| Record ID | marc_columbia/Columbia-extract-20221130-031.mrc:60909834:4658 |
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LEADER: 04658cam a2200649 a 4500
001 15075274
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020 $a9781420059496$q(electronic bk.)
020 $a1420059491$q(electronic bk.)
020 $z9781420059472
020 $z1420059475
035 $a(OCoLC)317674910$z(OCoLC)315139943$z(OCoLC)316790678$z(OCoLC)399755320$z(OCoLC)495932278$z(OCoLC)646806941$z(OCoLC)666903011$z(OCoLC)815761347$z(OCoLC)987757404$z(OCoLC)991903355$z(OCoLC)1048142545$z(OCoLC)1050967064$z(OCoLC)1065976606$z(OCoLC)1077302633
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049 $aZCUA
100 1 $aVarmuza, Kurt,$d1942-
245 10 $aIntroduction to multivariate statistical analysis in chemometrics /$cKurt Varmuza, Peter Filzmoser.
264 1 $aBoca Raton :$bCRC Press,$c©2009.
300 $a1 online resource (xiii, 321 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
504 $aIncludes bibliographical references and index.
588 0 $aPrint version record.
505 0 $aFront cover; Contents; Preface; Acknowledgments; Authors; Chapter 1. Introduction; Chapter 2. Multivariate Data; Chapter 3. Principal Component Analysis; Chapter 4. Calibration; Chapter 5. Classification; Chapter 6. Cluster Analysis; Chapter 7. Preprocessing; Appendix 1: Symbols and Abbreviations; Appendix 2: Matrix Algebra; Appendix 3: Introduction to R; Index; Back cover.
520 8 $aUsing formal descriptions, graphical illustrations, practical examples, and R software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. It includes discussions of various statistical methods, such as principal component analysis, regression analysis, classification methods, and clustering. Written by a chemometrician and a statistician, the book reflects the practical approach of chemometrics and the more formally oriented one of statistics. To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry. They also examine results of the different methods, comparing traditional approaches with their robust counterparts. In addition, the authors use the freely available R package to implement methods, encouraging readers to go through the examples and adapt the procedures to their own problems. Focusing on the practicality of the methods and the validity of the results, this book offers concise mathematical descriptions of many multivariate methods and employs graphical schemes to visualize key concepts. It effectively imparts a basic understanding of how to apply statistical methods to multivariate scientific data.
650 0 $aChemometrics.
650 0 $aMultivariate analysis.
650 0 $aChemistry.
650 12 $aMultivariate Analysis
650 12 $aChemistry
650 12 $aChemistry Techniques, Analytical
650 6 $aChimiométrie.
650 6 $aAnalyse multivariée.
650 6 $aChimie.
650 7 $achemistry.$2aat
650 7 $aSCIENCE$xChemistry$xAnalytic.$2bisacsh
650 7 $aChemistry.$2fast$0(OCoLC)fst00853344
650 7 $aChemometrics.$2fast$0(OCoLC)fst01736550
650 7 $aMultivariate analysis.$2fast$0(OCoLC)fst01029105
650 7 $aKemometri.$2sao
655 4 $aElectronic books.
700 1 $aFilzmoser, Peter.
776 08 $iPrint version:$aVarmuza, Kurt, 1942-$tIntroduction to multivariate statistical analysis in chemometrics.$dBoca Raton : CRC Press, ©2009$z9781420059472$z1420059475$w(DLC) 2008031581$w(OCoLC)144226514
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15075274$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS