|
|
| FBE5006 | Advanced Statistics | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Necati TÜYSÜZ | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | to aware the students about the statistical concepts and parameters, to have the students gain abilities to adapt the univariate and multivariate statistical methods to any kind of data set, to have the students gain abilities to solve the statistical problems using SPSS program |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | to know the statistical parameters and concepts and how to use them | | | | PO - 2 : | to find out the most suitable statistical method for the problem faced to | | | | PO - 3 : | to be able to solve any statistical problem using SPSS program. | | | | 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 | | |
| Basic statistical concepts and parameters, sampling and statistical estimation theory, statistical decision theory (t-test, F test etc.), ANOVA, corelation and simple linear regression, non parametric tests, multivariate normal distribution and hypothesis testing, Multi ANOVA, multivariate linear regression, Logistic regression, trend surface analysisi, cluster techniques, discriminant anaylsis, PCA, Factor analysis, multivariate scaling, correspondance analysis, canonical corelation. |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic statistic parameters | | | Week 2 | Statistical decision theory
T-test (Student test)
F-test | | | Week 3 | Analysis of variance
One-way ANOVA
| | | Week 4 | Correlation and regression analysis
Simple correlation
Simple linear regression
| | | Week 5 | Non-parametric test
Mann-Whitney U test
Friedman test | | | Week 6 |
Introduction to eigenvector methods including factor analysis
-Q-Mode factor analysis
-R-Mode factor analysis | | | Week 7 | multivariate normal distribution and hypothesis testing | | | Week 8 | Quiz | | | Week 9 | Principle component analysis | | | Week 10 | Discriminant Functions | | | Week 11 | Multi ANOVA | | | Week 12 | Multivariate linear regression, Logistic regression | | | Week 13 | Cluster Analysis | | | Week 14 | Correspondence analysis | | | Week 15 | Canonical correlations | | | Week 16 | Final exam | | | |
| 1 | Tüysüz, N., Yaylalı-Abanuz, G., 2012; Jeoistatistik-Kavramlar ve Bilgisayarlı Uygulamalar, KTÜ Yayınları, Yayın No: 220, 382 s. | | | |
| 1 | Özdamar, K., 2002; Paket programlar ile istatistiksel veri analizi, Kaan Kitabevi, 2 cilt. | | | 2 | Hinton, P.R., Brownlow, C., Mac Murray, I., Cozens, B., 2004; SPSS Explained, Routledge Yayınevi, 377 s. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 1 | | 2 | 30 | | In-term studies (second mid-term exam) | 14 | | 1 | 20 | | End-of-term exam | 1 | | 3 | 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 | 6 | 10 | 60 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 0 | 0 | 0 | | Arasınav | 1 | 1 | 1 | | Uygulama | 2 | 14 | 28 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 10 | 2 | 20 | | Dönem sonu sınavı | 1 | 2 | 2 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 153 |
|