2.   For each of the independent variables listed in part 1, use plain language to explain why the relationship shown in the scatterplot and regression equation make sense in the context of the situation you’re exploring. However, if either the slope or intercept of the equation seems counterintuitive given your intuition, also make a note of that in your explanation.

3.   Run a multiple regression for your data. Paste a screenshot of the results below.

Part Three- Generate a Revised Regression Equation

In order to satisfy this part of the project, you will complete the following.

Addressing nonlinear relationships:

1.   For each of the independent variables listed in part 1, paste a screenshot of its residual plot against the dependent variable Y here in your project document.

2.   Review the residual plots and determine which, if any, suggest a nonlinear relationship with the dependent variable. List suspected nonlinear variables in the table below.

3.   Use the Semilog and Log-log Transforms tool for the scatterplot for each independent variable listed, and include screenshots of the plots in the table.

4.   List the independent variables with nonlinear relationships.

 Possible NonLinearities

Independent variable

Transform used (log or semilog)

Screenshot of transform plot

Addressing Multicollinearity

5.   Include a correlation table for your independent variables. Paste a screenshot of your correlation table here.

6.   Determine if there are independent variables that may be sources of multicollinearity. List them here with an explanation of why you think they might be a source of multicollinearity.

Part Four—Validate Your Model

In order to satisfy this part of the project, you will complete the following.

1.   Share a one-page summary of your project.

In your summary, include a brief description of the context and the dependent variable of interest. Make an argument for the viability of your model. Aspects of your argument may be based on statistical details such as p-values for coefficients, signs and magnitudes of coefficients, and R-squared values.

For attributes you have included in your model, be sure to address the consistency of linear relationships and any nonlinearities. Where possible, draw a connection between these attributes and the working realities of the situation being described.

If there are any missing drivers or attributes that have been excluded and which seem relevant to the situation, make a note of these and explain the reasoning and potential impacts of excluding them. In this discussion, be sure to address not only attributes excluded on purpose as well as those excluded because of unavailability of data.