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Unit information: Quantitative Analysis in Management in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

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 data analysis tools and techniques and data sources used to solve problems in a business and management environment. The module focuses on how to use Excel to perform data analysis and how to interpret the resulting analyses 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 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:


Excel functions and tools for data analysis

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

Introduction to sampling

Hypothesis test for a mean

Simple Linear Regression Analysis

Intended learning outcomes

Students should be able to demonstrate knowledge and understanding of:

  1. The role of quantitative analysis in generating value from data
  2. The scope and nature of different quantitative techniques
  3. The role of probability theory in modelling uncertainty
  4. Basic concepts of statistical and mathematical analysis and inference models

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

5. Apply basic statistical and mathematical techniques to business and management problems

6. Use probability distributions to model uncertainty in real life problems

7. Communicate quantitative ideas effectively both in oral and written form

8. Use a variety of visual models to represent statistical results

9. Use Excel for data analysis and presentation.

Teaching details

30 hours of lectorials in groups of approximately 60 in a computing lab.

Assessment Details

Coursework - Portfolio of three activities (100%). Up to 20 pages. This assesses all of the learning outcomes.

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:

Anderson, D.R., Sweeney, D.J., Wiliams, T.A., Freeman, J and Shoesmith, E. (2017) Statistics for Business and Economics, Andover: Cengage Learning

Moore, D.S., McCabe, G.P. and Craig, B (2014). Introduction to the Practice of Statistics, Houndsmills: 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://www.math.com/

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

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