Hi @del ,
I'd say 'general concept' might serve sufficiently here for most folks. You're talking experiments, so the X1 & X2 are better represented by what you mean to control in your experiment, e.g. pH and temperature, your experimental factors (each a 'column vector' for the mathspeak).
Ideally, if you detect an effect of one of the factors via stats techniques like regression, you'd like to know the effect is solely from that factor (e.g. pH), and not due to the correlation of the factor with another (e.g. temp) induced by the design. Hence the desire for the design to provide orthogonality of the factors (or 'column vectors'); no correlation, effect source is clear. Not sure where you saw 'vector', but likely framing the [n,1] representation of the data for their dot product to assess orthogonality.
Hopefully that helps. I expect you'll find more along these lines in either (both?) the free, self-paced 1) ANOVA and Regression and 2) Custom Design of Experiments courses offered by our Education folks here: https://www.jmp.com/en_us/training/overview.html
As for drawing it, JMP's got the 3D Scatterplot under Graph, in case that helps you out.