长江中下游地区若干湖泊水质的多元分析与比较

1996年11月-1998年11月,对鄱阳湖等长江中下游若干浅水湖泊进行了水化学分析,并进行了主要水质指标(包括透明度、pH值、总氮、总磷、氨氮和硝酸盐等)的季节变动规律,并对这些指标进行了方差分析。结果表明：不同利用方式的水体间除硬度、碱度和T-N在几个湖区间的差异较小外,其它各项指标的差异均较显着;季节间除硬度、碱度、Ca、Mg和T-P外,其余各项指标在季节间的差异均较显着,说明其季节变动均不稳定。     

因子与巢式交配设计群体近交率的比较研究

探讨了因子和巢式交配设计群体近交率的形成机理,结果表明,这两种交配设计群体结构本身并不导致基近交率累积的差异。在育种选择情况下因子交配设计比巢式设计群体的近交率上升慢,而在完全随机留种和限制随机种情形这两种酱地群体近交率的影响没有差异,但在交配设计情况下,限制随机留种比完全随机留有有交地控制群体近交率的提高。这一结果显示,在动物的家畜遗传留种比完全随机留各 效地控制群体近交率的提高。这一结果显示,     

安徽荒漠植被的多元分析

应用数量分类和排序方法分析了安西砾石戈壁荒漠植被、并应用逐步回归分析方法确定了群落和环境因素的关系方程。结果表明,该区砾石戈壁荒漠植被可分为６个主要群落类型。这些群落类型与土壤总含盐量、氯化物含量、硫酸盐含量及年降水量有密切的关系。     

安西荒漠植被的多元分析

应用数量分类(TWINSPAN)和排序(DCA)方法分析了安西砾石戈壁荒漠植被,并应用逐步回归分析方法确定了群落和环境因素的关系方程。结果表明,该区砾石戈壁荒漠植被可分为6个主要群落类型。这些群落类型与土壤总含盐量、氯化物含量、硫酸盐含量及年降水量有密切的关系。群落按低盐到高盐和干旱到极旱的环境梯度分布。60个样地在DCA排序第一轴的位置(X_1)与土壤含盐量(S)、氯化物含量(Cl)、硫酸盐含量(SO)显著相关,而第二轴的位置(X_2)与年降水量(R)有密切的关系,其关系式为:1/X_1=2.1992 0.0225/S-0.0188/Cl-0.0567/SO(R=0.797,P<0.01);X_2=-82.8561 117.9219logR(R=0.817,P<0.01)。     

甘南玛曲植物群落的多元分析与环境解释

应用无偏对应分析（ＤＣＡ）及二岐指示种分析（ＴＷＩＮＳＰＡＮ）对玛曲１７８个植物群落样方资料进行了多斐邓和数量分类,并应用植物群落的排序值与环境参数的多元回归分析出各群类型的定量环境解释,探讨了该区植物群的基本类型生态梯度及其与环境因子的定量关系。结果表明,玛曲的植物群落类型及基分布主要取惟地热量和湿度,并可通过环境参数地数字表达式定量地确定。     

人类基因组中巢式基因对的系统分析

基因组上一个基因的所有或大部分外显子位于另一基因内含子和UTR中被称为巢式基因(Nested gene)。巢式基因对(Nested gene pair)是由主基因(Host gene)和巢式基因组成的基因对。文章针对人类基因组上的顺式巢式基因对(简称为巢式基因对)进行全基因组鉴定,并通过比较巢式基因对在人和小鼠基因组上的保守性分布,研究巢式基因对的进化方式。保守性分析表明基因转座、序列原位突变和主基因转录起始/终止位点突变是巢式基因对进化的主要原因,其中主基因转录起始/终止位点突变是巢式基因对独特的一种进化方式。Gene On-tology分析揭示大部分巢式基因与主基因的产物在功能上没有相关性。     

混杂试验的多元分析

本文首次提出混杂试验的多元统计分析方法,并以香茅肥料试验为实例,论述平方和与乘积和矩阵的分解及总自由度的裂分方法,各种WilksA统计量的构造及其显著性测验法．最后给出了笔者编制的SAS统计分析程序．     

塔里木河流域荒漠河岸林群落的多元分析与环境解释

应用无偏对应分析（DCA）及二歧指示种分析（TWINSPAN）对塔里木河流域荒漠河岸林33个植物群落方资料进行了多元分析－排序和数量分类,并应用植物群落的排序值与环境参数的多元回归分析给出各群落类型的定量环境解释,探讨了该区植物群落的基本类型,生态梯度及其与环境因子的定量关系,结果表明,塔里木河流域漠河岸林的群落类型及其分布主要取决于土壤湿度和盐分含量,并可通过环境参数的数字表达式定量的确定。     

