Consider a multiple regression model for predicting the total number of runs scored by a Major League Baseball (MLB) team during a season. Using data on number of walks (x1), singles (x2), doubles (x3), triples (x4), home runs (x5), stolen bases (x6), times caught stealing (x7), strike outs (x8), and ground outs (x9) for each of the 30 teams during the 2014 MLB season, a 1st-order model for total number of runs scored (y) was fit. The results are shown in the accompanying Minitab printout.

a. Write the least squares prediction equation for y = total number of runs scored by a team during the 2014 season.
b. Give practical interpretations of the beta estimates.
c. Conduct a test of H0: β7 = 0 against Hα: β7 d. Form a 95% confidence interval for β5. Interpret the results.