LEADER 05181nam 2200625Ia 450 001 9910139536203321 005 20170809151145.0 010 $a1-282-47203-8 010 $a9786612472039 010 $a0-470-01791-0 010 $a0-470-68481-X 035 $a(CKB)2550000000001254 035 $a(EBL)477886 035 $a(OCoLC)501316327 035 $a(SSID)ssj0000341714 035 $a(PQKBManifestationID)11252608 035 $a(PQKBTitleCode)TC0000341714 035 $a(PQKBWorkID)10396011 035 $a(PQKB)10669177 035 $a(MiAaPQ)EBC477886 035 $a(EXLCZ)992550000000001254 100 $a20090706d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 12$aA practical guide to scientific data analysis$b[electronic resource] /$fDavid Livingstone 210 $aHoboken, N.J. $cWiley$d2009 215 $a1 online resource (359 p.) 300 $aDescription based upon print version of record. 311 $a0-470-85153-8 320 $aIncludes bibliographical references and index. 327 $aA Practical Guide toScientific Data Analysis; Contents; Preface; Abbreviations; 1 Introduction: Data and Its Properties, Analytical Methods and Jargon; 1.1 Introduction; 1.2 Types of Data; 1.3 Sources of Data; 1.3.1 Dependent Data; 1.3.2 Independent Data; 1.4 The Nature of Data; 1.4.1 Types of Data and Scales of Measurement; 1.4.2 Data Distribution; 1.4.3 Deviations in Distribution; 1.5 Analytical Methods; 1.6 Summary; References; 2 Experimental Design - Experiment and Set Selection; 2.1 What is Experimental Design?; 2.2 Experimental Design Techniques; 2.2.1 Single-factor Design Methods 327 $a2.2.2 Factorial Design (Multiple-factor Design)2.2.3 D-optimal Design; 2.3 Strategies for Compound Selection; 2.4 High Throughput Experiments; 2.5 Summary; References; 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; 4 Data Display; 4.1 Introduction; 4.2 Linear Methods; 4.3 Nonlinear Methods; 4.3.1 Nonlinear Mapping; 4.3.2 Self-organizing Map; 4.4 Faces, Flowerplots and Friends; 4.5 Summary; References; 5 Unsupervised Learning; 5.1 Introduction 327 $a5.2 Nearest-neighbour Methods5.3 Factor Analysis; 5.4 Cluster Analysis; 5.5 Cluster Significance Analysis; 5.6 Summary; References; 6 Regression Analysis; 6.1 Introduction; 6.2 Simple Linear Regression; 6.3 Multiple Linear Regression; 6.3.1 Creating Multiple Regression Models; 6.3.1.1 Forward Inclusion; 6.3.1.2 Backward Elimination; 6.3.1.3 Stepwise Regression; 6.3.1.4 All Subsets; 6.3.1.5 Model Selection by Genetic Algorithm; 6.3.2 Nonlinear Regression Models; 6.3.3 Regression with Indicator Variables 327 $a6.4 Multiple Regression: Robustness, Chance Effects, the Comparison of Models and Selection Bias6.4.1 Robustness (Cross-validation); 6.4.2 Chance Effects; 6.4.3 Comparison of Regression Models; 6.4.4 Selection Bias; 6.5 Summary; References; 7 Supervised Learning; 7.1 Introduction; 7.2 Discriminant Techniques; 7.2.1 Discriminant Analysis; 7.2.2 SIMCA; 7.2.3 Confusion Matrices; 7.2.4 Conditions and Cautions for Discriminant Analysis; 7.3 Regression on Principal Components and PLS; 7.3.1 Regression on Principal Components; 7.3.2 Partial Least Squares; 7.3.3 Continuum Regression 327 $a7.4 Feature Selection7.5 Summary; References; 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; 9 Artificial Intelligence and Friends; 9.1 Introduction; 9.2 Expert Systems; 9.2.1 Log P Prediction; 9.2.2 Toxicity Prediction; 9.2.3 Reaction and Structure Prediction; 9.3 Neural Networks; 9.3.1 Data Display Using ANN; 9.3.2 Data Analysis Using ANN; 9.3.3 Building ANN Models; 9.3.4 Interrogating ANN Models 327 $a9.4 Miscellaneous AI Techniques 330 $aInspired by the author's need for practical guidance in the processes of data analysis, A Practical Guide to Scientific Data Analysis has been written as a statistical companion for the working scientist. This handbook of data analysis with worked examples focuses on the application of mathematical and statistical techniques and the interpretation of their results. Covering the most common statistical methods for examining and exploring relationships in data, the text includes extensive examples from a variety of scientific disciplines. The chapters are organised logically, from pl 606 $aScience$xStatistical methods 606 $aExperimental design 608 $aElectronic books. 615 0$aScience$xStatistical methods. 615 0$aExperimental design. 676 $a519.57 676 $a540.72 700 $aLivingstone$b D$g(David)$0862758 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139536203321 996 $aA practical guide to scientific data analysis$91926030 997 $aUNINA