MANAGEMENT ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
SEN4016 | Multivariate Data Analysis | Fall | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Prof. Dr. MEHMET ALPER TUNGA |
Recommended Optional Program Components: | None. |
Course Objectives: | The students will have the ability of applying specific techniques included in multivariate analysis such as principle component analysis, factor analysis, linear regression to specific problems. |
The students who have succeeded in this course; 1. Describe multivariate data analysis concepts 2. Define the properties and limitations of PCA and compute PCA through different ways 3. Describe the types of factoring and factor computation 4. Define metric and non-metric scales 5. Describe simple and multiple correspondence analysis and chi squared distances 6. Define variations of MANOVA 7. Evaluate regression coefficients, parameter estimation, hypothesis testing 8. Describe deduction, induction, estimation, tests, correlation 9. Define univariate and multivariate filters |
The course content is composed of principle component analysis (pca), factor analysis, multidimensional scaling, correspondence analysis, multivariate analysis of variance (manova), multiple linear regression, statistical inference, feature subset selection. |
Week | Subject | Related Preparation |
1) | Introduction | |
2) | Principle Component Analysis (PCA) | |
3) | Principle Component Analysis (PCA) | |
4) | Factor Analysis | |
5) | Factor Analysis | |
6) | Multidimensional Scaling | |
7) | Correspondence Analysis | |
8) | Multivariate Analysis of Variance (MANOVA) | |
9) | Multiple Linear Regression | |
10) | Multiple Linear Regression | |
11) | Statistical Inference | |
12) | Statistical Inference | |
13) | Feature Subset Selection | |
14) | Feature Subset Selection |
Course Notes / Textbooks: | Multivariate Data Analysis, 7/E, Joseph F. Hair, Jr, William C. Black, Barry J. Babin, Rolph E. Anderson, Pearson, 2010, 9780138132637 |
References: | Yok - None. |
Semester Requirements | Number of Activities | Level of Contribution |
Quizzes | 4 | % 20 |
Homework Assignments | 2 | % 10 |
Midterms | 1 | % 30 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 4 | 5 | 20 |
Homework Assignments | 2 | 5 | 10 |
Quizzes | 4 | 3 | 12 |
Midterms | 1 | 15 | 15 |
Final | 1 | 17 | 17 |
Total Workload | 116 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Build up a body of knowledge in mathematics, science and engineering subjects; use theoretical and applied information in these areas to model and solve engineering problems. | |
2) | identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose. | |
3) | Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.) | |
4) | Devise, select, and use modern techniques and tools needed for engineering management practice; employ information technologies effectively. | |
5) | Design and conduct experiments, collect data, analyze and interpret results for investigating engineering management problems. | |
6) | Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently. | |
7) | Demonstrate effective communication skills in both oral and written English and Turkish. | |
8) | Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself. | |
9) | Develop an awareness of professional and ethical responsibility. | |
10) | Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development. | |
11) | Know contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions. | |
12) | Develop effective and efficient managerial skills. |