deletions | additions       diff --git a/subsection_Method_for_Johnson_Noise__.tex b/subsection_Method_for_Johnson_Noise__.tex  index 8ecaf25..05cbab7 100644  --- a/subsection_Method_for_Johnson_Noise__.tex  +++ b/subsection_Method_for_Johnson_Noise__.tex 
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Nyquist found theoretically that the ratio found in Equation \ref{eq:Equatoin}, to produce $k_B$ even if you change $R$, just as long as you don't change $T$ or $\Delta f$. That means the slope should also remain constant.   As seen in equation \ref{eq:NyquistPredictionForJohnsonNoise2}, we will be measuring the $<(V_J + V_{\textrm{other noise}})^2>$.  \textbf{YOU NEED SOME SORT OF preliminary explanation} of what you are doing here before showering us with specific examples. For example, why data at $10 \textrm{ k}\Omega$, $1 \textrm{ k}\Omega$ and $1 \Omega$? The reader needs to know this before you start giving her the results for each of these cases.   This should be a reasonably straightforward explanation to provide if you refer to what you expect from the Johnson noise equation and an additional equation explaining how what you measure is a combination of Johnson noise and other sources. That explanation should include an explanation of why the formula involves $$ and $$ as opposed to $$ and $$ or $<(V_J + V_{\textrm{other noise}})^2>$, for example.