Introduction To Data Analysis With R For Forensic Scientists

by James Michael Curran

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 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 existFocusing 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.

Genres: Reference

Paperback

First published December 1, 2009

Reviews

[Sylvester · Rating 5 out of 5]

A well written guide on how to carry out statistical data analysis using the software R. It has detailed explanation for each function and examples well spaced between each chapters to allow for hands on experiences as the reader mimic the same procedure on their own computers. A must read for forensic scientists brushing up on their statistical knowledge.