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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 nequals 200 samples, the results below were obtained. Complete parts a through d.
1. Write the least squares prediction equation for y = total number of runs scored by a team in a season.
2. Interpret, practically, beta0 and beta1 in the model. Which statement below best interprets beta0 ?
A. For an increase of 1 in any variable, the runs scored changes by beta0.
B. For a change of beta0 in any variable, the runs scored decreases by 1.
C. For a decrease of 1 in any variable, the runs scored changes by beta0.
D. For a change of beta0 in any variable, the runs scored increases by 1.
E. This parameter does not have a practical interpretation.
3. Which statement below best interprets beta1 ?
A. For a change of beta1 in the number of walks, the runs scored decreases by 1.
B. For a change of ModifyingAbove beta1 in the number of walks, the runs scored increases by 1.
C. For an increase of 1 in the number of walks, the runs scored changes by beta1.
D. For a decrease of 1 in the number of walks, the runs scored changes by ModifyingAbove beta1.
E. This parameter does not have a practical interpretation.
4. Conduct a test of Upper H 0 : beta1 = 0 against Ha: beta1 != 0 at alpha = 0.01.
The test statistic is
Form a 90% confidence interval for beta 4. Interpret the results.