What we're going to do in this video is calculate a typical measure of how well the actual data points agree with a model, in this case, a linear model. And there's several names for it. We could consider this to be the standard deviation of the residuals, and that's essentially what we're going to calculate. 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?