MANAGEMENT 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

Basic information

Language of instruction:	English
Type of course:	Must Course
Course Level:	Bachelor’s Degree (First Cycle)
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
Recommended Optional Program Components:	N.A.
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 Outcomes

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 / Textbooks:	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
Quizzes	12	% 20
Project	1	% 20
Midterms	1	% 20
Final	1	% 40
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
Study Hours Out of Class	14	3	42
Project	1	20	20
Midterms	1	15	15
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 engineering subjects; use theoretical and applied information in these areas to model and solve engineering problems.	2
2)	identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose.	3
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. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.)
4)	Devise, select, and use modern techniques and tools needed for engineering management practice; employ information technologies effectively.
5)	Design and conduct experiments, collect data, analyze and interpret results for investigating engineering management problems.	5
6)	Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently.
7)	Demonstrate effective communication skills in both oral and written English and Turkish.
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.
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 engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions.
12)	Develop effective and efficient managerial skills.