**AFTER
FAMILIARIZING YOURSELF WITH THE DATA THROUGH PLOTS AND GRAPHS, THE NEXT
STEP IS TO START** building a model for the data.
The meaning of the word “model” is quite hazy, and I don’t want to spend
much time and effort attempting to define this concept in an abstract
way. For our purposes, a *model* is a mathematical
description of the data that ideally is guided by our understanding of
the system under consideration and that relates the various variables of
the system to each other: a “formula.”

Models like this are incredibly important. It is at this point
that we go from the merely *descriptive* (plots and
graphs) to the *prescriptive*: having a model
allows us to predict what the system will do under a certain set of
conditions. Furthermore, a good or truly useful model—because it helps
us to *understand* how the system works—allows us
to do so without resorting to the model itself or having to evaluate
any particular formula explicitly. A good model ties the different
variables that control the system together in such a way that we can
see how varying any one of them will influence the outcome. It is this
use of models—as an aide to or expression of our understanding—that is
the most important one. (Of course, we must still evaluate the model
formulas explicitly in order to obtain actual numbers for a specific
prediction.)

I should point out that this view of models and what they can do is not universal, and you will find the term used ...

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