- Department: Mathematics
- Module co-ordinator: Prof. Marina Knight
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2021-22
- See module specification for other years: 2022-23
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
Pre-requisites for Natural Sciences students: must have taken Statistics Option 1 MAT00033I.
Occurrence | Teaching period |
---|---|
A | Autumn Term 2021-22 |
B | Spring Term 2021-22 |
This module is to teach students how to derive, from first principles and using matrix algebra, theoretical results relating to fitting regression models by least squares, local least squares or maximum likelihood approach, how to select a regression model to fit a given data set and carry out related statistical inferences using appropriate computer software.
At the end of the module you should:
Task | Length | % of module mark |
---|---|---|
Essay/coursework Coursework |
N/A | 20 |
Online Exam -less than 24hrs (Centrally scheduled) Advanced Regression Analysis |
3 hours | 80 |
None
Task | Length | % of module mark |
---|---|---|
Essay/coursework Coursework |
N/A | 20 |
Online Exam -less than 24hrs (Centrally scheduled) Advanced Regression Analysis |
3 hours | 80 |
Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.
N. R. Draper and H. Smith, Applied Regression Analysis, Wiley (1966, 1981, 1998)
S. Chatterjee and B. Price, Regression Analysis by Example, Wiley (1977, 1991, 1999).
P. McCullagh, J . Nelder, Generalized Linear Models, Second Edition. Boca Raton: Chapman and Hall/CRC (1989).
Fan, J. and Gijbels, I. Local Polynomial Modelling and its Applications (341pp). Chapman and Hall, London (1996).