STAT 6130

Applied Multivariate Statistics

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Course Description

Pre-Requisite(s): Graduate standing in Statistics, or instructor permission

This course develops fundamental methodology to the analysis of multivariate data. Topics include the multivariate normal distributions, multivariate regression, multivariate analysis of variance (MANOVA), principal components analysis, factor analysis, and discriminant analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.


  • Xiwei Tang

     Rating

     Difficulty

    2.00

     GPA

    3.85

     Sections

    Last Taught

    Fall 2022

  • Tianxi Li

     Rating

     Difficulty

     GPA

    3.78

     Sections

    Last Taught

    Fall 2022

  • Jianhui Zhou

     Rating

     Difficulty

     GPA

    3.77

     Sections

    Last Taught

    Spring 2018

  • Chao Du

     Rating

     Difficulty

     GPA

    3.57

     Sections

    Last Taught

    Spring 2020

  • Lingxiao Wang

     Rating

     Difficulty

     GPA

    3.53

     Sections

    Last Taught

    Spring 2024

  • Faculty Staff

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Fall 2015

  • Shan Yu

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Spring 2025