Sentence:  The <WSD>covariance</WSD> of "X" and "Y" is the expected value of the product of two random variables, "X" − "E"("X") and "Y" − "E"("Y")....If two random variables tend to act like opposites, one is high when the other is low and vice versa, then the <WSD>covariance</WSD> will be negative. If two random variables tend to be high and low at the same time, then the <WSD>covariance</WSD> will be positive. In fact, the <WSD>covariance</WSD> measures the extent of a linear relationship between the two random variables. 
Meaning of the word marked with <WSD> tags:  A statistical measure defined as \scriptstyle\operatorname{Cov}(X, Y) = \operatorname{E}((X - \mu) (Y - \nu)) given two real-valued random variables "X" and "Y", with expected values \scriptstyle E(X)\,=\,\mu and \scriptstyle E(Y)\,=\,\nu.