Take Web data analysis to the next level with PHP
By Paul Meagher2004-06-15
Relate Web data to experimental design
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The application of inferential statistics to Web data streams involves more than learning the math underlying various statistical tests. Equally important is the ability to relate the data-collection process to critical distinctions in experimental design: What is my measurement scale? How representative is my sample? What's my population? What's the hypothesis I'm testing?
To apply inferential statistics to your Web data streams, you need to first think of your results as if they were generated by an experimental design; then select an analysis procedure appropriate to that experimental design. Even if you consider it a stretch to think of Web polls and access log data as the results of an experiment, it is critical for you to do so. Why?
1. It will help you select an appropriate statistical test.
2. It will help you draw the appropriate conclusions from your collected data.
One aspect of experimental design that is critical in determining the appropriate statistical test to use is the choice of measurement scale for data collection.
Tutorial Pages:
» Take Web data analysis to the next level with PHP
» Relate Web data to experimental design
» Examples of measurement scales
» Start with the sampling
» Test the hypothesis
» Model the null hypothesis: The Chi Square statistic
» Look at the Chi Square sampling distribution
» Chi Square instance variables
» The Constructor: Backbone of the Chi Square test
» Handle output issues
» Repoll
» Apply the knowledge
» Resources
First published by IBM developerWorks
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