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Abstract <p>This paper considers the problem on how to analyze statistical relationships between sets of natural numbers. It is assumed that the sets are divided into named ranges. The numerical values of psychological indicators obtained during focus group testing are an example of such sets. It is proposed to perform the analysis using decision trees. The aim of this study is to propose a mathematical tool that allows using decision trees to test hypotheses about the presence or absence of relationships between two or more sets of numbers. The sets are sorted in ascending order and then divided into ranges. A new approach is proposed for the analysis of statistical dependences between sets of numerical values of experimental indicators. The advantages of the new approach are described in comparison with the one previously proposed in the development of mathematical and algorithmic tools for the analysis of quantitative testing results.</p>

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Analyzing the Relationships between Sets of Numbers by Constructing Decision Trees

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • V. Y. Leonov,
  • M. M. Norokesku

摘要

Abstract

This paper considers the problem on how to analyze statistical relationships between sets of natural numbers. It is assumed that the sets are divided into named ranges. The numerical values of psychological indicators obtained during focus group testing are an example of such sets. It is proposed to perform the analysis using decision trees. The aim of this study is to propose a mathematical tool that allows using decision trees to test hypotheses about the presence or absence of relationships between two or more sets of numbers. The sets are sorted in ascending order and then divided into ranges. A new approach is proposed for the analysis of statistical dependences between sets of numerical values of experimental indicators. The advantages of the new approach are described in comparison with the one previously proposed in the development of mathematical and algorithmic tools for the analysis of quantitative testing results.