These are equivalent statements. So you could view this as being equal to, just using this over here, the sum of these two variances. So it's going to be equal to the sum of the variance of X plus the variance of negative Y. And what I need to show you is that the variance of negative Y, the negative of that random variable, is going to be the same thing as the variance of Y. So what is the variance of negative Y? The variance of negative Y is the same thing as the variance of negative Y, which is equal to the expected value of the distance between negative Y and the expected value of negative Y squared. That's all the variance actually is.