Module MATH2697: Statistical Modelling

Department: Mathematical Sciences

MATH2697: Statistical Modelling

Prerequisites

• None

Corequisites

• Data Science and Statistical Computing (MATH2687) OR Statistical Inference (MATH2671)

Excluded Combination of Modules

• None

Aims

• To provide a working knowledge of the theory, computation and practice of the linear model.

Content

• Linear models: Least squares estimation, properties, inference (hypothesis tests and CI’s), prediction.
• Analysis of variance (incl. design of experiments), full and partial F-tests.
• Model selection.
• Diagnostics.
• Transformation methods.

Learning Outcomes

• By the end of the module students will:
• be able to formulate a given problem in terms of the linear model and use the acquired skills to solve it;
• have developed a set of skills to assess the suitability of a given linear model, and to compare it with competing models;
• have a systematic and coherent understanding of the theory and mathematics underlying the statistical methods studied;
• be able to relate the conceptual framework to practical implementations of the methods;
• have acquired a coherent body of knowledge on regression methodology, based on which extensions of the linear model such as generalized models or nonparametric regression can be learnt and understood.

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

• Students will have basic skills in the following: 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.
• Problem classes show how to solve example problems in an ideal way, revealing also the thought processes behind such solutions.
• Computer practicals consolidate the studied material, explore theoretical ideas in practice, enhance practical understanding, and develop practical data analysis skills.
• Tutorials provide active problem-solving engagement and immediate feedback to the learning process.
• 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:

Weekly 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