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 Schneps, Leila, author.
 New York : Basic Books, [2013]
 Description
 Book — xi, 256 pages : illustrations, portraits ; 25 cm
 Summary

 Introduction
 Math error number 1 : multiplying nonindependent probabilities : the case of Sally Clark : motherhood under attack
 Math error number 2 : unjustified estimates : the case of Janet Collins : hairstyle probability
 Math error number 3 : trying to get something from nothing : the case of Joe Sneed : absent from the phone book
 Math error number 4 : double experiment : the case of Meredith Kercher : the test that wasn't done
 Math error number 5 : the birthday problem : the cold case of Diana Sylvester : cold hit analysis
 Math error number 6 : Simpson's paradox : the Berkeley sex bias case : discrimination detection
 Math error number 7 : the incredible coincidence : the case of Lucia de Berk : carer or killer?
 Math error number 8 : underestimation : the case of Charles Ponzi : American dream, American scheme
 Math error number 9 : choosing a wrong model : the case of Hetty Green : a battle of wills
 Math error number 10 : mathematical madness : the Dreyfus affair : spy or scapegoat?.
(source: Nielsen Book Data) 9780465032921 20160615
 Online
 Good, Phillip I.
 Boca Raton : Chapman & Hall/CRC, c2001.
 Description
 Book — xviii, 276 p. : ill. ; 25 cm.
 Summary

 Preface Interpreting Case Citations PART I: SAMPLES AND POPULATIONS Samples and Populations Audits Determining the Appropriate Population Representative Samples and Jury Selection Concepts Issues Composition of the Jury Pool Random Selection To Learn More Sample and Survey Methodology Concepts Sampling Methodology Increasing Sample Reliability Missing Data and Nonresponders Presenting Your Case Concepts The Center or Average Measures of Precision Changes in Rates PART II: PROBABILITY Probability Concepts EquallyLikely, Equally Probable, Equally Frequent MutuallyExclusive Events Conditional Probabilities Independence Bayes Theorem To Learn More Criminal Law Facts not Probabilities Observation vs Guesstimates Probable Cause Sentencing To Learn More Civil Law The Civil Paradigm Holdings Speculative Gains and Losses To Learn More Environmental Hazards Concepts Is the Evidence Admissible? Is the Evidence Sufficient? Risk v. Probability Use of Models Observations v. Experiments Multiple Defendants PART III: HYPOTHESIS TESTING AND ESTIMATION How Large is Large? Discrimination 80% Rule No Sample Too Small Methods of Analysis Comparing Two Samples The Underlying Population Distribution Theory Contingency Tables To Learn More Correlation Correlation Bias Testing Linear Regression Sidebar: Limitations of Regression Multivariate Regression Lost Earnings Multiple Applications Collinearity and Partial Correlation Defenses Rebuttal Decisions Alternate Forms of Regression Analysis When Statistics Don't Count CounterAttack PART IV: APPLYING STATISTICS IN THE COURTROOM Preventative Statistics Concepts Appropriate Controls Power of a Test Coincidence Recognizing Bad Statistics The Trial ProcessFor the Statistician Selecting the Case PreFiling Discovery Depositions PostDeposition, PreTrial In the Courtroom Appeals Making Effective Use of Statistics and StatisticiansFor the Attorney Selecting the Statistician PreFiling Preparation Discovery Presentation of Evidence Appeals TABLE OF AUTHORITIES BIBLIOGRAPHY INDEX.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781584882718 20160528
 Online
 New York : SpringerVerlag, c1989.
 Description
 Book — xvii, 357 p. ; 25 cm.
 Online
4. Statistical science in the courtroom [2000]
 New York : Springer, c2000.
 Description
 Book — xxii, 443 p. : ill. ; 24 cm.
 Summary

