mdeleon1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 6.1.0\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "import json\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "# Pandas is good with data tables\n",
    "import pandas as pd\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
    "import bs4\n",
    "# Nltk helps with some natural language tasks, like stemming\n",
    "import nltk\n",
    "# Bson is a binary format of json to be stored in databases\n",
    "import bson\n",
    "# Mongo is one of common nosql databases \n",
    "# it stores/searches json documents natively\n",
    "import pymongo\n",
    "print (\"IPython version:      %6.6s\", IPython.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[ 0.20354485  0.87353642  0.79226415]\n",
      " [ 0.26457656  0.23486214  0.8240387 ]]\n",
      "Add 5 to every element\n",
      "[[ 5.20354485  5.87353642  5.79226415]\n",
      " [ 5.26457656  5.23486214  5.8240387 ]]\n",
      "Get the first row\n",
      "[ 5.20354485  5.87353642  5.79226415]\n"
     ]
    }
   ],
   "source": [
    "#Here is what numpy can do\\n\",\n",
    "print (\"Make a 2 row x 3 column array of random numbers\")\n",
    "x = np.random.random((2, 3))\n",
    "print (x)\n",
    "\n",
    "#array operation (as in R)\n",
    "print (\"Add 5 to every element\")\n",
    "x = x + 5\n",
    "print (x)\n",
    "\n",
    "# get a slice (first row) (as in R)\n",
    "print (\"Get the first row\")\n",
    "print (x[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
    "#       on possible completions.\n",
    "#       In the code cell below, type x.<TAB>, to find built-in operations for x\n",
    "x.any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aIPsbgIu69+lFwOXj3P4RX/RJnsbg/4CrquqGofHXAK8ELqju5BkHcWuHw+lA2YdcBfxB\ntzzt2Z/H4HzqV5Ls6vLdleQXmf7sVNUTVfWjqvox8Nc8dapgarIfYH/ZDdzQnSr4EvBjBveNmZrc\nsOj7dBXw+8C1Q9NXQvZNwJPLf8+495dJXKCYli8GFzyuBD603/gZDG6pPLPf+Av5yYs8DzO5izwH\nyn7S0PKbgeu75bP5yYs8X5q2132/Obt46mLs1GcH1gwtX8TgHPHU7DOL5H498Kfd8gsYnDbItORe\nan/p3qtf2G9s6rMzOFf/sm75dODObnks+/ph/x8+TV/AbzG4KHIPcHf3dRaDizePDo19fOg5lzC4\niv8g3VXzKcv+KeDebvwfGFygfXKH+2iX/avA7LRl32/OcNFPfXbgb7ts9zC4r9Nw8U98n1kk99HA\n33X7zF3Ay6cp91L7C/BJ4PULPGeqs3fjdzL4hbQD+PVu/lj2df8yVpIad8Sfo5ek1ln0ktQ4i16S\nGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8ApNqW0K2lWVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a2433ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "heads = np.random.binomial(500, .5, size=500)\n",
