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Stata

Developer: StataCorp LP

Latest Release: Stata 14 (April 2015)

Operating System: Windows, Mac OS, Linux

New — Bayesian analysis commands / Treatment-effect analysis / IRT (Item Response Theory) Analysis / Support for Unicode / Stata in new languages / New time series commands / and much more…
End User License Agreement

Stata 14 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.

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

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 is a version of Stata/SE that runs on multiprocessor and multicore computers. 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, and the only difference between Stata/MP and Stata/SE, is that Stata/MP 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 1.6 times faster on two cores, 2.1 times faster on four cores, and 2.7 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 1.8 times faster on dual-core computers, 2.8 times faster on quad-core computers, 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);
  • Mac OS X (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

Stata SE performs in the same way as Stata/MP, allowing for the same number of variables and observations and the only difference is that it is not designed for parallel processing.

In addition, Stata/SE, Stata/IC and Small Stata 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/IC

Stata/IC allows datasets with as many as 2,047 variables. The maximum number of observations is 2.14 billion. Stata/IC can have at most 798 right-hand-side variables in a model.

Small Stata

Small Stata is limited to analysing datasets with a maximum of 99 variables and 1,200 observations. Small Stata can have at most 99 right-hand-side variables in a model.

Comparison of features

  Stata/MP Stata/SE Stata/IC Small Stata
Max. no. of variables 32,767 32,767 2,047 99
Max. no. of right-hand variables 10,998 10,998 798 99
Max. no. of observations 20 billion* 2.14 billion 2.14 billion 1,200
64-bit compatible? Yes Yes Yes Yes
Allows parallel processing? Yes No No No
Platforms Windows, Mac OS X (64-bit Intel), Unix
Minimum memory required 2 GB 1 GB 512 MB 512 MB
Minimum disk space required 900 MB 900 MB 900 MB 900 MB

* The Maximum number of observations is limited only by the amount of available RAM on your system.

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 include PDF documentation:

  • Stata/MP: The fastest version of Stata (for dual- and multicore/multiprocessor computers)
  • Stata/SE: Stata for large datasets
  • Stata/IC: Stata for moderate-sized datasets
  • Small Stata: A version of Stata that handles small datasets (for educational purchases only)

What Stata is right for me?

The summary above shows the Stata packages available.

Stata/MP is the fastest and largest version of Stata. Most computers purchased after mid-2006 can take advantage of the advanced multiprocessing capabilities of Stata/MP.

Stata/MP, Stata/SE, and Stata/IC all run on any machine, but Stata/MP runs faster. You can buy a Stata/MP license for up to the number of cores on your machine (the most is 64). For example, if your machine has eight cores, you can buy a Stata/MP license for either eight cores (Stata/MP8), four cores (Stata/MP4), or two cores (Stata/MP2).

Stata/MP can also analyse more data than any other flavour of Stata. Stata/MP can analyse 10 to 20 billion observations given the current largest computers, and is ready to analyse up to 281 trillion observations once computer hardware catches up.

Stata/SE, Stata/IC, and Small Stata 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 2,047 variables and 2 billion observations. Stata/IC can have at most 798 right-hand-side variables in a model.

Small Stata is limited to analysing datasets with a maximum of 99* variables and 1,200* observations. Small Stata can have at most 98 right-hand-side variables in a model.

Note: The number of variables and observations allowed by Small Stata includes the additional variables or observations generated during statistical computations.


New Features in Stata 14

Stata 14 has 102 new features and is one of the biggest new releases of Stata and offers new research capabilities for users in a variety of fields such as: economics, health researchers, epidemiologists, sociologists, psychologists, education researchers, political scientists, and econometricians.

Bayesian analysis commands

The introduction of Bayesian analysis commands (univariate and multivariate linear models, univariate GLM, univariate and generalized nonlinear models, etc.) supported by an all new Stata Bayesian Analysis reference manual.

Stata 14 includes 12 built-in likelihood models and 22 built-in prior distributions among other helpful features. More

Extended models of treatment effects

Treatment-effect analysis is now available for a much broader class of models. Endogenous treatment-effect estimation is now available for continuous, binary, count, and fractional outcomes.

Treatment effects can now also be estimated from observational survival data. More

IRT (item response theory) analysis

Stata 14 now supports IRT models for binary items (1-3 PL), categorical items (nominal response), ordinal items (graded response, rating scale and partial credit) and any combination of those models. More

Stata in new languages

Stata’s user interface is now available in Spanish and Japanese. More

More useful new features added in Stata 14 are:

  • You can fit a variety of multilevel survival models such as exponential and Weibull mixed-effects models. More
  • You can perform small-sample inference in linear mixed models using several denominator degrees-of-freedom methods, including the Kenward-Roger method. More
  • New time series commands. More
  • New and extended panel-data estimators. More
  • You can calculate power and sample size for epidemiological contingency table analyses. More
  • Stata now understands Unicode. More
  • You can conduct the Satorra-Bentler adjusted model test for SEMs with data that are not normally distributed. More
  • You can estimate models for rates, proportions, and other fractional responses using beta regression and fractional regression models.
  • You can estimate Poisson models with censored dependent variables.
  • Stata/MP now allows more than 2.1 billion observations – up to 20 billion observations given the current largest computer, and is ready for more once computer hardware catches up. More
  • ICD-10 codes. More
  • Stage-level weights. More
In addition to:

