CME8014 : Data Analysis and Reconciliation for Control
- Offered for Year: 2019/20
- Module Leader(s): Dr Jie Zhang
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
|Semester 2 Credit Value:||10|
To provide an understanding of the use of data analysis methods relevant to the process industries.
This module provides an introduction to linear and nonlinear data analysis techniques and data reconciliation techniques relevant to the process industries.
Outline Of Syllabus
Data gathering, Data conditioning, Graphical techniques (e.g. Scatter and Box-Whisker plots), Correlation techniques, Linear regression (Single and Multi-variate), Principal Component Analysis, Principal Component Regression, Partial least Square Regression, Non-linear regression, Quality of estimates (Confidence bounds, standard errors, Hypothesis tests, Analysis of variance), Model validation, Data reconciliation. Application of techniques to real problems. Inferential Measurement.
|Guided Independent Study||Assessment preparation and completion||30||1:00||30:00||Assessment preparation|
|Scheduled Learning And Teaching Activities||Lecture||24||1:00||24:00||N/A|
|Scheduled Learning And Teaching Activities||Practical||7||1:00||7:00||Computer lab|
|Scheduled Learning And Teaching Activities||Small group teaching||5||1:00||5:00||Tutorials|
|Guided Independent Study||Independent study||34||1:00||34:00||Review lecture material and prepare for small group teaching|
Teaching Rationale And Relationship
Lectures convey the underlying principles of data analysis, data modelling, and data reconciliation. Computing laboratories aid the understanding of materials taught in the lectures. Tutorials address the problem in understanding the taught material on individual basis.
The format of resits will be determined by the Board of Examiners
|Report||2||M||100||Assignment 20-40 pages. Issued week 1 semester 2|
Assessment Rationale And Relationship
The assignment enables more realistic engineering problems to be set and the knowledge and skill learnt to be assessed fully.