LECTURE: MWF 1:10 - 2:00 p.m., PhysGeo 148 DISCUSSION: R 12:10 - 1:00 p.m., Hoagland 168 INSTRUCTOR: Prof.
Jiming Jiang, 4228 MSB, (530) 754-8589, jiang@wald.ucdavis.edu;
Office hours: T 1-3 p.m. TEACHING ASSISTANT: Ms. Irina Udaltsova, 1117 MSB,
iudaltsova@wald.ucdavis.edu;
Office hours: TBA TEXT: Applied Multivariate Statistical Analysis
by R. A. Johnson & D. W. Wichern (6th ed.), ISBN-13: 978-0-13-187715-3;
ISBN-10: 0-13-187715-1. REQUIREMENTS:
HOMEWORK: Homework will be assigned in class and collected
each Friday during the lecture. The graded homework will be given
back during the discussion session on Thursday. The lowest two homework
grades will be dropped in calculating the overall homework score.
No late homework.
PROJECT: There will be two projects involving computer data
analysis. The projects will be assigned on the course website. Each
project is due in two weeks. Although we recommend that you use the R
software which is freely available to complete the projects; other
softwares (e.g., Splus, Minitab, SAS, SPSS) are also allowed. The TA
will provide instructions for R and Splus, which is very similar to R,
during her discussion sessions.
MIDTERM: There will be an in-class midterm exam. Exam date:
TBA (during the lecture).
FINAL: There will be an in-class final exam. Exam date:
Wed. June 10, 8:00 - 10:00 a.m.
GRADES:
FINAL 40%, MIDTERM 20%, PROJECT 20% (10% each),
HOMEWORK 20%.
(Grade Disputes will not be considered until the end of the
quarter. Write a cover note including the total number of points in
questions and your rationale. If the total points in question will
change your letter grade (including +/-), your rationale will be
considered.)
TOPIC:
Aspects of Multivariate Analysis
Random Sampling
Inference about A Mean Vector
Comparisons of Several Multivariate Means
Multivariate Linear Regression
Principal Components Analysis
Factor Analysis and Inference about Structured Covariance Matrices