Whether you’re a student or a seasoned research professional, we have a package designed to suit your needs:
- Stata/MP: The fastest version of Stata (for quad-core, dual-core, and multicore/multiprocessor computers) that can analyze the most data
- Stata/SE: Stata for large datasets
- Stata/IC: Stata for mid-sized datasets
- Numerics by Stata: Stata for embedded and web applications
Stata/MP is the fastest and largest version of Stata. Virtually any current computer can take advantage of the advanced multiprocessing of Stata/MP. This includes the Intel i3, i5, i7, Xeon, and Celeron, and AMD multi-core chips. On dual-core chips, Stata/MP runs 40% faster overall and 72% faster where it matters, on the time-consuming estimation commands. With more than two cores or processors, Stata/MP is even faster. Find out more about Stata/MP .
Stata/MP, Stata/SE, and Stata/IC all run on any machine, but Stata/MP runs faster. You can purchase a Stata/MP license for up to the number of cores on your machine (maximum is 64). For example, if your machine has eight cores, you can purchase a Stata/MP license for eight cores, four cores, or two cores.
Stata/MP can also analyze more data than any other flavor of Stata. Stata/MP can analyze 10 to 20 billion observations given the current largest computers, and is ready to analyze up to 1 trillion observations once computer hardware catches up.
Stata/SE and Stata/IC differ only in the dataset size that each can analyze. Stata/SE and Stata/MP can fit models with more independent variables than Stata/IC (up to 10,998). Stata/SE can analyze up to 2 billion observations.
Stata/IC allows datasets with as many as 2,048 variables and 2 billion observations. Stata/IC can have at most 798 independent variables in a model.
Numerics by Stata can support any of the data sizes listed above in an embedded environment.
All the above flavors have the same complete set of features and include PDF documentation.
| Product features | Stata/IC | Stata/SE | Stata/MP |
|---|
|
Maximum number of variables
|
2,048 |
32,767 |
120,000 |
|
Maximum number of observations
|
2.14 billion |
2.14 billion |
Up to 20 billion |
|
Maximum number of independent variables
|
798 |
10,998 |
10,998 |
|
Multicore support
Time to run logistic regression with 5 million obs and 10 covariates 1-core
|
1-core
10.0 sec
|
1-core
10.0 sec
|
2-core 4-core 4+
5.0 sec 2.6 sec Even faster
|
|
Complete suite of statistical features
|
 |
|
|
|
Publication-quality graphics
|
|
|
|
|
Matrix programming language
|
|
|
|
|
Complete PDF documentation
|
|
|
|
|
Exceptional technical support
|
|
|
|
|
Includes within-release updates
|
|
|
|
|
64-bit version available
|
|
|
|
| Disk space requirements |
1 GB |
1 GB |
1 GB |
| Memory Space Requirements |
1 GB |
2 GB |
4 GB |
STATA
System requirements
Stata will run on the platforms listed below. While Stata software is platform-specific, your Stata license is not; therefore, you need not specify your operating system when placing your order for a license.
Learn about running Stata on a dual-core, multicore, or multiprocessor computer.
Platforms
Stata for Windows
- Windows 10 *
- Windows 8 *
- Windows 7 *
- Windows Vista *
- Windows Server 2016, 2012, 2008, 2003 *
* 64-bit Windows varieties for x86-64 and x86 processors made by Intel® and AMD
Stata for Mac
- Stata for macOS requires 64-bit Intel® processors (Core™2 Duo or better) running macOS 10.9 or newer
Stata for Unix
- Linux
- Any 64-bit (x86-64 or compatible) running Linux
Hardware requirements
| Package | Memory | Disk space |
|---|
| Stata/MP |
4 GB |
1 GB |
|---|
| Stata/SE |
2 GB |
1 GB |
|---|
| Stata/IC |
1 GB |
1 GB |
|---|
Stata for Unix requires a video card that can display thousands of colors or more (16-bit or 24-bit color)
For xstata, you need to have GTK 2.24 installed.
