When we build regression models, we need to check the correlations between attributes. Could anyone explain the difference between checking the correlation between pairwise attributes and multicolinearity. What I found was that some attributes have high pairwise correlation, say greater than 0.6. But when I am checking their VIF, which is less than 3, showing they are not correlated.
What's the correct way of checking correlation and decide the attributes to be dropped?