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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2010/2011
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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Data Analysis (MATH10011)

Course Outline
School School of Mathematics College College of Science and Engineering
Course type Standard Availability Available to all students
Credit level (Normal year taken) SCQF Level 10 (Year 4 Undergraduate) Credits 20
Home subject area Mathematics Other subject area Specialist Mathematics & Statistics (Honours)
Course website https://info.maths.ed.ac.uk/teaching.html Taught in Gaelic? No
Course description Course for Honours Degrees involving Statistics.

The syllabus may change from year to year according to what other courses in Statistics are offered, but it is likely to contain most of the following topics.

1. Two-way and three-way classifications, blocking, interaction
2. Models with categorical and continuous variables, analysis of covariance
3. Generalized linear models for binary and count data
4. Repeated measures, emphasising the use of summary statistics
5. Discriminant analysis, especially Normal-based methods and logistic discrimination
6. Random effect models, emphasising REML estimation for Normal models
7. Non-linear regression
Entry Requirements
Pre-requisites Students MUST have passed: Linear Statistical Modelling (MATH10005) AND Likelihood (MATH10004)
Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisites None
Displayed in Visiting Students Prospectus? Yes
Course Delivery Information
Delivery period: 2010/11 Semester 1, Available to all students (SV1) WebCT enabled:  Yes Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
King's BuildingsLaboratory1-11 15:00 - 15:50
King's BuildingsLecture1-11 09:00 - 09:50
King's BuildingsLaboratory1-11 16:10 - 17:00
King's BuildingsLecture1-11 15:00 - 15:50
First Class Week 1, Monday, 15:00 - 15:50, Zone: King's Buildings. JCMB, room 4312
No Exam Information
Summary of Intended Learning Outcomes
1. Knowledge of S-Plus commands for plotting and annotation (including interaction plots and methods for repeated measures), fitting linear models, model selection, summarising multivariate data, discriminant analysis, variance component estimation and non-linear regression.
2. Ability to choose and apply appropriate statistical models and methods for the topics listed in the Syllabus Summary.
3. Ability to prepare typed reports of statistical analyses using LaTeX (or MS Word) and selected S-Plus output.
Assessment Information
Coursework only.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus Not entered
Transferable skills Not entered
Reading list Not entered
Study Abroad Not entered
Study Pattern Not entered
Keywords DAn
Contacts
Course organiser Dr Tom Mackay
Tel: (0131 6)50 5058
Email: T.Mackay@ed.ac.uk
Course secretary Mrs Alison Fairgrieve
Tel: (0131 6)50 6427
Email: Alison.Fairgrieve@ed.ac.uk
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copyright 2011 The University of Edinburgh - 13 January 2011 6:20 am