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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2014/2015
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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Linear Statistical Modelling (MATH10005)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryCore 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.
Course description 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Several Variable Calculus and Differential Equations (MATH08063) AND Fundamentals of Pure Mathematics (MATH08064) AND Probability (MATH08066) AND Statistics (Year 2) (MATH08051)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Academic year 2014/15, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
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 )
Assessment (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 5%, Examination 95%
Feedback Not entered
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
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.
Reading List
http://www.readinglists.co.uk
Additional Information
Course URL https://info.maths.ed.ac.uk/teaching.html
Graduate Attributes and Skills Not entered
Study Abroad Not Applicable.
KeywordsLSM
Contacts
Course organiserDr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk		 Course secretaryMrs Kathryn Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk
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