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CONTÁCTENOS

Developer: StataCorp LLC

Latest Release: Stata 15 (June 2017)

Operating System: Windows, Mac OS, Linux

New - Extended Regression Models / Spatial Autoregressive Models / Linearized DSGE (Dynamic Stochastic General Equilibrium Models / 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:**The fastest version of Stata (for dual-core and multicore/multiprocessor computers);**Stata/SE:**Stata for large datasets;**Stata/IC:**Stata for moderate-sized datasets;**Comparison of features**

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 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 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.

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

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

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

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.

Panel data tobit

Multilevel regression for interval-censored data

Multilevel tobit regression for censored data

Panel-data cointegration tests

Tests for multiple breaks in time series

Multiple-group SEM for continuous, binary, ordered, and count outcomes

Multiple-group multilevel SEM

ICD-10-CM/PCS

Power analysis for cluster randomized designs

Power analysis for linear regression models

Heteroskedastic linear regression

Stata in Swedish

Poisson with sample selection

Zero-inflated ordered probit

Add your own power and sample-size methods

Transparency on graphs

Stream random-number generator

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.

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

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 |

- 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
- 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 *

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

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

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

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

ESTUDIANTES may purchase **Stata/MP**, **Stata/SE**, **Stata/IC** and **Small Stata** at a discounted price through the **Stata GradPlan** programme. For more information about available licence types, click here.