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6 Great Linux Statistical Analysis Tools

6 Great Linux Statistical Analysis Tools

Science is the effort of seeking to comprehend how the physical world works. From observation and experimentation, science uses physical evidence of natural phenomena to compile data and analyze the collated information.

In modern research it is essential for scientists to keep abreast of the latest statistical software. Just like the fast moving world of research, developments in statistical software and methods continue to abound. Making full use of the improvements in computer software helps to advance the pace of research.

Science really prospers and advances when individuals share the results of their experiments with others in the scientific community. There is a certain logic that scientific software should therefore be released in a freely distributable environment.

Linux is particularly strong in the field of open source statistical software. The purpose of this article is to identify software for performing statistical analysis. This type of software helps to summarize data in a shorter form, and helps scientists understand a concept or representation and make possible predictions based on this understanding.


Now, let's explore the 6 statistical analysis tools at hand. For each title we have compiled its own portal page, a full description with an in-depth analysis of its features, a screenshot of the software in action, together with links to relevant resources and reviews.

We give our strongest recommendation to R, an open source programming language and software environment for statistical computing and graphics. It consists of a language together with a run-time environment with a debugger, graphics, access to system functions, and scripting. If you are looking for a really easy-to-use package, look no further than SOFA Statistics. Its presentation is an example to developers of how to design and implement intuitive software.

Statistical Analysis Tools
R Environment for statistical computing and graphics (see graphical interfaces)
PSPP Free replacement of the proprietary program, SPSS
SOFA Statistics Extremely user-friendly statistics, analysis and reporting package
ROOT Solves the data analysis challenges of high-energy physics
MacAnova Multi-platform statistical analysis and matrix alegbra
PAW Analyze and graphically present experimental data

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Last Updated Sunday, April 13 2014 @ 09:03 AM EDT


We have written a range of guides highlighting excellent free books for popular programming languages. Check out the following guides: C, C++, C#, Java, JavaScript, CoffeeScript, HTML, Python, Ruby, Perl, Haskell, PHP, Lisp, R, Prolog, Scala, Scheme, Forth, SQL, Node.js (new), Fortran (new), Erlang (new), Pascal (new), and Ada (new).


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