A Practical Guide to Scientific Data Analysis
Published on 21. November 2009
358 pages
978-0-470-68481-8 (ISBN)
Description
Inspired by the author's need for practical guidance in theprocesses of data analysis, A Practical Guide to Scientific DataAnalysis has been written as a statistical companion for theworking scientist. This handbook of data analysis with workedexamples focuses on the application of mathematical and statisticaltechniques and the interpretation of their results.
Covering the most common statistical methods for examining andexploring relationships in data, the text includes extensiveexamples from a variety of scientific disciplines.
The chapters are organised logically, from planning anexperiment, through examining and displaying the data, toconstructing quantitative models. Each chapter is intended to standalone so that casual users can refer to the section that is mostappropriate to their problem.
Written by a highly qualified and internationally respectedauthor this text:
* Presents statistics for the non-statistician
* Explains a variety of methods to extract information fromdata
* Describes the application of statistical methods to the designof "performance chemicals"
* Emphasises the application of statistical techniques and theinterpretation of their results
Of practical use to chemists, biochemists, pharmacists,biologists and researchers from many other scientific disciplinesin both industry and academia.
Reviews / Votes
"Written by a highly qualified internationally respectedauthor this text is of practical use to chemists, biochemists,pharmacists, biologists and researchers from many other scientificdisciplines in both industry and academia." (International Journal Microstructure & MaterialsProperties, 1 October 2011) "At the same time, the highly detailed, thoughtful and readableexplanation of statistical and data-mining concepts throughout thebook will make it a valuable addition to the libraries of a widerange of researchers . . . It is definitely worth its purchaseprice and may be considered seriously as a textbook for nonmajorstatistics students and research scientists in a wide variety offields." (The American Statistician, 1 May 2011) "The book is recommended for readers interested, but notexperienced, in data analysis methods used in drug design,pharmaceutical research or related areas. It provides an almostmathematical-free introduction to some multivariate statisticalmethods applied in these fields. Also the great experience and thepersonal views of a highly qualified author may be interesting formany scientists." (Zentralblatt Math, 2010) "This book should provide those engaged in multidimensionalexperimentation a relatively compact (under 400 pages) oversight ofthe relative merits of numerous techniques, all of which areheavily computer dependent, and will be of especial interest tothose working in the field of pharmaceutical research. It shouldalso draw their attention to the roots of complex methods by meansof 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 examplesin the book are all chemical and hence it will be of most interestand value to chemistry researchers." (Chemistry World,May 2010)More details
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A Practical Guide to Scientific Data Analysis
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12/2009
Wiley
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A Practical Guide to Scientific Data Analysis
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11/2009
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David J. Livingstone is the author of A Practical Guide to Scientific Data Analysis, published by Wiley.
Content
Preface.
Abbreviations.
1 Introduction: Data and it's Properties, AnalyticalMethods 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 SetSelection.
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, theComparison 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.
