ECMZ202 Regression Analysis

6 ECTS - 3-0 Duration (T+A)- 4. Semester- 3 National Credit

Information

Unit FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES
ECONOMETRICS PR. (ENGLISH)
Code ECMZ202
Name Regression Analysis
Term 2019-2020 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
Label C Compulsory
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Dr. Öğr. Üyesi FELA ÖZBEY
Course Instructor Dr. Öğr. Üyesi Çiğdem KOŞAR TAŞ (Bahar) (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

Resources

Notes



Course Learning Outcomes

Order Course Learning Outcomes
LO01 Defines concepts of statistics, data, population, sample, parameter, estimation, estimator.
LO02 Distinguishes between qualitative and quantitative data.
LO03 Applies the Ordinary Least Squares method to estimate linear regression model.
LO04 Chooses the most appropriate model for the data.
LO05 Tests the validity of a model estimate.
LO06 Performs the variance anasysis.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 - Explains Econometric concepts 5
PLO02 - Acquires basic Mathematics, Statistics and Operation Research concepts 4
PLO03 - Equipped with the foundations of Economics, and develops Economic models 2
PLO04 - Describes the necessary concepts of Business 0
PLO05 - Models problems with Mathematics, Statistics, and Econometrics 5
PLO06 - Estimates the model consistently and analyzes & interprets its results 5
PLO07 - Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 5
PLO08 - Collects, edits, and analyzes data 5
PLO09 - Uses a package program of Econometrics, Statistics, and Operation Research 2
PLO10 - Effectively works, take responsibility, and the leadership individually or as a member of a team 0
PLO11 - Awareness towards life-long learning and follow-up of the new information and knowledge in the field of study 2
PLO12 - Develops the ability of using different resources in the form of academic rules, synthesis the information gathered, and effective presentation in an area which has not been studied 2
PLO13 - Uses Turkish and at least one other foreign language, academically and in the business context 2
PLO14 - Good understanding, interpretation, efficient written and oral expression of the people involved 2
PLO15 - Improves his/herself constantly by defining educational requirements considering interests and talents in scientific, cultural, art and social fields besides career development 2
PLO16 - Questions traditional approaches and their implementation while developing alternative study programs when required 2
PLO17 - Recognizes and implements social, scientific, and professional ethic values 2
PLO18 - Follows actuality, and interprets the data about economic and social events 3


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
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
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
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
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
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
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
8 Mid-Term Exam
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
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
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
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
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
14 Chapter 5:Model Building Students will be prepared by studying relevant subjects from source books according to the weekly program
15 Chapter 6: Variable screening methods Students will be prepared by studying relevant subjects from source books according to the weekly program
16 Term Exams
17 Term Exams


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 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

Update Time: 01.05.2025 12:20