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

Module aims

To show that statistical procedures can be confidently applied and the results reported in a professional manner revealing both good understanding and interpretation.

ECTS units:  6   
Contributing to Course Elements: 6 to ME3-LCTVS or ME4-LCTVS

Learning outcomes

On successfully completing this module, students will be able to:

  • Use MATLAB for data processing, visualization, simulation and analysis
  • Apply probability models, estimate their parameters and test their fit to data
  • Apply reliability theory to devices and networks
  • Carry out 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

  • Duration: Autumn and Spring terms (21 weeks)
  • Lectures: 1 x 1hr per week
  • Tutorials: one drop in class every fortnight
  • Coursework: The student is expected to use statistical methods to address a real-life application.  This will include using MATLAB for statistical computing.  A report on the task should be presented, including commented MATLAB code

Summary of student timetabled hours

Autumn

Spring

Summer

Lectures

10

11

-

Tutorials

5

6

-

Total

32 hrs (if all tutorials attended)

Expected private study time

3-4 hrs per week (excluding exam revision)

Assessments

Written examinations:

Date (approx.)

Max. mark

Pass mark

Statistics (3h)

A statistics formula sheet is provided.

This is a CLOSED BOOK Examination

April/May

180

n/a

 

Coursework (including progress tests, oral presentations etc.)

Submission date

Max. mark

Pass mark

Submission

Feedback

Report on applied statistics problem

Returned for viewing, with written comments

wk23

20

n/a

Total marks

200

Reading list

Module leaders

Dr Ioanna Papatsouma