{"product_id":"applied-multiway-data-analysis-hardback-9780470164976","title":"Applied Multiway Data Analysis (Hardback) 9780470164976","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eApplied Multiway Data Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003ePieter M. Kroonenberg (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470164976, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 15 February 2008\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e612 pages, Charts: 2 B\u0026amp;W, 0 Color; Drawings: 53 B\u0026amp;W, 0 Color; Tables: 0 B\u0026amp;W, 0 Color; Graphs: 106 B\u0026amp;W, 0 Color\u003cbr\u003e24.4 x 16.5 x 3.6 cm, 0.998 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"All topics are well illustrated with good examples from a fairly wide range of applications... the book’s usefulness is enhanced by a glossary of multiway terminology, a good index and references to extension work... this is a well-crafted and highly readable book.\" (\u003ci\u003eJournal of the Royal Statistical Society- Series A\u003c\/i\u003e, 2009)  \u003cp\u003e“All in all, Kroonenberg’s book constitutes an extremely valuable tool for applied researchers in almost all domains of investigation, whenever they are faced with the task of analyzing complex statistical data in view of obtaining useful information in their areas of interest.” (\u003ci\u003eBiometrics\u003c\/i\u003e, June 2009)\u003c\/p\u003e \u003cp\u003e“This book is focused primarily toward graduate students in the areas of chemistry, social and behavioral sciences, and environmental sciences, although the techniques and methods used can be more broadly used in other areas, such as finance and engineering, as well.” (\u003ci\u003eTechnometrics\u003c\/i\u003e, May 2009)\u003c\/p\u003e \u003cp\u003e\"Kroonenberg’s book constitutes an extremely valuable tool for applied researchers in almost all domains of investigation (from economics to psychology, from biomedicine to technology and physical sciences), whenever they are faced with the task of analyzing complex statistical data in view of obtaining useful information in their areas of interest.\" (\u003ci\u003eBiometrics\u003c\/i\u003e 2009)\u003c\/p\u003e \u003cp\u003e\"...the combination of worked-out examples alongside descriptions and critical considerations on the theory behind those analyses make AMDA an interesting book for researchers and practitioners in both academia and industry. (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e 2009)\u003c\/p\u003e \u003cp\u003e\"The book is written in a clear style and mostly in conceptual rather than mathematical level. It emphasized the author's over thirty years' personal experience and practical side of performing multiway data analyses. It is easy to recommend this book, as it really open news views of the world.\" (\u003ci\u003eInternational Statistical Review\u003c\/i\u003e, December 2008)\u003c\/p\u003e \u003cp\u003e\"Good things take time - and this hold for this book as well…Pieter Kroonenberg is one of the few with a profound knowledge of multiway analysis. It is meritorious that he took the effort to share his knowledge. It is to be hoped that a next edition will appear soon...the book deserves a broad reading public.\" (\u003ci\u003eVereniging voor Ordinatie en Classificatie, Nieuwsbrief\u003c\/i\u003e, no 41, November 2008)\u003c\/p\u003e \u003cp\u003e\"We believe that this book will offer applied researchers a lot of good advice for using three-way techniques.  In addition, \u003ci\u003eApplied Multiway Data-Analysis\u003c\/i\u003e will turn out to be a valuable resource of reference for three-way specialists.