# 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:

## Other useful textbooks

Here is a list of other textbooks that provide further (or complementary) details to the lecture notes

- Rencher (1998).
*Multivariate Statistical Inference and Applications*. - Timm (2002).
*Applied Multivariate Analysis*. - Mardia, Kent, Bibby (1980).
*Multivariate Analysis*.

## Resources on Matrix algebra

- The first appendix of Mardia, Kent, Bibby (1980) provides a good review of basic matrix algebra.
- Puntanen, Styan, Isotalo (2011)
*Matrix Tricks for Linear Statistical Models* - Golub, Van Loan (2013)
*Matrix Computations*- Highly recommended book on numerical linear algebra. Excellent introduction to important algorithms for matrix computations.

- Notes on quadratic forms and ellipses