INDUSTRIAL ENGINEERING (ENGLISH, PHD)
PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
INE6103	Multivariate Data Analysis	Spring	3	0	3	9

Basic information

Language of instruction:	English
Type of course:	Must Course
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :
Course Lecturer(s):	Prof. Dr. SELİM ZAİM Dr. Öğr. Üyesi YÜCEL BATU SALMAN
Recommended Optional Program Components:	None
Course Objectives:	The purpose of this doctorate course is to broaden and enrich the student's knowledge and understanding of various topics in multivariate analysis and to provide some practical experience in their applications and interpretation. The focus will be on practical issues such as selecting the appropriate analysis, preparing data for analysis, interpreting output, and presenting results of a complex nature.

Learning Outcomes

The students who have succeeded in this course;
1. Develop skills with a range of procedures and programs for multivariate data analysis.
2. Determine which multivariate technique is appropriate for a specific research problem.

Course Content

This course covers linear regression models, multiple regression models, dummy variable regression models, multicollinearity and its remedial measures, and multivariate statistical techniques such as; structural equation modelling, factor analysis, analysis of covariance and discriminant analysis.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to course
2)	Data examination and fundamentals of data manipulation
3)	Multiple Regression analysis
4)	Exploratory factor analysis
5)	Covariance based structural equation modeling technique
6)	Confirmatory factor analysis
7)	Review, Midterm Exam
8)	Path analysis using covariance based structural equation modeling technique
9)	Variance based structural equation modeling technique
10)	Path analysis using variance based structural equation modeling technique
11)	Neural network analysis
12)	Discriminant analysis
13)	Cluster analysis
14)	Review, Problem Session

Sources

Course Notes / Textbooks:	• Multivariate Data Analysis by Joseph F. Hair, Jr,, William C. Black, Barry J. Babin, Rolph E. Anderson, 7/E, Pearson, 2010. • Applied Multivariate Techniques by Subhash Sharma. John Wiley & Sons, Inc. 1996. • Regression Analysis by Example by Samprit Chatterjee and Ali S. Hadi. John Wiley & Sons, Inc. 2006.
References:	Various

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Final	1	% 60
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	13	3	39
Study Hours Out of Class	14	6	84
Homework Assignments	5	5	25
Midterms	1	15	15
Final	1	25	25
Total Workload			188

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To understand and implement areas that are related with basic sciences, mathematics, and industrial engineering at a high level.
2)	To have expanded and deeper knowledge in the related field including the most recent developments.
3)	To use and evaluate knowledge with a systematic approach.
4)	To have high level proficiency of necessary methods and skills to reach the latest knowledge in the field and to understand the knowledge for making research studies.
5)	To make a comprehensive study innovating science and technology, developing new scientific method or technological product/process, implementing a known method to a new field.
6)	To be able to detect, design, implement, and finalize an original independent research process; to manage this process
7)	To be able to contribute science and technology by publishing outcomes of academic studies in reputable scholarly environments.
8)	To be able to evaluate scientific, technological, social and cultural developments, and to transfer these developments to society with scientific objectivity and ethical responsibility.
9)	To be able to conduct critical analysis, synthesis and evaluation of thoughts and developments in focusing field.
10)	To be Able to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
11)	To be able to conduct functional interaction to solve the problems related to the field by using the strategic decision making processes
12)	To have effective and efficient management capabilities