Week 4
assignment
Multiple Regression Models Case Study: Web Video on Demand
Review “Multiple Regression Models Case Study: Web
Video on Demand” for this topic’s case study, predicting advertising sales
for an Internet video-on-demand streaming service.
After developing Regression Model A and Regression Model B,
prepare a 250-500-word executive summary of your findings. Explain your
approach and evaluate the outcomes of your regression models.
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.
Use an Excel spreadsheet file for the calculations and
explanations. Cells should contain the formulas (i.e., if a formula was used to
calculate the entry in that cell). Students are highly encouraged to use the
“Multiple Regression Dataset” Excel resource to complete this
assignment.
Mac users can use StatPlus:mac LE, free of charge, from
AnalystSoft.
Prepare the written portion of this assignment according to
the guidelines found in the APA Style Guide, located in the Student Success
Center. An abstract is not required.
This assignment uses a rubric. Please review the rubric
prior to beginning the assignment to become familiar with the expectations for
successful completion.
You are required to submit this assignment to Turnitin.
Please refer to the directions in the Student Success Center.
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 Demand would 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 also like 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 Demand may 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.
