Test 1

Thursday, March 4 2010
Econ 3200  Introduction to Econometrics
Spring 2010
Cornell University
Prof. Molinari
This exam consists of TWO parts totaling 100 points.
This is a CLOSED BOOK exam.
However,
useful formulas are provided on separate pages.
Tables for the Normal,
t
, and
°
2
distributions are
provided on separate pages.
Calculators are permitted °but graphic calculators are prohibited.
You have 75 minutes
to complete the exam.
SHOW ALL your work as partial credit will be given.
Answers without explanations might NOT be given (full) credit even if they are correct.
THINK
before you answer, and GOOD LUCK!
Part I: Short Answer (20 points, 4 points each)
In a few sentences, brie±y de²ne/describe the following. Use formulas if you think they will make
your answers more precise, but a formula without a written explanation is not an acceptable answer.
1.
R
2
:
2. Explained Sum of Squares.
3. Mean Squared Error.
4. Interpreting the slope coe¢ cient of a linear regression.
5. Convergence in mean squared.
1
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Part II: 2 Long Questions, 80 Points (40 points each)
1. There has been a trend in college students³life style by which they tend to exercise more and
more regularly.
In fact, there has also been a growing interest among educators on whether
the old Latin saying ´Mens sana in corpore sanoµ(which means ´A healthy mind in a healthy
bodyµ) is true, and in particular on whether students who exercise regularly perform better
academically.
In order to better understand the relationship between physical exercise and
academic performance, an Ivy League University collected the gym attendance records of 650
randomly chosen students.
Using the gym³s records, the university computed the numbers
of hours per week (
hours
i
) each student in the sample spent in the gym.
Using data on the
GPAs of these 650 students, the following regression model estimates were obtained using
OLS. (Standard errors for each estimate are given in parentheses.)
G
b
PA
i
=
2
:
36
+
0
:
0768
hours
i
R
2
= 0
:
1958
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 Spring '08
 NEILSEN
 Econometrics, Linear Regression, Regression Analysis, Estimation theory, Yi, Mean squared error

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