3. /15 points/: According to the World Health Organization, obesity has reached epidemic proportions globally. While obesity has generally been linked with chronic disease and disability, researchers argue that it may also affect wages. Body Mass Index (BMI) is a widely used weight measure that also adjusts for height. A person is considered normal weight if BMI is between 18.5 to 25, overweight if BMI is between 25 to 30, and obese if BMI is over 30. The dataset contains the salary (expressed in $1,000s) of 30 college-educated men with their respective BMIs. We consider a linear (Model 1) and an exponential (Model 2) model. Dependent Variable: Salary Dependent Variable: In(Salary) Variable Model 1 Model 2 Intercept 66.1852 (0.000) 4.3277 (0.000) BMI -0.9931 (0.000) -0.0248 (0.000) se 4.1702 0.1111 R2 0.626 0.5944 Notes: Parameter estimates are in the top part of the table with the p- values in parentheses a. [10 points]: What is the estimated salary of a college-educated man with BMI of 20? What is the estimated salary of a college-educated man with BMI of 40? Make predictions for each model. Interpret the estimated slope in each model. b. [5 points]: Which of the above two models is more appropriate for this application? Can we compare the R's of the two models? Explain your reason