The distribution of 1 and 0- 1: E(X)=p, D(X)=p( 1-p).

2. Binomial distribution b (n, p): p (x = k) = c (k \ n) p k (1-p) (n-k), E(X)=np, d (x) = NP (1).

3. Poisson distribution x ~ p (x = k) = (λ k/k! )e^-λ,E(X)=λ,D(X)=λ。

4. Uniform distribution U(a, b):X~f(x)= 1/(b-a), A0; E(X)= 1/λ,D(X)=θ^2。

6. Normal distribution n (μ, σ 2): f (x) = (1√ (2π) σ) e-((x-μ) 2/2σ 2), e (x) = μ, d (x) = σ 2.

Extended data:

A measure of the degree of dispersion when probability theory and statistical variance measure random variables or a set of data. In probability theory, variance is used to measure the deviation between random variables and their mathematical expectations (that is, the average 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 describes the dispersion degree between the value of random variable and its mathematical expectation. (The greater the standard deviation and variance, the greater the dispersion).

If the values of x are concentrated, the variance D(X) is small, and if the values of x are scattered, the variance D(X) is large.