So it takes care of that issue of negatives and positives canceling out with each other. And when you square a number, things with large residuals are gonna become even larger, relatively speaking. If you square a large, if you think about it this way, let me put regular numbers, one, two, three, four, these are all one apart from each other. 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.