Continuous probability distribution
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
1
d
2
{\displaystyle +\left({\tfrac {d_{1}+d_{2}}{2}}\right)\psi \left({\tfrac {d_{1}+d_{2}}{2}}\right)+\ln {\frac {d_{1}}{d_{2}}}\!}
[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]
Definition
The F -distribution with d 1 and d 2 degrees of freedom is the distribution of
X
=
S
1
/
d
1
S
2
/
d
2
{\displaystyle X={\frac {S_{1}/d_{1}}{S_{2}/d_{2}}}}
where
S
1
{\textstyle S_{1}}
and
S
2
{\textstyle S_{2}}
are independent
random variables with
chi-square distributions with respective degrees of freedom
d
1
{\textstyle d_{1}}
and
d
2
{\textstyle d_{2}}
.
It can be shown to follow that the
probability density function (pdf) for X is given by
f
(
x
;
d
1
,
d
2
)
=
(
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
)
=
1
B
(
d
1
2
,
d
2
2
)
(
d
1
d
2
)
d
1
2
x
d
1
2
−
1
(
1
+
d
1
d
2
x
)
−
d
1
+
d
2
2
{\displaystyle {\begin{aligned}f(x;d_{1},d_{2})&={\frac {\sqrt {\frac {(d_{1}x)^{d_{1}}\,\,d_{2}^{d_{2}}}{(d_{1}x+d_{2})^{d_{1}+d_{2}}}}}{x\operatorname {B} \left({\frac {d_{1}}{2}},{\frac {d_{2}}{2}}\right)}}\\[5pt]&={\frac {1}{\operatorname {B} \left({\frac {d_{1}}{2}},{\frac {d_{2}}{2}}\right)}}\left({\frac {d_{1}}{d_{2}}}\right)^{\frac {d_{1}}{2}}x^{{\frac {d_{1}}{2}}-1}\left(1+{\frac {d_{1}}{d_{2}}}\,x\right)^{-{\frac {d_{1}+d_{2}}{2}}}\end{aligned}}}
for
real x > 0. Here
B
{\displaystyle \mathrm {B} }
is the
beta function . In many applications, the parameters d 1 and d 2 are
positive integers , but the distribution is well-defined for positive real values of these parameters.
The
cumulative distribution function is
F
(
x
;
d
1
,
d
2
)
=
I
d
1
x
/
(
d
1
x
+
d
2
)
(
d
1
2
,
d
2
2
)
,
{\displaystyle F(x;d_{1},d_{2})=I_{d_{1}x/(d_{1}x+d_{2})}\left({\tfrac {d_{1}}{2}},{\tfrac {d_{2}}{2}}\right),}
where I is the
regularized incomplete beta function .
The expectation, variance, and other details about the F(d 1 , d 2 ) are given in the sidebox; for d 2 > 8, the
excess kurtosis is
γ
2
=
12
d
1
(
5
d
2
−
22
)
(
d
1
+
d
2
−
2
)
+
(
d
2
−
4
)
(
d
2
−
2
)
2
d
1
(
d
2
−
6
)
(
d
2
−
8
)
(
d
1
+
d
2
−
2
)
.
{\displaystyle \gamma _{2}=12{\frac {d_{1}(5d_{2}-22)(d_{1}+d_{2}-2)+(d_{2}-4)(d_{2}-2)^{2}}{d_{1}(d_{2}-6)(d_{2}-8)(d_{1}+d_{2}-2)}}.}
The k -th moment of an F(d 1 , d 2 ) distribution exists and is finite only when 2k < d 2 and it is equal to
μ
X
(
k
)
=
(
d
2
d
1
)
k
Γ
(
d
1
2
+
k
)
Γ
(
d
1
2
)
Γ
(
d
2
2
−
k
)
Γ
(
d
2
2
)
.
{\displaystyle \mu _{X}(k)=\left({\frac {d_{2}}{d_{1}}}\right)^{k}{\frac {\Gamma \left({\tfrac {d_{1}}{2}}+k\right)}{\Gamma \left({\tfrac {d_{1}}{2}}\right)}}{\frac {\Gamma \left({\tfrac {d_{2}}{2}}-k\right)}{\Gamma \left({\tfrac {d_{2}}{2}}\right)}}.}
[6]
The F -distribution is a particular
parametrization of the
beta prime distribution , which is also called the beta distribution of the second kind.
