1, and the expected formula: E(X+Y)=E(X)+E(Y).

2. product expectation formula: E(XY)=E(X)×E(Y).

3. Variance formula: Variance is the average of the square of the difference between each data and the average, that is, S2 = (1/n) [(x1-x _) 2+(x2-x _) 2+...+(xn-x _) 2], x.

4. Covariance formula: Covariance is the total error of measuring two variables, expressed as Cov(X, Y)=E{[X-E(X)][Y-E(Y)]}. Covariance is related to correlation. When two variables are the same, covariance is variance.

5. Zero expectation formula: random variables x, x 1, x2, ..., xn are 1, and the probability of multiplying each value by this value is 0, that is, e (x) = x1p (x1)+x2p (x2)+.

6. Define the expectation formula: expectation is the "average" under probability weighting, that is, E(X)=∑[x*p(x)], x is all possible values of random variable x, and p(x) is the probability of corresponding values. This formula is the basic definition of expectation and the basis for calculating expectation.