A Practical Guide to Scientific Data Analysis
Published on 20. November 2009
Book
Hardback
358 pages
978-0-470-85153-1 (ISBN)
Description
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
Reviews / Votes
"Written by a highly qualified internationally respected author this text is of practical use to chemists, biochemists, pharmacists, biologists and researchers from many other scientific disciplines in both industry and academia." (International Journal Microstructure & Materials Properties, 1 October 2011)"At the same time, the highly detailed, thoughtful and readable explanation of statistical and data-mining concepts throughout the book will make it a valuable addition to the libraries of a wide range of researchers . . . It is definitely worth its purchase price and may be considered seriously as a textbook for nonmajor statistics students and research scientists in a wide variety of fields." (The American Statistician, 1 May 2011)
"The book is recommended for readers interested, but not experienced, in data analysis methods used in drug design, pharmaceutical research or related areas. It provides an almost mathematical-free introduction to some multivariate statistical methods applied in these fields. Also the great experience and the personal views of a highly qualified author may be interesting for many scientists." (Zentralblatt Math, 2010)
"This book should provide those engaged in multidimensional experimentation a relatively compact (under 400 pages) oversight of the relative merits of numerous techniques, all of which are heavily computer dependent, and will be of especial interest to those working in the field of pharmaceutical research. It should also draw their attention to the roots of complex methods by means of 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 examples in the book are all chemical and hence it will be of most interest and value to chemistry researchers." (Chemistry World, May 2010)
More details
Edition
1. Auflage
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Unsewn / adhesive bound
Paper over boards
Dimensions
Height: 237 mm
Width: 162 mm
Thickness: 29 mm
Weight
638 gr
ISBN-13
978-0-470-85153-1 (9780470851531)
Schweitzer Classification
Other editions
Additional editions
David N. Livingstone
A Practical Guide to Scientific Data Analysis
E-Book
11/2009
Wiley
€71.99
Available for download
Person
David J. Livingstone is the author of A Practical Guide to Scientific Data Analysis, published by Wiley.
Content
Preface
Abbreviations
Chapter 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
References
Chapter 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
Chapter 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
Chapter 4 Data Display
4.1 Introduction
4.2 Linear Methods
4.3 Non-linear Methods
4.4 Faces, Flowerplots & Friends
4.5 Summary
References
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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