 Evidence Interpretation and Sample Size Determination, C.G.G. Aitken. Statistical Issues in the Application of the Federal Sentencing Guidelines in Drug, Pornography, and Fraud Cases, Alan J. Izenman. Interpreting DNA Evidence: Can Probability Theory Help? David J. Balding. Statistics, Litigation, and Conduct Unbecoming, Seymour Geisser. The Consequences of Defending DNA Statistics, Bruce S. Weir. DNA Statistics Under Trial in the Australia Adversarial System, Janet Chaseling. A Likelihood Approach to DNA Evidence, Beverly Mellen. The Choice of Hypotheses When Evaluating DNA Profile Evidence, Anders Stockmarr. On the Evolution of Analytical Proof, Statistics and the Use of Experts in EEO Litigation, Marc Rosenblum. A Connecticut Jury Array Challenge, David Pollard. Issues Arising in the Use of Statistical Evidence in Discrimination Cases, Joseph L. Gastwirth. Statistical Consulting in the Legal Environment, Charles R. Mann. Epidemiological Causation in the Legal Context: Substance and Procedures, Sana Loue. Judicial Review of Statistical Analyses in Environmental Rulemakings, Wendy E. Wagner. Assessing Costs of Smoking for Minnesota  vs.  Tobacco The Perspective of Statistical Experts in a Landmark Civil Case, Scott L. Zeger, Timothy Wyant, Leonard Miller, and Jonathan Samet. Statistical Issues in the Estimation of the Causal Effects of Smoking Due to the Conduct of the Tobacco Industry, Donald B. Rubin. Forensic Statistics and Multiparty Bayesianism, Joseph B. Kadane. Warranty Contracts and Equilibrium Probabilities, Nozer D. Singpurwalla. Death and Deterrence: Notes on a Still Inchoate Judicial Inquiry, Robert J. Cottrol. Introduction to Two Views on the Shonubi Case, Alan J. Izenman. Assessing the Statistical Evidence in the Shonubi Case, Alan Izenman. The Shonubi Case as Example of the Legal System's Failure to Appreciate Statistical Evidence, Joseph L. Gastwirth, Boris Freidlin, and Weiwen. Assessing the Statistical Evidence in the Shonubi Case, Alan J. Izenman.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387989976 20160528
 Online
 New York : Springer, ©2000.
 Description
 Book — 1 online resource (xxii, 443 pages) : illustrations.
 Summary

 Evidence Interpretation and Sample Size Determination, C.G.G. Aitken. Statistical Issues in the Application of the Federal Sentencing Guidelines in Drug, Pornography, and Fraud Cases, Alan J. Izenman. Interpreting DNA Evidence: Can Probability Theory Help? David J. Balding. Statistics, Litigation, and Conduct Unbecoming, Seymour Geisser. The Consequences of Defending DNA Statistics, Bruce S. Weir. DNA Statistics Under Trial in the Australia Adversarial System, Janet Chaseling. A Likelihood Approach to DNA Evidence, Beverly Mellen. The Choice of Hypotheses When Evaluating DNA Profile Evidence, Anders Stockmarr. On the Evolution of Analytical Proof, Statistics and the Use of Experts in EEO Litigation, Marc Rosenblum. A Connecticut Jury Array Challenge, David Pollard. Issues Arising in the Use of Statistical Evidence in Discrimination Cases, Joseph L. Gastwirth. Statistical Consulting in the Legal Environment, Charles R. Mann. Epidemiological Causation in the Legal Context: Substance and Procedures, Sana Loue. Judicial Review of Statistical Analyses in Environmental Rulemakings, Wendy E. Wagner. Assessing Costs of Smoking for Minnesota  vs.  Tobacco The Perspective of Statistical Experts in a Landmark Civil Case, Scott L. Zeger, Timothy Wyant, Leonard Miller, and Jonathan Samet. Statistical Issues in the Estimation of the Causal Effects of Smoking Due to the Conduct of the Tobacco Industry, Donald B. Rubin. Forensic Statistics and Multiparty Bayesianism, Joseph B. Kadane. Warranty Contracts and Equilibrium Probabilities, Nozer D. Singpurwalla. Death and Deterrence: Notes on a Still Inchoate Judicial Inquiry, Robert J. Cottrol. Introduction to Two Views on the Shonubi Case, Alan J. Izenman. Assessing the Statistical Evidence in the Shonubi Case, Alan Izenman. The Shonubi Case as Example of the Legal System's Failure to Appreciate Statistical Evidence, Joseph L. Gastwirth, Boris Freidlin, and Weiwen. Assessing the Statistical Evidence in the Shonubi Case, Alan J. Izenman.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387989976 20160528
 Gastwirth, Joseph L.
 Boston : Academic Press, c1988.
 Description
 Book — 2 v. : ill. ; 24 cm.
 Summary

 v.
 1. Statistical concepts and issues of fairness.
 v.
 2. Tort law, evidence, and health.
 Online
 Barnes, David W.
 Boston : Little, Brown, c1986.
 Description
 Book — xxxv, 651 p. : ill. ; 24 cm.
 Online
 Peterson, David W., 1940
 1st ed.  Morrisville, NC : Published by David W. Peterson via Lulu Enterprises, c2007.
 Description
 Book — viii, 211 p. : ill. ; 24 cm.
 Summary

