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모두를 위한 열린 강좌 KOCW

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  • 주제분류
    자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
  • 강의학기
    2018년 1학기
  • 조회수
    2,999
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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

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1. 문서 2018’ Welcome to Multivariate Statistics(I) Course description/Practice time/Final Exam/Term project URL
2. 문서 Lecture 1. Multivariate Data Analysis (MDA) 1.1. Multivariate data analysis 1.2 Types of multivariate analysis techniques URL
3. 문서 Lecture 1. Multivariate Data Analysis (MDA) 1.3 Introduction and visualization of multivariate data 1.4 Matrix Representation and Descriptive Statistics URL
4. 문서 Lecture 1. Multivariate Data Analysis (MDA) 1.5 Distances and Correlation of multivariate data 1.6 Multivariate normal distribution and its useful property URL
5. 문서 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 URL
6. 문서 Lecture 2. Principal Component Analysis (PCA) 2.1 Comprehension of PCA 2.2 Concepts of pc 2.3 Algebraic inducement of pc URL
7. 문서 Lecture 2. Principal Component Analysis (PCA) 2.4 Selection and explanation of pc 2.5 Algebraic inducement of sample pc URL
8. 문서 Lecture 2. Principal Component Analysis (PCA) 2.6 Visualizations of PCA 2.7 R for PCA: Practice Time URL
9. 문서 Lecture 3. Factor Analysis (FA) 3.1 Comprehension of FA 3.2 Conseptof common factor URL
10. 문서 Lecture 3. Factor Analysis (FA) 3.3 Factor model 3.4 Estimation of factor model URL
11. 문서 Lecture 3. Factor Analysis (FA) 3.5 Factor rotation and factor loadings plot 3.6 Application of factor scores URL
12. 문서 Lecture 3. Factor Analysis (FA) 3.7 Visualizations of FA 3.8 R for FA: Practice Time URL
13. 문서 Lecture 5. Cluster Analysis (CA) 5.1 Comprehension of CA 5.2 Similarity measures URL
14. 문서 Lecture 5. Cluster Analysis (CA) 5.3 Hierarchical clustering methods URL
15. 문서 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 URL

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