An Implementation of Rough Information Analysis Tool and Its Application to Data by Questionnaires

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In this research, we have developed a data analysis tool, which can handle rough sets based concepts, and we applied this tool to analyzing data by questionnaires. For tables with numerical values, multivariate analysis has mainly been employed, and especially regression analysis is applied to generate regression lines from tables with numerical values. In tables with numerical values, it is possible to obtain mean and variance of data. However in tables with categorical data, mean and variance may not be defined, and multivariate analysis may not be applied to analyzing tables with categorical values. For such tables, we apply rough sets based concepts, and we have realized tool programs, which can handle rough sets based concepts.We employed C and Prolog language, and realized programs handling rough sets based concepts, like a program for the definability of a set, a program for manipulating equivalence relations in tables, a program for checking data dependency, a program for calculating criteria support,. accuracy and coverage, a program for generating rules from tables, etc. According to these basic programs, we have realized several tool programs.We have applied these tool programs to analyzing class evaluation data by students, which consists of evaluation on 16 questions by 60 students. We obtained several results, and the most characteristic rule is such that "If every student felt that the teacher was polite in every class, the evaluation of this student is good." The impression of a teacher to every student seems to strongly influence the evaluation of the class.