A Practical Guide To Scientific Data Analysis

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. More

David J. Livingstone is the author of A Practical Guide to Scientific Data Analysis, published by Wiley.

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. More