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快速,简单并易于使用
点击式的界面和强大,直观的命令语言让Stata使用起来快速,并易于使用。
所有的分析结果都可以被复制和存档,并用来出版和审查。不管您什么时候写的内容,版本控制系统确保统计程序可
继续生成同样的结果。

完整的数据管理功能
Stata的数据管理功能让您控制所有类型
的数据。
您可以重组数据,管理变量,并收集各组
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出版质量的图形
Stata轻松生成出版质量、风格迥异的图形。您可以编写脚本并以可复制的方式生成成百上千个图形,并且可以以EPS
或TIF格式输出打印、以PNG格式或SVG格式输出放到网上、或PDF格式输出预览。使用这个图形编辑器可更改图形的任何
方面,或添加标题、注释、横线、箭头和文本。

All of these sequences could have been generated in one line with generate and with the use of
the int and mod functions. The variables b through e are obtained with
. gen b = 1 + int((_n - 1)/2)
. gen c = 1 + mod(_n - 1, 6)
. gen d = 10 + mod(_n - 1, 3)
. gen e = 3 - mod(_n - 1, 3)
Nevertheless, seq() may save users from puzzling out such solutions or from typing in the needed
values.
In general, the sequences produced depend on the sort order of observations, following three rules:
1. observations excluded by if or in are not counted;
2. observations are sorted by varlist, if specified; and
3. otherwise, the order is that specified when seq() is called.

In Bayesian analysis, we can use previous information, either belief or experimental evidence, in
a data model to acquire more balanced results for a particular problem. For example, incorporating
prior information can mitigate the effect of a small sample size. Importantly, the use of the prior
evidence is achieved in a theoretically sound and principled way.
By using the knowledge of the entire posterior distribution of model parameters, Bayesian inference
is far more comprehensive and flexible than the traditional inference.
Bayesian inference is exact, in the sense that estimation and prediction are based on the posterior
distribution. The latter is either known analytically or can be estimated numerically with an arbitrary
precision. In contrast, many frequentist estimation procedures such as maximum likelihood rely on
the assumption of asymptotic normality for inference.
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