Quality through design: experimental design, off-line quality control, and Taguchi's contributions
From a review by P. D. T. O'Connor in the Journal of Quality and Reliability Engineering: 'The authors clearly state their objectives, to explain the Taguchi methods, describing their advantages and disadvantages, and to describe extensions to the ideas, such as the use of simulations drivenby computer aided design, and response surface and other optimization methods. They seek to make friends among engineers whilst pleading for mercy from statisticians. In fact they do rather more, since the book covers the general principles of experimental design in some detail, thereby settingTaguchi's contribution in context. The result is a very good book, the best so far on the Taguchi phenomenon.'This important and acclaimed text is not available in paperback. This book describes the theoretical background to the techniques of experimental design and quality control that are now ssen of fundamental importance in the engineering and and process industries. The approach is two-fold; firstthe authors emphasise the importance of examples - mostly from the engineering industry - to illustrate the principles of Taguchi's methods. Secondly, they draw on methods available in statistics which together with the special Taguchi methodology and philosophy should form the backbone of apost-Taguchi methodology in off-line quality improvements.This paperback edition will be welcomed by the many students and teachers on undergraduate and graduate courses in design of experiments and quality control.
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Fundamentals of data analysis
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2-level factors 3-level analysis ANOVA Appendix assigned assuming block breakdown voltages calculated case-study Chapter CM Cl CM CM CM coefficients components confidence interval considered contribution ratios controllable factors corresponding cost data values decibel degrees of freedom design of Table dummy level error sum error variance estimate example experiment experimental design experimental trials F-ratio F-test factor levels full-factorial function hypothesis interaction columns Latin squares linear effect logit transformation main effect matrix mean response method minimize missing values noise factors normal distribution number of observations number of replications obtained optimal optimum orthogonal array orthogonal polynomials parameter design phosphor-type procedure process average random variables regression regression analysis represents response surface Section shown in Table significant Source df standard deviation statistical sum of squares Taguchi method target target-control factors technique tolerance transformation variation wafer