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Exploring Data Tables, Trends, and Shapes
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Exploring Data Tables, Trends, and Shapes (EDTTS) was written as a companion volume to the same editors' book, Understanding Robust and Exploratory Data Analysis (UREDA). Whereas UREDA is a collection of exploratory and resistant methods of estimation and display, EDTTS goes a step further, describing multivariate and more complicated techniques . . . I feel that the authors have made a very significant contribution in the area of multivariate nonparametric methods. This book [is] a valuable source of reference to researchers in the area." —Technometrics "This edited volume . . . provides an important theoretical and philosophical extension to the currently popular statistical area of Exploratory Data Analysis, which seeks to reveal structure, or simple descriptions, in data . . . It is . . . an important reference volume which any statistical library should consider seriously." —The Statistician This newly available and affordably priced paperback version of Exploring Data Tables, Trends, and Shapes presents major advances in exploratory data analysis and robust regression methods and explains the techniques, relating them to classical methods. The book addresses the role of exploratory and robust techniques in the overall data-analytic enterprise, and it also presents new methods such as fitting by organized comparisons using the square combining table and identifying extreme cells in a sizable contingency table with probabilistic and exploratory approaches. The book features a chapter on using robust regression in less technical language than available elsewhere. Conceptual support for each technique is also provided.
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Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA
An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA Complete reference for applied statisticians and data analysts that uniquely covers the new statistical methodologies that enable deeper data analysis An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA provides readers with powerful new statistical methodologies that enable deeper data analysis. The book offers applied statisticians an introduction to the latest topics in nonparametrics. The worked examples with supporting R code provide analysts the tools they need to apply these methods to their own problems. Co-authored by an internationally recognised expert in the field and an early career researcher with broad skills including data analysis and R programming, the book discusses key topics such as: NP ANOVA methodologyCochran-Mantel-Haenszel (CMH) methodology and designLatin squares and balanced incomplete block designsParametric ANOVA F tests for continuous dataNonparametric rank tests (the Kruskal-Wallis and Friedman tests)CMH MS tests for the nonparametric analysis of categorical response data Applied statisticians and data analysts, as well as students and professors in data analysis, can use this book to gain a complete understanding of the modern statistical methodologies that are allowing for deeper data analysis.
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Understanding Least Squares Estimation and Geomatics Data Analysis
Provides a modern approach to least squares estimation and data analysis for undergraduate land surveying and geomatics programs Rich in theory and concepts, this comprehensive book on least square estimation and data analysis provides examples that are designed to help students extend their knowledge to solving more practical problems. The sample problems are accompanied by suggested solutions, and are challenging, yet easy enough to manually work through using simple computing devices, and chapter objectives provide an overview of the material contained in each section. Understanding Least Squares Estimation and Geomatics Data Analysis begins with an explanation of survey observables, observations, and their stochastic properties. It reviews matrix structure and construction and explains the needs for adjustment. Next, it discusses analysis and error propagation of survey observations, including the application of heuristic rule for covariance propagation. Then, the important elements of statistical distributions commonly used in geomatics are discussed. Main topics of the book include: concepts of datum definitions; the formulation and linearization of parametric, conditional and general model equations involving typical geomatics observables; geomatics problems; least squares adjustments of parametric, conditional and general models; confidence region estimation; problems of network design and pre-analysis; three-dimensional geodetic network adjustment; nuisance parameter elimination and the sequential least squares adjustment; post-adjustment data analysis and reliability; the problems of datum; mathematical filtering and prediction; an introduction to least squares collocation and the kriging methods; and more. Contains ample concepts/theory and content, as well as practical and workable examplesBased on the author's manual, which he developed as a complete and comprehensive book for his Adjustment of Surveying Measurements and Special Topics in Adjustments coursesProvides geomatics undergraduates and geomatics professionals with required foundational knowledgeAn excellent companion to Precision Surveying: The Principles and Geomatics Practice Understanding Least Squares Estimation and Geomatics Data Analysis is recommended for undergraduates studying geomatics, and will benefit many readers from a variety of geomatics backgrounds, including practicing surveyors/engineers who are interested in least squares estimation and data analysis, geomatics researchers, and software developers for geomatics.
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Business Intelligence
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies.Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.
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Real Work of Data Science
The essential guide for data scientists and for leaders who must get more from their data science teams The Economist boldly claims that data are now "the world's most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is "the most valuable resource." "These two authors are world-class experts on analytics, data management, and data quality; they've forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it."—Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy "I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data."—Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University "Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers." —A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University
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Business Intelligence
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies.Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.
R 3,190.00
Machine Learning for Business Analytics
MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. readers will also find: Updated material which improves the book’s usefulness as a reference for professionals beyond the classroomFour new chapters, covering topics including Text Mining and Responsible Data ScienceAn updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbookA guide to JMP Pro®’s new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.
