LEADER 05600nam 2200673 450 001 9910494590703321 005 20170822122300.0 010 $a0-8213-9584-X 035 $a(CKB)2670000000599148 035 $a(EBL)1974010 035 $a(SSID)ssj0001424024 035 $a(PQKBManifestationID)11801971 035 $a(PQKBTitleCode)TC0001424024 035 $a(PQKBWorkID)11440726 035 $a(PQKB)11008818 035 $a(PQKBManifestationID)16037712 035 $a(PQKB)22446661 035 $a(MiAaPQ)EBC1974010 035 $a(DLC) 2015006080 035 $a(EXLCZ)992670000000599148 100 $a20150314h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNational assessments of educational achievement$hVolume 4$iAnalyzing data from a national assessment of educational achievement /$fGerry Shiel, Fernando Cartwright ; Vincent Greaney and Thomas Kellaghan, series editors 210 1$aWashington, District of Columbia :$cWorld Bank Group,$d2015. 210 4$dİ2015 215 $a1 online resource (297 p.) 225 0 $aNational assessments of educational achievement ;$vv. 4 300 $aDescription based upon print version of record. 311 $a0-8213-9583-1 311 $a1-336-13829-7 320 $aIncludes bibliographical references at the end of each chapters. 327 $aCover; CONTENTS; PREFACE; ABOUT THE AUTHORS AND EDITORS; ACKNOWLEDGMENTS; ABBREVIATIONS; INTRODUCTION; Note; Part I: An Introduction to the Statistical Analysis of National Assessment Data; 1. THE DATABASE FOR ANALYSES; Saving the CD Files to Your Hard Drive or Server; Survey Instruments; TABLES; 1.1 Mathematics Test: Distribution of Items by Content Area and Process; Sampling Weights; 1.2 Abbreviated Questionnaire Descriptions; SPSS; EXERCISES; 1.1 Running Descriptive Statistics in SPSS and Saving the Files; EXERCISE FIGURES; 1.1.A: Weight Cases Dialog Box 327 $a1.1.B: SPSS Descriptives Dialog BoxWesVar; Notes; 2. EXPLORING NATIONAL ASSESSMENT DATA USING SPSS; Measures of Central Tendency; Measures of Spread; Measures of Position; Measures of Shape; FIGURES; 2.1 Normal Distribution Showing Standard Deviation Units; 2.2 Examples of Distributions with Positive, Negative, and No Skews; Exploring a Data Set Using SPSS; 2.1 Running Explore in SPSS, Single Dependent Variable (One Level); EXERCISE TABLES; 2.1.A: Case-Processing Summary; 2.1.B: Descriptive Statistics; 2.1.A: Stem-and-Leaf Plot for Mathematics Scale Scores 327 $a2.1.B: Box Plot for Mathematics Scale Scores2.2 Running Explore in SPSS, Single Dependent Variable (More Than One Level); 2.2.A: Box Plots for Mathematics Scale Scores by Region; Notes; 3. AN INTRODUCTION TO WESVAR; Setting Up a Data File in WesVar; Adding Variable Labels; Computing Descriptive Statistics in WesVar; 3.1 Adding Variable Labels in WesVar; 3.1 Generating Descriptive Statistics in WesVar; 3.1.A: New WesVar Workbook; 3.1.B: Specifying Variables for Analysis in WesVar Descriptives; 3.1.C: Output from WesVar Descriptives; 3.1.D: Exporting a WesVar File 327 $aCalculating a Mean Score and Its Standard Error3.2 Computing a Mean Score and Its Standard Error in WesVar; 3.2.A: Specifying a Computed Statistic in a WesVar Table; 3.2.B: Output for WesVar Tables: Computing Mean Score; Computing Mean Scores and Standard Errors for Subgroups in the Population; 3.3 Computing Mean Scores and Standard Errors in WesVar, Four Regions; 3.3.A: WesVar Workbook before Computing Mean Scores by Region; Notes; 3.3.B: WesVar Output for Computing Mean Scores by Region; 4. COMPARING THE ACHIEVEMENTS OF TWO OR MORE GROUPS; Examining the Difference Between Two Mean Scores 327 $a4.1 Evaluating the Difference between Two Mean Scores4.1.A: WesVar Workbook before Assessing the Difference between Two Mean Scores; 4.1.B: WesVar Output: Mean Mathematics Scores of Students with and without Electricity at Home; 4.1.C: WesVar Output: Mean Score Difference in Mathematics between Students with and without Electricity at Home; 4.1.A: Comparison of Mean Mathematics Scores of Students with and without Electricity at Home; Examining Differences Between Three or More Mean Scores; 4.2 Evaluating Differences among Three or More Mean Scores 327 $a4.2.A: WesVar Workbook Showing Adjustment to Alpha Level 330 $aThis is the fourth and last volume in the set 'National Assessments of Educational Achievement.' Effective assessment of the performance of educational systems is a key component in developing policies to optimize the development of human capital around the world. The five books in the National Assessments of Educational Achievement series introduce key concepts in national assessments of student achievement levels, from policy issues to address when designing and carrying out assessments through test development, questionnaire design, sampling, organizing and carrying out data collection, dat 606 $aEducational tests and measurements 606 $aEducational evaluation 608 $aElectronic books. 