Stata 是一套提供其使用者数据分析、数据管理以及绘制专业图表的完整及整合性统计软件。它提供许许多多功能，包含线性混合模型、均衡重复反复及多项式普罗比模式。用Stata绘制的统计图形相当精美。
Stata is a general-purpose statistical software package created in 1985 by StataCorp. Most of its users work in research, especially in the fields of economics, sociology, political science, biomedicine and epidemiology.[citation needed]Stata’s capabilities include data management, statistical analysis, graphics, simulations, regression analysis (linear and multiple), and custom programming.
The name Stata is a portmanteau of the words statistics and data. The correct English pronunciation of Stata “must remain a mystery”; any of “Stay-ta”, “Sta-ta” or “Stah-ta” are considered acceptable.
There are four major builds of each version of Stata:
– Stata/MP for multiprocessor computers (including dual-core and multicore processors)
– Stata/SE for large databases
– Stata/IC, which is the standard version
– Small Stata, which is a smaller, student version for educational purchase only
User interface
Stata has always emphasized a command-line interface, which facilitates replicable analyses. Starting with version 8.0, however, Stata has included a graphical user interface which uses menus and dialog boxes to give access to nearly all built-in commands. This generates code which is always displayed, easing the transition to the command line interface and more flexible scripting language. The dataset can be viewed or edited in spreadsheet format. From version 11 on, other commands can be executed while the data browser or editor is opened.
Data structure and storage
Stata can only open a single dataset at any one time. Stata holds the entire dataset in (random-access or virtual) memory, which limits its use with extremely large datasets. This is mitigated to some extent by efficient internal storage, as there are integer storage types which occupy only one or two bytes rather than four, and single-precision (4 bytes) rather than double-precision (8 bytes) is the default for floating-point numbers.
Stata is offered in many licenses. From student to professional use. A “small” version is available for small datasets at a lower price point.
The dataset is always rectangular in format, that is, all variables hold the same number of observations (in more mathematical terms, all vectors have the same length, although some entries may be missing values).
Linear models
regression • censored outcomes • endogenous regressors • bootstrap, jackknife, and robust and cluster–robust variance • instrumental variables • three-stage least squares • constraints • quantile regression • GLS • more
Panel/longitudinal 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 • instrumental variables • panel unit-root tests • more
Multilevel mixed-effects models
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 • more
Binary, count, and limited 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 • more
Extended regression models (ERMs)
combine endogenous covariates, sample selection, and nonrandom treatment in models for continuous, interval-censored, binary, and ordinal outcomes • more
and more.
https://www.anonymz.com/…//www.stata.com
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