### 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 language | English |
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Pages | 49-53 |

Number of pages | 5 |

Publication status | Published - 1998 Jan 1 |

Event | Proceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2) - Beijing, China Duration: 1997 Oct 28 → 1997 Oct 31 |

### Other

Other | Proceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2) |
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City | Beijing, China |

Period | 97/10/28 → 97/10/31 |

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### ASJC Scopus subject areas

- Computer Science(all)
- Engineering(all)

### Cite this

*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, .