Design and analysis of experiments
 Responsibility
 Klaus Hinkelmann, Oscar Kempthorne.
 Language
 English.
 Edition
 2nd ed.
 Imprint
 Hoboken, N.J. : WileyInterscience, c2008<c2012>
 Physical description
 v. <1, 3> : ill. ; 25 cm.
 Series
 Wiley series in probability and statistics
Access
Available online
 Vol. 1 Wiley Online Library
 Vol. 2 Wiley Online Library
 Vol. 3 Wiley Online Library
 Vol. 3 ebrary
Science Library (Li and Ma)
Stacks
Library has: v.13
Call number  Status 

QA279 .K45 2008 V.1  Unknown 
QA279 .K45 2008 V.2  Unknown 
QA279 .K45 2008 V.3  Unknown 
More options
Creators/Contributors
 Contributor
 Hinkelmann, Klaus, 1932
 Kempthorne, Oscar.
Contents/Summary
 Bibliography
 Includes bibliographical references and indexes.
 Contents

 Preface. 1. General Incomplete Block Design. 1.1 Introduction and Examples. 1.2 General Remarks on the Analysis of Incomplete Block Designs. 1.3 The Intrablock Analysis. 1.4 Incomplete Designs with Variable Block Size. 1.5 Disconnected Incomplete Block Designs. 1.6 Randomization Analysis. 1.7 Interblock Information in an Incomplete Block Design. 1.8 Combined Intra and Interblock Analysis. 1.9 Relationships Among Intrablock, Interblock, and Combined Estimation. 1.10 Estimation of Weights for the Combined Analysis. 1.11 MaximumLikelihood Type Estimation. 1.12 Efficiency Factor of an Incomplete Block Design. 1.13 Optimal Designs. 1.14 Computational Procedures. 2. Balanced Incomplete Block Designs. 2.1 Introduction. 2.2 Definition of the BIB Design. 2.3 Properties of BIB Designs. 2.4 Analysis of BIB Designs. 2.5 Estimation of rho. 2.6 Significance Tests. 2.7 Some Special Arrangements. 2.8 Resistant and Susceptible BIB Designs. 3. Construction of Balanced Incomplete Block Designs. 3.1 Introduction. 3.2 Difference Methods. 3.3 Other Methods. 3.4 Listing of Existing BIB Designs. 4. Partially Balanced Incomplete Block Designs. 4.1 Introduction. 4.2 Preliminaries. 4.3 Definition and Properties of PBIB Designs. 4.4 Association Schemes and Linear Associative Algebras. 4.5 Analysis of PBIB Designs. 4.6 Classification of PBIB Designs. 4.7 Estimation of rho for PBIB(2) Designs. 5. Construction of Partially Balanced Incomplete Block Designs. 5.1 GroupDivisible PBIB(2) Designs. 5.2 Construction of Other PBIB(2) Designs. 5.3 Cyclic PBIB Designs. 5.4 Kronecker Product Designs. 5.5 Extended GroupDivisible PBIB Designs. 5.6 Hypercubic PBIB Designs. 6. More Block Designs and Blocking Structures. 6.1 Introduction. 6.2 Alpha Designs. 6.3 Generalized Cyclic Incomplete Block Designs. 6.4 Designs Based on the Successive Diagonalizing Method. 6.5 Comparing Treatments with a Control. 6.6 RowColumn Designs. 7. TwoLevel Factorial Designs. 7.1 Introduction. 7.2 Case of Two Factors. 7.3 Case of Three Factors. 7.4 General Case. 7.5 Interpretation of Effects and Interactions. 7.6 Analysis of Factorial Experiments. 8. Confounding in 2n Factorial Designs. 8.1 Introduction. 8.2 Systems of Confounding. 8.3 Composition of Blocks for a Particular System of Confounding. 8.4 Detecting a System of Confounding. 8.5 Using SAS for Constructing Systems of Confounding. 8.6 Analysis of Experiments with Confounding. 8.7 Interblock Information in Confounded Experiments. 8.8 Numerical Example Using SAS. 9. Partial Confounding in 2n Factorial Designs. 9.1 Introduction. 9.2 Simple Case of Partial Confounding. 9.3 Partial Confounding as an Incomplete Block Design. 9.4 Efficiency of Partial Confounding. 9.5 Partial Confounding in a 23 Experiment. 9.6 Partial Confounding in a 24 Experiment. 9.7 General Case. 9.7.1 Intrablock Information. 9.8 Double Confounding. 