- Basic tabulations and summaries
- Case-control analysis
- ARIMA
- ANOVA and MANOVA
- Linear regression
- Time-series smoothers
- Generalized linear models (GLM)
- Cluster analysis
- Contrasts and comparisons
- Power analysis
- Sample selection
- Choice modeling

With both a point-and-click interface and a powerful, intuitive command syntax, Stata is fast, accurate, and easy to use.

All analyses can be reproduced and documented for publication and review. Version control ensures statistical programs will continue to produce the same results no matter when you wrote them. See certification results and FDA document compliance for accuracy details.

Stata puts hundreds of statistical tools at your fingertips:

- Basic tabulations and summaries
- Case-control analysis
- ARIMA
- ANOVA and MANOVA
- Linear regression
- Time-series smoothers
- Generalized linear models (GLM)
- Cluster analysis
- Contrasts and comparisons
- Power analysis
- Sample selection
- Choice modeling

- Multilevel models
- Survival models with frailty
- Dynamic panel data (DPD) regressions
- SEM (Structural equation modeling)
- Binary count and censored outcomes
- ARCH
- Multiple imputation
- Survey data
- Treatment effects
- Exact data science
- Bayesian analysis
- Latent class analysis (LCA)
- Finite mixture models (FMM)

Mata is a full-blown programming language that compiles what you type into bytecode, optimizes it, and executes it fast.Though you don't need to program to use Stata, it is comforting to know that a fast and complete matrix 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.

**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 code. Technical supportStata 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.

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 capabilities, new features and official updates can be installed over the Internet with a single click.

Stata's data-management features give you complete control of all types of data.

You can combine and reshape datasets, manage variables, and collect data science across groups or replicates. You can work with byte, integer, long, float, double, and string variables (including BLOBs and strings up to 2 billion characters). Stata also has advanced tools for managing specialised data such as survival/duration data, time-series data, panel/longitudinal data, categorical data, multiple-imputation data, and survey data.

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 TIF 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.

- Regression fit graphs
- Distributional plots
- Time-series graphs
- Survival plots
- Contour plots

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 Quick Starts 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, indices, and even community-contributed packages to bring you everything you need to know about *your topic*. Everything is available right within Stata.

Stata will run on Windows, Mac and Linux/Unix computers; however, 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.

The Stata Journal is a quarterly publication containing articles about data science, 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 material of interest to researchers applying data science in a variety of disciplines.

Stata JournalStata Press® publishes books, manuals, and journals about Stata and general data science topics for professional researchers of all disciplines. Stata Press® publications are available to purchase in our Bookshop

Visit BookshopThe *Stata News* is a free publication containing articles on using Stata, announcements of new releases and updates, training schedules, new books, Conferences and Users Group meetings, new products, and other announcements of interest to Stata users.

The offical Stata Blog, Not Elsewhere Classified (NEC), will keep you up to date about all things related to Stata, including product announcements, service announcements such as on-site and public training, and timely tips and comments related to the use of Stata. Individually signed, the articles in NEC are written by the same people who develop, support, and sell Stata. NEC is informal but useful, and even entertaining.

Latest Stata blogThere are a multitude of training options available to become proficient at Stata quickly. Stata provides hands-on public training courses, one-hour feature webinars, customized on-site training courses, and online training through NetCourses and video tutorials.

All coursesWhether you are a beginner or an expert, you will find something just for you at the Users Group meetings (UGM's), which are held each year in various locations around the world. These meetings showcase in-depth presentations from StataCorp experts and experienced Stata users that focus on helping you use Stata more effectively.

Upcoming meetingsStata is a complete, integrated software package that provides all your data science needs—data manipulation, visualization, statistics, and reproducible reporting. Stata is not sold in modules, which means you get everything you need in one package. And, you can choose a perpetual licence, with nothing more to buy ever. Annual licences are also available.

Whether you're a student or a seasoned research professional, a range of Stata packages are available and designed to suit all needs.

All of the following flavours of Stata have the same complete set of commands and features and manuals included as PDF documentation within Stata.

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, 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.

Stata/MP is faster-much faster. Stata/MP lets you analyze data in one-half to two-thirds of the time compared to Stata/SE on inexpensive dual-core laptops and in one-quarter to one-half the time on quad-core desktops and laptops.

Stata/MP runs even faster on multiprocessor servers. Stata/MP supports up to 64 processors/cores.

Speed is often most crucial when performing computationally intense estimation procedures. A few of Stata’s estimation procedures, including linear regression, are nearly perfectly parallelized, meaning they run twice as fast on two cores, four times as fast on four cores, eight times as fast on eight cores, and so on. Some estimation commands can be parallelized more than others. Taken at the median, estimation commands runs 1.9 times faster on two cores, 3.1 times faster on four cores, and 4.3 times faster on eight cores.

