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Further Regression Analysis - HEA00002M

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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 an awareness of effect modification and confounding. By means of lectures and hands-on analysis of data from real healthrelated studies, using the statistical software package STATA the student is guided through the full range of standard statistical parametric and non-parametric techniques, ranging from frequency tables to Cox's regression. Special attention is paid to the conditions under which the technique may or not may be applied.

Module learning outcomes

Students will be able to:

  1. Make effective use of the statistical package STATA for analysis.
  2. Interpret the results of using such packages and generate functional reports.
  3. Utilise descriptive and inferential statistical tests of difference and association.
  4. Correctly construct multivariate linear, logistic and Poisson regression models and to undertake survival analysis and Cox-regression modelling.

Module content

Introduction to STATA

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
Online Exam - 24 hrs (Centrally scheduled) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Online Exam - 24 hrs (Centrally scheduled) 100

Module feedback

Students are provided with collective exam feedback relating to their cohort, within the timescale specified in the programme handbook.

Indicative reading

  • Belsley, David A. Regression diagnostics. Wiley-Interscience
  • 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.