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Stata—数据统计分析软件包
Stata 是一套集数据分析、数据管理以及绘制专业图表于一体的整合性统计软件，也是当前三大权威同机软件之一，功能非常强大，具有操作灵活、简单、易学易用、运行速度极快等优点。他有多个版本。Stata/MP，这是运行最快和处理数据集最大的Stata版本，允许数据集最多可达120,000个变量。
Stata功能非常强大，具有操作灵活、简单、易学易用、运行速度极快等优点。通过STATA可以进行各种预测，包括线性预测、非线性预测、动态预测等；或者对参数和模型进行各种检验，比如似然比、Hausman检验、Granger因果检验、嵌套Cox检验等。除了STATA内设的OLS、GLS、NLS、ML、TSLS、GMM等估计方法之外，用户还可以自己设定似然估值函数进行NLS、ML估计和GMM估计，或者利用MATA的最优化模块进行数值最优化或者模型最优化。
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《使用putdocx输出list的结果》
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《命令sum2docx输出统计量表到docx文件》
《用reg2docx报告你的实证结果吧！》
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更多推文持续更新中~
一、统计功能
Stata 的统计功能十分强大，除了传统的统计分析方法外，还收集了近20年发展起来的新方法，如 Cox 比例风险回归，指数与 Weibull 回归，多类结果与有序结果的 logistic 回归， Poisson 回归，负二项回归及广义负二项回归，随机效应模型等。Stata 具有如下详细的统计分析能力：
统计分析能力
能力介绍
数值变量资料的一般分析
参数估计，t检验，单因素和多因素的方差分析，协方差分析，交互效应模型，平衡和非平衡设计，嵌套设计，随机效应，多个均数的两两比较，缺项数据的处理，方差齐性检验，正态性检验，变量变换等。
分类资料的一般分析
参数估计，列联表分析 ( 列联系数，确切概率 ) ，流行病学表格分析等。
等级资料的一般分析
秩变换，秩和检验，秩相关等。
相关与回归分析
简单相关，偏相关，典型相关，以及多达数十种的回归分析方法，如多元线性回归，逐步回归，加权回归，稳键回归，二阶段回归，百分位数 ( 中位数 ) 回归，残差分析、强影响点分析，曲线拟合，随机效应的线性回归模型等。
其他方法
质量控制，整群抽样的设计效率，诊断试验评价， kappa 等。
二、作图功能
Stata 的作图模块，15版本增加了多个新图形制作:
这些图形的巧妙应用，可以满足绝大多数用户的统计作图要求。在有些非绘图命令中，也提供了专门绘制某种图形的功能，如在生存分析中，提供了绘制生存曲线图，回归分析中提供了残差图等。
三、矩阵运算功能
矩阵代数是多元统计分析的重要工具， Stata 提供了多元统计分析中所需的矩阵基本运算，如矩阵的加、积、逆、 Cholesky 分解、 Kronecker 内积等；还提供了一些高级运算，如特征根、特征向量、奇异值分解等；在执行完某些统计分析命令后，还提供了一些系统矩阵，如估计系数向量、估计系数的协方差矩阵等。
四、程序设计功能
Stata 是一个统计分析软件，但它也具有很强的程序语言功能，这给用户提供了一个广阔的开发应用的天地，用户可以充分发挥自己的聪明才智，熟练应用各种技巧，真正做到随心所欲。Stata 的 ado 文件 ( 高级统计部分 ) 是用 Stata 自己的语言编写的。
Stata 其统计分析能力远远超过了 SPSS ，在许多方面也超过了 SAS 。由于 Stata 在分析时是将数据全部读入内存，在计算全部完成后才和磁盘交换数据，因此计算速度极快（一般来说， SAS 的运算速度要比 SPSS 至少快一个数量级，而 Stata 的某些模块和执行同样功能的 SAS 模块比，其速度又比 SAS 快将近一个数量级。） Stata 也是采用命令行方式来操作，但使用上远比 SAS 简单。其生存数据分析、纵向数据（重复测量数据）分析等模块的功能甚至超过了 SAS 。用 Stata 绘制的统计图形相当精美，很有特色。
五、功能列表
功能名称
功能介绍
线性模型
(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
面板/纵向数据
(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
多级混合效应模型
(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
二进制、计数和有限结果
(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
扩展回归模型（ERMs）
(Extended regression models (ERMs))
combine endogenous covariates, sample selection, and nonrandom treatment in models for continuous, interval-censored, binary, and ordinal outcomes • more
广义线性模型（GLMs）
(Generalized linear models (GLMs))
ten link functions • user-defined links • seven distributions • ML and IRLS estimation • nine variance estimators • seven residuals • more
有限混合模型（FMMs）
(Finite mixture models (FMMs))
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 • more
空间自回归模型
(Spatial autoregressive models)
spatial lags of dependent variable, independent variables, and autoregressive errors • fixed and random effects in panel data • endogenous covariates • analyze spillover effects • more
方差分析/多变量方差分析
(ANOVA/MANOVA)
balanced and unbalanced designs • factorial, nested, and mixed designs • repeated measures • marginal means • contrasts • more
精确统计
(Exact statistics)
exact logistic and Poisson regression • exact case–control statistics • binomial tests • Fisher’s exact test for r × c tables • more
线性动态随机一般均衡模型
(Linearized DSGE models)
specify models algebraically • solve models • estimate parameters • identification diagnostics • policy and transition matrices • IRFs • dynamic forecasts • more
测试、预测和结果
(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 • more
差异、成对比较和差数
(Contrasts, pairwise comparisons, and margins)
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 • more
简单最大概似法
(Simple maximum likelihood)
specify likelihood using simple expressions • no programming required • survey data • standard, robust, bootstrap, and jackknife SEs • matrix estimators • more
可编程最大概似法
(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 • more
再抽样及模拟方法
(Resampling and simulation methods)