    "histogram = plt.hist(heads, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Task 1\n",
    "## write a program to produce Fibonacci numbers up to 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "1\n",
      "1\n",
      "2\n",
      "3\n",
      "5\n",
      "8\n",
      "13\n",
      "21\n",
      "34\n",
      "55\n",
      "89\n",
      "144\n",
      "233\n",
      "377\n",
      "610\n",
      "987\n",
      "1597\n",
      "2584\n",
      "4181\n",
      "6765\n",
      "10946\n",
      "17711\n",
      "28657\n",
      "46368\n",
      "75025\n",
      "121393\n",
      "196418\n",
      "317811\n",
      "514229\n",
      "832040\n"
     ]
    }
   ],
   "source": [
    "a=0\n",
    "b=1\n",
    "print(a)\n",
    "print(b)\n",
    "result = 1\n",
    "while(result <= 1000000):\n",
    "    print(result)\n",
    "    result = a + b\n",
    "    a = b\n",
    "    b = result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
    "## Calculate the mean and standard deviation of that sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  0.515\n",
      "Standard Deviation:  0.49977494935220584\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "n, p = 1, 0.5\n",
    "s = np.random.binomial(n,p, 1000)\n",
    "\n",
    "print(\"Mean: \", np.mean(s))\n",
    "print(\"Standard Deviation: \", np.std(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3\n",
    "## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
    "### use numpy.random.normal to generate gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEGCAYAAABsLkJ6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbqElEQVR4nO3df5DcdX3H8dc7l0MuoB6WE+HkmkgxjBAheCI1rWOQEioIJ2LFgfqjnUnriAqDYS7KDDiDQ8bYYqe1zqRKdUbUUMAThRpB4jilg3LhgiGQVBQJLCDHyNFKDrjk3v3jds+9ve9397u7393Pd/f7fMxkuPvu3u5nubnP+/t5fz6f98fcXQCA/FkSugEAgDAIAACQUwQAAMgpAgAA5BQBAAByamnoBtTjyCOP9OXLl4duBgB0lB07djzr7gOV1zsqACxfvlzj4+OhmwEAHcXMHou6TgoIAHKKAAAAOUUAAICcIgAAQE4RAAAgpzpqFRAA5M3YREGbt+3Vk1PTOqa/TxvWrdTI6sFUXpsAAAAZNTZR0MZbd2l65qAkqTA1rY237pKkVIIAKSAAyKjN2/bOd/4l0zMHtXnb3lRenwAAABn15NR0XdfrRQAAgIw6pr+vruv1IgAAQEZtWLdSfb