#create dataset
df <- data.frame(hours=c(1, 2, 4, 5, 5, 6, 6, 7, 8, 10, 11, 11, 12, 12, 14),
                 score=c(64, 66, 76, 73, 74, 81, 83, 82, 80, 88, 84, 82, 91, 93, 89))

#view first six rows
head(df)

#attach dataset to make it easier to work with
attach(df)

#create a scatterplot to check for linear relationship
scatter.smooth(hours, score, main='Hours studied vs. Exam Score')

#create a boxplot to check for outliers
boxplot(score)

#fit simple linear regression model
model <- lm(score~hours)

#view model summary
summary(model)

#define residuals of model
res <- resid(model)

#produce residual vs. fitted plot
plot(fitted(model), res)

#add a horizontal line at 0 
abline(0,0)

#create Q-Q plot for residuals
qqnorm(res)

#add a straight diagonal line to the plot
qqline(res)