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On Blackboard you will find a data file TeachingRatings that contains data on course evaluations, course characteristics, and professor characteristics for 463 courses. A detailed description is given in TeachingRatings-Description. One of the characteristics is an index of the professor’s “beauty” as rated by a panel of six judges. In this exercise you will investigate how course evaluations are related to the professor’s beauty.
1. Run a regression of average course evaluations (Course_Eval) on the professor’s beauty (Beauty) and answer the following questions.
i. Explain how the parameters of the regression of Course_Eval on Beauty can be estimated by ordinary least squares (OLS).
ii. Explain why the estimated intercept is equal to the sample mean of Course_Eval.
iii. Is the estimated effect of Beauty on Course_Eval large or small? Explain.
iv. Does Beauty explain a large fraction of the variance in evaluations across courses? Explain.
v. Is the estimated regression slope coefficient statistically significant? What is the p-value associated with the coefficient’s t-statistic?
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2. Run a regression of Course_Eval on Beauty, including some additional variables to control for the type of course and professor characteristics. In particular, include as additional regressors Intro, OneCredit, Female, Minority and NNEnglish.
i. Is the estimated effect of Beauty on Course_Eval substantially different from the regression in (1)? Based on this, does the regression in (1) seem to suffer from important omitted variable bias? Explain.
ii. Professor Smith is a black male with average beauty and is native English speaker. He teaches a three-credit upper-division course. Predict Professor Smith’s course evaluation.
iii. Consider the various control variables in the data set. Which do you think should be included in the regression? Explain.
iv. Given your answer in part (iii) what is a reasonable 95% confidence interval for the effect of Beauty on Course_Eval?
3. Estimate a regression of Course_Eval on Beauty, Intro, OneCredit, Female, Minority and NNEnglish.
i. Add Age and Age2 to the regression. Is there evidence that Age has a nonlinear effect on Course_Eval? Is there evidence that Age has any effect on Course_Eval? Explain.
ii. Modify the regression so that the effect of Beauty on Course_Eval is different for men and women. Is the male-female difference in the effect of Beauty statistically significant? Explain.
iii. Professor Smith is a man. He has cosmetic surgery that increases his beauty index from one standard deviation below the average to one standard deviation above the average. What is his value of Beauty before the surgery? After the surgery? Using the regression in (ii), construct a 95% confidence interval for the increase in his course evaluation.
iv. Repeat (iii) for Professor Jones, who is a woman.
TeachingRatings_Description.pdf
(17.41 KB)
TeachingRatings.rar
(2.46 KB)
本附件包括：- TeachingRatings.dta
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