Reference no: EM131358128

Problem -

For its first 2 decades of existence, the NBA's Orlando Magic basketball team set seat prices for its 41-game home schedule the same for each game. If a lower-deck seat sold for $150, that was the price charged, regardless of the opponent, day of the week, or time of the season. If an upper-deck seat sold for $10 in the first game of the year, it likewise sold for $10 for every game. But when Anthony Perez, director of business strategy, finished his MBA at the University of Florida, he developed a valuable database of ticket sales. Analysis of the data led him to build a forecasting model he hoped would increase ticket revenue. Perez hypothesized that selling a ticket for similar seats should differ based on demand. Studying individual sales of Magic tickets on the open Stub Hub marketplace during the prior season, Perez determined the additional potential sales revenue the Magic could have made had they charged prices the fans had proven they were willing to pay on Stub Hub. This became his dependent variable, y , in a multiple-regression model. He also found that three variables would help him build the "true market" seat price for every game.

With his model, it was possible that the same seat in the arena would have as many as seven different prices created at season onset-sometimes higher than expected on average and sometimes lower. The major factors he found to be statistically significant in determining how high the demand for a game ticket, and hence, its price, would be were:  The day of the week (x1) A rating of how popular the opponent was (x2) The time of the year (x3) For the day of the week, Perez found that Mondays were the least-favored game days (and he assigned them a value of 1). The rest of the weekdays increased in popularity, up to a Saturday game, which he rated a 6. Sundays and Fridays received 5 ratings, and holidays a 3 (refer to the footnote in Table 4.3). His ratings of opponents, done just before the start of the season, were subjective and range from a low of 0 to a high of 8. A very high-rated team in that particular season may have had one or more superstars on its roster, or have won the NBA finals the prior season, making it a popular fan draw. Finally, Perez believed that the NBA season could be divided into four periods in popularity:

Discussion Questions

1. Use the data in Table 4.3 to build a regression model with day of the week as the only independent variable.

Southwestern University Football Game Attendance, 2010-2015
Game	2010		2011		2012
	Attendees	Opponent	Attendees	Opponent	Attendees	Opponent
1	34,200	Rice	36,100	Miami	35,900	USC
2a	39,800	Texas	40,200	Nebraska	46,500	Texas Tech
3	38,200	Duke	39,100	Ohio State	43,100	Alaska
4b	26,900	Arkansas	25,300	Nevada	27,900	Arizona
5	35,100	TCU	36,200	Boise State	39,200	Baylor

Game	2013		2014		2015
	Attendees	Opponent	Attendees	Opponent	Attendees	Opponent
1	41,900	Arkansas	42,500	Indiana	46,900	LSU
2a	46,100	Missouri	48,200	North Texas	50,100	Texas
3	43,900	Florida	44,200	Texas A&M	45,900	South Florida
4b	30,100	Central Florida	33,900	Southern	36,300	Montana
5	40,500	LSU	47,800	Oklahoma	49,900	Arizona State

2. Use the data to build a model with rating of the opponent as the sole independent variable.

3. Using Perez's multiple-regression model, what would be the additional sales potential of a Thursday Miami Heat game played during the Christmas holiday?

4. What additional independent variables might you suggest to include in Perez's model?