Stat 462 Exam 3 Outline
Chapter 10
 Variable added plot – Be able to describe how to do create one, and know what it
indicates about a multiple regression.
 deleted residual – Be able to describe how it’s determined (for instance, how would we
get an unstandardized value?).
 hi (leverage) values – What do they measure? What does it mean if a value is large?
 DFFITS - Be able to describe how they are determined (for instance, how would we get
an unstandardized value?). What’s the criterion for a large DFFIT?
 Cook’s D – What does it measure? What does it mean if the value is relatively large.
 Variance Inflation Factor (VIF) – What does it mean if the value is large? How is it
calculated? What is the book’s suggested criterion for a large value?
Section 11.1
 What is weighed least squares?
 What problem with the data and/or model does the use of weighted least squares
correct?
 Theoretically, what is the correct set of weights to use?
 What are some strategies for determining an appropriate set of weights?
Sections 12.1-12.4
 Know what is meant by the phrase “autocorrelation in the errors.”
 Know how to use the Durbin-Watson table in the text.
 What is the null hypothesis for a Durbin-Watson test?
 What are some other ways to identify autocorrelation in the errors (besides the Durbin
Watson test)?
 What is the purpose of the Cochrane-Orcutt procedure?
Section 14.1-14.4
 Know what the model logistic model is.
 Know how to interpret Minitab output. For instance, in the ouput below, the response is
whether respondents think marijuana should be not legal or legal, and the predictor
variable is age. The event being predicted is the probability of saying not legal.
 Write the logistic equation.
 Show how to calculate the probability of saying “Not Legal” for age = 20. Show how to
calculate the odds of saying not legal.
 Explain how we know that the variable age is a “significant” predictor.
 Explain how the value under “Odds Ratio” is computed and what it measures.
Logistic Regression Table
Predictor
Constant
age
Coef
0.2782
0.020432
SE Coef
0.2147
0.004686
Z
P
1.30 0.195
4.36 0.000
Odds
Ratio
1.02
95% CI
Lower
Upper
1.01
1.03