多元数据分析（英文版）[按需印刷]

　　 本书介绍了多元数据分析的现代方法，主要讲解多元统计学中的最新方法及其应用。作者通过大量的示例说明每种技术的工作方式以及应用方法，还应用几何图形的方法来开发学生的直觉力，帮助读者对各种方法有一个比较形象的认识。书中大量习题和示例采用了来源于心理学，社会学和营销学等各个学科的真实数据。
　　 因为本书提供了各种类型的应用，所以适用于很多专业的教学，不仅适合营销学、组织行为学、会计学专业，还适合工程学、教育学、经济学、心理学、社会学和统计学等专业。
　　
　　

part 1
overview
1 introduction
1.1 the nature of multivariate data
1.2 overview of multivariate methods
1.3 format of succeeding chapters
2 vectors and matrices
2.1 introduction
2.2 definitions
2.3 geometric interpretation of operations
2.4 matrix properties
2.5 learning summary
exercises
3 regression analysis
3.1 introduction
3.2 regression analysis: how it works
3.3 sample problem: leslie salt property
3.4 questions regarding the application of regression analysis
3.5 learning summary
part ii

　　 Once upon a time, over two decades ago now, two gentlemen (Paul Green and Doug Carroll) collaborated on a textbook titled Analyzing Multivariate Data. Their objec-tive was to produce a book with a pragmatic orientation--"a book for the data ana-lyzer." Quoting from the preface of that book,
　　 Most users of multivariate statistical techniques are not professional statisticians. They
　　are applications-oriented researchers--psychologists, sociologists, marketing research-ers, management scientists, and so on--who, from time to time, need the techniques to help them in their work. This text has been written for them and for students of these disciplines .... As implied by the title, emphasis on data analysis and the objectives of people who do data analysis has shaped the character of the whole enterprise.
　　Many people adopted the book, including a young professor (Jim Lattin) who was teaching a course on multivariate data analysis for the very first time. The level of the text seemed quite appropriate for the mix of graduate students taking the course (mainly first- and some second-year graduate students from different parts of the uni-versity). It was not too difficult (i.e., it did not rely too heavily on mathematics be-yond the preparation of the typical student) and not too simplistic (i.e., it was not a"cookbook,). Because the book presented a variety of applications, it appealed to a relatively broad cross-section of students (not only students in marketing, organiza-tional behavior, and accounting from the Graduate School of Business, but also stu-dents in engineering, education, economics, food research, psychology, sociology,
　　and statistics).
　　 But perhaps the best feature of the book (in the opinion of the young professor) was the way the authors used the geometry underlying the mathematics to show how the techniques really worked. Even a student with only a tentative grasp of matrix al-gebra can see what is happening when he or she understands that each matrix opera-tion corresponds to a stretching (or shrinking) and rotation of the data. After the orig-inal text went out of print, the young professor continued to teach the course from the notes he had developed. Many things about the course changed (e.g., topics were added, dropped, and rearranged; new examples and larger data sets were included to keep pace with the increased computational capabilities of today's software pack-
　　 ages), but the underlying pedagogy remained the same.
　　 This new book is the result of the collaboration between the now not-so-young professor and the two authors of the original text. It is not so much a revision as it is a rebirth: a fresh look at multivariate techniques more than 20 years later, with new examples, new data, and some new methods, but grounded in the same pedagogical approach (applications-oriented, intuitively motivated using the underlying geometry of the method) that guided the creation of the original.
　　Organization The book is organized into three parts. By way of introduction, Part I (Chapters 1
　　 through 3) provides a general overview of multivariate methods, some helpful back- ground on vectors and matrices and their geometrical interpretation, and a review of multiple regression analysis. Part II (Chapters 4 through 8) focuses on the analysis of interdependence, both among variables (principal components, factor analysis) and among objects (multidimensional scaling, cluster analysis). Part III (Chapters 9 through 13) covers canonical correlation and methods used in the analysis of depen- dence, including structural equation models with latent variables, logit choice mod- els, and special cases of the general linear model (analysis of variance, discriminant analysis).
　　 Objectives
　　 Our objective is to make students intelligent users of these multivariate techniques and good critics of multivariate analyses performed by others. If students are to be intelligent users and good critics of the techniques discussed in this book, they must have some grasp of theory, application, and interpretation. In other words, they must Have some intuition as to how the technique works. To this end, we use a geo- metric interpretation to provide the students with a mental picture of how each method works. We use mathematics to support the underlying intuition (rather than as a substitute for it).
　　 Be able to apply the technique. We take a hands-on approach, providing illus- trative examples in each chapter based on real-world data. To facilitate the ap- plication of these methods, we have developed student workbooks specific to