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| Course Description |
|
| Course Name |
: |
Simulation and Modelling |
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| Course Code |
: |
İSB461 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
First Cycle |
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| Year of Study |
: |
4 |
|
| Course Semester |
: |
Fall (16 Weeks) |
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| ECTS |
: |
5 |
|
| Name of Lecturer(s) |
: |
Asst.Prof.Dr. HÜSEYİN GÜLER
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| Learning Outcomes of the Course |
: |
Knows the purpose of Simulation and importance of it in Statistics.
Produces random numbers using a random number generator.
Simulates random variables by their distributions.
Simulates using MATLAB.
Models a statistical problem using random variables.
Designs a Monte Carlo experiment to solve the problem modelled.
Obtains a Monte Carlo estimate for the result of a problem using MATLAB.
Simulates a regression model.
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
To bring the ability of modelling statistical problems that are hard to solve analytically and designing a virtual experiment on this model to obtain an empirical solution for the problem. |
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| Course Contents |
: |
In this course, modelling a problem mathematically, drawing virtual samples from the distributions of random variables in the model, and obtaining consistent Monte Carlo estimates of the parameters with this sample is considered. |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Faculty of Arts and Sciences Classrooms |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Basic Topics, A Review of Probability and Random Variables |
Lecture Notes, Chapter 1 |
Lecture, Homework |
|
2 |
Random Number Generators |
Lecture Notes, Chapter 2; Reference book p.123-140 and p.147-154 |
Lecture, Homework |
|
3 |
Simulation for Some Distributions |
Lecture Notes; 3.1-3.3; Reference Book, Chapters 3.1, 3.5 and 3.6 |
Lecture, Homework |
|
4 |
Simulation for Some Distributions |
Lecture Notes, Chapter 3.4; Reference Book, Chapter 3.4 |
Lecture, Homework |
|
5 |
Algorithms and MATLAB |
Lecture Notes, Chapter 4 |
Lecture, Package Program |
|
6 |
Producing Random Numbers and Simulating from Some Distributions using MATLAB |
Lecture Notes, Chapter 4 |
Lecture, Package Program, Homework |
|
7 |
Monte Carlo Experiment and the Simulation Method, Parameter Estimates with Simulation |
Lecture Notes, Chapter 5.1-5.3; Reference Book, p. 246-250 |
Lecture, Package Program |
|
8 |
Midterm exam |
Review of topics |
Essay |
|
9 |
Parameter Estimates with Simulation |
Lecture Notes, Chapter 5 |
Lecture, Package Program |
|
10 |
Applications of Simulation #1: Parameter Estimates |
Lecture Notes, Chapter 5 |
Lecture, Package Program, Group Study |
|
11 |
Applications of Simulation #2: Parameter Estimates, Estimation of a Probability, |
Lecture Notes, Chapter 5 |
Lecture, Package Program, Group Study |
|
12 |
Applications of Simulation #3: Estimation of a Probability, Hypothesis Testing |
Lecture Notes, Chapter 5; Reference Book, p.250-261 |
Lecture, Package Program, Group Study |
|
13 |
Applications of Simulation #4: Hypothesis Testing, Simulation of a Regression Model |
Lecture Notes, Chapter 5 |
Lecture, Package Program, Group Study |
|
14 |
Applications of Simulation #5: Simulation of a Regression Model, Monte Carlo Integration |
Lecture Notes, Chapter 5 |
Lecture, Package Program, Group Study |
|
15 |
Applications of Simulation #6: Simulation of a Regression Model, Monte Carlo Integration |
Lecture Notes, Chapter 5 |
Lecture, Package Program, Group Study |
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16/17 |
Final exam |
Review of topics |
Essay |
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|
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 "Istatistiksel Simulasyon", Lecture Notes, Huseyin GULER, Adana, 2006.
Matematiksel Modelleme ve Simülasyon, Fikri ÖZTÜRK, Levent ÖZBEK, Gazi Kitabevi, Ankara, 2004.
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| |
| Required Course Material(s) |
Simulation Modelling and Analysis, 2. ed., A.M. Law, W.D. Kelton, New York, McGraw-Hill, 1991.
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
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Homeworks/Projects/Others |
8 |
50 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Utilize computer systems and softwares |
4 |
|
2 |
Apply the statistical analyze methods |
2 |
|
3 |
Make statistical inference(estimation, hypothesis tests etc.) |
3 |
|
4 |
Generate solutions for the problems in other disciplines by using statistical techniques |
2 |
|
5 |
Discover the visual, database and web programming techniques and posses the ability of writing programme |
0 |
|
6 |
Construct a model and analyze it by using statistical packages |
5 |
|
7 |
Distinguish the difference between the statistical methods |
1 |
|
8 |
Be aware of the interaction between the disciplines related to statistics |
3 |
|
9 |
Make oral and visual presentation for the results of statistical methods |
4 |
|
10 |
Have capability on effective and productive work in a group and individually |
4 |
|
11 |
Develop scientific and ethical values in the fields of statistics-and scientific data collection |
0 |
|
12 |
Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics |
3 |
|
13 |
Emphasize the importance of Statistics in life |
3 |
|
14 |
Define basic principles and concepts in the field of Law and Economics |
0 |
|
15 |
Produce numeric and statistical solutions in order to overcome the problems |
4 |
|
16 |
Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events |
4 |
|
17 |
Use proper methods and techniques to gather and/or to arrange the data |
0 |
|
18 |
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 |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
2 |
28 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
8 |
3 |
24 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
|
Final Exam |
1 |
15 |
15 |
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Total Workload: | 119 |
| Total Workload / 25 (h): | 4.76 |
| ECTS Credit: | 5 |
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