Does prior mathematics knowledge really lead to variation in elementary statistics performance? Evidence from a developing country

A model incorporating prerequisite mathematics performance and other variables deemed to be associated with learning elementary statistics (ES) is developed. The relationship between ES performance and the explanatory variables is well represented by the logistics form. Aptitude, effort and motivation are the only significant explanatory variables of ES performance. Since prerequisite mathematics is not significant, statistical thinking at the tertiary level may be mostly intuitive and non-mathematical. Students with low aptitude experience increasing returns to effort over the first half of the feasible effort interval, while high-aptitude students experience diminishing returns at all levels of effort. The levels of effort required to achieve a minimum pass are interpreted.     

Four New Corrected Statistics for SEM With Small Samples and Nonnormally Distributed Data

When the assumption of multivariate normality is violated and the sample sizes are relatively small, existing test statistics such as the likelihood ratio statistic and Satorra–Bentler’s rescaled and adjusted statistics often fail to provide reliable assessment of overall model fit. This article proposes four new corrected statistics, aiming for better model evaluation with nonnormally distributed data at small sample sizes. A Monte Carlo study is conducted to compare the performances of the four corrected statistics against those of existing statistics regarding Type I error rate. Results show that the performances of the four new statistics are relatively stable compared with those of existing statistics. In particular, Type I error rates of a new statistic are close to the nominal level across all sample sizes under a condition of asymptotic robustness. Other new statistics also exhibit improved Type I error control, especially with nonnormally distributed data at small sample sizes.     

金融数学模型概述

本文对现代金融理论中应用到的金融数学模型进行了概括性综述,并就金融数学模型的发展趋势进行了分析.     

基于局部投影统计法的螺纹缺陷检测

为解决传统接触式螺纹测量方法费时且程序冗长的缺陷,提出一种基于机器视觉技术的螺纹缺陷检测算法。对捕获的螺纹图像进行中值滤波、迭代法二值化与Canny边缘提取处理|通过分析螺栓图像中螺纹缺陷断口位置灰度值的变化,提出一种基于DOG模型的螺纹自动检测方法。为验证该算法性能,用基于形状的模板匹配算法作为对照进行实验。结果表明,局部投影统计算法能有效提取螺纹缺陷图像的缺陷信息,螺纹缺陷图像识别率在95%以上。该方法可快速有效地降低噪声,准确迅速地定位缺陷点,提高生产线螺栓可替换性。     

图书馆信息服务热点问题探究

对5种图书馆专业核心期刊2003—2005年服务类论文的发文情况进行统计，从中发现图书馆服务技术、服务模式和服务方法及图书馆服务现状、趋势和实践问题是图书馆服务领域的研究热点。     

《统计学基础》教学模式改革初探

随着社会经济的不断发展,传统的统计课程的教学方法已经不能满足教育发展的需要。针对传统统计教学方式的特点及其不足,结合自己在教学中的实际经验,提出了新形势下统计学基础的教学模式改革的一些设想。     

熵概念的发展及其应用

熵是物理化学中的重要概念，完成了热力学第二定律的量化问题。熵在现代科学技术中的应用将越 越广泛。本文论述了熵的概念及其熵的统计解释，探讨了熵在信息科学、社会经济和工程技术中的应用。     

统计学的发展趋势与金融统计课程的改革

要培养出新型的21世纪的人才,统计教育必须高瞻远瞩。统计学的发展趋势要求金融统计教学急需改革,要从教学目标、教学方法、教学手段和课程考核四方面进行探讨,提出相应的措施。     

不同英语教材教学效果差异性检验方法研究

就外语教学中不同英语教材教学效果的差异性检验问题进行了探讨，研究给出了一种数理统计检验方法——联列表中相互独立性检验方法，并据此进行了示例检验，取得了令人满意的效果。该检验方法的提出，填补了在该领域应用的空白，具有重要的理论意义和现实意义。     

基于录像分析背景下的初中统计课堂教学提问研究

在课堂教学中,教师的提问是最重要的活动.对两节初中统计课的课堂录像进行实录和量化分析,结合教师课堂提问的类型与学生回答之间的联系,得出:在统计课堂教学中(1)教师提问的数量相对比较少,提示类和理解类的提问类型所占比例较高;(2)学生无答的情况较少,但在问题回答中,学生机械性回答占了较大的比例,教师创新性问题较少;(3)教师提问难度与学生回答水平有直接的关系;(4)教师应围绕统计的特点进行提问,使学生经历统计的全过程,有助于提高课堂教学的有效性.