ST3240 Multivariate Statistics
Lec 16
est mini TPM for Unequal sigma
est mini TPM for equal-covariance Nor pop
11.6 Fisher Method for discriminating among several pop.
Lec 17
Classification with Normal Population
Using Fisher's Discriminants to classify objects
Fisher's sample Linear Discriminats
PCA
Lec 10
Lec 11
Interpretation Sample Principal Component
Standardizing Sample Principal Components
8.4 Graphing Principal Components
8.5 Large Sample Inference
Lec 6
12.3 Hierarchical clustering Method
Lec 7
12.4 Nonhierarchical clustering Method
K means method
PCA
8.1 Introduction
8.2 Population Principal Components
Lec 8
Result 8.1 Cov matix, eigenvalue & eigenvector
Result 8.2 total variance of ith p.c. is trace
Result 8.3 correlation coefficients btw p.c.Yi & variable Xk
Result 8.4 P.C. from standardize variable Z
Lec 9
Covariance Matrices with special structure (diagonal matrix)
Summarize sample variation by P.C.
Need to study again
Lec 9 to p.7
Number of Principal Component (how many ?)
scree plot
DA(Discrimination & classification)
Lec 11