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Information
| Code | ECMZ202 |
| Name | Regression Analysis |
| Term | 2023-2024 Academic Year |
| Semester | 4. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi FELA ÖZBEY |
| Course Instructor |
Dr. Öğr. Üyesi FELA ÖZBEY
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to give the students a good theoretical and empirical understanding of statistical methods used in regression analysis.
Course Content
Concepts of statistics, data, population, sample, parameter, estimation, estimator; Qualitative and quantitative data; The normal probability distirbution; Sampling distirbutions and central limit theorem; Simple linear regression; Multiple linear regression; Fitting the model: the method of least squares; Assumptions of the model; Estimation of the variances (the error variance and parameter estimates variances); The coefficient of correlation; The coefficient of determination; Testing the validity of a model; The analysis of variance; Model building; Variable screening methods.
Course Precondition
None
Resources
William Mendenhan, Terry Sincich (2003), A Second Course in Statistics: Regression Analysis, Pearson Ecucation Inc., ISBN: 0-13-122810-2
Notes
Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining(2012) Introduction to Linear Regression Analysis, Fifth Edition, ISBN: 978-0470542811
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Distinguishes between qualitative and quantitative data. |
| LO02 | Applies the Ordinary Least Squares method to estimate linear regression model. |
| LO03 | Chooses the most appropriate model for the data. |
| LO04 | Tests the validity of a model estimate. |
| LO05 | Performs the variance anasysis. |
| LO06 | Tests the statistical significance of the estimated parameters. |
| LO07 | Lists the assumptions of the classical linear regression model. |
| LO08 | Estimates a simple linear regression model using ordinary least squares. |
| LO09 | Estimates a multiple linear regression model using ordinary least squares. |
| LO10 | Calculates the determination coefficient. |
| LO11 | Calculates the residual sum of squares. |
| LO12 | Calculates the total sum of squares. |
| LO13 | Calculates the sum of squares of regression. |
| LO14 | Estimates the confidence intervals for model parameters. |
| LO15 | Evaluates the confidence interval for the prediction. |
| LO16 | Evaluates the confidence interval for the estimation. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the basic concepts and theorems in the fields of Econometrics, Statistics and Operations research | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquires basic Mathematics, Statistics and Operation Research concepts | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Describes the necessary concepts of Business | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Equipped with the foundations of Economics, and develops Economic models | 3 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Has the ability to analyze/interpret at the conceptual level to develop solutions to problems | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Collects/analyses data | 5 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Interprets the results analyzed with the model | 5 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Combines the information obtained from different sources within the framework of academic rules in a field which does not research | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | It develops traditional approaches, practices and methods into new working methods when it deems necessary | 3 |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | In addition to herself/himself professional development, constantly improves in scientific, cultural, artistic and social fields in line with interests and abilities | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it follows the current developments in the field / constantly renews itself | 2 |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses a package program of Econometrics, Statistics, and Operation Research | |
| PLO15 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses Turkish and at least one other foreign language, academically and in the business context | 4 |
| PLO16 | 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 | |
| PLO17 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on current economic and social issues | 3 |
| PLO18 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Chapter 1: A Review of Basic Concepts: Concepts of statistics, data, population, sample, parameter, estimation, estimator | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 2 | Chapter 1: A Review of Basic Concepts: Qualitative and quantitative data; The normal probability distirbution; Sampling distirbutions and central limit theorem | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 3 | Chapter 2: Introduction to Regression Analysis: Modeling a response, Overiew of regression analysis | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 4 | Chapter 3: Simple Linear Regression Model: The method of least squares, model assumptions | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 5 | Chapter 3: Simple Linear Regression Model: An estimator of the variance, making inferences about the slope | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 6 | Chapter 3: Simple Linear Regression Model: the coefficient of correlation, the coefficient of determination, using the model for estimation and prediction. | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 7 | Chapter 3: Simple Linear Regression Model: Regression through the origin | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Chapter 4: Multiple Linear Regression Model: General form of a multiple regression model, model assumptions, model fitting of a first-order multiple regression model with quantitative regressors | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 10 | Chapter 4: Multiple Linear Regression Model: Estimation of the error variance, inferences about the parameters | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 11 | Chapter 4: Multiple Linear Regression Model: The multiple coefficient of determination, The analysis of variance, F test | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 12 | Chapter 4: Multiple Linear Regression Model: More complex multiple regression models | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 13 | Chapter 4: Multiple Linear Regression Model: Using the model for estimation and prediction, A test for comparing nested models | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 14 | Chapter 5:Model Building | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 15 | Chapter 6: Variable screening methods | Students will be prepared by studying relevant subjects from source books according to the weekly program | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 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 | ||