ST3PR-Statistics Project

Module Provider: Mathematics and Statistics
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Autumn / Spring term module
Pre-requisites: ST2LM Linear Models or ST2LMD Linear Models and Data Analysis
Non-modular pre-requisites:
Modules excluded: MA3PRO Part 3 Project and MA3PAL Peer Assisted Learning
Current from: 2023/4

Module Convenor: Prof Sue Todd

Type of module:

Summary module description:
This module focuses on independent learning of a statistical topic, and application of the relevant methods to a dataset.


The ability to research a new topic independently is a key skill in statistics. This module aims to provide students with the experience of independent research, and to develop the skills required for working independently on a project which involves an element of data analysis. A list of available datasets for analysis will be distributed to students. The datasets are grouped according to the new statistical topic to be researched. These topics will include survival analysis, time series and modern regression techniques. Students are expected to research independently the topic they have chosen, and apply it to their dataset. They should keep a diary outlining their progress on the project, and submit a final dissertation in week 9 of the Spring term that explains fully the statistical methods used, and interprets the results obtained. At the end of the Spring term, students will be required to produce a poster on their project and give an accompanying presentation.

Assessable learning outcomes:

On completion of this module the student will have acquired the ability:

• to formulate questions in a statistical framework;

• to research independently an advanced statistical topic, and apply it appropriately;

• to manage and document project work effectively, and to complete the work on time;

• to report the results of an investigation thoroughly, neatly and succinctly and to present them to other interested parties.

Additional outcomes:
Students will need to develop good time management skills in order to complete their project to a high standard. To obtain effective feedback throughout the duration of the project, students will need to be able to accurately and succinctly describe their work on a regular basis in the form of an online research diary. Depending on the topic chosen, there may be the opportunity to learn a new statistical software package.

Outline content:
Research Methods - project planning and documenting of work, time management, library & information systems, reading techniques, ethics, avoiding plagiarism and copyright infringement, referencing, writing a report, presentation skills. Statistical content defined by individual projects.

Brief description of teaching and learning methods:
Primarily self-study. Lectures are given on general research methods, and some advice is given in group help sessions, and through feedback on e-portfolio diary entries.

Contact hours:
  Autumn Spring Summer
Lectures 2 2
Project Supervision 4 3
Guided independent study: 94 95
Total hours by term 100 100 0
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Dissertation 80
Oral assessment and presentation 20

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:
Dissertation (80%) and poster presentation (20%).

Formative assessment methods:
Weekly research diary entries and regular group help sessions.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
A mark of 40% overall.

Reassessment arrangements:
Resubmission of dissertation.

Additional Costs (specified where applicable):

1) Required text books: 

2) Specialist equipment or materials: 

3) Specialist clothing, footwear or headgear: 

4) Printing and binding: 

5) Computers and devices with a particular specification: 

6) Travel, accommodation and subsistence: 

Last updated: 30 March 2023


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