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The traditional least-squares fitting or chi-squared minimisation route of fitting a straight line makes the implicit assumption that the errors on the x-axis quantity are negligible. If that is so, then there is no reason why you can't use the uncertainty in the gradient as the uncertainty in $R$. I guess from your question though, that this is not the ...


Yes, there will be a difference in how you label the axes. This sounds trivial, but loads of people get it wrong. Imagine you have a luminosity, measured in solar luminosities, on the y-axis of a graph. If you plot it on a log scale, the label should read "Luminosity ($L_{\odot}$)" and equal steps along the y-axis will have numbers like 0.1,1,10,100 etc. ...

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