Mathematics

MTHM017 - Advanced Topics in Statistics (2019)

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MODULE TITLEAdvanced Topics in Statistics CREDIT VALUE15
MODULE CODEMTHM017 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 15
DESCRIPTION - summary of the module content

This module offers an insight to cutting-edge statistical techniques that are at the forefront of current research and application. You will have opportunity to explore a range of topics from time series modelling and forecasting, geostatistics, modelling of extreme values, hierarchical modelling, data fusion, multivariate analysis, computational statistics, data mining methods, survival analysis, sample survey and experimental design. The choice of topics in any year may change to ensure that the content of the module reflects the rapid change in this exciting area.

AIMS - intentions of the module

The aims are to expose the student to some recent developments in statistics; to allow the student to study one or more advanced topics in some depth and to gain some insight into areas of postgraduate research in statistics.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Demonstrate an understanding of current developments in statistics;

2 Demonstrate an understanding of the strengths and limitations of different statistical approaches;

3 Demonstrate the ability to apply advanced statistical methodology across a variety of settings;

Discipline Specific Skills and Knowledge:

4 Demonstrate an understanding of advanced regression modelling;

5 Demonstrate an understanding of modelling data with dependence;

6 Demonstrate the ability to self-learn further details of the methodology introduced within topics;

Personal and Key Transferable/ Employment Skills and Knowledge:

7 Statistical analysis skills;

8 Self-learning and making effective use of learning resources;

9 Effective use of learning resources;

10 Report writing and presentation.

SYLLABUS PLAN - summary of the structure and academic content of the module

The syllabus will depend upon the module topic(s) offered and will be specified in detail by the lecturer(s) and agreed by the module coordinator for any particular year. Examples of topics include time series modelling and forecasting; geostatistics; modelling of extreme values; hierarchical modelling; data fusion; multivariate analysis; computational statistics; data mining methods; survival analysis; survey sampling and experimental design. Other suitable topics may also be offered.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36.00 Guided Independent Study 114.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 24 Lectures
Scheduled Learning and Teaching Activities 12 Problem-solving sessions
Guided Independent Study 50 Self-study & background reading
Guided Independent Study 64 Coursework

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Feedback on unassessed problem sheets and data analyses 24 All Oral

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 80 4 pieces of coursework based around individual topics All Oral & Written
Presentation 20 One piece of the coursework will have a presentation associated with it All Oral & Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic Reading:

ELE: http://vle.exeter.ac.uk/

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Faraway, J.J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models Chapman & Hall 2006 158488424X [Library]
Set Venables, W.N., Ripley, B.D. Modern Applied Statistics with S 2nd Springer 2003 978-0387954578 [Library]
Set Wakefield, J. Bayesian and Frequentist Regression Methods Springer 2013 978-1441909244 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Tuesday 20 August 2019
KEY WORDS SEARCH None Defined