IN THIS CHAPTER
Modeling and fitting
Working with the Monte Carlo method
“Model” is a term that gets thrown around a lot these days. Simply put, a model is something you know and can work with that helps you understand something you know little about. A model is supposed to mimic, in some way, the thing it’s modeling. A globe, for example, is a model of the earth. A street map is a model of a neighborhood. A blueprint is a model of a building.
Researchers use models to help them understand natural processes and phenomena. Business analysts use models to help them understand business processes. The models these people use might include concepts from mathematics and statistics — concepts that are so well known they can shed light on the unknown. The idea is to create a model that consists of concepts you understand, put the model through its paces, and see if the results look like real-world results.
In this chapter, I discuss modeling. My goal is to show how you can harness Excel’s statistical capabilities to help you understand processes in your world.
In one approach to modeling, you gather data and group them into a distribution. Next, you try to figure out a process that results in that kind of a distribution. Restate that process in statistical terms so that it can generate a distribution, and then see how well the generated distribution matches up to the real one. This “process you figure out ...