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 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
