The formula can be used to calculate: E(XY)=∑i*j*(Pij), where I is the value of x, j is the value of y, Pij is the relative probability in the joint distribution list corresponding to X=i and Y=j, and the sum is the sum of any I and J. Furthermore, in E(XY)=∑i*j*(Pij),

Therefore:

e(XY)= 1 * 1 * 0.06+ 1 * 2 * 0.07+ 1 * 3 * 0.04+2 * 1 * 0.07+2 * 2 * 0. 14+2 * 3 * 0. 16+3 * 1 * 0.0 1+3 * 2 *

Extended data:

The essential difference between the uncertainty of random variables and the uncertainty of fuzzy variables is that the measurement results of the latter are still uncertain, that is, fuzzy. If a variable can take any real number in a certain interval, that is, the value of the variable can be continuous, then this random variable is called a continuous random variable.

In practical problems, it is often used to represent the probability characteristics of multiple independent random test results or multiple independent random factors, so it is very important for the application of probability statistics. If x and y are independent, then: E(XY)=E(X)*E(Y) If not independent, it can be calculated by definition: first, find the joint probability density of x and macro-denier y, and then use the definition.

E(XY) in the mathematical cycle represents the mathematical expectation of XY multiplication. At first, x and y are random variables, and E(x) represents the "average" of x, that is, mathematical expectation. Now, it is equivalent to treating xy as a number (x and y are randomly selected). If two random variables x and y are independent of each other, then E[(X-E(X))(Y-E(Y))]=0.