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David J. Livingstone is the author of A Practical Guide to Scientific Data Analysis, published by Wiley.
Preface | p. xi |
Abbreviations | p. xiii |
Introduction: Data and Its Properties, Analytical Methods and Jargon | p. 1 |
Introduction | p. 2 |
Types of Data | p. 3 |
Sources of Datap5 | |
Dependent Data | p. 5 |
Independent Data | p. 6 |
The Nature of Data | p. 7 |
Types of Data and Scales of Measurement | p. 8 |
Data Distribution | p. 10 |
Deviations in Distribution | p. 15 |
Analytical Methods | p. 19 |
Summary | p. 23 |
References | p. 23 |
Experimental Design - Experiment and Set Selection | p. 25 |
What is Experimental Design? | p. 25 |
Experimental-Design Techniques | p. 27 |
Single-factor Design Methods | p. 31 |
Factorial Design (Multiple-factor Design) | p. 33 |
D-optimal Design | p. 38 |
Strategies for Compound Selection | p. 40 |
High Throughput Experiments | p. 51 |
Summary | p. 53 |
References | p. 54 |
Data Pre-treatment and Variable Selection | p. 57 |
Introduction | p. 57 |
Data Distribution | p. 58 |
Scaling | p. 60 |
Correlations | p. 62 |
Data Reduction | p. 63 |
Variable Selection | p. 67 |
Summary | p. 72 |
References | p. 73 |
Data Display | p. 75 |
Introduction | p. 75 |
Linear Methods | p. 77 |
Nonlinear Methods | p. 94 |
Nonlinear Mapping | p. 94 |
Self-organizing Map | p. 105 |
Faces, Flowerplots and Friends | p. 110 |
Summary | p. 113 |
References | p. 116 |
Unsupervised Learning | p. 119 |
Introduction | p. 119 |
Nearest-neighbour Methods | p. 120 |
Factor Analysis | p. 125 |
Cluster Analysis | p. 135 |
Cluster Significance Analysis | p. 140 |
Summary | p. 143 |
References | p. 144 |
Regression Analysis | p. 145 |
Introduction | p. 145 |
Simple Linear Regression | p. 146 |
Multiple Linear Regression | p. 154 |
Creating Multiple Regression Models | p. 159 |
Forward Inclusion | p. 159 |
Backward Elimination | p. 161 |
Stepwise Regression | p. 163 |
All Subsets | p. 164 |
Model Selection by Genetic Algorithm | p. 165 |
Nonlinear Regression Models | p. 167 |
Regression with Indicator Variables | p. 169 |
Multiple Regression: Robustness, Chance Effects, the Comparison of Models and Selection Bias | p. 174 |
Robustness (Cross-validation) | p. 174 |
Chance Effects | p. 177 |
Comparison of Regression Models | p. 178 |
Selection Bias | p. 180 |
Summary | p. 183 |
References | p. 184 |
Supervised Learning | p. 187 |
Introduction | p. 187 |
Discriminant Techniques | p. 188 |
Discriminant Analysis | p. 188 |
SIMCA | p. 195 |
Confusion Matrices | p. 198 |
Conditions and Cautions for Discriminant Analysis | p. 201 |
Regression on Principal Components and PLS | p. 202 |
Regression on Principal Components | p. 203 |
Partial Least Squares | p. 206 |
Continuum Regression | p. 211 |
Feature Selection | p. 214 |
Summary | p. 216 |
References | p. 217 |
Multivariate Dependent Data | p. 219 |
Introduction | p. 219 |
Principal Components and Factor Analysis | p. 221 |
Cluster Analysis | p. 230 |
Spectral Map Analysis | p. 233 |
Models with Multivariate Dependent and Independent Data | p. 238 |
Summary | p. 246 |
References | p. 247 |
Artificial Intelligence and Friends | p. 249 |
Introduction | p. 250 |
Expert Systems | p. 251 |
LogP Prediction | p. 252 |
Toxicity Prediction | p. 261 |
Reaction and Structure Prediction | p. 268 |
Neural Networks | p. 273 |
Data Display Using ANN | p. 277 |
Data Analysis Using ANN | p. 280 |
Building ANN Models | p. 287 |
Interrogating ANN Models | p. 292 |
Miscellaneous AI Techniques | p. 295 |
Genetic Methods | p. 301 |
Consensus Models | p. 303 |
Summary | p. 304 |
References | p. 305 |
Molecular Design | p. 309 |
The Need for Molecular Design | p. 309 |
What is QSAR/QSPR? | p. 310 |
Why Look for Quantitative Relationships? | p. 321 |
Modelling Chemistry | p. 323 |
Molecular Fields and Surfaces | p. 325 |
Mixtures | p. 327 |
Summary | p. 329 |
References | p. 330 |
Index | p. 333 |
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