Complexity and redundancy

Visualization is not the only reason why we need data reduction methods. Quite often, multivariate data stem from the administration of a questionnaire to a sample of respondents; each question corresponds to a single variable, and a set of answers by a single respondent is a multivariate observation. It is customary to ask respondents many related questions, possibly in order to check the coherence in their answers. However, an unpleasing consequence is that some variables may be strictly related, if not redundant. On the one hand, this motivates the use of data reduction methods further. On the other hand, this may complicate the application of rather standard approaches, such as multiple linear regression. We will see that a strong correlation between variables may result in unreliable regressed models; this issue is known as collinearity. By reducing the number of explanatory variables in the regression model, we may ease collinearity issues. Another common issue is that when a problem has multiple dimensions, it is difficult to group similar observations together. For instance, a common task in marketing is customer segmentation. We also outline clustering methods that may be used to this aim.


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