| Course Title | Code | Semester | L+U Hour | Credits | ECTS |
|---|---|---|---|---|---|
| Applied Multivariate Statistical Methods | MAT614 | 3 + 0 | 3.0 | 8.0 |
| Prerequisites | None |
| Language of Instruction | Turkish |
| Course Level | Graduate |
| Course Type | |
| Mode of delivery | Lecturing |
| Course Coordinator |
Assist. Prof. Dr. Pınar ZENGİN ALP |
| Instructors | |
| Assistants | |
| Goals | Ensuring high level of knowledge related to the topics in the content of the course, giving the ability of using this konwledge in discussion and research environments to students. |
| Course Content | Basic matrix information,linear equation systems,homogen linear equation systems, Applications related to eigenvalues and eigenvectors, Data matrix and descriptive statistics in multivariable analysis, Average vector, variance,covariance and variance- covariance matrix, Correlation coefficient and correlation matrix,Scatter graphs, Ensure the linearity, Extreme values, Generalized variance standardization and Generalized variance,Analysis of groups , Standardization and distribution pattern,multivariate normal distribution , Mahalobis distance, Kontur and ellipse concept, p-dimensional ellipsoid, Ellipsoid concept with more than two variables, Investigation whether ellipsoid is suitable for normal distribution or not, Investigation whether univariable is suitable for normal distribution or not,Interval method, Normality tests,Missing datas and reviewing of them,Approches for overcome data problem , Multivariate hypotesis tests,Multi linear regrassion,Reliability |
| Learning Outcomes |
- Students will learn theoretical concepts in mathematics - Students will learn how to read academical journals |
| Week | Topics | Learning Methods |
|---|---|---|
| 1. Week | Basic matrix information,linear equation systems,homogen linear equation systems | |
| 2. Week | Applications related to eigenvalues and eigenvectors | |
| 3. Week | Data matrix and descriptive statistics in multivariable analysis | |
| 4. Week | Average vector, variance,covariance and variance- covariance matrix | |
| 5. Week | Correlation coefficient and correlation matrix,Scatter graphs | |
| 6. Week | Ensure the linearity, Extreme values | |
| 7. Week | Generalized variance standardization and Generalized variance,Analysis of groups | |
| 8. Week | Midterm | |
| 9. Week | Standardization and distribution pattern,multivariate normal distribution | |
| 10. Week | Mahalobis distance, Kontur and ellipse concept, p-dimensional ellipsoid | |
| 11. Week | Ellipsoid concept with more than two variables, Investigation whether ellipsoid is suitable for normal distribution or not | |
| 12. Week | Investigation whether univariable is suitable for normal distribution or not,Interval method | |
| 13. Week | Normality tests,Missing datas and reviewing of them,Approches for overcome data problem | |
| 14. Week | Multivariate hypotesis tests,Multi linear regrassion,Reliability |
| 1.Multivariate Techniques, Sharma, S., John Wiley and Sons Inc. (1996). |
| 2.Çok Değişkenli İstatistik Yöntemlere Giriş, Alpar, Reha., Nobel Yayınevi. (2003). |
| 3.Regresyon Yöntemleri ve Sorunları, Işıkara, Baki., İ.Ü.Fen Fakültesi Basımevi. (1975). |
| Program Requirements | Contribution Level | DK1 | DK2 | Measurement Method |
|---|---|---|---|---|
| PY1 | 4 | 0 | 0 | 40 |
| PY2 | 4 | 0 | 0 | 40 |
| PY3 | 4 | 0 | 0 | 40 |
| PY4 | 4 | 0 | 0 | 40 |
| PY5 | 4 | 0 | 0 | 40 |
| PY6 | 4 | 0 | 0 | 40 |
| PY7 | 4 | 0 | 0 | 40 |
| PY8 | 4 | 0 | 0 | 40 |
| PY9 | 4 | 0 | 0 | 40 |
| PY10 | 4 | 0 | 0 | 40 |
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| Course's Level of contribution | None | Very Low | Low | Fair | High | Very High |
| Method of assessment/evaluation | Written exam | Oral Exams | Assignment/Project | Laboratory work | Presentation/Seminar |
| Event | Quantity | Duration (Hour) | Total Workload (Hour) |
|---|---|---|---|
| Course Hours | 14 | 3 | 42 |
| Midterm 1 | 1 | 2 | 2 |
| Homework 1 | 15 | 2 | 30 |
| Homework 2 | 15 | 2 | 30 |
| Quiz 1 | 4 | 1 | 4 |
| Quiz 2 | 4 | 1 | 4 |
| Final | 1 | 2 | 2 |
| Practice | 15 | 3 | 45 |
| Classroom Activities | 15 | 3 | 45 |
| Total Workload | 204 | ||
| ECTS Credit of the Course | 8.0 | ||
