Using Stata 11 & higher for Logistic Regression
http://www3.nd.edu/~rwilliam/stats2/LogisticStata.pdf
Model fit: How often is the model right?
Create a classification table
% predicted correctly
page 6: “To get the equivalent of SPSS’s classification table, you can use the estat clas command (lstat also works). This command shows you how many cases were classified correctly and incorrectly, using a cutoff point of 50% for the predicted probability.”
UCLA:
Stata FAQ: How can I perform the likelihood ratio, Wald, and Lagrange multiplier (score) test in Stata?
http://www.ats.ucla.edu/stat/stata/faq/nested_tests.htm
Model significance: Use likelihood ratio test to compare two models (m1 nested in m2):
logit hiwrite female read estimates store m1 logit hiwrite female read math science estimates store m2 lrtest m1 m2
If the test is significant, the bigger model is a better fit.
Check assumptions:
Correction: This webpage is about linear regression, not logistic regression. But still, the measures described are useful – just choose wisely. There are two things to do for logistic regression:

Assumptions for logistic regression: linearity, independence of errors, multicollinearity.

Identify outliers & influential observations.
http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter2/statareg2.htm
From this webpage:
The following table summarizes the general rules of thumb we use for these measures to identify observations worthy of further investigation (where k is the number of predictors and n is the number of observations).
Measure  Value 
leverage  >(2k+2)/n 
abs(rstu)  > 2 
Cook’s D  > 4/n 
abs(DFITS)  > 2*sqrt(k/n) 
abs(DFBETA)  > 2/sqrt(n) 
VIF: > 10
tolerance: < 0.1
Definition and interaction:
http://www.ats.ucla.edu/stat/stata/seminars/interaction_sem/interaction_sem.htm
Visualizing Main Effects and Interactions for Binary Logit Models in Stata:
http://www.ats.ucla.edu/stat/stata/seminars/stata_vibl/default.htm
Logistic Regression with Stata (Xiao Chen, Phil Ender, Michael Mitchell & Christine Wells):
http://www.ats.ucla.edu/stat/stata/webbooks/logistic/
Applied Logistic Regression (David Hosmer and Stanley Lemeshow):
http://www.ats.ucla.edu/stat/examples/alr2/default.htm
Stata Programs for Data Analysis:
http://www.ats.ucla.edu/stat/stata/ado/analysis/
Resources:
http://www.ats.ucla.edu/stat/stata/topics/logistic_regression.htm
http://www.socwkp.sinica.edu.tw/doc/Day5Session1.pdf
Princeton:
Intro:
http://dss.princeton.edu/training/Logit.pdf
Resources:
http://www.princeton.edu/~otorres/Stata/statnotes
STATWING: