## 주메뉴

### Multivariate Statistics (II) - With R

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자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
• 강의학기
2018년2학기
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강의계획서
This lecture is designed to learn some statistical analysis techniques (Discriminant and Classification Analysis(DCA), Multidimensional Scaling(MDS), Correpondence Analysis(CRA), Biplots)

#### 차시별 강의

 1 2018’ Welcome to Multivariate Statistics(II) Course description/Practice time/Final Exam/Term project 2 Lecture 6. Discrimination and Classification Analysis(DCA) 6.1 Introduction 6.2 DCA with two clusters 3 Lecture 6. Discrimination and Classification Analysis(DCA) 6.2 DCA with two clusters 4 Lecture 6. Discrimination and Classification Analysis(DCA) 6.3 DCA with two multivariate normal clusters 5 Lecture 6. Discrimination and Classification Analysis(DCA) 6.4 DCA with several clusters 6 Lecture 6. Discrimination and Classification Analysis(DCA) 6.5 DCA with several multivariate normal clusters 7 Lecture 6. Discrimination and Classification Analysis(DCA) 6.6 Evaluating classification function 8 Lecture 6. Discrimination and Classification Analysis(DCA) 6.11 R for DCA: Practice Time 9 Lecture 7. Multidimensional Scaling(MDS) 7.1 Introduction 10 Lecture 7. Multidimensional Scaling(MDS) 7.2 Metric MDS 11 Lecture 7. Multidimensional Scaling(MDS) 7.3 Non-metric MDS 7.6 R for MDS: Practice Time 12 Lecture 8. Correspondence Analysis(CRA) 8.1 Introduction 13 Lecture 8. Correspondence Analysis(CRA) 8.2 Simple CRA 8.3 Independence and homogeneity in CRA 14 Lecture 8. Correspondence Analysis(CRA) 8.4 Multiple CRA 15 Lecture 8. Correspondence Analysis(CRA) 8.5 MCRA of classification variables data 8.6 R for CRA: Practice Time

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