Resources

• Get the source code for this article at Datavore Productions.
  
  
• Check out the popular undergraduate textbook, Statistics, 9th ed., by James T. McClave and Terry Sincich (Prentice-Hall, online) that was consulted for the algorithm steps and the "Burnout Study" example in this article.
  
  
• Look at the PEAR repository's low-level PHP math classes. Eventually, it would be nice to see PEAR contain packages that implement standard higher-level numerical methods, such as SimpleLinearRegression, MultipleRegression, TimeSeries, ANOVA, FactorAnalysis, or FourierAnalysis.
  
  
• Visit the Numerical Python Project which extends Python with a full scientific array language complete with sophisticated indexing. Mathematical operations with this extension are close to what one would expect from a compiled language.
  
  
• Explore several math resources available for Perl, including an index of CPAN Math modules and the modules in the Algorithm section at CPAN, as well as the Perl Data Language, designed to deliver to Perl the ability to compactly store and speedily manipulate large N-dimensional data arrays.
  
  
• For more on John Chambers' S programming language, check out these links to his publications and various research projects at Bell Labs.
  
  
• R is a language and environment for statistical computing and graphics, similar to the award-winning S system, that provides such statistical and graphical techniques as linear and nonlinear modeling, statistical tests, time series analysis, classification, and clustering. Learn about R at the R Project homepage.
  
  
• If you are new to PHP, read Amol Hatwar's developerWorks series, "Develop rock-solid code in PHP:" Part 1: Laying the Foundation", "Part 2: Use variables effectively", and "Part 3: Write reusable functions".
  
  
• Discover a catalog of code-optimization techniques in Steven Gould's IBM tutorial "Writing Efficient PHP" (developerWorks, July 2002).
  
  
• Read this developerWorks roundup of math library articles:
  • Lou Grinzo's "Mondo math libs" for Linux (December 1999)
  • Michael Juntao Yuan's "Building dynamic Web sites with mathematical content" (February 2002)
  • The IBM Accurate Portable Mathlib Project
  
  
• Learn more on PHP, read the IBM tutorial "Creating dynamic Web sites with PHP and MySQL" (developerWorks, May 2001).
  
  
• Visit John Pezzullo's excellent site dedicated to Web pages that perfom statistical calculations. The PHP-based probability functions were based upon code found on John's probability functions page.
  
  
• Learn more about the M. Abramowitz and I.A. Stegun book, The Handbook of Mathematical Functions (also known as the AMS55), at the Digital Library of Mathematical Functions.
  
  
• Check out the JpGraph site for a wealth of information about PHP's premier OO Graph Library.
  
  
• Read The Engineering Handbook of Statistics, published by the National Institute of Standards (NIST), which has an excellent section on Exploratory Data Analysis.
  
  
• Try these useful references, if you are interested in studying the topic of Regression in more detail:
  • Hamilton, L. C. (1992). Regression with Graphics. Pacific Grove, California: Brooks/Cole Publishing Company.
  • Neter, J, Kutner, M.H., Wasserman, W. (1990). Applied Linear Regression Models (3th Edition). Irwin, Chicago.
  • Pedhazur, E. J. (1982). Multiple regression in behavioral research. New York, NY: Holt, Rinehart and Winston.
  
  
• Read Cameron Laird's article "Open source in the biosciences." PHP needs better math tools to participate in this growth market (developerWorks, November 2002).
  
  
• Check out RWeb, a Web-based interface to R.