Resources
Design and Analysis of Experiments
Taback, N. (2019). Design of Experiments and Observational Studies. is the main textbook for the course. Below are other textbooks that provide supplementary information.
- Box, G.E.P., Hunter, J.S., and Hunter, W.G. (2005) Statistics for Experimenters: Design, Innovation, and Discovery. Wiley. 2nd Ed.
- Dean, A., Voss, D., and Draguljić, D. (2017) Design and Analysis of Experiments. Springer. 2nd Ed. University of Toronto Library Link . Publisher Link .
- Rosenbaum, P.R. (2010) Design of Observational Studies. Springer. University of Toronto Library Link .
- Wu, C.F.J. and Hamada, M.S. (2009) Experiments: Planning, Analysis, and Optimization. Wiley. 2nd Ed.
- Imbens, G.W. and Rubin, D.B. (2015) Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. University of Toronto Library Link .
R & R Markdown
The online books below are good resources for programming in R.
- Grolemund, G. (2014). Hands-on Programming with R.
- Grolemund, G. and Wickham, H. (2017) R for Data Science
There are plenty of online tutorials such as Quick-R by Kabacoff, R. as well.
R Markdown allows you to create documents in PDF and HTML with code and text weaved together. You can find R Markdown Cheetsheet from RStudio here .