Postgraduate Programme and Module Handbook 2024-2025
Module MATH31320: Advanced Statistical Modelling
Department: Mathematical Sciences
MATH31320: Advanced Statistical Modelling
| Type | Tied | Level | 3 | Credits | 20 | Availability | Not available in 2024/2025 | Module Cap | None. |
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| Tied to | G1K509 |
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Prerequisites
- Statistical Inference, Statistical Modelling
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To provide advanced methodological and practical knowledge in the field of statistical modelling, covering a wide range of modelling techniques which are essential for the professional statistician.
Content
- To provide advanced methodological and practical knowledge in the field of statistical modelling, covering a wide range of modelling techniques which are essential for the professional statistician.
Learning Outcomes
Subject-specific Knowledge:
- By the end of the module students will:
- be able to formulate a given problem in terms of a suitable statistical model and use the acquired skills to solve it;
- have a systematic and coherent understanding of the theory and mathematics underlying the statistical methods studied;
- have developed a set of skills to assess the suitability of a given model, and to compare it with competing models;
- understand how the conceptual framework relates to practical implementations of the methods;
- have acquired a coherent body of knowledge on modelling and estimation, based on which modern developments in this field can be followed and understood.
Subject-specific Skills:
- Students will have advanced mathematical skills in the following areas: modelling, computation.
Key Skills:
- Students will have advanced skills in the following areas: problem solving, synthesis of data, critical and analytical thinking, computer skills.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Computer practicals consolidate the studied material and enhance practical understanding.
- Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
- Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
- Computer-based examinations assess the ability to use statistical software and basic programming to solve predictable and unpredictable problems.
- The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 42 | 2 per week for 21 weeks | 1 hour | 42 | |
| Computer Practicals | 8 | Weeks 3, 5, 7, 9, 13, 15, 17, 19 | 1 hour | 8 | ■ |
| Preparation and Reading | 150 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 70% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Written Examination | 2 hours and 30 minutes | 100% | |
| Component: Practical Assessment | Component Weighting: 30% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Computer-based examination | 2 hours | 50% | |
| Computer-based examination | 2 hours | 50% | |
Formative Assessment:
Eight written or electronic assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University