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 (x5)

1.51

0.05

Stolen bases (x6)

.26

0.05

Caught stealing (x7)

-.14

0.14

Strikeouts (x8)

-.10

0.01

Outs (x9)

-.10

0.01

a) Write the least squares prediction equation for y = total number of runs scored by a team in a season. 
b) Interpret, practically, the β estimates in the model. 
c) Conduct a test of Ho: β7 = 0 against Ha: β7 < 0 at α = 0.05. Interpret the results. 
d) Form a 95% confidence interval for β5.  Interpret the results. 

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