《多元统计分析综述》由会员分享，可在线阅读，更多相关《多元统计分析综述（38页珍藏版）》请在金锄头文库上搜索。
1、Preface to the 1st Edition Most of the observable phenomenafinmin in the empirical (empirikl经验 )sciences are of a multivariate nature. In financial studies, assets in stock markets are observed simultaneously and their joint development is analyzed to better understand general tendencies（趋势） and to
2、track indices（路灯）. The underlying theoretical structure of these and many other quantitative studies of applied sciences is multivariate. This book on Applied Multivariate Statistical Analysis presents the tools and concepts of multivariate data analysis with a strong focus on applications. The aim
3、of the book is to present multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who are （面对） by statistical data analysis. This is achieved by focusing on the practical relevance and through the e-book character of this text. All practical examples may b
4、e recalculated and modified by the reader using a standard web browser and without reference or application of any specific software. Most of the observable phenomenafinmin in the empirical (empirikl经验)sciences are of a multivariate nature. The underlying theoretical structure of these and many othe
5、r quantitative studies of applied sciences is multivariate. This book on Applied Multivariate Statistical Analysis presents the tools and concepts of multivariate,mlti vereit data analysis with a strong focus on applications. The book is divided into three main parts. The first part is devoted to gr
6、aphical techniques describing the distributions of the variables involved. The second part deals with multivariate random variables and presents from a theoretical point of view distributions, estimators and tests for various practical situations. The last part is on multivariate techniques and intr
7、oduces the reader to the wide selection of tools available for multivariate data analysis. All data sets are given in the appendix and are downloadable from www.md- . The text contains a wide variety of exercises the solutions of which are given in a separate textbook. In addition a full set of tran
8、sparencies on www.md- is provided making it easier for an instructor to present the materials in this book. All transparencies contain hyper links to the statistical web service so that students and instructors alike may recompute all examples via a standard web browser. 1-2 week UNIT-I Descriptive
9、Techniques(描述技术) 1 Comparison（对照） of Batches 1.1 Boxplots 4 1.2 Histograms 10 1.3 Scatterplots 17 1.4 Data Set -Boston Housing 35 1 Comparison of Batches Multivariate statistical analysis is concerned with analyzing and understanding data in high dimensions. We suppose that we are given a set xini=1
10、 of n observations of a variable vector X in Rp. That is, we suppose that each observation xi has p dimensions: xi = (xi1, xi2, ., xip), and that it is an observed value of a variable vector X Rp. Therefore, X is composed of p random variables: X = (X1,X2, .,Xp) where Xj, for j = 1, . . . , p, is a
11、one-dimensional random variable. 1 Comparison of Batches Multivariate statistical analysis is concerned with analyzing and understanding data in high dimensions. How do we begin to analyze this kind of data? Before we investigate questions on what inferences we can reach from the data, we should thi
12、nk about how to look at the data. This involves descriptive techniques. Questions that we could answer by descriptive techniques are: Are there components of X that are more spread out than others? Are there some elements of X that indicate subgroups of the data? Are there outliers in the components
13、 of X? How “normal” is the distribution of the data? 1.1 Boxplots 1 Comparison of Batches Genuine denjuin 真正的 X6 X1 The median and mean bars are measures of locations. The relative location of the median (and the mean) in the box is a measure of skewness. The length of the box and whiskers are a mea
14、sure of spread. The length of the whiskers indicate the tail length of the distribution. The outlying points are indicated with a “” or “” depending on if they are outside of FUL 1.5dF or FUL 3dF respectively. The boxplots do not indicate multi modality or clusters. If we compare the relative size a
15、nd location of the boxes, we are comparing distributions. Summary Reading material 21. data capacity数据容量kpsiti 22. data handling数据处理hndli 23. data reduction数据缩减分析ridkn 24. data transformation数据变换 25. density function密度函数 26. description描述 27. descriptive描述性的 28. deviation from average均值离差,di:viein背离
16、 29. Df. Fit拟合差值 30.df.(degree of freedom)自由度 31. distribution shape分布形状eip 32. double logarithmic双对数,l:grimik 33. eigenvector特征向量aign,vekt(r) 34. error of estimate估计误差estimeit 35. estimation估计量estimein重音差别 36. Euclidean distance欧式距离ju:klidin 37. expected value期望值ikspektid 38. experimental sampling实验抽样ik,sperimentl s
