Undergraduate Course: Statistical Methodology (MATH10095)
Course Outline
| 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 |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| 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. |
| Course description |
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 | This is a Year 3, Honours level course. Visiting students are expected to have an academic profile equivalent to the first two years of the BSc (Hons) Mathematics programme (UTMATHB). Students should have passed courses equivalent to Several Variable Calculus and Differential Equations (MATH08063) and Statistics (Year 2) (MATH08051). |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
81 )
|
| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 20%; Examination 80% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S1 (December) | Statistical Methodology (MATH10095) | 120 | |
Learning Outcomes
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.
- Demonstrate a time management skill to make the coursework deadlines.
|
Reading List
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. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | StMe,Statistics |
Contacts
| Course organiser | Dr Victor Elvira Arregui
Tel:
Email: victor.elvira@ed.ac.uk |
Course secretary | Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk |
|
|
University Homepage