Should I Use Total or Partial Differentiation in Absolute Error Calculation?

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The absolute error of the quantity p, defined as p = a*b, should be calculated using differentiation. The discussion highlights that whether to use total or partial differentiation is not crucial, as both approaches represent the error in measuring the variables. The correct differentiation method is clarified as using d, since it differentiates ln p with respect to p, regardless of its dependencies. The preferred method for calculating absolute errors is presented as E[f(x_1,x_2,...,x_n)] = √∑(∂f/∂x_k * dx_k)², which effectively captures the maximum change in the function based on changes in its variables. This method is favored for its clarity and effectiveness in error analysis.
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The absolute error of the quantity p should be calculated.
p=a*b where a and b are two variables.
By taking ln on both sides:
lnp=lna+lnb, then differentiating:
dp/p=da/a+db/b
dp=abolute error
Should I have used total or partial differential in the differentiation step?
 
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Doesn't matter. da or \partial a or \Delta a, they all represent the error in measuring a.
 
Which is the most correct to use when I differentiate a function of two variables? Partial derivative?
 
Well, now that I think, d is the correct one. Because you're actually differentiating \ln p w.r.t. p regardless of what p depends on! The same for other two logarithms.
Anyway, I've heard several methods for calculating absolute errors. But the one I prefer, is the following:
E[f(x_1,x_2,...,x_n)]=\sqrt{\sum_{k=1}^n (\frac{\partial f}{\partial x_k} dx_k)^2}
Because it obviously gives the maximum change in f for given changes in its arguments.
 
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