Introduction  I
•
Econometrics – Use of statistical tools to analyze
economic related data.
•
Econ 41 – primarily covers statistical analysis of a
single variable
•
Econ 103 – analysis of relationships between two
(or more) variables.
•
Main tool in Econ 103:
Regression analysis
Introduction  II
•
Regression analysis
is used to determine the
effect of one variable (e.g.
X
) on another variable
(e.g.
Y
).
Graphical illustration.
•
It is definitely the statistical procedure of choice
in formal economic research.
•
However, it is also used quite often in the real
world, as a tool to use data to inform better
decisions.
•
Many, many examples of this….
Introduction  III
–
Entrepreneur trying to learn the effect of price on demand for his/her
product.
–
Marketer assessing the impact of product design decisions on sales.
–
Lender measuring the effect of credit scores on loan default.
–
Investment banker trying to learn how economic variables affect a
particular industries profitability.
–
Education administrator learning how class size affects student
achievement.
–
Lawyer establish some cause and effect using data.
–
Manager trying to assess relative productivity of his/her workers.
–
Stock broker learning how stocks covary for hedging purposes.
–
Development office determining the best design of donation
solicitations.
–
Peace Corps workers trying to learn how to best distribute
contraceptives or drugs
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Introduction  IV
•
Regression analysis is an extremely
powerful tool that allows one to use data to
answer questions like this and help you
make important decisions.
•
Of course, it is not the only tool to make
decisions, but it is one valuable input.
Introduction  V
•
Regression analysis is “hard” in one sense
(lots of mathematical theory involved),
“easy” in another (input data press a button
in
STATA
).
•
Most of this course will involve focusing
on the theory.
Why?
Introduction  VI
•
While very powerful, regression analysis has
many caveats.
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 Spring '07
 SandraBlack
 Statistics, Econometrics, Normal Distribution, Regression Analysis, Standard Deviation, Variance, µY

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