Statistical Modelling (Part II, Michaelmas 2021)
General information

This course consists of 16 lectures and 8 practical sessions. It complements the Part II Principles of Statistics, but takes a more applied perspective.

Prerequisites: Part IB Statistics.

Location: MR5.

Please email me or leave a comment below if you find any mistakes or have any questions.

Lectures will be recorded and the recordings can be found on Moodle.
Lectures
By Chapter
 Chapter 1: Scope and approach;
 Chapter 2: Linear models.
 Chapter 3: Exponential families.
 Chapter 4: Generalised linear models.
 Chapter 5: Review and look forward.
Combined
Lecture Notes on Statistical Modelling.
Practicals
Number  Date  Topic  Optional Reading 

P1  <2021109 Sat>  Basic R; Solution  CRAN Intro to R 1,2,5,8 
P2  Writing functions, linear models; Code; Solution  CRAN Intro to R 6, 10  
P3  Linear models; Code  CRAN Intro to R 11.1–3  
P4  Model selection; Code; Solution  
P5  ANOVA and ANCOVA; Code; Solution  2019 notes 1.2.5  
P6  Binomial GLMs; Code; Solution  
P7  Binomial and Poisson GLMs; Code; Solution  
P8  Contigency tables and Gamma GLMs; Code; Solution  Agresti 4.7 
Example sheets
Readings
Theory for LM and GLM

A. Agresti. Foundations of Linear and Generalized Linear Models. Wiley 2015.

G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning (with Applications in R). Springer 2013.

Prof Richard Weber’s notes for IB Statistics.
R and statistical computing

W. N. Venables, D. M. Smith and the R Core Team. An Introduction to R.

H. Wickham. Advanced R (for anyone who wants to really understand R as a programming language).