In the last several videos, we did some fairly hairy mathematics, and you might have even skipped them. But we got to a pretty neat result. We got to a formula for the slope and y-intercept of the best-fitting regression line when you measure the error by the squared distance to that line. And our formula is, and I'll just rewrite it here just so we have something neat to look at. So the slope of that line is going to be the mean of x's times the mean of the y's minus the mean of the xy's. And don't worry, this seems really confusing. We're going to actually do an example of this in a few seconds.