Research on Human Behavior Management (Screening and Comprehensive Evaluation of Non-Numeric Variables)

Author: Dong Fenggu, Lu Wanzhong, et al.
Publisher:
Publishing Date: 2006-04-01
Features: This book innovatively applies the chi-square test technique of nonparametric statistics to design data mining skills for filtering massive data, laying a foundation for comprehensive analysis of variables with high independence in massive data. Secondly, this book innovatively applies the static aggregate index method to non-numeric variables to compile individual indices, class indices, and total indices, in order to evaluate and predict human behavior phenomena and issues across different aspects, levels, and categories. The academic or practical value of the systematic innovation in index theory presented in this book primarily lies in: once again proving the objectivity of the academic contribution of "the classification definition and expansion of statistical index theory for single-phenomenon dynamic aggregate indices and multi-phenomenon static aggregate indices," innovating the calculation method for static indices designed for quality variables (non-numeric variables), solving the theoretical problem of index calculation for qualitative variables, and using index theory to describe the changes and differences in quality variables, which holds significant implications for reverse guidance in derivative theory exploration. Due to the fact that the compilation of statistical indices has historically been mostly focused on numeric variables in economic and social management, with little involvement in non-numeric variables related to human behavior, the methodological system for compiling indices for non-numeric variables has broad application prospects. This book innovatively applies the chi-square test technique of nonparametric statistics to design data mining skills for filtering massive data, laying a foundation for comprehensive analysis of variables with high independence in massive data. Secondly, this book innovatively applies the static aggregate index method to non-numeric variables to compile individual indices, class indices, and total indices, in order to evaluate and predict human behavior phenomena and issues across different aspects, levels, and categories. The academic or practical value of the systematic innovation in index theory presented in this book primarily lies in: once again proving the objectivity of the academic contribution of "the classification definition and expansion of statistical index theory for single-phenomenon dynamic aggregate indices and multi-phenomenon static aggregate indices," innovating the calculation method for static indices designed for quality variables (non-numeric variables), solving the theoretical problem of index calculation for qualitative variables, and using index theory to describe the changes and differences in quality variables, which holds significant implications for reverse guidance in derivative theory exploration. Due to the fact that the compilation of statistical indices has historically been mostly focused on numeric variables in economic and social management, with little involvement in non-numeric variables related to human behavior, the methodological system for compiling indices for non-numeric variables has broad application prospects.