A Practical Guide to Scientific Data Analysis

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ISBN: 9780470851531 | 0470851538
Cover: Hardcover
Copyright: 12/21/2009

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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	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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A Practical Guide to Scientific Data Analysis

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