{"product_id":"experiments-isbn-9781119470106","title":"Experiments","description":"\u003cp\u003e\u003cb\u003ePraise for the \u003ci\u003eFirst Edition\u003c\/i\u003e:\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"If you ... want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library.\"\u003cbr\u003e\u003cb\u003e—\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA COMPREHENSIVE REVIEW OF MODERN EXPERIMENTAL DESIGN\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eExperiments: Planning, Analysis, and Optimization, Third Edition\u003c\/i\u003e provides a complete discussion of modern experimental design for product and process improvement—the design and analysis of experiments and their applications for system optimization, robustness, and treatment comparison. While maintaining the same easy-to-follow style as the previous editions, this book continues to present an integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. New chapters provide modern updates on practical optimal design and computer experiments, an explanation of computer simulations as an alternative to physical experiments. Each chapter begins with a real-world example of an experiment followed by the methods required to design that type of experiment. The chapters conclude with an application of the methods to the experiment, bridging the gap between theory and practice.\u003c\/p\u003e \u003cp\u003eThe authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays.\u003c\/p\u003e \u003cp\u003eThe third edition includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eInformation on the design and analysis of computer experiments\u003c\/li\u003e \u003cli\u003eA discussion of practical optimal design of experiments\u003c\/li\u003e \u003cli\u003eAn introduction to conditional main effect (CME) analysis and definitive screening designs (DSDs)\u003c\/li\u003e \u003cli\u003eNew exercise problems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book includes valuable exercises and problems, allowing the reader to gauge their progress and retention of the book's subject matter as they complete each chapter.\u003c\/p\u003e \u003cp\u003eDrawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eExperiments: Planning, Analysis, and Optimization, Third Edition\u003c\/i\u003e is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.\u003c\/p\u003e \u003cp\u003ePreface to the Third Edition xvii\u003c\/p\u003e \u003cp\u003ePreface to the Second Edition xix\u003c\/p\u003e \u003cp\u003ePreface to the First Edition xxi\u003c\/p\u003e \u003cp\u003eSuggestions of Topics for Instructors xxv\u003c\/p\u003e \u003cp\u003eList of Experiments and Data Sets xxvii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Basic Concepts for Experimental Design and Introductory Regression Analysis 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction and Historical Perspective 1\u003c\/p\u003e \u003cp\u003e1.2 A Systematic Approach to the Planning and Implementation of Experiments 4\u003c\/p\u003e \u003cp\u003e1.3 Fundamental Principles: Replication, Randomization, and Blocking 8\u003c\/p\u003e \u003cp\u003e1.4 Simple Linear Regression 11\u003c\/p\u003e \u003cp\u003e1.5 Testing of Hypothesis and Interval Estimation 14\u003c\/p\u003e \u003cp\u003e1.6 Multiple Linear Regression 20\u003c\/p\u003e \u003cp\u003e1.7 Variable Selection in Regression Analysis 26\u003c\/p\u003e \u003cp\u003e1.8 Analysis of Air Pollution Data 28\u003c\/p\u003e \u003cp\u003e1.9 Practical Summary 34\u003c\/p\u003e \u003cp\u003eExercises 35\u003c\/p\u003e \u003cp\u003eReferences 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Experiments with a Single Factor 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 One-Way