Industrial Engineering

Course Code	Course Name	Year	Period	Semester	T+A+L	Credit	ECTS
88600010	Probability and Statistics	2025	Autumn	3	3+0+0	3	5

Course Type	Compulsory
Course Cycle	Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6)
Course Language	Turkish
Methods and Techniques	-
Mode of Delivery	Face to Face
Prerequisites	-
Coordinator	Prof. Murat DARÇIN
Instructor(s)	Asst. Prof. Sümeyye BAKIM
Instructor Assistant(s)	-

Name and Surname	Room	E-Mail Address	Internal	Meeting Hours
Asst. Prof. Sümeyye BAKIM	A-130	[email protected]	7483	Monday 09.30-11.00

Basic Vocational Courses	X
Specialization / Field Courses
Support Courses
Transferable Skills Courses
Humanities, Communication and Management Skills Courses

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Program Learning Outcomes	Level
P1	Knowledge of mathematics, natural sciences, fundamental engineering, computational sciences, and industrial engineering-specific subjects; the ability to apply this knowledge to solve complex industrial engineering problems.	5
P5	The ability to use research methods, including literature review, experimental design, experiment execution, data collection, analysis, and interpretation of results, to investigate complex industrial engineering problems.	5

Upon the successful completion of this course, students will be able to:
No	Learning Outcomes	Outcome Relationship	Measurement Method **
O1	Able to apply basic combinatorial counting methods (permutations, combinations, etc.) and use them in engineering applications.	P.1.104	1
O2	Defines discrete and continuous random variables, determines their distributions, and performs probability calculations based on them.	P.1.105	1
O3	Analyzes the joint behavior of random variables based on concepts such as joint, marginal, and conditional distributions, and draws conclusions.	P.1.106	1
O4	Understands the mathematical structure of the most commonly used discrete and continuous distributions in engineering and their appropriate applications.	P.1.107	1
O5	Calculates moments such as expected value, variance, and covariance theoretically for use in statistical applications.	P.1.108	1
O6	Applies basic combinatorial counting techniques (permutations, combinations, etc.) in engineering applications.	P.5.27	1
O7	Defines discrete and continuous random variables, determines their distributions, and performs probability calculations based on them.	P.5.28	1
O8	Analyzes the joint behavior of random variables based on concepts such as joint, marginal, and conditional distributions, and draws conclusions.	P.5.29	1
O9	Understands the mathematical structure of the most commonly used discrete and continuous distributions in engineering and their appropriate applications.	P.5.30	1
O10	Calculates moments such as expected value, variance, and covariance theoretically for use in statistical applications.	P.5.31	1
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7

Week	Topics
1	Descriptive Statistics, Population and Sampling, Frequency Distributions for Qualitative Data, Frequency Distributions for Quantitative Data, Central Tendency
2	Arithmetic Mean, Geometric Mean, Harmonic Mean, Quadratic Mean, Standard Deviation and Variance, Sets, Sample Space, Events, Counting Sample Points
3	Probability of an Event, Additive Rules, Multiplicative Rules, Conditional Probability
4	Dependent and Independent Events, Bayes Theorem
5	Discrete Probability Distribution, First Midyear Exam
5	Measures of Dispersion, Variance and Standard Deviation
6	Continuous Probability Distribution, Joint Probability Distributions, Marginal Probability Distributions, Statistical Dependency
7	Mathematical Expectation, Expected value
8	Ara sınav
9	Variance, Expected Value of Variance, Covariance
10	Variance, Expected Value of Variance, Covariance
11	Variance, Expected Value of Variance, Covariance
12	Some Discrete Probability Distributions,Uniform Distribution, Binomial Distribution, Hypergeometric Distribution
13	Negative Binomial Distribution, Geometric Distribution, Poisson Distributions
14	Normal Distribution
15	Normal Distribution Approximation to Binomial Distribution, Gamma Distribution, Chi-squared Distribution, Weibull Distribution

Resources

WALPOLE R.E., MYERS R.H., MYERS S.L. YE K.E., Probability and Statistics for Engineers and Scientists, 9th Edition, Prentice Hall, 2011.

In-Term Studies	Quantity	Percentage
Attendance	-	-
Laboratory	-	-
Practice	-	-
Field Study	-	-
Course Specific Internship (If Any)	-	-
Homework	-	-
Presentation	-	-
Projects	-	-
Seminar	-	-
Quiz	-	-
Listening	-	-
Midterms	1	40 (%)
Final Exam	1	60 (%)
Total		100 (%)

	Quantity	Duration	Total Work Load
Course Week Number and Time	14	3	42
Out-of-Class Study Time (Pre-study, Library, Reinforcement)	14	4	56
Midterms	1	20	20
Quiz	0	0	0
Homework	0	0	0
Practice	0	0	0
Laboratory	0	0	0
Project	0	0	0
Workshop	0	0	0
Presentation/Seminar Preparation	0	0	0
Fieldwork	0	0	0
Final Exam	1	32	32
Other	0	0	0
Total Work Load:			150
Total Work Load / 30			5
Course ECTS Credits:			5

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Learning Outcomes	P1	P5
O1	Able to apply basic combinatorial counting methods (permutations, combinations, etc.) and use them in engineering applications.	5	-
O2	Defines discrete and continuous random variables, determines their distributions, and performs probability calculations based on them.	5	-
O3	Analyzes the joint behavior of random variables based on concepts such as joint, marginal, and conditional distributions, and draws conclusions.	5	-
O4	Understands the mathematical structure of the most commonly used discrete and continuous distributions in engineering and their appropriate applications.	5	-
O5	Calculates moments such as expected value, variance, and covariance theoretically for use in statistical applications.	5	-
O6	Applies basic combinatorial counting techniques (permutations, combinations, etc.) in engineering applications.	-	5
O7	Defines discrete and continuous random variables, determines their distributions, and performs probability calculations based on them.	-	5
O8	Analyzes the joint behavior of random variables based on concepts such as joint, marginal, and conditional distributions, and draws conclusions.	-	5
O9	Understands the mathematical structure of the most commonly used discrete and continuous distributions in engineering and their appropriate applications.	-	5
O10	Calculates moments such as expected value, variance, and covariance theoretically for use in statistical applications.	-	5