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Applied Mathematics for Scientists and Engineers (Textbooks in Mathematics)
by Youssef N. RaffoulAfter many years of teaching graduate courses in applied mathematics, Youssef N. Raffoul saw a need among his students for a book reviewing topics from undergraduate courses to help them recall what they had learned, while his students urged him to publish a brief and approachable book on the topic. Thus, the author used his lecture notes from his graduate course in applied mathematical methods, which comprises three chapters on linear algebra, calculus of variations, and integral equations, to serve as the foundation for this work. These notes have undergone continuous revision. Applied Mathematics for Scientists and Engineers is designed to be used as a graduate textbook for one semester. The five chapters in the book can be used by the instructor to create a one-semester, three-chapter course. The only prerequisites for this self-contained book are a basic understanding of calculus and differential equations. In order to make the book accessible to a broad audience, the author endeavored to strike a balance between rigor and presentation of the most challenging content in a simple format by adopting friendlier, more approachable notations and using numerous examples to clarify complex themes. The hope is both instructors and students will find, in this single volume, a refresher on topics necessary to further their courses and study.
Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB
by Peter J. CostaFeatures a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® toolbox for the collection, visualization, and evaluation of experimental and real-life data Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® presents a practical approach to the task that biological scientists face when analyzing data.The primary focus is on the application of mathematical models and scientific computingmethods to provide insight into the behavior of biological systems. The author draws upon hisexperience in academia, industry, and government–sponsored research as well as his expertisein MATLAB to produce a suite of computer programs with applications in epidemiology,machine learning, and biostatistics. These models are derived from real–world data andconcerns. Among the topics included are the spread of infectious disease (HIV/AIDS) througha population, statistical pattern recognition methods to determine the presence of disease in adiagnostic sample, and the fundamentals of hypothesis testing. In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book’s technical merit and value to industry professionals. Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code. In addition, the book features: • Real–world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen • Clear delineation of topics to accelerate access to data analysis • Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® is an excellent textbook for students in mathematics, biostatistics, the life and social sciences,and quantitative, computational, and mathematical biology. This book is also an ideal referencefor industrial scientists, biostatisticians, product development scientists, and practitionerswho use mathematical models of biological systems in biomedical research, medical devicedevelopment, and pharmaceutical submissions. PETER J. COSTA, PhD, is Senior Applied Mathematician at Hologic Incorporated in Marlborough, MA. Dr. Costa is the co-creator of MATLAB's Symbolic Math Toolbox. He has developed mathematical models for the spread of HIV, the outbreak of AIDS, the transmission of an infectious respiratory disease throughout a population, and the diagnosis of cervical cancer. His research interests include scientific computing and mathematical biology. He received a PhD in Applied Mathematics from the University of Massachusetts at Amherst. Edit
Applied Mathematics in Engineering and Reliability: Proceedings of the 1st International Conference on Applied Mathematics in Engineering and Reliability (Ho Chi Minh City, Vietnam, 4-6 May 2016)
by Radim Briš & Václav Snášel Chu Duc Khanh Phan DaoApplied Mathematics in Engineering and Reliability contains papers presented at the International Conference on Applied Mathematics in Engineering and Reliability (ICAMER 2016, Ho Chi Minh City, Viet Nam, 4-6 May 2016). The book covers a wide range of topics within mathematics applied in reliability, risk and engineering, including:- Risk and Relia
Applied Mathematics in Hydrogeology
by Tien-Chang LeeAs introduced in Dr. Lee's 10-week class, Applied Mathematics in Hydrogeology is written for professionals and graduate students who have a keen interest in the application of mathematics in hydrogeology.Its first seven chapters cover analytical solutions for problems commonly encountered in the study of quantitative hydrogeology, while the final three chapters focus on solving linear simultaneous equations, finite element analysis, and inversion for parameter determination.Dr. Lee provides various equation-solving methods that are of interest to hydrogeologists, geophysicists, soil scientists, and civil engineers, as well as applied physicists and mathematicians. In the classroom, this same information will help students realize how familiar equations in hydrogeology are derived-an important step toward development of a student's own mathematical models.Unlike other applied mathematics books that are structured according to systematic methodology, Applied Mathematics in Hydrogeology emphasizes equation-solving methods according to topics. Hydrogeological problems and governing differential equations are introduced, including hydraulic responses to pumping in confined and unconfined aquifers, as well as transport of heat and solute in flowing groundwater.
