# What are the implications of the significance of two variables decreasing when in a model together and increasing when adding an interaction term?

In my data set, I have two primary predictors A and B. I also have the outcome Y.
I have created four multivariate models that include other covariates.

• Model 1 includes A and covariates,

• Model 2 includes B and covariates,

• Model 3 includes both A and B and covariates, and

• Model 4 includes A and B and covariates and an A**B interaction term.

In Model 1, A is highly associated with Y (Beta=0.037, p<.0001).
In Model 2, B is highly associated with Y (Beta=0.234, P<.0001).
In Model 3, the parameter estimates of both A and B shift closer to 0 and significance decreases (A Beta=0.029, p=.0003; B Beta=0.157, p=.009).
In Model 4, significance and magnitude of both predictors are the highest out of all models (A Beta=0.237, p<.0001; B Beta=0.399, p<.0001; A*B interaction Beta=-0.066, p<.0001). . I also know that A and B are correlated (Spearman's r=0.484, p>.0001). I am looking for assistance in interpretation of these results.
I hypothesize that there may some collinearity in Model 3 without the interaction term.

Does the interaction term serve to diminish that? In general, I am unsure why significance decreases in Model 3 and increases in Model 4.

Model 3 includes both A and B. I suspect that if you look at the variance-covariance matrix of the coefficient estimates (often hidden from view but eventually accessible with a function applied to the model, like vcov() in R), there will be a high-magnitude correlation between their coefficients. I also suspect that predictions based on Model 3 would be superior to those based on either Model 1 or Model 2, even if the individual coefficients for A and B appear to be less "significant."