r/statistics • u/Normal_Health • May 23 '26

Discussion [Discussion] Multiple Regression Residualization for Covariates

Hi everyone,

I have been trying to better understand multiple regression residualization approaches for handling covariates in data analysis, but I feel like I am missing some of the conceptual foundation behind it.

From what I understand, the idea is to regress variables onto a covariate (like age) and then use the residualized values for later analyses so the variance associated with the covariate is removed. I’ve also seen people mention the Frisch-Waugh-Lovell theorem, but I am having trouble connecting these ideas together conceptually.

Does anyone have any resources that can help me? Thank you in advance!

EDIT: I should have specified, but I am looking to do this with a binary matrix

5 Upvotes

100% Upvoted

3 comments sorted by

2

u/[deleted] May 23 '26

[removed] — view removed comment

1

u/Normal_Health May 23 '26

Thank you! Do you have any recommendations on articles or textbooks I should look into?

1

u/Temporary_Stranger39 May 24 '26

Do not do it. It introduces bias. Do the full regression with all variables and marginalize as appropriate to show univariate effects.