MULTIPLE REGRESSION

PLEASE HELP ME UNDERSTAND HOW SOLVE PROBLEM IF YOU KNOW HOW TO DO MULTIPLE REGRESSION.

***I NEED HELP WITH PART C AT THE BOTTOM OF THE SNIPPET****

An association that supports people who research, design, and evaluate the user experience of products and services periodically conducts a salary survey of its members. One of the authors of the report investigated how much having a Ph.D. affects salaries of people in the profession. The author t a rst-order multiple regression model for salary (y, in dollars) as a function of years of experience (x1), Ph.D, status (x2 = 1 if Ph.D., 0 if not). and manager status (x3 = 1 if manager. 0 if not). The following prediction equation was obtained. Complete parts a through c. = 50,925 + 3,461x1 +1e,7o1x2 +12,1ta7x3 a. Predict the salary of a member of the association with 10 years of experience. who does have a Ph.D., and is a manager, $ 114.404 (Round to the nearest dollar as needed.) b. Predict the salary of a member of the association with 10 years of experience. who does not have a Ph.D., and is not a manager. 3 35.536 (Round to the nearest dollar as needed.) c. Why is a 95% prediction interval preferred over the predicted values given in parts a and b? O A. Instead of just being a single value, the prediction interval gives information about the likely salary range of members that t exactly to the given values of x1, x2, and x3. O B. Although the predicted value is accurate for members that fit exactly to the given values of x1, x2, and x3, a prediction interval is more accurate for a range of possible values. 0 C. The accuracy of the single predicted value is unknown, but the prediction interval's accuracy is 95%. O 0. Since the independent variables cannot be known exactly, the prediction interval accounts for the extra variability introduced when using approximations