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Re: st: Interpretation of Interaction terms in log-lin
From
David Hoaglin <[email protected]>
To
[email protected]
Subject
Re: st: Interpretation of Interaction terms in log-lin
Date
Tue, 22 May 2012 06:44:46 -0400
Dear Lukas,
Please tell us more about what you are trying to do. The subject line
starts with "Interpretation." What do you wish to interpret?
If, for example, you want to interpret the individual coefficients,
you must take into account that the definition of each regression
coefficient includes the set of other explanatory variables in the
model.
Further, the popular (but oversimplified) interpretation that involves
changing a predictor by 1 unit while holding the other predictors
constant cannot be used in your model. Changing x1 by 1 unit must
also change x1_x2.
It may be helpful to choose a grid of values of x1 and x2, calculate
the predicted value of y at each point in the grid, and then
exponentiate those predicted values.
David Hoaglin
On Tue, May 22, 2012 at 6:24 AM, Lukas Borkowski <[email protected]> wrote:
> Dear all,
>
> my simplified model can be written as y = ß0 + ß1x1 + ß2x2 + ß3x1_x2 with the last expression being an interaction term.
>
> However, the dependent variable is in logs and the explanatory variables are not. I now wonder whether I have to add ß2 and ß3 before putting them into the e-function or to exponantiate each coeffecient seperately and then do the addition?
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