Studying at York
Regression Analysis - HEA00158M
- Department: Health Sciences
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
Module summary
This module will enable you to develop your understanding and skills in using statistical analysis techniques that are widely used in quantiative health research, such as understanding the impact of socio-economic status on wellbeing. You will be able to develop these skills by means of lectures and hands-on analysis of data from real health related studies, using the statistical software package STATA. You will be guided through the use of statistical techniques such as linear and logistic regression. You will be able to assess the conditions under which a technique may or may not be applied and define commonly used terms in regression analysis and non-parametric statistics. You will also gain knowledge and skills that enable you to evaluate the use of these statistical techniques in published research.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 1 2024-25 |
Module aims
To provide understanding and skills in using linear and logistic regression and non-parametric statistics. 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 the full range of standard statistical parametric and non-parametric techniques with emphasis on linear and logistic regression. Special attention is paid to the conditions under which a technique may or may not be applied.
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
By the end of the module, students will be able to:
-
Demonstrate understanding of the principles underlying inferential statistics with an emphasis on linear and logistic regression and non-parametric statistics.
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Critically appraise results of research.
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Interpret the results of research.
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Describe data and carry out linear and logistic regression and non-parametric statistics.
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Critically appraise reports of research which have used a range of methods including linear and logistic regression.
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Use STATA for analysing data.
Module content
The module assumes basic knowledge of statistics.
Module content:
Refresher session
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Summary statistics and basic inferential statistics
Linear Regression Analysis
- Simple linear regression
- Multiple linear regression
- Model diagnostics for Linear regression
Logistic Regression Analysis
- Revisit binary outcomes: OR, RR, Risk difference
- Logistic regression
- Model diagnostics for Logistic regression
Further topics
- Introduction to Non-parametric tests
- Sample Size Issues
- Writing a statistical report
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Special assessment rules
None
Additional assessment information
The four formative quizzes are spaced out across the Semester starting from week 3. Exact release dates will be communicated at the module introduction and through the VLE.
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
Written feedback for the summative assessment is provided on the standard proforma, 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
Rabe-Hesketh, S. and Everitt, B. A handbook of statistical analyses using Stata. Chapman & Hall.
Peacock, Janet . Presenting medical statistics from proposal to publication: a step-by-step guide. Oxford : Oxford University Press