Multiple Regression Models Case Study Web Video On De 402713

Multiple Regression Models Case Study Web Video On Demandweb Video On

Multiple Regression Models Case Study Web Video On Demand Web Video On 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,.,.,.,.,.,.,.,.,.,.,.,.,.,.,. 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. © 2016. Grand Canyon University. All Rights Reserved. 2 Essay Assignment 3: How would you define pluralism? Write a 1-page essay on pluralism, concentrating on religion and religious oppression. Use concepts and specific examples from this course, history, current events, the media, or other external sources, and the textbook to either support or oppose the following statement: "Pluralism is the greatest philosophical ideal of our time." Remember to cite your sources and use appropriate formatting as per APA (6th ed.): Data Advertising Sales ($) Average # of Viewers (Millions) Length of Program (Minutes) Average Viewer Age (years) 28,.,.,.,.,.,.,.,.,.,.,.,.,.,.,. A B C Advertising Sales ($) Average # of Viewers (Millions) Length of Program (Minutes) 28,.,.,.,.,.,.5 30

Paper For Above instruction

The case study on Web Video On Demand (WVOD) provides a comprehensive context for developing multiple regression models to analyze advertising revenue based on user engagement and programming characteristics. The primary objective involves constructing two regression models to predict advertising sales, facilitating strategic decisions about new programming acquisitions, especially those targeting social issues and diverse audiences.

Introduction

Regression analysis serves as a vital statistical tool in understanding the relationships between dependent and independent variables. In the context of WVOD, constructing regression models helps predict advertising sales depending on program features like viewer count, program length, and viewer demographics. These insights enable WVOD to make informed choices regarding programming investments, marketing strategies, and partnerships with filmmakers producing socially conscious content.

Development of Regression Model A

The first regression model, referred to as “Regression Model A,” predicts advertising sales based solely on the number of viewers and the program length. The variables involved are defined as follows:

- Dependent variable: Advertising Sales ($)

- Independent variables: Average Number of Viewers (Millions), Length of Program (Minutes)

Assuming the data provided in the case, a multiple linear regression analysis would be performed to estimate the coefficients corresponding to each predictor. For instance, suppose the model produces the following regression equation:

Advertising Sales = β₀ + β₁(Viewers) + β₂(Program Length) + ε

Where β₀ is the intercept, and β₁ and β₂ are the coefficients for the number of viewers and program length, respectively. Although the specific coefficients are not provided here, this model provides a framework to incorporate available data for predictive purposes.

Prediction for a 60-minute Documentary Special

Based on Regression Model A, the company seeks to predict advertising sales for a 60-minute documentary about social media and bullying, which is estimated to attract 3.2 million viewers. By plugging in these values into the regression equation, WVOD can estimate the advertising revenue generated by this program.

For example, if the regression equation was determined to be:

Advertising Sales = 10 + 8(Volume of Viewers) + 2(Program Length)

then plugging in the specific values:

Advertising Sales = 10 + 8(3.2) + 2(60) = 10 + 25.6 + 120 = 155.6 (thousand dollars or appropriate units)

This prediction informs WVOD about the potential profitability of the documentary based on its expected viewership and length.

Development of Regression Model B

The second regression model, “Regression Model B,” extends the first by including the average viewer age as an additional predictor. The variables are:

- Dependent variable: Advertising Sales ($)

- Independent variables: Average Number of Viewers (Millions), Length of Program (Minutes), Average Viewer Age (Years)

This model accounts for the demographic profile of viewers, which could influence advertising revenue based on targeted advertising strategies or viewer engagement levels.

Similar to the first, this model would be estimated through multiple linear regression analysis to derive coefficients for each predictor:

Advertising Sales = β₀ + β₁(Viewers) + β₂(Program Length) + β₃(Viewer Age) + ε

Using the parameters estimated from the data, WVOD can predict advertising sales for a 2-hour film with predicted 14.1 million viewers and an average viewer age of 32 years, thus assessing how this title might impact advertising revenue given inclusion of demographic considerations.

Prediction for a 2-Hour Film

Supposing the regression equation is:

Advertising Sales = 15 + 7(14.1) + 1.5(120) + 0.8(32)

we would calculate:

Advertising Sales = 15 + 98.7 + 180 + 25.6 = 319.3 (thousand dollars or relevant units)

This figure guides WVOD’s decision on whether acquiring such a film aligns with revenue expectations and strategic goals.

Conclusion

The development of these regression models provides WVOD with quantitative tools to forecast advertising revenue based on programming features and viewer demographics. Such predictive analysis supports strategic planning in content acquisition, especially for socially conscious and diverse programming that addresses current societal issues. Employing regression analysis enhances WVOD’s ability to optimize its programming portfolio and maximize advertising income effectively.

References

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
• Meyers, L. S., Gamst, G., & Guo, M. (2013). Regression Analysis and Other Multivariable Methods. Sage Publications.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. Pearson.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
• Cook, R. D., & Weisberg, S. (2004). Applied Regression Including Computing and Graphics. Wiley.
• Brooks, C. (2014). Introductory Econometrics for Finance. Cambridge University Press.
• Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Sage Publications.
• Minar, P., & Beer, C. (2014). Regression analysis in practice. Journal of Data Science, 12(3), 189-205.
• Wooldridge, J. M. (2012). Introductory Econometrics: A Modern Approach. South-Western Cengage Learning.