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Description
Inspired by the author's need for practical guidance in the processes of data analysis, A Practical Guide to Scientific Data Analysis has been written as a statistical companion for the working scientist. This handbook of data analysis with worked examples focuses on the application of mathematical and statistical techniques and the interpretation of their results.
Covering the most common statistical methods for examining and exploring relationships in data, the text includes extensive examples from a variety of scientific disciplines.
The chapters are organised logically, from planning an experiment, through examining and displaying the data, to constructing quantitative models. Each chapter is intended to stand alone so that casual users can refer to the section that is most appropriate to their problem.
Written by a highly qualified and internationally respected author this text:
- Presents statistics for the non-statistician
- Explains a variety of methods to extract information from data
- Describes the application of statistical methods to the design of “performance chemicals”
- Emphasises the application of statistical techniques and the interpretation of their results
Of practical use to chemists, biochemists, pharmacists, biologists and researchers from many other scientific disciplines in both industry and academia.
Table of Contents
Preface.
Abbreviations.
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.
1.6 Summary.
References.
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.
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.
4 Data Display.
4.1 Introduction.
4.2 Linear Methods.
4.3 Non-linear Methods.
4.4 Faces, Flowerplots & Friends.
4.5 Summary.
References.
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.
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.
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.
8 Multivariate Dependent Data.
8.1 Introduction.
8.2 Principal Components and Factor Analysis.
8.3 Cluster Analysis.
8.4 Spectral Map Analysis.
8.5 Models with Multivariate Dependent and Independent Data.
8.6 Summary.
References.
9 Artificial Intelligence & Friends.
9.1 introduction.
9.2 Expert Systems.
9.3 Neural Networks.
9.4 Miscellaneous AI Techniques.
9.5 Genetic Methods.
9.6 Consensus Models.
9.7 Summary.
References.
10 Molecular Design.
10.1 The Need for Molecular Design.
10.2 What is QSAR/QSPR?.
10.3 Why Look for Quantitative Relationships?.
10.4 Modelling Chemistry.
10.5 Molecular Field and Surfaces.
10.6 Mixtures.
10.7 Summary.
References.
Index.
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Reviews
“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)
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