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More About This Title Statistical Analysis of Designed Experiments: Theory and Applications
English
The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences.
The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests.
Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab® software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets.
With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.
English
Ajit C. Tamhane, PhD, is Professor of Industrial Engineering and Management Sciences at Northwestern University. A Fellow of the American Statistical Society, Institute of Mathematical Statistics, American Association for Advancement of Science and an elected member of the International Statistical Institute, Dr. Tamhane has over forty years of academic and consulting experience in the areas of applied and mathematical statistics. He is the coauthor of Multiple Comparison Procedures and a forthcoming book on Predictive Analytics: Parametric Models for Regression and Classification Using R, also published by Wiley. He is also the coauthor of Statistics and Data Analysis: From Elementary to Intermediate.
English
Preface xv
Abbreviations xxi
1 Introduction 1
1.1 Observational Studies and Experiments 1
1.2 Brief Historical Remarks 4
1.3 Basic Terminology and Concepts of Experimentation 5
1.4 Basic Principles of Experimentation 9
1.4.1 How to Minimize Biases and Variability? 9
1.4.2 Sequential Experimentation 14
1.5 Chapter Summary 15
Exercises 16
2 Review of Elementary Statistics 20
2.1 Experiments for a Single Treatment 20
2.1.1 Summary Statistics and Graphical Plots 21
2.1.2 Confidence Intervals and Hypothesis Tests 25
2.1.3 Power and Sample Size Calculation 27
2.2 Experiments for Comparing Two Treatments 28
2.2.1 Independent Samples Design 29
2.2.2 Matched Pairs Design 38
2.3 Linear Regression 41
2.3.1 Simple Linear Regression 42
2.3.2 Multiple Linear Regression 50
2.4 Chapter Summary 62
Exercises 62
3 Single Factor Experiments: Completely Randomized Designs 70
3.1 Summary Statistics and Graphical Displays 71
3.2 Model 73
3.3 Statistical Analysis 75
3.3.1 Estimation 75
3.3.2 Analysis of Variance 76
3.3.3 Confidence Intervals and Hypothesis Tests 78
3.4 Model Diagnostics 79
3.4.1 Checking Homoscedasticity 80
3.4.2 Checking Normality 81
3.4.3 Checking Independence 81
3.4.4 Checking Outliers 81
3.5 Data Transformations 85
3.6 Power of F-Test and Sample Size Determination 87
3.7 Quantitative Treatment Factors 90
3.8 One-Way Analysis of Covariance 96
3.8.1 Randomized Block Design versus Analysis of Covariance 96
3.8.2 Model 96
3.8.3 Statistical Analysis 98
3.9 Chapter Notes 106
3.9.1 Randomization Distribution of F-Statistic 106
3.9.2 F-Test for Heteroscedastic Treatment Variances 108
3.9.3 Derivations of Formulas for Orthogonal Polynomials 110
3.9.4 Derivation of LS Estimators for One-Way Analysis of Covariance 112
3.10 Chapter Summary 113
Exercises 114
4 Single-Factor Experiments: Multiple Comparison and Selection Procedures 126
4.1 Basic Concepts of Multiple Comparisons 127
4.1.1 Family 127
4.1.2 Familywise Error Rate 128
4.1.3 Bonferroni Method 129
4.1.4 Union–Intersection Method 130
4.1.5 Closure Method 131
4.2 Pairwise Comparisons 132
4.2.1 Least Significant Difference and Bonferroni Procedures 133
4.2.2 Tukey Procedure for Pairwise Comparisons 134
4.2.3 Step-Down Procedures for Pairwise Comparisons 136
4.3 Comparisons with a Control 139
4.3.1 Dunnett Procedure for Comparisons with a Control 139
4.3.2 Step-Down Procedures for Comparisons with a Control 142
4.4 General Contrasts 144
4.4.1 Tukey Procedure for Orthogonal Contrasts 145
4.4.2 Scheff´e Procedure for All Contrasts 146
4.5 Ranking and Selection Procedures 148
4.5.1 Indifference-Zone Formulation 148
