Back Links and Google Page Rank - Data Gathering and Analysis
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The data of the 100 sample websites has been gathered. See screen shot below for the snapshot:
A complete list of raw data is available.
To examine the relationship between total inbound links pointing to the URL and the Google Page Rank, we will use statistical regression analysis. Below is the plot of the raw data:
The Y-axis uses a logarithmic scale to properly show all the data in the graph. The regression plot helps answer the first question:
“What is the current relationship between inbound links and Google Page Rank?”
The answer is a direct/strong logarithmic relationship between Google Page Rank (toolbar) and total inbound links to the URL. The R squared (coefficient of determination in statistics) is 67%. This means that currently, back links account for 67% of the variation for Google Page Rank. It also says that the Google Page Rank algorithm is 67% back link dependent.
How does Google convert inbound link data to Google PR in the toolbar?
In the above plot, the regression model is:
Y = 13.418e1.4717x
Where:
Y is the total inbound links pointing to the URL and X is the Google Toolbar PR.
In practical application, we will be attempting to express Google Toolbar PR as a function of the inbound links. So we will solve for X.
Taking the logarithm of both sides (using the common logarithm, base 10):
Log10Y=Log1013.41e1.4717x
Applying properties of the logarithm and solving for X:
X= (log10(y/13.418))/ (1.4717log10e)
e is a constant in natural logarithm which is equal to 2.71828…
Punch that into the Excel spreadsheet and it will approximately compute the Google PR toolbar value given the total inbound links (y variable).
Next: Computing Degree of Difficulty >>
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