Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST404APPLICATIONS OF MULTIVARIATE STATISTICSCompulsory487
Level of Course Unit
First Cycle
Objectives of the Course
To make the students learn the Multivariate Statistical Analysis Methods and apply them in computer environment
Name of Lecturer(s)
Assoc. Prof. Dr. Hakan Savaş SAZAK
Learning Outcomes
1To know which Multivariate Statistical Analysis is appropriate for a problem in a research project
2To know how to conduct the required operations on data and prepare the required tables and graphics using MS Excel software
3To be able to determine whether Univariate or Multivariate data are normally distributed
4To be able to detect possible outliers in Multivariate data
5To be able to conduct the application of ANOVA and MANOVA in computer environment
6To be able to conduct the application of Multivariate Regression in computer environment
7To be able to conduct Principal Components and Factor Analysis in computer environment
8To be able to conduct Classification and Discriminant Analysis in computer environment
9To be able to conduct Cluster Analysis in computer environment
10To be able to conduct Multidimensional Scaling in computer environment
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
It is advised that students took the course Multivariate Statistics
Course Contents
To know which Multivariate Statistical Analysis is appropriate for a problem in a research project ; To know how to conduct the required operations on data and prepare the required tables and graphics using MS Excel software; To be able to determine whether Univariate or Multivariate data are normally distributed; To be able to detect possible outliers in Multivariate data; To be able to conduct the application of ANOVA and MANOVA in computer environment; To be able to conduct the application of Multivariate Regression in computer environment; To be able to conduct Principal Components and Factor Analysis in computer environment; To be able to conduct Classification and Discriminant Analysis in computer environment; To be able to conduct Cluster Analysis in computer environment; To be able to conduct Multidimensional Scaling in computer environment
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Introduction of Multivariate Statistical Analysis Methods
2Introduction of MS Excel
3Preparation of Tables, creation of various statistical functions and graphs using MS Excel
4Construction of Q-Q graph and detection of possible outliers using MS Excel
5Obtaining the mean vector, the variance-covariance matrix and the Mahalanobis distance of data using MS Excel
6Check of Multivariate normality and Box-Cox transformation using MS Excel
7Conducting tests based on means and constructing confidence intervals using MS Excel
8Midterm Exam
9MANOVA
10Multivariate Regression
11Principal Components Analysis
12Factor Analysis
13Classification and Discriminant Analysis
14Cluster Analysis
15Multidimensional Scaling
16Final 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
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
English
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination122
Final Examination122
Attending Lectures14342
Tutorial5525
Self Study51050
Individual Study for Mid term Examination13535
Individual Study for Final Examination15050
TOTAL WORKLOAD (hours)206
Contribution of Learning Outcomes to Programme Outcomes
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LO1    4          3    5   
LO2  2 3 2 23  3324    53  
LO3  3 2   2    5 3    43  
LO434  2       3  42   2  3
LO523       3   2 32   22 3
LO622  23 2 2  2  42   2  3
LO712   2 2    2  32   2  3
LO81   2          42   2  3
LO9     3   2     22      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