ECTS - Probability and Statistics II

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)
  • Dr. Öğr. Üyesi 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
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. X
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.

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