Quantitative Methods

Review “Multiple Regression Models Case Study: Web Video on Demand” (Attached Link) for this topic’s case study, predicting advertising sales for an

Internet video-on-demand streaming service.

1.) Submit a copy of the Excel spreadsheet file you used to design your regression model and to determine statistical significance.

*******Note: Students should use Excel’s regression option to perform the regression. Apply all Excel file calculation and explanations. Cells

should contain the formulas (i.e., if a formula was used to calculate the entry in that cell). Use the “Multiple Regression Dataset” Excel resource

to complete this assignment.*******

2.) After developing both Regression Model A and Regression Model B, prepare a 250-500-word executive summary of your findings on a word document.

Please explain your approach and evaluate the outcomes of your regression models.

Advertising Sales ($) Average # of Viewers (Millions) Length of Program (Minutes) Average Viewer Age (years)
28,000 10.1 30 30
25,500 11.4 30 25
31,000 19.9 60 30
29,000 13.6 60 38
20,500 12.5 60 20
14,500 3.5 30 15
27,000 15.1 60 24
23,500 3.7 30 17
19,500 4.3 30 19
23,000 12.2 120 45
18,000 5.1 120 19
29,500 15.9 60 28
30,000 16.8 120 31
25,000 8.5 120 58
22,500 9.1 30 43

Multiple Regression Models Case Study: Web Video on Demand
Web Video on Demand (WVOD) is an Internet video-on-demand streaming service. The company offers a subscription service for $5.99/month, which

includes access to all programming and 30-second commercial intervals.
In the last year, the company has recently begun producing its own programming, including 30-, 60-, and 120-minute television shows, specials, and

films. Programming has been developed for teen audiences as well as adults.
The following data represent the amount of money brought in through advertising sales, the average number of viewers, length of the program, and the

average viewer age per program.

Advertising Sales
($) Average # of Viewers
(Millions) Length of Program (Minutes) Average Viewer Age
(Years)
28,000 10.1 30 30
25,500 11.4 30 25
31,000 19.9 60 30
29,000 13.6 60 38
20,500 12.5 60 20
14,500 3.5 30 15
27,000 15.1 60 24
23,500 3.7 30 17
19,500 4.3 30 19
23,000 12.2 120 45
18,000 5.1 120 19
29,500 15.9 60 28
30,000 16.8 120 31
25,000 8.5 120 58
22,500 9.1 30 43

The WVOD executives are in the process of evaluating a partnership with several independent filmmakers to fund and distribute socially conscious and

diverse programming. The executives have asked for regression models to be developed based on specific needs. The three regression model requests

and programming details are included below.
The WVOD executives would like to see a regression model that predicts the amount of advertising sales based on the number of viewers and the length

of the program. Develop this regression model (“Regression Model A”). Web Video on Demandwould like to acquire a 60-minute documentary special about

social media and bullying. The special is aimed at teen viewers and is estimated to bring in 3.2 million viewers. Based on the regression model,

predict the advertising sales that could be generated by the special.
The WVOD executives would alsolike to see a regression model that predicts the amount of advertising sales based on the number of viewers, the

length of the program, and the average viewer age. Develop this regression model (“Regression Model B”). Web Video on Demandmay acquire a 2-hour

film that was a hit with critics and audiences at several international film festivals. Initial customer surveys indicate that the film could bring

in 14.1 viewers and the average viewer age would be 32. Use this information to predict the advertising sales.

WE ACCEPT