Principal Components Analysis

Author: George H. Dunteman
Editor: SAGE
ISBN: 9780803931046
File Size: 33,70 MB
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For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use.

Principal Component Analysis

Author: I.T. Jolliffe
Editor: Springer Science & Business Media
ISBN: 1475719043
File Size: 51,68 MB
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Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.

A User S Guide To Principal Components

Author: J. Edward Jackson
Editor: John Wiley & Sons
ISBN: 0471725323
File Size: 74,15 MB
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WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selectedbooks that have been made more accessible to consumers in an effortto increase global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. From the Reviews of A User’s Guide to PrincipalComponents "The book is aptly and correctly named–A User’sGuide. It is the kind of book that a user at any level, novice orskilled practitioner, would want to have at hand for autotutorial,for refresher, or as a general-purpose guide through the maze ofmodern PCA." –Technometrics "I recommend A User’s Guide to Principal Components toanyone who is running multivariate analyses, or who contemplatesperforming such analyses. Those who write their own software willfind the book helpful in designing better programs. Those who useoff-the-shelf software will find it invaluable in interpreting theresults." –Mathematical Geology

Comparing Maximum Likelihood Ordination With Principal Components Analysis And Correspondence Analysis For Equicorrelated Data

Author: Henry Sui Heng Lynn
Editor:
ISBN:
File Size: 67,55 MB
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Practical Guide To Principal Component Methods In R

Author: Alboukadel KASSAMBARA
Editor: STHDA
ISBN: 1975721136
File Size: 64,88 MB
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Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.

Principal Components Analysis On Glacier Climatological Data For Sentinel Glacier British Columbia

Author: Stephen Fogarasi
Editor: [ottawa] : minister of supply and services
ISBN:
File Size: 72,71 MB
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"Glacier runoff, an important contributor to water balance, depends heavily on the interaction of various climatological variables influencing glacier melt. Linear regression techniques frequently used in runoff estimations do not usually reveal the intricate association between runoff and some of the weather-related variables"--Introduction, page 1.

Response Of Fish Communities To Cropland Density And Natural Environmental Setting In The Eastern Highland Rim Ecoregion Of The Lower Tennessee River Basin Alabama And Tennessee 1999

Author: Jeffrey R. Powell
Editor:
ISBN:
File Size: 24,32 MB
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Environmental Gradient Analysis Ordination And Classification In Environmental Impact Assessments

Author: Anthony J. Krzysik
Editor:
ISBN:
File Size: 16,49 MB
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This report develops the theoretical foundation for analytical description and quantification of habitat structure. The analytical description of environmental gradients is shown to be an eigenanalysis problem, mathematically equivalent to the largest eigenvector (or first principal component) of a principal components analysis. The analytical representation of an environmental gradient, itself a single variable, is empirically demonstrated to have similar ecological information as the combination of all the original 58 habitat variables describing five Mojave Desert study sites. Two vastly different data bases were analyzed to explore the effects of sample sizes and variable selection on the ordination of study sites in both principal components and canonical variate space. Merits and shortcomings of principal components analysis, canonical analysis of discriminance, and cluster analysis for the ordination and classification of samples are reviewed in detail. Canonical analysis of discriminance is a very effective mechanism for classifying samples into a priori established groups, or for identifying variables that contribute significantly to group discrimination.

Report Of Investigations

Author:
Editor:
ISBN:
File Size: 64,86 MB
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Geochemistry Of Some Petroleum Associated Waters From Louisiana

Author: A. Gene Collins
Editor:
ISBN:
File Size: 14,91 MB
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Applied Multivariate Statistical Analysis

Author: Wolfgang Karl Härdle
Editor: Springer Science & Business Media
ISBN: 3540722440
File Size: 60,23 MB
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With a wealth of examples and exercises, this is a brand new edition of a classic work on multivariate data analysis. A key advantage of the work is its accessibility. This is because, in its focus on applications, the book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. In this second edition a wider scope of methods and applications of multivariate statistical analysis is introduced. All quantlets have been translated into the R and Matlab language and are made available online.

