Resources

CSS 211 has no required readings. However, students might benefit from exploring additional content on course topics. The course schedule lists suggested readings for each week; here, I include those readings, as well as additional resources students might benefit from, organized by topic.

General Resources

• Hadley Wickham’s R for Data Science(R4DS).
• Introduction to Statistical Learning (James et al.)

Data wrangling and visualization.

• Wickham, H. (2014). “Tidy data” (Journal of Statistical Software).
• Wickham, H. & Grolemund, G. (2017). R for Data Science, [Chapters 5, 10-12](R4DS, data wrangling.
• Healy, K. (2018). Data Visualization, Chapters 1-3

Regression

• James, G. et al. (2021). Introduction to Statistical Learning, Chapter 3
• James, G. et al. (2021). Introduction to Statistical Learning, Chapter 4.1-4.3
• James, G. et al. (2021). Introduction to Statistical Learning, Chapter 6.1-6.2

Mixed Effects Models

• Winter, B. (2013). “Linear models and linear mixed effects models in R” (tutorial)
• Gelman, A. & Hill, J. (2006). Data Analysis Using Regression, Chapters 11-12
• Bates, D. et al. (2015). “Fitting linear mixed-effects models using lme4” (Journal of Statistical Software)

Resampling Methods

• James, G. et al. (2021). Introduction to Statistical Learning, Chapter 5

Best Practices and Research Design

• Gelman, A. & Loken, E. (2014). “The garden of forking paths: Why multiple comparisons can be a problem” (American Scientist)