TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB411 |
| Name | Simulation and Modeling |
| Term | 2020-2021 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 | Uzaktan Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU |
| Course Instructor |
The current term course schedule has not been prepared yet. Previous term groups and teaching staff are shown.
|
Course Goal / Objective
Time series modeling, forecasting and prediction, and the use of a variety of package programs related to them
Course Content
The components of the time series, the time series graphics, the decomposition methods, the regression models in time series, exponential smoothing techniques, Box-Jenkins Models, the statistical package programs
Course Precondition
Yok
Resources
Notes
1. Kadılar, C. (2005), SPSS Uygulamalı Zaman Serileri Analizine Giriş. Bizim Büro Basımevi 2. Sevüktekin, M., Nargeleçekenler, M. (2005), Zaman Serileri Analizi. Nobel Yayın Dağıtım 3. Cryer, J. D. (1986), Time Series Analysis. PWS-KENT Publishing Company
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Distinguish the components of the time series |
| LO02 | Comment the time series graphics |
| LO03 | Apply the decomposition methods |
| LO04 | Determine the regression model that fits the data |
| LO05 | Distinguish the difference between smoothing techniques |
| LO06 | Explain the statistical basics of Box Jenkins models |
| LO07 | Distinguish between the Box Jenkins models that fit the time series data |
| LO08 | Apply the necessary methods for time-series forecasting and prediction |
| LO09 | Use the statistical package programs necessary for time series analysis |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics | 1 |
| PLO02 | - | Emphasize the importance of Statistics in life | 5 |
| PLO03 | - | Define basic principles and concepts in the field of Law and Economics | 0 |
| PLO04 | - | Produce numeric and statistical solutions in order to overcome the problems | 4 |
| PLO05 | - | Use proper methods and techniques to gather and/or to arrange the data | 5 |
| PLO06 | - | Utilize computer systems and softwares | 5 |
| PLO07 | - | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | 4 |
| PLO08 | - | Apply the statistical analyze methods | 5 |
| PLO09 | - | Make statistical inference(estimation, hypothesis tests etc.) | 1 |
| PLO10 | - | Generate solutions for the problems in other disciplines by using statistical techniques | 4 |
| PLO11 | - | Discover the visual, database and web programming techniques and posses the ability of writing programme | 1 |
| PLO12 | - | Construct a model and analyze it by using statistical packages | 5 |
| PLO13 | - | Distinguish the difference between the statistical methods | 3 |
| PLO14 | - | Be aware of the interaction between the disciplines related to statistics | 3 |
| PLO15 | - | Make oral and visual presentation for the results of statistical methods | 4 |
| PLO16 | - | Have capability on effective and productive work in a group and individually | 2 |
| PLO17 | - | 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 |
| PLO18 | - | Develop scientific and ethical values in the fields of statistics-and scientific data collection | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Interpretation of time series and time-series graphics | Source reading | |
| 2 | Autocorrelation and partial autocorrelation functions | Source reading | |
| 3 | Examination of stationary | Source reading | |
| 4 | Portmanteau tests, the index numbers | Source reading | |
| 5 | Decomposition methods | Source reading | |
| 6 | Introduction to time series regression analysis, normality tests, the problem of heteroscedasticity | Source reading | |
| 7 | autocorrelation test, regression analysis in non-seasonal time series | Source reading | |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | |
| 9 | Regression analysis in seasonal tiem series | Source reading | |
| 10 | Regression analysis in seasonal tiem series | Source reading | |
| 11 | Exponential smoothing methods | Source reading | |
| 12 | Autoregression (AR) models and properties | Source reading | |
| 13 | Moving average (MA) models and properties | Source reading | |
| 14 | ARIMA models, parameter estimation | Source reading | |
| 15 | Dickey-Fuller unit root test | Source reading | |
| 16 | Term Exams | Review the topics discussed in the lecture notes and sources | |
| 17 | Term Exams | Review the topics discussed in the lecture notes and sources |
Assessment (Exam) Methods and Criteria
Current term shares have not yet been determined. Shares of the previous term are shown.
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 80 | 32 |
| 1. Homework | 20 | 8 |
| 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 | ||