ST411      Half Unit
Generalised Linear Modelling and Survival Analysis

This information is for the 2020/21 session.

Teacher responsible

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.


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.

Course content

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 and lectures totalling a minimum of 20 hours across Michaelmas Term. This year, some or all 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.

Formative coursework

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

Indicative reading

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 (80%, duration: 2 hours) in the summer exam period.
Continuous assessment (20%) in the MT.

Student performance results

(2016/17 - 2018/19 combined)

Classification % of students
Distinction 27.7
Merit 20.5
Pass 39.8
Fail 12

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Statistics

Total students 2019/20: 17

Average class size 2019/20: 18

Controlled access 2019/20: Yes

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Problem solving
  • Application of numeracy skills