Module manager: Panagiotis Stamolampros
Email: P.Stamolampros@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2018/19
A-Level Mathematics or Statistics Grade B
| LUBS1525 | Analytical Methods |
| LUBS2230 | Mathematics for Business and Economics 2 |
| LUBS2670 | Statistics for Business and Economics 2 |
| LUBS3210 | Advanced Modelling Techniques for Business Analytics |
This module is not approved as a discovery module
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.
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 – 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
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.
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
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 22 | 1 | 22 |
| Tutorial | 21 | 1 | 21 |
| Private study hours | 157 | ||
| Total Contact hours | 43 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Private Study
3 hours reading per lecture = 66 hours
3 hours preparation per tutorial = 63 hours
Revision = 28 hours
Total private study = 157 hours
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.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 3.0 Hrs Mins | 100 |
| Total percentage (Assessment Exams) | 100 | |
The resit for this module will be 100% by 3 hour examination.
The reading list is available from the Library website
Last updated: 05/07/2019
Errors, omissions, failed links etc should be notified to the Catalogue Team