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

    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.


    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?
    Simple descriptive statistics
    Summarizing data
    Installing R on your computer
    Reading data into R
    The dafs package
    R tutorial
    Who should read this chapter?
    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
    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
    The design of experiments
    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).