Chapter_3.R

# Exercise 3.2.R1

1-pnorm(0.9, 0.5, 0.2) # for 0.9 because the p value is 0.0227
1-pnorm(0.8, 0.5, 0.2) # the answer is 0.8 because the p value is 0.0668 > 0.05

# Exercise 3.2.R2

# Exercise 3.3

#The more the pertubations the better to test the model


# Exercise 3.5R2
#0.029+0.0011*50


#taken from page 89

# Linear Regression in R
library(MASS)
library(ISLR)
names(Boston)
plot(medv~lstat,Boston)
fit1 <- lm(medv~lstat,data=Boston)
fit1
summary(fit1)
abline(fit1, col ="green")
names(fit1)
confint(fit1)
predict(fit1,data.frame(lstat=c(5,10,15)),interval="confidence")

# # Multiple linear regression
fit2 <- lm(medv~lstat+age,data=Boston)
summary(fit2)
fit3 <- lm(medv~.,Boston)
summary(fit)
par(mfrow=c(2,2))
plot(fit3)
fit4 <- update(fit3,~.-age-indus)
summary(fit4)

# # Nonlinear terms and Interactions
fit5 <- lm(medv~lstat*age,Boston)
summary(fit5)
fit6 <- lm(medv~lstat + I(lstat^2) ,Boston); summary(fit6)
attach(Boston)
par(mfrow=c(1,1))
plot(medv~lstat)
points(lstat,fitted(fit6),col="red",pch=20)
fit7 <- lm(medv~poly(lstat,4))
points(lstat,fitted(fit7),col="blue",pch=20)
plot(1:20,1:20,pch = 1:20,cex=2)

# Qualitative Predictors
fix(Carseats)
names(Carseats)
summary(Carseats)
fitl <- lm(Sales~.+Income:Advertising+Age:Price,Carseats)
summary(fit1)
contrasts(Carseats$ShelveLoc)

#Writing R functions
regplot = function(x,y){
  fit <- lm(y~x)
  plot(x,y)
  abline(fit,col="red")
}
attach(Carseats)
regplot(Price,Sales)

#Writing R functions
regplot = function(x,y,...){
  fit <- lm(y~x)
  plot(x,y,...)
  abline(fit,col="red")
}
attach(Carseats)
regplot(Price,Sales,xlab="Price",ylab="Sales",col="blue",pch=10)