- 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

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

- 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 statistics
- Bayesian analysis

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.Extensible Resources Community

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

You can combine and reshape datasets, manage variables, and collect statistics 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 write scripts to produce hundreds or thousands of graphs in a reproducible manner and export them to EPS or TIF for publication, to PNG 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 user-written 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 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 material of interest to researchers applying statistics in a variety of disciplines.

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

Visit the Timberlake BookshopThe *Stata News* is a free quarterly 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 thingsrelated 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.

Read the latest Stata blog postThere are a multitude of training options available to become proficient at Stata quickly. Stata provides hands-on public training courses, customized on-site training courses, and online training through NetCourses and video tutorials.

View available trainingsWhether 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.

View upcoming meetingsStata 15 is a complete, integrated statistical package that provides everything you need for data analysis, data management, and graphics. 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. Most computers purchased since mid 2006 can take advantage of the advanced multiprocessing of Stata/MP. This includes the Intel Core™ 2 Duo, i3, i5, i7, and the AMD X2 dual-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 provides the most extensive support for multiprocessor computers and multicore computers of any statistics and data-management package.

The exciting thing about Stata/MP is that it runs faster—much faster. Stata/MP lets you analyse data in one-half to two-thirds of the time compared with Stata/SE on inexpensive dual-core desktops and laptops and in one-quarter to one-half the time on quad-core desktops. Stata/MP runs even faster on multiprocessor servers. Stata/MP supports up to 64 processors/cores.

In a perfect world, software would run twice as fast on two cores, four times as fast on four cores, eight times as fast on eight cores, and so on. Across all commands, Stata/MP runs 1.7 times faster on two cores, 2.4 times faster on four cores, and 3.2 times faster on eight cores. These values are median speed improvements. Half the commands run even faster.

On the other side of the distribution, a few commands do not run faster, often because they are inherently sequential, such as time-series commands.

Stata worked hard to make sure that the performance gains for commands that take longer to run would be greater. Across all estimation commands, Stata/MP runs 1.9 times faster on two cores, 3.1 times faster on four cores, and 4.1 times faster on computers with 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 (32- and 64-bit processors);
- macOS (64-bit Intel processors);
- Linux (32- and 64-bit processors);
- Solaris (64-bit SPARC and x86-64).

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.

There are many video tutorials in using Stata. Below you will find the most recent additions that relate to Stata 15, as well as a list of all other resources currently available.

Converting string variables to numeric | Partial dataset | How to download and install Stata for Windows |

Tour of the Stata 15 interface | PDF documentation in Stata 15 | Bayesian analysis in Stata |

Censored Poisson regression in Stata | Endogenous treatment effects in Stata | Graphical user interface for Bayesian analysis in Stata |

IRT (item response theory) models in Stata | Japanese and Spanish interface in Stata | Markov-switching models in Stata |

Multilevel models for survey data in Stata | Multilevel survival analysis in Stata | New power and sample-size features in Stata |

Panel-data survival models in Stata | Postestimation Selector in Stata | Regression models for fractional data in Stata |

Satorra–Bentler adjustments for SEM | Small-sample inference for mixed-effects models in Stata | Survey data support for SEM in Stata |

Survival models for SEM in Stata | Treatment effects for survival models in Stata | Unicode in Stata |

- Tour of the Stata 15 interface
- Quick help in Stata
- PDF documentation in Stata 15
- Example data included with Stata
- How to download and install community-contributed commands in Stata
- Tour of Stata Project Manager
- Postestimation Selector in Stata
- Copy/paste data from Excel into Stata
- Import Excel data into Stata
- Converting data to Stata with Stat/Transfer
- Stata's Expression Builder
- Tour of long strings and BLOBs
- Importing delimited data
- Saving estimation results to Excel
- Unicode in Stata
- Bar graphs in Stata
- Box plots in Stata
- Basic scatterplots in Stata
- Histograms in Stata
- Pie charts in Stata
- Contour plots in Stata
- Stata's Expression Builder
- 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 in Stata
- One-sample
*t*test in Stata *t*test for two paired samples in Stata*t*test for two independent samples in Stata- Descriptive statistics in Stata
- Tables and cross-tabulations in Stata
- Combining cross-tabulations and descriptives in Stata
- Pearson’s chi2 and Fisher’s exact test in Stata
- Confidence intervals calculator for normal data
- Confidence intervals calculator for binomial data
- Confidence intervals calculator for Poisson data
- Cross-tabulations and chi-squared tests calculator
- One-sample
*t*tests calculator - Two-sample
*t*tests calculator - Incidence-rate ratios calculator
- Risk-ratios calculator
- Odds-ratios calculator
**Linear models**- One-way ANOVA in Stata
- Two-way ANOVA in Stata
- Pearson’s correlation coefficient in Stata
- Simple linear regression in Stata
- Analysis of covariance in Stata
- Nominal response (NRM) models
- Graded response (GRM) models
- Rating scale (RSM) 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
- Introduction to contrasts in Stata: One-way ANOVA
- Introduction to multilevel linear models, part 1
- Introduction to multilevel linear models, part 2
- Tour of multilevel GLMs
- Multilevel survival analysis in Stata
- Multilevel models for survey data in Stata
- Small-sample inference for mixed-effects models in Stata
- Setup, imputation, estimation—regression imputation
- Setup, imputation, estimation—predictive mean matching
- Setup, imputation, estimation—logistic regression
- Tour of power and sample size
- A conceptual introduction to power and sample size using Stata
- Sample-size calculation for comparing a sample mean to a reference value using Stata
- Power calculation for comparing a sample mean to a reference value using Stata
- Find the minimum detectable effect size for comparing a sample mean to a reference value using Stata
- Sample-size calculation for comparing a sample proportion to a reference value using Stata
- Power calculation for comparing a sample proportion to a reference value using Stata
- Minimum detectable effect size for comparing a sample proportion to a reference value using Stata
- How to calculate sample size for two independent proportions using Stata
- How to calculate power for two independent proportions using Stata
- How to calculate minimum detectable effect size for two independent proportions using Stata
- 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 using Stata
- Power calculation for one-way analysis of variance using Stata
- Minimum detectable effect size for one-way analysis of variance using Stata
- New power and sample-size features in Stata
- Tour of multilevel generalized SEM
- SEM Builder in Stata
- Satorra–Bentler adjustments for SEM
- Survey data support for SEM in Stata
- Survival models for SEM in Stata
- How to download, import, and merge multiple datasets from the NHANES website
- How to download, import, and prepare data from the NHANES website
- Basic introduction to the analysis of complex survey data
- Specifying the poststratification of survey data
- Specifying the design of your survey data
- Multilevel models for survey data in Stata
- Survey data support for SEM in Stata
- Learn how to set up your data for survival analysis
- How to describe and summarize survival data
- How to construct life tables using Stata
- How to calculate the Kaplan-Meier survivor and Nelson-Aalen cumulative hazard functions with Stata
- How to graph survival curves using Stata
- How to test the equality of survivor functions using nonparametric tests using Stata
- How to calculate incidence rates and incidence-rate ratios using Stata
- How to fit a Cox proportional hazards model and check proportional-hazards assumption with Stata
- Multilevel survival analysis in Stata
- Treatment effects for survival models in Stata
- Panel-data survival models in Stata
- Survival models for SEM in Stata
- Tour of forecasting
- Formatting and managing dates
- Line graphs and tin()
- Time-series operators
- Correlograms and partial correlograms
- Introduction to ARMA/ARIMA models
- Moving-average smoothers
- Using freduse to download time-series data from the Federal Reserve
- Markov-switching models in Stata
- Tour of treatment effects
- Introduction to treatment effects in Stata: Part 1
- Introduction to treatment effects in Stata: Part 2
- Treatment effects in Stata: Regression adjustment
- Treatment effects in Stata: Inverse probability weights
- Treatment effects in Stata: Inverse probability weights with regression adjustment
- Treatment effects in Stata: Augmented inverse probability weights
- Treatment effects in Stata: Nearest-neighbor matching
- Treatment effects in Stata: Propensity-score matching
- Treatment effects for survival models in Stata
- Endogenous treatment effects in Stata

Below you will find a list of all video tutorial resources available. The links will take you to YouTube.

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

- Windows 10 *
- Windows 8 *
- Windows 7 *
- Windows Vista *
- Windows Server 2012 *
- Windows Server 2008 *
- Windows Server 2003 *
- Windows Server 2016 *

* 64-bit and 32-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) or 32-bit (x86 or compatible) running Linux.

- 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.timberlake.co.uk/software/stata/

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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 LP, College Station TX, USA, and is used with permission of StataCorp LP.

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