Numerical Methods:
Numerical differentiation, integration, interpolation and curve fitting (regression analysis). Solution of linear and non-linear algebraic equations. Matrix operations, and applications to solution of systems of linear equations, elimination and tridiagonal 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 and F-tests. Simple and multiple linear regression and analysis of variance. Design and analysis of experiments including an introduction to factorial designs. Statistical quality control. Applications will be drawn from mechanical, mining, photovoltaic and chemical engineering and surveying. Matlab will be used extensively in this course.

Note: Available only to students for whom it is specifically required as part of their program