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## Binary, fractional, count, and limited outcomes

Is your response binary (for example, employed or unemployed), ordinal
(education level), count (number of children), or censored (ticket sales in an existing venue)? Stata has maximum likelihood
estimators—logistic, probit, ordered probit, multinomial logit,
Poisson, tobit, and many others—that estimate the relationship
between such outcomes and their determinants. A vast array of tools is
available to analyze such models. Predict outcomes and their confidence
intervals. Test equality of parameters or any linear or nonlinear
combination of parameters. And much more.

- Basic (dichotomous) ML logistic regression with influence statistics
- Fit diagnostics and ROC curve
- Classification table and sensitivity-versus-specificity graph
- Complementary log-log regression
- Skewed logistic regression
- Grouped-data logistic regression
- GLM for the binomial family
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Multiple imputation
- Bayesian estimation
- Finite mixture models

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
- 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
- Bayesian estimation

- Beta regression
- Fractional probit regression
- Fractional logistic regression
- Heteroskedastic fractional probit regression
- Bayesian estimation
- Finite mixture models

- Ordered logistic (proportional-odds model)
- Ordered probit
- Heteroskedastic ordered probit
- Zero-inflated ordered logit regression New
- Zero-inflated ordered probit regression
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Bayesian estimation
- Finite mixture models

- Lower and upper limits of censoring
- Specify censoring points that vary by observation
- 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
- Linear constraints
- Bayesian estimation
- Finite mixture models

- 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
- Bayesian estimation
- Finite mixture models

- 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
- Bayesian estimation
- Finite mixture models

**Poisson and negative binomial regression **

- Predict expected counts, incidence rates, and probabilities of counts
- Poisson goodness-of-fit tests
- Poisson model with endogenous regressors
- Poisson with sample selection
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Bayesian estimation
- Finite mixture models

- 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
- Bayesian estimation

**Zero-inflated ordinal models** Updated

- Zero-inflated ordered logit New
- Zero-inflated ordered probit
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Predictions of marginal probabilities of levels, joint probabilities of levels and susceptibility, probability of susceptibility, probability of susceptibility, linear prediction, and more New
- Predictions of marginal probabilities of levels, joint probabilities of levels and participation, probability of participation, probability of nonparticipation, linear prediction, and more
- Bayesian estimation

**Truncated count models**

- Zero-truncated, left-truncated, right-truncated, interval-truncated Poisson
- Zero-truncated and left-truncated 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
- Bayesian estimation
- Finite mixture models

- Combine endogeneity, Heckman-style selection, and treatment effects
- Interval regression, including tobit
- Probit regression
- Ordered probit regression
- Random effects in one or all equations
- Exogenous or endogenous treatment assignment
- Endogenous selection using probit or tobit
- All standard postestimation command available, including
**predict**and**margins**

- McFadden's choice model
**Mixed logit model**- Panel-data mixed logit
- Multinomial probit model
- Nested logit model
- Rank-ordered probit model
- Rank-ordered logit model
- Alternative-specific and case-specific variables
- Advanced inference using
**margins**

**Multinomial logistic regression**

- Predicted probabilities of each outcome
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Bayesian estimation
- Finite mixture models

**Probit regression**

- Dichotomous outcome with ML estimates
- Bivariate probit regression
- Endogenous regressors
- Grouped-data probit regression
- Heteroskedastic probit regression
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Bayesian estimation
- Finite mixture models

Watch Probit regression tutorials

- Predicted probabilities of each category
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints

**Sample-selection models for continuous outcomes **

- Two-step (Heckman method) and maximum likelihood (ML)
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Bootstrap and jackknife standard errors
- Linear constraints
- Predictions available for Mills’ ratio, expected value, conditional expected value, probability of selection, nonselection hazard, and more
- Bayesian estimation

**Sample 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
- Bayesian estimation

**Sample 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 prediction, and more
- Bayesian estimation

**Sample selection for Poisson regression **

- Robust, cluster—robust, bootstrap, and jackknife standard errors
- Linear constraints
- Predictions of number of events, incidence rate, probability of selection, linear prediction, and more

**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
- 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 resources

*Base Reference Manual**Extended Regression Models Reference Manual**Choice Models Reference Manual**Regression Models for Categorical Dependent Variables Using Stata, Third Edition*by J. Scott Long and Jeremy Freese- The Stata Blog: probit or logit: ladies and gentlemen, pick your weapon
- The Stata Blog: regress, probit, or logit?

See tests, predictions, and effects.

See
**New in Stata 17**
to learn about what was added in Stata 17.