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PU5559: UNDERSTANDING AND APPLYING REGRESSION MODELS (2022-2023)

Last modified: 01 Jul 2022 10:25


Course Overview

This course intends to develop a student’s statistical skills and understanding so that they can apply common multivariate regression modelling techniques to a range of health research data. The course will focus on the application, interpretation and communication of common regression models, including general linear models, log-linear models, logistic regression, and survival analysis. It assumes that students will already have completed a first course in statistics and have an understanding of bivariate techniques and basic mathematical skills.

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • David Mclernon

What courses & programmes must have been taken before this course?

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

Health research often involves complex data which needs multivariate statistical techniques to answer research questions. This course will introduce you to key concepts in advanced regression modelling which can be applied to a range of data. It will cover some of the theory underlying different regression models and then take a practical approach to teach you how to investigate data in different contexts. You will use the statistical software package (SPSS) to apply different multivariate regression models, including how to check model assumptions, adjust for confounding and assess the model suitability. You will have the opportunity to analyse a variety of data sets and practice communicating the rationale for choice of statistical method and interpretation of results for a scientific audience.


Details, including assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

Summative Assessments

Short data analysis reports (three)

Assessment Type Summative Weighting 60
Assessment Weeks 33,35,39 Feedback Weeks 28,32

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Feedback

Three short data analysis reports - each 20% = 60%

Students will submit a short data analysis report for each of 3 main topics in teaching weeks 8, 10, 12 with feedback in weeks 10,12,14. Each topic is self contained and the feedback from each topic is not necessary before commencing the next topic.

Learning Outcomes
Knowledge LevelThinking SkillOutcome
FactualEvaluateCheck a model’s assumptions, adjust for confounding and use strategies to assess a model’s suitability and fit
ProceduralAnalyseSelect and apply an appropriate regression model and interpret its results.
ProceduralApplyEmploy the statistical package SPSS to analyse data using regression methods
ReflectionCreateCommunicate the process and results of regression models using written, tabular and graphical displays as appropriate for a scientific audience.

Formative Assessment

Project plan and report

Assessment Type Formative Weighting 40
Assessment Weeks 27,30 Feedback Weeks 28,32,34

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Feedback

Students will submit a brief project plan in teaching week 2 with written feedback by teaching week 3 and optional consultation with tutor; final report will be in teaching week 5 with final feedback in teaching week 7.

Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand and describe the rationale for using multivariate regression models.
FactualEvaluateCheck a model’s assumptions, adjust for confounding and use strategies to assess a model’s suitability and fit
ProceduralAnalyseSelect and apply an appropriate regression model and interpret its results.
ProceduralApplyEmploy the statistical package SPSS to analyse data using regression methods
ReflectionCreateCommunicate the process and results of regression models using written, tabular and graphical displays as appropriate for a scientific audience.

Resit Assessments

Exam

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

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Feedback

Feedback will be released with grade and will provide additional written commentary to facilitate ongoing learning 

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand and describe the rationale for using multivariate regression models.
FactualEvaluateCheck a model’s assumptions, adjust for confounding and use strategies to assess a model’s suitability and fit
ProceduralApplyEmploy the statistical package SPSS to analyse data using regression methods
ReflectionCreateCommunicate the process and results of regression models using written, tabular and graphical displays as appropriate for a scientific audience.
ProceduralAnalyseSelect and apply an appropriate regression model and interpret its results.

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