Week 9 lab

We will be looking at some regression diagnostics

1. Start by fitting a linear regression model using the mtcars data set. mpg as the response and wt as the predictor. Create a scatter plot of mpg and wt and plot the linear model fit on top. Explain how well you think the fit looks.
2. plot() the regression model you created to look at the diagnostic plots. Explain what you see.
3. Use rstudent() to get the studentized residuals and plot them. Do you see any trends or outliers?
4. Use the shapiro.test() to test for Normality of the studentized residuals. Comment on the results.