This course has two parts. The first part provides students with a solid foundation of statistical concepts, theory, and methods including probability theory, statistical estimation and inference methods, and multivariate statistics and linear models. Emphasis will be placed on intuitive and rigorous understanding of the fundamentals of statistics but implementation of the statistical methods via computer programming in R or Matlab will be an important part of the course as well. The second part teaches students to simulate real-world systems and analyse the simulation model. In particular, this part will cover some basic introduction of simulation and some statistical aspects of simulation such as input analysis, random variate generation, output analysis, and variance reduction techniques.

Continuous Assessment: 75%

Examination: 25%