What does a significant interaction mean in regression?
The presence of a significant interaction indicates that the effect of one predictor variable on the response variable is different at different values of the other predictor variable. It is tested by adding a term to the model in which the two predictor variables are multiplied.
Can you have a significant interaction if neither main effect is significant?
There is really only one situation possible in which an interaction is significant, but the main effects are not: a cross-over interaction. In most data sets, this difference would not be significant. But there clearly is an interaction.
When to add interaction terms in an ANOVA?
In an ANOVA, adding interaction terms still leaves the main effects as main effects. That is, as long as the data are balanced, the main effects and the interactions are independent. The main effect is still telling you if there is an overall effect of that variable after accounting for other variables in the model.
When is ANOVA significant in a regression table?
That just tells you that the combination of included predictors is significantly better than nothing, not that any individual predictor is “significant.” It seems that all models significant in the tables labeled “ANOVA” include age, a significant predictor in the multiple regression.
When to drop interaction terms in a regression?
In a Regression model, should you drop interaction terms if they’re not significant? In an ANOVA, adding interaction terms still leaves the main effects as main effects. That is, as long as the data are balanced, the main effects and the interactions are independent.
Can a model with d1 be significant in ANOVA?
The significant ANOVA results that trouble you seem to be those presented in the tables labeled “ANOVA.” Yes, the models that contain D1 are significant. But these are tests of a model with all of the specified variables against a model with no variables, as the associated degrees of freedom indicate.