Probability and Statistics II (IE202) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Probability and Statistics II	IE202	4. Semester	3	1	0	3	6.5

Pre-requisite Course(s)
IE201

Course Language	English
Course Type	Compulsory Departmental Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Discussion, Question and Answer, Problem Solving, Team/Group, Project Design/Management.
Course Coordinator
Course Lecturer(s)	Asst. Prof. Dr. Danışment VURAL Research Assistant Şevval KILIÇOĞLU
Course Assistants
Course Objectives	The course aims to expose students to basic concepts of statistical inference, linear regression and correlation, forecasting and experimental design.
Course Learning Outcomes	The students who succeeded in this course; Students will be exposed to several types of decision making problems of industry that can be solved by statistical inference and hypothesis testing. Students will be able to develop simple and multiple-parameter linear models that can be utilized for prediction and forecasting in industrial planning and management. Students will reinforce their problem solving skills and their analytical thinking ability. Students will become familiar with a suitable statistical package through computer-based statistical analysis.
Course Content	Sampling distributions, point estimation, confidence intervals and interval estimation, hypothesis testing, simple linear regression and correlation, multiple linear regression, analysis of variance with a single factor

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Fundamentals of Sampling Distributions and Data Descriptions	[1] pages 224-244
2	One-and-two-sample estimation problems	[1] pages 253-276
3	One-and-two-sample estimation problems	[1] pages 253-276
4	One-and-two-sample estimation problems	[1] pages 253-276
5	One-and-two-sample tests of hypotheses	[1] pages 283-344
6	One-and-two-sample tests of hypotheses	[1] pages 283-344
7	One-and-two-sample tests of hypotheses	[1] pages 283-344
8	Midterm I
9	Simple Linear Regression	[1] pages 401-440
10	Simple Linear Regression	[1] pages 401-440
11	ANOVA and its applications	[1] pages 449-502
12	Multiple Linear Regression	[1] pages 449-502
13	Midterm II
14	Design of Experiments	[1] pages 514-544
15	Industrial Engineering Topics: Forecasting, Quality and Simulation Applications
16	Industrial Engineering Topics: Forecasting, Quality and Simulation Applications

Sources

Course Book	1. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, 5th Edition, John Wiley and Sons, 2011.
Other Sources	2. Walpole, R.E., Myers, R.H., Myers, S.L. and Ye, K., Probability and Statistics for Engineers and Scientists, Prentice Hall, 2007.
	3. Milton, J.S. and Arnold, J.C., Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, McGraw-Hill, 4th edition, 2002.
	4. Ross, S. Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, 3rd edition, 2004.
	5. Triola, M.F., Essentials of Statistics, Addison Wesley, 2nd edition, 2004.
	6. Hines, W.W. and Montgomery, D.A., Probability and Statistics in Engineering and Management Science, John Wiley, 1990.
	7. Navidi,W. Statistics for Engineers and Scientists, McGraw-Hill, 2008.
	8. Mendenhall, W. and Sincich, T. Statistics for Engineering and the Sciences. Prentice Hall, 2007.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	1	10
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	3	15
Presentation	-	-
Project	1	15
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	2	40
Final Exam/Final Jury	1	20
Toplam	8	100

Percentage of Semester Work	80
Percentage of Final Work	20
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
#	Program Qualifications / Competencies	1	2	3	4	5
1	Gains adequate knowledge in mathematics, science, and relevant engineering disciplines and acquires the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems.				X
2	Gains the ability to identify, formulate, and solve complex engineering problems and the ability 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 under realistic constraints and conditions to meet specific requirements and to apply modern design methods for this purpose.
4	Gains the ability to select and use modern techniques and tools necessary for the analysis and solution of complex engineering problems encountered in industrial engineering applications and the ability to use information technologies effectively.				X
5	Gains the ability to design experiments, conduct experiments, collect data, analyze results, and interpret findings for investigating complex engineering problems or discipline specific research questions.			X
6	Gains the ability to work effectively in intra-disciplinary and multi-disciplinary teams and the ability to work individually.
7	Gains the ability to communicate effectively in written and oral form, acquires proficiency in at least one foreign language, the ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8	Gains awareness of the need for lifelong learning and the ability to access information, follow developments in science and technology, and to continue to educate him/herself.
9	Gains knowledge about behaviour in accordance with ethical principles, professional and ethical responsibility and standards used in industrial engineering applications
10	Gains knowledge about business practices such as project management, risk management, and change management and develops awareness of entrepreneurship, innovation, and sustainable development.
11	Gains knowledge about the global and social effects of industrial engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; 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.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application	14	2	28
Special Course Internship
Field Work
Study Hours Out of Class	16	2	32
Presentation/Seminar Prepration
Project	1	15	15
Report
Homework Assignments	3	6	18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	10	10
Prepration of Final Exams/Final Jury	1	12	12
Total Workload			163