IND452 Experiment Design and R&D StatisticsInstitutional InformationDegree Programs Industrial Engineering(English)Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Industrial Engineering(English)

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Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course General Introduction Information

Course Code: IND452
Course Name: Experiment Design and R&D Statistics
Course Semester: Spring
Course Credits:
ECTS
5
Language of instruction:
Course Requirement:
Does the Course Require Work Experience?: No
Type of course: Area Ellective
Course Level:
Bachelor TR-NQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Ar.Gör. FATMANUR GÖÇER
Course Lecturer(s): Dr. Mustafa YILDIRIM
Course Assistants:

Course Purpose and Content

Course Objectives: The aim of this course is to design experiments using statistical methods in order to obtain the most information from the experimental results by doing the least number of experiments.
Course Content: Review of applied statistics topics, Experimental design principles, randomness, planned experiment and information, Linear model approach to designed experiments, Deterministic and probabilistic models, linear models, Random Block Design and Latin Square Block Design, Factorial experiments

Learning Outcomes

The students who have succeeded in this course;
1) Students learn basic statistical methods,
2) Students learn experimental design methods.
3) Students learn the application of experimental design methods to data.

Course Flow Plan

Week Subject Related Preparation
1) Variability, expected value, Simple linear correlation, Inference, prediction and hypothesis testing, -
2) Randomization and Design -
3) Completely Random Designs -
4) contrasts -
5) Multiple Comparisons -
6) Testing Assumptions -
7) Factorial Design: Main effect and Interaction -
8) Midterm -
9) Variance Analysis for Balanced Factors -
10) Completely Random Block Design -
11) Latin Square Design -
12) Repeated-Latin Squares -
13) Applications of variance analysis for random block designs and Latin square designs. -
14) Applications of analysis of variance in factorial experiments -
15) Final -

Sources

Course Notes / Textbooks: Ders Notları/Lecture Notes
References: D.C.Montgomery, Design and Analysis of Experiments, Fourth Edition, Wiley, 1997.
W.G.Cochran, G.M.Cox, Experimental Desings, Second Ed., Wiley,1957.
G.Cochran, G.M.Cox, Experimental Desings, Second Ed., Wiley,1957.

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. 2
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. 3
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. 2
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. 3
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. 2
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. 1

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 Workload
Course Hours 16 32
Midterms 1 2
Final 1 2
Total Workload 36