Principles Of Multivariate Analysis

Author: W. J. Krzanowski
Publisher: Oxford University Press
ISBN: 0198507089
Size: 41.99 MB
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Principles Of Multivariate Analysis from the Author: W. J. Krzanowski. "Overall this volume provides an up-to-date and readable account of the subject, both for students of statistics and for research workers in subjects as diverse as anthropology, education, industry, medicine, and taxonomy."--BOOK JACKET.

Principles Of Multivariate Analysis

Author: W. J. Krzanowski
Publisher: Oxford University Press
ISBN: 9780198522300
Size: 39.90 MB
Format: PDF
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Principles Of Multivariate Analysis from the Author: W. J. Krzanowski. This book is an introduction to the principles and methodology of modern multivariate statistical analysis. It is written for the user and potential user of multivariate techniques as well as for postgraduate students coming to the subject for the first time. The author's emphasis is problem-oriented; he stresses geometrical intuition in preference to algebraic manipulation. Mathematical sections which are not essential for a practical understanding of technique are clearly indicated so they may be skipped by nonspecialists. The book covers recent developments concerning discrete and mixed variable techniques, as well as continuous variable techniques and other new ideas. This is an up-to-date and very readable account--with a practical emphasis--for research workers in subjects as diverse as anthropology, education, industry, medicine, and taxonomy.

Principles Of Plant Genetics And Breeding

Author: George Acquaah
Publisher: John Wiley & Sons
ISBN: 1444309013
Size: 36.78 MB
Format: PDF
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Principles Of Plant Genetics And Breeding from the Author: George Acquaah. Until recently, plant breeders have depended primarily on classical tools to develop new and improved products for producers and consumers. However, with the advent of biotechnology, breeders are increasingly incorporating molecular tools in their breeding work. In recognition of the current state of methods and their application, this text introduces both classical and molecular tools for plant breeding. Topics such as biotechnology in plant breeding, intellectual property, risks, emerging concepts (decentralized breeding, organic breeding), and more are addressed in this state of the art text. The final 8 chapters provide a useful reference on breeding the largest and most common crops. In addition, over 25 plant breeders share their professional experiences while illustrating concepts in the text. Features include: Comprehensive presentation of both classical and molecular plant breeding tools Industry highlight essays from over 25 professional plant breeders Chapter introductions, summaries and discussion questions Easy reference glossary Reference chapters on breeding 8 of the largest and most common crops Artwork from the book is available to instructors online at An Instructor manual CD-ROM for this title is available. Please contact our Higher Education team at [email protected] for more information.

Exploratory Multivariate Analysis By Example Using R Second Edition

Author: Francois Husson
Publisher: CRC Press
ISBN: 1315301865
Size: 73.22 MB
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Exploratory Multivariate Analysis By Example Using R Second Edition from the Author: Francois Husson. Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors. The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.

Multi And Megavariate Data Analysis Basic Principles And Applications

Author: L. Eriksson
Publisher: Umetrics Academy
ISBN: 9197373052
Size: 61.36 MB
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Multi And Megavariate Data Analysis Basic Principles And Applications from the Author: L. Eriksson. To understand the world around us, as well as ourselves, we need to measure many things, many variables, many properties of the systems and processes we investigate. Hence, data collected in science, technology, and almost everywhere else are multivariate, a data table with multiple variables measured on multiple observations (cases, samples, items, process time points, experiments). This book describes a remarkably simple minimalistic and practical approach to the analysis of data tables (multivariate data). The approach is based on projection methods, which are PCA (principal components analysis), and PLS (projection to latent structures) and the book shows how this works in science and technology for a wide variety of applications. In particular, it is shown how the great information content in well collected multivariate data can be expressed in terms of simple but illuminating plots, facilitating the understanding and interpretation of the data. The projection approach applies to a variety of data-analytical objectives, i.e., (i) summarizing and visualizing a data set, (ii) multivariate classification and discriminant analysis, and (iii) finding quantitative relationships among the variables. This works with any shape of data table, with many or few variables (columns), many or few observations (rows), and complete or incomplete data tables (missing data). In particular, projections handle data matrices with more variables than observations very well, and the data can be noisy and highly collinear. Authors: The five authors are all connected to the Umetrics company ( which has developed and sold software for multivariate analysis since 1987, as well as supports customers with training and consultations. Umetrics' customers include most large and medium sized companies in the pharmaceutical, biopharm, chemical, and semiconductor sectors.

