| INDUSTRIAL PRODUCTS DESIGN | |||||
| 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 | Spring | 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) | Having the theoretical and practical knowledge proficiency in the discipline of industrial product design | |
| 2) | Applying professional knowledge to the fields of product, service and experience design development | |
| 3) | Understanding, using, interpreting and evaluating the design concepts, knowledge and language | |
| 4) | Knowing the research methods in the discipline of industrial product design, collecting information with these methods, interpreting and applying the collected knowledge | |
| 5) | Identifying the problems of industrial product design, evaluating the conditions and requirements of problems, producing proposals of solutions to them | |
| 6) | Developing the solutions with the consideration of social, cultural, environmental, economic and humanistic values; being sensitive to personal differences and ability levels | |
| 7) | Having the ability of communicating the knowledge about design concepts and solutions through written, oral and visual methods | |
| 8) | To identify and apply the relation among material, form giving, detailing, maintenance and manufacturing methods of design solutions | |
| 9) | Using the computer aided information and communication technologies for the expression of industrial product design solutions and applications | |
| 10) | Having the knowledge and methods in disciplines like management, engineering, psychology, ergonomics, visual communication which support the solutions of industrial product design; having the ability of searching, acquiring and using the knowledge that belong these disciplines when necessary. | |
| 11) | Using a foreign language to command the jargon of industrial product design and communicate with the colleagues from different cultures | |
| 12) | Following and evaluating the new topics and trends that industrial product design needs to integrate according to technological and scientific developments |