This course aims to develop students’ abilities in understanding and solving practical statistical problems, and to teach them how to use appropriate models and techniques for analysing data especially in applications related to linear and generalised linear models.
The course will consist of a mixture of lectures and practical work.
Lectures will focus on statistical modelling, including the selection of appropriate models and the analysis and interpretation of results. Exploratory and graphical techniques will be considered, as well as more formal statistical procedures. Both parametric procedures (e.g. linear and generalised linear models) and nonparametric methods will be discussed, as will modern robust techniques.
There will be considerable emphasis on examples, applications, and case studies, especially for continuous response variables. Computing facilities, especially R, will be used extensively.
Course Description: Link to Official Course Descriptor.
Location: Edinburgh, Malaysia.
Learning Outcomes: Subject Mastery
At the end of the course, students should:
- be able to construct statistical models appropriate to practical problems;
- be able to understand, select and use appropriate graphical and summary techniques for exploratory data analysis;
- understand in detail the issues involved in the modelling of a continuous response variable with one or more explanatory variables, with particular regard to model selection and fitting and diagnostic procedures;
- understand the theory and techniques for the analysis of categorical data;
- choose appropriate techniques, e.g. generalised linear models, to analyse categorical data and present results;
- be able to write clear, concise and well-structured reports involving the application of the above skills to practical data-analytic problems.
It is highly desirable that students should have good access to the following books:
- Garthwaite, P.H., Jolliffe, I.T. and Jones, B. (2002) Statistical Inference, 2nd edn. Prentice Hall.
- Faraway, J. Linear models with R (1st or later edition), Taylor & Francis.
- Faraway, J. (2006) Extending the Linear Model with R :Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC.
- Weisberg, S. (2005) Applied Linear Regression, 3rd edn, Wiley/Interscience.
The following texts will be useful for occasional reference:
- Dobson, A.J. An Introduction to Generalized Linear Models (2nd or later edition), Taylor & Francis.
- Verzani, J. Using R for Introductory Statistics (1st or later edition), Taylor & Francis.
Assessment Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22
SCQF Level: 9.
Help: If you have any problems or questions regarding the course, you are encouraged to contact the course leader.
Canvas: further information and course materials are available on Canvas