| Module Code | STU33002 |
| Module Name | Statistical Analysis III |
| ECTS Weighting[1] | 5 ECTS |
| Semester taught | Semester 1 |
| Module Coordinator/s | Dr. Cornelius Fritz |
| Academic Year | 2026-2027 |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- LO1. Understand and put into practice merging and cleaning of datasets
- LO2. Understand and put into practice use of inbuilt and user written functions
- LO3. Understand how to interpret and differentiate between different graphics capabilities of R and use these methods to visualise data and create reports
- LO4. Understand the use of Generalized linear models and their application to different data types
- LO5. Understand how to optimize likelihood functions
- LO6. Understand the principles of effective report writing and how to present research and analysis.
Module Content
This module aims to provide an opportunity for students to develop their hands on
skills in data analysis. Specific methods will be explored to illustrate these
approaches. Students will become very familiar with the R statistical computing
language. After this course, students will have a toolbox of skills for data analysis. In
particular, students should be able to apply their statistical knowledge to a
real scenario, do analysis and make recommendations.
Teaching and learning Methods
1 hour lecture and 2 hours of lab per week
Lectures will introduce theory, methods, and examples. Labs will put these methods
into practice in RStudio.
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of total | Week set | |
| Lab Work | Lab assessments | all | 30 | 6 | |
| Exam | all | 70 |
Reassessment Details
Reassessment is by an assigned project.
Contact Hours and Indicative Student Workload
| Contact Hours (scheduled hours per student over full module), broken down by: | 31 hours |
| Lecture | 11 hours |
| Laboratory | 20 hours |
| Tutorial or seminar | 0 hours |
| Other | 0 hours |
| Independent study (outside scheduled contact hours), broken down by: | 70 hours |
| Preparation for classes and review of material (including preparation for examination, if applicable | 30 hours |
| completion of assessments (including examination, if applicable) | 50 hours |
| Total Hours | 101 hours |
Recommended Reading List
Stuart, M. An Introduction to Statistical Analysis for Business and Industry A
problem Solving approach. London: Hodder Arnold, 2003
Moore, D.S, McCabe G.P & Craig, B.A. An Introduction to the practice of
Statistics 6th ed. New York: W. H. Freeman, 2009
R for Data Science (available free online at https://r4ds.had.co.nz/)
Module Pre-requisites
Prerequisite modules: None.
Other/alternative non-module prerequisites: None.
Module Co-requisites
None