4.5.2 Subset Selection Formulation 154
4.5.3 Multiple Comparisons with the Best 155
4.5.4 Connection between Multiple Comparisons with Best and Selection of Best Treatment 157
4.6 Chapter Summary 158
Exercises 159
5 Randomized Block Designs and Extensions 168
5.1 Randomized Block Designs 169
5.1.1 Model 169
5.1.2 Statistical Analysis 171
5.1.3 Randomized Block Designs with Replicates 177
5.2 Balanced Incomplete Block Designs 180
5.2.1 Statistical Analysis 182
5.2.2 Interblock Analysis 185
5.3 Youden Square Designs 188
5.3.1 Statistical Analysis 189
5.4 Latin Square Designs 192
5.4.1 Choosing a Latin Square 192
5.4.2 Model 195
5.4.3 Statistical Analysis 195
5.4.4 Crossover Designs 198
5.4.5 Graeco–Latin Square Designs 202
5.5 Chapter Notes 205
5.5.1 Restriction Error Model for Randomized Block Designs 205
5.5.2 Derivations of Formulas for BIB Design 206
5.6 Chapter Summary 211
Exercises 212
6 General Factorial Experiments 224
6.1 Factorial versus One-Factor-at-a-Time Experiments 225
6.2 Balanced Two-Way Layouts 227
6.2.1 Summary Statistics and Graphical Plots 227
6.2.2 Model 230
6.2.3 Statistical Analysis 231
6.2.4 Model Diagnostics 235
6.2.5 Tukey’s Test for Interaction for Singly Replicated Two-Way Layouts 236
6.3 Unbalanced Two-Way Layouts 240
6.3.1 Statistical Analysis 240
6.4 Chapter Notes 245
6.4.1 Derivation of LS Estimators of Parameters for Balanced Two-Way Layouts 245
6.4.2 Derivation of ANOVA Sums of Squares and F-Tests for Balanced Two-Way Layouts 246
6.4.3 Three- and Higher Way Layouts 248
6.5 Chapter Summary 250
Exercises 250
7 Two-Level Factorial Experiments 256
7.1 Estimation of Main Effects and Interactions 257
7.1.1 22 Designs 257
7.1.2 23 Designs 261
7.1.3 2p Designs 266
7.2 Statistical Analysis 267
7.2.1 Confidence Intervals and Hypothesis Tests 267
7.2.2 Analysis of Variance 268
7.2.3 Model Fitting and Diagnostics 270
7.3 Single-Replicate Case 272
7.3.1 Normal and Half-Normal Plots of Estimated Effects 272
7.3.2 Lenth Method 278
7.3.3 Augmenting a 2p Design with Observations at the Center Point 279
7.4 2p Factorial Designs in Incomplete Blocks: Confounding of Effects 282
7.4.1 Construction of Designs 282
7.4.2 Statistical Analysis 286
7.5 Chapter Notes 287
7.5.1 Yates Algorithm 287
7.5.2 Partial Confounding 288
7.6 Chapter Summary 289
Exercises 290
8 Two-Level Fractional Factorial Experiments 300
8.1 2p−q Fractional Factorial Designs 301
8.1.1 2p−1 Fractional Factorial Design 301
8.1.2 General 2p−q Fractional Factorial Designs 307
8.1.3 Statistical Analysis 312
8.1.4 Minimum Aberration Designs 316
8.2 Plackett–Burman Designs 317
8.3 Hadamard Designs 323
8.4 Supersaturated Designs 325
8.4.1 Construction of Supersaturated Designs 325
8.4.2 Statistical Analysis 327
8.5 Orthogonal Arrays 329
8.6 Sequential Assemblies of Fractional Factorials 333
8.6.1 Foldover of Resolution III Designs 334
8.6.2 Foldover of Resolution IV Designs 337
8.7 Chapter Summary 338
Exercises 339
9 Three-Level and Mixed-Level Factorial Experiments 351
9.1 Three-Level Full Factorial Designs 351
9.1.1 Linear–Quadratic System 353
9.1.2 Orthogonal Component System 361
9.2 Three-Level Fractional Factorial Designs 364
9.3 Mixed-Level Factorial Designs 372
9.3.1 2p4q Designs 373
9.3.2 2p3q Designs 378
9.4 Chapter Notes 386
9.4.1 Alternative Derivations of Estimators of Linear and Quadratic Effects 386
9.5 Chapter Summary 388
Exercises 389
10 Experiments for Response Optimization 395
10.1 Response Surface Methodology 396
10.1.1 Outline of Response Surface Methodology 396
10.1.2 First-Order Experimentation Phase 397
10.1.3 Second-Order Experimentation Phase 402
10.2 Mixture Experiments 412
10.2.1 Designs for Mixture Experiments 414
10.2.2 Analysis of Mixture Experiments 416
10.3 Taguchi Method of Quality Improvement 419
10.3.1 Philosophy Underlying Taguchi Method 422
