
GNU PSPP
Sign in to saveAlso known as PSPP, pspp
PSPP is a free software application for analysis of sampled data, intended as a free alternative for IBM SPSS Statistics. It has a graphical user interface and conventional command-line interface. It is written in C and uses GNU Scientific Library for its mathematical routines. The name has "no official acronymic expansion".
Key facts
- Software.name
- PSPP
- Software.logo
- Pspplogo.png
- Software.logo_size
- 99px
- Software.logo_caption
- PSPP logo
- Software.screenshot
- PSPP-home-screen.png
- Software.caption
- Screenshot of PSPP
- Software.developer
- GNU Project
- Software.programming language
- C
- Software.genre
- Statistics
- Software.license
- GNU General Public License
- Software.operating_system
- GNU, macOS, Microsoft Windows, Linux
via Wikipedia infobox
Wikidata facts
- Official website
- www.gnu.org/software/pspp
- Image
- GNU PSPP.png
Show 7 more facts
- software version identifier
- 2.1.1
- source code repository URL
- git.savannah.gnu.org/gitweb/?p=pspp.git
- Commons category
- GNU PSPP
- user manual URL
- www.gnu.org/software/pspp/manual
- issue tracker URL
- savannah.gnu.org/bugs/?group=pspp
- mailing list archive URL
- lists.gnu.org/archive/html/pspp-users
- inception
- 1995-00-00
Sources (10)
via Wikidata · CC0
~3 min read
Article
10 sectionsContents
- Features
- Origins
- Third Party Reviews
- Research about PSPP
- Examples of Research Performed using PSPP
- See also
- External Resources
- References
- External links
- Third-party resources
PSPP is a free software application for analysis of sampled data, intended as a free alternative for IBM SPSS Statistics. It has a graphical user interface and conventional command-line interface. It is written in C and uses GNU Scientific Library for its mathematical routines. The name has "no official acronymic expansion".
==Features== This software provides a comprehensive set of capabilities including frequencies, cross-tabs comparison of means (t-tests and one-way ANOVA), linear regression, logistic regression, reliability (Cronbach's alpha, not failure or Weibull), and re-ordering data, non-parametric tests, factor analysis, cluster analysis, principal components analysis, chi-square analysis and more.