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## Binary, fractional, count, and limited outcomes

- Basic (dichotomous) ML logistic regression with influence statistics
- Fit diagnostics and ROC curve
- Classification table and sensitivity-versus-specificity graph
- Skewed logistic regression
- Grouped-data logistic regression
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Multiple imputation

Watch Logistic regression tutorials

**Conditional logistic regression**

- Conditional fixed-effects logit models (m:k matching) with exact likelihood (no limit on panel size)
- For matched case–control groups
- McFadden’s choice model Updated
- 1:1 and 1:k matching
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Predictions for influence and lack-of-fit statistics and Pearson residuals

**Multinomial logistic regression**

**Probit regression**

- Dichotomous outcome with ML estimates
- Bivariate probit regression
- Endogenous regressors
- Grouped-data probit regression
- Heteroskedastic probit regression
- Rank-ordered with alternative-specific and case-specific variables Updated
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints

- Beta regression
- Fractional probit regression
- Fractional logistic regression
- Heteroskedastic fractional probit regression

- Ordered logistic (proportional-odds model)
- Ordered probit
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints

- 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
- Random effects and random coefficients
- Treatment effects (ATEs)
- Multivariate models
- Unobserved components
- Endogenous switching models
- Robust, cluster–robust, bootstrap, and jackknife standard errors

- 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

- Open and closed intervals
- Endogenous regressors
- Selection models
- Random effects and random coefficients
- Treatment effects (ATEs)
- Multivariate models
- Unobserved components
- Endogenous switching models
- 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 goodness-of-fit tests
- Poisson model with endogenous regressors
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints

**Censored Poisson regression** New

- Left, right, and interval censoring
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints

Watch Censored Poisson regression.

**Zero-inflated count models**

- Zero-inflated Poisson
- Zero-inflated negative binomial
- Predict expected counts, incidence rates, and probabilities of counts
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints

**Left-truncated count models**

- Random-utilities maximization model
- Full maximum-likelihood 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 inclusive-value parameters

- Alternative-specific and case-specific variables Updated
- Homo- or heteroskedastic variances
- Various correlation structures, including user-specified
- Probabilities based on GHK simulator
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints

- Two-step and maximum likelihood (ML)
- Robust, cluster–robust, bootstrap, and jackknife standard errors (ML only)
- Bootstrap and jackknife standard errors (two-step)
- 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, cluster-robust, 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

**Rank-ordered logistic regression **

- Plackett–Luce model, exploded logit, choice-based 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

- View and run all postestimation features for your command
- Automatically updated as estimation commands are run

Watch Postestimation Selector.

- Automatically create indicators based on categorical variables
- Form interactions among discrete and continuous variables
- Include polynomial terms
- Perform contrasts of categories/levels

- Estimated marginal means
- Marginal and partial effects
- Average marginal and partial effects
- Least-squares means
- Predictive margins
- Adjusted predictions, means, and effects
- Works with multiple outcomes simultaneously New
- 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

A single categorical variable

A single continuous variable

Interactions of categorical variables

Interactions of categorical and continuous variables

Interactions of two continuous variables

A single continuous variable

Interactions of categorical variables

Interactions of categorical and continuous variables

Interactions of two continuous variables

Additional resource

*Regression Models for Categorical Dependent Variables Using Stata, Third Edition*by J. Scott Long and Jeremy Freese

See tests, predictions, and effects.

See
**New in Stata 14**
for more about what was added in Stata 14.