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Introduction to Statistics for Forensic Scientists |  |
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Introduction to Statistics for Forensic Scientists | |
Opening with a brief overview of the history and use of statistics within forensic science, the text then goes on to introduce statistical techniques commonly used to examine data obtained during laboratory experiments. There is a strong emphasis on the evaluation of scientific observation as evidence and modern Bayesian approaches to interpreting forensic data for the courts. The analysis of key forms of evidence are discussed throughout with a particular focus on DNA, fibres and glass.
An invaluable introduction to the statistical interpretation of forensic evidence; this book will be invaluable for all undergraduates taking courses in forensic science. Introduction to the key statistical techniques used in the evaluation of forensic evidence Includes end of chapter exercises to enhance student understanding Numerous examples taken from forensic science to put the subject into context
作者简介 Dr. David Lucy, School of Mathematics, The University of Edinburgh
媒体推荐 "…useful for those who are becoming introduced to forensic science." (The American Statistician, August 2007)
"...the book is an easy read...it would appeal to students of forensic science at both introductory and advanced levels." (Journal of Tropical Pedriatrics, 2nd February 2006)
" … deserves a place in the library of any serious forensic scientist and I congratulate the author on his achievement." (Significance, Issue 3, 2006)
" … an easy read with many complex concepts described in a lucid style." (Journal of Tropical Pediatrics: Vol. 52; 4, 2006)
"One of the most important issues in using likelihood ratios in a forensic context may well be determining the relevant population of a sample. This is an are that is discussed throughout the text…gives insight…" (Canadian Society of Forensic Science, October 2006)
专业书评 From the Back Cover
Introduction to Statistics for Forensic Scientists gently guides the reader through the key statistical techniques used to evaluate various types of forensic evidence. Assuming only a modest mathematical background, the book uses real-life examples from forensic science literature and forensic case-work to illustrate relevant statistical concepts and methods.
Opening with a brief overview of the history and use of statistics within forensic science, the text goes on to introduce statistical techniques commonly used to examine data obtained during laboratory experiments. There is a strong emphasis of the evaluation of scientific observation as evidence and modern Bayesian approaches to interpreting forensic data for the courts. The analysis of key forms of evidence is discussed throughout with a particular forces on DNA, fibres and glass. Introduces key statistical techniques used in the evaluation of forensic evidence. Included the end of chapter exercises to enhance student understanding. Numerous examples taken from forensic science put the subject into context.
An essential introduction to the statistical interpretation of forensic evidence, this book be invaluable for all undergraduates taking courses in forensic science.
目录
Preface.
List of Figures.
List of Tables.
1. A short history of statistics in the law.
1.1 History.
1.2 Some recent uses of statistics in forensic science.
1.3 What is probability?.
2. Data types, location and dispersion.
2.1 Types of data.
2.2 Populations and samples.
2.3 Distributions.
2.4 Location.
2.5 Dispersion.
2.6 Hierarchies of variation.
3. Probability.
3.1 Aleatory probability.
3.2 Binomial probability.
3.3 Poisson probability.
3.4 Empirical probability.
4. The normal distribution.
4.1 The normal distribution.
4.2 Standard deviation and standard error of the mean.
4.3 Percentage points of the normal distribution.
4.5 t-testing between two independent samples.
4.6 Testing between paired observations.
4.7 Confidence, significance and p-values.
5. Measures of nominal and ordinal association.
5.1 Association between discrete variables.
5.2 X2 test for a 2 x 2 table
5.3 Yules Q.
5.4 X2 tests for greater than 2 x 2 tables.
5.5 02 and crammers V2.
5.6 The limitations of X2 testing.
5.7 Interpretation and conclusions.
6. Correlation.
6.1 Significance tests for correlation coefficients.
6.2 Correlation coefficients for non-linear data.
6.3 The coefficient of determination.
6.4 Partial correlation.
6.5 Partial correlation controlling for two of more covariates.
7. Regression and calibration.
7.1 Linear models.
7.2 Calculation of a linear regression model.
7.3 Testing 'goodness of fit'.
7.4 Testing coefficients a and b.
7.5 Residuals.
7.6 Calibration.
7.7 Points to remember.
8. Evidence evaluation.
8.1 Verbal statements of evidential value.
8.2 Evidence types.
8.3 The value of evidence.
8.4 Significance testing and evidence evaluation.
9. Conditional probability and Bayes' theorem.
9.1 Conditional probability.
9.2 Bayes' theorem.
9.3 The value of evidence.
10. Relevance and the formulation of propositions.
10.1 Relevance.
10.2 Hierarchy of propositions.
10.3 Likelihood ratios and relevance.
10.4 The logic of relevance.
10.5 The formulation of propositions.
10.6 What kind of propositions can we not evaluate.
11. Evaluation of evidence in practice.
11.1 Which database to use.
11.2 Verbal equivalence of the likelihood ratio.
11.3 Some common criticisms of statistical approaches.
12. Evidence evaluation examples/
12.1 Blood group frequencies.
12.2 Trouser fibres.
12.3 Shoe types.
12.4 Airweapon projectiles.
12.5 Height description from eyewitness.
13. Errors in interpretation.
13.1 Statistically based errors of interpretation.
13.2 Methodological errors of interpretation.
14. DNA I.
14.1 Loci and alleles.
14.2 Simple case genotypic frequencies.
14.3 Hardy-weinberg equilibrium.
14.4 Simple case allelic frequencies.
14.5 Accounting for sub-populations.
15. DNA II.
15.1Paternity -mother and father unrelated.
15.2 Database searches and value of evidence.
15.3 Discussion.
16. Sampling and sample size estimation.
16.1 Estimation of a mean.
16.2 Sample sizes for t-tests.
16.3 How many drugs to sample.
16.4 Concluding comments.
17. Epilogue.
17.1 Graphical models and Bayesian Networks.
17.2 Kernel density estimation.
17.3 Multivariate continuous matching.
Appendix A: Worked solutions to questions.
Appendix B: Percentage pints of the standard normal distribution.
Appendix C: Percentage points of t-distributions.
Appendix D: Percentage points of X2-distributions.
Appendix E: Percentage points of beta-beta distributions.
Appendix F: Percentage points of f-distributions.
Appendix G: Calculating partial correlations using 'Excel' software.
Appendix H: Further algebra using the "third law".
References.
Index.
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