Experiments

C. F. JEFF WU, PHD, 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. MICHAEL S. HAMADA, PHD, 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.

• Preface to the Third Edition xviiPreface to the Second Edition xixPreface to the First Edition xxiSuggestions of Topics for Instructors xxvList of Experiments and Data Sets xxviiAbout the Companion Website xxxiii1 Basic Concepts for Experimental Design and Introductory Regression Analysis 11.1 Introduction and Historical Perspective 11.2 A Systematic Approach to the Planning and Implementation of Experiments 41.3 Fundamental Principles: Replication, Randomization, and Blocking 81.4 Simple Linear Regression 111.5 Testing of Hypothesis and Interval Estimation 141.6 Multiple Linear Regression 201.7 Variable Selection in Regression Analysis 261.8 Analysis of Air Pollution Data 281.9 Practical Summary 342 Experiments with a Single Factor 452.1 One-Way Layout 452.2 Multiple Comparisons 522.3 Quantitative Factors and Orthogonal Polynomials 562.4 Expected Mean Squares and Sample Size Determination 612.5 One-Way Random Effects Model 682.6 Residual Analysis: Assessment of Model Assumptions 712.7 Practical Summary 763 Experiments with More than One Factor 853.1 Paired Comparison Designs 853.2 Randomized Block Designs 883.3 Two-Way Layout: Factors with Fixed Levels 92*3.4 Two-Way Layout: Factors with Random Levels 983.5 Multi-Way Layouts 1053.6 Latin Square Designs: Two Blocking Variables 1083.7 Graeco-Latin Square Designs 112*3.8 Balanced Incomplete Block Designs 113*3.9 Split-Plot Designs 1183.10 Analysis of Covariance: Incorporating Auxiliary Information 126*3.11 Transformation of the Response 1303.12 Practical Summary 1344 Full Factorial Experiments at Two Levels 1514.1 An Epitaxial Layer Growth Experiment 1514.2 Full Factorial Designs at Two Levels: A General Discussion 1534.3 Factorial Effects and Plots 1574.4 Using Regression to Compute Factorial Effects 165*4.5 ANOVA Treatment of Factorial Effects 1674.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity 1684.7 Comparisons with the "One-Factor-at-a-Time" Approach 1694.8 Normal and Half-Normal Plots for Judging Effect Significance 1724.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates 1744.10 Nominal-the-Best Problem and Quadratic Loss Function 1784.11 Use of Log Sample Variance for Dispersion Analysis 1794.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment 181*4.13 Test of Variance Homogeneity and Pooled Estimate of Variance 184*4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments With Variance Estimates 1854.15 Blocking and Optimal Arrangement of 2k Factorial Designs in 2q Blocks 1884.16 Practical Summary 1935 Fractional Factorial Experiments at Two Levels 2055.1 A Leaf Spring Experiment 2055.2 Fractional Factorial Designs: Effect Aliasing and the Criteria of Resolution and Minimum Aberration 2065.3 Analysis of Fractional Factorial Experiments 2125.4 Techniques for Resolving the Ambiguities in Aliased Effects 2175.5 Conditional Main Effect (CME) Analysis: A Method to Unravel Aliased Interactions 2275.6 Selection of 2k −p Designs Using Minimum Aberration and Related Criteria 2325.7 Blocking in Fractional Factorial Designs 2365.8 Practical Summary 2386 Full Factorial and Fractional Factorial Experiments at Three Levels 2656.1 A Seat-Belt Experiment 2656.2 Larger-the-Better and Smaller-the-Better Problems 2676.3 3k Full Factorial Designs 2686.4 3k −p Fractional Factorial Designs 2736.5 Simple Analysis Methods: Plots and Analysis of Variance 2776.6 An Alternative Analysis Method 2826.7 Analysis Strategies for Multiple Responses I: Out-Of-Spec Probabilities 2916.8 Blocking in 3k and 3k −p Designs 2996.9 Practical Summary 3017 Other Design and Analysis Techniques for Experiments at More than Two Levels 3157.