Detecting Multicollinearity in Categorical Variables

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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).

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Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has close to 7 years of experience in data science and predictive modeling. During his tenure, he has worked with global clients in various domains like retail and commercial banking, Telecom, HR and Automotive.


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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.

    ReplyDelete

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