So what's its expected value going to be? The expected value of A is going to be equal to the expected value of X minus Y, which is equal to, you can either view it as the expected value of X plus the expected value of negative Y, or the expected value of X minus the expected value of Y, which is the same thing as the mean of X minus the mean of Y. So this is what the mean of our random variable A would be equal to. All of this is review, and I'm going to use this when we start talking about distributions that are sums and differences of other distributions. Now let's think about what the variance of random variable Z is and what the variance of random variable A is. So the variance of Z, and just to kind of always focus back on the intuition, it makes sense. If X is completely independent of Y, and if I have some random variable that is the sum of the two, then the expected value of that variable, of that new variable, is going to be the sum of the expected values of the other two, because they are unrelated.