This is just a review of things we already know, but I just want to reintroduce it, because I'll use this to build up some of our tools. So you do the same thing with this with random variable Y. The variance of random variable Y is the expected value of the squared difference between our random variable Y and the mean of Y, or the expected value of Y, squared. And that's the same thing as sigma squared of Y. There's a variance of Y. Now, you may or may not already know these properties of expected values and variances, but I will reintroduce them to you, and I won't go into some rigorous proof. Actually, I think they're fairly easy to digest.