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Stata

Why

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 »

 

                                                      data-editor-paste-special  



Broad suite of statistical features

stata features

Publication-quality graphics

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 15,000 pages in 31 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.

Trusted

We don't just program 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 3.2 million lines of testing code that produces 4.9 million lines of output. We certify every number and piece of text from those 4.9 million lines of output.

Easy to use

All of Stata's features can be accessed through menus, dialogs, control panels, a Data Editor, a Variables Manager, a Graph Editor, and even an SEM Diagram Builder. You can point and click your way through any analysis.

If you don't want to write commands and scripts, you don't have to.

Even when you are pointing and clicking, you can record all your results and later include them in reports. You can even save the commands created by your actions and reproduce your complete analysis later.

SEM Builder screenshot

Easy to grow with

Stata's commands for performing tasks are intuitive and easy to learn. Even better, everything you learn about performing a task can be applied to other tasks. For example, you simply add if gender=="female" to any command to limit your analysis to females in your sample. You simply add vce(robust)to any estimator to obtain standard errors and hypothesis tests that are robust to many common assumptions.

The consistency goes even deeper. What you learn about data management commands often applies to estimation commands, and vice versa. There is also a full suite of postestimation commands to perform hypothesis tests, form linear and nonlinear combinations, make predictions, form contrasts, and even perform marginal analysis with interaction plots. These commands work the same way after virtually every estimator.

Sequencing commands to read and clean data, then to perform statistical tests and estimation, and finally to report results is at the heart of reproducible research. Stata makes this process accessible to all researchers.

Find out how »

Easy to automate

Everyone has tasks that they do all the time—create a particular kind of variable, produce a particular table, perform a sequence of statistical steps, compute an RMSE, etc. The possibilities are endless. Stata has thousands of built-in procedures, but you may have tasks that are relatively unique or that you want done in a specific way.

If you have written a script to perform your task on a given dataset, it is easy to transform that script into something that can be used on all your datasets, on any set of variables, and on any set of observations.

See how easy automation is in Stata »

Easy to extend

Some of the things you automate may be so useful that you want to share them with colleagues or even make them available to all Stata users. That's also easy. With just a little code, you can turn an automation script into a Stata command. A command that supports standard features that Stata's official commands support. A command that can be used in the same way official commands are used.

Take a look »

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 also has comprehensive Python integration, allowing you to harness all the power of Python directly from your Stata code.

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

                
             

Stata/MP

Get the most out of your multicore computer.
No other statistical software comes close.
Enjoy the new features of Stata 16 at top speed.

Learn more »

Community-contributed features

Stata is so programmable that developers and users add new features every day to respond to the growing demands of today's researchers.

 With Stata's Internet capabilitiesnew features and official updates can be installed over the Internet with a single click.

World-class technical support

Stata technical support is free to registered users, which means you get 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 and from explaining a robust standard error to specifying your multilevel model, we have your answers.


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.

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

Biostatistics

Data Science

Economics

Public policy

Sociology

Education

Epidemiology

Finance, business, and marketing

Institutional Research

Medicine

Political science

Public health

Can't find your discipline? See who else is using Stata »

Comprehensive resources

Video tutorials

Stata's YouTube channel is the perfect resource for new users to Stata, users wanting to learn a new feature in Stata, and professors looking for aids in teaching with Stata. We have over 250 videos on our YouTube channel that have been viewed over 6 million times by Stata users wanting to learn how to label variables, merge datasets, create scatterplots, fit regression models, work with time-series or panel data, fit multilevel models, analyze survival data, perform Bayesian analysis, and use many other features of Stata. View the complete list of videos.

Visit our YouTube channel »

Stata Blog

We write the official Stata Blog, Not Elsewhere Classified (NEC), to share things we think you will find instructive, informative, or just plain entertaining. We have written about how to interpret statistical results; export results into Word, Excel, and LaTeX; perform Monte Carlo simulations; program your own estimators; and more. We also post service and product announcements. Individually signed, the articles in NEC are written by the same people who develop and support Stata.

Read our latest blog post »

Free Stata webinars

Stata webinars offer something for everyone. Those new to Stata will get a head start when they join our Ready. Set. Go Stata webinar. Both new and experienced users will want to join our Tips and Tricks webinar and our one-hour feature webinars; each one provides an in-depth look at one of Stata's statistical, graphical, data management, or reporting features.

