# Multiple Regression Models Case Study: Web Video on Demand, case study help

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### Question Description

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.

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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.

### Unformatted Attachment Preview

Advertising Sales (\$) 28.000 25.500 31.000 29.000 20.500 14.500 27.000 23.500 19.500 23.000 18.000 29.500 30.000 25.000 22.500 Average # of Viewers (Millions) 10,1 11,4 19,9 13,6 12,5 3,5 15,1 3,7 4,3 12,2 5,1 15,9 16,8 8,5 9,1 Length of Program (Minutes) Average Viewer Age (years) 30 30 60 60 60 30 60 30 30 120 120 60 120 120 30 30 25 30 38 20 15 24 17 19 45 19 28 31 58 43 ...
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FinAccGuru
School: Carnegie Mellon University

Regression Model A
Regression Model A:
Advertising Sales (Y) = 16583.3338 + 796.7551* Average # of Viewers (X1)
- 11.5472*Length of Program (X2)

The regression coefficient to of the Average Number of Viewers is found out as
statistically significant, whereas regression coefficient to of the Ave Length of Program is not
significant. The p value of regression coefficient of the Average # of Viewers is lower than the
level of significance, but the p value of regression coefficient of the Ave Length of Program is
higher than level of significance. Thus, true association is said to exist in between Average # of
Viewers and Advertising Sales, but not in between Average Number of Viewers and Advertising
Sales. But the Regression Model A as a whole is statistically significant as implied from the
lower value of F significance (0.0011) [From ANOVA Table] than the level of significance (0.05).
Thus, it can be said that Average Number of Viewers, and Length of Program in combine affect
the advertising sales of the company.
Given estimate of the Average # of Viewers of 3.2 million and Length of Program of 60
minutes, the predicted advertising sales of the Web Video on Demand (WVOD) is \$18440.11
[16583.3338 + (796.7551* 3.2) – (11.5472*60)].
Regression Model B
Regression Model B:
Advertising Sales (Y) = 15041.5642 + 756.1511* Average # of Viewers (X1)
- 24.5612*Length of Program (X2) + 95.4427* Average Viewer Age (X3)

The regression coefficient to of the Average Number of Viewers, and Average Viewer
Age are found out as...

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