试验设计 (DOE)

通过系统的数据采集制定试验计划

被动的数据收集在统计建模中产生许多问题。在响应变量中观察到的变化也许跟在个体因子（过程变量）中观察到的变化有关，但不是由其引起的。多个因子的同时变化可能产生交互作用，难以分成独立的效应。观测值可能是相关的，而数据模型却认为它们是无关的。

经过设计的试验可以解决这些问题。在经过设计的试验中，会主动操控生成数据的过程，从而提高信息的质量并消除冗余数据。所有试验设计的一个共同目标就是既要尽可能少地收集数据，又要为准确估计模型参数提供足够的信息。

函数

`ff2n`	二水平完全析因设计
`fullfact`	完全析因设计

`fracfact`	Fractional factorial design
`fracfactgen`	Fractional factorial design generators

`bbdesign`	Box-Behnken design
`ccdesign`	Central composite design

`candexch`	D-optimal design from candidate set using row exchanges
`candgen`	Candidate set generation
`cordexch`	Coordinate exchange
`daugment`	D-optimal augmentation
`dcovary`	D-optimal design with fixed covariates
`rowexch`	Row exchange
`rsmdemo`	Interactive response surface demonstration

`lhsdesign`	拉丁超立方样本
`lhsnorm`	来自正态分布的拉丁超立方样本

`haltonset`	Halton quasirandom point set
`qrandstream`	Construct quasi-random number stream
`sobolset`	Sobol quasirandom point set

`interactionplot`	Interaction plot for grouped data
`maineffectsplot`	Main effects plot for grouped data
`multivarichart`	Multivari chart for grouped data
`rsmdemo`	Interactive response surface demonstration
`rstool`	Interactive response surface modeling

Full Factorial Designs
Designs for all treatments
Fractional Factorial Designs
Designs for selected treatments
Response Surface Designs
Quadratic polynomial models
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D-Optimal Designs
Minimum variance parameter estimates