Statistical Analysis Software provides the automation and processing power needed to facilitate the manipulation and analysis of data sets.
Top Seven Free and Open Source Statistical Analysis Software:
JASP – Free Statistical Software. Our main goal is to help statistical practitioners in academia and business reach maximally informative conclusions with a minimum of fuss. This is why we have developed JASP, a cross platform statistical software program with a state-of-the-art graphical user interface.
SOFA Statistics Open For All. SOFA is a user-friendly statistics, analysis, & reporting program. It is free, with an emphasis on ease of use, learn as you go, and beautiful output.
GNU PSPP is a program for statistical analysis of sampled data. It is a free as in freedom replacement for the proprietary program SPSS, and appears very similar to it with a few exceptions.
Salstat, friendly open source statistics program. Salstat is useful whenever you need to crunch numbers into something manageable. Salstat has been used in these industries:
Business and commerce
- Website analytics, business intelligence, marketing, finance;
- Agriculture, biology, medicine, biostatistics, psychology/psychometrics;
- Economics, sociology, politics, social policy, education;
Research and development
- Product development, market research, analytics, process management techniques like Six Sigma;
RStudio is an active member of the R community. We believe free and open source data analysis software is a foundation for innovative and important work in science, education, and industry. The many customers who value our professional software capabilities help us contribute to this community.
Is a cross-platform software package for econometric analysis, written in the C programming language. It is free, open-source software. You may redistribute it and/or modify it under the terms of the GNU General Public License (GPL).
MacAnova is a free, open source, interactive statistical analysis program for Windows, Macintosh, and Linux written by Gary W. Oehlert and Christopher Bingham, both of the School of Statistics, University of Minnesota.
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