Consider all 40 observations on the delivery time data. Delete \(10 \%\) (4) of the observations at random. Fit a model to the remaining 36 observations, predict the four deleted values, and calculate \(R^{2}\) for prediction. Repeat these calculations 100 times. Calculate the average \(R^{2}\) for prediction. What information does this convey about the predictive capability of the model? How does the average of the \(100 R^{2}\) for prediction values compare to \(R^{2}\) for prediction based on PRESS for all 40 observations?