Many legal disputes turn on some form of the question, Why did they do that? Using examples involving employment discrimination, political redistricting, jury selection and computer code theft, we demonstrate that a novel analytical framework connects these diverse cases. When this framework is applied to pay discrimination cases, it yields information that is more relevant to the issues in dispute than does the traditional framework.
(source: Nielsen Book Data) 9781430306535 20160528
 Online
9. Statistical analysis in forensic science : evidential value of multivariate physicochemical data [2014]
 Zadora, Grzegorz, author.
 Chichester, West Sussex : John Wiley & Sons, 2014.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Preface xiii
 1 Physicochemical data obtained in forensic science laboratories
 1 1.1 Introduction
 1 1.2 Glass
 2 1.3 Flammable liquids: ATDGC/MS technique
 8 1.4 Car paints: PyGC/MS technique
 10 1.5 Fibres and inks: MSPDAD technique
 13 References
 15
 2 Evaluation of evidence in the form of physicochemical data
 19 2.1 Introduction
 19 2.2 Comparison problem
 21 2.3 Classification problem
 27 2.4 Likelihood ratio and Bayes theorem
 31 References
 32
 3 Continuous data
 35 3.1 Introduction
 35 3.2 Data transformations
 37 3.3 Descriptive statistics
 39 3.4 Hypothesis testing
 59 3.5 Analysis of variance
 78 3.6 Cluster analysis
 85 3.7 Dimensionality reduction
 92 References
 105
 4 Likelihood ratio models for comparison problems
 107 4.1 Introduction
 107 4.2 Normal betweenobject distribution
 108 4.3 Betweenobject distribution modelled by kernel density estimation
 110 4.4 Examples
 112 4.5 R Software
 140 References
 149
 5 Likelihood ratio models for classification problems
 151 5.1 Introduction
 151 5.2 Normal betweenobject distribution
 152 5.3 Betweenobject distribution modelled by kernel density estimation
 155 5.4 Examples
 157 5.5 R software
 172 References
 179
 6 Performance of likelihood ratio methods
 181 6.1 Introduction
 181 6.2 Empirical measurement of the performance of likelihood ratios
 182 6.3 Histograms and Tippett plots
 183 6.4 Measuring discriminating power
 186 6.5 Accuracy equals discriminating power plus calibration: Empirical crossentropy plots
 192 6.6 Comparison of the performance of different methods for LR computation
 200 6.7 Conclusions: What to measure, and how
 214 6.8 Software
 215 References
 216 Appendix A Probability
 218 A.1 Laws of probability
 218 A.2 Bayes theorem and the likelihood ratio
 222 A.3 Probability distributions for discrete data
 225 A.4 Probability distributions for continuous data
 227 References
 227 Appendix B Matrices: An introduction to matrix algebra
 228 B.1 Multiplication by a constant
 228 B.2 Adding matrices
 229 B.3 Multiplying matrices
 230 B.4 Matrix transposition
 232 B.5 Determinant of a matrix
 232 B.6 Matrix inversion
 233 B.7 Matrix equations
 235 B.8 Eigenvectors and eigenvalues
 237 Reference
 239 Appendix C Pool adjacent violators algorithm
 240 References
 243 Appendix D Introduction to R software
 244 D.1 Becoming familiar with R
 244 D.2 Basic mathematical operations in R
 246 D.3 Data input
 252 D.4 Functions in R
 254 D.5 Dereferencing
 255 D.6 Basic statistical functions
 257 D.7 Graphics with R
 258 D.8 Saving data
 266 D.9 R codes used in Chapters
 4 and
 5
 266 D.10 Evaluating the performance of LR models
 289 Reference
 293 Appendix E Bayesian network models
 294 E.1 Introduction to Bayesian networks
 294 E.2 Introduction to Hugin ResearcherTM software
 296 References
 308 Appendix F Introduction to calcuLatoR software
 309 F.1 Introduction
 309 F.2 Manual
 309 Reference
 314 Index 315.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781118763155 20160711
 Zadora, Grzegorz.
 Wiley, 2013.
 Description
 Book — 1 online resource ()
 Summary

