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Business analytics, applied modelling and prediction ST2187

The course extends and reinforces existing knowledge and introduces new areas of interest and applications of modelling in the ever-widening field of management.


Prerequisite: A course that you must have ordinarily attempted all elements of before you are permitted to register for another particular course.

If taken as part of a BSc degree, the following courses must be attempted before this course may be taken:

  • ST104a Statistics 1
  • MT105a Mathematics 1 or MT1174 Calculus or MT1186 Mathematical methods

Topics covered 

  • Introduction to data analysis and decision-making.
  • Time series data.
  • Outliers and missing values.
  • Pivot tables.
  • Probability distributions.
  • Decision making under uncertainty.
  • Methods for selecting random samples.
  • Nonparametric tests.
  • Stepwise regression.
  • Time series forecasting.
  • Regression-based trend models.
  • The random walk model.
  • Autoregressive and moving average models.
  • Exponential smoothing.
  • Seasonal models.
  • Introduction to linear programming.
  • Product mix models.
  • Sensitivity analysis.
  • Monte Carlo simulation.
  • Applied simulation examples.

Learning outcomes

At the end of the course and having completed the essential reading and activities students should be able to:

  • apply modelling at varying levels to aid decision-making
  • understand basic principles of how to analyse complex multivariate datasets with the aim of extracting the important message contained within the large amount of data which is often available
  • demonstrate the wide applicability of mathematical models while, at the same time, identifying their limitations and possible misuse.


An individual case study piece of coursework (30%) and a 2 hr unseen written examination (70%).

Essential reading

Albright, S., W. Winston and C.J. Zappe. Data Analysis and Decision Making, South-Western, fourth edition (2010) [ISBN 9780538476126].