  • churdle to estimate linear and exponential hurdle models
  • betareg and fracreg for fractional responses, proportions, rates, etc.
  • cpoisson to estimate censored Poisson models
  • ztest and ztesti commands to compute z-statistics
  • Postestimation Selector that greatly simplifies postestimation analysis
  • Nearly all estimation commands in Stata now support factor variables
  • A multitude of improvements to margins, such as the ability to make multiple predictions at a time and having the default predictions reflect the best choice for marginal analysis
  • Several new utilities to help you better manage graphs
  • New Quick start section of the manuals
  • New Stata Functions Reference Manual

Programming your thing...? You'll be interested in these new features in Stata 14.

  • Stata now uses the 64-bit Mersenne twister as its default random-number generator
  • New statistical, random-number distribution, and string functions
  • All new functions added to Stata are also available in Mata

There are many video tutorials in using Stata. Below you will find the most recent additions that relate to Stata 14, as well as a list of all other resources currently available.

Quick tips

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

Tour of Stata 14

Tour of the Stata 14 interface PDF documentation in Stata 14 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 14 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


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

Stata basics

Tour of the Stata 14 interface
Quick help in Stata
PDF documentation in Stata 14
Example data included with Stata
How to download and install user-written commands in Stata
Tour of Stata Project Manager
Postestimation Selector in Stata

Data management

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

Graphics

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

Bayesian analysis

Bayesian analysis in Stata
Graphical user interface for Bayesian analysis in Stata

Binary, count, and fractional outcomes

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

Case–control studies

Stratified analysis of case–control data
Odds ratios for case–control data

Classical hypothesis tests

One-sample t test in Stata
t test for two paired samples in Stata
t test for two independent samples in Stata

Descriptive statistics, tables, and cross-tabulations

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

Econometrics

Instrumental-variables regression using Stata

Effect sizes

Tour of effect sizes

Factor variables

The basics
Interactions
More interactions
Factor variable labels to results

Immediate commands

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

IRT (item response theory)

IRT (item response theory) models in Stata
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

Marginal means, predictive margins, and contrasts

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

Multilevel mixed-effects models

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

Multiple imputation

Setup, imputation, estimation—regression imputation
Setup, imputation, estimation—predictive mean matching
Setup, imputation, estimation—logistic regression

Panel data

Ordered logistic and probit for panel data
Panel-data survival models in Stata

Power and sample size

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

Structural equation modeling

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

Survey data analysis

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

Survival analysis

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

Time series

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

Treatment effects

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

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

Stata for Windows

  • Windows 10 *
  • Windows 8 *
  • Windows 7 *
  • Windows Vista *
  • Windows Server 2012 *
  • Windows Server 2008 *
  • Windows Server 2003 *

* 64-bit and 32-bit Windows varieties for x86-64 and x86 processors made by Intel® and AMD.

Stata for Mac

  • Stata for Mac requires 64-bit Intel® processors (Core™2 Duo or better) running OS X 10.7 or newer

Stata for Unix

  • Linux: Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux.

Hardware requirements

  • Minimum of 512 MB of RAM
  • Minimum of 900 MB of disk space
  • Stata for Unix requires a video card that can display thousands of colours or more (16-bit or 24-bit colour)

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Stata 14 Documentation

Every installation of Stata includes all the documentation in PDF format. Stata’s documentation consists of over 12,000 pages detailing each feature in Stata including the methods and formulas and fully worked examples. You can transition seamlessly across entries using the links within each entry.

Stata 14 Manuals


Base Reference Manual Bayesian Analysis
Reference Manual
Data-Management
Reference Manual
Functions Reference Manual
Graphics Reference Manual Item Response Theory Reference Manual Longitudinal-Data/Panel-Data Reference Manual Mata Reference Manual
Multilevel Mixed-Effects Reference Manual Multiple-Imputation Reference Manual Multivariate Statistics Reference Manual Power and Sample-Size Reference Manual
Programming
Reference Manual
Structural Equation Modeling Reference Manual Survey Data Reference Manual Survival Analysis Reference Manual
Time-Series Reference Manual Treatment-Effects Reference Manual User’s Guide Glossary and Index
Getting Started
with Stata for Mac
Getting Started
with Stata for Unix
Getting Started
with Stata for Windows


The Stata 14 documentation is copyright of StataCorp LP, College Station TX, USA, and is used with permission of StataCorp LP.

Students may purchase Stata/MPStata/SEStata/IC and Small Stata at a discounted price through the Stata GradPlan programme. For more information about available licence types, click here.

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