Industrial Engineering

Probability(ING241)

Content
Course Code Course Name Semester Theory Practice Lab Credit ECTS
ING241 Probability 3 3 0 0 3 4
Prerequisites
Admission Requirements
Language of Instruction French
Course Type Compulsory
Course Level Bachelor Degree
Course Instructor(s) Mehtap DURSUN KARAHÜSEYİN mdursun@gsu.edu.tr (Email)
Assistant Muhammed Emre DEMİRCİOĞLU edemircioglu@gsu.edu.tr (Email)
Objective This course, which is offered as a compulsory course in the program, will help students perceive the basic concepts of probability theory and achieve proficiency in using the methods related to this discipline (probabilities of events, rules regarding random variables and the concept of moment, transformations of random variables, Gauss' suggestions). In this context, the objectives of the course are determined as follows:
• Introduce the student to the concept of probability, especially random variables in relation to uncertain events.
• To ensure that students master different probability distributions
• To enable students to benefit from probability theory in the problems they may encounter in the business world, especially in the analysis of uncertainty.
Content Week 1: Course introduction and introduction to probability
Week 2: Probability of an event, probability axioms, conditional probability, independent events, Bayes theorem
Week 3: Random variables and probability distributions
Week 4: Probability distribution function, probability mass function, probability density function
Week 5: Expected value, variance and standard deviation
Week 6: Two and higher dimensional random variables
Week 7: Moments
Week 8: Midterm Exam
Week 9: Some important discrete distributions
Week 10: Some important discrete distributions (continued)
Week 11: Some important discrete distributions (continued)
Week 12: Some important sustained distributions
Week 13: Some important continuous distributions (continued)
Week 14: Some important continuous distributions (continued)
Course Learning Outcomes The student who successfully completes this course:
1. Explain the relationship between set theory and probability axioms.
2. Can distinguish different functions of random variables.
3. Describe the characteristics of random variables.
4. List the important discrete and continuous distributions.
5. Apply probability theory to real life examples with the help of probability distribution functions.
Teaching and Learning Methods Lecture, discussion, question-answer.
References • Soong, T.T., Fundamentals of Probability and Statistics for Engineers, John Wiley & Sons, 2004.
• Akdeniz, F., Olasılık ve İstatistik, Baki Kitapevi, Eylül 1998.
• Ross, S.M., Introduction to probability models, Academic Press, 2003, 8th Ed.
• Lipschutz, S., Lipson, M., Olasılık, Schaum serisi, Nobel Akademik Yayıncılık, 2013.
Print the course contents
Weekly Contents
Theory Topics
Week Weekly Contents
1 Course introduction and introduction to probability
2 Probability of an event, probability axioms, conditional probability, independent events, Bayes theorem
3 Random variables and probability distributions
4 Probability distribution function, probability mass function, probability density function
5 Expected value, variance and standard deviation
6 Two and higher dimensional random variables
7 Moments
8 Midterm Exam
9 Some important discrete distributions
10 Some important discrete distributions
11 Some important discrete distributions
12 Some important continuous distributions
13 Some important continuous distributions
14 Some important continuous distributions
Practice Topics
Week Weekly Contents
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Assessment System
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 1 40
Contribution of final exam to overall grade 1 60
Toplam 2 100
In-Term Studies
  Number Contribution
Assignments 0 0
Presentation 0 0
Midterm Examinations (including preparation) 0 0
Project 0 0
Laboratory 0 0
Other Applications 0 0
Quiz 0 0
Term Paper/ Project 0 0
Portfolio Study 0 0
Reports 0 0
Learning Diary 0 0
Thesis/ Project 0 0
Seminar 0 0
Other 0 0
Toplam 0 0
Relation of Proficiency
No Program Learning Outcomes Contribution
1 2 3 4 5
1 Knowledge and understanding of a wide range of basic sciences (math, physics, ...) and the main concepts of engineering X
2 Ability to combine the knowledge and skills to solve engineering problems and provide reliable solutions X
3 Ability to select and apply methods of analysis and modeling to ask, reformulate and solve the complex problems of industrial engineering
4 Ability to conceptualize complex systems, processes or products under practical constraints to improve their performance, ability to use innovative methods of design
5 Ability to design, select and apply methods and tools needed to solve problems related to the practice of industrial engineering, ability to use computer technology
6 Ability to design experiments, collect and interpret data and analyze results
7 Ability to work independently, ability to participate in working groups and have a multidisciplinary team spirit
8 Ability to communicate effectively, ability to speak at least two foreign languages
9 Awareness of the need for continuous improvement of lifelong learning, ability to keep abreast of scientific and technological developments to use the tools of information management
10 Awareness of professional and ethical responsibility
11 Knowledge of the concepts of professional life as "project management", "risk management" and "management of change"
12 Knowledge on entrepreneurship, innovation and sustainability
13 Understanding of the effects of Industrial Engineering applications on global and social health, environment and safety.
14 Knowledge of the problems of contemporary society
15 Knowledge of the legal implications of the practice of industrial engineering
ECTS
Activities Number Period Total Workload
Class Hours 14 3 42
Working Hours out of Class 13 2 26
Midterm Examinations (including preparation) 1 10 10
Final Examinations (including preparation) 1 17 17
Total Workload 95
Total Workload / 25 3.80
Credits ECTS 4
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