Understanding Spearman

In conclusion to Issue #244:

Throughout my research on this topic, I have found that there is minimal correlation between who is a sheriff and what their contribution is to the issues that they may or may not own. In early portions of my research on this issue, I was able to analyze the correlations between developers with sheriff hours and centrality values.

In later portions of my research, I was able draw conclusions about the correlation (if any found) between sheriff hours and participation degree-- how active a developer is on an issue, etc. By looking further into our data, I was really able to understand and address the concepts that lead into our degree metric. Concluding this segment of my research, I have found that it is not valid in defining that a sheriff is central or holds sheriff hours based on their degree metric in our data.

This research has brought me to question a few more things about our developers and their degree. I will be conducting research to answer the question:

Are developers with high participation/degree more likely to have missed vulnerabilities? I will be working with the vuln_missed metric and analyze how it is related with the Spearman's rank correlation coefficient. You can find more about this research topic here.

I have been learning about the Spearman Correlation Coefficient and how we use it in our code. The file dev_analysis.rb allows me to understand how to use Spearman with Ruby, however, I would like to understand more about how the cor() function works and how exactly the Spearman Correlation Coefficient is correlating its data.