{"product_id":"design-and-analysis-of-experiments-volume-3-isbn-9780470530689","title":"Design and Analysis of Experiments, Volume 3","description":"\u003cb\u003eProvides timely applications, modifications, and extensions of experimental designs for a variety of disciplines\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e   \u003cp\u003eDesign and Analysis of Experiments, Volume 3: Special Designs and Applications continues building upon the philosophical foundations of experimental design by providing important, modern applications of experimental design to the many fields that utilize them. The book also presents optimal and efficient designs for practice and covers key topics in current statistical research.\u003c\/p\u003e \u003cp\u003eFeaturing contributions from leading researchers and academics, the book demonstrates how the presented concepts are used across various fields from genetics and medicinal and pharmaceutical research to manufacturing, engineering, and national security. Each chapter includes an introduction followed by the historical background as well as in-depth procedures that aid in the construction and analysis of the discussed designs. Topical coverage includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGenetic cross experiments, microarray experiments, and variety trials\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eClinical trials, group-sequential designs, and adaptive designs\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eFractional factorial and search, choice, and optimal designs for generalized linear models\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eComputer experiments with applications to homeland security\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eRobust parameter designs and split-plot type response surface designs\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eAnalysis of directional data experiments\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThroughout the book, illustrative and numerical examples utilize SAS®, JMP®, and R software programs to demonstrate the discussed techniques. Related data sets and software applications are available on the book's related FTP site.\u003c\/p\u003e \u003cp\u003eDesign and Analysis of Experiments, Volume 3 is an ideal textbook for graduate courses in experimental design and also serves as a practical, hands-on reference for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering, medicine, and business.\u003c\/p\u003e Preface xvii \u003cp\u003eContributors xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Genetic Crosses Experiments 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMurari Singh, Sudhir Gupta, and Rajender Parsad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction, 1\u003c\/p\u003e \u003cp\u003e1.2 Basic Objectives and Models, 2\u003c\/p\u003e \u003cp\u003e1.3 Diallel Mating Design of Type I, 8\u003c\/p\u003e \u003cp\u003e1.4 Diallel Crosses: Type II Designs, 14\u003c\/p\u003e \u003cp\u003e1.5 Partial Diallel Crosses: No Blocking or Complete Blocks, 25\u003c\/p\u003e \u003cp\u003e1.6 Partial Diallel Crosses in Incomplete Blocks, 32\u003c\/p\u003e \u003cp\u003e1.7 Optimality, 44\u003c\/p\u003e \u003cp\u003e1.8 Robustness, 59\u003c\/p\u003e \u003cp\u003e1.9 Three- or Higher-Way Crosses, 61\u003c\/p\u003e \u003cp\u003e1.10 Computation, 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Design of Gene Expression Microarray Experiments 73\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDan Nettleton\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction, 73\u003c\/p\u003e \u003cp\u003e2.2 Gene Expression Microarray Technology, 74\u003c\/p\u003e \u003cp\u003e2.3 Preprocessing of Microarray Fluorescence Intensities, 76\u003c\/p\u003e \u003cp\u003e2.4 Introduction to Gene Expression Microarray Experimental Design, 80\u003c\/p\u003e \u003cp\u003e2.5 Two-Treatment Experiments Using Two-Color Microarrays, 81\u003c\/p\u003e \u003cp\u003e2.6 Two-Color Microarray Experiments Involving More Than Two Treatments, 86\u003c\/p\u003e \u003cp\u003e2.7 