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

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... Read more

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

Biography

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).