Remember slope-intercept form from basic algebra? y=mx+b shows the relationship between y and x, which are both variables. In this equation, b is the *constant*.

*eyes glaze over* – “WAKE UP!”

Okay, so now think really hard. We have two variables that we are trying to show a relationship between. Sound important? If we can model the relationship between two variables, we can *make predictions* and provide insight into *why there is a relationship!*

So the m in this equation is called the *coefficient* and it is important because it quantifies the relationship. It allows us to model the relationship! Things get more complicated though, and then much *more* complicated (we’ll talk in a bit how to use generalize this idea of slop-intercept form to think about neural networks).

Think about adding more variables. Instead of a relationship between two variables, such as height and weight, now we add other features of a person, such as hair color. Some of them won’t have an impact on what we are trying to predict (height), in which case the coefficient will be zero. So then we have multiple m’s/coefficients.

Now’s the kicker. Take each one of those m’s/coefficients and put another equation inside of it, with it’s own m’s/coefficients. Now consider that maybe there’s a relationship between the m’s/coefficients in that m/coefficient and the m’s/coefficients of the other m’s/coefficients. Mmmm.. M&M’s.