MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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Estimation
A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.
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A random sample of 1,000 students at a large college included 458 who had one or more credit cards. For this sample, \( \hat{p} = \frac{458}{1,000} = 0.458 \). If another random sample of 1,000 students from this university were selected, would you expect that \( \hat{p} \) for that sample would also be 0.458? Explain why or why not. - ○ I would not expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on 1,000 samples. - ○ I would expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on the entire population. - ○ I would not expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on one particular sample. - ○ I would not expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on the entire population. - ○ I would expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on one particular sample.
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Transcribed Image Text:A random sample of 1,000 students at a large college included 458 who had one or more credit cards. For this sample, \( \hat{p} = \frac{458}{1,000} = 0.458 \). If another random sample of 1,000 students from this university were selected, would you expect that \( \hat{p} \) for that sample would also be 0.458? Explain why or why not. - ○ I would not expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on 1,000 samples. - ○ I would expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on the entire population. - ○ I would not expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on one particular sample. - ○ I would not expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on the entire population. - ○ I would expect that \( \hat{p} \) for a different random sample would also be equal to 0.458 because that specific value for \( \hat{p} \) was based on one particular sample.
Expert Solution
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Step 1

A sample is a set of individuals or objects collected or selected from a statistical population with the help of a defined procedure. The sample proportion is the no. of observed events divided by the sample size.

Here, A random sample of 1000 students at a large college included 458 who had one or more credit cards.

p⏞=4581000=0.458

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