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What is multicollinearity and how you can overcome it?
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Multicollinearity refers to high correlation between independent variables in a regression model, when they are supposed to be independent among themselves. This causes problems in fitting the model, redundancy and and interpreting the results.

Multicollinearity can be dealt with by:

1.    Assessing logically whether each independent variable included in a model is really unique and measuring something different from the others.

2.    Determining the correlation between independent variables

3.    Looking at scatter plots.

4.    Eliminating the redundant independent variable(s).
by Diamond (39,212 points)

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