The analysis and interpretation of research data arising from more complex designs. Review of matrix algebra-random vectors, mean vectors and covariance matrices. Multivariate normal distribution. Generalised linear models. Maximum likelihood estimation and inferences for GLMs. Introduction to multivariate statistical methods using R and SAS. Methods for categorical data.