Stata/MP is 100% compatible other versions of with Stata. Analyses do not have to be reformulated or modified in any way to obtain Stata/MP’s speed improvements.

Stata/MP is available for the following operating systems:

- Windows (64-bit processors);
- macOS (64-bit Intel processors);
- Linux (64-bit processors);

To run Stata/MP, you can use a desktop computer with a dual-core or quad-core processor, or you can use a server with multiple processors. Whether a computer has separate processors or one processor with multiple cores makes no difference. More processors or cores makes Stata/MP run faster.

For more advice on purchasing/upgrading to Stata/MP or for hardware queries, please contact our sales team.

Stata/SE and Stata/IC differ only in the dataset size that each can analyse. Stata/SE and Stata/MP can fit models with more independent variables than Stata/IC (up to 10,998). Stata/SE can analyse up to 2 billion observations.

Stata/IC allows datasets with as many as 2,048 variables. The maximum number of observations is 2.14 billion. Stata/IC can have at most 798 independent variables in a model.

Multicore support

Time to run logistic regression with 5 million obs and 10 covariates Info**1-core**

**1-core**

**2 core**

**4 core**

**4+**

Matrix programming language

Exceptional technical support

Includes within-release updates

64-bit version available

Memory requirements

1 GB

2 GB

4 GB

Disk space requirements

1 GB

1 GB

1 GB

Stata 15 has something for everyone. Below we list the highlights of the release. This release is unique because most of the new features can be used by researchers in every discipline.

PDF documentation in Stata 15

Automatic production of web pages from dynamic Markdown documents

Create PDF reports from within Stata

Create Word documents from within Stata

Extended regression models (ERMs)

Finite mixture models (FMMs)

Heteroskedastic linear regression

Import FRED (Import Federal Reserve Economic Data)

Interval-censored survival models

Latent class analysis (LCA)

Linearized DSGEs

Mixed logit models

Multilevel tobit and interval regression

Multiple-group generalized SEM

Nonlinear mixed-effects models

Nonparametric regression

Panel-data cointegration tests

Poisson with sample selection

Power analysis for cluster randomized designs and linear regression

A prefix for Bayesian regression

Spatial autoregressive models

Tests for multiple breaks in time series

Threshold regression

Transparency in Stata graphs

Zero-inflated ordered probit

Import FRED (Import Federal Reserve Economic Data)

Copy/paste data from Excel into Stata

Import Excel data into Stata

Saving estimation results to Excel

Importing delimited data

Changing and renaming variables

Convert a string variable to a numeric variable

Convert categorical string variables to labeled numeric variables

Create a categorical variable from a continuous variable

Convert missing value codes to missing values

Combining data

How to merge files into a single dataset

How to append files into a single dataset

Creating and dropping variables

Create a new variable that is calculated from other variables

Identify and replace unusual data values

Create a date variable from a date stored as a string

Optimize the storage of variables

Round a continuous variable

Stata's Expression Builder

Examining data

Identify and remove duplicate observations

Labeling, display formats, and notes

Label variables

Label the values of categorical variables

Change the display format of a variable

Add notes to a variable

Reshaping datasets

Reshape data from wide format to long format

Reshape data from long format to wide format

Strings

Unicode

Tour of long strings and BLOBs

A prefix for Bayesian regression

Bayesian linear regression using the bayes prefix

Bayesian linear regression using the bayes prefix: How to specify custom priors

Bayesian linear regression using the bayes prefix: Checking convergence of the MCMC chain>

Bayesian linear regression using the bayes prefix: How to customize the MCMC chain>

Bayesian analysis

Graphical user interface for Bayesian analysis

Introduction to Bayesian statistics, part 1: The basic concepts

Introduction to Bayesian statistics, part 2: MCMC and the Metropolis-Hastings algorithm

Mixed logit models

Poisson with sample selection

Zero-inflated ordered probit

Logistic regression in Stata, part 1: Binary predictors

Logistic regression in Stata, part 2: Continuous predictors

Logistic regression in Stata, part 3: Factor variables

Regression models for fractional data

Probit regression with categorical covariates New

Probit regression with continuous covariates New

Probit regression with categorical and continuous covariates New

Nonparametric regression

Spatial autoregressive models

Heteroskedastic linear regression

Mixed logit models

Multilevel tobit and interval regression

Extended regression models (ERMs)

