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Simple linear regression with PHP: Part 1
By Paul Meagher - 2004-05-12 Page:  1 2 3 4 5 6 7 8

Resources

  • The popular undergraduate textbook, Statistics, 9th ed., by James T. McClave and Terry Sincich (Prentice-Hall, online) was consulted for the algorithm steps and for the "Burnout Study" example used in this article.

  • Consult the PEAR repository, which currently contains a small number of 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, FourierAnalysis, and others.

  • View or download all of the source code for the author's SimpleLinearRegression class.

  • Look at 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 a number of 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. Also read about its ACM Award in 1998 for language design.

  • 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, clustering, and such. 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".

  • Try Steven Gould's IBM tutorial on "Writing Efficient PHP" for a catalog of code-optimization techniques (developerWorks, July 2002).

  • Read this developerWorks roundup of math library articles:


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First published by IBM developerWorks


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