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Fisher's z
	Probability density function
Parameters	deg. of freedom
Support
PDF
Mode

Fisher's z-distribution is the statistical distribution of half the logarithm of an F-distribution variate:

z={\frac {1}{2}}\log F

It was first described by Ronald Fisher in a paper delivered at the International Mathematical Congress of 1924 in Toronto.^[1] Nowadays one usually uses the F-distribution instead.

The probability density function and cumulative distribution function can be found by using the F-distribution at the value of $x'=e^{2x}\,$ . However, the mean and variance do not follow the same transformation.

The probability density function is^[2]^[3]

f(x;d_{1},d_{2})={\frac {2d_{1}^{d_{1}/2}d_{2}^{d_{2}/2}}{B(d_{1}/2,d_{2}/2)}}{\frac {e^{d_{1}x}}{\left(d_{1}e^{2x}+d_{2}\right)^{(d_{1}+d_{2})/2}}},

where B is the beta function.

When the degrees of freedom becomes large ( $d_{1},d_{2}\rightarrow \infty$ ), the distribution approaches normality with mean^[2]

{\bar {x}}={\frac {1}{2}}\left({\frac {1}{d_{2}}}-{\frac {1}{d_{1}}}\right)

and variance

\sigma _{x}^{2}={\frac {1}{2}}\left({\frac {1}{d_{1}}}+{\frac {1}{d_{2}}}\right).

Related distribution

If $X\sim \operatorname {FisherZ} (n,m)$ then $e^{2X}\sim \operatorname {F} (n,m)\,$ (F-distribution)
If $X\sim \operatorname {F} (n,m)$ then ${\tfrac {\log X}{2}}\sim \operatorname {FisherZ} (n,m)$

Probability density function
Parameters	$d_{1}>0,\ d_{2}>0$ deg. of freedom
Support	$x\in (-\infty ;+\infty )\!$
PDF	${\frac {2d_{1}^{d_{1}/2}d_{2}^{d_{2}/2}}{B(d_{1}/2,d_{2}/2)}}{\frac {e^{d_{1}x}}{\left(d_{1}e^{2x}+d_{2}\right)^{\left(d_{1}+d_{2}\right)/2}}}\!$
Mode	$0$