Variance of Chi Distribution
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Theorem
Let $n$ be a strictly positive integer.
Let $X \sim \chi_n$ where $\chi_n$ is the chi distribution with $n$ degrees of freedom.
Then the variance of $X$ is given by:
- $\var X = n - 2 \paren {\dfrac {\map \Gamma {\paren {n + 1} / 2} } {\map \Gamma {n / 2} } }^2$
where $\Gamma$ is the gamma function.
Proof
By Variance as Expectation of Square minus Square of Expectation, we have:
- $\var X = \expect {X^2} - \paren {\expect X}^2$
By Square of Chi Random Variable has Chi-Squared Distribution:
- $X^2 \sim \chi^2_n$
So, by Expectation of Chi-Squared Distribution:
- $\expect {X^2} = n$
By Expectation of Chi Distribution:
- $\expect X = \sqrt 2 \dfrac {\map \Gamma {\paren {n + 1} / 2} } {\map \Gamma {n / 2} }$
Hence the result.
$\blacksquare$