SOCR Analyses - an Instructional Java Web-based Statistical Analysis Toolkit.

Annie Chu, Jenny Cui, Ivo D Dinov
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引用次数: 0

Abstract

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test.The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website.In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models.

SOCR分析-一个基于Java的指导性统计分析工具包。
统计在线计算资源(SOCR)设计了基于网络的工具,用于各种本科课程的教育用途(Dinov 2006)。几项研究表明,这些资源显著提高了学生的学习动机和学习体验(Dinov et al. 2008)。SOCR分析是一个新的组件,专注于数据建模和分析,使用图形模型诊断支持的参数和非参数技术。目前实现的分析包括本科统计学课程中常用的模型,如线性模型(简单线性回归、多元线性回归、单向和双向方差分析)。此外,我们实施了样本比较检验,如参数类别中的t检验;和Wilcoxon秩和检验,Kruskal-Wallis检验,Friedman检验,在非参数类别。SOCR分析还包括一些假设检验模型,如列联表、弗里德曼检验和费雪精确检验。代码本身是开源的(http://socr.googlecode.com/),希望对统计计算社区的工作有所贡献。该代码包括每个特定分析模型的功能,并且具有可应用于各种统计计算任务的通用实用程序。例如,用API(应用程序编程接口)实现了统计汇总、一般线性模型的最小二乘解、等级计算等具体方法。HTML界面、教程、源代码、活动和数据都可以通过web (www.SOCR.ucla.edu)免费获得。在SOCR Wiki网站上提供了开发人员的代码示例和教育工作者的演示。在这篇文章中,讨论了SOCR分析的教学应用,以及底层的设计框架。随着SOCR项目的进行,越来越多的功能和工具被添加到其中,这些资源也在不断得到改进。强烈建议读者查看SOCR站点以获取最新信息和新添加的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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