Applied Multivariate Statistics

This course aims to introduce the students to concepts and techniques of summarising, visualising the geometry, and analysing such dependent, multivariate data

Data in most disciplines are multidimensional in nature, be it field surveys pertaining to demographic details, socio-economic profile and preferences or financial market data, where stocks exhibit joint movement. To understand consumption behaviour, variables ranging from income to expenditure on a diverse basket of goods are collected. Understanding and extracting structures of dependence in higher dimensions requires sophisticated mathematical machinery. The methods can be applied to a wide array of settings where the assumptions on the data hold. The course will cover the following topics and techniques: Theory of Multivariate Normal Distributions, Multivariate Central Limit Theorem, Techniques such as Principal Component Analysis, Factor Analysis, Clustering Methods and Classification techniques.