5. The data file TeachingRatings contains data on course evaluations, course characteristics, and professor characteristics for 463 courses at the University of Texas at Austin. A detailed descrip- tion 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. (a) Run a regression of average course evaluations (Course_Eval) on the professor's beauty (Beauty). What is the estimated intercept? What is the estimated slope? Explain why the estimated intercept is equal to the sample mean of Course Eval. (Hint: What is the sample mean of Beauty?) (b) Professor Watson has an average value of Beauty, while Professor Stock's value of Beauty is one standard deviation above the average. Predict Professor Stock's and Professor Watson's course evaluations. (c) Does Beauty explain a large fraction of the variance in evaluations across courses? Explain.
5. The data file TeachingRatings contains data on course evaluations, course characteristics, and professor characteristics for 463 courses at the University of Texas at Austin. A detailed descrip- tion 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. (a) Run a regression of average course evaluations (Course_Eval) on the professor's beauty (Beauty). What is the estimated intercept? What is the estimated slope? Explain why the estimated intercept is equal to the sample mean of Course Eval. (Hint: What is the sample mean of Beauty?) (b) Professor Watson has an average value of Beauty, while Professor Stock's value of Beauty is one standard deviation above the average. Predict Professor Stock's and Professor Watson's course evaluations. (c) Does Beauty explain a large fraction of the variance in evaluations across courses? Explain.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Can you answer A,B,C with clear answers. You can use the data in the second photo
Transcribed Image Text:III Empirical
Note: Include Eviews output for each question. Feel free to use guidelines on the D2L where I
provide detailed steps to perform estimation in EViews using an example. The datasets are in the
"03 Datasets" folder on the D2L page. There are three files for each dataset. (1) the Eviews file, (2)
the csv file (for drag and drop in Eviews, if the other file leads to an error), (3) and the description
file.
5. The data file TeachingRatings contains data on course evaluations, course characteristics, and
professor characteristics for 463 courses at the University of Texas at Austin. A detailed descrip-
tion 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.
(a) Run a regression of average course evaluations (Course_Eval) on the professor's beauty
(Beauty). What is the estimated intercept? What is the estimated slope? Explain why the
estimated intercept is equal to the sample mean of Course Eval. (Hint: What is the sample
mean of Beauty?)
(b) Professor Watson has an average value of Beauty, while Professor Stock's value of Beauty is
one standard deviation above the average. Predict Professor Stock's and Professor Watson's
course evaluations.
(c) Does Beauty explain a large fraction of the variance in evaluations across courses? Explain.
Transcribed Image Text:W
Equation: UNTITLED Workfile: TEACHINGRATINGS (4)::Unti...
View Proc Object Print Name Freeze Estimate Forecast Stats Resids
Dependent Variable: COURSE_EVAL
Method: Least Squares
Date: 09/26/22 Time: 21:51
Sample: 1 463
Included observations: 463
Variable
BEAUTY
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Coefficient
3.998272
0.133001
Std. Error t-Statistic
0.025349 157.7272 0.0000
0.032178 4.133368
0.0000
Mean dependent var
S.D. dependent var
Akaike info criterion
Prob.
0.035736
0.033644
0.545452
137.1556 Schwarz criterion
-375.3231 Hannan-Quinn criter.
17.08473
Durbin-Watson stat
0.000042
3.998272
0.554866
1.629905
1.647779
1.636942
1.410317
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