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A Practical Guide to Scientific Data Analysisby David Livingstone
Synopses & Reviews
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:
Of practical use to chemists, biochemists, pharmacists, biologists and researchers from many other scientific disciplines in both industry and academia.
Book News Annotation:
For scientists with little statistics background, such as chemists, biochemists, pharmacists, and biologists, Livingstone (U. of Portsmouth, UK) provides a guide to scientific data analysis that focuses on the application of mathematical and statistical techniques and the interpretation of their results. He covers the most common multivariate statistical methods for examining and exploring relationships in data, with many examples from different scientific disciplines, including the design of foods, drugs, and cosmetics, and chapters covering aspects from planning an experiment, to examining and displaying data, to constructing quantitative models. Discussion of theory is minimized. Elementary knowledge of statistics is assumed. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)
A practical handbook aimed at the working scientist, it covers the application of statistical and mathematical methods to the design of performance chemicals, such as pharmaceuticals, agrochemicals, fragrances, flavours and paints. This volume will have wide appeal, not only to chemists, but biochemists, pharmacists and other researchers within the field of statistical analysis of experimental results. The first book in this field to address this topic The statistics book for the non-statistician Highly qualified and internationally respected author
“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)
Table of Contents
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.
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