Distribution  class=Logistic name=Logistic dimension=1 alpha=-0.5 beta=1.5
Distribution  Logistic(alpha = -0.5, beta = 1.5)
Elliptical =  True
Continuous =  True
oneRealization= class=Point name=Unnamed dimension=1 values=[0.297531]
oneSample first= class=Point name=Unnamed dimension=1 values=[2.5289]  last= class=Point name=Unnamed dimension=1 values=[-0.955127]
mean= class=Point name=Unnamed dimension=1 values=[-0.472451]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[7.49258]
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]
ddf     = class=Point name=Unnamed dimension=1 values=[-0.0403812]
ddf (FD)= class=Point name=Unnamed dimension=1 values=[-0.0403812]
log pdf=-2.031988
pdf     =0.131075
pdf (FD)=0.131075
cdf=0.731059
ccdf=0.268941
characteristic function= (0.0743069833187-0.0405940899998j)
pdf gradient     = class=Point name=Unnamed dimension=2 values=[0.0403812,-0.0470019]
pdf gradient (FD)= class=Point name=Unnamed dimension=2 values=[0.0403812,-0.0470019]
cdf gradient     = class=Point name=Unnamed dimension=2 values=[-0.131075,-0.131075]
cdf gradient (FD)= class=Point name=Unnamed dimension=2 values=[-0.131075,-0.131075]
quantile= class=Point name=Unnamed dimension=1 values=[3.91666]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-4.91666]
Survival(inverseSurvival)=0.950000
Minimum volume interval= [-5.99534, 4.99534]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 4.11966} with f=
MinimumVolumeLevelSetEvaluation(Logistic(alpha = -0.5, beta = 1.5))
beta= [0.01625]
Bilateral confidence interval= [-5.99534, 4.99534]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-48.8543, 3.91666]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-4.91666, 47.8543]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[-0.5]
standard deviation= class=Point name=Unnamed dimension=1 values=[2.7207]
skewness= class=Point name=Unnamed dimension=1 values=[0]
kurtosis= class=Point name=Unnamed dimension=1 values=[4.2]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[7.4022]
parameters= [class=PointWithDescription name=X0 dimension=2 description=[alpha,beta] values=[-0.5,1.5]]
standard moment n= 0  value= [1]
standard moment n= 1  value= [0]
standard moment n= 2  value= [3.28987]
standard moment n= 3  value= [0]
standard moment n= 4  value= [45.4576]
standard moment n= 5  value= [0]
Standard representative= Logistic(alpha = 0, beta = 1)
