Jrodeghi.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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\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": [
    "prev = 0\n",
    "prev1 = 1\n",
    "print(0)\n",
    "print(1)\n",
    "\n",
    "while prev+prev1 < 1000000:\n",
    "    out = prev + prev1;\n",
    "    print(out);\n",
    "    prev = prev1\n",
    "    prev1 = out\n",
    "    "
   ]
  },
  {
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  0.524\n",
      "Standard deviation:  0.49942366784124287\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "tosses = np.random.binomial(1,0.5,1000)\n",
    "print(\"Mean: \",np.mean(tosses))\n",
    "print(\"Standard deviation: \",np.std(tosses))"
   ]
  },
  {
   "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": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7facc8b10be0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAc2ElEQVR4nO3df4wc5XkH8O+X4woHCbkknBJztmOUIEcCE1xWhNRVRVxRCBBzAlJAJY0rIos2iB+hjuwoggQR4cRqAhVtkAsoEBA4NdQyP1IXyaYEhAl7+Iz5KdGEgC+oPn7YicWFns9P/9g9s17P7M7Mzo933vl+pBN3u8Puu3PnZ9555nmfoZlBRETK75CiByAiIulQQBcR8YQCuoiIJxTQRUQ8oYAuIuKJQ4t646OPPtrmzZtX1NuLiJTS6OjoW2Y2FPRcYQF93rx5qNfrRb29iEgpkfxt2HORUy4k+0huJflQwHNLSU6QHGt+fT3pYEVEJJk4M/QrAbwE4KiQ59ea2eW9D0lERJKINEMnORvA2QBuy3Y4IiKSVNSUy00AvgVgX4dtzif5HMl1JOcEbUByGck6yfrExETMoYqISCddAzrJcwDsNLPRDps9CGCemZ0I4FEAdwZtZGZrzKxmZrWhocCLtCIiklCUHPoiAEtIngXgcABHkbzbzC6Z2cDM3m7Z/jYAP0x3mCLSi/Vbx7F64yv43a5JHDM4gOVnzMfIwuGihyUp6xrQzWwlgJUAQPI0AP/YGsybj88yszebPy5B4+KpiDhg/dZxrHxgOyanpgEA47smsfKB7QBQ6qCug9TBEq8UJXk9ySXNH68g+QLJbQCuALA0jcGJSO9Wb3xlfzCfMTk1jdUbXyloRL2bOUiN75qE4YOD1Pqt40UPrVCxFhaZ2WMAHmt+f23L4/tn8SLilt/tmoz1eBl0OkhVeZauXi4injtmcCDW42Xg40EqDQroIp5bfsZ8DPT3HfDYQH8flp8xv6AR9c7Hg1QaFNBFPDeycBg3nrcAw4MDIIDhwQHceN6CUqcmfDxIpaGw5lwikp+RhcOlDuDtZj6LqlwOpIAuIqXk20EqDUq5iIh4QgFdRMQTCugiIp5QQBcR8YQCuoiIJxTQRUQ8obJFEc+pK2F1KKCLeMzX1rkSTCkXEY/52DpXwimgi3hMXQmrRQFdxGPqSlgtkQM6yT6SW0k+FPDcYSTXknyV5NMk56U6ShFJRF0JqyXORdEr0bhX6FEBz10K4F0z+wzJiwD8AMCFKYxPRHrgQ1dCVelEFymgk5wN4GwA3wfwzYBNzgXw3eb36wDcQpJmZmkMUkSSK3NXQlXpxBM15XITgG8B2Bfy/DCANwDAzPYC2A3g4+0bkVxGsk6yPjExEX+0IlIpqtKJp+sMneQ5AHaa2SjJ03p5MzNbA2ANANRqNc3eRaSjpFU6VU3TRJmhLwKwhORrAO4DsJjk3W3bjAOYAwAkDwXwEQBvpzhOEamgJFU6M2ma8V2TMHyQplm/dTyjUbqja0A3s5VmNtvM5gG4CMAmM7ukbbMNAL7W/P6C5jaagYtIT5JU6VQ5TZN46T/J6wHUzWwDgNsB/IzkqwDeQSPwi4j0JEmVTpUXU8UK6Gb2GIDHmt9f2/L4HwF8Jc2BiYgA8at0jhkcwHhA8K7CYiqtFBURr1R5MZW6LYqIV3xYTJWUArqIeKfMi6l6oZSLiIgnFNBFRDyhgC4i4gnl0EWk8nxpFaCALiKV5lNHR6VcRKTSfGoVoIAuIpXmU6sApVyk1PLMffqSZ5UD+dQqQDN0Ka0826RWuSWr73xqFaCALqWVZ+7TpzyrHGhk4TBuPG8BhgcHQADDgwO48bwFpTz7UspFSivP3KdPeVY5mC+tAjRDl9JKcjebMryXSFIK6FJaeeY+fcqzir+UcpHSyrNNapVbskp5sNutP0keDuBxAIehcQBYZ2bXtW2zFMBqNG4WDQC3mNltnV63VqtZvV5POGwRkWoiOWpmtaDnoszQ3wew2Mz2kOwH8ATJX5jZlrbt1prZ5b0OVsQnql2XPHUN6NaYwu9p/tjf/Oo8rRcRr3qESDlEuihKso/kGICdAB41s6cDNjuf5HMk15GcE/I6y0jWSdYnJiaSj1qkBFS7LnmLFNDNbNrMTgIwG8ApJE9o2+RBAPPM7EQAjwK4M+R11phZzcxqQ0NDPQxbxH1hNerjuyaxaNUmrTKV1MWqcjGzXSQ3AzgTwPMtj7/dstltAH6YzvBEkis6fx3WIwRQ+kWy0XWGTnKI5GDz+wEApwN4uW2bWS0/LgHwUopjFInNhd4rQbXrrZR+kbRFSbnMArCZ5HMAnkEjh/4QyetJLmlucwXJF0huA3AFgKXZDFckGhfy1609QsKodYCkKUqVy3MAFgY8fm3L9ysBrEx3aCLJudJ7ZaZHyKJVm7xp0Sru0kpR8ZJrPa6XnzH/gBJGwL/WAUVfsyiDrPeRermIl1zrveJTi9YgLlyzcF0e+0gzdPGSi71XfGnRGqTTNQtfP3NceewjBXTxVpQAqjRBOly5ZuGyPPaRUi5SWUoTpEf94rvLYx8poEtluVDa6AvXrlm4KI99pJSLVJbSBOlx8ZqFa/LYRwroUlmulTaWnc8XfdOS9T5SQJdKab0IOnhEP/oPIab2fdANutdTYF1klSIpoFdclgHIteDW3p/83fem0N9HDA70Y/fkVM9jVP9zKZoCeoVlGYBcDG5BF0Gnpg1HHnYoxq77q0xeX7XYkidVuVRYWAC65ufbcOyKh3vq2e1iBUnWF0F1kVWKpoBeYWGBZtqs57psF4Nb1nXAqsWWoimgV1iUQJN0Vu1icMu6Drjb66/fOo5Fqzb1fPYjEkYBvcK63YBhRpJZtYsLTbJukNXp9bUqtVzKevClmXXfKgO1Ws3q9Xoh7y0faK1EOYTEdMjfw3CCChDXqlyKFNYPfXhwAE+uWFzAiCRM+wV9oDEZcaU7JslRM6sFPde1yoXk4QAeB3BYc/t1ZnZd2zaHAbgLwMkA3gZwoZm91uO4JQetCx2C/pBnJKlSyWIRRVkPEi5eU8hL2X5nZa5WipJyeR/AYjP7HICTAJxJ8tS2bS4F8K6ZfQbAjwH8INVRSi663TKt6CqVMqctwq4dHEKWYvxJlfF3VuaDb9eAbg17mj/2N7/az8vPBXBn8/t1AP6SJFMbpeRmZOEwnlyxGGG/vCL/qF0shYwq7HrFtJnzAa4XZfyduXhBP6pIF0VJ9pEcA7ATjZtEP922yTCANwDAzPYC2A3g4wGvs4xknWR9YmKip4FLtlz8oy7zzGnm7KcvYJ7jeoDrRRl/Zy5e0I8qUkA3s2kzOwnAbACnkDwhyZuZ2Rozq5lZbWhoKMlLSE5c/KN28SATx8jCYewLuejscoDrRRl/Z2W+XWCspf9mtovkZgBnAni+5alxAHMA7CB5KICPoHFxVErKxXaoZbzRcvsFwcEj+vHue1MHbedygOtFGX9nQHk7R0apchkCMNUM5gMATsfBFz03APgagKcAXABgkxVVDympce2P2sWDTCdB/Wz6DyH6+4ip6fQ6PGYljeqUsv3Oyq5rHTrJE9G44NmHRorm52Z2PcnrAdTNbEOztPFnABYCeAfARWb2606vqzr08ipbGVpRwmrPBwf6ceRhhzq9/1yvxa6ynurQzew5NAJ1++PXtnz/RwBf6WWQUg4udlF0VVhefPfkVCrdHbNU5lrsKtPSf4mljGVoRSnjBcEZYQej8V2T3pZY+kABXWIpYxlaUVysFIqq00Hn6rVj+M767TmORqJSQJdYyjzrzFuZy986NW4zAPdseV0zdQfpjkUSS1nL0IriWqVQVDNjvmrtWODzBiif7iDN0CWWMs86kyprK9VejSwcDu3rAyjN5iLN0CW2ss46k6haVU97SeoXPzuEe7a8flDzJkBpNhdphi7SQZWqeoI6I94/Oo4/+/THDmrWpjSbmxTQRTqoUlVP2MHrtbcn8eMLT6pUmq2slHIR6eCYwYHA1Z4+phs6Hbzab4SyeuMruHrtmLMrXatKM3SRDspcSx5XlJLUMt6wokoU0KUQZakcqVJVT5SDV5WuKZSRUi6Su7JVjlSlqidKZ8ROaRk1bSte126LWVG3xeoK60I4PDiAJ1csLmBEElWnDpLv792n7ow56NRtUSkXyV2alSNlSd34IiwtQ0KpGAco5SKhsjqFjlo50u39y5a68UFYWubqkBYBaZd3Kq3TmQK6BMoyWEbpBxPl/eP27M4rGPgedIKuKaze+Erm5Z06gHfXNeVCcg7JzSRfJPkCySsDtjmN5G6SY82va4NeS8ojy2qGKJUjUd4/Tuomr3K7qpb15VHeqQqb7qLM0PcCuMbMniX5YQCjJB81sxfbtvulmZ2T/hClCFmvkOxWORLl/eMs+snrDjxVvdNPHvcOrdKq3aSi3ILuTQBvNr//A8mXAAwDaA/o4pGiV0hGef84rXzzCgZVDjpZl3cW/TdZBrGqXEjOQ+P+ok8HPP0FkttI/oLk8SH//zKSdZL1iYmJ+KOV3OS5QjKoUiXK+8dZ9JPXjTl0A5DsVGnVblKR69BJfgjAfwP4vpk90PbcUQD2mdkekmcBuNnMjuv0eqpDd18eF/c63V0eAL734At4970pAI1a5+8uOT7RGPK6i33W7+P7Bdduqv75gc516JECOsl+AA8B2GhmP4qw/WsAamb2Vtg2CugCdF5kFJZSSRocy17lktdBSdzWU0AnSQB3AnjHzK4K2eaTAP7XzIzkKQDWAfiUdXhxBXQBgGNXPBx48wQiPGda1RWlWmErQOeAHqXKZRGArwLYTnKs+di3AcwFADO7FcAFAP6e5F4AkwAu6hTMRWZ0utBV5QuMQbQ/OlM6JlqVyxPAQTcsad/mFgC3pDUoqY5OlSp5LFYpE1V5hNOiowatFHWIizOMrMfUrX45alliHC7u5yjilGlWTVXr/9spoDvCxRlGXmMKq1/OYrGKi/s5qjwW75SV0lENCuiOKGKG0W2m6sKsJ+3FKi58pk66/U6q0ps9LqWjGhTQHZHVDCMsQESZqeY964mbCkmSOnF5JlfU2UNZU1CtlI5qUEB3RBYzjE4BIspMNc9ZT9xgljT4ZfWZ1m8d73kRVB5nD+3B+4ufHcL9o+OlTEG1UjqqobR3LPJhVtEqi0UjneqWf9fsBhhkpga8/R97GmNKMtagGuukNdlZ7Of1W8exfN02TE0fuEf7DyFWf+VzkV+3U03+b1adnWhs7eNs/+wEAt9Tte3u8u6ORT62KM3iZsSd0gudZqQz+/T+0XGcf/JwLjdIjpsKSZo6yWI/r974ykHBHACm9lms1q5Z94EJOgMIO6i7kIKS+EqZcnH9wlZSaV/w6pReCMo5tpucmsbmlydymanFTYX0kjpJez93Cn5xAmPWeeA4Y+k11efT2XOZlHKG7vKFLZd06k7XPlMNk9c+jdtJz6XOe52CX5zAmMXZQ5SxtP/+e9mPPp49l0kpZ+gqUYqm24Wi1plqWE661336nfXbce/Tb2DaDH0kLv78HNwwsiD2WFvNzAAnp6bRR2LabH8zryJmgsvPmB+aQ48bGLMsSww7Azj/5GFsfnkilRm1r2fPZVHKgF7lEqW4p7NRA0QW+/Q767fj7i2v7/952mz/z2FBvdtY2y/sTZsdcNZRhJn3TavVb1JRatgB3VXIZ6pyKZGy9dr+9MpHMB3w99VH4n9uPCvReML6u1S9KsOV1rrqCJm9XrstOqmKK+binM4mCc5p79OgYN7p8VZhdeZhF3GrPgN0JdVR5bNnF5Q2oFdR1NNZV/qVzOS3gx7vJixAhb1m1a+fuJLq0AKfYimgl0jUi8GuzNYu/vycA3LorY93ExaIZnLmmgEeyKVCgSqePbuilGWLVRW1VM+V2doNIwtwyalz98/I+0hccurcwAui7cIC0UwpXx6LncrEpTJOKY5m6CUS9XTWpdnaDSMLIgXwdp1ysUEzwLJdJE97vEp1CBDtnqJzANwF4BNorBReY2Y3t21DADcDOAvAewCWmtmznV5X9xTNjisVD72KGvTWbx3H8n/fhql9H/wtx+2jkidffj9SjF6rXPYCuMbMniX5YQCjJB81sxdbtvkSgOOaX58H8JPmf6UAvszWouZiv7vhhQOCOdDoo3L12rH9r+MSV65xiH+i3FP0TQBvNr//A8mXAAwDaA3o5wK4q3lj6C0kB0nOav6/UoAqXZjaNTkV+LgBTraCdeUah/gn1kVRkvMALATwdNtTwwDeaPl5R/Ox9v9/Gck6yfrExETMoYrENzPzdUnWXRWluiIHdJIfAnA/gKvM7PdJ3szM1phZzcxqQ0NDSV5C5CAfPaK/4/OuzXxVkSJZiRTQSfajEczvMbMHAjYZB9BaXDy7+ZhI5q778vHo7wtfrOTazDfrropSXV1z6M0KltsBvGRmPwrZbAOAy0neh8bF0N3Kn0tegppjzXB15lulaxySnyhVLosAfBXAdpJjzce+DWAuAJjZrQAeQaNk8VU0yhb/LvWRinQwEyDLVo8ukqbSdlsUdymolod+V+XjZbdFcZMrjcGkO/2u/KOA7piyzphmxh3UciDqopmyfvay0gIn/yigO6SsM6agpeztupUOlvWzl5kWOPlH3RYd0mnG5LKgcbfrVjpY1s/uqvVbx7Fo1SYcu+JhLFq1KfAmzVrg5B/N0B0SNjMa3zWJRas2OZuK6Daji1I6GGW2qJRMNFHPdnR3If9ohu6QsJkR0fhHafjgH2fQjKsonWZ0URfNdJstzgQpl/eDK6Ke7WiBk38U0B0StCScaDSZauVaKiJsKftNF56EJ1csjhQgui2HV0omuji58ZGFw3hyxWL8ZtXZkX9X4i6lXBwS1PY2qGoEcOvCVRrteru9hi7gRefSDU4kXwrojmlfEr5o1aZS/ONMYyl7p9dQkIpOufHqUsrFcerM16D9EJ1y49WlGbrjfLn7UBoOO/SQ/bPOjx7Rj+u+fHwl90MUav5VTQroJVD1f5xBC5f+OLWvwBGJuEkBXZyX9hJ11bOLrxTQJRe9BNE0K1zUYkB8poAumes1iKZZ4RI227/m59sijycKnQVIEVTlIpnrdVFQmhUuYbP6abPUVp5qVasUpWtAJ3kHyZ0knw95/jSSu0mONb+uTX+YUma9pkzSLMPrNKtPa+WpVrVKUaKkXH4K4BYAd3XY5pdmdk4qIxLvpJEy6VbpEzXFEbToplUaK0+1qlWK0nWGbmaPA3gnh7GIp3pJmURpAxsnxTEz2+8jA98vjZWnaksrRUkrh/4FkttI/oLk8WEbkVxGsk6yPjExkdJbS96iBNlWSVMmUQN13BTHyMJh/NNffy6zlada1SpFSaPK5VkAnzKzPSTPArAewHFBG5rZGgBrgMZNolN4b8lZ0oqVJIujotSfr986nqiBWZYrcLW6V4rSc0A3s9+3fP8IyX8lebSZvdXra4t78rwPZbdc9MzBJUy3FEeWK3CrvrpXitFzyoXkJ8lGQpLkKc3XfLvX1xU35XnBr1suutOt75TikCqKUrZ4L4CnAMwnuYPkpSQvI3lZc5MLADxPchuAfwZwkZkpneKpPC/4dctFdzqIqLugVFHXlIuZXdzl+VvQKGuUCsiz13a3XHRYOeTw4ICCuVSSlv5LLHlf8OuUi9aNHEQOpIAusblywU/VJCIHUkBPSM2X3ODKwUXEBQroCagFq4i4SN0WE1DzJRFxkQJ6Amq+JCIuUkBPQM2XRMRFCugJqPmSiLhIF0UTcLFcTlU3IqKAnpBL5XJFVd3oICLiFqVcPFBE1Y3umyniHgV0DxRRdaPSTRH3KOXigTTu2RlXnIOIUjMi+dAMPQdxb9kWVxFVN1FLN5WaEcmPAnrG8ghoSe/Z2YuoBxGlZkTyo5RLxvK6ZVveVTdRSze1qlYkP10DOsk7AJwDYKeZnRDwPAHcDOAsAO8BWGpmz6Y90LLyOaBFOYgUkd8XqaooKZefAjizw/NfAnBc82sZgJ/0Pix/VL1NgFbViuSna0A3s8cBvNNhk3MB3GUNWwAMkpyV1gDLruoBrYj8vkhVpZFDHwbwRsvPO5qPvZnCax+gjOVvSdsElPGzhnFpVa2Iz3K9KEpyGRppGcydOzfW/1vmm0q0BrSZQH312rHQQB30Wa9eO4b6b9/BDSMLch+/iJRDGmWL4wDmtPw8u/nYQcxsjZnVzKw2NDQU6018KH+LWsIY9FkNwD1bXo9c7ph17buIuCeNgL4BwN+y4VQAu80s9XSLD9UiUQ9KYZ/Jmq/RjRbziFRT14BO8l4ATwGYT3IHyUtJXkbysuYmjwD4NYBXAfwbgH/IYqA+VItEPSh1+kxRDmA+nM2ISHxRqlwuNrNZZtZvZrPN7HYzu9XMbm0+b2b2DTP7tJktMLN6FgP1oVok6kFp+RnzwZiv0cqHsxkRia80S/99KH+LelAaWTiMvzl17kFBPeoBzIezGRGJr1RL/8te/hanhPGGkQWofepjiUoXl58x/4AqGaB8ZzMiEl+pAnoZBdWTP7licaT/N+kBzMVb5IlI9hTQM1Rk7XzZz2ZEJL7S5NDLSNUmIpInBfQMqdpERPKkgJ4hVZuISJ4U0DPkQ+28iJSHLopmSNUmIpInBfSMqdpERPKilIuIiCcU0EVEPKGALiLiCQV0ERFPKKCLiHhCAV1ExBMK6CIinogU0EmeSfIVkq+SXBHw/FKSEyTHml9fT3+obtFNmEXENV0XFpHsA/AvAE4HsAPAMyQ3mNmLbZuuNbPLMxijc4psiysiEibKDP0UAK+a2a/N7P8A3Afg3GyH5Ta1xRURF0UJ6MMA3mj5eUfzsXbnk3yO5DqSc4JeiOQyknWS9YmJiQTDdYPa4oqIi9K6KPoggHlmdiKARwHcGbSRma0xs5qZ1YaGhlJ66/ypLa6IuChKQB8H0Drjnt18bD8ze9vM3m/+eBuAk9MZnpvUFldEXBQloD8D4DiSx5L8EwAXAdjQugHJWS0/LgHwUnpDdM/IwmHceN4CDA8OgACGBwdw43kLdEFURArVtcrFzPaSvBzARgB9AO4wsxdIXg+gbmYbAFxBcgmAvQDeAbA0wzE7QW1xRcQ1NLNC3rhWq1m9Xi/kvUVEyorkqJnVgp7TSlEREU8ooIuIeEIBXUTEEwroIiKeKOyiKMkJAL8t5M3zdTSAt4oeRMG0D7QPAO2DtD7/p8wscGVmYQG9KkjWw65IV4X2gfYBoH2Qx+dXykVExBMK6CIinlBAz96aogfgAO0D7QNA+yDzz68cuoiIJzRDFxHxhAK6iIgnFNAzQvIOkjtJPl/0WIpAcg7JzSRfJPkCySuLHlPeSB5O8lcktzX3wfeKHlNRSPaR3EryoaLHUgSSr5HcTnKMZGZdCZVDzwjJvwCwB8BdZnZC0ePJW7NH/iwze5bkhwGMAhgJuLm4t0gSwJFmtodkP4AnAFxpZlsKHlruSH4TQA3AUWZ2TtHjyRvJ1wDUzCzThVWaoWfEzB5Hozd8JZnZm2b2bPP7P6Bx05NKNZC3hj3NH/ubX5WbQZGcDeBsNO5mJhlSQJfMkZwHYCGApwseSu6aqYYxADsBPGpmldsHAG4C8C0A+woeR5EMwH+RHCW5LKs3UUCXTJH8EID7AVxlZr8vejx5M7NpMzsJjXvxnkKyUuk3kucA2Glmo0WPpWB/bmZ/CuBLAL7RTMmmTgFdMtPMG98P4B4ze6Do8RTJzHYB2AzgzIKHkrdFAJY0c8j3AVhM8u5ih5Q/Mxtv/ncngP8AcEoW76OALploXhC8HcBLZvajosdTBJJDJAeb3w8AOB3Ay4UOKmdmttLMZpvZPDRuML/JzC4peFi5InlkszAAJI8E8FcAMql+U0DPCMl7ATwFYD7JHSQvLXpMOVsE4KtozMjGml9nFT2onM0CsJnkcwCeQSOHXsmyvYr7BIAnSG4D8CsAD5vZf2bxRipbFBHxhGboIiKeUEAXEfGEArqIiCcU0EVEPKGALiLiCQV0ERFPKKCLiHji/wFBzdnq+FHZBgAAAABJRU5ErkJggg==\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 = abs(np.random.normal(2.5,1,size = 100))\n",
    "e = abs(np.random.normal(0.5,1,size = 100))\n",
    "\n",
    "y = 0.5*x+e\n",
    "\n",
    "plt.scatter(x,y)"
   ]
  }
 ],
 "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
}