STATISTICAL METHODS WITH FINANCIAL APPLICATIONS - 2018/9

Module code: MAT3012

Module provider

Mathematics

Module Leader

KUEH A Dr (Maths)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

JACs code: G300

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Module content

Assessment pattern

Assessment type Unit of assessment Weighting
Examination EXAMINATION 80
Coursework ASSESSED COURSEWORK 20

Alternative Assessment

N/A

Assessment Strategy

Module aims

  • Introduce students to fundamental concepts of Univariate Time Series as well as provide tools to analyse and interpret analyses of data arising from such time series to determine appropriate model fit of the dataset.
  • Enable students to fit datasets to Autoregressive Integrated Moving Average (ARIMA) models.
  • Illustrate key concepts of modelling datasets by demonstrating theory and enable students to deduce similar theoretical models in order to model the behaviour of a dataset with the intention of forecasting future values.

Learning outcomes

Attributes Developed
1 Understand results and methods of univariate time series. K
2 Apply these results and methods to analyse appropriate data. KCP
3 Interpret the results from such analyses and critically evaluate viable models to recommend the best choice. KCP
4 Know the limitations of modelling time series data and apply statistical inference to eliminate unsuitable models. KC
5 Use the statistical software R to create data summaries and to deduce best-fit models for forecasting future data values. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

Methods of Teaching / Learning

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

Reading list for STATISTICAL METHODS WITH FINANCIAL APPLICATIONS : http://aspire.surrey.ac.uk/modules/mat3012

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Economics and Mathematics BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Mathematics BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Mathematics with Music BSc (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Financial Mathematics BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mathematics with Statistics BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mathematics MMath 2 Optional A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2018/9 academic year.