    "- H1: p > 0.52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4eb4017a",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1018\n",
    "pnull = 0.52\n",
    "phat = 0.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a57c616c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.571067795759113, 0.005069273865860533)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.proportions_ztest(phat * n,n,pnull,alternative=\"larger\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "81cc4fe6",
   "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>SEQN</th>\n",
       "      <th>ALQ101</th>\n",
       "      <th>ALQ110</th>\n",
       "      <th>ALQ130</th>\n",
       "      <th>SMQ020</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>RIDRETH1</th>\n",
       "      <th>DMDCITZN</th>\n",
       "      <th>DMDEDUC2</th>\n",
       "      <th>...</th>\n",
       "      <th>BPXSY2</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>BMXWT</th>\n",
       "      <th>BMXHT</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>BMXLEG</th>\n",
       "      <th>BMXARML</th>\n",
       "      <th>BMXARMC</th>\n",
       "      <th>BMXWAIST</th>\n",
       "      <th>HIQ210</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83732</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>124.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>94.8</td>\n",
       "      <td>184.5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>43.3</td>\n",
       "      <td>43.6</td>\n",
       "      <td>35.9</td>\n",
       "      <td>101.1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>90.4</td>\n",
       "      <td>171.4</td>\n",
       "      <td>30.8</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>107.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83734</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>83.4</td>\n",
       "      <td>170.1</td>\n",
       "      <td>28.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>116.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83735</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>134.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>109.8</td>\n",
       "      <td>160.9</td>\n",
       "      <td>42.4</td>\n",
       "      <td>38.5</td>\n",
       "      <td>37.7</td>\n",
       "      <td>38.3</td>\n",
       "      <td>110.1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83736</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>55.2</td>\n",
       "      <td>164.9</td>\n",
       "      <td>20.3</td>\n",
       "      <td>37.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>80.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SEQN  ALQ101  ALQ110  ALQ130  SMQ020  RIAGENDR  RIDAGEYR  RIDRETH1  \\\n",
       "0  83732     1.0     NaN     1.0       1         1        62         3   \n",
       "1  83733     1.0     NaN     6.0       1         1        53         3   \n",
       "2  83734     1.0     NaN     NaN       1         1        78         3   \n",
       "3  83735     2.0     1.0     1.0       2         2        56         3   \n",
       "4  83736     2.0     1.0     1.0       2         2        42         4   \n",
       "\n",
       "   DMDCITZN  DMDEDUC2  ...  BPXSY2  BPXDI2  BMXWT  BMXHT  BMXBMI  BMXLEG  \\\n",
       "0       1.0       5.0  ...   124.0    64.0   94.8  184.5    27.8    43.3   \n",
       "1       2.0       3.0  ...   140.0    88.0   90.4  171.4    30.8    38.0   \n",
       "2       1.0       3.0  ...   132.0    44.0   83.4  170.1    28.8    35.6   \n",
       "3       1.0       5.0  ...   134.0    68.0  109.8  160.9    42.4    38.5   \n",
       "4       1.0       4.0  ...   114.0    54.0   55.2  164.9    20.3    37.4   \n",
       "\n",
       "   BMXARML  BMXARMC  BMXWAIST  HIQ210  \n",
       "0     43.6     35.9     101.1     2.0  \n",
       "1     40.0     33.2     107.9     NaN  \n",
       "2     37.0     31.0     116.5     2.0  \n",
       "3     37.7     38.3     110.1     2.0  \n",
       "4     36.0     27.2      80.4     2.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/nhanes_2015_2016.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c312206e",
   "metadata": {},
   "outputs": [],
   "source": [
    "females = df[df[\"RIAGENDR\"] == 2].copy()\n",
    "male = df[df[\"RIAGENDR\"] == 1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "96df8e74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2976, 29.939945652173996, 7.75331880954568)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = len(females)\n",
    "mu1 = females[\"BMXBMI\"].mean()\n",
    "sd1 = females[\"BMXBMI\"].std()\n",
    "\n",
    "n1,mu1,sd1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2cf7b907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2759, 28.778072111846985, 6.252567616801485)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2 = len(male)\n",
    "mu2 = male[\"BMXBMI\"].mean()\n",
    "sd2 = male[\"BMXBMI\"].std()\n",
    "\n",
    "n2,mu2,sd2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eb3239c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.1755933531383205, 6.591544431126401e-10)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.ztest(females[\"BMXBMI\"].dropna(),male[\"BMXBMI\"].dropna(),alternative=\"two-sided\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4c6cfffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.ztest(females[\"BMXBMI\"].dropna(),male[\"BMXBMI\"].dropna(),alternative=\"two-sided\")[-1] < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9f8388fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(females[\"BMXBMI\"].dropna(),color=\"pink\",bins=25)\n",
    "plt.show()\n",
    "\n",
    "plt.hist(male[\"BMXBMI\"].dropna(),color=\"blue\",bins=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c7eb0c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184a4c731c0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 467.95x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,\n",
    "            x=\"BMXBMI\",\n",
    "            hue=\"RIAGENDR\",\n",
    "            alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9c9742",
   "metadata": {},
   "source": [
    "# Hypothesis Testing for One Proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3308aa1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats.distributions as dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e2d5f2d8",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd614a64",
   "metadata": {},
   "source": [
    "- H0: p = 0.42\n",
    "- H1: p > 0.42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4f49248e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5131707317073171"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_us = len(df[df[\"target\"] == 1]) / len(df)\n",
    "p_us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b58bee8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015416177755376521"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se = np.sqrt(0.42 * (1 - 0.42) / len(df))\n",
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4f7702d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.04369858636477"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "be = p_us\n",
    "\n",
    "he = 0.42\n",
    "\n",
    "test_stat = (be - he) / se\n",
    "test_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "97c68855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5062078087269162e-09"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvalue = 2 * dist.norm.cdf(-np.abs(test_stat))\n",
    "pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "25c7e960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvalue < 0.05"
   ]
  }
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