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This text is an accessible, student-friendly introduction to thewide range of mathematical and statistical tools needed by theforensic scientist in the analysis, interpretation and presentationof experimental measurements. From a basis of high school mathematics, the book developsessential quantitative analysis techniques within the context of abroad range of forensic applications. This clearly structured textfocuses on developing core mathematical skills together with anunderstanding of the calculations associated with the analysis ofexperimental work, including an emphasis on the use of…mehr

Produktbeschreibung
This text is an accessible, student-friendly introduction to thewide range of mathematical and statistical tools needed by theforensic scientist in the analysis, interpretation and presentationof experimental measurements. From a basis of high school mathematics, the book developsessential quantitative analysis techniques within the context of abroad range of forensic applications. This clearly structured textfocuses on developing core mathematical skills together with anunderstanding of the calculations associated with the analysis ofexperimental work, including an emphasis on the use of graphs andthe evaluation of uncertainties. Through a broad study ofprobability and statistics, the reader is led ultimately to the useof Bayesian approaches to the evaluation of evidence within thecourt. In every section, forensic applications such as ballisticstrajectories, post-mortem cooling, aspects of forensicpharmacokinetics, the matching of glass evidence, the formation ofbloodstains and the interpretation of DNA profiles are discussedand examples of calculations are worked through. In every chapterthere are numerous self-assessment problems to aid studentlearning. Its broad scope and forensically focused coverage make this bookan essential text for students embarking on any degree course inforensic science or forensic analysis, as well as an invaluablereference for post-graduate students and forensicprofessionals. Key features: * Offers a unique mix of mathematics and statistics topics,specifically tailored to a forensic science undergraduatedegree. * All topics illustrated with examples from the forensic sciencediscipline. * Written in an accessible, student-friendly way to engageinterest and enhance learning and confidence. * Assumes only a basic high-school level prior mathematicalknowledge.

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 366
  • Erscheinungstermin: 15. März 2010
  • Englisch
  • ISBN-13: 9780470710357
  • Artikelnr.: 37298859
Autorenporträt
Craig Adam has over twenty years experience in teaching mathematics within the context of science at degree level. Initially this was within the physics discipline, but more recently he has developed and taught courses in mathematics and statistics for students in forensic science. As head of natural sciences at Staffordshire University in 1998, he led the initial development of forensic science degrees at that institution. Once at Keele University he worked within physics before committing himself principally to forensic science from 2004. His current research interests are focused on the use of chemometrics in the interpretation and evaluation of data from the analysis of forensic materials, particularly those acquired from spectroscopy. His teaching expertise areas within forensic science, apart from mathematics and statistics, include blood dynamics and pattern analysis, enhancement of marks and impressions, all aspects of document analysis, trace evidence analysis and evidence evaluation.
Inhaltsangabe
Preface. 1 Getting the basics right. Introduction: Why forensic science is a quantitative science. 1.1 Numbers, their representation and meaning. Self
assessment exercises and problems. 1.2 Units of measurement and their conversion. Self
assessment problems. 1.3 Uncertainties in measurement and how to deal with them. Self
assessment problems. 1.4 Basic chemical calculations. Self
assessment exercises and problems. Chapter summary. 2 Functions, formulae and equations. Introduction: Understanding and using functions, formulae and equations. 2.1 Algebraic manipulation of equations. Self
assessment exercises. 2.2 Applications involving the manipulation of formulae. Self
assessment exercises and problems. 2.3 Polynomial functions. Self
assessment exercises and problems. 2.4 The solution of linear simultaneous equations. Self
assessment exercises and problems. 2.5 Quadratic functions. Self
assessment problems. 2.6 Powers and indices. Self
assessment problems. Chapter summary. 3 The exponential and logarithmic functions and their applications. Introduction: Two special functions in forensic science. 3.1 Origin and definition of the exponential function. Self
assessment exercises. 3.2 Origin and definition of the logarithmic function. Self
assessment exercises and problems. Self
assessment exercises. 3.3 Application: the pH scale. Self
assessment exercises. 3.4 The "decaying" exponential. Self
assessment problems. 3.5 Application: post
mortem body cooling. Self
assessment problems. 3.6 Application: forensic pharmacokinetics. Self
assessment problems. Chapter summary. 4 Trigonometric methods in forensic science. Introduction: Why trigonometry is needed in forensic science. 4.1 Pythagoras's theorem. Self
assessment exercises and problems. 4.2 The trigonometric functions. Self
