How much of the, we could even say total variation, how much of the total variation in y? So let's just figure out what the total variation in y is. The total variation, and it's really just a tool for measuring, total variation in y, well, when we think about variation, and this is even true when we talk about variance, which was the mean variation in y, is we think about the square distance from some central tendency, and the best central measure we can have of y is the arithmetic mean. So we could just say the total variation in y is just going to be the sum of the distances of each of the y's, so you get y1, let me do this in another color, you get y1, this y1 over here, this is y1 over here, you get y1 minus the mean of all the y's, minus the mean of all the y's squared, plus y2, plus y2, minus the mean of all of the y squared, plus, and you just keep going all the way to the nth y value, to yn minus the mean of all the y's squared. This gives you the total variation in y. You can just take out all the y values, find their mean, it'll be some value, maybe it's right over here someplace, maybe that is the mean value of all the y's, and so you can even visualize it the same way we visualized the squared error from the line. So if you visualize it, you can imagine a line that's y is equal to the mean of y, which would look just like that, and what we're measuring over here, this error right over here is the square of this distance right over here, between this point vertically and this line.