# Detailed module information

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:  5

### 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