Undergraduate Course: Categorical Data Analysis (MATH10055)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 10 (Year 4 Undergraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
Summary	Course 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.
Course description	Not entered

Entry Requirements (not applicable to Visiting Students)
Pre-requisites	Students MUST have passed: ( Several Variable Calculus and Differential Equations (MATH08063) AND Fundamentals of Pure Mathematics (MATH08064) AND Probability (MATH08066) AND Likelihood (MATH10004) AND Linear Statistical Modelling (MATH10005)) AND ( Statistics (Year 2) (MATH08051) OR Statistics (Yr 3) (MATH09022))	Co-requisites
Prohibited Combinations		Other requirements	None

Information for Visiting Students
Pre-requisites	None

Course Delivery Information

Academic year 2015/16, Available to all students (SV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
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 )
Assessment (Further Info)	Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Additional Information (Assessment)	Coursework 5%, Examination 95%
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S1 (December)	MATH10055 Categorical Data Analysis		2:00

Academic year 2015/16, Part-year visiting students only (VV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
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 )
Assessment (Further Info)	Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Additional Information (Assessment)	Coursework 5%, Examination 95%
Feedback	Not entered
No Exam Information

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.