A Practical Guide to Scientific Data Analysis

Preface
 
Abbreviations
 
Chapter 1 Introduction: Data and it's Properties, Analytical Methods and Jargon
 
1.1 Introduction
 
1.2 Types of Data
 
1.3 Sources of Data
 
1.4 The nature of data
 
1.5 Analytical methods
 
References
 
Chapter 2 Experimental Design - Experiment and Set Selection
 
2.1 What is Experimental Design?
 
2.2 Experimental Design Techniques
 
2.3 Strategies for Compound Selection
 
2.4 High Throughput Experiments
 
2.5 Summary
 
References
 
Chapter 3 Data Pre-treatment and Variable Selection
 
3.1 Introduction
 
3.2 Data Distribution
 
3.3 Scaling
 
3.4 Correlations
 
3.5 Data Reduction
 
3.6 Variable Selection
 
3.7 Summary
 
References
 
Chapter 4 Data Display
 
4.1 Introduction
 
4.2 Linear Methods
 
4.3 Non-linear Methods
 
4.4 Faces, Flowerplots & Friends
 
4.5 Summary
 
References
 
Chapter 5 Unsupervised Learning
 
5.1 Introduction
 
5.2 Nearest-neighbour Methods
 
5.3 Factor Analysis
 
5.4 Cluster Analysis
 
5.5 Cluster Significance Analysis
 
5.6 Summary
 
References
 
Chapter 6 Regression analysis
 
6.1 Introduction
 
6.2 Simple Linear Regression
 
6.3 Multiple Linear Regression
 
6.4 Multiple regression - Robustness, Chance Effects, the Comparison of Models and Selection Bias
 
6.5 Summary
 
References
 
Chapter 7 Supervised Learning
 
7.1 Introduction
 
7.2 Discriminant Techniques
 
7.3 Regression on principal Components & PLS
 
7.4 Feature Selection.
 
7.5 Summary
 
References
 
Chapter 8 Multivariate dependent data
 
8.1 Introduction
 
8.2 Principal Components and Factor Analysis
 
8.3 Cluster Analysis
 
8.4 Spectral Map

"Written by a highly qualified internationally respected author this text is of practical use to chemists, biochemists, pharmacists, biologists and researchers from many other scientific disciplines in both industry and academia." ( International Journal Microstructure & Materials Properties , 1 October 2011)

"At the same time, the highly detailed, thoughtful and readable explanation of statistical and data-mining concepts throughout the book will make it a valuable addition to the libraries of a wide range of researchers . . . It is definitely worth its purchase price and may be considered seriously as a textbook for nonmajor statistics students and research scientists in a wide variety of fields." (The American Statistician, 1 May 2011)

"The book is recommended for readers interested, but not experienced, in data analysis methods used in drug design, pharmaceutical research or related areas. It provides an almost mathematical-free introduction to some multivariate statistical methods applied in these fields. Also the great experience and the personal views of a highly qualified author may be interesting for many scientists." (Zentralblatt Math, 2010)

"This book should provide those engaged in multidimensional experimentation a relatively compact (under 400 pages) oversight of the relative merits of numerous techniques, all of which are heavily computer dependent, and will be of especial interest to those working in the field of pharmaceutical research. It should also draw their attention to the roots of complex methods by means of its introductory chapters." (Chromatographia, October 2010)

"This book is a guide to the wide range of methods available. Not surprisingly given the author's background, the examples in the book are all chemical and hence it will be of most interest and value to chemistry researchers." ( Chemistry World , May 2010)