Logistic/logit regression
 Basic (dichotomous) ML logistic regression
with influence statistics
 Fit diagnostics and ROC curve
 Classification table and sensitivityversusspecificity graph
 Skewed logistic regression
 Groupeddata logistic regression
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
 Multiple imputation
Watch Logistic regression tutorials
Conditional logistic regression
 Conditional fixedeffects logit models (m:k matching) with exact likelihood (no limit on panel size)
 For matched casecontrol groups
 McFadden’s choice model
 1:1 and 1:k matching
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
 Predictions for influence and lackoffit statistics and Pearson residuals
Multinomial logistic regression
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Probit regression
 Dichotomous outcome with ML estimates
 Bivariate probit regression
 Endogenous regressors
 Groupeddata probit regression
 Heteroskedastic probit regression
 Rankordered with alternativespecific and casespecific variables
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Ordinal regression models
 Ordered logistic (proportionalodds model)
 Ordered probit
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Tobit/censored regression
 Lower and upper limits of censoring
 Predictions available for expected value, conditional expected value, censored expected value, and probability of censoring
 Endogenous regressors
 Selection models New
 Random effects and random coefficients New
 Treatment effects (ATEs) New
 Multivariate models New
 Unobserved components New
 Endogenous switching models New
 Robust, cluster–robust, bootstrap, and jackknife standard errors
Truncated regression
 Lower and upper limits of censoring
 Differing limits for each observation
 Predictions available for expected value, conditional expected value, censored expected value, and probability of censoring
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Interval regression
 Open and closed intervals
 Endogenous regressors New
 Selection models New
 Random effects and random coefficients New
 Treatment effects (ATEs) New
 Multivariate models New
 Unobserved components New
 Endogenous switching models New
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Poisson and negative binomial regression
 Predict expected counts, incidence rates, and probabilities of counts
 Poisson goodnessoffit tests
 Poisson model with endogenous regressors
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Zeroinflated count models
 Zeroinflated Poisson
 Zeroinflated negative binomial
 Predict expected counts, incidence rates, and probabilities of counts
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Lefttruncated count models
 Zerotruncated Poisson
 Zerotruncated negative binomial
 Lefttruncated Poisson
 Lefttruncated negative binomial
 Truncation varying by observation
 Predict expected counts, incidence rates, and probabilities of counts
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints

Nested logit
 Randomutilities maximization model
 Full maximumlikelihood estimation
 Up to eight nested levels
 Facilities to set up the data and display the tree structure
 Predictions available for utility functions, probabilities, conditional probabilities, and inclusive values
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints, including constraints on inclusivevalue parameters
Multinomial probit regression
 Alternativespecific and casespecific variables
 Homo or heteroskedastic variances
 Various correlation structures, including userspecified
 Probabilities based on GHK simulator
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Heckman selection models
 Twostep and maximum likelihood (ML)
 Robust, cluster–robust, bootstrap, and jackknife standard errors (ML only)
 Bootstrap and jackknife standard errors (twostep)
 Linear constraints (ML only)
 Predictions available for Mills’ ratio, expected value,
conditional expected value, probability of selection, nonselection
hazard, and more
Heckman selection with a binary outcome
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
 Predictions available for probability of binary outcome, all four
combinations of outcome and selection, probability of selection,
conditional probability of outcome, and more
Heckman selection for ordered probit
 Robust, clusterrobust, bootstrap, and jackknife standard errors
 Linear constraints
 Predictions available for marginal and bivariate probabilities, probabilities of levels conditional on selection or no selection, selection probability, linear production, and more
Rankordered logistic regression
 Plackett–Luce model, exploded logit, choicebased conjoint analysis
 Complete rankings of ordered outcome
 Incomplete rankings of ordered outcome
 Ties (“indifference”)
 Prediction of probability that alternatives are ranked first
 Robust, cluster–robust, bootstrap, and jackknife standard errors
Stereotype logistic regression
 Predictions of probabilities of outcomes
 Robust, cluster–robust, bootstrap, and jackknife standard errors
 Linear constraints
Factor variables
 Automatically create indicators based on categorical variables
 Form interactions among discrete and continuous variables
 Include polynomial terms
 Perform contrasts of categories/levels
Marginal analysis
 Estimated marginal means
 Marginal and partial effects
 Average marginal and partial effects
 Leastsquares means
 Predictive margins
 Adjusted predictions, means, and effects
 Contrasts of margins
 Pairwise comparisons of margins
 Profile plots
 Graphs of margins and marginal effects
Watch Introduction to margins in Stata tutorials
Watch Profile plots and interaction plots in Stata tutorials
Additional resource
