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 quadcore, dualcore, and multicore/multiprocessor computers) that can analyze the most data
 Stata/SE: Stata for large datasets
 Stata/IC: Stata for midsized 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 multicore chips. On dualcore chips, Stata/MP runs 40% faster overall and 72% faster where it matters, on the timeconsuming 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 1core

1core
10.0 sec

1core
10.0 sec

2core 4core 4+
5.0 sec 2.6 sec Even faster

Complete suite of statistical features




Publicationquality graphics




Matrix programming language




Complete PDF documentation




Exceptional technical support




Includes withinrelease updates




64bit 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 platformspecific, 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 dualcore, multicore, or multiprocessor computer.
Platforms
Stata for Windows
 Windows 10 *
 Windows 8 *
 Windows 7 *
 Windows Vista *
 Windows Server 2016, 2012, 2008, 2003 *
* 64bit and 32bit Windows varieties for x8664 and x86 processors made by Intel® and AMD
Stata for Mac
 Stata for macOS requires 64bit Intel® processors (Core™2 Duo or better) running macOS 10.9 or newer
Stata for Unix
 Linux
 Any 64bit (x8664 or compatible) or 32bit (x86 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 (16bit or 24bit color)
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 • threestage least squares • constraints • quantile regression • GLS • more

Panel/longitudinal data
random and fixed effects with robust standard errors • linear mixed models • randomeffects probit • GEE • random and fixedeffects Poisson • dynamic paneldata models • instrumental variables • panel unitroot tests • more

Multilevel mixedeffects models
continuous, binary, count, and survival outcomes • two, three, and higherlevel 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, rankordered, and stereotype logistic • multinomial probit • zeroinflated and lefttruncated count models • selection models • marginal effects • more

Extended regression models (ERMs)
combine endogenous covariates, sample selection, and nonrandom treatment in models for continuous, intervalcensored, binary, and ordinal outcomes • more

Generalized linear models (GLMs)
ten link functions • userdefined 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 • leastsquares 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 • multiplecomparison 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
userspecified 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 • unobservedcomponents model • dynamic factors • statespace models • Markovswitching models • business calendars • tests for structural breaks • threshold regression • forecasts • impulse–response functions • unitroot 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 • timevarying covariates • left, right, and intervalcensoring • Weibull, exponential, and Gompertz models • more

Bayesian analysis
thousands of builtin 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 • propensityscore matching • regression adjustment • covariate matching • multilevel treatments • endogenous treatments • average treatment effects (ATEs) • ATEs on the treated (ATETs) • potentialoutcome 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 • userextensible 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 • userextensible analyses • more

Data management
data transformations • matchmerge • import/export data • ODBC • SQL • Unicode • bygroup 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 • Dofile 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 • crosstabulations • correlations • z and t tests • equalityofvariance 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 • ICD10 • 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 • randomnumber • mathematical • string • date and time • more

Internet capabilities
ability to install new commands • web updating • web file sharing • latest Stata news • more

communitycontributed commands
communitycontributed commands for metaanalysis, data management, survival, econometrics, more

Programming features
adding new commands • command scripting • objectoriented programming • menu and dialogbox programming • dynamic documents • Markdown • Project Manager • plugins • more

Matrix programming—Mata
interactive sessions • largescale 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 • objectoriented 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 paneldata models • Mixed logit choice models • Multilevel Bayesian analysis • Nonparametric regression • Intervalcensored survival models • and much more