{"product_id":"experiments-planning-analysis-and-optimization-third-edition-9781119470106","title":"Experiments  Planning Analysis and Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\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","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407061164375,"sku":"9781119470106","price":98.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119470106.jpg?v=1730498037","url":"https:\/\/bookcurl.com\/products\/experiments-planning-analysis-and-optimization-third-edition-9781119470106","provider":"Book Curl","version":"1.0","type":"link"}