Bivariate regression analysis is an excellent tool to help you answer questions about a business. When you use bivariate analysis, you can discover whether there is a strong correlation between a dependent and an independent variable. As a business consultant, you will probably want to test a hypothesis for cause and effect when you use a scatterplot and a line of best fit, which will show you the strength of the correlation.

In this scenario, you will continue to work as a business consultant trainee with the superstore client. The superstore would like to know which key attributes have an impact on its sales revenue and the number of orders. Your vice president would like you to perform two bivariate 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. Then you will write a report for your vice president of operations in which you describe the regression models and the key attributes you chose to analyze. Additionally, you will explain why you chose to analyze those key attributes.

Prompt
Your task is to create two bivariate regressions using Excel. You will also write a short report that describes the regression model you used and why you chose to analyze your selected independent variables.

Perform two bivariate regressions on the data using the Superstore Excel Workbook to complete this step. This workbook contains your work from previous modules. Both bivariate regressions should analyze Sales with the independent variables of your choice.
Create one bivariate regression that is placed within the Bivariate_Regression_1 worksheet
Create one bivariate regression that is placed within the Bivariate_Regression_2 worksheet.
Explain the results of the bivariate regressions. For each bivariate regression performed, address the following:
Why did you choose your selected independent variable?
Explain the regression model used.
Include the key regression output values that include: R2, p value, intercept, and coefficients.
Explain the regression equation performed.