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IE 547

Course ID:
Course Code & Number
IE 547
Course Title
Modelling and Analysis of Uncertainty
Level
MS
Credit Hours/ ECTS Credits
(3+0+0) 3 TEDU Credits, 7.5 ECTS Credits
Year of Study:
Master
Semester:
Fall
Type of Course:
Compulsory
Mode of Delivery:
Face-to-face
Language of Instruction:
English
Pre-requisite / Co-requisite::
Pre-requisites: NONE
Co-requisites: NONE
Catalog Description
Probability and fundamental theorems of probability. Conditional probability. Random variables. Discrete and continuous distributions. Expected value. Hypothesis tests. Variance analysis. Simple and multiple regression. Correlation analysis.
Course Objectives

The goal of this course is to teach the fundamentals of analysis and modeling under uncertainty. The course introduces the nature of uncertainty and basic concepts of probability including distributions and expectations. Another goal is to develop statistical analysis skills that help understanding and modeling uncertain events in practice. The course also aims to teach students how to use spreadsheet software for various data analysis.

Software Usage
Course Learning Outcomes

Upon succesful completion of this course, a student will be able to
1. Calculate the mean and variance of a random variable and apply general properties of the expectation and variance operators.
2. Describe the main properties of probability distributions and random variables and apply the concepts of discrete and continuous probability distributions.
3. Identify the random variable(s) of interest in a given scenario and construct the probability distribution based on a real-world situation.
4. Perform hypothesis tests to statistically prove or disprove claims involving one or more populations
5. Construct simple and multiple regression models
6. Draw conclusions based on statistical models

Learning Activities and Teaching Methods:
Telling/Explaining Discussion/Debate Questioning Reading Problem Solving Inquiry Predict-Observe-Explain Case Study/Scenarion Analysis
Assessment Methods and Criteria:
Test / Exam Quiz Case Studies / Homework
Assessment Methods and Criteria Others:
Design Content
Recommended Reading
Required Reading
1. Walpole, R. E., Myers, R. H., Myers S. L., Ye, K. Probability and Statistics for Engineers and Scientists (9th edition)
Grading

Homework Assignments - 20%
Term Project - 25%
Midterm Exam - 25%
Final Exam - 30%

Learning Activities and Teaching Methods Others:
Course Coordinator:
Student Workload:
Workload Hrs
Course Readings 30
Hands-on Work 10
Exams/Quizzes 45
Case Study Analysis 36
Course & Program Learning Outcome Matching: