TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF SOCIAL SCIENCES |
| ECONOMETRICS (PhD) | |
| Code | IEM1840 |
| Name | Regression Theory and Methods |
| Term | 2024-2025 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 8 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim is to create the necessary theoretical infrastructure for regression analysis, to ensure that a dataset is modeled in the best way possible using statistical package programs, and to provide the ability to make statistical comments about the proposed model.
Course Content
It covers advanced regression methods for research problems within the framework of General Regression Theory based on Matrices and Linear Models.
Course Precondition
There are no prerequisites.
Resources
Rawlings, J. O. (1988). Applied regression analysis: a research tool. Wadsworth & Brooks. Pacific Grove, CA. Chatterjee, S., & Hadi, A. S. (2000). B. Price Regression analysis by example.
Notes
ALPAR, C. (2017). Uygulamalı çok değişkenli istatistiksel yöntemler. Mendenhall, W., Sincich, T., & Boudreau, N. S. (2003). A second course in statistics: Regression analysis (Vol. 6). Upper Saddle River, NJ: Prentice Hall.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Defines the theoretical background that needs to be known in regression. |
| LO02 | Defines statistical package programs. |
| LO03 | Statistical comments about the proposed model. |
| LO04 | Determines Multiple Linear Regression. |
| LO05 | Determines Polynomial Regression Models. |
| LO06 | Defines the measures related to the adequacy of the model in regression. |
| LO07 | Determines the Calculation Techniques for Variable Selection. |
| LO08 | Principal Components Defines regression. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Identify an econometric problem and propose a new solution to it | 2 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Develops new knowledge using current concepts in Econometrics, Statistics and Operations Research | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explain for what purpose and how econometric methods are applied to other fields and disciplines | 2 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Using her knowledge, brings original solutions to problems in Economics, Business Administration and other social sciences | 2 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Creates a new model using mathematics, statistics and econometrics knowledge to solve the problem encountered | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Collects data on purpose | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field of research | 2 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | 3 |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | It researches current approaches and methods to solve the problems it encounters and proposes new solutions | 3 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops long-term plans and strategies using econometric and statistical methods | 3 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Uses a package program/writes a new code for Econometrics, Statistics, and Operation Research | 3 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs self-study using knowledge of Econometrics, Statistics and Operations to solve a problem | |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads the team by taking responsibility | 4 |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it constantly renews itself by following the current developments in the field of study | 3 |
| PLO18 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | 3 |
| PLO19 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | 4 |
| PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values | 4 |
| PLO21 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Multiple Linear Regression | Reading | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Polynomial Regression Models | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap |
| 3 | Measures Related to the Adequacy of the Model in Regression | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Calculation Techniques for Variable Selection | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap |
| 5 | Principal Components Regression | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | The Methods Shown will be Coded in S-plus and Minitab Package Programs and Application will be made | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap |
| 7 | Midterm exam subject repetition quiz application | Midterm Subject Repetition | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Midterm exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Biased Estimation of Regression Coefficients (Ridge Reg)(2 weeks) | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Variable Selection Methods (2 weeks) | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Logistic Regression (2 weeks) | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Probit Regression | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Topit Regression | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Tartışma |
| 14 | Final topic review | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Final topic review and quiz application | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Examining the relevant chapter in the book | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Examining the relevant chapter in the book | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 8 | 112 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 4 | 8 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 24 | 24 |
| Total Workload (Hour) | 212 | ||
| Total Workload / 25 (h) | 8,48 | ||
| ECTS | 8 ECTS | ||