Module MATH3411: Advanced Statistical Modelling III

Department: Mathematical Sciences

MATH3411: Advanced Statistical Modelling III

Prerequisites

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

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

• 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

• 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.

• Students will have advanced mathematical skills in the following areas: modelling, computation.

• 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

Summative Assessment

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