11.11 Game-day spending. Game-day spending (ticket sales and food and beverage purchases) is critical for the sustainability of many professional sports teams. In the National Hockey League (NHL), nearly half the franchises generate more than two-thirds of their annual income from game-day spending. Understanding and possibly predicting this spending would allow teams to respond with appropriate marketing and pricing strategies. To investigate this possibility, a group of researchers looked at data from one NHL team over a three-season period (n = 123 home games).³ The following table summarizes the multiple regression used to predict ticket sales. Each explanatory variable is an indicator variable taking the value 1 for the condition specified and 0 otherwise.

Explanatory variables	b	t
Constant	12,493.47	12.13
Division	−788.74	−2.01
Nonconference	−474.83	−1.04
November	−1800.81	−2.65
December	−559.24	−0.82
January	−925.56	−1.54
February	−35.59	−0.05
March	−131.62	−0.21
Weekend	2992.75	8.48
Night	1460.31	2.13
Promotion	2162.45	5.65
Season 2	−754.56	−1.85
Season 3	−779.81	−1.84

(a) Which of the explanatory variables significantly aid prediction in the presence of all the explanatory variables? Show your work.
(b) The overall F statistic was 11.59. What are the degrees of freedom and P-value of this statistic?
(c) The value of R² is 0.52. What percent of the variance in ticket sales is explained by these explanatory variables?
(d) The constant predicts the number of tickets sold for a nondivisional, conference game with no promotions played during the day on a weekday in October of Season 1. What is the predicted number of tickets sold for a divisional conference game with no promotions played on a weekend evening in March during Season 3?
(e) Would a 95% confidence interval for the mean response or a 95% prediction interval be more appropriate to include with your answer to part (d)? Explain your reasoning.