D SUMMARY OUTPUT F G H M N N D Regression Statistics Multiple R 0.320648466 R Square 0.102815439 Adjusted R Squi 0.03677

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D Summary Output F G H M N N D Regression Statistics Multiple R 0 320648466 R Square 0 102815439 Adjusted R Squi 0 03677 1 (34.54 KiB) Viewed 44 times

D Summary Output F G H M N N D Regression Statistics Multiple R 0 320648466 R Square 0 102815439 Adjusted R Squi 0 03677 2 (62.56 KiB) Viewed 44 times

D SUMMARY OUTPUT F G H M N N D Regression Statistics Multiple R 0.320648466 R Square 0.102815439 Adjusted R Squi 0.036773792 Standard Error 0.542187704 Observations 300 1 2 3 4 5 5 7 B 9 10 11 12 13 14 15 16 17 18 ANOVA ar Regression Residual Total 55 MS F Significance 2 10.00535 5.002675 17.01778 1.00681E-07 297 87.30835 0.293968 299 97.3137 Intercept beauty PRENAS Coefficients andard Errt Star p.value 3.864376031 0.157435 24.54592 1.93E-73 0.234049729 0.040344 5.8013 1.68E-08 0.003993586 0.003258 1.225783 0.221251 Lower 95 Upper 95% Lower 95.01er 95.00 3.55454739 4. 174204672 3.55454739 4.174205 0.1546527 0.313446759 0.1546527 0.313447 -0.00241808 0.010405253 0.00241808 0.010405 20 21 22 23 24 25 27

P (1.26x1) -4 3. In this exercise we look again at the relationship between the look of instructors and their course evaluation. The dataset contains several variables, but for the purpose of this problem we are only considering course evaluations, beauty and age. Download the file at the following link. Open the file in Excel and estimate the following three linear regression models: KA) COURSE_EVAL = betal + beta1 BEAUTY + error -intercept You B) COURSE_EVAL = beta1 + beta1 AGE + error + (C) COURSE_EVAL = beta0 + beta1 BEAUTY + beta2.AGE + error a) 3.8 where: CORN= 3.8 • COURSE_EVAL: average course evaluation of the instructor • BEAUTY: beauty index of the instructor (between-2 and 2 with representing average look) • AGEage (in years) of the instructors Answer the following questions: Vladimir and Igor are identical twins and they both happen to be teachers at the same university: based on model Chow much do you expect to be the difference in COURSE_EVAL between them? Mario and Luigi are longtime college friends from the class of 1960 and they now happen to be teachers at the same university Mario has always been considered the better looking of the two with a BEAUTY index of 1.9 while Luigi's index is 1.5. Based on model C, Mario is expected to have (type HIGHER or LOWER) COURSE_EVAL relative to Luigi by 1 4.093 What is the estimated effect of BEAUTY on COURSE_EVAL in regression A? and in regression C? Comparing the estimates for regression A and C do you find evidence that regression A might ?