# Thread: Interactions in regression analysis

1. ## Interactions in regression analysis

Is their anyone who knows how to identify interactions between independent variables in a regression analysis? How does one use the output diagnostics to determine that there is an interaction.

Thanks

2. You can look at pairwise correlations between the independent variables, if r values are greater than 0.80 (or smaller than -0.80) the variables are strongly correlated.

3. Hi Quark

Thank you for your response. From what you say, I infer that if two variables correlate with one another then they will interact? I have not come across this in my statistical studies. If two variables are highly correlated I am inclined to consider removing one from the analysis rather than try to model an interaction between them.

But I know that interactions are possible between variables that are not correlated; how would I detect interaction effects between such variables?

4. Originally Posted by firefly
But I know that interactions are possible between variables that are not correlated; how would I detect interaction effects between such variables?
There's a VIF（Variance Inflation Factor), which measures the impact of collinearity among the X's in a regression model. Most statistics software will show it in the multiple regression output. You can google it to learn more.

5. Hi Quark

Two points here.

1) Does correlation imply interactions? your responses so far seem to imply so, but I have never heard of this before.

2) Yes, VIF has a lot to do with collinearity, but how is it related to interaction effects, especially when the variables are not correlated as mentioned in your quote from my previous mail

6. 1) In multiple regression context, if two IVs are significantly correlated, then chances are there are significant interactions. There's no need for a broad statement like "correlation imply interactions".

2) Multicollinearity is interaction between IVs. Typically a VIF value greater than 10 MAY suggest multicollinearity.

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