Variance - Definition

Definition

If a random variable X has the expected value (mean) μ = E, then the variance of X is the covariance of X with itself, given by:


\begin{align}
\operatorname{Var}(X)
&= \operatorname{Cov}(X, X) \\
&= \operatorname{E}\left \\
&= \operatorname{E}\left
\end{align}

That is, the variance is the expected value of the squared difference between the variable's realization and the variable's mean. This definition encompasses random variables that are discrete, continuous, neither, or mixed. From the corresponding expression for Covariance, it can be expanded:

\begin{align} \operatorname{Var}(X) &= \operatorname{Cov}(X, X) \\ &= \operatorname{E}\left - \operatorname{E} \operatorname{E} \\ &= \operatorname{E}\left - (\operatorname{E})^2 \end{align}

A mnemonic for the above expression is "mean of square minus square of mean". The variance of random variable X is typically designated as Var(X), or simply σ2 (pronounced "sigma squared").

Read more about this topic:  Variance

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