Presentations, Statistical Distributions, Quality Tools and Relationship to DoE -- Samples and Populations: Statistical Tests for Significance of Mean and Variability -- Regression, Treatments, DoE Design and Modelling Tools -- Two-Level Factorial Design and Analysis Techniques -- Three-Level Factorial Design and Analysis Techniques -- DoE Error Handling, Significance and Goal Setting -- DoE Reduction Using Confounding and Professional Experience -- Multiple Level Factorial Design and DoE Sequencing Techniques -- Variability Reduction Techniques and Combining with Mean Analysis -- Strategies for Multiple Outcome Analysis and Summary of DoE Case Studies and Techniques. .

This textbook provides the tools, techniques, and industry examples needed for the successful implementation of design of experiments (DoE) in engineering and manufacturing applications. It contains a high-level engineering analysis of key issues in the design, development, and successful analysis of industrial DoE, focusing on the design aspect of the experiment and then on interpreting the results. Statistical analysis is shown without formula derivation, and readers are directed as to the meaning of each term in the statistical analysis. Industrial Design of Experiments: A Case Study Approach for Design and Process Optimization is designed for graduate-level DoE, engineering design, and general statistical courses, as well as professional education and certification classes. Practicing engineers and managers working in multidisciplinary product development will find it to be an invaluable reference that provides all the information needed to accomplish a successful DoE. Presents classical versus Taguchi DoE methodologies as well as techniques developed by the author for successful DoE; Offers a step-wise approach to DoE optimization and interpretation of results; Includes industrial case studies, worked examples and detailed solutions to problems.