Statistical Thinking, 3rd Edition

CHAPTER 7Building and Using Models

All models are wrong, but some are useful.

—George E. P. Box

Model building is one of the methods used in the improvement frameworks discussed in Chapter 5. Model building is a broad topic, and in this chapter we will develop a basic understanding of the types and uses of models and develop functional capability to apply basic regression analysis to real problems. More extensive study and applications experience will be required to develop mastery of the entire field of regression analysis, or with other, more complex model building methods, such as machine learning.

The power of statistical thinking is in developing process knowledge that can be used to manage and improve processes. The most effective way to create process knowledge is to develop a model that describes the behavior of the process. Webster's New World College Dictionary defines model as “a generalized, hypothetical description, often based on an analogy, used in analyzing and explaining something.” In this chapter we learn how to integrate our frameworks and tools (see Chapters 5 and 6) and enhance them by building models. Model development is an iterative process in which we move back and forth between hypotheses about what process variables are important and data that confirm or disprove these hypotheses.

Our overall strategy is as follows: We build a model that relates the process outputs (y's) to input and process variables (x's) that cause systematic behavior in y. That ...