過去傳統迴歸分析確實成功的詮釋了傳統現象的因果關係。然而，由於社會現象的漸趨繁雜，有些資料具有模糊現象，使得傳統的分析方法難以適用。而自Tanaka 等人將傳統迴歸分析拓展至模糊環境後，便有越來越多的學者紛紛投入於模式的建立與分析，但多數文獻的共同特色是所求解出的迴歸係數為模糊數值，使得進行反應變數的估計時，估計值的展度隨著解釋變數數值的增加而擴大，因而降低了模式的估計準確度。
本研究提出兩種求解模式來建構模糊迴歸模式，第一種求解模式的觀念乃基於 截集的概念直接對觀察值求取出上下限值，利用最小平方估計法建立模糊迴歸模式。而第二種求解模式則是利用數學規劃法建構出模糊迴歸模式，其觀念為利用Chen與Lu之模糊排序評估準則尋求最佳配適的迴歸係數建立模糊迴歸模式。與過去文獻不同的是，本研究所提之兩種求解模式所求解出的迴歸係數均為明確數值，除了避免先前文獻的共同缺失之外，並使得整體模式之估計誤差降低，使決策者之決策品質可以大幅的提升。

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王景南(民 89)：模糊迴歸分析在籃球比賽攻防技術之應用。國家科學委員會研究彙刊：人文及社會科學，10(3)，287-298。

邱清爐(民 91)：模糊迴歸分析中最小平方法之求解與應用。國立成功大學工業管理科學研究所博士論文。

曹勝雄、曾國雄、江勁毅(民 85)：傳統計量迴歸、模糊迴歸、GMDH、類神經網路四種方法在預測應用之比較－以國人赴港旅客需求之預測為例。中國統計學報，34(2)，132-161。

曾能芳(民 91)：模糊隨機變數在線性迴歸模式上的應用。國立政治大學統計研究所博士論文。

張乃斌、陳育成(民 86)：以模糊目標迴歸法建立垃圾衛生掩埋場沼氣回收設備之建造成本模式。模糊系統月刊，3，23-48。

謝邦昌、林燦隆、呂理添(民 85) ：甘蔗產量之模糊迴歸預測模式。中國農藝，3 (6)，180-189。

Buckley, J.J. and T. Feuring, 2000, Linear and non-linear regression : Evolutionary algorithm solutions, Fuzzy Sets and Systems 112, 381-394.

Chang, P.T. and E.S. Lee, 1996, A generalized fuzzy weighted least-squares regression, Fuzzy Sets and Systems 82, 289-298.

Chen, L.H. and H.W. Lu, 2001, An approximate approach for ranking fuzzy numbers based on left and right dominance, Computers and Mathematics with applications 41, 1589-1602.

Diamond, P., 1988, Fuzzy least squares, Information Sciences 46, 141-157.

Diamond, P. and R. Körner, 1997, Extended fuzzy linear models and least squares estimates, Computers and Mathematics with Applications 33, 15-32.

Dunyak, J.P. and D. Wunsch, 2000, Fuzzy regression by fuzzy number neural networks, Fuzzy Sets and Systems 112, 371-380.

Kacprzyk, J. and M. Fedrizzi, 1992, Fuzzy Regression Analysis, Omnitech Press and Physica-Verlag, Heidelberg.

Kao, C. and C.L. Chyu, 2002, A fuzzy linear regression model with better explanatory power, Fuzzy Sets and Systems 126, 401-409.

Kim, B. and R.R. Bishu, 1996, On assessing operator response time in human reliability analysis (HBR) using a possibilistic fuzzy regression model, Reliability Engineering System Safety 52, 27-34.

Kim, B. and R.R. Bishu, 1998, Evaluation of fuzzy linear regression models by comparing membership functions, Fuzzy Sets and Systems 100, 343-352.

Körner, R., 1997, On the variance of fuzzy random variables, Fuzzy Sets and Systems 92, 83-93.

Körner, R. and W. Näther, 1998, Linear regression with random fuzzy variables: extended classical estimates, best linear estimates, least squares estimates, Information Sciences 109, 95-118.

Kruse, R. and K.D. Meyer, 1987, Statictics with Vague Data, Reidel, Dordrecht, Boston.

