11.51 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 ( home games).12 The following table summarizes the multiple regression used to predict ticket sales.
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Explanatory variables | ||
---|---|---|
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 |
11.51
(a) Using and (use 100), for significance we need . So Division, November, Weekend, Night, and Promotion are all significant in the presence of all the other explanatory variables. (b) . (c) 52%. (d) 15246.36. (e) Because we don’t expect the same setting for very many games, the mean response interval doesn’t make sense, so a prediction interval is more appropriate to represent this particular game and its specific settings.