Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
STATISTICAL INFERENCE 801000725570 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery Oral presentation
Course Coordinator
Instructors
Assistants
Goals To introduce methods for obtaining point estimators, properties of estimators, interval estimation, hypothesis tests
Course Content 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
Learning Outcomes 1) Reinforces the basic information about the estimation problems
2) Increases the theoretical knowledge about the desired properties of the estimators such as unbiasedness and efficiency
3) Understand the importance of the principles of data reduction
4) Applies the methods for obtaining estimators for parametric distribution families
5) Obtains estimators for various statistical distributions and compares them
6) Obtains best unbiased estimator for some parametric distributions
7) Reinforces the theoretical knowledge about point estimation and interval estimation
8) Perceives concept of hypothesis tests and their requirements
9) Obtains uniformly most powerful test

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Sampling statistics and their distributions Lecture; Question Answer; Discussion

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2. Week Parameter estimation Methods Lecture; Question Answer; Discussion

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3. Week Parameter estimation Methods Lecture; Question Answer; Discussion

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4. Week Desired properties of parameter estimators Lecture; Question Answer; Discussion

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5. Week Desired properties of parameter estimators Lecture; Question Answer; Discussion

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6. Week Data Reduction Principles Lecture; Question Answer; Discussion

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7. Week Data Reduction Principles Lecture; Question Answer; Discussion

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8. Week Properties of the estimators for small and large sample Lecture; Question Answer; Discussion

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9. Week hypothesis Testing Lecture; Question Answer; Discussion

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10. Week Neyman-Pearson Lemma Lecture; Question Answer; Discussion

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11. Week Monotone likelihood ratios Lecture; Question Answer; Discussion

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12. Week test functions based on the likelihood ratio Lecture; Question Answer; Discussion

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13. Week Bayesian Tests Lecture; Question Answer; Discussion

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14. Week Interval Estimations Lecture; Question Answer; Discussion

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Sources Used in This Course
Recommended Sources
Bain, L..J. and Engelhardt, M. (1992). Introduction to Probability and Mathematical Statistics. Second Edition. Duxbury, Canada
Casella, G. and Berger, R.L. (2002). Statistical Inference. Second Edition. Duxbury, Canada.
Öztürk, F., Akdi, Y., Aydoğdu, H. Ve Karabulut, İ. (2006). Parametre Tahmini ve Hipotez Testi. Bıçaklar kitabevi, ANKARA.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 7
Work Hour outside Classroom (Preparation, strengthening) 14 6
Homework 5 6
Midterm Exam 1 6
Time to prepare for Midterm Exam 1 14
Final Exam 1 6
Time to prepare for Final Exam 1 12
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • ECTS credits and course workload