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How to deal with multicollinearity

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How to Deal with Multicollinearity
  1. Remove some of the highly correlated independent variables.
  2. Linearly combine the independent variables, such as adding them together.
  3. Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.

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Multicollinearity occurs when there is a high correlation between the independent variables in the regression analysis which impacts the overall interpretation ...
2013年4月16日 — How Can I Deal With Multicollinearity? · Remove highly correlated predictors from the model. · Use Partial Least Squares Regression (PLS) or ...
作者:GJ Chen2012被引用次数:35 — The Staged Regression approach is more convenient in handling these cases. It is arguable whether a coefficients estimate under multicollinearity can be ...
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2017年2月22日 — However, methods have been developed to mitigate its effects. Perhaps the most effective way to remedy multicollinearity is to make a priori ...

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