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. If I think, if my expected value here is 5 and my expected value here is 7, it's completely reasonable that my expected value here is 12, assuming that they are completely independent. Now, if we have a situation, so what is the variance of my random variable Z? And once again, I'm not going to do a rigorous proof here, this is really just a property of variances, but I'm going to use this to establish what the variance of our random variable A is.