By default, Evince uses variable mean centering when preparing a DataSet prior to creating a multivariate PCA or PLS model. In variable mean centering the mean value of a variable (i.e. the mean value of all observations) is subtracted from each individual value. Visually, this can be seen as the center of the multidimensional variable space is shifted towards the origin of coordinates. Variable mean centering implies that the subsequent multivariate modeling is more straightforward, as the variable means, if not removed, would have added one component to the model. This simplifies both the model complexity and the interpretation of data.