Multivariate Data Analysis: In Practice : an Introduction to Multivariate Data Analysis and Experimental Design

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Multivariate Data Analysis, 2002 - Experimental design - 598 pages
2 Reviews
"Multivariate Data Analysis - in practice adopts a practical, non-mathematical approach to multivariate data analysis. The book's principal objective is to provide a conceptual framework for multivariate data analysis techniques, enabling the reader to apply these in his or her own field. Features: Focuses on the practical application of multivariate techniques such as PCA, PCR and PLS and experimental design. Non-mathematical approach - ideal for analysts with little or no background in statistics. Step by step introduction of new concepts and techniques promotes ease of learning. Theory supported by hands-on exercises based on real-world data. A full training copy of The Unscrambler (for Windows 95, Windows NT 3.51 or later versions) including data sets for the exercises is available. Tutorial exercises based on data from real-world applications are used throughout the book to illustrate the use of the techniques introduced, providing the reader with a working knowledge of modern multivariate data analysis and experimental design. All exercises use The Unscrambler, a de facto industry standard for multivariate data analysis software packages. Multivariate Data Analysis in Practice is an excellent self-study text for scientists, chemists and engineers from all disciplines (non-statisticians) wishing to exploit the power of practical multivariate methods. It is very suitable for teaching purposes at the introductory level, and it can always be supplemented with higher level theoretical literature."Résumé de l'éditeur.
 

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User Review - Flag as inappropriate

This book in general is very difficult to read and understand for beginners in this area. With no background in statistics, the students have to search for a lot of information on internet or from other books, instead of getting answer from this book.The sentences are in such a disorder that students have to spend at least 3 times more efforts than they have to in understanding the concept and principle, compared with other books. On the other hand, the book is very much related to the software Unscramber- this means it is actually a tutorial for the software, other than a book in "Multivariate data analysis" for beginners. Not recommended at all if you are not using the Unscramber.  

User Review - Flag as inappropriate

This book is incredible for those who don't need to go in-depth about mathematical modeling in PCA. It's a very easy read, and explains the concepts of PCA exceptionally well. A must for anyone using the Unscrambler X (Camo)!

Contents

lntroduction to Multivariate Data Analysis
1
Getting Started with Descriptive Statistics
13
Principal Component Analysis PCA
19
21
48
Principal Component Analysis PCA ln Practice
75
30
81
PCA Exercises RealWorld Application Examples
105
Multivariate Calibration PCRPLS
115
31
279
32
295
Interim Examination
303
Uncertainty Estimates Significance and Stability
327
An lntroduction to Classification
335
lntroduction to Experimental Design
361
Complex Experimental Design Problems
447
Comparison of Methods for Multivariate Data
489

Mandatory Performance Testing
155
How to Perform PCR and PLSR
171
RealWorld Application
221
PLS PCR Multivariate Calibration ln Practice
241
RealWorld Applications ll273
273
Literature
513
Algorithms
519
Software lnstallation and User
527
Glossary of Terms
549
lndex
587

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Page 515 - The algorithm extracts one factor at a time. Each factor is obtained iteratively by repeated regressions of X on scores t to obtain improved p and of X on these p to obtain improved t . The algorithm proceeds as follows: Pre-scale the X-variables to ensure comparable noise-levels. Then center the X-variables, eg by subtracting the calibration means x', forming XQ.

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