Julia,
You are correct that most Q-Q plots will use the sample data quantiles on the Y axis and the theoretical distribution quantiles on X-axis. See https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot Also a Q-Q plot typically uses the same scale, since the quantiles of the sample data is plotted against the quantiles of the theoretical, hence, its name.
JMP plots are called probability plots, and the sample (Actual) data scale is used for one axis. The reason, I documented using Fit Y by X is because it allows you to to create an Actual by Quantile or a Quantile by Actual.
If you want a Q-Q plot as documented by the url above, you need to compute the standardized data values, or compute the sample quantile (0 to 1, sort the data and compute the cdf ) and use that data in Fit Y by X, and plot Actual by Quantile and then they will have the same scale.
However, I like the probability plot, because typically the consumer of the reported plot typically relates to actual sample data scale. Also, the key to any probability or Q-Q plot is to asses whether the data appoximates a linear relationship.
Because I first learned to use probability paper in physics prior to the computer age (yes in the late 60's we still used slide rules), plotting Quantile by Actual is my preference. However, I recommend you plot the data in a format that is expected or typical. If there is no "norm" or expectation then just add a blurb to your paper or report, describing the plot.
Hope that helps!
