Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
5İKT343Statistical Modelling2+0+02318.08.2026
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Economics
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course This course aims to introduce statistical modeling techniques and enhance students' ability to conduct data-driven analysis. Students will learn various statistical modeling methods such as regression analysis, time series analysis, and factor analysis, and apply these techniques to real-world datasets.
Course Content The course focuses on teaching the fundamentals of statistical modeling techniques. Students will learn methods such as regression analysis, time series analysis, factor analysis, and machine learning to perform data-driven analyses. Additionally, model evaluation, validation methods, and interpretation of results are key components of the course. Students will apply theoretical knowledge to real-world data using statistical software (R, Stata) and gain the ability to accurately report analysis results.
Course Methods and Techniques Lecturing-Q&A-Application
Prerequisites and co-requisities None
Course Coordinator Sekreter Turgay Bozan
Name of Lecturers None
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis.
Course Notes At least 70% attendance is mandatory.

Course Category
Social Sciences %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 15 2 30
Mid-terms 1 30 30
Final examination 1 30 30
Total Work Load   Number of ECTS Credits 3 90

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Understand and apply statistical modeling techniques and analysis methods.
2 Analyze datasets using statistical methods such as regression analysis, time series analysis, and factor analysis.
3 Create and evaluate models using statistical software (R, Stata, etc.).

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Statistical Modeling
2 Regression Analysis
3 Multiple Regression and Model Selection
4 Time Series Analysis
5 Correlation and Factor Analysis
6 Model Evaluation and Diagnostics
7 Advanced Regression Models
8 Midterm
9 Time Series and Econometric Models
10 Machine Learning and Modeling
11 Statistical Software: R and Stata
12 Data Mining and Modeling
13 Interpreting Model Results
14 Closing and Final Exam Preparation
15 General Review

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 3 4 3 4 4 3 4 3 3 4 3 1
C1 3 3 4 4 4 3 4 4 3 4 4 1
C2 3 3 4 4 3 3 4 4 3 4 4 1
C3 5 4 5 4 4 3 4 4 5 3 3 1

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant