Author Information

Craig A. Wilson
Mark E. Payton

Abstract

The coefficient of variation (CV) has long been used as a measure of the relative consistency of sample data. However, little attention has been paid to using the CV to make conclusions about the relative consistency of the population(s) from which the data are drawn, particularly when the data are observed in the context of a designed factorial experiment. This research focused on using three approximations to the exact distribution of the sample CV of normally distributed data (McKay's, David's, and Iglewicz and Myers') in the context of the generalized linear model to develop a method for detecting main effects and interactions among factors when the population characteristic of interest is the CV.

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Apr 26th, 2:30 PM

MODELLING THE COEFFICIENT OF VARIATION IN FACTORIAL EXPERIMENTS

The coefficient of variation (CV) has long been used as a measure of the relative consistency of sample data. However, little attention has been paid to using the CV to make conclusions about the relative consistency of the population(s) from which the data are drawn, particularly when the data are observed in the context of a designed factorial experiment. This research focused on using three approximations to the exact distribution of the sample CV of normally distributed data (McKay's, David's, and Iglewicz and Myers') in the context of the generalized linear model to develop a method for detecting main effects and interactions among factors when the population characteristic of interest is the CV.