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Zusatztext "...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) Informationen zum Autor Dr. David Lucy , School of Mathematics, The University of Edinburgh. Klappentext 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. Zusammenfassung Introduction to Statistics for Forensic Scientists is an essential introduction to the subject, gently guiding the reader through the key statistical techniques used to evaluate various types of forensic evidence. Inhaltsverzeichnis 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. Conditio...
