- Book
- xi, 256 pages : illustrations, portraits ; 25 cm

- Introduction
- Math error number 1 : multiplying non-independent 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

- Introduction
- Math error number 1 : multiplying non-independent 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

- Book
- xviii, 276 p. : ill. ; 25 cm.

- 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 Equally-Likely, Equally Probable, Equally Frequent Mutually-Exclusive 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 Counter-Attack PART IV: APPLYING STATISTICS IN THE COURTROOM Preventative Statistics Concepts Appropriate Controls Power of a Test Coincidence Recognizing Bad Statistics The Trial Process-For the Statistician Selecting the Case Pre-Filing Discovery Depositions Post-Deposition, Pre-Trial In the Courtroom Appeals Making Effective Use of Statistics and Statisticians-For the Attorney Selecting the Statistician Pre-Filing Preparation Discovery Presentation of Evidence Appeals TABLE OF AUTHORITIES BIBLIOGRAPHY INDEX.
- (source: Nielsen Book Data)9781584882718 20160528

(source: Nielsen Book Data)9781584882718 20160528

- 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 Equally-Likely, Equally Probable, Equally Frequent Mutually-Exclusive 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 Counter-Attack PART IV: APPLYING STATISTICS IN THE COURTROOM Preventative Statistics Concepts Appropriate Controls Power of a Test Coincidence Recognizing Bad Statistics The Trial Process-For the Statistician Selecting the Case Pre-Filing Discovery Depositions Post-Deposition, Pre-Trial In the Courtroom Appeals Making Effective Use of Statistics and Statisticians-For the Attorney Selecting the Statistician Pre-Filing Preparation Discovery Presentation of Evidence Appeals TABLE OF AUTHORITIES BIBLIOGRAPHY INDEX.
- (source: Nielsen Book Data)9781584882718 20160528

(source: Nielsen Book Data)9781584882718 20160528

- Book
- xvii, 357 p. ; 25 cm.

### 4. Statistical science in the courtroom [2000]

- Book
- xxii, 443 p. : ill. ; 24 cm.

- 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)9780387989976 20160528

(source: Nielsen Book Data)9780387989976 20160528

- 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)9780387989976 20160528

(source: Nielsen Book Data)9780387989976 20160528

- Book
- 1 online resource (xxii, 443 pages) : illustrations.

- 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)9780387989976 20160528

(source: Nielsen Book Data)9780387989976 20160528

- (source: Nielsen Book Data)9780387989976 20160528

(source: Nielsen Book Data)9780387989976 20160528

- Book
- 2 v. : ill. ; 24 cm.

- v. 1. Statistical concepts and issues of fairness.
- v. 2. Tort law, evidence, and health.

- v. 1. Statistical concepts and issues of fairness.
- v. 2. Tort law, evidence, and health.

- Book
- xxxv, 651 p. : ill. ; 24 cm.

- Book
- viii, 211 p. : ill. ; 24 cm.

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

(source: Nielsen Book Data)9781430306535 20160528

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

(source: Nielsen Book Data)9781430306535 20160528

### 9. Statistical analysis in forensic science : evidential value of multivariate physicochemical data [2014]

- Book
- 1 online resource (338 pages) : illustrations

### 10. Statistical analysis in forensic science : evidential value of multivariate physicochemical data [2014]

- Book
- 1 online resource (1 volume) : illustrations

- Preface xiii 1 Physicochemical data obtained in forensic science laboratories 1 1.1 Introduction 1 1.2 Glass 2 1.3 Flammable liquids: ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and inks: MSP-DAD 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 between-object distribution 108 4.3 Between-object 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 between-object distribution 152 5.3 Between-object 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 cross-entropy 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)9781118763155 20160711

(source: Nielsen Book Data)9781118763155 20160711

- Preface xiii 1 Physicochemical data obtained in forensic science laboratories 1 1.1 Introduction 1 1.2 Glass 2 1.3 Flammable liquids: ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and inks: MSP-DAD 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 between-object distribution 108 4.3 Between-object 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 between-object distribution 152 5.3 Between-object 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 cross-entropy 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)9781118763155 20160711

(source: Nielsen Book Data)9781118763155 20160711

- Book
- 1 online resource ()

- Preface xiii 1 Physicochemical data obtained in forensic science laboratories 1 1.1 Introduction 1 1.2 Glass 2 1.2.1 SEM-EDX technique 4 1.2.2 GRIM technique 5 1.3 Flammable liquids: ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and inks: MSP-DAD 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 Two-stage approach 21 2.2.2 Likelihood ratio approach 23 2.2.3 Difference between an application of two-stage 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 between-object distribution 108 4.2.1 Multivariate data 109 4.2.2 Univariate data 110 4.3 Between-object 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 between-object distribution -- R software 112 4.4.2 Univariate casework data -- normal between-object 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 between-object 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 between-object distribution 152 5.2.1 Multivariate data 153 5.2.2 Univariate data 153 5.2.3 One-level models 154 5.3 Between-object distribution modelled by kernel density estimation 155 5.3.1 Multivariate data 155 5.3.2 Univariate data 156 5.3.3 One-level models 156 5.4 Examples 157 5.4.1 Univariate casework data -- normal between-object 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 cross-entropy 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 MSP-DAD data from comparison of inks 200 6.6.2 Py-GC/MS data from comparison of car paints 205 6.6.3 SEM-EDX 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 Box-plots 258 D.7.2 Q-Q 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)9781118763155 20160612

