D. Use stepwise regression approaches to t a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable). I (1) Based on the fonNard modeling criterion determine which independent variables should be included in your regression model. I (2) Based on the backward selection modeling criterion determine which independent variables should be included in your regression model. I (3) Based on the mixed selection modeling criterion determine which independent variables should be included in your regression model. I (4) Based on the Adjusted r2 criterion determine which independent variables should be included in your regression model. E. Comment on the consistency of your ndings in Step 2D (1)-(4). F. Paste screenshots of (1), (2), and (3) outputs from Step 2D above into your report. G. Based on Step 20 (along with the principle of parsimony if necessary) select a \"best"multiple regression model. Note your nding. H. Using the predictor variables from your selected "best" multiple regression model, rerun the multiple regression model in order to assess its assumptions. You may use Excel or JMP for this step. I. Look at the set of residual plots. cut and paste them into the report, and briefly comment on the appropriateness of your fitted model. I (1) if the assumptions are met and the fitted model is appropriate, continue to Step 2J. I (2) If the normality assumption is problematic, state this but continue to Step 2.] with caution because your sample size is large enough for the central limit theorem to enable the use of classical inferential methods. 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. I (3) 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 2H. J. Assess the significance of the overall tted model. Note your observation. K. Assess the significance of each predictor variable. Note your observations. . Write the sample multiple regression equation for the "final best" model you have developed. A. Interpret the meaning of the Y intercept and interpret the meaning of all the slopes for your tted model (but do this in whatever units you used for Y to build this model). B. Interpret and describe the meaning of the coefficient of multiple determination r 2. C. Interpret and describe the meaning of the standard error of the estimate SYX (in the units you used to build this model)