Department of Industrial Engineering

IE 336 | Course Introduction and Application Information

Course Name

Advanced Statistics for Industrial Engineering

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
IE 336	SPRING	3	0	3	6

Prerequisites	MATH 236 or IE 234 To succeed (To get a grade of at least DD)
Course Language	English
Course Type	Required (Core Course)
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	Problem solving Lectrure / Presentation Application
National Occupational Classification Code	-
Course Coordinator	Prof. Dr. Ahmet Sermet Anagün
Course Lecturer(s)	Prof. Dr. Ahmet Sermet Anagün
Assistant(s)	-

Course Objectives

The objective of this course is to introduce students advanced topics in statistical approaches and areas of usage for Industrial Engineering.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Students will be able to determine the appropriate distribution for observed data.			X
LO2	Students will be able to analyze the independence and homogeneity relationships between criteria.			X
LO3	Students will be able to model linear and nonlinear relationships between variables.				X
LO4	Students will be able to investigate complex multivariate models.			X
LO5	Students will be able to examine the effects of one or more factors.					X

Course Description

Chi-square distribution and applications, parameter estimation, goodness of fit test, simple linear regression and correlation analysis, multiple regression analysis, non-linear regression analysis, determining model in multiple regression, single factor analysis of variance, multiple comparisons, multi-factor analysis of variance.

Related Sustainable Development Goals

Course Category	Core Courses	X
	Major Area Courses
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Tests of Chi-square	Textbook, Chapter 9	LO1
2	Independence and Homogeneity Tests	Textbook, Chapter 9	LO2
3	Parameter Estimation and Goodness of Fit Test	Textbook, Chapter 9	LO2
4	Introduction to Regression	Textbook, Chapter 11	LO3
5	Analysis of Simple Regression and Correlation	Textbook, Chapter 11	LO3
6	Analysis of Multiple Linear Regression	Textbook, Chapter 12	LO3
7	Analysis of Multiple Linear Regression	Textbook, Chapter 12	LO3
8	Mid-Term Exam		-
9	Methods Used for Defining Multiple Regression	Textbook, Chapter 12	LO3
10	Analysis of Non-linear Regression	Textbook, Chapter 11	LO3
11	Single-factor Analysis of Variance	Textbook, Chapter 13	LO4
12	Multiple Comparisons	Textbook, Chapter 13	LO4
13	The Models of Two-factor Analysis of Variance	Textbook, Chapter 14	LO5
14	Two- factor Analysis of Variance	Textbook, Chapter 14	LO5
15	Review of the semester		-
16	Final Exam		-

Course Notes/Textbooks	Montgomery D.C. & Runger G.C. (2014). Applied Statistics and Probability for Engineers 6th Ed. John Wiley&Sons.; ISBN:9781118744123
Suggested Readings/Materials	Navidi W. (2014). Statistics for Engineers and Scientists 4th Ed. McGraw Hill. ISBN:9781259251603

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Quizzes / Studio Critiques	3	15	X	X	X	X	X
Project	1	15	X	X	X	X	X
Midterm	1	30	X	X	X
Final Exam	1	40	X	X	X	X	X
Total	6	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	3	42
Field Work	-	-	-
Quizzes / Studio Critiques	3	5	15
Portfolio	-	-	-
Homework / Assignments	-	-	-
Presentation / Jury	-	-	-
Project	1	15	15
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	25	25
Final Exam	1	35	35
		Total	180