- Preface xiii 1 Physicochemical data obtained in forensic sciencelaboratories 1 1.1 Introduction 1 1.2 Glass 2 1.3 Flammable liquids: ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and inks: MSP-DAD 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 between-object distribution 108 4.3 Between-object 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 between-object distribution 152 5.3 Between-object 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 cross-entropy 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)9780470972106 20160612

(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, pre-computed 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 empiricalcross-entropy plots, for assessing the performance of likelihoodratio-based methods for evidence evaluation. * Routines written using the open-source 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

- Preface xiii 1 Physicochemical data obtained in forensic science laboratories 1 1.1 Introduction 1 1.2 Glass 2 1.2.1 SEM-EDX technique 4 1.2.2 GRIM technique 5 1.3 Flammable liquids: ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and inks: MSP-DAD 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 Two-stage approach 21 2.2.2 Likelihood ratio approach 23 2.2.3 Difference between an application of two-stage 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 between-object distribution 108 4.2.1 Multivariate data 109 4.2.2 Univariate data 110 4.3 Between-object 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 between-object distribution -- R software 112 4.4.2 Univariate casework data -- normal between-object 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 between-object 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 between-object distribution 152 5.2.1 Multivariate data 153 5.2.2 Univariate data 153 5.2.3 One-level models 154 5.3 Between-object distribution modelled by kernel density estimation 155 5.3.1 Multivariate data 155 5.3.2 Univariate data 156 5.3.3 One-level models 156 5.4 Examples 157 5.4.1 Univariate casework data -- normal between-object 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 cross-entropy 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 MSP-DAD data from comparison of inks 200 6.6.2 Py-GC/MS data from comparison of car paints 205 6.6.3 SEM-EDX 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 Box-plots 258 D.7.2 Q-Q 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)9781118763155 20160612

- Preface xiii 1 Physicochemical data obtained in forensic sciencelaboratories 1 1.1 Introduction 1 1.2 Glass 2 1.3 Flammable liquids: ATD-GC/MS technique 8 1.4 Car paints: Py-GC/MS technique 10 1.5 Fibres and inks: MSP-DAD 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 between-object distribution 108 4.3 Between-object 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 between-object distribution 152 5.3 Between-object 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 cross-entropy 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)9780470972106 20160612

(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, pre-computed 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 empiricalcross-entropy plots, for assessing the performance of likelihoodratio-based methods for evidence evaluation. * Routines written using the open-source 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

### 12. Measuring the frequency occurrence of handwriting and hand-printing characteristics [2017] Online

- Book
- 1 online resource (84 pages) : color illustrations Digital: text file.
- Collection
- Government Information United States Federal Collection

"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 pre-specified 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.

"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 pre-specified 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.

### 13. The use of statistics in forensic science [1991]

- Book
- 242 p. ; 25 cm.

- Probability-- populations and samples-- weight of evidence and the Bayesian likelihood ratio-- transfer evidence-- application of statistics to particular areas of forensic science-- knowledge-based systems-- quality- based systems.
- (source: Nielsen Book Data)9780139337482 20160528

(source: Nielsen Book Data)9780139337482 20160528

- Probability-- populations and samples-- weight of evidence and the Bayesian likelihood ratio-- transfer evidence-- application of statistics to particular areas of forensic science-- knowledge-based systems-- quality- based systems.
- (source: Nielsen Book Data)9780139337482 20160528

(source: Nielsen Book Data)9780139337482 20160528

- Book
- 242 p. : ill.

### 15. Symposium : forensic statistics [2005 - 2006]

- Book
- 2 volumes ; 23 cm

- 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 bullet-lead evidence : did the NRC report miss the target / William C. Thompson
- The NRC bullet-lead 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. Power-law 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.

- 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 bullet-lead evidence : did the NRC report miss the target / William C. Thompson
- The NRC bullet-lead 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. Power-law 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.

- Book
- xxi, 649 p. : ill.

- 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)9789814583688 20160617

(source: Nielsen Book Data)9789814583688 20160617

- 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)9789814583688 20160617

(source: Nielsen Book Data)9789814583688 20160617

- Book
- xxx, 509 p. : ill. ; 24 cm.

- 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.

- 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.

dx.doi.org Wiley Online Library

- dx.doi.org Wiley Online Library
- Google Books (Full view)

- Book
- xv, 436 p. : ill. ; 26 cm.

- 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.

- 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.

### 19. Courtroom use and misuse of mathematics, physics and finance : cases, lessons and materials [2013]

- Book
- xv, 534 pages : illustrations ; 27 cm

- 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.

- 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.

- Book
- 1 online resource (p. 1436-1440) : digital, PDF file.

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 quasi-striated 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.

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 quasi-striated 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.

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