Layout 45\u003c\/p\u003e \u003cp\u003e*2.1.1 Constraint on the Parameters 50\u003c\/p\u003e \u003cp\u003e2.2 Multiple Comparisons 52\u003c\/p\u003e \u003cp\u003e2.3 Quantitative Factors and Orthogonal Polynomials 56\u003c\/p\u003e \u003cp\u003e2.4 Expected Mean Squares and Sample Size Determination 61\u003c\/p\u003e \u003cp\u003e2.5 One-Way Random Effects Model 68\u003c\/p\u003e \u003cp\u003e2.6 Residual Analysis: Assessment of Model Assumptions 71\u003c\/p\u003e \u003cp\u003e2.7 Practical Summary 76\u003c\/p\u003e \u003cp\u003eExercises 77\u003c\/p\u003e \u003cp\u003eReferences 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Experiments with More than One Factor 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Paired Comparison Designs 85\u003c\/p\u003e \u003cp\u003e3.2 Randomized Block Designs 88\u003c\/p\u003e \u003cp\u003e3.3 Two-Way Layout: Factors with Fixed Levels 92\u003c\/p\u003e \u003cp\u003e3.3.1 Two Qualitative Factors: A Regression Modeling Approach 95\u003c\/p\u003e \u003cp\u003e*3.4 Two-Way Layout: Factors with Random Levels 98\u003c\/p\u003e \u003cp\u003e3.5 Multi-Way Layouts 105\u003c\/p\u003e \u003cp\u003e3.6 Latin Square Designs: Two Blocking Variables 108\u003c\/p\u003e \u003cp\u003e3.7 Graeco-Latin Square Designs 112\u003c\/p\u003e \u003cp\u003e*3.8 Balanced Incomplete Block Designs 113\u003c\/p\u003e \u003cp\u003e*3.9 Split-Plot Designs 118\u003c\/p\u003e \u003cp\u003e3.10 Analysis of Covariance: Incorporating Auxiliary Information 126\u003c\/p\u003e \u003cp\u003e*3.11 Transformation of the Response 130\u003c\/p\u003e \u003cp\u003e3.12 Practical Summary 134\u003c\/p\u003e \u003cp\u003eExercises 135\u003c\/p\u003e \u003cp\u003eAppendix 3A: Table of Latin Squares, Graeco-Latin Squares, and Hyper-Graeco-Latin Squares 147\u003c\/p\u003e \u003cp\u003eReferences 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Full Factorial Experiments at Two Levels 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 An Epitaxial Layer Growth Experiment 151\u003c\/p\u003e \u003cp\u003e4.2 Full Factorial Designs at Two Levels: A General Discussion 153\u003c\/p\u003e \u003cp\u003e4.3 Factorial Effects and Plots 157\u003c\/p\u003e \u003cp\u003e4.3.1 Main Effects 158\u003c\/p\u003e \u003cp\u003e4.3.2 Interaction Effects 159\u003c\/p\u003e \u003cp\u003e4.4 Using Regression to Compute Factorial Effects 165\u003c\/p\u003e \u003cp\u003e*4.5 ANOVA Treatment of Factorial Effects 167\u003c\/p\u003e \u003cp\u003e4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity 168\u003c\/p\u003e \u003cp\u003e4.7 Comparisons with the “One-Factor-at-a-Time” Approach 169\u003c\/p\u003e \u003cp\u003e4.8 Normal and Half-Normal Plots for Judging Effect Significance 172\u003c\/p\u003e \u003cp\u003e4.9 Lenth’s Method: Testing Effect Significance for Experiments Without Variance Estimates 174\u003c\/p\u003e \u003cp\u003e4.10 Nominal-the-Best Problem and Quadratic Loss Function 178\u003c\/p\u003e \u003cp\u003e4.11 Use of Log Sample Variance for Dispersion Analysis 179\u003c\/p\u003e \u003cp\u003e4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment 181\u003c\/p\u003e \u003cp\u003e*4.13 Test of Variance Homogeneity and Pooled Estimate of Variance 184\u003c\/p\u003e \u003cp\u003e*4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments With Variance Estimates 185\u003c\/p\u003e \u003cp\u003e4.15 Blocking and Optimal Arrangement of 2\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e \u003c\/i\u003eFactorial Designs in 2\u003ci\u003e\u003csup\u003eq\u003c\/sup\u003e \u003c\/i\u003eBlocks 188\u003c\/p\u003e \u003cp\u003e4.16 Practical Summary 193\u003c\/p\u003e \u003cp\u003eExercises 195\u003c\/p\u003e \u003cp\u003eAppendix 4A: Table of 