Hi to all.
I was reading in the text "Stat methods in the SS" by Agresti, and he was explaining with an example how in a linear regression model, for example with 2 predicors, the p value of the predictors may change dramatically between the interaction and the non-interaction model.
y=a + b1x1 + b2x2
vs.
y=a + b1x1 + b2x2 + b3x1x2
He was explaining that you may get similar results for the interaction and no-interaction model by centering the explanatory variables around zero and then running the analyses using the centered explanatory variables.
I have the following model
y=a + b1x1 + b2x2
y and x2 are continuous
x1=categorical with 3 levels
I find a large difference in the p-values for the explanatory variables between the interaction and no-interaction model.
I centered for the continuous variable x2 ((sum x2, gen cx2=x2-r(mean)).
I get similar p values for interaction and no-interaction models.
Question: How about the categorical explanatory variable? Is it appropriate to center this variable also? Does it make any sense?
Many thanks,
Nick
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