# All material presented in the Estimation Chapter 1. When would the mean

All material presented in the Estimation Chapter
1. When would the mean grade in a class on a final exam be considered a statistic?
When would it be considered a parameter?
2. Define bias in terms of expected value.
3. Is it possible for a statistic to be unbiased yet very imprecise? How about being very accurate but biased?
4. Why is a 99% confidence interval wider than a 95% confidence interval?
5. When you construct a 95% confidence interval, what are you 95% confident about?
6. What is the difference in the computation of a confidence interval between cases in which you know the population standard deviation and cases in which you have to estimate it?
7. Assume a researcher found that the correlation between a test he or she developed and job performance was 0.55 in a study of 28 employees. If correlations under .35 are considered unacceptable, would you have any reservations about using this test to screen job applicants?
8. What is the effect of sample size on the width of a confidence interval?
9. How does the t distribution compare with the normal distribution? How does this difference affect the size of confidence intervals constructed using z relative to those constructed using t? Does sample size make a difference?
10. The effectiveness of a blood-pressure drug is being investigated. How might an experimenter demonstrate that, on average, the reduction in systolic blood pressure is 20 or more?
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11. A population is known to be normally distributed with a standard deviation of 2.8. (a) Compute the 95% confidence interval on the mean based on the following sample of nine: 8, 9, 10, 13, 14, 16, 17, 20, 21. (b) Now compute the 99% confidence interval using the same data.
12. A person claims to be able to predict the outcome of flipping a coin. This person is correct 16/25 times. Compute the 95% confidence interval on the proportion of times this person can predict coin flips correctly. What