Sampling Distribution
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Department
Operations Management and Information System
Course
OMIS 2010
Professor
Alan Marshall
Semester
Fall

Description
Sampling DistributionsSampling Distributions of the MeanIs created by sampling draw sample of same size from a population or use rules of probability and laws of expected value and variance to derive sampling distributionSampling distribution of rolling a die can be created by drawing samples of size 2 tossing two diceoMean of sampling distribution of x bar is same as mean of population of toss of a die oVariance of sampling distribution of x bar is half of variance of population of the toss of a dieVariance of sampling distribution of sample mean is variance of population divided by sample size Standard error of the mean standard deviation of sampling distribution for infinitely large populationsAsof throws of the die increases probability that sample mean will be close to population mean increases oSampling distribution of x bar becomes narrower as n increases sampling distribution becomes increasingly bellshapedCentral limit theorem sampling distribution of mean of random sample drawn from any population is about normal for sufficiently large sample sizeoLarger the sample size more closely t
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