09C6719fZow7qVqbw+k8AAkFGliV5WAQFADo2sHkytw68UNAVkZpeb2W4ze9DMvm1mh4ZsDwDkSbAAYGaDkj4padjdT5LUI+miUO0BgLwJPQm8VFKfmS2VtEzSk4HbAwC5ESwAuHtB0hcl7ZP0lKTn3f1Hlc8zs/VmNm5m45OTk+1uJgB0rZApoCMknS9phaRjJB1mZpdUPs/dt7j7sLsPDwwsOtEMANCgkCmgMyU96u6T7j4j6VZJbw/YHgDIlZABYJ+k081smZmZpHdJejhgewAgV0LOAfxM0s2S7pe0q9iWLaHaAwB5E3QjmLtfLenqkG0AgLwKvQwUABAIAQAAcooAAAA5RQAAgJwiAABAThEAACCnCAAAkFMEAADIKQIAAOQUAQAAcooAAAA5RQAAgJwiAABAThEAACCnCAAAkFMEAADIKQIAAORU0ABgZv1mdrOZ7TGzh83sT0O2BwDyJOiRkJL+SdIP3f1CMztE0rLA7QGA3AgWAMzs1ZLeIekjkuTuL0t6OVR7ACBvQqaAVkialPTvZjZhZl81s8MCtgcAciVkAFgq6VRJX3H31ZJekDRa+SQzW29m42Y2Pjk52e42AkDXChkAnpD0hLv/rPj9zZoLCAu4+xZ3H3b34YGBgbY2EAC6WbAA4O5PS3rczFYWL71L0kOh2gMAeRN6FdAnJN1YXAH0a0kfDdweAMiNoAHA3XdKGg7ZBgDIK3YCA0BOEQAAIKcIAACQUwQAAMip0KuAAHSJsYmCNm/bqyenpnVMf582rFupkdWDoZuFKggAAJo2NlHQxlt3aXrmoCSpMDWtjbfukiSCQIaRAgLQtM3b9s53/iXTMwe1edveQC1CEgQAAE17cmq6ruvIBgIAgKYd099X13VkAwEAQNM2rFupvt6eBdf6enu0Yd3KmJ9AFjAJDORIq1bqlF6DVUCdhQAA5ESrV+qMrB6kw+8wBAAgJ6qt1Om0jps9B+kgAAA50S0rddhzkB4mgYGc6JaVOuw5SA8BAMiJqJU6JmntCZ111Gq3jGSygAAA5MTI6kG97y2DsrJrLumWHQWNTRTa2paxiYLWbLpbK0Zv15pNd9f1/t0ykskCAgCQI9v3TMorrqWdPqnVuZdy+IWpabn+kMNPGgTYc5Ce4AHAzHrMbMLMfhC6LUC3a3X6JEnn3mwOf2T1oK67YJUG+/tkkgb7+3TdBauYAG5AFlYBfUrSw5JeFbohQLc7pr9PhYjOPq30SZKlpmkEIfYcpCPoCMDMXi/pHElfDdkOIC9anT5J0rmTw8+O0CmgL0m6UtJs3BPMbL2ZjZvZ+OTkZNsaBnSjVqdPknTu5PCzI1gKyMzOlfSMu+8ws3fGPc/dt0jaIknDw8OV81cA6tTK9MmGdSsXbNKSFnfu1A3KjpBzAGsknWdm75Z0qKRXmdk33f2SgG0C0ISknTs5/Gww9/A31cURwKfd/dxqzxseHvbx8fG2tAkAuoWZ7XD34crrWVgFBKABFERDszIRANz9J5J+ErgZQObEdfIUREMaMhEAACx21dgu3Xjvvvmdu+WdfKtLOzO6yIfQy0ABRBibKCzo/EtKnXzcevvC1HTdtXWi3ruZUg3oHAQAIIM2b9u7qPMvKd2Vx2m2w6bccn4QAIAMqlYWoZSSqdxMVa6ZDptyy/nBHACQQXE1e0xakI/fvG1v5POkuQ67kVx+q+sFITsYAQAZUFlCee0JA5GHt1x8+tB8Bz6yelD3jJ6hwZiO+dDeJbp86866c/mUasgPAgAQWNSk6y07CnrfWwYX1Oy5/gOn6NqRVYt+PqrD7l1imp6Zbaj2P+WW84MUEBBY3KTr9j2Tumf0jJo/H1V+Yf/LB/Tc/pnI5yfJ5VOqIR8IAEBgraiPv2L09tjnkstHCQEACKzapGujG7JqTSIDEnMAQHBxk65rTxhItCEr6gzeqNesnEQGCABAYHGTrtv3TNbckBW3a1fSoteMm0RGfpECAgKpld65fOvOyJ8rFNf3j6werLpr957RM7jbR1UEAKBMu4qgJanmGZfHlzT/XHbtohmkgICiVhVBi8rRJ6m3U63cQ+m5HLCOZhAAgKJWFEGLCyrVyjeUlOYG4jw5Nc2uXTSFAAAU1ZNOibqrjxIXVJZYdBv6l/Uu+H5k9WBsqYdj+vvYtYumMAcAFCUtglbPaVxxQWXWpZ4lpoOzC4s1/P7FA/MTvCUb1q1c8H7Swrt8du2iUcFGAGZ2rJltN7OHzGy3mX0qVFsAKXkRtHpSRdVy8ZWdvyTNzPqi12n0Lj/pKAX5FXIEcEDSFe5+v5m