 Preface xiii
 1 Physicochemical data obtained in forensic science laboratories
 1 1.1 Introduction
 1 1.2 Glass
 2 1.2.1 SEMEDX technique
 4 1.2.2 GRIM technique
 5 1.3 Flammable liquids: ATDGC/MS technique
 8 1.4 Car paints: PyGC/MS technique
 10 1.5 Fibres and inks: MSPDAD technique
 13 References
 15
 2 Evaluation of evidence in the form of physicochemical data
 19 2.1 Introduction
 19 2.2 Comparison problem
 21 2.2.1 Twostage approach
 21 2.2.2 Likelihood ratio approach
 23 2.2.3 Difference between an application of twostage approach and likelihood ratio approach
 26 2.3 Classification problem
 27 2.3.1 Chemometric approach
 27 2.3.2 Likelihood ratio approach
 31 2.4 Likelihood ratio and Bayes' theorem
 31 References
 32
 3 Continuous data
 35 3.1 Introduction
 35 3.2 Data transformations
 37 3.3 Descriptive statistics
 39 3.3.1 Measures of location
 39 3.3.2 Dispersion: Variance estimation
 42 3.3.3 Data distribution
 44 3.3.4 Correlation
 45 3.3.5 Continuous probability distributions
 49 3.4 Hypothesis testing
 59 3.4.1 Introduction
 59 3.4.2 Hypothesis test for a population mean for samples with known variance sigma2 from a normal distribution
 60 3.4.3 Hypothesis test for a population mean for small samples with unknown variance sigma2 from a normal distribution
 63 3.4.4 Relation between tests and confidence intervals
 67 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
 68 3.4.6 Paired comparisons
 72 3.4.7 Hotelling's T
 2 test
 75 3.4.8 Significance test for correlation coefficient
 77 3.5 Analysis of variance
 78 3.5.1 Principles of ANOVA
 78 3.5.2 Feature selection with application of ANOVA
 82 3.5.3 Testing of the equality of variances
 85 3.6 Cluster analysis
 85 3.6.1 Similarity measurements
 86 3.6.2 Hierarchical cluster analysis
 89 3.7 Dimensionality reduction
 92 3.7.1 Principal component analysis
 93 3.7.2 Graphical models
 99 References
 105
 4 Likelihood ratio models for comparison problems
 107 4.1 Introduction
 107 4.2 Normal betweenobject distribution
 108 4.2.1 Multivariate data
 109 4.2.2 Univariate data
 110 4.3 Betweenobject distribution modelled by kernel density estimation
 110 4.3.1 Multivariate data
 111 4.3.2 Univariate data
 111 4.4 Examples
 112 4.4.1 Univariate research data
 normal betweenobject distribution
 R software
 112 4.4.2 Univariate casework data
 normal betweenobject distribution
 Bayesian network
 116 4.4.3 Univariate research data
 kernel density estimation
 R software
 119 4.4.4 Univariate casework data
 kernel density estimation
 calcuLatoR software
 125 4.4.5 Multivariate research data
 normal betweenobject distribution
 R software
 127 4.4.6 Multivariate research data
 kernel density estimation procedure
 R software
 129 4.4.7 Multivariate casework data
 kernel density estimation
 R software
 137 4.5 R Software
 140 4.5.1 Routines for casework applications
 140 4.5.2 Routines for research applications
 144 References
 149
 5 Likelihood ratio models for classification problems
 151 5.1 Introduction
 151 5.2 Normal betweenobject distribution
 152 5.2.1 Multivariate data
 153 5.2.2 Univariate data
 153 5.2.3 Onelevel models
 154 5.3 Betweenobject distribution modelled by kernel density estimation
 155 5.3.1 Multivariate data
 155 5.3.2 Univariate data
 156 5.3.3 Onelevel models
 156 5.4 Examples
 157 5.4.1 Univariate casework data
 normal betweenobject distribution
 Bayesian network
 158 5.4.2 Univariate research data
 kernel density estimation procedure
 R software
 161 5.4.3 Multivariate research data
 kernel density estimation
 R software
 164 5.4.4 Multivariate casework data
 kernel density estimation
 R software
 169 5.5 R software
 172 5.5.1 Routines for casework applications
 172 5.5.2 Routines for research applications
 175 References
 179
 6 Performance of likelihood ratio methods
 181 6.1 Introduction
 181 6.2 Empirical measurement of the performance of likelihood ratios
 182 6.3 Histograms and Tippett plots
 183 6.4 Measuring discriminating power
 186 6.4.1 False positive and false negative rates
 187 6.4.2 Discriminating power: A definition
 188 6.4.3 Measuring discriminating power with DET curves
 190 6.4.4 Is discriminating power enough?
 192 6.5 Accuracy equals discriminating power plus calibration: Empirical crossentropy plots
 192 6.5.1 Accuracy in a classical example: Weather forecasting
 193 6.5.2 Calibration
 195 6.5.3 Adaptation to forensic inference using likelihood ratios
 196 6.6 Comparison of the performance of different methods for LR computation
 200 6.6.1 MSPDAD data from comparison of inks
 200 6.6.2 PyGC/MS data from comparison of car paints
 205 6.6.3 SEMEDX data for classification of glass objects
 209 6.7 Conclusions: What to measure, and how
 214 6.8 Software
 215 References
 216 Appendix A Probability
 218 A.1 Laws of probability
 218 A.2 Bayes' theorem and the likelihood ratio
 222 A.3 Probability distributions for discrete data
 225 A.4 Probability distributions for continuous data
 227 References
 227 Appendix B Matrices: An introduction to matrix algebra
 228 B.1 Multiplication by a constant
 228 B.2 Adding matrices
 229 B.3 Multiplying matrices
 230 B.4 Matrix transposition
 232 B.5 Determinant of a matrix
 232 B.6 Matrix inversion
 233 B.7 Matrix equations
 235 B.8 Eigenvectors and eigenvalues
 237 Reference
 239 Appendix C Pool adjacent violators algorithm
 240 References
 243 Appendix D Introduction to R software
 244 D.1 Becoming familiar with R
 244 D.2 Basic mathematical operations in R
 246 D.2.1 Vector algebra
 248 D.2.2 Matrix algebra
 250 D.3 Data input
 252 D.4 Functions in R
 254 D.5 Dereferencing
 255 D.6 Basic statistical functions
 257 D.7 Graphics with R
 258 D.7.1 Boxplots
 258 D.7.2 QQ plots
 259 D.7.3 Normal distribution
 260 D.7.4 Histograms
 262 D.7.5 Kernel density estimation
 263 D.7.6 Correlation between variables
 263 D.8 Saving data
