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
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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? |
Yes |
Course Delivery Information
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| Academic year 2020/21, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
|
| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 30%; Examination 70% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | | 2:00 | |
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.
- Undertake unsupervised study of the online content and 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 Serveh Sharifi Far
Tel: (0131 6)50 5051
Email: Serveh.Sharifi@ed.ac.uk |
Course secretary | Mr Christopher Palmer
Tel: (0131 6)50 5060
Email: chris.palmer@ed.ac.uk |
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