Statistics of Research Methods II| Statistics of Research Methods II
Identification of collinearity relies on examining relationships among explanatory variables through techniques such as correlation & variance inflation factor (VIF) tests etc., which can help detect any significant correlations present within the data set. Additionally, looking at scatterplots/heat maps is also beneficial here – as these can provide further insights into how different features are related and if they could potentially be impacting regression results.
Overall there are several ways of detecting collinearity issues within a given data set however eliminating them altogether isn’t always necessary either. It may be more advantageous to combine highly-correlated features in order to decrease their influence and create better predictors to help models perform better.