Continuous probability distribution
Not to be confused with
F -statistics as used in population genetics.
Fisher–Snedecor
Probability density function
Cumulative distribution function
Parameters
d 1 , d 2 > 0 deg. of freedom Support
x
∈
(
0
,
+
∞
)
{\displaystyle x\in (0,+\infty )\;}
if
d
1
=
1
{\displaystyle d_{1}=1}
, otherwise
x
∈
[
0
,
+
∞
)
{\displaystyle x\in [0,+\infty )\;}
PDF
(
d
1
x
)
d
1
d
2
d
2
(
d
1
x
+
d
2
)
d
1
+
d
2
x
B
(
d
1
2
,
d
2
2
)
{\displaystyle {\frac {\sqrt {\frac {(d_{1}x)^{d_{1}}d_{2}^{d_{2}}}{(d_{1}x+d_{2})^{d_{1}+d_{2}}}}}{x\,\mathrm {B} \!\left({\frac {d_{1}}{2}},{\frac {d_{2}}{2}}\right)}}\!}
CDF
I
d
1
x
d
1
x
+
d
2
(
d
1
2
,
d
2
2
)
{\displaystyle I_{\frac {d_{1}x}{d_{1}x+d_{2}}}\left({\tfrac {d_{1}}{2}},{\tfrac {d_{2}}{2}}\right)}
Mean
d
2
d
2
−
2
{\displaystyle {\frac {d_{2}}{d_{2}-2}}\!}
for d 2 > 2 Mode
d
1
−
2
d
1
d
2
d
2
+
2
{\displaystyle {\frac {d_{1}-2}{d_{1}}}\;{\frac {d_{2}}{d_{2}+2}}}
for d 1 > 2 Variance
2
d
2
2
(
d
1
+
d
2
−
2
)
d
1
(
d
2
−
2
)
2
(
d
2
−
4
)
{\displaystyle {\frac {2\,d_{2}^{2}\,(d_{1}+d_{2}-2)}{d_{1}(d_{2}-2)^{2}(d_{2}-4)}}\!}
for d 2 > 4 Skewness
(
2
d
1
+
d
2
−
2
)
8
(
d
2
−
4
)
(
d
2
−
6
)
d
1
(
d
1
+
d
2
−
2
)
{\displaystyle {\frac {(2d_{1}+d_{2}-2){\sqrt {8(d_{2}-4)}}}{(d_{2}-6){\sqrt {d_{1}(d_{1}+d_{2}-2)}}}}\!}
for d 2 > 6 Excess kurtosis
see text Entropy
ln
Γ
(
d
1
2
)
+
ln
Γ
(
d
2
2
)
−
ln
Γ
(
d
1
+
d
2
2
)
+
{\displaystyle \ln \Gamma \left({\tfrac {d_{1}}{2}}\right)+\ln \Gamma \left({\tfrac {d_{2}}{2}}\right)-\ln \Gamma \left({\tfrac {d_{1}+d_{2}}{2}}\right)+\!}
(
1
−
d
1
2
)
ψ
(
1
+
d
1
2
)
−
(
1
+
d
2
2
)
ψ
(
1
+
d
2
2
)
{\displaystyle \left(1-{\tfrac {d_{1}}{2}}\right)\psi \left(1+{\tfrac {d_{1}}{2}}\right)-\left(1+{\tfrac {d_{2}}{2}}\right)\psi \left(1+{\tfrac {d_{2}}{2}}\right)\!}
+
(
d
1
+
d
2
2
)
ψ
(
d
1
+
d
2
2
)
+
ln
d
2
d
1
{\displaystyle +\left({\tfrac {d_{1}+d_{2}}{2}}\right)\psi \left({\tfrac {d_{1}+d_{2}}{2}}\right)+\ln {\frac {d_{2}}{d_{1}}}\!}
[ 1] MGF
does not exist, raw moments defined in text and in [ 2] [ 3] CF
see text
In probability theory and statistics , the F -distribution or F -ratio , also known as Snedecor's F distribution or the Fisher–Snedecor distribution (after Ronald Fisher and George W. Snedecor ), is a continuous probability distribution that arises frequently as the null distribution of a test statistic , most notably in the analysis of variance (ANOVA) and other F -tests .[ 2] [ 3] [ 4] [ 5]
^ Lazo, A.V.; Rathie, P. (1978). "On the entropy of continuous probability distributions". IEEE Transactions on Information Theory . 24 (1). IEEE: 120–122. doi :10.1109/tit.1978.1055832 .
^ a b Johnson, Norman Lloyd; Samuel Kotz; N. Balakrishnan (1995). Continuous Univariate Distributions, Volume 2 (Second Edition, Section 27) . Wiley. ISBN 0-471-58494-0 .
^ a b Abramowitz, Milton ; Stegun, Irene Ann , eds. (1983) [June 1964]. "Chapter 26" . Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables . Applied Mathematics Series. Vol. 55 (Ninth reprint with additional corrections of tenth original printing with corrections (December 1972); first ed.). Washington D.C.; New York: United States Department of Commerce, National Bureau of Standards; Dover Publications. p. 946. ISBN 978-0-486-61272-0 . LCCN 64-60036 . MR 0167642 . LCCN 65-12253 .
^ NIST (2006). Engineering Statistics Handbook – F Distribution
^ Mood, Alexander; Franklin A. Graybill; Duane C. Boes (1974). Introduction to the Theory of Statistics (Third ed.). McGraw-Hill. pp. 246–249. ISBN 0-07-042864-6 .