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简介
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。
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。
目录
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
.analysis of interdependence
4 principal components analysis
4.1 introduction
4.2 principal components: how it works
4.3 sample problem: gross state product
4.4 questions regarding the application of principal components
4.5 learning summary
5 exploratory factor analysis
5.1 introduction
5.2 exploratory factor analysis: how it works
5.3 sample problem: perceptions of ready-to-eat cereals
5.4 questions regarding the application of factor analysis
5.5 learning summary
6 confirmatory factor analysis
6.1 introduction
6.2 confirmatory factor analysis: how it works
6.3 sample problems
6.4 questions regarding the application of confirmatory factor analysis
6.5 learning summary
7 multidimensional scaling
7.1 introduction
7.2 classical metric mds: how it works
7.3 nonmetric mds: how it works
7.4 the indscal model and method for individual differences scaling:
how it works
7.5 multidimensional analysis of preference: how it works
7.6 learning summary
7.7 selected readings
8 cluster analysis
8.1 introduction
8.2 objectives of cluster analysis
8.3 measures of distance, dissimilarity, and density
8.4 agglomerative clustering: how it works
8.5 partitioning: how it works
8.6 sample problem: preference segmentation
8.7 questions regarding the application of cluster analysis
8.8 learning summary
part iii
analysis of dependence
9 canonical correlation
9.1 introduction
9.2 canonical correlation: how it works
9.3 sample problem
9.4 questions regarding the application of canonical correlation
9.5 learning summary
10 structural equation models with
latent variables
10.1 introduction
10.2 structural equation models with latent variables: how it works
10.3 sample problem: modeling the adoption of innovation
10.4 questions regarding the application of structural equations with
latent variables
10.5 learning summary
11 analysis of variance
11.1 introduction
11.2 anova/ancova: how it works
11.3 sample problem: test marketing a new product
11.4 multiple analysis of variance (manova): how it works
11.5 sample problem: testing advertising message strategy
11.6 questions regarding the application of anova
11.7 learning summary
12 discriminant analysis
12.1 introduction
12.2 two-group discriminant analysis: how it works
12.3 sample problem: books by mail
12.4 questions regarding the application of two-group discriminant analysis
12.5 multiple discriminant analysis: how it works
12.6 sample problem: real estate
12.7 questions regarding the application of multiple discriminant analysis
12.8 learning summary
13 logit choice models
13.1 introduction
13.2 binary logit model: how it works
13.3 sample problem: books by mail
13.4 multinomial logit model: how it works
13.5 sample problem: brand choice
13.6 questions regarding the application of logit choice models
13.7 learning summary
statistical tables
bibliography
index
particular statistical packages (e.g., sas and spss). these workbooks explain
how the concepts in the text are linked to the application software and show the
student how to perform the analyses presented in each chapter. the program
templates provided in the workbooks enable students to run their own analyses
of the more than 100 data sets (most taken from real applications in the pub-
lished literature) contained the cd-rom that accompanies the text.
be able to interpret the results of the analysis. in each chapter, we raise the im-
portant issues and problems that tend to come up with the application of each
method. we place special emphasis on assessing the generalizability of the re-
sults of an analysis, and suggest ways in which students can test the validity of
their findings.
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
.analysis of interdependence
4 principal components analysis
4.1 introduction
4.2 principal components: how it works
4.3 sample problem: gross state product
4.4 questions regarding the application of principal components
4.5 learning summary
5 exploratory factor analysis
5.1 introduction
5.2 exploratory factor analysis: how it works
5.3 sample problem: perceptions of ready-to-eat cereals
5.4 questions regarding the application of factor analysis
5.5 learning summary
6 confirmatory factor analysis
6.1 introduction
6.2 confirmatory factor analysis: how it works
6.3 sample problems
6.4 questions regarding the application of confirmatory factor analysis
6.5 learning summary
7 multidimensional scaling
7.1 introduction
7.2 classical metric mds: how it works
7.3 nonmetric mds: how it works
7.4 the indscal model and method for individual differences scaling:
how it works
7.5 multidimensional analysis of preference: how it works
7.6 learning summary
7.7 selected readings
8 cluster analysis
8.1 introduction
8.2 objectives of cluster analysis
8.3 measures of distance, dissimilarity, and density
8.4 agglomerative clustering: how it works
8.5 partitioning: how it works
8.6 sample problem: preference segmentation
8.7 questions regarding the application of cluster analysis
8.8 learning summary
part iii
analysis of dependence
9 canonical correlation
9.1 introduction
9.2 canonical correlation: how it works
9.3 sample problem
9.4 questions regarding the application of canonical correlation
9.5 learning summary
10 structural equation models with
latent variables
10.1 introduction
10.2 structural equation models with latent variables: how it works
10.3 sample problem: modeling the adoption of innovation
10.4 questions regarding the application of structural equations with
latent variables
10.5 learning summary
11 analysis of variance
11.1 introduction
11.2 anova/ancova: how it works
11.3 sample problem: test marketing a new product
11.4 multiple analysis of variance (manova): how it works
11.5 sample problem: testing advertising message strategy
11.6 questions regarding the application of anova
11.7 learning summary
12 discriminant analysis
12.1 introduction
12.2 two-group discriminant analysis: how it works
12.3 sample problem: books by mail
12.4 questions regarding the application of two-group discriminant analysis
12.5 multiple discriminant analysis: how it works
12.6 sample problem: real estate
12.7 questions regarding the application of multiple discriminant analysis
12.8 learning summary
13 logit choice models
13.1 introduction
13.2 binary logit model: how it works
13.3 sample problem: books by mail
13.4 multinomial logit model: how it works
13.5 sample problem: brand choice
13.6 questions regarding the application of logit choice models
13.7 learning summary
statistical tables
bibliography
index
particular statistical packages (e.g., sas and spss). these workbooks explain
how the concepts in the text are linked to the application software and show the
student how to perform the analyses presented in each chapter. the program
templates provided in the workbooks enable students to run their own analyses
of the more than 100 data sets (most taken from real applications in the pub-
lished literature) contained the cd-rom that accompanies the text.
be able to interpret the results of the analysis. in each chapter, we raise the im-
portant issues and problems that tend to come up with the application of each
method. we place special emphasis on assessing the generalizability of the re-
sults of an analysis, and suggest ways in which students can test the validity of
their findings.
多元数据分析
- 名称
- 类型
- 大小
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