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Review Exam: Concepts in Estimation, Hypothesis Testing, Regression

Closed book/notes exam focused on interpreting results and concepts in estimation, hypothesis testing, 1-sample/2-sample t-tests, ANOVA, and regression. Some simple calculations included.

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Review Exam: Concepts in Estimation, Hypothesis Testing, Regression

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  1. Review Exam Next Wednesday – March 9 • Closed book/notes – bring front/back of page of notes • short answer, multiple choice • focus on concepts and interpreting results • some very simple calculations

  2. What is NOT on Test Exam Next Wednesday – March 9 • UNIX commands • How to read in data in SAS

  3. What to know • Concepts – understand estimation, hypothesis testing, CIs, p-values • 1-sample, 2-sample t-tests, ANOVA, regression • Know assumptions for tests and how to test assumptions (where discussed in class) • Understand meaning of bs • Understand SAS output

  4. Example Question • In simple linear regression which is NOT an assumption of the model: • The values of Y are normally distributed for each value of X • The values of X are normally distributed • The variance of Y is the same for each value of X • The means of Y is linearly related to X

  5. Example Question • In regression analyses, the variable that is being predicted is the : • Dependent variable • Independent variable • Intervention variable • Is usually X

  6. Example Question • In a regression analyses if SSE = 200 and SSR = 300, then the R2 value would be : • 0.667 • 0.600 • 0.400 • 1.500 • Give the meaning of R2 from a regression model

  7. Example Question • A residual from a regression analyses is the • The difference of the point from the regression line • The squared distance of the point from the regression line • The difference of the point from the mean of Y • The difference of the point from the mean of X

  8. Example Question Give an example of matched pair design:

  9. Example Question A regression analysis between sales (Y in $1000) and advertising (X in dollars) resulted in the following equation: Y = 30,000 + 4 X What is the interpretation of the value 30,000 in the equation? What is the interpretation of the value 4 in the equation? What is the predicted sales when advertising costs is 10,000?

  10. Example Question A 95% confidence interval for the mean student loan debt of U of M graduates based on a sample size of n=100 is calculated as (10,000 to 15,000). Assume the variance in the population was $200. If the sample size were increased to 200 what would happen to the size of the 95% CI: If the researcher wanted a 90% CI what would happen to the size of the CI If the researcher took a sample size of n=100 from a population with variance of $400 what would happen to the size of the 95% CI

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