Stata is widely used in the following domains:
- Behavioral sciences
- Biostatistics
- Economics
- Education
- Epidemiology
|
- Finance, business, and marketing
- Medicine
- Political science
- Public health
- Public policy
- Sociology
|
By category
Linear models
|
regression • censored outcomes • endogenous regressors • bootstrap, jackknife, and robust and cluster–robust variance • instrumental variables • three-stage least squares • constraints • quantile regression • GLS • more
|
Panel/longitudinal data
|
random and fixed effects with robust standard errors • linear mixed models • random-effects probit • GEE • random- and fixed-effects Poisson • dynamic panel-data models • instrumental variables • panel unit-root tests • more
|
Multilevel mixed-effects models
|
continuous, binary, count, and survival outcomes • two-, three-, and higher-level models • generalized linear models • nonlinear models • random intercepts • random slopes • crossed random effects • BLUPs of effects and fitted values • hierarchical models • residual error structures • DDF adjustments • support for survey data • more
|
Binary, count, and limited outcomes
|
logistic, probit, tobit • Poisson and negative binomial • conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic • multinomial probit • zero-inflated and left-truncated count models • selection models • marginal effects • more
|
Extended regression models (ERMs)
|
combine endogenous covariates, sample selection, and nonrandom treatment in models for continuous, interval-censored, binary, and ordinal outcomes • more
|
Generalized linear models (GLMs)
|
ten link functions • user-defined links • seven distributions • ML and IRLS estimation • nine variance estimators • seven residuals • more
|
Finite mixture models (FMMs)
|
fmm: prefix for 17 estimators • mixtures of a single estimator • mixtures combining multiple estimators or distributions • continuous, binary, count, ordinal, categorical, censored, truncated, and survival outcomes • more
|
Spatial autoregressive models
|
spatial lags of dependent variable, independent variables, and autoregressive errors • fixed and random effects in panel data • endogenous covariates • analyze spillover effects • more
|
ANOVA/MANOVA
|
balanced and unbalanced designs • factorial, nested, and mixed designs • repeated measures • marginal means • contrasts • more
|
Exact statistics
|
exact logistic and Poisson regression • exact case–control statistics • binomial tests • Fisher’s exact test for r x c tables • more
|
Linearized DSGE models
|
specify models algebraically • solve models • estimate parameters • identification diagnostics • policy and transition matrices • IRFs • dynamic forecasts • more
|
Tests, predictions, and effects
|
Wald tests • LR tests • linear and nonlinear combinations • predictions and generalized predictions • marginal means • least-squares means • adjusted means • marginal and partial effects • forecast models • Hausman tests • more
|
Contrasts, pairwise comparisons, and margins
|
compare means, intercepts, or slopes • compare with reference category, adjacent category, grand mean, etc. • orthogonal polynomials • multiple-comparison adjustments • graph estimated means and contrasts • interaction plots • more
|
Simple maximum likelihood
|
specify likelihood using simple expressions • no programming required • survey data • standard, robust, bootstrap, and jackknife SEs • matrix estimators • more
|
Programmable maximum likelihood
|
user-specified functions • NR, DFP, BFGS, BHHH • OIM, OPG, robust, bootstrap, and jackknife SEs • Wald tests • survey data • numeric or analytic derivatives • more
|
Resampling and simulation methods
|
bootstrap • jackknife • Monte Carlo simulation • permutation tests • more
|
Time series
|
ARIMA • ARFIMA • ARCH/GARCH • VAR • VECM • multivariate GARCH • unobserved-components model • dynamic factors • state-space models • Markov-switching models • business calendars • tests for structural breaks • threshold regression • forecasts • impulse–response functions • unit-root tests • filters and smoothers • rolling and recursive estimation • more
|
Survival analysis
|
Kaplan–Meier and Nelson–Aalen estimators, • Cox regression (frailty) • parametric models (frailty, random effects) • competing risks • hazards • time-varying covariates • left-, right-, and interval-censoring • Weibull, exponential, and Gompertz models • more
|
Bayesian analysis
|
thousands of built-in models • univariate and multivariate models • linear and nonlinear models • multilevel models • continuous, binary, ordinal, and count outcomes • bayes: prefix for 45 estimation commands • continuous univariate, multivariate, and discrete priors • add your own models • convergence diagnostics • posterior summaries • hypothesis testing • model comparison • more
|
Power and sample size
|
power • sample size • effect size • minimum detectable effect • means • proportions • variances • correlations • ANOVA • regression • cluster randomized designs • case–control studies • cohort studies • contingency tables • survival analysis • balanced or unbalanced designs • results in tables or graphs • more
|
Treatment effects/Causal inference
|