The
characteristic function is listed incorrectly in many standard references (e.g.,
[3] ). The correct expression
[7] is
φ
d
1
,
d
2
F
(
s
)
=
Γ
(
d
1
+
d
2
2
)
Γ
(
d
2
2
)
U
(
d
1
2
,
1
−
d
2
2
,
−
d
2
d
1
ı
s
)
{\displaystyle \varphi _{d_{1},d_{2}}^{F}(s)={\frac {\Gamma \left({\frac {d_{1}+d_{2}}{2}}\right)}{\Gamma \left({\tfrac {d_{2}}{2}}\right)}}U\!\left({\frac {d_{1}}{2}},1-{\frac {d_{2}}{2}},-{\frac {d_{2}}{d_{1}}}\imath s\right)}
where U (a , b , z ) is the
confluent hypergeometric function of the second kind.
Characterization
A
random variate of the F -distribution with parameters
d
1
{\displaystyle d_{1}}
and
d
2
{\displaystyle d_{2}}
arises as the ratio of two appropriately scaled
chi-squared variates:
[8]
X
=
U
1
/
d
1
U
2
/
d
2
{\displaystyle X={\frac {U_{1}/d_{1}}{U_{2}/d_{2}}}}
where
In instances where the F -distribution is used, for example in the
analysis of variance , independence of
U
1
{\displaystyle U_{1}}
and
U
2
{\displaystyle U_{2}}
might be demonstrated by applying
Cochran's theorem .
Equivalently, the random variable of the F -distribution may also be written
X
=
s
1
2
σ
1
2
÷
s
2
2
σ
2
2
,
{\displaystyle X={\frac {s_{1}^{2}}{\sigma _{1}^{2}}}\div {\frac {s_{2}^{2}}{\sigma _{2}^{2}}},}
where
s
1
2
=
S
1
2
d
1
{\displaystyle s_{1}^{2}={\frac {S_{1}^{2}}{d_{1}}}}
and
s
2
2
=
S
2
2
d
2
{\displaystyle s_{2}^{2}={\frac {S_{2}^{2}}{d_{2}}}}
,
S
1
2
{\displaystyle S_{1}^{2}}
is the sum of squares of
d
1
{\displaystyle d_{1}}
random variables from normal distribution
N
(
0
,
σ
1
2
)
{\displaystyle N(0,\sigma _{1}^{2})}
and
S
2
2
{\displaystyle S_{2}^{2}}
is the sum of squares of
d
2
{\displaystyle d_{2}}
random variables from normal distribution
N
(
0
,
σ
2
2
)
{\displaystyle N(0,\sigma _{2}^{2})}
.
[
discuss ] [
citation needed ]
In a
frequentist context, a scaled F -distribution therefore gives the probability
p
(
s
1
2
/
s
2
2
∣
σ
1
2
,
σ
2
2
)
{\displaystyle p(s_{1}^{2}/s_{2}^{2}\mid \sigma _{1}^{2},\sigma _{2}^{2})}
, with the F -distribution itself, without any scaling, applying where
σ
1
2
{\displaystyle \sigma _{1}^{2}}
is being taken equal to
σ
2
2
{\displaystyle \sigma _{2}^{2}}
. This is the context in which the F -distribution most generally appears in
F -tests : where the null hypothesis is that two independent normal variances are equal, and the observed sums of some appropriately selected squares are then examined to see whether their ratio is significantly incompatible with this null hypothesis.
The quantity
X
{\displaystyle X}
has the same distribution in Bayesian statistics, if an uninformative rescaling-invariant
Jeffreys prior is taken for the
prior probabilities of
σ
1
2
{\displaystyle \sigma _{1}^{2}}
and
σ
2
2
{\displaystyle \sigma _{2}^{2}}
.
[9] In this context, a scaled F -distribution thus gives the posterior probability
p
(
σ
2
2
/
σ
1
2
∣
s
1
2
,
s
2
2
)
{\displaystyle p(\sigma _{2}^{2}/\sigma _{1}^{2}\mid s_{1}^{2},s_{2}^{2})}
, where the observed sums
s
1
2
{\displaystyle s_{1}^{2}}
and
s
2
2
{\displaystyle s_{2}^{2}}
are now taken as known.
