Error Propagation (uncertainty)
This module is useful when exploring statistical behavior of a variable which is not measured but computed from measured variables or from variables with a known statistical behavior. Using the module, range of the resulting variable, its mean and confidence interval can be estimated. The individual measured variables contributions to the resulting variable variability can be determined as well. The approach is sometimes called uncertainity evaluation or error propagation. Resulting variable properties are evaluated by the Monte Carlo simulation and by the second order Taylor expansion. Simulation - Pdf manual

Example
Data:
 AP 3.488 3.328 3.298 3.493 3.353 .... Text Output:
 Error Propagation, Uncertainty Task name : Sheet1 Function : (X1-X2)*ln(Y1) No. of simulations : 1000 Input variables : Mean Std. deviation 95% interval X1 : 40 4 32.16 47.84 X2 : 50 10 30.4.2007 69.6 Input data : Mean Std. deviation 95% interval Y1 : 4.851617133 1.406598457 2.094684157 7.608550109 Output value : Median : -14.71772521 Mean Std. deviation 95% interval +-3sigma -15.38768532 17.20265039 -49.10488009 18.32950945 -66.9956365 Interval of generated values -51.98832859 17.66283193 Sensitivity analysis Absolute sensitivity : x1: 1.711697033 x2: 1.711697033 y1: 1.805592704 Relative sensitivity : x1: 26.8396 x2: 67.099 y1: 15.92717428 Taylor expansion approximation Simple mean : -15.79312079 Corrected mean : -15.37284183 Corrected mean (w/covar.) : -15.37284183 Corrected std. deviation : 17.25502425 Corr. std. dev. (w/covar.) : 17.25502425 95% interval : -49.19268936 18.44700569 Interval +-3sigma : -67.13791457 36.3922309

Graphical Output: 