ISB461 Simulation and Modelling

5 ECTS - 3-0 Duration (T+A)- 7. Semester- 3 National Credit

Information

Code ISB461
Name Simulation and Modelling
Term 2024-2025 Academic Year
Semester 7. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 5 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. HÜSEYİN GÜLER
Course Instructor Prof. Dr. HÜSEYİN GÜLER (A Group) (Ins. in Charge)


Course Goal / Objective

The aim of this course is to provide students with the knowledge of modelling analytically difficult problems encountered in Statistics. In addition to this, it is also aimed to bring the knowledge of obtaining an empirical solution of the problem with a simulation by creating a virtual experiment

Course Content

The definition and objective of simulation, techniques for simulation, mathematical modelling, programming in MATLAB, Monte Carlo estimation of a parameter of interest.

Course Precondition

None

Resources

İstatistiksel Simülasyon Ders Notları, Hüseyin Güler, Adana, 2015.

Notes

Benzetim, Beşinci Basımdan Çeviri, Sheldon Ross, Çevirenler: Mustafa Yavuz Ata, M. Akif Bakır, Osman Ufuk Ekiz, Nobel Kitabevi, 2015. Matematiksel Modelleme ve Simülasyon, Fikri Öztürk, Levent Özbek, Gazi Kitabevi, 2004.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Students will be able to determine the difference between classical sampling approaches and simulation.
LO02 Students will be able to generate random numbers from the uniform distribution within range (0,1) with random number generators.
LO03 Students will be able to determine how they can generate random numbers from a distribution with probability integral transformation.
LO04 Students will be able to model a real life phenomena with random variables.
LO05 Students will be able to determine how they can estimate the model parameters with simulation.
LO06 Students will be able to estimate the parameters of a large scale problem with simulations by using MATLAB.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explain the essence fundamentals and concepts in the field of Statistics
PLO02 Bilgi - Kuramsal, Olgusal Emphasize the importance of Statistics in life
PLO03 Bilgi - Kuramsal, Olgusal Define basic principles and concepts in the field of Law and Economics
PLO04 Bilgi - Kuramsal, Olgusal Produce numeric and statistical solutions in order to overcome the problems
PLO05 Bilgi - Kuramsal, Olgusal Use proper methods and techniques to gather and/or to arrange the data
PLO06 Bilgi - Kuramsal, Olgusal Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization
PLO07 Bilgi - Kuramsal, Olgusal Apply the statistical analyze methods
PLO08 Bilgi - Kuramsal, Olgusal Make statistical inference (estimation, hypothesis tests etc.)
PLO09 Bilgi - Kuramsal, Olgusal Generate solutions for the problems in other disciplines by using statistical techniques and gain insight
PLO10 Bilgi - Kuramsal, Olgusal Discover the visual, database and web programming techniques and posses the ability of writing programs
PLO11 Beceriler - Bilişsel, Uygulamalı Distinguish the difference between the statistical methods
PLO12 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Make oral and visual presentation for the results of statistical methods
PLO13 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Have capability on effective and productive work in a group and individually 2
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs
PLO15 Yetkinlikler - Öğrenme Yetkinliği Develop scientific and ethical values in the fields of statistics-and scientific data collection


Week Plan

Week Topic Preparation Methods
1 Definitions and a review of probability and random variables Lecture notes: Chapter 1 Öğretim Yöntemleri:
Anlatım
2 Random number generators Lecture notes: Chapter 2, Reference book: p.123-140 and p.147-157 Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
3 Simulating from some distributions - 1 Lecture notes: Chapters 3.1 to 3.3, Reference book: Chapters 3.1, 3.5 and 3.6 Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
4 Simulating from some distributions - 2 Lecture notes: Chapters 3.1 to 3.3, Reference book: Chapters 3.1, 3.5 and 3.6 Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
5 Algorithms and MATLAB Lecture notes: Chapter 4 Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 Generating random numbers and simulating some distributions with MATLAB Lecture notes: Chapter 4 Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
7 Monte Carlo experiment and simulation, estimation of a parameter with simulation Lecture notes: Chapters 5.1 to 5.3, Reference book: p.246-250 Öğretim Yöntemleri:
Anlatım, Tartışma, Alıştırma ve Uygulama, Problem Çözme
8 Mid-Term Exam A review of topics about the midterm exam Ölçme Yöntemleri:
Yazılı Sınav
9 Estimation of a parameter with simulation - 1 Lecture notes: Chapter 5 Öğretim Yöntemleri:
Anlatım, Tartışma, Alıştırma ve Uygulama
10 Estimation of a parameter with simulation - 2 Lecture notes: Chapter 5 Öğretim Yöntemleri:
Anlatım, Tartışma, Alıştırma ve Uygulama
11 Applications of simulation 1: Estimation of a parameter, estimation of a probability Reference book: p.250-261 Öğretim Yöntemleri:
Anlatım, Tartışma, Grup Çalışması, Proje Temelli Öğrenme
12 Applications of simulation 2: Estimation of a probability, hypothesis testing Reference book: p.250-261 Öğretim Yöntemleri:
Anlatım, Tartışma, Grup Çalışması, Proje Temelli Öğrenme
13 Applications of simulation 3: Hypothesis testing, simulating a regression equation Lecture notes: Chapter 5 Öğretim Yöntemleri:
Anlatım, Tartışma, Grup Çalışması, Proje Temelli Öğrenme
14 Applications of simulation 4: Simulating a regression equation, Monte Carlo Integral Lecture notes: Chapter 5 Öğretim Yöntemleri:
Anlatım, Tartışma, Grup Çalışması, Proje Temelli Öğrenme
15 Applications of simulation 5: Monte Carlo Integral Lecture notes: Chapter 5 Öğretim Yöntemleri:
Anlatım, Tartışma, Grup Çalışması, Proje Temelli Öğrenme
16 Term Exams A review of topics about the final exam Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams A review of topics about the final exam Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 3 42
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 12 12
Final Exam 1 18 18
Total Workload (Hour) 114
Total Workload / 25 (h) 4,56
ECTS 5 ECTS

Update Time: 09.05.2024 02:23