Application of the Model

It is argued that models are conceptualized, designed, developed, and validated to understand complex behaviour of larger entities. Models provide indicative measurements, and their validations in real life situation need careful considerations of relevant ambient conditions. Models also provide suggestive and causal relationships among their qualitative and quantitative influencers for better predictability. Generally, predictive models provide structural equations, measurement equations with associated random errors. These errors do play vital roles in relating abstracted behavior of the model outputs with the real life situations. In order to reduce these errors to an agreed level, case-based validations of models are quite important. This chapter discusses derived measurement and structural equations that the model has produced and presents some cases to examine the appropriateness of the application of the model developed.