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The interaction self and the marker are insignificant. When the interaction is dropped, the effect of a marker is significant. … How legitimate is it in statistics in general to drop the interaction, just to show that the marker is overall a significant predictor of an outcome? …

There is some reason to believe that there would be an interaction between these two fixed effects. When I run the model with both main effects and their interaction, none of them are significant. … It seems to me that I should go with the model with the interaction in it, but I am concerned that I am missing the effect of the variable that becomes very significant once the interaction is removed. …

Will I need to put all the interaction terms together with the main effects at once, then drop the insignificant interaction parameters and do the likelihood test (model including all interaction parameters … compared to model with all insignificant interaction parameter removed). …

Can I drop the interaction term from the model if the interaction term is insignificant? …

I have a regression model, which consists of 4 predictors and 1 interaction - i dont know what are the correct symbols and terms, so I will drop my model here: Y=B0+B1x1+B2X2+B3X3+B4X4+B5X3X4 So X4 … is insignificant ( p-value is 0.90), while interaction is significant (p-value is 0.00000 ...) …

Basically this is what I got: After deciding to leave the variable $age$ and $risk$ in my model, I created this interaction term. … We see that the test statistics of $old*standard$ is insignificant. Question 1: Does it make sense to drop this particular level of interactions and run regression on the others? …

I have to analyse if there is an interaction between gender and age keeping gender as the main effects. I dont know how to proceed. … I have to fit a survival model to see if there is interaction. How to do this, can anyone tell me how to proceed further? …

That is to say, there is not necessarily a different slope for all four groups (the interaction). … My suggestion would be to first rerun the model dropping the insignificant interaction terms (which sounds like $A\times B$ and the three-way interaction). …

One should not drop variables from model just because p values for those variables are not significant. … On p.262, after fitting a saturated model, he made the comment that "parch clearly insignificant, so drop". This seems to go against my understanding #2. What am I missing in my reading? …

The statistics alone might not provide sufficient guidance here, because the interaction term can be statistically insignificant even when the effect of X1 does in fact depend on X2 (but the sample size … /general/1315734-insignificant-interaction-term-should-we-still-look-at-the-marginal-effects. …

I thought about running a regression with all possible interactions, and then drop those with insignificant p-values. … That didn't really work out because everytime I drop one, the p-value of the variables in the new model would alter drastically. I apologize for such a long post. …

My question: Do stepwise procedures (such as the stepwise elimination of variables with insignificant coefficients) invalidate inference regarding $\beta$? … Estimate $Y_i = \alpha + \beta D_i + \gamma X_i$, using OLS Drop from $X_i$ all entries variables for which the coefficient estimate in 2 had a p-value below, say, 5% If any variables were dropped in …

You shouldn't do model selection (ie. use AIC or ANOVA F-test) to decide whether to keep or drop the interaction term. … From a statistics point of view: You know that the interaction term is insignificant because you performed a hypothesis test. …

So generally speaking, the error will always increase when you drop a predictor. 2) Can it increase to some substantively meaningful degree even if the predictor you drop is insignificant? … Yes, though the drop will always be less than if you dropped a significant predictor. …

A term should not be removed from the model if a higher order interaction involving that term is significant at the $0.25$ level. … While "drop the terms with insignificant $p$-values" is one such method, it does not seem to have much support theoretically in general. …