Multifactor Two-Color Microarray Experiments, 89\u003c\/p\u003e \u003cp\u003e2.8 Phase 2 Designs for Complex Phase 1 Designs, 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Spatial Analysis of Agricultural Field Experiments 109\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJoanne K. Stringer, Alison B. Smith, and Brian R. Cullis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction, 109\u003c\/p\u003e \u003cp\u003e3.2 Methods to Account for Spatial Variation, 110\u003c\/p\u003e \u003cp\u003e3.3 A Spatial Linear Mixed Model, 116\u003c\/p\u003e \u003cp\u003e3.4 Analysis of Examples, 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Optimal Designs for Generalized Linear Models 137\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJohn Stufken and Min Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction, 137\u003c\/p\u003e \u003cp\u003e4.2 Notation and Basic Concepts, 141\u003c\/p\u003e \u003cp\u003e4.3 Tools for Finding Locally Optimal Designs, 145\u003c\/p\u003e \u003cp\u003e4.4 GLMs with Two Parameters, 149\u003c\/p\u003e \u003cp\u003e4.5 GLMs with Multiple Parameters, 155\u003c\/p\u003e \u003cp\u003e4.6 Summary and Concluding Comments, 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Design and Analysis of Randomized Clinical Trials 165\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJanet Wittes and Zi-Fan Yu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Overview, 165\u003c\/p\u003e \u003cp\u003e5.2 Components of a Randomized Clinical Trial, 168\u003c\/p\u003e \u003cp\u003e5.3 Bias, 175\u003c\/p\u003e \u003cp\u003e5.4 Statistical Analysis of Randomized Clinical Trials, 182\u003c\/p\u003e \u003cp\u003e5.5 Failure Time Studies, 184\u003c\/p\u003e \u003cp\u003e5.6 Other Topics, 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Monitoring Randomized Clinical Trials 213\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEric S. Leifer and Nancy L. Geller\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction, 213\u003c\/p\u003e \u003cp\u003e6.2 Normally Distributed Outcomes, 215\u003c\/p\u003e \u003cp\u003e6.3 Brownian Motion Properties, 217\u003c\/p\u003e \u003cp\u003e6.4 Brief Historical Overview of Group Sequential Methods, 219\u003c\/p\u003e \u003cp\u003e6.5 Dichotomous Outcomes, 223\u003c\/p\u003e \u003cp\u003e6.6 Time-to-Event Outcomes, 225\u003c\/p\u003e \u003cp\u003e6.7 Unconditional Power, 227\u003c\/p\u003e \u003cp\u003e6.8 Conditional Power, 229\u003c\/p\u003e \u003cp\u003e6.9 Spending Functions, 232\u003c\/p\u003e \u003cp\u003e6.10 Flexibility and Properties of Spending Functions, 233\u003c\/p\u003e \u003cp\u003e6.11 Modifying the Trial’s Sample Size Based on a Nuisance Parameter, 235\u003c\/p\u003e \u003cp\u003e6.12 Sample Size Modification Based on the Interim Treatment Effect, 240\u003c\/p\u003e \u003cp\u003e6.13 Concluding Remarks, 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Adaptive Randomization in Clinical Trials 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLanju Zhang and William F. Rosenberger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction, 251\u003c\/p\u003e \u003cp\u003e7.2 Adaptive Randomization Procedures, 252\u003c\/p\u003e \u003cp\u003e7.3 Likelihood-Based Inference, 264\u003c\/p\u003e \u003cp\u003e7.4 Randomization-Based Inference, 269\u003c\/p\u003e \u003cp\u003e7.5 Conclusions and Practical Considerations, 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Search Linear Model for Identification and Discrimination 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSubir Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction, 283\u003c\/p\u003e \u003cp\u003e8.2 General Linear Model with Fixed Effects, 284\u003c\/p\u003e \u003cp\u003e8.3 Search Linear Model, 285\u003c\/p\u003e \u003cp\u003e8.4 Applications, 288\u003c\/p\u003e \u003cp\u003e8.5 Effects of Noise in Performance Comparison, 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Minimum Aberration and Related Criteria for Fractional Factorial Designs 299\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHegang H. Chen and Ching-Shui