Livingstone A Practical Guide to Scientific Data Analysis

1. Auflage 2009
ISBN: 978-0-470-68481-8
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 358 Seiten, E-Book

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.

Autoren/Hrsg.

Livingstone, David N.

Livingstone, David J.

Inhaltsverzeichnis

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.