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Data analysis is a vital part of science today, and in assessing quality, multivariate analysis is often necessary in order to avoid loss of essential information. Martens provides a powerful and versatile methodology that enables researchers to design their investigations and analyse data effectively and safely, without the need for formal statistical training. Offers an introductory explanation of multivariate analysis by graphical ‘soft modelling’ Minimises mathematics, providing all technical details in the appendix Presents itself in an accessible style with cartoons, self-assessment questions and a wide range of practical examples Demonstrates the methodology for various types of quality assessment, ranging from human quality perception via industrial quality monitoring to environmental quality and its molecular basis All data sets available FREE online on “Chemometrics World” (http://www wiley.co.uk/wileychi/chemometrics)
Author Biography
Harald Martens, Professor of Chemometrics, Norwegian University of Science and Technology, and Technical University of Denmark. He is co-author of the best selling book, Multivariate Calibration, also published by John Wiley & Sons. He has won many awards for his work in data analysis including in 1999 the Galactic Industries Prize and the Herman World medal. His present developments have also been incorporated by CAMO Inc in their latest version of The Unscrambler.
Magni Martens, Professor of Sensory Science, Royal Veterinary & Agricultural University, Denmark. She has also won awards for her work in sensory science including in 2000 the prestigious Carlsberg Research Foundation proze. She has published numerous papers on multivariate data analysis for relating “soft” human quality perception to “hard” facts and measurements.We are committed to protecting your rights under the Consumer Guarantees Act and working with our suppliers to assist with warranty claims. Products sold by Mighty Ape will be covered by a Manufacturer's Warranty for at least a one-year period from the date of purchase.
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