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Unformatted text preview: estimator we will focus on for now is the sample mean. Under some conditions it is
successful to approximate the population mean:
i =1 xi
x1 + x2 + · · · + xn
n Notice how x is a function of the the particular sample values we have.
¯ Utku Suleymanoglu (UMich) Sampling Distributions 3 / 21 Introduction Sampling We want x to be a good guess of the unknown µ. So it is important how the x ’s are
Drawing a sample from a population is called sampling. So what is the best way to
A course on its own. We will focus on the basic but most important way.
Simple Random Sample: For ﬁnite populations. Each population member has the
same probability to be chosen.
Random Sample: For inﬁnite populations. Each draw is independent from the
For most of our purposes we can think of random sampling as randomly picking
independent numbers from a population with some distribution (not known) with mean µ
and variance σ 2 (neither is known). Utku Suleymanoglu (UMich) Sampling Distributions 4 / 21 Introduction Point Estimation
We talked about the population parameters and est...
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- Spring '08