ECON 390 Kean University Documentation for College Distance Data Analysis
Description
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Documentation for CollegeDistance Data
These data are taken from the HighSchool and Beyond survey conducted by the
Department of Education in 1980, with a follow-up in 1986. The survey included
students from approximately 1100 high schools.
The data used here were supplied by Professor Cecilia Rouse of Princeton University and
were used in her paper Democratization or Diversion? The Effect of Community
Colleges on Educational Attainment, Journal of Business and Economic Statistics, April
1995, Vol. 12, No. 2, pp 217-224.
The data in CollegeDistance exclude students in the western states. The data in
CollegeDistanceWest includes only those students in the western states.
Series in Data Set
Name
ed
female
black
Hispanic
bytest
Description
Years of Education Completed (See below)
1 = Female/0 = Male
1 = Black/0 = Not-Black
1 = Hispanic/0 = Not-Hispanic
Base Year Composite Test Score. (These are achievement tests given to high
school seniors in the sample)
dadcoll
1 = Father is a College Graduate/ 0 = Father is not a College Graduate
momcoll 1 = Mother is a College Graduate/ 0 = Mother is not a College Graduate
incomehi 1 = Family Income > $25,000 per year/ 0 = Income ? $25,000 per year.
ownhome 1= Family Owns Home / 0 = Family Does not Own Home
urban
1 = School in Urban Area / = School not in Urban Area
cue80
County Unemployment rate in 1980
stwmfg80 State Hourly Wage in Manufacturing in 1980
dist
Distance from 4yr College in 10’s of miles
tuition
Avg. State 4yr College Tuition in $1000’s
Years of Education: Rouse computed years of education by assigning 12 years to all
members of the senior class. Each additional year of secondary education counted as a
one year. Students with vocational degrees were assigned 13 years, AA degrees were
assigned 14 years, BA degrees were assigned 16 years, those with some graduate
education were assigned 17 years, and those with a graduate degree were assigned 18
years.
Documentation for TeachingRatings Data
TeachingRatings contains data on course evaluations, course characteristics, and professor
characteristics for 463 courses for the academic years 2000-2002 at the University of Texas at
Austin. These data were provided by Professor Daniel Hamermesh of the University of Texas at
Austin and were used in his paper with Amy Parker, Beauty in the Classroom: Instructors
Pulchritude and Putative Pedagogical Productivity, Economics of Education Review, August
2005, Vol. 24, No. 4, pp. 369-376.
Variable Definitions
Variable
Definition
Course_eval Course overall teaching evaluation score, on a scale of 1 (very unsatisfactory) to
5 (excellent)
Rating of instructor physical appearance by a panel of six students, averaged across
Beauty
the six panelists, shifted to have mean zero.
Female
?1 if the instructor is female
??
?0 if the instructor is male
Minority
?1 if the instructor is a non-White
??
?0 if the instructor is White
NNenglish
?1 if the instructor is not a native English speaker
??
?0 if the instructor is a native English speaker
intro
?1 if the course is introductory (mainly large Freshman and Sophomore courses)
??
?0 if the course is not introductory
onecredit
?1 if the course is a single-credit elective (yoga, aerobics, dance, etc.)
??
?0 otherwise
Professors age
age
Professor’s years of experience in teaching position
experience
Documentation for TeachingRatings Data
TeachingRatings contains data on course evaluations, course characteristics, and professor
characteristics for 463 courses for the academic years 2000-2002 at the University of Texas at
Austin. These data were provided by Professor Daniel Hamermesh of the University of Texas at
Austin and were used in his paper with Amy Parker, Beauty in the Classroom: Instructors
Pulchritude and Putative Pedagogical Productivity, Economics of Education Review, August
2005, Vol. 24, No. 4, pp. 369-376.
Variable Definitions
Variable
Definition
Course_eval Course overall teaching evaluation score, on a scale of 1 (very unsatisfactory) to
5 (excellent)
Rating of instructor physical appearance by a panel of six students, averaged across
Beauty
the six panelists, shifted to have mean zero.
Female
?1 if the instructor is female
??
?0 if the instructor is male
Minority
?1 if the instructor is a non-White
??
?0 if the instructor is White
NNenglish
?1 if the instructor is not a native English speaker
??
?0 if the instructor is a native English speaker
intro
?1 if the course is introductory (mainly large Freshman and Sophomore courses)
??
?0 if the course is not introductory
onecredit
?1 if the course is a single-credit elective (yoga, aerobics, dance, etc.)
??
?0 otherwise
Professors age
age
Professor’s years of experience in teaching position
experience
Econ 390
Assignment 12 (Module 12)
Late submissions are not accepted.
1. Open the TeachingsRatings data in RStudio.
2. Read the Description of the data uploaded on Canvas.
3. Provide a summary of variables: Course-eval, Beauty, female, age, minority, intro, and onecredit
(what they are, what their unit is, what their mean is?)
4. Run a single regression of Course-eval over Beauty.
5. What is the model?
6. What is the predicted equation?
7. Is the coefficient of beauty significant? Why?
8. Run a multiple regression of Course-eval over Beauty, female, age, minority, intro, and onecredit.
(the 6 variables must be included in 1 regression and not separately).
9. What is the population equation?
10. What is the predicted equation?
11. What is the effect of beauty on Course-eval in the multiple regression.
12. In the multiple regression, which one of the regressors (coefficients) are statistically significant?
Why?
13. What is the new R2 and what does it mean?
14. In the multiple regression, provide a test of the significance of course characteristics on the
course evaluations.
(hint: if you keep the order of the variables I wrote in part. 8 the same, the null of this test would
H0: B5=B6=0, meaning in your MLRM you should check the significance of intro and onecredit at
the same time. You should run a F-Test. Once with these variables and once without them and
create your restricted and unrestricted models)
VERY IMPORTANT: You should calculate and provide the FSTAT on PAPER (even if you use
RStudio to get the Fstat, you should confirm it again. I want you to do it once to learn it)
15. What is the overall Fstat and the conclusion in the MLRM (the model of part 8). What does this
mean? Explain?
VERY IMPORTANT: You should calculate and provide the FSTAT on PAPER.
The first part of the assignment is similar to the sample-midterm. That is on purpose to guide you through
the material and to encourage you to think about the similarities/difference between the null of F-Test and
the previous tests we had ran.
The new content is Q14, Q15.
Purchase answer to see full
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Statistical analysis
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predicted equation
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