In Multivariate Statistics (I) with R, our aim is to learn some statistical analysis and visualization 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. Recently, multivariate statistics provides some absolute and essential techniques for data mining. In this lecture, we have a good chance to raise our understanding multivariate data and to study some powerful analysis techniques. With practice, we will be experienced in results' interpretations of data analysis. Also Beam projector help to understand all process and interpretations for data analysis.

Next semester, Multivariate Statistics (II) with R will give some statistical analysis and visualization techniques : Discrimination Analysis and Classification Tree (DACT), Multidimensional Scaling (MDS), Correspondence Analysis (CRA) and Machine Lernings(SVM, ANN, DNN).

- Lecture Overview

1. Multivaiate Data Analysis(MDA)

2. Principal Component Analysis(PCA)

3. Factor Analysis(FA)

5. Cluster Analysis(CA)

- Required Textbook

Choi, Y.S.(2021). Multivariate Data Analysis with R, 1st Edition(2nd Printing), Kyungmoon, Seoul.

- Requirements and Grading

Total Score = Attendance(10%) + Reports(20%) + Exam(40%) + Term Project(30%)

- Competitors and Knowledge

- Matrix Algebra (I), (II)

- R

- References

[1] Anderson, T.W.(1984). An Introduction to Multivariate Statistical Analysis (2nd ed.), John Wiley, New York.

[2] Chatfield, C. and Collins, A. J. (1980). Introducton to Multivariate Analysis, Chapman and Hall, London.

[3] Choi, Y.S. and Shin, S.M.(2013). Understanding of Biplot Analysis using R, Free Academy, Seoul.

[4] Choi, Y.S.(2014). Understanding Statistics with R, Kyobo, Seoul.

[5] Choi, Y.S.(2014). Walk in Multidimensional Scaling, Free Academy, Seoul.

[6] Dillon, W. R. and M. Goldstein (1984). Multivariate Analysis -Methods and Applications-, John Wiley & Sons, New York.

[7] Jobson, J. D. (1992). Applied Multivariate Data Analysis, Springer-Verlag, New York.

[8] Johnson, R. A. and Wichern, D. W. (2002). Applied Multivariate Statistical Analysis, 5th Edition, Prentice Hall Inc, London.

[9] Rencher, A. V. (1995). Methods of Multivariate Aanlysis. John Wiley & Sons, Inc., New York.

[10] Srivastava, M.S. (2002). Methods of Multivariate Statistics, Wiley-Interscience, John Wiley & Sons, Inc., USA.