Statistics Online Computational Resource

Statistics Online Computational Resource (SOCR)

Founded| January 5, 2002
Type| not-for-profit
Focus| scientific computing, data analytics, visualization, inference, education
Coordinates|  [ ⚑ ] 42°17′03″N 83°44′17″W﻿ / ﻿42.284199°N 83.738072°W﻿ / 42.284199; -83.738072
Website| www.socr.umich.edu

The Statistics Online Computational Resource (SOCR)[1] is an online multi-institutional research and education organization. SOCR designs, validates and broadly shares a suite of online tools for statistical computing, and interactive materials for hands-on learning and teaching concepts in data science, statistical analysis and probability theory. The SOCR resources are platform agnostic based on HTML, XML and Java, and all materials, tools and services are freely available over the Internet.

The core SOCR components include interactive distribution calculators, statistical analysis modules, tools for data modeling, graphics visualization, instructional resources, learning activities and other resources.[2][3]

All SOCR resources are licensed under either the Lesser GNU Public License or CC BY;[4] peer-reviewed, integrated internally and interoperate with independent digital libraries developed by other professional societies and scientific organizations[5] like NSDL, Open Educational Resources, Mathematical Association of America, California Digital Library, LONI Pipeline, etc.

## See also

* List of statistical packages
* Comparison of statistical packages

## External links

* SOCR University of Michigan site
* SOCR UCLA site

## References

1. ↑ Dinov, Ivo D.; Sanchez, Juana; Christou, Nicolas (2008). "Pedagogical Utilization and Assessment of the Statistic Online Computational Resource in Introductory Probability and Statistics Courses". Computers and Education 50 (1): 284–300. doi:10.1016/j.compedu.2006.06.003. PMID 19750185.
2. ↑ Dinov, Ivo D. (2006). "Statistics Online Computational Resource". Journal of Statistical Software 16 (1): 1–16. doi:10.18637/jss.v016.i11. PMID 21451741. PMC 3065362. http://www.jstatsoft.org/v16/i11.
3. ↑ Christou, Nicolas; Dinov, I. (2010). "A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education". Journal of Online Learning and Teaching 6 (3): 546–572. PMID 21603097. PMC 3098746. http://jolt.merlot.org/vol6no3/dinov_0910.pdf.
4. ↑ "SOCR Citations and Licenses". http://socr.umich.edu/html/SOCR_CitingLicense.html.
5. ↑ Peer-reviewed SOCR papers and SOCR integration in international digital libraries

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