# Choosing between two parameters in a model

I have a few parameters that are related (let's call them X1 and X2), and I want to use whichever one will provide the strongest model. The model has many other parameters. Would I simply be able to compare the AICc of these two models?

Model using X1:

Y ~ X1 + X3 + X4 + X5 + X6


Model using X2:

Y ~ X2 + X3 + X4 + X5 + X6


This is confusing me as I'm not sure what other parameters will ultimately belong in the "correct" model. Ultimately, X3, X4, etc. may be thrown out. So would I want to test this with potentially "bad" parameters?

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What does "strongest" mean in terms of models? Having parameters that don't contribute much explanatory power in your model doesn't usually cause much harm (some inflation of standard errors, when there's multicollinearity may be an issue) - but removing predictors that don't achieve significance certainly can cause problems. – Glen_b Aug 31 '14 at 23:14
This is a chapter in a regression textbook. The people here are fairly good a condensing information so you might get something in one of the answers, but I think you'd be better served by revisiting a textbook. (why are you using AICc, how many outcomes do you have, why is this a mixed-model, what is the goal of model building??) – charles Sep 1 '14 at 0:01