Practical Guide to Scientific Data Analysis

Informationen zum Autor David J. Livingstone is the author of A Practical Guide to Scientific Data Analysis, published by Wiley. Klappentext 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 Zusammenfassung 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 Inhaltsverzeichnis Preface xi Abbreviations xiii 1 Introduction: Data and Its Properties, Analytical Methods and Jargon 1 1.1 Introduction 2 1.2 Types of Data 3 1.3 Sources of Data 5 1.3.1 Dependent Data 5 1.3.2 Independent Data 6 1.4 The Nature of Data 7 1.4.1 Types of Data and Scales of Measurement 8 1.4.2 Data Distribution 10 1.4.3 Deviations in Distribution 15 1.5 Analytical Methods 19 1.6 Summary 23 References 23 2 Experimental Design - Experiment and Set Selection 25 2.1 What is Experimental Design? 25 2.2 Experimental Design Techniques 27 2.2.1 Single-factor Design Methods 31 2.2.2 Factorial Design (Multiple-factor Design) 33 2.2.3 D-optimal Design 38 2.3 Strategies for Compound Selection 40 2.4 High Throughput Experiments 51 2.5 Summary 53 References 54 3 Data Pre-treatment and Variable Selection 57 3.1 Introduction 57 3.2 Data Distribution 58 3.3 Scaling 60 3.4 Correlations 62 3.5 Data Reduction 63 3.6 Variable Selection 67 3.7 Summary 72 References 73 4 Data Display 75 4.1 Introduction 75 4.2 Linear Methods 77 4.3 Nonlinear Methods 94 4.3.1 Nonlinear Mapping 94 4.3.2 Self-organizing Map 105 4.4 Faces, Flowerplots and Friends 110 4.5 Summary 113 References 116 5 Unsupervised Learning 119 5.1 Introduction 119 5.2 Nearest-neighbour Methods 120 5.3 Factor Analysis 125 5.4 Cluster Analysis 135 5.5 Cluster Significance Analysis 140 5.6 Summary 143 References 144 6 Regression Analysis 145 6.1 Introduction 145 6.2 Simple Linear Regression 146 6.3 Multiple Linear Regression 154 6.3.1 Creating Multiple Regression Models 159 6.3.1.1 Forward Inclusion 159 6.3.1.2 Backward Elimination 161 6.3.1.3 Stepwise Regression 163 6.3.1.4 All Subsets 164 6.3.1.5 Model Selection by Genetic Algorithm 165 6.3.2 Nonlinear Regression Models 167 6.3.3 Regression with Indicator Variables 169 6.4 Multiple Regression: Robustness, Chance Effects, the Comparison of Models and Selection Bias 174 6.4.1 Robustness (Cross-validation) 174 6.4.2 Chance Effects 177 6.4.3 Comparison of Regression Models 178 6.4.4 Selection Bias 180 6.5 Summary 183 References 184 7 Supervised Learning 187 7.1 Introduction 187 7.2...

List of contents

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

Report

"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)

Product details

• Authors: David J. Livingstone, Livingstone, David Livingstone, David J Livingstone, David J. Livingstone, David J. (University of Portsmouth Livingstone, David N Livingstone, David N. Livingstone, DJ Livingstone, LIVINGSTONE DAVID J, Livingstone David J.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 20.11.2009
• EAN: 9780470851531
• ISBN: 978-0-470-85153-1
• No. of pages: 368
• Subjects: Natural sciences, medicine, IT, technology > Chemistry > Theoretical chemistry; Statistik, Chemie, Datenanalyse, Statistics, Biostatistik, chemistry, Biostatistics, Laborautomatisierung u. Miniaturisierung, Lab Automation & Miniaturization, Chemometrik