Istanbul Health and Technology University INFORMATION PACKAGE / COURSE CATALOGUE
| Industrial Engineering(English) | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course General Introduction Information
| Course Code: | IND206 | ||||
| Course Name: | Statistical Methods in Industrial Engineering | ||||
| Course Semester: | Spring | ||||
| Course Credits: |
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| Language of instruction: | |||||
| Course Requirement: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Necessary | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator : | Ar.Gör. FATMANUR GÖÇER | ||||
| Course Lecturer(s): | Prof. Dr. Şükrü Alp BARAY | ||||
| Course Assistants: |
Course Purpose and Content
| Course Objectives: | This course aims to provide students with information on data analysis, classification and empirical model development for industrial engineering problems within production/service fields. It is aimed that the student will be able to distinguish different statistical techniques and apply them using the package program. |
| Course Content: | Applications of simple and multiple linear regression, experimental design and analysis, multivariate analysis and nonparametric tests in solving industrial engineering problems. |
Learning Outcomes
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The students who have succeeded in this course;
1) Students will be able to improve their problem solving and analytical thinking skills. 2) Students will be able to familiarize themselves with an appropriate statistical package program and perform computer-based statistical analysis. 3) Students will be able to improve their project goals by learning how to collect and analyze data. 4) Students will be able to learn and distinguish the general uses and misuses of statistics in business and industrial engineering applications. 5) Students will be able to identify and distinguish industrial and systems engineering problems that can be solved with statistical techniques. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Curriculum Introduction | - |
| 2) | Review of Some Statistical Topics | Lecture Notes |
| 3) | Simple Linear Regression | Lecture Notes |
| 4) | Multiple Linear Regression | Lecture Notes |
| 5) | Single Factor Experimental Design and Analysis | Lecture Notes |
| 6) | Single Factor Experimental Design and Analysis | Lecture Notes |
| 7) | Multifactor Experimental Design | Lecture Notes |
| 8) | Midterm | - |
| 9) | Multivariate Statistical Analysis | Lecture Notes |
| 10) | Multivariate Statistical Analysis | Lecture Notes |
| 11) | Nonparametric Tests | Lecture Notes |
| 12) | Nonparametric Tests | Lecture Notes |
| 13) | Case Studies and Applications | - |
| 14) | Case Studies and Applications | - |
| 15) | Final | - |
Sources
| Course Notes / Textbooks: | Ders Notları-Lecture Notes |
| References: | Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, John Wiley and Sons, Inc., 4th Edition, June 2006. Editors, Coleman,S.,Greenfield,T.,Stewardson,D. and Montgomery,D. Statistical Practice in Business and Industry, Wiley, 2008. |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Medium | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Adequate knowledge in mathematics, science, and related engineering discipline; ability to use theoretical and practical knowledge in these areas in complex engineering problems. | 1 |
| 2) | An ability to detect, identify, formulate, and solve complex engineering problems; the ability to select and apply appropriate analysis and modelling methods for this purpose. | 1 |
| 3) | An ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; the ability to apply modern design methods for this purpose. | 3 |
| 4) | An ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems in engineering applications. | 2 |
| 5) | An ability to use information technologies effectively. | 2 |
| 6) | Ability to design, conduct experiments, collect data, analyse, and interpret results to investigate complex engineering problems or discipline-specific research topics. | 2 |
| 7) | Ability to work effectively in disciplinary and multidisciplinary teams; ability to work individually. | 2 |
| 8) | Ability to communicate effectively in oral and written Turkish. | 1 |
| 9) | Knowledge of at least one foreign language. | 2 |
| 10) | Ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give clear and understandable instructions. | 3 |
| 11) | Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and ability to renew themselves. | 3 |
Learning Activity and Teaching Methods
| Alan Çalışması | |
| Akran Değerlendirmesi | |
| Anlatım | |
| Beyin fırtınası /Altı şapka | |
| Bireysel çalışma ve ödevi | |
| Course | |
| Grup çalışması ve ödevi | |
| Labs | |
| Okuma | |
| Homework | |
| Problem Çözme | |
| Proje Hazırlama | |
| Rapor Yazma | |
| Rol oynama | |
| Seminar | |
| Soru cevap/ Tartışma | |
| Sosyal Faaliyet | |
| Teknik gezi | |
| Tez Hazırlama | |
| Uygulama (Modelleme, Tasarım, Maket, Simülasyon, Deney vs.) | |
| Örnek olay çalışması | |
| Web Tabanlı Öğrenme | |
| Staj/Yerinde Uygulama |
Measurement and Evaluation Methods and Criteria
| Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
| Sözlü sınav | |
| Homework | |
| Uygulama | |
| Gözlem | |
| Bireysel Proje | |
| Grup Projesi | |
| Sunum | |
| Raporlama | |
| Akran Değerlendirmesi | |
| Bilgisayar Destekli Sunum | |
| Tez Sunma | |
| Uzman / Jüri Değerlendirmesi | |
| Örnek olay sunma | |
| Staj/ Yerinde Uygulama Değerlendirmesi | |
| Yarışma |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 1 | % 10 |
| Project | 1 | % 20 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| total | % 100 | |
İş Yükü ve AKTS Kredisi Hesaplaması
| Activities | Number of Activities | Aktiviteye Hazırlık | Aktivitede Harçanan Süre | Aktivite Gereksinimi İçin Süre | Workload | ||
| Course Hours | 14 | 3 | 42 | ||||
| Application | 1 | 3 | 3 | ||||
| Homework Assignments | 1 | 3 | 3 | ||||
| Midterms | 1 | 2 | 2 | ||||
| Final | 1 | 2 | 2 | ||||
| Total Workload | 52 | ||||||