Hello,

I am not a statistician, so I will provide my two cents on the matter, waiting for stat guys to jump in and possibly correct me.

As forquestion n.1, it is quite interesting and I myself look forward to hearing from more experienced guys here...

As forquestion n.2, I think that writing or not the Logistic Regression equation depends on individual choice: as a matter of fact, as long as you provide the constant and the beta coefficients, anyone would be able to work out the LR equation.

As forquestion n.3, this is a HUGE topic and I think that you should read some literature: it would be easy to search for some references in Google Scholar or JSTOR. What I can say is that judging the performance of a model can be seen from two different standpoints: one is the performance in relation to the data you already have, i.e. the data on which your model is built. The second standpoint is the performance of the model in relation to 'future' observations. For the first case, maybe a good starting point is the analysis of the ROC curve (LINK). IN the second case, cross-validation should be used (LINK).

Hope this helps,

gm