基于粗集和熵值的卷烟消费市场潜力评价

运用粗糙集属性约简算法对初始因素指标进行约简,运用熵值法确定指标权重并建立综合评价模型,对贵州省各地区卷烟消费市场潜力进行综合评价,得出各地区消费潜力排序.应用实例表明两种方法结合使用将有效提高评价质量.     

变量聚类—全局主成分分析在我国普通高等教育发展水平评价中的应用

本文根据1997年~2003年共同构成的关于我国普通高等教育发展水平的时序立体数据表,对各地区高等教育发展整体状况的各项指标进行变量聚类,以区分对我国高教发展背景与现状分别产生影响的各项指标,然后对每一类作主成分分析,选出能够代表该类的主成分。在此基础上,计算出各地区各年度主成分的增长度;再对各年度主成分的增长度作主成分分析,以期对我国高教发展背景与现状的总体变化趋势做出综合评价。     

江西省旅游资源环境承载力评价的科学计算

科学地评价江西省各地区旅游资源环境承载力是制定旅游可持续发展战略的基础.基于评价指标体系的构建原则,建立了江西省旅游资源环境承载力的评价指标体系.结合实例,运用主成分分析法对江西省旅游资源环境承载力进行综合评价.通过Spss和Matlab软件的计算.说明了该评价模型的合理性、有效性及实用性,研究为旅游资源的合理开发提供了一种新的评价方法.     

一种循环修正的组合评价方法

提出了一种循环修正的组合评价方法.首先,对所研究的问题选取几种综合评价方法进行综合评价.其次,用Sperm an等级相关系数法检验几种综合评价法的一致性.进一步重复采用平均值法、Boarda法、C om peland法和模糊Board法对上述综合评价结果进行组合评价,直到标准差均收敛于0为止,得到最优的组合评价结果.最后以辽宁省2001年实际统计资料为例,对各地区的经济实力进行了综合评价和排序.     

基于面板数据的伊犁州直县(市)居民消费水平的聚类分析研究

运用多元统计分析中聚类分析的方法,对新疆伊犁州直十个县(市)2012年社会综合消费水平的数据进行了比较分析,综合评价了各地区的居民消费水平,并根据得到的分类情况提出了相应的改进意见,使之进一步改善和提高.     

基于因子分析的省会城市和计划单列市综合实力评价模型

改革开放40多年来,省会城市和计划单列市作为国家城市发展的核心城市,城市发展水平备受关注.文章从人口、资源环境、经济发展、科技创新、人民生活、公共服务6个方面选取28个核心指标构建城市综合实力评价体系.综合运用因子分析和聚类分析方法,对各城市综合实力进行比较和分析,结果表明:中国不同地区城市综合实力存在显著差异,东部地区城市整体实力较为强大,而西部地区城市相对较弱.在此基础上,提出了各地区城市综合实力的提升建议.     

基于多元统计的可持续发展动态评价模型研究与应用

给出了一种基于多元统计的可持续发展动态评价模型方法,该模型方法以全局主成分分析(GPCA)、聚类分析等方法为主,将之与层次分析法(AHP)进行有效集成。再以中国各地区可持续发展环境子系统为例,根据环境污染、环境保护与治理、生态环境质量与保护、可持续发展综合环境支持能力4个方面对中国各地区1999-2005年可持续发展环境支持能力状态进行了定量评价和动态分析。指数化的评价结果表明,中国各地区以上4个方面的可持续发展环境支持能力逐年增强,可持续发展综合环境支持能力综合指数均值从1999年的0.021上升到2005年的0.446,2001年以后有加速提高的迹象。     

基于因子分析法的各地城镇就业情况综合评价模型

对各地区城镇单位就业情况进行综合评价,以期为各地区长期就业规划提供客观依据.构建各地区城镇单位行业就业指标体系评价模型,选择全国29个省市2008年的相关数据采用因子分析法,计算各省市的各因子得分和综合得分.将就业指标归结为第一产业信息因子、第二产业信息因子和第三产业信息因子3个因子,得出各省市在各因子上的排名和就业情况综合排名.客观地分析了各地区城镇单位就业情况在各行业间的优势和劣势,为各地认清优劣,做出适合的产业结构调整,构建扩大就业发展规划提供借鉴.     

广西地区经济指标评价

杨善朝，李志友．广西地区经济指标评价．数理统计与管理，１９９７，１６，（４），１～４本文试用主成分分析方法，对广西七个地区的经济生产情况作出综合评价，分析各地区在广西的经济地位。并提出了一种构造加权综合量的方法     

基于相关性分析与DEA模型的寿险公司效率分析

选取国内较有影响力的八家寿险公司作为研究对象,提出了基于相关性分析的DEA组合评价方法,该方法综合了相关性分析和DEA两种方法的优点.利用相关性分析的方法设计出评价寿险公司经营效率的投入和产出指标.然后综合运用DEA模型对这八家公司的经营效率进行研究,分析影响效率有效性的因素.     

