中国农村居民消费类型及其特征对应分析

本文采用多元统计中的星座聚类分析与对应分析方法，对中国农村居民的消费类型进行了划分，得到了我国不同地区、不同时期农村居民的消费对应特征，为科学把握农村居民消费规律提供了依据。     

地区恶性肿瘤死亡率的对应分析

目的—了解山东省某县2000-2002年恶性肿瘤的地区分布和肿瘤类型分布特征.方法—应用分组对应分析对该县恶性肿瘤死亡资料进行分析.结果—得到各地区和各肿瘤类型的公因子及其负荷系数,并根据第一、二因子负荷系数绘制因子负荷平面图,可以清楚看出恶性肿瘤死亡率的聚集性及其高发地与低发地的分布.结论—将变量与样本结合起来的对应分析是对因子分析的有益补充,它可以分析二维数据阵的行因素与列因素之关系,达到研究目的.     

多元对应分析在抽样调查分析中的应用

本文应用多元对应分析、聚类分析等数理统计方法 ,对抽样调查问卷进行分析 ,从而得出各类读者群的特征及它们之间的差异     

紧空间上上半连续对应的不变紧子集

证明了对于T2紧空间上的上半连续对应，闭集对应，闭对应和闭值对应是等价的，并由此证明了T1紧空间上的上半连续闭集对应（闭对应）存在不变紧子集。作为一个推论，得到了T2紧空间上的上半连续闭值对应存在不变紧子集，同时给出一个反例说明，这里T2甚至不能减弱为T1，从而说明Klein和Thompson关于不附加T2分离性公理，紧空间上的上半连续闭值对应存在不变紧子集是不成立的。     

教师课堂教学质量的对应分析

教师课堂教学质量的优劣,直接影响着学生的学习效果和人才培养的质量,关系到学校的办学水平.科学分析研究教师的课堂教学质量,优化教学过程,实现教师和学生的全面发展,对提高整个高等教育的质量意义重大.以重庆第二师范学院经济与工商管理系为例,运用对应分析方法,分析了不同教师课堂教学的现状和特点,找出了影响课堂教学质量的因素,并针对各个不同类型的教师组提出分类引导,及时改进教学工作,提高课堂教学质量的建议.     

多元统计在海南农业区域经济分析中的应用

利用多元统计中的主成分和聚类分析对海南各市县的农业产量进行研究,得出代表海南的传统农业产量区及热带作物产量区,并对上述产量区进行分类.此外,还对各市县农业现状进行对应分析,得出各市县农业产业结构特征和主导产业,结论较为合理.     

正投影法在解析几何教学中的应用

本文探讨了正投影与面积、外积、仿射对应等概念之间的联系,运用正投影思想研究了双曲抛物面的直母线的几何性质,得到了判别二次曲面与平面截交线类型的统一方法,从而说明在解析几何教学中融入正投影法的重要性.     

分段函数问题的类型及解法

分段函数由于是分段定义的 ,在不同的区间上函数有着不同的对应法则 ,与一般函数有着明显的区别 .学生往往受负迁移影响对分段函数问题认识不清或思维片面产生解题错误 ,本文就分段函数问题的类型进行归类解析 .1　判定分段函数的奇偶性例 1　判定分段函数f (x) =(110 ) x,x >0     

Glauberman对应诱导的Morita等价

假设G是一个p-可解群，A是一个可解群，它作用在G上且它的阶与G的阶互素，对适当的Dedekind整环R和RG的一个块B，A平凡地作用在它的某一个亏群上，我们证明B与它的Watanabe对应之间存在一个Morita等价．     

积商看点定位法

乘，除运算前，先看准积、商的个位分别在被乘数，被除数的对应档，着上点，用以布盘或推算，简快易行。     

加权模糊C-均值聚类

将经典的模糊C-均值聚类中的欧氏距离推广到广义欧氏距离,得到了加权模糊C-均值聚类的迭代公式,实证分析表明加权模糊C-均值聚类的结果与主成分分析的排序基本一致,特别适用于大样本的聚类与排序。     

湖南省各地市州居民生活水平比较分析

本文结合恩格尔系数研究居民生活水平的优点，采用聚类分析法与主成分分析法对2004年湖南省各地市州居民生活水平进行比较分析，给出了这些城市居民生活水平的综合得分及其排序，从而更真实地衡量湖南省各地市州居民生活水平．     

统计分析方法在市场调研前期的应用

本文基于聚类分析和判别分析 ,对在众多的区域市场中如何选取一个或几个有代表性的区域市场进行了一些探索性的研究。并利用这种方法进行了一个关于在新产品上市的营销策划中如何进行营销试点市场的选取的实证分析     

适合中小型制造企业的客户信用评估的研究

本在对中小型制造企业的客户管理调研基础上，建立了适合中小型制造企业的信用评估指标体系，提出了基于AHP、TOPSIS和聚类分析的客户信用评估方法，并在上海某企业进行了实践，取得了较好的效果。     

Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis,DEA and Neural Networks

Data Envelopment Analysis (DEA) is a powerful data analytic tool that is widely used by researchers and practitioners alike to assess relative performance of Decision Making Units (DMU). Commonly, the difference in the scores of relative performance of DMUs in the sample is considered to reflect their differences in the efficiency of conversion of inputs into outputs. In the presence of scale heterogeneity, however, the source of the difference in scores becomes less clear, for it is also possible that the difference in scores is caused by heterogeneity of the levels of inputs and outputs of DMUs in the sample. By augmenting DEA with Cluster Analysis (CA) and Neural Networks (NN), we propose a five-step methodology allowing an investigator to determine whether the difference in the scores of scale heterogeneous DMUs is due to the heterogeneity of the levels of inputs and outputs, or whether it is caused by their efficiency of conversion of inputs into outputs. An illustrative example demonstrates the application of the proposed methodology in action.     

