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Colleges

Multivariate statistical methods

Course Description: Matrix Algebra and Random Vectors - Multivariate Normal Distribution - Hypothesis Testing on the Mean of a Multivariate Distribution - Hotelling's T-squared Statistic and Comparing Several Mean Vectors - One-Way and Two-Way Analysis of Variance - Principal Components - Discrimination and Classification - Using Computer Software for Data Analysis to Apply the Studied Methods (SAS-SAS/IML-SPSS-Minitab-S-plus).
Credit hours: 3
Prerequisites: 332 STAT
Objectives of the course :

This course aims to strongly equip the student with statistical concepts related to multiple random variables as an extension of what has been previously studied.

Course outputs :

Acquire knowledge by learning key concepts and properties of common multivariate random variables and their associated tests. Enhance the ability to use available software packages and programs for simulating multivariate random variables and their distributions. Gain cognitive skills to solve real-life problems that can be formulated using correlated multivariate random variables. Improve the capacity for further advanced studies in statistics generally, as needed.

Additional information:

Statistical Analysis of Multiple Variables from an Applied Perspective – Author: Richard Johnson and Dean Wichern Translation: Dr. Abdul Rahman Hamed Azzam – Publisher: Dar Al-Marikh Year 1998
Applied Multivariate Statistical Analysis, Richard A. Johnson, 1-Dean W. Wichern, Fifth Edition 2002 Pearson Education International
J.E. Gentle: Matrix Algebra, Exercises, Computations, and Applications in Statistics. Springer Verlag.

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