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Klappentext Design and Analysis of Experiments provides a rigorous introduction to product and process design improvement through quality and performance optimization. Clear demonstration of widely practiced techniques and procedures allows readers to master fundamental concepts, develop design and analysis skills, and use experimental models and results in real-world applications. Detailed coverage of factorial and fractional factorial design, response surface techniques, regression analysis, biochemistry and biotechnology, single factor experiments, and other critical topics offer highly-relevant guidance through the complexities of the field.Stressing the importance of both conceptual knowledge and practical skills, this text adopts a balanced approach to theory and application. Extensive discussion of modern software tools integrate data from real-world studies, while examples illustrate the efficacy of designed experiments across industry lines, from service and transactional organizations to heavy industry and biotechnology. Broad in scope yet deep in detail, this text is both an essential student resource and an invaluable reference for professionals in engineering, science, manufacturing, statistics, and business management. Inhaltsverzeichnis Preface iii 1 Introduction 1 1.1 Strategy of Experimentation 1 1.2 Some Typical Applications of Experimental Design 7 1.3 Basic Principles 11 1.4 Guidelines for Designing Experiments 13 1.5 A Brief History of Statistical Design 19 1.6 Summary: Using Statistical Techniques in Experimentation 20 2 Simple Comparative Experiments 22 2.1 Introduction 22 2.2 Basic Statistical Concepts 23 2.3 Sampling and Sampling Distributions 27 2.4 Inferences About the Differences in Means, Randomized Designs 32 2.5 Inferences About the Differences in Means, Paired Comparison Designs 47 2.6 Inferences About the Variances of Normal Distributions 52 3 Experiments with a Single Factor: The Analysis of Variance 55 3.1 An Example 55 3.2 The Analysis of Variance 58 3.3 Analysis of the Fixed Effects Model 59 3.4 Model Adequacy Checking 68 3.5 Practical Interpretation of Results 76 3.6 Sample Computer Output 89 3.7 Determining Sample Size 93 3.8 Other Examples of Single-Factor Experiments 95 3.9 The Random Effects Model 101 3.10 The Regression Approach to the Analysis of Variance 109 3.11 Nonparametric Methods in the Analysis of Variance 113 4 Randomized Blocks, Latin Squares, and Related Designs 115 4.1 The Randomized Complete Block Design 115 4.2 The Latin Square Design 133 4.3 The Graeco-Latin Square Design 140 4.4 Balanced Incomplete Block Designs 142 5 Introduction to Factorial Designs 152 5.1 Basic Definitions and Principles 152 5.2 The Advantage of Factorials 155 5.3 The Two-Factor Factorial Design 156 5.4 The General Factorial Design 174 5.5 Fitting Response Curves and Surfaces 179 5.6 Blocking in a Factorial Design 188 6 The 2k Factorial Design 194 6.1 Introduction 194 6.2 The 22 Design 195 6.3 The 23 Design 203 6.4 The General 2k Design 215 6.5 A Single Replicate of the 2k Design 218 6.6 Additional Examples of Unreplicated 2k Designs 231 6.7 2k Designs are Optimal Designs 243 6.8 The Addition of Center Points to the 2k Design 248 6.9 Why We Work with Coded Design Variables 253 7 Blocking and Confounding in the 2k Factorial Design 256 7.1 Introduction 256 7.2 Blocking a Replicated 2k Factorial Design 256 7.3 Confounding in the 2k Factorial Design 259 7.4 Confounding the 2k Factorial Design in Two Blocks 259 7.5 Another Illustration of Why Blocking is Im...
