Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
3ENM203Probability3+0+03611.11.2026
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Industrial Engineering
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course Understanding the concept of probability, applying the properties of probability to both simple and complex probability calculations, and grasping the mathematical foundations of discrete and continuous distributions.
Course Content Descriptive Statistics, Introduction to Sets and Probability, Concept of Sample Space, Concept of Probability, Conditional Probability, Bayes ` Theorem, Random Variables, Discrete and Continuous Probability Distributions, Joint Distributions, Concept of Expected Value, Variance and the Expected Value of Variance, Chebyshev ` s Theorem, Selected Discrete Probability Distributions, Selected Continuous Probability Distributions, Functions of Random Variables, Moment-Generating Functions
Course Methods and Techniques Face-to-face
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Şakire Nesli Demircioğlu nesli.demircioglu@hku.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources 1-Olasılık ve İstatistik - Fikri Akdeniz, Akademisyen Kitabevi 2-Ross, S. M. (2014). Introduction to probability models. Academic press. 3- Devore, J. L. (2011). Probability and Statistics for Engineering and the Sciences. Cengage learning 4- Statistics for Engineers and Scientists, William Navidi, 4th Ed., Mc-Graw Hill 5- Walpole R.E., Myers R.H., Myers S.L. Ye K.E., Probability and Statistics for Engineers and Scientists, 9th Edition, Prentice Hall, 2011.
Course Notes 1- Olasılık ve İstatistik - Fikri Akdeniz, Akademisyen Kitabevi
2- Ross, S. M. (2014). Introduction to probability models. Academic press.
3- Devore, J. L. (2011). Probability and Statistics for Engineering and the Sciences. Cengage learning
4- Statistics for Engineers and Scientists, William Navidi, 4th Ed., Mc-Graw Hill
5- Walpole R.E., Myers R.H., Myers S.L. Ye K.E., Probability and Statistics for Engineers and Scientists, 9th Edition, Prentice Hall, 2011.

Course Category
Mathematics and Basic Sciences %60
Engineering %40

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Assignment 1 % 10
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 15 3 45
Hours for off-the-c.r.stud 15 8 120
Assignments 1 10 10
Mid-terms 1 1,5 1,5
Final examination 1 1,5 1,5
Total Work Load   Number of ECTS Credits 6 178

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
Bilgi 
5 The student identifies appropriate distributions for analyzing data.
Beceri 
1 The student acquires the ability to apply an extensive body of knowledge in Mathematics, Science, and Engineering to problems related to Industrial Engineering.
2 Student data can be visualized using appropriate graphical methods.
3 The student acquires the ability to use knowledge of probability and statistics, which is necessary for applying fundamental modeling and decision-making techniques in engineering.
Yetkinlik 
4 The student acquires the ability to think analytically.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Descriptive Statistics, Introduction to Probability Lecturer ` s notes
2 Set Theory, Concept of Sample Space, Concept of Event, Counting Sample Points, Permutation and Combination Lecturer ` s notes
3 Probability of an Event, Addition Rule in Probability, Multiplication Rule in Probability, Conditional Probability Lecturer ` s notes
4 Dependent and Independent Events, Bayes ` Theorem Lecturer ` s notes
5 The Concept of a Random Variable, Discrete and Continuous Random Variables, Discrete and Continuous Sample Spaces, Discrete Probability Distributions Lecturer ` s notes
6 Discrete Probability Distributions Lecturer ` s notes
7 Continuous Probability Distributions, Joint Probability Distributions, Marginal Probability Distributions, Statistical Dependence Lecturer ` s notes
8 Midterm
9 Mean and Variance of Discrete and Random Variables ISO 3534-1: Statistics — Vocabulary and Symbols — Part 1: General statistical terms and terms used in probability Lecturer ` s notes
10 Means and Variances of Linear Combinations of Random Variables, Chebyshev ` s Theorem Lecturer ` s notes
11 Some Discrete Probability Distributions: Uniform Distribution, Binomial Distribution, Hypergeometric Distribution Lecturer ` s notes
12 Negative Binomial Distribution, Geometric Distribution, Poisson Distribution, Some Continuous Probability Distributions Lecturer ` s notes
13 Normal Distribution Lecturer ` s notes
14 Normal Curve Approximation to the Binomial Distribution, Exponential Distribution, Gamma Distribution Lecturer ` s notes
15 Exponential Distribution, Chi-Square Distribution, Weibull Distribution. Lecturer ` s notes

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24
All 4 4
In5
Sk1
Sk2
Sk3
Co4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant