= E{X？ y？ -2XYE(XY)+E？ (XY)}

= E(X？ )E(Y？ )-2E？ (X)E？ (Y)+E？ (X)E？ (Y)

= E(X？ )E(Y？ )-E？ (X)E？ (Y)？

What if? E(X) = E(Y) = 0，

So what? D(XY) = E(X？ )E(Y？ )= D(X)D(Y)，？

That is, when X and Y are independent of each other and their mathematical expectations are all zero, the variance d (XY) of the product xy of X and Y is equal to:

D(XY) = D(X)D(Y)。 ?

//:is formula (3)

Variance) is a measure of the degree of dispersion when probability theory and statistical variance measure random variables or a group of data. Variance in probability theory is used to measure the deviation between random variables and their mathematical expectations (that is, the mean value). The variance (sample variance) in statistics is the average value of the square of the difference between each sample value and the average value of all sample values. In many practical problems, it is of great significance to study variance or deviation.

Variance is a measure of the difference between the source data and the expected value.