Abstract
The aim of the paper is to examine procedures of descriptive statistics in the case when the values of relevant attribute in a sample are set in the form of fuzzy categories. The paper provides alternative definitions of a fuzzy random variable, and describes corresponding procedures for calculating the analogues of location and spread parameters. The paper also presents some illustrative examples and analyses the results obtained. Based on the result analysis, practical recommendations are given on how to use procedures of fuzzy statistics.
Highlights
Classical statistics is a powerful tool for processing a different kind of initial data
By means of procedures of inference statistics, the obtained results can be transferred to the population, and potential errors related to such transfer can be evaluated
One limitation of classical statistics is that the values of relevant attribute in the sample and/or population have to be set in the form of real numbers
Summary
Classical statistics is a powerful tool for processing a different kind of initial data. Based on the descriptive statistical procedures, it is possible to calculate parameters of location and spread for sample initial data distribution. By means of procedures of inference statistics, the obtained results can be transferred to the population, and potential errors related to such transfer can be evaluated. One limitation of classical statistics is that the values of relevant attribute in the sample and/or population have to be set in the form of real numbers. In many practical situations, it is impossible to get them. Relevant evaluations can only be obtained in uncertain form as intervals or fuzzy categories
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