LEADER 01580nam0 22003491i 450 001 SUN0019182 005 20050301120000.0 010 $a88-7002-601-9 100 $a20040708d1994 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aPerversioni e quasi-perversioni nella pratica clinica$enuove prospettive psicoanalitiche$fa cura di Gerald I. Fogel e Wayne A. Myers$gtraduzione di Carola Catenacci e Tatiana Petrovich Njegosh 210 $aRoma$cIl pensiero scientifico$d1994 215 $aX, 260 p.$d24 cm. 500 1$3SUN0078076$aPerversions and near-perversions in clinical practice $91401065 606 $aPervertimenti sessuali$xPsicanalisi$2FI$3SUNC009432 620 $dRoma$3SUNL000360 676 $a616.8583$v21 702 1$aFogel$b, Gerald I.$3SUNV015329 702 1$aMyers$b, Wayne A.$3SUNV015330 702 1$aCatenacci$b, Carola$3SUNV015331 702 1$aPetrovich Njegosh$b, Tatiana$3SUNV015332 712 $aIl pensiero scientifico$3SUNV000063$4650 790 1$aFogel, G.I.$zFogel, Gerald I.$3SUNV062014 790 1$aFogel, G. I.$zFogel, Gerald I.$3SUNV062015 801 $aIT$bSOL$c20181109$gRICA 912 $aSUN0019182 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$d16 CONS 2320 $e16 LET6753 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI PSICOLOGIA$bIT-CE0119$gLET$h6753$kCONS 2320$op$qa 996 $aPerversions and near-perversions in clinical practice$91401065 997 $aUNICAMPANIA LEADER 05240nam 2200661Ia 450 001 9910139536203321 005 20200520144314.0 010 $a9786612472039 010 $a9781282472037 010 $a1282472038 010 $a9780470017913 010 $a0470017910 010 $a9780470684818 010 $a047068481X 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(Perlego)2749893 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 /$fDavid Livingstone 210 $aHoboken, N.J. $cWiley$d2009 215 $a1 online resource (359 p.) 300 $aDescription based upon print version of record. 311 08$a9780470851531 311 08$a0470851538 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 $aExperimental design 615 0$aScience$xStatistical methods. 615 0$aExperimental design. 676 $a519.5/7 700 $aLivingstone$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