Error uncertainty for power law

AI Thread Summary
To determine error uncertainty for a power law function, one can transform the data using logarithms, resulting in a linear relationship between log(y) and log(x). This allows for the calculation of uncertainties in the parameters by analyzing the slope and intercept of the log-transformed data. It is important to ensure that error bars are correctly represented, as they may not be symmetric. For more precise uncertainty estimates, rigorous statistical methods can be employed, though simpler approaches may suffice for basic analysis. Understanding these methods is crucial for accurately interpreting data that follows a power law.
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So I have a series of 5 data points let's say that they are (1,1),(2,3),(3,4),(4,4.5),(5,4.75) that create a power function that has the equation y=1.2x^.97. Let's also say that the error uncertainty for every number is 0.1. I know that for a linear line you can take the uncertainty of the slope by finding the largest possible slope and the smallest possible slope but how would you do it for a power function?
 
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What do you want to find the uncertainty of?

Guessing:

For data suspected to be of form ##y=ax^b## ... where a and b are to be found...
notice that: ##\log(y)=\log(a)+b\log(x)##

... a plot of log(y) vs log(x) should yield a line with slope b and intercept log(a).
Find the uncertainties normally ... make sure your errorbars are correct, they are no longer symmetric.There are also more rigorous statistical approaches to getting uncertainties in the parameters of a regressed curve but I'm guessing you don't need to go that far.
 
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