Title: Robust analysis of variance: process design and quality improvement
Author: Avi Giloni, Sridhar Seshadri, Jeffrey S. Simonoff
Sy Syms School of Business, Yeshiva University, 500 West 185th Street, New York, NY 10033, USA.
Leonard N. Stern School of Business, New York University, 44 West 4th Street, New York, NY 10012, USA.
Leonard N. Stern School of Business, New York University, 44 West 4th Street, New York, NY 10012, USA
Journal: Int. J. of Productivity and Quality Management, 2006 Vol.1, No.3, pp.306 - 319
Abstract: We discuss the use of robust Analysis Of Variance (ANOVA) techniques as applied to quality engineering. ANOVA is the cornerstone for uncovering the effects of design factors on performance. Our goal is to utilise methodologies that yield similar results to standard methods when the underlying assumptions are satisfied, but are also relatively unaffected by outliers (observations that are inconsistent with the general pattern in the data). We do this by utilising statistical software to implement robust ANOVA methods, which are no more difficult to perform than ordinary ANOVA. We study several examples to illustrate how using standard techniques can lead to misleading inferences about the process being examined, which are avoided when using a robust analysis. We further demonstrate that assessments of the importance of factors for quality design can be seriously compromised when utilising standard methods as opposed to robust methods.
Keywords: robust ANOVA; Taguchi methods; robust design; quality engineering; robust statistics; outliers; signal-to-noise ratio; S/N ratio; M-estimator; LAD regression; median; analysis of variance; process design; quality improvement; quality design.
Available online 21 Dec 2005