Unit name | Linear Models |
---|---|
Unit code | MATH35110 |
Credit points | 10 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 1A (weeks 1 - 6) |
Unit director | Professor. Beaumont |
Open unit status | Not open |
Pre-requisites |
MATH11300 Probability 1 and MATH 11400 Statistics 1; MATH 20800 Statistics 2 is desirable but not essential; what is actually needed is a thorough grasp of basic ideas of estimation, hypothesis testing and confidence intervals, from either course. |
Co-requisites |
None |
School/department | School of Mathematics |
Faculty | Faculty of Science |
Unit aims
The aims of this unit are:
General Description of the Unit
This unit explores the role of linear models as a statistical tool for modelling data. Theoretical aspects of such models are explored, and most proofs require familiarity with basic results in linear algebra. The emphasis is on strategies and methodology for model selection, estimation, inference and checking. Models covered include simple and multiple regression, and one- and two-way analysis of variance for factorial experiments. Inference will be based largely on the least-squares criterion, exploiting the Gauss-Markov theorem, but connections will also be made with likelihood-based approaches. The use of R for modelling data via linear models will be integral to the course.
Relation to Other Units
This unit builds on the basic ideas of linear models introduced in Statistics 1 and 2. Generalisations are studied in Generalised Linear Models.Other related units are Bayesian Modelling and Theory of Inference.
Further information is available on the School of Mathematics website: http://www.maths.bris.ac.uk/study/undergrad/
Learning Objectives
By the end of the unit the student should be able to:
Transferable Skills
Computing skills (use of an advanced package, simple programming, interpretation of computational results in problem context). Relation of numerical results to mathematical and statistical theory. Building models for uncertain phenomena. Data analysis. Self assessment by working through examples sheets and using solutions provided.
Lectures supported by problem and solution sheets.
100% Examination
Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.
Additionally, the following may be useful to consult: W. N. Venables and B. D. Ripley, Modern applied statistics with S-Plus, Springer, 1994