1. A computer-implemented method for evaluating process robustness, wherein the computer performs the following steps comprising: accepting a definition of one or more independent variables of a process-related experiment; receiving experimental results observed after the varying of the one or more independent variables during the process-related experiment; calculating a prediction model by the computer from the experimental results for the one or more independent variables, the prediction model capable of generating predicted results; calculating a setpoint for each of the one or more independent variables that optimizes the predicted results to a defined target result; defining the variation around each setpoint under normal operation, the variation comprising a range encompassing each setpoint; calculating a process robustness metric using at least the variation(s) around each setpoint, the prediction model, and the defined target result; and displaying a representation of the process robustness, at least in part using the process robustness metric, wherein a parameter-based propagation-of-error (PPOE) template allows a robustness estimation based on the same data construct for all independent variables, wherein the parameter-based propagation-of-error matrix template (PPOE template) is created based on the experiment variables and their associated control limits; wherein the PPOE template is utilized to generate a joint probability of occurrence matrix; wherein using the PPOE template is utilized to generate a response prediction matrix; transforming acceptance limits for the response to actual lower and upper acceptance limits around the response prediction value calculated for the setpoint level settings of the variables in preparation of calculating a response robustness; for each element in the response prediction matrix, centering the response prediction model error distribution about the mean predicted value and calculating the proportional amount of the distribution that is outside the response acceptance limits is transferred; transforming the data distribution of the matrix values is transferred from a uniform distribution to a Gaussian distribution; calculating the failure rate at setpoint (FRSP), wherein the FRSP is the proportion of the predicted response distribution that is outside the acceptance limits; calculating the standard deviation of the predicted response distribution using the FRSP; calculating the confidence interval of the predicted response distribution; and calculating a first data set from based on the predicted response distribution resulting in robustness data from which a robustness prediction model is derived.