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Stata 16版本更新22条新功能。比以往的版本更强大,更值得您拥有。
Stata 是一套提供其使用者数据分析、数据管理以及绘制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。
尽管您不需要使用Stata进行编程,
但是它作为一个快速完成矩阵的编程
语言,是Stata功能中不可或缺的一部
分。Mata既是一个操作矩阵的互动环
境,也是一个完整开发环境,可以生
产编译和优化代码。它还包含了一些
功能来处理面板数据、执行真实
或复制的矩阵运算,提供完整的支持
面向对象的编程,并完全兼容Stata。
What is Bayesian analysis?
Bayesian analysis is a statistical analysis that answers research questions about unknown parameters
of statistical models by using probability statements. Bayesian analysis rests on the assumption that
all model parameters are random quantities and thus are subjects to prior knowledge. This assumption
is in sharp contrast with the more traditional, also called frequentist, statistical inference where all
parameters are considered unknown but fixed quantities. Bayesian analysis follows a simple rule
of probability, the Bayes rule, which provides a formalism for combining prior information with
evidence from the data at hand. The Bayes rule is used to form the so called posterior distribution of
model parameters. The posterior distribution results from updating the prior knowledge about model
parameters with evidence from the observed data. Bayesian analysis uses the posterior distribution to
form various summaries for the model parameters including point estimates such as posterior means,
medians, percentiles, and interval estimates such as credible intervals. Moreover, all statistical tests
about model parameters can be expressed as probability statements based on the estimated posterior
distribution.
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