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FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES

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IST4020Multivariate Statistical Analysis4+0+0ECTS:6
Year / SemesterSpring Semester
Level of CourseFirst Cycle
Status Compulsory
DepartmentDEPARTMENT of STATISTICS and COMPUTER SCIENCES
Prerequisites and co-requisitesNone
Mode of Delivery
Contact Hours14 weeks - 4 hours of lectures per week
LecturerDr. Öğr. Üyesi Uğur ŞEVİK
Co-LecturerPROF. DR. Türkan ERBAY DALKILIÇ, PROF. DR. Zafer KÜÇÜK
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
The aim of the course is to help students use techniques that will help explain the relationships between multiple variables and to help them evaluate the results obtained.
 
Learning OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : To learn the basic concepts of multivariate statistics.1,2,3,41
LO - 2 : To be able to evaluate a scientific research in terms of multivariate statistical techniques. 1,2,3,41
LO - 3 : To be able to decide which multivariate statistical method to use in a scientific research. 1,2,3,41
LO - 4 : To be able to analyze multidimensional data on computer. 1,2,3,41
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome

 
Contents of the Course
Basic Matrix Knowledge, Data Matrix in Multivariate Analysis and Descriptive Statistics, Multivariate Graphs, Standardization, Multivariate Normal Distribution and Multivariate Oversights, Examination and Assignment Methods of Missing Data, Distance and Similarity Measures, Multivariate Hypothesis Tests, Factor Analysis, Cluster Analysis.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Basic definitions and concepts: Matrices, determinants, eigenvalues and eigenvectors
 Week 2Basic definitions and concepts: Matrices, determinants, eigenvalues and eigenvectors
 Week 3Data Matrix and Descriptive Statistics in Multivariate Analysis
 Week 4Multivariate Graphics
 Week 5Standardization,
 Week 6Multivariate Normal Distribution and Multivariate Extremes.
 Week 7Examination and Assignment Methods of Missing (Lost) Data.
 Week 8Distance and Similarity Measures
 Week 9Mid-term exam
 Week 10Correlation coefficient: simple, partial, multi
 Week 11Multivariate Hypothesis Tests
 Week 12Factor Analysis
 Week 13Factor Analysis
 Week 14Cluster Analysis
 Week 15Cluster Analysis
 Week 16End-of-term exam
 
Textbook / Material
1Alpar, R. 2020, Uygulamalı Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık
 
Recommended Reading
1Tatlidil, H. (1996). Uygulamalı Çok Değişkenli İstatistiksel Analiz, Akademi Matbaası, Ankara.
2Tuncer, Y. (2002). Çok değişkenli İstatistik Analize Giriş: Normal Teori, Bıçaklar Kitapevi, Ankara.
3Johnson, R. A. and Wichern, D. W. (1982). Applied Multivariate Statistical Analysis, Prentice-Hall.
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 08/04/2022 1,5 50
End-of-term exam 16 28/05/2022 1,5 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 4 14 56
Sınıf dışı çalışma 2 14 28
Laboratuar çalışması 0 0 0
Arasınav için hazırlık 10 1 10
Arasınav 1.5 1 1.5
Uygulama 0 0 0
Klinik Uygulama 0 0 0
Ödev 0 0 0
Proje 0 0 0
Kısa sınav 0 0 0
Dönem sonu sınavı için hazırlık 12 1 12
Dönem sonu sınavı 1.5 2 3
Diğer 1 0 0 0
Diğer 2 0 0 0
Total work load110.5