Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Institute of Graduate Programmes
  • Department of Statistics
  • Master of Science (MS) Degree
  • Program in Applied Statistics
  • Course Structure Diagram with Credits
  • Sampling Theory and Methods
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title İST542 - Sampling Theory and Methods
Course Type Required Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOÇENT DOKTOR KADİR ÖZGÜR PEKER
Mode of Delivery The mode of delivery of this course is Face to face
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended There is no recommended optional programme component for this course.
Required or Recommended Resources * Thompson, S.K., Sampling, Third Ed., Wiley, USA, (2012).
Recommended Reading List * Çıngı, H., Örnekleme Kuramı, 3. Baskı, Bizim Büro Basımevi, Ankara, (2009).
Assessment methods and criteria 1 Mid-term and 1 Final Exams. Exams will be applied by classical method.
Work Placement Not available.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Estimating Population Mean and Total Under Simple Random Sampling
Week - 2 Confidence Intervals and Sample Size
Week - 3 Unequal Probability Sampling
Week - 4 Auxiliary Data and Ratio Estimation
Week - 5 Auxiliary Data and Regression Estimation
Week - 6 Stratified Sampling
Week - 7 Cluster Sampling and Systematic Sampling
Week - 8 Cluster Sampling and Systematic Sampling
Week - 9 Multi-stage Designs
Week - 10 Two-Phase Sampling
Week - 11 Applied Problems for Survey Sampling
Week - 12 Applied Problems for Survey Sampling
Week - 13 Random Response Model
Week - 14 Random Response Model

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Demonstration
  • Drill - Practise
  • Case Study
  • Problem Solving
  • Brain Storming
  • Competences
  • Rational
  • Follow ethical and moral rules
  • Use time effectively
  • Abstract analysis and synthesis
  • Problem solving
  • Applying theoretical knowledge into practice
  • Information Management
  • Organization and planning
  • Elementary computing skills
  • Decision making
  • To work in interdisciplinary projects

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100
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