Question

Suppose a statistician built a multiple regression model for predicting the total number of runs scored by a baseball team during a season. Using data for n=200 samples, the results below were obtained. Complete parts a throughd.

Ind. Var.Ind. Var.	β estimate	Standard Error	Ind. Var.	β estimate	Standard Error
InterceptIntercept	3.88	17.03	Doubles (x3)	0.74	0.04
Walks (x1)	0.37	0.05	Triples (x4)	1.17	0.23
Singles (x2)	0.51	0.05	Home Runs x5	1.44	0.04

a. Write the least squares prediction equation for y=total a number of runs scored by a team in a season.

y=(3.88)+(0.37)x1+(0.51)x2+(0.74)x3+(1.17)x4+(1.44 )x5 (Type integers or decimals.)

b. Interpret, practically, β0 and β1 in the model. Which statement below best interprets β0?

A. For a change of β0 in any variable, the runs scored increases by 1.

B. For a decrease of 1 in any variable, the runs scored changes by β0.

C. For an increase of 1 in any variable, the runs scored changes by β0.

D. For a change of β0 in any variable, the runs scored decreases by 1.

E. This parameter does not have a practical interpretation. Your answer is correct.

Which statement below best interprets β1 ?

A. For an increase of 1 in the number of walks, the runs scored changes by β1. Your answer is correct.

B. For a change of β1 in the number of walks, the runs scored increases by 1.

C. For a decrease of 1 in the number of walks, the runs scored changes by β1.

D. For a change of β1 in the number of walks, the runs scored decreases by 1.

E. This parameter does not have a practical interpretation.

c. Conduct a test of H0: β3=0 against Ha: β3>0 at α=0.10.

The test statistic is _____________. (Round to three decimal places as needed.)

Answer 1