SEO Information

To properly discuss statistical experiments for Internet marketing it is important to begin with a brief overview of what exactly an experiment is. For our purposes, an experiment is when we take websites and randomly assign changes to them and look for changes in results. Any detected changes can then be said to be caused by the changes we made. In the real world, experiments tend to include variables about people which prevent researches from assigning individuals to groups. For instance, if a big drug company is testing the effect of a medicine, they could take 50 people and select 25 to receive the new medicine and 25 to get nothing for their headaches (the “control group”). They would then randomly select each person to go into a group. Then if the people in the group getting the medicine have…
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Model validation requires us to consider multiple variables and models across many different data sets all at the same time. Overall variables selection can be done by comparing the best models among the various datasets. We do not consider all datasets together because there is a chance that some data sets will lack any accurate use of a certain variable, and consequently make us unlikely to find it in an overall model. Rather we consider individual models from each data set and then analyze each variable to understand why it is or is not present in each model. It would be simple if all variables were consistent across all datasets. In practice there is not a single variable which is significant for all datasets. All variables fluctuate from dataset to dataset. What we are really looking for then is variables…
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Using multiple linear regression and other techniques previously described we can now begin to make a full model based on one single dataset (or google search). There are a lot of ways to determine the variables that should be included. The best way to include variables is based on subject knowledge. Because Google is not forthcoming about the variables they use, we are left to base our variable choices only on search results. We begin by performing a Factor Analysis (FA). This analysis will allow us to divide the variables up into a number of groups. Recall that root domain trust and subdomain trust were closely related to each other (“trust” is an estimation of the quality of a domain or subdomain). Remember, that Google continues to disfavor subdomains over root domains in search results. FA would indicate that these…
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The previous blog posts on using regression analysis for SEO focused on a single variable only, backlinks for instance. Of course, the actual Google algorithm involves many variables, perhaps thousands. We now begin to discuss the issues surrounding multiple regressions for SEO. Multiple regressions simply refer to any regression model which includes more than one predictor variable. In the case of SEO actual site rank is the dependent variable and the variables on a website are all potential predictor variables. Of course site rank is not as easy to determine when you are considering personalized search results. For this exercise we are using a generic geographically neutral ranking. Simple linear regression has an easy to see form. We simply graph points on a two-dimensional table. With multiple regressions we can only visualize a model with two predictors. Two predictors would…
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