A Practical Guide to Scientific Data Analysis
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.
1 Introduction: Data and it’s Properties, Analytical Methods and Jargon.
1.2 Types of Data.
1.3 Sources of Data.
1.4 The Nature of Data.
1.5 Analytical Methods.
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.
3 Data Pre-treatment and Variable Selection.
3.2 Data Distribution.
3.5 Data Reduction.
3.6 Variable Selection.
4 Data Display.
4.2 Linear Methods.
4.3 Non-linear Methods.
4.4 Faces, Flowerplots & Friends.
5 Unsupervised Learning.
5.2 Nearest-neighbour Methods.
5.3 Factor Analysis.
5.4 Cluster Analysis.
5.5 Cluster Significance Analysis.
6 Regression analysis.
6.2 Simple Linear Regression.
6.3 Multiple Linear Regression.
6.4 Multiple Regression - Robustness, Chance Effects, the Comparison of Models and Selection Bias.
7 Supervised Learning.
7.2 Discriminant Techniques.
7.3 Regression on principal Components & PLS.
7.4 Feature Selection.
8 Multivariate Dependent Data.
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.
9 Artificial Intelligence & Friends.
9.2 Expert Systems.
9.3 Neural Networks.
9.4 Miscellaneous AI Techniques.
9.5 Genetic Methods.
9.6 Consensus Models.
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.
“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)
Wiley is pleased to announce the publication of A Practical Guide to Scientific Data Analysis (January 2010). This ‘statistics book for the non-statistician’ is the first book in the field to address this important topic.
The application of statistical and mathematical methods to the design of performance chemicals such as pharmaceuticals, agrochemicals, fragrances, flavors and paints is an increasingly important area. The process requires an understanding of the mechanism of action, the relationship between performance and chemical structure, the dependence of certain chemical and physicochemical properties on chemical structure and the design of experiments.
A Practical Guide to Scientific Data Analysis is a practical handbook aimed at the working scientist involved in the design of performance chemicals. It will have wide appeal, not only to chemists, but also to biochemists, pharmacists and other researchers within the field of statistical analysis of experimental results.
A Practical Guide to Scientific Data Analysis will prove invaluable for professional scientists in the pharmaceutical, agrochemical, chemical and biotechnology industries. It is also written specifically for post-doctoral researchers and PhD students from disciplines where mathematical modeling and statistics is not fundamentally taught, such as analytical chemistry, computational chemistry, and general chemistry for experimental physical chemistry options.
Chapter 1: Introduction: Data and its Properties, Analytical Methods and Jargon
Chapter 2: Experimental Design -- Experiment and Set Selection
Chapter 3: Data Pre-treatment and Variable Selection
Chapter 4: Data Display
Chapter 5: Unsupervised Learning
Chapter 6: Regression Analysis
Chapter 7: Supervised Learning
Chapter 8: Multivariate Dependent Data
Chapter 9: Artificial Intelligence & Friends
Chapter 10: Molecular Design