Design and Analysis of Experiments, 8th Edition
April 2012, ©2013
The eighth edition of Design and Analysis of Experiments maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book.
Continuing to place a strong focus on the use of the computer, this edition includes software examples taken from the four most dominant programs in the field: Design-Expert, Minitab, JMP, and SAS.
1 Introduction 1
1.1 Strategy of Experimentation 1
1.2 Some Typical Applications of Experimental Design 8
1.3 Basic Principles 11
1.4 Guidelines for Designing Experiments 14
1.5 A Brief History of Statistical Design 21
1.6 Summary: Using Statistical Techniques in Experimentation 22
1.7 Problems 23
2 Simple Comparative Experiments 25
2.1 Introduction 25
2.2 Basic Statistical Concepts 27
2.3 Sampling and Sampling Distributions 30
2.4 Inferences About the Differences in Means, Randomized Designs 36
2.5 Inferences About the Differences in Means, Paired Comparison Designs 53
2.6 Inferences About the Variances of Normal Distributions 57
2.7 Problems 59
3 Experiments with a Single Factor: The Analysis of Variance 65
3.1 An Example 66
3.2 The Analysis of Variance 68
3.3 Analysis of the Fixed Effects Model 70
3.4 Model Adequacy Checking 80
3.5 Practical Interpretation of Results 89
3.6 Sample Computer Output 102
3.7 Determining Sample Size 105
3.8 Other Examples of Single-Factor Experiments 110
3.9 The Random Effects Model 116
3.10 The Regression Approach to the Analysis of Variance 125
3.11 Nonparametric Methods in the Analysis of Variance 128
3.12 Problems 130
4 Randomized Blocks, Latin Squares, and Related Designs 139
4.1 The Randomized Complete Block Design 139
4.2 The Latin Square Design 158
4.3 The Graeco-Latin Square Design 165
4.4 Balanced Incomplete Block Designs 168
4.5 Problems 177
5 Introduction to Factorial Designs 183
5.1 Basic Definitions and Principles 183
5.2 The Advantage of Factorials 186
5.3 The Two-Factor Factorial Design 187
5.4 The General Factorial Design 206
5.5 Fitting Response Curves and Surfaces 211
5.6 Blocking in a Factorial Design 219
5.7 Problems 225
6 The 2k Factorial Design 233
6.1 Introduction 233
6.2 The 22 Design 234
6.3 The 23 Design 241
6.4 The General 2k Design 253
6.5 A Single Replicate of the 2k Design 255
6.6 Additional Examples of Unreplicated 2k Design 269
6.7 2k Designs are Optimal Designs 280
6.8 The Addition of Center Points to the 2k Design 285
6.9 Why We Work with Coded Design Variables 290
6.10 Problems 292
7 Blocking and Confounding in the 2k Factorial Design 304
7.1 Introduction 304
7.2 Blocking a Replicated 2k Factorial Design 305
7.3 Confounding in the 2k Factorial Design 306
7.4 Confounding the 2k Factorial Design in Two Blocks 306
7.5 Another Illustration of Why Blocking Is Important 312
7.6 Confounding the 2k Factorial Design in Four Blocks 313
7.7 Confounding the 2k Factorial Design in 2p Blocks 315
7.8 Partial Confounding 316
7.9 Problems 319
8 Two-Level Fractional Factorial Designs 320
8.1 Introduction 320
8.2 The One-Half Fraction of the 2k Design 321
8.3 The One-Quarter Fraction of the 2k Design 333
8.4 The General 2k_p Fractional Factorial Design 340
8.5 Alias Structures in Fractional Factorials and other Designs 349
8.6 Resolution III Designs 351
8.7 Resolution IV and V Designs 366
8.8 Supersaturated Designs 374
8.9 Summary 375
8.10 Problems 376
9 Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs 394
9.1 The 3k Factorial Design 395
9.2 Confounding in the 3k Factorial Design 402
9.3 Fractional Replication of the 3k Factorial Design 408
9.4 Factorials with Mixed Levels 412
9.5 Nonregular Fractional Factorial Designs 415
9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool 431
9.7 Problems 444
10 Fitting Regression Models 449
10.1 Introduction 449
10.2 Linear Regression Models 450
10.3 Estimation of the Parameters in Linear Regression Models 451
10.4 Hypothesis Testing in Multiple Regression 462
10.5 Confidence Intervals in Multiple Regression 467
10.6 Prediction of New Response Observations 468
10.7 Regression Model Diagnostics 470
