Resources

Primary Textbooks

These textbooks are great resources for some of the topics we will cover.

• Plane Answers to Complex Questions, Ronald Christensen. eBook in Duke Library

• Linear Regression Analysis, George A.F Seber and Alan J. Lee eBook in Duke Library. Duke Library is aware the link is broken

• The Linear Model and Hypothesis, George A. F. Seber eBook in Duke Library

Supplementary Textbooks on Linear/Matrix Algebra

• Gilbert Strang’s Online Course at MIT
• Video Lectures
  • Introduction to Linear Algebra. Strang, Gilbert. 4th ed. Wellesley, MA: Wellesley-Cambridge Press, 2009. ISBN: 9780980232714. Buy @ Amazon
• Matrix Algebra from a Statistician’s Perspective. Harville, David A. eBook in Duke Library

R and R Markdown Resources

Quarto/R Markdown/LaTeX can be used to create high quality reports and presentations with embedded chunks of R code and LaTeX equations! You are required to use Quarto in RStudio to type up your homework assignments that involve Data Analysis/Simulation for this course, but you are welcome to use any word processor of your choice for those. To learn more about Quarto/R Markdown and for other resources for programming in R, see the links below.

• Using R in Quarto for Documents
• R for Data Science (by Hadley Wickham & Garrett Grolemund)
• Introduction to R Markdown (Article by Garrett Grolemund)
• Introduction to R Markdown (Slides by Andrew Cho)
• R Markdown Cheat Sheet
• Data Visualization with ggplot2 Cheat Sheet
• Other Useful Cheat Sheets
• A very (very!) basic R Markdown template

LaTeX

You may also use LaTeX to type up your assignments. You may find it easier to create your TeX and LaTeX documents using online editors such as Overleaf (simply create a free account and you are good to go!). However, that need not be the case. If you prefer to create them locally/offline on your personal computers, you will need to download a TeX distribution (the most popular choices are MiKTeX for Windows and MacTeX for macOS) plus an editor (I personally prefer TeXstudio but feel free to download any editor of your choice). Follow the links below for some options, and to also learn how to use LaTeX.

1. Learn LaTeX in 30 minutes
2. Choosing a LaTeX Compiler.

Interesting Articles

I will add articles I find interesting below. These are articles I find useful as supplementary readings for topics covered in class, or as good sources that cover concepts I think you should know, but which we may not have time to cover. I strongly suggest you find time to (at the very least) take a “quick peek” at each article.

1. Efron, B., 1986. Why isn’t everyone a Bayesian?. The American Statistician, 40(1), pp. 1-5.
2. Gelman, A., 2008. Objections to Bayesian statistics. Bayesian Analysis, 3(3), pp. 445-449.
3. Diaconis, P., 1977. Finite forms of de Finetti’s theorem on exchangeability. Synthese, 36(2), pp. 271-281.
4. Gelman, A., Meng, X. L. and Stern, H., 1996. Posterior predictive assessment of model fitness via realized discrepancies. Statistica sinica, pp. 733-760.
5. Dunson, D. B., 2018. Statistics in the big data era: Failures of the machine. Statistics & Probability Letters, 136, pp. 4-9.