Data Analysis And Decision Support

Author: Daniel Baier
Editor: Springer Science & Business Media
ISBN: 9783540260073
File Size: 26,40 MB
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It is a great privilege and pleasure to write a foreword for a book honor ing Wolfgang Gaul on the occasion of his sixtieth birthday. Wolfgang Gaul is currently Professor of Business Administration and Management Science and the Head of the Institute of Decision Theory and Management Science, Faculty of Economics, University of Karlsruhe (TH), Germany. He is, by any measure, one of the most distinguished and eminent scholars in the world today. Wolfgang Gaul has been instrumental in numerous leading research initia tives and has achieved an unprecedented level of success in facilitating com munication among researchers in diverse disciplines from around the world. A particularly remarkable and unique aspect of his work is that he has been a leading scholar in such diverse areas of research as graph theory and net work models, reliability theory, stochastic optimization, operations research, probability theory, sampling theory, cluster analysis, scaling and multivariate data analysis. His activities have been directed not only at these and other theoretical topics, but also at applications of statistical and mathematical tools to a multitude of important problems in computer science (e.g., w- mining), business research (e.g., market segmentation), management science (e.g., decision support systems) and behavioral sciences (e.g., preference mea surement and data mining). All of his endeavors have been accomplished at the highest level of professional excellence.

An Easy Guide To Factor Analysis

Author: Paul Kline
Editor: Routledge
ISBN: 1317725603
File Size: 11,98 MB
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Factor analysis is a statistical technique widely used in psychology and the social sciences. With the advent of powerful computers, factor analysis and other multivariate methods are now available to many more people. An Easy Guide to Factor Analysis presents and explains factor analysis as clearly and simply as possible. The author, Paul Kline, carefully defines all statistical terms and demonstrates step-by-step how to work out a simple example of principal components analysis and rotation. He further explains other methods of factor analysis, including confirmatory and path analysis, and concludes with a discussion of the use of the technique with various examples. An Easy Guide to Factor Analysis is the clearest, most comprehensible introduction to factor analysis for students. All those who need to use statistics in psychology and the social sciences will find it invaluable. Paul Kline is Professor of Psychometrics at the University of Exeter. He has been using and teaching factor analysis for thirty years. His previous books include Intelligence: the psychometric view (Routledge 1990) and The Handbook of Psychological Testing (Routledge 1992).

Multivariate Statistical Methods

Author: Bryan F.J. Manly
Editor: CRC Press
ISBN: 9781584884149
File Size: 22,84 MB
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Multivariate methods are now widely used in the quantitative sciences as well as in statistics because of the ready availability of computer packages for performing the calculations. While access to suitable computer software is essential to using multivariate methods, using the software still requires a working knowledge of these methods and how they can be used. Multivariate Statistical Methods: A Primer, Third Edition introduces these methods and provides a general overview of the techniques without overwhelming you with comprehensive details. This thoroughly revised, updated edition of a best-selling introductory text retains the author's trademark clear, concise style but includes a range of new material, new exercises, and supporting materials on the Web. New in the Third Edition: Fully updated references Additional examples and exercises from the social and environmental sciences A comparison of the various statistical software packages, including Stata, Statistica, SAS Minitab, and Genstat, particularly in terms of their ease of use by beginners In his efforts to produce a book that is as short as possible and that enables you to begin to use multivariate methods in an intelligent manner, the author has produced a succinct and handy reference. With updated information on multivariate analyses, new examples using the latest software, and updated references, this book provides a timely introduction to useful tools for statistical analysis.

Functional Data Analysis

Author: James Ramsay
Editor: Springer Science & Business Media
ISBN: 147577107X
File Size: 38,63 MB
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Included here are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drawn from growth analysis, meteorology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, applied data analysts, and to experienced researchers; and as such is of value both within statistics and across a broad spectrum of other fields. Much of the material appears here for the first time.

