Linkscape provides to produce some interesting data

[zihadhasan019]

The corrected line shows unique domains linking to be much more important, so I also added this line to the combined über score vs individual features chart. My apologies for the error. -Ben] First, some stats on the latest Linkscape index: Release date: Oct. 6th, 2009 (exactly 1 year after our initial launch) Root Domains: 57,422,144 (57 million) Subdomains: 215,675,235 (215 million) URLs: 40,596,773,936 (40.5 billion) Links: 456,939,586,207 (456 billion; if we also include pages that 301, that number climbs to 461 billion) Link Attributes: No-follow link, internal: 6,965,314,198 (1.


51% of total) No-follow links, external: 2,765,319,261 email lists australia (0.60% of total) No-follow links, total: 9,730,633,459 (2.11% of total) 301'ing URLs: 384,092,425 (0.08% of total) 302'ing URLs: 2,721,705,173 (0.59% of total) URLs employing 'rel=canonical': 52,148,989 (0.01% of total) Average Correlation between mozRank + Google PageRank Mean absolute error: 0.54 Average Correlation between Domain mozRank (DmR) and Homepage PageRank Mean absolute error: 0.37 Now let's get into some of the research around correlation data and talk about how we can use the features .





These first charts use raw correlation - just the relationship between the ranking positions and the individual feature. As noted above, please read the descriptions of each carefully before drawing conclusions and remember that correlation IS NOT causation. These charts are not meant to say that if you do these things, you will instantly get better rankings - they merely show what features apply to pages/sites ranking in the top positions. Understanding the Charts: Mean Index By Value: These are used for the y-axises of many charts.