Review \"Multiple Regression Models Case Study: Web Video on Demand\" for this t

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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 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.

Explanation / Answer

Regression model A:

The result of the regression function of excel is:

The model would be = y = 16583.33382+796.7551062x1-11.54719579x2

where y = sales, x1 = number of viewers and x2 = program length.

Now we have to predict sales for a  60-minute documentary special with an expected 3.2 million viewers. Thus x1 = 3.2 and x2 = 60. Thus y (sales) = 16583.33382+796.7551062x1-11.54719579x2 = 16583.33382+(796.7551062*3.2)+(-11.54719579*60) = $18,440.

Regression Model B:

The result of the regression function of excel is:

The model will be: y = 15041.5642+756.1510757x1-24.56120666x2+95.44268428x3

where y = sales, x1 = no. of viewers, x2 = program length and x3 = viewer's age.

Now we have to predict sales for a 2 hour film with 14.1 million viewers and age of 32 years. Thus x1 = 14.1, x2 = 120 minutes (2 hours) and x3 = 32.

Thus y = 15041.5642+756.1510757*14.1-24.56120666*120+95.44268428*32

= $25,810

Executive summary: The linear regression equation y = ax1+bx2+c has been used for regression model A. Here the intercept is represented by "c" and "a" and "b" are slopes for the x1 and x2 variables. "a", "b" and "c" are together known as coefficients and give the least square estimate. By using the "data analysis" - regression function in excel we are able to determine the regression model for sales, number of viewers and the length of the program. The regression function of excel gives the following model: y = 16583.33382+796.7551062x1-11.54719579x2. In this model, y = sales, x1 = number of viewers and x2 = program length. The model can be used to predict sales for any given level of viewership and length of program.

For regression model B the linear regression equation y = ax1+bx2+cx3+d has been used.  Here the intercept is represented by "d" and "a", "b", "c" are slopes for the x1,x2 and x3 variables. "a", "b","c" and "d" are together known as coefficients and give the least square estimate. By using the "data analysis" - regression function in excel we are able to determine the regression model for sales, number of viewers, length of the program and age of the viewer. The regression function of excel gives the following model: y = 15041.5642+756.1510757x1-24.56120666x2+95.44268428x3. In this model, y = sales, x1 = number of viewers , x2 = program length and x3 = age of the viewer. The model can be used to predict sales for any given level of viewership and length of program.

Sales No. of viewers Program length 28000 10.1 30 25500 11.4 30 31000 19.9 60 29000 13.6 60 20500 12.5 60 14500 3.5 30 27000 15.1 60 23500 3.7 30 19500 4.3 30 23000 12.2 120 18000 5.1 120 29500 15.9 60 30000 16.8 120 25000 8.5 120 22500 9.1 30

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