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Fast. Accurate. Easy to use. Stata is a complete, integrated software package that provides all your data science needs—data manipulation, visualization, statistics, and reproducible reporting.

Stata interface


Master your data

Stata's data management features give you complete control.

And much more, to support all your data science needs.

Explore all of Stata's data management features »



Broad suite of statistical features

stata features

Explore all of Stata's features »

Publication-quality graphics

Stata Stata Stata
Stata Stata Stata

Stata makes it easy to generate publication-quality, distinctly styled graphs.

You can point and click to create a custom graph. Or you can write scripts to produce hundreds or thousands of graphs in a reproducible manner. Export graphs to EPS or TIFF for publication, to PNG or SVG for the web, or to PDF for viewing. With the integrated Graph Editor, you click to change anything about your graph or to add titles, notes, lines, arrows, and text.

Discover Stata's publication-quality graphics »


Truly reproducible research

Lots of folks talk about reproducible research.
Stata has been dedicated to it for over 30 years.

We constantly add new features; we have even fundamentally changed language elements. No matter. Stata is the only statistical package with integrated versioning. If you wrote a script to perform an analysis in 1985, that same script will still run and still produce the same results today. Any dataset you created in 1985, you can read today. And the same will be true in 2050. Stata will be able to run anything you do today.

We take reproducibility seriously.



Real documentation


When it comes time to perform your analyses or understand the methods you are using, Stata does not leave you high and dry or ordering books to learn every detail.

Each of our data management features is fully explained and documented and shown in practice on real examples. Each estimator is fully documented and includes several examples on real data, with real discussions of how to interpret the results. The examples give you the data so you can work along in Stata and even extend the analyses. We give you a Quick start for every feature, showing some of the most common uses. Want even more detail? Our Methods and formulas sections provide the specifics of what is being computed, and our References point you to even more information.

Stata is a big package and so has lots of documentation – over 14,000 pages in 27 volumes. But don't worry, type help my topic, and Stata will search its keywords, indexes, and even community-contributed packages to bring you everything you need to know about your topic. Everything is available right within Stata.

Access the documentation online »



We don't just write statistical methods, we validate them.

The results you see from a Stata estimator rest on comparisons with other estimators, Monte Carlo simulations of consistency and coverage, and extensive testing by our statisticians. Every Stata we ship has passed a certification suite that includes 2.3 million lines of testing code that produces 4.3 million lines of output. We certify every number and piece of text from those 4.3 million lines of output.

Advanced programming

Stata also includes an advanced programming language—Mata.

Mata has the structures, pointers, and classes that you
 expect in your programming language and adds direct
 support for matrix programming.

Though you don't need to program to use Stata, it is comforting to know that a fast and complete
programming language is an integral part of Stata. Mata is both an interactive environment for
manipulating matrices and a full development environment that can produce compiled and
optimized code. It includes special features for processing panel data, performs operations on
real or complex matrices, provides complete support for object-oriented programming, and is fully integrated with every aspect of Stata.

Learn more about Mata »

Stata even let's you incorporate CC++, and Java plugins in your Stata programs via a native API
for each language.




World-class technical support

Stata technical support is free to registered users. And, this is a case of getting much more than you pay for.

We have a dedicated staff of expert Stata programmers and Statisticians to answer your technical questions. From tricky data management solutions to getting your graph looking just right. From explaining a robust standard error to specifying your multilevel model. We have your answers.

See what our customers are saying about our technical support »

Cross-platform compatible

Stata will run on WindowsMac, and Linux/Unix computers; however, our licenses are not platform specific.

That means if you have a Mac laptop and a Windows desktop, you don't need two separate licenses to run Stata. You can install your Stata license on any of the supported platforms. Stata datasets, programs, and other data can be shared across platforms without translation. You can also quickly and easily import datasets from other statistical packages, spreadsheets, and databases.

View compatible operating systems »



Stata is not sold in modules, which means you get everything in one package!

Stata offers several purchase options to fit your budget. You can choose between a perpetual license, with nothing more to buy ever, or an annual license. Contact a sales representative or browse our products to find out more about our affordable options. You can also download a product brochure.


Widely used

Used by researchers for more than 30 years, Stata provides everything you need for data science—data manipulation, visualization, statistics, and reproducible reporting.

Select your discipline and see how Stata can work for you.


Behavioral sciences


Data Science


Public policy




Finance, business, and marketing

Institutional Research


Political science

Public health

ERM = Endogeneity
+ Selection
+ Treatment

Combine endogenous covariates, sample selection, and endogenous treatment in models for continuous, binary, ordered, and censored outcomes.

Ermistatas Eureka Take your causal inference to a whole new level.

Latent class analysis (LCA)

lca diagram

Discover and understand the unobserved groupings in your data. Use LCA's model-based classification to find out

  • how many groups you have,
  • who is in those groups, and
  • what makes those groups distinct.

bayes: logistic ...
and 44 more

Bayesian regression

Type bayes: in front of any of 45 Stata estimation commands to fit a Bayesian regression model.

