Undergraduate Course: Data Analysis (MATH10011)
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 | 20 |
ECTS Credits | 10 |
| Summary | Course for Honours Degrees involving Statistics. Note that this course is not about 'Big Data'.
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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| Course description |
Not entered
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Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2014/15, 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:
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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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework 100% |
| Feedback |
Not entered |
| No Exam Information |
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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Contacts
| Course organiser | Dr Chris Theobald
Tel: (0131 6)51 7032
Email: c.theobald@ed.ac.uk |
Course secretary | Mrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: Alison.Fairgrieve@ed.ac.uk |
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© Copyright 2014 The University of Edinburgh - 12 January 2015 4:21 am
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