inverse probability weight (IPW) • doubly robust methods • propensity-score matching • regression adjustment • covariate matching • multilevel treatments • endogenous treatments • average treatment effects (ATEs) • ATEs on the treated (ATETs) • potential-outcome means (POMs) • continuous, binary, count, fractional, and survival outcomes • more
|
SEM (structural equation modeling)
|
graphical path diagram builder • standardized and unstandardized estimates • modification indices • direct and indirect effects • continuous, binary, count, ordinal, and survival outcomes • multilevel models • random slopes and intercepts • factor scores, empirical Bayes, and other predictions • groups and tests of invariance • goodness of fit • handles MAR data by FIML • correlated data • survey data • more
|
Latent class analysis
|
binary, ordinal, continuous, count, categorical, fractional, and survival items • add covariates to model class membership • combine with SEM path models • expected class proportions • goodness of fit • predictions of class membership • more
|
Multiple imputation
|
nine univariate imputation methods • multivariate normal imputation • chained equations • explore pattern of missingness • manage imputed datasets • fit model and pool results • transform parameters • joint tests of parameter estimates • predictions • more
|
Survey methods
|
multistage designs • bootstrap, BRR, jackknife, linearized, and SDR variance estimation • poststratification • DEFF • predictive margins • means, proportions, ratios, totals • summary tables • almost all estimators supported • more
|
Cluster analysis
|
hierarchical clustering • kmeans and kmedian nonhierarchical clustering • dendrograms • stopping rules • user-extensible analyses • more
|
IRT (item response theory)
|
binary (1PL, 2PL, 3PL), ordinal, and categorical response models • item characteristic curves • test characteristic curves • item information functions • test information functions • differential item functioning (DIF) • more
|
Multivariate methods
|
factor analysis • principal components • discriminant analysis • rotation • multidimensional scaling • Procrustean analysis • correspondence analysis • biplots • dendrograms • user-extensible analyses • more
|
Data management
|
data transformations • match-merge • import/export data • ODBC • SQL • Unicode • by-group processing • append files • sort • row–column transposition • labeling • save results • more
|
Graphics
|
lines • bars • areas • ranges • contours • confidence intervals • interaction plots • survival plots • publication quality • customize anything • Graph Editor • more
|
Graphical user interface
|
menus and dialogs for all features • Data Editor • Variables Manager • Graph Editor • Project Manager • Do-file Editor • Clipboard Preview Tool • multiple preference sets • more
|
Documentation
|
27 manuals • 14,000+ pages • seamless navigation • thousands of worked examples • quick starts • methods and formulas • references • more
|
Basic statistics
|
summaries • cross-tabulations • correlations • z and t tests • equality-of-variance tests • tests of proportions • confidence intervals • factor variables • more
|
Nonparametric methods
|
nonparametric regression • Wilcoxon–Mann–Whitney, Wilcoxon signed ranks, and Kruskal–Wallis tests • Spearman and Kendall correlations • Kolmogorov–Smirnov tests • exact binomial CIs • survival data • ROC analysis • smoothing • bootstrapping • more
|
Epidemiology
|
standardization of rates • case–control • cohort • matched case–control • Mantel–Haenszel • pharmacokinetics • ROC analysis • ICD-10 • more
|
GMM and nonlinear regression
|
generalized method of moments (GMM) • nonlinear regression • more
|
Other statistical methods
|
kappa measure of interrater agreement • Cronbach's alpha • stepwise regression • tests of normality • more
|
Functions
|
statistical • random-number • mathematical • string • date and time • more
|
Internet capabilities
|
ability to install new commands • web updating • web file sharing • latest Stata news • more
|
community-contributed commands
|
community-contributed commands for meta-analysis, data management, survival, econometrics, more
|
Programming features
|
adding new commands • command scripting • object-oriented programming • menu and dialog-box programming • dynamic documents • Markdown • Project Manager • plugins • more
|
Matrix programming—Mata
|
interactive sessions • large-scale development projects • optimization • matrix inversions • decompositions • eigenvalues and eigenvectors • LAPACK engine • real and complex numbers • string matrices • interface to Stata datasets and matrices • numerical derivatives • object-oriented programming • more
|
Embedded statistical computations
Installation Qualification
|
IQ report for regulatory agencies such as the FDA • installation verification
|
Accessibility
|
Section 508 compliance, accessibility for persons with disabilities
|
Sample session
New in Stata 15—Latent class analysis • Bayes prefix • Combine endogenous regressors, treatment effects, and selection • Spatial autoregressive models • Finite mixture models (FMM) • Markdown—create web pages with intermixed text, Stata output, and graphs • DSGE models • Nonlinear multilevel and panel-data models • Mixed logit choice models • Multilevel Bayesian analysis • Nonparametric regression • Interval-censored survival models • and much more