Markdown & dynamic documents

Type this,
Get this,
markdown thumbnail
  • Create webpages from Stata
  • Intermix text, regressions, results, graphs, etc.
  • See changes in data or commands automatically reflected on webpage

Linearized DSGEs

Write your model in simple algebraic form. Stata does the rest: solve model, estimate parameters, estimate policy and transition matrices (with CIs), estimate and graph IRFs, and perform forecasts.

Finite mixture models (FMMs)

  • 17 estimators and combinations
  • Continuous, binary, count, ordinal, categorical, censored, and truncated outcomes
  • Survival outcomes

Spatial autoregressive models



                  where you are


Interval-censored survival models


Fit any of Stata's six parametric survival models to interval-censored data. All the usual survival features are supported: stratified estimation, robust and clustered SEs, survey data, graphs, and more.

Nonlinear multilevel
mixed-effects models

      When ...
      your science ...
      says ...
      your model ...
      is ...
      nonlinear in its parameters

Mixed logit models: Advanced choice modeling

Do you walk to work, ride a bus, or drive your car? Which of three insurance plans do you buy? Which political party do you vote for?

We make dozens of choices every day. Researchers have access to gaggles of data about those choices. Mixed logit introduces random effects into choice modeling and thereby relaxes the IIA assumption and increases model flexibility.

Nonparametric regression

Nonparametric regression

When you know something matters.

But have no idea how.

Create Word documents from Stata

  • Automate your reports
  • Write paragraphs and tables to Word documents
  • Embed Stata results and graphs in paragraphs and tables
  • Customize formatting of text, tables, and cells

Bayesian multilevel models


Small number of groups?
Many hierarchical levels?
Prefer making probability statements?

Consider Bayesian multilevel modeling.

Threshold regression


Your time-series regression may change parameters at some point in time or at multiple points in time. The activity of foraging animals might follow a completely different pattern at temperatures above some threshold. You may not know the value of that threshold. Finding such thresholds and estimating the parameters within the regimes is what threshold regression does.

Panel-data tobit with random coefficients


Stata has long had estimators for random effects (random intercepts) in panel data.

Now you can have random coefficients, too.

Search, browse, and import FRED data


The St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data.

Multilevel regression for interval-measured outcomes

Incomes are sometimes recorded in groupings, as are people's weights, insect counts, grade-point averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So ... we need multilevel regression for interval-measured (interval-censored) outcomes.

Multilevel tobit regression for censored outcomes

  • Left-censoring, right-censoring, both
  • Censoring that varies by observation
  • Make inferences about either the uncensored or the censored outcome
  • Robust and clustered SEs
  • Support for survey data

Panel-data cointegration tests

  • Tests
    • Kao
    • Pedroni
    • Westerlund
  • Total of nine variants of tests

Tests for multiple breaks in time series

  • Cumulative sum (CUSUM) test for parameter stability
    • CUSUM of recursive residuals
    • CUSUM of OLS residuals
  • Plots with CIs

Multiple-group generalized SEM


Generalized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include

  • continuous, binary, ordinal, count, categorical, and even survival outcomes
  • multilevel models


  • NCHS's ICD-10-CM diagnosis codes
  • CMS's ICD-10-PCS procedure codes
  • Verify codes are valid
  • Create new variables based on codes

Power for cluster randomized designs

power twomeans graph

Power analysis for comparing

  • One- and two-sample means
  • One- and two-sample proportions
  • Two-sample survivor curves

when you randomize clusters instead of individuals

Power for linear regression models

screenshot of dialog box
  • Solve for
    • Power
    • Sample size
    • Effect size
  • Specify lists of
    • Alpha values
    • Power levels
    • Beta values
    • Sample sizes
    • And more
  • Automated tables and graphs

Heteroskedastic linear regression

  • Model for the variance
  • Robust and cluster SEs
  • Survey-data support

Poisson models with sample selection

Counts are common. How many:

Fish did you catch?
Accidents occurred?
Patents does a firm generate?

Outcomes are not always seen.

Folks evade the game warden.
Accidents are not always reported.
Some firms prefer trade secrets to patents.

So you need Poisson models with sample selection.

Stata/MP Stata/SE Stata/IC Numerics by Stata

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 featuresStata/ICStata/SEStata/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


10.0 sec


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


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.


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

PackageMemoryDisk 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
  • Data Science
  • Economics
  • Education
  • Epidemiology
  • Finance, business, and marketing
  • Institutional Research
  • 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


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


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


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


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


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

Mata - Stata's serious programming language

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

Numerics by Stata

Installation Qualification

IQ report for regulatory agencies such as the FDA  •  installation verification


Section 508 compliance, accessibility for persons with disabilities

Sample session

A sample session of Stata for Mac, Unix, or Windows.

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


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Price From: $117.00