1999 대학원 다변량통계해석론 (Multivariate Analysis)
You can download both programs and results which are related with my lecture.
In time, I will add everything related with my lecture to here.
Multivariate Analysis deals with onservations on more than one variable where there is some inherent interdependence betweeen the variables.
In my lecture, I will teach the various approches, techniques and algorithms for this mutivariate analysis.
If you know the SAS package, this shall help you to understand these multivariate techniques.
강의내용
Lec 1) Aspects of Multivariate Analysis: Data Matrix, R-technique-Q.
Lec 2) Matrix Algebra and Random vectors: Singular Value Decomposition.
Lec 3) Goemetric views in multivariate analysis.
Lec 4) Multivariate Normal Distribution, Likelihood function. Wishart Dist.
Lec 5) Hotelling's squared-T test and Manova.
Lec 6) Principal Component Analysis.
Lec 7) Factor Analaysis: Principal FA, ML FA, Factor rotations.
Lec 8) Canonical Correlation Analysis
Lec 9) Discrimination and Classificaton
Lec 10) Clustering.
Lec 11) Multidimensional Scaling: Metric and Nonmetric MDS.
Lec 12) Biplots and Correpondence Analysis.
Lec 13) Path Analysis and LISREL.
평가
발표(100) + 중간시험(100) + 기말시험(100) + 과제(100) = 400
중간시험: 구두시험
Main Text
References
Jobson, J.D.(1992). Applied Multivariate Data Analysis, Spring-Velag, New York.
최용석(1999). [행렬도의 이해와 응용], 부산대학교 출판부
Jolliffe, I.T.(1986). Principal Component Analysis, Spring-Velag, New York.
최용석(1995). SAS 다차원척도법 , 자유아카데미, 서울.