# Odds ratio for continuous variable

I have a question

How would you interpret the odds ratios for a continuous variable?

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It might help if you clarified the context. It seems like you're talking about a continuous predictor in logistic regression (as I've assumed in the answer below) but more information would be helpful. I have the same comment regarding your interaction question - stats.stackexchange.com/questions/22036/… –  Macro Feb 1 '12 at 2:27

It's not completely clear from your question, but I'm assuming you're talking about the situation where you have a single binary response $Y$ and a continuous predictor $X$ and fit a logistic regression model:
$$\log \left( \frac{ P(Y_{i}=1|X_{i}) }{P(Y_{i}=0|X_{i})} \right) = \beta_{0} + \beta_{1} X_{i}$$
Then the odds ratio, $e^{\beta_{1}}$, is the odds ratio associated with a one unit increase in $X$.
Essentially you can think of it as the odds ratio between $Y$ and the dummy variable $B$ defined such that $B=1$ if $X=x+1$ and $B=0$ if $X=x$.
Note: The assumption underlying the logistic model is that this odds ratio does not depend on $x$.