Review of Sampling Distributions
Submitted by gfj100 on Wed, 11/11/2009  12:01
In later part of the last lesson we discussed finding the probability for a continuous random
variable that followed a normal distribution. We did so by converting the observed score to a
standardized zscore and then applying
Standard Normal Table
. For example:
IQ scores are normally distributed with mean, μ, of 110 and standard deviation, σ, equal to 25.
Let the random variable
X
be a randomly chosen score. Find the probability of a randomly
chosen score exceeding a 100. That is, find P(X > 100). To solve,
But what about situations when we have more than one sample, that is the sample size is greater
than 1? In practice, usually just one random sample is taken from a population of quantitative or
qualitative values and the statistic
the sample mean or
the sample proportion, respectively,
is measured  one time only. For instance, if we wanted to estimate what proportion of PSU
students agreed with the President's explanation to the rising tuition costs we would only take
one random sample, of some size, and use this sample to make an estimate. We would not
continue to take samples and make estimates as this would be costly and inefficient. For samples
taken at random, sample mean {or sample proportion} is a
random variable
. To get an idea of
how such a random variable behaves we consider this variable's
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 Spring '11
 AndyRegards
 Normal Distribution, Probability, Standard Deviation, 64 inches

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