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.
Author(s): James Michael Curran
Series: International Forensic Science and Investigation
Publisher: CRC Press
Year: 2010
Language: English
Pages: 317
Tags: Библиотека;Компьютерная литература;R;
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
