whannon1.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": 5,
   "metadata": {},
   "outputs": [],
   "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",
    "import numpy as np\n",
    "x = np.abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "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": 54,
   "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": [
    "fiblist = [0, 1]\n",
    "x = 100\n",
    "for y in range(3, x + 1):\n",
    "    z = fiblist[y-2] + fiblist[y-3]   \n",
    "    if z >= 1000000:\n",
    "        break\n",
    "    fiblist.append(z)\n",
    "for i in fiblist:\n",
    "    print(i)"
   ]
  },
  {
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.477\n",
      "Standard deviation: 0.4994707198625361\n"
     ]
    }
   ],
   "source": [
    "x = np.random.binomial(1, .5, 1000)\n",
    "#print(x)\n",
    "#print(sum(x))\n",
    "print(\"Mean: {}\".format(np.mean(x)))\n",
    "print(\"Standard deviation: {}\".format(np.std(x)))"
   ]
  },
  {
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v43551pccfvrlTE3PThLjY6M8tG3HrEKG3Sp1XtRaGeutvq3ziIiPAh+tOnS7pOVF38fmnqK7QGo/GddT740c6s+6+sw1v9j5++TUNKdccD3Q+FN2bQxZ98vTtXR/ncRROf6hVx3es24kbyU73ApPHpLek/HQ+4q+l80debtAWkk09QaQa2W1PFoxsz04/SubGr45txID5OtaajTLq5eFDPsx28x6p+FsK0nzq7+36HdVX9tJpvYubTM+MyDfgrtmax0qmn0CHhsd4TV/eMCsGVN51BtjyBNDJY48M7QGpbDhoMRh3dFsqu6/SVpAsslTSyLig1Vf7weeBzy+gxjNcnWBtJpoGn0Crmx7+o8rDpu1HWqRsmIYkdrefnVQtnAdlDisOzK7rdItZ9cD3wE+Jem5dbaebcVCYP824zMD8nWBtJposnbCq32Dq+3qyRoIrqfZBjStxpDXoOyzMShxWPEatTwqE0pybcAk6QZJP0q/NgG3AB9uP0SzfF0gWZ/ma4+3+8m4XixZmo2WVGIYHxvdeWzBaFsl58x6KrPlERHfknQCSXn1f42IVruujqn6eRvwq4jY1kGMO0k6mmSl+whwdkSsLuK6NvjyLLjLs7d2O5+M68Xy4NZtdcc3Wu3memjbjp0/3/fgjNdD2MBruM5D0oKI2FL5nuvC0qOBBZXfI+IXDU5v5XojwP8ALwTuJCnO+JqI+HHWc7zOY+6qnW21/NBFba3ByHO/drufvB7CBkkh6zwqCSNP4pD0UuCDwH7APcDvATcBT271GhmeCfw0In6e3ud84FggM3nY3FXdoujFSudWWkZZ04e9HsLKqBubQf0D8CzgGxGxLF0g+LoCrrsYuKPq9zuBP6w9SdJKYCXAkiVLCritlV2j2VftJI+sJNCoC6xRAssqGzK+cHTWMbNB0Y2RuZmI+F9gnqR5EXEl0LQJVJSIWBsRExExsWjRol7d1gZYkZ/sW11DUqtRAsvqOS64cpBZoVpueUh6NknX0Y0RcXmDU6ck7Ql8GzhP0j0kCwY7NQkcUPX7/ukxs13UtgyyPtnPk1i3cTJX66PdVkw7CSyrzIjZIMhseUj6QdXPJwIfA/YCTpO0qsE1jwWmgZOBS4GfAX9aQKw/BA6WdGC6De6rgYsLuK4NkXotg99u2cboyOwZ59sjWmo1VGu3FdNo+nCrU4vNBkmjlkd1h+tK4IURsVnSB4BrgLrTZCOiupVxbuch7rzuNklvBS4jmap7TkRsKur6Vh6N6lbVaxnM7AjGx0Z5YMu2WXWq8o595K3X1OpeIK1OLTYbFI3GPOZJ2lvSPiRTejfDzuSQuW5D0gOSflPzdYekL6X7mbctIr4aEU+MiIPS0ic2xzQbc8hqAdw/PcOOAirW5lmsWB0r7LritnpBYu1ixb0XjjJ/t3mc/PnrvM+4DaxGLY9HkuwAKCAkPS4i7k7HMxqtOv8wyUyoz6bnvRo4CLgWOIek1pVZW5qNOWS1DCqbN9WrkJuneyjPYsV6sQb1129Ukog3ULKyaLTCfGnGQzuAlzW45ksj4mlVv6+VdF1EvFPSu9qI0WynZmMO9VaXV9RLHO10D7W6Kr2d8ZGipxWbdUvudR4R8SBwa4NTHpT0SuCC9Pc/AyqLDD35sEQGcf/pZmMO1S2DrOKF0sPTYOfv1ny2erv/HdrZz8ILBq0s2lokKOm/IuKYjIdfS1J/6iySZHEN8DpJY8Bb24rSem5Qu08a1a2qfZPPUt0AmZpuXEeq0X8HaNx9lafGVoU3ULKyaHeF+YlZD6TlQ7Km5n63zftZjw1q90nWmAMw602+dnZTlqzXtW7jJO/4wvV1Z2id/pVNbJnZ0TC5rli2mPW3/5rPff8OtkcwIvHyZyxuWLJk+aGLuHDDpGde2cBrKXlIehRARPw6/X53N4Oy/hvk7pN6Yw5HrL6i7uB0qwmk9nVVWhxZW9DWW3RYm4TWbZzkwg2TO6+xPYILN0wy8XuPyhwcv3DDJC9/xuKuFnE0K0KjzaCWAGcCzwemkkN6BHAFsCoibutFgNYfZes+yUpqldlNzTZvqn1dre4t3iiOZq23rMevvHmzq+nawGs0Wvh54EvAYyPi4Ih4AvA4YB1wfg9isz4ahP2n122c5IjVV3DgqkuarnfISmqVabGN9tWo97oatbAazVOvjqNZ622QW3dmzTTqtto3Ij5ffSAitgPnS/qHrCdJmg+8HFhaff2IeF9noVov5VnPUE+nM7XyDtg3G0h/cGv9da3jY6O896VPnnXNrJYXZHeD1SahZq23srXuzKo1Sh4bJJ1FUmKkUgr9AOANwMYGz/sycD/JAsOHigjS+qPd/aeLmKmVd8C+XrJbfugi3nvxJqbqFBjMShoV9ZJRo/GTxW3MtmpnNpbZoGiUPF4PvBk4nWQvDUhWjn8F+GSD5+0fEUcXE56VUREztdrp0mm0AVStPebv1jCWeskoc90I1B2jaNZ667R1Z9ZPjVaYbwU+nn7l8T1Jh0XEDc1PtWFURF9+p106zQa8W4mltuWVtV1so5iatd7abd2Z9VthOwlKuoGkVb8bcIKkn5N0WwmIiHhqUfey7ilirGJekxpS1fcYXzhKRFK4sHrNRr0xijxdOu2WSG/E3UxmDytyG9qsFedWEp2OVTRaG1E9eF19j+r1EpNT05xywfUQSRn1as3GKGo16mZq9w3f3UxmDysseUTE7QCS/jMijq9+TNJ/AsfXfaINjE7HKrK6ikakneXH6y3mqzazvf6QdLMxilpZBRL3XjjKaX/aehKq5W4ms0SrK8yPjIgrKt+bnP7kmueOAM9oN0DrnU7HKrLO2xGx8w233TUMeZ/nVoJZd7Xa8vgA8PSq77NIOhV4FzAm6TeVw8BWYG2HcVoPdDpI3crzG3UnNbt2Xm4lmHVP83rUu8pcXBsRZ0TEXsCaiHhE+rVXROwTEad2Fqb1Qqerylt5fr1zqo2OiHmafcyD0maDpcjZVodGxM3AFyXNap1ExLUdXHsNSaXercDPgBMiYqrd