The dummy variable trap is a statistical phenomenon that can occur when using dummy variables in regression analysis. It occurs when one or more of the dummy variables are perfectly collinear with the other independent variables in the model. This can lead to biased and unstable coefficient estimates, and can make it difficult to interpret the results of the regression analysis.
There are a few different ways to avoid the dummy variable trap. One way is to center the dummy variables before entering them into the regression model. This can be done by subtracting the mean of the dummy variable from each value of the variable. Another way to avoid the dummy variable trap is to use a reduced set of dummy variables. This can be done by creating a new dummy variable that represents the contrast between the two groups that are being compared.
