Advanced Regression Analysis - MAT00042M

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Additional information

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

Module will run

Occurrence	Teaching period
A	Autumn Term 2022-23

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.

Indicative assessment

Task	% of module mark
Closed/in-person Exam (Centrally scheduled)	80
Essay/coursework	20

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Closed/in-person Exam (Centrally scheduled)	80
Essay/coursework	20

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).