We have multivariate data when more that 3 dependent variables are varying agains one.
Very often real world cases cope with situations where relationships between more than three variables must be analysed. Let's think about an analysis of factors such as age, living place, job, and sex on the appearance of cancer on a sample of patients. This is a challenging aspect in IV, because some proprieties of images have to be explored to distinguish between several variables in a 2D drawing plane. For this purpose several methods have been proposed.