Module title: Statistics, Probability and Risk

SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: FIN11101
Module leader: Paul Gallacher
School The Business School
Subject area group: Accountancy Finance and Law
Prerequisites

There are no pre-requisites for this module to be added

2019/0, Trimester 1, Face-to-Face, Edinburgh Napier University
Occurrence: 001
Primary mode of delivery: Face-to-Face
Location of delivery: CRAIGLOCKHAR
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Paul Gallacher
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
Learning & teaching methods including their alignment to LOs
Use of computer packages and IT will allow students to access, retrieve, interpret, communicate and present technical financial information effectively

Embedding of employability/PDP/Scholarship Skills
Key employability skills will be developed and enhanced through the practical experiences associated with assessments. Students will be exposed to the importance of academic rigour in terms of sourcing relevant information, structuring this information to support their own work, and communicating this effectively through a written report.

Assessment (formative & summative)
Formative assessments include evidence of information retrieval and the production of a business related report. Tasks relating to standard software packages will enhance student's IT skills.

Research/teaching linkages
Students are engaged in research throughout the module, to support their work through accessing and retrieving relevant academic information, and with the undertaking of a report based in their subject area.

Supporting equality and diversity
The module content will be broad based and applicable to all societies and also in an international and global context. Mature, part-time and international students will be able to contribute to class exercise and discussion sessions. Disabled students will have appropriate adjustments made to enable them to participate fully in the module.


Internationalisation
Material is drawn from some of the major world markets. The expected number of international students present on the module will enrich the understanding of all students as they collaborate and support each other in terms of their own learning.





Formative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Summative Assessment:
The University is currently undertaking work to improve the quality of information provided on methods of assessment and feedback. Please refer to the section on Learning and Teaching Approaches above for further information about this module’s learning, teaching and assessment practices, including formative and summative approaches.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 24
Face To Face Practical classes and workshops 12
Face To Face Practical classes and workshops 12
Independent Learning Guided independent study 152
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 50 1, 2, 3, 5 9 HOURS= 0, WORDS= 2500
Report 50 1, 2, 4 13 HOURS= 0, WORDS= 2500
Component 1 subtotal: 50
Component 2 subtotal: 50
Module subtotal: 100

Description of module content:

Nature of economic and financial data
Graphical and numerical summaries of data. Applications to market efficiency
Discrete probability distributions. Applications to option pricing and default risk
Continuous probability distributions. Applications to stock prices and returns
Bayesian probability and behavioural finance.
Sampling distributions and statistical inference
Simple and multiple regression. Potential difficulties, and applications to CAPM
Forecasting with time series
Use of software packages
Report writing

Learning Outcomes for module:

LO1: Identify, search and retrieve relevant academic and finance based information.
LO2: Evaluate secondary information collecting techniques, analytical tools, interpretation and presentation.
LO3: Investigate and compare a range of software packages for the analysis of economic/financial data.
LO4: Explore techniques for managing risk.
LO5: Develop report writing skills in a technical context.

Indicative References and Reading List - URL:

Please contact your Module Leader for details
Click here to view the LibrarySearch.