Question

For a long time, a western football team set seat prices for its 15-game home schedule the same for each game. But when Nawaf Abdulaziz, director of business strategy, finished his IE at the King Abdulaziz University, 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. Studying individual sales of Western tickets on the open Makani marketplace during the prior season, Nawaf determined the additional potential sales revenue the Western team could have made had they charged prices the fans had proven they were willing to pay on Makani. This became his dependent variable, y , in a multiple-regression model.

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 temperature of the day ( x1 )
• Distance from where a team guest team come from ( x2 )
• The humidity of the day ( x3 )

Table 1 illustrates, for brevity in this case study, a sample

of 12 games that year (out of the total 15 home game regular season), including the potential extra revenue per game ( y ) to be expected using the variable pricing model.

A leader in football game variable pricing, the western team have learned that regression analysis is indeed a profitable forecasting tool.

Discussion Questions * (Solve using Minitab/other statistical software)

1. Use the data in Table 1 to build a regression model with three independent variables mentioned above.
2. Check the model adequacy
3. Which variables are significant? Justify your answer using ANOVA table
4. Use the data to build a model with the temperature of the day as the sole independent variable.
5. Using the multiple-regression model from question 1, what would be the additional sales potential of a 42 °C , 528.36 Km, and 79% ?
6. What additional independent variables might you suggest to include in Nawaf’s model?

Table1. Data for Last Year’s Western Team Ticket Sales Pricing Model

Temp (°C)	Distance (KM)	Humidity%	Additional Sales Potential (S.R.)
43.36	1050.15	73.712	46,241.25
41.18	1108.37	70.006	108,765.00
37.27	899.25	63.359	410,295.00
42.53	52.36	72.301	284,208.75
43.15	1011.59	73.355	159,588.75
38.24	900.25	65.008	450,795.00
29.58	1086	50.286	76,721.25
34.11	3.15	57.987	866,325.00
37.45	795.25	63.665	106,706.25
43.49	75.25	73.933	414,603.75
39.22	958.25	66.674	168,641.25
32.14	952.88	54.638	113,463.75
30.27	1025.66	51.459	91,983.75
42.49	400.25	72.233	372,967.50
28.43	582.32	48.331	217,327.50

Answer 1