View the current webinar offerings »

Training

A multitude of training options are available to become proficient at Stata quickly. Stata provides hands-on classroom and web-based training courses, customized on-site training courses, and online training through NetCourses, webinars, and video tutorials.

View available trainings »

Stata Press

Stata Press® publishes books, manuals, and journals about Stata and general statistics topics for professional researchers of all disciplines. Stata Press® publications, along with books recommended by StataCorp, can be found in the Stata Bookstore.

Visit the Stata Bookstore »

Stata News

The Stata News is a free publication with columns such as the popular In the Spotlight, where Stata developers give insight into specific Stata features, and the User's corner, where we share unique, helpful, and fun contributions from the user community. The News also contains announcements such as new releases and updates, training schedules, new books, Conferences, and Users Group meetings.

Sign up to receive the Stata News »

Stata Journal

The Stata Journal is a quarterly publication containing articles about statistics, data analysis, teaching methods, and effective use of Stata's language. The Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other materials of interest to researchers applying statistics in a variety of disciplines.

Subscribe to the Stata Journal »

Affordable

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.

New in

Lasso


coefpath
cvplot

All the expected tools for model selection and prediction

  • Cross-validation

  • Goodness of fit

  • Coefficient paths

  • Knot analysis

  • Lasso and elastic net

Alongside cutting-edge inferential methods

  • Robust to mistakes in variable selection

  • Proper inference for coefficients of interest

  • Double selection, partialing out, and cross-fitting

Continuous, binary, count outcomes

LEARN MORE

Reproducible reporting

Create Word, HTML, PDF, and Excel files with Stata results and graphs.

pdf

word html  excel

Stata's integrated versioning gives you truly reproducible reporting. Want dynamic documents? All of these reports can be updated as your data change.

LEARN MORE

Meta-analysis

meta-sum
meta-cp
meta-forest
meta-funnel

With Stata's new meta-analysis suite, you can easily summarize results from multiple studies.

  • Estimate an overall effect size

  • Perform random-effects, fixed-effects, or common-effect meta-analysis

  • Display results in a forest plot

  • Analyze subgroups

  • Perform meta-regression

  • Explore small-study effects

  • Evaluate publication bias

  • Perform cumulative meta-analysis

LEARN MORE

Choice models

With Stata's new and unique choice-model analysis features, you can truly interpret the results of your choice model.

modes-of-transport What proportion of travelers are expected to choose air travel?
car-travel How does each additional $10,000 of income change the probability of traveling by air?
select-modes Wait times at the airport increase by 30 minutes. How does this affect the probability of traveling by air? By train? By bus? By car?

LEARN MORE

Python integration

Use any Python package from within Stata

python


  • Matplotlib for 3-D graphs

  • Scrapy for scraping data

  • TensorFlow for machine learning

LEARN MORE

New in Bayesian analysis

Multiple chains Gelman-Rubin convergence diagnostics
grubin ac
trace
Bayesian predictions Posterior predictive p-values

LEARN MORE

Panel-data ERMs

xterm

Endogeneity +

Selection +

Treatment +

Panel data


Handle all of these complications simultaneously.

LEARN MORE

Import data from

SAS

SPSS

sas spss
LEARN MORE LEARN MORE

Nonparametric series regression

Agnostic about functional form? Poisson or negative binomial? Cubic or quadratic on a covariate?

No problem.

With nonparametric regression, you can explore the response surface, estimate population-averaged effects, perform tests, and obtain confidence intervals.

Frames—multiple datasets in memory

fr-manager data-ed
fr-link

Load datasets simultaneously into frames.

Link related frames.

Multitask.

Record results in another frame.

Make code run faster.

LEARN MORE

Sample-size analysis for CIs

How many subjects are needed to achieve a confidence interval of the desired width?

ci-db ci-panel

Perform sample size-analysis CIs for

  • One mean
  • One variance
  • Two independent means
  • Two paired means
  • Your own method

ci-table ci-graph

Tabular results and automated graphs

LEARN MORE

Panel-data mixed logit

You choose dinner everyday.

You choose your car insurance every year.

You choose where to vacation each summer.

