Moment about the mean - Definition and Overview

The k^th moment about the mean (or k^th central moment) of a real-valued random variable X is the quantity E[(X − E[X])^k], where E is the expectation operator. Some random variables have no mean, in which case the moment about the mean is not defined. The k^th moment about the mean is often denoted μ_k. For a continuous univariate probability distribution with probability density function f(x) the moment about the mean μ is

<math>

\mu_k = \left\langle ( x - \langle x \rangle )^k \right\rangle = \int_{-\infty}^{+\infty} (x - \mu)^k f(x)\,dx. <math>

Sometimes it is convenient to convert moments about the origin to moments about the mean. The general equation for converting the n^th-order moment about the origin to the moment about the mean is

<math>

\mu_n = \sum_{j=0}^n {n \choose j} (-1) ^{n-j} \mu'_j m^{n-j}, <math>

where m is the mean of the distribution, and the moment about the origin is given by

<math>

\mu'_j = \int_{-\infty}^{+\infty} x^n f(x)\,dx. <math>

The first moment about the mean is zero. The second moment about the mean is called the variance, and is usually denoted σ², where σ represents the standard deviation. The third and fourth moments about the mean are used to define the standardized moments which are in turn used to define skewness and kurtosis, respectively.

See also

moment (mathematics), cumulant