What additional independent variables might you suggest

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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?

Reference no: EM131358128

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