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Distribution class=FisherSnedecor name=FisherSnedecor dimension=1 d1=5.5 d2=10.5
Distribution FisherSnedecor(d1 = 5.5, d2 = 10.5)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[1.85322]
Point= class=Point name=Unnamed dimension=1 values=[1]
log pdf=-0.659362
pdf =0.517181
cdf=0.531405
ccdf=0.468595
characteristic function=(0.385738+0.612601j)
pdf gradient = class=Point name=Unnamed dimension=2 values=[0.0333323,0.00890862]
cdf gradient = class=Point name=Unnamed dimension=2 values=[-0.0101307,0.00377782]
log-pdf gradient = class=Point name=Unnamed dimension=2 values=[0.0644499,0.0172253]
quantile= class=Point name=Unnamed dimension=1 values=[3.2027]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.230549]
Survival(inverseSurvival)=0.950000
entropy=1.100253
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 3.14683} with f=
MinimumVolumeLevelSetEvaluation(FisherSnedecor(d1 = 5.5, d2 = 10.5))
beta= [0.0429883]
Bilateral confidence interval= [0.168551, 4.04601]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0, 3.2027]
beta= [0.95]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[1.23529]
standard deviation= class=Point name=Unnamed dimension=1 values=[1.09323]
skewness= class=Point name=Unnamed dimension=1 values=[3.56105]
kurtosis= class=Point name=Unnamed dimension=1 values=[42.039]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.19515]
parameters= [class=PointWithDescription name=X0 dimension=2 description=[d1,d2] values=[5.5,10.5]]
Standard representative= FisherSnedecor(d1 = 5.5, d2 = 10.5)
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