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
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Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2014/15 Semester 1, Available to all students (SV1)
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Learn enabled: Yes |
Quota: None |
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Web Timetable |
Web Timetable |
| Course Start Date |
15/09/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 19,
Supervised Practical/Workshop/Studio Hours 16,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
161 )
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| Additional Notes |
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| No Exam Information |
Summary of Intended Learning Outcomes
1. Knowledge of R 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 R output.
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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 |
| Keywords | DAn |
Contacts
| Course organiser | Dr Chris Theobald
Tel: (0131 6)50 4878
Email: |
Course secretary | Mrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: |
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