Brannick, Spring 2002

 

Exam 1

 

  1. What happens to the mean, median, and mode when the distribution becomes skewed?  Draw a picture to illustrate your answer.
  2. What is a sampling distribution?
  3. Describe the standard error of the mean, , when samples are drawn from a normal distribution.  What determines its size?
  4. Why is the number 1.96 such a big deal for the normal distribution?
  5. How does the null hypothesis help specify the rejection region?
  6. What is statistical power?  Why is it important?
  7. How are the distributions of z and t related? 
  8. What are the mean and variance of the chi-square distribution?
  9. Describe the F distribution.  What is it made of?  What affects its shape (what are the parameter(s) of the F distribution)?
  10. Give a concrete example of the use of the dependent samples t-test.  State why the particular test is the right one to choose, that is, what it is about the design that makes the dependent samples t appropriate.

 

Exam 2

 

  1. What is the difference between fixed and random factors in ANOVA?  Give an example of each.
  2. Why is the sum of squares for error important in ANOVA significance tests?
  3. Describe (make up) a concrete example of a one-way ANOVA where it makes sense to use an overall F test.  Explain why ANOVA (not t, chi-square or something else) is the best method for the analysis.
  4. Describe (make up) a concrete example where you would use a post hoc test.  Explain why the post hoc test is needed (not the specific choice of post hoc test, but rather why post hoc test at all).
  5. Define (label) each term in this linear model:

  6. Describe a concrete example of a randomized block design.  You should have 1 factor as the blocking factor and one other factor as the factor of main interest.
  7. What is the purpose of the Fisher r to z transformation?

 

 

 

  1. What is the relation between r in correlation and R-square in regression with 1 independent variable?
  2. What is the problem in regression/ANOVA if the homogeneity of variance / homoscedasticity assumption is violated?  What will happen in practice?

10.  What is the difference between a per comparison and a familywise error rate?

 

 

 

 

 

Exam 3

 

  1. What is the difference between a standardized and unstandardized regression (b) weight?  Why (under what circumstances) might you prefer one to the other?
  2. Why is regression more closely related to the semipartial correlation than to the partial correlation?
  3. Draw an illustration of the confidence interval for the regression line.  Why does it have the shape that it does?
  4. What is cross validation?  How does it relate to the ‘adjusted R-square’ contained in the SAS regression output?
  5. Why does the order of entry into a hierarchical regression change the increment in variance due to a particular variable?
  6. What is the variance inflation factor (VIF)?  What is its major use in regression analysis?
  7. Suppose you were predicting employee salary based on the financial responsibility of the job (that is, the yearly salary for a job is predicted by the size of the budget controlled by the job).  Upon looking at the residuals of the linear regression, you notice that salary for jobs at the high budget end are under-predicted.  How can you solve this problem?  Describe how you would modify the regression equation.
  8. Why should you avoid dichotomizing continuous variables for analysis (describe 2 reasons)?
  9. What are least squares means?  Describe a concrete situation in which you might want to use them.
  10. What is the connection between homogeneity of variance in ANOVA and homoscedasticity in regression?
  11. Suppose you have two unbiased statistics, A and B.  A has a standard error that is twice that of B.  Which statistic are you likely to prefer and why?
  12. What is the difference between the t distribution and the unit normal (z)?  When do we prefer to use t to z?
  13. Why do we examine studentized residuals in regression?  What do they help us to do?
  14. We could use the t-test for comparing single means or ANOVA to test groups of means in post hoc test.  Why don’t we?

15.  Describe the chi-square distribution.  What is it made of?  Describe its shape.  What is its expected mean value?