Prof Jouni Kuha

This course is available on the MSc in Data Science, MSc in Financial Statistics, MSc in Financial Statistics (Research), MSc in Health Data Science, MSc in Statistics and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

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 models for survival (duration) data. The R software package will be used in computer workshops.

20 hours of computer workshop and 20 hours of lectures in the Autumn Term.

This course has a reading week in Week 6 of Autumn 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: 120 Minutes in the January exam period.