Statistical design of experiments with engineering applications
著者
書誌事項
Statistical design of experiments with engineering applications
（Statistics : textbooks and monographs, v. 182）
Champan & Hall/CRC, Taylor & Francis Group, 2005
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
In today's high-technology world, with flourishing e-business and intense competition at a global level, the search for the competitive advantage has become a crucial task of corporate executives. Quality, formerly considered a secondary expense, is now universally recognized as a necessary tool. Although many statistical methods are available for determining quality, there has been no guide to easy learning and implementation until now. Filling that gap, Statistical Design of Experiments with Engineering Applications, provides a ready made, quick and easy-to-learn approach for applying design of experiments techniques to problems. The book uses quality as the main theme to explain various design of experiments concepts.
The authors examine the entire product lifecycle and the tools and techniques necessary to measure quality at each stage. They explain topics such as optimization, Taguchi's method, variance reduction, and graphical applications based on statistical techniques. Wherever applicable the book supplies practical rules of thumb, step-wise procedures that allow you to grasp concepts quickly and apply them appropriately, and examples that demonstrate how to apply techniques. Emphasizing the importance of quality to products and services, the authors include concepts from the field of Quality Engineering. Written with an emphasis on application and not on bogging you down with the theoretical underpinnings, the book enables you to solve 80% of design problems without worrying about the derivation of mathematical formulas.
目次
Preface. Introduction. Designing and Conducting Experiments. Optimization of the Location Parameter. Minimization of the Dispersion. Taguchi's Approach to the Design of Experiments. Statistical Optimization of the Location Parameter. Statistical Minimization of the Dispersion Parameter. Validity of the Prediction Equation. Three-Level Factorial Designs. Second-Order Analysis. Appendices.
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