Econometrics 9
The mean and variance of all of the values are the mean and variance of its
sampling distribution,
E( ) and var( ).
(remember: is a sample statistic.)
VIP: The concept of the samplin
Econometrics 7
1 corr(X,Z) 1
corr(X,Z) = 1 mean perfect positive linear association
corr(X,Z) = 1 means perfect negative linear association
corr(X,Z) = 0 means no linear association
Correlation coeffi
Econometrics 6
= P(Y=1| X=0) = ? (hint: recall the answer above)
Conditional Distribution (cont.)
Question from previous slide (cont.)
prob. of short commute (Y=1) if you know its raining (X=0)
No
Econometrics 5
Moments (cont.) (note 1-17)
kurtosis =
= measure of mass in tails
= measure of probability of large values
kurtosis = 3: normal distribution
kurtosis > 3: heavy tails (leptokurtotic)
Econometrics 4
(a) Single random variable (note 1-10)
Population
The group or collection of all possible entities of interest (school districts)
We will think of populations as infinitely large ( i
Econometrics 3
Standard deviation across districts = 19.1
Is this a big enough difference to be important for school reform discussions, for parents,
or for a school committee?
What does this tell
Econometrics 2
Learn to evaluate the regression analysis of others this means you will be able to
read/understand empirical economics papers in other econ courses;
Get some hands-on experience with
Econometrics 1
Introduction to Econometrics is title of text
What is econometrics?
What is it?
Science (& art!)
Broadly, using theory and statistical methods to analyze data
What are some uses?
T
Econometrics 10
1.1.
var( ) is inversely proportional to n
1.1.1.
the spread (standard deviation) of the sampling
distribution is proportional to 1/
1.1.2.
Thus the sampling uncertainty associated wit
Econometrics 8
We will assume simple random sampling
Choose an individual (district, entity) at random from the population
Randomness and data
Prior to sample selection, the value of Y is random bec