Intended for forensic scientists and students of the discipline, Forensic Interpretation of Glass Evidence provides the practicing forensic scientist with the necessary statistical tools and methodology to introduce forensic glass evidence into the laboratory. With free software available for downloading at the author's Web site, scientists can apply their own data and draw conclusions using principles practiced in the text.
This book contains an introductory chapter on glass evidence procedures and analysis before covering topics such as classical approaches to handling glass evidence, the application of Bayesian statistics to forensic science, and the use of histograms.
By presenting both the physical and chemical examinations performed on glass along with a recommended interpretation, the author allows readers the luxury of having all reference materials contained within a single book. Useful for case-working forensic scientists, this book is ideal for students of forensic science at both the undergraduate and graduate levels, as well anyone currently working in the field.

Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research.

Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers:

• A refresher on basic statistics and an introduction to R
• Considerations and techniques for the visual display of data through graphics
• An overview of statistical hypothesis tests and the reasoning behind them
• A comprehensive guide to the use of the linear model, the foundation of most statistics encountered
• An introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regression
• Instruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may exist

Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.

Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research. Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers: A refresher on basic statistics and an introduction to R Considerations and techniques for the visual display of data through graphics An overview of statistical hypothesis tests and the reasoning behind them A comprehensive guide to the use of the linear model, the foundation of most statistics encountered An introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regression Instruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may exist Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.