The covariance between X and Y is intended to measure the degree to which X and Y tend to be large at the same time or the degree to which one tends to be large while the other is small. For example, suppose that is positive. Then X> and Y> must occur together and/or X< and Y< must occur together to a larger extent than X< occur with Y> and X> occur with Y< . Otherwise, the mean would be negative. But the magnitude of Cov(X,V) is also influenced by the overall magnitudes of X and Y . For example, in Exercise 5 in this section, you can prove that Cov(2X,Y) =2Cov(X,Y). In order to obtain a measure of association between X and Y that is not driven by arbitrary changes in the scales of one or the other random variable, we define a slightly different quantity next.