In JMP, stepwise regression is a personality of the Fit Model platform. The Stepwise feature computes estimates that are the same as those of other least squares platforms, but it facilitates searching and selecting among many models.
The approach has side effects of which you need to be aware. The significance levels on the statistics for selected models violate the standard statistical assumptions because the model has been selected rather than tested within a fixed model. On the positive side, the approach has been helpful for 30 years in reducing the number of terms. The book Subset Selection in Regression, by A. J. Miller (1990), brings statistical sense to model selection statistics.
This chapter uses the term significance probability in a mechanical way to represent that the calculation would be valid in a fixed model, recognizing that the true significance probability could be nowhere near the reported one.