10.3.2 Implementation of Taguchi Method 425
10.3.3 Critique of Taguchi Method 432
10.4 Chapter Summary 436
Exercises 437
11 Random and Mixed Crossed-Factors Experiments 448
11.1 One-Way Layouts 449
11.1.1 Random-Effects Model 449
11.1.2 Analysis of Variance 450
11.1.3 Estimation of Variance Components 452
11.2 Two-Way Layouts 455
11.2.1 Random-Effects Model 455
11.2.2 Mixed-Effects Model 459
11.3 Three-Way Layouts 464
11.3.1 Random- and Mixed-Effects Models 464
11.3.2 Analysis of Variance 465
11.3.3 Approximate F-Tests 468
11.4 Chapter Notes 472
11.4.1 Maximum Likelihood and Restricted Maximum Likelihood (REML) Estimation of Variance Components 472
11.4.2 Derivations of Results for One- and Two-Way Random-Effects Designs 475
11.4.3 Relationship between Unrestricted and Restricted Models 478
11.5 Chapter Summary 479
Exercises 480
12 Nested, Crossed–Nested, and Split-Plot Experiments 487
12.1 Two-Stage Nested Designs 488
12.1.1 Model 488
12.1.2 Analysis of Variance 489
12.2 Three-Stage Nested Designs 490
12.2.1 Model 491
12.2.2 Analysis of Variance 492
12.3 Crossed and Nested Designs 495
12.3.1 Model 495
12.3.2 Analysis of Variance 496
12.4 Split-Plot Designs 501
12.4.1 Model 504
12.4.2 Analysis of Variance 505
12.4.3 Extensions of Split-Plot Designs 508
12.5 Chapter Notes 515
12.5.1 Derivations of E(MS) Expressions for Two-Stage Nested Design of Section 12.1 with Both Factors
Random 515
12.5.2 Derivations of E(MS) Expressions for Design of Section 12.3 with Crossed and Nested Factors 517
12.5.3 Derivations of E(MS) Expressions for Split-Plot Design 520
12.6 Chapter Summary 523
Exercises 524
13 Repeated Measures Experiments 536
13.1 Univariate Approach 536
13.1.1 Model 537
13.1.2 Univariate Analysis of Variance for RM Designs 537
13.2 Multivariate Approach 548
13.2.1 One-Way Multivariate Analysis of Variance 548
13.2.2 Multivariate Analysis of Variance for RM Designs 549
13.3 Chapter Notes 555
13.3.1 Derivations of E(MS) Expressions for Repeated Measures Design Assuming Compound Symmetry 555
13.4 Chapter Summary 558
Exercises 559
14 Theory of Linear Models with Fixed Effects 566
14.1 Basic Linear Model and Least Squares Estimation 566
14.1.1 Geometric Interpretation of Least Squares Estimation 568
14.1.2 Least Squares Estimation in Singular Case 570
14.1.3 Least Squares Estimation in Orthogonal Case 572
14.2 Confidence Intervals and Hypothesis Tests 573
14.2.1 Sampling Distribution of_β 573
14.2.2 Sampling Distribution of s2 574
14.2.3 Inferences on Scalar Parameters 575
14.2.4 Inferences on Vector Parameters 575
14.2.5 Extra Sum of Squares Method 577
14.2.6 Analysis of Variance 579
14.3 Power of F-Test 583
14.4 Chapter Notes 586
14.4.1 Proof of Theorem 14.1 (Gauss–Markov Theorem) 586
14.4.2 Proof of Theorem 14.2 586
14.5 Chapter Summary 587
Exercises 588
Appendix A Vector-Valued Random Variables and Some Distribution Theory 595
A.1 Mean Vector and Covariance Matrix of Random Vector 596
A.2 Covariance Matrix of Linear Transformation of Random Vector 597
A.3 Multivariate Normal Distribution 598
A.4 Chi-Square, F-, and t-Distributions 599
A.5 Distributions of Quadratic Forms 601
A.6 Multivariate t-Distribution 605
A.7 Multivariate Normal Sampling Distribution Theory 606
Appendix B Case Studies 608
B.1 Case Study 1: Effects of Field Strength and Flip Angle on MRI Contrast 608
B.1.1 Introduction 608
B.1.2 Design 609
B.1.3 Data Analysis 610
B.1.4 Results 612
B.2 Case Study 2: Growing Stem Cells for Bone Implants 613
B.2.1 Introduction 613
B.2.2 Design 614
B.2.3 Data Analysis 614
B.2.4 Results 614
B.3 Case Study 3: Router Bit Experiment 619
B.3.1 Introduction 619
B.3.2 Design 619
B.3.3 Data Analysis 623
B.3.4 Results 624
Appendix C Statistical Tables 627
Answers to Selected Exercises 644
References 664
Index 675