Properties and related distributions
If
X
∼
χ
d
1
2
{\displaystyle X\sim \chi _{d_{1}}^{2}}
and
Y
∼
χ
d
2
2
{\displaystyle Y\sim \chi _{d_{2}}^{2}}
(
Chi squared distribution ) are
independent , then
X
/
d
1
Y
/
d
2
∼
F
(
d
1
,
d
2
)
{\displaystyle {\frac {X/d_{1}}{Y/d_{2}}}\sim \mathrm {F} (d_{1},d_{2})}
If
X
k
∼
Γ
(
α
k
,
β
k
)
{\displaystyle X_{k}\sim \Gamma (\alpha _{k},\beta _{k})\,}
(
Gamma distribution ) are independent, then
α
2
β
1
X
1
α
1
β
2
X
2
∼
F
(
2
α
1
,
2
α
2
)
{\displaystyle {\frac {\alpha _{2}\beta _{1}X_{1}}{\alpha _{1}\beta _{2}X_{2}}}\sim \mathrm {F} (2\alpha _{1},2\alpha _{2})}
If
X
∼
Beta
(
d
1
/
2
,
d
2
/
2
)
{\displaystyle X\sim \operatorname {Beta} (d_{1}/2,d_{2}/2)}
(
Beta distribution ) then
d
2
X
d
1
(
1
−
X
)
∼
F
(
d
1
,
d
2
)
{\displaystyle {\frac {d_{2}X}{d_{1}(1-X)}}\sim \operatorname {F} (d_{1},d_{2})}
Equivalently, if
X
∼
F
(
d
1
,
d
2
)
{\displaystyle X\sim F(d_{1},d_{2})}
, then
d
1
X
/
d
2
1
+
d
1
X
/
d
2
∼
Beta
(
d
1
/
2
,
d
2
/
2
)
{\displaystyle {\frac {d_{1}X/d_{2}}{1+d_{1}X/d_{2}}}\sim \operatorname {Beta} (d_{1}/2,d_{2}/2)}
.
If
X
∼
F
(
d
1
,
d
2
)
{\displaystyle X\sim F(d_{1},d_{2})}
, then
d
1
d
2
X
{\displaystyle {\frac {d_{1}}{d_{2}}}X}
has a
beta prime distribution :
d
1
d
2
X
∼
β
′
(
d
1
2
,
d
2
2
)
{\displaystyle {\frac {d_{1}}{d_{2}}}X\sim \operatorname {\beta ^{\prime }} \left({\tfrac {d_{1}}{2}},{\tfrac {d_{2}}{2}}\right)}
.
If
X
∼
F
(
d
1
,
d
2
)
{\displaystyle X\sim F(d_{1},d_{2})}
then
Y
=
lim
d
2
→
∞
d
1
X
{\displaystyle Y=\lim _{d_{2}\to \infty }d_{1}X}
has the
chi-squared distribution
χ
d
1
2
{\displaystyle \chi _{d_{1}}^{2}}
F
(
d
1
,
d
2
)
{\displaystyle F(d_{1},d_{2})}
is equivalent to the scaled
Hotelling's T-squared distribution
d
2
d
1
(
d
1
+
d
2
−
1
)
T
2
(
d
1
,
d
1
+
d
2
−
1
)
{\displaystyle {\frac {d_{2}}{d_{1}(d_{1}+d_{2}-1)}}\operatorname {T} ^{2}(d_{1},d_{1}+d_{2}-1)}
.
If
X
∼
F
(
d
1
,
d
2
)
{\displaystyle X\sim F(d_{1},d_{2})}
then
X
−
1
∼
F
(
d
2
,
d
1
)
{\displaystyle X^{-1}\sim F(d_{2},d_{1})}
.
If
X
∼
t
(
n
)
{\displaystyle X\sim t_{(n)}}
—
Student's t-distribution — then:
X
2
∼
F
(
1
,
n
)
X
−
2
∼
F
(
n
,
1
)
{\displaystyle {\begin{aligned}X^{2}&\sim \operatorname {F} (1,n)\\X^{-2}&\sim \operatorname {F} (n,1)\end{aligned}}}
F -distribution is a special case of type 6
Pearson distribution
If
X
{\displaystyle X}
and
Y
{\displaystyle Y}
are independent, with
X
,
Y
∼
{\displaystyle X,Y\sim }
Laplace(μ , b ) then
|
X
−
μ
|
|
Y
−
μ
|
∼
F
(
2
,
2
)
{\displaystyle {\frac {|X-\mu |}{|Y-\mu |}}\sim \operatorname {F} (2,2)}
If
X
∼
F
(
n
,
m
)
{\displaystyle X\sim F(n,m)}
then
log
X
2
∼
FisherZ
(
n
,
m
)
{\displaystyle {\tfrac {\log {X}}{2}}\sim \operatorname {FisherZ} (n,m)}
(
Fisher's z-distribution )
The
noncentral F -distribution simplifies to the F -distribution if
λ
=
0
{\displaystyle \lambda =0}
.
