Design and analysis of experiments
 Responsibility
 Douglas C. Montgomery.
 Language
 English.
 Edition
 Eighth edition.
 Imprint
 Hoboken, NJ : John Wiley & Sons, Inc., [2013], ©2013.
 Physical description
 xvii, 730 pages : illustrations ; 27 cm
Access
Available online
Course reserve
 Course
 STATS20101  Design and Analysis of Experiments
 Instructor(s)
 Ledolter, Johannes
Math & Statistics Library
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Creators/Contributors
 Author/Creator
 Montgomery, Douglas C.
Contents/Summary
 Bibliography
 Includes bibliographical references (pages 719724) and index.
 Contents

 Preface v 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 SingleFactor 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 GraecoLatin 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 TwoFactor 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 TwoLevel Fractional Factorial Designs 320 8.1 Introduction 320 8.2 The OneHalf Fraction of the 2k Design 321 8.3 The OneQuarter Fraction of the 2k Design 333 8.4 The General 2kp 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 SecondOrder 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 TwoFactor Factorial with Random Factors 574 13.3 The TwoFactor 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 SplitPlot Designs 604 14.1 The TwoStage Nested Design 604 14.2 The General mStage Nested Design 614 14.3 Designs with Both Nested and Factorial Factors 616 14.4 The SplitPlot Design 621 14.5 Other Variations of the SplitPlot 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 Appendix 681 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 2kp Fractional Factorial Designs with k 15 and n 64 704 Bibliography 717 Index 723.
 (source: Nielsen Book Data)9781118146927 20160608
 Preface 1 Introduction to Designed Experiments 1.1 Strategy of Experimentation 1.2 Some Typical Applications of Experimental Design 1.3 Basic Principles 1.4 Guidelines for Designing Experiments 1.5 A Brief History of Statistical Design 1.6 Summary: Using Statistical Techniques in Experimentation 1.7 Problems 2 Basic Statistical Methods 2.1 Introduction 2.2 Basic Statistical Concepts 2.3 Sampling and Sampling Distributions 2.4 Inferences About the Differences in Means, Randomized Designs 2.5 Inferences About the Differences in Means, Paired Comparison Designs 2.6 Inferences About the Variances of Normal Distributions 2.7 Problems 3 Analysis of Variance 3.1 An Example 3.2 The Analysis of Variance 3.3 Analysis of the Fixed Effects Model 3.4 Model Adequacy Checking 3.5 Practical Interpretation of Results 3.6 Sample Computer Output 3.7 Determining Sample Size 3.8 Other Examples of SingleFactor Experiments 3.9 The Random Effects Model 3.10 The Regression Approach to the Analysis of Variance 3.11 Nonparametric Methods in the Analysis of Variance 3.12 Problems 4 Experiments with Blocking Factors 4.1 The Randomized Complete Block Design 4.2 The Latin Square Design 4.3 The GraecoLatin Square Design 4.4 Balanced Incomplete Block Designs 4.5 Problems 5 Factorial Experiments 5.1 Basic Definitions and Principles 5.2 The Advantage of Factorials 5.3 The TwoFactor Factorial Design 5.4 The General Factorial Design 5.5 Fitting Response Curves and Surfaces 5.6 Blocking in a Factorial Design 5.7 Problems 6 TwoLevel Factorial Designs 6.1 Introduction 6.2 The 22 Design 6.3 The 23 Design 6.4 The General 2k Design 6.5 A Single Replicate of the 2k Design 6.6 Additional Examples of Unreplicated 2k Design 6.7 2k Designs are Optimal Designs 6.8 The Addition of Center Points to the 2k Design 6.9 Why We Work with Coded Design Variables 6.10 Problems 7 Blocking and Confounding Systems for TwoLevel Factorials 7.1 Introduction 7.2 Blocking a Replicated 2k Factorial Design 7.3 Confounding in the 2k Factorial Design 7.4 Confounding the 2k Factorial Design in Two Blocks 7.5 Another Illustration of Why Blocking Is Important 7.6 Confounding the 2k Factorial Design in Four Blocks 