Abstract
This article deals with multivariate categorical data analysis in industrial experiments. We must identify factors that affect the level of a production process (location effects) and variability of the output (dispersion effects). Association models with location-dispersion type scores are proposed for identifying the location and dispersion effects separately from experimental data for quality improvement. Such modeling approach can enable us to set the factors with important location and/or dispersion effects at their optimal conditions.