 266 D.9 R codes used in Chapters
 4 and
 5
 266 D.9.1 Comparison problems in casework studies
 266 D.9.2 Comparison problems in research studies
 273 D.9.3 Classification problems in casework studies
 278 D.9.4 Classification problems in research studies
 285 D.10 Evaluating the performance of LR models
 289 D.10.1 Histograms
 289 D.10.2 Tippett plots
 290 D.10.3 DET plots
 291 D.10.4 ECE plots
 292 Reference
 293 Appendix E Bayesian network models
 294 E.1 Introduction to Bayesian networks
 294 E.2 Introduction to Hugin ResearcherTM software
 296 E.2.1 Basic functions
 297 E.2.2 Creating a new Bayesian network
 298 E.2.3 Calculations
 302 References
 308 Appendix F Introduction to calcuLatoR software
 309 F.1 Introduction
 309 F.2 Manual
 309 Reference
 314 Index 315.
 (source: Nielsen Book Data)
 Preface xiii
 1 Physicochemical data obtained in forensic sciencelaboratories
 1 1.1 Introduction
 1 1.2 Glass
 2 1.3 Flammable liquids: ATDGC/MS technique
 8 1.4 Car paints: PyGC/MS technique
 10 1.5 Fibres and inks: MSPDAD technique
 13 References
 15
 2 Evaluation of evidence in the form of physicochemical data19 2.1 Introduction
 19 2.2 Comparison problem
 21 2.3 Classification problem
 27 2.4 Likelihood ratio and Bayes theorem
 31 References
 32
 3 Continuous data
 35 3.1 Introduction
 35 3.2 Data transformations
 37 3.3 Descriptive statistics
 39 3.4 Hypothesis testing
 59 3.5 Analysis of variance
 78 3.6 Cluster analysis
 85 3.7 Dimensionality reduction
 92 References
 105
 4 Likelihood ratio models for comparison problems
 107 4.1 Introduction
 107 4.2 Normal betweenobject distribution
 108 4.3 Betweenobject distribution modelled by kernel densityestimation
 110 4.4 Examples
 112 4.5 R Software
 140 References
 149
 5 Likelihood ratio models for classification problems151 5.1 Introduction
 151 5.2 Normal betweenobject distribution
 152 5.3 Betweenobject distribution modelled by kernel densityestimation
 155 5.4 Examples
 157 5.5 R software
 172 References
 179
 6 Performance of likelihood ratio methods
 181 6.1 Introduction
 181 6.2 Empirical measurement of the performance of likelihoodratios
 182 6.3 Histograms and Tippett plots
 183 6.4 Measuring discriminating power
 186 6.5 Accuracy equals discriminating power plus calibration:Empirical crossentropy plots
 192 6.6 Comparison of the performance of different methods for LRcomputation
 200 6.7 Conclusions: What to measure, and how
 214 6.8 Software
 215 References
 216 Appendix A Probability
 218 A.1 Laws of probability
 218 A.2 Bayes theorem and the likelihood ratio
 222 A.3 Probability distributions for discrete data
 225 A.4 Probability distributions for continuous data
 227 References
 227 Appendix B Matrices: An introduction to matrix algebra228 B.1 Multiplication by a constant
 228 B.2 Adding matrices
 229 B.3 Multiplying matrices
 230 B.4 Matrix transposition
 232 B.5 Determinant of a matrix
 232 B.6 Matrix inversion
 233 B.7 Matrix equations
 235 B.8 Eigenvectors and eigenvalues
 237 Reference
 239 Appendix C Pool adjacent violators algorithm
 240 References
 243 Appendix D Introduction to R software
 244 D.1 Becoming familiar with R
 244 D.2 Basic mathematical operations in R
 246 D.3 Data input
 252 D.4 Functions in R
 254 D.5 Dereferencing
 255 D.6 Basic statistical functions
 257 D.7 Graphics with R
 258 D.8 Saving data
 266 D.9 R codes used in Chapters
 4 and
 5
 266 D.10 Evaluating the performance of LR models
 289 Reference
 293 Appendix E Bayesian network models
 294 E.1 Introduction to Bayesian networks
 294 E.2 Introduction to Hugin ResearcherTM software
 296 References
 308 Appendix F Introduction to calcuLatoR software
 309 F.1 Introduction
 309 F.2 Manual
 309 Reference
 314 Index 315.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781118763155 20160612
A practical guide for determining the evidential value ofphysicochemical data Microtraces of various materials (e.g. glass, paint, fibres, andpetroleum products) are routinely subjected to physicochemicalexamination by forensic experts, whose role is to evaluate suchphysicochemical data in the context of the prosecution and defencepropositions. Such examinations return various kinds ofinformation, including quantitative data. From the forensic pointof view, the most suitable way to evaluate evidence is thelikelihood ratio. This book provides a collection of recentapproaches to the determination of likelihood ratios and describessuitable software, with documentation and examples of their use inpractice. The statistical computing and graphics softwareenvironment R, precomputed Bayesian networks using HuginResearcher and a new package, calcuLatoR, for thecomputation of likelihood ratios are all explored. Statistical Analysis in Forensic Science will provide aninvaluable practical guide for forensic experts and practitioners, forensic statisticians, analytical chemists, andchemometricians. Key features include: * Description of the physicochemical analysis of forensic traceevidence. * Detailed description of likelihood ratio models for determiningthe evidential value of multivariate physicochemicaldata. * Detailed description of methods, such as empiricalcrossentropy plots, for assessing the performance of likelihoodratiobased methods for evidence evaluation. * Routines written using the opensource R software, aswell as Hugin Researcher and calcuLatoR. * Practical examples and recommendations for the use of all thesemethods in practice.
(source: Nielsen Book Data) 9780470972106 20160612
Online 11. Measuring the frequency occurrence of handwriting and handprinting characteristics [2017]
 Johnson, Mark E. author.
 [United States] : [Publisher not identified], January 2017. [Rockville, MD] : National Criminal Justice Reference Service
 Description
 Book — 1 online resource (84 pages) : color illustrations Digital: text file.
 Summary

