Time: Monday, Wednesday, Friday at 10:00 a.m. - 10:50 a.m.
Location (in person and on campus): Taft Hall (826 S Halsted St, Chicago, IL 60607), Room 208
Instructor: Jie Yang
Office: SEO 513
Phone: (312) 413-3748
E-Mail: jyang06 AT uic DOT edu
Office Hours: Monday, Wednesday, Friday at 11:00 a.m. - 12:00 p.m. at UIC Zoom or by appointment
Textbook (required): Richard A. Johnson, Dean W. Wichern,
Applied Multivariate Statistical Analysis, Pearson Prentice Hall, 6th edition, 2007
Reference Books (optional):
Attendance/Participation Policy:
Students are expected to attend the lectures and participate in the discussions. Attendance will be counted at least six times during the course period and the students who present may receive half credit point each time and up to three extra credit points in total on the final grade at a 100-point scale. Students who actively participate in the discussion may receive half or one credit point each time and up to five credit points in total. If for any reason you could not present in class on time, please send me an email at your earliest convenience. If you need special accommodations due to disabilities, please contact the Disability Resource Center for a Letter of Accommodation (LOA).
Assignments, Due Dates, and Deadlines:
Homework will be assigned at a weekly basis;
turn in your homework every Wednesday before class via UIC Blackboard;
half of the grade counts for completeness;
half of the grade counts for correctness of one selected problem
Policy for Missed or Late Homework: Students may request up to two days' extension for each homework; late homework without request ahead of time or longer than two days will not be accepted; the lowest two homework scores will be dropped for final grade.
Exams: This course will require students to be on campus for in-person exams on February 12th and March 19th, Wednesdays, 10:00 a.m. - 10:50 a.m.
Project: Students are required to work by themselves or in groups on course projects and submit their final reports before May 2nd, 2025, Friday, 10:00am.
Each group should consist of at most three students. The projects may come from the project problems assigned by the instructor or be proposed by the students themselves upon the approval of the instructor.
Grading: Homework 20%, Two Exams 25% each, Project 30%
Grading Scale: 90% A , 80% B , 70% C , 60% D
Format of All Exams: Each exam is based on the homework and the examples discussed in class. The last class session before each exam is a review session. Please prepare any questions that you may have.
No makeup exam will be given without a valid excuse.
| WEEK | SECTIONS | BRIEF DESCRIPTION |
| 01/13 - 01/17 | 1.1, 1.2, 1.3; 2.1, 2.2, 2.3; 2.5, 2.6 | Aspects of Multivariate Analysis; Some Basics of Matrix and Vector Algebra, Positive Definite Matrices; Random Vectors and Matrices, Mean Vectors and Covariance Matrices |
| 01/20 - 01/24 | Holiday; 3.3, 3.5, 3.6; 4.1, 4.2 | Sample Geometry and Random Sampling; Multivariate Normal Density and Its Properties |
| 01/27 - 01/31 | 4.3; 4.4, 4.5; 4.6 | Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation; Sampling Distribution of Xbar and S, Large-Sample Behavior of Xbar and S; Assessing the Assumption of Normality |
| 02/03 - 02/07 | 5.1, 5.2; 5.3; 5.4 | Plausibility of mu0 as a Value for a Normal Population Mean; Hotelling's T^2 and Likelihood Ratio Tests; Confidence Regions and Simultaneous Comparisons |
| 02/10 - 02/14 | Review; Exam-I; 5.5 | Large Sample Inferences about a Population Mean Vector |
| 02/17 - 02/21 | 6.1, 6.2; 6.3; 6.4 | Paired Comparisons and a Repeated Measures Design; Comparing Mean Vectors from Two Populations; Comparing Several Multivariate Population Means |
| 02/24 - 02/28 | 6.5, 6.6; 6.7; 7.1, 7.2, 7.3 | Simultaneous Confidence Intervals for Treatment Effects, Testing for Equality of Covariance Matrices; Two-Way Multivariate Analysis of Variance; Classical Linear Regression Model, Least Squares Estimation |
| 03/03 - 03/07 | 7.4; 7.6; 7.7 | Inferences about the Regression Model; Model Checking and Other Aspects of Regression; Multivariate Multiple Regression |
| 03/10 - 03/14 | 7.7; 7A; 7.10 | Multivariate Multiple Regression; Distribution of the Likelihood Ratio; Multiple Regression Models with Time Dependent Errors |
| 03/17 - 03/21 | Review; Exam-II; 8.1, 8.2 | Population Principal Components |
| 03/31 - 04/04 | 8.2; 8.3; 8.5, 8A | Population Principal Components; Summarizing Sample Variation by Principal Components; Large Sample Inferences, Geometry of Sample Principal Component Approximation |
| 04/07 - 04/11 | 9.1, 9.2; 9.3; 9.3 | Orthogonal Factor Model; Methods of Estimation; Methods of Estimation |
| 04/14 - 04/18 | 10.1, 10.2; 10.3; 10.4 | Canonical Variates and Canonical Correlations; Interpreting the Population Canonical Variables; Sample Canonical Variates and Sample Canonical Correlations |
| 04/21 - 04/25 | 11.1, 11.2; 11.3; 11.4 | Introduction, Separation and Classification for Two Populations; Classification with Two Multivariate Normal Populations; Evaluating Classification Functions |
| 04/28 - 05/02 | 11.6; 11.7; 11.7 | Fisher's Method for Discriminating, Logistic Regression and Classification |