The course is designed to develop further the understanding of selected multivariate statistical analysis techniques; their purpose, areas of application, assumptions and limitations.
Prerequisite(s)
Probabity
Corequisite(s)
None
Special Requisite(s)
None
Instructor(s)
Assist. Prof. Dr. Alper ÜLKER
Course Assistant(s)
Res. Asst. Ayşe Nur ALTUNSOY
Schedule
Tuesday, 09:00-10:45
Wednesday, 11:00-12:45
Office Hour(s)
Tuesday, 09:00-10:45 via IKU CATS
Teaching Methods and Techniques
Lectures, application exercises by topic in classroom and tutorials, applications using MATLAB in tutorials in the lab where applicable, assignments.
Principle Sources
Analyzing Multivariate Data - Paul E.Green
Other Sources
Uygulamalı Çok değişkenli istatistik analiz. Doç.Dr.Hüseyin Tatlıdil
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to Multivariate Statistics
Theory and application
2. Week
Analysis of Variance
Theory and application
3. Week
R Programming
LAB.
4. Week
Analysis of Covariance
Theory and application
5. Week
R Programming
LAB.
6. Week
Linear Discriminant Analysis
Theory and application
7. Week
R Programming
LAB.
8. Week
Midterm
Exam
9. Week
Principal Component Analysis
Theory and application
10. Week
R Programming
LAB.
11. Week
Cluster analysis
Theory and application
12. Week
R Programming
LAB.
13. Week
Factor Analysis
Theory and application
14. Week
R Programming
LAB.
15. Week
Final Exam
Exam
16. Week
Final Exam
Exam
17. Week
Final Exam
Exam
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
40
Project(s)
1
10
Final Exam
1
50
Program Outcomes
PO-1
Interpreting advanced theoretical and applied knowledge in Mathematics and Computer Science.
PO-2
Critiquing and evaluating data by implementing the acquired knowledge and skills in Mathematics and Computer Science.
PO-3
Recognizing, describing, and analyzing problems in Mathematics and Computer Science; producing solution proposals based on research and evidence.
PO-4
Understanding the operating logic of computer and recognizing computational-based thinking using mathematics as a discipline.
PO-5
Collaborating as a team-member, as well as individually, to produce solutions to problems in Mathematics and Computer Science.
PO-6
Communicating in a foreign language, and interpreting oral and written communicational abilities in Turkish.
PO-7
Using time effectively in inventing solutions by implementing analytical thinking.
PO-8
Understanding professional ethics and responsibilities.
PO-9
Having the ability to behave independently, to take initiative, and to be creative.
PO-10
Understanding the importance of lifelong learning and developing professional skills continuously.
PO-11
Using professional knowledge for the benefit of the society.
Learning Outcomes
LO-1
Apply selected multivariate statistical analysis techniques; their purpose, areas of application, assumptions and limitations.
