Postgraduate Course: Statistical Regression Models (MATH11086)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | Statistical modelling and motivation.
Relationships between variables, transformations to linearity, residual and regression sums of squares, analysis of variance in simple linear regression, residual analysis.
Multiple regression, matrix notation, distributions of sums of squares, inferences about regression parameters, analysis-of-variance models.
Use of R for statistical analysis. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2013/14 Semester 1, Not available to visiting students (SS1)
|
Learn enabled: Yes |
Quota: None |
Web Timetable |
Web Timetable |
| Course Start Date |
16/09/2013 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
|
|
| Main Exam Diet S2 (April/May) | MSc Statistical Regression Models | 2:00 | | |
Summary of Intended Learning Outcomes
1.Familiarity with simple linear regression and multiple linear regression.
2. Knowledge of the definition and properties of the Normal Linear Model.
3. Familiarity with some examples of the Normal Linear Model and ability to recognise other special cases.
4. Ability to use statistical software R for data analysis, particularly regression analysis and analysis of variance.
5. Ability to interpret the results of statistical analyses. |
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 | SRM |
Contacts
| Course organiser | Dr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
|
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|
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