Modelling a non-normal response for quality improvement

Efforts to improve quality require that the factors affecting it be identified. This allows either removal of root-causes for low quality or finding optimal settings for the investigated product or process. When the common assumptions of the normal scenario are not met, two alternative approaches are commonly pursued: normalization of data and the use of generalized linear models (GLM). Recently, a third alternative has been developed, that models a response subject to self-generated random variation and externally generated systematic and random variation. It is assumed that the relationship between the response and the externally generated variation is uniformly convex (or concave). A unique feature of the new model is that both its structure and the parameters' values are determined solely by the data on hand (no theory-based arguments are required). Here, we compare the effectiveness of the new methodology relative to current approaches when applied to response modelling in quality improvement efforts. We do this by using published data sets which have been formerly analysed within the framework of either the normalizing approach or the GLM approach (or both). The relative merits of the new methodology are demonstrated and discussed.