of estimation for this model is conditional ML
estimation.T F 4. Until recently, the most widely available com-
puter packages for fitting the logistic model
have used unconditional procedures.
T F 5. In a matched case-control study involving
50 cases and 2-to-1 matching, a logistic model
used to analyze the data will contain a small
number of parameters relative to the total num-
ber of subjects studied.
T F 6. If a likelihood function for a logistic model con-
tains ten parameters, then the ML solution
solves a system of ten equations in ten unknowns
by using an iterative procedure.
T F 7. The conditional likelihood function reflects the
probability of the observed data configuration
relative to the probability of all possible config-
urations of the data.
T F 8. The nuisance parameter a is not estimated
using an unconditional ML program.
T F 9. The likelihood ratio test is a chi-square test that
uses the maximized likelihood valueL^in its
computation.
T F 10. The Wald test and the likelihood ratio test of the
same hypothesis give approximately the same
results in large samples.
T F 11. The variance–covariance matrix printed out for
a fitted logistic model gives the variances of
each variable in the model and the covariances
of each pair of variables in the model.
T F 12. Confidence intervals for odds ratio estimates
obtained from the fit of a logistic model use
large sample formulae that involve variances
and possibly covariances from the variance–
covariance matrix.
Test 125