9.9 Confounding in Squares. 9.10 Numerical Examples Using SAS. 10. Designs with Factors at Three Levels. 10.1 Introduction. 10.2 Definition of Main Effects and Interactions. 10.3 Parameterization in Terms of Main Effects and Interactions. 10.4 Analysis of 3n Experiments. 10.5 Confounding in a 3n Factorial. 10.6 Useful Systems of Confounding. 10.7 Analysis of Confounded 3n Factorials. 10.8 Numerical Example. 11. General Symmetrical Factorial Design. 11.1 Introduction. 11.2 Representation of Effects and Interactions. 11.3 Generalized Interactions. 11.4 Systems of Confounding. 11.5 Intrablock Subgroup. 11.6 Enumerating Systems of Confounding. 11.7 Fisher Plans. 11.8 Symmetrical Factorials and Finite Geometries. 11.9 Parameterization of Treatment Responses. 11.10 Analysis of pn Factorial Experiments. 11.11 Interblock Analysis. 11.12 Combined Intra and Interblock Information. 11.13 The sn Factorial. 11.14 General Method of Confounding for the Symmetrical Factorial Experiment. 11.15 Choice of Initial Block. 12. Confounding in Asymmetrical Factorial Designs. 12.1 Introduction. 12.2 Combining Symmetrical Systems of Confounding. 12.3 The GC/n Method. 12.4 Method of Finite Rings. 12.5 Balanced Factorial Designs (BFD). 13. Fractional Factorial Designs. 13.1 Introduction. 13.2 Simple Example of Fractional Replication. 13.3 Fractional Replicates for 2n Factorial Designs. 13.4 Fractional Replicates for 3n Factorial Designs. 13.5 General Case of Fractional Replication. 13.6 Characterization of Fractional Factorial Designs of Resolution III, IV, and V. 13.7 Fractional Factorials and Combinatorial Arrays. 13.8 Blocking in Fractional Factorials. 13.9 Analysis of Unreplicated Factorials. 14. Main Effect Plans. 14.1 Introduction. 14.2 Orthogonal Resolution III Designs for Symmetrical Factorials. 14.3 Orthogonal Resolution III Designs for Asymmetrical Factorials. 14.4 Nonorthogonal Resolution III Designs. 15. Supersaturated Designs. 15.1 Introduction and Rationale. 15.2 Random Balance Designs. 15.3 Definition and Properties of Supersaturated Designs. 15.4 Construction of TwoLevel Supersaturated Designs. 15.5 ThreeLevel Supersaturated Designs. 15.6 Analysis of Supersaturated Experiments. 16. Search Designs. 16.1 Introduction and Rationale. 16.2 Definition of Search Design. 16.3 Properties of Search Designs. 16.4 Listing of Search Designs. 16.5 Analysis of Search Experiments. 16.6 Search Probabilities. 17. RobustDesign Experiments. 17.1 OffLine Quality Control. 17.2 Design and Noise Factors. 17.3 Measuring Loss. 17.4 RobustDesign Experiments. 17.5 Modeling of Data. 18. Lattice Designs. 18.1 Definition of QuasiFactorial Designs. 18.2 Types of Lattice Designs. 18.3 Construction of OneRestrictional Lattice Designs. 18.4 General Method of Analysis for OneRestrictional Lattice Designs. 18.5 Effects of Inaccuracies in the Weights. 18.6 Analysis of Lattice Designs as Randomized Complete Block Designs. 18.7 Lattice Designs as Partially Balanced Incomplete Block Designs. 18.8 Lattice Designs with Blocks of Size Kl. 18.9 TwoRestrictional Lattices. 18.10 Lattice Rectangles. 18.11 Rectangular Lattices. 18.12 Efficiency Factors. 19. Crossover Designs. 19.1 Introduction. 19.2 Residual Effects. 19.3 The Model. 19.4 Properties of Crossover Designs. 19.5 Construction of Crossover Designs. 19.6 Optimal Designs. 19.7 Analysis of Crossover Designs. 19.8 Comments on Other Models. Appendix A: Fields and Galois Fields. Appendix B: Finite Geometries. Appendix C: Orthogonal and Balanced Arrays. Appendix D: Selected Asymmetrical Balanced Factorial Designs. Appendix E: Exercises. References. Author Index. Subject Index.
