Let X and Y be two random variable s defined on the same sample space . The marginal density function for X is Notice that the marginal density is the same as the ordinary density for the single randome variable.
Covariance extends The notion of variance to two dimensions.
Its value measures the linear relationship between r.v. Positive covariance indicates that when X is above its mean then Y tends to be above its mean as well - positive statistical relationship. X and Y independent. Correlation avoids this scaling effect. You can get as much practice as you want with this kind of example by running the tutorial for week 2 .