Applied Mathematics in Tunisia: International Conference on Advances in Applied Mathematics (ICAAM), Hammamet, Tunisia, December 2013 (Springer Proceedings in Mathematics & Statistics #131)
by Aref Jeribi Mohamed Ali Hammami Afif MasmoudiThis contributed volume presents some recent theoretical advances in mathematics and its applications in various areas of science and technology. Written by internationally recognized scientists and researchers, the chapters in this book are based on talks given at the International Conference on Advances in Applied Mathematics (ICAAM), which took place December 16-19, 2013, in Hammamet, Tunisia. Topics discussed at the conference included spectral theory, operator theory, optimization, numerical analysis, ordinary and partial differential equations, dynamical systems, control theory, probability, and statistics. These proceedings aim to foster and develop further growth in all areas of applied mathematics.
Applied Mathematics with Open-Source Software: Operational Research Problems with Python and R (Chapman & Hall/CRC Series in Operations Research)
by Vincent Knight Geraint PalmerApplied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references. This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic. Features An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software. Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered. The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them. An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.
Applied Matrix Algebra in the Statistical Sciences
by Alexander BasilevskyThis comprehensive text covers both applied and theoretical branches of matrix algebra in the statistical sciences. It also provides a bridge between linear algebra and statistical models. Appropriate for advanced undergraduate and graduate students, the self-contained treatment also constitutes a handy reference for researchers. The only mathematical background necessary is a sound knowledge of high school mathematics and a first course in statistics.Consisting of two interrelated parts, this volume begins with the basic structure of vectors and vector spaces. The latter part emphasizes the diverse properties of matrices and their associated linear transformations--and how these, in turn, depend upon results derived from linear vector spaces. An overview of introductory concepts leads to more advanced topics such as latent roots and vectors, generalized inverses, and nonnegative matrices. Each chapter concludes with a section on real-world statistical applications, plus exercises that offer concrete examples of the applications of matrix algebra.
Applied Matrix and Tensor Variate Data Analysis (SpringerBriefs in Statistics)
by Toshio SakataThis book provides comprehensive reviews of recent progress in matrix variate and tensor variate data analysis from applied points of view. Matrix and tensor approaches for data analysis are known to be extremely useful for recently emerging complex and high-dimensional data in various applied fields. The reviews contained herein cover recent applications of these methods in psychology (Chap. 1), audio signals (Chap. 2) , image analysis from tensor principal component analysis (Chap. 3), and image analysis from decomposition (Chap. 4), and genetic data (Chap. 5) . Readers will be able to understand the present status of these techniques as applicable to their own fields. In Chapter 5 especially, a theory of tensor normal distributions, which is a basic in statistical inference, is developed, and multi-way regression, classification, clustering, and principal component analysis are exemplified under tensor normal distributions. Chapter 6 treats one-sided tests under matrix variate and tensor variate normal distributions, whose theory under multivariate normal distributions has been a popular topic in statistics since the books of Barlow et al. (1972) and Robertson et al. (1988). Chapters 1, 5, and 6 distinguish this book from ordinary engineering books on these topics.
Applied Measurement with jMetrik
by J. Patrick MeyerjMetrik is a computer program for implementing classical and modern psychometric methods. It is designed to facilitate work in a production environment and to make advanced psychometric procedures accessible to every measurement practitioner. Applied Measurement with jMetrik reviews psychometric theory and describes how to use jMetrik to conduct a comprehensive psychometric analysis. Each chapter focuses on a topic in measurement, describes the steps for using jMetrik, and provides one or more examples of conducting an analysis on the topic. Recommendations and guidance for practice is provided throughout the book.
Applied Medical Statistics Using SAS
by Brian S. Everitt Geoff DerWritten with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudi
Applied Meta-Analysis with R and Stata (Chapman & Hall/CRC Biostatistics Series)
by Ding-Geng (Din) Chen Karl E. PeaceReview of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis… A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.
Applied Metal Forming
by Henry S. ValbergApplied Metal Forming: Using FEM Analysis describes metal forming theory and how experimental techniques can be used to study any metal forming operation with great accuracy. For each primary class of processes, such as forging, rolling, extrusion, wiredrawing, and sheet-metal forming, it explains how FEA (Finite Elements Analysis) can be applied with great precision to characterize the forming condition and in this way optimize the processes. FEA has made it possible to build very realistic FEM-models of any metal forming process, including complex three-dimensional forming operations, in which complex products are shaped by complex dies. Thus, using FEA it is now possible to visualize any metal forming process and to study strain, stresses, and other forming conditions inside the parts being manufactured as they develop throughout the process.
