The Statistics Ontology defines the basic classes and properties needed to model data related to descriptive statistics and probability distributions.
A description of an early version of the ontology (named VffStochasticResource) can be found in:
- Terkaj W, Pedrielli G, Sacco M (2012) Virtual Factory Data Model. Workshop on Ontology and Semantic Web for Manufacturing OSEMA 2012, CEUR Workshop Proceedings, vol. 886, pp 29-43.
This version of the ontology is mentioned in:
- Pellegrinelli S, Terkaj W, Urgo M (2016) A Concept for a Pallet Configuration Approach Using Zero-point Clamping Systems. Procedia CIRP 41:123-128.
For an alternative ontology you may refer to the STATO ontology (http://stato-ontology.org/).
license: http://creativecommons.org/licenses/by/3.0/
If you wish to use this ontology, please acknowledge Walter Terkaj (ITIA-CNR) and related publications.
For any query please write to walter.terkaj@itia.cnr.it or walter@terkaj.com
Walter Terkaj
Definition of the outcomes of a categorical probability distribution.
Definition of the first shape parameter for a Beta probability distribution.
hasAlpha
Definition of the second shape parameter for a Beta probability distribution.
hasBeta
Definition of the first degrees of freedom for an F- probability distribution.
hasD1
Definition of the second degrees of freedom for an F- probability distribution.
hasD2
Kurtosis of the probability distribution.
Mean of the probability distribution.
Median of the probability distribution.
Mean of the probability distribution.
Skewness of the probability distribution.
Variance of the probability distribution.
Definition of the degrees of freedom for a Chi-squared probability distribution.
hasKappa
Definition of the shape parameter for a Gamma probability distribution.
hasKappa
Definition of the shape parameter for a Weibull probability distribution.
hasKappa
Definition of the rate parameter for an Exponential probability distribution.
hasLambda
Definition of the lambda parameter for a Poisson probability distribution.
hasLambda
Definition of the scale parameter for a Weibull probability distribution.
hasLambda
Definition of the lower limit parameter (a) for a Triangular probability distribution.
hasLowerLimit
Definition of the lower limit parameter (a) for a Uniform probability distribution.
hasLowerLimit
Definition of the location parameter for a Log-Normal probability distribution.
hasMi
Definition of the mean parameter for a Normal probability distribution.
hasMi
Definition of the mode parameter (c) for a Triangular probability distribution.
hasMode
Definition of the label of an outcome of categorical distribution.
Definition of the probability of an outcome of categorical distribution.
First quartile of the sample.
Kurtosis of the sample.
Maximum value in the sample.
Sample arithmetic mean.
Median of the sample.
Minimum value in the sample.
Mode of the sample.
Difference between the largest and smallest values in the sample.
Size of the sample.
Skewness of the sample.
Third quartile of the sample.
Sample variance.
Definition of the scale parameter for a Log-Normal probability distribution.
hasSigma
Definition of the standard deviation parameter for a Normal probability distribution.
hasSigma
Definition of the success probability for a Bernoulli probability distribution.
hasSuccessProbability
Definition of the success probability for a Geometric probability distribution.
hasSuccessProbability
Definition of the scale parameter for the Gamma probability distribution.
hasTheta
Definition of the upper limit parameter (b) for a Triangular probability distribution.
hasUpperLimit
Definition of the upper limit parameter (b) for a Uniform probability distribution.
hasUpperLimit
0.0
1.0
Bernoulli probability distribution.
0.0
0.0
Beta probability distribution that is defined by alpha and beta parameters.
Categorical probability distribution.
0.0
1.0
1
Outcome of a categorical probability distribution.
0
Chi-squared distribution defined by the degrees of freedom parameter.
continuous probability distribution.
Abstract superclass
0
Descriptive statistics of a data sample that is taken from a statistical population.
discrete probability distribution.
Abstract superclass
0.0
Exponenltial probability distribution that is defined by lambda parameter.
0
0
F-distribution defined by parameters d1 and d2.
0.0
0.0
Gamma probability distribution that is defined by kappa and theta parameters.
0.0
1.0
Geometric probability distribution modeling number of failures until the first success.
0.0
Log-normal probability distribution that is defined by mean and standard deviation parameters.
0.0
Normal (gaussian) probability distribution that is defined by mean and standard deviation parameters.
0.0
Poisson probability distribution.
generic probability distribution.
Abstract superclass
Triangular probability distribution that is defined by lower limit (a), upper limit (b) and mode (c) parameters.
Uniform continuous probability distribution that is defined by lower (a) and upper (b) limit parameters.
0.0
0.0
Weibull probability distribution that is defined by lambda and kappa parameters.