Distribution  class=Burr name=Burr dimension=1 c=1.5 k=4.5
Distribution  Burr(c = 1.5, k = 4.5)
Elliptical =  False
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[0.393842]
oneSample first= class=Point name=Unnamed dimension=1 values=[0.719504]  last= class=Point name=Unnamed dimension=1 values=[0.257588]
mean= class=Point name=Unnamed dimension=1 values=[0.383125]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.0960551]
Kolmogorov test for the generator, sample size= 100  is accepted
Kolmogorov test for the generator, sample size= 1000  is accepted
Point=  class=Point name=Unnamed dimension=1 values=[1.5]
ddf (FD)= class=Point name=Unnamed dimension=1 values=[-0.0861923]
pdf     = 0.0267008010312
pdf (FD)= 0.026700801
cdf= 0.990836694178
pdf gradient     = class=Point name=Unnamed dimension=2 values=[-0.00992994,-0.0219098]
pdf gradient (FD)= class=Point name=Unnamed dimension=2 values=[-0.00992994,-0.0219098]
cdf gradient     = class=Point name=Unnamed dimension=2 values=[0.0108262,0.00955539]
cdf gradient (FD)= class=Point name=Unnamed dimension=2 values=[0.0108262,0.00955539]
quantile= class=Point name=Unnamed dimension=1 values=[0.963592]
cdf(quantile)= 0.95
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.0508413]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [0.000634361, 0.964014]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 1.77199} with f=
MinimumVolumeLevelSetEvaluation(Burr(c = 1.5, k = 4.5))
beta= [0.169994]
Bilateral confidence interval= [0.0316926, 1.1727]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0, 0.963592]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.0508413, 118.536]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[0.37922]
standard deviation= class=Point name=Unnamed dimension=1 values=[0.310221]
skewness= class=Point name=Unnamed dimension=1 values=[2.16221]
kurtosis= class=Point name=Unnamed dimension=1 values=[13.2624]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.0962368]
parameters= [class=PointWithDescription name=X0 dimension=2 description=[c,k] values=[1.5,4.5]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [0.37922]
standard moment n= 2  value= [0.240044]
standard moment n= 3  value= [0.228571]
standard moment n= 4  value= [0.324466]
standard moment n= 5  value= [0.738598]
Standard representative= Burr(c = 1.5, k = 4.5)
