Recommended reading

  1. The R for data science (Wickham and Grolemund 2016) book covers data management and exploration using the tools from the tidyverse packages.

  2. Dalgaard (2008): Introductory statistics with R. 2nd edition. New York: Springer. This textbook offers an introductory level to probability and basic linear models with a focus on the software (R) and with only a minimal discussion of the mathematical fundamentals. The code shown is base R and mostly differs from what we are using in the classes (tidyverse).

  3. Sheather (2009): A Modern Approach to Regression with R. New York, NY: Springer New York (Springer Texts in Statistics). Available at: https://doi.org/10.1007/978-0-387-09608-7.

    The Textbook focuses on linear regression models and develops the mathematical fundametals alongside with examples using R.

  4. Gelman, Hill, and Vehtari (2021): Regression and other stories. Cambridge New York, NY Port Melbourne, VIC New Delhi Singapore: Cambridge University Press (Analytical methods for social research). Available at: https://doi.org/10.1017/9781139161879.

    Another introduction into basic probability theory and linear models. The references to Bayesian inference in the book are not relevant to our course.

  5. Faraway (2015): Linear models with R. Second edition. Boca Raton: CRC Press, Taylor & Francis Group.

    Another introduction into linear models with examples in R.