615 0$aEducational tests and measurements. 615 0$aEducational evaluation. 676 $a378.167 700 $aShiel$b Gerry$01039034 702 $aCartwright$b Fernando 702 $aGreaney$b Vincent 702 $aKellaghan$b Thomas 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910494590703321 996 $aNational assessments of educational achievement$92460959 997 $aUNINA LEADER 04026nam 2200757z- 450 001 9910404080803321 005 20231214132823.0 010 $a3-03928-211-5 035 $a(CKB)4100000011302330 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/55528 035 $a(EXLCZ)994100000011302330 100 $a20202102d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOvercoming Data Scarcity in Earth Science 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (94 p.) 311 $a3-03928-210-7 330 $aheavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response?s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales. 610 $ageophysical monitoring 610 $adata scarcity 610 $amissing data 610 $aclimate extreme indices (CEIs) 610 $arule extraction 610 $aDataset Licensedatabase 610 $adata assimilation 610 $adata imputation 610 $asupport vector machines 610 $aenvironmental observations 610 $amulti-class classification 610 $aearth-science data 610 $aremote sensing 610 $amagnetotelluric monitoring 610 $asoil texture calculator 610 $amachine learning 610 $aClimPACT 610 $ainvasive species 610 $aspecies distribution modeling 610 $a3D-Var 610 $aensemble learning 610 $adata quality 610 $awater quality 610 $amicrohabitat 610 $ak-Nearest Neighbors 610 $aExpert Team on Climate Change Detection and Indices (ETCCDI) 610 $adecision trees 610 $aprocessing 610 $aattribute reduction 610 $aExpert Team on Sector-specific Climate Indices (ET-SCI) 610 $acore attribute 610 $arough set theory 610 $aGLDAS 610 $aarthropod vector 610 $aenvironmental modeling 610 $astatistical methods 700 $aEtcheverry Venturini$b Lorena$4auth$01325393 702 $aChreties Ceriani$b Christian$4auth 702 $aCastro Casales$b Alberto$4auth 702 $aGorgoglione$b Angela$4auth 906 $aBOOK 912 $a9910404080803321 996 $aOvercoming Data Scarcity in Earth Science$93036840 997 $aUNINA LEADER 02995nam 22007211 450 001 9910823914803321 005 20230803031736.0 010 $a3-11-028119-8 024 7 $a10.1515/9783110281194 035 $a(CKB)2670000000432706 035 $a(EBL)1121580 035 $a(OCoLC)858762119 035 $a(SSID)ssj0000957981 035 $a(PQKBManifestationID)11566253 035 $a(PQKBTitleCode)TC0000957981 035 $a(PQKBWorkID)10985313 035 $a(PQKB)10816496 035 $a(MiAaPQ)EBC1121580 035 $a(DE-B1597)175639 035 $a(OCoLC)881295752 035 $a(DE-B1597)9783110281194 035 $a(Au-PeEL)EBL1121580 035 $a(CaPaEBR)ebr10786106 035 $a(CaONFJC)MIL783415 035 $a(EXLCZ)992670000000432706 100 $a20130117h20132013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBiomimetics $ea molecular perspective /$fRaz Jelinek 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d[2013] 210 4$dİ2013 215 $a1 online resource (260 p.) 225 0 $aDe Gruyter Textbook 225 0$aDe Gruyter graduate 300 $aDescription based upon print version of record. 311 $a3-11-028117-1 320 $aIncludes bibliographical references and index. 320 $aIncludes bibliographical references and index. 327 $aBio-inspired and bio-hybrid materials -- Biomimetic surfaces -- Tissue engineering -- Biomineralization -- Synthetic biology -- Artificial cells -- Drug delivery -- DNA and RNA nanotechnology -- Mimicking biological phenomena and concepts. 330 $aBiological systems have always inspired mankind in the creation of new systems and technologies. In recent years the interface between the biological and non-biological worlds appears increasingly blurred due to significant advances both in our understanding of biological phenomena, as well as the development of sophisticated means to manipulate molecular systems for varied applications. Biomimetics as a distinct discipline shows how biology and biological processes are manifested in diverse aspects of chemistry, physics and engineering. This book aims to methodically describe artificial and synthetic assemblies mimicking biological and living systems - from biomaterials to drug discovery to microelectronics and computer sciences. 410 3$aDe Gruyter Textbook 606 $aBiomimetic materials 606 $aBiomimetics 610 $aBiochemistry. 610 $aBiomimetics. 610 $aBiophysics. 610 $aChemical Engineering. 610 $aMaterials Science. 615 0$aBiomimetic materials. 615 0$aBiomimetics. 676 $a610.82/4 686 $aWD 2350$2rvk 700 $aJelinek$b Raz$0767194 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910823914803321 996 $aBiomimetics$92764984 997 $aUNINA