Diez, D.M., Barr, C.D., & Çetinkaya-Rundel, M. (2019). OpenIntro Statistics (4th Ed).
Navarro, D. (2018, version 0.6). Learning Statistics with R
This is free textbook that supplements a lot of the material covered in Diez and Barr. We will use the chapter on Bayesian analysis. You can download a PDF version, Bookdown version, or visit the author's website at learningstatisticswithr.com.
OpenStax.org is a great resource for free textbooks. The following books will be helpful to have as a reference and to supplement, and get an alternative explanition, for many of the topics covered in this course:
- Calculus - We will very briefly explain the concepts of limits, derivatives, and integrals that underlie some important statistical concepts. This books will provide much more detail.
- College Algebra - For those who need a refresher in algebra, this is a good resource.
Wickham, H., & Grolemund, G. (2016) R for Data Science. O'Reilly.
Wickham, H. Advanced R. Baca Raton, FL: Taylor & Francis Group.
Kruschke, J.K. (2014). Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan (2nd Ed). London: Academic Press.
This book can be purchased from Amazon, but also check out the author's webiste (doingbayesiandataanalysis.blogspot.com/) for additional resources.