Calculating uncertainty of a function in Mathematica

In summary, The conversation discusses a problem with determining the uncertainty of a function using Mathematica. The individual has noticed that their uncertainty has the same value as the function, but with a factor of 10^7 higher. They are seeking help in identifying where they have made a mistake. Another individual points out that the formula for the propagation of errors is incorrect and provides a correction.
  • #1
Shukie
95
0
I have a function of which I'm trying to determine the uncertainty. I'm using Mathematica and I can't get it to work. However, I don't see what I'm doing wrong. My uncertainty has the same value as the function itself, only a factor 10^7 higher. That is of course completely wrong, but I don't see where I'm going wrong. Could anyone please take a look?

http://home.orange.nl/~kuip3148/Differentieermethode.nb
 
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  • #2
Hi Shukie,

This is probably a little late, but your formula for the propagation of errors is wrong. Each term should be of the form SDx²(df/dx)² where SDx is the standard deviation of x. You calculate the (df/dx)² part of each term but never use the SDx² part. Also, you forgot to square the one for R.
 
  • #3


I understand the importance of accurately calculating uncertainties in scientific calculations. In this case, it seems like the issue may lie in the method used to calculate the uncertainty in Mathematica. It is important to ensure that the method being used is appropriate for the function being analyzed.

I would suggest checking the documentation for Mathematica to see if there are any specific instructions or examples for calculating uncertainties for the type of function you are working with. Additionally, it may be helpful to seek assistance from Mathematica experts or forums to troubleshoot the issue and find a solution.

It is also important to carefully check the input and parameters used in the function to ensure accuracy. Small errors in input values can result in significant differences in the calculated uncertainty.

In conclusion, I recommend double-checking the method and parameters used in the Mathematica function and seeking assistance from experts to accurately calculate the uncertainty. With careful attention to detail and the appropriate techniques, the uncertainty can be determined accurately and confidently.
 

Related to Calculating uncertainty of a function in Mathematica

1. How do I calculate the uncertainty of a function in Mathematica?

To calculate the uncertainty of a function in Mathematica, you can use the built-in function "ErrorBar" or the "UncertaintyPropagation" function. Both of these methods require you to input the function and the corresponding error values for each variable used in the function.

2. Can I calculate the uncertainty of a function with multiple variables in Mathematica?

Yes, you can calculate the uncertainty of a function with multiple variables in Mathematica by using the "UncertaintyPropagation" function. This function allows you to input the function and the corresponding error values for each variable.

3. How do I interpret the uncertainty of a function in Mathematica?

The uncertainty of a function in Mathematica represents the range of possible values that the function can take due to the error in the input variables. It is important to note that the uncertainty is not a definitive value, but rather a measure of the potential variability in the function's output.

4. Can I plot the uncertainty of a function in Mathematica?

Yes, you can plot the uncertainty of a function in Mathematica by using the "ErrorListPlot" function. This function allows you to input the function, the corresponding error values, and the range of values for the independent variable.

5. How can I use the uncertainty of a function in Mathematica for further analysis?

The uncertainty of a function in Mathematica can be used for further analysis by incorporating it into other calculations or by applying statistical methods to analyze the variability in the function's output. It can also be used to determine the sensitivity of the function to different input variables and to identify any potential sources of error.

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