# BUS 660 Grand Canyon Week 4 Complete Work

BUS 660 Grand Canyon Week 4 Complete Work

BUS 660 Grand Canyon Week 4 Discussion 1

Provide an example based on your professional experience of a situation in which using a multiple regression model or nonlinear regression model may have helped your organization make a better decision.

BUS 660 Grand Canyon Week 4 Discussion 2

What types of business situations or problems might best lend themselves to multiple linear regression? What types may not? When do you anticipate using a multiple linear regression model in your postgraduate, professional experience? Explain.

BUS 660 Grand Canyon Week 4 Assignment

Multiple Regression Models Case Study: Web Video on Demand

Details:

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.

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.

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