Pub. Date:
Taylor & Francis
Introduction to Data Analysis with R for Forensic Scientists / Edition 1

Introduction to Data Analysis with R for Forensic Scientists / Edition 1

by James Michael Curran


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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.

Product Details

ISBN-13: 9781420088267
Publisher: Taylor & Francis
Publication date: 08/05/2010
Series: International Forensic Science and Investigation Series , #21
Edition description: New Edition
Pages: 331
Product dimensions: 6.00(w) x 9.30(h) x 0.90(d)

About the Author

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 (

Table of Contents

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
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

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