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Stat Tutor

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0:39

Incorrect. Bushels per acre is a quantitative response variable.

Correct. Bushels per acre is a quantitative response variable.

Incorrect. Try again.

2

Incorrect. We analyze a mean if the response variable is quantitative and we analyze proportion if the response variable is categorical.

Correct. We analyze a mean if the response variable is quantitative and we analyze proportion if the response variable is categorical.

Incorrect. Try again.

2

Incorrect. Since the response variable is quantitative, we want to estimate a mean. In context, we want the mean yield in bushels per acre. Without the word "mean," we have not stated a parameter. "Yield" by itself is a statement of the response variable, not the parameter.

Correct. Since the response variable is quantitative, we want to estimate a mean. In context, we want the mean yield in bushels per acre. Without the word "mean," we have not stated a parameter. "Yield" by itself is a statement of the response variable, not the parameter.

Incorrect. Try again.

2

1:44

Incorrect. \(\overline{x} \) almost never equals µ. This is one reason why we need sampling distributions.

Correct. \(\overline{x} \) almost never equals µ. This is one reason why we need sampling distributions.

Incorrect. Try again.

2

Incorrect. A different sample will almost always give a different value for \(\overline{x} \). This is another reason why we need sampling distributions.

Correct. A different sample will almost always give a different value for \(\overline{x} \). This is another reason why we need sampling distributions.

Incorrect. Try again.

2

Incorrect. Because \(\overline{x} \) varies from one sample to the next, we usually get a different value for \(\overline{x} \) for each sample.

Correct. Because \(\overline{x} \) varies from one sample to the next, we usually get a different value for \(\overline{x} \) for each sample.

Incorrect. Try again.

2

Incorrect. The opposite is true; the larger the sample, the closer \(\overline{x} \) should be to µ. If you think about it, as we continue to increase sample size, we will eventually sample the entire population and the mean with equal µ. This fact has a name as you will see in the next slide.

Correct. The opposite is true; the larger the sample, the closer \(\overline{x} \) should be to µ. If you think about it, as we continue to increase sample size, we will eventually sample the entire population and the mean with equal µ. This fact has a name as you will see in the next slide.

Incorrect. Try again.

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2:18

Incorrect. Answers B and C are false statements. As you increase the sample size, you sample more and more of the population. Eventually, you will sample the entire population so the mean will equal the population mean.

Correct. Answers B and C are false statements. As you increase the sample size, you sample more and more of the population. Eventually, you will sample the entire population so the mean will equal the population mean.

Incorrect. Try again.

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