Prof Jouni Kuha COL.8.04

This course is compulsory on the MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available on the MPhil/PhD in Statistics, MSc in Data Science, MSc in Marketing, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

This course has a limited number of places (it is controlled access). In previous years we have been able to provide places for all students that apply but that may not continue to be the case.

Mathematics to the level of Mathematical Methods (MA100) and probability to the level of Probability, Distribution Theory and Inference (ST202). Some knowledge of linear regression.

An introduction to the theory and application of generalised linear models for the analysis of continuous, categorical and count data, and regression models for survival data.  Topics include: general theory of regression and generalised linear models, linear regression, logistic regression for binary data, models for ordered and unordered (nominal) responses, log-linear models for count data and contingency tables, and models for survival (duration) data. The R software package will be used in computer workshops.

This course will be delivered through a combination of classes, lectures and Q&A sessions, totalling a minimum of 20 hours across Michaelmas Term. This year, some of this teaching may be delivered through a combination of virtual classes and flipped lectures delivered as short online videos. This course includes a reading week in Week 6 of Michaelmas Term.

Answers to questions based on theoretical and data analysis exercises can be submitted for formative feedback.

Dobson, A.J. & Barnett, A.G. (2002)  An Introduction to Generalised Linear Modelling. 2nd edition. Chapman & Hall.

McCullagh, P. & Nelder, J.A. (1989) Generalized Linear Models. 2nd edition. Chapman & Hall.

Agresti, A. (2015) Foundations of Linear and Generalized Linear Models. Wiley [Available as electronic resource from LSE library].

Hosmer, D.W. & Lemeshow, S. (1999)  Applied Survival Analysis, Regression Modeling of Time-to-Event Data. Wiley.

Long, J.S. and Freese, J. (2006) Regression  Models for Categorical Dependent Variables Using Stata. 2nd edition. Stata Press.

Exam (100%, duration: 2 hours) in the summer exam period.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.