You could also call it the root mean square error, and you'll see why it's called this, because this really describes how we calculate it. So what we're going to do is look at the residuals for each of these points, and then we're going to find the standard deviation of them. So just as a bit of review, the ith residual is going to be equal to the ith y value for a given x minus the predicted y value for a given x. Now when I say y hat right over here, this just says what would the linear regression predict for a given x? And this is the actual y for a given x. So for example, and we've done this in other videos, this is all review, the residual here, when x is equal to one, we have y is equal to one, but what was predicted by the model is 2.5 times one minus two, which is.5. So one minus.5, so this residual here, this residual is equal to one minus 0.5, which is equal to 0.5, and it's a positive 0.5.