INE6103 Multivariate Data AnalysisBahçeşehir UniversityDegree Programs INDUSTRIAL ENGINEERING (ENGLISH, THESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
INDUSTRIAL ENGINEERING (ENGLISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
INE6103 Multivariate Data Analysis Spring 3 0 3 9
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Departmental Elective
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) Process view and analytic thinking
2) managerial thinking with technical background
3) To have theoretical knowledge on operations research.
4) Awareness about the applications of operations research
5) To have ability of selection and efficient use of modern techniques, equipments and information technologies for industrial engineering
6) To be capable of designing and conducting experiments and collecting data, analyzing and interpreting results
7) To have verbal and oral effective communication skills by using visual methods in Turkish and English
8) To be aware of entrepreneurship, sustainability and innovation
9) To lead disciplinary and multi-disciplinary teams, to develop solution approaches in complex situations, to work individually and to take responsibility.
10) To have conscious of professional and ethical responsibility
11) To have conscious of necessity to lifelong learning
12) To be aware of economic and legal implications of engineering solutions
13) Economic, social and environmental responsibility while solving management problems