Handbook

This course gives an introduction to numerical methods and statistics essential in a wide range of engineering disciplines.

Numerical methods: Computing with real numbers. Numerical differentiation, integration, interpolation and curve fitting (regression analysis). Solution of linear and nonlinear algebraic equations. Matrix operations and applications to solution of systems of linear equations, elimination and tri-diagonal matrix algorithms. Introduction to numerical solution of ordinary and partial differential equations.

Statistics: Exploratory data analysis. Probability and distribution theory including the Binomial, Poisson and Normal distributions. Large sample theory including the Central Limit Theorem. Elements of statistical inference including estimation, confidence intervals and hypothesis testing. One-sample and two-sample t-tests. Simple linear regression and analysis of variance.

In each component, applications will be drawn from a variety of engineering disciplines. MATLAB or Python will be used as a practical tool for both numerical and statistical computations and to illustrate theoretical concepts that are introduced during lectures and tutorials.