Course overview
Statistics and data analysis are an essential part of a modern engineer's toolkit. So are numerical methods for solving a variety of mathematical problems that arise in engineering practice. The course provides an introduction to probability and statistics; inference for population means, multiple population means and categorical variables; and linear regression. The course also covers interpolation methods, numerical integration, linear systems and factorisations, iterative solutions of linear and nonlinear systems, and numerical methods in ordinary differential equations. Applications in engineering are emphasised throughout.
Course learning outcomes
- Demonstrate understanding of the probability and statistical foundations of data analysis
- Demonstrate understanding of the importance of assumption checking for valid statistical analysis, and be able to perform assumption checking
- Demonstrate understanding of common numerical methods and how they are used to obtain approximate solutions to otherwise intractable mathematical problems
- Derive numerical methods for various mathematical operations and tasks, such as interpolation, differentiation, integration, the solution of linear and nonlinear equations, and the solution of differential equations
- Analyse and evaluate the accuracy of common numerical methods
- Apply standard statistical and numerical methods using Matlab
- Interpret results from the application of standard statistical and numerical methods
- Write efficient well-documented Matlab code and present statistical and numerical results in an informative way