MA2405 - Advanced Statistical Modelling

This subject includes theories and practicals on statistical methods in the applied sciences. The first part of the subject focuses on various aspects of the least square method, including structure of linear models, parameter estimation, inference techniques (including bootstrapping and permutation test), prediction, model diagnostics, and model selection. In the second part of this subject, students will be introduced to models for different data types in the framework of Generalised Linear Models. This includes modelling of binary, count and positive continuous data.

Learning Outcomes

• integrate statistical principles, methods, techniques and tools covered in this course to plan and execute a statistical analysis;
• demonstrate sound knowledge of the theories that underpin the ordinary least squares regression and generalised linear models;
• effectively integrate and execute statistical theories in R;
• retrieve, analyse, synthesise and evaluate outputs produced from R;
• evaluate, synthesise and communicate findings from statistical modelling to end-users.

Subject Assessment

• Written > Examination (centrally administered) - (50%) - Individual
• Written > Examination - In class - (15%) - Individual
• Written > Project report - (35%) - Individual.

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.