QM Stata 
University of Bristol
School of Economics, Finance and Management
Course: MSc Accounting, Finance and Management
ECONM1012 Quantitative Methods
Summery of “Beauty in the classroom: instructors’ pulchritude and putative pedagogical productivity”
The report is written by Daniel S. Hamermesh, Amy Parker of analyzing the relationship between beauty and teaching productivity.
Q1
(i) Command: regress Course_Eval beauty, robust
OLS is a kind of method of estimating the unknown parameters (ie: , ) of a linear regression model. The simplest one-linear regression model has the form of . is 3.998272. β1=0.133
So Course_ Eval = 4.00+0.133×Beauty.
(ii) The variable of beauty is shifted to have mean zero. The intercept is the point where beauty is zero, course evaluation is equal to it’s mean at this point respectively. Thus estimated intercept is equal to the sample mean of course evaluation.
(iii) The estimated effect of Beauty on Course_Eval is small. In statistics,can describe how well the OLS regression line fits the data. The R square of beauty is 0.0357. In another word, Beauty can only explain small fraction of the Course_Eval. There are many other factors can influence the Course_Eval, such as gender, minority status, non-native English, tenure track and age. The effect of beauty on course evaluation is not small in this estimated model. Since absolute value of beta 1 is bigger than 0.1.
(iv) Command: su Course_Eval
R-square is this estimated model is 0.0357. R-square measures the fraction of variance of Y that is explained by X, ranges between zero (no fit) and one (perfect fit). 0.0357 is small, thus beauty does not explain a large fraction of the variance in evaluations across courses.
(V)
No, P value is associated with a test statistic.
Course_ Eval = 4.00+0.133×Beauty. (0.025) (0.032) so the t-statistic is 0.13/0.032=4.06,
The smaller the P value, the more strongly the test rejects the
School of Economics, Finance and Management
Course: MSc Accounting, Finance and Management
ECONM1012 Quantitative Methods
Summery of “Beauty in the classroom: instructors’ pulchritude and putative pedagogical productivity”
The report is written by Daniel S. Hamermesh, Amy Parker of analyzing the relationship between beauty and teaching productivity.
Q1
(i) Command: regress Course_Eval beauty, robust
OLS is a kind of method of estimating the unknown parameters (ie: , ) of a linear regression model. The simplest one-linear regression model has the form of . is 3.998272. β1=0.133
So Course_ Eval = 4.00+0.133×Beauty.
(ii) The variable of beauty is shifted to have mean zero. The intercept is the point where beauty is zero, course evaluation is equal to it’s mean at this point respectively. Thus estimated intercept is equal to the sample mean of course evaluation.
(iii) The estimated effect of Beauty on Course_Eval is small. In statistics,can describe how well the OLS regression line fits the data. The R square of beauty is 0.0357. In another word, Beauty can only explain small fraction of the Course_Eval. There are many other factors can influence the Course_Eval, such as gender, minority status, non-native English, tenure track and age. The effect of beauty on course evaluation is not small in this estimated model. Since absolute value of beta 1 is bigger than 0.1.
(iv) Command: su Course_Eval
R-square is this estimated model is 0.0357. R-square measures the fraction of variance of Y that is explained by X, ranges between zero (no fit) and one (perfect fit). 0.0357 is small, thus beauty does not explain a large fraction of the variance in evaluations across courses.
(V)
No, P value is associated with a test statistic.
Course_ Eval = 4.00+0.133×Beauty. (0.025) (0.032) so the t-statistic is 0.13/0.032=4.06,
The smaller the P value, the more strongly the test rejects the