\" (\u003ci\u003eMathematical Reviews\u003c\/i\u003e, 2008)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eFrom a preeminent authority—a modern and applied treatment of multiway data analysis\u003c\/b\u003e \u003cp\u003eThis groundbreaking book is the first of its kind to present methods for analyzing multiway data by applying multiway component techniques. Multiway analysis is a specialized branch of the larger field of multivariate statistics that extends the standard methods for two-way data, such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data. Applied Multiway Data Analysis presents a unique, thorough, and authoritative treatment of this relatively new and emerging approach to data analysis that is applicable across a range of fields, from the social and behavioral sciences to agriculture, environmental sciences, and chemistry.\u003c\/p\u003e \u003cp\u003eGeneral introductions to multiway data types, methods, and estimation procedures are provided in addition to detailed explanations and advice for readers who would like to learn more about applying multiway methods. Using carefully laid out examples and engaging applications, the book begins with an introductory chapter that serves as a general overview of multiway analysis, including the types of problems it can address. Next, the process of setting up, carrying out, and evaluating multiway analyses is discussed along with commonly encountered issues, such as preprocessing, missing data, model and dimensionality selection, postprocessing, and transformation, as well as robustness and stability issues.\u003c\/p\u003e \u003cp\u003eExtensive examples are presented within a unified framework consisting of a five-step structure: objectives; data description and design; model and dimensionality selection; results and their interpretation; and validation. Procedures featured in the book are conducted using 3WayPack, which is software developed by the author, and analyses can also be carried out within the R and MATLAB systems. Several data sets and 3WayPack can be downloaded via the book's related Web site.\u003c\/p\u003e \u003cp\u003eThe author presents the material in a clear, accessible style without unnecessary or complex formalism, assuring a smooth transition from well-known standard two-analysis to multiway analysis for readers from a wide range of backgrounds. An understanding of linear algebra, statistics, and principal component analyses and related techniques is assumed, though the author makes an effort to keep the presentation at a conceptual, rather than mathematical, level wherever possible. Applied Multiway Data Analysis is an excellent supplement for component analysis and statistical multivariate analysis courses at the upper-undergraduate and beginning graduate levels. The book can also serve as a primary reference for statisticians, data analysts, methodologists, applied mathematicians, and social science researchers working in academia or industry.\u003c\/p\u003e \u003cp\u003eVisit the Related Website: http:\/\/three-mode.leidenuniv.nl\/ to view data from the book.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eForeword xv\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I DATA, MODELS, AND ALGORITHMS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overture 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Three-way and multiway data 4\u003c\/p\u003e \u003cp\u003e1.2 Multiway data analysis 5\u003c\/p\u003e \u003cp\u003e1.3 Before the arrival of three-mode analysis 6\u003c\/p\u003e \u003cp\u003e1.4 Three-mode data-analytic techniques 7\u003c\/p\u003e \u003cp\u003e1.5 Example: Judging Chopin's preludes 7\u003c\/p\u003e \u003cp\u003e1.6 Birth of the Tucker model 12\u003c\/p\u003e \u003cp\u003e1.7 Current status of multiway analysis 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Overview 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 What are multiway data? 15\u003c\/p\u003e \u003cp\u003e2.2 Why multiway analysis? 17\u003c\/p\u003e \u003cp\u003e2.3 What is a model? 