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 1. The Processes of Science. 1.1 Introduction. 1.2 Development of Theory. 1.3 The Nature and Role of Theory in Science. 1.4 Varieties of Theory. 1.5 The Problem of General Science. 1.6 Causality. 1.7 The Upshot. 1.8 What Is An Experiment?. 1.9 Statistical Inference. 2. Principles of Experimental Design. 2.1 Confirmatory and Exploratory Experiments. 2.2 Steps of Designed Investigations. 2.3 The Linear Model. 2.4 Illustrating Individual Steps: Study 1. 2.5 Three Principles of Experimental Design. 2.6 The Statistical Triangle and Study 2. 2.7 Planning the Experiment. 2.8 Cooperation between Scientist and Statistician. 2.9 General Principle of Inference. 2.10 Other Considerations for Experimental Designs. 3. Survey of Designs and Analyses. 3.1 Introduction. 3.2 ErrorControl Designs. 3.3 Treatment Designs. 3.4 Combining Ideas. 3.5 Sampling Designs. 3.6 Analysis and Statistical Software. 3.7 Summary. 4. Linear Model Theory. 4.1 Introduction. 4.2 Representation of Linear Models. 4.3 Functional and Classificatory Linear Models. 4.4 The Fitting Of Y .= X. 4.5 The MoorePenrose Generalized Inverse. 4.6 The Conditioned Linear Model. 4.7 The TwoPart Linear Model. 4.8 A Special Case of a Partitioned Model. 4.9 ThreePart Models. 4.10 The TwoWay Classification Without Interaction. 4.11 The KPart Linear Model. 4.12 Balanced Classificatory Structures. 4.13 Unbalanced Data Structures. 4.14 Analysis of Covariance Model. 4.15 From Data Analysis to Statistical Inference. 4.16 The Simple Normal Stochastic Linear Model. 4.17 Distribution Theory with GMNLM. 4.18 Mixed Models. 5. Randomization. 5.1 Introduction. 5.2 The Tea Tasting Lady. 5.3 A Triangular Experiment. 5.4 The Simple Arithmetical Experiment. 5.5 Randomization Ideas for Intervention Experiments. 5.6 The General Idea of the Experiment Randomization Test. 5.7 Introduction to Subsequent. 6. The Completely Randomized Design. 6.1 Introduction and Definition. 6.2 The Randomization Process. 6.3 The Derived Linear Model. 6.4 Analysis Of Variance. 6.5 Statistical Tests. 6.6 Approximating the Randomization Test. 6.7 CRD with Unequal Numbers of Replications. 6.8 Number of Replications. 6.9 Subsampling In A CRD. 6.10 Transformations. 6.11 Examples Using SASR. 7. Comparisons of Treatments. 7.1 Introduction. 7.2 Comparisons for Qualitative Treatments. 7.3 Orthogonality and Orthogonal Comparisons. 7.4 Comparisons for Quantitative Treatments. 7.5 Multiple Comparison Procedures. 7.6 Grouping Treatments. 7.7 Examples Using SAS. 8. Use of Supplementary Information. 8.1 Introduction. 8.2 Motivation of the Procedure. 8.3 Analysis of Covariance Procedure. 8.4 Treatment Comparisons. 8.5 Violation of Assumptions. 8.6 Analysis of Covariance with Subsampling. 8.7 The Case of Several Covariates. 8.8 Examples Using SASR. 9. Randomized Block Designs. 9.1 Introduction. 9.2 Randomized Complete Block Design. 9.3 Relative Efficiency of the RCBD. 9.4 Analysis of Covariance. 9.5 Missing Observations. 9.6 Nonadditivity in the RCBD. 9.7 The Generalized Randomized Block Design. 9.8 Incomplete Block Designs. 9.9 Systematic Block Designs. 9.10 Examples Using SASR. 10. Latin Square Type Designs. 10.1 Introduction and Motivation. 10.2 Latin Square Design. 10.3 Replicated Latin Squares. 10.4 Latin Rectangles. 10.5 Incomplete Latin Squares. 10.6 Orthogonal Latin Squares. 10.7 ChangeOver Designs. 10.8 Examples Using SAS. 11. Factorial Experiments: Basic Ideas. 11.1 Introduction. 11.2 Inferences from Factorial Experiments. 11.3 Experiments with Factors at Two Levels. 11.4 The Interpretation of Effects and Interactions. 11.5 Interactions: A Case Study. 11.6 2n Factorials in Incomplete Blocks. 11.7 Fractions of 2n Factorials. 11.8 Orthogonal Main Effect Plans for 2n Factorials. 11.9 Experiments with Factors at Three Levels. 11.10experimentswith Factors at Two and Three Levels. 11.11examples Using SAS. 12. Response Surface Designs. 12.1 Introduction. 12.2 Formulation of the Problem. 12.3 FirstOrder Models and Designs. 12.4 SecondOrder Models and Designs. 12.5 Integrated Mean Squared Error Designs. 12.6 Searching For an Optimum. 12.7 Experiments with Mixtures. 12.8 Examples Using SAS. 13. SplitPlot Type Designs. 13.1 Introduction. 13.2 The Simple SplitPlot Design. 13.3 Relative Efficiency of SplitPlot Design. 13.4 Other Forms of SplitPlot Designs. 13.5 SplitBlock Design. 13.6 The SplitSplitPlot Design. 13.7 Examples Using SAS. 14. Designs with Repeated Measures. 14.1 Introduction. 14.2 Methods for Analyzing Repeated Measures Data. 14.3 Examples Using SAS. 14.4 Exercises.
