4. Process Modeling 4.1. Introduction to Process Modeling
Overview of Section 4.1	The goal of this section is to give the big picture of function-based process modeling. This includes discussion of what process modeling is, the goals of process modeling, and a comparison of the different statistical methods used for model building. Detailed information on how to collect data, construct appropriate models, interpret output, and use process models is covered in the following sections. The final section of the chapter contains case studies that illustrate the general information presented in the first five sections using data from a variety of scientific and engineering applications.
Contents of Section 4.1	What is process modeling? What terminology do statisticians use to describe process models? What are process models used for? Prediction Calibration Optimization What are the some of the different statistical methods for model building? Linear Least Sum of Squares Regression Nonlinear Least Sum of Squares Regression Weighted Least Sum of Squares Regression LOESS (aka LOWESS)