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
  • Graduate School of Sciences
  • Department of Statistics
  • Master of Science (MS) Degree
  • Course Structure Diagram with Credits
  • Statistical Simulation
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications

Course Introduction Information

Code - Course Title İST539 - Statistical Simulation
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) PROFESÖR DOKTOR İLHAN USTA
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.
Recommended Reading List
Assessment methods and criteria 1 (or 2) Midterm Exam, 1 Final Exam.
Work Placement N/A
Sustainability Development Goals

Content

Weeks Topics
Week - 1 System, Model and Simulation Concepts
Week - 2 Random Number Generators; Linear Congruential Generators
Week - 3 Random Number Generators; Non-Linear Generators
Week - 4 Examination of Some of The Properties of The Generated Numbers
Week - 5 Ki-kare, Kolmogorov-Smirnov, Run and Gap Tests
Week - 6 Generation of Random Numbers From Probability Distribution: Inverse Transformation
Week - 7 Generation of Random Numbers From Probability Distribution: Acceptance-Rejection and Decomposition Methods
Week - 8 Simulation of Some Discrete And Continuous Distributions and Matlab Applications: Binom, Poisson, Gamma; Exponantial Distribution
Week - 9 Simulation of Univariate and Multivariate Normal Distribution and Matlab Applications
Week - 10 Simulation for Statistical Inference and Matlab Applications: Parameter Estimation
Week - 11 Simulation for Statistical Inference and Matlab Applications: Hypothesis Test
Week - 12 Bootstrap and Jacknife Methods and Matlab Applications
Week - 13 Monte Carlo Integration
Week - 14 Markov Chain Monte Carlo Simulation

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Drill - Practise
  • Case Study
  • Competences
  • Productive
  • True to core values
  • Rational
  • Questoning
  • Follow ethical and moral rules
  • Effective use of a foreign language
  • To work autonomously
  • Elementary computing skills

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%) 0
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