Figure 1. Schematic representation of uncertainty analysis and sensitivity analysis. In mathematical modeling, uncertainty arises from a variety of sources - errors in input data, parameter estimation and approximation procedure, underlying hypothesis, choice of model, alternative model structures and so on. They propagate through the model and have an impact on the output. The uncertainty on the output is described via uncertainty analysis (represented
Figure 2. Sampling-based sensitivity analysis by scatterplots. Y (vertical axis) is a function of four factors. The points in the four scatterplots are always the same though sorted differently, i.e. by Z1, Z2, Z3, Z4 in turn. Note that the abscissa is different for each plot: (−5, +5) for Z1, (−8, +8) for Z2, (−10, +10) for Z3 and Z4. Z4 is most important in influencing Y as it imparts more 'shape' on Y.