多變量分析
Multivariate Analysis
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5 13:20–14:10 | 多變量分析 A304 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
The aims of this course are: (1) To illustrate extensions of univariate statistical methodology to multivariate data. (2) To introduce students to some of the distinctive statistical methodologies which arise only in multivariate data. (3) To introduce students to some of the computational techniques required for multivariate analysis available in standard statistical packages. Topics include multivariate techniques and analyses, multivariate analysis of variance, principal component analysis, and factor analysis, cluster analysis, discrimination and classification, and machine learning. The course uses the R software for statistical computing. Students are expected to be familiar with the usage of the software.
Students are expected to have a background in undergraduate linear algebra, probability, mathematical statistics, and linear regression. Computer programming knowledge on R and/or C/C++ is required.
Class website: https://ghuang.stat.nycu.edu.tw/course/multivariate26/
The course grade will be based on 3 homework assignments (30%), 1 final data analysis competition (20%), 1 midterm exam (20%), and 1 final exam (30%).
- Aspects of multivariate analysis
- Random vectors and random sampling
- Multivariate normal distribution
- Inferences about a mean vector
- Comparisons of several multivariate means
- Principal components
- Factor analysis
- Canonical correlation analysis
- Clustering
- Discrimination and classification
- Machine learning
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Handouts corresponding to each lecture will be available on the class website before each class. The required textbook for this course is: Johnson, R.A. and Wichern, D.W., 2007. Applied Multivariate Statistical Analysis (6th Edition). Prentice Hall, Upper Saddle River, NJ. (AMSA) The following book is recommended for further reading: Hastie, Tibshirani and Friedman, 2009. The Elements of Statistical Learning (2nd edition). Springer, New York, NY, USA. (ESL) Reading assignments will be made primarily in these two books.
- 地點
- A423 Joint Education Hall
- 時間
- By appointment
- 聯絡方式
- Email: ghuang@nycu.edu.tw