1st Edition

# Introduction to Data Analysis with R for Forensic Scientists

By James Michael Curran Copyright 2010
331 Pages 95 B/W Illustrations
by CRC Press

331 Pages 95 B/W Illustrations
by CRC Press

331 Pages
by CRC Press

Also available as eBook on:

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 this book was written
Why R?
Basic statistics
Introduction
Definitions
Simple descriptive statistics
Summarizing data
The dafs package
R tutorial
Graphics
Introduction
Why are we doing this?
Flexible versus \canned"
Drawing simple graphs
Annotating and embellishing plots
R graphics tutorial
Hypothesis tests and sampling theory
Topics covered in this chapter
Statistical distributions
Introduction to statistical hypothesis testing
Tutorial
The linear model
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
Introduction to GLMs
Poisson regression or Poisson GLMs
The negative binomial GLM
Logistic regression or the binomial GLM
Deviance
The design of experiments
Introduction