2\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e \u003c\/i\u003eFactorial Designs in 2\u003ci\u003e\u003csup\u003eq\u003c\/sup\u003e \u003c\/i\u003eBlocks 201\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Fractional Factorial Experiments at Two Levels 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 A Leaf Spring Experiment 205\u003c\/p\u003e \u003cp\u003e5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria of Resolution and Minimum Aberration 206\u003c\/p\u003e \u003cp\u003e5.3 Analysis of Fractional Factorial Experiments 212\u003c\/p\u003e \u003cp\u003e5.4 Techniques for Resolving the Ambiguities in Aliased Effects 217\u003c\/p\u003e \u003cp\u003e5.4.1 Fold-Over Technique for Follow-Up Experiments 218\u003c\/p\u003e \u003cp\u003e5.4.2 Optimal Design Approach for Follow-Up Experiments 222\u003c\/p\u003e \u003cp\u003e5.5 Conditional Main Effect (CME) Analysis: A Method to Unravel Aliased Interactions 227\u003c\/p\u003e \u003cp\u003e5.6 Selection of 2\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eDesigns Using Minimum Aberration and Related Criteria 232\u003c\/p\u003e \u003cp\u003e5.7 Blocking in Fractional Factorial Designs 236\u003c\/p\u003e \u003cp\u003e5.8 Practical Summary 238\u003c\/p\u003e \u003cp\u003eExercises 240\u003c\/p\u003e \u003cp\u003eAppendix 5A: Tables of 2\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eFractional Factorial Designs 252\u003c\/p\u003e \u003cp\u003eAppendix 5B: Tables of 2\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eFractional Factorial Designs in 2\u003ci\u003eq \u003c\/i\u003eBlocks 258\u003c\/p\u003e \u003cp\u003eReferences 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Full Factorial and Fractional Factorial Experiments at Three Levels 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 A Seat-Belt Experiment 265\u003c\/p\u003e \u003cp\u003e6.2 Larger-the-Better and Smaller-the-Better Problems 267\u003c\/p\u003e \u003cp\u003e6.3 3\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e \u003c\/i\u003eFull Factorial Designs 268\u003c\/p\u003e \u003cp\u003e6.4 3\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep\u003c\/i\u003e\u003c\/sup\u003eFractional Factorial Designs 273\u003c\/p\u003e \u003cp\u003e6.5 Simple Analysis Methods: Plots and Analysis of Variance 277\u003c\/p\u003e \u003cp\u003e6.6 An Alternative Analysis Method 282\u003c\/p\u003e \u003cp\u003e6.7 Analysis Strategies for Multiple Responses I: Out-Of-Spec Probabilities 291\u003c\/p\u003e \u003cp\u003e6.8 Blocking in 3\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e \u003c\/i\u003eand 3\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eDesigns 299\u003c\/p\u003e \u003cp\u003e6.9 Practical Summary 301\u003c\/p\u003e \u003cp\u003eExercises 303\u003c\/p\u003e \u003cp\u003eAppendix 6A: Tables of 3\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eFractional Factorial Designs 309\u003c\/p\u003e \u003cp\u003eAppendix 6B: Tables of 3\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eFractional Factorial Designs in 3\u003ci\u003e\u003csup\u003eq\u003c\/sup\u003e \u003c\/i\u003eBlocks 310\u003c\/p\u003e \u003cp\u003eReferences 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Other Design and Analysis Techniques for Experiments at More than Two Levels 315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design 315\u003c\/p\u003e \u003cp\u003e7.2 Method of Replacement and Construction of 2\u003ci\u003e\u003csup\u003em\u003c\/sup\u003e\u003c\/i\u003e4\u003ci\u003e\u003csup\u003en\u003c\/sup\u003e \u003c\/i\u003eDesigns 318\u003c\/p\u003e \u003cp\u003e7.3 Minimum Aberration 2\u003ci\u003e\u003csup\u003em\u003c\/sup\u003e\u003c\/i\u003e4\u003ci\u003e\u003csup\u003en\u003c\/sup\u003e \u003c\/i\u003eDesigns with \u003ci\u003en \u003c\/i\u003e= 1, 2, 321\u003c\/p\u003e \u003cp\u003e7.4 An Analysis Strategy for 2\u003ci\u003e\u003csup\u003em\u003c\/sup\u003e\u003c\/i\u003e4\u003ci\u003e\u003csup\u003en\u003c\/sup\u003e \u003c\/i\u003eExperiments 324\u003c\/p\u003e \u003cp\u003e7.5 Analysis of the Router Bit Experiment 326\u003c\/p\u003e \u003cp\u003e7.6 A Paint Experiment Based on a Mixed Two-Level and Three-Level Design 329\u003c\/p\u003e \u003cp\u003e7.7 Design and Analysis of 36-Run Experiments at Two And Three Levels 332\u003c\/p\u003e \u003cp\u003e7.8 \u003ci\u003er\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep\u003c\/i\u003e\u003c\/sup\u003eFractional Factorial Designs for any Prime Number \u003ci\u003er\u003c\/i\u003e 337\u003c\/p\u003e \u003cp\u003e7.8.1 25-Run Fractional Factorial Designs at Five Levels 337\u003c\/p\u003e \u003cp\u003e7.8.2 49-Run Fractional Factorial Designs at Seven Levels 340\u003c\/p\u003e \u003cp\u003e7.8.3 General Construction 340\u003c\/p\u003e \u003cp\u003e7.9 Definitive Screening Designs 341\u003c\/p\u003e \u003cp\u003e*7.10 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling 343\u003c\/p\u003e \u003cp\u003e7.10.1 Nested Effects Modeling 346\u003c\/p\u003e \u003cp\u003e7.10.2 Analysis of Light Bulb Experiment 347\u003c\/p\u003e \u003cp\u003e7.10.3 Response Surface Modeling 349\u003c\/p\u003e \u003cp\u003e7.10.4 Symmetric and Asymmetric Relationships Between Related Factors 352\u003c\/p\u003e \u003cp\u003e7.11 Practical Summary 352\u003c\/p\u003e \u003cp\u003eExercises 353\u003c\/p\u003e \u003cp\u003eAppendix 7A: Tables of 2\u003ci\u003e\u003csup\u003em\u003c\/sup\u003e\u003c\/i\u003e4\u003csup\u003e1\u003c\/sup\u003e Minimum Aberration Designs 361\u003c\/p\u003e \u003cp\u003eAppendix 7B: Tables of 2\u003ci\u003e\u003csup\u003em\u003c\/sup\u003e\u003c\/i\u003e4\u003csup\u003e2\u003c\/sup\u003e Minimum Aberration Designs 362\u003c\/p\u003e \u003cp\u003eAppendix 7C: OA(25, 5\u003csup\u003e6\u003c\/sup\u003e) 364\u003c\/p\u003e \u003cp\u003eAppendix 7D: OA(49, 7\u003csup\u003e8\u003c\/sup\u003e) 364\u003c\/p\u003e \u003cp\u003eAppendix 7E: Conference Matrices C6 C8 C10 C12 C14 and C16 366\u003c\/p\u003e \u003cp\u003eReferences 368\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Nonregular Designs: Construction and Properties 369\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Two Experiments: Weld-Repaired Castings and Blood Glucose Testing 369\u003c\/p\u003e \u003cp\u003e8.2 Some Advantages of Nonregular Designs Over the 2\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eAND 3\u003ci\u003e\u003csup\u003ek\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003ep \u003c\/i\u003e\u003c\/sup\u003eSeries of Designs 370\u003c\/p\u003e \u003cp\u003e8.3 A Lemma on Orthogonal Arrays 372\u003c\/p\u003e \u003cp\u003e8.4 Plackett–Burman Designs and Hall’s Designs 373\u003c\/p\u003e \u003cp\u003e8.5 A Collection of Useful Mixed-Level Orthogonal Arrays 377\u003c\/p\u003e \u003cp\u003e*8.6 Construction of Mixed-Level Orthogonal Arrays Based on Difference Matrices 379\u003c\/p\u003e \u003cp\u003e8.6.1 General Method for Constructing Asymmetrical Orthogonal Arrays 380\u003c\/p\u003e \u003cp\u003e*8.7 Construction of Mixed-Level Orthogonal Arrays Through the Method of Replacement 382\u003c\/p\u003e \u003cp\u003e8.8 Orthogonal Main-Effect Plans Through Collapsing Factors 384\u003c\/p\u003e \u003cp\u003e8.9 Practical Summary 388\u003c\/p\u003e \u003cp\u003eExercises 389\u003c\/p\u003e \u003cp\u003eAppendix 8A: Plackett–Burman Designs OA(\u003ci\u003eN\u003c\/i\u003e, 2\u003ci\u003e\u003csup\u003eN\u003c\/sup\u003e\u003c\/i\u003e\u003csup\u003e−1\u003c\/sup\u003e) with 12 ≤ \u003ci\u003eN \u003c\/i\u003e≤ 48 and \u003ci\u003eN \u003c\/i\u003e= 4 \u003ci\u003ek \u003c\/i\u003ebut not a Power of 2 394\u003c\/p\u003e \u003cp\u003eAppendix 8B: Hall’S 16-Run Orthogonal Arrays of Types II to V 397\u003c\/p\u003e \u003cp\u003eAppendix 8C: Some Useful Mixed-Level Orthogonal Arrays 399\u003c\/p\u003e \u003cp\u003eAppendix 8D: Some Useful Difference Matrices 411\u003c\/p\u003e \u003cp\u003eAppendix 8E: Some Useful Orthogonal Main-Effect Plans 413\u003c\/p\u003e \u003cp\u003eReferences 414\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Experiments with Complex Aliasing 417\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Partial Aliasing of Effects and the Alias Matrix 417\u003c\/p\u003e \u003cp\u003e9.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis 420\u003c\/p\u003e \u003cp\u003e9.3 Simplification of Complex Aliasing via Effect Sparsity 421\u003c\/p\u003e \u003cp\u003e9.4 An Analysis Strategy for Designs with Complex Aliasing 422\u003c\/p\u003e \u003cp\u003e9.4.1 Some Limitations 428\u003c\/p\u003e \u003cp\u003e*9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing 429\u003c\/p\u003e \u003cp\u003e9.5.1 Bayesian Model Priors 431\u003c\/p\u003e \u003cp\u003e9.5.2 Gibbs Sampling 432\u003c\/p\u003e \u003cp\u003e9.5.3 Choice of Prior Tuning Constants 434\u003c\/p\u003e \u003cp\u003e9.5.4 Blood Glucose Experiment Revisited 435\u003c\/p\u003e \u003cp\u003e9.5.5 Other Applications 437\u003c\/p\u003e \u003cp\u003e*9.6 Supersaturated Designs: Design Construction and Analysis 437\u003c\/p\u003e \u003cp\u003e9.7 Practical Summary 441\u003c\/p\u003e \u003cp\u003eExercises 442\u003c\/p\u003e \u003cp\u003eAppendix 9A: Further Details for the Full Conditional Distributions 451\u003c\/p\u003e \u003cp\u003eReferences 453\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Response Surface Methodology 455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 A Ranitidine Separation Experiment 455\u003c\/p\u003e \u003cp\u003e10.2 Sequential Nature of Response Surface Methodology 457\u003c\/p\u003e \u003cp\u003e10.3 From First-Order Experiments to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid Search 460\u003c\/p\u003e \u003cp\u003e10.3.1 Curvature Check 460\u003c\/p\u003e \u003cp\u003e10.3.2 Steepest Ascent Search 461\u003c\/p\u003e \u003cp\u003e10.3.3 Rectangular Grid Search 466\u003c\/p\u003e \u003cp\u003e10.4 Analysis of Second-Order Response Surfaces 469\u003c\/p\u003e \u003cp\u003e10.4.1 Ridge Systems 470\u003c\/p\u003e \u003cp\u003e10.5 Analysis of the Ranitidine Experiment 472\u003c\/p\u003e \u003cp\u003e10.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions 475\u003c\/p\u003e \u003cp\u003e10.7 Central Composite Designs 478\u003c\/p\u003e \u003cp\u003e10.8 Box–Behnken Designs and Uniform Shell Designs 483\u003c\/p\u003e \u003cp\u003e10.9 Practical Summary 486\u003c\/p\u003e \u003cp\u003eExercises 488\u003c\/p\u003e \u003cp\u003eAppendix 10A: Table of Central Composite Designs 498\u003c\/p\u003e \u003cp\u003eAppendix 10B: Table of Box–Behnken Designs 500\u003c\/p\u003e \u003cp\u003eAppendix 10C: Table of Uniform Shell Designs 501\u003c\/p\u003e \u003cp\u003eReferences 502\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Introduction to Robust Parameter Design 503\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments 503\u003c\/p\u003e \u003cp\u003e11.1.1 Layer Growth Experiment Revisited 503\u003c\/p\u003e \u003cp\u003e11.1.2 Leaf Spring Experiment Revisited 504\u003c\/p\u003e \u003cp\u003e11.2 Strategies for Reducing Variation 506\u003c\/p\u003e \u003cp\u003e11.3 Noise (Hard-to-Control) Factors 508\u003c\/p\u003e \u003cp\u003e11.4 Variation Reduction Through Robust Parameter Design 510\u003c\/p\u003e \u003cp\u003e11.5 Experimentation and Modeling