Testing for interaction effect btw two continous variables.

Hi, I have 3 variables, A, B and C. All of them are continuous, assymetrical in distibution.

Using sperman correlations, I've found that A/B (A divided by B) is significantly
correlated with C. A by itself does not correlate significantly with C, neither does B correlate significantly with C by itself.
(Of note A*B does not correlate with C using Spearman's rho.)

Because of my finding of A/B correlating with C, I suspect that there is an interaction
effect going on. I wonder how I should investigate if this is the case in a meningful way.

My hypothesis - which would fit nicely with previous research and studies - is that higher values of A, combined with lower values of B (thereby creating a higher A/B fraction) interact so that A/B correlates positively with C.

I have tried visualizing and examining this using all sorts of plots and/or calculations, and it seems like my hypothesis holds - but I need help finding a way to calculate or visualize this that would be statistically sound, so that it actually could be published in an article.

All help appriciated!


Omega Contributor
Based on the context, does your hypothesis make sense?

I may recommend creating scatter graphs for all of these relationships so you can better see what is going on. I slightly wonder if there is a non-linear shape that you are able to get rid of with the division.
What kind of regression model should I use?
Does it matter if the dependant variable ("C") is continous or a discrete variable?
Sorry, foor my noobness :)
why not build a multiple regression model C~A+B+A*B and see what you get?

What kind of regression model should I use? Does it matter if variable C is discrete or not?

Linear Enter Regression with two blocks (block one A+B, block two A*B) ?

Edit: So I did the Linear Enter Regression with two blocks (block one A+B, block two A*B). Variable C is dependant variable and discrete in nature. (Does this matter? Does it have to be continuous for this to work?)

The R square change for the second model is 0.001 compared to 0.099 for model 1. Sig F. change is much above 0.05 for both models.

So I think it is safe to say that there is no significant interaction effect between a and b if I have made this correctly?
What I still wonder though is why A/B correlates with C significantly when neither A or B alone does. Shouldn't this
impy some sort of interaction going on? Why don't I find any? :\
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