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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2016/2017

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Sampling Theory and Applications (MATH10061)

This course will be closed from 13 January 2017

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummarySampling theory is relevant to a wide range of applications ranging from (finite population) sample surveys to Monte Carlo methods and simulation. The course will cover the statistical theory underlying sample surveys including multistage and longitudinal sample designs. Both design and analysis ideas will be illustrated further with reference to financial auditing, compliance and simulation studies. Practical aspects of population sampling (e.g. non-response) will be covered together with methods of dealing with related problems.
Course description 1. Key concepts in sampling finite populations.
2. Elements of probability sampling.
3. Stratification, cluster sampling.
4. Multistage designs.
5. Two-phase and longitudinal designs.
6. Use of auxiliary information in design and analysis.
7. Imputation methods.
8. Applications in financial auditing and compliance.
9. Applications in simulation.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Algebra (MATH10021) AND Complex Variable & Differential Equations (MATH10033) AND Pure & Applied Analysis (MATH10008) AND ( Statistics (Year 2) (MATH08051) OR Statistics (Year 3) (MATH09021))
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
Course Delivery Information
Not being delivered
Learning Outcomes
1. Understanding of the principles and theory of probability sampling.
2. Application of these to population surveys.
3. Understanding sampling for rare events.
4. Ability to apply sampling methods to more general problems in statistics.
5. Ability to identify appropriate use of imputation and implementation of imputation algorithms.
6. Ability to analyse and interpret results of statistical sampling.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsSTA
Contacts
Course organiserProf Jim Wright
Tel: (0131 6)50 8570
Email: J.R.Wright@ed.ac.uk
Course secretaryMrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: Alison.Fairgrieve@ed.ac.uk
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