Strategies I: Cross Array 512\u003c\/p\u003e \u003cp\u003e11.5.1 Location and Dispersion Modeling 513\u003c\/p\u003e \u003cp\u003e11.5.2 Response Modeling 518\u003c\/p\u003e \u003cp\u003e11.6 Experimentation and Modeling Strategies II: Single Array and Response Modeling 523\u003c\/p\u003e \u003cp\u003e11.7 Cross Arrays: Estimation Capacity and Optimal Selection 526\u003c\/p\u003e \u003cp\u003e11.8 Choosing Between Cross Arrays and Single Arrays 529\u003c\/p\u003e \u003cp\u003e*11.8.1 Compound Noise Factor 533\u003c\/p\u003e \u003cp\u003e11.9 Signal-to-Noise Ratio and Its Limitations for Parameter Design Optimization 534\u003c\/p\u003e \u003cp\u003e11.9.1 SN Ratio Analysis of Layer Growth Experiment 536\u003c\/p\u003e \u003cp\u003e*11.10 Further Topics 537\u003c\/p\u003e \u003cp\u003e11.11 Practical Summary 539\u003c\/p\u003e \u003cp\u003eExercises 541\u003c\/p\u003e \u003cp\u003eReferences 550\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Analysis of Experiments with Nonnormal Data 553\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 A Wave Soldering Experiment with Count Data 553\u003c\/p\u003e \u003cp\u003e12.2 Generalized Linear Models 554\u003c\/p\u003e \u003cp\u003e12.2.1 The Distribution of the Response 555\u003c\/p\u003e \u003cp\u003e12.2.2 The Form of the Systematic Effects 557\u003c\/p\u003e \u003cp\u003e12.2.3 GLM versus Transforming the Response 558\u003c\/p\u003e \u003cp\u003e12.3 Likelihood-Based Analysis of Generalized Linear Models 558\u003c\/p\u003e \u003cp\u003e12.4 Likelihood-Based Analysis of theWave Soldering Experiment 562\u003c\/p\u003e \u003cp\u003e12.5 Bayesian Analysis of Generalized Linear Models 564\u003c\/p\u003e \u003cp\u003e12.6 Bayesian Analysis of the Wave Soldering Experiment 565\u003c\/p\u003e \u003cp\u003e12.7 Other Uses and Extensions of Generalized Linear Models and Regression Models for Nonnormal Data 567\u003c\/p\u003e \u003cp\u003e*12.8 Modeling and Analysis for Ordinal Data 567\u003c\/p\u003e \u003cp\u003e12.8.1 The Gibbs Sampler for Ordinal Data 569\u003c\/p\u003e \u003cp\u003e*12.9 Analysis of Foam Molding Experiment 572\u003c\/p\u003e \u003cp\u003e12.10 Scoring: A Simple Method for Analyzing Ordinal Data 575\u003c\/p\u003e \u003cp\u003e12.11 Practical Summary 576\u003c\/p\u003e \u003cp\u003eExercises 577\u003c\/p\u003e \u003cp\u003eReferences 587\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Practical Optimal Design 589\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 589\u003c\/p\u003e \u003cp\u003e13.2 A Design Criterion 590\u003c\/p\u003e \u003cp\u003e13.3 Continuous and Exact Design 590\u003c\/p\u003e \u003cp\u003e13.4 Some Design Criteria 592\u003c\/p\u003e \u003cp\u003e13.4.1 Nonlinear Regression Model, Generalized Linear Model, and Bayesian Criteria 593\u003c\/p\u003e \u003cp\u003e13.5 Design Algorithms 595\u003c\/p\u003e \u003cp\u003e13.5.1 Point Exchange Algorithm 595\u003c\/p\u003e \u003cp\u003e13.5.2 Coordinate Exchange Algorithm 596\u003c\/p\u003e \u003cp\u003e13.5.3 Point and Coordinate Exchange Algorithms for Bayesian Designs 596\u003c\/p\u003e \u003cp\u003e13.5.4 Some Design Software 597\u003c\/p\u003e \u003cp\u003e13.5.5 Some Practical Considerations 597\u003c\/p\u003e \u003cp\u003e13.6 Examples 598\u003c\/p\u003e \u003cp\u003e13.6.1 A Quadratic Regression Model in One Factor 598\u003c\/p\u003e \u003cp\u003e13.6.2 Handling a Constrained Design Region 598\u003c\/p\u003e \u003cp\u003e13.6.3 Augmenting an Existing Design 598\u003c\/p\u003e \u003cp\u003e13.6.4 Handling an Odd-Sized Run Size 600\u003c\/p\u003e \u003cp\u003e13.6.5 Blocking from Initially Running a Subset of a Designed Experiment 601\u003c\/p\u003e \u003cp\u003e13.6.6 A Nonlinear Regression Model 605\u003c\/p\u003e \u003cp\u003e13.6.7 A Generalized Linear Model 605\u003c\/p\u003e \u003cp\u003e13.7 Practical Summary 606\u003c\/p\u003e \u003cp\u003eExercises 607\u003c\/p\u003e \u003cp\u003eReferences 608\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Computer Experiments 611\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 An Airfoil Simulation Experiment 611\u003c\/p\u003e \u003cp\u003e14.2 Latin Hypercube Designs (LHDs) 613\u003c\/p\u003e \u003cp\u003e14.2.1 Orthogonal Array-Based Latin Hypercube Designs 617\u003c\/p\u003e \u003cp\u003e14.3 Latin Hypercube Designs with Maximin Distance or Maximum Projection Properties 619\u003c\/p\u003e \u003cp\u003e14.4 Kriging: The Gaussian Process Model 622\u003c\/p\u003e \u003cp\u003e14.5 Kriging: Prediction and Uncertainty Quantification 625\u003c\/p\u003e \u003cp\u003e14.5.1 Known Model Parameters 626\u003c\/p\u003e \u003cp\u003e14.5.2 Unknown Model Parameters 627\u003c\/p\u003e \u003cp\u003e14.5.3 Analysis of Airfoil Simulation Experiment 629\u003c\/p\u003e \u003cp\u003e14.6 Expected Improvement 631\u003c\/p\u003e \u003cp\u003e14.6.1 Optimization of Airfoil Simulation Experiment 633\u003c\/p\u003e \u003cp\u003e14.7 Further Topics 634\u003c\/p\u003e \u003cp\u003e14.8 Practical Summary 636\u003c\/p\u003e \u003cp\u003eExercises 637\u003c\/p\u003e \u003cp\u003eAppendix 14A: Derivation of the Kriging Equations (14.10) and (14.11) 643\u003c\/p\u003e \u003cp\u003eAppendix 14B: Derivation of the EI Criterion (14.22) 644 References 645\u003c\/p\u003e \u003cp\u003eAppendix A Upper Tail Probabilities of the Standard Normal Distribution ∫ \u003csup\u003e∞\u003c\/sup\u003e\u003ci\u003e\u003csub\u003ez \u003c\/sub\u003e\u003c\/i\u003e1\/√2\u003ci\u003e𝜋e\u003c\/i\u003e\u003csup\u003e−\u003ci\u003eu\u003c\/i\u003e2\u003c\/sup\u003e∕\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003edu \u003c\/i\u003e647\u003c\/p\u003e \u003cp\u003eAppendix B Upper Percentiles of the \u003ci\u003et \u003c\/i\u003eDistribution 649\u003c\/p\u003e \u003cp\u003eAppendix C Upper Percentiles of the \u003ci\u003e𝜒 \u003csup\u003e2\u003c\/sup\u003e \u003c\/i\u003eDistribution 651\u003c\/p\u003e \u003cp\u003eAppendix D Upper Percentiles of the \u003ci\u003eF \u003c\/i\u003eDistribution 653\u003c\/p\u003e \u003cp\u003eAppendix E Upper Percentiles of the Studentized Range Distribution 661\u003c\/p\u003e \u003cp\u003eAppendix F Upper Percentiles of the Studentized Maximum Modulus Distribution 669\u003c\/p\u003e \u003cp\u003eAppendix G Coefficients of Orthogonal Contrast Vectors 683\u003c\/p\u003e \u003cp\u003eAppendix H Critical Values for Lenth’s Method 685\u003c\/p\u003e \u003cp\u003eAuthor Index 689\u003c\/p\u003e \u003cp\u003eSubject Index 693\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eC. F. JEFF WU, P\u003csmall\u003eH\u003c\/small\u003eD\u003c\/b\u003e, is Coca-Cola Professor in Engineering Statistics at the Georgia Institute of Technology. Dr. Wu has published more than 180 papers and is the recipient of numerous accolades, including the National Academy of Engineering membership and the COPSS Presidents' Award. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMICHAEL S. HAMADA, P\u003csmall\u003eH\u003c\/small\u003eD\u003c\/b\u003e, is Senior Scientist at Los Alamos National Laboratory (LANL) in New Mexico. Dr. Hamada is a Fellow of the American Statistical Association, a LANL Fellow, and has published more than 160 papers.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989186396389,"sku":"NP9781119470106","price":139.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119470106.jpg?v=1761783133","url":"https:\/\/k12savings.com\/es\/products\/experiments-isbn-9781119470106","provider":"K12savings","version":"1.0","type":"link"}