2022/23 Taught Postgraduate Module Catalogue
JALJ0006 Random Phenomena
10 creditsClass Size: 20
Module manager: Professor Edvard Govekar
Email: .
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2022/23
Pre-requisite qualifications
Min 2(i) Engineering or Physical SciencesThis module is not approved as an Elective
Objectives
On completion of this module, students should be able tointroduce the concepts of variability, description, characterisation and interpretation of random data
acquaint the students with the concept of probability and the basic statistical methods for the description and characterisation of random data
equip the students with the basic knowledge for the analysis, description, characterisation and interpretation of experimental data
use the Matlab environment
Learning outcomes
Knowledge and understanding
The students obtain the knowledge about:
the fundamentals of probability and statistical description,
of random phenomena,
the fundamentals of time series/sensor signal analysis,
using the statistical methods of description, deduction and interpretation of data variability in the characterisation and design of products, systems and processes.
The students will understand:
the concepts of variability, randomness, probability, probability distribution of random variables,
the assumptions and principles of statistical analysis and deduction,
the assumptions of stationariness and the ergodicity principles in the analysis of random processes.
Usage
for the empirical description, analysis and characterisation of experimental measurement data,
for finding empirical laws,
for analyses and empirical characterisation of existing products and for designing new products, systems and processes.
Reflection
The presented concepts and methods will be used to attain new knowledge and solve scientific and industrial problems.
Transferrable skills related to more than one course
Using domestic and foreign literature. Problem identification and problem solving methodology in different fields.
Skills outcomes
Competences:
the ability to analyse, characterise and interpret variable random data
the ability to conduct linear analysis and characterise sensor signals
using the methods of technical statistics for the description, analysis, characterisation, design and planning of products, technical systems and processes
Syllabus
- Introduction: examples of random phenomena and the problem of variability in the engineering, experiments and randomness of experiment outcomes, events.
- Probability: relations and event probabilities; random variables and probability distributions, functions of random variables; means and moments of random variables.
- Technical statistics: important statistics, properties and probability distributions of statistics; point estimates; methods for determining estimates; interval estimates; statistical hypotheses, tests and deduction; errors of deduction; goodness-of-fit tests, tests for dependence and homogeneity; analysis of variance; function estimates: parametric and non-parametric regression.
Random processes: examples of random processes; moments and characteristics; stationariness and ergodicity; linear transforms of random processes, autocorrelation function, spectral density, coherence function, examples of random process characterisation.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lecture | 30 | 1.00 | 30.00 |
| Tutorial | 30 | 1.00 | 30.00 |
| Private study hours | 60.00 | ||
| Total Contact hours | 60.00 | ||
| Total hours (100hr per 10 credits) | 120.00 | ||
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Tutorial Performance | . | 50.00 |
| Total percentage (Assessment Coursework) | 50.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Exams
| Exam type | Exam duration | % of formal assessment |
| Standard exam (closed essays, MCQs etc) | 1 hr | 50.00 |
| Total percentage (Assessment Exams) | 50.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading list
There is no reading list for this moduleLast updated: 29/04/2022 15:31:27
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