Problems with propagation of error for multiple variables

In summary, the person is asking for feedback on their approach to solving problems related to vector-distance and total differentials. The other person agrees that their approach seems good, but cautions them about considering the limits of their triangle quantities and the behavior of the function around their point. They suggest looking at later derivatives for more global information and recommend studying numerical analysis and differential equations for error analysis.
  • #1
bobey
32
0
please help by telling me whether my approach to solve the problems are right or wrong. please refer to the ATTACHMENT for the questions and my approaches...

your help is highly appreciated!
 

Attachments

  • ERROR ANALYSIS-2.doc
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  • #2
Hey bobey.

Your approach seems like you are using a vector-distance based on finding the length of the sum of all independent basis "errors".

The total differential of a multi-variable function describes the infinitesimal change of that function given the infinitesimal changes of all the variables with respect to its derivatives.

What you are doing looks good, but the only thing I would caution you about is what limits you put on the triangle quantities (i.e. the fixed deltas not the infinitesimal values).

Using a small enough value for the triangle delta's of your independent variables should always be OK, but if the function is changing wildly over some region, your errors will be way off. To look at whether this is the case you see how many first derivatives the function has (if any) that are equal to zero and also how the function behaves around your point (whether its derivative suddenly gets really high or really low just after your point).

The method though (calculating the error vector and finding its length) is a sound approach, but the important thing to be aware of is how local this information is and if it's not local (i.e. the stuff that I talked about above happens) then you need to re-consider looking at more global information like later derivatives at that point.

Looking at later derivatives is exactly what is done in numerical analysis when you get the "crazy functions" that have the potential to go wild and if you are interested in error analysis to take into account this behaviour, get some material on numerical analysis and differential equations.
 

1. What is the propagation of error for multiple variables?

The propagation of error for multiple variables is a method used in scientific research to estimate the uncertainty or error in a final result that is calculated from multiple measured variables. This approach takes into account the uncertainties of each individual variable and how they contribute to the overall uncertainty of the final result.

2. Why is it important to consider propagation of error for multiple variables?

It is important to consider propagation of error for multiple variables because it provides a more accurate representation of the true value of the final result. Ignoring the uncertainties of individual variables can lead to an incorrect or misleading conclusion about the validity of the final result.

3. How is propagation of error for multiple variables calculated?

The propagation of error for multiple variables is typically calculated using the partial derivative method. This involves taking the partial derivatives of the final result with respect to each individual variable, multiplying them by the uncertainties of those variables, and then adding them in quadrature to obtain the overall uncertainty of the final result.

4. What are some common sources of error in propagation of error for multiple variables?

Common sources of error in propagation of error for multiple variables include measurement errors, systematic errors, and model assumptions. Measurement errors can arise from imprecise or inaccurate measuring instruments, while systematic errors can occur due to biases in the experimental setup or data analysis. Model assumptions, such as assuming a linear relationship between variables, can also introduce error in the final result.

5. How can propagation of error for multiple variables be minimized?

To minimize the effects of error in propagation of error for multiple variables, scientists can use more precise and accurate measuring instruments, carefully control for sources of systematic error, and critically evaluate the assumptions made in their models. Additionally, taking multiple measurements and averaging the results can help to reduce the overall uncertainty in the final result.

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