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 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
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 Ability to apply the acquired knowledge in mathematics, science and engineering
2 Ability to identify, formulate and solve complex engineering problems
3 Ability to accomplish the integration of systems
4 Ability to design, develop, implement and improve complex systems, components, or processes
5 Ability to select/develop and use suitable modern engineering techniques and tools
6 Ability to design/conduct experiments and collect/analyze/interpret data
7 Ability to function independently and in teams
8 Ability to make use of oral and written communication skills effectively
9 Ability to recognize the need for and engage in life-long learning
10 Ability to understand and exercise professional and ethical responsibility
11 Ability to understand the impact of engineering solutions
12 Ability to have knowledge of contemporary issues

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