“…Recently, the optimization of multiple responses in the Taguchi method has received significant research attention. Several techniques are adopted for this purpose, including a trade-off between quality loss and productivity (Phadke, 1989), multiple response S/N ratio (Antony, 2001;Lin, 2002;Al-Refaie et al, 2010), data envelopment analysis (Al-Refaie et al, 2009), grey relational analysis and desirability function approach (Lin, 2004;Palanikumar et al, 2006;Pan et al, 2007;Al-Refaie et al, 2008;Lin & Lee, 2009;Chen et al, 2010), principal components analysis (Tong et al, 2005;Fung & Kang, 2005;Huang & Lin, 2008;Gauri & Chakraborty, 2009), fuzzy logic approach (Tarng et al, 2000;Lin & Lin, 2005;Wang & Jean, 2006;Chang & Lu, 2007), and multiple regression-based integration approach (Gopalsamy et al, 2009;Pal & Gauri, 2010). Although statistical regression has many applications (Shu et al, 2006;Gopalsamy et al, 2009), problems can still occur in the following situations, including number of observations is inadequate (small data set), difficulties verifying distribution assumptions, vagueness in the relationship between input and output variables, ambiguity of events or degree to which they occur, and inaccuracy and distortion introduced by linearization.…”