Time: Monday, Wednesday, Friday at 10:00 a.m. - 10:50 a.m.
Location: Taft Hall 301
Textbook: Richard A. Johnson, Dean W. Wichern,
Applied Multivariate Statistical Analysis, Pearson Prentice Hall, 6th edition, 2007
Turn in every Friday before class;
half of the grade counts for completeness;
half of the grade counts for correctness of one selected problem
Midterms: February 15th (Friday), and March 20th (Wednesday), 10:00 a.m. - 10:50 a.m.
Final Exam: May 10th (Friday), 10:30 a.m. - 12:30 p.m.
Grading: Homework 10%, midterms 25% each, final exam 40%
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.
|01/14 - 01/18||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|
|01/21 - 01/25||Holiday; 3.3, 3.5, 3.6; 4.1, 4.2||Sample Geometry and Random Sampling; Multivariate Normal Density and Its Properties|
|01/28 - 02/01||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|
|02/04 - 02/08||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|
|02/11 - 02/15||Review; 5.5; Midterm-1 exam;||Large Sample Inferences about a Population Mean Vector|
|02/18 - 02/22||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|
|02/25 - 03/01||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|
|03/04 - 03/08||7.4; 7.6; 7.7||Inferences about the Regression Model; Model Checking and Other Aspects of Regression; Multivariate Multiple Regression|
|03/11 - 03/15||7.7; 7A; 7.10||Multivariate Multiple Regression; Distribution of the Likelihood Ratio; Multiple Regression Models with Time Dependent Errors|
|03/18 - 03/22||Review; Midterm-2 exam; 8.1, 8.2||Population Principal Components|
|04/01 - 04/05||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|
|04/08 - 04/12||9.1, 9.2; 9.3; 9.3||Orthogonal Factor Model; Methods of Estimation; Methods of Estimation|
|04/15 - 04/19||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|
|04/22 - 04/26||11.1, 11.2; 11.3; 11.4||Introduction, Separation and Classification for Two Populations; Classification with Two Multivariate Normal Populations; Evaluating Classification Functions|
|04/29 - 05/03||11.6, 11.7; Review; Review||Fisher's Method for Discriminating, Logistic Regression and Classification|
|05/06 - 05/10||Exam week||(Final exam)|