Course literature

Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press. https://doi.org/10.1017/9781139161879
Harrell, F. (2001). Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer-Verlag. https://doi.org/10.1007/978-1-4757-3462-1

Cole, S. R., Platt, R. W., Schisterman, E. F., Chu, H., Westreich, D., Richardson, D., & Poole, C. (2010). Illustrating bias due to conditioning on a collider. International Journal of Epidemiology, 39(2), 417–420. https://doi.org/10.1093/ije/dyp334
Cook, R. D. (1977). Detection of Influential Observation in Linear Regression. Technometrics, 19(1), 15–18. https://doi.org/10.2307/1268249
Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models (Third edition). SAGE Publications, Inc.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3
King, G., & Roberts, M. E. (2015). How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It. Political Analysis, 23(2), 159–179.
Shmueli, G. (2010). To Explain or To Predict? (SSRN Scholarly Paper ID 1351252). Social Science Research Network. https://doi.org/10.2139/ssrn.1351252