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Predicting runs scored in baseball. In Chance (Fall 2000),
Chapter 12, Problem 14E(choose chapter or problem)
Predicting runs scored in baseball. 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.
a. Write the least squares prediction equation for y = total number of runs scored by a team in a season.
b. Interpret, practically, the \(\beta\) estimates in the model.
c. Conduct a test of \(H_0: \beta_7=0\) against \(H_{\mathrm{a}}: \beta_7<0\) at \(\alpha=.05\). Interpret the results.
d. Form a 95% confidence interval for \(\beta_5\). Interpret the results.
Questions & Answers
QUESTION:
Predicting runs scored in baseball. 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.
a. Write the least squares prediction equation for y = total number of runs scored by a team in a season.
b. Interpret, practically, the \(\beta\) estimates in the model.
c. Conduct a test of \(H_0: \beta_7=0\) against \(H_{\mathrm{a}}: \beta_7<0\) at \(\alpha=.05\). Interpret the results.
d. Form a 95% confidence interval for \(\beta_5\). Interpret the results.
ANSWER:Step 1 of 4
a) The least squares prediction equation for total number of runs scored by a team in
a season is:
where 
independent variables.