mdenise.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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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slkeeF+bM/CwG86ZfSrKvy3Vnkl9hfDNTVQ9X1U+q6qfA3/DYtMDYZmZwtXhDNzXwBeCnDL7cauSZj/DcWwP8AXDt0OYjzwuHzbwFeHT5HxjVebESLzyslh8GL3R8EvjQrPEzgHuBiVnjz+VnX9S5n5V9EepweU8eWn4zcH23fBY/+4LOF8blMZ61zT4ee0F1bDMDJw0tX8xgDnjk58U8md8A/Gm3/BwG0wQZdeYjnRfd8+9zs8bG+THeA5zWLZ8O7O6WV/RcXrEHYjX8AC9l8GLI3cBd3c+ZDF6seXBo7GND+1zK4JX6++heIR+DvJ8CvtKN/yODF1kfPRk/0uX9MjA1Lo/xrG2Gy31sMwN/12W6m8F3Jw2X/cjOi3kyHwv8fXd+3Am8fBwyH+m8AD4BvGGOfcb1MX4psJvBH59dwG+N4lz26wckqUHOuUtSgyx3SWqQ5S5JDbLcJalBlrskNchyl6QGWe6S1KD/B0H4fbkUF39bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 42,
   "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": [
    "x = 0;\n",
    "y = 1;\n",
    "print(x)\n",
    "while(y <= 1000000):\n",
    "    print(y);\n",
    "    tmp = x + y;\n",
    "    x = y;\n",
    "    y = tmp;"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean = 0.501000\n",
      "Standard Deviation = 0.499999\n"
     ]
    }
   ],
   "source": [
    "flip = np.random.binomial(1, .5, 1000)\n",
    "mean = np.mean(flip)\n",
    "print(\"Mean = %f\"%mean);\n",
    "\n",
    "stdv = np.std(flip)\n",
    "print(\"Standard Deviation = %f\"%stdv);"
   ]
  },
  {
   "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": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYrklEQVR4nO3df5BddXnH8c+TZWM3yLg4iSILadIpwkAZia7UmbQdQYbgL4jYVp3asbUzaZ3q+IPGhmIVah1TmRaZattJldYZqZAirlAcAwjVKVPQjRuM4ceUURFWLXF026mssEme/rH3Jjd3z7n33HO+5/f7NZMhe3+ce+4uefa5z3me79fcXQCA+lpV9gkAALIhkANAzRHIAaDmCOQAUHMEcgCouRPKeNG1a9f6hg0bynhpAKitvXv3/tjd1/XfXkog37Bhg2ZnZ8t4aQCoLTN7POp2SisAUHMEcgCoOQI5ANQcgRwAao5ADgA1V0rXCgC0zczcvK7d86h+sLCoUycntH3Lmdq6aSrIsQnkAJCzmbl5XXnrfi0uHZYkzS8s6spb90tSkGBOaQUAcnbtnkePBvGuxaXDunbPo0GOTyAHgJz9YGFxpNtHRWkFQKvlWbvuOnVyQvMRQfvUyYkgxycjB9Ba3dr1/MKiXMdq1zNz80FfZ/uWMzUxPnbcbRPjY9q+5cwgxyeQA2itvGvXXVs3Temjl5+rqckJmaSpyQl99PJzq9W1YmaTkj4l6VckuaS3u/t/hjg2AOQl79p1