Chu Annie, Cui Jenny, Dinov Ivo D
The 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/
J Stat Softw. 2009 Apr 1;30(3):1-19. doi: 10.18637/jss.v030.i03.
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.
基于网络、用Java编写的SOCR（统计在线计算资源）工具至今已在许多本科和研究生层次的统计学课程中使用了七年（迪诺夫，2006年；迪诺夫等人，2008b）。事实证明，这些资源能够成功提高学生的学习效果（迪诺夫等人，2008b）。SOCR分析于2005年首次在线发布，是一个相对较新的组件，它专注于参数和非参数数据分析的数据建模以及图形模型诊断。SOCR分析的主要目的之一是促进高中和本科学生的统计学学习。由于我们已经实现了SOCR分布和实验，SOCR分析和图表完善了标准统计学课程的其余部分。目前，SOCR分析有四个核心组件。SOCR分析中包含的线性模型有简单线性回归、多元线性回归、单向和双向方差分析。样本比较的检验包括参数类别中的t检验。SOCR分析在非参数类别中的一些例子有威尔科克森秩和检验、克鲁斯卡尔 - 沃利斯检验、弗里德曼检验、柯尔莫哥洛夫 - 斯米尔诺夫检验和弗利格纳 - 基林检验。假设检验模型包括列联表、弗里德曼检验和费舍尔精确检验。分析的最后一个组件是一个用于计算正态分布样本量的实用工具。在本文中，我们介绍SOCR分析的设计框架、计算实现和应用。
