Excel: https://docs.google.com/spreadsheets/d/1d1BPy5CKNp_cjhwlWz6fLDuUYQkStdDO/edit?usp=sharing&ouid=113068968791942309329&rtpof=true&sd=true The fileFTMBAcontains data from a sample of full time MBA programs offered by private universities. The variables collected for this sample are average starting salary upon graduation ($), the percentage of applicants to the full-time program who were accepted, the average GMAT test score of students entering the program, program per-year tuition ($), and percent of students with job offers at time of graduation.

Develop the most appropriate multiple regression model to predict the mean starting salary upon graduation. Developing a good regression model is an art as well as a science that requires significant practice. Please answer the following questions in your quest for the most appropriate model.

1. Build a simple linear regression model with each of the predictors and the dependent variable. Provide the R2 for each simple linear regression model. Which predictor has the highest correlation with the dependent variable?
2. Build at least five different multiple regression models with different combinations of predictors and transformations. Provide the regression equation for each model and its Adjusted R2 and F statistic.
3. From the models considered in the previous questions, what model is the most appropriate one? Justify your selection.
4. Interpret the coefficients in the regression equation of your selected model within the context of the problem.
5. What is the significance (p-value) for overall fit and each predictor in the model? Explain.
6. Conduct a residual analysis for your selected model. Create residual plots for all the predictors and the predicted values. Does your selected model meet the necessary assumptions? Are there any areas of concerns with your model? Include residual plots and comment on the information shown on them