EMÜ667 - REGRESSION ANALYSIS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| REGRESSION ANALYSIS | EMÜ667 | Any Semester/Year | 3 | 0 | 3 | 10 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving Other: Lecture, question and answer, problem solving, individual work. | |||||
| Instructor (s) | To be determined by the department | |||||
| Course objective | Develop skills to build simple and advanced regression models for engineering problems | |||||
| Learning outcomes |
| |||||
| Course Content | Regression and model building Simple and multiple linear regression Least squares estimation of parameters Prediction of new observations Model adequacy checking Variable transformations Variable selection | |||||
| References | Montgomery, D.C., Peck, E.A., Vining, G.G. (2012) Introduction to Linear Regression Analysis. 5th ed., Wiley Interscience. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Regression and model building |
| Week 2 | Simple linear regression |
| Week 3 | Simple linear regression |
| Week 4 | Multiple linear regression |
| Week 5 | Multiple linear regression |
| Week 6 | Model adequacy checking |
| Week 7 | Model adequacy checking |
| Week 8 | Diagnostics for leverage and influence |
| Week 9 | Variable selection and model building |
| Week 10 | Midterm exam |
| Week 11 | Indicator variables |
| Week 12 | Multicollinearitity |
| Week 13 | Regression applications |
| Week 14 | Project presentations |
| Week 15 | Study for the Final Exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 13 | 10 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 1 | 20 |
| Seminar | 0 | 0 |
| Midterms | 1 | 20 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 15 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 7 | 91 |
| Presentation / Seminar Preparation | 1 | 15 | 15 |
| Project | 1 | 62 | 62 |
| Homework assignment | 0 | 0 | 0 |
| Midterms (Study duration) | 1 | 40 | 40 |
| Final Exam (Study duration) | 1 | 50 | 50 |
| Total Workload | 31 | 177 | 300 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Reach the necessary knowledge and methods in engineering within the scope of advanced industrial engineering studies through scientific research and evaluate knowledge and methods and implement them. | X | ||||
| 2. Implement advanced analytical methods and modeling techniques to design processes, products and systems in an innovative and original way and improve them | X | ||||
| 3. Have the competency to plan, manage and monitor processes, products and systems. | X | ||||
| 4. Evaluate the data obtained from analysis of the processes, products and systems, complete limited or missing data through scientific methods, develop data driven solution approaches. | X | ||||
| 5. Develop original methods for the efficient integration of the scarce resources such as man, machine, and material, energy, capital and time to the systems and implement these. | X | ||||
| 6. Effectively utilize computer programming languages, computer software, information and communication technology to solve problems in the field of industrial engineering. | X | ||||
| 7. Report and present advanced studies, outcomes/results and the evaluations on the design, analysis, planning, monitoring and improvement of processes, products and systems. | X | ||||
| 8. Are aware of the professional responsibility, describe the technological, economic and environmental effects of the industrial engineering applications, work as an individual independently and as a team member having an understanding of the scientific ethical values, take responsibility and lead the team. | X | ||||
| 9. Are aware of the up-to-date engineering applications, follow the necessary literature for advanced researches, have the competency to reach knowledge in a foreign language, to quote and implement them. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest