STAT40510 Applied Statistical Modelling

Academic Year 2022/2023

This module aims to develop students real-world skills in data analysis and modeling.

Students will perform independent analysis of real datasets using the techniques of applied statistics and mathematical modeling learned from previous study.

This module will develop students ability to work in a group, choose and apply appropriate analysis techniques, and present their results in a clear and rigorous fashion.

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Curricular information is subject to change

Learning Outcomes:

Students will develop skills in:

Design of data analysis plans to answer research questions
Exploratory and descriptive analysis of data
Statistical and mathematical data modeling
Presentation of research results
Academic writing, presentation, and producing reports of modeling results

Indicative Module Content:

Application of mathematical and statistical modelling techniques to real data sets including:
Linear Modelling, Generalised Linear Modelling, Time Series, Survival Analysis, Multivariate Analysis

Application of applied mathematical modeling techniques including:
Epidemiological models, Pharmacokinetic models

Research methods, report writing and presentation skills

Student Effort Hours: 
Student Effort Type Hours
Lectures

2
Specified Learning Activities

50
Autonomous Student Learning

50
Total

102
Approaches to Teaching and Learning:
This is a project-based module, which focuses on application of analysis techniques to data in group projects.

The module includes some lectures to revise or introduce relevant research methods, optional workshops covering certain analysis techniques, and presentation sessions where students will show the results of their analysis and receive feedback from lecturers.

Students will be graded on their group presentations and research reports, with emphasis on both validity of analysis and presentation and communication of results.  
Requirements, Exclusions and Recommendations
Learning Requirements:

Students should have a thorough understanding of basic statistical inference including hypothesis testing and regression models. Students should also have a basic knowledge of Ordinary Differential Equations.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Group Project: Written reports Varies over the Trimester n/a Other No

50
Group Project: Oral presentations Varies over the Trimester n/a Other No

50

Carry forward of passed components
Yes
 
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Written feedback will be provided to each group by email.