We have the covariance of these two random variables, X and Y, are equal to the expected value of, I'll switch back to my colors just because this is the final result, the expected value of X times the expected value of the product of XY, the expected value of the product, the expected value of the product, minus, what is this? The expected value of Y, the expected value of Y times the expected value of X, times the expected value of X. Now, you can calculate these expected values if you know everything about the probability distribution or density functions for each of these random variables, or if you had the entire population that you're sampling from whenever you take an instantiation of these random variables. But let's say you just had a sample of these random variables. How could you estimate them? Well, if you were estimating it, the expected value, let's say you just have a bunch of data points, a bunch of coordinates, and I think you'll start to see how this relates to what we did with regression. The expected value of X times Y, it can be approximated by the sample mean of the products of X and Y.