Time: Monday, Wednesday, Friday at 11:00 a.m. - 11:50 a.m.
Location: Addams Hall 302 (830 S Halsted St, Chicago, IL 60607)
Textbook: Richard A. Johnson, Dean W. Wichern,
Applied Multivariate Statistical Analysis, Pearson Prentice Hall, 6th edition, 2007
Turn in 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
Exams: This course will require students to be on campus for in-person exams on February 8th and March 15th, Wednesdays, 11:00 a.m. - 11:50 a.m.
Project: Students are required to work by themselves or in groups on course projects and submit their final reports before April 28th, 2023, Friday, 11:00am. 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.
|01/09 - 01/13||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|
|01/16 - 01/20||Holiday; 3.3, 3.5, 3.6; 4.1, 4.2||Sample Geometry and Random Sampling; Multivariate Normal Density and Its Properties|
|01/23 - 01/27||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|
|01/30 - 02/03||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|
|02/06 - 02/10||Review; Exam-I; 5.5||Large Sample Inferences about a Population Mean Vector|
|02/13 - 02/17||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|
|02/20 - 02/24||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|
|02/27 - 03/03||7.4; 7.6; 7.7||Inferences about the Regression Model; Model Checking and Other Aspects of Regression; Multivariate Multiple Regression|
|03/06 - 03/10||7.7; 7A; 7.10||Multivariate Multiple Regression; Distribution of the Likelihood Ratio; Multiple Regression Models with Time Dependent Errors|
|03/13 - 03/17||Review; Exam-II; 8.1, 8.2||Population Principal Components|
|03/27 - 03/31||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|
|04/03 - 04/07||9.1, 9.2; 9.3; 9.3||Orthogonal Factor Model; Methods of Estimation; Methods of Estimation|
|04/10 - 04/14||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|
|04/17 - 04/21||11.1, 11.2; 11.3; 11.4||Introduction, Separation and Classification for Two Populations; Classification with Two Multivariate Normal Populations; Evaluating Classification Functions|
|04/24 - 04/28||11.6; 11.7; 11.7||Fisher's Method for Discriminating, Logistic Regression and Classification|