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Statistical Data Analysis: A Practical Guide (Hardback)

Milan Meloun, J. Militky

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ISBN 10: 0857091093 / ISBN 13: 9780857091093
Edité par ELSEVIER SCIENCE TECHNOLOGY, United Kingdom, 2012
Neuf(s) Etat : New Couverture rigide
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A propos de cet article

Language: English . Brand New Book. Over the past decade, computer supported data analysis by statistical methods has been one of the fastest growth areas in chemometrics, biometrics and other related branches of natural, technical and social sciences. This has been strongly supported by the development of exploratory data analysis, testing assumptions about data, model and statistical methods and computer intensive techniques. This book presents a combination of individual topics with solved problems and a collection of experimental tasks. Methods suitable for extreme or small and large datasets are described. * Presents a combination of individual topics in one complete volume featuring statistical analysis of univariate and multivariate data* Interspersed throughout with solved problems and experimental tasks suitable for extreme or small and large datasets* Features the interpretation of results based on the comprehensive information about data behaviour and validity of used assumptions. N° de réf. du libraire AAS9780857091093

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Détails bibliographiques

Titre : Statistical Data Analysis: A Practical Guide...

Éditeur : ELSEVIER SCIENCE TECHNOLOGY, United Kingdom

Date d'édition : 2012

Reliure : Hardback

Etat du livre :New

A propos de ce titre

Synopsis :

Over the past decade, computer supported data analysis by statistical methods has been one of the fastest growth areas in chemometrics, biometrics and other related branches of natural, technical and social sciences. This has been strongly supported by the development of exploratory data analysis, testing assumptions about data, model and statistical methods and computer intensive techniques. This book presents a combination of individual topics with solved problems and a collection of experimental tasks. Methods suitable for extreme or small and large datasets are described.

  • Presents a combination of individual topics in one complete volume featuring statistical analysis of univariate and multivariate data
  • Interspersed throughout with solved problems and experimental tasks suitable for extreme or small and large datasets
  • Features the interpretation of results based on the comprehensive information about data behaviour and validity of used assumptions

About the Author:

Milan Meloun is Professor of Chemometrics at Department of Analytical Chemistry, Faculty of Chemical Technology, Technical University of Liberec, Czech Republic.

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