Now you can account for the you-ness in those decisions.

LEARN MORE

Nonlinear dynamic stochastic general equilibrium (DSGE) models

Now you can leave the linearization to us.

dsge

irf

Specify your nonlinear model and evaluate policy implications

  • Solve
  • Calibrate
  • Estimate
  • Graph

LEARN MORE

Multiple-group IRT

irtgrp1
irtgrp2
irtgrp3

Fit multiple-group IRT models

  • 1PL, 2PL, and 3PL models for binary outcomes
  • Graded response, partial credit, and rating scale models for ordinal outcomes
  • Nominal response models for categorical outcomes

Compare estimates across groups

  • Item difficulty
  • Item discrimination

Graph group differences

  • ICCs, TCCs, IIFs, TIFs

Perform model-based tests of differential item functioning

LEARN MORE

xtcheckman

If you know what this means ...

... you know that you want it.

LEARN MORE

Nonlinear mixed-effects models with lags, leads, and differences

pk

Multiple-dose pharmacokinetic models

Growth models

More

LEARN MORE

Heteroskedastic ordered probit

Model differences in variance among subjects or groups

hetero-prob

LEARN MORE

Point sizes for graphics

wwwsa

Now specify the size of text, markers, margins, lines, and more
using printer points, centimeters, or inches.

LEARN MORE

Numerical integration

quad quad2

LEARN MORE

Linear programming

linear-prog

LEARN MORE

Stata in Korean

korean korean2

이제부터 Stata의 모든 인터페이스 (모든 메뉴 및 모든 대화 상자)는 한국어로 가능합니다.

LEARN MORE

New in the Mac interface

Dark mode

Native tabbed windows

gui-man

LEARN MORE

Autocompletion in the Do-file editor

You type

    sum

    re

We type

  marize

  gress

auto-comp

LEARN MORE

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, i9, Xeon, 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 (up to 10,998) and Stata/MP (up to 65,532) can fit models with more independent variables than Stata/IC (up to 798). 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   65,532

Multicore support

Time to run logistic regression with 10 million observations and 20 covariates

1-core

20 sec

1-core

20 sec

2-core

10 sec

4-core

5.2 sec

6+

<5.2 sec

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 Server 2016, 2012, 2008, 2003 *

Stata requires 64-bit Windows for x86-64 processors made by Intel® and AMD

Stata for Mac

  • Stata for macOS requires 64-bit Intel® processors (Core™2 Duo or better) running macOS 10.11 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:

By discipline

  • 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

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

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

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

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 46 estimation commands  •  continuous univariate, multivariate, and discrete priors  •  add your own models  •  multiple chains  •  convergence diagnostics  •  posterior summaries  •  hypothesis testing  •  model fit  •  model comparison  •  predictions  •  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

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

Meta-analysis

effect sizes  •  common, fixed, and random effects  •  forest, funnel, and more plots  •  subgroup and cumulative analysis  •  meta-regression  •  small-study effects  •  publication bias  •  more

Documentation

31 manuals  •  15,000+ pages  •  seamless navigation  •  thousands of worked examples  •  quick starts  •  methods and formulas  •  references  •  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

Power, precision, and sample size

power  •  sample size  •  effect size  •  minimum detectable effect  •  CI width  •  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

Basic statistics

summaries  •  cross-tabulations  •  correlations  •  z and t tests  •  equality-of-variance tests  •  tests of proportions  •  confidence intervals  •  factor variables  •  more

Choice models

discrete choice  •  rank-ordered alternatives  •  conditional logit  •  multinomial probit  •  nested logit  •  mixed logit  •  panel data  •  case-specific and alternative-specific predictors  •  interpret results—expected probabilities, covariate effects, comparisons across alternatives  •  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  •  panel data  •  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

Extended regression models (ERMs)

endogenous covariates  •  sample selection  •  nonrandom treatment  •  panel data  •  account for problems alone or in combination  •  continuous, interval-censored, binary, and ordinal outcomes  •  more

Lasso

lasso  •  elastic net  •  model selection  •  prediction  •  inference  •  continuous, binary, and count outcomes  •  cross-validation  •  adaptive lasso  •  double selection  •  partialing out  •  cross-fit partialing out  •  double machine learning  •  endogenous covariates  •  more