18\u003c\/p\u003e \u003cp\u003e2.4 Some history 20\u003c\/p\u003e \u003cp\u003e2.5 Multiway models and methods 24\u003c\/p\u003e \u003cp\u003e2.6 Conclusions 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Three-Way and Multiway Data 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Chapter preview 27\u003c\/p\u003e \u003cp\u003e3.2 Terminology 28\u003c\/p\u003e \u003cp\u003e3.3 Two-way solutions to three-way data 30\u003c\/p\u003e \u003cp\u003e3.4 Classification principles 31\u003c\/p\u003e \u003cp\u003e3.5 Overview of three-way data designs 33\u003c\/p\u003e \u003cp\u003e3.6 Fully crossed designs 33\u003c\/p\u003e \u003cp\u003e3.7 Nested designs 38\u003c\/p\u003e \u003cp\u003e3.8 Scaling designs 40\u003c\/p\u003e \u003cp\u003e3.9 Categorical data 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Component Models for Fully-Crossed Designs 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 43\u003c\/p\u003e \u003cp\u003e4.2 Chapter preview 45\u003c\/p\u003e \u003cp\u003e4.3 Two-mode modeling of three-way data 45\u003c\/p\u003e \u003cp\u003e4.4 Extending two-mode component models to three-mode models 47\u003c\/p\u003e \u003cp\u003e4.5 Tucker models 51\u003c\/p\u003e \u003cp\u003e4.6 Parafac models 57\u003c\/p\u003e \u003cp\u003e4.7 ParaTuck2 model 63\u003c\/p\u003e \u003cp\u003e4.8 Core arrays 64\u003c\/p\u003e \u003cp\u003e4.9 Relationships between component models 66\u003c\/p\u003e \u003cp\u003e4.10 Multiway component modeling under constraints 68\u003c\/p\u003e \u003cp\u003e4.11 Conclusions 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5\u003c\/b\u003e \u003cb\u003eAlgorithms for Multiway Models 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 77\u003c\/p\u003e \u003cp\u003e5.2 Chapter preview 78\u003c\/p\u003e \u003cp\u003e5.3 Terminology and general issues 79\u003c\/p\u003e \u003cp\u003e5.4 An example of an iterative algorithm 81\u003c\/p\u003e \u003cp\u003e5.5 General behavior of multiway algorithms 84\u003c\/p\u003e \u003cp\u003e5.6 The Parallel factor model - Parafac 85\u003c\/p\u003e \u003cp\u003e5.7 The Tucker models 97\u003c\/p\u003e \u003cp\u003e5.8 STATIS 105\u003c\/p\u003e \u003cp\u003e5.9 Conclusions 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II DATA HANDLING, MODEL SELECTION, AND INTERPRETATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Preprocessing 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 109\u003c\/p\u003e \u003cp\u003e6.2 Chapter preview 112\u003c\/p\u003e \u003cp\u003e6.3 General considerations 112\u003c\/p\u003e \u003cp\u003e6.4 Model-based arguments for preprocessing choices 117\u003c\/p\u003e \u003cp\u003e6.5 Content-based arguments for preprocessing choices 128\u003c\/p\u003e \u003cp\u003e6.6 Preprocessing and specific multiway data designs 130\u003c\/p\u003e \u003cp\u003e6.7 Centering and analysis-of-variance models: Two-way data 134\u003c\/p\u003e \u003cp\u003e6.8 Centering and analysis-of-variance models: Three-way data 137\u003c\/p\u003e \u003cp\u003e6.9 Recommendations 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Missing Data in Multiway Analysis 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 143\u003c\/p\u003e \u003cp\u003e7.2 Chapter preview 147\u003c\/p\u003e \u003cp\u003e7.3 Handling missing data in two-mode \u003cb\u003ePCA\u003c\/b\u003e 148\u003c\/p\u003e \u003cp\u003e7.4 Handling missing data in multiway analysis 154\u003c\/p\u003e \u003cp\u003e7.5 Multiple imputation in multiway analysis: Data matters 156\u003c\/p\u003e \u003cp\u003e7.6 Missing data in multiway analysis: Practice 157\u003c\/p\u003e \u003cp\u003e7.7 Example: Spanjer's Chromatography data 159\u003c\/p\u003e \u003cp\u003e7.8 Example: NICHD Child care data 168\u003c\/p\u003e \u003cp\u003e7.9 Further applications 172\u003c\/p\u003e \u003cp\u003e7.10 Computer programs for multiple imputation 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Model and Dimensionality Selection 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 175\u003c\/p\u003e \u003cp\u003e8.2 Chapter preview 176\u003c\/p\u003e \u003cp\u003e8.3 Sample