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