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Over the past few weeks, we have studied a number of different hypothesis tests including the following:
1. One-sample z- and t-tests for the mean2. One-sample z-test for the proportion3. Two-sample t-tests for the mean for related and independent samples4. Two-sample z-test for the proportion5. Two-sample chi-square test for the proportion6. Three-sample chi-square test for the proportion7. Chi-square test of independence8. ANOVA
During the course, you could normally tell which test to use by identifying which chapter or section in the text it came from. That usually works when you are taking a class, but suppose you are out there in the "real" world and decide to collect some data and do a hypothesis test to impress your boss with your knowledge of statistics. Or maybe there is a problem on the final exam that doesn't tell you specifically which test to use. How would you determine which test would be appropriate to use? What characteristics of the problem would you use to determine which hypothesis test you should use? Explain your answer.
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Develop an appropriate forecast model for the given problem whereas the student population has remained constant during this period.
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You are given an annual time series with 40 consecutive values and asked to fit a fifth-order autoregressive model.
Perform an appropriate hypothesis test, showing the necessary calculations and/or explaining the process used to obtain the results any group over 30 needs z test.
The standard deviation of the population of adult female height scores is 3 inches. A random sample of 50 women yields a mean height of 64 inches. Calculate the standard error of the mean.
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A random sample of 100 entering freshmen is selected. What is the distribution of the mean composite ACT score, for the University?
The mean is a useful measure, but it can be misleading. Describe a circumstance when the mean is very useful as the average and a circumstance when the mean is very misleading as the average.
Do you think that these types of statistics can ever be accurate? How could this statistic be biased? In what ways could scientists misinterpret the results of their studies and come up with an incorrect statistic? What are your thoughts?
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