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Description
The course covers statistical principles and techniques for analysing engineering data, with an emphasis on advanced least-squares estimation techniques for determining the coefficients of mathematical models from experimental data. The level of complexity and sophistication increases as the module progresses. Essential tools such as matrix algebra and calculus will be revised in the early sessions. The course then proceeds to cover basic ideas on the nature of errors, probability distributions and statistical tests, followed by the theory of error propagation and the correlation of errors in space and time. The course then moves on to least-squares estimation, including both linear and non-linear problems, constrained solutions, reliability and quality control procedures, and Kalman filtering. The examples focus mainly on geospatial engineering, but the mathematical techniques we teach are applicable to any branch of science and engineering. Please note that this module does not cover data science, data analytics or machine learning.
Learning Outcomes
- Apply statistical tests to experimental data
- Understand the generic concept of least squares
- Generate quality indicators and interpret results
- Apply these techniques to a range of suitable engineering problems with an emphasis on the geospatial sciences
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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