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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2024/2025

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Statistical Methodology (MATH10095)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Several Variable Calculus and Differential Equations (MATH08063) AND Statistics (Year 2) (MATH08051)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting 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
Academic year 2024/25, 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 Hours & Minutes
Main Exam Diet S1 (December)Statistical Methodology (MATH10095)2:120
Learning Outcomes
On completion of this course, the student will be able to:
  1. Apply likelihood-based methods to derive estimates and confidence intervals, and conduct hypothesis tests
  2. Fit normal linear models to data, analyse the model assumptions, and derive the theoretical computations of the models.
  3. Conduct analyses using R.
  4. 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
KeywordsStMe,Statistics
Contacts
Course organiserDr Victor Elvira Arregui
Tel:
Email: victor.elvira@ed.ac.uk
Course secretaryMiss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk
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