Statistical Modelling (Part II, Michaelmas 2026)

General information

There will be 24 one-hour sessions, comprising approximately 16 hours of lectures and eight hours of practical classes. The bulk of the lectures will focus on classical statistical models including linear models, generalised linear models, and some important variations of them. We will also spend a few lectures on alternative perspectives of statistical modelling that focus on predictive performance or causality.

In the practical sessions, we will learn how to implement the techniques and ideas covered in the lectures by analysing several real data sets. We will be making extensive use of the statistical computer programming language R, which can be downloaded free of charge and for a variety of platforms. Most students will find it useful to write and execute their code in RStudio, an integrated development environment for R that is also free to use. Please bring a laptop with R installed to the practical sessions.

• Time: Monday, Wednesday and Friday, 10am.
• Location: MR9.
• Please email me if you find any mistakes or have any questions.
• Lectures will be recorded and the recordings can be found on Moodle.

Useful materials

• Lecture notes (Chapter 1).
• R code in lecture demos.
• Yule data used in the notes and demos.
• Example sheet 1.
• Example sheet 2.
• Example sheet 3.
• Example sheet 4.
• Lecture notes from 2024.
• Lecture notes for IB Statistics.

Further readings

• A. Agresti. Foundations of Linear and Generalized Linear Models. Wiley 2015. Introduction to the classical theory of LM and GLM.
• D. Freedman. Statistical Models: Theory and Practice. CUP 2012. Less technical but excellent book to reflect on how statistical models should be used for real-world scientific problems.
• G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning (with Applications in R). Springer 2013. Modern, more algorithmic perspective on statistical modelling, with R labs.
• B. Efron, T. Hastie. Computer Age Statistical Inference: Algorithms, Evidence and Data Science. CUP 2016. Not in the schedules but expand course materials in many ways.
• H. Wickham. Advanced R. For anyone who wants to seriously learn R.