"This report describes the results from a National Institute of Justice funded statistical research project through the National Center of Forensic Science at the University of Central Florida. The motivation of the study was to strengthen the statistical basis for handwriting comparisons, following the recognition that the discipline of forensic document examination was facing increasing judicial scrutiny under the Daubert guidelines as recognized by the profession and subsequently reported in the National Research Council report, Strengthening Forensic Science in the United States: A Path Forward (2009). In response, this project's objectives were to develop statistically valid frequency occurrence proportions for selected characteristics of handwriting and hand printing based on specimen samples representative of the United States population, to provide practitioners of forensic document examination with a statistical basis for reliability and measurement validity and to provide courts with the requested supporting data The project produced an initial set of over 2500 precise handwriting and hand printing features that were subsequently reduced to 903 features which passed an attribute agreement analysis and to 786 that were utilized in this project. These attribute features (presence/absence) can be unambiguously identified by forensic document examiners. Handwriting samples from over 1500 writers were collected representing a broad spectrum of contributors intended to be representative of the US adult population. Meeting the prespecified population representation led to the selection of a subset of 880 cursive specimens and 839 hand printed specimens that closely approximated the demographic proportions represented in the US. The analysis of these specimens yielded numerous specific frequency occurrence proportions. Additional analyses have shown quantitatively the extent to which demographic features such as age, gender, ethnicity, education, location of second/third grade training and handedness impact the presence/absence of features. An immediate benefit of the databases analysis has been a detailed assessment of the scope of the appropriateness of the product rule. This project relied heavily on international standards and appropriate statistical methodology to develop the sampling protocols."Abstract.
 Collection
 Government Information United States Federal Collection
 Also online at
12. The use of statistics in forensic science [1991]
 Aitken, C. G. G.
 New York : E. Horwood, 1991.
 Description
 Book — 242 p. ; 25 cm.
 Summary

