Q1. Multiple Linear Regression Using a Built-in R Dataset (30 pts) The built-in swiss dataset in R contains data from 47 French-speaking provinces of Switzerland. For each province, we have a standardized fertility measure (Fertility, index of birth rate) and several socioeconomic indicators: percentage of males involved in agriculture (Agriculture), percentage of draftees receiving the highest exam mark (Examination), percentage of draftees with education beyond primary school (Education), percentage Catholic (Catholic), and infant mortality rate per 1000 live births (Infant.Mortality). These variables have been used historically to study associations between fertility and modernization / education / religion. data(swiss) # loads the dataset summary(swiss) # glimpse the data ## Fertility Agriculture Examination Education ## Min. :35.00 Min. : 1.20 Min. : 3.00 Min. : 1.00 ## 1st Qu.:64.70 1st Qu.:35.90 1st Qu.:12.00 1st Qu.: 6.00 ## Median :70.40 Median :54.10 Median :16.00 Median : 8.00 ## Mean :70.14 Mean :50.66 Mean :16.49 Mean :10.98 ## 3rd Qu.:78.45 3rd Qu.:67.65 3rd Qu.:22.00 3rd Qu.:12.00 ## Max. :92.50 Max. :89.70 Max. :37.00 Max. :53.00 ## Catholic Infant.Mortality ## Min. : 2.150 Min. :10.80 ## 1st Qu.: 5.195 1st Qu.:18.15 ## Median : 15.140 Median :20.00 ## Mean : 41.144 Mean :19.94 ## 3rd Qu.: 93.125 3rd Qu.:21.70 ## Max. :100.000 Max. :26.60 We will model Fertility as a linear function of all other variables. (a) Use lm() to fit a multiple linear regression model (8 pts). ######################################################### # Insert your codes here #########################################################