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Im working on a panel data set and have difficulties to understand the odds ratio of a
fixed effect logit model. I prepared an cross sectional exapmle. I suppose the interpretation is identical:
data(cars) attach(cars) fit<-glm(Sound ~ Mileage+Price, family=binomial(link="logit")) summary(fit) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.581e+00 2.811e-01 5.623 1.88e-08 *** Mileage -1.189e-05 9.428e-06 -1.261 0.207205 Price -2.734e-05 7.564e-06 -3.614 0.000301 *** require(MASS) exp(cbind(coef(fit), confint(fit))) ## for odds ratios 2.5 % 97.5 % (Intercept) 4.8578575 2.8174158 8.4927615 Mileage 0.9999881 0.9999695 1.0000065 Price 0.9999727 0.9999577 0.9999874
How can I interpret odds ratios as an increase/decrease in chance or probability?