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Traffic estimators give you an estimate of how many readers you can expect to get with a published article. All of this depends on the type of keywords you use and how well you use them in your article. Most estimators will help you generate good keywords to put into your articles as well.

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Q: How does a traffic estimator operate?
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Related questions

How does one become a traffic estimator?

Using online tools, anyone can be a traffic estimator of their own site, however, to be a traffic estimator as a career, one would seek employment with a large website which was in requirement of such services to monitor site usage.


How is a Google Adwords Traffic Estimator useful for a website?

Google Adwords Traffic Estimator is useful because it helps owners of websites figure out the level of traffic they should expect to see on their website. Since it is an estimate, it will not be 100% correct, but can be used as a good basis for the website operators.


What is the purpose of a traffic estimator?

How to use the web estimation tool to interact with a specialist site. Notices for information about the Website, notices of information on the Site, notices of notices, notices (organines, ad listings, or posted advertisements), date and time notices, notices, and notices.


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Estimator is the correct spelling.


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In statistics, an efficient estimator is an estimator that estimates the quantity of interest in some "best possible" manner


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Is www-sitelogr-com accurate?

Hi! I have found another website traffic and value estimator site. I'm talking about http://www.estimix.com. The estimation provided by estimix is the result of a complex analysis based on factors like: the age of the website, the demographic structure of the traffic, the countries where the website is popular and sources of the traffic.


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There are four main properties associated with a "good" estimator. These are: 1) Unbiasedness: the expected value of the estimator (or the mean of the estimator) is simply the figure being estimated. In statistical terms, E(estimate of Y) = Y. 2) Consistency: the estimator converges in probability with the estimated figure. In other words, as the sample size approaches the population size, the estimator gets closer and closer to the estimated. 3) Efficiency: The estimator has a low variance, usually relative to other estimators, which is called relative efficiency. Otherwise, the variance of the estimator is minimized. 4) Robustness: The mean-squared errors of the estimator are minimized relative to other estimators.


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