## 주메뉴

### 다변량통계학(I)

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자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
• 강의학기
2022년 1학기
• 조회수
624
•
강의계획서
1.1. Multivariate data analysis 1.2 Types of multivariate analysis techniques

#### 차시별 강의

 1. 1.1. Multivariate data analysis 1.2 Types of multivariate analysis techniques 2. 1.3 Introduction and visualization of multivariate data 1.4 Matrix representation and descriptive statistics of multivariate data 1.4 Matrix representation and descriptive statistics of multivariate data 3. 1.4 Matrix representation and descriptive statistics of multivariate data 1.5 Distances and Correlation of multivariate data 1.6 Multivariate normal distribution and its useful property 4. 1.6 Multivariate normal distribution and its useful property 1.7 Wishart W-dist and Hotelling’s -dist 1.7 Wishart W-dist and Hotelling’s -dist 1.8 Testing multivariate normality 5. 2. Principal Component Analysis (PCA) 2.1 Comprehension of PCA 2.1 Comprehension of PCA 6. 2.1 Comprehension of PCA 2.2 Concepts of pc 2.3 Algebraic inducement of pc 7. 2.2 Concepts of pc 2.3 Algebraic inducement of pc 2.4 Selection and explanation of pc 2.5 Algebraic inducement of sample pc 8. 2.4 Selection and explanation of pc 2.5 Algebraic inducement of sample pc 9. 2.6 Visualizations of PCA 10. 3.1 Comprehension of FA 3.2 Conseptof common factor 3.3 Factor model 3.4 Estimation of factor model 3.5 Factor rotation and factor loadings plot 11. 3.1 Comprehension of FA 3.2 Conseptof common factor 3.3 Factor model 3.4 Estimation of factor model 3.5 Factor rotation and factor loadings plot 12. 5.1 Comprehension of CA 5.2 Similarity measures 13. 5.3 Hierarchical clustering methods 14. 5.3 Hierarchical clustering methods 5.4 Non-hierachical clustering methods 15. 5.4 Non-hierachical clustering methods 5.5 Numbers of Clusters

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