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Introduction to Regression Analysis - HEA00093M

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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

  • None

Co-requisite modules

  • None

Prohibited combinations


Module will run

Occurrence Teaching period
B Spring Term 2022-23

Module aims

To provide understanding and skills in using linear and logistic regression and non-parametric statistics. To be able to define commonly used terms in regression analysis and non-parametric statistics. To evaluate the use of statistical analysis in published research.

Module learning outcomes

Knowledge and understanding of the subject area:

  • Demonstrate understanding of the principles underlying inferential statistics with an emphasis on linear and logistic regression and non-parametric statistics.

Cognitive and intellectual skills:

  • Critically appraise results of research.
  • Interpret the results of research.

Subject-specific skills:

  • Be able to describe data and carry out linear and logistic regression and non-parametric statistics.
  • Be able to critically appraise reports of research which have used a range of methods including linear and logistic regression.

Key transferable skills:

  • Be able to use SPSS for analysing data.

Module content

The module assumes basic knowledge of descriptive statistics and basic inferential statistics.

Module content:

Estimation

  • Standard error and confidence intervals

Linear Regression Analysis

  • Introduction to Simple Linear regression
  • Introduction to Multiple Linear regression
  • Analysis of Variance and Linear regression

Logistic Regression Analysis

  • Revisit binary outcomes: OR, RR, Risk difference
  • Introduction to Logistic regression

Further topics:

  • Introduction to Non-parametric tests
  • Sample Size Issues
  • Writing a statistical report

Indicative assessment

Task % of module mark
Coursework - extensions not feasible/practicable 10
Essay/coursework 90

Special assessment rules

Non-compensatable

Indicative reassessment

Task % of module mark
Essay/coursework 90

Module feedback

Written feedback for summative assessment is provided on the standard proforma, within the timescale specified in the programme handbook.

Indicative reading

Recommended text for students

  • Altman DG. Practical statistics for medical research. London: Chapman and Hall, 1995.
  • Bland M. An introduction to medical statistics; Oxford: Oxford University Press
  • Cumming, Geoff . Understanding the new statistics: effect sizes, confidence intervals, and meta-analysis. New York ; London : Routledge

  • Other recommended reading

  • Field, A. Discovering statistics using SPSS for Windows. Sage.
  • Norman, Geoffrey R. Biostatistics: the bare essentials. Shelton, Conn : People’s Medical Pub. House
  • Peacock, Janet . Presenting medical statistics from proposal to publication: a step-by-step guide. Oxford : Oxford University Press


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.