1) (50 points total) In this exercise we will examine whether a teacher's physical attractiveness has any impact on student course evaluations. The dataset to use is called teachingratings, and a description of the variables is available as well. You can access these through the course website. Use an =.05 for hypothesis testing.

We'll first examine whether our control variables seem to have any predictive power for ratings. a.) (10 points) We'll start with some basic dummy variable regressions to test for differences between groups: 1) Regress course_eval on minority. Report the coefficients and p-values. Do minority instructors have significantly different course evaluations compared to non- minorities? 2) Regress course_eval on female. Report the coefficients and p-values. Do females have significantly different course evaluations from males? 3) Run a final regression to test whether the effect of being a minority changes depending on whether the person is female. Report the coefficients and p-values. Can you reject the hypothesis of no effect? b.) (10 points) Now regress course_eval on beauty, age, age^2 [you'll need to create this], nenglish, female, minority, onecredit. Are any variables not statistically significant? Which? This is Model 1. Use Model 1 to answer the following: 1) Calculate an F-test for the hypothesis that age and age^2 jointly have no effect on course_eval. You can execute this test however you prefer. 2) Calculate an F-test that female and minority have an equal impact on course_eval. You can execute this test however you prefer. c.) (15 points) Modify Model 1 so that you can determine whether beauty has a different effect for women than for men. This is Model 2. Report the coefficient and p-value for the interaction variable. Can you reject the hypothesis of no effect of the interaction? Use Model 2 to answer the following: 1) Professor Smith is a man. He isn't very handsome. He undergoes plastic surgery, which raises his beauty from one standard deviation below the mean to one standard deviation above. What is the predicted change in his course evaluations? [Note: you will have to figure out how to calculate a standard deviation in STATA. It isn't hard. Also, note that the average beauty level in the data has been scaled to equal zero.] 2) Repeat part 1) for Professor Jones, who is a woman. d.) (15 points) Go back to Model 1. Add two variables to Model 1 that will allow you to determine whether age has a different effect for women than for men on course evaluations. [Note: since age appears twice in Model 1, you will need to create the interaction for both age and age^2.] This is Model 3. Report the coefficients and p- values for the interaction terms. Can you reject the hypothesis of no effect of the interactions? Use Model 3 results to answer the following: 1) Professor Thomas, a man, begins his teaching career at age 32. Assuming his beauty measure (and all other variables) is unchanged over his career, what is the predicted change in his course_eval score when he retires at age 65? 2) Repeat 1) for Professor Gomez, who is a woman.