Principles Of Multivariate Analysis

Author: W. J. Krzanowski
Publisher: Oxford University Press
ISBN: 0198507089
Size: 28.41 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: 23.86 MB
Format: PDF, Docs
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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.

The Chicago Guide To Writing About Multivariate Analysis Second Edition

Author: Jane E. Miller
Publisher: University of Chicago Press
ISBN: 022603819X
Size: 80.55 MB
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The Chicago Guide To Writing About Multivariate Analysis Second Edition from the Author: Jane E. Miller. Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. Researchers use these advanced statistical techniques to analyze relationships among multiple variables, such as how exercise and weight relate to the risk of heart disease, or how unemployment and interest rates affect economic growth. Yet, despite the widespread need to plainly and effectively explain the results of multivariate analyses to varied audiences, few are properly taught this critical skill. The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results. For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses. This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.

Multivariate Analysis

Author: William R. Dillon
Publisher: John Wiley & Sons Inc
ISBN: 9780471083177
Size: 49.47 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.

Multi And Megavariate Data Analysis Basic Principles And Applications

Author: L. Eriksson
Publisher: Umetrics Academy
ISBN: 9197373052
Size: 44.38 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 (www.umetrics.com) 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.

Exploratory Multivariate Analysis By Example Using R Second Edition

Author: Francois Husson
Publisher: CRC Press
ISBN: 1315301865
Size: 11.46 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.

Nonlinear Multivariate Analysis

Author: Albert Gifi
Publisher: John Wiley & Sons Incorporated
ISBN: 9780471926207
Size: 40.37 MB
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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.

Multivariate Statistical Methods In Behavioral Research

Author: Richard Darrell Bock
Publisher: McGraw-Hill Companies
ISBN:
Size: 25.26 MB
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Multivariate Statistical Methods In Behavioral Research from the Author: Richard Darrell Bock. The role of multivariate statistical methods in behavioral research; Mathematical prerequisites for multivariate analysis; The multivariate normal distribution; Principles and methods of multivariate least-squares estimation; Linear models for designed experiments; Linear models in nonexperimental studies; Analysis of repeated measurements; Multivariate analysis of qualitative data.

Multivariate Analysis In Community Ecology

Author: Hugh G. Gauch
Publisher: Cambridge University Press
ISBN: 9780521282406
Size: 52.70 MB
Format: PDF, Mobi
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Multivariate Analysis In Community Ecology from the Author: Hugh G. Gauch. A full description of computer-based methods of analysis used to define and solve ecological problems. Multivariate techniques permit summary of complex sets of data and allow investigation of many problems which cannot be tackled experimentally because of practical restraints.