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st: Re: Poisson vs. Linear regression for comparing rates

From   "David Davis" <>
To   <>
Subject   st: Re: Poisson vs. Linear regression for comparing rates
Date   Tue, 4 Aug 2009 20:31:14 -0500

A fractional logit is likely appropriate.

Here is a reference:

Econometric Methods for Fractional Response Variables With an Application to 401 (K) Plan Participation Rates, Leslie E. Papke and Jeffrey M. Wooldridge Journal of Applied Econometrics, Vol. 11, No. 6 (Nov. - Dec., 1996), pp. 619-632

Here is the code in stata with robust standard errors.

glm y x1 x2 ... xk, fam(bin) link(logit) robust

----- Original Message ----- From: "Ashwin Ananthakrishnan" <>
To: <>
Sent: Tuesday, August 04, 2009 7:56 PM
Subject: st: Poisson vs. Linear regression for comparing rates


I'm trying to do a state-level analysis looking at rates of a particular cancer and it's relationship to some state-level variables such as proportion uninsured in the state, racial composition of the state and some other state-level economic variables.

However, I'm not sure if I should be using Poisson regression or Linear regression to assess the relationship between the rate of cancer (independent variable) and the dependent variables.

What determines which regression model is the best?

Should I automatically start with Poisson regression, and use the goodness of fit test after that to see if that's the appropriate model (i.e. if gof p > 0.05). If so, should I do it after univariate analysis with each variable?

If the rate of cancer is not normally distributed, should I log transform it and then use linear regression?

Thanks in advance for your help.


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