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design 3157.2 Method of Replacement and Construction of 2m 4n Designs 3187.3 Minimum Aberration 2m 4n Designs with n = 1, 2, 3217.4 An Analysis Strategy for 2m 4n Experiments 3247.5 Analysis of the Router Bit Experiment 3267.6 A Paint Experiment Based on a Mixed Two-Level and Three-Level Design 3297.7 Design and Analysis of 36-Run Experiments at Two And Three Levels 3327.8 rk −p Fractional Factorial Designs for any Prime Number r 3377.9 Definitive Screening Designs 341*7.10 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling 3437.11 Practical Summary 3528 Nonregular Designs: Construction and Properties 3698.1 Two Experiments: Weld-Repaired Castings and Blood Glucose Testing 3698.2 Some Advantages of Nonregular Designs Over the 2k −p AND 3k −p Series of Designs 3708.3 A Lemma on Orthogonal Arrays 3728.4 Plackett–Burman Designs and Hall's Designs 3738.5 A Collection of Useful Mixed-Level Orthogonal Arrays 377*8.6 Construction of Mixed-Level Orthogonal Arrays Based on Difference Matrices 379*8.7 Construction of Mixed-Level Orthogonal Arrays Through the Method of Replacement 3828.8 Orthogonal Main-Effect Plans Through Collapsing Factors 3848.9 Practical Summary 3889 Experiments with Complex Aliasing 4179.1 Partial Aliasing of Effects and the Alias Matrix 4179.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis 4209.3 Simplification of Complex Aliasing via Effect Sparsity 4219.4 An Analysis Strategy for Designs with Complex Aliasing 422*9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing 429*9.6 Supersaturated Designs: Design Construction and Analysis 4379.7 Practical Summary 44110 Response Surface Methodology 45510.1 A Ranitidine Separation Experiment 45510.2 Sequential Nature of Response Surface Methodology 45710.3 From First-Order Experiments to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid Search 46010.4 Analysis of Second-Order Response Surfaces 46910.5 Analysis of the Ranitidine Experiment 47210.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions 47510.7 Central Composite Designs 47810.8 Box–Behnken Designs and Uniform Shell Designs 48310.9 Practical Summary 48611 Introduction to Robust Parameter Design 50311.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments 50311.2 Strategies for Reducing Variation 50611.3 Noise (Hard-to-Control) Factors 50811.4 Variation Reduction Through Robust Parameter Design 51011.5 Experimentation and Modeling Strategies I: Cross Array 51211.6 Experimentation and Modeling Strategies II: Single Array and Response Modeling 52311.7 Cross Arrays: Estimation Capacity and Optimal Selection 52611.8 Choosing Between Cross Arrays and Single Arrays 52911.9 Signal-to-Noise Ratio and Its Limitations for Parameter Design Optimization 534*11.10 Further Topics 53711.11 Practical Summary 53912 Analysis of Experiments with Nonnormal Data 55312.1 A Wave Soldering Experiment with Count Data 55312.2 Generalized Linear Models 55412.3 Likelihood-Based Analysis of Generalized Linear Models 55812.4 Likelihood-Based Analysis of the Wave Soldering Experiment 56212.5 Bayesian Analysis of Generalized Linear Models 56412.6 Bayesian Analysis of the Wave Soldering Experiment 56512.7 Other Uses and Extensions of Generalized Linear Models and Regression Models for Nonnormal Data 567*12.8 Modeling and Analysis for Ordinal Data 567*12.9 Analysis of Foam Molding Experiment 57212.10 Scoring: A Simple Method for Analyzing Ordinal Data 57512.11 Practical Summary 57613 Practical Optimal Design 58913.1 Introduction 58913.2 A Design Criterion 59013.3 Continuous and Exact Design 59013.4 Some Design Criteria 59213.5 Design Algorithms 59513.6 Examples 59813.7 Practical Summary 60614 Computer Experiments 61114.1 An Airfoil Simulation Experiment 61114.2 Latin Hypercube Designs (LHDs) 61314.3 Latin Hypercube Designs with Maximin Distance or Maximum Projection Properties 61914.4 Kriging: The Gaussian Process Model 62214.5 Kriging: Prediction and Uncertainty Quantification 62514.6 Expected Improvement 63114.7 Further Topics 63414.8 Practical Summary 636Author Index 689Subject Index 693

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