Stata SE

Trialversion of Stata

The producer provides a free 30-day trialversion on their website. The trialversion contains all the features of Stata. You can register for this license simply by visiting the following link: http://www.stata.com/customer-service/evaluate-stata/

 

 

Compatible operating systems

Stata will run on the platforms listed below. While Stata software is platform-specific, your Stata license is not; therefore, you need not specify your operating system when placing your order for a license.

 running Stata on a dual-core, multicore, or multiprocessor computer.

Platforms

Stata for Windows®

• Windows 10 *
• Windows 8 *
• Windows Server 2019, 2016, 2012 *

* Stata requires 64-bit Windows for x86-64 processors made by Intel® or AMD
 

Stata for Mac®

• Mac with Apple Silicon or 64-bit Intel processor
• macOS 11.0 (Big Sur) or newer for Macs with Apple Silicon and macOS 10.12 (Sierra) or newer for Macs with 64-bit Intel processors

Stata for Linux

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

 

Hardware requirements

Package	Memory	Disk space
Stata/MP	4 GB	2 GB
Stata/SE	2 GB	2 GB
Stata/BE	1 GB	2 GB

Stata for Linux requires a video card that can display thousands of colors or more (16-bit or 24-bit color)

Interval-censored survival models Fit any of Stata's six parametric survival models to interval-censored data. All the usual survival features are supported: stratified estimation, robust and clustered SEs, survey data, graphs, and more.

Nonlinear multilevel mixed-effects models When ... your science ... says ... your model ... is ... nonlinear in its parameters

Mixed logit models: Advanced choice modeling Do you walk to work, ride a bus, or drive your car? Which of three insurance plans do you buy? Which political party do you vote for? We make dozens of choices every day. Researchers have access to gaggles of data about those choices. Mixed logit introduces random effects into choice modeling and thereby relaxes the IIA assumption and increases model flexibility.

Nonparametric regression When you know something matters. But have no idea how.

Create Word documents from Stata Automate your reports Write paragraphs and tables to Word documents Embed Stata results and graphs in paragraphs and tables Customize formatting of text, tables, and cells   Create PDFs, too!

Bayesian multilevel models Small number of groups? Many hierarchical levels? Prefer making probability statements? Consider Bayesian multilevel modeling.

Threshold regression Your time-series regression may change parameters at some point in time or at multiple points in time. The activity of foraging animals might follow a completely different pattern at temperatures above some threshold. You may not know the value of that threshold. Finding such thresholds and estimating the parameters within the regimes is what threshold regression does.

Panel-data tobit with random coefficients Stata has long had estimators for random effects (random intercepts) in panel data.

Search, browse, and import FRED data The St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data.

Multilevel regression for interval-measured outcomes Incomes are sometimes recorded in groupings, as are people's weights, insect counts, grade-point averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So ... we need multilevel regression for interval-measured (interval-censored) outcomes.

Multilevel tobit regression for censored outcomes Left-censoring, right-censoring, both Censoring that varies by observation Make inferences about either the uncensored or the censored outcome Robust and clustered SEs Support for survey data

Panel-data cointegration tests Tests Kao Pedroni Westerlund Total of nine variants of tests

Tests for multiple breaks in time series Cumulative sum (CUSUM) test for parameter stability CUSUM of recursive residuals CUSUM of OLS residuals Plots with CIs

Multiple-group generalized SEM Generalized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include continuous, binary, ordinal, count, categorical, and even survival outcomes multilevel models

ICD-10-CM/PCS NCHS's ICD-10-CM diagnosis codes CMS's ICD-10-PCS procedure codes Verify codes are valid Create new variables based on codes

Power for cluster randomized designs Power analysis for comparing One- and two-sample means One- and two-sample proportions Two-sample survivor curves when you randomize clusters instead of individuals

Power for linear regression models Solve for Power Sample size Effect size Specify lists of Alpha values Power levels Beta values Sample sizes And more Automated tables and graphs

Heteroskedastic linear regression Model for the variance Robust and cluster SEs Survey-data support

Poisson models with sample selection Counts are common. How many: Fish did you catch? Accidents occurred? Patents does a firm generate? Outcomes are not always seen. Folks evade the game warden. Accidents are not always reported. Some firms prefer trade secrets to patents. So you need Poisson models with sample selection.

