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Chapter 20
Each term in the model,plus the model as a whole, is tested for whether its effect equals 0. Terms
with significancevalues less than 0.05 have some discernible effect. Thus, age,employ,debtinc,
and creddebtcontribute to the model, while the other main effects do not. In a further analysis of
the data, you would probably remove ed,address,income,andothdebt from model consideration.
Parameter Estimates
Figure 20-9
Parameter estimates
The parameter estimates table summarizes theeffect of each predictor. Notethat parameter values
affectthe likelihood of the “did default” category relative to the “did not default” category. Thus,
parameters with positive coefficients increase the likelihood of default, while parameters with
negative coefficientsdecrease the likelihood of default.
The meaning of a logistic regression coefficientis not as straightforward as that of a linear
regression coefficient. While Bis convenient for testing the model effects, Exp(B) is easier to
interpret. Exp(B) represents the ratio change in the odds of the event of interest attributable to a
one-unitincrease in the predictor for predictors that are not part of interaction terms. Forexample,
Exp(B) for employ is equal to 0.798, which means that the odds of default for people who have
been with their currentemployer for two years are 0.798 times the odds of default for those who
have been with theircurrent employer for one year, all other things being equal.
The design effects indicatethat some of the standard errors computed for these parameter
estimates are larger than those you would obtain if you assumed that these observations came
from a simple random sample,while others are smaller. It is vitally important to incorporatethe
sampling design informationin your analysis because you might otherwise infer, for example,
that the age coefficient is no differentfrom 0!