 Probability populations and samples weight of evidence and the Bayesian likelihood ratio transfer evidence application of statistics to particular areas of forensic science knowledgebased systems quality based systems.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780139337482 20160528
 Online
13. The use of statistics in forensic science [2002]
 New York : E. Horwood, 2002.
 Description
 Book — 1 online resource (242 pages) : illustrations.
 Summary

 Interpretation : a personal odyssey / I.W. Evett
 Introduction / C.G.G. Aitken and D.A. Stoney
 Probability / D.V. Lindley
 Populations and samples / C.G.G. Aitken
 Weight of evidence and the Bayesian likelihood ratio / I.J. Good
 Transfer evidence / D.A. Stoney
 Application of statistics to particular areas of forensic science. Evidence from blood / G.A.F. Seber ; DNA fingerprinting / R.N. Curnow ; Probability of paternity / D.A. Berry ; Forensic identification of soil, using statistical models / G. Gettinby ; Human hairs / C.G.G. Aitken and J. Robertson ; Statistics in forensic ballistics / W.F. Rowe ; The use of statistics in the investigation of post mortem interval estimation from body temperature / J.C. Wright and M.A. Green
 Knowledgebased systems / J. Buckleton and K. Walsh
 Quality assurance in the forensic laboratory / T.A. Kubic and J. Buscaglia.
14. Symposium : forensic statistics [2005  2006]
 Symposium (Jurimetrics)
 [Chicago, Illinois] : Section of Science & Technology Law, American Bar Association ; Tempe, Arizona : Center for the Study of Law, Science, and Technology, Arizona State University College of Law, [2005][2006]
 Description
 Book — 2 volumes ; 23 cm
 Summary

