Let's just say that we say that Y is the thing that we're trying to predict. So this is the hours spent studying, hours studying, and then let's say X is what we think explains the hours studying or is one of the things that explains the hours studying, and this is the amount of caffeine ingested. So this is caffeine consumed in milligrams. And so our regression line would have the form Y hat. This tells us that this is a linear regression. It's trying to estimate the actual Y values for given Xs. It's going to be equal to MX plus B.