# Poisson or Linear Regression for Time Data

I am trying to model time-elapsed data (time from event A to event B) and am stuck on deciding between standard multiple linear regression vs. Poisson regression. A number of papers published on a similar topic seem to use Poisson regression, but in my head I've always associated the Poisson distribution with "count" data. What are your thoughts?

Thanks!

Back to your question: if you have the difference in time between event A to event B for each object, i.e., $y =time_B-time_A$, then you could likely use $y$ as a dependent variable in linear regression, providing the residual values $e_i=y_i-\hat{y}_i$ derived from results are normally-distributed, where $\hat{y}_i$ are the predicted values of $y$.
If you are modelling the time between events, this can be thought of as being exponentially distributed, i.e a Poisson distribution can be thought of as being the number of events, while the exponential as the time between these events, https://en.wikipedia.org/wiki/Exponential_distribution. Based on this, $y=time_B−time_A$ probably follows an Exponential distribution.