 (source: Nielsen Book Data)9780471727569 20160528
 Publisher's Summary
 This title offers a comprehensive overview of experimental design at the advanced level. The development and introduction of new experimental designs in the last fifty years has been quite staggering and was brought about largely by an everwidening field of applications. "Design and Analysis of Experiments, Volume 2: Advanced Experimental Design" is the second of a twovolume body of work that builds upon the philosophical foundations of experimental design set forth half a century ago by Oscar Kempthorne, and features the latest developments in the field. Volume 1: "An Introduction to Experimental Design" introduced students at the MS level to the principles of experimental design, including the groundbreaking work of R. A. Fisher and Frank Yates, and Kempthorne's work in randomization theory with the development of derived linear models. "Design and Analysis of Experiments, Volume 2" provides more detail about aspects of error control and treatment design, with emphasis on their historical development and practical significance, and the connections between them. Designed for advancedlevel graduate students and industry professionals, this text includes coverage of: incomplete block and rowcolumn designs; symmetrical and asymmetrical factorial designs; systems of confounding; fractional factorial designs, including main effect plans; supersaturated designs; robust design or Taguchi experiments; lattice designs; and, crossover designs. In order to facilitate the application of text material to a broad range of fields, the authors take a general approach to their discussions. To aid in the construction and analysis of designs, many procedures are illustrated using Statistical Analysis System (SAS[registered]) software.
(source: Nielsen Book Data)9780471551775 20160528  This userfriendly new edition reflects a modern and accessible approach to experimental design and analysis. "Design and Analysis of Experiments, Volume 1, Second Edition" provides a general introduction to the philosophy, theory, and practice of designing scientific comparative experiments and also details the intricacies that are often encountered throughout the design and analysis processes. With the addition of extensive numerical examples and expanded treatment of key concepts, this book further addresses the needs of practitioners and successfully provides a solid understanding of the relationship between the quality of experimental design and the validity of conclusions. This second edition continues to provide the theoretical basis of the principles of experimental design in conjunction with the statistical framework within which to apply the fundamental concepts. The difference between experimental studies and observational studies is addressed, along with a discussion of the various components of experimental design: the errorcontrol design, the treatment design, and the observation design. A series of errorcontrol designs are presented based on fundamental design principles, such as randomization, local control (blocking), the Latin square principle, the splitunit principle, and the notion of factorial treatment structure. This book also emphasizes the practical aspects of designing and analyzing experiments and features: increased coverage of the practical aspects of designing and analyzing experiments, complete with the steps needed to plan and construct an experiment; and, a case study that explores the various types of interaction between both treatment and blocking factors, and numerical and graphical techniques are provided to analyze and interpret these interactions. It also covers: discussion of the important distinctions between two types of blocking factors and their role in the process of drawing statistical inferences from an experiment; a new chapter devoted entirely to repeated measures, highlighting its relationship to splitplot and splitblock designs; and, numerical examples using SAS[registered] to illustrate the analyses of data from various designs and to construct factorial designs that relate the results to the theoretical derivations. "Design and Analysis of Experiments, Volume 1, Second Edition" is an ideal textbook for firstyear graduate courses in experimental design and also serves as a practical, handson reference for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering, medicine, pharmacology, psychology, and business.
(source: Nielsen Book Data)9780471727569 20160528
Subjects
Bibliographic information
 Beginning date
 2008
 Note
 Rev. ed. of: Design and analysis of experiments / Klaus Hinkelmann, Oscar Kempthorne. c1994c2005. 2 v.
 Vol. 3 lacks edition statement.
 Related Work
 Hinkelmann, Klaus, 1932 Design and analysis of experiments.
 ISBN
 9780471727569 (v. 1 : cloth)
 0471727563 (v. 1 : cloth)
 9780470530689 (v. 3)
 0470530685 (v. 3)
 9780471551775 (v. 2)
 0471551775 (v. 2)