ECTS - Statistical Applications in Industrial Engineering
Statistical Applications in Industrial Engineering (IE442) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Statistical Applications in Industrial Engineering | IE442 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration, Experiment, Problem Solving. |
| Course Lecturer(s) |
|
| Course Objectives | The course aims to prepare the student to analyze and classify data and develop empirical models for industrial engineering problems under service/production contexts. The student will be able to distinguish between different statistical techniques and implement them using a statistical software package. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Applications of simple and multiple linear regression, design and analysis of experiments, multivariate analysis and nonparametric tests for the solution of industrial engineering problems. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Syllabus Introduction | |
| 2 | Review of Some Statistical Topics | |
| 3 | Simple Linear Regression | |
| 4 | Multiple Linear Regression | |
| 5 | Design and Analysis of Single Factor Experiments | |
| 6 | Design and Analysis of Single Factor Experiments | |
| 7 | Design of Experiments with Several Factors | |
| 8 | Design of Experiments with Several Factors | |
| 9 | Multivariate Statistical Analysis | |
| 10 | Multivariate Statistical Analysis | |
| 11 | Midterm | |
| 12 | Non-parametric Tests | |
| 13 | Non-parametric tests | |
| 14 | Case studies and Applications | |
| 15 | Final Examination Period | |
| 16 | Final Examination Period |
Sources
| Other Sources | 1. Editors, Coleman,S.,Greenfield,T.,Stewardson,D. and Montgomery,D. Statistical Practice in Business and Industry, Wiley, 2008. |
|---|---|
| 3. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, John Wiley and Sons, Inc., 4th Edition, June 2006. | |
| 4. Czitron,V., Spagon, P.O., Statistical case studies for industrial process improvement, SIAM,1997 | |
| 5. Ross, S. Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, 3rd edition, 2004. | |
| 7. Schuyler,W. Reading Statistics and Research, Pearson,4th edition,2004. | |
| 9. Tabachnick, B.G. and Fidell, L.S.Using multivariate statistics, Pearson, 4th edition, 2001. | |
| 11. Editors, Tinsley, Howard E.A., Brown, S.D.Handbook of Applied Multivariate Statistics and mathematical modelling, Academic Press, 2000. | |
| 14. Allison, P. Multiple Regression: A primer, Pine Forge, 1999. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | 1 | 10 |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 5 | 15 |
| Presentation | - | - |
| Project | 1 | 10 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 35 |
| Toplam | 9 | 100 |
| Percentage of Semester Work | 65 |
|---|---|
| Percentage of Final Work | 35 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Attains knowledge through wide and in-depth investigations his/her field and surveys, evaluates, interprets, and applies the knowledge thus acquired. | X | ||||
| 2 | Has a critical and comprehensive knowledge of contemporary engineering techniques and methods of application. | X | ||||
| 3 | By using unfamiliar, ambiguous, or incompletely defined data, completes and utilizes the required knowledge by scientific methods; is able to fuse and make use of knowledge from different disciplines. | |||||
| 4 | Has the awareness of new and emerging technologies in his/her branch of engineering profession, studies and learns these when needed. | |||||
| 5 | Defines and formulates problems in his/her branch of engineering, develops methods of solution, and applies innovative methods of solution. | X | ||||
| 6 | Devises new and/or original ideas and methods; designs complex systems and processes and proposes innovative/alternative solutions for their design. | |||||
| 7 | Has the ability to design and conduct theoretical, experimental, and model-based investigations; is able to use judgment to solve complex problems that may be faced in this process. | |||||
| 8 | Functions effectively as a member or as a leader in teams that may be interdisciplinary, devises approaches of solving complex situations, can work independently and can assume responsibility. | X | ||||
| 9 | Has the oral and written communication skills in one foreign language at the B2 general level of European Language Portfolio. | X | ||||
| 10 | Can present the progress and the results of his investigations clearly and systematically in national or international contexts both orally and in writing. | |||||
| 11 | Knows social, environmental, health, safety, and legal dimensions of engineering applications as well as project management and business practices; and is aware of the limitations and the responsibilities these impose on engineering practices. | X | ||||
| 12 | Commits to social, scientific, and professional ethics during data acquisition, interpretation, and publication as well as in all professional activities. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 2 | 32 |
| Laboratory | |||
| Application | 16 | 1 | 16 |
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 2 | 28 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 18 | 18 |
| Report | |||
| Homework Assignments | 5 | 5 | 25 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 3 | 3 |
| Prepration of Final Exams/Final Jury | 1 | 3 | 3 |
| Total Workload | 125 | ||