Undergraduate Course: Linear Statistical Modelling (MATH10005)
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 3 Undergraduate) |
Credits | 10 |
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 | Core course for Honours Degrees involving Statistics; optional course for Honours degrees involving Mathematics. Syllabus summary: Simple linear regression, relationships between variables, transformations to linearity, residual and regression sums of squares, analysis of variance and residual analysis. Multiple regression, matrix notation, distributions of sums of squares, inferences about regression parameters, and analysis-of-variance models. Use of R for statistical analysis. |
Information for Visiting Students
Pre-requisites | None |
Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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Delivery period: 2013/14 Semester 1, Available to all students (SV1)
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Learn enabled: Yes |
Quota: None |
Web Timetable |
Web Timetable |
Course Start Date |
17/09/2013 |
Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
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Additional Notes |
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Breakdown of Assessment Methods (Further Info) |
Written Exam
95 %,
Coursework
5 %,
Practical Exam
0 %
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Exam Information |
Exam Diet |
Paper Name |
Hours:Minutes |
|
|
Main Exam Diet S2 (April/May) | Linear Statistical Modelling (MATH10005) | 2:00 | | | Main Exam Diet S1 (December) | Linear Statistical Modelling (For visiting students only) | 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 R for data analysis, particularly regression analysis and analysis of variance.
5. Ability to interpret the results of statistical analyses.
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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 |
Simple linear regression, relationships between variables, transformations to linearity, residual and regression sums of squares, analysis of variance and residual analysis.
Multiple regression, matrix notation, distributions of sums of squares, inferences about regression parameters, and analysis-of-variance models.
Use of R for statistical analysis. |
Transferable skills |
Not entered |
Reading list |
http://www.readinglists.co.uk |
Study Abroad |
Not Applicable. |
Study Pattern |
See 'Breakdown of Learning and Teaching activities' above |
Keywords | LSM |
Contacts
Course organiser | Dr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk |
Course secretary | Mrs Kathryn Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 10 October 2013 4:51 am
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