GMM and nonlinear regression

generalized method of moments (GMM)  •  nonlinear regression  •  more

Generalized linear models (GLMs)

ten link functions  •  user-defined links  •  seven distributions  •  ML and IRLS estimation  •  nine variance estimators  •  seven residuals  •  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

Simple maximum likelihood

specify likelihood using simple expressions  •  no programming required  •  survey data  •  standard, robust, bootstrap, and jackknife SEs  •  matrix estimators  •  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

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

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

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

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

Other statistical methods

kappa measure of interrater agreement  •  Cronbach's alpha  •  stepwise regression  •  tests of normality  •  more

ANOVA/MANOVA

balanced and unbalanced designs  •  factorial, nested, and mixed designs  •  repeated measures  •  marginal means  •  contrasts  •  more

Survey methods

multistage designs  •  bootstrap, BRR, jackknife, linearized, and SDR variance estimation  •  poststratification  •  raking  •  calibration  •  DEFF  •  predictive margins  •  means, proportions, ratios, totals  •  summary tables  •  almost all estimators supported  •  more

Functions

statistical  •  random-number  •  mathematical  •  string  •  date and time  •  regular expressions  •  Unicode  •  more

Exact statistics

exact logistic and Poisson regression  •  exact case–control statistics  •  binomial tests  •  Fisher’s exact test for r × c tables  •  more

Cluster analysis

hierarchical clustering  •  kmeans and kmedian nonhierarchical clustering  •  dendrograms  •  stopping rules  •  user-extensible analyses  •  more

Internet capabilities

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

Epidemiology

standardization of rates  •  case–control  •  cohort  •  matched case–control  •  Mantel–Haenszel  •  pharmacokinetics  •  ROC analysis  •  ICD-10  •  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  •  multiple-group models  •  differential item functioning (DIF)  •  more

Community-contributed commands

search and download thousands of free additions  •  discover new features in the Stata Journal  •  share commands by posting to the SSC  •  discuss community-contributed commands on Statalist  •  more

DSGE models

specify models algebraically  •  solve models  •  estimate parameters  •  identification diagnostics  •  policy and transition matrices  •  IRFs  •  dynamic forecasts  •  more

Multivariate methods

factor analysis  •  principal components  •  discriminant analysis  •  rotation  •  multidimensional scaling  •  Procrustean analysis  •  correspondence analysis  •  biplots  •  dendrograms  •  user-extensible analyses  •  more

Embedded statistical computations

Numerics by Stata

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

Data wrangling

data transformations  •  data frames  •  match-merge  •  import/export data  •  ODBC  •  SQL  •  Unicode  •  by-group processing  •  append files  •  sort  •  row–column transposition  •  labeling  •  save results  •  more

Installation Qualification

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

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

Reporting

reproducible reports  •  Word  •  Excel  •  PDF  •  HTML  •  dynamic documents  •  Markdown  •  Stata results and graphs  •  SVG  •  EPS  •  PNG  •  TIF  •  formatted text and tables  •  more

Accessibility

Section 508 compliance, accessibility for persons with disabilities

Resampling and simulation methods

bootstrap  •  jackknife  •  Monte Carlo simulation  •  permutation tests  •  more

Graphics

lines  •  bars  •  areas  •  ranges  •  contours  •  confidence intervals  •  interaction plots  •  survival plots  •  publication quality  •  customize anything  •  Graph Editor  •  more

Sample session

A sample session of Stata for MacUnix, or Windows.

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

Programming features

adding new commands  •  scripting  •  object-oriented programming  •  menu and dialog-box programming  •  dynamic documents  •  Markdown  •  Project Manager  •  Python integration  •  Java plugins  •  C/C++ plugins  •  more

New in Stata 16— Lasso  •  Truly reproducible reporting  •  Meta-analysis  •  Choice models  •  Python integration  •  Bayes—multiple chains, predictions, ...  •  Import data from SAS and SPSS  •  Panel-data extended regression models  •  Frames—multiple datasets in memory  •  Nonlinear DSGE models  •  Multiple-group IRT  •  xtheckman  •  NLME models with lags: Pharmacokinetic models, ...  •  Linear programming  •  Do-file Editor autocompletion  •  and more

 

Price From: $117.00

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Price From: $117.00