多元分析方法分辨细齿小塔塔鼠的下臼齿

啮齿类梳趾鼠科小塔塔鼠的下臼齿的形态非常接近。本文根据已有的一百多个牙齿,用齿冠的长度和宽度两个性状,在坐标系中画出了M_1、M_2和M_3的一系列椭圆,代表着这些牙齿一定比例的样本分布范围,并以此得出了细齿小塔塔鼠这三种牙齿的判别线。     

黄土高原马栏林区植物群落的多元分析与环境解释

应用双向指示种分析(TW IN SPAN)及除趋势对应分析(DCA)对黄土高原马栏林区40个植物群落样地资料进行了多元分析——数量分类和排序,并结合群落排序值与环境参数的回归分析给出各群落类型的定量环境解释,探讨了该区植物群落的基本类型、生态梯度及其与环境因子的定量关系.结果表明:在大尺度范围内,决定马栏林区植物群落宏观分布格局的主导因子是坡向和坡位;小尺度范围内,决定该区植物群落类型和分布的主导因子是枯枝落叶层厚度、土壤腐殖质厚度、土壤全磷、速效磷含量及土壤的pH值.     

Model Building and Analysis for Block Designs with Nested Rows and Columns

The paper deals with two main problems: it gives a unified approach to model building for observations obtained in a block design with nested rows and columns and it provides the analysis of variance for such data. Also, some statistical properties of the design are examined.     

The Polynomial Model in the Multivariate Analysis of Variance

Assuming that the independent variables (factors) are quantitative, there exist besides the coding schemes generally used for the multivariate analysis of variance (dummy-coded or effect-coded design matrices) the so-called polynomial models. The advantage of these polynomial models are the full rank design matrices, which allow a more comprehensible analysis, i.e. the unambiguous interpretation of tested hypotheses and simultaneous confidence intervals.     

生物医学动物实验中的实验设计和统计分析

实验设计和统计分析在动物实验研究的启动、实施和结果评价中起着关键的作用。我们对实验设计的因素、原则及实验设计类型进行了综述,阐明了统计分析在整个研究所有环节中的重要意义,并提出在生物医学动物实验中容易忽视的统计学分析的问题。     

Analysis of Nested Designs with Binomially-Distributed Outcome Variables

A model and methods are presented for the analysis of a nested design with binomially-distributed outcome variables. The proposed method may also be applicable to the analysis of singly-ordered contingency tables.     

Application of some Multivariate Analysis Techniques to Data on Effect of Air-Pollution on Morbidity

In this article an attempt has been made to analyze the data obtained from an air-pollution study to investigate the effects of the levels of air pollution exposure in causing morbidity, using some multivariate statistical techniques. Considering the importance of the theoretical aspect of the problem, the usual theory related to the application of these methods has also been described.     

Profile Analysis of Variance as a Tool for Analyzing Correlated Responses in Experimental Ecology

In ecological studies experiments are often designed in which the variables to be compared are not statistically independent. Examples include repeated measures of the same response by the same individual at different times, measurement of several traits on the same individual, and measurements taken from two or more types of organisms present together in the same experimental unit (e.g.) plot, cage, pond, etc.). This type of design violates several assumptions of the standard analysis of variance. These assumptions are examined and profile analysis, a modification of the standard analysis of variance which does not depend upon these assumptions, is presented. Simple instructions for performing profile analysis of variance using two common statistical packages for mainframe computers are provided in an appendix.     

Applications of a Vectorized MANOVA-Model

If the observation matrix of n independent p-dimensional normal vectors with common covariance matrix is transformed into an np-vector, linear models and hypotheses on the expectations can be formulated, that are not attainable in standard MANOVA or GMANOVA models. The distribution of an earlier proposed test procedure for this vectorized model is derived for p = 2, and its application is demonstrated for crossover and repeated measurement designs. Numerical results are compared in situations, in which both the standard and the vectorized model are applicable.     

A Generalization of the GMANOVA-Model

It is shown that in some experimental designs the MANOVA- and the GMANOVA-model are too restrictive either to yield all hypothesis tests of interest or to reflect all known features of the design. An extension of these models is derived by relating the response vectors with the unknown model parameters by linear equations which may be completely different for each of the p components of the response vector and for each of the n independent vectors. For situations, in which a Wishart-distributed estimator for the underlying common covariance matrix is attainable, a test for any s-dimensional linear hypothesis on the model parameters is derived.