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Further Regression Analysis (online) - HEA00094M

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  • Department: Health Sciences
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2022-23

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations


Module will run

Occurrence Teaching period
C Summer Term 2022-23

Module aims

To equip students with the necessary skills and knowledge to allow analysis of data with the ability to handle effect modification, confounding and model diagnostics. By means of lectures and hands-on analysis of data from real health-related studies, using the statistical software package STATA the student is guided through a range of generalised linear models, semi-parametric models such as Cox regression, and bootstrapping. Special attention is paid to the conditions under which the technique may or may not be applied

Module learning outcomes

At the end of the module, students will be able to:

  1. Make effective use of the statistical package STATA for analysis and interpret the results.
  2. Correctly construct multivariate generalised linear models, semi-parametric and non-parametric models with emphasis on handling effect modification and carrying regression diagnostics

Module content

Module content

Introduction to STATA

Further multiple regression

Further multiple regression: Introducing interaction terms, more on diagnostics tools for multiple regression including transformations and collinearity

Multiple Logistic regression

Multiple Logistic regression including interaction terms, goodness-of- fit for multiple logistic regression and discrimination

Survival analysis

Principles of survival analysis and introduction of Cox’s regression for time related data

Poisson regression

Poisson regression for count data

Further non-parametric tests and bootstrapping

Indicative assessment

Task % of module mark
Open Exam (1 day) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Open Exam (1 day) 100

Module feedback

Cohort feedback will be provided in line with Departmental policy for examinations. The exam is computer-based. 

Indicative reading

Fox, John . Applied regression analysis and generalized linear models. Sage

Hamilton, L. Statistics with Stata, Wadsworth.

Harrell, Frank E . Regression modeling strategies. Springer-Verlag New York Inc

Hosmer, David W.  et al  Applied logistic regression. Wiley

Hosmer, David W et al. Applied survival analysis. Wiley-Interscience

Rabe-Hesketh, S. and Everitt, B. A handbook of statistical analyses using Stata, Chapman & Hall.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.