Type of Credit: Elective
Credit(s)
Number of Students
Multivariate statistical analysis refers to statistical techniques that are used to study the joint behavior of many variables at the same time.
能力項目說明
In this two-semester course, we'll give the multivariate statistical techniques such as multiple linear regression models, principle component analysis, factor analysis, canonical analysis and discrimination and classification and their theory behind these techniques. In addition, students will also learn how to use statistical software to analyze the data. It is expected that students should be able to determine and use the appropriate technique to answer and explain the multivariate data they are given.
| 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 |
|---|---|---|
Week Topic Content and Reading Teaching Activities Student workload Assignment and Homework _______________________________________________________________________________________________
1 Aspects of Multivariate Analysis 1 introduction and multivariate techniques Homework will be Students are expected assigned in the class to spend 9 hours/week . . outside of class
2 Aspects of Multivariate Analysis 2 organization of data
3. National Holiday
4. Aspects of Multivariate Analysis 3 pictorial representations and distance
5. Matrix algebra and random vector 1 properties of matrix and vector algebra
6. Matrix algebra and random vector 2 Random vectors and matrices
7. Sample geometry and random sampling 1 geometry of sample
8. Sample geometry and random sampling 2 properties of sample mean and covariance matrix
9. Multivariate normal distribution 1 density and properties
10. Multivariate normal distribution 2 sampling distributions and MLE
11. Multivariate normal distribution 3 assessing the normality assumption
12. Multivariate linear regression models 1 classical linear regression model
13. Multivariate linear regression models 2 estimation
14. Multivariate linear regression models 3 inferences
15. Multivariate linear regression models 4 diagnostics and remedial measures
16. Preview of principle components and factor analysis
17. Regression project 1
18. Regression project 2
1. class participation: 20%
2. home work: 40%
3. projects: 40%
All are subject to change.
Applied Mltivariate Statistical Analysis (2014) by Johnson and Wichern 6 th edition
Reference book: Applied Linear Regression Models (2004) by Kutner, Nachtssheim, and Neter 4th edition
| 書名 Book Title | 作者 Author | 出版年 Publish Year | 出版者 Publisher | ISBN | 館藏來源* | 備註 Note |
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