• Student home
• Things to know this week
• Student news
• Student events
• New students' welcome
• Studying at York
  • Semesters
  • Teaching hours
  • Enrolment
  • Manage your studies
    • Enrol or re-enrol
    • Your student record
    • Your timetable
    • Change your plan
    • Programmes and modules
      • Programme specifications
      • Studying beyond your department
      • Module catalogue
    • University study spaces
  • Student Visa holders
  • Study skills
  • Assessment and examination
  • Academic progress issues
  • Graduation
• Support and advice
• Health and wellbeing
• Work, volunteering and career planning
• Study and work abroad
• Accommodation
• IT and online services
• Finance
• Student life in York
• If things go wrong
• Student Centre

Regression Analysis - HEA00158M

« Back to module search

• Department: Health Sciences
• Module co-ordinator: Prof. Mona Kanaan
• 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:

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

2. Critically appraise results of research.

3. Interpret the results of research.

4. Describe data and carry out linear and logistic regression and non-parametric statistics.

5. Critically appraise reports of research which have used a range of methods including linear and logistic regression.

6. Use STATA for analysing data.

Module content

The module assumes basic knowledge of statistics.

Module content:

Refresher session

• 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

Assessment

Task	Length	% of module mark
Essay/coursework Quizzes	N/A	10
Essay/coursework Report	N/A	90

Special assessment rules

None

Reassessment

Task	Length	% of module mark
Essay/coursework Report	N/A	90

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