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Essential Mathematics and Statistics for Forensic Science

ISBN: 978-0-470-74253-2
Paperback
368 pages
May 2010
List Price: US $104.95
Government Price: US $72.92
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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 χ2 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 χ2 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.

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