The Knowledge Content of Statistical Data
Copyright policy )The Knowledge Content of Statistical Data
An information-theoretic framework is used to analyze the knowledge content in multivariate cross classified data. Several related measures based directly on the information concept are proposed: the knowledge content (S) of a cross classification, its terseness (Zeta), and the separability (GammaX) of one variable, given all others. Exemplary applications are presented which illustrate the solutions obtained where classical analysis is unsatisfactory, such as optimal grouping, the analysis of very skew tables, or the interpretation of well-known paradoxes. Further, the separability suggests a solution for the classic problem of inductive inference which is independent of sample size.
- University of Fribourg Switzerland
paradox, association, info:eu-repo/classification/udc/61, significance, Statistical aspects of information-theoretic topics, entropy, Applications of statistics to psychology
paradox, association, info:eu-repo/classification/udc/61, significance, Statistical aspects of information-theoretic topics, entropy, Applications of statistics to psychology
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