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?
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.
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.