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| IST4020 | Multivariate Statistical Analysis | 4+0+0 | ECTS:6 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Uğur ŞEVİK | | Co-Lecturer | PROF. DR. Türkan ERBAY DALKILIÇ, PROF. DR. Zafer KÜÇÜK | | Language of instruction | Turkish | | 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 Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | To learn the basic concepts of multivariate statistics. | 1,2,3,4 | 1 | | LO - 2 : | To be able to evaluate a scientific research in terms of multivariate statistical techniques. | 1,2,3,4 | 1 | | LO - 3 : | To be able to decide which multivariate statistical method to use in a scientific research. | 1,2,3,4 | 1 | | LO - 4 : | To be able to analyze multidimensional data on computer. | 1,2,3,4 | 1 | | 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 | | |
| 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. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic definitions and concepts: Matrices, determinants, eigenvalues and eigenvectors | | | Week 2 | Basic definitions and concepts: Matrices, determinants, eigenvalues and eigenvectors | | | Week 3 | Data Matrix and Descriptive Statistics in Multivariate Analysis | | | Week 4 | Multivariate Graphics | | | Week 5 | Standardization, | | | Week 6 | Multivariate Normal Distribution and Multivariate Extremes. | | | Week 7 | Examination and Assignment Methods of Missing (Lost) Data. | | | Week 8 | Distance and Similarity Measures | | | Week 9 | Mid-term exam | | | Week 10 | Correlation coefficient: simple, partial, multi | | | Week 11 | Multivariate Hypothesis Tests | | | Week 12 | Factor Analysis | | | Week 13 | Factor Analysis | | | Week 14 | Cluster Analysis | | | Week 15 | Cluster Analysis | | | Week 16 | End-of-term exam | | | |
| 1 | Alpar, R. 2020, Uygulamalı Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık | | | |
| 1 | Tatlidil, H. (1996). Uygulamalı Çok Değişkenli İstatistiksel Analiz, Akademi Matbaası, Ankara. | | | 2 | Tuncer, Y. (2002). Çok değişkenli İstatistik Analize Giriş: Normal Teori, Bıçaklar Kitapevi, Ankara. | | | 3 | Johnson, R. A. and Wichern, D. W. (1982). Applied Multivariate Statistical Analysis, Prentice-Hall. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | 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 work | Duration (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 load | | | 110.5 |
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