Supplementary Material

On this page, I will post additional resources and supplementary material for the course.

Resources for R

In this course, we will use R as the main computational tool. Below are some resources:

• R for Data Science by Grolemund and Wickham.
• Hands-On Programming with R by Grolemund.
• Jenny Bryan’s course on Data wrangling, exploration, and analysis with R
  • In particular, I recommend the notes on The Many Flavours of R objects
• Mine Cetinkaya’s curated list of resources for R
• If you’re looking for a deep understanding of R, I recommend Hadley Wickham’s book Advanced R
• RStudio has several great cheatsheets on their website. In particular, I recommend:
  • Data transformation
  • Data visualization
  • Rmarkdown
    

Other useful textbooks

Here is a list of other textbooks that provide further (or complementary) details to Johnson & Wichern.

• Rencher (1998). Multivariate Statistical Inference and Applications.
• Anderson (2003). An Introduction to Multivariate Statistical Analysis.
• Timm (2002). Applied Multivariate Analysis.
• Muirhead (1982). Aspects of Multivariate Statistical Theory.
  • This one is a classic text, but it is much more advanced than the other textbooks.