  Information visualization significantly enhances human perception by
graphically representing complex data sets. The variety of visualization
designs makes it challenging to efficiently evaluate all possible designs
catering to users' preferences and characteristics. Most of existing evaluation
methods perform user studies to obtain multivariate qualitative responses from
users via questionnaires and interviews. However, these methods cannot support
online evaluation of designs as they are often time-consuming. A statistical
model is desired to predict users' preferences on visualization designs based
on non-interference measurements (i.e., wearable sensor signals). In this work,
we propose a multivariate regression of mixed responses (MRMR) to facilitate
quantitative evaluation of visualization designs. The proposed MRMR method is
able to provide accurate model prediction with meaningful variable selection. A
simulation study and a user study of evaluating visualization designs with 14
effective participants are conducted to illustrate the merits of the proposed
model.
