The dummy variable trap refers to a scenario in regression analysis where the inclusion of dummy variables for all categories of a categorical variable leads to perfect multicollinearity. This situation arises when one category can be perfectly predicted by the other categories, causing issues with estimating the coefficients of the model accurately.
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