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Predicting runs scored in baseball. Refer to the Chance
Chapter 12, Problem 26E(choose chapter or problem)
Problem 26E
Predicting runs scored in baseball. Refer to the Chance (Fall 2000) study of runs scored in Major League Baseball games, Exercise 12.14 (p. 681). 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 b 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? [Note: You can find data on your favorite team on the Internet at www.mlb.com.]
Questions & Answers
QUESTION:
Problem 26E
Predicting runs scored in baseball. Refer to the Chance (Fall 2000) study of runs scored in Major League Baseball games, Exercise 12.14 (p. 681). 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 b 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? [Note: You can find data on your favorite team on the Internet at www.mlb.com.]
ANSWER:
Step 1 of 2
The least squares prediction equation for total number of runs scored by a team in a
season is:
Where 
variables.