Introduction to multivariate statistical analysis. Review of matrix algebra. Random vectors, mean vectors, covariance and correlation matrices. Multivariate normal distribution and its properties. Random samples from the multivariate normal and sampling distributions thereof. Inference for multivariate normal parameters, maximum likelihood estimation, generalised linear models (GLM) and inferences for GLMs, one sample and two sample test of hypotheses on the mean vector: comparison of several mean vectors - multivariate analysis of variance (MANOVA), discriminant analysis. Analysis of multivariate data using statistical software like R and SAS (Statistical Analysis System). -- Course Website
Prerequisites: 8128 (v.6)<br/> Linear Algebra 202<br/> <br/> or any previous version<br/> <br/> <br/><br/> <br/> AND<br/><br/> <br/> 8393 (v.9)<br/> Experimental Design and Analysis 202<br/> <br/> or any previous version<br/> <br/> <br/><br/> <br/> AND<br/><br/> <br/> 3
