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
 STATS26301  Design of Experiments
 Instructor(s)
 Owen, Art B
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 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)
 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)  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.)