Term
| Sampling distribution model |
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Definition
| different random samples give different values for a statistic; the sampling distribution model shows the behavior of the statistic over all the possible samples for the same size n |
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Term
| Sampling variability/sampling error |
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Definition
| the variability we expect to see from one random sample to another |
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Term
| Sampling distribution model for a proportion |
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Definition
| if assumptions of independence and random sampling are met, and we expect at least 10 successes and 10 failure, then the sampling distribution of a proportion is modeled by a Normal model with a mean equal to the true proportion p and a standard deviation equal to sqaure root(pq/n) |
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Term
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Definition
| CLT states that the sampling distribution model of the sample mean (and proportion) from a random sample is approximately Normal for large n, regardless of the distribution of the population, as long as the observations are independent |
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Term
| Sampling distribution model for a mean |
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Definition
| if assumptions and random sampling are met, and the sample size is large enough, the sampling distribution of the sample mean is modeled by a Normal model with a mean equal to the population mean and a standard deviation equal to SD/sqrt(n) |
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