A Practical Guide to Scientific Data Analysis

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.