Craig Adam
Essential Mathematics and Statistics for Forensic Science
Craig Adam
Essential Mathematics and Statistics for Forensic Science
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This text is an accessible, student-friendly introduction to the wide range of mathematical and statistical tools needed by the forensic scientist in the analysis, interpretation and presentation of experimental measurements.
From a basis of high school mathematics, the book develops essential quantitative analysis techniques within the context of a broad range of forensic applications. This clearly structured text focuses on developing core mathematical skills together with an understanding of the calculations associated with the analysis of experimental work, including an emphasis on the…mehr
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This text is an accessible, student-friendly introduction to the wide range of mathematical and statistical tools needed by the forensic scientist in the analysis, interpretation and presentation of experimental measurements.
From a basis of high school mathematics, the book develops essential quantitative analysis techniques within the context of a broad range of forensic applications. This clearly structured text focuses on developing core mathematical skills together with an understanding of the calculations associated with the analysis of experimental work, including an emphasis on the use of graphs and the evaluation of uncertainties. Through a broad study of probability and statistics, the reader is led ultimately to the use of Bayesian approaches to the evaluation of evidence within the court. In every section, forensic applications such as ballistics trajectories, post-mortem cooling, aspects of forensic pharmacokinetics, the matching of glass evidence, the formation of bloodstains and the interpretation of DNA profiles are discussed and examples of calculations are worked through. In every chapter there are numerous self-assessment problems to aid student learning.
Its broad scope and forensically focused coverage make this book an essential text for students embarking on any degree course in forensic science or forensic analysis, as well as an invaluable reference for post-graduate students and forensic professionals.
Key features:
Offers a unique mix of mathematics and statistics topics, specifically tailored to a forensic science undergraduate degree.
All topics illustrated with examples from the forensic science discipline.
Written in an accessible, student-friendly way to engage interest and enhance learning and confidence.
Assumes only a basic high-school level prior mathematical knowledge.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
From a basis of high school mathematics, the book develops essential quantitative analysis techniques within the context of a broad range of forensic applications. This clearly structured text focuses on developing core mathematical skills together with an understanding of the calculations associated with the analysis of experimental work, including an emphasis on the use of graphs and the evaluation of uncertainties. Through a broad study of probability and statistics, the reader is led ultimately to the use of Bayesian approaches to the evaluation of evidence within the court. In every section, forensic applications such as ballistics trajectories, post-mortem cooling, aspects of forensic pharmacokinetics, the matching of glass evidence, the formation of bloodstains and the interpretation of DNA profiles are discussed and examples of calculations are worked through. In every chapter there are numerous self-assessment problems to aid student learning.
Its broad scope and forensically focused coverage make this book an essential text for students embarking on any degree course in forensic science or forensic analysis, as well as an invaluable reference for post-graduate students and forensic professionals.
Key features:
Offers a unique mix of mathematics and statistics topics, specifically tailored to a forensic science undergraduate degree.
All topics illustrated with examples from the forensic science discipline.
Written in an accessible, student-friendly way to engage interest and enhance learning and confidence.
Assumes only a basic high-school level prior mathematical knowledge.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14574253000
- 1. Auflage
- Seitenzahl: 368
- Erscheinungstermin: 16. April 2010
- Englisch
- Abmessung: 244mm x 170mm x 20mm
- Gewicht: 600g
- ISBN-13: 9780470742532
- ISBN-10: 0470742534
- Artikelnr.: 28162253
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14574253000
- 1. Auflage
- Seitenzahl: 368
- Erscheinungstermin: 16. April 2010
- Englisch
- Abmessung: 244mm x 170mm x 20mm
- Gewicht: 600g
- ISBN-13: 9780470742532
- ISBN-10: 0470742534
- Artikelnr.: 28162253
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.
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.
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.
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.
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.