But if I were to square them, one, four, nine, 16, they get further and further apart. And so something, the larger the residual is, when you square it, when the sum of squares is gonna represent a bigger proportion of the sum. And so what we'll see in future videos is that there's a technique called least squares regression, least squares regression, where you can find an M and a B for a given set of data so it minimizes the sum of the squares of the residual. And that's valuable. And the reason why this is used most is it really tries to take into account things that are significant outliers, things that sit from pretty far away from the model. Something like this is going to really, with a least squares regression, is going to try to be minimized, or it's going to be weighted a little bit heavier because when you square it, it becomes even a bigger factor in this. But this is just a conceptual introduction.