notes 26 - Review of Sampling Distributions Submitted by...

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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 z-score 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 sampling distribution
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notes 26 - Review of Sampling Distributions Submitted by...

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