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First Course Mathematical Statistics
This book provides the mathematical foundations of statistics. Its aim is to explain the principles, to prove the formulae to give validity to the methods employed in the interpretation of statistical data. Many examples are included but, since the primary emphasis is on the underlying theory, it is of interest to students of a wide variety of subjects: biology, psychology, agriculture, economics, physics, chemistry, and (of course) mathematics.
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Structured Credit Portfolio Analysis, Baskets and CDOs
The financial industry is swamped by credit products whose economic performance is linked to the performance of some underlying portfolio of credit-risky instruments, like loans, bonds, swaps, or asset-backed securities. Financial institutions continuously use these products for tailor-made long and short positions in credit risks. Based on a steadily growing market, there is a high demand for concepts and techniques applicable to the evaluation of structured credit products.Written from the perspective of practitioners who apply mathematical concepts to structured credit products, Structured Credit Portfolio Analysis, Baskets & CDOs starts with a brief wrap-up on basic concepts of credit risk modeling and then quickly moves on to more advanced topics such as the modeling and evaluation of basket products, credit-linked notes referenced to credit portfolios, collateralized debt obligations, and index tranches. The text is written in a self-contained style so readers with a basic understanding of probability will have no difficulties following it. In addition, many examples and calculations have been included to keep the discussion close to business applications. Practitioners as well as academics will find ideas and tools in the book that they can use for their daily work.
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Selected Works of Norman Levinson
The deep and original ideas of Norman Levinson have had a lasting impact on fields as diverse as differential & integral equations, harmonic, complex & stochas tic analysis, and analytic number theory during more than half a century. Yet, the extent of his contributions has not always been fully recognized in the mathematics community. For example, the horseshoe mapping constructed by Stephen Smale in 1960 played a central role in the development of the modern theory of dynami cal systems and chaos. The horseshoe map was directly stimulated by Levinson's research on forced periodic oscillations of the Van der Pol oscillator, and specifi cally by his seminal work initiated by Cartwright and Littlewood. In other topics, Levinson provided the foundation for a rigorous theory of singularly perturbed dif ferential equations. He also made fundamental contributions to inverse scattering theory by showing the connection between scattering data and spectral data, thus relating the famous Gel'fand-Levitan method to the inverse scattering problem for the Schrodinger equation. He was the first to analyze and make explicit use of wave functions, now widely known as the Jost functions. Near the end of his life, Levinson returned to research in analytic number theory and made profound progress on the resolution of the Riemann Hypothesis. Levinson's papers are typically tightly crafted and masterpieces of brevity and clarity. It is our hope that the publication of these selected papers will bring his mathematical ideas to the attention of the larger mathematical community.
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Questions of Uniqueness and Resolution in Reconstruction from Projections
Reconstruction from projections has revolutionized radiology and has now become one of the most important tools of medical diagnosis The E. M. I. Scanner is one example. In this text, some fundamental theoretical and practical questions are resolved. Despite recent research activity in the area, the crucial subject of the uniqueness of the reconstruction and the effect of noise in the data posed some unsettled fundamental questions. In particular, Kennan Smith proved that if we describe an object by a C^info function, i.e., infinitely differentiable with compact support, then there are other objects with the same shape, i.e., support, which can differ almost arbitrarily and still have the same projections in finitely many directions. On the other hand, he proved that objects in finite dimensional function spaces are uniquely determined by a single projection for almost all angles, i.e., except on a set of measure zero. Along these lines, Herman and Rowland in "Three Methods for reconstructing objects from x-rays: a comparative study" (1973) showed that reconstructions obtained from the commonly used algorithms can grossly misrepresent the object and that the algorithm which produced the best reconstruction when using noiseless data gave unsatisfactory results with noisy data. Equally important are reports in Science, and personal communications by radiologists indicating that in medical practice failure rates of reconstruction vary from four to twenty percent. within this work, the mathematical dilemma posed by Kennan Smith's result is discussed and clarified.
R 1,606.82
Competition for Space and the Structure of Ecological Communities
This volume is an investigation of interspecific competition for space, particularly among sessile organisms, both plant and animal, and its consequences for community structure. While my own contribu tion ----and the bulk of this volume --- lies in mathematical analysis of the phenomenon, I have also tried to summarize the most important natural historical aspects of these communities, and have devoted much effort to relating the mathematical results to observations of the natural world. Thus, the volume has both a synthetic and an analytic aspect. On the one hand, I have been struck by certain similarities among many communities, from forests to mussel beds, in which spatial com petition is important. On the other hand, I have analyzed this pheno menon by means of reaction-dispersal models. Finally, the mathematical analysis has suggested a conceptual framework for these communities which, I believe, further unifies and illuminates the field data. A focal perception of this work is that, just as niche relations provide an appropriate expression of the influence of resource compe tition on community structure, so do dominance relations provide an appropriate expression of the influence of spatial competition.
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