Simple linear regression analysis for fuzzy input-output data and its application to psychological study

Research output: Contribution to conferencePaper

Abstract

A simple linear regression analysis using the least square method under some constraints, where both input data and output data are represented by triangular fuzzy numbers, was proposed and then compared to the possibilistic linear regression analysis proposed by Sakawa and Yano (1992) using fuzzy rating data in a psychological study. The major finding of the comparison were as follows: (1) Under the proposed analysis, the width between the upper and lower values of the predicted model was nearer to the width of the dependent variable than that of the possibilistic linear regression analysis, (2) As well, the representative value of the predicted value by the proposed analysis was also nearer to that of the dependent variable, compared with that of the possibilistic linear regression analysis.

Original languageEnglish
Pages49-53
Number of pages5
Publication statusPublished - 1998 Jan 1
Externally publishedYes
EventProceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2) - Beijing, China
Duration: 1997 Oct 281997 Oct 31

Other

OtherProceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2)
CityBeijing, China
Period97/10/2897/10/31

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Fingerprint Dive into the research topics of 'Simple linear regression analysis for fuzzy input-output data and its application to psychological study'. Together they form a unique fingerprint.

  • Cite this

    Takemura, K. (1998). Simple linear regression analysis for fuzzy input-output data and its application to psychological study. 49-53. Paper presented at Proceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2), Beijing, China, .