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. And if the actual point is above the model, you are going to have a positive residual. Now the residual over here, you also have the actual point being higher than the model. So this is also going to be a positive residual.