Distribution  class=Trapezoidal name=Trapezoidal dimension=1 a=1 b=1.2 c=3 d=14 h=0.135135
Distribution  Trapezoidal(a = 1, b = 1.2, c = 3, d = 14)
Mean=  class=Point name=Unnamed dimension=1 values=[5.48108]
Covariance=  class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[9.1428]
Elliptical =  False
oneRealization= class=Point name=Unnamed dimension=1 values=[6.23752]
oneSample first= class=Point name=Unnamed dimension=1 values=[9.63201]  last= class=Point name=Unnamed dimension=1 values=[4.32244]
mean= class=Point name=Unnamed dimension=1 values=[5.52363]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[9.34744]
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=[1.1]
ddf     = class=Point name=Unnamed dimension=1 values=[0.675676]
ddf (FD)= class=Point name=Unnamed dimension=1 values=[0.675676]
log pdf=-2.694627
pdf     =0.067568
pdf (FD)=0.067568
cdf=0.003378
ccdf=0.996622
characteristic function= (-0.115059431951+0.0386019620712j)
pdf gradient     = class=Point name=Unnamed dimension=4 values=[-0.333272,-0.333272,-0.00456538,-0.00456538]
pdf gradient (FD)= class=Point name=Unnamed dimension=4 values=[-0.333272,-0.333272,-0.00456538,-0.00456538]
log-pdf gradient     = class=Point name=Unnamed dimension=4 values=[-4.93243,-4.93243,-0.0675676,-0.0675676]
log-pdf gradient (FD)= class=Point name=Unnamed dimension=4 values=[-4.93243,-4.93243,-0.0675676,-0.0675676]
cdf gradient     = class=Point name=Unnamed dimension=4 values=[-0.0504474,-0.0166636,-0.000228269,-0.000228269]
cdf gradient (FD)= class=Point name=Unnamed dimension=4 values=[-0.0504474,-0.0166636,-0.000228269,-0.000228269]
quantile= class=Point name=Unnamed dimension=1 values=[11.1469]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[1.47]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [1.05141, 11.1725]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 3.35999} with f=
MinimumVolumeLevelSetEvaluation(Trapezoidal(a = 1, b = 1.2, c = 3, d = 14))
beta= [0.0347356]
Bilateral confidence interval= [1.285, 11.9826]
beta= [0.95]
Unilateral confidence interval (lower tail)= [1, 11.1469]
beta= [0.95]
Unilateral confidence interval (upper tail)= [1.47, 14]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[5.48108]
standard deviation= class=Point name=Unnamed dimension=1 values=[3.02371]
skewness= class=Point name=Unnamed dimension=1 values=[0.548305]
kurtosis= class=Point name=Unnamed dimension=1 values=[2.39403]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[9.1428]
parameters= [class=PointWithDescription name=X0 dimension=4 description=[a,b,c,d] values=[1,1.2,3,14]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [-0.310603]
standard moment n= 2  value= [0.312872]
standard moment n= 3  value= [-0.176411]
standard moment n= 4  value= [0.1781]
standard moment n= 5  value= [-0.120255]
Standard representative= Trapezoidal(a = -1, b = -0.969231, c = -0.692308, d = 1)
roughness= [0.101047]
