Perfect collinearity of independent variables
Channel: Mikko Rönkkö
Date: 2019-07-24

In the video, the concept of the no perfect collinearity assumption in regression analysis is explained. This assumption requires that each independent variable contributes unique information to the model, preventing the prediction of one variable's values based on others. The video uses the prestige data set as an example, focusing on the creation of dummy variables for a categorical variable 'type'. It demonstrates that including all dummy variables (blue collar, white collar, professional) leads to a violation of the no perfect collinearity assumption. This is evidenced by the software's inability to estimate the model, resulting in a warning about singularities and the dropping of one variable from the analysis. The video emphasizes that such issues indicate either a violation of this assumption or potential data coding or preparation errors, such as duplicating a variable under different names.

Slides: https://osf.io/ztm79