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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Likelihood (MATH10004)

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
SummaryCore course for Honours Degrees involving Statistics; optional course for Honours degrees involving Mathematics. There are two lectures each week, and a workshop each fortnight
Course description The course involves likelihood based statistical theory. The course covers both theoretical aspects of likelihood theory, as well as practical applications of the approaches.

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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Several Variable Calculus and Differential Equations (MATH08063) AND Fundamentals of Pure Mathematics (MATH08064) AND Probability (MATH08066) AND Statistics (Year 2) (MATH08051)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) 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 )
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
On completion of this course, the student will be able to:
  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.
Reading List
None
Additional Information
Course URL https://info.maths.ed.ac.uk/teaching.html
Graduate Attributes and Skills Not entered
Study Abroad Not Applicable.
KeywordsLik
Contacts
Course organiserDr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk
Course secretaryMr Thomas Robinson
Tel: (0131 6)50 4885
Email: Thomas.Robinson@ed.ac.uk
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