Cheng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction, 299\u003c\/p\u003e \u003cp\u003e9.2 Projections of Fractional Factorial Designs, 302\u003c\/p\u003e \u003cp\u003e9.3 Estimation Capacity, 304\u003c\/p\u003e \u003cp\u003e9.4 Clear Two-Factor Interactions, 307\u003c\/p\u003e \u003cp\u003e9.5 Estimation Index, 310\u003c\/p\u003e \u003cp\u003e9.6 Estimation Index, Minimum Aberration, and Maximum Estimation Capacity, 314\u003c\/p\u003e \u003cp\u003e9.7 Complementary Design Theory for Minimum Aberration Designs, 315\u003c\/p\u003e \u003cp\u003e9.8 Nonregular Designs and Orthogonal Arrays, 317\u003c\/p\u003e \u003cp\u003e9.9 Generalized Minimum Aberration, 320\u003c\/p\u003e \u003cp\u003e9.10 Optimal Fractional Factorial Block Designs, 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Designs for Choice Experiments for the Multinomial Logit Model 331\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDeborah J. Street and Leonie Burgess\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction, 331\u003c\/p\u003e \u003cp\u003e10.2 Definitions, 332\u003c\/p\u003e \u003cp\u003e10.3 The MNL Model, 335\u003c\/p\u003e \u003cp\u003e10.4 Design Comparisons, 338\u003c\/p\u003e \u003cp\u003e10.5 Optimal Designs for DCEs, 340\u003c\/p\u003e \u003cp\u003e10.6 Using Combinatorial Designs to Construct DCEs, 364\u003c\/p\u003e \u003cp\u003e10.7 Bayesian Work, 368\u003c\/p\u003e \u003cp\u003e10.8 Best–Worst Experiments, 368\u003c\/p\u003e \u003cp\u003e10.9 Miscellaneous Topics, 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Computer Experiments 379\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMax D. Morris\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction, 379\u003c\/p\u003e \u003cp\u003e11.2 Sensitivity\/Uncertainty Analysis, 382\u003c\/p\u003e \u003cp\u003e11.3 Gaussian Stochastic Process Models, 385\u003c\/p\u003e \u003cp\u003e11.4 Inference, 389\u003c\/p\u003e \u003cp\u003e11.5 Experimental Designs, 398\u003c\/p\u003e \u003cp\u003e11.6 Multivariate Output, 403\u003c\/p\u003e \u003cp\u003e11.7 Multiple Data Sources, 406\u003c\/p\u003e \u003cp\u003e11.8 Conclusion, 409\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Designs for Large-Scale Simulation Experiments, with Applications to Defense and Homeland Security 413\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSusan M. Sanchez, Thomas W. Lucas, Paul J. Sanchez, Christopher J. Nannini, and Hong Wan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction, 413\u003c\/p\u003e \u003cp\u003e12.2 Philosophy: Evolution of Computational Experiments, 414\u003c\/p\u003e \u003cp\u003e12.3 Application: U.S. Army Unmanned Aerial Vehicle (UAV) Mix Study, 422\u003c\/p\u003e \u003cp\u003e12.4 Parting Thoughts, 437\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Robust Parameter Designs 443\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTimothy J. Robinson and Christine M. Anderson-Cook\u003cbr\u003e \u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction, 443\u003c\/p\u003e \u003cp\u003e13.2 Taguchi Signal-to-Noise Ratio Approach, 445\u003c\/p\u003e \u003cp\u003e13.3 Dual Model Response Surface Methodology, 448\u003c\/p\u003e \u003cp\u003e13.4 Single Model Response Surface Methods Using Combined Arrays, 451\u003c\/p\u003e \u003cp\u003e13.5 Computer Generated Combined Arrays, 461\u003c\/p\u003e \u003cp\u003e13.6 RPD Involving Quantitative and Qualitative Factors, 465\u003c\/p\u003e \u003cp\u003e13.7 Conclusions, 466\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Split-Plot Response Surface Designs 471\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Geoffrey Vining\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction, 471\u003c\/p\u003e \u003cp\u003e14.2 Differences between Agricultural and Industrial Experimentation, 472\u003c\/p\u003e \u003cp\u003e14.3 OLS–GLS Equivalent Second-Order Split-Plot Designs and Analysis, 482\u003c\/p\u003e \u003cp\u003e14.4 Exact Tests for the Coeffi cients, 488\u003c\/p\u003e \u003cp\u003e14.5 Proper Residuals for Checking Assumptions, 493\u003c\/p\u003e \u003cp\u003e14.6 \"Optimal\" Second-Order Split-Plot Designs, 496\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Design and Analysis of Experiments for Directional Data 501\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSango B. Otieno and Christine M. Anderson-Cook\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Summary, 501\u003c\/p\u003e \u003cp\u003e15.2 Introduction and Historical Background, 501\u003c\/p\u003e \u003cp\u003e15.3 ANOVA for Circular Data, 509\u003c\/p\u003e \u003cp\u003e15.4 ANOVA for Cylindrical Data, 521\u003c\/p\u003e \u003cp\u003e15.5 ANOVA for Spherical Data, 524\u003c\/p\u003e \u003cp\u003e15.6 Conclusions, 530\u003c\/p\u003e \u003cp\u003eReferences, 531\u003c\/p\u003e \u003cp\u003eAuthor Index 533\u003c\/p\u003e \u003cp\u003eSubject Index 545\u003c\/p\u003e  \u003cp\u003e“The presentation by Wiley is superb, as we have come to expect. All in all, this is a first class offering.”\u003ci\u003e( International Statistical Review\u003c\/i\u003e, 1 October 2012)\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKLAUS HINKELMANN, PhD\u003c\/b\u003e, is Professor Emeritus of Statistics at the Virginia Polytechnic Institute and State University Department of Statistics. A Fellow of the American Statistical Association and the American Association for the Advancement of Science, Dr. Hinkelmann has published extensively in the areas of design of experiments, statistical methods, and biometry.\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eProvides timely applications, modifications, and extensions of experimental designs for a variety of disciplines\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eDesign and Analysis of Experiments, Volume 3: Special Designs and Applications\u003c\/i\u003e continues building upon the philosophical foundations of experimental design by providing important, modern applications of experimental design to the many fields that utilize them. The book also presents optimal and efficient designs for practice and covers key topics in current statistical research.\u003c\/p\u003e \u003cp\u003eFeaturing contributions from leading researchers and academics, the book demonstrates how the presented concepts are used across various fields from genetics and medicinal and pharmaceutical research to manufacturing, engineering, and national security. Each chapter includes an introduction followed by the historical background as well as in-depth procedures that aid in the construction and analysis of the discussed designs. Topical coverage includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eGenetic cross experiments, microarray experiments, and variety trials\u003c\/li\u003e \u003cli\u003eClinical trials, group-sequential designs, and adaptive designs\u003c\/li\u003e \u003cli\u003eFractional factorial, search, and choice designs, and optimal designs for generalized linear models\u003c\/li\u003e \u003cli\u003eComputer experiments with applications to homeland security\u003c\/li\u003e \u003cli\u003eRobust parameter designs and split-plot type response surface designs\u003c\/li\u003e \u003cli\u003eAnalysis of directional data experiments\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThroughout the book, illustrative and numerical examples utilize SAS®, JMP®, and R software programs to demonstrate the discussed techniques. Related data sets and software applications are available on the book's related FTP site.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eDesign and Analysis of Experiments, Volume 3\u003c\/i\u003e is an ideal textbook for graduate courses in experimental design and also serves as a practical, hands-on reference for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering, medicine, and business.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989043298533,"sku":"NP9780470530689","price":162.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470530689.jpg?v=1761782557","url":"https:\/\/k12savings.com\/es\/products\/design-and-analysis-of-experiments-volume-3-isbn-9780470530689","provider":"K12savings","version":"1.0","type":"link"}