deletions | additions       diff --git a/subsection_Method_for_Johnson_Noise__.tex b/subsection_Method_for_Johnson_Noise__.tex  index 40670a0..39c600d 100644  --- a/subsection_Method_for_Johnson_Noise__.tex  +++ b/subsection_Method_for_Johnson_Noise__.tex 
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To get the best measurement of Johnson Noise, we wanted to vary the $R_{in}$ from $10 \textrm{ k}\Omega$, $1 \textrm{ k}\Omega$ and $1 \Omega$. This is important because if our apparatus and measurements are correct, all of the different $R_{in}$ should generate the same value for the Johnson Noise, $k_B$. The $10 \textrm{ k}\Omega$ and $1 \textrm{ k}\Omega$ were used to calculate the $k_B$ while the $1 \Omega$ measurement was used as an error source that needed to be subtracted. As with all electronics, there is some innate error associated with the device, in order to subtract this, we took data with the $1 \Omega$ resistor.  In order to get the desired data, we decided to vary $\Delta f$ in order to vary the bandwidth. To do this, we kept the resistance ($R$) constant, but did three different trials where each trial had a different $R$ value. The values for $R$ are as follows: $10 \textrm{ k}\Omega$, $1 \textrm{ k}\Omega$, and $1 \Omega$.  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.