DSC 410/510 - Multivariate Statistical Methods
In order to make informed decisions, business decision-makers need to identify and quantify relationships between multiple information sources that are usually interrelated. In striving to make some sense out of our environment, to seek some order to the chaos of inter-relationships, there are many approaches, including intuition, logical reasoning, and making simplifying assumptions. This course instead relies on the accepted framework of modern multivariate analysis techniques applied to business decisions. It is designed for graduate students and upper-level undergraduates in business (although graduate students from other disciplines are also welcome), and places emphasis on real-world applications using actual data, with hands-on experience using SAS statistical software. The major topics to be covered include conjoint analysis, cluster analysis, discriminant analysis, logistic regression, multidimensional scaling, correspondence analysis, and discrete choice analysis; all can be very powerful means of achieving parsimonious descriptions, explanations, and predictions of reality. The course website is here.