The H-L goodness of fit test tests something different from the overall model fit test. You want the H-L test to be non-significant, or, more precisely, you want it to be small. A large value of H-L indicates a problem with your model. SAS prints a table with details. The overall model test says whether your null can be rejected. But be careful; statistical significance does NOT mean what many think it means. It is NOT the likelihood of the parameters being 0, it is the probability of getting results as extreme or more extreme as you got in a sample of your size drawn from a population where the parameter is 0. This is rarely a useful question. Whether a pseudo R2 of .18 is "large" depends on the field. In social sciences, it is pretty darn good. In physics, it would be lousy. All of which illustrates the point that it is hard to answer a question like this sensibly without context.
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