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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| STATISTICS PR. | |
| Code | ISB462 |
| Name | Biostatistics |
| Term | 2019-2020 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 |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU |
| Course Instructor |
Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Statistical modeling and interpeting the econometric data
Course Content
Multiple linear regression model, heteroscedasticity, multicollineairt problem, dummy variable models, distributed lag models
Course Precondition
Yok
Resources
Notes
1. Gujarati, D. N. (çev. Şenesen, Ü., Şenesen, G. G.) (1999), Temel Ekonometri. Literatür Yayıncılık 2. 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 | - | Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics | 2 |
| 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 | 0 |
| PLO07 | - | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | 5 |
| PLO08 | - | Apply the statistical analyze methods | 5 |
| PLO09 | - | Make statistical inference(estimation, hypothesis tests etc.) | 4 |
| 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 | 0 |
| PLO12 | - | Construct a model and analyze it by using statistical packages | 3 |
| PLO13 | - | Distinguish the difference between the statistical methods | 4 |
| 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 | 2 |
| PLO16 | - | Have capability on effective and productive work in a group and individually | 1 |
| 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 | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Econometrics, examination of the deviations from the assumptions of multiple regression analysis | Source reading | |
| 2 | Investigate the properties of the estimators, hypothesis testing in multiple lnear regession model | Source reading | |
| 3 | Confidence interval in multiple lnear regession model, matrix approximaitons to multiple linear regression model | Source reading | |
| 4 | Multicollinearity problem (identification and correction of multicollinearity) | Source reading | |
| 5 | Some biased estimators in the problem of multicollinearity | Source reading | |
| 6 | Determination of heteroscedasticity, systematic and non-systematic tests (Goldfeld Quant, Park ve Glejser testsi) | Source reading | |
| 7 | Breusch Pagan Godfrey test from systematic test and correction of heteroscedasticity | Source reading | |
| 8 | Mid-Term Exam | Review the topics discussed in the lecture notes and sources | |
| 9 | Dummy variable models | Source reading | |
| 10 | Dummy variable models | Source reading | |
| 11 | Qualitative dependent variable regression models (DOM and Logit models) | Source reading | |
| 12 | Qualitative dependent variable regression models (Logit and Probit models) | Source reading | |
| 13 | Distributed Lag models (estimation by least squares, Koyck model and Almon polynomial lag model) | Source reading | |
| 14 | Distributed Lag models (estimation by Nerlove s partial adjustment model and Cagan s adptive expectation model) | Source reading | |
| 15 | Autoregressive models | 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
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 20 |
| General Assessment | ||
| Midterm / Year Total | 100 | 20 |
| 1. Final Exam | - | 80 |
| 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 | ||