This book presents a model-based approach to quality improvement through design of experiments. After a description of statistical methods for data analysis and design of experiments it addresses the following topics: Taguchi's approach to quality improvement; Reduction of errors transmitted from factors to the response; Robustness against errors in product/process parameters and external noise factors; Optimization procedures for product/process design; Quality improvement through mechanistic models; Quality improvement of products with both qualitative and quantitative factors; and Quality improvement based on replicated observations. The book provides systematic and detailed practical guidance to a model-based approach to quality engineering problems. All methods are illustrated by real-world examples that make them readily accessible to readers. All mathematical proofs are given in appendices to the relevant chapters. The book is written for a wide range of engineers, quality engineering professionals, engineering designers, engineering statisticians, and all those who want to apply design of experiments to solving quality improvement problems. The text is appropriate for undergraduate and graduate students in engineering and statistics.
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