Undergraduate Course: Likelihood (MATH10004)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | Specialist Mathematics & Statistics (Honours) |
| Course website |
https://info.maths.ed.ac.uk/teaching.html |
Taught in Gaelic? | No |
| Course description | Core course for Honours Degrees involving Statistics; optional course for Honours degrees involving Mathematics.
Syllabus summary: Likelihood function and exponential family. Likelihood based inference, score, Wald and likelihood ratio tests, and related confidence regions. Maximum likelihood, iterative estimation and Fisher's method of scoring. Generalized linear models, estimation, analysis of deviance, residuals, log linear and logistic linear models. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 2, Available to all students (SV1)
|
Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| Breakdown of 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 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
95 %,
Coursework
5 %,
Practical Exam
0 %
|
| No Exam Information |
Summary of Intended Learning Outcomes
1. Familiarity with likelihood based inference.
2. Ability to apply likelihood methods to derive estimates, confidence intervals and hypothesis tests.
3. Familiarity with examples of generalized linear models, including Poisson regression and logistic regression.
4. Ability to use R for statistical modelling and data analysis.
5. Ability to analyse data and interpret results of statistical analyses.
|
Assessment Information
| See 'Breakdown of Assessment Methods' and 'Additional Notes' above. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Likelihood function and exponential family.
Likelihood based inference, score, Wald and likelihood ratio tests, and related confidence regions.
Maximum likelihood, iterative estimation and Fisher's method of scoring.
Generalized linear models, estimation, analysis of deviance, residuals, log linear and logistic linear models. |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not Applicable. |
| Study Pattern |
See 'Breakdown of Learning and Teaching activities' above. |
| Keywords | Lik |
Contacts
| Course organiser | Dr Bruce Worton
Tel: (0131 6)50 4884
Email: |
Course secretary | Dr Jenna Mann
Tel: (0131 6)50 4885
Email: |
|
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