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SOCR Analyses: Implementation and Demonstration of a New Graphical Statistics Educational Toolkit.
Annie Chu1, Jenny Cui, Ivo D Dinov
1The SOCR Resource, Department of Statistics, and Center for Computational Biology, 8125 Mathematical Science Bldg. University of California, Los Angeles, Los Angeles, CA 90095-1554, United States of America, Telephone: +1/310/825-8430, /310/206-5658, URL: http://www.SOCR.ucla.edu/
Journal of Statistical Software
|September 28, 2011
Summary
The Statistical Online Computational Resource (SOCR) Analyses tool enhances statistical learning for students. This web-based resource offers data modeling, graphical diagnostics, and various statistical tests to improve understanding of statistical concepts.
Area of Science:
- Statistics Education
- Computational Statistics
Background:
- The Statistical Online Computational Resource (SOCR) tools have been used in statistics education for seven years.
- Previous SOCR components (Distributions and Experiments) have proven effective in improving student learning.
Purpose of the Study:
- Introduce SOCR Analyses, a new component for data modeling and graphical diagnostics.
- Facilitate statistical learning for high school and undergraduate students.
- Complete the SOCR suite to cover standard statistics curricula.
Main Methods:
- Developed SOCR Analyses as a web-based, Java-written tool.
- Implemented components for parametric and non-parametric data analyses.
- Included modules for linear models, sample comparisons, hypothesis testing, and sample size computation.
Main Results:
- SOCR Analyses offers a comprehensive set of statistical tools including regression, ANOVA, t-tests, and non-parametric tests.
- The tool provides graphical model diagnostics for enhanced data analysis.
- Includes utilities for sample size calculations for normal distributions.
Conclusions:
- SOCR Analyses effectively supports statistical education by providing accessible computational tools.
- The integration of SOCR Analyses with other SOCR components offers a complete curriculum resource.
- The presented framework, implementation, and utilization demonstrate the value of SOCR Analyses in statistics learning.
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