{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sKYKeP72Ym/a7gtnl21dKSYNpX2x+IeTUISbcpxSM9srf1Fs0f3cFsZBDXTbXH8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\nCAkQ+F9IpyPjuys0kwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Redo the small example from class\n",
    "\n",
    "mydata=data.frame(X=c(1,2,3,4), Y=c(0.5,2,3,3.5) )\n",
    "plot(mydata$X,mydata$Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LJo/JDlK7XEhJDCzo1pvt70DIiOnF9/f/V0SyFEvQmnIqwdD0IKOTEwc5fpGRAl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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We calculated this in class:\n",
    "\n",
    "plot(mydata$X,mydata$Y)\n",
    "lines(mydata$X,mydata$X*1-0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = Y ~ X^2 + X, data = mydata)\n",
       "\n",
       "Coefficients:\n",
       "(Intercept)            X  \n",
       "      -0.25         1.00  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a linear model in R\n",
    "\n",
    "lm(formula = Y ~ X^2 + X, data = mydata)  # lm: linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}