Computer-aided engineering (CAE) has become an indispensable tool for the pursuit of automotive NVH (noise, vibration, and harshness), collision safety, and so on. However, generally speaking, CAE is believed to be a deductive and deterministic tool that uniquely quantifies input-output relationships in models and cannot be used for stochastic evaluation, considering the various uncertainties of actual automobiles. That being said, in order to promote prototype-less methods (proactive application of hypothetical prototypes using CAE), which are gaining traction in the automotive industry, stochastic evaluation is important. Through joint research with automotive manufacturers, I use NVH robust design methods to conduct DOE and multiple regression analysis and build probabilistic projection models using simulations in numerical models with JMP. I will introduce a case example of this, as well as a case example of DOE, cluster analysis, neural networks using JMP for pattern recognition and machine learning for the deformation mode control of high-order, nonlinear and indeterminate automotive structures.