Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST401MULTIVARIATE STATISTICSCompulsory478
Level of Course Unit
First Cycle
Objectives of the Course
To make the students learn the Multivariate Statistics concepts and required terminology and to give them the theoretical fundamentals which will be needed for higher level analysis in the area.
Name of Lecturer(s)
Assoc. Prof. Dr. Hakan Savaş SAZAK
Learning Outcomes
1To know the Multivariate Statistics Data Structure and how to express them in matrix format
2To know the calculation of Multivariate Descriptive Statistics
3To know the concepts and properties of the mean vector, the variance-covariance matrix, the correlation matrix etc. based on population and sample
4To know the Multivariate Graphical Display methods
5To know the calculation of eigenvalues and eigenvectors and their properties and usage in the area
6To know the statistical distance concept and its usage in the area
7To know the properties of Multivariate Normal distribution
8To know the Multivariate Moment and Cumulant Generating Functions and their properties
9To know the transitions and relationships between the Multivariate distributions
10To be able to conduct the Multivariate tests and construct the related confidence intervals
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
The Multivariate Statistics Data Structure and their expression matrix format; The Multivariate Descriptive Statistics and their calculation; The concepts and properties of the mean vector, the variance-covariance matrix, the correlation matrix etc. based on population and sample; Graphical Displays; Eigenvalues and eigenvectors; The statistical distance concept; The Multivariate Normal distribution and its properties; The Multivariate Moment and Cumulant Generating Functions and their properties; Check of Multivariate Normality and data transformation ; The transitions and relationships between the Multivariate distributions; Marginal and Conditional distributions; Hotelling T2 and simultaneous confidence intervals; The tests based on the comparison of various samples and the related confidence intervals ; The tests related to matched comparisons for Multivariate Normal distribution and the related confidence intervals ; The hypothesis tests based on the Repeated Measures Design and the related confidence intervals ; MANOVA
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
0The Multivariate Statistics Data Structure and their expression matrix format, the Multivariate Descriptive Statistics and their calculation
1The concepts and properties of the mean vector, the variance-covariance matrix, the correlation matrix etc. based on population and sample
2Graphical displays, eigenvalues and eigenvectors and their properties
3The statistical distance concept
4The Multivariate Normal distribution and its properties
5The Multivariate Moment and Cumulant Generating Functions and their properties
6Check of Multivariate Normality and data transformation
7Midterm Exam
8The transitions and relationships between the Multivariate distributions
9Marginal and Conditional distributions
10Hotelling T2 and simultaneous confidence intervals
11The tests based on the comparison of various samples and the related confidence intervals
12The tests related to matched comparisons for Multivariate Normal distribution and the related confidence intervals
13The hypothesis tests based on the Repeated Measures Design and the related confidence intervals
14MANOVA
15Final Exam
Recommended or Required Reading
Applied Multivariate Statistical Analysis (R. A. Johnson & D. W. Wichern, Fifth Edition, 2002) Özdamar, K., Paket Programlar ile İstatistiksel Veri Analizi Tatlıdil, H., Uygulamalı Çok Değişkenli İstatistiksel Analiz
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Sınavı1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Language of Instruction
English
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Attending Lectures14456
Individual Study for Mid term Examination16666
Individual Study for Final Examination1114114
TOTAL WORKLOAD (hours)240
Contribution of Learning Outcomes to Programme Outcomes
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LO132  3    2  2    2  2   
LO24322221222  52  2   2  2
LO3                    3  2
LO43 2  2   3  2    2      
LO54 3222   3  42   2  2   
LO65 3      2   3          
LO75  2        4           
LO85        3  5    2      
LO93 2  22  2  23  33  33 3
LO10                        
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr