MATH 4339 - Multivariate Statistics
***This is a course guideline. Students should contact instructor for the updated information on current course syllabus, textbooks, and course content***
Prerequisites: MATH 3349
Course Description: Multivariate analysis is a set of techniques used for analysis of data sets that contain more than one variable, and the techniques are especially valuable when working with correlated variables. The techniques provide a method for information extraction, regression, or classification. This includes applications of data sets using statistical software.
Texts:
- Applied Multivariate Statistical Analysis (6th Edition), Pearson. Richard A. Johnson, Dean W. Wichern. ISBN: 978-0131877153 (Required)
- Using R With Multivariate Statistics (1st Edition). Schumacker, R. E. SAGE Publications. ISBN: 978-1483377964 (Optional)
Course Objectives:
- Understand how to use R and R Markdown
- Understand matrix algebra using R
- Understand the geometry of a sample and random sampling
- Understand the properties of multivariate normal distribution
- Make inferences about a mean vector
- Compare several multivariate means
- Identify and interpret multivariate linear regression models
Course Topics:
| Introduction to R Markdown, Review of R commands | Notes |
| Introduction to Multivariate Analysis | Ch.1 |
| Matrix Algebra, R Matrix Commands | Ch.2 |
| Sample Geometry and Random Sampling | Ch.3 |
| Multivariate Normal Distribution | Ch.4 |
| MANOVA | Ch.6 |
| Multiple Regression | Ch.7 |
| Logistic Regression | Notes |
| Classification | Ch.11 |
Grading: Please consult your instructor's syllabus regarding any and all grading guidelines.
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