duplicate categorical variables with different values for multiple linear regression I am building a regression model and have run into an issue. I'm trying to build a model that predicts the weight of a baby when it is born. One of the explanatory variables is the race of the parents.
In some cases, the baby's parents will have one race and in some, it will have multiple. There is no distinction between the mother or father's race, so essentially I have Parent 1 and Parent 2 as variables. However, the order does not matter, meaning Parent 1 and Parent 2 could have their values swapped with no change to the predictor variable.
I know I have to encode the values categorically but I'm not sure how to handle the Parent 1 and Parent 2 variables such that order doesn't matter. I'm not sure if my issue is coming off as clear so please ask follow-up questions if you're not sure what I'm talking about.

It seems to me that the mother and the father don't contribute equally to the baby's weight at birth.  Certainly, both contribute nuclear DNA, which would have some influence, but the mother's nutritional status potentially matters more.  Thus, I would analyze the data using two categorical variables (viz., mothers_race, and fathers_race).  If you are really committed to only caring about what the combination of the races is, then make just one categorical variable, with all possible combinations (irrespective of order) as the levels (i.e., both_white, both_black, both_asian, ..., white_black, etc.).
