MATH 4339 - Multivariate Statistics - University of Houston
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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.


  • 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.

Justin Dart Jr. Center Accommodations:

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