Module Search

MATH3411: Advanced Statistical Modelling III

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback. Current modules are subject to change in light of the ongoing disruption caused by Covid-19.

Type	Open
Level	3
Credits	20
Availability	Available in 2024/2025
Module Cap	None.
Location	Durham
Department	Mathematical Sciences

Prerequisites

Statistical Inference (MATH2711) and Statistical Modelling (MATH2697)

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

Categorical data analysis: Investigating associations between categorical variables presented and cross-classified in contingency tables. Formal modelling of such data using log-linear models.
Generalised linear models (GLMs): Introduction and practical application of Generalised Linear Models: topics include binary regression, components of a GLM, inference, residual analysis and analysis of deviance.
Repeated Measurements Analysis: random intercept models, Linear Mixed Models (LMMs), and Estimation of parameters for LMMs.

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.
Workshops consolidate the studied material, explore theoretical ideas in practice, enhance practical understanding, and develop practical data analysis skills.
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
Lectures	42	2 per week in weeks 1-10, 11-20, 21	1 hour	42
Workshops	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	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 assignments to be submitted.

More information

If you have a question about Durham's modular degree programmes, please visit our FAQ webpages, Help page or our glossary of terms. If you have a question about modular programmes that is not covered by the FAQ, or a query about the on-line Undergraduate 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.