LEADER 05647nam 2200757 450 001 9910820309003321 005 20210916124325.0 010 $a1-118-76318-1 010 $a1-118-76315-7 010 $a1-118-76317-3 035 $a(CKB)2550000001159905 035 $a(EBL)1557286 035 $a(OCoLC)858778356 035 $a(SSID)ssj0001041546 035 $a(PQKBManifestationID)11577322 035 $a(PQKBTitleCode)TC0001041546 035 $a(PQKBWorkID)11010091 035 $a(PQKB)10257915 035 $a(DLC) 2013038429 035 $a(Au-PeEL)EBL1557286 035 $a(CaPaEBR)ebr10804687 035 $a(CaONFJC)MIL543110 035 $a(CaSebORM)9781118763186 035 $a(MiAaPQ)EBC1557286 035 $a(PPN)191455814 035 $a(EXLCZ)992550000001159905 100 $a20130813d2014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical analysis in forensic science $eevidential value of multivariate physicochemical data /$fGrzegorz Zadora [and three others] 205 $a1st edition 210 1$aChichester, West Sussex :$cWiley,$d2014. 215 $a1 online resource (338 p.) 300 $aDescription based upon print version of record. 311 $a0-470-97210-6 311 $a1-306-11859-X 320 $aIncludes bibliographical references and index. 327 $aStatistical Analysis in Forensic Science; Contents; Preface; 1 Physicochemical data obtained in forensic science laboratories; 1.1 Introduction; 1.2 Glass; 1.2.1 SEM-EDX technique; 1.2.2 GRIM technique; 1.3 Flammable liquids: ATD-GC/MS technique; 1.4 Car paints: Py-GC/MS technique; 1.5 Fibres and inks: MSP-DAD technique; References; 2 Evaluation of evidence in the form of physicochemical data; 2.1 Introduction; 2.2 Comparison problem; 2.2.1 Two-stage approach; 2.2.2 Likelihood ratio approach; 2.2.3 Difference between an application of two-stage approach and likelihood ratio approach 327 $a2.3 Classification problem2.3.1 Chemometric approach; 2.3.2 Likelihood ratio approach; 2.4 Likelihood ratio and Bayes' theorem; References; 3 Continuous data; 3.1 Introduction; 3.2 Data transformations; 3.3 Descriptive statistics; 3.3.1 Measures of location; 3.3.2 Dispersion: Variance estimation; 3.3.3 Data distribution; 3.3.4 Correlation; 3.3.5 Continuous probability distributions; 3.4 Hypothesis testing; 3.4.1 Introduction; 3.4.2 Hypothesis test for a population mean for samples with known variance from a normal distribution 327 $a3.4.3 Hypothesis test for a population mean for small samples with unknown variance from a normal distribution3.4.4 Relation between tests and confidence intervals; 3.4.5 Hypothesis test based on small samples for a difference in the means of two independent populations with unknown variances from normal distributions; 3.4.6 Paired comparisons; 3.4.7 Hotelling's test; 3.4.8 Significance test for correlation coefficient; 3.5 Analysis of variance; 3.5.1 Principles of ANOVA; 3.5.2 Feature selection with application of ANOVA; 3.5.3 Testing of the equality of variances; 3.6 Cluster analysis 327 $a3.6.1 Similarity measurements3.6.2 Hierarchical cluster analysis; 3.7 Dimensionality reduction; 3.7.1 Principal component analysis; 3.7.2 Graphical models; References; 4 Likelihood ratio models for comparison problems; 4.1 Introduction; 4.2 Normal between-object distribution; 4.2.1 Multivariate data; 4.2.2 Univariate data; 4.3 Between-object distribution modelled by kernel density estimation; 4.3.1 Multivariate data; 4.3.2 Univariate data; 4.4 Examples; 4.4.1 Univariate research data - normal between-object distribution - R software 327 $a4.4.2 Univariate casework data - normal between-object distribution - Bayesian network4.4.3 Univariate research data - kernel density estimation - R software; 4.4.4 Univariate casework data - kernel density estimation - calcuLatoR software; 4.4.5 Multivariate research data - normal between-object distribution - R software; 4.4.6 Multivariate research data - kernel density estimation procedure - R software; 4.4.7 Multivariate casework data - kernel density estimation - R software; 4.5 R Software; 4.5.1 Routines for casework applications; 4.5.2 Routines for research applications; References 327 $a5 Likelihood ratio models for classification problems 330 $aA practical guide for determining the evidential value of physicochemical data Microtraces of various materials (e.g. glass, paint, fibres, and petroleum products) are routinely subjected to physicochemical examination by forensic experts, whose role is to evaluate such physicochemical data in the context of the prosecution and defence propositions. Such examinations return various kinds of information, including quantitative data. From the forensic point of view, the most suitable way to evaluate evidence is the likelihood ratio. This book provides a collection of recent approaches t 606 $aChemistry, Forensic 606 $aForensic statistics 606 $aChemometrics 615 0$aChemistry, Forensic. 615 0$aForensic statistics. 615 0$aChemometrics. 676 $a614/.12 700 $aZadora$b Grzegorz$01600512 701 $aZadora$b Grzegorz$01600512 701 $aMartyna$b Agnieszka$01600513 701 $aRamos$b Daniel$01600514 701 $aAitken$b Colin$01600515 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910820309003321 996 $aStatistical analysis in forensic science$93923647 997 $aUNINA