1	Course Title:	STATISTICAL MODELLING
2	Course Code:	EKO5124
3	Type of Course:	Optional
4	Level of Course:	Second Cycle
5	Year of Study:	1
6	Semester:	2
7	ECTS Credits Allocated:	4
8	Theoretical (hour/week):	2
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. SEVDA GÜRSAKAL
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	E-posta : sdalgic@uludag.edu.tr Telefon: 0 224 29 41112 Adres: Bursa Uludağ Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, Ekonometri Bölümü,16059, Görükle/Bursa.
18	Website:
19	Objective of the Course:	This course covers generalized linear models, some basic statistical learning tools, and statistical models for complex causal relationships, especially in social science contexts. In addition to the theoretical foundations of the models, they will also be discussed in practice. These applications are implemented using the statistical software environment R. The course uses a hands-on approach through analysis using statistical software R. Practices are mostly selected from real social science research questions, but examples from other disciplines such as biology, medicine, and engineering are also given.
20	Contribution of the Course to Professional Development	It has a contribution to lay the groundwork for students to develop their professional skills related to statistical modeling and application.

21

Learning Outcomes:

1 Be able to describe the basic concepts and assumptions of statistical models; 2 Be able to describe statistical distributions; 3 Be able to describe linear models through the framework of generalized linear models; 4 Be able to recognize and predicting nonlinear models; 5 Be able to analyze multidimensional data through dimension reduction, clustering and discriminant analysis; 6 Be able to use the statistical model suitable for the data structure in different disciplines;; 7 Be able to interpret the results by analyzing the predicted statistical model; 8 Be able to use the R software for data management, data analysis and data visualization.;

22	Course Content:

Week	Theoretical	Practical
1	Introduction to statistics; Population and Sample; Random Sampling; Some important statistics; Data description and visualization techniques.
2	Introduction of linear and nonlinear models
3	R essentials (import, export, manipulate, data); R data visualization functions;
4	Generalized Linear Models
5	Implementing Generalized Linear Models with R
6	Hierarchical Models
7	Principal Component Analysis and Implementations With R
8	Nonlinear Principal Component Analysis and Implementations With R
9	Factor Analysis and Implementations With R
10	Clustering Analysis
11	R implementations for Clustering Analysis
12	Discriminant Analysis
13	R implementations for Discriminant Analysis
14	Logistic Regression Analysis and Implementations With R

23	Textbooks, References and/or Other Materials:	1. David A. Freedman, Statistical Models: Theory and Practice, Cambridge University Press, 2005. 2. C. Davison, Statistical Models, Cambridge University Press, 2003. 3. Harvey Goldstein, Multilevel Statistical Models, London: Institute of Education, Multilevel Models Project, 1999. 4. William H. Crown, Statistical Models for the Social and the Behavioral Sciences, Praeger Puslishers, 1998.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	0	0
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	100
Total	1	100
Contribution of Term (Year) Learning Activities to Success Grade		0
Contribution of Final Exam to Success Grade		100
Total		100
Measurement and Evaluation Techniques Used in the Course	Measurement and evaluation are made with multiple choice test questions and written questions.
Information	This course is evaluated with a absolute evaluation system.

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ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	2	28
Practicals/Labs	0	0	0
Self Study and Preparation	14	3	42
Homeworks, Performances	0	5	20
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	10	10
Others	0	0	0
Final Exams	1	20	20
Total WorkLoad			120
Total workload/ 30 hr			4
ECTS Credit of the Course			4

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12 LO1 3 3 4 3 4 4 4 4 4 3 4 3 LO2 4 3 4 4 4 4 5 4 4 4 4 4 LO3 4 3 4 4 4 4 4 4 4 4 4 4 LO4 4 3 3 3 3 4 4 4 3 4 4 3 LO5 4 3 3 4 4 4 4 4 4 4 4 5 LO6 4 4 4 4 4 4 4 4 5 5 5 5 LO7 4 4 4 4 5 5 5 5 5 5 5 5 LO8 5 5 5 5 5 5 5 5 5 5 5 5

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High