Multivariate Statistics (I) - With R
- 부산대학교
- 최용석
페이스북
트위터
카카오톡
네이버밴드
링크복사
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- 주제분류
- 자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
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- 강의학기
- 2018년 1학기
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- 조회수
- 3,957
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- 강의계획서
- 강의계획서
This lecture is designed to learn some statistical analysis techniques (Principal Component Analysis(PCA), Factor Analysis(FA), Cluster Analysis(CA)) for multivariate data which are measuring the various social present situations by many variables and observations.
차시별 강의
| 1. | |
2018’ Welcome to Multivariate Statistics(I) | Course description/Practice time/Final Exam/Term project |  |
| 2. | |
Lecture 1. Multivariate Data Analysis (MDA) | 1.1. Multivariate data analysis 1.2 Types of multivariate analysis techniques | |
| 3. | |
Lecture 1. Multivariate Data Analysis (MDA) | 1.3 Introduction and visualization of multivariate data 1.4 Matrix Representation and Descriptive Statistics | |
| 4. | |
Lecture 1. Multivariate Data Analysis (MDA) | 1.5 Distances and Correlation of multivariate data 1.6 Multivariate normal distribution and its useful property | |
| 5. | |
Lecture 1. Multivariate Data Analysis (MDA) | 1.7 Wishart and Hotelling Distributions 1.8 Test of multivariate normality 1.9 R for EDA: Practice Time | |
| 6. | |
Lecture 2. Principal Component Analysis (PCA) | 2.1 Comprehension of PCA 2.2 Concepts of pc 2.3 Algebraic inducement of pc | |
| 7. | |
Lecture 2. Principal Component Analysis (PCA) | 2.4 Selection and explanation of pc 2.5 Algebraic inducement of sample pc | |
| 8. | |
Lecture 2. Principal Component Analysis (PCA) | 2.6 Visualizations of PCA 2.7 R for PCA: Practice Time | |
| 9. | |
Lecture 3. Factor Analysis (FA) | 3.1 Comprehension of FA 3.2 Conseptof common factor | |
| 10. | |
Lecture 3. Factor Analysis (FA) | 3.3 Factor model 3.4 Estimation of factor model | |
| 11. | |
Lecture 3. Factor Analysis (FA) | 3.5 Factor rotation and factor loadings plot 3.6 Application of factor scores | |
| 12. | |
Lecture 3. Factor Analysis (FA) | 3.7 Visualizations of FA 3.8 R for FA: Practice Time | |
| 13. | |
Lecture 5. Cluster Analysis (CA) | 5.1 Comprehension of CA 5.2 Similarity measures | |
| 14. | |
Lecture 5. Cluster Analysis (CA) | 5.3 Hierarchical clustering methods | |
| 15. | |
Lecture 5. Cluster Analysis (CA) | 5.4 Non-hierachical clustering methods 5.5 Numbers of Clusters 5.7 R for CA:Practice Time 5.7 R for CA: Practice Time | |
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