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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2014/2015
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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Categorical Data Analysis (MATH10055)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) Credits10
Home subject areaMathematics Other subject areaSpecialist Mathematics & Statistics (Honours)
Course website http://student.maths.ed.ac.uk Taught in Gaelic?No
Course descriptionCourse for final year students in Honours programmes in Mathematics.

The syllabus will include transformations; 2-by-2 tables, k-by-2 tables; conditional and profile likelihoods; several 2-by-2 tables; logistic regression; loglinear models for two-way tables; and three-way tables.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Foundations of Calculus (MATH08005) AND Several Variable Calculus (MATH08006) AND Linear Algebra (MATH08007) AND Methods of Applied Mathematics (MATH08035) AND ( Probability (Year 2) (MATH08008) OR Probability (Year 3) (MATH09004)) AND Likelihood (MATH10004) AND Linear Statistical Modelling (MATH10005) AND Pure & Applied Analysis (MATH10008) AND ( Statistics (Year 2) (MATH08051) OR Statistics (Year 3) (MATH09021))
Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2014/15 Semester 1, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 15/09/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 31, Seminar/Tutorial Hours 2, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 63 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
No Exam Information
Summary of Intended Learning Outcomes
1. Appreciation of difference between linear models and logistic and loglinear models.

2. Knowledge of models for categorical data analysis and ability to fit and analyse them.

3. Awareness of dependence relationships amongst categorial variables.

4. Ability to use R to fit models for categorial data.
Assessment Information
See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
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
KeywordsCAT
Contacts
Course organiserProf Colin Aitken
Tel: (0131 6)50 4877
Email: 		Course secretaryMrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email:
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