Multivariate Data Analysis: In Practice : an Introduction to Multivariate Data Analysis and Experimental Design"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. |
Contents
Introduction to Multivariate Data Analysis | 1 |
Getting Started with Descriptive Statistics | 13 |
Introduction | 19 |
Principal Component Analysis PCA In Practice | 75 |
PCA Exercises RealWorld Application Examples | 105 |
Multivariate Calibration PCRPLS | 115 |
Mandatory Performance Testing | 155 |
How to Perform PCR and PLSR | 171 |
Interim Examination | 303 |
Uncertainty Estimates Significance and Stability | 327 |
An Introduction to Classification | 335 |
Introduction to Experimental Design | 361 |
Complex Experimental Design Problems | 447 |
Comparison of Methods for Multivariate Data | 489 |
Literature | 513 |
Algorithms | 519 |
9 | 181 |
RealWorld Application | 221 |
PLS PCR Multivariate Calibration In Practice | 241 |
RealWorld Applications II | 273 |
Software Installation and User | 527 |
Glossary of Terms | 549 |
| 587 | |
Common terms and phrases
application average calculated calibration called chapter choose classification combinations complete contains correction correlation corresponding course cross validation data analysis data set data structure defined described design variables determine display distribution effects error estimate example exercise experimental experiments explained fact Figure give groups important included interactions interested interpret levels leverage loading look matrix means measurements method mixture multivariate normal Note objects observed optimal option original outliers particular perform points possible practical prediction prediction error present Principal Component problem projection range reference region regression relationships replicates representative residual response RMSEP samples scaling score plot screening shows significant similar situation specific square standard statistical structure test set Unscrambler usually validation values variance variation View weights X-variables
Popular passages
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.