Postgraduate Programme and Module Handbook 2020-2021 (archived)
Module MATH42420: Topics in Statistics
Department: Mathematical Sciences
MATH42420: Topics in Statistics
| Type | Tied | Level | 4 | Credits | 20 | Availability | Available in 2020/21 | Module Cap | None. |
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| Tied to | G1K509 |
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Prerequisites
- Statistical Methods.
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To provide a working knowledge of the theory, computation and practice of a number of specialised statistical tools, complementing Statistical Methods III.
Content
- Likelihood-based inference
- Generalised linear models
- Log-linear modelling of contingency tables
- Advanced topic: one of multivariate analysis, time series analysis, medical statistics.
- Reading material in an advanced area of statistics chosen by the lecturer.
Learning Outcomes
Subject-specific Knowledge:
- By the end of the module students will:
- be aware of a wide range of applicable statistical methodology.
- have a systematic and coherent understanding of the theory, computation and application of the mathematics underlying the statistical topics studied.
- have acquired a coherent body of applicable knowledge on likelihood methods as a general approach to inference.
- have acquired a coherent body og knowledge of generalised linear methods and log-linear modelling.
- have a knowledge and understanding of a substantial topic in an advanced area of statistics obtained by independent study.
Subject-specific Skills:
- In addition students will have specialised mathematical skills in the following areas which can be used with minimal guidance: Modelling, Computation.
Key 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.
- Formatively assessed assignments provide practice in the application of logic and high level of rigour as well as feedback for the students and the lecturer on students' progress.
- 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 | 40 | 2 per week for 20 weeks (omitting two slots) and 2 in term 3 | 1 Hour | 40 | |
| Computer Practicals | 2 | In unused lecture slots in first two terms | 1 Hour | 2 | |
| Problems Classes | 8 | Four in each of terms 1 and 2 | 1 Hour | 8 | |
| Preparation and Reading | 150 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 90% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Written examination | 3 hours | 100% | |
| Component: Continuous Assessment | Component Weighting: 10% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| 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. | 100% | ||
Formative Assessment:
■ 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