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Unformatted text preview: he variance
is, and even the what the distribution is.
We will focus on x . Let’s change the notation so that we know we’re dealing with a
random variable: X is a random variable, x is a realization of it for a given random
sample. Utku Suleymanoglu (UMich) Sampling Distributions 7 / 21 Sampling Distributions Random Variables and Sampling Distributions
The way we will think about the random sampling is as following:
The aspect of the population that we are interested in (e.g. incomes or heights of
people) is generated by a random variable, X .
This random variable has an unknown distribution (maybe Normal, maybe not).
This random variable has a mean µ and variance σ 2 .
We are interested in producing a guess for µ. Expected value of the random
variable, µ, is the population parameter we are interested in.
We randomly choose a random sample from the population. Each of the n values we
draw is a realization of X .
So each number in your sample is comes from this random variable with mean µ and
variance σ 2 .
Another way to think ab...
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This note was uploaded on 03/17/2014 for the course ECON 404 taught by Professor Staff during the Spring '08 term at University of Michigan.
- Spring '08