One way of interpreting the product variable is to think of it as examining the the difference in the regression slopes for, in this instance, males and females. Since the product variable is not statistically significant, the diffenence in the regression coefficients for males would not be significantly different from the coefficient for females. Here is what the separate regressions would look like in Stata. Contrary to how it sounds, curvilinear regression uses a linear model to fit a curved line to data points. Curvilinear regression makes use of various transformations of variables to achieve its fit.
Nonlinear regression fits arbitrary nonlinear functions to the dependent variable.
Inspection of the y vs x plot reveals a degree of curvilinearity. From the above analysis, it appears that model 2 appears to be our best bet. A plot of y vs x with the predicted scores connect by a curved line is displayed above.