Essential mathematics and statistics for forensic science. (2011)

• Record Type: Book
• Further Information: Note: Craig Adam.
• Other Names: Adam, Craig
• Contents: 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 χ 2 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 probab
• Publisher Details: Place of publication not identified : Wiley
• Publication Date: 2011
• Extent: 1 online resource (366 pages)
• Subjects: 510; Mathematical statistics; Forensic statistics; Mathematics
• Languages: English
• ISBNs: 9781119964186
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.