logistic

Interpreting the coefficient estimators in a logistic regression is straightforward. The binary logistic regression model is \[y = \mathrm{logit}(\pi) = \ln\left(\frac{\pi}{1 - \pi}\right) = X\beta\] A \(\delta = x_1 - x_0\) unit change in \(x\) in a estimated regression \(\hat{y} = X\hat{\beta}\) is associated with a \(\delta\hat{\beta}\) factor change in the log odds of \(y\). More commonly, you take the exponent of the coefficient estimate and say a \(\delta\) unit change is associated with a \(e^{\delta\hat{\beta}}\) factor change in the odds of \(y\) (see my handbook).