I have a set of observations of a variable Z (shown as the colormap) as a function of two other variables A and B. I want to study how Z varies with respect to A, B, and both A and B (eg. if A increases, does Z increase or decrease? Same with respect to B?)

The only problem is that my observations of Z are highly biased, i.e., for low A, B is high, and vice-versa.

Is there any way I can disentangle both dimensions (A and B)?

Right now, I have just studied Z for A < 1.75, as A doesn't vary too much in this window.

enter image description here

  • $\begingroup$ What do you mean by Z being biased for low A - high B? How do you know that? Is it really biased or did you mean that the variability is different? Also a 3D plot of the three variables might help. $\endgroup$ Feb 12 at 14:54
  • $\begingroup$ From what you are describing, both A and B and correlated somehow. Have you explored this further ? $\endgroup$
    – CaroZ
    Feb 12 at 16:06
  • $\begingroup$ To my understanding of this plot, the observations of Z are biased (maybe not the best term here ^^) in the sense that when observing Z for low A, B is high (vice-versa). This observation bias makes any analysis of the trend of Z versus A and B hard. And the plot already show a 3D dataset? And to answer @CaroZ, indeed the observations of A and B are correlated. $\endgroup$ Feb 13 at 8:08
  • $\begingroup$ How strongly are A and B correlated, could you upload a plot ? And what exactly is your research question ? $\endgroup$
    – CaroZ
    Feb 13 at 10:51
  • $\begingroup$ You may want to look in the direction of PCA in particular and dimensionality reduction in general. $\endgroup$
    – Cryo
    Feb 14 at 5:25


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