This course is to help students, primarily from business and social sciences, learn the application of the multivariate data analytic techniques without being intimidated with mathematical derivations or rigorous proofs. We will emphasize the concepts of a given technique and its application, and will concentrate more on interpretation of the results and less on derivation of the technique. At the end of the course, students will be introduced to the various multivariate techniques and will be able to learn when to use a particular technique and how to interpret the resulting output obtained from the most widely used statistical packages (e.g. SPSS, SAS, and Statistica).

1. (知識) 建立學生多變量統計資料分析的能力
2. (技能) 使學生能將多變量統計資料分析方法正確的應用在商業及管理上
3. (態度) 使學生能具備統計資料分析之專業態度
4. (其他) 使學生能瞭解統計資料分析在科學研究上的重要性

1. 多變量分析簡介

2. 主成份分析
3. 探索性因素分析
4. 信度與效度分析
5. 典型相關分析

6. 假設之檢查
7. 兩群組 區別分析
8. 多群組 區別分析
9. 邏輯斯迴歸分析
10. 多類別邏輯模型

11. 集群分析

12. 多變量變異數分析

13. 驗證性因素分析
14. 線性結構關係模式

1. Introduction
Part I：Interdependence Methods(I)－Relations among Variables
2. Principal Components Analysis
3. Exploratory Factor Analysis (EFA)
4. Reliability and Validity Analysis
5. Canonical Correlation Analysis
Part II：Dependence Methods(I)－Categorical (Nonmetric) Response Variable
6. Assumptions Checking
7. Two-Group Discriminant Analysis
8. Multiple-Group Discriminant Analysis
9. Logistic Regression Analysis
10. Multi-category Logit Models
Part III：Interdependence Methods(II)－Relations among Observations
11. Cluster Analysis
Part IV：Dependence Methods(II)－Analytical (Metric) Response Variable
12. Multivariate Analysis of Variance (MANOVA)
Part V：A glimpse of Structural Equation Modeling (SEM)
13. Confirmatory Factor Analysis (CFA)
14. Linear Structural Relations (LISREL) Modeling

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