When x and y are irrelevant, E(XY)=E(X)E(Y) and d (x) = e (x 2)-(e (x)) 2. At this time, e (x (x+y-2)) = e (x 2+xy-2x.

D(x) refers to variance, and E(x) refers to expectation. Variance is a measure of dispersion when probability theory and statistical variance measure random variables or a set of data. Variance in probability theory is used to measure the deviation between random variables and their mathematical expectations (that is, the mean value).

In probability theory and statistics

Exponential distribution (also called negative exponential distribution) is a probability distribution that describes the time interval between events in Poisson process, that is, the process in which events occur continuously and independently at a constant average rate. This is a special case of gamma distribution. It is a continuous simulation of geometric distribution and has the key characteristic of memoryless. Besides analyzing Poisson process, it can also be found in various other environments.