Discuss the below:
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) Conduct a test of Ho: β7 = 0 against Ha: β7 < 0 at α= 0.05. Interpret the results.
c) Form a 95% confidence interval for β5. Interpret the results.
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
|
|
|
|
|