anchor.diff:        <h4>Top Histogram 12/2008</h4>
         <a href="/top"><img src="/assets/top-histogram.png" width=100% /></a>
         <p>
  -        Blurp blurp blurp blurp blurp
  -        Description/Analysis
  + In this experiment we sought to see trends in our data and if certain current events caused spikes in page views. In order to do this we displayed the top 50 pages for each month and a breakdown of how viewership of those pages changed over time. As presented above it is clear to see that Wikipedia page viewership was drastically affected by current events (Ponzi Scheme, Twilight, Facebook). It is encouraging to see that people turn to sources like Wikipedia to become informed about what is happening in the world.
         </p>
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positive.code:       <h4>Top Regression 12/2008</h4>
        <a href="/top"><img src="/assets/top-regression.png" width=100% /></a>
        <p>
          Blurp blurp blurp blurp blurp
          Description/Analysis
        </p>
      </div>
    </div>
  
negative.code:       <h4>Cluster 10/2008</h4>
        <a href="/cluster"><img src="/assets/cluster.png" width=100% /></a>
        <p>
          We had two key metrics available for use in clustering: page views and size. We sought to take the data and determine whether a larger page size indicated a larger viewership, or if there was any sort of relationship. The results of the clustering experiment were not very useful. The most interesting result from clustering the data was that there tended to be an enormous number of small pages with low views. The larger page sizes still tended to be viewed a low number of times. This also indicated that the great majority of our data has low page views. On a data set of this size, clustering was not the ideal analysis tool. 
        </p>
      </div>
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