Stata Guide Logistic Regression

Logistic Regression and Related Models. Logistic regression models deal with categorical dependent variables. Depending on the number of categories and on whether or not these categories are ordered, different models are available. Model overview. Binary logistic regression. Here are three examples with variable "vote" (yes/no) as the dependent variable: logit vote age education gender. logistic vote age education gender. logit vote age education gender, or. The first command will produce the model estimates in terms of logit coefficients; the second and third command will yield what some people call "effect coefficients", i.e. the effect the independent variables have on the odds. Alternatively, you may write. logistic vote age education gender logit. Here, logit will "translate" the immediately preceding model (with effect coefficients) into a model with logit coefficients. Multinomial logistic regression. With Stata procedure mlogit , you may estimate the influence of variables on a dependent variable with several categories (such as "Brand A", "Brand B", "Brand C", "Brand D"). Note that if these categories are ordered (such as in statements like "strongly agree" . "strongly disagree"), an ordered logistic regression model should usually be preferred. The option baseoutcome is necessary only if you wish to depart from Stata's default, i.e., the most frequent category. Another option is rrr , which causes stata to display the odds ratios (and the associated confidence intervals) instead of the logit coefficients. Ordered logistic regression. Actually, Stata offers several possibilities to analyze an ordered dependent variable, say, an attitude towards abortion. The most common model is based on cumulative logits and goes like this: Option or will again produce influences in terms of odds. Probit models. Probit models are alternatives to logistic regression models (or logit models). The commands for the binary, multinomial and ordered case go like this: Interpretation of effects with "margins" Stata can compute the effects of independent variables on the outcome in terms of probabilities, either literally (predicted probabilities) or as marginal effects (predicted changes of probability).