wc.at <- read.csv(“D:\t\drive-download-20190806T055124Z-001\data.csv”) attach(wc.at) View(wc.at) summary(wc.at)

qqnorm(Weight) cor(Height,Weight) plot(Height,Weight)

#Model 1 –STANDARD REGRESSION MODEL

m1 <- lm(Weight ~ Height,data=wc.at) summary(m1) pv1 <- predict(m1,wc.at)

pv1 <- as.data.frame(pv1) f1 <- cbind(wc.at,pv1) View(f1)

#Model 2 – LOGARITHMETIC MODEL

m2 <- lm(Weight ~ log(Height),data=wc.at) summary(m2)

pv2 <- predict(m2,wc.at) View(pv2) f2 <- cbind(wc.at,pv2) View(f2)

Model 3 – Exponential Model

m3 <- lm(log(Weight) ~ Height,data = wc.at) summary(m3)

pv3 <- predict(m3,wc.at) View(pv3) pv3 <- as.data.frame(pv3)

f3 <- cbind(wc.at,pv3) View(f3)

Model 4 –Quadratic Model

m4 <- lm(Weight ~ sqrt(Height),data = wc.at) summary(m4)

pv4 <- predict(m4,wc.at) pv4 <- as.data.frame(pv4) f4 <- cbind(wc.at,pv4) View(f4)

Model 5 – Power Model

m5 <- lm(log(Weight) ~ log(Height),data=wc.at) summary(m5)

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