Analysis of unreplicated factorials allowing for possibly faulty observations
Design, data & analysis
Robust regression and outlier detection
Robust regression and outlier detection
Quick and easy analysis of unreplicated factorials
Technometrics
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The existing methods for analyzing unreplicated fractional factorial experiments that do not contemplate the possibility of outliers in the data have a poor performance for detecting the active effects when that contingency becomes a reality. There are some methods to detect active effects under this experimental setup that consider outliers. We propose a new procedure based on robust regression methods to estimate the effects that allows for outliers. We perform a simulation study to compare its behavior relative to existing methods and find that the new method has a very competitive or even better power. The relative power improves as the contamination and size of outliers increase when the number of active effects is up to four. Copyright © 2012 John Wiley & Sons, Ltd.