多元统计分析在大学生综合素质评价中的应用

德育和智育是衡量大学生综合素质的重要因素,本文根据天津工业大学某年度某班级学生的各科成绩和影响学生综合素质的相关因素的实际数据,应用因子分析对影响学生综合素质的各因素进行主成份分析,计算各个学生的因子综合得分并按得分高低进行排序,把它和常见的的两种评价方法进行比较,结果发现该方法能够弥补仅仅依靠平均积点分和按综合测评总分排序的不足。最后,以因子综合得分和平均积点分和综合测评总分为指标采用聚类分析对所有学生进行分类,得出了令人满意的结果。实证分析结果表明因子分析和聚类分析是衡量学生综合素质行之有效的方法。     

西藏农村初中办学条件的计量与评价

教育均衡是我国义务教育发展的重要战略,其重点关注对象是教育欠发达地区.从地域方位看,应重点关注西部地区;从地域的行政区划看,应重点关注农村地区.故选取西藏农村地区初中学校办学条件作为研究对象,运用因子分析和ward聚类分析法对2019年西藏自治区农村地区68个县级行政区进行综合排序和分类,并从基础教育教学资源、知识拓展...     

On the Cost of Data Analysis

Abstract

A regression analysis usually consists of several stages, such as variable selection, transformation and residual diagnosis. Inference is often made from the selected model without regard to the model selection methods that preceeded it. This can result in overoptimistic and biased inferences. We first characterize data-analytic actions as functions acting on regression models. We investigate the extent of the problem and test bootstrap, jackknife, and sample-splitting methods for ameliorating it. We also demonstrate an interactive LISP-STAT system for assessing the cost of the data analysis while it is taking place.     

受教育程度与就业之间的对应分析

利用对应分析理论和方法,对受教育程度与就业之间进行综合数据分析,从理论上研究了受教育程度与就业之间的对应关系,并为教育政策和就业政策制定者提供数据分析方法和合理的、可行的促进就业的建议.     

Implementing and interpreting a data envelopment analysis model to assess the efficiency of health systems in developing countries

This paper presents a data envelopment analysis model that canbe implemented by public sector management for assessing theefficiency of a health system within a developing country. Toillustrate the practical implementation and interpretation ofthe model this study compares health systems across 51 developingcountries using 1998–99 data. The results of the analysisgenerated empirical indicators of efficiency, and we demonstratehow these may then be used by management in order to understandthe factors associated with good performance of a health system.     

Evaluation of pressure levels in pipelines due to solar heat gains

A detailed numerical model based on a lumped capacity analysis, is developed for assessing potential pressure increases in pipelines due to solar energy gains. Test computations for gasoline and water show that for operational conditions of no pipeline flow, pressures reach levels for which structural damage may occur.     

A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units

This paper is concerned with the comparison of two popular non-parametric methodologies—data envelopment analysis and artificial neural networks—as tools for assessing performance. Data envelopment analysis has been established since 1978 as a superior alternative to traditional parametric methodologies, such as regression analysis, for assessing performance. Neural networks have recently been proposed as a method for assessing performance. In this paper, we use a simulated production technology of two inputs and one output for testing the success of the two methods for assessing efficiency. The two methods are also compared on their practical use as performance measurement tools on a set of bank branches, having multiple input and output criteria. The results demonstrate that, despite their differences, both methods offer a useful range of information regarding the assessment of performance.     

Assessing the comparative spatial disadvantage (CSD) of regions in the European Union using non-radial data envelopment analysis methods

In this paper we propose non-radial data envelopment analysis models for assessing the comparative spatial disadvantage (CSD) of the Level II regions of the European Union (EU). CSD is considered as a relatively new element of spatial efficiency which seeks to enhance the spectrum of the current methods used for assessing spatial inequality and dispersion. The paper also proposes a goal programming formulation for assessing “production” function coefficients of the social cohesion among the regions of the EU assuming elimination of the levels of comparative disadvantage.     

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

This paper proposes data envelopment analysis (DEA) as a quick-and-easy tool for assessing corporate bankruptcy. DEA is a non-parametric method that measures weight estimates (not parameter estimates) of a classification function for separating default and non-default firms. Using a recent sample of large corporate failures in the United States, we examine the capability of DEA in assessing corporate bankruptcy by comparing it with logistic regression (LR). We find that DEA outperforms LR in evaluating bankruptcy out-of-sample. This feature of DEA is appealing and has practical relevance for investors. Another advantage of DEA over LR is that it does not have assumptions associated with statistical and econometric methods. Furthermore, DEA does not need a large sample size for bankruptcy evaluation, usually required by such statistical and econometric approaches. The need for such a large sample size is a significant disadvantage to practitioners when investment decisions are made using small samples. DEA can bypass such a difficulty related to a sample size. Thus, DEA is a practically appealing method for bankruptcy assessment.