wc.at <- read.csv(“D:\t\drive-download-20190806T055124Z-001\Salary_Data.csv”) attach(wc.at) dim(wc.at) summary(wc.at) View(wc.at) qqnorm(Salary) cor(YearsExperience,Salary)

model 1 Standard regression model

m1 <- lm(Salary ~ YearsExperience,data=wc.at) summary(m1)

p1 <- predict(m1,wc.at) p1 <- as.data.frame(p1) f1 <- cbind(wc.at,p1) View(f1)

model 2 Logarithmetic model

m2 <- lm(Salary ~ log(YearsExperience),data=wc.at) summary(m2)

p2 <- predict(m2,wc.at) p2 <- as.data.frame(p2) f2 <- cbind(wc.at,p2) View(f2)

model 3 exponential model

m3 <- lm(log(Salary) ~ YearsExperience,data=wc.at) summary(m3)

p3 <- predict(m3,wc.at) p3 <- as.data.frame(p3) f3 <- cbind(wc.at,p3) View(f3)

model 4 Quadratic model

m4 <- lm(Salary ~ sqrt(YearsExperience),data=wc.at) summary(m4)

p4 <- predict(m4,wc.at) p4 <- as.data.frame(p4) f4 <- cbind(wc.at,p4) View(f4)

model 5 power model

m5 <- lm(log(Salary) ~ log(YearsExperience),data=wc.at) summary(m5)

p5 <- predict(m5,wc.at) p5 <- as.data.frame(p5) f5 <- cbind(wc.at,p5) View(f5)

model 6 Reciprocal model

m6 <- lm(1/Salary ~ YearsExperience,data=wc.at) summary(m6)

p6 <- predict(m6,wc.at) p6 <- as.data.frame(p6) f6 <- cbind(wc.at,p6) View(f6)