Undergraduate Course: Statistical Methodology (MATH10095)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||This course provides many of the underlying concepts and theory for Likelihood based statistical analyses, and is required for further Year 3-5 courses in Statistics.
Topics to be covered include:
- likelihood function
- maximum likelihood estimation
- likelihood ratio tests
- Bayes theorem and posterior distribution
- Iterative estimation of the MLE (Fisher's method of scoring)
- normal linear models
Information for Visiting Students
|Pre-requisites||Visiting students are advised to check that they have studied the material covered in the syllabus of any pre-requisite course listed above before enrolling.
|High Demand Course?
Course Delivery Information
|Academic year 2021/22, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 20%; Examination 80%
||Hours & Minutes
|Main Exam Diet S1 (December)||2:00|
On completion of this course, the student will be able to:
- Apply likelihood-based methods to derive estimates and confidence intervals, and conduct hypothesis tests
- Fit normal linear models to data, analyse the model assumptions, and derive the theoretical computations of the models.
- Conduct analyses using R.
- Undertake unsupervised study of the online content and demonstrate a time management skill to make the coursework deadlines.
|Recommended, but not essential: |
1. Wood, S. N., Core Statistics, Cambridge University Press, 2015.
2. Azzalini, A., Statistical Inference Based on the Likelihood, Chapman & Hall, 1996.
3. Held, L. & Bove, D. S., Applied Statistical Inference: Likelihood and Bayes, Springer, 2014.
4. Christensen, R. et al., Bayesian Ideas and Data Analysis, An Introduction for Scientists and Statisticians, Chapman & Hall, 2011.
5. Weisberg, S., Applied Linear Regression, 2nd Edition, Wiley, 2005.
6. Crawley, M. J. The R Book, Wiley, 2013.
|Graduate Attributes and Skills
|Course organiser||Dr Natalia Bochkina
Tel: 0131 650 8597
|Course secretary||Miss Greta Mazelyte