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Advanced Regression Analysis - MAT00042M

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  • Department: Mathematics
  • Module co-ordinator: Dr. Marina Knight
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2020-21

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations


Additional information

Pre-requisites for Natural Sciences students: must have taken Statistics Option 1 MAT00033I.

Module will run

Occurrence Teaching cycle
A Autumn Term 2020-21

Module aims

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.

Module learning outcomes

At the end of the module you should:

  • Have a reasonable ability to derive theoretical results relating to fitting regression models.
  • Have a reasonable ability to fit regression models to data, and carry out related statistical inferences using appropriate computer software.
  • Have a reasonable ability to use residual plots and other techniques to check the assumptions underlying regression analysis.
  • Have a reasonable ability to choose between alternative models for sets of data.

Assessment

Task Length % of module mark
University - closed examination
Advanced Regression Analysis
3 hours 100

Special assessment rules

None

Reassessment

Task Length % of module mark
University - closed examination
Advanced Regression Analysis
3 hours 100

Module feedback

Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.

Indicative reading

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).



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.

Coronavirus (COVID-19): changes to courses

The 2020/21 academic year will start in September. We aim to deliver as much face-to-face teaching as we can, supported by high quality online alternatives where we must.

Find details of the measures we're planning to protect our community.

Course changes for new students