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Error Propagation (uncertainty) Print E-mail
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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.

PDF Simulation - Pdf manual

Example
Data:
AP
3.488
3.328
3.298
3.493
3.353
....

Error Propagation

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:
Error Propagation
 
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