Module information on this degree can be found below, separated by year of study.

The module information below applies for the current academic year. The academic year runs from August to July; the 'current year' switches over at the end of July.

Students select optional courses subject to rules specified in the Mechanical Engineering Student Handbook,  for example at most three Design and Business courses. Please note that numbers are limited on some optional courses and selection criteria will apply.

Statistics A

Module aims

This module aims to develop  statistical methods and procedures that can be confidently applied and the results reported in a professional manner revealing both good understanding and interpretation. This is a level 6 version of the enhanced level 7 Statistics module and students cannot take both for credit towards their final degree.

ECTS units:  5     

Learning outcomes

On completion of this module, students should be able to:

1. Use MATLAB for data processing, visualization, simulation and analysis

2. Apply probability models, estimate their parameters and test their fit to data

3. Apply reliability theory to devices and networks

4. Perform predictive modelling tasks using regression and time series analysis.

Module syllabus

Statistical techniques for summarising, interpreting and displaying data, including computer processing
Probability theory for events
Discrete probability models (Poisson, binomial, geometric), including computer simulation, fitting parameters and testing the fit
Continuous probability models (uniform, exponential, normal, student t, chi-squared, Weibull including simulations, fitting and applications
Failure analysis, reliability of devices and systems
Covariance and correlation
Sampling distributions, unbiasedness, standard error and mean square error
Maximum likelihood estimation, confidence bounds and hypothesis testing
Linear models, simple and multiple regression.

Teaching methods

Students will be introduced to the main topics through lectures, supported by technology (PowerPoint, Panapto and Blackboard). Short activities (using interactive pedagogies) will occasionally be introduced in the classroom setting to reinforce learning, for example through mentimeter and the like. You will be provided with problem solving sheets and should complete these as part of your independent study. Tutorials sessions will provide an opportunity for interaction with teaching staff where you can discuss specific problems. 


Assessment details        
      Pass mark   
Grading method Numeric   40%
Assessment type Assessment description Weighting Pass mark Must pass?
Examination 3 Hour exam 90% 40% N
Coursework Report on applied statistics problem 10% 40% N

Reading list

Module leaders

Dr Ioanna Papatsouma