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 2015/16, Not available to visiting students (SS1)		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.