Detecting and Remedying Multicollinearity in Your Data Analysis

This post introduces and details methods for detecting and remedying multicollinearity in data analysis. It covers the consequences of multicollinearity, including difficulty in interpretation and reduced predictive power. Detection methods explained include Variance Inflation Factor (VIF), correlation matrices, heatmaps, clustermaps, and eigenvalues. The post also touches upon fixing multicollinearity, though the specific techniques for fixing are not deeply detailed in this excerpt.