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