10.8 Testing for Lack of Fit 473
10.9 Problems 475
11 Response Surface Methods and Designs 478
11.1 Introduction to Response Surface Methodology 478
11.2 The Method of Steepest Ascent 480
11.3 Analysis of a Second-Order Response Surface 486
11.4 Experimental Designs for Fitting Response Surfaces 500
11.5 Experiments with Computer Models 523
11.6 Mixture Experiments 530
11.7 Evolutionary Operation 540
11.8 Problems 544
12 Robust Parameter Design and Process Robustness Studies 554
12.1 Introduction 554
12.2 Crossed Array Designs 556
12.3 Analysis of the Crossed Array Design 558
12.4 Combined Array Designs and the Response Model Approach 561
12.5 Choice of Designs 567
12.6 Problems 570
13 Experiments with Random Factors 573
13.1 Random Effects Models 573
13.2 The Two-Factor Factorial with Random Factors 574
13.3 The Two-Factor Mixed Model 581
13.4 Sample Size Determination with Random Effects 587
13.5 Rules for Expected Mean Squares 588
13.6 Approximate F Tests 592
13.7 Some Additional Topics on Estimation of Variance Components 596
13.8 Problems 601
14 Nested and Split-Plot Designs 604
14.1 The Two-Stage Nested Design 604
14.2 The General m-Stage Nested Design 614
14.3 Designs with Both Nested and Factorial Factors 616
14.4 The Split-Plot Design 621
14.5 Other Variations of the Split-Plot Design 627
14.6 Problems 637
15 Other Design and Analysis Topics 642
15.1 Nonnormal Responses and Transformations 643
15.2 Unbalanced Data in a Factorial Design 652
15.3 The Analysis of Covariance 655
15.4 Repeated Measures 675
15.5 Problems 677
Table I. Cumulative Standard Normal Distribution 682
Table II. Percentage Points of the t Distribution 684
Table III. Percentage Points of the _2 Distribution 685
Table IV. Percentage Points of the F Distribution 686
Table V. Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance 691
Table VI. Operating Characteristic Curves for the Random Effects Model Analysis of Variance 695
Table VII. Percentage Points of the Studentized Range Statistic 699
Table VIII. Critical Values for Dunnett's Test for Comparing Treatments with a Control 701
Table IX. Coefficients of Orthogonal Polynomials 703
Table X. Alias Relationships for 2k_p Fractional Factorial Designs with k 15 and n 64 704
- The residual maximum likelihood (REML) method is now emphasized throughout the book.
- 83 new homework problems (including in the areas of biochemistry and biotechnology).
- Additional examples of single-factor experiments, such as a study involving chocolate consumption and cardiovascular health (Chapter 3)
- New section on the random effects model (Chapter 3)
- New material on nonregular fractions as alternatives to traditional minimum aberration fractions in 16 runs and analysis methods for those designs discussed and illustrated (Chapter 9).
- New material on constructing factorial and fractional factorial designs using an optimal design tool (Chapter 9).
- New topics and problems in the area of response surface, including designs that combine screening and optimization and use of optimal designs (Chapter 11).
- New topics in nested and split-plot design, including the use of REML in the analysis (Chapter 14).
- Includes software examples taken from the four most dominant programs in the field: Design-Expert, Minitab, JMP, and SAS.
- Focuses on the connection between the experiment and the model that the experimenter can develop from the results of the experiment.
- Stresses the importance of experimental design as a tool for engineers and scientists to use for product design and development as well as process development and improvement. The use of experimental design in developing products that are robust to environmental factors and other sources of variability is illustrated. The use of experimental design early in the product cycle can substantially reduce development lead time and cost, leading to processes and products that perform better in the field and have higher reliability than those developed using other approaches.
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