Crazy Dave, a well-known baseball analyst, would like to study various team statistics for the 2009 baseball season to determine which variables might be useful in predicting the number of wins achieved by teams during the season. He has decided to begin by using a team's earned run average (ERA), a measure of pitching performance, to predict the number of wins. The data for the 30 Major League Baseball teams are stored in BB2009.
a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b0 and b1.
b. Interpret the meaning of the Y intercept, b0, and the slope, b1, in this problem.
c. Use the prediction line developed in (a) to predict the number of wins for a team with an ERA of 4.50.
d. Compute the coefficient of determination, r2, and interpret its meaning.
e. Perform a residual analysis on your results and determine the adequacy of the fit of the model.
f. At the 0.05 level of significance, is there evidence of a linear relationship between the number of wins and the ERA?
g. Construct a 95% confidence interval estimate of the mean number of wins expected for teams with an ERA of 4.50.
h. Construct a 95% prediction interval of the number of wins for an individual team that has an ERA of 4.50.
i. Construct a 95% confidence interval estimate of the population slope.
j. The 30 teams constitute a population. In order to use statistical inference, as in (f) through (i), the data must be assumed to represent a random sample. What "population" would this sample be drawing conclusions about?
k. What other independent variables might you consider for inclusion in the model?