Lecture Notes
Required Text
- Faraway, J. (2014) Linear Models with R, Second Edition. Chapman and Hall/CRC [amazon] [R code]
Recommended Text
- Kutner, M., Nachtsheim, C. and Netter, J. (2004) Applied Linear Statistical Models (fifth edition). McGraw-Hill. [amazon]
- This is a huge book and contains a lot of information (1396 pages!). It's worth owning, but expensive. The fourth edition (1996) is less expensive and should be sufficient for this course [amazon].
Required Software
- R Core Team (2016) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing [link]
- RStudio Team (2016). RStudio: Integrated Development for R. RStudio, Inc. [link]
- MiKTex [link] (for Windows) MacTex (for OS X) [link]
Supplementary Material
Matrix algebra for statisticians
- Banerjee, S. and Roy, A (2015) Linear Algebra and Matrix Analysis for Statistics. Chapman and Hall/CRC [amazon]
- Fieller, N. (2015) Basics of Matrix Algebra for Statistics with R. Chapman and Hall/CRC [amazon]
- Minka (2000). Old and New Matrix Algebra Useful for Statistics. Unpublished paper [pdf]
Matrix algebra
- Strang, G. (2010) Linear Algebra. MIT Open Courseware [link]
Mathematical notation
- Scheinerman, E. (2011) Mathematical Notation: A Guide for Engineers and Scientists. CreateSpace [amazon]
Reproducible research
- Xie, Y. (2013) Dynamic Documents with R and knitr, Second Edition. Chapman and Hall/CRC [amazon]