When you usingWith logistic regression what you analyse is the association of a binary outcome towith a set of predictors. In particular you are modelling the $\log(\text{odds})$ of that particular outcome. The odds themselves are simply: $\text{odds} = \frac{\text{Pr(of Occurring)}}{\text{Pr(of Not Occurring)}} = \frac{\text{Pr(of Occurring)}}{1 - \text{Pr(of Occurring)}}$ where $\text{Pr}$ refers to proportions (or loosely speaking probability). Therefore the estimated coefficient $\hat{\beta}$ in your model shows the difference in $\log(\text{odds})$ between two subjects that differ by one unit of your predictor (here: hours
) when all other predictors are constant (or as in your case just absent).
If ones wants toTo find the change in terms of the proportions that are modelled he needsyou need to:
- Get the $\log(\text{odds})$ estimate.
- Exponentiate it to get the $\text{odds}$.
- Get the new proportions as: $\text{Pr}_{\text{new}} = \frac{\text{odds}}{1 + \text{odds}} $. (This later follows from the equality shown above.)