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Information
| Code | ISB462 |
| Name | Biostatistics |
| Term | 2024-2025 Academic Year |
| Semester | 8. 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. MAHMUDE REVAN ÖZKALE |
| Course Instructor |
1 2 |
Course Goal / Objective
The aim of this course is statistical modeling and interpeting the econometric data.
Course Content
In this couse, multiple linear regression model, heteroscedasticity, multicollineairt problem, dummy variable models, distributed lag models are covered.
Course Precondition
none
Resources
Gujarati, D. N. (çev. Şenesen, Ü., Şenesen, G. G.) (1999), Temel Ekonometri. Literatür Yayıncılık
Notes
Koutsoyiannis, A. (çev. Şenesen, Ü., Şenesen, G. G.) (1989), Ekonometri Kuramı. Verso Yayıncılık
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Describe econometrics and econometric model |
| LO02 | Check the validity of the assumptions |
| LO03 | Use appropriate methods in case of deviation from the model assumptions |
| LO04 | Distinguish appropriate estimation methods of models |
| LO05 | Select the correct model that fits the data for statistical analysis |
| LO06 | Comment on the results obtained using the statistical package programs |
| LO07 | Evaluate the results of analysis |
| LO08 | Explain the difference between the models |
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 | 4 |
| 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 | 4 |
| 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 | 4 |
| 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 | 2 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | 1 |
| 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 | 1 |
| 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 | Introduction to Econometrics, examination of the deviations from the assumptions of multiple regression analysis | Source reading | Öğretim Yöntemleri: Anlatım |
| 2 | Investigate the properties of the estimators, hypothesis testing in multiple lnear regession model | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Confidence interval in multiple lnear regession model, matrix approximaitons to multiple linear regression model | Source reading | Öğretim Yöntemleri: Anlatım |
| 4 | Multicollinearity problem (identification and correction of multicollinearity) | Source reading | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 5 | Some biased estimators in the problem of multicollinearity | Source reading | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Determination of heteroscedasticity, systematic and non-systematic tests (Goldfeld Quant, Park ve Glejser testsi) | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Breusch Pagan Godfrey test from systematic test and correction of heteroscedasticity | Source reading | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Dummy variable models | Source reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Dummy variable models, more than one dummy variable | Source reading | Öğretim Yöntemleri: Anlatım |
| 11 | Qualitative dependent variable regression models (DOM and Logit models) | Source reading | Öğretim Yöntemleri: Anlatım |
| 12 | Qualitative dependent variable regression models (Logit and Probit models) | Source reading | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 13 | Distributed Lag models (estimation by least squares, Koyck model and Almon polynomial lag model) | Source reading | Öğretim Yöntemleri: Anlatım, Örnek Olay |
| 14 | Distributed Lag models (estimation by Nerlove s partial adjustment model and Cagan s adptive expectation model) | Source reading | Öğretim Yöntemleri: Anlatım |
| 15 | Autoregressive models | Source reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Problem solutions | Review the topics discussed in the lecture notes and sources | Öğretim Yöntemleri: Tartışma |
| 17 | Term Exams | Review the topics discussed in the lecture notes and sources | 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 | ||