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| IST7012 | Parametrical Statistics | 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 | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Fatma Gül AKGÜL | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To introduce methods for obtaining point estimators, properties of estimators, interval estimation, hypothesis tests |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Reinforces the basic information about the estimation problems | 1,8,9 | 1, | | PO - 2 : | Increases the theoretical knowledge about the desired properties of the estimators such as unbiasedness and efficiency | 1,8,9 | 1, | | PO - 3 : | Understand the importance of the principles of data reduction | 1,8,9 | 1, | | PO - 4 : | Reinforces the theoretical knowledge about point estimation and interval estimation | 1,8,9 | 1, | | PO - 5 : | Perceives concept of hypothesis tests and their requirements | 1,8,9 | 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), PO : Learning Outcome | | |
| Sample Statistics and sampling distributions, parameter estimation and methods, small and large sample properties of estimators, hypothesis testing, Neyman-Pearson Lemma, monotone likelihood ratios, similar tests and likelihood ratio tests, Bayesian Parameter Estimation, Bayesian Interval Estimation and hypothesis testing. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Sampling statistics and their distributions | | | Week 2 | Parameter estimation methods | | | Week 3 | Parameter estimation methods | | | Week 4 | Desired properties of parameter estimators | | | Week 5 | Desired properties of parameter estimators | | | Week 6 | Data Reduction Principles | | | Week 7 | Data Reduction Principles | | | Week 8 | Properties of the estimators for small and large sample | | | Week 9 | Mid term exam | | | Week 10 | hypothesis Testing | | | Week 11 | Neyman-Pearson Lemma | | | Week 12 | Monotone likelihood ratios | | | Week 13 | test functions based on the likelihood ratio | | | Week 14 | Bayesian Tests | | | Week 15 | interval estimation | | | Week 16 | Final exam | | | |
| 1 | Bain, L..J. and Engelhardt, M. (1992). Introduction to Probability and Mathematical Statistics. Second Edition. Duxbury, Canada | | | |
| 1 | Casella, G. and Berger, R.L. (2002). Statistical Inference. Second Edition. Duxbury, Canada. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Quiz | 12 | | 1 | 20 | | End-of-term exam | 16 | | 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 | 3 | 14 | 42 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 4 | 12 | 48 | | Arasınav | 2 | 1 | 2 | | Kısa sınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 3 | 8 | 24 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 161 |
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