Covariance 

In probability theory and statistics, the covariance between two random variables <math>X</math> and <math>Y</math>, with respective expected values <math>\mu</math> and <math>\nu</math> is:

<math>\operatorname{cov}(X, Y) = E((X - \mu) (Y - \nu))</math>

<math>\operatorname{cov}(X, Y) = E(X Y) - \mu \nu</math>

If <math>X</math> and <math>Y</math> are independent, then their covariance is zero. The converse however isn't true. The covariance is sometimes called a measure of "linear dependence" between the two random variables. That phrase doesn't mean the same thing that is usually means in mathematics, as explicated in the Wikipedia article titled linear dependence, although the meaning isn't unrelated. The correlation is a closely related concept used to measure the degree of linear dependence between two variables.

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