Nonlinear Multivariate Analysis

Author: Albert Gifi
Publisher: John Wiley & Sons Incorporated
ISBN: 9780471926207
Size: 26.67 MB
Format: PDF, Kindle
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Nonlinear Multivariate Analysis from the Author: Albert Gifi. Conventions and controversies in multivariate analysis; Coding of categorical data; Homogeneity analysis; Nonlinear principal components analysis; Nonlinear generalized canonical analysis; Nonlinear canonical correlation analysis; Asymmetric treatment of sets: some special cases, some future programs; Multidimensional scaling and correspondende analysis; Models as gauges for the analysis of binary data; Reflections on restrictions; Nonlinear multivariate analysis: principles and possibilities; The study of stability; The proof of the pudding.

Marker Assisted Plant Breeding Principles And Practices

Author: B.D. Singh
Publisher: Springer
ISBN: 8132223160
Size: 40.93 MB
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Marker Assisted Plant Breeding Principles And Practices from the Author: B.D. Singh. Marker-assisted plant breeding involves the application of molecular marker techniques and statistical and bioinformatics tools to achieve plant breeding objectives in a cost-effective and time-efficient manner. This book is intended for beginners in the field who have little or no prior exposure to molecular markers and their applications, but who do have a basic knowledge of genetics and plant breeding, and some exposure to molecular biology. An attempt has been made to provide sufficient basic information in an easy-to-follow format, and also to discuss current issues and developments so as to offer comprehensive coverage of the subject matter. The book will also be useful for breeders and research workers, as it offers a broad range of up-to-the-year information, including aspects like the development of different molecular markers and their various applications. In the first chapter, the field of marker-assisted plant breeding is introduced and placed in the proper perspective in relation to plant breeding. The next three chapters describe the various molecular marker systems, while mapping populations and mapping procedures including high-throughput genotyping are discussed in the subsequent five chapters. Four chapters are devoted to various applications of markers, e.g. marker-assisted selection, genomic selection, diversity analysis, finger printing and positional cloning. In closing, the last two chapters provide information on relevant bioinformatics tools and the rapidly evolving field of phenomics.

Multivariate Analysis

Author: William R. Dillon
Publisher: John Wiley & Sons Inc
ISBN: 9780471083177
Size: 25.37 MB
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Multivariate Analysis from the Author: William R. Dillon. Structural Sensitivity in Econometric Models Edwin Kuh, John W. Neese and Peter Hollinger Provides a pathbreaking assessment of the worth of linear dynamic systems methods for probing the behavior of complex macroeconomic models. Representing a major improvement upon the standard "black box" approach to analyzing economic model structure, it introduces the powerful concept of parameter sensitivity analysis within a linear systems root/vector framework. The approach is illustrated with a good mediumsize econometric model (Michigan Quarterly Econometric Model of the United States). EISPACK, the Fortran code for computing characteristic roots and vectors has been upgraded and augmented by a model linearization code and a broader algorithmic framework. Also features an interface between the algorithmic code and the interactive modeling system (TROLL), making an unusually wide range of linear systems methods accessible to economists, operations researchers, engineers and physical scientists. 1985 (0-471-81930-1) 324 pp. Linear Statistical Models and Related Methods With Applications to Social Research John Fox A comprehensive, modern treatment of linear models and their variants and extensions, combining statistical theory with applied data analysis. Considers important methodological principles underlying statistical methods. Designed for researchers and students who wish to apply these models to their own work in a flexible manner. 1984 (0 471-09913-9) 496 pp. Statistical Methods for Forecasting Bovas Abraham and Johannes Ledolter This practical, user-oriented book treats the statistical methods and models used to produce short-term forecasts. Provides an intermediate level discussion of a variety of statistical forecasting methods and models and explains their interconnections, linking theory and practice. Includes numerous time-series, autocorrelations, and partial autocorrelation plots. 1983 (0 471-86764-0) 445 pp.

Applied Multivariate Analysis

Author: Neil H. Timm
Publisher: Springer Science & Business Media
ISBN: 0387227717
Size: 16.58 MB
Format: PDF, ePub, Docs
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Applied Multivariate Analysis from the Author: Neil H. Timm. This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.