Multivariate and Megavariate Data Analysis Basic Principles and Applications by L. Eriksson (2006-01-01)

L. Eriksson; E. Johansson; N. Kettaneh-Wold; J. Trygg; C. Wikstrom; S. Wold

ISBN 10: 9197373028 / ISBN 13: 9789197373029

Published by Umetrics, 1656

About this Item

Moderate wear. Clean with no highlighting or writing detected. No hassle 30-day return policy and free shipping within Canada. For shipments outside Canada, customers are responsible for any custom duties charged by their home country. ; Part I; 8vo 8" - 9" tall.

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Bibliographic Details

• Title: Multivariate and Megavariate Data Analysis Basic Principles and Applications by L. Eriksson (2006-01-01)
• Publisher: Umetrics
• Publication Date: 1656
• Language: English
• ISBN 10: 9197373028
• ISBN 13: 9789197373029
• Binding: Softcover
• Condition: Very Good

About this title

Synopsis

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,in two volumes,describes a remarkably simple minimalistic and practical approach to the analysis of data tables (multivariate data). The approach is based on so called 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. Volume 1: Basic Principles and Applications Authors: The six authors are all connected to the Umetrics company (www.umetrics.com) which develops and sells software for multivariate analysis since 1987, as well as supports customers with training and consultations. Umetrics’ customers include most large and medim sized companies in the pharmaceutical, chemical, and semiconductor sectors.