MATH3411: Advanced Statistical Modelling III

• Statistical Inference (MATH2711) and Statistical Modelling (MATH2697)

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• 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.

• 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.

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.

• 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.

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.

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