So then, so then we're going to have our r squared will be close, close to one, which tells us that a lot of the variation in y is described by the variation in x, which makes sense because the line is a good fit. You take the opposite case. If the squared error of the line is huge, if this number over here is huge, if this number over here is huge, then that means there's a lot of error between the data points and the line. And so if this number is huge, then this number over here is going to be huge. One minus, or it's going to be a percentage close to one, and one minus that is going to be close to zero. And so if this, if the squared error of the line is large, is large, is large, if this is large, this whole thing is going to be close to one. And if this whole thing is close to one, the whole coefficient of determination, the whole r squared is going to be close to zero, which makes sense.