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Statistics of Predicting Runs Scored in Baseball

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Predicting runs scored in baseball.

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 attached table were obtained (see attached table).

a) Write the least squares prediction equation for y = total number of runs scored by a team in a season.
b) Interpret, practically, the ? estimates in the model.
c) Conduct a test of Ho: ?7 = 0 against Ha: ?7 < 0 at ? = 0.05. Interpret the results.
d) Form a 95% confidence interval for ?5. Interpret the results.

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The expert predicts runs scored in baseball using statistics.

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Predicting runs scored in baseball.

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 attached table were obtained (see table).
Independent Variable β Estimate Standard Error
Intercept 3.70 15.00
Walks (x1) .34 0.02
Singles (x2) .49 0.03
Doubles (x3) .72 0.05
Triples (x4) 1.14 0.19
Home runs ...

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