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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Incomplete Data Analysis (MATH11185)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThis course is for MSc students who already have some undergraduate level background in statistics.
The course focuses on different techniques for dealing with missing data within a formal frequentist statistical framework.
Course description Topics to be covered may include :
- types of missingness;
- single and multiple imputation;
- mixture models;
- hidden Markov models; and
- the EM algorithm.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Statistical Methodology (MATH10095) OR ( Linear Statistical Modelling (MATH10005) AND Likelihood (MATH10004))
Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) 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 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 S1 (December) Incomplete Data Analysis (MATH11185)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand different types of missingness.
  2. Understand different statistical techniques for dealing with missing data and associated advantages and disadvantages.
  3. Fit models to data with missing observations.
  4. Interpret the output from statistical analyses.
Reading List
Statistical Analysis with Missing Data. Little and Rubin. Wiley.
Additional Information
Graduate Attributes and Skills Not entered
Special Arrangements These Postgraduate Taught courses may be taken by Undergraduate students *without* requiring a concession (NB. students on Postgraduate taught programmes are given priority in the allocation of places). For all other Postgraduate Taught courses the student and/or Personal Tutor must seek a concession.
KeywordsIDAn,Data Analysis,Statistics
Contacts
Course organiserDr Vanda Fernandes Inacio De Carvalho
Tel: (0131 6)50 4877
Email: Vanda.Inacio@ed.ac.uk
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk
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