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Stata

Developer: StataCorp LLC

Latest Release: Stata 15 (June 2017)

Operating System: Windows, macOS, Linux

New Stata 15 - Extended Regression Models / Spatial Autoregressive Models / Linearized DSGE / Embed Stata results, graphs in Word and PDF documents, Markdown to HTML / Transparent Graphics and a lot more...
End User License Agreement

Stata 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.

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 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

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/IC

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.

Comparison of features

Product Features

Stata/IC

Stata/SE

Stata/MP

Maximum number of variables

Info

2,048

32,767

120,000

Maximum number of observations

Info

2.14 billion

2.14 billion

Up to 20 billion

Maximum number of independent variables

Info

798

10,998

10,998

Multicore support

Time to run logistic regression with 5 million obs and 10 covariates Info

1-core

10.0 sec

1-core

10.0 sec

2 core

5.0 sec

4 core

2.6 sec

4+

even faster

Complete suite of statistical features

Info

Yes

Yes

Yes

Yes

Yes

Publication-quality graphics

Info

Yes

Yes

Yes

Yes

Yes

Matrix programming language

Yes

Yes

Yes

Yes

Yes

Complete PDF documentation

Info

Yes

Yes

Yes

Yes

Yes

Exceptional technical support

Yes

Yes

Yes

Yes

Yes

Includes within-release updates

Yes

Yes

Yes

Yes

Yes

64-bit version available

Yes

Yes

Yes

Yes

Yes

Windows, Mac, or Unix

Info

Yes

Yes

Yes

Yes

Yes

Memory requirements

1 GB

2 GB

4 GB

Disk space requirements

1 GB

1 GB

1 GB

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 mid-sized datasets

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 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 and 2 billion observations. Stata/IC can have at most 798 independent variables in a model.

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.

Quick tips

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

Tour of Stata 15

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


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 15 interface
Quick help in Stata
PDF documentation in Stata 15
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 *
  • Windows Server 2016 *

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

Stata for Mac

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

Stata for Unix

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

Hardware requirements

  • 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)

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

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Stata 15 Documentation

Every installation of Stata includes all the documentation in PDF format. Stata’s documentation consists of over 14,000 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 15 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 15 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. For more information about available licence types, click here.

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