All well and good, but the primary flaw in this work is that the principle determinant of wait time, drink complexity, is poorly coded. This is not addressed in your response. In addition, I'm not sure the statistical tests applied were appropriate.

Consider the non-fancy drinks. The orders in this set should be most equivalent - there are many possible fancy drinks but only a few non-fancy ones. Therefore this set best reduces drink complexity as a source of error.

Looking at this data, which was provided in figure 1, it seems the difference in wait times was minimal. Most of the difference between the male and female means is due to 4 (?) orders with wait times over 150 seconds. Without those 4 orders, the wait time for women is substantially less than for men. I tried to estimate the underlying numbers (assigning the average value for each bin to every observation in that bin - including the 4 long waits) and apply a Wilcoxon ranksum test for equal medians. The resulting p-value was .88. I realize this is largely BS -- I don't have the real underlying data. Perhaps you could run the same analysis and report the p-value?

In general however, it seems that for the most controlled comparison there is no significant difference. It might have been a better idea to pick a typical drink often ordered by both men and women and examine the wait times for those groups, ignoring special orders.

If persisting with the regression method of analysis, it might be a good idea to toss the insignificant terms in the model and re-evaluate the model with only the significant terms.