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。
新版本的STATA采用较具亲和力的窗口接口，使用者自行建立程序时，软件能提供具有直接命令式的语法。Stata提供完整的使用手册，包含统计样本建立、解释、模型与语法、文献等**过一万余页的出版品。
除此之外，Stata软件可以透过网络实时更新每天的较新功能，更可以得知世界各地的使用者对于STATA公司提出的问题与解决之道。使用者也可以透过Stata Journal获得许许多多的相关讯息以及书籍介绍等。另外一个获取庞大资源的管道就是Statalist，它是一个独立的listserver，每月交替提供使用者**过1000个讯息以及50个程序。
Stata的统计功能很强，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。具体说， Stata具有如下统计分析能力：
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。
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Stata’s reporting features allow you to create Word, PDF, Excel, and HTML documents that incorporate Stata results and graphs with formatted text and tables. Regardless of the type of document you create, you can rely on Stata’s integrated versioning features to ensure that your reports are reproducible.
Want dynamic reports that are updated as your data change? Stata’s reporting features make this easy too. Rerun the command or do-file that created your report with the updated dataset, and all Stata results in the report are updated automatically.
Stata 16 has new and improved reporting features, of course, but just as importantly, all of Stata's reporting features are now documented in a new Reporting Reference Manual. The manual includes many new examples that demonstrate workflows and provide guidance on customizing the Word, PDF, Excel, and HTML documents you create using Stata.
Stata 16 New in Bayesian analysis—Multiple chains, predictions, and more
Multiple chains.
Bayesian inference based on an MCMC (Markov chain Monte Carlo) sample is valid only if the Markov chain has converged. One way we can evaluate this convergence is to simulate and compare multiple chains.
The new nchains() option can be used with both the bayes: prefix and the bayesmh command. For instance, you type
. bayes, nchains(4): regress y x1 x2
and four chains will be produced. The chains will be combined to produce a more accurate final result. Before interpreting the result, however, you can compare the chains graphically to evaluate convergence. You can also evaluate convergence using the Gelman–Rubin convergence diagnostic that is now reported by bayes: regress and other Bayesian estimation commands when multiple chains are simulated. When you are concerned about noncovergence, you can investigate further using the bayesstats grubin command to obtain individual Gelman–Rubin diagnostics for each parameter in your model.
Bayesian predictions.
Bayesian predictions are simulated values from the posterior predictive distribution. These predictions are useful for checking model fit and for predicting out-of-sample observations. After you fit a model with bayesmh, you can use bayespredict to compute these simulated values or functions of them and save those in a new Stata dataset. For instance, you can type
. bayespredict (ymin:@min({_ysim})) (ymax:@max({_ysim})), saving(yminmax)
to compute minimums and maximums of the simulated values. You can then use other postestimation commands such as bayesgraph to obtain summaries of the predictions.
The dataset created by bayespredict may include thousands of simulated values for each observation in your dataset. Sometimes, you do not need all of these individual values. To instead obtain posterior summaries such as posterior means or medians, you can use bayespredict, pmean or bayespredict, pmedian. Alternatively, you may be interested in a random sample of the simulated values. You can use, for instance, bayesreps, nreps(100) to obtain 100 replicates.
Finally, you may want to evaluate model goodness of fit using posterior predictive p-values, also known as PPPs or as Bayesian predictive p-values. PPPs measure agreement between observed and replicated data and can be computed using the new bayesstats ppvalues command. For instance, using our earlier example
. bayesstats ppvalues {ymin} {ymax} using yminmax
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