Refer to the Chance (Fall 2000) study of runs scored in Major League Baseball games, Exercise 12.14. Multiple regression was used to model total number of runs scored (y) of a team during the season as a function of number of walks (x1), number of singles (x2), number of doubles (x3), number of triples (x4), number of home runs (x5), number of stolen bases (x6), number of times caught stealing (x7), number of strikeouts (x8), and total number of outs (x9). Using the β estimates given in Exercise 12.14, predict the number of runs scored by your favorite Major League Baseball team last year. How close is the predicted value to the actual number of runs scored by your team?
Exercise 12.14
In Chance (Fall 2000), statistician Scott Berry built a multiple regression model for predicting total number of runs scored by a Major League Baseball team during a season. Using data on all teams over a 9-year period (a sample of n = 234), the results in the following table were obtained.

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Standard Independent Variable B Estimate Error Intercept Walks (x) Singles (2) Doubles (x3) Triples (%) Home runs (s) Stolen bases () Caught stealing (x) Strikeouts (g) Outs () 3.70 34 49 -72 1.14 1.51 15.00 .02 03 .05 .19 .05 .05 .14 .01 .01 .14 -10 -.10