Module Search

MATH31320: Advanced Statistical Modelling

It is possible that changes to modules or programmes might need to be made during the academic year, in response to the impact of Covid-19 and/or any further changes in public health advice.

Type	Tied
Level	3
Credits	20
Availability	Not available in 2024/2025
Module Cap	None.
Location	Durham
Department	Mathematical Sciences

Prerequisites

Statistical Inference, Statistical Modelling

Corequisites

None

Excluded Combinations 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	Monitored
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	Yes
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.

More information

If you have a question about Durham's modular degree programmes, please visit our Help page. If you have a question about modular programmes that is not covered by the Help page, or a query about the on-line Postgraduate Module Handbook, please contact us.

Prospective Students: If you have a query about a specific module or degree programme, please Ask Us.

Current Students: Please contact your department.