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

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
INE2002 Statistics in Engineering Spring 3 2 4 7
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Must Course
Course Level: Bachelor
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. SABRİ TANKUT ATAN
Course Lecturer(s): Prof. Dr. SELİM ZAİM
Dr. Öğr. Üyesi ETHEM ÇANAKOĞLU
Prof. Dr. CENGİZ KAHRAMAN
RA ESRA ADIYEKE
Course Objectives: The aim of the course is to provide the fundamentals of engineering statistics such as random sampling, data analysis, sampling distribution theory, estimation, confidence intervals, hypothesis tests, goodness of fit tests, and regression and correlation analysis.

Learning Outputs

The students who have succeeded in this course;
I. Explain the concept of random sampling, evaluate sample mean and sample variance, construct and interpret visual data displays (stem-and-leaf display, histogram, box plot) and normal probability plot
II. Explain statistical inference, point estimation, interval estimation. Use the Central Limit Theorem to identify the sampling distributions of means computed from samples. Determine the distribution of point estimators. Construct certain confidence intervals and interpret the results.
III. Explain hypothesis testing and error types. Generates hypothesis testing procedure and interprets the results.
IV. Construct certain confidence intervals and hypothesis tests for comparing two samples, and interpret the results.
V. Construct and analyze simple linear regression models.

Course Content

Population vs sample, sample mean, sample variance, stem-and-leaf display, histogram, box plot, normal probability plot, sampling distributions and point estimation of parameters, statistical intervals for a single sample, tests of hypotheses for a single sample, statistical inference for two samples, linear regression.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to statistics.
2) Population vs sample, sample mean, sample variance.
3) Stem-and-leaf display, histogram, box plot, normal probability plot
4) Sampling distributions and point estimation of parameters
5) Sampling distributions and point estimation of parameters
6) Statistical intervals for a single sample
7) Statistical intervals for a single sample
8) Tests of hypotheses for a single sample
9) Midterm
10) Statistical inference for two samples
11) Statistical inference for two samples
12) Simple linear regression
13) Simple linear regression
14) Nonparametric tests

Sources

Course Notes: Allan G. Bluman, Elementary Statistics, McGraw Hill, Most recent edition.
References: • Douglas C. Montgomery and George C. Runger. Applied Statistics and Probability for Engineers, John Wiley & Sons, 7th Edition. • Ross, S. M. (2017). Introductory Statistics. Academic Press, 4th Edition. • Ross, S. M. (2014). Introduction to Probability and Statistics for Engineers and Scientists. Academic Press. 5th Edition.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes 12 % 20
Homework Assignments % 0
Presentation % 0
Project 1 % 20
Seminar % 0
Midterms 1 % 20
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 14 1 14
Application 14 1 14
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 3 42
Presentations / Seminar 0 0 0
Project 1 20 20
Homework Assignments 0 0 0
Quizzes 0 0 0
Preliminary Jury 0 0 0
Midterms 1 15 15
Paper Submission 0 0 0
Jury 0 0 0
Final 1 30 30
Total Workload 177

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Build up a body of knowledge in mathematics, science and industrial engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems. 5
2) Identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose. 4
3) Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. The ability to apply modern design methods to meet this objective.
4) Devise, select, and use modern techniques and tools needed for solving complex problems in industrial engineering practice; employ information technologies effectively.
5) Design and conduct experiments, collect data, analyze and interpret results for investigating the complex problems specific to industrial engineering. 5
6) Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently. 3
7) Demonstrate effective communication skills in both oral and written English and Turkish. Writing and understanding reports, preparing design and production reports, making effective presentations, giving and receiving clear and understandable instructions.
8) Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself.
9) Develop an awareness of professional and ethical responsibility, and behaving accordingly. Information about the standards used in engineering applications.
10) Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11) Know contemporary issues and the global and societal effects of modern age engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions.
12) Develop effective and efficient managerial skills.