61l9nXb1tPL82nNqZ1stP3QRn//BHbtuGVvgLjCDuEeJWRkpMqqG7nKSdG1EPF3SxohYlnHO2ohYKenKOg9HRLRd6U3Si4ArImKbpH9OL/jOZs+bmJiI9evXt3tby6GoN+WstRT1tm7NG1NWufPKYL6ZgaQNETHR7LwiZ1utTL8vL+qaVde+vOrXa0h2J7QBUeTGUUUsMFy3cZLTv7Jp1jTg8675xaxGzCDsUWJWRq2Oefw2/f5AsxMlfVfS+yUdLWmv9kPL9Cbgaw3uv1LSeknrN2/e3IXbW61GU3zzyhoYb3XAvJLI6u23kdXGdhVbs/xaSh4R8dzq700cD9xCUln3e+kb+YeaPUnSNyTdWOfr2Kpz3g1sA85rEOvaiJiIiIlFixa1EK51qsjS4p0O2rezD4er2JrlV+QKcwAi4lZJW0gGt7cCy4Hfb+F5L2j0uKQ3kqxif360MlBjPdPJFN/KuMTk1DQjaVmTvReOMn+3ebuULOm0oGJFbWVclxcxa0/eqbpNSfoZyYZRjyGpvvuUTqvsSjoa+FvgpRHxYMdBWqHabS1UupgqiadS1uS+B2d4aNsOPvSqw7lq1ZG5xiMaJayx0RFe+6wlLB4fQySD8B4sN2tP4S0P4KPAs4HXAMuAb0n6dkT8rINrfgyYD3xdEsA1EfGWjiO1THlmT7U7xbdRF1O7A9lZZUny1sYys8aaJg9JJwGfiYj7WrlgRHwE+IikPYETgPcC+wPZK8OaX/MJ7T7X8mtn9lQ7q7mbdTG1M2bisiRmvdFKy+MxwA8lXQucA1zWaMxB0gdJWh57At8D3gN8p4BYrUeK2MypFc1KlbQ7kO2yJGbd13TMIyL+DjiYZPzijcBPJP2TpIMynnI1ydjEkyPixIg4NyJ+XljE1nVZn/gnp6Y5cNUlHLH6CtZtnOz4Po1KlXgg22ywtTpVN4Bfpl/bgL2BCySdWefcCyLiV4VGaT3V6BN/8HA3VqcJZMWyxZxx3GEsTu83koxneSDbrASalieR9HaS/czvBc4G1kXEjKR5wE8iIqsF0ncuT9KeZvt/V7RTMsTMBluR5UkeBRxX2eypIiJ2SPLugUOodtDZK7PNrFbT5BERpzV47KZ6xyU9Gzg4Ij4laRGwZ0Tc2n6Y1mvVg85ZxQq9Mtts7urGIsHTgHcClT08RoHPFH0f651OS4aY2fDpxiLBl5EsDrwWICLu6lKBROsRr50ws1rdSB5bIyIkBYCkPbpwD+uxbq2d8OZMZuXUjeTxBUn/DoxLOpGkhPonunAfK7ki9wExs97qRlXdD0h6IfAb4BDgPRHx9aLvY4Oj3dZDr1aym1nxitzDXJWyJWmymJUwqs+x4dBJ66HIfUDMrLeKnG11paSTJC2pPihpd0lHSjoXeEOB97MBkNV6+KvPX9e0jEmnuwaaWf8UmTyOBrYDn5N0l6QfS7oV+AlJefYPR8SnC7yfDYBGrYRmZUw8BdisvArrtoqILcBZwFmSRoF9gemImCrqHjZ4mlXGbTSG4SnAZuXVjdlWRMQMcHc3rm2DJWvzpWqNWicun25WTl1JHjZ3VLceslogHsMwGz6lSh6S3gF8AFgUEff2O55+qkyPnZyaZkRiewSLm3T7dGtBXqX1UK8ar8cwzIZTaZKHpAOAFwG/6Hcs0N+V0bVv0tvT2c+Npsn2YkGexzDM5o7SJA/gQ8DfAl/udyD9Xhldb3psRdYAda8W5HkMw2xuKLyqbjdIOhaYjIjrWzh3paT1ktZv3ry5K/E0eiPuhWaL6Oo97gV5ZlakgWl5SPoG8Ng6D70beBdJl1VTEbEWWAvJToKFBVil32/EzabH1hugznqOB7PNrB0D0/KIiBdExFNqv4CfAwcC10u6DdgfuFZSvUTTE/1eGV1vcV1F1gC1F+SZWZEGJnlkiYgbIuLREbE0IpYCdwJPj4hf9iumfr8Rr1i2mDOOO4zFabIakXZ+r3Sf1a7qrn6OSPYfP+O4wzw+YWZtUdnqFKatj4lWpupOTEzE+vXruxLHIO1DkTVF1snBzPKStCEiJpqdNzBjHq1KWx99N0izilza3Mx6beC7ray5fg/gm9nc4+QxBPo9gG9mc4+TxxDo9wC+mc09pRvzmOsaDdQPygC+mQ0/J48SaVYWxcnCzHrF3VYl0u+yKGZmFU4eJeJZVWY2KJw8SsSzqsxsUDh5lIhnVZnZoPCAeYl4VpWZDQonj5LxrCozGwROHh2qt+4C3Dows+Hm5NGBeusuTrngegiY2dF8X3Ezs7Jy8uhAvXUXM9tnl7ivV+F2kEq6m5nl5eTRgTzrK6rPbbZS3Mxs0HmqbgfyrK+oPtcrxc2s7Jw8OlBv3cXoiBidp12O1a7F8EpxMyu70iQPSSdJulnSJkln9jseqL8v+Jo/exprXvG0hnuFe6W4mZVdKcY8JC0HjgWeFhEPSXp0v2OqyFp3UXuseoB8fOEoo/O0c0YWeKW4mZVLKZIH8OfA6oh4CCAi7ulzPLnUDpDf9+AMoyNifGyU+6dnPNvKzEqnLMnjicBzJL0f2AL8TUT8sN6JklYCKwGWLFnSuwgbyJrSu8f83bjutBf1KSozs/YNTPKQ9A3gsXUeejdJnI8CngX8AfAFSY+PiFmLKiJiLbAWYGJiYvaiiz7wALmZDZuBSR4R8YKsxyT9OXBRmix+IGkHsC+wuVfxdWK/8TEm6yQKD5CbWVmVZbbVOmA5gKQnArsD9/YzoDxcSt3Mhs3AtDyaOAc4R9KNwFbgDfW6rAaVS6mb2bApRfKIiK3A6/odRydcSt3MhklZuq3MzGyAOHmYmVluTh5mZpabk4eZmeXm5GFmZrk5eZiZWW5OHmZmlpuTh5mZ5VaKRYK9VL3vhleCm5nV5+RRpXbfjcmpaU696AZg9uZOZmZzmbutqtTbd2N6ZjtrLrulTxGZmQ0mJ48q3nfDzKw1Th5VsvbX8L4bZma7cvKo4n03zMxa4wHzKt53w8ysNU4eNbzvhplZc+62MjOz3Jw8zMwst1IkD0mHS7pG0nWS1kt6Zr9jMjOby0qRPIAzgdMj4nDgPenvZmbWJ2VJHgE8Iv35kcBdfYzFzGzOU0T0O4amJP0+cBkgkoT3xxFxe8a5K4GV6a+HAINQW2Rf4N5+B9FDc+n1+rUOr7n0eqtf6+9FxKJmTxiY5CHpG8Bj6zz0buD5wLci4kJJrwRWRsQLehpgByStj4iJfsfRK3Pp9fq1Dq+59Hrbea0Ds86jUTKQ9B/A29Nfvwic3ZOgzMysrrKMedwF/J/05yOBn/QxFjOzOW9gWh5NnAh8RNJuwBYeHtMoi7X9DqDH5tLr9WsdXnPp9eZ+rQMz5mFmZuVRlm4rMzMbIE4eZmaWm5NHF0l6haRNknZImqh57FRJP5V0i6Sj+hVjN0h6r6TJtJzMdZJe3O+Yiibp6PTf7qeSVvU7nm6TdJukGyolgvodT5EknSPpHkk3Vh17lKSvS/pJ+n3vfsZYpIzXm/tv1smju24EjgO+XX1Q0pOAVwNPBo4GzpI0MvvppfahiDg8/fpqv4MpUvpv9a/AnwBPAl6T/psOu+Xpv+ewrX34NMnfYbVVwH9HxMHAf6e/D4tPM/v1Qs6/WSePLoqImyKi3gr3Y4HzI+KhiLgV+CngYo/l8UzgpxHx84jYCpxP8m9qJRQR3wZ+XXP4WODc9OdzgRW9jKmbMl5vbk4e/bEYuKPq9zvTY8PkrZJ+lDaRh6bJn5oL/361Arhc0oa0BNCwe0xE3J3+/EvgMf0Mpkdy/c06eXRI0jck3Vjna6g/iTZ53R8HDgIOB+4GPtjPWK0Qz46Ip5N01f2lpOf2O6BeiWQ9w7Cvacj9N1uWRYIDq80aW5PAAVW/758eK41WX7ekTwD/1eVweq30/355RcRk+v0eSV8i6br7duNnldqvJD0uIu6W9Djgnn4H1E0R8avKz63+zbrl0R8XA6+WNF/SgcDBwA/6HFNh0j+2ipeRTBwYJj8EDpZ0oKTdSSY/XNznmLpG0h6S9qr8DLyI4fs3rXUx8Ib05zcAX+5jLF3Xzt+sWx5dJOllwP8DFgGXSLouIo6KiE2SvgD8GNgG