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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
- Mixed logit regression New
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Multiple imputation
- Bayesian estimation New
- Finite mixture models New

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
- 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 New

- Also known as
- Mixed multinomial logit models
- Mixed discrete choice models
- Discrete choice models with random coefficients

- Random-effect and random-coefficient distributions
- Normal
- Correlated normal
- Lognormal
- Truncated normal
- Uniform
- Triangular

- Robust and cluster–robust standard errors
- Survey data support

Watch Mixed logit models.

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

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

- Lower and upper limits of censoring
- Specify censoring points that vary by observation New
- Predictions available for expected value, conditional expected value, censored expected value, and probability of censoring
- Endogenous regressors Updated
- 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 New
- Bayesian estimation New
- Finite mixture models New

- 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 New
- Finite mixture models New

- 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 New
- Finite mixture models New

**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 New
- Finite mixture models 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
- Bayesian estimation New

**Zero-inflated ordered probit regression ** New

- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Predictions of marginal probabilities of levels, joint probabilities of levels and participation, probability of participation, probability of nonparticipation, linear prediction, and more
- Bayesian estimation

Watch Zero-inflated orderd probit.

**Truncated count models**

- Zero-truncated, left-truncated, right-truncated, interval-truncated Poisson Updated
- 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 New
- Finite mixture models New

**Extended regression models** New

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

- 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

**Multinomial logistic regression**

- Alternative-specific New and case-specific variables
- Mixed multinomial logit regression New
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Bayesian estimation New
- Finite mixture models New

**Probit regression**

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

Watch Probit regression tutorials

- Alternative-specific and case-specific variables
- 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

**Sample-selection models for continuous outcomes ** Updated

- 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 New

**Sample selection with a binary outcome ** Updated

- 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 New

**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 New

**Sample selection for Poisson regression New**

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

**Factor variables ** Updated

- 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

See tests, predictions, and effects.

See
**New in Stata 15**
for more about what was added in Stata 15.