跳到内容ScholarGate
文库我的文库桌面Review Studio助手
登录
Multi-response full factorial design/证据
方法证据记录

Multi-response full factorial design

Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously.

Sources recorded, not reviewed

源记录

引文逐字复制自方法源记录。这些引文不代表任何层级的验证。

Multi-Response Full Factorial Design of Experiments
分类方法记录 · process-pipeline / experimental-design
  • Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. · ISBN 978-1119492443
  • Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. · DOI 10.1080/00224065.1980.11980968
打开完整方法

精选声明

声明已持久化到证据分类账中,每个声明都有自己的评估。

尚无精选声明

当分类账中没有声明时,此视图不会自行创建声明评估。

相关方法

从方法图中生成,显示为机器建议的关系 — 不推断任何证据声明。

Taxonomic bucketDesign of experimentsmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMulti-response Response Surface Methodologymachine-suggested · Relational suggestion, not evidence.Used in the same domainResponse Surface Methodologymachine-suggested · Relational suggestion, not evidence.

证据状态

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

来源

从方法源记录复制的 2 条记录的引文。

操作

打开方法页面
ScholarGate

以内容为本的研究方法参考文库——每种方法是什么、如何运作、源自何处。

开放数据(CC-BY)

探索

  • 文库
  • 搜索方法…
  • 按领域浏览
  • 学科领域
  • 历程
  • 对比
  • 该用哪种方法?

参考

  • 学科
  • 图集
  • 术语表
  • 方法论
  • 哲学

工作区

  • 我的文库
  • 桌面
  • 聊天

公司

  • 关于
  • 价格
  • 联系我们
  • 建议新方法

本词条系根据已发表文献整理,仅供参考。核实任何信息的准确性及其是否适用于您的具体用途,仍由您自行负责。

© 2026 ScholarGate · 研究方法参考文库
  • 隐私
  • Cookie
  • 条款
  • 删除账户