Conduct Web experiments using PHP, Part 2
By Paul Meagher2005-03-18
Conclusions
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Computer simulation and mathematical analysis are two major approaches categorical data analysts use to understand contingency table data. In this article, you learned how to both simulate and analyze contingency table data arising from a two-factor Web experiment.
You can achieve data simulation by sampling from a theoretical probability distribution with an appropriate set of parameter estimates. You learned that data simulation can be used to:
* Assist in the planning stages of a Web experiment
* Determine the number of subjects needed to detect an effect of a given size (also known as power analysis)
* Rigorously elicit subjective estimates of the lambda parameters so you can derive the expected counts to use in a test of the information value of an experiment
* Help determine whether a proposed Web experiment is worth running
In this series, I have discussed the application of DOE and CDA techniques to the task of improving the quality of Web site offers. In particular, I looked at improving the quality of the ad banner component by manipulating image and text channel factors and examining the response counts for the different factor-level combinations.
The simplest ad banner experiment involves manipulating one factor, namely, the ad banner version (the version factor) and conducting a one-dimensional chi-square analysis on the resulting response counts. Such one-variable-at-a-time experiments can be inefficient and ineffective ways to accumulate knowledge about your Web site. Factorial Web experiments are more efficient and effective knowledge acquisition tools when planned, administered, and analyzed appropriately.
Tutorial Pages:
» Categorical data analysis
» 2x2 contingency tables
» Sampling model
» Discrete probability distributions
» Binomial sampling model
» Poisson sampling model
» Envisioning your results
» Eliciting your prior distribution
» Model fitting with chi-square
» Null effects model
» Independence model
» Prior model
» DOE explorer
» Explorer output
» Conclusions
» Resources
First published by IBM developerWorks
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