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`	Two-level 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 D-optimal design
`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`	Quasirandom 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
Improve an Engine Cooling Fan Using Design for Six Sigma Techniques
This example shows how to improve the performance of an engine cooling fan through a Design for Six Sigma approach using Define, Measure, Analyze, Improve, and Control.
D-Optimal Designs
Minimum variance parameter estimates