Principles Of Multivariate Analysis
DOWNLOAD
Download Principles Of Multivariate Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Principles Of Multivariate Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Principles Of Multivariate Analysis
DOWNLOAD
Author : W. J. Krzanowski
language : en
Publisher: Oxford University Press
Release Date : 2000-09-28
Principles Of Multivariate Analysis written by W. J. Krzanowski and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-09-28 with Mathematics categories.
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.
Multivariate Analysis
DOWNLOAD
Author : W. J. Krzanowski
language : en
Publisher: Wiley-Blackwell
Release Date : 1994
Multivariate Analysis written by W. J. Krzanowski and has been published by Wiley-Blackwell this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Mathematics categories.
The second of a two-part work on multivariate analysis, this volume covers more specialized techniques; topics include discriminant analysis, cluster analysis, path analysis, graphical modelling, latent variable techniques, repeated measures analysis and growth curve models. Modern problems and techniques, such as handling of high dimensional data and the use of neural networks are featured and the book concludes with a discussion of strategic aspects of multivariate analysis.
Principles Of Multivariate Analysis
DOWNLOAD
Author : W. J. Krzanowski
language : en
Publisher:
Release Date : 2023
Principles Of Multivariate Analysis written by W. J. Krzanowski and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Mutivariate analysis categories.
This work offers an introduction to the principles and methodology of modern multivariate statistical analysis. Written for the user of multivariate techniques and for students coming to the subject, the author's emphasis is problem-orientated.
A First Course In Multivariate Statistics
DOWNLOAD
Author : Bernhard Flury
language : en
Publisher: Springer Science & Business Media
Release Date : 1997-08-15
A First Course In Multivariate Statistics written by Bernhard Flury and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-08-15 with Mathematics categories.
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.
Multi And Megavariate Data Analysis Basic Principles And Applications
DOWNLOAD
Author : L. Eriksson
language : en
Publisher: Umetrics Academy
Release Date : 2013-07-01
Multi And Megavariate Data Analysis Basic Principles And Applications written by L. Eriksson and has been published by Umetrics Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-01 with Mathematics categories.
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.
Applied Multivariate Analysis
DOWNLOAD
Author : Neil H. Timm
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-21
Applied Multivariate Analysis written by Neil H. Timm and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-21 with Mathematics categories.
Univariate statistical analysis is concerned with techniques for the analysis of a single random variable. This book is about applied multivariate analysis. It was written to p- vide students and researchers with an introduction to statistical techniques for the ana- sis of continuous quantitative measurements on several random variables simultaneously. While quantitative measurements may be obtained from any population, the material in this text is primarily concerned with techniques useful for the analysis of continuous obser- tions from multivariate normal populations with linear structure. While several multivariate methods are extensions of univariate procedures, a unique feature of multivariate data an- ysis techniques is their ability to control experimental error at an exact nominal level and to provide information on the covariance structure of the data. These features tend to enhance statistical inference, making multivariate data analysis superior to univariate analysis. While in a previous edition of my textbook on multivariate analysis, I tried to precede a multivariate method with a corresponding univariate procedure when applicable, I have not taken this approach here. Instead, it is assumed that the reader has taken basic courses in multiple linear regression, analysis of variance, and experimental design. While students may be familiar with vector spaces and matrices, important results essential to multivariate analysis are reviewed in Chapter 2. I have avoided the use of calculus in this text.
The Chicago Guide To Writing About Multivariate Analysis
DOWNLOAD
Author : Jane E. Miller
language : en
Publisher: University of Chicago Press
Release Date : 2008-04-15
The Chicago Guide To Writing About Multivariate Analysis written by Jane E. Miller and has been published by University of Chicago Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-15 with Reference categories.
Writing about multivariate analysis is a surprisingly common task. Researchers use these advanced statistical techniques to examine relationships among multiple variables, such as exercise, diet, and heart disease, or to forecast information such as future interest rates or unemployment. Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. At the same time, many researchers have trouble communicating the purpose and findings of these models. Too often, explanations become bogged down in statistical jargon and technical details, and audiences are left struggling to make sense of both the numbers and their interpretation. Here, Jane Miller offers much-needed help to academic researchers as well as to analysts who write for general audiences. The Chicago Guide to Writing about Multivariate Analysis brings together advanced statistical methods with good expository writing. Starting with twelve core principles for writing about numbers, Miller goes on to discuss how to use tables, charts, examples, and analogies to write a clear, compelling argument using multivariate results as evidence. Writers will repeatedly look to this book for guidance on how to express their ideas in scientific papers, grant proposals, speeches, issue briefs, chartbooks, posters, and other documents. Communicating with multivariate models need never appear so complicated again.
Multi And Megavariate Data Analysis
DOWNLOAD
Author : L. Eriksson
language : en
Publisher:
Release Date : 2001
Multi And Megavariate Data Analysis written by L. Eriksson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Multivariate analysis categories.
Making Sense Of Multivariate Data Analysis
DOWNLOAD
Author : John Spicer
language : en
Publisher: SAGE
Release Date : 2005
Making Sense Of Multivariate Data Analysis written by John Spicer and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Mathematics categories.
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.
Exploratory Multivariate Analysis By Example Using R
DOWNLOAD
Author : Francois Husson
language : en
Publisher: CRC Press
Release Date : 2020-09-30
Exploratory Multivariate Analysis By Example Using R written by Francois Husson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-30 with categories.
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.