Recommended reading

• Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
  A comprehensive and applied introduction to regression modeling, emphasizing causal inference and hierarchical data structures. Excellent bridge between classical and modern approaches.

• Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models (3rd ed.). Sage.
  Clear exposition of linear and multiple regression, diagnostics, and model interpretation. Useful for both theory and R-based practice.

• Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2004). Applied Linear Regression Models (4th ed.). McGraw-Hill.
  A widely used undergraduate/graduate text emphasizing linear model formulation, assumptions, and practical applications.

• Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.
  The standard reference for experimental design, randomization, and treatment effect estimation ,from randomized blocks to factorial designs.

• Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
  Focuses on causal inference and treatment effects through the lens of linear models, including instrumental variables and difference-in-differences.

• Freedman, D. A. (2009). Statistical Models: Theory and Practice. Cambridge University Press.
  Conceptually sharp and rigorous introduction to what regression models mean and how assumptions affect inference.

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (2nd ed.). Springer.
  Chapters 3–4 provide a gentle, modern introduction to simple and multiple regression with R code examples.

• Agresti, A. (2013). Categorical Data Analysis (3rd ed.).
  Authoritative resource on association tests and categorical data.

• Dobson, A. J., & Barnett, A. G. (2018). An Introduction to Generalized Linear Models (4th ed.).
  A clear, accessible introduction to GLMs.

• McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.).
  The classic and definitive text on GLMs; more theoretical but essential for deep understanding.

• Tyler Vigen. Spurious Correlations.
  A lighthearted but instructive look at how unrelated variables can appear strongly correlated. Helps develop critical thinking about association versus causation.
  Website

• Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal Diagrams for Epidemiologic Research. Epidemiology, 10(1), 37–48.
  A foundational paper explaining confounding and causal thinking using directed acyclic graphs (DAGs).

• Rothman, K. J., Greenland, S., & Lash, T. L. (2021). Modern Epidemiology (4th ed.).
  A thorough reference on confounding, bias, and study design, often used in epidemiology and public health.