Multiple Regression Analysis Assignment Work in assigned teams on this assignmen

Multiple Regression Analysis Assignment
Work in assigned teams on this assignment, but each student must submit separately. For this assignment you will develop a brief report based on your multiple regression analysis of business valuation data. Use the Excel project file Business Valuation.xlsx Download Business Valuation.xlsx(Files> Datasets> Lec02_Regression analysis) which lists evaluation metrics for 71 Pharmaceutical companies. The DATA worksheet contains a YELLOW column for ROE (Return on Equity) representing the numerical response (dependent) variable. Use all the other White column numerical variables as potential predictor variables to include in your multiple regression analysis. The categorical variables have been recoded as 0 and 1 in the dataset. Use only one of the categorical dummy variables in your analysis. Ignore columns with data labels.
The report will be documented in MS Word (in less than 8 pages) and will have the following sections
Title Page
Background: is a section describing the numerical dependent (i.e., response) variable of interest as well all the potential numerical and categorical predictor variables that will be used to develop the multiple regression model.
Multiple Regression Modeling: is a section, on our class lectures demonstrating the complete development of a multiple regression model using the numerical predictor variable, several other numerical predictors and one categorical (dummy) predictor variable as necessary. The step-by-step modeling process should be followed..See below for the modeling steps.
Appropriate tables and charts should be included in the body of this section or in an Appendix to the report.
Summary of Finding: The report should end with a short paragraph that describes your insight from this finding.
Modeling Steps
Open the Excel worksheet containing your Team Project Data.
As we learned in class, will be using the set of potentially meaningful numerical independent variables and the one selected “two-category” dummy categorical variable in your study to develop a “best” multiple regression model for predicting your numerical response variable Y. Follow the step by step modeling process described in the PowerPoints for Multiple Regression
A. Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing the scatterplot matrix (use JMP) and paste this into your report.
B. Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable.
C. Then assess collinearity with VIF until you are satisfied that you have a final set of possible predictors that are “independent,” i.e., not unduly correlated with each other.
D. Use stepwise regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable).
(1) Based on the forward modeling criterion determine which independent variables should be included in your regression model.
(2) Based on the backward selection modeling criterion determine which independent variables should be included in your regression model.
(3) Based on the mixed selection modeling criterion determine which independent variables should be included in your regression model.
(4) Based on the Adjusted r2 criterion determine which independent variables should be included in your regression model.
3. Comment on the consistency of your findings in Step 2D (1)-(4).
4. Based on Step 2D (along with the principle of parsimony if necessary) select a “best”multiple regression model.
5. Using the predictor variables from your selected “best” multiple regression model, rerun the multiple regression model in order to assess its assumptions.
6. Look at the set of residual plots, cut and pasted them into the report, and briefly comment on the appropriateness of your fitted model.
(1) If the assumptions are met and the fitted model is appropriate, continue to Step 7.
Note: You do not need to check the assumption of independence in your project. That assumption is met because your project is not time-dependent.
(2) If either the linearity or equality of variance assumption is violated in one or two scatter plots of Y with individual predictors then transform the particular independent variables involved following Tukey’s “ladder of powers” and rerun the multiple regression model as in Step 5.
7. Assess the significance of the overall fitted model.
8. Assess the significance of each predictor variable.
9. Write the sample multiple regression equation for the “final best” model you have developed.
(1) Interpret the meaning of the Y intercept and interpret the meaning of all the slopes for your fitted model (but do this in whatever units you used for Y to build this model).
(2) Interpret the meaning of the coefficient of multiple determination r 2 .
(3) Interpret the meaning of the standard error of the estimate SYX (in the units you used to build this model).
Assigned Teams for Fall 2023
Teams for Multiple Linear Regression Project(Glaxo)