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
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
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
||Environment for statistical computing and graphics (see
replacement of the proprietary program, SPSS
||Extremely user-friendly statistics, analysis and
the data analysis challenges of high-energy physics
||Multi-platform statistical analysis and matrix alegbra
and graphically present experimental data
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Last Updated Sunday, September 09 2012 @ 05:05 AM EDT