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MATH2697: Statistical Modelling II

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Type	Open
Level	2
Credits	10
Availability	Available in 2023/24
Module Cap	None.
Location	Durham
Department	Mathematical Sciences

Prerequisites

None

Corequisites

Data Science and Statistical Computing (MATH2687) OR Statistical Inference (MATH2711)

Excluded Combinations 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 CIs), prediction.
Analysis of variance (incl. design of experiments), full and partial F-tests.
Model selection.
Diagnostics.
Transformation methods.

Learning Outcomes

Subject-specific Knowledge:

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.

Subject-specific Skills:

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

Key Skills:

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

Activity	Number	Frequency	Duration	Total	Monitored
Lectures	23	Three in weeks 11, 12; two in weeks 13-20; one in week 21	1 hour	23
Tutorials	6	Weeks 12, 14, 16, 18, 20, 22	1 hour	6	Yes
Problem Classes	4	One in weeks 12, 14, 16, 18	1 hour	4
Computer Practicals	4	Weeks 13, 15, 17, 19	1 hour	4	Yes
Preparation and Reading				63
Total				100

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	100

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.

More information

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