Split-Plot Designs: What, Why, and How

被引：167

作者：

Jones, Bradley ^{[1
]}

Nachtsheim, Christopher J. ^{[2
]}

机构：

[1] SAS Inst, Cary, NC 27513 USA

[2] Univ Minnesota, Carlson Sch Management, Operat & Management Sci Dept, Minneapolis, MN 55455 USA

来源：

JOURNAL OF QUALITY TECHNOLOGY | 2009年 / 41卷 / 04期

关键词：

Block Designs; Completely Randomized Designs; Factorial Designs; Fractional Factorial Designs; Optimal Designs; Random-Effects Model; Random-Run-Order Designs; Response Surface Designs; Robust Parameter Designs; RESPONSE-SURFACE DESIGNS; HARD-TO-CHANGE; FACTORIAL-EXPERIMENTS; PROCESS VARIABLES; RANDOMIZATION; INFERENCE; RESTRICTIONS; ALGORITHM; MIXTURE;

D O I：

10.1080/00224065.2009.11917790

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The past decade has seen rapid advances in the development of new methods for the design and analysis of split-plot experiments. Unfortunately, the value of these designs for industrial experimentation has not been fully appreciated. In this paper, we review recent developments and provide guidelines for the use of split-plot designs in industrial applications.

引用

页码：340 / 361

页数：22

共 48 条

[1] Follow-up designs to resolve confounding in split-plot experiments [J].