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Author: Wojtek Krzanowski Publisher: OUP Oxford ISBN: 0191053945 Category : Mathematics Languages : en Pages : 609
Book Description
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 students coming to the subject for the first time. The author's emphasis is problem-orientated and he is at pains to stress geometrical intuition in preference to algebraic manipulation. Mathematical sections that are not essential for a practical understanding of the techniques are clearly indicated so that they may be skipped by the non-specialist. Discrete and mixed variable techniques are presented as well as continuous variable techniques to give a comprehensive coverage of the subject. This updated edition includes a new appendix which traces developments that have taken place in the years since the publication of the first edition and which clarifies some issues raised by readers of the original text. References to about 60 recent books and articles supplement the material in this appendix. Overall, this volume provides an up-to-date and readable practical account of the subject, both for students of statistics and for research workers in subjects as diverse as anthropology, education, industry, medicine and taxonomy. The new edition includes a survey of the most recent developments in the subject.
Author: Wojtek Krzanowski Publisher: OUP Oxford ISBN: 0191053945 Category : Mathematics Languages : en Pages : 609
Book Description
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 students coming to the subject for the first time. The author's emphasis is problem-orientated and he is at pains to stress geometrical intuition in preference to algebraic manipulation. Mathematical sections that are not essential for a practical understanding of the techniques are clearly indicated so that they may be skipped by the non-specialist. Discrete and mixed variable techniques are presented as well as continuous variable techniques to give a comprehensive coverage of the subject. This updated edition includes a new appendix which traces developments that have taken place in the years since the publication of the first edition and which clarifies some issues raised by readers of the original text. References to about 60 recent books and articles supplement the material in this appendix. Overall, this volume provides an up-to-date and readable practical account of the subject, both for students of statistics and for research workers in subjects as diverse as anthropology, education, industry, medicine and taxonomy. The new edition includes a survey of the most recent developments in the subject.
Author: W. J. Krzanowski Publisher: Oxford University Press ISBN: 9780198522300 Category : Science Languages : en Pages : 563
Book Description
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.
Author: L. Eriksson Publisher: Umetrics Academy ISBN: 9197373052 Category : Mathematics Languages : en Pages : 509
Book Description
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.
Author: Jane E. Miller Publisher: University of Chicago Press ISBN: 022603819X Category : Mathematics Languages : en Pages : 576
Book Description
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.
Author: Francois Husson Publisher: CRC Press ISBN: 1315301865 Category : Mathematics Languages : en Pages : 263
Book Description
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) a
Author: Bernard Flury Publisher: Springer Science & Business Media ISBN: 1475727658 Category : Mathematics Languages : en Pages : 723
Book Description
A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate distributions from first principles. After a chapter on the multivariate normal distribution reviewing the classical parametric theory, methods of estimation are explored using the plug-in principles as well as maximum likelihood. Two chapters on discrimination and classification, including logistic regression, form the core of the book, followed by methods of testing hypotheses developed from heuristic principles, likelihood ratio tests and permutation tests. Finally, the powerful self-consistency principle is used to introduce principal components as a method of approximation, rounded off by a chapter on finite mixture analysis.
Author: W. J. Krzanowski Publisher: Oxford University Press ISBN: 0198507089 Category : Mathematics Languages : en Pages : 609
Book Description
Multivariate analysis is necessary whenever more than one characteristic is observed on each individual under study. Applications arise in very many areas of study. This book provides a comprehensive introduction to available techniques for analysing date of this form, written in a style that should appeal to non-specialists as well as to statisticians. In particular, geometric intuition is emphasized in preference to algebraic manipulation wherever possible. The new edition includes a survey of the most recent developments in the subject.
Author: Brian Everitt Publisher: Springer Science & Business Media ISBN: 1441996508 Category : Mathematics Languages : en Pages : 284
Book Description
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Author: John Spicer Publisher: SAGE ISBN: 9781412904018 Category : Mathematics Languages : en Pages : 256
Book Description
A short introduction to the subject, this text is aimed at students & practitioners in the behavioural & social sciences. It offers a conceptual overview of the foundations of MDA & of a range of specific techniques including multiple regression, logistic regression & log-linear analysis.
Author: Wojtek Krzanowski
Author: Wojtek Krzanowski
Author: W. J. Krzanowski
Author: L. Eriksson
Author: Jane E. Miller
Author: Francois Husson
Author: Bernard Flury
Author: W. J. Krzanowski
Author: 
Author: Brian Everitt
Author: John Spicer