| Statistics 656 |
| Applied Multivariate Analysis |
| |
|
Instructor
Dr. Catherine Calder
Grader
Linchao Chen
|
Lectures MWF
8:30-9:48am in 0080 Derby Hall
|
Course Description
Statistics 656 is
an introductory multivariate statistical analysis course designed for
graduate students in the Department of Statistics, as well as from
other disciplines. The aim of the course is to introduce a variety of
standard statistical methods used to analyze multivariate data,
emphasizing the implementation and interpretations of these
methods. Topics covered include matrix computation of summary
statistics, graphical techniques, the geometry of sample data, the
multivariate normal distribution, principal components analysis,
factor analysis, classification/discrimination, as well as cluster
analysis if time permits. We will use the R statistical computing
environment and the GGobi data visualization system; no previous
experience using these software packages is required.
|
Syllabus syllabus.pdf |
Course Materials
The class schedule,
important announcements, lecture notes, computing references, homework problems and
solutions, and other information about the course will be posted
on Carmen.
|
|
|
|
|
