Type of Credit: Partially Required
Credit(s)
Number of Students
Multivariate analysis is concerned with statistical methods of analyzing data consisting of observations on two or more variables for each individual or unit. The course intends to introduce students to the various topics and concepts in multivariate data analysis, with an emphasis on their applications, interpretation, and practical skills. Principles will be illustrated using the R package, the emphasis being on the interpretation of results.
能力項目說明
After completing this course, students should be able to:
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
每週學習投入時數約六至八小時（含正課三小時及實習課兩小時）
不定期作業與報告，配合上課進度與內容。
第1週 Introduction
第2-3週 Chapter 1 Overview of Multivariate Methods
第3-4週 Chapter 2 Examining Your Data
第5-6週 Chapter 3 Exploratory Factor Analysis
第7-8週 Chapter 4 Cluster Analysis
第9週 期中考
第10-11週 Chapter 6 MANOVA: Extending ANOVA
第12-13週 Chapter 7 Multiple Discriminant Analysis
第14-15週 Chapter 8 Structural Equation Modeling: An Introduction
第16-17週 Chapter 9 SEM: Confirmatory Factor Analysis
第18週 期末考
Coursework and class participation (30%), midterm exam (35%), and final exam ( and/or report) (35%).
Textbook:
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2014). Multivariate data analysis. 8th ed., Pearson new international edition.
References:
Additional material related to the textbook can be found on the dedicated website: www.mvstats.com