Information
Code | IEM729 |
Name | Regression Theory |
Term | 2022-2023 Academic Year |
Semester | . Semester |
Duration (T+A) | 3-0 (T-A) (17 Week) |
ECTS | 6 ECTS |
National Credit | 3 National Credit |
Teaching Language | Türkçe |
Level | Yüksek Lisans Dersi |
Type | Normal |
Mode of study | Yüz Yüze Öğretim |
Catalog Information Coordinator | Prof. Dr. GÜLSEN KIRAL |
Course Goal / Objective
The purpose of this course is to explain the advance regression methods for the research problems within the framework of general regression theory depending on the matrix and linear models.
Course Content
The course covers multiple linear regression, polynomial regression, principal component regression, logistic regression, probit and tobit regression.
Course Precondition
There are no prerequisites.
Resources
Doğrusal Regresyon Analizine Giriş (D.C. Montgomery, E.A. Peck and G.G. Vining (2001) Introduction to Linear Regression Analysis) Çeviri Editör : Prof.Dr. M Aydın Erar Nobel Yayın Evi.
Notes
Source books, statistical packages
Course Learning Outcomes
Order | Course Learning Outcomes |
---|---|
LO01 | Specifies the relationships between variables. |
LO02 | Evaluates model building based on the relationships between variables. |
LO03 | Evaluates the model. |
LO04 | It predicts based on established models. |
LO05 | Defines the theoretical background of regression. |
Relation with Program Learning Outcome
Order | Type | Program Learning Outcomes | Level |
---|---|---|---|
PLO01 | Bilgi - Kuramsal, Olgusal | Explains contemporary concepts about Econometrics, Statistics, and Operation Research | |
PLO02 | Bilgi - Kuramsal, Olgusal | Explains relationships between acquired knowledge about Econometrics, Statistics, and Operation Research | 3 |
PLO03 | Bilgi - Kuramsal, Olgusal | Explains how to apply acquired knowledge in the field to Economics, Business, and other social sciences | 2 |
PLO04 | Beceriler - Bilişsel, Uygulamalı | Performs conceptual analysis to develop solutions to problems | 4 |
PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 3 |
PLO06 | Beceriler - Bilişsel, Uygulamalı | Interprets the results obtained from the most appropriate method to predict the model | 2 |
PLO07 | Beceriler - Bilişsel, Uygulamalı | Synthesizes the information obtained by using different sources within the framework of academic rules in a field that does not research | 2 |
PLO08 | Beceriler - Bilişsel, Uygulamalı | Uses acquired knowledge in the field to determine the vision, aim, and goals for an organization/institution | 4 |
PLO09 | Beceriler - Bilişsel, Uygulamalı | Searches for new approaches and methods to solve problems being faced | 3 |
PLO10 | Beceriler - Bilişsel, Uygulamalı | Presents analysis results conveniently | 4 |
PLO11 | Beceriler - Bilişsel, Uygulamalı | Collects/analyzes data in a purposeful way | 4 |
PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Converts its findings into a master's thesis or a professional report in Turkish or a foreign language | 3 |
PLO13 | Beceriler - Bilişsel, Uygulamalı | Develops solutions for organizations using Econometrics, Statistics, and Operation Research | 1 |
PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs an individual work to solve a problem with Econometrics, Statistics, and Operation Research | |
PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | 3 |
PLO16 | 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 |
PLO17 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses a package program of Econometrics, Statistics, and Operation Research or writes a new code | 2 |
PLO18 | 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 | 2 |
PLO19 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on economic and social events by following current issues | |
PLO20 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values | 2 |
Week Plan
Week | Topic | Preparation | Methods |
---|---|---|---|
1 | Introduction to regression analysis, regression analysis, definition and the purposes of regression analysis, data types, Regression and Correlation Analysis | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
2 | Simple Linear Regression, the estimation of Regression coefficients with the OLS (Ordinary Least Squares Method) | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
3 | the standard error of the regression model and coefficients, significance tests and confidence intervals, analysis of variance | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
4 | The correlation coefficient, the coefficient of determination, and their significance tests | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
5 | Random error term (residues-residues) assumptions about, examining the assumption of normality of the error term | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
6 | To investigate the validity and reliability of the coefficients, elasticity coefficients, Multiple coefficient of determination | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
7 | For the validity of the regression model analysis of varianceSimple and multiple regression models of non-linear autocorrelation | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
8 | Mid-Term Exam | Reading the relevant sections of the source book | Ölçme Yöntemleri: Yazılı Sınav |
9 | Assumptions about Random error term (residues-residues) , examining the assumption of normality of the error term | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
10 | Autocorrelation problem identification and solutions. Multicollinearity problem | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
11 | Constant variance assumption (Homoskedasite), variable variance (Heterodskedasite) state of constant variance revealed problems and solutions | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
12 | problems and solutions of linear multicollinearity, example | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
13 | Multiple linear regression models, alternative methods of selection of variables to be included in the model. Dummy variable models | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
14 | Minitab and SPlus applications in solving regression models. Dummy dependent variable models | Reading the relevant sections of the source book | Öğretim Yöntemleri: Anlatım |
15 | Homework presentation | Reading the relevant sections of the source book | Öğretim Yöntemleri: Soru-Cevap |
16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
17 | Term Exams | 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 | 5 | 70 |
Assesment Related Works | |||
Homeworks, Projects, Others | 0 | 0 | 0 |
Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
Final Exam | 1 | 30 | 30 |
Total Workload (Hour) | 157 | ||
Total Workload / 25 (h) | 6,28 | ||
ECTS | 6 ECTS |