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

Author

Takemura, Kazuhisa

Author_Institution

Inst. of Policy & Planning Sci., Tsukuba Univ., Ibaraki, Japan

Volume

1

fYear

1997

fDate

28-31 Oct 1997

Firstpage

49

Abstract

A simple linear regression analysis using the least squares method under some constraints, where both input data and output data are represented by triangular fuzzy numbers, is 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 findings of the comparison were as follows: 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. Also, 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

Keywords

data analysis; fuzzy set theory; least squares approximations; possibility theory; psychology; statistical analysis; constraints; fuzzy input-output data; fuzzy rating data; least squares method; linear regression analysis; possibilistic linear regression analysis; predicted value; psychological study; triangular fuzzy numbers; Data analysis; Fuzzy set theory; Fuzzy sets; Humans; Least squares methods; Linear programming; Linear regression; Predictive models; Psychology; Temperature measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.672737

Filename

672737