The doubly
noncentral F -distribution simplifies to the F -distribution if
λ
1
=
λ
2
=
0
{\displaystyle \lambda _{1}=\lambda _{2}=0}
If
Q
X
(
p
)
{\displaystyle \operatorname {Q} _{X}(p)}
is the quantile p for
X
∼
F
(
d
1
,
d
2
)
{\displaystyle X\sim F(d_{1},d_{2})}
and
Q
Y
(
1
−
p
)
{\displaystyle \operatorname {Q} _{Y}(1-p)}
is the quantile
1
−
p
{\displaystyle 1-p}
for
Y
∼
F
(
d
2
,
d
1
)
{\displaystyle Y\sim F(d_{2},d_{1})}
, then
Q
X
(
p
)
=
1
Q
Y
(
1
−
p
)
.
{\displaystyle \operatorname {Q} _{X}(p)={\frac {1}{\operatorname {Q} _{Y}(1-p)}}.}
F -distribution is an instance of
ratio distributions
W -distribution
[10] is a unique parametrization of F-distribution.
See also
Beta prime distribution
Chi-square distribution
Chow test
Gamma distribution
Hotelling's T-squared distribution
Wilks' lambda distribution
Wishart distribution
Modified half-normal distribution
[11] with the pdf on
(
0
,
∞
)
{\displaystyle (0,\infty )}
is given as
f
(
x
)
=
2
β
α
2
x
α
−
1
exp
(
−
β
x
2
+
γ
x
)
Ψ
(
α
2
,
γ
β
)
{\displaystyle f(x)={\frac {2\beta ^{\frac {\alpha }{2}}x^{\alpha -1}\exp(-\beta x^{2}+\gamma x)}{\Psi {\left({\frac {\alpha }{2}},{\frac {\gamma }{\sqrt {\beta }}}\right)}}}}
, where
Ψ
(
α
,
z
)
=
1
Ψ
1
(
(
α
,
1
2
)
(
1
,
0
)
;
z
)
{\displaystyle \Psi (\alpha ,z)={}_{1}\Psi _{1}\left({\begin{matrix}\left(\alpha ,{\frac {1}{2}}\right)\\(1,0)\end{matrix}};z\right)}
denotes the
Fox–Wright Psi function .
References
^ 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
c
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 .
^ Taboga, Marco.
"The F distribution" .
^ Phillips, P. C. B. (1982) "The true characteristic function of the F distribution,"
Biometrika , 69: 261–264
JSTOR
2335882
^ DeGroot, M. H. (1986). Probability and Statistics (2nd ed.). Addison-Wesley. p. 500.
ISBN
0-201-11366-X .
^ Box, G. E. P.; Tiao, G. C. (1973). Bayesian Inference in Statistical Analysis . Addison-Wesley. p. 110.
ISBN
0-201-00622-7 .
^ Mahmoudi, Amin; Javed, Saad Ahmed (October 2022).
"Probabilistic Approach to Multi-Stage Supplier Evaluation: Confidence Level Measurement in Ordinal Priority Approach" . Group Decision and Negotiation . 31 (5): 1051–1096.
doi :
10.1007/s10726-022-09790-1 .
ISSN
0926-2644 .
PMC
9409630 .
PMID
36042813 .
^ Sun, Jingchao; Kong, Maiying; Pal, Subhadip (22 June 2021).
"The Modified-Half-Normal distribution: Properties and an efficient sampling scheme" (PDF) . Communications in Statistics - Theory and Methods . 52 (5): 1591–1613.
doi :
10.1080/03610926.2021.1934700 .
ISSN
0361-0926 .
S2CID
237919587 .
External links
Discrete univariate
with finite support with infinite support
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supported on a bounded interval supported on a semi-infinite interval supported on the whole real line with support whose type varies
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Degenerate and
singular Families