deletions | additions       diff --git a/section_Statistics_The_data_points__.tex b/section_Statistics_The_data_points__.tex  index 24a6d46..ba84b9e 100644  --- a/section_Statistics_The_data_points__.tex  +++ b/section_Statistics_The_data_points__.tex 
...
There are about 200 pairs per projected separation bin and the bins are randomly cut to be a similar size. The sample space is cut into 6 bins. The errors in the projected separations of our data points are neglected neglected because the selected bins width ($\sigma_{rp} << 20 kpc$) are much larger than the projected separation uncertainties (as seen in figure 11 and 12).    In Figure 11, we plot the bin averages of radio luminosity against projected separation. Using the binomial theorem each of the y-error bars is calculated using  σ^2= np(1-p) $\sigma = \sqrt{np(1-p)}$  Where n is the number of data points in the particular bin and p is the proportion of success. The x-errors are taken to be zero.  The median radio luminosity of each bin was plotted against the projected separation in figure 12. Since this data is obtained in nature via random sample (and since we have a large sample), we approximate the distribution of the radio luminosities to a normal distribution. The error in the measurement of these luminosities is the conventional standard deviation of a normal distribution/Gaussian.