Undergraduate Course: Likelihood (MATH10004)
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 | 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. |
| Course description |
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.
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Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2014/15, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
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 )
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| Assessment (Further Info) |
Written Exam
95 %,
Coursework
5 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 5%, Examination 95% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Likelihood (MATH10004) | 2:00 | |
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.
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Contacts
| Course organiser | Dr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk |
Course secretary | Mrs Kathryn Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk |
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© Copyright 2014 The University of Edinburgh - 12 January 2015 4:21 am
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