Applied Microbiome Statistics: Correlation, Association, Interaction and Composition (Chapman & Hall/CRC Biostatistics Series)
by Jun Sun Yinglin XiaThis unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts. Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction, and composition adopted from classical statistics and ecology and specifically designed for microbiome research. Performs step-by-step statistical analysis of correlation, association, interaction, and composition in microbiome data. Discusses the issues of statistical analysis of microbiome data: high dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity. Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data. Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot. Employs the Kruskal–Wallis rank-sum test to perform model selection for further multi-omics data integration. Offers R code and the datasets from the authors’ real microbiome research and publicly available data for the analysis used. Remarks on the advantages and disadvantages of each of the methods used.
Applied Missing Data Analysis in the Health Sciences
by Chuan Zhou Xiao-Hua Zhou Danping Lui Xaiobo DingA modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatisticsWith an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine.Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book's subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features:Multiple data sets that can be replicated using the SAS®, Stata®, R, and WinBUGS software packagesNumerous examples of case studies in the field of biostatistics to illustrate real-world scenarios and demonstrate applications of discussed methodologiesDetailed appendices to guide readers through the use of the presented data in various software environmentsApplied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.
Applied Mixed Model Analysis: A Practical Guide (Practical Guides to Biostatistics and Epidemiology)
by Jos W. TwiskThis practical book is designed for applied researchers who want to use mixed models with their data. It discusses the basic principles of mixed model analysis, including two-level and three-level structures, and covers continuous outcome variables, dichotomous outcome variables, and categorical and survival outcome variables. Emphasizing interpretation of results, the book develops the most important applications of mixed models, such as the study of group differences, longitudinal data analysis, multivariate mixed model analysis, IPD meta-analysis, and mixed model predictions. All examples are analyzed with STATA, and an extensive overview and comparison of alternative software packages is provided. All datasets used in the book are available for download, so readers can re-analyze the examples to gain a strong understanding of the methods. Although most examples are taken from epidemiological and clinical studies, this book is also highly recommended for researchers working in other fields.
Applied Mixed Models in Medicine (Statistics in Practice #28)
by Helen Brown Robin PrescottA fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott’s groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data. Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists. Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output. Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, and further material. This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.
Applied Multi-objective Optimization (Springer Tracts in Nature-Inspired Computing)
by Nilanjan DeyThe book explains basic ideas behind several kinds of applied multi-objective optimization and shows how it will be applied in practical contexts in the domain of healthcare, engineering design, and manufacturing. The book discusses how meta-heuristic algorithms are successful in resolving challenging, multi-objective optimization issues in various disciplines, including engineering, economics, medical and environmental management. The topic is useful for graduates, researchers and lecturers in optimization, engineering, management science and computer science.
Applied Multidimensional Scaling and Unfolding (SpringerBriefs In Statistics)
by Ingwer Borg Patrick J.F. Groenen Patrick MairProvides a concise, largely conceptual introduction to multidimensional scaling and unfolding.<p><p> Focuses on how to actually run and interpret MDS and unfolding in applied research (with examples from psychology, the social sciences, and market research).<p> Explains with several examples how to use the R-package smacof for MDS/unfolding and Proxscal in SPSS.<p> Includes numerous R-scripts that show how to run MDS and unfolding analyses (a file containing all scripts, and more, can be downloaded).<p>This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers. MDS is used for the analysis of proximity data on a set of objects, representing the data as distances between points in a geometric space (usually of two dimensions). Unfolding is a related method that maps preference data (typically evaluative ratings of different persons on a set of objects) as distances between two sets of points (representing the persons and the objects, resp.).<p> This second edition has been completely revised to reflect new developments and the coverage of unfolding has also been substantially expanded. Intended for applied researchers whose main interests are in using these methods as tools for building substantive theories, it discusses numerous applications (classical and recent), highlights practical issues (such as evaluating model fit), presents ways to enforce theoretical expectations for the scaling solutions, and addresses the typical mistakes that MDS/unfolding users tend to make. Further, it shows how MDS and unfolding can be used in practical research work, primarily by using the smacof package in the R environment but also Proxscal in SPSS. It is a valuable resource for psychologists, social scientists, and market researchers, with a basic understanding of multivariate statistics (such as multiple regression and factor analysis).