size and stochastics 176\u003c\/p\u003e \u003cp\u003e8.4 Degrees of freedom 177\u003c\/p\u003e \u003cp\u003e8.5 Selecting the dimensionality of a Tucker model 179\u003c\/p\u003e \u003cp\u003e8.6 Selecting the dimensionality of a Parafac model 184\u003c\/p\u003e \u003cp\u003e8.7 Model selection from a hierarchy 186\u003c\/p\u003e \u003cp\u003e8.8 Model stability and predictive power 187\u003c\/p\u003e \u003cp\u003e8.9 Example: Chopin prelude data 190\u003c\/p\u003e \u003cp\u003e8.10 Conclusions 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Interpreting Component Models 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Chapter preview 209\u003c\/p\u003e \u003cp\u003e9.2 General principles 210\u003c\/p\u003e \u003cp\u003e9.3 Representations of component models 215\u003c\/p\u003e \u003cp\u003e9.4 Scaling of components 218\u003c\/p\u003e \u003cp\u003e9.5 Interpreting core arrays 225\u003c\/p\u003e \u003cp\u003e9.6 Interpreting extended core arrays 231\u003c\/p\u003e \u003cp\u003e9.7 Special topics 232\u003c\/p\u003e \u003cp\u003e9.8 Validation 233\u003c\/p\u003e \u003cp\u003e9.9 Conclusions 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Improving Interpretation through Rotations 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 237\u003c\/p\u003e \u003cp\u003e10.2 Chapter preview 240\u003c\/p\u003e \u003cp\u003e10.3 Rotating components 241\u003c\/p\u003e \u003cp\u003e10.4 Rotating full core arrays 244\u003c\/p\u003e \u003cp\u003e10.5 Theoretical simplicity of core arrays 254\u003c\/p\u003e \u003cp\u003e10.6 Conclusions 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Graphical Displays for Components 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 257\u003c\/p\u003e \u003cp\u003e11.2 Chapter preview 258\u003c\/p\u003e \u003cp\u003e11.3 General considerations 259\u003c\/p\u003e \u003cp\u003e11.4 Plotting single modes 260\u003c\/p\u003e \u003cp\u003e11.5 Plotting different modes together 270\u003c\/p\u003e \u003cp\u003e11.6 Conclusions 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Residuals, Outliers, and Robustness 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 281\u003c\/p\u003e \u003cp\u003e12.2 Chapter preview 282\u003c\/p\u003e \u003cp\u003e12.3 Goals 283\u003c\/p\u003e \u003cp\u003e12.4 Procedures for analyzing residuals 284\u003c\/p\u003e \u003cp\u003e12.5 Decision schemes for analyzing multiway residuals 287\u003c\/p\u003e \u003cp\u003e12.6 Structured squared residuals 287\u003c\/p\u003e \u003cp\u003e12.7 Unstructured residuals 292\u003c\/p\u003e \u003cp\u003e12.8 Robustness: Basics 294\u003c\/p\u003e \u003cp\u003e12.9 Robust methods of multiway analysis 297\u003c\/p\u003e \u003cp\u003e12.10 Examples 301\u003c\/p\u003e \u003cp\u003e12.1 1 Conclusions 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III MULTIWAY DATA AND THEIR ANALYSIS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Modeling Multiway Profile Data 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 311\u003c\/p\u003e \u003cp\u003e13.2 Chapter preview 313\u003c\/p\u003e \u003cp\u003e13.3 Example: Judging parents' behavior 313\u003c\/p\u003e \u003cp\u003e13.4 Multiway profile data: General issues 320\u003c\/p\u003e \u003cp\u003e13.5 Multiway profile data: Parafac in practice 322\u003c\/p\u003e \u003cp\u003e13.6 Multiway profile data: Tucker analyses in practice 331\u003c\/p\u003e \u003cp\u003e13.7 Conclusions 342\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Modeling Multiway Rating Scale Data 345\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 345\u003c\/p\u003e \u003cp\u003e14.2 Chapter preview 346\u003c\/p\u003e \u003cp\u003e14.3 Three-way rating scale data: Theory 346\u003c\/p\u003e \u003cp\u003e14.4 Example: Coping at school 354\u003c\/p\u003e \u003cp\u003e14.5 Analyzing three-way rating scales: Practice 360\u003c\/p\u003e \u003cp\u003e14.6 Example: Differences within a multiple personality 361\u003c\/p\u003e \u003cp\u003e14.7 Conclusions 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Exploratory Multivariate Longitudinal Analysis 373\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 373\u003c\/p\u003e \u003cp\u003e15.2 Chapter preview 375\u003c\/p\u003e \u003cp\u003e15.3 Overview of longitudinal modeling 375\u003c\/p\u003e \u003cp\u003e15.4 Longitudinal three-mode modeling 378\u003c\/p\u003e \u003cp\u003e15.5 Example: Organizational changes in Dutch hospitals 385\u003c\/p\u003e \u003cp\u003e15.6 Example: Morphological development of French girls 394\u003c\/p\u003e \u003cp\u003e15.7 Further reading 400\u003c\/p\u003e \u003cp\u003e15.8 Conclusions 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Three-Mode Clustering 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 403\u003c\/p\u003e \u003cp\u003e16.2 Chapter preview 405\u003c\/p\u003e \u003cp\u003e16.3 Three-mode clustering analysis: Theory 405\u003c\/p\u003e \u003cp\u003e16.4 Example: Identifying groups of diseased blue crabs 409\u003c\/p\u003e \u003cp\u003e16.5 Three-mode cluster analysis: Practice 411\u003c\/p\u003e \u003cp\u003e16.6 Example: Behavior of children in the Strange Situation 424\u003c\/p\u003e \u003cp\u003e16.7 Extensions and special topics 430\u003c\/p\u003e \u003cp\u003e16.8 Conclusions 432\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Multiway Contingency Tables 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 433\u003c\/p\u003e \u003cp\u003e17.2 Chapter preview 434\u003c\/p\u003e \u003cp\u003e17.3 Three-way correspondence analysis: Theory 435\u003c\/p\u003e \u003cp\u003e17.4 Example: Sources of happiness 444\u003c\/p\u003e \u003cp\u003e17.5 Three-way correspondence analysis: Practice 448\u003c\/p\u003e \u003cp\u003e17.6 Example: Playing with peers 454\u003c\/p\u003e \u003cp\u003e17.7 Conclusions 458\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Three-Way Binary Data 459\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 459\u003c\/p\u003e \u003cp\u003e18.2 Chapter preview 460\u003c\/p\u003e \u003cp\u003e18.3 A graphical introduction 460\u003c\/p\u003e \u003cp\u003e18.4 Formal description of the Tucker-HICLAS models 462\u003c\/p\u003e \u003cp\u003e18.5 Additional issues 465\u003c\/p\u003e \u003cp\u003e18.6 Example: Hostile behavior in frustrating situations 465\u003c\/p\u003e \u003cp\u003e18.7 Conclusion 467\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 From Three-Way Data to Four-Way Data and Beyond 469\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 469\u003c\/p\u003e \u003cp\u003e19.2 Chapter preview 471\u003c\/p\u003e \u003cp\u003e19.3 Examples of multiway data 471\u003c\/p\u003e \u003cp\u003e19.4 Multiway techniques: Theory 474\u003c\/p\u003e \u003cp\u003e19.5 Example: Differences within a multiple personality 476\u003c\/p\u003e \u003cp\u003e19.6 Example: Austrian aerosol particles 480\u003c\/p\u003e \u003cp\u003e19.7 Further reading and computer programs 487\u003c\/p\u003e \u003cp\u003e19.8 Conclusions 488\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Standard Notation for Multiway Analysis 489\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: Biplots and Their Interpretation 491\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB. 1 Introduction 492\u003c\/p\u003e \u003cp\u003eB.2 Singular value decomposition 492\u003c\/p\u003e \u003cp\u003eB.3 Biplots 494\u003c\/p\u003e \u003cp\u003eB.4 Relationship with PCA 499\u003c\/p\u003e \u003cp\u003eB.5 Basic vector geometry relevant to biplots 499\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 501\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eGlossary 527\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcronyms 543\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAuthor Index 545\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSubject Index 553\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand 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