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
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Information for Visiting Students
Pre-requisites | None |
Course Delivery Information
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Academic year 2015/16, Available to all students (SV1)
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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 )
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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 |
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Main Exam Diet S1 (December) | MATH10055 Categorical Data Analysis | 2:00 | |
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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.
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Contacts
Course organiser | Prof Colin Aitken
Tel: (0131 6)50 4877
Email: |
Course secretary | Mrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: |
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© Copyright 2015 The University of Edinburgh - 27 July 2015 11:35 am
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