01103nam0-22003131i-450-99000813300040332120160204113724.0000813300FED01000813300(Aleph)000813300FED0100081330020050620d1605----km-y0itay50------balatITa-------001yyTractatus de coniecturis vltimarum voluntatum, in libros duodecim distinctus; auctore Francisco Mantica ...Hac tertia editione diligentius quam antea Venetijs impressum. Cum indice rerum, ac materiarum locupletissimoVenetiisapud Damianum Zenarium1605[42], 399, [1], 18, [2] c.ill.fol.Marca (Salamandra tra le fiamme. Motto: Virtuti sic cedit invidia) simile a Z1016 sul front.Segn.: *6 a-f6 A-3V6 3X4, [2]A-C6 D2Italia.VeneziaMantica,Francesco287305ITUNINARICAUNIMARCAQ990008133000403321V N a 604408FGBCFGBCUNINA05240nam 2200661Ia 450 991013953620332120200520144314.0978661247203997812824720371282472038978047001791304700179109780470684818047068481X(CKB)2550000000001254(EBL)477886(OCoLC)501316327(SSID)ssj0000341714(PQKBManifestationID)11252608(PQKBTitleCode)TC0000341714(PQKBWorkID)10396011(PQKB)10669177(MiAaPQ)EBC477886(Perlego)2749893(EXLCZ)99255000000000125420090706d2009 uy 0engur|n|---|||||txtccrA practical guide to scientific data analysis /David LivingstoneHoboken, N.J. Wiley20091 online resource (359 p.)Description based upon print version of record.9780470851531 0470851538 Includes bibliographical references and index.A 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 Methods2.2.2 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 Introduction5.2 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 Variables6.4 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 Regression7.4 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 Models9.4 Miscellaneous AI TechniquesInspired 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 plScienceStatistical methodsExperimental designScienceStatistical methods.Experimental design.519.5/7Livingstone D(David)862758MiAaPQMiAaPQMiAaPQBOOK9910139536203321A practical guide to scientific data analysis1926030UNINA03557nam 22005295 450 991015545300332120200713104206.03-658-16492-110.1007/978-3-658-16492-8(CKB)4340000000018273(DE-He213)978-3-658-16492-8(MiAaPQ)EBC4747273(EXLCZ)99434000000001827320161123d2017 u| 0gerurnn|008mamaatxtrdacontentcrdamediacrrdacarrierBilingualer Unterricht im Fokus der Biologiedidaktik Auswirkungen von Unterrichtssprache und -kontext auf Motivation und Wissenserwerb /von Petra Duske1st ed. 2017.Wiesbaden :Springer Fachmedien Wiesbaden :Imprint: Springer VS,2017.1 online resource (XIV, 191 S. 44 Abb.) Research3-658-16491-3 Includes bibliographical references.Bilingualer Biologieunterricht/Sachfachunterricht -- Kontext- und Kompetenzorientierung -- Leistungsmotivation -- Lernerfolg.Petra Duske zeigt, dass sich bilingualer Fachunterricht zur Aufrechterhaltung der Schülermotivation im Sachfach und zum Erwerb vergleichbaren Wissens wie im deutschsprachigen Biologieunterricht eignet. Der Unterrichtskontext im Sinne einer thematischen Einbettung scheint eine untergeordnete Rolle für Motivation und Wissenserwerb zu spielen. Dies sind die Ergebnisse einer vergleichenden Untersuchung mit ca. 800 Schülerinnen und Schülern anhand eines bilingualen bzw. deutschsprachigen Moduls im Fach Biologie. Die Studie kann als Entscheidungshilfe für Lehrkräfte, Schulleitungen, Bildungsadministrationen, Schülerinnen und Schülern sowie deren Eltern bei der Einführung von oder Teilnahme an bilingualen Bildungsgängen dienen. Der Inhalt Bilingualer Biologieunterricht/Sachfachunterricht Kontext- und Kompetenzorientierung Leistungsmotivation Lernerfolg Die Zielgruppen Lehrkräfte im Fach Englisch sowie bilingualer Sachfächer Lehrkräfte an Lehrerbildungsseminaren Dozierende und Studierende der Anglistik, Fachdidaktiken und des bilingualen Sachfachunterrichts Die Autorin Petra Duske lehrte bis 2014 an der Pädagogischen Hochschule Weingarten im Fach Biologie (Schwerpunkt Bilingualer Biologieunterricht, Humanbiologie). Derzeit unterrichtet sie die Fächer Biologie, Naturwissenschaft und Technik, Chemie sowie Englisch am Gymnasium Überlingen. Seit 2002 unterrichtet sie Biologie auch bilingual. .Research (Wiesbaden, Germany)ScienceStudy and teachingMultilingualismLanguage and languages—Study and teachingScience Educationhttps://scigraph.springernature.com/ontologies/product-market-codes/O27000Multilingualismhttps://scigraph.springernature.com/ontologies/product-market-codes/N55000Language Teachinghttps://scigraph.springernature.com/ontologies/product-market-codes/O46000ScienceStudy and teaching.Multilingualism.Language and languages—Study and teaching.Science Education.Multilingualism.Language Teaching.507.1Duske Petraauthttp://id.loc.gov/vocabulary/relators/aut1226456BOOK9910155453003321Bilingualer Unterricht im Fokus der Biologiedidaktik2847730UNINA