Abstract
The web-based, Java-written SOCR (Statistical Online Computational Resource) tools have been utilized in many undergraduate and graduate level statistics courses for seven years now (Dinov 2006; Dinov et al. 2008b). It has been proven that these resources can successfully improve students' learning (Dinov et al. 2008b). Being first published online in 2005, SOCR Analyses is a somewhat new component and it concentrate on data modeling for both parametric and non-parametric data analyses with graphical model diagnostics. One of the main purposes of SOCR Analyses is to facilitate statistical learning for high school and undergraduate students. As we have already implemented SOCR Distributions and Experiments, SOCR Analyses and Charts fulfill the rest of a standard statistics curricula. Currently, there are four core components of SOCR Analyses. Linear models included in SOCR Analyses are simple linear regression, multiple linear regression, one-way and two-way ANOVA. Tests for sample comparisons include t-test in the parametric category. Some examples of SOCR Analyses' in the non-parametric category are Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, Kolmogorov-Smirnoff test and Fligner-Killeen test. Hypothesis testing models include contingency table, Friedman's test and Fisher's exact test. The last component of Analyses is a utility for computing sample sizes for normal distribution. In this article, we present the design framework, computational implementation and the utilization of SOCR Analyses.
Highlights
The Statistics Online Computational Resource http://www.SOCR.ucla.edu/, an NSF-funded project, provides integrated tools for probability and statistics education (Dinov 2006; Dinov et al 2008a)
The SOCR Analyses innovation extends of the core SOCR development principles: (1) it is web-based so it is available to everyone with no cost; (2) it has virtually all materials needed for an introductory statistics class to reduce instructors’ preparation time; (3) it is graphics-based and lowers the learning curve; (4) the source code is available for interested researchers to use and extend
A commonly taught topic in a standard statistics, Multiple Linear Regression, is used as the first example to demonstrate the operation of SOCR Analyses
Summary
The Statistics Online Computational Resource http://www.SOCR.ucla.edu/, an NSF-funded project, provides integrated tools for probability and statistics education (Dinov 2006; Dinov et al 2008a) This project is to provide a free, web-based and browser-independent suite of tools; to develop a well-designed, extensible and open-sources library; to introduce a graphical user interface (GUI) to statistical resources; and to present an integrated framework for course-material, simulation and computation. SOCR Charts and Modeler, not part of SOCR Analyses and not a main topic of this paper, provide visual demonstration of descriptive statistics These tools all together have built a complete standard curriculum. Another SOCR facility, a newly developed Interactive Hypergraph SOCR viewer provides a graphical overview of the entire SOCR infrastructure and relationship among all the computational tools, learning materials and instructional resources. The Interactive Hypergraph SOCR viewer is available at: http://www.SOCR.ucla.edu/SOCR_HT_ResourceViewer.html
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