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Unit information: Quantitative Analysis in Management in 2017/18

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Unit name Quantitative Analysis in Management
Unit code EFIM10014
Credit points 20
Level of study C/4
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Holland
Open unit status Not open
Pre-requisites

None

Co-requisites

None

School/department School of Management
Faculty Faculty of Social Sciences and Law

Description

The aim of this module is to provide students with an understanding of the use of different statistical techniques and data sources used to solve problems in a business and management environment. The module focuses on how to interpret data involving uncertainty and variability; how to model and analyse the relationships within business data; and how to make correct inferences from the data (and recognise incorrect inferences). The module utilises advanced computer modelling tools available in Microsoft Excel and other modelling software packages to analyse and present quantitative data. It therefore develops practical skills in statistical and mathematical techniques commonly used in business and management decision-making. It draws on fundamental quantitative analysis and business statistics theories with contemporary computational skills to critically evaluate complex business problems and to cross-examine them through computer technologies. The module will also prepare students for the reading, comprehension and interpretation of original business and management research articles that are based on quantitative data and statistical analysis.
Indicative course content:
Quantitative Modelling in Business
Algebraic Expressions and Equations (including financial applications)
Introduction to Statistical Variables (types and data collection)
Statistical Summaries (measures of central tendency and dispersion – means, variance and skewness)
Elementary Probability
Correlation and Association
Regression Analysis
Differentiation
Linear Programming
Simultaneous Equations

Intended learning outcomes

Students should be able to demonstrate knowledge and understanding of:

  • The role of quantitative analysis in generating value from data
  • The scope and nature of different quantitative techniques
  • The role of probability theory in modelling uncertainty
  • Basic concepts of statistical analysis and inference models

Having successfully completed the module, students will be able to:

  • Apply basic statistical techniques to business and management problems
  • Apply basic statistical techniques to business and management problems
  • Use probability distributions to model uncertainty in real life problems
  • Apply basic statistical analysis and inference models to business and management problems
  • Communicate quantitative ideas effectively both in oral and written form
  • Use a variety of visual models to representing statistical results

Teaching details

Weekly lectures and seminars/computer-based workshops.

Assessment Details

From Week 4 of the module, students will be required to undertake four weekly on-line (data analysis) assessment tests that will each contribute 10% towards the overall unit mark (40% in total). These on-line tests are designed to ensure cumulative learning of key statistical techniques and how they are applied in practice using organisational data. A 2-hour written exam (60% in total) will assess both the interpretation of (research) data and the student’s technical skills in statistical analysis.

Reading and References

Students will follow a range of standard statistics/quantitative analysis textbooks for business and management, and there is any number of on-line support websites for maths and statistics. The following is indicative of the reading/resources available:

Moore, D.S., McCabe, G.P. and Craig, B. (2014) Introduction to the Practice of Statistics, Houndmills: Palgrave Macmillan.

Swift, L. and Piff, S. (2014) Quantitative Methods for Business, Management and Finance, Basingstoke: Palgrave Macmillan.

http://www.mathtutor.ac.uk/

http://www.mathcentre.ac.uk/about/

http://nrich.maths.org/public/index.php

http://www.math.com/

http://www.geogebra.org/cms/en/download

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