Data visualization is a quick, easy, and effective way to convey concepts in a universal manner. Visualized data helps you understand large and complex data sets easily so you can make decisions based on the analysis. It also helps you identify new patterns, trends, and correlations in the attributes.

In this scenario, you are continuing to work as a business consultant trainee with the superstore client. Now you will take a deeper dive into the analysis with measures of central tendency. After having spent time reviewing your initial discoveries, your vice president has asked you to create an in-depth visualization of the data. You will be required to create multiple charts of each type: trend, pie, and column. Your analysis should now focus on the subcategories: product types, states, and shipping modes. Different visualizations can help one discover new aspects of the data. Examining the subcategories will provide further insight as you begin to identify new patterns, trends, and relationships or correlations in the key attributes.

Prompt
You will create multiple charts of each type: trend, pie, and column. Then explain the new insights that you have gathered during your continued analysis.

Create multiple charts to represent the subcategories of the data.
Use the Superstore Excel Workbook to complete this step. This workbook also contains your work from previous modules.
The charts should be created in the Data_Visualization worksheet.
Each of the charts you create should have two attributes. You can choose any two of the subcategories: product types, states, and shipping modes.
Explain the identified patterns, trends, and correlations between the key attributes and subcategories in a short paper.
Describe your observations of the subcategories (product types, states, and shipping modes) and their relationship to the key attributes within the data set.
For example, if you are describing product types, what trends across other subcategories or key attributes are you seeing (product types as they relate to profit, to shipping modes, etc.)?

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.

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.

While bivariate regression is used to predict the impact of one independent variable on one dependent variable, multivariate regression is used to predict the impact of two or more independent variables on one dependent variable.

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.

Prompt
Your task is to perform multivariate regressions using excel.

Perform two multivariate regressions on the data using the Superstore Excel Workbook 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.
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.
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.