Discuss the remedial measures of multicollinearity.

Multicollinearity is a common issue in regression analysis that occurs when two or more independent variables are highly correlated with each other. This can result in several problems, including unstable and poorly estimated regression coefficients, difficulty in interpreting the results, and increased standard errors. The following are some of the remedial measures that can be taken to address multicollinearity:

1. Variable Selection: One of the simplest ways to reduce multicollinearity is to remove one of the highly correlated variables from the model. This can be done through a process known as variable selection, which involves identifying the variables with the _____ _____ _____ ____ _________ __________ ______ ____ _____ _______.
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