cgpa <- c(2.70, 2.66, 2.61, 2.23, 2.04, 3.19, 3.35, 2.80,
          2.33, 3.61, 2.60, 2.44, 2.44, 2.83, 2.64, 2.40,
          3.12, 2.73, 2.66, 2.22, 2.31, 2.33, 2.68, 3.30,
          2.54, 3.27, 3.48, 2.43, 2.81, 2.90)


salary <- c(29.29,  28.79, 28.98,  24.07, 22.57,  31.37, 31.73,  27.67,
            26.88,  29.34, 28.46,  26.44,  26.75,  30.05, 28.58,  27.16,
            30.16,  29.80, 27.83,  26.63,  25.97,  26.58, 29.82,  31.99,
            29.24,  31.45, 30.66,  27.61,  31.85,  29.73)

CollegeData <- data.frame(cgpa,salary)

rm(cgpa,salary)


College.Fit <- lm(salary ~ cgpa, data = CollegeData)
summary(College.Fit)
anova(College.Fit)


 # a plot with the raw data, the fitted regression line, and a lowess line
plot(CollegeData$cgpa,CollegeData$salary,xlab="College GPA",ylab="Salary",main="Regression with College data")
abline(College.Fit)
lines(lowess(CollegeData$cgpa,CollegeData$salary))

windows()   # on a Windows computer, opens a new window for plotting


  # this code creates a residual-by-predicted plot and a normal plot of the residuals
par(mfrow=c(2,1))                                 # makes all future plots appear two 
                                                  # per page, vertically
plot(fitted(College.Fit),resid(College.Fit),xlab="Predicted Values",
     ylab="Residuals",main="Residual by Predicted plot")
qqnorm(resid(College.Fit))
qqline(resid(College.Fit))
par(mfrow=c(1,1))                                 # restores one plot per page