Detecting Multicollinearity in Categorical Variables

Data Science: Machine Learning A-Z: Hands-On Python & R In Data Science

In regression and tree models, it is required to meet assumptions of multicollinearity. Multicollinearity means "Independent variables are highly correlated to each other".

For categorical variables, multicollinearity can be detected with Spearman rank correlation coefficient (ordinal variables) and chi-square test (nominal variables).

For a categorical and a continuous variable, multicollinearity can be measured by t-test (if the categorical variable has 2 categories) or ANOVA (more than 2 categories).
Coursera Data Science

Statistics Tutorials : 50 Statistics Tutorials

Get Free Email Updates :
*Please confirm your email address by clicking on the link sent to your Email*

Related Posts:

1 Response to "Detecting Multicollinearity in Categorical Variables"

  1. Hi,

    Thanks for your great work. Could please explain this in more detail. Like age group and income group. Both are co-related but which test we would we use in SAS to quantify this relationship.


Next → ← Prev