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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB461 |
| Name | Simulation and Modelling |
| Term | 2017-2018 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 |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. HÜSEYİN GÜLER |
| Course Instructor |
Prof. Dr. HÜSEYİN GÜLER
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
In this course, modelling analytically difficult problems encountered in Statistics will be investigated. In addition to this, obtaining the empirical solution of the problem with a simulation by creating a virtual experiment will be emphasized.
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
Yok
Resources
Notes
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 | |
| 2 | Random number generators | Lecture notes: Chapter 2, Reference book: p.123-140 and p.147-157 | |
| 3 | Simulating from some distributions | Lecture notes: Chapters 3.1 to 3.3, Reference book: Chapters 3.1, 3.5 and 3.6 | |
| 4 | Simulating from some distributions | Lecture notes: Chapter 3.4, Reference book: Chapter 3.4 | |
| 5 | Algorithms and MATLAB | Lecture notes: Chapter 4 | |
| 6 | Algorithms and MATLAB | Lecture notes: Chapter 4 | |
| 7 | Producing random numbers and simulating some distributions with MATLAB | Lecture notes: Chapter 4 | |
| 8 | Producing random numbers and simulating some distributions with MATLAB | Lecture notes: Chapters 5.1 to 5.3, Reference book: p.246-250 | |
| 9 | Midterm exam | ||
| 10 | Estimation of a parameter with simulation | Lecture notes: Chapter 5 | |
| 11 | Estimation of a parameter with simulation | Lecture notes: Chapter 5 | |
| 12 | Applications of simulation 1: Estimation of a parameter, estimation of a probability | Reference book: p.250-261 | |
| 13 | Applications of simulation 2: Estimation of a probability, hypothesis testing | Reference book: p.250-261 | |
| 14 | Applications of simulation 3: Hypothesis testing, simulating a regression equation | Lecture notes: Chapter 5 | |
| 15 | Applications of simulation 4: Simulating a regression equation, Monte Carlo Integral | Lecture notes: Chapter 5 | |
| 16 | Applications of simulation 5: Monte Carlo Integral | Lecture notes: Chapter 5 | |
| 17 | Applications of simulation 5: Monte Carlo Integral | Lecture notes: Chapter 5 |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
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 | ||