9UtIOM7vT3R8K2CbkWFRNnahVQPWkijasW6nLYpZzxol6nXrv8jkzGEkECwDu/pSkp4pf/5+ZPSxpUBIBAE1rdDlnko62nnr5I6sHdc1tuzU1HV2YLUrlPEAjWn1mMLpDJuYAzGy5pNWSfhbx2HpJ6yVpaGiovQ1DR2r13W+tnHylc08+Wt+8d1/i13+x7HXLA1n/sl65S89Pz9QMauwPQBLBVwGZ2eGSbpF0mbv/b+Xj7r7F3YfdfXhgYKD9DUTHafWZtvXm5Lfvmazr9adnZiUtXkL63P4ZTU3PJNqjwP4AJBF0BGBmvZrr/G9091tDtgXdox13v/Xk5Bt936hAVq5aSqfeUQryKVgAMDOT9DVJD7v7P4ZqB7pPK860baZERLWSDlGOKM4BJAkccc9JOqGNfAs5Algj6a8l7TKzncVrn3H3O8I1CWlpV02dKGnf/TY7p1DPSqDeHtPV7zlRUrLAUS2osT8AtQSbA3D3/3J3c/c3u/spxX90/l2gVTV1kkp7d2yzcwojqwfV3xe9sqe/r3dBOzdfeLIkac2mu1WYmlbMhmFJpHTQvEysAkJ3ycISxGp3v/WOTtKYU7jmvBMjRyXXnHfigveuHG245g5ycc2lhpKuAgKSIAAgdVlegthIOicuFbPETCtGb0/UGSfNyUcFT9fc6CDJAfFAPQgASF0rJmHT0sjoJGpOQZIO+lwph8LUtC7fulOXbd2pwSrBIElOPsvBE90n+D4AdJ8slyhupIOtnFPoscWZ+VJVn2bnO1i/j3aqGQDM7BNmdkQ7GoPukOUSxY12sCOrB3XP6Bl6dNM5mvXFRdzKTc8c1GVbd8YWYLtqbJeO23iHlo/eruM23qGrxnbNP5bl4InukyQFdJSk+8zsfkk3SNrmXuMvALmXlSWIlRO+a08Y0C07Ck0tEU26rj9qfuGqsV0LykIcdJ///tqRVYnnCkIus0X3sCR9eXHT1lmSPippWNJNkr7m7r9qbfMWGh4e9vHx8Xa+JTpY5YSvNNfZv+8tg9q+Z7LhzjPqdaspn8A9buMd83MH5XrM9Kvr3t3w+/f19mRmlIXsMbMd7j5ceT3RJLC7u5k9LelpzZVxPkLSzcXyzVem21QgHXETvtv3TDa1oqb8Lr20Vr/abVT5/EJU51/tepQsLLNFd6gZAIoHtXxI0rOSvippg7vPmNkSSb+URABAJiWd8E2aTol7Xul6XFqofH6hxyx2BJD25wJqSTICeI2kC9z9sfKL7j5rZue2pllA85IsR026L6DW80qBoFYJig++7djI0tCnv+EIrdl0d6K0VJaX2aKz1FwF5O5XV3b+ZY89nH6TgOaNTRS0/+UDi65XdshJyzwkeV6S1U/XjqzSJacPzd/x95hpzXGv0f37nk9cOoOVQkgLG8HQdeImafv7eudLL9RK21SmU5KmXZKsfrp2ZJWuHVk1//2aTXfXldOn0ifSQgBA14mro3/YK5bGpmoqVaZTWpl2aXRzGh0+msVOYHSNsYnCfBXNKKUOtdZBK1HplKRpl1IbVozeHrsRrBK7fxEKAQBdobwEdZxSh1rtzjpu13KS/H6jZbDJ6SOURBvBsoKNYIhT7c5fknqXmA4/dKmm9s9oScxSzGYrbsa1IcnrsrMXrdTURjAg66rd1ff39eqFlw/ouf0zkqI3XaVxx93M+nxy+giBAIBUhL6DjZuk7THT1PRM5M/0mGnWPbX2sj4fnSboHICZnW1me83sETMbDdkWNC70EZBSdB5dql5iYdZdj246R/eMnpFKsKonl9/IZDGQtmAjADPrkfRlSX8h6QnNVRy9zd0fCtUmNOZz398dvDZN5dr4uDx/uVbcmR/au2T+/0V/X6/OPflobd62V5dv3Tk/0pDU1CHzQFpCpoBOk/SIu/9akszsO5LOl0QA6CBjE4X53HqldtemKc+jLx+9vepz015lE7W34IWXDmjrfY9r5uAfTg7beOuuBUGihGJuCCFkABiU9HjZ909Ielvlk8xsvaT1kjQ0NNSeliGxypIJ5ULlvscmClUrdFY7trHe96k24piZXdyC6ZmDsXsQKOaGdsv8JLC7b5G0RZpbBhq4OahQrdMKtY5987a9sZ3/JacPLSjDECXJhHblHX895ZzjMFmMdgs5CVyQdGzZ968vXkMHieu0+vt6g6UzqgWl7Xsmq/5s0gntWruJq+nv6100WWzF92JCGO0UMgDcJ+l4M1thZodIukjSbQHbgwbErXy55rwTA7Wo+p10rTRLXNXPK256YEHHnCRd07vE1NuzsM5/6f9NaVexpAXpqhArqJBfwQKAux+QdKmkbZIelnSTu+8O1R40Jq5EgqRgyxw3rFupuONVaqVZ4jr2g+4LOua41+kxm///sPn9J2vzhSdHlo8oHTI/2N+3KF0VVY4aaIWgcwDufoekO0K2Ac2r3MWa9JCVVrZn/LHf6cZ79y3oXJOs/Kl24Hv5Sp0N61YmPpe32mfmdC+ERDE4pC7pISutdO3IKl3/gVOqFm+LErehrKTUMScpDpcElUARUuZXAaHzZOWutpH6OqXnX3HTA5Ere8o75jTq98SNJKgEinYgACB1rayJ046aQ6XXa0fHzOleCIkAgNQluattpCNv59xCOztmKoEiFAIAUler82y0I682t0DHDNSPAICWqNZ5NtKRj00UEh/gDiAZVgGh7eqdJC6NGOKwYgZoDCMApCZpXr/eSeJqZReqTcyGPqQGyDpGAEhFPYfC1HsIerUUT9za+ywcUgNkHQEAqahn81e9m6jiRgaD/X0NzTMAmEMKCKmIu0svTE1rbKIQWR4haTqmkc1SWdmMBmQZAQCpqFZDZ8N/PKDPfX+3pvbPNJSLb2RNPge0A7URAJCKqLv0kplZnz82stHNW/WuyafEAlAbAQCpKHXOl23dWfO5aW7eilvpQ4kFoDYCAFIzsnpQm7ftjU0FlUvynFpq7ShmJy9QHauAkKpa5ZRLeizuyJbkWOkDNIcRAFJVmXqJOyo9jUPUWekDNIcRABIbmygkOuaxdNzho5vOmT/3NkqzR0VymArQnCABwMw2m9keM/uFmX3XzPpDtCNPknbe1X6+kZ211VJChalpXb51p64ai6/zU+9rs9IHSC7UCOBOSSe5+5sl/Y+kjYHakQtplEVoNN9evus3iku68d59DY0E0jqWEcirIHMA7v6jsm/vlXRhiHbkRRp19JvJt5dW4ywfvT3ycS+2sZGOm5U+QOOyMAfwN5L+M+5BM1tvZuNmNj45OdnGZnWPNCZL4/LqS8wSp5Wqrfxh4hZov5YFADO7y8wejPh3ftlzPivpgKQb417H3be4+7C7Dw8MDLSquV0tjcnSuFz+QffEaaVqK3+YuAXar2UBwN3PdPeTIv59T5LM7COSzpV0sXsKawIRK43J0sp8e9TdfK05gbh5ACu2EUB7hVoFdLakKyWd5+77Q7QhT9KaLC1f3jkbE7OrpXKiApFJuvj0IfL4QAChNoL9i6RXSLrT5u4k73X3vw/Ulo7TyElXaU+WNlJtk/o8QLaEWgX0JyHetxvUqn9T62fT6nwbrbbJqh0gOygF0WEaXdIZFzjGH/udtu+ZVGFqWj1mOuiuwQTBoZvv5jlLGHlBAOgw9S7pLHVmUema6ZmDuvHeffP1ekqrdJKOKtK4m89aZ9vMCAvoNFnYB4A61LOks3wHcJy45VftqKqZxYPbqTCKPCEAdJiolTS9PaYXXjqwaENWVGdWj8LUdFP1g2rJYmdLhVHkCSmgDlOZe+9f1qvfv3hAU9OLj1ys1WmZ4kcApcdLo4dWpEKy2NlyljDyhBFABypfj7/skKWamV3YjZfuoqt1WoP9fbr49KHYSp1RwSHtu/MslnOmwijyhADQ4ardRcd1Zl/6wCm6Z/QMXTuyakGlztLu3sH+vtiRQZp351nsbKkwijwhBdRhKlfN9C/r1XP7ZxY975j+vkRLNeNW8qzZdHdqqZBOO7idvQrIC+ukMjzDw8M+Pj4euhnBVC5RlKTeJSaZNHPwD7/Hvt6epu9ao96rkddN63UANM7Mdrj7cOV1UkAdJGrVzMys67BDlqaeskgrFZLFlT4A5pAC6iBx+ffnp2e08+qzIh9rZqNVGqmQLK70ATCHEUAHqXfVTBY2WmVxpQ+AOQSADlLvqpkspF+yuNIHwBxSQB2k3lUzWUi/ZHWlDwACQMepJy+flV2tLKsEsokUUBcj/QKgGkYAXYz0C4BqggYAM7tC0hclDbj7syHb0q1IvwCIEywFZGbHSjpL0r5QbQCAPAs5B3C9pCtVvSIxAKBFgqSAzOx8SQV3f8CKFSiRrqwdtQgge1oWAMzsLkmvi3jos5I+o7n0T5LXWS9pvSQNDQ2l1r5mZK1zrWzP2hMGdMuOAufaAqiq7dVAzWyVpB9L2l+89HpJT0o6zd2frvazoaqBlnewr+7r1QsvH0i9+mYzbausthl30tdgf5/uGT2jbW0DkA2ZqQbq7rvc/bXuvtzdl0t6QtKptTr/UCrr6UxNzyzo/KWw1S2jyj204zAXAJ2PjWA1JD1YPVTnWs/7UoANQLngG8GKo4DMStrBtqJzTTLXEFfuoTINxA5gAJUYAdSQpGNvReeatJRzXLmHi08f4lxbAFUFHwFk3YZ1KyOPYTz80KWa2j/TslVA1Uo5V57pW3p+VlYlAegMBIAaQnWw9ZRyptwDgEYQABII0cFmpZQzgO7FHEBGUcoZQKsxAsgocvsAWo0AkGHk9gG0EikgAMgpAgAA5BQBAAByigAAADlFAACAnCIAAEBO5WIZaNZO8AKALOj6AFB5YhbHIwLAnK5PAV1z2+7YqpoAkGddHQDGJgqamp6JfIzjEQHkXVcHgGp3+VTVBJB3wQKAmX3CzPaY2W4z+0Ir3qPaXT5VNQHkXZBJYDNbK+l8SSe7+0tm9tpWvE9cTf0jlvUyAQwg90KNAD4maZO7vyRJ7v5MK94krqb+1e85sRVvBwAdJdQy0DdK+nMz+7ykFyV92t3vi3qima2XtF6ShoaG6nqTTqipzx4FAKGYu7fmhc3ukvS6iIc+K+nzkrZL+qSkt0raKukNXqMxw8PDPj4+nnZTg6ncoyDNjVCuu2AVQQBAasxsh7sPV15v2QjA3c+s0piPSbq12OH/3MxmJR0pabJV7cmizdv2xu5RIAAAaLVQcwBjktZKkpm9UdIhkp4N1JZg4lYpsUcBQDuECgA3SHqDmT0o6TuSPlwr/dON4vYisEcBQDsECQDu/rK7X+LuJ7n7qe5+d4h2hBa3Sok9CgDaoeuLwWVZJ6xSAtC9CACBjawepMMHEERX1wICAMQjAABAThEAACCnCAAAkFMEAADIqZbVAmoFM5uU9FjAJhypfOxY5nN2jzx8Rikfn7OZz/jH7j5QebGjAkBoZjYeVVCp2/A5u0cePqOUj8/Zis9ICggAcooAAAA5RQCoz5bQDWgTPmf3yMNnlPLxOVP/jMwBAEBOMQIAgJwiAABAThEAEjCz95vZbjObNbPhisc2mtkjZrbXzNaFamPazOwaMyuY2c7iv3eHblNazOzs4u/rETMbDd2eVjGz35jZruLvr2sO0zazG8zsmeKBUqVrrzGzO83sl8X/HhGyjc2K+Yyp/00SAJJ5UNIFkn5aftHM3iTpIkknSjpb0r+aWc/iH+9Y17v7KcV/d4RuTBqKv58vS/pLSW+S9MHi77FbrS3+/rppjfzXNff3Vm5U0o/d/XhJPy5+38m+rsWfUUr5b5IAkIC7P+zueyMeOl/Sd9z9JXd/VNIjkk5rb+tQp9MkPeLuv3b3lzV3JOn5gduEOrj7TyX9ruLy+ZK+Ufz6G5JG2tmmtMV8xtQRAJozKOnxsu+fKF7rFpea2S+Kw9GOHlKX6fbfWTmX9CMz22Fm60M3psWOcvenil8/LemokI1poVT/JgkARWZ2l5k9GPGva+8Oa3zmr0g6TtIpkp6S9A8h24qG/Jm7n6q5dNfHzewdoRvUDj63tr0b17en/jfJkZBF7n5mAz9WkHRs2fevL17rCEk/s5n9m6QftLg57dLRv7N6uHuh+N9nzOy7mkt//bT6T3Ws35rZ0e7+lJkdLemZ0A1Km7v/tvR1Wn+TjACac5uki8zsFWa2QtLxkn4euE2pKP4RlbxXcxPh3eA+Sceb2QozO0Rzk/i3BW5T6szsMDN7ZelrSWepe36HUW6T9OHi1x+W9L2AbWmJVvxNMgJIwMzeK+mfJQ1Iut3Mdrr7OnffbWY3SXpI0gFJH3f3gyHbmqIvmNkpmhtK/0bS3wVtTUrc/YCZXSppm6QeSTe4++7AzWqFoyR918ykub/zb7n7D8M2KR1m9m1J75R0pJk9IelqSZsk3WRmf6u5kvF/Fa6FzYv5jO9M+2+SUhAAkFOkgAAgpwgAAJBTBAAAyCkCAADkFAEAAHKKAAAAOUUAAICcIgAATTCztxaLcx1a3H2728xOCt0uIAk2ggFNMrNrJR0qqU/SE+5+XeAmAYkQAIAmFWsK3SfpRUlv76JyIOhypICA5v2RpMMlvVJzIwGgIzACAJpkZrdp7mSxFZKOdvdLAzcJSIRqoEATzOxDkmbc/VvF84b/28zOcPe7Q7cNqIURAADkFHMAAJBTBAAAyCkCAADkFAEAAHKKAAAAOUUAAICcIgAAQE79P9njH/qmpnouAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.random.normal(0,5, size=100)\n",
    "e = np.random.normal(0,1, size=100)\n",
    "\n",
    "plt.scatter(x, 0.5*x+e)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}