r62bpoKXbLpCZeTXS/qyu58l6SWSHg50XADITVyNOlTtuiiZA7mZPU/Sb0j6tCS5+7PuvpD1uACQt7xr10UJkZFvlHRQ0j+Z2ZyZfcrMTux/kJltM7NZM5s9ePBggJcFgGzyrl0Xxdw92wHMpiXdL2mzuz9gZtdL+l93//O450xPTzsbSwDAaMxsr7tP998eIiN/UtKT7v5A5+tbJL00wHEBAAlkDuTu/iNJT5hZt6j0KkkPZT0uACCZUJOd75J0o5mtlvQdSb8f6LgAgCGCBHJ33ydpRd0GAJA/JjsBoOYI5ABQcwRyAKg5AjkA1ByBHABqjo0lACBHRWxcQSAHUEtFBMis8t50uYvSCoDaKWpnn6yK2riCQA6gdooKkFkVtXEFgRxA7RS5s08WRW1cQSAHUDt12dmnqI0rCOQAaqcuO/sUtXEFXSsAaqcbCKvetSLlu+lyF4EcQHBFtAYWESDrgkAOIKiieqdxDDVyAEHVpTWwScjIAQSVtjWwDpOaVUUgBxDUqZMTmo8I2oNaA0OVY9r6y4DSCoCg0rQGhijH1GVsPw8EcgBBjdI7PTM3r80774nM4KXRJjXbXJsPVloxszFJs5Lm3f11oY4LoH6StAb2l1OijDKpWZex/TyEzMjfLenhgMcD0GBRGXSvUSc16zK2n4cgGbmZnSbptZI+Iul9IY4JoNkGZcpTCS9U9l7cfN7EuMbHTEuH/ej9VRzbz0Oo0srHJb1f0kmBjgegRtJ0i8R1t0xNTui+HRcmes3e0szC4pLGV5lOXjOuhaeXWtW1kjmQm9nrJD3l7nvN7JUDHrdN0jZJWr9+fdaXBVARaVsHt285c0WNfJQMOqo0s3TEtWb1CZr74MWjvo1aC1Ej3yzpUjP7nqSbJF1oZp/tf5C773L3aXefXrduXYCXBVAFabtFsq4M2OaLm/0yZ+TufqWkKyWpk5H/ibu/NetxAdRDloCaZeGrNINHTUUfOYBMyuoWqcua5EUIGsjd/d/pIQfapayAWtSmDXXAWisAMilzkwfWJF9GIAeQGQG1XNTIAaDmyMgBSGrHErBNfY8EcqBAVQ0kbdiercnvkdIKUJAqr5fdhiVgm/weyciBggwKJEVkhIM+DTR5SrL7vkOseV5VBHKgIGUGy2FlhaZOSYZe87yqCORAQYoOlr0Z+CozHXY/7v7FpcO65vYDR7NVk9T7iCZMSYZe87yqqJEDBSlyArK/Ht8fxLt++vTS0V8uLsk6tzdlSnLYmudNeI8SGTlQmCInIIdlonFcydcDr4Osa57XBYEcKFBRE5BZ6u551+yLbMHMuuZ5XVBaARooru4+ZnZ0gamJ8eh//nle/Cu6BbMtC2uRkQMNFJeJdoPYzNy8tt/y4Irnja+yXLPVMlow27AODIEcaKBh9fhr9zx63CbFXc/9hRNyDXpN7lcvE4EcaKhBmWhc4Fx4einPU2psv3rZCORAA4x6ATFLQM1ysbItFx+LxsVOoObSXEBM29Oe9WJlWy4+Fo2MHChAni13aS4gpu1pD3Gxsg0XH4tGIAdylvfyqWkvIKYJqFysrCZKK0DO8l4+tchd7It8LSSXOZCb2elmdq+ZPWRmB8zs3SFODGiKvLPYItdwKfK1kFyI0sohSVe4+zfN7CRJe83sLnd/KMCxgdrLu+WuyDVcinwtJGcesypa6gOafVHSJ9z9rrjHTE9P++zsbNDXBaoqak3s3ilLICkz2+vu0/23B73YaWYbJG2S9EDEfdskbZOk9evXh3xZoHSDulKKyGKruhcoihEsIzez50r6qqSPuPutgx5LRo4mKTvjLvv1UZxcM3IzG5f0eUk3DgviQNOE6K0elFEPy7bL3gsU5cscyM3MJH1a0sPu/jfZTwmoj5m5+cyb+g7qM5c0tAed3m6E6CPfLOl3JV1oZvs6f14T4LhApXUDcJykXSmDMuokPehxr7PKTBt33KHNO+/Jbb1vVEPmjNzd/0PHtvoDWmPQdmqj9Fanyah774taiEo6tk9n6EnSLLgomw8mO4GUBgXaUS40DpqWTDJJ2b8Q1ZitzKsWlw7rPTfvKzU7L3p3oDYhkAMpxQXZqcmJkbLMQdOSSScpt26a0n07LtR3d75WRwZ0omUNnjNz89q8855UJZu8lypoMwI5kFKocfVBS7umWfb1eRPjA18vbfDMmlFzUTY/rH4IpBQ36CNJm3feM1IdeNBKhElXKZyZm9fVtx3QwuLwXX7SBM+sbY7sDpQfAjmQQX+QzXvJ2jhRQ0GDpAmeWTNqdgfKD6UVIKCy6sCDOmj6pQ2eWZewZXeg/JCRAwGVVQcedvwxMx1xz9TyFyKjZnegfBDIgYDi6sCTa8ZHrpuHeF0p3LorLGFbXcGXsU2CRbPQVFG16vExk1xaOnLs31roRa3iauQnrxnXh15/DsG2IeIWzSKQA4H1Ty/+7JlDsZ0kUwGzWqYmm49ADpRk4447NOhf2SjZOcG63eICOV0raJQsk4d5GdbVkbSrhRF3xCGQozGqGuiiJkD7JelqiWttvGL3g6W/R5SLQI7GqOpaHr3903GS9GLHBfvD7pX4hYXyEMjRGFl6uPMuyXQXtfr4m85LvT7LoGBfhV9YKA+BHI2RdvKwyJJMlunGYSUaFp9qLwaCkIsyuivSTh4Wvedl2unG7nOu2P3g0U0jerH4VHsRyBFcWQtHpZ08LGKsPtQvtu5zWHwKvQjkCK7MXd3TZLt5L68a+hcbo/LoRyBHcHXbQCDv5VXz+MXG4lPoxcVOBJd1udOi5b28at1+saF+gmTkZnaJpOsljUn6lLvvDHFc1FMdNxCIynBD1bXZGQd5y5yRm9mYpE9KerWksyW9xczOznpc1FeeGW5RI/ghWxJD7e0JxAmRkZ8v6TF3/44kmdlNki6T9FCAY6Om8qjhFtkNE7Ku3Xtxcn5hUWNmxw3wUOtGViFq5FOSnuj5+snObccxs21mNmtmswcPHgzwsmibrCP4o2TzoevaWzdNHc3Muz3gVVkLBvVX2MVOd9/l7tPuPr1u3bqiXhYNkiS4xgXrmbl5bb/lweNKJdtviV9sKq5+PblmPPX5V3UtGNRfiEA+L+n0nq9P69wGBDWsG+YDM/v13pv3Rda1r7n9gJYOHz8NuXTYdc3tByKPuX3Lmcs7+/T5v58fSp1B072CvIQI5N+QdIaZbTSz1ZLeLOm2AMcFjjPoouHM3LxuvP/7KzZw6Ga8P306eoeeuNu3bprSiatXXkJaOuKpM+i6tWWiPjIHcnc/JOmdkvZIeljSbnePTnOADAZ1w1y759HYXXjSZrz/E7M9W9rj0b2CvATpI3f3L0n6UohjAYPEdcMMCq6rzLT6BNPi0pEV901OxNe8Q/d/M1qPvDCij0aIC7rS8sYLh44sf/zsDeXjq0xXX3pO7DHzGGxitB55IJCjVKGmJ6OCbq+lw66T14xrzeoTjvZy99a7o14zdAbNxsnIi3nEusZ5m56e9tnZ2cJfF9XSP+AjjbajfNTxukM3UUzSdW86L+hrjnJuZbwumsXM9rr7dP/tLJqF0ozSV51kmKe7nVrc3pinTk6U1stNDznyRGkFhekvLcRlzvMLi9q4446j5QdJI43mD6ptv/fmfZGvmXcvNz3kyBOBHIWIWifFpNiWwd6BnuecsCoym71i94OSVgbzQbXtuNJL3r3crICIPBHIkVh3QrI7RDM5Ma6rLz0nUY03qrTg0sBgLi0H7LgLmIfdYzPzuO6QC85at2JwqIhe7jou7Yv6oEaORLprlfROQi4sLmn7v8avV9IrroTg0tEBnzRGXTTr83vnjwviJumNL8u/JTDvzSvQbmTkSOTaPY+uWKtEOjayPiwgxZUWpiYndN+OCyVJm3feE/mYk9eM6+dLR2Iz86R15rhPBfc+UsxqnPSQIy9k5EhkULBMEkiTjKfHPeZDrz9HH738XI1ZdN6etM7MBUc0FRk5EhnUZdIbSOOGXpIM1yR5TJY6c9oLjgzyoOoI5CWpW3DYvuVMvW/3Ph2JuDJ5wVnL68sP28Ena2kh66RlmguORe5KBKTFZGcJ6jrlt+kv7oxc9rVb546rcffWwQcp4vsy6i/QrO8JCCluspOMvAQh94Ms0kLM2t3dGvOoNej+oPqzZw7l/n0Z9VMBdXXUARc7S1DX4DBsY4S4+11ase3aedfcqff07eazEHj97xDYDAJ1QCAvQV2Dw7DOk6j7u7q15Q/M7NeVt+6PDdpR8vy+DFvDhc0gUAeUVkpQ1ym/YRcbe++PqisvLh3W5x544ugu8kmM8n0Ztf6d5EImm0GgDrjYWZK6da2MauOOOwaO3sdZM75KJ5/4nJG/L2kulHIhE3XDxc6KafqUX1zP9pjZwIx86Yin+qWW5gJyXa9VAP2okSMXcbXlt/zq6bF1dGl5J580a3SnCcp1vVYB9MsUyM3sWjN7xMy+ZWZfMLPJQOeFmotbJOovt5579PY4aTLiNEGZC5loikw1cjO7WNI97n7IzP5Kktz9T4c9jxp5Mapehw9Zo047TFT17xHQK5caubvf2fPl/ZJ+M8vxEE4dRstDdu+k7S5p+rUKtEOwrhUzu13Sze7+2Zj7t0naJknr169/2eOPPx7kdRFt0JKwcx+8uIQzikZGDCSXOiM3s7slnRJx11Xu/sXOY66SdEjSjXHHcfddknZJy6WVhOeNlOLqzD99ekkzc/OJg2WaQJv0OcMeR5AHkhkayN39okH3m9nvSXqdpFd5GU3piDRo2dlrbj+Qqjc7SXkm6XOGPS7u/tnHf6J7HzlIcAd6ZO1auUTS+yVd6u5PhzklhDCoztzNyocZ1Jud9Tlxj3vPzfu0eec9uub2A5H333j/949bn+XKW/cnei9Ak2XtI/+EpJMk3WVm+8zsHwKcEwLYumlKkxPjsfcn6dVO05ud9DmDjjG/sBi5XK60cqPmUfbsBJoqUyB3919299Pd/bzOnz8KdWLI7upLz4m9L0mvdpre7KTPCTl0wyQm2o7JzgYblJUnCaRpBmainjO+yvT0s4eOW2Fw0EqJcaJ37GQSEyCQN9zVl56Tenoxbjpz0MXF/udMToxLtlyX761rSxo64dlrcmJcv/OK9UxiAhFY/bAFQrbx5bFVWtRUZtzjaUlEm7H6YYuFml5M046Y5OLnsHXMex/PJCawEqUVJJamHTHpxc+tm6Z0344LY0st1MGBeARyJJYku+7fOu2Cs9aNVNdmRUJgdARyJDYsu+6WXnoHdj6/d15vfNlU4gumaS6wAm1HjRyRoi4qDlutMK70cu8jB0dalpY6ODAaMnKsEJVZ97cMRmXLbJ0GlINAjhWG7X95344Ldd2bzpMkvbezNsrM3DxbpwElIZBjhWGZdVzGPuqFTQBhEMgL0t/NUeUV+4Zl1oNq4VyoBIrHxc4C1GHbtV7DLmoOyti5UAkUj4y8AGkGaco0rAWQWjhQLWTkBahjN8egzPqCs9bps/d/P/J2AMUjkBcgbtu1umaw9z5ycKTbi8SiWmgjSisFaNrYeVU/YcR101T5wjIQAoE8g6SdKE0bO69qjbxu1yKAUCitpDRqJ0qTujmGdbWUpaqfFIC8kZGnFDr7q1OfeVU/YVT1kwKQNzLylEJmf3XrM5eq+Qmjqp8UgLwFycjN7AozczNbG+J4dRAy+6O2G0ZVPykAecuckZvZ6ZIulrSysbjBQmZ/bantFtEaWMVPCkDeQmTk10l6v6Tid3EuUcjsrw21XVoDgfxkysjN7DJJ8+7+oJkNe+w2Sdskaf369VletjJCZX9tqO0OWxoXQHpDA7mZ3S3plIi7rpL0Z1ouqwzl7rsk7ZKk6enpVmXvw/TuIt/UicS2lI+AMgwN5O5+UdTtZnaupI2Sutn4aZK+aWbnu/uPgp5lCzS9ttu0ZQqAKkldI3f3/e7+Anff4O4bJD0p6aUEcURp2jIFQJXQR16iNi3w1IbyEVCWYIG8k5UjoToOAWXV9PIRUBZG9EvCEBCAUAjkJaGLA0Ao1MhLUnYXR5vq80DTkZGXpMwuDqYsgWYhkJekzAWeqM8DzdKq0krVyglldXFQnweapTUZOeWEY9qwSBfQJq0J5JQTjmHKEmiW1pRWKCccw5Ql0CytCeRlt/tVDVOWQHO0prRCOQFAU7UmI6ecAKCpWhPIJcoJAJqpNaUVAGiqVmXkTVS1IScAxSOQ11gb1zQHsBKllRpjyAmARCCvNYacAEgE8lpjzRQAEoG81hhyAiAFCORm9i4ze8TMDpjZx0KcFJIpc01zANWRqWvFzC6QdJmkl7j7M2b2gjCnhaQYcgKQNSN/h6Sd7v6MJLn7U9lPCQAwiqyB/MWSft3MHjCzr5rZy+MeaGbbzGzWzGYPHjyY8WUBAF1DSytmdrekUyLuuqrz/OdLeoWkl0vabWa/5O7e/2B33yVplyRNT0+vuL/tmNAEkNbQQO7uF8XdZ2bvkHRrJ3B/3cyOSForiZR7BExoAsgia2llRtIFkmRmL5a0WtKPMx6zdZjQBJBF1rVWbpB0g5l9W9Kzkt4WVVYJocmlByY0AWSRKZC7+7OS3hroXGI1vfTANnQAsqjFZGfTSw9MaALIohbL2Da99MA2dACyqEUgb0PpgQlNAGnVorRC6QEA4tUiI6f0AADxahHIpeqXHprcHgmg2moTyKus6e2RAKqtFjXyqmt6eySAaiOQB9D09kgA1UYgD4C9MwGUiUAeAO2RAMrExc4AaI8EUCYCeSBVb48E0FyUVgCg5gjkAFBzBHIAqDkCOQDUHIEcAGrOctpic/CLmh2U9HjhLxzGWrV3g+k2v3ep3e+/ze9dqs77/0V3X9d/YymBvM7MbNbdp8s+jzK0+b1L7X7/bX7vUvXfP6UVAKg5AjkA1ByBfHS7yj6BErX5vUvtfv9tfu9Sxd8/NXIAqDkycgCoOQI5ANQcgTwhM/stMztgZkfMbLrvvivN7DEze9TMtpR1jkUws6vNbN7M9nX+vKbsc8qbmV3S+dk+ZmY7yj6fopnZ98xsf+fnPVv2+eTNzG4ws6fM7Ns9tz3fzO4ys//q/PfkMs+xH4E8uW9LulzS13pvNLOzJb1Z0jmSLpH0d2Y2tvLpjXKdu5/X+fOlsk8mT52f5SclvVrS2ZLe0vmZt80FnZ93ZXupA/pnLf9b7rVD0lfc/QxJX+l8XRkE8oTc/WF3j9pN+TJJN7n7M+7+XUmPSTq/2LNDjs6X9Ji7f8fdn5V0k5Z/5mgod/+apJ/03XyZpM90/v4ZSVuLPKdhCOTZTUl6oufrJzu3Ndk7zexbnY+glfqImYM2/nz7uaQ7zWyvmW0r+2RK8kJ3/2Hn7z+S9MIyT6YfOwT1MLO7JZ0ScddV7v7Fos+nLIO+D5L+XtKHtfyP+8OS/lrS24s7O5Tg19x93sxeIOkuM3ukk7W2kru7mVWqb5tA3sPdL0rxtHlJp/d8fVrnttpK+n0ws3+U9G85n07ZGvfzHZW7z3f++5SZfUHL5aa2BfL/NrMXufsPzexFkp4q+4R6UVrJ7jZJbzaz55jZRklnSPp6yeeUm87/xF1v0PJF4Cb7hqQzzGyjma3W8oXt20o+p8KY2YlmdlL375IuVvN/5lFuk/S2zt/fJqlSn9DJyBMyszdI+ltJ6yTdYWb73H2Lux8ws92SHpJ0SNIfu/vhMs81Zx8zs/O0XFr5nqQ/LPVscubuh8zsnZL2SBqTdIO7Hyj5tIr0QklfMDNpOV78i7t/udxTypeZfU7SKyWtNbMnJX1I0k5Ju83sD7S8BPdvl3eGKzGiDwA1R2kFAGqOQA4ANUcgB4CaI5ADQM0RyAGg5gjkAFBzBHIAqLn/BwEmKcWrNhRHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.random.normal(0, 5, 100)\n",
    "e = np.random.normal(0, 1, 100)\n",
    "\n",
    "y = 0.5*x+e\n",
    "\n",
    "plt.scatter(x,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
}