The X Z score was zero. And so that would have taken away a little bit from our correlation coefficient. The reason why it would take away, even though it's not negative, you're not contributing to the sum, but you're going to be dividing by a slightly higher value by including that extra pair. If you had a data point where, let's say X was below the mean and Y was above the mean, something like this, if this was one of the points, this term would have been negative because the Y Z score would have been positive and the X Z score would have been negative. And so when you put it in the sum, it would have actually taken away from the sum. And so it would have made the R score even lower. Similarly, something like this would have done, would have made the R score even lower because you would have a positive Z score for X and a negative Z score for Y.