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| IST7061 | Multivariate Statistical Inference | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Uğur ŞEVİK | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this lecture is to understand the main features of multivariate data, to be able to use exploratory and confirmatory multivariate statistical methods properly and to be able to carry out multivariate statistical techniques and methods efficiently and effectively. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Explore and summarize multivariate data using graphical and numerical methods and techniques to uncover hidden information and patterns. | | | | PO - 2 : | Describe properties of multivariate distributions. | | | | PO - 3 : | Use principal component analysis effectively for data exploration and data dimension reduction. | | | | PO - 4 : | Use factor analysis effectively for exploratory and confirmatory data analysis. | | | | PO - 5 : | Discriminate between groups and classify new observations. | | | | PO - 6 : | Find groupings and associations using cluster and correspondence analysis. | | | | 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), PO : Learning Outcome | | |
| Vector spaces; random vectors; multivariate distributions; MANOVA; Wishart distribution; multiple linear regression model; Canonical Correlation; Analysis of covariance; Principal Components; Factor Analysis; Classification and grouping techniques; multivariate hypothesis testing. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic Multivariate Mathematics and Statistics | | | Week 2 | Multivariate Distributions | | | Week 3 | Principle Component Analysis | | | Week 4 | Explanatory Factor Analysis | | | Week 5 | Confirmatory Factor Analysis | | | Week 6 | MANOVA | | | Week 7 | Discriminant Analysis | | | Week 8 | Limited Dependent Regression Models | | | Week 9 | Midterm Exam | | | Week 10 | Limited Dependent Regression Models | | | Week 11 | Cluster Analysis | | | Week 12 | Canonical Correlation | | | Week 13 | Conjoint Analysis | | | Week 14 | Multidimensional Scaling | | | Week 15 | Multivariate Hypothesis Testing | | | |
| 1 | Johnson R.A , Wıchern D.W. (2007) Applied Multivariate Statistical Analysis, Prentice Hall | | | |
| 1 | Dugard, Pat, John B. Todman, and Harry Staines, (2010) Approaching multivariate analysis: A practical introduction. Routledge. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 01/01/2016 | 2 | 50 | | End-of-term exam | 16 | 01/01/2016 | 2 | 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 | 3 | 14 | 42 | | Sınıf dışı çalışma | 8 | 14 | 112 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 6 | 1 | 6 | | Arasınav | 2 | 1 | 2 | | Uygulama | 0 | 0 | 0 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 5 | 6 | 30 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 20 | 1 | 20 | | Dönem sonu sınavı | 0 | 0 | 0 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 212 |
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