2019/20 Undergraduate Module Catalogue

LUBS1525 Analytical Methods

20 creditsClass Size: 60

Module manager: Prof Barbara Summers
Email: B.A.Summers@lubs.leeds.ac.uk

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

Year running 2019/20

Pre-requisite qualifications

A-Level Mathematics or Statistics Grade B

This module is mutually exclusive with

LUBS1275	Mathematics and Statistics for Economics and Business 1A
LUBS1285	Mathematics and Statistics for Economics and Business 1B
LUBS1535	Excel for Business Analytics
LUBS1630	Introductory Statistics for Business
LUBS2925	Modelling Techniques for Business Analytics

This module is not approved as a discovery module

Module summary

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.

Objectives

This module aims to give students an introduction to the application of statistical analysis and other related analytical techniques used in business analytics.

Learning outcomes
Upon completion of this module students will be able to:
- Describe statistical and other related analytical techniques
- Accurately apply these techniques to business problems

Skills outcomes
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

Syllabus

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

Teaching methods

Delivery type	Number	Length hours	Student hours
Workshop	22	2.00	44.00
Private study hours			156.00
Total Contact hours			44.00
Total hours (100hr per 10 credits)			200.00

Private study

Private Study
2 hours reading per workshop = 44 hours
Total private study = 156 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 3 hour examination.

Reading list

The reading list is available from the Library website

Last updated: 30/04/2019

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