Postgraduate Course: Incomplete Data Analysis (MATH11185)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This 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 statistical framework. |
| Course description |
Topics to be covered include:
- types of missingness;
- single imputation;
- likelihood based approaches for dealing with missing data (including the EM algorithm); and
- multiple imputation.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Methodology (MATH10095)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Note that PGT students on School of Mathematics MSc programmes are not required to have taken pre-requisite courses, but they are advised to check that they have studied the material covered in the syllabus of each pre-requisite course before enrolling. |
Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 2 |
| Course Start Date |
12/01/2026 |
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
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 20%
Exam 80% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | Incomplete Data Analysis (MATH11185) | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate an understanding of the different types of missingness.
- Demonstrate an understand different statistical techniques for dealing with missing data and associated advantages and disadvantages.
- Demonstrate an ability to fit models to data with missing observations.
- Demonstrate an ability to interpret the output from statistical analyses.
- Demonstrate an ability to use the R statistical software to implement statistical procedures that can handle missing values.
|
Reading List
Statistical Analysis with Missing Data. Little and Rubin. Wiley.
Applied Missing Data Analysis. Enders. Guilford Press.
Applied Multiple Imputation: Advantages, Pitfalls, New Developments, and Applications in R. Kleinke, Reinecke, Salfran and Spiess. Springer.
Flexible Imputation of Missing Data. Van Buuren. Chapman & Hall/CRC Press. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | IDAn,Data Analysis,Statistics |
Contacts
| Course organiser | Dr Maarya Sharif
Tel: 01316505060
Email: |
Course secretary | Miss Kirstie Paterson
Tel:
Email: |
|
|
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