Note: For the first question the key tools are described in the help file that was created for Exercise #2 -- that is, we can use "cc" with the "by" option to obtain stratified odds ratios, or the "mhodds" procedure. Also, for some descriptives you may want to use "table" and "tabulate". See the DO file for the second question of Exercise #2 -- there I use "table" to compute means for each clinic. Also: If we want to stratify on more than one variable, then we can use "mhodds" to do this. We'd specify: mhodds Dvar Evar Svar1 Svar2 And this would return an odds ratio adjusted for both Svar1 and Svar2. Since I can't make "cc" take 2 variables with the "by" option, I could as an alternative to mhodds, simply create a new combined variable -- AgeAlc = 1000*age + alc, and use this as the stratifying variable. By creating this new variable we create a unique value for every (Age,Alc) combination. I could then use this as the "by" argument with "cc". **************** **************** ** logistic ** logistic regression **************** **************** 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" 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". (2) "xi: logistic y i.x1 x2" -- this is used to create dummy variables for variable x1.