PROBABILITY CONCEPTS AND PROBABILITY THEORY FOR INDUSTRIAL ENGINEERS
- Course
- INDE203 - PROBABILITY CONCEPTS AND PROBABILITY THEORY FOR INDUSTRIAL ENGINEERS
- Department
- Industrial Engineering - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 7
- T+P+L
- 3 + 1 + 0
- Course Coordinator(s)
- Assoc. Prof. Dr. Mazyar GHADIRI NEJAD
- Prerequisite
- -
Course Description
THIS COURSE AIMS TO HELP INDUSTRIAL ENGINEERING STUDENTS TO UNDERSTAND FUNDAMENTAL CONCEPT OF PROBABILITY THEORY AND GAIN THE ABILITY TO USE METHODS OF THE DISCIPLINE. THE MAIN CONTENT INCLUDES INTRODUCING THE PROBABILITY THEORY - GIVING PRIOIRITY TO RANDOM VARIABLES RELATED TO UNCERTAIN EVENTS; ENSURING THAT SRUDENTS HAVE A DEEP UNDERSTANDING OF PROBABILITY DISTRIBUTIONS AND THEIR IMPLICATIONS IN INDUSTRIAL ENGINEERING; HELPING THE STUDENTS TO USE PROBABILITY THEORY TO BUILD AND ANALYZE INDUSTRIAL ENGINEERING MODELS/PROBLEMS ENCOUNTERED IN REAL LIFE, ESPECIALLY FOR THE PROBLEMS INCLUDING UNCERTAINTY.
PROBABILITY CONCEPTS AND PROBABILITY THEORY FOR INDUSTRIAL ENGINEERS
Evaluation Tools (Active Term)
| Item | Type | Weight (%) |
|---|---|---|
| Final | Final | 40 |
| Midterm | Midterm | 30 |
| Quiz 1 | Quiz | 10 |
| Quiz 2 | Quiz | 10 |
| LAB | Assignment | 10 |
| Total | 100 | |
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction |
| Week 2 | A review of Sets |
| Week 3 | Probabilities (Population, Samples, and Procedures included) |
| Week 4 | Sample Spaces and Events |
| Week 5 | Definition and axioms of probability |
| Week 6 | Combination of Events (including Bayesian Theorem) |
| Week 7 | Conditional probability and independence (including decision trees) |
| Week 8 | Midterm Exam |
| Week 9 | Random variables (discrete and continuous) |
| Week 10 | Expectation of a random variable |
| Week 11 | Discrete Probability Distributions |
| Week 12 | Continuous Probability Distributions |
| Week 13 | Joint probability distributions and conditional expectations |
| Week 14 | The normal distribution and the Central Limit Theorem |
| Week 15 | Final Exam |
Reference Books & Course Materials
- 01 Hayter, A., (2013). Probability and Statistics for Engineering and Sciences, 4th Edition., Brooks/Cole Cengage Learning.
- 02 Mendenhall, W., Beaver, R. J., & Beaver, B. M. (2020). Introduction to probability and statistics. Cengage.
- 03 Walpole, R.E., Myers, R.H., Myers, S.L., &.Ye, K., (2007). Probability and Statistics for Engineers and Scientists, 7th edition. Prentice Hall.
- 04 Devore, J.L.,(2012). Probability and Statistics for Engineering and Sciences, 8th ed., Brooks/Cole Cengage Learning.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- P01 Knowledge of mathematics, natural sciences, basic engineering, computer-based computation, and topics specific to the relevant engineering discipline.
- P02 Ability to apply knowledge of mathematics, natural sciences, basic engineering, computer-based computation, and topics specific to the relevant engineering discipline to the solution of complex engineering problems.
- P03 Ability to define complex engineering problems by using knowledge of basic sciences, mathematics, and engineering, while considering the relevant United Nations Sustainable Development Goals (SDGs) related to the problem addressed.
- P04 Ability to formulate complex engineering problems using knowledge of basic sciences, mathematics, and engineering, while considering the relevant United Nations Sustainable Development Goals (SDGs) associated with the problem addressed.
- P05 Ability to analyse and solve complex engineering problems using knowledge of basic sciences, mathematics, and engineering, while considering the relevant United Nations Sustainable Development Goals (SDGs) associated with the problem addressed.
- P06 Ability to design creative solutions to complex engineering problems.
- P07 Ability to design complex systems, processes, devices, or products in a way that meets present and future needs while considering realistic constraints and conditions.
- P08 Ability to select and use appropriate techniques and resources—including estimation and modelling—for the analysis and solution of complex engineering problems, while being aware of their limitations.
- P09 Ability to select and use modern engineering and computational tools—including estimation and modelling—for the analysis and solution of complex engineering problems, while being aware of their limitations.
- P10 Ability to conduct literature research and use appropriate research methods for the investigation of complex engineering problems.
- P11 Ability to design experiments for the investigation of complex engineering problems.
- P12 Ability to conduct experiments, collect data, analyse results, and interpret findings for the investigation of complex engineering problems.
- P13 Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the framework of the United Nations Sustainable Development Goals (SDGs).
- P14 Awareness of the legal implications of engineering solutions within the framework of the United Nations Sustainable Development Goals (SDGs).
- P15 Knowledge of ethical responsibility and adherence to the principles of professional engineering conduct.
- P16 Awareness of acting impartially without discrimination in any matter and of being inclusive of diversity.
- P17 Ability to work effectively as an individual.
- P18 Ability to work effectively as a team member or leader in intra-disciplinary teams (face-to-face, remote, or hybrid).
- P19 Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote, or hybrid).
- P20 Ability to communicate effectively in spoken form on technical matters, taking into account the diverse characteristics of the target audience (such as education, language, and profession).
- P21 Ability to communicate effectively in written form on technical matters, taking into account the diverse characteristics of the target audience (such as education, language, and profession).
- P22 Knowledge of professional practices such as project management and economic feasibility analysis.
- P23 Awareness of entrepreneurship and innovation.
- P24 Ability for independent and lifelong learning.
- P25 Ability to adapt to new and emerging technologies.
- P26 Lifelong learning ability that includes the capacity to think critically about technological changes.
Po-Lo Matrix
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