Postgraduate Course: Statistical Regression Models (MATH11086)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | 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. |
| Course description |
Not entered
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2014/15, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| 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 )
|
| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
See 'Breakdown of Assessment Methods' and 'Additional Notes', above. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | MSc Statistical Regression Models | 2:00 | |
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.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SRM |
Contacts
| Course organiser | Dr Natalia Bochkina
Tel: 0131 650 8597
Email: n.bochkina@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
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