In this scenario, you will continue to work as a business consultant trainee with the superstore client. The superstore would like to know which variables have an impact on its sales revenue and number of orders. Your vice president would like you to perform two multivariate regressions to analyze the data. Remember that the superstore is interested in whether specific trends are identified that can help grow its business through improved operations and sales. You have decided that the best analysis will be to perform multivariate regressions.
For each of the regressions, your dependent variables will be sales revenue and number of orders, respectively, and you will be selecting two independent variables. Then you will write a report for the superstore in which you describe the regression modules and the variables you chose to analyze. Additionally, you will explain why you chose to analyze those independent variables.
Your task is to perform multivariate regressions using Excel. You will also write a short report that describes the regression model you used and why you chose to analyze these selected independent variables.
- Perform two multivariate regressions on the data using the Superstore Excel Workbook (attached) to complete this step. This workbook also contains your work from previous modules. Both multivariate regressions should analyze Sales with the two independent variables of your choice. DO NOT DELETE ANY OTHER WORKBOOK PAGES
- Create one multivariate regression that is placed in the Multivariate_Regression_1 worksheet.
- Create one multivariate regression that is placed in the Multivariate_Regression_2 worksheet.
- Submit a Word document using double spacing, 12-point Times New Roman font, and one-inch margins. This assignment should be 1 to 2 pages in length. If references are included, they should be cited in APA format. Explain the results of the multivariate regression. For each multivariate regression performed, address the following:
- Why did you choose your selected independent variables?
- Explain the regression model used.
- Include the key regression output values that include: R2, p value, intercept, and coefficients.
- Explain the regression equation performed.