In probability and statistics, the *variance* of a random
variable is the average value of the square distance from the mean
value. It represents the how the random variable is distributed near
the mean value. Small variance indicates that the random variable is
distributed near the mean value. Big variance indicates that the
random variable is distributed far from the mean value. For example,
with normal distribution, narrow bell curve will have small variance
and wide bell curve will have big variance.

The variance of random variable X is the expected value of squares of difference of X and the expected value μ.

σ^{2} = *Var *(* X *) = *E *
[(*X*^{ }- *μ*)^{2}]

From the definition of the variance we can get

σ^{2} = *Var *(* X *) = *E*(*X*^{
2}) - *μ*^{2}

For continuous random variable with mean value μ and probability density function f(x):

or

For discrete random variable X with mean value μ and probability mass function P(x):

or

When X and Y are independent random variables:

- Basic probability
- Expectation
- Variance
- Standard deviation
- Probability distribution
- Normal distribution
- Statistics symbols