MATH 4339 - Multivariate Statistics - University of Houston

# 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