Appendix 2: Online Resources for Getting Started with Statistical Modeling in R

Roughly, in order from most elementary to most advanced

Learning Statistics with R by Danielle Navarro and adapted to Bookdown (for web viewing) by Emily Kothe.

Statististical Thinking for the 21st Century by Russell A. Poldrack

Regression Models for Data Science in R by Brian Caffo

Broadening Your Statistical Horizons: Generalized Linear Models and Multilevel Models by J. Legler and P. Roback

Modern Statistics for Modern Biology

The Art of Data Science by Roger D. Peng and Elizabeth Matsui

  1. Corrigan, June K., et al. “A big-data approach to understanding metabolic rate and response to obesity in laboratory mice.” Elife 9 (2020): e53560.)↩︎

  2. “The Pretty Good House - Finding the right balance between construction cost and energy performance”.↩︎

  3. Amrhein, V., Trafimow, D. and Greenland, S., 2019. Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), pp.262-270.↩︎


  5. the variance is less than that expected by the probability model↩︎

  6. the variance is greater than that expected by the probability model↩︎