For two random variables X and Y, this is the difference between the expected value of their product and the product of their separate expected values. It is denoted by Cov (X,Y):![population covariance](Images/oree/doc/10.1093/acref/9780199679188.001.0001/acref-9780199679188-math-0526-full.gif)
If X and Y are independent then Cov(X, Y)=0. However, if Cov(X, Y)=0 then X and Y may not be independent. A useful result is
Var(aX+bY)=a2Var(X)+2ab Cov(X, Y)+b2Var(Y), where Var denotes variance, and a and b are constants. The term 'covariance' was used by Sir Ronald Fisher in 1930. See also population correlation coefficient.