Introductory description

Business processes have to be monitored and understood if they are to be controlled and the usual way of doing this is to collect (often numerical) information about them. Unfortunately, all human activity is to a greater or lesser extent subject to randomness and this complicates understanding and decision making. Statistics is the science of collecting data, describing and presenting it, understanding and quantifying randomness present in it, finding facts from it and making informed decisions on the basis of it. This module covers the general concepts and techniques of statistics that apply to industrial and commercial disciplines and shows how these methods may be applied to specific problems. The philosophy of the module is to concentrate on the concepts and applications of statistical processes, rather than the mathematical theory behind them.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• Basic Parameters (Descriptive Statistics & Probability)
• Distributions: e.g. Binomial, Poisson, Exponential and Normal.
• Exploratory Data Analysis (EDA)
• Sampling & Significance Testing.
• Goodness of Fit testing.
• Analysis of Variance (ANOVA)
• Correlation & Regression Analysis.
• Design of Experiments (DoE).
• Distribution-free (non-parametric) statistics.
• Contingency Tables
• Statistics on the computer.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a comprehensive understanding of the nature and significance of variation and evaluate its occurrence in practical situations.
• Independently interpret and evaluate data analysis situations to enable the appropriate selection and implementation of a range of statistical techniques.
• Develop hypotheses that may be successfully analysed using appropriate statistical techniques; implement these analyses and interpret the outcome in a practical problem-solving situation.
• Recognise when additional, more complex analytical techniques are required to address unpredictable situations of data-based problem solving.

Indicative reading list

• “Statistical Methods for Engineers” (3rd Edition) Vining & Kowalski; Duxbury Press , 2010.
  ISBN: 139780538737234
• “Introduction to Probability and Statistics” (13th Edition) Mendenhall, Beaver & Beaver; Cengage Learning, 2012.
  ISBN: 139780495389538
• “Introduction to Statistical Quality Control” (7th Edition) Montgomery, Wiley, 2012.
  ISBN: 139781118146811
• “Applied Statistics for Engineers & Scientists” (3rd Edition) Devore, Farnum & Doi; Cengage Learning, 2014.
  ISBN: 1285972422.

View reading list on Talis Aspire

Subject specific skills

Understand the nature and significance of variation, interpret and evaluate data analysis situations, select and implement appropriate statistical techniques, develop hypotheses that may be analysed using statistical technique, implement statistical analyses and interpret the outcomes, recognise when more complex analytical techniques are required.

Transferable skills

Communication, statistical analysis, problem solving