Teaching Multivariate Analysis to Business-Major Students

During the last two or three decades, multivariate statistical analysis has become increasingly popular. The theory has made great progress, and with the rapid advances in computer technology, routine applications of multivariate statistical methods are implemented in several statistical software packages, making it simple even for the novice to undertake fairly sophisticated multivariate statistical analysis of data at their disposal. While this is certainly a welcome development, we find, on the other hand, that many users of statistical packages are unable to appreciate, what they are doing, and this is particularly true for multivariate statistical methods. With the increasing use of multivariate statistical methods by business analysts, it is important for business-major students to develop an understanding of multivariate statistical methods: Even though business executives are not generally required to undertake sophisticated statistical analysis themselves, they are often presented with reports and articles based on such analysis. Furthermore, many executives have heard about these techniques and would like to use them as analytic and decision support tools. The traditional approach to the teaching of multivariate statistical analysis, as exemplified by Anderson (1958), relies heavily on advanced matrix mathematics. On the other hand, Hury and Riedwyl(1988) suggest that it is possible to understand most of the basic ideas underlying multivariate statistical analysis without a mastery of such mathematics, provided that these are conveyed with the help of real data sets. Since most business data do not follow the usual normality assumption, there are often (possibly severe) limitations in the use of some of the standard multivariate statistical techniques. Real data sets are therefore required not only to illustrate the statistical techniques concerned, but also to clarify the assumptions needed for these techniques to be valid. In this paper, we propose a non-mathematical data-driven approach for teaching multivariate statistical methods to business-major students. Despite this, we are mindful of the need for students to know some basic linear algebra and univariate statistical concepts. Such basic knowledge provides students with the foundation necessary for the application of the appropriate multivariate statistical procedures and for the interpretation of results.