2linearRegression.r

#Perform linear regressing on the builtin cars dataset for the expression
dist = Intercept + (β ∗ speed)


head(cars)  
scatter.smooth(x=cars$speed, y=cars$dist, main="Dist ~ Speed")  
par(mfrow=c(1, 2))  
boxplot(cars$speed, main="Speed", sub=paste("Outlier rows: ", boxplot.stats(cars$speed)$out))  
boxplot(cars$dist, main="Distance", sub=paste("Outlier rows: ", boxplot.stats(cars$dist)$out))  
#Density plot – Check if the response variable is close to normality
library(e1071)
par(mfrow=c(1, 2))  
plot(density(cars$speed), main="Density Plot: Speed", ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(cars$speed), 2)))  
polygon(density(cars$speed), col="red")
plot(density(cars$dist), main="Density Plot: Distance", ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(cars$dist), 2)))  
polygon(density(cars$dist), col="red")
cor(cars$speed, cars$dist)  
linearMod <- lm(dist ~ speed, data=cars)  
print(linearMod)
summary(linearMod)





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