Les fonctionnalités de STATA
Largement utilisé
Stata est largement utilisé dans les domaines suivants :
- Sciences comportementales
- Bio statistique
- Économie
- Éducation
- Épidémiologie
- Finance, Affaires, et marketing
- Médecine
- Sciences politiques
- Santé publique
- Politique publique
- Sociologie
Par catégorie
regression • censored outcomes • endogenous regressors • bootstrap, jackknife, and robust and cluster–robust variance • instrumental variables • three-stage least squares • constraints • quantile regression • GLS
random and fixed effects with robust standard errors • linear mixed models • random-effects probit • GEE • random- and fixed-effects Poisson • dynamic panel-data models • instrumental variables • panel unit-root tests
continuous, binary, count, and survival outcomes • two-, three-, and higher-level models • generalized linear models • nonlinear models • random intercepts • random slopes • crossed random effects • BLUPs of effects and fitted values • hierarchical models • residual error structures • DDF adjustments • support for survey data
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
combine endogenous covariates, sample selection, and nonrandom treatment in models for continuous, interval-censored, binary, and ordinal outcomes
ten link functions • user-defined links • seven distributions • ML and IRLS estimation • nine variance estimators • seven residuals
fmm: prefix for 17 estimators • mixtures of a single estimator • mixtures combining multiple estimators or distributions • continuous, binary, count, ordinal, categorical, censored, truncated, and survival outcomes
spatial lags of dependent variable, independent variables, and autoregressive errors • fixed and random effects in panel data • endogenous covariates • analyze spillover effects
balanced and unbalanced designs • factorial, nested, and mixed designs • repeated measures • marginal means • contrasts
exact logistic and Poisson regression • exact case–control statistics • binomial tests • Fisher’s exact test for r x c tables
specify models algebraically • solve models • estimate parameters • identification diagnostics • policy and transition matrices • IRFs • dynamic forecasts
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
compare means, intercepts, or slopes • compare with reference category, adjacent category, grand mean, etc. • orthogonal polynomials • multiple-comparison adjustments • graph estimated means and contrasts • interaction plots
specify likelihood using simple expressions • no programming required • survey data • standard, robust, bootstrap, and jackknife SEs • matrix estimators
user-specified functions • NR, DFP, BFGS, BHHH • OIM, OPG, robust, bootstrap, and jackknife SEs • Wald tests • survey data • numeric or analytic derivatives
bootstrap • jackknife • Monte Carlo simulation • permutation tests
ARIMA • ARFIMA • ARCH/GARCH • VAR • VECM • multivariate GARCH • unobserved-components model • dynamic factors • state-space models • Markov-switching models • business calendars • tests for structural breaks • threshold regression • forecasts • impulse–response functions • unit-root tests • filters and smoothers • rolling and recursive estimation
Kaplan–Meier and Nelson–Aalen estimators, • Cox regression (frailty) • parametric models (frailty, random effects) • competing risks • hazards • time-varying covariates • left-, right-, and interval-censoring • Weibull, exponential, and Gompertz models
thousands of built-in models • univariate and multivariate models • linear and nonlinear models • multilevel models • continuous, binary, ordinal, and count outcomes • bayes: prefix for 45 estimation commands • continuous univariate, multivariate, and discrete priors • add your own models • convergence diagnostics • posterior summaries • hypothesis testing • model comparison
power • sample size • effect size • minimum detectable effect • means • proportions • variances • correlations • ANOVA • regression • cluster randomized designs • case–control studies • cohort studies • contingency tables • survival analysis • balanced or unbalanced designs • results in tables or graphs
inverse probability weight (IPW) • doubly robust methods • propensity-score matching • regression adjustment • covariate matching • multilevel treatments • endogenous treatments • average treatment effects (ATEs) • ATEs on the treated (ATETs) • potential-outcome means (POMs) • continuous, binary, count, fractional, and survival outcomes
graphical path diagram builder • standardized and unstandardized estimates • modification indices • direct and indirect effects • continuous, binary, count, ordinal, and survival outcomes • multilevel models • random slopes and intercepts • factor scores, empirical Bayes, and other predictions • groups and tests of invariance • goodness of fit • handles MAR data by FIML • correlated data • survey data
binary, ordinal, continuous, count, categorical, fractional, and survival items • add covariates to model class membership • combine with SEM path models • expected class proportions • goodness of fit • predictions of class membership
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
multistage designs • bootstrap, BRR, jackknife, linearized, and SDR variance estimation • poststratification • DEFF • predictive margins • means, proportions, ratios, totals • summary tables • almost all estimators supported
hierarchical clustering • kmeans and kmedian nonhierarchical clustering • dendrograms • stopping rules • user-extensible analyses
binary (1PL, 2PL, 3PL), ordinal, and categorical response models • item characteristic curves • test characteristic curves • item information functions • test information functions • differential item functioning (DIF)
factor analysis • principal components • discriminant analysis • rotation • multidimensional scaling • Procrustean analysis • correspondence analysis • biplots • dendrograms • user-extensible analyses
data transformations • match-merge • import/export data • ODBC • SQL • Unicode • by-group processing • append files • sort • row–column transposition • labeling • save results
menus and dialogs for all features • Data Editor • Variables Manager • Graph Editor • Project Manager • Do-file Editor • Clipboard Preview Tool • multiple preference sets
27 manuals • 14,000+ pages • seamless navigation • thousands of worked examples • quick starts • methods and formulas • references
summaries • cross-tabulations • correlations • z and t tests • equality-of-variance tests • tests of proportions • confidence intervals • factor variables
nonparametric regression • 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
standardization of rates • case–control • cohort • matched case–control • Mantel–Haenszel • pharmacokinetics • ROC analysis • ICD-10
kappa measure of interrater agreement • Cronbach’s alpha • stepwise regression • tests of normality
ability to install new commands • web updating • web file sharing • latest Stata news
user-written commands for meta-analysis, data management, survival, econometrics
adding new commands • command scripting • object-oriented programming • menu and dialog-box programming • dynamic documents • Markdown • Project Manager • plugins
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
IQ report for regulatory agencies such as the FDA • installation verification
Latent class analysis • Bayes prefix • Combine endogenous regressors, treatment effects, and selection • Spatial autoregressive models • Finite mixture models (FMM) • Markdown—create web pages with intermixed text, Stata output, and graphs • DSGE models • Nonlinear multilevel and panel-data models • Mixed logit choice models • Multilevel Bayesian analysis • Nonparametric regression • Interval-censored survival models