We've discussed various types of models.

That was the language that I introduced in modeling.

We talked about deterministic and stochastic.

We talked about discrete and continuous.

We talked about static and dynamic models.

So those terms are important to understand because you might at some

point have a conversation as you create more of these models with someone and say,

well did you create a discrete time or continuous time model?

And being able to understand those words is very helpful

if you want to be able to participate in those conversations.

The final part of the module was reviewing some essential

mathematics in particular seeing the functions that we're going to be using

as we create our quantitative models there were four key functions.

Linear, power, exponential, and log.

And from a modeling point of view,

what you want to understand about these functions is how they relate

changes in the input to changes in the output and whether or

not those changes are being thought of in absolute terms or relative terms.

So recall, a straight line is characterized by its constant slope.

And that tells us that absolute changes in X are always

accompanied with the same absolute change in Y of M.

That's what the slope of M equals.

Whereas if we had a power function, then we would have a 1%

change in X is associated with an approximate M% change in Y.

So percent change in X is percent change in Y.

And you simply have to think about and understand your business process and ask

yourself, well, on which type of change is this process most readily modeled?

In terms of absolute change or percent change.

And by doing that,

you're able to think which of these functions should be used in the model.