The key to modeling anything correctly is to be aware of exactly what assumptions your choice of model corresponds to and how well they match the real-world processes underlying your data.
So you've fitted some curve, but why that curve? What are the implications of assuming linearity between your input and output domains? What have you assumed about the distribution of noise over your predictions? How about over your input features? How would you expect the bias and variance of your predictions to degrade if any of these assumptions no longer held?
As an engineer, if you don't understand the statistics behind your models enough to answer to such questions, their real-world applications aren't going to go far beyond wishful garbage-in-garbage-out number crunching.