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Module details

MA208-5-SP: LINEAR MODELS

Year: 2013/14
Department: Mathematical Sciences
Essex credit: 15
ECTS credit: 7.5
Available to Study Abroad / Exchange Students: Yes
Pre-requisites: EC252 OR MA108
Co-requisites: MA207-5-AU

Staff
Supervisor:  
Teaching Staff: Dr. Aris Perperoglou, email aperpe@essex.ac.uk  
Contact details: Miss Camilla Thomsen, Departmental Administrator, Tel. 01206 873040, email cthomsj (Non essex users should add @essex.ac.uk to create the full email address) 

Module is taught during the following terms
AutumnnoSpringyesSummerno

Module Description

Aims

To bring together and develop the ideas concerning regression, the analysis of variance, the analysis of contingency tables, under the common umbrella of the general linear model.

On completion of the course students should be able to:

- represent a model in matrix form

- write down and manipulate matrix expressions for least squares estimates and their properties

- appreciate the problems of model selection and know standard methods such as backward elimination

- be able to formulate a test procedure for handling a discrete random variable

understand what is meant by log-linear model and how it applies to multidimensional contingency tables

- use R for data analysis

Syllabus:

- General linear model: least Squares, properties of estimators, Gauss-Markov theorem, ANOVA, interval estimates of parameters, WLS, linearising.

- Multiple regression: regression diagnostics, residuals, leverage and influence, AIC, Mallows' Cp. Imputation of missing values.

- Logits and logistic regression: Newton-Raphson method, fit of the model, hypothesis testing, diagnostics and polytomous response variables.

- Log-linear models: two-, three- and multi-way contingency tables, Deming-Stephan algorithm, hypothesis testing, choice of model, diagnostics and model search.

Learning & Teaching Methods

The module has 30 contact hours in total. 2 lectures each week for 9 weeks, 1 lab in weeks 16-18 and 1 class in weeks 19-23 and 24, during the spring term.

In the summer term 3 revision lectures are given. A project is undertaken in (about) 5-person groups.

Assessment

30 per cent Coursework Mark, 70 per cent Exam Mark

Coursework:
Course work assessment: best 2 (each 2.5%) out of 4 problem sets; group project (written report and oral presentation) count for 25%.

Other details:
Information about coursework deadlines can be found in the "Coursework Information" section of the Current Students, Useful Information Maths web pages: Coursework Information

Exam Duration and Period

2:00 hour exam during Summer Examination period.

Other information

Available to Socrates/IP students spending all relevant terms at Essex.

Bibliography

  • Main texts:
  • Dobson, A. J., Barnett, A.G. (2008), An Introduction to Generalized Linear Models, 3rd edition, Chapman & Hall.
  • Additional Reading:
  • Andersen, E.B. (1998), Introduction to the Statistical Analysis of Categorical Data, Springer-Verlag.
  • Draper, N. R. and Smith, H. (1998), Applied Regression Analysis, 3rd edition, Wiley.
  • Fox, J. (1997), "Applied Regression Analysis, Linear Models, and Related
  • Methods", Sage.
  • Robert I. Kabacoff (2011), R in Action - Data Analysis and Graphics with R, Manning Publications.
  • Krzanowski, K. (1998), An Introduction to Statistical Modelling, Arnold.
  • Sawitzki, G. (2009), Computational Statistics - An Introduction to R, Chapman & Hall.

Further information

External Examiner Information

  • Name: Dr Prakash Patil
  • Institution: THE UNIVERSITY OF BIRMINGHAM
  • Academic Role: Reader in Statistics

Should you have any queries about the Module Directory pages, please contact the Course Record Team, Systems Administration Office, Academic Section; email: crt (non Essex users should add @essex.ac.uk)