Kwakernaak, H., 1978, Fuzzy random variables－part I : Definitions and theorems, Information Sciences 15, 1-29.

Lee, H.T. and S.H. Chen, 2001, Fuzzy regression model with fuzzy input and output data for manpower forecasting, Fuzzy Sets and Systems 119, 205-213.

Ma, M., M. Friedman, and A. Kandel, 1997, General fuzzy least squares, Fuzzy Sets and Systems 88, 107-118.

Moskowitz, H. and K. Kim, 1993, On assessing the h value in fuzzy linear regression, Fuzzy Sets and Systems 58, 303-327.

Näther, W., 1997, Linear statistical inference for random fuzzy data, Statistics 29, 221-240.

Peters, G., 1994, Fuzzy linear regression with fuzzy intervals, Fuzzy Sets and Systems 63, 45-55.

Redden, D.T. and W.H. Woodall, 1994, Properties of certain fuzzy linear regression methods, Fuzzy Sets and Systems 64, 361-375.

Redden, D.T. and W.H. Woodall, 1996, Further examination of fuzzy linear regression, Fuzzy Sets and Systems 79, 203-211.

Sakawa, M. and H. Yano, 1992, Multiobjective fuzzy linear regression analysis for fuzzy input-output data, Fuzzy Sets and Systems 47, 173-181.

Savic, D. and W. Pedrycz, 1991, Evaluation of fuzzy linear regression models, Fuzzy Sets and Systems 39, 51-63.

Stein, W.E. and K. Talati, 1981, Convex fuzzy random variables, Fuzzy Sets and Systems 6, 271-283.

Tanaka, H., 1987, Fuzzy data analysis by possibilistic linear models, Fuzzy Sets and Systems 24, 363-375.

Tanaka, H., I. Hayashi, and J. Watada, 1989, Possibilistic linear regression analysis for fuzzy data, European Journal of Operational Research 40, 389-396.

Tanaka, H., H. Ishibuchi, and S. Yoshikawa, 1995, Exponential possibility regression analysis, Fuzzy Sets and Systems 69, 305-318.

Tanaka, H. and H. Lee, 1999, Exponential possibility regression analysis by identification method of possibilistic coefficients, Fuzzy Sets and Systems 106, 155-165.

Tanaka, H., S. Uegima, and K. Asai, 1982, Linear regression analysis with fuzzy model, IEEE Transactions on Systems, Man and Cybernetics 12, 903-907.

Tanaka, H. and J. Watada, 1988, Possibilistic linear systems and their application to the linear regression model, Fuzzy Sets and Systems 27, 275-289.

Tsaur, R.C. and H.F. Wang, 1999, Outliers in Fuzzy Regression Analysis, International Journal of Fuzzy Systems 1, 113-119.

Tseng, F.M. and G.H. Tzeng, 2002, A fuzzy seasonal ARIMA model for forecasting, Fuzzy Sets and Systems 126, 367-376.

Tseng, F.M., G.H. Tzeng, H.C. Yu, and B.J.C. Yuan, 2001, Fuzzy ARIMA model for forecasting the foreign exchange market, Fuzzy Sets and Systems 118, 9-19.

Wu B. and N.F. Tseng, 2002, A new approach to fuzzy regression model with application to business cycle analysis, Fuzzy Sets and Systems 130, 33-42.

Wu, H.C., 2003, Linear regression analysis for fuzzy input and output data using the extension principle, Computers and Mathematics with applications 45, 1849-1859.

Xu, R., 1997, S-curve regression model in fuzzy environment, Fuzzy Sets and Systems 90, 317-326.

Yang, M.S. and C.H. Ko, 1997, On cluster-wise fuzzy regression analysis, IEEE Transactions on Systems, Man and Cybernetics 27, 1-13.

Yang, M.S. and T.S. Lin, 2002, Fuzzy least-squares linear regression analysis for fuzzy input-output data, Fuzzy Sets and Systems 126, 389-399.

Yen, K.K., S. Ghoshray, and G. Roig, 1999, A linear regression model using triangular fuzzy number coefficients, Fuzzy Sets and Systems 106, 167-177.

Zadeh, L.A., 1978, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems 1, 3-28.