assessment exercises and problems. 4.3 Trigonometric rules. Self
assessment exercises. 4.4 Application: heights and distances. Self
assessment problems. 4.5 Application: ricochet analysis. Self
assessment problems. 4.6 Application: aspects of ballistics. Self
assessment problems. 4.7 Suicide, accident or murder? Self
assessment problems. 4.8 Application: bloodstain shape. Self
assessment problems. 4.9 Bloodstain pattern analysis. Self
assessment problems. Chapter summary. 5 Graphs
their construction and interpretation. Introduction: Why graphs are important in forensic science. 5.1 Representing data using graphs. 5.2 Linearizing equations. Self
assessment exercises. 5.3 Linear regression. Self
assessment exercises. 5.4 Application: shotgun pellet patterns in firearms incidents. Self
assessment problem. 5.5 Application: bloodstain formation. Self
assessment problem. 5.6 Application: the persistence of hair, fibres and flints on clothing. Self
assessment problem. 5.7 Application: determining the time since death by fly egg hatching. 5.8 Application: determining age from bone or tooth material Self
assessment problem. 5.9 Application: kinetics of chemical reactions. Self
assessment problems. 5.10 Graphs for calibration. Self
assessment problems. 5.11 Excel and the construction of graphs. Chapter summary. 6 The statistical analysis of data. Introduction: Statistics and forensic science. 6.1 Describing a set of data. Self
assessment problems. 6.2 Frequency statistics. Self
assessment problems. 6.3 Probability density functions. Self
assessment problems. 6.4 Excel and basic statistics. Chapter summary. 7 Probability in forensic science. Introduction: Theoretical and empirical probabilities. 7.1 Calculating probabilities. Self
assessment problems. 7.2 Application: the matching of hair evidence. Self
assessment problems. 7.3 Conditional probability. Self
assessment problems. 7.4 Probability tree diagrams. Self
assessment problems. 7.5 Permutations and combinations. Self
assessment problems. 7.6 The binomial probability distribution. Self
assessment problems. Chapter summary. 8 Probability and infrequent events. Introduction: Dealing with infrequent events. 8.1 The Poisson probability distribution. Self
assessment exercises. 8.2 Probability and the uniqueness of fingerprints. Self
assessment problems. 8.3 Probability and human teeth marks. Self
assessment problems. 8.4 Probability and forensic genetics. 8.5 Worked problems of genotype and allele calculations. Self
assessment problems. 8.6 Genotype frequencies and subpopulations. Self
assessment problems. Chapter summary. 9 Statistics in the evaluation of experimental data: comparison and confidence. How can statistics help in the interpretation of experimental data? 9.1 The normal distribution. Self
assessment problems. 9.2 The normal distribution and frequency histograms. 9.3 The standard error in the mean. Self
assessment problems. 9.4 The t
distribution. Self
assessment exercises and problems. 9.5 Hypothesis testing. Self
assessment problems. 9.6 Comparing two datasets using the t
test. Self
assessment problems. 9.7 The t
test applied to paired measurements. Self
assessment problems. 9.8 Pearson's Ç2 test. Self
assessment problems. Chapter summary. 10 Statistics in the evaluation of experimental data: computation and calibration. Introduction: What more can we do with statistics and uncertainty? 10.1 The propagation of uncertainty in calculations. Self
assessment exercises and problems. Self
assessment exercises and problems. 10.2 Application: physicochemical measurements. Self
assessment problems. 10.3 Measurement of density by Archimedes' upthrust. Self
assessment problems. 10.4 Application: bloodstain impact angle. Self
assessment problems. 10.5 Application: bloodstain formation. Self
assessment problems. 10.6 Statistical approaches to outliers. Self
assessment problems. 10.7 Introduction to robust statistics. Self
assessment problems. 10.8 Statistics and linear regression. Self
assessment problems. 10.9 Using linear calibration graphs and the calculation of standard error. Self
assessment problems. Chapter summary. 11 Statistics and the significance of evidence. Introduction: Where do we go from here?
Interpretation and significance. 11.1 A case study in the interpretation and significance of forensic evidence. 11.2 A probabilistic basis for interpreting evidence. Self
assessment problems. 11.3 Likelihood ratio, Bayes' rule and weight of evidence. Self
assessment problems. 11.4 Population data and interpretive databases. Self
assessment problems. 11.5 The probability of accepting the prosecution case
given the evidence. Self
assessment problems. 11.6 Likelihood ratios from continuous data. Self
assessment problems. 11.7 Likelihood ratio and transfer evidence. Self
assessment problems. 11.8 Application: double cot
death or double murder? Self
assessment problems. Chapter summary. References. Bibliography. Answers to self
assessment exercises and problems. Appendix I: The definitions of non
SI units and their relationship to the equivalent SI units. Appendix II: Constructing graphs using Microsoft Excel. Appendix III: Using Microsoft Excel for statistics calculations. Appendix IV: Cumulative z
probability table for the standard normal distribution. Appendix V: Student's t
test: tables of critical values for the t
statistic. Appendix VI: Chi squared Ç2 test: table of critical values. Appendix VII: Some values of Qcrit for Dixon's Q test. Some values for Gcrit for Grubbs' two
tailed test. Index.