Identical Twins Raised Apart

This article describes a bivariate data set that is interesting to students. Indeed, this particular data set, which involves twins and IQ, has sparked more student interest than any other set that I have presented. Specific uses of the data set are presented.     

二元线性回归分析在物理实验数据处理中的应用研究

本文阐述了多元线性回归的基本原理,以物理实验数据处理为例,介绍了二元线性回归的具体应用方法,该方法使得曲线的拟合计算简单易行,在物理实验中分析多个变量的线性关系时有很好的应用价值。     

缺失数据下线性模型响应变量均值估计的渐近正态性

考虑响应变量随机缺失下线性模型响应变量均值的估计问题,分别获得了基于完全观测样本数据、线性回归插补后的"完全样本"和逆概率加权插补后的"完全样本"得到的响应变量均值估计,并证明了其渐近正态性.     

两个回归模型的比较

证明了在经典线性回归中 ,对同一组样本数据选取不同变量时 ,所得两条回归直线不重合 ,得出了这两条回归直线都通过点 ( x,y)及两直线夹角的正切计算公式 ,讨论了样本相关系数与回归直线的位置关系     

Assumptions are Important

The relationship between the test statistics for the paired and pooled t tests, when applied to the same data set, is obtained. This assists us to understand why each is preferred over the other in appropriate circumstances.     

工程量清单计价模式和报价技巧探讨

工程量清单计价模式越来越多地用于我们的工程报价中,从如何编制工程量清单和报价技巧两个方面进行探讨,可为工程技术人员提供一定参考。     

函数系数部分线性模型的B样条估计

函数系数部分线性模型是一个比较广泛的模型,其常数项函数和系数函数具有不同的自变量．这给模型的估计带来了不小的挑战。利用B样条方法同时给出该模型的常数项函数和系数函数的估计,给出了估计的相合性、渐近正态性以及收敛速度,且该收敛速度达到了非参数最优收敛速度;最后,模拟说明了B样条方法对该模型的估计是有效的。     

Adding a Data Point to a Scatterplot

Students who have been taught regression properly are certainly familiar with the dangers and curiosities to which the regression analysis leads when only two data points are available. However, one may ask whether students are familiar with the effects of adding a third observation into this data set. The purpose of this article is to illustrate some unexpected phenomena related to this situation using a specific map technique.     

变系数回归模型的参数估计

讨论了变系数回归模型中变系数的加权最小二乘估计和变系数的加权可估函数的线性估计的最优性;推广了Gauss-Markov定理;并且构造出参数σ2的估计量.     

Bivariate Data: Lessons from Students' Coursework

This article examines some of the difficulties frequently encountered by students when analysing bivariate data and suggests how they might be overcome.     

实现ArcGIS环境下的确认性空间数据分析功能

在地理信息科学领域,空间分析扮演着中心的作用.但目前的地理信息系统商业软件中,空间分析功能仍是一个薄弱环节;即便是美国ESR I公司的ArcGIS,其空间分析模块也仅仅局限于探索性空间数据分析的功能,从而极大地限制了地理信息系统作为空间决策支持平台的作用.本研究的目标便是解决这一问题.我们经过反复实践实现了ArcGIS环境下的确认性空间数据分析功能,包括古典线性回归模型和空间计量经济学模型的标准诊断、估计、以及可靠性检测的复杂算法.通过对ArcGIS的二次开发,真正实现了地理信息系统和空间分析功能的全面集成.     

房地产定价模型的研究

房价作为一种价格杠杆，在引导住房消费，促进房地产可持续发展将起到积极的作用。科学合理地制定房价，对房地产发展具有重要意义。本对房地产价格的定价模型进行了数量分析，通过对房地产市场情况的研究，分别针对整个市场只有一个房地产商开发、两个房地产商开发和多个房地产商开发的情况，运用博弈论的方法给出不同的模型，并以经济理论加以解释，得出较为合理的结论。     

现实环境下权证定价方式探讨

权证是一种特殊期权,B-S模型是迄今为止被认为最精确有效的期权定价模型,但该模型必须建立在许多假设下,而这些假设与现实世界是不尽相符的,使得此模型在实际运作上易产生价格偏差的现象。人工神经网络由于能模拟人脑的思维方式,具有高速计算和学习的特性,使它在预测、评价等方面有很好的应用效果。应用基于BP神经网络的权证定价方法可以缩小对权证价格预测的误差。     

变量的选取对回归直线的影响

从理论上证明了一元线性回归中选取不同的变量时，所得两条回归直线的关系。讨论了两直线夹角的正切。     

基于面板数据分位数回归模型的房地产价格分析

采用安徽省13个地市2001～2010年的面板数据,对影响安徽省房地产价格的因素进行实证分析。研究结果表明：银行年度基准贷款利率和房屋销售面积与房地产价格不存在协整关系,用面板数据模型及分位数回归模型分析的结果均表明人均可支配收入对房地产价格影响的弹性最大。最后提出3点政策性建议,为政府调控房地产市场价格作参考。     

Using Real Life Examples to Teach Abstract Statistical Concepts

This article provides real life examples that can be used to explain statistical concepts. It does not attempt to be exhaustive, but rather, provide a few examples for selected concepts based on what students should know after taking a statistics course.     

Bayesian Quantile Structural Equation Models

Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. Basic structural equation models consist of a measurement equation for characterizing latent variables through multiple observed variables and a mean regression-type structural equation for investigating how explanatory latent variables influence outcomes of interest. However, the conventional structural equation does not provide a comprehensive analysis of the relationship between latent variables. In this article, we introduce the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The estimation is conducted in a Bayesian framework with Markov Chain Monte Carlo algorithm. The posterior inference is performed with the help of asymmetric Laplace distribution. A simulation shows that the proposed method performs satisfactorily. An application to a study of chronic kidney disease is presented.     

Analyzing Mixed-Dyadic Data Using Structural Equation Models

Mixed-dyadic data, collected from distinguishable (nonexchangeable) or indistinguishable (exchangeable) dyads, require statistical analysis techniques that model the variation within dyads and between dyads appropriately. The purpose of this article is to provide a tutorial for performing structural equation modeling analyses of cross-sectional and longitudinal models for mixed independent variable dyadic data, and to clarify questions regarding various dyadic data analysis specifications that have not been addressed elsewhere. Artificially generated data similar to the Newlywed Project and the Swedish Adoption Twin Study on Aging were used to illustrate analysis models for distinguishable and indistinguishable dyads, respectively. Due to their widespread use among applied researchers, the AMOS and Mplus statistical analysis software packages were used to analyze the dyadic data structural equation models illustrated here. These analysis models are presented in sufficient detail to allow researchers to perform these analyses using their preferred statistical analysis software package.     

“数据结构”教学改革探究

数据结构课程是计算机软件专业和计算机应用专业的核心基础课程,在各个专业课程体系中起着承上启下的作用。但该课程具有相当的抽象性和动态性,容易造成教学效果不良和学时超标等问题。根据学生的特性和目前在教学中存在的各种实际问题,提出如何更好地实施教学过程以提高教学效果的一些方法。