Skip to main content
Unit of study_

QBUS2810: Statistical Modelling for Business

Statistical analysis of quantitative data is a fundamental aspect of modern business. The pervasiveness of information technology in all aspects of business means that managers are able to use very large and rich data sets. This unit covers a range of methods to model and analyse statistical dependencies in such data, extending the introductory methods in BUSS1020. The methods are useful for detecting, analysing and making inference about patterns and dependences within the data so as to support business decisions. This unit offers an insight into the main statistical methodologies for modelling statistical dependence in both discrete and continuous business data. This provides the information required for a range of specific tasks, e.g. in financial asset valuation and risk measurement, market research, demand and sales forecasting and financial analysis, among others. The unit emphasises real empirical applications in business, finance, accounting and marketing, using modern software tools.

Code QBUS2810
Academic unit Business Analytics
Credit points 6
Students commencing from 2018: QBUS1040. Pre-2018 continuing students: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905
Assumed knowledge:
This unit relies on mathematical knowledge at the level of the Maths in Business program, including calculus and matrix algebra. Students who do not meet this requirement are strongly encouraged to acquire the needed mathematical skills prior to enrolling in this unit

At the completion of this unit, you should be able to:

  • LO1. develop an understanding of the principles of statistical modelling of business-related variables
  • LO2. develop a deeper understanding of statistically measuring and analysing relationships between business variables via a range of quantitative models and methods
  • LO3. develop proficiency in using relationships between variables and analytic methods to inform and assist business decision making
  • LO4. develop introductory skills in how to manage data and in how to extract objective quantitative information from them
  • LO5. develop proficiency in a software package, e.g. Python, for analysing and assessing relationships between business variables, and in dealing with large data sets
  • LO6. communicate empirical findings using adequate statistical reporting methods and appropriate technical language, as well as layman’s terms.