 Volume 46, number 1. Symposium on forensic statistics : a foreword / D. H. Kaye
 The importance of checking the assumptions underlying statistical analysis : graphical methods for assessing normality / Yulia Gel, Weiwen Miao, Joseph L. Gastwirth
 Assessing spatial heterogeneity in the refractive index of float glass / Geva Maimon, Russell J. Steele, James M. Curran
 An introduction to data fusion, data mining, and pattern recognition applied to fiber analysis / Jennifer Wiseman Mercer, Suzanne C. Bell
 Analyzing the relevance and admissibility of bulletlead evidence : did the NRC report miss the target / William C. Thompson
 The NRC bulletlead report : should science committees make legal findings / D. H. Kaye
 To tell the truth : on the probative value of polygraph search evidence / Stephen E. Fienberg.
 Volume 46, number 2. "Implicit testing" : can casework validate forensic techniques? / Simon A. Cole
 Statistical assessment of damages in breach of contract litigation / Duane L. Steffey, Stephen E. Fienberg, Robert H. Sturgess
 Articles. Powerlaw distributions and the federal judiciary / Thomas Bak
 Assessing the implications for close relatives in the event of similar but nonmatching DNA profiles / David R. Paoletti, Travis E. Doom, Michael L. Raymer, Dan E. Krane
 Beyond the ken? : testing jurors' understanding of eyewitness reliability evidence / Richard S. Schmechel, Timothy P. O'Toole, Catharine Easterly, Elizabeth F. Loftus.
 Online
 Kossovsky, Alex Ely.
 Singapore ; Hackensack, N.J. : World Scientific Pub. Co., c2015.
 Description
 Book — xxi, 649 p. : ill.
 Summary

 Benford's Law Forensic Digital Analysis & Fraud Detection Data Compliance Tests Conceptual and Mathematical Foundations Benford's Law in the Physical Sciences Topics in Benford's Law The Law of Relative Quantities.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9789814583688 20160617
 Aitken, C. G. G.
 2nd ed.  Chichester, England : Wiley, c2004.
 Description
 Book — xxx, 509 p. : ill. ; 24 cm.
 Summary

 Uncertainty in forensic science
 Variation
 The evaluation of evidence
 Historical review
 Bayesian inference
 Sampling
 Interpretation
 Transfer evidence
 Discrete data
 Continuous data
 Multivariate analysis
 Fibres
 DNA profiling
 Bayesian networks.
 Online

 dx.doi.org Wiley Online Library
 Google Books (Full view)
 Lipson, Ashley S.
 Durham, N.C. : Carolina Academic Press, c2011.
 Description
 Book — xv, 436 p. : ill. ; 26 cm.
 Summary

 Introducing numbers to the legal profession
 Math basics
 Logic and set theory
 Probability theory
 Statistics
 Graphs and diagrams
 Classical physics
 Modern physics
 Accounting and finance.
 Online
18. Courtroom use and misuse of mathematics, physics and finance : cases, lessons and materials [2013]
 Mathematics, physics and finance for the legal profession
 Lipson, Ashley S., author.
 Durham, North Carolina : Carolina Academic Press, [2013]
 Description
 Book — xv, 534 pages : illustrations ; 27 cm
 Summary

 Introducing numbers to the legal profession
 Math basics
 Logic and set theory
 Probability theory
 Statistics
 Graphs and diagrams
 Classical physics
 Modern physics
 Accounting and finance.
 Online
 Washington, D.C. : United States. Dept. of Energy. ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2015
 Description
 Book — 1 online resource (p. 14361440) : digital, PDF file.
 Summary

Historical and recent challenges to the practice of comparative forensic examination have created a driving force for the formation of objective methods for toolmark identification. In this study, fifty sequentially manufactured chisels were used to create impression toolmarks in lead (500 toolmarks total). An algorithm previously used to statistically separate known matching and nonmatching striated screwdriver marks and quasistriated plier marks was used to evaluate the chisel marks. Impression toolmarks, a more complex form of toolmark, pose a more difficult test for the algorithm that was originally designed for striated toolmarks. Lastly, results show in this instance that the algorithm can separate matching and nonmatching impression marks, providing further validation of the assumption that toolmarks are identifiably unique.
 Online
 Bell, Suzanne, author.
 Boca Raton : CRC Press, [2017]
 Description
 Book — 1 online resource : text file, PDF
 Summary

 1. Forensic Measurements, Metrology, and Uncertainty
 2. Sources of Uncertainty
 3. Foundational Concepts
 4. Processes and Procedures
 5. Measurement Assurance: Distances, Crime Scenes, and Firearms
 6. Uncertainty and Weighing
 7. Breath Alcohol
 8. Miscellaneous Topics.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781498721165 20171218
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