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Multivariate Statistics (II) - With R

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

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

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