Event Related Potentials

Author: Todd C. Handy
Editor: MIT Press
ISBN: 9780262083331
File Size: 26,28 MB
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The first comprehensive handbook to detail ERP methodology, covering experimental design, data analysis, and special applications. The study of event-related potentials (ERPs)--signal-averaged EEG recordings that are time-locked to perceptual, cognitive, and motor events--has increased dramatically in recent years, but until now there has been no comprehensive guide to ERP methodology comparable to those available for fMRI techniques. Event-Related Potentials meets the need for a practical and concise handbook of ERP methods that is suitable for both the novice user of an ERP system and a researcher more experienced in cognitive electrophysiology. The chapters in the first section discuss the design of ERP experiments, providing a practical foundation for understanding the design of ERP experiments and interpreting ERP data. Topics covered include quantification of ERP data and theoretical and practical aspects of ANOVAs as applied to ERP datasets. The second section presents a variety of approaches to ERP data analysis and includes chapters on digital filtering, artifact removal, source localization, and wavelet analysis. The chapters in the final section of the book cover the use of ERPs in relation to such specific participant populations as children and neuropsychological patients and the ways in which ERPs can be combined with related methodologies, including intracranial ERPs and hemodynamic imaging.

Methods Of Multivariate Analysis

Author: Alvin C. Rencher
Editor: John Wiley & Sons
ISBN: 0471461725
File Size: 26,52 MB
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Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. Methods ofMultivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit,it is often necessary to analyze the variables simultaneously,rather than isolate them and consider them individually.Multivariate analysis enables researchers to explore the jointperformance of such variables and to determine the effect of eachvariable in the presence of the others. The Second Edition of AlvinRencher's Methods of Multivariate Analysis provides studentsof all statistical backgrounds with both the fundamental and moresophisticated skills necessary to master the discipline. To illustrate multivariate applications, the author providesexamples and exercises based on fifty-nine real data sets from awide variety of scientific fields. Rencher takes a "methods"approach to his subject, with an emphasis on how students andpractitioners can employ multivariate analysis in real-lifesituations. The Second Edition contains revised and updatedchapters from the critically acclaimed First Edition as well asbrand-new chapters on: Cluster analysis Multidimensional scaling Correspondence analysis Biplots Each chapter contains exercises, with corresponding answers andhints in the appendix, providing students the opportunity to testand extend their understanding of the subject. Methods ofMultivariate Analysis provides an authoritative reference forstatistics students as well as for practicing scientists andclinicians.

Performing Data Analysis Using Ibm Spss

Author: Lawrence S. Meyers
Editor: John Wiley & Sons
ISBN: 1118363574
File Size: 33,37 MB
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Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS® Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical output Descriptive statistics procedures Score distribution assumption evaluations Bivariate correlation Regressing (predicting) quantitative and categorical variables Survival analysis t Test ANOVA and ANCOVA Multivariate group differences Multidimensional scaling Cluster analysis Nonparametric procedures for frequency data Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.

Environment Planning

Author:
Editor:
ISBN:
File Size: 32,26 MB
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Multivariate Analysis In The Human Services

Author: J.R. Schuerman
Editor: Springer Science & Business Media
ISBN: 9780898381054
File Size: 47,98 MB
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Research and evaluation in the human services usually involves a relatively large number of variables. We are interested in phenomena that have many aspects and many causes. The techniques needed to deal with many variables go beyond those of introductory statistics. Elementary procedures in statistics are limited in usefulness to situations in which we have two or three variables. When we have more than that, application of elementary techniques will often yield mis leading results. Why are elementary techniques inadequate when applied to many variables? Why, for example, should we not simply interpret a series of correlations of independent and dependent variables? The answer lies in the fact that these correlations are not independent pieces of information. The correlations of vari ables x and z with yare affected by the association of x with z. Hence, talk about the "effect" of x on y will be somewhat ambiguous, since we will be in cluding in that effect some of the effects of z. We would like to be able to sort out these effects. This is the problem of "estimation," that is, estimating the relationships or effects between variables, taking into account their relationships with other variables.