Professor Mumford
Econ 360  Fall 2010
[email protected]
Problem Set 1 Answers
True/False
(20 points)
1.
TRUE
If
{
a
1
,a
2
,...,a
n
}
are constants and
{
X
1
,X
2
,...,X
n
}
are random variables
then:
E
n
X
i
=1
a
i
X
i
!
=
n
X
i
=1
a
i
E (
X
i
)
2.
FALSE
For a random variable
X
, let
μ
= E (
X
). The variance of X can be expressed
as:
V ar
(
X
) = E
(
X
2
)

μ
2
3.
TRUE
An estimator,
W
, of
θ
is an unbiased estimator if
E (
W
) =
θ
for all possible values of
θ
.
4.
FALSE
The central limit theorem states that the average from a random sample
for any population (with ﬁnite variance)
when it is standardized, by subtracting the
mean and then dividing by the standard deviation,
has an asymptotic standard normal
distribution.
5.
FALSE
Inferring causality is sometimes possible without a designed experiment. Using
observational data to infer causality is what econometrics is all about.
1
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View Full DocumentMultiple Choice Questions
(20 points)
6. The idea of holding “all else equal” is known as
(a)
ceteris paribus
(b) correlation
(c) causal eﬀect
(d) independence
7. If our dataset has one observation for every state for the year 2000, then our dataset is
(a)
crosssectional data
(b) pooled crosssectional data
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 Spring '10
 NA
 Normal Distribution, Standard Deviation, Variance, time series data, Crosssectional Data

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