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several different, competing logistic regression models. The coefficient here can be interpreted in the
same way as R 2 in an ordinary regression. Hence, in this case, the model is considered relatively good,
since the independent variables (the variables of productivity and age) explain about 53% to 74%
variation of the variation in the dependent variable.
Table: 3 Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 153.771 a .480 .671
2 130.066 b .526 .736
a. Estimation terminated at iteration number 5 because parameter estimates changed by
less than .001.
b. Estimation terminated at iteration number 6 because parameter estimates changed by
less than .001.
Table: 4 Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 .83 1 .12
2 1.301 2 .254
The table Hosmer and Lemeshow test provides a formal test for determining whether the
predicted probabilities for a covariate match the observed probabilities. A large p-value indicates a
good match (column “Sig.”), whereas a small p-value indicates the opposite, indicates to look for some
alternative logistic models to describe the relationship between this covariate and the outcome
variable. Table 4 indicates that the p-values are relatively large 0.254, therefore, indicating support for
the predicted and observed probabilities.
HL test, a further contingency table (Table 5) for the HL test can produce more details.
This test divides the data up into ten groups, which are defined by increasing the order of
estimated probability. The first group corresponds to those subjects who have the lowest
predicted probability.
Table: 5 Contingency Table for Hosmer and Lemeshow Test
Demography = Demography =
Male
Female
Observed Expected Observed Expected Total
Step 1 1 29 29.000 53 53.000 82
2 22 22.000 152 152.000 174
The findings of the Logit models indicate that increasing age decreases the log odds of being
better performance. In addition, being female workers are increases the log odds of being better
performance relative to those being male workers.
A classification table (Table 6) is useful for logistic regression models which involve
diagnostic testing. The classification table displays the agreement between predicted (vertical column)
and actual results (horizontal row), and basically indicates that the incorrect answer is never predicted.
This would be the same as the intercept-only model, without independent variables, where the
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