Extended regression models, part 1: Endogenous covariates

Extended regression models, part 2: Nonrandom treatment assignment

Extended regression models, part 3: Endogenous sample selection

Extended regression models, part 4: Interpreting the model

Probit regression with categorical covariates New

Probit regression with continuous covariates New

Probit regression with categorical and continuous covariates New

Item response theory using Stata: One-parameter logistic (1PL) models

Item response theory using Stata: Two-parameter logistic (2PL) models

Item response theory using Stata: Three-parameter logistic (3PL) models

Item response theory using Stata: Nominal response (NRM) models

Item response theory using Stata: Rating scale (RSM) models

Item response theory using Stata: Graded response (GRM) models

Introduction to margins in Stata, part 1: Categorical variables

Introduction to margins in Stata, part 2: Continuous variables

Introduction to margins in Stata, part 3: Interactions

Profile plots and interaction plots in Stata, part 1: A single categorical variable

Profile plots and interaction plots in Stata, part 2: A single continuous variable

Profile plots and interaction plots in Stata, part 3: Interactions of categorical variables

Profile plots and interaction plots in Stata, part 4: Interactions of continuous and categorical variables

Profile plots and interaction plots in Stata, part 5: Interactions of two continuous variables

Multilevel tobit and interval regression

Nonlinear mixed-effects models

Introduction to multilevel linear models, part 1

Introduction to multilevel linear models, part 2

Tour of multilevel GLMs

Multilevel models for survey data

Multilevel survival analysis

Small-sample inference for mixed-effects models

Power analysis for cluster randomized designs and linear regression

Tour of power and sample size

A conceptual introduction to power and sample size

New power and sample-size features in Stata 14

Sample-size calculation for comparing a sample mean to a reference value

Power calculation for comparing a sample mean to a reference value

Find the minimum detectable effect size for comparing a sample mean to a reference value

Sample-size calculation for comparing a sample proportion to a reference value

Power calculation for comparing a sample proportion to a reference value

Minimum detectable effect size for comparing a sample proportion to a reference value

How to calculate sample size for two independent proportions

How to calculate power for two independent proportions

How to calculate minimum detectable effect size for two independent proportions

Sample-size calculation for comparing sample means from two paired samples

Power calculation for comparing sample means from two paired samples

How to calculate the minimum detectable effect size for comparing the means from two paired samples

Sample-size calculation for one-way analysis of variance

Power calculation for one-way analysis of variance

Minimum detectable effect size for one-way analysis of variance

Basic introduction to the analysis of complex survey data

Specifying the design of your survey data

How to download, import, and merge multiple datasets from the NHANES website

How to download, import, and prepare data from the NHANES website

Multilevel models for survey data

Survey data support for SEM

Interval-censored survival models

Learn how to set up your data for survival analysis

How to describe and summarize survival data

How to construct life tables

How to calculate incidence rates and incidence-rate ratios

How to calculate the Kaplan-Meier survivor and Nelson-Aalen cumulative hazard functions

How to graph survival curves

How to test the equality of survivor functions using nonparametric tests

How to fit a Cox proportional hazards model and check proportional-hazards assumption

Multilevel survival analysis

Panel-data survival models

Survival models for SEM

Treatment effects for survival models

Import FRED (Import Federal Reserve Economic Data)

Threshold regression

Tests for multiple breaks in time series

Tour of forecasting

Formatting and managing dates

Time-series operators

Correlograms and partial correlograms

Line graphs and tin()

Introduction to ARMA/ARIMA models

Markov-switching models

Moving-average smoothers

Introduction to treatment effects in Stata: Part 1

Introduction to treatment effects in Stata: Part 2

Treatment effects: Regression adjustment

Treatment effects: Inverse-probability weighting

Treatment effects: Inverse-probability weighted regression adjustment

Treatment effects: Augmented inverse-probability weighting

Treatment effects: Nearest-neighbor matching

Treatment effects: Propensity-score matching

Treatment effects for survival models

Endogenous treatment effects

All versions of Stata run on dual-core, multi-core and multi-processor computers.

- 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 macOS requires 64-bit Intel® processors (Core™2 Duo or better) running macOS 10.9 or newer

**Linux:**Any 64-bit (x86-64 or compatible) running Linux.**For xstata**, you need to have GTK 2.24 installed

Please note: The 32-bit and Solaris download options are still available, but by request only.

- Minimum of 1 GB of RAM for Stata/IC, 2GB for Stata/SE and 4GB for Stata/MP
- Minimum of 1 GB of disk space for all versions
- Stata for Unix requires a video card that can display thousands of colours or more (16-bit or 24-bit colour)

Find out all about Stata’s expansive range of statistical features using the table of contents below. Each section links to further details and examples to help users get the best out of their software.

http://www.stata-uk.com/software/stata/

Start AgainPlease select a licence type:

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Single User / Volume Single Users Network (Concurrent Use) Student LabI currently own a Stata license for:

The Stata 15 documentation is copyright of StataCorp LLC, College Station TX, USA, and is used with permission of StataCorp LLC.

Students may purchase **Stata/MP**, **Stata/SE** and **Stata/IC** at a discounted price. For more information about available licence types, click here.