A Comparison on the Application of Prediction of Traditional Linear Regression and Fuzzy Linear Regression
| Autor: | Hung-Yi Lee, 李鴻毅 |
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| Rok vydání: | 2005 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 93 Regression analysis is a method that uses the relationship of two or more quantified variables to make one of variables that can be predicted by another. At present, regression analysis is widely used to construct models for business, industry, society, and physical education. However, the variables in these models do not present completely as crisp values. They often have the quality of fuzzy. Hence, we do analysis by fuzzy regression. The uncertainty of observable values of traditional regression is due to randomness, and residuals of traditional regression come from error of measurement or observation. However the uncertainty of observable values of fuzzy regression is due to the membership. The objective of this article is to compare these two different analytical methods with the traditional regression and fuzzy regression. Finally, the author takes an example of motor industry in Taiwan to make a comparison on the application of prediction of these two methods. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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