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STAT6632 - Multivariate Statistical Analysis

Course Name: 
Multivariate Statistical Analysis
Course Code: 
STAT6632
Course Credits: 
4
Level: 
Graduate
Prerequisites: 
none
Course Description: 

The aim of the course is to introduce a variety of standard statistical methods used to analyze multivariate data. Emphasis will be placed on developing the theory of these methods as well as the various interpretations of results derived from applying these methods. The (free) R statistical computing package will be used for data analyses.

Content: 
Linear Algebra (Matrix Theory) Review; Random vectors; Multivariate distributions–normal, Wishart, Hotelling’s-T, Skew-T, Skew-Normal; Estimation and testing of multivariate distribution parameters; Multivariate Analysis of Variance (MANOVA); Predictive Discriminant Analysis (PDA); Principal Components Analysis (PCA); Exploratory Factor Analysis (EFA);Cluster Analysis Wishart, Hotelling’s-T, Skew-T, Skew-Normal; Estimation and testing of multivariate distribution parameters; Multivariate Analysis of Variance (MANOVA); Predictive Discriminant Analysis (PDA); Principal Components Analysis (PCA); Exploratory Factor Analysis (EFA);Cluster Analysis
Assessment: 
The course assessment has two components consisting of coursework (50%) and a final exam (50%) Two In-course and in-class tests – 20% of overall grade; One Laboratory assignment (project and report) – 20% of overall grade; Two graded (at home) assignments – 10% of overall grade; One Final exam – 50% of overall grade.
Categoryofcourses: 
Typeofcourse: 
Electives
coursedegree: 
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