Postgraduate Course: Statistical Regression Models (MATH11086)
|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
|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.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Academic year 2015/16, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||MSc Statistical Regression Models||2:00|
| 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.
|Graduate Attributes and Skills
|Course organiser||Dr Jonathan Gair
Tel: (0131 6)50 4897
|Course secretary||Mrs Frances Reid
Tel: (0131 6)50 4883
© Copyright 2015 The University of Edinburgh - 18 January 2016 4:25 am