variance

Variance measures how spread out a random variable's values are around its mean; covariance measures how two random variables spread out together.

The variance of a random variable $X$ , $\operatorname{Var}(X)=E[(X-\mu)^2]$ , measures how far its values typically spread from the mean $\mu$ . Covariance is the two-variable version, $\operatorname{Cov}(X,Y)=E[(X-\mu_X)(Y-\mu_Y)]$ , and reduces to variance when $X=Y$ .