Statistical Design - Chemometrics

Other | January 1, 2006

byBruns, Roy E, Roy E Bruns

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Statistical Design-Chemometrics is applicable to researchers and professionals who wish to perform experiments in chemometrics and carry out analysis of the data in the most efficient way possible. The language is clear, direct and oriented towards real applications. The book provides 106 exercises with answers to accompany the study of theoretical principles. Forty two cases studies with real data are presented showing designs and the complete statistical analyses for problems in the areas chromatography, electroanalytical and electrochemistry, calibration, polymers, gas adsorption, semiconductors, food technology, biotechnology, photochemistry, catalysis, detergents and ceramics. These studies serve as a guide that the reader can use to perform correct data analyses.

-Provides 42 case studies containing step-by-step descriptions of calculational procedures that can be applied to most real optimization problems
-Contains 106 theoretical exercises to test individual learning and to provide classroom exercises and material for written tests and exams
-Written in a language that facilitates learning for physical and biological scientists and engineers
-Takes a practical approach for those involved in industrial optimization problems

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From the Publisher

Statistical Design-Chemometrics is applicable to researchers and professionals who wish to perform experiments in chemometrics and carry out analysis of the data in the most efficient way possible. The language is clear, direct and oriented towards real applications. The book provides 106 exercises with answers to accompany the study o...

Format:OtherDimensions:422 pages, 1 × 1 × 1 inPublished:January 1, 2006Publisher:Elsevier ScienceLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0080462138

ISBN - 13:9780080462134

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Table of Contents

1. How statistics can help
2. When the situation is normal
3. Changing everything at the same time
4. When there are many variables
5. Empirical model-building
6. Exploring the response surface
7. Mixtures modeling
8. Simplex optimization