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