毕业论文thesis[英][ˈθi:sɪs][美][ˈθisɪs]论文，毕业论文; 论点，论题; 命题; 复数：theses易混淆单词：THESIS例句:He completed his doctorate in 1999 with his thesis on the technical subject of structural 1999年，朱竞翔获得博士学位，博士论文写的是结构设计的技术问题。It is a beguilingly simple thesis， one particularly attractive to the western business executives who have joinedthe china gold 但这是一个具有欺骗性的简单论点，对参与中国淘金浪潮的西方企业高管尤其有吸引力。We have a grand new thesis of the emerging 我们现在得出了一套全新的新兴市场理论。The question now is whether the overstretch thesis was wrong or simply 目前问题是，过度扩张说是错误命题还是只是言之过早。The thesis is that women still do so badly at work mainly because we are not ambitious 书的主题是：女性的工作表现仍如此糟糕，主要是因为我们不够有雄心。同义词：dissertation[英][ˌdɪsəˈteɪʃn][美][ˌdɪsərˈteɪʃn]专题论文，学位论文; 学术演讲; essay[英][ˈeseɪ][美][ˈɛsˌe， ɛˈse]散文; 随笔，杂记文; 尝试，企图; 试验; 尝试; 试验; 经常说的：English dissertation(英语论文)Graduation thesis(毕业论文)
Principal Components Analysis 主成分分析When a researcher is beginning to think about analyzing a new data set， several questions about the data should be Important questions include these: (1) Are there any aspects of the data that are strange or unusual? (2) Can the data be assumed to be distributed normally? (3) Are there any abnormalities in the data? (4) Are there outliers in the data? Experimental units whose measured variables seem inconsistent with measurements on other experimental units are usually called 当研究人员开始思考分析了一款新型的数据集、数据有关的几个问题要考虑。这些重要的问题包括:(1)有什么方面的数据，这些数据是陌生的或不寻常的事?(2)数据可以被假定是分布式的正常吗?(3)是否有任何异常数据吗?(4)有野值的数据吗?实验测量变量的单位似乎不符合在测量其他实验单位通常被称为概要图。By far， the most important reason for performing a principal components analysis (PCA) is to use it as a tool for screening multivariate New variables， called principal component scores， can be These new variables can be used as input for graphing and plotting programs， and an examination of the resulting graphical displays will often reveal abnormalities in the data that you are planning to For example， plots of principal component scores can help identify outliers in the data when they In addition， the principal component scores can be analyzed individually to see whether distributional assumptions such as normality of the variables and independence of the experimental units Such assumptions are often required for certain kinds of statistical analyses of the data to be 到目前为止，最重要的原因为执行一个主成分分析(PCA)是使用它当作一个工具，用于筛选的多元数据。新的变数，所谓的主成分得分，可以被创建了。这些新的变量可以用来作为输入为图及计算机绘图程序，和考试产生出来的图形的显示会经常展现躯体异常数据，你正在计划与分析。例如，主成分得分的情节，可以帮助鉴别孤立点数据，当他们存在。此外，通过主成分得分可以分析单独去看看是否如常态分布假设的变量和独立的实验单位持有。这样的假设通常需要对某些种类的统计分析的数据是有效的Principal components analysis uses a mathematical procedure that transforms a set of correlated response variables into a new set of uncorrelated variables that are called principal Principal components analysis can be performed on either a sample variance-covariance matrix or a correlation The type of matrix that is best often depends on the variables being Occasionally， but not often， the newly created variables are One cannot always expect to be able to interpret the newly created In fact， it is considered to be a bonus when the principal component variables can actually be When using PCA to screen a multivariate data set， you do not need to be able to interpret the principal components because PCA is extremely useful regardless of whether the new variables can be 主成分分析使用了一个数学程式，从而使一组相关反应变量到一套新的独立变量叫做主成分。主成分分析方法也可以执行一个样品variance-covariance矩阵或相关矩阵。该类型的矩阵里，他是最好的，这往往取决于把变量被测量。偶尔，但不经常，新创建的变量被识别的。一个人不能总是期望能够解释新创建的变量。事实上，它被认为是一种奖励当主成分变量实际上可以被破译。当使用PCA掩护多元数据集时，你不需要能够解释的多解性，利用主成分分析法(PCA)主成分是非常有用的，因为无论是否自己的新变量可以解释。to partition experimental units into subgroup so that similar experimental units belong to the same In this case， principal component scores can be used as input to clustering This often increases the effectiveness of the clustering programs， while reducing the cost of using such Furthermore， the principal component scores can and should always be used to help validate the results of clustering 主成分分析是研究人员通常很有帮助那些想要隔断实验单位成为小组，这样类似的试验单元属于同一情况。在这种情况下，主成分得分可以用作输入到聚类的节目。这通常会增加对聚类的程序的有效性，同时减少成本的使用这样的计划。此外，通过主成分得分可以而且应该总是被用来帮助验证了结果的聚类的节目。
