4. Process Modeling 4.1. Introduction to Process Modeling 4.1.3. What are process models used for?
Three Main Purposes	Process models are used for three main purposes: prediction, calibration, and optimization. The rest of this page lists brief explanations of the different uses of process models. More detailed explanations of the uses for process models are given in the subsections of this section listed at the bottom of this page.
Prediction	The goal of prediction is to determine either the value of the regression function (i.e. the average value of the response variable), the value of a new observation of the response variable, or the values of a specified proportion of all future observations of the response variable for a particular combination of the values of the predictor variables. Predictions can be made for any combination of predictor variable values, including values for which no data has been measured or observed. Predictions made within the observed space of predictor variable values are called are sometimes called interpolations. Predictions made outside the observed space of predictor variable values, called extrapolations, are sometimes necessary, but require caution.
Calibration	The goal of calibration is to quantitatively relate measurements made using one measurement system to those of another measurement system. This is done so that measurements can be compared in common units or to tie results from a relative measurement method to absolute units.
Optimization	Optimization is done to determine what values of process inputs should be used to obtain the desired process output. Typical optimzation goals might be to maximize the yield of a process, to minimize the processing time required to fabricate a product, or to hit a target product specification with minimum variation to maintain specified tolerances.
Further Details	Prediction Calibration Optimization