One of the assumptions lying behind our use of regression is the assumption of homogeneity of variance in arrays. One way to examine the data for violations of this assumption is to calculate...

One of the assumptions lying behind our use of regression is the assumption of homogeneity of variance in arrays. One way to examine the data for violations of this assumption is to calculate predicted values of Y and the corresponding residuals (Y 2 ). If you plot the residuals against the predicted values, you should see a more or less random collection of points. The vertical dispersion should not increase or decrease systematically as you move from right to left, nor should there be any other apparent pattern. Create the scatterplot for the data from Cancer.dat at the Web site for this book. Most computer packages let you request this plot. If not, you can easily generate the appropriate variables by first determining the regression equation and then feeding that equation back into the program in a “compute statement” (e.g., “set Pred 5 0.256*GSIT 1 4.65,” and “set Resid 5 TotBPT 2 Pred”).