For what type of response variable do we compute proportion?
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What is the symbol for sample proportion?
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Suppose a random sample of 300 students at a state university were asked whether they attended their university's last home football game. Thirteen percent said they had. Is "13%" a statistic or a parameter?
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How does the sampling distribution of \(\widehat{p} \) differ from the sampling distribution of \(\overline{x}\)?
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For what do we examine a histogram representing the approximate sampling distribution of \(\widehat{p} \)?
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What is the response variable for this example and is it categorical or quantitative?
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What is the parameter for this example?
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True or false: The center of the histogram is about 0.21.
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True or false: The estimated sampling distribution of \(\widehat{p} \) for samples of size 40 is closer to Normal than the estimated sampling distribution of \(\widehat{p} \) for samples of size 400.
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Fill in the blank: As the sample size increases, the spread of the sampling distribution of \(\widehat{p} \) ______________.
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Fill in the blank: The proportion of voters for Lincoln is _____________ the proportion of voters for Douglas.
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What is the shape of the estimated sampling distributions of \(\widehat{p} \) for samples of size n = 40 and n = 400?
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Fill in the blank: The value of p for Lincoln is _____________ the value of p for Douglas.
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The value of p for Douglas is p = 0.21. What is the mean of the sampling distribution of \(\widehat{p} \) created by taking repeated samples of size n = 100 and computing the proportion for Douglas?
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The value of p for Douglas is p = 0.21. What is the standard deviation of the sampling distribution of \(\widehat{p} \) created by taking repeated samples of size n=100 and computing the proportion for Douglas?
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The value of p for Douglas is p = 0.21. What is the shape of the sampling distribution of \(\widehat{p} \) created by taking repeated samples of size n = 100 and computing the proportion for Douglas?
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True or false: The standard deviations differ because p for Douglas is p = 0.21 and p for Lincoln is p = 0.40.
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True or false: The requirement for "n large" for applying the Central Limit Theorem depends on np and n(1 - p).
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In order to apply the Central Limit Theorem to the shape of the sampling distribution of \(\widehat{p} \), which of the following conditions must be met?
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Why was the shape of the sampling distribution of \(\widehat{p} \) created from samples of size 40 of those for Lincoln closer to Normal than the shape of the sampling distribution of \(\widehat{p} \) created from samples of size 40 of those for Douglas?
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True or false: The shape of the sampling distribution of \(\widehat{p} \) gets closer to Normal as n increases.
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Why is the shape of the sampling distribution of \(\widehat{p} \) approximately Normal?
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On the basis of this probability, can we reject H0: p = 0.5?
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Why doesn't the population from which we sample have a mean and a standard deviation?
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What type of graph is used for categorical data?
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