ECTSStatistical 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 Elective Courses 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 Coordinator
Course Lecturer(s)
  • Instructor Dr. Nilüfer PEKİN ALAKOÇ
Course Assistants
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;
  • Students will improve their problem solving skills and their analytical thinking ability.
  • Students will become familiar with a suitable statistical package through computer-based statistical analysis.
  • Students will learn how to collect and analyze data and use statistics to enhance their project objectives.
  • Students will learn to differentiate the common uses and misuses of statistics in business and industrial applications.
  • Students will be able to define and differentiate industrial and systems engineering problems that can be solved using statistical techniques.
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 Having accumulated knowledge on mathematics, science and engineering and an ability to apply these knowledge to solve Civil engineering problems.
2 Ability to design Civil Engineering systems fulfilling sustainability in environment and manufacturability and economic constraints
3 An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems.
4 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment
5 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications
6 Ability to carry out independent research in the field and to report the results of the research effectively and be able to present the research results at scientific meetings.
7 Sufficient oral and written English knowledge to follow scientific conferences in the field and communicate with colleagues.
8 Ability to effectively use knowledge in the field to work in disciplinary/multidisciplinary teams and the skill to lead these teams
9 Consciousness on the necessity of improvement and sustainability as a result of life-long learning,ability for continuous renovation and monitoring the developments on science and technology and awareness on entrepreneurship and innovation
10 Professional and ethical responsibility to gather and interpret data, apply and announce solutions to Civil Engineering problems.
11 An ability to investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary.

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