TABLE OF CONTENTS The RMM model was derived axiomatically in Chapter 7. It has been emphasized that RMM represents well relational models of monotone convex relationships, which one can find in a myriad of current engineering and scientific disciplines. Furthermore, the RMM error distribution represents well existing models of random variation, namely, known statistical distributions.
In this chapter, it is shown that RMM may in fact deliver exact representation to various models in engineering and the sciences, as well as to statistical distributions that one often encounters in the application of statistical models. Demonstrating that known models are exact special cases of RMM would confer further validity on the new methodology as a general platform for empirical modeling. We conduct this study pursuing the dual distinction that has been made earlier in the book between "Systematic Variation" (Section 12.1) and "Random Variation" (Section 12.2).
In Chapter 2 current mainstream relational models in various branches of science and engineering have been introduced. By examining more closely three particular examples (Chapter 3), we have studied commonly shared properties. These properties indeed formed the basis that led to the development of the RMM model. In this section, we reexamine a subset of the models in Chapter 2, and show how these may be derived as special cases of the RMM model.
Four different subject areas are addressed: Chemistry and chemical engineering, physics, electric engineering and non-linear growth modeling. Since all related models have already been...
