 | Term | Definition |
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sampling distribution model |
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 <em>n</em> |
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sampling distribution model for a proportion |
1. sampled values must be independent of each other 2. the sampled size must be large enough |
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central limit theorem [ CLT ] |
states that the sampling distribution model of the sample mean (and proportion) is approximately Normal for large <em>n</em>, regardless of the distrabution of the population, as long as the observations are independent |
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sampling distribution model for a mean |
if assumptions of independence 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 [ s/[(sqr)n] ] |
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standard error [ sampling varability ] |
its not really an error at all, but just variability you'd expect to see from one sample to another. |
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what is <em>q</em> |
it is the probability of a failure; 1 - p = q |
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^p |
is the observed portion |
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^q |
is the observed value |
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what is the law of large numbers? |
it says that as the sample size gets larger, each sample average is more likely to be closer to the population mean; similar to the [ CLT ] Central Limit Theorem |
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