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Introduction to multivariate analysi...

Konishi, Sadanori.

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Introduction to multivariate analysis : = linear and nonlinear modeling /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to multivariate analysis :/ Sadanori Konishi.
Reminder of title:	linear and nonlinear modeling /
Author:	Konishi, Sadanori.
Published:	Boca Raton :CRC Press, Taylor & Francis Group, : c2014.,
Description:	xxv, 312 p. :ill. ;24 cm.
Subject:	Multivariate analysis. -
ISBN:	1466567287

Introduction to multivariate analysis : = linear and nonlinear modeling /
Konishi, Sadanori.

Introduction to multivariate analysis :linear and nonlinear modeling /Sadanori Konishi. - Boca Raton :CRC Press, Taylor & Francis Group,c2014. - xxv, 312 p. :ill. ;24 cm. - Chapman & Hall/CRC Texts in Statistical Science series..

Includes bibliographical references and index.

"Multivariate techniques are used to analyze data that arise from more than one variable in which there are relationships between the variables. Mainly based on the linearity of observed variables, these techniques are useful for extracting information and patterns from multivariate data as well as for the understanding the structure of random phenomena. This book describes the concepts of linear and nonlinear multivariate techniques, including regression modeling, classification, discrimination, dimension reduction, and clustering"--

ISBN: 1466567287GBP60.00

LCCN: 2014013362Subjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278 / .K597 2014

Dewey Class. No.: 519.5/35

Introduction to multivariate analysis : = linear and nonlinear modeling /
LDR:02873cam a2200217 a 4500 001 2012591
005 20140905182110.0
008 160811s2014 flua b 001 0 eng
010 $a 2014013362
020 $a 1466567287 $q (hbk.) : $c GBP60.00
020 $a 9781466567283 $q (hbk.)
040 $a DLC $b eng $c DLC $d DLC
042 $a pcc
050 0 0 $a QA278 $b .K597 2014
082 0 0 $a 519.5/35 $2 23
100 1 $a Konishi, Sadanori. $3 2170817
245 1 0 $a Introduction to multivariate analysis : $b linear and nonlinear modeling / $c Sadanori Konishi.
260 $a Boca Raton : $b CRC Press, Taylor & Francis Group, $c c2014.
300 $a xxv, 312 p. : $b ill. ; $c 24 cm.
490 0 $a Chapman & Hall/CRC Texts in Statistical Science series.
504 $a Includes bibliographical references and index.
520 $a "Multivariate techniques are used to analyze data that arise from more than one variable in which there are relationships between the variables. Mainly based on the linearity of observed variables, these techniques are useful for extracting information and patterns from multivariate data as well as for the understanding the structure of random phenomena. This book describes the concepts of linear and nonlinear multivariate techniques, including regression modeling, classification, discrimination, dimension reduction, and clustering"-- $c Provided by publisher.
520 $a "The aim of statistical science is to develop the methodology and the theory for extracting useful information from data and for reasonable inference to elucidate phenomena with uncertainty in various fields of the natural and social sciences. The data contain information about the random phenomenon under consideration and the objective of statistical analysis is to express this information in an understandable form using statistical procedures. We also make inferences about the unknown aspects of random phenomena and seek an understanding of causal relationships. Multivariate analysis refers to techniques used to analyze data that arise from multiple variables between which there are some relationships. Multivariate analysis has been widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Techniques would include regression, discriminant analysis, principal component analysis, clustering, etc., and are mainly based on the linearity of observed variables. In recent years, the wide availability of fast and inexpensive computers enables us to accumulate a huge amount of data with complex structure and/or high-dimensional data. Such data accumulation is also accelerated by the development and proliferation of electronic measurement and instrumentation technologies. Such data sets arise in various fields of science and industry, including bioinformatics, medicine, pharmaceuticals, systems engineering, pattern recognition, earth and environmental sciences, economics and marketing. "-- $c Provided by publisher.
650 0 $a Multivariate analysis. $3 517467