STAB22H3 Chapter Notes - Chapter 18: Central Limit Theorem, Standard Deviation, Sampling Distribution
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STAB22H3 Full Course Notes
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Sampling distribution of the proportions: distribution in which we can see all the proportions form all possible samples. Sampling distribution model: for how a sample proportion varies from sample to sample allows us to quantify that variation and to talk about how likely it is that we"d observe a sample proportion in any particular interval. To use a normal model we need to specify its mean and standard deviation. The centre of the histogram is naturally at so we"ll put , the mean of the. Once we know the mean, , we automatically know the standard deviation of pp, the proportion of successes: (pp) = sd (pp) = (1 ) = q n n. Sampling error/ sampling variability: expected variability of sample proportions about the true population proportion as we imagine going from one random sample to another. The independence assumption: the sampled values must be independent random draws from the population under study.
