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10 Cards in this Set
- Front
- Back
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What is the implication for the sampling proportion distribution? |
The sample proportion is an unbiased estimator of the population proportion. |
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What does the sampling proportion distribution follow? |
A Binomial Distribution |
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What are the three conditions to assume normality of a sampling proportion distribution? What is another method? |
N > 30, np (sample mean) > 5 n(1-p) (sample variance) > 5 The other method: If both the number of events of interest as well as the number of events that are not of interest are both > 5, it approximately follows a normal distribution. |
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How is the sample proportion calculated? |
X/n, where X is the number of items of interest and n is the sample size. |
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When can the normal distribution be used to approximate the binomial distribution? |
When the sample size is large enough. |
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Given that the sample size is not large enough, what distribution must be used to calculate the proportion? |
The Binomial Distribution. |
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What is the standard error of the proportion? How is it calculated? |
It is the equivalent of the standard error of deviation, but for the proportion. sqrt([pi(1-pi)/n]) |
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What is the proportion used for? In terms variable type? |
In order to calculated the proportion of a population with exhibiting a certain characteristic, either yes or no. It is used for categorical variables. |
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What is the relationship of the sample size to the probability of the proportion being less than x%? |
As n approaches infinity, the Z-stat gets larger. In effect, the probability for the proportion being less than x% gets closer to 1. However, for values greater than x%, it gets closer to 0. |
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Confidence interval: why is the t-distribution not necessary?
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Because the standard error of the proportion is a function of of the hypothesized pi, then it is known, implying that the t-distribution is unnecessary. |