/GVEbO9nrAU7U9LhJE3924D/29doChYR2yS9lWSbgBHgnIjY1OewuukxwJckQfKe8dmIuLS/IRVH0ueA5wH7SroTOA1YDXxB0puB24FX9i/CYmW83ufl/Zt1eRIzM8vN3VZmZpabk4eZmeXm5GFmZrk5eZiZWW5OHmZmlpuTh1lK0lcljadff1F1/HmSBnaho6Rlkj6Z/ixJH02r/f5I0tNbeP430wrBlYqqj06Pv1XSm7odv5WTk4dZKiJeHBFTwDjwF43PHijvAj6a/vwnJItODybZrvnjLV7jtVUVVSurqc8BTio0UhsaTh5WepL+IP2UvSBdDb1J0lNqzjlF0tvSnz8k6Yr05yMlnZf+fJukfUkWiB2Ufgpfk15iT0kXSLpZ0nlKV8xlxHGdpDWV/RIkLZX0HUnXpl9/nB7fpUUj6WOS3pj+vFrSj9PrfSA99oq0ftj1kr6dHtsLeGpEXJ9e5ljgPyJxDTBes3q4ZRHxIHCbJFd8tlm8wtxKLyJ+KOli4B+BMeAzEVFbXuE7wDtIPqFPAPMljQLPYXaNplXAUyLicEje5IFlJPuv3AVcBRwBfLfmeZ8CToyIqyWtrjp+D/DCiNgi6WDgc2kMdUnah6RExKEREZLG04feAxwVEZNVxybYtZREVsXfu2nsU5K2AxcC/xgPrx5eT/LfaGjK51gx3PKwYfE+4IUkb6Zn1nl8A/AMSY8AHgKuTs99DkliaeYHEXFnROwArgOWVj+YvpnvFRFXp4c+W/XwKPAJSTcAXyTZQKqR+4EtwCclHQc8mB6/Cvi0pBNJyqIAPA7Y3EL8jbw2Ig4j+W/xHOD4qsfuAfbr8Po2hJw8bFjsA+wJ7AUsqH0wImaAW4E3At8jSRjLgScAN7Vw/Yeqft5Ovlb7ycCvgKeRJKzd0+Pb2PVvcEEa6zaSqrUXAMcAl6bH3wL8HUlF3w1pC2WaXV9v7oq/VRVzHyBJetXdVAvSe5jtwsnDhsW/A38PnAf8c8Y53wH+hqSb6jvAW4CNVV00FQ+QJKGWpQPtD0j6w/TQq6sefiRwd9pqOZ6HWw23A09KqyuPA88HkLQn8Mh0K9CTSZIOkg6KiO9HxHtIWhsHkCS+J1Td62Lg9emsq2cB91c2NZJ0c23cknZLx3lIu/GOYddusCcy/BV0rQ0e87DSk/R6YCYiPqtkf/HvSToyIq6oOfU7wLuBqyPid5K2UKfLKiL+V9JV6YD314BLWgzlzSTdUzuAb5F0PwGcBVyYxnkp8Lv0Pnek1ZVvJGkVbUzP3wv4sqQFgIC/To+vScdMRLKv9vXpmMgjJe2Vthy+CryYZGvjB4ET0v9G+6bPqzUfuCxNHCPAN4BPVD1+BPDeFl+/zSGuqmtWEEl7RsRv059XAY+LiLf34L4nAw9ExNkNzjkGeHxEfDTrnDrPWQb8dUQc3/Rkm3OcPMwKIulVwKkkLfrbgTdGRKeD2a3cdwHwioj4z4Kv+0LgJxFxW5HXteHg5GFmZrl5wNzMzHJz8jAzs9ycPMzMLDcnDzMzy83Jw8zMcvv/lJUWPmsnZAsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "a = np.random.normal(0, 5, 100)\n",
    "#print(a)\n",
    "b = np.random.normal(0,1, 100)\n",
    "#print(b)\n",
    "y = [ ]\n",
    "for x in range (0,100):\n",
    "    temp = a[x] * 0.5\n",
    "    temp += b[x]\n",
    "    y.append(temp)\n",
    "plt.xlabel(\"x with gauss(0, 5)\")\n",
    "plt.ylabel(\"y = 0.5 * x + e \\n(e with gauss (0,1))\")\n",
    "plt.scatter(a, 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
}