ST411      Half Unit
Generalised Linear Modelling and Survival Analysis

This information is for the 2023/24 session.

Teacher responsible

Dr Anastasia Kakou

Availability

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) (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.

Pre-requisites

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

Teaching

This course will be delivered through a combination of classes, lectures and Q&A sessions, totalling a minimum of 20 hours across Autumn Term. This course includes a reading week in Week 6 of Autumn 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.

 

Assessment

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

Student performance results

(2019/20 - 2021/22 combined)

Classification % of students
Distinction 47.2
Merit 33.3
Pass 8.3
Fail 11.1

Key facts

Department: Statistics

Total students 2022/23: 10

Average class size 2022/23: 11

Controlled access 2022/23: Yes

Lecture capture used 2022/23: Yes (MT)

Value: Half Unit

Guidelines for interpreting course guide information

Course selection videos

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

Personal development skills

  • Problem solving
  • Application of numeracy skills