Module manager: Prof Bill Gerrard
Email: W.J.Gerrard@lubs.leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2016/17
A-Level Mathematics or Statistics Grade B
| LUBS1535 | Excel for Business Analytics |
| LUBS2925 | |
| LUBS3210 |
This module is not approved as a discovery module
This module provides you with an introduction to the application of statistical analysis and other related analytical techniques used in business analytics. Analytical techniques to be covered include correlation and regression, analysis of variance, segmentation analysis, Bayesian approaches, non-parametric tests, and multi-level models.
This module aims to give students an introduction to the application of statistical analysis and other related analytical techniques used in business analytics.
Upon completion of this module students will be able to:
- Describe statistical and other related analytical techniques
- Accurately apply these techniques to business problems
Upon completion of this module students will be able to apply in context the following skills:
Transferable
- Analytical – mathematical; numerical; and statistical
- Communication – written and presentational
- Critical thinking – reviewing evidence; and interpreting result
- Use of knowledge
- Creative problem solving
- Research skills
Subject Specific
- Apply appropriate statistical and other related techniques to analyse business data to support management decision making
Indicative content:
1. Review of basic mathematics: linear algebra; univariate and multivariate calculus
2. Further topics in mathematics: constrained optimisation; linear programming; matrix algebra
3. Review of basic statistics: exploratory data analysis; probability and probability distributions; sampling and sampling distributions; confidence intervals; hypothesis testing
4. Analysis of variance (ANOVA)
5. Categorical data, contingency tables and chi-square tests
6. Correlation and simple bivariate regression
7. Multiple regression
8. Non-parametric tests
9. Segmentation analysis
10. Bayesian statistics and decision making
11. Extensions to regression analysis: diagnostic testing; non-linearities; moderation and mediation; specification searches
12. Extensions to ANOVA: repeated-measure analysis; multivariate analysis (MANOVA)
13. Multilevel models
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 44 | 1 | 44 |
| Tutorial | 18 | 1 | 18 |
| Private study hours | 138 | ||
| Total Contact hours | 62 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Private Study
2 hours reading per lecture = 88 hours
2 hours preparation per tutorial = 36 hours
Revision = 14 hours
Total private study = 138 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 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 8/5/2016
Errors, omissions, failed links etc should be notified to the Catalogue Team