2. The property of covariance: Cov(X, Y)=Cov(Y, x).

3.Cov(aX, bY)=abCov(X, y), (a, b are constants).

4.Cov(X 1+X2，Y)=Cov(X 1，Y)+Cov(X2，Y).

5. As can be seen from the definition of covariance, Cov(X, X)=D(X), Cov(Y, Y)=D(Y).

6. Let x and y be random variables. If e (x k) and k = 1, 2 exist, it is called the K-order origin moment of X, or K-order moment for short.

7. If E{[X-E(X)]k}, k = 1, 2 exists, it is called the k-order central moment of X. ..

8. if e {(x k) (y p)}, k and l = 1, 2 exist, it is called the k+p mixed origin moment of x and y.

9. If e {[x-e (x)] k [y-e (y)] l}, k and l = 1, 2 exist, it is called the k+l mixed central moment of x and y. ..

10. Obviously, the mathematical expectation of X is that E(X) is the first-order origin moment of X, the variance D(X) is the second-order central moment of X, and the covariance Cov(X, y) is the second-order mixed central moment of X and Y. ..