7.7 Confounding the 2k Factorial Design in 2p Blocks 7.8 Partial Confounding 7.9 Problems 8 TwoLevel Fractional Factorial Designs 8.1 Introduction 8.2 The OneHalf Fraction of the 2k Design 8.3 The OneQuarter Fraction of the 2k Design 8.4 The General 2kp Fractional Factorial Design 8.5 Alias Structures in Fractional Factorials and other Designs 8.6 Resolution III Designs 8.7 Resolution IV and V Designs 8.8 Supersaturated Designs 8.9 Summary 8.10 Problems 9 Other Topics on Factorial and Fractional Factorial Designs 9.1 The 3k Factorial Design 9.2 Confounding in the 3k Factorial Design 9.3 Fractional Replication of the 3k Factorial Design 9.4 Factorials with Mixed Levels 9.5 Nonregular Fractional Factorial Designs 9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool 9.7 Problems 10 Regression Modeling 10.1 Introduction 10.2 Linear Regression Models 10.3 Estimation of the Parameters in Linear Regression Models 10.4 Hypothesis Testing in Multiple Regression 10.5 Confidence Intervals in Multiple Regression 10.6 Prediction of New Response Observations 10.7 Regression Model Diagnostics 10.8 Testing for Lack of Fit 10.9 Problems 11 Response Surface Methodology 11.1 Introduction to Response Surface Methodology 11.2 The Method of Steepest Ascent 11.3 Analysis of a SecondOrder Response Surface 11.4 Experimental Designs for Fitting Response Surfaces 11.5 Experiments with Computer Models 11.6 Mixture Experiments 11.7 Evolutionary Operation 11.8 Problems 12 Robust Design 12.1 Introduction 12.2 Crossed Array Designs 12.3 Analysis of the Crossed Array Design 12.4 Combined Array Designs and the Response Model Approach 12.5 Choice of Designs 12.6 Problems 13 Random Effects Models 13.1 Random Effects Models 13.2 The TwoFactor Factorial with Random Factors 13.3 The TwoFactor Mixed Model 13.4 Sample Size Determination with Random Effects 13.5 Rules for Expected Mean Squares 13.6 Approximate F Tests 13.7 Some Additional Topics on Estimation of Variance Components 13.8 Problems 14 Experiments with Nested Factors and HardtoChange Factors 14.1 The TwoStage Nested Design 14.2 The General mStage Nested Design 14.3 Designs with Both Nested and Factorial Factors 14.4 The SplitPlot Design 14.5 Other Variations of the SplitPlot Design 14.6 Problems 15 Other Topics 15.1 Nonnormal Responses and Transformations 15.2 Unbalanced Data in a Factorial Design 15.3 The Analysis of Covariance 15.4 Repeated Measures 15.5 Problems Appendix Table I. Cumulative Standard Normal Distribution Table II. Percentage Points of the t Distribution Table III. Percentage Points of the X2 Distribution Table IV. Percentage Points of the F Distribution Table V. Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance Table VI. Operating Characteristic Curves for the Random Effects Model Analysis of Variance Table VII. Percentage Points of the Studentized Range Statistic Table VIII. Critical Values for Dunnett's Test for Comparing Treatments with a Control Table IX. Coefficients of Orthogonal Polynomials Table X. Alias Relationships for 2kp Fractional Factorial Designs with k <= 15 and n <=64 Bibliography Index.
 (source: Nielsen Book Data)9781118097939 20160614
 Publisher's Summary
 The eighth edition of Design and Analysis of Experiments continues to provide extensive and indepth information on engineering, business, and statisticsas well as informative ways to help readers design and analyze experiments for improving the quality, efficiency and performance of working systems. Furthermore, the text 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 splitplot design; and the residual maximum likelihood method is now emphasized throughout the book.
(source: Nielsen Book Data)9781118146927 20160608  Supplemental links

Table of contents only
Publisher description
Subjects
 Subject
 Experimental design.
Bibliographic information
 Publication date
 2013
 Copyright date
 2013
 ISBN
 9781118146927
 1118146921
 9781118097939 (pbk.)
 1118097939 (pbk.)