ST411 Half Unit
Generalised Linear Modelling and Survival Analysis
This information is for the 2019/20 session.
Teacher responsible
Dr Jouni Kuha COL.8.04
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) (ÐÓ°ÉÂÛ̳ 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.
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, count and survival data. Topics include: linear regression, analysis of variance (ANOVA), 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.
Teaching
20 hours of lectures and 15 hours of computer workshops in the MT.
Week 6 will be used as a reading week.
Formative coursework
Coursework assigned weekly based on the computer sessions and returned to students with comments/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 ÐÓ°ÉÂÛ̳ 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 summer exam period.
Student performance results
(2015/16 - 2017/18 combined)
| Classification | % of students |
|---|---|
| Distinction | 32.5 |
| Merit | 18.8 |
| Pass | 38.8 |
| Fail | 10 |
Key facts
Department: Statistics
Total students 2018/19: 23
Average class size 2018/19: 23
Controlled access 2018/19: No
Value: Half Unit
Personal development skills
- Problem solving
- Application of numeracy skills