bootstrap • jackknife • Monte Carlo simulation • permutation tests • more
时间序列
(Time series)
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 • more
生存分析
(Survival analysis)
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 • more
贝叶斯分析
(Bayesian analysis)
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 • more
功效和样本大小
(Power and sample size)
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 • more
治疗效果/因果推断
(Treatment effects/Causal inference)
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 • more
结构方程模型（SEM）
(SEM (structural equation modeling))
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 • more
潜伏组分析
(Latent class analysis)
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 • more
多重估算
(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 • more
调查方法
(Survey methods)
multistage designs • bootstrap, BRR, jackknife, linearized, and SDR variance estimation • poststratification • DEFF • predictive margins • means, proportions, ratios, totals • summary tables • almost all estimators supported • more
聚类分析
(Cluster analysis)
hierarchical clustering • kmeans and kmedian nonhierarchical clustering • dendrograms • stopping rules • user-extensible analyses • more
项目反应理论（IRT）
(IRT (item response theory))
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) • more
多变量方法
(Multivariate methods)
factor analysis • principal components • discriminant analysis • rotation • multidimensional scaling • Procrustean analysis • correspondence analysis • biplots • dendrograms • user-extensible analyses • more
数据[资料]管理
(Data management)
data transformations • match-merge • import/export data • ODBC • SQL • Unicode • by-group processing • append files • sort • row–column transposition • labeling • save results • more
绘图
(Graphics)
lines • bars • areas • ranges • contours • confidence intervals • interaction plots • survival plots • publication quality • customize anything • Graph Editor • more
图形用户界面
(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 • more
参考资料
(Documentation)
27 manuals • 14,000+ pages • seamless navigation • thousands of worked examples • quick starts • methods and formulas • references • more
基本统计
(Basic statistics)
summaries • cross-tabulations • correlations • z and t tests • equality-of-variance tests • tests of proportions • confidence intervals • factor variables • more
非参数方法
(Nonparametric methods)
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 • more
流行病学
(Epidemiology)
standardization of rates • case–control • cohort • matched case–control • Mantel–Haenszel • pharmacokinetics • ROC analysis • ICD-10 • more
GMM与非线性回归
(GMM and nonlinear regression)
generalized method of moments (GMM) • nonlinear regression • more
其他统计方法
(Other statistical methods)
kappa measure of interrater agreement • Cronbach's alpha • stepwise regression • tests of normality • more
函数
(Functions)
statistical • random-number • mathematical • string • date and time • more
互联网功能
(Internet capabilities)
ability to install new commands • web updating • web file sharing • latest Stata news • more
用户编写的命令
(User-written commands)
user-written commands for meta-analysis, data management, survival, econometrics, more
编程特点
(Programming features)
adding new commands • command scripting • object-oriented programming • menu and dialog-box programming • dynamic documents • Markdown • Project Manager • plugins • more
矩阵编程-Mata
(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 • more
嵌入式统计计算
(Embedded statistical computations)
Numerics by Stata
系统要求
Stata for Windows
Windows 10 *
Windows 8 *
Windows 7 *
Windows Vista *
Windows Server 2016, 2012, 2008, 2003 *
* 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 Linux
Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux
For xstata, you need to have GTK 2.24 installed
Hardware requirements
Package
Memory
Disk space
Stata/MP
4GB
1GB
Stata/SE
2GB
1GB
Stata/IC
1GB
1GB