More in panel data Nonlinear models with random effects, including random coefficients Bayesian panel-data models Interval regression with random intercepts and random coefficients

More in graphics Transparency in graphs SVG export

More in statistics Bayesian survival models Zero-inflated ordered probit Add your own power and sample-size methods Bayesian sample-selection models And yet more

More in the interface Stata in Swedish Stata in Chinese Improvements to the Do-file Editor

And, even more

Stream random-number generator Improvements for Java plugins

 

Die gesamte Feature Liste finden Sie auch afu der Seite von Stata.com:

https://www.stata.com/features/

 

Stata Features

Data management

data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results

Basic statistics

summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables

Linear models

regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS

Multilevel mixed-effects models

generalized linear models;continuous, binary, and count outcomes; two-, three-, and higher-level models; random-intercepts; random-slopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models

Binary, count, and discrete outcomes

logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and left-truncated count models; selection models; marginal effects

Longitudinal data/panel data

random and fixed effects with robust standard errors; linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances

Generalized linear models (GLMs)

ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals

Nonparametric methods

Wilcoxon-Mann-Whitney, Wilcoxon signed ranks and Kruskal-Wallis tests; Spearman and Kendall correlations; Kolmogorov-Smirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping

Exact statistics

exact logistic and Poisson regression, exact case-control statistics, binomial tests, Fisher's exact test for r × c tables

ANOVA/MANOVA

balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts

Multivariate methods

factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses

Cluster analysis

hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses

Resampling and simulation methods

bootstrapping, jackknife and Monte Carlo simulation; permutation tests

Tests, predictions, and effects

Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, least-squares means, adjusted means; marginal and partial effects; forecast models; Hausman tests

Graphics

line charts, scatterplots, bar charts, pie charts, hi-lo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q-Q plots

Survey methods

multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression

Survival analysis

Kaplan-Meier and Nelson-Aalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left- and right-censoring, Weibull, exponential, and Gompertz analysis

Epidemiology

standardization of rates, case–control, cohort, matched case-control, Mantel-Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM

Time series

ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; state-space models; business calendars; correlograms; periodograms; forecasts; impulse-response functions; unit-root tests; filters and smoothers; rolling and recursive estimation

Multiple imputation

nine univariate imputation methods; multivariate normal imputation; chained equations; explore pattern of missingness; manage imputed datasets; fit model and pool results; transform parameters; joint tests of parameter estimates; predictions

Simple maximum likelihood

specify likelihood using simple expressions; no programming required; survey data; standard, robust, bootstrap, and jackknife SEs; matrix estimators

Programmable maximum likelihood

user-specified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife SEs; Wald tests; survey data; numeric or analytic derivatives

Other statistical methods

kappa measure of interrater agreement; Cronbach's alpha; stepwise regression; tests of normality

Programming features

adding new commands; command scripting; object-oriented programming; menu and dialog-box programming; Project Manager; plugins

Matrix programming-Mata

interactive sessions, large-scale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, object-oriented programming

Internet capabilities

ability to install new commands, web updating, web file sharing, latest Stata news

Accessibility

Section 508 compliance, accessibility for persons with disabilities

Sample session

A sample session of Stata for Mac, Unix, or Windows.

User-written commands

User-written commands for meta-analysis, data management, survival, econometrics

Graphical user interface

menus and dialogs for all features; Data Editor; Variables Manager; Graph Editor; Project Manager; Do-file Editor; Clipboard Preview Tool; multiple preference sets

Graphics

line charts; scatterplots; bar charts; pie charts; hi-lo charts; contour plots; GUI Editor; regression diagnostic graphs; survival plots; nonparametric smoothers; distribution Q-Q plots

Documentation

20 manuals20 manuals; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references

Power and sample size

power; sample size; effect size; minimum detectable effect; means; proportions; variances; correlations; case-control studies; cohort studies; survival analysis; balanced or unbalanced designs; results in tables or graphs

Treatment effects

inverse probability weight (IPW); doubly robust methods; propensity score matching; regression adjustment; covariate matching; multilevel treatments; average treatment effects (ATEs); average treatment effects on the treated (ATETs); potential-outcome means (POMs)

SEM (Structural equation modeling)

graphical path diagram builder; standardized and unstandardized estimates; modification indices; direct and indirect effects; continuous, binary, count, and ordinal outcomes (GLM); multilevel models; random slopes and intercepts; factors scores, empirical Bayes, and other predictions; groups and tests of invariance; goodness of fit; handles MAR data by FIML; correlated data

Functions

statistical; random-number; mathematical; string; date and time

Embedded statistical computations

Numerics by Stata

Contrasts, pairwise comparisons, and margins

compare means, intercepts, or slopes; compare to reference category, adjacent category, grand mean, etc.; orthogonal polynomials; multiple comparison adjustments; graph estimated means and contrasts; interaction plots

GMM an nonlinear regression

generalized method of moments (GMM); nonlinear regression