index.Rmd

--- 
title: "Introduction to Statistical Methodology"
subtitle: "First Edition"
author: "Derek L. Sonderegger"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
output: bookdown::gitbook
documentclass: book
bibliography: [book.bib, packages.bib]
biblio-style: apalike
link-citations: yes
github-repo: dereksonderegger/570_I
description: ""
---

# Preface {-}
The problem with most introductory statistics courses is that they don't prepare the student for the use of advanced statistics. Rote hand calculation is easy to test, easy to grade, and easy for students to learn to do, but is useless for actually understanding how to apply statistics. Because students pursuing a Ph.D. will likely be using statistics for the rest of their professional careers, I feel that this sort of course should attempt to steer away from a “cookbook” undergraduate pedagogy, and give the student enough theoretical background to continue their statistical studies at a high level while staying away from the painful mathematical details that statisticians must work through.

Statistical software has progressed by leaps and bounds over the last decades. Scientists need access to reliable software that is flexible enough to handle new problems, with minimal headaches. R has become a widely used, and extremely robust Open Source platform for statistical computing and most new methodologies will appear in R before being incorporated into commercial software. Second, data exploration is the first step of any analysis and a user friendly yet powerful mechanism for graphing is a critical component in a researchers toolbox. R succeeds in this area as R has the most flexible graphing library of any statistical software I know of and the basic plots can created quickly and easily. The only downside is that there is a substantial learning curve to learning a scripting language, particularly for students without any programming background. I attempt to introduce the software with as little pain as possible, but some frustration is inevitable. 

Because the mathematical and statistical background of typical students varies widely, the course seems to have a split-personality disorder. We wish to talk about using calculus to maximize the likelihood function and define the expectation of a continuous random variable, but also must spend time defining how to calculate the a mean. I attempt to address both audiences, but recognize that it is not ideal. 

These notes were originally written for an introductory statistics course for grad students in biological sciences. As such, many of the examples are biological or ecological in nature, but don't require extreme in-depth knowledge of biology. The concepts are widely applicable and this book should be suitable for any college student.


I hope you'll find these notes useful.

## Acknowledgements {-}
I have had the pleasure of interacting with a great number of talented mathematicians and statisticians in my schooling.  In particular I am deeply indebted to Dr Robert Boik and Dr Warren Esty as well as my Ph.D. adviser Dr Jan Hannig. 

As a professor at Northern Arizona University, I am grateful for the feedback and comradery of my fellow statisticans, particularly Dr St. Laurent.

Finally, I am deeply appreciative of the support given to me by my wife, Aubrey.