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** logistic   **  logistic regression
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  Overview:  The "logistic" command estimates a logistic regression for 
             a binary outcome variable.  The default summary is in terms
             of exp(beta_j), which is an odds ratio when beta_j is the 
             coefficient of a main effect in the model.


  Usage:     "logistic Dvar X1 X2 X3 [fweight]"
             Where - Dvar is the disease variable (1=disease, 0=control)
                     Xj are predictor variables 


  Summaries: The "logistic" command returns estimates of adjusted odds 
             ratos, exp( beta_j ).

             After fitting the model using logistic, the command "logit" will
             display the regression coefficients, beta_j.

  Options:   (1) "logistic y x1 x2 [freq=count]"  -- this is used when the 
                 data are in a "grouped" format with the number of 
                 cases/controls that have a certain covariate combination 
                 given by the variable "freq".  (e.g. the tuyns data)

             (2) "xi: logistic y i.x1 x2"  -- this is used to create
                 dummy variables for variable x1.



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**** logit  **** logistic regression
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  Overview:  The "logit" command estimates a logistic regression for 
             a binary outcome variable.  The default summary is in terms
             of beta_j, where is a log odds ratio when beta_j is the 
             coefficient of a main effect in the model. Usage is similar
             to logistic given above.



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** lrtest     **  logistic regression and likelihood ratio tests
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  Overview:  The "logistic" command estimates a logistic regression for 
             a binary outcome variable.  We can save the maximized 
             log-likelihood for any fitted model using "lrtest".  Then
             we can compare nested models using "lrtest".

  Usage:     "logistic Y X1"
             "estimates store model1"

             "logistic Y X1 X2"
             "estimates store model2"

             "logistic Y X1 X2 X3 X4"
             "estimates store model3"

             "lrtest model1 model2"
             "lrtest model2 model3"


  Summaries: The "lrtest" command tests for zero coefficients in nested
             models that have been saved with "estimates store". The 
             first model in the lrtest command must be nested in the 
             second model.