2017/18 Undergraduate Module Catalogue

LUBS2920 Advanced Analytical Methods

20 creditsClass Size: 20

Module manager: Christina Phillips
Email: C.Phillips1@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2017/18

Pre-requisite qualifications

A-Level Mathematics or Statistics Grade B

Pre-requisites

LUBS1525

Analytical Methods

This module is mutually exclusive with

LUBS2230	Mathematics for Business and Economics 2
LUBS2670	Statistics for Business and Economics 2
LUBS3210

This module is not approved as a discovery module

Module summary

This module extends the knowledge and experience of the application of more advanced statistical analysis and other related analytical techniques used in business analytics. Analytical techniques to be covered include time-series analysis, discriminant analysis, logistic regression, non-linear techniques and neural networks.

Objectives

This modules aims to further extend the knowledge and experience of students in the application of more advanced statistical analysis and other related analytical techniques used in business analytics.

Learning outcomes
Learning Outcomes – Knowledge/Application
Upon completion of this module students will be able to:

- Describe and explain more advanced statistical and other related analytical techniques (Knowledge)
- Accurately apply these techniques to business problems (Application)

Learning Outcomes – Skills
Upon completion of this module students will be able to:

Subject specific
1. Apply appropriate statistical and other related techniques to analyse business data to support management decision making

Transferable
1. Analytical skills – mathematical/numerical/statistical
2. Creative problem solving
3. Critical thinking – reviewing evidence; interpreting results
4. Research skills
5. Use of knowledge

Skills outcomes
Upon completion of this module students will be able to apply appropriate advanced statistical and other related techniques to analyse business data in support of management decision making.

Syllabus

Indicative content:
1. Dynamic optimisation and stochastic calculus
2. Time-series analysis
3. Discriminant analysis
4. Factor analysis, principal components and structural equation modelling
5. Statistical process control
6. Panel data analysis
7. Logistic regression
8. Survival analysis
9. Non-linear techniques
10. Cluster analysis
11. Neural networks
12. Machine learning

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	22	1.00	22.00
Tutorial	21	1.00	21.00
Private study hours			157.00
Total Contact hours			43.00
Total hours (100hr per 10 credits)			200.00

Private study

Private Study
3 hours reading per lecture = 66 hours
3 hours preparation per tutorial = 63 hours
Revision = 28 hours
Total private study = 157 hours

Opportunities for Formative Feedback

Student progress will be monitored principally by tutorial performance. All tutorials will require the completion of a practical assignment in advance. Selected assignments will be submitted and marked to provide feedback on student performance (including written communication skills). In addition there will be regular VLE progress tests.

Methods of assessment

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	3 hr	100.00
Total percentage (Assessment Exams)		100.00

The resit for this module will be 100% by examination.

Reading list

The reading list is available from the Library website

Last updated: 08/12/2017

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