load("kruiser.RData")

View(kruiser)

options(scipen = 999)


initialmodel<-with(kruiser,lm(Price ~ Mileage + Cylinder + Sound + Leather))
summary(initialmodel)


library(lm.beta)
initialmodel<-lm.beta(initialmodel)
summary(initialmodel)



initialmodel2<-with(kruiser,lm(Price ~ Mileage + Liter + Sound + Leather))
summary(initialmodel2)

initialmodel3<-with(kruiser,lm(Price ~ Mileage + Cylinder + Liter + Sound + Leather))
summary(initialmodel3)



# Useful link: https://www.stat.berkeley.edu/~s133/factors.html


kruiser$Cylinder<-as.factor(kruiser$Cylinder)

str(kruiser)


initialmodel4<-with(kruiser,lm(Price ~ Mileage + Cylinder + Sound + Leather))

summary(initialmodel4)

# We finished here. Nevertheless it is worthwile to run the following lines

improvedmodel<-with(kruiser,lm(Price ~ Mileage + Cylinder + Sound + Make+ Mileage:Cylinder))
summary(improvedmodel)

kruiser$Priceestimated<-predict(improvedmodel,kruiser)

kruiser$Priceestimated2<-fitted(improvedmodel)

View(kruiser)

plot(kruiser$Price,kruiser$Priceestimated)