deletions | additions       diff --git a/section_Statistics_The_data_points__.tex b/section_Statistics_The_data_points__.tex  index 96f9427..b92a1f9 100644  --- a/section_Statistics_The_data_points__.tex  +++ b/section_Statistics_The_data_points__.tex 
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The probability of a success is 2572/138070≈ 0.0186 . We further assume that thek trial are independent from each other (under the guidance of our selection criteria). Our variable of interest is the number of successes observed during the n trials.  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 sizes of the bins are much larger than the errors in the projected separations of our data points, and thus these errors (as seen in figure 11 and 12) are approximated by zero.    L1p4std[b] = np.sqrt(((radiolumrms - np.mean(radiolumrms))**2).sum())/ (len(radiolumrms[mask][radiolumrms[mask]>0])-1) For figure 11 we plotted the binned averages of radio luminosity with separation. Using the binomial theorem each of the y-error bars is calculated using  σ^2= 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.