FACULTY OF ENGINEERING

Department of Food Engineering

MATH 240 | Course Introduction and Application Information

Course Name
Probability for Engineers
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
MATH 240
Fall/Spring
3
0
3
6

Prerequisites
  MATH 154 To get a grade of at least FD
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives This course aims to introduce students the theory of probability and its applications to engineering problems.
Learning Outcomes The students who succeeded in this course;
  • use fundamental concepts such as sample space, events and counting techniques.
  • explain concepts of probability.
  • use conditional probability, the total probability rule and Bayes' theorem.
  • compute discrete and continuous random variables.
  • investigate the advantages of joint probability distributions.
  • find mean and variance of random variables.
  • apply discrete and continuous distributions.
Course Description In this course some important theorems about probability are investigated. In addition, applications of random variables and their probability distributions are discussed.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Sample space and events Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Probability”, Chap. 2 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 55-63.
2 Events and counting sample points Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Probability”, Chap. 2 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 58-71.
3 Counting sample points, probability of an event and additive rules Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Probability”, Chap. 2 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 64-79.
4 Additive rules, conditional probability of an event Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Probability”, Chap. 2 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 76-89.
5 Bayes’ rule Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Probability”, Chap. 2 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 92-97
6 Concept of random variable and discrete probability distributions Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Random Variables and Probability Distributions”, Chap. 3 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 101-106.
7 Discrete probability distributions and continuous probability distributions .Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Random Variables and Probability Distributions”, Chap. 3 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 104-111.
8 Midterm
9 Joint probability distributions Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Mathematical Expectation”, Chap. 4 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 114-124.
10 Mean and Variance of a Random variable Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Some Discrete Probability Distributions”, Chap. 5 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 131-147
11 Binomial and multinomial distributions Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Some Discrete Probability Distributions”, Chap. 5 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 163-170..
12 Binomial and multinomial distributions Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Some Discrete Probability Distributions”, Chap. 5 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 172-184.
13 Uniform, Normal, areas under the normal curve, applications of the normal dist. and exponential distribution Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Some Continuous Probability Distributions”, Chap. 6 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 191-205.
14 Uniform, normal, areas under the normal curve, applications of the normal dist. and exponential distribution Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, “Some Continuous Probability Distributions”, Chap. 6 Probability & Statistics for Engineers and Scientists, 9th Edition (Pearson, 2017), 191-205.
15 Semester review
16 Final Exam

 

Course Notes/Textbooks

Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, Probability and Statistics for Engineers and Scientists, 9th Edition (United States of America: Pearson, 2017).

ISBN-13: 978-0134115856

Suggested Readings/Materials

William Navidi, Statistics for Engineers and Scientists, 5th Ed. (Mc-Graw Hill, 2019)  ISBN-13: 978-1260547887

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
2
10
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
40
Final Exam
1
50
Total

Weighting of Semester Activities on the Final Grade
3
50
Weighting of End-of-Semester Activities on the Final Grade
1
50
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
2
10
20
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
30
30
Final Exam
1
40
40
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Being able to transfer knowledge and skills acquired in mathematics and science into engineering,
2 Being able to identify and solve problem areas related to Food Engineering,
3 Being able to design projects and production systems related to Food Engineering, gather data, analyze them and utilize their outcomes in practice,
4

Having the necessary skills to develop and use novel technologies and equipment in the field of food engineering,

5

Being able to take part actively in team work, express his/her ideas freely, make efficient decisions as well as working individually,

6

Being able to follow universal developments and innovations, improve himself/herself continuously and have an awareness to enhance the quality,

7

Having professional and ethical awareness,

8 Being aware of universal issues such as environment, health, occupational safety in solving problems related to Food Engineering,
9

Being able to apply entrepreneurship, innovativeness and sustainability in the profession,

10

Being able to use software programs in Food Engineering and have the necessary knowledge and skills to use information and communication technologies that may be encountered in practice (European Computer Driving License, Advanced Level),

11

Being able to gather information about food engineering and communicate with colleagues using a foreign language ("European Language Portfolio Global Scale", Level B1)

12

Being able to speak a second foreign language at intermediate level.

13

Being able to relate the knowledge accumulated during the history of humanity to the field of expertise

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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