Introduction to Data Analysis with R for Forensic Scientists

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.

Introduction Who is this book for? What this book is not about How to read this book How this book was written Why R? Basic statistics Who should read this chapter? Introduction Definitions Simple descriptive statistics Summarizing data Installing R on your computer Reading data into R The dafs package R tutorial Graphics Who should read this chapter? Introduction Why are we doing this? Flexible versus \canned" Drawing simple graphs Annotating and embellishing plots R graphics tutorial Further reading Hypothesis tests and sampling theory Who should read this chapter? Topics covered in this chapter Additional reading Statistical distributions Introduction to statistical hypothesis testing Tutorial The linear model Who should read this? How to read this chapter Simple linear regression Multiple linear regression Calibration in the simple linear regression case Regression with factors Linear models for grouped data - One way ANOVA Two way ANOVA Unifying the linear model Modeling count and proportion data Who should read this? How to read this chapter Introduction to GLMs Poisson regression or Poisson GLMs The negative binomial GLM Logistic regression or the binomial GLM Deviance The design of experiments Introduction Who should read this chapter? What is an experiment? The components of an experiment The principles of experimental design The description and analysis of experiments Fixed and random effects Completely randomized designs Randomized complete block designs Designs with fewer experimental units Further reading

James M. Curran is currently an Associate Professor of Statistics in the Department of Statistics at the University of Auckland (Auckland, New Zealand). Dr. Curran is also the co-director of the New Zealand Bioinformatics Institute at the University of Auckland (www.bioinformatics.org.nz).