Logistic Regression: A Self-learning Text, Third Edition (Statistics in the Health Sciences)

Practice Exercises

Exercises

True or False (Circle T or F)

T F 1. When estimating the parameters of the logistic

model, least squares estimation is the preferred

method of estimation.

T F 2. Two alternative maximum likelihood approaches

are called unconditional and conditional meth-

ods of estimation.

T F 3. The conditional approach is preferred if the

number of parameters in one’s model is small

relative to the number of subjects in one’s data

set.

T F 4. Conditional ML estimation should be used to

estimate logistic model parameters if matching

has been carried out in one’s study.

T F 5. Unconditional ML estimation gives unbiased

results always.

T F 6. The likelihood function L(u) represents the

joint probability of observing the data that has

been collected for analysis.

T F 7. The maximum likelihood method maximizes

the function ln L(u).

T F 8. The likelihood function formulae for both the

unconditional and conditional approaches are

the same.

T F 9. The maximized likelihood valueLð^uÞis used for

confidence interval estimation of parameters in

the logistic model.

T F 10. The likelihood ratio test is the preferred method

for testing hypotheses about parameters in the

logistic model.

Test True or False (Circle T or F)

T F 1. Maximum likelihood estimation is preferred to

least squares estimation for estimating the

parameters of the logistic and other nonlinear

models.

T F 2. If discriminant function analysis is used to esti-

mate logistic model parameters, biased esti-

mates can be obtained that result in estimated

odds ratios that are too high.

T F 3. In a case-control study involving 1,200 subjects,

a logistic model involving 1 exposure variable,

3 potential confounders, and 3 potential effect

modifiers is to be estimated. Assuming no

matching has been done, the preferred method

124 4. Maximum Likelihood Techniques: An Overview