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实验设计 (DOE)

通过系统的数据采集制定实验计划

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。

经过设计的实验可以解决这些问题。在经过设计的实验中,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。所有实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

全部展开

ff2nTwo-level full factorial design
fullfactFull factorial design
fracfactFractional factorial design
fracfactgenFractional factorial design generators
bbdesignBox-Behnken design
ccdesignCentral composite design
candexchD-optimal design from candidate set using row exchanges
candgenCandidate set generation
cordexchCoordinate exchange
daugmentD-optimal augmentation
dcovaryD-optimal design with fixed covariates
rowexchRow exchange
rsmdemoInteractive response surface demonstration
lhsdesignLatin hypercube sample
lhsnormLatin hypercube sample from normal distribution
haltonsetHalton quasirandom point set
qrandstreamConstruct quasi-random number stream
sobolsetSobol quasirandom point set
interactionplotInteraction plot for grouped data
maineffectsplotMain effects plot for grouped data
multivarichartMultivari chart for grouped data
rsmdemoInteractive response surface demonstration
rstoolInteractive response surface modeling

主题

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 (DMAIC).

D-Optimal Designs

Minimum variance parameter estimates