田口方法(Taguchi Method)是以產品品質特性的平均值與變異數合併的信號雜訊比(signal-to-noise ratio)作為衡量產品品質優劣的指標，利用此指標找出最佳的因子水準組合。然而，此指標缺乏較精確的演算程序，造成信號雜訊比的使用在統計學界較具有爭議性。本研究將產品品質特性的平均值與變異數視為兩個反應值，構成兩個反應曲面系統，使用雙反應曲面法(Dual Response Surface Method)發展出一套實用的分析程序，最後，以一個實例研究分別以田口方法與本研究提出的分析程序作比較，驗證本研究所提出的雙反應曲面法之最佳化參數設計流程確實有效可行。

Taguchi method uses signal-to-noise ratio which combines the mean and the variance of the quality characteristic of a product as an index to evaluate the quality of products and utilizes this index to find out the optimal combination of factor levels. However, lack of accuracy in the procedure of making mathematical calculations on this index causes a dispute about using signal-to-noise ratio in parameter design. This research considers the mean and the variance of the quality characteristic of a product as two response values to constitute two curved surface systems of responses, and to use dual response surface method to develop a set of practical analysis procedures. At last, we use a practical problem to make a comparison between Taguchi method and the analytic procedure proposed in this study to demonstrate that the procedure of dual response surface method is effective and feasible for parameter design.

中文部份
[1]蘇朝墩 (2004)，「專訪世界品質大師-田口玄一博士」，品質月刊，第40卷第3期，頁30-32。
[2]傅和彥　黃士滔 (1999)，「品質管理」，前程企業。
[3]鄭春生 (1997)，「品質管理」，育友。
[4]葉怡成 (2001)，「實驗計劃法-製程與產品最佳化」，五南。
[5]郭朝洲 (1994)，「田口式實驗設計於有機鍍鋁之應用」，國立臺灣工業技術學院機械工程技術研究所碩士論文。

英文部份
[6]Vining, G. G. and R. H. Myers (1990), “Combining Taguchi and Response Surface Philosophies：A Dual Response Approach,” Journal of Quality Technology, 22: 38-45.
[7]Myers, R. H, Jr. and W. H. Carter (1973), “Response Surface Techniques for Dual Response System,” Technometrics, 15(2): 301-317.
[8]Ross, P. J. (1988), Taguchi Techniques for Quality Engineering, McGraw-Hill, NY.
[9]Del Castillo, E. and D. C. Montgomery (1993), “A Nonlinear Programming Solution to the Dual Response Problem,” Journal of Quality Technology, 25: 199-204.
[10]Lin, D. K. J. and W. Tu (1995), “Dual Response Surface Optimization,” Journal of Quality Technology, 27(1): 34-39.
[11]Copeland, K. A. F. and P. R. Nelson (1996), “Dual Response Optimization via Direct Function Minimization,” Journal of Quality Technology, 28: 331-336.
[12]Kim, K. and D. K. J. Lin (1998), “Dual Response Surface Optimization: A Fuzzy Modeling Approach,” Journal of Quality Technology, 30: 1-10.
[13]Koksoy, O. and N. Doganaksoy (2003), “Joint Optimization of Mean and Standard Deviation Using Response Surface Methods,” Journal of Quality Technology, 35(3): 239-252.
[14]Montgomery, D. C. (2005), Design and Analysis of Experiments, John Wiley& Sons, NY.
[15]Neter, Kutner, Nachtsheim and Wasserman (1996), Applied Linear Statistical Models, Irwin.
[16]Myers, R. H. and D. C. Montgomery (1995), Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley& Sons, NY.
[17]Montgomery, D. C. (1999), “Experimental Design for Product and Process Design and Development,” The Statistician, 48(2): 159-177.
[18]Derringer, G. and R. Suich (1980), “Simultaneous Optimization of Several Response Variables,” Journal of Quality Technology, 12: 214-219.