A Practical Guide to Scientific Data Analysis
1. Edition November 2009
358 Pages, Hardcover
Wiley & Sons Ltd
Short Description
Written by a well-respected author in the area who has worked in industry (Pfizer) and has run training courses in both industry and academia on this topic, Scientific Data Analysis is the first book in the field to address this subject. This statistics book for the non-statistician is designed to have broad appeal, not only to chemists, but biochemists, pharmacists and other researchers within the field of statistical analysis of experimental results.
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
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
