# 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 N/A Bachelor’s Degree (First Cycle) Face To Face Lecture, Demonstration, Experiment, Problem Solving. Instructor Dr. Nilüfer PEKİN ALAKOÇ 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. 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. 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

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 35 100

### Course Category

Core Courses X

### The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Acquires sufficient knowledge in mathematics, natural sciences, and related engineering disciplines; gains the ability to use theoretical and applied knowledge in these fields in solving complex engineering problems.
2 Gains the ability to identify, define, formulate, and solve complex engineering problems; acquires the skill to select and apply appropriate analysis and modeling methods for this purpose. X
3 Gains the ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions, and applies modern design methods for this purpose.
4 Develops the skills to develop, select, and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; gains the ability to effectively use information technologies. X
5 Gains the ability to design experiments, conduct experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics. X
6 Acquires the ability to work effectively in intra-disciplinary and multidisciplinary teams, as well as individual work skills.
7 Acquires effective oral and written communication skills in Turkish; at least one foreign language proficiency; gains the ability to write effective reports, understand written reports, prepare design and production reports, make effective presentations, and give and receive clear instructions.
8 Develops awareness of the necessity of lifelong learning; gains the ability to access information, follow developments in science and technology, and continuously renew oneself.
9 Acquires the consciousness of adhering to ethical principles, and gains professional and ethical responsibility awareness. Gains knowledge about the standards used in industrial engineering applications.
10 Gains knowledge about practices in the business life such as project management, risk management, and change management. Develops awareness about entrepreneurship and innovation. Gains knowledge about sustainable development.
11 Gains knowledge about the universal and social dimensions of the impacts of industrial engineering applications on health, environment, and safety, as well as the problems reflected in the engineering field of the era. Gains awareness of the legal consequences of engineering solutions.
12 Gains skills in the design, development, implementation, and improvement of integrated systems involving human, material, information, equipment, and energy.
13 Gains knowledge about appropriate analytical and experimental methods, as well as computational methods, for ensuring system integration.

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