Question: Many factors determine the attendance at Major League Baseball games. These factors can include when the game is played, the weather, the opponent, whether or not the team is having a good season, and whether or not a mar­keting promotion is held. Popular promotions during the 2002 season were the traditional hat days and poster days, and the new craze, bobble-heads of star players (T. C. Boyd and T. C. Krehbiel, "Promotion Timing in Major League Baseball and the Stacking Effects of Factors That Increase Game Attractiveness," Sport Marketing Quarterly, 12, 2003, 173-184).

The data file BASEBALL includes the fol­lowing variables for the 2002 major league baseball season:

TEAM = (Kansas City Royals, Philadelphia Phillies, Chicago Cubs, or Cincinnati Reds)

ATTENDANCE = Paid attendance for the game

TEMP = High temperature for the day

WIN% = Team's winning percentage at the time of the game

OPWIN% = Opponent team's winning percentage at the time of the game

WEEKEND: 1 if game played on Friday, Saturday or Sunday; 0 otherwise

PROMOTION: 1 = if a promotion was held; 0 = if no promotion was held

(a) Construct a multiple regression model for the Kansas City Royals, using attendance as the dependent variable and the remaining five variables as the independent variables.

(b) State the multiple regression equation.

(c) Interpret the meaning of the regression coefficients.

(d) At the 0.05 level of significance, determine whether each independent variable makes a significant contribu­tion to the regression model.

(e) Determine and interpret the adjusted r².

(f) Do a residual analysis on the results and determine the adequacy of the model.

(g) Based on the best-subsets approach, delete any indepen­dent variables that are not making a significant contribu­tion to the regression model. Repeat (b) through (f) using this more parsimonious model. Which model do you think is better? Explain.