ST411      Half Unit
Generalised Linear Modelling and Survival Analysis

This information is for the 2017/18 session.

Teacher responsible

Prof Christopher Skinner COL. 7.13

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 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.

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 Stata software package will be used in computer workshops.

Teaching

20 hours of lectures and 15 hours of computer workshops in the LT.

Week 6 will be used as a reading week.

Formative coursework

Coursework assigned weekly and returned to students with comments/feedback during the computer sessions.

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 main exam period.

Student performance results

(2013/14 - 2015/16 combined)

Classification % of students
Distinction 34.5
Merit 22.4
Pass 32.8
Fail 10.3

Key facts

Department: Statistics

Total students 2016/17: 22

Average class size 2016/17: 23

Controlled access 2016/17: No

Lecture capture used 2016/17: Yes (LT)

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Problem solving
  • Application of numeracy skills