Applied Multidimensional Scaling of Asymmetric Relationships (Behaviormetrics: Quantitative Approaches to Human Behavior #19)
by Akinori Okada Tadashi ImaizumiThis is the first book that focusses the attention on applying asymmetric multidimensional scaling (MDS) and describes how to apply it in a practical manner. The book provides the know-how for prominent methods of asymmetric MDS: analysis of asymmetric relationships and the efficient use of programs of asymmetric MDS based on a distance model. These features will facilitate the work of researchers and enlighten students in disciplines such as statistics, psychology, sociology, education, marketing science, medicine, education, and other fields. Support is provided for practitioners in these areas who are, for instance, developing new products or services, investigating dominance relationships among brands or products, researching the use of antibiotics against resistance bacteria, and developing items for tests. This informative book demonstrates the analyses of data from diverse areas to show how asymmetric relationships are prevalent in real-world relationships and helps readers grasp the meanings and implications of those relationships. The results of the analyses illustrate the importance of analyzing asymmetric relationships that cannot be perceived by analysis using traditional methods, which ignore asymmetry in the data.
Applied Multilevel Analysis
by Jos W. R. TwiskThis is a practical introduction to multilevel analysis suitable for all those doing research. Most books on multilevel analysis are written by statisticians; those books are difficult for non-mathematical researchers. In contrast, this volume provides an accessible account on the practical application of multilevel analysis in research. Many worked examples, with computer output, are given to illustrate and explain this subject. Datasets of the examples are available on the internet, so the reader can reanalyze the data. This approach will help bridge the conceptual and communication gap that exists between researchers and statisticians.
Applied Multiple Imputation: Advantages, Pitfalls, New Developments and Applications in R (Statistics for Social and Behavioral Sciences)
by Jost Reinecke Kristian Kleinke Daniel Salfrán Martin SpiessThis book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice). The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master’s and PhD students with a sound basic knowledge of statistics.
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences Third Edition
by Jacob Cohen Patricia Cohen Stephen G. West Leona S. AikenA textbooks for behavioral scientists in statistics.
Applied Multiregional Demography: Migration and Population Redistribution (SpringerBriefs in Population Studies #0)
by Andrei RogersThis book shows the effectiveness of multiregional demography for studying the spatial dynamics of migration and population redistribution. It examines important questions in demographic analysis and shows how the techniques of multiregional analysis can lead to answers that sometimes contradict conventional wisdom. The book reconsiders conclusions reached in the literature regarding several fundamental common sense demographic questions in migration and population redistribution, including: Is it mostly migration or "aging-in-place" that has been driving Florida's elderly population growth? Do the elderly return "home" after retirement more than the non-elderly do? Does longer life lead to longer ill-health? Do simple population projection models outperform complex ones? For each demographic question it reconsiders, the book begins with a simple empirical numerical example and with it illustrates how a uniregional specification can bias findings to favor a particular, and possibly incorrect, conclusion. It then goes on to show how a multiregional analysis can better illuminate the dynamics that underlie the observed population totals and lead to a more informed conclusion. Offering insights into the effectiveness of multiregional demography, this book serves as a valuable resource for students and researchers searching for a better way to answer questions in demographic analysis and population dynamics.
Applied Multiregional Demography Through Problems: A Programmed Learning Workbook with Exercises and Solutions
by Andrei RogersWritten by the 2018 Mindel C. Sheps Award winner, this textbook offers a unique method for teaching how to model spatial (multiregional) population dynamics through models of increasing complexity. Each chapter in this programmed workbook starts with a descriptive text, followed by a sequence of exercises focused on particular multiregional models, of increasing complexity, and then ends with the solutions.It extends the current developments in the spatial analysis of social data towards improving our understanding of dynamics and interacting change across multiple populations in space. Frameworks for analyzing such dynamics were first proposed in multiregional demography, over 40 years ago. This book revisits these methods and then illustrates how they may be used to analyze spatial data and study spatial population dynamics.Topics covered include spatial population dynamics, population projections and estimations, spatial and age structure of migration flows and much more. As such this innovative textbook is a great teaching and learning tool for teachers, students as well as individuals who want to study demographic processes across space.
Applied Multivariate Analysis: Using Bayesian and Frequentist Methods of Inference, Second Edition (Dover Books on Mathematics)
by S. James PressGeared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications. Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations, with particular focus on multidimensional scaling and clustering.In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.