Distribution of the inter and intra inertia in conditional MCA

Conditional Multiple Correspondence Analysis (MCA), where a control variable plays the role of a partition model, allows us to decompose global inertia into between inertia and within inertia. The problem is to assess when the conditioning variable gives different results with respect to the unconditional analysis. In this paper, we study the asymptotic distribution function of these inertias, which can allow us to determine whether conditioning is significant. Some simulations were performed to corroborate the established results.     

Cluster analysis and mathematical programming

Given a set of entities, Cluster Analysis aims at finding subsets, called clusters, which are homogeneous and/or well separated. As many types of clustering and criteria for homogeneity or separation are of interest, this is a vast field. A survey is given from a mathematical programming viewpoint. Steps of a clustering study, types of clustering and criteria are discussed. Then algorithms for hierarchical, partitioning, sequential, and additive clustering are studied. Emphasis is on solution methods, i.e., dynamic programming, graph theoretical algorithms, branch-and-bound, cutting planes, column generation and heuristics. Research supported by ONR grant N00014-95-1-0917, FCAR grant 95-ER-1048 and NSERC grants GP0105574 and GP0036426. The authors thank Olivier Gascuel and an anonymous referee for insightful remarks.     

Learning the Learning Cycle: The Differential Effect on Elementary Preservice Teachers

This study examined the difficulties and factors that led to understanding the learning cycle teaching strategy. Participants included 83 preservice elementary teachers (PTs) enrolled in multiple sections of a science methods course taught by the same professor, one of the authors of this study. Analysis revealed that there were four categories of PTs, ranging from Enthusiastic to Fearful students, distinguished by their science content background and attitudes to science. High achieving students, successful in science courses, felt confused by the learning cycle that was so different from their previous science learning experiences and formed mindsets against learning it. Average students who expressed disinterest or even fear of science embraced it as their first successful science learning experience. Multiple exposures to the learning cycle were necessary to overcome these mindsets. Most PTs in all categories increased in their understanding of the learning cycle and perceived it as an effective method for allowing students to construct their understanding of science.     

The role of multiple solution tasks in developing knowledge and creativity in geometry

This paper describes changes in students’ geometrical knowledge and their creativity associated with implementation of Multiple Solution Tasks (MSTs) in school geometry courses. Three hundred and three students from 14 geometry classes participated in the study, of whom 229 students from 11 classes learned in an experimental environment that employed MSTs while the rest learned without any special intervention in the course of one school year. This longitudinal study compares the development of knowledge and creativity between the experimental and control groups as reflected in students’ written tests. Geometry knowledge was measured by the correctness and connectedness of the solutions presented. The criteria for creativity were: fluency, flexibility, and originality. The findings show that students’ connectedness as well as their fluency and flexibility benefited from implementation of MSTs. The study supports the idea that originality is a more internal characteristic than fluency and flexibility, and therefore more related with creativity and less dynamic. Nevertheless, the MSTs approach provides greater opportunity for potentially creative students to present their creative products than conventional learning environment. Cluster analysis of the experimental group identified three clusters that correspond to three levels of student performance, according to the five measured criteria in pre- and post-tests, and showed that, with the exception of originality, performance in all three clusters generally improved on the various criteria.     

Predicting performance in a first engineering calculus course: implications for interventions

At the University of Louisville, a large, urban institution in the south-east United States, undergraduate engineering students take their mathematics courses from the school of engineering. In the fall of their freshman year, engineering students take Engineering Analysis I, a calculus-based engineering analysis course. After the first two weeks of the semester, many students end up leaving Engineering Analysis I and moving to a mathematics intervention course. In an effort to retain more students in Engineering Analysis I, the department collaborated with university academic support services to create a summer intervention programme. Students were targeted for the summer programme based on their score on an algebra readiness exam (ARE). In a previous study, the ARE scores were found to be a significant predictor of retention and performance in Engineering Analysis I. This study continues that work, analysing data from students who entered the engineering school in the fall of 2012. The predictive validity of the ARE was verified, and a hierarchical linear regression model was created using math American College Testing (ACT) scores, ARE scores, summer intervention participation, and several metacognitive and motivational factors as measured by subscales of the Motivated Strategies for Learning Questionnaire. In the regression model, ARE score explained an additional 5.1% of the variation in exam performance in Engineering Analysis I beyond math ACT score. Students took the ARE before and after the summer interventions and scores were significantly higher following the intervention. However, intervention participants nonetheless had lower exam scores in Engineering Analysis I. The following factors related to motivation and learning strategies were found to significantly predict exam scores in Engineering Analysis I: time and study environment management, internal goal orientation, and test anxiety. The adjusted R² for the full model was 0.42, meaning that the model could explain 42% of the variation in Engineering Analysis I exam scores.